From 509aa29a5f619db9effd06b0447346ebc8b32457 Mon Sep 17 00:00:00 2001
From: joshfactorial <jallen17@illinois.edu>
Date: Tue, 7 Jan 2020 10:58:39 -0600
Subject: [PATCH 001/323] A bunch of changes to meet minimum python 3
 requirements

---
 genReads.py                   | 1222 +++++++++++-----------
 py/OutputFileWriter.py        |  484 ++++-----
 py/SequenceContainer.py       | 1860 +++++++++++++++++----------------
 py/__init__.py                |    0
 py/biopython_modified_bgzf.py |  169 ++-
 py/inputChecking.py           |   56 +-
 py/neat_cigar.py              |  157 ++-
 py/probability.py             |  260 ++---
 py/refFunc.py                 |  361 ++++---
 py/vcfFunc.py                 |  350 +++----
 10 files changed, 2472 insertions(+), 2447 deletions(-)
 create mode 100644 py/__init__.py

diff --git a/genReads.py b/genReads.py
index 7879583..426f89b 100644
--- a/genReads.py
+++ b/genReads.py
@@ -21,20 +21,20 @@
 import re
 import time
 import bisect
-import cPickle as pickle
+import pickle
 import numpy as np
 import argparse
 
 # absolute path to this script
 SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-1])
-sys.path.append(SIM_PATH+'/py/')
+sys.path.append(SIM_PATH + '/py/')
 
-from inputChecking		import requiredField, checkFileOpen, checkDir, isInRange
-from refFunc			import indexRef, readRef, getAllRefRegions, partitionRefRegions, ALLOWED_NUCL
-from vcfFunc			import parseVCF
-from OutputFileWriter	import OutputFileWriter
-from probability		import DiscreteDistribution, mean_ind_of_weighted_list
-from SequenceContainer	import SequenceContainer, ReadContainer, parseInputMutationModel
+from py.inputChecking import requiredField, checkFileOpen, checkDir, isInRange
+from py.refFunc import indexRef, readRef, getAllRefRegions, partitionRefRegions
+from py.vcfFunc import parseVCF
+from py.OutputFileWriter import OutputFileWriter
+from py.probability import DiscreteDistribution, mean_ind_of_weighted_list
+from py.SequenceContainer import SequenceContainer, ReadContainer, parseInputMutationModel
 
 # if coverage val for a given window/position is below this value, consider it effectively zero.
 LOW_COV_THRESH = 50
@@ -43,213 +43,215 @@
 ////////////    PARSE INPUT ARGUMENTS    ////////////
 //////////////////////////////////////////////////"""
 
-
 parser = argparse.ArgumentParser(description='NEAT-genReads V2.0')
-parser.add_argument('-r', type=str,   required=True,  metavar='<str>',                  help="* ref.fa")
-parser.add_argument('-R', type=int,   required=True,  metavar='<int>',                  help="* read length")
-parser.add_argument('-o', type=str,   required=True,  metavar='<str>',                  help="* output prefix")
-parser.add_argument('-c', type=float, required=False, metavar='<float>', default=10.,   help="average coverage")
-parser.add_argument('-e', type=str,   required=False, metavar='<str>',   default=None,  help="sequencing error model")
-parser.add_argument('-E', type=float, required=False, metavar='<float>', default=-1,    help="rescale avg sequencing error rate to this")
-parser.add_argument('-p', type=int,   required=False, metavar='<int>',   default=2,     help="ploidy")
-parser.add_argument('-t', type=str,   required=False, metavar='<str>',   default=None,  help="bed file containing targeted regions")
-parser.add_argument('-to',type=float, required=False, metavar='<float>', default=0.00,  help="off-target coverage scalar")
-parser.add_argument('-m', type=str,   required=False, metavar='<str>',   default=None,  help="mutation model pickle file")
-parser.add_argument('-M', type=float, required=False, metavar='<float>', default=-1,    help="rescale avg mutation rate to this")
-parser.add_argument('-Mb',type=str,   required=False, metavar='<str>',   default=None,  help="bed file containing positional mut rates")
-parser.add_argument('-N', type=int,   required=False, metavar='<int>',   default=-1,    help="below this qual, replace base-calls with 'N's")
-#parser.add_argument('-s', type=str,   required=False, metavar='<str>',   default=None,  help="input sample model")
-parser.add_argument('-v', type=str,   required=False, metavar='<str>',   default=None,  help="input VCF file")
-
-parser.add_argument('--pe',       nargs=2, type=int,   required=False, metavar=('<int>','<int>'), default=(None,None), help='paired-end fragment length mean and std')
-parser.add_argument('--pe-model',          type=str,   required=False, metavar='<str>',           default=None,  help='empirical fragment length distribution')
-#parser.add_argument('--cancer',                        required=False, action='store_true',       default=False, help='produce tumor/normal datasets')
-#parser.add_argument('-cm',                 type=str,   required=False, metavar='<str>',           default=None,  help="cancer mutation model directory")
-#parser.add_argument('-cp',                 type=float, required=False, metavar='<float>',         default=0.8,   help="tumor sample purity")
-parser.add_argument('--gc-model',          type=str,   required=False, metavar='<str>',           default=None,  help='empirical GC coverage bias distribution')
-parser.add_argument('--job',      nargs=2, type=int,   required=False, metavar=('<int>','<int>'), default=(0,0), help='jobs IDs for generating reads in parallel')
-parser.add_argument('--nnr',                           required=False, action='store_true',       default=False, help='save non-N ref regions (for parallel jobs)')
-parser.add_argument('--bam',                           required=False, action='store_true',       default=False, help='output golden BAM file')
-parser.add_argument('--vcf',                           required=False, action='store_true',       default=False, help='output golden VCF file')
-parser.add_argument('--fa',                            required=False, action='store_true',       default=False, help='output FASTA instead of FASTQ')
-parser.add_argument('--rng',               type=int,   required=False, metavar='<int>',           default=-1,    help='rng seed value; identical RNG value should produce identical runs of the program, so things like read locations, variant positions, error positions, etc, should all be the same.')
-parser.add_argument('--gz',                            required=False, action='store_true',       default=False, help='gzip output FQ and VCF')
-parser.add_argument('--no-fastq',                      required=False, action='store_true',       default=False, help='bypass fastq generation')
+parser.add_argument('-r', type=str, required=True, metavar='<str>', help="* ref.fa")
+parser.add_argument('-R', type=int, required=True, metavar='<int>', help="* read length")
+parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output prefix")
+parser.add_argument('-c', type=float, required=False, metavar='<float>', default=10., help="average coverage")
+parser.add_argument('-e', type=str, required=False, metavar='<str>', default=None, help="sequencing error model")
+parser.add_argument('-E', type=float, required=False, metavar='<float>', default=-1,
+                    help="rescale avg sequencing error rate to this")
+parser.add_argument('-p', type=int, required=False, metavar='<int>', default=2, help="ploidy")
+parser.add_argument('-t', type=str, required=False, metavar='<str>', default=None,
+                    help="bed file containing targeted regions")
+parser.add_argument('-to', type=float, required=False, metavar='<float>', default=0.00,
+                    help="off-target coverage scalar")
+parser.add_argument('-m', type=str, required=False, metavar='<str>', default=None, help="mutation model pickle file")
+parser.add_argument('-M', type=float, required=False, metavar='<float>', default=-1,
+                    help="rescale avg mutation rate to this")
+parser.add_argument('-Mb', type=str, required=False, metavar='<str>', default=None,
+                    help="bed file containing positional mut rates")
+parser.add_argument('-N', type=int, required=False, metavar='<int>', default=-1,
+                    help="below this qual, replace base-calls with 'N's")
+# parser.add_argument('-s', type=str,   required=False, metavar='<str>',   default=None,  help="input sample model")
+parser.add_argument('-v', type=str, required=False, metavar='<str>', default=None, help="input VCF file")
+
+parser.add_argument('--pe', nargs=2, type=int, required=False, metavar=('<int>', '<int>'), default=(None, None),
+                    help='paired-end fragment length mean and std')
+parser.add_argument('--pe-model', type=str, required=False, metavar='<str>', default=None,
+                    help='empirical fragment length distribution')
+# parser.add_argument('--cancer',                        required=False, action='store_true',       default=False, help='produce tumor/normal datasets')
+# parser.add_argument('-cm',                 type=str,   required=False, metavar='<str>',           default=None,  help="cancer mutation model directory")
+# parser.add_argument('-cp',                 type=float, required=False, metavar='<float>',         default=0.8,   help="tumor sample purity")
+parser.add_argument('--gc-model', type=str, required=False, metavar='<str>', default=None,
+                    help='empirical GC coverage bias distribution')
+parser.add_argument('--job', nargs=2, type=int, required=False, metavar=('<int>', '<int>'), default=(0, 0),
+                    help='jobs IDs for generating reads in parallel')
+parser.add_argument('--nnr', required=False, action='store_true', default=False,
+                    help='save non-N ref regions (for parallel jobs)')
+parser.add_argument('--bam', required=False, action='store_true', default=False, help='output golden BAM file')
+parser.add_argument('--vcf', required=False, action='store_true', default=False, help='output golden VCF file')
+parser.add_argument('--fa', required=False, action='store_true', default=False, help='output FASTA instead of FASTQ')
+parser.add_argument('--rng', type=int, required=False, metavar='<int>', default=-1,
+                    help='rng seed value; identical RNG value should produce identical runs of the program, so things like read locations, variant positions, error positions, etc, should all be the same.')
+parser.add_argument('--gz', required=False, action='store_true', default=False, help='gzip output FQ and VCF')
+parser.add_argument('--no-fastq', required=False, action='store_true', default=False, help='bypass fastq generation')
 args = parser.parse_args()
 
 # required args
 (REFERENCE, READLEN, OUT_PREFIX) = (args.r, args.R, args.o)
 # various dataset parameters
-(COVERAGE, PLOIDS, INPUT_BED, SE_MODEL, SE_RATE, MUT_MODEL, MUT_RATE, MUT_BED, INPUT_VCF) = (args.c, args.p, args.t, args.e, args.E, args.m, args.M, args.Mb, args.v)
+(COVERAGE, PLOIDS, INPUT_BED, SE_MODEL, SE_RATE, MUT_MODEL, MUT_RATE, MUT_BED, INPUT_VCF) = (
+args.c, args.p, args.t, args.e, args.E, args.m, args.M, args.Mb, args.v)
 # cancer params (disabled currently)
-#(CANCER, CANCER_MODEL, CANCER_PURITY) = (args.cancer, args.cm, args.cp)
+# (CANCER, CANCER_MODEL, CANCER_PURITY) = (args.cancer, args.cm, args.cp)
 (CANCER, CANCER_MODEL, CANCER_PURITY) = (False, None, 0.8)
 (OFFTARGET_SCALAR) = (args.to)
 # important flags
 (SAVE_BAM, SAVE_VCF, FASTA_INSTEAD, GZIPPED_OUT, NO_FASTQ) = (args.bam, args.vcf, args.fa, args.gz, args.no_fastq)
 
-ONLY_VCF = (NO_FASTQ and SAVE_VCF and not(SAVE_BAM))
+ONLY_VCF = (NO_FASTQ and SAVE_VCF and not (SAVE_BAM))
 if ONLY_VCF:
-	print 'Only producing VCF output, that should speed things up a bit...'
+    print('Only producing VCF output, that should speed things up a bit...')
 
 # sequencing model parameters
 (FRAGMENT_SIZE, FRAGMENT_STD) = args.pe
 FRAGLEN_MODEL = args.pe_model
 GC_BIAS_MODEL = args.gc_model
-N_MAX_QUAL    = args.N
+N_MAX_QUAL = args.N
 
 # if user specified no fastq, no bam, no vcf, then inform them of their wasteful ways and exit
 if NO_FASTQ == True and SAVE_BAM == False and SAVE_VCF == False:
-        print '\nError: No files will be written when --no-fastq is specified without --vcf or --bam.'
-        exit(1)
+    print('\nError: No files will be written when --no-fastq is specified without --vcf or --bam.')
+    exit(1)
 
 # if user specified mean/std, use artificial fragment length distribution, otherwise use
 # the empirical model specified. If neither, then we're doing single-end reads.
 PAIRED_END = False
 PAIRED_END_ARTIFICIAL = False
 if FRAGMENT_SIZE != None and FRAGMENT_STD != None:
-	PAIRED_END = True
-	PAIRED_END_ARTIFICIAL = True
+    PAIRED_END = True
+    PAIRED_END_ARTIFICIAL = True
 elif FRAGLEN_MODEL != None:
-	PAIRED_END = True
-	PAIRED_END_ARTIFICIAL = False
+    PAIRED_END = True
+    PAIRED_END_ARTIFICIAL = False
 
 (MYJOB, NJOBS) = args.job
 if MYJOB == 0:
-	MYJOB = 1
-	NJOBS = 1
+    MYJOB = 1
+    NJOBS = 1
 SAVE_NON_N = args.nnr
 
 RNG_SEED = args.rng
 if RNG_SEED == -1:
-	RNG_SEED = random.randint(1,99999999)
+    RNG_SEED = random.randint(1, 99999999)
 random.seed(RNG_SEED)
 
-
 """************************************************
 ****            INPUT ERROR CHECKING
 ************************************************"""
 
-
-checkFileOpen(REFERENCE,'ERROR: could not open reference',required=True)
-checkFileOpen(INPUT_VCF,'ERROR: could not open input VCF',required=False)
-checkFileOpen(INPUT_BED,'ERROR: could not open input BED',required=False)
-requiredField(OUT_PREFIX,'ERROR: no output prefix provided')
+checkFileOpen(REFERENCE, 'ERROR: could not open reference', required=True)
+checkFileOpen(INPUT_VCF, 'ERROR: could not open input VCF', required=False)
+checkFileOpen(INPUT_BED, 'ERROR: could not open input BED', required=False)
+requiredField(OUT_PREFIX, 'ERROR: no output prefix provided')
 if (FRAGMENT_SIZE == None and FRAGMENT_STD != None) or (FRAGMENT_SIZE != None and FRAGMENT_STD == None):
-	print '\nError: --pe argument takes 2 space-separated arguments.\n'
-	exit(1)
-
+    print('\nError: --pe argument takes 2 space-separated arguments.\n')
+    exit(1)
 
 """************************************************
 ****             LOAD INPUT MODELS
 ************************************************"""
 
-
 #	mutation models
 #
-MUT_MODEL = parseInputMutationModel(MUT_MODEL,1)
+MUT_MODEL = parseInputMutationModel(MUT_MODEL, 1)
 if CANCER:
-	CANCER_MODEL = parseInputMutationModel(CANCER_MODEL,2)
+    CANCER_MODEL = parseInputMutationModel(CANCER_MODEL, 2)
 if MUT_RATE < 0.:
-	MUT_RATE = None
+    MUT_RATE = None
 
 #	sequencing error model
 #
 if SE_RATE < 0.:
-	SE_RATE = None
+    SE_RATE = None
 if SE_MODEL == None:
-	print 'Using default sequencing error model.'
-	SE_MODEL = SIM_PATH+'/models/errorModel_toy.p'
-	SE_CLASS = ReadContainer(READLEN,SE_MODEL,SE_RATE)
+    print('Using default sequencing error model.')
+    SE_MODEL = SIM_PATH + '/models/errorModel_toy.p'
+    SE_CLASS = ReadContainer(READLEN, SE_MODEL, SE_RATE)
 else:
-	# probably need to do some sanity checking
-	SE_CLASS = ReadContainer(READLEN,SE_MODEL,SE_RATE)
+    # probably need to do some sanity checking
+    SE_CLASS = ReadContainer(READLEN, SE_MODEL, SE_RATE)
 
 #	GC-bias model
 #
 if GC_BIAS_MODEL == None:
-	print 'Using default gc-bias model.'
-	GC_BIAS_MODEL = SIM_PATH+'/models/gcBias_toy.p'
-	[GC_SCALE_COUNT, GC_SCALE_VAL] = pickle.load(open(GC_BIAS_MODEL,'rb'))
-	GC_WINDOW_SIZE = GC_SCALE_COUNT[-1]
+    print('Using default gc-bias model.')
+    GC_BIAS_MODEL = SIM_PATH + '/models/gcBias_toy.p'
+    [GC_SCALE_COUNT, GC_SCALE_VAL] = pickle.load(open(GC_BIAS_MODEL, 'rb'))
+    GC_WINDOW_SIZE = GC_SCALE_COUNT[-1]
 else:
-	[GC_SCALE_COUNT, GC_SCALE_VAL] = pickle.load(open(GC_BIAS_MODEL,'rb'))
-	GC_WINDOW_SIZE = GC_SCALE_COUNT[-1]
+    [GC_SCALE_COUNT, GC_SCALE_VAL] = pickle.load(open(GC_BIAS_MODEL, 'rb'))
+    GC_WINDOW_SIZE = GC_SCALE_COUNT[-1]
 
 #	fragment length distribution
 #
-if PAIRED_END and not(PAIRED_END_ARTIFICIAL):
-	print 'Using empirical fragment length distribution.'
-	[potential_vals, potential_prob] = pickle.load(open(FRAGLEN_MODEL,'rb'))
-	FRAGLEN_VALS = []
-	FRAGLEN_PROB = []
-	for i in xrange(len(potential_vals)):
-		if potential_vals[i] > READLEN:
-			FRAGLEN_VALS.append(potential_vals[i])
-			FRAGLEN_PROB.append(potential_prob[i])
-	# should probably add some validation and sanity-checking code here...
-	FRAGLEN_DISTRIBUTION = DiscreteDistribution(FRAGLEN_PROB,FRAGLEN_VALS)
-	FRAGMENT_SIZE = FRAGLEN_VALS[mean_ind_of_weighted_list(FRAGLEN_PROB)]
+if PAIRED_END and not (PAIRED_END_ARTIFICIAL):
+    print('Using empirical fragment length distribution.')
+    [potential_vals, potential_prob] = pickle.load(open(FRAGLEN_MODEL, 'rb'))
+    FRAGLEN_VALS = []
+    FRAGLEN_PROB = []
+    for i in range(len(potential_vals)):
+        if potential_vals[i] > READLEN:
+            FRAGLEN_VALS.append(potential_vals[i])
+            FRAGLEN_PROB.append(potential_prob[i])
+    # should probably add some validation and sanity-checking code here...
+    FRAGLEN_DISTRIBUTION = DiscreteDistribution(FRAGLEN_PROB, FRAGLEN_VALS)
+    FRAGMENT_SIZE = FRAGLEN_VALS[mean_ind_of_weighted_list(FRAGLEN_PROB)]
 
 #	Indicate not writing FASTQ reads
 #
 if NO_FASTQ:
-	print 'Bypassing FASTQ generation...'
+    print('Bypassing FASTQ generation...')
 
 """************************************************
 ****            HARD-CODED CONSTANTS
 ************************************************"""
 
-
 # target window size for read sampling. how many times bigger than read/frag length
 WINDOW_TARGET_SCALE = 100
 # sub-window size for read sampling windows. this is basically the finest resolution
 # that can be obtained for targeted region boundaries and GC% bias
-SMALL_WINDOW        = 20
+SMALL_WINDOW = 20
 # is the mutation model constant throughout the simulation? If so we can save a lot of time
-CONSTANT_MUT_MODEL  = True
-
+CONSTANT_MUT_MODEL = True
 
 """************************************************
 ****               DEFAULT MODELS
 ************************************************"""
 
-
 # fragment length distribution: normal distribution that goes out to +- 6 standard deviations
 if PAIRED_END and PAIRED_END_ARTIFICIAL:
-	print 'Using artificial fragment length distribution. mean='+str(FRAGMENT_SIZE)+', std='+str(FRAGMENT_STD)
-	if FRAGMENT_STD == 0:
-		FRAGLEN_DISTRIBUTION = DiscreteDistribution([1],[FRAGMENT_SIZE],degenerateVal=FRAGMENT_SIZE)
-	else:
-		potential_vals = range(FRAGMENT_SIZE-6*FRAGMENT_STD,FRAGMENT_SIZE+6*FRAGMENT_STD+1)
-		FRAGLEN_VALS   = []
-		for i in xrange(len(potential_vals)):
-			if potential_vals[i] > READLEN:
-				FRAGLEN_VALS.append(potential_vals[i])
-		FRAGLEN_PROB = [np.exp(-(((n-float(FRAGMENT_SIZE))**2)/(2*(FRAGMENT_STD**2)))) for n in FRAGLEN_VALS]
-		FRAGLEN_DISTRIBUTION = DiscreteDistribution(FRAGLEN_PROB,FRAGLEN_VALS)
-
+    print('Using artificial fragment length distribution. mean=' + str(FRAGMENT_SIZE) + ', std=' + str(FRAGMENT_STD))
+    if FRAGMENT_STD == 0:
+        FRAGLEN_DISTRIBUTION = DiscreteDistribution([1], [FRAGMENT_SIZE], degenerateVal=FRAGMENT_SIZE)
+    else:
+        potential_vals = range(FRAGMENT_SIZE - 6 * FRAGMENT_STD, FRAGMENT_SIZE + 6 * FRAGMENT_STD + 1)
+        FRAGLEN_VALS = []
+        for i in range(len(potential_vals)):
+            if potential_vals[i] > READLEN:
+                FRAGLEN_VALS.append(potential_vals[i])
+        FRAGLEN_PROB = [np.exp(-(((n - float(FRAGMENT_SIZE)) ** 2) / (2 * (FRAGMENT_STD ** 2)))) for n in FRAGLEN_VALS]
+        FRAGLEN_DISTRIBUTION = DiscreteDistribution(FRAGLEN_PROB, FRAGLEN_VALS)
 
 """************************************************
 ****          MORE INPUT ERROR CHECKING
 ************************************************"""
 
-
-isInRange(READLEN,           10,1000000, 'Error: -R must be between 10 and 1,000,000')
-isInRange(COVERAGE,          0,1000000,  'Error: -c must be between 0 and 1,000,000')
-isInRange(PLOIDS,            1,100,      'Error: -p must be between 1 and 100')
-isInRange(OFFTARGET_SCALAR,  0,1,        'Error: -to must be between 0 and 1')
+isInRange(READLEN, 10, 1000000, 'Error: -R must be between 10 and 1,000,000')
+isInRange(COVERAGE, 0, 1000000, 'Error: -c must be between 0 and 1,000,000')
+isInRange(PLOIDS, 1, 100, 'Error: -p must be between 1 and 100')
+isInRange(OFFTARGET_SCALAR, 0, 1, 'Error: -to must be between 0 and 1')
 if MUT_RATE != -1 and MUT_RATE != None:
-	isInRange(MUT_RATE,      0,0.3,      'Error: -M must be between 0 and 0.3')
+    isInRange(MUT_RATE, 0, 0.3, 'Error: -M must be between 0 and 0.3')
 if SE_RATE != -1 and SE_RATE != None:
-	isInRange(SE_RATE,       0,0.3,      'Error: -E must be between 0 and 0.3')
+    isInRange(SE_RATE, 0, 0.3, 'Error: -E must be between 0 and 0.3')
 if NJOBS != 1:
-	isInRange(NJOBS,         1,1000,     'Error: --job must be between 1 and 1,000')
-	isInRange(MYJOB,         1,1000,     'Error: --job must be between 1 and 1,000')
-	isInRange(MYJOB,         1,NJOBS,    'Error: job id must be less than or equal to number of jobs')
+    isInRange(NJOBS, 1, 1000, 'Error: --job must be between 1 and 1,000')
+    isInRange(MYJOB, 1, 1000, 'Error: --job must be between 1 and 1,000')
+    isInRange(MYJOB, 1, NJOBS, 'Error: job id must be less than or equal to number of jobs')
 if N_MAX_QUAL != -1:
-	isInRange(N_MAX_QUAL,    1,40,       'Error: -N must be between 1 and 40')
-
+    isInRange(N_MAX_QUAL, 1, 40, 'Error: -N must be between 1 and 40')
 
 """************************************************
 ****                   MAIN()
@@ -257,490 +259,508 @@
 
 
 def main():
-
-	# index reference
-	refIndex = indexRef(REFERENCE)
-	if PAIRED_END:
-		N_HANDLING = ('random',FRAGMENT_SIZE)
-	else:
-		N_HANDLING = ('ignore',READLEN)
-	indices_by_refName = {refIndex[n][0]:n for n in xrange(len(refIndex))}
-
-	# parse input variants, if present
-	inputVariants = []
-	if INPUT_VCF != None:
-		if CANCER:
-			(sampNames, inputVariants) = parseVCF(INPUT_VCF,tumorNormal=True,ploidy=PLOIDS)
-			tumorInd  = sampNames.index('TUMOR')
-			normalInd = sampNames.index('NORMAL')
-		else:
-			(sampNames, inputVariants) = parseVCF(INPUT_VCF,ploidy=PLOIDS)
-		for k in sorted(inputVariants.keys()):
-			inputVariants[k].sort()
-
-	# parse input targeted regions, if present
-	inputRegions = {}
-	refList      = [n[0] for n in refIndex]
-	if INPUT_BED != None:
-		with open(INPUT_BED,'r') as f:
-			for line in f:
-				[myChr,pos1,pos2] = line.strip().split('\t')[:3]
-				if myChr not in inputRegions:
-					inputRegions[myChr] = [-1]
-				inputRegions[myChr].extend([int(pos1),int(pos2)])
-		# some validation
-		nInBedOnly = 0
-		nInRefOnly = 0
-		for k in refList:
-			if k not in inputRegions:
-				nInRefOnly += 1
-		for k in inputRegions.keys():
-			if not k in refList:
-				nInBedOnly += 1
-				del inputRegions[k]
-		if nInRefOnly > 0:
-			print 'Warning: Reference contains sequences not found in targeted regions BED file.'
-		if nInBedOnly > 0:
-			print 'Warning: Targeted regions BED file contains sequence names not found in reference (regions ignored).'
-
-
-	# parse input mutation rate rescaling regions, if present
-	mutRateRegions = {}
-	mutRateValues  = {}
-	if MUT_BED != None:
-		with open(MUT_BED,'r') as f:
-			for line in f:
-				[myChr,pos1,pos2,metaData] = line.strip().split('\t')[:4]
-				mutStr = re.findall(r"MUT_RATE=.*?(?=;)",metaData+';')
-				(pos1,pos2) = (int(pos1),int(pos2))
-				if len(mutStr) and (pos2-pos1) > 1:
-					# mutRate = #_mutations / length_of_region, let's bound it by a reasonable amount
-					mutRate = max([0.0,min([float(mutStr[0][9:]),0.3])])
-					if myChr not in mutRateRegions:
-						mutRateRegions[myChr] = [-1]
-						mutRateValues[myChr]  = [0.0]
-					mutRateRegions[myChr].extend([pos1,pos2])
-					mutRateValues.extend([mutRate*(pos2-pos1)]*2)
-
-	# initialize output files (part I)
-	bamHeader = None
-	if SAVE_BAM:
-		bamHeader = [copy.deepcopy(refIndex)]
-	vcfHeader = None
-	if SAVE_VCF:
-		vcfHeader = [REFERENCE]
-	
-	# If processing jobs in parallel, precompute the independent regions that can be process separately
-	if NJOBS > 1:
-		parallelRegionList  = getAllRefRegions(REFERENCE,refIndex,N_HANDLING,saveOutput=SAVE_NON_N)
-		(myRefs, myRegions) = partitionRefRegions(parallelRegionList,refIndex,MYJOB,NJOBS)
-		if not len(myRegions):
-			print 'This job id has no regions to process, exiting...'
-			exit(1)
-		for i in xrange(len(refIndex)-1,-1,-1):	# delete reference not used in our job
-			if not refIndex[i][0] in myRefs:
-				del refIndex[i]
-		# if value of NJOBS is too high, let's change it to the maximum possible, to avoid output filename confusion
-		corrected_nJobs = min([NJOBS,sum([len(n) for n in parallelRegionList.values()])])
-	else:
-		corrected_nJobs = 1
-
-	# initialize output files (part II)
-	if CANCER:
-		OFW = OutputFileWriter(OUT_PREFIX+'_normal',paired=PAIRED_END,BAM_header=bamHeader,VCF_header=vcfHeader,gzipped=GZIPPED_OUT,noFASTQ=NO_FASTQ,FASTA_instead=FASTA_INSTEAD)
-		OFW_CANCER = OutputFileWriter(OUT_PREFIX+'_tumor',paired=PAIRED_END,BAM_header=bamHeader,VCF_header=vcfHeader,gzipped=GZIPPED_OUT,jobTuple=(MYJOB,corrected_nJobs),noFASTQ=NO_FASTQ,FASTA_instead=FASTA_INSTEAD)
-	else:
-		OFW = OutputFileWriter(OUT_PREFIX,paired=PAIRED_END,BAM_header=bamHeader,VCF_header=vcfHeader,gzipped=GZIPPED_OUT,jobTuple=(MYJOB,corrected_nJobs),noFASTQ=NO_FASTQ,FASTA_instead=FASTA_INSTEAD)
-	OUT_PREFIX_NAME = OUT_PREFIX.split('/')[-1]
-
-
-	"""************************************************
+    ALLOWED_NUCL = ['A', 'C', 'G', 'T']
+    # index reference
+    refIndex = indexRef(REFERENCE)
+    if PAIRED_END:
+        N_HANDLING = ('random', FRAGMENT_SIZE)
+    else:
+        N_HANDLING = ('ignore', READLEN)
+    indices_by_refName = {refIndex[n][0]: n for n in range(len(refIndex))}
+
+    # parse input variants, if present
+    inputVariants = []
+    if INPUT_VCF != None:
+        if CANCER:
+            (sampNames, inputVariants) = parseVCF(INPUT_VCF, tumorNormal=True, ploidy=PLOIDS)
+            tumorInd = sampNames.index('TUMOR')
+            normalInd = sampNames.index('NORMAL')
+        else:
+            (sampNames, inputVariants) = parseVCF(INPUT_VCF, ploidy=PLOIDS)
+        for k in sorted(inputVariants.keys()):
+            inputVariants[k].sort()
+
+    # parse input targeted regions, if present
+    inputRegions = {}
+    refList = [n[0] for n in refIndex]
+    if INPUT_BED != None:
+        with open(INPUT_BED, 'r') as f:
+            for line in f:
+                [myChr, pos1, pos2] = line.strip().split('\t')[:3]
+                if myChr not in inputRegions:
+                    inputRegions[myChr] = [-1]
+                inputRegions[myChr].extend([int(pos1), int(pos2)])
+        # some validation
+        nInBedOnly = 0
+        nInRefOnly = 0
+        for k in refList:
+            if k not in inputRegions:
+                nInRefOnly += 1
+        for k in inputRegions.keys():
+            if not k in refList:
+                nInBedOnly += 1
+                del inputRegions[k]
+        if nInRefOnly > 0:
+            print('Warning: Reference contains sequences not found in targeted regions BED file.')
+        if nInBedOnly > 0:
+            print(
+                'Warning: Targeted regions BED file contains sequence names not found in reference (regions ignored).')
+
+    # parse input mutation rate rescaling regions, if present
+    mutRateRegions = {}
+    mutRateValues = {}
+    if MUT_BED != None:
+        with open(MUT_BED, 'r') as f:
+            for line in f:
+                [myChr, pos1, pos2, metaData] = line.strip().split('\t')[:4]
+                mutStr = re.findall(r"MUT_RATE=.*?(?=;)", metaData + ';')
+                (pos1, pos2) = (int(pos1), int(pos2))
+                if len(mutStr) and (pos2 - pos1) > 1:
+                    # mutRate = #_mutations / length_of_region, let's bound it by a reasonable amount
+                    mutRate = max([0.0, min([float(mutStr[0][9:]), 0.3])])
+                    if myChr not in mutRateRegions:
+                        mutRateRegions[myChr] = [-1]
+                        mutRateValues[myChr] = [0.0]
+                    mutRateRegions[myChr].extend([pos1, pos2])
+                    mutRateValues.extend([mutRate * (pos2 - pos1)] * 2)
+
+    # initialize output files (part I)
+    bamHeader = None
+    if SAVE_BAM:
+        bamHeader = [copy.deepcopy(refIndex)]
+    vcfHeader = None
+    if SAVE_VCF:
+        vcfHeader = [REFERENCE]
+
+    # If processing jobs in parallel, precompute the independent regions that can be process separately
+    if NJOBS > 1:
+        parallelRegionList = getAllRefRegions(REFERENCE, refIndex, N_HANDLING, saveOutput=SAVE_NON_N)
+        (myRefs, myRegions) = partitionRefRegions(parallelRegionList, refIndex, MYJOB, NJOBS)
+        if not len(myRegions):
+            print('This job id has no regions to process, exiting...')
+            exit(1)
+        for i in range(len(refIndex) - 1, -1, -1):  # delete reference not used in our job
+            if not refIndex[i][0] in myRefs:
+                del refIndex[i]
+        # if value of NJOBS is too high, let's change it to the maximum possible, to avoid output filename confusion
+        corrected_nJobs = min([NJOBS, sum([len(n) for n in parallelRegionList.values()])])
+    else:
+        corrected_nJobs = 1
+
+    # initialize output files (part II)
+    if CANCER:
+        OFW = OutputFileWriter(OUT_PREFIX + '_normal', paired=PAIRED_END, BAM_header=bamHeader, VCF_header=vcfHeader,
+                               gzipped=GZIPPED_OUT, noFASTQ=NO_FASTQ, FASTA_instead=FASTA_INSTEAD)
+        OFW_CANCER = OutputFileWriter(OUT_PREFIX + '_tumor', paired=PAIRED_END, BAM_header=bamHeader,
+                                      VCF_header=vcfHeader, gzipped=GZIPPED_OUT, jobTuple=(MYJOB, corrected_nJobs),
+                                      noFASTQ=NO_FASTQ, FASTA_instead=FASTA_INSTEAD)
+    else:
+        OFW = OutputFileWriter(OUT_PREFIX, paired=PAIRED_END, BAM_header=bamHeader, VCF_header=vcfHeader,
+                               gzipped=GZIPPED_OUT, jobTuple=(MYJOB, corrected_nJobs), noFASTQ=NO_FASTQ,
+                               FASTA_instead=FASTA_INSTEAD)
+    OUT_PREFIX_NAME = OUT_PREFIX.split('/')[-1]
+
+    """************************************************
 	****        LET'S GET THIS PARTY STARTED...
 	************************************************"""
 
-
-	readNameCount = 1	# keep track of the number of reads we've sampled, for read-names
-	unmapped_records = []
-
-	for RI in xrange(len(refIndex)):
-
-		# read in reference sequence and notate blocks of Ns
-		(refSequence,N_regions) = readRef(REFERENCE,refIndex[RI],N_HANDLING)
-
-		# if we're processing jobs in parallel only take the regions relevant for the current job
-		if NJOBS > 1:
-			for i in xrange(len(N_regions['non_N'])-1,-1,-1):
-				if not (refIndex[RI][0],N_regions['non_N'][i][0],N_regions['non_N'][i][1]) in myRegions:
-					del N_regions['non_N'][i]
-
-		# count total bp we'll be spanning so we can get an idea of how far along we are (for printing progress indicators)
-		total_bp_span   = sum([n[1]-n[0] for n in N_regions['non_N']])
-		currentProgress = 0
-		currentPercent  = 0
-		havePrinted100  = False
-
-		# prune invalid input variants, e.g variants that:
-		#		- try to delete or alter any N characters
-		#		- don't match the reference base at their specified position
-		#		- any alt allele contains anything other than allowed characters
-		validVariants = []
-		nSkipped = [0,0,0]
-		if refIndex[RI][0] in inputVariants:
-			for n in inputVariants[refIndex[RI][0]]:
-				span = (n[0],n[0]+len(n[1]))
-				rseq = str(refSequence[span[0]-1:span[1]-1])	# -1 because going from VCF coords to array coords
-				anyBadChr = any((nn not in ALLOWED_NUCL) for nn in [item for sublist in n[2] for item in sublist])
-				if rseq != n[1]:
-					nSkipped[0] += 1
-					continue
-				elif 'N' in rseq:
-					nSkipped[1] += 1
-					continue
-				elif anyBadChr:
-					nSkipped[2] += 1
-					continue
-				#if bisect.bisect(N_regions['big'],span[0])%2 or bisect.bisect(N_regions['big'],span[1])%2:
-				#	continue
-				validVariants.append(n)
-			print 'found',len(validVariants),'valid variants for '+refIndex[RI][0]+' in input VCF...'
-			if any(nSkipped):
-				print sum(nSkipped),'variants skipped...'
-				print ' - ['+str(nSkipped[0])+'] ref allele does not match reference'
-				print ' - ['+str(nSkipped[1])+'] attempting to insert into N-region'
-				print ' - ['+str(nSkipped[2])+'] alt allele contains non-ACGT characters'
-
-
-		# add large random structural variants
-		#
-		#	TBD!!!
-
-
-		# determine sampling windows based on read length, large N regions, and structural mutations.
-		# in order to obtain uniform coverage, windows should overlap by:
-		#		- READLEN, if single-end reads
-		#		- FRAGMENT_SIZE (mean), if paired-end reads
-		# ploidy is fixed per large sampling window,
-		# coverage distributions due to GC% and targeted regions are specified within these windows
-		samplingWindows  = []
-		ALL_VARIANTS_OUT = {}
-		sequences        = None
-		if PAIRED_END:
-			targSize = WINDOW_TARGET_SCALE*FRAGMENT_SIZE
-			overlap  = FRAGMENT_SIZE
-			overlap_minWindowSize = max(FRAGLEN_DISTRIBUTION.values) + 10
-		else:
-			targSize = WINDOW_TARGET_SCALE*READLEN
-			overlap  = READLEN
-			overlap_minWindowSize = READLEN + 10
-
-		print '--------------------------------'
-		if ONLY_VCF:
-			print 'generating vcf...'
-		else:
-			print 'sampling reads...'
-		tt = time.time()
-
-		for i in xrange(len(N_regions['non_N'])):
-			(pi,pf) = N_regions['non_N'][i]
-			nTargWindows = max([1,(pf-pi)/targSize])
-			bpd = int((pf-pi)/float(nTargWindows))
-			#bpd += GC_WINDOW_SIZE - bpd%GC_WINDOW_SIZE
-
-			#print len(refSequence), (pi,pf), nTargWindows
-			#print structuralVars
-
-			# if for some reason our region is too small to process, skip it! (sorry)
-			if nTargWindows == 1 and (pf-pi) < overlap_minWindowSize:
-				#print 'Does this ever happen?'
-				continue
-
-			start = pi
-			end   = min([start+bpd,pf])
-			#print '------------------RAWR:', (pi,pf), nTargWindows, bpd
-			varsFromPrevOverlap = []
-			varsCancerFromPrevOverlap = []
-			vindFromPrev = 0
-			isLastTime = False
-			havePrinted100 = False
-
-			while True:
-				
-				# which inserted variants are in this window?
-				varsInWindow = []
-				updated = False
-				for j in xrange(vindFromPrev,len(validVariants)):
-					vPos = validVariants[j][0]
-					if vPos > start and vPos < end:	# update: changed >= to >, so variant cannot be inserted in first position
-						varsInWindow.append(tuple([vPos-1]+list(validVariants[j][1:])))	# vcf --> array coords
-					if vPos >= end-overlap-1 and updated == False:
-						updated = True
-						vindFromPrev = j
-					if vPos >= end:
-						break
-
-				# determine which structural variants will affect our sampling window positions
-				structuralVars = []
-				for n in varsInWindow:
-					bufferNeeded = max([max([abs(len(n[1])-len(alt_allele)),1]) for alt_allele in n[2]]) # change: added abs() so that insertions are also buffered.
-					structuralVars.append((n[0]-1,bufferNeeded))	# -1 because going from VCF coords to array coords
-
-				# adjust end-position of window based on inserted structural mutations
-				buffer_added = 0
-				keepGoing = True
-				while keepGoing:
-					keepGoing = False
-					for n in structuralVars:
-						# adding "overlap" here to prevent SVs from being introduced in overlap regions
-						# (which can cause problems if random mutations from the previous window land on top of them)
-						delta = (end-1) - (n[0] + n[1]) - 2 - overlap
-						if delta < 0:
-							#print 'DELTA:', delta, 'END:', end, '-->',
-							buffer_added = -delta
-							end += buffer_added
-							####print end
-							keepGoing = True
-							break
-				next_start = end-overlap
-				next_end   = min([next_start+bpd,pf])
-				if next_end-next_start < bpd:
-					end = next_end
-					isLastTime = True
-
-				# print progress indicator
-				####print 'PROCESSING WINDOW:',(start,end), [buffer_added], 'next:', (next_start,next_end)
-				currentProgress += end-start
-				newPercent = int((currentProgress*100)/float(total_bp_span))
-				if newPercent > currentPercent:
-					if newPercent <= 99 or (newPercent == 100 and not havePrinted100):
-						sys.stdout.write(str(newPercent)+'% ')
-						sys.stdout.flush()
-					currentPercent = newPercent
-					if currentPercent == 100:
-						havePrinted100 = True
-
-				skip_this_window = False
-
-				# compute coverage modifiers
-				coverage_avg = None
-				coverage_dat = [GC_WINDOW_SIZE,GC_SCALE_VAL,[]]
-				if INPUT_BED == None:
-					coverage_dat[2] = [1.0]*(end-start)
-				else:
-					if refIndex[RI][0] not in inputRegions:
-						coverage_dat[2] = [OFFTARGET_SCALAR]*(end-start)
-					else:
-						for j in xrange(start,end):
-							if not(bisect.bisect(inputRegions[refIndex[RI][0]],j)%2):
-								coverage_dat[2].append(1.0)
-							else:
-								coverage_dat[2].append(OFFTARGET_SCALAR)
-
-				#print len(coverage_dat[2]), sum(coverage_dat[2])
-				if sum(coverage_dat[2]) < LOW_COV_THRESH:
-					coverage_avg = 0.0
-					skip_this_window = True
-
-				# check for small window sizes
-				if (end-start) < overlap_minWindowSize:
-					skip_this_window = True
-
-				if skip_this_window:
-					# skip window, save cpu time
-					start = next_start
-					end   = next_end
-					if isLastTime:
-						break
-					if end >= pf:
-						isLastTime = True
-					varsFromPrevOverlap = []
-					continue
-
-				# construct sequence data that we will sample reads from
-				if sequences == None:
-					sequences = SequenceContainer(start,refSequence[start:end],PLOIDS,overlap,READLEN,[MUT_MODEL]*PLOIDS,MUT_RATE,onlyVCF=ONLY_VCF)
-				else:
-					sequences.update(start,refSequence[start:end],PLOIDS,overlap,READLEN,[MUT_MODEL]*PLOIDS,MUT_RATE)
-
-				# insert variants
-				sequences.insert_mutations(varsFromPrevOverlap + varsInWindow)
-				all_inserted_variants = sequences.random_mutations()
-				#print all_inserted_variants
-
-				# init coverage
-				if sum(coverage_dat[2]) >= LOW_COV_THRESH:
-					if PAIRED_END:
-						coverage_avg = sequences.init_coverage(tuple(coverage_dat),fragDist=FRAGLEN_DISTRIBUTION)
-					else:
-						coverage_avg = sequences.init_coverage(tuple(coverage_dat))
-
-				# unused cancer stuff
-				if CANCER:
-					tumor_sequences = SequenceContainer(start,refSequence[start:end],PLOIDS,overlap,READLEN,[CANCER_MODEL]*PLOIDS,MUT_RATE,coverage_dat)
-					tumor_sequences.insert_mutations(varsCancerFromPrevOverlap + all_inserted_variants)
-					all_cancer_variants = tumor_sequences.random_mutations()
-
-				# which variants do we need to keep for next time (because of window overlap)?
-				varsFromPrevOverlap       = []
-				varsCancerFromPrevOverlap = []
-				for n in all_inserted_variants:
-					if n[0] >= end-overlap-1:
-						varsFromPrevOverlap.append(n)
-				if CANCER:
-					for n in all_cancer_variants:
-						if n[0] >= end-overlap-1:
-							varsCancerFromPrevOverlap.append(n)
-
-				# if we're only producing VCF, no need to go through the hassle of generating reads
-				if ONLY_VCF:
-					pass
-				else:
-					if PAIRED_END:
-						readsToSample = int(((end-start)*float(COVERAGE)*coverage_avg)/(2*READLEN))+1
-					else:
-						readsToSample = int(((end-start)*float(COVERAGE)*coverage_avg)/(READLEN))+1
-
-					# if coverage is so low such that no reads are to be sampled, skip region
-					#      (i.e., remove buffer of +1 reads we add to every window)
-					if readsToSample == 1 and sum(coverage_dat[2]) < LOW_COV_THRESH:
-						readsToSample = 0
-
-					# sample reads
-					ASDF2_TT = time.time()
-					for i in xrange(readsToSample):
-
-						isUnmapped = []
-						if PAIRED_END:
-							myFraglen = FRAGLEN_DISTRIBUTION.sample()
-							myReadData = sequences.sample_read(SE_CLASS,myFraglen)
-							if myReadData == None:	# skip if we failed to find a valid position to sample read
-								continue
-							if myReadData[0][0] == None:
-								isUnmapped.append(True)
-							else:
-								isUnmapped.append(False)
-								myReadData[0][0] += start	# adjust mapping position based on window start
-							if myReadData[1][0] == None:
-								isUnmapped.append(True)
-							else:
-								isUnmapped.append(False)
-								myReadData[1][0] += start
-						else:
-							myReadData = sequences.sample_read(SE_CLASS)
-							if myReadData == None:	# skip if we failed to find a valid position to sample read
-								continue
-							if myReadData[0][0] == None:	# unmapped read (lives in large insertion)
-								isUnmapped = [True]
-							else:
-								isUnmapped = [False]
-								myReadData[0][0] += start	# adjust mapping position based on window start
-
-						if NJOBS > 1:
-							myReadName = OUT_PREFIX_NAME+'-j'+str(MYJOB)+'-'+refIndex[RI][0]+'-r'+str(readNameCount)
-						else:
-							myReadName = OUT_PREFIX_NAME+'-'+refIndex[RI][0]+'-'+str(readNameCount)
-						readNameCount += len(myReadData)
-
-						# if desired, replace all low-quality bases with Ns
-						if N_MAX_QUAL > -1:
-							for j in xrange(len(myReadData)):
-								myReadString = [n for n in myReadData[j][2]]
-								for k in xrange(len(myReadData[j][3])):
-									adjusted_qual = ord(myReadData[j][3][k])-SE_CLASS.offQ
-									if adjusted_qual <= N_MAX_QUAL:
-										myReadString[k] = 'N'
-								myReadData[j][2] = ''.join(myReadString)
-
-						# if read (or read + mate for PE) are unmapped, put them at end of bam file
-						if all(isUnmapped):
-							if PAIRED_END:
-								unmapped_records.append((myReadName+'/1',myReadData[0],109))
-								unmapped_records.append((myReadName+'/2',myReadData[1],157))
-							else:
-								unmapped_records.append((myReadName+'/1',myReadData[0],4))
-
-						# write read data out to FASTQ and BAM files, bypass FASTQ if option specified
-						myRefIndex = indices_by_refName[refIndex[RI][0]]
-						if len(myReadData) == 1:
-							if NO_FASTQ != True:
-								OFW.writeFASTQRecord(myReadName,myReadData[0][2],myReadData[0][3])
-							if SAVE_BAM:
-								if isUnmapped[0] == False:
-									OFW.writeBAMRecord(myRefIndex, myReadName+'/1', myReadData[0][0], myReadData[0][1], myReadData[0][2], myReadData[0][3], samFlag=0)
-						elif len(myReadData) == 2:
-							if NO_FASTQ != True:
-								OFW.writeFASTQRecord(myReadName,myReadData[0][2],myReadData[0][3],read2=myReadData[1][2],qual2=myReadData[1][3])
-							if SAVE_BAM:
-								if isUnmapped[0] == False and isUnmapped[1] == False:
-									OFW.writeBAMRecord(myRefIndex, myReadName+'/1', myReadData[0][0], myReadData[0][1], myReadData[0][2], myReadData[0][3], samFlag=99,  matePos=myReadData[1][0])
-									OFW.writeBAMRecord(myRefIndex, myReadName+'/2', myReadData[1][0], myReadData[1][1], myReadData[1][2], myReadData[1][3], samFlag=147, matePos=myReadData[0][0])
-								elif isUnmapped[0] == False and isUnmapped[1] == True:
-									OFW.writeBAMRecord(myRefIndex, myReadName+'/1', myReadData[0][0], myReadData[0][1], myReadData[0][2], myReadData[0][3], samFlag=105,  matePos=myReadData[0][0])
-									OFW.writeBAMRecord(myRefIndex, myReadName+'/2', myReadData[0][0], myReadData[1][1], myReadData[1][2], myReadData[1][3], samFlag=149, matePos=myReadData[0][0], alnMapQual=0)
-								elif isUnmapped[0] == True and isUnmapped[1] == False:
-									OFW.writeBAMRecord(myRefIndex, myReadName+'/1', myReadData[1][0], myReadData[0][1], myReadData[0][2], myReadData[0][3], samFlag=101,  matePos=myReadData[1][0], alnMapQual=0)
-									OFW.writeBAMRecord(myRefIndex, myReadName+'/2', myReadData[1][0], myReadData[1][1], myReadData[1][2], myReadData[1][3], samFlag=153, matePos=myReadData[1][0])
-						else:
-							print '\nError: Unexpected number of reads generated...\n'
-							exit(1)
-					#print 'READS:',time.time()-ASDF2_TT
-
-					if not isLastTime:
-						OFW.flushBuffers(bamMax=next_start)
-					else:
-						OFW.flushBuffers(bamMax=end+1)
-
-				# tally up all the variants that got successfully introduced
-				for n in all_inserted_variants:
-					ALL_VARIANTS_OUT[n] = True
-
-				# prepare indices of next window
-				start = next_start
-				end   = next_end
-				if isLastTime:
-					break
-				if end >= pf:
-					isLastTime = True
-
-		if currentPercent != 100 and not havePrinted100:
-			print '100%'
-		else:
-			print ''
-		if ONLY_VCF:
-			print 'VCF generation completed in',
-		else:
-			print 'Read sampling completed in',
-		print int(time.time()-tt),'(sec)'
-
-		# write all output variants for this reference
-		if SAVE_VCF:
-			print 'Writing output VCF...'
-			for k in sorted(ALL_VARIANTS_OUT.keys()):
-				currentRef = refIndex[RI][0]
-				myID       = '.'
-				myQual     = '.'
-				myFilt     = 'PASS'
-				# k[0] + 1 because we're going back to 1-based vcf coords
-				OFW.writeVCFRecord(currentRef, str(int(k[0])+1), myID, k[1], k[2], myQual, myFilt, k[4])
-
-		#break
-
-	# write unmapped reads to bam file
-	if SAVE_BAM and len(unmapped_records):
-		print 'writing unmapped reads to bam file...'
-		for umr in unmapped_records:
-			if PAIRED_END:
-				OFW.writeBAMRecord(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], samFlag=umr[2], matePos=0, alnMapQual=0)
-			else:
-				OFW.writeBAMRecord(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], samFlag=umr[2], alnMapQual=0)
-
-	# close output files
-	OFW.closeFiles()
-	if CANCER:
-		OFW_CANCER.closeFiles()
+    readNameCount = 1  # keep track of the number of reads we've sampled, for read-names
+    unmapped_records = []
+
+    for RI in range(len(refIndex)):
+
+        # read in reference sequence and notate blocks of Ns
+        (refSequence, N_regions) = readRef(REFERENCE, refIndex[RI], N_HANDLING)
+
+        # if we're processing jobs in parallel only take the regions relevant for the current job
+        if NJOBS > 1:
+            for i in range(len(N_regions['non_N']) - 1, -1, -1):
+                if not (refIndex[RI][0], N_regions['non_N'][i][0], N_regions['non_N'][i][1]) in myRegions:
+                    del N_regions['non_N'][i]
+
+        # count total bp we'll be spanning so we can get an idea of how far along we are (for printing progress indicators)
+        total_bp_span = sum([n[1] - n[0] for n in N_regions['non_N']])
+        currentProgress = 0
+        currentPercent = 0
+        havePrinted100 = False
+
+        # prune invalid input variants, e.g variants that:
+        #		- try to delete or alter any N characters
+        #		- don't match the reference base at their specified position
+        #		- any alt allele contains anything other than allowed characters
+        validVariants = []
+        nSkipped = [0, 0, 0]
+        if refIndex[RI][0] in inputVariants:
+            for n in inputVariants[refIndex[RI][0]]:
+                span = (n[0], n[0] + len(n[1]))
+                rseq = str(refSequence[span[0] - 1:span[1] - 1])  # -1 because going from VCF coords to array coords
+                anyBadChr = any((nn not in ALLOWED_NUCL) for nn in [item for sublist in n[2] for item in sublist])
+                if rseq != n[1]:
+                    nSkipped[0] += 1
+                    continue
+                elif 'N' in rseq:
+                    nSkipped[1] += 1
+                    continue
+                elif anyBadChr:
+                    nSkipped[2] += 1
+                    continue
+                # if bisect.bisect(N_regions['big'],span[0])%2 or bisect.bisect(N_regions['big'],span[1])%2:
+                #	continue
+                validVariants.append(n)
+            print('found', len(validVariants), 'valid variants for ' + refIndex[RI][0] + ' in input VCF...')
+            if any(nSkipped):
+                print(sum(nSkipped), 'variants skipped...')
+                print(' - [' + str(nSkipped[0]) + '] ref allele does not match reference')
+                print(' - [' + str(nSkipped[1]) + '] attempting to insert into N-region')
+                print(' - [' + str(nSkipped[2]) + '] alt allele contains non-ACGT characters')
+
+        # add large random structural variants
+        #
+        #	TBD!!!
+
+        # determine sampling windows based on read length, large N regions, and structural mutations.
+        # in order to obtain uniform coverage, windows should overlap by:
+        #		- READLEN, if single-end reads
+        #		- FRAGMENT_SIZE (mean), if paired-end reads
+        # ploidy is fixed per large sampling window,
+        # coverage distributions due to GC% and targeted regions are specified within these windows
+        samplingWindows = []
+        ALL_VARIANTS_OUT = {}
+        sequences = None
+        if PAIRED_END:
+            targSize = WINDOW_TARGET_SCALE * FRAGMENT_SIZE
+            overlap = FRAGMENT_SIZE
+            overlap_minWindowSize = max(FRAGLEN_DISTRIBUTION.values) + 10
+        else:
+            targSize = WINDOW_TARGET_SCALE * READLEN
+            overlap = READLEN
+            overlap_minWindowSize = READLEN + 10
+
+        print('--------------------------------')
+        if ONLY_VCF:
+            print('generating vcf...')
+        else:
+            print('sampling reads...')
+        tt = time.time()
+
+        for i in range(len(N_regions['non_N'])):
+            (pi, pf) = N_regions['non_N'][i]
+            nTargWindows = max([1, (pf - pi) / targSize])
+            bpd = int((pf - pi) / float(nTargWindows))
+            # bpd += GC_WINDOW_SIZE - bpd%GC_WINDOW_SIZE
+
+            # print len(refSequence), (pi,pf), nTargWindows
+            # print structuralVars
+
+            # if for some reason our region is too small to process, skip it! (sorry)
+            if nTargWindows == 1 and (pf - pi) < overlap_minWindowSize:
+                # print 'Does this ever happen?'
+                continue
+
+            start = pi
+            end = min([start + bpd, pf])
+            # print '------------------RAWR:', (pi,pf), nTargWindows, bpd
+            varsFromPrevOverlap = []
+            varsCancerFromPrevOverlap = []
+            vindFromPrev = 0
+            isLastTime = False
+            havePrinted100 = False
+
+            while True:
+
+                # which inserted variants are in this window?
+                varsInWindow = []
+                updated = False
+                for j in range(vindFromPrev, len(validVariants)):
+                    vPos = validVariants[j][0]
+                    if vPos > start and vPos < end:  # update: changed >= to >, so variant cannot be inserted in first position
+                        varsInWindow.append(tuple([vPos - 1] + list(validVariants[j][1:])))  # vcf --> array coords
+                    if vPos >= end - overlap - 1 and updated == False:
+                        updated = True
+                        vindFromPrev = j
+                    if vPos >= end:
+                        break
+
+                # determine which structural variants will affect our sampling window positions
+                structuralVars = []
+                for n in varsInWindow:
+                    bufferNeeded = max([max([abs(len(n[1]) - len(alt_allele)), 1]) for alt_allele in
+                                        n[2]])  # change: added abs() so that insertions are also buffered.
+                    structuralVars.append((n[0] - 1, bufferNeeded))  # -1 because going from VCF coords to array coords
+
+                # adjust end-position of window based on inserted structural mutations
+                buffer_added = 0
+                keepGoing = True
+                while keepGoing:
+                    keepGoing = False
+                    for n in structuralVars:
+                        # adding "overlap" here to prevent SVs from being introduced in overlap regions
+                        # (which can cause problems if random mutations from the previous window land on top of them)
+                        delta = (end - 1) - (n[0] + n[1]) - 2 - overlap
+                        if delta < 0:
+                            # print 'DELTA:', delta, 'END:', end, '-->',
+                            buffer_added = -delta
+                            end += buffer_added
+                            ####print end
+                            keepGoing = True
+                            break
+                next_start = end - overlap
+                next_end = min([next_start + bpd, pf])
+                if next_end - next_start < bpd:
+                    end = next_end
+                    isLastTime = True
+
+                # print progress indicator
+                ####print 'PROCESSING WINDOW:',(start,end), [buffer_added], 'next:', (next_start,next_end)
+                currentProgress += end - start
+                newPercent = int((currentProgress * 100) / float(total_bp_span))
+                if newPercent > currentPercent:
+                    if newPercent <= 99 or (newPercent == 100 and not havePrinted100):
+                        sys.stdout.write(str(newPercent) + '% ')
+                        sys.stdout.flush()
+                    currentPercent = newPercent
+                    if currentPercent == 100:
+                        havePrinted100 = True
+
+                skip_this_window = False
+
+                # compute coverage modifiers
+                coverage_avg = None
+                coverage_dat = [GC_WINDOW_SIZE, GC_SCALE_VAL, []]
+                if INPUT_BED == None:
+                    coverage_dat[2] = [1.0] * (end - start)
+                else:
+                    if refIndex[RI][0] not in inputRegions:
+                        coverage_dat[2] = [OFFTARGET_SCALAR] * (end - start)
+                    else:
+                        for j in range(start, end):
+                            if not (bisect.bisect(inputRegions[refIndex[RI][0]], j) % 2):
+                                coverage_dat[2].append(1.0)
+                            else:
+                                coverage_dat[2].append(OFFTARGET_SCALAR)
+
+                # print len(coverage_dat[2]), sum(coverage_dat[2])
+                if sum(coverage_dat[2]) < LOW_COV_THRESH:
+                    coverage_avg = 0.0
+                    skip_this_window = True
+
+                # check for small window sizes
+                if (end - start) < overlap_minWindowSize:
+                    skip_this_window = True
+
+                if skip_this_window:
+                    # skip window, save cpu time
+                    start = next_start
+                    end = next_end
+                    if isLastTime:
+                        break
+                    if end >= pf:
+                        isLastTime = True
+                    varsFromPrevOverlap = []
+                    continue
+
+                # construct sequence data that we will sample reads from
+                if sequences == None:
+                    sequences = SequenceContainer(start, refSequence[start:end], PLOIDS, overlap, READLEN,
+                                                  [MUT_MODEL] * PLOIDS, MUT_RATE, onlyVCF=ONLY_VCF)
+                else:
+                    sequences.update(start, refSequence[start:end], PLOIDS, overlap, READLEN, [MUT_MODEL] * PLOIDS,
+                                     MUT_RATE)
+
+                # insert variants
+                sequences.insert_mutations(varsFromPrevOverlap + varsInWindow)
+                all_inserted_variants = sequences.random_mutations()
+                # print all_inserted_variants
+
+                # init coverage
+                if sum(coverage_dat[2]) >= LOW_COV_THRESH:
+                    if PAIRED_END:
+                        coverage_avg = sequences.init_coverage(tuple(coverage_dat), fragDist=FRAGLEN_DISTRIBUTION)
+                    else:
+                        coverage_avg = sequences.init_coverage(tuple(coverage_dat))
+
+                # unused cancer stuff
+                if CANCER:
+                    tumor_sequences = SequenceContainer(start, refSequence[start:end], PLOIDS, overlap, READLEN,
+                                                        [CANCER_MODEL] * PLOIDS, MUT_RATE, coverage_dat)
+                    tumor_sequences.insert_mutations(varsCancerFromPrevOverlap + all_inserted_variants)
+                    all_cancer_variants = tumor_sequences.random_mutations()
+
+                # which variants do we need to keep for next time (because of window overlap)?
+                varsFromPrevOverlap = []
+                varsCancerFromPrevOverlap = []
+                for n in all_inserted_variants:
+                    if n[0] >= end - overlap - 1:
+                        varsFromPrevOverlap.append(n)
+                if CANCER:
+                    for n in all_cancer_variants:
+                        if n[0] >= end - overlap - 1:
+                            varsCancerFromPrevOverlap.append(n)
+
+                # if we're only producing VCF, no need to go through the hassle of generating reads
+                if ONLY_VCF:
+                    pass
+                else:
+                    if PAIRED_END:
+                        readsToSample = int(((end - start) * float(COVERAGE) * coverage_avg) / (2 * READLEN)) + 1
+                    else:
+                        readsToSample = int(((end - start) * float(COVERAGE) * coverage_avg) / (READLEN)) + 1
+
+                    # if coverage is so low such that no reads are to be sampled, skip region
+                    #      (i.e., remove buffer of +1 reads we add to every window)
+                    if readsToSample == 1 and sum(coverage_dat[2]) < LOW_COV_THRESH:
+                        readsToSample = 0
+
+                    # sample reads
+                    ASDF2_TT = time.time()
+                    for i in range(readsToSample):
+
+                        isUnmapped = []
+                        if PAIRED_END:
+                            myFraglen = FRAGLEN_DISTRIBUTION.sample()
+                            myReadData = sequences.sample_read(SE_CLASS, myFraglen)
+                            if myReadData == None:  # skip if we failed to find a valid position to sample read
+                                continue
+                            if myReadData[0][0] == None:
+                                isUnmapped.append(True)
+                            else:
+                                isUnmapped.append(False)
+                                myReadData[0][0] += start  # adjust mapping position based on window start
+                            if myReadData[1][0] == None:
+                                isUnmapped.append(True)
+                            else:
+                                isUnmapped.append(False)
+                                myReadData[1][0] += start
+                        else:
+                            myReadData = sequences.sample_read(SE_CLASS)
+                            if myReadData == None:  # skip if we failed to find a valid position to sample read
+                                continue
+                            if myReadData[0][0] == None:  # unmapped read (lives in large insertion)
+                                isUnmapped = [True]
+                            else:
+                                isUnmapped = [False]
+                                myReadData[0][0] += start  # adjust mapping position based on window start
+
+                        if NJOBS > 1:
+                            myReadName = OUT_PREFIX_NAME + '-j' + str(MYJOB) + '-' + refIndex[RI][0] + '-r' + str(
+                                readNameCount)
+                        else:
+                            myReadName = OUT_PREFIX_NAME + '-' + refIndex[RI][0] + '-' + str(readNameCount)
+                        readNameCount += len(myReadData)
+
+                        # if desired, replace all low-quality bases with Ns
+                        if N_MAX_QUAL > -1:
+                            for j in range(len(myReadData)):
+                                myReadString = [n for n in myReadData[j][2]]
+                                for k in range(len(myReadData[j][3])):
+                                    adjusted_qual = ord(myReadData[j][3][k]) - SE_CLASS.offQ
+                                    if adjusted_qual <= N_MAX_QUAL:
+                                        myReadString[k] = 'N'
+                                myReadData[j][2] = ''.join(myReadString)
+
+                        # if read (or read + mate for PE) are unmapped, put them at end of bam file
+                        if all(isUnmapped):
+                            if PAIRED_END:
+                                unmapped_records.append((myReadName + '/1', myReadData[0], 109))
+                                unmapped_records.append((myReadName + '/2', myReadData[1], 157))
+                            else:
+                                unmapped_records.append((myReadName + '/1', myReadData[0], 4))
+
+                        # write read data out to FASTQ and BAM files, bypass FASTQ if option specified
+                        myRefIndex = indices_by_refName[refIndex[RI][0]]
+                        if len(myReadData) == 1:
+                            if NO_FASTQ != True:
+                                OFW.writeFASTQRecord(myReadName, myReadData[0][2], myReadData[0][3])
+                            if SAVE_BAM:
+                                if isUnmapped[0] == False:
+                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/1', myReadData[0][0],
+                                                       myReadData[0][1], myReadData[0][2], myReadData[0][3], samFlag=0)
+                        elif len(myReadData) == 2:
+                            if NO_FASTQ != True:
+                                OFW.writeFASTQRecord(myReadName, myReadData[0][2], myReadData[0][3],
+                                                     read2=myReadData[1][2], qual2=myReadData[1][3])
+                            if SAVE_BAM:
+                                if isUnmapped[0] == False and isUnmapped[1] == False:
+                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/1', myReadData[0][0],
+                                                       myReadData[0][1], myReadData[0][2], myReadData[0][3], samFlag=99,
+                                                       matePos=myReadData[1][0])
+                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/2', myReadData[1][0],
+                                                       myReadData[1][1], myReadData[1][2], myReadData[1][3],
+                                                       samFlag=147, matePos=myReadData[0][0])
+                                elif isUnmapped[0] == False and isUnmapped[1] == True:
+                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/1', myReadData[0][0],
+                                                       myReadData[0][1], myReadData[0][2], myReadData[0][3],
+                                                       samFlag=105, matePos=myReadData[0][0])
+                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/2', myReadData[0][0],
+                                                       myReadData[1][1], myReadData[1][2], myReadData[1][3],
+                                                       samFlag=149, matePos=myReadData[0][0], alnMapQual=0)
+                                elif isUnmapped[0] == True and isUnmapped[1] == False:
+                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/1', myReadData[1][0],
+                                                       myReadData[0][1], myReadData[0][2], myReadData[0][3],
+                                                       samFlag=101, matePos=myReadData[1][0], alnMapQual=0)
+                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/2', myReadData[1][0],
+                                                       myReadData[1][1], myReadData[1][2], myReadData[1][3],
+                                                       samFlag=153, matePos=myReadData[1][0])
+                        else:
+                            print('\nError: Unexpected number of reads generated...\n')
+                            exit(1)
+                    # print 'READS:',time.time()-ASDF2_TT
+
+                    if not isLastTime:
+                        OFW.flushBuffers(bamMax=next_start)
+                    else:
+                        OFW.flushBuffers(bamMax=end + 1)
+
+                # tally up all the variants that got successfully introduced
+                for n in all_inserted_variants:
+                    ALL_VARIANTS_OUT[n] = True
+
+                # prepare indices of next window
+                start = next_start
+                end = next_end
+                if isLastTime:
+                    break
+                if end >= pf:
+                    isLastTime = True
+
+        if currentPercent != 100 and not havePrinted100:
+            print('100%')
+        else:
+            print('')
+        if ONLY_VCF:
+            print('VCF generation completed in', end=' ')
+        else:
+            print('Read sampling completed in', end=' ')
+        print(int(time.time() - tt), '(sec)')
+
+        # write all output variants for this reference
+        if SAVE_VCF:
+            print('Writing output VCF...')
+            for k in sorted(ALL_VARIANTS_OUT.keys()):
+                currentRef = refIndex[RI][0]
+                myID = '.'
+                myQual = '.'
+                myFilt = 'PASS'
+                # k[0] + 1 because we're going back to 1-based vcf coords
+                OFW.writeVCFRecord(currentRef, str(int(k[0]) + 1), myID, k[1], k[2], myQual, myFilt, k[4])
+
+    # break
+
+    # write unmapped reads to bam file
+    if SAVE_BAM and len(unmapped_records):
+        print('writing unmapped reads to bam file...')
+        for umr in unmapped_records:
+            if PAIRED_END:
+                OFW.writeBAMRecord(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], samFlag=umr[2], matePos=0,
+                                   alnMapQual=0)
+            else:
+                OFW.writeBAMRecord(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], samFlag=umr[2], alnMapQual=0)
+
+    # close output files
+    OFW.closeFiles()
+    if CANCER:
+        OFW_CANCER.closeFiles()
 
 
 if __name__ == '__main__':
-	main()
-
-
-
+    main()
diff --git a/py/OutputFileWriter.py b/py/OutputFileWriter.py
index 5b77db0..f72d079 100644
--- a/py/OutputFileWriter.py
+++ b/py/OutputFileWriter.py
@@ -4,30 +4,36 @@
 import gzip
 from struct import pack
 
-from biopython_modified_bgzf import BgzfWriter
+from py.biopython_modified_bgzf import BgzfWriter
 
 BAM_COMPRESSION_LEVEL = 6
 
-# return the reverse complement of a string
-RC_DICT = {'A':'T','C':'G','G':'C','T':'A','N':'N'}
+
+
+
 def RC(s):
-	return ''.join(RC_DICT[n] for n in s[::-1])
+    # return the reverse complement of a string
+    RC_DICT = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'N': 'N'}
+    return ''.join(RC_DICT[n] for n in s[::-1])
+
 
 # SAMtools reg2bin function
-def reg2bin(a,b):
-	b -= 1
-	if (a>>14 == b>>14): return ((1<<15)-1)/7 + (a>>14)
-	if (a>>17 == b>>17): return ((1<<12)-1)/7 + (a>>17)
-	if (a>>20 == b>>20): return  ((1<<9)-1)/7 + (a>>20)
-	if (a>>23 == b>>23): return  ((1<<6)-1)/7 + (a>>23)
-	if (a>>26 == b>>26): return  ((1<<3)-1)/7 + (a>>26)
-	return 0
+def reg2bin(a, b):
+    b -= 1
+    if (a >> 14 == b >> 14): return ((1 << 15) - 1) / 7 + (a >> 14)
+    if (a >> 17 == b >> 17): return ((1 << 12) - 1) / 7 + (a >> 17)
+    if (a >> 20 == b >> 20): return ((1 << 9) - 1) / 7 + (a >> 20)
+    if (a >> 23 == b >> 23): return ((1 << 6) - 1) / 7 + (a >> 23)
+    if (a >> 26 == b >> 26): return ((1 << 3) - 1) / 7 + (a >> 26)
+    return 0
+
 
-CIGAR_PACKED = {'M':0, 'I':1, 'D':2, 'N':3, 'S':4, 'H':5, 'P':6, '=':7, 'X':8}
-SEQ_PACKED   = {'=':0, 'A':1, 'C':2, 'M':3, 'G':4, 'R':5, 'S':6, 'V':7,
-                'T':8, 'W':9, 'Y':10,'H':11,'K':12,'D':13,'B':14,'N':15}
+CIGAR_PACKED = {'M': 0, 'I': 1, 'D': 2, 'N': 3, 'S': 4, 'H': 5, 'P': 6, '=': 7, 'X': 8}
+SEQ_PACKED = {'=': 0, 'A': 1, 'C': 2, 'M': 3, 'G': 4, 'R': 5, 'S': 6, 'V': 7,
+              'T': 8, 'W': 9, 'Y': 10, 'H': 11, 'K': 12, 'D': 13, 'B': 14, 'N': 15}
+
+BUFFER_BATCH_SIZE = 1000  # write out to file after this many reads
 
-BUFFER_BATCH_SIZE = 1000		# write out to file after this many reads
 
 #
 #	outFQ      = path to output FASTQ prefix
@@ -37,223 +43,229 @@ def reg2bin(a,b):
 #	gzipped    = True for compressed FASTQ/VCF, False for uncompressed
 #
 class OutputFileWriter:
-	def __init__(self, outPrefix, paired=False, BAM_header=None, VCF_header=None, gzipped=False, jobTuple=(1,1), noFASTQ=False, FASTA_instead=False):
-		
-		jobSuffix = ''
-		if jobTuple[1] > 1:
-			jsl = len(str(jobTuple[1]))
-			jsb = '0'*(jsl-len(str(jobTuple[0])))
-			jobSuffix = '.job'+jsb+str(jobTuple[0])+'of'+str(jobTuple[1])
-
-		self.FASTA_instead = FASTA_instead
-		if FASTA_instead:
-			fq1 = outPrefix+'_read1.fa'+jobSuffix
-			fq2 = outPrefix+'_read2.fa'+jobSuffix
-		else:
-			fq1 = outPrefix+'_read1.fq'+jobSuffix
-			fq2 = outPrefix+'_read2.fq'+jobSuffix
-		bam = outPrefix+'_golden.bam'+jobSuffix
-		vcf = outPrefix+'_golden.vcf'+jobSuffix
-
-		self.noFASTQ = noFASTQ
-		if not self.noFASTQ:
-			if gzipped:
-				self.fq1_file = gzip.open(fq1+'.gz', 'wb')
-			else:
-				self.fq1_file = open(fq1,'w')
-
-			self.fq2_file = None
-			if paired:
-				if gzipped:
-					self.fq2_file = gzip.open(fq2+'.gz', 'wb')
-				else:
-					self.fq2_file = open(fq2,'w')
-
-		#
-		#	VCF OUTPUT
-		#
-		self.vcf_file = None
-		if VCF_header != None:
-			if gzipped:
-				self.vcf_file = gzip.open(vcf+'.gz', 'wb')
-			else:
-				self.vcf_file = open(vcf, 'wb')
-
-			# WRITE VCF HEADER (if parallel: only for first job)
-			if jobTuple[0] == 1:
-				self.vcf_file.write('##fileformat=VCFv4.1\n')
-				self.vcf_file.write('##reference='+VCF_header[0]+'\n')
-				self.vcf_file.write('##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">\n')
-				self.vcf_file.write('##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">\n')
-				#self.vcf_file.write('##INFO=<ID=READS,Number=1,Type=String,Description="Names of Reads Covering this Variant">\n')
-				self.vcf_file.write('##INFO=<ID=VMX,Number=1,Type=String,Description="SNP is Missense in these Read Frames">\n')
-				self.vcf_file.write('##INFO=<ID=VNX,Number=1,Type=String,Description="SNP is Nonsense in these Read Frames">\n')
-				self.vcf_file.write('##INFO=<ID=VFX,Number=1,Type=String,Description="Indel Causes Frameshift">\n')
-				self.vcf_file.write('##INFO=<ID=WP,Number=A,Type=Integer,Description="NEAT-GenReads ploidy indicator">\n')
-				self.vcf_file.write('##ALT=<ID=DEL,Description="Deletion">\n')
-				self.vcf_file.write('##ALT=<ID=DUP,Description="Duplication">\n')
-				self.vcf_file.write('##ALT=<ID=INS,Description="Insertion of novel sequence">\n')
-				self.vcf_file.write('##ALT=<ID=INV,Description="Inversion">\n')
-				self.vcf_file.write('##ALT=<ID=CNV,Description="Copy number variable region">\n')
-				self.vcf_file.write('##ALT=<ID=TRANS,Description="Translocation">\n')
-				self.vcf_file.write('##ALT=<ID=INV-TRANS,Description="Inverted translocation">\n')
-				self.vcf_file.write('#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\n')
-
-		#
-		#	BAM OUTPUT
-		#
-		self.bam_file = None
-		if BAM_header != None:
-			self.bam_file = BgzfWriter(bam, 'w', compresslevel=BAM_COMPRESSION_LEVEL)
-
-			# WRITE BAM HEADER (if parallel: only for first job)
-			if True or jobTuple[0] == 1:
-				self.bam_file.write("BAM\1")
-				header = '@HD\tVN:1.5\tSO:coordinate\n'
-				for n in BAM_header[0]:
-					header += '@SQ\tSN:'+n[0]+'\tLN:'+str(n[3])+'\n'
-				header += '@RG\tID:NEAT\tSM:NEAT\tLB:NEAT\tPL:NEAT\n'
-				headerBytes = len(header)
-				numRefs     = len(BAM_header[0])
-				self.bam_file.write(pack('<i',headerBytes))
-				self.bam_file.write(header)
-				self.bam_file.write(pack('<i',numRefs))
-
-				for n in BAM_header[0]:
-					l_name = len(n[0])+1
-					self.bam_file.write(pack('<i',l_name))
-					self.bam_file.write(n[0]+'\0')
-					self.bam_file.write(pack('<i',n[3]))
-
-		# buffers for more efficient writing
-		self.fq1_buffer = []
-		self.fq2_buffer = []
-		self.bam_buffer = []
-
-	def writeFASTQRecord(self,readName,read1,qual1,read2=None,qual2=None):
-		if self.FASTA_instead:
-			self.fq1_buffer.append('>'+readName+'/1\n'+read1+'\n')
-			if read2 != None:
-				self.fq2_buffer.append('>'+readName+'/2\n'+RC(read2)+'\n')
-		else:
-			self.fq1_buffer.append('@'+readName+'/1\n'+read1+'\n+\n'+qual1+'\n')
-			if read2 != None:
-				self.fq2_buffer.append('@'+readName+'/2\n'+RC(read2)+'\n+\n'+qual2[::-1]+'\n')
-
-	def writeVCFRecord(self, chrom, pos, idStr, ref, alt, qual, filt, info):
-		self.vcf_file.write(str(chrom)+'\t'+str(pos)+'\t'+str(idStr)+'\t'+str(ref)+'\t'+str(alt)+'\t'+str(qual)+'\t'+str(filt)+'\t'+str(info)+'\n')
-
-	def writeBAMRecord(self, refID, readName, pos_0, cigar, seq, qual, samFlag, matePos=None, alnMapQual=70):
-
-		myBin     = reg2bin(pos_0,pos_0+len(seq))
-		#myBin     = 0	# or just use a dummy value, does this actually matter?
-
-		myMapQual = alnMapQual
-		cig_letters = re.split(r"\d+",cigar)[1:]
-		cig_numbers = [int(n) for n in re.findall(r"\d+",cigar)]
-		cig_ops     = len(cig_letters)
-		next_refID = refID
-		if matePos == None:
-			next_pos = 0
-			my_tlen  = 0
-		else:
-			next_pos = matePos
-			if pos_0 < next_pos:
-				my_tlen = next_pos + len(seq) - pos_0
-			else:
-				my_tlen = -pos_0 - len(seq) + next_pos
-
-		encodedCig = ''
-		for i in xrange(cig_ops):
-			encodedCig += pack('<I',(cig_numbers[i]<<4) + CIGAR_PACKED[cig_letters[i]])
-		encodedSeq = ''
-		encodedLen = (len(seq)+1)/2
-		seqLen     = len(seq)
-		if seqLen&1:
-			seq += '='
-		for i in xrange(encodedLen):
-			encodedSeq += pack('<B',(SEQ_PACKED[seq[2*i]]<<4) + SEQ_PACKED[seq[2*i+1]])
-
-		# apparently samtools automatically adds 33 to the quality score string...
-		encodedQual = ''.join([chr(ord(n)-33) for n in qual])
-
-		#blockSize = 4 +		# refID 		int32
-		#            4 +		# pos			int32
-		#            4 +		# bin_mq_nl		uint32
-		#            4 +		# flag_nc		uint32
-		#            4 +		# l_seq			int32
-		#            4 +		# next_refID	int32
-		#            4 +		# next_pos		int32
-		#            4 +		# tlen			int32
-		#            len(readName)+1 +
-		#            4*cig_ops +
-		#            encodedLen +
-		#            len(seq)
-
-		#blockSize = 32 + len(readName)+1 + 4*cig_ops + encodedLen + len(seq)
-		blockSize = 32 + len(readName)+1 + len(encodedCig) + len(encodedSeq) + len(encodedQual)
-
-		####self.bam_file.write(pack('<i',blockSize))
-		####self.bam_file.write(pack('<i',refID))
-		####self.bam_file.write(pack('<i',pos_0))
-		####self.bam_file.write(pack('<I',(myBin<<16) + (myMapQual<<8) + len(readName)+1))
-		####self.bam_file.write(pack('<I',(samFlag<<16) + cig_ops))
-		####self.bam_file.write(pack('<i',seqLen))
-		####self.bam_file.write(pack('<i',next_refID))
-		####self.bam_file.write(pack('<i',next_pos))
-		####self.bam_file.write(pack('<i',my_tlen))
-		####self.bam_file.write(readName+'\0')
-		####self.bam_file.write(encodedCig)
-		####self.bam_file.write(encodedSeq)
-		####self.bam_file.write(encodedQual)
-
-		# a horribly compressed line, I'm sorry.
-		# (ref_index, position, data)
-		self.bam_buffer.append((refID, pos_0, pack('<i',blockSize) + pack('<i',refID) + pack('<i',pos_0) + pack('<I',(myBin<<16) + (myMapQual<<8) + len(readName)+1) + pack('<I',(samFlag<<16) + cig_ops) + pack('<i',seqLen) + pack('<i',next_refID) + pack('<i',next_pos) + pack('<i',my_tlen) + readName+'\0' + encodedCig + encodedSeq + encodedQual))
-
-
-	def flushBuffers(self,bamMax=None,lastTime=False):
-		if (len(self.fq1_buffer) >= BUFFER_BATCH_SIZE or len(self.bam_buffer) >= BUFFER_BATCH_SIZE) or (len(self.fq1_buffer) and lastTime) or (len(self.bam_buffer) and lastTime):
-			# fq
-			if not self.noFASTQ:
-				self.fq1_file.write(''.join(self.fq1_buffer))
-				if len(self.fq2_buffer):
-					self.fq2_file.write(''.join(self.fq2_buffer))
-			# bam
-			if len(self.bam_buffer):
-				bam_data = sorted(self.bam_buffer)
-				if lastTime:
-					self.bam_file.write(''.join([n[2] for n in bam_data]))
-					self.bam_buffer = []
-				else:
-					ind_to_stop_at = 0
-					for i in xrange(0,len(bam_data)):
-						# if we are from previous reference, or have coordinates lower than next window position, it's safe to write out to file
-						if bam_data[i][0] != bam_data[-1][0] or bam_data[i][1] < bamMax:
-							ind_to_stop_at = i+1
-						else:
-							break
-					self.bam_file.write(''.join([n[2] for n in bam_data[:ind_to_stop_at]]))
-					####print 'BAM WRITING:',ind_to_stop_at,'/',len(bam_data)
-					if ind_to_stop_at >= len(bam_data):
-						self.bam_buffer = []
-					else:
-						self.bam_buffer = bam_data[ind_to_stop_at:]
-			self.fq1_buffer = []
-			self.fq2_buffer = []
-
-
-	def closeFiles(self):
-		self.flushBuffers(lastTime=True)
-		if not self.noFASTQ:
-			self.fq1_file.close()
-			if self.fq2_file != None:
-				self.fq2_file.close()
-		if self.vcf_file != None:
-			self.vcf_file.close()
-		if self.bam_file != None:
-			self.bam_file.close()
-
-
-
-
+    def __init__(self, outPrefix, paired=False, BAM_header=None, VCF_header=None, gzipped=False, jobTuple=(1, 1),
+                 noFASTQ=False, FASTA_instead=False):
+
+        jobSuffix = ''
+        if jobTuple[1] > 1:
+            jsl = len(str(jobTuple[1]))
+            jsb = '0' * (jsl - len(str(jobTuple[0])))
+            jobSuffix = '.job' + jsb + str(jobTuple[0]) + 'of' + str(jobTuple[1])
+
+        self.FASTA_instead = FASTA_instead
+        if FASTA_instead:
+            fq1 = outPrefix + '_read1.fa' + jobSuffix
+            fq2 = outPrefix + '_read2.fa' + jobSuffix
+        else:
+            fq1 = outPrefix + '_read1.fq' + jobSuffix
+            fq2 = outPrefix + '_read2.fq' + jobSuffix
+        bam = outPrefix + '_golden.bam' + jobSuffix
+        vcf = outPrefix + '_golden.vcf' + jobSuffix
+
+        self.noFASTQ = noFASTQ
+        if not self.noFASTQ:
+            if gzipped:
+                self.fq1_file = gzip.open(fq1 + '.gz', 'wb')
+            else:
+                self.fq1_file = open(fq1, 'w')
+
+            self.fq2_file = None
+            if paired:
+                if gzipped:
+                    self.fq2_file = gzip.open(fq2 + '.gz', 'wb')
+                else:
+                    self.fq2_file = open(fq2, 'w')
+
+        #
+        #	VCF OUTPUT
+        #
+        self.vcf_file = None
+        if VCF_header != None:
+            if gzipped:
+                self.vcf_file = gzip.open(vcf + '.gz', 'wb')
+            else:
+                self.vcf_file = open(vcf, 'wb')
+
+            # WRITE VCF HEADER (if parallel: only for first job)
+            if jobTuple[0] == 1:
+                self.vcf_file.write('##fileformat=VCFv4.1\n')
+                self.vcf_file.write('##reference=' + VCF_header[0] + '\n')
+                self.vcf_file.write('##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">\n')
+                self.vcf_file.write('##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">\n')
+                # self.vcf_file.write('##INFO=<ID=READS,Number=1,Type=String,Description="Names of Reads Covering this Variant">\n')
+                self.vcf_file.write(
+                    '##INFO=<ID=VMX,Number=1,Type=String,Description="SNP is Missense in these Read Frames">\n')
+                self.vcf_file.write(
+                    '##INFO=<ID=VNX,Number=1,Type=String,Description="SNP is Nonsense in these Read Frames">\n')
+                self.vcf_file.write('##INFO=<ID=VFX,Number=1,Type=String,Description="Indel Causes Frameshift">\n')
+                self.vcf_file.write(
+                    '##INFO=<ID=WP,Number=A,Type=Integer,Description="NEAT-GenReads ploidy indicator">\n')
+                self.vcf_file.write('##ALT=<ID=DEL,Description="Deletion">\n')
+                self.vcf_file.write('##ALT=<ID=DUP,Description="Duplication">\n')
+                self.vcf_file.write('##ALT=<ID=INS,Description="Insertion of novel sequence">\n')
+                self.vcf_file.write('##ALT=<ID=INV,Description="Inversion">\n')
+                self.vcf_file.write('##ALT=<ID=CNV,Description="Copy number variable region">\n')
+                self.vcf_file.write('##ALT=<ID=TRANS,Description="Translocation">\n')
+                self.vcf_file.write('##ALT=<ID=INV-TRANS,Description="Inverted translocation">\n')
+                self.vcf_file.write('#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\n')
+
+        #
+        #	BAM OUTPUT
+        #
+        self.bam_file = None
+        if BAM_header != None:
+            self.bam_file = BgzfWriter(bam, 'w', compresslevel=BAM_COMPRESSION_LEVEL)
+
+            # WRITE BAM HEADER (if parallel: only for first job)
+            if True or jobTuple[0] == 1:
+                self.bam_file.write("BAM\1")
+                header = '@HD\tVN:1.5\tSO:coordinate\n'
+                for n in BAM_header[0]:
+                    header += '@SQ\tSN:' + n[0] + '\tLN:' + str(n[3]) + '\n'
+                header += '@RG\tID:NEAT\tSM:NEAT\tLB:NEAT\tPL:NEAT\n'
+                headerBytes = len(header)
+                numRefs = len(BAM_header[0])
+                self.bam_file.write(pack('<i', headerBytes))
+                self.bam_file.write(header)
+                self.bam_file.write(pack('<i', numRefs))
+
+                for n in BAM_header[0]:
+                    l_name = len(n[0]) + 1
+                    self.bam_file.write(pack('<i', l_name))
+                    self.bam_file.write(n[0] + '\0')
+                    self.bam_file.write(pack('<i', n[3]))
+
+        # buffers for more efficient writing
+        self.fq1_buffer = []
+        self.fq2_buffer = []
+        self.bam_buffer = []
+
+    def writeFASTQRecord(self, readName, read1, qual1, read2=None, qual2=None):
+        if self.FASTA_instead:
+            self.fq1_buffer.append('>' + readName + '/1\n' + read1 + '\n')
+            if read2 != None:
+                self.fq2_buffer.append('>' + readName + '/2\n' + RC(read2) + '\n')
+        else:
+            self.fq1_buffer.append('@' + readName + '/1\n' + read1 + '\n+\n' + qual1 + '\n')
+            if read2 != None:
+                self.fq2_buffer.append('@' + readName + '/2\n' + RC(read2) + '\n+\n' + qual2[::-1] + '\n')
+
+    def writeVCFRecord(self, chrom, pos, idStr, ref, alt, qual, filt, info):
+        self.vcf_file.write(
+            str(chrom) + '\t' + str(pos) + '\t' + str(idStr) + '\t' + str(ref) + '\t' + str(alt) + '\t' + str(
+                qual) + '\t' + str(filt) + '\t' + str(info) + '\n')
+
+    def writeBAMRecord(self, refID, readName, pos_0, cigar, seq, qual, samFlag, matePos=None, alnMapQual=70):
+
+        myBin = reg2bin(pos_0, pos_0 + len(seq))
+        # myBin     = 0	# or just use a dummy value, does this actually matter?
+
+        myMapQual = alnMapQual
+        cig_letters = re.split(r"\d+", cigar)[1:]
+        cig_numbers = [int(n) for n in re.findall(r"\d+", cigar)]
+        cig_ops = len(cig_letters)
+        next_refID = refID
+        if matePos == None:
+            next_pos = 0
+            my_tlen = 0
+        else:
+            next_pos = matePos
+            if pos_0 < next_pos:
+                my_tlen = next_pos + len(seq) - pos_0
+            else:
+                my_tlen = -pos_0 - len(seq) + next_pos
+
+        encodedCig = ''
+        for i in range(cig_ops):
+            encodedCig += pack('<I', (cig_numbers[i] << 4) + CIGAR_PACKED[cig_letters[i]])
+        encodedSeq = ''
+        encodedLen = (len(seq) + 1) / 2
+        seqLen = len(seq)
+        if seqLen & 1:
+            seq += '='
+        for i in range(encodedLen):
+            encodedSeq += pack('<B', (SEQ_PACKED[seq[2 * i]] << 4) + SEQ_PACKED[seq[2 * i + 1]])
+
+        # apparently samtools automatically adds 33 to the quality score string...
+        encodedQual = ''.join([chr(ord(n) - 33) for n in qual])
+
+        # blockSize = 4 +		# refID 		int32
+        #            4 +		# pos			int32
+        #            4 +		# bin_mq_nl		uint32
+        #            4 +		# flag_nc		uint32
+        #            4 +		# l_seq			int32
+        #            4 +		# next_refID	int32
+        #            4 +		# next_pos		int32
+        #            4 +		# tlen			int32
+        #            len(readName)+1 +
+        #            4*cig_ops +
+        #            encodedLen +
+        #            len(seq)
+
+        # blockSize = 32 + len(readName)+1 + 4*cig_ops + encodedLen + len(seq)
+        blockSize = 32 + len(readName) + 1 + len(encodedCig) + len(encodedSeq) + len(encodedQual)
+
+        ####self.bam_file.write(pack('<i',blockSize))
+        ####self.bam_file.write(pack('<i',refID))
+        ####self.bam_file.write(pack('<i',pos_0))
+        ####self.bam_file.write(pack('<I',(myBin<<16) + (myMapQual<<8) + len(readName)+1))
+        ####self.bam_file.write(pack('<I',(samFlag<<16) + cig_ops))
+        ####self.bam_file.write(pack('<i',seqLen))
+        ####self.bam_file.write(pack('<i',next_refID))
+        ####self.bam_file.write(pack('<i',next_pos))
+        ####self.bam_file.write(pack('<i',my_tlen))
+        ####self.bam_file.write(readName+'\0')
+        ####self.bam_file.write(encodedCig)
+        ####self.bam_file.write(encodedSeq)
+        ####self.bam_file.write(encodedQual)
+
+        # a horribly compressed line, I'm sorry.
+        # (ref_index, position, data)
+        self.bam_buffer.append((refID, pos_0, pack('<i', blockSize) + pack('<i', refID) + pack('<i', pos_0) +
+                                pack('<I', (myBin << 16) + (myMapQual << 8) + len(readName) + 1) +
+                                pack('<I', (samFlag << 16) + cig_ops) + pack('<i', seqLen) + pack('<i', next_refID) +
+                                pack('<i', next_pos) + pack('<i', my_tlen) + readName +
+                                '\0' + encodedCig + encodedSeq + encodedQual))
+
+    def flushBuffers(self, bamMax=None, lastTime=False):
+        if (len(self.fq1_buffer) >= BUFFER_BATCH_SIZE or len(self.bam_buffer) >= BUFFER_BATCH_SIZE) or (
+                len(self.fq1_buffer) and lastTime) or (len(self.bam_buffer) and lastTime):
+            # fq
+            if not self.noFASTQ:
+                self.fq1_file.write(''.join(self.fq1_buffer))
+                if len(self.fq2_buffer):
+                    self.fq2_file.write(''.join(self.fq2_buffer))
+            # bam
+            if len(self.bam_buffer):
+                bam_data = sorted(self.bam_buffer)
+                if lastTime:
+                    self.bam_file.write(''.join([n[2] for n in bam_data]))
+                    self.bam_buffer = []
+                else:
+                    ind_to_stop_at = 0
+                    for i in range(0, len(bam_data)):
+                        # if we are from previous reference, or have coordinates lower
+                        # than next window position, it's safe to write out to file
+                        if bam_data[i][0] != bam_data[-1][0] or bam_data[i][1] < bamMax:
+                            ind_to_stop_at = i + 1
+                        else:
+                            break
+                    self.bam_file.write(''.join([n[2] for n in bam_data[:ind_to_stop_at]]))
+                    ####print 'BAM WRITING:',ind_to_stop_at,'/',len(bam_data)
+                    if ind_to_stop_at >= len(bam_data):
+                        self.bam_buffer = []
+                    else:
+                        self.bam_buffer = bam_data[ind_to_stop_at:]
+            self.fq1_buffer = []
+            self.fq2_buffer = []
+
+    def closeFiles(self):
+        self.flushBuffers(lastTime=True)
+        if not self.noFASTQ:
+            self.fq1_file.close()
+            if self.fq2_file != None:
+                self.fq2_file.close()
+        if self.vcf_file != None:
+            self.vcf_file.close()
+        if self.bam_file != None:
+            self.bam_file.close()
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index d87f763..70e3d71 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -3,11 +3,11 @@
 import re
 import os
 import bisect
-import cPickle as pickle
+import pickle
 import numpy as np
 
-from probability import DiscreteDistribution, poisson_list, quantize_list
-from neat_cigar import CigarString
+from py.probability import DiscreteDistribution, poisson_list, quantize_list
+from py.neat_cigar import CigarString
 
 MAX_ATTEMPTS = 100	# max attempts to insert a mutation into a valid position
 MAX_MUTFRAC  = 0.3	# the maximum percentage of a window that can contain mutations
@@ -16,8 +16,8 @@
 TRI_IND = {'AA':0,  'AC':1,  'AG':2,   'AT':3,  'CA':4,  'CC':5,  'CG':6,  'CT':7,
            'GA':8,  'GC':9,  'GG':10,  'GT':11, 'TA':12, 'TC':13, 'TG':14, 'TT':15}
 NUC_IND = {'A':0, 'C':1, 'G':2, 'T':3}
-ALL_TRI = [NUCL[i]+NUCL[j]+NUCL[k] for i in xrange(len(NUCL)) for j in xrange(len(NUCL)) for k in xrange(len(NUCL))]
-ALL_IND = {ALL_TRI[i]:i for i in xrange(len(ALL_TRI))}
+ALL_TRI = [NUCL[i]+NUCL[j]+NUCL[k] for i in range(len(NUCL)) for j in range(len(NUCL)) for k in range(len(NUCL))]
+ALL_IND = {ALL_TRI[i]:i for i in range(len(ALL_TRI))}
 
 # DEBUG
 IGNORE_TRINUC = False
@@ -30,775 +30,779 @@
 #	Container for reference sequences, applies mutations
 #
 class SequenceContainer:
-	def __init__(self, xOffset, sequence, ploidy, windowOverlap, readLen, mutationModels=[], mutRate=None, onlyVCF=False):
-		# initialize basic variables
-		self.onlyVCF = onlyVCF
-		self.init_basicVars(xOffset, sequence, ploidy, windowOverlap, readLen)
-		# initialize mutation models
-		self.init_mutModels(mutationModels, mutRate)
-		# sample the number of variants that will be inserted into each ploid
-		self.init_poisson()
-		self.indelsToAdd = [n.sample() for n in self.ind_pois]
-		self.snpsToAdd   = [n.sample() for n in self.snp_pois]
-		# initialize trinuc snp bias
-		self.init_trinucBias()
-
-	def init_basicVars(self, xOffset, sequence, ploidy, windowOverlap, readLen):
-		self.x         = xOffset
-		self.ploidy    = ploidy
-		self.readLen   = readLen
-		self.sequences = [bytearray(sequence) for n in xrange(self.ploidy)]
-		self.seqLen    = len(sequence)
-		self.indelList = [[] for n in xrange(self.ploidy)]
-		self.snpList   = [[] for n in xrange(self.ploidy)]
-		self.allCigar  = [[] for n in xrange(self.ploidy)]
-		self.FM_pos    = [[] for n in xrange(self.ploidy)]
-		self.FM_span   = [[] for n in xrange(self.ploidy)]
-		# blackList[ploid][pos] = 0		safe to insert variant here
-		# blackList[ploid][pos] = 1		indel inserted here
-		# blackList[ploid][pos] = 2		snp inserted here
-		# blackList[ploid][pos] = 3		invalid position for various processing reasons
-		self.blackList = [np.zeros(self.seqLen,dtype='<i4') for n in xrange(self.ploidy)]
-
-		# disallow mutations to occur on window overlap points
-		self.winBuffer = windowOverlap
-		for p in xrange(self.ploidy):
-			self.blackList[p][-self.winBuffer]   = 3
-			self.blackList[p][-self.winBuffer-1] = 3
-
-	def init_coverage(self,coverageDat,fragDist=None):
-		# if we're only creating a vcf, skip some expensive initialization related to coverage depth
-		if not self.onlyVCF:
-			(self.windowSize, gc_scalars, targetCov_vals) = coverageDat
-			gcCov_vals = [[] for n in self.sequences]
-			trCov_vals = [[] for n in self.sequences]
-			self.coverage_distribution = []
-			avg_out = []
-			for i in xrange(len(self.sequences)):
-				max_coord = min([len(self.sequences[i])-self.readLen, len(self.allCigar[i])-self.readLen])
-				# compute gc-bias
-				j = 0
-				while j+self.windowSize < len(self.sequences[i]):
-					gc_c = self.sequences[i][j:j+self.windowSize].count('G') + self.sequences[i][j:j+self.windowSize].count('C')
-					gcCov_vals[i].extend([gc_scalars[gc_c]]*self.windowSize)
-					j += self.windowSize
-				gc_c = self.sequences[i][-self.windowSize:].count('G') + self.sequences[i][-self.windowSize:].count('C')
-				gcCov_vals[i].extend([gc_scalars[gc_c]]*(len(self.sequences[i])-len(gcCov_vals[i])))
-				#
-				trCov_vals[i].append(targetCov_vals[0])
-				prevVal = self.FM_pos[i][0]
-				for j in xrange(1,max_coord):
-					if self.FM_pos[i][j] == None:
-						trCov_vals[i].append(targetCov_vals[prevVal])
-					elif self.FM_span[i][j]-self.FM_pos[i][j] <= 1:
-						trCov_vals[i].append(targetCov_vals[prevVal])
-					else:
-						trCov_vals[i].append(sum(targetCov_vals[self.FM_pos[i][j]:self.FM_span[i][j]])/float(self.FM_span[i][j]-self.FM_pos[i][j]))
-						prevVal = self.FM_pos[i][j]
-					#print (i,j), self.adj[i][j], self.allCigar[i][j], self.FM_pos[i][j], self.FM_span[i][j]
-				# shift by half of read length
-				if len(trCov_vals[i]) > int(self.readLen/2.):
-					trCov_vals[i] = [0.0]*int(self.readLen/2) + trCov_vals[i][:-int(self.readLen/2.)]
-				# fill in missing indices
-				trCov_vals[i].extend([0.0]*(len(self.sequences[i])-len(trCov_vals[i])))
-
-				#
-				covvec = np.cumsum([trCov_vals[i][nnn]*gcCov_vals[i][nnn] for nnn in xrange(len(trCov_vals[i]))])
-				coverage_vals = []
-				for j in xrange(0,max_coord):
-					coverage_vals.append(covvec[j+self.readLen] - covvec[j])
-				avg_out.append(np.mean(coverage_vals)/float(self.readLen))
-
-				if fragDist == None:
-					self.coverage_distribution.append(DiscreteDistribution(coverage_vals,range(len(coverage_vals))))
-			
-				# fragment length nightmare
-				else:
-					currentThresh = 0.
-					index_list    = [0]
-					for j in xrange(len(fragDist.cumP)):
-						if fragDist.cumP[j] >= currentThresh + COV_FRAGLEN_PERCENTILE/100.0:
-							currentThresh = fragDist.cumP[j]
-							index_list.append(j)
-					flq = [fragDist.values[nnn] for nnn in index_list]
-					if fragDist.values[-1] not in flq:
-						flq.append(fragDist.values[-1])
-					flq.append(LARGE_NUMBER)
-
-					self.fraglens_indMap = {}
-					for j in fragDist.values:
-						bInd = bisect.bisect(flq,j)
-						if abs(flq[bInd-1] - j) <= abs(flq[bInd] - j):
-							self.fraglens_indMap[j] = flq[bInd-1]
-						else:
-							self.fraglens_indMap[j] = flq[bInd]
-
-					self.coverage_distribution.append({})
-					for flv in sorted(list(set(self.fraglens_indMap.values()))):
-						buffer_val = self.readLen
-						for j in fragDist.values:
-							if self.fraglens_indMap[j] == flv and j > buffer_val:
-								buffer_val = j
-						max_coord = min([len(self.sequences[i])-buffer_val-1, len(self.allCigar[i])-buffer_val+self.readLen-2])
-						#print 'BEFORE:', len(self.sequences[i])-buffer_val
-						#print 'AFTER: ', len(self.allCigar[i])-buffer_val+self.readLen-2
-						#print 'AFTER2:', max_coord
-						coverage_vals = []
-						for j in xrange(0,max_coord):
-							coverage_vals.append(covvec[j+self.readLen] - covvec[j] + covvec[j+flv] - covvec[j+flv-self.readLen])
-
-						# EXPERIMENTAL
-						#quantized_covVals = quantize_list(coverage_vals)
-						#self.coverage_distribution[i][flv] = DiscreteDistribution([n[2] for n in quantized_covVals],[(n[0],n[1]) for n in quantized_covVals])
-
-						# TESTING
-						#import matplotlib.pyplot as mpl
-						#print len(coverage_vals),'-->',len(quantized_covVals)
-						#mpl.figure(0)
-						#mpl.plot(range(len(coverage_vals)),coverage_vals)
-						#for qcv in quantized_covVals:
-						#	mpl.plot([qcv[0],qcv[1]+1],[qcv[2],qcv[2]],'r')
-						#mpl.show()
-						#exit(1)
-
-						self.coverage_distribution[i][flv] = DiscreteDistribution(coverage_vals,range(len(coverage_vals)))
-
-			return np.mean(avg_out)
-
-	def init_mutModels(self,mutationModels,mutRate):
-		if mutationModels == []:
-			ml = [copy.deepcopy(DEFAULT_MODEL_1) for n in xrange(self.ploidy)]
-			self.modelData = ml[:self.ploidy]
-		else:
-			if len(mutationModels) != self.ploidy:
-				print '\nError: Number of mutation models recieved is not equal to specified ploidy\n'
-				exit(1)
-			self.modelData = copy.deepcopy(mutationModels)
-
-		# do we need to rescale mutation frequencies?
-		mutRateSum = sum([n[0] for n in self.modelData])
-		self.mutRescale = mutRate
-		if self.mutRescale == None:
-			self.mutScalar = 1.0
-		else:
-			self.mutScalar = float(self.mutRescale)/(mutRateSum/float(len(self.modelData)))
-
-		# how are mutations spread to each ploid, based on their specified mut rates?
-		self.ploidMutFrac  = [float(n[0])/mutRateSum for n in self.modelData]
-		self.ploidMutPrior = DiscreteDistribution(self.ploidMutFrac,range(self.ploidy))
-
-		# init mutation models
-		#
-		# self.models[ploid][0] = average mutation rate
-		# self.models[ploid][1] = p(mut is homozygous | mutation occurs)
-		# self.models[ploid][2] = p(mut is indel | mut occurs)
-		# self.models[ploid][3] = p(insertion | indel occurs)
-		# self.models[ploid][4] = distribution of insertion lengths
-		# self.models[ploid][5] = distribution of deletion lengths
-		# self.models[ploid][6] = distribution of trinucleotide SNP transitions
-		# self.models[ploid][7] = p(trinuc mutates)
-		self.models = []
-		for n in self.modelData:
-			self.models.append([self.mutScalar*n[0],n[1],n[2],n[3],DiscreteDistribution(n[5],n[4]),DiscreteDistribution(n[7],n[6]),[]])
-			for m in n[8]:
-				self.models[-1][6].append([DiscreteDistribution(m[0],NUCL),
-					                       DiscreteDistribution(m[1],NUCL),
-					                       DiscreteDistribution(m[2],NUCL),
-					                       DiscreteDistribution(m[3],NUCL)])
-			self.models[-1].append([m for m in n[9]])
-
-	def init_poisson(self):
-		ind_l_list = [self.seqLen*self.models[i][0]*self.models[i][2]*self.ploidMutFrac[i] for i in xrange(len(self.models))]
-		snp_l_list = [self.seqLen*self.models[i][0]*(1.-self.models[i][2])*self.ploidMutFrac[i] for i in xrange(len(self.models))]
-		k_range    = range(int(self.seqLen*MAX_MUTFRAC))
-		self.ind_pois = [poisson_list(k_range,ind_l_list[n]) for n in xrange(len(self.models))]
-		self.snp_pois = [poisson_list(k_range,snp_l_list[n]) for n in xrange(len(self.models))]
-
-	def init_trinucBias(self):
-		# compute mutation positional bias given trinucleotide strings of the sequence (ONLY AFFECTS SNPs)
-		#
-		# note: since indels are added before snps, it's possible these positional biases aren't correctly utilized
-		#       at positions affected by indels. At the moment I'm going to consider this negligible.
-		trinuc_snp_bias  = [[0. for n in xrange(self.seqLen)] for m in xrange(self.ploidy)]
-		self.trinuc_bias = [None for n in xrange(self.ploidy)]
-		for p in xrange(self.ploidy):
-			for i in xrange(self.winBuffer+1,self.seqLen-1):
-				trinuc_snp_bias[p][i] = self.models[p][7][ALL_IND[str(self.sequences[p][i-1:i+2])]]
-			self.trinuc_bias[p] = DiscreteDistribution(trinuc_snp_bias[p][self.winBuffer+1:self.seqLen-1],range(self.winBuffer+1,self.seqLen-1))
-
-	def update(self, xOffset, sequence, ploidy, windowOverlap, readLen, mutationModels=[], mutRate=None):
-		# if mutation model is changed, we have to reinitialize it...
-		if ploidy != self.ploidy or mutRate != self.mutRescale or mutationModels != []:
-			self.ploidy = ploidy
-			self.mutRescale = mutRate
-			self.init_mutModels(mutationModels, mutRate)
-		# if sequence length is different than previous window, we have to redo snp/indel poissons
-		if len(sequence) != self.seqLen:
-			self.seqLen = len(sequence)
-			self.init_poisson()
-		# basic vars
-		self.init_basicVars(xOffset, sequence, ploidy, windowOverlap, readLen)
-		self.indelsToAdd = [n.sample() for n in self.ind_pois]
-		self.snpsToAdd   = [n.sample() for n in self.snp_pois]
-		# initialize trinuc snp bias
-		if not IGNORE_TRINUC:
-			self.init_trinucBias()
-
-	def insert_mutations(self, inputList):
-		for inpV in inputList:
-			whichPloid = []
-			wps = inpV[4][0]
-			if wps == None:	# if no genotype given, assume heterozygous and choose a single ploid based on their mut rates
-				whichPloid.append(self.ploidMutPrior.sample())
-				whichAlt = [0]
-			else:
-				if '/' in wps or '|' in wps:
-					if '/' in wps:
-						splt = wps.split('/')
-					else:
-						splt = wps.split('|')
-					whichPloid = []
-					whichAlt   = []
-					for i in xrange(len(splt)):
-						if splt[i] == '1':
-							whichPloid.append(i)
-					# assume we're just using first alt for inserted variants?
-					whichAlt = [0 for n in whichPloid]
-				else:	# otherwise assume monoploidy
-					whichPloid = [0]
-					whichAlt   = [0]
-
-			# ignore invalid ploids
-			for i in xrange(len(whichPloid)-1,-1,-1):
-				if whichPloid[i] >= self.ploidy:
-					del whichPloid[i]
-						
-			for i in xrange(len(whichPloid)):
-				p = whichPloid[i]
-				myAlt = inpV[2][whichAlt[i]]
-				myVar = (inpV[0]-self.x,inpV[1],myAlt)
-				inLen = max([len(inpV[1]),len(myAlt)])
-
-				if myVar[0] < 0 or myVar[0] >= len(self.blackList[p]):
-					print '\nError: Attempting to insert variant out of window bounds:'
-					print myVar, '--> blackList[0:'+str(len(self.blackList[p]))+']\n'
-					exit(1)
-				if len(inpV[1]) == 1 and len(myAlt) == 1:
-					if self.blackList[p][myVar[0]]:
-						continue
-					self.snpList[p].append(myVar)
-					self.blackList[p][myVar[0]] = 2
-				else:
-					indel_failed = False
-					for k in xrange(myVar[0],myVar[0]+inLen+1):
-						if k >= len(self.blackList[p]):
-							indel_failed = True
-							continue
-						if self.blackList[p][k]:
-							indel_failed = True
-							continue
-					if indel_failed:
-						continue
-					for k in xrange(myVar[0],myVar[0]+inLen+1):
-						self.blackList[p][k] = 1
-					self.indelList[p].append(myVar)
-
-	def random_mutations(self):
-		
-		#	add random indels
-		all_indels  = [[] for n in self.sequences]
-		for i in xrange(self.ploidy):
-			for j in xrange(self.indelsToAdd[i]):
-				if random.random() <= self.models[i][1]:	# insert homozygous indel
-					whichPloid = range(self.ploidy)
-				else:								# insert heterozygous indel
-					whichPloid = [self.ploidMutPrior.sample()]
-
-				# try to find suitable places to insert indels
-				eventPos = -1
-				for attempt in xrange(MAX_ATTEMPTS):
-					eventPos = random.randint(self.winBuffer,self.seqLen-1)
-					for p in whichPloid:
-						if self.blackList[p][eventPos]:
-							eventPos = -1
-					if eventPos != -1:
-						break
-				if eventPos == -1:
-					continue
-
-				if random.random() <= self.models[i][3]:	# insertion
-					inLen   = self.models[i][4].sample()
-					# sequence content of random insertions is uniformly random (change this later, maybe)
-					inSeq   = ''.join([random.choice(NUCL) for n in xrange(inLen)])
-					refNucl = chr(self.sequences[i][eventPos])
-					myIndel = (eventPos,refNucl,refNucl+inSeq)
-				else:										# deletion
-					inLen   = self.models[i][5].sample()
-					if eventPos+inLen+1 >= len(self.sequences[i]):	# skip if deletion too close to boundary
-						continue
-					if inLen == 1:
-						inSeq = chr(self.sequences[i][eventPos+1])
-					else:
-						inSeq = str(self.sequences[i][eventPos+1:eventPos+inLen+1])
-					refNucl = chr(self.sequences[i][eventPos])
-					myIndel = (eventPos,refNucl+inSeq,refNucl)
-
-				# if event too close to boundary, skip. if event conflicts with other indel, skip.
-				skipEvent = False
-				if eventPos+len(myIndel[1]) >= self.seqLen-self.winBuffer-1:
-					skipEvent = True
-				if skipEvent:
-					continue
-				for p in whichPloid:
-					for k in xrange(eventPos,eventPos+inLen+1):
-						if self.blackList[p][k]:
-							skipEvent = True
-				if skipEvent:
-					continue
-
-				for p in whichPloid:
-					for k in xrange(eventPos,eventPos+inLen+1):
-						self.blackList[p][k] = 1
-					all_indels[p].append(myIndel)
-
-		#	add random snps
-		all_snps  = [[] for n in self.sequences]
-		for i in xrange(self.ploidy):
-			for j in xrange(self.snpsToAdd[i]):
-				if random.random() <= self.models[i][1]:	# insert homozygous SNP
-					whichPloid = range(self.ploidy)
-				else:								# insert heterozygous SNP
-					whichPloid = [self.ploidMutPrior.sample()]
-
-				# try to find suitable places to insert snps
-				eventPos = -1
-				for attempt in xrange(MAX_ATTEMPTS):
-					# based on the mutation model for the specified ploid, choose a SNP location based on trinuc bias
-					# (if there are multiple ploids, choose one at random)
-					if IGNORE_TRINUC:
-						eventPos = random.randint(self.winBuffer+1,self.seqLen-2)
-					else:
-						ploid_to_use = whichPloid[random.randint(0,len(whichPloid)-1)]
-						eventPos     = self.trinuc_bias[ploid_to_use].sample()
-					for p in whichPloid:
-						if self.blackList[p][eventPos]:
-							eventPos = -1
-					if eventPos != -1:
-						break
-				if eventPos == -1:
-					continue
-
-				refNucl = chr(self.sequences[i][eventPos])
-				context = str(chr(self.sequences[i][eventPos-1])+chr(self.sequences[i][eventPos+1]))
-				# sample from tri-nucleotide substitution matrices to get SNP alt allele
-				newNucl = self.models[i][6][TRI_IND[context]][NUC_IND[refNucl]].sample()
-				mySNP   = (eventPos,refNucl,newNucl)
-
-				for p in whichPloid:
-					all_snps[p].append(mySNP)
-					self.blackList[p][mySNP[0]] = 2
-
-		# combine random snps with inserted snps, remove any snps that overlap indels
-		for p in xrange(len(all_snps)):
-			all_snps[p].extend(self.snpList[p])
-			all_snps[p] = [n for n in all_snps[p] if self.blackList[p][n[0]] != 1]
-
-		# MODIFY REFERENCE STRING: SNPS
-		for i in xrange(len(all_snps)):
-			for j in xrange(len(all_snps[i])):
-				vPos = all_snps[i][j][0]
-
-				if all_snps[i][j][1] != chr(self.sequences[i][vPos]):
-					print '\nError: Something went wrong!\n', all_snps[i][j], chr(self.sequences[i][vPos]),'\n'
-					exit(1)
-				else:
-					self.sequences[i][vPos] = all_snps[i][j][2]
-
-		# organize the indels we want to insert
-		for i in xrange(len(all_indels)):
-			all_indels[i].extend(self.indelList[i])
-		all_indels_ins = [sorted([list(m) for m in n]) for n in all_indels]
-
-		# MODIFY REFERENCE STRING: INDELS
-		adjToAdd = [[] for n in xrange(self.ploidy)]
-		for i in xrange(len(all_indels_ins)):
-			rollingAdj = 0
-			tempSymbolString = ['M' for n in self.sequences[i]]
-			# there's an off-by-one error somewhere in the position sampling routines.. this might fix it
-			tempSymbolString.append('M')
-			for j in xrange(len(all_indels_ins[i])):
-				vPos        = all_indels_ins[i][j][0] + rollingAdj
-				vPos2       = vPos + len(all_indels_ins[i][j][1])
-				rollingAdj += len(all_indels_ins[i][j][2])-len(all_indels_ins[i][j][1])
-				
-				if all_indels_ins[i][j][1] != str(self.sequences[i][vPos:vPos2]):
-					print '\nError: Something went wrong!\n', all_indels_ins[i][j], [vPos,vPos2], str(self.sequences[i][vPos:vPos2]),'\n'
-					exit(1)
-				else:
-					# alter reference sequence
-					self.sequences[i] = self.sequences[i][:vPos] + bytearray(all_indels_ins[i][j][2]) + self.sequences[i][vPos2:]
-					# notate indel positions for cigar computation
-					d = len(all_indels_ins[i][j][2]) - len(all_indels_ins[i][j][1])
-					if d > 0:
-						tempSymbolString = tempSymbolString[:vPos+1] + ['I']*d + tempSymbolString[vPos2+1:]
-					elif d < 0:
-						tempSymbolString[vPos+1] = 'D'*abs(d)+'M'
-
-			# precompute cigar strings
-			for j in xrange(len(tempSymbolString)-self.readLen):
-				self.allCigar[i].append(CigarString(listIn=tempSymbolString[j:j+self.readLen]).getString())
-
-			# create some data structures we will need later:
-			# --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e. corresponding to the unmodified reference genome)
-			# --- self.FM_span[ploid][pos]: number of reference positions spanned by a read originating from this coordinate
-			MD_soFar = 0
-			for j in xrange(len(tempSymbolString)):
-				self.FM_pos[i].append(MD_soFar)
-				# fix an edge case with deletions
-				if 'D' in tempSymbolString[j]:
-					self.FM_pos[i][-1] += tempSymbolString[j].count('D')
-				# compute number of ref matches for each read
-				span_dif = len([n for n in tempSymbolString[j:j+self.readLen] if 'M' in n])
-				self.FM_span[i].append(self.FM_pos[i][-1] + span_dif)
-				MD_soFar += tempSymbolString[j].count('M') + tempSymbolString[j].count('D')
-
-		# tally up all the variants we handled...
-		countDict = {}
-		all_variants = [sorted(all_snps[i]+all_indels[i]) for i in xrange(self.ploidy)]
-		for i in xrange(len(all_variants)):
-			for j in xrange(len(all_variants[i])):
-				all_variants[i][j] = tuple([all_variants[i][j][0]+self.x])+all_variants[i][j][1:]
-				t = tuple(all_variants[i][j])
-				if t not in countDict:
-					countDict[t] = []
-				countDict[t].append(i)
-
-		#
-		#	TODO: combine multiple variants that happened to occur at same position into single vcf entry?
-		#
-
-		output_variants = []
-		for k in sorted(countDict.keys()):
-			output_variants.append(k+tuple([len(countDict[k])/float(self.ploidy)]))
-			ploid_string = ['0' for n in xrange(self.ploidy)]
-			for k2 in [n for n in countDict[k]]:
-				ploid_string[k2] = '1'
-			output_variants[-1] += tuple(['WP='+'/'.join(ploid_string)])
-		return output_variants
-
-
-	def sample_read(self, sequencingModel, fragLen=None):
-		
-		# choose a ploid
-		myPloid = random.randint(0,self.ploidy-1)
-
-		# stop attempting to find a valid position if we fail enough times
-		MAX_READPOS_ATTEMPTS = 100
-		attempts_thus_far    = 0
-
-		# choose a random position within the ploid, and generate quality scores / sequencing errors
-		readsToSample = []
-		if fragLen == None:
-			rPos = self.coverage_distribution[myPloid].sample()
-
-			# sample read position and call function to compute quality scores / sequencing errors
-			rDat = self.sequences[myPloid][rPos:rPos+self.readLen]
-			(myQual, myErrors) = sequencingModel.getSequencingErrors(rDat)
-			readsToSample.append([rPos,myQual,myErrors,rDat])
-
-		else:
-			rPos1 = self.coverage_distribution[myPloid][self.fraglens_indMap[fragLen]].sample()
-			
-			# EXPERIMENTAL
-			#coords_to_select_from = self.coverage_distribution[myPloid][self.fraglens_indMap[fragLen]].sample()
-			#rPos1 = random.randint(coords_to_select_from[0],coords_to_select_from[1])
-
-			rPos2 = rPos1 + fragLen - self.readLen
-			rDat1 = self.sequences[myPloid][rPos1:rPos1+self.readLen]
-			rDat2 = self.sequences[myPloid][rPos2:rPos2+self.readLen]
-			(myQual1, myErrors1) = sequencingModel.getSequencingErrors(rDat1)
-			(myQual2, myErrors2) = sequencingModel.getSequencingErrors(rDat2,isReverseStrand=True)
-			readsToSample.append([rPos1,myQual1,myErrors1,rDat1])
-			readsToSample.append([rPos2,myQual2,myErrors2,rDat2])
-
-		# error format:
-		# myError[i] = (type, len, pos, ref, alt)
-
-		# examine sequencing errors to-be-inserted.
-		#	- remove deletions that don't have enough bordering sequence content to "fill in"
-		# if error is valid, make the changes to the read data
-		rOut = []
-		for r in readsToSample:
-			try:
-				myCigar = self.allCigar[myPloid][r[0]]
-			except IndexError:
-				print 'Index error when attempting to find cigar string.'
-				print myPloid, len(self.allCigar[myPloid]), r[0]
-				if fragLen != None:
-					print (rPos1, rPos2)
-					print fragLen, self.fraglens_indMap[fragLen]
-				exit(1)
-			totalD  = sum([error[1] for error in r[2] if error[0] == 'D'])
-			totalI  = sum([error[1] for error in r[2] if error[0] == 'I'])
-			availB  = len(self.sequences[myPloid]) - r[0] - self.readLen - 1
-			# add buffer sequence to fill in positions that get deleted
-			r[3] += self.sequences[myPloid][r[0]+self.readLen:r[0]+self.readLen+totalD]
-			expandedCigar = []
-			extraCigar    = []
-			adj           = 0
-			sse_adj       = [0 for n in xrange(self.readLen + max(sequencingModel.errP[3]))]
-			anyIndelErr   = False
-
-			# sort by letter (D > I > S) such that we introduce all indel errors before substitution errors
-			# secondarily, sort by index
-			arrangedErrors = {'D':[],'I':[],'S':[]}
-			for error in r[2]:
-				arrangedErrors[error[0]].append((error[2],error))
-			sortedErrors = []
-			for k in sorted(arrangedErrors.keys()):
-				sortedErrors.extend([n[1] for n in sorted(arrangedErrors[k])])
-
-			skipIndels = False
-
-
-			#FIXED TdB 05JUN2018
-			#Moved this outside the for error loop, since it messes up the CIGAR string when more than one deletion is in the same read
-			extraCigarVal = []
-			#END FIXED TdB
-			
-			for error in sortedErrors:
-				eLen = error[1]
-				ePos = error[2]
-				if error[0] == 'D' or error[0] == 'I':
-					anyIndelErr   = True
-					
-					#FIXED TdB 05JUN2018
-					#Moved this OUTSIDE the for error loop, since it messes up the CIGAR string when more than one deletion is in the same read
-					#extraCigarVal = []
-					#END FIXED TdB
-					
-					if totalD > availB:	# if not enough bases to fill-in deletions, skip all indel erors
-						continue
-					if expandedCigar == []:
-						expandedCigar = CigarString(stringIn=myCigar).getList()
-						fillToGo = totalD - totalI + 1
-						if fillToGo > 0:
-							try:
-								extraCigarVal = CigarString(stringIn=self.allCigar[myPloid][r[0]+fillToGo]).getList()[-fillToGo:]
-							except IndexError:	# applying the deletions we want requires going beyond region boundaries. skip all indel errors
-								skipIndels = True
-								
-
-					if skipIndels:
-						continue
-
-					# insert deletion error into read and update cigar string accordingly
-					if error[0] == 'D':
-						myadj = sse_adj[ePos]
-						pi = ePos+myadj
-						pf = ePos+myadj+eLen+1
-						if str(r[3][pi:pf]) == str(error[3]):
-							r[3] = r[3][:pi+1] + r[3][pf:]
-							expandedCigar = expandedCigar[:pi+1] + expandedCigar[pf:]
-							if pi+1 == len(expandedCigar):	# weird edge case with del at very end of region. Make a guess and add a "M"
-								expandedCigar.append('M')
-							expandedCigar[pi+1] = 'D'*eLen + expandedCigar[pi+1]
-						else:
-							print '\nError, ref does not match alt while attempting to insert deletion error!\n'
-							exit(1)
-						adj -= eLen
-						for i in xrange(ePos,len(sse_adj)):
-							sse_adj[i] -= eLen
-
-					# insert insertion error into read and update cigar string accordingly
-					else:
-						myadj = sse_adj[ePos]
-						if chr(r[3][ePos+myadj]) == error[3]:
-							r[3] = r[3][:ePos+myadj] + error[4] + r[3][ePos+myadj+1:]
-							expandedCigar = expandedCigar[:ePos+myadj] + ['I']*eLen + expandedCigar[ePos+myadj:]
-						else:
-							print '\nError, ref does not match alt while attempting to insert insertion error!\n'
-							print '---',chr(r[3][ePos+myadj]), '!=', error[3]
-							exit(1)
-						adj += eLen
-						for i in xrange(ePos,len(sse_adj)):
-							sse_adj[i] += eLen
-
-				else:	# substitution errors, much easier by comparison...
-					if chr(r[3][ePos+sse_adj[ePos]]) == error[3]:
-						r[3][ePos+sse_adj[ePos]] = error[4]
-					else:
-						print '\nError, ref does not match alt while attempting to insert substitution error!\n'
-						exit(1)
-
-			if anyIndelErr:
-				if len(expandedCigar):
-					relevantCigar = (expandedCigar+extraCigarVal)[:self.readLen]
-					myCigar = CigarString(listIn=relevantCigar).getString()
-
-				r[3] = r[3][:self.readLen]
-
-			rOut.append([self.FM_pos[myPloid][r[0]],myCigar,str(r[3]),str(r[1])])
-
-		# rOut[i] = (pos, cigar, read_string, qual_string)
-		return rOut
+    def __init__(self, xOffset, sequence, ploidy, windowOverlap, readLen, mutationModels=[], mutRate=None, onlyVCF=False):
+        # initialize basic variables
+        self.onlyVCF = onlyVCF
+        self.init_basicVars(xOffset, sequence, ploidy, windowOverlap, readLen)
+        # initialize mutation models
+        self.init_mutModels(mutationModels, mutRate)
+        # sample the number of variants that will be inserted into each ploid
+        self.init_poisson()
+        self.indelsToAdd = [n.sample() for n in self.ind_pois]
+        self.snpsToAdd   = [n.sample() for n in self.snp_pois]
+        # initialize trinuc snp bias
+        self.init_trinucBias()
+
+    def init_basicVars(self, xOffset, sequence, ploidy, windowOverlap, readLen):
+        self.x         = xOffset
+        self.ploidy    = ploidy
+        self.readLen   = readLen
+        self.sequences = [bytearray(sequence) for n in range(self.ploidy)]
+        self.seqLen    = len(sequence)
+        self.indelList = [[] for n in range(self.ploidy)]
+        self.snpList   = [[] for n in range(self.ploidy)]
+        self.allCigar  = [[] for n in range(self.ploidy)]
+        self.FM_pos    = [[] for n in range(self.ploidy)]
+        self.FM_span   = [[] for n in range(self.ploidy)]
+        # blackList[ploid][pos] = 0		safe to insert variant here
+        # blackList[ploid][pos] = 1		indel inserted here
+        # blackList[ploid][pos] = 2		snp inserted here
+        # blackList[ploid][pos] = 3		invalid position for various processing reasons
+        self.blackList = [np.zeros(self.seqLen,dtype='<i4') for n in range(self.ploidy)]
+
+        # disallow mutations to occur on window overlap points
+        self.winBuffer = windowOverlap
+        for p in range(self.ploidy):
+            self.blackList[p][-self.winBuffer]   = 3
+            self.blackList[p][-self.winBuffer-1] = 3
+
+    def init_coverage(self,coverageDat,fragDist=None):
+        # if we're only creating a vcf, skip some expensive initialization related to coverage depth
+        if not self.onlyVCF:
+            (self.windowSize, gc_scalars, targetCov_vals) = coverageDat
+            gcCov_vals = [[] for n in self.sequences]
+            trCov_vals = [[] for n in self.sequences]
+            self.coverage_distribution = []
+            avg_out = []
+            for i in range(len(self.sequences)):
+                max_coord = min([len(self.sequences[i])-self.readLen, len(self.allCigar[i])-self.readLen])
+                # compute gc-bias
+                j = 0
+                while j+self.windowSize < len(self.sequences[i]):
+                    gc_c = self.sequences[i][j:j+self.windowSize].count('G') + self.sequences[i][j:j+self.windowSize].count('C')
+                    gcCov_vals[i].extend([gc_scalars[gc_c]]*self.windowSize)
+                    j += self.windowSize
+                gc_c = self.sequences[i][-self.windowSize:].count('G') + self.sequences[i][-self.windowSize:].count('C')
+                gcCov_vals[i].extend([gc_scalars[gc_c]]*(len(self.sequences[i])-len(gcCov_vals[i])))
+                #
+                trCov_vals[i].append(targetCov_vals[0])
+                prevVal = self.FM_pos[i][0]
+                for j in range(1,max_coord):
+                    if self.FM_pos[i][j] == None:
+                        trCov_vals[i].append(targetCov_vals[prevVal])
+                    elif self.FM_span[i][j]-self.FM_pos[i][j] <= 1:
+                        trCov_vals[i].append(targetCov_vals[prevVal])
+                    else:
+                        trCov_vals[i].append(sum(targetCov_vals[self.FM_pos[i][j]:self.FM_span[i][j]])/float(self.FM_span[i][j]-self.FM_pos[i][j]))
+                        prevVal = self.FM_pos[i][j]
+                    #print (i,j), self.adj[i][j], self.allCigar[i][j], self.FM_pos[i][j], self.FM_span[i][j]
+                # shift by half of read length
+                if len(trCov_vals[i]) > int(self.readLen/2.):
+                    trCov_vals[i] = [0.0]*int(self.readLen/2) + trCov_vals[i][:-int(self.readLen/2.)]
+                # fill in missing indices
+                trCov_vals[i].extend([0.0]*(len(self.sequences[i])-len(trCov_vals[i])))
+
+                #
+                covvec = np.cumsum([trCov_vals[i][nnn]*gcCov_vals[i][nnn] for nnn in range(len(trCov_vals[i]))])
+                coverage_vals = []
+                for j in range(0,max_coord):
+                    coverage_vals.append(covvec[j+self.readLen] - covvec[j])
+                avg_out.append(np.mean(coverage_vals)/float(self.readLen))
+
+                if fragDist == None:
+                    self.coverage_distribution.append(DiscreteDistribution(coverage_vals,range(len(coverage_vals))))
+
+                # fragment length nightmare
+                else:
+                    currentThresh = 0.
+                    index_list    = [0]
+                    for j in range(len(fragDist.cumP)):
+                        if fragDist.cumP[j] >= currentThresh + COV_FRAGLEN_PERCENTILE/100.0:
+                            currentThresh = fragDist.cumP[j]
+                            index_list.append(j)
+                    flq = [fragDist.values[nnn] for nnn in index_list]
+                    if fragDist.values[-1] not in flq:
+                        flq.append(fragDist.values[-1])
+                    flq.append(LARGE_NUMBER)
+
+                    self.fraglens_indMap = {}
+                    for j in fragDist.values:
+                        bInd = bisect.bisect(flq,j)
+                        if abs(flq[bInd-1] - j) <= abs(flq[bInd] - j):
+                            self.fraglens_indMap[j] = flq[bInd-1]
+                        else:
+                            self.fraglens_indMap[j] = flq[bInd]
+
+                    self.coverage_distribution.append({})
+                    for flv in sorted(list(set(self.fraglens_indMap.values()))):
+                        buffer_val = self.readLen
+                        for j in fragDist.values:
+                            if self.fraglens_indMap[j] == flv and j > buffer_val:
+                                buffer_val = j
+                        max_coord = min([len(self.sequences[i])-buffer_val-1, len(self.allCigar[i])-buffer_val+self.readLen-2])
+                        #print 'BEFORE:', len(self.sequences[i])-buffer_val
+                        #print 'AFTER: ', len(self.allCigar[i])-buffer_val+self.readLen-2
+                        #print 'AFTER2:', max_coord
+                        coverage_vals = []
+                        for j in range(0,max_coord):
+                            coverage_vals.append(covvec[j+self.readLen] - covvec[j] + covvec[j+flv] - covvec[j+flv-self.readLen])
+
+                        # EXPERIMENTAL
+                        #quantized_covVals = quantize_list(coverage_vals)
+                        #self.coverage_distribution[i][flv] = DiscreteDistribution([n[2] for n in quantized_covVals],[(n[0],n[1]) for n in quantized_covVals])
+
+                        # TESTING
+                        #import matplotlib.pyplot as mpl
+                        #print len(coverage_vals),'-->',len(quantized_covVals)
+                        #mpl.figure(0)
+                        #mpl.plot(range(len(coverage_vals)),coverage_vals)
+                        #for qcv in quantized_covVals:
+                        #	mpl.plot([qcv[0],qcv[1]+1],[qcv[2],qcv[2]],'r')
+                        #mpl.show()
+                        #exit(1)
+
+                        self.coverage_distribution[i][flv] = DiscreteDistribution(coverage_vals,range(len(coverage_vals)))
+
+            return np.mean(avg_out)
+
+    def init_mutModels(self,mutationModels,mutRate):
+        if mutationModels == []:
+            ml = [copy.deepcopy(DEFAULT_MODEL_1) for n in range(self.ploidy)]
+            self.modelData = ml[:self.ploidy]
+        else:
+            if len(mutationModels) != self.ploidy:
+                print('\nError: Number of mutation models recieved is not equal to specified ploidy\n')
+                exit(1)
+            self.modelData = copy.deepcopy(mutationModels)
+
+        # do we need to rescale mutation frequencies?
+        mutRateSum = sum([n[0] for n in self.modelData])
+        self.mutRescale = mutRate
+        if self.mutRescale == None:
+            self.mutScalar = 1.0
+        else:
+            self.mutScalar = float(self.mutRescale)/(mutRateSum/float(len(self.modelData)))
+
+        # how are mutations spread to each ploid, based on their specified mut rates?
+        self.ploidMutFrac  = [float(n[0])/mutRateSum for n in self.modelData]
+        self.ploidMutPrior = DiscreteDistribution(self.ploidMutFrac,range(self.ploidy))
+
+        # init mutation models
+        #
+        # self.models[ploid][0] = average mutation rate
+        # self.models[ploid][1] = p(mut is homozygous | mutation occurs)
+        # self.models[ploid][2] = p(mut is indel | mut occurs)
+        # self.models[ploid][3] = p(insertion | indel occurs)
+        # self.models[ploid][4] = distribution of insertion lengths
+        # self.models[ploid][5] = distribution of deletion lengths
+        # self.models[ploid][6] = distribution of trinucleotide SNP transitions
+        # self.models[ploid][7] = p(trinuc mutates)
+        self.models = []
+        for n in self.modelData:
+            self.models.append([self.mutScalar*n[0],n[1],n[2],n[3],DiscreteDistribution(n[5],n[4]),DiscreteDistribution(n[7],n[6]),[]])
+            for m in n[8]:
+                self.models[-1][6].append([DiscreteDistribution(m[0],NUCL),
+                                           DiscreteDistribution(m[1],NUCL),
+                                           DiscreteDistribution(m[2],NUCL),
+                                           DiscreteDistribution(m[3],NUCL)])
+            self.models[-1].append([m for m in n[9]])
+
+    def init_poisson(self):
+        ind_l_list = [self.seqLen*self.models[i][0]*self.models[i][2]*self.ploidMutFrac[i] for i in range(len(self.models))]
+        snp_l_list = [self.seqLen*self.models[i][0]*(1.-self.models[i][2])*self.ploidMutFrac[i] for i in range(len(self.models))]
+        k_range    = range(int(self.seqLen*MAX_MUTFRAC))
+        self.ind_pois = [poisson_list(k_range,ind_l_list[n]) for n in range(len(self.models))]
+        self.snp_pois = [poisson_list(k_range,snp_l_list[n]) for n in range(len(self.models))]
+
+    def init_trinucBias(self):
+        # compute mutation positional bias given trinucleotide strings of the sequence (ONLY AFFECTS SNPs)
+        #
+        # note: since indels are added before snps, it's possible these positional biases aren't correctly utilized
+        #       at positions affected by indels. At the moment I'm going to consider this negligible.
+        trinuc_snp_bias  = [[0. for n in range(self.seqLen)] for m in range(self.ploidy)]
+        self.trinuc_bias = [None for n in range(self.ploidy)]
+        for p in range(self.ploidy):
+            for i in range(self.winBuffer+1,self.seqLen-1):
+                trinuc_snp_bias[p][i] = self.models[p][7][ALL_IND[str(self.sequences[p][i-1:i+2])]]
+            self.trinuc_bias[p] = DiscreteDistribution(trinuc_snp_bias[p][self.winBuffer+1:self.seqLen-1],range(self.winBuffer+1,self.seqLen-1))
+
+    def update(self, xOffset, sequence, ploidy, windowOverlap, readLen, mutationModels=[], mutRate=None):
+        # if mutation model is changed, we have to reinitialize it...
+        if ploidy != self.ploidy or mutRate != self.mutRescale or mutationModels != []:
+            self.ploidy = ploidy
+            self.mutRescale = mutRate
+            self.init_mutModels(mutationModels, mutRate)
+        # if sequence length is different than previous window, we have to redo snp/indel poissons
+        if len(sequence) != self.seqLen:
+            self.seqLen = len(sequence)
+            self.init_poisson()
+        # basic vars
+        self.init_basicVars(xOffset, sequence, ploidy, windowOverlap, readLen)
+        self.indelsToAdd = [n.sample() for n in self.ind_pois]
+        self.snpsToAdd   = [n.sample() for n in self.snp_pois]
+        # initialize trinuc snp bias
+        if not IGNORE_TRINUC:
+            self.init_trinucBias()
+
+    def insert_mutations(self, inputList):
+        for inpV in inputList:
+            whichPloid = []
+            wps = inpV[4][0]
+            if wps == None:	# if no genotype given, assume heterozygous and choose a single ploid based on their mut rates
+                whichPloid.append(self.ploidMutPrior.sample())
+                whichAlt = [0]
+            else:
+                if '/' in wps or '|' in wps:
+                    if '/' in wps:
+                        splt = wps.split('/')
+                    else:
+                        splt = wps.split('|')
+                    whichPloid = []
+                    whichAlt   = []
+                    for i in range(len(splt)):
+                        if splt[i] == '1':
+                            whichPloid.append(i)
+                    # assume we're just using first alt for inserted variants?
+                    whichAlt = [0 for n in whichPloid]
+                else:	# otherwise assume monoploidy
+                    whichPloid = [0]
+                    whichAlt   = [0]
+
+            # ignore invalid ploids
+            for i in range(len(whichPloid)-1,-1,-1):
+                if whichPloid[i] >= self.ploidy:
+                    del whichPloid[i]
+
+            for i in range(len(whichPloid)):
+                p = whichPloid[i]
+                myAlt = inpV[2][whichAlt[i]]
+                myVar = (inpV[0]-self.x,inpV[1],myAlt)
+                inLen = max([len(inpV[1]),len(myAlt)])
+
+                if myVar[0] < 0 or myVar[0] >= len(self.blackList[p]):
+                    print('\nError: Attempting to insert variant out of window bounds:')
+                    print(myVar, '--> blackList[0:' + str(len(self.blackList[p])) + ']\n')
+                    exit(1)
+                if len(inpV[1]) == 1 and len(myAlt) == 1:
+                    if self.blackList[p][myVar[0]]:
+                        continue
+                    self.snpList[p].append(myVar)
+                    self.blackList[p][myVar[0]] = 2
+                else:
+                    indel_failed = False
+                    for k in range(myVar[0],myVar[0]+inLen+1):
+                        if k >= len(self.blackList[p]):
+                            indel_failed = True
+                            continue
+                        if self.blackList[p][k]:
+                            indel_failed = True
+                            continue
+                    if indel_failed:
+                        continue
+                    for k in range(myVar[0],myVar[0]+inLen+1):
+                        self.blackList[p][k] = 1
+                    self.indelList[p].append(myVar)
+
+    def random_mutations(self):
+
+        #	add random indels
+        all_indels  = [[] for n in self.sequences]
+        for i in range(self.ploidy):
+            for j in range(self.indelsToAdd[i]):
+                if random.random() <= self.models[i][1]:	# insert homozygous indel
+                    whichPloid = range(self.ploidy)
+                else:								# insert heterozygous indel
+                    whichPloid = [self.ploidMutPrior.sample()]
+
+                # try to find suitable places to insert indels
+                eventPos = -1
+                for attempt in range(MAX_ATTEMPTS):
+                    eventPos = random.randint(self.winBuffer,self.seqLen-1)
+                    for p in whichPloid:
+                        if self.blackList[p][eventPos]:
+                            eventPos = -1
+                    if eventPos != -1:
+                        break
+                if eventPos == -1:
+                    continue
+
+                if random.random() <= self.models[i][3]:	# insertion
+                    inLen   = self.models[i][4].sample()
+                    # sequence content of random insertions is uniformly random (change this later, maybe)
+                    inSeq   = ''.join([random.choice(NUCL) for n in range(inLen)])
+                    refNucl = chr(self.sequences[i][eventPos])
+                    myIndel = (eventPos,refNucl,refNucl+inSeq)
+                else:										# deletion
+                    inLen   = self.models[i][5].sample()
+                    if eventPos+inLen+1 >= len(self.sequences[i]):	# skip if deletion too close to boundary
+                        continue
+                    if inLen == 1:
+                        inSeq = chr(self.sequences[i][eventPos+1])
+                    else:
+                        inSeq = str(self.sequences[i][eventPos+1:eventPos+inLen+1])
+                    refNucl = chr(self.sequences[i][eventPos])
+                    myIndel = (eventPos,refNucl+inSeq,refNucl)
+
+                # if event too close to boundary, skip. if event conflicts with other indel, skip.
+                skipEvent = False
+                if eventPos+len(myIndel[1]) >= self.seqLen-self.winBuffer-1:
+                    skipEvent = True
+                if skipEvent:
+                    continue
+                for p in whichPloid:
+                    for k in range(eventPos,eventPos+inLen+1):
+                        if self.blackList[p][k]:
+                            skipEvent = True
+                if skipEvent:
+                    continue
+
+                for p in whichPloid:
+                    for k in range(eventPos,eventPos+inLen+1):
+                        self.blackList[p][k] = 1
+                    all_indels[p].append(myIndel)
+
+        #	add random snps
+        all_snps  = [[] for n in self.sequences]
+        for i in range(self.ploidy):
+            for j in range(self.snpsToAdd[i]):
+                if random.random() <= self.models[i][1]:	# insert homozygous SNP
+                    whichPloid = range(self.ploidy)
+                else:								# insert heterozygous SNP
+                    whichPloid = [self.ploidMutPrior.sample()]
+
+                # try to find suitable places to insert snps
+                eventPos = -1
+                for attempt in range(MAX_ATTEMPTS):
+                    # based on the mutation model for the specified ploid, choose a SNP location based on trinuc bias
+                    # (if there are multiple ploids, choose one at random)
+                    if IGNORE_TRINUC:
+                        eventPos = random.randint(self.winBuffer+1,self.seqLen-2)
+                    else:
+                        ploid_to_use = whichPloid[random.randint(0,len(whichPloid)-1)]
+                        eventPos     = self.trinuc_bias[ploid_to_use].sample()
+                    for p in whichPloid:
+                        if self.blackList[p][eventPos]:
+                            eventPos = -1
+                    if eventPos != -1:
+                        break
+                if eventPos == -1:
+                    continue
+
+                refNucl = chr(self.sequences[i][eventPos])
+                context = str(chr(self.sequences[i][eventPos-1])+chr(self.sequences[i][eventPos+1]))
+                # sample from tri-nucleotide substitution matrices to get SNP alt allele
+                newNucl = self.models[i][6][TRI_IND[context]][NUC_IND[refNucl]].sample()
+                mySNP   = (eventPos,refNucl,newNucl)
+
+                for p in whichPloid:
+                    all_snps[p].append(mySNP)
+                    self.blackList[p][mySNP[0]] = 2
+
+        # combine random snps with inserted snps, remove any snps that overlap indels
+        for p in range(len(all_snps)):
+            all_snps[p].extend(self.snpList[p])
+            all_snps[p] = [n for n in all_snps[p] if self.blackList[p][n[0]] != 1]
+
+        # MODIFY REFERENCE STRING: SNPS
+        for i in range(len(all_snps)):
+            for j in range(len(all_snps[i])):
+                vPos = all_snps[i][j][0]
+
+                if all_snps[i][j][1] != chr(self.sequences[i][vPos]):
+                    print('\nError: Something went wrong!\n', all_snps[i][j], chr(self.sequences[i][vPos]), '\n')
+                    exit(1)
+                else:
+                    self.sequences[i][vPos] = all_snps[i][j][2]
+
+        # organize the indels we want to insert
+        for i in range(len(all_indels)):
+            all_indels[i].extend(self.indelList[i])
+        all_indels_ins = [sorted([list(m) for m in n]) for n in all_indels]
+
+        # MODIFY REFERENCE STRING: INDELS
+        adjToAdd = [[] for n in range(self.ploidy)]
+        for i in range(len(all_indels_ins)):
+            rollingAdj = 0
+            tempSymbolString = ['M' for n in self.sequences[i]]
+            # there's an off-by-one error somewhere in the position sampling routines.. this might fix it
+            tempSymbolString.append('M')
+            for j in range(len(all_indels_ins[i])):
+                vPos        = all_indels_ins[i][j][0] + rollingAdj
+                vPos2       = vPos + len(all_indels_ins[i][j][1])
+                rollingAdj += len(all_indels_ins[i][j][2])-len(all_indels_ins[i][j][1])
+
+                if all_indels_ins[i][j][1] != str(self.sequences[i][vPos:vPos2]):
+                    print('\nError: Something went wrong!\n', all_indels_ins[i][j], [vPos, vPos2],
+                          str(self.sequences[i][vPos:vPos2]), '\n')
+                    exit(1)
+                else:
+                    # alter reference sequence
+                    self.sequences[i] = self.sequences[i][:vPos] + bytearray(all_indels_ins[i][j][2]) + self.sequences[i][vPos2:]
+                    # notate indel positions for cigar computation
+                    d = len(all_indels_ins[i][j][2]) - len(all_indels_ins[i][j][1])
+                    if d > 0:
+                        tempSymbolString = tempSymbolString[:vPos+1] + ['I']*d + tempSymbolString[vPos2+1:]
+                    elif d < 0:
+                        tempSymbolString[vPos+1] = 'D'*abs(d)+'M'
+
+            # precompute cigar strings
+            for j in range(len(tempSymbolString)-self.readLen):
+                self.allCigar[i].append(CigarString(listIn=tempSymbolString[j:j+self.readLen]).getString())
+
+            # create some data structures we will need later:
+            # --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e. corresponding to the unmodified reference genome)
+            # --- self.FM_span[ploid][pos]: number of reference positions spanned by a read originating from this coordinate
+            MD_soFar = 0
+            for j in range(len(tempSymbolString)):
+                self.FM_pos[i].append(MD_soFar)
+                # fix an edge case with deletions
+                if 'D' in tempSymbolString[j]:
+                    self.FM_pos[i][-1] += tempSymbolString[j].count('D')
+                # compute number of ref matches for each read
+                span_dif = len([n for n in tempSymbolString[j:j+self.readLen] if 'M' in n])
+                self.FM_span[i].append(self.FM_pos[i][-1] + span_dif)
+                MD_soFar += tempSymbolString[j].count('M') + tempSymbolString[j].count('D')
+
+        # tally up all the variants we handled...
+        countDict = {}
+        all_variants = [sorted(all_snps[i]+all_indels[i]) for i in range(self.ploidy)]
+        for i in range(len(all_variants)):
+            for j in range(len(all_variants[i])):
+                all_variants[i][j] = tuple([all_variants[i][j][0]+self.x])+all_variants[i][j][1:]
+                t = tuple(all_variants[i][j])
+                if t not in countDict:
+                    countDict[t] = []
+                countDict[t].append(i)
+
+        #
+        #	TODO: combine multiple variants that happened to occur at same position into single vcf entry?
+        #
+
+        output_variants = []
+        for k in sorted(countDict.keys()):
+            output_variants.append(k+tuple([len(countDict[k])/float(self.ploidy)]))
+            ploid_string = ['0' for n in range(self.ploidy)]
+            for k2 in [n for n in countDict[k]]:
+                ploid_string[k2] = '1'
+            output_variants[-1] += tuple(['WP='+'/'.join(ploid_string)])
+        return output_variants
+
+
+    def sample_read(self, sequencingModel, fragLen=None):
+
+        # choose a ploid
+        myPloid = random.randint(0,self.ploidy-1)
+
+        # stop attempting to find a valid position if we fail enough times
+        MAX_READPOS_ATTEMPTS = 100
+        attempts_thus_far    = 0
+
+        # choose a random position within the ploid, and generate quality scores / sequencing errors
+        readsToSample = []
+        if fragLen == None:
+            rPos = self.coverage_distribution[myPloid].sample()
+
+            # sample read position and call function to compute quality scores / sequencing errors
+            rDat = self.sequences[myPloid][rPos:rPos+self.readLen]
+            (myQual, myErrors) = sequencingModel.getSequencingErrors(rDat)
+            readsToSample.append([rPos,myQual,myErrors,rDat])
+
+        else:
+            rPos1 = self.coverage_distribution[myPloid][self.fraglens_indMap[fragLen]].sample()
+
+            # EXPERIMENTAL
+            #coords_to_select_from = self.coverage_distribution[myPloid][self.fraglens_indMap[fragLen]].sample()
+            #rPos1 = random.randint(coords_to_select_from[0],coords_to_select_from[1])
+
+            rPos2 = rPos1 + fragLen - self.readLen
+            rDat1 = self.sequences[myPloid][rPos1:rPos1+self.readLen]
+            rDat2 = self.sequences[myPloid][rPos2:rPos2+self.readLen]
+            (myQual1, myErrors1) = sequencingModel.getSequencingErrors(rDat1)
+            (myQual2, myErrors2) = sequencingModel.getSequencingErrors(rDat2,isReverseStrand=True)
+            readsToSample.append([rPos1,myQual1,myErrors1,rDat1])
+            readsToSample.append([rPos2,myQual2,myErrors2,rDat2])
+
+        # error format:
+        # myError[i] = (type, len, pos, ref, alt)
+
+        # examine sequencing errors to-be-inserted.
+        #	- remove deletions that don't have enough bordering sequence content to "fill in"
+        # if error is valid, make the changes to the read data
+        rOut = []
+        for r in readsToSample:
+            try:
+                myCigar = self.allCigar[myPloid][r[0]]
+            except IndexError:
+                print('Index error when attempting to find cigar string.')
+                print(myPloid, len(self.allCigar[myPloid]), r[0])
+                if fragLen is not None:
+                    print((rPos1, rPos2))
+                    print(fragLen, self.fraglens_indMap[fragLen])
+                exit(1)
+            totalD  = sum([error[1] for error in r[2] if error[0] == 'D'])
+            totalI  = sum([error[1] for error in r[2] if error[0] == 'I'])
+            availB  = len(self.sequences[myPloid]) - r[0] - self.readLen - 1
+            # add buffer sequence to fill in positions that get deleted
+            r[3] += self.sequences[myPloid][r[0]+self.readLen:r[0]+self.readLen+totalD]
+            expandedCigar = []
+            extraCigar    = []
+            adj           = 0
+            sse_adj       = [0 for n in range(self.readLen + max(sequencingModel.errP[3]))]
+            anyIndelErr   = False
+
+            # sort by letter (D > I > S) such that we introduce all indel errors before substitution errors
+            # secondarily, sort by index
+            arrangedErrors = {'D':[],'I':[],'S':[]}
+            for error in r[2]:
+                arrangedErrors[error[0]].append((error[2],error))
+            sortedErrors = []
+            for k in sorted(arrangedErrors.keys()):
+                sortedErrors.extend([n[1] for n in sorted(arrangedErrors[k])])
+
+            skipIndels = False
+
+
+            #FIXED TdB 05JUN2018
+            #Moved this outside the for error loop, since it messes up the CIGAR string when more than one deletion is in the same read
+            extraCigarVal = []
+            #END FIXED TdB
+
+            for error in sortedErrors:
+                eLen = error[1]
+                ePos = error[2]
+                if error[0] == 'D' or error[0] == 'I':
+                    anyIndelErr   = True
+
+                    #FIXED TdB 05JUN2018
+                    #Moved this OUTSIDE the for error loop, since it messes up the CIGAR string when more than one deletion is in the same read
+                    #extraCigarVal = []
+                    #END FIXED TdB
+
+                    if totalD > availB:	# if not enough bases to fill-in deletions, skip all indel erors
+                        continue
+                    if expandedCigar == []:
+                        expandedCigar = CigarString(stringIn=myCigar).getList()
+                        fillToGo = totalD - totalI + 1
+                        if fillToGo > 0:
+                            try:
+                                extraCigarVal = CigarString(stringIn=self.allCigar[myPloid][r[0]+fillToGo]).getList()[-fillToGo:]
+                            except IndexError:	# applying the deletions we want requires going beyond region boundaries. skip all indel errors
+                                skipIndels = True
+
+
+                    if skipIndels:
+                        continue
+
+                    # insert deletion error into read and update cigar string accordingly
+                    if error[0] == 'D':
+                        myadj = sse_adj[ePos]
+                        pi = ePos+myadj
+                        pf = ePos+myadj+eLen+1
+                        if str(r[3][pi:pf]) == str(error[3]):
+                            r[3] = r[3][:pi+1] + r[3][pf:]
+                            expandedCigar = expandedCigar[:pi+1] + expandedCigar[pf:]
+                            if pi+1 == len(expandedCigar):	# weird edge case with del at very end of region. Make a guess and add a "M"
+                                expandedCigar.append('M')
+                            expandedCigar[pi+1] = 'D'*eLen + expandedCigar[pi+1]
+                        else:
+                            print('\nError, ref does not match alt while attempting to insert deletion error!\n')
+                            exit(1)
+                        adj -= eLen
+                        for i in range(ePos,len(sse_adj)):
+                            sse_adj[i] -= eLen
+
+                    # insert insertion error into read and update cigar string accordingly
+                    else:
+                        myadj = sse_adj[ePos]
+                        if chr(r[3][ePos+myadj]) == error[3]:
+                            r[3] = r[3][:ePos+myadj] + error[4] + r[3][ePos+myadj+1:]
+                            expandedCigar = expandedCigar[:ePos+myadj] + ['I']*eLen + expandedCigar[ePos+myadj:]
+                        else:
+                            print('\nError, ref does not match alt while attempting to insert insertion error!\n')
+                            print('---', chr(r[3][ePos + myadj]), '!=', error[3])
+                            exit(1)
+                        adj += eLen
+                        for i in range(ePos,len(sse_adj)):
+                            sse_adj[i] += eLen
+
+                else:	# substitution errors, much easier by comparison...
+                    if chr(r[3][ePos+sse_adj[ePos]]) == error[3]:
+                        r[3][ePos+sse_adj[ePos]] = error[4]
+                    else:
+                        print('\nError, ref does not match alt while attempting to insert substitution error!\n')
+                        exit(1)
+
+            if anyIndelErr:
+                if len(expandedCigar):
+                    relevantCigar = (expandedCigar+extraCigarVal)[:self.readLen]
+                    myCigar = CigarString(listIn=relevantCigar).getString()
+
+                r[3] = r[3][:self.readLen]
+
+            rOut.append([self.FM_pos[myPloid][r[0]],myCigar,str(r[3]),str(r[1])])
+
+        # rOut[i] = (pos, cigar, read_string, qual_string)
+        return rOut
 
 
 #
 #	Container for read data, computes quality scores and positions to insert errors
 #
 class ReadContainer:
-	def __init__(self, readLen, errorModel, reScaledError):
-
-		self.readLen = readLen
-
-		errorDat = pickle.load(open(errorModel,'rb'))
-		self.UNIFORM = False
-		if len(errorDat) == 4:		# uniform-error SE reads (e.g. PacBio)
-			self.UNIFORM = True
-			[Qscores,offQ,avgError,errorParams] = errorDat
-			self.uniform_qscore = int(-10.*np.log10(avgError)+0.5)
-			print 'Using uniform sequencing error model. (q='+str(self.uniform_qscore)+'+'+str(offQ)+', p(err)={0:0.2f}%)'.format(100.*avgError)
-		if len(errorDat) == 6:		# only 1 q-score model present, use same model for both strands
-			[initQ1,probQ1,Qscores,offQ,avgError,errorParams] = errorDat
-			self.PE_MODELS = False
-		elif len(errorDat) == 8:	# found a q-score model for both forward and reverse strands
-			#print 'Using paired-read quality score profiles...'
-			[initQ1,probQ1,initQ2,probQ2,Qscores,offQ,avgError,errorParams] = errorDat
-			self.PE_MODELS = True
-			if len(initQ1) != len(initQ2) or len(probQ1) != len(probQ2):
-				print '\nError: R1 and R2 quality score models are of different length.\n'
-				exit(1)
-
-
-		self.qErrRate = [0.]*(max(Qscores)+1)
-		for q in Qscores:
-			self.qErrRate[q] = 10.**(-q/10.)
-		self.offQ = offQ
-
-		# errorParams = [SSE_PROB, SIE_RATE, SIE_PROB, SIE_VAL, SIE_INS_FREQ, SIE_INS_NUCL]
-		self.errP   = errorParams
-		self.errSSE = [DiscreteDistribution(n,NUCL) for n in self.errP[0]]
-		self.errSIE = DiscreteDistribution(self.errP[2],self.errP[3])
-		self.errSIN = DiscreteDistribution(self.errP[5],NUCL)
-
-		# adjust sequencing error frequency to match desired rate
-		if reScaledError == None:
-			self.errorScale = 1.0
-		else:
-			self.errorScale = reScaledError/avgError
-			print 'Warning: Quality scores no longer exactly representative of error probability. Error model scaled by {0:.3f} to match desired rate...'.format(self.errorScale)
-
-		if self.UNIFORM == False:
-			# adjust length to match desired read length
-			if self.readLen == len(initQ1):
-				self.qIndRemap = range(self.readLen)
-			else:
-				print 'Warning: Read length of error model ('+str(len(initQ1))+') does not match -R value ('+str(self.readLen)+'), rescaling model...'
-				self.qIndRemap = [max([1,len(initQ1)*n/readLen]) for n in xrange(readLen)]
-
-			# initialize probability distributions
-			self.initDistByPos1        = [DiscreteDistribution(initQ1[i],Qscores) for i in xrange(len(initQ1))]
-			self.probDistByPosByPrevQ1 = [None]
-			for i in xrange(1,len(initQ1)):
-				self.probDistByPosByPrevQ1.append([])
-				for j in xrange(len(initQ1[0])):
-					if np.sum(probQ1[i][j]) <= 0.:	# if we don't have sufficient data for a transition, use the previous qscore
-						self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution([1],[Qscores[j]],degenerateVal=Qscores[j]))
-					else:
-						self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution(probQ1[i][j],Qscores))
-
-			if self.PE_MODELS:
-				self.initDistByPos2        = [DiscreteDistribution(initQ2[i],Qscores) for i in xrange(len(initQ2))]
-				self.probDistByPosByPrevQ2 = [None]
-				for i in xrange(1,len(initQ2)):
-					self.probDistByPosByPrevQ2.append([])
-					for j in xrange(len(initQ2[0])):
-						if np.sum(probQ2[i][j]) <= 0.:	# if we don't have sufficient data for a transition, use the previous qscore
-							self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution([1],[Qscores[j]],degenerateVal=Qscores[j]))
-						else:
-							self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution(probQ2[i][j],Qscores))
-
-	def getSequencingErrors(self, readData, isReverseStrand=False):
-
-		qOut = [0]*self.readLen
-		sErr = []
-
-		if self.UNIFORM:
-			myQ  = [self.uniform_qscore + self.offQ for n in xrange(self.readLen)]
-			qOut = ''.join([chr(n) for n in myQ])
-			for i in xrange(self.readLen):
-				if random.random() < self.errorScale*self.qErrRate[self.uniform_qscore]:
-					sErr.append(i)
-		else:
-
-			if self.PE_MODELS and isReverseStrand:
-				myQ = self.initDistByPos2[0].sample()
-			else:
-				myQ = self.initDistByPos1[0].sample()
-			qOut[0] = myQ
-
-			for i in xrange(1,self.readLen):
-				if self.PE_MODELS and isReverseStrand:
-					myQ = self.probDistByPosByPrevQ2[self.qIndRemap[i]][myQ].sample()
-				else:
-					myQ = self.probDistByPosByPrevQ1[self.qIndRemap[i]][myQ].sample()
-				qOut[i] = myQ
-
-			if isReverseStrand:
-				qOut = qOut[::-1]
-
-			for i in xrange(self.readLen):
-				if random.random() < self.errorScale * self.qErrRate[qOut[i]]:
-					sErr.append(i)
-
-			qOut = ''.join([chr(n + self.offQ) for n in qOut])
-
-		if self.errorScale == 0.0:
-			return (qOut,[])
-
-		sOut = []
-		nDelSoFar = 0
-		# don't allow indel errors to occur on subsequent positions
-		prevIndel = -2
-		# don't allow other sequencing errors to occur on bases removed by deletion errors
-		delBlacklist = []
-
-		for ind in sErr[::-1]:	# for each error that we're going to insert...
-
-			# determine error type
-			isSub = True
-			if ind != 0 and ind != self.readLen-1-max(self.errP[3]) and abs(ind-prevIndel) > 1:
-				if random.random() < self.errP[1]:
-					isSub = False
-
-			# errorOut = (type, len, pos, ref, alt)
-
-			if isSub:								# insert substitution error
-				myNucl  = chr(readData[ind])
-				newNucl = self.errSSE[NUC_IND[myNucl]].sample()
-				sOut.append(('S',1,ind,myNucl,newNucl))
-			else:									# insert indel error
-				indelLen = self.errSIE.sample()
-				if random.random() < self.errP[4]:		# insertion error
-					myNucl  = chr(readData[ind])
-					newNucl = myNucl + ''.join([self.errSIN.sample() for n in xrange(indelLen)])
-					sOut.append(('I',len(newNucl)-1,ind,myNucl,newNucl))
-				elif ind < self.readLen-2-nDelSoFar:	# deletion error (prevent too many of them from stacking up)
-					myNucl  = str(readData[ind:ind+indelLen+1])
-					newNucl = chr(readData[ind])
-					nDelSoFar += len(myNucl)-1
-					sOut.append(('D',len(myNucl)-1,ind,myNucl,newNucl))
-					for i in xrange(ind+1,ind+indelLen+1):
-						delBlacklist.append(i)
-				prevIndel = ind
-
-		# remove blacklisted errors
-		for i in xrange(len(sOut)-1,-1,-1):
-			if sOut[i][2] in delBlacklist:
-				del sOut[i]
-
-		return (qOut,sOut)
+    def __init__(self, readLen, errorModel, reScaledError):
+
+        self.readLen = readLen
+
+        errorDat = pickle.load(open(errorModel,'rb'))
+        self.UNIFORM = False
+        if len(errorDat) == 4:		# uniform-error SE reads (e.g. PacBio)
+            self.UNIFORM = True
+            [Qscores,offQ,avgError,errorParams] = errorDat
+            self.uniform_qscore = int(-10.*np.log10(avgError)+0.5)
+            print('Using uniform sequencing error model. (q=' + str(self.uniform_qscore) + '+' + str(
+                offQ) + ', p(err)={0:0.2f}%)'.format(100. * avgError))
+        if len(errorDat) == 6:		# only 1 q-score model present, use same model for both strands
+            [initQ1,probQ1,Qscores,offQ,avgError,errorParams] = errorDat
+            self.PE_MODELS = False
+        elif len(errorDat) == 8:	# found a q-score model for both forward and reverse strands
+            #print 'Using paired-read quality score profiles...'
+            [initQ1,probQ1,initQ2,probQ2,Qscores,offQ,avgError,errorParams] = errorDat
+            self.PE_MODELS = True
+            if len(initQ1) != len(initQ2) or len(probQ1) != len(probQ2):
+                print('\nError: R1 and R2 quality score models are of different length.\n')
+                exit(1)
+
+
+        self.qErrRate = [0.]*(max(Qscores)+1)
+        for q in Qscores:
+            self.qErrRate[q] = 10.**(-q/10.)
+        self.offQ = offQ
+
+        # errorParams = [SSE_PROB, SIE_RATE, SIE_PROB, SIE_VAL, SIE_INS_FREQ, SIE_INS_NUCL]
+        self.errP   = errorParams
+        self.errSSE = [DiscreteDistribution(n,NUCL) for n in self.errP[0]]
+        self.errSIE = DiscreteDistribution(self.errP[2],self.errP[3])
+        self.errSIN = DiscreteDistribution(self.errP[5],NUCL)
+
+        # adjust sequencing error frequency to match desired rate
+        if reScaledError == None:
+            self.errorScale = 1.0
+        else:
+            self.errorScale = reScaledError/avgError
+            print('Warning: Quality scores no longer exactly representative of error probability. '
+                  'Error model scaled by {0:.3f} to match desired rate...'.format(self.errorScale))
+
+        if self.UNIFORM == False:
+            # adjust length to match desired read length
+            if self.readLen == len(initQ1):
+                self.qIndRemap = range(self.readLen)
+            else:
+                print('Warning: Read length of error model (' + str(len(initQ1)) + ') does not match -R value (' + str(
+                    self.readLen) + '), rescaling model...')
+                self.qIndRemap = [max([1,len(initQ1)*n/readLen]) for n in range(readLen)]
+
+            # initialize probability distributions
+            self.initDistByPos1        = [DiscreteDistribution(initQ1[i],Qscores) for i in range(len(initQ1))]
+            self.probDistByPosByPrevQ1 = [None]
+            for i in range(1,len(initQ1)):
+                self.probDistByPosByPrevQ1.append([])
+                for j in range(len(initQ1[0])):
+                    if np.sum(probQ1[i][j]) <= 0.:	# if we don't have sufficient data for a transition, use the previous qscore
+                        self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution([1],[Qscores[j]],degenerateVal=Qscores[j]))
+                    else:
+                        self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution(probQ1[i][j],Qscores))
+
+            if self.PE_MODELS:
+                self.initDistByPos2        = [DiscreteDistribution(initQ2[i],Qscores) for i in range(len(initQ2))]
+                self.probDistByPosByPrevQ2 = [None]
+                for i in range(1,len(initQ2)):
+                    self.probDistByPosByPrevQ2.append([])
+                    for j in range(len(initQ2[0])):
+                        if np.sum(probQ2[i][j]) <= 0.:	# if we don't have sufficient data for a transition, use the previous qscore
+                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution([1],[Qscores[j]],degenerateVal=Qscores[j]))
+                        else:
+                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution(probQ2[i][j],Qscores))
+
+    def getSequencingErrors(self, readData, isReverseStrand=False):
+
+        qOut = [0]*self.readLen
+        sErr = []
+
+        if self.UNIFORM:
+            myQ  = [self.uniform_qscore + self.offQ for n in range(self.readLen)]
+            qOut = ''.join([chr(n) for n in myQ])
+            for i in range(self.readLen):
+                if random.random() < self.errorScale*self.qErrRate[self.uniform_qscore]:
+                    sErr.append(i)
+        else:
+
+            if self.PE_MODELS and isReverseStrand:
+                myQ = self.initDistByPos2[0].sample()
+            else:
+                myQ = self.initDistByPos1[0].sample()
+            qOut[0] = myQ
+
+            for i in range(1,self.readLen):
+                if self.PE_MODELS and isReverseStrand:
+                    myQ = self.probDistByPosByPrevQ2[self.qIndRemap[i]][myQ].sample()
+                else:
+                    myQ = self.probDistByPosByPrevQ1[self.qIndRemap[i]][myQ].sample()
+                qOut[i] = myQ
+
+            if isReverseStrand:
+                qOut = qOut[::-1]
+
+            for i in range(self.readLen):
+                if random.random() < self.errorScale * self.qErrRate[qOut[i]]:
+                    sErr.append(i)
+
+            qOut = ''.join([chr(n + self.offQ) for n in qOut])
+
+        if self.errorScale == 0.0:
+            return (qOut,[])
+
+        sOut = []
+        nDelSoFar = 0
+        # don't allow indel errors to occur on subsequent positions
+        prevIndel = -2
+        # don't allow other sequencing errors to occur on bases removed by deletion errors
+        delBlacklist = []
+
+        for ind in sErr[::-1]:	# for each error that we're going to insert...
+
+            # determine error type
+            isSub = True
+            if ind != 0 and ind != self.readLen-1-max(self.errP[3]) and abs(ind-prevIndel) > 1:
+                if random.random() < self.errP[1]:
+                    isSub = False
+
+            # errorOut = (type, len, pos, ref, alt)
+
+            if isSub:								# insert substitution error
+                myNucl  = chr(readData[ind])
+                newNucl = self.errSSE[NUC_IND[myNucl]].sample()
+                sOut.append(('S',1,ind,myNucl,newNucl))
+            else:									# insert indel error
+                indelLen = self.errSIE.sample()
+                if random.random() < self.errP[4]:		# insertion error
+                    myNucl  = chr(readData[ind])
+                    newNucl = myNucl + ''.join([self.errSIN.sample() for n in range(indelLen)])
+                    sOut.append(('I',len(newNucl)-1,ind,myNucl,newNucl))
+                elif ind < self.readLen-2-nDelSoFar:	# deletion error (prevent too many of them from stacking up)
+                    myNucl  = str(readData[ind:ind+indelLen+1])
+                    newNucl = chr(readData[ind])
+                    nDelSoFar += len(myNucl)-1
+                    sOut.append(('D',len(myNucl)-1,ind,myNucl,newNucl))
+                    for i in range(ind+1,ind+indelLen+1):
+                        delBlacklist.append(i)
+                prevIndel = ind
+
+        # remove blacklisted errors
+        for i in range(len(sOut)-1,-1,-1):
+            if sOut[i][2] in delBlacklist:
+                del sOut[i]
+
+        return (qOut,sOut)
 
 
 
@@ -809,145 +813,145 @@ def getSequencingErrors(self, readData, isReverseStrand=False):
 
 # parse mutation model pickle file
 def parseInputMutationModel(model=None, whichDefault=1):
-	if whichDefault == 1:
-		outModel = [copy.deepcopy(n) for n in DEFAULT_MODEL_1]
-	elif whichDefault == 2:
-		outModel = [copy.deepcopy(n) for n in DEFAULT_MODEL_2]
-	else:
-		print '\nError: Unknown default mutation model specified\n'
-		exit(1)
-
-	if model != None:
-		pickle_dict = pickle.load(open(model,"rb"))
-		outModel[0] = pickle_dict['AVG_MUT_RATE']
-		outModel[2] = 1. - pickle_dict['SNP_FREQ']
-
-		insList     = pickle_dict['INDEL_FREQ']
-		if len(insList):
-			insCount = sum([insList[k] for k in insList.keys() if k >= 1])
-			delCount = sum([insList[k] for k in insList.keys() if k <= -1])
-			insVals  = [k for k in sorted(insList.keys()) if k >= 1]
-			insWght  = [insList[k]/float(insCount) for k in insVals]
-			delVals  = [k for k in sorted([abs(k) for k in insList.keys() if k <= -1])]
-			delWght  = [insList[-k]/float(delCount) for k in delVals]
-		else:	# degenerate case where no indel stats are provided
-			insCount = 1
-			delCount = 1
-			insVals  = [1]
-			insWght  = [1.0]
-			delVals  = [1]
-			delWght  = [1.0]
-		outModel[3] = insCount/float(insCount + delCount)
-		outModel[4] = insVals
-		outModel[5] = insWght
-		outModel[6] = delVals
-		outModel[7] = delWght
-
-		trinuc_trans_prob = pickle_dict['TRINUC_TRANS_PROBS']
-		for k in sorted(trinuc_trans_prob.keys()):
-			myInd   = TRI_IND[k[0][0]+k[0][2]]
-			(k1,k2) = (NUC_IND[k[0][1]],NUC_IND[k[1][1]])
-			outModel[8][myInd][k1][k2] = trinuc_trans_prob[k]
-		for i in xrange(len(outModel[8])):
-			for j in xrange(len(outModel[8][i])):
-				for l in xrange(len(outModel[8][i][j])):
-					# if trinuc not present in input mutation model, assign it uniform probability
-					if float(sum(outModel[8][i][j])) < 1e-12:
-						outModel[8][i][j] = [0.25,0.25,0.25,0.25]
-					else:
-						outModel[8][i][j][l] /= float(sum(outModel[8][i][j]))
-
-		trinuc_mut_prob    = pickle_dict['TRINUC_MUT_PROB']
-		which_have_we_seen = {n:False for n in ALL_TRI}
-		trinuc_mean        = np.mean(trinuc_mut_prob.values())
-		for trinuc in trinuc_mut_prob.keys():
-			outModel[9][ALL_IND[trinuc]] = trinuc_mut_prob[trinuc]
-			which_have_we_seen[trinuc]   = True
-		for trinuc in which_have_we_seen.keys():
-			if which_have_we_seen[trinuc] == False:
-				outModel[9][ALL_IND[trinuc]] = trinuc_mean
-
-	return outModel
+    if whichDefault == 1:
+        outModel = [copy.deepcopy(n) for n in DEFAULT_MODEL_1]
+    elif whichDefault == 2:
+        outModel = [copy.deepcopy(n) for n in DEFAULT_MODEL_2]
+    else:
+        print('\nError: Unknown default mutation model specified\n')
+        exit(1)
+
+    if model != None:
+        pickle_dict = pickle.load(open(model,"rb"))
+        outModel[0] = pickle_dict['AVG_MUT_RATE']
+        outModel[2] = 1. - pickle_dict['SNP_FREQ']
+
+        insList     = pickle_dict['INDEL_FREQ']
+        if len(insList):
+            insCount = sum([insList[k] for k in insList.keys() if k >= 1])
+            delCount = sum([insList[k] for k in insList.keys() if k <= -1])
+            insVals  = [k for k in sorted(insList.keys()) if k >= 1]
+            insWght  = [insList[k]/float(insCount) for k in insVals]
+            delVals  = [k for k in sorted([abs(k) for k in insList.keys() if k <= -1])]
+            delWght  = [insList[-k]/float(delCount) for k in delVals]
+        else:	# degenerate case where no indel stats are provided
+            insCount = 1
+            delCount = 1
+            insVals  = [1]
+            insWght  = [1.0]
+            delVals  = [1]
+            delWght  = [1.0]
+        outModel[3] = insCount/float(insCount + delCount)
+        outModel[4] = insVals
+        outModel[5] = insWght
+        outModel[6] = delVals
+        outModel[7] = delWght
+
+        trinuc_trans_prob = pickle_dict['TRINUC_TRANS_PROBS']
+        for k in sorted(trinuc_trans_prob.keys()):
+            myInd   = TRI_IND[k[0][0]+k[0][2]]
+            (k1,k2) = (NUC_IND[k[0][1]],NUC_IND[k[1][1]])
+            outModel[8][myInd][k1][k2] = trinuc_trans_prob[k]
+        for i in range(len(outModel[8])):
+            for j in range(len(outModel[8][i])):
+                for l in range(len(outModel[8][i][j])):
+                    # if trinuc not present in input mutation model, assign it uniform probability
+                    if float(sum(outModel[8][i][j])) < 1e-12:
+                        outModel[8][i][j] = [0.25,0.25,0.25,0.25]
+                    else:
+                        outModel[8][i][j][l] /= float(sum(outModel[8][i][j]))
+
+        trinuc_mut_prob    = pickle_dict['TRINUC_MUT_PROB']
+        which_have_we_seen = {n:False for n in ALL_TRI}
+        trinuc_mean        = np.mean(trinuc_mut_prob.values())
+        for trinuc in trinuc_mut_prob.keys():
+            outModel[9][ALL_IND[trinuc]] = trinuc_mut_prob[trinuc]
+            which_have_we_seen[trinuc]   = True
+        for trinuc in which_have_we_seen.keys():
+            if which_have_we_seen[trinuc] == False:
+                outModel[9][ALL_IND[trinuc]] = trinuc_mean
+
+    return outModel
 
 
 # parse mutation model files, returns default model if no model directory is specified
 #
 # OLD FUNCTION THAT PROCESSED OUTDATED TEXTFILE MUTATION MODELS
 def parseInputMutationModel_deprecated(prefix=None, whichDefault=1):
-	if whichDefault == 1:
-		outModel = [copy.deepcopy(n) for n in DEFAULT_MODEL_1]
-	elif whichDefault == 2:
-		outModel = [copy.deepcopy(n) for n in DEFAULT_MODEL_2]
-	else:
-		print '\nError: Unknown default mutation model specified\n'
-		exit(1)
-
-	if prefix != None:
-		if prefix[-1] != '/':
-			prefix += '/'
-		if not os.path.isdir(prefix):
-			'\nError: Input mutation model directory not found:',prefix,'\n'
-			exit(1)
-
-		print 'Reading in mutation model...'
-		listing1 = [n for n in os.listdir(prefix) if n[-5:] == '.prob']
-		listing2 = [n for n in os.listdir(prefix) if n[-7:] == '.trinuc']
-		listing  = sorted(listing1) + sorted(listing2)
-		for l in listing:
-			f = open(prefix+l,'r')
-			fr = [n.split('\t') for n in f.read().split('\n')]
-			f.close()
-
-			if '_overall.prob' in l:
-				myIns = None
-				myDel = None
-				for dat in fr[1:]:
-					if len(dat) == 2:
-						if dat[0] == 'insertion':
-							myIns = float(dat[1])
-						elif dat[0] == 'deletion':
-							myDel = float(dat[1])
-				if myIns != None and myDel != None:
-					outModel[2] = myIns + myDel
-					outModel[3] = myIns / (myIns + myDel)
-					print '-',l
-
-			if '_insLength.prob' in l:
-				insVals = {}
-				for dat in fr[1:]:
-					if len(dat) == 2:
-						insVals[int(dat[0])] = float(dat[1])
-				if len(insVals):
-					outModel[4] = sorted(insVals.keys())
-					outModel[5] = [insVals[n] for n in outModel[4]]
-					print '-',l
-
-			if '_delLength.prob' in l:
-				delVals = {}
-				for dat in fr[1:]:
-					if len(dat) == 2:
-						delVals[int(dat[0])] = float(dat[1])
-				if len(delVals):
-					outModel[6] = sorted(delVals.keys())
-					outModel[7] = [delVals[n] for n in outModel[6]]
-					print '-',l
-
-			if '.trinuc' == l[-7:]:
-				context_ind = TRI_IND[l[-10]+l[-8]]
-				p_matrix    = [[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1]]
-				for i in xrange(len(p_matrix)):
-					for j in xrange(len(fr[i])):
-						p_matrix[i][j] = float(fr[i][j])
-				anyNone = False
-				for i in xrange(len(p_matrix)):
-					for j in xrange(len(p_matrix[i])):
-						if p_matrix[i][j] == -1:
-							anyNone = True
-				if not anyNone:
-					outModel[8][context_ind] = copy.deepcopy(p_matrix)
-					print '-',l
-
-	return outModel
+    if whichDefault == 1:
+        outModel = [copy.deepcopy(n) for n in DEFAULT_MODEL_1]
+    elif whichDefault == 2:
+        outModel = [copy.deepcopy(n) for n in DEFAULT_MODEL_2]
+    else:
+        print('\nError: Unknown default mutation model specified\n')
+        exit(1)
+
+    if prefix is not None:
+        if prefix[-1] != '/':
+            prefix += '/'
+        if not os.path.isdir(prefix):
+            print('\nError: Input mutation model directory not found:', prefix, '\n')
+            exit(1)
+
+        print('Reading in mutation model...')
+        listing1 = [n for n in os.listdir(prefix) if n[-5:] == '.prob']
+        listing2 = [n for n in os.listdir(prefix) if n[-7:] == '.trinuc']
+        listing  = sorted(listing1) + sorted(listing2)
+        for l in listing:
+            f = open(prefix+l,'r')
+            fr = [n.split('\t') for n in f.read().split('\n')]
+            f.close()
+
+            if '_overall.prob' in l:
+                myIns = None
+                myDel = None
+                for dat in fr[1:]:
+                    if len(dat) == 2:
+                        if dat[0] == 'insertion':
+                            myIns = float(dat[1])
+                        elif dat[0] == 'deletion':
+                            myDel = float(dat[1])
+                if myIns != None and myDel != None:
+                    outModel[2] = myIns + myDel
+                    outModel[3] = myIns / (myIns + myDel)
+                    print('-', l)
+
+            if '_insLength.prob' in l:
+                insVals = {}
+                for dat in fr[1:]:
+                    if len(dat) == 2:
+                        insVals[int(dat[0])] = float(dat[1])
+                if len(insVals):
+                    outModel[4] = sorted(insVals.keys())
+                    outModel[5] = [insVals[n] for n in outModel[4]]
+                    print('-', l)
+
+            if '_delLength.prob' in l:
+                delVals = {}
+                for dat in fr[1:]:
+                    if len(dat) == 2:
+                        delVals[int(dat[0])] = float(dat[1])
+                if len(delVals):
+                    outModel[6] = sorted(delVals.keys())
+                    outModel[7] = [delVals[n] for n in outModel[6]]
+                    print('-', l)
+
+            if '.trinuc' == l[-7:]:
+                context_ind = TRI_IND[l[-10]+l[-8]]
+                p_matrix    = [[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1]]
+                for i in range(len(p_matrix)):
+                    for j in range(len(fr[i])):
+                        p_matrix[i][j] = float(fr[i][j])
+                anyNone = False
+                for i in range(len(p_matrix)):
+                    for j in range(len(p_matrix[i])):
+                        if p_matrix[i][j] == -1:
+                            anyNone = True
+                if not anyNone:
+                    outModel[8][context_ind] = copy.deepcopy(p_matrix)
+                    print('-', l)
+
+    return outModel
 
 ######################
 #	DEFAULT VALUES   #
@@ -962,45 +966,45 @@ def parseInputMutationModel_deprecated(prefix=None, whichDefault=1):
 DEFAULT_1_DEL_LENGTH_VALUES  = [1,2,3,4,5]
 DEFAULT_1_DEL_LENGTH_WEIGHTS = [0.3,0.2,0.2,0.2,0.1]
 example_matrix_1             = [[0.0, 0.15, 0.7, 0.15],
-						        [0.15, 0.0, 0.15, 0.7],
-						        [0.7, 0.15, 0.0, 0.15],
-						        [0.15, 0.7, 0.15, 0.0]]
-DEFAULT_1_TRI_FREQS          = [copy.deepcopy(example_matrix_1) for n in xrange(16)]
+                                [0.15, 0.0, 0.15, 0.7],
+                                [0.7, 0.15, 0.0, 0.15],
+                                [0.15, 0.7, 0.15, 0.0]]
+DEFAULT_1_TRI_FREQS          = [copy.deepcopy(example_matrix_1) for n in range(16)]
 DEFAULT_1_TRINUC_BIAS        = [1./float(len(ALL_TRI)) for n in ALL_TRI]
 DEFAULT_MODEL_1              = [DEFAULT_1_OVERALL_MUT_RATE,
-							    DEFAULT_1_HOMOZYGOUS_FREQ,
-							    DEFAULT_1_INDEL_FRACTION,
-							    DEFAULT_1_INS_VS_DEL,
-							    DEFAULT_1_INS_LENGTH_VALUES,
-							    DEFAULT_1_INS_LENGTH_WEIGHTS,
-							    DEFAULT_1_DEL_LENGTH_VALUES,
-							    DEFAULT_1_DEL_LENGTH_WEIGHTS,
-							    DEFAULT_1_TRI_FREQS,
-							    DEFAULT_1_TRINUC_BIAS]
+                                DEFAULT_1_HOMOZYGOUS_FREQ,
+                                DEFAULT_1_INDEL_FRACTION,
+                                DEFAULT_1_INS_VS_DEL,
+                                DEFAULT_1_INS_LENGTH_VALUES,
+                                DEFAULT_1_INS_LENGTH_WEIGHTS,
+                                DEFAULT_1_DEL_LENGTH_VALUES,
+                                DEFAULT_1_DEL_LENGTH_WEIGHTS,
+                                DEFAULT_1_TRI_FREQS,
+                                DEFAULT_1_TRINUC_BIAS]
 
 DEFAULT_2_OVERALL_MUT_RATE   = 0.002
 DEFAULT_2_HOMOZYGOUS_FREQ    = 0.200
 DEFAULT_2_INDEL_FRACTION     = 0.1
 DEFAULT_2_INS_VS_DEL         = 0.3
-DEFAULT_2_INS_LENGTH_VALUES  = [1,2,3,4,5,6,7,8,9,10]
+DEFAULT_2_INS_LENGTH_VALUES  = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
 DEFAULT_2_INS_LENGTH_WEIGHTS = [0.1, 0.1, 0.2, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]
 DEFAULT_2_DEL_LENGTH_VALUES  = [1,2,3,4,5]
 DEFAULT_2_DEL_LENGTH_WEIGHTS = [0.3,0.2,0.2,0.2,0.1]
 example_matrix_2             = [[0.0, 0.15, 0.7, 0.15],
-						        [0.15, 0.0, 0.15, 0.7],
-						        [0.7, 0.15, 0.0, 0.15],
-						        [0.15, 0.7, 0.15, 0.0]]
-DEFAULT_2_TRI_FREQS          = [copy.deepcopy(example_matrix_2) for n in xrange(16)]
+                                [0.15, 0.0, 0.15, 0.7],
+                                [0.7, 0.15, 0.0, 0.15],
+                                [0.15, 0.7, 0.15, 0.0]]
+DEFAULT_2_TRI_FREQS          = [copy.deepcopy(example_matrix_2) for n in range(16)]
 DEFAULT_2_TRINUC_BIAS        = [1./float(len(ALL_TRI)) for n in ALL_TRI]
 DEFAULT_MODEL_2              = [DEFAULT_2_OVERALL_MUT_RATE,
-							    DEFAULT_2_HOMOZYGOUS_FREQ,
-							    DEFAULT_2_INDEL_FRACTION,
-							    DEFAULT_2_INS_VS_DEL,
-							    DEFAULT_2_INS_LENGTH_VALUES,
-							    DEFAULT_2_INS_LENGTH_WEIGHTS,
-							    DEFAULT_2_DEL_LENGTH_VALUES,
-							    DEFAULT_2_DEL_LENGTH_WEIGHTS,
-							    DEFAULT_2_TRI_FREQS,
-							    DEFAULT_2_TRINUC_BIAS]
+                                DEFAULT_2_HOMOZYGOUS_FREQ,
+                                DEFAULT_2_INDEL_FRACTION,
+                                DEFAULT_2_INS_VS_DEL,
+                                DEFAULT_2_INS_LENGTH_VALUES,
+                                DEFAULT_2_INS_LENGTH_WEIGHTS,
+                                DEFAULT_2_DEL_LENGTH_VALUES,
+                                DEFAULT_2_DEL_LENGTH_WEIGHTS,
+                                DEFAULT_2_TRI_FREQS,
+                                DEFAULT_2_TRINUC_BIAS]
 
 
diff --git a/py/__init__.py b/py/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/py/biopython_modified_bgzf.py b/py/biopython_modified_bgzf.py
index 4e84afe..7da4c31 100755
--- a/py/biopython_modified_bgzf.py
+++ b/py/biopython_modified_bgzf.py
@@ -1,103 +1,102 @@
-#!/usr/bin/env python
+#!/user/env/bin python
+
 # Copyright 2010-2013 by Peter Cock.
 # All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 
-""" ############################################################################
-#######                                                                  #######
-#######    06/02/2015:                                                   #######
-#######        - I picked out the bits and pieces of code needed         #######
-#######          to write BAM files, removed python 3.0 compatibility    #######
-#######                                                                  #######
-############################################################################ """
+
+##########################################################################
+#                                                                  #######
+#    06/02/2015:                                                   #######
+#        - I picked out the bits and pieces of code needed         #######
+#          to write BAM files, removed python 3.0 compatibility    #######
+#                                                                  #######
+##########################################################################
 
 import zlib
 import struct
 
-_bgzf_header = b"\x1f\x8b\x08\x04\x00\x00\x00\x00\x00\xff\x06\x00\x42\x43\x02\x00"
-_bgzf_eof    = b"\x1f\x8b\x08\x04\x00\x00\x00\x00\x00\xff\x06\x00\x42\x43\x02\x00\x1b\x00\x03\x00\x00\x00\x00\x00\x00\x00\x00\x00"
-
-class BgzfWriter(object):
-
-	def __init__(self, filename=None, mode="w", fileobj=None, compresslevel=6):
-		if fileobj:
-			assert filename is None
-			handle = fileobj
-		else:
-			if "w" not in mode.lower() \
-			and "a" not in mode.lower():
-				raise ValueError("Must use write or append mode, not %r" % mode)
-			if "a" in mode.lower():
-				handle = open(filename, "ab")
-			else:
-				handle = open(filename, "wb")
-		self._text = "b" not in mode.lower()
-		self._handle = handle
-		self._buffer = b""
-		self.compresslevel = compresslevel
 
-	def _write_block(self, block):
-		start_offset = self._handle.tell()
-		assert len(block) <= 65536
-		# Giving a negative window bits means no gzip/zlib headers, -15 used in samtools
-		c = zlib.compressobj(self.compresslevel,
-							 zlib.DEFLATED,
-							 -15,
-							 zlib.DEF_MEM_LEVEL,
-							 0)
-		compressed = c.compress(block) + c.flush()
-		del c
-		assert len(compressed) < 65536, "TODO - Didn't compress enough, try less data in this block"
-		crc = zlib.crc32(block)
-		# Should cope with a mix of Python platforms...
-		if crc < 0:
-			crc = struct.pack("<i", crc)
-		else:
-			crc = struct.pack("<I", crc)
-		bsize = struct.pack("<H", len(compressed) + 25)  # includes -1
-		crc = struct.pack("<I", zlib.crc32(block) & 0xffffffff)
-		uncompressed_length = struct.pack("<I", len(block))
-		data = _bgzf_header + bsize + compressed + crc + uncompressed_length
-		self._handle.write(data)
+class BgzfWriter:
 
-	def write(self, data):
-		data_len = len(data)
-		if len(self._buffer) + data_len < 65536:
-			self._buffer += data
-			return
-		else:
-			self._buffer += data
-			while len(self._buffer) >= 65536:
-				self._write_block(self._buffer[:65536])
-				self._buffer = self._buffer[65536:]
+    def __init__(self, filename=None, mode="w", fileobj=None, compresslevel=6):
+        if fileobj:
+            assert filename is None
+            handle = fileobj
+        else:
+            if "w" not in mode.lower() \
+                    and "a" not in mode.lower():
+                raise ValueError("Must use write or append mode, not %r" % mode)
+            if "a" in mode.lower():
+                handle = open(filename, "ab")
+            else:
+                handle = open(filename, "wb")
+        self._text = "b" not in mode.lower()
+        self._handle = handle
+        self._buffer = b""
+        self.compresslevel = compresslevel
 
-	def flush(self):
-		while len(self._buffer) >= 65536:
-			self._write_block(self._buffer[:65535])
-			self._buffer = self._buffer[65535:]
-		self._write_block(self._buffer)
-		self._buffer = b""
-		self._handle.flush()
+    def _write_block(self, block):
+        _bgzf_header = b"\x1f\x8b\x08\x04\x00\x00\x00\x00\x00\xff\x06\x00\x42\x43\x02\x00"
+        start_offset = self._handle.tell()
+        assert len(block) <= 65536
+        # Giving a negative window bits means no gzip/zlib headers, -15 used in samtools
+        c = zlib.compressobj(self.compresslevel,
+                             zlib.DEFLATED,
+                             -15,
+                             zlib.DEF_MEM_LEVEL,
+                             0)
+        compressed = c.compress(block) + c.flush()
+        del c
+        assert len(compressed) < 65536, "TODO - Didn't compress enough, try less data in this block"
+        crc = zlib.crc32(block)
+        # Should cope with a mix of Python platforms...
+        if crc < 0:
+            crc = struct.pack("<i", crc)
+        else:
+            crc = struct.pack("<I", crc)
+        bsize = struct.pack("<H", len(compressed) + 25)  # includes -1
+        crc = struct.pack("<I", zlib.crc32(block) & 0xffffffff)
+        uncompressed_length = struct.pack("<I", len(block))
+        data = _bgzf_header + bsize + compressed + crc + uncompressed_length
+        self._handle.write(data)
 
-	def close(self):
-		"""Flush data, write 28 bytes empty BGZF EOF marker, and close the BGZF file."""
-		if self._buffer:
-			self.flush()
-		# samtools will look for a magic EOF marker, just a 28 byte empty BGZF block,
-		# and if it is missing warns the BAM file may be truncated. In addition to
-		# samtools writing this block, so too does bgzip - so we should too.
-		self._handle.write(_bgzf_eof)
-		self._handle.flush()
-		self._handle.close()
+    def write(self, data):
+        data_len = len(data)
+        if len(self._buffer) + data_len < 65536:
+            self._buffer += data
+            return
+        else:
+            self._buffer += data
+            while len(self._buffer) >= 65536:
+                self._write_block(self._buffer[:65536])
+                self._buffer = self._buffer[65536:]
 
-	def __enter__(self):
-		return self
+    def flush(self):
+        while len(self._buffer) >= 65536:
+            self._write_block(self._buffer[:65535])
+            self._buffer = self._buffer[65535:]
+        self._write_block(self._buffer)
+        self._buffer = b""
+        self._handle.flush()
 
-	def __exit__(self, type, value, traceback):
-		self.close()
+    def close(self):
+        """Flush data, write 28 bytes empty BGZF EOF marker, and close the BGZF file."""
+        _bgzf_eof = b"\x1f\x8b\x08\x04\x00\x00\x00\x00\x00\xff\x06\x00\x42\x43\x02\x00\x1b\x00\x03\x00\x00\x00\x00\x00" \
+                    b"\x00\x00\x00\x00"
+        if self._buffer:
+            self.flush()
+        # samtools will look for a magic EOF marker, just a 28 byte empty BGZF block,
+        # and if it is missing warns the BAM file may be truncated. In addition to
+        # samtools writing this block, so too does bgzip - so we should too.
+        self._handle.write(_bgzf_eof)
+        self._handle.flush()
+        self._handle.close()
 
+    def __enter__(self):
+        return self
 
-if __name__ == "__main__":
-	pass
+    def __exit__(self, type, value, traceback):
+        self.close()
\ No newline at end of file
diff --git a/py/inputChecking.py b/py/inputChecking.py
index 30cddaf..e47e849 100644
--- a/py/inputChecking.py
+++ b/py/inputChecking.py
@@ -1,29 +1,33 @@
 import os
 import sys
 
-def requiredField(s,errString):
-	if s == None:
-		print '\n'+errString+'\n'
-		exit(1)
-
-def checkFileOpen(fn,errString,required=False):
-	if required or fn != None:
-		if fn == None:
-			print '\n'+errString+'\n'
-			exit(1)
-		else:
-			try:
-				open(fn,'r')
-			except:
-				print '\n'+errString+'\n'
-				exit(1)
-
-def checkDir(dir,errString):
-	if not os.path.isdir(dir):
-		print '\n'+errString+'\n'
-		exit(1)
-
-def isInRange(val,lb,ub,errString):
-	if val < lb or val > ub:
-		print '\n'+errString+'\n'
-		exit(1)
+
+def requiredField(s, errString):
+    if s is None:
+        print('\n' + errString + '\n')
+        exit(1)
+
+
+def checkFileOpen(fn, errString, required=False):
+    if required or fn is not None:
+        if fn is None:
+            print('\n' + errString + '\n')
+            exit(1)
+        else:
+            try:
+                open(fn, 'r')
+            except:
+                print('\n' + errString + '\n')
+                exit(1)
+
+
+def checkDir(dir, errString):
+    if not os.path.isdir(dir):
+        print('\n' + errString + '\n')
+        exit(1)
+
+
+def isInRange(val, lb, ub, errString):
+    if val < lb or val > ub:
+        print('\n' + errString + '\n')
+        exit(1)
diff --git a/py/neat_cigar.py b/py/neat_cigar.py
index 80d79b2..ab588f9 100644
--- a/py/neat_cigar.py
+++ b/py/neat_cigar.py
@@ -1,108 +1,103 @@
 import re
 
-class CigarString:
-	def __init__(self, stringIn=None, listIn=None):
-
-		if stringIn == None and listIn == None:
-			print '\nError: CigarString object not initialized.\n'
-			exit(1)
-
-		self.cigarData = []
-
-		if stringIn != None:
-			self.joinCigar(j_stringIn=stringIn)
-
-		if listIn != None:
-			self.joinCigar(j_listIn=listIn)
 
+class CigarString:
+    def __init__(self, stringIn=None, listIn=None):
 
-	def stringToList(self, s):
+        if stringIn is None and listIn is None:
+            print('\nError: CigarString object not initialized.\n')
+            exit(1)
 
-		cigarDat = []
-		letters = re.split(r"\d+",s)[1:]
-		numbers = [int(n) for n in re.findall(r"\d+",s)]
-		dReserve = 0
-		for i in xrange(len(letters)):
-			if letters[i] == 'D':
-				dReserve = numbers[i]
-			if letters[i] == 'M' or letters[i] == 'I':
-				if dReserve:
-					cigarDat += ['D'*dReserve+letters[i]] + [letters[i]]*(int(numbers[i])-1)
-				else:
-					cigarDat += [letters[i]]*int(numbers[i])
-				dReserve = 0
-		return cigarDat
+        self.cigarData = []
 
+        if stringIn is not None:
+            self.joinCigar(j_stringIn=stringIn)
 
-	def listToString(self, l):
+        if listIn is not None:
+            self.joinCigar(j_listIn=listIn)
 
-		symbols      = ''
-		currentSym   = l[0]
-		currentCount = 1
-		if 'D' in currentSym:
-			currentSym   = currentSym[-1]
-		for k in xrange(1,len(l)):
-			nextSym = l[k]
-			if len(nextSym) == 1 and nextSym == currentSym:
-				currentCount += 1
-			else:
-				symbols += str(currentCount) + currentSym
-				if 'D' in nextSym:
-					symbols += str(nextSym.count('D')) + 'D'
-					currentSym   = nextSym[-1]
-				else:
-					currentSym   = nextSym
-				currentCount = 1
-		symbols += str(currentCount) + currentSym
-		return symbols
+    def stringToList(self, s):
 
-	def getList(self):
+        cigarDat = []
+        letters = re.split(r"\d+", s)[1:]
+        numbers = [int(n) for n in re.findall(r"\d+", s)]
+        dReserve = 0
+        for i in range(len(letters)):
+            if letters[i] == 'D':
+                dReserve = numbers[i]
+            if letters[i] == 'M' or letters[i] == 'I':
+                if dReserve:
+                    cigarDat += ['D' * dReserve + letters[i]] + [letters[i]] * (int(numbers[i]) - 1)
+                else:
+                    cigarDat += [letters[i]] * int(numbers[i])
+                dReserve = 0
+        return cigarDat
 
-		return self.cigarData
+    def listToString(self, l):
 
+        symbols = ''
+        currentSym = l[0]
+        currentCount = 1
+        if 'D' in currentSym:
+            currentSym = currentSym[-1]
+        for k in range(1, len(l)):
+            nextSym = l[k]
+            if len(nextSym) == 1 and nextSym == currentSym:
+                currentCount += 1
+            else:
+                symbols += str(currentCount) + currentSym
+                if 'D' in nextSym:
+                    symbols += str(nextSym.count('D')) + 'D'
+                    currentSym = nextSym[-1]
+                else:
+                    currentSym = nextSym
+                currentCount = 1
+        symbols += str(currentCount) + currentSym
+        return symbols
 
-	def getString(self):
+    def getList(self):
 
-		return self.listToString(self.cigarData)
+        return self.cigarData
 
+    def getString(self):
 
-	def joinCigar(self, j_stringIn=None, j_listIn=None):
+        return self.listToString(self.cigarData)
 
-		if j_stringIn == None and j_listIn == None:
-			print '\nError: Invalid join operation in CigarString\n'
-			exit(1)
+    def joinCigar(self, j_stringIn=None, j_listIn=None):
 
-		if j_stringIn != None:
-			self.cigarData += self.stringToList(j_stringIn)
+        if j_stringIn is None and j_listIn is None:
+            print('\nError: Invalid join operation in CigarString\n')
+            exit(1)
 
-		if j_listIn != None:
-			self.cigarData += j_listIn
+        if j_stringIn is not None:
+            self.cigarData += self.stringToList(j_stringIn)
 
+        if j_listIn is not None:
+            self.cigarData += j_listIn
 
-	def insertCigarElement(self, pos, i_stringIn=None, i_listIn=None):
+    def insertCigarElement(self, pos, i_stringIn=None, i_listIn=None):
 
-		if i_stringIn == None and i_listIn == None:
-			print '\nError: Invalid insertion operation in CigarString\n'
-			exit(1)
+        if i_stringIn == None and i_listIn == None:
+            print('\nError: Invalid insertion operation in CigarString\n')
+            exit(1)
 
-		if pos < 0 or pos >= len(self.cigarData):
-			print '\nError: Invalid insertion position in CigarString\n'
-			exit(1)
+        if pos < 0 or pos >= len(self.cigarData):
+            print('\nError: Invalid insertion position in CigarString\n')
+            exit(1)
 
-		if i_stringIn != None:
-			self.cigarData = self.cigarData[:pos] + self.stringToList(i_stringIn) + self.cigarData[pos:]
+        if i_stringIn != None:
+            self.cigarData = self.cigarData[:pos] + self.stringToList(i_stringIn) + self.cigarData[pos:]
 
-		if i_listIn != None:
-			self.cigarData = self.cigarData[:pos] + i_listIn + self.cigarData[pos:]
+        if i_listIn != None:
+            self.cigarData = self.cigarData[:pos] + i_listIn + self.cigarData[pos:]
 
 
 if __name__ == '__main__':
-	print 'testing CigarString class...'
-
-	str1 = '50M10D7I23M'
-	str2 = '10I25M'
-	iPos = 20
-	myCigar  = CigarString(stringIn=str1)
-	myCigar.insertCigarElement(iPos,i_stringIn=str2)
-	print str1,'+',str2,'[inserted at position',str(iPos)+']','=',myCigar.getString()
-
+    print('testing CigarString class...')
+
+    str1 = '50M10D7I23M'
+    str2 = '10I25M'
+    iPos = 20
+    myCigar = CigarString(stringIn=str1)
+    myCigar.insertCigarElement(iPos, i_stringIn=str2)
+    print(str1, '+', str2, '[inserted at position', str(iPos) + ']', '=', myCigar.getString())
diff --git a/py/probability.py b/py/probability.py
index 789e98c..699a966 100644
--- a/py/probability.py
+++ b/py/probability.py
@@ -6,141 +6,143 @@
 
 LOW_PROB_THRESH = 1e-12
 
+
 def mean_ind_of_weighted_list(l):
-	myMid = sum(l)/2.0
-	mySum = 0.0
-	for i in xrange(len(l)):
-		mySum += l[i]
-		if mySum >= myMid:
-			return i
+    myMid = sum(l) / 2.0
+    mySum = 0.0
+    for i in range(len(l)):
+        mySum += l[i]
+        if mySum >= myMid:
+            return i
+
 
 class DiscreteDistribution:
-	def __init__(self, weights, values, degenerateVal=None, method='bisect'):
-
-		# some sanity checking
-		if not len(weights) or not len(values):
-			print '\nError: weight or value vector given to DiscreteDistribution() are 0-length.\n'
-			asdf = intentional_crash[0]
-			exit(1)
-
-		self.method  = method
-		sumWeight    = float(sum(weights))
-		
-		# if probability of all input events is 0, consider it degenerate and always return the first value
-		if sumWeight < LOW_PROB_THRESH:
-			self.degenerate = values[0]
-		else:
-			self.weights = [n/sumWeight for n in weights]
-			self.values  = copy.deepcopy(values)
-			if len(self.values) != len(self.weights):
-				print '\nError: length and weights and values vectors must be the same.\n'
-				exit(1)
-			self.degenerate = degenerateVal
-			# prune values with probability too low to be worth using [DOESN'T REALLY IMPROVE PERFORMANCE]
-			####if self.degenerate != None:
-			####	for i in xrange(len(self.weights)-1,-1,-1):
-			####		if self.weights[i] < LOW_PROB_THRESH:
-			####			del self.weights[i]
-			####			del self.values[i]
-			####	if len(self.weights) == 0:
-			####		print '\nError: probability distribution has no usable values.\n'
-			####		exit(1)
-
-			if self.method == 'alias':
-				K       = len(self.weights)
-				q       = np.zeros(K)
-				J       = np.zeros(K, dtype=np.int)
-				smaller = []
-				larger  = []
-				for kk, prob in enumerate(self.weights):
-					q[kk] = K*prob
-					if q[kk] < 1.0:
-						smaller.append(kk)
-					else:
-						larger.append(kk)
-				while len(smaller) > 0 and len(larger) > 0:
-					small = smaller.pop()
-					large = larger.pop()
-					J[small] = large
-					q[large] = (q[large] + q[small]) - 1.0
-					if q[large] < 1.0:
-						smaller.append(large)
-					else:
-						larger.append(large)
-
-				self.a1 = len(J)-1
-				self.a2 = J.tolist()
-				self.a3 = q.tolist()
-
-			elif self.method == 'bisect':
-				self.cumP = np.cumsum(self.weights).tolist()[:-1]
-				self.cumP.insert(0,0.)
-
-	def __str__(self):
-		return str(self.weights)+' '+str(self.values)+' '+self.method
-
-	def sample(self):
-
-		if self.degenerate != None:
-			return self.degenerate
-
-		else:
-
-			if self.method == 'alias':
-				r1 = random.randint(0,self.a1)
-				r2 = random.random()
-				if r2 < self.a3[r1]:
-					return self.values[r1]
-				else:
-					return self.values[self.a2[r1]]
-
-			elif self.method == 'bisect':
-				r = random.random()
-				return self.values[bisect.bisect(self.cumP,r)-1]
+    def __init__(self, weights, values, degenerateVal=None, method='bisect'):
+
+        # some sanity checking
+        if not len(weights) or not len(values):
+            print('\nError: weight or value vector given to DiscreteDistribution() are 0-length.\n')
+            exit(1)
+
+        self.method = method
+        sumWeight = float(sum(weights))
+
+        # if probability of all input events is 0, consider it degenerate and always return the first value
+        if sumWeight < LOW_PROB_THRESH:
+            self.degenerate = values[0]
+        else:
+            self.weights = [n / sumWeight for n in weights]
+            self.values = copy.deepcopy(values)
+            if len(self.values) != len(self.weights):
+                print('\nError: length and weights and values vectors must be the same.\n')
+                exit(1)
+            self.degenerate = degenerateVal
+            # prune values with probability too low to be worth using [DOESN'T REALLY IMPROVE PERFORMANCE]
+            ####if self.degenerate != None:
+            ####	for i in xrange(len(self.weights)-1,-1,-1):
+            ####		if self.weights[i] < LOW_PROB_THRESH:
+            ####			del self.weights[i]
+            ####			del self.values[i]
+            ####	if len(self.weights) == 0:
+            ####		print '\nError: probability distribution has no usable values.\n'
+            ####		exit(1)
+
+            if self.method == 'alias':
+                K = len(self.weights)
+                q = np.zeros(K)
+                J = np.zeros(K, dtype=np.int)
+                smaller = []
+                larger = []
+                for kk, prob in enumerate(self.weights):
+                    q[kk] = K * prob
+                    if q[kk] < 1.0:
+                        smaller.append(kk)
+                    else:
+                        larger.append(kk)
+                while len(smaller) > 0 and len(larger) > 0:
+                    small = smaller.pop()
+                    large = larger.pop()
+                    J[small] = large
+                    q[large] = (q[large] + q[small]) - 1.0
+                    if q[large] < 1.0:
+                        smaller.append(large)
+                    else:
+                        larger.append(large)
+
+                self.a1 = len(J) - 1
+                self.a2 = J.tolist()
+                self.a3 = q.tolist()
+
+            elif self.method == 'bisect':
+                self.cumP = np.cumsum(self.weights).tolist()[:-1]
+                self.cumP.insert(0, 0.)
+
+    def __str__(self):
+        return str(self.weights) + ' ' + str(self.values) + ' ' + self.method
+
+    def sample(self):
+
+        if self.degenerate != None:
+            return self.degenerate
+
+        else:
+
+            if self.method == 'alias':
+                r1 = random.randint(0, self.a1)
+                r2 = random.random()
+                if r2 < self.a3[r1]:
+                    return self.values[r1]
+                else:
+                    return self.values[self.a2[r1]]
+
+            elif self.method == 'bisect':
+                r = random.random()
+                return self.values[bisect.bisect(self.cumP, r) - 1]
 
 
 # takes k_range, lambda, [0,1,2,..], returns a DiscreteDistribution object with the corresponding to a poisson distribution
-MIN_WEIGHT = 1e-12
-def poisson_list(k_range,l):
-	if l < MIN_WEIGHT:
-		return DiscreteDistribution([1],[0],degenerateVal=0)
-	logFactorial_list = [0.0]
-	for k in k_range[1:]:
-		logFactorial_list.append(np.log(float(k))+logFactorial_list[k-1])
-	w_range = [np.exp(k*np.log(l) - l - logFactorial_list[k]) for k in k_range]
-	w_range = [n for n in w_range if n >= MIN_WEIGHT]
-	if len(w_range) <= 1:
-		return DiscreteDistribution([1],[0],degenerateVal=0)
-	return DiscreteDistribution(w_range,k_range[:len(w_range)])
+
+def poisson_list(k_range, l):
+    MIN_WEIGHT = 1e-12
+    if l < MIN_WEIGHT:
+        return DiscreteDistribution([1], [0], degenerateVal=0)
+    logFactorial_list = [0.0]
+    for k in k_range[1:]:
+        logFactorial_list.append(np.log(float(k)) + logFactorial_list[k - 1])
+    w_range = [np.exp(k * np.log(l) - l - logFactorial_list[k]) for k in k_range]
+    w_range = [n for n in w_range if n >= MIN_WEIGHT]
+    if len(w_range) <= 1:
+        return DiscreteDistribution([1], [0], degenerateVal=0)
+    return DiscreteDistribution(w_range, k_range[:len(w_range)])
+
 
 # quantize a list of values into blocks
-MIN_PROB = 1e-12
-QUANT_BLOCKS = 10
 def quantize_list(l):
-	suml = float(sum(l))
-	ls = sorted([n for n in l if n >= MIN_PROB*suml])
-	if len(ls) == 0:
-		return None
-	qi = []
-	for i in xrange(QUANT_BLOCKS):
-		#qi.append(ls[int((i)*(len(ls)/float(QUANT_BLOCKS)))])
-		qi.append(ls[0]+(i/float(QUANT_BLOCKS))*(ls[-1]-ls[0]))
-	qi.append(1e12)
-	runningList = []
-	prevBi = None
-	previ  = None
-	for i in xrange(len(l)):
-		if l[i] >= MIN_PROB*suml:
-			bi = bisect.bisect(qi,l[i])
-			#print i, l[i], qi[bi-1]
-			if prevBi != None:
-				if bi == prevBi and previ == i-1:
-					runningList[-1][1] += 1
-				else:
-					runningList.append([i,i,qi[bi-1]])
-			else:
-				runningList.append([i,i,qi[bi-1]])
-			prevBi = bi
-			previ  = i
-	return runningList
-
+    MIN_PROB = 1e-12
+    QUANT_BLOCKS = 10
+    suml = float(sum(l))
+    ls = sorted([n for n in l if n >= MIN_PROB * suml])
+    if len(ls) == 0:
+        return None
+    qi = []
+    for i in range(QUANT_BLOCKS):
+        # qi.append(ls[int((i)*(len(ls)/float(QUANT_BLOCKS)))])
+        qi.append(ls[0] + (i / float(QUANT_BLOCKS)) * (ls[-1] - ls[0]))
+    qi.append(1e12)
+    runningList = []
+    prevBi = None
+    previ = None
+    for i in range(len(l)):
+        if l[i] >= MIN_PROB * suml:
+            bi = bisect.bisect(qi, l[i])
+            # print i, l[i], qi[bi-1]
+            if prevBi != None:
+                if bi == prevBi and previ == i - 1:
+                    runningList[-1][1] += 1
+                else:
+                    runningList.append([i, i, qi[bi - 1]])
+            else:
+                runningList.append([i, i, qi[bi - 1]])
+            prevBi = bi
+            previ = i
+    return runningList
diff --git a/py/refFunc.py b/py/refFunc.py
index 6ade0c8..597498f 100644
--- a/py/refFunc.py
+++ b/py/refFunc.py
@@ -3,62 +3,57 @@
 import os
 import random
 
-OK_CHR_ORD   = {ord('A'):True,ord('C'):True,ord('G'):True,ord('T'):True,ord('U'):True}
-ALLOWED_NUCL = ['A','C','G','T']
 
-#
 #	Index reference fasta
-#
 def indexRef(refPath):
-
-	tt = time.time()
-
-	fn = None
-	if os.path.isfile(refPath+'i'):
-		print 'found index '+refPath+'i'
-		fn = refPath+'i'
-	if os.path.isfile(refPath+'.fai'):
-		print 'found index '+refPath+'.fai'
-		fn = refPath+'.fai'
-
-	ref_inds = []
-	if fn != None:
-		fai = open(fn,'r')
-		for line in fai:
-			splt = line[:-1].split('\t')
-			seqLen = int(splt[1])
-			offset = int(splt[2])
-			lineLn = int(splt[3])
-			nLines = seqLen/lineLn
-			if seqLen%lineLn != 0:
-				nLines += 1
-			ref_inds.append((splt[0],offset,offset+seqLen+nLines,seqLen))
-		fai.close()
-		return ref_inds
-
-	sys.stdout.write('index not found, creating one... ')
-	sys.stdout.flush()
-	refFile = open(refPath,'r')
-	prevR   = None
-	prevP   = None
-	seqLen  = 0
-	while 1:
-		data = refFile.readline()
-		if not data:
-			ref_inds.append( (prevR, prevP, refFile.tell()-len(data), seqLen) )
-			break
-		if data[0] == '>':
-			if prevP != None:
-				ref_inds.append( (prevR, prevP, refFile.tell()-len(data), seqLen) )
-			seqLen = 0
-			prevP  = refFile.tell()
-			prevR  = data[1:-1]
-		else:
-			seqLen += len(data)-1
-	refFile.close()
-
-	print '{0:.3f} (sec)'.format(time.time()-tt)
-	return ref_inds
+    tt = time.time()
+
+    fn = None
+    if os.path.isfile(refPath + 'i'):
+        print('found index ' + refPath + 'i')
+        fn = refPath + 'i'
+    if os.path.isfile(refPath + '.fai'):
+        print('found index ' + refPath + '.fai')
+        fn = refPath + '.fai'
+
+    ref_inds = []
+    if fn is not None:
+        fai = open(fn, 'r')
+        for line in fai:
+            splt = line[:-1].split('\t')
+            seqLen = int(splt[1])
+            offset = int(splt[2])
+            lineLn = int(splt[3])
+            nLines = seqLen / lineLn
+            if seqLen % lineLn != 0:
+                nLines += 1
+            ref_inds.append((splt[0], offset, offset + seqLen + nLines, seqLen))
+        fai.close()
+        return ref_inds
+
+    sys.stdout.write('index not found, creating one... ')
+    sys.stdout.flush()
+    refFile = open(refPath, 'r')
+    prevR = None
+    prevP = None
+    seqLen = 0
+    while 1:
+        data = refFile.readline()
+        if not data:
+            ref_inds.append((prevR, prevP, refFile.tell() - len(data), seqLen))
+            break
+        if data[0] == '>':
+            if prevP is not None:
+                ref_inds.append((prevR, prevP, refFile.tell() - len(data), seqLen))
+            seqLen = 0
+            prevP = refFile.tell()
+            prevR = data[1:-1]
+        else:
+            seqLen += len(data) - 1
+    refFile.close()
+
+    print('{0:.3f} (sec)'.format(time.time() - tt))
+    return ref_inds
 
 
 #
@@ -73,138 +68,136 @@ def indexRef(refPath):
 #                                           of 'chr's
 #		- ('ignore')                    --> do not alter nucleotides in N regions
 #
-def readRef(refPath,ref_inds_i,N_handling,N_unknowns=True,quiet=False):
-
-	tt = time.time()
-	if not quiet:
-		sys.stdout.write('reading '+ref_inds_i[0]+'... ')
-		sys.stdout.flush()
-
-	refFile = open(refPath,'r')
-	refFile.seek(ref_inds_i[1])
-	myDat = ''.join(refFile.read(ref_inds_i[2]-ref_inds_i[1]).split('\n'))
-	myDat = bytearray(myDat.upper())
-
-	# find N regions
-	# data explanation: myDat[N_atlas[0][0]:N_atlas[0][1]] = solid block of Ns
-	prevNI = 0
-	nCount = 0
-	N_atlas = []
-	for i in xrange(len(myDat)):
-		if myDat[i] == ord('N') or (N_unknowns and myDat[i] not in OK_CHR_ORD):
-			if nCount == 0:
-				prevNI = i
-			nCount += 1
-			if i == len(myDat)-1:
-				N_atlas.append((prevNI,prevNI+nCount))
-		else:
-			if nCount > 0:
-				N_atlas.append((prevNI,prevNI+nCount))
-			nCount = 0
-
-	# handle N base-calls as desired
-	N_info = {}
-	N_info['all']   = []
-	N_info['big']   = []
-	N_info['non_N'] = []
-	if N_handling[0] == 'random':
-		for region in N_atlas:
-			N_info['all'].extend(region)
-			if region[1]-region[0] <= N_handling[1]:
-				for i in xrange(region[0],region[1]):
-					myDat[i] = random.choice(ALLOWED_NUCL)
-			else:
-				N_info['big'].extend(region)
-	elif N_handling[0] == 'allChr' and N_handling[2] in OK_CHR_ORD:
-		for region in N_atlas:
-			N_info['all'].extend(region)
-			if region[1]-region[0] <= N_handling[1]:
-				for i in xrange(region[0],region[1]):
-					myDat[i] = N_handling[2]
-			else:
-				N_info['big'].extend(region)
-	elif N_handling[0] == 'ignore':
-		for region in N_atlas:
-			N_info['all'].extend(region)
-			N_info['big'].extend(region)
-	else:
-		print '\nERROR: UNKNOWN N_HANDLING MODE\n'
-		exit(1)
-
-	habitableRegions = []
-	if N_info['big'] == []:
-		N_info['non_N'] = [(0,len(myDat))]
-	else:
-		for i in xrange(0,len(N_info['big']),2):
-			if i == 0:
-				habitableRegions.append((0,N_info['big'][0]))
-			else:
-				habitableRegions.append((N_info['big'][i-1],N_info['big'][i]))
-		habitableRegions.append((N_info['big'][-1],len(myDat)))
-	for n in habitableRegions:
-		if n[0] != n[1]:
-			N_info['non_N'].append(n)
-
-	if not quiet:
-		print '{0:.3f} (sec)'.format(time.time()-tt)
-	return (myDat,N_info)
+def readRef(refPath, ref_inds_i, N_handling, N_unknowns=True, quiet=False):
+    OK_CHR_ORD = {ord('A'): True, ord('C'): True, ord('G'): True, ord('T'): True, ord('U'): True}
+    ALLOWED_NUCL = ['A', 'C', 'G', 'T']
+    tt = time.time()
+    if not quiet:
+        sys.stdout.write('reading ' + ref_inds_i[0] + '... ')
+        sys.stdout.flush()
+
+    refFile = open(refPath, 'r')
+    refFile.seek(ref_inds_i[1])
+    myDat = ''.join(refFile.read(ref_inds_i[2] - ref_inds_i[1]).split('\n'))
+    myDat = bytearray(myDat.upper())
+
+    # find N regions
+    # data explanation: myDat[N_atlas[0][0]:N_atlas[0][1]] = solid block of Ns
+    prevNI = 0
+    nCount = 0
+    N_atlas = []
+    for i in range(len(myDat)):
+        if myDat[i] == ord('N') or (N_unknowns and myDat[i] not in OK_CHR_ORD):
+            if nCount == 0:
+                prevNI = i
+            nCount += 1
+            if i == len(myDat) - 1:
+                N_atlas.append((prevNI, prevNI + nCount))
+        else:
+            if nCount > 0:
+                N_atlas.append((prevNI, prevNI + nCount))
+            nCount = 0
+
+    # handle N base-calls as desired
+    N_info = {}
+    N_info['all'] = []
+    N_info['big'] = []
+    N_info['non_N'] = []
+    if N_handling[0] == 'random':
+        for region in N_atlas:
+            N_info['all'].extend(region)
+            if region[1] - region[0] <= N_handling[1]:
+                for i in range(region[0], region[1]):
+                    myDat[i] = random.choice(ALLOWED_NUCL)
+            else:
+                N_info['big'].extend(region)
+    elif N_handling[0] == 'allChr' and N_handling[2] in OK_CHR_ORD:
+        for region in N_atlas:
+            N_info['all'].extend(region)
+            if region[1] - region[0] <= N_handling[1]:
+                for i in range(region[0], region[1]):
+                    myDat[i] = N_handling[2]
+            else:
+                N_info['big'].extend(region)
+    elif N_handling[0] == 'ignore':
+        for region in N_atlas:
+            N_info['all'].extend(region)
+            N_info['big'].extend(region)
+    else:
+        print('\nERROR: UNKNOWN N_HANDLING MODE\n')
+        exit(1)
+
+    habitableRegions = []
+    if N_info['big'] == []:
+        N_info['non_N'] = [(0, len(myDat))]
+    else:
+        for i in range(0, len(N_info['big']), 2):
+            if i == 0:
+                habitableRegions.append((0, N_info['big'][0]))
+            else:
+                habitableRegions.append((N_info['big'][i - 1], N_info['big'][i]))
+        habitableRegions.append((N_info['big'][-1], len(myDat)))
+    for n in habitableRegions:
+        if n[0] != n[1]:
+            N_info['non_N'].append(n)
+
+    if not quiet:
+        print('{0:.3f} (sec)'.format(time.time() - tt))
+    return (myDat, N_info)
+
 
-#
 #	find all non-N regions in reference sequence ahead of time, for computing jobs in parallel
-#
-def getAllRefRegions(refPath,ref_inds,N_handling,saveOutput=False):
-	outRegions = {}
-	fn = refPath+'.nnr'
-	if os.path.isfile(fn) and not(saveOutput):
-		print 'found list of preidentified non-N regions...'
-		f = open(fn,'r')
-		for line in f:
-			splt = line.strip().split('\t')
-			if splt[0] not in outRegions:
-				outRegions[splt[0]] = []
-			outRegions[splt[0]].append((int(splt[1]),int(splt[2])))
-		f.close()
-		return outRegions
-	else:
-		print 'enumerating all non-N regions in reference sequence...'
-		for RI in xrange(len(ref_inds)):
-			(refSequence,N_regions) = readRef(refPath,ref_inds[RI],N_handling,quiet=True)
-			refName = ref_inds[RI][0]
-			outRegions[refName] = [n for n in N_regions['non_N']]
-		if saveOutput:
-			f = open(fn,'w')
-			for k in outRegions.keys():
-				for n in outRegions[k]:
-					f.write(k+'\t'+str(n[0])+'\t'+str(n[1])+'\n')
-			f.close()
-		return outRegions
+def getAllRefRegions(refPath, ref_inds, N_handling, saveOutput=False):
+    outRegions = {}
+    fn = refPath + '.nnr'
+    if os.path.isfile(fn) and not (saveOutput):
+        print('found list of preidentified non-N regions...')
+        f = open(fn, 'r')
+        for line in f:
+            splt = line.strip().split('\t')
+            if splt[0] not in outRegions:
+                outRegions[splt[0]] = []
+            outRegions[splt[0]].append((int(splt[1]), int(splt[2])))
+        f.close()
+        return outRegions
+    else:
+        print('enumerating all non-N regions in reference sequence...')
+        for RI in range(len(ref_inds)):
+            (refSequence, N_regions) = readRef(refPath, ref_inds[RI], N_handling, quiet=True)
+            refName = ref_inds[RI][0]
+            outRegions[refName] = [n for n in N_regions['non_N']]
+        if saveOutput:
+            f = open(fn, 'w')
+            for k in outRegions.keys():
+                for n in outRegions[k]:
+                    f.write(k + '\t' + str(n[0]) + '\t' + str(n[1]) + '\n')
+            f.close()
+        return outRegions
+
 
-#
 #	find which of the non-N regions are going to be used for this job
-#
-def partitionRefRegions(inRegions,ref_inds,myjob,njobs):
-
-	totSize = 0
-	for RI in xrange(len(ref_inds)):
-		refName = ref_inds[RI][0]
-		for region in inRegions[refName]:
-			totSize += region[1] - region[0]
-	sizePerJob = int(totSize/float(njobs)-0.5)
-
-	regionsPerJob = [[] for n in xrange(njobs)]
-	refsPerJob    = [{} for n in xrange(njobs)]
-	currentInd    = 0
-	currentCount  = 0
-	for RI in xrange(len(ref_inds)):
-		refName = ref_inds[RI][0]
-		for region in inRegions[refName]:
-			regionsPerJob[currentInd].append((refName,region[0],region[1]))
-			refsPerJob[currentInd][refName] = True
-			currentCount += region[1] - region[0]
-			if currentCount >= sizePerJob:
-				currentCount = 0
-				currentInd   = min([currentInd+1,njobs-1])
-
-	relevantRefs = refsPerJob[myjob-1].keys()
-	relevantRegs = regionsPerJob[myjob-1]
-	return (relevantRefs,relevantRegs)
+def partitionRefRegions(inRegions, ref_inds, myjob, njobs):
+    totSize = 0
+    for RI in range(len(ref_inds)):
+        refName = ref_inds[RI][0]
+        for region in inRegions[refName]:
+            totSize += region[1] - region[0]
+    sizePerJob = int(totSize / float(njobs) - 0.5)
+
+    regionsPerJob = [[] for n in range(njobs)]
+    refsPerJob = [{} for n in range(njobs)]
+    currentInd = 0
+    currentCount = 0
+    for RI in range(len(ref_inds)):
+        refName = ref_inds[RI][0]
+        for region in inRegions[refName]:
+            regionsPerJob[currentInd].append((refName, region[0], region[1]))
+            refsPerJob[currentInd][refName] = True
+            currentCount += region[1] - region[0]
+            if currentCount >= sizePerJob:
+                currentCount = 0
+                currentInd = min([currentInd + 1, njobs - 1])
+
+    relevantRefs = refsPerJob[myjob - 1].keys()
+    relevantRegs = regionsPerJob[myjob - 1]
+    return (relevantRefs, relevantRegs)
diff --git a/py/vcfFunc.py b/py/vcfFunc.py
index fe2bcd7..f9e2ff7 100644
--- a/py/vcfFunc.py
+++ b/py/vcfFunc.py
@@ -8,181 +8,177 @@
 INCLUDE_FAIL = False
 CHOOSE_RANDOM_PLOID_IF_NO_GT_FOUND = True
 
-def parseLine(splt,colDict,colSamp):
-	
-	#	check if we want to proceed..
-	ra = splt[colDict['REF']]
-	aa = splt[colDict['ALT']]
-	# enough columns?
-	if len(splt) != len(colDict):
-		return None
-	# exclude homs / filtered?
-	if not(INCLUDE_HOMS) and (aa == '.' or aa == '' or aa == ra):
-		return None
-	if not(INCLUDE_FAIL) and (splt[colDict['FILTER']] != 'PASS' and splt[colDict['FILTER']] != '.'):
-		return None
-
-	#	default vals
-	alt_alleles = [aa]
-	alt_freqs   = []
-	
-	gt_perSamp  = []
-
-	#	any alt alleles?
-	alt_split = aa.split(',')
-	if len(alt_split) > 1:
-		alt_alleles = alt_split
-
-	#	check INFO for AF
-	af = None
-	if 'INFO' in colDict and ';AF=' in ';'+splt[colDict['INFO']]:
-		info = splt[colDict['INFO']]+';'
-		af   = re.findall(r"AF=.*?(?=;)",info)[0][3:]
-	if af != None:
-		af_splt = af.split(',')
-		while(len(af_splt) < len(alt_alleles)):	# are we lacking enough AF values for some reason?
-			af_splt.append(af_splt[-1])			# phone it in.
-		if len(af_splt) != 0 and af_splt[0] != '.' and af_splt[0] != '':		# missing data, yay
-			alt_freqs = [float(n) for n in af_splt]
-	else:
-		alt_freqs = [None]*max([len(alt_alleles),1])
-
-	gt_perSamp = None
-	#	if available (i.e. we simulated it) look for WP in info
-	if len(colSamp) == 0 and 'INFO' in colDict and 'WP=' in splt[colDict['INFO']]:
-		info       = splt[colDict['INFO']]+';'
-		gt_perSamp = [re.findall(r"WP=.*?(?=;)",info)[0][3:]]
-	else:
-		#	if no sample columns, check info for GT
-		if len(colSamp) == 0 and 'INFO' in colDict and 'GT=' in splt[colDict['INFO']]:
-			info       = splt[colDict['INFO']]+';'
-			gt_perSamp = [re.findall(r"GT=.*?(?=;)",info)[0][3:]]
-		elif len(colSamp):
-			fmt = ':'+splt[colDict['FORMAT']]+':'
-			if ':GT:' in fmt:
-				gtInd = fmt.split(':').index('GT')
-				gt_perSamp = [splt[colSamp[iii]].split(':')[gtInd-1] for iii in xrange(len(colSamp))]
-				for i in xrange(len(gt_perSamp)):
-					gt_perSamp[i] = gt_perSamp[i].replace('.','0')
-		if gt_perSamp == None:
-			gt_perSamp = [None]*max([len(colSamp),1])
-
-	return (alt_alleles, alt_freqs, gt_perSamp)
-
-
-
-def parseVCF(vcfPath,tumorNormal=False,ploidy=2):
-
-	tt = time.time()
-	print '--------------------------------'
-	sys.stdout.write('reading input VCF...\n')
-	sys.stdout.flush()
-	
-	colDict   = {}
-	colSamp   = []
-	nSkipped  = 0
-	nSkipped_becauseHash = 0
-	allVars   = {}	# [ref][pos]
-	sampNames = []
-	alreadyPrintedWarning = False
-	f = open(vcfPath,'r')
-	for line in f:
-
-		if line[0] != '#':
-			if len(colDict) == 0:
-				print '\n\nERROR: VCF has no header?\n'+VCF_FILENAME+'\n\n'
-				f.close()
-				exit(1)
-			splt = line[:-1].split('\t')
-			plOut = parseLine(splt,colDict,colSamp)
-			if plOut == None:
-				nSkipped += 1
-			else:
-				(aa, af, gt) = plOut
-
-				# make sure at least one allele somewhere contains the variant
-				if tumorNormal:
-					gtEval = gt[:2]
-				else:
-					gtEval = gt[:1]
-				if None in gtEval:
-					if CHOOSE_RANDOM_PLOID_IF_NO_GT_FOUND:
-						if not alreadyPrintedWarning:
-							print 'Warning: Found variants without a GT field, assuming heterozygous...'
-							alreadyPrintedWarning = True
-						for i in xrange(len(gtEval)):
-							tmp = ['0']*ploidy
-							tmp[random.randint(0,ploidy-1)] = '1'
-							gtEval[i] = '/'.join(tmp)
-					else:
-						# skip because no GT field was found
-						nSkipped += 1
-						continue
-				isNonReference = False
-				for gtVal in gtEval:
-					if gtVal != None:
-						if '1' in gtVal:
-							isNonReference = True
-				if not isNonReference:
-					# skip if no genotype actually contains this variant
-					nSkipped += 1
-					continue
-
-				chrom = splt[0]
-				pos   = int(splt[1])
-				ref   = splt[3]
-				# skip if position is <= 0
-				if pos <= 0:
-					nSkipped += 1
-					continue
-
-				# hash variants to avoid inserting duplicates (there are some messy VCFs out there...)
-				if chrom not in allVars:
-					allVars[chrom] = {}
-				if pos not in allVars[chrom]:
-					allVars[chrom][pos] = (pos,ref,aa,af,gtEval)
-				else:
-					nSkipped_becauseHash += 1
-			
-		else:
-			if line[1] != '#':
-				cols = line[1:-1].split('\t')
-				for i in xrange(len(cols)):
-					if 'FORMAT' in colDict:
-						colSamp.append(i)
-					colDict[cols[i]] = i
-				if len(colSamp):
-					sampNames = cols[-len(colSamp):]
-					if len(colSamp) == 1:
-						pass
-					elif len(colSamp) == 2 and tumorNormal:
-						print 'Detected 2 sample columns in input VCF, assuming tumor/normal.'
-					else:
-						print 'Warning: Multiple sample columns present in input VCF. By default genReads uses only the first column.'
-				else:
-					sampNames = ['Unknown']
-				if tumorNormal:
-					#tumorInd  = sampNames.index('TUMOR')
-					#normalInd = sampNames.index('NORMAL')
-					if 'NORMAL' not in sampNames or 'TUMOR' not in sampNames:
-						print '\n\nERROR: Input VCF must have a "NORMAL" and "TUMOR" column.\n'
-	f.close()
-
-	varsOut = {}
-	for r in allVars.keys():
-		varsOut[r] = [list(allVars[r][k]) for k in sorted(allVars[r].keys())]
-		# prune unnecessary sequence from ref/alt alleles
-		for i in xrange(len(varsOut[r])):
-			while len(varsOut[r][i][1]) > 1 and all([n[-1] == varsOut[r][i][1][-1] for n in varsOut[r][i][2]]):
-				varsOut[r][i][1] = varsOut[r][i][1][:-1]
-				varsOut[r][i][2] = [n[:-1] for n in varsOut[r][i][2]]
-			varsOut[r][i] = tuple(varsOut[r][i])
-	
-	print 'found',sum([len(n) for n in allVars.values()]),'valid variants in input vcf.'
-	print ' *',nSkipped,'variants skipped: (qual filtered / ref genotypes / invalid syntax)'
-	print ' *',nSkipped_becauseHash,'variants skipped due to multiple variants found per position'
-	print '--------------------------------'
-	return (sampNames, varsOut)
-
-
 
+def parseLine(splt, colDict, colSamp):
+    #	check if we want to proceed..
+    ra = splt[colDict['REF']]
+    aa = splt[colDict['ALT']]
+    # enough columns?
+    if len(splt) != len(colDict):
+        return None
+    # exclude homs / filtered?
+    if not (INCLUDE_HOMS) and (aa == '.' or aa == '' or aa == ra):
+        return None
+    if not (INCLUDE_FAIL) and (splt[colDict['FILTER']] != 'PASS' and splt[colDict['FILTER']] != '.'):
+        return None
+
+    #	default vals
+    alt_alleles = [aa]
+    alt_freqs = []
+
+    gt_perSamp = []
+
+    #	any alt alleles?
+    alt_split = aa.split(',')
+    if len(alt_split) > 1:
+        alt_alleles = alt_split
+
+    #	check INFO for AF
+    af = None
+    if 'INFO' in colDict and ';AF=' in ';' + splt[colDict['INFO']]:
+        info = splt[colDict['INFO']] + ';'
+        af = re.findall(r"AF=.*?(?=;)", info)[0][3:]
+    if af != None:
+        af_splt = af.split(',')
+        while (len(af_splt) < len(alt_alleles)):  # are we lacking enough AF values for some reason?
+            af_splt.append(af_splt[-1])  # phone it in.
+        if len(af_splt) != 0 and af_splt[0] != '.' and af_splt[0] != '':  # missing data, yay
+            alt_freqs = [float(n) for n in af_splt]
+    else:
+        alt_freqs = [None] * max([len(alt_alleles), 1])
+
+    gt_perSamp = None
+    #	if available (i.e. we simulated it) look for WP in info
+    if len(colSamp) == 0 and 'INFO' in colDict and 'WP=' in splt[colDict['INFO']]:
+        info = splt[colDict['INFO']] + ';'
+        gt_perSamp = [re.findall(r"WP=.*?(?=;)", info)[0][3:]]
+    else:
+        #	if no sample columns, check info for GT
+        if len(colSamp) == 0 and 'INFO' in colDict and 'GT=' in splt[colDict['INFO']]:
+            info = splt[colDict['INFO']] + ';'
+            gt_perSamp = [re.findall(r"GT=.*?(?=;)", info)[0][3:]]
+        elif len(colSamp):
+            fmt = ':' + splt[colDict['FORMAT']] + ':'
+            if ':GT:' in fmt:
+                gtInd = fmt.split(':').index('GT')
+                gt_perSamp = [splt[colSamp[iii]].split(':')[gtInd - 1] for iii in range(len(colSamp))]
+                for i in range(len(gt_perSamp)):
+                    gt_perSamp[i] = gt_perSamp[i].replace('.', '0')
+        if gt_perSamp == None:
+            gt_perSamp = [None] * max([len(colSamp), 1])
+
+    return (alt_alleles, alt_freqs, gt_perSamp)
+
+
+def parseVCF(vcfPath, tumorNormal=False, ploidy=2):
+    tt = time.time()
+    print('--------------------------------')
+    sys.stdout.write('reading input VCF...\n')
+    sys.stdout.flush()
+
+    colDict = {}
+    colSamp = []
+    nSkipped = 0
+    nSkipped_becauseHash = 0
+    allVars = {}  # [ref][pos]
+    sampNames = []
+    alreadyPrintedWarning = False
+    f = open(vcfPath, 'r')
+    for line in f:
+
+        if line[0] != '#':
+            if len(colDict) == 0:
+                print('\n\nERROR: VCF has no header?\n' + vcfPath + '\n\n')
+                f.close()
+                exit(1)
+            splt = line[:-1].split('\t')
+            plOut = parseLine(splt, colDict, colSamp)
+            if plOut == None:
+                nSkipped += 1
+            else:
+                (aa, af, gt) = plOut
+
+                # make sure at least one allele somewhere contains the variant
+                if tumorNormal:
+                    gtEval = gt[:2]
+                else:
+                    gtEval = gt[:1]
+                if None in gtEval:
+                    if CHOOSE_RANDOM_PLOID_IF_NO_GT_FOUND:
+                        if not alreadyPrintedWarning:
+                            print('Warning: Found variants without a GT field, assuming heterozygous...')
+                            alreadyPrintedWarning = True
+                        for i in range(len(gtEval)):
+                            tmp = ['0'] * ploidy
+                            tmp[random.randint(0, ploidy - 1)] = '1'
+                            gtEval[i] = '/'.join(tmp)
+                    else:
+                        # skip because no GT field was found
+                        nSkipped += 1
+                        continue
+                isNonReference = False
+                for gtVal in gtEval:
+                    if gtVal != None:
+                        if '1' in gtVal:
+                            isNonReference = True
+                if not isNonReference:
+                    # skip if no genotype actually contains this variant
+                    nSkipped += 1
+                    continue
+
+                chrom = splt[0]
+                pos = int(splt[1])
+                ref = splt[3]
+                # skip if position is <= 0
+                if pos <= 0:
+                    nSkipped += 1
+                    continue
+
+                # hash variants to avoid inserting duplicates (there are some messy VCFs out there...)
+                if chrom not in allVars:
+                    allVars[chrom] = {}
+                if pos not in allVars[chrom]:
+                    allVars[chrom][pos] = (pos, ref, aa, af, gtEval)
+                else:
+                    nSkipped_becauseHash += 1
+
+        else:
+            if line[1] != '#':
+                cols = line[1:-1].split('\t')
+                for i in range(len(cols)):
+                    if 'FORMAT' in colDict:
+                        colSamp.append(i)
+                    colDict[cols[i]] = i
+                if len(colSamp):
+                    sampNames = cols[-len(colSamp):]
+                    if len(colSamp) == 1:
+                        pass
+                    elif len(colSamp) == 2 and tumorNormal:
+                        print('Detected 2 sample columns in input VCF, assuming tumor/normal.')
+                    else:
+                        print(
+                            'Warning: Multiple sample columns present in input VCF. By default genReads uses only the first column.')
+                else:
+                    sampNames = ['Unknown']
+                if tumorNormal:
+                    # tumorInd  = sampNames.index('TUMOR')
+                    # normalInd = sampNames.index('NORMAL')
+                    if 'NORMAL' not in sampNames or 'TUMOR' not in sampNames:
+                        print('\n\nERROR: Input VCF must have a "NORMAL" and "TUMOR" column.\n')
+    f.close()
+
+    varsOut = {}
+    for r in allVars.keys():
+        varsOut[r] = [list(allVars[r][k]) for k in sorted(allVars[r].keys())]
+        # prune unnecessary sequence from ref/alt alleles
+        for i in range(len(varsOut[r])):
+            while len(varsOut[r][i][1]) > 1 and all([n[-1] == varsOut[r][i][1][-1] for n in varsOut[r][i][2]]):
+                varsOut[r][i][1] = varsOut[r][i][1][:-1]
+                varsOut[r][i][2] = [n[:-1] for n in varsOut[r][i][2]]
+            varsOut[r][i] = tuple(varsOut[r][i])
+
+    print('found', sum([len(n) for n in allVars.values()]), 'valid variants in input vcf.')
+    print(' *', nSkipped, 'variants skipped: (qual filtered / ref genotypes / invalid syntax)')
+    print(' *', nSkipped_becauseHash, 'variants skipped due to multiple variants found per position')
+    print('--------------------------------')
+    return (sampNames, varsOut)

From fe6700304de80ba8a0d90330f4483b394681bb84 Mon Sep 17 00:00:00 2001
From: joshfactorial <jallen17@illinois.edu>
Date: Tue, 7 Jan 2020 16:14:39 -0600
Subject: [PATCH 002/323] Bringing scripts up to par with minimal Python3

---
 mergeJobs.py                  |  214 +++---
 utilities/__init__.py         |    0
 utilities/computeFraglen.py   |  104 +--
 utilities/computeGC.py        |  129 ++--
 utilities/genMutModel.py      |  942 +++++++++++------------
 utilities/genSeqErrorModel.py |  522 ++++++-------
 utilities/plotMutModel.py     |  366 ++++-----
 utilities/validateBam.py      |  124 +--
 utilities/validateFQ.py       |  111 +--
 utilities/vcf_compare_OLD.py  | 1333 +++++++++++++++++----------------
 10 files changed, 1946 insertions(+), 1899 deletions(-)
 create mode 100644 utilities/__init__.py

diff --git a/mergeJobs.py b/mergeJobs.py
index 5f3b04e..55c9237 100644
--- a/mergeJobs.py
+++ b/mergeJobs.py
@@ -2,59 +2,66 @@
 import os
 import argparse
 
-def getListOfFiles(inDir,pattern):
-	return [inDir+n for n in os.listdir(inDir) if (pattern in n and os.path.getsize(inDir+n))]
+
+def getListOfFiles(inDir, pattern):
+    return [inDir + n for n in os.listdir(inDir) if (pattern in n and os.path.getsize(inDir + n))]
+
 
 TEMP_IND = 0
+
+
 def stripVCF_header(fn):
-	global TEMP_IND
-	f = open(fn,'r')
-	ftn = fn+'_temp'+str(TEMP_IND)
-	f_t = open(ftn,'w')
-	hasHeader = False
-	for line in f:
-		if line[0] == '#':
-			if not hasHeader:
-				TEMP_IND += 1
-			hasHeader = True
-		elif hasHeader:
-			f_t.write(line)
-		else:
-			break
-	f_t.close()
-	f.close()
-	if hasHeader:
-		return ftn
-	else:
-		os.system('rm '+ftn)
-		return fn
-
-def catListOfFiles(l,outName,gzipped=False):
-	for n in l:
-		if n[-3:] == '.gz' or n[-5:] == '.gzip':
-			gzipped = True
-	if gzipped:
-		for n in l:
-			if not n[-3:] == '.gz' and not n[-5:] == '.gzip':
-				print '\nError: Found a mixture of compressed and decompressed files with the specified prefix. Abandoning ship...\n'
-				for m in l:
-					print m
-				print ''
-				exit(1)
-		cmd = 'cat '+' '.join(sorted(l))+' > '+outName+'.gz'
-	else:
-		cmd = 'cat '+' '.join(sorted(l))+' > '+outName
-	print cmd
-	os.system(cmd)
-
-def catBams(l,outName,samtools_exe):
-	l_sort = sorted(l)
-	tmp = outName+'.tempHeader.sam'
-	os.system(samtools_exe+' view -H '+l_sort[0]+' > '+tmp)
-	cmd = samtools_exe+' cat -h '+tmp+' '+' '.join(l_sort)+' > '+outName
-	print cmd
-	os.system(cmd)
-	os.system('rm '+tmp)
+    global TEMP_IND
+    f = open(fn, 'r')
+    ftn = fn + '_temp' + str(TEMP_IND)
+    f_t = open(ftn, 'w')
+    hasHeader = False
+    for line in f:
+        if line[0] == '#':
+            if not hasHeader:
+                TEMP_IND += 1
+            hasHeader = True
+        elif hasHeader:
+            f_t.write(line)
+        else:
+            break
+    f_t.close()
+    f.close()
+    if hasHeader:
+        return ftn
+    else:
+        os.system('rm ' + ftn)
+        return fn
+
+
+def catListOfFiles(l, outName, gzipped=False):
+    for n in l:
+        if n[-3:] == '.gz' or n[-5:] == '.gzip':
+            gzipped = True
+    if gzipped:
+        for n in l:
+            if not n[-3:] == '.gz' and not n[-5:] == '.gzip':
+                print('\nError: Found a mixture of compressed and decompressed files with the specified prefix.'
+                      'Abandoning ship...\n')
+                for m in l:
+                    print(m)
+                print('')
+                exit(1)
+        cmd = 'cat ' + ' '.join(sorted(l)) + ' > ' + outName + '.gz'
+    else:
+        cmd = 'cat ' + ' '.join(sorted(l)) + ' > ' + outName
+    print(cmd)
+    os.system(cmd)
+
+
+def catBams(l, outName, samtools_exe):
+    l_sort = sorted(l)
+    tmp = outName + '.tempHeader.sam'
+    os.system(samtools_exe + ' view -H ' + l_sort[0] + ' > ' + tmp)
+    cmd = samtools_exe + ' cat -h ' + tmp + ' ' + ' '.join(l_sort) + ' > ' + outName
+    print(cmd)
+    os.system(cmd)
+    os.system('rm ' + tmp)
 
 
 #####################################
@@ -62,60 +69,59 @@ def catBams(l,outName,samtools_exe):
 #####################################
 
 def main():
-
-	parser = argparse.ArgumentParser(description='mergeJobs.py')
-	parser.add_argument('-i', type=str, required=True, metavar='<str>', nargs='+', help="* input prefix: [prefix_1] [prefix_2] ...")
-	parser.add_argument('-o', type=str, required=True, metavar='<str>',            help="* output prefix")
-	parser.add_argument('-s', type=str, required=True, metavar='<str>',            help="* /path/to/samtools")
-
-	args = parser.parse_args()
-	(INP,OUP,SAMTOOLS) = (args.i,args.o,args.s)
-
-	inDir = '/'.join(INP[0].split('/')[:-1])+'/'
-	if inDir == '/':
-		inDir = './'
-	#print inDir
-
-	INP_LIST = []
-	for n in INP:
-		if n[-1] == '/':
-			n = n[:-1]
-		INP_LIST.append(n.split('/')[-1])
-	listing_r1 = []
-	listing_r2 = []
-	listing_b  = []
-	listing_v  = []
-	for n in INP_LIST:
-		listing_r1 += getListOfFiles(inDir,n+'_read1.fq.job')
-		listing_r2 += getListOfFiles(inDir,n+'_read2.fq.job')
-		listing_b  += getListOfFiles(inDir,n+'_golden.bam.job')
-		if len(listing_v):	# remove headers from vcf files that aren't the first being processed
-			initList   = getListOfFiles(inDir,n+'_golden.vcf.job')
-			listing_v += [stripVCF_header(n) for n in initList]
-		else:
-			listing_v  += getListOfFiles(inDir,n+'_golden.vcf.job')
-	
-	#
-	#	merge fq files
-	#
-	if len(listing_r1):
-		catListOfFiles(listing_r1,OUP+'_read1.fq')
-	if len(listing_r2):
-		catListOfFiles(listing_r2,OUP+'_read2.fq')
-
-	#
-	#	merge golden alignments, if present
-	#
-	if len(listing_b):
-		catBams(listing_b,OUP+'_golden.bam',SAMTOOLS)
-
-	#
-	#	merge golden vcfs, if present
-	#
-	if len(listing_v):
-		catListOfFiles(listing_v,OUP+'_golden.vcf')
+    parser = argparse.ArgumentParser(description='mergeJobs.py')
+    parser.add_argument('-i', type=str, required=True, metavar='<str>', nargs='+',
+                        help="* input prefix: [prefix_1] [prefix_2] ...")
+    parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output prefix")
+    parser.add_argument('-s', type=str, required=True, metavar='<str>', help="* /path/to/samtools")
+
+    args = parser.parse_args()
+    (INP, OUP, SAMTOOLS) = (args.i, args.o, args.s)
+
+    inDir = '/'.join(INP[0].split('/')[:-1]) + '/'
+    if inDir == '/':
+        inDir = './'
+    # print inDir
+
+    INP_LIST = []
+    for n in INP:
+        if n[-1] == '/':
+            n = n[:-1]
+        INP_LIST.append(n.split('/')[-1])
+    listing_r1 = []
+    listing_r2 = []
+    listing_b = []
+    listing_v = []
+    for n in INP_LIST:
+        listing_r1 += getListOfFiles(inDir, n + '_read1.fq.job')
+        listing_r2 += getListOfFiles(inDir, n + '_read2.fq.job')
+        listing_b += getListOfFiles(inDir, n + '_golden.bam.job')
+        if len(listing_v):  # remove headers from vcf files that aren't the first being processed
+            initList = getListOfFiles(inDir, n + '_golden.vcf.job')
+            listing_v += [stripVCF_header(n) for n in initList]
+        else:
+            listing_v += getListOfFiles(inDir, n + '_golden.vcf.job')
+
+    #
+    #	merge fq files
+    #
+    if len(listing_r1):
+        catListOfFiles(listing_r1, OUP + '_read1.fq')
+    if len(listing_r2):
+        catListOfFiles(listing_r2, OUP + '_read2.fq')
+
+    #
+    #	merge golden alignments, if present
+    #
+    if len(listing_b):
+        catBams(listing_b, OUP + '_golden.bam', SAMTOOLS)
+
+    #
+    #	merge golden vcfs, if present
+    #
+    if len(listing_v):
+        catListOfFiles(listing_v, OUP + '_golden.vcf')
 
 
 if __name__ == "__main__":
-	main()
-
+    main()
diff --git a/utilities/__init__.py b/utilities/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/utilities/computeFraglen.py b/utilities/computeFraglen.py
index 21a691b..f3715a1 100644
--- a/utilities/computeFraglen.py
+++ b/utilities/computeFraglen.py
@@ -10,79 +10,79 @@
 
 import sys
 import fileinput
-import cPickle as pickle
+import pickle
 import numpy as np
 
-FILTER_MAPQUAL  = 10	# only consider reads that are mapped with at least this mapping quality
-FILTER_MINREADS = 100	# only consider fragment lengths that have at least this many read pairs supporting it
-FILTER_MEDDEV_M = 10	# only consider fragment lengths this many median deviations above the median
+FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality
+FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it
+FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median
+
 
 def quick_median(countDict):
-	midPoint = sum(countDict.values())/2
-	mySum    = 0
-	myInd    = 0
-	sk       = sorted(countDict.keys())
-	while mySum < midPoint:
-		mySum += countDict[sk[myInd]]
-		if mySum >= midPoint:
-			break
-		myInd += 1
-	return myInd
+    midPoint = sum(countDict.values()) / 2
+    mySum = 0
+    myInd = 0
+    sk = sorted(countDict.keys())
+    while mySum < midPoint:
+        mySum += countDict[sk[myInd]]
+        if mySum >= midPoint:
+            break
+        myInd += 1
+    return myInd
+
 
 def median_deviation_from_median(countDict):
-	myMedian = quick_median(countDict)
-	deviations = {}
-	for k in sorted(countDict.keys()):
-		d = abs(k-myMedian)
-		deviations[d] = countDict[k]
-	return quick_median(deviations)
+    myMedian = quick_median(countDict)
+    deviations = {}
+    for k in sorted(countDict.keys()):
+        d = abs(k - myMedian)
+        deviations[d] = countDict[k]
+    return quick_median(deviations)
+
 
 if len(sys.argv) != 1:
-	print "Usage: samtools view normal.bam | python computeFraglen.py"
-	exit(1)
+    print("Usage: samtools view normal.bam | python computeFraglen.py")
+    exit(1)
 
 all_tlens = {}
 PRINT_EVERY = 100000
 BREAK_AFTER = 1000000
 i = 0
 for line in fileinput.input():
-	splt = line.strip().split('\t')
-	samFlag = int(splt[1])
-	myRef   = splt[2]
-	mapQual = int(splt[4])
-	mateRef = splt[6]
-	myTlen  = abs(int(splt[8]))
+    splt = line.strip().split('\t')
+    samFlag = int(splt[1])
+    myRef = splt[2]
+    mapQual = int(splt[4])
+    mateRef = splt[6]
+    myTlen = abs(int(splt[8]))
 
-	if samFlag&1 and samFlag&64 and mapQual > FILTER_MAPQUAL:	# if read is paired, and is first in pair, and is confidently mapped...
-		if mateRef == '=' or mateRef == myRef:					# and mate is mapped to same reference
-			if myTlen not in all_tlens:
-				all_tlens[myTlen] = 0
-			all_tlens[myTlen] += 1
-			i += 1
-			if i%PRINT_EVERY == 0:
-				print '---',i, quick_median(all_tlens), median_deviation_from_median(all_tlens)
-				#for k in sorted(all_tlens.keys()):
-				#	print k, all_tlens[k]
-
-			#if i > BREAK_AFTER:
-			#	break
+    if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:  # if read is paired, and is first in pair, and is confidently mapped...
+        if mateRef == '=' or mateRef == myRef:  # and mate is mapped to same reference
+            if myTlen not in all_tlens:
+                all_tlens[myTlen] = 0
+            all_tlens[myTlen] += 1
+            i += 1
+            if i % PRINT_EVERY == 0:
+                print('---', i, quick_median(all_tlens), median_deviation_from_median(all_tlens))
+            # for k in sorted(all_tlens.keys()):
+            #	print k, all_tlens[k]
 
+        # if i > BREAK_AFTER:
+        #	break
 
 med = quick_median(all_tlens)
 mdm = median_deviation_from_median(all_tlens)
 
-outVals  = []
+outVals = []
 outProbs = []
 for k in sorted(all_tlens.keys()):
-	if k > 0 and k < med + FILTER_MEDDEV_M * mdm:
-		if all_tlens[k] >= FILTER_MINREADS:
-			print k, all_tlens[k]
-			outVals.append(k)
-			outProbs.append(all_tlens[k])
+    if k > 0 and k < med + FILTER_MEDDEV_M * mdm:
+        if all_tlens[k] >= FILTER_MINREADS:
+            print(k, all_tlens[k])
+            outVals.append(k)
+            outProbs.append(all_tlens[k])
 countSum = float(sum(outProbs))
-outProbs = [n/countSum for n in outProbs]
-
-print '\nsaving model...'
-pickle.dump([outVals, outProbs],open('fraglen.p','wb'))
-
+outProbs = [n / countSum for n in outProbs]
 
+print('\nsaving model...')
+pickle.dump([outVals, outProbs], open('fraglen.p', 'wb'))
diff --git a/utilities/computeGC.py b/utilities/computeGC.py
index ea7ea8e..5f4294d 100644
--- a/utilities/computeGC.py
+++ b/utilities/computeGC.py
@@ -15,101 +15,100 @@
 import sys
 import argparse
 import numpy as np
-import cPickle as pickle
+import pickle
 
 parser = argparse.ArgumentParser(description='computeGC.py')
-parser.add_argument('-i', type=str, required=True,  metavar='<str>', help="* input.genomecov")
-parser.add_argument('-r', type=str, required=True,  metavar='<str>', help="* reference.fa")
-parser.add_argument('-o', type=str, required=True,  metavar='<str>', help="* output.p")
+parser.add_argument('-i', type=str, required=True, metavar='<str>', help="* input.genomecov")
+parser.add_argument('-r', type=str, required=True, metavar='<str>', help="* reference.fa")
+parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output.p")
 parser.add_argument('-w', type=int, required=False, metavar='<int>', help="sliding window length [50]", default=50)
 args = parser.parse_args()
 
 (IN_GCB, REF_FILE, WINDOW_SIZE, OUT_P) = (args.i, args.r, args.w, args.o)
 
-GC_BINS = {n:[] for n in range(WINDOW_SIZE+1)}
+GC_BINS = {n: [] for n in range(WINDOW_SIZE + 1)}
 
-print 'reading ref...'
+print('reading ref...')
 allRefs = {}
-f = open(REF_FILE,'r')
+f = open(REF_FILE, 'r')
 for line in f:
-	if line[0] == '>':
-		refName = line.strip()[1:]
-		allRefs[refName] = []
-		print refName
-		#if refName == 'chr2':
-		#	break
-	else:
-		allRefs[refName].append(line.strip())
+    if line[0] == '>':
+        refName = line.strip()[1:]
+        allRefs[refName] = []
+        print(refName)
+    # if refName == 'chr2':
+    #	break
+    else:
+        allRefs[refName].append(line.strip())
 f.close()
 
-print 'capitalizing ref...'
+print('capitalizing ref...')
 for k in sorted(allRefs.keys()):
-	print k
-	allRefs[k] = ''.join(allRefs[k])
-	allRefs[k] = allRefs[k].upper()
+    print(k)
+    allRefs[k] = ''.join(allRefs[k])
+    allRefs[k] = allRefs[k].upper()
 
-print 'reading genomecov file...'
+print('reading genomecov file...')
 tt = time.time()
-f = open(IN_GCB,'r')
+f = open(IN_GCB, 'r')
 currentLine = 0
-currentRef  = None
-currentCov  = 0
+currentRef = None
+currentCov = 0
 linesProcessed = 0
-PRINT_EVERY    = 1000000
-STOP_AFTER     = 1000000
+PRINT_EVERY = 1000000
+STOP_AFTER = 1000000
 for line in f:
-	splt = line.strip().split('\t')
-	if linesProcessed%PRINT_EVERY == 0:
-		print linesProcessed
-	linesProcessed += 1
+    splt = line.strip().split('\t')
+    if linesProcessed % PRINT_EVERY == 0:
+        print(linesProcessed)
+    linesProcessed += 1
 
-	#if linesProcessed > STOP_AFTER:
-	#	break
+    # if linesProcessed > STOP_AFTER:
+    #	break
 
-	if currentLine == 0:
-		currentRef = splt[0]
-		sPos       = int(splt[1])-1
+    if currentLine == 0:
+        currentRef = splt[0]
+        sPos = int(splt[1]) - 1
 
-	if currentRef not in allRefs:
-		continue
+    if currentRef not in allRefs:
+        continue
 
-	currentLine += 1
-	currentCov  += float(splt[2])
+    currentLine += 1
+    currentCov += float(splt[2])
 
-	if currentLine == WINDOW_SIZE:
-		currentLine = 0
-		seq         = allRefs[currentRef][sPos:sPos+WINDOW_SIZE]
-		if 'N' not in seq:
-			gc_count = seq.count('G') + seq.count('C')
-			GC_BINS[gc_count].append(currentCov)
-		currentCov = 0
+    if currentLine == WINDOW_SIZE:
+        currentLine = 0
+        seq = allRefs[currentRef][sPos:sPos + WINDOW_SIZE]
+        if 'N' not in seq:
+            gc_count = seq.count('G') + seq.count('C')
+            GC_BINS[gc_count].append(currentCov)
+        currentCov = 0
 
 f.close()
 
 runningTot = 0
-allMean    = 0.0
+allMean = 0.0
 for k in sorted(GC_BINS.keys()):
-	if len(GC_BINS[k]) == 0:
-		print '{0:0.2%}'.format(k/float(WINDOW_SIZE)), 0.0, 0
-		GC_BINS[k] = 0
-	else:
-		myMean = np.mean(GC_BINS[k])
-		myLen  = len(GC_BINS[k])
-		print '{0:0.2%}'.format(k/float(WINDOW_SIZE)), myMean, myLen
-		allMean += myMean * myLen
-		runningTot += myLen
-		GC_BINS[k] = myMean
-
-avgCov = allMean/float(runningTot)
-print 'AVERAGE COVERAGE =',avgCov
+    if len(GC_BINS[k]) == 0:
+        print('{0:0.2%}'.format(k / float(WINDOW_SIZE)), 0.0, 0)
+        GC_BINS[k] = 0
+    else:
+        myMean = np.mean(GC_BINS[k])
+        myLen = len(GC_BINS[k])
+        print('{0:0.2%}'.format(k / float(WINDOW_SIZE)), myMean, myLen)
+        allMean += myMean * myLen
+        runningTot += myLen
+        GC_BINS[k] = myMean
+
+avgCov = allMean / float(runningTot)
+print('AVERAGE COVERAGE =', avgCov)
 
 y_out = []
 for k in sorted(GC_BINS.keys()):
-	GC_BINS[k] /= avgCov
-	y_out.append(GC_BINS[k])
+    GC_BINS[k] /= avgCov
+    y_out.append(GC_BINS[k])
 
-print 'saving model...'
-pickle.dump([range(WINDOW_SIZE+1),y_out],open(OUT_P,'wb'))
-
-print time.time()-tt,'(sec)'
+print('saving model...')
+pickle.dump([range(WINDOW_SIZE + 1), y_out], open(OUT_P, 'wb'))
 
+print(time.time() - tt, '(sec)')
diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index 6aa1097..12ef023 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -12,12 +12,12 @@
 SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2])
 sys.path.append(SIM_PATH+'/py/')
 
-from refFunc import indexRef
+from py.refFunc import indexRef
 
-REF_WHITELIST =  [str(n) for n in xrange(1,30)] + ['x','y','X','Y','mt','Mt','MT']
+REF_WHITELIST =  [str(n) for n in range(1,30)] + ['x','y','X','Y','mt','Mt','MT']
 REF_WHITELIST += ['chr'+n for n in REF_WHITELIST]
 VALID_NUCL    =  ['A','C','G','T']
-VALID_TRINUC  =  [VALID_NUCL[i]+VALID_NUCL[j]+VALID_NUCL[k] for i in xrange(len(VALID_NUCL)) for j in xrange(len(VALID_NUCL)) for k in xrange(len(VALID_NUCL))]
+VALID_TRINUC  =  [VALID_NUCL[i]+VALID_NUCL[j]+VALID_NUCL[k] for i in range(len(VALID_NUCL)) for j in range(len(VALID_NUCL)) for k in range(len(VALID_NUCL))]
 # if parsing a dbsnp vcf, and no CAF= is found in info tag, use this as default val for population freq
 VCF_DEFAULT_POP_FREQ = 0.00001
 
@@ -29,64 +29,64 @@
 
 # given a reference index, grab the sequence string of a specified reference
 def getChrFromFasta(refPath,ref_inds,chrName):
-	for i in xrange(len(ref_inds)):
-		if ref_inds[i][0] == chrName:
-			ref_inds_i = ref_inds[i]
-			break
-	refFile = open(refPath,'r')
-	refFile.seek(ref_inds_i[1])
-	myDat = ''.join(refFile.read(ref_inds_i[2]-ref_inds_i[1]).split('\n'))
-	return myDat
+    for i in range(len(ref_inds)):
+        if ref_inds[i][0] == chrName:
+            ref_inds_i = ref_inds[i]
+            break
+    refFile = open(refPath,'r')
+    refFile.seek(ref_inds_i[1])
+    myDat = ''.join(refFile.read(ref_inds_i[2]-ref_inds_i[1]).split('\n'))
+    return myDat
 
 # cluster a sorted list
 def clusterList(l,delta):
-	outList    = [[l[0]]]
-	prevVal    = l[0]
-	currentInd = 0
-	for n in l[1:]:
-		if n-prevVal <= delta:
-			outList[currentInd].append(n)
-		else:
-			currentInd += 1
-			outList.append([])
-			outList[currentInd].append(n)
-		prevVal = n
-	return outList
+    outList    = [[l[0]]]
+    prevVal    = l[0]
+    currentInd = 0
+    for n in l[1:]:
+        if n-prevVal <= delta:
+            outList[currentInd].append(n)
+        else:
+            currentInd += 1
+            outList.append([])
+            outList[currentInd].append(n)
+        prevVal = n
+    return outList
 
 def list_2_countDict(l):
-	cDict = {}
-	for n in l:
-		if n not in cDict:
-			cDict[n] = 0
-		cDict[n] += 1
-	return cDict
+    cDict = {}
+    for n in l:
+        if n not in cDict:
+            cDict[n] = 0
+        cDict[n] += 1
+    return cDict
 
 def getBedTracks(fn):
-	f = open(fn,'r')
-	trackDict = {}
-	for line in f:
-		splt = line.strip().split('\t')
-		if splt[0] not in trackDict:
-			trackDict[splt[0]] = []
-		trackDict[splt[0]].extend([int(splt[1]),int(splt[2])])
-	f.close()
-	return trackDict
+    f = open(fn,'r')
+    trackDict = {}
+    for line in f:
+        splt = line.strip().split('\t')
+        if splt[0] not in trackDict:
+            trackDict[splt[0]] = []
+        trackDict[splt[0]].extend([int(splt[1]),int(splt[2])])
+    f.close()
+    return trackDict
 
 def getTrackLen(trackDict):
-	totSum = 0
-	for k in trackDict.keys():
-		for i in xrange(0,len(trackDict[k]),2):
-			totSum += trackDict[k][i+1] - trackDict[k][i] + 1
-	return totSum
+    totSum = 0
+    for k in trackDict.keys():
+        for i in range(0,len(trackDict[k]),2):
+            totSum += trackDict[k][i+1] - trackDict[k][i] + 1
+    return totSum
 
 def isInBed(track,ind):
-	myInd = bisect.bisect(track,ind)
-	if myInd&1:
-		return True
-	if myInd < len(track):
-		if track[myInd-1] == ind:
-			return True
-	return False
+    myInd = bisect.bisect(track,ind)
+    if myInd&1:
+        return True
+    if myInd < len(track):
+        if track[myInd-1] == ind:
+            return True
+    return False
 
 ## return the mean distance to the median of a cluster
 #def mean_dist_from_median(c):
@@ -138,19 +138,19 @@ def isInBed(track,ind):
 
 MYBED = None
 if args.bi != None:
-	print 'only considering variants in specified bed regions...'
-	MYBED = (getBedTracks(args.bi),True)
+    print('only considering variants in specified bed regions...')
+    MYBED = (getBedTracks(args.bi),True)
 elif args.be != None:
-	print 'only considering variants outside of specified bed regions...'
-	MYBED = (getBedTracks(args.be),False)
+    print('only considering variants outside of specified bed regions...')
+MYBED = (getBedTracks(args.be),False)
 
 if TSV[-4:] == '.vcf':
-	IS_VCF = True
+    IS_VCF = True
 elif TSV[-4:] == '.tsv':
-	IS_VCF = False
+    IS_VCF = False
 else:
-	print '\nError: Unknown format for mutation input.\n'
-	exit(1)
+    print('\nError: Unknown format for mutation input.\n')
+    exit(1)
 
 
 #####################################
@@ -160,421 +160,423 @@ def isInBed(track,ind):
 
 def main():
 
-	ref_inds = indexRef(REF)
-	refList  = [n[0] for n in ref_inds]
-
-	# how many times do we observe each trinucleotide in the reference (and input bed region, if present)?
-	TRINUC_REF_COUNT = {}
-	TRINUC_BED_COUNT = {}
-	printBedWarning  = True
-	# [(trinuc_a, trinuc_b)] = # of times we observed a mutation from trinuc_a into trinuc_b
-	TRINUC_TRANSITION_COUNT = {}
-	# total count of SNPs
-	SNP_COUNT = 0
-	# overall SNP transition probabilities
-	SNP_TRANSITION_COUNT = {}
-	# total count of indels, indexed by length
-	INDEL_COUNT = {}
-	# tabulate how much non-N reference sequence we've eaten through
-	TOTAL_REFLEN = 0
-	# detect variants that occur in a significant percentage of the input samples (pos,ref,alt,pop_fraction)
-	COMMON_VARIANTS = []
-	# tabulate how many unique donors we've encountered (this is useful for identifying common variants)
-	TOTAL_DONORS = {}
-	# identify regions that have significantly higher local mutation rates than the average
-	HIGH_MUT_REGIONS = []
-
-	# load and process variants in each reference sequence individually, for memory reasons...
-	for refName in refList:
-
-		if (refName not in REF_WHITELIST) and (not NO_WHITELIST):
-			print refName,'is not in our whitelist, skipping...'
-			continue
-
-		print 'reading reference "'+refName+'"...'
-		refSequence = getChrFromFasta(REF,ref_inds,refName).upper()
-		TOTAL_REFLEN += len(refSequence) - refSequence.count('N')
-
-		# list to be used for counting variants that occur multiple times in file (i.e. in multiple samples)
-		VDAT_COMMON = []
-
-
-		""" ##########################################################################
-		###						COUNT TRINUCLEOTIDES IN REF						   ###
-		########################################################################## """
-
-
-		if MYBED != None:
-			if printBedWarning:
-				print "since you're using a bed input, we have to count trinucs in bed region even if you specified a trinuc count file for the reference..."
-				printBedWarning = False
-			if refName in MYBED[0]:
-				refKey = refName
-			elif ('chr' in refName) and (refName not in MYBED[0]) and (refName[3:] in MYBED[0]):
-				refKey = refName[3:]
-			elif ('chr' not in refName) and (refName not in MYBED[0]) and ('chr'+refName in MYBED[0]):
-				refKey = 'chr'+refName
-			if refKey in MYBED[0]:
-				subRegions = [(MYBED[0][refKey][n],MYBED[0][refKey][n+1]) for n in xrange(0,len(MYBED[0][refKey]),2)]
-				for sr in subRegions:
-					for i in xrange(sr[0],sr[1]+1-2):
-						trinuc = refSequence[i:i+3]
-						if not trinuc in VALID_TRINUC:
-							continue	# skip if trinuc contains invalid characters, or not in specified bed region
-						if trinuc not in TRINUC_BED_COUNT:
-							TRINUC_BED_COUNT[trinuc] = 0
-						TRINUC_BED_COUNT[trinuc] += 1
-
-		if not os.path.isfile(REF+'.trinucCounts'):
-			print 'counting trinucleotides in reference...'
-			for i in xrange(len(refSequence)-2):
-				if i%1000000 == 0 and i > 0:
-					print i,'/',len(refSequence)
-					#break
-				trinuc = refSequence[i:i+3]
-				if not trinuc in VALID_TRINUC:
-					continue	# skip if trinuc contains invalid characters
-				if trinuc not in TRINUC_REF_COUNT:
-					TRINUC_REF_COUNT[trinuc] = 0
-				TRINUC_REF_COUNT[trinuc] += 1
-		else:
-			print 'skipping trinuc counts (for whole reference) because we found a file...'
-
-
-		""" ##########################################################################
-		###							READ INPUT VARIANTS							   ###
-		########################################################################## """
-
-
-		print 'reading input variants...'
-		f = open(TSV,'r')
-		isFirst = True
-		for line in f:
-
-			if IS_VCF and line[0] == '#':
-				continue
-			if isFirst:
-				if IS_VCF:
-					# hard-code index values based on expected columns in vcf
-					(c1,c2,c3,m1,m2,m3) = (0,1,1,3,3,4)
-				else:
-					# determine columns of fields we're interested in
-					splt = line.strip().split('\t')
-					(c1,c2,c3) = (splt.index('chromosome'),splt.index('chromosome_start'),splt.index('chromosome_end'))
-					(m1,m2,m3) = (splt.index('reference_genome_allele'),splt.index('mutated_from_allele'),splt.index('mutated_to_allele'))
-					(d_id) = (splt.index('icgc_donor_id'))
-				isFirst = False
-				continue
-
-			splt = line.strip().split('\t')
-			# we have -1 because tsv/vcf coords are 1-based, and our reference string index is 0-based
-			[chrName,chrStart,chrEnd] = [splt[c1],int(splt[c2])-1,int(splt[c3])-1]
-			[allele_ref,allele_normal,allele_tumor] = [splt[m1].upper(),splt[m2].upper(),splt[m3].upper()]
-			if IS_VCF:
-				if len(allele_ref) != len(allele_tumor):
-					# indels in tsv don't include the preserved first nucleotide, so lets trim the vcf alleles
-					[allele_ref,allele_normal,allele_tumor] = [allele_ref[1:],allele_normal[1:],allele_tumor[1:]]
-				if not allele_ref: allele_ref = '-'
-				if not allele_normal: allele_normal = '-'
-				if not allele_tumor: allele_tumor = '-'
-				# if alternate alleles are present, lets just ignore this variant. I may come back and improve this later
-				if ',' in allele_tumor:
-					continue
-				vcf_info = ';'+splt[7]+';'
-			else:
-				[donor_id] = [splt[d_id]]
-
-			# if we encounter a multi-np (i.e. 3 nucl --> 3 different nucl), let's skip it for now...
-			if ('-' not in allele_normal and '-' not in allele_tumor) and (len(allele_normal) > 1 or len(allele_tumor) > 1):
-				print 'skipping a complex variant...'
-				continue
-
-			# to deal with '1' vs 'chr1' references, manually change names. this is hacky and bad.
-			if 'chr' not in chrName:
-				chrName = 'chr'+chrName
-			if 'chr' not in refName:
-				refName = 'chr'+refName
-			# skip irrelevant variants
-			if chrName != refName:
-				continue
-
-			# if variant is outside the regions we're interested in (if specified), skip it...
-			if MYBED != None:
-				refKey = refName
-				if not refKey in MYBED[0] and refKey[3:] in MYBED[0]:	# account for 1 vs chr1, again...
-					refKey = refKey[3:]
-				if refKey not in MYBED[0]:
-					inBed = False
-				else:
-					inBed = isInBed(MYBED[0][refKey],chrStart)
-				if inBed != MYBED[1]:
-					continue
-
-			# we want only snps
-			# so, no '-' characters allowed, and chrStart must be same as chrEnd
-			if '-' not in allele_normal and '-' not in allele_tumor and chrStart == chrEnd:
-				trinuc_ref = refSequence[chrStart-1:chrStart+2]
-				if not trinuc_ref in VALID_TRINUC:
-					continue	# skip ref trinuc with invalid characters
-				# only consider positions where ref allele in tsv matches the nucleotide in our reference
-				if allele_ref == trinuc_ref[1]:
-					trinuc_normal    = refSequence[chrStart-1] + allele_normal + refSequence[chrStart+1]
-					trinuc_tumor     = refSequence[chrStart-1] + allele_tumor + refSequence[chrStart+1]
-					if not trinuc_normal in VALID_TRINUC or not trinuc_tumor in VALID_TRINUC:
-						continue	# skip if mutation contains invalid char
-					key = (trinuc_normal,trinuc_tumor)
-					if key not in TRINUC_TRANSITION_COUNT:
-						TRINUC_TRANSITION_COUNT[key] = 0
-					TRINUC_TRANSITION_COUNT[key] += 1
-					SNP_COUNT += 1
-					key2 = (allele_normal,allele_tumor)
-					if key2 not in SNP_TRANSITION_COUNT:
-						SNP_TRANSITION_COUNT[key2] = 0
-					SNP_TRANSITION_COUNT[key2] += 1
-
-					if IS_VCF:
-						myPopFreq = VCF_DEFAULT_POP_FREQ
-						if ';CAF=' in vcf_info:
-							cafStr = re.findall(r";CAF=.*?(?=;)",vcf_info)[0]
-							if ',' in cafStr:
-								myPopFreq = float(cafStr[5:].split(',')[1])
-						VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor,myPopFreq))
-					else:
-						VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor))
-						TOTAL_DONORS[donor_id] = True
-				else:
-					print '\nError: ref allele in variant call does not match reference.\n'
-					exit(1)
-
-			# now let's look for indels...
-			if '-' in allele_normal: len_normal = 0
-			else: len_normal = len(allele_normal)
-			if '-' in allele_tumor: len_tumor = 0
-			else: len_tumor = len(allele_tumor)
-			if len_normal != len_tumor:
-				indel_len = len_tumor - len_normal
-				if indel_len not in INDEL_COUNT:
-					INDEL_COUNT[indel_len] = 0
-				INDEL_COUNT[indel_len] += 1
-
-				if IS_VCF:
-					myPopFreq = VCF_DEFAULT_POP_FREQ
-					if ';CAF=' in vcf_info:
-						cafStr = re.findall(r";CAF=.*?(?=;)",vcf_info)[0]
-						if ',' in cafStr:
-							myPopFreq = float(cafStr[5:].split(',')[1])
-					VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor,myPopFreq))
-				else:
-					VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor))
-					TOTAL_DONORS[donor_id] = True
-		f.close()
-
-		# if we didn't find anything, skip ahead along to the next reference sequence
-		if not len(VDAT_COMMON):
-			print 'Found no variants for this reference, moving along...'
-			continue
-
-		#
-		# identify common mutations
-		#
-		percentile_var = 95
-		if IS_VCF:
-			minVal = np.percentile([n[4] for n in VDAT_COMMON],percentile_var)
-			for k in sorted(VDAT_COMMON):
-				if k[4] >= minVal:
-					COMMON_VARIANTS.append((refName,k[0],k[1],k[3],k[4]))
-			VDAT_COMMON = {(n[0],n[1],n[2],n[3]):n[4] for n in VDAT_COMMON}
-		else:
-			N_DONORS = len(TOTAL_DONORS)
-			VDAT_COMMON = list_2_countDict(VDAT_COMMON)
-			minVal = int(np.percentile(VDAT_COMMON.values(),percentile_var))
-			for k in sorted(VDAT_COMMON.keys()):
-				if VDAT_COMMON[k] >= minVal:
-					COMMON_VARIANTS.append((refName,k[0],k[1],k[3],VDAT_COMMON[k]/float(N_DONORS)))
-
-		#
-		# identify areas that have contained significantly higher random mutation rates
-		#
-		dist_thresh      = 2000
-		percentile_clust = 97
-		qptn             = 1000
-		# identify regions with disproportionately more variants in them
-		VARIANT_POS = sorted([n[0] for n in VDAT_COMMON.keys()])
-		clustered_pos = clusterList(VARIANT_POS,dist_thresh)
-		byLen  = [(len(clustered_pos[i]),min(clustered_pos[i]),max(clustered_pos[i]),i) for i in xrange(len(clustered_pos))]
-		#byLen  = sorted(byLen,reverse=True)
-		#minLen = int(np.percentile([n[0] for n in byLen],percentile_clust))
-		#byLen  = [n for n in byLen if n[0] >= minLen]
-		candidate_regions = []
-		for n in byLen:
-			bi = int((n[1]-dist_thresh)/float(qptn))*qptn
-			bf = int((n[2]+dist_thresh)/float(qptn))*qptn
-			candidate_regions.append((n[0]/float(bf-bi),max([0,bi]),min([len(refSequence),bf])))
-		minVal = np.percentile([n[0] for n in candidate_regions],percentile_clust)
-		for n in candidate_regions:
-			if n[0] >= minVal:
-				HIGH_MUT_REGIONS.append((refName,n[1],n[2],n[0]))
-		# collapse overlapping regions
-		for i in xrange(len(HIGH_MUT_REGIONS)-1,0,-1):
-			if HIGH_MUT_REGIONS[i-1][2] >= HIGH_MUT_REGIONS[i][1] and HIGH_MUT_REGIONS[i-1][0] == HIGH_MUT_REGIONS[i][0]:
-				avgMutRate = 0.5*HIGH_MUT_REGIONS[i-1][3]+0.5*HIGH_MUT_REGIONS[i][3]	# not accurate, but I'm lazy
-				HIGH_MUT_REGIONS[i-1] = (HIGH_MUT_REGIONS[i-1][0], HIGH_MUT_REGIONS[i-1][1], HIGH_MUT_REGIONS[i][2], avgMutRate)
-				del HIGH_MUT_REGIONS[i]
-
-	#
-	# if we didn't count ref trinucs because we found file, read in ref counts from file now
-	#
-	if os.path.isfile(REF+'.trinucCounts'):
-		print 'reading pre-computed trinuc counts...'
-		f = open(REF+'.trinucCounts','r')
-		for line in f:
-			splt = line.strip().split('\t')
-			TRINUC_REF_COUNT[splt[0]] = int(splt[1])
-		f.close()
-	# otherwise, save trinuc counts to file, if desired
-	elif SAVE_TRINUC:
-		if MYBED != None:
-			print 'unable to save trinuc counts to file because using input bed region...'
-		else:
-			print 'saving trinuc counts to file...'
-			f = open(REF+'.trinucCounts','w')
-			for trinuc in sorted(TRINUC_REF_COUNT.keys()):
-				f.write(trinuc+'\t'+str(TRINUC_REF_COUNT[trinuc])+'\n')
-			f.close()
-
-	#
-	# if using an input bed region, make necessary adjustments to trinuc ref counts based on the bed region trinuc counts
-	#
-	if MYBED != None:
-		if MYBED[1] == True:	# we are restricting our attention to bed regions, so ONLY use bed region trinuc counts
-			TRINUC_REF_COUNT = TRINUC_BED_COUNT
-		else:					# we are only looking outside bed regions, so subtract bed region trinucs from entire reference trinucs
-			for k in TRINUC_REF_COUNT.keys():
-				if k in TRINUC_BED_COUNT:
-					TRINUC_REF_COUNT[k] -= TRINUC_BED_COUNT[k]
-
-	# if for some reason we didn't find any valid input variants, exit gracefully...
-	totalVar = SNP_COUNT + sum(INDEL_COUNT.values())
-	if totalVar == 0:
-		print '\nError: No valid variants were found, model could not be created. (Are you using the correct reference?)\n'
-		exit(1)
-
-	""" ##########################################################################
-	###							COMPUTE PROBABILITIES						   ###
-	########################################################################## """
-
-
-	#for k in sorted(TRINUC_REF_COUNT.keys()):
-	#		print k, TRINUC_REF_COUNT[k]
-	#
-	#for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
-	#	print k, TRINUC_TRANSITION_COUNT[k]
-
-	# frequency that each trinuc mutated into anything else
-	TRINUC_MUT_PROB = {}
-	# frequency that a trinuc mutates into another trinuc, given that it mutated
-	TRINUC_TRANS_PROBS = {}
-	# frequency of snp transitions, given a snp occurs.
-	SNP_TRANS_FREQ = {}
-
-	for trinuc in sorted(TRINUC_REF_COUNT.keys()):
-		myCount = 0
-		for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
-			if k[0] == trinuc:
-				myCount += TRINUC_TRANSITION_COUNT[k]
-		TRINUC_MUT_PROB[trinuc] = myCount / float(TRINUC_REF_COUNT[trinuc])
-		for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
-			if k[0] == trinuc:
-				TRINUC_TRANS_PROBS[k] = TRINUC_TRANSITION_COUNT[k] / float(myCount)
-
-	for n1 in VALID_NUCL:
-		rollingTot = sum([SNP_TRANSITION_COUNT[(n1,n2)] for n2 in VALID_NUCL if (n1,n2) in SNP_TRANSITION_COUNT])
-		for n2 in VALID_NUCL:
-			key2 = (n1,n2)
-			if key2 in SNP_TRANSITION_COUNT:
-				SNP_TRANS_FREQ[key2] = SNP_TRANSITION_COUNT[key2] / float(rollingTot)
-
-	# compute average snp and indel frequencies
-	SNP_FREQ       = SNP_COUNT/float(totalVar)
-	AVG_INDEL_FREQ = 1.-SNP_FREQ
-	INDEL_FREQ     = {k:(INDEL_COUNT[k]/float(totalVar))/AVG_INDEL_FREQ for k in INDEL_COUNT.keys()}
-	if MYBED != None:
-		if MYBED[1] == True:
-			AVG_MUT_RATE = totalVar/float(getTrackLen(MYBED[0]))
-		else:
-			AVG_MUT_RATE = totalVar/float(TOTAL_REFLEN - getTrackLen(MYBED[0]))
-	else:
-		AVG_MUT_RATE = totalVar/float(TOTAL_REFLEN)
-
-	#
-	#	if values weren't found in data, appropriately append null entries
-	#
-	printTrinucWarning = False
-	for trinuc in VALID_TRINUC:
-		trinuc_mut = [trinuc[0]+n+trinuc[2] for n in VALID_NUCL if n != trinuc[1]]
-		if trinuc not in TRINUC_MUT_PROB:
-			TRINUC_MUT_PROB[trinuc] = 0.
-			printTrinucWarning = True
-		for trinuc2 in trinuc_mut:
-			if (trinuc,trinuc2) not in TRINUC_TRANS_PROBS:
-				TRINUC_TRANS_PROBS[(trinuc,trinuc2)] = 0.
-				printTrinucWarning = True
-	if printTrinucWarning:
-		print 'Warning: Some trinucleotides transitions were not encountered in the input dataset, probabilities of 0.0 have been assigned to these events.'
-
-	#
-	#	print some stuff
-	#
-	for k in sorted(TRINUC_MUT_PROB.keys()):
-		print 'p('+k+' mutates) =',TRINUC_MUT_PROB[k]
-
-	for k in sorted(TRINUC_TRANS_PROBS.keys()):
-		print 'p('+k[0]+' --> '+k[1]+' | '+k[0]+' mutates) =',TRINUC_TRANS_PROBS[k]
-
-	for k in sorted(INDEL_FREQ.keys()):
-		if k > 0:
-			print 'p(ins length = '+str(abs(k))+' | indel occurs) =',INDEL_FREQ[k]
-		else:
-			print 'p(del length = '+str(abs(k))+' | indel occurs) =',INDEL_FREQ[k]
-
-	for k in sorted(SNP_TRANS_FREQ.keys()):
-		print 'p('+k[0]+' --> '+k[1]+' | SNP occurs) =',SNP_TRANS_FREQ[k]
-
-	#for n in COMMON_VARIANTS:
-	#	print n
-
-	#for n in HIGH_MUT_REGIONS:
-	#	print n
-
-	print 'p(snp)   =',SNP_FREQ
-	print 'p(indel) =',AVG_INDEL_FREQ
-	print 'overall average mut rate:',AVG_MUT_RATE
-	print 'total variants processed:',totalVar
-
-	#
-	# save variables to file
-	#
-	if SKIP_COMMON:
-		OUT_DICT = {'AVG_MUT_RATE':AVG_MUT_RATE,
-		            'SNP_FREQ':SNP_FREQ,
-		            'SNP_TRANS_FREQ':SNP_TRANS_FREQ,
-		            'INDEL_FREQ':INDEL_FREQ,
-		            'TRINUC_MUT_PROB':TRINUC_MUT_PROB,
-		            'TRINUC_TRANS_PROBS':TRINUC_TRANS_PROBS}
-	else:
-		OUT_DICT = {'AVG_MUT_RATE':AVG_MUT_RATE,
-		            'SNP_FREQ':SNP_FREQ,
-		            'SNP_TRANS_FREQ':SNP_TRANS_FREQ,
-		            'INDEL_FREQ':INDEL_FREQ,
-		            'TRINUC_MUT_PROB':TRINUC_MUT_PROB,
-		            'TRINUC_TRANS_PROBS':TRINUC_TRANS_PROBS,
-		            'COMMON_VARIANTS':COMMON_VARIANTS,
-		            'HIGH_MUT_REGIONS':HIGH_MUT_REGIONS}
-	pickle.dump( OUT_DICT, open( OUT_PICKLE, "wb" ) )
+    ref_inds = indexRef(REF)
+    refList  = [n[0] for n in ref_inds]
+
+    # how many times do we observe each trinucleotide in the reference (and input bed region, if present)?
+    TRINUC_REF_COUNT = {}
+    TRINUC_BED_COUNT = {}
+    printBedWarning  = True
+    # [(trinuc_a, trinuc_b)] = # of times we observed a mutation from trinuc_a into trinuc_b
+    TRINUC_TRANSITION_COUNT = {}
+    # total count of SNPs
+    SNP_COUNT = 0
+    # overall SNP transition probabilities
+    SNP_TRANSITION_COUNT = {}
+    # total count of indels, indexed by length
+    INDEL_COUNT = {}
+    # tabulate how much non-N reference sequence we've eaten through
+    TOTAL_REFLEN = 0
+    # detect variants that occur in a significant percentage of the input samples (pos,ref,alt,pop_fraction)
+    COMMON_VARIANTS = []
+    # tabulate how many unique donors we've encountered (this is useful for identifying common variants)
+    TOTAL_DONORS = {}
+    # identify regions that have significantly higher local mutation rates than the average
+    HIGH_MUT_REGIONS = []
+
+    # load and process variants in each reference sequence individually, for memory reasons...
+    for refName in refList:
+
+        if (refName not in REF_WHITELIST) and (not NO_WHITELIST):
+            print(refName, 'is not in our whitelist, skipping...')
+            continue
+
+        print('reading reference "' + refName + '"...')
+        refSequence = getChrFromFasta(REF,ref_inds,refName).upper()
+        TOTAL_REFLEN += len(refSequence) - refSequence.count('N')
+
+        # list to be used for counting variants that occur multiple times in file (i.e. in multiple samples)
+        VDAT_COMMON = []
+
+
+        """ ##########################################################################
+        ###						COUNT TRINUCLEOTIDES IN REF						   ###
+        ########################################################################## """
+
+
+        if MYBED != None:
+            if printBedWarning:
+                print(
+                    "since you're using a bed input, we have to count trinucs in bed region even if you specified a trinuc count file for the reference...")
+                printBedWarning = False
+            if refName in MYBED[0]:
+                refKey = refName
+            elif ('chr' in refName) and (refName not in MYBED[0]) and (refName[3:] in MYBED[0]):
+                refKey = refName[3:]
+            elif ('chr' not in refName) and (refName not in MYBED[0]) and ('chr'+refName in MYBED[0]):
+                refKey = 'chr'+refName
+            if refKey in MYBED[0]:
+                subRegions = [(MYBED[0][refKey][n],MYBED[0][refKey][n+1]) for n in range(0,len(MYBED[0][refKey]),2)]
+                for sr in subRegions:
+                    for i in range(sr[0],sr[1]+1-2):
+                        trinuc = refSequence[i:i+3]
+                        if not trinuc in VALID_TRINUC:
+                            continue	# skip if trinuc contains invalid characters, or not in specified bed region
+                        if trinuc not in TRINUC_BED_COUNT:
+                            TRINUC_BED_COUNT[trinuc] = 0
+                        TRINUC_BED_COUNT[trinuc] += 1
+
+        if not os.path.isfile(REF+'.trinucCounts'):
+            print('counting trinucleotides in reference...')
+            for i in range(len(refSequence)-2):
+                if i%1000000 == 0 and i > 0:
+                    print(i, '/', len(refSequence))
+                    #break
+                trinuc = refSequence[i:i+3]
+                if not trinuc in VALID_TRINUC:
+                    continue	# skip if trinuc contains invalid characters
+                if trinuc not in TRINUC_REF_COUNT:
+                    TRINUC_REF_COUNT[trinuc] = 0
+                TRINUC_REF_COUNT[trinuc] += 1
+        else:
+            print('skipping trinuc counts (for whole reference) because we found a file...')
+
+        """ ##########################################################################
+        ###							READ INPUT VARIANTS							   ###
+        ########################################################################## """
+
+        print('reading input variants...')
+        f = open(TSV,'r')
+        isFirst = True
+        for line in f:
+
+            if IS_VCF and line[0] == '#':
+                continue
+            if isFirst:
+                if IS_VCF:
+                    # hard-code index values based on expected columns in vcf
+                    (c1,c2,c3,m1,m2,m3) = (0,1,1,3,3,4)
+                else:
+                    # determine columns of fields we're interested in
+                    splt = line.strip().split('\t')
+                    (c1,c2,c3) = (splt.index('chromosome'),splt.index('chromosome_start'),splt.index('chromosome_end'))
+                    (m1,m2,m3) = (splt.index('reference_genome_allele'),splt.index('mutated_from_allele'),splt.index('mutated_to_allele'))
+                    (d_id) = (splt.index('icgc_donor_id'))
+                isFirst = False
+                continue
+
+            splt = line.strip().split('\t')
+            # we have -1 because tsv/vcf coords are 1-based, and our reference string index is 0-based
+            [chrName,chrStart,chrEnd] = [splt[c1],int(splt[c2])-1,int(splt[c3])-1]
+            [allele_ref,allele_normal,allele_tumor] = [splt[m1].upper(),splt[m2].upper(),splt[m3].upper()]
+            if IS_VCF:
+                if len(allele_ref) != len(allele_tumor):
+                    # indels in tsv don't include the preserved first nucleotide, so lets trim the vcf alleles
+                    [allele_ref,allele_normal,allele_tumor] = [allele_ref[1:],allele_normal[1:],allele_tumor[1:]]
+                if not allele_ref: allele_ref = '-'
+                if not allele_normal: allele_normal = '-'
+                if not allele_tumor: allele_tumor = '-'
+                # if alternate alleles are present, lets just ignore this variant. I may come back and improve this later
+                if ',' in allele_tumor:
+                    continue
+                vcf_info = ';'+splt[7]+';'
+            else:
+                [donor_id] = [splt[d_id]]
+
+            # if we encounter a multi-np (i.e. 3 nucl --> 3 different nucl), let's skip it for now...
+            if ('-' not in allele_normal and '-' not in allele_tumor) and (len(allele_normal) > 1 or len(allele_tumor) > 1):
+                print('skipping a complex variant...')
+                continue
+
+            # to deal with '1' vs 'chr1' references, manually change names. this is hacky and bad.
+            if 'chr' not in chrName:
+                chrName = 'chr'+chrName
+            if 'chr' not in refName:
+                refName = 'chr'+refName
+            # skip irrelevant variants
+            if chrName != refName:
+                continue
+
+            # if variant is outside the regions we're interested in (if specified), skip it...
+            if MYBED != None:
+                refKey = refName
+                if not refKey in MYBED[0] and refKey[3:] in MYBED[0]:	# account for 1 vs chr1, again...
+                    refKey = refKey[3:]
+                if refKey not in MYBED[0]:
+                    inBed = False
+                else:
+                    inBed = isInBed(MYBED[0][refKey],chrStart)
+                if inBed != MYBED[1]:
+                    continue
+
+            # we want only snps
+            # so, no '-' characters allowed, and chrStart must be same as chrEnd
+            if '-' not in allele_normal and '-' not in allele_tumor and chrStart == chrEnd:
+                trinuc_ref = refSequence[chrStart-1:chrStart+2]
+                if not trinuc_ref in VALID_TRINUC:
+                    continue	# skip ref trinuc with invalid characters
+                # only consider positions where ref allele in tsv matches the nucleotide in our reference
+                if allele_ref == trinuc_ref[1]:
+                    trinuc_normal    = refSequence[chrStart-1] + allele_normal + refSequence[chrStart+1]
+                    trinuc_tumor     = refSequence[chrStart-1] + allele_tumor + refSequence[chrStart+1]
+                    if not trinuc_normal in VALID_TRINUC or not trinuc_tumor in VALID_TRINUC:
+                        continue	# skip if mutation contains invalid char
+                    key = (trinuc_normal,trinuc_tumor)
+                    if key not in TRINUC_TRANSITION_COUNT:
+                        TRINUC_TRANSITION_COUNT[key] = 0
+                    TRINUC_TRANSITION_COUNT[key] += 1
+                    SNP_COUNT += 1
+                    key2 = (allele_normal,allele_tumor)
+                    if key2 not in SNP_TRANSITION_COUNT:
+                        SNP_TRANSITION_COUNT[key2] = 0
+                    SNP_TRANSITION_COUNT[key2] += 1
+
+                    if IS_VCF:
+                        myPopFreq = VCF_DEFAULT_POP_FREQ
+                        if ';CAF=' in vcf_info:
+                            cafStr = re.findall(r";CAF=.*?(?=;)",vcf_info)[0]
+                            if ',' in cafStr:
+                                myPopFreq = float(cafStr[5:].split(',')[1])
+                        VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor,myPopFreq))
+                    else:
+                        VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor))
+                        TOTAL_DONORS[donor_id] = True
+                else:
+                    print('\nError: ref allele in variant call does not match reference.\n')
+                    exit(1)
+
+            # now let's look for indels...
+            if '-' in allele_normal: len_normal = 0
+            else: len_normal = len(allele_normal)
+            if '-' in allele_tumor: len_tumor = 0
+            else: len_tumor = len(allele_tumor)
+            if len_normal != len_tumor:
+                indel_len = len_tumor - len_normal
+                if indel_len not in INDEL_COUNT:
+                    INDEL_COUNT[indel_len] = 0
+                INDEL_COUNT[indel_len] += 1
+
+                if IS_VCF:
+                    myPopFreq = VCF_DEFAULT_POP_FREQ
+                    if ';CAF=' in vcf_info:
+                        cafStr = re.findall(r";CAF=.*?(?=;)",vcf_info)[0]
+                        if ',' in cafStr:
+                            myPopFreq = float(cafStr[5:].split(',')[1])
+                    VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor,myPopFreq))
+                else:
+                    VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor))
+                    TOTAL_DONORS[donor_id] = True
+        f.close()
+
+        # if we didn't find anything, skip ahead along to the next reference sequence
+        if not len(VDAT_COMMON):
+            print('Found no variants for this reference, moving along...')
+            continue
+
+        #
+        # identify common mutations
+        #
+        percentile_var = 95
+        if IS_VCF:
+            minVal = np.percentile([n[4] for n in VDAT_COMMON],percentile_var)
+            for k in sorted(VDAT_COMMON):
+                if k[4] >= minVal:
+                    COMMON_VARIANTS.append((refName,k[0],k[1],k[3],k[4]))
+            VDAT_COMMON = {(n[0],n[1],n[2],n[3]):n[4] for n in VDAT_COMMON}
+        else:
+            N_DONORS = len(TOTAL_DONORS)
+            VDAT_COMMON = list_2_countDict(VDAT_COMMON)
+            minVal = int(np.percentile(VDAT_COMMON.values(),percentile_var))
+            for k in sorted(VDAT_COMMON.keys()):
+                if VDAT_COMMON[k] >= minVal:
+                    COMMON_VARIANTS.append((refName,k[0],k[1],k[3],VDAT_COMMON[k]/float(N_DONORS)))
+
+        #
+        # identify areas that have contained significantly higher random mutation rates
+        #
+        dist_thresh      = 2000
+        percentile_clust = 97
+        qptn             = 1000
+        # identify regions with disproportionately more variants in them
+        VARIANT_POS = sorted([n[0] for n in VDAT_COMMON.keys()])
+        clustered_pos = clusterList(VARIANT_POS,dist_thresh)
+        byLen  = [(len(clustered_pos[i]),min(clustered_pos[i]),max(clustered_pos[i]),i) for i in range(len(clustered_pos))]
+        #byLen  = sorted(byLen,reverse=True)
+        #minLen = int(np.percentile([n[0] for n in byLen],percentile_clust))
+        #byLen  = [n for n in byLen if n[0] >= minLen]
+        candidate_regions = []
+        for n in byLen:
+            bi = int((n[1]-dist_thresh)/float(qptn))*qptn
+            bf = int((n[2]+dist_thresh)/float(qptn))*qptn
+            candidate_regions.append((n[0]/float(bf-bi),max([0,bi]),min([len(refSequence),bf])))
+        minVal = np.percentile([n[0] for n in candidate_regions],percentile_clust)
+        for n in candidate_regions:
+            if n[0] >= minVal:
+                HIGH_MUT_REGIONS.append((refName,n[1],n[2],n[0]))
+        # collapse overlapping regions
+        for i in range(len(HIGH_MUT_REGIONS)-1,0,-1):
+            if HIGH_MUT_REGIONS[i-1][2] >= HIGH_MUT_REGIONS[i][1] and HIGH_MUT_REGIONS[i-1][0] == HIGH_MUT_REGIONS[i][0]:
+                avgMutRate = 0.5*HIGH_MUT_REGIONS[i-1][3]+0.5*HIGH_MUT_REGIONS[i][3]	# not accurate, but I'm lazy
+                HIGH_MUT_REGIONS[i-1] = (HIGH_MUT_REGIONS[i-1][0], HIGH_MUT_REGIONS[i-1][1], HIGH_MUT_REGIONS[i][2], avgMutRate)
+                del HIGH_MUT_REGIONS[i]
+
+    #
+    # if we didn't count ref trinucs because we found file, read in ref counts from file now
+    #
+    if os.path.isfile(REF+'.trinucCounts'):
+        print('reading pre-computed trinuc counts...')
+        f = open(REF+'.trinucCounts','r')
+        for line in f:
+            splt = line.strip().split('\t')
+            TRINUC_REF_COUNT[splt[0]] = int(splt[1])
+        f.close()
+    # otherwise, save trinuc counts to file, if desired
+    elif SAVE_TRINUC:
+        if MYBED != None:
+            print('unable to save trinuc counts to file because using input bed region...')
+        else:
+            print('saving trinuc counts to file...')
+            f = open(REF+'.trinucCounts','w')
+            for trinuc in sorted(TRINUC_REF_COUNT.keys()):
+                f.write(trinuc+'\t'+str(TRINUC_REF_COUNT[trinuc])+'\n')
+            f.close()
+
+    #
+    # if using an input bed region, make necessary adjustments to trinuc ref counts based on the bed region trinuc counts
+    #
+    if MYBED != None:
+        if MYBED[1] == True:	# we are restricting our attention to bed regions, so ONLY use bed region trinuc counts
+            TRINUC_REF_COUNT = TRINUC_BED_COUNT
+        else:					# we are only looking outside bed regions, so subtract bed region trinucs from entire reference trinucs
+            for k in TRINUC_REF_COUNT.keys():
+                if k in TRINUC_BED_COUNT:
+                    TRINUC_REF_COUNT[k] -= TRINUC_BED_COUNT[k]
+
+    # if for some reason we didn't find any valid input variants, exit gracefully...
+    totalVar = SNP_COUNT + sum(INDEL_COUNT.values())
+    if totalVar == 0:
+        print(
+            '\nError: No valid variants were found, model could not be created. (Are you using the correct reference?)\n')
+        exit(1)
+
+    """ ##########################################################################
+    ###							COMPUTE PROBABILITIES						   ###
+    ########################################################################## """
+
+
+    #for k in sorted(TRINUC_REF_COUNT.keys()):
+    #		print k, TRINUC_REF_COUNT[k]
+    #
+    #for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
+    #	print k, TRINUC_TRANSITION_COUNT[k]
+
+    # frequency that each trinuc mutated into anything else
+    TRINUC_MUT_PROB = {}
+    # frequency that a trinuc mutates into another trinuc, given that it mutated
+    TRINUC_TRANS_PROBS = {}
+    # frequency of snp transitions, given a snp occurs.
+    SNP_TRANS_FREQ = {}
+
+    for trinuc in sorted(TRINUC_REF_COUNT.keys()):
+        myCount = 0
+        for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
+            if k[0] == trinuc:
+                myCount += TRINUC_TRANSITION_COUNT[k]
+        TRINUC_MUT_PROB[trinuc] = myCount / float(TRINUC_REF_COUNT[trinuc])
+        for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
+            if k[0] == trinuc:
+                TRINUC_TRANS_PROBS[k] = TRINUC_TRANSITION_COUNT[k] / float(myCount)
+
+    for n1 in VALID_NUCL:
+        rollingTot = sum([SNP_TRANSITION_COUNT[(n1,n2)] for n2 in VALID_NUCL if (n1,n2) in SNP_TRANSITION_COUNT])
+        for n2 in VALID_NUCL:
+            key2 = (n1,n2)
+            if key2 in SNP_TRANSITION_COUNT:
+                SNP_TRANS_FREQ[key2] = SNP_TRANSITION_COUNT[key2] / float(rollingTot)
+
+    # compute average snp and indel frequencies
+    SNP_FREQ       = SNP_COUNT/float(totalVar)
+    AVG_INDEL_FREQ = 1.-SNP_FREQ
+    INDEL_FREQ     = {k:(INDEL_COUNT[k]/float(totalVar))/AVG_INDEL_FREQ for k in INDEL_COUNT.keys()}
+    if MYBED != None:
+        if MYBED[1] == True:
+            AVG_MUT_RATE = totalVar/float(getTrackLen(MYBED[0]))
+        else:
+            AVG_MUT_RATE = totalVar/float(TOTAL_REFLEN - getTrackLen(MYBED[0]))
+    else:
+        AVG_MUT_RATE = totalVar/float(TOTAL_REFLEN)
+
+    #
+    #	if values weren't found in data, appropriately append null entries
+    #
+    printTrinucWarning = False
+    for trinuc in VALID_TRINUC:
+        trinuc_mut = [trinuc[0]+n+trinuc[2] for n in VALID_NUCL if n != trinuc[1]]
+        if trinuc not in TRINUC_MUT_PROB:
+            TRINUC_MUT_PROB[trinuc] = 0.
+            printTrinucWarning = True
+        for trinuc2 in trinuc_mut:
+            if (trinuc,trinuc2) not in TRINUC_TRANS_PROBS:
+                TRINUC_TRANS_PROBS[(trinuc,trinuc2)] = 0.
+                printTrinucWarning = True
+    if printTrinucWarning:
+        print(
+            'Warning: Some trinucleotides transitions were not encountered in the input dataset, '
+            'probabilities of 0.0 have been assigned to these events.')
+
+    #
+    #	print some stuff
+    #
+    for k in sorted(TRINUC_MUT_PROB.keys()):
+        print('p(' + k + ' mutates) =', TRINUC_MUT_PROB[k])
+
+    for k in sorted(TRINUC_TRANS_PROBS.keys()):
+        print('p(' + k[0] + ' --> ' + k[1] + ' | ' + k[0] + ' mutates) =', TRINUC_TRANS_PROBS[k])
+
+    for k in sorted(INDEL_FREQ.keys()):
+        if k > 0:
+            print('p(ins length = ' + str(abs(k)) + ' | indel occurs) =', INDEL_FREQ[k])
+        else:
+            print('p(del length = ' + str(abs(k)) + ' | indel occurs) =', INDEL_FREQ[k])
+
+    for k in sorted(SNP_TRANS_FREQ.keys()):
+        print('p(' + k[0] + ' --> ' + k[1] + ' | SNP occurs) =', SNP_TRANS_FREQ[k])
+
+    #for n in COMMON_VARIANTS:
+    #	print n
+
+    #for n in HIGH_MUT_REGIONS:
+    #	print n
+
+    print('p(snp)   =', SNP_FREQ)
+    print('p(indel) =', AVG_INDEL_FREQ)
+    print('overall average mut rate:', AVG_MUT_RATE)
+    print('total variants processed:', totalVar)
+
+    #
+    # save variables to file
+    #
+    if SKIP_COMMON:
+        OUT_DICT = {'AVG_MUT_RATE':AVG_MUT_RATE,
+                    'SNP_FREQ':SNP_FREQ,
+                    'SNP_TRANS_FREQ':SNP_TRANS_FREQ,
+                    'INDEL_FREQ':INDEL_FREQ,
+                    'TRINUC_MUT_PROB':TRINUC_MUT_PROB,
+                    'TRINUC_TRANS_PROBS':TRINUC_TRANS_PROBS}
+    else:
+        OUT_DICT = {'AVG_MUT_RATE':AVG_MUT_RATE,
+                    'SNP_FREQ':SNP_FREQ,
+                    'SNP_TRANS_FREQ':SNP_TRANS_FREQ,
+                    'INDEL_FREQ':INDEL_FREQ,
+                    'TRINUC_MUT_PROB':TRINUC_MUT_PROB,
+                    'TRINUC_TRANS_PROBS':TRINUC_TRANS_PROBS,
+                    'COMMON_VARIANTS':COMMON_VARIANTS,
+                    'HIGH_MUT_REGIONS':HIGH_MUT_REGIONS}
+    pickle.dump(OUT_DICT, open(OUT_PICKLE, "wb"))
 
 
 if __name__ == "__main__":
-	main()
+    main()
 
 
 
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 7860178..dfd2680 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -17,284 +17,290 @@
 import random
 import numpy as np
 import argparse
-import cPickle as pickle
+import pickle
 
 # absolute path to this script
-SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2])+'/py/'
+SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2]) + '/py/'
 sys.path.append(SIM_PATH)
 
-from probability		import DiscreteDistribution
+from py.probability import DiscreteDistribution
+
 
 def parseFQ(inf):
-	print 'reading '+inf+'...'
-	if inf[-3:] == '.gz':
-		print 'detected gzip suffix...'
-		f = gzip.open(inf,'r')
-	else:
-		f = open(inf,'r')
-
-	IS_SAM = False
-	if inf[-4:] == '.sam':
-		print 'detected sam input...'
-		IS_SAM = True
-
-	rRead  = 0
-	actual_readlen = 0
-	qDict  = {}
-	while True:
-
-		if IS_SAM:
-			data4 = f.readline()
-			if not len(data4):
-				break
-			try:
-				data4 = data4.split('\t')[10]
-			except IndexError:
-				break
-			# need to add some input checking here? Yup, probably.
-		else:
-			data1 = f.readline()
-			data2 = f.readline()
-			data3 = f.readline()
-			data4 = f.readline()
-			if not all([data1,data2,data3,data4]):
-				break
-
-		if actual_readlen == 0:
-			if inf[-3:] != '.gz' and not IS_SAM:
-				totalSize = os.path.getsize(inf)
-				entrySize = sum([len(n) for n in [data1,data2,data3,data4]])
-				print 'estimated number of reads in file:',int(float(totalSize)/entrySize)
-			actual_readlen = len(data4)-1
-			print 'assuming read length is uniform...'
-			print 'detected read length (from first read found):',actual_readlen
-			priorQ = np.zeros([actual_readlen,RQ])
-			totalQ = [None] + [np.zeros([RQ,RQ]) for n in xrange(actual_readlen-1)]
-
-		# sanity-check readlengths
-		if len(data4)-1 != actual_readlen:
-			print 'skipping read with unexpected length...'
-			continue
-
-		for i in range(len(data4)-1):
-			q = ord(data4[i])-offQ
-			qDict[q] = True
-			if i == 0:
-				priorQ[i][q] += 1
-			else:
-				totalQ[i][prevQ,q] += 1
-				priorQ[i][q] += 1
-			prevQ = q
-
-		rRead += 1
-		if rRead%PRINT_EVERY == 0:
-			print rRead
-		if MAX_READS > 0 and rRead >= MAX_READS:
-			break
-	f.close()
-
-	# some sanity checking again...
-	QRANGE = [min(qDict.keys()),max(qDict.keys())]
-	if QRANGE[0] < 0:
-		print '\nError: Read in Q-scores below 0\n'
-		exit(1)
-	if QRANGE[1] > RQ:
-		print '\nError: Read in Q-scores above specified maximum:',QRANGE[1],'>',RQ,'\n'
-		exit(1)
-
-	print 'computing probabilities...'
-	probQ  = [None] + [[[0. for m in xrange(RQ)] for n in xrange(RQ)] for p in xrange(actual_readlen-1)]
-	for p in xrange(1,actual_readlen):
-		for i in xrange(RQ):
-			rowSum = float(np.sum(totalQ[p][i,:]))+PROB_SMOOTH*RQ
-			if rowSum <= 0.:
-				continue
-			for j in xrange(RQ):
-				probQ[p][i][j] = (totalQ[p][i][j]+PROB_SMOOTH)/rowSum
-
-	initQ  = [[0. for m in xrange(RQ)] for n in xrange(actual_readlen)]
-	for i in xrange(actual_readlen):
-		rowSum = float(np.sum(priorQ[i,:]))+INIT_SMOOTH*RQ
-		if rowSum <= 0.:
-			continue
-		for j in xrange(RQ):
-			initQ[i][j] = (priorQ[i][j]+INIT_SMOOTH)/rowSum
-
-	if PLOT_STUFF:
-		mpl.rcParams.update({'font.size': 14, 'font.weight':'bold', 'lines.linewidth': 3})
-
-		mpl.figure(1)
-		Z = np.array(initQ).T
-		X, Y = np.meshgrid( range(0,len(Z[0])+1), range(0,len(Z)+1) )
-		mpl.pcolormesh(X,Y,Z,vmin=0.,vmax=0.25)
-		mpl.axis([0,len(Z[0]),0,len(Z)])
-		mpl.yticks(range(0,len(Z),10),range(0,len(Z),10))
-		mpl.xticks(range(0,len(Z[0]),10),range(0,len(Z[0]),10))
-		mpl.xlabel('Read Position')
-		mpl.ylabel('Quality Score')
-		mpl.title('Q-Score Prior Probabilities')
-		mpl.colorbar()
-
-		mpl.show()
-
-		VMIN_LOG = [-4,0]
-		minVal   = 10**VMIN_LOG[0]
-		qLabels  = [str(n) for n in range(QRANGE[0],QRANGE[1]+1) if n%5==0]
-		print qLabels
-		qTicksx  = [int(n)+0.5 for n in qLabels]
-		qTicksy  = [(RQ-int(n))-0.5 for n in qLabels]
-
-		for p in xrange(1,actual_readlen,10):
-			currentDat = np.array(probQ[p])
-			for i in xrange(len(currentDat)):
-				for j in xrange(len(currentDat[i])):
-					currentDat[i][j] = max(minVal,currentDat[i][j])
-
-			# matrix indices:		pcolormesh plotting:	plot labels and axes:
-			#
-			#      y				   ^					   ^
-			#	   -->				 x |					 y |
-			#  x |					    -->					    -->
-			#    v 					    y					    x
-			#
-			# to plot a MxN matrix 'Z' with rowNames and colNames we need to:
-			#
-			# pcolormesh(X,Y,Z[::-1,:])		# invert x-axis
-			# # swap x/y axis parameters and labels, remember x is still inverted:
-			# xlim([yMin,yMax])
-			# ylim([M-xMax,M-xMin])
-			# xticks()
-			#
-
-			mpl.figure(p+1)
-			Z = np.log10(currentDat)
-			X, Y = np.meshgrid( range(0,len(Z[0])+1), range(0,len(Z)+1) )
-			mpl.pcolormesh(X,Y,Z[::-1,:],vmin=VMIN_LOG[0],vmax=VMIN_LOG[1],cmap='jet')
-			mpl.xlim([QRANGE[0],QRANGE[1]+1])
-			mpl.ylim([RQ-QRANGE[1]-1,RQ-QRANGE[0]])
-			mpl.yticks(qTicksy,qLabels)
-			mpl.xticks(qTicksx,qLabels)
-			mpl.xlabel('\n' + r'$Q_{i+1}$')
-			mpl.ylabel(r'$Q_i$')
-			mpl.title('Q-Score Transition Frequencies [Read Pos:'+str(p)+']')
-			cb = mpl.colorbar()
-			cb.set_ticks([-4,-3,-2,-1,0])
-			cb.set_ticklabels([r'$10^{-4}$',r'$10^{-3}$',r'$10^{-2}$',r'$10^{-1}$',r'$10^{0}$'])
-
-		#mpl.tight_layout()
-		mpl.show()
-
-	print 'estimating average error rate via simulation...'
-	Qscores = range(RQ)
-	#print (len(initQ), len(initQ[0]))
-	#print (len(probQ), len(probQ[1]), len(probQ[1][0]))
-
-	initDistByPos        = [DiscreteDistribution(initQ[i],Qscores) for i in xrange(len(initQ))]
-	probDistByPosByPrevQ = [None]
-	for i in xrange(1,len(initQ)):
-		probDistByPosByPrevQ.append([])
-		for j in xrange(len(initQ[0])):
-			if np.sum(probQ[i][j]) <= 0.:	# if we don't have sufficient data for a transition, use the previous qscore
-				probDistByPosByPrevQ[-1].append(DiscreteDistribution([1],[Qscores[j]],degenerateVal=Qscores[j]))
-			else:
-				probDistByPosByPrevQ[-1].append(DiscreteDistribution(probQ[i][j],Qscores))
-
-	countDict = {}
-	for q in Qscores:
-		countDict[q] = 0
-	for samp in xrange(1,N_SAMP+1):
-		if samp%PRINT_EVERY == 0:
-			print samp
-		myQ = initDistByPos[0].sample()
-		countDict[myQ] += 1
-		for i in xrange(1,len(initQ)):
-			myQ = probDistByPosByPrevQ[i][myQ].sample()
-			countDict[myQ] += 1
-
-	totBases = float(sum(countDict.values()))
-	avgError = 0.
-	for k in sorted(countDict.keys()):
-		eVal = 10.**(-k/10.)
-		#print k, eVal, countDict[k]
-		avgError += eVal * (countDict[k]/totBases)
-	print 'AVG ERROR RATE:',avgError
-
-	return (initQ, probQ, avgError)
+    print('reading ' + inf + '...')
+    if inf[-3:] == '.gz':
+        print('detected gzip suffix...')
+        f = gzip.open(inf, 'r')
+    else:
+        f = open(inf, 'r')
+
+    IS_SAM = False
+    if inf[-4:] == '.sam':
+        print('detected sam input...')
+        IS_SAM = True
+
+    rRead = 0
+    actual_readlen = 0
+    qDict = {}
+    while True:
+
+        if IS_SAM:
+            data4 = f.readline()
+            if not len(data4):
+                break
+            try:
+                data4 = data4.split('\t')[10]
+            except IndexError:
+                break
+        # need to add some input checking here? Yup, probably.
+        else:
+            data1 = f.readline()
+            data2 = f.readline()
+            data3 = f.readline()
+            data4 = f.readline()
+            if not all([data1, data2, data3, data4]):
+                break
+
+        if actual_readlen == 0:
+            if inf[-3:] != '.gz' and not IS_SAM:
+                totalSize = os.path.getsize(inf)
+                entrySize = sum([len(n) for n in [data1, data2, data3, data4]])
+                print('estimated number of reads in file:', int(float(totalSize) / entrySize))
+            actual_readlen = len(data4) - 1
+            print('assuming read length is uniform...')
+            print('detected read length (from first read found):', actual_readlen)
+            priorQ = np.zeros([actual_readlen, RQ])
+            totalQ = [None] + [np.zeros([RQ, RQ]) for n in range(actual_readlen - 1)]
+
+        # sanity-check readlengths
+        if len(data4) - 1 != actual_readlen:
+            print('skipping read with unexpected length...')
+            continue
+
+        for i in range(len(data4) - 1):
+            q = ord(data4[i]) - offQ
+            qDict[q] = True
+            if i == 0:
+                priorQ[i][q] += 1
+            else:
+                totalQ[i][prevQ, q] += 1
+                priorQ[i][q] += 1
+            prevQ = q
+
+        rRead += 1
+        if rRead % PRINT_EVERY == 0:
+            print(rRead)
+        if MAX_READS > 0 and rRead >= MAX_READS:
+            break
+    f.close()
+
+    # some sanity checking again...
+    QRANGE = [min(qDict.keys()), max(qDict.keys())]
+    if QRANGE[0] < 0:
+        print('\nError: Read in Q-scores below 0\n')
+        exit(1)
+    if QRANGE[1] > RQ:
+        print('\nError: Read in Q-scores above specified maximum:', QRANGE[1], '>', RQ, '\n')
+        exit(1)
+
+    print('computing probabilities...')
+    probQ = [None] + [[[0. for m in range(RQ)] for n in range(RQ)] for p in range(actual_readlen - 1)]
+    for p in range(1, actual_readlen):
+        for i in range(RQ):
+            rowSum = float(np.sum(totalQ[p][i, :])) + PROB_SMOOTH * RQ
+            if rowSum <= 0.:
+                continue
+            for j in range(RQ):
+                probQ[p][i][j] = (totalQ[p][i][j] + PROB_SMOOTH) / rowSum
+
+    initQ = [[0. for m in range(RQ)] for n in range(actual_readlen)]
+    for i in range(actual_readlen):
+        rowSum = float(np.sum(priorQ[i, :])) + INIT_SMOOTH * RQ
+        if rowSum <= 0.:
+            continue
+        for j in range(RQ):
+            initQ[i][j] = (priorQ[i][j] + INIT_SMOOTH) / rowSum
+
+    if PLOT_STUFF:
+        mpl.rcParams.update({'font.size': 14, 'font.weight': 'bold', 'lines.linewidth': 3})
+
+        mpl.figure(1)
+        Z = np.array(initQ).T
+        X, Y = np.meshgrid(range(0, len(Z[0]) + 1), range(0, len(Z) + 1))
+        mpl.pcolormesh(X, Y, Z, vmin=0., vmax=0.25)
+        mpl.axis([0, len(Z[0]), 0, len(Z)])
+        mpl.yticks(range(0, len(Z), 10), range(0, len(Z), 10))
+        mpl.xticks(range(0, len(Z[0]), 10), range(0, len(Z[0]), 10))
+        mpl.xlabel('Read Position')
+        mpl.ylabel('Quality Score')
+        mpl.title('Q-Score Prior Probabilities')
+        mpl.colorbar()
+
+        mpl.show()
+
+        VMIN_LOG = [-4, 0]
+        minVal = 10 ** VMIN_LOG[0]
+        qLabels = [str(n) for n in range(QRANGE[0], QRANGE[1] + 1) if n % 5 == 0]
+        print(qLabels)
+        qTicksx = [int(n) + 0.5 for n in qLabels]
+        qTicksy = [(RQ - int(n)) - 0.5 for n in qLabels]
+
+        for p in range(1, actual_readlen, 10):
+            currentDat = np.array(probQ[p])
+            for i in range(len(currentDat)):
+                for j in range(len(currentDat[i])):
+                    currentDat[i][j] = max(minVal, currentDat[i][j])
+
+            # matrix indices:		pcolormesh plotting:	plot labels and axes:
+            #
+            #      y				   ^					   ^
+            #	   -->				 x |					 y |
+            #  x |					    -->					    -->
+            #    v 					    y					    x
+            #
+            # to plot a MxN matrix 'Z' with rowNames and colNames we need to:
+            #
+            # pcolormesh(X,Y,Z[::-1,:])		# invert x-axis
+            # # swap x/y axis parameters and labels, remember x is still inverted:
+            # xlim([yMin,yMax])
+            # ylim([M-xMax,M-xMin])
+            # xticks()
+            #
+
+            mpl.figure(p + 1)
+            Z = np.log10(currentDat)
+            X, Y = np.meshgrid(range(0, len(Z[0]) + 1), range(0, len(Z) + 1))
+            mpl.pcolormesh(X, Y, Z[::-1, :], vmin=VMIN_LOG[0], vmax=VMIN_LOG[1], cmap='jet')
+            mpl.xlim([QRANGE[0], QRANGE[1] + 1])
+            mpl.ylim([RQ - QRANGE[1] - 1, RQ - QRANGE[0]])
+            mpl.yticks(qTicksy, qLabels)
+            mpl.xticks(qTicksx, qLabels)
+            mpl.xlabel('\n' + r'$Q_{i+1}$')
+            mpl.ylabel(r'$Q_i$')
+            mpl.title('Q-Score Transition Frequencies [Read Pos:' + str(p) + ']')
+            cb = mpl.colorbar()
+            cb.set_ticks([-4, -3, -2, -1, 0])
+            cb.set_ticklabels([r'$10^{-4}$', r'$10^{-3}$', r'$10^{-2}$', r'$10^{-1}$', r'$10^{0}$'])
+
+        # mpl.tight_layout()
+        mpl.show()
+
+    print('estimating average error rate via simulation...')
+    Qscores = range(RQ)
+    # print (len(initQ), len(initQ[0]))
+    # print (len(probQ), len(probQ[1]), len(probQ[1][0]))
+
+    initDistByPos = [DiscreteDistribution(initQ[i], Qscores) for i in range(len(initQ))]
+    probDistByPosByPrevQ = [None]
+    for i in range(1, len(initQ)):
+        probDistByPosByPrevQ.append([])
+        for j in range(len(initQ[0])):
+            if np.sum(probQ[i][j]) <= 0.:  # if we don't have sufficient data for a transition, use the previous qscore
+                probDistByPosByPrevQ[-1].append(DiscreteDistribution([1], [Qscores[j]], degenerateVal=Qscores[j]))
+            else:
+                probDistByPosByPrevQ[-1].append(DiscreteDistribution(probQ[i][j], Qscores))
+
+    countDict = {}
+    for q in Qscores:
+        countDict[q] = 0
+    for samp in range(1, N_SAMP + 1):
+        if samp % PRINT_EVERY == 0:
+            print(samp)
+        myQ = initDistByPos[0].sample()
+        countDict[myQ] += 1
+        for i in range(1, len(initQ)):
+            myQ = probDistByPosByPrevQ[i][myQ].sample()
+            countDict[myQ] += 1
+
+    totBases = float(sum(countDict.values()))
+    avgError = 0.
+    for k in sorted(countDict.keys()):
+        eVal = 10. ** (-k / 10.)
+        # print k, eVal, countDict[k]
+        avgError += eVal * (countDict[k] / totBases)
+    print('AVG ERROR RATE:', avgError)
+
+    return initQ, probQ, avgError
+
 
 parser = argparse.ArgumentParser(description='genSeqErrorModel.py')
-parser.add_argument('-i',  type=str, required=True,  metavar='<str>',                      help="* input_read1.fq (.gz) / input_read1.sam")
-parser.add_argument('-o',  type=str, required=True,  metavar='<str>',                      help="* output.p")
-parser.add_argument('-i2', type=str, required=False, metavar='<str>',     default=None,    help="input_read2.fq (.gz) / input_read2.sam")
-parser.add_argument('-p',  type=str, required=False, metavar='<str>',     default=None,    help="input_alignment.pileup")
-parser.add_argument('-q',  type=int, required=False, metavar='<int>',     default=33,      help="quality score offset [33]")
-parser.add_argument('-Q',  type=int, required=False, metavar='<int>',     default=41,      help="maximum quality score [41]")
-parser.add_argument('-n',  type=int, required=False, metavar='<int>',     default=-1,      help="maximum number of reads to process [all]")
-parser.add_argument('-s',  type=int, required=False, metavar='<int>',     default=1000000, help="number of simulation iterations [1000000]")
-parser.add_argument('--plot',        required=False, action='store_true', default=False,   help='perform some optional plotting')
+parser.add_argument('-i', type=str, required=True, metavar='<str>', help="* input_read1.fq (.gz) / input_read1.sam")
+parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output.p")
+parser.add_argument('-i2', type=str, required=False, metavar='<str>', default=None,
+                    help="input_read2.fq (.gz) / input_read2.sam")
+parser.add_argument('-p', type=str, required=False, metavar='<str>', default=None, help="input_alignment.pileup")
+parser.add_argument('-q', type=int, required=False, metavar='<int>', default=33, help="quality score offset [33]")
+parser.add_argument('-Q', type=int, required=False, metavar='<int>', default=41, help="maximum quality score [41]")
+parser.add_argument('-n', type=int, required=False, metavar='<int>', default=-1,
+                    help="maximum number of reads to process [all]")
+parser.add_argument('-s', type=int, required=False, metavar='<int>', default=1000000,
+                    help="number of simulation iterations [1000000]")
+parser.add_argument('--plot', required=False, action='store_true', default=False, help='perform some optional plotting')
 args = parser.parse_args()
 
 (INF, OUF, offQ, maxQ, MAX_READS, N_SAMP) = (args.i, args.o, args.q, args.Q, args.n, args.s)
 (INF2, PILEUP) = (args.i2, args.p)
 
-RQ = maxQ+1
+RQ = maxQ + 1
 
 INIT_SMOOTH = 0.
 PROB_SMOOTH = 0.
 PRINT_EVERY = 10000
-PLOT_STUFF  = args.plot
+PLOT_STUFF = args.plot
 if PLOT_STUFF:
-	print 'plotting is desired, lets import matplotlib...'
-	import matplotlib.pyplot as mpl
+    print('plotting is desired, lets import matplotlib...')
+    import matplotlib.pyplot as mpl
+
 
 def main():
+    Qscores = range(RQ)
+    if INF2 == None:
+        (initQ, probQ, avgError) = parseFQ(INF)
+    else:
+        (initQ, probQ, avgError1) = parseFQ(INF)
+        (initQ2, probQ2, avgError2) = parseFQ(INF2)
+        avgError = (avgError1 + avgError2) / 2.
+
+    #
+    #	embed some default sequencing error parameters if no pileup is provided
+    #
+    if PILEUP == None:
+
+        print('Using default sequencing error parameters...')
+
+        # sequencing substitution transition probabilities
+        SSE_PROB = [[0., 0.4918, 0.3377, 0.1705],
+                    [0.5238, 0., 0.2661, 0.2101],
+                    [0.3754, 0.2355, 0., 0.3890],
+                    [0.2505, 0.2552, 0.4942, 0.]]
+        # if a sequencing error occurs, what are the odds it's an indel?
+        SIE_RATE = 0.01
+        # sequencing indel error length distribution
+        SIE_PROB = [0.999, 0.001]
+        SIE_VAL = [1, 2]
+        # if a sequencing indel error occurs, what are the odds it's an insertion as opposed to a deletion?
+        SIE_INS_FREQ = 0.4
+        # if a sequencing insertion error occurs, what's the probability of it being an A, C, G, T...
+        SIE_INS_NUCL = [0.25, 0.25, 0.25, 0.25]
+
+    #
+    #	otherwise we need to parse a pileup and compute statistics!
+    #
+    else:
+        print('\nPileup parsing coming soon!\n')
+        exit(1)
+
+    errorParams = [SSE_PROB, SIE_RATE, SIE_PROB, SIE_VAL, SIE_INS_FREQ, SIE_INS_NUCL]
+
+    #
+    #	finally, let's save our output model
+    #
+    print('saving model...')
+    if INF2 == None:
+        pickle.dump([initQ, probQ, Qscores, offQ, avgError, errorParams], open(OUF, 'wb'))
+    else:
+        pickle.dump([initQ, probQ, initQ2, probQ2, Qscores, offQ, avgError, errorParams], open(OUF, 'wb'))
 
-	Qscores = range(RQ)
-	if INF2 == None:
-		(initQ, probQ, avgError) = parseFQ(INF)
-	else:
-		(initQ, probQ, avgError1)   = parseFQ(INF)
-		(initQ2, probQ2, avgError2) = parseFQ(INF2)
-		avgError = (avgError1+avgError2)/2.
-
-	#
-	#	embed some default sequencing error parameters if no pileup is provided
-	#
-	if PILEUP == None:
-
-		print 'Using default sequencing error parameters...'
-
-		# sequencing substitution transition probabilities
-		SSE_PROB   = [[0.,     0.4918, 0.3377, 0.1705 ],
-					  [0.5238,     0., 0.2661, 0.2101 ],
-					  [0.3754, 0.2355,     0., 0.3890 ],
-					  [0.2505, 0.2552, 0.4942, 0.     ]]
-		# if a sequencing error occurs, what are the odds it's an indel?
-		SIE_RATE     = 0.01
-		# sequencing indel error length distribution
-		SIE_PROB     = [0.999,0.001]
-		SIE_VAL      = [1,2]
-		# if a sequencing indel error occurs, what are the odds it's an insertion as opposed to a deletion?
-		SIE_INS_FREQ = 0.4
-		# if a sequencing insertion error occurs, what's the probability of it being an A, C, G, T...
-		SIE_INS_NUCL = [0.25, 0.25, 0.25, 0.25]
-
-	#
-	#	otherwise we need to parse a pileup and compute statistics!
-	#
-	else:
-		print '\nPileup parsing coming soon!\n'
-		exit(1)
-
-	errorParams  = [SSE_PROB, SIE_RATE, SIE_PROB, SIE_VAL, SIE_INS_FREQ, SIE_INS_NUCL]
-
-	#
-	#	finally, let's save our output model
-	#
-	print 'saving model...'
-	if INF2 == None:
-		pickle.dump([initQ,probQ,Qscores,offQ,avgError,errorParams],open(OUF,'wb'))
-	else:
-		pickle.dump([initQ,probQ,initQ2,probQ2,Qscores,offQ,avgError,errorParams],open(OUF,'wb'))
 
 if __name__ == '__main__':
-	main()
+    main()
diff --git a/utilities/plotMutModel.py b/utilities/plotMutModel.py
index 8d1e83d..57c838b 100644
--- a/utilities/plotMutModel.py
+++ b/utilities/plotMutModel.py
@@ -15,151 +15,156 @@
 import matplotlib.cm as cmx
 import argparse
 
-#mpl.rc('text',usetex=True)
-#mpl.rcParams['text.latex.preamble']=[r"\usepackage{amsmath}"]
-
-parser = argparse.ArgumentParser(description='Plot and compare mutation models from genMutModel.py Usage: python plotMutModel.py -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix')
-parser.add_argument('-i',  type=str,   required=True,   metavar='<str>',   nargs='+',                help="* mutation_model_1.p [mutation_model_2.p] [mutation_model_3] ...")
-parser.add_argument('-l',  type=str,   required=True,   metavar='<str>',   nargs='+',                help="* legend labels: model1_name [model2_name] [model3_name]...")
-parser.add_argument('-o',  type=str,   required=True,   metavar='<str>',                             help="* output pdf prefix")
+# mpl.rc('text',usetex=True)
+# mpl.rcParams['text.latex.preamble']=[r"\usepackage{amsmath}"]
+
+parser = argparse.ArgumentParser(
+    description='Plot and compare mutation models from genMutModel.py Usage: python plotMutModel.py -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix')
+parser.add_argument('-i', type=str, required=True, metavar='<str>', nargs='+',
+                    help="* mutation_model_1.p [mutation_model_2.p] [mutation_model_3] ...")
+parser.add_argument('-l', type=str, required=True, metavar='<str>', nargs='+',
+                    help="* legend labels: model1_name [model2_name] [model3_name]...")
+parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output pdf prefix")
 args = parser.parse_args()
 
 
+def getColor(i, N, colormap='jet'):
+    cm = mpl.get_cmap(colormap)
+    cNorm = colors.Normalize(vmin=0, vmax=N + 1)
+    scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
+    colorVal = scalarMap.to_rgba(i)
+    return colorVal
+
+
+def isInBed(track, ind):
+    if ind in track:
+        return True
+    elif bisect.bisect(track, ind) % 1 == 1:
+        return True
+    else:
+        return False
+
+
+def getBedOverlap(track, ind_s, ind_e):
+    if ind_s in track:
+        myInd = track.index(ind_s)
+        return min([track[myInd + 1] - ind_s + 1, ind_e - ind_s + 1])
+    else:
+        myInd = bisect.bisect(track, ind_s)
+        if myInd % 1 and myInd < len(track) - 1:
+            return min([track[myInd + 1] - ind_s + 1, ind_e - ind_s + 1])
+    return 0
 
-def getColor(i,N,colormap='jet'):
-	cm = mpl.get_cmap(colormap) 
-	cNorm  = colors.Normalize(vmin=0, vmax=N+1)
-	scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
-	colorVal = scalarMap.to_rgba(i)
-	return colorVal
-
-def isInBed(track,ind):
-	if ind in track:
-		return True
-	elif bisect.bisect(track,ind)%1 == 1:
-		return True
-	else:
-		return False
-
-def getBedOverlap(track,ind_s,ind_e):
-	if ind_s in track:
-		myInd = track.index(ind_s)
-		return min([track[myInd+1]-ind_s+1,ind_e-ind_s+1])
-	else:
-		myInd = bisect.bisect(track,ind_s)
-		if myInd%1 and myInd < len(track)-1:
-			return min([track[myInd+1]-ind_s+1,ind_e-ind_s+1])
-	return 0
 
 # a waaaaaaay slower version of the above function ^^
-#def getTrackOverlap(track1,track2):
-#	otrack = [0 for n in xrange(max(track1+track2)+1)]
-#	for i in xrange(0,len(track1),2):
-#		for j in xrange(track1[i],track1[i+1]+1):
+# def getTrackOverlap(track1,track2):
+#	otrack = [0 for n in range(max(track1+track2)+1)]
+#	for i in range(0,len(track1),2):
+#		for j in range(track1[i],track1[i+1]+1):
 #			otrack[j] = 1
 #	ocount = 0
-#	for i in xrange(0,len(track2),2):
-#		for j in xrange(track2[i],track2[i+1]+1):
+#	for i in range(0,len(track2),2):
+#		for j in range(track2[i],track2[i+1]+1):
 #			if otrack[j]:
 #				ocount += 1
 #	return ocount
 
-OUP  = args.o
+OUP = args.o
 LAB = args.l
-#print LAB
-INP  = args.i
+# print LAB
+INP = args.i
 
 N_FILES = len(INP)
 
-mpl.rcParams.update({'font.size': 13, 'font.weight':'bold', 'lines.linewidth': 3})
+mpl.rcParams.update({'font.size': 13, 'font.weight': 'bold', 'lines.linewidth': 3})
 
 #################################################
 #
 #	BASIC STATS
 #
 #################################################
-mpl.figure(0,figsize=(12,10))
+mpl.figure(0, figsize=(12, 10))
 
-mpl.subplot(2,2,1)
+mpl.subplot(2, 2, 1)
 colorInd = 0
 for fn in INP:
-	myCol = getColor(colorInd,N_FILES)
-	colorInd += 1
-	DATA_DICT = pickle.load( open( fn, "rb" ) )
-	[AVG_MUT_RATE, SNP_FREQ, INDEL_FREQ] = [DATA_DICT['AVG_MUT_RATE'], DATA_DICT['SNP_FREQ'], DATA_DICT['INDEL_FREQ']]
-	mpl.bar([colorInd-1],[AVG_MUT_RATE],1.,color=myCol)
-mpl.xlim([-1,N_FILES+1])
+    myCol = getColor(colorInd, N_FILES)
+    colorInd += 1
+    DATA_DICT = pickle.load(open(fn, "rb"))
+    [AVG_MUT_RATE, SNP_FREQ, INDEL_FREQ] = [DATA_DICT['AVG_MUT_RATE'], DATA_DICT['SNP_FREQ'], DATA_DICT['INDEL_FREQ']]
+    mpl.bar([colorInd - 1], [AVG_MUT_RATE], 1., color=myCol)
+mpl.xlim([-1, N_FILES + 1])
 mpl.grid()
-mpl.xticks([],[])
+mpl.xticks([], [])
 mpl.ylabel('Frequency')
 mpl.title('Overall mutation rate (1/bp)')
 
-mpl.subplot(2,2,2)
+mpl.subplot(2, 2, 2)
 colorInd = 0
 for fn in INP:
-	myCol = getColor(colorInd,N_FILES)
-	colorInd += 1
-	DATA_DICT = pickle.load( open( fn, "rb" ) )
-	[AVG_MUT_RATE, SNP_FREQ, INDEL_FREQ] = [DATA_DICT['AVG_MUT_RATE'], DATA_DICT['SNP_FREQ'], DATA_DICT['INDEL_FREQ']]
-	mpl.bar([colorInd-1],[SNP_FREQ],1.,color=myCol)
-	mpl.bar([colorInd-1],[1.-SNP_FREQ],1.,color=myCol,bottom=[SNP_FREQ],hatch='/')
-mpl.axis([-1,N_FILES+1,0,1.2])
+    myCol = getColor(colorInd, N_FILES)
+    colorInd += 1
+    DATA_DICT = pickle.load(open(fn, "rb"))
+    [AVG_MUT_RATE, SNP_FREQ, INDEL_FREQ] = [DATA_DICT['AVG_MUT_RATE'], DATA_DICT['SNP_FREQ'], DATA_DICT['INDEL_FREQ']]
+    mpl.bar([colorInd - 1], [SNP_FREQ], 1., color=myCol)
+    mpl.bar([colorInd - 1], [1. - SNP_FREQ], 1., color=myCol, bottom=[SNP_FREQ], hatch='/')
+mpl.axis([-1, N_FILES + 1, 0, 1.2])
 mpl.grid()
-mpl.xticks([],[])
-mpl.yticks([0,.2,.4,.6,.8,1.],[0,0.2,0.4,0.6,0.8,1.0])
+mpl.xticks([], [])
+mpl.yticks([0, .2, .4, .6, .8, 1.], [0, 0.2, 0.4, 0.6, 0.8, 1.0])
 mpl.ylabel('Frequency')
 mpl.title('SNP freq [  ] & indel freq [//]')
 
-mpl.subplot(2,1,2)
+mpl.subplot(2, 1, 2)
 colorInd = 0
-legText  = LAB
+legText = LAB
 for fn in INP:
-	myCol = getColor(colorInd,N_FILES)
-	colorInd += 1
-	DATA_DICT = pickle.load( open( fn, "rb" ) )
-	[AVG_MUT_RATE, SNP_FREQ, INDEL_FREQ] = [DATA_DICT['AVG_MUT_RATE'], DATA_DICT['SNP_FREQ'], DATA_DICT['INDEL_FREQ']]
-	x = sorted(INDEL_FREQ.keys())
-	y = [INDEL_FREQ[n] for n in x]
-	mpl.plot(x,y,color=myCol)
-	#legText.append(fn)
+    myCol = getColor(colorInd, N_FILES)
+    colorInd += 1
+    DATA_DICT = pickle.load(open(fn, "rb"))
+    [AVG_MUT_RATE, SNP_FREQ, INDEL_FREQ] = [DATA_DICT['AVG_MUT_RATE'], DATA_DICT['SNP_FREQ'], DATA_DICT['INDEL_FREQ']]
+    x = sorted(INDEL_FREQ.keys())
+    y = [INDEL_FREQ[n] for n in x]
+    mpl.plot(x, y, color=myCol)
+# legText.append(fn)
 mpl.grid()
 mpl.xlabel('Indel size (bp)', fontweight='bold')
 mpl.ylabel('Frequency')
 mpl.title('Indel frequency by size (- deletion, + insertion)')
 mpl.legend(legText)
-#mpl.show()
-mpl.savefig(OUP+'_plot1_mutRates.pdf')
+# mpl.show()
+mpl.savefig(OUP + '_plot1_mutRates.pdf')
 
 #################################################
 #
 #	TRINUC PRIOR PROB
 #
 #################################################
-mpl.figure(1,figsize=(14,6))
+mpl.figure(1, figsize=(14, 6))
 colorInd = 0
-legText  = LAB
+legText = LAB
 for fn in INP:
-	myCol = getColor(colorInd,N_FILES)
-	colorInd += 1
-	DATA_DICT = pickle.load( open( fn, "rb" ) )
-	TRINUC_MUT_PROB = DATA_DICT['TRINUC_MUT_PROB']
-
-	x  = range(colorInd-1,len(TRINUC_MUT_PROB)*N_FILES,N_FILES)
-	xt = sorted(TRINUC_MUT_PROB.keys())
-	y  = [TRINUC_MUT_PROB[k] for k in xt]
-	markerline, stemlines, baseline = mpl.stem(x,y,'-.')
-	mpl.setp(markerline, 'markerfacecolor', myCol)
-	mpl.setp(markerline, 'markeredgecolor', myCol)
-	mpl.setp(baseline, 'color', myCol, 'linewidth', 0)
-	mpl.setp(stemlines, 'color', myCol, 'linewidth', 3)
-	if colorInd == 1:
-		mpl.xticks(x,xt,rotation=90)
-	#legText.append(fn)
+    myCol = getColor(colorInd, N_FILES)
+    colorInd += 1
+    DATA_DICT = pickle.load(open(fn, "rb"))
+    TRINUC_MUT_PROB = DATA_DICT['TRINUC_MUT_PROB']
+
+    x = range(colorInd - 1, len(TRINUC_MUT_PROB) * N_FILES, N_FILES)
+    xt = sorted(TRINUC_MUT_PROB.keys())
+    y = [TRINUC_MUT_PROB[k] for k in xt]
+    markerline, stemlines, baseline = mpl.stem(x, y, '-.')
+    mpl.setp(markerline, 'markerfacecolor', myCol)
+    mpl.setp(markerline, 'markeredgecolor', myCol)
+    mpl.setp(baseline, 'color', myCol, 'linewidth', 0)
+    mpl.setp(stemlines, 'color', myCol, 'linewidth', 3)
+    if colorInd == 1:
+        mpl.xticks(x, xt, rotation=90)
+# legText.append(fn)
 mpl.grid()
 mpl.ylabel('p(trinucleotide mutates)')
 mpl.legend(legText)
-#mpl.show()
-mpl.savefig(OUP+'_plot2_trinucPriors.pdf')
+# mpl.show()
+mpl.savefig(OUP + '_plot2_trinucPriors.pdf')
 
 #################################################
 #
@@ -168,45 +173,45 @@ def getBedOverlap(track,ind_s,ind_e):
 #################################################
 plotNum = 3
 for fn in INP:
-	fig = mpl.figure(plotNum,figsize=(12,10))
-	DATA_DICT = pickle.load( open( fn, "rb" ) )
-	TRINUC_TRANS_PROBS = DATA_DICT['TRINUC_TRANS_PROBS']
-
-	xt2 = [m[3] for m in sorted([(n[0],n[2],n[1],n) for n in xt])]
-	reverse_dict = {xt2[i]:i for i in xrange(len(xt2))}
-	Z = np.zeros((64,64))
-	L = [['' for n in xrange(64)] for m in xrange(64)]
-	for k in TRINUC_TRANS_PROBS:
-		i = reverse_dict[k[0]]
-		j = reverse_dict[k[1]]
-		Z[i][j] = TRINUC_TRANS_PROBS[k]
-
-	HARDCODED_LABEL = ['A_A','A_C','A_G','A_T',
-	                   'C_A','C_C','C_G','C_T',
-	                   'G_A','G_C','G_G','G_T',
-	                   'T_A','T_C','T_G','T_T']
-
-	for pi in xrange(16):
-		mpl.subplot(4,4,pi+1)
-		Z2 = Z[pi*4:(pi+1)*4,pi*4:(pi+1)*4]
-		X, Y = np.meshgrid( range(0,len(Z2[0])+1), range(0,len(Z2)+1) )
-		im = mpl.pcolormesh(X,Y,Z2[::-1,:],vmin=0.0,vmax=0.5)
-		mpl.axis([0,4,0,4])
-		mpl.xticks([0.5,1.5,2.5,3.5],['A','C','G','T'])
-		mpl.yticks([0.5,1.5,2.5,3.5],['T','G','C','A'])
-		mpl.text(1.6, 1.8, HARDCODED_LABEL[pi], color='white')
-
-	# colorbar haxx
-	fig.subplots_adjust(right=0.8)
-	cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
-	cb = fig.colorbar(im,cax=cbar_ax)
-	cb.set_label(r"p(X$Y_1$Z->X$Y_2$Z | X_Z mutates)")
-
-	#mpl.tight_layout()
-	#mpl.figtext(0.24,0.94,'Trinucleotide Mutation Frequency',size=20)
-	#mpl.show()
-	mpl.savefig(OUP+'_plot'+str(plotNum)+'_trinucTrans.pdf')
-	plotNum += 1
+    fig = mpl.figure(plotNum, figsize=(12, 10))
+    DATA_DICT = pickle.load(open(fn, "rb"))
+    TRINUC_TRANS_PROBS = DATA_DICT['TRINUC_TRANS_PROBS']
+
+    xt2 = [m[3] for m in sorted([(n[0], n[2], n[1], n) for n in xt])]
+    reverse_dict = {xt2[i]: i for i in range(len(xt2))}
+    Z = np.zeros((64, 64))
+    L = [['' for n in range(64)] for m in range(64)]
+    for k in TRINUC_TRANS_PROBS:
+        i = reverse_dict[k[0]]
+        j = reverse_dict[k[1]]
+        Z[i][j] = TRINUC_TRANS_PROBS[k]
+
+    HARDCODED_LABEL = ['A_A', 'A_C', 'A_G', 'A_T',
+                       'C_A', 'C_C', 'C_G', 'C_T',
+                       'G_A', 'G_C', 'G_G', 'G_T',
+                       'T_A', 'T_C', 'T_G', 'T_T']
+
+    for pi in range(16):
+        mpl.subplot(4, 4, pi + 1)
+        Z2 = Z[pi * 4:(pi + 1) * 4, pi * 4:(pi + 1) * 4]
+        X, Y = np.meshgrid(range(0, len(Z2[0]) + 1), range(0, len(Z2) + 1))
+        im = mpl.pcolormesh(X, Y, Z2[::-1, :], vmin=0.0, vmax=0.5)
+        mpl.axis([0, 4, 0, 4])
+        mpl.xticks([0.5, 1.5, 2.5, 3.5], ['A', 'C', 'G', 'T'])
+        mpl.yticks([0.5, 1.5, 2.5, 3.5], ['T', 'G', 'C', 'A'])
+        mpl.text(1.6, 1.8, HARDCODED_LABEL[pi], color='white')
+
+    # colorbar haxx
+    fig.subplots_adjust(right=0.8)
+    cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
+    cb = fig.colorbar(im, cax=cbar_ax)
+    cb.set_label(r"p(X$Y_1$Z->X$Y_2$Z | X_Z mutates)")
+
+    # mpl.tight_layout()
+    # mpl.figtext(0.24,0.94,'Trinucleotide Mutation Frequency',size=20)
+    # mpl.show()
+    mpl.savefig(OUP + '_plot' + str(plotNum) + '_trinucTrans.pdf')
+    plotNum += 1
 
 #################################################
 #
@@ -214,38 +219,41 @@ def getBedOverlap(track,ind_s,ind_e):
 #
 #################################################
 track_byFile_byChr = [{} for n in INP]
-bp_total_byFile    = [0 for n in INP]
+bp_total_byFile = [0 for n in INP]
 colorInd = 0
 for fn in INP:
-	DATA_DICT = pickle.load( open( fn, "rb" ) )
-	HIGH_MUT_REGIONS = DATA_DICT['HIGH_MUT_REGIONS']
-	for region in HIGH_MUT_REGIONS:
-		if region[0] not in track_byFile_byChr[colorInd]:
-			track_byFile_byChr[colorInd][region[0]] = []
-		track_byFile_byChr[colorInd][region[0]].extend([region[1],region[2]])
-		bp_total_byFile[colorInd] += region[2]-region[1]+1
-	colorInd += 1
+    DATA_DICT = pickle.load(open(fn, "rb"))
+    HIGH_MUT_REGIONS = DATA_DICT['HIGH_MUT_REGIONS']
+    for region in HIGH_MUT_REGIONS:
+        if region[0] not in track_byFile_byChr[colorInd]:
+            track_byFile_byChr[colorInd][region[0]] = []
+        track_byFile_byChr[colorInd][region[0]].extend([region[1], region[2]])
+        bp_total_byFile[colorInd] += region[2] - region[1] + 1
+    colorInd += 1
 
 bp_overlap_count = [[0 for m in INP] for n in INP]
-for i in xrange(N_FILES):
-	bp_overlap_count[i][i] = bp_total_byFile[i]
-	for j in xrange(i+1,N_FILES):
-		for k in track_byFile_byChr[i].keys():
-			if k in track_byFile_byChr[j]:
-				for ii in xrange(len(track_byFile_byChr[i][k][::2])):
-					bp_overlap_count[i][j] += getBedOverlap(track_byFile_byChr[j][k],track_byFile_byChr[i][k][ii*2],track_byFile_byChr[i][k][ii*2+1])
-
-print ''				
-print 'HIGH_MUT_REGION OVERLAP BETWEEN '+str(N_FILES)+' MODELS...'
-for i in xrange(N_FILES):
-	for j in xrange(i,N_FILES):
-		nDissimilar = (bp_overlap_count[i][i]-bp_overlap_count[i][j]) + (bp_overlap_count[j][j]-bp_overlap_count[i][j])
-		if bp_overlap_count[i][j] == 0:
-			percentageV = 0.0
-		else:
-			percentageV = bp_overlap_count[i][j]/float(bp_overlap_count[i][j]+nDissimilar)
-		print 'overlap['+str(i)+','+str(j)+'] = '+str(bp_overlap_count[i][j])+' bp ({0:.3f}%)'.format(percentageV*100.)
-print ''
+for i in range(N_FILES):
+    bp_overlap_count[i][i] = bp_total_byFile[i]
+    for j in range(i + 1, N_FILES):
+        for k in track_byFile_byChr[i].keys():
+            if k in track_byFile_byChr[j]:
+                for ii in range(len(track_byFile_byChr[i][k][::2])):
+                    bp_overlap_count[i][j] += getBedOverlap(track_byFile_byChr[j][k], track_byFile_byChr[i][k][ii * 2],
+                                                            track_byFile_byChr[i][k][ii * 2 + 1])
+
+print('')
+print('HIGH_MUT_REGION OVERLAP BETWEEN ' + str(N_FILES) + ' MODELS...')
+for i in range(N_FILES):
+    for j in range(i, N_FILES):
+        nDissimilar = (bp_overlap_count[i][i] - bp_overlap_count[i][j]) + (
+                    bp_overlap_count[j][j] - bp_overlap_count[i][j])
+        if bp_overlap_count[i][j] == 0:
+            percentageV = 0.0
+        else:
+            percentageV = bp_overlap_count[i][j] / float(bp_overlap_count[i][j] + nDissimilar)
+    print('overlap[' + str(i) + ',' + str(j) + '] = ' + str(bp_overlap_count[i][j]) + ' bp ({0:.3f}%)'.format(
+        percentageV * 100.))
+print('')
 
 #################################################
 #
@@ -255,24 +263,22 @@ def getBedOverlap(track,ind_s,ind_e):
 setofVars = [set([]) for n in INP]
 colorInd = 0
 for fn in INP:
-	DATA_DICT = pickle.load( open( fn, "rb" ) )
-	COMMON_VARIANTS = DATA_DICT['COMMON_VARIANTS']
-	for n in COMMON_VARIANTS:
-		setofVars[colorInd].add(n)
-	colorInd += 1
-
-print ''
-print 'COMMON_VARIANTS OVERLAP BETWEEN '+str(N_FILES)+' MODELS...'
-for i in xrange(N_FILES):
-	for j in xrange(i,N_FILES):
-		overlapCount = len(setofVars[i].intersection(setofVars[j]))
-		nDissimilar  = (len(setofVars[i])-overlapCount) + (len(setofVars[j])-overlapCount)
-		if overlapCount == 0:
-			percentageV = 0.0
-		else:
-			percentageV = overlapCount/float(overlapCount+nDissimilar)
-		print 'overlap['+str(i)+','+str(j)+'] = '+str(overlapCount)+' variants ({0:.3f}%)'.format(percentageV*100.)
-print ''
-
-
-
+    DATA_DICT = pickle.load(open(fn, "rb"))
+    COMMON_VARIANTS = DATA_DICT['COMMON_VARIANTS']
+    for n in COMMON_VARIANTS:
+        setofVars[colorInd].add(n)
+    colorInd += 1
+
+print('')
+print('COMMON_VARIANTS OVERLAP BETWEEN ' + str(N_FILES) + ' MODELS...')
+for i in range(N_FILES):
+    for j in range(i, N_FILES):
+        overlapCount = len(setofVars[i].intersection(setofVars[j]))
+        nDissimilar = (len(setofVars[i]) - overlapCount) + (len(setofVars[j]) - overlapCount)
+        if overlapCount == 0:
+            percentageV = 0.0
+        else:
+            percentageV = overlapCount / float(overlapCount + nDissimilar)
+    print('overlap[' + str(i) + ',' + str(j) + '] = ' + str(overlapCount) + ' variants ({0:.3f}%)'.format(
+        percentageV * 100.))
+print('')
diff --git a/utilities/validateBam.py b/utilities/validateBam.py
index 47da72e..97c777c 100644
--- a/utilities/validateBam.py
+++ b/utilities/validateBam.py
@@ -5,80 +5,82 @@
 import gzip
 from struct import unpack
 
-BAM_EOF = ['1f', '8b', '08', '04', '00', '00', '00', '00', '00', 'ff', '06', '00', '42', '43', '02', '00', '1b', '00', '03', '00', '00', '00', '00', '00', '00', '00', '00', '00']
+BAM_EOF = ['1f', '8b', '08', '04', '00', '00', '00', '00', '00', 'ff', '06', '00', '42', '43', '02', '00', '1b', '00',
+           '03', '00', '00', '00', '00', '00', '00', '00', '00', '00']
+
+
+def getBytes(fmt, amt):
+    if fmt == '<i' or fmt == '<I':
+        mySize = 4
+    elif fmt == '<c' or fmt == '<b' or fmt == '<B':
+        mySize = 1
+    else:
+        print('\nError, unknown format:', fmt, '\n')
+        exit(1)
+    if amt == 1:
+        fread = f.read(mySize)
+        if not fread:
+            return None
+        return unpack(fmt, fread)[0]
+    else:
+        fread = f.read(mySize * amt)
+        if not fread:
+            return None
+        return unpack(fmt, fread)
 
-def getBytes(fmt,amt):
-	if fmt == '<i' or fmt == '<I':
-		mySize = 4
-	elif fmt == '<c' or fmt == '<b' or fmt == '<B':
-		mySize = 1
-	else:
-		print '\nError, unknown format:',fmt,'\n'
-		exit(1)
-	if amt == 1:
-		fread = f.read(mySize)
-		if not fread:
-			return None
-		return unpack(fmt,fread)[0]
-	else:
-		fread = f.read(mySize*amt)
-		if not fread:
-			return None
-		return unpack(fmt,fread)
 
 # check eof
 IN_BAM = sys.argv[1]
-f = open(IN_BAM,'rb')
-f.seek(os.path.getsize(IN_BAM)-28)
-EOF = [format(ord(n),'02x') for n in f.read()]
-print 'EOF_MARKER:  ', ' '.join(EOF)
+f = open(IN_BAM, 'rb')
+f.seek(os.path.getsize(IN_BAM) - 28)
+EOF = [format(ord(n), '02x') for n in f.read()]
+print('EOF_MARKER:  ', ' '.join(EOF))
 if EOF != BAM_EOF:
-	print '\nWARNING: BAM EOF DOES NOT MATCH EXPECTED STRING.\n'
+    print('\nWARNING: BAM EOF DOES NOT MATCH EXPECTED STRING.\n')
 f.close()
 
 # check other stuff
-f = gzip.open(IN_BAM,'rb')
+f = gzip.open(IN_BAM, 'rb')
 
-print 'MAGIC STRING:', f.read(4)
-l_text = getBytes('<i',1)
-print 'l_text:      ', l_text
-print 'text:      \n', f.read(l_text)
-n_ref = getBytes('<i',1)
-print 'n_ref:       ', n_ref
+print('MAGIC STRING:', f.read(4))
+l_text = getBytes('<i', 1)
+print('l_text:      ', l_text)
+print('text:      \n', f.read(l_text))
+n_ref = getBytes('<i', 1)
+print('n_ref:       ', n_ref)
 
-for i in xrange(n_ref):
-	l_name = getBytes('<i',1)
-	print 'ref'+str(i)+' - l_name:', l_name
-	print 'ref'+str(i)+' - name:  ', f.read(l_name)
-	print 'ref'+str(i)+' - l_ref: ', getBytes('<i',1)
+for i in range(n_ref):
+    l_name = getBytes('<i', 1)
+    print('ref' + str(i) + ' - l_name:', l_name)
+    print('ref' + str(i) + ' - name:  ', f.read(l_name))
+    print('ref' + str(i) + ' - l_ref: ', getBytes('<i', 1))
 
-print '\nEXAMINING ALIGNMENT DATA:\n'
+print('\nEXAMINING ALIGNMENT DATA:\n')
 aln_N = 0
 while True:
-	aln_N += 1
-	blockSize = getBytes('<i',1)
-	if blockSize == None:
-		break
-	print '['+str(aln_N)+']:', 'blockSize:', blockSize
-	print '-- refID:', getBytes('<i',1)
-	print '-- pos:  ', getBytes('<i',1)
-	bmqnl = getBytes('<I',1)
-	binv  = (bmqnl>>16)&65535
-	mapq  = (bmqnl>>8)&255
-	lrn   = bmqnl&255
-	print '-- bmqnl:', bmqnl, '(bin='+str(binv)+', mapq='+str(mapq)+', l_readname+1='+str(lrn)+')'
-	flgnc = getBytes('<I',1)
-	flag  = (flgnc>>16)&65535
-	ncig  = flgnc&65535
-	print '-- flgnc:', flgnc, '(flag='+str(flag)+', ncig='+str(ncig)+')'
-	print '-- l_seq:', getBytes('<i',1)
-	print '-- nxtID:', getBytes('<i',1)
-	print '-- nxtPo:', getBytes('<i',1)
-	print '-- tlen: ', getBytes('<i',1)
-	print '-- rname:', str([f.read(lrn)])[1:-1]
-
-	f.read(blockSize-32-lrn)
-	#print [block]
+    aln_N += 1
+    blockSize = getBytes('<i', 1)
+    if blockSize == None:
+        break
+    print('[' + str(aln_N) + ']:', 'blockSize:', blockSize)
+    print('-- refID:', getBytes('<i', 1))
+    print('-- pos:  ', getBytes('<i', 1))
+    bmqnl = getBytes('<I', 1)
+    binv = (bmqnl >> 16) & 65535
+    mapq = (bmqnl >> 8) & 255
+    lrn = bmqnl & 255
+    print('-- bmqnl:', bmqnl, '(bin=' + str(binv) + ', mapq=' + str(mapq) + ', l_readname+1=' + str(lrn) + ')')
+    flgnc = getBytes('<I', 1)
+    flag = (flgnc >> 16) & 65535
+    ncig = flgnc & 65535
+    print('-- flgnc:', flgnc, '(flag=' + str(flag) + ', ncig=' + str(ncig) + ')')
+    print('-- l_seq:', getBytes('<i', 1))
+    print('-- nxtID:', getBytes('<i', 1))
+    print('-- nxtPo:', getBytes('<i', 1))
+    print('-- tlen: ', getBytes('<i', 1))
+    print('-- rname:', str([f.read(lrn)])[1:-1])
 
+    f.read(blockSize - 32 - lrn)
+# print [block]
 
 f.close()
diff --git a/utilities/validateFQ.py b/utilities/validateFQ.py
index b4d7b8d..047d7f2 100644
--- a/utilities/validateFQ.py
+++ b/utilities/validateFQ.py
@@ -8,71 +8,78 @@
 
 import sys
 
+
 def get4lines(fn):
-	l1 = fn.readline().strip()
-	l2 = fn.readline().strip()
-	l3 = fn.readline().strip()
-	l4 = fn.readline().strip()
-	if any([l1,l2,l3,l4]) and not all([l1,l2,l3,l4]):
-		print '\nError: missing lines:\n'
-		print l1+'\n'+l2+'\n'+l3+'\n'+l4+'\n'
-		exit(1)
-	return (l1,l2,l3,l4)
+    l1 = fn.readline().strip()
+    l2 = fn.readline().strip()
+    l3 = fn.readline().strip()
+    l4 = fn.readline().strip()
+    if any([l1, l2, l3, l4]) and not all([l1, l2, l3, l4]):
+        print('\nError: missing lines:\n')
+        print(l1 + '\n' + l2 + '\n' + l3 + '\n' + l4 + '\n')
+        exit(1)
+    return (l1, l2, l3, l4)
+
 
 ALLOWED_QUAL = '!\"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJ'
 ALLOWED_NUCL = 'ACGTN'
 
-def validate4lines(l1,l2,l3,l4):
-	failed = 0
-	# make sure lines contain correct delimiters
-	if l1[0] != '@' or l1[-2] != '/' or l3[0] != '+':
-		failed = 1
-	# make sure seq len == qual length
-	if len(l2) != len(l4):
-		failed = 2
-	# make sure seq string contains only valid characters
-	for n in l2:
-		if n not in ALLOWED_NUCL:
-			failed = 3
-	# make sure qual string contains only valid characters
-	for n in l4:
-		if n not in ALLOWED_QUAL:
-			failed = 4
-	if failed:
-		print '\nError: malformed lines:'
-		if failed == 1: print ' ---- invalid delimiters\n'
-		elif failed == 2: print ' ---- seq len != qual len\n'
-		elif failed == 3: print ' ---- seq contains invalid characters\n'
-		elif failed == 4: print ' ---- qual contains invalid characters\n'
-		print l1+'\n'+l2+'\n'+l3+'\n'+l4+'\n'
-		exit(1)
 
-f1 = open(sys.argv[1],'r')
+def validate4lines(l1, l2, l3, l4):
+    failed = 0
+    # make sure lines contain correct delimiters
+    if l1[0] != '@' or l1[-2] != '/' or l3[0] != '+':
+        failed = 1
+    # make sure seq len == qual length
+    if len(l2) != len(l4):
+        failed = 2
+    # make sure seq string contains only valid characters
+    for n in l2:
+        if n not in ALLOWED_NUCL:
+            failed = 3
+    # make sure qual string contains only valid characters
+    for n in l4:
+        if n not in ALLOWED_QUAL:
+            failed = 4
+    if failed:
+        print('\nError: malformed lines:')
+        if failed == 1:
+            print(' ---- invalid delimiters\n')
+        elif failed == 2:
+            print(' ---- seq len != qual len\n')
+        elif failed == 3:
+            print(' ---- seq contains invalid characters\n')
+    elif failed == 4:
+        print(' ---- qual contains invalid characters\n')
+        print(l1 + '\n' + l2 + '\n' + l3 + '\n' + l4 + '\n')
+        exit(1)
+
+
+f1 = open(sys.argv[1], 'r')
 (l1_r1, l2_r1, l3_r1, l4_r1) = get4lines(f1)
 f2 = None
 if len(sys.argv) == 3:
-	f2 = open(sys.argv[2],'r')
-	(l1_r2, l2_r2, l3_r2, l4_r2) = get4lines(f2)
+    f2 = open(sys.argv[2], 'r')
+    (l1_r2, l2_r2, l3_r2, l4_r2) = get4lines(f2)
 
 while l1_r1:
-	# check line syntax
-	validate4lines(l1_r1,l2_r1,l3_r1,l4_r1)
-	if f2 != None:
-		validate4lines(l1_r2,l2_r2,l3_r2,l4_r2)
-		# make sure seq id is same for r1/r2
-		if l1_r1[:-1] != l1_r2[:-1]:
-			print '\nError: mismatched r1/r2 name:\n'
-			print l1_r1+'\n'+l1_r2+'\n'
-			exit(1)
+    # check line syntax
+    validate4lines(l1_r1, l2_r1, l3_r1, l4_r1)
+    if f2 != None:
+        validate4lines(l1_r2, l2_r2, l3_r2, l4_r2)
+        # make sure seq id is same for r1/r2
+        if l1_r1[:-1] != l1_r2[:-1]:
+            print('\nError: mismatched r1/r2 name:\n')
+            print(l1_r1 + '\n' + l1_r2 + '\n')
+            exit(1)
 
-	# grab next 4 lines...
-	(l1_r1, l2_r1, l3_r1, l4_r1) = get4lines(f1)
-	if f2 != None:
-		(l1_r2, l2_r2, l3_r2, l4_r2) = get4lines(f2)
+    # grab next 4 lines...
+    (l1_r1, l2_r1, l3_r1, l4_r1) = get4lines(f1)
+    if f2 != None:
+        (l1_r2, l2_r2, l3_r2, l4_r2) = get4lines(f2)
 
 if f2 != None:
-	f2.close()
+    f2.close()
 f1.close()
 
-print '\nPASSED WITH FLYING COLORS. GOOD DAY.\n'
-
+print('\nPASSED WITH FLYING COLORS. GOOD DAY.\n')
diff --git a/utilities/vcf_compare_OLD.py b/utilities/vcf_compare_OLD.py
index d451ee6..fa9d061 100644
--- a/utilities/vcf_compare_OLD.py
+++ b/utilities/vcf_compare_OLD.py
@@ -22,700 +22,719 @@
 import numpy as np
 import optparse
 
+EV_BPRANGE = 50  # how far to either side of a particular variant location do we want to check for equivalents?
 
-EV_BPRANGE = 50		# how far to either side of a particular variant location do we want to check for equivalents?
+DEFAULT_QUAL = -666  # if we can't find a qual score, use this instead so we know it's missing
 
-DEFAULT_QUAL = -666	# if we can't find a qual score, use this instead so we know it's missing
+MAX_VAL = 9999999999999  # an unreasonably large value that no reference fasta could concievably be longer than
 
-MAX_VAL = 9999999999999	# an unreasonably large value that no reference fasta could concievably be longer than
+DESC = """%prog: vcf comparison script."""
+VERS = 0.1
 
-DESC   = """%prog: vcf comparison script."""
-VERS   = 0.1
-
-PARSER = optparse.OptionParser('python %prog [options] -r <ref.fa> -g <golden.vcf> -w <workflow.vcf>',description=DESC,version="%prog v"+str(VERS))
+PARSER = optparse.OptionParser('python %prog [options] -r <ref.fa> -g <golden.vcf> -w <workflow.vcf>', description=DESC,
+                               version="%prog v" + str(VERS))
 
 PARSER.add_option('-r', help='* Reference Fasta', dest='REFF', action='store', metavar='<ref.fa>')
-PARSER.add_option('-g', help='* Golden VCF',      dest='GVCF', action='store', metavar='<golden.vcf>')
-PARSER.add_option('-w', help='* Workflow VCF',    dest='WVCF', action='store', metavar='<workflow.vcf>')
-PARSER.add_option('-o', help='* Output Prefix',   dest='OUTF', action='store', metavar='<prefix>')
+PARSER.add_option('-g', help='* Golden VCF', dest='GVCF', action='store', metavar='<golden.vcf>')
+PARSER.add_option('-w', help='* Workflow VCF', dest='WVCF', action='store', metavar='<workflow.vcf>')
+PARSER.add_option('-o', help='* Output Prefix', dest='OUTF', action='store', metavar='<prefix>')
 PARSER.add_option('-m', help='Mappability Track', dest='MTRK', action='store', metavar='<track.bed>')
-PARSER.add_option('-M', help='Maptrack Min Len',  dest='MTMM', action='store', metavar='<int>')
+PARSER.add_option('-M', help='Maptrack Min Len', dest='MTMM', action='store', metavar='<int>')
 PARSER.add_option('-t', help='Targetted Regions', dest='TREG', action='store', metavar='<regions.bed>')
-PARSER.add_option('-T', help='Min Region Len',    dest='MTRL', action='store', metavar='<int>')
-PARSER.add_option('-c', help='Coverage Filter Threshold [%default]',       dest='DP_THRESH', default=15, action='store', metavar='<int>')
-PARSER.add_option('-a', help='Allele Freq Filter Threshold [%default]',    dest='AF_THRESH', default=0.3, action='store', metavar='<float>')
-
-PARSER.add_option('--vcf-out',   help="Output Match/FN/FP variants [%default]",       dest='VCF_OUT', default=False, action='store_true')
-PARSER.add_option('--no-plot',   help="No plotting [%default]",                       dest='NO_PLOT', default=False, action='store_true')
-PARSER.add_option('--incl-homs', help="Include homozygous ref calls [%default]",      dest='INCL_H',  default=False, action='store_true')
-PARSER.add_option('--incl-fail', help="Include calls that failed filters [%default]", dest='INCL_F',  default=False, action='store_true')
-PARSER.add_option('--fast',      help="No equivalent variant detection [%default]",   dest='FAST',    default=False, action='store_true')
-
-(OPTS,ARGS) = PARSER.parse_args()
-
-REFERENCE    = OPTS.REFF
-GOLDEN_VCF   = OPTS.GVCF
+PARSER.add_option('-T', help='Min Region Len', dest='MTRL', action='store', metavar='<int>')
+PARSER.add_option('-c', help='Coverage Filter Threshold [%default]', dest='DP_THRESH', default=15, action='store',
+                  metavar='<int>')
+PARSER.add_option('-a', help='Allele Freq Filter Threshold [%default]', dest='AF_THRESH', default=0.3, action='store',
+                  metavar='<float>')
+
+PARSER.add_option('--vcf-out', help="Output Match/FN/FP variants [%default]", dest='VCF_OUT', default=False,
+                  action='store_true')
+PARSER.add_option('--no-plot', help="No plotting [%default]", dest='NO_PLOT', default=False, action='store_true')
+PARSER.add_option('--incl-homs', help="Include homozygous ref calls [%default]", dest='INCL_H', default=False,
+                  action='store_true')
+PARSER.add_option('--incl-fail', help="Include calls that failed filters [%default]", dest='INCL_F', default=False,
+                  action='store_true')
+PARSER.add_option('--fast', help="No equivalent variant detection [%default]", dest='FAST', default=False,
+                  action='store_true')
+
+(OPTS, ARGS) = PARSER.parse_args()
+
+REFERENCE = OPTS.REFF
+GOLDEN_VCF = OPTS.GVCF
 WORKFLOW_VCF = OPTS.WVCF
-OUT_PREFIX   = OPTS.OUTF
-MAPTRACK     = OPTS.MTRK
-MIN_READLEN  = OPTS.MTMM
-BEDFILE      = OPTS.TREG
-DP_THRESH    = int(OPTS.DP_THRESH)
-AF_THRESH    = float(OPTS.AF_THRESH)
-
-VCF_OUT      = OPTS.VCF_OUT
-NO_PLOT      = OPTS.NO_PLOT
+OUT_PREFIX = OPTS.OUTF
+MAPTRACK = OPTS.MTRK
+MIN_READLEN = OPTS.MTMM
+BEDFILE = OPTS.TREG
+DP_THRESH = int(OPTS.DP_THRESH)
+AF_THRESH = float(OPTS.AF_THRESH)
+
+VCF_OUT = OPTS.VCF_OUT
+NO_PLOT = OPTS.NO_PLOT
 INCLUDE_HOMS = OPTS.INCL_H
 INCLUDE_FAIL = OPTS.INCL_F
-FAST         = OPTS.FAST
+FAST = OPTS.FAST
 
 if len(sys.argv[1:]) == 0:
-	PARSER.print_help()
-	exit(1)
+    PARSER.print_help()
+    exit(1)
 
 if OPTS.MTRL != None:
-	MINREGIONLEN = int(OPTS.MTRL)
+    MINREGIONLEN = int(OPTS.MTRL)
 else:
-	MINREGIONLEN = None
+    MINREGIONLEN = None
 
 if MIN_READLEN == None:
-	MIN_READLEN = 0
+    MIN_READLEN = 0
 else:
-	MIN_READLEN = int(MIN_READLEN)
+    MIN_READLEN = int(MIN_READLEN)
 
 if REFERENCE == None:
-	print 'Error: No reference provided.'
-	exit(1)
+    print('Error: No reference provided.')
+    exit(1)
 if GOLDEN_VCF == None:
-	print 'Error: No golden VCF provided.'
-	exit(1)
+    print('Error: No golden VCF provided.')
+    exit(1)
 if WORKFLOW_VCF == None:
-	print 'Error: No workflow VCF provided.'
-	exit(1)
+    print('Error: No workflow VCF provided.')
+    exit(1)
 if OUT_PREFIX == None:
-	print 'Error: No output prefix provided.'
-	exit(1)
+    print('Error: No output prefix provided.')
+    exit(1)
 if (BEDFILE != None and MINREGIONLEN == None) or (BEDFILE == None and MINREGIONLEN != None):
-	print 'Error: Both -t and -T must be specified'
-	exit(1)
+    print('Error: Both -t and -T must be specified')
+    exit(1)
 
 if NO_PLOT == False:
-	import matplotlib
-	matplotlib.use('Agg')
-	import matplotlib.pyplot as mpl
-	from matplotlib_venn import venn2, venn3
-	import warnings
-	warnings.filterwarnings("ignore", category=UserWarning, module='matplotlib_venn')
+    import matplotlib
+
+    matplotlib.use('Agg')
+    import matplotlib.pyplot as mpl
+    from matplotlib_venn import venn2, venn3
+    import warnings
+
+    warnings.filterwarnings("ignore", category=UserWarning, module='matplotlib_venn')
 
 AF_STEPS = 20
-AF_KEYS  = np.linspace(0.0,1.0,AF_STEPS+1)
+AF_KEYS = np.linspace(0.0, 1.0, AF_STEPS + 1)
+
 
 def quantize_AF(af):
-	if af >= 1.0:
-		return AF_STEPS
-	elif af <= 0.0:
-		return 0
-	else:
-		return int(af*AF_STEPS)
-
-VCF_HEADER = '##fileformat=VCFv4.1\n##reference='+REFERENCE+'##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">\n##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">\n'
-
-DP_TOKENS = ['DP','DPU','DPI']	# in the order that we'll look for them
-
-def parseLine(splt,colDict,colSamp):
-
-	#	check if we want to proceed..
-	ra = splt[colDict['REF']]
-	aa = splt[colDict['ALT']]
-	if not(INCLUDE_HOMS) and (aa == '.' or aa == '' or aa == ra):
-		return None
-	if not(INCLUDE_FAIL) and (splt[colDict['FILTER']] != 'PASS' and splt[colDict['FILTER']] != '.'):
-		return None
-
-	#	default vals
-	cov  = None
-	qual = DEFAULT_QUAL
-	alt_alleles = []
-	alt_freqs   = [None]
-
-	#	any alt alleles?
-	alt_split = aa.split(',')
-	if len(alt_split) > 1:
-		alt_alleles = alt_split
-
-	#	cov
-	for dp_tok in DP_TOKENS:
-		#	check INFO for DP first
-		if 'INFO' in colDict and dp_tok+'=' in splt[colDict['INFO']]:
-			cov = int(re.findall(re.escape(dp_tok)+r"=[0-9]+",splt[colDict['INFO']])[0][3:])
-		#	check FORMAT/SAMPLE for DP second:
-		elif 'FORMAT' in colDict and len(colSamp):
-			format = splt[colDict['FORMAT']]+':'
-			if ':'+dp_tok+':' in format:
-				dpInd = format.split(':').index(dp_tok)
-				cov   = int(splt[colSamp[0]].split(':')[dpInd])
-		if cov != None:
-			break
-
-	#	check INFO for AF first
-	af = None
-	if 'INFO' in colDict and ';AF=' in ';'+splt[colDict['INFO']]:
-		info = splt[colDict['INFO']]+';'
-		af  = re.findall(r"AF=.*?(?=;)",info)[0][3:]
-	#	check FORMAT/SAMPLE for AF second:
-	elif 'FORMAT' in colDict and len(colSamp):
-		format = splt[colDict['FORMAT']]+':'
-		if ':AF:' in format:
-			afInd = splt[colDict['FORMAT']].split(':').index('AF')
-			af    = splt[colSamp[0]].split(':')[afInd]
-
-	if af != None:
-		af_splt = af.split(',')
-		while(len(af_splt) < len(alt_alleles)):	# are we lacking enough AF values for some reason?
-			af_splt.append(af_splt[-1])			# phone it in.
-		if len(af_splt) != 0 and af_splt[0] != '.' and af_splt[0] != '':		# missing data, yay
-			alt_freqs = [float(n) for n in af_splt]
-	else:
-		alt_freqs = [None]*max([len(alt_alleles),1])
-
-	#	get QUAL if it's interesting
-	if 'QUAL' in colDict and splt[colDict['QUAL']] != '.':
-		qual = float(splt[colDict['QUAL']])
-
-	return (cov, qual, alt_alleles, alt_freqs)
-
-
-def parseVCF(VCF_FILENAME,refName,targRegionsFl,outFile,outBool):
-	v_Hashed      = {}
-	v_posHash     = {}
-	v_Alts        = {}
-	v_Cov         = {}
-	v_AF          = {}
-	v_Qual        = {}
-	v_TargLen     = {}
-	nBelowMinRLen = 0
-	line_unique   = 0	# number of lines in vcf file containing unique variant
-	hash_coll     = 0	# number of times we saw a hash collision ("per line" so non-unique alt alleles don't get counted multiple times)
-	var_filtered  = 0	# number of variants excluded due to filters (e.g. hom-refs, qual)
-	var_merged    = 0	# number of variants we merged into another due to having the same position specified
-	colDict       = {}
-	colSamp       = []
-	for line in open(VCF_FILENAME,'r'):
-		if line[0] != '#':
-			if len(colDict) == 0:
-				print '\n\nError: VCF has no header?\n'+VCF_FILENAME+'\n\n'
-				exit(1)
-			splt = line[:-1].split('\t')
-			if splt[0] == refName:
-
-				var  = (int(splt[1]),splt[3],splt[4])
-				targInd = bisect.bisect(targRegionsFl,var[0])
-
-				if targInd%2 == 1:
-					targLen = targRegionsFl[targInd]-targRegionsFl[targInd-1]
-					if (BEDFILE != None and targLen >= MINREGIONLEN) or BEDFILE == None:
-						
-						pl_out = parseLine(splt,colDict,colSamp)
-						if pl_out == None:
-							var_filtered += 1
-							continue
-						(cov, qual, aa, af) = pl_out
-
-						if var not in v_Hashed:
-
-							vpos = var[0]
-							if vpos in v_posHash:
-								if len(aa) == 0:
-									aa = [var[2]]
-								aa.extend([n[2] for n in v_Hashed.keys() if n[0] == vpos])
-								var_merged += 1
-							v_posHash[vpos] = 1
-							
-							if len(aa):
-								allVars = [(var[0],var[1],n) for n in aa]
-								for i in xrange(len(allVars)):
-									v_Hashed[allVars[i]] = 1
-									#if allVars[i] not in v_Alts:
-									#	v_Alts[allVars[i]] = []
-									#v_Alts[allVars[i]].extend(allVars)
-									v_Alts[allVars[i]] = allVars
-							else:
-								v_Hashed[var] = 1
-
-							if cov != None:
-								v_Cov[var] = cov
-							v_AF[var]      = af[0]		# only use first AF, even if multiple. fix this later?
-							v_Qual[var]    = qual
-							v_TargLen[var] = targLen
-							line_unique += 1
-
-						else:
-							hash_coll += 1
-
-					else:
-						nBelowMinRLen += 1
-		else:
-			if line[1] != '#':
-				cols = line[1:-1].split('\t')
-				for i in xrange(len(cols)):
-					if 'FORMAT' in colDict:
-						colSamp.append(i)
-					colDict[cols[i]] = i
-				if VCF_OUT and outBool:
-					outBool = False
-					outFile.write(line)
-
-	return (v_Hashed, v_Alts, v_Cov, v_AF, v_Qual, v_TargLen, nBelowMinRLen, line_unique, var_filtered, var_merged, hash_coll)
+    if af >= 1.0:
+        return AF_STEPS
+    elif af <= 0.0:
+        return 0
+    else:
+        return int(af * AF_STEPS)
+
+
+VCF_HEADER = '##fileformat=VCFv4.1\n##reference=' + REFERENCE + '##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">\n##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">\n'
+
+DP_TOKENS = ['DP', 'DPU', 'DPI']  # in the order that we'll look for them
+
+
+def parseLine(splt, colDict, colSamp):
+    #	check if we want to proceed..
+    ra = splt[colDict['REF']]
+    aa = splt[colDict['ALT']]
+    if not (INCLUDE_HOMS) and (aa == '.' or aa == '' or aa == ra):
+        return None
+    if not (INCLUDE_FAIL) and (splt[colDict['FILTER']] != 'PASS' and splt[colDict['FILTER']] != '.'):
+        return None
+
+    #	default vals
+    cov = None
+    qual = DEFAULT_QUAL
+    alt_alleles = []
+    alt_freqs = [None]
+
+    #	any alt alleles?
+    alt_split = aa.split(',')
+    if len(alt_split) > 1:
+        alt_alleles = alt_split
+
+    #	cov
+    for dp_tok in DP_TOKENS:
+        #	check INFO for DP first
+        if 'INFO' in colDict and dp_tok + '=' in splt[colDict['INFO']]:
+            cov = int(re.findall(re.escape(dp_tok) + r"=[0-9]+", splt[colDict['INFO']])[0][3:])
+        #	check FORMAT/SAMPLE for DP second:
+        elif 'FORMAT' in colDict and len(colSamp):
+            format = splt[colDict['FORMAT']] + ':'
+            if ':' + dp_tok + ':' in format:
+                dpInd = format.split(':').index(dp_tok)
+                cov = int(splt[colSamp[0]].split(':')[dpInd])
+        if cov != None:
+            break
+
+    #	check INFO for AF first
+    af = None
+    if 'INFO' in colDict and ';AF=' in ';' + splt[colDict['INFO']]:
+        info = splt[colDict['INFO']] + ';'
+        af = re.findall(r"AF=.*?(?=;)", info)[0][3:]
+    #	check FORMAT/SAMPLE for AF second:
+    elif 'FORMAT' in colDict and len(colSamp):
+        format = splt[colDict['FORMAT']] + ':'
+        if ':AF:' in format:
+            afInd = splt[colDict['FORMAT']].split(':').index('AF')
+            af = splt[colSamp[0]].split(':')[afInd]
+
+    if af != None:
+        af_splt = af.split(',')
+        while (len(af_splt) < len(alt_alleles)):  # are we lacking enough AF values for some reason?
+            af_splt.append(af_splt[-1])  # phone it in.
+        if len(af_splt) != 0 and af_splt[0] != '.' and af_splt[0] != '':  # missing data, yay
+            alt_freqs = [float(n) for n in af_splt]
+    else:
+        alt_freqs = [None] * max([len(alt_alleles), 1])
+
+    #	get QUAL if it's interesting
+    if 'QUAL' in colDict and splt[colDict['QUAL']] != '.':
+        qual = float(splt[colDict['QUAL']])
+
+    return (cov, qual, alt_alleles, alt_freqs)
+
+
+def parseVCF(VCF_FILENAME, refName, targRegionsFl, outFile, outBool):
+    v_Hashed = {}
+    v_posHash = {}
+    v_Alts = {}
+    v_Cov = {}
+    v_AF = {}
+    v_Qual = {}
+    v_TargLen = {}
+    nBelowMinRLen = 0
+    line_unique = 0  # number of lines in vcf file containing unique variant
+    hash_coll = 0  # number of times we saw a hash collision ("per line" so non-unique alt alleles don't get counted multiple times)
+    var_filtered = 0  # number of variants excluded due to filters (e.g. hom-refs, qual)
+    var_merged = 0  # number of variants we merged into another due to having the same position specified
+    colDict = {}
+    colSamp = []
+    for line in open(VCF_FILENAME, 'r'):
+        if line[0] != '#':
+            if len(colDict) == 0:
+                print('\n\nError: VCF has no header?\n' + VCF_FILENAME + '\n\n')
+                exit(1)
+            splt = line[:-1].split('\t')
+            if splt[0] == refName:
+
+                var = (int(splt[1]), splt[3], splt[4])
+                targInd = bisect.bisect(targRegionsFl, var[0])
+
+                if targInd % 2 == 1:
+                    targLen = targRegionsFl[targInd] - targRegionsFl[targInd - 1]
+                    if (BEDFILE != None and targLen >= MINREGIONLEN) or BEDFILE == None:
+
+                        pl_out = parseLine(splt, colDict, colSamp)
+                        if pl_out == None:
+                            var_filtered += 1
+                            continue
+                        (cov, qual, aa, af) = pl_out
+
+                        if var not in v_Hashed:
+
+                            vpos = var[0]
+                            if vpos in v_posHash:
+                                if len(aa) == 0:
+                                    aa = [var[2]]
+                                aa.extend([n[2] for n in v_Hashed.keys() if n[0] == vpos])
+                                var_merged += 1
+                            v_posHash[vpos] = 1
+
+                            if len(aa):
+                                allVars = [(var[0], var[1], n) for n in aa]
+                                for i in range(len(allVars)):
+                                    v_Hashed[allVars[i]] = 1
+                                    # if allVars[i] not in v_Alts:
+                                    #	v_Alts[allVars[i]] = []
+                                    # v_Alts[allVars[i]].extend(allVars)
+                                    v_Alts[allVars[i]] = allVars
+                            else:
+                                v_Hashed[var] = 1
+
+                            if cov != None:
+                                v_Cov[var] = cov
+                            v_AF[var] = af[0]  # only use first AF, even if multiple. fix this later?
+                            v_Qual[var] = qual
+                            v_TargLen[var] = targLen
+                            line_unique += 1
+
+                        else:
+                            hash_coll += 1
+
+                    else:
+                        nBelowMinRLen += 1
+        else:
+            if line[1] != '#':
+                cols = line[1:-1].split('\t')
+                for i in range(len(cols)):
+                    if 'FORMAT' in colDict:
+                        colSamp.append(i)
+                    colDict[cols[i]] = i
+                if VCF_OUT and outBool:
+                    outBool = False
+                    outFile.write(line)
+
+    return (
+    v_Hashed, v_Alts, v_Cov, v_AF, v_Qual, v_TargLen, nBelowMinRLen, line_unique, var_filtered, var_merged, hash_coll)
 
 
 def condenseByPos(listIn):
-	varListOfInterest = [n for n in listIn]
-	indCount = {}
-	for n in varListOfInterest:
-		c = n[0]
-		if c not in indCount:
-			indCount[c] = 0
-		indCount[c] += 1
-	#nonUniqueDict = {n:[] for n in sorted(indCount.keys()) if indCount[n] > 1}		# the python 2.7 way
-	nonUniqueDict = {}
-	for n in sorted(indCount.keys()):
-		if indCount[n] > 1:
-			nonUniqueDict[n] = []
-	delList = []
-	for i in xrange(len(varListOfInterest)):
-		if varListOfInterest[i][0] in nonUniqueDict:
-			nonUniqueDict[varListOfInterest[i][0]].append(varListOfInterest[i])
-			delList.append(i)
-	delList = sorted(delList,reverse=True)
-	for di in delList:
-		del varListOfInterest[di]
-	for v in nonUniqueDict.values():
-		var = (v[0][0],v[0][1],','.join([n[2] for n in v[::-1]]))
-		varListOfInterest.append(var)
-	return varListOfInterest
+    varListOfInterest = [n for n in listIn]
+    indCount = {}
+    for n in varListOfInterest:
+        c = n[0]
+        if c not in indCount:
+            indCount[c] = 0
+        indCount[c] += 1
+    # nonUniqueDict = {n:[] for n in sorted(indCount.keys()) if indCount[n] > 1}		# the python 2.7 way
+    nonUniqueDict = {}
+    for n in sorted(indCount.keys()):
+        if indCount[n] > 1:
+            nonUniqueDict[n] = []
+    delList = []
+    for i in range(len(varListOfInterest)):
+        if varListOfInterest[i][0] in nonUniqueDict:
+            nonUniqueDict[varListOfInterest[i][0]].append(varListOfInterest[i])
+            delList.append(i)
+    delList = sorted(delList, reverse=True)
+    for di in delList:
+        del varListOfInterest[di]
+    for v in nonUniqueDict.values():
+        var = (v[0][0], v[0][1], ','.join([n[2] for n in v[::-1]]))
+        varListOfInterest.append(var)
+    return varListOfInterest
 
 
 def main():
-
-	ref = []
-	f = open(REFERENCE,'r')
-	nLines = 0
-	prevR = None
-	prevP = None
-	ref_inds = []
-	sys.stdout.write('\nindexing reference fasta... ')
-	sys.stdout.flush()
-	tt = time.time()
-	while 1:
-		nLines += 1
-		data = f.readline()
-		if not data:
-			ref_inds.append( (prevR, prevP, f.tell()-len(data)) )
-			break
-		if data[0] == '>':
-			if prevP != None:
-				ref_inds.append( (prevR, prevP, f.tell()-len(data)) )
-			prevP = f.tell()
-			prevR = data[1:-1]
-	print '{0:.3f} (sec)'.format(time.time()-tt)
-	#ref_inds = [('chrM', 6, 16909), ('chr1', 16915, 254252549), ('chr2', 254252555, 502315916), ('chr3', 502315922, 704298801), ('chr4', 704298807, 899276169), ('chr5', 899276175, 1083809741), ('chr6', 1083809747, 1258347116), ('chr7', 1258347122, 1420668559), ('chr8', 1420668565, 1569959868), ('chr9', 1569959874, 1713997574), ('chr10', 1713997581, 1852243023), ('chr11', 1852243030, 1989949677), ('chr12', 1989949684, 2126478617), ('chr13', 2126478624, 2243951900), ('chr14', 2243951907, 2353448438), ('chr15', 2353448445, 2458030465), ('chr16', 2458030472, 2550192321), ('chr17', 2550192328, 2633011443), ('chr18', 2633011450, 2712650243), ('chr19', 2712650250, 2772961813), ('chr20', 2772961820, 2837247851), ('chr21', 2837247858, 2886340351), ('chr22', 2886340358, 2938671016), ('chrX', 2938671022, 3097046994), ('chrY', 3097047000, 3157608038)]
-
-	ztV = 0	# total golden variants
-	ztW = 0	# total workflow variants
-	znP = 0	# total perfect matches
-	zfP = 0	# total false positives
-	znF = 0	# total false negatives
-	znE = 0	# total equivalent variants detected
-	zgF = 0	# total golden variants that were filtered and excluded
-	zgR = 0	# total golden variants that were excluded for being redundant
-	zgM = 0	# total golden variants that were merged into a single position
-	zwF = 0	# total workflow variants that were filtered and excluded
-	zwR = 0	# total workflow variants that were excluded for being redundant
-	zwM = 0	# total workflow variants that were merged into a single position
-	if BEDFILE != None:
-		zbM = 0
-
-	mappability_vs_FN = {0:0, 1:0}	# [0] = # of FNs that were in mappable regions, [1] = # of FNs that were in unmappable regions
-	coverage_vs_FN    = {}			# [C] = # of FNs that were covered by C reads
-	alleleBal_vs_FN   = {}			# [AF] = # of FNs that were heterozygous genotypes with allele freq AF (quantized to multiples of 1/AF_STEPS)
-	for n in AF_KEYS:
-		alleleBal_vs_FN[n] = 0
-
-	#
-	#	read in mappability track
-	#
-	mappability_tracks = {}		# indexed by chr string (e.g. 'chr1'), has boolean array
-	prevRef            = ''
-	relevantRegions    = []
-	if MAPTRACK != None:
-		mtf = open(MAPTRACK,'r')
-		for line in mtf:
-			splt = line.strip().split('\t')
-			if prevRef != '' and splt[0] != prevRef:
-				# do stuff
-				if len(relevantRegions):
-					myTrack = [0]*(relevantRegions[-1][1]+100)
-					for r in relevantRegions:
-						for ri in xrange(r[0],r[1]):
-							myTrack[ri] = 1
-					mappability_tracks[prevRef] = [n for n in myTrack]
-				#
-				relevantRegions = []
-			if int(splt[3]) >= MIN_READLEN:
-				relevantRegions.append((int(splt[1]),int(splt[2])))
-			prevRef = splt[0]
-		mtf.close()
-		# do stuff
-		if len(relevantRegions):
-			myTrack = [0]*(relevantRegions[-1][1]+100)
-			for r in relevantRegions:
-				for ri in xrange(r[0],r[1]):
-					myTrack[ri] = 1
-			mappability_tracks[prevRef] = [n for n in myTrack]
-		#
-
-	#
-	#	init vcf output, if desired
-	#
-	vcfo2 = None
-	vcfo3 = None
-	global vcfo2_firstTime
-	global vcfo3_firstTime
-	vcfo2_firstTime = False
-	vcfo3_firstTime = False
-	if VCF_OUT:
-		vcfo2 = open(OUT_PREFIX+'_FN.vcf','w')
-		vcfo3 = open(OUT_PREFIX+'_FP.vcf','w')
-		vcfo2_firstTime = True
-		vcfo3_firstTime = True
-
-	#
-	#	data for plotting FN analysis
-	#
-	set1 = []
-	set2 = []
-	set3 = []
-	varAdj = 0
-
-	#
-	#
-	#	For each sequence in reference fasta...
-	#
-	#
-	for n_RI in ref_inds:
-
-		refName = n_RI[0]
-		if FAST == False:
-			f.seek(n_RI[1])
-			print 'reading '+refName+'...',
-			myDat  = f.read(n_RI[2]-n_RI[1]).split('\n')
-			myLen  = sum([len(m) for m in myDat])
-			if sys.version_info >= (2,7):
-				print '{:,} bp'.format(myLen)
-			else:
-				print '{0:} bp'.format(myLen)
-			inWidth = len(myDat[0])
-			if len(myDat[-1]) == 0:	# if last line is empty, remove it.
-				del myDat[-1]
-			if inWidth*(len(myDat)-1)+len(myDat[-1]) != myLen:
-				print 'fasta column-width not consistent.'
-				print myLen, inWidth*(len(myDat)-1)+len(myDat[-1])
-				for i in xrange(len(myDat)):
-					if len(myDat[i]) != inWidth:
-						print i, len(myDat[i]), inWidth
-				exit(1)
-
-			myDat = bytearray(''.join(myDat)).upper()
-			myLen = len(myDat)
-
-		#
-		#	Parse relevant targeted regions
-		#
-		targRegionsFl = []
-		if BEDFILE != None:
-			bedfile = open(BEDFILE,'r')
-			for line in bedfile:
-				splt = line.split('\t')
-				if splt[0] == refName:
-					targRegionsFl.extend((int(splt[1]),int(splt[2])))
-			bedfile.close()
-		else:
-			targRegionsFl = [-1,MAX_VAL+1]
-
-		#
-		#	Parse vcf files
-		#
-		sys.stdout.write('comparing variation in '+refName+'... ')
-		sys.stdout.flush()
-		tt = time.time()
-
-		(correctHashed, correctAlts, correctCov, correctAF, correctQual, correctTargLen, correctBelowMinRLen, correctUnique, gFiltered, gMerged, gRedundant)        = parseVCF(GOLDEN_VCF, refName, targRegionsFl, vcfo2, vcfo2_firstTime)
-		(workflowHashed, workflowAlts, workflowCov, workflowAF, workflowQual, workflowTarLen, workflowBelowMinRLen, workflowUnique, wFiltered, wMerged, wRedundant) = parseVCF(WORKFLOW_VCF, refName, targRegionsFl, vcfo3, vcfo3_firstTime)
-		zgF += gFiltered
-		zgR += gRedundant
-		zgM += gMerged
-		zwF += wFiltered
-		zwR += wRedundant
-		zwM += wMerged
-
-		#
-		#	Deduce which variants are FP / FN
-		#
-		solvedInds = {}
-		for var in correctHashed.keys():
-			if var in workflowHashed or var[0] in solvedInds:
-				correctHashed[var]  = 2
-				workflowHashed[var] = 2
-				solvedInds[var[0]] = True
-		for var in correctHashed.keys()+workflowHashed.keys():
-			if var[0] in solvedInds:
-				correctHashed[var]  = 2
-				workflowHashed[var] = 2
-		nPerfect = len(solvedInds)
-		
-		# correctHashed[var] = 1: were not found
-		#                    = 2: should be discluded because we were found
-		#                    = 3: should be discluded because an alt was found
-		notFound   = [n for n in sorted(correctHashed.keys()) if correctHashed[n] == 1]
-		FPvariants = [n for n in sorted(workflowHashed.keys()) if workflowHashed[n] == 1]
-
-		#
-		#	condense all variants who have alternate alleles and were *not* found to have perfect matches
-		#	into a single variant again. These will not be included in the candidates for equivalency checking. Sorry!
-		#
-		notFound   = condenseByPos(notFound)
-		FPvariants = condenseByPos(FPvariants)
-
-		#
-		#	tally up some values, if there are no golden variants lets save some CPU cycles and move to the next ref
-		#
-		totalGoldenVariants   = nPerfect + len(notFound)
-		totalWorkflowVariants = nPerfect + len(FPvariants)
-		if totalGoldenVariants == 0:
-			zfP += len(FPvariants)
-			ztW += totalWorkflowVariants
-			print '{0:.3f} (sec)'.format(time.time()-tt)
-			continue
-
-		#
-		#	let's check for equivalent variants
-		#
-		if FAST == False:
-			delList_i = []
-			delList_j = []
-			regionsToCheck = []
-			for i in xrange(len(FPvariants)):
-				pos = FPvariants[i][0]
-				regionsToCheck.append((max([pos-EV_BPRANGE-1,0]),min([pos+EV_BPRANGE,len(myDat)-1])))
-
-			for n in regionsToCheck:
-				refSection = myDat[n[0]:n[1]]
-
-				fpWithin = []
-				for i in xrange(len(FPvariants)):
-					m  = FPvariants[i]
-					if (m[0] > n[0] and m[0] < n[1]):
-						fpWithin.append((m,i))
-				fpWithin = sorted(fpWithin)
-				adj = 0
-				altSection = copy.deepcopy(refSection)
-				for (m,i) in fpWithin:
-					lr = len(m[1])
-					la = len(m[2])
-					dpos = m[0]-n[0]+adj
-					altSection = altSection[:dpos-1] + m[2] + altSection[dpos-1+lr:]
-					adj += la-lr
-
-				nfWithin = []
-				for j in xrange(len(notFound)):
-					m = notFound[j]
-					if (m[0] > n[0] and m[0] < n[1]):
-						nfWithin.append((m,j))
-				nfWithin = sorted(nfWithin)
-				adj = 0
-				altSection2 = copy.deepcopy(refSection)
-				for (m,j) in nfWithin:
-					lr = len(m[1])
-					la = len(m[2])
-					dpos = m[0]-n[0]+adj
-					altSection2 = altSection2[:dpos-1] + m[2] + altSection2[dpos-1+lr:]
-					adj += la-lr
-
-				if altSection == altSection2:
-					for (m,i) in fpWithin:
-						if i not in delList_i:
-							delList_i.append(i)
-					for (m,j) in nfWithin:
-						if j not in delList_j:
-							delList_j.append(j)
-
-			nEquiv = 0
-			for i in sorted(list(set(delList_i)),reverse=True):
-				del FPvariants[i]
-			for j in sorted(list(set(delList_j)),reverse=True):
-				del notFound[j]
-				nEquiv += 1
-			nPerfect += nEquiv
-
-		#
-		#	Tally up errors and whatnot
-		#
-		ztV += totalGoldenVariants
-		ztW += totalWorkflowVariants
-		znP += nPerfect
-		zfP += len(FPvariants)
-		znF += len(notFound)
-		if FAST == False:
-			znE += nEquiv
-		if BEDFILE != None:
-			zbM += correctBelowMinRLen
-
-		#
-		#	try to identify a reason for FN variants:
-		#
-
-		venn_data = [[0,0,0] for n in notFound]		# [i] = (unmappable, low cov, low het)
-		for i in xrange(len(notFound)):
-			var = notFound[i]
-
-			noReason = True
-
-			#	mappability?
-			if MAPTRACK != None:
-				if refName in mappability_tracks and var[0] < len(mappability_tracks[refName]):
-					if mappability_tracks[refName][var[0]]:
-						mappability_vs_FN[1] += 1
-						venn_data[i][0] = 1
-						noReason = False
-					else:
-						mappability_vs_FN[0] += 1
-
-			#	coverage?
-			if var in correctCov:
-				c = correctCov[var]
-				if c != None:
-					if c not in coverage_vs_FN:
-						coverage_vs_FN[c] = 0
-					coverage_vs_FN[c] += 1
-					if c < DP_THRESH:
-						venn_data[i][1] = 1
-						noReason = False
-
-			#	heterozygous genotype messing things up?
-			#if var in correctAF:
-			#	a = correctAF[var]
-			#	if a != None:
-			#		a = AF_KEYS[quantize_AF(a)]
-			#		if a not in alleleBal_vs_FN:
-			#			alleleBal_vs_FN[a] = 0
-			#		alleleBal_vs_FN[a] += 1
-			#		if a < AF_THRESH:
-			#			venn_data[i][2] = 1
-
-			#	no reason?
-			if noReason:
-				venn_data[i][2] += 1
-
-		for i in xrange(len(notFound)):
-			if venn_data[i][0]: set1.append(i+varAdj)
-			if venn_data[i][1]: set2.append(i+varAdj)
-			if venn_data[i][2]: set3.append(i+varAdj)
-		varAdj += len(notFound)
-
-		#
-		#	if desired, write out vcf files.
-		#
-		notFound   = sorted(notFound)
-		FPvariants = sorted(FPvariants)
-		if VCF_OUT:
-			for line in open(GOLDEN_VCF,'r'):
-				if line[0] != '#':
-					splt = line.split('\t')
-					if splt[0] == refName:
-						var  = (int(splt[1]),splt[3],splt[4])
-						if var in notFound:
-							vcfo2.write(line)
-			for line in open(WORKFLOW_VCF,'r'):
-				if line[0] != '#':
-					splt = line.split('\t')
-					if splt[0] == refName:
-						var  = (int(splt[1]),splt[3],splt[4])
-						if var in FPvariants:
-							vcfo3.write(line)
-
-		print '{0:.3f} (sec)'.format(time.time()-tt)
-
-	#
-	#	close vcf output
-	#
-	print ''
-	if VCF_OUT:
-		print OUT_PREFIX+'_FN.vcf'
-		print OUT_PREFIX+'_FP.vcf'
-		vcfo2.close()
-		vcfo3.close()
-
-	#
-	#	plot some FN stuff
-	#
-	if NO_PLOT == False:
-		nDetected = len(set(set1+set2+set3))
-		set1 = set(set1)
-		set2 = set(set2)
-		set3 = set(set3)
-
-		if len(set1): s1 = 'Unmappable'
-		else: s1 = ''
-		if len(set2): s2 = 'DP < '+str(DP_THRESH)
-		else: s2 = ''
-		#if len(set3): s3 = 'AF < '+str(AF_THRESH)
-		if len(set3): s3 = 'Unknown'
-		else: s3 = ''
-
-		mpl.figure(0)
-		tstr1 = 'False Negative Variants (Missed Detections)'
-		#tstr2 = str(nDetected)+' / '+str(znF)+' FN variants categorized'
-		tstr2 = ''
-		if MAPTRACK != None:
-			v = venn3([set1, set2, set3], (s1, s2, s3))
-		else:
-			v = venn2([set2, set3], (s2, s3))
-		mpl.figtext(0.5,0.95,tstr1,fontdict={'size':14,'weight':'bold'},horizontalalignment='center')
-		mpl.figtext(0.5,0.03,tstr2,fontdict={'size':14,'weight':'bold'},horizontalalignment='center')
-
-		ouf = OUT_PREFIX+'_FNvenn.pdf'
-		print ouf
-		mpl.savefig(ouf)
-
-	#
-	#	spit out results to console
-	#
-	print '\n**********************************\n'
-	if BEDFILE != None:
-		print 'ONLY CONSIDERING VARIANTS FOUND WITHIN TARGETED REGIONS\n\n'
-	print 'Total Golden Variants:  ',ztV,'\t[',zgF,'filtered,',zgM,'merged,',zgR,'redundant ]'
-	print 'Total Workflow Variants:',ztW,'\t[',zwF,'filtered,',zwM,'merged,',zwR,'redundant ]'
-	print ''
-	if ztV > 0 and ztW > 0:
-		print 'Perfect Matches:',znP,'({0:.2f}%)'.format(100.*float(znP)/ztV)
-		print 'FN variants:    ',znF,'({0:.2f}%)'.format(100.*float(znF)/ztV)
-		print 'FP variants:    ',zfP#,'({0:.2f}%)'.format(100.*float(zfP)/ztW)
-	if FAST == False:
-		print '\nNumber of equivalent variants denoted differently between the two vcfs:',znE
-	if BEDFILE != None:
-		print '\nNumber of golden variants located in targeted regions that were too small to be sampled from:',zbM
-	if FAST:
-		print "\nWarning! Running with '--fast' means that identical variants denoted differently between the two vcfs will not be detected! The values above may be lower than the true accuracy."
-	#if NO_PLOT:
-	if True:
-		print '\n#unmappable:  ',len(set1)
-		print '#low_coverage:',len(set2)
-		print '#unknown:     ',len(set3)
-	print '\n**********************************\n'
-
-
-
+    ref = []
+    f = open(REFERENCE, 'r')
+    nLines = 0
+    prevR = None
+    prevP = None
+    ref_inds = []
+    sys.stdout.write('\nindexing reference fasta... ')
+    sys.stdout.flush()
+    tt = time.time()
+    while 1:
+        nLines += 1
+        data = f.readline()
+        if not data:
+            ref_inds.append((prevR, prevP, f.tell() - len(data)))
+            break
+        if data[0] == '>':
+            if prevP != None:
+                ref_inds.append((prevR, prevP, f.tell() - len(data)))
+            prevP = f.tell()
+            prevR = data[1:-1]
+    print('{0:.3f} (sec)'.format(time.time() - tt))
+    # ref_inds = [('chrM', 6, 16909), ('chr1', 16915, 254252549), ('chr2', 254252555, 502315916), ('chr3', 502315922, 704298801), ('chr4', 704298807, 899276169), ('chr5', 899276175, 1083809741), ('chr6', 1083809747, 1258347116), ('chr7', 1258347122, 1420668559), ('chr8', 1420668565, 1569959868), ('chr9', 1569959874, 1713997574), ('chr10', 1713997581, 1852243023), ('chr11', 1852243030, 1989949677), ('chr12', 1989949684, 2126478617), ('chr13', 2126478624, 2243951900), ('chr14', 2243951907, 2353448438), ('chr15', 2353448445, 2458030465), ('chr16', 2458030472, 2550192321), ('chr17', 2550192328, 2633011443), ('chr18', 2633011450, 2712650243), ('chr19', 2712650250, 2772961813), ('chr20', 2772961820, 2837247851), ('chr21', 2837247858, 2886340351), ('chr22', 2886340358, 2938671016), ('chrX', 2938671022, 3097046994), ('chrY', 3097047000, 3157608038)]
+
+    ztV = 0  # total golden variants
+    ztW = 0  # total workflow variants
+    znP = 0  # total perfect matches
+    zfP = 0  # total false positives
+    znF = 0  # total false negatives
+    znE = 0  # total equivalent variants detected
+    zgF = 0  # total golden variants that were filtered and excluded
+    zgR = 0  # total golden variants that were excluded for being redundant
+    zgM = 0  # total golden variants that were merged into a single position
+    zwF = 0  # total workflow variants that were filtered and excluded
+    zwR = 0  # total workflow variants that were excluded for being redundant
+    zwM = 0  # total workflow variants that were merged into a single position
+    if BEDFILE != None:
+        zbM = 0
+
+    mappability_vs_FN = {0: 0,
+                         1: 0}  # [0] = # of FNs that were in mappable regions, [1] = # of FNs that were in unmappable regions
+    coverage_vs_FN = {}  # [C] = # of FNs that were covered by C reads
+    alleleBal_vs_FN = {}  # [AF] = # of FNs that were heterozygous genotypes with allele freq AF (quantized to multiples of 1/AF_STEPS)
+    for n in AF_KEYS:
+        alleleBal_vs_FN[n] = 0
+
+    #
+    #	read in mappability track
+    #
+    mappability_tracks = {}  # indexed by chr string (e.g. 'chr1'), has boolean array
+    prevRef = ''
+    relevantRegions = []
+    if MAPTRACK != None:
+        mtf = open(MAPTRACK, 'r')
+        for line in mtf:
+            splt = line.strip().split('\t')
+            if prevRef != '' and splt[0] != prevRef:
+                # do stuff
+                if len(relevantRegions):
+                    myTrack = [0] * (relevantRegions[-1][1] + 100)
+                    for r in relevantRegions:
+                        for ri in range(r[0], r[1]):
+                            myTrack[ri] = 1
+                    mappability_tracks[prevRef] = [n for n in myTrack]
+                #
+                relevantRegions = []
+            if int(splt[3]) >= MIN_READLEN:
+                relevantRegions.append((int(splt[1]), int(splt[2])))
+            prevRef = splt[0]
+        mtf.close()
+        # do stuff
+        if len(relevantRegions):
+            myTrack = [0] * (relevantRegions[-1][1] + 100)
+            for r in relevantRegions:
+                for ri in range(r[0], r[1]):
+                    myTrack[ri] = 1
+            mappability_tracks[prevRef] = [n for n in myTrack]
+    #
+
+    #
+    #	init vcf output, if desired
+    #
+    vcfo2 = None
+    vcfo3 = None
+    global vcfo2_firstTime
+    global vcfo3_firstTime
+    vcfo2_firstTime = False
+    vcfo3_firstTime = False
+    if VCF_OUT:
+        vcfo2 = open(OUT_PREFIX + '_FN.vcf', 'w')
+        vcfo3 = open(OUT_PREFIX + '_FP.vcf', 'w')
+        vcfo2_firstTime = True
+        vcfo3_firstTime = True
+
+    #
+    #	data for plotting FN analysis
+    #
+    set1 = []
+    set2 = []
+    set3 = []
+    varAdj = 0
+
+    #
+    #
+    #	For each sequence in reference fasta...
+    #
+    #
+    for n_RI in ref_inds:
+
+        refName = n_RI[0]
+        if FAST == False:
+            f.seek(n_RI[1])
+            print('reading ' + refName + '...', end=' ')
+            myDat = f.read(n_RI[2] - n_RI[1]).split('\n')
+            myLen = sum([len(m) for m in myDat])
+            if sys.version_info >= (2, 7):
+                print('{:,} bp'.format(myLen))
+            else:
+                print('{0:} bp'.format(myLen))
+            inWidth = len(myDat[0])
+            if len(myDat[-1]) == 0:  # if last line is empty, remove it.
+                del myDat[-1]
+            if inWidth * (len(myDat) - 1) + len(myDat[-1]) != myLen:
+                print('fasta column-width not consistent.')
+                print(myLen, inWidth * (len(myDat) - 1) + len(myDat[-1]))
+                for i in range(len(myDat)):
+                    if len(myDat[i]) != inWidth:
+                        print(i, len(myDat[i]), inWidth)
+                exit(1)
+
+            myDat = bytearray(''.join(myDat)).upper()
+            myLen = len(myDat)
+
+        #
+        #	Parse relevant targeted regions
+        #
+        targRegionsFl = []
+        if BEDFILE != None:
+            bedfile = open(BEDFILE, 'r')
+            for line in bedfile:
+                splt = line.split('\t')
+                if splt[0] == refName:
+                    targRegionsFl.extend((int(splt[1]), int(splt[2])))
+            bedfile.close()
+        else:
+            targRegionsFl = [-1, MAX_VAL + 1]
+
+        #
+        #	Parse vcf files
+        #
+        sys.stdout.write('comparing variation in ' + refName + '... ')
+        sys.stdout.flush()
+        tt = time.time()
+
+        (correctHashed, correctAlts, correctCov, correctAF, correctQual, correctTargLen, correctBelowMinRLen,
+         correctUnique, gFiltered, gMerged, gRedundant) = parseVCF(GOLDEN_VCF, refName, targRegionsFl, vcfo2,
+                                                                   vcfo2_firstTime)
+        (workflowHashed, workflowAlts, workflowCov, workflowAF, workflowQual, workflowTarLen, workflowBelowMinRLen,
+         workflowUnique, wFiltered, wMerged, wRedundant) = parseVCF(WORKFLOW_VCF, refName, targRegionsFl, vcfo3,
+                                                                    vcfo3_firstTime)
+        zgF += gFiltered
+        zgR += gRedundant
+        zgM += gMerged
+        zwF += wFiltered
+        zwR += wRedundant
+        zwM += wMerged
+
+        #
+        #	Deduce which variants are FP / FN
+        #
+        solvedInds = {}
+        for var in correctHashed.keys():
+            if var in workflowHashed or var[0] in solvedInds:
+                correctHashed[var] = 2
+                workflowHashed[var] = 2
+                solvedInds[var[0]] = True
+        for var in correctHashed.keys() + workflowHashed.keys():
+            if var[0] in solvedInds:
+                correctHashed[var] = 2
+                workflowHashed[var] = 2
+        nPerfect = len(solvedInds)
+
+        # correctHashed[var] = 1: were not found
+        #                    = 2: should be discluded because we were found
+        #                    = 3: should be discluded because an alt was found
+        notFound = [n for n in sorted(correctHashed.keys()) if correctHashed[n] == 1]
+        FPvariants = [n for n in sorted(workflowHashed.keys()) if workflowHashed[n] == 1]
+
+        #
+        #	condense all variants who have alternate alleles and were *not* found to have perfect matches
+        #	into a single variant again. These will not be included in the candidates for equivalency checking. Sorry!
+        #
+        notFound = condenseByPos(notFound)
+        FPvariants = condenseByPos(FPvariants)
+
+        #
+        #	tally up some values, if there are no golden variants lets save some CPU cycles and move to the next ref
+        #
+        totalGoldenVariants = nPerfect + len(notFound)
+        totalWorkflowVariants = nPerfect + len(FPvariants)
+        if totalGoldenVariants == 0:
+            zfP += len(FPvariants)
+            ztW += totalWorkflowVariants
+            print('{0:.3f} (sec)'.format(time.time() - tt))
+            continue
+
+        #
+        #	let's check for equivalent variants
+        #
+        if FAST == False:
+            delList_i = []
+            delList_j = []
+            regionsToCheck = []
+            for i in range(len(FPvariants)):
+                pos = FPvariants[i][0]
+                regionsToCheck.append((max([pos - EV_BPRANGE - 1, 0]), min([pos + EV_BPRANGE, len(myDat) - 1])))
+
+            for n in regionsToCheck:
+                refSection = myDat[n[0]:n[1]]
+
+                fpWithin = []
+                for i in range(len(FPvariants)):
+                    m = FPvariants[i]
+                    if (m[0] > n[0] and m[0] < n[1]):
+                        fpWithin.append((m, i))
+                fpWithin = sorted(fpWithin)
+                adj = 0
+                altSection = copy.deepcopy(refSection)
+                for (m, i) in fpWithin:
+                    lr = len(m[1])
+                    la = len(m[2])
+                    dpos = m[0] - n[0] + adj
+                    altSection = altSection[:dpos - 1] + m[2] + altSection[dpos - 1 + lr:]
+                    adj += la - lr
+
+                nfWithin = []
+                for j in range(len(notFound)):
+                    m = notFound[j]
+                    if (m[0] > n[0] and m[0] < n[1]):
+                        nfWithin.append((m, j))
+                nfWithin = sorted(nfWithin)
+                adj = 0
+                altSection2 = copy.deepcopy(refSection)
+                for (m, j) in nfWithin:
+                    lr = len(m[1])
+                    la = len(m[2])
+                    dpos = m[0] - n[0] + adj
+                    altSection2 = altSection2[:dpos - 1] + m[2] + altSection2[dpos - 1 + lr:]
+                    adj += la - lr
+
+                if altSection == altSection2:
+                    for (m, i) in fpWithin:
+                        if i not in delList_i:
+                            delList_i.append(i)
+                    for (m, j) in nfWithin:
+                        if j not in delList_j:
+                            delList_j.append(j)
+
+            nEquiv = 0
+            for i in sorted(list(set(delList_i)), reverse=True):
+                del FPvariants[i]
+            for j in sorted(list(set(delList_j)), reverse=True):
+                del notFound[j]
+                nEquiv += 1
+            nPerfect += nEquiv
+
+        #
+        #	Tally up errors and whatnot
+        #
+        ztV += totalGoldenVariants
+        ztW += totalWorkflowVariants
+        znP += nPerfect
+        zfP += len(FPvariants)
+        znF += len(notFound)
+        if FAST == False:
+            znE += nEquiv
+        if BEDFILE != None:
+            zbM += correctBelowMinRLen
+
+        #
+        #	try to identify a reason for FN variants:
+        #
+
+        venn_data = [[0, 0, 0] for n in notFound]  # [i] = (unmappable, low cov, low het)
+        for i in range(len(notFound)):
+            var = notFound[i]
+
+            noReason = True
+
+            #	mappability?
+            if MAPTRACK != None:
+                if refName in mappability_tracks and var[0] < len(mappability_tracks[refName]):
+                    if mappability_tracks[refName][var[0]]:
+                        mappability_vs_FN[1] += 1
+                        venn_data[i][0] = 1
+                        noReason = False
+                    else:
+                        mappability_vs_FN[0] += 1
+
+            #	coverage?
+            if var in correctCov:
+                c = correctCov[var]
+                if c != None:
+                    if c not in coverage_vs_FN:
+                        coverage_vs_FN[c] = 0
+                    coverage_vs_FN[c] += 1
+                    if c < DP_THRESH:
+                        venn_data[i][1] = 1
+                        noReason = False
+
+            #	heterozygous genotype messing things up?
+            # if var in correctAF:
+            #	a = correctAF[var]
+            #	if a != None:
+            #		a = AF_KEYS[quantize_AF(a)]
+            #		if a not in alleleBal_vs_FN:
+            #			alleleBal_vs_FN[a] = 0
+            #		alleleBal_vs_FN[a] += 1
+            #		if a < AF_THRESH:
+            #			venn_data[i][2] = 1
+
+            #	no reason?
+            if noReason:
+                venn_data[i][2] += 1
+
+        for i in range(len(notFound)):
+            if venn_data[i][0]: set1.append(i + varAdj)
+            if venn_data[i][1]: set2.append(i + varAdj)
+            if venn_data[i][2]: set3.append(i + varAdj)
+        varAdj += len(notFound)
+
+        #
+        #	if desired, write out vcf files.
+        #
+        notFound = sorted(notFound)
+        FPvariants = sorted(FPvariants)
+        if VCF_OUT:
+            for line in open(GOLDEN_VCF, 'r'):
+                if line[0] != '#':
+                    splt = line.split('\t')
+                    if splt[0] == refName:
+                        var = (int(splt[1]), splt[3], splt[4])
+                        if var in notFound:
+                            vcfo2.write(line)
+            for line in open(WORKFLOW_VCF, 'r'):
+                if line[0] != '#':
+                    splt = line.split('\t')
+                    if splt[0] == refName:
+                        var = (int(splt[1]), splt[3], splt[4])
+                        if var in FPvariants:
+                            vcfo3.write(line)
+
+        print('{0:.3f} (sec)'.format(time.time() - tt))
+
+    #
+    #	close vcf output
+    #
+    print('')
+    if VCF_OUT:
+        print(OUT_PREFIX + '_FN.vcf')
+        print(OUT_PREFIX + '_FP.vcf')
+        vcfo2.close()
+        vcfo3.close()
+
+    #
+    #	plot some FN stuff
+    #
+    if NO_PLOT == False:
+        nDetected = len(set(set1 + set2 + set3))
+        set1 = set(set1)
+        set2 = set(set2)
+        set3 = set(set3)
+
+        if len(set1):
+            s1 = 'Unmappable'
+        else:
+            s1 = ''
+        if len(set2):
+            s2 = 'DP < ' + str(DP_THRESH)
+        else:
+            s2 = ''
+        # if len(set3): s3 = 'AF < '+str(AF_THRESH)
+        if len(set3):
+            s3 = 'Unknown'
+        else:
+            s3 = ''
+
+        mpl.figure(0)
+        tstr1 = 'False Negative Variants (Missed Detections)'
+        # tstr2 = str(nDetected)+' / '+str(znF)+' FN variants categorized'
+        tstr2 = ''
+        if MAPTRACK != None:
+            v = venn3([set1, set2, set3], (s1, s2, s3))
+        else:
+            v = venn2([set2, set3], (s2, s3))
+        mpl.figtext(0.5, 0.95, tstr1, fontdict={'size': 14, 'weight': 'bold'}, horizontalalignment='center')
+        mpl.figtext(0.5, 0.03, tstr2, fontdict={'size': 14, 'weight': 'bold'}, horizontalalignment='center')
+
+        ouf = OUT_PREFIX + '_FNvenn.pdf'
+        print(ouf)
+        mpl.savefig(ouf)
+
+    #
+    #	spit out results to console
+    #
+    print('\n**********************************\n')
+    if BEDFILE != None:
+        print('ONLY CONSIDERING VARIANTS FOUND WITHIN TARGETED REGIONS\n\n')
+    print('Total Golden Variants:  ', ztV, '\t[', zgF, 'filtered,', zgM, 'merged,', zgR, 'redundant ]')
+    print('Total Workflow Variants:', ztW, '\t[', zwF, 'filtered,', zwM, 'merged,', zwR, 'redundant ]')
+    print('')
+    if ztV > 0 and ztW > 0:
+        print('Perfect Matches:', znP, '({0:.2f}%)'.format(100. * float(znP) / ztV))
+        print('FN variants:    ', znF, '({0:.2f}%)'.format(100. * float(znF) / ztV))
+        print('FP variants:    ', zfP)  # ,'({0:.2f}%)'.format(100.*float(zfP)/ztW)
+    if FAST == False:
+        print('\nNumber of equivalent variants denoted differently between the two vcfs:', znE)
+    if BEDFILE != None:
+        print('\nNumber of golden variants located in targeted regions that were too small to be sampled from:', zbM)
+    if FAST:
+        print(
+            "\nWarning! Running with '--fast' means that identical variants denoted differently between the two vcfs will not be detected! The values above may be lower than the true accuracy.")
+    # if NO_PLOT:
+    if True:
+        print('\n#unmappable:  ', len(set1))
+        print('#low_coverage:', len(set2))
+        print('#unknown:     ', len(set3))
+    print('\n**********************************\n')
 
 
 if __name__ == '__main__':
-	main()
+    main()

From 01ddc58c01094f7064058b40e2606934b5aea536 Mon Sep 17 00:00:00 2001
From: joshfactorial <jallen17@illinois.edu>
Date: Wed, 8 Jan 2020 12:56:19 -0600
Subject: [PATCH 003/323] Making some basic modifications to bring it inline
 with Py3

---
 py/biopython_modified_bgzf.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/py/biopython_modified_bgzf.py b/py/biopython_modified_bgzf.py
index 7da4c31..e74e87e 100755
--- a/py/biopython_modified_bgzf.py
+++ b/py/biopython_modified_bgzf.py
@@ -26,8 +26,7 @@ def __init__(self, filename=None, mode="w", fileobj=None, compresslevel=6):
             assert filename is None
             handle = fileobj
         else:
-            if "w" not in mode.lower() \
-                    and "a" not in mode.lower():
+            if "w" not in mode.lower() and "a" not in mode.lower():
                 raise ValueError("Must use write or append mode, not %r" % mode)
             if "a" in mode.lower():
                 handle = open(filename, "ab")
@@ -99,4 +98,4 @@ def __enter__(self):
         return self
 
     def __exit__(self, type, value, traceback):
-        self.close()
\ No newline at end of file
+        self.close()

From badfe5607f36b990de48b0228053c77d7dd99b29 Mon Sep 17 00:00:00 2001
From: joshfactorial <jallen17@illinois.edu>
Date: Tue, 11 Feb 2020 16:20:45 -0600
Subject: [PATCH 004/323] Added docstrings to inputChecking.py

---
 py/SequenceContainer.py |  5 +++--
 py/inputChecking.py     | 44 ++++++++++++++++++++++++++++++++++++-----
 2 files changed, 42 insertions(+), 7 deletions(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 70e3d71..7a5259c 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -17,7 +17,7 @@
            'GA':8,  'GC':9,  'GG':10,  'GT':11, 'TA':12, 'TC':13, 'TG':14, 'TT':15}
 NUC_IND = {'A':0, 'C':1, 'G':2, 'T':3}
 ALL_TRI = [NUCL[i]+NUCL[j]+NUCL[k] for i in range(len(NUCL)) for j in range(len(NUCL)) for k in range(len(NUCL))]
-ALL_IND = {ALL_TRI[i]:i for i in range(len(ALL_TRI))}
+ALL_IND = {ALL_TRI[i]: i for i in range(len(ALL_TRI))}
 
 # DEBUG
 IGNORE_TRINUC = False
@@ -161,7 +161,8 @@ def init_coverage(self,coverageDat,fragDist=None):
                         #mpl.show()
                         #exit(1)
 
-                        self.coverage_distribution[i][flv] = DiscreteDistribution(coverage_vals,range(len(coverage_vals)))
+                        self.coverage_distribution[i][flv] = DiscreteDistribution(coverage_vals,
+                                                                                  range(len(coverage_vals)))
 
             return np.mean(avg_out)
 
diff --git a/py/inputChecking.py b/py/inputChecking.py
index e47e849..45ec25c 100644
--- a/py/inputChecking.py
+++ b/py/inputChecking.py
@@ -1,14 +1,33 @@
+"""
+This file contains several standard functions that will be used throughout the program. Each function checks input
+and issues an error if there is something wrong.
+"""
+
 import os
 import sys
 
 
-def requiredField(s, errString):
+def requiredField(s: any, errString: str) -> None:
+    """
+    If required field s is empty, issues an error. Otherwise this does nothing
+
+    :param s: Any input type
+    :param errString: A string with the error message
+    :return: None
+    """
     if s is None:
         print('\n' + errString + '\n')
         exit(1)
 
 
-def checkFileOpen(fn, errString, required=False):
+def checkFileOpen(fn: str, errString: str, required: bool = False) -> None:
+    """
+    Checks that the filename is not empty and that it is indeed a  file
+    :param fn: file name, string
+    :param errString: string of the error if it is not a file
+    :param required: If not required, skips the check
+    :return: None
+    """
     if required or fn is not None:
         if fn is None:
             print('\n' + errString + '\n')
@@ -16,18 +35,33 @@ def checkFileOpen(fn, errString, required=False):
         else:
             try:
                 open(fn, 'r')
-            except:
+            except ValueError:
                 print('\n' + errString + '\n')
                 exit(1)
 
 
-def checkDir(dir, errString):
+def checkDir(dir: str, errString: str) -> None:
+    """
+    Checks that directory exists and is a directory
+    :param dir: string of the directory path
+    :param errString: string of the error in case it is not a directory or doesn't exist
+    :return: None
+    """
     if not os.path.isdir(dir):
         print('\n' + errString + '\n')
         exit(1)
 
 
-def isInRange(val, lb, ub, errString):
+def isInRange(val: float, lb: float, ub: float, errString: str) -> None:
+    """
+    Checks that value (val) is between the lower bound (lb) and upper bound (ub), and if not prints an error message
+    (errString) and exits the program.
+    :param val: float for the value
+    :param lb: float for the upper bound
+    :param ub: float for the lower bound
+    :param errString: string of the error message to print if the value is out of range
+    :return: None
+    """
     if val < lb or val > ub:
         print('\n' + errString + '\n')
         exit(1)

From ea9d3cddd3f94b2e1f72fd6451346bd6f14ea4d4 Mon Sep 17 00:00:00 2001
From: Zachary Stephens <zach@Zacharys-MacBook-Pro.local>
Date: Thu, 27 Feb 2020 17:28:39 -0600
Subject: [PATCH 005/323] encoding required to load this particular pickle in
 python3

---
 py/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 7a5259c..0d619df 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -654,7 +654,7 @@ def __init__(self, readLen, errorModel, reScaledError):
 
         self.readLen = readLen
 
-        errorDat = pickle.load(open(errorModel,'rb'))
+        errorDat = pickle.load(open(errorModel,'rb'), encoding='bytes')
         self.UNIFORM = False
         if len(errorDat) == 4:		# uniform-error SE reads (e.g. PacBio)
             self.UNIFORM = True

From 5afe2324f6a059405a70092713bd026eaf5ee320 Mon Sep 17 00:00:00 2001
From: jallen17 <joshfactorial@pop-os.localdomain>
Date: Fri, 28 Feb 2020 13:56:48 -0600
Subject: [PATCH 006/323] Progress on python3 conversion

---
 py/SequenceContainer.py | 2 +-
 py/refFunc.py           | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 0d619df..560efb3 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -224,7 +224,7 @@ def init_trinucBias(self):
         self.trinuc_bias = [None for n in range(self.ploidy)]
         for p in range(self.ploidy):
             for i in range(self.winBuffer+1,self.seqLen-1):
-                trinuc_snp_bias[p][i] = self.models[p][7][ALL_IND[str(self.sequences[p][i-1:i+2])]]
+                trinuc_snp_bias[p][i] = self.models[p][7][ALL_IND[self.sequences[p][i-1:i+2].decode()]]
             self.trinuc_bias[p] = DiscreteDistribution(trinuc_snp_bias[p][self.winBuffer+1:self.seqLen-1],range(self.winBuffer+1,self.seqLen-1))
 
     def update(self, xOffset, sequence, ploidy, windowOverlap, readLen, mutationModels=[], mutRate=None):
diff --git a/py/refFunc.py b/py/refFunc.py
index 597498f..7114e35 100644
--- a/py/refFunc.py
+++ b/py/refFunc.py
@@ -79,7 +79,7 @@ def readRef(refPath, ref_inds_i, N_handling, N_unknowns=True, quiet=False):
     refFile = open(refPath, 'r')
     refFile.seek(ref_inds_i[1])
     myDat = ''.join(refFile.read(ref_inds_i[2] - ref_inds_i[1]).split('\n'))
-    myDat = bytearray(myDat.upper())
+    myDat = bytearray(myDat.upper(), 'utf8')
 
     # find N regions
     # data explanation: myDat[N_atlas[0][0]:N_atlas[0][1]] = solid block of Ns

From fa175fea937e65a3ce1c376528a975324c9d8881 Mon Sep 17 00:00:00 2001
From: jallen17 <joshfactorial@pop-os.localdomain>
Date: Fri, 28 Feb 2020 14:02:36 -0600
Subject: [PATCH 007/323] More progress on python3 conversion

---
 py/SequenceContainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 560efb3..2ccf482 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -79,7 +79,7 @@ def init_coverage(self,coverageDat,fragDist=None):
                 # compute gc-bias
                 j = 0
                 while j+self.windowSize < len(self.sequences[i]):
-                    gc_c = self.sequences[i][j:j+self.windowSize].count('G') + self.sequences[i][j:j+self.windowSize].count('C')
+                    gc_c = self.sequences[i][j:j+self.windowSize].count(ord('G')) + self.sequences[i][j:j+self.windowSize].count(ord('C'))
                     gcCov_vals[i].extend([gc_scalars[gc_c]]*self.windowSize)
                     j += self.windowSize
                 gc_c = self.sequences[i][-self.windowSize:].count('G') + self.sequences[i][-self.windowSize:].count('C')
@@ -413,7 +413,7 @@ def random_mutations(self):
                     print('\nError: Something went wrong!\n', all_snps[i][j], chr(self.sequences[i][vPos]), '\n')
                     exit(1)
                 else:
-                    self.sequences[i][vPos] = all_snps[i][j][2]
+                    self.sequences[i][vPos] = ord(all_snps[i][j][2])
 
         # organize the indels we want to insert
         for i in range(len(all_indels)):

From 0cbd868554cfa3990301f79d163b5086743a648e Mon Sep 17 00:00:00 2001
From: joshfactorial <jallen17@illinois.edu>
Date: Wed, 4 Mar 2020 10:12:18 -0600
Subject: [PATCH 008/323] minor python 3 changes

---
 py/OutputFileWriter.py        | 33 +++++++++++++++++----------------
 py/SequenceContainer.py       |  6 +++---
 py/biopython_modified_bgzf.py |  2 ++
 py/refFunc.py                 |  2 +-
 4 files changed, 23 insertions(+), 20 deletions(-)

diff --git a/py/OutputFileWriter.py b/py/OutputFileWriter.py
index f72d079..00a4726 100644
--- a/py/OutputFileWriter.py
+++ b/py/OutputFileWriter.py
@@ -88,26 +88,27 @@ def __init__(self, outPrefix, paired=False, BAM_header=None, VCF_header=None, gz
 
             # WRITE VCF HEADER (if parallel: only for first job)
             if jobTuple[0] == 1:
-                self.vcf_file.write('##fileformat=VCFv4.1\n')
-                self.vcf_file.write('##reference=' + VCF_header[0] + '\n')
-                self.vcf_file.write('##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">\n')
-                self.vcf_file.write('##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">\n')
+                self.vcf_file.write('##fileformat=VCFv4.1\n'.encode('utf-8'))
+                reference = '##reference=' + VCF_header[0] + '\n'
+                self.vcf_file.write(reference.encode('utf-8'))
+                self.vcf_file.write('##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">\n'.encode('utf-8'))
+                self.vcf_file.write('##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">\n'.encode('utf-8'))
                 # self.vcf_file.write('##INFO=<ID=READS,Number=1,Type=String,Description="Names of Reads Covering this Variant">\n')
                 self.vcf_file.write(
-                    '##INFO=<ID=VMX,Number=1,Type=String,Description="SNP is Missense in these Read Frames">\n')
+                    '##INFO=<ID=VMX,Number=1,Type=String,Description="SNP is Missense in these Read Frames">\n'.encode('utf-8'))
                 self.vcf_file.write(
-                    '##INFO=<ID=VNX,Number=1,Type=String,Description="SNP is Nonsense in these Read Frames">\n')
-                self.vcf_file.write('##INFO=<ID=VFX,Number=1,Type=String,Description="Indel Causes Frameshift">\n')
+                    '##INFO=<ID=VNX,Number=1,Type=String,Description="SNP is Nonsense in these Read Frames">\n'.encode('utf-8'))
+                self.vcf_file.write('##INFO=<ID=VFX,Number=1,Type=String,Description="Indel Causes Frameshift">\n'.encode('utf-8'))
                 self.vcf_file.write(
-                    '##INFO=<ID=WP,Number=A,Type=Integer,Description="NEAT-GenReads ploidy indicator">\n')
-                self.vcf_file.write('##ALT=<ID=DEL,Description="Deletion">\n')
-                self.vcf_file.write('##ALT=<ID=DUP,Description="Duplication">\n')
-                self.vcf_file.write('##ALT=<ID=INS,Description="Insertion of novel sequence">\n')
-                self.vcf_file.write('##ALT=<ID=INV,Description="Inversion">\n')
-                self.vcf_file.write('##ALT=<ID=CNV,Description="Copy number variable region">\n')
-                self.vcf_file.write('##ALT=<ID=TRANS,Description="Translocation">\n')
-                self.vcf_file.write('##ALT=<ID=INV-TRANS,Description="Inverted translocation">\n')
-                self.vcf_file.write('#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\n')
+                    '##INFO=<ID=WP,Number=A,Type=Integer,Description="NEAT-GenReads ploidy indicator">\n'.encode('utf-8'))
+                self.vcf_file.write('##ALT=<ID=DEL,Description="Deletion">\n'.encode('utf-8'))
+                self.vcf_file.write('##ALT=<ID=DUP,Description="Duplication">\n'.encode('utf-8'))
+                self.vcf_file.write('##ALT=<ID=INS,Description="Insertion of novel sequence">\n'.encode('utf-8'))
+                self.vcf_file.write('##ALT=<ID=INV,Description="Inversion">\n'.encode('utf-8'))
+                self.vcf_file.write('##ALT=<ID=CNV,Description="Copy number variable region">\n'.encode('utf-8'))
+                self.vcf_file.write('##ALT=<ID=TRANS,Description="Translocation">\n'.encode('utf-8'))
+                self.vcf_file.write('##ALT=<ID=INV-TRANS,Description="Inverted translocation">\n'.encode('utf-8'))
+                self.vcf_file.write('#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\n'.encode('utf-8'))
 
         #
         #	BAM OUTPUT
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 2ccf482..834e4bc 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -66,7 +66,7 @@ def init_basicVars(self, xOffset, sequence, ploidy, windowOverlap, readLen):
             self.blackList[p][-self.winBuffer]   = 3
             self.blackList[p][-self.winBuffer-1] = 3
 
-    def init_coverage(self,coverageDat,fragDist=None):
+    def init_coverage(self, coverageDat, fragDist=None):
         # if we're only creating a vcf, skip some expensive initialization related to coverage depth
         if not self.onlyVCF:
             (self.windowSize, gc_scalars, targetCov_vals) = coverageDat
@@ -82,7 +82,7 @@ def init_coverage(self,coverageDat,fragDist=None):
                     gc_c = self.sequences[i][j:j+self.windowSize].count(ord('G')) + self.sequences[i][j:j+self.windowSize].count(ord('C'))
                     gcCov_vals[i].extend([gc_scalars[gc_c]]*self.windowSize)
                     j += self.windowSize
-                gc_c = self.sequences[i][-self.windowSize:].count('G') + self.sequences[i][-self.windowSize:].count('C')
+                gc_c = self.sequences[i][-self.windowSize:].count(ord('G')) + self.sequences[i][-self.windowSize:].count(ord('C'))
                 gcCov_vals[i].extend([gc_scalars[gc_c]]*(len(self.sequences[i])-len(gcCov_vals[i])))
                 #
                 trCov_vals[i].append(targetCov_vals[0])
@@ -743,7 +743,7 @@ def getSequencingErrors(self, readData, isReverseStrand=False):
                 myQ = self.initDistByPos1[0].sample()
             qOut[0] = myQ
 
-            for i in range(1,self.readLen):
+            for i in range(1, self.readLen):
                 if self.PE_MODELS and isReverseStrand:
                     myQ = self.probDistByPosByPrevQ2[self.qIndRemap[i]][myQ].sample()
                 else:
diff --git a/py/biopython_modified_bgzf.py b/py/biopython_modified_bgzf.py
index e74e87e..2177a01 100755
--- a/py/biopython_modified_bgzf.py
+++ b/py/biopython_modified_bgzf.py
@@ -63,6 +63,8 @@ def _write_block(self, block):
         self._handle.write(data)
 
     def write(self, data):
+        if type(data) is str:
+            data = data.encode('utf-8')
         data_len = len(data)
         if len(self._buffer) + data_len < 65536:
             self._buffer += data
diff --git a/py/refFunc.py b/py/refFunc.py
index 7114e35..7a94925 100644
--- a/py/refFunc.py
+++ b/py/refFunc.py
@@ -108,7 +108,7 @@ def readRef(refPath, ref_inds_i, N_handling, N_unknowns=True, quiet=False):
             N_info['all'].extend(region)
             if region[1] - region[0] <= N_handling[1]:
                 for i in range(region[0], region[1]):
-                    myDat[i] = random.choice(ALLOWED_NUCL)
+                    myDat[i] = ord(random.choice(ALLOWED_NUCL))
             else:
                 N_info['big'].extend(region)
     elif N_handling[0] == 'allChr' and N_handling[2] in OK_CHR_ORD:

From 6bce0f6add6912de40530cb849159e71b4a2bf9d Mon Sep 17 00:00:00 2001
From: joshfactorial <jallen17@illinois.edu>
Date: Wed, 4 Mar 2020 10:57:11 -0600
Subject: [PATCH 009/323] Found places where integer division made more sense
 and converted / to // in those places.

---
 genReads.py             |  4 ++--
 py/OutputFileWriter.py  | 12 ++++++------
 py/SequenceContainer.py |  6 +++---
 py/refFunc.py           |  2 +-
 4 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/genReads.py b/genReads.py
index 426f89b..0d307e8 100644
--- a/genReads.py
+++ b/genReads.py
@@ -728,9 +728,9 @@ def main():
         else:
             print('')
         if ONLY_VCF:
-            print('VCF generation completed in', end=' ')
+            print('VCF generation completed in', end='')
         else:
-            print('Read sampling completed in', end=' ')
+            print('Read sampling completed in', end='')
         print(int(time.time() - tt), '(sec)')
 
         # write all output variants for this reference
diff --git a/py/OutputFileWriter.py b/py/OutputFileWriter.py
index 00a4726..97f89f2 100644
--- a/py/OutputFileWriter.py
+++ b/py/OutputFileWriter.py
@@ -20,11 +20,11 @@ def RC(s):
 # SAMtools reg2bin function
 def reg2bin(a, b):
     b -= 1
-    if (a >> 14 == b >> 14): return ((1 << 15) - 1) / 7 + (a >> 14)
-    if (a >> 17 == b >> 17): return ((1 << 12) - 1) / 7 + (a >> 17)
-    if (a >> 20 == b >> 20): return ((1 << 9) - 1) / 7 + (a >> 20)
-    if (a >> 23 == b >> 23): return ((1 << 6) - 1) / 7 + (a >> 23)
-    if (a >> 26 == b >> 26): return ((1 << 3) - 1) / 7 + (a >> 26)
+    if (a >> 14 == b >> 14): return ((1 << 15) - 1) // 7 + (a >> 14)
+    if (a >> 17 == b >> 17): return ((1 << 12) - 1) // 7 + (a >> 17)
+    if (a >> 20 == b >> 20): return ((1 << 9) - 1) // 7 + (a >> 20)
+    if (a >> 23 == b >> 23): return ((1 << 6) - 1) // 7 + (a >> 23)
+    if (a >> 26 == b >> 26): return ((1 << 3) - 1) // 7 + (a >> 26)
     return 0
 
 
@@ -180,7 +180,7 @@ def writeBAMRecord(self, refID, readName, pos_0, cigar, seq, qual, samFlag, mate
         for i in range(cig_ops):
             encodedCig += pack('<I', (cig_numbers[i] << 4) + CIGAR_PACKED[cig_letters[i]])
         encodedSeq = ''
-        encodedLen = (len(seq) + 1) / 2
+        encodedLen = (len(seq) + 1) // 2
         seqLen = len(seq)
         if seqLen & 1:
             seq += '='
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 834e4bc..554f69a 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -98,7 +98,7 @@ def init_coverage(self, coverageDat, fragDist=None):
                     #print (i,j), self.adj[i][j], self.allCigar[i][j], self.FM_pos[i][j], self.FM_span[i][j]
                 # shift by half of read length
                 if len(trCov_vals[i]) > int(self.readLen/2.):
-                    trCov_vals[i] = [0.0]*int(self.readLen/2) + trCov_vals[i][:-int(self.readLen/2.)]
+                    trCov_vals[i] = [0.0]*int(self.readLen//2) + trCov_vals[i][:-int(self.readLen/2.)]
                 # fill in missing indices
                 trCov_vals[i].extend([0.0]*(len(self.sequences[i])-len(trCov_vals[i])))
 
@@ -182,7 +182,7 @@ def init_mutModels(self,mutationModels,mutRate):
         if self.mutRescale == None:
             self.mutScalar = 1.0
         else:
-            self.mutScalar = float(self.mutRescale)/(mutRateSum/float(len(self.modelData)))
+            self.mutScalar = float(self.mutRescale)//(mutRateSum/float(len(self.modelData)))
 
         # how are mutations spread to each ploid, based on their specified mut rates?
         self.ploidMutFrac  = [float(n[0])/mutRateSum for n in self.modelData]
@@ -700,7 +700,7 @@ def __init__(self, readLen, errorModel, reScaledError):
             else:
                 print('Warning: Read length of error model (' + str(len(initQ1)) + ') does not match -R value (' + str(
                     self.readLen) + '), rescaling model...')
-                self.qIndRemap = [max([1,len(initQ1)*n/readLen]) for n in range(readLen)]
+                self.qIndRemap = [max([1,len(initQ1)*n//readLen]) for n in range(readLen)]
 
             # initialize probability distributions
             self.initDistByPos1        = [DiscreteDistribution(initQ1[i],Qscores) for i in range(len(initQ1))]
diff --git a/py/refFunc.py b/py/refFunc.py
index 7a94925..a9b9484 100644
--- a/py/refFunc.py
+++ b/py/refFunc.py
@@ -24,7 +24,7 @@ def indexRef(refPath):
             seqLen = int(splt[1])
             offset = int(splt[2])
             lineLn = int(splt[3])
-            nLines = seqLen / lineLn
+            nLines = seqLen // lineLn
             if seqLen % lineLn != 0:
                 nLines += 1
             ref_inds.append((splt[0], offset, offset + seqLen + nLines, seqLen))

From 5f61a2618945b097f24e18f8d2119a4d86300470 Mon Sep 17 00:00:00 2001
From: joshfactorial <jallen17@illinois.edu>
Date: Fri, 6 Mar 2020 10:38:58 -0600
Subject: [PATCH 010/323] Added a comment

---
 py/OutputFileWriter.py  | 9 +++++----
 py/SequenceContainer.py | 6 ++++--
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/py/OutputFileWriter.py b/py/OutputFileWriter.py
index 97f89f2..1918d6c 100644
--- a/py/OutputFileWriter.py
+++ b/py/OutputFileWriter.py
@@ -176,16 +176,17 @@ def writeBAMRecord(self, refID, readName, pos_0, cigar, seq, qual, samFlag, mate
             else:
                 my_tlen = -pos_0 - len(seq) + next_pos
 
-        encodedCig = ''
+        encodedCig = bytearray()
         for i in range(cig_ops):
-            encodedCig += pack('<I', (cig_numbers[i] << 4) + CIGAR_PACKED[cig_letters[i]])
-        encodedSeq = ''
+            encodedCig.extend(pack('<I', (cig_numbers[i] << 4) + CIGAR_PACKED[cig_letters[i]]))
+        encodedSeq = bytearray()
         encodedLen = (len(seq) + 1) // 2
         seqLen = len(seq)
         if seqLen & 1:
             seq += '='
         for i in range(encodedLen):
-            encodedSeq += pack('<B', (SEQ_PACKED[seq[2 * i]] << 4) + SEQ_PACKED[seq[2 * i + 1]])
+            print(seq[2*i], seq[2*i+1])
+            encodedSeq.extend(pack('<B', (SEQ_PACKED[seq[2 * i].capitalize()] << 4) + SEQ_PACKED[seq[2 * i + 1].capitalize()]))
 
         # apparently samtools automatically adds 33 to the quality score string...
         encodedQual = ''.join([chr(ord(n) - 33) for n in qual])
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 554f69a..bbf6175 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -410,6 +410,8 @@ def random_mutations(self):
                 vPos = all_snps[i][j][0]
 
                 if all_snps[i][j][1] != chr(self.sequences[i][vPos]):
+                    print(all_snps[i][j])
+                    print(self.sequences[i][vPos])
                     print('\nError: Something went wrong!\n', all_snps[i][j], chr(self.sequences[i][vPos]), '\n')
                     exit(1)
                 else:
@@ -628,7 +630,7 @@ def sample_read(self, sequencingModel, fragLen=None):
 
                 else:	# substitution errors, much easier by comparison...
                     if chr(r[3][ePos+sse_adj[ePos]]) == error[3]:
-                        r[3][ePos+sse_adj[ePos]] = error[4]
+                        r[3][ePos+sse_adj[ePos]] = ord(error[4])
                     else:
                         print('\nError, ref does not match alt while attempting to insert substitution error!\n')
                         exit(1)
@@ -640,7 +642,7 @@ def sample_read(self, sequencingModel, fragLen=None):
 
                 r[3] = r[3][:self.readLen]
 
-            rOut.append([self.FM_pos[myPloid][r[0]],myCigar,str(r[3]),str(r[1])])
+            rOut.append([self.FM_pos[myPloid][r[0]], myCigar, r[3].decode(), str(r[1])])
 
         # rOut[i] = (pos, cigar, read_string, qual_string)
         return rOut

From 90f96d0f63b7bc485f6739690e6d8aa9680902e1 Mon Sep 17 00:00:00 2001
From: joshfactorial <jallen17@illinois.edu>
Date: Tue, 10 Mar 2020 14:11:59 -0500
Subject: [PATCH 011/323] Moving bulk of genReads.py within the main clause and
 cleaning up some bytearray problems

---
 genReads.py             | 482 +++++++++++++++++++---------------------
 py/OutputFileWriter.py  |   5 +
 py/SequenceContainer.py |  20 +-
 py/refFunc.py           |  12 +-
 4 files changed, 254 insertions(+), 265 deletions(-)

diff --git a/genReads.py b/genReads.py
index 0d307e8..3d15f07 100644
--- a/genReads.py
+++ b/genReads.py
@@ -25,10 +25,6 @@
 import numpy as np
 import argparse
 
-# absolute path to this script
-SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-1])
-sys.path.append(SIM_PATH + '/py/')
-
 from py.inputChecking import requiredField, checkFileOpen, checkDir, isInRange
 from py.refFunc import indexRef, readRef, getAllRefRegions, partitionRefRegions
 from py.vcfFunc import parseVCF
@@ -36,229 +32,235 @@
 from py.probability import DiscreteDistribution, mean_ind_of_weighted_list
 from py.SequenceContainer import SequenceContainer, ReadContainer, parseInputMutationModel
 
-# if coverage val for a given window/position is below this value, consider it effectively zero.
-LOW_COV_THRESH = 50
-
 """//////////////////////////////////////////////////
 ////////////    PARSE INPUT ARGUMENTS    ////////////
 //////////////////////////////////////////////////"""
 
-parser = argparse.ArgumentParser(description='NEAT-genReads V2.0')
-parser.add_argument('-r', type=str, required=True, metavar='<str>', help="* ref.fa")
-parser.add_argument('-R', type=int, required=True, metavar='<int>', help="* read length")
-parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output prefix")
-parser.add_argument('-c', type=float, required=False, metavar='<float>', default=10., help="average coverage")
-parser.add_argument('-e', type=str, required=False, metavar='<str>', default=None, help="sequencing error model")
-parser.add_argument('-E', type=float, required=False, metavar='<float>', default=-1,
-                    help="rescale avg sequencing error rate to this")
-parser.add_argument('-p', type=int, required=False, metavar='<int>', default=2, help="ploidy")
-parser.add_argument('-t', type=str, required=False, metavar='<str>', default=None,
-                    help="bed file containing targeted regions")
-parser.add_argument('-to', type=float, required=False, metavar='<float>', default=0.00,
-                    help="off-target coverage scalar")
-parser.add_argument('-m', type=str, required=False, metavar='<str>', default=None, help="mutation model pickle file")
-parser.add_argument('-M', type=float, required=False, metavar='<float>', default=-1,
-                    help="rescale avg mutation rate to this")
-parser.add_argument('-Mb', type=str, required=False, metavar='<str>', default=None,
-                    help="bed file containing positional mut rates")
-parser.add_argument('-N', type=int, required=False, metavar='<int>', default=-1,
-                    help="below this qual, replace base-calls with 'N's")
-# parser.add_argument('-s', type=str,   required=False, metavar='<str>',   default=None,  help="input sample model")
-parser.add_argument('-v', type=str, required=False, metavar='<str>', default=None, help="input VCF file")
-
-parser.add_argument('--pe', nargs=2, type=int, required=False, metavar=('<int>', '<int>'), default=(None, None),
-                    help='paired-end fragment length mean and std')
-parser.add_argument('--pe-model', type=str, required=False, metavar='<str>', default=None,
-                    help='empirical fragment length distribution')
-# parser.add_argument('--cancer',                        required=False, action='store_true',       default=False, help='produce tumor/normal datasets')
-# parser.add_argument('-cm',                 type=str,   required=False, metavar='<str>',           default=None,  help="cancer mutation model directory")
-# parser.add_argument('-cp',                 type=float, required=False, metavar='<float>',         default=0.8,   help="tumor sample purity")
-parser.add_argument('--gc-model', type=str, required=False, metavar='<str>', default=None,
-                    help='empirical GC coverage bias distribution')
-parser.add_argument('--job', nargs=2, type=int, required=False, metavar=('<int>', '<int>'), default=(0, 0),
-                    help='jobs IDs for generating reads in parallel')
-parser.add_argument('--nnr', required=False, action='store_true', default=False,
-                    help='save non-N ref regions (for parallel jobs)')
-parser.add_argument('--bam', required=False, action='store_true', default=False, help='output golden BAM file')
-parser.add_argument('--vcf', required=False, action='store_true', default=False, help='output golden VCF file')
-parser.add_argument('--fa', required=False, action='store_true', default=False, help='output FASTA instead of FASTQ')
-parser.add_argument('--rng', type=int, required=False, metavar='<int>', default=-1,
-                    help='rng seed value; identical RNG value should produce identical runs of the program, so things like read locations, variant positions, error positions, etc, should all be the same.')
-parser.add_argument('--gz', required=False, action='store_true', default=False, help='gzip output FQ and VCF')
-parser.add_argument('--no-fastq', required=False, action='store_true', default=False, help='bypass fastq generation')
-args = parser.parse_args()
-
-# required args
-(REFERENCE, READLEN, OUT_PREFIX) = (args.r, args.R, args.o)
-# various dataset parameters
-(COVERAGE, PLOIDS, INPUT_BED, SE_MODEL, SE_RATE, MUT_MODEL, MUT_RATE, MUT_BED, INPUT_VCF) = (
-args.c, args.p, args.t, args.e, args.E, args.m, args.M, args.Mb, args.v)
-# cancer params (disabled currently)
-# (CANCER, CANCER_MODEL, CANCER_PURITY) = (args.cancer, args.cm, args.cp)
-(CANCER, CANCER_MODEL, CANCER_PURITY) = (False, None, 0.8)
-(OFFTARGET_SCALAR) = (args.to)
-# important flags
-(SAVE_BAM, SAVE_VCF, FASTA_INSTEAD, GZIPPED_OUT, NO_FASTQ) = (args.bam, args.vcf, args.fa, args.gz, args.no_fastq)
-
-ONLY_VCF = (NO_FASTQ and SAVE_VCF and not (SAVE_BAM))
-if ONLY_VCF:
-    print('Only producing VCF output, that should speed things up a bit...')
-
-# sequencing model parameters
-(FRAGMENT_SIZE, FRAGMENT_STD) = args.pe
-FRAGLEN_MODEL = args.pe_model
-GC_BIAS_MODEL = args.gc_model
-N_MAX_QUAL = args.N
-
-# if user specified no fastq, no bam, no vcf, then inform them of their wasteful ways and exit
-if NO_FASTQ == True and SAVE_BAM == False and SAVE_VCF == False:
-    print('\nError: No files will be written when --no-fastq is specified without --vcf or --bam.')
-    exit(1)
-
-# if user specified mean/std, use artificial fragment length distribution, otherwise use
-# the empirical model specified. If neither, then we're doing single-end reads.
-PAIRED_END = False
-PAIRED_END_ARTIFICIAL = False
-if FRAGMENT_SIZE != None and FRAGMENT_STD != None:
-    PAIRED_END = True
-    PAIRED_END_ARTIFICIAL = True
-elif FRAGLEN_MODEL != None:
-    PAIRED_END = True
+
+def main():
+    parser = argparse.ArgumentParser(description='NEAT-genReads V2.0')
+    parser.add_argument('-r', type=str, required=True, metavar='<str>', help="* ref.fa")
+    parser.add_argument('-R', type=int, required=True, metavar='<int>', help="* read length")
+    parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output prefix")
+    parser.add_argument('-c', type=float, required=False, metavar='<float>', default=10., help="average coverage")
+    parser.add_argument('-e', type=str, required=False, metavar='<str>', default=None, help="sequencing error model")
+    parser.add_argument('-E', type=float, required=False, metavar='<float>', default=-1,
+                        help="rescale avg sequencing error rate to this")
+    parser.add_argument('-p', type=int, required=False, metavar='<int>', default=2, help="ploidy")
+    parser.add_argument('-t', type=str, required=False, metavar='<str>', default=None,
+                        help="bed file containing targeted regions")
+    parser.add_argument('-to', type=float, required=False, metavar='<float>', default=0.00,
+                        help="off-target coverage scalar")
+    parser.add_argument('-m', type=str, required=False, metavar='<str>', default=None,
+                        help="mutation model pickle file")
+    parser.add_argument('-M', type=float, required=False, metavar='<float>', default=-1,
+                        help="rescale avg mutation rate to this")
+    parser.add_argument('-Mb', type=str, required=False, metavar='<str>', default=None,
+                        help="bed file containing positional mut rates")
+    parser.add_argument('-N', type=int, required=False, metavar='<int>', default=-1,
+                        help="below this qual, replace base-calls with 'N's")
+    parser.add_argument('-v', type=str, required=False, metavar='<str>', default=None, help="input VCF file")
+
+    parser.add_argument('--pe', nargs=2, type=int, required=False, metavar=('<int>', '<int>'), default=(None, None),
+                        help='paired-end fragment length mean and std')
+    parser.add_argument('--pe-model', type=str, required=False, metavar='<str>', default=None,
+                        help='empirical fragment length distribution')
+    parser.add_argument('--gc-model', type=str, required=False, metavar='<str>', default=None,
+                        help='empirical GC coverage bias distribution')
+    parser.add_argument('--job', nargs=2, type=int, required=False, metavar=('<int>', '<int>'), default=(0, 0),
+                        help='jobs IDs for generating reads in parallel')
+    parser.add_argument('--nnr', required=False, action='store_true', default=False,
+                        help='save non-N ref regions (for parallel jobs)')
+    parser.add_argument('--bam', required=False, action='store_true', default=False, help='output golden BAM file')
+    parser.add_argument('--vcf', required=False, action='store_true', default=False, help='output golden VCF file')
+    parser.add_argument('--fa', required=False, action='store_true', default=False,
+                        help='output FASTA instead of FASTQ')
+    parser.add_argument('--rng', type=int, required=False, metavar='<int>', default=-1,
+                        help='rng seed value; identical RNG value should produce identical runs of the program, so '
+                             'things like read locations, variant positions, error positions, etc, '
+                             'should all be the same.')
+    parser.add_argument('--gz', required=False, action='store_true', default=False, help='gzip output FQ and VCF')
+    parser.add_argument('--no-fastq', required=False, action='store_true', default=False,
+                        help='bypass fastq generation')
+    args = parser.parse_args()
+
+
+    """
+    Set variables for processing
+    """
+    # absolute path to this script
+    SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-1])
+    sys.path.append(SIM_PATH + '/py/')
+
+    # if coverage val for a given window/position is below this value, consider it effectively zero.
+    LOW_COV_THRESH = 50
+
+
+    # required args
+    (REFERENCE, READLEN, OUT_PREFIX) = (args.r, args.R, args.o)
+    # various dataset parameters
+    (COVERAGE, PLOIDS, INPUT_BED, SE_MODEL, SE_RATE, MUT_MODEL, MUT_RATE, MUT_BED, INPUT_VCF) = \
+        (args.c, args.p, args.t, args.e, args.E, args.m, args.M, args.Mb, args.v)
+    (OFFTARGET_SCALAR) = (args.to)
+    # important flags
+    (SAVE_BAM, SAVE_VCF, FASTA_INSTEAD, GZIPPED_OUT, NO_FASTQ) = \
+        (args.bam, args.vcf, args.fa, args.gz, args.no_fastq)
+
+    ONLY_VCF = (NO_FASTQ and SAVE_VCF and not (SAVE_BAM))
+    if ONLY_VCF:
+        print('Only producing VCF output, that should speed things up a bit...')
+
+    # sequencing model parameters
+    (FRAGMENT_SIZE, FRAGMENT_STD) = args.pe
+    FRAGLEN_MODEL = args.pe_model
+    GC_BIAS_MODEL = args.gc_model
+    N_MAX_QUAL = args.N
+
+    # if user specified no fastq, no bam, no vcf, then inform them of their wasteful ways and exit
+    if NO_FASTQ is True and SAVE_BAM is False and SAVE_VCF is False:
+        print('\nError: No files will be written when --no-fastq is specified without --vcf or --bam.')
+        exit(1)
+
+    # if user specified mean/std, use artificial fragment length distribution, otherwise use
+    # the empirical model specified. If neither, then we're doing single-end reads.
+    PAIRED_END = False
     PAIRED_END_ARTIFICIAL = False
+    if FRAGMENT_SIZE is not None and FRAGMENT_STD is not None:
+        PAIRED_END = True
+        PAIRED_END_ARTIFICIAL = True
+    elif FRAGLEN_MODEL is not None:
+        PAIRED_END = True
+        PAIRED_END_ARTIFICIAL = False
+
+    (MYJOB, NJOBS) = args.job
+    if MYJOB == 0:
+        MYJOB = 1
+        NJOBS = 1
+    SAVE_NON_N = args.nnr
+
+    RNG_SEED = args.rng
+    if RNG_SEED == -1:
+        RNG_SEED = random.randint(1, 99999999)
+    random.seed(RNG_SEED)
+
+    """************************************************
+    ****            INPUT ERROR CHECKING
+    ************************************************"""
 
-(MYJOB, NJOBS) = args.job
-if MYJOB == 0:
-    MYJOB = 1
-    NJOBS = 1
-SAVE_NON_N = args.nnr
-
-RNG_SEED = args.rng
-if RNG_SEED == -1:
-    RNG_SEED = random.randint(1, 99999999)
-random.seed(RNG_SEED)
-
-"""************************************************
-****            INPUT ERROR CHECKING
-************************************************"""
-
-checkFileOpen(REFERENCE, 'ERROR: could not open reference', required=True)
-checkFileOpen(INPUT_VCF, 'ERROR: could not open input VCF', required=False)
-checkFileOpen(INPUT_BED, 'ERROR: could not open input BED', required=False)
-requiredField(OUT_PREFIX, 'ERROR: no output prefix provided')
-if (FRAGMENT_SIZE == None and FRAGMENT_STD != None) or (FRAGMENT_SIZE != None and FRAGMENT_STD == None):
-    print('\nError: --pe argument takes 2 space-separated arguments.\n')
-    exit(1)
-
-"""************************************************
-****             LOAD INPUT MODELS
-************************************************"""
-
-#	mutation models
-#
-MUT_MODEL = parseInputMutationModel(MUT_MODEL, 1)
-if CANCER:
-    CANCER_MODEL = parseInputMutationModel(CANCER_MODEL, 2)
-if MUT_RATE < 0.:
-    MUT_RATE = None
-
-#	sequencing error model
-#
-if SE_RATE < 0.:
-    SE_RATE = None
-if SE_MODEL == None:
-    print('Using default sequencing error model.')
-    SE_MODEL = SIM_PATH + '/models/errorModel_toy.p'
-    SE_CLASS = ReadContainer(READLEN, SE_MODEL, SE_RATE)
-else:
-    # probably need to do some sanity checking
-    SE_CLASS = ReadContainer(READLEN, SE_MODEL, SE_RATE)
-
-#	GC-bias model
-#
-if GC_BIAS_MODEL == None:
-    print('Using default gc-bias model.')
-    GC_BIAS_MODEL = SIM_PATH + '/models/gcBias_toy.p'
-    [GC_SCALE_COUNT, GC_SCALE_VAL] = pickle.load(open(GC_BIAS_MODEL, 'rb'))
-    GC_WINDOW_SIZE = GC_SCALE_COUNT[-1]
-else:
-    [GC_SCALE_COUNT, GC_SCALE_VAL] = pickle.load(open(GC_BIAS_MODEL, 'rb'))
-    GC_WINDOW_SIZE = GC_SCALE_COUNT[-1]
-
-#	fragment length distribution
-#
-if PAIRED_END and not (PAIRED_END_ARTIFICIAL):
-    print('Using empirical fragment length distribution.')
-    [potential_vals, potential_prob] = pickle.load(open(FRAGLEN_MODEL, 'rb'))
-    FRAGLEN_VALS = []
-    FRAGLEN_PROB = []
-    for i in range(len(potential_vals)):
-        if potential_vals[i] > READLEN:
-            FRAGLEN_VALS.append(potential_vals[i])
-            FRAGLEN_PROB.append(potential_prob[i])
-    # should probably add some validation and sanity-checking code here...
-    FRAGLEN_DISTRIBUTION = DiscreteDistribution(FRAGLEN_PROB, FRAGLEN_VALS)
-    FRAGMENT_SIZE = FRAGLEN_VALS[mean_ind_of_weighted_list(FRAGLEN_PROB)]
-
-#	Indicate not writing FASTQ reads
-#
-if NO_FASTQ:
-    print('Bypassing FASTQ generation...')
-
-"""************************************************
-****            HARD-CODED CONSTANTS
-************************************************"""
-
-# target window size for read sampling. how many times bigger than read/frag length
-WINDOW_TARGET_SCALE = 100
-# sub-window size for read sampling windows. this is basically the finest resolution
-# that can be obtained for targeted region boundaries and GC% bias
-SMALL_WINDOW = 20
-# is the mutation model constant throughout the simulation? If so we can save a lot of time
-CONSTANT_MUT_MODEL = True
-
-"""************************************************
-****               DEFAULT MODELS
-************************************************"""
-
-# fragment length distribution: normal distribution that goes out to +- 6 standard deviations
-if PAIRED_END and PAIRED_END_ARTIFICIAL:
-    print('Using artificial fragment length distribution. mean=' + str(FRAGMENT_SIZE) + ', std=' + str(FRAGMENT_STD))
-    if FRAGMENT_STD == 0:
-        FRAGLEN_DISTRIBUTION = DiscreteDistribution([1], [FRAGMENT_SIZE], degenerateVal=FRAGMENT_SIZE)
+    checkFileOpen(REFERENCE, 'ERROR: could not open reference', required=True)
+    checkFileOpen(INPUT_VCF, 'ERROR: could not open input VCF', required=False)
+    checkFileOpen(INPUT_BED, 'ERROR: could not open input BED', required=False)
+    requiredField(OUT_PREFIX, 'ERROR: no output prefix provided')
+    if (FRAGMENT_SIZE is None and FRAGMENT_STD is not None) or (FRAGMENT_SIZE is not None and FRAGMENT_STD is None):
+        print('\nError: --pe argument takes 2 space-separated arguments.\n')
+        exit(1)
+
+    """************************************************
+    ****             LOAD INPUT MODELS
+    ************************************************"""
+
+    #	mutation models
+    #
+    MUT_MODEL = parseInputMutationModel(MUT_MODEL, 1)
+    if MUT_RATE < 0.:
+        MUT_RATE = None
+
+    #	sequencing error model
+    #
+    if SE_RATE < 0.:
+        SE_RATE = None
+    if SE_MODEL is None:
+        print('Using default sequencing error model.')
+        SE_MODEL = SIM_PATH + '/models/errorModel_toy.p'
+        SE_CLASS = ReadContainer(READLEN, SE_MODEL, SE_RATE)
+    else:
+        # probably need to do some sanity checking
+        SE_CLASS = ReadContainer(READLEN, SE_MODEL, SE_RATE)
+
+    #	GC-bias model
+    #
+    if GC_BIAS_MODEL is None:
+        print('Using default gc-bias model.')
+        GC_BIAS_MODEL = SIM_PATH + '/models/gcBias_toy.p'
+        [GC_SCALE_COUNT, GC_SCALE_VAL] = pickle.load(open(GC_BIAS_MODEL, 'rb'))
+        GC_WINDOW_SIZE = GC_SCALE_COUNT[-1]
     else:
-        potential_vals = range(FRAGMENT_SIZE - 6 * FRAGMENT_STD, FRAGMENT_SIZE + 6 * FRAGMENT_STD + 1)
+        [GC_SCALE_COUNT, GC_SCALE_VAL] = pickle.load(open(GC_BIAS_MODEL, 'rb'))
+        GC_WINDOW_SIZE = GC_SCALE_COUNT[-1]
+
+    #	fragment length distribution
+    #
+    if PAIRED_END and not (PAIRED_END_ARTIFICIAL):
+        print('Using empirical fragment length distribution.')
+        [potential_vals, potential_prob] = pickle.load(open(FRAGLEN_MODEL, 'rb'))
         FRAGLEN_VALS = []
+        FRAGLEN_PROB = []
         for i in range(len(potential_vals)):
             if potential_vals[i] > READLEN:
                 FRAGLEN_VALS.append(potential_vals[i])
-        FRAGLEN_PROB = [np.exp(-(((n - float(FRAGMENT_SIZE)) ** 2) / (2 * (FRAGMENT_STD ** 2)))) for n in FRAGLEN_VALS]
+                FRAGLEN_PROB.append(potential_prob[i])
+        # should probably add some validation and sanity-checking code here...
         FRAGLEN_DISTRIBUTION = DiscreteDistribution(FRAGLEN_PROB, FRAGLEN_VALS)
+        FRAGMENT_SIZE = FRAGLEN_VALS[mean_ind_of_weighted_list(FRAGLEN_PROB)]
 
-"""************************************************
-****          MORE INPUT ERROR CHECKING
-************************************************"""
-
-isInRange(READLEN, 10, 1000000, 'Error: -R must be between 10 and 1,000,000')
-isInRange(COVERAGE, 0, 1000000, 'Error: -c must be between 0 and 1,000,000')
-isInRange(PLOIDS, 1, 100, 'Error: -p must be between 1 and 100')
-isInRange(OFFTARGET_SCALAR, 0, 1, 'Error: -to must be between 0 and 1')
-if MUT_RATE != -1 and MUT_RATE != None:
-    isInRange(MUT_RATE, 0, 0.3, 'Error: -M must be between 0 and 0.3')
-if SE_RATE != -1 and SE_RATE != None:
-    isInRange(SE_RATE, 0, 0.3, 'Error: -E must be between 0 and 0.3')
-if NJOBS != 1:
-    isInRange(NJOBS, 1, 1000, 'Error: --job must be between 1 and 1,000')
-    isInRange(MYJOB, 1, 1000, 'Error: --job must be between 1 and 1,000')
-    isInRange(MYJOB, 1, NJOBS, 'Error: job id must be less than or equal to number of jobs')
-if N_MAX_QUAL != -1:
-    isInRange(N_MAX_QUAL, 1, 40, 'Error: -N must be between 1 and 40')
-
-"""************************************************
-****                   MAIN()
-************************************************"""
+    #	Indicate not writing FASTQ reads
+    #
+    if NO_FASTQ:
+        print('Bypassing FASTQ generation...')
 
+    """************************************************
+    ****            HARD-CODED CONSTANTS
+    ************************************************"""
+
+    # target window size for read sampling. how many times bigger than read/frag length
+    WINDOW_TARGET_SCALE = 100
+    # sub-window size for read sampling windows. this is basically the finest resolution
+    # that can be obtained for targeted region boundaries and GC% bias
+    SMALL_WINDOW = 20
+    # is the mutation model constant throughout the simulation? If so we can save a lot of time
+    CONSTANT_MUT_MODEL = True
+
+    """************************************************
+    ****               DEFAULT MODELS
+    ************************************************"""
+
+    # fragment length distribution: normal distribution that goes out to +- 6 standard deviations
+    if PAIRED_END and PAIRED_END_ARTIFICIAL:
+        print('Using artificial fragment length distribution. mean=' + str(FRAGMENT_SIZE) + ', std=' + str(FRAGMENT_STD))
+        if FRAGMENT_STD == 0:
+            FRAGLEN_DISTRIBUTION = DiscreteDistribution([1], [FRAGMENT_SIZE], degenerateVal=FRAGMENT_SIZE)
+        else:
+            potential_vals = range(FRAGMENT_SIZE - 6 * FRAGMENT_STD, FRAGMENT_SIZE + 6 * FRAGMENT_STD + 1)
+            FRAGLEN_VALS = []
+            for i in range(len(potential_vals)):
+                if potential_vals[i] > READLEN:
+                    FRAGLEN_VALS.append(potential_vals[i])
+            FRAGLEN_PROB = [np.exp(-(((n - float(FRAGMENT_SIZE)) ** 2) / (2 * (FRAGMENT_STD ** 2)))) for n in FRAGLEN_VALS]
+            FRAGLEN_DISTRIBUTION = DiscreteDistribution(FRAGLEN_PROB, FRAGLEN_VALS)
+
+    """************************************************
+    ****          MORE INPUT ERROR CHECKING
+    ************************************************"""
+
+    isInRange(READLEN, 10, 1000000, 'Error: -R must be between 10 and 1,000,000')
+    isInRange(COVERAGE, 0, 1000000, 'Error: -c must be between 0 and 1,000,000')
+    isInRange(PLOIDS, 1, 100, 'Error: -p must be between 1 and 100')
+    isInRange(OFFTARGET_SCALAR, 0, 1, 'Error: -to must be between 0 and 1')
+    if MUT_RATE != -1 and MUT_RATE is not None:
+        isInRange(MUT_RATE, 0, 0.3, 'Error: -M must be between 0 and 0.3')
+    if SE_RATE != -1 and SE_RATE is not None:
+        isInRange(SE_RATE, 0, 0.3, 'Error: -E must be between 0 and 0.3')
+    if NJOBS != 1:
+        isInRange(NJOBS, 1, 1000, 'Error: --job must be between 1 and 1,000')
+        isInRange(MYJOB, 1, 1000, 'Error: --job must be between 1 and 1,000')
+        isInRange(MYJOB, 1, NJOBS, 'Error: job id must be less than or equal to number of jobs')
+    if N_MAX_QUAL != -1:
+        isInRange(N_MAX_QUAL, 1, 40, 'Error: -N must be between 1 and 40')
+
+    """************************************************
+    ****   Process Inputs
+    ************************************************"""
 
-def main():
     ALLOWED_NUCL = ['A', 'C', 'G', 'T']
     # index reference
     refIndex = indexRef(REFERENCE)
@@ -270,20 +272,15 @@ def main():
 
     # parse input variants, if present
     inputVariants = []
-    if INPUT_VCF != None:
-        if CANCER:
-            (sampNames, inputVariants) = parseVCF(INPUT_VCF, tumorNormal=True, ploidy=PLOIDS)
-            tumorInd = sampNames.index('TUMOR')
-            normalInd = sampNames.index('NORMAL')
-        else:
-            (sampNames, inputVariants) = parseVCF(INPUT_VCF, ploidy=PLOIDS)
+    if INPUT_VCF is not None:
+        (sampNames, inputVariants) = parseVCF(INPUT_VCF, ploidy=PLOIDS)
         for k in sorted(inputVariants.keys()):
             inputVariants[k].sort()
 
     # parse input targeted regions, if present
     inputRegions = {}
     refList = [n[0] for n in refIndex]
-    if INPUT_BED != None:
+    if INPUT_BED is not None:
         with open(INPUT_BED, 'r') as f:
             for line in f:
                 [myChr, pos1, pos2] = line.strip().split('\t')[:3]
@@ -309,7 +306,7 @@ def main():
     # parse input mutation rate rescaling regions, if present
     mutRateRegions = {}
     mutRateValues = {}
-    if MUT_BED != None:
+    if MUT_BED is not None:
         with open(MUT_BED, 'r') as f:
             for line in f:
                 [myChr, pos1, pos2, metaData] = line.strip().split('\t')[:4]
@@ -348,16 +345,9 @@ def main():
         corrected_nJobs = 1
 
     # initialize output files (part II)
-    if CANCER:
-        OFW = OutputFileWriter(OUT_PREFIX + '_normal', paired=PAIRED_END, BAM_header=bamHeader, VCF_header=vcfHeader,
-                               gzipped=GZIPPED_OUT, noFASTQ=NO_FASTQ, FASTA_instead=FASTA_INSTEAD)
-        OFW_CANCER = OutputFileWriter(OUT_PREFIX + '_tumor', paired=PAIRED_END, BAM_header=bamHeader,
-                                      VCF_header=vcfHeader, gzipped=GZIPPED_OUT, jobTuple=(MYJOB, corrected_nJobs),
-                                      noFASTQ=NO_FASTQ, FASTA_instead=FASTA_INSTEAD)
-    else:
-        OFW = OutputFileWriter(OUT_PREFIX, paired=PAIRED_END, BAM_header=bamHeader, VCF_header=vcfHeader,
-                               gzipped=GZIPPED_OUT, jobTuple=(MYJOB, corrected_nJobs), noFASTQ=NO_FASTQ,
-                               FASTA_instead=FASTA_INSTEAD)
+    OFW = OutputFileWriter(OUT_PREFIX, paired=PAIRED_END, BAM_header=bamHeader, VCF_header=vcfHeader,
+                           gzipped=GZIPPED_OUT, jobTuple=(MYJOB, corrected_nJobs), noFASTQ=NO_FASTQ,
+                           FASTA_instead=FASTA_INSTEAD)
     OUT_PREFIX_NAME = OUT_PREFIX.split('/')[-1]
 
     """************************************************
@@ -461,7 +451,6 @@ def main():
             end = min([start + bpd, pf])
             # print '------------------RAWR:', (pi,pf), nTargWindows, bpd
             varsFromPrevOverlap = []
-            varsCancerFromPrevOverlap = []
             vindFromPrev = 0
             isLastTime = False
             havePrinted100 = False
@@ -527,7 +516,7 @@ def main():
                 # compute coverage modifiers
                 coverage_avg = None
                 coverage_dat = [GC_WINDOW_SIZE, GC_SCALE_VAL, []]
-                if INPUT_BED == None:
+                if INPUT_BED is None:
                     coverage_dat[2] = [1.0] * (end - start)
                 else:
                     if refIndex[RI][0] not in inputRegions:
@@ -560,7 +549,7 @@ def main():
                     continue
 
                 # construct sequence data that we will sample reads from
-                if sequences == None:
+                if sequences is None:
                     sequences = SequenceContainer(start, refSequence[start:end], PLOIDS, overlap, READLEN,
                                                   [MUT_MODEL] * PLOIDS, MUT_RATE, onlyVCF=ONLY_VCF)
                 else:
@@ -579,23 +568,11 @@ def main():
                     else:
                         coverage_avg = sequences.init_coverage(tuple(coverage_dat))
 
-                # unused cancer stuff
-                if CANCER:
-                    tumor_sequences = SequenceContainer(start, refSequence[start:end], PLOIDS, overlap, READLEN,
-                                                        [CANCER_MODEL] * PLOIDS, MUT_RATE, coverage_dat)
-                    tumor_sequences.insert_mutations(varsCancerFromPrevOverlap + all_inserted_variants)
-                    all_cancer_variants = tumor_sequences.random_mutations()
-
                 # which variants do we need to keep for next time (because of window overlap)?
                 varsFromPrevOverlap = []
-                varsCancerFromPrevOverlap = []
                 for n in all_inserted_variants:
                     if n[0] >= end - overlap - 1:
                         varsFromPrevOverlap.append(n)
-                if CANCER:
-                    for n in all_cancer_variants:
-                        if n[0] >= end - overlap - 1:
-                            varsCancerFromPrevOverlap.append(n)
 
                 # if we're only producing VCF, no need to go through the hassle of generating reads
                 if ONLY_VCF:
@@ -619,23 +596,23 @@ def main():
                         if PAIRED_END:
                             myFraglen = FRAGLEN_DISTRIBUTION.sample()
                             myReadData = sequences.sample_read(SE_CLASS, myFraglen)
-                            if myReadData == None:  # skip if we failed to find a valid position to sample read
+                            if myReadData is None:  # skip if we failed to find a valid position to sample read
                                 continue
-                            if myReadData[0][0] == None:
+                            if myReadData[0][0] is None:
                                 isUnmapped.append(True)
                             else:
                                 isUnmapped.append(False)
                                 myReadData[0][0] += start  # adjust mapping position based on window start
-                            if myReadData[1][0] == None:
+                            if myReadData[1][0] is None:
                                 isUnmapped.append(True)
                             else:
                                 isUnmapped.append(False)
                                 myReadData[1][0] += start
                         else:
                             myReadData = sequences.sample_read(SE_CLASS)
-                            if myReadData == None:  # skip if we failed to find a valid position to sample read
+                            if myReadData is None:  # skip if we failed to find a valid position to sample read
                                 continue
-                            if myReadData[0][0] == None:  # unmapped read (lives in large insertion)
+                            if myReadData[0][0] is None:  # unmapped read (lives in large insertion)
                                 isUnmapped = [True]
                             else:
                                 isUnmapped = [False]
@@ -758,9 +735,6 @@ def main():
 
     # close output files
     OFW.closeFiles()
-    if CANCER:
-        OFW_CANCER.closeFiles()
-
 
 if __name__ == '__main__':
     main()
diff --git a/py/OutputFileWriter.py b/py/OutputFileWriter.py
index 1918d6c..35e0665 100644
--- a/py/OutputFileWriter.py
+++ b/py/OutputFileWriter.py
@@ -3,6 +3,11 @@
 import re
 import gzip
 from struct import pack
+from Bio.Seq import Seq
+from Bio.Alphabet import IUPAC
+from Bio import SeqIO
+import gzip
+from Bio.bgzf import *
 
 from py.biopython_modified_bgzf import BgzfWriter
 
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index bbf6175..3c67a1e 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -1,10 +1,14 @@
 import random
 import copy
-import re
 import os
 import bisect
 import pickle
 import numpy as np
+from Bio.Seq import Seq
+from Bio.Alphabet import IUPAC
+from Bio import SeqIO
+import gzip
+from Bio.bgzf import *
 
 from py.probability import DiscreteDistribution, poisson_list, quantize_list
 from py.neat_cigar import CigarString
@@ -47,7 +51,7 @@ def init_basicVars(self, xOffset, sequence, ploidy, windowOverlap, readLen):
         self.x         = xOffset
         self.ploidy    = ploidy
         self.readLen   = readLen
-        self.sequences = [bytearray(sequence) for n in range(self.ploidy)]
+        self.sequences = [Seq(sequence).tomutable() for n in range(self.ploidy)]
         self.seqLen    = len(sequence)
         self.indelList = [[] for n in range(self.ploidy)]
         self.snpList   = [[] for n in range(self.ploidy)]
@@ -79,10 +83,10 @@ def init_coverage(self, coverageDat, fragDist=None):
                 # compute gc-bias
                 j = 0
                 while j+self.windowSize < len(self.sequences[i]):
-                    gc_c = self.sequences[i][j:j+self.windowSize].count(ord('G')) + self.sequences[i][j:j+self.windowSize].count(ord('C'))
+                    gc_c = self.sequences[i][j:j+self.windowSize].count('G') + self.sequences[i][j:j+self.windowSize].count('C')
                     gcCov_vals[i].extend([gc_scalars[gc_c]]*self.windowSize)
                     j += self.windowSize
-                gc_c = self.sequences[i][-self.windowSize:].count(ord('G')) + self.sequences[i][-self.windowSize:].count(ord('C'))
+                gc_c = self.sequences[i][-self.windowSize:].count('G') + self.sequences[i][-self.windowSize:].count('C')
                 gcCov_vals[i].extend([gc_scalars[gc_c]]*(len(self.sequences[i])-len(gcCov_vals[i])))
                 #
                 trCov_vals[i].append(targetCov_vals[0])
@@ -415,7 +419,7 @@ def random_mutations(self):
                     print('\nError: Something went wrong!\n', all_snps[i][j], chr(self.sequences[i][vPos]), '\n')
                     exit(1)
                 else:
-                    self.sequences[i][vPos] = ord(all_snps[i][j][2])
+                    self.sequences[i][vPos] = all_snps[i][j][2]
 
         # organize the indels we want to insert
         for i in range(len(all_indels)):
@@ -440,7 +444,9 @@ def random_mutations(self):
                     exit(1)
                 else:
                     # alter reference sequence
-                    self.sequences[i] = self.sequences[i][:vPos] + bytearray(all_indels_ins[i][j][2]) + self.sequences[i][vPos2:]
+                    self.sequences[i] = self.sequences[i][:vPos] + Seq(all_indels_ins[i][j][2],
+                                                                       IUPAC.unambiguous_dna).tomutable() + \
+                                        self.sequences[i][vPos2:]
                     # notate indel positions for cigar computation
                     d = len(all_indels_ins[i][j][2]) - len(all_indels_ins[i][j][1])
                     if d > 0:
@@ -630,7 +636,7 @@ def sample_read(self, sequencingModel, fragLen=None):
 
                 else:	# substitution errors, much easier by comparison...
                     if chr(r[3][ePos+sse_adj[ePos]]) == error[3]:
-                        r[3][ePos+sse_adj[ePos]] = ord(error[4])
+                        r[3][ePos+sse_adj[ePos]] = error[4]
                     else:
                         print('\nError, ref does not match alt while attempting to insert substitution error!\n')
                         exit(1)
diff --git a/py/refFunc.py b/py/refFunc.py
index a9b9484..aac3f0f 100644
--- a/py/refFunc.py
+++ b/py/refFunc.py
@@ -2,6 +2,9 @@
 import time
 import os
 import random
+from Bio.Seq import Seq
+from Bio.Alphabet import IUPAC
+from Bio.bgzf import *
 
 
 #	Index reference fasta
@@ -69,7 +72,7 @@ def indexRef(refPath):
 #		- ('ignore')                    --> do not alter nucleotides in N regions
 #
 def readRef(refPath, ref_inds_i, N_handling, N_unknowns=True, quiet=False):
-    OK_CHR_ORD = {ord('A'): True, ord('C'): True, ord('G'): True, ord('T'): True, ord('U'): True}
+    OK_CHR_ORD = {'A': True, 'C': True, 'G': True, 'T': True, 'U': True}
     ALLOWED_NUCL = ['A', 'C', 'G', 'T']
     tt = time.time()
     if not quiet:
@@ -79,7 +82,8 @@ def readRef(refPath, ref_inds_i, N_handling, N_unknowns=True, quiet=False):
     refFile = open(refPath, 'r')
     refFile.seek(ref_inds_i[1])
     myDat = ''.join(refFile.read(ref_inds_i[2] - ref_inds_i[1]).split('\n'))
-    myDat = bytearray(myDat.upper(), 'utf8')
+    myDat = Seq(myDat.upper(), IUPAC.unambiguous_dna)
+    myDat = myDat.tomutable()
 
     # find N regions
     # data explanation: myDat[N_atlas[0][0]:N_atlas[0][1]] = solid block of Ns
@@ -87,7 +91,7 @@ def readRef(refPath, ref_inds_i, N_handling, N_unknowns=True, quiet=False):
     nCount = 0
     N_atlas = []
     for i in range(len(myDat)):
-        if myDat[i] == ord('N') or (N_unknowns and myDat[i] not in OK_CHR_ORD):
+        if myDat[i] == 'N' or (N_unknowns and myDat[i] not in OK_CHR_ORD):
             if nCount == 0:
                 prevNI = i
             nCount += 1
@@ -108,7 +112,7 @@ def readRef(refPath, ref_inds_i, N_handling, N_unknowns=True, quiet=False):
             N_info['all'].extend(region)
             if region[1] - region[0] <= N_handling[1]:
                 for i in range(region[0], region[1]):
-                    myDat[i] = ord(random.choice(ALLOWED_NUCL))
+                    myDat[i] = random.choice(ALLOWED_NUCL)
             else:
                 N_info['big'].extend(region)
     elif N_handling[0] == 'allChr' and N_handling[2] in OK_CHR_ORD:

From 876af86d758c28037c67fbb4b31699939f632fee Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 25 Mar 2020 10:22:25 -0500
Subject: [PATCH 012/323] changed bytearray to sequence container

---
 py/SequenceContainer.py | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 2ccf482..7a0ed6f 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -5,6 +5,8 @@
 import bisect
 import pickle
 import numpy as np
+from Bio.Seq import Seq
+from Bio.Alphabet import IUPAC
 
 from py.probability import DiscreteDistribution, poisson_list, quantize_list
 from py.neat_cigar import CigarString
@@ -47,7 +49,7 @@ def init_basicVars(self, xOffset, sequence, ploidy, windowOverlap, readLen):
         self.x         = xOffset
         self.ploidy    = ploidy
         self.readLen   = readLen
-        self.sequences = [bytearray(sequence) for n in range(self.ploidy)]
+        self.sequences = [Seq(sequence, IUPAC.unambiguous_dna).tomutable() for n in range(self.ploidy)]
         self.seqLen    = len(sequence)
         self.indelList = [[] for n in range(self.ploidy)]
         self.snpList   = [[] for n in range(self.ploidy)]
@@ -79,7 +81,7 @@ def init_coverage(self,coverageDat,fragDist=None):
                 # compute gc-bias
                 j = 0
                 while j+self.windowSize < len(self.sequences[i]):
-                    gc_c = self.sequences[i][j:j+self.windowSize].count(ord('G')) + self.sequences[i][j:j+self.windowSize].count(ord('C'))
+                    gc_c = self.sequences[i][j:j+self.windowSize].count('G') + self.sequences[i][j:j+self.windowSize].count('C')
                     gcCov_vals[i].extend([gc_scalars[gc_c]]*self.windowSize)
                     j += self.windowSize
                 gc_c = self.sequences[i][-self.windowSize:].count('G') + self.sequences[i][-self.windowSize:].count('C')
@@ -331,17 +333,17 @@ def random_mutations(self):
                     inLen   = self.models[i][4].sample()
                     # sequence content of random insertions is uniformly random (change this later, maybe)
                     inSeq   = ''.join([random.choice(NUCL) for n in range(inLen)])
-                    refNucl = chr(self.sequences[i][eventPos])
+                    refNucl = self.sequences[i][eventPos]
                     myIndel = (eventPos,refNucl,refNucl+inSeq)
                 else:										# deletion
                     inLen   = self.models[i][5].sample()
                     if eventPos+inLen+1 >= len(self.sequences[i]):	# skip if deletion too close to boundary
                         continue
                     if inLen == 1:
-                        inSeq = chr(self.sequences[i][eventPos+1])
+                        inSeq = self.sequences[i][eventPos+1]
                     else:
                         inSeq = str(self.sequences[i][eventPos+1:eventPos+inLen+1])
-                    refNucl = chr(self.sequences[i][eventPos])
+                    refNucl = self.sequences[i][eventPos]
                     myIndel = (eventPos,refNucl+inSeq,refNucl)
 
                 # if event too close to boundary, skip. if event conflicts with other indel, skip.
@@ -389,8 +391,8 @@ def random_mutations(self):
                 if eventPos == -1:
                     continue
 
-                refNucl = chr(self.sequences[i][eventPos])
-                context = str(chr(self.sequences[i][eventPos-1])+chr(self.sequences[i][eventPos+1]))
+                refNucl = self.sequences[i][eventPos]
+                context = str(self.sequences[i][eventPos-1])+chr(self.sequences[i][eventPos+1])
                 # sample from tri-nucleotide substitution matrices to get SNP alt allele
                 newNucl = self.models[i][6][TRI_IND[context]][NUC_IND[refNucl]].sample()
                 mySNP   = (eventPos,refNucl,newNucl)
@@ -409,11 +411,11 @@ def random_mutations(self):
             for j in range(len(all_snps[i])):
                 vPos = all_snps[i][j][0]
 
-                if all_snps[i][j][1] != chr(self.sequences[i][vPos]):
-                    print('\nError: Something went wrong!\n', all_snps[i][j], chr(self.sequences[i][vPos]), '\n')
+                if all_snps[i][j][1] != self.sequences[i][vPos]:
+                    print('\nError: Something went wrong!\n', all_snps[i][j], self.sequences[i][vPos], '\n')
                     exit(1)
                 else:
-                    self.sequences[i][vPos] = ord(all_snps[i][j][2])
+                    self.sequences[i][vPos] = all_snps[i][j][2]
 
         # organize the indels we want to insert
         for i in range(len(all_indels)):

From b89d704e753800912832690fd9c2c8a0dcac4051 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 25 Mar 2020 10:31:20 -0500
Subject: [PATCH 013/323] Merged online changes with local

---
 py/SequenceContainer.py | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index fc3d7a7..ab8cec7 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -6,12 +6,9 @@
 import numpy as np
 from Bio.Seq import Seq
 from Bio.Alphabet import IUPAC
-<<<<<<< HEAD
-=======
 from Bio import SeqIO
 import gzip
 from Bio.bgzf import *
->>>>>>> 90f96d0f63b7bc485f6739690e6d8aa9680902e1
 
 from py.probability import DiscreteDistribution, poisson_list, quantize_list
 from py.neat_cigar import CigarString
@@ -54,11 +51,7 @@ def init_basicVars(self, xOffset, sequence, ploidy, windowOverlap, readLen):
         self.x         = xOffset
         self.ploidy    = ploidy
         self.readLen   = readLen
-<<<<<<< HEAD
         self.sequences = [Seq(sequence, IUPAC.unambiguous_dna).tomutable() for n in range(self.ploidy)]
-=======
-        self.sequences = [Seq(sequence).tomutable() for n in range(self.ploidy)]
->>>>>>> 90f96d0f63b7bc485f6739690e6d8aa9680902e1
         self.seqLen    = len(sequence)
         self.indelList = [[] for n in range(self.ploidy)]
         self.snpList   = [[] for n in range(self.ploidy)]
@@ -420,15 +413,10 @@ def random_mutations(self):
             for j in range(len(all_snps[i])):
                 vPos = all_snps[i][j][0]
 
-<<<<<<< HEAD
                 if all_snps[i][j][1] != self.sequences[i][vPos]:
                     print('\nError: Something went wrong!\n', all_snps[i][j], self.sequences[i][vPos], '\n')
-=======
-                if all_snps[i][j][1] != chr(self.sequences[i][vPos]):
                     print(all_snps[i][j])
                     print(self.sequences[i][vPos])
-                    print('\nError: Something went wrong!\n', all_snps[i][j], chr(self.sequences[i][vPos]), '\n')
->>>>>>> 90f96d0f63b7bc485f6739690e6d8aa9680902e1
                     exit(1)
                 else:
                     self.sequences[i][vPos] = all_snps[i][j][2]

From 8b5783e620dae95f73b1d32751035d1ca9a870f9 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 27 Mar 2020 10:30:15 -0500
Subject: [PATCH 014/323] Converted a utility to python 3 (plutMutModel.py)

---
 py/refFunc.py               | 2 +-
 utilities/computeFraglen.py | 1 +
 utilities/genMutModel.py    | 8 ++++++--
 utilities/plotMutModel.py   | 5 +++--
 4 files changed, 11 insertions(+), 5 deletions(-)

diff --git a/py/refFunc.py b/py/refFunc.py
index aac3f0f..df0174a 100644
--- a/py/refFunc.py
+++ b/py/refFunc.py
@@ -4,7 +4,7 @@
 import random
 from Bio.Seq import Seq
 from Bio.Alphabet import IUPAC
-from Bio.bgzf import *
+# from Bio.bgzf import *
 
 
 #	Index reference fasta
diff --git a/utilities/computeFraglen.py b/utilities/computeFraglen.py
index f3715a1..96f3780 100644
--- a/utilities/computeFraglen.py
+++ b/utilities/computeFraglen.py
@@ -7,6 +7,7 @@
 #      Usage: samtools view normal.bam | python computeFraglen.py
 #
 #
+# Python 3 ready
 
 import sys
 import fileinput
diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index 12ef023..f448660 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -12,7 +12,7 @@
 SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2])
 sys.path.append(SIM_PATH+'/py/')
 
-from py.refFunc import indexRef
+from refFunc import indexRef
 
 REF_WHITELIST =  [str(n) for n in range(1,30)] + ['x','y','X','Y','mt','Mt','MT']
 REF_WHITELIST += ['chr'+n for n in REF_WHITELIST]
@@ -38,6 +38,7 @@ def getChrFromFasta(refPath,ref_inds,chrName):
     myDat = ''.join(refFile.read(ref_inds_i[2]-ref_inds_i[1]).split('\n'))
     return myDat
 
+
 # cluster a sorted list
 def clusterList(l,delta):
     outList    = [[l[0]]]
@@ -53,6 +54,7 @@ def clusterList(l,delta):
         prevVal = n
     return outList
 
+
 def list_2_countDict(l):
     cDict = {}
     for n in l:
@@ -61,6 +63,7 @@ def list_2_countDict(l):
         cDict[n] += 1
     return cDict
 
+
 def getBedTracks(fn):
     f = open(fn,'r')
     trackDict = {}
@@ -72,6 +75,7 @@ def getBedTracks(fn):
     f.close()
     return trackDict
 
+
 def getTrackLen(trackDict):
     totSum = 0
     for k in trackDict.keys():
@@ -142,7 +146,7 @@ def isInBed(track,ind):
     MYBED = (getBedTracks(args.bi),True)
 elif args.be != None:
     print('only considering variants outside of specified bed regions...')
-MYBED = (getBedTracks(args.be),False)
+    MYBED = (getBedTracks(args.be),False)
 
 if TSV[-4:] == '.vcf':
     IS_VCF = True
diff --git a/utilities/plotMutModel.py b/utilities/plotMutModel.py
index 57c838b..7a70693 100644
--- a/utilities/plotMutModel.py
+++ b/utilities/plotMutModel.py
@@ -5,6 +5,7 @@
 #
 #	python plotMutModel.py -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix 
 #
+# Python 3 ready
 
 import sys
 import pickle
@@ -90,7 +91,7 @@ def getBedOverlap(track, ind_s, ind_e):
 for fn in INP:
     myCol = getColor(colorInd, N_FILES)
     colorInd += 1
-    DATA_DICT = pickle.load(open(fn, "rb"))
+    DATA_DICT = pickle.load(open(fn, "rb"), encoding="utf-8")
     [AVG_MUT_RATE, SNP_FREQ, INDEL_FREQ] = [DATA_DICT['AVG_MUT_RATE'], DATA_DICT['SNP_FREQ'], DATA_DICT['INDEL_FREQ']]
     mpl.bar([colorInd - 1], [AVG_MUT_RATE], 1., color=myCol)
 mpl.xlim([-1, N_FILES + 1])
@@ -104,7 +105,7 @@ def getBedOverlap(track, ind_s, ind_e):
 for fn in INP:
     myCol = getColor(colorInd, N_FILES)
     colorInd += 1
-    DATA_DICT = pickle.load(open(fn, "rb"))
+    DATA_DICT = pickle.load(open(fn, "rb"), encoding='utf-8')
     [AVG_MUT_RATE, SNP_FREQ, INDEL_FREQ] = [DATA_DICT['AVG_MUT_RATE'], DATA_DICT['SNP_FREQ'], DATA_DICT['INDEL_FREQ']]
     mpl.bar([colorInd - 1], [SNP_FREQ], 1., color=myCol)
     mpl.bar([colorInd - 1], [1. - SNP_FREQ], 1., color=myCol, bottom=[SNP_FREQ], hatch='/')

From 024e65faad4c7b81d400af67d7c96849a113b1b9 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 27 Mar 2020 11:33:48 -0500
Subject: [PATCH 015/323] computeGC.py checked for python 3 compatability

---
 utilities/computeGC.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/utilities/computeGC.py b/utilities/computeGC.py
index 5f4294d..1cc0b15 100644
--- a/utilities/computeGC.py
+++ b/utilities/computeGC.py
@@ -6,13 +6,12 @@
 #   Takes output file from bedtools genomecov to generate GC/coverage model
 #
 #   Usage: bedtools genomecov -d -ibam input.bam -g reference.fa > genomeCov.dat
-#          python computeGC.py -r reference.fa -i genomeCov.dat -W [sliding window length] -o output_name.p
-#
+#          python computeGC.py -r reference.fa -i genomeCov.dat -w [sliding window length] -o output_name.p
 #
 #
+# Python 3 ready
 
 import time
-import sys
 import argparse
 import numpy as np
 import pickle

From 7690877b4fdd5c5377504f3cf6844e62079d1234 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 30 Mar 2020 10:17:38 -0500
Subject: [PATCH 016/323] Checking python 3 compatability of validateBam.py and
 validateFQ.py

---
 utilities/validateBam.py | 4 +++-
 utilities/validateFQ.py  | 1 +
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/utilities/validateBam.py b/utilities/validateBam.py
index 97c777c..9c164f7 100644
--- a/utilities/validateBam.py
+++ b/utilities/validateBam.py
@@ -1,5 +1,7 @@
 #!/usr/bin/env python
 
+# Python 3 ready
+
 import sys
 import os
 import gzip
@@ -33,7 +35,7 @@ def getBytes(fmt, amt):
 IN_BAM = sys.argv[1]
 f = open(IN_BAM, 'rb')
 f.seek(os.path.getsize(IN_BAM) - 28)
-EOF = [format(ord(n), '02x') for n in f.read()]
+EOF = [format(n, '02x') for n in f.read()]
 print('EOF_MARKER:  ', ' '.join(EOF))
 if EOF != BAM_EOF:
     print('\nWARNING: BAM EOF DOES NOT MATCH EXPECTED STRING.\n')
diff --git a/utilities/validateFQ.py b/utilities/validateFQ.py
index 047d7f2..bc2db60 100644
--- a/utilities/validateFQ.py
+++ b/utilities/validateFQ.py
@@ -5,6 +5,7 @@
 #
 #	python validateFQ.py read1.fq [read2.fq]
 #
+# Python 3 ready
 
 import sys
 

From 83a85db872a4848b3b210d20cafb5fbb9c5056b6 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 30 Mar 2020 11:05:20 -0500
Subject: [PATCH 017/323] Preparing vcf_compare_OLD.py for python 3
 compatability

---
 utilities/vcf_compare_OLD.py | 63 +++++++++++++++++++-----------------
 1 file changed, 34 insertions(+), 29 deletions(-)

diff --git a/utilities/vcf_compare_OLD.py b/utilities/vcf_compare_OLD.py
index fa9d061..acdced3 100644
--- a/utilities/vcf_compare_OLD.py
+++ b/utilities/vcf_compare_OLD.py
@@ -1,6 +1,8 @@
 #!/usr/bin/env python
 # encoding: utf-8
 
+# Python 3 ready
+
 """ **************************************************
 
 vcf_compare.py
@@ -14,7 +16,6 @@
 ************************************************** """
 
 import sys
-import os
 import copy
 import time
 import bisect
@@ -22,6 +23,9 @@
 import numpy as np
 import optparse
 
+from Bio.Seq import Seq
+from Bio.Alphabet import IUPAC
+
 EV_BPRANGE = 50  # how far to either side of a particular variant location do we want to check for equivalents?
 
 DEFAULT_QUAL = -666  # if we can't find a qual score, use this instead so we know it's missing
@@ -79,33 +83,33 @@
     PARSER.print_help()
     exit(1)
 
-if OPTS.MTRL != None:
+if OPTS.MTRL is not None:
     MINREGIONLEN = int(OPTS.MTRL)
 else:
     MINREGIONLEN = None
 
-if MIN_READLEN == None:
+if MIN_READLEN is None:
     MIN_READLEN = 0
 else:
     MIN_READLEN = int(MIN_READLEN)
 
-if REFERENCE == None:
+if REFERENCE is None:
     print('Error: No reference provided.')
     exit(1)
-if GOLDEN_VCF == None:
+if GOLDEN_VCF is None:
     print('Error: No golden VCF provided.')
     exit(1)
-if WORKFLOW_VCF == None:
+if WORKFLOW_VCF is None:
     print('Error: No workflow VCF provided.')
     exit(1)
-if OUT_PREFIX == None:
+if OUT_PREFIX is None:
     print('Error: No output prefix provided.')
     exit(1)
-if (BEDFILE != None and MINREGIONLEN == None) or (BEDFILE == None and MINREGIONLEN != None):
+if (BEDFILE is not None and MINREGIONLEN is None) or (BEDFILE is None and MINREGIONLEN is not None):
     print('Error: Both -t and -T must be specified')
     exit(1)
 
-if NO_PLOT == False:
+if NO_PLOT is False:
     import matplotlib
 
     matplotlib.use('Agg')
@@ -164,7 +168,7 @@ def parseLine(splt, colDict, colSamp):
             if ':' + dp_tok + ':' in format:
                 dpInd = format.split(':').index(dp_tok)
                 cov = int(splt[colSamp[0]].split(':')[dpInd])
-        if cov != None:
+        if cov is not None:
             break
 
     #	check INFO for AF first
@@ -179,7 +183,7 @@ def parseLine(splt, colDict, colSamp):
             afInd = splt[colDict['FORMAT']].split(':').index('AF')
             af = splt[colSamp[0]].split(':')[afInd]
 
-    if af != None:
+    if af is not None:
         af_splt = af.split(',')
         while (len(af_splt) < len(alt_alleles)):  # are we lacking enough AF values for some reason?
             af_splt.append(af_splt[-1])  # phone it in.
@@ -223,10 +227,10 @@ def parseVCF(VCF_FILENAME, refName, targRegionsFl, outFile, outBool):
 
                 if targInd % 2 == 1:
                     targLen = targRegionsFl[targInd] - targRegionsFl[targInd - 1]
-                    if (BEDFILE != None and targLen >= MINREGIONLEN) or BEDFILE == None:
+                    if (BEDFILE is not None and targLen >= MINREGIONLEN) or BEDFILE is None:
 
                         pl_out = parseLine(splt, colDict, colSamp)
-                        if pl_out == None:
+                        if pl_out is None:
                             var_filtered += 1
                             continue
                         (cov, qual, aa, af) = pl_out
@@ -252,7 +256,7 @@ def parseVCF(VCF_FILENAME, refName, targRegionsFl, outFile, outBool):
                             else:
                                 v_Hashed[var] = 1
 
-                            if cov != None:
+                            if cov is not None:
                                 v_Cov[var] = cov
                             v_AF[var] = af[0]  # only use first AF, even if multiple. fix this later?
                             v_Qual[var] = qual
@@ -276,7 +280,8 @@ def parseVCF(VCF_FILENAME, refName, targRegionsFl, outFile, outBool):
                     outFile.write(line)
 
     return (
-    v_Hashed, v_Alts, v_Cov, v_AF, v_Qual, v_TargLen, nBelowMinRLen, line_unique, var_filtered, var_merged, hash_coll)
+        v_Hashed, v_Alts, v_Cov, v_AF, v_Qual, v_TargLen, nBelowMinRLen, line_unique, var_filtered, var_merged,
+        hash_coll)
 
 
 def condenseByPos(listIn):
@@ -323,7 +328,7 @@ def main():
             ref_inds.append((prevR, prevP, f.tell() - len(data)))
             break
         if data[0] == '>':
-            if prevP != None:
+            if prevP is not None:
                 ref_inds.append((prevR, prevP, f.tell() - len(data)))
             prevP = f.tell()
             prevR = data[1:-1]
@@ -342,7 +347,7 @@ def main():
     zwF = 0  # total workflow variants that were filtered and excluded
     zwR = 0  # total workflow variants that were excluded for being redundant
     zwM = 0  # total workflow variants that were merged into a single position
-    if BEDFILE != None:
+    if BEDFILE is not None:
         zbM = 0
 
     mappability_vs_FN = {0: 0,
@@ -358,7 +363,7 @@ def main():
     mappability_tracks = {}  # indexed by chr string (e.g. 'chr1'), has boolean array
     prevRef = ''
     relevantRegions = []
-    if MAPTRACK != None:
+    if MAPTRACK is not None:
         mtf = open(MAPTRACK, 'r')
         for line in mtf:
             splt = line.strip().split('\t')
@@ -436,14 +441,14 @@ def main():
                         print(i, len(myDat[i]), inWidth)
                 exit(1)
 
-            myDat = bytearray(''.join(myDat)).upper()
+            myDat = Seq(''.join(myDat)).upper().tomutable()
             myLen = len(myDat)
 
         #
         #	Parse relevant targeted regions
         #
         targRegionsFl = []
-        if BEDFILE != None:
+        if BEDFILE is not None:
             bedfile = open(BEDFILE, 'r')
             for line in bedfile:
                 splt = line.split('\t')
@@ -480,9 +485,9 @@ def main():
         for var in correctHashed.keys():
             if var in workflowHashed or var[0] in solvedInds:
                 correctHashed[var] = 2
-                workflowHashed[var] = 2
+                workflowHashed[var] = 20
                 solvedInds[var[0]] = True
-        for var in correctHashed.keys() + workflowHashed.keys():
+        for var in list(correctHashed.keys()) + list(workflowHashed.keys()):
             if var[0] in solvedInds:
                 correctHashed[var] = 2
                 workflowHashed[var] = 2
@@ -580,9 +585,9 @@ def main():
         znP += nPerfect
         zfP += len(FPvariants)
         znF += len(notFound)
-        if FAST == False:
+        if FAST is False:
             znE += nEquiv
-        if BEDFILE != None:
+        if BEDFILE is not None:
             zbM += correctBelowMinRLen
 
         #
@@ -596,7 +601,7 @@ def main():
             noReason = True
 
             #	mappability?
-            if MAPTRACK != None:
+            if MAPTRACK is not None:
                 if refName in mappability_tracks and var[0] < len(mappability_tracks[refName]):
                     if mappability_tracks[refName][var[0]]:
                         mappability_vs_FN[1] += 1
@@ -608,7 +613,7 @@ def main():
             #	coverage?
             if var in correctCov:
                 c = correctCov[var]
-                if c != None:
+                if c is not None:
                     if c not in coverage_vs_FN:
                         coverage_vs_FN[c] = 0
                     coverage_vs_FN[c] += 1
@@ -697,7 +702,7 @@ def main():
         tstr1 = 'False Negative Variants (Missed Detections)'
         # tstr2 = str(nDetected)+' / '+str(znF)+' FN variants categorized'
         tstr2 = ''
-        if MAPTRACK != None:
+        if MAPTRACK is not None:
             v = venn3([set1, set2, set3], (s1, s2, s3))
         else:
             v = venn2([set2, set3], (s2, s3))
@@ -712,7 +717,7 @@ def main():
     #	spit out results to console
     #
     print('\n**********************************\n')
-    if BEDFILE != None:
+    if BEDFILE is not None:
         print('ONLY CONSIDERING VARIANTS FOUND WITHIN TARGETED REGIONS\n\n')
     print('Total Golden Variants:  ', ztV, '\t[', zgF, 'filtered,', zgM, 'merged,', zgR, 'redundant ]')
     print('Total Workflow Variants:', ztW, '\t[', zwF, 'filtered,', zwM, 'merged,', zwR, 'redundant ]')
@@ -723,7 +728,7 @@ def main():
         print('FP variants:    ', zfP)  # ,'({0:.2f}%)'.format(100.*float(zfP)/ztW)
     if FAST == False:
         print('\nNumber of equivalent variants denoted differently between the two vcfs:', znE)
-    if BEDFILE != None:
+    if BEDFILE is not None:
         print('\nNumber of golden variants located in targeted regions that were too small to be sampled from:', zbM)
     if FAST:
         print(

From e1a01a8c54cc515252829d483dc9005e5bced3b1 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 30 Mar 2020 11:32:30 -0500
Subject: [PATCH 018/323] Update model software for python 3

---
 py/refFunc.py                 | 14 --------------
 utilities/genMutModel.py      |  4 +++-
 utilities/genSeqErrorModel.py |  4 ++--
 3 files changed, 5 insertions(+), 17 deletions(-)

diff --git a/py/refFunc.py b/py/refFunc.py
index df0174a..1672998 100644
--- a/py/refFunc.py
+++ b/py/refFunc.py
@@ -4,7 +4,6 @@
 import random
 from Bio.Seq import Seq
 from Bio.Alphabet import IUPAC
-# from Bio.bgzf import *
 
 
 #	Index reference fasta
@@ -58,19 +57,6 @@ def indexRef(refPath):
     print('{0:.3f} (sec)'.format(time.time() - tt))
     return ref_inds
 
-
-#
-#	Read in sequence data from reference fasta
-#
-#	N_unknowns  = True --> all ambiguous characters will be treated as Ns
-#	N_handling  = (mode,params)
-#		- ('random',read/frag len)      --> all regions of Ns smaller than read or fragment
-#                                           length (whichever is bigger) will be replaced
-#                                           with uniformly random nucleotides
-#		- ('allChr',read/frag len, chr) --> same as above, but replaced instead with a string
-#                                           of 'chr's
-#		- ('ignore')                    --> do not alter nucleotides in N regions
-#
 def readRef(refPath, ref_inds_i, N_handling, N_unknowns=True, quiet=False):
     OK_CHR_ORD = {'A': True, 'C': True, 'G': True, 'T': True, 'U': True}
     ALLOWED_NUCL = ['A', 'C', 'G', 'T']
diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index f448660..a105473 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -1,5 +1,7 @@
 #!/usr/bin/env python
 
+# Python 3 ready
+
 import sys
 import os
 import re
@@ -158,7 +160,7 @@ def isInBed(track,ind):
 
 
 #####################################
-#				main()				#
+#				main()				#c
 #####################################
 
 
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index dfd2680..867c975 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -9,12 +9,12 @@
 #          Usage: python genSeqErrorModel.py -i input_reads.fq -o path/to/output_name.p
 #
 #
+# Python 3 ready
 
 
 import os
 import sys
 import gzip
-import random
 import numpy as np
 import argparse
 import pickle
@@ -23,7 +23,7 @@
 SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2]) + '/py/'
 sys.path.append(SIM_PATH)
 
-from py.probability import DiscreteDistribution
+from probability import DiscreteDistribution
 
 
 def parseFQ(inf):

From 792cf6b9c2de816bcabf3d786a37a5afe093e9db Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 30 Mar 2020 13:09:28 -0500
Subject: [PATCH 019/323] cleaning up functions

---
 py/refFunc.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/py/refFunc.py b/py/refFunc.py
index 1672998..58aca20 100644
--- a/py/refFunc.py
+++ b/py/refFunc.py
@@ -4,6 +4,7 @@
 import random
 from Bio.Seq import Seq
 from Bio.Alphabet import IUPAC
+# from Bio.bgzf import *
 
 
 #	Index reference fasta
@@ -57,6 +58,7 @@ def indexRef(refPath):
     print('{0:.3f} (sec)'.format(time.time() - tt))
     return ref_inds
 
+
 def readRef(refPath, ref_inds_i, N_handling, N_unknowns=True, quiet=False):
     OK_CHR_ORD = {'A': True, 'C': True, 'G': True, 'T': True, 'U': True}
     ALLOWED_NUCL = ['A', 'C', 'G', 'T']

From 1318d8a26f279d0164f36b056972bad21b1a43e3 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 30 Mar 2020 15:30:09 -0500
Subject: [PATCH 020/323] minor updates to sequence container

---
 genReads.py             | 2 +-
 py/SequenceContainer.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/genReads.py b/genReads.py
index 3d15f07..7571aad 100644
--- a/genReads.py
+++ b/genReads.py
@@ -25,7 +25,7 @@
 import numpy as np
 import argparse
 
-from py.inputChecking import requiredField, checkFileOpen, checkDir, isInRange
+from py.inputChecking import requiredField, checkFileOpen, isInRange
 from py.refFunc import indexRef, readRef, getAllRefRegions, partitionRefRegions
 from py.vcfFunc import parseVCF
 from py.OutputFileWriter import OutputFileWriter
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index ab8cec7..b9a0435 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -662,7 +662,7 @@ def __init__(self, readLen, errorModel, reScaledError):
 
         self.readLen = readLen
 
-        errorDat = pickle.load(open(errorModel,'rb'), encoding='bytes')
+        errorDat = pickle.load(open(errorModel, 'rb'), encoding='latin1')
         self.UNIFORM = False
         if len(errorDat) == 4:		# uniform-error SE reads (e.g. PacBio)
             self.UNIFORM = True

From 2748272383994e52920f5196c4943882e494a0af Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 9 Apr 2020 21:51:17 -0500
Subject: [PATCH 021/323] Updates to python 3 compatability

---
 genReads.py             |  3 ++-
 py/SequenceContainer.py | 24 ++++++++++++------------
 2 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/genReads.py b/genReads.py
index 7571aad..5a23798 100644
--- a/genReads.py
+++ b/genReads.py
@@ -505,7 +505,8 @@ def main():
                 newPercent = int((currentProgress * 100) / float(total_bp_span))
                 if newPercent > currentPercent:
                     if newPercent <= 99 or (newPercent == 100 and not havePrinted100):
-                        sys.stdout.write(str(newPercent) + '% ')
+                        if newPercent % 5 == 0:
+                            sys.stdout.write(str(newPercent) + '% ')
                         sys.stdout.flush()
                     currentPercent = newPercent
                     if currentPercent == 100:
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index b9a0435..89b9c79 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -7,8 +7,8 @@
 from Bio.Seq import Seq
 from Bio.Alphabet import IUPAC
 from Bio import SeqIO
-import gzip
-from Bio.bgzf import *
+# import gzip
+# from Bio.bgzf import *
 
 from py.probability import DiscreteDistribution, poisson_list, quantize_list
 from py.neat_cigar import CigarString
@@ -51,7 +51,7 @@ def init_basicVars(self, xOffset, sequence, ploidy, windowOverlap, readLen):
         self.x         = xOffset
         self.ploidy    = ploidy
         self.readLen   = readLen
-        self.sequences = [Seq(sequence, IUPAC.unambiguous_dna).tomutable() for n in range(self.ploidy)]
+        self.sequences = [Seq(str(sequence), IUPAC.unambiguous_dna).tomutable() for n in range(self.ploidy)]
         self.seqLen    = len(sequence)
         self.indelList = [[] for n in range(self.ploidy)]
         self.snpList   = [[] for n in range(self.ploidy)]
@@ -228,7 +228,7 @@ def init_trinucBias(self):
         self.trinuc_bias = [None for n in range(self.ploidy)]
         for p in range(self.ploidy):
             for i in range(self.winBuffer+1,self.seqLen-1):
-                trinuc_snp_bias[p][i] = self.models[p][7][ALL_IND[self.sequences[p][i-1:i+2].decode()]]
+                trinuc_snp_bias[p][i] = self.models[p][7][ALL_IND[str(self.sequences[p][i-1:i+2])]]
             self.trinuc_bias[p] = DiscreteDistribution(trinuc_snp_bias[p][self.winBuffer+1:self.seqLen-1],range(self.winBuffer+1,self.seqLen-1))
 
     def update(self, xOffset, sequence, ploidy, windowOverlap, readLen, mutationModels=[], mutRate=None):
@@ -394,7 +394,7 @@ def random_mutations(self):
                     continue
 
                 refNucl = self.sequences[i][eventPos]
-                context = str(self.sequences[i][eventPos-1])+chr(self.sequences[i][eventPos+1])
+                context = str(self.sequences[i][eventPos-1])+str(self.sequences[i][eventPos+1])
                 # sample from tri-nucleotide substitution matrices to get SNP alt allele
                 newNucl = self.models[i][6][TRI_IND[context]][NUC_IND[refNucl]].sample()
                 mySNP   = (eventPos,refNucl,newNucl)
@@ -623,7 +623,7 @@ def sample_read(self, sequencingModel, fragLen=None):
                     # insert insertion error into read and update cigar string accordingly
                     else:
                         myadj = sse_adj[ePos]
-                        if chr(r[3][ePos+myadj]) == error[3]:
+                        if str(r[3][ePos+myadj]) == error[3]:
                             r[3] = r[3][:ePos+myadj] + error[4] + r[3][ePos+myadj+1:]
                             expandedCigar = expandedCigar[:ePos+myadj] + ['I']*eLen + expandedCigar[ePos+myadj:]
                         else:
@@ -635,7 +635,7 @@ def sample_read(self, sequencingModel, fragLen=None):
                             sse_adj[i] += eLen
 
                 else:	# substitution errors, much easier by comparison...
-                    if chr(r[3][ePos+sse_adj[ePos]]) == error[3]:
+                    if str(r[3][ePos+sse_adj[ePos]]) == error[3]:
                         r[3][ePos+sse_adj[ePos]] = error[4]
                     else:
                         print('\nError, ref does not match alt while attempting to insert substitution error!\n')
@@ -648,7 +648,7 @@ def sample_read(self, sequencingModel, fragLen=None):
 
                 r[3] = r[3][:self.readLen]
 
-            rOut.append([self.FM_pos[myPloid][r[0]], myCigar, r[3].decode(), str(r[1])])
+            rOut.append([self.FM_pos[myPloid][r[0]], myCigar, str(r[3]), str(r[1])])
 
         # rOut[i] = (pos, cigar, read_string, qual_string)
         return rOut
@@ -662,7 +662,7 @@ def __init__(self, readLen, errorModel, reScaledError):
 
         self.readLen = readLen
 
-        errorDat = pickle.load(open(errorModel, 'rb'), encoding='latin1')
+        errorDat = pickle.load(open(errorModel, 'rb'), encoding="bytes")
         self.UNIFORM = False
         if len(errorDat) == 4:		# uniform-error SE reads (e.g. PacBio)
             self.UNIFORM = True
@@ -788,18 +788,18 @@ def getSequencingErrors(self, readData, isReverseStrand=False):
             # errorOut = (type, len, pos, ref, alt)
 
             if isSub:								# insert substitution error
-                myNucl  = chr(readData[ind])
+                myNucl  = str(readData[ind])
                 newNucl = self.errSSE[NUC_IND[myNucl]].sample()
                 sOut.append(('S',1,ind,myNucl,newNucl))
             else:									# insert indel error
                 indelLen = self.errSIE.sample()
                 if random.random() < self.errP[4]:		# insertion error
-                    myNucl  = chr(readData[ind])
+                    myNucl  = str(readData[ind])
                     newNucl = myNucl + ''.join([self.errSIN.sample() for n in range(indelLen)])
                     sOut.append(('I',len(newNucl)-1,ind,myNucl,newNucl))
                 elif ind < self.readLen-2-nDelSoFar:	# deletion error (prevent too many of them from stacking up)
                     myNucl  = str(readData[ind:ind+indelLen+1])
-                    newNucl = chr(readData[ind])
+                    newNucl = str(readData[ind])
                     nDelSoFar += len(myNucl)-1
                     sOut.append(('D',len(myNucl)-1,ind,myNucl,newNucl))
                     for i in range(ind+1,ind+indelLen+1):

From 40abc841a0b7efa11cd6d0c20ab12946f571ce34 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 20 Apr 2020 09:59:24 -0500
Subject: [PATCH 022/323] Some minor bug fixes for python3

---
 genReads.py                   | 6 +++---
 py/SequenceContainer.py       | 7 ++-----
 utilities/genSeqErrorModel.py | 2 +-
 3 files changed, 6 insertions(+), 9 deletions(-)

diff --git a/genReads.py b/genReads.py
index 5a23798..548b7e3 100644
--- a/genReads.py
+++ b/genReads.py
@@ -435,7 +435,7 @@ def main():
 
         for i in range(len(N_regions['non_N'])):
             (pi, pf) = N_regions['non_N'][i]
-            nTargWindows = max([1, (pf - pi) / targSize])
+            nTargWindows = max([1, (pf - pi) // targSize])
             bpd = int((pf - pi) / float(nTargWindows))
             # bpd += GC_WINDOW_SIZE - bpd%GC_WINDOW_SIZE
 
@@ -706,9 +706,9 @@ def main():
         else:
             print('')
         if ONLY_VCF:
-            print('VCF generation completed in', end='')
+            print('VCF generation completed in ', end='')
         else:
-            print('Read sampling completed in', end='')
+            print('Read sampling completed in ', end='')
         print(int(time.time() - tt), '(sec)')
 
         # write all output variants for this reference
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 89b9c79..8c0b5a3 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -6,9 +6,6 @@
 import numpy as np
 from Bio.Seq import Seq
 from Bio.Alphabet import IUPAC
-from Bio import SeqIO
-# import gzip
-# from Bio.bgzf import *
 
 from py.probability import DiscreteDistribution, poisson_list, quantize_list
 from py.neat_cigar import CigarString
@@ -432,7 +429,7 @@ def random_mutations(self):
             rollingAdj = 0
             tempSymbolString = ['M' for n in self.sequences[i]]
             # there's an off-by-one error somewhere in the position sampling routines.. this might fix it
-            tempSymbolString.append('M')
+            # tempSymbolString.append('M')
             for j in range(len(all_indels_ins[i])):
                 vPos        = all_indels_ins[i][j][0] + rollingAdj
                 vPos2       = vPos + len(all_indels_ins[i][j][1])
@@ -701,7 +698,7 @@ def __init__(self, readLen, errorModel, reScaledError):
             print('Warning: Quality scores no longer exactly representative of error probability. '
                   'Error model scaled by {0:.3f} to match desired rate...'.format(self.errorScale))
 
-        if self.UNIFORM == False:
+        if not self.UNIFORM:
             # adjust length to match desired read length
             if self.readLen == len(initQ1):
                 self.qIndRemap = range(self.readLen)
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 867c975..3013295 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -78,7 +78,7 @@ def parseFQ(inf):
             continue
 
         for i in range(len(data4) - 1):
-            q = ord(data4[i]) - offQ
+            q = ord(str(data4[i])) - offQ
             qDict[q] = True
             if i == 0:
                 priorQ[i][q] += 1

From bd384687063933c3c93076f669eeec565576e608 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 20 Apr 2020 10:00:26 -0500
Subject: [PATCH 023/323] Some minor bug fixes for python3

---
 utilities/genSeqErrorModel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 3013295..95439a5 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -78,7 +78,7 @@ def parseFQ(inf):
             continue
 
         for i in range(len(data4) - 1):
-            q = ord(str(data4[i])) - offQ
+            q = ord(chr(data4[i])) - offQ
             qDict[q] = True
             if i == 0:
                 priorQ[i][q] += 1

From 4266b41f6b13653c2bf7262e38dbf6b734b291c7 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 22 Apr 2020 10:31:18 -0500
Subject: [PATCH 024/323] Patch to a bug I was encountering

---
 py/SequenceContainer.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 8c0b5a3..7eddee8 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -77,6 +77,9 @@ def init_coverage(self, coverageDat, fragDist=None):
             avg_out = []
             for i in range(len(self.sequences)):
                 max_coord = min([len(self.sequences[i])-self.readLen, len(self.allCigar[i])-self.readLen])
+                # Trying to fix a problem wherein the above line gives a negative answer
+                if max_coord <= 0:
+                    max_coord = min([len(self.sequences[i]), len(self.allCigar[i])])
                 # compute gc-bias
                 j = 0
                 while j+self.windowSize < len(self.sequences[i]):
@@ -89,7 +92,7 @@ def init_coverage(self, coverageDat, fragDist=None):
                 trCov_vals[i].append(targetCov_vals[0])
                 prevVal = self.FM_pos[i][0]
                 for j in range(1,max_coord):
-                    if self.FM_pos[i][j] == None:
+                    if self.FM_pos[i][j] is None:
                         trCov_vals[i].append(targetCov_vals[prevVal])
                     elif self.FM_span[i][j]-self.FM_pos[i][j] <= 1:
                         trCov_vals[i].append(targetCov_vals[prevVal])

From 62f63825fabeb578ed5980532e597e939077641a Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 22 Apr 2020 14:44:47 -0500
Subject: [PATCH 025/323] eliminated an unnecessary import

---
 genReads.py       | 3 +--
 py/probability.py | 1 -
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/genReads.py b/genReads.py
index 548b7e3..684b624 100644
--- a/genReads.py
+++ b/genReads.py
@@ -505,8 +505,7 @@ def main():
                 newPercent = int((currentProgress * 100) / float(total_bp_span))
                 if newPercent > currentPercent:
                     if newPercent <= 99 or (newPercent == 100 and not havePrinted100):
-                        if newPercent % 5 == 0:
-                            sys.stdout.write(str(newPercent) + '% ')
+                        sys.stdout.write(str(newPercent) + '% ')
                         sys.stdout.flush()
                     currentPercent = newPercent
                     if currentPercent == 100:
diff --git a/py/probability.py b/py/probability.py
index 699a966..f7d0355 100644
--- a/py/probability.py
+++ b/py/probability.py
@@ -1,4 +1,3 @@
-import math
 import random
 import bisect
 import copy

From 2d0fa6ea504dfa94fc334cef85644e0d72095022 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 27 Apr 2020 11:39:48 -0500
Subject: [PATCH 026/323] Minor changes to Sequence Container

---
 py/SequenceContainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 7eddee8..a5873d4 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -78,8 +78,8 @@ def init_coverage(self, coverageDat, fragDist=None):
             for i in range(len(self.sequences)):
                 max_coord = min([len(self.sequences[i])-self.readLen, len(self.allCigar[i])-self.readLen])
                 # Trying to fix a problem wherein the above line gives a negative answer
-                if max_coord <= 0:
-                    max_coord = min([len(self.sequences[i]), len(self.allCigar[i])])
+                # if max_coord <= 0:
+                #     max_coord = min([len(self.sequences[i]), len(self.allCigar[i])])
                 # compute gc-bias
                 j = 0
                 while j+self.windowSize < len(self.sequences[i]):

From 7c17cfe8a57a0109da3322df25eaeea6be891372 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 27 Apr 2020 11:40:08 -0500
Subject: [PATCH 027/323] Minor changes to Sequence Container

---
 py/SequenceContainer.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index a5873d4..926cd2c 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -78,9 +78,9 @@ def init_coverage(self, coverageDat, fragDist=None):
             for i in range(len(self.sequences)):
                 max_coord = min([len(self.sequences[i])-self.readLen, len(self.allCigar[i])-self.readLen])
                 # Trying to fix a problem wherein the above line gives a negative answer
-                # if max_coord <= 0:
-                #     max_coord = min([len(self.sequences[i]), len(self.allCigar[i])])
-                # compute gc-bias
+                if max_coord <= 0:
+                    max_coord = min([len(self.sequences[i]), len(self.allCigar[i])])
+                compute gc-bias
                 j = 0
                 while j+self.windowSize < len(self.sequences[i]):
                     gc_c = self.sequences[i][j:j+self.windowSize].count('G') + self.sequences[i][j:j+self.windowSize].count('C')

From 2830f6387f1aee27b597554407b14fde0d5aa57e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 27 Apr 2020 11:40:34 -0500
Subject: [PATCH 028/323] Minor changes to Sequence Container

---
 py/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 926cd2c..7eddee8 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -80,7 +80,7 @@ def init_coverage(self, coverageDat, fragDist=None):
                 # Trying to fix a problem wherein the above line gives a negative answer
                 if max_coord <= 0:
                     max_coord = min([len(self.sequences[i]), len(self.allCigar[i])])
-                compute gc-bias
+                # compute gc-bias
                 j = 0
                 while j+self.windowSize < len(self.sequences[i]):
                     gc_c = self.sequences[i][j:j+self.windowSize].count('G') + self.sequences[i][j:j+self.windowSize].count('C')

From 2b7833f630dee5a35c6461bf015ddc347194ccb2 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 1 May 2020 11:37:54 -0500
Subject: [PATCH 029/323] a few minor changes

---
 utilities/computeFraglen.py | 111 ++++++++++++++++++++----------------
 1 file changed, 62 insertions(+), 49 deletions(-)

diff --git a/utilities/computeFraglen.py b/utilities/computeFraglen.py
index 96f3780..cf4c5ef 100644
--- a/utilities/computeFraglen.py
+++ b/utilities/computeFraglen.py
@@ -18,72 +18,85 @@
 FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it
 FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median
 
-
-def quick_median(countDict):
-    midPoint = sum(countDict.values()) / 2
+def quick_median(count_dict):
+    midPoint = sum(count_dict.values()) // 2
     mySum = 0
     myInd = 0
-    sk = sorted(countDict.keys())
+    sk = sorted(count_dict.keys())
     while mySum < midPoint:
-        mySum += countDict[sk[myInd]]
+        mySum += count_dict[sk[myInd]]
         if mySum >= midPoint:
             break
         myInd += 1
     return myInd
 
 
-def median_deviation_from_median(countDict):
-    myMedian = quick_median(countDict)
+def median_deviation_from_median(count_dict):
+    myMedian = quick_median(count_dict)
     deviations = {}
-    for k in sorted(countDict.keys()):
+    for k in sorted(count_dict.keys()):
         d = abs(k - myMedian)
-        deviations[d] = countDict[k]
+        deviations[d] = count_dict[k]
     return quick_median(deviations)
 
 
-if len(sys.argv) != 1:
-    print("Usage: samtools view normal.bam | python computeFraglen.py")
-    exit(1)
+def count_frags(file: str) -> dict:
+    count_dict = {}
+    PRINT_EVERY = 100000
+    i = 0
+    for line in fileinput.input():
+        # Skip all comments and headers
+        if line[0] == '#' or line[0] == '@':
+            continue
+        splt = line.strip().split('\t')
+        samFlag = int(splt[1])
+        myRef = splt[2]
+        mapQual = int(splt[4])
+        mateRef = splt[6]
+        myTlen = abs(int(splt[8]))
+
+        # if read is paired, and is first in pair, and is confidently mapped...
+        if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:
+            # and mate is mapped to same reference
+            if mateRef == '=' or mateRef == myRef:
+                if myTlen not in count_dict:
+                    count_dict[myTlen] = 0
+                count_dict[myTlen] += 1
+                i += 1
+                if i % PRINT_EVERY == 0:
+                    print('---', i, quick_median(count_dict), median_deviation_from_median(count_dict))
+    return count_dict
+
 
-all_tlens = {}
-PRINT_EVERY = 100000
-BREAK_AFTER = 1000000
-i = 0
-for line in fileinput.input():
-    splt = line.strip().split('\t')
-    samFlag = int(splt[1])
-    myRef = splt[2]
-    mapQual = int(splt[4])
-    mateRef = splt[6]
-    myTlen = abs(int(splt[8]))
+def compute_probs(count_dict: dict) -> (list, list):
+    values = []
+    probabilities = []
+    med = quick_median(count_dict)
+    mdm = median_deviation_from_median(count_dict)
 
-    if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:  # if read is paired, and is first in pair, and is confidently mapped...
-        if mateRef == '=' or mateRef == myRef:  # and mate is mapped to same reference
-            if myTlen not in all_tlens:
-                all_tlens[myTlen] = 0
-            all_tlens[myTlen] += 1
-            i += 1
-            if i % PRINT_EVERY == 0:
-                print('---', i, quick_median(all_tlens), median_deviation_from_median(all_tlens))
-            # for k in sorted(all_tlens.keys()):
-            #	print k, all_tlens[k]
+    for k in sorted(count_dict.keys()):
+        if k > 0 and k < med + FILTER_MEDDEV_M * mdm:
+            if count_dict[k] >= FILTER_MINREADS:
+                print(k, count_dict[k])
+                values.append(k)
+                probabilities.append(count_dict[k])
+    countSum = float(sum(probabilities))
+    probabilities = [n / countSum for n in probabilities]
+    return values, probabilities
 
-        # if i > BREAK_AFTER:
-        #	break
+def main():
+    # TODO implement a more robust checking
+    if len(sys.argv) > 2:
+        print("Usage: samtools view normal.bam | python computeFraglen.py")
+        print("Usage: python computeFraglen.py normal.sam")
+        exit(1)
 
-med = quick_median(all_tlens)
-mdm = median_deviation_from_median(all_tlens)
+    all_tlens = count_frags(sys.arv[1])
+    print('\nsaving model...')
+    out_vals, out_probs = compute_probs(all_tlens)
+    print(out_probs)
+    pickle.dump([out_vals, out_probs], open('fraglen.p', 'wb'))
 
-outVals = []
-outProbs = []
-for k in sorted(all_tlens.keys()):
-    if k > 0 and k < med + FILTER_MEDDEV_M * mdm:
-        if all_tlens[k] >= FILTER_MINREADS:
-            print(k, all_tlens[k])
-            outVals.append(k)
-            outProbs.append(all_tlens[k])
-countSum = float(sum(outProbs))
-outProbs = [n / countSum for n in outProbs]
 
-print('\nsaving model...')
-pickle.dump([outVals, outProbs], open('fraglen.p', 'wb'))
+if __name__ == "__main()":
+    main()

From 57593c58897fa85c650328c0685a907252972d91 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 1 May 2020 11:50:19 -0500
Subject: [PATCH 030/323] Bug fix for reading mut models'

---
 py/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 7eddee8..62fc636 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -872,7 +872,7 @@ def parseInputMutationModel(model=None, whichDefault=1):
 
         trinuc_mut_prob    = pickle_dict['TRINUC_MUT_PROB']
         which_have_we_seen = {n:False for n in ALL_TRI}
-        trinuc_mean        = np.mean(trinuc_mut_prob.values())
+        trinuc_mean        = np.mean(list(trinuc_mut_prob.values()))
         for trinuc in trinuc_mut_prob.keys():
             outModel[9][ALL_IND[trinuc]] = trinuc_mut_prob[trinuc]
             which_have_we_seen[trinuc]   = True

From 4a3178dfcb347654c89d4d6b7da62eb2107b6478 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 1 May 2020 13:36:21 -0500
Subject: [PATCH 031/323] fixing some byte issues in outputfilewriter.py

---
 py/OutputFileWriter.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/py/OutputFileWriter.py b/py/OutputFileWriter.py
index 35e0665..435ec1d 100644
--- a/py/OutputFileWriter.py
+++ b/py/OutputFileWriter.py
@@ -190,7 +190,7 @@ def writeBAMRecord(self, refID, readName, pos_0, cigar, seq, qual, samFlag, mate
         if seqLen & 1:
             seq += '='
         for i in range(encodedLen):
-            print(seq[2*i], seq[2*i+1])
+            # print(seq[2*i], seq[2*i+1])
             encodedSeq.extend(pack('<B', (SEQ_PACKED[seq[2 * i].capitalize()] << 4) + SEQ_PACKED[seq[2 * i + 1].capitalize()]))
 
         # apparently samtools automatically adds 33 to the quality score string...
@@ -231,8 +231,8 @@ def writeBAMRecord(self, refID, readName, pos_0, cigar, seq, qual, samFlag, mate
         self.bam_buffer.append((refID, pos_0, pack('<i', blockSize) + pack('<i', refID) + pack('<i', pos_0) +
                                 pack('<I', (myBin << 16) + (myMapQual << 8) + len(readName) + 1) +
                                 pack('<I', (samFlag << 16) + cig_ops) + pack('<i', seqLen) + pack('<i', next_refID) +
-                                pack('<i', next_pos) + pack('<i', my_tlen) + readName +
-                                '\0' + encodedCig + encodedSeq + encodedQual))
+                                pack('<i', next_pos) + pack('<i', my_tlen) + readName.encode('utf-8') +
+                                b'\0' + encodedCig + encodedSeq + encodedQual.encode('utf-8')))
 
     def flushBuffers(self, bamMax=None, lastTime=False):
         if (len(self.fq1_buffer) >= BUFFER_BATCH_SIZE or len(self.bam_buffer) >= BUFFER_BATCH_SIZE) or (
@@ -257,7 +257,7 @@ def flushBuffers(self, bamMax=None, lastTime=False):
                             ind_to_stop_at = i + 1
                         else:
                             break
-                    self.bam_file.write(''.join([n[2] for n in bam_data[:ind_to_stop_at]]))
+                    self.bam_file.write(b''.join([n[2] for n in bam_data[:ind_to_stop_at]]))
                     ####print 'BAM WRITING:',ind_to_stop_at,'/',len(bam_data)
                     if ind_to_stop_at >= len(bam_data):
                         self.bam_buffer = []

From e50b2c2dd05fe074164f21e8380e1ea63cc4e888 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 1 May 2020 13:46:54 -0500
Subject: [PATCH 032/323] fixing some byte issues in outputfilewriter.py

---
 py/OutputFileWriter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/py/OutputFileWriter.py b/py/OutputFileWriter.py
index 435ec1d..2904292 100644
--- a/py/OutputFileWriter.py
+++ b/py/OutputFileWriter.py
@@ -246,7 +246,7 @@ def flushBuffers(self, bamMax=None, lastTime=False):
             if len(self.bam_buffer):
                 bam_data = sorted(self.bam_buffer)
                 if lastTime:
-                    self.bam_file.write(''.join([n[2] for n in bam_data]))
+                    self.bam_file.write(b''.join([n[2] for n in bam_data]))
                     self.bam_buffer = []
                 else:
                     ind_to_stop_at = 0

From f5af61c9db2b3d16b3320a11793b938c91f1f122 Mon Sep 17 00:00:00 2001
From: joshfactorial <jallen17@illinois.edu>
Date: Mon, 4 May 2020 13:39:07 -0500
Subject: [PATCH 033/323] Small bugfix in genSeqErrorModel.py

---
 utilities/genSeqErrorModel.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 95439a5..de16d1e 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -20,10 +20,10 @@
 import pickle
 
 # absolute path to this script
-SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2]) + '/py/'
-sys.path.append(SIM_PATH)
+# SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2]) + '/py/'
+# sys.path.append(SIM_PATH)
 
-from probability import DiscreteDistribution
+from py.probability import DiscreteDistribution
 
 
 def parseFQ(inf):
@@ -78,7 +78,7 @@ def parseFQ(inf):
             continue
 
         for i in range(len(data4) - 1):
-            q = ord(chr(data4[i])) - offQ
+            q = ord(data4[i]) - offQ
             qDict[q] = True
             if i == 0:
                 priorQ[i][q] += 1

From 33d8a016c18a8c7fa321289d640c98ae361a9423 Mon Sep 17 00:00:00 2001
From: joshfactorial <jallen17@illinois.edu>
Date: Mon, 4 May 2020 14:55:24 -0500
Subject: [PATCH 034/323] Updating computeFraglen.py

---
 utilities/computeFraglen.py | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/utilities/computeFraglen.py b/utilities/computeFraglen.py
index cf4c5ef..02c44c4 100644
--- a/utilities/computeFraglen.py
+++ b/utilities/computeFraglen.py
@@ -9,10 +9,9 @@
 #
 # Python 3 ready
 
-import sys
 import fileinput
 import pickle
-import numpy as np
+import argparse
 
 FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality
 FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it
@@ -85,17 +84,20 @@ def compute_probs(count_dict: dict) -> (list, list):
     return values, probabilities
 
 def main():
-    # TODO implement a more robust checking
-    if len(sys.argv) > 2:
-        print("Usage: samtools view normal.bam | python computeFraglen.py")
-        print("Usage: python computeFraglen.py normal.sam")
-        exit(1)
+    parser = argparse.ArgumentParser(description="computeFraglen.py")
+    parser.add_argument('-i', type=str, required=True, default=None, help="Sam file input (samtools view name.bam > name.sam")
+    parser.add_argument('-o', type=str, required=True, default=None, help="Prefix for output")
 
-    all_tlens = count_frags(sys.arv[1])
+    args = parser.parse_args()
+    input_file = args.i
+    output_prefix = args.o
+    output = output_prefix + '.p'
+
+    all_tlens = count_frags(input_file)
     print('\nsaving model...')
     out_vals, out_probs = compute_probs(all_tlens)
     print(out_probs)
-    pickle.dump([out_vals, out_probs], open('fraglen.p', 'wb'))
+    pickle.dump([out_vals, out_probs], open(output, 'wb'))
 
 
 if __name__ == "__main()":

From 88abba53bae54e045dde0fe1acdf2135b2ce34b7 Mon Sep 17 00:00:00 2001
From: joshfactorial <jallen17@illinois.edu>
Date: Tue, 5 May 2020 05:12:06 -0500
Subject: [PATCH 035/323] updated argparse arguments for computeFraglen.py

---
 utilities/computeFraglen.py | 50 +++++++++++++++++++------------------
 1 file changed, 26 insertions(+), 24 deletions(-)

diff --git a/utilities/computeFraglen.py b/utilities/computeFraglen.py
index 02c44c4..136cea1 100644
--- a/utilities/computeFraglen.py
+++ b/utilities/computeFraglen.py
@@ -43,27 +43,28 @@ def count_frags(file: str) -> dict:
     count_dict = {}
     PRINT_EVERY = 100000
     i = 0
-    for line in fileinput.input():
-        # Skip all comments and headers
-        if line[0] == '#' or line[0] == '@':
-            continue
-        splt = line.strip().split('\t')
-        samFlag = int(splt[1])
-        myRef = splt[2]
-        mapQual = int(splt[4])
-        mateRef = splt[6]
-        myTlen = abs(int(splt[8]))
-
-        # if read is paired, and is first in pair, and is confidently mapped...
-        if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:
-            # and mate is mapped to same reference
-            if mateRef == '=' or mateRef == myRef:
-                if myTlen not in count_dict:
-                    count_dict[myTlen] = 0
-                count_dict[myTlen] += 1
-                i += 1
-                if i % PRINT_EVERY == 0:
-                    print('---', i, quick_median(count_dict), median_deviation_from_median(count_dict))
+    with open(file, 'r') as f:
+        for line in f:
+            # Skip all comments and headers
+            if line[0] == '#' or line[0] == '@':
+                continue
+            splt = line.strip().split('\t')
+            samFlag = int(splt[1])
+            myRef = splt[2]
+            mapQual = int(splt[4])
+            mateRef = splt[6]
+            myTlen = abs(int(splt[8]))
+
+            # if read is paired, and is first in pair, and is confidently mapped...
+            if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:
+                # and mate is mapped to same reference
+                if mateRef == '=' or mateRef == myRef:
+                    if myTlen not in count_dict:
+                        count_dict[myTlen] = 0
+                    count_dict[myTlen] += 1
+                    i += 1
+                    if i % PRINT_EVERY == 0:
+                        print('---', i, quick_median(count_dict), median_deviation_from_median(count_dict))
     return count_dict
 
 
@@ -83,10 +84,11 @@ def compute_probs(count_dict: dict) -> (list, list):
     probabilities = [n / countSum for n in probabilities]
     return values, probabilities
 
+
 def main():
     parser = argparse.ArgumentParser(description="computeFraglen.py")
-    parser.add_argument('-i', type=str, required=True, default=None, help="Sam file input (samtools view name.bam > name.sam")
-    parser.add_argument('-o', type=str, required=True, default=None, help="Prefix for output")
+    parser.add_argument('-i', type=str, metavar="input", required=True, default=None, help="Sam file input (samtools view name.bam > name.sam)")
+    parser.add_argument('-o', type=str, metavar="output", required=True, default=None, help="Prefix for output")
 
     args = parser.parse_args()
     input_file = args.i
@@ -100,5 +102,5 @@ def main():
     pickle.dump([out_vals, out_probs], open(output, 'wb'))
 
 
-if __name__ == "__main()":
+if __name__ == "__main__":
     main()

From a2130e2e3b324208e0efb9da1698a1339cbf56de Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 5 May 2020 13:42:04 -0500
Subject: [PATCH 036/323] playing at installing pysam for compute fraglen so it
 can read bams as well as sams

---
 utilities/computeFraglen.py | 50 +++++++++++++++++++++++++++++++------
 1 file changed, 43 insertions(+), 7 deletions(-)

diff --git a/utilities/computeFraglen.py b/utilities/computeFraglen.py
index 136cea1..22dfa3f 100644
--- a/utilities/computeFraglen.py
+++ b/utilities/computeFraglen.py
@@ -12,12 +12,15 @@
 import fileinput
 import pickle
 import argparse
+import pysam
 
-FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality
-FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it
-FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median
 
-def quick_median(count_dict):
+def quick_median(count_dict: dict) -> int:
+    """
+    Finds the median of a counting dictionary
+    :param count_dict: the counting dictionary to find the median of
+    :return: integer index of the location of the median
+    """
     midPoint = sum(count_dict.values()) // 2
     mySum = 0
     myInd = 0
@@ -30,7 +33,13 @@ def quick_median(count_dict):
     return myInd
 
 
-def median_deviation_from_median(count_dict):
+def median_deviation_from_median(count_dict: dict) -> int:
+    """
+    calculates the deviation from the median of each element of counting dictionary,
+    then returns the median of that dictionary
+    :param count_dict: Counting dictionary to analyze
+    :return: index of median of the deviations
+    """
     myMedian = quick_median(count_dict)
     deviations = {}
     for k in sorted(count_dict.keys()):
@@ -40,6 +49,13 @@ def median_deviation_from_median(count_dict):
 
 
 def count_frags(file: str) -> dict:
+    """
+    Takes a sam file input and creates a counting dictionary of the number of reads that are paired,
+    first in the pair, confidently mapped and whose pair is mapped to the same reference
+    :param file: A sam input file
+    :return: A dictionary of the counts of the above reads
+    """
+    FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality
     count_dict = {}
     PRINT_EVERY = 100000
     i = 0
@@ -69,13 +85,21 @@ def count_frags(file: str) -> dict:
 
 
 def compute_probs(count_dict: dict) -> (list, list):
+    """
+    Computes the probabilities for fragments with at least 100 pairs supporting it and that are at least 10 median
+    deviations from the median.
+    :param count_dict: A dictionary of fragments with counts
+    :return: A list of values that meet the criteria and a list of -their associated probabilities
+    """
+    FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it
+    FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median
     values = []
     probabilities = []
     med = quick_median(count_dict)
     mdm = median_deviation_from_median(count_dict)
 
     for k in sorted(count_dict.keys()):
-        if k > 0 and k < med + FILTER_MEDDEV_M * mdm:
+        if 0 < k < med + FILTER_MEDDEV_M * mdm:
             if count_dict[k] >= FILTER_MINREADS:
                 print(k, count_dict[k])
                 values.append(k)
@@ -86,8 +110,20 @@ def compute_probs(count_dict: dict) -> (list, list):
 
 
 def main():
+    """
+    Main function takes 2 arguments:
+        input - a samfile input that can be formed by applying samtools to a bam file
+        in the follawing way: samtools view nameof.bam > nameof.sam
+
+        output - the prefix of the output. The actual output will be the prefix plus ".p" at the end
+        for pickle file. The list of values and list of probabilities are dumped as a list of lists
+        into a pickle file on completion of the analysis
+
+    :return: None
+    """
     parser = argparse.ArgumentParser(description="computeFraglen.py")
-    parser.add_argument('-i', type=str, metavar="input", required=True, default=None, help="Sam file input (samtools view name.bam > name.sam)")
+    parser.add_argument('-i', type=str, metavar="input", required=True, default=None,
+                        help="Sam file input (samtools view name.bam > name.sam)")
     parser.add_argument('-o', type=str, metavar="output", required=True, default=None, help="Prefix for output")
 
     args = parser.parse_args()

From c93a6ab2181a892d7c80425621b96f305e5ef583 Mon Sep 17 00:00:00 2001
From: Joshua Allen <joshfactorial@pop-os.localdomain>
Date: Tue, 5 May 2020 15:43:24 -0500
Subject: [PATCH 037/323] added bam reading capability to computeFraglen.py

---
 utilities/computeFraglen.py | 53 +++++++++++++++++++++----------------
 1 file changed, 30 insertions(+), 23 deletions(-)

diff --git a/utilities/computeFraglen.py b/utilities/computeFraglen.py
index 22dfa3f..502fa80 100644
--- a/utilities/computeFraglen.py
+++ b/utilities/computeFraglen.py
@@ -9,7 +9,6 @@
 #
 # Python 3 ready
 
-import fileinput
 import pickle
 import argparse
 import pysam
@@ -59,28 +58,36 @@ def count_frags(file: str) -> dict:
     count_dict = {}
     PRINT_EVERY = 100000
     i = 0
-    with open(file, 'r') as f:
-        for line in f:
-            # Skip all comments and headers
-            if line[0] == '#' or line[0] == '@':
-                continue
-            splt = line.strip().split('\t')
-            samFlag = int(splt[1])
-            myRef = splt[2]
-            mapQual = int(splt[4])
-            mateRef = splt[6]
-            myTlen = abs(int(splt[8]))
-
-            # if read is paired, and is first in pair, and is confidently mapped...
-            if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:
-                # and mate is mapped to same reference
-                if mateRef == '=' or mateRef == myRef:
-                    if myTlen not in count_dict:
-                        count_dict[myTlen] = 0
-                    count_dict[myTlen] += 1
-                    i += 1
-                    if i % PRINT_EVERY == 0:
-                        print('---', i, quick_median(count_dict), median_deviation_from_median(count_dict))
+    if file[-4:] == ".sam":
+        file_to_parse = open(file, 'r')
+    elif file[-4:] == ".bam":
+        file_to_parse = pysam.AlignmentFile(file, 'rb')
+    else:
+        print("Unknown file type, must be bam or sam")
+        exit(1)
+
+    for item in file_to_parse:
+        line = str(item)
+        # Skip all comments and headers
+        if line[0] == '#' or line[0] == '@':
+            continue
+        splt = line.strip().split('\t')
+        samFlag = int(splt[1])
+        myRef = splt[2]
+        mapQual = int(splt[4])
+        mateRef = splt[6]
+        myTlen = abs(int(splt[8]))
+
+        # if read is paired, and is first in pair, and is confidently mapped...
+        if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:
+            # and mate is mapped to same reference
+            if mateRef == '=' or mateRef == myRef:
+                if myTlen not in count_dict:
+                    count_dict[myTlen] = 0
+                count_dict[myTlen] += 1
+                i += 1
+                if i % PRINT_EVERY == 0:
+                    print('---', i, quick_median(count_dict), median_deviation_from_median(count_dict))
     return count_dict
 
 

From 84e93e82dd36a802885ba46dfa59a9ed0dddf58c Mon Sep 17 00:00:00 2001
From: Joshua Allen <joshfactorial@pop-os.localdomain>
Date: Wed, 6 May 2020 10:16:47 -0500
Subject: [PATCH 038/323] Added a comment to computeFraglen.py

---
 utilities/computeFraglen.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/utilities/computeFraglen.py b/utilities/computeFraglen.py
index 502fa80..04d131d 100644
--- a/utilities/computeFraglen.py
+++ b/utilities/computeFraglen.py
@@ -58,6 +58,7 @@ def count_frags(file: str) -> dict:
     count_dict = {}
     PRINT_EVERY = 100000
     i = 0
+    # Check if the file is sam or bam and decide how to open based on that
     if file[-4:] == ".sam":
         file_to_parse = open(file, 'r')
     elif file[-4:] == ".bam":
@@ -67,6 +68,7 @@ def count_frags(file: str) -> dict:
         exit(1)
 
     for item in file_to_parse:
+        # Need to convert bam iterable objects into strings for the next part
         line = str(item)
         # Skip all comments and headers
         if line[0] == '#' or line[0] == '@':

From b26a527dfabb195889aa9cd14aee59d0ce4bc1ad Mon Sep 17 00:00:00 2001
From: Joshua Allen <joshfactorial@pop-os.localdomain>
Date: Wed, 6 May 2020 10:25:13 -0500
Subject: [PATCH 039/323] Added a comment to computeFraglen.py

---
 utilities/computeFraglen.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utilities/computeFraglen.py b/utilities/computeFraglen.py
index 04d131d..89e9b6c 100644
--- a/utilities/computeFraglen.py
+++ b/utilities/computeFraglen.py
@@ -7,7 +7,7 @@
 #      Usage: samtools view normal.bam | python computeFraglen.py
 #
 #
-# Python 3 ready
+# Upgraded 5/6/2020 to match Python 3 standards and refactored for easier reading
 
 import pickle
 import argparse

From fce987409603cf26e26ddcf9fe80de7be4363cd2 Mon Sep 17 00:00:00 2001
From: Joshua Allen <joshfactorial@pop-os.localdomain>
Date: Wed, 6 May 2020 11:44:20 -0500
Subject: [PATCH 040/323] trying out seqio for reading the fasta in
 computeGC.py

---
 utilities/computeGC.py | 211 ++++++++++++++++++++++-------------------
 1 file changed, 115 insertions(+), 96 deletions(-)

diff --git a/utilities/computeGC.py b/utilities/computeGC.py
index 1cc0b15..67126c2 100644
--- a/utilities/computeGC.py
+++ b/utilities/computeGC.py
@@ -15,99 +15,118 @@
 import argparse
 import numpy as np
 import pickle
-
-parser = argparse.ArgumentParser(description='computeGC.py')
-parser.add_argument('-i', type=str, required=True, metavar='<str>', help="* input.genomecov")
-parser.add_argument('-r', type=str, required=True, metavar='<str>', help="* reference.fa")
-parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output.p")
-parser.add_argument('-w', type=int, required=False, metavar='<int>', help="sliding window length [50]", default=50)
-args = parser.parse_args()
-
-(IN_GCB, REF_FILE, WINDOW_SIZE, OUT_P) = (args.i, args.r, args.w, args.o)
-
-GC_BINS = {n: [] for n in range(WINDOW_SIZE + 1)}
-
-print('reading ref...')
-allRefs = {}
-f = open(REF_FILE, 'r')
-for line in f:
-    if line[0] == '>':
-        refName = line.strip()[1:]
-        allRefs[refName] = []
-        print(refName)
-    # if refName == 'chr2':
-    #	break
-    else:
-        allRefs[refName].append(line.strip())
-f.close()
-
-print('capitalizing ref...')
-for k in sorted(allRefs.keys()):
-    print(k)
-    allRefs[k] = ''.join(allRefs[k])
-    allRefs[k] = allRefs[k].upper()
-
-print('reading genomecov file...')
-tt = time.time()
-f = open(IN_GCB, 'r')
-currentLine = 0
-currentRef = None
-currentCov = 0
-linesProcessed = 0
-PRINT_EVERY = 1000000
-STOP_AFTER = 1000000
-for line in f:
-    splt = line.strip().split('\t')
-    if linesProcessed % PRINT_EVERY == 0:
-        print(linesProcessed)
-    linesProcessed += 1
-
-    # if linesProcessed > STOP_AFTER:
-    #	break
-
-    if currentLine == 0:
-        currentRef = splt[0]
-        sPos = int(splt[1]) - 1
-
-    if currentRef not in allRefs:
-        continue
-
-    currentLine += 1
-    currentCov += float(splt[2])
-
-    if currentLine == WINDOW_SIZE:
-        currentLine = 0
-        seq = allRefs[currentRef][sPos:sPos + WINDOW_SIZE]
-        if 'N' not in seq:
-            gc_count = seq.count('G') + seq.count('C')
-            GC_BINS[gc_count].append(currentCov)
-        currentCov = 0
-
-f.close()
-
-runningTot = 0
-allMean = 0.0
-for k in sorted(GC_BINS.keys()):
-    if len(GC_BINS[k]) == 0:
-        print('{0:0.2%}'.format(k / float(WINDOW_SIZE)), 0.0, 0)
-        GC_BINS[k] = 0
-    else:
-        myMean = np.mean(GC_BINS[k])
-        myLen = len(GC_BINS[k])
-        print('{0:0.2%}'.format(k / float(WINDOW_SIZE)), myMean, myLen)
-        allMean += myMean * myLen
-        runningTot += myLen
-        GC_BINS[k] = myMean
-
-avgCov = allMean / float(runningTot)
-print('AVERAGE COVERAGE =', avgCov)
-
-y_out = []
-for k in sorted(GC_BINS.keys()):
-    GC_BINS[k] /= avgCov
-    y_out.append(GC_BINS[k])
-
-print('saving model...')
-pickle.dump([range(WINDOW_SIZE + 1), y_out], open(OUT_P, 'wb'))
-
-print(time.time() - tt, '(sec)')
+from Bio import SeqIO
+
+
+def process_fasta(file: str) -> dict:
+    ref_dict = {}
+
+    try:
+        ref_dict = SeqIO.to_dict(SeqIO.parse(file, 'fasta'))
+        # ref_dict = {rec.id: rec.seq for rec in SeqIO.parse(file, "fasta")}
+    except UnicodeDecodeError:
+        print("Input file incorrect: -r should specify the reference fasta")
+        exit(1)
+
+    if not ref_dict:
+        print("Input file incorrect: -r should specify the reference fasta")
+        exit(1)
+
+    return ref_dict
+
+
+def capitalize_ref(input_dict: dict) -> dict:
+    for k in sorted(input_dict.keys()):
+        print("Capitalizing " + k)
+        input_dict[k] = ''.join(input_dict[k])
+        input_dict[k] = input_dict[k].upper()
+
+
+def main():
+    parser = argparse.ArgumentParser(description='computeGC.py')
+    parser.add_argument('-i', type=str, required=True, metavar='input', help="input.genomecov")
+    parser.add_argument('-r', type=str, required=True, metavar='reference', help="reference.fasta")
+    parser.add_argument('-o', type=str, required=True, metavar='output prefix',
+                        help="prefix for output (/path/to/output)")
+    parser.add_argument('-w', type=int, required=False, metavar='sliding window',
+                        help="sliding window length [50]", default=50)
+    args = parser.parse_args()
+
+    (IN_GCB, REF_FILE, WINDOW_SIZE, OUT_P) = (args.i, args.r, args.w, args.o)
+
+    GC_BINS = {n: [] for n in range(WINDOW_SIZE + 1)}
+
+    print('reading ref...')
+    allrefs = process_fasta(REF_FILE)
+
+    print('capitalizing ref...')
+    allrefs = capitalize_ref(allrefs)
+
+    print('reading genomecov file...')
+    tt = time.time()
+    f = open(IN_GCB, 'r')
+    currentLine = 0
+    currentRef = None
+    currentCov = 0
+    linesProcessed = 0
+    PRINT_EVERY = 1000000
+    STOP_AFTER = 1000000
+    for line in f:
+        splt = line.strip().split('\t')
+        if linesProcessed % PRINT_EVERY == 0:
+            print(linesProcessed)
+        linesProcessed += 1
+
+        # if linesProcessed > STOP_AFTER:
+        #	break
+
+        if currentLine == 0:
+            currentRef = splt[0]
+            sPos = int(splt[1]) - 1
+
+        if currentRef not in allrefs:
+            continue
+
+        currentLine += 1
+        currentCov += float(splt[2])
+
+        if currentLine == WINDOW_SIZE:
+            currentLine = 0
+            seq = allrefs[currentRef][sPos:sPos + WINDOW_SIZE]
+            if 'N' not in seq:
+                gc_count = seq.count('G') + seq.count('C')
+                GC_BINS[gc_count].append(currentCov)
+            currentCov = 0
+
+    f.close()
+
+    runningTot = 0
+    allMean = 0.0
+    for k in sorted(GC_BINS.keys()):
+        if len(GC_BINS[k]) == 0:
+            print('{0:0.2%}'.format(k / float(WINDOW_SIZE)), 0.0, 0)
+            GC_BINS[k] = 0
+        else:
+            myMean = np.mean(GC_BINS[k])
+            myLen = len(GC_BINS[k])
+            print('{0:0.2%}'.format(k / float(WINDOW_SIZE)), myMean, myLen)
+            allMean += myMean * myLen
+            runningTot += myLen
+            GC_BINS[k] = myMean
+
+    avgCov = allMean / float(runningTot)
+    print('AVERAGE COVERAGE =', avgCov)
+
+    y_out = []
+    for k in sorted(GC_BINS.keys()):
+        GC_BINS[k] /= avgCov
+        y_out.append(GC_BINS[k])
+
+    print('saving model...')
+    pickle.dump([range(WINDOW_SIZE + 1), y_out], open(OUT_P, 'wb'))
+
+    print(time.time() - tt, '(sec)')
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file

From 97235d1cf3b9d1e2764ac25c1f63a1145fe7de39 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 7 May 2020 13:46:56 -0500
Subject: [PATCH 041/323] Brining computeGC.py up to python 3 standards

---
 utilities/computeGC.py | 157 +++++++++++++++++++++++------------------
 1 file changed, 88 insertions(+), 69 deletions(-)

diff --git a/utilities/computeGC.py b/utilities/computeGC.py
index 67126c2..2ca14c5 100644
--- a/utilities/computeGC.py
+++ b/utilities/computeGC.py
@@ -19,30 +19,98 @@
 
 
 def process_fasta(file: str) -> dict:
+    """
+    Takes a fasta file, converts it into a dictionary of upper case sequences. Does some basic error checking,
+    like the file is readable and the reference dictionary is not empty
+    :param file: path to a fasta file
+    :return: dictionary form of the sequences indexed by chromosome
+    """
     ref_dict = {}
 
     try:
-        ref_dict = SeqIO.to_dict(SeqIO.parse(file, 'fasta'))
-        # ref_dict = {rec.id: rec.seq for rec in SeqIO.parse(file, "fasta")}
+        # reads in fasta file, converts sequence to upper case
+        ref_dict = {rec.id: rec.seq.upper() for rec in SeqIO.parse(file, "fasta")}
     except UnicodeDecodeError:
+        # if the file isn't readable, this exception should catch it
         print("Input file incorrect: -r should specify the reference fasta")
         exit(1)
 
     if not ref_dict:
+        # if the file was readable by SeqIO but wasn't a fasta file, this should catch it
         print("Input file incorrect: -r should specify the reference fasta")
         exit(1)
 
     return ref_dict
 
 
-def capitalize_ref(input_dict: dict) -> dict:
-    for k in sorted(input_dict.keys()):
-        print("Capitalizing " + k)
-        input_dict[k] = ''.join(input_dict[k])
-        input_dict[k] = input_dict[k].upper()
+def process_genomecov(file: str, ref_dict: dict, window) -> dict:
+
+    gc_bins = {n: [] for n in range(window + 1)}
+
+    # variables needed to parse coverage file
+    current_line = 0
+    current_ref = None
+    current_cov = 0
+    lines_processed = 0
+    print_every = 1000000
+
+    f = open(file, 'r')
+    for line in f:
+        splt = line.strip().split('\t')
+        if lines_processed % print_every == 0:
+            print(lines_processed)
+        lines_processed += 1
+        if current_line == 0:
+            current_ref = splt[0]
+            sPos = int(splt[1]) - 1
+
+        if current_ref not in ref_dict:
+            continue
+
+        current_line += 1
+        current_cov += float(splt[2])
+
+        if current_line == window:
+            current_line = 0
+            seq = str(ref_dict[current_ref][sPos:sPos + window])
+            if 'N' not in seq:
+                gc_count = seq.count('G') + seq.count('C')
+                gc_bins[gc_count].append(current_cov)
+            current_cov = 0
+
+    f.close()
+    return gc_bins
+
+
+def calculate_coverage(bin_dict: dict, window: int) -> float:
+    running_total = 0
+    all_mean = 0.0
+    for k in sorted(bin_dict.keys()):
+        if len(bin_dict[k]) == 0:
+            print('{0:0.2%}'.format(k / float(window)), 0.0, 0)
+            bin_dict[k] = 0
+        else:
+            myMean = np.mean(bin_dict[k])
+            myLen = len(bin_dict[k])
+            print('{0:0.2%}'.format(k / float(window)), myMean, myLen)
+            all_mean += myMean * myLen
+            running_total += myLen
+            bin_dict[k] = myMean
+
+    return all_mean / float(running_total)
 
 
 def main():
+    """
+    Reads in arguments and processes the inputs to a GC count for the sequence.
+    Parameters:
+        -i is the genome coverage input file
+        -r is the reference file
+        -o is the prefix for the output
+        -w is the sliding window length. The default is 50, but you can declare any reasonable integer
+
+    :return: None
+    """
     parser = argparse.ArgumentParser(description='computeGC.py')
     parser.add_argument('-i', type=str, required=True, metavar='input', help="input.genomecov")
     parser.add_argument('-r', type=str, required=True, metavar='reference', help="reference.fasta")
@@ -52,81 +120,32 @@ def main():
                         help="sliding window length [50]", default=50)
     args = parser.parse_args()
 
-    (IN_GCB, REF_FILE, WINDOW_SIZE, OUT_P) = (args.i, args.r, args.w, args.o)
+    (in_gcb, ref_file, window_size, out_p) = (args.i, args.r, args.w, args.o)
 
-    GC_BINS = {n: [] for n in range(WINDOW_SIZE + 1)}
 
-    print('reading ref...')
-    allrefs = process_fasta(REF_FILE)
 
-    print('capitalizing ref...')
-    allrefs = capitalize_ref(allrefs)
+    print('Reading ref...')
+    allrefs = process_fasta(ref_file)
 
-    print('reading genomecov file...')
     tt = time.time()
-    f = open(IN_GCB, 'r')
-    currentLine = 0
-    currentRef = None
-    currentCov = 0
-    linesProcessed = 0
-    PRINT_EVERY = 1000000
-    STOP_AFTER = 1000000
-    for line in f:
-        splt = line.strip().split('\t')
-        if linesProcessed % PRINT_EVERY == 0:
-            print(linesProcessed)
-        linesProcessed += 1
-
-        # if linesProcessed > STOP_AFTER:
-        #	break
-
-        if currentLine == 0:
-            currentRef = splt[0]
-            sPos = int(splt[1]) - 1
-
-        if currentRef not in allrefs:
-            continue
-
-        currentLine += 1
-        currentCov += float(splt[2])
-
-        if currentLine == WINDOW_SIZE:
-            currentLine = 0
-            seq = allrefs[currentRef][sPos:sPos + WINDOW_SIZE]
-            if 'N' not in seq:
-                gc_count = seq.count('G') + seq.count('C')
-                GC_BINS[gc_count].append(currentCov)
-            currentCov = 0
-
-    f.close()
+    print('Reading genome coverage file...')
+    gc_bins = process_genomecov(in_gcb, allrefs, window_size)
 
-    runningTot = 0
-    allMean = 0.0
-    for k in sorted(GC_BINS.keys()):
-        if len(GC_BINS[k]) == 0:
-            print('{0:0.2%}'.format(k / float(WINDOW_SIZE)), 0.0, 0)
-            GC_BINS[k] = 0
-        else:
-            myMean = np.mean(GC_BINS[k])
-            myLen = len(GC_BINS[k])
-            print('{0:0.2%}'.format(k / float(WINDOW_SIZE)), myMean, myLen)
-            allMean += myMean * myLen
-            runningTot += myLen
-            GC_BINS[k] = myMean
+    print("Calculating average coverage")
+    average_coverage = calculate_coverage(gc_bins, window_size)
 
-    avgCov = allMean / float(runningTot)
-    print('AVERAGE COVERAGE =', avgCov)
+    print('AVERAGE COVERAGE =', average_coverage)
 
     y_out = []
-    for k in sorted(GC_BINS.keys()):
-        GC_BINS[k] /= avgCov
-        y_out.append(GC_BINS[k])
+    for k in sorted(gc_bins.keys()):
+        gc_bins[k] /= average_coverage
+        y_out.append(gc_bins[k])
 
     print('saving model...')
-    pickle.dump([range(WINDOW_SIZE + 1), y_out], open(OUT_P, 'wb'))
+    pickle.dump([range(window_size + 1), y_out], open(out_p, 'wb'))
 
     print(time.time() - tt, '(sec)')
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()

From 0f90919fff329822f04c7c849a4a30a00d3f565d Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 May 2020 10:01:49 -0500
Subject: [PATCH 042/323] Bringing computeGC.py up to python 3 standards

---
 utilities/computeGC.py | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/utilities/computeGC.py b/utilities/computeGC.py
index 2ca14c5..c642dfd 100644
--- a/utilities/computeGC.py
+++ b/utilities/computeGC.py
@@ -9,7 +9,7 @@
 #          python computeGC.py -r reference.fa -i genomeCov.dat -w [sliding window length] -o output_name.p
 #
 #
-# Python 3 ready
+# Updated to Python 3 standards
 
 import time
 import argparse
@@ -44,7 +44,6 @@ def process_fasta(file: str) -> dict:
 
 
 def process_genomecov(file: str, ref_dict: dict, window) -> dict:
-
     gc_bins = {n: [] for n in range(window + 1)}
 
     # variables needed to parse coverage file
@@ -57,8 +56,6 @@ def process_genomecov(file: str, ref_dict: dict, window) -> dict:
     f = open(file, 'r')
     for line in f:
         splt = line.strip().split('\t')
-        if lines_processed % print_every == 0:
-            print(lines_processed)
         lines_processed += 1
         if current_line == 0:
             current_ref = splt[0]
@@ -122,8 +119,6 @@ def main():
 
     (in_gcb, ref_file, window_size, out_p) = (args.i, args.r, args.w, args.o)
 
-
-
     print('Reading ref...')
     allrefs = process_fasta(ref_file)
 
@@ -131,7 +126,7 @@ def main():
     print('Reading genome coverage file...')
     gc_bins = process_genomecov(in_gcb, allrefs, window_size)
 
-    print("Calculating average coverage")
+    print("Calculating average coverage...")
     average_coverage = calculate_coverage(gc_bins, window_size)
 
     print('AVERAGE COVERAGE =', average_coverage)

From 49119cac6377e5a36d30e5cb889c9be423989e5b Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 May 2020 15:46:13 -0500
Subject: [PATCH 043/323] renaming some modules, reworking genMutModel.py

---
 genReads.py                                   |   4 +-
 py/{refFunc.py => ref_func.py}                |   3 +-
 .../{computeFraglen.py => compute_fraglen.py} |   6 +-
 utilities/{computeGC.py => compute_gc.py}     |   6 +-
 ...indNucleotideContextOnReference.healthy.pl | 508 ------------------
 .../FindNucleotideContextOnReference.pl       | 307 -----------
 utilities/deprecated/README.md                |  16 -
 utilities/genMutModel.py                      | 466 ++++++++--------
 8 files changed, 229 insertions(+), 1087 deletions(-)
 rename py/{refFunc.py => ref_func.py} (99%)
 rename utilities/{computeFraglen.py => compute_fraglen.py} (96%)
 rename utilities/{computeGC.py => compute_gc.py} (95%)
 delete mode 100755 utilities/deprecated/FindNucleotideContextOnReference.healthy.pl
 delete mode 100755 utilities/deprecated/FindNucleotideContextOnReference.pl
 delete mode 100644 utilities/deprecated/README.md

diff --git a/genReads.py b/genReads.py
index 684b624..cdf00f6 100644
--- a/genReads.py
+++ b/genReads.py
@@ -26,7 +26,7 @@
 import argparse
 
 from py.inputChecking import requiredField, checkFileOpen, isInRange
-from py.refFunc import indexRef, readRef, getAllRefRegions, partitionRefRegions
+from py.ref_func import index_ref, readRef, getAllRefRegions, partitionRefRegions
 from py.vcfFunc import parseVCF
 from py.OutputFileWriter import OutputFileWriter
 from py.probability import DiscreteDistribution, mean_ind_of_weighted_list
@@ -263,7 +263,7 @@ def main():
 
     ALLOWED_NUCL = ['A', 'C', 'G', 'T']
     # index reference
-    refIndex = indexRef(REFERENCE)
+    refIndex = index_ref(REFERENCE)
     if PAIRED_END:
         N_HANDLING = ('random', FRAGMENT_SIZE)
     else:
diff --git a/py/refFunc.py b/py/ref_func.py
similarity index 99%
rename from py/refFunc.py
rename to py/ref_func.py
index 58aca20..e00f383 100644
--- a/py/refFunc.py
+++ b/py/ref_func.py
@@ -4,11 +4,10 @@
 import random
 from Bio.Seq import Seq
 from Bio.Alphabet import IUPAC
-# from Bio.bgzf import *
 
 
 #	Index reference fasta
-def indexRef(refPath):
+def index_ref(refPath):
     tt = time.time()
 
     fn = None
diff --git a/utilities/computeFraglen.py b/utilities/compute_fraglen.py
similarity index 96%
rename from utilities/computeFraglen.py
rename to utilities/compute_fraglen.py
index 89e9b6c..bfbaa4b 100644
--- a/utilities/computeFraglen.py
+++ b/utilities/compute_fraglen.py
@@ -1,10 +1,10 @@
 #
 #
 #      Compute Fragment Length Model for genReads.py
-#                  computeFraglen.py
+#                  compute_fraglen.py
 #
 #
-#      Usage: samtools view normal.bam | python computeFraglen.py
+#      Usage: samtools view normal.bam | python compute_fraglen.py
 #
 #
 # Upgraded 5/6/2020 to match Python 3 standards and refactored for easier reading
@@ -130,7 +130,7 @@ def main():
 
     :return: None
     """
-    parser = argparse.ArgumentParser(description="computeFraglen.py")
+    parser = argparse.ArgumentParser(description="compute_fraglen.py")
     parser.add_argument('-i', type=str, metavar="input", required=True, default=None,
                         help="Sam file input (samtools view name.bam > name.sam)")
     parser.add_argument('-o', type=str, metavar="output", required=True, default=None, help="Prefix for output")
diff --git a/utilities/computeGC.py b/utilities/compute_gc.py
similarity index 95%
rename from utilities/computeGC.py
rename to utilities/compute_gc.py
index c642dfd..5d9b7bc 100644
--- a/utilities/computeGC.py
+++ b/utilities/compute_gc.py
@@ -1,12 +1,12 @@
 #
 #
-#   computeGC.py
+#   compute_gc.py
 #   Compute GC and coverage model for genReads.py
 #
 #   Takes output file from bedtools genomecov to generate GC/coverage model
 #
 #   Usage: bedtools genomecov -d -ibam input.bam -g reference.fa > genomeCov.dat
-#          python computeGC.py -r reference.fa -i genomeCov.dat -w [sliding window length] -o output_name.p
+#          python compute_gc.py -r reference.fa -i genomeCov.dat -w [sliding window length] -o output_name.p
 #
 #
 # Updated to Python 3 standards
@@ -108,7 +108,7 @@ def main():
 
     :return: None
     """
-    parser = argparse.ArgumentParser(description='computeGC.py')
+    parser = argparse.ArgumentParser(description='compute_gc.py')
     parser.add_argument('-i', type=str, required=True, metavar='input', help="input.genomecov")
     parser.add_argument('-r', type=str, required=True, metavar='reference', help="reference.fasta")
     parser.add_argument('-o', type=str, required=True, metavar='output prefix',
diff --git a/utilities/deprecated/FindNucleotideContextOnReference.healthy.pl b/utilities/deprecated/FindNucleotideContextOnReference.healthy.pl
deleted file mode 100755
index ee982cb..0000000
--- a/utilities/deprecated/FindNucleotideContextOnReference.healthy.pl
+++ /dev/null
@@ -1,508 +0,0 @@
-#!/usr/bin/perl
-
-use strict;
-use Math::Round;
-
-
-if ($#ARGV < 1) {
-   print "parameter mismatch\nTo run type this command:\nperl $0 fastahack reference input_pos_file output_file human_gff_file\n\n";
-
-   print " first argument = full path to fastahack\n"; 
-   print " second argument = full path to reference genome\n"; 
-   print " third argument = input file with arbitrary number of columns, but 1st col=chromosome name and 2nd col=position\n"; 
-   print " fourth argument = output file with three columns: chromosome name, position of the center nucleotide, and the thre-nucleotide context for that position\n";
-   print " fifth argument = full path to human gff file\n\n\n"; 
-   exit 1;
-}
-
-
-my $Fastahack=$ARGV[0];
-my $Reference=$ARGV[1];
-open(InputPositions,             '<', $ARGV[2]) || die("Could not open file!");
-open(OutputTrinucleotideContext, '>', $ARGV[3]) || die("Could not open file!");
-open(HumanGFF,                   '<', $ARGV[4]) || die("Could not open file!");
-
-
-
-################ read in one coordinate at a time and execute fastahack on it
-
-# reading the header
-my $head = <InputPositions>;
-$head =~ s/\n|\r//;
-print OutputTrinucleotideContext "$head\tContext\n";
-my $gffHead = <HumanGFF>;
-chomp $gffHead;
-
-# creating trinucleotide context data hash, insertion and deletion counts
-my %trinucleotide_context_data;
-my %context_tally_across_mutated_to;
-my %gff_hash;
-my $gffMatch;
-my %location;
-# my %genotype_hash;
-my %insertion_hash;
-my %deletion_hash;   
-my $insertion_total;
-my $deletion_total;
-my $zygotes_total;
-my %annotation_hash;
-my $annotation_total;
-my %exonic_consequence_hash;
-my $intronic;
-my $exonic;
-my $intergenic;
-
-# reading the positional information
-my $line_count = 1;
-while (<InputPositions>) {
-   $_ =~ s/\n|\r//;
-   #print "$_\n";
-   my @line = split('\t', $_);
-
-   # getting the chromosome and coordinate fields from input file
-   # fastahack will need to the chromosome and coordinate to read the information from the reference
-   my $chromosome = $line[0];
-   my $coordinate = $line[1];
-
-   # get coordinates of first and last character in the context
-   my $start_region = $coordinate - 1;
-   my $end_region = $coordinate + 1;
-
-   # if the coordinate is the very first letter on the chromosome, then do not read before that position
-   # the context becomes 2 letter code, as opposed to a trinucleotide
-   if ( $start_region == 0 ) {
-      $start_region = 1;
-      $end_region = 2;
-   }
-
-   #print "$Fastahack -r $chromosome:$start_region..$end_region $Reference\n";
-   my $context = `$Fastahack -r $chromosome:$start_region..$end_region $Reference`;
-   
-   # capitalize context letters
-   $context = uc($context);
-
-   #### IF USING CONTROLLED DATA, split germline column into germline allele and mutated_to allele
-   # my @germline = split ('/', $line[6]);
-
-   # if germline allele does not equal reference allele, print "start_region germline allele end_region"
-   # specifically, replace the middle letter of the context with the germline allele
-   #print "$germline[0], $germline[1]\n";
-   # if ($germline[0] ne $germline[1]) {
-      # print "germline/reference mismatch, line number $line_count\n";
-      # if ($coordinate != 1) {
-         # substr($context,1,1)= $germline[1];   
-      # }
-      # else {
-        #  substr($context,0,1)= $germline[1];
-      # }
-   # }
-
-   print OutputTrinucleotideContext "$_\t$context";
-   
-
-   ###############################
-   # new section: forming the data structure
-   ###############################
-
-   # to create N_N contexts for data structure, context_code is defined as the trinucleotide context with a blank middle allele
-   my $context_code=$context;
-   $context_code =~ s/\n|\r//;
-   substr($context_code,1,1) = "_";
-   
-   # create variables for mutated_from and  mutated_to nucleotides
-   my $mutated_from = $line[3];
-   my $mutated_to = $line[4];
-
-   # creating genotype variable from column 10 of VCF
-   my $genotype = $line[9];
-
-   # incrementing each genotype
-   # $genotype_hash{$genotype} = $genotype_hash{$genotype} + 1;
-
-   # splitting heterozygosity by comma, defining heterozygosity total
-   my @zygotes = split (',', $mutated_to);
-   my $zygotes_length = scalar(@zygotes);
-
-   # identify heterozygosity, choose one at random to use. Count heterozygosity instances
-   if ($zygotes_length > 1) {
-      my $zygotesRand = $zygotes_length*rand();
-      my $zygotesRound = round($zygotesRand) - 1;
-      $zygotes_total = $zygotes_total + 1;
-
-      $mutated_to = $zygotes[$zygotesRound];
-      # print "@zygotes\t$mutated_to\n";
-   }  
-   # print "@zygotes\t$mutated_to\n";
-   
-   # my $round_rand_test = round(rand());
-   # print $round_rand_test;
-
-   # define length of insertions and deletions
-   my $insertion_length;
-   my $deletion_length;
-   if ($mutated_from eq "-") {
-      $insertion_length = length( $mutated_to );
-   }
-   else {
-      $insertion_length = length( $mutated_to ) - 1;
-   }
-   if ($mutated_to eq "-") {
-      $deletion_length = length( $mutated_from );
-   }
-   else {
-      $deletion_length = length( $mutated_from ) - 1;
-   }
-
-   # context_codes are totalled
-   $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to} = $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to} + 1; 
-   $context_tally_across_mutated_to{$context_code}{$mutated_from} = $context_tally_across_mutated_to{$context_code}{$mutated_from} + 1; 
-
-   # insertion and deletion lengths are totalled
-   if ($insertion_length > $deletion_length) {
-      $insertion_hash{$insertion_length} = $insertion_hash{$insertion_length} + 1;
-   }
-   if ($deletion_length > $insertion_length) {
-      $deletion_hash{$deletion_length} = $deletion_hash{$deletion_length} + 1;
-   }
-
-   # total insertions and deletions
-   if ($insertion_length != $deletion_length) {
-      if ($insertion_length > $deletion_length) {
-         $insertion_total = $insertion_total + 1;
-      }
-      elsif ($deletion_length > $insertion_length) {
-         $deletion_total = $deletion_total + 1;
-      } 
-   }
-
-   # Find variant annotation and exonic consequence in ANNOVAR outfile
-   my $annotation = $line[7];
-   if ( $annotation =~ /Func.refGene=(.{1,30});Gene\.refGene/ ) {
-      # print "$1\n";
-      $annotation_hash{$1}++;
-      $annotation_total++;
-   }   
-   if ( $annotation =~ /ExonicFunc.refGene=(.{1,30});AAChange\.refGene/ ) {
-      # print "$1\n";
-      $exonic_consequence_hash{$1}++;
-   } 
-   if ( $annotation =~ /Func.refGene=.{0,15}intronic\;/ ) {
-      $intronic++;
-   }
-   if ( $annotation !~ /Func.refGene=ncRNA_exonic/ ) {
-      if ( $annotation =~ /Func.refGene=.{0,15}exonic\;/ ) {
-         $exonic++;
-      }
-   }
-   if ( $annotation =~ /Func.refGene=.{0,15}intergenic\;/ ) {
-      $intergenic++;
-   }
-   elsif ( $annotation =~ /Func.refGene=.{0,15}ncRNA_splicing\;/ ) {
-      $intergenic++;
-   }
-   elsif ( $annotation =~ /Func.refGene=.{0,15}upstream\;/ ) {
-      $intergenic++;
-   }
-   elsif ( $annotation =~ /Func.refGene=.{0,15}downstream\;/ ) {
-      $intergenic++;
-   }
-   
-   $location{$coordinate}++;
-   # Reading input gff file, incrementing gff variant region hash
-   # while (<HumanGFF>) {
-      # $_ =~ s/\n|\r//;
-      # my @line = split('\t', $_);
-      # my $region_name = "$line[3]-$line[4]";
-      # if ($coordinate >= $line[3] && $coordinate <= $line[4]) {
-         # $gff_hash{$region_name}++;
-         # $gffMatch++;
-         # print "$coordinate $region_name\n";
-      # }
-   # }
-   #print "$region_name, $gff_hash{$region_name}\n";
-   
-
-   # to keep track of progress
-   # 1000000 for LARGE dbsnp vcfs, 10000 for smaller vcf/tsv tumor mutation files
-   unless ($line_count%10000) {
-      print "processed $line_count lines\n";
-   }
-   $line_count++; 
-} 
-# end working through the input file
-
-# print total number of mutations
-my $mutation_total = $line_count;
-print "Number of Mutations -- $mutation_total\n";
-
-
-################### Reading the input gff and creating custom BED file ####################
-
-my $gffBED = "vars.bed";
-open(my $bed_handle, '>', $gffBED) || die("Could not open file!");
-
-# Print BED file Header
-print $bed_handle "START\tEND\tVariant_Frequency\n";
-
-# Reading input gff file, incrementing gff variant region hash
-while (<HumanGFF>) {
-   $_ =~ s/\n|\r//;
-   my @line = split('\t', $_);
-   my $region_name = "$line[3]-$line[4]";
-   my $region_length = $line[4] - $line[3];
-   my $region_freq = 0;
-   foreach my $coordinate (sort(keys %location)) {
-      if ($coordinate >= $line[3] && $coordinate <= $line[4]) {
-         $gff_hash{$region_name}++;
-         $gffMatch++;
-         # print "$coordinate $region_name\n";
-      }
-   }
-   if ($gff_hash{$region_name} == 0) {
-      print $bed_handle "$line[3]\t$line[4]\t$region_freq\n";
-   }
-   if ($gff_hash{$region_name} > 0) {
-      $region_freq = $gff_hash{$region_name} / $region_length;
-      print $bed_handle "$line[3]\t$line[4]\t$region_freq\n";
-      print "Region $region_name variant frequency -- $region_freq\n";
-      print "Total variants in region $region_name -- $gff_hash{$region_name}\n";
-   }
-}
-   #print "$region_name, $gff_hash{$region_name}\n";
-
-print "GFF Match -- $gffMatch\n";
-
-
-######################### open files for writing ##########################
-
-
-# my $genotype_name = "zygosity.prob";
-# open(my $genotype_handle, '>', $genotype_name) || die("Could not open file!");
-   
-my $insertion_file_name = "SSM_insLength.prob";
-open(my $insertion_prob_handle, '>', $insertion_file_name) || die("Could not open file!");
-
-my $deletion_file_name = "SSM_delLength.prob";
-open(my $deletion_prob_handle, '>', $deletion_file_name) || die("Could not open file!");
-
-my $overall_file_name = "SSM_overall.prob";
-open(my $overall_prob_handle, '>', $overall_file_name) || die("Could not open file!");
-
-my $heterozygosity_file_name = "heterozygosity.prob";
-open(my $heterozygosity_prob_handle, '>', $heterozygosity_file_name) || die("Could not open file!");
-
-my $annotation_file_name = "annofreq.prob";
-open(my $annotation_handle, '>', $annotation_file_name) || die("Could not open file!");
-
-my $exonic_con_file_name = "exonic_consequences.prob";
-open(my $exonic_con_handle, '>', $exonic_con_file_name) || die("Could not open file!");
-
-my $intronic_file_name = "intronic_vars.prob";
-open(my $intronic_handle, '>', $intronic_file_name) || die("Could not open file!");
-
-my $exonic_file_name = "exonic_vars.prob";
-open(my $exonic_handle, '>', $exonic_file_name) || die ("Could not open file!");
-
-my $intergenic_file_name = "intergenic_vars.prob";
-open(my $intergenic_handle, '>', $intergenic_file_name) || die ("Could not open file!");
-
-
-######################### Calculate frequency models ####################### 
-
-
-# calculate zygosity ratio frequency, print to file
-# foreach my $genotype (sort(keys %genotype_hash)) {
-   # my $zygosity_frequency;
-   # $zygosity_frequency = $genotype_hash{$genotype}/$mutation_total;
-   # print $genotype_handle "$genotype\t$zygosity_frequency\n";
-   # print "Genotype, $genotype -- $genotype_hash{$genotype}\n";
-# }
-
-# print annotation and exonic consequence frequencies
-foreach $1 (sort(keys %annotation_hash)) {
-   my $annotation_frequency;
-   $annotation_frequency = $annotation_hash{$1}/$mutation_total;
-   print "$1 -- $annotation_hash{$1}, $annotation_frequency\n";
-   print $annotation_handle "$1\t$annotation_frequency\n";
-}
-foreach $1 (sort(keys %exonic_consequence_hash)) {
-   my $exonic_con_freq;
-   if ( $1 ne "." ) {
-      $exonic_con_freq = $exonic_consequence_hash{$1}/$mutation_total;
-      print "Exonic Consequence: $1 -- $exonic_consequence_hash{$1}, $exonic_con_freq\n";
-      print $exonic_con_handle "$1\t$exonic_con_freq\n";
-   }
-}
-
-# Calculating exonic, intronic, and intergenic frequencies, printing to files
-my $intronic_freq;
-my $exonic_freq;
-my $intergenic_freq;
-$intronic_freq = $intronic/$mutation_total;
-$exonic_freq = $exonic/$mutation_total;
-$intergenic_freq = $intergenic/$mutation_total;
-print $intronic_handle "$intronic_freq\n";
-print $exonic_handle "$exonic_freq\n";
-print $intergenic_handle "$intergenic_freq\n";
-
-print "Intronic -- $intronic\nExonic -- $exonic\nIntergenic -- $intergenic\n";
-#print "Total Annotations -- $annotation_total\n";
-
-# print overall likelihood file headers
-print $overall_prob_handle "mutation_type\tprobability\n";
-
-# print insertions and deletion probabilities out of all mutations
-my $insertion_prob_all = $insertion_total / $mutation_total;
-my $deletion_prob_all = $deletion_total / $mutation_total;
-print $overall_prob_handle "insertion\t$insertion_prob_all\ndeletion\t$deletion_prob_all\n";
-# print $overall_prob_handle "Deletion Probability -- $deletion_prob_all\n";
-
-# print InDel totals
-print "Insertions $insertion_total\n";
-print "Deletions $deletion_total\n";
-
-# print insertion and deletion headers
-print $insertion_prob_handle "insertion_length\tprobability\n";
-print $deletion_prob_handle "deletion_length\tprobability\n";
-
-# calculate InDel length totals and probability out of total number of insertions/deletions. Print probabilities to file.
-foreach my $insertion_length (sort(keys %insertion_hash)) {
-   my $insertion_probability;
-   $insertion_probability = $insertion_hash{$insertion_length}/$insertion_total;
-   print $insertion_prob_handle "$insertion_length\t$insertion_probability\n";
-   # print "Insertion, $insertion_length, total , $insertion_hash{$insertion_length}\n";
-}
-foreach my $deletion_length (sort(keys %deletion_hash)) {
-   my $deletion_probability;
-   $deletion_probability = $deletion_hash{$deletion_length}/$deletion_total;
-   print $deletion_prob_handle "$deletion_length\t$deletion_probability\n";
-   # print "Deletion, $deletion_length, total, $deletion_hash{$deletion_length}\n";
-}
- 
-# print heterozygosity frequency to file
-my $zygote_frequency = $zygotes_total / $mutation_total;
-print $heterozygosity_prob_handle "$zygote_frequency\n";
-
-print "heterozygous alleles -- $zygotes_total\n";
-
-
-# define nucleotide array
-my @nucleotides = ("A", "C", "G", "T");
-   
-foreach my $nt1 (@nucleotides) {
-   foreach my $nt3 (@nucleotides) {
-
-      # define the output file name and open it for writing
-      my $trinucleotide_SNP_probability_file_name = "Context".$nt1."-".$nt3.".trinuc";
-      open(my $trinuc_prob_handle, '>', $trinucleotide_SNP_probability_file_name) || die("Could not open file!");
-
-
-      # print trinucleotide contexts and corresponding totals for every mutated_to nucleotide
-      my $context_code=$nt1."_".$nt3;
-
-         #foreach my $mutated_from_nucl_key (keys %{ $trinucleotide_context_data{$context_code} }) {
-         foreach my $mutated_from (@nucleotides) {
-            # define the "mutated_to" keys in trinuc context hash
-            # my $mutated_to_nucl_key;
-
-            # the sum is only across mutated_to, and will be redefined for each mutated_from
-            my $context_sum_across_mutated_to = 0;
-            my $context_sum_across_indel = 0;
-
-            # print "\nRaw counts for mutated_from $mutated_from \n";
-
-
-            # foreach $mutated_to_nucl_key (keys %{ $trinucleotide_context_data{$context_code}{$mutated_from_nucl_key} }) {
-            foreach my $mutated_to (@nucleotides) {
-               my $mutated_from_length = length( $mutated_from );
-               my $mutated_to_length = length( $mutated_to );
-               if ( $mutated_from_length == 1 ) {
-                  if ( $mutated_from ne "-" ) {
-                     if ( $mutated_to_length == 1 ) {
-                        if ( $mutated_to ne "-" ) {
-                           # print "$context_code, $mutated_from_nucl_key, $mutated_to_nucl_key -- $trinucleotide_context_data{$context_code}{$mutated_from_nucl_key}{$mutated_to_nucl_key}\n";
-                           $context_sum_across_mutated_to = $context_sum_across_mutated_to + $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to};
-                        }# end if statement
-                        else {
-                           $context_sum_across_indel = $context_sum_across_indel + $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to};
-                        }# end else statement
-                     }# end if statement
-                     else {
-                        $context_sum_across_indel = $context_sum_across_indel + $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to};
-                     }# end else statement
-                  }# end if statement
-                  else {
-                     $context_sum_across_indel = $context_sum_across_indel + $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to};
-                  }# end else statement
-               }# end if statement
-               else {
-                  $context_sum_across_indel = $context_sum_across_indel + $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to};
-               }# end else statement
-               # print "$context_code, $mutated_from, $mutated_to-- $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to}\n";
-            }# end of loop over mutated_to
-
-            # print "\nProbabilities for mutated_from $mutated_from:\n";
-
-
-            foreach my $mutated_to (@nucleotides) {
-            #foreach $mutated_to_nucl_key (keys %{ $trinucleotide_context_data{$context_code}{$mutated_from_nucl_key} }) {
-               my $mutated_from_length = length( $mutated_from);
-               my $mutated_to_length = length( $mutated_to);
-               if ( $mutated_from_length == 1 ) {
-                  if ( $mutated_from ne "-" ) {
-                     if ( $mutated_to_length == 1 ) {
-                        if ( $mutated_to ne "-" ) {
-                           my $SNP_probability;
-                           if ( $context_sum_across_mutated_to == 0 ) {
-                              $SNP_probability = 0;
-                           }
-                           else {
-                              $SNP_probability = $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to}/$context_sum_across_mutated_to;
-                           }
-                           if ( $mutated_to eq "T" ) {
-                              print $trinuc_prob_handle "$SNP_probability";
-                           }
-                           else {
-                              # print "$context_code, $mutated_from, $mutated_to, context_sum_across_mutated_to=$context_sum_across_mutated_to -- $SNP_probability\n";
-                              print $trinuc_prob_handle "$SNP_probability\t";
-                           }
-                        }# end of if statement
-                        else {
-                           my $indel_probability = $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to}/$context_sum_across_indel;
-                           # print $indel_prob_handle "$context_code, $mutated_from, $mutated_to, context_sum_across_indel=$context_sum_across_indel -- $indel_probability\n";
-                        }# end else statement
-                     }# end of if statement
-                     else {
-                        # my $indel_probability = $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to}/$context_sum_across_indel;
-                        # print $indel_prob_handle "$context_code, $mutated_from, $mutated_to, context_sum_across_indel=$context_sum_across_indel -- $indel_probability\n";
-                     }# end else statement
-                  }# end of if statement
-                  else {
-                     # my $indel_probability = $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to}/$context_sum_across_indel;
-                     # print $indel_prob_handle "$context_code, $mutated_from, $mutated_to, context_sum_across_indel=$context_sum_across_indel -- $indel_probability\n";
-                  }# end else statement
-               }# end of if statement
-               else {
-                  my $indel_probability;
-                  if ( $context_sum_across_indel = 0 ) {
-                     $indel_probability = 0;
-                  }
-                  else {
-                     # $indel_probability = $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to}/$context_sum_across_indel;
-                     # print $indel_prob_handle "$context_code, $mutated_from, $mutated_to, context_sum_across_indel=$context_sum_across_indel -- $indel_probability\n";
-                  }
-               }# end else statement
-            }# end of loop over mutated_to
-            print $trinuc_prob_handle "\n";
-
-         }# end of loop over mutated_from
-
-     # print "\n\n";
-     
-
-  }# end loop over nt3
-}# end loop over nt1
-
-
-
-
diff --git a/utilities/deprecated/FindNucleotideContextOnReference.pl b/utilities/deprecated/FindNucleotideContextOnReference.pl
deleted file mode 100755
index 61fed2c..0000000
--- a/utilities/deprecated/FindNucleotideContextOnReference.pl
+++ /dev/null
@@ -1,307 +0,0 @@
-#!/usr/bin/perl
-
-use strict;
-
-
-if ($#ARGV < 1) {
-   print "parameter mismatch\nTo run type this command:\nperl $0 fastahack reference input_pos_file output_file\n\n";
-
-   print " first argument = full path to fastahack\n"; 
-   print " second argument = full path to reference genome\n"; 
-   print " third argument = input file with arbitrary number of columns, but 1st col=chromosome name and 2nd col=position\n"; 
-   print " fourth argument = output file with three columns: chromosome name, position of the center nucleotide, and the thre-nucleotide context for that position\n\n\n"; 
-   exit 1;
-}
-
-
-my $Fastahack=$ARGV[0];
-my $Reference=$ARGV[1];
-open(InputPositions,             '<', $ARGV[2]) || die("Could not open file!");
-open(OutputTrinucleotideContext, '>', $ARGV[3]) || die("Could not open file!");
-
-
-
-
-################ read in one coordinate at a time and execute fastahack on it
-
-# reading the header
-my $head = <InputPositions>;
-$head =~ s/\n|\r//;
-print OutputTrinucleotideContext "$head\tContext\n";
-
-# creating trinucleotide context data hash, insertion and deletion counts
-my %trinucleotide_context_data;
-my %context_tally_across_mutated_to;
-my %insertion_hash;
-my %deletion_hash;   
-my $insertion_total;
-my $deletion_total;
-
-
-# reading the positional information
-my $line_count = 1;
-while (<InputPositions>) {
-   $_ =~ s/\n|\r//;
-   #print "$_\n";
-   my @line = split('\t', $_);
-
-   # getting the chromosome and coordinate fields from input file
-   # fastahack will need to the chromosome and coordinate to read the information from the reference
-   my $chromosome = $line[0];
-   my $coordinate = $line[1];
-
-   # get coordinates of first and last character in the context
-   my $start_region = $coordinate - 1;
-   my $end_region = $coordinate + 1;
-
-   # if the coordinate is the very first letter on the chromosome, then do not read before that position
-   # the context becomes 2 letter code, as opposed to a trinucleotide
-   if ( $start_region == 0 ) {
-      $start_region = 1;
-      $end_region = 2;
-   }
-
-   #print "$Fastahack -r $chromosome:$start_region..$end_region $Reference\n";
-   my $context = `$Fastahack -r $chromosome:$start_region..$end_region $Reference`;
-   
-   # capitalize context letters
-   $context = uc($context);
-
-   # split germline column into germline allele and mutated_to allele
-   # my @germline = split ('/', $line[6]);
-
-   # if germline allele does not equal reference allele, print "start_region germline allele end_region"
-   # specifically, replace the middle letter of the context with the germline allele
-   #print "$germline[0], $germline[1]\n";
-   # if ($germline[0] ne $germline[1]) {
-      # print "germline/reference mismatch, line number $line_count\n";
-      # if ($coordinate != 1) {
-         # substr($context,1,1)= $germline[1];   
-      # }
-      # else {
-        #  substr($context,0,1)= $germline[1];
-      # }
-   # }
-
-   print OutputTrinucleotideContext "$_\t$context";
-   
-
-
-   ###############################
-   # new section: forming the data structure
-   ###############################
-
-   # to create N_N contexts for data structure, context_code is defined as the trinucleotide context with a blank middle allele
-   my $context_code=$context;
-   $context_code =~ s/\n|\r//;
-   substr($context_code,1,1) = "_";
-   
-   # create variables for mutated_from and  mutated_to nucleotides
-   my $mutated_from = $line[9];
-   my $mutated_to = $line[10];
-
-   # define length of insertions and deletions
-   # if ($mutated_from eq "-") {
-   my $insertion_length = length( $mutated_to );
-   # }
-
-   # if ($mutated_to eq "-") {
-   my $deletion_length = length( $mutated_from );
-   # }
-
-   # context_codes are totalled
-   $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to} = $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to} + 1; 
-   $context_tally_across_mutated_to{$context_code}{$mutated_from} = $context_tally_across_mutated_to{$context_code}{$mutated_from} + 1; 
-
-   # insertion and deletion lengths are totalled
-   if ($mutated_from eq "-") {
-      $insertion_hash{$insertion_length} = $insertion_hash{$insertion_length} + 1;
-   }
-   if ($mutated_to eq "-") {
-      $deletion_hash{$deletion_length} = $deletion_hash{$deletion_length} + 1;
-   }
-
-   # total insertions and deletions
-   if ($mutated_from eq "-") {
-      $insertion_total = $insertion_total + 1;
-   }
-   if ($mutated_to eq "-") {
-      $deletion_total = $deletion_total + 1;
-   }
-  
-
- 
-   # to keep track of progress
-   unless ($line_count%10000) {
-      print "processed $line_count lines\n";
-   }
-   $line_count++; 
-} 
-# end working through the input file
-
-# print total number of mutations
-my $mutation_total = $line_count - 1;
-print "Number of Mutations -- $mutation_total\n";
-   
-# define the output file name for insertions, deletions, and overall likelihoods. Open files for writing
-my $insertion_file_name = "Leukemia_OPEN_insLength.prob";
-open(my $insertion_prob_handle, '>', $insertion_file_name) || die("Could not open file!");
-
-my $deletion_file_name = "Leukemia_OPEN_delLength.prob";
-open(my $deletion_prob_handle, '>', $deletion_file_name) || die("Could not open file!");
-
-my $overall_file_name = "Leukemia_OPEN_overall.prob";
-open(my $overall_prob_handle, '>', $overall_file_name) || die("Could not open file!");
-
-# print overall likelihood file headers
-print $overall_prob_handle "mutation_type\tprobability\n";
-
-# print insertions and deletion probabilities out of all mutations
-my $insertion_prob_all = $insertion_total / $mutation_total;
-my $deletion_prob_all = $deletion_total / $mutation_total;
-print $overall_prob_handle "insertion\t$insertion_prob_all\ndeletion\t$deletion_prob_all\n";
-# print $overall_prob_handle "Deletion Probability -- $deletion_prob_all\n";
-
-# print InDel totals
-print "Insertions $insertion_total\n";
-print "Deletions $deletion_total\n";
-
-# print insertion and deletion headers
-print $insertion_prob_handle "insertion_length\tprobability\n";
-print $deletion_prob_handle "deletion_length\tprobability\n";
-
-# calculate InDel length totals and probability out of total number of insertions/deletions. Print probabilities to file.
-foreach my $insertion_length (sort(keys %insertion_hash)) {
-   my $insertion_probability;
-   $insertion_probability = $insertion_hash{$insertion_length}/$insertion_total;
-   print $insertion_prob_handle "$insertion_length\t$insertion_probability\n";
-   print "Insertion, $insertion_length, total , $insertion_hash{$insertion_length}\n";
-}
-foreach my $deletion_length (sort(keys %deletion_hash)) {
-   my $deletion_probability;
-   $deletion_probability = $deletion_hash{$deletion_length}/$deletion_total;
-   print $deletion_prob_handle "$deletion_length\t$deletion_probability\n";
-   print "Deletion, $deletion_length, total, $deletion_hash{$deletion_length}\n";
-}
- 
-
-# define nucleotide array
-my @nucleotides = ("A", "C", "G", "T");
-   
-foreach my $nt1 (@nucleotides) {
-   foreach my $nt3 (@nucleotides) {
-
-      # define the output file name and open it for writing
-      my $trinucleotide_SNP_probability_file_name = "Leukemia_OPEN_".$nt1."-".$nt3.".trinuc";
-      open(my $trinuc_prob_handle, '>', $trinucleotide_SNP_probability_file_name) || die("Could not open file!");
-
-
-      # print trinucleotide contexts and corresponding totals for every mutated_to nucleotide
-      my $context_code=$nt1."_".$nt3;
-
-         #foreach my $mutated_from_nucl_key (keys %{ $trinucleotide_context_data{$context_code} }) {
-         foreach my $mutated_from (@nucleotides) {
-            # define the "mutated_to" keys in trinuc context hash
-            # my $mutated_to_nucl_key;
-
-            # the sum is only across mutated_to, and will be redefined for each mutated_from
-            my $context_sum_across_mutated_to = 0;
-            my $context_sum_across_indel = 0;
-
-            # print "\nRaw counts for mutated_from $mutated_from \n";
-
-
-            # foreach $mutated_to_nucl_key (keys %{ $trinucleotide_context_data{$context_code}{$mutated_from_nucl_key} }) {
-            foreach my $mutated_to (@nucleotides) {
-               my $mutated_from_length = length( $mutated_from );
-               my $mutated_to_length = length( $mutated_to );
-               if ( $mutated_from_length == 1 ) {
-                  if ( $mutated_from ne "-" ) {
-                     if ( $mutated_to_length == 1 ) {
-                        if ( $mutated_to ne "-" ) {
-                           # print "$context_code, $mutated_from_nucl_key, $mutated_to_nucl_key -- $trinucleotide_context_data{$context_code}{$mutated_from_nucl_key}{$mutated_to_nucl_key}\n";
-                           $context_sum_across_mutated_to = $context_sum_across_mutated_to + $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to};
-                        }# end if statement
-                        else {
-                           $context_sum_across_indel = $context_sum_across_indel + $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to};
-                        }# end else statement
-                     }# end if statement
-                     else {
-                        $context_sum_across_indel = $context_sum_across_indel + $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to};
-                     }# end else statement
-                  }# end if statement
-                  else {
-                     $context_sum_across_indel = $context_sum_across_indel + $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to};
-                  }# end else statement
-               }# end if statement
-               else {
-                  $context_sum_across_indel = $context_sum_across_indel + $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to};
-               }# end else statement
-               # print "$context_code, $mutated_from, $mutated_to-- $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to}\n";
-            }# end of loop over mutated_to
-
-            # print "\nProbabilities for mutated_from $mutated_from:\n";
-
-
-            foreach my $mutated_to (@nucleotides) {
-            #foreach $mutated_to_nucl_key (keys %{ $trinucleotide_context_data{$context_code}{$mutated_from_nucl_key} }) {
-               my $mutated_from_length = length( $mutated_from);
-               my $mutated_to_length = length( $mutated_to);
-               if ( $mutated_from_length == 1 ) {
-                  if ( $mutated_from ne "-" ) {
-                     if ( $mutated_to_length == 1 ) {
-                        if ( $mutated_to ne "-" ) {
-                           my $SNP_probability;
-                           if ( $context_sum_across_mutated_to == 0 ) {
-                              $SNP_probability = 0;
-                           }
-                           else {
-                              $SNP_probability = $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to}/$context_sum_across_mutated_to;
-                           }
-                           if ( $mutated_to eq "T" ) {
-                              print $trinuc_prob_handle "$SNP_probability";
-                           }
-                           else {
-                              # print "$context_code, $mutated_from, $mutated_to, context_sum_across_mutated_to=$context_sum_across_mutated_to -- $SNP_probability\n";
-                              print $trinuc_prob_handle "$SNP_probability\t";
-                           }
-                        }# end of if statement
-                        else {
-                           my $indel_probability = $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to}/$context_sum_across_indel;
-                           # print $indel_prob_handle "$context_code, $mutated_from, $mutated_to, context_sum_across_indel=$context_sum_across_indel -- $indel_probability\n";
-                        }# end else statement
-                     }# end of if statement
-                     else {
-                        # my $indel_probability = $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to}/$context_sum_across_indel;
-                        # print $indel_prob_handle "$context_code, $mutated_from, $mutated_to, context_sum_across_indel=$context_sum_across_indel -- $indel_probability\n";
-                     }# end else statement
-                  }# end of if statement
-                  else {
-                     # my $indel_probability = $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to}/$context_sum_across_indel;
-                     # print $indel_prob_handle "$context_code, $mutated_from, $mutated_to, context_sum_across_indel=$context_sum_across_indel -- $indel_probability\n";
-                  }# end else statement
-               }# end of if statement
-               else {
-                  my $indel_probability;
-                  if ( $context_sum_across_indel = 0 ) {
-                     $indel_probability = 0;
-                  }
-                  else {
-                     # $indel_probability = $trinucleotide_context_data{$context_code}{$mutated_from}{$mutated_to}/$context_sum_across_indel;
-                     # print $indel_prob_handle "$context_code, $mutated_from, $mutated_to, context_sum_across_indel=$context_sum_across_indel -- $indel_probability\n";
-                  }
-               }# end else statement
-            }# end of loop over mutated_to
-            print $trinuc_prob_handle "\n";
-
-         }# end of loop over mutated_from
-
-     # print "\n\n";
-     
-
-  }# end loop over nt3
-}# end loop over nt1
-
-
-
-
diff --git a/utilities/deprecated/README.md b/utilities/deprecated/README.md
deleted file mode 100644
index 01613ce..0000000
--- a/utilities/deprecated/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
-#Deprecated Perl Scripts
-These scripts were updated and rewritten in python to improve ease of use and speed. Usage and a quick description of the deprecated scripts can be found below. Please use genMutModel.py to generate mutation models.
-
-##FindNucleotideContextOnReference.pl
-This script takes in VCF files and generates variant frequency models for NEAT. Coordinates for each variant are located within the HG19 human reference. The corresponding trinucleotide context around that location on the reference is returned into a new column.
-
-## Running the Script
-The script requires 5 arguments to be entered after the full path to FindNucleotideContextOnReference.healthy.pl
-
-```
-1. Full path to Fastahack
-2. Full path to Reference Genome
-3. Full path to input VCF
-4. Full path to output file
-5. Full path to human GFF
-```
diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index a105473..ba054d1 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -9,19 +9,12 @@
 import pickle
 import argparse
 import numpy as np
+from Bio import SeqIO
+import pyfaidx
 
-# absolute path to the directory above this script
 SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2])
-sys.path.append(SIM_PATH+'/py/')
-
-from refFunc import indexRef
-
-REF_WHITELIST =  [str(n) for n in range(1,30)] + ['x','y','X','Y','mt','Mt','MT']
-REF_WHITELIST += ['chr'+n for n in REF_WHITELIST]
-VALID_NUCL    =  ['A','C','G','T']
-VALID_TRINUC  =  [VALID_NUCL[i]+VALID_NUCL[j]+VALID_NUCL[k] for i in range(len(VALID_NUCL)) for j in range(len(VALID_NUCL)) for k in range(len(VALID_NUCL))]
-# if parsing a dbsnp vcf, and no CAF= is found in info tag, use this as default val for population freq
-VCF_DEFAULT_POP_FREQ = 0.00001
+sys.path.append(SIM_PATH)
+print(sys.path)
 
 
 #########################################################
@@ -29,25 +22,13 @@
 #########################################################
 
 
-# given a reference index, grab the sequence string of a specified reference
-def getChrFromFasta(refPath,ref_inds,chrName):
-    for i in range(len(ref_inds)):
-        if ref_inds[i][0] == chrName:
-            ref_inds_i = ref_inds[i]
-            break
-    refFile = open(refPath,'r')
-    refFile.seek(ref_inds_i[1])
-    myDat = ''.join(refFile.read(ref_inds_i[2]-ref_inds_i[1]).split('\n'))
-    return myDat
-
-
 # cluster a sorted list
-def clusterList(l,delta):
-    outList    = [[l[0]]]
-    prevVal    = l[0]
+def cluster_list(l: list, delta: float) -> list:
+    outList = [[l[0]]]
+    prevVal = l[0]
     currentInd = 0
     for n in l[1:]:
-        if n-prevVal <= delta:
+        if n - prevVal <= delta:
             outList[currentInd].append(n)
         else:
             currentInd += 1
@@ -57,7 +38,7 @@ def clusterList(l,delta):
     return outList
 
 
-def list_2_countDict(l):
+def list_2_countDict(l: list) -> dict:
     cDict = {}
     for n in l:
         if n not in cDict:
@@ -66,98 +47,35 @@ def list_2_countDict(l):
     return cDict
 
 
-def getBedTracks(fn):
-    f = open(fn,'r')
+def getBedTracks(fn: str) -> dict:
+    f = open(fn, 'r')
     trackDict = {}
     for line in f:
         splt = line.strip().split('\t')
         if splt[0] not in trackDict:
             trackDict[splt[0]] = []
-        trackDict[splt[0]].extend([int(splt[1]),int(splt[2])])
+        trackDict[splt[0]].extend([int(splt[1]), int(splt[2])])
     f.close()
     return trackDict
 
 
-def getTrackLen(trackDict):
+def getTrackLen(trackDict: dict) -> float:
     totSum = 0
     for k in trackDict.keys():
-        for i in range(0,len(trackDict[k]),2):
-            totSum += trackDict[k][i+1] - trackDict[k][i] + 1
+        for i in range(0, len(trackDict[k]), 2):
+            totSum += trackDict[k][i + 1] - trackDict[k][i] + 1
     return totSum
 
-def isInBed(track,ind):
-    myInd = bisect.bisect(track,ind)
-    if myInd&1:
+
+def isInBed(track, ind):
+    myInd = bisect.bisect(track, ind)
+    if myInd & 1:
         return True
     if myInd < len(track):
-        if track[myInd-1] == ind:
+        if track[myInd - 1] == ind:
             return True
     return False
 
-## return the mean distance to the median of a cluster
-#def mean_dist_from_median(c):
-#	centroid = np.median([n for n in c])
-#	dists    = []
-#	for n in c:
-#		dists.append(abs(n-centroid))
-#	return np.mean(dists)
-#
-## get median value from counting dictionary
-#def quick_median(countDict):
-#	midPoint = sum(countDict.values())/2
-#	mySum    = 0
-#	myInd    = 0
-#	sk       = sorted(countDict.keys())
-#	while mySum < midPoint:
-#		mySum += countDict[sk[myInd]]
-#		if mySum >= midPoint:
-#			break
-#		myInd += 1
-#	return myInd
-#
-## get median deviation from median of counting dictionary
-#def median_deviation_from_median(countDict):
-#	myMedian = quick_median(countDict)
-#	deviations = {}
-#	for k in sorted(countDict.keys()):
-#		d = abs(k-myMedian)
-#		deviations[d] = countDict[k]
-#	return quick_median(deviations)
-
-
-#################################################
-#				PARSE INPUT OPTIONS				#
-#################################################
-
-
-parser = argparse.ArgumentParser(description='genMutModel.py')
-parser.add_argument('-r',  type=str, required=True,  metavar='<str>',                   help="* ref.fa")
-parser.add_argument('-m',  type=str, required=True,  metavar='<str>',                   help="* mutations.tsv [.vcf]")
-parser.add_argument('-o',  type=str, required=True,  metavar='<str>',                   help="* output.p")
-parser.add_argument('-bi', type=str, required=False, metavar='<str>',    default=None,  help="only_use_these_regions.bed")
-parser.add_argument('-be', type=str, required=False, metavar='<str>',    default=None,  help="exclude_these_regions.bed")
-parser.add_argument('--save-trinuc', required=False,action='store_true', default=False, help='save trinuc counts for ref')
-parser.add_argument('--no-whitelist',required=False,action='store_true', default=False, help='allow any non-standard ref')
-parser.add_argument('--skip-common', required=False,action='store_true', default=False, help='do not save common snps + high mut regions')
-args = parser.parse_args()
-(REF, TSV, OUT_PICKLE, SAVE_TRINUC, NO_WHITELIST, SKIP_COMMON) = (args.r, args.m, args.o, args.save_trinuc, args.no_whitelist, args.skip_common)
-
-MYBED = None
-if args.bi != None:
-    print('only considering variants in specified bed regions...')
-    MYBED = (getBedTracks(args.bi),True)
-elif args.be != None:
-    print('only considering variants outside of specified bed regions...')
-    MYBED = (getBedTracks(args.be),False)
-
-if TSV[-4:] == '.vcf':
-    IS_VCF = True
-elif TSV[-4:] == '.tsv':
-    IS_VCF = False
-else:
-    print('\nError: Unknown format for mutation input.\n')
-    exit(1)
-
 
 #####################################
 #				main()				#c
@@ -165,14 +83,65 @@ def isInBed(track,ind):
 
 
 def main():
+    # Some constants we'll need later
+    REF_WHITELIST = [str(n) for n in range(1, 30)] + ['x', 'y', 'X', 'Y', 'mt', 'Mt', 'MT']
+    REF_WHITELIST += ['chr' + n for n in REF_WHITELIST]
+    VALID_NUCL = ['A', 'C', 'G', 'T']
+    VALID_TRINUC = [VALID_NUCL[i] + VALID_NUCL[j] + VALID_NUCL[k] for i in range(len(VALID_NUCL)) for j in
+                    range(len(VALID_NUCL)) for k in range(len(VALID_NUCL))]
+    # if parsing a dbsnp vcf, and no CAF= is found in info tag, use this as default val for population freq
+    VCF_DEFAULT_POP_FREQ = 0.00001
+
+    parser = argparse.ArgumentParser(description='genMutModel.py')
+    parser.add_argument('-r', type=str, required=True, metavar='<str>', help="* ref.fa")
+    parser.add_argument('-m', type=str, required=True, metavar='<str>', help="* mutations.tsv [.vcf]")
+    parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output.p")
+    parser.add_argument('-bi', type=str, required=False, metavar='<str>', default=None,
+                        help="only_use_these_regions.bed")
+    parser.add_argument('-be', type=str, required=False, metavar='<str>', default=None,
+                        help="exclude_these_regions.bed")
+    parser.add_argument('--save-trinuc', required=False, action='store_true', default=False,
+                        help='save trinuc counts for ref')
+    # parser.add_argument('--no-whitelist', required=False, action='store_true', default=False,
+    #                     help='allow any non-standard ref')
+    parser.add_argument('--skip-common', required=False, action='store_true', default=False,
+                        help='do not save common snps + high mut regions')
+    args = parser.parse_args()
+    # (ref, tsv, out_pickle, save_trinuc, no_whitelist, skip_common) = (
+    #     args.r, args.m, args.o, args.save_trinuc, args.no_whitelist, args.skip_common)
+
+    (ref, tsv, out_pickle, save_trinuc, skip_common) = (
+        args.r, args.m, args.o, args.save_trinuc, args.skip_common)
+
+    mybed = None
+    if args.bi is not None:
+        print('only considering variants in specified bed regions...')
+        mybed = (getBedTracks(args.bi), True)
+    elif args.be is not None:
+        print('only considering variants outside of specified bed regions...')
+        mybed = (getBedTracks(args.be), False)
+
+    if tsv[-4:] == '.vcf':
+        is_vcf = True
+    elif tsv[-4:] == '.tsv':
+        is_vcf = False
+    else:
+        print('\nError: Unknown format for mutation input.\n')
+        exit(1)
+
+    ref_inds = SeqIO.to_dict(SeqIO.parse(ref, "fasta"))
+
+    # simplify naming
+    for key in ref_inds.keys():
+        splt = key.split("|")
+        ref_inds[splt[0]] = ref_inds.pop(key)
 
-    ref_inds = indexRef(REF)
-    refList  = [n[0] for n in ref_inds]
+    ref_list = list(ref_inds.keys())
 
     # how many times do we observe each trinucleotide in the reference (and input bed region, if present)?
     TRINUC_REF_COUNT = {}
     TRINUC_BED_COUNT = {}
-    printBedWarning  = True
+    printBedWarning = True
     # [(trinuc_a, trinuc_b)] = # of times we observed a mutation from trinuc_a into trinuc_b
     TRINUC_TRANSITION_COUNT = {}
     # total count of SNPs
@@ -191,56 +160,55 @@ def main():
     HIGH_MUT_REGIONS = []
 
     # load and process variants in each reference sequence individually, for memory reasons...
-    for refName in refList:
+    for ref_name in ref_list:
 
-        if (refName not in REF_WHITELIST) and (not NO_WHITELIST):
-            print(refName, 'is not in our whitelist, skipping...')
-            continue
+        # if (ref_name not in REF_WHITELIST) and (not no_whitelist):
+        #     print(ref_name, 'is not in our whitelist, skipping...')
+        #     continue
 
-        print('reading reference "' + refName + '"...')
-        refSequence = getChrFromFasta(REF,ref_inds,refName).upper()
-        TOTAL_REFLEN += len(refSequence) - refSequence.count('N')
+        # Count the number of non-N nucleotides for the reference
+        TOTAL_REFLEN += len(ref_inds[ref_name].seq) - ref_inds[ref_name].seq.count('N')
 
         # list to be used for counting variants that occur multiple times in file (i.e. in multiple samples)
         VDAT_COMMON = []
 
-
         """ ##########################################################################
-        ###						COUNT TRINUCLEOTIDES IN REF						   ###
+        ###						COUNT TRINUCLEOTIDES IN ref						   ###
         ########################################################################## """
 
-
-        if MYBED != None:
+        if mybed is not None:
             if printBedWarning:
-                print(
-                    "since you're using a bed input, we have to count trinucs in bed region even if you specified a trinuc count file for the reference...")
+                print("since you're using a bed input, "
+                      "we have to count trinucs in bed region even if "
+                      "you specified a trinuc count file for the reference...")
                 printBedWarning = False
-            if refName in MYBED[0]:
-                refKey = refName
-            elif ('chr' in refName) and (refName not in MYBED[0]) and (refName[3:] in MYBED[0]):
-                refKey = refName[3:]
-            elif ('chr' not in refName) and (refName not in MYBED[0]) and ('chr'+refName in MYBED[0]):
-                refKey = 'chr'+refName
-            if refKey in MYBED[0]:
-                subRegions = [(MYBED[0][refKey][n],MYBED[0][refKey][n+1]) for n in range(0,len(MYBED[0][refKey]),2)]
+            if ref_name in mybed[0]:
+                refKey = ref_name
+            elif ('chr' in ref_name) and (ref_name not in mybed[0]) and (ref_name[3:] in mybed[0]):
+                refKey = ref_name[3:]
+            elif ('chr' not in ref_name) and (ref_name not in mybed[0]) and ('chr' + ref_name in mybed[0]):
+                refKey = 'chr' + ref_name
+            if refKey in mybed[0]:
+                subRegions = [(mybed[0][refKey][n], mybed[0][refKey][n + 1]) for n in
+                              range(0, len(mybed[0][refKey]), 2)]
                 for sr in subRegions:
-                    for i in range(sr[0],sr[1]+1-2):
-                        trinuc = refSequence[i:i+3]
+                    for i in range(sr[0], sr[1] + 1 - 2):
+                        trinuc = ref_inds[ref_name][i:i + 3]
                         if not trinuc in VALID_TRINUC:
-                            continue	# skip if trinuc contains invalid characters, or not in specified bed region
+                            continue  # skip if trinuc contains invalid characters, or not in specified bed region
                         if trinuc not in TRINUC_BED_COUNT:
                             TRINUC_BED_COUNT[trinuc] = 0
                         TRINUC_BED_COUNT[trinuc] += 1
 
-        if not os.path.isfile(REF+'.trinucCounts'):
+        if not os.path.isfile(ref + '.trinucCounts'):
             print('counting trinucleotides in reference...')
-            for i in range(len(refSequence)-2):
-                if i%1000000 == 0 and i > 0:
-                    print(i, '/', len(refSequence))
-                    #break
-                trinuc = refSequence[i:i+3]
+            for i in range(len(ref_inds[ref_name]) - 2):
+                if i % 1000000 == 0 and i > 0:
+                    print(i, '/', len(ref_inds[ref_name]))
+                    # break
+                trinuc = ref_inds[ref_name][i:i + 3]
                 if not trinuc in VALID_TRINUC:
-                    continue	# skip if trinuc contains invalid characters
+                    continue  # skip if trinuc contains invalid characters
                 if trinuc not in TRINUC_REF_COUNT:
                     TRINUC_REF_COUNT[trinuc] = 0
                 TRINUC_REF_COUNT[trinuc] += 1
@@ -252,125 +220,132 @@ def main():
         ########################################################################## """
 
         print('reading input variants...')
-        f = open(TSV,'r')
+        f = open(tsv, 'r')
         isFirst = True
         for line in f:
 
-            if IS_VCF and line[0] == '#':
+            if is_vcf and line[0] == '#':
                 continue
             if isFirst:
-                if IS_VCF:
+                if is_vcf:
                     # hard-code index values based on expected columns in vcf
-                    (c1,c2,c3,m1,m2,m3) = (0,1,1,3,3,4)
+                    (c1, c2, c3, m1, m2, m3) = (0, 1, 1, 3, 3, 4)
                 else:
                     # determine columns of fields we're interested in
                     splt = line.strip().split('\t')
-                    (c1,c2,c3) = (splt.index('chromosome'),splt.index('chromosome_start'),splt.index('chromosome_end'))
-                    (m1,m2,m3) = (splt.index('reference_genome_allele'),splt.index('mutated_from_allele'),splt.index('mutated_to_allele'))
+                    (c1, c2, c3) = (
+                        splt.index('chromosome'), splt.index('chromosome_start'), splt.index('chromosome_end'))
+                    (m1, m2, m3) = (splt.index('reference_genome_allele'), splt.index('mutated_from_allele'),
+                                    splt.index('mutated_to_allele'))
                     (d_id) = (splt.index('icgc_donor_id'))
                 isFirst = False
                 continue
 
             splt = line.strip().split('\t')
             # we have -1 because tsv/vcf coords are 1-based, and our reference string index is 0-based
-            [chrName,chrStart,chrEnd] = [splt[c1],int(splt[c2])-1,int(splt[c3])-1]
-            [allele_ref,allele_normal,allele_tumor] = [splt[m1].upper(),splt[m2].upper(),splt[m3].upper()]
-            if IS_VCF:
+            [chrName, chrStart, chrEnd] = [splt[c1], int(splt[c2]) - 1, int(splt[c3]) - 1]
+            [allele_ref, allele_normal, allele_tumor] = [splt[m1].upper(), splt[m2].upper(), splt[m3].upper()]
+            if is_vcf:
                 if len(allele_ref) != len(allele_tumor):
                     # indels in tsv don't include the preserved first nucleotide, so lets trim the vcf alleles
-                    [allele_ref,allele_normal,allele_tumor] = [allele_ref[1:],allele_normal[1:],allele_tumor[1:]]
+                    [allele_ref, allele_normal, allele_tumor] = [allele_ref[1:], allele_normal[1:], allele_tumor[1:]]
                 if not allele_ref: allele_ref = '-'
                 if not allele_normal: allele_normal = '-'
                 if not allele_tumor: allele_tumor = '-'
                 # if alternate alleles are present, lets just ignore this variant. I may come back and improve this later
                 if ',' in allele_tumor:
                     continue
-                vcf_info = ';'+splt[7]+';'
+                vcf_info = ';' + splt[7] + ';'
             else:
                 [donor_id] = [splt[d_id]]
 
             # if we encounter a multi-np (i.e. 3 nucl --> 3 different nucl), let's skip it for now...
-            if ('-' not in allele_normal and '-' not in allele_tumor) and (len(allele_normal) > 1 or len(allele_tumor) > 1):
+            if ('-' not in allele_normal and '-' not in allele_tumor) and (
+                    len(allele_normal) > 1 or len(allele_tumor) > 1):
                 print('skipping a complex variant...')
                 continue
 
             # to deal with '1' vs 'chr1' references, manually change names. this is hacky and bad.
             if 'chr' not in chrName:
-                chrName = 'chr'+chrName
-            if 'chr' not in refName:
-                refName = 'chr'+refName
+                chrName = 'chr' + chrName
+            if 'chr' not in ref_name:
+                ref_name = 'chr' + ref_name
             # skip irrelevant variants
-            if chrName != refName:
+            if chrName != ref_name:
                 continue
 
             # if variant is outside the regions we're interested in (if specified), skip it...
-            if MYBED != None:
-                refKey = refName
-                if not refKey in MYBED[0] and refKey[3:] in MYBED[0]:	# account for 1 vs chr1, again...
+            if mybed is not None:
+                refKey = ref_name
+                if not refKey in mybed[0] and refKey[3:] in mybed[0]:  # account for 1 vs chr1, again...
                     refKey = refKey[3:]
-                if refKey not in MYBED[0]:
+                if refKey not in mybed[0]:
                     inBed = False
                 else:
-                    inBed = isInBed(MYBED[0][refKey],chrStart)
-                if inBed != MYBED[1]:
+                    inBed = isInBed(mybed[0][refKey], chrStart)
+                if inBed != mybed[1]:
                     continue
 
             # we want only snps
             # so, no '-' characters allowed, and chrStart must be same as chrEnd
             if '-' not in allele_normal and '-' not in allele_tumor and chrStart == chrEnd:
-                trinuc_ref = refSequence[chrStart-1:chrStart+2]
+                trinuc_ref = ref_inds[ref_name][chrStart - 1:chrStart + 2]
                 if not trinuc_ref in VALID_TRINUC:
-                    continue	# skip ref trinuc with invalid characters
+                    continue  # skip ref trinuc with invalid characters
                 # only consider positions where ref allele in tsv matches the nucleotide in our reference
                 if allele_ref == trinuc_ref[1]:
-                    trinuc_normal    = refSequence[chrStart-1] + allele_normal + refSequence[chrStart+1]
-                    trinuc_tumor     = refSequence[chrStart-1] + allele_tumor + refSequence[chrStart+1]
+                    trinuc_normal = ref_inds[ref_name][chrStart - 1] + allele_normal + ref_inds[ref_name][chrStart + 1]
+                    trinuc_tumor = ref_inds[ref_name][chrStart - 1] + allele_tumor + ref_inds[ref_name][chrStart + 1]
                     if not trinuc_normal in VALID_TRINUC or not trinuc_tumor in VALID_TRINUC:
-                        continue	# skip if mutation contains invalid char
-                    key = (trinuc_normal,trinuc_tumor)
+                        continue  # skip if mutation contains invalid char
+                    key = (trinuc_normal, trinuc_tumor)
                     if key not in TRINUC_TRANSITION_COUNT:
                         TRINUC_TRANSITION_COUNT[key] = 0
                     TRINUC_TRANSITION_COUNT[key] += 1
                     SNP_COUNT += 1
-                    key2 = (allele_normal,allele_tumor)
+                    key2 = (allele_normal, allele_tumor)
                     if key2 not in SNP_TRANSITION_COUNT:
                         SNP_TRANSITION_COUNT[key2] = 0
                     SNP_TRANSITION_COUNT[key2] += 1
 
-                    if IS_VCF:
+                    if is_vcf:
                         myPopFreq = VCF_DEFAULT_POP_FREQ
                         if ';CAF=' in vcf_info:
-                            cafStr = re.findall(r";CAF=.*?(?=;)",vcf_info)[0]
+                            cafStr = re.findall(r";CAF=.*?(?=;)", vcf_info)[0]
                             if ',' in cafStr:
                                 myPopFreq = float(cafStr[5:].split(',')[1])
-                        VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor,myPopFreq))
+                        VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_tumor, myPopFreq))
                     else:
-                        VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor))
+                        VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_tumor))
                         TOTAL_DONORS[donor_id] = True
                 else:
                     print('\nError: ref allele in variant call does not match reference.\n')
                     exit(1)
 
             # now let's look for indels...
-            if '-' in allele_normal: len_normal = 0
-            else: len_normal = len(allele_normal)
-            if '-' in allele_tumor: len_tumor = 0
-            else: len_tumor = len(allele_tumor)
+            if '-' in allele_normal:
+                len_normal = 0
+            else:
+                len_normal = len(allele_normal)
+            if '-' in allele_tumor:
+                len_tumor = 0
+            else:
+                len_tumor = len(allele_tumor)
             if len_normal != len_tumor:
                 indel_len = len_tumor - len_normal
                 if indel_len not in INDEL_COUNT:
                     INDEL_COUNT[indel_len] = 0
                 INDEL_COUNT[indel_len] += 1
 
-                if IS_VCF:
+                if is_vcf:
                     myPopFreq = VCF_DEFAULT_POP_FREQ
                     if ';CAF=' in vcf_info:
-                        cafStr = re.findall(r";CAF=.*?(?=;)",vcf_info)[0]
+                        cafStr = re.findall(r";CAF=.*?(?=;)", vcf_info)[0]
                         if ',' in cafStr:
                             myPopFreq = float(cafStr[5:].split(',')[1])
-                    VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor,myPopFreq))
+                    VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_tumor, myPopFreq))
                 else:
-                    VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor))
+                    VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_tumor))
                     TOTAL_DONORS[donor_id] = True
         f.close()
 
@@ -383,77 +358,81 @@ def main():
         # identify common mutations
         #
         percentile_var = 95
-        if IS_VCF:
-            minVal = np.percentile([n[4] for n in VDAT_COMMON],percentile_var)
+        if is_vcf:
+            minVal = np.percentile([n[4] for n in VDAT_COMMON], percentile_var)
             for k in sorted(VDAT_COMMON):
                 if k[4] >= minVal:
-                    COMMON_VARIANTS.append((refName,k[0],k[1],k[3],k[4]))
-            VDAT_COMMON = {(n[0],n[1],n[2],n[3]):n[4] for n in VDAT_COMMON}
+                    COMMON_VARIANTS.append((ref_name, k[0], k[1], k[3], k[4]))
+            VDAT_COMMON = {(n[0], n[1], n[2], n[3]): n[4] for n in VDAT_COMMON}
         else:
             N_DONORS = len(TOTAL_DONORS)
             VDAT_COMMON = list_2_countDict(VDAT_COMMON)
-            minVal = int(np.percentile(VDAT_COMMON.values(),percentile_var))
+            minVal = int(np.percentile(VDAT_COMMON.values(), percentile_var))
             for k in sorted(VDAT_COMMON.keys()):
                 if VDAT_COMMON[k] >= minVal:
-                    COMMON_VARIANTS.append((refName,k[0],k[1],k[3],VDAT_COMMON[k]/float(N_DONORS)))
+                    COMMON_VARIANTS.append((ref_name, k[0], k[1], k[3], VDAT_COMMON[k] / float(N_DONORS)))
 
         #
         # identify areas that have contained significantly higher random mutation rates
         #
-        dist_thresh      = 2000
+        dist_thresh = 2000
         percentile_clust = 97
-        qptn             = 1000
+        qptn = 1000
         # identify regions with disproportionately more variants in them
         VARIANT_POS = sorted([n[0] for n in VDAT_COMMON.keys()])
-        clustered_pos = clusterList(VARIANT_POS,dist_thresh)
-        byLen  = [(len(clustered_pos[i]),min(clustered_pos[i]),max(clustered_pos[i]),i) for i in range(len(clustered_pos))]
-        #byLen  = sorted(byLen,reverse=True)
-        #minLen = int(np.percentile([n[0] for n in byLen],percentile_clust))
-        #byLen  = [n for n in byLen if n[0] >= minLen]
+        clustered_pos = cluster_list(VARIANT_POS, dist_thresh)
+        byLen = [(len(clustered_pos[i]), min(clustered_pos[i]), max(clustered_pos[i]), i) for i in
+                 range(len(clustered_pos))]
+        # byLen  = sorted(byLen,reverse=True)
+        # minLen = int(np.percentile([n[0] for n in byLen],percentile_clust))
+        # byLen  = [n for n in byLen if n[0] >= minLen]
         candidate_regions = []
         for n in byLen:
-            bi = int((n[1]-dist_thresh)/float(qptn))*qptn
-            bf = int((n[2]+dist_thresh)/float(qptn))*qptn
-            candidate_regions.append((n[0]/float(bf-bi),max([0,bi]),min([len(refSequence),bf])))
-        minVal = np.percentile([n[0] for n in candidate_regions],percentile_clust)
+            bi = int((n[1] - dist_thresh) / float(qptn)) * qptn
+            bf = int((n[2] + dist_thresh) / float(qptn)) * qptn
+            candidate_regions.append((n[0] / float(bf - bi), max([0, bi]), min([len(ref_inds[ref_name]), bf])))
+        minVal = np.percentile([n[0] for n in candidate_regions], percentile_clust)
         for n in candidate_regions:
             if n[0] >= minVal:
-                HIGH_MUT_REGIONS.append((refName,n[1],n[2],n[0]))
+                HIGH_MUT_REGIONS.append((ref_name, n[1], n[2], n[0]))
         # collapse overlapping regions
-        for i in range(len(HIGH_MUT_REGIONS)-1,0,-1):
-            if HIGH_MUT_REGIONS[i-1][2] >= HIGH_MUT_REGIONS[i][1] and HIGH_MUT_REGIONS[i-1][0] == HIGH_MUT_REGIONS[i][0]:
-                avgMutRate = 0.5*HIGH_MUT_REGIONS[i-1][3]+0.5*HIGH_MUT_REGIONS[i][3]	# not accurate, but I'm lazy
-                HIGH_MUT_REGIONS[i-1] = (HIGH_MUT_REGIONS[i-1][0], HIGH_MUT_REGIONS[i-1][1], HIGH_MUT_REGIONS[i][2], avgMutRate)
+        for i in range(len(HIGH_MUT_REGIONS) - 1, 0, -1):
+            if HIGH_MUT_REGIONS[i - 1][2] >= HIGH_MUT_REGIONS[i][1] and HIGH_MUT_REGIONS[i - 1][0] == \
+                    HIGH_MUT_REGIONS[i][0]:
+                avgMutRate = 0.5 * HIGH_MUT_REGIONS[i - 1][3] + 0.5 * HIGH_MUT_REGIONS[i][
+                    3]  # not accurate, but I'm lazy
+                HIGH_MUT_REGIONS[i - 1] = (
+                    HIGH_MUT_REGIONS[i - 1][0], HIGH_MUT_REGIONS[i - 1][1], HIGH_MUT_REGIONS[i][2], avgMutRate)
                 del HIGH_MUT_REGIONS[i]
 
     #
     # if we didn't count ref trinucs because we found file, read in ref counts from file now
     #
-    if os.path.isfile(REF+'.trinucCounts'):
+    if os.path.isfile(ref + '.trinucCounts'):
         print('reading pre-computed trinuc counts...')
-        f = open(REF+'.trinucCounts','r')
+        f = open(ref + '.trinucCounts', 'r')
         for line in f:
             splt = line.strip().split('\t')
             TRINUC_REF_COUNT[splt[0]] = int(splt[1])
         f.close()
     # otherwise, save trinuc counts to file, if desired
-    elif SAVE_TRINUC:
-        if MYBED != None:
+    elif save_trinuc:
+        if mybed is not None:
             print('unable to save trinuc counts to file because using input bed region...')
         else:
             print('saving trinuc counts to file...')
-            f = open(REF+'.trinucCounts','w')
+            f = open(ref + '.trinucCounts', 'w')
             for trinuc in sorted(TRINUC_REF_COUNT.keys()):
-                f.write(trinuc+'\t'+str(TRINUC_REF_COUNT[trinuc])+'\n')
+                f.write(trinuc + '\t' + str(TRINUC_REF_COUNT[trinuc]) + '\n')
             f.close()
 
     #
     # if using an input bed region, make necessary adjustments to trinuc ref counts based on the bed region trinuc counts
     #
-    if MYBED != None:
-        if MYBED[1] == True:	# we are restricting our attention to bed regions, so ONLY use bed region trinuc counts
+    if mybed is not None:
+        if mybed[1] == True:  # we are restricting our attention to bed regions, so ONLY use bed region trinuc counts
             TRINUC_REF_COUNT = TRINUC_BED_COUNT
-        else:					# we are only looking outside bed regions, so subtract bed region trinucs from entire reference trinucs
+        else:  # we are only looking outside bed regions, so subtract bed region trinucs from entire reference trinucs
             for k in TRINUC_REF_COUNT.keys():
                 if k in TRINUC_BED_COUNT:
                     TRINUC_REF_COUNT[k] -= TRINUC_BED_COUNT[k]
@@ -469,11 +448,10 @@ def main():
     ###							COMPUTE PROBABILITIES						   ###
     ########################################################################## """
 
-
-    #for k in sorted(TRINUC_REF_COUNT.keys()):
+    # for k in sorted(TRINUC_REF_COUNT.keys()):
     #		print k, TRINUC_REF_COUNT[k]
     #
-    #for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
+    # for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
     #	print k, TRINUC_TRANSITION_COUNT[k]
 
     # frequency that each trinuc mutated into anything else
@@ -494,36 +472,36 @@ def main():
                 TRINUC_TRANS_PROBS[k] = TRINUC_TRANSITION_COUNT[k] / float(myCount)
 
     for n1 in VALID_NUCL:
-        rollingTot = sum([SNP_TRANSITION_COUNT[(n1,n2)] for n2 in VALID_NUCL if (n1,n2) in SNP_TRANSITION_COUNT])
+        rollingTot = sum([SNP_TRANSITION_COUNT[(n1, n2)] for n2 in VALID_NUCL if (n1, n2) in SNP_TRANSITION_COUNT])
         for n2 in VALID_NUCL:
-            key2 = (n1,n2)
+            key2 = (n1, n2)
             if key2 in SNP_TRANSITION_COUNT:
                 SNP_TRANS_FREQ[key2] = SNP_TRANSITION_COUNT[key2] / float(rollingTot)
 
     # compute average snp and indel frequencies
-    SNP_FREQ       = SNP_COUNT/float(totalVar)
-    AVG_INDEL_FREQ = 1.-SNP_FREQ
-    INDEL_FREQ     = {k:(INDEL_COUNT[k]/float(totalVar))/AVG_INDEL_FREQ for k in INDEL_COUNT.keys()}
-    if MYBED != None:
-        if MYBED[1] == True:
-            AVG_MUT_RATE = totalVar/float(getTrackLen(MYBED[0]))
+    SNP_FREQ = SNP_COUNT / float(totalVar)
+    AVG_INDEL_FREQ = 1. - SNP_FREQ
+    INDEL_FREQ = {k: (INDEL_COUNT[k] / float(totalVar)) / AVG_INDEL_FREQ for k in INDEL_COUNT.keys()}
+    if mybed is not None:
+        if mybed[1] == True:
+            AVG_MUT_RATE = totalVar / float(getTrackLen(mybed[0]))
         else:
-            AVG_MUT_RATE = totalVar/float(TOTAL_REFLEN - getTrackLen(MYBED[0]))
+            AVG_MUT_RATE = totalVar / float(TOTAL_REFLEN - getTrackLen(mybed[0]))
     else:
-        AVG_MUT_RATE = totalVar/float(TOTAL_REFLEN)
+        AVG_MUT_RATE = totalVar / float(TOTAL_REFLEN)
 
     #
     #	if values weren't found in data, appropriately append null entries
     #
     printTrinucWarning = False
     for trinuc in VALID_TRINUC:
-        trinuc_mut = [trinuc[0]+n+trinuc[2] for n in VALID_NUCL if n != trinuc[1]]
+        trinuc_mut = [trinuc[0] + n + trinuc[2] for n in VALID_NUCL if n != trinuc[1]]
         if trinuc not in TRINUC_MUT_PROB:
             TRINUC_MUT_PROB[trinuc] = 0.
             printTrinucWarning = True
         for trinuc2 in trinuc_mut:
-            if (trinuc,trinuc2) not in TRINUC_TRANS_PROBS:
-                TRINUC_TRANS_PROBS[(trinuc,trinuc2)] = 0.
+            if (trinuc, trinuc2) not in TRINUC_TRANS_PROBS:
+                TRINUC_TRANS_PROBS[(trinuc, trinuc2)] = 0.
                 printTrinucWarning = True
     if printTrinucWarning:
         print(
@@ -548,10 +526,10 @@ def main():
     for k in sorted(SNP_TRANS_FREQ.keys()):
         print('p(' + k[0] + ' --> ' + k[1] + ' | SNP occurs) =', SNP_TRANS_FREQ[k])
 
-    #for n in COMMON_VARIANTS:
+    # for n in COMMON_VARIANTS:
     #	print n
 
-    #for n in HIGH_MUT_REGIONS:
+    # for n in HIGH_MUT_REGIONS:
     #	print n
 
     print('p(snp)   =', SNP_FREQ)
@@ -562,28 +540,24 @@ def main():
     #
     # save variables to file
     #
-    if SKIP_COMMON:
-        OUT_DICT = {'AVG_MUT_RATE':AVG_MUT_RATE,
-                    'SNP_FREQ':SNP_FREQ,
-                    'SNP_TRANS_FREQ':SNP_TRANS_FREQ,
-                    'INDEL_FREQ':INDEL_FREQ,
-                    'TRINUC_MUT_PROB':TRINUC_MUT_PROB,
-                    'TRINUC_TRANS_PROBS':TRINUC_TRANS_PROBS}
+    if skip_common:
+        OUT_DICT = {'AVG_MUT_RATE': AVG_MUT_RATE,
+                    'SNP_FREQ': SNP_FREQ,
+                    'SNP_TRANS_FREQ': SNP_TRANS_FREQ,
+                    'INDEL_FREQ': INDEL_FREQ,
+                    'TRINUC_MUT_PROB': TRINUC_MUT_PROB,
+                    'TRINUC_TRANS_PROBS': TRINUC_TRANS_PROBS}
     else:
-        OUT_DICT = {'AVG_MUT_RATE':AVG_MUT_RATE,
-                    'SNP_FREQ':SNP_FREQ,
-                    'SNP_TRANS_FREQ':SNP_TRANS_FREQ,
-                    'INDEL_FREQ':INDEL_FREQ,
-                    'TRINUC_MUT_PROB':TRINUC_MUT_PROB,
-                    'TRINUC_TRANS_PROBS':TRINUC_TRANS_PROBS,
-                    'COMMON_VARIANTS':COMMON_VARIANTS,
-                    'HIGH_MUT_REGIONS':HIGH_MUT_REGIONS}
-    pickle.dump(OUT_DICT, open(OUT_PICKLE, "wb"))
+        OUT_DICT = {'AVG_MUT_RATE': AVG_MUT_RATE,
+                    'SNP_FREQ': SNP_FREQ,
+                    'SNP_TRANS_FREQ': SNP_TRANS_FREQ,
+                    'INDEL_FREQ': INDEL_FREQ,
+                    'TRINUC_MUT_PROB': TRINUC_MUT_PROB,
+                    'TRINUC_TRANS_PROBS': TRINUC_TRANS_PROBS,
+                    'COMMON_VARIANTS': COMMON_VARIANTS,
+                    'HIGH_MUT_REGIONS': HIGH_MUT_REGIONS}
+    pickle.dump(OUT_DICT, open(out_pickle, "wb"))
 
 
 if __name__ == "__main__":
     main()
-
-
-
-

From 8d8f232ebfe80fbc79eb48bfd6b50dc4046aff47 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 14 May 2020 16:12:43 -0500
Subject: [PATCH 044/323] Working on updating genMutModel.py

---
 utilities/genMutModel.py      | 337 +++++++++++++++++-----------------
 utilities/genSeqErrorModel.py |   8 +-
 2 files changed, 174 insertions(+), 171 deletions(-)

diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index ba054d1..8202445 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -10,7 +10,7 @@
 import argparse
 import numpy as np
 from Bio import SeqIO
-import pyfaidx
+import pandas as pd
 
 SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2])
 sys.path.append(SIM_PATH)
@@ -93,55 +93,26 @@ def main():
     VCF_DEFAULT_POP_FREQ = 0.00001
 
     parser = argparse.ArgumentParser(description='genMutModel.py')
-    parser.add_argument('-r', type=str, required=True, metavar='<str>', help="* ref.fa")
-    parser.add_argument('-m', type=str, required=True, metavar='<str>', help="* mutations.tsv [.vcf]")
-    parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output.p")
-    parser.add_argument('-bi', type=str, required=False, metavar='<str>', default=None,
+    parser.add_argument('-r', type=str, required=True, metavar='Reference, in fasta format', help="* ref.fa")
+    parser.add_argument('-m', type=str, required=True, metavar='A vcf with mutations to model', help="* mutations.vcf")
+    parser.add_argument('-o', type=str, required=True, metavar='The name of the output model', help="* output.p")
+    parser.add_argument('-bi', type=str, required=False, metavar='Bed file of regions to include', default=None,
                         help="only_use_these_regions.bed")
-    parser.add_argument('-be', type=str, required=False, metavar='<str>', default=None,
+    parser.add_argument('-be', type=str, required=False, metavar='Bed file of regions to exclued', default=None,
                         help="exclude_these_regions.bed")
     parser.add_argument('--save-trinuc', required=False, action='store_true', default=False,
                         help='save trinuc counts for ref')
-    # parser.add_argument('--no-whitelist', required=False, action='store_true', default=False,
-    #                     help='allow any non-standard ref')
     parser.add_argument('--skip-common', required=False, action='store_true', default=False,
                         help='do not save common snps + high mut regions')
     args = parser.parse_args()
-    # (ref, tsv, out_pickle, save_trinuc, no_whitelist, skip_common) = (
-    #     args.r, args.m, args.o, args.save_trinuc, args.no_whitelist, args.skip_common)
 
     (ref, tsv, out_pickle, save_trinuc, skip_common) = (
         args.r, args.m, args.o, args.save_trinuc, args.skip_common)
 
-    mybed = None
-    if args.bi is not None:
-        print('only considering variants in specified bed regions...')
-        mybed = (getBedTracks(args.bi), True)
-    elif args.be is not None:
-        print('only considering variants outside of specified bed regions...')
-        mybed = (getBedTracks(args.be), False)
-
-    if tsv[-4:] == '.vcf':
-        is_vcf = True
-    elif tsv[-4:] == '.tsv':
-        is_vcf = False
-    else:
-        print('\nError: Unknown format for mutation input.\n')
-        exit(1)
-
-    ref_inds = SeqIO.to_dict(SeqIO.parse(ref, "fasta"))
-
-    # simplify naming
-    for key in ref_inds.keys():
-        splt = key.split("|")
-        ref_inds[splt[0]] = ref_inds.pop(key)
-
-    ref_list = list(ref_inds.keys())
-
     # how many times do we observe each trinucleotide in the reference (and input bed region, if present)?
     TRINUC_REF_COUNT = {}
     TRINUC_BED_COUNT = {}
-    printBedWarning = True
+    print_bed_warning = True
     # [(trinuc_a, trinuc_b)] = # of times we observed a mutation from trinuc_a into trinuc_b
     TRINUC_TRANSITION_COUNT = {}
     # total count of SNPs
@@ -159,15 +130,63 @@ def main():
     # identify regions that have significantly higher local mutation rates than the average
     HIGH_MUT_REGIONS = []
 
-    # load and process variants in each reference sequence individually, for memory reasons...
+    mybed = None
+    if args.bi is not None:
+        print('only considering variants in specified bed regions...')
+        mybed = (getBedTracks(args.bi), True)
+    elif args.be is not None:
+        print('only considering variants outside of specified bed regions...')
+        mybed = (getBedTracks(args.be), False)
+
+    if tsv[-4:] == '.vcf':
+        is_vcf = True
+    elif tsv[-4:] == '.tsv':
+        is_vcf = False
+    else:
+        print('\nError: Unknown format for mutation input.\n')
+        exit(1)
+
+    reference = SeqIO.to_dict(SeqIO.parse(ref, "fasta"))
+
+    # simplify naming
+    ref_dict = {}
+    for key in reference.keys():
+        key_split = key.split("|")
+        ref_dict[key_split[0]] = reference[key]
+
+    ref_list = list(ref_dict.keys())
+
+    # Pre-parsing
+    print('reading input variants...')
+    header = ["CHROM", 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO']
+    variants = pd.read_csv(tsv, sep='\t', comment='#', names=header)
+    # hard-code index values based on expected columns in vcf
+    (c1, c2, c3, m1, m2, m3) = (0, 1, 1, 3, 3, 4)
+    variant_chroms = variants['CHROM'].to_list()
+    matching_chromosomes = []
     for ref_name in ref_list:
+        if ref_name not in variant_chroms:
+            print("Warning: {} not in reference. Skipping this chromosome".format(ref_name))
+            print("(Note that if this behavior is unexpected, check that chromosome names in VCF match the reference)")
+            continue
+        else:
+            matching_chromosomes.append(ref_name)
 
-        # if (ref_name not in REF_WHITELIST) and (not no_whitelist):
-        #     print(ref_name, 'is not in our whitelist, skipping...')
-        #     continue
+    # Check to make sure there are some matches
+    if not matching_chromosomes:
+        print("Found no chromosomes in common between VCF and Fasta. Please fix the chromosome names and try again")
+        exit(-1)
 
+    matching_variants = variants[variants['CHROM'].isin(matching_chromosomes)]
+
+    if not matching_variants:
+        print("No variants matched with the Reference. This may be a chromosome naming problem.")
+        exit(-1)
+
+    # load and process variants in each reference sequence individually, for memory reasons...
+    for ref_name in ref_list:
         # Count the number of non-N nucleotides for the reference
-        TOTAL_REFLEN += len(ref_inds[ref_name].seq) - ref_inds[ref_name].seq.count('N')
+        TOTAL_REFLEN += len(ref_dict[ref_name].seq) - ref_dict[ref_name].seq.count('N')
 
         # list to be used for counting variants that occur multiple times in file (i.e. in multiple samples)
         VDAT_COMMON = []
@@ -177,23 +196,23 @@ def main():
         ########################################################################## """
 
         if mybed is not None:
-            if printBedWarning:
+            if print_bed_warning:
                 print("since you're using a bed input, "
                       "we have to count trinucs in bed region even if "
                       "you specified a trinuc count file for the reference...")
-                printBedWarning = False
+                print_bed_warning = False
             if ref_name in mybed[0]:
-                refKey = ref_name
+                ref_key = ref_name
             elif ('chr' in ref_name) and (ref_name not in mybed[0]) and (ref_name[3:] in mybed[0]):
-                refKey = ref_name[3:]
+                ref_key = ref_name[3:]
             elif ('chr' not in ref_name) and (ref_name not in mybed[0]) and ('chr' + ref_name in mybed[0]):
-                refKey = 'chr' + ref_name
-            if refKey in mybed[0]:
-                subRegions = [(mybed[0][refKey][n], mybed[0][refKey][n + 1]) for n in
-                              range(0, len(mybed[0][refKey]), 2)]
+                ref_key = 'chr' + ref_name
+            if ref_key in mybed[0]:
+                subRegions = [(mybed[0][ref_key][n], mybed[0][ref_key][n + 1]) for n in
+                              range(0, len(mybed[0][ref_key]), 2)]
                 for sr in subRegions:
                     for i in range(sr[0], sr[1] + 1 - 2):
-                        trinuc = ref_inds[ref_name][i:i + 3]
+                        trinuc = ref_dict[ref_name][i:i + 3].seq
                         if not trinuc in VALID_TRINUC:
                             continue  # skip if trinuc contains invalid characters, or not in specified bed region
                         if trinuc not in TRINUC_BED_COUNT:
@@ -202,14 +221,14 @@ def main():
 
         if not os.path.isfile(ref + '.trinucCounts'):
             print('counting trinucleotides in reference...')
-            for i in range(len(ref_inds[ref_name]) - 2):
+            for i in range(len(ref_dict[ref_name]) - 2):
                 if i % 1000000 == 0 and i > 0:
-                    print(i, '/', len(ref_inds[ref_name]))
+                    print(i, '/', len(ref_dict[ref_name]))
                     # break
-                trinuc = ref_inds[ref_name][i:i + 3]
-                if not trinuc in VALID_TRINUC:
+                trinuc = ref_dict[ref_name][i:i + 3].seq
+                if not str(trinuc) in VALID_TRINUC:
                     continue  # skip if trinuc contains invalid characters
-                if trinuc not in TRINUC_REF_COUNT:
+                if str(trinuc) not in TRINUC_REF_COUNT:
                     TRINUC_REF_COUNT[trinuc] = 0
                 TRINUC_REF_COUNT[trinuc] += 1
         else:
@@ -220,122 +239,78 @@ def main():
         ########################################################################## """
 
         print('reading input variants...')
-        f = open(tsv, 'r')
-        isFirst = True
-        for line in f:
 
-            if is_vcf and line[0] == '#':
-                continue
-            if isFirst:
-                if is_vcf:
-                    # hard-code index values based on expected columns in vcf
-                    (c1, c2, c3, m1, m2, m3) = (0, 1, 1, 3, 3, 4)
-                else:
-                    # determine columns of fields we're interested in
-                    splt = line.strip().split('\t')
-                    (c1, c2, c3) = (
-                        splt.index('chromosome'), splt.index('chromosome_start'), splt.index('chromosome_end'))
-                    (m1, m2, m3) = (splt.index('reference_genome_allele'), splt.index('mutated_from_allele'),
-                                    splt.index('mutated_to_allele'))
-                    (d_id) = (splt.index('icgc_donor_id'))
-                isFirst = False
-                continue
+        # TODO change all this to pandas
+        # we have -1 because tsv/vcf coords are 1-based, and our reference string index is 0-based
 
-            splt = line.strip().split('\t')
-            # we have -1 because tsv/vcf coords are 1-based, and our reference string index is 0-based
-            [chrName, chrStart, chrEnd] = [splt[c1], int(splt[c2]) - 1, int(splt[c3]) - 1]
-            [allele_ref, allele_normal, allele_tumor] = [splt[m1].upper(), splt[m2].upper(), splt[m3].upper()]
-            if is_vcf:
-                if len(allele_ref) != len(allele_tumor):
-                    # indels in tsv don't include the preserved first nucleotide, so lets trim the vcf alleles
-                    [allele_ref, allele_normal, allele_tumor] = [allele_ref[1:], allele_normal[1:], allele_tumor[1:]]
-                if not allele_ref: allele_ref = '-'
-                if not allele_normal: allele_normal = '-'
-                if not allele_tumor: allele_tumor = '-'
-                # if alternate alleles are present, lets just ignore this variant. I may come back and improve this later
-                if ',' in allele_tumor:
-                    continue
-                vcf_info = ';' + splt[7] + ';'
-            else:
-                [donor_id] = [splt[d_id]]
 
-            # if we encounter a multi-np (i.e. 3 nucl --> 3 different nucl), let's skip it for now...
-            if ('-' not in allele_normal and '-' not in allele_tumor) and (
-                    len(allele_normal) > 1 or len(allele_tumor) > 1):
-                print('skipping a complex variant...')
+        [chrName, chrStart, chrEnd] = [splt[c1], int(splt[c2]) - 1, int(splt[c3]) - 1]
+        [allele_ref, allele_normal, allele_alternate] = [splt[m1].upper(), splt[m2].upper(), splt[m3].upper()]
+        if is_vcf:
+            if len(allele_ref) != len(allele_alternate):
+                # indels in tsv don't include the preserved first nucleotide, so lets trim the vcf alleles
+                [allele_ref, allele_normal, allele_alternate] = [allele_ref[1:], allele_normal[1:], allele_alternate[1:]]
+            if not allele_ref:
+                allele_ref = '-'
+            if not allele_normal:
+                allele_normal = '-'
+            if not allele_alternate:
+                allele_alternate = '-'
+            # if alternate alleles are present, lets just ignore this variant. I may come back and improve this later
+            if ',' in allele_alternate:
                 continue
+            vcf_info = ';' + splt[7] + ';'
+        else:
+            [donor_id] = [splt[d_id]]
 
-            # to deal with '1' vs 'chr1' references, manually change names. this is hacky and bad.
-            if 'chr' not in chrName:
-                chrName = 'chr' + chrName
-            if 'chr' not in ref_name:
-                ref_name = 'chr' + ref_name
-            # skip irrelevant variants
-            if chrName != ref_name:
-                continue
+        # if we encounter a multi-np (i.e. 3 nucl --> 3 different nucl), let's skip it for now...
+        if ('-' not in allele_normal and '-' not in allele_alternate) and (
+                len(allele_normal) > 1 or len(allele_alternate) > 1):
+            print('skipping a complex variant...')
+            continue
 
-            # if variant is outside the regions we're interested in (if specified), skip it...
-            if mybed is not None:
-                refKey = ref_name
-                if not refKey in mybed[0] and refKey[3:] in mybed[0]:  # account for 1 vs chr1, again...
-                    refKey = refKey[3:]
-                if refKey not in mybed[0]:
-                    inBed = False
-                else:
-                    inBed = isInBed(mybed[0][refKey], chrStart)
-                if inBed != mybed[1]:
-                    continue
-
-            # we want only snps
-            # so, no '-' characters allowed, and chrStart must be same as chrEnd
-            if '-' not in allele_normal and '-' not in allele_tumor and chrStart == chrEnd:
-                trinuc_ref = ref_inds[ref_name][chrStart - 1:chrStart + 2]
-                if not trinuc_ref in VALID_TRINUC:
-                    continue  # skip ref trinuc with invalid characters
-                # only consider positions where ref allele in tsv matches the nucleotide in our reference
-                if allele_ref == trinuc_ref[1]:
-                    trinuc_normal = ref_inds[ref_name][chrStart - 1] + allele_normal + ref_inds[ref_name][chrStart + 1]
-                    trinuc_tumor = ref_inds[ref_name][chrStart - 1] + allele_tumor + ref_inds[ref_name][chrStart + 1]
-                    if not trinuc_normal in VALID_TRINUC or not trinuc_tumor in VALID_TRINUC:
-                        continue  # skip if mutation contains invalid char
-                    key = (trinuc_normal, trinuc_tumor)
-                    if key not in TRINUC_TRANSITION_COUNT:
-                        TRINUC_TRANSITION_COUNT[key] = 0
-                    TRINUC_TRANSITION_COUNT[key] += 1
-                    SNP_COUNT += 1
-                    key2 = (allele_normal, allele_tumor)
-                    if key2 not in SNP_TRANSITION_COUNT:
-                        SNP_TRANSITION_COUNT[key2] = 0
-                    SNP_TRANSITION_COUNT[key2] += 1
-
-                    if is_vcf:
-                        myPopFreq = VCF_DEFAULT_POP_FREQ
-                        if ';CAF=' in vcf_info:
-                            cafStr = re.findall(r";CAF=.*?(?=;)", vcf_info)[0]
-                            if ',' in cafStr:
-                                myPopFreq = float(cafStr[5:].split(',')[1])
-                        VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_tumor, myPopFreq))
-                    else:
-                        VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_tumor))
-                        TOTAL_DONORS[donor_id] = True
-                else:
-                    print('\nError: ref allele in variant call does not match reference.\n')
-                    exit(1)
+        # to deal with '1' vs 'chr1' references, manually change names. this is hacky and bad.
+        if 'chr' not in chrName:
+            chrName = 'chr' + chrName
+        if 'chr' not in ref_name:
+            ref_name = 'chr' + ref_name
+        # skip irrelevant variants
+        if chrName != ref_name:
+            continue
 
-            # now let's look for indels...
-            if '-' in allele_normal:
-                len_normal = 0
-            else:
-                len_normal = len(allele_normal)
-            if '-' in allele_tumor:
-                len_tumor = 0
+        # if variant is outside the regions we're interested in (if specified), skip it...
+        if mybed is not None:
+            ref_key = ref_name
+            if not ref_key in mybed[0] and ref_key[3:] in mybed[0]:  # account for 1 vs chr1, again...
+                ref_key = ref_key[3:]
+            if ref_key not in mybed[0]:
+                inBed = False
             else:
-                len_tumor = len(allele_tumor)
-            if len_normal != len_tumor:
-                indel_len = len_tumor - len_normal
-                if indel_len not in INDEL_COUNT:
-                    INDEL_COUNT[indel_len] = 0
-                INDEL_COUNT[indel_len] += 1
+                inBed = isInBed(mybed[0][ref_key], chrStart)
+            if inBed != mybed[1]:
+                continue
+
+        # we want only snps
+        # so, no '-' characters allowed, and chrStart must be same as chrEnd
+        if '-' not in allele_normal and '-' not in allele_alternate and chrStart == chrEnd:
+            trinuc_ref = ref_dict[ref_name][chrStart - 1:chrStart + 2]
+            if not trinuc_ref in VALID_TRINUC:
+                continue  # skip ref trinuc with invalid characters
+            # only consider positions where ref allele in tsv matches the nucleotide in our reference
+            if allele_ref == trinuc_ref[1]:
+                trinuc_normal = ref_dict[ref_name][chrStart - 1] + allele_normal + ref_dict[ref_name][chrStart + 1]
+                trinuc_tumor = ref_dict[ref_name][chrStart - 1] + allele_alternate + ref_dict[ref_name][chrStart + 1]
+                if not trinuc_normal in VALID_TRINUC or not trinuc_tumor in VALID_TRINUC:
+                    continue  # skip if mutation contains invalid char
+                key = (trinuc_normal, trinuc_tumor)
+                if key not in TRINUC_TRANSITION_COUNT:
+                    TRINUC_TRANSITION_COUNT[key] = 0
+                TRINUC_TRANSITION_COUNT[key] += 1
+                SNP_COUNT += 1
+                key2 = (allele_normal, allele_alternate)
+                if key2 not in SNP_TRANSITION_COUNT:
+                    SNP_TRANSITION_COUNT[key2] = 0
+                SNP_TRANSITION_COUNT[key2] += 1
 
                 if is_vcf:
                     myPopFreq = VCF_DEFAULT_POP_FREQ
@@ -343,11 +318,39 @@ def main():
                         cafStr = re.findall(r";CAF=.*?(?=;)", vcf_info)[0]
                         if ',' in cafStr:
                             myPopFreq = float(cafStr[5:].split(',')[1])
-                    VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_tumor, myPopFreq))
+                    VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_alternate, myPopFreq))
                 else:
-                    VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_tumor))
+                    VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_alternate))
                     TOTAL_DONORS[donor_id] = True
-        f.close()
+            else:
+                print('\nError: ref allele in variant call does not match reference.\n')
+                exit(1)
+
+        # now let's look for indels...
+        if '-' in allele_normal:
+            len_normal = 0
+        else:
+            len_normal = len(allele_normal)
+        if '-' in allele_alternate:
+            len_tumor = 0
+        else:
+            len_tumor = len(allele_alternate)
+        if len_normal != len_tumor:
+            indel_len = len_tumor - len_normal
+            if indel_len not in INDEL_COUNT:
+                INDEL_COUNT[indel_len] = 0
+            INDEL_COUNT[indel_len] += 1
+
+            if is_vcf:
+                myPopFreq = VCF_DEFAULT_POP_FREQ
+                if ';CAF=' in vcf_info:
+                    cafStr = re.findall(r";CAF=.*?(?=;)", vcf_info)[0]
+                    if ',' in cafStr:
+                        myPopFreq = float(cafStr[5:].split(',')[1])
+                VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_alternate, myPopFreq))
+            else:
+                VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_alternate))
+                TOTAL_DONORS[donor_id] = True
 
         # if we didn't find anything, skip ahead along to the next reference sequence
         if not len(VDAT_COMMON):
@@ -390,7 +393,7 @@ def main():
         for n in byLen:
             bi = int((n[1] - dist_thresh) / float(qptn)) * qptn
             bf = int((n[2] + dist_thresh) / float(qptn)) * qptn
-            candidate_regions.append((n[0] / float(bf - bi), max([0, bi]), min([len(ref_inds[ref_name]), bf])))
+            candidate_regions.append((n[0] / float(bf - bi), max([0, bi]), min([len(ref_dict[ref_name]), bf])))
         minVal = np.percentile([n[0] for n in candidate_regions], percentile_clust)
         for n in candidate_regions:
             if n[0] >= minVal:
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index de16d1e..02e57f4 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -13,17 +13,17 @@
 
 
 import os
-import sys
 import gzip
 import numpy as np
 import argparse
+import sys
 import pickle
 
 # absolute path to this script
-# SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2]) + '/py/'
-# sys.path.append(SIM_PATH)
+SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2]) + '/py/'
+sys.path.append(SIM_PATH)
 
-from py.probability import DiscreteDistribution
+from probability import DiscreteDistribution
 
 
 def parseFQ(inf):

From ce95aae2a007f55d0829c2d2895606956618b259 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 15 May 2020 14:49:24 -0500
Subject: [PATCH 045/323] adding pandas functionality to genMutModel.py'

---
 utilities/genMutModel.py | 99 ++++++++++++++++++++++------------------
 1 file changed, 54 insertions(+), 45 deletions(-)

diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index 8202445..e5b8370 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -11,10 +11,10 @@
 import numpy as np
 from Bio import SeqIO
 import pandas as pd
-
-SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2])
-sys.path.append(SIM_PATH)
-print(sys.path)
+#
+# SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2])
+# sys.path.append(SIM_PATH)
+# print(sys.path)
 
 
 #########################################################
@@ -93,22 +93,29 @@ def main():
     VCF_DEFAULT_POP_FREQ = 0.00001
 
     parser = argparse.ArgumentParser(description='genMutModel.py')
-    parser.add_argument('-r', type=str, required=True, metavar='Reference, in fasta format', help="* ref.fa")
-    parser.add_argument('-m', type=str, required=True, metavar='A vcf with mutations to model', help="* mutations.vcf")
-    parser.add_argument('-o', type=str, required=True, metavar='The name of the output model', help="* output.p")
+    parser.add_argument('-r', type=str, required=True, metavar='/path/to/reference.fasta',
+                        help="Reference file for organism in fasta format")
+    parser.add_argument('-m', type=str, required=True, metavar='/path/to/mutations.vcf',
+                        help="Mutation file for organism in VCF format")
+    parser.add_argument('-o', type=str, required=True, metavar='/path/to/output/and/prefix',
+                        help="Name of output file (final model will append \'.p\')")
     parser.add_argument('-bi', type=str, required=False, metavar='Bed file of regions to include', default=None,
                         help="only_use_these_regions.bed")
     parser.add_argument('-be', type=str, required=False, metavar='Bed file of regions to exclued', default=None,
                         help="exclude_these_regions.bed")
     parser.add_argument('--save-trinuc', required=False, action='store_true', default=False,
-                        help='save trinuc counts for ref')
+                        help='save trinucleotide counts for reference')
+    parser.add_argument('--human-sample', required=False, action='store_true', default=False,
+                        help='To skip unnumbered scaffolds in human references')
     parser.add_argument('--skip-common', required=False, action='store_true', default=False,
-                        help='do not save common snps + high mut regions')
+                        help='Do not save common snps + high mut regions')
     args = parser.parse_args()
 
-    (ref, tsv, out_pickle, save_trinuc, skip_common) = (
+    (ref, vcf, out_pickle, save_trinuc, skip_common) = (
         args.r, args.m, args.o, args.save_trinuc, args.skip_common)
 
+    is_human = args.human_sample
+
     # how many times do we observe each trinucleotide in the reference (and input bed region, if present)?
     TRINUC_REF_COUNT = {}
     TRINUC_BED_COUNT = {}
@@ -138,13 +145,8 @@ def main():
         print('only considering variants outside of specified bed regions...')
         mybed = (getBedTracks(args.be), False)
 
-    if tsv[-4:] == '.vcf':
-        is_vcf = True
-    elif tsv[-4:] == '.tsv':
-        is_vcf = False
-    else:
-        print('\nError: Unknown format for mutation input.\n')
-        exit(1)
+    if vcf[-4:] == '.tsv':
+        print("Warning! TSV file must follow VCF specifications.")
 
     reference = SeqIO.to_dict(SeqIO.parse(ref, "fasta"))
 
@@ -152,22 +154,26 @@ def main():
     ref_dict = {}
     for key in reference.keys():
         key_split = key.split("|")
-        ref_dict[key_split[0]] = reference[key]
+        if is_human:
+            if key_split[0] in REF_WHITELIST:
+                ref_dict[key_split[0]] = reference[key]
+            else:
+                continue
+        else:
+            ref_dict[key_split[0]] = reference[key]
 
     ref_list = list(ref_dict.keys())
 
     # Pre-parsing
     print('reading input variants...')
     header = ["CHROM", 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO']
-    variants = pd.read_csv(tsv, sep='\t', comment='#', names=header)
+    variants = pd.read_csv(vcf, sep='\t', comment='#', names=header)
     # hard-code index values based on expected columns in vcf
     (c1, c2, c3, m1, m2, m3) = (0, 1, 1, 3, 3, 4)
     variant_chroms = variants['CHROM'].to_list()
     matching_chromosomes = []
     for ref_name in ref_list:
         if ref_name not in variant_chroms:
-            print("Warning: {} not in reference. Skipping this chromosome".format(ref_name))
-            print("(Note that if this behavior is unexpected, check that chromosome names in VCF match the reference)")
             continue
         else:
             matching_chromosomes.append(ref_name)
@@ -179,18 +185,27 @@ def main():
 
     matching_variants = variants[variants['CHROM'].isin(matching_chromosomes)]
 
-    if not matching_variants:
+    if matching_variants.empty:
         print("No variants matched with the Reference. This may be a chromosome naming problem.")
         exit(-1)
 
+    # Rename variants dataframe for processing
+    matching_variants = matching_variants.rename(columns={'POS': 'chr_start'})
+    # Change the indexing by -1 to match VCF format indexing
+    matching_variants['chr_start'] = matching_variants['chr_start'] - 1
+    matching_variants['chr_end'] = matching_variants['chr_start']
+
     # load and process variants in each reference sequence individually, for memory reasons...
-    for ref_name in ref_list:
+    for ref_name in matching_chromosomes:
         # Count the number of non-N nucleotides for the reference
         TOTAL_REFLEN += len(ref_dict[ref_name].seq) - ref_dict[ref_name].seq.count('N')
 
         # list to be used for counting variants that occur multiple times in file (i.e. in multiple samples)
         VDAT_COMMON = []
 
+        # narrow variants list to current ref
+        df_to_process = matching_variants[matching_variants["CHROM"] == ref_name]
+
         """ ##########################################################################
         ###						COUNT TRINUCLEOTIDES IN ref						   ###
         ########################################################################## """
@@ -241,27 +256,21 @@ def main():
         print('reading input variants...')
 
         # TODO change all this to pandas
-        # we have -1 because tsv/vcf coords are 1-based, and our reference string index is 0-based
-
-
-        [chrName, chrStart, chrEnd] = [splt[c1], int(splt[c2]) - 1, int(splt[c3]) - 1]
-        [allele_ref, allele_normal, allele_alternate] = [splt[m1].upper(), splt[m2].upper(), splt[m3].upper()]
-        if is_vcf:
-            if len(allele_ref) != len(allele_alternate):
-                # indels in tsv don't include the preserved first nucleotide, so lets trim the vcf alleles
-                [allele_ref, allele_normal, allele_alternate] = [allele_ref[1:], allele_normal[1:], allele_alternate[1:]]
-            if not allele_ref:
-                allele_ref = '-'
-            if not allele_normal:
-                allele_normal = '-'
-            if not allele_alternate:
-                allele_alternate = '-'
-            # if alternate alleles are present, lets just ignore this variant. I may come back and improve this later
-            if ',' in allele_alternate:
-                continue
-            vcf_info = ';' + splt[7] + ';'
-        else:
-            [donor_id] = [splt[d_id]]
+        # we have -1 because vcf/vcf coords are 1-based, and our reference string index is 0-based
+
+        if len(allele_ref) != len(allele_alternate):
+            # indels in vcf don't include the preserved first nucleotide, so lets trim the vcf alleles
+            [allele_ref, allele_normal, allele_alternate] = [allele_ref[1:], allele_normal[1:], allele_alternate[1:]]
+        if not allele_ref:
+            allele_ref = '-'
+        if not allele_normal:
+            allele_normal = '-'
+        if not allele_alternate:
+            allele_alternate = '-'
+        # if alternate alleles are present, lets just ignore this variant. I may come back and improve this later
+        if ',' in allele_alternate:
+            continue
+        vcf_info = ';' + splt[7] + ';'
 
         # if we encounter a multi-np (i.e. 3 nucl --> 3 different nucl), let's skip it for now...
         if ('-' not in allele_normal and '-' not in allele_alternate) and (
@@ -296,7 +305,7 @@ def main():
             trinuc_ref = ref_dict[ref_name][chrStart - 1:chrStart + 2]
             if not trinuc_ref in VALID_TRINUC:
                 continue  # skip ref trinuc with invalid characters
-            # only consider positions where ref allele in tsv matches the nucleotide in our reference
+            # only consider positions where ref allele in vcf matches the nucleotide in our reference
             if allele_ref == trinuc_ref[1]:
                 trinuc_normal = ref_dict[ref_name][chrStart - 1] + allele_normal + ref_dict[ref_name][chrStart + 1]
                 trinuc_tumor = ref_dict[ref_name][chrStart - 1] + allele_alternate + ref_dict[ref_name][chrStart + 1]

From b3f543fd99115280cefb06aa73769c851c7fdce2 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Sun, 17 May 2020 04:57:59 -0500
Subject: [PATCH 046/323] Minor changes to genMutModel.py to add pandas
 functionality

---
 utilities/genMutModel.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index e5b8370..c994a1c 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -256,7 +256,6 @@ def main():
         print('reading input variants...')
 
         # TODO change all this to pandas
-        # we have -1 because vcf/vcf coords are 1-based, and our reference string index is 0-based
 
         if len(allele_ref) != len(allele_alternate):
             # indels in vcf don't include the preserved first nucleotide, so lets trim the vcf alleles

From 1bad90182658c58fcff47fb034290536f17a2eb9 Mon Sep 17 00:00:00 2001
From: Joshua Allen <joshfactorial@pop-os.localdomain>
Date: Mon, 18 May 2020 10:49:36 -0500
Subject: [PATCH 047/323] Updating genMutModel.py to pandas compatible to try
 to speed it up

---
 utilities/genMutModel.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index c994a1c..2fecc91 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -256,14 +256,20 @@ def main():
         print('reading input variants...')
 
         # TODO change all this to pandas
+        indices_to_indels = df_to_process.loc[df_to_process.ALT != df_to_process.REF].index
+
+        # indels in vcf don't include the preserved first nucleotide, so lets trim the vcf alleles
+        ref_values_to_change = df_to_process.loc[indices_to_indels, 'REF'].str[1:]
+        alt_values_to_change = df_to_process.loc[indices_to_indels, 'ALT'].str[1:]
+        df_to_process.loc[indices_to_indels, "REF"] = ref_values_to_change
+        df_to_process.loc[indices_to_indels, "ALT"] = alt_values_to_change
+
 
         if len(allele_ref) != len(allele_alternate):
             # indels in vcf don't include the preserved first nucleotide, so lets trim the vcf alleles
             [allele_ref, allele_normal, allele_alternate] = [allele_ref[1:], allele_normal[1:], allele_alternate[1:]]
         if not allele_ref:
             allele_ref = '-'
-        if not allele_normal:
-            allele_normal = '-'
         if not allele_alternate:
             allele_alternate = '-'
         # if alternate alleles are present, lets just ignore this variant. I may come back and improve this later

From 2d2c7168a6afce9031d61dee950073316eb72313 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 19 May 2020 10:05:53 -0500
Subject: [PATCH 048/323] further pandas conversion for genMutModel

---
 utilities/genMutModel.py | 61 +++++++++++++++++-----------------------
 1 file changed, 26 insertions(+), 35 deletions(-)

diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index 2fecc91..1465985 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -166,11 +166,11 @@ def main():
 
     # Pre-parsing
     print('reading input variants...')
-    header = ["CHROM", 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO']
-    variants = pd.read_csv(vcf, sep='\t', comment='#', names=header)
+    variants = pd.read_csv(vcf, sep='\t', comment='#', index_col=None)
     # hard-code index values based on expected columns in vcf
     (c1, c2, c3, m1, m2, m3) = (0, 1, 1, 3, 3, 4)
-    variant_chroms = variants['CHROM'].to_list()
+    variant_chroms = variants[0].to_list()
+    variant_chroms = list(set(variant_chroms))
     matching_chromosomes = []
     for ref_name in ref_list:
         if ref_name not in variant_chroms:
@@ -183,14 +183,15 @@ def main():
         print("Found no chromosomes in common between VCF and Fasta. Please fix the chromosome names and try again")
         exit(-1)
 
-    matching_variants = variants[variants['CHROM'].isin(matching_chromosomes)]
+    matching_variants = variants[variants[0].isin(matching_chromosomes)]
 
     if matching_variants.empty:
         print("No variants matched with the Reference. This may be a chromosome naming problem.")
         exit(-1)
 
-    # Rename variants dataframe for processing
-    matching_variants = matching_variants.rename(columns={'POS': 'chr_start'})
+    # Rename header in dataframe for processing
+    matching_variants = matching_variants.rename(columns={0: "CHROM", 1: 'chr_start', 2: 'ID', 3: 'REF', 4: 'ALT',
+                                                          5: 'QUAL', 6: 'FILTER', 7: 'INFO'})
     # Change the indexing by -1 to match VCF format indexing
     matching_variants['chr_start'] = matching_variants['chr_start'] - 1
     matching_variants['chr_end'] = matching_variants['chr_start']
@@ -204,7 +205,7 @@ def main():
         VDAT_COMMON = []
 
         # narrow variants list to current ref
-        df_to_process = matching_variants[matching_variants["CHROM"] == ref_name]
+        df_to_process = matching_variants[matching_variants["CHROM"] == ref_name].copy()
 
         """ ##########################################################################
         ###						COUNT TRINUCLEOTIDES IN ref						   ###
@@ -255,43 +256,33 @@ def main():
 
         print('reading input variants...')
 
-        # TODO change all this to pandas
-        indices_to_indels = df_to_process.loc[df_to_process.ALT != df_to_process.REF].index
+        indices_to_indels = df_to_process.loc[df_to_process.ALT.apply(len) != df_to_process.REF.apply(len)].index
 
         # indels in vcf don't include the preserved first nucleotide, so lets trim the vcf alleles
-        ref_values_to_change = df_to_process.loc[indices_to_indels, 'REF'].str[1:]
-        alt_values_to_change = df_to_process.loc[indices_to_indels, 'ALT'].str[1:]
+        ref_values_to_change = df_to_process.loc[indices_to_indels, 'REF'].copy().str[1:]
+        alt_values_to_change = df_to_process.loc[indices_to_indels, 'ALT'].copy().str[1:]
         df_to_process.loc[indices_to_indels, "REF"] = ref_values_to_change
         df_to_process.loc[indices_to_indels, "ALT"] = alt_values_to_change
+        df_to_process.replace('', '-', inplace=True)
 
-
-        if len(allele_ref) != len(allele_alternate):
-            # indels in vcf don't include the preserved first nucleotide, so lets trim the vcf alleles
-            [allele_ref, allele_normal, allele_alternate] = [allele_ref[1:], allele_normal[1:], allele_alternate[1:]]
-        if not allele_ref:
-            allele_ref = '-'
-        if not allele_alternate:
-            allele_alternate = '-'
         # if alternate alleles are present, lets just ignore this variant. I may come back and improve this later
-        if ',' in allele_alternate:
-            continue
-        vcf_info = ';' + splt[7] + ';'
+        indices_to_ignore = df_to_process[df_to_process['ALT'].str.contains(',')].index
+        df_to_process = df_to_process.drop(indices_to_ignore)
 
-        # if we encounter a multi-np (i.e. 3 nucl --> 3 different nucl), let's skip it for now...
-        if ('-' not in allele_normal and '-' not in allele_alternate) and (
-                len(allele_normal) > 1 or len(allele_alternate) > 1):
-            print('skipping a complex variant...')
-            continue
+        # Not sure what the point of this line is
+        # vcf_info = ';' + splt[7] + ';'
 
-        # to deal with '1' vs 'chr1' references, manually change names. this is hacky and bad.
-        if 'chr' not in chrName:
-            chrName = 'chr' + chrName
-        if 'chr' not in ref_name:
-            ref_name = 'chr' + ref_name
-        # skip irrelevant variants
-        if chrName != ref_name:
-            continue
+        # if we encounter a multi-np (i.e. 3 nucl --> 3 different nucl), let's skip it for now...
+        # Alt and Ref contain no dashes
+        no_dashes = df_to_process[
+            ~df_to_process['REF'].str.contains('-') & ~df_to_process['ALT'].str.contains('-')].index
+        # Alt and Ref lengths are greater than 1
+        long_variants = df_to_process[
+            (df_to_process['REF'].apply(len) > 1) & (df_to_process['ALT'].apply(len) > 1)].index
+        complex_variants = list(set(no_dashes) & set(long_variants))
+        df_to_process = df_to_process.drop(complex_variants)
 
+        # TODO change all this to pandas
         # if variant is outside the regions we're interested in (if specified), skip it...
         if mybed is not None:
             ref_key = ref_name

From 249579d74728bcb0dc405a0a30612917ecfe60ff Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 21 May 2020 09:07:24 -0500
Subject: [PATCH 049/323] Further updates to genMutModel

---
 utilities/genMutModel.py | 236 +++++++++++++++++++++------------------
 1 file changed, 130 insertions(+), 106 deletions(-)

diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index 1465985..e420352 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -101,8 +101,9 @@ def main():
                         help="Name of output file (final model will append \'.p\')")
     parser.add_argument('-bi', type=str, required=False, metavar='Bed file of regions to include', default=None,
                         help="only_use_these_regions.bed")
-    parser.add_argument('-be', type=str, required=False, metavar='Bed file of regions to exclued', default=None,
-                        help="exclude_these_regions.bed")
+    # This was not implemented correctly in the original file. Will circle back to it.
+    # parser.add_argument('-be', type=str, required=False, metavar='Bed file of regions to exclued', default=None,
+    #                     help="exclude_these_regions.bed")
     parser.add_argument('--save-trinuc', required=False, action='store_true', default=False,
                         help='save trinucleotide counts for reference')
     parser.add_argument('--human-sample', required=False, action='store_true', default=False,
@@ -119,7 +120,6 @@ def main():
     # how many times do we observe each trinucleotide in the reference (and input bed region, if present)?
     TRINUC_REF_COUNT = {}
     TRINUC_BED_COUNT = {}
-    print_bed_warning = True
     # [(trinuc_a, trinuc_b)] = # of times we observed a mutation from trinuc_a into trinuc_b
     TRINUC_TRANSITION_COUNT = {}
     # total count of SNPs
@@ -137,20 +137,29 @@ def main():
     # identify regions that have significantly higher local mutation rates than the average
     HIGH_MUT_REGIONS = []
 
+    # Process bed file
+    is_bed = False
     mybed = None
     if args.bi is not None:
-        print('only considering variants in specified bed regions...')
-        mybed = (getBedTracks(args.bi), True)
-    elif args.be is not None:
-        print('only considering variants outside of specified bed regions...')
-        mybed = (getBedTracks(args.be), False)
-
-    if vcf[-4:] == '.tsv':
-        print("Warning! TSV file must follow VCF specifications.")
-
-    reference = SeqIO.to_dict(SeqIO.parse(ref, "fasta"))
+        print('Processing bed file...')
+        try:
+            mybed = pd.read_csv(args.bi, sep='\t', header=None, index_col=None)
+            is_bed = True
+        except ValueError:
+            print('Problem parsing bed file. Ensure bed file is tab separated, standard bed format')
+            exit(1)
+        mybed = mybed.rename(columns={0: 'chrom', 1: 'start', 2: 'end'})
+        mybed['coords'] = list(zip(mybed.start, mybed.end))
+
+    # Process reference file
+    print('Processing reference...')
+    try:
+        reference = SeqIO.to_dict(SeqIO.parse(ref, "fasta"))
+    except ValueError:
+        print("Problems parsing reference file. Ensure reference is in proper fasta format")
+        exit(1)
 
-    # simplify naming
+    # simplify naming and filter out actual human genomes from scaffolding
     ref_dict = {}
     for key in reference.keys():
         key_split = key.split("|")
@@ -164,11 +173,20 @@ def main():
 
     ref_list = list(ref_dict.keys())
 
-    # Pre-parsing
-    print('reading input variants...')
-    variants = pd.read_csv(vcf, sep='\t', comment='#', index_col=None)
-    # hard-code index values based on expected columns in vcf
-    (c1, c2, c3, m1, m2, m3) = (0, 1, 1, 3, 3, 4)
+    # Process VCF file. First check if it's been entered as a TSV
+    if vcf[-3:] == 'tsv':
+        print("Warning! TSV file must follow VCF specifications.")
+
+    # Pre-parsing to find all the matching chromosomes between ref and vcf
+    print('Processing VCF file...')
+    try:
+        variants = pd.read_csv(vcf, sep='\t', comment='#', index_col=None)
+    except ValueError:
+        print("VCF must be in standard VCF format with tab-separated columns")
+        exit(1)
+
+    # Narrow chromosomes to those matching the reference
+    # This is in part to make sure the names match
     variant_chroms = variants[0].to_list()
     variant_chroms = list(set(variant_chroms))
     matching_chromosomes = []
@@ -181,22 +199,104 @@ def main():
     # Check to make sure there are some matches
     if not matching_chromosomes:
         print("Found no chromosomes in common between VCF and Fasta. Please fix the chromosome names and try again")
-        exit(-1)
-
-    matching_variants = variants[variants[0].isin(matching_chromosomes)]
+        exit(1)
 
+    # Double check that there are matches
+    try:
+        matching_variants = variants[variants[0].isin(matching_chromosomes)]
+    except ValueError:
+        print("Problem matching variants with reference.")
+        exit(1)
     if matching_variants.empty:
-        print("No variants matched with the Reference. This may be a chromosome naming problem.")
-        exit(-1)
+        print("There is no overlap between reference and variant file. This could be a chromosome naming problem")
+        exit(1)
+
 
     # Rename header in dataframe for processing
     matching_variants = matching_variants.rename(columns={0: "CHROM", 1: 'chr_start', 2: 'ID', 3: 'REF', 4: 'ALT',
                                                           5: 'QUAL', 6: 'FILTER', 7: 'INFO'})
-    # Change the indexing by -1 to match VCF format indexing
+
+    # Change the indexing by -1 to match python format indexing (0-based)
     matching_variants['chr_start'] = matching_variants['chr_start'] - 1
     matching_variants['chr_end'] = matching_variants['chr_start']
 
-    # load and process variants in each reference sequence individually, for memory reasons...
+    # Process the variant table
+    indices_to_indels = \
+        matching_variants.loc[matching_variants.ALT.apply(len) != matching_variants.REF.apply(len)].index
+
+    # indels in vcf don't include the preserved first nucleotide, so lets trim the vcf alleles
+    ref_values_to_change = matching_variants.loc[indices_to_indels, 'REF'].copy().str[1:]
+    alt_values_to_change = matching_variants.loc[indices_to_indels, 'ALT'].copy().str[1:]
+    matching_variants.loc[indices_to_indels, "REF"] = ref_values_to_change
+    matching_variants.loc[indices_to_indels, "ALT"] = alt_values_to_change
+    matching_variants.replace('', '-', inplace=True)
+
+    # If multi-alternate alleles are present, lets just ignore this variant. I may come back and improve this later
+    indices_to_ignore = matching_variants[matching_variants['ALT'].str.contains(',')].index
+    matching_variants = matching_variants.drop(indices_to_ignore)
+
+    # Not sure what the point of this line is
+    # TODO take this out if it's not needed
+    # vcf_info = ';' + splt[7] + ';'
+
+    # if we encounter a multi-np (i.e. 3 nucl --> 3 different nucl), let's skip it for now...
+
+    # Alt and Ref contain no dashes
+    no_dashes = matching_variants[
+        ~matching_variants['REF'].str.contains('-') & ~matching_variants['ALT'].str.contains('-')].index
+    # Alt and Ref lengths are greater than 1
+    long_variants = matching_variants[
+        (matching_variants['REF'].apply(len) > 1) & (matching_variants['ALT'].apply(len) > 1)].index
+    complex_variants = list(set(no_dashes) & set(long_variants))
+    matching_variants = matching_variants.drop(complex_variants)
+
+    # Now we check that the bed and vcf have matching regions
+    # This also checks that the vcf and bed have the same naming conventions and cuts out scaffolding.
+    if is_bed:
+        bed_chroms = list(set(mybed['chrom']))
+        matching_bed_keys = list(set(bed_chroms) & set(variant_chroms))
+        try:
+            matching_bed = mybed[mybed['chrom'].isin(matching_bed_keys)]
+        except ValueError:
+            print('Problem matching bed chromosomes to variant file.')
+            exit(1)
+        if matching_bed.empty:
+            print("There is no overlap between bed and variant file. "
+                  "This could be a chromosome naming problem")
+            exit(1)
+
+    # Count Trinucleotides in reference, based on bed or not
+    print('Counting trinucleotides in reference...')
+    if is_bed:
+        print("since you're using a bed input, we have to count trinucs in bed region even if\n"
+              "you already have a trinuc count file for the reference...")
+        sub_regions = matching_bed['coords'].to_list()
+        for sr in sub_regions:
+            for i in range(sr[0], sr[1] + 1):
+                for ref_name in matching_chromosomes:
+                    trinuc = ref_dict[ref_name][i:i + 3].seq
+                    # skip if trinuc contains invalid characters, or not in specified bed region
+                    if not trinuc in VALID_TRINUC:
+                        continue
+                    if trinuc not in TRINUC_BED_COUNT:
+                        TRINUC_BED_COUNT[trinuc] = 0
+                    TRINUC_BED_COUNT[trinuc] += 1
+
+    elif not os.path.isfile(ref + '.trinucCounts'):
+        for i in range(len(ref_dict[ref_name]) - 2):
+            if i % 1000000 == 0 and i > 0:
+                print(i, '/', len(ref_dict[ref_name]))
+            trinuc = ref_dict[ref_name][i:i + 3].seq
+            if not str(trinuc) in VALID_TRINUC:
+                continue  # skip if trinuc contains invalid characters
+            if str(trinuc) not in TRINUC_REF_COUNT:
+                TRINUC_REF_COUNT[trinuc] = 0
+            TRINUC_REF_COUNT[trinuc] += 1
+    else:
+        print('Found trinucCounts file, using that.')
+
+    # Load and process variants in each reference sequence individually, for memory reasons...
+    print('Creating mutational model...')
     for ref_name in matching_chromosomes:
         # Count the number of non-N nucleotides for the reference
         TOTAL_REFLEN += len(ref_dict[ref_name].seq) - ref_dict[ref_name].seq.count('N')
@@ -204,94 +304,18 @@ def main():
         # list to be used for counting variants that occur multiple times in file (i.e. in multiple samples)
         VDAT_COMMON = []
 
-        # narrow variants list to current ref
+        # Create a view that narrows variants list to current ref
         df_to_process = matching_variants[matching_variants["CHROM"] == ref_name].copy()
+        bed_to_process = matching_bed[matching_bed['chrom'] == ref_name].copy()
 
-        """ ##########################################################################
-        ###						COUNT TRINUCLEOTIDES IN ref						   ###
-        ########################################################################## """
-
-        if mybed is not None:
-            if print_bed_warning:
-                print("since you're using a bed input, "
-                      "we have to count trinucs in bed region even if "
-                      "you specified a trinuc count file for the reference...")
-                print_bed_warning = False
-            if ref_name in mybed[0]:
-                ref_key = ref_name
-            elif ('chr' in ref_name) and (ref_name not in mybed[0]) and (ref_name[3:] in mybed[0]):
-                ref_key = ref_name[3:]
-            elif ('chr' not in ref_name) and (ref_name not in mybed[0]) and ('chr' + ref_name in mybed[0]):
-                ref_key = 'chr' + ref_name
-            if ref_key in mybed[0]:
-                subRegions = [(mybed[0][ref_key][n], mybed[0][ref_key][n + 1]) for n in
-                              range(0, len(mybed[0][ref_key]), 2)]
-                for sr in subRegions:
-                    for i in range(sr[0], sr[1] + 1 - 2):
-                        trinuc = ref_dict[ref_name][i:i + 3].seq
-                        if not trinuc in VALID_TRINUC:
-                            continue  # skip if trinuc contains invalid characters, or not in specified bed region
-                        if trinuc not in TRINUC_BED_COUNT:
-                            TRINUC_BED_COUNT[trinuc] = 0
-                        TRINUC_BED_COUNT[trinuc] += 1
-
-        if not os.path.isfile(ref + '.trinucCounts'):
-            print('counting trinucleotides in reference...')
-            for i in range(len(ref_dict[ref_name]) - 2):
-                if i % 1000000 == 0 and i > 0:
-                    print(i, '/', len(ref_dict[ref_name]))
-                    # break
-                trinuc = ref_dict[ref_name][i:i + 3].seq
-                if not str(trinuc) in VALID_TRINUC:
-                    continue  # skip if trinuc contains invalid characters
-                if str(trinuc) not in TRINUC_REF_COUNT:
-                    TRINUC_REF_COUNT[trinuc] = 0
-                TRINUC_REF_COUNT[trinuc] += 1
-        else:
-            print('skipping trinuc counts (for whole reference) because we found a file...')
-
-        """ ##########################################################################
-        ###							READ INPUT VARIANTS							   ###
-        ########################################################################## """
-
-        print('reading input variants...')
-
-        indices_to_indels = df_to_process.loc[df_to_process.ALT.apply(len) != df_to_process.REF.apply(len)].index
-
-        # indels in vcf don't include the preserved first nucleotide, so lets trim the vcf alleles
-        ref_values_to_change = df_to_process.loc[indices_to_indels, 'REF'].copy().str[1:]
-        alt_values_to_change = df_to_process.loc[indices_to_indels, 'ALT'].copy().str[1:]
-        df_to_process.loc[indices_to_indels, "REF"] = ref_values_to_change
-        df_to_process.loc[indices_to_indels, "ALT"] = alt_values_to_change
-        df_to_process.replace('', '-', inplace=True)
-
-        # if alternate alleles are present, lets just ignore this variant. I may come back and improve this later
-        indices_to_ignore = df_to_process[df_to_process['ALT'].str.contains(',')].index
-        df_to_process = df_to_process.drop(indices_to_ignore)
-
-        # Not sure what the point of this line is
-        # vcf_info = ';' + splt[7] + ';'
-
-        # if we encounter a multi-np (i.e. 3 nucl --> 3 different nucl), let's skip it for now...
-        # Alt and Ref contain no dashes
-        no_dashes = df_to_process[
-            ~df_to_process['REF'].str.contains('-') & ~df_to_process['ALT'].str.contains('-')].index
-        # Alt and Ref lengths are greater than 1
-        long_variants = df_to_process[
-            (df_to_process['REF'].apply(len) > 1) & (df_to_process['ALT'].apply(len) > 1)].index
-        complex_variants = list(set(no_dashes) & set(long_variants))
-        df_to_process = df_to_process.drop(complex_variants)
 
         # TODO change all this to pandas
         # if variant is outside the regions we're interested in (if specified), skip it...
         if mybed is not None:
-            ref_key = ref_name
-            if not ref_key in mybed[0] and ref_key[3:] in mybed[0]:  # account for 1 vs chr1, again...
-                ref_key = ref_key[3:]
-            if ref_key not in mybed[0]:
+            if ref_name not in mybed[0]:
                 inBed = False
             else:
-                inBed = isInBed(mybed[0][ref_key], chrStart)
+                inBed = isInBed(mybed[0][ref_name], chrStart)
             if inBed != mybed[1]:
                 continue
 

From 6f859e83a0b4f66ae10ec119ca8918ead569b39e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 1 Jun 2020 17:03:59 -0500
Subject: [PATCH 050/323] Updating genMutModel to pandas

---
 utilities/genMutModel.py | 128 ++++++++++++++++++---------------------
 1 file changed, 60 insertions(+), 68 deletions(-)

diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index e420352..c62db9d 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -235,10 +235,6 @@ def main():
     indices_to_ignore = matching_variants[matching_variants['ALT'].str.contains(',')].index
     matching_variants = matching_variants.drop(indices_to_ignore)
 
-    # Not sure what the point of this line is
-    # TODO take this out if it's not needed
-    # vcf_info = ';' + splt[7] + ';'
-
     # if we encounter a multi-np (i.e. 3 nucl --> 3 different nucl), let's skip it for now...
 
     # Alt and Ref contain no dashes
@@ -250,6 +246,10 @@ def main():
     complex_variants = list(set(no_dashes) & set(long_variants))
     matching_variants = matching_variants.drop(complex_variants)
 
+    # This is solely to make regex easier later, since we can't predict where in the line a string will be
+    new_info = ';' + matching_variants['INFO'].copy() + ';'
+    matching_variants['INFO'] = new_info
+
     # Now we check that the bed and vcf have matching regions
     # This also checks that the vcf and bed have the same naming conventions and cuts out scaffolding.
     if is_bed:
@@ -305,81 +305,73 @@ def main():
         VDAT_COMMON = []
 
         # Create a view that narrows variants list to current ref
-        df_to_process = matching_variants[matching_variants["CHROM"] == ref_name].copy()
+        variants_to_process = matching_variants[matching_variants["CHROM"] == ref_name].copy()
         bed_to_process = matching_bed[matching_bed['chrom'] == ref_name].copy()
+        ref_sequence = str(ref_dict[ref_name].seq)
 
 
         # TODO change all this to pandas
-        # if variant is outside the regions we're interested in (if specified), skip it...
-        if mybed is not None:
-            if ref_name not in mybed[0]:
-                inBed = False
-            else:
-                inBed = isInBed(mybed[0][ref_name], chrStart)
-            if inBed != mybed[1]:
-                continue
 
         # we want only snps
         # so, no '-' characters allowed, and chrStart must be same as chrEnd
-        if '-' not in allele_normal and '-' not in allele_alternate and chrStart == chrEnd:
-            trinuc_ref = ref_dict[ref_name][chrStart - 1:chrStart + 2]
-            if not trinuc_ref in VALID_TRINUC:
-                continue  # skip ref trinuc with invalid characters
+        snp_df = variants_to_process[~variants_to_process.index.isin(indices_to_indels)]
+        snp_df = snp_df.loc[snp_df['chr_start'] == snp_df['chr_end']]
+        if not snp_df.empty:
             # only consider positions where ref allele in vcf matches the nucleotide in our reference
-            if allele_ref == trinuc_ref[1]:
-                trinuc_normal = ref_dict[ref_name][chrStart - 1] + allele_normal + ref_dict[ref_name][chrStart + 1]
-                trinuc_tumor = ref_dict[ref_name][chrStart - 1] + allele_alternate + ref_dict[ref_name][chrStart + 1]
-                if not trinuc_normal in VALID_TRINUC or not trinuc_tumor in VALID_TRINUC:
-                    continue  # skip if mutation contains invalid char
-                key = (trinuc_normal, trinuc_tumor)
-                if key not in TRINUC_TRANSITION_COUNT:
-                    TRINUC_TRANSITION_COUNT[key] = 0
-                TRINUC_TRANSITION_COUNT[key] += 1
-                SNP_COUNT += 1
-                key2 = (allele_normal, allele_alternate)
-                if key2 not in SNP_TRANSITION_COUNT:
-                    SNP_TRANSITION_COUNT[key2] = 0
-                SNP_TRANSITION_COUNT[key2] += 1
-
-                if is_vcf:
-                    myPopFreq = VCF_DEFAULT_POP_FREQ
-                    if ';CAF=' in vcf_info:
-                        cafStr = re.findall(r";CAF=.*?(?=;)", vcf_info)[0]
-                        if ',' in cafStr:
-                            myPopFreq = float(cafStr[5:].split(',')[1])
-                    VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_alternate, myPopFreq))
+            for index, row in snp_df.iterrows():
+                trinuc_to_analyze = str(ref_sequence[row.chr_start - 1: row.chr_start + 2])
+                if not trinuc_to_analyze in VALID_TRINUC:
+                    continue
+                if row.REF == trinuc_to_analyze[1]:
+                    trinuc_ref = trinuc_to_analyze
+                    trinuc_alt = trinuc_to_analyze[0] + snp_df.loc[index, 'ALT'] + trinuc_to_analyze[2]
+                    if not trinuc_alt in VALID_TRINUC:
+                        continue
+                    key = (trinuc_ref, trinuc_alt)
+                    if key not in TRINUC_TRANSITION_COUNT:
+                        TRINUC_TRANSITION_COUNT[key] = 0
+                    TRINUC_TRANSITION_COUNT[key] += 1
+                    SNP_COUNT += 1
+                    key2 = (str(row.REF), str(row.ALT))
+                    if key2 not in SNP_TRANSITION_COUNT:
+                        SNP_TRANSITION_COUNT[key2] = 0
+                    SNP_TRANSITION_COUNT[key2] += 1
+
+                    my_pop_freq = VCF_DEFAULT_POP_FREQ
+                    if ';CAF=' in snp_df.loc[index, 'INFO']:
+                        caf_str = re.findall(r";CAF=.*?(?=;)", row.INFO)[0]
+                        if ',' in caf_str:
+                            my_pop_freq = float(caf_str[5:].split(',')[1])
+                    VDAT_COMMON.append(
+                        (row.chr_start, row.REF, row.ALT, my_pop_freq))
                 else:
-                    VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_alternate))
-                    TOTAL_DONORS[donor_id] = True
-            else:
-                print('\nError: ref allele in variant call does not match reference.\n')
-                exit(1)
+                    print('\nError: ref allele in variant call does not match reference.\n')
+                    exit(1)
 
         # now let's look for indels...
-        if '-' in allele_normal:
-            len_normal = 0
-        else:
-            len_normal = len(allele_normal)
-        if '-' in allele_alternate:
-            len_tumor = 0
-        else:
-            len_tumor = len(allele_alternate)
-        if len_normal != len_tumor:
-            indel_len = len_tumor - len_normal
-            if indel_len not in INDEL_COUNT:
-                INDEL_COUNT[indel_len] = 0
-            INDEL_COUNT[indel_len] += 1
-
-            if is_vcf:
-                myPopFreq = VCF_DEFAULT_POP_FREQ
-                if ';CAF=' in vcf_info:
-                    cafStr = re.findall(r";CAF=.*?(?=;)", vcf_info)[0]
-                    if ',' in cafStr:
-                        myPopFreq = float(cafStr[5:].split(',')[1])
-                VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_alternate, myPopFreq))
-            else:
-                VDAT_COMMON.append((chrStart, allele_ref, allele_normal, allele_alternate))
-                TOTAL_DONORS[donor_id] = True
+        indel_df = variants_to_process[variants_to_process.index.isin(indices_to_indels)]
+        if not indel_df.empty:
+            for index, row in indel_df.iterrows():
+                if "-" in row.REF:
+                    len_ref = 0
+                else:
+                    len_ref = len(row.REF)
+                if "-" in row.ALT:
+                    len_alt = 0
+                else:
+                    len_alt = len(row.ALT)
+                if len_ref != len_alt:
+                    indel_len = len_alt - len_ref
+                    if indel_len not in INDEL_COUNT:
+                        INDEL_COUNT[indel_len] = 0
+                    INDEL_COUNT[indel_len] += 1
+
+                    my_pop_freq = VCF_DEFAULT_POP_FREQ
+                    if ';CAF=' in row.INFO:
+                        caf_str = re.findall(r";CAF=.*?(?=;))", row.INFO)[0]
+                        if ',' in caf_str:
+                            my_pop_freq = float(caf_str[5:].split(',')[1])
+                    VDAT_COMMON.append((row.chr_start, row.REF, row.ALT, my_pop_freq))
 
         # if we didn't find anything, skip ahead along to the next reference sequence
         if not len(VDAT_COMMON):

From 32112d7e1ae4ba69c30b9ed1ebf5913676c3fba5 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 8 Jun 2020 14:03:24 -0500
Subject: [PATCH 051/323] Committing some updates to genMutModel

---
 utilities/genMutModel.py | 104 ++++++++++++++++++---------------------
 1 file changed, 48 insertions(+), 56 deletions(-)

diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index c62db9d..79982ea 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -22,20 +22,25 @@
 #########################################################
 
 
-# cluster a sorted list
-def cluster_list(l: list, delta: float) -> list:
-    outList = [[l[0]]]
-    prevVal = l[0]
-    currentInd = 0
-    for n in l[1:]:
-        if n - prevVal <= delta:
-            outList[currentInd].append(n)
+def cluster_list(list_to_cluster: list, delta: float) -> list:
+    """
+    Clusters a sorted list
+    :param list_to_cluster: a sorted list
+    :param delta: the value to compare list items to
+    :return: a clustered list of values
+    """
+    out_list = [[list_to_cluster[0]]]
+    previous_value = list_to_cluster[0]
+    current_index = 0
+    for item in list_to_cluster[1:]:
+        if item - previous_value <= delta:
+            out_list[current_index].append(item)
         else:
-            currentInd += 1
-            outList.append([])
-            outList[currentInd].append(n)
-        prevVal = n
-    return outList
+            current_index += 1
+            out_list.append([])
+            out_list[current_index].append(item)
+        previous_value = item
+    return out_list
 
 
 def list_2_countDict(l: list) -> dict:
@@ -309,9 +314,6 @@ def main():
         bed_to_process = matching_bed[matching_bed['chrom'] == ref_name].copy()
         ref_sequence = str(ref_dict[ref_name].seq)
 
-
-        # TODO change all this to pandas
-
         # we want only snps
         # so, no '-' characters allowed, and chrStart must be same as chrEnd
         snp_df = variants_to_process[~variants_to_process.index.isin(indices_to_indels)]
@@ -343,7 +345,7 @@ def main():
                         if ',' in caf_str:
                             my_pop_freq = float(caf_str[5:].split(',')[1])
                     VDAT_COMMON.append(
-                        (row.chr_start, row.REF, row.ALT, my_pop_freq))
+                        (row.chr_start, row.REF, row.REF, row.ALT, my_pop_freq))
                 else:
                     print('\nError: ref allele in variant call does not match reference.\n')
                     exit(1)
@@ -368,70 +370,58 @@ def main():
 
                     my_pop_freq = VCF_DEFAULT_POP_FREQ
                     if ';CAF=' in row.INFO:
-                        caf_str = re.findall(r";CAF=.*?(?=;))", row.INFO)[0]
+                        caf_str = re.findall(r";CAF=.*?(?=;)", row.INFO)[0]
                         if ',' in caf_str:
                             my_pop_freq = float(caf_str[5:].split(',')[1])
-                    VDAT_COMMON.append((row.chr_start, row.REF, row.ALT, my_pop_freq))
+                    VDAT_COMMON.append((row.chr_start, row.REF, row.REF, row.ALT, my_pop_freq))
 
         # if we didn't find anything, skip ahead along to the next reference sequence
         if not len(VDAT_COMMON):
-            print('Found no variants for this reference, moving along...')
+            print('Found no variants for this reference.')
             continue
 
-        #
         # identify common mutations
-        #
         percentile_var = 95
-        if is_vcf:
-            minVal = np.percentile([n[4] for n in VDAT_COMMON], percentile_var)
-            for k in sorted(VDAT_COMMON):
-                if k[4] >= minVal:
-                    COMMON_VARIANTS.append((ref_name, k[0], k[1], k[3], k[4]))
-            VDAT_COMMON = {(n[0], n[1], n[2], n[3]): n[4] for n in VDAT_COMMON}
-        else:
-            N_DONORS = len(TOTAL_DONORS)
-            VDAT_COMMON = list_2_countDict(VDAT_COMMON)
-            minVal = int(np.percentile(VDAT_COMMON.values(), percentile_var))
-            for k in sorted(VDAT_COMMON.keys()):
-                if VDAT_COMMON[k] >= minVal:
-                    COMMON_VARIANTS.append((ref_name, k[0], k[1], k[3], VDAT_COMMON[k] / float(N_DONORS)))
-
-        #
+        min_value = np.percentile([n[4] for n in VDAT_COMMON], percentile_var)
+        for k in sorted(VDAT_COMMON):
+            if k[4] >= min_value:
+                COMMON_VARIANTS.append((ref_name, k[0], k[1], k[3], k[4]))
+        VDAT_COMMON = {(n[0], n[1], n[2], n[3]): n[4] for n in VDAT_COMMON}
+
         # identify areas that have contained significantly higher random mutation rates
-        #
         dist_thresh = 2000
         percentile_clust = 97
-        qptn = 1000
+        scaler = 1000
         # identify regions with disproportionately more variants in them
         VARIANT_POS = sorted([n[0] for n in VDAT_COMMON.keys()])
         clustered_pos = cluster_list(VARIANT_POS, dist_thresh)
-        byLen = [(len(clustered_pos[i]), min(clustered_pos[i]), max(clustered_pos[i]), i) for i in
+        by_len = [(len(clustered_pos[i]), min(clustered_pos[i]), max(clustered_pos[i]), i) for i in
                  range(len(clustered_pos))]
-        # byLen  = sorted(byLen,reverse=True)
-        # minLen = int(np.percentile([n[0] for n in byLen],percentile_clust))
-        # byLen  = [n for n in byLen if n[0] >= minLen]
+        # Not sure what this was intended to do or why it is commented out. Leaving it here for now.
+        # by_len  = sorted(by_len,reverse=True)
+        # minLen = int(np.percentile([n[0] for n in by_len],percentile_clust))
+        # by_len  = [n for n in by_len if n[0] >= minLen]
         candidate_regions = []
-        for n in byLen:
-            bi = int((n[1] - dist_thresh) / float(qptn)) * qptn
-            bf = int((n[2] + dist_thresh) / float(qptn)) * qptn
+        for n in by_len:
+            bi = int((n[1] - dist_thresh) / float(scaler)) * scaler
+            bf = int((n[2] + dist_thresh) / float(scaler)) * scaler
             candidate_regions.append((n[0] / float(bf - bi), max([0, bi]), min([len(ref_dict[ref_name]), bf])))
-        minVal = np.percentile([n[0] for n in candidate_regions], percentile_clust)
+        minimum_value = np.percentile([n[0] for n in candidate_regions], percentile_clust)
         for n in candidate_regions:
-            if n[0] >= minVal:
+            if n[0] >= minimum_value:
                 HIGH_MUT_REGIONS.append((ref_name, n[1], n[2], n[0]))
         # collapse overlapping regions
         for i in range(len(HIGH_MUT_REGIONS) - 1, 0, -1):
             if HIGH_MUT_REGIONS[i - 1][2] >= HIGH_MUT_REGIONS[i][1] and HIGH_MUT_REGIONS[i - 1][0] == \
                     HIGH_MUT_REGIONS[i][0]:
+                # Might need to research a more accurate way to get the mutation rate for this region
                 avgMutRate = 0.5 * HIGH_MUT_REGIONS[i - 1][3] + 0.5 * HIGH_MUT_REGIONS[i][
-                    3]  # not accurate, but I'm lazy
+                    3]
                 HIGH_MUT_REGIONS[i - 1] = (
                     HIGH_MUT_REGIONS[i - 1][0], HIGH_MUT_REGIONS[i - 1][1], HIGH_MUT_REGIONS[i][2], avgMutRate)
                 del HIGH_MUT_REGIONS[i]
 
-    #
     # if we didn't count ref trinucs because we found file, read in ref counts from file now
-    #
     if os.path.isfile(ref + '.trinucCounts'):
         print('reading pre-computed trinuc counts...')
         f = open(ref + '.trinucCounts', 'r')
@@ -450,13 +440,15 @@ def main():
                 f.write(trinuc + '\t' + str(TRINUC_REF_COUNT[trinuc]) + '\n')
             f.close()
 
-    #
-    # if using an input bed region, make necessary adjustments to trinuc ref counts based on the bed region trinuc counts
-    #
+    # if using an input bed region, make necessary adjustments to
+    # trinuc ref counts based on the bed region trinuc counts
+    #TODO figure out how to make this next part work
     if mybed is not None:
-        if mybed[1] == True:  # we are restricting our attention to bed regions, so ONLY use bed region trinuc counts
+        # we are restricting our attention to bed regions, so ONLY use bed region trinuc counts
+        if mybed[1] == True:
             TRINUC_REF_COUNT = TRINUC_BED_COUNT
-        else:  # we are only looking outside bed regions, so subtract bed region trinucs from entire reference trinucs
+        # we are only looking outside bed regions, so subtract bed region trinucs from entire reference trinucs
+        else:
             for k in TRINUC_REF_COUNT.keys():
                 if k in TRINUC_BED_COUNT:
                     TRINUC_REF_COUNT[k] -= TRINUC_BED_COUNT[k]

From e55096b98d0c2edf633d31c501cbeef185bcc2a3 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 12 Jun 2020 12:24:43 -0500
Subject: [PATCH 052/323] Cleaning up some names in compute_fraglen

---
 utilities/compute_fraglen.py | 36 +++++++++++------------
 utilities/genMutModel.py     | 56 ++++++++++--------------------------
 2 files changed, 33 insertions(+), 59 deletions(-)

diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index bfbaa4b..c056e44 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -20,16 +20,16 @@ def quick_median(count_dict: dict) -> int:
     :param count_dict: the counting dictionary to find the median of
     :return: integer index of the location of the median
     """
-    midPoint = sum(count_dict.values()) // 2
-    mySum = 0
-    myInd = 0
+    mid_point = sum(count_dict.values()) // 2
+    my_sum = 0
+    my_ind = 0
     sk = sorted(count_dict.keys())
-    while mySum < midPoint:
-        mySum += count_dict[sk[myInd]]
-        if mySum >= midPoint:
+    while my_sum < mid_point:
+        my_sum += count_dict[sk[my_ind]]
+        if my_sum >= mid_point:
             break
-        myInd += 1
-    return myInd
+        my_ind += 1
+    return my_ind
 
 
 def median_deviation_from_median(count_dict: dict) -> int:
@@ -39,11 +39,11 @@ def median_deviation_from_median(count_dict: dict) -> int:
     :param count_dict: Counting dictionary to analyze
     :return: index of median of the deviations
     """
-    myMedian = quick_median(count_dict)
+    my_median = quick_median(count_dict)
     deviations = {}
-    for k in sorted(count_dict.keys()):
-        d = abs(k - myMedian)
-        deviations[d] = count_dict[k]
+    for key in sorted(count_dict.keys()):
+        delta = abs(key - my_median)
+        deviations[delta] = count_dict[key]
     return quick_median(deviations)
 
 
@@ -78,15 +78,15 @@ def count_frags(file: str) -> dict:
         myRef = splt[2]
         mapQual = int(splt[4])
         mateRef = splt[6]
-        myTlen = abs(int(splt[8]))
+        my_tlen = abs(int(splt[8]))
 
         # if read is paired, and is first in pair, and is confidently mapped...
         if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:
             # and mate is mapped to same reference
             if mateRef == '=' or mateRef == myRef:
-                if myTlen not in count_dict:
-                    count_dict[myTlen] = 0
-                count_dict[myTlen] += 1
+                if my_tlen not in count_dict:
+                    count_dict[my_tlen] = 0
+                count_dict[my_tlen] += 1
                 i += 1
                 if i % PRINT_EVERY == 0:
                     print('---', i, quick_median(count_dict), median_deviation_from_median(count_dict))
@@ -113,8 +113,8 @@ def compute_probs(count_dict: dict) -> (list, list):
                 print(k, count_dict[k])
                 values.append(k)
                 probabilities.append(count_dict[k])
-    countSum = float(sum(probabilities))
-    probabilities = [n / countSum for n in probabilities]
+    count_sum = float(sum(probabilities))
+    probabilities = [n / count_sum for n in probabilities]
     return values, probabilities
 
 
diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index 79982ea..ba42ad8 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -52,8 +52,8 @@ def list_2_countDict(l: list) -> dict:
     return cDict
 
 
-def getBedTracks(fn: str) -> dict:
-    f = open(fn, 'r')
+def getBedTracks(file_name: str) -> dict:
+    f = open(file_name, 'r')
     trackDict = {}
     for line in f:
         splt = line.strip().split('\t')
@@ -63,7 +63,7 @@ def getBedTracks(fn: str) -> dict:
     f.close()
     return trackDict
 
-
+#TODO Convert this function to pandas
 def getTrackLen(trackDict: dict) -> float:
     totSum = 0
     for k in trackDict.keys():
@@ -83,7 +83,7 @@ def isInBed(track, ind):
 
 
 #####################################
-#				main()				#c
+#				main()				#
 #####################################
 
 
@@ -104,11 +104,10 @@ def main():
                         help="Mutation file for organism in VCF format")
     parser.add_argument('-o', type=str, required=True, metavar='/path/to/output/and/prefix',
                         help="Name of output file (final model will append \'.p\')")
-    parser.add_argument('-bi', type=str, required=False, metavar='Bed file of regions to include', default=None,
+    parser.add_argument('-bi', type=str, required=False, metavar='Bed file of regions to include '
+                                                                 '(use bedtools complement if you have a '
+                                                                 'bed of exclusion areas)', default=None,
                         help="only_use_these_regions.bed")
-    # This was not implemented correctly in the original file. Will circle back to it.
-    # parser.add_argument('-be', type=str, required=False, metavar='Bed file of regions to exclued', default=None,
-    #                     help="exclude_these_regions.bed")
     parser.add_argument('--save-trinuc', required=False, action='store_true', default=False,
                         help='save trinucleotide counts for reference')
     parser.add_argument('--human-sample', required=False, action='store_true', default=False,
@@ -124,7 +123,6 @@ def main():
 
     # how many times do we observe each trinucleotide in the reference (and input bed region, if present)?
     TRINUC_REF_COUNT = {}
-    TRINUC_BED_COUNT = {}
     # [(trinuc_a, trinuc_b)] = # of times we observed a mutation from trinuc_a into trinuc_b
     TRINUC_TRANSITION_COUNT = {}
     # total count of SNPs
@@ -142,7 +140,7 @@ def main():
     # identify regions that have significantly higher local mutation rates than the average
     HIGH_MUT_REGIONS = []
 
-    # Process bed file
+    # Process bed file,
     is_bed = False
     mybed = None
     if args.bi is not None:
@@ -283,9 +281,9 @@ def main():
                     # skip if trinuc contains invalid characters, or not in specified bed region
                     if not trinuc in VALID_TRINUC:
                         continue
-                    if trinuc not in TRINUC_BED_COUNT:
-                        TRINUC_BED_COUNT[trinuc] = 0
-                    TRINUC_BED_COUNT[trinuc] += 1
+                    if trinuc not in TRINUC_REF_COUNT:
+                        TRINUC_REF_COUNT[trinuc] = 0
+                    TRINUC_REF_COUNT[trinuc] += 1
 
     elif not os.path.isfile(ref + '.trinucCounts'):
         for i in range(len(ref_dict[ref_name]) - 2):
@@ -440,19 +438,6 @@ def main():
                 f.write(trinuc + '\t' + str(TRINUC_REF_COUNT[trinuc]) + '\n')
             f.close()
 
-    # if using an input bed region, make necessary adjustments to
-    # trinuc ref counts based on the bed region trinuc counts
-    #TODO figure out how to make this next part work
-    if mybed is not None:
-        # we are restricting our attention to bed regions, so ONLY use bed region trinuc counts
-        if mybed[1] == True:
-            TRINUC_REF_COUNT = TRINUC_BED_COUNT
-        # we are only looking outside bed regions, so subtract bed region trinucs from entire reference trinucs
-        else:
-            for k in TRINUC_REF_COUNT.keys():
-                if k in TRINUC_BED_COUNT:
-                    TRINUC_REF_COUNT[k] -= TRINUC_BED_COUNT[k]
-
     # if for some reason we didn't find any valid input variants, exit gracefully...
     totalVar = SNP_COUNT + sum(INDEL_COUNT.values())
     if totalVar == 0:
@@ -460,15 +445,8 @@ def main():
             '\nError: No valid variants were found, model could not be created. (Are you using the correct reference?)\n')
         exit(1)
 
-    """ ##########################################################################
-    ###							COMPUTE PROBABILITIES						   ###
-    ########################################################################## """
 
-    # for k in sorted(TRINUC_REF_COUNT.keys()):
-    #		print k, TRINUC_REF_COUNT[k]
-    #
-    # for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
-    #	print k, TRINUC_TRANSITION_COUNT[k]
+    ###	COMPUTE PROBABILITIES
 
     # frequency that each trinuc mutated into anything else
     TRINUC_MUT_PROB = {}
@@ -498,17 +476,13 @@ def main():
     SNP_FREQ = SNP_COUNT / float(totalVar)
     AVG_INDEL_FREQ = 1. - SNP_FREQ
     INDEL_FREQ = {k: (INDEL_COUNT[k] / float(totalVar)) / AVG_INDEL_FREQ for k in INDEL_COUNT.keys()}
-    if mybed is not None:
-        if mybed[1] == True:
-            AVG_MUT_RATE = totalVar / float(getTrackLen(mybed[0]))
-        else:
-            AVG_MUT_RATE = totalVar / float(TOTAL_REFLEN - getTrackLen(mybed[0]))
+    #TODO fix this get track len function
+    if is_bed:
+        AVG_MUT_RATE = totalVar / float(getTrackLen(mybed))
     else:
         AVG_MUT_RATE = totalVar / float(TOTAL_REFLEN)
 
-    #
     #	if values weren't found in data, appropriately append null entries
-    #
     printTrinucWarning = False
     for trinuc in VALID_TRINUC:
         trinuc_mut = [trinuc[0] + n + trinuc[2] for n in VALID_NUCL if n != trinuc[1]]

From 5fbfb3221642cd97b2336454cad7762a10762fc7 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 12 Jun 2020 14:11:20 -0500
Subject: [PATCH 053/323] Cleaning up some names in compute_fraglen

---
 utilities/compute_fraglen.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index c056e44..773300e 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -107,12 +107,12 @@ def compute_probs(count_dict: dict) -> (list, list):
     med = quick_median(count_dict)
     mdm = median_deviation_from_median(count_dict)
 
-    for k in sorted(count_dict.keys()):
-        if 0 < k < med + FILTER_MEDDEV_M * mdm:
-            if count_dict[k] >= FILTER_MINREADS:
-                print(k, count_dict[k])
-                values.append(k)
-                probabilities.append(count_dict[k])
+    for key in sorted(count_dict.keys()):
+        if 0 < key < med + FILTER_MEDDEV_M * mdm:
+            if count_dict[key] >= FILTER_MINREADS:
+                print(key, count_dict[key])
+                values.append(key)
+                probabilities.append(count_dict[key])
     count_sum = float(sum(probabilities))
     probabilities = [n / count_sum for n in probabilities]
     return values, probabilities

From af869aaeb82db4ed757c03f127e150fbea771d3d Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 15 Jun 2020 11:13:58 -0500
Subject: [PATCH 054/323] fixing the median functions

---
 utilities/compute_fraglen.py | 27 ++++++++++++++-------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index 773300e..9c4cd9a 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -20,16 +20,17 @@ def quick_median(count_dict: dict) -> int:
     :param count_dict: the counting dictionary to find the median of
     :return: integer index of the location of the median
     """
-    mid_point = sum(count_dict.values()) // 2
-    my_sum = 0
-    my_ind = 0
-    sk = sorted(count_dict.keys())
-    while my_sum < mid_point:
-        my_sum += count_dict[sk[my_ind]]
-        if my_sum >= mid_point:
-            break
-        my_ind += 1
-    return my_ind
+    list = []
+    for key in count_dict.keys():
+        for i in range(count_dict[key]):
+            list.append(key)
+    list = sorted(list)
+    midpoint = len(list)//2
+    if len(list) % 2 == 0:
+        median = (list[midpoint] + list[midpoint-1])//2
+    else:
+        median = list[midpoint]
+    return median
 
 
 def median_deviation_from_median(count_dict: dict) -> int:
@@ -42,11 +43,11 @@ def median_deviation_from_median(count_dict: dict) -> int:
     my_median = quick_median(count_dict)
     deviations = {}
     for key in sorted(count_dict.keys()):
-        delta = abs(key - my_median)
-        deviations[delta] = count_dict[key]
+        X_value = abs(key - my_median)
+        deviations[X_value] = count_dict[key]
+    print(deviations)
     return quick_median(deviations)
 
-
 def count_frags(file: str) -> dict:
     """
     Takes a sam file input and creates a counting dictionary of the number of reads that are paired,

From bc0adf28bdcf8449ef34b59d2bd3c110e3e2dc0e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 15 Jun 2020 11:27:19 -0500
Subject: [PATCH 055/323] fixing the median functions

---
 utilities/compute_fraglen.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index 9c4cd9a..a2ae677 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -18,7 +18,7 @@ def quick_median(count_dict: dict) -> int:
     """
     Finds the median of a counting dictionary
     :param count_dict: the counting dictionary to find the median of
-    :return: integer index of the location of the median
+    :return: The median of the set
     """
     list = []
     for key in count_dict.keys():
@@ -33,6 +33,19 @@ def quick_median(count_dict: dict) -> int:
     return median
 
 
+def quick_median_old(count_dict: dict) -> int:
+    mid_point = sum(count_dict.values()) / 2
+    my_sum = 0
+    my_ind = 0
+    sk = sorted(count_dict.keys())
+    while my_sum < mid_point:
+        my_sum += count_dict[sk[my_ind]]
+        if my_sum >= mid_point:
+            break
+        my_ind += 1
+    return sk[my_ind]
+
+
 def median_deviation_from_median(count_dict: dict) -> int:
     """
     calculates the deviation from the median of each element of counting dictionary,
@@ -45,7 +58,6 @@ def median_deviation_from_median(count_dict: dict) -> int:
     for key in sorted(count_dict.keys()):
         X_value = abs(key - my_median)
         deviations[X_value] = count_dict[key]
-    print(deviations)
     return quick_median(deviations)
 
 def count_frags(file: str) -> dict:

From d20dbde4e4a3a3ef8d5a6f272f33ac9e7d7adc91 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 15 Jun 2020 13:03:43 -0500
Subject: [PATCH 056/323] fixing the median functions

---
 utilities/compute_fraglen.py | 23 ++++++++++++++---------
 1 file changed, 14 insertions(+), 9 deletions(-)

diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index a2ae677..4a2dfc7 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -20,16 +20,21 @@ def quick_median(count_dict: dict) -> int:
     :param count_dict: the counting dictionary to find the median of
     :return: The median of the set
     """
-    list = []
-    for key in count_dict.keys():
-        for i in range(count_dict[key]):
-            list.append(key)
-    list = sorted(list)
-    midpoint = len(list)//2
-    if len(list) % 2 == 0:
-        median = (list[midpoint] + list[midpoint-1])//2
+    mid_point = sum(count_dict.values()) / 2
+    my_sum = 0
+    my_ind = 0
+    sk = sorted(count_dict.keys())
+    while my_sum < mid_point:
+        my_sum += count_dict[sk[my_ind]]
+        if my_sum >= mid_point:
+            break
+        my_ind += 1
+    if sum(count_dict.values())%2 == 0:
+        print(sk[my_ind])
+        print(sk[my_ind-1])
+        median = (sk[my_ind] + sk[my_ind-1])//2
     else:
-        median = list[midpoint]
+        median = sk[my_ind]
     return median
 
 

From 40fa82dad67b80674d9754499a3fedf9e7e10747 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 15 Jun 2020 13:04:59 -0500
Subject: [PATCH 057/323] fixing the median functions

---
 utilities/compute_fraglen.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index 4a2dfc7..79bda95 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -29,9 +29,7 @@ def quick_median(count_dict: dict) -> int:
         if my_sum >= mid_point:
             break
         my_ind += 1
-    if sum(count_dict.values())%2 == 0:
-        print(sk[my_ind])
-        print(sk[my_ind-1])
+    if sum(count_dict.values()) % 2 == 0:
         median = (sk[my_ind] + sk[my_ind-1])//2
     else:
         median = sk[my_ind]

From f36cd6ee2bb8370b85f3af6dc7c76175b2afe5e2 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 15 Jun 2020 13:10:07 -0500
Subject: [PATCH 058/323] fixing the median functions

---
 utilities/compute_fraglen.py | 17 +++--------------
 1 file changed, 3 insertions(+), 14 deletions(-)

diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index 79bda95..0993410 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -16,7 +16,9 @@
 
 def quick_median(count_dict: dict) -> int:
     """
-    Finds the median of a counting dictionary
+    Finds the median of a counting dictionary. A counting dictionary is a representation of a list of data.
+    For example, the data [1,1,2,2,2] could be represented in dictionary form as {1:2, 2:3}, indicating that 1 is
+    repeated twice and 2 is repeating three times.
     :param count_dict: the counting dictionary to find the median of
     :return: The median of the set
     """
@@ -36,19 +38,6 @@ def quick_median(count_dict: dict) -> int:
     return median
 
 
-def quick_median_old(count_dict: dict) -> int:
-    mid_point = sum(count_dict.values()) / 2
-    my_sum = 0
-    my_ind = 0
-    sk = sorted(count_dict.keys())
-    while my_sum < mid_point:
-        my_sum += count_dict[sk[my_ind]]
-        if my_sum >= mid_point:
-            break
-        my_ind += 1
-    return sk[my_ind]
-
-
 def median_deviation_from_median(count_dict: dict) -> int:
     """
     calculates the deviation from the median of each element of counting dictionary,

From aefed5bfcd21050f824a9270615ffa92ba8ed657 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 15 Jun 2020 13:17:57 -0500
Subject: [PATCH 059/323] fixing the median functions

---
 utilities/compute_fraglen.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index 0993410..945199c 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -22,15 +22,20 @@ def quick_median(count_dict: dict) -> int:
     :param count_dict: the counting dictionary to find the median of
     :return: The median of the set
     """
+    # The sum of the values of the counting dictionary tells us how long the expanded list is. To find the midpoint
+    # We divide by 2
     mid_point = sum(count_dict.values()) / 2
     my_sum = 0
     my_ind = 0
     sk = sorted(count_dict.keys())
+    # Here we basically count up one group of dictionary values at a time until we're at the midpoint
     while my_sum < mid_point:
         my_sum += count_dict[sk[my_ind]]
         if my_sum >= mid_point:
             break
         my_ind += 1
+    # Once we've found the midpoint, we calculate the median, which is just the middle value if there are an
+    # odd number of values, or the average of the two middle valuse if there are an even number
     if sum(count_dict.values()) % 2 == 0:
         median = (sk[my_ind] + sk[my_ind-1])//2
     else:
@@ -40,16 +45,21 @@ def quick_median(count_dict: dict) -> int:
 
 def median_deviation_from_median(count_dict: dict) -> int:
     """
-    calculates the deviation from the median of each element of counting dictionary,
-    then returns the median of that dictionary
+    Calculates the deviation from the median of each element of counting dictionary,
+    then returns the median of that dictionary. A counting dictionary such as {2: 3, 5:2} expands to [2,2,2,5,5]
     :param count_dict: Counting dictionary to analyze
     :return: index of median of the deviations
     """
     my_median = quick_median(count_dict)
     deviations = {}
     for key in sorted(count_dict.keys()):
+        # We take the absolute difference between the value and the median
         X_value = abs(key - my_median)
+        # We retain the number of items for each element of the dictionary
+        # This is equivalent to subtracting the median from each value of the expanded dataset
         deviations[X_value] = count_dict[key]
+    # Taking the median of this dataset gives us the median deviation from the median
+    # (also called the Median Absolute Deviation)
     return quick_median(deviations)
 
 def count_frags(file: str) -> dict:

From c3c360764fcafb60c1e99dfe495c325298b698fc Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Mon, 15 Jun 2020 13:38:55 -0500
Subject: [PATCH 060/323] Update compute_fraglen.py

---
 utilities/compute_fraglen.py | 35 +++++++++++++++++++----------------
 1 file changed, 19 insertions(+), 16 deletions(-)

diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index 945199c..e38d066 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -24,23 +24,26 @@ def quick_median(count_dict: dict) -> int:
     """
     # The sum of the values of the counting dictionary tells us how long the expanded list is. To find the midpoint
     # We divide by 2
-    mid_point = sum(count_dict.values()) / 2
-    my_sum = 0
-    my_ind = 0
-    sk = sorted(count_dict.keys())
-    # Here we basically count up one group of dictionary values at a time until we're at the midpoint
-    while my_sum < mid_point:
-        my_sum += count_dict[sk[my_ind]]
-        if my_sum >= mid_point:
-            break
-        my_ind += 1
-    # Once we've found the midpoint, we calculate the median, which is just the middle value if there are an
-    # odd number of values, or the average of the two middle valuse if there are an even number
-    if sum(count_dict.values()) % 2 == 0:
-        median = (sk[my_ind] + sk[my_ind-1])//2
+    if not count_dict:
+        raise Exception('This Counting Dictionary is Empty. Please input a dictionary with at least one entry.')
     else:
-        median = sk[my_ind]
-    return median
+        mid_point = sum(count_dict.values()) / 2
+        my_sum = 0
+        my_ind = 0
+        sk = sorted(count_dict.keys())
+        # Here we basically count up one group of dictionary values at a time until we're at the midpoint
+        while my_sum < mid_point:
+            my_sum += count_dict[sk[my_ind]]
+            if my_sum >= mid_point:
+                break
+            my_ind += 1
+        # Once we've found the midpoint, we calculate the median, which is just the middle value if there are an
+        # odd number of values, or the average of the two middle valuse if there are an even number
+        if sum(count_dict.values()) % 2 == 0:
+            median = (sk[my_ind] + sk[my_ind-1])//2
+        else:
+            median = sk[my_ind]
+        return median
 
 
 def median_deviation_from_median(count_dict: dict) -> int:

From 8d347b07714bb9337d60a57d86388584d1186739 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 15 Jun 2020 16:39:07 -0500
Subject: [PATCH 061/323] Added a notebook for testing compute_fraglen.py

---
 Testing_compute_fraglen.ipynb | 881 ++++++++++++++++++++++++++++++++++
 utilities/compute_fraglen.py  |  39 +-
 utilities/genMutModel.py      |  10 +-
 3 files changed, 907 insertions(+), 23 deletions(-)
 create mode 100644 Testing_compute_fraglen.ipynb

diff --git a/Testing_compute_fraglen.ipynb b/Testing_compute_fraglen.ipynb
new file mode 100644
index 0000000..730aa16
--- /dev/null
+++ b/Testing_compute_fraglen.ipynb
@@ -0,0 +1,881 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 363,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = {1:4, 2:6, 3:1, 4:8, 5:3, 10:2}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 392,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list = []\n",
+    "for key in cd.keys():\n",
+    "    for i in range(cd[key]):\n",
+    "        list.append(key)\n",
+    "list = sorted(list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 393,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[202,\n",
+       " 221,\n",
+       " 234,\n",
+       " 240,\n",
+       " 245,\n",
+       " 251,\n",
+       " 251,\n",
+       " 252,\n",
+       " 254,\n",
+       " 255,\n",
+       " 256,\n",
+       " 258,\n",
+       " 258,\n",
+       " 259,\n",
+       " 259,\n",
+       " 263,\n",
+       " 263,\n",
+       " 264,\n",
+       " 266,\n",
+       " 267,\n",
+       " 268,\n",
+       " 269,\n",
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+       " 270,\n",
+       " 271,\n",
+       " 273,\n",
+       " 273,\n",
+       " 274,\n",
+       " 274,\n",
+       " 275,\n",
+       " 276,\n",
+       " 276,\n",
+       " 277,\n",
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+       " 280,\n",
+       " 280,\n",
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+       " 284,\n",
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+       " 301,\n",
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+       " 303,\n",
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+       " 304,\n",
+       " 304,\n",
+       " 304,\n",
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+       " 223639769,\n",
+       " 223816267,\n",
+       " 242562011]"
+      ]
+     },
+     "execution_count": 393,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 369,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def quick_median(count_dict: dict) -> int:\n",
+    "    \"\"\"\n",
+    "    Finds the median of a counting dictionary\n",
+    "    :param count_dict: the counting dictionary to find the median of\n",
+    "    :return: integer index of the location of the median\n",
+    "    \"\"\"\n",
+    "    list = []\n",
+    "    for key in count_dict.keys():\n",
+    "        for i in range(count_dict[key]):\n",
+    "            list.append(key)\n",
+    "    list = sorted(list)\n",
+    "    midpoint = len(list)//2\n",
+    "    if len(list)%2 == 0:\n",
+    "        median = (list[midpoint] + list[midpoint-1])//2\n",
+    "    else:\n",
+    "        median = list[midpoint]\n",
+    "    return median"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def quick_median_old(count_dict: dict) -> int:\n",
+    "    mid_point = sum(count_dict.values()) / 2\n",
+    "    my_sum = 0\n",
+    "    my_ind = 0\n",
+    "    sk = sorted(count_dict.keys())\n",
+    "    while my_sum < mid_point:\n",
+    "        my_sum += count_dict[sk[my_ind]]\n",
+    "        if my_sum >= mid_point:\n",
+    "            break\n",
+    "        my_ind += 1\n",
+    "    return sk[my_ind]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = {4: 10, 236: 13, 99: 43, 88: 43, 127: 15}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 370,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 370,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quick_median(cd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 325,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "99"
+      ]
+     },
+     "execution_count": 325,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quick_median_old(cd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 326,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd2 = {4: 10, 236: 13, 99: 43, 88: 43, 127: 14, 8:102}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 327,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "88"
+      ]
+     },
+     "execution_count": 327,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quick_median(cd2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 328,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "88"
+      ]
+     },
+     "execution_count": 328,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quick_median_old(cd2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_median1 = quick_median(cd)\n",
+    "my_median2 = quick_median(cd2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 309,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def median_deviation_from_median(count_dict: dict) -> int:\n",
+    "    \"\"\"\n",
+    "    calculates the deviation from the median of each element of counting dictionary,\n",
+    "    then returns the median of that dictionary\n",
+    "    :param count_dict: Counting dictionary to analyze\n",
+    "    :return: index of median of the deviations\n",
+    "    \"\"\"\n",
+    "    my_median = quick_median(count_dict)\n",
+    "    deviations = {}\n",
+    "    for key in sorted(count_dict.keys()):\n",
+    "        X_value = abs(key - my_median)\n",
+    "        deviations[X_value] = count_dict[key]\n",
+    "    print(deviations)\n",
+    "    return quick_median(deviations)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 342,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{95: 10, 11: 43, 0: 43, 28: 15, 137: 13}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 342,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median_deviation_from_median(cd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 353,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sorted([abs(x-99) for x in list]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 329,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd3 = {1:100, 100:100}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 330,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 330,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quick_median(cd3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 331,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 331,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quick_median_old(cd3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 401,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def quick_median_new(count_dict: dict) -> int:\n",
+    "    mid_point = sum(count_dict.values()) / 2\n",
+    "    my_sum = 0\n",
+    "    my_ind = 0\n",
+    "    sk = sorted(count_dict.keys())\n",
+    "    while my_sum < mid_point:\n",
+    "        my_sum += count_dict[sk[my_ind]]\n",
+    "        if my_sum >= mid_point:\n",
+    "            break\n",
+    "        my_ind += 1\n",
+    "    if sum(count_dict.values())%2 == 0:\n",
+    "        median = (sk[my_ind] + sk[my_ind-1])//2\n",
+    "    else:\n",
+    "        median = sk[my_ind]\n",
+    "    return median"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 378,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12.0\n",
+      "4\n",
+      "10\n",
+      "11\n",
+      "19\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 378,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quick_median_new(cd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 315,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def count_frags(file: str) -> dict:\n",
+    "    \"\"\"\n",
+    "    Takes a sam file input and creates a counting dictionary of the number of reads that are paired,\n",
+    "    first in the pair, confidently mapped and whose pair is mapped to the same reference\n",
+    "    :param file: A sam input file\n",
+    "    :return: A dictionary of the counts of the above reads\n",
+    "    \"\"\"\n",
+    "    FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality\n",
+    "    count_dict = {}\n",
+    "    PRINT_EVERY = 100000\n",
+    "    i = 0\n",
+    "    # Check if the file is sam or bam and decide how to open based on that\n",
+    "    if file[-4:] == \".sam\":\n",
+    "        file_to_parse = open(file, 'r')\n",
+    "    elif file[-4:] == \".bam\":\n",
+    "        file_to_parse = pysam.AlignmentFile(file, 'rb')\n",
+    "    else:\n",
+    "        print(\"Unknown file type, must be bam or sam\")\n",
+    "        exit(1)\n",
+    "\n",
+    "    for item in file_to_parse:\n",
+    "        # Need to convert bam iterable objects into strings for the next part\n",
+    "        line = str(item)\n",
+    "        # Skip all comments and headers\n",
+    "        if line[0] == '#' or line[0] == '@':\n",
+    "            continue\n",
+    "        splt = line.strip().split('\\t')\n",
+    "        samFlag = int(splt[1])\n",
+    "        myRef = splt[2]\n",
+    "        mapQual = int(splt[4])\n",
+    "        mateRef = splt[6]\n",
+    "        myTlen = abs(int(splt[8]))\n",
+    "\n",
+    "        # if read is paired, and is first in pair, and is confidently mapped...\n",
+    "        if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:\n",
+    "            # and mate is mapped to same reference\n",
+    "            if mateRef == '=' or mateRef == myRef:\n",
+    "                if myTlen not in count_dict:\n",
+    "                    count_dict[myTlen] = 0\n",
+    "                count_dict[myTlen] += 1\n",
+    "                i += 1\n",
+    "                if i % PRINT_EVERY == 0:\n",
+    "                    print('---', i, quick_median(count_dict), median_deviation_from_median(count_dict))\n",
+    "    return count_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 391,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = count_frags(\"C:/Users/joshf/Documents/work_stuff/neat_data/baby.sam\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 317,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality\n",
+    "count_dict = {}\n",
+    "PRINT_EVERY = 100000\n",
+    "i = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for item in file_to_parse:\n",
+    "    # Need to convert bam iterable objects into strings for the next part\n",
+    "    line = str(item)\n",
+    "    # Skip all comments and headers\n",
+    "    if line[0] == '#' or line[0] == '@':\n",
+    "        continue\n",
+    "    splt = line.strip().split('\\t')\n",
+    "    samFlag = int(splt[1])\n",
+    "    myRef = splt[2]\n",
+    "    mapQual = int(splt[4])\n",
+    "    mateRef = splt[6]\n",
+    "    myTlen = abs(int(splt[8]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "128"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(count_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 361,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_probs(count_dict: dict) -> (list, list):\n",
+    "    \"\"\"\n",
+    "    Computes the probabilities for fragments with at least 100 pairs supporting it and that are at least 10 median\n",
+    "    deviations from the median.\n",
+    "    :param count_dict: A dictionary of fragments with counts\n",
+    "    :return: A list of values that meet the criteria and a list of -their associated probabilities\n",
+    "    \"\"\"\n",
+    "    FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it\n",
+    "    FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median\n",
+    "    values = []\n",
+    "    probabilities = []\n",
+    "    med = quick_median(count_dict)\n",
+    "    mdm = median_deviation_from_median(count_dict)\n",
+    "\n",
+    "    for key in sorted(count_dict.keys()):\n",
+    "        if 0 < key < med + FILTER_MEDDEV_M * mdm:\n",
+    "            if count_dict[key] >= FILTER_MINREADS:\n",
+    "                print(key, count_dict[key])\n",
+    "                values.append(key)\n",
+    "                probabilities.append(count_dict[key])\n",
+    "    count_sum = float(sum(probabilities))\n",
+    "    probabilities = [n / count_sum for n in probabilities]\n",
+    "    return values, probabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 362,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{84: 10, 80: 102, 0: 43, 11: 43, 39: 14, 148: 13}\n",
+      "8 102\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([8], [1.0])"
+      ]
+     },
+     "execution_count": 362,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compute_probs(cd2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 394,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import timeit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 395,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def wrapper(func, *args, **kwargs):\n",
+    "    def wrapped():\n",
+    "        return func(*args, **kwargs)\n",
+    "    return wrapped"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 404,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pysam'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-404-5a05a53419d8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpysam\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mcd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcount_frags\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"C:/Users/joshf/Documents/work_stuff/neat_data/WGS_chr20_21_22_normal.bam\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pysam'"
+     ]
+    }
+   ],
+   "source": [
+    "import pysam\n",
+    "cd = count_frags(\"C:/Users/joshf/Documents/work_stuff/neat_data/WGS_chr20_21_22_normal.bam\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 397,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wrapped = wrapper(quick_median, cd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 399,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.06138989998726174"
+      ]
+     },
+     "execution_count": 399,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "timeit.timeit(wrapped, number=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 402,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.01676309999311343"
+      ]
+     },
+     "execution_count": 402,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wrapped2 = wrapper(quick_median_new, cd)\n",
+    "timeit.timeit(wrapped2, number=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index 945199c..e616067 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -24,26 +24,29 @@ def quick_median(count_dict: dict) -> int:
     """
     # The sum of the values of the counting dictionary tells us how long the expanded list is. To find the midpoint
     # We divide by 2
-    mid_point = sum(count_dict.values()) / 2
-    my_sum = 0
-    my_ind = 0
-    sk = sorted(count_dict.keys())
-    # Here we basically count up one group of dictionary values at a time until we're at the midpoint
-    while my_sum < mid_point:
-        my_sum += count_dict[sk[my_ind]]
-        if my_sum >= mid_point:
-            break
-        my_ind += 1
-    # Once we've found the midpoint, we calculate the median, which is just the middle value if there are an
-    # odd number of values, or the average of the two middle valuse if there are an even number
-    if sum(count_dict.values()) % 2 == 0:
-        median = (sk[my_ind] + sk[my_ind-1])//2
+    if not count_dict:
+        raise Exception('Empty counting dictionary. Check that sam input was correctly formatted.')
     else:
-        median = sk[my_ind]
+        mid_point = sum(count_dict.values()) / 2
+        my_sum = 0
+        my_ind = 0
+        sk = sorted(count_dict.keys())
+        # Here we basically count up one group of dictionary values at a time until we're at the midpoint
+        while my_sum < mid_point:
+            my_sum += count_dict[sk[my_ind]]
+            if my_sum >= mid_point:
+                break
+            my_ind += 1
+        # Once we've found the midpoint, we calculate the median, which is just the middle value if there are an
+        # odd number of values, or the average of the two middle values if there are an even number
+        if sum(count_dict.values()) % 2 == 0:
+            median = (sk[my_ind] + sk[my_ind-1])//2
+        else:
+            median = sk[my_ind]
     return median
 
 
-def median_deviation_from_median(count_dict: dict) -> int:
+def median_absolute_deviation(count_dict: dict) -> int:
     """
     Calculates the deviation from the median of each element of counting dictionary,
     then returns the median of that dictionary. A counting dictionary such as {2: 3, 5:2} expands to [2,2,2,5,5]
@@ -104,7 +107,7 @@ def count_frags(file: str) -> dict:
                 count_dict[my_tlen] += 1
                 i += 1
                 if i % PRINT_EVERY == 0:
-                    print('---', i, quick_median(count_dict), median_deviation_from_median(count_dict))
+                    print('---', i, quick_median(count_dict), median_absolute_deviation(count_dict))
     return count_dict
 
 
@@ -120,7 +123,7 @@ def compute_probs(count_dict: dict) -> (list, list):
     values = []
     probabilities = []
     med = quick_median(count_dict)
-    mdm = median_deviation_from_median(count_dict)
+    mdm = median_absolute_deviation(count_dict)
 
     for key in sorted(count_dict.keys()):
         if 0 < key < med + FILTER_MEDDEV_M * mdm:
diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index ba42ad8..901ae3b 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -64,11 +64,11 @@ def getBedTracks(file_name: str) -> dict:
     return trackDict
 
 #TODO Convert this function to pandas
-def getTrackLen(trackDict: dict) -> float:
+def get_track_len(track_df: pd.DataFrame) -> float:
     totSum = 0
-    for k in trackDict.keys():
-        for i in range(0, len(trackDict[k]), 2):
-            totSum += trackDict[k][i + 1] - trackDict[k][i] + 1
+    for k in track_df.keys():
+        for i in range(0, len(track_df[k]), 2):
+            totSum += track_df[k][i + 1] - track_df[k][i] + 1
     return totSum
 
 
@@ -478,7 +478,7 @@ def main():
     INDEL_FREQ = {k: (INDEL_COUNT[k] / float(totalVar)) / AVG_INDEL_FREQ for k in INDEL_COUNT.keys()}
     #TODO fix this get track len function
     if is_bed:
-        AVG_MUT_RATE = totalVar / float(getTrackLen(mybed))
+        AVG_MUT_RATE = totalVar / float(get_track_len(mybed))
     else:
         AVG_MUT_RATE = totalVar / float(TOTAL_REFLEN)
 

From 24155679bf393d4991bb86967c2f15bb64131972 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 16 Jun 2020 13:22:08 -0500
Subject: [PATCH 062/323] Eliminated a stray line

---
 utilities/compute_gc.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/utilities/compute_gc.py b/utilities/compute_gc.py
index 5d9b7bc..9bd695e 100644
--- a/utilities/compute_gc.py
+++ b/utilities/compute_gc.py
@@ -51,7 +51,6 @@ def process_genomecov(file: str, ref_dict: dict, window) -> dict:
     current_ref = None
     current_cov = 0
     lines_processed = 0
-    print_every = 1000000
 
     f = open(file, 'r')
     for line in f:

From af5e28131d2e14ad2bb4024d2b5e1b460b4cb960 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 16 Jun 2020 18:07:44 -0500
Subject: [PATCH 063/323] Updating compute_fraglen.py with the datalist method.

---
 Testing_compute_fraglen.ipynb | 1396 ++++++++++++++++++++++-----------
 utilities/compute_fraglen.py  |  124 ++-
 2 files changed, 975 insertions(+), 545 deletions(-)

diff --git a/Testing_compute_fraglen.ipynb b/Testing_compute_fraglen.ipynb
index 730aa16..c187b88 100644
--- a/Testing_compute_fraglen.ipynb
+++ b/Testing_compute_fraglen.ipynb
@@ -2,16 +2,59 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 363,
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Collecting ipython-autotime\n",
+      "  Downloading ipython-autotime-0.1.tar.bz2 (1.2 kB)\n",
+      "Could not build wheels for ipython-autotime, since package 'wheel' is not installed.\n",
+      "Installing collected packages: ipython-autotime\n",
+      "    Running setup.py install for ipython-autotime ... \u001b[?25ldone\n",
+      "\u001b[?25hSuccessfully installed ipython-autotime-0.1\n",
+      "\u001b[33mWARNING: You are using pip version 20.1; however, version 20.1.1 is available.\n",
+      "You should consider upgrading via the '/usr/bin/python3.8 -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install ipython-autotime\n",
+    "%load_ext autotime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 628 µs\n"
+     ]
+    }
+   ],
    "source": [
     "cd = {1:4, 2:6, 3:1, 4:8, 5:3, 10:2}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 392,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,246 +67,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 393,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[202,\n",
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-       " 242562011]"
-      ]
-     },
-     "execution_count": 393,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "cd = list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "list"
+    "cd"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 369,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 305,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -307,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 323,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -316,47 +140,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 370,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 370,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "quick_median(cd)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 325,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "99"
-      ]
-     },
-     "execution_count": 325,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "quick_median_old(cd)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 326,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -365,47 +167,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 327,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "88"
-      ]
-     },
-     "execution_count": 327,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "quick_median(cd2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 328,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "88"
-      ]
-     },
-     "execution_count": 328,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "quick_median_old(cd2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 308,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -415,75 +195,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 309,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def median_deviation_from_median(count_dict: dict) -> int:\n",
+    "def median_deviation_from_median1(count_dict: dict) -> int:\n",
     "    \"\"\"\n",
     "    calculates the deviation from the median of each element of counting dictionary,\n",
     "    then returns the median of that dictionary\n",
     "    :param count_dict: Counting dictionary to analyze\n",
     "    :return: index of median of the deviations\n",
     "    \"\"\"\n",
-    "    my_median = quick_median(count_dict)\n",
+    "    my_median = quick_median1(count_dict)\n",
     "    deviations = {}\n",
     "    for key in sorted(count_dict.keys()):\n",
     "        X_value = abs(key - my_median)\n",
     "        deviations[X_value] = count_dict[key]\n",
     "    print(deviations)\n",
-    "    return quick_median(deviations)"
+    "    return quick_median1(deviations)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 342,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{95: 10, 11: 43, 0: 43, 28: 15, 137: 13}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "11"
-      ]
-     },
-     "execution_count": 342,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "median_deviation_from_median(cd)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 353,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(sorted([abs(x-99) for x in list]))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 329,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -492,51 +246,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 330,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "50"
-      ]
-     },
-     "execution_count": 330,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "quick_median(cd3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 331,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 331,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "quick_median_old(cd3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 401,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def quick_median_new(count_dict: dict) -> int:\n",
+    "def quick_median1(count_dict: dict) -> int:\n",
     "    mid_point = sum(count_dict.values()) / 2\n",
     "    my_sum = 0\n",
     "    my_ind = 0\n",
@@ -555,42 +287,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 378,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12.0\n",
-      "4\n",
-      "10\n",
-      "11\n",
-      "19\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 378,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "quick_median_new(cd)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 315,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def count_frags(file: str) -> dict:\n",
+    "def count_frags1(file: str) -> dict:\n",
     "    \"\"\"\n",
     "    Takes a sam file input and creates a counting dictionary of the number of reads that are paired,\n",
     "    first in the pair, confidently mapped and whose pair is mapped to the same reference\n",
@@ -600,7 +310,6 @@
     "    FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality\n",
     "    count_dict = {}\n",
     "    PRINT_EVERY = 100000\n",
-    "    i = 0\n",
     "    # Check if the file is sam or bam and decide how to open based on that\n",
     "    if file[-4:] == \".sam\":\n",
     "        file_to_parse = open(file, 'r')\n",
@@ -630,15 +339,12 @@
     "                if myTlen not in count_dict:\n",
     "                    count_dict[myTlen] = 0\n",
     "                count_dict[myTlen] += 1\n",
-    "                i += 1\n",
-    "                if i % PRINT_EVERY == 0:\n",
-    "                    print('---', i, quick_median(count_dict), median_deviation_from_median(count_dict))\n",
     "    return count_dict"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 391,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -647,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 317,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -659,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -679,31 +385,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "128"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "len(count_dict)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 361,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def compute_probs(count_dict: dict) -> (list, list):\n",
+    "def compute_probs1(count_dict: dict) -> (list, list):\n",
     "    \"\"\"\n",
     "    Computes the probabilities for fragments with at least 100 pairs supporting it and that are at least 10 median\n",
     "    deviations from the median.\n",
@@ -714,8 +409,8 @@
     "    FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median\n",
     "    values = []\n",
     "    probabilities = []\n",
-    "    med = quick_median(count_dict)\n",
-    "    mdm = median_deviation_from_median(count_dict)\n",
+    "    med = quick_median1(count_dict)\n",
+    "    mdm = median_deviation_from_median1(count_dict)\n",
     "\n",
     "    for key in sorted(count_dict.keys()):\n",
     "        if 0 < key < med + FILTER_MEDDEV_M * mdm:\n",
@@ -730,35 +425,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 362,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{84: 10, 80: 102, 0: 43, 11: 43, 39: 14, 148: 13}\n",
-      "8 102\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([8], [1.0])"
-      ]
-     },
-     "execution_count": 362,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "compute_probs(cd2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 394,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -767,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 395,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -779,74 +455,846 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 404,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'pysam'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-404-5a05a53419d8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpysam\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mcd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcount_frags\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"C:/Users/joshf/Documents/work_stuff/neat_data/WGS_chr20_21_22_normal.bam\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pysam'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import pysam\n",
-    "cd = count_frags(\"C:/Users/joshf/Documents/work_stuff/neat_data/WGS_chr20_21_22_normal.bam\")"
+    "import pysam"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 397,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "wrapped = wrapper(quick_median, cd)"
+    "cd = count_frags(\"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 399,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.06138989998726174"
-      ]
-     },
-     "execution_count": 399,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "timeit.timeit(wrapped, number=1000)"
+    "wrapped = wrapper(quick_median, cd)\n",
+    "timeit.timeit(wrapped, number=100)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 402,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.01676309999311343"
-      ]
-     },
-     "execution_count": 402,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "wrapped2 = wrapper(quick_median_new, cd)\n",
-    "timeit.timeit(wrapped2, number=1000)"
+    "timeit.timeit(wrapped2, number=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 21.5 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "def quick_median_new2(count_dict: dict) -> int:\n",
+    "    mid_point = sum(count_dict.values()) / 2\n",
+    "    print(mid_point)\n",
+    "    my_sum = 0\n",
+    "    my_ind = 0\n",
+    "    sk = sorted(count_dict.keys())\n",
+    "    while my_sum < mid_point:\n",
+    "        my_sum += count_dict[sk[my_ind]]\n",
+    "        if my_sum >= mid_point:\n",
+    "            my_ind += 1\n",
+    "            break\n",
+    "        my_ind += 1\n",
+    "    # correction if we miss the mark too far\n",
+    "    if sum(count_dict.values())%2 == 0:\n",
+    "        median = (sk[my_ind] + sk[my_ind-1])//2\n",
+    "    else:\n",
+    "        median = sk[my_ind]\n",
+    "    return median"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 7.22 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "count_d = {1:4, 2:6, 3:3, 4:8, 5:3, 10:2}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 4.65 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "list = []\n",
+    "for key in count_d.keys():\n",
+    "    for i in range(count_d[key]):\n",
+    "        list.append(key)\n",
+    "list = sorted(list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 5.68 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "(list[13] + list[12])//2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 6.58 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "quick_median_new2(count_d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def count_frags_new(file: str) -> list:\n",
+    "    \"\"\"\n",
+    "    Takes a sam file input and creates a counting dictionary of the number of reads that are paired,\n",
+    "    first in the pair, confidently mapped and whose pair is mapped to the same reference\n",
+    "    :param file: A sam input file\n",
+    "    :return: A dictionary of the counts of the above reads\n",
+    "    \"\"\"\n",
+    "    FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality\n",
+    "    count_list = []\n",
+    "    PRINT_EVERY = 100000\n",
+    "    i = 0\n",
+    "    # Check if the file is sam or bam and decide how to open based on that\n",
+    "    if file[-4:] == \".sam\":\n",
+    "        file_to_parse = open(file, 'r')\n",
+    "    elif file[-4:] == \".bam\":\n",
+    "        file_to_parse = pysam.AlignmentFile(file, 'rb')\n",
+    "    else:\n",
+    "        print(\"Unknown file type, must be bam or sam\")\n",
+    "        exit(1)\n",
+    "\n",
+    "    for item in file_to_parse:\n",
+    "        # Need to convert bam iterable objects into strings for the next part\n",
+    "        line = str(item)\n",
+    "        # Skip all comments and headers\n",
+    "        if line[0] == '#' or line[0] == '@':\n",
+    "            continue\n",
+    "        splt = line.strip().split('\\t')\n",
+    "        samFlag = int(splt[1])\n",
+    "        myRef = splt[2]\n",
+    "        mapQual = int(splt[4])\n",
+    "        mateRef = splt[6]\n",
+    "        myTlen = abs(int(splt[8]))\n",
+    "\n",
+    "        # if read is paired, and is first in pair, and is confidently mapped...\n",
+    "        if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:\n",
+    "            # and mate is mapped to same reference\n",
+    "            if mateRef == '=' or mateRef == myRef:\n",
+    "                count_list.append(myTlen)\n",
+    "    count_list = sorted(count_list)\n",
+    "    return count_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wrapped3 = wrapper(count_frags, \"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")\n",
+    "timeit.timeit(wrapped3, number=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wrapped4 = wrapper(count_frags_new, \"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")\n",
+    "timeit.timeit(wrapped4, number=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def quick_median_new(datalist: list) -> float:\n",
+    "    \"\"\"\n",
+    "    Finds the median of a counting dictionary\n",
+    "    :param count_dict: the counting dictionary to find the median of\n",
+    "    :return: integer index of the location of the median\n",
+    "    \"\"\"\n",
+    "    midpoint = len(datalist)//2\n",
+    "    if len(datalist) % 2 == 0:\n",
+    "        median = (datalist[midpoint] + datalist[midpoint-1])/2\n",
+    "    else:\n",
+    "        median = datalist[midpoint]\n",
+    "    return median"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quick_median(cd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def median_deviation_from_median_new(datalist: list) -> float:\n",
+    "    \"\"\"\n",
+    "    calculates the deviation from the median of each element of counting dictionary,\n",
+    "    then returns the median of that dictionary\n",
+    "    :param count_dict: Counting dictionary to analyze\n",
+    "    :return: index of median of the deviations\n",
+    "    \"\"\"\n",
+    "    my_median = quick_median_new(datalist)\n",
+    "    deviations = []\n",
+    "    for item in datalist:\n",
+    "        X_value = abs(item - my_median)\n",
+    "        deviations.append(X_value)\n",
+    "    return quick_median_new(sorted(deviations))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "median_deviation_from_median(cd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_probs_new(datalist: list) -> (list, list):\n",
+    "    \"\"\"\n",
+    "    Computes the probabilities for fragments with at least 100 pairs supporting it and that are at least 10 median\n",
+    "    deviations from the median.\n",
+    "    :param count_dict: A dictionary of fragments with counts\n",
+    "    :return: A list of values that meet the criteria and a list of -their associated probabilities\n",
+    "    \"\"\"\n",
+    "    FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it\n",
+    "    FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median\n",
+    "    values = []\n",
+    "    probabilities = []\n",
+    "    med = quick_median_new(datalist)\n",
+    "    mdm = median_deviation_from_median_new(datalist)\n",
+    "    \n",
+    "    for item in list(set(datalist)):\n",
+    "        if 0 < item < med + FILTER_MEDDEV_M * mdm:\n",
+    "            if datalist.count(item) >= FILTER_MINREADS:\n",
+    "                values.append(item)\n",
+    "                probabilities.append(datalist.count(item))\n",
+    "    count_sum = float(sum(probabilities))\n",
+    "    probabilities = [n / count_sum for n in probabilities]\n",
+    "    return values, probabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum(x == 10 for x in cd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list.count(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "computing probs\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([81,\n",
+       "  82,\n",
+       "  83,\n",
+       "  84,\n",
+       "  85,\n",
+       "  86,\n",
+       "  87,\n",
+       "  88,\n",
+       "  89,\n",
+       "  90,\n",
+       "  91,\n",
+       "  92,\n",
+       "  93,\n",
+       "  94,\n",
+       "  95,\n",
+       "  96,\n",
+       "  97,\n",
+       "  98,\n",
+       "  99,\n",
+       "  100],\n",
+       " [0.0014269858886951008,\n",
+       "  0.0019132181174356536,\n",
+       "  0.0021669044976481157,\n",
+       "  0.0028539717773902015,\n",
+       "  0.0024734422070715077,\n",
+       "  0.007071507848422388,\n",
+       "  0.00800169124253475,\n",
+       "  0.007927699381639449,\n",
+       "  0.008033402040061307,\n",
+       "  0.007853707520744147,\n",
+       "  0.03276782411077639,\n",
+       "  0.0322287405528249,\n",
+       "  0.03289466730088262,\n",
+       "  0.03376142909994186,\n",
+       "  0.02844458538132234,\n",
+       "  0.23078061413244544,\n",
+       "  0.21153216003382486,\n",
+       "  0.1821996723217589,\n",
+       "  0.16061518947201522,\n",
+       "  0.005052587072564875])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "counting_list = count_frags_new(\"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")\n",
+    "print(\"computing probs\")\n",
+    "compute_probs_new(counting_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below are the two competing compute frag length programs, so that I can run side-by-side time comparisons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 22.8 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Count_dict method\n",
+    "\n",
+    "def quick_median1(count_dict: dict) -> int:\n",
+    "    mid_point = sum(count_dict.values()) / 2\n",
+    "    my_sum = 0\n",
+    "    my_ind = 0\n",
+    "    sk = sorted(count_dict.keys())\n",
+    "    while my_sum < mid_point:\n",
+    "        my_sum += count_dict[sk[my_ind]]\n",
+    "        if my_sum >= mid_point:\n",
+    "            break\n",
+    "        my_ind += 1\n",
+    "    if sum(count_dict.values())%2 == 0:\n",
+    "        median = (sk[my_ind] + sk[my_ind-1])//2\n",
+    "    else:\n",
+    "        median = sk[my_ind]\n",
+    "    return median\n",
+    "\n",
+    "\n",
+    "def median_deviation_from_median1(count_dict: dict) -> int:\n",
+    "    \"\"\"\n",
+    "    calculates the deviation from the median of each element of counting dictionary,\n",
+    "    then returns the median of that dictionary\n",
+    "    :param count_dict: Counting dictionary to analyze\n",
+    "    :return: index of median of the deviations\n",
+    "    \"\"\"\n",
+    "    my_median = quick_median1(count_dict)\n",
+    "    deviations = {}\n",
+    "    for key in sorted(count_dict.keys()):\n",
+    "        X_value = abs(key - my_median)\n",
+    "        deviations[X_value] = count_dict[key]\n",
+    "    return quick_median1(deviations)\n",
+    "\n",
+    "\n",
+    "def count_frags1(file: str) -> dict:\n",
+    "    \"\"\"\n",
+    "    Takes a sam file input and creates a counting dictionary of the number of reads that are paired,\n",
+    "    first in the pair, confidently mapped and whose pair is mapped to the same reference\n",
+    "    :param file: A sam input file\n",
+    "    :return: A dictionary of the counts of the above reads\n",
+    "    \"\"\"\n",
+    "    FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality\n",
+    "    count_dict = {}\n",
+    "    PRINT_EVERY = 100000\n",
+    "    # Check if the file is sam or bam and decide how to open based on that\n",
+    "    if file[-4:] == \".sam\":\n",
+    "        file_to_parse = open(file, 'r')\n",
+    "    elif file[-4:] == \".bam\":\n",
+    "        file_to_parse = pysam.AlignmentFile(file, 'rb')\n",
+    "    else:\n",
+    "        print(\"Unknown file type, must be bam or sam\")\n",
+    "        exit(1)\n",
+    "\n",
+    "    for item in file_to_parse:\n",
+    "        # Need to convert bam iterable objects into strings for the next part\n",
+    "        line = str(item)\n",
+    "        # Skip all comments and headers\n",
+    "        if line[0] == '#' or line[0] == '@':\n",
+    "            continue\n",
+    "        splt = line.strip().split('\\t')\n",
+    "        samFlag = int(splt[1])\n",
+    "        myRef = splt[2]\n",
+    "        mapQual = int(splt[4])\n",
+    "        mateRef = splt[6]\n",
+    "        myTlen = abs(int(splt[8]))\n",
+    "\n",
+    "        # if read is paired, and is first in pair, and is confidently mapped...\n",
+    "        if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:\n",
+    "            # and mate is mapped to same reference\n",
+    "            if mateRef == '=' or mateRef == myRef:\n",
+    "                if myTlen not in count_dict:\n",
+    "                    count_dict[myTlen] = 0\n",
+    "                count_dict[myTlen] += 1\n",
+    "    return count_dict\n",
+    "\n",
+    "\n",
+    "def compute_probs1(count_dict: dict) -> (list, list):\n",
+    "    \"\"\"\n",
+    "    Computes the probabilities for fragments with at least 100 pairs supporting it and that are at least 10 median\n",
+    "    deviations from the median.\n",
+    "    :param count_dict: A dictionary of fragments with counts\n",
+    "    :return: A list of values that meet the criteria and a list of -their associated probabilities\n",
+    "    \"\"\"\n",
+    "    FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it\n",
+    "    FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median\n",
+    "    values = []\n",
+    "    probabilities = []\n",
+    "    med = quick_median1(count_dict)\n",
+    "    mdm = median_deviation_from_median1(count_dict)\n",
+    "\n",
+    "    for key in sorted(count_dict.keys()):\n",
+    "        if 0 < key < med + FILTER_MEDDEV_M * mdm:\n",
+    "            if count_dict[key] >= FILTER_MINREADS:\n",
+    "                values.append(key)\n",
+    "                probabilities.append(count_dict[key])\n",
+    "    count_sum = float(sum(probabilities))\n",
+    "    probabilities = [n / count_sum for n in probabilities]\n",
+    "    return values, probabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 24min 3s\n"
+     ]
+    }
+   ],
+   "source": [
+    "counting_list = count_frags1(\"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{81: 13, 80: 3, 79: 8, 78: 4, 77: 2, 76: 5, 75: 6, 74: 7, 73: 9, 72: 13, 71: 3, 70: 5, 69: 9, 68: 3, 67: 9, 66: 2, 65: 8, 64: 6, 63: 7, 62: 6, 61: 7, 60: 10, 59: 10, 58: 5, 57: 12, 56: 8, 55: 5, 54: 5, 53: 12, 52: 8, 51: 12, 50: 5, 49: 11, 48: 11, 47: 7, 46: 10, 45: 10, 44: 14, 43: 8, 42: 5, 41: 10, 40: 7, 39: 13, 38: 7, 37: 10, 36: 16, 35: 6, 34: 11, 33: 9, 32: 16, 31: 14, 30: 9, 29: 25, 28: 19, 27: 20, 26: 17, 25: 23, 24: 34, 23: 55, 22: 42, 21: 37, 20: 81, 19: 135, 18: 181, 17: 205, 16: 270, 15: 234, 14: 669, 13: 757, 12: 750, 11: 760, 10: 743, 9: 3100, 8: 3049, 7: 3112, 6: 3194, 5: 2691, 4: 21833, 3: 20012, 2: 17237, 1: 12661399, 0: 478}\n",
+      "81 135\n",
+      "82 181\n",
+      "83 205\n",
+      "84 270\n",
+      "85 234\n",
+      "86 669\n",
+      "87 757\n",
+      "88 750\n",
+      "89 760\n",
+      "90 743\n",
+      "91 3100\n",
+      "92 3049\n",
+      "93 3112\n",
+      "94 3194\n",
+      "95 2691\n",
+      "96 21833\n",
+      "97 20012\n",
+      "98 17237\n",
+      "99 15195\n",
+      "100 478\n",
+      "101 12661399\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([81,\n",
+       "  82,\n",
+       "  83,\n",
+       "  84,\n",
+       "  85,\n",
+       "  86,\n",
+       "  87,\n",
+       "  88,\n",
+       "  89,\n",
+       "  90,\n",
+       "  91,\n",
+       "  92,\n",
+       "  93,\n",
+       "  94,\n",
+       "  95,\n",
+       "  96,\n",
+       "  97,\n",
+       "  98,\n",
+       "  99,\n",
+       "  100,\n",
+       "  101],\n",
+       " [1.0583251620178232e-05,\n",
+       "  1.4189396616683407e-05,\n",
+       "  1.607086357138176e-05,\n",
+       "  2.1166503240356464e-05,\n",
+       "  1.8344302808308934e-05,\n",
+       "  5.244589136221657e-05,\n",
+       "  5.934460352944386e-05,\n",
+       "  5.879584233432351e-05,\n",
+       "  5.9579786898781155e-05,\n",
+       "  5.824708113920316e-05,\n",
+       "  0.0002430228149818705,\n",
+       "  0.0002390246977031365,\n",
+       "  0.00024396354845921967,\n",
+       "  0.0002503918938877724,\n",
+       "  0.00021095948229555274,\n",
+       "  0.00171158616758038,\n",
+       "  0.0015688298623926426,\n",
+       "  0.0013512852457556457,\n",
+       "  0.0011912037656933942,\n",
+       "  3.747255018107551e-05,\n",
+       "  0.9925834924479484])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 21.4 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "compute_probs1(counting_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 22.3 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Datalist method\n",
+    "\n",
+    "def quick_median_new(datalist: list) -> float:\n",
+    "    \"\"\"\n",
+    "    Finds the median of a counting dictionary\n",
+    "    :param count_dict: the counting dictionary to find the median of\n",
+    "    :return: integer index of the location of the median\n",
+    "    \"\"\"\n",
+    "    midpoint = len(datalist)//2\n",
+    "    if len(datalist) % 2 == 0:\n",
+    "        median = (datalist[midpoint] + datalist[midpoint-1])/2\n",
+    "    else:\n",
+    "        median = datalist[midpoint]\n",
+    "    return median\n",
+    "\n",
+    "\n",
+    "def median_deviation_from_median_new(datalist: list) -> float:\n",
+    "    \"\"\"\n",
+    "    calculates the deviation from the median of each element of counting dictionary,\n",
+    "    then returns the median of that dictionary\n",
+    "    :param count_dict: Counting dictionary to analyze\n",
+    "    :return: index of median of the deviations\n",
+    "    \"\"\"\n",
+    "    my_median = quick_median_new(datalist)\n",
+    "    deviations = []\n",
+    "    for item in datalist:\n",
+    "        X_value = abs(item - my_median)\n",
+    "        deviations.append(X_value)\n",
+    "    return quick_median_new(sorted(deviations))\n",
+    "\n",
+    "\n",
+    "def count_frags_new(file: str) -> list:\n",
+    "    \"\"\"\n",
+    "    Takes a sam file input and creates a counting dictionary of the number of reads that are paired,\n",
+    "    first in the pair, confidently mapped and whose pair is mapped to the same reference\n",
+    "    :param file: A sam input file\n",
+    "    :return: A dictionary of the counts of the above reads\n",
+    "    \"\"\"\n",
+    "    FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality\n",
+    "    count_list = []\n",
+    "    PRINT_EVERY = 100000\n",
+    "    i = 0\n",
+    "    # Check if the file is sam or bam and decide how to open based on that\n",
+    "    if file[-4:] == \".sam\":\n",
+    "        file_to_parse = open(file, 'r')\n",
+    "    elif file[-4:] == \".bam\":\n",
+    "        file_to_parse = pysam.AlignmentFile(file, 'rb')\n",
+    "    else:\n",
+    "        print(\"Unknown file type, must be bam or sam\")\n",
+    "        exit(1)\n",
+    "\n",
+    "    for item in file_to_parse:\n",
+    "        # Need to convert bam iterable objects into strings for the next part\n",
+    "        line = str(item)\n",
+    "        # Skip all comments and headers\n",
+    "        if line[0] == '#' or line[0] == '@':\n",
+    "            continue\n",
+    "        splt = line.strip().split('\\t')\n",
+    "        samFlag = int(splt[1])\n",
+    "        myRef = splt[2]\n",
+    "        mapQual = int(splt[4])\n",
+    "        mateRef = splt[6]\n",
+    "        myTlen = abs(int(splt[8]))\n",
+    "\n",
+    "        # if read is paired, and is first in pair, and is confidently mapped...\n",
+    "        if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:\n",
+    "            # and mate is mapped to same reference\n",
+    "            if mateRef == '=' or mateRef == myRef:\n",
+    "                count_list.append(myTlen)\n",
+    "    count_list = sorted(count_list)\n",
+    "    file_to_parse.close()\n",
+    "    return count_list\n",
+    "\n",
+    "\n",
+    "def compute_probs_new(datalist: list) -> (list, list):\n",
+    "    \"\"\"\n",
+    "    Computes the probabilities for fragments with at least 100 pairs supporting it and that are at least 10 median\n",
+    "    deviations from the median.\n",
+    "    :param count_dict: A dictionary of fragments with counts\n",
+    "    :return: A list of values that meet the criteria and a list of -their associated probabilities\n",
+    "    \"\"\"\n",
+    "    FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it\n",
+    "    FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median\n",
+    "    values = []\n",
+    "    probabilities = []\n",
+    "    med = quick_median_new(datalist)\n",
+    "    mdm = median_deviation_from_median_new(datalist)\n",
+    "    \n",
+    "    for item in list(set(datalist)):\n",
+    "        if 0 < item < med + FILTER_MEDDEV_M * mdm:\n",
+    "            data_count = datalist.count(item)\n",
+    "            if data_count >= FILTER_MINREADS:\n",
+    "                values.append(item)\n",
+    "                probabilities.append(data_count)\n",
+    "    count_sum = float(sum(probabilities))\n",
+    "    probabilities = [n / count_sum for n in probabilities]\n",
+    "    return values, probabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 18min 4s\n"
+     ]
+    }
+   ],
+   "source": [
+    "counting_list = count_frags_new(\"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([81,\n",
+       "  82,\n",
+       "  83,\n",
+       "  84,\n",
+       "  85,\n",
+       "  86,\n",
+       "  87,\n",
+       "  88,\n",
+       "  89,\n",
+       "  90,\n",
+       "  91,\n",
+       "  92,\n",
+       "  93,\n",
+       "  94,\n",
+       "  95,\n",
+       "  96,\n",
+       "  97,\n",
+       "  98,\n",
+       "  99,\n",
+       "  100],\n",
+       " [0.0014269858886951008,\n",
+       "  0.0019132181174356536,\n",
+       "  0.0021669044976481157,\n",
+       "  0.0028539717773902015,\n",
+       "  0.0024734422070715077,\n",
+       "  0.007071507848422388,\n",
+       "  0.00800169124253475,\n",
+       "  0.007927699381639449,\n",
+       "  0.008033402040061307,\n",
+       "  0.007853707520744147,\n",
+       "  0.03276782411077639,\n",
+       "  0.0322287405528249,\n",
+       "  0.03289466730088262,\n",
+       "  0.03376142909994186,\n",
+       "  0.02844458538132234,\n",
+       "  0.23078061413244544,\n",
+       "  0.21153216003382486,\n",
+       "  0.1821996723217589,\n",
+       "  0.16061518947201522,\n",
+       "  0.005052587072564875])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 1min 13s\n"
+     ]
+    }
+   ],
+   "source": [
+    "compute_probs_new(counting_list)"
    ]
   },
   {
@@ -873,7 +1321,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,
diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index e616067..d968256 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -14,75 +14,61 @@
 import pysam
 
 
-def quick_median(count_dict: dict) -> int:
+def median(datalist: list) -> float:
     """
-    Finds the median of a counting dictionary. A counting dictionary is a representation of a list of data.
-    For example, the data [1,1,2,2,2] could be represented in dictionary form as {1:2, 2:3}, indicating that 1 is
-    repeated twice and 2 is repeating three times.
-    :param count_dict: the counting dictionary to find the median of
+    Finds the median of a list of data. For this function, the data are expected to be a list of
+    numbers, either float or int.
+    :param datalist: the list of data to find the median of. This should be a set of numbers.
     :return: The median of the set
     """
-    # The sum of the values of the counting dictionary tells us how long the expanded list is. To find the midpoint
-    # We divide by 2
-    if not count_dict:
-        raise Exception('Empty counting dictionary. Check that sam input was correctly formatted.')
+    # using integer division here gives the index of the midpoint, due to zero-based indexing.
+    midpoint = len(datalist)//2
+
+    # Once we've found the midpoint, we calculate the median, which is just the middle value if there are an
+    # odd number of values, or the average of the two middle values if there are an even number
+    if len(datalist) % 2 == 0:
+        median = (datalist[midpoint] + datalist[midpoint-1])/2
     else:
-        mid_point = sum(count_dict.values()) / 2
-        my_sum = 0
-        my_ind = 0
-        sk = sorted(count_dict.keys())
-        # Here we basically count up one group of dictionary values at a time until we're at the midpoint
-        while my_sum < mid_point:
-            my_sum += count_dict[sk[my_ind]]
-            if my_sum >= mid_point:
-                break
-            my_ind += 1
-        # Once we've found the midpoint, we calculate the median, which is just the middle value if there are an
-        # odd number of values, or the average of the two middle values if there are an even number
-        if sum(count_dict.values()) % 2 == 0:
-            median = (sk[my_ind] + sk[my_ind-1])//2
-        else:
-            median = sk[my_ind]
+        median = datalist[midpoint]
     return median
 
 
-def median_absolute_deviation(count_dict: dict) -> int:
+
+def median_absolute_deviation(datalist: list) -> float:
     """
-    Calculates the deviation from the median of each element of counting dictionary,
+    Calculates the median deviation from the median for each element of of a datalist.
     then returns the median of that dictionary. A counting dictionary such as {2: 3, 5:2} expands to [2,2,2,5,5]
-    :param count_dict: Counting dictionary to analyze
+    :param datalist: A list of data to find the MAD of
     :return: index of median of the deviations
     """
-    my_median = quick_median(count_dict)
-    deviations = {}
-    for key in sorted(count_dict.keys()):
+    my_median = median(datalist)
+    deviations = []
+    for item in datalist:
         # We take the absolute difference between the value and the median
-        X_value = abs(key - my_median)
-        # We retain the number of items for each element of the dictionary
-        # This is equivalent to subtracting the median from each value of the expanded dataset
-        deviations[X_value] = count_dict[key]
-    # Taking the median of this dataset gives us the median deviation from the median
-    # (also called the Median Absolute Deviation)
-    return quick_median(deviations)
-
-def count_frags(file: str) -> dict:
+        X_value = abs(item - my_median)
+        # This creates a dataset that is the absolute deviations about the median
+        deviations.append(X_value)
+    # The median of the absolute deviations is the median absolute deviation
+    return median(sorted(deviations))
+
+
+def count_frags(file: str) -> list:
     """
-    Takes a sam file input and creates a counting dictionary of the number of reads that are paired,
+    Takes a sam or bam file input and creates a list of the number of reads that are paired,
     first in the pair, confidently mapped and whose pair is mapped to the same reference
     :param file: A sam input file
-    :return: A dictionary of the counts of the above reads
+    :return: A list of the tlens from the bam/sam file
     """
     FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality
-    count_dict = {}
-    PRINT_EVERY = 100000
-    i = 0
+    count_list = []
     # Check if the file is sam or bam and decide how to open based on that
     if file[-4:] == ".sam":
         file_to_parse = open(file, 'r')
     elif file[-4:] == ".bam":
+        print("WARNING: Must have pysam installed to read bam files. Pysam does not work on Windows OS.")
         file_to_parse = pysam.AlignmentFile(file, 'rb')
     else:
-        print("Unknown file type, must be bam or sam")
+        print("Unknown file type, file extension must be bam or sam")
         exit(1)
 
     for item in file_to_parse:
@@ -96,41 +82,38 @@ def count_frags(file: str) -> dict:
         myRef = splt[2]
         mapQual = int(splt[4])
         mateRef = splt[6]
-        my_tlen = abs(int(splt[8]))
+        myTlen = abs(int(splt[8]))
 
         # if read is paired, and is first in pair, and is confidently mapped...
         if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:
             # and mate is mapped to same reference
             if mateRef == '=' or mateRef == myRef:
-                if my_tlen not in count_dict:
-                    count_dict[my_tlen] = 0
-                count_dict[my_tlen] += 1
-                i += 1
-                if i % PRINT_EVERY == 0:
-                    print('---', i, quick_median(count_dict), median_absolute_deviation(count_dict))
-    return count_dict
+                count_list.append(myTlen)
+    count_list = sorted(count_list)
+    file_to_parse.close()
+    return count_list
 
 
-def compute_probs(count_dict: dict) -> (list, list):
+def compute_probs(datalist: list) -> (list, list):
     """
     Computes the probabilities for fragments with at least 100 pairs supporting it and that are at least 10 median
     deviations from the median.
-    :param count_dict: A dictionary of fragments with counts
-    :return: A list of values that meet the criteria and a list of -their associated probabilities
+    :param datalist: A list of fragments with counts
+    :return: A list of values that meet the criteria and a list of their associated probabilities
     """
     FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it
     FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median
     values = []
     probabilities = []
-    med = quick_median(count_dict)
-    mdm = median_absolute_deviation(count_dict)
-
-    for key in sorted(count_dict.keys()):
-        if 0 < key < med + FILTER_MEDDEV_M * mdm:
-            if count_dict[key] >= FILTER_MINREADS:
-                print(key, count_dict[key])
-                values.append(key)
-                probabilities.append(count_dict[key])
+    med = median(datalist)
+    mad = median_absolute_deviation(datalist)
+
+    for item in list(set(datalist)):
+        if 0 < item < med + FILTER_MEDDEV_M * mad:
+            data_count = datalist.count(item)
+            if data_count >= FILTER_MINREADS:
+                values.append(item)
+                probabilities.append(data_count)
     count_sum = float(sum(probabilities))
     probabilities = [n / count_sum for n in probabilities]
     return values, probabilities
@@ -139,10 +122,10 @@ def compute_probs(count_dict: dict) -> (list, list):
 def main():
     """
     Main function takes 2 arguments:
-        input - a samfile input that can be formed by applying samtools to a bam file
+        input - a path to a sam or bam file input. Note that sam files can be formed by applying samtools to a bam file
         in the follawing way: samtools view nameof.bam > nameof.sam
 
-        output - the prefix of the output. The actual output will be the prefix plus ".p" at the end
+        output - the string prefix of the output. The actual output will be the prefix plus ".p" at the end
         for pickle file. The list of values and list of probabilities are dumped as a list of lists
         into a pickle file on completion of the analysis
 
@@ -159,11 +142,10 @@ def main():
     output = output_prefix + '.p'
 
     all_tlens = count_frags(input_file)
-    print('\nsaving model...')
+    print('\nSaving model...')
     out_vals, out_probs = compute_probs(all_tlens)
-    print(out_probs)
     pickle.dump([out_vals, out_probs], open(output, 'wb'))
-
+    print('\nModel successfully saved.')
 
 if __name__ == "__main__":
     main()

From 0fd2890d093b0a1bed2276863caf3e8adebd1b99 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 17 Jun 2020 09:15:32 -0500
Subject: [PATCH 064/323] fixi

---
 utilities/compute_gc.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/utilities/compute_gc.py b/utilities/compute_gc.py
index 9bd695e..c339674 100644
--- a/utilities/compute_gc.py
+++ b/utilities/compute_gc.py
@@ -43,7 +43,7 @@ def process_fasta(file: str) -> dict:
     return ref_dict
 
 
-def process_genomecov(file: str, ref_dict: dict, window) -> dict:
+def process_genomecov(file: str, ref_dict: dict, window: int) -> dict:
     gc_bins = {n: [] for n in range(window + 1)}
 
     # variables needed to parse coverage file
@@ -58,7 +58,7 @@ def process_genomecov(file: str, ref_dict: dict, window) -> dict:
         lines_processed += 1
         if current_line == 0:
             current_ref = splt[0]
-            sPos = int(splt[1]) - 1
+            current_pos = int(splt[1]) - 1
 
         if current_ref not in ref_dict:
             continue
@@ -68,7 +68,7 @@ def process_genomecov(file: str, ref_dict: dict, window) -> dict:
 
         if current_line == window:
             current_line = 0
-            seq = str(ref_dict[current_ref][sPos:sPos + window])
+            seq = str(ref_dict[current_ref][current_pos:current_pos + window])
             if 'N' not in seq:
                 gc_count = seq.count('G') + seq.count('C')
                 gc_bins[gc_count].append(current_cov)

From 8b2e8956234141d72a0b3b5d45ac5a2e9626d8a5 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 17 Jun 2020 09:17:52 -0500
Subject: [PATCH 065/323] fixing a variable name in compute_gc

---
 utilities/compute_gc.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/utilities/compute_gc.py b/utilities/compute_gc.py
index c339674..d2af2c9 100644
--- a/utilities/compute_gc.py
+++ b/utilities/compute_gc.py
@@ -86,12 +86,12 @@ def calculate_coverage(bin_dict: dict, window: int) -> float:
             print('{0:0.2%}'.format(k / float(window)), 0.0, 0)
             bin_dict[k] = 0
         else:
-            myMean = np.mean(bin_dict[k])
-            myLen = len(bin_dict[k])
-            print('{0:0.2%}'.format(k / float(window)), myMean, myLen)
-            all_mean += myMean * myLen
-            running_total += myLen
-            bin_dict[k] = myMean
+            my_mean = np.mean(bin_dict[k])
+            my_len = len(bin_dict[k])
+            print('{0:0.2%}'.format(k / float(window)), my_mean, my_len)
+            all_mean += my_mean * my_len
+            running_total += my_len
+            bin_dict[k] = my_mean
 
     return all_mean / float(running_total)
 

From 9355c2e72130db7ef9e8217ae6c0d14af1d194f7 Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Wed, 17 Jun 2020 15:09:28 -0500
Subject: [PATCH 066/323] Update compute_gc.py

---
 utilities/compute_gc.py | 53 +++++++++++++++++++++++++++++++----------
 1 file changed, 41 insertions(+), 12 deletions(-)

diff --git a/utilities/compute_gc.py b/utilities/compute_gc.py
index 5d9b7bc..a30d037 100644
--- a/utilities/compute_gc.py
+++ b/utilities/compute_gc.py
@@ -43,7 +43,15 @@ def process_fasta(file: str) -> dict:
     return ref_dict
 
 
-def process_genomecov(file: str, ref_dict: dict, window) -> dict:
+def process_genomecov(file: str, ref_dict: dict, window: int) -> dict:
+    """
+    Takes a genomecov file and converts it into a dictionary made up of 'window' sized sections 
+    that record the number of GCs and the coverage measure for each section.
+    :param file: path to a genomecov file
+    :param ref_dict: dictionary created from using the process_fasta function
+    :param window: Length of each section of base pairs to count in the reference dictionary
+    :return: dictionary form of genomecov file based on window size and ref_dict data
+    """
     gc_bins = {n: [] for n in range(window + 1)}
 
     # variables needed to parse coverage file
@@ -51,7 +59,6 @@ def process_genomecov(file: str, ref_dict: dict, window) -> dict:
     current_ref = None
     current_cov = 0
     lines_processed = 0
-    print_every = 1000000
 
     f = open(file, 'r')
     for line in f:
@@ -59,7 +66,7 @@ def process_genomecov(file: str, ref_dict: dict, window) -> dict:
         lines_processed += 1
         if current_line == 0:
             current_ref = splt[0]
-            sPos = int(splt[1]) - 1
+            current_pos = int(splt[1]) - 1
 
         if current_ref not in ref_dict:
             continue
@@ -69,7 +76,7 @@ def process_genomecov(file: str, ref_dict: dict, window) -> dict:
 
         if current_line == window:
             current_line = 0
-            seq = str(ref_dict[current_ref][sPos:sPos + window])
+            seq = str(ref_dict[current_ref][current_pos:current_pos + window])
             if 'N' not in seq:
                 gc_count = seq.count('G') + seq.count('C')
                 gc_bins[gc_count].append(current_cov)
@@ -80,6 +87,28 @@ def process_genomecov(file: str, ref_dict: dict, window) -> dict:
 
 
 def calculate_coverage(bin_dict: dict, window: int) -> float:
+    """
+    Takes the dictionary created in process_genomecov and finds the average coverage value.
+    Also ouputs the average coverage value for each window, along with the number of entries in that window.
+    :param bin_dict: dictionary created from using the process_genomecov function
+    :param window: Length of each section of base pairs to count, 
+                   should be the same as the window value in process_genomecov
+    :return: Average coverage value for the whole sample, along with average coverage values for each window.
+    
+
+    Parameters
+    ----------
+    bin_dict : dict
+        DESCRIPTION.
+    window : int
+        DESCRIPTION.
+
+    Returns
+    -------
+    float
+        DESCRIPTION.
+
+    """
     running_total = 0
     all_mean = 0.0
     for k in sorted(bin_dict.keys()):
@@ -87,12 +116,12 @@ def calculate_coverage(bin_dict: dict, window: int) -> float:
             print('{0:0.2%}'.format(k / float(window)), 0.0, 0)
             bin_dict[k] = 0
         else:
-            myMean = np.mean(bin_dict[k])
-            myLen = len(bin_dict[k])
-            print('{0:0.2%}'.format(k / float(window)), myMean, myLen)
-            all_mean += myMean * myLen
-            running_total += myLen
-            bin_dict[k] = myMean
+            my_mean = np.mean(bin_dict[k])
+            my_len = len(bin_dict[k])
+            print('{0:0.2%}'.format(k / float(window)), my_mean, my_len)
+            all_mean += my_mean * my_len
+            running_total += my_len
+            bin_dict[k] = my_mean
 
     return all_mean / float(running_total)
 
@@ -101,8 +130,8 @@ def main():
     """
     Reads in arguments and processes the inputs to a GC count for the sequence.
     Parameters:
-        -i is the genome coverage input file
-        -r is the reference file
+        -i is the genome coverage input file (genomecov)
+        -r is the reference file (fasta)
         -o is the prefix for the output
         -w is the sliding window length. The default is 50, but you can declare any reasonable integer
 

From 382562d5606e6f02af7be1b8d5787e79571916f5 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 17 Jun 2020 16:29:08 -0500
Subject: [PATCH 067/323] changed a less than to less than equal to, to cover
 edge cases

---
 Testing_compute_fraglen.ipynb | 425 +++++++---------------------------
 utilities/compute_fraglen.py  |   2 +-
 2 files changed, 79 insertions(+), 348 deletions(-)

diff --git a/Testing_compute_fraglen.ipynb b/Testing_compute_fraglen.ipynb
index c187b88..02b519c 100644
--- a/Testing_compute_fraglen.ipynb
+++ b/Testing_compute_fraglen.ipynb
@@ -2,27 +2,11 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Defaulting to user installation because normal site-packages is not writeable\n",
-      "Collecting ipython-autotime\n",
-      "  Downloading ipython-autotime-0.1.tar.bz2 (1.2 kB)\n",
-      "Could not build wheels for ipython-autotime, since package 'wheel' is not installed.\n",
-      "Installing collected packages: ipython-autotime\n",
-      "    Running setup.py install for ipython-autotime ... \u001b[?25ldone\n",
-      "\u001b[?25hSuccessfully installed ipython-autotime-0.1\n",
-      "\u001b[33mWARNING: You are using pip version 20.1; however, version 20.1.1 is available.\n",
-      "You should consider upgrading via the '/usr/bin/python3.8 -m pip install --upgrade pip' command.\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install ipython-autotime\n",
     "%load_ext autotime"
@@ -30,17 +14,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 628 µs\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cd = {1:4, 2:6, 3:1, 4:8, 5:3, 10:2}"
    ]
@@ -455,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -493,17 +469,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 21.5 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def quick_median_new2(count_dict: dict) -> int:\n",
     "    mid_point = sum(count_dict.values()) / 2\n",
@@ -527,34 +495,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 7.22 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "count_d = {1:4, 2:6, 3:3, 4:8, 5:3, 10:2}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 4.65 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "list = []\n",
     "for key in count_d.keys():\n",
@@ -565,68 +517,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 5.68 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "(list[13] + list[12])//2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13.0\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 6.58 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "quick_median_new2(count_d)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -694,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -723,7 +632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -753,7 +662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -801,66 +710,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "computing probs\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([81,\n",
-       "  82,\n",
-       "  83,\n",
-       "  84,\n",
-       "  85,\n",
-       "  86,\n",
-       "  87,\n",
-       "  88,\n",
-       "  89,\n",
-       "  90,\n",
-       "  91,\n",
-       "  92,\n",
-       "  93,\n",
-       "  94,\n",
-       "  95,\n",
-       "  96,\n",
-       "  97,\n",
-       "  98,\n",
-       "  99,\n",
-       "  100],\n",
-       " [0.0014269858886951008,\n",
-       "  0.0019132181174356536,\n",
-       "  0.0021669044976481157,\n",
-       "  0.0028539717773902015,\n",
-       "  0.0024734422070715077,\n",
-       "  0.007071507848422388,\n",
-       "  0.00800169124253475,\n",
-       "  0.007927699381639449,\n",
-       "  0.008033402040061307,\n",
-       "  0.007853707520744147,\n",
-       "  0.03276782411077639,\n",
-       "  0.0322287405528249,\n",
-       "  0.03289466730088262,\n",
-       "  0.03376142909994186,\n",
-       "  0.02844458538132234,\n",
-       "  0.23078061413244544,\n",
-       "  0.21153216003382486,\n",
-       "  0.1821996723217589,\n",
-       "  0.16061518947201522,\n",
-       "  0.005052587072564875])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "counting_list = count_frags_new(\"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")\n",
     "print(\"computing probs\")\n",
@@ -876,17 +728,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 22.8 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Count_dict method\n",
     "\n",
@@ -990,130 +834,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 24min 3s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "counting_list = count_frags1(\"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{81: 13, 80: 3, 79: 8, 78: 4, 77: 2, 76: 5, 75: 6, 74: 7, 73: 9, 72: 13, 71: 3, 70: 5, 69: 9, 68: 3, 67: 9, 66: 2, 65: 8, 64: 6, 63: 7, 62: 6, 61: 7, 60: 10, 59: 10, 58: 5, 57: 12, 56: 8, 55: 5, 54: 5, 53: 12, 52: 8, 51: 12, 50: 5, 49: 11, 48: 11, 47: 7, 46: 10, 45: 10, 44: 14, 43: 8, 42: 5, 41: 10, 40: 7, 39: 13, 38: 7, 37: 10, 36: 16, 35: 6, 34: 11, 33: 9, 32: 16, 31: 14, 30: 9, 29: 25, 28: 19, 27: 20, 26: 17, 25: 23, 24: 34, 23: 55, 22: 42, 21: 37, 20: 81, 19: 135, 18: 181, 17: 205, 16: 270, 15: 234, 14: 669, 13: 757, 12: 750, 11: 760, 10: 743, 9: 3100, 8: 3049, 7: 3112, 6: 3194, 5: 2691, 4: 21833, 3: 20012, 2: 17237, 1: 12661399, 0: 478}\n",
-      "81 135\n",
-      "82 181\n",
-      "83 205\n",
-      "84 270\n",
-      "85 234\n",
-      "86 669\n",
-      "87 757\n",
-      "88 750\n",
-      "89 760\n",
-      "90 743\n",
-      "91 3100\n",
-      "92 3049\n",
-      "93 3112\n",
-      "94 3194\n",
-      "95 2691\n",
-      "96 21833\n",
-      "97 20012\n",
-      "98 17237\n",
-      "99 15195\n",
-      "100 478\n",
-      "101 12661399\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([81,\n",
-       "  82,\n",
-       "  83,\n",
-       "  84,\n",
-       "  85,\n",
-       "  86,\n",
-       "  87,\n",
-       "  88,\n",
-       "  89,\n",
-       "  90,\n",
-       "  91,\n",
-       "  92,\n",
-       "  93,\n",
-       "  94,\n",
-       "  95,\n",
-       "  96,\n",
-       "  97,\n",
-       "  98,\n",
-       "  99,\n",
-       "  100,\n",
-       "  101],\n",
-       " [1.0583251620178232e-05,\n",
-       "  1.4189396616683407e-05,\n",
-       "  1.607086357138176e-05,\n",
-       "  2.1166503240356464e-05,\n",
-       "  1.8344302808308934e-05,\n",
-       "  5.244589136221657e-05,\n",
-       "  5.934460352944386e-05,\n",
-       "  5.879584233432351e-05,\n",
-       "  5.9579786898781155e-05,\n",
-       "  5.824708113920316e-05,\n",
-       "  0.0002430228149818705,\n",
-       "  0.0002390246977031365,\n",
-       "  0.00024396354845921967,\n",
-       "  0.0002503918938877724,\n",
-       "  0.00021095948229555274,\n",
-       "  0.00171158616758038,\n",
-       "  0.0015688298623926426,\n",
-       "  0.0013512852457556457,\n",
-       "  0.0011912037656933942,\n",
-       "  3.747255018107551e-05,\n",
-       "  0.9925834924479484])"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 21.4 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "compute_probs1(counting_list)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 22.3 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Datalist method\n",
     "\n",
@@ -1204,7 +945,7 @@
     "    mdm = median_deviation_from_median_new(datalist)\n",
     "    \n",
     "    for item in list(set(datalist)):\n",
-    "        if 0 < item < med + FILTER_MEDDEV_M * mdm:\n",
+    "        if 0 < item <= med + FILTER_MEDDEV_M * mdm:\n",
     "            data_count = datalist.count(item)\n",
     "            if data_count >= FILTER_MINREADS:\n",
     "                values.append(item)\n",
@@ -1216,85 +957,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "counting_list = count_frags_new(\"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "compute_probs_new(counting_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = {50:10, 75: 10, 100:1000}\n",
+    "\n",
+    "datalist = []\n",
+    "for key in cd.keys():\n",
+    "    for i in range(cd[key]):\n",
+    "        datalist.append(key)\n",
+    "datalist = sorted(datalist)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 18min 4s\n"
-     ]
+     "data": {
+      "text/plain": [
+       "100.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "counting_list = count_frags_new(\"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")"
+    "quick_median_new(datalist)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "([81,\n",
-       "  82,\n",
-       "  83,\n",
-       "  84,\n",
-       "  85,\n",
-       "  86,\n",
-       "  87,\n",
-       "  88,\n",
-       "  89,\n",
-       "  90,\n",
-       "  91,\n",
-       "  92,\n",
-       "  93,\n",
-       "  94,\n",
-       "  95,\n",
-       "  96,\n",
-       "  97,\n",
-       "  98,\n",
-       "  99,\n",
-       "  100],\n",
-       " [0.0014269858886951008,\n",
-       "  0.0019132181174356536,\n",
-       "  0.0021669044976481157,\n",
-       "  0.0028539717773902015,\n",
-       "  0.0024734422070715077,\n",
-       "  0.007071507848422388,\n",
-       "  0.00800169124253475,\n",
-       "  0.007927699381639449,\n",
-       "  0.008033402040061307,\n",
-       "  0.007853707520744147,\n",
-       "  0.03276782411077639,\n",
-       "  0.0322287405528249,\n",
-       "  0.03289466730088262,\n",
-       "  0.03376142909994186,\n",
-       "  0.02844458538132234,\n",
-       "  0.23078061413244544,\n",
-       "  0.21153216003382486,\n",
-       "  0.1821996723217589,\n",
-       "  0.16061518947201522,\n",
-       "  0.005052587072564875])"
+       "([100], [1.0])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 1min 13s\n"
-     ]
     }
    ],
    "source": [
-    "compute_probs_new(counting_list)"
+    "compute_probs_new(datalist)"
    ]
   },
   {
@@ -1321,7 +1052,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,
diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index d968256..e9598e2 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -109,7 +109,7 @@ def compute_probs(datalist: list) -> (list, list):
     mad = median_absolute_deviation(datalist)
 
     for item in list(set(datalist)):
-        if 0 < item < med + FILTER_MEDDEV_M * mad:
+        if 0 < item <= med + FILTER_MEDDEV_M * mad:
             data_count = datalist.count(item)
             if data_count >= FILTER_MINREADS:
                 values.append(item)

From cac18c0d7ba346a25248df112dfc6b6edc55924a Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 18 Jun 2020 13:27:50 -0500
Subject: [PATCH 068/323] Changing camel case variable names to python3
 preferred format

---
 Testing_compute_fraglen.ipynb | 34 +++++++++++++++++++---------------
 utilities/compute_fraglen.py  | 16 ++++++++--------
 2 files changed, 27 insertions(+), 23 deletions(-)

diff --git a/Testing_compute_fraglen.ipynb b/Testing_compute_fraglen.ipynb
index 02b519c..e130f57 100644
--- a/Testing_compute_fraglen.ipynb
+++ b/Testing_compute_fraglen.ipynb
@@ -975,11 +975,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "cd = {50:10, 75: 10, 100:1000}\n",
+    "cd = {50:100, 75: 100, 100:100}\n",
     "\n",
     "datalist = []\n",
     "for key in cd.keys():\n",
@@ -990,36 +990,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "100.0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "75.0 25.0\n"
+     ]
     }
    ],
    "source": [
-    "quick_median_new(datalist)"
+    "print(quick_median_new(datalist), median_deviation_from_median_new(datalist))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "([100], [1.0])"
+       "([50, 75, 100], [0.3333333333333333, 0.3333333333333333, 0.3333333333333333])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1028,6 +1025,13 @@
     "compute_probs_new(datalist)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index e9598e2..8124e8d 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -78,17 +78,17 @@ def count_frags(file: str) -> list:
         if line[0] == '#' or line[0] == '@':
             continue
         splt = line.strip().split('\t')
-        samFlag = int(splt[1])
-        myRef = splt[2]
-        mapQual = int(splt[4])
-        mateRef = splt[6]
-        myTlen = abs(int(splt[8]))
+        sam_flag = int(splt[1])
+        my_ref = splt[2]
+        map_qual = int(splt[4])
+        mate_ref = splt[6]
+        my_tlen = abs(int(splt[8]))
 
         # if read is paired, and is first in pair, and is confidently mapped...
-        if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:
+        if sam_flag & 1 and sam_flag & 64 and map_qual > FILTER_MAPQUAL:
             # and mate is mapped to same reference
-            if mateRef == '=' or mateRef == myRef:
-                count_list.append(myTlen)
+            if mate_ref == '=' or mate_ref == my_ref:
+                count_list.append(my_tlen)
     count_list = sorted(count_list)
     file_to_parse.close()
     return count_list

From 1963f56639829c4023a15f3a3491b5ae0e71281e Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Thu, 18 Jun 2020 14:49:13 -0500
Subject: [PATCH 069/323] Add not about pysam not working on windows

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index c731a72..bc90c3d 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,8 @@ NEAT-genReads is a fine-grained read simulator. GenReads simulates real-looking
 
 This is an in-progress v2.0 of the software. For a previous stable release please see: [genReads1](https://github.com/zstephens/genReads1)
 
+***PLEASE NOTE: The module "pysam" does not work on Windows OS. If you are using a windows system and wish to input a BAM file, you will first have to use samtools to convert the BAM to a SAM file.****
+
 
 Table of Contents
 =================

From 31370adb15e18af80ab879b8ec0ff3e129de8cd0 Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Thu, 18 Jun 2020 14:51:45 -0500
Subject: [PATCH 070/323] Update compute_fraglen.py

---
 utilities/compute_fraglen.py | 151 +++++++++++++++++------------------
 1 file changed, 73 insertions(+), 78 deletions(-)

diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index e38d066..4ba6c7f 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -12,77 +12,76 @@
 import pickle
 import argparse
 import pysam
+import doctest
 
 
-def quick_median(count_dict: dict) -> int:
+def median(datalist: list) -> float:
     """
-    Finds the median of a counting dictionary. A counting dictionary is a representation of a list of data.
-    For example, the data [1,1,2,2,2] could be represented in dictionary form as {1:2, 2:3}, indicating that 1 is
-    repeated twice and 2 is repeating three times.
-    :param count_dict: the counting dictionary to find the median of
+    Finds the median of a list of data. For this function, the data are expected to be a list of
+    numbers, either float or int.
+    :param datalist: the list of data to find the median of. This should be a set of numbers.
     :return: The median of the set
+    >>> median([2])
+    2
+    >>> median([2183, 2292, 4064, 4795, 7471, 12766, 14603, 15182, 16803, 18704, 21504, 21677, 23347, 23586, 24612, 24878, 25310, 25993, 26448, 28018, 28352, 28373, 28786, 30037, 31659, 31786, 33487, 33531, 34442, 39138, 39718, 39815, 41518, 41934, 43301])
+    25993
+    >>> median([1,2,4,6,8,12,14,15,17,21])
+    10.0
     """
-    # The sum of the values of the counting dictionary tells us how long the expanded list is. To find the midpoint
-    # We divide by 2
-    if not count_dict:
-        raise Exception('This Counting Dictionary is Empty. Please input a dictionary with at least one entry.')
+    # using integer division here gives the index of the midpoint, due to zero-based indexing.
+    midpoint = (len(datalist))//2
+
+    # Once we've found the midpoint, we calculate the median, which is just the middle value if there are an
+    # odd number of values, or the average of the two middle values if there are an even number
+    if len(datalist) % 2 == 0:
+        median = (datalist[midpoint] + datalist[midpoint-1])/2
     else:
-        mid_point = sum(count_dict.values()) / 2
-        my_sum = 0
-        my_ind = 0
-        sk = sorted(count_dict.keys())
-        # Here we basically count up one group of dictionary values at a time until we're at the midpoint
-        while my_sum < mid_point:
-            my_sum += count_dict[sk[my_ind]]
-            if my_sum >= mid_point:
-                break
-            my_ind += 1
-        # Once we've found the midpoint, we calculate the median, which is just the middle value if there are an
-        # odd number of values, or the average of the two middle valuse if there are an even number
-        if sum(count_dict.values()) % 2 == 0:
-            median = (sk[my_ind] + sk[my_ind-1])//2
-        else:
-            median = sk[my_ind]
-        return median
-
-
-def median_deviation_from_median(count_dict: dict) -> int:
+        median = datalist[midpoint]
+    return median
+
+
+
+def median_absolute_deviation(datalist: list) -> float:
     """
-    Calculates the deviation from the median of each element of counting dictionary,
-    then returns the median of that dictionary. A counting dictionary such as {2: 3, 5:2} expands to [2,2,2,5,5]
-    :param count_dict: Counting dictionary to analyze
+    Calculates the absolute value of the median deviation from the median for each element of of a datalist. 
+    Then returns the median of these values.
+    :param datalist: A list of data to find the MAD of
     :return: index of median of the deviations
+    >>> median_absolute_deviation([2183, 2292, 4064, 4795, 7471, 12766, 14603, 15182, 16803, 18704, 21504, 21677, 23347, 23586, 24612, 24878, 25310, 25993, 26448, 28018, 28352, 28373, 28786, 30037, 31659, 31786, 33487, 33531, 34442, 39138, 39718, 39815, 41518, 41934, 43301])
+    7494
+    >>> median_absolute_deviation([1,2,4,6,8,12,14,15,17,21])
+    5.5
+    >>> median_absolute_deviation([0,2])
+    1.0
     """
-    my_median = quick_median(count_dict)
-    deviations = {}
-    for key in sorted(count_dict.keys()):
+    my_median = median(datalist)
+    deviations = []
+    for item in datalist:
         # We take the absolute difference between the value and the median
-        X_value = abs(key - my_median)
-        # We retain the number of items for each element of the dictionary
-        # This is equivalent to subtracting the median from each value of the expanded dataset
-        deviations[X_value] = count_dict[key]
-    # Taking the median of this dataset gives us the median deviation from the median
-    # (also called the Median Absolute Deviation)
-    return quick_median(deviations)
-
-def count_frags(file: str) -> dict:
+        X_value = abs(item - my_median)
+        # This creates a dataset that is the absolute deviations about the median
+        deviations.append(X_value)
+    # The median of the absolute deviations is the median absolute deviation
+    return median(sorted(deviations))
+
+
+def count_frags(file: str) -> list:
     """
-    Takes a sam file input and creates a counting dictionary of the number of reads that are paired,
+    Takes a sam or bam file input and creates a list of the number of reads that are paired,
     first in the pair, confidently mapped and whose pair is mapped to the same reference
     :param file: A sam input file
-    :return: A dictionary of the counts of the above reads
+    :return: A list of the tlens from the bam/sam file
     """
     FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality
-    count_dict = {}
-    PRINT_EVERY = 100000
-    i = 0
+    count_list = []
     # Check if the file is sam or bam and decide how to open based on that
     if file[-4:] == ".sam":
         file_to_parse = open(file, 'r')
     elif file[-4:] == ".bam":
+        print("WARNING: Must have pysam installed to read bam files. Pysam does not work on Windows OS.")
         file_to_parse = pysam.AlignmentFile(file, 'rb')
     else:
-        print("Unknown file type, must be bam or sam")
+        print("Unknown file type, file extension must be bam or sam")
         exit(1)
 
     for item in file_to_parse:
@@ -96,41 +95,38 @@ def count_frags(file: str) -> dict:
         myRef = splt[2]
         mapQual = int(splt[4])
         mateRef = splt[6]
-        my_tlen = abs(int(splt[8]))
+        myTlen = abs(int(splt[8]))
 
         # if read is paired, and is first in pair, and is confidently mapped...
         if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:
             # and mate is mapped to same reference
             if mateRef == '=' or mateRef == myRef:
-                if my_tlen not in count_dict:
-                    count_dict[my_tlen] = 0
-                count_dict[my_tlen] += 1
-                i += 1
-                if i % PRINT_EVERY == 0:
-                    print('---', i, quick_median(count_dict), median_deviation_from_median(count_dict))
-    return count_dict
+                count_list.append(myTlen)
+    count_list = sorted(count_list)
+    file_to_parse.close()
+    return count_list
 
 
-def compute_probs(count_dict: dict) -> (list, list):
+def compute_probs(datalist: list) -> (list, list):
     """
     Computes the probabilities for fragments with at least 100 pairs supporting it and that are at least 10 median
     deviations from the median.
-    :param count_dict: A dictionary of fragments with counts
-    :return: A list of values that meet the criteria and a list of -their associated probabilities
+    :param datalist: A list of fragments with counts
+    :return: A list of values that meet the criteria and a list of their associated probabilities
     """
     FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it
     FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median
     values = []
     probabilities = []
-    med = quick_median(count_dict)
-    mdm = median_deviation_from_median(count_dict)
-
-    for key in sorted(count_dict.keys()):
-        if 0 < key < med + FILTER_MEDDEV_M * mdm:
-            if count_dict[key] >= FILTER_MINREADS:
-                print(key, count_dict[key])
-                values.append(key)
-                probabilities.append(count_dict[key])
+    med = median(datalist)
+    mad = median_absolute_deviation(datalist)
+
+    for item in list(set(datalist)):
+        if 0 < item < med + FILTER_MEDDEV_M * mad:
+            data_count = datalist.count(item)
+            if data_count >= FILTER_MINREADS:
+                values.append(item)
+                probabilities.append(data_count)
     count_sum = float(sum(probabilities))
     probabilities = [n / count_sum for n in probabilities]
     return values, probabilities
@@ -139,13 +135,13 @@ def compute_probs(count_dict: dict) -> (list, list):
 def main():
     """
     Main function takes 2 arguments:
-        input - a samfile input that can be formed by applying samtools to a bam file
+        input - a path to a sam or bam file input. Note that sam files can be formed by applying samtools to a bam file
         in the follawing way: samtools view nameof.bam > nameof.sam
-
-        output - the prefix of the output. The actual output will be the prefix plus ".p" at the end
+​
+        output - the string prefix of the output. The actual output will be the prefix plus ".p" at the end
         for pickle file. The list of values and list of probabilities are dumped as a list of lists
         into a pickle file on completion of the analysis
-
+​
     :return: None
     """
     parser = argparse.ArgumentParser(description="compute_fraglen.py")
@@ -159,11 +155,10 @@ def main():
     output = output_prefix + '.p'
 
     all_tlens = count_frags(input_file)
-    print('\nsaving model...')
+    print('\nSaving model...')
     out_vals, out_probs = compute_probs(all_tlens)
-    print(out_probs)
     pickle.dump([out_vals, out_probs], open(output, 'wb'))
-
+    print('\nModel successfully saved.')
 
 if __name__ == "__main__":
-    main()
+    main()
\ No newline at end of file

From ee555a7c9e14a86b7fa8fef4c4f9566734eae8f5 Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Thu, 18 Jun 2020 14:57:27 -0500
Subject: [PATCH 071/323] Update compute_gc.py

---
 utilities/compute_gc.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/utilities/compute_gc.py b/utilities/compute_gc.py
index a30d037..b13efef 100644
--- a/utilities/compute_gc.py
+++ b/utilities/compute_gc.py
@@ -95,19 +95,19 @@ def calculate_coverage(bin_dict: dict, window: int) -> float:
                    should be the same as the window value in process_genomecov
     :return: Average coverage value for the whole sample, along with average coverage values for each window.
     
-
+​
     Parameters
     ----------
     bin_dict : dict
         DESCRIPTION.
     window : int
         DESCRIPTION.
-
+​
     Returns
     -------
     float
         DESCRIPTION.
-
+​
     """
     running_total = 0
     all_mean = 0.0
@@ -134,7 +134,7 @@ def main():
         -r is the reference file (fasta)
         -o is the prefix for the output
         -w is the sliding window length. The default is 50, but you can declare any reasonable integer
-
+​
     :return: None
     """
     parser = argparse.ArgumentParser(description='compute_gc.py')
@@ -172,4 +172,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()
\ No newline at end of file

From a70a334f30a9f609df87386da2895da1a8d076d1 Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Thu, 18 Jun 2020 15:47:26 -0500
Subject: [PATCH 072/323] Update README.md

---
 utilities/README.md | 39 +++++++++++++++++++++++++++++----------
 1 file changed, 29 insertions(+), 10 deletions(-)

diff --git a/utilities/README.md b/utilities/README.md
index 1864d6c..3ac998a 100644
--- a/utilities/README.md
+++ b/utilities/README.md
@@ -1,4 +1,4 @@
-# computeGC.py
+# compute_gc.py
 
 Takes .genomecov files produced by BEDtools genomeCov (with -d option).
 
@@ -10,20 +10,39 @@ bedtools genomecov
 ```
 
 ```
-python computeGC.py                 \
-        -r reference.fa             \
-        -i genomecovfile            \
-        -w [sliding window length]  \
-        -o /path/to/model.p
+python computeGC.py                	\
+        -r reference.fasta         	\
+        -i genomecov            	\
+        -w [sliding window length]  	\
+        -o /path/to/output
 ```
 
-# computeFraglen.py
+The main function in this file processes the inputs (reference.fasta, genome.cov, window length), and outputs a GC count for the sequence in the form of a pickle file at the location and with the name from the path the user provides with the -o command.
 
-Takes SAM file via stdin:
 
-./samtools view toy.bam | python computeFraglen.py
 
-and creates fraglen.p model in working directory.
+# compute_fraglen.py
+
+Takes SAM or BAM files and uses console commands for processing:
+
+```
+python computeGC.py		\
+	-i path to sam file	\
+	-o path/to/output
+```
+
+The main function in this file will save a pickle (.p) in the location and with the name from the path the user provides with the -o command.
+
+**Please be aware that pysam is not usable on windows, so any BAM file will need to be turned into a SAM file using samtools beforehand.
+
+To use samtools, use the following command:
+
+```
+Turn SAM file into BAM file:			\
+	samtools view nameof.bam > nameof.sam
+```
+
+
 
 
 # genMutModel.py

From 309b0bb579f00310e0e752c9fe3cf4d8fab81c41 Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Thu, 18 Jun 2020 15:50:36 -0500
Subject: [PATCH 073/323] Update README.md

---
 utilities/README.md | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/utilities/README.md b/utilities/README.md
index 3ac998a..d5f425c 100644
--- a/utilities/README.md
+++ b/utilities/README.md
@@ -4,13 +4,13 @@ Takes .genomecov files produced by BEDtools genomeCov (with -d option).
 
 ```
 bedtools genomecov 
-	-d                          \
-	-ibam normal.bam            \
+	-d				\
+	-ibam normal.bam		\
         -g reference.fa             
 ```
 
 ```
-python computeGC.py                	\
+python computeGC.py			\
         -r reference.fasta         	\
         -i genomecov            	\
         -w [sliding window length]  	\
@@ -26,8 +26,8 @@ The main function in this file processes the inputs (reference.fasta, genome.cov
 Takes SAM or BAM files and uses console commands for processing:
 
 ```
-python computeGC.py		\
-	-i path to sam file	\
+python computeGC.py			\
+	-i path to sam file		\
 	-o path/to/output
 ```
 
@@ -35,11 +35,10 @@ The main function in this file will save a pickle (.p) in the location and with
 
 **Please be aware that pysam is not usable on windows, so any BAM file will need to be turned into a SAM file using samtools beforehand.
 
-To use samtools, use the following command:
+To convert a BAM file to a SAM file using samtools, use the following command:
 
 ```
-Turn SAM file into BAM file:			\
-	samtools view nameof.bam > nameof.sam
+samtools view nameof.bam > nameof.sam
 ```
 
 

From 6f6d88a9e3c6b477333c543672687ed84c3b41c3 Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Fri, 19 Jun 2020 13:27:00 -0500
Subject: [PATCH 074/323] Update compute_fraglen.py

---
 utilities/compute_fraglen.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index 0a5745b..e8351a3 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -11,8 +11,11 @@
 
 import pickle
 import argparse
-import pysam
+import platform
 import doctest
+os = platform.system()
+if os !='Windows':
+    import pysam
 
 
 def median(datalist: list) -> float:
@@ -79,7 +82,10 @@ def count_frags(file: str) -> list:
         file_to_parse = open(file, 'r')
     elif file[-4:] == ".bam":
         print("WARNING: Must have pysam installed to read bam files. Pysam does not work on Windows OS.")
-        file_to_parse = pysam.AlignmentFile(file, 'rb')
+        if os != 'Windows':
+            file_to_parse = pysam.AlignmentFile(file, 'rb')
+        else:
+            raise Exception("Your machine is running Windows. Please convert any BAM files to SAM files using samtools prior to input")
     else:
         print("Unknown file type, file extension must be bam or sam")
         exit(1)

From 2a60aefb93c5a817acee271b50d7cdd3aa9b9134 Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Fri, 19 Jun 2020 16:40:50 -0500
Subject: [PATCH 075/323] variable renaming

---
 utilities/genSeqErrorModel.py | 229 +++++++++++++++++-----------------
 1 file changed, 115 insertions(+), 114 deletions(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 02e57f4..a3072c0 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -20,13 +20,13 @@
 import pickle
 
 # absolute path to this script
-SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2]) + '/py/'
-sys.path.append(SIM_PATH)
+sim_path = '/'.join(os.path.realpath(__file__).split('/')[:-2]) + '/py/'
+sys.path.append(sim_path)
 
 from probability import DiscreteDistribution
 
 
-def parseFQ(inf):
+def parse_fq(inf):
     print('reading ' + inf + '...')
     if inf[-3:] == '.gz':
         print('detected gzip suffix...')
@@ -34,17 +34,17 @@ def parseFQ(inf):
     else:
         f = open(inf, 'r')
 
-    IS_SAM = False
+    is_sam = False
     if inf[-4:] == '.sam':
         print('detected sam input...')
-        IS_SAM = True
+        is_sam = True
 
-    rRead = 0
+    r_read = 0
     actual_readlen = 0
-    qDict = {}
+    q_dict = {}
     while True:
 
-        if IS_SAM:
+        if is_sam:
             data4 = f.readline()
             if not len(data4):
                 break
@@ -62,15 +62,15 @@ def parseFQ(inf):
                 break
 
         if actual_readlen == 0:
-            if inf[-3:] != '.gz' and not IS_SAM:
-                totalSize = os.path.getsize(inf)
-                entrySize = sum([len(n) for n in [data1, data2, data3, data4]])
-                print('estimated number of reads in file:', int(float(totalSize) / entrySize))
+            if inf[-3:] != '.gz' and not is_sam:
+                total_size = os.path.getsize(inf)
+                entry_size = sum([len(n) for n in [data1, data2, data3, data4]])
+                print('estimated number of reads in file:', int(float(total_size) / entry_size))
             actual_readlen = len(data4) - 1
             print('assuming read length is uniform...')
             print('detected read length (from first read found):', actual_readlen)
-            priorQ = np.zeros([actual_readlen, RQ])
-            totalQ = [None] + [np.zeros([RQ, RQ]) for n in range(actual_readlen - 1)]
+            prior_q = np.zeros([actual_readlen, r_q])
+            total_q = [None] + [np.zeros([r_q, r_q]) for n in range(actual_readlen - 1)]
 
         # sanity-check readlengths
         if len(data4) - 1 != actual_readlen:
@@ -78,54 +78,55 @@ def parseFQ(inf):
             continue
 
         for i in range(len(data4) - 1):
-            q = ord(data4[i]) - offQ
-            qDict[q] = True
+            q = ord(data4[i]) - off_q
+            q_dict[q] = True
+            prev_q = q
             if i == 0:
-                priorQ[i][q] += 1
+                prior_q[i][q] += 1
             else:
-                totalQ[i][prevQ, q] += 1
-                priorQ[i][q] += 1
-            prevQ = q
-
-        rRead += 1
-        if rRead % PRINT_EVERY == 0:
-            print(rRead)
-        if MAX_READS > 0 and rRead >= MAX_READS:
+                total_q[i][prev_q, q] += 1
+                prior_q[i][q] += 1
+            
+
+        r_read += 1
+        if r_read % print_every == 0:
+            print(r_read)
+        if max_reads > 0 and r_read >= max_reads:
             break
     f.close()
 
     # some sanity checking again...
-    QRANGE = [min(qDict.keys()), max(qDict.keys())]
-    if QRANGE[0] < 0:
+    q_range = [min(q_dict.keys()), max(q_dict.keys())]
+    if q_range[0] < 0:
         print('\nError: Read in Q-scores below 0\n')
         exit(1)
-    if QRANGE[1] > RQ:
-        print('\nError: Read in Q-scores above specified maximum:', QRANGE[1], '>', RQ, '\n')
+    if q_range[1] > r_q:
+        print('\nError: Read in Q-scores above specified maximum:', q_range[1], '>', r_q, '\n')
         exit(1)
 
     print('computing probabilities...')
-    probQ = [None] + [[[0. for m in range(RQ)] for n in range(RQ)] for p in range(actual_readlen - 1)]
+    prob_q = [None] + [[[0. for m in range(r_q)] for n in range(r_q)] for p in range(actual_readlen - 1)]
     for p in range(1, actual_readlen):
-        for i in range(RQ):
-            rowSum = float(np.sum(totalQ[p][i, :])) + PROB_SMOOTH * RQ
-            if rowSum <= 0.:
+        for i in range(r_q):
+            row_sum = float(np.sum(total_q[p][i, :])) + prob_smooth * r_q
+            if row_sum <= 0.:
                 continue
-            for j in range(RQ):
-                probQ[p][i][j] = (totalQ[p][i][j] + PROB_SMOOTH) / rowSum
+            for j in range(r_q):
+                prob_q[p][i][j] = (total_q[p][i][j] + prob_smooth) / row_sum
 
-    initQ = [[0. for m in range(RQ)] for n in range(actual_readlen)]
+    init_q = [[0. for m in range(r_q)] for n in range(actual_readlen)]
     for i in range(actual_readlen):
-        rowSum = float(np.sum(priorQ[i, :])) + INIT_SMOOTH * RQ
-        if rowSum <= 0.:
+        row_sum = float(np.sum(prior_q[i, :])) + INIT_SMOOTH * r_q
+        if row_sum <= 0.:
             continue
-        for j in range(RQ):
-            initQ[i][j] = (priorQ[i][j] + INIT_SMOOTH) / rowSum
+        for j in range(r_q):
+            init_q[i][j] = (prior_q[i][j] + INIT_SMOOTH) / row_sum
 
-    if PLOT_STUFF:
+    if plot_stuff:
         mpl.rcParams.update({'font.size': 14, 'font.weight': 'bold', 'lines.linewidth': 3})
 
         mpl.figure(1)
-        Z = np.array(initQ).T
+        Z = np.array(init_q).T
         X, Y = np.meshgrid(range(0, len(Z[0]) + 1), range(0, len(Z) + 1))
         mpl.pcolormesh(X, Y, Z, vmin=0., vmax=0.25)
         mpl.axis([0, len(Z[0]), 0, len(Z)])
@@ -138,18 +139,18 @@ def parseFQ(inf):
 
         mpl.show()
 
-        VMIN_LOG = [-4, 0]
-        minVal = 10 ** VMIN_LOG[0]
-        qLabels = [str(n) for n in range(QRANGE[0], QRANGE[1] + 1) if n % 5 == 0]
-        print(qLabels)
-        qTicksx = [int(n) + 0.5 for n in qLabels]
-        qTicksy = [(RQ - int(n)) - 0.5 for n in qLabels]
+        v_min_log = [-4, 0]
+        min_val = 10 ** v_min_log[0]
+        q_labels = [str(n) for n in range(q_range[0], q_range[1] + 1) if n % 5 == 0]
+        print(q_labels)
+        q_ticks_x = [int(n) + 0.5 for n in q_labels]
+        qTicksy = [(r_q - int(n)) - 0.5 for n in q_labels]
 
         for p in range(1, actual_readlen, 10):
-            currentDat = np.array(probQ[p])
-            for i in range(len(currentDat)):
-                for j in range(len(currentDat[i])):
-                    currentDat[i][j] = max(minVal, currentDat[i][j])
+            current_data = np.array(prob_q[p])
+            for i in range(len(current_data)):
+                for j in range(len(current_data[i])):
+                    current_data[i][j] = max(min_val, current_data[i][j])
 
             # matrix indices:		pcolormesh plotting:	plot labels and axes:
             #
@@ -168,13 +169,13 @@ def parseFQ(inf):
             #
 
             mpl.figure(p + 1)
-            Z = np.log10(currentDat)
-            X, Y = np.meshgrid(range(0, len(Z[0]) + 1), range(0, len(Z) + 1))
-            mpl.pcolormesh(X, Y, Z[::-1, :], vmin=VMIN_LOG[0], vmax=VMIN_LOG[1], cmap='jet')
-            mpl.xlim([QRANGE[0], QRANGE[1] + 1])
-            mpl.ylim([RQ - QRANGE[1] - 1, RQ - QRANGE[0]])
-            mpl.yticks(qTicksy, qLabels)
-            mpl.xticks(qTicksx, qLabels)
+            z = np.log10(current_data)
+            x, y = np.meshgrid(range(0, len(Z[0]) + 1), range(0, len(Z) + 1))
+            mpl.pcolormesh(x, y, z[::-1, :], vmin=v_min_log[0], vmax=v_min_log[1], cmap='jet')
+            mpl.xlim([q_range[0], q_range[1] + 1])
+            mpl.ylim([r_q - q_range[1] - 1, r_q - q_range[0]])
+            mpl.yticks(qTicksy, q_labels)
+            mpl.xticks(q_ticks_x, q_labels)
             mpl.xlabel('\n' + r'$Q_{i+1}$')
             mpl.ylabel(r'$Q_i$')
             mpl.title('Q-Score Transition Frequencies [Read Pos:' + str(p) + ']')
@@ -186,41 +187,41 @@ def parseFQ(inf):
         mpl.show()
 
     print('estimating average error rate via simulation...')
-    Qscores = range(RQ)
-    # print (len(initQ), len(initQ[0]))
-    # print (len(probQ), len(probQ[1]), len(probQ[1][0]))
-
-    initDistByPos = [DiscreteDistribution(initQ[i], Qscores) for i in range(len(initQ))]
-    probDistByPosByPrevQ = [None]
-    for i in range(1, len(initQ)):
-        probDistByPosByPrevQ.append([])
-        for j in range(len(initQ[0])):
-            if np.sum(probQ[i][j]) <= 0.:  # if we don't have sufficient data for a transition, use the previous qscore
-                probDistByPosByPrevQ[-1].append(DiscreteDistribution([1], [Qscores[j]], degenerateVal=Qscores[j]))
+    q_scores = range(r_q)
+    # print (len(init_q), len(init_q[0]))
+    # print (len(prob_q), len(prob_q[1]), len(prob_q[1][0]))
+
+    init_dist_by_pos = [DiscreteDistribution(init_q[i], q_scores) for i in range(len(init_q))]
+    prob_dist_by_pos_by_prev_q = [None]
+    for i in range(1, len(init_q)):
+        prob_dist_by_pos_by_prev_q.append([])
+        for j in range(len(init_q[0])):
+            if np.sum(prob_q[i][j]) <= 0.:  # if we don't have sufficient data for a transition, use the previous qscore
+                prob_dist_by_pos_by_prev_q[-1].append(DiscreteDistribution([1], [q_scores[j]], degenerateVal=q_scores[j]))
             else:
-                probDistByPosByPrevQ[-1].append(DiscreteDistribution(probQ[i][j], Qscores))
+                prob_dist_by_pos_by_prev_q[-1].append(DiscreteDistribution(prob_q[i][j], q_scores))
 
-    countDict = {}
-    for q in Qscores:
-        countDict[q] = 0
-    for samp in range(1, N_SAMP + 1):
-        if samp % PRINT_EVERY == 0:
+    count_dict = {}
+    for q in q_scores:
+        count_dict[q] = 0
+    for samp in range(1, n_samp + 1):
+        if samp % print_every == 0:
             print(samp)
-        myQ = initDistByPos[0].sample()
-        countDict[myQ] += 1
-        for i in range(1, len(initQ)):
-            myQ = probDistByPosByPrevQ[i][myQ].sample()
-            countDict[myQ] += 1
-
-    totBases = float(sum(countDict.values()))
-    avgError = 0.
-    for k in sorted(countDict.keys()):
+        my_q = init_dist_by_pos[0].sample()
+        count_dict[my_q] += 1
+        for i in range(1, len(init_q)):
+            my_q = prob_dist_by_pos_by_prev_q[i][my_q].sample()
+            count_dict[my_q] += 1
+
+    tot_bases = float(sum(count_dict.values()))
+    avg_err = 0.
+    for k in sorted(count_dict.keys()):
         eVal = 10. ** (-k / 10.)
-        # print k, eVal, countDict[k]
-        avgError += eVal * (countDict[k] / totBases)
-    print('AVG ERROR RATE:', avgError)
+        # print k, eVal, count_dict[k]
+        avg_err += eVal * (count_dict[k] / tot_bases)
+    print('AVG ERROR RATE:', avg_err)
 
-    return initQ, probQ, avgError
+    return init_q, prob_q, avg_err
 
 
 parser = argparse.ArgumentParser(description='genSeqErrorModel.py')
@@ -238,50 +239,50 @@ def parseFQ(inf):
 parser.add_argument('--plot', required=False, action='store_true', default=False, help='perform some optional plotting')
 args = parser.parse_args()
 
-(INF, OUF, offQ, maxQ, MAX_READS, N_SAMP) = (args.i, args.o, args.q, args.Q, args.n, args.s)
-(INF2, PILEUP) = (args.i2, args.p)
+(inf, ouf, off_q, max_q, max_reads, n_samp) = (args.i, args.o, args.q, args.Q, args.n, args.s)
+(inf2, pileup) = (args.i2, args.p)
 
-RQ = maxQ + 1
+r_q = max_q + 1
 
-INIT_SMOOTH = 0.
-PROB_SMOOTH = 0.
-PRINT_EVERY = 10000
-PLOT_STUFF = args.plot
-if PLOT_STUFF:
+init_smooth = 0.
+prob_smooth = 0.
+print_every = 10000
+plot_stuff = args.plot
+if plot_stuff:
     print('plotting is desired, lets import matplotlib...')
     import matplotlib.pyplot as mpl
 
 
 def main():
-    Qscores = range(RQ)
-    if INF2 == None:
-        (initQ, probQ, avgError) = parseFQ(INF)
+    q_scores = range(r_q)
+    if inf2 == None:
+        (init_q, prob_q, avg_err) = parse_fq(inf)
     else:
-        (initQ, probQ, avgError1) = parseFQ(INF)
-        (initQ2, probQ2, avgError2) = parseFQ(INF2)
-        avgError = (avgError1 + avgError2) / 2.
+        (init_q, prob_q, avg_err1) = parse_fq(inf)
+        (init_q2, prob_q2, avg_err2) = parse_fq(inf2)
+        avg_err = (avg_err1 + avg_err2) / 2.
 
     #
     #	embed some default sequencing error parameters if no pileup is provided
     #
-    if PILEUP == None:
+    if pileup == None:
 
         print('Using default sequencing error parameters...')
 
         # sequencing substitution transition probabilities
-        SSE_PROB = [[0., 0.4918, 0.3377, 0.1705],
+        sse_prob = [[0., 0.4918, 0.3377, 0.1705],
                     [0.5238, 0., 0.2661, 0.2101],
                     [0.3754, 0.2355, 0., 0.3890],
                     [0.2505, 0.2552, 0.4942, 0.]]
         # if a sequencing error occurs, what are the odds it's an indel?
-        SIE_RATE = 0.01
+        sie_rate = 0.01
         # sequencing indel error length distribution
-        SIE_PROB = [0.999, 0.001]
-        SIE_VAL = [1, 2]
+        sie_prob = [0.999, 0.001]
+        sie_val = [1, 2]
         # if a sequencing indel error occurs, what are the odds it's an insertion as opposed to a deletion?
-        SIE_INS_FREQ = 0.4
+        sie_ins_freq = 0.4
         # if a sequencing insertion error occurs, what's the probability of it being an A, C, G, T...
-        SIE_INS_NUCL = [0.25, 0.25, 0.25, 0.25]
+        sie_ins_nucl = [0.25, 0.25, 0.25, 0.25]
 
     #
     #	otherwise we need to parse a pileup and compute statistics!
@@ -290,16 +291,16 @@ def main():
         print('\nPileup parsing coming soon!\n')
         exit(1)
 
-    errorParams = [SSE_PROB, SIE_RATE, SIE_PROB, SIE_VAL, SIE_INS_FREQ, SIE_INS_NUCL]
+    err_params = [sse_prob, sie_rate, sie_prob, sie_val, sie_ins_freq, sie_ins_nucl]
 
     #
     #	finally, let's save our output model
     #
     print('saving model...')
-    if INF2 == None:
-        pickle.dump([initQ, probQ, Qscores, offQ, avgError, errorParams], open(OUF, 'wb'))
+    if inf2 == None:
+        pickle.dump([init_q, prob_q, q_scores, off_q, avg_err, err_params], open(ouf, 'wb'))
     else:
-        pickle.dump([initQ, probQ, initQ2, probQ2, Qscores, offQ, avgError, errorParams], open(OUF, 'wb'))
+        pickle.dump([init_q, prob_q, init_q2, prob_q2, q_scores, off_q, avg_err, err_params], open(ouf, 'wb'))
 
 
 if __name__ == '__main__':

From e262b41081cd9ddcc85335a9a36cd10c69589bdc Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 24 Jun 2020 16:12:22 -0500
Subject: [PATCH 076/323] finished rough draft of genMutModel.py

---
 Testing_compute_fraglen.ipynb | 1064 ---------------------------------
 utilities/genMutModel.py      |   46 +-
 2 files changed, 5 insertions(+), 1105 deletions(-)
 delete mode 100644 Testing_compute_fraglen.ipynb

diff --git a/Testing_compute_fraglen.ipynb b/Testing_compute_fraglen.ipynb
deleted file mode 100644
index e130f57..0000000
--- a/Testing_compute_fraglen.ipynb
+++ /dev/null
@@ -1,1064 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "!pip install ipython-autotime\n",
-    "%load_ext autotime"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd = {1:4, 2:6, 3:1, 4:8, 5:3, 10:2}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list = []\n",
-    "for key in cd.keys():\n",
-    "    for i in range(cd[key]):\n",
-    "        list.append(key)\n",
-    "list = sorted(list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "cd = list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def quick_median(count_dict: dict) -> int:\n",
-    "    \"\"\"\n",
-    "    Finds the median of a counting dictionary\n",
-    "    :param count_dict: the counting dictionary to find the median of\n",
-    "    :return: integer index of the location of the median\n",
-    "    \"\"\"\n",
-    "    list = []\n",
-    "    for key in count_dict.keys():\n",
-    "        for i in range(count_dict[key]):\n",
-    "            list.append(key)\n",
-    "    list = sorted(list)\n",
-    "    midpoint = len(list)//2\n",
-    "    if len(list)%2 == 0:\n",
-    "        median = (list[midpoint] + list[midpoint-1])//2\n",
-    "    else:\n",
-    "        median = list[midpoint]\n",
-    "    return median"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def quick_median_old(count_dict: dict) -> int:\n",
-    "    mid_point = sum(count_dict.values()) / 2\n",
-    "    my_sum = 0\n",
-    "    my_ind = 0\n",
-    "    sk = sorted(count_dict.keys())\n",
-    "    while my_sum < mid_point:\n",
-    "        my_sum += count_dict[sk[my_ind]]\n",
-    "        if my_sum >= mid_point:\n",
-    "            break\n",
-    "        my_ind += 1\n",
-    "    return sk[my_ind]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd = {4: 10, 236: 13, 99: 43, 88: 43, 127: 15}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quick_median(cd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quick_median_old(cd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd2 = {4: 10, 236: 13, 99: 43, 88: 43, 127: 14, 8:102}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quick_median(cd2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quick_median_old(cd2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_median1 = quick_median(cd)\n",
-    "my_median2 = quick_median(cd2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def median_deviation_from_median1(count_dict: dict) -> int:\n",
-    "    \"\"\"\n",
-    "    calculates the deviation from the median of each element of counting dictionary,\n",
-    "    then returns the median of that dictionary\n",
-    "    :param count_dict: Counting dictionary to analyze\n",
-    "    :return: index of median of the deviations\n",
-    "    \"\"\"\n",
-    "    my_median = quick_median1(count_dict)\n",
-    "    deviations = {}\n",
-    "    for key in sorted(count_dict.keys()):\n",
-    "        X_value = abs(key - my_median)\n",
-    "        deviations[X_value] = count_dict[key]\n",
-    "    print(deviations)\n",
-    "    return quick_median1(deviations)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "median_deviation_from_median(cd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(sorted([abs(x-99) for x in list]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd3 = {1:100, 100:100}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quick_median(cd3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quick_median_old(cd3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def quick_median1(count_dict: dict) -> int:\n",
-    "    mid_point = sum(count_dict.values()) / 2\n",
-    "    my_sum = 0\n",
-    "    my_ind = 0\n",
-    "    sk = sorted(count_dict.keys())\n",
-    "    while my_sum < mid_point:\n",
-    "        my_sum += count_dict[sk[my_ind]]\n",
-    "        if my_sum >= mid_point:\n",
-    "            break\n",
-    "        my_ind += 1\n",
-    "    if sum(count_dict.values())%2 == 0:\n",
-    "        median = (sk[my_ind] + sk[my_ind-1])//2\n",
-    "    else:\n",
-    "        median = sk[my_ind]\n",
-    "    return median"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quick_median_new(cd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def count_frags1(file: str) -> dict:\n",
-    "    \"\"\"\n",
-    "    Takes a sam file input and creates a counting dictionary of the number of reads that are paired,\n",
-    "    first in the pair, confidently mapped and whose pair is mapped to the same reference\n",
-    "    :param file: A sam input file\n",
-    "    :return: A dictionary of the counts of the above reads\n",
-    "    \"\"\"\n",
-    "    FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality\n",
-    "    count_dict = {}\n",
-    "    PRINT_EVERY = 100000\n",
-    "    # Check if the file is sam or bam and decide how to open based on that\n",
-    "    if file[-4:] == \".sam\":\n",
-    "        file_to_parse = open(file, 'r')\n",
-    "    elif file[-4:] == \".bam\":\n",
-    "        file_to_parse = pysam.AlignmentFile(file, 'rb')\n",
-    "    else:\n",
-    "        print(\"Unknown file type, must be bam or sam\")\n",
-    "        exit(1)\n",
-    "\n",
-    "    for item in file_to_parse:\n",
-    "        # Need to convert bam iterable objects into strings for the next part\n",
-    "        line = str(item)\n",
-    "        # Skip all comments and headers\n",
-    "        if line[0] == '#' or line[0] == '@':\n",
-    "            continue\n",
-    "        splt = line.strip().split('\\t')\n",
-    "        samFlag = int(splt[1])\n",
-    "        myRef = splt[2]\n",
-    "        mapQual = int(splt[4])\n",
-    "        mateRef = splt[6]\n",
-    "        myTlen = abs(int(splt[8]))\n",
-    "\n",
-    "        # if read is paired, and is first in pair, and is confidently mapped...\n",
-    "        if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:\n",
-    "            # and mate is mapped to same reference\n",
-    "            if mateRef == '=' or mateRef == myRef:\n",
-    "                if myTlen not in count_dict:\n",
-    "                    count_dict[myTlen] = 0\n",
-    "                count_dict[myTlen] += 1\n",
-    "    return count_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd = count_frags(\"C:/Users/joshf/Documents/work_stuff/neat_data/baby.sam\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality\n",
-    "count_dict = {}\n",
-    "PRINT_EVERY = 100000\n",
-    "i = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for item in file_to_parse:\n",
-    "    # Need to convert bam iterable objects into strings for the next part\n",
-    "    line = str(item)\n",
-    "    # Skip all comments and headers\n",
-    "    if line[0] == '#' or line[0] == '@':\n",
-    "        continue\n",
-    "    splt = line.strip().split('\\t')\n",
-    "    samFlag = int(splt[1])\n",
-    "    myRef = splt[2]\n",
-    "    mapQual = int(splt[4])\n",
-    "    mateRef = splt[6]\n",
-    "    myTlen = abs(int(splt[8]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(count_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_probs1(count_dict: dict) -> (list, list):\n",
-    "    \"\"\"\n",
-    "    Computes the probabilities for fragments with at least 100 pairs supporting it and that are at least 10 median\n",
-    "    deviations from the median.\n",
-    "    :param count_dict: A dictionary of fragments with counts\n",
-    "    :return: A list of values that meet the criteria and a list of -their associated probabilities\n",
-    "    \"\"\"\n",
-    "    FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it\n",
-    "    FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median\n",
-    "    values = []\n",
-    "    probabilities = []\n",
-    "    med = quick_median1(count_dict)\n",
-    "    mdm = median_deviation_from_median1(count_dict)\n",
-    "\n",
-    "    for key in sorted(count_dict.keys()):\n",
-    "        if 0 < key < med + FILTER_MEDDEV_M * mdm:\n",
-    "            if count_dict[key] >= FILTER_MINREADS:\n",
-    "                print(key, count_dict[key])\n",
-    "                values.append(key)\n",
-    "                probabilities.append(count_dict[key])\n",
-    "    count_sum = float(sum(probabilities))\n",
-    "    probabilities = [n / count_sum for n in probabilities]\n",
-    "    return values, probabilities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compute_probs(cd2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import timeit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def wrapper(func, *args, **kwargs):\n",
-    "    def wrapped():\n",
-    "        return func(*args, **kwargs)\n",
-    "    return wrapped"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pysam"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd = count_frags(\"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wrapped = wrapper(quick_median, cd)\n",
-    "timeit.timeit(wrapped, number=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wrapped2 = wrapper(quick_median_new, cd)\n",
-    "timeit.timeit(wrapped2, number=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def quick_median_new2(count_dict: dict) -> int:\n",
-    "    mid_point = sum(count_dict.values()) / 2\n",
-    "    print(mid_point)\n",
-    "    my_sum = 0\n",
-    "    my_ind = 0\n",
-    "    sk = sorted(count_dict.keys())\n",
-    "    while my_sum < mid_point:\n",
-    "        my_sum += count_dict[sk[my_ind]]\n",
-    "        if my_sum >= mid_point:\n",
-    "            my_ind += 1\n",
-    "            break\n",
-    "        my_ind += 1\n",
-    "    # correction if we miss the mark too far\n",
-    "    if sum(count_dict.values())%2 == 0:\n",
-    "        median = (sk[my_ind] + sk[my_ind-1])//2\n",
-    "    else:\n",
-    "        median = sk[my_ind]\n",
-    "    return median"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "count_d = {1:4, 2:6, 3:3, 4:8, 5:3, 10:2}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list = []\n",
-    "for key in count_d.keys():\n",
-    "    for i in range(count_d[key]):\n",
-    "        list.append(key)\n",
-    "list = sorted(list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(list[13] + list[12])//2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quick_median_new2(count_d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def count_frags_new(file: str) -> list:\n",
-    "    \"\"\"\n",
-    "    Takes a sam file input and creates a counting dictionary of the number of reads that are paired,\n",
-    "    first in the pair, confidently mapped and whose pair is mapped to the same reference\n",
-    "    :param file: A sam input file\n",
-    "    :return: A dictionary of the counts of the above reads\n",
-    "    \"\"\"\n",
-    "    FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality\n",
-    "    count_list = []\n",
-    "    PRINT_EVERY = 100000\n",
-    "    i = 0\n",
-    "    # Check if the file is sam or bam and decide how to open based on that\n",
-    "    if file[-4:] == \".sam\":\n",
-    "        file_to_parse = open(file, 'r')\n",
-    "    elif file[-4:] == \".bam\":\n",
-    "        file_to_parse = pysam.AlignmentFile(file, 'rb')\n",
-    "    else:\n",
-    "        print(\"Unknown file type, must be bam or sam\")\n",
-    "        exit(1)\n",
-    "\n",
-    "    for item in file_to_parse:\n",
-    "        # Need to convert bam iterable objects into strings for the next part\n",
-    "        line = str(item)\n",
-    "        # Skip all comments and headers\n",
-    "        if line[0] == '#' or line[0] == '@':\n",
-    "            continue\n",
-    "        splt = line.strip().split('\\t')\n",
-    "        samFlag = int(splt[1])\n",
-    "        myRef = splt[2]\n",
-    "        mapQual = int(splt[4])\n",
-    "        mateRef = splt[6]\n",
-    "        myTlen = abs(int(splt[8]))\n",
-    "\n",
-    "        # if read is paired, and is first in pair, and is confidently mapped...\n",
-    "        if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:\n",
-    "            # and mate is mapped to same reference\n",
-    "            if mateRef == '=' or mateRef == myRef:\n",
-    "                count_list.append(myTlen)\n",
-    "    count_list = sorted(count_list)\n",
-    "    return count_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wrapped3 = wrapper(count_frags, \"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")\n",
-    "timeit.timeit(wrapped3, number=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wrapped4 = wrapper(count_frags_new, \"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")\n",
-    "timeit.timeit(wrapped4, number=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def quick_median_new(datalist: list) -> float:\n",
-    "    \"\"\"\n",
-    "    Finds the median of a counting dictionary\n",
-    "    :param count_dict: the counting dictionary to find the median of\n",
-    "    :return: integer index of the location of the median\n",
-    "    \"\"\"\n",
-    "    midpoint = len(datalist)//2\n",
-    "    if len(datalist) % 2 == 0:\n",
-    "        median = (datalist[midpoint] + datalist[midpoint-1])/2\n",
-    "    else:\n",
-    "        median = datalist[midpoint]\n",
-    "    return median"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quick_median(cd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def median_deviation_from_median_new(datalist: list) -> float:\n",
-    "    \"\"\"\n",
-    "    calculates the deviation from the median of each element of counting dictionary,\n",
-    "    then returns the median of that dictionary\n",
-    "    :param count_dict: Counting dictionary to analyze\n",
-    "    :return: index of median of the deviations\n",
-    "    \"\"\"\n",
-    "    my_median = quick_median_new(datalist)\n",
-    "    deviations = []\n",
-    "    for item in datalist:\n",
-    "        X_value = abs(item - my_median)\n",
-    "        deviations.append(X_value)\n",
-    "    return quick_median_new(sorted(deviations))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "median_deviation_from_median(cd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_probs_new(datalist: list) -> (list, list):\n",
-    "    \"\"\"\n",
-    "    Computes the probabilities for fragments with at least 100 pairs supporting it and that are at least 10 median\n",
-    "    deviations from the median.\n",
-    "    :param count_dict: A dictionary of fragments with counts\n",
-    "    :return: A list of values that meet the criteria and a list of -their associated probabilities\n",
-    "    \"\"\"\n",
-    "    FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it\n",
-    "    FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median\n",
-    "    values = []\n",
-    "    probabilities = []\n",
-    "    med = quick_median_new(datalist)\n",
-    "    mdm = median_deviation_from_median_new(datalist)\n",
-    "    \n",
-    "    for item in list(set(datalist)):\n",
-    "        if 0 < item < med + FILTER_MEDDEV_M * mdm:\n",
-    "            if datalist.count(item) >= FILTER_MINREADS:\n",
-    "                values.append(item)\n",
-    "                probabilities.append(datalist.count(item))\n",
-    "    count_sum = float(sum(probabilities))\n",
-    "    probabilities = [n / count_sum for n in probabilities]\n",
-    "    return values, probabilities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sum(x == 10 for x in cd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list.count(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "counting_list = count_frags_new(\"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")\n",
-    "print(\"computing probs\")\n",
-    "compute_probs_new(counting_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below are the two competing compute frag length programs, so that I can run side-by-side time comparisons."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Count_dict method\n",
-    "\n",
-    "def quick_median1(count_dict: dict) -> int:\n",
-    "    mid_point = sum(count_dict.values()) / 2\n",
-    "    my_sum = 0\n",
-    "    my_ind = 0\n",
-    "    sk = sorted(count_dict.keys())\n",
-    "    while my_sum < mid_point:\n",
-    "        my_sum += count_dict[sk[my_ind]]\n",
-    "        if my_sum >= mid_point:\n",
-    "            break\n",
-    "        my_ind += 1\n",
-    "    if sum(count_dict.values())%2 == 0:\n",
-    "        median = (sk[my_ind] + sk[my_ind-1])//2\n",
-    "    else:\n",
-    "        median = sk[my_ind]\n",
-    "    return median\n",
-    "\n",
-    "\n",
-    "def median_deviation_from_median1(count_dict: dict) -> int:\n",
-    "    \"\"\"\n",
-    "    calculates the deviation from the median of each element of counting dictionary,\n",
-    "    then returns the median of that dictionary\n",
-    "    :param count_dict: Counting dictionary to analyze\n",
-    "    :return: index of median of the deviations\n",
-    "    \"\"\"\n",
-    "    my_median = quick_median1(count_dict)\n",
-    "    deviations = {}\n",
-    "    for key in sorted(count_dict.keys()):\n",
-    "        X_value = abs(key - my_median)\n",
-    "        deviations[X_value] = count_dict[key]\n",
-    "    return quick_median1(deviations)\n",
-    "\n",
-    "\n",
-    "def count_frags1(file: str) -> dict:\n",
-    "    \"\"\"\n",
-    "    Takes a sam file input and creates a counting dictionary of the number of reads that are paired,\n",
-    "    first in the pair, confidently mapped and whose pair is mapped to the same reference\n",
-    "    :param file: A sam input file\n",
-    "    :return: A dictionary of the counts of the above reads\n",
-    "    \"\"\"\n",
-    "    FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality\n",
-    "    count_dict = {}\n",
-    "    PRINT_EVERY = 100000\n",
-    "    # Check if the file is sam or bam and decide how to open based on that\n",
-    "    if file[-4:] == \".sam\":\n",
-    "        file_to_parse = open(file, 'r')\n",
-    "    elif file[-4:] == \".bam\":\n",
-    "        file_to_parse = pysam.AlignmentFile(file, 'rb')\n",
-    "    else:\n",
-    "        print(\"Unknown file type, must be bam or sam\")\n",
-    "        exit(1)\n",
-    "\n",
-    "    for item in file_to_parse:\n",
-    "        # Need to convert bam iterable objects into strings for the next part\n",
-    "        line = str(item)\n",
-    "        # Skip all comments and headers\n",
-    "        if line[0] == '#' or line[0] == '@':\n",
-    "            continue\n",
-    "        splt = line.strip().split('\\t')\n",
-    "        samFlag = int(splt[1])\n",
-    "        myRef = splt[2]\n",
-    "        mapQual = int(splt[4])\n",
-    "        mateRef = splt[6]\n",
-    "        myTlen = abs(int(splt[8]))\n",
-    "\n",
-    "        # if read is paired, and is first in pair, and is confidently mapped...\n",
-    "        if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:\n",
-    "            # and mate is mapped to same reference\n",
-    "            if mateRef == '=' or mateRef == myRef:\n",
-    "                if myTlen not in count_dict:\n",
-    "                    count_dict[myTlen] = 0\n",
-    "                count_dict[myTlen] += 1\n",
-    "    return count_dict\n",
-    "\n",
-    "\n",
-    "def compute_probs1(count_dict: dict) -> (list, list):\n",
-    "    \"\"\"\n",
-    "    Computes the probabilities for fragments with at least 100 pairs supporting it and that are at least 10 median\n",
-    "    deviations from the median.\n",
-    "    :param count_dict: A dictionary of fragments with counts\n",
-    "    :return: A list of values that meet the criteria and a list of -their associated probabilities\n",
-    "    \"\"\"\n",
-    "    FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it\n",
-    "    FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median\n",
-    "    values = []\n",
-    "    probabilities = []\n",
-    "    med = quick_median1(count_dict)\n",
-    "    mdm = median_deviation_from_median1(count_dict)\n",
-    "\n",
-    "    for key in sorted(count_dict.keys()):\n",
-    "        if 0 < key < med + FILTER_MEDDEV_M * mdm:\n",
-    "            if count_dict[key] >= FILTER_MINREADS:\n",
-    "                values.append(key)\n",
-    "                probabilities.append(count_dict[key])\n",
-    "    count_sum = float(sum(probabilities))\n",
-    "    probabilities = [n / count_sum for n in probabilities]\n",
-    "    return values, probabilities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "counting_list = count_frags1(\"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compute_probs1(counting_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Datalist method\n",
-    "\n",
-    "def quick_median_new(datalist: list) -> float:\n",
-    "    \"\"\"\n",
-    "    Finds the median of a counting dictionary\n",
-    "    :param count_dict: the counting dictionary to find the median of\n",
-    "    :return: integer index of the location of the median\n",
-    "    \"\"\"\n",
-    "    midpoint = len(datalist)//2\n",
-    "    if len(datalist) % 2 == 0:\n",
-    "        median = (datalist[midpoint] + datalist[midpoint-1])/2\n",
-    "    else:\n",
-    "        median = datalist[midpoint]\n",
-    "    return median\n",
-    "\n",
-    "\n",
-    "def median_deviation_from_median_new(datalist: list) -> float:\n",
-    "    \"\"\"\n",
-    "    calculates the deviation from the median of each element of counting dictionary,\n",
-    "    then returns the median of that dictionary\n",
-    "    :param count_dict: Counting dictionary to analyze\n",
-    "    :return: index of median of the deviations\n",
-    "    \"\"\"\n",
-    "    my_median = quick_median_new(datalist)\n",
-    "    deviations = []\n",
-    "    for item in datalist:\n",
-    "        X_value = abs(item - my_median)\n",
-    "        deviations.append(X_value)\n",
-    "    return quick_median_new(sorted(deviations))\n",
-    "\n",
-    "\n",
-    "def count_frags_new(file: str) -> list:\n",
-    "    \"\"\"\n",
-    "    Takes a sam file input and creates a counting dictionary of the number of reads that are paired,\n",
-    "    first in the pair, confidently mapped and whose pair is mapped to the same reference\n",
-    "    :param file: A sam input file\n",
-    "    :return: A dictionary of the counts of the above reads\n",
-    "    \"\"\"\n",
-    "    FILTER_MAPQUAL = 10  # only consider reads that are mapped with at least this mapping quality\n",
-    "    count_list = []\n",
-    "    PRINT_EVERY = 100000\n",
-    "    i = 0\n",
-    "    # Check if the file is sam or bam and decide how to open based on that\n",
-    "    if file[-4:] == \".sam\":\n",
-    "        file_to_parse = open(file, 'r')\n",
-    "    elif file[-4:] == \".bam\":\n",
-    "        file_to_parse = pysam.AlignmentFile(file, 'rb')\n",
-    "    else:\n",
-    "        print(\"Unknown file type, must be bam or sam\")\n",
-    "        exit(1)\n",
-    "\n",
-    "    for item in file_to_parse:\n",
-    "        # Need to convert bam iterable objects into strings for the next part\n",
-    "        line = str(item)\n",
-    "        # Skip all comments and headers\n",
-    "        if line[0] == '#' or line[0] == '@':\n",
-    "            continue\n",
-    "        splt = line.strip().split('\\t')\n",
-    "        samFlag = int(splt[1])\n",
-    "        myRef = splt[2]\n",
-    "        mapQual = int(splt[4])\n",
-    "        mateRef = splt[6]\n",
-    "        myTlen = abs(int(splt[8]))\n",
-    "\n",
-    "        # if read is paired, and is first in pair, and is confidently mapped...\n",
-    "        if samFlag & 1 and samFlag & 64 and mapQual > FILTER_MAPQUAL:\n",
-    "            # and mate is mapped to same reference\n",
-    "            if mateRef == '=' or mateRef == myRef:\n",
-    "                count_list.append(myTlen)\n",
-    "    count_list = sorted(count_list)\n",
-    "    file_to_parse.close()\n",
-    "    return count_list\n",
-    "\n",
-    "\n",
-    "def compute_probs_new(datalist: list) -> (list, list):\n",
-    "    \"\"\"\n",
-    "    Computes the probabilities for fragments with at least 100 pairs supporting it and that are at least 10 median\n",
-    "    deviations from the median.\n",
-    "    :param count_dict: A dictionary of fragments with counts\n",
-    "    :return: A list of values that meet the criteria and a list of -their associated probabilities\n",
-    "    \"\"\"\n",
-    "    FILTER_MINREADS = 100  # only consider fragment lengths that have at least this many read pairs supporting it\n",
-    "    FILTER_MEDDEV_M = 10  # only consider fragment lengths this many median deviations above the median\n",
-    "    values = []\n",
-    "    probabilities = []\n",
-    "    med = quick_median_new(datalist)\n",
-    "    mdm = median_deviation_from_median_new(datalist)\n",
-    "    \n",
-    "    for item in list(set(datalist)):\n",
-    "        if 0 < item <= med + FILTER_MEDDEV_M * mdm:\n",
-    "            data_count = datalist.count(item)\n",
-    "            if data_count >= FILTER_MINREADS:\n",
-    "                values.append(item)\n",
-    "                probabilities.append(data_count)\n",
-    "    count_sum = float(sum(probabilities))\n",
-    "    probabilities = [n / count_sum for n in probabilities]\n",
-    "    return values, probabilities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "counting_list = count_frags_new(\"/home/joshfactorial/Documents/neat_data/WGS_chr20_21_22_normal.bam\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compute_probs_new(counting_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd = {50:100, 75: 100, 100:100}\n",
-    "\n",
-    "datalist = []\n",
-    "for key in cd.keys():\n",
-    "    for i in range(cd[key]):\n",
-    "        datalist.append(key)\n",
-    "datalist = sorted(datalist)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "75.0 25.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(quick_median_new(datalist), median_deviation_from_median_new(datalist))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([50, 75, 100], [0.3333333333333333, 0.3333333333333333, 0.3333333333333333])"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_probs_new(datalist)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/utilities/genMutModel.py b/utilities/genMutModel.py
index 901ae3b..2782eb4 100644
--- a/utilities/genMutModel.py
+++ b/utilities/genMutModel.py
@@ -43,45 +43,6 @@ def cluster_list(list_to_cluster: list, delta: float) -> list:
     return out_list
 
 
-def list_2_countDict(l: list) -> dict:
-    cDict = {}
-    for n in l:
-        if n not in cDict:
-            cDict[n] = 0
-        cDict[n] += 1
-    return cDict
-
-
-def getBedTracks(file_name: str) -> dict:
-    f = open(file_name, 'r')
-    trackDict = {}
-    for line in f:
-        splt = line.strip().split('\t')
-        if splt[0] not in trackDict:
-            trackDict[splt[0]] = []
-        trackDict[splt[0]].extend([int(splt[1]), int(splt[2])])
-    f.close()
-    return trackDict
-
-#TODO Convert this function to pandas
-def get_track_len(track_df: pd.DataFrame) -> float:
-    totSum = 0
-    for k in track_df.keys():
-        for i in range(0, len(track_df[k]), 2):
-            totSum += track_df[k][i + 1] - track_df[k][i] + 1
-    return totSum
-
-
-def isInBed(track, ind):
-    myInd = bisect.bisect(track, ind)
-    if myInd & 1:
-        return True
-    if myInd < len(track):
-        if track[myInd - 1] == ind:
-            return True
-    return False
-
-
 #####################################
 #				main()				#
 #####################################
@@ -152,7 +113,9 @@ def main():
             print('Problem parsing bed file. Ensure bed file is tab separated, standard bed format')
             exit(1)
         mybed = mybed.rename(columns={0: 'chrom', 1: 'start', 2: 'end'})
+        # Adding a couple of columns we'll need for later calculations
         mybed['coords'] = list(zip(mybed.start, mybed.end))
+        mybed['track_len'] = mybed.end - mybed.start + 1
 
     # Process reference file
     print('Processing reference...')
@@ -476,9 +439,10 @@ def main():
     SNP_FREQ = SNP_COUNT / float(totalVar)
     AVG_INDEL_FREQ = 1. - SNP_FREQ
     INDEL_FREQ = {k: (INDEL_COUNT[k] / float(totalVar)) / AVG_INDEL_FREQ for k in INDEL_COUNT.keys()}
-    #TODO fix this get track len function
+
     if is_bed:
-        AVG_MUT_RATE = totalVar / float(get_track_len(mybed))
+        track_sum = float(mybed['track_len'].sum())
+        AVG_MUT_RATE = totalVar / track_sum
     else:
         AVG_MUT_RATE = totalVar / float(TOTAL_REFLEN)
 

From e7c017d0fddf747f9d873b967875c929047d17be Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 24 Jun 2020 16:16:20 -0500
Subject: [PATCH 077/323] Changed name of genMutModel.py to gen_mut_model.py,
 in keeping with PEP 8.

---
 utilities/{genMutModel.py => gen_mut_model.py} | 13 +------------
 utilities/plotMutModel.py                      |  2 +-
 2 files changed, 2 insertions(+), 13 deletions(-)
 rename utilities/{genMutModel.py => gen_mut_model.py} (98%)

diff --git a/utilities/genMutModel.py b/utilities/gen_mut_model.py
similarity index 98%
rename from utilities/genMutModel.py
rename to utilities/gen_mut_model.py
index 2782eb4..4c72b6f 100644
--- a/utilities/genMutModel.py
+++ b/utilities/gen_mut_model.py
@@ -2,19 +2,13 @@
 
 # Python 3 ready
 
-import sys
 import os
 import re
-import bisect
 import pickle
 import argparse
 import numpy as np
 from Bio import SeqIO
 import pandas as pd
-#
-# SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2])
-# sys.path.append(SIM_PATH)
-# print(sys.path)
 
 
 #########################################################
@@ -58,7 +52,7 @@ def main():
     # if parsing a dbsnp vcf, and no CAF= is found in info tag, use this as default val for population freq
     VCF_DEFAULT_POP_FREQ = 0.00001
 
-    parser = argparse.ArgumentParser(description='genMutModel.py')
+    parser = argparse.ArgumentParser(description='gen_mut_model.py')
     parser.add_argument('-r', type=str, required=True, metavar='/path/to/reference.fasta',
                         help="Reference file for organism in fasta format")
     parser.add_argument('-m', type=str, required=True, metavar='/path/to/mutations.vcf',
@@ -480,11 +474,6 @@ def main():
     for k in sorted(SNP_TRANS_FREQ.keys()):
         print('p(' + k[0] + ' --> ' + k[1] + ' | SNP occurs) =', SNP_TRANS_FREQ[k])
 
-    # for n in COMMON_VARIANTS:
-    #	print n
-
-    # for n in HIGH_MUT_REGIONS:
-    #	print n
 
     print('p(snp)   =', SNP_FREQ)
     print('p(indel) =', AVG_INDEL_FREQ)
diff --git a/utilities/plotMutModel.py b/utilities/plotMutModel.py
index 7a70693..6301943 100644
--- a/utilities/plotMutModel.py
+++ b/utilities/plotMutModel.py
@@ -20,7 +20,7 @@
 # mpl.rcParams['text.latex.preamble']=[r"\usepackage{amsmath}"]
 
 parser = argparse.ArgumentParser(
-    description='Plot and compare mutation models from genMutModel.py Usage: python plotMutModel.py -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix')
+    description='Plot and compare mutation models from gen_mut_model.py Usage: python plotMutModel.py -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix')
 parser.add_argument('-i', type=str, required=True, metavar='<str>', nargs='+',
                     help="* mutation_model_1.p [mutation_model_2.p] [mutation_model_3] ...")
 parser.add_argument('-l', type=str, required=True, metavar='<str>', nargs='+',

From b88644edb35466fe8364ab96f7e93bcaecf840d8 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 25 Jun 2020 09:29:53 -0500
Subject: [PATCH 078/323] Changed name of genMutModel.py to gen_mut_model.py,
 in keeping with PEP 8.

---
 utilities/gen_mut_model.py | 57 +++++++++++++++++++-------------------
 1 file changed, 29 insertions(+), 28 deletions(-)

diff --git a/utilities/gen_mut_model.py b/utilities/gen_mut_model.py
index 4c72b6f..ca64c66 100644
--- a/utilities/gen_mut_model.py
+++ b/utilities/gen_mut_model.py
@@ -78,6 +78,7 @@ def main():
 
     # how many times do we observe each trinucleotide in the reference (and input bed region, if present)?
     TRINUC_REF_COUNT = {}
+    TRINUC_BED_COUNT = {}
     # [(trinuc_a, trinuc_b)] = # of times we observed a mutation from trinuc_a into trinuc_b
     TRINUC_TRANSITION_COUNT = {}
     # total count of SNPs
@@ -140,7 +141,7 @@ def main():
     # Pre-parsing to find all the matching chromosomes between ref and vcf
     print('Processing VCF file...')
     try:
-        variants = pd.read_csv(vcf, sep='\t', comment='#', index_col=None)
+        variants = pd.read_csv(vcf, sep='\t', comment='#', index_col=None, header=None)
     except ValueError:
         print("VCF must be in standard VCF format with tab-separated columns")
         exit(1)
@@ -232,10 +233,10 @@ def main():
               "you already have a trinuc count file for the reference...")
         sub_regions = matching_bed['coords'].to_list()
         for sr in sub_regions:
-            for i in range(sr[0], sr[1] + 1):
-                for ref_name in matching_chromosomes:
-                    trinuc = ref_dict[ref_name][i:i + 3].seq
-                    # skip if trinuc contains invalid characters, or not in specified bed region
+            for ref_name in matching_chromosomes:
+                for i in range(sr[0], sr[1] - 2):
+                    trinuc = str(ref_dict[ref_name][i:i + 3].seq)
+                    # skip if trinuc contains invalid characters
                     if not trinuc in VALID_TRINUC:
                         continue
                     if trinuc not in TRINUC_REF_COUNT:
@@ -243,15 +244,15 @@ def main():
                     TRINUC_REF_COUNT[trinuc] += 1
 
     elif not os.path.isfile(ref + '.trinucCounts'):
-        for i in range(len(ref_dict[ref_name]) - 2):
-            if i % 1000000 == 0 and i > 0:
-                print(i, '/', len(ref_dict[ref_name]))
-            trinuc = ref_dict[ref_name][i:i + 3].seq
-            if not str(trinuc) in VALID_TRINUC:
-                continue  # skip if trinuc contains invalid characters
-            if str(trinuc) not in TRINUC_REF_COUNT:
-                TRINUC_REF_COUNT[trinuc] = 0
-            TRINUC_REF_COUNT[trinuc] += 1
+        for ref_name in matching_chromosomes:
+            for i in range(len(ref_dict[ref_name]) - 2):
+                trinuc = str(ref_dict[ref_name][i:i + 3].seq)
+                # skip if trinuc contains invalid characters
+                if not trinuc in VALID_TRINUC:
+                    continue
+                if trinuc not in TRINUC_REF_COUNT:
+                    TRINUC_REF_COUNT[trinuc] = 0
+                TRINUC_REF_COUNT[trinuc] += 1
     else:
         print('Found trinucCounts file, using that.')
 
@@ -277,12 +278,12 @@ def main():
             # only consider positions where ref allele in vcf matches the nucleotide in our reference
             for index, row in snp_df.iterrows():
                 trinuc_to_analyze = str(ref_sequence[row.chr_start - 1: row.chr_start + 2])
-                if not trinuc_to_analyze in VALID_TRINUC:
+                if trinuc_to_analyze not in VALID_TRINUC:
                     continue
                 if row.REF == trinuc_to_analyze[1]:
                     trinuc_ref = trinuc_to_analyze
                     trinuc_alt = trinuc_to_analyze[0] + snp_df.loc[index, 'ALT'] + trinuc_to_analyze[2]
-                    if not trinuc_alt in VALID_TRINUC:
+                    if trinuc_alt not in VALID_TRINUC:
                         continue
                     key = (trinuc_ref, trinuc_alt)
                     if key not in TRINUC_TRANSITION_COUNT:
@@ -386,7 +387,7 @@ def main():
         f.close()
     # otherwise, save trinuc counts to file, if desired
     elif save_trinuc:
-        if mybed is not None:
+        if is_bed:
             print('unable to save trinuc counts to file because using input bed region...')
         else:
             print('saving trinuc counts to file...')
@@ -396,8 +397,8 @@ def main():
             f.close()
 
     # if for some reason we didn't find any valid input variants, exit gracefully...
-    totalVar = SNP_COUNT + sum(INDEL_COUNT.values())
-    if totalVar == 0:
+    total_var = SNP_COUNT + sum(INDEL_COUNT.values())
+    if total_var == 0:
         print(
             '\nError: No valid variants were found, model could not be created. (Are you using the correct reference?)\n')
         exit(1)
@@ -430,28 +431,28 @@ def main():
                 SNP_TRANS_FREQ[key2] = SNP_TRANSITION_COUNT[key2] / float(rollingTot)
 
     # compute average snp and indel frequencies
-    SNP_FREQ = SNP_COUNT / float(totalVar)
+    SNP_FREQ = SNP_COUNT / float(total_var)
     AVG_INDEL_FREQ = 1. - SNP_FREQ
-    INDEL_FREQ = {k: (INDEL_COUNT[k] / float(totalVar)) / AVG_INDEL_FREQ for k in INDEL_COUNT.keys()}
+    INDEL_FREQ = {k: (INDEL_COUNT[k] / float(total_var)) / AVG_INDEL_FREQ for k in INDEL_COUNT.keys()}
 
     if is_bed:
         track_sum = float(mybed['track_len'].sum())
-        AVG_MUT_RATE = totalVar / track_sum
+        AVG_MUT_RATE = total_var / track_sum
     else:
-        AVG_MUT_RATE = totalVar / float(TOTAL_REFLEN)
+        AVG_MUT_RATE = total_var / float(TOTAL_REFLEN)
 
     #	if values weren't found in data, appropriately append null entries
-    printTrinucWarning = False
+    print_trinuc_warning = False
     for trinuc in VALID_TRINUC:
         trinuc_mut = [trinuc[0] + n + trinuc[2] for n in VALID_NUCL if n != trinuc[1]]
         if trinuc not in TRINUC_MUT_PROB:
             TRINUC_MUT_PROB[trinuc] = 0.
-            printTrinucWarning = True
+            print_trinuc_warning = True
         for trinuc2 in trinuc_mut:
             if (trinuc, trinuc2) not in TRINUC_TRANS_PROBS:
                 TRINUC_TRANS_PROBS[(trinuc, trinuc2)] = 0.
-                printTrinucWarning = True
-    if printTrinucWarning:
+                print_trinuc_warning = True
+    if print_trinuc_warning:
         print(
             'Warning: Some trinucleotides transitions were not encountered in the input dataset, '
             'probabilities of 0.0 have been assigned to these events.')
@@ -478,7 +479,7 @@ def main():
     print('p(snp)   =', SNP_FREQ)
     print('p(indel) =', AVG_INDEL_FREQ)
     print('overall average mut rate:', AVG_MUT_RATE)
-    print('total variants processed:', totalVar)
+    print('total variants processed:', total_var)
 
     #
     # save variables to file

From 2055880b55ddcbcd4caf83ea64f58530b0a632d5 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 25 Jun 2020 13:06:44 -0500
Subject: [PATCH 079/323] minor changes to gen_mut_model.py

---
 utilities/gen_mut_model.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/utilities/gen_mut_model.py b/utilities/gen_mut_model.py
index ca64c66..41a00d2 100644
--- a/utilities/gen_mut_model.py
+++ b/utilities/gen_mut_model.py
@@ -78,7 +78,6 @@ def main():
 
     # how many times do we observe each trinucleotide in the reference (and input bed region, if present)?
     TRINUC_REF_COUNT = {}
-    TRINUC_BED_COUNT = {}
     # [(trinuc_a, trinuc_b)] = # of times we observed a mutation from trinuc_a into trinuc_b
     TRINUC_TRANSITION_COUNT = {}
     # total count of SNPs

From 74cc02b849916c0e099ffd093218e11d3cea40cf Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 25 Jun 2020 13:17:35 -0500
Subject: [PATCH 080/323] Removed some spurious lines of code

---
 utilities/gen_mut_model.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/utilities/gen_mut_model.py b/utilities/gen_mut_model.py
index 41a00d2..cf1bee1 100644
--- a/utilities/gen_mut_model.py
+++ b/utilities/gen_mut_model.py
@@ -105,7 +105,7 @@ def main():
             is_bed = True
         except ValueError:
             print('Problem parsing bed file. Ensure bed file is tab separated, standard bed format')
-            exit(1)
+
         mybed = mybed.rename(columns={0: 'chrom', 1: 'start', 2: 'end'})
         # Adding a couple of columns we'll need for later calculations
         mybed['coords'] = list(zip(mybed.start, mybed.end))
@@ -117,7 +117,6 @@ def main():
         reference = SeqIO.to_dict(SeqIO.parse(ref, "fasta"))
     except ValueError:
         print("Problems parsing reference file. Ensure reference is in proper fasta format")
-        exit(1)
 
     # simplify naming and filter out actual human genomes from scaffolding
     ref_dict = {}
@@ -143,7 +142,6 @@ def main():
         variants = pd.read_csv(vcf, sep='\t', comment='#', index_col=None, header=None)
     except ValueError:
         print("VCF must be in standard VCF format with tab-separated columns")
-        exit(1)
 
     # Narrow chromosomes to those matching the reference
     # This is in part to make sure the names match
@@ -166,7 +164,7 @@ def main():
         matching_variants = variants[variants[0].isin(matching_chromosomes)]
     except ValueError:
         print("Problem matching variants with reference.")
-        exit(1)
+
     if matching_variants.empty:
         print("There is no overlap between reference and variant file. This could be a chromosome naming problem")
         exit(1)
@@ -219,7 +217,7 @@ def main():
             matching_bed = mybed[mybed['chrom'].isin(matching_bed_keys)]
         except ValueError:
             print('Problem matching bed chromosomes to variant file.')
-            exit(1)
+
         if matching_bed.empty:
             print("There is no overlap between bed and variant file. "
                   "This could be a chromosome naming problem")

From b98006b311a1107108e3d9268fd9276decf31d9e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 29 Jun 2020 16:22:41 -0500
Subject: [PATCH 081/323] Minor changes to gen_mut_model.py

---
 utilities/gen_mut_model.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/utilities/gen_mut_model.py b/utilities/gen_mut_model.py
index cf1bee1..2b1a14d 100644
--- a/utilities/gen_mut_model.py
+++ b/utilities/gen_mut_model.py
@@ -59,9 +59,9 @@ def main():
                         help="Mutation file for organism in VCF format")
     parser.add_argument('-o', type=str, required=True, metavar='/path/to/output/and/prefix',
                         help="Name of output file (final model will append \'.p\')")
-    parser.add_argument('-bi', type=str, required=False, metavar='Bed file of regions to include '
-                                                                 '(use bedtools complement if you have a '
-                                                                 'bed of exclusion areas)', default=None,
+    parser.add_argument('-b', type=str, required=False, metavar='Bed file of regions to include '
+                                                                '(use bedtools complement if you have a '
+                                                                'bed of exclusion areas)', default=None,
                         help="only_use_these_regions.bed")
     parser.add_argument('--save-trinuc', required=False, action='store_true', default=False,
                         help='save trinucleotide counts for reference')
@@ -98,10 +98,10 @@ def main():
     # Process bed file,
     is_bed = False
     mybed = None
-    if args.bi is not None:
+    if args.b is not None:
         print('Processing bed file...')
         try:
-            mybed = pd.read_csv(args.bi, sep='\t', header=None, index_col=None)
+            mybed = pd.read_csv(args.b, sep='\t', header=None, index_col=None)
             is_bed = True
         except ValueError:
             print('Problem parsing bed file. Ensure bed file is tab separated, standard bed format')

From 0a5d4ca417d19e83076beba317e988558e9d94de Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Tue, 30 Jun 2020 14:28:40 -0500
Subject: [PATCH 082/323] Renamed variables to current standards

---
 utilities/genSeqErrorModel.py |  12 +-
 utilities/gen_mut_model.py    |  32 +--
 utilities/plotMutModel.py     | 132 ++++-----
 utilities/validateBam.py      |  50 ++--
 utilities/validateFQ.py       |  16 +-
 utilities/vcf_compare_OLD.py  | 496 +++++++++++++++++-----------------
 6 files changed, 369 insertions(+), 369 deletions(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index a3072c0..7625b05 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -116,11 +116,11 @@ def parse_fq(inf):
 
     init_q = [[0. for m in range(r_q)] for n in range(actual_readlen)]
     for i in range(actual_readlen):
-        row_sum = float(np.sum(prior_q[i, :])) + INIT_SMOOTH * r_q
+        row_sum = float(np.sum(prior_q[i, :])) + init_smooth * r_q
         if row_sum <= 0.:
             continue
         for j in range(r_q):
-            init_q[i][j] = (prior_q[i][j] + INIT_SMOOTH) / row_sum
+            init_q[i][j] = (prior_q[i][j] + init_smooth) / row_sum
 
     if plot_stuff:
         mpl.rcParams.update({'font.size': 14, 'font.weight': 'bold', 'lines.linewidth': 3})
@@ -144,7 +144,7 @@ def parse_fq(inf):
         q_labels = [str(n) for n in range(q_range[0], q_range[1] + 1) if n % 5 == 0]
         print(q_labels)
         q_ticks_x = [int(n) + 0.5 for n in q_labels]
-        qTicksy = [(r_q - int(n)) - 0.5 for n in q_labels]
+        q_ticks_y = [(r_q - int(n)) - 0.5 for n in q_labels]
 
         for p in range(1, actual_readlen, 10):
             current_data = np.array(prob_q[p])
@@ -174,7 +174,7 @@ def parse_fq(inf):
             mpl.pcolormesh(x, y, z[::-1, :], vmin=v_min_log[0], vmax=v_min_log[1], cmap='jet')
             mpl.xlim([q_range[0], q_range[1] + 1])
             mpl.ylim([r_q - q_range[1] - 1, r_q - q_range[0]])
-            mpl.yticks(qTicksy, q_labels)
+            mpl.yticks(q_ticks_y, q_labels)
             mpl.xticks(q_ticks_x, q_labels)
             mpl.xlabel('\n' + r'$Q_{i+1}$')
             mpl.ylabel(r'$Q_i$')
@@ -240,7 +240,7 @@ def parse_fq(inf):
 args = parser.parse_args()
 
 (inf, ouf, off_q, max_q, max_reads, n_samp) = (args.i, args.o, args.q, args.Q, args.n, args.s)
-(inf2, pileup) = (args.i2, args.p)
+(inf2, pile_up) = (args.i2, args.p)
 
 r_q = max_q + 1
 
@@ -265,7 +265,7 @@ def main():
     #
     #	embed some default sequencing error parameters if no pileup is provided
     #
-    if pileup == None:
+    if pile_up == None:
 
         print('Using default sequencing error parameters...')
 
diff --git a/utilities/gen_mut_model.py b/utilities/gen_mut_model.py
index 2b1a14d..d9d0688 100644
--- a/utilities/gen_mut_model.py
+++ b/utilities/gen_mut_model.py
@@ -97,19 +97,19 @@ def main():
 
     # Process bed file,
     is_bed = False
-    mybed = None
+    my_bed = None
     if args.b is not None:
         print('Processing bed file...')
         try:
-            mybed = pd.read_csv(args.b, sep='\t', header=None, index_col=None)
+            my_bed = pd.read_csv(args.b, sep='\t', header=None, index_col=None)
             is_bed = True
         except ValueError:
             print('Problem parsing bed file. Ensure bed file is tab separated, standard bed format')
 
-        mybed = mybed.rename(columns={0: 'chrom', 1: 'start', 2: 'end'})
+        my_bed = my_bed.rename(columns={0: 'chrom', 1: 'start', 2: 'end'})
         # Adding a couple of columns we'll need for later calculations
-        mybed['coords'] = list(zip(mybed.start, mybed.end))
-        mybed['track_len'] = mybed.end - mybed.start + 1
+        my_bed['coords'] = list(zip(my_bed.start, my_bed.end))
+        my_bed['track_len'] = my_bed.end - my_bed.start + 1
 
     # Process reference file
     print('Processing reference...')
@@ -211,10 +211,10 @@ def main():
     # Now we check that the bed and vcf have matching regions
     # This also checks that the vcf and bed have the same naming conventions and cuts out scaffolding.
     if is_bed:
-        bed_chroms = list(set(mybed['chrom']))
+        bed_chroms = list(set(my_bed['chrom']))
         matching_bed_keys = list(set(bed_chroms) & set(variant_chroms))
         try:
-            matching_bed = mybed[mybed['chrom'].isin(matching_bed_keys)]
+            matching_bed = my_bed[my_bed['chrom'].isin(matching_bed_keys)]
         except ValueError:
             print('Problem matching bed chromosomes to variant file.')
 
@@ -368,10 +368,10 @@ def main():
             if HIGH_MUT_REGIONS[i - 1][2] >= HIGH_MUT_REGIONS[i][1] and HIGH_MUT_REGIONS[i - 1][0] == \
                     HIGH_MUT_REGIONS[i][0]:
                 # Might need to research a more accurate way to get the mutation rate for this region
-                avgMutRate = 0.5 * HIGH_MUT_REGIONS[i - 1][3] + 0.5 * HIGH_MUT_REGIONS[i][
+                avg_mut_rate = 0.5 * HIGH_MUT_REGIONS[i - 1][3] + 0.5 * HIGH_MUT_REGIONS[i][
                     3]
                 HIGH_MUT_REGIONS[i - 1] = (
-                    HIGH_MUT_REGIONS[i - 1][0], HIGH_MUT_REGIONS[i - 1][1], HIGH_MUT_REGIONS[i][2], avgMutRate)
+                    HIGH_MUT_REGIONS[i - 1][0], HIGH_MUT_REGIONS[i - 1][1], HIGH_MUT_REGIONS[i][2], avg_mut_rate)
                 del HIGH_MUT_REGIONS[i]
 
     # if we didn't count ref trinucs because we found file, read in ref counts from file now
@@ -411,21 +411,21 @@ def main():
     SNP_TRANS_FREQ = {}
 
     for trinuc in sorted(TRINUC_REF_COUNT.keys()):
-        myCount = 0
+        my_count = 0
         for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
             if k[0] == trinuc:
-                myCount += TRINUC_TRANSITION_COUNT[k]
-        TRINUC_MUT_PROB[trinuc] = myCount / float(TRINUC_REF_COUNT[trinuc])
+                my_count += TRINUC_TRANSITION_COUNT[k]
+        TRINUC_MUT_PROB[trinuc] = my_count / float(TRINUC_REF_COUNT[trinuc])
         for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
             if k[0] == trinuc:
-                TRINUC_TRANS_PROBS[k] = TRINUC_TRANSITION_COUNT[k] / float(myCount)
+                TRINUC_TRANS_PROBS[k] = TRINUC_TRANSITION_COUNT[k] / float(my_count)
 
     for n1 in VALID_NUCL:
-        rollingTot = sum([SNP_TRANSITION_COUNT[(n1, n2)] for n2 in VALID_NUCL if (n1, n2) in SNP_TRANSITION_COUNT])
+        rolling_tot = sum([SNP_TRANSITION_COUNT[(n1, n2)] for n2 in VALID_NUCL if (n1, n2) in SNP_TRANSITION_COUNT])
         for n2 in VALID_NUCL:
             key2 = (n1, n2)
             if key2 in SNP_TRANSITION_COUNT:
-                SNP_TRANS_FREQ[key2] = SNP_TRANSITION_COUNT[key2] / float(rollingTot)
+                SNP_TRANS_FREQ[key2] = SNP_TRANSITION_COUNT[key2] / float(rolling_tot)
 
     # compute average snp and indel frequencies
     SNP_FREQ = SNP_COUNT / float(total_var)
@@ -433,7 +433,7 @@ def main():
     INDEL_FREQ = {k: (INDEL_COUNT[k] / float(total_var)) / AVG_INDEL_FREQ for k in INDEL_COUNT.keys()}
 
     if is_bed:
-        track_sum = float(mybed['track_len'].sum())
+        track_sum = float(my_bed['track_len'].sum())
         AVG_MUT_RATE = total_var / track_sum
     else:
         AVG_MUT_RATE = total_var / float(TOTAL_REFLEN)
diff --git a/utilities/plotMutModel.py b/utilities/plotMutModel.py
index 6301943..159b532 100644
--- a/utilities/plotMutModel.py
+++ b/utilities/plotMutModel.py
@@ -29,15 +29,15 @@
 args = parser.parse_args()
 
 
-def getColor(i, N, colormap='jet'):
+def get_color(i, N, colormap='jet'):
     cm = mpl.get_cmap(colormap)
-    cNorm = colors.Normalize(vmin=0, vmax=N + 1)
-    scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
-    colorVal = scalarMap.to_rgba(i)
-    return colorVal
+    c_norm = colors.Normalize(vmin=0, vmax=N + 1)
+    scalar_map = cmx.ScalarMappable(norm=c_norm, cmap=cm)
+    color_val = scalar_map.to_rgba(i)
+    return color_val
 
 
-def isInBed(track, ind):
+def is_in_bed(track, ind):
     if ind in track:
         return True
     elif bisect.bisect(track, ind) % 1 == 1:
@@ -46,14 +46,14 @@ def isInBed(track, ind):
         return False
 
 
-def getBedOverlap(track, ind_s, ind_e):
+def get_bed_overlap(track, ind_s, ind_e):
     if ind_s in track:
-        myInd = track.index(ind_s)
-        return min([track[myInd + 1] - ind_s + 1, ind_e - ind_s + 1])
+        my_ind = track.index(ind_s)
+        return min([track[my_ind + 1] - ind_s + 1, ind_e - ind_s + 1])
     else:
-        myInd = bisect.bisect(track, ind_s)
-        if myInd % 1 and myInd < len(track) - 1:
-            return min([track[myInd + 1] - ind_s + 1, ind_e - ind_s + 1])
+        my_ind = bisect.bisect(track, ind_s)
+        if my_ind % 1 and my_ind < len(track) - 1:
+            return min([track[my_ind + 1] - ind_s + 1, ind_e - ind_s + 1])
     return 0
 
 
@@ -87,13 +87,13 @@ def getBedOverlap(track, ind_s, ind_e):
 mpl.figure(0, figsize=(12, 10))
 
 mpl.subplot(2, 2, 1)
-colorInd = 0
+color_ind = 0
 for fn in INP:
-    myCol = getColor(colorInd, N_FILES)
-    colorInd += 1
+    my_col = get_color(color_ind, N_FILES)
+    color_ind += 1
     DATA_DICT = pickle.load(open(fn, "rb"), encoding="utf-8")
     [AVG_MUT_RATE, SNP_FREQ, INDEL_FREQ] = [DATA_DICT['AVG_MUT_RATE'], DATA_DICT['SNP_FREQ'], DATA_DICT['INDEL_FREQ']]
-    mpl.bar([colorInd - 1], [AVG_MUT_RATE], 1., color=myCol)
+    mpl.bar([color_ind - 1], [AVG_MUT_RATE], 1., color=my_col)
 mpl.xlim([-1, N_FILES + 1])
 mpl.grid()
 mpl.xticks([], [])
@@ -101,14 +101,14 @@ def getBedOverlap(track, ind_s, ind_e):
 mpl.title('Overall mutation rate (1/bp)')
 
 mpl.subplot(2, 2, 2)
-colorInd = 0
+color_ind = 0
 for fn in INP:
-    myCol = getColor(colorInd, N_FILES)
-    colorInd += 1
+    my_col = get_color(color_ind, N_FILES)
+    color_ind += 1
     DATA_DICT = pickle.load(open(fn, "rb"), encoding='utf-8')
     [AVG_MUT_RATE, SNP_FREQ, INDEL_FREQ] = [DATA_DICT['AVG_MUT_RATE'], DATA_DICT['SNP_FREQ'], DATA_DICT['INDEL_FREQ']]
-    mpl.bar([colorInd - 1], [SNP_FREQ], 1., color=myCol)
-    mpl.bar([colorInd - 1], [1. - SNP_FREQ], 1., color=myCol, bottom=[SNP_FREQ], hatch='/')
+    mpl.bar([color_ind - 1], [SNP_FREQ], 1., color=my_col)
+    mpl.bar([color_ind - 1], [1. - SNP_FREQ], 1., color=my_col, bottom=[SNP_FREQ], hatch='/')
 mpl.axis([-1, N_FILES + 1, 0, 1.2])
 mpl.grid()
 mpl.xticks([], [])
@@ -117,22 +117,22 @@ def getBedOverlap(track, ind_s, ind_e):
 mpl.title('SNP freq [  ] & indel freq [//]')
 
 mpl.subplot(2, 1, 2)
-colorInd = 0
-legText = LAB
+color_ind = 0
+leg_text = LAB
 for fn in INP:
-    myCol = getColor(colorInd, N_FILES)
-    colorInd += 1
+    my_col = get_color(color_ind, N_FILES)
+    color_ind += 1
     DATA_DICT = pickle.load(open(fn, "rb"))
     [AVG_MUT_RATE, SNP_FREQ, INDEL_FREQ] = [DATA_DICT['AVG_MUT_RATE'], DATA_DICT['SNP_FREQ'], DATA_DICT['INDEL_FREQ']]
     x = sorted(INDEL_FREQ.keys())
     y = [INDEL_FREQ[n] for n in x]
-    mpl.plot(x, y, color=myCol)
-# legText.append(fn)
+    mpl.plot(x, y, color=my_col)
+# leg_text.append(fn)
 mpl.grid()
 mpl.xlabel('Indel size (bp)', fontweight='bold')
 mpl.ylabel('Frequency')
 mpl.title('Indel frequency by size (- deletion, + insertion)')
-mpl.legend(legText)
+mpl.legend(leg_text)
 # mpl.show()
 mpl.savefig(OUP + '_plot1_mutRates.pdf')
 
@@ -142,28 +142,28 @@ def getBedOverlap(track, ind_s, ind_e):
 #
 #################################################
 mpl.figure(1, figsize=(14, 6))
-colorInd = 0
-legText = LAB
+color_ind = 0
+leg_text = LAB
 for fn in INP:
-    myCol = getColor(colorInd, N_FILES)
-    colorInd += 1
+    my_col = get_color(color_ind, N_FILES)
+    color_ind += 1
     DATA_DICT = pickle.load(open(fn, "rb"))
     TRINUC_MUT_PROB = DATA_DICT['TRINUC_MUT_PROB']
 
-    x = range(colorInd - 1, len(TRINUC_MUT_PROB) * N_FILES, N_FILES)
+    x = range(color_ind - 1, len(TRINUC_MUT_PROB) * N_FILES, N_FILES)
     xt = sorted(TRINUC_MUT_PROB.keys())
     y = [TRINUC_MUT_PROB[k] for k in xt]
     markerline, stemlines, baseline = mpl.stem(x, y, '-.')
-    mpl.setp(markerline, 'markerfacecolor', myCol)
-    mpl.setp(markerline, 'markeredgecolor', myCol)
-    mpl.setp(baseline, 'color', myCol, 'linewidth', 0)
-    mpl.setp(stemlines, 'color', myCol, 'linewidth', 3)
-    if colorInd == 1:
+    mpl.setp(markerline, 'markerfacecolor', my_col)
+    mpl.setp(markerline, 'markeredgecolor', my_col)
+    mpl.setp(baseline, 'color', my_col, 'linewidth', 0)
+    mpl.setp(stemlines, 'color', my_col, 'linewidth', 3)
+    if color_ind == 1:
         mpl.xticks(x, xt, rotation=90)
-# legText.append(fn)
+# leg_text.append(fn)
 mpl.grid()
 mpl.ylabel('p(trinucleotide mutates)')
-mpl.legend(legText)
+mpl.legend(leg_text)
 # mpl.show()
 mpl.savefig(OUP + '_plot2_trinucPriors.pdf')
 
@@ -172,9 +172,9 @@ def getBedOverlap(track, ind_s, ind_e):
 #	TRINUC TRANS PROB
 #
 #################################################
-plotNum = 3
+plot_num = 3
 for fn in INP:
-    fig = mpl.figure(plotNum, figsize=(12, 10))
+    fig = mpl.figure(plot_num, figsize=(12, 10))
     DATA_DICT = pickle.load(open(fn, "rb"))
     TRINUC_TRANS_PROBS = DATA_DICT['TRINUC_TRANS_PROBS']
 
@@ -211,8 +211,8 @@ def getBedOverlap(track, ind_s, ind_e):
     # mpl.tight_layout()
     # mpl.figtext(0.24,0.94,'Trinucleotide Mutation Frequency',size=20)
     # mpl.show()
-    mpl.savefig(OUP + '_plot' + str(plotNum) + '_trinucTrans.pdf')
-    plotNum += 1
+    mpl.savefig(OUP + '_plot' + str(plot_num) + '_trinucTrans.pdf')
+    plot_num += 1
 
 #################################################
 #
@@ -221,16 +221,16 @@ def getBedOverlap(track, ind_s, ind_e):
 #################################################
 track_byFile_byChr = [{} for n in INP]
 bp_total_byFile = [0 for n in INP]
-colorInd = 0
+color_ind = 0
 for fn in INP:
     DATA_DICT = pickle.load(open(fn, "rb"))
     HIGH_MUT_REGIONS = DATA_DICT['HIGH_MUT_REGIONS']
     for region in HIGH_MUT_REGIONS:
-        if region[0] not in track_byFile_byChr[colorInd]:
-            track_byFile_byChr[colorInd][region[0]] = []
-        track_byFile_byChr[colorInd][region[0]].extend([region[1], region[2]])
-        bp_total_byFile[colorInd] += region[2] - region[1] + 1
-    colorInd += 1
+        if region[0] not in track_byFile_byChr[color_ind]:
+            track_byFile_byChr[color_ind][region[0]] = []
+        track_byFile_byChr[color_ind][region[0]].extend([region[1], region[2]])
+        bp_total_byFile[color_ind] += region[2] - region[1] + 1
+    color_ind += 1
 
 bp_overlap_count = [[0 for m in INP] for n in INP]
 for i in range(N_FILES):
@@ -239,21 +239,21 @@ def getBedOverlap(track, ind_s, ind_e):
         for k in track_byFile_byChr[i].keys():
             if k in track_byFile_byChr[j]:
                 for ii in range(len(track_byFile_byChr[i][k][::2])):
-                    bp_overlap_count[i][j] += getBedOverlap(track_byFile_byChr[j][k], track_byFile_byChr[i][k][ii * 2],
+                    bp_overlap_count[i][j] += get_bed_overlap(track_byFile_byChr[j][k], track_byFile_byChr[i][k][ii * 2],
                                                             track_byFile_byChr[i][k][ii * 2 + 1])
 
 print('')
 print('HIGH_MUT_REGION OVERLAP BETWEEN ' + str(N_FILES) + ' MODELS...')
 for i in range(N_FILES):
     for j in range(i, N_FILES):
-        nDissimilar = (bp_overlap_count[i][i] - bp_overlap_count[i][j]) + (
+        n_dissimilar = (bp_overlap_count[i][i] - bp_overlap_count[i][j]) + (
                     bp_overlap_count[j][j] - bp_overlap_count[i][j])
         if bp_overlap_count[i][j] == 0:
-            percentageV = 0.0
+            percentage_v = 0.0
         else:
-            percentageV = bp_overlap_count[i][j] / float(bp_overlap_count[i][j] + nDissimilar)
+            percentage_v = bp_overlap_count[i][j] / float(bp_overlap_count[i][j] + n_dissimilar)
     print('overlap[' + str(i) + ',' + str(j) + '] = ' + str(bp_overlap_count[i][j]) + ' bp ({0:.3f}%)'.format(
-        percentageV * 100.))
+        percentage_v * 100.))
 print('')
 
 #################################################
@@ -261,25 +261,25 @@ def getBedOverlap(track, ind_s, ind_e):
 #	COMMON VARIANTS
 #
 #################################################
-setofVars = [set([]) for n in INP]
-colorInd = 0
+set_of_vars = [set([]) for n in INP]
+color_ind = 0
 for fn in INP:
     DATA_DICT = pickle.load(open(fn, "rb"))
     COMMON_VARIANTS = DATA_DICT['COMMON_VARIANTS']
     for n in COMMON_VARIANTS:
-        setofVars[colorInd].add(n)
-    colorInd += 1
+        set_of_vars[color_ind].add(n)
+    color_ind += 1
 
 print('')
 print('COMMON_VARIANTS OVERLAP BETWEEN ' + str(N_FILES) + ' MODELS...')
 for i in range(N_FILES):
     for j in range(i, N_FILES):
-        overlapCount = len(setofVars[i].intersection(setofVars[j]))
-        nDissimilar = (len(setofVars[i]) - overlapCount) + (len(setofVars[j]) - overlapCount)
-        if overlapCount == 0:
-            percentageV = 0.0
+        overlap_count = len(set_of_vars[i].intersection(set_of_vars[j]))
+        n_dissimilar = (len(set_of_vars[i]) - overlap_count) + (len(set_of_vars[j]) - overlap_count)
+        if overlap_count == 0:
+            percentage_v = 0.0
         else:
-            percentageV = overlapCount / float(overlapCount + nDissimilar)
-    print('overlap[' + str(i) + ',' + str(j) + '] = ' + str(overlapCount) + ' variants ({0:.3f}%)'.format(
-        percentageV * 100.))
+            percentage_v = overlap_count / float(overlap_count + n_dissimilar)
+    print('overlap[' + str(i) + ',' + str(j) + '] = ' + str(overlap_count) + ' variants ({0:.3f}%)'.format(
+        percentage_v * 100.))
 print('')
diff --git a/utilities/validateBam.py b/utilities/validateBam.py
index 9c164f7..f4d85fb 100644
--- a/utilities/validateBam.py
+++ b/utilities/validateBam.py
@@ -11,24 +11,24 @@
            '03', '00', '00', '00', '00', '00', '00', '00', '00', '00']
 
 
-def getBytes(fmt, amt):
+def get_bytes(fmt, amt):
     if fmt == '<i' or fmt == '<I':
-        mySize = 4
+        my_size = 4
     elif fmt == '<c' or fmt == '<b' or fmt == '<B':
-        mySize = 1
+        my_size = 1
     else:
         print('\nError, unknown format:', fmt, '\n')
         exit(1)
     if amt == 1:
-        fread = f.read(mySize)
-        if not fread:
+        f_read = f.read(my_size)
+        if not f_read:
             return None
-        return unpack(fmt, fread)[0]
+        return unpack(fmt, f_read)[0]
     else:
-        fread = f.read(mySize * amt)
-        if not fread:
+        f_read = f.read(my_size * amt)
+        if not f_read:
             return None
-        return unpack(fmt, fread)
+        return unpack(fmt, f_read)
 
 
 # check eof
@@ -45,44 +45,44 @@ def getBytes(fmt, amt):
 f = gzip.open(IN_BAM, 'rb')
 
 print('MAGIC STRING:', f.read(4))
-l_text = getBytes('<i', 1)
+l_text = get_bytes('<i', 1)
 print('l_text:      ', l_text)
 print('text:      \n', f.read(l_text))
-n_ref = getBytes('<i', 1)
+n_ref = get_bytes('<i', 1)
 print('n_ref:       ', n_ref)
 
 for i in range(n_ref):
-    l_name = getBytes('<i', 1)
+    l_name = get_bytes('<i', 1)
     print('ref' + str(i) + ' - l_name:', l_name)
     print('ref' + str(i) + ' - name:  ', f.read(l_name))
-    print('ref' + str(i) + ' - l_ref: ', getBytes('<i', 1))
+    print('ref' + str(i) + ' - l_ref: ', get_bytes('<i', 1))
 
 print('\nEXAMINING ALIGNMENT DATA:\n')
 aln_N = 0
 while True:
     aln_N += 1
-    blockSize = getBytes('<i', 1)
-    if blockSize == None:
+    block_size = get_bytes('<i', 1)
+    if block_size == None:
         break
-    print('[' + str(aln_N) + ']:', 'blockSize:', blockSize)
-    print('-- refID:', getBytes('<i', 1))
-    print('-- pos:  ', getBytes('<i', 1))
-    bmqnl = getBytes('<I', 1)
+    print('[' + str(aln_N) + ']:', 'block_size:', block_size)
+    print('-- refID:', get_bytes('<i', 1))
+    print('-- pos:  ', get_bytes('<i', 1))
+    bmqnl = get_bytes('<I', 1)
     binv = (bmqnl >> 16) & 65535
     mapq = (bmqnl >> 8) & 255
     lrn = bmqnl & 255
     print('-- bmqnl:', bmqnl, '(bin=' + str(binv) + ', mapq=' + str(mapq) + ', l_readname+1=' + str(lrn) + ')')
-    flgnc = getBytes('<I', 1)
+    flgnc = get_bytes('<I', 1)
     flag = (flgnc >> 16) & 65535
     ncig = flgnc & 65535
     print('-- flgnc:', flgnc, '(flag=' + str(flag) + ', ncig=' + str(ncig) + ')')
-    print('-- l_seq:', getBytes('<i', 1))
-    print('-- nxtID:', getBytes('<i', 1))
-    print('-- nxtPo:', getBytes('<i', 1))
-    print('-- tlen: ', getBytes('<i', 1))
+    print('-- l_seq:', get_bytes('<i', 1))
+    print('-- nxtID:', get_bytes('<i', 1))
+    print('-- nxtPo:', get_bytes('<i', 1))
+    print('-- tlen: ', get_bytes('<i', 1))
     print('-- rname:', str([f.read(lrn)])[1:-1])
 
-    f.read(blockSize - 32 - lrn)
+    f.read(block_size - 32 - lrn)
 # print [block]
 
 f.close()
diff --git a/utilities/validateFQ.py b/utilities/validateFQ.py
index bc2db60..20d0a94 100644
--- a/utilities/validateFQ.py
+++ b/utilities/validateFQ.py
@@ -10,7 +10,7 @@
 import sys
 
 
-def get4lines(fn):
+def get_4_lines(fn):
     l1 = fn.readline().strip()
     l2 = fn.readline().strip()
     l3 = fn.readline().strip()
@@ -26,7 +26,7 @@ def get4lines(fn):
 ALLOWED_NUCL = 'ACGTN'
 
 
-def validate4lines(l1, l2, l3, l4):
+def validate_4_lines(l1, l2, l3, l4):
     failed = 0
     # make sure lines contain correct delimiters
     if l1[0] != '@' or l1[-2] != '/' or l3[0] != '+':
@@ -57,17 +57,17 @@ def validate4lines(l1, l2, l3, l4):
 
 
 f1 = open(sys.argv[1], 'r')
-(l1_r1, l2_r1, l3_r1, l4_r1) = get4lines(f1)
+(l1_r1, l2_r1, l3_r1, l4_r1) = get_4_lines(f1)
 f2 = None
 if len(sys.argv) == 3:
     f2 = open(sys.argv[2], 'r')
-    (l1_r2, l2_r2, l3_r2, l4_r2) = get4lines(f2)
+    (l1_r2, l2_r2, l3_r2, l4_r2) = get_4_lines(f2)
 
 while l1_r1:
     # check line syntax
-    validate4lines(l1_r1, l2_r1, l3_r1, l4_r1)
+    validate_4_lines(l1_r1, l2_r1, l3_r1, l4_r1)
     if f2 != None:
-        validate4lines(l1_r2, l2_r2, l3_r2, l4_r2)
+        validate_4_lines(l1_r2, l2_r2, l3_r2, l4_r2)
         # make sure seq id is same for r1/r2
         if l1_r1[:-1] != l1_r2[:-1]:
             print('\nError: mismatched r1/r2 name:\n')
@@ -75,9 +75,9 @@ def validate4lines(l1, l2, l3, l4):
             exit(1)
 
     # grab next 4 lines...
-    (l1_r1, l2_r1, l3_r1, l4_r1) = get4lines(f1)
+    (l1_r1, l2_r1, l3_r1, l4_r1) = get_4_lines(f1)
     if f2 != None:
-        (l1_r2, l2_r2, l3_r2, l4_r2) = get4lines(f2)
+        (l1_r2, l2_r2, l3_r2, l4_r2) = get_4_lines(f2)
 
 if f2 != None:
     f2.close()
diff --git a/utilities/vcf_compare_OLD.py b/utilities/vcf_compare_OLD.py
index acdced3..d2d16e3 100644
--- a/utilities/vcf_compare_OLD.py
+++ b/utilities/vcf_compare_OLD.py
@@ -68,7 +68,7 @@
 WORKFLOW_VCF = OPTS.WVCF
 OUT_PREFIX = OPTS.OUTF
 MAPTRACK = OPTS.MTRK
-MIN_READLEN = OPTS.MTMM
+MIN_READ_LEN = OPTS.MTMM
 BEDFILE = OPTS.TREG
 DP_THRESH = int(OPTS.DP_THRESH)
 AF_THRESH = float(OPTS.AF_THRESH)
@@ -84,14 +84,14 @@
     exit(1)
 
 if OPTS.MTRL is not None:
-    MINREGIONLEN = int(OPTS.MTRL)
+    MIN_REGION_LEN = int(OPTS.MTRL)
 else:
-    MINREGIONLEN = None
+    MIN_REGION_LEN = None
 
-if MIN_READLEN is None:
-    MIN_READLEN = 0
+if MIN_READ_LEN is None:
+    MIN_READ_LEN = 0
 else:
-    MIN_READLEN = int(MIN_READLEN)
+    MIN_READ_LEN = int(MIN_READ_LEN)
 
 if REFERENCE is None:
     print('Error: No reference provided.')
@@ -105,7 +105,7 @@
 if OUT_PREFIX is None:
     print('Error: No output prefix provided.')
     exit(1)
-if (BEDFILE is not None and MINREGIONLEN is None) or (BEDFILE is None and MINREGIONLEN is not None):
+if (BEDFILE is not None and MIN_REGION_LEN is None) or (BEDFILE is None and MIN_REGION_LEN is not None):
     print('Error: Both -t and -T must be specified')
     exit(1)
 
@@ -137,13 +137,13 @@ def quantize_AF(af):
 DP_TOKENS = ['DP', 'DPU', 'DPI']  # in the order that we'll look for them
 
 
-def parseLine(splt, colDict, colSamp):
+def parse_line(splt, col_dict, col_samp):
     #	check if we want to proceed..
-    ra = splt[colDict['REF']]
-    aa = splt[colDict['ALT']]
+    ra = splt[col_dict['REF']]
+    aa = splt[col_dict['ALT']]
     if not (INCLUDE_HOMS) and (aa == '.' or aa == '' or aa == ra):
         return None
-    if not (INCLUDE_FAIL) and (splt[colDict['FILTER']] != 'PASS' and splt[colDict['FILTER']] != '.'):
+    if not (INCLUDE_FAIL) and (splt[col_dict['FILTER']] != 'PASS' and splt[col_dict['FILTER']] != '.'):
         return None
 
     #	default vals
@@ -160,28 +160,28 @@ def parseLine(splt, colDict, colSamp):
     #	cov
     for dp_tok in DP_TOKENS:
         #	check INFO for DP first
-        if 'INFO' in colDict and dp_tok + '=' in splt[colDict['INFO']]:
-            cov = int(re.findall(re.escape(dp_tok) + r"=[0-9]+", splt[colDict['INFO']])[0][3:])
+        if 'INFO' in col_dict and dp_tok + '=' in splt[col_dict['INFO']]:
+            cov = int(re.findall(re.escape(dp_tok) + r"=[0-9]+", splt[col_dict['INFO']])[0][3:])
         #	check FORMAT/SAMPLE for DP second:
-        elif 'FORMAT' in colDict and len(colSamp):
-            format = splt[colDict['FORMAT']] + ':'
+        elif 'FORMAT' in col_dict and len(col_samp):
+            format = splt[col_dict['FORMAT']] + ':'
             if ':' + dp_tok + ':' in format:
-                dpInd = format.split(':').index(dp_tok)
-                cov = int(splt[colSamp[0]].split(':')[dpInd])
+                dp_ind = format.split(':').index(dp_tok)
+                cov = int(splt[col_samp[0]].split(':')[dp_ind])
         if cov is not None:
             break
 
     #	check INFO for AF first
     af = None
-    if 'INFO' in colDict and ';AF=' in ';' + splt[colDict['INFO']]:
-        info = splt[colDict['INFO']] + ';'
+    if 'INFO' in col_dict and ';AF=' in ';' + splt[col_dict['INFO']]:
+        info = splt[col_dict['INFO']] + ';'
         af = re.findall(r"AF=.*?(?=;)", info)[0][3:]
     #	check FORMAT/SAMPLE for AF second:
-    elif 'FORMAT' in colDict and len(colSamp):
-        format = splt[colDict['FORMAT']] + ':'
+    elif 'FORMAT' in col_dict and len(col_samp):
+        format = splt[col_dict['FORMAT']] + ':'
         if ':AF:' in format:
-            afInd = splt[colDict['FORMAT']].split(':').index('AF')
-            af = splt[colSamp[0]].split(':')[afInd]
+            af_ind = splt[col_dict['FORMAT']].split(':').index('AF')
+            af = splt[col_samp[0]].split(':')[af_ind]
 
     if af is not None:
         af_splt = af.split(',')
@@ -193,43 +193,43 @@ def parseLine(splt, colDict, colSamp):
         alt_freqs = [None] * max([len(alt_alleles), 1])
 
     #	get QUAL if it's interesting
-    if 'QUAL' in colDict and splt[colDict['QUAL']] != '.':
-        qual = float(splt[colDict['QUAL']])
+    if 'QUAL' in col_dict and splt[col_dict['QUAL']] != '.':
+        qual = float(splt[col_dict['QUAL']])
 
     return (cov, qual, alt_alleles, alt_freqs)
 
 
-def parseVCF(VCF_FILENAME, refName, targRegionsFl, outFile, outBool):
+def parse_vcf(VCF_FILENAME, ref_name, targ_regions_FL, out_file, out_bool):
     v_Hashed = {}
-    v_posHash = {}
+    v_pos_Hash = {}
     v_Alts = {}
     v_Cov = {}
     v_AF = {}
     v_Qual = {}
-    v_TargLen = {}
-    nBelowMinRLen = 0
+    v_Targ_Len = {}
+    n_below_min_R_Len = 0
     line_unique = 0  # number of lines in vcf file containing unique variant
     hash_coll = 0  # number of times we saw a hash collision ("per line" so non-unique alt alleles don't get counted multiple times)
     var_filtered = 0  # number of variants excluded due to filters (e.g. hom-refs, qual)
     var_merged = 0  # number of variants we merged into another due to having the same position specified
-    colDict = {}
-    colSamp = []
+    col_dict = {}
+    col_samp = []
     for line in open(VCF_FILENAME, 'r'):
         if line[0] != '#':
-            if len(colDict) == 0:
+            if len(col_dict) == 0:
                 print('\n\nError: VCF has no header?\n' + VCF_FILENAME + '\n\n')
                 exit(1)
             splt = line[:-1].split('\t')
-            if splt[0] == refName:
+            if splt[0] == ref_name:
 
                 var = (int(splt[1]), splt[3], splt[4])
-                targInd = bisect.bisect(targRegionsFl, var[0])
+                targ_ind = bisect.bisect(targ_regions_FL, var[0])
 
-                if targInd % 2 == 1:
-                    targLen = targRegionsFl[targInd] - targRegionsFl[targInd - 1]
-                    if (BEDFILE is not None and targLen >= MINREGIONLEN) or BEDFILE is None:
+                if targ_ind % 2 == 1:
+                    targ_Len = targ_regions_FL[targ_ind] - targ_regions_FL[targ_ind - 1]
+                    if (BEDFILE is not None and targ_Len >= MIN_REGION_LEN) or BEDFILE is None:
 
-                        pl_out = parseLine(splt, colDict, colSamp)
+                        pl_out = parse_line(splt, col_dict, col_samp)
                         if pl_out is None:
                             var_filtered += 1
                             continue
@@ -237,22 +237,22 @@ def parseVCF(VCF_FILENAME, refName, targRegionsFl, outFile, outBool):
 
                         if var not in v_Hashed:
 
-                            vpos = var[0]
-                            if vpos in v_posHash:
+                            v_pos = var[0]
+                            if v_pos in v_pos_Hash:
                                 if len(aa) == 0:
                                     aa = [var[2]]
-                                aa.extend([n[2] for n in v_Hashed.keys() if n[0] == vpos])
+                                aa.extend([n[2] for n in v_Hashed.keys() if n[0] == v_pos])
                                 var_merged += 1
-                            v_posHash[vpos] = 1
+                            v_pos_Hash[v_pos] = 1
 
                             if len(aa):
-                                allVars = [(var[0], var[1], n) for n in aa]
-                                for i in range(len(allVars)):
-                                    v_Hashed[allVars[i]] = 1
-                                    # if allVars[i] not in v_Alts:
-                                    #	v_Alts[allVars[i]] = []
-                                    # v_Alts[allVars[i]].extend(allVars)
-                                    v_Alts[allVars[i]] = allVars
+                                all_vars = [(var[0], var[1], n) for n in aa]
+                                for i in range(len(all_vars)):
+                                    v_Hashed[all_vars[i]] = 1
+                                    # if all_vars[i] not in v_Alts:
+                                    #	v_Alts[all_vars[i]] = []
+                                    # v_Alts[all_vars[i]].extend(all_vars)
+                                    v_Alts[all_vars[i]] = all_vars
                             else:
                                 v_Hashed[var] = 1
 
@@ -260,78 +260,78 @@ def parseVCF(VCF_FILENAME, refName, targRegionsFl, outFile, outBool):
                                 v_Cov[var] = cov
                             v_AF[var] = af[0]  # only use first AF, even if multiple. fix this later?
                             v_Qual[var] = qual
-                            v_TargLen[var] = targLen
+                            v_Targ_Len[var] = targ_Len
                             line_unique += 1
 
                         else:
                             hash_coll += 1
 
                     else:
-                        nBelowMinRLen += 1
+                        n_below_min_R_Len += 1
         else:
             if line[1] != '#':
                 cols = line[1:-1].split('\t')
                 for i in range(len(cols)):
-                    if 'FORMAT' in colDict:
-                        colSamp.append(i)
-                    colDict[cols[i]] = i
-                if VCF_OUT and outBool:
-                    outBool = False
-                    outFile.write(line)
+                    if 'FORMAT' in col_dict:
+                        col_samp.append(i)
+                    col_dict[cols[i]] = i
+                if VCF_OUT and out_bool:
+                    out_bool = False
+                    out_file.write(line)
 
     return (
-        v_Hashed, v_Alts, v_Cov, v_AF, v_Qual, v_TargLen, nBelowMinRLen, line_unique, var_filtered, var_merged,
+        v_Hashed, v_Alts, v_Cov, v_AF, v_Qual, v_Targ_Len, n_below_min_R_Len, line_unique, var_filtered, var_merged,
         hash_coll)
 
 
-def condenseByPos(listIn):
-    varListOfInterest = [n for n in listIn]
-    indCount = {}
-    for n in varListOfInterest:
+def condense_by_pos(list_in):
+    var_list_of_interest = [n for n in list_in]
+    ind_count = {}
+    for n in var_list_of_interest:
         c = n[0]
-        if c not in indCount:
-            indCount[c] = 0
-        indCount[c] += 1
-    # nonUniqueDict = {n:[] for n in sorted(indCount.keys()) if indCount[n] > 1}		# the python 2.7 way
-    nonUniqueDict = {}
-    for n in sorted(indCount.keys()):
-        if indCount[n] > 1:
-            nonUniqueDict[n] = []
-    delList = []
-    for i in range(len(varListOfInterest)):
-        if varListOfInterest[i][0] in nonUniqueDict:
-            nonUniqueDict[varListOfInterest[i][0]].append(varListOfInterest[i])
-            delList.append(i)
-    delList = sorted(delList, reverse=True)
-    for di in delList:
-        del varListOfInterest[di]
-    for v in nonUniqueDict.values():
+        if c not in ind_count:
+            ind_count[c] = 0
+        ind_count[c] += 1
+    # non_unique_dict = {n:[] for n in sorted(ind_count.keys()) if ind_count[n] > 1}		# the python 2.7 way
+    non_unique_dict = {}
+    for n in sorted(ind_count.keys()):
+        if ind_count[n] > 1:
+            non_unique_dict[n] = []
+    del_list = []
+    for i in range(len(var_list_of_interest)):
+        if var_list_of_interest[i][0] in non_unique_dict:
+            non_unique_dict[var_list_of_interest[i][0]].append(var_list_of_interest[i])
+            del_list.append(i)
+    del_list = sorted(del_list, reverse=True)
+    for di in del_list:
+        del var_list_of_interest[di]
+    for v in non_unique_dict.values():
         var = (v[0][0], v[0][1], ','.join([n[2] for n in v[::-1]]))
-        varListOfInterest.append(var)
-    return varListOfInterest
+        var_list_of_interest.append(var)
+    return var_list_of_interest
 
 
 def main():
     ref = []
     f = open(REFERENCE, 'r')
-    nLines = 0
-    prevR = None
-    prevP = None
+    n_lines = 0
+    prev_R = None
+    prev_P = None
     ref_inds = []
     sys.stdout.write('\nindexing reference fasta... ')
     sys.stdout.flush()
     tt = time.time()
     while 1:
-        nLines += 1
+        n_lines += 1
         data = f.readline()
         if not data:
-            ref_inds.append((prevR, prevP, f.tell() - len(data)))
+            ref_inds.append((prev_R, prev_P, f.tell() - len(data)))
             break
         if data[0] == '>':
-            if prevP is not None:
-                ref_inds.append((prevR, prevP, f.tell() - len(data)))
-            prevP = f.tell()
-            prevR = data[1:-1]
+            if prev_P is not None:
+                ref_inds.append((prev_R, prev_P, f.tell() - len(data)))
+            prev_P = f.tell()
+            prev_R = data[1:-1]
     print('{0:.3f} (sec)'.format(time.time() - tt))
     # ref_inds = [('chrM', 6, 16909), ('chr1', 16915, 254252549), ('chr2', 254252555, 502315916), ('chr3', 502315922, 704298801), ('chr4', 704298807, 899276169), ('chr5', 899276175, 1083809741), ('chr6', 1083809747, 1258347116), ('chr7', 1258347122, 1420668559), ('chr8', 1420668565, 1569959868), ('chr9', 1569959874, 1713997574), ('chr10', 1713997581, 1852243023), ('chr11', 1852243030, 1989949677), ('chr12', 1989949684, 2126478617), ('chr13', 2126478624, 2243951900), ('chr14', 2243951907, 2353448438), ('chr15', 2353448445, 2458030465), ('chr16', 2458030472, 2550192321), ('chr17', 2550192328, 2633011443), ('chr18', 2633011450, 2712650243), ('chr19', 2712650250, 2772961813), ('chr20', 2772961820, 2837247851), ('chr21', 2837247858, 2886340351), ('chr22', 2886340358, 2938671016), ('chrX', 2938671022, 3097046994), ('chrY', 3097047000, 3157608038)]
 
@@ -353,41 +353,41 @@ def main():
     mappability_vs_FN = {0: 0,
                          1: 0}  # [0] = # of FNs that were in mappable regions, [1] = # of FNs that were in unmappable regions
     coverage_vs_FN = {}  # [C] = # of FNs that were covered by C reads
-    alleleBal_vs_FN = {}  # [AF] = # of FNs that were heterozygous genotypes with allele freq AF (quantized to multiples of 1/AF_STEPS)
+    allele_bal_vs_FN = {}  # [AF] = # of FNs that were heterozygous genotypes with allele freq AF (quantized to multiples of 1/AF_STEPS)
     for n in AF_KEYS:
-        alleleBal_vs_FN[n] = 0
+        allele_bal_vs_FN[n] = 0
 
     #
     #	read in mappability track
     #
     mappability_tracks = {}  # indexed by chr string (e.g. 'chr1'), has boolean array
-    prevRef = ''
-    relevantRegions = []
+    prev_Ref = ''
+    relevant_regions = []
     if MAPTRACK is not None:
         mtf = open(MAPTRACK, 'r')
         for line in mtf:
             splt = line.strip().split('\t')
-            if prevRef != '' and splt[0] != prevRef:
+            if prev_Ref != '' and splt[0] != prev_Ref:
                 # do stuff
-                if len(relevantRegions):
-                    myTrack = [0] * (relevantRegions[-1][1] + 100)
-                    for r in relevantRegions:
+                if len(relevant_regions):
+                    my_track = [0] * (relevant_regions[-1][1] + 100)
+                    for r in relevant_regions:
                         for ri in range(r[0], r[1]):
-                            myTrack[ri] = 1
-                    mappability_tracks[prevRef] = [n for n in myTrack]
+                            my_track[ri] = 1
+                    mappability_tracks[prev_Ref] = [n for n in my_track]
                 #
-                relevantRegions = []
-            if int(splt[3]) >= MIN_READLEN:
-                relevantRegions.append((int(splt[1]), int(splt[2])))
-            prevRef = splt[0]
+                relevant_regions = []
+            if int(splt[3]) >= MIN_READ_LEN:
+                relevant_regions.append((int(splt[1]), int(splt[2])))
+            prev_Ref = splt[0]
         mtf.close()
         # do stuff
-        if len(relevantRegions):
-            myTrack = [0] * (relevantRegions[-1][1] + 100)
-            for r in relevantRegions:
+        if len(relevant_regions):
+            my_track = [0] * (relevant_regions[-1][1] + 100)
+            for r in relevant_regions:
                 for ri in range(r[0], r[1]):
-                    myTrack[ri] = 1
-            mappability_tracks[prevRef] = [n for n in myTrack]
+                    my_track[ri] = 1
+            mappability_tracks[prev_Ref] = [n for n in my_track]
     #
 
     #
@@ -395,15 +395,15 @@ def main():
     #
     vcfo2 = None
     vcfo3 = None
-    global vcfo2_firstTime
-    global vcfo3_firstTime
-    vcfo2_firstTime = False
-    vcfo3_firstTime = False
+    global vcfo2_first_time
+    global vcfo3_first_time
+    vcfo2_first_time = False
+    vcfo3_first_time = False
     if VCF_OUT:
         vcfo2 = open(OUT_PREFIX + '_FN.vcf', 'w')
         vcfo3 = open(OUT_PREFIX + '_FP.vcf', 'w')
-        vcfo2_firstTime = True
-        vcfo3_firstTime = True
+        vcfo2_first_time = True
+        vcfo3_first_time = True
 
     #
     #	data for plotting FN analysis
@@ -411,7 +411,7 @@ def main():
     set1 = []
     set2 = []
     set3 = []
-    varAdj = 0
+    var_adj = 0
 
     #
     #
@@ -420,99 +420,99 @@ def main():
     #
     for n_RI in ref_inds:
 
-        refName = n_RI[0]
+        ref_name = n_RI[0]
         if FAST == False:
             f.seek(n_RI[1])
-            print('reading ' + refName + '...', end=' ')
-            myDat = f.read(n_RI[2] - n_RI[1]).split('\n')
-            myLen = sum([len(m) for m in myDat])
+            print('reading ' + ref_name + '...', end=' ')
+            my_dat = f.read(n_RI[2] - n_RI[1]).split('\n')
+            my_len = sum([len(m) for m in my_dat])
             if sys.version_info >= (2, 7):
-                print('{:,} bp'.format(myLen))
+                print('{:,} bp'.format(my_len))
             else:
-                print('{0:} bp'.format(myLen))
-            inWidth = len(myDat[0])
-            if len(myDat[-1]) == 0:  # if last line is empty, remove it.
-                del myDat[-1]
-            if inWidth * (len(myDat) - 1) + len(myDat[-1]) != myLen:
+                print('{0:} bp'.format(my_len))
+            in_width = len(my_dat[0])
+            if len(my_dat[-1]) == 0:  # if last line is empty, remove it.
+                del my_dat[-1]
+            if in_width * (len(my_dat) - 1) + len(my_dat[-1]) != my_len:
                 print('fasta column-width not consistent.')
-                print(myLen, inWidth * (len(myDat) - 1) + len(myDat[-1]))
-                for i in range(len(myDat)):
-                    if len(myDat[i]) != inWidth:
-                        print(i, len(myDat[i]), inWidth)
+                print(my_len, in_width * (len(my_dat) - 1) + len(my_dat[-1]))
+                for i in range(len(my_dat)):
+                    if len(my_dat[i]) != in_width:
+                        print(i, len(my_dat[i]), in_width)
                 exit(1)
 
-            myDat = Seq(''.join(myDat)).upper().tomutable()
-            myLen = len(myDat)
+            my_dat = Seq(''.join(my_dat)).upper().tomutable()
+            my_len = len(my_dat)
 
         #
         #	Parse relevant targeted regions
         #
-        targRegionsFl = []
+        targ_regions_FL = []
         if BEDFILE is not None:
             bedfile = open(BEDFILE, 'r')
             for line in bedfile:
                 splt = line.split('\t')
-                if splt[0] == refName:
-                    targRegionsFl.extend((int(splt[1]), int(splt[2])))
+                if splt[0] == ref_name:
+                    targ_regions_FL.extend((int(splt[1]), int(splt[2])))
             bedfile.close()
         else:
-            targRegionsFl = [-1, MAX_VAL + 1]
+            targ_regions_FL = [-1, MAX_VAL + 1]
 
         #
         #	Parse vcf files
         #
-        sys.stdout.write('comparing variation in ' + refName + '... ')
+        sys.stdout.write('comparing variation in ' + ref_name + '... ')
         sys.stdout.flush()
         tt = time.time()
 
-        (correctHashed, correctAlts, correctCov, correctAF, correctQual, correctTargLen, correctBelowMinRLen,
-         correctUnique, gFiltered, gMerged, gRedundant) = parseVCF(GOLDEN_VCF, refName, targRegionsFl, vcfo2,
-                                                                   vcfo2_firstTime)
-        (workflowHashed, workflowAlts, workflowCov, workflowAF, workflowQual, workflowTarLen, workflowBelowMinRLen,
-         workflowUnique, wFiltered, wMerged, wRedundant) = parseVCF(WORKFLOW_VCF, refName, targRegionsFl, vcfo3,
-                                                                    vcfo3_firstTime)
-        zgF += gFiltered
-        zgR += gRedundant
-        zgM += gMerged
-        zwF += wFiltered
-        zwR += wRedundant
-        zwM += wMerged
+        (correct_hashed, correct_alts, correct_cov, correct_AF, correct_qual, correct_targ_len, correct_below_min_R_len,
+         correct_unique, g_filtered, g_merged, g_redundant) = parse_vcf(GOLDEN_VCF, ref_name, targ_regions_FL, vcfo2,
+                                                                   vcfo2_first_time)
+        (workflow_hashed, workflow_alts, workflow_COV, workflow_AF, workflow_qual, workflow_tar_len, workflow_below_min_R_len,
+         workflow_unique, w_filtered, w_merged, w_redundant) = parse_vcf(WORKFLOW_VCF, ref_name, targ_regions_FL, vcfo3,
+                                                                    vcfo3_first_time)
+        zgF += g_filtered
+        zgR += g_redundant
+        zgM += g_merged
+        zwF += w_filtered
+        zwR += w_redundant
+        zwM += w_merged
 
         #
         #	Deduce which variants are FP / FN
         #
-        solvedInds = {}
-        for var in correctHashed.keys():
-            if var in workflowHashed or var[0] in solvedInds:
-                correctHashed[var] = 2
-                workflowHashed[var] = 20
-                solvedInds[var[0]] = True
-        for var in list(correctHashed.keys()) + list(workflowHashed.keys()):
-            if var[0] in solvedInds:
-                correctHashed[var] = 2
-                workflowHashed[var] = 2
-        nPerfect = len(solvedInds)
-
-        # correctHashed[var] = 1: were not found
+        solved_inds = {}
+        for var in correct_hashed.keys():
+            if var in workflow_hashed or var[0] in solved_inds:
+                correct_hashed[var] = 2
+                workflow_hashed[var] = 20
+                solved_inds[var[0]] = True
+        for var in list(correct_hashed.keys()) + list(workflow_hashed.keys()):
+            if var[0] in solved_inds:
+                correct_hashed[var] = 2
+                workflow_hashed[var] = 2
+        n_perfect = len(solved_inds)
+
+        # correct_hashed[var] = 1: were not found
         #                    = 2: should be discluded because we were found
         #                    = 3: should be discluded because an alt was found
-        notFound = [n for n in sorted(correctHashed.keys()) if correctHashed[n] == 1]
-        FPvariants = [n for n in sorted(workflowHashed.keys()) if workflowHashed[n] == 1]
+        not_found = [n for n in sorted(correct_hashed.keys()) if correct_hashed[n] == 1]
+        FP_variants = [n for n in sorted(workflow_hashed.keys()) if workflow_hashed[n] == 1]
 
         #
         #	condense all variants who have alternate alleles and were *not* found to have perfect matches
         #	into a single variant again. These will not be included in the candidates for equivalency checking. Sorry!
         #
-        notFound = condenseByPos(notFound)
-        FPvariants = condenseByPos(FPvariants)
+        not_found = condense_by_pos(not_found)
+        FP_variants = condense_by_pos(FP_variants)
 
         #
         #	tally up some values, if there are no golden variants lets save some CPU cycles and move to the next ref
         #
-        totalGoldenVariants = nPerfect + len(notFound)
-        totalWorkflowVariants = nPerfect + len(FPvariants)
-        if totalGoldenVariants == 0:
-            zfP += len(FPvariants)
+        tot_golden_variants = n_perfect + len(not_found)
+        totalWorkflowVariants = n_perfect + len(FP_variants)
+        if tot_golden_variants == 0:
+            zfP += len(FP_variants)
             ztW += totalWorkflowVariants
             print('{0:.3f} (sec)'.format(time.time() - tt))
             continue
@@ -521,146 +521,146 @@ def main():
         #	let's check for equivalent variants
         #
         if FAST == False:
-            delList_i = []
-            delList_j = []
-            regionsToCheck = []
-            for i in range(len(FPvariants)):
-                pos = FPvariants[i][0]
-                regionsToCheck.append((max([pos - EV_BPRANGE - 1, 0]), min([pos + EV_BPRANGE, len(myDat) - 1])))
-
-            for n in regionsToCheck:
-                refSection = myDat[n[0]:n[1]]
-
-                fpWithin = []
-                for i in range(len(FPvariants)):
-                    m = FPvariants[i]
+            del_list_i = []
+            del_list_j = []
+            regions_to_check = []
+            for i in range(len(FP_variants)):
+                pos = FP_variants[i][0]
+                regions_to_check.append((max([pos - EV_BPRANGE - 1, 0]), min([pos + EV_BPRANGE, len(my_dat) - 1])))
+
+            for n in regions_to_check:
+                ref_section = my_dat[n[0]:n[1]]
+
+                FP_within = []
+                for i in range(len(FP_variants)):
+                    m = FP_variants[i]
                     if (m[0] > n[0] and m[0] < n[1]):
-                        fpWithin.append((m, i))
-                fpWithin = sorted(fpWithin)
+                        FP_within.append((m, i))
+                FP_within = sorted(FP_within)
                 adj = 0
-                altSection = copy.deepcopy(refSection)
-                for (m, i) in fpWithin:
+                alt_section = copy.deepcopy(ref_section)
+                for (m, i) in FP_within:
                     lr = len(m[1])
                     la = len(m[2])
-                    dpos = m[0] - n[0] + adj
-                    altSection = altSection[:dpos - 1] + m[2] + altSection[dpos - 1 + lr:]
+                    d_pos = m[0] - n[0] + adj
+                    alt_section = alt_section[:d_pos - 1] + m[2] + alt_section[d_pos - 1 + lr:]
                     adj += la - lr
 
-                nfWithin = []
-                for j in range(len(notFound)):
-                    m = notFound[j]
+                nf_within = []
+                for j in range(len(not_found)):
+                    m = not_found[j]
                     if (m[0] > n[0] and m[0] < n[1]):
-                        nfWithin.append((m, j))
-                nfWithin = sorted(nfWithin)
+                        nf_within.append((m, j))
+                nf_within = sorted(nf_within)
                 adj = 0
-                altSection2 = copy.deepcopy(refSection)
-                for (m, j) in nfWithin:
+                alt_section2 = copy.deepcopy(ref_section)
+                for (m, j) in nf_within:
                     lr = len(m[1])
                     la = len(m[2])
-                    dpos = m[0] - n[0] + adj
-                    altSection2 = altSection2[:dpos - 1] + m[2] + altSection2[dpos - 1 + lr:]
+                    d_pos = m[0] - n[0] + adj
+                    alt_section2 = alt_section2[:d_pos - 1] + m[2] + alt_section2[d_pos - 1 + lr:]
                     adj += la - lr
 
-                if altSection == altSection2:
-                    for (m, i) in fpWithin:
-                        if i not in delList_i:
-                            delList_i.append(i)
-                    for (m, j) in nfWithin:
-                        if j not in delList_j:
-                            delList_j.append(j)
-
-            nEquiv = 0
-            for i in sorted(list(set(delList_i)), reverse=True):
-                del FPvariants[i]
-            for j in sorted(list(set(delList_j)), reverse=True):
-                del notFound[j]
-                nEquiv += 1
-            nPerfect += nEquiv
+                if alt_section == alt_section2:
+                    for (m, i) in FP_within:
+                        if i not in del_list_i:
+                            del_list_i.append(i)
+                    for (m, j) in nf_within:
+                        if j not in del_list_j:
+                            del_list_j.append(j)
+
+            n_equiv = 0
+            for i in sorted(list(set(del_list_i)), reverse=True):
+                del FP_variants[i]
+            for j in sorted(list(set(del_list_j)), reverse=True):
+                del not_found[j]
+                n_equiv += 1
+            n_perfect += n_equiv
 
         #
         #	Tally up errors and whatnot
         #
-        ztV += totalGoldenVariants
+        ztV += tot_golden_variants
         ztW += totalWorkflowVariants
-        znP += nPerfect
-        zfP += len(FPvariants)
-        znF += len(notFound)
+        znP += n_perfect
+        zfP += len(FP_variants)
+        znF += len(not_found)
         if FAST is False:
-            znE += nEquiv
+            znE += n_equiv
         if BEDFILE is not None:
-            zbM += correctBelowMinRLen
+            zbM += correct_below_min_R_len
 
         #
         #	try to identify a reason for FN variants:
         #
 
-        venn_data = [[0, 0, 0] for n in notFound]  # [i] = (unmappable, low cov, low het)
-        for i in range(len(notFound)):
-            var = notFound[i]
+        venn_data = [[0, 0, 0] for n in not_found]  # [i] = (unmappable, low cov, low het)
+        for i in range(len(not_found)):
+            var = not_found[i]
 
-            noReason = True
+            no_reason = True
 
             #	mappability?
             if MAPTRACK is not None:
-                if refName in mappability_tracks and var[0] < len(mappability_tracks[refName]):
-                    if mappability_tracks[refName][var[0]]:
+                if ref_name in mappability_tracks and var[0] < len(mappability_tracks[ref_name]):
+                    if mappability_tracks[ref_name][var[0]]:
                         mappability_vs_FN[1] += 1
                         venn_data[i][0] = 1
-                        noReason = False
+                        no_reason = False
                     else:
                         mappability_vs_FN[0] += 1
 
             #	coverage?
-            if var in correctCov:
-                c = correctCov[var]
+            if var in correct_cov:
+                c = correct_cov[var]
                 if c is not None:
                     if c not in coverage_vs_FN:
                         coverage_vs_FN[c] = 0
                     coverage_vs_FN[c] += 1
                     if c < DP_THRESH:
                         venn_data[i][1] = 1
-                        noReason = False
+                        no_reason = False
 
             #	heterozygous genotype messing things up?
-            # if var in correctAF:
-            #	a = correctAF[var]
+            # if var in correct_AF:
+            #	a = correct_AF[var]
             #	if a != None:
             #		a = AF_KEYS[quantize_AF(a)]
-            #		if a not in alleleBal_vs_FN:
-            #			alleleBal_vs_FN[a] = 0
-            #		alleleBal_vs_FN[a] += 1
+            #		if a not in allele_bal_vs_FN:
+            #			allele_bal_vs_FN[a] = 0
+            #		allele_bal_vs_FN[a] += 1
             #		if a < AF_THRESH:
             #			venn_data[i][2] = 1
 
             #	no reason?
-            if noReason:
+            if no_reason:
                 venn_data[i][2] += 1
 
-        for i in range(len(notFound)):
-            if venn_data[i][0]: set1.append(i + varAdj)
-            if venn_data[i][1]: set2.append(i + varAdj)
-            if venn_data[i][2]: set3.append(i + varAdj)
-        varAdj += len(notFound)
+        for i in range(len(not_found)):
+            if venn_data[i][0]: set1.append(i + var_adj)
+            if venn_data[i][1]: set2.append(i + var_adj)
+            if venn_data[i][2]: set3.append(i + var_adj)
+        var_adj += len(not_found)
 
         #
         #	if desired, write out vcf files.
         #
-        notFound = sorted(notFound)
-        FPvariants = sorted(FPvariants)
+        not_found = sorted(not_found)
+        FP_variants = sorted(FP_variants)
         if VCF_OUT:
             for line in open(GOLDEN_VCF, 'r'):
                 if line[0] != '#':
                     splt = line.split('\t')
-                    if splt[0] == refName:
+                    if splt[0] == ref_name:
                         var = (int(splt[1]), splt[3], splt[4])
-                        if var in notFound:
+                        if var in not_found:
                             vcfo2.write(line)
             for line in open(WORKFLOW_VCF, 'r'):
                 if line[0] != '#':
                     splt = line.split('\t')
-                    if splt[0] == refName:
+                    if splt[0] == ref_name:
                         var = (int(splt[1]), splt[3], splt[4])
-                        if var in FPvariants:
+                        if var in FP_variants:
                             vcfo3.write(line)
 
         print('{0:.3f} (sec)'.format(time.time() - tt))
@@ -679,7 +679,7 @@ def main():
     #	plot some FN stuff
     #
     if NO_PLOT == False:
-        nDetected = len(set(set1 + set2 + set3))
+        n_detected = len(set(set1 + set2 + set3))
         set1 = set(set1)
         set2 = set(set2)
         set3 = set(set3)
@@ -700,7 +700,7 @@ def main():
 
         mpl.figure(0)
         tstr1 = 'False Negative Variants (Missed Detections)'
-        # tstr2 = str(nDetected)+' / '+str(znF)+' FN variants categorized'
+        # tstr2 = str(n_detected)+' / '+str(znF)+' FN variants categorized'
         tstr2 = ''
         if MAPTRACK is not None:
             v = venn3([set1, set2, set3], (s1, s2, s3))

From 40044fd2fae2928d82a2a568163edb340dcdc98d Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 1 Jul 2020 16:15:17 -0500
Subject: [PATCH 083/323] Fixed a bug that was slowing down gen_mut_model.py

---
 utilities/genSeqErrorModel.py |  4 ++--
 utilities/gen_mut_model.py    | 22 +++++++++++-----------
 2 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index a3072c0..9fc247f 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -116,11 +116,11 @@ def parse_fq(inf):
 
     init_q = [[0. for m in range(r_q)] for n in range(actual_readlen)]
     for i in range(actual_readlen):
-        row_sum = float(np.sum(prior_q[i, :])) + INIT_SMOOTH * r_q
+        row_sum = float(np.sum(prior_q[i, :])) + init_smooth * r_q
         if row_sum <= 0.:
             continue
         for j in range(r_q):
-            init_q[i][j] = (prior_q[i][j] + INIT_SMOOTH) / row_sum
+            init_q[i][j] = (prior_q[i][j] + init_smooth) / row_sum
 
     if plot_stuff:
         mpl.rcParams.update({'font.size': 14, 'font.weight': 'bold', 'lines.linewidth': 3})
diff --git a/utilities/gen_mut_model.py b/utilities/gen_mut_model.py
index 2b1a14d..37f2a71 100644
--- a/utilities/gen_mut_model.py
+++ b/utilities/gen_mut_model.py
@@ -225,23 +225,23 @@ def main():
 
     # Count Trinucleotides in reference, based on bed or not
     print('Counting trinucleotides in reference...')
-    if is_bed:
-        print("since you're using a bed input, we have to count trinucs in bed region even if\n"
-              "you already have a trinuc count file for the reference...")
-        sub_regions = matching_bed['coords'].to_list()
-        for sr in sub_regions:
-            for ref_name in matching_chromosomes:
+    for ref_name in matching_chromosomes:
+        if is_bed:
+            print("since you're using a bed input, we have to count trinucs in bed region even if "
+                  "you already have a trinuc count file for the reference...")
+            sub_bed = matching_bed[matching_bed['chrom'] == ref_name]
+            sub_regions = sub_bed['coords'].to_list()
+            for sr in sub_regions:
                 for i in range(sr[0], sr[1] - 2):
                     trinuc = str(ref_dict[ref_name][i:i + 3].seq)
                     # skip if trinuc contains invalid characters
-                    if not trinuc in VALID_TRINUC:
+                    if trinuc not in VALID_TRINUC:
                         continue
                     if trinuc not in TRINUC_REF_COUNT:
                         TRINUC_REF_COUNT[trinuc] = 0
                     TRINUC_REF_COUNT[trinuc] += 1
 
-    elif not os.path.isfile(ref + '.trinucCounts'):
-        for ref_name in matching_chromosomes:
+        elif not os.path.isfile(ref + '.trinucCounts'):
             for i in range(len(ref_dict[ref_name]) - 2):
                 trinuc = str(ref_dict[ref_name][i:i + 3].seq)
                 # skip if trinuc contains invalid characters
@@ -250,8 +250,8 @@ def main():
                 if trinuc not in TRINUC_REF_COUNT:
                     TRINUC_REF_COUNT[trinuc] = 0
                 TRINUC_REF_COUNT[trinuc] += 1
-    else:
-        print('Found trinucCounts file, using that.')
+        else:
+            print('Found trinucCounts file, using that.')
 
     # Load and process variants in each reference sequence individually, for memory reasons...
     print('Creating mutational model...')

From e49f9b13feb1a1c10bb9aa39626f081e8bf022bb Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Thu, 2 Jul 2020 13:20:06 -0500
Subject: [PATCH 084/323] Changed r_q to RQ

---
 utilities/genSeqErrorModel.py | 35 +++++++++++++++++++----------------
 1 file changed, 19 insertions(+), 16 deletions(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 7625b05..83d8c13 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -27,6 +27,7 @@
 
 
 def parse_fq(inf):
+    #Takes a gzip or sam file and returns the simulation's average error rate, 
     print('reading ' + inf + '...')
     if inf[-3:] == '.gz':
         print('detected gzip suffix...')
@@ -69,8 +70,8 @@ def parse_fq(inf):
             actual_readlen = len(data4) - 1
             print('assuming read length is uniform...')
             print('detected read length (from first read found):', actual_readlen)
-            prior_q = np.zeros([actual_readlen, r_q])
-            total_q = [None] + [np.zeros([r_q, r_q]) for n in range(actual_readlen - 1)]
+            prior_q = np.zeros([actual_readlen, RQ])
+            total_q = [None] + [np.zeros([RQ, RQ]) for n in range(actual_readlen - 1)]
 
         # sanity-check readlengths
         if len(data4) - 1 != actual_readlen:
@@ -100,26 +101,26 @@ def parse_fq(inf):
     if q_range[0] < 0:
         print('\nError: Read in Q-scores below 0\n')
         exit(1)
-    if q_range[1] > r_q:
-        print('\nError: Read in Q-scores above specified maximum:', q_range[1], '>', r_q, '\n')
+    if q_range[1] > RQ:
+        print('\nError: Read in Q-scores above specified maximum:', q_range[1], '>', RQ, '\n')
         exit(1)
 
     print('computing probabilities...')
-    prob_q = [None] + [[[0. for m in range(r_q)] for n in range(r_q)] for p in range(actual_readlen - 1)]
+    prob_q = [None] + [[[0. for m in range(RQ)] for n in range(RQ)] for p in range(actual_readlen - 1)]
     for p in range(1, actual_readlen):
-        for i in range(r_q):
-            row_sum = float(np.sum(total_q[p][i, :])) + prob_smooth * r_q
+        for i in range(RQ):
+            row_sum = float(np.sum(total_q[p][i, :])) + prob_smooth * RQ
             if row_sum <= 0.:
                 continue
-            for j in range(r_q):
+            for j in range(RQ):
                 prob_q[p][i][j] = (total_q[p][i][j] + prob_smooth) / row_sum
 
-    init_q = [[0. for m in range(r_q)] for n in range(actual_readlen)]
+    init_q = [[0. for m in range(RQ)] for n in range(actual_readlen)]
     for i in range(actual_readlen):
-        row_sum = float(np.sum(prior_q[i, :])) + init_smooth * r_q
+        row_sum = float(np.sum(prior_q[i, :])) + init_smooth * RQ
         if row_sum <= 0.:
             continue
-        for j in range(r_q):
+        for j in range(RQ):
             init_q[i][j] = (prior_q[i][j] + init_smooth) / row_sum
 
     if plot_stuff:
@@ -144,7 +145,7 @@ def parse_fq(inf):
         q_labels = [str(n) for n in range(q_range[0], q_range[1] + 1) if n % 5 == 0]
         print(q_labels)
         q_ticks_x = [int(n) + 0.5 for n in q_labels]
-        q_ticks_y = [(r_q - int(n)) - 0.5 for n in q_labels]
+        q_ticks_y = [(RQ - int(n)) - 0.5 for n in q_labels]
 
         for p in range(1, actual_readlen, 10):
             current_data = np.array(prob_q[p])
@@ -173,7 +174,7 @@ def parse_fq(inf):
             x, y = np.meshgrid(range(0, len(Z[0]) + 1), range(0, len(Z) + 1))
             mpl.pcolormesh(x, y, z[::-1, :], vmin=v_min_log[0], vmax=v_min_log[1], cmap='jet')
             mpl.xlim([q_range[0], q_range[1] + 1])
-            mpl.ylim([r_q - q_range[1] - 1, r_q - q_range[0]])
+            mpl.ylim([RQ - q_range[1] - 1, RQ - q_range[0]])
             mpl.yticks(q_ticks_y, q_labels)
             mpl.xticks(q_ticks_x, q_labels)
             mpl.xlabel('\n' + r'$Q_{i+1}$')
@@ -187,7 +188,7 @@ def parse_fq(inf):
         mpl.show()
 
     print('estimating average error rate via simulation...')
-    q_scores = range(r_q)
+    q_scores = range(RQ)
     # print (len(init_q), len(init_q[0]))
     # print (len(prob_q), len(prob_q[1]), len(prob_q[1][0]))
 
@@ -242,7 +243,7 @@ def parse_fq(inf):
 (inf, ouf, off_q, max_q, max_reads, n_samp) = (args.i, args.o, args.q, args.Q, args.n, args.s)
 (inf2, pile_up) = (args.i2, args.p)
 
-r_q = max_q + 1
+RQ = max_q + 1
 
 init_smooth = 0.
 prob_smooth = 0.
@@ -254,7 +255,7 @@ def parse_fq(inf):
 
 
 def main():
-    q_scores = range(r_q)
+    q_scores = range(RQ)
     if inf2 == None:
         (init_q, prob_q, avg_err) = parse_fq(inf)
     else:
@@ -305,3 +306,5 @@ def main():
 
 if __name__ == '__main__':
     main()
+    
+main('C:/Users/Meridith/Documents/SPIN Internship/Python Code/test.fasta')

From b9c56534cf1455115fcbb384524b3f27b7b88d46 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 2 Jul 2020 14:12:24 -0500
Subject: [PATCH 085/323] Minor fixes to genSeqErrorModel.py

---
 utilities/genSeqErrorModel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 7625b05..8d8176a 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -23,7 +23,7 @@
 sim_path = '/'.join(os.path.realpath(__file__).split('/')[:-2]) + '/py/'
 sys.path.append(sim_path)
 
-from probability import DiscreteDistribution
+from py.probability import DiscreteDistribution
 
 
 def parse_fq(inf):

From a4eb025b6d24add254d0787c6d56c6741bf18eac Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 2 Jul 2020 14:14:30 -0500
Subject: [PATCH 086/323] Minor fixes to genSeqErrorModel.py

---
 utilities/genSeqErrorModel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 66a8d94..83d8c13 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -23,7 +23,7 @@
 sim_path = '/'.join(os.path.realpath(__file__).split('/')[:-2]) + '/py/'
 sys.path.append(sim_path)
 
-from py.probability import DiscreteDistribution
+from probability import DiscreteDistribution
 
 
 def parse_fq(inf):

From 3dd5b3d3d7bc420f2ec1b9667a1ef05ee5e1bf96 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 2 Jul 2020 14:41:14 -0500
Subject: [PATCH 087/323] Minor fixes to genSeqErrorModel.py

---
 utilities/genSeqErrorModel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 83d8c13..8706227 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -79,7 +79,7 @@ def parse_fq(inf):
             continue
 
         for i in range(len(data4) - 1):
-            q = ord(data4[i]) - off_q
+            q = data4[i] - off_q
             q_dict[q] = True
             prev_q = q
             if i == 0:

From 0f946c4b279231f3817820169e0874d575770429 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 2 Jul 2020 15:17:12 -0500
Subject: [PATCH 088/323] eliminated copy-paster error

---
 utilities/genSeqErrorModel.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 8706227..71f706b 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -306,5 +306,3 @@ def main():
 
 if __name__ == '__main__':
     main()
-    
-main('C:/Users/Meridith/Documents/SPIN Internship/Python Code/test.fasta')

From 24cef73dc0afd7d3b5305e7437e14bea0db3dc75 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 2 Jul 2020 16:16:56 -0500
Subject: [PATCH 089/323] Fixed a problem of gen_mut_model.py being very slow

---
 utilities/gen_mut_model.py | 34 +++++++++++++++-------------------
 1 file changed, 15 insertions(+), 19 deletions(-)

diff --git a/utilities/gen_mut_model.py b/utilities/gen_mut_model.py
index a4c4018..2312048 100644
--- a/utilities/gen_mut_model.py
+++ b/utilities/gen_mut_model.py
@@ -225,33 +225,29 @@ def main():
 
     # Count Trinucleotides in reference, based on bed or not
     print('Counting trinucleotides in reference...')
-    for ref_name in matching_chromosomes:
-        if is_bed:
-            print("since you're using a bed input, we have to count trinucs in bed region even if "
-                  "you already have a trinuc count file for the reference...")
+
+    if is_bed:
+        print("since you're using a bed input, we have to count trinucs in bed region even if "
+              "you already have a trinuc count file for the reference...")
+        for ref_name in matching_chromosomes:
             sub_bed = matching_bed[matching_bed['chrom'] == ref_name]
             sub_regions = sub_bed['coords'].to_list()
             for sr in sub_regions:
-                for i in range(sr[0], sr[1] - 2):
-                    trinuc = str(ref_dict[ref_name][i:i + 3].seq)
-                    # skip if trinuc contains invalid characters
-                    if trinuc not in VALID_TRINUC:
-                        continue
+                sub_seq = ref_dict[ref_name][sr[0]: sr[1]].seq
+                for trinuc in VALID_TRINUC:
                     if trinuc not in TRINUC_REF_COUNT:
                         TRINUC_REF_COUNT[trinuc] = 0
-                    TRINUC_REF_COUNT[trinuc] += 1
+                    TRINUC_REF_COUNT[trinuc] += sub_seq.count_overlap(trinuc)
 
-        elif not os.path.isfile(ref + '.trinucCounts'):
-            for i in range(len(ref_dict[ref_name]) - 2):
-                trinuc = str(ref_dict[ref_name][i:i + 3].seq)
-                # skip if trinuc contains invalid characters
-                if not trinuc in VALID_TRINUC:
-                    continue
+    elif not os.path.isfile(ref + '.trinucCounts'):
+        for ref_name in matching_chromosomes:
+            sub_seq = ref_dict[ref_name].seq
+            for trinuc in VALID_TRINUC:
                 if trinuc not in TRINUC_REF_COUNT:
                     TRINUC_REF_COUNT[trinuc] = 0
-                TRINUC_REF_COUNT[trinuc] += 1
-        else:
-            print('Found trinucCounts file, using that.')
+                TRINUC_REF_COUNT[trinuc] += sub_seq.count_overlap(trinuc)
+    else:
+        print('Found trinucCounts file, using that.')
 
     # Load and process variants in each reference sequence individually, for memory reasons...
     print('Creating mutational model...')

From f1f26befeda547e8e94d67d928e9bb6b35c63b4f Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 6 Jul 2020 16:13:31 -0500
Subject: [PATCH 090/323] refactoring genSeqErrorModel.py

---
 utilities/genSeqErrorModel.py | 107 +++++++++++++++++-----------------
 1 file changed, 53 insertions(+), 54 deletions(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 71f706b..a5535b0 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -18,16 +18,21 @@
 import argparse
 import sys
 import pickle
+import matplotlib.pyplot as mpl
 
-# absolute path to this script
-sim_path = '/'.join(os.path.realpath(__file__).split('/')[:-2]) + '/py/'
-sys.path.append(sim_path)
+# # absolute path to this script
+# sim_path = '/'.join(os.path.realpath(__file__).split('/')[:-2]) + '/py/'
+# sys.path.append(sim_path)
 
-from probability import DiscreteDistribution
+from py.probability import DiscreteDistribution
 
 
-def parse_fq(inf):
-    #Takes a gzip or sam file and returns the simulation's average error rate, 
+def parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff):
+    init_smooth = 0.
+    prob_smooth = 0.
+    print_every = 10000
+
+    #Takes a gzip or sam file and returns the simulation's average error rate,
     print('reading ' + inf + '...')
     if inf[-3:] == '.gz':
         print('detected gzip suffix...')
@@ -70,8 +75,8 @@ def parse_fq(inf):
             actual_readlen = len(data4) - 1
             print('assuming read length is uniform...')
             print('detected read length (from first read found):', actual_readlen)
-            prior_q = np.zeros([actual_readlen, RQ])
-            total_q = [None] + [np.zeros([RQ, RQ]) for n in range(actual_readlen - 1)]
+            prior_q = np.zeros([actual_readlen, real_q])
+            total_q = [None] + [np.zeros([real_q, real_q]) for n in range(actual_readlen - 1)]
 
         # sanity-check readlengths
         if len(data4) - 1 != actual_readlen:
@@ -87,7 +92,7 @@ def parse_fq(inf):
             else:
                 total_q[i][prev_q, q] += 1
                 prior_q[i][q] += 1
-            
+
 
         r_read += 1
         if r_read % print_every == 0:
@@ -101,26 +106,26 @@ def parse_fq(inf):
     if q_range[0] < 0:
         print('\nError: Read in Q-scores below 0\n')
         exit(1)
-    if q_range[1] > RQ:
-        print('\nError: Read in Q-scores above specified maximum:', q_range[1], '>', RQ, '\n')
+    if q_range[1] > real_q:
+        print('\nError: Read in Q-scores above specified maximum:', q_range[1], '>', real_q, '\n')
         exit(1)
 
     print('computing probabilities...')
-    prob_q = [None] + [[[0. for m in range(RQ)] for n in range(RQ)] for p in range(actual_readlen - 1)]
+    prob_q = [None] + [[[0. for m in range(real_q)] for n in range(real_q)] for p in range(actual_readlen - 1)]
     for p in range(1, actual_readlen):
-        for i in range(RQ):
-            row_sum = float(np.sum(total_q[p][i, :])) + prob_smooth * RQ
+        for i in range(real_q):
+            row_sum = float(np.sum(total_q[p][i, :])) + prob_smooth * real_q
             if row_sum <= 0.:
                 continue
-            for j in range(RQ):
+            for j in range(real_q):
                 prob_q[p][i][j] = (total_q[p][i][j] + prob_smooth) / row_sum
 
-    init_q = [[0. for m in range(RQ)] for n in range(actual_readlen)]
+    init_q = [[0. for m in range(real_q)] for n in range(actual_readlen)]
     for i in range(actual_readlen):
-        row_sum = float(np.sum(prior_q[i, :])) + init_smooth * RQ
+        row_sum = float(np.sum(prior_q[i, :])) + init_smooth * real_q
         if row_sum <= 0.:
             continue
-        for j in range(RQ):
+        for j in range(real_q):
             init_q[i][j] = (prior_q[i][j] + init_smooth) / row_sum
 
     if plot_stuff:
@@ -145,7 +150,7 @@ def parse_fq(inf):
         q_labels = [str(n) for n in range(q_range[0], q_range[1] + 1) if n % 5 == 0]
         print(q_labels)
         q_ticks_x = [int(n) + 0.5 for n in q_labels]
-        q_ticks_y = [(RQ - int(n)) - 0.5 for n in q_labels]
+        q_ticks_y = [(real_q - int(n)) - 0.5 for n in q_labels]
 
         for p in range(1, actual_readlen, 10):
             current_data = np.array(prob_q[p])
@@ -174,7 +179,7 @@ def parse_fq(inf):
             x, y = np.meshgrid(range(0, len(Z[0]) + 1), range(0, len(Z) + 1))
             mpl.pcolormesh(x, y, z[::-1, :], vmin=v_min_log[0], vmax=v_min_log[1], cmap='jet')
             mpl.xlim([q_range[0], q_range[1] + 1])
-            mpl.ylim([RQ - q_range[1] - 1, RQ - q_range[0]])
+            mpl.ylim([real_q - q_range[1] - 1, real_q - q_range[0]])
             mpl.yticks(q_ticks_y, q_labels)
             mpl.xticks(q_ticks_x, q_labels)
             mpl.xlabel('\n' + r'$Q_{i+1}$')
@@ -188,7 +193,7 @@ def parse_fq(inf):
         mpl.show()
 
     print('estimating average error rate via simulation...')
-    q_scores = range(RQ)
+    q_scores = range(real_q)
     # print (len(init_q), len(init_q[0]))
     # print (len(prob_q), len(prob_q[1]), len(prob_q[1][0]))
 
@@ -225,42 +230,36 @@ def parse_fq(inf):
     return init_q, prob_q, avg_err
 
 
-parser = argparse.ArgumentParser(description='genSeqErrorModel.py')
-parser.add_argument('-i', type=str, required=True, metavar='<str>', help="* input_read1.fq (.gz) / input_read1.sam")
-parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output.p")
-parser.add_argument('-i2', type=str, required=False, metavar='<str>', default=None,
-                    help="input_read2.fq (.gz) / input_read2.sam")
-parser.add_argument('-p', type=str, required=False, metavar='<str>', default=None, help="input_alignment.pileup")
-parser.add_argument('-q', type=int, required=False, metavar='<int>', default=33, help="quality score offset [33]")
-parser.add_argument('-Q', type=int, required=False, metavar='<int>', default=41, help="maximum quality score [41]")
-parser.add_argument('-n', type=int, required=False, metavar='<int>', default=-1,
-                    help="maximum number of reads to process [all]")
-parser.add_argument('-s', type=int, required=False, metavar='<int>', default=1000000,
-                    help="number of simulation iterations [1000000]")
-parser.add_argument('--plot', required=False, action='store_true', default=False, help='perform some optional plotting')
-args = parser.parse_args()
-
-(inf, ouf, off_q, max_q, max_reads, n_samp) = (args.i, args.o, args.q, args.Q, args.n, args.s)
-(inf2, pile_up) = (args.i2, args.p)
-
-RQ = max_q + 1
-
-init_smooth = 0.
-prob_smooth = 0.
-print_every = 10000
-plot_stuff = args.plot
-if plot_stuff:
-    print('plotting is desired, lets import matplotlib...')
-    import matplotlib.pyplot as mpl
-
-
 def main():
-    q_scores = range(RQ)
+    parser = argparse.ArgumentParser(description='genSeqErrorModel.py')
+    parser.add_argument('-i', type=str, required=True, metavar='<str>', help="* input_read1.fq (.gz) / input_read1.sam")
+    parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output.p")
+    parser.add_argument('-i2', type=str, required=False, metavar='<str>', default=None,
+                        help="input_read2.fq (.gz) / input_read2.sam")
+    parser.add_argument('-p', type=str, required=False, metavar='<str>', default=None, help="input_alignment.pileup")
+    parser.add_argument('-q', type=int, required=False, metavar='<int>', default=33, help="quality score offset [33]")
+    parser.add_argument('-Q', type=int, required=False, metavar='<int>', default=41, help="maximum quality score [41]")
+    parser.add_argument('-n', type=int, required=False, metavar='<int>', default=-1,
+                        help="maximum number of reads to process [all]")
+    parser.add_argument('-s', type=int, required=False, metavar='<int>', default=1000000,
+                        help="number of simulation iterations [1000000]")
+    parser.add_argument('--plot', required=False, action='store_true', default=False,
+                        help='perform some optional plotting')
+    args = parser.parse_args()
+
+    (inf, ouf, off_q, max_q, max_reads, n_samp) = (args.i, args.o, args.q, args.Q, args.n, args.s)
+    (inf2, pile_up) = (args.i2, args.p)
+
+    real_q = max_q + 1
+
+    plot_stuff = args.plot
+
+    q_scores = range(real_q)
     if inf2 == None:
-        (init_q, prob_q, avg_err) = parse_fq(inf)
+        (init_q, prob_q, avg_err) = parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff)
     else:
-        (init_q, prob_q, avg_err1) = parse_fq(inf)
-        (init_q2, prob_q2, avg_err2) = parse_fq(inf2)
+        (init_q, prob_q, avg_err1) = parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff)
+        (init_q2, prob_q2, avg_err2) = parse_fq(inf2, real_q, off_q, max_reads, n_samp, plot_stuff)
         avg_err = (avg_err1 + avg_err2) / 2.
 
     #

From e388153f53450e0a9764e380c421b8bd3bc05675 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 6 Jul 2020 16:54:29 -0500
Subject: [PATCH 091/323] Added a very basic bacterial wrapper.

---
 bacterial_genreads_wrapper.py | 41 +++++++++++++++++++++++++++++++++++
 1 file changed, 41 insertions(+)
 create mode 100644 bacterial_genreads_wrapper.py

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
new file mode 100644
index 0000000..b672437
--- /dev/null
+++ b/bacterial_genreads_wrapper.py
@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+
+import genReads
+import argparse
+
+def cull(bacteria):
+    """
+    The purpose of this function will be to cull the bacteria created in the model
+    :param bacteria:
+    :return:
+    """
+    return None
+
+def evolve(reference, read_length, output_prefix):
+    """
+    The purpose of this function is to evolve the bacteria
+    :param bacteria:
+    :return:
+    """
+    args = ['-r', reference, '-R', read_length, '-o', output_prefix]
+    new_fasta = genReads.main(args)
+    return new_fasta
+
+
+def main():
+    parser = argparse.ArgumentParser(description='bacterial_genreads_wrapper.py')
+    parser.add_argument('-r', type=str, required=True, metavar='/path/to/reference.fasta',
+                        help="Reference file for organism in fasta format")
+    parser.add_argument('-R', type=int, required=True, metavar='<int>', help="read length")
+    parser.add_argument('-o', type=str, required=True, metavar='<str>', help="output prefix")
+    parser.add_argument('-C', type=str, required=True, metavar='<str>', help="Number of cycles to run")
+    args = parser.parse_args()
+
+    (ref_fasta, read_len, out_pref) = (args.r, args.R, args.o)
+    cycles = args.C
+
+    old_fasta = ref_fasta
+    for i in range(cycles):
+        new_fasta = evolve(old_fasta, read_len, out_pref)
+        cull(new_fasta)
+        old_fasta = new_fasta

From d5ebcda3bb56c904b7b1b5a5a92b9b4fa55b28b5 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 8 Jul 2020 16:21:18 -0500
Subject: [PATCH 092/323] updated bacterial wrapper and genReads for
 compatability

---
 bacterial_genreads_wrapper.py | 14 +++++++++-----
 genReads.py                   |  5 ++---
 2 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index b672437..84b76e0 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -3,21 +3,25 @@
 import genReads
 import argparse
 
-def cull(bacteria):
+
+def cull(fasta):
     """
     The purpose of this function will be to cull the bacteria created in the model
-    :param bacteria:
+    :param fasta:
     :return:
     """
-    return None
+    return fasta
+
 
 def evolve(reference, read_length, output_prefix):
     """
     The purpose of this function is to evolve the bacteria
-    :param bacteria:
+    :param reference:
+    :param read_length:
+    :param output_prefix
     :return:
     """
-    args = ['-r', reference, '-R', read_length, '-o', output_prefix]
+    args = ['-r', reference, '-R', str(read_length), '-o', output_prefix]
     new_fasta = genReads.main(args)
     return new_fasta
 
diff --git a/genReads.py b/genReads.py
index cdf00f6..0bfc859 100644
--- a/genReads.py
+++ b/genReads.py
@@ -37,7 +37,7 @@
 //////////////////////////////////////////////////"""
 
 
-def main():
+def main(raw_args=None):
     parser = argparse.ArgumentParser(description='NEAT-genReads V2.0')
     parser.add_argument('-r', type=str, required=True, metavar='<str>', help="* ref.fa")
     parser.add_argument('-R', type=int, required=True, metavar='<int>', help="* read length")
@@ -82,8 +82,7 @@ def main():
     parser.add_argument('--gz', required=False, action='store_true', default=False, help='gzip output FQ and VCF')
     parser.add_argument('--no-fastq', required=False, action='store_true', default=False,
                         help='bypass fastq generation')
-    args = parser.parse_args()
-
+    args = parser.parse_args(raw_args)
 
     """
     Set variables for processing

From 987d4cfc311405cb7abe84db1d7eda7686e5a743 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 9 Jul 2020 15:38:48 -0500
Subject: [PATCH 093/323] put a bed line in an if is_bed statement

---
 utilities/gen_mut_model.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/utilities/gen_mut_model.py b/utilities/gen_mut_model.py
index 2312048..3ce5eda 100644
--- a/utilities/gen_mut_model.py
+++ b/utilities/gen_mut_model.py
@@ -260,8 +260,9 @@ def main():
 
         # Create a view that narrows variants list to current ref
         variants_to_process = matching_variants[matching_variants["CHROM"] == ref_name].copy()
-        bed_to_process = matching_bed[matching_bed['chrom'] == ref_name].copy()
         ref_sequence = str(ref_dict[ref_name].seq)
+        if is_bed:
+            bed_to_process = matching_bed[matching_bed['chrom'] == ref_name].copy()
 
         # we want only snps
         # so, no '-' characters allowed, and chrStart must be same as chrEnd

From 7b5befae06cb32c94db46a4bcbf00859df29fd2d Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 14 Jul 2020 15:13:39 -0500
Subject: [PATCH 094/323] Updates to genreads and bacterial wrapper

---
 bacterial_genreads_wrapper.py | 57 +++++++++++++++++++++++++++--------
 genReads.py                   |  2 +-
 utilities/gen_mut_model.py    |  7 +++--
 3 files changed, 50 insertions(+), 16 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 84b76e0..ec1d280 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -4,26 +4,52 @@
 import argparse
 
 
-def cull(fasta):
+def cull(population: list) -> list:
     """
     The purpose of this function will be to cull the bacteria created in the model
-    :param fasta:
+    :param population:
     :return:
     """
-    return fasta
+    return population
 
 
-def evolve(reference, read_length, output_prefix):
+def create_fasta(neat_input):
+    """
+    The purpose of this function will be to convert the output from neat-genreads into something
+    that can be an input back into neat genreads
+    :param neat_input:
+    :return fasta_output:
+    """
+    fasta_output = None
+    return fasta_output
+
+
+def crossover(population: list) -> list:
+    """
+    This function will take a list of individuals and do crossover type mixing
+    :param population:
+    :return new_population:
+    """
+    new_population = population
+    return new_population
+
+
+def evolve(reference, read_length, output_prefix, pop_size):
     """
     The purpose of this function is to evolve the bacteria
     :param reference:
     :param read_length:
-    :param output_prefix
-    :return:
+    :param output_prefix:
+    :param cycles:
+    :return population:
     """
     args = ['-r', reference, '-R', str(read_length), '-o', output_prefix]
-    new_fasta = genReads.main(args)
-    return new_fasta
+    population = []
+    for i in range(pop_size):
+        new_evolution = genReads.main(args)
+        new_member = create_fasta(new_evolution)
+        population.append(new_member)
+    return population
 
 
 def main():
@@ -32,14 +58,19 @@ def main():
                         help="Reference file for organism in fasta format")
     parser.add_argument('-R', type=int, required=True, metavar='<int>', help="read length")
     parser.add_argument('-o', type=str, required=True, metavar='<str>', help="output prefix")
-    parser.add_argument('-C', type=str, required=True, metavar='<str>', help="Number of cycles to run")
+    parser.add_argument('-C', type=int, required=True, metavar='<str>', help="Number of cycles to run")
+    parser.add_argument('-p', type=int, required=True, metavar='<str>', help="Population size per cycle")
     args = parser.parse_args()
 
-    (ref_fasta, read_len, out_pref) = (args.r, args.R, args.o)
+    (ref_fasta, read_len, out_pref, population_size) = (args.r, args.R, args.o, args.p)
     cycles = args.C
 
     old_fasta = ref_fasta
+    candidate_population = evolve(old_fasta, read_len, out_pref, population_size)
     for i in range(cycles):
-        new_fasta = evolve(old_fasta, read_len, out_pref)
-        cull(new_fasta)
-        old_fasta = new_fasta
+        new_population = cull(candidate_population)
+        # If all elements get culled, then break and quit
+        if not new_population:
+            break
+        new_population = crossover(new_population)
+        candidate_population = evolve(new_population)
diff --git a/genReads.py b/genReads.py
index 0bfc859..9a90d7f 100644
--- a/genReads.py
+++ b/genReads.py
@@ -89,7 +89,7 @@ def main(raw_args=None):
     """
     # absolute path to this script
     SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-1])
-    sys.path.append(SIM_PATH + '/py/')
+    print("The sim path is: ", SIM_PATH)
 
     # if coverage val for a given window/position is below this value, consider it effectively zero.
     LOW_COV_THRESH = 50
diff --git a/utilities/gen_mut_model.py b/utilities/gen_mut_model.py
index 3ce5eda..948ff18 100644
--- a/utilities/gen_mut_model.py
+++ b/utilities/gen_mut_model.py
@@ -261,13 +261,16 @@ def main():
         # Create a view that narrows variants list to current ref
         variants_to_process = matching_variants[matching_variants["CHROM"] == ref_name].copy()
         ref_sequence = str(ref_dict[ref_name].seq)
-        if is_bed:
-            bed_to_process = matching_bed[matching_bed['chrom'] == ref_name].copy()
 
         # we want only snps
         # so, no '-' characters allowed, and chrStart must be same as chrEnd
         snp_df = variants_to_process[~variants_to_process.index.isin(indices_to_indels)]
         snp_df = snp_df.loc[snp_df['chr_start'] == snp_df['chr_end']]
+        if is_bed:
+            bed_to_process = matching_bed[matching_bed['chrom'] == ref_name].copy()
+            # TODO fix this line (need the intersection of these two, I think)
+            snp_df = bed_to_process.join(snp_df)
+
         if not snp_df.empty:
             # only consider positions where ref allele in vcf matches the nucleotide in our reference
             for index, row in snp_df.iterrows():

From c1b4fe08c7dea2f971fddc76215a39dc4d316e0f Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Thu, 16 Jul 2020 15:56:39 -0500
Subject: [PATCH 095/323] renamed variables

---
 genReads.py                                   |   7 +-
 py/SequenceContainer.py                       | 455 +++++++++---------
 .../OutputFileWriter.cpython-37.pyc           | Bin 0 -> 7674 bytes
 .../SequenceContainer.cpython-37.pyc          | Bin 0 -> 31358 bytes
 py/__pycache__/__init__.cpython-37.pyc        | Bin 0 -> 173 bytes
 .../biopython_modified_bgzf.cpython-37.pyc    | Bin 0 -> 2695 bytes
 py/__pycache__/inputChecking.cpython-37.pyc   | Bin 0 -> 2391 bytes
 py/__pycache__/neat_cigar.cpython-37.pyc      | Bin 0 -> 3011 bytes
 py/__pycache__/probability.cpython-37.pyc     | Bin 0 -> 3974 bytes
 py/__pycache__/ref_func.cpython-37.pyc        | Bin 0 -> 4818 bytes
 py/__pycache__/vcfFunc.cpython-37.pyc         | Bin 0 -> 4688 bytes
 py/vcfFunc.py                                 | 168 +++----
 12 files changed, 316 insertions(+), 314 deletions(-)
 create mode 100644 py/__pycache__/OutputFileWriter.cpython-37.pyc
 create mode 100644 py/__pycache__/SequenceContainer.cpython-37.pyc
 create mode 100644 py/__pycache__/__init__.cpython-37.pyc
 create mode 100644 py/__pycache__/biopython_modified_bgzf.cpython-37.pyc
 create mode 100644 py/__pycache__/inputChecking.cpython-37.pyc
 create mode 100644 py/__pycache__/neat_cigar.cpython-37.pyc
 create mode 100644 py/__pycache__/probability.cpython-37.pyc
 create mode 100644 py/__pycache__/ref_func.cpython-37.pyc
 create mode 100644 py/__pycache__/vcfFunc.cpython-37.pyc

diff --git a/genReads.py b/genReads.py
index 0bfc859..825d1ed 100644
--- a/genReads.py
+++ b/genReads.py
@@ -37,7 +37,7 @@
 //////////////////////////////////////////////////"""
 
 
-def main(raw_args=None):
+def main():
     parser = argparse.ArgumentParser(description='NEAT-genReads V2.0')
     parser.add_argument('-r', type=str, required=True, metavar='<str>', help="* ref.fa")
     parser.add_argument('-R', type=int, required=True, metavar='<int>', help="* read length")
@@ -82,7 +82,7 @@ def main(raw_args=None):
     parser.add_argument('--gz', required=False, action='store_true', default=False, help='gzip output FQ and VCF')
     parser.add_argument('--no-fastq', required=False, action='store_true', default=False,
                         help='bypass fastq generation')
-    args = parser.parse_args(raw_args)
+    args = parser.parse_args()
 
     """
     Set variables for processing
@@ -90,7 +90,7 @@ def main(raw_args=None):
     # absolute path to this script
     SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-1])
     sys.path.append(SIM_PATH + '/py/')
-
+    print('The SIM_PATH variable is {}.'.format(SIM_PATH))
     # if coverage val for a given window/position is below this value, consider it effectively zero.
     LOW_COV_THRESH = 50
 
@@ -170,6 +170,7 @@ def main(raw_args=None):
         SE_RATE = None
     if SE_MODEL is None:
         print('Using default sequencing error model.')
+        print('The SIM_PATH variable is {}.'.format(SIM_PATH))
         SE_MODEL = SIM_PATH + '/models/errorModel_toy.p'
         SE_CLASS = ReadContainer(READLEN, SE_MODEL, SE_RATE)
     else:
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 62fc636..4f5c349 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -8,7 +8,7 @@
 from Bio.Alphabet import IUPAC
 
 from py.probability import DiscreteDistribution, poisson_list, quantize_list
-from py.neat_cigar import CigarString
+from py.neat_cigar import cigar_string
 
 MAX_ATTEMPTS = 100	# max attempts to insert a mutation into a valid position
 MAX_MUTFRAC  = 0.3	# the maximum percentage of a window that can contain mutations
@@ -31,12 +31,12 @@
 #	Container for reference sequences, applies mutations
 #
 class SequenceContainer:
-    def __init__(self, xOffset, sequence, ploidy, windowOverlap, readLen, mutationModels=[], mutRate=None, onlyVCF=False):
+    def __init__(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_models=[], mut_rate=None, only_vcf=False):
         # initialize basic variables
-        self.onlyVCF = onlyVCF
-        self.init_basicVars(xOffset, sequence, ploidy, windowOverlap, readLen)
+        self.only_vcf = only_vcf
+        self.init_basicVars(x_offset, sequence, ploidy, window_overlap, read_len)
         # initialize mutation models
-        self.init_mutModels(mutationModels, mutRate)
+        self.init_mutModels(mut_models, mut_rate)
         # sample the number of variants that will be inserted into each ploid
         self.init_poisson()
         self.indelsToAdd = [n.sample() for n in self.ind_pois]
@@ -44,112 +44,112 @@ def __init__(self, xOffset, sequence, ploidy, windowOverlap, readLen, mutationMo
         # initialize trinuc snp bias
         self.init_trinucBias()
 
-    def init_basicVars(self, xOffset, sequence, ploidy, windowOverlap, readLen):
-        self.x         = xOffset
+    def init_basicVars(self, x_offset, sequence, ploidy, window_overlap, read_len):
+        self.x         = x_offset
         self.ploidy    = ploidy
-        self.readLen   = readLen
+        self.read_len   = read_len
         self.sequences = [Seq(str(sequence), IUPAC.unambiguous_dna).tomutable() for n in range(self.ploidy)]
-        self.seqLen    = len(sequence)
+        self.seq_Len    = len(sequence)
         self.indelList = [[] for n in range(self.ploidy)]
         self.snpList   = [[] for n in range(self.ploidy)]
-        self.allCigar  = [[] for n in range(self.ploidy)]
+        self.all_cigar  = [[] for n in range(self.ploidy)]
         self.FM_pos    = [[] for n in range(self.ploidy)]
         self.FM_span   = [[] for n in range(self.ploidy)]
         # blackList[ploid][pos] = 0		safe to insert variant here
         # blackList[ploid][pos] = 1		indel inserted here
         # blackList[ploid][pos] = 2		snp inserted here
         # blackList[ploid][pos] = 3		invalid position for various processing reasons
-        self.blackList = [np.zeros(self.seqLen,dtype='<i4') for n in range(self.ploidy)]
+        self.blackList = [np.zeros(self.seq_Len, dtype='<i4') for n in range(self.ploidy)]
 
         # disallow mutations to occur on window overlap points
-        self.winBuffer = windowOverlap
+        self.winBuffer = window_overlap
         for p in range(self.ploidy):
             self.blackList[p][-self.winBuffer]   = 3
             self.blackList[p][-self.winBuffer-1] = 3
 
-    def init_coverage(self, coverageDat, fragDist=None):
+    def init_coverage(self, coverage_data, frag_dist=None):
         # if we're only creating a vcf, skip some expensive initialization related to coverage depth
-        if not self.onlyVCF:
-            (self.windowSize, gc_scalars, targetCov_vals) = coverageDat
+        if not self.only_vcf:
+            (self.window_size, gc_scalars, target_cov_vals) = coverage_data
             gcCov_vals = [[] for n in self.sequences]
-            trCov_vals = [[] for n in self.sequences]
+            tr_cov_vals = [[] for n in self.sequences]
             self.coverage_distribution = []
             avg_out = []
             for i in range(len(self.sequences)):
-                max_coord = min([len(self.sequences[i])-self.readLen, len(self.allCigar[i])-self.readLen])
+                max_coord = min([len(self.sequences[i])-self.read_len, len(self.all_cigar[i])-self.read_len])
                 # Trying to fix a problem wherein the above line gives a negative answer
                 if max_coord <= 0:
-                    max_coord = min([len(self.sequences[i]), len(self.allCigar[i])])
+                    max_coord = min([len(self.sequences[i]), len(self.all_cigar[i])])
                 # compute gc-bias
                 j = 0
-                while j+self.windowSize < len(self.sequences[i]):
-                    gc_c = self.sequences[i][j:j+self.windowSize].count('G') + self.sequences[i][j:j+self.windowSize].count('C')
-                    gcCov_vals[i].extend([gc_scalars[gc_c]]*self.windowSize)
-                    j += self.windowSize
-                gc_c = self.sequences[i][-self.windowSize:].count('G') + self.sequences[i][-self.windowSize:].count('C')
+                while j+self.window_size < len(self.sequences[i]):
+                    gc_c = self.sequences[i][j:j+self.window_size].count('G') + self.sequences[i][j:j+self.window_size].count('C')
+                    gcCov_vals[i].extend([gc_scalars[gc_c]]*self.window_size)
+                    j += self.window_size
+                gc_c = self.sequences[i][-self.window_size:].count('G') + self.sequences[i][-self.window_size:].count('C')
                 gcCov_vals[i].extend([gc_scalars[gc_c]]*(len(self.sequences[i])-len(gcCov_vals[i])))
                 #
-                trCov_vals[i].append(targetCov_vals[0])
-                prevVal = self.FM_pos[i][0]
+                tr_cov_vals[i].append(target_cov_vals[0])
+                prev_val = self.FM_pos[i][0]
                 for j in range(1,max_coord):
                     if self.FM_pos[i][j] is None:
-                        trCov_vals[i].append(targetCov_vals[prevVal])
+                        tr_cov_vals[i].append(target_cov_vals[prev_val])
                     elif self.FM_span[i][j]-self.FM_pos[i][j] <= 1:
-                        trCov_vals[i].append(targetCov_vals[prevVal])
+                        tr_cov_vals[i].append(target_cov_vals[prev_val])
                     else:
-                        trCov_vals[i].append(sum(targetCov_vals[self.FM_pos[i][j]:self.FM_span[i][j]])/float(self.FM_span[i][j]-self.FM_pos[i][j]))
-                        prevVal = self.FM_pos[i][j]
-                    #print (i,j), self.adj[i][j], self.allCigar[i][j], self.FM_pos[i][j], self.FM_span[i][j]
+                        tr_cov_vals[i].append(sum(target_cov_vals[self.FM_pos[i][j]:self.FM_span[i][j]])/float(self.FM_span[i][j]-self.FM_pos[i][j]))
+                        prev_val = self.FM_pos[i][j]
+                    #print (i,j), self.adj[i][j], self.all_cigar[i][j], self.FM_pos[i][j], self.FM_span[i][j]
                 # shift by half of read length
-                if len(trCov_vals[i]) > int(self.readLen/2.):
-                    trCov_vals[i] = [0.0]*int(self.readLen//2) + trCov_vals[i][:-int(self.readLen/2.)]
+                if len(tr_cov_vals[i]) > int(self.read_len/2.):
+                    tr_cov_vals[i] = [0.0]*int(self.read_len//2) + tr_cov_vals[i][:-int(self.read_len/2.)]
                 # fill in missing indices
-                trCov_vals[i].extend([0.0]*(len(self.sequences[i])-len(trCov_vals[i])))
+                tr_cov_vals[i].extend([0.0]*(len(self.sequences[i])-len(tr_cov_vals[i])))
 
                 #
-                covvec = np.cumsum([trCov_vals[i][nnn]*gcCov_vals[i][nnn] for nnn in range(len(trCov_vals[i]))])
+                covvec = np.cumsum([tr_cov_vals[i][nnn]*gcCov_vals[i][nnn] for nnn in range(len(tr_cov_vals[i]))])
                 coverage_vals = []
                 for j in range(0,max_coord):
-                    coverage_vals.append(covvec[j+self.readLen] - covvec[j])
-                avg_out.append(np.mean(coverage_vals)/float(self.readLen))
+                    coverage_vals.append(covvec[j+self.read_len] - covvec[j])
+                avg_out.append(np.mean(coverage_vals)/float(self.read_len))
 
-                if fragDist == None:
+                if frag_dist == None:
                     self.coverage_distribution.append(DiscreteDistribution(coverage_vals,range(len(coverage_vals))))
 
                 # fragment length nightmare
                 else:
-                    currentThresh = 0.
+                    current_thresh = 0.
                     index_list    = [0]
-                    for j in range(len(fragDist.cumP)):
-                        if fragDist.cumP[j] >= currentThresh + COV_FRAGLEN_PERCENTILE/100.0:
-                            currentThresh = fragDist.cumP[j]
+                    for j in range(len(frag_dist.cumP)):
+                        if frag_dist.cumP[j] >= current_thresh + COV_FRAGLEN_PERCENTILE/100.0:
+                            current_thresh = frag_dist.cumP[j]
                             index_list.append(j)
-                    flq = [fragDist.values[nnn] for nnn in index_list]
-                    if fragDist.values[-1] not in flq:
-                        flq.append(fragDist.values[-1])
+                    flq = [frag_dist.values[nnn] for nnn in index_list]
+                    if frag_dist.values[-1] not in flq:
+                        flq.append(frag_dist.values[-1])
                     flq.append(LARGE_NUMBER)
 
-                    self.fraglens_indMap = {}
-                    for j in fragDist.values:
+                    self.fraglens_ind_map = {}
+                    for j in frag_dist.values:
                         bInd = bisect.bisect(flq,j)
                         if abs(flq[bInd-1] - j) <= abs(flq[bInd] - j):
-                            self.fraglens_indMap[j] = flq[bInd-1]
+                            self.fraglens_ind_map[j] = flq[bInd-1]
                         else:
-                            self.fraglens_indMap[j] = flq[bInd]
+                            self.fraglens_ind_map[j] = flq[bInd]
 
                     self.coverage_distribution.append({})
-                    for flv in sorted(list(set(self.fraglens_indMap.values()))):
-                        buffer_val = self.readLen
-                        for j in fragDist.values:
-                            if self.fraglens_indMap[j] == flv and j > buffer_val:
+                    for flv in sorted(list(set(self.fraglens_ind_map.values()))):
+                        buffer_val = self.read_len
+                        for j in frag_dist.values:
+                            if self.fraglens_ind_map[j] == flv and j > buffer_val:
                                 buffer_val = j
-                        max_coord = min([len(self.sequences[i])-buffer_val-1, len(self.allCigar[i])-buffer_val+self.readLen-2])
+                        max_coord = min([len(self.sequences[i])-buffer_val-1, len(self.all_cigar[i])-buffer_val+self.read_len-2])
                         #print 'BEFORE:', len(self.sequences[i])-buffer_val
-                        #print 'AFTER: ', len(self.allCigar[i])-buffer_val+self.readLen-2
+                        #print 'AFTER: ', len(self.all_cigar[i])-buffer_val+self.read_len-2
                         #print 'AFTER2:', max_coord
                         coverage_vals = []
                         for j in range(0,max_coord):
-                            coverage_vals.append(covvec[j+self.readLen] - covvec[j] + covvec[j+flv] - covvec[j+flv-self.readLen])
+                            coverage_vals.append(covvec[j+self.read_len] - covvec[j] + covvec[j+flv] - covvec[j+flv-self.read_len])
 
                         # EXPERIMENTAL
                         #quantized_covVals = quantize_list(coverage_vals)
@@ -159,9 +159,9 @@ def init_coverage(self, coverageDat, fragDist=None):
                         #import matplotlib.pyplot as mpl
                         #print len(coverage_vals),'-->',len(quantized_covVals)
                         #mpl.figure(0)
-                        #mpl.plot(range(len(coverage_vals)),coverage_vals)
+                        #mpl.plot(range(len(coverage_vals)), coverage_vals)
                         #for qcv in quantized_covVals:
-                        #	mpl.plot([qcv[0],qcv[1]+1],[qcv[2],qcv[2]],'r')
+                        #	mpl.plot([qcv[0], qcv[1]+1], [qcv[2],qcv[2]], 'r')
                         #mpl.show()
                         #exit(1)
 
@@ -170,27 +170,28 @@ def init_coverage(self, coverageDat, fragDist=None):
 
             return np.mean(avg_out)
 
-    def init_mutModels(self,mutationModels,mutRate):
-        if mutationModels == []:
+    
+    def init_mutModels(self,mut_models,mut_rate):
+        if mut_models == []:
             ml = [copy.deepcopy(DEFAULT_MODEL_1) for n in range(self.ploidy)]
-            self.modelData = ml[:self.ploidy]
+            self.model_data = ml[:self.ploidy]
         else:
-            if len(mutationModels) != self.ploidy:
+            if len(mut_models) != self.ploidy:
                 print('\nError: Number of mutation models recieved is not equal to specified ploidy\n')
                 exit(1)
-            self.modelData = copy.deepcopy(mutationModels)
+            self.model_data = copy.deepcopy(mut_models)
 
         # do we need to rescale mutation frequencies?
-        mutRateSum = sum([n[0] for n in self.modelData])
-        self.mutRescale = mutRate
-        if self.mutRescale == None:
-            self.mutScalar = 1.0
+        mut_rateSum = sum([n[0] for n in self.model_data])
+        self.mut_rescale = mut_rate
+        if self.mut_rescale == None:
+            self.mut_scalar = 1.0
         else:
-            self.mutScalar = float(self.mutRescale)//(mutRateSum/float(len(self.modelData)))
+            self.mut_scalar = float(self.mut_rescale)//(mut_rateSum/float(len(self.model_data)))
 
         # how are mutations spread to each ploid, based on their specified mut rates?
-        self.ploidMutFrac  = [float(n[0])/mutRateSum for n in self.modelData]
-        self.ploidMutPrior = DiscreteDistribution(self.ploidMutFrac,range(self.ploidy))
+        self.ploid_mut_frac  = [float(n[0])/mut_rateSum for n in self.model_data]
+        self.ploid_mut_prior = DiscreteDistribution(self.ploid_mut_frac,range(self.ploidy))
 
         # init mutation models
         #
@@ -203,19 +204,19 @@ def init_mutModels(self,mutationModels,mutRate):
         # self.models[ploid][6] = distribution of trinucleotide SNP transitions
         # self.models[ploid][7] = p(trinuc mutates)
         self.models = []
-        for n in self.modelData:
-            self.models.append([self.mutScalar*n[0],n[1],n[2],n[3],DiscreteDistribution(n[5],n[4]),DiscreteDistribution(n[7],n[6]),[]])
+        for n in self.model_data:
+            self.models.append([self.mut_scalar*n[0], n[1], n[2], n[3], DiscreteDistribution(n[5], n[4]), DiscreteDistribution(n[7], n[6]), []])
             for m in n[8]:
-                self.models[-1][6].append([DiscreteDistribution(m[0],NUCL),
-                                           DiscreteDistribution(m[1],NUCL),
-                                           DiscreteDistribution(m[2],NUCL),
-                                           DiscreteDistribution(m[3],NUCL)])
+                self.models[-1][6].append([DiscreteDistribution(m[0], NUCL),
+                                           DiscreteDistribution(m[1], NUCL),
+                                           DiscreteDistribution(m[2], NUCL),
+                                           DiscreteDistribution(m[3], NUCL)])
             self.models[-1].append([m for m in n[9]])
 
     def init_poisson(self):
-        ind_l_list = [self.seqLen*self.models[i][0]*self.models[i][2]*self.ploidMutFrac[i] for i in range(len(self.models))]
-        snp_l_list = [self.seqLen*self.models[i][0]*(1.-self.models[i][2])*self.ploidMutFrac[i] for i in range(len(self.models))]
-        k_range    = range(int(self.seqLen*MAX_MUTFRAC))
+        ind_l_list = [self.seq_Len*self.models[i][0]*self.models[i][2]*self.ploid_mut_frac[i] for i in range(len(self.models))]
+        snp_l_list = [self.seq_Len*self.models[i][0]*(1.-self.models[i][2])*self.ploid_mut_frac[i] for i in range(len(self.models))]
+        k_range    = range(int(self.seq_Len*MAX_MUTFRAC))
         self.ind_pois = [poisson_list(k_range,ind_l_list[n]) for n in range(len(self.models))]
         self.snp_pois = [poisson_list(k_range,snp_l_list[n]) for n in range(len(self.models))]
 
@@ -224,25 +225,25 @@ def init_trinucBias(self):
         #
         # note: since indels are added before snps, it's possible these positional biases aren't correctly utilized
         #       at positions affected by indels. At the moment I'm going to consider this negligible.
-        trinuc_snp_bias  = [[0. for n in range(self.seqLen)] for m in range(self.ploidy)]
+        trinuc_snp_bias  = [[0. for n in range(self.seq_Len)] for m in range(self.ploidy)]
         self.trinuc_bias = [None for n in range(self.ploidy)]
         for p in range(self.ploidy):
-            for i in range(self.winBuffer+1,self.seqLen-1):
+            for i in range(self.winBuffer+1,self.seq_Len-1):
                 trinuc_snp_bias[p][i] = self.models[p][7][ALL_IND[str(self.sequences[p][i-1:i+2])]]
-            self.trinuc_bias[p] = DiscreteDistribution(trinuc_snp_bias[p][self.winBuffer+1:self.seqLen-1],range(self.winBuffer+1,self.seqLen-1))
+            self.trinuc_bias[p] = DiscreteDistribution(trinuc_snp_bias[p][self.winBuffer+1:self.seq_Len-1],range(self.winBuffer+1,self.seq_Len-1))
 
-    def update(self, xOffset, sequence, ploidy, windowOverlap, readLen, mutationModels=[], mutRate=None):
+    def update(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_models=[], mut_rate=None):
         # if mutation model is changed, we have to reinitialize it...
-        if ploidy != self.ploidy or mutRate != self.mutRescale or mutationModels != []:
+        if ploidy != self.ploidy or mut_rate != self.mut_rescale or mut_models != []:
             self.ploidy = ploidy
-            self.mutRescale = mutRate
-            self.init_mutModels(mutationModels, mutRate)
+            self.mut_rescale = mut_rate
+            self.init_mutModels(mut_models, mut_rate)
         # if sequence length is different than previous window, we have to redo snp/indel poissons
-        if len(sequence) != self.seqLen:
-            self.seqLen = len(sequence)
+        if len(sequence) != self.seq_Len:
+            self.seq_Len = len(sequence)
             self.init_poisson()
         # basic vars
-        self.init_basicVars(xOffset, sequence, ploidy, windowOverlap, readLen)
+        self.init_basicVars(x_offset, sequence, ploidy, window_overlap, read_len)
         self.indelsToAdd = [n.sample() for n in self.ind_pois]
         self.snpsToAdd   = [n.sample() for n in self.snp_pois]
         # initialize trinuc snp bias
@@ -254,7 +255,7 @@ def insert_mutations(self, inputList):
             whichPloid = []
             wps = inpV[4][0]
             if wps == None:	# if no genotype given, assume heterozygous and choose a single ploid based on their mut rates
-                whichPloid.append(self.ploidMutPrior.sample())
+                whichPloid.append(self.ploid_mut_prior.sample())
                 whichAlt = [0]
             else:
                 if '/' in wps or '|' in wps:
@@ -317,12 +318,12 @@ def random_mutations(self):
                 if random.random() <= self.models[i][1]:	# insert homozygous indel
                     whichPloid = range(self.ploidy)
                 else:								# insert heterozygous indel
-                    whichPloid = [self.ploidMutPrior.sample()]
+                    whichPloid = [self.ploid_mut_prior.sample()]
 
                 # try to find suitable places to insert indels
                 eventPos = -1
                 for attempt in range(MAX_ATTEMPTS):
-                    eventPos = random.randint(self.winBuffer,self.seqLen-1)
+                    eventPos = random.randint(self.winBuffer,self.seq_Len-1)
                     for p in whichPloid:
                         if self.blackList[p][eventPos]:
                             eventPos = -1
@@ -335,8 +336,8 @@ def random_mutations(self):
                     inLen   = self.models[i][4].sample()
                     # sequence content of random insertions is uniformly random (change this later, maybe)
                     inSeq   = ''.join([random.choice(NUCL) for n in range(inLen)])
-                    refNucl = self.sequences[i][eventPos]
-                    myIndel = (eventPos,refNucl,refNucl+inSeq)
+                    ref_nucl = self.sequences[i][eventPos]
+                    myIndel = (eventPos, ref_nucl, ref_nucl+inSeq)
                 else:										# deletion
                     inLen   = self.models[i][5].sample()
                     if eventPos+inLen+1 >= len(self.sequences[i]):	# skip if deletion too close to boundary
@@ -345,17 +346,17 @@ def random_mutations(self):
                         inSeq = self.sequences[i][eventPos+1]
                     else:
                         inSeq = str(self.sequences[i][eventPos+1:eventPos+inLen+1])
-                    refNucl = self.sequences[i][eventPos]
-                    myIndel = (eventPos,refNucl+inSeq,refNucl)
+                    ref_nucl = self.sequences[i][eventPos]
+                    myIndel = (eventPos, ref_nucl+inSeq, ref_nucl)
 
                 # if event too close to boundary, skip. if event conflicts with other indel, skip.
                 skipEvent = False
-                if eventPos+len(myIndel[1]) >= self.seqLen-self.winBuffer-1:
+                if eventPos+len(myIndel[1]) >= self.seq_Len-self.winBuffer-1:
                     skipEvent = True
                 if skipEvent:
                     continue
                 for p in whichPloid:
-                    for k in range(eventPos,eventPos+inLen+1):
+                    for k in range(eventPos, eventPos+inLen+1):
                         if self.blackList[p][k]:
                             skipEvent = True
                 if skipEvent:
@@ -373,7 +374,7 @@ def random_mutations(self):
                 if random.random() <= self.models[i][1]:	# insert homozygous SNP
                     whichPloid = range(self.ploidy)
                 else:								# insert heterozygous SNP
-                    whichPloid = [self.ploidMutPrior.sample()]
+                    whichPloid = [self.ploid_mut_prior.sample()]
 
                 # try to find suitable places to insert snps
                 eventPos = -1
@@ -381,7 +382,7 @@ def random_mutations(self):
                     # based on the mutation model for the specified ploid, choose a SNP location based on trinuc bias
                     # (if there are multiple ploids, choose one at random)
                     if IGNORE_TRINUC:
-                        eventPos = random.randint(self.winBuffer+1,self.seqLen-2)
+                        eventPos = random.randint(self.winBuffer+1,self.seq_Len-2)
                     else:
                         ploid_to_use = whichPloid[random.randint(0,len(whichPloid)-1)]
                         eventPos     = self.trinuc_bias[ploid_to_use].sample()
@@ -393,11 +394,11 @@ def random_mutations(self):
                 if eventPos == -1:
                     continue
 
-                refNucl = self.sequences[i][eventPos]
+                ref_nucl = self.sequences[i][eventPos]
                 context = str(self.sequences[i][eventPos-1])+str(self.sequences[i][eventPos+1])
                 # sample from tri-nucleotide substitution matrices to get SNP alt allele
-                newNucl = self.models[i][6][TRI_IND[context]][NUC_IND[refNucl]].sample()
-                mySNP   = (eventPos,refNucl,newNucl)
+                new_nucl = self.models[i][6][TRI_IND[context]][NUC_IND[ref_nucl]].sample()
+                mySNP   = (eventPos,ref_nucl,new_nucl)
 
                 for p in whichPloid:
                     all_snps[p].append(mySNP)
@@ -411,15 +412,15 @@ def random_mutations(self):
         # MODIFY REFERENCE STRING: SNPS
         for i in range(len(all_snps)):
             for j in range(len(all_snps[i])):
-                vPos = all_snps[i][j][0]
+                v_pos = all_snps[i][j][0]
 
-                if all_snps[i][j][1] != self.sequences[i][vPos]:
-                    print('\nError: Something went wrong!\n', all_snps[i][j], self.sequences[i][vPos], '\n')
+                if all_snps[i][j][1] != self.sequences[i][v_pos]:
+                    print('\nError: Something went wrong!\n', all_snps[i][j], self.sequences[i][v_pos], '\n')
                     print(all_snps[i][j])
-                    print(self.sequences[i][vPos])
+                    print(self.sequences[i][v_pos])
                     exit(1)
                 else:
-                    self.sequences[i][vPos] = all_snps[i][j][2]
+                    self.sequences[i][v_pos] = all_snps[i][j][2]
 
         # organize the indels we want to insert
         for i in range(len(all_indels)):
@@ -427,80 +428,80 @@ def random_mutations(self):
         all_indels_ins = [sorted([list(m) for m in n]) for n in all_indels]
 
         # MODIFY REFERENCE STRING: INDELS
-        adjToAdd = [[] for n in range(self.ploidy)]
+        adj_to_add = [[] for n in range(self.ploidy)]
         for i in range(len(all_indels_ins)):
-            rollingAdj = 0
-            tempSymbolString = ['M' for n in self.sequences[i]]
+            rolling_adj = 0
+            temp_symbol_string = ['M' for n in self.sequences[i]]
             # there's an off-by-one error somewhere in the position sampling routines.. this might fix it
-            # tempSymbolString.append('M')
+            # temp_symbol_string.append('M')
             for j in range(len(all_indels_ins[i])):
-                vPos        = all_indels_ins[i][j][0] + rollingAdj
-                vPos2       = vPos + len(all_indels_ins[i][j][1])
-                rollingAdj += len(all_indels_ins[i][j][2])-len(all_indels_ins[i][j][1])
+                v_pos        = all_indels_ins[i][j][0] + rolling_adj
+                v_pos2       = v_pos + len(all_indels_ins[i][j][1])
+                rolling_adj += len(all_indels_ins[i][j][2])-len(all_indels_ins[i][j][1])
 
-                if all_indels_ins[i][j][1] != str(self.sequences[i][vPos:vPos2]):
-                    print('\nError: Something went wrong!\n', all_indels_ins[i][j], [vPos, vPos2],
-                          str(self.sequences[i][vPos:vPos2]), '\n')
+                if all_indels_ins[i][j][1] != str(self.sequences[i][v_pos:v_pos2]):
+                    print('\nError: Something went wrong!\n', all_indels_ins[i][j], [v_pos, v_pos2],
+                          str(self.sequences[i][v_pos:v_pos2]), '\n')
                     exit(1)
                 else:
                     # alter reference sequence
-                    self.sequences[i] = self.sequences[i][:vPos] + Seq(all_indels_ins[i][j][2],
+                    self.sequences[i] = self.sequences[i][:v_pos] + Seq(all_indels_ins[i][j][2],
                                                                        IUPAC.unambiguous_dna).tomutable() + \
-                                        self.sequences[i][vPos2:]
+                                        self.sequences[i][v_pos2:]
                     # notate indel positions for cigar computation
                     d = len(all_indels_ins[i][j][2]) - len(all_indels_ins[i][j][1])
                     if d > 0:
-                        tempSymbolString = tempSymbolString[:vPos+1] + ['I']*d + tempSymbolString[vPos2+1:]
+                        temp_symbol_string = temp_symbol_string[:v_pos+1] + ['I']*d + temp_symbol_string[v_pos2+1:]
                     elif d < 0:
-                        tempSymbolString[vPos+1] = 'D'*abs(d)+'M'
+                        temp_symbol_string[v_pos+1] = 'D'*abs(d)+'M'
 
             # precompute cigar strings
-            for j in range(len(tempSymbolString)-self.readLen):
-                self.allCigar[i].append(CigarString(listIn=tempSymbolString[j:j+self.readLen]).getString())
+            for j in range(len(temp_symbol_string)-self.read_len):
+                self.all_cigar[i].append(cigar_string(listIn=temp_symbol_string[j:j+self.read_len]).getString())
 
             # create some data structures we will need later:
             # --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e. corresponding to the unmodified reference genome)
             # --- self.FM_span[ploid][pos]: number of reference positions spanned by a read originating from this coordinate
-            MD_soFar = 0
-            for j in range(len(tempSymbolString)):
-                self.FM_pos[i].append(MD_soFar)
+            MD_so_far = 0
+            for j in range(len(temp_symbol_string)):
+                self.FM_pos[i].append(MD_so_far)
                 # fix an edge case with deletions
-                if 'D' in tempSymbolString[j]:
-                    self.FM_pos[i][-1] += tempSymbolString[j].count('D')
+                if 'D' in temp_symbol_string[j]:
+                    self.FM_pos[i][-1] += temp_symbol_string[j].count('D')
                 # compute number of ref matches for each read
-                span_dif = len([n for n in tempSymbolString[j:j+self.readLen] if 'M' in n])
+                span_dif = len([n for n in temp_symbol_string[j:j+self.read_len] if 'M' in n])
                 self.FM_span[i].append(self.FM_pos[i][-1] + span_dif)
-                MD_soFar += tempSymbolString[j].count('M') + tempSymbolString[j].count('D')
+                MD_so_far += temp_symbol_string[j].count('M') + temp_symbol_string[j].count('D')
 
         # tally up all the variants we handled...
-        countDict = {}
+        count_dict = {}
         all_variants = [sorted(all_snps[i]+all_indels[i]) for i in range(self.ploidy)]
         for i in range(len(all_variants)):
             for j in range(len(all_variants[i])):
                 all_variants[i][j] = tuple([all_variants[i][j][0]+self.x])+all_variants[i][j][1:]
                 t = tuple(all_variants[i][j])
-                if t not in countDict:
-                    countDict[t] = []
-                countDict[t].append(i)
+                if t not in count_dict:
+                    count_dict[t] = []
+                count_dict[t].append(i)
 
         #
         #	TODO: combine multiple variants that happened to occur at same position into single vcf entry?
         #
 
         output_variants = []
-        for k in sorted(countDict.keys()):
-            output_variants.append(k+tuple([len(countDict[k])/float(self.ploidy)]))
+        for k in sorted(count_dict.keys()):
+            output_variants.append(k+tuple([len(count_dict[k])/float(self.ploidy)]))
             ploid_string = ['0' for n in range(self.ploidy)]
-            for k2 in [n for n in countDict[k]]:
+            for k2 in [n for n in count_dict[k]]:
                 ploid_string[k2] = '1'
             output_variants[-1] += tuple(['WP='+'/'.join(ploid_string)])
         return output_variants
 
 
-    def sample_read(self, sequencingModel, fragLen=None):
+    def sample_read(self, sequencingModel, frag_len=None):
 
         # choose a ploid
-        myPloid = random.randint(0,self.ploidy-1)
+        my_ploid = random.randint(0,self.ploidy-1)
 
         # stop attempting to find a valid position if we fail enough times
         MAX_READPOS_ATTEMPTS = 100
@@ -508,28 +509,28 @@ def sample_read(self, sequencingModel, fragLen=None):
 
         # choose a random position within the ploid, and generate quality scores / sequencing errors
         readsToSample = []
-        if fragLen == None:
-            rPos = self.coverage_distribution[myPloid].sample()
+        if frag_len == None:
+            r_pos = self.coverage_distribution[my_ploid].sample()
 
             # sample read position and call function to compute quality scores / sequencing errors
-            rDat = self.sequences[myPloid][rPos:rPos+self.readLen]
-            (myQual, myErrors) = sequencingModel.getSequencingErrors(rDat)
-            readsToSample.append([rPos,myQual,myErrors,rDat])
+            r_dat = self.sequences[my_ploid][r_pos:r_pos+self.read_len]
+            (myQual, myErrors) = sequencingModel.getSequencingErrors(r_dat)
+            readsToSample.append([r_pos,myQual,myErrors,r_dat])
 
         else:
-            rPos1 = self.coverage_distribution[myPloid][self.fraglens_indMap[fragLen]].sample()
+            r_pos1 = self.coverage_distribution[my_ploid][self.fraglens_ind_map[frag_len]].sample()
 
             # EXPERIMENTAL
-            #coords_to_select_from = self.coverage_distribution[myPloid][self.fraglens_indMap[fragLen]].sample()
-            #rPos1 = random.randint(coords_to_select_from[0],coords_to_select_from[1])
+            #coords_to_select_from = self.coverage_distribution[my_ploid][self.fraglens_ind_map[frag_len]].sample()
+            #r_pos1 = random.randint(coords_to_select_from[0],coords_to_select_from[1])
 
-            rPos2 = rPos1 + fragLen - self.readLen
-            rDat1 = self.sequences[myPloid][rPos1:rPos1+self.readLen]
-            rDat2 = self.sequences[myPloid][rPos2:rPos2+self.readLen]
-            (myQual1, myErrors1) = sequencingModel.getSequencingErrors(rDat1)
-            (myQual2, myErrors2) = sequencingModel.getSequencingErrors(rDat2,isReverseStrand=True)
-            readsToSample.append([rPos1,myQual1,myErrors1,rDat1])
-            readsToSample.append([rPos2,myQual2,myErrors2,rDat2])
+            r_pos2 = r_pos1 + frag_len - self.read_len
+            r_dat1 = self.sequences[my_ploid][r_pos1:r_pos1+self.read_len]
+            r_dat2 = self.sequences[my_ploid][r_pos2:r_pos2+self.read_len]
+            (myQual1, myErrors1) = sequencingModel.getSequencingErrors(r_dat1)
+            (myQual2, myErrors2) = sequencingModel.getSequencingErrors(r_dat2,isReverseStrand=True)
+            readsToSample.append([r_pos1,myQual1,myErrors1,r_dat1])
+            readsToSample.append([r_pos2,myQual2,myErrors2,r_dat2])
 
         # error format:
         # myError[i] = (type, len, pos, ref, alt)
@@ -537,27 +538,27 @@ def sample_read(self, sequencingModel, fragLen=None):
         # examine sequencing errors to-be-inserted.
         #	- remove deletions that don't have enough bordering sequence content to "fill in"
         # if error is valid, make the changes to the read data
-        rOut = []
+        r_out = []
         for r in readsToSample:
             try:
-                myCigar = self.allCigar[myPloid][r[0]]
+                myCigar = self.all_cigar[my_ploid][r[0]]
             except IndexError:
                 print('Index error when attempting to find cigar string.')
-                print(myPloid, len(self.allCigar[myPloid]), r[0])
-                if fragLen is not None:
-                    print((rPos1, rPos2))
-                    print(fragLen, self.fraglens_indMap[fragLen])
+                print(my_ploid, len(self.all_cigar[my_ploid]), r[0])
+                if frag_len is not None:
+                    print((r_pos1, r_pos2))
+                    print(frag_len, self.fraglens_ind_map[frag_len])
                 exit(1)
             totalD  = sum([error[1] for error in r[2] if error[0] == 'D'])
             totalI  = sum([error[1] for error in r[2] if error[0] == 'I'])
-            availB  = len(self.sequences[myPloid]) - r[0] - self.readLen - 1
+            availB  = len(self.sequences[my_ploid]) - r[0] - self.read_len - 1
             # add buffer sequence to fill in positions that get deleted
-            r[3] += self.sequences[myPloid][r[0]+self.readLen:r[0]+self.readLen+totalD]
-            expandedCigar = []
+            r[3] += self.sequences[my_ploid][r[0]+self.read_len:r[0]+self.read_len+totalD]
+            expanded_cigar = []
             extraCigar    = []
             adj           = 0
-            sse_adj       = [0 for n in range(self.readLen + max(sequencingModel.errP[3]))]
-            anyIndelErr   = False
+            sse_adj       = [0 for n in range(self.read_len + max(sequencingModel.errP[3]))]
+            any_indel_err   = False
 
             # sort by letter (D > I > S) such that we introduce all indel errors before substitution errors
             # secondarily, sort by index
@@ -568,7 +569,7 @@ def sample_read(self, sequencingModel, fragLen=None):
             for k in sorted(arrangedErrors.keys()):
                 sortedErrors.extend([n[1] for n in sorted(arrangedErrors[k])])
 
-            skipIndels = False
+            skip_indels = False
 
 
             #FIXED TdB 05JUN2018
@@ -580,7 +581,7 @@ def sample_read(self, sequencingModel, fragLen=None):
                 eLen = error[1]
                 ePos = error[2]
                 if error[0] == 'D' or error[0] == 'I':
-                    anyIndelErr   = True
+                    any_indel_err   = True
 
                     #FIXED TdB 05JUN2018
                     #Moved this OUTSIDE the for error loop, since it messes up the CIGAR string when more than one deletion is in the same read
@@ -589,17 +590,17 @@ def sample_read(self, sequencingModel, fragLen=None):
 
                     if totalD > availB:	# if not enough bases to fill-in deletions, skip all indel erors
                         continue
-                    if expandedCigar == []:
-                        expandedCigar = CigarString(stringIn=myCigar).getList()
+                    if expanded_cigar == []:
+                        expanded_cigar = cigar_string(stringIn=myCigar).getList()
                         fillToGo = totalD - totalI + 1
                         if fillToGo > 0:
                             try:
-                                extraCigarVal = CigarString(stringIn=self.allCigar[myPloid][r[0]+fillToGo]).getList()[-fillToGo:]
+                                extraCigarVal = cigar_string(stringIn=self.all_cigar[my_ploid][r[0]+fillToGo]).getList()[-fillToGo:]
                             except IndexError:	# applying the deletions we want requires going beyond region boundaries. skip all indel errors
-                                skipIndels = True
+                                skip_indels = True
 
 
-                    if skipIndels:
+                    if skip_indels:
                         continue
 
                     # insert deletion error into read and update cigar string accordingly
@@ -609,10 +610,10 @@ def sample_read(self, sequencingModel, fragLen=None):
                         pf = ePos+myadj+eLen+1
                         if str(r[3][pi:pf]) == str(error[3]):
                             r[3] = r[3][:pi+1] + r[3][pf:]
-                            expandedCigar = expandedCigar[:pi+1] + expandedCigar[pf:]
-                            if pi+1 == len(expandedCigar):	# weird edge case with del at very end of region. Make a guess and add a "M"
-                                expandedCigar.append('M')
-                            expandedCigar[pi+1] = 'D'*eLen + expandedCigar[pi+1]
+                            expanded_cigar = expanded_cigar[:pi+1] + expanded_cigar[pf:]
+                            if pi+1 == len(expanded_cigar):	# weird edge case with del at very end of region. Make a guess and add a "M"
+                                expanded_cigar.append('M')
+                            expanded_cigar[pi+1] = 'D'*eLen + expanded_cigar[pi+1]
                         else:
                             print('\nError, ref does not match alt while attempting to insert deletion error!\n')
                             exit(1)
@@ -625,7 +626,7 @@ def sample_read(self, sequencingModel, fragLen=None):
                         myadj = sse_adj[ePos]
                         if str(r[3][ePos+myadj]) == error[3]:
                             r[3] = r[3][:ePos+myadj] + error[4] + r[3][ePos+myadj+1:]
-                            expandedCigar = expandedCigar[:ePos+myadj] + ['I']*eLen + expandedCigar[ePos+myadj:]
+                            expanded_cigar = expanded_cigar[:ePos+myadj] + ['I']*eLen + expanded_cigar[ePos+myadj:]
                         else:
                             print('\nError, ref does not match alt while attempting to insert insertion error!\n')
                             print('---', chr(r[3][ePos + myadj]), '!=', error[3])
@@ -641,41 +642,41 @@ def sample_read(self, sequencingModel, fragLen=None):
                         print('\nError, ref does not match alt while attempting to insert substitution error!\n')
                         exit(1)
 
-            if anyIndelErr:
-                if len(expandedCigar):
-                    relevantCigar = (expandedCigar+extraCigarVal)[:self.readLen]
-                    myCigar = CigarString(listIn=relevantCigar).getString()
+            if any_indel_err:
+                if len(expanded_cigar):
+                    relevantCigar = (expanded_cigar+extraCigarVal)[:self.read_len]
+                    myCigar = cigar_string(listIn=relevantCigar).getString()
 
-                r[3] = r[3][:self.readLen]
+                r[3] = r[3][:self.read_len]
 
-            rOut.append([self.FM_pos[myPloid][r[0]], myCigar, str(r[3]), str(r[1])])
+            r_out.append([self.FM_pos[my_ploid][r[0]], myCigar, str(r[3]), str(r[1])])
 
-        # rOut[i] = (pos, cigar, read_string, qual_string)
-        return rOut
+        # r_out[i] = (pos, cigar, read_string, qual_string)
+        return r_out
 
 
 #
 #	Container for read data, computes quality scores and positions to insert errors
 #
 class ReadContainer:
-    def __init__(self, readLen, errorModel, reScaledError):
+    def __init__(self, read_len, errorModel, reScaledError):
 
-        self.readLen = readLen
+        self.read_len = read_len
 
-        errorDat = pickle.load(open(errorModel, 'rb'), encoding="bytes")
+        error_dat = pickle.load(open(errorModel, 'rb'), encoding="bytes")
         self.UNIFORM = False
-        if len(errorDat) == 4:		# uniform-error SE reads (e.g. PacBio)
+        if len(error_dat) == 4:		# uniform-error SE reads (e.g. PacBio)
             self.UNIFORM = True
-            [Qscores,offQ,avgError,errorParams] = errorDat
-            self.uniform_qscore = int(-10.*np.log10(avgError)+0.5)
+            [Qscores, offQ, avgError, errorParams] = error_dat
+            self.uniform_qscore = int(-10. * np.log10(avgError) + 0.5)
             print('Using uniform sequencing error model. (q=' + str(self.uniform_qscore) + '+' + str(
                 offQ) + ', p(err)={0:0.2f}%)'.format(100. * avgError))
-        if len(errorDat) == 6:		# only 1 q-score model present, use same model for both strands
-            [initQ1,probQ1,Qscores,offQ,avgError,errorParams] = errorDat
+        if len(error_dat) == 6:		# only 1 q-score model present, use same model for both strands
+            [initQ1, probQ1, Qscores, offQ, avgError, errorParams] = error_dat
             self.PE_MODELS = False
-        elif len(errorDat) == 8:	# found a q-score model for both forward and reverse strands
+        elif len(error_dat) == 8:	# found a q-score model for both forward and reverse strands
             #print 'Using paired-read quality score profiles...'
-            [initQ1,probQ1,initQ2,probQ2,Qscores,offQ,avgError,errorParams] = errorDat
+            [initQ1 ,probQ1, initQ2, probQ2, Qscores, offQ, avgError, errorParams] = error_dat
             self.PE_MODELS = True
             if len(initQ1) != len(initQ2) or len(probQ1) != len(probQ2):
                 print('\nError: R1 and R2 quality score models are of different length.\n')
@@ -689,9 +690,9 @@ def __init__(self, readLen, errorModel, reScaledError):
 
         # errorParams = [SSE_PROB, SIE_RATE, SIE_PROB, SIE_VAL, SIE_INS_FREQ, SIE_INS_NUCL]
         self.errP   = errorParams
-        self.errSSE = [DiscreteDistribution(n,NUCL) for n in self.errP[0]]
-        self.errSIE = DiscreteDistribution(self.errP[2],self.errP[3])
-        self.errSIN = DiscreteDistribution(self.errP[5],NUCL)
+        self.errSSE = [DiscreteDistribution(n, NUCL) for n in self.errP[0]]
+        self.errSIE = DiscreteDistribution(self.errP[2], self.errP[3])
+        self.errSIN = DiscreteDistribution(self.errP[5], NUCL)
 
         # adjust sequencing error frequency to match desired rate
         if reScaledError == None:
@@ -703,45 +704,45 @@ def __init__(self, readLen, errorModel, reScaledError):
 
         if not self.UNIFORM:
             # adjust length to match desired read length
-            if self.readLen == len(initQ1):
-                self.qIndRemap = range(self.readLen)
+            if self.read_len == len(initQ1):
+                self.qIndRemap = range(self.read_len)
             else:
                 print('Warning: Read length of error model (' + str(len(initQ1)) + ') does not match -R value (' + str(
-                    self.readLen) + '), rescaling model...')
-                self.qIndRemap = [max([1,len(initQ1)*n//readLen]) for n in range(readLen)]
+                    self.read_len) + '), rescaling model...')
+                self.qIndRemap = [max([1, len(initQ1) * n // read_len]) for n in range(read_len)]
 
             # initialize probability distributions
-            self.initDistByPos1        = [DiscreteDistribution(initQ1[i],Qscores) for i in range(len(initQ1))]
+            self.initDistByPos1        = [DiscreteDistribution(initQ1[i], Qscores) for i in range(len(initQ1))]
             self.probDistByPosByPrevQ1 = [None]
-            for i in range(1,len(initQ1)):
+            for i in range(1, len(initQ1)):
                 self.probDistByPosByPrevQ1.append([])
                 for j in range(len(initQ1[0])):
                     if np.sum(probQ1[i][j]) <= 0.:	# if we don't have sufficient data for a transition, use the previous qscore
-                        self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution([1],[Qscores[j]],degenerateVal=Qscores[j]))
+                        self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution([1], [Qscores[j]], degenerateVal=Qscores[j]))
                     else:
-                        self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution(probQ1[i][j],Qscores))
+                        self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution(probQ1[i][j], Qscores))
 
             if self.PE_MODELS:
-                self.initDistByPos2        = [DiscreteDistribution(initQ2[i],Qscores) for i in range(len(initQ2))]
+                self.initDistByPos2        = [DiscreteDistribution(initQ2[i], Qscores) for i in range(len(initQ2))]
                 self.probDistByPosByPrevQ2 = [None]
-                for i in range(1,len(initQ2)):
+                for i in range(1, len(initQ2)):
                     self.probDistByPosByPrevQ2.append([])
                     for j in range(len(initQ2[0])):
                         if np.sum(probQ2[i][j]) <= 0.:	# if we don't have sufficient data for a transition, use the previous qscore
-                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution([1],[Qscores[j]],degenerateVal=Qscores[j]))
+                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution([1], [Qscores[j]], degenerateVal=Qscores[j]))
                         else:
-                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution(probQ2[i][j],Qscores))
+                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution(probQ2[i][j], Qscores))
 
     def getSequencingErrors(self, readData, isReverseStrand=False):
 
-        qOut = [0]*self.readLen
+        qOut = [0]*self.read_len
         sErr = []
 
         if self.UNIFORM:
-            myQ  = [self.uniform_qscore + self.offQ for n in range(self.readLen)]
+            myQ  = [self.uniform_qscore + self.offQ for n in range(self.read_len)]
             qOut = ''.join([chr(n) for n in myQ])
-            for i in range(self.readLen):
-                if random.random() < self.errorScale*self.qErrRate[self.uniform_qscore]:
+            for i in range(self.read_len):
+                if random.random() < self.errorScale * self.qErrRate[self.uniform_qscore]:
                     sErr.append(i)
         else:
 
@@ -751,7 +752,7 @@ def getSequencingErrors(self, readData, isReverseStrand=False):
                 myQ = self.initDistByPos1[0].sample()
             qOut[0] = myQ
 
-            for i in range(1, self.readLen):
+            for i in range(1, self.read_len):
                 if self.PE_MODELS and isReverseStrand:
                     myQ = self.probDistByPosByPrevQ2[self.qIndRemap[i]][myQ].sample()
                 else:
@@ -761,14 +762,14 @@ def getSequencingErrors(self, readData, isReverseStrand=False):
             if isReverseStrand:
                 qOut = qOut[::-1]
 
-            for i in range(self.readLen):
+            for i in range(self.read_len):
                 if random.random() < self.errorScale * self.qErrRate[qOut[i]]:
                     sErr.append(i)
 
             qOut = ''.join([chr(n + self.offQ) for n in qOut])
 
         if self.errorScale == 0.0:
-            return (qOut,[])
+            return (qOut, [])
 
         sOut = []
         nDelSoFar = 0
@@ -781,28 +782,28 @@ def getSequencingErrors(self, readData, isReverseStrand=False):
 
             # determine error type
             isSub = True
-            if ind != 0 and ind != self.readLen-1-max(self.errP[3]) and abs(ind-prevIndel) > 1:
+            if ind != 0 and ind != self.read_len - 1 - max(self.errP[3]) and abs(ind-prevIndel) > 1:
                 if random.random() < self.errP[1]:
                     isSub = False
 
-            # errorOut = (type, len, pos, ref, alt)
+            # error_out = (type, len, pos, ref, alt)
 
             if isSub:								# insert substitution error
                 myNucl  = str(readData[ind])
-                newNucl = self.errSSE[NUC_IND[myNucl]].sample()
-                sOut.append(('S',1,ind,myNucl,newNucl))
+                new_nucl = self.errSSE[NUC_IND[myNucl]].sample()
+                sOut.append(('S', 1, ind, myNucl, new_nucl))
             else:									# insert indel error
                 indelLen = self.errSIE.sample()
                 if random.random() < self.errP[4]:		# insertion error
                     myNucl  = str(readData[ind])
-                    newNucl = myNucl + ''.join([self.errSIN.sample() for n in range(indelLen)])
-                    sOut.append(('I',len(newNucl)-1,ind,myNucl,newNucl))
-                elif ind < self.readLen-2-nDelSoFar:	# deletion error (prevent too many of them from stacking up)
+                    new_nucl = myNucl + ''.join([self.errSIN.sample() for n in range(indelLen)])
+                    sOut.append(('I', len(new_nucl) - 1, ind, myNucl, new_nucl))
+                elif ind < self.read_len - 2 - nDelSoFar:	# deletion error (prevent too many of them from stacking up)
                     myNucl  = str(readData[ind:ind+indelLen+1])
-                    newNucl = str(readData[ind])
+                    new_nucl = str(readData[ind])
                     nDelSoFar += len(myNucl)-1
-                    sOut.append(('D',len(myNucl)-1,ind,myNucl,newNucl))
-                    for i in range(ind+1,ind+indelLen+1):
+                    sOut.append(('D', len(myNucl) - 1, ind, myNucl, new_nucl))
+                    for i in range(ind+1, ind + indelLen+1):
                         delBlacklist.append(i)
                 prevIndel = ind
 
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diff --git a/py/__pycache__/SequenceContainer.cpython-37.pyc b/py/__pycache__/SequenceContainer.cpython-37.pyc
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diff --git a/py/vcfFunc.py b/py/vcfFunc.py
index f9e2ff7..7c80ec4 100644
--- a/py/vcfFunc.py
+++ b/py/vcfFunc.py
@@ -9,24 +9,24 @@
 CHOOSE_RANDOM_PLOID_IF_NO_GT_FOUND = True
 
 
-def parseLine(splt, colDict, colSamp):
+def parseLine(splt, col_dict, col_samp):
     #	check if we want to proceed..
-    ra = splt[colDict['REF']]
-    aa = splt[colDict['ALT']]
+    ra = splt[col_dict['REF']]
+    aa = splt[col_dict['ALT']]
     # enough columns?
-    if len(splt) != len(colDict):
+    if len(splt) != len(col_dict):
         return None
     # exclude homs / filtered?
     if not (INCLUDE_HOMS) and (aa == '.' or aa == '' or aa == ra):
         return None
-    if not (INCLUDE_FAIL) and (splt[colDict['FILTER']] != 'PASS' and splt[colDict['FILTER']] != '.'):
+    if not (INCLUDE_FAIL) and (splt[col_dict['FILTER']] != 'PASS' and splt[col_dict['FILTER']] != '.'):
         return None
 
     #	default vals
     alt_alleles = [aa]
     alt_freqs = []
 
-    gt_perSamp = []
+    gt_per_samp = []
 
     #	any alt alleles?
     alt_split = aa.split(',')
@@ -35,8 +35,8 @@ def parseLine(splt, colDict, colSamp):
 
     #	check INFO for AF
     af = None
-    if 'INFO' in colDict and ';AF=' in ';' + splt[colDict['INFO']]:
-        info = splt[colDict['INFO']] + ';'
+    if 'INFO' in col_dict and ';AF=' in ';' + splt[col_dict['INFO']]:
+        info = splt[col_dict['INFO']] + ';'
         af = re.findall(r"AF=.*?(?=;)", info)[0][3:]
     if af != None:
         af_splt = af.split(',')
@@ -47,83 +47,83 @@ def parseLine(splt, colDict, colSamp):
     else:
         alt_freqs = [None] * max([len(alt_alleles), 1])
 
-    gt_perSamp = None
+    gt_per_samp = None
     #	if available (i.e. we simulated it) look for WP in info
-    if len(colSamp) == 0 and 'INFO' in colDict and 'WP=' in splt[colDict['INFO']]:
-        info = splt[colDict['INFO']] + ';'
-        gt_perSamp = [re.findall(r"WP=.*?(?=;)", info)[0][3:]]
+    if len(col_samp) == 0 and 'INFO' in col_dict and 'WP=' in splt[col_dict['INFO']]:
+        info = splt[col_dict['INFO']] + ';'
+        gt_per_samp = [re.findall(r"WP=.*?(?=;)", info)[0][3:]]
     else:
         #	if no sample columns, check info for GT
-        if len(colSamp) == 0 and 'INFO' in colDict and 'GT=' in splt[colDict['INFO']]:
-            info = splt[colDict['INFO']] + ';'
-            gt_perSamp = [re.findall(r"GT=.*?(?=;)", info)[0][3:]]
-        elif len(colSamp):
-            fmt = ':' + splt[colDict['FORMAT']] + ':'
+        if len(col_samp) == 0 and 'INFO' in col_dict and 'GT=' in splt[col_dict['INFO']]:
+            info = splt[col_dict['INFO']] + ';'
+            gt_per_samp = [re.findall(r"GT=.*?(?=;)", info)[0][3:]]
+        elif len(col_samp):
+            fmt = ':' + splt[col_dict['FORMAT']] + ':'
             if ':GT:' in fmt:
-                gtInd = fmt.split(':').index('GT')
-                gt_perSamp = [splt[colSamp[iii]].split(':')[gtInd - 1] for iii in range(len(colSamp))]
-                for i in range(len(gt_perSamp)):
-                    gt_perSamp[i] = gt_perSamp[i].replace('.', '0')
-        if gt_perSamp == None:
-            gt_perSamp = [None] * max([len(colSamp), 1])
+                gt_ind = fmt.split(':').index('GT')
+                gt_per_samp = [splt[col_samp[iii]].split(':')[gt_ind - 1] for iii in range(len(col_samp))]
+                for i in range(len(gt_per_samp)):
+                    gt_per_samp[i] = gt_per_samp[i].replace('.', '0')
+        if gt_per_samp == None:
+            gt_per_samp = [None] * max([len(col_samp), 1])
 
-    return (alt_alleles, alt_freqs, gt_perSamp)
+    return (alt_alleles, alt_freqs, gt_per_samp)
 
 
-def parseVCF(vcfPath, tumorNormal=False, ploidy=2):
+def parseVCF(vcf_path, tumor_normal=False, ploidy=2):
     tt = time.time()
     print('--------------------------------')
     sys.stdout.write('reading input VCF...\n')
     sys.stdout.flush()
 
-    colDict = {}
-    colSamp = []
-    nSkipped = 0
-    nSkipped_becauseHash = 0
-    allVars = {}  # [ref][pos]
-    sampNames = []
-    alreadyPrintedWarning = False
-    f = open(vcfPath, 'r')
+    col_dict = {}
+    col_samp = []
+    n_skipped = 0
+    n_skipped_because_hash = 0
+    all_vars = {}  # [ref][pos]
+    samp_names = []
+    printed_warning = False
+    f = open(vcf_path, 'r')
     for line in f:
 
         if line[0] != '#':
-            if len(colDict) == 0:
-                print('\n\nERROR: VCF has no header?\n' + vcfPath + '\n\n')
+            if len(col_dict) == 0:
+                print('\n\nERROR: VCF has no header?\n' + vcf_path + '\n\n')
                 f.close()
                 exit(1)
             splt = line[:-1].split('\t')
-            plOut = parseLine(splt, colDict, colSamp)
-            if plOut == None:
-                nSkipped += 1
+            pl_out = parseLine(splt, col_dict, col_samp)
+            if pl_out == None:
+                n_skipped += 1
             else:
-                (aa, af, gt) = plOut
+                (aa, af, gt) = pl_out
 
                 # make sure at least one allele somewhere contains the variant
-                if tumorNormal:
-                    gtEval = gt[:2]
+                if tumor_normal:
+                    gt_eval = gt[:2]
                 else:
-                    gtEval = gt[:1]
-                if None in gtEval:
+                    gt_eval = gt[:1]
+                if None in gt_eval:
                     if CHOOSE_RANDOM_PLOID_IF_NO_GT_FOUND:
-                        if not alreadyPrintedWarning:
+                        if not printed_warning:
                             print('Warning: Found variants without a GT field, assuming heterozygous...')
-                            alreadyPrintedWarning = True
-                        for i in range(len(gtEval)):
+                            printed_warning = True
+                        for i in range(len(gt_eval)):
                             tmp = ['0'] * ploidy
                             tmp[random.randint(0, ploidy - 1)] = '1'
-                            gtEval[i] = '/'.join(tmp)
+                            gt_eval[i] = '/'.join(tmp)
                     else:
                         # skip because no GT field was found
-                        nSkipped += 1
+                        n_skipped += 1
                         continue
-                isNonReference = False
-                for gtVal in gtEval:
+                non_reference = False
+                for gtVal in gt_eval:
                     if gtVal != None:
                         if '1' in gtVal:
-                            isNonReference = True
-                if not isNonReference:
+                            non_reference = True
+                if not non_reference:
                     # skip if no genotype actually contains this variant
-                    nSkipped += 1
+                    n_skipped += 1
                     continue
 
                 chrom = splt[0]
@@ -131,54 +131,54 @@ def parseVCF(vcfPath, tumorNormal=False, ploidy=2):
                 ref = splt[3]
                 # skip if position is <= 0
                 if pos <= 0:
-                    nSkipped += 1
+                    n_skipped += 1
                     continue
 
                 # hash variants to avoid inserting duplicates (there are some messy VCFs out there...)
-                if chrom not in allVars:
-                    allVars[chrom] = {}
-                if pos not in allVars[chrom]:
-                    allVars[chrom][pos] = (pos, ref, aa, af, gtEval)
+                if chrom not in all_vars:
+                    all_vars[chrom] = {}
+                if pos not in all_vars[chrom]:
+                    all_vars[chrom][pos] = (pos, ref, aa, af, gt_eval)
                 else:
-                    nSkipped_becauseHash += 1
+                    n_skipped_because_hash += 1
 
         else:
             if line[1] != '#':
                 cols = line[1:-1].split('\t')
                 for i in range(len(cols)):
-                    if 'FORMAT' in colDict:
-                        colSamp.append(i)
-                    colDict[cols[i]] = i
-                if len(colSamp):
-                    sampNames = cols[-len(colSamp):]
-                    if len(colSamp) == 1:
+                    if 'FORMAT' in col_dict:
+                        col_samp.append(i)
+                    col_dict[cols[i]] = i
+                if len(col_samp):
+                    samp_names = cols[-len(col_samp):]
+                    if len(col_samp) == 1:
                         pass
-                    elif len(colSamp) == 2 and tumorNormal:
+                    elif len(col_samp) == 2 and tumor_normal:
                         print('Detected 2 sample columns in input VCF, assuming tumor/normal.')
                     else:
                         print(
                             'Warning: Multiple sample columns present in input VCF. By default genReads uses only the first column.')
                 else:
-                    sampNames = ['Unknown']
-                if tumorNormal:
-                    # tumorInd  = sampNames.index('TUMOR')
-                    # normalInd = sampNames.index('NORMAL')
-                    if 'NORMAL' not in sampNames or 'TUMOR' not in sampNames:
+                    samp_names = ['Unknown']
+                if tumor_normal:
+                    # tumorInd  = samp_names.index('TUMOR')
+                    # normalInd = samp_names.index('NORMAL')
+                    if 'NORMAL' not in samp_names or 'TUMOR' not in samp_names:
                         print('\n\nERROR: Input VCF must have a "NORMAL" and "TUMOR" column.\n')
     f.close()
 
-    varsOut = {}
-    for r in allVars.keys():
-        varsOut[r] = [list(allVars[r][k]) for k in sorted(allVars[r].keys())]
+    vars_out = {}
+    for r in all_vars.keys():
+        vars_out[r] = [list(all_vars[r][k]) for k in sorted(all_vars[r].keys())]
         # prune unnecessary sequence from ref/alt alleles
-        for i in range(len(varsOut[r])):
-            while len(varsOut[r][i][1]) > 1 and all([n[-1] == varsOut[r][i][1][-1] for n in varsOut[r][i][2]]):
-                varsOut[r][i][1] = varsOut[r][i][1][:-1]
-                varsOut[r][i][2] = [n[:-1] for n in varsOut[r][i][2]]
-            varsOut[r][i] = tuple(varsOut[r][i])
-
-    print('found', sum([len(n) for n in allVars.values()]), 'valid variants in input vcf.')
-    print(' *', nSkipped, 'variants skipped: (qual filtered / ref genotypes / invalid syntax)')
-    print(' *', nSkipped_becauseHash, 'variants skipped due to multiple variants found per position')
+        for i in range(len(vars_out[r])):
+            while len(vars_out[r][i][1]) > 1 and all([n[-1] == vars_out[r][i][1][-1] for n in vars_out[r][i][2]]):
+                vars_out[r][i][1] = vars_out[r][i][1][:-1]
+                vars_out[r][i][2] = [n[:-1] for n in vars_out[r][i][2]]
+            vars_out[r][i] = tuple(vars_out[r][i])
+
+    print('found', sum([len(n) for n in all_vars.values()]), 'valid variants in input vcf.')
+    print(' *', n_skipped, 'variants skipped: (qual filtered / ref genotypes / invalid syntax)')
+    print(' *', n_skipped_because_hash, 'variants skipped due to multiple variants found per position')
     print('--------------------------------')
-    return (sampNames, varsOut)
+    return (samp_names, vars_out)

From a96f180cdbcbf009f22c010faf264762013e9188 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 17 Jul 2020 11:23:45 -0500
Subject: [PATCH 096/323] Minor changes to genreads

---
 genReads.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/genReads.py b/genReads.py
index 9a90d7f..414a311 100644
--- a/genReads.py
+++ b/genReads.py
@@ -87,9 +87,10 @@ def main(raw_args=None):
     """
     Set variables for processing
     """
+    # TODO change these to pathlib.Path objects so they are machine agnostic
     # absolute path to this script
     SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-1])
-    print("The sim path is: ", SIM_PATH)
+    print("sim path is ", SIM_PATH)
 
     # if coverage val for a given window/position is below this value, consider it effectively zero.
     LOW_COV_THRESH = 50

From 5bf8a63327c6a06584a1123e7cc0ec03b8b1259d Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 17 Jul 2020 11:28:30 -0500
Subject: [PATCH 097/323] minor change to neat_cigar

---
 py/neat_cigar.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/py/neat_cigar.py b/py/neat_cigar.py
index ab588f9..8965116 100644
--- a/py/neat_cigar.py
+++ b/py/neat_cigar.py
@@ -1,7 +1,7 @@
 import re
 
 
-class CigarString:
+class cigar_string:
     def __init__(self, stringIn=None, listIn=None):
 
         if stringIn is None and listIn is None:

From 6c54fbf5c98890ff1e43b183817118452736300d Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 17 Jul 2020 11:29:00 -0500
Subject: [PATCH 098/323] minor change to genreads

---
 genReads.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/genReads.py b/genReads.py
index 414a311..66dac5d 100644
--- a/genReads.py
+++ b/genReads.py
@@ -90,7 +90,6 @@ def main(raw_args=None):
     # TODO change these to pathlib.Path objects so they are machine agnostic
     # absolute path to this script
     SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-1])
-    print("sim path is ", SIM_PATH)
 
     # if coverage val for a given window/position is below this value, consider it effectively zero.
     LOW_COV_THRESH = 50

From 2e163fb718521ae058e5e21e7c01564f042adaee Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 17 Jul 2020 11:48:53 -0500
Subject: [PATCH 099/323] renaming in genReads

---
 genReads.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/genReads.py b/genReads.py
index 66dac5d..39e7afa 100644
--- a/genReads.py
+++ b/genReads.py
@@ -550,7 +550,7 @@ def main(raw_args=None):
                 # construct sequence data that we will sample reads from
                 if sequences is None:
                     sequences = SequenceContainer(start, refSequence[start:end], PLOIDS, overlap, READLEN,
-                                                  [MUT_MODEL] * PLOIDS, MUT_RATE, onlyVCF=ONLY_VCF)
+                                                  [MUT_MODEL] * PLOIDS, MUT_RATE, only_vcf=ONLY_VCF)
                 else:
                     sequences.update(start, refSequence[start:end], PLOIDS, overlap, READLEN, [MUT_MODEL] * PLOIDS,
                                      MUT_RATE)

From e88b60496b6b0eb5da99572769247fcadca2526d Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 17 Jul 2020 14:26:30 -0500
Subject: [PATCH 100/323] minor fix to genreads

---
 genReads.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/genReads.py b/genReads.py
index 39e7afa..e84f5f3 100644
--- a/genReads.py
+++ b/genReads.py
@@ -563,7 +563,7 @@ def main(raw_args=None):
                 # init coverage
                 if sum(coverage_dat[2]) >= LOW_COV_THRESH:
                     if PAIRED_END:
-                        coverage_avg = sequences.init_coverage(tuple(coverage_dat), fragDist=FRAGLEN_DISTRIBUTION)
+                        coverage_avg = sequences.init_coverage(tuple(coverage_dat), frag_dist=FRAGLEN_DISTRIBUTION)
                     else:
                         coverage_avg = sequences.init_coverage(tuple(coverage_dat))
 

From e474b07917254bdb95297d573ef73fc36cfe1ad0 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 6 Aug 2020 15:01:05 -0500
Subject: [PATCH 101/323] Bringing py/input_checking.py into PEP8 compliance.

---
 genReads.py                                 |  30 ++++-----
 py/__pycache__/inputChecking.cpython-37.pyc | Bin 2391 -> 0 bytes
 py/inputChecking.py                         |  67 --------------------
 py/input_checking.py                        |  66 +++++++++++++++++++
 4 files changed, 81 insertions(+), 82 deletions(-)
 delete mode 100644 py/__pycache__/inputChecking.cpython-37.pyc
 delete mode 100644 py/inputChecking.py
 create mode 100644 py/input_checking.py

diff --git a/genReads.py b/genReads.py
index e84f5f3..2b700d4 100644
--- a/genReads.py
+++ b/genReads.py
@@ -25,7 +25,7 @@
 import numpy as np
 import argparse
 
-from py.inputChecking import requiredField, checkFileOpen, isInRange
+from py.input_checking import required_field, check_file_open, is_in_range
 from py.ref_func import index_ref, readRef, getAllRefRegions, partitionRefRegions
 from py.vcfFunc import parseVCF
 from py.OutputFileWriter import OutputFileWriter
@@ -146,10 +146,10 @@ def main(raw_args=None):
     ****            INPUT ERROR CHECKING
     ************************************************"""
 
-    checkFileOpen(REFERENCE, 'ERROR: could not open reference', required=True)
-    checkFileOpen(INPUT_VCF, 'ERROR: could not open input VCF', required=False)
-    checkFileOpen(INPUT_BED, 'ERROR: could not open input BED', required=False)
-    requiredField(OUT_PREFIX, 'ERROR: no output prefix provided')
+    check_file_open(REFERENCE, 'ERROR: could not open reference', required=True)
+    check_file_open(INPUT_VCF, 'ERROR: could not open input VCF', required=False)
+    check_file_open(INPUT_BED, 'ERROR: could not open input BED', required=False)
+    required_field(OUT_PREFIX, 'ERROR: no output prefix provided')
     if (FRAGMENT_SIZE is None and FRAGMENT_STD is not None) or (FRAGMENT_SIZE is not None and FRAGMENT_STD is None):
         print('\nError: --pe argument takes 2 space-separated arguments.\n')
         exit(1)
@@ -241,20 +241,20 @@ def main(raw_args=None):
     ****          MORE INPUT ERROR CHECKING
     ************************************************"""
 
-    isInRange(READLEN, 10, 1000000, 'Error: -R must be between 10 and 1,000,000')
-    isInRange(COVERAGE, 0, 1000000, 'Error: -c must be between 0 and 1,000,000')
-    isInRange(PLOIDS, 1, 100, 'Error: -p must be between 1 and 100')
-    isInRange(OFFTARGET_SCALAR, 0, 1, 'Error: -to must be between 0 and 1')
+    is_in_range(READLEN, 10, 1000000, 'Error: -R must be between 10 and 1,000,000')
+    is_in_range(COVERAGE, 0, 1000000, 'Error: -c must be between 0 and 1,000,000')
+    is_in_range(PLOIDS, 1, 100, 'Error: -p must be between 1 and 100')
+    is_in_range(OFFTARGET_SCALAR, 0, 1, 'Error: -to must be between 0 and 1')
     if MUT_RATE != -1 and MUT_RATE is not None:
-        isInRange(MUT_RATE, 0, 0.3, 'Error: -M must be between 0 and 0.3')
+        is_in_range(MUT_RATE, 0, 0.3, 'Error: -M must be between 0 and 0.3')
     if SE_RATE != -1 and SE_RATE is not None:
-        isInRange(SE_RATE, 0, 0.3, 'Error: -E must be between 0 and 0.3')
+        is_in_range(SE_RATE, 0, 0.3, 'Error: -E must be between 0 and 0.3')
     if NJOBS != 1:
-        isInRange(NJOBS, 1, 1000, 'Error: --job must be between 1 and 1,000')
-        isInRange(MYJOB, 1, 1000, 'Error: --job must be between 1 and 1,000')
-        isInRange(MYJOB, 1, NJOBS, 'Error: job id must be less than or equal to number of jobs')
+        is_in_range(NJOBS, 1, 1000, 'Error: --job must be between 1 and 1,000')
+        is_in_range(MYJOB, 1, 1000, 'Error: --job must be between 1 and 1,000')
+        is_in_range(MYJOB, 1, NJOBS, 'Error: job id must be less than or equal to number of jobs')
     if N_MAX_QUAL != -1:
-        isInRange(N_MAX_QUAL, 1, 40, 'Error: -N must be between 1 and 40')
+        is_in_range(N_MAX_QUAL, 1, 40, 'Error: -N must be between 1 and 40')
 
     """************************************************
     ****   Process Inputs
diff --git a/py/__pycache__/inputChecking.cpython-37.pyc b/py/__pycache__/inputChecking.cpython-37.pyc
deleted file mode 100644
index 00142fac6a5a7a6c274d1d3cf6818e725a7739b9..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/py/inputChecking.py b/py/inputChecking.py
deleted file mode 100644
index 45ec25c..0000000
--- a/py/inputChecking.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""
-This file contains several standard functions that will be used throughout the program. Each function checks input
-and issues an error if there is something wrong.
-"""
-
-import os
-import sys
-
-
-def requiredField(s: any, errString: str) -> None:
-    """
-    If required field s is empty, issues an error. Otherwise this does nothing
-
-    :param s: Any input type
-    :param errString: A string with the error message
-    :return: None
-    """
-    if s is None:
-        print('\n' + errString + '\n')
-        exit(1)
-
-
-def checkFileOpen(fn: str, errString: str, required: bool = False) -> None:
-    """
-    Checks that the filename is not empty and that it is indeed a  file
-    :param fn: file name, string
-    :param errString: string of the error if it is not a file
-    :param required: If not required, skips the check
-    :return: None
-    """
-    if required or fn is not None:
-        if fn is None:
-            print('\n' + errString + '\n')
-            exit(1)
-        else:
-            try:
-                open(fn, 'r')
-            except ValueError:
-                print('\n' + errString + '\n')
-                exit(1)
-
-
-def checkDir(dir: str, errString: str) -> None:
-    """
-    Checks that directory exists and is a directory
-    :param dir: string of the directory path
-    :param errString: string of the error in case it is not a directory or doesn't exist
-    :return: None
-    """
-    if not os.path.isdir(dir):
-        print('\n' + errString + '\n')
-        exit(1)
-
-
-def isInRange(val: float, lb: float, ub: float, errString: str) -> None:
-    """
-    Checks that value (val) is between the lower bound (lb) and upper bound (ub), and if not prints an error message
-    (errString) and exits the program.
-    :param val: float for the value
-    :param lb: float for the upper bound
-    :param ub: float for the lower bound
-    :param errString: string of the error message to print if the value is out of range
-    :return: None
-    """
-    if val < lb or val > ub:
-        print('\n' + errString + '\n')
-        exit(1)
diff --git a/py/input_checking.py b/py/input_checking.py
new file mode 100644
index 0000000..4e8d895
--- /dev/null
+++ b/py/input_checking.py
@@ -0,0 +1,66 @@
+"""
+This file contains several standard functions that will be used throughout the program. Each function checks input
+and issues an error if there is something wrong.
+"""
+
+import os
+
+
+def required_field(variable_to_test: any, err_string: str) -> None:
+    """
+    If required field variable_to_test is empty, issues an error. Otherwise this does nothing
+
+    :param variable_to_test: Any input type
+    :param err_string: A string with the error message
+    :return: None
+    """
+    if variable_to_test is None:
+        print('\n' + err_string + '\n')
+        exit(1)
+
+
+def check_file_open(filename: str, err_string: str, required: bool = False) -> None:
+    """
+    Checks that the filename is not empty and that it is indeed a  file
+    :param filename: file name, string
+    :param err_string: string of the error if it is not a file
+    :param required: If not required, skips the check
+    :return: None
+    """
+    if required or filename is not None:
+        if filename is None:
+            print('\n' + err_string + '\n')
+            exit(1)
+        else:
+            try:
+                open(filename, 'r')
+            except ValueError:
+                print('\n' + err_string + '\n')
+                exit(1)
+
+
+def check_dir(directory: str, err_string: str) -> None:
+    """
+    Checks that directory exists and is a directory
+    :param directory: string of the directory path
+    :param err_string: string of the error in case it is not a directory or doesn't exist
+    :return: None
+    """
+    if not os.path.isdir(directory):
+        print('\n' + err_string + '\n')
+        exit(1)
+
+
+def is_in_range(value: float, lower_bound: float, upper_bound: float, err_string: str) -> None:
+    """
+    Checks that value (val) is between the lower bound (lb) and upper bound (ub), and if not prints an error message
+    (errString) and exits the program.
+    :param value: float for the value
+    :param lower_bound: float for the upper bound
+    :param upper_bound: float for the lower bound
+    :param err_string: string of the error message to print if the value is out of range
+    :return: None
+    """
+    if value < lower_bound or value > upper_bound:
+        print('\n' + err_string + '\n')
+        exit(1)

From 94b310eeab6e1fd155518741d325adf2225ee24b Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 6 Aug 2020 15:40:41 -0500
Subject: [PATCH 102/323] Started bringing neat_cigar.py up to PEP8 standards

---
 py/SequenceContainer.py |  10 ++--
 py/neat_cigar.py        | 105 +++++++++++++++++++++-------------------
 2 files changed, 59 insertions(+), 56 deletions(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 4f5c349..bd35c33 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -8,7 +8,7 @@
 from Bio.Alphabet import IUPAC
 
 from py.probability import DiscreteDistribution, poisson_list, quantize_list
-from py.neat_cigar import cigar_string
+from py.neat_cigar import CigarString
 
 MAX_ATTEMPTS = 100	# max attempts to insert a mutation into a valid position
 MAX_MUTFRAC  = 0.3	# the maximum percentage of a window that can contain mutations
@@ -457,7 +457,7 @@ def random_mutations(self):
 
             # precompute cigar strings
             for j in range(len(temp_symbol_string)-self.read_len):
-                self.all_cigar[i].append(cigar_string(listIn=temp_symbol_string[j:j+self.read_len]).getString())
+                self.all_cigar[i].append(CigarString(listIn=temp_symbol_string[j:j + self.read_len]).get_string())
 
             # create some data structures we will need later:
             # --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e. corresponding to the unmodified reference genome)
@@ -591,11 +591,11 @@ def sample_read(self, sequencingModel, frag_len=None):
                     if totalD > availB:	# if not enough bases to fill-in deletions, skip all indel erors
                         continue
                     if expanded_cigar == []:
-                        expanded_cigar = cigar_string(stringIn=myCigar).getList()
+                        expanded_cigar = CigarString(stringIn=myCigar).get_list()
                         fillToGo = totalD - totalI + 1
                         if fillToGo > 0:
                             try:
-                                extraCigarVal = cigar_string(stringIn=self.all_cigar[my_ploid][r[0]+fillToGo]).getList()[-fillToGo:]
+                                extraCigarVal = CigarString(stringIn=self.all_cigar[my_ploid][r[0] + fillToGo]).get_list()[-fillToGo:]
                             except IndexError:	# applying the deletions we want requires going beyond region boundaries. skip all indel errors
                                 skip_indels = True
 
@@ -645,7 +645,7 @@ def sample_read(self, sequencingModel, frag_len=None):
             if any_indel_err:
                 if len(expanded_cigar):
                     relevantCigar = (expanded_cigar+extraCigarVal)[:self.read_len]
-                    myCigar = cigar_string(listIn=relevantCigar).getString()
+                    myCigar = CigarString(listIn=relevantCigar).get_string()
 
                 r[3] = r[3][:self.read_len]
 
diff --git a/py/neat_cigar.py b/py/neat_cigar.py
index 8965116..e6f821e 100644
--- a/py/neat_cigar.py
+++ b/py/neat_cigar.py
@@ -1,95 +1,97 @@
 import re
 
 
-class cigar_string:
+class CigarString:
     def __init__(self, stringIn=None, listIn=None):
 
         if stringIn is None and listIn is None:
             print('\nError: CigarString object not initialized.\n')
             exit(1)
 
-        self.cigarData = []
+        self.cigar_data = []
 
         if stringIn is not None:
-            self.joinCigar(j_stringIn=stringIn)
+            self.join_cigar(j_string_in=stringIn)
 
         if listIn is not None:
-            self.joinCigar(j_listIn=listIn)
+            self.join_cigar(j_list_in=listIn)
 
-    def stringToList(self, s):
+    @staticmethod
+    def string_to_list(string_to_covert: str) -> list:
 
-        cigarDat = []
-        letters = re.split(r"\d+", s)[1:]
-        numbers = [int(n) for n in re.findall(r"\d+", s)]
-        dReserve = 0
+        cigar_dat = []
+        letters = re.split(r"\d+", string_to_covert)[1:]
+        numbers = [int(n) for n in re.findall(r"\d+", string_to_covert)]
+        d_reserve = 0
         for i in range(len(letters)):
             if letters[i] == 'D':
-                dReserve = numbers[i]
+                d_reserve = numbers[i]
             if letters[i] == 'M' or letters[i] == 'I':
-                if dReserve:
-                    cigarDat += ['D' * dReserve + letters[i]] + [letters[i]] * (int(numbers[i]) - 1)
+                if d_reserve:
+                    cigar_dat += ['D' * d_reserve + letters[i]] + [letters[i]] * (int(numbers[i]) - 1)
                 else:
-                    cigarDat += [letters[i]] * int(numbers[i])
-                dReserve = 0
-        return cigarDat
+                    cigar_dat += [letters[i]] * int(numbers[i])
+                d_reserve = 0
+        return cigar_dat
 
-    def listToString(self, l):
+    @staticmethod
+    def list_to_string(list_to_convert: list) -> str:
 
         symbols = ''
-        currentSym = l[0]
-        currentCount = 1
-        if 'D' in currentSym:
-            currentSym = currentSym[-1]
-        for k in range(1, len(l)):
-            nextSym = l[k]
-            if len(nextSym) == 1 and nextSym == currentSym:
-                currentCount += 1
+        current_sym = list_to_convert[0]
+        current_count = 1
+        if 'D' in current_sym:
+            current_sym = current_sym[-1]
+        for k in range(1, len(list_to_convert)):
+            next_sym = list_to_convert[k]
+            if len(next_sym) == 1 and next_sym == current_sym:
+                current_count += 1
             else:
-                symbols += str(currentCount) + currentSym
-                if 'D' in nextSym:
-                    symbols += str(nextSym.count('D')) + 'D'
-                    currentSym = nextSym[-1]
+                symbols += str(current_count) + current_sym
+                if 'D' in next_sym:
+                    symbols += str(next_sym.count('D')) + 'D'
+                    current_sym = next_sym[-1]
                 else:
-                    currentSym = nextSym
-                currentCount = 1
-        symbols += str(currentCount) + currentSym
+                    current_sym = next_sym
+                current_count = 1
+        symbols += str(current_count) + current_sym
         return symbols
 
-    def getList(self):
+    def get_list(self):
 
-        return self.cigarData
+        return self.cigar_data
 
-    def getString(self):
+    def get_string(self):
 
-        return self.listToString(self.cigarData)
+        return self.list_to_string(self.cigar_data)
 
-    def joinCigar(self, j_stringIn=None, j_listIn=None):
+    def join_cigar(self, j_string_in=None, j_list_in=None):
 
-        if j_stringIn is None and j_listIn is None:
+        if j_string_in is None and j_list_in is None:
             print('\nError: Invalid join operation in CigarString\n')
             exit(1)
 
-        if j_stringIn is not None:
-            self.cigarData += self.stringToList(j_stringIn)
+        if j_string_in is not None:
+            self.cigar_data += self.string_to_list(j_string_in)
 
-        if j_listIn is not None:
-            self.cigarData += j_listIn
+        if j_list_in is not None:
+            self.cigar_data += j_list_in
 
-    def insertCigarElement(self, pos, i_stringIn=None, i_listIn=None):
+    def insert_cigar_element(self, pos, i_string_in=None, i_list_in=None):
 
-        if i_stringIn == None and i_listIn == None:
+        if i_string_in is None and i_list_in is None:
             print('\nError: Invalid insertion operation in CigarString\n')
             exit(1)
 
-        if pos < 0 or pos >= len(self.cigarData):
+        if pos < 0 or pos >= len(self.cigar_data):
             print('\nError: Invalid insertion position in CigarString\n')
             exit(1)
 
-        if i_stringIn != None:
-            self.cigarData = self.cigarData[:pos] + self.stringToList(i_stringIn) + self.cigarData[pos:]
+        if i_string_in is not None:
+            self.cigar_data = self.cigar_data[:pos] + self.string_to_list(i_string_in) + self.cigar_data[pos:]
 
-        if i_listIn != None:
-            self.cigarData = self.cigarData[:pos] + i_listIn + self.cigarData[pos:]
+        if i_list_in is not None:
+            self.cigar_data = self.cigar_data[:pos] + i_list_in + self.cigar_data[pos:]
 
 
 if __name__ == '__main__':
@@ -98,6 +100,7 @@ def insertCigarElement(self, pos, i_stringIn=None, i_listIn=None):
     str1 = '50M10D7I23M'
     str2 = '10I25M'
     iPos = 20
-    myCigar = CigarString(stringIn=str1)
-    myCigar.insertCigarElement(iPos, i_stringIn=str2)
-    print(str1, '+', str2, '[inserted at position', str(iPos) + ']', '=', myCigar.getString())
+    my_cigar = CigarString(stringIn=str1)
+    my_cigar.insert_cigar_element(iPos, i_string_in=str2)
+    assert(my_cigar.get_string() == "20M10I55M10D7I23M")
+    print("passed")

From eb4ac2e9fce2e408dcc6f8018ad4083483a3cbbd Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 7 Aug 2020 10:59:37 -0500
Subject: [PATCH 103/323] basic pep8 conversion of output_file_writer.py.

---
 genReads.py                                   |  68 +++---
 .../OutputFileWriter.cpython-37.pyc           | Bin 7674 -> 0 bytes
 ...putFileWriter.py => output_file_writer.py} | 226 ++++++++++--------
 3 files changed, 158 insertions(+), 136 deletions(-)
 delete mode 100644 py/__pycache__/OutputFileWriter.cpython-37.pyc
 rename py/{OutputFileWriter.py => output_file_writer.py} (52%)

diff --git a/genReads.py b/genReads.py
index 2b700d4..fe10bfd 100644
--- a/genReads.py
+++ b/genReads.py
@@ -28,7 +28,7 @@
 from py.input_checking import required_field, check_file_open, is_in_range
 from py.ref_func import index_ref, readRef, getAllRefRegions, partitionRefRegions
 from py.vcfFunc import parseVCF
-from py.OutputFileWriter import OutputFileWriter
+from py.output_file_writer import OutputFileWriter
 from py.probability import DiscreteDistribution, mean_ind_of_weighted_list
 from py.SequenceContainer import SequenceContainer, ReadContainer, parseInputMutationModel
 
@@ -344,9 +344,9 @@ def main(raw_args=None):
         corrected_nJobs = 1
 
     # initialize output files (part II)
-    OFW = OutputFileWriter(OUT_PREFIX, paired=PAIRED_END, BAM_header=bamHeader, VCF_header=vcfHeader,
-                           gzipped=GZIPPED_OUT, jobTuple=(MYJOB, corrected_nJobs), noFASTQ=NO_FASTQ,
-                           FASTA_instead=FASTA_INSTEAD)
+    OFW = OutputFileWriter(OUT_PREFIX, paired=PAIRED_END, bam_header=bamHeader, vcf_header=vcfHeader,
+                           gzipped=GZIPPED_OUT, job_tuple=(MYJOB, corrected_nJobs), no_fastq=NO_FASTQ,
+                           fasta_instead=FASTA_INSTEAD)
     OUT_PREFIX_NAME = OUT_PREFIX.split('/')[-1]
 
     """************************************************
@@ -646,46 +646,46 @@ def main(raw_args=None):
                         myRefIndex = indices_by_refName[refIndex[RI][0]]
                         if len(myReadData) == 1:
                             if NO_FASTQ != True:
-                                OFW.writeFASTQRecord(myReadName, myReadData[0][2], myReadData[0][3])
+                                OFW.write_fastq_record(myReadName, myReadData[0][2], myReadData[0][3])
                             if SAVE_BAM:
                                 if isUnmapped[0] == False:
-                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/1', myReadData[0][0],
-                                                       myReadData[0][1], myReadData[0][2], myReadData[0][3], samFlag=0)
+                                    OFW.write_bam_record(myRefIndex, myReadName + '/1', myReadData[0][0],
+                                                         myReadData[0][1], myReadData[0][2], myReadData[0][3], sam_flag=0)
                         elif len(myReadData) == 2:
                             if NO_FASTQ != True:
-                                OFW.writeFASTQRecord(myReadName, myReadData[0][2], myReadData[0][3],
-                                                     read2=myReadData[1][2], qual2=myReadData[1][3])
+                                OFW.write_fastq_record(myReadName, myReadData[0][2], myReadData[0][3],
+                                                       read2=myReadData[1][2], qual2=myReadData[1][3])
                             if SAVE_BAM:
                                 if isUnmapped[0] == False and isUnmapped[1] == False:
-                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/1', myReadData[0][0],
-                                                       myReadData[0][1], myReadData[0][2], myReadData[0][3], samFlag=99,
-                                                       matePos=myReadData[1][0])
-                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/2', myReadData[1][0],
-                                                       myReadData[1][1], myReadData[1][2], myReadData[1][3],
-                                                       samFlag=147, matePos=myReadData[0][0])
+                                    OFW.write_bam_record(myRefIndex, myReadName + '/1', myReadData[0][0],
+                                                         myReadData[0][1], myReadData[0][2], myReadData[0][3], sam_flag=99,
+                                                         mate_pos=myReadData[1][0])
+                                    OFW.write_bam_record(myRefIndex, myReadName + '/2', myReadData[1][0],
+                                                         myReadData[1][1], myReadData[1][2], myReadData[1][3],
+                                                         sam_flag=147, mate_pos=myReadData[0][0])
                                 elif isUnmapped[0] == False and isUnmapped[1] == True:
-                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/1', myReadData[0][0],
-                                                       myReadData[0][1], myReadData[0][2], myReadData[0][3],
-                                                       samFlag=105, matePos=myReadData[0][0])
-                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/2', myReadData[0][0],
-                                                       myReadData[1][1], myReadData[1][2], myReadData[1][3],
-                                                       samFlag=149, matePos=myReadData[0][0], alnMapQual=0)
+                                    OFW.write_bam_record(myRefIndex, myReadName + '/1', myReadData[0][0],
+                                                         myReadData[0][1], myReadData[0][2], myReadData[0][3],
+                                                         sam_flag=105, mate_pos=myReadData[0][0])
+                                    OFW.write_bam_record(myRefIndex, myReadName + '/2', myReadData[0][0],
+                                                         myReadData[1][1], myReadData[1][2], myReadData[1][3],
+                                                         sam_flag=149, mate_pos=myReadData[0][0], aln_map_qual=0)
                                 elif isUnmapped[0] == True and isUnmapped[1] == False:
-                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/1', myReadData[1][0],
-                                                       myReadData[0][1], myReadData[0][2], myReadData[0][3],
-                                                       samFlag=101, matePos=myReadData[1][0], alnMapQual=0)
-                                    OFW.writeBAMRecord(myRefIndex, myReadName + '/2', myReadData[1][0],
-                                                       myReadData[1][1], myReadData[1][2], myReadData[1][3],
-                                                       samFlag=153, matePos=myReadData[1][0])
+                                    OFW.write_bam_record(myRefIndex, myReadName + '/1', myReadData[1][0],
+                                                         myReadData[0][1], myReadData[0][2], myReadData[0][3],
+                                                         sam_flag=101, mate_pos=myReadData[1][0], aln_map_qual=0)
+                                    OFW.write_bam_record(myRefIndex, myReadName + '/2', myReadData[1][0],
+                                                         myReadData[1][1], myReadData[1][2], myReadData[1][3],
+                                                         sam_flag=153, mate_pos=myReadData[1][0])
                         else:
                             print('\nError: Unexpected number of reads generated...\n')
                             exit(1)
                     # print 'READS:',time.time()-ASDF2_TT
 
                     if not isLastTime:
-                        OFW.flushBuffers(bamMax=next_start)
+                        OFW.flush_buffers(bam_max=next_start)
                     else:
-                        OFW.flushBuffers(bamMax=end + 1)
+                        OFW.flush_buffers(bam_max=end + 1)
 
                 # tally up all the variants that got successfully introduced
                 for n in all_inserted_variants:
@@ -718,7 +718,7 @@ def main(raw_args=None):
                 myQual = '.'
                 myFilt = 'PASS'
                 # k[0] + 1 because we're going back to 1-based vcf coords
-                OFW.writeVCFRecord(currentRef, str(int(k[0]) + 1), myID, k[1], k[2], myQual, myFilt, k[4])
+                OFW.write_vcf_record(currentRef, str(int(k[0]) + 1), myID, k[1], k[2], myQual, myFilt, k[4])
 
     # break
 
@@ -727,13 +727,13 @@ def main(raw_args=None):
         print('writing unmapped reads to bam file...')
         for umr in unmapped_records:
             if PAIRED_END:
-                OFW.writeBAMRecord(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], samFlag=umr[2], matePos=0,
-                                   alnMapQual=0)
+                OFW.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], sam_flag=umr[2], mate_pos=0,
+                                     aln_map_qual=0)
             else:
-                OFW.writeBAMRecord(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], samFlag=umr[2], alnMapQual=0)
+                OFW.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], sam_flag=umr[2], aln_map_qual=0)
 
     # close output files
-    OFW.closeFiles()
+    OFW.close_files()
 
 if __name__ == '__main__':
     main()
diff --git a/py/__pycache__/OutputFileWriter.cpython-37.pyc b/py/__pycache__/OutputFileWriter.cpython-37.pyc
deleted file mode 100644
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diff --git a/py/OutputFileWriter.py b/py/output_file_writer.py
similarity index 52%
rename from py/OutputFileWriter.py
rename to py/output_file_writer.py
index 2904292..6301d48 100644
--- a/py/OutputFileWriter.py
+++ b/py/output_file_writer.py
@@ -14,22 +14,37 @@
 BAM_COMPRESSION_LEVEL = 6
 
 
-
-
-def RC(s):
-    # return the reverse complement of a string
-    RC_DICT = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'N': 'N'}
-    return ''.join(RC_DICT[n] for n in s[::-1])
+def reverse_complement(dna_string: str) -> str:
+    """
+    Return the reverse complement of a string from a DNA strand
+    :param dna_string: string of DNA
+    :return: the reverse compliment of the above string
+    """
+    rc_dict = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'N': 'N'}
+    return ''.join(rc_dict[n] for n in dna_string[::-1])
 
 
 # SAMtools reg2bin function
-def reg2bin(a, b):
-    b -= 1
-    if (a >> 14 == b >> 14): return ((1 << 15) - 1) // 7 + (a >> 14)
-    if (a >> 17 == b >> 17): return ((1 << 12) - 1) // 7 + (a >> 17)
-    if (a >> 20 == b >> 20): return ((1 << 9) - 1) // 7 + (a >> 20)
-    if (a >> 23 == b >> 23): return ((1 << 6) - 1) // 7 + (a >> 23)
-    if (a >> 26 == b >> 26): return ((1 << 3) - 1) // 7 + (a >> 26)
+def reg2bin(beg: int, end: int):
+    """
+    Finds the largest superset bin of region. Numeric values taken from hts-specs
+    Note: description of this function taken from source code for bamnostic.bai
+        (https://bamnostic.readthedocs.io/en/latest/_modules/bamnostic/bai.html)
+    :param beg: inclusive beginning position of region
+    :param end: exclusive end position of region
+    :return: distinct bin ID or largest superset bin of region
+    """
+    end -= 1
+    if beg >> 14 == end >> 14:
+        return ((1 << 15) - 1) // 7 + (beg >> 14)
+    if beg >> 17 == end >> 17:
+        return ((1 << 12) - 1) // 7 + (beg >> 17)
+    if beg >> 20 == end >> 20:
+        return ((1 << 9) - 1) // 7 + (beg >> 20)
+    if beg >> 23 == end >> 23:
+        return ((1 << 6) - 1) // 7 + (beg >> 23)
+    if beg >> 26 == end >> 26:
+        return ((1 << 3) - 1) // 7 + (beg >> 26)
     return 0
 
 
@@ -48,27 +63,27 @@ def reg2bin(a, b):
 #	gzipped    = True for compressed FASTQ/VCF, False for uncompressed
 #
 class OutputFileWriter:
-    def __init__(self, outPrefix, paired=False, BAM_header=None, VCF_header=None, gzipped=False, jobTuple=(1, 1),
-                 noFASTQ=False, FASTA_instead=False):
-
-        jobSuffix = ''
-        if jobTuple[1] > 1:
-            jsl = len(str(jobTuple[1]))
-            jsb = '0' * (jsl - len(str(jobTuple[0])))
-            jobSuffix = '.job' + jsb + str(jobTuple[0]) + 'of' + str(jobTuple[1])
-
-        self.FASTA_instead = FASTA_instead
-        if FASTA_instead:
-            fq1 = outPrefix + '_read1.fa' + jobSuffix
-            fq2 = outPrefix + '_read2.fa' + jobSuffix
+    def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, gzipped=False, job_tuple=(1, 1),
+                 no_fastq=False, fasta_instead=False):
+
+        job_suffix = ''
+        if job_tuple[1] > 1:
+            jsl = len(str(job_tuple[1]))
+            jsb = '0' * (jsl - len(str(job_tuple[0])))
+            job_suffix = '.job' + jsb + str(job_tuple[0]) + 'of' + str(job_tuple[1])
+
+        self.fasta_instead = fasta_instead
+        if fasta_instead:
+            fq1 = out_prefix + '_read1.fa' + job_suffix
+            fq2 = out_prefix + '_read2.fa' + job_suffix
         else:
-            fq1 = outPrefix + '_read1.fq' + jobSuffix
-            fq2 = outPrefix + '_read2.fq' + jobSuffix
-        bam = outPrefix + '_golden.bam' + jobSuffix
-        vcf = outPrefix + '_golden.vcf' + jobSuffix
+            fq1 = out_prefix + '_read1.fq' + job_suffix
+            fq2 = out_prefix + '_read2.fq' + job_suffix
+        bam = out_prefix + '_golden.bam' + job_suffix
+        vcf = out_prefix + '_golden.vcf' + job_suffix
 
-        self.noFASTQ = noFASTQ
-        if not self.noFASTQ:
+        self.no_fastq = no_fastq
+        if not self.no_fastq:
             if gzipped:
                 self.fq1_file = gzip.open(fq1 + '.gz', 'wb')
             else:
@@ -85,27 +100,32 @@ def __init__(self, outPrefix, paired=False, BAM_header=None, VCF_header=None, gz
         #	VCF OUTPUT
         #
         self.vcf_file = None
-        if VCF_header != None:
+        if vcf_header is not None:
             if gzipped:
                 self.vcf_file = gzip.open(vcf + '.gz', 'wb')
             else:
                 self.vcf_file = open(vcf, 'wb')
 
             # WRITE VCF HEADER (if parallel: only for first job)
-            if jobTuple[0] == 1:
+            if job_tuple[0] == 1:
                 self.vcf_file.write('##fileformat=VCFv4.1\n'.encode('utf-8'))
-                reference = '##reference=' + VCF_header[0] + '\n'
+                reference = '##reference=' + vcf_header[0] + '\n'
                 self.vcf_file.write(reference.encode('utf-8'))
                 self.vcf_file.write('##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">\n'.encode('utf-8'))
-                self.vcf_file.write('##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">\n'.encode('utf-8'))
+                self.vcf_file.write(
+                    '##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">\n'.encode('utf-8'))
                 # self.vcf_file.write('##INFO=<ID=READS,Number=1,Type=String,Description="Names of Reads Covering this Variant">\n')
                 self.vcf_file.write(
-                    '##INFO=<ID=VMX,Number=1,Type=String,Description="SNP is Missense in these Read Frames">\n'.encode('utf-8'))
+                    '##INFO=<ID=VMX,Number=1,Type=String,Description="SNP is Missense in these Read Frames">\n'.encode(
+                        'utf-8'))
+                self.vcf_file.write(
+                    '##INFO=<ID=VNX,Number=1,Type=String,Description="SNP is Nonsense in these Read Frames">\n'.encode(
+                        'utf-8'))
                 self.vcf_file.write(
-                    '##INFO=<ID=VNX,Number=1,Type=String,Description="SNP is Nonsense in these Read Frames">\n'.encode('utf-8'))
-                self.vcf_file.write('##INFO=<ID=VFX,Number=1,Type=String,Description="Indel Causes Frameshift">\n'.encode('utf-8'))
+                    '##INFO=<ID=VFX,Number=1,Type=String,Description="Indel Causes Frameshift">\n'.encode('utf-8'))
                 self.vcf_file.write(
-                    '##INFO=<ID=WP,Number=A,Type=Integer,Description="NEAT-GenReads ploidy indicator">\n'.encode('utf-8'))
+                    '##INFO=<ID=WP,Number=A,Type=Integer,Description="NEAT-GenReads ploidy indicator">\n'.encode(
+                        'utf-8'))
                 self.vcf_file.write('##ALT=<ID=DEL,Description="Deletion">\n'.encode('utf-8'))
                 self.vcf_file.write('##ALT=<ID=DUP,Description="Duplication">\n'.encode('utf-8'))
                 self.vcf_file.write('##ALT=<ID=INS,Description="Insertion of novel sequence">\n'.encode('utf-8'))
@@ -119,23 +139,23 @@ def __init__(self, outPrefix, paired=False, BAM_header=None, VCF_header=None, gz
         #	BAM OUTPUT
         #
         self.bam_file = None
-        if BAM_header != None:
+        if bam_header is not None:
             self.bam_file = BgzfWriter(bam, 'w', compresslevel=BAM_COMPRESSION_LEVEL)
 
             # WRITE BAM HEADER (if parallel: only for first job)
-            if True or jobTuple[0] == 1:
+            if True or job_tuple[0] == 1:
                 self.bam_file.write("BAM\1")
                 header = '@HD\tVN:1.5\tSO:coordinate\n'
-                for n in BAM_header[0]:
+                for n in bam_header[0]:
                     header += '@SQ\tSN:' + n[0] + '\tLN:' + str(n[3]) + '\n'
                 header += '@RG\tID:NEAT\tSM:NEAT\tLB:NEAT\tPL:NEAT\n'
-                headerBytes = len(header)
-                numRefs = len(BAM_header[0])
-                self.bam_file.write(pack('<i', headerBytes))
+                header_bytes = len(header)
+                num_refs = len(bam_header[0])
+                self.bam_file.write(pack('<i', header_bytes))
                 self.bam_file.write(header)
-                self.bam_file.write(pack('<i', numRefs))
+                self.bam_file.write(pack('<i', num_refs))
 
-                for n in BAM_header[0]:
+                for n in bam_header[0]:
                     l_name = len(n[0]) + 1
                     self.bam_file.write(pack('<i', l_name))
                     self.bam_file.write(n[0] + '\0')
@@ -146,106 +166,108 @@ def __init__(self, outPrefix, paired=False, BAM_header=None, VCF_header=None, gz
         self.fq2_buffer = []
         self.bam_buffer = []
 
-    def writeFASTQRecord(self, readName, read1, qual1, read2=None, qual2=None):
-        if self.FASTA_instead:
-            self.fq1_buffer.append('>' + readName + '/1\n' + read1 + '\n')
-            if read2 != None:
-                self.fq2_buffer.append('>' + readName + '/2\n' + RC(read2) + '\n')
+    def write_fastq_record(self, read_name, read1, qual1, read2=None, qual2=None):
+        if self.fasta_instead:
+            self.fq1_buffer.append('>' + read_name + '/1\n' + read1 + '\n')
+            if read2 is not None:
+                self.fq2_buffer.append('>' + read_name + '/2\n' + reverse_complement(read2) + '\n')
         else:
-            self.fq1_buffer.append('@' + readName + '/1\n' + read1 + '\n+\n' + qual1 + '\n')
-            if read2 != None:
-                self.fq2_buffer.append('@' + readName + '/2\n' + RC(read2) + '\n+\n' + qual2[::-1] + '\n')
+            self.fq1_buffer.append('@' + read_name + '/1\n' + read1 + '\n+\n' + qual1 + '\n')
+            if read2 is not None:
+                self.fq2_buffer.append(
+                    '@' + read_name + '/2\n' + reverse_complement(read2) + '\n+\n' + qual2[::-1] + '\n')
 
-    def writeVCFRecord(self, chrom, pos, idStr, ref, alt, qual, filt, info):
+    def write_vcf_record(self, chrom, pos, id_str, ref, alt, qual, filt, info):
         self.vcf_file.write(
-            str(chrom) + '\t' + str(pos) + '\t' + str(idStr) + '\t' + str(ref) + '\t' + str(alt) + '\t' + str(
+            str(chrom) + '\t' + str(pos) + '\t' + str(id_str) + '\t' + str(ref) + '\t' + str(alt) + '\t' + str(
                 qual) + '\t' + str(filt) + '\t' + str(info) + '\n')
 
-    def writeBAMRecord(self, refID, readName, pos_0, cigar, seq, qual, samFlag, matePos=None, alnMapQual=70):
+    def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag, mate_pos=None, aln_map_qual=70):
 
-        myBin = reg2bin(pos_0, pos_0 + len(seq))
-        # myBin     = 0	# or just use a dummy value, does this actually matter?
+        my_bin = reg2bin(pos_0, pos_0 + len(seq))
+        # my_bin     = 0	# or just use a dummy value, does this actually matter?
 
-        myMapQual = alnMapQual
+        my_map_qual = aln_map_qual
         cig_letters = re.split(r"\d+", cigar)[1:]
         cig_numbers = [int(n) for n in re.findall(r"\d+", cigar)]
         cig_ops = len(cig_letters)
-        next_refID = refID
-        if matePos == None:
+        next_ref_id = ref_id
+        if mate_pos is None:
             next_pos = 0
             my_tlen = 0
         else:
-            next_pos = matePos
+            next_pos = mate_pos
             if pos_0 < next_pos:
                 my_tlen = next_pos + len(seq) - pos_0
             else:
                 my_tlen = -pos_0 - len(seq) + next_pos
 
-        encodedCig = bytearray()
+        encoded_cig = bytearray()
         for i in range(cig_ops):
-            encodedCig.extend(pack('<I', (cig_numbers[i] << 4) + CIGAR_PACKED[cig_letters[i]]))
-        encodedSeq = bytearray()
-        encodedLen = (len(seq) + 1) // 2
-        seqLen = len(seq)
-        if seqLen & 1:
+            encoded_cig.extend(pack('<I', (cig_numbers[i] << 4) + CIGAR_PACKED[cig_letters[i]]))
+        encoded_seq = bytearray()
+        encoded_len = (len(seq) + 1) // 2
+        seq_len = len(seq)
+        if seq_len & 1:
             seq += '='
-        for i in range(encodedLen):
+        for i in range(encoded_len):
             # print(seq[2*i], seq[2*i+1])
-            encodedSeq.extend(pack('<B', (SEQ_PACKED[seq[2 * i].capitalize()] << 4) + SEQ_PACKED[seq[2 * i + 1].capitalize()]))
+            encoded_seq.extend(
+                pack('<B', (SEQ_PACKED[seq[2 * i].capitalize()] << 4) + SEQ_PACKED[seq[2 * i + 1].capitalize()]))
 
         # apparently samtools automatically adds 33 to the quality score string...
-        encodedQual = ''.join([chr(ord(n) - 33) for n in qual])
+        encoded_qual = ''.join([chr(ord(n) - 33) for n in qual])
 
-        # blockSize = 4 +		# refID 		int32
+        # block_size = 4 +		# refID 		int32
         #            4 +		# pos			int32
         #            4 +		# bin_mq_nl		uint32
         #            4 +		# flag_nc		uint32
         #            4 +		# l_seq			int32
-        #            4 +		# next_refID	int32
+        #            4 +		# next_ref_id	int32
         #            4 +		# next_pos		int32
         #            4 +		# tlen			int32
         #            len(readName)+1 +
         #            4*cig_ops +
-        #            encodedLen +
+        #            encoded_len +
         #            len(seq)
 
-        # blockSize = 32 + len(readName)+1 + 4*cig_ops + encodedLen + len(seq)
-        blockSize = 32 + len(readName) + 1 + len(encodedCig) + len(encodedSeq) + len(encodedQual)
+        # block_size = 32 + len(readName)+1 + 4*cig_ops + encoded_len + len(seq)
+        block_size = 32 + len(read_name) + 1 + len(encoded_cig) + len(encoded_seq) + len(encoded_qual)
 
-        ####self.bam_file.write(pack('<i',blockSize))
+        ####self.bam_file.write(pack('<i',block_size))
         ####self.bam_file.write(pack('<i',refID))
         ####self.bam_file.write(pack('<i',pos_0))
-        ####self.bam_file.write(pack('<I',(myBin<<16) + (myMapQual<<8) + len(readName)+1))
+        ####self.bam_file.write(pack('<I',(my_bin<<16) + (my_map_qual<<8) + len(readName)+1))
         ####self.bam_file.write(pack('<I',(samFlag<<16) + cig_ops))
-        ####self.bam_file.write(pack('<i',seqLen))
-        ####self.bam_file.write(pack('<i',next_refID))
+        ####self.bam_file.write(pack('<i',seq_len))
+        ####self.bam_file.write(pack('<i',next_ref_id))
         ####self.bam_file.write(pack('<i',next_pos))
         ####self.bam_file.write(pack('<i',my_tlen))
         ####self.bam_file.write(readName+'\0')
-        ####self.bam_file.write(encodedCig)
-        ####self.bam_file.write(encodedSeq)
-        ####self.bam_file.write(encodedQual)
+        ####self.bam_file.write(encoded_cig)
+        ####self.bam_file.write(encoded_seq)
+        ####self.bam_file.write(encoded_qual)
 
         # a horribly compressed line, I'm sorry.
         # (ref_index, position, data)
-        self.bam_buffer.append((refID, pos_0, pack('<i', blockSize) + pack('<i', refID) + pack('<i', pos_0) +
-                                pack('<I', (myBin << 16) + (myMapQual << 8) + len(readName) + 1) +
-                                pack('<I', (samFlag << 16) + cig_ops) + pack('<i', seqLen) + pack('<i', next_refID) +
-                                pack('<i', next_pos) + pack('<i', my_tlen) + readName.encode('utf-8') +
-                                b'\0' + encodedCig + encodedSeq + encodedQual.encode('utf-8')))
+        self.bam_buffer.append((ref_id, pos_0, pack('<i', block_size) + pack('<i', ref_id) + pack('<i', pos_0) +
+                                pack('<I', (my_bin << 16) + (my_map_qual << 8) + len(read_name) + 1) +
+                                pack('<I', (sam_flag << 16) + cig_ops) + pack('<i', seq_len) + pack('<i', next_ref_id) +
+                                pack('<i', next_pos) + pack('<i', my_tlen) + read_name.encode('utf-8') +
+                                b'\0' + encoded_cig + encoded_seq + encoded_qual.encode('utf-8')))
 
-    def flushBuffers(self, bamMax=None, lastTime=False):
+    def flush_buffers(self, bam_max=None, last_time=False):
         if (len(self.fq1_buffer) >= BUFFER_BATCH_SIZE or len(self.bam_buffer) >= BUFFER_BATCH_SIZE) or (
-                len(self.fq1_buffer) and lastTime) or (len(self.bam_buffer) and lastTime):
+                len(self.fq1_buffer) and last_time) or (len(self.bam_buffer) and last_time):
             # fq
-            if not self.noFASTQ:
+            if not self.no_fastq:
                 self.fq1_file.write(''.join(self.fq1_buffer))
                 if len(self.fq2_buffer):
                     self.fq2_file.write(''.join(self.fq2_buffer))
             # bam
             if len(self.bam_buffer):
                 bam_data = sorted(self.bam_buffer)
-                if lastTime:
+                if last_time:
                     self.bam_file.write(b''.join([n[2] for n in bam_data]))
                     self.bam_buffer = []
                 else:
@@ -253,7 +275,7 @@ def flushBuffers(self, bamMax=None, lastTime=False):
                     for i in range(0, len(bam_data)):
                         # if we are from previous reference, or have coordinates lower
                         # than next window position, it's safe to write out to file
-                        if bam_data[i][0] != bam_data[-1][0] or bam_data[i][1] < bamMax:
+                        if bam_data[i][0] != bam_data[-1][0] or bam_data[i][1] < bam_max:
                             ind_to_stop_at = i + 1
                         else:
                             break
@@ -266,13 +288,13 @@ def flushBuffers(self, bamMax=None, lastTime=False):
             self.fq1_buffer = []
             self.fq2_buffer = []
 
-    def closeFiles(self):
-        self.flushBuffers(lastTime=True)
-        if not self.noFASTQ:
+    def close_files(self):
+        self.flush_buffers(last_time=True)
+        if not self.no_fastq:
             self.fq1_file.close()
-            if self.fq2_file != None:
+            if self.fq2_file is not None:
                 self.fq2_file.close()
-        if self.vcf_file != None:
+        if self.vcf_file is not None:
             self.vcf_file.close()
-        if self.bam_file != None:
+        if self.bam_file is not None:
             self.bam_file.close()

From af3bdfeba91b587e091ac24c3a37fa3d1703d77a Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 7 Aug 2020 12:14:11 -0500
Subject: [PATCH 104/323] refactoring probability.py to PEP8 standards

---
 genReads.py                   |   2 +-
 py/SequenceContainer.py       |  10 +--
 py/probability.py             | 126 +++++++++++++++++-----------------
 utilities/genSeqErrorModel.py |   2 +-
 4 files changed, 70 insertions(+), 70 deletions(-)

diff --git a/genReads.py b/genReads.py
index fe10bfd..2eea68e 100644
--- a/genReads.py
+++ b/genReads.py
@@ -227,7 +227,7 @@ def main(raw_args=None):
     if PAIRED_END and PAIRED_END_ARTIFICIAL:
         print('Using artificial fragment length distribution. mean=' + str(FRAGMENT_SIZE) + ', std=' + str(FRAGMENT_STD))
         if FRAGMENT_STD == 0:
-            FRAGLEN_DISTRIBUTION = DiscreteDistribution([1], [FRAGMENT_SIZE], degenerateVal=FRAGMENT_SIZE)
+            FRAGLEN_DISTRIBUTION = DiscreteDistribution([1], [FRAGMENT_SIZE], degenerate_val=FRAGMENT_SIZE)
         else:
             potential_vals = range(FRAGMENT_SIZE - 6 * FRAGMENT_STD, FRAGMENT_SIZE + 6 * FRAGMENT_STD + 1)
             FRAGLEN_VALS = []
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index bd35c33..cf23c89 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -120,9 +120,9 @@ def init_coverage(self, coverage_data, frag_dist=None):
                 else:
                     current_thresh = 0.
                     index_list    = [0]
-                    for j in range(len(frag_dist.cumP)):
-                        if frag_dist.cumP[j] >= current_thresh + COV_FRAGLEN_PERCENTILE/100.0:
-                            current_thresh = frag_dist.cumP[j]
+                    for j in range(len(frag_dist.cum_prob)):
+                        if frag_dist.cum_prob[j] >= current_thresh + COV_FRAGLEN_PERCENTILE/100.0:
+                            current_thresh = frag_dist.cum_prob[j]
                             index_list.append(j)
                     flq = [frag_dist.values[nnn] for nnn in index_list]
                     if frag_dist.values[-1] not in flq:
@@ -718,7 +718,7 @@ def __init__(self, read_len, errorModel, reScaledError):
                 self.probDistByPosByPrevQ1.append([])
                 for j in range(len(initQ1[0])):
                     if np.sum(probQ1[i][j]) <= 0.:	# if we don't have sufficient data for a transition, use the previous qscore
-                        self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution([1], [Qscores[j]], degenerateVal=Qscores[j]))
+                        self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution([1], [Qscores[j]], degenerate_val=Qscores[j]))
                     else:
                         self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution(probQ1[i][j], Qscores))
 
@@ -729,7 +729,7 @@ def __init__(self, read_len, errorModel, reScaledError):
                     self.probDistByPosByPrevQ2.append([])
                     for j in range(len(initQ2[0])):
                         if np.sum(probQ2[i][j]) <= 0.:	# if we don't have sufficient data for a transition, use the previous qscore
-                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution([1], [Qscores[j]], degenerateVal=Qscores[j]))
+                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution([1], [Qscores[j]], degenerate_val=Qscores[j]))
                         else:
                             self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution(probQ2[i][j], Qscores))
 
diff --git a/py/probability.py b/py/probability.py
index f7d0355..5fb39f3 100644
--- a/py/probability.py
+++ b/py/probability.py
@@ -6,17 +6,17 @@
 LOW_PROB_THRESH = 1e-12
 
 
-def mean_ind_of_weighted_list(l):
-    myMid = sum(l) / 2.0
-    mySum = 0.0
-    for i in range(len(l)):
-        mySum += l[i]
-        if mySum >= myMid:
+def mean_ind_of_weighted_list(candidate_list: list) -> int:
+    my_mid = sum(candidate_list) / 2.0
+    my_sum = 0.0
+    for i in range(len(candidate_list)):
+        my_sum += candidate_list[i]
+        if my_sum >= my_mid:
             return i
 
 
 class DiscreteDistribution:
-    def __init__(self, weights, values, degenerateVal=None, method='bisect'):
+    def __init__(self, weights, values, degenerate_val=None, method='bisect'):
 
         # some sanity checking
         if not len(weights) or not len(values):
@@ -24,18 +24,18 @@ def __init__(self, weights, values, degenerateVal=None, method='bisect'):
             exit(1)
 
         self.method = method
-        sumWeight = float(sum(weights))
+        sum_weight = float(sum(weights))
 
         # if probability of all input events is 0, consider it degenerate and always return the first value
-        if sumWeight < LOW_PROB_THRESH:
+        if sum_weight < LOW_PROB_THRESH:
             self.degenerate = values[0]
         else:
-            self.weights = [n / sumWeight for n in weights]
+            self.weights = [n / sum_weight for n in weights]
             self.values = copy.deepcopy(values)
             if len(self.values) != len(self.weights):
                 print('\nError: length and weights and values vectors must be the same.\n')
                 exit(1)
-            self.degenerate = degenerateVal
+            self.degenerate = degenerate_val
             # prune values with probability too low to be worth using [DOESN'T REALLY IMPROVE PERFORMANCE]
             ####if self.degenerate != None:
             ####	for i in xrange(len(self.weights)-1,-1,-1):
@@ -47,101 +47,101 @@ def __init__(self, weights, values, degenerateVal=None, method='bisect'):
             ####		exit(1)
 
             if self.method == 'alias':
-                K = len(self.weights)
-                q = np.zeros(K)
-                J = np.zeros(K, dtype=np.int)
+                len_weights = len(self.weights)
+                prob_vector = np.zeros(len_weights)
+                count_vector = np.zeros(len_weights, dtype=np.int)
                 smaller = []
                 larger = []
                 for kk, prob in enumerate(self.weights):
-                    q[kk] = K * prob
-                    if q[kk] < 1.0:
+                    prob_vector[kk] = len_weights * prob
+                    if prob_vector[kk] < 1.0:
                         smaller.append(kk)
                     else:
                         larger.append(kk)
                 while len(smaller) > 0 and len(larger) > 0:
                     small = smaller.pop()
                     large = larger.pop()
-                    J[small] = large
-                    q[large] = (q[large] + q[small]) - 1.0
-                    if q[large] < 1.0:
+                    count_vector[small] = large
+                    prob_vector[large] = (prob_vector[large] + prob_vector[small]) - 1.0
+                    if prob_vector[large] < 1.0:
                         smaller.append(large)
                     else:
                         larger.append(large)
 
-                self.a1 = len(J) - 1
-                self.a2 = J.tolist()
-                self.a3 = q.tolist()
+                self.a1 = len(count_vector) - 1
+                self.a2 = count_vector.tolist()
+                self.a3 = prob_vector.tolist()
 
             elif self.method == 'bisect':
-                self.cumP = np.cumsum(self.weights).tolist()[:-1]
-                self.cumP.insert(0, 0.)
+                self.cum_prob = np.cumsum(self.weights).tolist()[:-1]
+                self.cum_prob.insert(0, 0.)
 
     def __str__(self):
         return str(self.weights) + ' ' + str(self.values) + ' ' + self.method
 
     def sample(self):
 
-        if self.degenerate != None:
+        if self.degenerate is not None:
             return self.degenerate
 
         else:
 
             if self.method == 'alias':
-                r1 = random.randint(0, self.a1)
-                r2 = random.random()
-                if r2 < self.a3[r1]:
-                    return self.values[r1]
+                random1 = random.randint(0, self.a1)
+                random2 = random.random()
+                if random2 < self.a3[random1]:
+                    return self.values[random1]
                 else:
-                    return self.values[self.a2[r1]]
+                    return self.values[self.a2[random1]]
 
             elif self.method == 'bisect':
                 r = random.random()
-                return self.values[bisect.bisect(self.cumP, r) - 1]
+                return self.values[bisect.bisect(self.cum_prob, r) - 1]
 
 
 # takes k_range, lambda, [0,1,2,..], returns a DiscreteDistribution object with the corresponding to a poisson distribution
 
-def poisson_list(k_range, l):
-    MIN_WEIGHT = 1e-12
-    if l < MIN_WEIGHT:
-        return DiscreteDistribution([1], [0], degenerateVal=0)
-    logFactorial_list = [0.0]
+def poisson_list(k_range, input_lambda):
+    min_weight = 1e-12
+    if input_lambda < min_weight:
+        return DiscreteDistribution([1], [0], degenerate_val=0)
+    log_factorial_list = [0.0]
     for k in k_range[1:]:
-        logFactorial_list.append(np.log(float(k)) + logFactorial_list[k - 1])
-    w_range = [np.exp(k * np.log(l) - l - logFactorial_list[k]) for k in k_range]
-    w_range = [n for n in w_range if n >= MIN_WEIGHT]
+        log_factorial_list.append(np.log(float(k)) + log_factorial_list[k - 1])
+    w_range = [np.exp(k * np.log(input_lambda) - input_lambda - log_factorial_list[k]) for k in k_range]
+    w_range = [n for n in w_range if n >= min_weight]
     if len(w_range) <= 1:
-        return DiscreteDistribution([1], [0], degenerateVal=0)
+        return DiscreteDistribution([1], [0], degenerate_val=0)
     return DiscreteDistribution(w_range, k_range[:len(w_range)])
 
 
 # quantize a list of values into blocks
-def quantize_list(l):
-    MIN_PROB = 1e-12
-    QUANT_BLOCKS = 10
-    suml = float(sum(l))
-    ls = sorted([n for n in l if n >= MIN_PROB * suml])
-    if len(ls) == 0:
+def quantize_list(list_to_quantize):
+    min_prob = 1e-12
+    quant_blocks = 10
+    sum_list = float(sum(list_to_quantize))
+    sorted_list = sorted([n for n in list_to_quantize if n >= min_prob * sum_list])
+    if len(sorted_list) == 0:
         return None
     qi = []
-    for i in range(QUANT_BLOCKS):
-        # qi.append(ls[int((i)*(len(ls)/float(QUANT_BLOCKS)))])
-        qi.append(ls[0] + (i / float(QUANT_BLOCKS)) * (ls[-1] - ls[0]))
+    for i in range(quant_blocks):
+        # qi.append(sorted_list[int((i)*(len(sorted_list)/float(quant_blocks)))])
+        qi.append(sorted_list[0] + (i / float(quant_blocks)) * (sorted_list[-1] - sorted_list[0]))
     qi.append(1e12)
-    runningList = []
-    prevBi = None
-    previ = None
-    for i in range(len(l)):
-        if l[i] >= MIN_PROB * suml:
-            bi = bisect.bisect(qi, l[i])
+    running_list = []
+    prev_bi = None
+    prev_i = None
+    for i in range(len(list_to_quantize)):
+        if list_to_quantize[i] >= min_prob * sum_list:
+            bi = bisect.bisect(qi, list_to_quantize[i])
             # print i, l[i], qi[bi-1]
-            if prevBi != None:
-                if bi == prevBi and previ == i - 1:
-                    runningList[-1][1] += 1
+            if prev_bi is not None:
+                if bi == prev_bi and prev_i == i - 1:
+                    running_list[-1][1] += 1
                 else:
-                    runningList.append([i, i, qi[bi - 1]])
+                    running_list.append([i, i, qi[bi - 1]])
             else:
-                runningList.append([i, i, qi[bi - 1]])
-            prevBi = bi
-            previ = i
-    return runningList
+                running_list.append([i, i, qi[bi - 1]])
+            prev_bi = bi
+            prev_i = i
+    return running_list
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index a5535b0..467fd38 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -203,7 +203,7 @@ def parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff):
         prob_dist_by_pos_by_prev_q.append([])
         for j in range(len(init_q[0])):
             if np.sum(prob_q[i][j]) <= 0.:  # if we don't have sufficient data for a transition, use the previous qscore
-                prob_dist_by_pos_by_prev_q[-1].append(DiscreteDistribution([1], [q_scores[j]], degenerateVal=q_scores[j]))
+                prob_dist_by_pos_by_prev_q[-1].append(DiscreteDistribution([1], [q_scores[j]], degenerate_val=q_scores[j]))
             else:
                 prob_dist_by_pos_by_prev_q[-1].append(DiscreteDistribution(prob_q[i][j], q_scores))
 

From 131110f920eeb596279e6a4d18874a862abc57b2 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 7 Aug 2020 12:32:27 -0500
Subject: [PATCH 105/323] Some basic PEP8 refactoring of ref_func.py

---
 genReads.py    |   8 +-
 py/ref_func.py | 231 ++++++++++++++++++++++++-------------------------
 2 files changed, 118 insertions(+), 121 deletions(-)

diff --git a/genReads.py b/genReads.py
index 2eea68e..0cc265e 100644
--- a/genReads.py
+++ b/genReads.py
@@ -26,7 +26,7 @@
 import argparse
 
 from py.input_checking import required_field, check_file_open, is_in_range
-from py.ref_func import index_ref, readRef, getAllRefRegions, partitionRefRegions
+from py.ref_func import index_ref, read_ref, get_all_ref_regions, partition_ref_regions
 from py.vcfFunc import parseVCF
 from py.output_file_writer import OutputFileWriter
 from py.probability import DiscreteDistribution, mean_ind_of_weighted_list
@@ -330,8 +330,8 @@ def main(raw_args=None):
 
     # If processing jobs in parallel, precompute the independent regions that can be process separately
     if NJOBS > 1:
-        parallelRegionList = getAllRefRegions(REFERENCE, refIndex, N_HANDLING, saveOutput=SAVE_NON_N)
-        (myRefs, myRegions) = partitionRefRegions(parallelRegionList, refIndex, MYJOB, NJOBS)
+        parallelRegionList = get_all_ref_regions(REFERENCE, refIndex, N_HANDLING, save_output=SAVE_NON_N)
+        (myRefs, myRegions) = partition_ref_regions(parallelRegionList, refIndex, MYJOB, NJOBS)
         if not len(myRegions):
             print('This job id has no regions to process, exiting...')
             exit(1)
@@ -359,7 +359,7 @@ def main(raw_args=None):
     for RI in range(len(refIndex)):
 
         # read in reference sequence and notate blocks of Ns
-        (refSequence, N_regions) = readRef(REFERENCE, refIndex[RI], N_HANDLING)
+        (refSequence, N_regions) = read_ref(REFERENCE, refIndex[RI], N_HANDLING)
 
         # if we're processing jobs in parallel only take the regions relevant for the current job
         if NJOBS > 1:
diff --git a/py/ref_func.py b/py/ref_func.py
index e00f383..6ff8c8a 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -7,58 +7,58 @@
 
 
 #	Index reference fasta
-def index_ref(refPath):
+def index_ref(reference_path):
     tt = time.time()
 
-    fn = None
-    if os.path.isfile(refPath + 'i'):
-        print('found index ' + refPath + 'i')
-        fn = refPath + 'i'
-    if os.path.isfile(refPath + '.fai'):
-        print('found index ' + refPath + '.fai')
-        fn = refPath + '.fai'
+    filename = None
+    if os.path.isfile(reference_path + 'i'):
+        print('found index ' + reference_path + 'i')
+        filename = reference_path + 'i'
+    if os.path.isfile(reference_path + '.fai'):
+        print('found index ' + reference_path + '.fai')
+        filename = reference_path + '.fai'
 
     ref_inds = []
-    if fn is not None:
-        fai = open(fn, 'r')
+    if filename is not None:
+        fai = open(filename, 'r')
         for line in fai:
             splt = line[:-1].split('\t')
-            seqLen = int(splt[1])
+            seq_len = int(splt[1])
             offset = int(splt[2])
-            lineLn = int(splt[3])
-            nLines = seqLen // lineLn
-            if seqLen % lineLn != 0:
-                nLines += 1
-            ref_inds.append((splt[0], offset, offset + seqLen + nLines, seqLen))
+            line_ln = int(splt[3])
+            n_lines = seq_len // line_ln
+            if seq_len % line_ln != 0:
+                n_lines += 1
+            ref_inds.append((splt[0], offset, offset + seq_len + n_lines, seq_len))
         fai.close()
         return ref_inds
 
     sys.stdout.write('index not found, creating one... ')
     sys.stdout.flush()
-    refFile = open(refPath, 'r')
-    prevR = None
-    prevP = None
-    seqLen = 0
+    ref_file = open(reference_path, 'r')
+    prev_r = None
+    prev_p = None
+    seq_len = 0
     while 1:
-        data = refFile.readline()
+        data = ref_file.readline()
         if not data:
-            ref_inds.append((prevR, prevP, refFile.tell() - len(data), seqLen))
+            ref_inds.append((prev_r, prev_p, ref_file.tell() - len(data), seq_len))
             break
         if data[0] == '>':
-            if prevP is not None:
-                ref_inds.append((prevR, prevP, refFile.tell() - len(data), seqLen))
-            seqLen = 0
-            prevP = refFile.tell()
-            prevR = data[1:-1]
+            if prev_p is not None:
+                ref_inds.append((prev_r, prev_p, ref_file.tell() - len(data), seq_len))
+            seq_len = 0
+            prev_p = ref_file.tell()
+            prev_r = data[1:-1]
         else:
-            seqLen += len(data) - 1
-    refFile.close()
+            seq_len += len(data) - 1
+    ref_file.close()
 
     print('{0:.3f} (sec)'.format(time.time() - tt))
     return ref_inds
 
 
-def readRef(refPath, ref_inds_i, N_handling, N_unknowns=True, quiet=False):
+def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
     OK_CHR_ORD = {'A': True, 'C': True, 'G': True, 'T': True, 'U': True}
     ALLOWED_NUCL = ['A', 'C', 'G', 'T']
     tt = time.time()
@@ -66,129 +66,126 @@ def readRef(refPath, ref_inds_i, N_handling, N_unknowns=True, quiet=False):
         sys.stdout.write('reading ' + ref_inds_i[0] + '... ')
         sys.stdout.flush()
 
-    refFile = open(refPath, 'r')
-    refFile.seek(ref_inds_i[1])
-    myDat = ''.join(refFile.read(ref_inds_i[2] - ref_inds_i[1]).split('\n'))
-    myDat = Seq(myDat.upper(), IUPAC.unambiguous_dna)
-    myDat = myDat.tomutable()
+    ref_file = open(ref_path, 'r')
+    ref_file.seek(ref_inds_i[1])
+    my_dat = ''.join(ref_file.read(ref_inds_i[2] - ref_inds_i[1]).split('\n'))
+    my_dat = Seq(my_dat.upper(), IUPAC.unambiguous_dna)
+    my_dat = my_dat.tomutable()
 
     # find N regions
-    # data explanation: myDat[N_atlas[0][0]:N_atlas[0][1]] = solid block of Ns
-    prevNI = 0
-    nCount = 0
-    N_atlas = []
-    for i in range(len(myDat)):
-        if myDat[i] == 'N' or (N_unknowns and myDat[i] not in OK_CHR_ORD):
-            if nCount == 0:
-                prevNI = i
-            nCount += 1
-            if i == len(myDat) - 1:
-                N_atlas.append((prevNI, prevNI + nCount))
+    # data explanation: my_dat[n_atlas[0][0]:n_atlas[0][1]] = solid block of Ns
+    prev_ni = 0
+    n_count = 0
+    n_atlas = []
+    for i in range(len(my_dat)):
+        if my_dat[i] == 'N' or (n_unknowns and my_dat[i] not in OK_CHR_ORD):
+            if n_count == 0:
+                prev_ni = i
+            n_count += 1
+            if i == len(my_dat) - 1:
+                n_atlas.append((prev_ni, prev_ni + n_count))
         else:
-            if nCount > 0:
-                N_atlas.append((prevNI, prevNI + nCount))
-            nCount = 0
+            if n_count > 0:
+                n_atlas.append((prev_ni, prev_ni + n_count))
+            n_count = 0
 
     # handle N base-calls as desired
-    N_info = {}
-    N_info['all'] = []
-    N_info['big'] = []
-    N_info['non_N'] = []
-    if N_handling[0] == 'random':
-        for region in N_atlas:
-            N_info['all'].extend(region)
-            if region[1] - region[0] <= N_handling[1]:
+    n_info = {'all': [], 'big': [], 'non_N': []}
+    if n_handling[0] == 'random':
+        for region in n_atlas:
+            n_info['all'].extend(region)
+            if region[1] - region[0] <= n_handling[1]:
                 for i in range(region[0], region[1]):
-                    myDat[i] = random.choice(ALLOWED_NUCL)
+                    my_dat[i] = random.choice(ALLOWED_NUCL)
             else:
-                N_info['big'].extend(region)
-    elif N_handling[0] == 'allChr' and N_handling[2] in OK_CHR_ORD:
-        for region in N_atlas:
-            N_info['all'].extend(region)
-            if region[1] - region[0] <= N_handling[1]:
+                n_info['big'].extend(region)
+    elif n_handling[0] == 'allChr' and n_handling[2] in OK_CHR_ORD:
+        for region in n_atlas:
+            n_info['all'].extend(region)
+            if region[1] - region[0] <= n_handling[1]:
                 for i in range(region[0], region[1]):
-                    myDat[i] = N_handling[2]
+                    my_dat[i] = n_handling[2]
             else:
-                N_info['big'].extend(region)
-    elif N_handling[0] == 'ignore':
-        for region in N_atlas:
-            N_info['all'].extend(region)
-            N_info['big'].extend(region)
+                n_info['big'].extend(region)
+    elif n_handling[0] == 'ignore':
+        for region in n_atlas:
+            n_info['all'].extend(region)
+            n_info['big'].extend(region)
     else:
         print('\nERROR: UNKNOWN N_HANDLING MODE\n')
         exit(1)
 
-    habitableRegions = []
-    if N_info['big'] == []:
-        N_info['non_N'] = [(0, len(myDat))]
+    habitable_regions = []
+    if not n_info['big']:
+        n_info['non_N'] = [(0, len(my_dat))]
     else:
-        for i in range(0, len(N_info['big']), 2):
+        for i in range(0, len(n_info['big']), 2):
             if i == 0:
-                habitableRegions.append((0, N_info['big'][0]))
+                habitable_regions.append((0, n_info['big'][0]))
             else:
-                habitableRegions.append((N_info['big'][i - 1], N_info['big'][i]))
-        habitableRegions.append((N_info['big'][-1], len(myDat)))
-    for n in habitableRegions:
+                habitable_regions.append((n_info['big'][i - 1], n_info['big'][i]))
+        habitable_regions.append((n_info['big'][-1], len(my_dat)))
+    for n in habitable_regions:
         if n[0] != n[1]:
-            N_info['non_N'].append(n)
+            n_info['non_N'].append(n)
 
     if not quiet:
         print('{0:.3f} (sec)'.format(time.time() - tt))
-    return (myDat, N_info)
+    return my_dat, n_info
 
 
 #	find all non-N regions in reference sequence ahead of time, for computing jobs in parallel
-def getAllRefRegions(refPath, ref_inds, N_handling, saveOutput=False):
-    outRegions = {}
-    fn = refPath + '.nnr'
-    if os.path.isfile(fn) and not (saveOutput):
+def get_all_ref_regions(ref_path, ref_inds, n_handling, save_output=False):
+    out_regions = {}
+    fn = ref_path + '.nnr'
+    if os.path.isfile(fn) and not (save_output):
         print('found list of preidentified non-N regions...')
         f = open(fn, 'r')
         for line in f:
             splt = line.strip().split('\t')
-            if splt[0] not in outRegions:
-                outRegions[splt[0]] = []
-            outRegions[splt[0]].append((int(splt[1]), int(splt[2])))
+            if splt[0] not in out_regions:
+                out_regions[splt[0]] = []
+            out_regions[splt[0]].append((int(splt[1]), int(splt[2])))
         f.close()
-        return outRegions
+        return out_regions
     else:
         print('enumerating all non-N regions in reference sequence...')
         for RI in range(len(ref_inds)):
-            (refSequence, N_regions) = readRef(refPath, ref_inds[RI], N_handling, quiet=True)
-            refName = ref_inds[RI][0]
-            outRegions[refName] = [n for n in N_regions['non_N']]
-        if saveOutput:
+            (refSequence, N_regions) = read_ref(ref_path, ref_inds[RI], n_handling, quiet=True)
+            ref_name = ref_inds[RI][0]
+            out_regions[ref_name] = [n for n in N_regions['non_N']]
+        if save_output:
             f = open(fn, 'w')
-            for k in outRegions.keys():
-                for n in outRegions[k]:
+            for k in out_regions.keys():
+                for n in out_regions[k]:
                     f.write(k + '\t' + str(n[0]) + '\t' + str(n[1]) + '\n')
             f.close()
-        return outRegions
+        return out_regions
 
 
 #	find which of the non-N regions are going to be used for this job
-def partitionRefRegions(inRegions, ref_inds, myjob, njobs):
-    totSize = 0
+def partition_ref_regions(in_regions, ref_inds, my_job, n_jobs):
+    tot_size = 0
     for RI in range(len(ref_inds)):
-        refName = ref_inds[RI][0]
-        for region in inRegions[refName]:
-            totSize += region[1] - region[0]
-    sizePerJob = int(totSize / float(njobs) - 0.5)
-
-    regionsPerJob = [[] for n in range(njobs)]
-    refsPerJob = [{} for n in range(njobs)]
-    currentInd = 0
-    currentCount = 0
+        ref_name = ref_inds[RI][0]
+        for region in in_regions[ref_name]:
+            tot_size += region[1] - region[0]
+    size_per_job = int(tot_size / float(n_jobs) - 0.5)
+
+    regions_per_job = [[] for n in range(n_jobs)]
+    refs_per_job = [{} for n in range(n_jobs)]
+    current_ind = 0
+    current_count = 0
     for RI in range(len(ref_inds)):
-        refName = ref_inds[RI][0]
-        for region in inRegions[refName]:
-            regionsPerJob[currentInd].append((refName, region[0], region[1]))
-            refsPerJob[currentInd][refName] = True
-            currentCount += region[1] - region[0]
-            if currentCount >= sizePerJob:
-                currentCount = 0
-                currentInd = min([currentInd + 1, njobs - 1])
-
-    relevantRefs = refsPerJob[myjob - 1].keys()
-    relevantRegs = regionsPerJob[myjob - 1]
-    return (relevantRefs, relevantRegs)
+        ref_name = ref_inds[RI][0]
+        for region in in_regions[ref_name]:
+            regions_per_job[current_ind].append((ref_name, region[0], region[1]))
+            refs_per_job[current_ind][ref_name] = True
+            current_count += region[1] - region[0]
+            if current_count >= size_per_job:
+                current_count = 0
+                current_ind = min([current_ind + 1, n_jobs - 1])
+
+    relevant_refs = refs_per_job[my_job - 1].keys()
+    relevant_regs = regions_per_job[my_job - 1]
+    return relevant_refs, relevant_regs

From 681f3baf2b3ced275e248e2874b030736b9fece3 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 10 Aug 2020 15:26:00 -0500
Subject: [PATCH 106/323] Working on the bacterial wrapper and updating some of
 SequenceContainer to PEP8

---
 bacterial_genreads_wrapper.py                 |  48 +-
 py/SequenceContainer.py                       | 707 +++++++++---------
 .../SequenceContainer.cpython-37.pyc          | Bin 31358 -> 31477 bytes
 py/__pycache__/__init__.cpython-37.pyc        | Bin 173 -> 146 bytes
 .../biopython_modified_bgzf.cpython-37.pyc    | Bin 2695 -> 2668 bytes
 py/__pycache__/neat_cigar.cpython-37.pyc      | Bin 3011 -> 3114 bytes
 py/__pycache__/probability.cpython-37.pyc     | Bin 3974 -> 4082 bytes
 py/__pycache__/ref_func.cpython-37.pyc        | Bin 4818 -> 4837 bytes
 py/__pycache__/vcfFunc.cpython-37.pyc         | Bin 4688 -> 4668 bytes
 9 files changed, 381 insertions(+), 374 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index ec1d280..2f427d6 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -2,6 +2,7 @@
 
 import genReads
 import argparse
+from Bio import SeqIO
 
 
 def cull(population: list) -> list:
@@ -13,17 +14,6 @@ def cull(population: list) -> list:
     return population
 
 
-def create_fasta(neat_input):
-    """
-    The purpose of this function will be to convert the output from neat-genreads into something
-    that can be an input back into neat genreads
-    :param neat_input:
-    :return fasta_output:
-    """
-    fasta_output = None
-    return fasta_output
-
-
 def crossover(population: list) -> list:
     """
     This function will take a list of individuals and do crossover type mixing
@@ -34,43 +24,47 @@ def crossover(population: list) -> list:
     return new_population
 
 
-def evolve(reference, read_length, output_prefix, pop_size):
+def initialize_population(reference: str, pop_size):
     """
     The purpose of this function is to evolve the bacteria
     :param reference:
-    :param read_length:
-    :param output_prefix:
-    :param cycles:
+    :param pop_size:
     :return population:
     """
-    args = ['-r', reference, '-R', str(read_length), '-o', output_prefix]
+    names = []
+    for j in range(pop_size):
+        names.append("name" + str(j))
     population = []
     for i in range(pop_size):
-        new_evolution = genReads.main(args)
-        new_member = create_fasta(new_evolution)
+        args = ['-r', reference, '-R', '151', '-o', names[i], '--fa']
+        new_member = genReads.main(args)
         population.append(new_member)
     return population
 
 
+def evolve(new_population, param):
+    pass
+
+
 def main():
     parser = argparse.ArgumentParser(description='bacterial_genreads_wrapper.py')
     parser.add_argument('-r', type=str, required=True, metavar='/path/to/reference.fasta',
                         help="Reference file for organism in fasta format")
-    parser.add_argument('-R', type=int, required=True, metavar='<int>', help="read length")
-    parser.add_argument('-o', type=str, required=True, metavar='<str>', help="output prefix")
     parser.add_argument('-C', type=int, required=True, metavar='<str>', help="Number of cycles to run")
-    parser.add_argument('-p', type=int, required=True, metavar='<str>', help="Population size per cycle")
+    parser.add_argument('-i', type=int, required=True, metavar='<str>', help="Initial population size")
+    parser.add_argument('-c', type=float, required=False, metavar='<float>',
+                        help="Percentage of population to cull each cycle (0.5 will keep population relatively stable)",
+                        default=0.5)
     args = parser.parse_args()
 
-    (ref_fasta, read_len, out_pref, population_size) = (args.r, args.R, args.o, args.p)
-    cycles = args.C
+    (ref_fasta, init_population_size, cycles) = (args.r, args.i, args.C)
+    cull_percentage = args.c
 
-    old_fasta = ref_fasta
-    candidate_population = evolve(old_fasta, read_len, out_pref, population_size)
+    population = initialize_population(ref_fasta, init_population_size)
     for i in range(cycles):
-        new_population = cull(candidate_population)
+        new_population = cull(population)
         # If all elements get culled, then break and quit
         if not new_population:
             break
         new_population = crossover(new_population)
-        candidate_population = evolve(new_population)
+        population = evolve(new_population, 2)
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index cf23c89..bfa4bc7 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -10,14 +10,14 @@
 from py.probability import DiscreteDistribution, poisson_list, quantize_list
 from py.neat_cigar import CigarString
 
-MAX_ATTEMPTS = 100	# max attempts to insert a mutation into a valid position
-MAX_MUTFRAC  = 0.3	# the maximum percentage of a window that can contain mutations
-
-NUCL    = ['A','C','G','T']
-TRI_IND = {'AA':0,  'AC':1,  'AG':2,   'AT':3,  'CA':4,  'CC':5,  'CG':6,  'CT':7,
-           'GA':8,  'GC':9,  'GG':10,  'GT':11, 'TA':12, 'TC':13, 'TG':14, 'TT':15}
-NUC_IND = {'A':0, 'C':1, 'G':2, 'T':3}
-ALL_TRI = [NUCL[i]+NUCL[j]+NUCL[k] for i in range(len(NUCL)) for j in range(len(NUCL)) for k in range(len(NUCL))]
+MAX_ATTEMPTS = 100  # max attempts to insert a mutation into a valid position
+MAX_MUTFRAC = 0.3  # the maximum percentage of a window that can contain mutations
+
+NUCL = ['A', 'C', 'G', 'T']
+TRI_IND = {'AA': 0, 'AC': 1, 'AG': 2, 'AT': 3, 'CA': 4, 'CC': 5, 'CG': 6, 'CT': 7,
+           'GA': 8, 'GC': 9, 'GG': 10, 'GT': 11, 'TA': 12, 'TC': 13, 'TG': 14, 'TT': 15}
+NUC_IND = {'A': 0, 'C': 1, 'G': 2, 'T': 3}
+ALL_TRI = [NUCL[i] + NUCL[j] + NUCL[k] for i in range(len(NUCL)) for j in range(len(NUCL)) for k in range(len(NUCL))]
 ALL_IND = {ALL_TRI[i]: i for i in range(len(ALL_TRI))}
 
 # DEBUG
@@ -27,101 +27,107 @@
 COV_FRAGLEN_PERCENTILE = 10.
 LARGE_NUMBER = 9999999999
 
+
 #
 #	Container for reference sequences, applies mutations
 #
 class SequenceContainer:
-    def __init__(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_models=[], mut_rate=None, only_vcf=False):
+    def __init__(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_models=[], mut_rate=None,
+                 only_vcf=False):
         # initialize basic variables
         self.only_vcf = only_vcf
-        self.init_basicVars(x_offset, sequence, ploidy, window_overlap, read_len)
+        self.init_basic_vars(x_offset, sequence, ploidy, window_overlap, read_len)
         # initialize mutation models
-        self.init_mutModels(mut_models, mut_rate)
+        self.init_mut_models(mut_models, mut_rate)
         # sample the number of variants that will be inserted into each ploid
         self.init_poisson()
-        self.indelsToAdd = [n.sample() for n in self.ind_pois]
-        self.snpsToAdd   = [n.sample() for n in self.snp_pois]
+        self.indels_to_add = [n.sample() for n in self.ind_pois]
+        self.snps_to_add = [n.sample() for n in self.snp_pois]
         # initialize trinuc snp bias
-        self.init_trinucBias()
+        self.init_trinuc_bias()
 
-    def init_basicVars(self, x_offset, sequence, ploidy, window_overlap, read_len):
-        self.x         = x_offset
-        self.ploidy    = ploidy
-        self.read_len   = read_len
+    def init_basic_vars(self, x_offset, sequence, ploidy, window_overlap, read_len):
+        self.x = x_offset
+        self.ploidy = ploidy
+        self.read_len = read_len
         self.sequences = [Seq(str(sequence), IUPAC.unambiguous_dna).tomutable() for n in range(self.ploidy)]
-        self.seq_Len    = len(sequence)
-        self.indelList = [[] for n in range(self.ploidy)]
-        self.snpList   = [[] for n in range(self.ploidy)]
-        self.all_cigar  = [[] for n in range(self.ploidy)]
-        self.FM_pos    = [[] for n in range(self.ploidy)]
-        self.FM_span   = [[] for n in range(self.ploidy)]
+        self.seq_len = len(sequence)
+        self.indel_list = [[] for n in range(self.ploidy)]
+        self.snp_list = [[] for n in range(self.ploidy)]
+        self.all_cigar = [[] for n in range(self.ploidy)]
+        self.fm_pos = [[] for n in range(self.ploidy)]
+        self.fm_span = [[] for n in range(self.ploidy)]
         # blackList[ploid][pos] = 0		safe to insert variant here
         # blackList[ploid][pos] = 1		indel inserted here
         # blackList[ploid][pos] = 2		snp inserted here
         # blackList[ploid][pos] = 3		invalid position for various processing reasons
-        self.blackList = [np.zeros(self.seq_Len, dtype='<i4') for n in range(self.ploidy)]
+        self.black_list = [np.zeros(self.seq_len, dtype='<i4') for n in range(self.ploidy)]
 
         # disallow mutations to occur on window overlap points
-        self.winBuffer = window_overlap
+        self.win_buffer = window_overlap
         for p in range(self.ploidy):
-            self.blackList[p][-self.winBuffer]   = 3
-            self.blackList[p][-self.winBuffer-1] = 3
+            self.black_list[p][-self.win_buffer] = 3
+            self.black_list[p][-self.win_buffer - 1] = 3
 
     def init_coverage(self, coverage_data, frag_dist=None):
         # if we're only creating a vcf, skip some expensive initialization related to coverage depth
         if not self.only_vcf:
             (self.window_size, gc_scalars, target_cov_vals) = coverage_data
-            gcCov_vals = [[] for n in self.sequences]
+            gc_cov_vals = [[] for n in self.sequences]
             tr_cov_vals = [[] for n in self.sequences]
             self.coverage_distribution = []
             avg_out = []
             for i in range(len(self.sequences)):
-                max_coord = min([len(self.sequences[i])-self.read_len, len(self.all_cigar[i])-self.read_len])
+                max_coord = min([len(self.sequences[i]) - self.read_len, len(self.all_cigar[i]) - self.read_len])
                 # Trying to fix a problem wherein the above line gives a negative answer
                 if max_coord <= 0:
                     max_coord = min([len(self.sequences[i]), len(self.all_cigar[i])])
                 # compute gc-bias
                 j = 0
-                while j+self.window_size < len(self.sequences[i]):
-                    gc_c = self.sequences[i][j:j+self.window_size].count('G') + self.sequences[i][j:j+self.window_size].count('C')
-                    gcCov_vals[i].extend([gc_scalars[gc_c]]*self.window_size)
+                while j + self.window_size < len(self.sequences[i]):
+                    gc_c = self.sequences[i][j:j + self.window_size].count('G') + self.sequences[i][
+                                                                                  j:j + self.window_size].count('C')
+                    gc_cov_vals[i].extend([gc_scalars[gc_c]] * self.window_size)
                     j += self.window_size
-                gc_c = self.sequences[i][-self.window_size:].count('G') + self.sequences[i][-self.window_size:].count('C')
-                gcCov_vals[i].extend([gc_scalars[gc_c]]*(len(self.sequences[i])-len(gcCov_vals[i])))
+                gc_c = self.sequences[i][-self.window_size:].count('G') + self.sequences[i][-self.window_size:].count(
+                    'C')
+                gc_cov_vals[i].extend([gc_scalars[gc_c]] * (len(self.sequences[i]) - len(gc_cov_vals[i])))
                 #
                 tr_cov_vals[i].append(target_cov_vals[0])
-                prev_val = self.FM_pos[i][0]
-                for j in range(1,max_coord):
-                    if self.FM_pos[i][j] is None:
+                prev_val = self.fm_pos[i][0]
+                for j in range(1, max_coord):
+                    if self.fm_pos[i][j] is None:
                         tr_cov_vals[i].append(target_cov_vals[prev_val])
-                    elif self.FM_span[i][j]-self.FM_pos[i][j] <= 1:
+                    elif self.fm_span[i][j] - self.fm_pos[i][j] <= 1:
                         tr_cov_vals[i].append(target_cov_vals[prev_val])
                     else:
-                        tr_cov_vals[i].append(sum(target_cov_vals[self.FM_pos[i][j]:self.FM_span[i][j]])/float(self.FM_span[i][j]-self.FM_pos[i][j]))
-                        prev_val = self.FM_pos[i][j]
-                    #print (i,j), self.adj[i][j], self.all_cigar[i][j], self.FM_pos[i][j], self.FM_span[i][j]
+                        tr_cov_vals[i].append(sum(target_cov_vals[self.fm_pos[i][j]:self.fm_span[i][j]]) / float(
+                            self.fm_span[i][j] - self.fm_pos[i][j]))
+                        prev_val = self.fm_pos[i][j]
+                    # print (i,j), self.adj[i][j], self.all_cigar[i][j], self.FM_pos[i][j], self.FM_span[i][j]
                 # shift by half of read length
-                if len(tr_cov_vals[i]) > int(self.read_len/2.):
-                    tr_cov_vals[i] = [0.0]*int(self.read_len//2) + tr_cov_vals[i][:-int(self.read_len/2.)]
+                if len(tr_cov_vals[i]) > int(self.read_len / 2.):
+                    tr_cov_vals[i] = [0.0] * int(self.read_len // 2) + tr_cov_vals[i][:-int(self.read_len / 2.)]
                 # fill in missing indices
-                tr_cov_vals[i].extend([0.0]*(len(self.sequences[i])-len(tr_cov_vals[i])))
+                tr_cov_vals[i].extend([0.0] * (len(self.sequences[i]) - len(tr_cov_vals[i])))
 
                 #
-                covvec = np.cumsum([tr_cov_vals[i][nnn]*gcCov_vals[i][nnn] for nnn in range(len(tr_cov_vals[i]))])
+                coverage_vector = np.cumsum([tr_cov_vals[i][nnn] *
+                                             gc_cov_vals[i][nnn] for nnn in range(len(tr_cov_vals[i]))])
                 coverage_vals = []
-                for j in range(0,max_coord):
-                    coverage_vals.append(covvec[j+self.read_len] - covvec[j])
-                avg_out.append(np.mean(coverage_vals)/float(self.read_len))
+                for j in range(0, max_coord):
+                    coverage_vals.append(coverage_vector[j + self.read_len] - coverage_vector[j])
+                avg_out.append(np.mean(coverage_vals) / float(self.read_len))
 
-                if frag_dist == None:
-                    self.coverage_distribution.append(DiscreteDistribution(coverage_vals,range(len(coverage_vals))))
+                if frag_dist is None:
+                    self.coverage_distribution.append(DiscreteDistribution(coverage_vals, range(len(coverage_vals))))
 
                 # fragment length nightmare
                 else:
                     current_thresh = 0.
-                    index_list    = [0]
+                    index_list = [0]
                     for j in range(len(frag_dist.cum_prob)):
-                        if frag_dist.cum_prob[j] >= current_thresh + COV_FRAGLEN_PERCENTILE/100.0:
+                        if frag_dist.cum_prob[j] >= current_thresh + COV_FRAGLEN_PERCENTILE / 100.0:
                             current_thresh = frag_dist.cum_prob[j]
                             index_list.append(j)
                     flq = [frag_dist.values[nnn] for nnn in index_list]
@@ -131,9 +137,9 @@ def init_coverage(self, coverage_data, frag_dist=None):
 
                     self.fraglens_ind_map = {}
                     for j in frag_dist.values:
-                        bInd = bisect.bisect(flq,j)
-                        if abs(flq[bInd-1] - j) <= abs(flq[bInd] - j):
-                            self.fraglens_ind_map[j] = flq[bInd-1]
+                        bInd = bisect.bisect(flq, j)
+                        if abs(flq[bInd - 1] - j) <= abs(flq[bInd] - j):
+                            self.fraglens_ind_map[j] = flq[bInd - 1]
                         else:
                             self.fraglens_ind_map[j] = flq[bInd]
 
@@ -143,38 +149,39 @@ def init_coverage(self, coverage_data, frag_dist=None):
                         for j in frag_dist.values:
                             if self.fraglens_ind_map[j] == flv and j > buffer_val:
                                 buffer_val = j
-                        max_coord = min([len(self.sequences[i])-buffer_val-1, len(self.all_cigar[i])-buffer_val+self.read_len-2])
-                        #print 'BEFORE:', len(self.sequences[i])-buffer_val
-                        #print 'AFTER: ', len(self.all_cigar[i])-buffer_val+self.read_len-2
-                        #print 'AFTER2:', max_coord
+                        max_coord = min([len(self.sequences[i]) - buffer_val - 1,
+                                         len(self.all_cigar[i]) - buffer_val + self.read_len - 2])
+                        # print 'BEFORE:', len(self.sequences[i])-buffer_val
+                        # print 'AFTER: ', len(self.all_cigar[i])-buffer_val+self.read_len-2
+                        # print 'AFTER2:', max_coord
                         coverage_vals = []
-                        for j in range(0,max_coord):
-                            coverage_vals.append(covvec[j+self.read_len] - covvec[j] + covvec[j+flv] - covvec[j+flv-self.read_len])
+                        for j in range(0, max_coord):
+                            coverage_vals.append(coverage_vector[j + self.read_len] - coverage_vector[j] + coverage_vector[j + flv] - coverage_vector[
+                                j + flv - self.read_len])
 
                         # EXPERIMENTAL
-                        #quantized_covVals = quantize_list(coverage_vals)
-                        #self.coverage_distribution[i][flv] = DiscreteDistribution([n[2] for n in quantized_covVals],[(n[0],n[1]) for n in quantized_covVals])
+                        # quantized_covVals = quantize_list(coverage_vals)
+                        # self.coverage_distribution[i][flv] = DiscreteDistribution([n[2] for n in quantized_covVals],[(n[0],n[1]) for n in quantized_covVals])
 
                         # TESTING
-                        #import matplotlib.pyplot as mpl
-                        #print len(coverage_vals),'-->',len(quantized_covVals)
-                        #mpl.figure(0)
-                        #mpl.plot(range(len(coverage_vals)), coverage_vals)
-                        #for qcv in quantized_covVals:
+                        # import matplotlib.pyplot as mpl
+                        # print len(coverage_vals),'-->',len(quantized_covVals)
+                        # mpl.figure(0)
+                        # mpl.plot(range(len(coverage_vals)), coverage_vals)
+                        # for qcv in quantized_covVals:
                         #	mpl.plot([qcv[0], qcv[1]+1], [qcv[2],qcv[2]], 'r')
-                        #mpl.show()
-                        #exit(1)
+                        # mpl.show()
+                        # exit(1)
 
                         self.coverage_distribution[i][flv] = DiscreteDistribution(coverage_vals,
                                                                                   range(len(coverage_vals)))
 
             return np.mean(avg_out)
 
-    
-    def init_mutModels(self,mut_models,mut_rate):
-        if mut_models == []:
-            ml = [copy.deepcopy(DEFAULT_MODEL_1) for n in range(self.ploidy)]
-            self.model_data = ml[:self.ploidy]
+    def init_mut_models(self, mut_models, mut_rate):
+        if not mut_models:
+            default_model = [copy.deepcopy(DEFAULT_MODEL_1) for n in range(self.ploidy)]
+            self.model_data = default_model[:self.ploidy]
         else:
             if len(mut_models) != self.ploidy:
                 print('\nError: Number of mutation models recieved is not equal to specified ploidy\n')
@@ -187,11 +194,11 @@ def init_mutModels(self,mut_models,mut_rate):
         if self.mut_rescale == None:
             self.mut_scalar = 1.0
         else:
-            self.mut_scalar = float(self.mut_rescale)//(mut_rateSum/float(len(self.model_data)))
+            self.mut_scalar = float(self.mut_rescale) // (mut_rateSum / float(len(self.model_data)))
 
         # how are mutations spread to each ploid, based on their specified mut rates?
-        self.ploid_mut_frac  = [float(n[0])/mut_rateSum for n in self.model_data]
-        self.ploid_mut_prior = DiscreteDistribution(self.ploid_mut_frac,range(self.ploidy))
+        self.ploid_mut_frac = [float(n[0]) / mut_rateSum for n in self.model_data]
+        self.ploid_mut_prior = DiscreteDistribution(self.ploid_mut_frac, range(self.ploidy))
 
         # init mutation models
         #
@@ -205,7 +212,8 @@ def init_mutModels(self,mut_models,mut_rate):
         # self.models[ploid][7] = p(trinuc mutates)
         self.models = []
         for n in self.model_data:
-            self.models.append([self.mut_scalar*n[0], n[1], n[2], n[3], DiscreteDistribution(n[5], n[4]), DiscreteDistribution(n[7], n[6]), []])
+            self.models.append([self.mut_scalar * n[0], n[1], n[2], n[3], DiscreteDistribution(n[5], n[4]),
+                                DiscreteDistribution(n[7], n[6]), []])
             for m in n[8]:
                 self.models[-1][6].append([DiscreteDistribution(m[0], NUCL),
                                            DiscreteDistribution(m[1], NUCL),
@@ -214,166 +222,169 @@ def init_mutModels(self,mut_models,mut_rate):
             self.models[-1].append([m for m in n[9]])
 
     def init_poisson(self):
-        ind_l_list = [self.seq_Len*self.models[i][0]*self.models[i][2]*self.ploid_mut_frac[i] for i in range(len(self.models))]
-        snp_l_list = [self.seq_Len*self.models[i][0]*(1.-self.models[i][2])*self.ploid_mut_frac[i] for i in range(len(self.models))]
-        k_range    = range(int(self.seq_Len*MAX_MUTFRAC))
-        self.ind_pois = [poisson_list(k_range,ind_l_list[n]) for n in range(len(self.models))]
-        self.snp_pois = [poisson_list(k_range,snp_l_list[n]) for n in range(len(self.models))]
-
-    def init_trinucBias(self):
+        ind_l_list = [self.seq_len * self.models[i][0] * self.models[i][2] * self.ploid_mut_frac[i] for i in
+                      range(len(self.models))]
+        snp_l_list = [self.seq_len * self.models[i][0] * (1. - self.models[i][2]) * self.ploid_mut_frac[i] for i in
+                      range(len(self.models))]
+        k_range = range(int(self.seq_len * MAX_MUTFRAC))
+        self.ind_pois = [poisson_list(k_range, ind_l_list[n]) for n in range(len(self.models))]
+        self.snp_pois = [poisson_list(k_range, snp_l_list[n]) for n in range(len(self.models))]
+
+    def init_trinuc_bias(self):
         # compute mutation positional bias given trinucleotide strings of the sequence (ONLY AFFECTS SNPs)
         #
         # note: since indels are added before snps, it's possible these positional biases aren't correctly utilized
         #       at positions affected by indels. At the moment I'm going to consider this negligible.
-        trinuc_snp_bias  = [[0. for n in range(self.seq_Len)] for m in range(self.ploidy)]
+        trinuc_snp_bias = [[0. for n in range(self.seq_len)] for m in range(self.ploidy)]
         self.trinuc_bias = [None for n in range(self.ploidy)]
         for p in range(self.ploidy):
-            for i in range(self.winBuffer+1,self.seq_Len-1):
-                trinuc_snp_bias[p][i] = self.models[p][7][ALL_IND[str(self.sequences[p][i-1:i+2])]]
-            self.trinuc_bias[p] = DiscreteDistribution(trinuc_snp_bias[p][self.winBuffer+1:self.seq_Len-1],range(self.winBuffer+1,self.seq_Len-1))
+            for i in range(self.win_buffer + 1, self.seq_len - 1):
+                trinuc_snp_bias[p][i] = self.models[p][7][ALL_IND[str(self.sequences[p][i - 1:i + 2])]]
+            self.trinuc_bias[p] = DiscreteDistribution(trinuc_snp_bias[p][self.win_buffer + 1:self.seq_len - 1],
+                                                       range(self.win_buffer + 1, self.seq_len - 1))
 
     def update(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_models=[], mut_rate=None):
         # if mutation model is changed, we have to reinitialize it...
         if ploidy != self.ploidy or mut_rate != self.mut_rescale or mut_models != []:
             self.ploidy = ploidy
             self.mut_rescale = mut_rate
-            self.init_mutModels(mut_models, mut_rate)
+            self.init_mut_models(mut_models, mut_rate)
         # if sequence length is different than previous window, we have to redo snp/indel poissons
-        if len(sequence) != self.seq_Len:
-            self.seq_Len = len(sequence)
+        if len(sequence) != self.seq_len:
+            self.seq_len = len(sequence)
             self.init_poisson()
         # basic vars
-        self.init_basicVars(x_offset, sequence, ploidy, window_overlap, read_len)
-        self.indelsToAdd = [n.sample() for n in self.ind_pois]
-        self.snpsToAdd   = [n.sample() for n in self.snp_pois]
+        self.init_basic_vars(x_offset, sequence, ploidy, window_overlap, read_len)
+        self.indels_to_add = [n.sample() for n in self.ind_pois]
+        self.snps_to_add = [n.sample() for n in self.snp_pois]
         # initialize trinuc snp bias
         if not IGNORE_TRINUC:
-            self.init_trinucBias()
+            self.init_trinuc_bias()
 
-    def insert_mutations(self, inputList):
-        for inpV in inputList:
-            whichPloid = []
+    def insert_mutations(self, input_list):
+        for inpV in input_list:
+            which_ploid = []
             wps = inpV[4][0]
-            if wps == None:	# if no genotype given, assume heterozygous and choose a single ploid based on their mut rates
-                whichPloid.append(self.ploid_mut_prior.sample())
-                whichAlt = [0]
+            if wps is None:  # if no genotype given, assume heterozygous and choose a single ploid based on their mut rates
+                which_ploid.append(self.ploid_mut_prior.sample())
+                which_alt = [0]
             else:
                 if '/' in wps or '|' in wps:
                     if '/' in wps:
                         splt = wps.split('/')
                     else:
                         splt = wps.split('|')
-                    whichPloid = []
-                    whichAlt   = []
+                    which_ploid = []
+                    which_alt = []
                     for i in range(len(splt)):
                         if splt[i] == '1':
-                            whichPloid.append(i)
+                            which_ploid.append(i)
                     # assume we're just using first alt for inserted variants?
-                    whichAlt = [0 for n in whichPloid]
-                else:	# otherwise assume monoploidy
-                    whichPloid = [0]
-                    whichAlt   = [0]
+                    which_alt = [0 for n in which_ploid]
+                else:  # otherwise assume monoploidy
+                    which_ploid = [0]
+                    which_alt = [0]
 
             # ignore invalid ploids
-            for i in range(len(whichPloid)-1,-1,-1):
-                if whichPloid[i] >= self.ploidy:
-                    del whichPloid[i]
+            for i in range(len(which_ploid) - 1, -1, -1):
+                if which_ploid[i] >= self.ploidy:
+                    del which_ploid[i]
 
-            for i in range(len(whichPloid)):
-                p = whichPloid[i]
-                myAlt = inpV[2][whichAlt[i]]
-                myVar = (inpV[0]-self.x,inpV[1],myAlt)
-                inLen = max([len(inpV[1]),len(myAlt)])
+            for i in range(len(which_ploid)):
+                p = which_ploid[i]
+                myAlt = inpV[2][which_alt[i]]
+                myVar = (inpV[0] - self.x, inpV[1], myAlt)
+                inLen = max([len(inpV[1]), len(myAlt)])
 
-                if myVar[0] < 0 or myVar[0] >= len(self.blackList[p]):
+                if myVar[0] < 0 or myVar[0] >= len(self.black_list[p]):
                     print('\nError: Attempting to insert variant out of window bounds:')
-                    print(myVar, '--> blackList[0:' + str(len(self.blackList[p])) + ']\n')
+                    print(myVar, '--> blackList[0:' + str(len(self.black_list[p])) + ']\n')
                     exit(1)
                 if len(inpV[1]) == 1 and len(myAlt) == 1:
-                    if self.blackList[p][myVar[0]]:
+                    if self.black_list[p][myVar[0]]:
                         continue
-                    self.snpList[p].append(myVar)
-                    self.blackList[p][myVar[0]] = 2
+                    self.snp_list[p].append(myVar)
+                    self.black_list[p][myVar[0]] = 2
                 else:
                     indel_failed = False
-                    for k in range(myVar[0],myVar[0]+inLen+1):
-                        if k >= len(self.blackList[p]):
+                    for k in range(myVar[0], myVar[0] + inLen + 1):
+                        if k >= len(self.black_list[p]):
                             indel_failed = True
                             continue
-                        if self.blackList[p][k]:
+                        if self.black_list[p][k]:
                             indel_failed = True
                             continue
                     if indel_failed:
                         continue
-                    for k in range(myVar[0],myVar[0]+inLen+1):
-                        self.blackList[p][k] = 1
-                    self.indelList[p].append(myVar)
+                    for k in range(myVar[0], myVar[0] + inLen + 1):
+                        self.black_list[p][k] = 1
+                    self.indel_list[p].append(myVar)
 
     def random_mutations(self):
 
         #	add random indels
-        all_indels  = [[] for n in self.sequences]
+        all_indels = [[] for n in self.sequences]
         for i in range(self.ploidy):
-            for j in range(self.indelsToAdd[i]):
-                if random.random() <= self.models[i][1]:	# insert homozygous indel
+            for j in range(self.indels_to_add[i]):
+                if random.random() <= self.models[i][1]:  # insert homozygous indel
                     whichPloid = range(self.ploidy)
-                else:								# insert heterozygous indel
+                else:  # insert heterozygous indel
                     whichPloid = [self.ploid_mut_prior.sample()]
 
                 # try to find suitable places to insert indels
                 eventPos = -1
                 for attempt in range(MAX_ATTEMPTS):
-                    eventPos = random.randint(self.winBuffer,self.seq_Len-1)
+                    eventPos = random.randint(self.win_buffer, self.seq_len - 1)
                     for p in whichPloid:
-                        if self.blackList[p][eventPos]:
+                        if self.black_list[p][eventPos]:
                             eventPos = -1
                     if eventPos != -1:
                         break
                 if eventPos == -1:
                     continue
 
-                if random.random() <= self.models[i][3]:	# insertion
-                    inLen   = self.models[i][4].sample()
+                if random.random() <= self.models[i][3]:  # insertion
+                    inLen = self.models[i][4].sample()
                     # sequence content of random insertions is uniformly random (change this later, maybe)
-                    inSeq   = ''.join([random.choice(NUCL) for n in range(inLen)])
+                    inSeq = ''.join([random.choice(NUCL) for n in range(inLen)])
                     ref_nucl = self.sequences[i][eventPos]
-                    myIndel = (eventPos, ref_nucl, ref_nucl+inSeq)
-                else:										# deletion
-                    inLen   = self.models[i][5].sample()
-                    if eventPos+inLen+1 >= len(self.sequences[i]):	# skip if deletion too close to boundary
+                    myIndel = (eventPos, ref_nucl, ref_nucl + inSeq)
+                else:  # deletion
+                    inLen = self.models[i][5].sample()
+                    if eventPos + inLen + 1 >= len(self.sequences[i]):  # skip if deletion too close to boundary
                         continue
                     if inLen == 1:
-                        inSeq = self.sequences[i][eventPos+1]
+                        inSeq = self.sequences[i][eventPos + 1]
                     else:
-                        inSeq = str(self.sequences[i][eventPos+1:eventPos+inLen+1])
+                        inSeq = str(self.sequences[i][eventPos + 1:eventPos + inLen + 1])
                     ref_nucl = self.sequences[i][eventPos]
-                    myIndel = (eventPos, ref_nucl+inSeq, ref_nucl)
+                    myIndel = (eventPos, ref_nucl + inSeq, ref_nucl)
 
                 # if event too close to boundary, skip. if event conflicts with other indel, skip.
                 skipEvent = False
-                if eventPos+len(myIndel[1]) >= self.seq_Len-self.winBuffer-1:
+                if eventPos + len(myIndel[1]) >= self.seq_len - self.win_buffer - 1:
                     skipEvent = True
                 if skipEvent:
                     continue
                 for p in whichPloid:
-                    for k in range(eventPos, eventPos+inLen+1):
-                        if self.blackList[p][k]:
+                    for k in range(eventPos, eventPos + inLen + 1):
+                        if self.black_list[p][k]:
                             skipEvent = True
                 if skipEvent:
                     continue
 
                 for p in whichPloid:
-                    for k in range(eventPos,eventPos+inLen+1):
-                        self.blackList[p][k] = 1
+                    for k in range(eventPos, eventPos + inLen + 1):
+                        self.black_list[p][k] = 1
                     all_indels[p].append(myIndel)
 
         #	add random snps
-        all_snps  = [[] for n in self.sequences]
+        all_snps = [[] for n in self.sequences]
         for i in range(self.ploidy):
-            for j in range(self.snpsToAdd[i]):
-                if random.random() <= self.models[i][1]:	# insert homozygous SNP
+            for j in range(self.snps_to_add[i]):
+                if random.random() <= self.models[i][1]:  # insert homozygous SNP
                     whichPloid = range(self.ploidy)
-                else:								# insert heterozygous SNP
+                else:  # insert heterozygous SNP
                     whichPloid = [self.ploid_mut_prior.sample()]
 
                 # try to find suitable places to insert snps
@@ -382,12 +393,12 @@ def random_mutations(self):
                     # based on the mutation model for the specified ploid, choose a SNP location based on trinuc bias
                     # (if there are multiple ploids, choose one at random)
                     if IGNORE_TRINUC:
-                        eventPos = random.randint(self.winBuffer+1,self.seq_Len-2)
+                        eventPos = random.randint(self.win_buffer + 1, self.seq_len - 2)
                     else:
-                        ploid_to_use = whichPloid[random.randint(0,len(whichPloid)-1)]
-                        eventPos     = self.trinuc_bias[ploid_to_use].sample()
+                        ploid_to_use = whichPloid[random.randint(0, len(whichPloid) - 1)]
+                        eventPos = self.trinuc_bias[ploid_to_use].sample()
                     for p in whichPloid:
-                        if self.blackList[p][eventPos]:
+                        if self.black_list[p][eventPos]:
                             eventPos = -1
                     if eventPos != -1:
                         break
@@ -395,19 +406,19 @@ def random_mutations(self):
                     continue
 
                 ref_nucl = self.sequences[i][eventPos]
-                context = str(self.sequences[i][eventPos-1])+str(self.sequences[i][eventPos+1])
+                context = str(self.sequences[i][eventPos - 1]) + str(self.sequences[i][eventPos + 1])
                 # sample from tri-nucleotide substitution matrices to get SNP alt allele
                 new_nucl = self.models[i][6][TRI_IND[context]][NUC_IND[ref_nucl]].sample()
-                mySNP   = (eventPos,ref_nucl,new_nucl)
+                mySNP = (eventPos, ref_nucl, new_nucl)
 
                 for p in whichPloid:
                     all_snps[p].append(mySNP)
-                    self.blackList[p][mySNP[0]] = 2
+                    self.black_list[p][mySNP[0]] = 2
 
         # combine random snps with inserted snps, remove any snps that overlap indels
         for p in range(len(all_snps)):
-            all_snps[p].extend(self.snpList[p])
-            all_snps[p] = [n for n in all_snps[p] if self.blackList[p][n[0]] != 1]
+            all_snps[p].extend(self.snp_list[p])
+            all_snps[p] = [n for n in all_snps[p] if self.black_list[p][n[0]] != 1]
 
         # MODIFY REFERENCE STRING: SNPS
         for i in range(len(all_snps)):
@@ -424,7 +435,7 @@ def random_mutations(self):
 
         # organize the indels we want to insert
         for i in range(len(all_indels)):
-            all_indels[i].extend(self.indelList[i])
+            all_indels[i].extend(self.indel_list[i])
         all_indels_ins = [sorted([list(m) for m in n]) for n in all_indels]
 
         # MODIFY REFERENCE STRING: INDELS
@@ -435,9 +446,9 @@ def random_mutations(self):
             # there's an off-by-one error somewhere in the position sampling routines.. this might fix it
             # temp_symbol_string.append('M')
             for j in range(len(all_indels_ins[i])):
-                v_pos        = all_indels_ins[i][j][0] + rolling_adj
-                v_pos2       = v_pos + len(all_indels_ins[i][j][1])
-                rolling_adj += len(all_indels_ins[i][j][2])-len(all_indels_ins[i][j][1])
+                v_pos = all_indels_ins[i][j][0] + rolling_adj
+                v_pos2 = v_pos + len(all_indels_ins[i][j][1])
+                rolling_adj += len(all_indels_ins[i][j][2]) - len(all_indels_ins[i][j][1])
 
                 if all_indels_ins[i][j][1] != str(self.sequences[i][v_pos:v_pos2]):
                     print('\nError: Something went wrong!\n', all_indels_ins[i][j], [v_pos, v_pos2],
@@ -446,17 +457,18 @@ def random_mutations(self):
                 else:
                     # alter reference sequence
                     self.sequences[i] = self.sequences[i][:v_pos] + Seq(all_indels_ins[i][j][2],
-                                                                       IUPAC.unambiguous_dna).tomutable() + \
+                                                                        IUPAC.unambiguous_dna).tomutable() + \
                                         self.sequences[i][v_pos2:]
                     # notate indel positions for cigar computation
                     d = len(all_indels_ins[i][j][2]) - len(all_indels_ins[i][j][1])
                     if d > 0:
-                        temp_symbol_string = temp_symbol_string[:v_pos+1] + ['I']*d + temp_symbol_string[v_pos2+1:]
+                        temp_symbol_string = temp_symbol_string[:v_pos + 1] + ['I'] * d + temp_symbol_string[
+                                                                                          v_pos2 + 1:]
                     elif d < 0:
-                        temp_symbol_string[v_pos+1] = 'D'*abs(d)+'M'
+                        temp_symbol_string[v_pos + 1] = 'D' * abs(d) + 'M'
 
             # precompute cigar strings
-            for j in range(len(temp_symbol_string)-self.read_len):
+            for j in range(len(temp_symbol_string) - self.read_len):
                 self.all_cigar[i].append(CigarString(listIn=temp_symbol_string[j:j + self.read_len]).get_string())
 
             # create some data structures we will need later:
@@ -464,21 +476,21 @@ def random_mutations(self):
             # --- self.FM_span[ploid][pos]: number of reference positions spanned by a read originating from this coordinate
             MD_so_far = 0
             for j in range(len(temp_symbol_string)):
-                self.FM_pos[i].append(MD_so_far)
+                self.fm_pos[i].append(MD_so_far)
                 # fix an edge case with deletions
                 if 'D' in temp_symbol_string[j]:
-                    self.FM_pos[i][-1] += temp_symbol_string[j].count('D')
+                    self.fm_pos[i][-1] += temp_symbol_string[j].count('D')
                 # compute number of ref matches for each read
-                span_dif = len([n for n in temp_symbol_string[j:j+self.read_len] if 'M' in n])
-                self.FM_span[i].append(self.FM_pos[i][-1] + span_dif)
+                span_dif = len([n for n in temp_symbol_string[j:j + self.read_len] if 'M' in n])
+                self.fm_span[i].append(self.fm_pos[i][-1] + span_dif)
                 MD_so_far += temp_symbol_string[j].count('M') + temp_symbol_string[j].count('D')
 
         # tally up all the variants we handled...
         count_dict = {}
-        all_variants = [sorted(all_snps[i]+all_indels[i]) for i in range(self.ploidy)]
+        all_variants = [sorted(all_snps[i] + all_indels[i]) for i in range(self.ploidy)]
         for i in range(len(all_variants)):
             for j in range(len(all_variants[i])):
-                all_variants[i][j] = tuple([all_variants[i][j][0]+self.x])+all_variants[i][j][1:]
+                all_variants[i][j] = tuple([all_variants[i][j][0] + self.x]) + all_variants[i][j][1:]
                 t = tuple(all_variants[i][j])
                 if t not in count_dict:
                     count_dict[t] = []
@@ -490,22 +502,21 @@ def random_mutations(self):
 
         output_variants = []
         for k in sorted(count_dict.keys()):
-            output_variants.append(k+tuple([len(count_dict[k])/float(self.ploidy)]))
+            output_variants.append(k + tuple([len(count_dict[k]) / float(self.ploidy)]))
             ploid_string = ['0' for n in range(self.ploidy)]
             for k2 in [n for n in count_dict[k]]:
                 ploid_string[k2] = '1'
-            output_variants[-1] += tuple(['WP='+'/'.join(ploid_string)])
+            output_variants[-1] += tuple(['WP=' + '/'.join(ploid_string)])
         return output_variants
 
-
     def sample_read(self, sequencingModel, frag_len=None):
 
         # choose a ploid
-        my_ploid = random.randint(0,self.ploidy-1)
+        my_ploid = random.randint(0, self.ploidy - 1)
 
         # stop attempting to find a valid position if we fail enough times
         MAX_READPOS_ATTEMPTS = 100
-        attempts_thus_far    = 0
+        attempts_thus_far = 0
 
         # choose a random position within the ploid, and generate quality scores / sequencing errors
         readsToSample = []
@@ -513,24 +524,24 @@ def sample_read(self, sequencingModel, frag_len=None):
             r_pos = self.coverage_distribution[my_ploid].sample()
 
             # sample read position and call function to compute quality scores / sequencing errors
-            r_dat = self.sequences[my_ploid][r_pos:r_pos+self.read_len]
+            r_dat = self.sequences[my_ploid][r_pos:r_pos + self.read_len]
             (myQual, myErrors) = sequencingModel.getSequencingErrors(r_dat)
-            readsToSample.append([r_pos,myQual,myErrors,r_dat])
+            readsToSample.append([r_pos, myQual, myErrors, r_dat])
 
         else:
             r_pos1 = self.coverage_distribution[my_ploid][self.fraglens_ind_map[frag_len]].sample()
 
             # EXPERIMENTAL
-            #coords_to_select_from = self.coverage_distribution[my_ploid][self.fraglens_ind_map[frag_len]].sample()
-            #r_pos1 = random.randint(coords_to_select_from[0],coords_to_select_from[1])
+            # coords_to_select_from = self.coverage_distribution[my_ploid][self.fraglens_ind_map[frag_len]].sample()
+            # r_pos1 = random.randint(coords_to_select_from[0],coords_to_select_from[1])
 
             r_pos2 = r_pos1 + frag_len - self.read_len
-            r_dat1 = self.sequences[my_ploid][r_pos1:r_pos1+self.read_len]
-            r_dat2 = self.sequences[my_ploid][r_pos2:r_pos2+self.read_len]
+            r_dat1 = self.sequences[my_ploid][r_pos1:r_pos1 + self.read_len]
+            r_dat2 = self.sequences[my_ploid][r_pos2:r_pos2 + self.read_len]
             (myQual1, myErrors1) = sequencingModel.getSequencingErrors(r_dat1)
-            (myQual2, myErrors2) = sequencingModel.getSequencingErrors(r_dat2,isReverseStrand=True)
-            readsToSample.append([r_pos1,myQual1,myErrors1,r_dat1])
-            readsToSample.append([r_pos2,myQual2,myErrors2,r_dat2])
+            (myQual2, myErrors2) = sequencingModel.getSequencingErrors(r_dat2, isReverseStrand=True)
+            readsToSample.append([r_pos1, myQual1, myErrors1, r_dat1])
+            readsToSample.append([r_pos2, myQual2, myErrors2, r_dat2])
 
         # error format:
         # myError[i] = (type, len, pos, ref, alt)
@@ -549,107 +560,108 @@ def sample_read(self, sequencingModel, frag_len=None):
                     print((r_pos1, r_pos2))
                     print(frag_len, self.fraglens_ind_map[frag_len])
                 exit(1)
-            totalD  = sum([error[1] for error in r[2] if error[0] == 'D'])
-            totalI  = sum([error[1] for error in r[2] if error[0] == 'I'])
-            availB  = len(self.sequences[my_ploid]) - r[0] - self.read_len - 1
+            total_d = sum([error[1] for error in r[2] if error[0] == 'D'])
+            total_i = sum([error[1] for error in r[2] if error[0] == 'I'])
+            availB = len(self.sequences[my_ploid]) - r[0] - self.read_len - 1
             # add buffer sequence to fill in positions that get deleted
-            r[3] += self.sequences[my_ploid][r[0]+self.read_len:r[0]+self.read_len+totalD]
+            r[3] += self.sequences[my_ploid][r[0] + self.read_len:r[0] + self.read_len + total_d]
             expanded_cigar = []
-            extraCigar    = []
-            adj           = 0
-            sse_adj       = [0 for n in range(self.read_len + max(sequencingModel.errP[3]))]
-            any_indel_err   = False
+            extraCigar = []
+            adj = 0
+            sse_adj = [0 for n in range(self.read_len + max(sequencingModel.errP[3]))]
+            any_indel_err = False
 
             # sort by letter (D > I > S) such that we introduce all indel errors before substitution errors
             # secondarily, sort by index
-            arrangedErrors = {'D':[],'I':[],'S':[]}
+            arrangedErrors = {'D': [], 'I': [], 'S': []}
             for error in r[2]:
-                arrangedErrors[error[0]].append((error[2],error))
+                arrangedErrors[error[0]].append((error[2], error))
             sortedErrors = []
             for k in sorted(arrangedErrors.keys()):
                 sortedErrors.extend([n[1] for n in sorted(arrangedErrors[k])])
 
             skip_indels = False
 
-
-            #FIXED TdB 05JUN2018
-            #Moved this outside the for error loop, since it messes up the CIGAR string when more than one deletion is in the same read
-            extraCigarVal = []
-            #END FIXED TdB
+            # FIXED TdB 05JUN2018
+            # Moved this outside the for error loop, since it messes up the CIGAR string when more than one deletion is in the same read
+            extra_cigar_val = []
+            # END FIXED TdB
 
             for error in sortedErrors:
-                eLen = error[1]
-                ePos = error[2]
+                e_len = error[1]
+                e_pos = error[2]
                 if error[0] == 'D' or error[0] == 'I':
-                    any_indel_err   = True
+                    any_indel_err = True
 
-                    #FIXED TdB 05JUN2018
-                    #Moved this OUTSIDE the for error loop, since it messes up the CIGAR string when more than one deletion is in the same read
-                    #extraCigarVal = []
-                    #END FIXED TdB
+                    # FIXED TdB 05JUN2018
+                    # Moved this OUTSIDE the for error loop, since it messes up the CIGAR string when more than one deletion is in the same read
+                    # extra_cigar_val = []
+                    # END FIXED TdB
 
-                    if totalD > availB:	# if not enough bases to fill-in deletions, skip all indel erors
+                    if total_d > availB:  # if not enough bases to fill-in deletions, skip all indel erors
                         continue
                     if expanded_cigar == []:
                         expanded_cigar = CigarString(stringIn=myCigar).get_list()
-                        fillToGo = totalD - totalI + 1
-                        if fillToGo > 0:
+                        fill_to_go = total_d - total_i + 1
+                        if fill_to_go > 0:
                             try:
-                                extraCigarVal = CigarString(stringIn=self.all_cigar[my_ploid][r[0] + fillToGo]).get_list()[-fillToGo:]
-                            except IndexError:	# applying the deletions we want requires going beyond region boundaries. skip all indel errors
+                                extra_cigar_val = CigarString(
+                                    stringIn=self.all_cigar[my_ploid][r[0] + fill_to_go]).get_list()[-fill_to_go:]
+                            except IndexError:  # applying the deletions we want requires going beyond region boundaries. skip all indel errors
                                 skip_indels = True
 
-
                     if skip_indels:
                         continue
 
                     # insert deletion error into read and update cigar string accordingly
                     if error[0] == 'D':
-                        myadj = sse_adj[ePos]
-                        pi = ePos+myadj
-                        pf = ePos+myadj+eLen+1
+                        my_adj = sse_adj[e_pos]
+                        pi = e_pos + my_adj
+                        pf = e_pos + my_adj + e_len + 1
                         if str(r[3][pi:pf]) == str(error[3]):
-                            r[3] = r[3][:pi+1] + r[3][pf:]
-                            expanded_cigar = expanded_cigar[:pi+1] + expanded_cigar[pf:]
-                            if pi+1 == len(expanded_cigar):	# weird edge case with del at very end of region. Make a guess and add a "M"
+                            r[3] = r[3][:pi + 1] + r[3][pf:]
+                            expanded_cigar = expanded_cigar[:pi + 1] + expanded_cigar[pf:]
+                            if pi + 1 == len(
+                                    expanded_cigar):  # weird edge case with del at very end of region. Make a guess and add a "M"
                                 expanded_cigar.append('M')
-                            expanded_cigar[pi+1] = 'D'*eLen + expanded_cigar[pi+1]
+                            expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
                         else:
                             print('\nError, ref does not match alt while attempting to insert deletion error!\n')
                             exit(1)
-                        adj -= eLen
-                        for i in range(ePos,len(sse_adj)):
-                            sse_adj[i] -= eLen
+                        adj -= e_len
+                        for i in range(e_pos, len(sse_adj)):
+                            sse_adj[i] -= e_len
 
                     # insert insertion error into read and update cigar string accordingly
                     else:
-                        myadj = sse_adj[ePos]
-                        if str(r[3][ePos+myadj]) == error[3]:
-                            r[3] = r[3][:ePos+myadj] + error[4] + r[3][ePos+myadj+1:]
-                            expanded_cigar = expanded_cigar[:ePos+myadj] + ['I']*eLen + expanded_cigar[ePos+myadj:]
+                        my_adj = sse_adj[e_pos]
+                        if str(r[3][e_pos + my_adj]) == error[3]:
+                            r[3] = r[3][:e_pos + my_adj] + error[4] + r[3][e_pos + my_adj + 1:]
+                            expanded_cigar = expanded_cigar[:e_pos + my_adj] + ['I'] * e_len + expanded_cigar[
+                                                                                               e_pos + my_adj:]
                         else:
                             print('\nError, ref does not match alt while attempting to insert insertion error!\n')
-                            print('---', chr(r[3][ePos + myadj]), '!=', error[3])
+                            print('---', chr(r[3][e_pos + my_adj]), '!=', error[3])
                             exit(1)
-                        adj += eLen
-                        for i in range(ePos,len(sse_adj)):
-                            sse_adj[i] += eLen
+                        adj += e_len
+                        for i in range(e_pos, len(sse_adj)):
+                            sse_adj[i] += e_len
 
-                else:	# substitution errors, much easier by comparison...
-                    if str(r[3][ePos+sse_adj[ePos]]) == error[3]:
-                        r[3][ePos+sse_adj[ePos]] = error[4]
+                else:  # substitution errors, much easier by comparison...
+                    if str(r[3][e_pos + sse_adj[e_pos]]) == error[3]:
+                        r[3][e_pos + sse_adj[e_pos]] = error[4]
                     else:
                         print('\nError, ref does not match alt while attempting to insert substitution error!\n')
                         exit(1)
 
             if any_indel_err:
                 if len(expanded_cigar):
-                    relevantCigar = (expanded_cigar+extraCigarVal)[:self.read_len]
+                    relevantCigar = (expanded_cigar + extra_cigar_val)[:self.read_len]
                     myCigar = CigarString(listIn=relevantCigar).get_string()
 
                 r[3] = r[3][:self.read_len]
 
-            r_out.append([self.FM_pos[my_ploid][r[0]], myCigar, str(r[3]), str(r[1])])
+            r_out.append([self.fm_pos[my_ploid][r[0]], myCigar, str(r[3]), str(r[1])])
 
         # r_out[i] = (pos, cigar, read_string, qual_string)
         return r_out
@@ -665,31 +677,30 @@ def __init__(self, read_len, errorModel, reScaledError):
 
         error_dat = pickle.load(open(errorModel, 'rb'), encoding="bytes")
         self.UNIFORM = False
-        if len(error_dat) == 4:		# uniform-error SE reads (e.g. PacBio)
+        if len(error_dat) == 4:  # uniform-error SE reads (e.g. PacBio)
             self.UNIFORM = True
             [Qscores, offQ, avgError, errorParams] = error_dat
             self.uniform_qscore = int(-10. * np.log10(avgError) + 0.5)
             print('Using uniform sequencing error model. (q=' + str(self.uniform_qscore) + '+' + str(
                 offQ) + ', p(err)={0:0.2f}%)'.format(100. * avgError))
-        if len(error_dat) == 6:		# only 1 q-score model present, use same model for both strands
+        if len(error_dat) == 6:  # only 1 q-score model present, use same model for both strands
             [initQ1, probQ1, Qscores, offQ, avgError, errorParams] = error_dat
             self.PE_MODELS = False
-        elif len(error_dat) == 8:	# found a q-score model for both forward and reverse strands
-            #print 'Using paired-read quality score profiles...'
-            [initQ1 ,probQ1, initQ2, probQ2, Qscores, offQ, avgError, errorParams] = error_dat
+        elif len(error_dat) == 8:  # found a q-score model for both forward and reverse strands
+            # print 'Using paired-read quality score profiles...'
+            [initQ1, probQ1, initQ2, probQ2, Qscores, offQ, avgError, errorParams] = error_dat
             self.PE_MODELS = True
             if len(initQ1) != len(initQ2) or len(probQ1) != len(probQ2):
                 print('\nError: R1 and R2 quality score models are of different length.\n')
                 exit(1)
 
-
-        self.qErrRate = [0.]*(max(Qscores)+1)
+        self.qErrRate = [0.] * (max(Qscores) + 1)
         for q in Qscores:
-            self.qErrRate[q] = 10.**(-q/10.)
+            self.qErrRate[q] = 10. ** (-q / 10.)
         self.offQ = offQ
 
         # errorParams = [SSE_PROB, SIE_RATE, SIE_PROB, SIE_VAL, SIE_INS_FREQ, SIE_INS_NUCL]
-        self.errP   = errorParams
+        self.errP = errorParams
         self.errSSE = [DiscreteDistribution(n, NUCL) for n in self.errP[0]]
         self.errSIE = DiscreteDistribution(self.errP[2], self.errP[3])
         self.errSIN = DiscreteDistribution(self.errP[5], NUCL)
@@ -698,7 +709,7 @@ def __init__(self, read_len, errorModel, reScaledError):
         if reScaledError == None:
             self.errorScale = 1.0
         else:
-            self.errorScale = reScaledError/avgError
+            self.errorScale = reScaledError / avgError
             print('Warning: Quality scores no longer exactly representative of error probability. '
                   'Error model scaled by {0:.3f} to match desired rate...'.format(self.errorScale))
 
@@ -712,34 +723,38 @@ def __init__(self, read_len, errorModel, reScaledError):
                 self.qIndRemap = [max([1, len(initQ1) * n // read_len]) for n in range(read_len)]
 
             # initialize probability distributions
-            self.initDistByPos1        = [DiscreteDistribution(initQ1[i], Qscores) for i in range(len(initQ1))]
+            self.initDistByPos1 = [DiscreteDistribution(initQ1[i], Qscores) for i in range(len(initQ1))]
             self.probDistByPosByPrevQ1 = [None]
             for i in range(1, len(initQ1)):
                 self.probDistByPosByPrevQ1.append([])
                 for j in range(len(initQ1[0])):
-                    if np.sum(probQ1[i][j]) <= 0.:	# if we don't have sufficient data for a transition, use the previous qscore
-                        self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution([1], [Qscores[j]], degenerate_val=Qscores[j]))
+                    if np.sum(probQ1[i][
+                                  j]) <= 0.:  # if we don't have sufficient data for a transition, use the previous qscore
+                        self.probDistByPosByPrevQ1[-1].append(
+                            DiscreteDistribution([1], [Qscores[j]], degenerate_val=Qscores[j]))
                     else:
                         self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution(probQ1[i][j], Qscores))
 
             if self.PE_MODELS:
-                self.initDistByPos2        = [DiscreteDistribution(initQ2[i], Qscores) for i in range(len(initQ2))]
+                self.initDistByPos2 = [DiscreteDistribution(initQ2[i], Qscores) for i in range(len(initQ2))]
                 self.probDistByPosByPrevQ2 = [None]
                 for i in range(1, len(initQ2)):
                     self.probDistByPosByPrevQ2.append([])
                     for j in range(len(initQ2[0])):
-                        if np.sum(probQ2[i][j]) <= 0.:	# if we don't have sufficient data for a transition, use the previous qscore
-                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution([1], [Qscores[j]], degenerate_val=Qscores[j]))
+                        if np.sum(probQ2[i][
+                                      j]) <= 0.:  # if we don't have sufficient data for a transition, use the previous qscore
+                            self.probDistByPosByPrevQ2[-1].append(
+                                DiscreteDistribution([1], [Qscores[j]], degenerate_val=Qscores[j]))
                         else:
                             self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution(probQ2[i][j], Qscores))
 
     def getSequencingErrors(self, readData, isReverseStrand=False):
 
-        qOut = [0]*self.read_len
+        qOut = [0] * self.read_len
         sErr = []
 
         if self.UNIFORM:
-            myQ  = [self.uniform_qscore + self.offQ for n in range(self.read_len)]
+            myQ = [self.uniform_qscore + self.offQ for n in range(self.read_len)]
             qOut = ''.join([chr(n) for n in myQ])
             for i in range(self.read_len):
                 if random.random() < self.errorScale * self.qErrRate[self.uniform_qscore]:
@@ -778,42 +793,41 @@ def getSequencingErrors(self, readData, isReverseStrand=False):
         # don't allow other sequencing errors to occur on bases removed by deletion errors
         delBlacklist = []
 
-        for ind in sErr[::-1]:	# for each error that we're going to insert...
+        for ind in sErr[::-1]:  # for each error that we're going to insert...
 
             # determine error type
             isSub = True
-            if ind != 0 and ind != self.read_len - 1 - max(self.errP[3]) and abs(ind-prevIndel) > 1:
+            if ind != 0 and ind != self.read_len - 1 - max(self.errP[3]) and abs(ind - prevIndel) > 1:
                 if random.random() < self.errP[1]:
                     isSub = False
 
             # error_out = (type, len, pos, ref, alt)
 
-            if isSub:								# insert substitution error
-                myNucl  = str(readData[ind])
+            if isSub:  # insert substitution error
+                myNucl = str(readData[ind])
                 new_nucl = self.errSSE[NUC_IND[myNucl]].sample()
                 sOut.append(('S', 1, ind, myNucl, new_nucl))
-            else:									# insert indel error
+            else:  # insert indel error
                 indelLen = self.errSIE.sample()
-                if random.random() < self.errP[4]:		# insertion error
-                    myNucl  = str(readData[ind])
+                if random.random() < self.errP[4]:  # insertion error
+                    myNucl = str(readData[ind])
                     new_nucl = myNucl + ''.join([self.errSIN.sample() for n in range(indelLen)])
                     sOut.append(('I', len(new_nucl) - 1, ind, myNucl, new_nucl))
-                elif ind < self.read_len - 2 - nDelSoFar:	# deletion error (prevent too many of them from stacking up)
-                    myNucl  = str(readData[ind:ind+indelLen+1])
+                elif ind < self.read_len - 2 - nDelSoFar:  # deletion error (prevent too many of them from stacking up)
+                    myNucl = str(readData[ind:ind + indelLen + 1])
                     new_nucl = str(readData[ind])
-                    nDelSoFar += len(myNucl)-1
+                    nDelSoFar += len(myNucl) - 1
                     sOut.append(('D', len(myNucl) - 1, ind, myNucl, new_nucl))
-                    for i in range(ind+1, ind + indelLen+1):
+                    for i in range(ind + 1, ind + indelLen + 1):
                         delBlacklist.append(i)
                 prevIndel = ind
 
         # remove blacklisted errors
-        for i in range(len(sOut)-1,-1,-1):
+        for i in range(len(sOut) - 1, -1, -1):
             if sOut[i][2] in delBlacklist:
                 del sOut[i]
 
-        return (qOut,sOut)
-
+        return (qOut, sOut)
 
 
 """************************************************
@@ -832,26 +846,26 @@ def parseInputMutationModel(model=None, whichDefault=1):
         exit(1)
 
     if model != None:
-        pickle_dict = pickle.load(open(model,"rb"))
+        pickle_dict = pickle.load(open(model, "rb"))
         outModel[0] = pickle_dict['AVG_MUT_RATE']
         outModel[2] = 1. - pickle_dict['SNP_FREQ']
 
-        insList     = pickle_dict['INDEL_FREQ']
+        insList = pickle_dict['INDEL_FREQ']
         if len(insList):
             insCount = sum([insList[k] for k in insList.keys() if k >= 1])
             delCount = sum([insList[k] for k in insList.keys() if k <= -1])
-            insVals  = [k for k in sorted(insList.keys()) if k >= 1]
-            insWght  = [insList[k]/float(insCount) for k in insVals]
-            delVals  = [k for k in sorted([abs(k) for k in insList.keys() if k <= -1])]
-            delWght  = [insList[-k]/float(delCount) for k in delVals]
-        else:	# degenerate case where no indel stats are provided
+            insVals = [k for k in sorted(insList.keys()) if k >= 1]
+            insWght = [insList[k] / float(insCount) for k in insVals]
+            delVals = [k for k in sorted([abs(k) for k in insList.keys() if k <= -1])]
+            delWght = [insList[-k] / float(delCount) for k in delVals]
+        else:  # degenerate case where no indel stats are provided
             insCount = 1
             delCount = 1
-            insVals  = [1]
-            insWght  = [1.0]
-            delVals  = [1]
-            delWght  = [1.0]
-        outModel[3] = insCount/float(insCount + delCount)
+            insVals = [1]
+            insWght = [1.0]
+            delVals = [1]
+            delWght = [1.0]
+        outModel[3] = insCount / float(insCount + delCount)
         outModel[4] = insVals
         outModel[5] = insWght
         outModel[6] = delVals
@@ -859,24 +873,24 @@ def parseInputMutationModel(model=None, whichDefault=1):
 
         trinuc_trans_prob = pickle_dict['TRINUC_TRANS_PROBS']
         for k in sorted(trinuc_trans_prob.keys()):
-            myInd   = TRI_IND[k[0][0]+k[0][2]]
-            (k1,k2) = (NUC_IND[k[0][1]],NUC_IND[k[1][1]])
+            myInd = TRI_IND[k[0][0] + k[0][2]]
+            (k1, k2) = (NUC_IND[k[0][1]], NUC_IND[k[1][1]])
             outModel[8][myInd][k1][k2] = trinuc_trans_prob[k]
         for i in range(len(outModel[8])):
             for j in range(len(outModel[8][i])):
                 for l in range(len(outModel[8][i][j])):
                     # if trinuc not present in input mutation model, assign it uniform probability
                     if float(sum(outModel[8][i][j])) < 1e-12:
-                        outModel[8][i][j] = [0.25,0.25,0.25,0.25]
+                        outModel[8][i][j] = [0.25, 0.25, 0.25, 0.25]
                     else:
                         outModel[8][i][j][l] /= float(sum(outModel[8][i][j]))
 
-        trinuc_mut_prob    = pickle_dict['TRINUC_MUT_PROB']
-        which_have_we_seen = {n:False for n in ALL_TRI}
-        trinuc_mean        = np.mean(list(trinuc_mut_prob.values()))
+        trinuc_mut_prob = pickle_dict['TRINUC_MUT_PROB']
+        which_have_we_seen = {n: False for n in ALL_TRI}
+        trinuc_mean = np.mean(list(trinuc_mut_prob.values()))
         for trinuc in trinuc_mut_prob.keys():
             outModel[9][ALL_IND[trinuc]] = trinuc_mut_prob[trinuc]
-            which_have_we_seen[trinuc]   = True
+            which_have_we_seen[trinuc] = True
         for trinuc in which_have_we_seen.keys():
             if which_have_we_seen[trinuc] == False:
                 outModel[9][ALL_IND[trinuc]] = trinuc_mean
@@ -906,9 +920,9 @@ def parseInputMutationModel_deprecated(prefix=None, whichDefault=1):
         print('Reading in mutation model...')
         listing1 = [n for n in os.listdir(prefix) if n[-5:] == '.prob']
         listing2 = [n for n in os.listdir(prefix) if n[-7:] == '.trinuc']
-        listing  = sorted(listing1) + sorted(listing2)
+        listing = sorted(listing1) + sorted(listing2)
         for l in listing:
-            f = open(prefix+l,'r')
+            f = open(prefix + l, 'r')
             fr = [n.split('\t') for n in f.read().split('\n')]
             f.close()
 
@@ -947,8 +961,8 @@ def parseInputMutationModel_deprecated(prefix=None, whichDefault=1):
                     print('-', l)
 
             if '.trinuc' == l[-7:]:
-                context_ind = TRI_IND[l[-10]+l[-8]]
-                p_matrix    = [[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1]]
+                context_ind = TRI_IND[l[-10] + l[-8]]
+                p_matrix = [[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1]]
                 for i in range(len(p_matrix)):
                     for j in range(len(fr[i])):
                         p_matrix[i][j] = float(fr[i][j])
@@ -963,58 +977,57 @@ def parseInputMutationModel_deprecated(prefix=None, whichDefault=1):
 
     return outModel
 
+
 ######################
 #	DEFAULT VALUES   #
 ######################
 
-DEFAULT_1_OVERALL_MUT_RATE   = 0.001
-DEFAULT_1_HOMOZYGOUS_FREQ    = 0.010
-DEFAULT_1_INDEL_FRACTION     = 0.05
-DEFAULT_1_INS_VS_DEL         = 0.6
-DEFAULT_1_INS_LENGTH_VALUES  = [1,2,3,4,5,6,7,8,9,10]
+DEFAULT_1_OVERALL_MUT_RATE = 0.001
+DEFAULT_1_HOMOZYGOUS_FREQ = 0.010
+DEFAULT_1_INDEL_FRACTION = 0.05
+DEFAULT_1_INS_VS_DEL = 0.6
+DEFAULT_1_INS_LENGTH_VALUES = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
 DEFAULT_1_INS_LENGTH_WEIGHTS = [0.4, 0.2, 0.1, 0.05, 0.05, 0.05, 0.05, 0.034, 0.033, 0.033]
-DEFAULT_1_DEL_LENGTH_VALUES  = [1,2,3,4,5]
-DEFAULT_1_DEL_LENGTH_WEIGHTS = [0.3,0.2,0.2,0.2,0.1]
-example_matrix_1             = [[0.0, 0.15, 0.7, 0.15],
-                                [0.15, 0.0, 0.15, 0.7],
-                                [0.7, 0.15, 0.0, 0.15],
-                                [0.15, 0.7, 0.15, 0.0]]
-DEFAULT_1_TRI_FREQS          = [copy.deepcopy(example_matrix_1) for n in range(16)]
-DEFAULT_1_TRINUC_BIAS        = [1./float(len(ALL_TRI)) for n in ALL_TRI]
-DEFAULT_MODEL_1              = [DEFAULT_1_OVERALL_MUT_RATE,
-                                DEFAULT_1_HOMOZYGOUS_FREQ,
-                                DEFAULT_1_INDEL_FRACTION,
-                                DEFAULT_1_INS_VS_DEL,
-                                DEFAULT_1_INS_LENGTH_VALUES,
-                                DEFAULT_1_INS_LENGTH_WEIGHTS,
-                                DEFAULT_1_DEL_LENGTH_VALUES,
-                                DEFAULT_1_DEL_LENGTH_WEIGHTS,
-                                DEFAULT_1_TRI_FREQS,
-                                DEFAULT_1_TRINUC_BIAS]
-
-DEFAULT_2_OVERALL_MUT_RATE   = 0.002
-DEFAULT_2_HOMOZYGOUS_FREQ    = 0.200
-DEFAULT_2_INDEL_FRACTION     = 0.1
-DEFAULT_2_INS_VS_DEL         = 0.3
-DEFAULT_2_INS_LENGTH_VALUES  = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+DEFAULT_1_DEL_LENGTH_VALUES = [1, 2, 3, 4, 5]
+DEFAULT_1_DEL_LENGTH_WEIGHTS = [0.3, 0.2, 0.2, 0.2, 0.1]
+example_matrix_1 = [[0.0, 0.15, 0.7, 0.15],
+                    [0.15, 0.0, 0.15, 0.7],
+                    [0.7, 0.15, 0.0, 0.15],
+                    [0.15, 0.7, 0.15, 0.0]]
+DEFAULT_1_TRI_FREQS = [copy.deepcopy(example_matrix_1) for n in range(16)]
+DEFAULT_1_TRINUC_BIAS = [1. / float(len(ALL_TRI)) for n in ALL_TRI]
+DEFAULT_MODEL_1 = [DEFAULT_1_OVERALL_MUT_RATE,
+                   DEFAULT_1_HOMOZYGOUS_FREQ,
+                   DEFAULT_1_INDEL_FRACTION,
+                   DEFAULT_1_INS_VS_DEL,
+                   DEFAULT_1_INS_LENGTH_VALUES,
+                   DEFAULT_1_INS_LENGTH_WEIGHTS,
+                   DEFAULT_1_DEL_LENGTH_VALUES,
+                   DEFAULT_1_DEL_LENGTH_WEIGHTS,
+                   DEFAULT_1_TRI_FREQS,
+                   DEFAULT_1_TRINUC_BIAS]
+
+DEFAULT_2_OVERALL_MUT_RATE = 0.002
+DEFAULT_2_HOMOZYGOUS_FREQ = 0.200
+DEFAULT_2_INDEL_FRACTION = 0.1
+DEFAULT_2_INS_VS_DEL = 0.3
+DEFAULT_2_INS_LENGTH_VALUES = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
 DEFAULT_2_INS_LENGTH_WEIGHTS = [0.1, 0.1, 0.2, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]
-DEFAULT_2_DEL_LENGTH_VALUES  = [1,2,3,4,5]
-DEFAULT_2_DEL_LENGTH_WEIGHTS = [0.3,0.2,0.2,0.2,0.1]
-example_matrix_2             = [[0.0, 0.15, 0.7, 0.15],
-                                [0.15, 0.0, 0.15, 0.7],
-                                [0.7, 0.15, 0.0, 0.15],
-                                [0.15, 0.7, 0.15, 0.0]]
-DEFAULT_2_TRI_FREQS          = [copy.deepcopy(example_matrix_2) for n in range(16)]
-DEFAULT_2_TRINUC_BIAS        = [1./float(len(ALL_TRI)) for n in ALL_TRI]
-DEFAULT_MODEL_2              = [DEFAULT_2_OVERALL_MUT_RATE,
-                                DEFAULT_2_HOMOZYGOUS_FREQ,
-                                DEFAULT_2_INDEL_FRACTION,
-                                DEFAULT_2_INS_VS_DEL,
-                                DEFAULT_2_INS_LENGTH_VALUES,
-                                DEFAULT_2_INS_LENGTH_WEIGHTS,
-                                DEFAULT_2_DEL_LENGTH_VALUES,
-                                DEFAULT_2_DEL_LENGTH_WEIGHTS,
-                                DEFAULT_2_TRI_FREQS,
-                                DEFAULT_2_TRINUC_BIAS]
-
-
+DEFAULT_2_DEL_LENGTH_VALUES = [1, 2, 3, 4, 5]
+DEFAULT_2_DEL_LENGTH_WEIGHTS = [0.3, 0.2, 0.2, 0.2, 0.1]
+example_matrix_2 = [[0.0, 0.15, 0.7, 0.15],
+                    [0.15, 0.0, 0.15, 0.7],
+                    [0.7, 0.15, 0.0, 0.15],
+                    [0.15, 0.7, 0.15, 0.0]]
+DEFAULT_2_TRI_FREQS = [copy.deepcopy(example_matrix_2) for n in range(16)]
+DEFAULT_2_TRINUC_BIAS = [1. / float(len(ALL_TRI)) for n in ALL_TRI]
+DEFAULT_MODEL_2 = [DEFAULT_2_OVERALL_MUT_RATE,
+                   DEFAULT_2_HOMOZYGOUS_FREQ,
+                   DEFAULT_2_INDEL_FRACTION,
+                   DEFAULT_2_INS_VS_DEL,
+                   DEFAULT_2_INS_LENGTH_VALUES,
+                   DEFAULT_2_INS_LENGTH_WEIGHTS,
+                   DEFAULT_2_DEL_LENGTH_VALUES,
+                   DEFAULT_2_DEL_LENGTH_WEIGHTS,
+                   DEFAULT_2_TRI_FREQS,
+                   DEFAULT_2_TRINUC_BIAS]
diff --git a/py/__pycache__/SequenceContainer.cpython-37.pyc b/py/__pycache__/SequenceContainer.cpython-37.pyc
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diff --git a/py/__pycache__/__init__.cpython-37.pyc b/py/__pycache__/__init__.cpython-37.pyc
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diff --git a/py/__pycache__/neat_cigar.cpython-37.pyc b/py/__pycache__/neat_cigar.cpython-37.pyc
index 92877fa16b6c8afe9279c3ef0667d2a8ee2ea5b7..ba8b5ebcce697495df97d073db744c705fa97ba3 100644
GIT binary patch
literal 3114
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delta 1597
zcmZuxO>7%Q6rP#=UpsD^IKP#s+W?81q~%YdiUbOE3ZW_%B0-HduA<blG8-rBbasN0
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diff --git a/py/__pycache__/probability.cpython-37.pyc b/py/__pycache__/probability.cpython-37.pyc
index 7f98effc8f7a3014453183b34262cdfe6105a4af..72c0c863e96e15f9a995fc697e12855ad24e1268 100644
GIT binary patch
delta 1336
zcmZux&2Jk;6rUNd_rqR0Nhxj8B9e-tun|E`sT73zLDLE@e6*@arB$l!+B0#s@$TBQ
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delta 1246
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diff --git a/py/__pycache__/ref_func.cpython-37.pyc b/py/__pycache__/ref_func.cpython-37.pyc
index 9660c918016c6e3b800f2331f9fdfdd3584ee6ee..1a4ba051edd81dcc444ed2938470b98ed11e20de 100644
GIT binary patch
delta 1548
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Zg?>fAk^qtM)DBTE%k0ZyDfWhP^j}~xv1kAQ

delta 1559
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AIRF3v

diff --git a/py/__pycache__/vcfFunc.cpython-37.pyc b/py/__pycache__/vcfFunc.cpython-37.pyc
index e996086a40ff29dfe7f006ac759f4d0d953e47d7..1e8645c6b6c9bf22cf28bfbc8586b0e12334a4a3 100644
GIT binary patch
delta 275
zcmcbhvPXr_iI<m)0SJCpYQ!7JZshyIEN|s(6%$&VT2vg9m0z5Z786jJoRL_R8&H&=
zm6}{qJlTfjJwHcseolOGVs61Lw)B$t%*ht4ZERq%l*xx#H95I~f(5BX@x`0JuxhZg
zaU|yC#FtH0<XywgQI=R#9G}1WGH)1@5Jy>ZT6{rbNk$Y;Noj6=QGDKHO95%CD9*h2
z;_S?Vg4C2KF*q|mDK$B<v^X_BBe6ImiVI|Ud|qO1YH<{QK~ZL2Noq=b`Q!-#qFQVP
zIq~_WB}FPgZxyKmiCawRB~k1^C#9Ap=G<Z~$t{TD&CAb=FWP)gfQykaaPmz-D<CN%
GWCH*Xg<fs|

delta 333
zcmXw!u}Z^G6oyUPG_^HF#o{7}SViAJii_G}9ZZX{ErdY0O|I#sH@V^5lR_sqAHeLs
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zq@6wM$CYY`UP8fMeCtPsZV=9gS<QSY8#4m3xA-uJg?nS>-}MPjoEoN)082nbxGE=H
zuwbqVh_IjgLL*<Q65CUjBs6fg{x5G#eUeJrC32!xWiDcoh)SnHI8YMI`@mwP!H7Vd
zKcKWbRpKnBu$}igdcqHTDME8eI9L)2Av)y{JG0!Oh*9FynY2WFvv^ytYMPyWZnQM}
Har5*K@^x|h


From 23e2de1219404cb757b0c14247cfa72d2883d065 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 10 Aug 2020 15:55:20 -0500
Subject: [PATCH 107/323] Updating bacterial wrapper

---
 bacterial_genreads_wrapper.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 2f427d6..4970b2a 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -36,8 +36,9 @@ def initialize_population(reference: str, pop_size):
         names.append("name" + str(j))
     population = []
     for i in range(pop_size):
-        args = ['-r', reference, '-R', '151', '-o', names[i], '--fa']
-        new_member = genReads.main(args)
+        args = ['-r', reference, '-R', '101', '-o', names[i], '--fa']
+        genReads.main(args)
+        new_member = names[i] + "_read1.fa"
         population.append(new_member)
     return population
 

From 0e1ef9597e7dd1fbe9c21c53ad6ce71e407e5972 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 11 Aug 2020 13:57:31 -0500
Subject: [PATCH 108/323] minor debugging change to
 baterial_genreads_wrapper.py

---
 bacterial_genreads_wrapper.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 4970b2a..491ee95 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -62,6 +62,7 @@ def main():
     cull_percentage = args.c
 
     population = initialize_population(ref_fasta, init_population_size)
+    print(population)
     for i in range(cycles):
         new_population = cull(population)
         # If all elements get culled, then break and quit

From 560b2c7feeb747d5932cdae91d7c1124cc4733cf Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 11 Aug 2020 14:22:42 -0500
Subject: [PATCH 109/323] debugging bacterial wrapper

---
 bacterial_genreads_wrapper.py | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 491ee95..a73a5c6 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -1,8 +1,8 @@
 #!/usr/bin/env python
 
-import genReads
+# import genReads
 import argparse
-from Bio import SeqIO
+# from Bio import SeqIO
 
 
 def cull(population: list) -> list:
@@ -35,11 +35,11 @@ def initialize_population(reference: str, pop_size):
     for j in range(pop_size):
         names.append("name" + str(j))
     population = []
-    for i in range(pop_size):
-        args = ['-r', reference, '-R', '101', '-o', names[i], '--fa']
-        genReads.main(args)
-        new_member = names[i] + "_read1.fa"
-        population.append(new_member)
+    # for i in range(pop_size):
+    #     args = ['-r', reference, '-R', '101', '-o', names[i], '--fa']
+    #     genReads.main(args)
+    #     new_member = names[i] + "_read1.fa"
+    #     population.append(new_member)
     return population
 
 
@@ -63,10 +63,10 @@ def main():
 
     population = initialize_population(ref_fasta, init_population_size)
     print(population)
-    for i in range(cycles):
-        new_population = cull(population)
-        # If all elements get culled, then break and quit
-        if not new_population:
-            break
-        new_population = crossover(new_population)
-        population = evolve(new_population, 2)
+    # for i in range(cycles):
+    #     new_population = cull(population)
+    #     # If all elements get culled, then break and quit
+    #     if not new_population:
+    #         break
+    #     new_population = crossover(new_population)
+    #     population = evolve(new_population, 2)

From 85f945fb6dbc744e8f6e08658bf3a8e55830d1d9 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 12 Aug 2020 15:43:30 -0500
Subject: [PATCH 110/323] Updates to bacterial wrapper

---
 bacterial_genreads_wrapper.py | 40 ++++++++++++++++++++---------------
 1 file changed, 23 insertions(+), 17 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index a73a5c6..b1cb919 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -1,7 +1,9 @@
 #!/usr/bin/env python
 
-# import genReads
+import genReads
 import argparse
+
+
 # from Bio import SeqIO
 
 
@@ -35,11 +37,11 @@ def initialize_population(reference: str, pop_size):
     for j in range(pop_size):
         names.append("name" + str(j))
     population = []
-    # for i in range(pop_size):
-    #     args = ['-r', reference, '-R', '101', '-o', names[i], '--fa']
-    #     genReads.main(args)
-    #     new_member = names[i] + "_read1.fa"
-    #     population.append(new_member)
+    for i in range(pop_size):
+        args = ['-r', reference, '-R', '101', '-o', names[i], '--fa']
+        genReads.main(args)
+        new_member = names[i] + "_read1.fa"
+        population.append(new_member)
     return population
 
 
@@ -49,11 +51,11 @@ def evolve(new_population, param):
 
 def main():
     parser = argparse.ArgumentParser(description='bacterial_genreads_wrapper.py')
-    parser.add_argument('-r', type=str, required=True, metavar='/path/to/reference.fasta',
+    parser.add_argument('-r', type=str, required=True, metavar='reference.fasta',
                         help="Reference file for organism in fasta format")
-    parser.add_argument('-C', type=int, required=True, metavar='<str>', help="Number of cycles to run")
-    parser.add_argument('-i', type=int, required=True, metavar='<str>', help="Initial population size")
-    parser.add_argument('-c', type=float, required=False, metavar='<float>',
+    parser.add_argument('-C', type=int, required=True, metavar='Cycles', help="Number of cycles to run")
+    parser.add_argument('-i', type=int, required=True, metavar='initial population', help="Initial population size")
+    parser.add_argument('-c', type=float, required=False, metavar='cull pct',
                         help="Percentage of population to cull each cycle (0.5 will keep population relatively stable)",
                         default=0.5)
     args = parser.parse_args()
@@ -63,10 +65,14 @@ def main():
 
     population = initialize_population(ref_fasta, init_population_size)
     print(population)
-    # for i in range(cycles):
-    #     new_population = cull(population)
-    #     # If all elements get culled, then break and quit
-    #     if not new_population:
-    #         break
-    #     new_population = crossover(new_population)
-    #     population = evolve(new_population, 2)
+    for i in range(cycles):
+        new_population = cull(population)
+        # If all elements get culled, then break and quit
+        if not new_population:
+            break
+        new_population = crossover(new_population)
+        population = evolve(new_population, 2)
+
+
+if __name__ == '__main__':
+    main()

From cccdebdc0cdf318a6e01d9421abed155ed0cbaaa Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 12 Aug 2020 16:48:22 -0500
Subject: [PATCH 111/323] updates to bacterial wrapper

---
 bacterial_genreads_wrapper.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index b1cb919..b38b32d 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -2,17 +2,24 @@
 
 import genReads
 import argparse
+import random
 
 
 # from Bio import SeqIO
 
 
-def cull(population: list) -> list:
+def cull(population: list, percentage: float = 0.5) -> list:
     """
     The purpose of this function will be to cull the bacteria created in the model
-    :param population:
-    :return:
+    :param percentage: percentage of the population to eliminate
+    :param population: the list of members to cull
+    :return: The list of remaining members
     """
+    cull_amount = round(len(population) * percentage)
+    print("Culling {} members from population".format(cull_amount))
+    for i in range(cull_amount):
+        selection = random.choice(population)
+        population.remove(selection)
     return population
 
 
@@ -35,7 +42,7 @@ def initialize_population(reference: str, pop_size):
     """
     names = []
     for j in range(pop_size):
-        names.append("name" + str(j))
+        names.append("name" + str(j+1))
     population = []
     for i in range(pop_size):
         args = ['-r', reference, '-R', '101', '-o', names[i], '--fa']
@@ -66,7 +73,7 @@ def main():
     population = initialize_population(ref_fasta, init_population_size)
     print(population)
     for i in range(cycles):
-        new_population = cull(population)
+        new_population = cull(population, cull_percentage)
         # If all elements get culled, then break and quit
         if not new_population:
             break

From 151a61a38ca6477f056482c8503ed85949230cd1 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 14 Aug 2020 13:47:03 -0500
Subject: [PATCH 112/323] Updating bacterial wrapper

---
 bacterial_genreads_wrapper.py | 39 ++++++++++++++++++++++++++++-------
 1 file changed, 31 insertions(+), 8 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index b38b32d..9bf9786 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -3,9 +3,18 @@
 import genReads
 import argparse
 import random
+import pathlib
+# from Bio import SeqIO
 
 
-# from Bio import SeqIO
+class Bacterium:
+    def __init__(self, reference: str, name: str, parent):
+        self.reference = pathlib.Path(reference)
+        self.parent = parent
+        self.name = name
+        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa']
+        genReads.main(args)
+        self.file = pathlib.Path().absolute() / (self.name + "_read1.fa")
 
 
 def cull(population: list, percentage: float = 0.5) -> list:
@@ -45,20 +54,33 @@ def initialize_population(reference: str, pop_size):
         names.append("name" + str(j+1))
     population = []
     for i in range(pop_size):
-        args = ['-r', reference, '-R', '101', '-o', names[i], '--fa']
-        genReads.main(args)
-        new_member = names[i] + "_read1.fa"
+        new_member = Bacterium(reference, names[i], None)
         population.append(new_member)
     return population
 
 
-def evolve(new_population, param):
-    pass
+def evolve(population: list) -> list:
+    """
+    This evolves an existing population by doubling them (binary fission), then introducing random mutation to
+    each member of the population.
+    :param population: A list of fasta files representing the bacteria.
+    :return:
+    """
+    new_population = population + population
+    names = []
+    for j in range(len(new_population)):
+        names.append("name" + str(j+1))
+    for i in range(len(new_population)):
+        args = ['-r', new_population[i], '-R', '101', '-o', names[i], '--fa']
+        genReads.main(args)
+        new_member = names[i] + "_read1.fa"
+        population.append(new_member)
+    return new_population
 
 
 def main():
     parser = argparse.ArgumentParser(description='bacterial_genreads_wrapper.py')
-    parser.add_argument('-r', type=str, required=True, metavar='reference.fasta',
+    parser.add_argument('-r', type=str, required=True, metavar='reference fasta',
                         help="Reference file for organism in fasta format")
     parser.add_argument('-C', type=int, required=True, metavar='Cycles', help="Number of cycles to run")
     parser.add_argument('-i', type=int, required=True, metavar='initial population', help="Initial population size")
@@ -74,11 +96,12 @@ def main():
     print(population)
     for i in range(cycles):
         new_population = cull(population, cull_percentage)
+        print(new_population)
         # If all elements get culled, then break and quit
         if not new_population:
             break
         new_population = crossover(new_population)
-        population = evolve(new_population, 2)
+        population = evolve(new_population)
 
 
 if __name__ == '__main__':

From 8322a0573f6db8fd471407deabaa717149bad636 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 14 Aug 2020 15:11:53 -0500
Subject: [PATCH 113/323] Adding bacterium class to bacterial wrapper.

---
 bacterial_genreads_wrapper.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 9bf9786..7d103d3 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -16,6 +16,12 @@ def __init__(self, reference: str, name: str, parent):
         genReads.main(args)
         self.file = pathlib.Path().absolute() / (self.name + "_read1.fa")
 
+    def __repr__(self):
+        return "Bacterium {}".format(self.name)
+
+    def __str__(self):
+        return "Bacterium {}".format(self.name)
+
 
 def cull(population: list, percentage: float = 0.5) -> list:
     """

From ef5c9241c2f30654f46f8fcdae792a296017030a Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 14 Aug 2020 17:10:34 -0500
Subject: [PATCH 114/323] bacterial genreads wrapper updates to class Bacterium

---
 bacterial_genreads_wrapper.py | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 7d103d3..72fcd2c 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -10,11 +10,14 @@
 class Bacterium:
     def __init__(self, reference: str, name: str, parent):
         self.reference = pathlib.Path(reference)
+        print(type(parent))
         self.parent = parent
         self.name = name
-        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa']
-        genReads.main(args)
-        self.file = pathlib.Path().absolute() / (self.name + "_read1.fa")
+        self.history = []
+        self.file = ""
+
+        if type(self.parent) is None:
+            self.analyze()
 
     def __repr__(self):
         return "Bacterium {}".format(self.name)
@@ -22,6 +25,11 @@ def __repr__(self):
     def __str__(self):
         return "Bacterium {}".format(self.name)
 
+    def analyze(self):
+        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa']
+        genReads.main(args)
+        self.file = pathlib.Path().absolute() / (self.name + "_read1.fa")
+
 
 def cull(population: list, percentage: float = 0.5) -> list:
     """
@@ -100,6 +108,7 @@ def main():
 
     population = initialize_population(ref_fasta, init_population_size)
     print(population)
+    bacterium3 = ()
     for i in range(cycles):
         new_population = cull(population, cull_percentage)
         print(new_population)

From fbe5d888a088d5cc7613dd8d4fde04267199d981 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 17 Aug 2020 13:23:22 -0500
Subject: [PATCH 115/323] updates to bacterial wrapper

---
 bacterial_genreads_wrapper.py | 51 ++++++++++++++++++-----------------
 1 file changed, 26 insertions(+), 25 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 72fcd2c..82bf94b 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -8,28 +8,28 @@
 
 
 class Bacterium:
-    def __init__(self, reference: str, name: str, parent):
+    def __init__(self, reference: str, name: str, analyze: bool):
         self.reference = pathlib.Path(reference)
-        print(type(parent))
-        self.parent = parent
         self.name = name
-        self.history = []
         self.file = ""
 
-        if type(self.parent) is None:
+        if analyze:
             self.analyze()
 
     def __repr__(self):
-        return "Bacterium {}".format(self.name)
+        return str(self.name)
 
     def __str__(self):
-        return "Bacterium {}".format(self.name)
+        return str(self.name)
 
     def analyze(self):
         args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa']
         genReads.main(args)
         self.file = pathlib.Path().absolute() / (self.name + "_read1.fa")
 
+    def remove(self):
+        pathlib.unlink(self.file)
+
 
 def cull(population: list, percentage: float = 0.5) -> list:
     """
@@ -43,6 +43,7 @@ def cull(population: list, percentage: float = 0.5) -> list:
     for i in range(cull_amount):
         selection = random.choice(population)
         population.remove(selection)
+        selection.remove()
     return population
 
 
@@ -65,30 +66,30 @@ def initialize_population(reference: str, pop_size):
     """
     names = []
     for j in range(pop_size):
-        names.append("name" + str(j+1))
+        names.append("bacterium_0_" + str(j+1))
     population = []
     for i in range(pop_size):
-        new_member = Bacterium(reference, names[i], None)
+        new_member = Bacterium(reference, names[i], True)
         population.append(new_member)
     return population
 
 
-def evolve(population: list) -> list:
+def evolve(population: list, generation: int) -> list:
     """
     This evolves an existing population by doubling them (binary fission), then introducing random mutation to
     each member of the population.
+    :param generation: Helps determine the starting point of the numbering system so the bacteria have unique names
     :param population: A list of fasta files representing the bacteria.
     :return:
     """
-    new_population = population + population
+    children_population = population + population
     names = []
-    for j in range(len(new_population)):
-        names.append("name" + str(j+1))
-    for i in range(len(new_population)):
-        args = ['-r', new_population[i], '-R', '101', '-o', names[i], '--fa']
-        genReads.main(args)
-        new_member = names[i] + "_read1.fa"
-        population.append(new_member)
+    new_population = []
+    for j in range(len(children_population)):
+        names.append("bacterium" + '_' + generation + '_' + str(j+1))
+    for i in range(len(children_population)):
+        child = Bacterium(children_population[i], names[i], True)
+        new_population.append(child)
     return new_population
 
 
@@ -96,7 +97,7 @@ def main():
     parser = argparse.ArgumentParser(description='bacterial_genreads_wrapper.py')
     parser.add_argument('-r', type=str, required=True, metavar='reference fasta',
                         help="Reference file for organism in fasta format")
-    parser.add_argument('-C', type=int, required=True, metavar='Cycles', help="Number of cycles to run")
+    parser.add_argument('-g', type=int, required=True, metavar='generations', help="Number of generations to run")
     parser.add_argument('-i', type=int, required=True, metavar='initial population', help="Initial population size")
     parser.add_argument('-c', type=float, required=False, metavar='cull pct',
                         help="Percentage of population to cull each cycle (0.5 will keep population relatively stable)",
@@ -107,16 +108,16 @@ def main():
     cull_percentage = args.c
 
     population = initialize_population(ref_fasta, init_population_size)
-    print(population)
-    bacterium3 = ()
+
     for i in range(cycles):
-        new_population = cull(population, cull_percentage)
-        print(new_population)
+        new_population = evolve(population, i + 1)
+
+        new_population = crossover(new_population)
+
+        new_population = cull(new_population, cull_percentage)
         # If all elements get culled, then break and quit
         if not new_population:
             break
-        new_population = crossover(new_population)
-        population = evolve(new_population)
 
 
 if __name__ == '__main__':

From bc51f6b1048fd221ee141503b22376d22ff43868 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 17 Aug 2020 13:43:41 -0500
Subject: [PATCH 116/323] updates to bacterial wrapper

---
 bacterial_genreads_wrapper.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 82bf94b..c4c9cbe 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -104,7 +104,7 @@ def main():
                         default=0.5)
     args = parser.parse_args()
 
-    (ref_fasta, init_population_size, cycles) = (args.r, args.i, args.C)
+    (ref_fasta, init_population_size, cycles) = (args.r, args.i, args.g)
     cull_percentage = args.c
 
     population = initialize_population(ref_fasta, init_population_size)
@@ -115,7 +115,8 @@ def main():
         new_population = crossover(new_population)
 
         new_population = cull(new_population, cull_percentage)
-        # If all elements get culled, then break and quit
+
+        # If all elements get culled, then break the loop
         if not new_population:
             break
 

From c8ec65302c6588966e535084c81f2042d38ccbe5 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 17 Aug 2020 13:56:55 -0500
Subject: [PATCH 117/323] updates to bacterial wrapper

---
 bacterial_genreads_wrapper.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index c4c9cbe..ff62fe1 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -66,7 +66,7 @@ def initialize_population(reference: str, pop_size):
     """
     names = []
     for j in range(pop_size):
-        names.append("bacterium_0_" + str(j+1))
+        names.append("bacterium_0_{}".format(j+1))
     population = []
     for i in range(pop_size):
         new_member = Bacterium(reference, names[i], True)
@@ -86,7 +86,7 @@ def evolve(population: list, generation: int) -> list:
     names = []
     new_population = []
     for j in range(len(children_population)):
-        names.append("bacterium" + '_' + generation + '_' + str(j+1))
+        names.append("bacterium_{}_{}".format(generation, j+1))
     for i in range(len(children_population)):
         child = Bacterium(children_population[i], names[i], True)
         new_population.append(child)

From acb57e23541e40e8e4a95d5a052772bd8a2a0769 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 17 Aug 2020 14:13:16 -0500
Subject: [PATCH 118/323] updates to bacterial wrapper

---
 bacterial_genreads_wrapper.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index ff62fe1..092ecc8 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -22,6 +22,9 @@ def __repr__(self):
     def __str__(self):
         return str(self.name)
 
+    def get_file(self):
+        return self.file
+
     def analyze(self):
         args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa']
         genReads.main(args)
@@ -57,7 +60,7 @@ def crossover(population: list) -> list:
     return new_population
 
 
-def initialize_population(reference: str, pop_size):
+def initialize_population(reference: str, pop_size) -> list:
     """
     The purpose of this function is to evolve the bacteria
     :param reference:
@@ -74,7 +77,7 @@ def initialize_population(reference: str, pop_size):
     return population
 
 
-def evolve(population: list, generation: int) -> list:
+def evolve_population(population: list, generation: int) -> list:
     """
     This evolves an existing population by doubling them (binary fission), then introducing random mutation to
     each member of the population.
@@ -88,7 +91,7 @@ def evolve(population: list, generation: int) -> list:
     for j in range(len(children_population)):
         names.append("bacterium_{}_{}".format(generation, j+1))
     for i in range(len(children_population)):
-        child = Bacterium(children_population[i], names[i], True)
+        child = Bacterium(children_population[i].get_file(), names[i], True)
         new_population.append(child)
     return new_population
 
@@ -104,13 +107,13 @@ def main():
                         default=0.5)
     args = parser.parse_args()
 
-    (ref_fasta, init_population_size, cycles) = (args.r, args.i, args.g)
+    (ref_fasta, init_population_size, generations) = (args.r, args.i, args.g)
     cull_percentage = args.c
 
     population = initialize_population(ref_fasta, init_population_size)
 
-    for i in range(cycles):
-        new_population = evolve(population, i + 1)
+    for i in range(generations):
+        new_population = evolve_population(population, i+1)
 
         new_population = crossover(new_population)
 

From 7479cd496a27dc57bc15a090b13a6c3157f38e65 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 17 Aug 2020 14:38:59 -0500
Subject: [PATCH 119/323] further work on the bacterial wrapper

---
 bacterial_genreads_wrapper.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 092ecc8..4130828 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -8,7 +8,7 @@
 
 
 class Bacterium:
-    def __init__(self, reference: str, name: str, analyze: bool):
+    def __init__(self, reference: str, name: str, analyze: bool = True):
         self.reference = pathlib.Path(reference)
         self.name = name
         self.file = ""
@@ -91,6 +91,7 @@ def evolve_population(population: list, generation: int) -> list:
     for j in range(len(children_population)):
         names.append("bacterium_{}_{}".format(generation, j+1))
     for i in range(len(children_population)):
+        print(children_population[i].get_file())
         child = Bacterium(children_population[i].get_file(), names[i], True)
         new_population.append(child)
     return new_population

From 1ce6aa5a8405f4a82cf39caba7d660cbf99f3c1a Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 17 Aug 2020 15:08:03 -0500
Subject: [PATCH 120/323] further work on the bacterial wrapper

---
 bacterial_genreads_wrapper.py | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 4130828..98daf51 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -8,13 +8,12 @@
 
 
 class Bacterium:
-    def __init__(self, reference: str, name: str, analyze: bool = True):
+    def __init__(self, reference: str, name: str):
         self.reference = pathlib.Path(reference)
         self.name = name
-        self.file = ""
-
-        if analyze:
-            self.analyze()
+        # Temporarily set the reference as the bacterium's file, until it is analyzed
+        self.file = pathlib.Path(reference)
+        self.analyze()
 
     def __repr__(self):
         return str(self.name)
@@ -29,9 +28,11 @@ def analyze(self):
         args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa']
         genReads.main(args)
         self.file = pathlib.Path().absolute() / (self.name + "_read1.fa")
+        new_name = self.name + "_read1.fa.gz"
+        self.file.rename(pathlib.Path(pathlib.Path().absolute(), new_name))
 
     def remove(self):
-        pathlib.unlink(self.file)
+        pathlib.Path.unlink(self.file)
 
 
 def cull(population: list, percentage: float = 0.5) -> list:
@@ -72,7 +73,7 @@ def initialize_population(reference: str, pop_size) -> list:
         names.append("bacterium_0_{}".format(j+1))
     population = []
     for i in range(pop_size):
-        new_member = Bacterium(reference, names[i], True)
+        new_member = Bacterium(reference, names[i])
         population.append(new_member)
     return population
 
@@ -92,7 +93,7 @@ def evolve_population(population: list, generation: int) -> list:
         names.append("bacterium_{}_{}".format(generation, j+1))
     for i in range(len(children_population)):
         print(children_population[i].get_file())
-        child = Bacterium(children_population[i].get_file(), names[i], True)
+        child = Bacterium(children_population[i].get_file(), names[i])
         new_population.append(child)
     return new_population
 

From b0a4cb789db58802f123034424255c4d5dde1dde Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 17 Aug 2020 15:43:22 -0500
Subject: [PATCH 121/323] further work on the bacterial wrapper

---
 bacterial_genreads_wrapper.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 98daf51..b132f10 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -4,6 +4,8 @@
 import argparse
 import random
 import pathlib
+import gzip
+import shutil
 # from Bio import SeqIO
 
 
@@ -27,14 +29,28 @@ def get_file(self):
     def analyze(self):
         args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa']
         genReads.main(args)
+
+        # The following workaround is due to the fact that genReads writes out a compressed
+        # fasta but does not put the .gz extension on it. Also, genReads cannot handle gzipped
+        # fasta files, so we further have to unzip it for it to actually work.
         self.file = pathlib.Path().absolute() / (self.name + "_read1.fa")
         new_name = self.name + "_read1.fa.gz"
         self.file.rename(pathlib.Path(pathlib.Path().absolute(), new_name))
+        true_path = pathlib.Path().absolute() / (self.name + "_read1.fa")
+        unzip_file(self.file, true_path)
+        self.file = true_path
+        # end workaround
 
     def remove(self):
         pathlib.Path.unlink(self.file)
 
 
+def unzip_file(zipped_file: pathlib, unzipped_file: pathlib):
+    with gzip.open(zipped_file, 'rb') as f_in:
+        with open(unzipped_file, 'wb') as f_out:
+            shutil.copyfileobj(f_in, f_out)
+
+
 def cull(population: list, percentage: float = 0.5) -> list:
     """
     The purpose of this function will be to cull the bacteria created in the model

From cbce52544edd5e5bab8fe1628e9ce59b66fed24f Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 17 Aug 2020 15:54:13 -0500
Subject: [PATCH 122/323] further work on the bacterial wrapper

---
 bacterial_genreads_wrapper.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index b132f10..3bb587e 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -34,8 +34,10 @@ def analyze(self):
         # fasta but does not put the .gz extension on it. Also, genReads cannot handle gzipped
         # fasta files, so we further have to unzip it for it to actually work.
         self.file = pathlib.Path().absolute() / (self.name + "_read1.fa")
+        print(self.file)
         new_name = self.name + "_read1.fa.gz"
         self.file.rename(pathlib.Path(pathlib.Path().absolute(), new_name))
+        print(self.file)
         true_path = pathlib.Path().absolute() / (self.name + "_read1.fa")
         unzip_file(self.file, true_path)
         self.file = true_path

From 9c5269cd4ccb1b315bf71e8327835a4f3ca7e882 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 17 Aug 2020 16:10:52 -0500
Subject: [PATCH 123/323] further work on the bacterial wrapper

---
 bacterial_genreads_wrapper.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 3bb587e..ccdafec 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -37,6 +37,7 @@ def analyze(self):
         print(self.file)
         new_name = self.name + "_read1.fa.gz"
         self.file.rename(pathlib.Path(pathlib.Path().absolute(), new_name))
+        self.file = pathlib.Path().absolute() / (self.name + "_read1.fa.gz")
         print(self.file)
         true_path = pathlib.Path().absolute() / (self.name + "_read1.fa")
         unzip_file(self.file, true_path)

From 54cd02784d2bb4cad9d317feb162882e10ee5186 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 17 Aug 2020 16:19:47 -0500
Subject: [PATCH 124/323] further work on the bacterial wrapper

---
 bacterial_genreads_wrapper.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index ccdafec..16c8111 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -41,6 +41,7 @@ def analyze(self):
         print(self.file)
         true_path = pathlib.Path().absolute() / (self.name + "_read1.fa")
         unzip_file(self.file, true_path)
+        pathlib.Path.unlink(pathlib.Path().absolute() / (self.name + "_read1.fa.gz"))
         self.file = true_path
         # end workaround
 

From bd0eed1a00a35c028cc4dfd893967cb896e41b24 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 17 Aug 2020 16:48:52 -0500
Subject: [PATCH 125/323] further work on the bacterial wrapper

---
 bacterial_genreads_wrapper.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 16c8111..6894136 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -41,10 +41,15 @@ def analyze(self):
         print(self.file)
         true_path = pathlib.Path().absolute() / (self.name + "_read1.fa")
         unzip_file(self.file, true_path)
-        pathlib.Path.unlink(pathlib.Path().absolute() / (self.name + "_read1.fa.gz"))
+        pathlib.Path.unlink(pathlib.Path().absolute() / (self.name + "_read1.fa.gz")) # deletes unused zip file
         self.file = true_path
         # end workaround
 
+        # Now we further have to fix the fasta file, which outputs in a form that doesn't make much sense,
+        # so that it can be properly analyzed in the next generation by genreads.
+        # TODO see above comment
+
+
     def remove(self):
         pathlib.Path.unlink(self.file)
 

From 0403d7e193a127671e3ad69ecb8f219142ca1fff Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 18 Aug 2020 11:29:59 -0500
Subject: [PATCH 126/323] further work on the bacterial wrapper

---
 bacterial_genreads_wrapper.py | 19 ++++++++++++++++++-
 1 file changed, 18 insertions(+), 1 deletion(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 6894136..c2cbe90 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -47,7 +47,24 @@ def analyze(self):
 
         # Now we further have to fix the fasta file, which outputs in a form that doesn't make much sense,
         # so that it can be properly analyzed in the next generation by genreads.
-        # TODO see above comment
+        chromosome_name = ""
+        sequence = ""
+        with self.file.open() as f:
+            first_line = f.readline()
+            if first_line.startswith(">"):
+                chromosome_name = first_line
+            else:
+                print("Something went wrong with the fasta file")
+                exit(1)
+            for line in f:
+                if line.startswith(">"):
+                    continue
+                else:
+                    sequence = sequence + line
+            f.close()
+        # re-write file with just the chrom name and sequence
+        self.file.open('w').write(chromosome_name + sequence)
+
 
 
     def remove(self):

From 2439d0ae99d8f6bb39fa753c5c5632a03bdc02be Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 18 Aug 2020 16:38:12 -0500
Subject: [PATCH 127/323] further work on the bacterial wrapper

---
 bacterial_genreads_wrapper.py | 54 ++++++++++++++++++++++-------------
 1 file changed, 34 insertions(+), 20 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index c2cbe90..56b04ba 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -27,6 +27,11 @@ def get_file(self):
         return self.file
 
     def analyze(self):
+        """
+        This function is supposed to just run genreads for the bacterium, but doing so requires some file
+        manipulation to unzip the file and fix genreads horribly formatted fasta file.
+        :return: None
+        """
         args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa']
         genReads.main(args)
 
@@ -34,14 +39,12 @@ def analyze(self):
         # fasta but does not put the .gz extension on it. Also, genReads cannot handle gzipped
         # fasta files, so we further have to unzip it for it to actually work.
         self.file = pathlib.Path().absolute() / (self.name + "_read1.fa")
-        print(self.file)
         new_name = self.name + "_read1.fa.gz"
         self.file.rename(pathlib.Path(pathlib.Path().absolute(), new_name))
         self.file = pathlib.Path().absolute() / (self.name + "_read1.fa.gz")
-        print(self.file)
         true_path = pathlib.Path().absolute() / (self.name + "_read1.fa")
         unzip_file(self.file, true_path)
-        pathlib.Path.unlink(pathlib.Path().absolute() / (self.name + "_read1.fa.gz")) # deletes unused zip file
+        pathlib.Path.unlink(pathlib.Path().absolute() / (self.name + "_read1.fa.gz"))  # deletes unused zip file
         self.file = true_path
         # end workaround
 
@@ -65,13 +68,21 @@ def analyze(self):
         # re-write file with just the chrom name and sequence
         self.file.open('w').write(chromosome_name + sequence)
 
-
+    def sample(self):
+        args = ['-r', str(self.reference), '-R', '101', '-o', self.name]
+        genReads.main(args)
 
     def remove(self):
         pathlib.Path.unlink(self.file)
 
 
 def unzip_file(zipped_file: pathlib, unzipped_file: pathlib):
+    """
+    This unzips a gzipped file, then saves the unzipped file as a new file.
+    :param zipped_file: pathlib object that points to the zipped file
+    :param unzipped_file: pathlib object that points to the unzipped file
+    :return:
+    """
     with gzip.open(zipped_file, 'rb') as f_in:
         with open(unzipped_file, 'wb') as f_out:
             shutil.copyfileobj(f_in, f_out)
@@ -93,22 +104,13 @@ def cull(population: list, percentage: float = 0.5) -> list:
     return population
 
 
-def crossover(population: list) -> list:
-    """
-    This function will take a list of individuals and do crossover type mixing
-    :param population:
-    :return new_population:
-    """
-    new_population = population
-    return new_population
-
-
 def initialize_population(reference: str, pop_size) -> list:
     """
-    The purpose of this function is to evolve the bacteria
-    :param reference:
-    :param pop_size:
-    :return population:
+    The purpose of this function is to evolve the initial population of bacteria. All bacteria are stored as
+    Bacterium objects.
+    :param reference: string path to the reference fasta file
+    :param pop_size: size of the population to initialize.
+    :return population: returns a list of bacterium objects.
     """
     names = []
     for j in range(pop_size):
@@ -140,6 +142,16 @@ def evolve_population(population: list, generation: int) -> list:
     return new_population
 
 
+def sample_population(population: list):
+    """
+    This will create a fastq based on each member of the population.
+    :param population: a list of bacteria
+    :return: None
+    """
+    for bacterium in population:
+        bacterium.sample()
+
+
 def main():
     parser = argparse.ArgumentParser(description='bacterial_genreads_wrapper.py')
     parser.add_argument('-r', type=str, required=True, metavar='reference fasta',
@@ -159,14 +171,16 @@ def main():
     for i in range(generations):
         new_population = evolve_population(population, i+1)
 
-        new_population = crossover(new_population)
-
         new_population = cull(new_population, cull_percentage)
 
         # If all elements get culled, then break the loop
         if not new_population:
             break
 
+        population = new_population
+
+    sample_population(population)
+
 
 if __name__ == '__main__':
     main()

From a3b185042ed609ddf380cb985f97397a316d6418 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 20 Aug 2020 12:10:37 -0500
Subject: [PATCH 128/323] Updating genreads code to pep8

---
 genReads.py              | 456 ++++++++++++++++++++-------------------
 py/SequenceContainer.py  | 284 ++++++++++++------------
 py/output_file_writer.py |   8 +-
 py/vcfFunc.py            |  18 +-
 4 files changed, 385 insertions(+), 381 deletions(-)

diff --git a/genReads.py b/genReads.py
index 0cc265e..10a3df1 100644
--- a/genReads.py
+++ b/genReads.py
@@ -27,10 +27,10 @@
 
 from py.input_checking import required_field, check_file_open, is_in_range
 from py.ref_func import index_ref, read_ref, get_all_ref_regions, partition_ref_regions
-from py.vcfFunc import parseVCF
+from py.vcfFunc import parse_vcf
 from py.output_file_writer import OutputFileWriter
 from py.probability import DiscreteDistribution, mean_ind_of_weighted_list
-from py.SequenceContainer import SequenceContainer, ReadContainer, parseInputMutationModel
+from py.SequenceContainer import SequenceContainer, ReadContainer, parse_input_mutation_model
 
 """//////////////////////////////////////////////////
 ////////////    PARSE INPUT ARGUMENTS    ////////////
@@ -94,7 +94,6 @@ def main(raw_args=None):
     # if coverage val for a given window/position is below this value, consider it effectively zero.
     LOW_COV_THRESH = 50
 
-
     # required args
     (REFERENCE, READLEN, OUT_PREFIX) = (args.r, args.R, args.o)
     # various dataset parameters
@@ -105,7 +104,7 @@ def main(raw_args=None):
     (SAVE_BAM, SAVE_VCF, FASTA_INSTEAD, GZIPPED_OUT, NO_FASTQ) = \
         (args.bam, args.vcf, args.fa, args.gz, args.no_fastq)
 
-    ONLY_VCF = (NO_FASTQ and SAVE_VCF and not (SAVE_BAM))
+    ONLY_VCF = (NO_FASTQ and SAVE_VCF and not SAVE_BAM)
     if ONLY_VCF:
         print('Only producing VCF output, that should speed things up a bit...')
 
@@ -160,7 +159,7 @@ def main(raw_args=None):
 
     #	mutation models
     #
-    MUT_MODEL = parseInputMutationModel(MUT_MODEL, 1)
+    MUT_MODEL = parse_input_mutation_model(MUT_MODEL, 1)
     if MUT_RATE < 0.:
         MUT_RATE = None
 
@@ -191,12 +190,12 @@ def main(raw_args=None):
     #
     if PAIRED_END and not (PAIRED_END_ARTIFICIAL):
         print('Using empirical fragment length distribution.')
-        [potential_vals, potential_prob] = pickle.load(open(FRAGLEN_MODEL, 'rb'))
+        [potential_values, potential_prob] = pickle.load(open(FRAGLEN_MODEL, 'rb'))
         FRAGLEN_VALS = []
         FRAGLEN_PROB = []
-        for i in range(len(potential_vals)):
-            if potential_vals[i] > READLEN:
-                FRAGLEN_VALS.append(potential_vals[i])
+        for i in range(len(potential_values)):
+            if potential_values[i] > READLEN:
+                FRAGLEN_VALS.append(potential_values[i])
                 FRAGLEN_PROB.append(potential_prob[i])
         # should probably add some validation and sanity-checking code here...
         FRAGLEN_DISTRIBUTION = DiscreteDistribution(FRAGLEN_PROB, FRAGLEN_VALS)
@@ -225,16 +224,18 @@ def main(raw_args=None):
 
     # fragment length distribution: normal distribution that goes out to +- 6 standard deviations
     if PAIRED_END and PAIRED_END_ARTIFICIAL:
-        print('Using artificial fragment length distribution. mean=' + str(FRAGMENT_SIZE) + ', std=' + str(FRAGMENT_STD))
+        print(
+            'Using artificial fragment length distribution. mean=' + str(FRAGMENT_SIZE) + ', std=' + str(FRAGMENT_STD))
         if FRAGMENT_STD == 0:
             FRAGLEN_DISTRIBUTION = DiscreteDistribution([1], [FRAGMENT_SIZE], degenerate_val=FRAGMENT_SIZE)
         else:
-            potential_vals = range(FRAGMENT_SIZE - 6 * FRAGMENT_STD, FRAGMENT_SIZE + 6 * FRAGMENT_STD + 1)
+            potential_values = range(FRAGMENT_SIZE - 6 * FRAGMENT_STD, FRAGMENT_SIZE + 6 * FRAGMENT_STD + 1)
             FRAGLEN_VALS = []
-            for i in range(len(potential_vals)):
-                if potential_vals[i] > READLEN:
-                    FRAGLEN_VALS.append(potential_vals[i])
-            FRAGLEN_PROB = [np.exp(-(((n - float(FRAGMENT_SIZE)) ** 2) / (2 * (FRAGMENT_STD ** 2)))) for n in FRAGLEN_VALS]
+            for i in range(len(potential_values)):
+                if potential_values[i] > READLEN:
+                    FRAGLEN_VALS.append(potential_values[i])
+            FRAGLEN_PROB = [np.exp(-(((n - float(FRAGMENT_SIZE)) ** 2) / (2 * (FRAGMENT_STD ** 2)))) for n in
+                            FRAGLEN_VALS]
             FRAGLEN_DISTRIBUTION = DiscreteDistribution(FRAGLEN_PROB, FRAGLEN_VALS)
 
     """************************************************
@@ -262,90 +263,90 @@ def main(raw_args=None):
 
     ALLOWED_NUCL = ['A', 'C', 'G', 'T']
     # index reference
-    refIndex = index_ref(REFERENCE)
+    ref_index = index_ref(REFERENCE)
     if PAIRED_END:
         N_HANDLING = ('random', FRAGMENT_SIZE)
     else:
         N_HANDLING = ('ignore', READLEN)
-    indices_by_refName = {refIndex[n][0]: n for n in range(len(refIndex))}
+    indices_by_ref_name = {ref_index[n][0]: n for n in range(len(ref_index))}
 
     # parse input variants, if present
-    inputVariants = []
+    input_variants = []
     if INPUT_VCF is not None:
-        (sampNames, inputVariants) = parseVCF(INPUT_VCF, ploidy=PLOIDS)
-        for k in sorted(inputVariants.keys()):
-            inputVariants[k].sort()
+        (sampNames, input_variants) = parse_vcf(INPUT_VCF, ploidy=PLOIDS)
+        for k in sorted(input_variants.keys()):
+            input_variants[k].sort()
 
     # parse input targeted regions, if present
-    inputRegions = {}
-    refList = [n[0] for n in refIndex]
+    input_regions = {}
+    ref_list = [n[0] for n in ref_index]
     if INPUT_BED is not None:
         with open(INPUT_BED, 'r') as f:
             for line in f:
-                [myChr, pos1, pos2] = line.strip().split('\t')[:3]
-                if myChr not in inputRegions:
-                    inputRegions[myChr] = [-1]
-                inputRegions[myChr].extend([int(pos1), int(pos2)])
+                [my_chr, pos1, pos2] = line.strip().split('\t')[:3]
+                if my_chr not in input_regions:
+                    input_regions[my_chr] = [-1]
+                input_regions[my_chr].extend([int(pos1), int(pos2)])
         # some validation
-        nInBedOnly = 0
-        nInRefOnly = 0
-        for k in refList:
-            if k not in inputRegions:
-                nInRefOnly += 1
-        for k in inputRegions.keys():
-            if not k in refList:
-                nInBedOnly += 1
-                del inputRegions[k]
-        if nInRefOnly > 0:
+        n_in_bed_only = 0
+        n_in_ref_only = 0
+        for k in ref_list:
+            if k not in input_regions:
+                n_in_ref_only += 1
+        for k in input_regions.keys():
+            if not k in ref_list:
+                n_in_bed_only += 1
+                del input_regions[k]
+        if n_in_ref_only > 0:
             print('Warning: Reference contains sequences not found in targeted regions BED file.')
-        if nInBedOnly > 0:
+        if n_in_bed_only > 0:
             print(
                 'Warning: Targeted regions BED file contains sequence names not found in reference (regions ignored).')
 
     # parse input mutation rate rescaling regions, if present
-    mutRateRegions = {}
-    mutRateValues = {}
+    mut_rate_regions = {}
+    mut_rate_values = {}
     if MUT_BED is not None:
         with open(MUT_BED, 'r') as f:
             for line in f:
-                [myChr, pos1, pos2, metaData] = line.strip().split('\t')[:4]
-                mutStr = re.findall(r"MUT_RATE=.*?(?=;)", metaData + ';')
+                [my_chr, pos1, pos2, meta_data] = line.strip().split('\t')[:4]
+                mut_str = re.findall(r"MUT_RATE=.*?(?=;)", meta_data + ';')
                 (pos1, pos2) = (int(pos1), int(pos2))
-                if len(mutStr) and (pos2 - pos1) > 1:
-                    # mutRate = #_mutations / length_of_region, let's bound it by a reasonable amount
-                    mutRate = max([0.0, min([float(mutStr[0][9:]), 0.3])])
-                    if myChr not in mutRateRegions:
-                        mutRateRegions[myChr] = [-1]
-                        mutRateValues[myChr] = [0.0]
-                    mutRateRegions[myChr].extend([pos1, pos2])
-                    mutRateValues.extend([mutRate * (pos2 - pos1)] * 2)
+                if len(mut_str) and (pos2 - pos1) > 1:
+                    # mut_rate = #_mutations / length_of_region, let's bound it by a reasonable amount
+                    mut_rate = max([0.0, min([float(mut_str[0][9:]), 0.3])])
+                    if my_chr not in mut_rate_regions:
+                        mut_rate_regions[my_chr] = [-1]
+                        mut_rate_values[my_chr] = [0.0]
+                    mut_rate_regions[my_chr].extend([pos1, pos2])
+                    mut_rate_values.extend([mut_rate * (pos2 - pos1)] * 2)
 
     # initialize output files (part I)
-    bamHeader = None
+    bam_header = None
     if SAVE_BAM:
-        bamHeader = [copy.deepcopy(refIndex)]
-    vcfHeader = None
+        bam_header = [copy.deepcopy(ref_index)]
+    vcf_header = None
     if SAVE_VCF:
-        vcfHeader = [REFERENCE]
+        vcf_header = [REFERENCE]
 
     # If processing jobs in parallel, precompute the independent regions that can be process separately
     if NJOBS > 1:
-        parallelRegionList = get_all_ref_regions(REFERENCE, refIndex, N_HANDLING, save_output=SAVE_NON_N)
-        (myRefs, myRegions) = partition_ref_regions(parallelRegionList, refIndex, MYJOB, NJOBS)
+        parallel_region_list = get_all_ref_regions(REFERENCE, ref_index, N_HANDLING, save_output=SAVE_NON_N)
+        (myRefs, myRegions) = partition_ref_regions(parallel_region_list, ref_index, MYJOB, NJOBS)
         if not len(myRegions):
             print('This job id has no regions to process, exiting...')
             exit(1)
-        for i in range(len(refIndex) - 1, -1, -1):  # delete reference not used in our job
-            if not refIndex[i][0] in myRefs:
-                del refIndex[i]
+        for i in range(len(ref_index) - 1, -1, -1):  # delete reference not used in our job
+            if not ref_index[i][0] in myRefs:
+                del ref_index[i]
         # if value of NJOBS is too high, let's change it to the maximum possible, to avoid output filename confusion
-        corrected_nJobs = min([NJOBS, sum([len(n) for n in parallelRegionList.values()])])
+        corrected_n_jobs = min([NJOBS, sum([len(n) for n in parallel_region_list.values()])])
     else:
-        corrected_nJobs = 1
+        corrected_n_jobs = 1
 
     # initialize output files (part II)
-    OFW = OutputFileWriter(OUT_PREFIX, paired=PAIRED_END, bam_header=bamHeader, vcf_header=vcfHeader,
-                           gzipped=GZIPPED_OUT, job_tuple=(MYJOB, corrected_nJobs), no_fastq=NO_FASTQ,
+    OFW = OutputFileWriter(OUT_PREFIX, paired=PAIRED_END, bam_header=bam_header, vcf_header=vcf_header,
+                           gzipped=GZIPPED_OUT, job_tuple=(MYJOB, corrected_n_jobs), no_fastq=NO_FASTQ,
                            fasta_instead=FASTA_INSTEAD)
     OUT_PREFIX_NAME = OUT_PREFIX.split('/')[-1]
 
@@ -353,55 +354,55 @@ def main(raw_args=None):
 	****        LET'S GET THIS PARTY STARTED...
 	************************************************"""
 
-    readNameCount = 1  # keep track of the number of reads we've sampled, for read-names
+    read_name_count = 1  # keep track of the number of reads we've sampled, for read-names
     unmapped_records = []
 
-    for RI in range(len(refIndex)):
+    for RI in range(len(ref_index)):
 
         # read in reference sequence and notate blocks of Ns
-        (refSequence, N_regions) = read_ref(REFERENCE, refIndex[RI], N_HANDLING)
+        (refSequence, n_regions) = read_ref(REFERENCE, ref_index[RI], N_HANDLING)
 
         # if we're processing jobs in parallel only take the regions relevant for the current job
         if NJOBS > 1:
-            for i in range(len(N_regions['non_N']) - 1, -1, -1):
-                if not (refIndex[RI][0], N_regions['non_N'][i][0], N_regions['non_N'][i][1]) in myRegions:
-                    del N_regions['non_N'][i]
+            for i in range(len(n_regions['non_N']) - 1, -1, -1):
+                if not (ref_index[RI][0], n_regions['non_N'][i][0], n_regions['non_N'][i][1]) in myRegions:
+                    del n_regions['non_N'][i]
 
         # count total bp we'll be spanning so we can get an idea of how far along we are (for printing progress indicators)
-        total_bp_span = sum([n[1] - n[0] for n in N_regions['non_N']])
-        currentProgress = 0
-        currentPercent = 0
-        havePrinted100 = False
+        total_bp_span = sum([n[1] - n[0] for n in n_regions['non_N']])
+        current_progress = 0
+        current_percent = 0
+        have_printed100 = False
 
         # prune invalid input variants, e.g variants that:
         #		- try to delete or alter any N characters
         #		- don't match the reference base at their specified position
         #		- any alt allele contains anything other than allowed characters
-        validVariants = []
-        nSkipped = [0, 0, 0]
-        if refIndex[RI][0] in inputVariants:
-            for n in inputVariants[refIndex[RI][0]]:
+        valid_variants = []
+        n_skipped = [0, 0, 0]
+        if ref_index[RI][0] in input_variants:
+            for n in input_variants[ref_index[RI][0]]:
                 span = (n[0], n[0] + len(n[1]))
-                rseq = str(refSequence[span[0] - 1:span[1] - 1])  # -1 because going from VCF coords to array coords
-                anyBadChr = any((nn not in ALLOWED_NUCL) for nn in [item for sublist in n[2] for item in sublist])
-                if rseq != n[1]:
-                    nSkipped[0] += 1
+                r_seq = str(refSequence[span[0] - 1:span[1] - 1])  # -1 because going from VCF coords to array coords
+                any_bad_chr = any((nn not in ALLOWED_NUCL) for nn in [item for sublist in n[2] for item in sublist])
+                if r_seq != n[1]:
+                    n_skipped[0] += 1
                     continue
-                elif 'N' in rseq:
-                    nSkipped[1] += 1
+                elif 'N' in r_seq:
+                    n_skipped[1] += 1
                     continue
-                elif anyBadChr:
-                    nSkipped[2] += 1
+                elif any_bad_chr:
+                    n_skipped[2] += 1
                     continue
-                # if bisect.bisect(N_regions['big'],span[0])%2 or bisect.bisect(N_regions['big'],span[1])%2:
-                #	continue
-                validVariants.append(n)
-            print('found', len(validVariants), 'valid variants for ' + refIndex[RI][0] + ' in input VCF...')
-            if any(nSkipped):
-                print(sum(nSkipped), 'variants skipped...')
-                print(' - [' + str(nSkipped[0]) + '] ref allele does not match reference')
-                print(' - [' + str(nSkipped[1]) + '] attempting to insert into N-region')
-                print(' - [' + str(nSkipped[2]) + '] alt allele contains non-ACGT characters')
+                # if bisect.bisect(n_regions['big'],span[0])%2 or bisect.bisect(n_regions['big'],span[1])%2:
+                # continue
+                valid_variants.append(n)
+            print('found', len(valid_variants), 'valid variants for ' + ref_index[RI][0] + ' in input VCF...')
+            if any(n_skipped):
+                print(sum(n_skipped), 'variants skipped...')
+                print(' - [' + str(n_skipped[0]) + '] ref allele does not match reference')
+                print(' - [' + str(n_skipped[1]) + '] attempting to insert into N-region')
+                print(' - [' + str(n_skipped[2]) + '] alt allele contains non-ACGT characters')
 
         # add large random structural variants
         #
@@ -413,17 +414,17 @@ def main(raw_args=None):
         #		- FRAGMENT_SIZE (mean), if paired-end reads
         # ploidy is fixed per large sampling window,
         # coverage distributions due to GC% and targeted regions are specified within these windows
-        samplingWindows = []
+        sampling_windows = []
         ALL_VARIANTS_OUT = {}
         sequences = None
         if PAIRED_END:
-            targSize = WINDOW_TARGET_SCALE * FRAGMENT_SIZE
+            target_size = WINDOW_TARGET_SCALE * FRAGMENT_SIZE
             overlap = FRAGMENT_SIZE
-            overlap_minWindowSize = max(FRAGLEN_DISTRIBUTION.values) + 10
+            overlap_min_window_size = max(FRAGLEN_DISTRIBUTION.values) + 10
         else:
-            targSize = WINDOW_TARGET_SCALE * READLEN
+            target_size = WINDOW_TARGET_SCALE * READLEN
             overlap = READLEN
-            overlap_minWindowSize = READLEN + 10
+            overlap_min_window_size = READLEN + 10
 
         print('--------------------------------')
         if ONLY_VCF:
@@ -432,56 +433,56 @@ def main(raw_args=None):
             print('sampling reads...')
         tt = time.time()
 
-        for i in range(len(N_regions['non_N'])):
-            (pi, pf) = N_regions['non_N'][i]
-            nTargWindows = max([1, (pf - pi) // targSize])
-            bpd = int((pf - pi) / float(nTargWindows))
+        for i in range(len(n_regions['non_N'])):
+            (pi, pf) = n_regions['non_N'][i]
+            n_target_windows = max([1, (pf - pi) // target_size])
+            bpd = int((pf - pi) / float(n_target_windows))
             # bpd += GC_WINDOW_SIZE - bpd%GC_WINDOW_SIZE
 
-            # print len(refSequence), (pi,pf), nTargWindows
-            # print structuralVars
+            # print len(refSequence), (pi,pf), n_target_windows
+            # print structural_vars
 
             # if for some reason our region is too small to process, skip it! (sorry)
-            if nTargWindows == 1 and (pf - pi) < overlap_minWindowSize:
+            if n_target_windows == 1 and (pf - pi) < overlap_min_window_size:
                 # print 'Does this ever happen?'
                 continue
 
             start = pi
             end = min([start + bpd, pf])
-            # print '------------------RAWR:', (pi,pf), nTargWindows, bpd
-            varsFromPrevOverlap = []
-            vindFromPrev = 0
-            isLastTime = False
-            havePrinted100 = False
+            # print '------------------RAWR:', (pi,pf), n_target_windows, bpd
+            vars_from_prev_overlap = []
+            vind_from_prev = 0
+            is_last_time = False
+            have_printed100 = False
 
             while True:
 
                 # which inserted variants are in this window?
-                varsInWindow = []
+                vars_in_window = []
                 updated = False
-                for j in range(vindFromPrev, len(validVariants)):
-                    vPos = validVariants[j][0]
-                    if vPos > start and vPos < end:  # update: changed >= to >, so variant cannot be inserted in first position
-                        varsInWindow.append(tuple([vPos - 1] + list(validVariants[j][1:])))  # vcf --> array coords
-                    if vPos >= end - overlap - 1 and updated == False:
+                for j in range(vind_from_prev, len(valid_variants)):
+                    v_pos = valid_variants[j][0]
+                    if start < v_pos < end:  # update: changed >= to >, so variant cannot be inserted in first position
+                        vars_in_window.append(tuple([v_pos - 1] + list(valid_variants[j][1:])))  # vcf --> array coords
+                    if v_pos >= end - overlap - 1 and updated is False:
                         updated = True
-                        vindFromPrev = j
-                    if vPos >= end:
+                        vind_from_prev = j
+                    if v_pos >= end:
                         break
 
                 # determine which structural variants will affect our sampling window positions
-                structuralVars = []
-                for n in varsInWindow:
-                    bufferNeeded = max([max([abs(len(n[1]) - len(alt_allele)), 1]) for alt_allele in
+                structural_vars = []
+                for n in vars_in_window:
+                    buffer_needed = max([max([abs(len(n[1]) - len(alt_allele)), 1]) for alt_allele in
                                         n[2]])  # change: added abs() so that insertions are also buffered.
-                    structuralVars.append((n[0] - 1, bufferNeeded))  # -1 because going from VCF coords to array coords
+                    structural_vars.append((n[0] - 1, buffer_needed))  # -1 because going from VCF coords to array coords
 
                 # adjust end-position of window based on inserted structural mutations
                 buffer_added = 0
-                keepGoing = True
-                while keepGoing:
-                    keepGoing = False
-                    for n in structuralVars:
+                keep_going = True
+                while keep_going:
+                    keep_going = False
+                    for n in structural_vars:
                         # adding "overlap" here to prevent SVs from being introduced in overlap regions
                         # (which can cause problems if random mutations from the previous window land on top of them)
                         delta = (end - 1) - (n[0] + n[1]) - 2 - overlap
@@ -490,25 +491,25 @@ def main(raw_args=None):
                             buffer_added = -delta
                             end += buffer_added
                             ####print end
-                            keepGoing = True
+                            keep_going = True
                             break
                 next_start = end - overlap
                 next_end = min([next_start + bpd, pf])
                 if next_end - next_start < bpd:
                     end = next_end
-                    isLastTime = True
+                    is_last_time = True
 
                 # print progress indicator
                 ####print 'PROCESSING WINDOW:',(start,end), [buffer_added], 'next:', (next_start,next_end)
-                currentProgress += end - start
-                newPercent = int((currentProgress * 100) / float(total_bp_span))
-                if newPercent > currentPercent:
-                    if newPercent <= 99 or (newPercent == 100 and not havePrinted100):
-                        sys.stdout.write(str(newPercent) + '% ')
+                current_progress += end - start
+                new_percent = int((current_progress * 100) / float(total_bp_span))
+                if new_percent > current_percent:
+                    if new_percent <= 99 or (new_percent == 100 and not have_printed100):
+                        sys.stdout.write(str(new_percent) + '% ')
                         sys.stdout.flush()
-                    currentPercent = newPercent
-                    if currentPercent == 100:
-                        havePrinted100 = True
+                    current_percent = new_percent
+                    if current_percent == 100:
+                        have_printed100 = True
 
                 skip_this_window = False
 
@@ -518,11 +519,11 @@ def main(raw_args=None):
                 if INPUT_BED is None:
                     coverage_dat[2] = [1.0] * (end - start)
                 else:
-                    if refIndex[RI][0] not in inputRegions:
+                    if ref_index[RI][0] not in input_regions:
                         coverage_dat[2] = [OFFTARGET_SCALAR] * (end - start)
                     else:
                         for j in range(start, end):
-                            if not (bisect.bisect(inputRegions[refIndex[RI][0]], j) % 2):
+                            if not (bisect.bisect(input_regions[ref_index[RI][0]], j) % 2):
                                 coverage_dat[2].append(1.0)
                             else:
                                 coverage_dat[2].append(OFFTARGET_SCALAR)
@@ -533,18 +534,18 @@ def main(raw_args=None):
                     skip_this_window = True
 
                 # check for small window sizes
-                if (end - start) < overlap_minWindowSize:
+                if (end - start) < overlap_min_window_size:
                     skip_this_window = True
 
                 if skip_this_window:
                     # skip window, save cpu time
                     start = next_start
                     end = next_end
-                    if isLastTime:
+                    if is_last_time:
                         break
                     if end >= pf:
-                        isLastTime = True
-                    varsFromPrevOverlap = []
+                        is_last_time = True
+                    vars_from_prev_overlap = []
                     continue
 
                 # construct sequence data that we will sample reads from
@@ -556,7 +557,7 @@ def main(raw_args=None):
                                      MUT_RATE)
 
                 # insert variants
-                sequences.insert_mutations(varsFromPrevOverlap + varsInWindow)
+                sequences.insert_mutations(vars_from_prev_overlap + vars_in_window)
                 all_inserted_variants = sequences.random_mutations()
                 # print all_inserted_variants
 
@@ -568,121 +569,123 @@ def main(raw_args=None):
                         coverage_avg = sequences.init_coverage(tuple(coverage_dat))
 
                 # which variants do we need to keep for next time (because of window overlap)?
-                varsFromPrevOverlap = []
+                vars_from_prev_overlap = []
                 for n in all_inserted_variants:
                     if n[0] >= end - overlap - 1:
-                        varsFromPrevOverlap.append(n)
+                        vars_from_prev_overlap.append(n)
 
                 # if we're only producing VCF, no need to go through the hassle of generating reads
                 if ONLY_VCF:
                     pass
                 else:
                     if PAIRED_END:
-                        readsToSample = int(((end - start) * float(COVERAGE) * coverage_avg) / (2 * READLEN)) + 1
+                        reads_to_sample = int(((end - start) * float(COVERAGE) * coverage_avg) / (2 * READLEN)) + 1
                     else:
-                        readsToSample = int(((end - start) * float(COVERAGE) * coverage_avg) / (READLEN)) + 1
+                        reads_to_sample = int(((end - start) * float(COVERAGE) * coverage_avg) / (READLEN)) + 1
 
                     # if coverage is so low such that no reads are to be sampled, skip region
                     #      (i.e., remove buffer of +1 reads we add to every window)
-                    if readsToSample == 1 and sum(coverage_dat[2]) < LOW_COV_THRESH:
-                        readsToSample = 0
+                    if reads_to_sample == 1 and sum(coverage_dat[2]) < LOW_COV_THRESH:
+                        reads_to_sample = 0
 
                     # sample reads
                     ASDF2_TT = time.time()
-                    for i in range(readsToSample):
+                    for i in range(reads_to_sample):
 
-                        isUnmapped = []
+                        is_unmapped = []
                         if PAIRED_END:
-                            myFraglen = FRAGLEN_DISTRIBUTION.sample()
-                            myReadData = sequences.sample_read(SE_CLASS, myFraglen)
-                            if myReadData is None:  # skip if we failed to find a valid position to sample read
+                            my_fraglen = FRAGLEN_DISTRIBUTION.sample()
+                            my_read_data = sequences.sample_read(SE_CLASS, my_fraglen)
+                            if my_read_data is None:  # skip if we failed to find a valid position to sample read
                                 continue
-                            if myReadData[0][0] is None:
-                                isUnmapped.append(True)
+                            if my_read_data[0][0] is None:
+                                is_unmapped.append(True)
                             else:
-                                isUnmapped.append(False)
-                                myReadData[0][0] += start  # adjust mapping position based on window start
-                            if myReadData[1][0] is None:
-                                isUnmapped.append(True)
+                                is_unmapped.append(False)
+                                my_read_data[0][0] += start  # adjust mapping position based on window start
+                            if my_read_data[1][0] is None:
+                                is_unmapped.append(True)
                             else:
-                                isUnmapped.append(False)
-                                myReadData[1][0] += start
+                                is_unmapped.append(False)
+                                my_read_data[1][0] += start
                         else:
-                            myReadData = sequences.sample_read(SE_CLASS)
-                            if myReadData is None:  # skip if we failed to find a valid position to sample read
+                            my_read_data = sequences.sample_read(SE_CLASS)
+                            if my_read_data is None:  # skip if we failed to find a valid position to sample read
                                 continue
-                            if myReadData[0][0] is None:  # unmapped read (lives in large insertion)
-                                isUnmapped = [True]
+                            if my_read_data[0][0] is None:  # unmapped read (lives in large insertion)
+                                is_unmapped = [True]
                             else:
-                                isUnmapped = [False]
-                                myReadData[0][0] += start  # adjust mapping position based on window start
+                                is_unmapped = [False]
+                                my_read_data[0][0] += start  # adjust mapping position based on window start
 
                         if NJOBS > 1:
-                            myReadName = OUT_PREFIX_NAME + '-j' + str(MYJOB) + '-' + refIndex[RI][0] + '-r' + str(
-                                readNameCount)
+                            my_read_name = OUT_PREFIX_NAME + '-j' + str(MYJOB) + '-' + ref_index[RI][0] + '-r' + str(
+                                read_name_count)
                         else:
-                            myReadName = OUT_PREFIX_NAME + '-' + refIndex[RI][0] + '-' + str(readNameCount)
-                        readNameCount += len(myReadData)
+                            my_read_name = OUT_PREFIX_NAME + '-' + ref_index[RI][0] + '-' + str(read_name_count)
+                        read_name_count += len(my_read_data)
 
                         # if desired, replace all low-quality bases with Ns
                         if N_MAX_QUAL > -1:
-                            for j in range(len(myReadData)):
-                                myReadString = [n for n in myReadData[j][2]]
-                                for k in range(len(myReadData[j][3])):
-                                    adjusted_qual = ord(myReadData[j][3][k]) - SE_CLASS.offQ
+                            for j in range(len(my_read_data)):
+                                my_read_string = [n for n in my_read_data[j][2]]
+                                for k in range(len(my_read_data[j][3])):
+                                    adjusted_qual = ord(my_read_data[j][3][k]) - SE_CLASS.offQ
                                     if adjusted_qual <= N_MAX_QUAL:
-                                        myReadString[k] = 'N'
-                                myReadData[j][2] = ''.join(myReadString)
+                                        my_read_string[k] = 'N'
+                                my_read_data[j][2] = ''.join(my_read_string)
 
                         # if read (or read + mate for PE) are unmapped, put them at end of bam file
-                        if all(isUnmapped):
+                        if all(is_unmapped):
                             if PAIRED_END:
-                                unmapped_records.append((myReadName + '/1', myReadData[0], 109))
-                                unmapped_records.append((myReadName + '/2', myReadData[1], 157))
+                                unmapped_records.append((my_read_name + '/1', my_read_data[0], 109))
+                                unmapped_records.append((my_read_name + '/2', my_read_data[1], 157))
                             else:
-                                unmapped_records.append((myReadName + '/1', myReadData[0], 4))
+                                unmapped_records.append((my_read_name + '/1', my_read_data[0], 4))
 
                         # write read data out to FASTQ and BAM files, bypass FASTQ if option specified
-                        myRefIndex = indices_by_refName[refIndex[RI][0]]
-                        if len(myReadData) == 1:
-                            if NO_FASTQ != True:
-                                OFW.write_fastq_record(myReadName, myReadData[0][2], myReadData[0][3])
+                        myRefIndex = indices_by_ref_name[ref_index[RI][0]]
+                        if len(my_read_data) == 1:
+                            if NO_FASTQ is not True:
+                                OFW.write_fastq_record(my_read_name, my_read_data[0][2], my_read_data[0][3])
                             if SAVE_BAM:
-                                if isUnmapped[0] == False:
-                                    OFW.write_bam_record(myRefIndex, myReadName + '/1', myReadData[0][0],
-                                                         myReadData[0][1], myReadData[0][2], myReadData[0][3], sam_flag=0)
-                        elif len(myReadData) == 2:
-                            if NO_FASTQ != True:
-                                OFW.write_fastq_record(myReadName, myReadData[0][2], myReadData[0][3],
-                                                       read2=myReadData[1][2], qual2=myReadData[1][3])
+                                if is_unmapped[0] is False:
+                                    OFW.write_bam_record(myRefIndex, my_read_name + '/1', my_read_data[0][0],
+                                                         my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
+                                                         sam_flag=0)
+                        elif len(my_read_data) == 2:
+                            if NO_FASTQ is not True:
+                                OFW.write_fastq_record(my_read_name, my_read_data[0][2], my_read_data[0][3],
+                                                       read2=my_read_data[1][2], qual2=my_read_data[1][3])
                             if SAVE_BAM:
-                                if isUnmapped[0] == False and isUnmapped[1] == False:
-                                    OFW.write_bam_record(myRefIndex, myReadName + '/1', myReadData[0][0],
-                                                         myReadData[0][1], myReadData[0][2], myReadData[0][3], sam_flag=99,
-                                                         mate_pos=myReadData[1][0])
-                                    OFW.write_bam_record(myRefIndex, myReadName + '/2', myReadData[1][0],
-                                                         myReadData[1][1], myReadData[1][2], myReadData[1][3],
-                                                         sam_flag=147, mate_pos=myReadData[0][0])
-                                elif isUnmapped[0] == False and isUnmapped[1] == True:
-                                    OFW.write_bam_record(myRefIndex, myReadName + '/1', myReadData[0][0],
-                                                         myReadData[0][1], myReadData[0][2], myReadData[0][3],
-                                                         sam_flag=105, mate_pos=myReadData[0][0])
-                                    OFW.write_bam_record(myRefIndex, myReadName + '/2', myReadData[0][0],
-                                                         myReadData[1][1], myReadData[1][2], myReadData[1][3],
-                                                         sam_flag=149, mate_pos=myReadData[0][0], aln_map_qual=0)
-                                elif isUnmapped[0] == True and isUnmapped[1] == False:
-                                    OFW.write_bam_record(myRefIndex, myReadName + '/1', myReadData[1][0],
-                                                         myReadData[0][1], myReadData[0][2], myReadData[0][3],
-                                                         sam_flag=101, mate_pos=myReadData[1][0], aln_map_qual=0)
-                                    OFW.write_bam_record(myRefIndex, myReadName + '/2', myReadData[1][0],
-                                                         myReadData[1][1], myReadData[1][2], myReadData[1][3],
-                                                         sam_flag=153, mate_pos=myReadData[1][0])
+                                if is_unmapped[0] is False and is_unmapped[1] is False:
+                                    OFW.write_bam_record(myRefIndex, my_read_name + '/1', my_read_data[0][0],
+                                                         my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
+                                                         sam_flag=99,
+                                                         mate_pos=my_read_data[1][0])
+                                    OFW.write_bam_record(myRefIndex, my_read_name + '/2', my_read_data[1][0],
+                                                         my_read_data[1][1], my_read_data[1][2], my_read_data[1][3],
+                                                         sam_flag=147, mate_pos=my_read_data[0][0])
+                                elif is_unmapped[0] == False and is_unmapped[1] == True:
+                                    OFW.write_bam_record(myRefIndex, my_read_name + '/1', my_read_data[0][0],
+                                                         my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
+                                                         sam_flag=105, mate_pos=my_read_data[0][0])
+                                    OFW.write_bam_record(myRefIndex, my_read_name + '/2', my_read_data[0][0],
+                                                         my_read_data[1][1], my_read_data[1][2], my_read_data[1][3],
+                                                         sam_flag=149, mate_pos=my_read_data[0][0], aln_map_quality=0)
+                                elif is_unmapped[0] == True and is_unmapped[1] == False:
+                                    OFW.write_bam_record(myRefIndex, my_read_name + '/1', my_read_data[1][0],
+                                                         my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
+                                                         sam_flag=101, mate_pos=my_read_data[1][0], aln_map_quality=0)
+                                    OFW.write_bam_record(myRefIndex, my_read_name + '/2', my_read_data[1][0],
+                                                         my_read_data[1][1], my_read_data[1][2], my_read_data[1][3],
+                                                         sam_flag=153, mate_pos=my_read_data[1][0])
                         else:
                             print('\nError: Unexpected number of reads generated...\n')
                             exit(1)
                     # print 'READS:',time.time()-ASDF2_TT
 
-                    if not isLastTime:
+                    if not is_last_time:
                         OFW.flush_buffers(bam_max=next_start)
                     else:
                         OFW.flush_buffers(bam_max=end + 1)
@@ -694,12 +697,12 @@ def main(raw_args=None):
                 # prepare indices of next window
                 start = next_start
                 end = next_end
-                if isLastTime:
+                if is_last_time:
                     break
                 if end >= pf:
-                    isLastTime = True
+                    is_last_time = True
 
-        if currentPercent != 100 and not havePrinted100:
+        if current_percent != 100 and not have_printed100:
             print('100%')
         else:
             print('')
@@ -713,12 +716,12 @@ def main(raw_args=None):
         if SAVE_VCF:
             print('Writing output VCF...')
             for k in sorted(ALL_VARIANTS_OUT.keys()):
-                currentRef = refIndex[RI][0]
-                myID = '.'
-                myQual = '.'
-                myFilt = 'PASS'
+                current_ref = ref_index[RI][0]
+                my_id = '.'
+                my_quality = '.'
+                my_filter = 'PASS'
                 # k[0] + 1 because we're going back to 1-based vcf coords
-                OFW.write_vcf_record(currentRef, str(int(k[0]) + 1), myID, k[1], k[2], myQual, myFilt, k[4])
+                OFW.write_vcf_record(current_ref, str(int(k[0]) + 1), my_id, k[1], k[2], my_quality, my_filter, k[4])
 
     # break
 
@@ -728,12 +731,13 @@ def main(raw_args=None):
         for umr in unmapped_records:
             if PAIRED_END:
                 OFW.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], sam_flag=umr[2], mate_pos=0,
-                                     aln_map_qual=0)
+                                     aln_map_quality=0)
             else:
-                OFW.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], sam_flag=umr[2], aln_map_qual=0)
+                OFW.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], sam_flag=umr[2], aln_map_quality=0)
 
     # close output files
     OFW.close_files()
 
+
 if __name__ == '__main__':
     main()
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index bfa4bc7..0b95005 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -525,7 +525,7 @@ def sample_read(self, sequencingModel, frag_len=None):
 
             # sample read position and call function to compute quality scores / sequencing errors
             r_dat = self.sequences[my_ploid][r_pos:r_pos + self.read_len]
-            (myQual, myErrors) = sequencingModel.getSequencingErrors(r_dat)
+            (myQual, myErrors) = sequencingModel.get_sequencing_errors(r_dat)
             readsToSample.append([r_pos, myQual, myErrors, r_dat])
 
         else:
@@ -538,8 +538,8 @@ def sample_read(self, sequencingModel, frag_len=None):
             r_pos2 = r_pos1 + frag_len - self.read_len
             r_dat1 = self.sequences[my_ploid][r_pos1:r_pos1 + self.read_len]
             r_dat2 = self.sequences[my_ploid][r_pos2:r_pos2 + self.read_len]
-            (myQual1, myErrors1) = sequencingModel.getSequencingErrors(r_dat1)
-            (myQual2, myErrors2) = sequencingModel.getSequencingErrors(r_dat2, isReverseStrand=True)
+            (myQual1, myErrors1) = sequencingModel.get_sequencing_errors(r_dat1)
+            (myQual2, myErrors2) = sequencingModel.get_sequencing_errors(r_dat2, is_reverse_strand=True)
             readsToSample.append([r_pos1, myQual1, myErrors1, r_dat1])
             readsToSample.append([r_pos2, myQual2, myErrors2, r_dat2])
 
@@ -671,7 +671,7 @@ def sample_read(self, sequencingModel, frag_len=None):
 #	Container for read data, computes quality scores and positions to insert errors
 #
 class ReadContainer:
-    def __init__(self, read_len, errorModel, reScaledError):
+    def __init__(self, read_len, errorModel, rescaled_error):
 
         self.read_len = read_len
 
@@ -679,64 +679,64 @@ def __init__(self, read_len, errorModel, reScaledError):
         self.UNIFORM = False
         if len(error_dat) == 4:  # uniform-error SE reads (e.g. PacBio)
             self.UNIFORM = True
-            [Qscores, offQ, avgError, errorParams] = error_dat
-            self.uniform_qscore = int(-10. * np.log10(avgError) + 0.5)
-            print('Using uniform sequencing error model. (q=' + str(self.uniform_qscore) + '+' + str(
-                offQ) + ', p(err)={0:0.2f}%)'.format(100. * avgError))
+            [q_scores, off_q, avg_error, error_params] = error_dat
+            self.uniform_q_score = int(-10. * np.log10(avg_error) + 0.5)
+            print('Using uniform sequencing error model. (q=' + str(self.uniform_q_score) + '+' + str(
+                off_q) + ', p(err)={0:0.2f}%)'.format(100. * avg_error))
         if len(error_dat) == 6:  # only 1 q-score model present, use same model for both strands
-            [initQ1, probQ1, Qscores, offQ, avgError, errorParams] = error_dat
+            [init_q1, prob_q1, q_scores, off_q, avg_error, error_params] = error_dat
             self.PE_MODELS = False
         elif len(error_dat) == 8:  # found a q-score model for both forward and reverse strands
             # print 'Using paired-read quality score profiles...'
-            [initQ1, probQ1, initQ2, probQ2, Qscores, offQ, avgError, errorParams] = error_dat
+            [init_q1, prob_q1, initQ2, probQ2, q_scores, off_q, avg_error, error_params] = error_dat
             self.PE_MODELS = True
-            if len(initQ1) != len(initQ2) or len(probQ1) != len(probQ2):
+            if len(init_q1) != len(initQ2) or len(prob_q1) != len(probQ2):
                 print('\nError: R1 and R2 quality score models are of different length.\n')
                 exit(1)
 
-        self.qErrRate = [0.] * (max(Qscores) + 1)
-        for q in Qscores:
+        self.qErrRate = [0.] * (max(q_scores) + 1)
+        for q in q_scores:
             self.qErrRate[q] = 10. ** (-q / 10.)
-        self.offQ = offQ
+        self.offQ = off_q
 
-        # errorParams = [SSE_PROB, SIE_RATE, SIE_PROB, SIE_VAL, SIE_INS_FREQ, SIE_INS_NUCL]
-        self.errP = errorParams
+        # error_params = [SSE_PROB, SIE_RATE, SIE_PROB, SIE_VAL, SIE_INS_FREQ, SIE_INS_NUCL]
+        self.errP = error_params
         self.errSSE = [DiscreteDistribution(n, NUCL) for n in self.errP[0]]
         self.errSIE = DiscreteDistribution(self.errP[2], self.errP[3])
         self.errSIN = DiscreteDistribution(self.errP[5], NUCL)
 
         # adjust sequencing error frequency to match desired rate
-        if reScaledError == None:
+        if rescaled_error is None:
             self.errorScale = 1.0
         else:
-            self.errorScale = reScaledError / avgError
+            self.errorScale = rescaled_error / avg_error
             print('Warning: Quality scores no longer exactly representative of error probability. '
                   'Error model scaled by {0:.3f} to match desired rate...'.format(self.errorScale))
 
         if not self.UNIFORM:
             # adjust length to match desired read length
-            if self.read_len == len(initQ1):
+            if self.read_len == len(init_q1):
                 self.qIndRemap = range(self.read_len)
             else:
-                print('Warning: Read length of error model (' + str(len(initQ1)) + ') does not match -R value (' + str(
+                print('Warning: Read length of error model (' + str(len(init_q1)) + ') does not match -R value (' + str(
                     self.read_len) + '), rescaling model...')
-                self.qIndRemap = [max([1, len(initQ1) * n // read_len]) for n in range(read_len)]
+                self.qIndRemap = [max([1, len(init_q1) * n // read_len]) for n in range(read_len)]
 
             # initialize probability distributions
-            self.initDistByPos1 = [DiscreteDistribution(initQ1[i], Qscores) for i in range(len(initQ1))]
+            self.initDistByPos1 = [DiscreteDistribution(init_q1[i], q_scores) for i in range(len(init_q1))]
             self.probDistByPosByPrevQ1 = [None]
-            for i in range(1, len(initQ1)):
+            for i in range(1, len(init_q1)):
                 self.probDistByPosByPrevQ1.append([])
-                for j in range(len(initQ1[0])):
-                    if np.sum(probQ1[i][
+                for j in range(len(init_q1[0])):
+                    if np.sum(prob_q1[i][
                                   j]) <= 0.:  # if we don't have sufficient data for a transition, use the previous qscore
                         self.probDistByPosByPrevQ1[-1].append(
-                            DiscreteDistribution([1], [Qscores[j]], degenerate_val=Qscores[j]))
+                            DiscreteDistribution([1], [q_scores[j]], degenerate_val=q_scores[j]))
                     else:
-                        self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution(probQ1[i][j], Qscores))
+                        self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution(prob_q1[i][j], q_scores))
 
             if self.PE_MODELS:
-                self.initDistByPos2 = [DiscreteDistribution(initQ2[i], Qscores) for i in range(len(initQ2))]
+                self.initDistByPos2 = [DiscreteDistribution(initQ2[i], q_scores) for i in range(len(initQ2))]
                 self.probDistByPosByPrevQ2 = [None]
                 for i in range(1, len(initQ2)):
                     self.probDistByPosByPrevQ2.append([])
@@ -744,90 +744,90 @@ def __init__(self, read_len, errorModel, reScaledError):
                         if np.sum(probQ2[i][
                                       j]) <= 0.:  # if we don't have sufficient data for a transition, use the previous qscore
                             self.probDistByPosByPrevQ2[-1].append(
-                                DiscreteDistribution([1], [Qscores[j]], degenerate_val=Qscores[j]))
+                                DiscreteDistribution([1], [q_scores[j]], degenerate_val=q_scores[j]))
                         else:
-                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution(probQ2[i][j], Qscores))
+                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution(probQ2[i][j], q_scores))
 
-    def getSequencingErrors(self, readData, isReverseStrand=False):
+    def get_sequencing_errors(self, read_data, is_reverse_strand=False):
 
-        qOut = [0] * self.read_len
-        sErr = []
+        q_out = [0] * self.read_len
+        s_err = []
 
         if self.UNIFORM:
-            myQ = [self.uniform_qscore + self.offQ for n in range(self.read_len)]
-            qOut = ''.join([chr(n) for n in myQ])
+            my_q = [self.uniform_q_score + self.offQ for n in range(self.read_len)]
+            q_out = ''.join([chr(n) for n in my_q])
             for i in range(self.read_len):
-                if random.random() < self.errorScale * self.qErrRate[self.uniform_qscore]:
-                    sErr.append(i)
+                if random.random() < self.errorScale * self.qErrRate[self.uniform_q_score]:
+                    s_err.append(i)
         else:
 
-            if self.PE_MODELS and isReverseStrand:
-                myQ = self.initDistByPos2[0].sample()
+            if self.PE_MODELS and is_reverse_strand:
+                my_q = self.initDistByPos2[0].sample()
             else:
-                myQ = self.initDistByPos1[0].sample()
-            qOut[0] = myQ
+                my_q = self.initDistByPos1[0].sample()
+            q_out[0] = my_q
 
             for i in range(1, self.read_len):
-                if self.PE_MODELS and isReverseStrand:
-                    myQ = self.probDistByPosByPrevQ2[self.qIndRemap[i]][myQ].sample()
+                if self.PE_MODELS and is_reverse_strand:
+                    my_q = self.probDistByPosByPrevQ2[self.qIndRemap[i]][my_q].sample()
                 else:
-                    myQ = self.probDistByPosByPrevQ1[self.qIndRemap[i]][myQ].sample()
-                qOut[i] = myQ
+                    my_q = self.probDistByPosByPrevQ1[self.qIndRemap[i]][my_q].sample()
+                q_out[i] = my_q
 
-            if isReverseStrand:
-                qOut = qOut[::-1]
+            if is_reverse_strand:
+                q_out = q_out[::-1]
 
             for i in range(self.read_len):
-                if random.random() < self.errorScale * self.qErrRate[qOut[i]]:
-                    sErr.append(i)
+                if random.random() < self.errorScale * self.qErrRate[q_out[i]]:
+                    s_err.append(i)
 
-            qOut = ''.join([chr(n + self.offQ) for n in qOut])
+            q_out = ''.join([chr(n + self.offQ) for n in q_out])
 
         if self.errorScale == 0.0:
-            return (qOut, [])
+            return q_out, []
 
-        sOut = []
-        nDelSoFar = 0
+        s_out = []
+        n_del_so_far = 0
         # don't allow indel errors to occur on subsequent positions
-        prevIndel = -2
+        prev_indel = -2
         # don't allow other sequencing errors to occur on bases removed by deletion errors
-        delBlacklist = []
+        del_blacklist = []
 
-        for ind in sErr[::-1]:  # for each error that we're going to insert...
+        for ind in s_err[::-1]:  # for each error that we're going to insert...
 
             # determine error type
-            isSub = True
-            if ind != 0 and ind != self.read_len - 1 - max(self.errP[3]) and abs(ind - prevIndel) > 1:
+            is_sub = True
+            if ind != 0 and ind != self.read_len - 1 - max(self.errP[3]) and abs(ind - prev_indel) > 1:
                 if random.random() < self.errP[1]:
-                    isSub = False
+                    is_sub = False
 
             # error_out = (type, len, pos, ref, alt)
 
-            if isSub:  # insert substitution error
-                myNucl = str(readData[ind])
-                new_nucl = self.errSSE[NUC_IND[myNucl]].sample()
-                sOut.append(('S', 1, ind, myNucl, new_nucl))
+            if is_sub:  # insert substitution error
+                my_nucl = str(read_data[ind])
+                new_nucl = self.errSSE[NUC_IND[my_nucl]].sample()
+                s_out.append(('S', 1, ind, my_nucl, new_nucl))
             else:  # insert indel error
-                indelLen = self.errSIE.sample()
+                indel_len = self.errSIE.sample()
                 if random.random() < self.errP[4]:  # insertion error
-                    myNucl = str(readData[ind])
-                    new_nucl = myNucl + ''.join([self.errSIN.sample() for n in range(indelLen)])
-                    sOut.append(('I', len(new_nucl) - 1, ind, myNucl, new_nucl))
-                elif ind < self.read_len - 2 - nDelSoFar:  # deletion error (prevent too many of them from stacking up)
-                    myNucl = str(readData[ind:ind + indelLen + 1])
-                    new_nucl = str(readData[ind])
-                    nDelSoFar += len(myNucl) - 1
-                    sOut.append(('D', len(myNucl) - 1, ind, myNucl, new_nucl))
-                    for i in range(ind + 1, ind + indelLen + 1):
-                        delBlacklist.append(i)
-                prevIndel = ind
+                    my_nucl = str(read_data[ind])
+                    new_nucl = my_nucl + ''.join([self.errSIN.sample() for n in range(indel_len)])
+                    s_out.append(('I', len(new_nucl) - 1, ind, my_nucl, new_nucl))
+                elif ind < self.read_len - 2 - n_del_so_far:  # deletion error (prevent too many of them from stacking up)
+                    my_nucl = str(read_data[ind:ind + indel_len + 1])
+                    new_nucl = str(read_data[ind])
+                    n_del_so_far += len(my_nucl) - 1
+                    s_out.append(('D', len(my_nucl) - 1, ind, my_nucl, new_nucl))
+                    for i in range(ind + 1, ind + indel_len + 1):
+                        del_blacklist.append(i)
+                prev_indel = ind
 
         # remove blacklisted errors
-        for i in range(len(sOut) - 1, -1, -1):
-            if sOut[i][2] in delBlacklist:
-                del sOut[i]
+        for i in range(len(s_out) - 1, -1, -1):
+            if s_out[i][2] in del_blacklist:
+                del s_out[i]
 
-        return (qOut, sOut)
+        return q_out, s_out
 
 
 """************************************************
@@ -836,76 +836,76 @@ def getSequencingErrors(self, readData, isReverseStrand=False):
 
 
 # parse mutation model pickle file
-def parseInputMutationModel(model=None, whichDefault=1):
-    if whichDefault == 1:
-        outModel = [copy.deepcopy(n) for n in DEFAULT_MODEL_1]
-    elif whichDefault == 2:
-        outModel = [copy.deepcopy(n) for n in DEFAULT_MODEL_2]
+def parse_input_mutation_model(model=None, which_default=1):
+    if which_default == 1:
+        out_model = [copy.deepcopy(n) for n in DEFAULT_MODEL_1]
+    elif which_default == 2:
+        out_model = [copy.deepcopy(n) for n in DEFAULT_MODEL_2]
     else:
         print('\nError: Unknown default mutation model specified\n')
         exit(1)
 
-    if model != None:
+    if model is not None:
         pickle_dict = pickle.load(open(model, "rb"))
-        outModel[0] = pickle_dict['AVG_MUT_RATE']
-        outModel[2] = 1. - pickle_dict['SNP_FREQ']
-
-        insList = pickle_dict['INDEL_FREQ']
-        if len(insList):
-            insCount = sum([insList[k] for k in insList.keys() if k >= 1])
-            delCount = sum([insList[k] for k in insList.keys() if k <= -1])
-            insVals = [k for k in sorted(insList.keys()) if k >= 1]
-            insWght = [insList[k] / float(insCount) for k in insVals]
-            delVals = [k for k in sorted([abs(k) for k in insList.keys() if k <= -1])]
-            delWght = [insList[-k] / float(delCount) for k in delVals]
+        out_model[0] = pickle_dict['AVG_MUT_RATE']
+        out_model[2] = 1. - pickle_dict['SNP_FREQ']
+
+        ins_list = pickle_dict['INDEL_FREQ']
+        if len(ins_list):
+            ins_count = sum([ins_list[k] for k in ins_list.keys() if k >= 1])
+            del_count = sum([ins_list[k] for k in ins_list.keys() if k <= -1])
+            ins_vals = [k for k in sorted(ins_list.keys()) if k >= 1]
+            ins_wght = [ins_list[k] / float(ins_count) for k in ins_vals]
+            del_vals = [k for k in sorted([abs(k) for k in ins_list.keys() if k <= -1])]
+            del_wght = [ins_list[-k] / float(del_count) for k in del_vals]
         else:  # degenerate case where no indel stats are provided
-            insCount = 1
-            delCount = 1
-            insVals = [1]
-            insWght = [1.0]
-            delVals = [1]
-            delWght = [1.0]
-        outModel[3] = insCount / float(insCount + delCount)
-        outModel[4] = insVals
-        outModel[5] = insWght
-        outModel[6] = delVals
-        outModel[7] = delWght
+            ins_count = 1
+            del_count = 1
+            ins_vals = [1]
+            ins_wght = [1.0]
+            del_vals = [1]
+            del_wght = [1.0]
+        out_model[3] = ins_count / float(ins_count + del_count)
+        out_model[4] = ins_vals
+        out_model[5] = ins_wght
+        out_model[6] = del_vals
+        out_model[7] = del_wght
 
         trinuc_trans_prob = pickle_dict['TRINUC_TRANS_PROBS']
         for k in sorted(trinuc_trans_prob.keys()):
-            myInd = TRI_IND[k[0][0] + k[0][2]]
+            my_ind = TRI_IND[k[0][0] + k[0][2]]
             (k1, k2) = (NUC_IND[k[0][1]], NUC_IND[k[1][1]])
-            outModel[8][myInd][k1][k2] = trinuc_trans_prob[k]
-        for i in range(len(outModel[8])):
-            for j in range(len(outModel[8][i])):
-                for l in range(len(outModel[8][i][j])):
+            out_model[8][my_ind][k1][k2] = trinuc_trans_prob[k]
+        for i in range(len(out_model[8])):
+            for j in range(len(out_model[8][i])):
+                for l in range(len(out_model[8][i][j])):
                     # if trinuc not present in input mutation model, assign it uniform probability
-                    if float(sum(outModel[8][i][j])) < 1e-12:
-                        outModel[8][i][j] = [0.25, 0.25, 0.25, 0.25]
+                    if float(sum(out_model[8][i][j])) < 1e-12:
+                        out_model[8][i][j] = [0.25, 0.25, 0.25, 0.25]
                     else:
-                        outModel[8][i][j][l] /= float(sum(outModel[8][i][j]))
+                        out_model[8][i][j][l] /= float(sum(out_model[8][i][j]))
 
         trinuc_mut_prob = pickle_dict['TRINUC_MUT_PROB']
         which_have_we_seen = {n: False for n in ALL_TRI}
         trinuc_mean = np.mean(list(trinuc_mut_prob.values()))
         for trinuc in trinuc_mut_prob.keys():
-            outModel[9][ALL_IND[trinuc]] = trinuc_mut_prob[trinuc]
+            out_model[9][ALL_IND[trinuc]] = trinuc_mut_prob[trinuc]
             which_have_we_seen[trinuc] = True
         for trinuc in which_have_we_seen.keys():
-            if which_have_we_seen[trinuc] == False:
-                outModel[9][ALL_IND[trinuc]] = trinuc_mean
+            if not which_have_we_seen[trinuc]:
+                out_model[9][ALL_IND[trinuc]] = trinuc_mean
 
-    return outModel
+    return out_model
 
 
 # parse mutation model files, returns default model if no model directory is specified
 #
 # OLD FUNCTION THAT PROCESSED OUTDATED TEXTFILE MUTATION MODELS
-def parseInputMutationModel_deprecated(prefix=None, whichDefault=1):
-    if whichDefault == 1:
-        outModel = [copy.deepcopy(n) for n in DEFAULT_MODEL_1]
-    elif whichDefault == 2:
-        outModel = [copy.deepcopy(n) for n in DEFAULT_MODEL_2]
+def parse_input_mutation_model_deprecated(prefix=None, which_default=1):
+    if which_default == 1:
+        out_model = [copy.deepcopy(n) for n in DEFAULT_MODEL_1]
+    elif which_default == 2:
+        out_model = [copy.deepcopy(n) for n in DEFAULT_MODEL_2]
     else:
         print('\nError: Unknown default mutation model specified\n')
         exit(1)
@@ -935,29 +935,29 @@ def parseInputMutationModel_deprecated(prefix=None, whichDefault=1):
                             myIns = float(dat[1])
                         elif dat[0] == 'deletion':
                             myDel = float(dat[1])
-                if myIns != None and myDel != None:
-                    outModel[2] = myIns + myDel
-                    outModel[3] = myIns / (myIns + myDel)
+                if myIns is not None and myDel is not None:
+                    out_model[2] = myIns + myDel
+                    out_model[3] = myIns / (myIns + myDel)
                     print('-', l)
 
             if '_insLength.prob' in l:
-                insVals = {}
+                ins_vals = {}
                 for dat in fr[1:]:
                     if len(dat) == 2:
-                        insVals[int(dat[0])] = float(dat[1])
-                if len(insVals):
-                    outModel[4] = sorted(insVals.keys())
-                    outModel[5] = [insVals[n] for n in outModel[4]]
+                        ins_vals[int(dat[0])] = float(dat[1])
+                if len(ins_vals):
+                    out_model[4] = sorted(ins_vals.keys())
+                    out_model[5] = [ins_vals[n] for n in out_model[4]]
                     print('-', l)
 
             if '_delLength.prob' in l:
-                delVals = {}
+                del_vals = {}
                 for dat in fr[1:]:
                     if len(dat) == 2:
-                        delVals[int(dat[0])] = float(dat[1])
-                if len(delVals):
-                    outModel[6] = sorted(delVals.keys())
-                    outModel[7] = [delVals[n] for n in outModel[6]]
+                        del_vals[int(dat[0])] = float(dat[1])
+                if len(del_vals):
+                    out_model[6] = sorted(del_vals.keys())
+                    out_model[7] = [del_vals[n] for n in out_model[6]]
                     print('-', l)
 
             if '.trinuc' == l[-7:]:
@@ -966,16 +966,16 @@ def parseInputMutationModel_deprecated(prefix=None, whichDefault=1):
                 for i in range(len(p_matrix)):
                     for j in range(len(fr[i])):
                         p_matrix[i][j] = float(fr[i][j])
-                anyNone = False
+                any_none = False
                 for i in range(len(p_matrix)):
                     for j in range(len(p_matrix[i])):
                         if p_matrix[i][j] == -1:
-                            anyNone = True
-                if not anyNone:
-                    outModel[8][context_ind] = copy.deepcopy(p_matrix)
+                            any_none = True
+                if not any_none:
+                    out_model[8][context_ind] = copy.deepcopy(p_matrix)
                     print('-', l)
 
-    return outModel
+    return out_model
 
 
 ######################
diff --git a/py/output_file_writer.py b/py/output_file_writer.py
index 6301d48..a8510f6 100644
--- a/py/output_file_writer.py
+++ b/py/output_file_writer.py
@@ -182,12 +182,12 @@ def write_vcf_record(self, chrom, pos, id_str, ref, alt, qual, filt, info):
             str(chrom) + '\t' + str(pos) + '\t' + str(id_str) + '\t' + str(ref) + '\t' + str(alt) + '\t' + str(
                 qual) + '\t' + str(filt) + '\t' + str(info) + '\n')
 
-    def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag, mate_pos=None, aln_map_qual=70):
+    def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag, mate_pos=None, aln_map_quality=70):
 
         my_bin = reg2bin(pos_0, pos_0 + len(seq))
         # my_bin     = 0	# or just use a dummy value, does this actually matter?
 
-        my_map_qual = aln_map_qual
+        my_map_quality = aln_map_quality
         cig_letters = re.split(r"\d+", cigar)[1:]
         cig_numbers = [int(n) for n in re.findall(r"\d+", cigar)]
         cig_ops = len(cig_letters)
@@ -237,7 +237,7 @@ def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag,
         ####self.bam_file.write(pack('<i',block_size))
         ####self.bam_file.write(pack('<i',refID))
         ####self.bam_file.write(pack('<i',pos_0))
-        ####self.bam_file.write(pack('<I',(my_bin<<16) + (my_map_qual<<8) + len(readName)+1))
+        ####self.bam_file.write(pack('<I',(my_bin<<16) + (my_map_quality<<8) + len(readName)+1))
         ####self.bam_file.write(pack('<I',(samFlag<<16) + cig_ops))
         ####self.bam_file.write(pack('<i',seq_len))
         ####self.bam_file.write(pack('<i',next_ref_id))
@@ -251,7 +251,7 @@ def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag,
         # a horribly compressed line, I'm sorry.
         # (ref_index, position, data)
         self.bam_buffer.append((ref_id, pos_0, pack('<i', block_size) + pack('<i', ref_id) + pack('<i', pos_0) +
-                                pack('<I', (my_bin << 16) + (my_map_qual << 8) + len(read_name) + 1) +
+                                pack('<I', (my_bin << 16) + (my_map_quality << 8) + len(read_name) + 1) +
                                 pack('<I', (sam_flag << 16) + cig_ops) + pack('<i', seq_len) + pack('<i', next_ref_id) +
                                 pack('<i', next_pos) + pack('<i', my_tlen) + read_name.encode('utf-8') +
                                 b'\0' + encoded_cig + encoded_seq + encoded_qual.encode('utf-8')))
diff --git a/py/vcfFunc.py b/py/vcfFunc.py
index 7c80ec4..cf02808 100644
--- a/py/vcfFunc.py
+++ b/py/vcfFunc.py
@@ -9,7 +9,7 @@
 CHOOSE_RANDOM_PLOID_IF_NO_GT_FOUND = True
 
 
-def parseLine(splt, col_dict, col_samp):
+def parse_line(splt, col_dict, col_samp):
     #	check if we want to proceed..
     ra = splt[col_dict['REF']]
     aa = splt[col_dict['ALT']]
@@ -38,7 +38,7 @@ def parseLine(splt, col_dict, col_samp):
     if 'INFO' in col_dict and ';AF=' in ';' + splt[col_dict['INFO']]:
         info = splt[col_dict['INFO']] + ';'
         af = re.findall(r"AF=.*?(?=;)", info)[0][3:]
-    if af != None:
+    if af is not None:
         af_splt = af.split(',')
         while (len(af_splt) < len(alt_alleles)):  # are we lacking enough AF values for some reason?
             af_splt.append(af_splt[-1])  # phone it in.
@@ -64,13 +64,13 @@ def parseLine(splt, col_dict, col_samp):
                 gt_per_samp = [splt[col_samp[iii]].split(':')[gt_ind - 1] for iii in range(len(col_samp))]
                 for i in range(len(gt_per_samp)):
                     gt_per_samp[i] = gt_per_samp[i].replace('.', '0')
-        if gt_per_samp == None:
+        if gt_per_samp is None:
             gt_per_samp = [None] * max([len(col_samp), 1])
 
-    return (alt_alleles, alt_freqs, gt_per_samp)
+    return alt_alleles, alt_freqs, gt_per_samp
 
 
-def parseVCF(vcf_path, tumor_normal=False, ploidy=2):
+def parse_vcf(vcf_path, tumor_normal=False, ploidy=2):
     tt = time.time()
     print('--------------------------------')
     sys.stdout.write('reading input VCF...\n')
@@ -92,8 +92,8 @@ def parseVCF(vcf_path, tumor_normal=False, ploidy=2):
                 f.close()
                 exit(1)
             splt = line[:-1].split('\t')
-            pl_out = parseLine(splt, col_dict, col_samp)
-            if pl_out == None:
+            pl_out = parse_line(splt, col_dict, col_samp)
+            if pl_out is None:
                 n_skipped += 1
             else:
                 (aa, af, gt) = pl_out
@@ -118,7 +118,7 @@ def parseVCF(vcf_path, tumor_normal=False, ploidy=2):
                         continue
                 non_reference = False
                 for gtVal in gt_eval:
-                    if gtVal != None:
+                    if gtVal is not None:
                         if '1' in gtVal:
                             non_reference = True
                 if not non_reference:
@@ -181,4 +181,4 @@ def parseVCF(vcf_path, tumor_normal=False, ploidy=2):
     print(' *', n_skipped, 'variants skipped: (qual filtered / ref genotypes / invalid syntax)')
     print(' *', n_skipped_because_hash, 'variants skipped due to multiple variants found per position')
     print('--------------------------------')
-    return (samp_names, vars_out)
+    return samp_names, vars_out

From 1fec80c4a15a6521bebb7878b325385f9c900542 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 20 Aug 2020 13:52:10 -0500
Subject: [PATCH 129/323] Converting gen_reads.py and associated files to pep8

---
 bacterial_genreads_wrapper.py | 6 +++---
 genReads.py => gen_reads.py   | 2 +-
 py/SequenceContainer.py       | 5 +----
 utilities/compute_fraglen.py  | 2 +-
 utilities/compute_gc.py       | 2 +-
 utilities/genSeqErrorModel.py | 2 +-
 6 files changed, 8 insertions(+), 11 deletions(-)
 rename genReads.py => gen_reads.py (99%)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 56b04ba..6de014c 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 
-import genReads
+import gen_reads
 import argparse
 import random
 import pathlib
@@ -33,7 +33,7 @@ def analyze(self):
         :return: None
         """
         args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa']
-        genReads.main(args)
+        gen_reads.main(args)
 
         # The following workaround is due to the fact that genReads writes out a compressed
         # fasta but does not put the .gz extension on it. Also, genReads cannot handle gzipped
@@ -70,7 +70,7 @@ def analyze(self):
 
     def sample(self):
         args = ['-r', str(self.reference), '-R', '101', '-o', self.name]
-        genReads.main(args)
+        gen_reads.main(args)
 
     def remove(self):
         pathlib.Path.unlink(self.file)
diff --git a/genReads.py b/gen_reads.py
similarity index 99%
rename from genReads.py
rename to gen_reads.py
index 10a3df1..96e79d8 100644
--- a/genReads.py
+++ b/gen_reads.py
@@ -2,7 +2,7 @@
 # encoding: utf-8
 """ ////////////////////////////////////////////////////////////////////////////////
    ///                                                                          ///
-  ///       genReads.py                                                        ///
+  ///       gen_reads.py                                                        ///
  ///        VERSION 2.0: HARDER, BETTER, FASTER, STRONGER!                    ///
 ///////                                                                      //////
    ///      Variant and read simulator for benchmarking NGS workflows          ///
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 0b95005..ff49507 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -215,10 +215,7 @@ def init_mut_models(self, mut_models, mut_rate):
             self.models.append([self.mut_scalar * n[0], n[1], n[2], n[3], DiscreteDistribution(n[5], n[4]),
                                 DiscreteDistribution(n[7], n[6]), []])
             for m in n[8]:
-                self.models[-1][6].append([DiscreteDistribution(m[0], NUCL),
-                                           DiscreteDistribution(m[1], NUCL),
-                                           DiscreteDistribution(m[2], NUCL),
-                                           DiscreteDistribution(m[3], NUCL)])
+                self.models[-1][6].append([DiscreteDistribution(m[0], NUCL), DiscreteDistribution(m[1], NUCL), DiscreteDistribution(m[2], NUCL), DiscreteDistribution(m[3], NUCL)])
             self.models[-1].append([m for m in n[9]])
 
     def init_poisson(self):
diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index e8351a3..1abda2d 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -1,6 +1,6 @@
 #
 #
-#      Compute Fragment Length Model for genReads.py
+#      Compute Fragment Length Model for gen_reads.py
 #                  compute_fraglen.py
 #
 #
diff --git a/utilities/compute_gc.py b/utilities/compute_gc.py
index 6bf4c7d..895b780 100644
--- a/utilities/compute_gc.py
+++ b/utilities/compute_gc.py
@@ -1,7 +1,7 @@
 #
 #
 #   compute_gc.py
-#   Compute GC and coverage model for genReads.py
+#   Compute GC and coverage model for gen_reads.py
 #
 #   Takes output file from bedtools genomecov to generate GC/coverage model
 #
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 467fd38..1dfe7fc 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -3,7 +3,7 @@
 #
 #
 #          genSeqErrorModel.py
-#          Computes sequencing error model for genReads.py
+#          Computes sequencing error model for gen_reads.py
 #
 #         
 #          Usage: python genSeqErrorModel.py -i input_reads.fq -o path/to/output_name.p

From ef302e93b702cae3227f0e5e67dc0bac9ec26ae0 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 20 Aug 2020 13:53:10 -0500
Subject: [PATCH 130/323] Converting gen_reads.py and associated files to pep8

---
 gen_reads.py                          |   2 +-
 py/__pycache__/vcfFunc.cpython-37.pyc | Bin 4668 -> 0 bytes
 py/{vcfFunc.py => vcf_func.py}        |   0
 3 files changed, 1 insertion(+), 1 deletion(-)
 delete mode 100644 py/__pycache__/vcfFunc.cpython-37.pyc
 rename py/{vcfFunc.py => vcf_func.py} (100%)

diff --git a/gen_reads.py b/gen_reads.py
index 96e79d8..ad33e11 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -27,7 +27,7 @@
 
 from py.input_checking import required_field, check_file_open, is_in_range
 from py.ref_func import index_ref, read_ref, get_all_ref_regions, partition_ref_regions
-from py.vcfFunc import parse_vcf
+from py.vcf_func import parse_vcf
 from py.output_file_writer import OutputFileWriter
 from py.probability import DiscreteDistribution, mean_ind_of_weighted_list
 from py.SequenceContainer import SequenceContainer, ReadContainer, parse_input_mutation_model
diff --git a/py/__pycache__/vcfFunc.cpython-37.pyc b/py/__pycache__/vcfFunc.cpython-37.pyc
deleted file mode 100644
index 1e8645c6b6c9bf22cf28bfbc8586b0e12334a4a3..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/py/vcfFunc.py b/py/vcf_func.py
similarity index 100%
rename from py/vcfFunc.py
rename to py/vcf_func.py

From 87b5fc590c87e693313cd8b6d5e486b7b55fa31f Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 20 Aug 2020 15:20:01 -0500
Subject: [PATCH 131/323] going over genreads and related code for pep8
 standards one last time.

---
 gen_reads.py | 36 +++++++++++++++++++-----------------
 1 file changed, 19 insertions(+), 17 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 8a33583..fc896a0 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -109,7 +109,8 @@ def main(raw_args=None):
     # cancer params (disabled currently)
     # (CANCER, CANCER_MODEL, CANCER_PURITY) = (args.cancer, args.cm, args.cp)
     (CANCER, CANCER_MODEL, CANCER_PURITY) = (False, None, 0.8)
-    (OFFTARGET_SCALAR, OFFTARGET_DISCARD, FORCE_COVERAGE, RESCALE_QUAL) = (args.to, args.discard_offtarget, args.force_coverage, args.rescale_qual)
+    (OFFTARGET_SCALAR, OFFTARGET_DISCARD, FORCE_COVERAGE, RESCALE_QUAL) = (args.to, args.discard_offtarget,
+                                                                           args.force_coverage, args.rescale_qual)
     # important flags
     (SAVE_BAM, SAVE_VCF, FASTA_INSTEAD, GZIPPED_OUT, NO_FASTQ) = \
         (args.bam, args.vcf, args.fa, args.gz, args.no_fastq)
@@ -200,7 +201,7 @@ def main(raw_args=None):
 
     #	fragment length distribution
     #
-    if PAIRED_END and not (PAIRED_END_ARTIFICIAL):
+    if PAIRED_END and not PAIRED_END_ARTIFICIAL:
         print('Using empirical fragment length distribution.')
         [potential_values, potential_prob] = pickle.load(open(FRAGLEN_MODEL, 'rb'))
         FRAGLEN_VALS = []
@@ -286,7 +287,7 @@ def main(raw_args=None):
     input_variants = []
     if INPUT_VCF is not None:
         if CANCER:
-            (sampNames, inputVariants) = parse_vcf(INPUT_VCF, tumorNormal=True, ploidy=PLOIDS)
+            (sampNames, inputVariants) = parse_vcf(INPUT_VCF, tumor_normal=True, ploidy=PLOIDS)
             tumor_ind = sampNames.index('TUMOR')
             normal_ind = sampNames.index('NORMAL')
         else:
@@ -346,6 +347,7 @@ def main(raw_args=None):
                         mut_rate_regions[my_chr] = [-1]
                         mut_rate_values[my_chr] = [0.0]
                     mut_rate_regions[my_chr].extend([pos1, pos2])
+                    # TODO figure out what the next line is supposed to do and fix
                     mut_rate_values.extend([mut_rate * (pos2 - pos1)] * 2)
 
     # initialize output files (part I)
@@ -359,12 +361,12 @@ def main(raw_args=None):
     # If processing jobs in parallel, precompute the independent regions that can be process separately
     if NJOBS > 1:
         parallel_region_list = get_all_ref_regions(REFERENCE, ref_index, N_HANDLING, save_output=SAVE_NON_N)
-        (myRefs, myRegions) = partition_ref_regions(parallel_region_list, ref_index, MYJOB, NJOBS)
-        if not len(myRegions):
+        (my_refs, my_regions) = partition_ref_regions(parallel_region_list, ref_index, MYJOB, NJOBS)
+        if not len(my_regions):
             print('This job id has no regions to process, exiting...')
             exit(1)
         for i in range(len(ref_index) - 1, -1, -1):  # delete reference not used in our job
-            if not ref_index[i][0] in myRefs:
+            if not ref_index[i][0] in my_refs:
                 del ref_index[i]
         # if value of NJOBS is too high, let's change it to the maximum possible, to avoid output filename confusion
         corrected_n_jobs = min([NJOBS, sum([len(n) for n in parallel_region_list.values()])])
@@ -373,11 +375,11 @@ def main(raw_args=None):
 
     # initialize output files (part II)
     if CANCER:
-        OFW = OutputFileWriter(OUT_PREFIX + '_normal', paired=PAIRED_END, BAM_header=bam_header, VCF_header=vcf_header,
-                               gzipped=GZIPPED_OUT, noFASTQ=NO_FASTQ, FASTA_instead=FASTA_INSTEAD)
-        OFW_CANCER = OutputFileWriter(OUT_PREFIX + '_tumor', paired=PAIRED_END, BAM_header=bam_header,
-                                      VCF_header=vcf_header, gzipped=GZIPPED_OUT, jobTuple=(MYJOB, corrected_n_jobs),
-                                      noFASTQ=NO_FASTQ, FASTA_instead=FASTA_INSTEAD)
+        OFW = OutputFileWriter(OUT_PREFIX + '_normal', paired=PAIRED_END, bam_header=bam_header, vcf_header=vcf_header,
+                               gzipped=GZIPPED_OUT, no_fastq=NO_FASTQ, fasta_instead=FASTA_INSTEAD)
+        OFW_CANCER = OutputFileWriter(OUT_PREFIX + '_tumor', paired=PAIRED_END, bam_header=bam_header,
+                                      vcf_header=vcf_header, gzipped=GZIPPED_OUT, job_tuple=(MYJOB, corrected_n_jobs),
+                                      no_fastq=NO_FASTQ, fasta_instead=FASTA_INSTEAD)
     else:
         OFW = OutputFileWriter(OUT_PREFIX, paired=PAIRED_END, bam_header=bam_header, vcf_header=vcf_header,
                                gzipped=GZIPPED_OUT, job_tuple=(MYJOB, corrected_n_jobs), no_fastq=NO_FASTQ,
@@ -399,7 +401,7 @@ def main(raw_args=None):
         # if we're processing jobs in parallel only take the regions relevant for the current job
         if NJOBS > 1:
             for i in range(len(n_regions['non_N']) - 1, -1, -1):
-                if not (ref_index[RI][0], n_regions['non_N'][i][0], n_regions['non_N'][i][1]) in myRegions:
+                if not (ref_index[RI][0], n_regions['non_N'][i][0], n_regions['non_N'][i][1]) in my_regions:
                     del n_regions['non_N'][i]
 
         # count total bp we'll be spanning so we can get an idea of how far along we are (for printing progress indicators)
@@ -485,7 +487,7 @@ def main(raw_args=None):
             end = min([start + bpd, pf])
             # print '------------------RAWR:', (pi,pf), n_target_windows, bpd
             vars_from_prev_overlap = []
-            varsCancerFromPrevOverlap = []
+            vars_cancer_from_prev_overlap = []
             vind_from_prev = 0
             is_last_time = False
             have_printed100 = False
@@ -614,19 +616,19 @@ def main(raw_args=None):
                 if CANCER:
                     tumor_sequences = SequenceContainer(start, refSequence[start:end], PLOIDS, overlap, READLEN,
                                                         [CANCER_MODEL] * PLOIDS, MUT_RATE, coverage_dat)
-                    tumor_sequences.insert_mutations(varsCancerFromPrevOverlap + all_inserted_variants)
+                    tumor_sequences.insert_mutations(vars_cancer_from_prev_overlap + all_inserted_variants)
                     all_cancer_variants = tumor_sequences.random_mutations()
 
                 # which variants do we need to keep for next time (because of window overlap)?
                 vars_from_prev_overlap = []
-                varsCancerFromPrevOverlap = []
+                vars_cancer_from_prev_overlap = []
                 for n in all_inserted_variants:
                     if n[0] >= end - overlap - 1:
                         vars_from_prev_overlap.append(n)
                 if CANCER:
                     for n in all_cancer_variants:
                         if n[0] >= end - overlap - 1:
-                            varsCancerFromPrevOverlap.append(n)
+                            vars_cancer_from_prev_overlap.append(n)
 
                 # if we're only producing VCF, no need to go through the hassle of generating reads
                 if ONLY_VCF:
@@ -857,7 +859,7 @@ def main(raw_args=None):
     # close output files
     OFW.close_files()
     if CANCER:
-        OFW_CANCER.closeFiles()
+        OFW_CANCER.close_files()
 
 
 if __name__ == '__main__':

From c6500f8efa042673b1ee77949861d4d4cb5e18fd Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 21 Aug 2020 10:17:08 -0500
Subject: [PATCH 132/323] Starting to reorg genreads

---
 gen_reads.py             | 512 ++++++++++++++++++---------------------
 py/output_file_writer.py |  96 ++++----
 py/vcf_func.py           |   3 +-
 3 files changed, 279 insertions(+), 332 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index fc896a0..bdb9d2f 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -14,7 +14,6 @@
  ///                                                                         ///
 /////////////////////////////////////////////////////////////////////////////// """
 
-import os
 import sys
 import copy
 import random
@@ -24,6 +23,7 @@
 import pickle
 import numpy as np
 import argparse
+import pathlib
 
 from py.input_checking import required_field, check_file_open, is_in_range
 from py.ref_func import index_ref, read_ref, get_all_ref_regions, partition_ref_regions
@@ -68,10 +68,6 @@ def main(raw_args=None):
                         help='empirical fragment length distribution')
     parser.add_argument('--gc-model', type=str, required=False, metavar='<str>', default=None,
                         help='empirical GC coverage bias distribution')
-    parser.add_argument('--job', nargs=2, type=int, required=False, metavar=('<int>', '<int>'), default=(0, 0),
-                        help='jobs IDs for generating reads in parallel')
-    parser.add_argument('--nnr', required=False, action='store_true', default=False,
-                        help='save non-N ref regions (for parallel jobs)')
     parser.add_argument('--bam', required=False, action='store_true', default=False, help='output golden BAM file')
     parser.add_argument('--vcf', required=False, action='store_true', default=False, help='output golden VCF file')
     parser.add_argument('--fa', required=False, action='store_true', default=False,
@@ -80,6 +76,7 @@ def main(raw_args=None):
                         help='rng seed value; identical RNG value should produce identical runs of the program, so '
                              'things like read locations, variant positions, error positions, etc, '
                              'should all be the same.')
+    # TODO check if this argument does anything at all.
     parser.add_argument('--gz', required=False, action='store_true', default=False, help='gzip output FQ and VCF')
     parser.add_argument('--no-fastq', required=False, action='store_true', default=False,
                         help='bypass fastq generation')
@@ -94,130 +91,134 @@ def main(raw_args=None):
     """
     Set variables for processing
     """
-    # TODO change these to pathlib.Path objects so they are machine agnostic
     # absolute path to this script
-    SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-1])
+    sim_path = pathlib.Path(__file__).resolve().parent
 
     # if coverage val for a given window/position is below this value, consider it effectively zero.
-    LOW_COV_THRESH = 50
+    low_cov_thresh = 50
 
     # required args
-    (REFERENCE, READLEN, OUT_PREFIX) = (args.r, args.R, args.o)
+    (reference, read_len, out_prefix) = (args.r, args.R, args.o)
     # various dataset parameters
-    (COVERAGE, PLOIDS, INPUT_BED, DISCARD_BED, SE_MODEL, SE_RATE, MUT_MODEL, MUT_RATE, MUT_BED, INPUT_VCF) = \
+    (coverage, ploids, input_bed, discard_bed, se_model, se_rate, mut_model, mut_rate, mut_bed, input_vcf) = \
         (args.c, args.p, args.t, args.d, args.e, args.E, args.m, args.M, args.Mb, args.v)
     # cancer params (disabled currently)
-    # (CANCER, CANCER_MODEL, CANCER_PURITY) = (args.cancer, args.cm, args.cp)
-    (CANCER, CANCER_MODEL, CANCER_PURITY) = (False, None, 0.8)
-    (OFFTARGET_SCALAR, OFFTARGET_DISCARD, FORCE_COVERAGE, RESCALE_QUAL) = (args.to, args.discard_offtarget,
-                                                                           args.force_coverage, args.rescale_qual)
+    # (cancer, cancer_model, cancer_purity) = (args.cancer, args.cm, args.cp)
+    (cancer, cancer_model, cancer_purity) = (False, None, 0.8)
+    (off_target_scalar, off_target_discard, force_coverage, rescale_qual) = (args.to, args.discard_offtarget,
+                                                                             args.force_coverage, args.rescale_qual)
     # important flags
-    (SAVE_BAM, SAVE_VCF, FASTA_INSTEAD, GZIPPED_OUT, NO_FASTQ) = \
+    (save_bam, save_vcf, fasta_instead, gzipped_out, no_fastq) = \
         (args.bam, args.vcf, args.fa, args.gz, args.no_fastq)
 
-    ONLY_VCF = (NO_FASTQ and SAVE_VCF and not SAVE_BAM)
-    if ONLY_VCF:
-        print('Only producing VCF output, that should speed things up a bit...')
-
     # sequencing model parameters
-    (FRAGMENT_SIZE, FRAGMENT_STD) = args.pe
-    FRAGLEN_MODEL = args.pe_model
-    GC_BIAS_MODEL = args.gc_model
-    N_MAX_QUAL = args.N
-
-    # if user specified no fastq, no bam, no vcf, then inform them of their wasteful ways and exit
-    if NO_FASTQ is True and SAVE_BAM is False and SAVE_VCF is False:
-        print('\nError: No files will be written when --no-fastq is specified without --vcf or --bam.')
-        exit(1)
+    (fragment_size, fragment_std) = args.pe
+    (fraglen_model, gc_bias_model) = args.pe_model, args.gc_model
+    n_max_qual = args.N
 
-    # if user specified mean/std, use artificial fragment length distribution, otherwise use
-    # the empirical model specified. If neither, then we're doing single-end reads.
-    PAIRED_END = False
-    PAIRED_END_ARTIFICIAL = False
-    if FRAGMENT_SIZE is not None and FRAGMENT_STD is not None:
-        PAIRED_END = True
-        PAIRED_END_ARTIFICIAL = True
-    elif FRAGLEN_MODEL is not None:
-        PAIRED_END = True
-        PAIRED_END_ARTIFICIAL = False
-
-    (MYJOB, NJOBS) = args.job
-    if MYJOB == 0:
-        MYJOB = 1
-        NJOBS = 1
-    SAVE_NON_N = args.nnr
-
-    RNG_SEED = args.rng
-    if RNG_SEED == -1:
-        RNG_SEED = random.randint(1, 99999999)
-    random.seed(RNG_SEED)
+    rng_seed = args.rng
 
     """************************************************
     ****            INPUT ERROR CHECKING
     ************************************************"""
-
-    check_file_open(REFERENCE, 'ERROR: could not open reference', required=True)
-    check_file_open(INPUT_VCF, 'ERROR: could not open input VCF', required=False)
-    check_file_open(INPUT_BED, 'ERROR: could not open input BED', required=False)
-    required_field(OUT_PREFIX, 'ERROR: no output prefix provided')
-    if (FRAGMENT_SIZE is None and FRAGMENT_STD is not None) or (FRAGMENT_SIZE is not None and FRAGMENT_STD is None):
+    if (fragment_size is None and fragment_std is not None) or (fragment_size is not None and fragment_std is None):
         print('\nError: --pe argument takes 2 space-separated arguments.\n')
         exit(1)
 
+    only_vcf = no_fastq and save_vcf and not save_bam and not fasta_instead
+    if only_vcf:
+        print('Only producing VCF output, that should speed things up a bit...')
+
+    check_file_open(reference, 'ERROR: could not open reference', required=True)
+    check_file_open(input_vcf, 'ERROR: could not open input VCF', required=False)
+    check_file_open(input_bed, 'ERROR: could not open input BED', required=False)
+    required_field(out_prefix, 'ERROR: no output prefix provided')
+
+    # if user specified mean/std, use artificial fragment length distribution, otherwise use
+    # the empirical model specified. If neither, then we're doing single-end reads.
+    if fragment_size is not None and fragment_std is not None:
+        paired_end = True
+        paired_end_artificial = True
+    elif fraglen_model is not None:
+        paired_end = True
+        paired_end_artificial = False
+    else:
+        paired_end = False
+        paired_end_artificial = False
+
+    # if user specified no fastq, not fasta only, and no bam and no vcf, then print error and exit.
+    if no_fastq and not fasta_instead and not save_bam and not save_vcf:
+        print('\nError: No files would be written.\n')
+        exit(1)
+
+    if no_fastq:
+        print('Bypassing FASTQ generation...')
+
+    if rng_seed == -1:
+        rng_seed = random.randint(1, 99999999)
+    random.seed(rng_seed)
+
     """************************************************
     ****             LOAD INPUT MODELS
     ************************************************"""
 
-    #	mutation models
-    #
-    MUT_MODEL = parse_input_mutation_model(MUT_MODEL, 1)
-    if CANCER:
-        CANCER_MODEL = parse_input_mutation_model(CANCER_MODEL, 2)
-    if MUT_RATE < 0.:
-        MUT_RATE = None
-
-    #	sequencing error model
-    #
-    if SE_RATE < 0.:
-        SE_RATE = None
-    if SE_MODEL is None:
+    # mutation models
+    mut_model = parse_input_mutation_model(mut_model, 1)
+    if cancer:
+        cancer_model = parse_input_mutation_model(cancer_model, 2)
+    if mut_rate < 0.:
+        mut_rate = None
+
+    # sequencing error model
+    if se_rate < 0.:
+        se_rate = None
+    if se_model is None:
         print('Using default sequencing error model.')
-        SE_MODEL = SIM_PATH + '/models/errorModel_toy.p'
-        SE_CLASS = ReadContainer(READLEN, SE_MODEL, SE_RATE)
+        se_model = sim_path / 'models/errorModel_toy.p'
+        se_class = ReadContainer(read_len, se_model, se_rate)
     else:
         # probably need to do some sanity checking
-        SE_CLASS = ReadContainer(READLEN, SE_MODEL, SE_RATE)
+        se_class = ReadContainer(read_len, se_model, se_rate)
 
-    #	GC-bias model
-    #
-    if GC_BIAS_MODEL is None:
+    # GC-bias model
+    if gc_bias_model is None:
         print('Using default gc-bias model.')
-        GC_BIAS_MODEL = SIM_PATH + '/models/gcBias_toy.p'
-        [GC_SCALE_COUNT, GC_SCALE_VAL] = pickle.load(open(GC_BIAS_MODEL, 'rb'))
-        GC_WINDOW_SIZE = GC_SCALE_COUNT[-1]
+        gc_bias_model = sim_path / 'models/gcBias_toy.p'
+        [gc_scale_count, gc_scale_val] = pickle.load(open(gc_bias_model, 'rb'))
+        gc_window_size = gc_scale_count[-1]
     else:
-        [GC_SCALE_COUNT, GC_SCALE_VAL] = pickle.load(open(GC_BIAS_MODEL, 'rb'))
-        GC_WINDOW_SIZE = GC_SCALE_COUNT[-1]
+        [gc_scale_count, gc_scale_val] = pickle.load(open(gc_bias_model, 'rb'))
+        gc_window_size = gc_scale_count[-1]
 
-    #	fragment length distribution
-    #
-    if PAIRED_END and not PAIRED_END_ARTIFICIAL:
+    # fragment length distribution
+    if paired_end and not paired_end_artificial:
         print('Using empirical fragment length distribution.')
-        [potential_values, potential_prob] = pickle.load(open(FRAGLEN_MODEL, 'rb'))
-        FRAGLEN_VALS = []
-        FRAGLEN_PROB = []
+        [potential_values, potential_prob] = pickle.load(open(fraglen_model, 'rb'))
+        fraglen_vals = []
+        fraglen_prob = []
         for i in range(len(potential_values)):
-            if potential_values[i] > READLEN:
-                FRAGLEN_VALS.append(potential_values[i])
-                FRAGLEN_PROB.append(potential_prob[i])
+            if potential_values[i] > read_len:
+                fraglen_vals.append(potential_values[i])
+                fraglen_prob.append(potential_prob[i])
         # should probably add some validation and sanity-checking code here...
-        FRAGLEN_DISTRIBUTION = DiscreteDistribution(FRAGLEN_PROB, FRAGLEN_VALS)
-        FRAGMENT_SIZE = FRAGLEN_VALS[mean_ind_of_weighted_list(FRAGLEN_PROB)]
+        fraglen_distribution = DiscreteDistribution(fraglen_prob, fraglen_vals)
+        fragment_size = fraglen_vals[mean_ind_of_weighted_list(fraglen_prob)]
 
-    #	Indicate not writing FASTQ reads
-    #
-    if NO_FASTQ:
-        print('Bypassing FASTQ generation...')
+    # fragment length distribution: normal distribution that goes out to +- 6 standard deviations
+    if paired_end and paired_end_artificial:
+        print(
+            'Using artificial fragment length distribution. mean=' + str(fragment_size) + ', std=' + str(fragment_std))
+        if fragment_std == 0:
+            fraglen_distribution = DiscreteDistribution([1], [fragment_size], degenerate_val=fragment_size)
+        else:
+            potential_values = range(fragment_size - 6 * fragment_std, fragment_size + 6 * fragment_std + 1)
+            fraglen_vals = []
+            for i in range(len(potential_values)):
+                if potential_values[i] > read_len:
+                    fraglen_vals.append(potential_values[i])
+            fraglen_prob = [np.exp(-(((n - float(fragment_size)) ** 2) / (2 * (fragment_std ** 2)))) for n in
+                            fraglen_vals]
+            fraglen_distribution = DiscreteDistribution(fraglen_prob, fraglen_vals)
 
     """************************************************
     ****            HARD-CODED CONSTANTS
@@ -225,81 +226,54 @@ def main(raw_args=None):
 
     # target window size for read sampling. how many times bigger than read/frag length
     WINDOW_TARGET_SCALE = 100
-    # sub-window size for read sampling windows. this is basically the finest resolution
-    # that can be obtained for targeted region boundaries and GC% bias
-    SMALL_WINDOW = 20
-    # is the mutation model constant throughout the simulation? If so we can save a lot of time
-    CONSTANT_MUT_MODEL = True
-
-    """************************************************
-    ****               DEFAULT MODELS
-    ************************************************"""
 
-    # fragment length distribution: normal distribution that goes out to +- 6 standard deviations
-    if PAIRED_END and PAIRED_END_ARTIFICIAL:
-        print(
-            'Using artificial fragment length distribution. mean=' + str(FRAGMENT_SIZE) + ', std=' + str(FRAGMENT_STD))
-        if FRAGMENT_STD == 0:
-            FRAGLEN_DISTRIBUTION = DiscreteDistribution([1], [FRAGMENT_SIZE], degenerate_val=FRAGMENT_SIZE)
-        else:
-            potential_values = range(FRAGMENT_SIZE - 6 * FRAGMENT_STD, FRAGMENT_SIZE + 6 * FRAGMENT_STD + 1)
-            FRAGLEN_VALS = []
-            for i in range(len(potential_values)):
-                if potential_values[i] > READLEN:
-                    FRAGLEN_VALS.append(potential_values[i])
-            FRAGLEN_PROB = [np.exp(-(((n - float(FRAGMENT_SIZE)) ** 2) / (2 * (FRAGMENT_STD ** 2)))) for n in
-                            FRAGLEN_VALS]
-            FRAGLEN_DISTRIBUTION = DiscreteDistribution(FRAGLEN_PROB, FRAGLEN_VALS)
+    # allowed nucleotides
+    ALLOWED_NUCL = ['A', 'C', 'G', 'T']
 
     """************************************************
     ****          MORE INPUT ERROR CHECKING
     ************************************************"""
 
-    is_in_range(READLEN, 10, 1000000, 'Error: -R must be between 10 and 1,000,000')
-    is_in_range(COVERAGE, 0, 1000000, 'Error: -c must be between 0 and 1,000,000')
-    is_in_range(PLOIDS, 1, 100, 'Error: -p must be between 1 and 100')
-    is_in_range(OFFTARGET_SCALAR, 0, 1, 'Error: -to must be between 0 and 1')
-    if MUT_RATE != -1 and MUT_RATE is not None:
-        is_in_range(MUT_RATE, 0, 0.3, 'Error: -M must be between 0 and 0.3')
-    if SE_RATE != -1 and SE_RATE is not None:
-        is_in_range(SE_RATE, 0, 0.3, 'Error: -E must be between 0 and 0.3')
-    if NJOBS != 1:
-        is_in_range(NJOBS, 1, 1000, 'Error: --job must be between 1 and 1,000')
-        is_in_range(MYJOB, 1, 1000, 'Error: --job must be between 1 and 1,000')
-        is_in_range(MYJOB, 1, NJOBS, 'Error: job id must be less than or equal to number of jobs')
-    if N_MAX_QUAL != -1:
-        is_in_range(N_MAX_QUAL, 1, 40, 'Error: -N must be between 1 and 40')
+    is_in_range(read_len, 10, 1000000, 'Error: -R must be between 10 and 1,000,000')
+    is_in_range(coverage, 0, 1000000, 'Error: -c must be between 0 and 1,000,000')
+    is_in_range(ploids, 1, 100, 'Error: -p must be between 1 and 100')
+    is_in_range(off_target_scalar, 0, 1, 'Error: -to must be between 0 and 1')
+    if mut_rate != -1 and mut_rate is not None:
+        is_in_range(mut_rate, 0, 0.3, 'Error: -M must be between 0 and 0.3')
+    if se_rate != -1 and se_rate is not None:
+        is_in_range(se_rate, 0, 0.3, 'Error: -E must be between 0 and 0.3')
+    if n_max_qual != -1:
+        is_in_range(n_max_qual, 1, 40, 'Error: -N must be between 1 and 40')
 
     """************************************************
     ****   Process Inputs
     ************************************************"""
 
-    ALLOWED_NUCL = ['A', 'C', 'G', 'T']
     # index reference
-    ref_index = index_ref(REFERENCE)
-    if PAIRED_END:
-        N_HANDLING = ('random', FRAGMENT_SIZE)
+    ref_index = index_ref(reference)
+    if paired_end:
+        n_handling = ('random', fragment_size)
     else:
-        N_HANDLING = ('ignore', READLEN)
+        n_handling = ('ignore', read_len)
     indices_by_ref_name = {ref_index[n][0]: n for n in range(len(ref_index))}
 
     # parse input variants, if present
     input_variants = []
-    if INPUT_VCF is not None:
-        if CANCER:
-            (sampNames, inputVariants) = parse_vcf(INPUT_VCF, tumor_normal=True, ploidy=PLOIDS)
-            tumor_ind = sampNames.index('TUMOR')
-            normal_ind = sampNames.index('NORMAL')
+    if input_vcf is not None:
+        if cancer:
+            (sample_names, input_variants) = parse_vcf(input_vcf, tumor_normal=True, ploidy=ploids)
+            tumor_ind = sample_names.index('TUMOR')
+            normal_ind = sample_names.index('NORMAL')
         else:
-            (sampNames, input_variants) = parse_vcf(INPUT_VCF, ploidy=PLOIDS)
+            (sample_names, input_variants) = parse_vcf(input_vcf, ploidy=ploids)
         for k in sorted(input_variants.keys()):
             input_variants[k].sort()
 
     # parse input targeted regions, if present
     input_regions = {}
     ref_list = [n[0] for n in ref_index]
-    if INPUT_BED is not None:
-        with open(INPUT_BED, 'r') as f:
+    if input_bed is not None:
+        with open(input_bed, 'r') as f:
             for line in f:
                 [my_chr, pos1, pos2] = line.strip().split('\t')[:3]
                 if my_chr not in input_regions:
@@ -322,8 +296,8 @@ def main(raw_args=None):
                 'Warning: Targeted regions BED file contains sequence names not found in reference (regions ignored).')
     # parse discard bed similarly
     discardRegions = {}
-    if DISCARD_BED != None:
-        f = open(DISCARD_BED, 'r')
+    if discard_bed != None:
+        f = open(discard_bed, 'r')
         for line in f:
             [myChr, pos1, pos2] = line.strip().split('\t')[:3]
             if myChr not in discardRegions:
@@ -334,11 +308,11 @@ def main(raw_args=None):
     # parse input mutation rate rescaling regions, if present
     mut_rate_regions = {}
     mut_rate_values = {}
-    if MUT_BED is not None:
-        with open(MUT_BED, 'r') as f:
+    if mut_bed is not None:
+        with open(mut_bed, 'r') as f:
             for line in f:
                 [my_chr, pos1, pos2, meta_data] = line.strip().split('\t')[:4]
-                mut_str = re.findall(r"MUT_RATE=.*?(?=;)", meta_data + ';')
+                mut_str = re.findall(r"mut_rate=.*?(?=;)", meta_data + ';')
                 (pos1, pos2) = (int(pos1), int(pos2))
                 if len(mut_str) and (pos2 - pos1) > 1:
                     # mut_rate = #_mutations / length_of_region, let's bound it by a reasonable amount
@@ -352,43 +326,28 @@ def main(raw_args=None):
 
     # initialize output files (part I)
     bam_header = None
-    if SAVE_BAM:
+    if save_bam:
         bam_header = [copy.deepcopy(ref_index)]
     vcf_header = None
-    if SAVE_VCF:
-        vcf_header = [REFERENCE]
-
-    # If processing jobs in parallel, precompute the independent regions that can be process separately
-    if NJOBS > 1:
-        parallel_region_list = get_all_ref_regions(REFERENCE, ref_index, N_HANDLING, save_output=SAVE_NON_N)
-        (my_refs, my_regions) = partition_ref_regions(parallel_region_list, ref_index, MYJOB, NJOBS)
-        if not len(my_regions):
-            print('This job id has no regions to process, exiting...')
-            exit(1)
-        for i in range(len(ref_index) - 1, -1, -1):  # delete reference not used in our job
-            if not ref_index[i][0] in my_refs:
-                del ref_index[i]
-        # if value of NJOBS is too high, let's change it to the maximum possible, to avoid output filename confusion
-        corrected_n_jobs = min([NJOBS, sum([len(n) for n in parallel_region_list.values()])])
-    else:
-        corrected_n_jobs = 1
+    if save_vcf:
+        vcf_header = [reference]
 
     # initialize output files (part II)
-    if CANCER:
-        OFW = OutputFileWriter(OUT_PREFIX + '_normal', paired=PAIRED_END, bam_header=bam_header, vcf_header=vcf_header,
-                               gzipped=GZIPPED_OUT, no_fastq=NO_FASTQ, fasta_instead=FASTA_INSTEAD)
-        OFW_CANCER = OutputFileWriter(OUT_PREFIX + '_tumor', paired=PAIRED_END, bam_header=bam_header,
-                                      vcf_header=vcf_header, gzipped=GZIPPED_OUT, job_tuple=(MYJOB, corrected_n_jobs),
-                                      no_fastq=NO_FASTQ, fasta_instead=FASTA_INSTEAD)
+    if cancer:
+        OFW = OutputFileWriter(out_prefix + '_normal', paired=paired_end, bam_header=bam_header, vcf_header=vcf_header,
+                               gzipped=gzipped_out, no_fastq=no_fastq, fasta_instead=fasta_instead)
+        OFW_CANCER = OutputFileWriter(out_prefix + '_tumor', paired=paired_end, bam_header=bam_header,
+                                      vcf_header=vcf_header, gzipped=gzipped_out,
+                                      no_fastq=no_fastq, fasta_instead=fasta_instead)
     else:
-        OFW = OutputFileWriter(OUT_PREFIX, paired=PAIRED_END, bam_header=bam_header, vcf_header=vcf_header,
-                               gzipped=GZIPPED_OUT, job_tuple=(MYJOB, corrected_n_jobs), no_fastq=NO_FASTQ,
-                               fasta_instead=FASTA_INSTEAD)
-    OUT_PREFIX_NAME = OUT_PREFIX.split('/')[-1]
+        OFW = OutputFileWriter(out_prefix, paired=paired_end, bam_header=bam_header, vcf_header=vcf_header,
+                               gzipped=gzipped_out, no_fastq=no_fastq,
+                               fasta_instead=fasta_instead)
+    OUT_PREFIX_NAME = out_prefix.split('/')[-1]
 
     """************************************************
-	****        LET'S GET THIS PARTY STARTED...
-	************************************************"""
+    ****        LET'S GET THIS PARTY STARTED...
+    ************************************************"""
 
     read_name_count = 1  # keep track of the number of reads we've sampled, for read-names
     unmapped_records = []
@@ -396,24 +355,19 @@ def main(raw_args=None):
     for RI in range(len(ref_index)):
 
         # read in reference sequence and notate blocks of Ns
-        (refSequence, n_regions) = read_ref(REFERENCE, ref_index[RI], N_HANDLING)
-
-        # if we're processing jobs in parallel only take the regions relevant for the current job
-        if NJOBS > 1:
-            for i in range(len(n_regions['non_N']) - 1, -1, -1):
-                if not (ref_index[RI][0], n_regions['non_N'][i][0], n_regions['non_N'][i][1]) in my_regions:
-                    del n_regions['non_N'][i]
+        (refSequence, n_regions) = read_ref(reference, ref_index[RI], n_handling)
 
-        # count total bp we'll be spanning so we can get an idea of how far along we are (for printing progress indicators)
+        # count total bp we'll be spanning so we can get an idea of how far along we are
+        # (for printing progress indicators)
         total_bp_span = sum([n[1] - n[0] for n in n_regions['non_N']])
         current_progress = 0
         current_percent = 0
         have_printed100 = False
 
-        # prune invalid input variants, e.g variants that:
-        #		- try to delete or alter any N characters
-        #		- don't match the reference base at their specified position
-        #		- any alt allele contains anything other than allowed characters
+        """prune invalid input variants, e.g variants that:
+                - try to delete or alter any N characters
+                - don't match the reference base at their specified position
+                - any alt allele contains anything other than allowed characters"""
         valid_variants = []
         n_skipped = [0, 0, 0]
         if ref_index[RI][0] in input_variants:
@@ -446,24 +400,24 @@ def main(raw_args=None):
 
         # determine sampling windows based on read length, large N regions, and structural mutations.
         # in order to obtain uniform coverage, windows should overlap by:
-        #		- READLEN, if single-end reads
-        #		- FRAGMENT_SIZE (mean), if paired-end reads
+        #		- read_len, if single-end reads
+        #		- fragment_size (mean), if paired-end reads
         # ploidy is fixed per large sampling window,
         # coverage distributions due to GC% and targeted regions are specified within these windows
         sampling_windows = []
         ALL_VARIANTS_OUT = {}
         sequences = None
-        if PAIRED_END:
-            target_size = WINDOW_TARGET_SCALE * FRAGMENT_SIZE
-            overlap = FRAGMENT_SIZE
-            overlap_min_window_size = max(FRAGLEN_DISTRIBUTION.values) + 10
+        if paired_end:
+            target_size = WINDOW_TARGET_SCALE * fragment_size
+            overlap = fragment_size
+            overlap_min_window_size = max(fraglen_distribution.values) + 10
         else:
-            target_size = WINDOW_TARGET_SCALE * READLEN
-            overlap = READLEN
-            overlap_min_window_size = READLEN + 10
+            target_size = WINDOW_TARGET_SCALE * read_len
+            overlap = read_len
+            overlap_min_window_size = read_len + 10
 
         print('--------------------------------')
-        if ONLY_VCF:
+        if only_vcf:
             print('generating vcf...')
         else:
             print('sampling reads...')
@@ -473,7 +427,7 @@ def main(raw_args=None):
             (pi, pf) = n_regions['non_N'][i]
             n_target_windows = max([1, (pf - pi) // target_size])
             bpd = int((pf - pi) / float(n_target_windows))
-            # bpd += GC_WINDOW_SIZE - bpd%GC_WINDOW_SIZE
+            # bpd += gc_window_size - bpd%gc_window_size
 
             # print len(refSequence), (pi,pf), n_target_windows
             # print structural_vars
@@ -511,8 +465,9 @@ def main(raw_args=None):
                 structural_vars = []
                 for n in vars_in_window:
                     buffer_needed = max([max([abs(len(n[1]) - len(alt_allele)), 1]) for alt_allele in
-                                        n[2]])  # change: added abs() so that insertions are also buffered.
-                    structural_vars.append((n[0] - 1, buffer_needed))  # -1 because going from VCF coords to array coords
+                                         n[2]])  # change: added abs() so that insertions are also buffered.
+                    structural_vars.append(
+                        (n[0] - 1, buffer_needed))  # -1 because going from VCF coords to array coords
 
                 # adjust end-position of window based on inserted structural mutations
                 buffer_added = 0
@@ -552,28 +507,28 @@ def main(raw_args=None):
 
                 # compute coverage modifiers
                 coverage_avg = None
-                coverage_dat = [GC_WINDOW_SIZE, GC_SCALE_VAL, []]
+                coverage_dat = [gc_window_size, gc_scale_val, []]
                 target_hits = 0
-                if INPUT_BED is None:
+                if input_bed is None:
                     coverage_dat[2] = [1.0] * (end - start)
                 else:
                     if ref_index[RI][0] not in input_regions:
-                        coverage_dat[2] = [OFFTARGET_SCALAR] * (end - start)
+                        coverage_dat[2] = [off_target_scalar] * (end - start)
                     else:
                         for j in range(start, end):
                             if not (bisect.bisect(input_regions[ref_index[RI][0]], j) % 2):
                                 coverage_dat[2].append(1.0)
                                 target_hits += 1
                             else:
-                                coverage_dat[2].append(OFFTARGET_SCALAR)
+                                coverage_dat[2].append(off_target_scalar)
 
                 # offtarget and we're not interested?
-                if OFFTARGET_DISCARD and target_hits <= READLEN:
+                if off_target_discard and target_hits <= read_len:
                     coverage_avg = 0.0
                     skip_this_window = True
 
                 # print len(coverage_dat[2]), sum(coverage_dat[2])
-                if sum(coverage_dat[2]) < LOW_COV_THRESH:
+                if sum(coverage_dat[2]) < low_cov_thresh:
                     coverage_avg = 0.0
                     skip_this_window = True
 
@@ -594,11 +549,11 @@ def main(raw_args=None):
 
                 # construct sequence data that we will sample reads from
                 if sequences is None:
-                    sequences = SequenceContainer(start, refSequence[start:end], PLOIDS, overlap, READLEN,
-                                                  [MUT_MODEL] * PLOIDS, MUT_RATE, only_vcf=ONLY_VCF)
+                    sequences = SequenceContainer(start, refSequence[start:end], ploids, overlap, read_len,
+                                                  [mut_model] * ploids, mut_rate, only_vcf=only_vcf)
                 else:
-                    sequences.update(start, refSequence[start:end], PLOIDS, overlap, READLEN, [MUT_MODEL] * PLOIDS,
-                                     MUT_RATE)
+                    sequences.update(start, refSequence[start:end], ploids, overlap, read_len, [mut_model] * ploids,
+                                     mut_rate)
 
                 # insert variants
                 sequences.insert_mutations(vars_from_prev_overlap + vars_in_window)
@@ -606,16 +561,16 @@ def main(raw_args=None):
                 # print all_inserted_variants
 
                 # init coverage
-                if sum(coverage_dat[2]) >= LOW_COV_THRESH:
-                    if PAIRED_END:
-                        coverage_avg = sequences.init_coverage(tuple(coverage_dat), frag_dist=FRAGLEN_DISTRIBUTION)
+                if sum(coverage_dat[2]) >= low_cov_thresh:
+                    if paired_end:
+                        coverage_avg = sequences.init_coverage(tuple(coverage_dat), frag_dist=fraglen_distribution)
                     else:
                         coverage_avg = sequences.init_coverage(tuple(coverage_dat))
 
                 # unused cancer stuff
-                if CANCER:
-                    tumor_sequences = SequenceContainer(start, refSequence[start:end], PLOIDS, overlap, READLEN,
-                                                        [CANCER_MODEL] * PLOIDS, MUT_RATE, coverage_dat)
+                if cancer:
+                    tumor_sequences = SequenceContainer(start, refSequence[start:end], ploids, overlap, read_len,
+                                                        [cancer_model] * ploids, mut_rate, coverage_dat)
                     tumor_sequences.insert_mutations(vars_cancer_from_prev_overlap + all_inserted_variants)
                     all_cancer_variants = tumor_sequences.random_mutations()
 
@@ -625,30 +580,30 @@ def main(raw_args=None):
                 for n in all_inserted_variants:
                     if n[0] >= end - overlap - 1:
                         vars_from_prev_overlap.append(n)
-                if CANCER:
+                if cancer:
                     for n in all_cancer_variants:
                         if n[0] >= end - overlap - 1:
                             vars_cancer_from_prev_overlap.append(n)
 
                 # if we're only producing VCF, no need to go through the hassle of generating reads
-                if ONLY_VCF:
+                if only_vcf:
                     pass
                 else:
-                    windowSpan = end - start
-                    if PAIRED_END:
-                        if FORCE_COVERAGE:
-                            reads_to_sample = int((windowSpan * float(COVERAGE)) / (2 * READLEN)) + 1
+                    window_span = end - start
+                    if paired_end:
+                        if force_coverage:
+                            reads_to_sample = int((window_span * float(coverage)) / (2 * read_len)) + 1
                         else:
-                            reads_to_sample = int((windowSpan * float(COVERAGE) * coverage_avg) / (2 * READLEN)) + 1
+                            reads_to_sample = int((window_span * float(coverage) * coverage_avg) / (2 * read_len)) + 1
                     else:
-                        if FORCE_COVERAGE:
-                            reads_to_sample = int((windowSpan * float(COVERAGE)) / READLEN) + 1
+                        if force_coverage:
+                            reads_to_sample = int((window_span * float(coverage)) / read_len) + 1
                         else:
-                            reads_to_sample = int((windowSpan * float(COVERAGE) * coverage_avg) / READLEN) + 1
+                            reads_to_sample = int((window_span * float(coverage) * coverage_avg) / read_len) + 1
 
                     # if coverage is so low such that no reads are to be sampled, skip region
                     #      (i.e., remove buffer of +1 reads we add to every window)
-                    if reads_to_sample == 1 and sum(coverage_dat[2]) < LOW_COV_THRESH:
+                    if reads_to_sample == 1 and sum(coverage_dat[2]) < low_cov_thresh:
                         reads_to_sample = 0
 
                     # sample reads
@@ -656,9 +611,9 @@ def main(raw_args=None):
                     for i in range(reads_to_sample):
 
                         is_unmapped = []
-                        if PAIRED_END:
-                            my_fraglen = FRAGLEN_DISTRIBUTION.sample()
-                            my_read_data = sequences.sample_read(SE_CLASS, my_fraglen)
+                        if paired_end:
+                            my_fraglen = fraglen_distribution.sample()
+                            my_read_data = sequences.sample_read(se_class, my_fraglen)
                             if my_read_data is None:  # skip if we failed to find a valid position to sample read
                                 continue
                             if my_read_data[0][0] is None:
@@ -672,7 +627,7 @@ def main(raw_args=None):
                                 is_unmapped.append(False)
                                 my_read_data[1][0] += start
                         else:
-                            my_read_data = sequences.sample_read(SE_CLASS)
+                            my_read_data = sequences.sample_read(se_class)
                             if my_read_data is None:  # skip if we failed to find a valid position to sample read
                                 continue
                             if my_read_data[0][0] is None:  # unmapped read (lives in large insertion)
@@ -683,13 +638,13 @@ def main(raw_args=None):
 
                         # are we discarding offtargets?
                         outside_boundaries = []
-                        if OFFTARGET_DISCARD and INPUT_BED != None:
+                        if off_target_discard and input_bed is not None:
                             outside_boundaries += [bisect.bisect(input_regions[ref_index[RI][0]], n[0]) % 2 for n
                                                    in my_read_data]
                             outside_boundaries += [
                                 bisect.bisect(input_regions[ref_index[RI][0]], n[0] + len(n[2])) % 2 for n in
                                 my_read_data]
-                        if DISCARD_BED != None:
+                        if discard_bed is not None:
                             outside_boundaries += [bisect.bisect(discardRegions[ref_index[RI][0]], n[0]) % 2 for
                                                    n in my_read_data]
                             outside_boundaries += [
@@ -698,107 +653,104 @@ def main(raw_args=None):
                         if len(outside_boundaries) and any(outside_boundaries):
                             continue
 
-                        if NJOBS > 1:
-                            my_read_name = OUT_PREFIX_NAME + '-j' + str(MYJOB) + '-' + ref_index[RI][0] + '-r' + str(
-                                read_name_count)
-                        else:
-                            my_read_name = OUT_PREFIX_NAME + '-' + ref_index[RI][0] + '-' + str(read_name_count)
+                        my_read_name = OUT_PREFIX_NAME + '-' + ref_index[RI][0] + '-' + str(read_name_count)
                         read_name_count += len(my_read_data)
 
                         # if desired, replace all low-quality bases with Ns
-                        if N_MAX_QUAL > -1:
+                        if n_max_qual > -1:
                             for j in range(len(my_read_data)):
                                 my_read_string = [n for n in my_read_data[j][2]]
                                 for k in range(len(my_read_data[j][3])):
-                                    adjusted_qual = ord(my_read_data[j][3][k]) - SE_CLASS.offQ
-                                    if adjusted_qual <= N_MAX_QUAL:
+                                    adjusted_qual = ord(my_read_data[j][3][k]) - se_class.offQ
+                                    if adjusted_qual <= n_max_qual:
                                         my_read_string[k] = 'N'
                                 my_read_data[j][2] = ''.join(my_read_string)
 
                         # flip a coin, are we forward or reverse strand?
-                        isForward = (random.random() < 0.5)
+                        is_forward = (random.random() < 0.5)
 
                         # if read (or read + mate for PE) are unmapped, put them at end of bam file
                         if all(is_unmapped):
-                            if PAIRED_END:
-                                if isForward:
+                            if paired_end:
+                                if is_forward:
                                     flag1 = sam_flag(['paired', 'unmapped', 'mate_unmapped', 'first', 'mate_reverse'])
                                     flag2 = sam_flag(['paired', 'unmapped', 'mate_unmapped', 'second', 'reverse'])
                                 else:
                                     flag1 = sam_flag(['paired', 'unmapped', 'mate_unmapped', 'second', 'mate_reverse'])
                                     flag2 = sam_flag(['paired', 'unmapped', 'mate_unmapped', 'first', 'reverse'])
-                                unmapped_records.append((my_read_name + '/1', my_read_data[0], 109))
-                                unmapped_records.append((my_read_name + '/2', my_read_data[1], 157))
+                                unmapped_records.append((my_read_name + '/1', my_read_data[0], flag1))
+                                unmapped_records.append((my_read_name + '/2', my_read_data[1], flag2))
                             else:
                                 flag1 = sam_flag(['unmapped'])
-                                unmapped_records.append((my_read_name + '/1', my_read_data[0], 4))
+                                unmapped_records.append((my_read_name + '/1', my_read_data[0], flag1))
 
-                        # write read data out to FASTQ and BAM files, bypass FASTQ if option specified
-                        myRefIndex = indices_by_ref_name[ref_index[RI][0]]
+                        my_ref_index = indices_by_ref_name[ref_index[RI][0]]
 
                         # write SE output
                         if len(my_read_data) == 1:
-                            if NO_FASTQ is not True:
-                                if isForward:
+                            if no_fastq is not True:
+                                if is_forward:
                                     OFW.write_fastq_record(my_read_name, my_read_data[0][2], my_read_data[0][3])
                                 else:
-                                    OFW.write_fastq_record(my_read_name, reverse_complement(my_read_data[0][2]), my_read_data[0][3][::-1])
-                            if SAVE_BAM:
+                                    OFW.write_fastq_record(my_read_name, reverse_complement(my_read_data[0][2]),
+                                                           my_read_data[0][3][::-1])
+                            if save_bam:
                                 if is_unmapped[0] is False:
-                                    if isForward:
+                                    if is_forward:
                                         flag1 = 0
-                                        OFW.write_bam_record(myRefIndex, my_read_name, my_read_data[0][0],
+                                        OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
                                                              my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
                                                              sam_flag=flag1)
                                     else:
                                         flag1 = sam_flag(['reverse'])
-                                        OFW.write_bam_record(myRefIndex, my_read_name, my_read_data[0][0],
-                                                         my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
-                                                         sam_flag=flag1)
+                                        OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
+                                                             my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
+                                                             sam_flag=flag1)
                         # write PE output
                         elif len(my_read_data) == 2:
-                            if NO_FASTQ is not True:
+                            if no_fastq is not True:
                                 OFW.write_fastq_record(my_read_name, my_read_data[0][2], my_read_data[0][3],
-                                                       read2=my_read_data[1][2], qual2=my_read_data[1][3], orientation=isForward)
-                            if SAVE_BAM:
+                                                       read2=my_read_data[1][2], qual2=my_read_data[1][3],
+                                                       orientation=is_forward)
+                            if save_bam:
                                 if is_unmapped[0] is False and is_unmapped[1] is False:
-                                    if isForward:
+                                    if is_forward:
                                         flag1 = sam_flag(['paired', 'proper', 'first', 'mate_reverse'])
                                         flag2 = sam_flag(['paired', 'proper', 'second', 'reverse'])
                                     else:
                                         flag1 = sam_flag(['paired', 'proper', 'second', 'mate_reverse'])
                                         flag2 = sam_flag(['paired', 'proper', 'first', 'reverse'])
-                                    OFW.write_bam_record(myRefIndex, my_read_name, my_read_data[0][0],
+                                    OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
                                                          my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
                                                          sam_flag=flag1,
                                                          mate_pos=my_read_data[1][0])
-                                    OFW.write_bam_record(myRefIndex, my_read_name, my_read_data[1][0],
+                                    OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[1][0],
                                                          my_read_data[1][1], my_read_data[1][2], my_read_data[1][3],
                                                          sam_flag=flag2, mate_pos=my_read_data[0][0])
                                 elif is_unmapped[0] is False and is_unmapped[1] is True:
-                                    if isForward:
+                                    if is_forward:
                                         flag1 = sam_flag(['paired', 'first', 'mate_unmapped', 'mate_reverse'])
                                         flag2 = sam_flag(['paired', 'second', 'unmapped', 'reverse'])
                                     else:
                                         flag1 = sam_flag(['paired', 'second', 'mate_unmapped', 'mate_reverse'])
                                         flag2 = sam_flag(['paired', 'first', 'unmapped', 'reverse'])
-                                    OFW.write_bam_record(myRefIndex, my_read_name, my_read_data[0][0],
+                                    OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
                                                          my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
                                                          sam_flag=flag1, mate_pos=my_read_data[0][0])
-                                    OFW.write_bam_record(myRefIndex, my_read_name, my_read_data[0][0],
+                                    OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
                                                          my_read_data[1][1], my_read_data[1][2], my_read_data[1][3],
                                                          sam_flag=flag2, mate_pos=my_read_data[0][0], aln_map_quality=0)
                                 elif is_unmapped[0] is True and is_unmapped[1] is False:
-                                    if isForward:
+                                    if is_forward:
                                         flag1 = sam_flag(['paired', 'first', 'unmapped', 'mate_reverse'])
                                         flag2 = sam_flag(['paired', 'second', 'mate_unmapped', 'reverse'])
                                     else:
                                         flag1 = sam_flag(['paired', 'second', 'unmapped', 'mate_reverse'])
                                         flag2 = sam_flag(['paired', 'first', 'mate_unmapped', 'reverse'])
-                                    OFW.write_bam_record(myRefIndex, my_read_name, my_read_data[1][0],
+                                    OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[1][0],
                                                          my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
                                                          sam_flag=flag1, mate_pos=my_read_data[1][0], aln_map_quality=0)
-                                    OFW.write_bam_record(myRefIndex, my_read_name, my_read_data[1][0],
+                                    OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[1][0],
                                                          my_read_data[1][1], my_read_data[1][2], my_read_data[1][3],
                                                          sam_flag=flag2, mate_pos=my_read_data[1][0])
                         else:
@@ -827,14 +779,14 @@ def main(raw_args=None):
             print('100%')
         else:
             print('')
-        if ONLY_VCF:
+        if only_vcf:
             print('VCF generation completed in ', end='')
         else:
             print('Read sampling completed in ', end='')
         print(int(time.time() - tt), '(sec)')
 
         # write all output variants for this reference
-        if SAVE_VCF:
+        if save_vcf:
             print('Writing output VCF...')
             for k in sorted(ALL_VARIANTS_OUT.keys()):
                 current_ref = ref_index[RI][0]
@@ -847,10 +799,10 @@ def main(raw_args=None):
     # break
 
     # write unmapped reads to bam file
-    if SAVE_BAM and len(unmapped_records):
+    if save_bam and len(unmapped_records):
         print('writing unmapped reads to bam file...')
         for umr in unmapped_records:
-            if PAIRED_END:
+            if paired_end:
                 OFW.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], sam_flag=umr[2], mate_pos=0,
                                      aln_map_quality=0)
             else:
@@ -858,7 +810,7 @@ def main(raw_args=None):
 
     # close output files
     OFW.close_files()
-    if CANCER:
+    if cancer:
         OFW_CANCER.close_files()
 
 
diff --git a/py/output_file_writer.py b/py/output_file_writer.py
index a84a34c..b3405a3 100644
--- a/py/output_file_writer.py
+++ b/py/output_file_writer.py
@@ -95,14 +95,10 @@ def sam_flag(l):
 #	gzipped    = True for compressed FASTQ/VCF, False for uncompressed
 #
 class OutputFileWriter:
-    def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, gzipped=False, job_tuple=(1, 1),
+    def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, gzipped=False,
                  no_fastq=False, fasta_instead=False):
 
         job_suffix = ''
-        if job_tuple[1] > 1:
-            jsl = len(str(job_tuple[1]))
-            jsb = '0' * (jsl - len(str(job_tuple[0])))
-            job_suffix = '.job' + jsb + str(job_tuple[0]) + 'of' + str(job_tuple[1])
 
         self.fasta_instead = fasta_instead
         if fasta_instead:
@@ -138,34 +134,33 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
             else:
                 self.vcf_file = open(vcf, 'wb')
 
-            # WRITE VCF HEADER (if parallel: only for first job)
-            if job_tuple[0] == 1:
-                self.vcf_file.write('##fileformat=VCFv4.1\n'.encode('utf-8'))
-                reference = '##reference=' + vcf_header[0] + '\n'
-                self.vcf_file.write(reference.encode('utf-8'))
-                self.vcf_file.write('##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">\n'.encode('utf-8'))
-                self.vcf_file.write(
-                    '##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">\n'.encode('utf-8'))
-                # self.vcf_file.write('##INFO=<ID=READS,Number=1,Type=String,Description="Names of Reads Covering this Variant">\n')
-                self.vcf_file.write(
-                    '##INFO=<ID=VMX,Number=1,Type=String,Description="SNP is Missense in these Read Frames">\n'.encode(
-                        'utf-8'))
-                self.vcf_file.write(
-                    '##INFO=<ID=VNX,Number=1,Type=String,Description="SNP is Nonsense in these Read Frames">\n'.encode(
-                        'utf-8'))
-                self.vcf_file.write(
-                    '##INFO=<ID=VFX,Number=1,Type=String,Description="Indel Causes Frameshift">\n'.encode('utf-8'))
-                self.vcf_file.write(
-                    '##INFO=<ID=WP,Number=A,Type=Integer,Description="NEAT-GenReads ploidy indicator">\n'.encode(
-                        'utf-8'))
-                self.vcf_file.write('##ALT=<ID=DEL,Description="Deletion">\n'.encode('utf-8'))
-                self.vcf_file.write('##ALT=<ID=DUP,Description="Duplication">\n'.encode('utf-8'))
-                self.vcf_file.write('##ALT=<ID=INS,Description="Insertion of novel sequence">\n'.encode('utf-8'))
-                self.vcf_file.write('##ALT=<ID=INV,Description="Inversion">\n'.encode('utf-8'))
-                self.vcf_file.write('##ALT=<ID=CNV,Description="Copy number variable region">\n'.encode('utf-8'))
-                self.vcf_file.write('##ALT=<ID=TRANS,Description="Translocation">\n'.encode('utf-8'))
-                self.vcf_file.write('##ALT=<ID=INV-TRANS,Description="Inverted translocation">\n'.encode('utf-8'))
-                self.vcf_file.write('#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\n'.encode('utf-8'))
+            # WRITE VCF HEADER
+            self.vcf_file.write('##fileformat=VCFv4.1\n'.encode('utf-8'))
+            reference = '##reference=' + vcf_header[0] + '\n'
+            self.vcf_file.write(reference.encode('utf-8'))
+            self.vcf_file.write('##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">\n'.encode('utf-8'))
+            self.vcf_file.write(
+                '##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">\n'.encode('utf-8'))
+            # self.vcf_file.write('##INFO=<ID=READS,Number=1,Type=String,Description="Names of Reads Covering this Variant">\n')
+            self.vcf_file.write(
+                '##INFO=<ID=VMX,Number=1,Type=String,Description="SNP is Missense in these Read Frames">\n'.encode(
+                    'utf-8'))
+            self.vcf_file.write(
+                '##INFO=<ID=VNX,Number=1,Type=String,Description="SNP is Nonsense in these Read Frames">\n'.encode(
+                    'utf-8'))
+            self.vcf_file.write(
+                '##INFO=<ID=VFX,Number=1,Type=String,Description="Indel Causes Frameshift">\n'.encode('utf-8'))
+            self.vcf_file.write(
+                '##INFO=<ID=WP,Number=A,Type=Integer,Description="NEAT-GenReads ploidy indicator">\n'.encode(
+                    'utf-8'))
+            self.vcf_file.write('##ALT=<ID=DEL,Description="Deletion">\n'.encode('utf-8'))
+            self.vcf_file.write('##ALT=<ID=DUP,Description="Duplication">\n'.encode('utf-8'))
+            self.vcf_file.write('##ALT=<ID=INS,Description="Insertion of novel sequence">\n'.encode('utf-8'))
+            self.vcf_file.write('##ALT=<ID=INV,Description="Inversion">\n'.encode('utf-8'))
+            self.vcf_file.write('##ALT=<ID=CNV,Description="Copy number variable region">\n'.encode('utf-8'))
+            self.vcf_file.write('##ALT=<ID=TRANS,Description="Translocation">\n'.encode('utf-8'))
+            self.vcf_file.write('##ALT=<ID=INV-TRANS,Description="Inverted translocation">\n'.encode('utf-8'))
+            self.vcf_file.write('#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\n'.encode('utf-8'))
 
         #
         #	BAM OUTPUT
@@ -174,24 +169,23 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
         if bam_header is not None:
             self.bam_file = BgzfWriter(bam, 'w', compresslevel=BAM_COMPRESSION_LEVEL)
 
-            # WRITE BAM HEADER (if parallel: only for first job)
-            if True or job_tuple[0] == 1:
-                self.bam_file.write("BAM\1")
-                header = '@HD\tVN:1.5\tSO:coordinate\n'
-                for n in bam_header[0]:
-                    header += '@SQ\tSN:' + n[0] + '\tLN:' + str(n[3]) + '\n'
-                header += '@RG\tID:NEAT\tSM:NEAT\tLB:NEAT\tPL:NEAT\n'
-                header_bytes = len(header)
-                num_refs = len(bam_header[0])
-                self.bam_file.write(pack('<i', header_bytes))
-                self.bam_file.write(header)
-                self.bam_file.write(pack('<i', num_refs))
-
-                for n in bam_header[0]:
-                    l_name = len(n[0]) + 1
-                    self.bam_file.write(pack('<i', l_name))
-                    self.bam_file.write(n[0] + '\0')
-                    self.bam_file.write(pack('<i', n[3]))
+            # WRITE BAM HEADER
+            self.bam_file.write("BAM\1")
+            header = '@HD\tVN:1.5\tSO:coordinate\n'
+            for n in bam_header[0]:
+                header += '@SQ\tSN:' + n[0] + '\tLN:' + str(n[3]) + '\n'
+            header += '@RG\tID:NEAT\tSM:NEAT\tLB:NEAT\tPL:NEAT\n'
+            header_bytes = len(header)
+            num_refs = len(bam_header[0])
+            self.bam_file.write(pack('<i', header_bytes))
+            self.bam_file.write(header)
+            self.bam_file.write(pack('<i', num_refs))
+
+            for n in bam_header[0]:
+                l_name = len(n[0]) + 1
+                self.bam_file.write(pack('<i', l_name))
+                self.bam_file.write(n[0] + '\0')
+                self.bam_file.write(pack('<i', n[3]))
 
         # buffers for more efficient writing
         self.fq1_buffer = []
diff --git a/py/vcf_func.py b/py/vcf_func.py
index cf02808..003d17f 100644
--- a/py/vcf_func.py
+++ b/py/vcf_func.py
@@ -157,7 +157,8 @@ def parse_vcf(vcf_path, tumor_normal=False, ploidy=2):
                         print('Detected 2 sample columns in input VCF, assuming tumor/normal.')
                     else:
                         print(
-                            'Warning: Multiple sample columns present in input VCF. By default genReads uses only the first column.')
+                            'Warning: Multiple sample columns present in input VCF. By default genReads uses '
+                            'only the first column.')
                 else:
                     samp_names = ['Unknown']
                 if tumor_normal:

From 5d2ba6aa6120246bc17996c02d53cf40477b98a1 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 21 Aug 2020 12:31:32 -0500
Subject: [PATCH 133/323] Starting to reorg genreads

---
 gen_reads.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index bdb9d2f..970c579 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -76,7 +76,7 @@ def main(raw_args=None):
                         help='rng seed value; identical RNG value should produce identical runs of the program, so '
                              'things like read locations, variant positions, error positions, etc, '
                              'should all be the same.')
-    # TODO check if this argument does anything at all.
+    # TODO check if this argument does anything at all. Near as I can tell the results are ALWAYS gzipped.
     parser.add_argument('--gz', required=False, action='store_true', default=False, help='gzip output FQ and VCF')
     parser.add_argument('--no-fastq', required=False, action='store_true', default=False,
                         help='bypass fastq generation')
@@ -93,6 +93,7 @@ def main(raw_args=None):
     """
     # absolute path to this script
     sim_path = pathlib.Path(__file__).resolve().parent
+    print(sim_path)
 
     # if coverage val for a given window/position is below this value, consider it effectively zero.
     low_cov_thresh = 50

From 14f519fee2a309dab2d858a3e3785e487647407e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 21 Aug 2020 15:48:49 -0500
Subject: [PATCH 134/323] Updated input_checking.py to use pathlib

---
 gen_reads.py                                  |  62 ++++++++++--------
 py/SequenceContainer.py                       |   3 +-
 .../SequenceContainer.cpython-37.pyc          | Bin 31477 -> 31522 bytes
 py/__pycache__/__init__.cpython-37.pyc        | Bin 146 -> 146 bytes
 .../biopython_modified_bgzf.cpython-37.pyc    | Bin 2668 -> 2668 bytes
 py/__pycache__/neat_cigar.cpython-37.pyc      | Bin 3114 -> 3114 bytes
 py/__pycache__/probability.cpython-37.pyc     | Bin 4082 -> 4082 bytes
 py/__pycache__/ref_func.cpython-37.pyc        | Bin 4837 -> 4837 bytes
 py/input_checking.py                          |  21 +++---
 9 files changed, 45 insertions(+), 41 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 970c579..7d36d5a 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -32,32 +32,37 @@
 from py.probability import DiscreteDistribution, mean_ind_of_weighted_list
 from py.SequenceContainer import SequenceContainer, ReadContainer, parse_input_mutation_model
 
-"""//////////////////////////////////////////////////
-////////////    PARSE INPUT ARGUMENTS    ////////////
-//////////////////////////////////////////////////"""
-
 
 def main(raw_args=None):
+    """//////////////////////////////////////////////////
+    ////////////    PARSE INPUT ARGUMENTS    ////////////
+    //////////////////////////////////////////////////"""
+
     parser = argparse.ArgumentParser(description='NEAT-genReads V2.0')
-    parser.add_argument('-r', type=str, required=True, metavar='<str>', help="* ref.fa")
-    parser.add_argument('-R', type=int, required=True, metavar='<int>', help="* read length")
-    parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output prefix")
-    parser.add_argument('-c', type=float, required=False, metavar='<float>', default=10., help="average coverage")
-    parser.add_argument('-e', type=str, required=False, metavar='<str>', default=None, help="sequencing error model")
-    parser.add_argument('-E', type=float, required=False, metavar='<float>', default=-1,
-                        help="rescale avg sequencing error rate to this")
-    parser.add_argument('-p', type=int, required=False, metavar='<int>', default=2, help="ploidy")
-    parser.add_argument('-t', type=str, required=False, metavar='<str>', default=None,
-                        help="bed file containing targeted regions")
-    parser.add_argument('-d', type=str, required=False, metavar='<str>', default=None, help="discard_regions.bed")
-    parser.add_argument('-to', type=float, required=False, metavar='<float>', default=0.00,
+    parser.add_argument('-r', type=str, required=True, metavar='reference', help="Path to reference fasta")
+    parser.add_argument('-R', type=int, required=True, metavar='read length', help="The desired read length")
+    parser.add_argument('-o', type=str, required=True, metavar='output_prefix',
+                        help="Prefix for the output files (can be a path)")
+    parser.add_argument('-c', type=float, required=False, metavar='coverage', default=10.0,
+                        help="Average coverage, default is 10.0")
+    parser.add_argument('-e', type=str, required=False, metavar='error_model', default=None,
+                        help="Location and name of sequencing error model (leave blank to use the default)")
+    parser.add_argument('-E', type=float, required=False, metavar='Error rate', default=-1,
+                        help="Rescale avg sequencing error rate to this, must be between 0.0 and 0.3")
+    parser.add_argument('-p', type=int, required=False, metavar='ploidy', default=2,
+                        help="Desired ploidy, default = 2")
+    parser.add_argument('-t', type=str, required=False, metavar='target.bed', default=None,
+                        help="Bed file containing targeted regions")
+    parser.add_argument('-d', type=str, required=False, metavar='discard_regions.bed', default=None,
+                        help="Bed file with regions to discard")
+    parser.add_argument('-to', type=float, required=False, metavar='off-target coverage scalar', default=0.00,
                         help="off-target coverage scalar")
-    parser.add_argument('-m', type=str, required=False, metavar='<str>', default=None,
-                        help="mutation model pickle file")
-    parser.add_argument('-M', type=float, required=False, metavar='<float>', default=-1,
-                        help="rescale avg mutation rate to this")
-    parser.add_argument('-Mb', type=str, required=False, metavar='<str>', default=None,
-                        help="bed file containing positional mut rates")
+    parser.add_argument('-m', type=str, required=False, metavar='model.p', default=None,
+                        help="Mutation model pickle file")
+    parser.add_argument('-M', type=float, required=False, metavar='avg mut rate', default=-1,
+                        help="Rescale avg mutation rate to this, must be between 0 and 0.3")
+    parser.add_argument('-Mb', type=str, required=False, metavar='mut_rates.bed', default=None,
+                        help="Bed file containing positional mut rates")
     parser.add_argument('-N', type=int, required=False, metavar='<int>', default=-1,
                         help="below this qual, replace base-calls with 'N's")
     parser.add_argument('-v', type=str, required=False, metavar='<str>', default=None, help="input VCF file")
@@ -93,7 +98,6 @@ def main(raw_args=None):
     """
     # absolute path to this script
     sim_path = pathlib.Path(__file__).resolve().parent
-    print(sim_path)
 
     # if coverage val for a given window/position is below this value, consider it effectively zero.
     low_cov_thresh = 50
@@ -119,9 +123,9 @@ def main(raw_args=None):
 
     rng_seed = args.rng
 
-    """************************************************
-    ****            INPUT ERROR CHECKING
-    ************************************************"""
+    """
+    INPUT ERROR CHECKING
+    """
     if (fragment_size is None and fragment_std is not None) or (fragment_size is not None and fragment_std is None):
         print('\nError: --pe argument takes 2 space-separated arguments.\n')
         exit(1)
@@ -159,9 +163,9 @@ def main(raw_args=None):
         rng_seed = random.randint(1, 99999999)
     random.seed(rng_seed)
 
-    """************************************************
-    ****             LOAD INPUT MODELS
-    ************************************************"""
+    """
+    LOAD INPUT MODELS
+    """
 
     # mutation models
     mut_model = parse_input_mutation_model(mut_model, 1)
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 89e0154..0c76619 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -215,7 +215,8 @@ def init_mut_models(self, mut_models, mut_rate):
             self.models.append([self.mut_scalar * n[0], n[1], n[2], n[3], DiscreteDistribution(n[5], n[4]),
                                 DiscreteDistribution(n[7], n[6]), []])
             for m in n[8]:
-                self.models[-1][6].append([DiscreteDistribution(m[0], NUCL), DiscreteDistribution(m[1], NUCL), DiscreteDistribution(m[2], NUCL), DiscreteDistribution(m[3], NUCL)])
+                self.models[-1][6].append([DiscreteDistribution(m[0], NUCL), DiscreteDistribution(m[1], NUCL),
+                                           DiscreteDistribution(m[2], NUCL), DiscreteDistribution(m[3], NUCL)])
             self.models[-1].append([m for m in n[9]])
 
     def init_poisson(self):
diff --git a/py/__pycache__/SequenceContainer.cpython-37.pyc b/py/__pycache__/SequenceContainer.cpython-37.pyc
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diff --git a/py/__pycache__/__init__.cpython-37.pyc b/py/__pycache__/__init__.cpython-37.pyc
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delta 19
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delta 19
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delta 20
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delta 20
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acmew)|4E+PiI<m)0SFxa*l*;1&JO@QW(C0j

delta 20
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index 1a4ba051edd81dcc444ed2938470b98ed11e20de..b8b4b688d0f76ac387e54f1ac6fa29e8a168de60 100644
GIT binary patch
delta 80
zcmaE=`c#$MiI<m)0SFxa*l*+(;A4!MEW>9FBqR8$7y~DBb4zZ1!#9(O%@?So$bWLT
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delta 80
zcmaE=`c#$MiI<m)0SM$K=x*c|;A6ZsS%%LVNJj8gF$PcO=9b+2hHoYln;%d~QQ+im
c!PAUklf#94xPw6gVIV?nav`7a<by&g07|74*8l(j

diff --git a/py/input_checking.py b/py/input_checking.py
index 4e8d895..bc59c39 100644
--- a/py/input_checking.py
+++ b/py/input_checking.py
@@ -3,7 +3,7 @@
 and issues an error if there is something wrong.
 """
 
-import os
+import pathlib
 
 
 def required_field(variable_to_test: any, err_string: str) -> None:
@@ -19,10 +19,10 @@ def required_field(variable_to_test: any, err_string: str) -> None:
         exit(1)
 
 
-def check_file_open(filename: str, err_string: str, required: bool = False) -> None:
+def check_file_open(filename: pathlib, err_string: str, required: bool = False) -> None:
     """
     Checks that the filename is not empty and that it is indeed a  file
-    :param filename: file name, string
+    :param filename: file name, pathlib object
     :param err_string: string of the error if it is not a file
     :param required: If not required, skips the check
     :return: None
@@ -30,25 +30,24 @@ def check_file_open(filename: str, err_string: str, required: bool = False) -> N
     if required or filename is not None:
         if filename is None:
             print('\n' + err_string + '\n')
-            exit(1)
+            raise ValueError
         else:
             try:
-                open(filename, 'r')
-            except ValueError:
+                filename.resolve(strict=True)
+            except FileNotFoundError:
                 print('\n' + err_string + '\n')
-                exit(1)
 
 
-def check_dir(directory: str, err_string: str) -> None:
+def check_dir(directory: pathlib, err_string: str) -> None:
     """
     Checks that directory exists and is a directory
     :param directory: string of the directory path
     :param err_string: string of the error in case it is not a directory or doesn't exist
     :return: None
     """
-    if not os.path.isdir(directory):
+    if not directory.is_dir():
         print('\n' + err_string + '\n')
-        exit(1)
+        raise NotADirectoryError
 
 
 def is_in_range(value: float, lower_bound: float, upper_bound: float, err_string: str) -> None:
@@ -63,4 +62,4 @@ def is_in_range(value: float, lower_bound: float, upper_bound: float, err_string
     """
     if value < lower_bound or value > upper_bound:
         print('\n' + err_string + '\n')
-        exit(1)
+        raise ValueError

From e9ebbea36b710701bbc101afe4093079a665c0f6 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 21 Aug 2020 15:58:12 -0500
Subject: [PATCH 135/323] Updated input_checking.py to use pathlib

---
 gen_reads.py         | 1 -
 py/input_checking.py | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 7d36d5a..7aad1e1 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -129,7 +129,6 @@ def main(raw_args=None):
     if (fragment_size is None and fragment_std is not None) or (fragment_size is not None and fragment_std is None):
         print('\nError: --pe argument takes 2 space-separated arguments.\n')
         exit(1)
-
     only_vcf = no_fastq and save_vcf and not save_bam and not fasta_instead
     if only_vcf:
         print('Only producing VCF output, that should speed things up a bit...')
diff --git a/py/input_checking.py b/py/input_checking.py
index bc59c39..0714705 100644
--- a/py/input_checking.py
+++ b/py/input_checking.py
@@ -16,7 +16,7 @@ def required_field(variable_to_test: any, err_string: str) -> None:
     """
     if variable_to_test is None:
         print('\n' + err_string + '\n')
-        exit(1)
+        raise ValueError
 
 
 def check_file_open(filename: pathlib, err_string: str, required: bool = False) -> None:

From ff7b27d7a858226ee1fb9594240c6beed6f92c85 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 21 Aug 2020 16:01:50 -0500
Subject: [PATCH 136/323] Updated input_checking.py to use pathlib

---
 py/input_checking.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/py/input_checking.py b/py/input_checking.py
index 0714705..cdc34f8 100644
--- a/py/input_checking.py
+++ b/py/input_checking.py
@@ -19,7 +19,7 @@ def required_field(variable_to_test: any, err_string: str) -> None:
         raise ValueError
 
 
-def check_file_open(filename: pathlib, err_string: str, required: bool = False) -> None:
+def check_file_open(filename: str, err_string: str, required: bool = False) -> None:
     """
     Checks that the filename is not empty and that it is indeed a  file
     :param filename: file name, pathlib object
@@ -33,19 +33,19 @@ def check_file_open(filename: pathlib, err_string: str, required: bool = False)
             raise ValueError
         else:
             try:
-                filename.resolve(strict=True)
+                pathlib.Path(filename).resolve(strict=True)
             except FileNotFoundError:
                 print('\n' + err_string + '\n')
 
 
-def check_dir(directory: pathlib, err_string: str) -> None:
+def check_dir(directory: str, err_string: str) -> None:
     """
     Checks that directory exists and is a directory
     :param directory: string of the directory path
     :param err_string: string of the error in case it is not a directory or doesn't exist
     :return: None
     """
-    if not directory.is_dir():
+    if not pathlib.Path(directory).is_dir():
         print('\n' + err_string + '\n')
         raise NotADirectoryError
 

From cb33c0dbe10fdf398b342c730f35655039230a23 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 21 Aug 2020 16:04:58 -0500
Subject: [PATCH 137/323] Updated input_checking.py to use pathlib

---
 py/input_checking.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/py/input_checking.py b/py/input_checking.py
index cdc34f8..7ac744f 100644
--- a/py/input_checking.py
+++ b/py/input_checking.py
@@ -17,6 +17,7 @@ def required_field(variable_to_test: any, err_string: str) -> None:
     if variable_to_test is None:
         print('\n' + err_string + '\n')
         raise ValueError
+        exit(1)
 
 
 def check_file_open(filename: str, err_string: str, required: bool = False) -> None:
@@ -31,11 +32,13 @@ def check_file_open(filename: str, err_string: str, required: bool = False) -> N
         if filename is None:
             print('\n' + err_string + '\n')
             raise ValueError
+            exit(1)
         else:
             try:
                 pathlib.Path(filename).resolve(strict=True)
             except FileNotFoundError:
                 print('\n' + err_string + '\n')
+                exit(1)
 
 
 def check_dir(directory: str, err_string: str) -> None:
@@ -48,6 +51,7 @@ def check_dir(directory: str, err_string: str) -> None:
     if not pathlib.Path(directory).is_dir():
         print('\n' + err_string + '\n')
         raise NotADirectoryError
+        exit(1)
 
 
 def is_in_range(value: float, lower_bound: float, upper_bound: float, err_string: str) -> None:
@@ -63,3 +67,4 @@ def is_in_range(value: float, lower_bound: float, upper_bound: float, err_string
     if value < lower_bound or value > upper_bound:
         print('\n' + err_string + '\n')
         raise ValueError
+        exit(1)

From 01a1ca6993f58c970f0687e2b9e3e6cf279bff7f Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 21 Aug 2020 16:06:16 -0500
Subject: [PATCH 138/323] Updated input_checking.py to use pathlib

---
 gen_reads.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 7aad1e1..d2164be 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -133,9 +133,9 @@ def main(raw_args=None):
     if only_vcf:
         print('Only producing VCF output, that should speed things up a bit...')
 
-    check_file_open(reference, 'ERROR: could not open reference', required=True)
-    check_file_open(input_vcf, 'ERROR: could not open input VCF', required=False)
-    check_file_open(input_bed, 'ERROR: could not open input BED', required=False)
+    check_file_open(reference, 'ERROR: could not open reference, {}'.format(reference), required=True)
+    check_file_open(input_vcf, 'ERROR: could not open input VCF, {}'.format(input_vcf), required=False)
+    check_file_open(input_bed, 'ERROR: could not open input BED, {}'.format(input_bed), required=False)
     required_field(out_prefix, 'ERROR: no output prefix provided')
 
     # if user specified mean/std, use artificial fragment length distribution, otherwise use

From 699eec163735982a6a022514c6ff5d8b5b69790f Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 24 Aug 2020 10:53:35 -0500
Subject: [PATCH 139/323] Starting to work on gen_reads and SequenceContainer

---
 gen_reads.py                                  |  97 +++----
 py/SequenceContainer.py                       | 250 ++++++------------
 .../SequenceContainer.cpython-37.pyc          | Bin 31522 -> 29131 bytes
 py/input_checking.py                          |   6 +-
 py/vcf_func.py                                |  58 ++--
 5 files changed, 163 insertions(+), 248 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index d2164be..1417199 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -33,6 +33,17 @@
 from py.SequenceContainer import SequenceContainer, ReadContainer, parse_input_mutation_model
 
 
+"""
+Some constants needed for analysis
+"""
+
+# target window size for read sampling. How many times bigger than read/frag length
+WINDOW_TARGET_SCALE = 100
+
+# allowed nucleotides
+ALLOWED_NUCL = ['A', 'C', 'G', 'T']
+
+
 def main(raw_args=None):
     """//////////////////////////////////////////////////
     ////////////    PARSE INPUT ARGUMENTS    ////////////
@@ -63,10 +74,10 @@ def main(raw_args=None):
                         help="Rescale avg mutation rate to this, must be between 0 and 0.3")
     parser.add_argument('-Mb', type=str, required=False, metavar='mut_rates.bed', default=None,
                         help="Bed file containing positional mut rates")
-    parser.add_argument('-N', type=int, required=False, metavar='<int>', default=-1,
-                        help="below this qual, replace base-calls with 'N's")
-    parser.add_argument('-v', type=str, required=False, metavar='<str>', default=None, help="input VCF file")
-
+    parser.add_argument('-N', type=int, required=False, metavar='min qual score', default=-1,
+                        help="below this quality score, replace base-calls with N's")
+    parser.add_argument('-v', type=str, required=False, metavar='vcf.file', default=None,
+                        help="Input VCF file of variants to include")
     parser.add_argument('--pe', nargs=2, type=int, required=False, metavar=('<int>', '<int>'), default=(None, None),
                         help='paired-end fragment length mean and std')
     parser.add_argument('--pe-model', type=str, required=False, metavar='<str>', default=None,
@@ -96,6 +107,7 @@ def main(raw_args=None):
     """
     Set variables for processing
     """
+
     # absolute path to this script
     sim_path = pathlib.Path(__file__).resolve().parent
 
@@ -126,17 +138,27 @@ def main(raw_args=None):
     """
     INPUT ERROR CHECKING
     """
-    if (fragment_size is None and fragment_std is not None) or (fragment_size is not None and fragment_std is None):
-        print('\nError: --pe argument takes 2 space-separated arguments.\n')
-        exit(1)
-    only_vcf = no_fastq and save_vcf and not save_bam and not fasta_instead
-    if only_vcf:
-        print('Only producing VCF output, that should speed things up a bit...')
 
+    # Check that files are real, if provided
     check_file_open(reference, 'ERROR: could not open reference, {}'.format(reference), required=True)
     check_file_open(input_vcf, 'ERROR: could not open input VCF, {}'.format(input_vcf), required=False)
     check_file_open(input_bed, 'ERROR: could not open input BED, {}'.format(input_bed), required=False)
-    required_field(out_prefix, 'ERROR: no output prefix provided')
+
+    # if user specified no fastq, not fasta only, and no bam and no vcf, then print error and exit.
+    if no_fastq and not fasta_instead and not save_bam and not save_vcf:
+        print('\nERROR: No files would be written.\n')
+        exit(1)
+
+    if no_fastq:
+        print('Bypassing FASTQ generation...')
+
+    only_vcf = no_fastq and save_vcf and not save_bam and not fasta_instead
+    if only_vcf:
+        print('Only producing VCF output...')
+
+    if (fragment_size is None and fragment_std is not None) or (fragment_size is not None and fragment_std is None):
+        print('\nERROR: --pe argument takes 2 space-separated arguments.\n')
+        exit(1)
 
     # if user specified mean/std, use artificial fragment length distribution, otherwise use
     # the empirical model specified. If neither, then we're doing single-end reads.
@@ -150,18 +172,23 @@ def main(raw_args=None):
         paired_end = False
         paired_end_artificial = False
 
-    # if user specified no fastq, not fasta only, and no bam and no vcf, then print error and exit.
-    if no_fastq and not fasta_instead and not save_bam and not save_vcf:
-        print('\nError: No files would be written.\n')
-        exit(1)
-
-    if no_fastq:
-        print('Bypassing FASTQ generation...')
-
     if rng_seed == -1:
         rng_seed = random.randint(1, 99999999)
     random.seed(rng_seed)
 
+    is_in_range(read_len, 10, 1000000, 'Error: -R must be between 10 and 1,000,000')
+    is_in_range(coverage, 0, 1000000, 'Error: -c must be between 0 and 1,000,000')
+    is_in_range(ploids, 1, 100, 'Error: -p must be between 1 and 100')
+    is_in_range(off_target_scalar, 0, 1, 'Error: -to must be between 0 and 1')
+
+    if se_rate != -1:
+        is_in_range(se_rate, 0, 0.3, 'Error: -E must be between 0 and 0.3')
+    else:
+        se_rate = None
+
+    if n_max_qual != -1:
+        is_in_range(n_max_qual, 1, 40, 'Error: -N must be between 1 and 40')
+
     """
     LOAD INPUT MODELS
     """
@@ -173,9 +200,10 @@ def main(raw_args=None):
     if mut_rate < 0.:
         mut_rate = None
 
+    if mut_rate != -1 and mut_rate is not None:
+        is_in_range(mut_rate, 0, 0.3, 'Error: -M must be between 0 and 0.3')
+
     # sequencing error model
-    if se_rate < 0.:
-        se_rate = None
     if se_model is None:
         print('Using default sequencing error model.')
         se_model = sim_path / 'models/errorModel_toy.p'
@@ -224,31 +252,6 @@ def main(raw_args=None):
                             fraglen_vals]
             fraglen_distribution = DiscreteDistribution(fraglen_prob, fraglen_vals)
 
-    """************************************************
-    ****            HARD-CODED CONSTANTS
-    ************************************************"""
-
-    # target window size for read sampling. how many times bigger than read/frag length
-    WINDOW_TARGET_SCALE = 100
-
-    # allowed nucleotides
-    ALLOWED_NUCL = ['A', 'C', 'G', 'T']
-
-    """************************************************
-    ****          MORE INPUT ERROR CHECKING
-    ************************************************"""
-
-    is_in_range(read_len, 10, 1000000, 'Error: -R must be between 10 and 1,000,000')
-    is_in_range(coverage, 0, 1000000, 'Error: -c must be between 0 and 1,000,000')
-    is_in_range(ploids, 1, 100, 'Error: -p must be between 1 and 100')
-    is_in_range(off_target_scalar, 0, 1, 'Error: -to must be between 0 and 1')
-    if mut_rate != -1 and mut_rate is not None:
-        is_in_range(mut_rate, 0, 0.3, 'Error: -M must be between 0 and 0.3')
-    if se_rate != -1 and se_rate is not None:
-        is_in_range(se_rate, 0, 0.3, 'Error: -E must be between 0 and 0.3')
-    if n_max_qual != -1:
-        is_in_range(n_max_qual, 1, 40, 'Error: -N must be between 1 and 40')
-
     """************************************************
     ****   Process Inputs
     ************************************************"""
@@ -665,7 +668,7 @@ def main(raw_args=None):
                             for j in range(len(my_read_data)):
                                 my_read_string = [n for n in my_read_data[j][2]]
                                 for k in range(len(my_read_data[j][3])):
-                                    adjusted_qual = ord(my_read_data[j][3][k]) - se_class.offQ
+                                    adjusted_qual = ord(my_read_data[j][3][k]) - se_class.off_q
                                     if adjusted_qual <= n_max_qual:
                                         my_read_string[k] = 'N'
                                 my_read_data[j][2] = ''.join(my_read_string)
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 0c76619..ad97a02 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -10,6 +10,9 @@
 from py.probability import DiscreteDistribution, poisson_list, quantize_list
 from py.neat_cigar import CigarString
 
+"""
+Constants needed for analysis
+"""
 MAX_ATTEMPTS = 100  # max attempts to insert a mutation into a valid position
 MAX_MUTFRAC = 0.3  # the maximum percentage of a window that can contain mutations
 
@@ -27,6 +30,60 @@
 COV_FRAGLEN_PERCENTILE = 10.
 LARGE_NUMBER = 9999999999
 
+"""
+DEFAULT MUTATION MODELS
+"""
+
+DEFAULT_1_OVERALL_MUT_RATE = 0.001
+DEFAULT_1_HOMOZYGOUS_FREQ = 0.010
+DEFAULT_1_INDEL_FRACTION = 0.05
+DEFAULT_1_INS_VS_DEL = 0.6
+DEFAULT_1_INS_LENGTH_VALUES = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+DEFAULT_1_INS_LENGTH_WEIGHTS = [0.4, 0.2, 0.1, 0.05, 0.05, 0.05, 0.05, 0.034, 0.033, 0.033]
+DEFAULT_1_DEL_LENGTH_VALUES = [1, 2, 3, 4, 5]
+DEFAULT_1_DEL_LENGTH_WEIGHTS = [0.3, 0.2, 0.2, 0.2, 0.1]
+example_matrix_1 = [[0.0, 0.15, 0.7, 0.15],
+                    [0.15, 0.0, 0.15, 0.7],
+                    [0.7, 0.15, 0.0, 0.15],
+                    [0.15, 0.7, 0.15, 0.0]]
+DEFAULT_1_TRI_FREQS = [copy.deepcopy(example_matrix_1) for n in range(16)]
+DEFAULT_1_TRINUC_BIAS = [1. / float(len(ALL_TRI)) for n in ALL_TRI]
+DEFAULT_MODEL_1 = [DEFAULT_1_OVERALL_MUT_RATE,
+                   DEFAULT_1_HOMOZYGOUS_FREQ,
+                   DEFAULT_1_INDEL_FRACTION,
+                   DEFAULT_1_INS_VS_DEL,
+                   DEFAULT_1_INS_LENGTH_VALUES,
+                   DEFAULT_1_INS_LENGTH_WEIGHTS,
+                   DEFAULT_1_DEL_LENGTH_VALUES,
+                   DEFAULT_1_DEL_LENGTH_WEIGHTS,
+                   DEFAULT_1_TRI_FREQS,
+                   DEFAULT_1_TRINUC_BIAS]
+
+DEFAULT_2_OVERALL_MUT_RATE = 0.002
+DEFAULT_2_HOMOZYGOUS_FREQ = 0.200
+DEFAULT_2_INDEL_FRACTION = 0.1
+DEFAULT_2_INS_VS_DEL = 0.3
+DEFAULT_2_INS_LENGTH_VALUES = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+DEFAULT_2_INS_LENGTH_WEIGHTS = [0.1, 0.1, 0.2, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]
+DEFAULT_2_DEL_LENGTH_VALUES = [1, 2, 3, 4, 5]
+DEFAULT_2_DEL_LENGTH_WEIGHTS = [0.3, 0.2, 0.2, 0.2, 0.1]
+example_matrix_2 = [[0.0, 0.15, 0.7, 0.15],
+                    [0.15, 0.0, 0.15, 0.7],
+                    [0.7, 0.15, 0.0, 0.15],
+                    [0.15, 0.7, 0.15, 0.0]]
+DEFAULT_2_TRI_FREQS = [copy.deepcopy(example_matrix_2) for n in range(16)]
+DEFAULT_2_TRINUC_BIAS = [1. / float(len(ALL_TRI)) for n in ALL_TRI]
+DEFAULT_MODEL_2 = [DEFAULT_2_OVERALL_MUT_RATE,
+                   DEFAULT_2_HOMOZYGOUS_FREQ,
+                   DEFAULT_2_INDEL_FRACTION,
+                   DEFAULT_2_INS_VS_DEL,
+                   DEFAULT_2_INS_LENGTH_VALUES,
+                   DEFAULT_2_INS_LENGTH_WEIGHTS,
+                   DEFAULT_2_DEL_LENGTH_VALUES,
+                   DEFAULT_2_DEL_LENGTH_WEIGHTS,
+                   DEFAULT_2_TRI_FREQS,
+                   DEFAULT_2_TRINUC_BIAS]
+
 
 #
 #	Container for reference sequences, applies mutations
@@ -156,8 +213,10 @@ def init_coverage(self, coverage_data, frag_dist=None):
                         # print 'AFTER2:', max_coord
                         coverage_vals = []
                         for j in range(0, max_coord):
-                            coverage_vals.append(coverage_vector[j + self.read_len] - coverage_vector[j] + coverage_vector[j + flv] - coverage_vector[
-                                j + flv - self.read_len])
+                            coverage_vals.append(
+                                coverage_vector[j + self.read_len] - coverage_vector[j] + coverage_vector[j + flv] -
+                                coverage_vector[
+                                    j + flv - self.read_len])
 
                         # EXPERIMENTAL
                         # quantized_covVals = quantize_list(coverage_vals)
@@ -665,16 +724,15 @@ def sample_read(self, sequencingModel, frag_len=None):
         return r_out
 
 
-
-#
-#	Container for read data, computes quality scores and positions to insert errors
-#
 class ReadContainer:
-    def __init__(self, read_len, errorModel, rescaled_error):
+    """
+    Container for read data, computes quality scores and positions to insert errors
+    """
+    def __init__(self, read_len, error_model, rescaled_error):
 
         self.read_len = read_len
 
-        error_dat = pickle.load(open(errorModel, 'rb'), encoding="bytes")
+        error_dat = pickle.load(open(error_model, 'rb'), encoding="bytes")
         self.UNIFORM = False
         if len(error_dat) == 4:  # uniform-error SE reads (e.g. PacBio)
             self.UNIFORM = True
@@ -682,21 +740,23 @@ def __init__(self, read_len, errorModel, rescaled_error):
             self.uniform_q_score = int(-10. * np.log10(avg_error) + 0.5)
             print('Using uniform sequencing error model. (q=' + str(self.uniform_q_score) + '+' + str(
                 off_q) + ', p(err)={0:0.2f}%)'.format(100. * avg_error))
-        if len(error_dat) == 6:  # only 1 q-score model present, use same model for both strands
+        elif len(error_dat) == 6:  # only 1 q-score model present, use same model for both strands
             [init_q1, prob_q1, q_scores, off_q, avg_error, error_params] = error_dat
             self.PE_MODELS = False
         elif len(error_dat) == 8:  # found a q-score model for both forward and reverse strands
-            # print 'Using paired-read quality score profiles...'
-            [init_q1, prob_q1, initQ2, probQ2, q_scores, off_q, avg_error, error_params] = error_dat
+            [init_q1, prob_q1, init_q2, prob_q2, q_scores, off_q, avg_error, error_params] = error_dat
             self.PE_MODELS = True
-            if len(init_q1) != len(initQ2) or len(prob_q1) != len(probQ2):
+            if len(init_q1) != len(init_q2) or len(prob_q1) != len(prob_q2):
                 print('\nError: R1 and R2 quality score models are of different length.\n')
                 exit(1)
-
-        self.qErrRate = [0.] * (max(q_scores) + 1)
+        else:
+            print('\nError: Something wrong with error model.\n')
+            exit(1)
+        print(q_scores)
+        self.q_err_rate = [0.] * (max(q_scores) + 1)
         for q in q_scores:
-            self.qErrRate[q] = 10. ** (-q / 10.)
-        self.offQ = off_q
+            self.q_err_rate[q] = 10. ** (-q / 10.)
+        self.off_q = off_q
 
         # error_params = [SSE_PROB, SIE_RATE, SIE_PROB, SIE_VAL, SIE_INS_FREQ, SIE_INS_NUCL]
         self.errP = error_params
@@ -735,17 +795,17 @@ def __init__(self, read_len, errorModel, rescaled_error):
                         self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution(prob_q1[i][j], q_scores))
 
             if self.PE_MODELS:
-                self.initDistByPos2 = [DiscreteDistribution(initQ2[i], q_scores) for i in range(len(initQ2))]
+                self.initDistByPos2 = [DiscreteDistribution(init_q2[i], q_scores) for i in range(len(init_q2))]
                 self.probDistByPosByPrevQ2 = [None]
-                for i in range(1, len(initQ2)):
+                for i in range(1, len(init_q2)):
                     self.probDistByPosByPrevQ2.append([])
-                    for j in range(len(initQ2[0])):
-                        if np.sum(probQ2[i][
+                    for j in range(len(init_q2[0])):
+                        if np.sum(prob_q2[i][
                                       j]) <= 0.:  # if we don't have sufficient data for a transition, use the previous qscore
                             self.probDistByPosByPrevQ2[-1].append(
                                 DiscreteDistribution([1], [q_scores[j]], degenerate_val=q_scores[j]))
                         else:
-                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution(probQ2[i][j], q_scores))
+                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution(prob_q2[i][j], q_scores))
 
     def get_sequencing_errors(self, read_data, is_reverse_strand=False):
 
@@ -753,10 +813,10 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
         s_err = []
 
         if self.UNIFORM:
-            my_q = [self.uniform_q_score + self.offQ for n in range(self.read_len)]
+            my_q = [self.uniform_q_score + self.off_q for n in range(self.read_len)]
             q_out = ''.join([chr(n) for n in my_q])
             for i in range(self.read_len):
-                if random.random() < self.errorScale * self.qErrRate[self.uniform_q_score]:
+                if random.random() < self.errorScale * self.q_err_rate[self.uniform_q_score]:
                     s_err.append(i)
         else:
 
@@ -777,10 +837,10 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
                 q_out = q_out[::-1]
 
             for i in range(self.read_len):
-                if random.random() < self.errorScale * self.qErrRate[q_out[i]]:
+                if random.random() < self.errorScale * self.q_err_rate[q_out[i]]:
                     s_err.append(i)
 
-            q_out = ''.join([chr(n + self.offQ) for n in q_out])
+            q_out = ''.join([chr(n + self.off_q) for n in q_out])
 
         if self.errorScale == 0.0:
             return q_out, []
@@ -829,13 +889,9 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
         return q_out, s_out
 
 
-"""************************************************
-****          DEFAULT MUTATION MODELS
-************************************************"""
-
-
 # parse mutation model pickle file
 def parse_input_mutation_model(model=None, which_default=1):
+
     if which_default == 1:
         out_model = [copy.deepcopy(n) for n in DEFAULT_MODEL_1]
     elif which_default == 2:
@@ -896,137 +952,3 @@ def parse_input_mutation_model(model=None, which_default=1):
 
     return out_model
 
-
-# parse mutation model files, returns default model if no model directory is specified
-#
-# OLD FUNCTION THAT PROCESSED OUTDATED TEXTFILE MUTATION MODELS
-def parse_input_mutation_model_deprecated(prefix=None, which_default=1):
-    if which_default == 1:
-        out_model = [copy.deepcopy(n) for n in DEFAULT_MODEL_1]
-    elif which_default == 2:
-        out_model = [copy.deepcopy(n) for n in DEFAULT_MODEL_2]
-    else:
-        print('\nError: Unknown default mutation model specified\n')
-        exit(1)
-
-    if prefix is not None:
-        if prefix[-1] != '/':
-            prefix += '/'
-        if not os.path.isdir(prefix):
-            print('\nError: Input mutation model directory not found:', prefix, '\n')
-            exit(1)
-
-        print('Reading in mutation model...')
-        listing1 = [n for n in os.listdir(prefix) if n[-5:] == '.prob']
-        listing2 = [n for n in os.listdir(prefix) if n[-7:] == '.trinuc']
-        listing = sorted(listing1) + sorted(listing2)
-        for l in listing:
-            f = open(prefix + l, 'r')
-            fr = [n.split('\t') for n in f.read().split('\n')]
-            f.close()
-
-            if '_overall.prob' in l:
-                myIns = None
-                myDel = None
-                for dat in fr[1:]:
-                    if len(dat) == 2:
-                        if dat[0] == 'insertion':
-                            myIns = float(dat[1])
-                        elif dat[0] == 'deletion':
-                            myDel = float(dat[1])
-                if myIns is not None and myDel is not None:
-                    out_model[2] = myIns + myDel
-                    out_model[3] = myIns / (myIns + myDel)
-                    print('-', l)
-
-            if '_insLength.prob' in l:
-                ins_vals = {}
-                for dat in fr[1:]:
-                    if len(dat) == 2:
-                        ins_vals[int(dat[0])] = float(dat[1])
-                if len(ins_vals):
-                    out_model[4] = sorted(ins_vals.keys())
-                    out_model[5] = [ins_vals[n] for n in out_model[4]]
-                    print('-', l)
-
-            if '_delLength.prob' in l:
-                del_vals = {}
-                for dat in fr[1:]:
-                    if len(dat) == 2:
-                        del_vals[int(dat[0])] = float(dat[1])
-                if len(del_vals):
-                    out_model[6] = sorted(del_vals.keys())
-                    out_model[7] = [del_vals[n] for n in out_model[6]]
-                    print('-', l)
-
-            if '.trinuc' == l[-7:]:
-                context_ind = TRI_IND[l[-10] + l[-8]]
-                p_matrix = [[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1]]
-                for i in range(len(p_matrix)):
-                    for j in range(len(fr[i])):
-                        p_matrix[i][j] = float(fr[i][j])
-                any_none = False
-                for i in range(len(p_matrix)):
-                    for j in range(len(p_matrix[i])):
-                        if p_matrix[i][j] == -1:
-                            any_none = True
-                if not any_none:
-                    out_model[8][context_ind] = copy.deepcopy(p_matrix)
-                    print('-', l)
-
-    return out_model
-
-
-######################
-#	DEFAULT VALUES   #
-######################
-
-DEFAULT_1_OVERALL_MUT_RATE = 0.001
-DEFAULT_1_HOMOZYGOUS_FREQ = 0.010
-DEFAULT_1_INDEL_FRACTION = 0.05
-DEFAULT_1_INS_VS_DEL = 0.6
-DEFAULT_1_INS_LENGTH_VALUES = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
-DEFAULT_1_INS_LENGTH_WEIGHTS = [0.4, 0.2, 0.1, 0.05, 0.05, 0.05, 0.05, 0.034, 0.033, 0.033]
-DEFAULT_1_DEL_LENGTH_VALUES = [1, 2, 3, 4, 5]
-DEFAULT_1_DEL_LENGTH_WEIGHTS = [0.3, 0.2, 0.2, 0.2, 0.1]
-example_matrix_1 = [[0.0, 0.15, 0.7, 0.15],
-                    [0.15, 0.0, 0.15, 0.7],
-                    [0.7, 0.15, 0.0, 0.15],
-                    [0.15, 0.7, 0.15, 0.0]]
-DEFAULT_1_TRI_FREQS = [copy.deepcopy(example_matrix_1) for n in range(16)]
-DEFAULT_1_TRINUC_BIAS = [1. / float(len(ALL_TRI)) for n in ALL_TRI]
-DEFAULT_MODEL_1 = [DEFAULT_1_OVERALL_MUT_RATE,
-                   DEFAULT_1_HOMOZYGOUS_FREQ,
-                   DEFAULT_1_INDEL_FRACTION,
-                   DEFAULT_1_INS_VS_DEL,
-                   DEFAULT_1_INS_LENGTH_VALUES,
-                   DEFAULT_1_INS_LENGTH_WEIGHTS,
-                   DEFAULT_1_DEL_LENGTH_VALUES,
-                   DEFAULT_1_DEL_LENGTH_WEIGHTS,
-                   DEFAULT_1_TRI_FREQS,
-                   DEFAULT_1_TRINUC_BIAS]
-
-DEFAULT_2_OVERALL_MUT_RATE = 0.002
-DEFAULT_2_HOMOZYGOUS_FREQ = 0.200
-DEFAULT_2_INDEL_FRACTION = 0.1
-DEFAULT_2_INS_VS_DEL = 0.3
-DEFAULT_2_INS_LENGTH_VALUES = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
-DEFAULT_2_INS_LENGTH_WEIGHTS = [0.1, 0.1, 0.2, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]
-DEFAULT_2_DEL_LENGTH_VALUES = [1, 2, 3, 4, 5]
-DEFAULT_2_DEL_LENGTH_WEIGHTS = [0.3, 0.2, 0.2, 0.2, 0.1]
-example_matrix_2 = [[0.0, 0.15, 0.7, 0.15],
-                    [0.15, 0.0, 0.15, 0.7],
-                    [0.7, 0.15, 0.0, 0.15],
-                    [0.15, 0.7, 0.15, 0.0]]
-DEFAULT_2_TRI_FREQS = [copy.deepcopy(example_matrix_2) for n in range(16)]
-DEFAULT_2_TRINUC_BIAS = [1. / float(len(ALL_TRI)) for n in ALL_TRI]
-DEFAULT_MODEL_2 = [DEFAULT_2_OVERALL_MUT_RATE,
-                   DEFAULT_2_HOMOZYGOUS_FREQ,
-                   DEFAULT_2_INDEL_FRACTION,
-                   DEFAULT_2_INS_VS_DEL,
-                   DEFAULT_2_INS_LENGTH_VALUES,
-                   DEFAULT_2_INS_LENGTH_WEIGHTS,
-                   DEFAULT_2_DEL_LENGTH_VALUES,
-                   DEFAULT_2_DEL_LENGTH_WEIGHTS,
-                   DEFAULT_2_TRI_FREQS,
-                   DEFAULT_2_TRINUC_BIAS]
diff --git a/py/__pycache__/SequenceContainer.cpython-37.pyc b/py/__pycache__/SequenceContainer.cpython-37.pyc
index 0569b166d46443ef5fd8ecf2e146943a4b69aaca..73184af3582177b539fa74dab2df1d193ed901f2 100644
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diff --git a/py/input_checking.py b/py/input_checking.py
index 7ac744f..25c4885 100644
--- a/py/input_checking.py
+++ b/py/input_checking.py
@@ -17,13 +17,12 @@ def required_field(variable_to_test: any, err_string: str) -> None:
     if variable_to_test is None:
         print('\n' + err_string + '\n')
         raise ValueError
-        exit(1)
 
 
 def check_file_open(filename: str, err_string: str, required: bool = False) -> None:
     """
     Checks that the filename is not empty and that it is indeed a  file
-    :param filename: file name, pathlib object
+    :param filename: file name, string
     :param err_string: string of the error if it is not a file
     :param required: If not required, skips the check
     :return: None
@@ -32,7 +31,6 @@ def check_file_open(filename: str, err_string: str, required: bool = False) -> N
         if filename is None:
             print('\n' + err_string + '\n')
             raise ValueError
-            exit(1)
         else:
             try:
                 pathlib.Path(filename).resolve(strict=True)
@@ -51,7 +49,6 @@ def check_dir(directory: str, err_string: str) -> None:
     if not pathlib.Path(directory).is_dir():
         print('\n' + err_string + '\n')
         raise NotADirectoryError
-        exit(1)
 
 
 def is_in_range(value: float, lower_bound: float, upper_bound: float, err_string: str) -> None:
@@ -67,4 +64,3 @@ def is_in_range(value: float, lower_bound: float, upper_bound: float, err_string
     if value < lower_bound or value > upper_bound:
         print('\n' + err_string + '\n')
         raise ValueError
-        exit(1)
diff --git a/py/vcf_func.py b/py/vcf_func.py
index 003d17f..55170ff 100644
--- a/py/vcf_func.py
+++ b/py/vcf_func.py
@@ -4,39 +4,36 @@
 import re
 import random
 
-INCLUDE_HOMS = False
-INCLUDE_FAIL = False
-CHOOSE_RANDOM_PLOID_IF_NO_GT_FOUND = True
 
-
-def parse_line(splt, col_dict, col_samp):
+def parse_line(vcf_line, col_dict, col_samp):
     #	check if we want to proceed..
-    ra = splt[col_dict['REF']]
-    aa = splt[col_dict['ALT']]
+    reference_allele = vcf_line[col_dict['REF']]
+    alternate_allele = vcf_line[col_dict['ALT']]
     # enough columns?
-    if len(splt) != len(col_dict):
+    if len(vcf_line) != len(col_dict):
         return None
     # exclude homs / filtered?
-    if not (INCLUDE_HOMS) and (aa == '.' or aa == '' or aa == ra):
+    # There was an attempt I think to make these optional that was never implemented.
+    if alternate_allele == '.' or alternate_allele == '' or alternate_allele == reference_allele:
         return None
-    if not (INCLUDE_FAIL) and (splt[col_dict['FILTER']] != 'PASS' and splt[col_dict['FILTER']] != '.'):
+    if vcf_line[col_dict['FILTER']] != 'PASS' and vcf_line[col_dict['FILTER']] != '.':
         return None
 
     #	default vals
-    alt_alleles = [aa]
+    alt_alleles = [alternate_allele]
     alt_freqs = []
 
     gt_per_samp = []
 
     #	any alt alleles?
-    alt_split = aa.split(',')
+    alt_split = alternate_allele.split(',')
     if len(alt_split) > 1:
         alt_alleles = alt_split
 
     #	check INFO for AF
     af = None
-    if 'INFO' in col_dict and ';AF=' in ';' + splt[col_dict['INFO']]:
-        info = splt[col_dict['INFO']] + ';'
+    if 'INFO' in col_dict and ';AF=' in ';' + vcf_line[col_dict['INFO']]:
+        info = vcf_line[col_dict['INFO']] + ';'
         af = re.findall(r"AF=.*?(?=;)", info)[0][3:]
     if af is not None:
         af_splt = af.split(',')
@@ -49,19 +46,19 @@ def parse_line(splt, col_dict, col_samp):
 
     gt_per_samp = None
     #	if available (i.e. we simulated it) look for WP in info
-    if len(col_samp) == 0 and 'INFO' in col_dict and 'WP=' in splt[col_dict['INFO']]:
-        info = splt[col_dict['INFO']] + ';'
+    if len(col_samp) == 0 and 'INFO' in col_dict and 'WP=' in vcf_line[col_dict['INFO']]:
+        info = vcf_line[col_dict['INFO']] + ';'
         gt_per_samp = [re.findall(r"WP=.*?(?=;)", info)[0][3:]]
     else:
         #	if no sample columns, check info for GT
-        if len(col_samp) == 0 and 'INFO' in col_dict and 'GT=' in splt[col_dict['INFO']]:
-            info = splt[col_dict['INFO']] + ';'
+        if len(col_samp) == 0 and 'INFO' in col_dict and 'GT=' in vcf_line[col_dict['INFO']]:
+            info = vcf_line[col_dict['INFO']] + ';'
             gt_per_samp = [re.findall(r"GT=.*?(?=;)", info)[0][3:]]
         elif len(col_samp):
-            fmt = ':' + splt[col_dict['FORMAT']] + ':'
+            fmt = ':' + vcf_line[col_dict['FORMAT']] + ':'
             if ':GT:' in fmt:
                 gt_ind = fmt.split(':').index('GT')
-                gt_per_samp = [splt[col_samp[iii]].split(':')[gt_ind - 1] for iii in range(len(col_samp))]
+                gt_per_samp = [vcf_line[col_samp[iii]].split(':')[gt_ind - 1] for iii in range(len(col_samp))]
                 for i in range(len(gt_per_samp)):
                     gt_per_samp[i] = gt_per_samp[i].replace('.', '0')
         if gt_per_samp is None:
@@ -103,19 +100,16 @@ def parse_vcf(vcf_path, tumor_normal=False, ploidy=2):
                     gt_eval = gt[:2]
                 else:
                     gt_eval = gt[:1]
+                # For some reason this had an additional "if True" inserted. I guess it was supposed to be an option
+                # the user could set but was never implemented.
                 if None in gt_eval:
-                    if CHOOSE_RANDOM_PLOID_IF_NO_GT_FOUND:
-                        if not printed_warning:
-                            print('Warning: Found variants without a GT field, assuming heterozygous...')
-                            printed_warning = True
-                        for i in range(len(gt_eval)):
-                            tmp = ['0'] * ploidy
-                            tmp[random.randint(0, ploidy - 1)] = '1'
-                            gt_eval[i] = '/'.join(tmp)
-                    else:
-                        # skip because no GT field was found
-                        n_skipped += 1
-                        continue
+                    if not printed_warning:
+                        print('Warning: Found variants without a GT field, assuming heterozygous...')
+                        printed_warning = True
+                    for i in range(len(gt_eval)):
+                        tmp = ['0'] * ploidy
+                        tmp[random.randint(0, ploidy - 1)] = '1'
+                        gt_eval[i] = '/'.join(tmp)
                 non_reference = False
                 for gtVal in gt_eval:
                     if gtVal is not None:

From 05e08090d9174f98ac6025ca44d9cc507a82d035 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 24 Aug 2020 13:48:47 -0500
Subject: [PATCH 140/323] debugging sequence container

---
 py/SequenceContainer.py                       |  25 +++++++++---------
 .../SequenceContainer.cpython-37.pyc          | Bin 29131 -> 29131 bytes
 2 files changed, 13 insertions(+), 12 deletions(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index ad97a02..5b7c45e 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -625,7 +625,7 @@ def sample_read(self, sequencingModel, frag_len=None):
             expanded_cigar = []
             extraCigar = []
             adj = 0
-            sse_adj = [0 for n in range(self.read_len + max(sequencingModel.errP[3]))]
+            sse_adj = [0 for n in range(self.read_len + max(sequencingModel.err_p[3]))]
             any_indel_err = False
 
             # sort by letter (D > I > S) such that we introduce all indel errors before substitution errors
@@ -752,17 +752,18 @@ def __init__(self, read_len, error_model, rescaled_error):
         else:
             print('\nError: Something wrong with error model.\n')
             exit(1)
-        print(q_scores)
+
         self.q_err_rate = [0.] * (max(q_scores) + 1)
         for q in q_scores:
             self.q_err_rate[q] = 10. ** (-q / 10.)
         self.off_q = off_q
 
+        print(error_params)
         # error_params = [SSE_PROB, SIE_RATE, SIE_PROB, SIE_VAL, SIE_INS_FREQ, SIE_INS_NUCL]
-        self.errP = error_params
-        self.errSSE = [DiscreteDistribution(n, NUCL) for n in self.errP[0]]
-        self.errSIE = DiscreteDistribution(self.errP[2], self.errP[3])
-        self.errSIN = DiscreteDistribution(self.errP[5], NUCL)
+        self.err_p = error_params
+        self.err_sse = [DiscreteDistribution(n, NUCL) for n in self.err_p[0]]
+        self.err_sie = DiscreteDistribution(self.err_p[2], self.err_p[3])
+        self.err_sin = DiscreteDistribution(self.err_p[5], NUCL)
 
         # adjust sequencing error frequency to match desired rate
         if rescaled_error is None:
@@ -856,21 +857,21 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
 
             # determine error type
             is_sub = True
-            if ind != 0 and ind != self.read_len - 1 - max(self.errP[3]) and abs(ind - prev_indel) > 1:
-                if random.random() < self.errP[1]:
+            if ind != 0 and ind != self.read_len - 1 - max(self.err_p[3]) and abs(ind - prev_indel) > 1:
+                if random.random() < self.err_p[1]:
                     is_sub = False
 
             # error_out = (type, len, pos, ref, alt)
 
             if is_sub:  # insert substitution error
                 my_nucl = str(read_data[ind])
-                new_nucl = self.errSSE[NUC_IND[my_nucl]].sample()
+                new_nucl = self.err_sse[NUC_IND[my_nucl]].sample()
                 s_out.append(('S', 1, ind, my_nucl, new_nucl))
             else:  # insert indel error
-                indel_len = self.errSIE.sample()
-                if random.random() < self.errP[4]:  # insertion error
+                indel_len = self.err_sie.sample()
+                if random.random() < self.err_p[4]:  # insertion error
                     my_nucl = str(read_data[ind])
-                    new_nucl = my_nucl + ''.join([self.errSIN.sample() for n in range(indel_len)])
+                    new_nucl = my_nucl + ''.join([self.err_sin.sample() for n in range(indel_len)])
                     s_out.append(('I', len(new_nucl) - 1, ind, my_nucl, new_nucl))
                 elif ind < self.read_len - 2 - n_del_so_far:  # deletion error (prevent too many of them from stacking up)
                     my_nucl = str(read_data[ind:ind + indel_len + 1])
diff --git a/py/__pycache__/SequenceContainer.cpython-37.pyc b/py/__pycache__/SequenceContainer.cpython-37.pyc
index 73184af3582177b539fa74dab2df1d193ed901f2..40c7ad84da8dc41dbad4cba5097f905ccd03b4e7 100644
GIT binary patch
delta 286
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From 585cca74e2e8dba38dcfb81601c8dd5352831759 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 24 Aug 2020 15:37:26 -0500
Subject: [PATCH 141/323] debugging sequence container

---
 py/SequenceContainer.py | 48 +++++++++++++++++++++--------------------
 py/probability.py       | 12 +++--------
 2 files changed, 28 insertions(+), 32 deletions(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 5b7c45e..aa8cf8e 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -733,19 +733,19 @@ def __init__(self, read_len, error_model, rescaled_error):
         self.read_len = read_len
 
         error_dat = pickle.load(open(error_model, 'rb'), encoding="bytes")
-        self.UNIFORM = False
+        self.uniform = False
         if len(error_dat) == 4:  # uniform-error SE reads (e.g. PacBio)
-            self.UNIFORM = True
+            self.uniform = True
             [q_scores, off_q, avg_error, error_params] = error_dat
             self.uniform_q_score = int(-10. * np.log10(avg_error) + 0.5)
             print('Using uniform sequencing error model. (q=' + str(self.uniform_q_score) + '+' + str(
                 off_q) + ', p(err)={0:0.2f}%)'.format(100. * avg_error))
         elif len(error_dat) == 6:  # only 1 q-score model present, use same model for both strands
             [init_q1, prob_q1, q_scores, off_q, avg_error, error_params] = error_dat
-            self.PE_MODELS = False
+            self.pe_models = False
         elif len(error_dat) == 8:  # found a q-score model for both forward and reverse strands
             [init_q1, prob_q1, init_q2, prob_q2, q_scores, off_q, avg_error, error_params] = error_dat
-            self.PE_MODELS = True
+            self.pe_models = True
             if len(init_q1) != len(init_q2) or len(prob_q1) != len(prob_q2):
                 print('\nError: R1 and R2 quality score models are of different length.\n')
                 exit(1)
@@ -758,29 +758,31 @@ def __init__(self, read_len, error_model, rescaled_error):
             self.q_err_rate[q] = 10. ** (-q / 10.)
         self.off_q = off_q
 
-        print(error_params)
-        # error_params = [SSE_PROB, SIE_RATE, SIE_PROB, SIE_VAL, SIE_INS_FREQ, SIE_INS_NUCL]
         self.err_p = error_params
+        # Selects a new nucleotide based on the error model
         self.err_sse = [DiscreteDistribution(n, NUCL) for n in self.err_p[0]]
+        # allows for selection of indel length based on the parameters of the model
         self.err_sie = DiscreteDistribution(self.err_p[2], self.err_p[3])
+        #allows for indel insertion based on the length above and the probability from the model
         self.err_sin = DiscreteDistribution(self.err_p[5], NUCL)
 
         # adjust sequencing error frequency to match desired rate
         if rescaled_error is None:
-            self.errorScale = 1.0
+            self.error_scale = 1.0
         else:
-            self.errorScale = rescaled_error / avg_error
+            self.error_scale = rescaled_error / avg_error
             print('Warning: Quality scores no longer exactly representative of error probability. '
-                  'Error model scaled by {0:.3f} to match desired rate...'.format(self.errorScale))
+                  'Error model scaled by {0:.3f} to match desired rate...'.format(self.error_scale))
 
-        if not self.UNIFORM:
+        if not self.uniform:
+            print('init_q1 = {init_q1}')
             # adjust length to match desired read length
             if self.read_len == len(init_q1):
-                self.qIndRemap = range(self.read_len)
+                self.q_ind_remap = range(self.read_len)
             else:
                 print('Warning: Read length of error model (' + str(len(init_q1)) + ') does not match -R value (' + str(
                     self.read_len) + '), rescaling model...')
-                self.qIndRemap = [max([1, len(init_q1) * n // read_len]) for n in range(read_len)]
+                self.q_ind_remap = [max([1, len(init_q1) * n // read_len]) for n in range(read_len)]
 
             # initialize probability distributions
             self.initDistByPos1 = [DiscreteDistribution(init_q1[i], q_scores) for i in range(len(init_q1))]
@@ -788,14 +790,14 @@ def __init__(self, read_len, error_model, rescaled_error):
             for i in range(1, len(init_q1)):
                 self.probDistByPosByPrevQ1.append([])
                 for j in range(len(init_q1[0])):
-                    if np.sum(prob_q1[i][
-                                  j]) <= 0.:  # if we don't have sufficient data for a transition, use the previous qscore
+                    # if we don't have sufficient data for a transition, use the previous qscore
+                    if np.sum(prob_q1[i][j]) <= 0.:
                         self.probDistByPosByPrevQ1[-1].append(
                             DiscreteDistribution([1], [q_scores[j]], degenerate_val=q_scores[j]))
                     else:
                         self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution(prob_q1[i][j], q_scores))
 
-            if self.PE_MODELS:
+            if self.pe_models:
                 self.initDistByPos2 = [DiscreteDistribution(init_q2[i], q_scores) for i in range(len(init_q2))]
                 self.probDistByPosByPrevQ2 = [None]
                 for i in range(1, len(init_q2)):
@@ -813,37 +815,37 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
         q_out = [0] * self.read_len
         s_err = []
 
-        if self.UNIFORM:
+        if self.uniform:
             my_q = [self.uniform_q_score + self.off_q for n in range(self.read_len)]
             q_out = ''.join([chr(n) for n in my_q])
             for i in range(self.read_len):
-                if random.random() < self.errorScale * self.q_err_rate[self.uniform_q_score]:
+                if random.random() < self.error_scale * self.q_err_rate[self.uniform_q_score]:
                     s_err.append(i)
         else:
 
-            if self.PE_MODELS and is_reverse_strand:
+            if self.pe_models and is_reverse_strand:
                 my_q = self.initDistByPos2[0].sample()
             else:
                 my_q = self.initDistByPos1[0].sample()
             q_out[0] = my_q
 
             for i in range(1, self.read_len):
-                if self.PE_MODELS and is_reverse_strand:
-                    my_q = self.probDistByPosByPrevQ2[self.qIndRemap[i]][my_q].sample()
+                if self.pe_models and is_reverse_strand:
+                    my_q = self.probDistByPosByPrevQ2[self.q_ind_remap[i]][my_q].sample()
                 else:
-                    my_q = self.probDistByPosByPrevQ1[self.qIndRemap[i]][my_q].sample()
+                    my_q = self.probDistByPosByPrevQ1[self.q_ind_remap[i]][my_q].sample()
                 q_out[i] = my_q
 
             if is_reverse_strand:
                 q_out = q_out[::-1]
 
             for i in range(self.read_len):
-                if random.random() < self.errorScale * self.q_err_rate[q_out[i]]:
+                if random.random() < self.error_scale * self.q_err_rate[q_out[i]]:
                     s_err.append(i)
 
             q_out = ''.join([chr(n + self.off_q) for n in q_out])
 
-        if self.errorScale == 0.0:
+        if self.error_scale == 0.0:
             return q_out, []
 
         s_out = []
diff --git a/py/probability.py b/py/probability.py
index 5fb39f3..2f34fa3 100644
--- a/py/probability.py
+++ b/py/probability.py
@@ -36,15 +36,6 @@ def __init__(self, weights, values, degenerate_val=None, method='bisect'):
                 print('\nError: length and weights and values vectors must be the same.\n')
                 exit(1)
             self.degenerate = degenerate_val
-            # prune values with probability too low to be worth using [DOESN'T REALLY IMPROVE PERFORMANCE]
-            ####if self.degenerate != None:
-            ####	for i in xrange(len(self.weights)-1,-1,-1):
-            ####		if self.weights[i] < LOW_PROB_THRESH:
-            ####			del self.weights[i]
-            ####			del self.values[i]
-            ####	if len(self.weights) == 0:
-            ####		print '\nError: probability distribution has no usable values.\n'
-            ####		exit(1)
 
             if self.method == 'alias':
                 len_weights = len(self.weights)
@@ -76,6 +67,9 @@ def __init__(self, weights, values, degenerate_val=None, method='bisect'):
                 self.cum_prob = np.cumsum(self.weights).tolist()[:-1]
                 self.cum_prob.insert(0, 0.)
 
+            else:
+                print("\nUnknown discreet distribution method.\n")
+
     def __str__(self):
         return str(self.weights) + ' ' + str(self.values) + ' ' + self.method
 

From 80a7ccbfe7c8a440881771b707d241df8caaeb99 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 24 Aug 2020 15:38:13 -0500
Subject: [PATCH 142/323] debugging sequence container

---
 py/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index aa8cf8e..ec56ffe 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -775,7 +775,7 @@ def __init__(self, read_len, error_model, rescaled_error):
                   'Error model scaled by {0:.3f} to match desired rate...'.format(self.error_scale))
 
         if not self.uniform:
-            print('init_q1 = {init_q1}')
+            print('init_q1 = {}'.format(init_q1))
             # adjust length to match desired read length
             if self.read_len == len(init_q1):
                 self.q_ind_remap = range(self.read_len)

From 2d941f7d0b3f2a9dd29c8d60f8dec829c4e9d8db Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 24 Aug 2020 18:29:01 -0500
Subject: [PATCH 143/323] debugging sequence container

---
 py/SequenceContainer.py | 37 +++++++++++++++++++------------------
 1 file changed, 19 insertions(+), 18 deletions(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index ec56ffe..306b9b2 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -757,13 +757,13 @@ def __init__(self, read_len, error_model, rescaled_error):
         for q in q_scores:
             self.q_err_rate[q] = 10. ** (-q / 10.)
         self.off_q = off_q
-
+        print(off_q)
         self.err_p = error_params
         # Selects a new nucleotide based on the error model
         self.err_sse = [DiscreteDistribution(n, NUCL) for n in self.err_p[0]]
         # allows for selection of indel length based on the parameters of the model
         self.err_sie = DiscreteDistribution(self.err_p[2], self.err_p[3])
-        #allows for indel insertion based on the length above and the probability from the model
+        # allows for indel insertion based on the length above and the probability from the model
         self.err_sin = DiscreteDistribution(self.err_p[5], NUCL)
 
         # adjust sequencing error frequency to match desired rate
@@ -785,30 +785,31 @@ def __init__(self, read_len, error_model, rescaled_error):
                 self.q_ind_remap = [max([1, len(init_q1) * n // read_len]) for n in range(read_len)]
 
             # initialize probability distributions
-            self.initDistByPos1 = [DiscreteDistribution(init_q1[i], q_scores) for i in range(len(init_q1))]
-            self.probDistByPosByPrevQ1 = [None]
+            self.init_dist_by_pos_1 = [DiscreteDistribution(init_q1[i], q_scores) for i in range(len(init_q1))]
+            self.prob_dist_by_pos_by_prev_q1 = [None]
             for i in range(1, len(init_q1)):
-                self.probDistByPosByPrevQ1.append([])
+                self.prob_dist_by_pos_by_prev_q1.append([])
                 for j in range(len(init_q1[0])):
-                    # if we don't have sufficient data for a transition, use the previous qscore
+                    # if we don't have sufficient data for a transition, use the previous quality score
                     if np.sum(prob_q1[i][j]) <= 0.:
-                        self.probDistByPosByPrevQ1[-1].append(
+                        self.prob_dist_by_pos_by_prev_q1[-1].append(
                             DiscreteDistribution([1], [q_scores[j]], degenerate_val=q_scores[j]))
                     else:
-                        self.probDistByPosByPrevQ1[-1].append(DiscreteDistribution(prob_q1[i][j], q_scores))
+                        self.prob_dist_by_pos_by_prev_q1[-1].append(DiscreteDistribution(prob_q1[i][j], q_scores))
 
+            # If paired-end, initialize probability distributions for the other strand
             if self.pe_models:
-                self.initDistByPos2 = [DiscreteDistribution(init_q2[i], q_scores) for i in range(len(init_q2))]
-                self.probDistByPosByPrevQ2 = [None]
+                self.init_dist_by_pos_2 = [DiscreteDistribution(init_q2[i], q_scores) for i in range(len(init_q2))]
+                self.prob_dist_by_pos_by_prev_q2 = [None]
                 for i in range(1, len(init_q2)):
-                    self.probDistByPosByPrevQ2.append([])
+                    self.prob_dist_by_pos_by_prev_q2.append([])
                     for j in range(len(init_q2[0])):
                         if np.sum(prob_q2[i][
                                       j]) <= 0.:  # if we don't have sufficient data for a transition, use the previous qscore
-                            self.probDistByPosByPrevQ2[-1].append(
+                            self.prob_dist_by_pos_by_prev_q2[-1].append(
                                 DiscreteDistribution([1], [q_scores[j]], degenerate_val=q_scores[j]))
                         else:
-                            self.probDistByPosByPrevQ2[-1].append(DiscreteDistribution(prob_q2[i][j], q_scores))
+                            self.prob_dist_by_pos_by_prev_q2[-1].append(DiscreteDistribution(prob_q2[i][j], q_scores))
 
     def get_sequencing_errors(self, read_data, is_reverse_strand=False):
 
@@ -816,7 +817,7 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
         s_err = []
 
         if self.uniform:
-            my_q = [self.uniform_q_score + self.off_q for n in range(self.read_len)]
+            my_q = [self.uniform_q_score + self.off_q] * self.read_len
             q_out = ''.join([chr(n) for n in my_q])
             for i in range(self.read_len):
                 if random.random() < self.error_scale * self.q_err_rate[self.uniform_q_score]:
@@ -824,16 +825,16 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
         else:
 
             if self.pe_models and is_reverse_strand:
-                my_q = self.initDistByPos2[0].sample()
+                my_q = self.init_dist_by_pos_2[0].sample()
             else:
-                my_q = self.initDistByPos1[0].sample()
+                my_q = self.init_dist_by_pos_1[0].sample()
             q_out[0] = my_q
 
             for i in range(1, self.read_len):
                 if self.pe_models and is_reverse_strand:
-                    my_q = self.probDistByPosByPrevQ2[self.q_ind_remap[i]][my_q].sample()
+                    my_q = self.prob_dist_by_pos_by_prev_q2[self.q_ind_remap[i]][my_q].sample()
                 else:
-                    my_q = self.probDistByPosByPrevQ1[self.q_ind_remap[i]][my_q].sample()
+                    my_q = self.prob_dist_by_pos_by_prev_q1[self.q_ind_remap[i]][my_q].sample()
                 q_out[i] = my_q
 
             if is_reverse_strand:

From 983f83dfda0e99a83f470696c9f79b6ed5e3dfd4 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 24 Aug 2020 18:42:01 -0500
Subject: [PATCH 144/323] debugging sequence container

---
 py/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 306b9b2..71b73ca 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -753,6 +753,7 @@ def __init__(self, read_len, error_model, rescaled_error):
             print('\nError: Something wrong with error model.\n')
             exit(1)
 
+        print(avg_error)
         self.q_err_rate = [0.] * (max(q_scores) + 1)
         for q in q_scores:
             self.q_err_rate[q] = 10. ** (-q / 10.)
@@ -775,7 +776,6 @@ def __init__(self, read_len, error_model, rescaled_error):
                   'Error model scaled by {0:.3f} to match desired rate...'.format(self.error_scale))
 
         if not self.uniform:
-            print('init_q1 = {}'.format(init_q1))
             # adjust length to match desired read length
             if self.read_len == len(init_q1):
                 self.q_ind_remap = range(self.read_len)

From 365896d4112d6450f510bc20ee390ebae73a3152 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 24 Aug 2020 18:50:38 -0500
Subject: [PATCH 145/323] debugging sequence container

---
 gen_reads.py            | 2 +-
 py/SequenceContainer.py | 2 --
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 1417199..b162a34 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -57,7 +57,7 @@ def main(raw_args=None):
     parser.add_argument('-c', type=float, required=False, metavar='coverage', default=10.0,
                         help="Average coverage, default is 10.0")
     parser.add_argument('-e', type=str, required=False, metavar='error_model', default=None,
-                        help="Location and name of sequencing error model (leave blank to use the default)")
+                        help="Location of the file for the sequencing error model (omit to use the default)")
     parser.add_argument('-E', type=float, required=False, metavar='Error rate', default=-1,
                         help="Rescale avg sequencing error rate to this, must be between 0.0 and 0.3")
     parser.add_argument('-p', type=int, required=False, metavar='ploidy', default=2,
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 71b73ca..351c2a4 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -753,12 +753,10 @@ def __init__(self, read_len, error_model, rescaled_error):
             print('\nError: Something wrong with error model.\n')
             exit(1)
 
-        print(avg_error)
         self.q_err_rate = [0.] * (max(q_scores) + 1)
         for q in q_scores:
             self.q_err_rate[q] = 10. ** (-q / 10.)
         self.off_q = off_q
-        print(off_q)
         self.err_p = error_params
         # Selects a new nucleotide based on the error model
         self.err_sse = [DiscreteDistribution(n, NUCL) for n in self.err_p[0]]

From 522ccf30877ddac58a7cbb6d4eb91896aa27b3e5 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 24 Aug 2020 18:59:27 -0500
Subject: [PATCH 146/323] debugging sequence container

---
 py/SequenceContainer.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 351c2a4..d16c994 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -817,6 +817,7 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
         if self.uniform:
             my_q = [self.uniform_q_score + self.off_q] * self.read_len
             q_out = ''.join([chr(n) for n in my_q])
+            print("self.error_scale = " + self.error_scale + "self.q_err_rate = " + self.q_err_rate)
             for i in range(self.read_len):
                 if random.random() < self.error_scale * self.q_err_rate[self.uniform_q_score]:
                     s_err.append(i)

From c2384cdc80ea4050e46072b5146aafe1c183b475 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 24 Aug 2020 19:06:25 -0500
Subject: [PATCH 147/323] debugging sequence container

---
 py/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index d16c994..dee9285 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -817,7 +817,7 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
         if self.uniform:
             my_q = [self.uniform_q_score + self.off_q] * self.read_len
             q_out = ''.join([chr(n) for n in my_q])
-            print("self.error_scale = " + self.error_scale + "self.q_err_rate = " + self.q_err_rate)
+            print("\nself.error_scale = " + str(self.error_scale) + "self.q_err_rate = " + str(self.q_err_rate) + '\n')
             for i in range(self.read_len):
                 if random.random() < self.error_scale * self.q_err_rate[self.uniform_q_score]:
                     s_err.append(i)

From 194f0bd6c85d2ec1bfb5e90b030a4ac46d6bbb43 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 13:53:10 -0500
Subject: [PATCH 148/323] Smoothing out genreads and adding todos

---
 gen_reads.py             | 332 +++++++++++++++++++++++----------------
 py/SequenceContainer.py  |  52 ++++--
 py/output_file_writer.py |  87 +++++-----
 py/ref_func.py           |  74 ++++++---
 4 files changed, 325 insertions(+), 220 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index b162a34..72399fd 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -30,8 +30,7 @@
 from py.vcf_func import parse_vcf
 from py.output_file_writer import OutputFileWriter, reverse_complement, sam_flag
 from py.probability import DiscreteDistribution, mean_ind_of_weighted_list
-from py.SequenceContainer import SequenceContainer, ReadContainer, parse_input_mutation_model
-
+from py.SequenceContainer import SequenceContainer, SequencingError, parse_input_mutation_model
 
 """
 Some constants needed for analysis
@@ -160,17 +159,12 @@ def main(raw_args=None):
         print('\nERROR: --pe argument takes 2 space-separated arguments.\n')
         exit(1)
 
-    # if user specified mean/std, use artificial fragment length distribution, otherwise use
-    # the empirical model specified. If neither, then we're doing single-end reads.
-    if fragment_size is not None and fragment_std is not None:
-        paired_end = True
-        paired_end_artificial = True
-    elif fraglen_model is not None:
+    # If user specified mean/std, or specified an empirical model, then the reads will be paired_ended
+    # If not, then we're doing single-end reads.
+    if (fragment_size is not None and fragment_std is not None) or (fraglen_model is not None) and not fasta_instead:
         paired_end = True
-        paired_end_artificial = False
     else:
         paired_end = False
-        paired_end_artificial = False
 
     if rng_seed == -1:
         rng_seed = random.randint(1, 99999999)
@@ -207,64 +201,87 @@ def main(raw_args=None):
     if se_model is None:
         print('Using default sequencing error model.')
         se_model = sim_path / 'models/errorModel_toy.p'
-        se_class = ReadContainer(read_len, se_model, se_rate)
+        se_class = SequencingError(read_len, se_model, se_rate)
     else:
         # probably need to do some sanity checking
-        se_class = ReadContainer(read_len, se_model, se_rate)
+        se_class = SequencingError(read_len, se_model, se_rate)
 
     # GC-bias model
     if gc_bias_model is None:
         print('Using default gc-bias model.')
         gc_bias_model = sim_path / 'models/gcBias_toy.p'
-        [gc_scale_count, gc_scale_val] = pickle.load(open(gc_bias_model, 'rb'))
+        try:
+            [gc_scale_count, gc_scale_val] = pickle.load(open(gc_bias_model, 'rb'))
+        except IOError:
+            print("\nProblem reading the default gc-bias model.\n")
+            exit(1)
         gc_window_size = gc_scale_count[-1]
     else:
-        [gc_scale_count, gc_scale_val] = pickle.load(open(gc_bias_model, 'rb'))
+        try:
+            [gc_scale_count, gc_scale_val] = pickle.load(open(gc_bias_model, 'rb'))
+        except IOError:
+            print("\nProblem reading the gc-bias model.\n")
+            exit(1)
         gc_window_size = gc_scale_count[-1]
 
-    # fragment length distribution
-    if paired_end and not paired_end_artificial:
-        print('Using empirical fragment length distribution.')
-        [potential_values, potential_prob] = pickle.load(open(fraglen_model, 'rb'))
-        fraglen_vals = []
-        fraglen_prob = []
-        for i in range(len(potential_values)):
-            if potential_values[i] > read_len:
-                fraglen_vals.append(potential_values[i])
-                fraglen_prob.append(potential_prob[i])
-        # should probably add some validation and sanity-checking code here...
-        fraglen_distribution = DiscreteDistribution(fraglen_prob, fraglen_vals)
-        fragment_size = fraglen_vals[mean_ind_of_weighted_list(fraglen_prob)]
-
-    # fragment length distribution: normal distribution that goes out to +- 6 standard deviations
-    if paired_end and paired_end_artificial:
-        print(
-            'Using artificial fragment length distribution. mean=' + str(fragment_size) + ', std=' + str(fragment_std))
-        if fragment_std == 0:
-            fraglen_distribution = DiscreteDistribution([1], [fragment_size], degenerate_val=fragment_size)
-        else:
-            potential_values = range(fragment_size - 6 * fragment_std, fragment_size + 6 * fragment_std + 1)
-            fraglen_vals = []
+    # Assign appropriate values to the needed variables if we're dealing with paired-ended data
+    if paired_end:
+        # Empirical fragment length distribution, if input model is specified
+        if fraglen_model is not None:
+            print('Using empirical fragment length distribution.')
+            try:
+                [potential_values, potential_prob] = pickle.load(open(fraglen_model, 'rb'))
+            except IOError:
+                print('\nProblem loading the empirical fragment length model.\n')
+                exit(1)
+
+            fraglen_values = []
+            fraglen_probability = []
             for i in range(len(potential_values)):
                 if potential_values[i] > read_len:
-                    fraglen_vals.append(potential_values[i])
-            fraglen_prob = [np.exp(-(((n - float(fragment_size)) ** 2) / (2 * (fragment_std ** 2)))) for n in
-                            fraglen_vals]
-            fraglen_distribution = DiscreteDistribution(fraglen_prob, fraglen_vals)
+                    fraglen_values.append(potential_values[i])
+                    fraglen_probability.append(potential_prob[i])
+
+            # TODO add some validation and sanity-checking code here...
+            fraglen_distribution = DiscreteDistribution(fraglen_probability, fraglen_values)
+            fragment_size = fraglen_values[mean_ind_of_weighted_list(fraglen_probability)]
 
-    """************************************************
-    ****   Process Inputs
-    ************************************************"""
+        # Using artificial fragment length distribution, if the parameters were specified
+        # fragment length distribution: normal distribution that goes out to +- 6 standard deviations
+        if fragment_size is not None and fragment_std is not None:
+            print(
+                'Using artificial fragment length distribution. mean=' + str(fragment_size) + ', std=' + str(
+                    fragment_std))
+            if fragment_std == 0:
+                fraglen_distribution = DiscreteDistribution([1], [fragment_size], degenerate_val=fragment_size)
+            else:
+                potential_values = range(fragment_size - 6 * fragment_std, fragment_size + 6 * fragment_std + 1)
+                fraglen_values = []
+                for i in range(len(potential_values)):
+                    if potential_values[i] > read_len:
+                        fraglen_values.append(potential_values[i])
+                fraglen_probability = [np.exp(-(((n - float(fragment_size)) ** 2) / (2 * (fragment_std ** 2)))) for n in
+                                       fraglen_values]
+                fraglen_distribution = DiscreteDistribution(fraglen_probability, fraglen_values)
+
+    """
+    Process Inputs
+    """
 
     # index reference
+    # TODO change reference processing to Biopython
     ref_index = index_ref(reference)
+
     if paired_end:
         n_handling = ('random', fragment_size)
     else:
         n_handling = ('ignore', read_len)
+
+    # TODO pretty sure all this can be handled by SeqIO
     indices_by_ref_name = {ref_index[n][0]: n for n in range(len(ref_index))}
 
     # parse input variants, if present
+    # TODO read this in as a pandas dataframe
     input_variants = []
     if input_vcf is not None:
         if cancer:
@@ -277,15 +294,22 @@ def main(raw_args=None):
             input_variants[k].sort()
 
     # parse input targeted regions, if present
+    # TODO convert bed to pandas dataframe
     input_regions = {}
     ref_list = [n[0] for n in ref_index]
     if input_bed is not None:
-        with open(input_bed, 'r') as f:
-            for line in f:
-                [my_chr, pos1, pos2] = line.strip().split('\t')[:3]
-                if my_chr not in input_regions:
-                    input_regions[my_chr] = [-1]
-                input_regions[my_chr].extend([int(pos1), int(pos2)])
+        try:
+            with open(input_bed, 'r') as f:
+                for line in f:
+                    [my_chr, pos1, pos2] = line.strip().split('\t')[:3]
+                    if my_chr not in input_regions:
+                        input_regions[my_chr] = [-1]
+                    input_regions[my_chr].extend([int(pos1), int(pos2)])
+        except IOError:
+            print("\nProblem reading input target BED file.\n")
+            exit(1)
+        finally:
+            f.close()
         # some validation
         n_in_bed_only = 0
         n_in_ref_only = 0
@@ -301,35 +325,48 @@ def main(raw_args=None):
         if n_in_bed_only > 0:
             print(
                 'Warning: Targeted regions BED file contains sequence names not found in reference (regions ignored).')
+
     # parse discard bed similarly
-    discardRegions = {}
-    if discard_bed != None:
-        f = open(discard_bed, 'r')
-        for line in f:
-            [myChr, pos1, pos2] = line.strip().split('\t')[:3]
-            if myChr not in discardRegions:
-                discardRegions[myChr] = [-1]
-            discardRegions[myChr].extend([int(pos1), int(pos2)])
-        f.close()
+    # TODO convert to pandas dataframe
+    discard_regions = {}
+    if discard_bed is not None:
+        try:
+            f = open(discard_bed, 'r')
+            for line in f:
+                [myChr, pos1, pos2] = line.strip().split('\t')[:3]
+                if myChr not in discard_regions:
+                    discard_regions[myChr] = [-1]
+                discard_regions[myChr].extend([int(pos1), int(pos2)])
+        except IOError:
+            print("\nProblem reading discard BED file.\n")
+        finally:
+            f.close()
 
     # parse input mutation rate rescaling regions, if present
+    # TODO convert to pandas dataframe
     mut_rate_regions = {}
     mut_rate_values = {}
     if mut_bed is not None:
-        with open(mut_bed, 'r') as f:
-            for line in f:
-                [my_chr, pos1, pos2, meta_data] = line.strip().split('\t')[:4]
-                mut_str = re.findall(r"mut_rate=.*?(?=;)", meta_data + ';')
-                (pos1, pos2) = (int(pos1), int(pos2))
-                if len(mut_str) and (pos2 - pos1) > 1:
-                    # mut_rate = #_mutations / length_of_region, let's bound it by a reasonable amount
-                    mut_rate = max([0.0, min([float(mut_str[0][9:]), 0.3])])
-                    if my_chr not in mut_rate_regions:
-                        mut_rate_regions[my_chr] = [-1]
-                        mut_rate_values[my_chr] = [0.0]
-                    mut_rate_regions[my_chr].extend([pos1, pos2])
-                    # TODO figure out what the next line is supposed to do and fix
-                    mut_rate_values.extend([mut_rate * (pos2 - pos1)] * 2)
+        try:
+            with open(mut_bed, 'r') as f:
+                for line in f:
+                    [my_chr, pos1, pos2, meta_data] = line.strip().split('\t')[:4]
+                    mut_str = re.findall(r"mut_rate=.*?(?=;)", meta_data + ';')
+                    (pos1, pos2) = (int(pos1), int(pos2))
+                    if len(mut_str) and (pos2 - pos1) > 1:
+                        # mut_rate = #_mutations / length_of_region, let's bound it by a reasonable amount
+                        mut_rate = max([0.0, min([float(mut_str[0][9:]), 0.3])])
+                        if my_chr not in mut_rate_regions:
+                            mut_rate_regions[my_chr] = [-1]
+                            mut_rate_values[my_chr] = [0.0]
+                        mut_rate_regions[my_chr].extend([pos1, pos2])
+                        # TODO figure out what the next line is supposed to do and fix
+                        mut_rate_values.extend([mut_rate * (pos2 - pos1)] * 2)
+        except IOError:
+            print("\nProblem reading mutational BED file.\n")
+            exit(1)
+        finally:
+            f.close()
 
     # initialize output files (part I)
     bam_header = None
@@ -340,25 +377,31 @@ def main(raw_args=None):
         vcf_header = [reference]
 
     # initialize output files (part II)
+    # TODO figure out how to do this more efficiently. Write the files at the end.
     if cancer:
-        OFW = OutputFileWriter(out_prefix + '_normal', paired=paired_end, bam_header=bam_header, vcf_header=vcf_header,
-                               gzipped=gzipped_out, no_fastq=no_fastq, fasta_instead=fasta_instead)
-        OFW_CANCER = OutputFileWriter(out_prefix + '_tumor', paired=paired_end, bam_header=bam_header,
-                                      vcf_header=vcf_header, gzipped=gzipped_out,
-                                      no_fastq=no_fastq, fasta_instead=fasta_instead)
+        output_file_writer = OutputFileWriter(out_prefix + '_normal', paired=paired_end, bam_header=bam_header,
+                                              vcf_header=vcf_header,
+                                              gzipped=gzipped_out, no_fastq=no_fastq, fasta_instead=fasta_instead)
+        output_file_writer_cancer = OutputFileWriter(out_prefix + '_tumor', paired=paired_end, bam_header=bam_header,
+                                                     vcf_header=vcf_header, gzipped=gzipped_out,
+                                                     no_fastq=no_fastq, fasta_instead=fasta_instead)
     else:
-        OFW = OutputFileWriter(out_prefix, paired=paired_end, bam_header=bam_header, vcf_header=vcf_header,
-                               gzipped=gzipped_out, no_fastq=no_fastq,
-                               fasta_instead=fasta_instead)
-    OUT_PREFIX_NAME = out_prefix.split('/')[-1]
-
-    """************************************************
-    ****        LET'S GET THIS PARTY STARTED...
-    ************************************************"""
+        output_file_writer = OutputFileWriter(out_prefix, paired=paired_end, bam_header=bam_header,
+                                              vcf_header=vcf_header,
+                                              gzipped=gzipped_out, no_fastq=no_fastq,
+                                              fasta_instead=fasta_instead)
+    # Using pathlib to make this more machine agnostic
+    out_prefix_name = pathlib.Path(out_prefix).name
 
-    read_name_count = 1  # keep track of the number of reads we've sampled, for read-names
+    """
+    LET'S GET THIS PARTY STARTED...
+    """
+    # keep track of the number of reads we've sampled, for read-names
+    read_name_count = 1
     unmapped_records = []
 
+    print(ref_index)
+
     for RI in range(len(ref_index)):
 
         # read in reference sequence and notate blocks of Ns
@@ -371,7 +414,7 @@ def main(raw_args=None):
         current_percent = 0
         have_printed100 = False
 
-        """prune invalid input variants, e.g variants that:
+        """Prune invalid input variants, e.g variants that:
                 - try to delete or alter any N characters
                 - don't match the reference base at their specified position
                 - any alt allele contains anything other than allowed characters"""
@@ -401,9 +444,7 @@ def main(raw_args=None):
                 print(' - [' + str(n_skipped[1]) + '] attempting to insert into N-region')
                 print(' - [' + str(n_skipped[2]) + '] alt allele contains non-ACGT characters')
 
-        # add large random structural variants
-        #
-        #	TBD!!!
+        # TODO add large random structural variants
 
         # determine sampling windows based on read length, large N regions, and structural mutations.
         # in order to obtain uniform coverage, windows should overlap by:
@@ -412,7 +453,7 @@ def main(raw_args=None):
         # ploidy is fixed per large sampling window,
         # coverage distributions due to GC% and targeted regions are specified within these windows
         sampling_windows = []
-        ALL_VARIANTS_OUT = {}
+        all_variants_out = {}
         sequences = None
         if paired_end:
             target_size = WINDOW_TARGET_SCALE * fragment_size
@@ -652,15 +693,15 @@ def main(raw_args=None):
                                 bisect.bisect(input_regions[ref_index[RI][0]], n[0] + len(n[2])) % 2 for n in
                                 my_read_data]
                         if discard_bed is not None:
-                            outside_boundaries += [bisect.bisect(discardRegions[ref_index[RI][0]], n[0]) % 2 for
+                            outside_boundaries += [bisect.bisect(discard_regions[ref_index[RI][0]], n[0]) % 2 for
                                                    n in my_read_data]
                             outside_boundaries += [
-                                bisect.bisect(discardRegions[ref_index[RI][0]], n[0] + len(n[2])) % 2 for n in
+                                bisect.bisect(discard_regions[ref_index[RI][0]], n[0] + len(n[2])) % 2 for n in
                                 my_read_data]
                         if len(outside_boundaries) and any(outside_boundaries):
                             continue
 
-                        my_read_name = OUT_PREFIX_NAME + '-' + ref_index[RI][0] + '-' + str(read_name_count)
+                        my_read_name = out_prefix_name + '-' + ref_index[RI][0] + '-' + str(read_name_count)
                         read_name_count += len(my_read_data)
 
                         # if desired, replace all low-quality bases with Ns
@@ -697,28 +738,36 @@ def main(raw_args=None):
                         if len(my_read_data) == 1:
                             if no_fastq is not True:
                                 if is_forward:
-                                    OFW.write_fastq_record(my_read_name, my_read_data[0][2], my_read_data[0][3])
+                                    output_file_writer.write_fastq_record(my_read_name, my_read_data[0][2],
+                                                                          my_read_data[0][3])
                                 else:
-                                    OFW.write_fastq_record(my_read_name, reverse_complement(my_read_data[0][2]),
-                                                           my_read_data[0][3][::-1])
+                                    output_file_writer.write_fastq_record(my_read_name,
+                                                                          reverse_complement(my_read_data[0][2]),
+                                                                          my_read_data[0][3][::-1])
                             if save_bam:
                                 if is_unmapped[0] is False:
                                     if is_forward:
                                         flag1 = 0
-                                        OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
-                                                             my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
-                                                             sam_flag=flag1)
+                                        output_file_writer.write_bam_record(my_ref_index, my_read_name,
+                                                                            my_read_data[0][0],
+                                                                            my_read_data[0][1], my_read_data[0][2],
+                                                                            my_read_data[0][3],
+                                                                            sam_flag=flag1)
                                     else:
                                         flag1 = sam_flag(['reverse'])
-                                        OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
-                                                             my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
-                                                             sam_flag=flag1)
+                                        output_file_writer.write_bam_record(my_ref_index, my_read_name,
+                                                                            my_read_data[0][0],
+                                                                            my_read_data[0][1], my_read_data[0][2],
+                                                                            my_read_data[0][3],
+                                                                            sam_flag=flag1)
                         # write PE output
                         elif len(my_read_data) == 2:
                             if no_fastq is not True:
-                                OFW.write_fastq_record(my_read_name, my_read_data[0][2], my_read_data[0][3],
-                                                       read2=my_read_data[1][2], qual2=my_read_data[1][3],
-                                                       orientation=is_forward)
+                                output_file_writer.write_fastq_record(my_read_name, my_read_data[0][2],
+                                                                      my_read_data[0][3],
+                                                                      read2=my_read_data[1][2],
+                                                                      qual2=my_read_data[1][3],
+                                                                      orientation=is_forward)
                             if save_bam:
                                 if is_unmapped[0] is False and is_unmapped[1] is False:
                                     if is_forward:
@@ -727,13 +776,15 @@ def main(raw_args=None):
                                     else:
                                         flag1 = sam_flag(['paired', 'proper', 'second', 'mate_reverse'])
                                         flag2 = sam_flag(['paired', 'proper', 'first', 'reverse'])
-                                    OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
-                                                         my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
-                                                         sam_flag=flag1,
-                                                         mate_pos=my_read_data[1][0])
-                                    OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[1][0],
-                                                         my_read_data[1][1], my_read_data[1][2], my_read_data[1][3],
-                                                         sam_flag=flag2, mate_pos=my_read_data[0][0])
+                                    output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
+                                                                        my_read_data[0][1], my_read_data[0][2],
+                                                                        my_read_data[0][3],
+                                                                        sam_flag=flag1,
+                                                                        mate_pos=my_read_data[1][0])
+                                    output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[1][0],
+                                                                        my_read_data[1][1], my_read_data[1][2],
+                                                                        my_read_data[1][3],
+                                                                        sam_flag=flag2, mate_pos=my_read_data[0][0])
                                 elif is_unmapped[0] is False and is_unmapped[1] is True:
                                     if is_forward:
                                         flag1 = sam_flag(['paired', 'first', 'mate_unmapped', 'mate_reverse'])
@@ -741,12 +792,15 @@ def main(raw_args=None):
                                     else:
                                         flag1 = sam_flag(['paired', 'second', 'mate_unmapped', 'mate_reverse'])
                                         flag2 = sam_flag(['paired', 'first', 'unmapped', 'reverse'])
-                                    OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
-                                                         my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
-                                                         sam_flag=flag1, mate_pos=my_read_data[0][0])
-                                    OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
-                                                         my_read_data[1][1], my_read_data[1][2], my_read_data[1][3],
-                                                         sam_flag=flag2, mate_pos=my_read_data[0][0], aln_map_quality=0)
+                                    output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
+                                                                        my_read_data[0][1], my_read_data[0][2],
+                                                                        my_read_data[0][3],
+                                                                        sam_flag=flag1, mate_pos=my_read_data[0][0])
+                                    output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
+                                                                        my_read_data[1][1], my_read_data[1][2],
+                                                                        my_read_data[1][3],
+                                                                        sam_flag=flag2, mate_pos=my_read_data[0][0],
+                                                                        aln_map_quality=0)
                                 elif is_unmapped[0] is True and is_unmapped[1] is False:
                                     if is_forward:
                                         flag1 = sam_flag(['paired', 'first', 'unmapped', 'mate_reverse'])
@@ -754,25 +808,28 @@ def main(raw_args=None):
                                     else:
                                         flag1 = sam_flag(['paired', 'second', 'unmapped', 'mate_reverse'])
                                         flag2 = sam_flag(['paired', 'first', 'mate_unmapped', 'reverse'])
-                                    OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[1][0],
-                                                         my_read_data[0][1], my_read_data[0][2], my_read_data[0][3],
-                                                         sam_flag=flag1, mate_pos=my_read_data[1][0], aln_map_quality=0)
-                                    OFW.write_bam_record(my_ref_index, my_read_name, my_read_data[1][0],
-                                                         my_read_data[1][1], my_read_data[1][2], my_read_data[1][3],
-                                                         sam_flag=flag2, mate_pos=my_read_data[1][0])
+                                    output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[1][0],
+                                                                        my_read_data[0][1], my_read_data[0][2],
+                                                                        my_read_data[0][3],
+                                                                        sam_flag=flag1, mate_pos=my_read_data[1][0],
+                                                                        aln_map_quality=0)
+                                    output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[1][0],
+                                                                        my_read_data[1][1], my_read_data[1][2],
+                                                                        my_read_data[1][3],
+                                                                        sam_flag=flag2, mate_pos=my_read_data[1][0])
                         else:
                             print('\nError: Unexpected number of reads generated...\n')
                             exit(1)
                     # print 'READS:',time.time()-ASDF2_TT
 
                     if not is_last_time:
-                        OFW.flush_buffers(bam_max=next_start)
+                        output_file_writer.flush_buffers(bam_max=next_start)
                     else:
-                        OFW.flush_buffers(bam_max=end + 1)
+                        output_file_writer.flush_buffers(bam_max=end + 1)
 
                 # tally up all the variants that got successfully introduced
                 for n in all_inserted_variants:
-                    ALL_VARIANTS_OUT[n] = True
+                    all_variants_out[n] = True
 
                 # prepare indices of next window
                 start = next_start
@@ -795,13 +852,14 @@ def main(raw_args=None):
         # write all output variants for this reference
         if save_vcf:
             print('Writing output VCF...')
-            for k in sorted(ALL_VARIANTS_OUT.keys()):
+            for k in sorted(all_variants_out.keys()):
                 current_ref = ref_index[RI][0]
                 my_id = '.'
                 my_quality = '.'
                 my_filter = 'PASS'
                 # k[0] + 1 because we're going back to 1-based vcf coords
-                OFW.write_vcf_record(current_ref, str(int(k[0]) + 1), my_id, k[1], k[2], my_quality, my_filter, k[4])
+                output_file_writer.write_vcf_record(current_ref, str(int(k[0]) + 1), my_id, k[1], k[2], my_quality,
+                                                    my_filter, k[4])
 
     # break
 
@@ -810,15 +868,17 @@ def main(raw_args=None):
         print('writing unmapped reads to bam file...')
         for umr in unmapped_records:
             if paired_end:
-                OFW.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], sam_flag=umr[2], mate_pos=0,
-                                     aln_map_quality=0)
+                output_file_writer.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], sam_flag=umr[2],
+                                                    mate_pos=0,
+                                                    aln_map_quality=0)
             else:
-                OFW.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], sam_flag=umr[2], aln_map_quality=0)
+                output_file_writer.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], sam_flag=umr[2],
+                                                    aln_map_quality=0)
 
     # close output files
-    OFW.close_files()
+    output_file_writer.close_files()
     if cancer:
-        OFW_CANCER.close_files()
+        output_file_writer_cancer.close_files()
 
 
 if __name__ == '__main__':
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index dee9285..211dce7 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -724,31 +724,44 @@ def sample_read(self, sequencingModel, frag_len=None):
         return r_out
 
 
-class ReadContainer:
+class SequencingError:
     """
-    Container for read data, computes quality scores and positions to insert errors
+    Container to model sequencing errors: computes quality scores and positions to insert errors
     """
     def __init__(self, read_len, error_model, rescaled_error):
 
         self.read_len = read_len
 
-        error_dat = pickle.load(open(error_model, 'rb'), encoding="bytes")
+        try:
+            error_dat = pickle.load(open(error_model, 'rb'), encoding="bytes")
+        except IOError:
+            print("\nProblem opening the sequencing error model.\n")
+            exit(1)
+
         self.uniform = False
-        if len(error_dat) == 4:  # uniform-error SE reads (e.g. PacBio)
+
+        # uniform-error SE reads (e.g., PacBio)
+        if len(error_dat) == 4:
             self.uniform = True
             [q_scores, off_q, avg_error, error_params] = error_dat
             self.uniform_q_score = int(-10. * np.log10(avg_error) + 0.5)
             print('Using uniform sequencing error model. (q=' + str(self.uniform_q_score) + '+' + str(
                 off_q) + ', p(err)={0:0.2f}%)'.format(100. * avg_error))
-        elif len(error_dat) == 6:  # only 1 q-score model present, use same model for both strands
+
+        # only 1 q-score model present, use same model for both strands
+        elif len(error_dat) == 6:
             [init_q1, prob_q1, q_scores, off_q, avg_error, error_params] = error_dat
             self.pe_models = False
-        elif len(error_dat) == 8:  # found a q-score model for both forward and reverse strands
+
+        # found a q-score model for both forward and reverse strands
+        elif len(error_dat) == 8:
             [init_q1, prob_q1, init_q2, prob_q2, q_scores, off_q, avg_error, error_params] = error_dat
             self.pe_models = True
             if len(init_q1) != len(init_q2) or len(prob_q1) != len(prob_q2):
                 print('\nError: R1 and R2 quality score models are of different length.\n')
                 exit(1)
+
+        # This serves as a sanity check for the input model
         else:
             print('\nError: Something wrong with error model.\n')
             exit(1)
@@ -810,6 +823,14 @@ def __init__(self, read_len, error_model, rescaled_error):
                             self.prob_dist_by_pos_by_prev_q2[-1].append(DiscreteDistribution(prob_q2[i][j], q_scores))
 
     def get_sequencing_errors(self, read_data, is_reverse_strand=False):
+        """
+        Inserts errors of type substitution, insertion, or deletion into read_data, and assigns a quality score
+        based on the container model.
+
+        :param read_data: sequence to insert errors into
+        :param is_reverse_strand: whether to treat this as the reverse strand or not
+        :return: modified sequence and associate quality scores
+        """
 
         q_out = [0] * self.read_len
         s_err = []
@@ -817,12 +838,10 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
         if self.uniform:
             my_q = [self.uniform_q_score + self.off_q] * self.read_len
             q_out = ''.join([chr(n) for n in my_q])
-            print("\nself.error_scale = " + str(self.error_scale) + "self.q_err_rate = " + str(self.q_err_rate) + '\n')
             for i in range(self.read_len):
                 if random.random() < self.error_scale * self.q_err_rate[self.uniform_q_score]:
                     s_err.append(i)
         else:
-
             if self.pe_models and is_reverse_strand:
                 my_q = self.init_dist_by_pos_2[0].sample()
             else:
@@ -863,19 +882,24 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
                 if random.random() < self.err_p[1]:
                     is_sub = False
 
-            # error_out = (type, len, pos, ref, alt)
-
-            if is_sub:  # insert substitution error
+            # insert substitution error
+            if is_sub:
                 my_nucl = str(read_data[ind])
                 new_nucl = self.err_sse[NUC_IND[my_nucl]].sample()
                 s_out.append(('S', 1, ind, my_nucl, new_nucl))
-            else:  # insert indel error
+
+            # insert indel error
+            else:
                 indel_len = self.err_sie.sample()
-                if random.random() < self.err_p[4]:  # insertion error
+
+                # insertion error
+                if random.random() < self.err_p[4]:
                     my_nucl = str(read_data[ind])
                     new_nucl = my_nucl + ''.join([self.err_sin.sample() for n in range(indel_len)])
                     s_out.append(('I', len(new_nucl) - 1, ind, my_nucl, new_nucl))
-                elif ind < self.read_len - 2 - n_del_so_far:  # deletion error (prevent too many of them from stacking up)
+
+                # deletion error (prevent too many of them from stacking up)
+                elif ind < self.read_len - 2 - n_del_so_far:
                     my_nucl = str(read_data[ind:ind + indel_len + 1])
                     new_nucl = str(read_data[ind])
                     n_del_so_far += len(my_nucl) - 1
diff --git a/py/output_file_writer.py b/py/output_file_writer.py
index b3405a3..906e65b 100644
--- a/py/output_file_writer.py
+++ b/py/output_file_writer.py
@@ -8,12 +8,14 @@
 from Bio import SeqIO
 import gzip
 from Bio.bgzf import *
+import pathlib
 
 from py.biopython_modified_bgzf import BgzfWriter
 
 BAM_COMPRESSION_LEVEL = 6
 
 
+# TODO figure out why these functions are in this file in the first place
 def reverse_complement(dna_string: str) -> str:
     """
     Return the reverse complement of a string from a DNA strand
@@ -49,35 +51,35 @@ def reg2bin(beg: int, end: int):
 
 
 # takes list of strings, returns numerical flag
-def sam_flag(l):
-    outVal = 0
-    l = list(set(l))
-    for n in l:
+def sam_flag(string_list: list) -> int:
+    out_val = 0
+    string_list = list(set(string_list))
+    for n in string_list:
         if n == 'paired':
-            outVal += 1
+            out_val += 1
         elif n == 'proper':
-            outVal += 2
+            out_val += 2
         elif n == 'unmapped':
-            outVal += 4
+            out_val += 4
         elif n == 'mate_unmapped':
-            outVal += 8
+            out_val += 8
         elif n == 'reverse':
-            outVal += 16
+            out_val += 16
         elif n == 'mate_reverse':
-            outVal += 32
+            out_val += 32
         elif n == 'first':
-            outVal += 64
+            out_val += 64
         elif n == 'second':
-            outVal += 128
+            out_val += 128
         elif n == 'not_primary':
-            outVal += 256
+            out_val += 256
         elif n == 'low_quality':
-            outVal += 512
+            out_val += 512
         elif n == 'duplicate':
-            outVal += 1024
+            out_val += 1024
         elif n == 'supplementary':
-            outVal += 2048
-    return outVal
+            out_val += 2048
+    return out_val
 
 
 CIGAR_PACKED = {'M': 0, 'I': 1, 'D': 2, 'N': 3, 'S': 4, 'H': 5, 'P': 6, '=': 7, 'X': 8}
@@ -87,50 +89,41 @@ def sam_flag(l):
 BUFFER_BATCH_SIZE = 1000  # write out to file after this many reads
 
 
-#
-#	outFQ      = path to output FASTQ prefix
-#	paired     = True for PE reads, False for SE
-#	BAM_header = [refIndex]
-#	VCF_header = [path_to_ref]
-#	gzipped    = True for compressed FASTQ/VCF, False for uncompressed
-#
+# TODO find a better way to write output files
 class OutputFileWriter:
     def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, gzipped=False,
                  no_fastq=False, fasta_instead=False):
 
-        job_suffix = ''
-
         self.fasta_instead = fasta_instead
-        if fasta_instead:
-            fq1 = out_prefix + '_read1.fa' + job_suffix
-            fq2 = out_prefix + '_read2.fa' + job_suffix
+        # TODO Eliminate paired end as an option for fastas
+        if self.fasta_instead:
+            fq1 = pathlib.Path(out_prefix + '.fasta')
+            fq2 = None
         else:
-            fq1 = out_prefix + '_read1.fq' + job_suffix
-            fq2 = out_prefix + '_read2.fq' + job_suffix
-        bam = out_prefix + '_golden.bam' + job_suffix
-        vcf = out_prefix + '_golden.vcf' + job_suffix
+            fq1 = pathlib.Path(out_prefix + '_read1.fq')
+            fq2 = pathlib.Path(out_prefix + '_read2.fq')
+        bam = pathlib.Path(out_prefix + '_golden.bam')
+        vcf = pathlib.Path(out_prefix + '_golden.vcf')
 
         self.no_fastq = no_fastq
         if not self.no_fastq:
             if gzipped:
-                self.fq1_file = gzip.open(fq1 + '.gz', 'wb')
+                self.fq1_file = gzip.open(fq1.with_suffix(fq1.suffix + '.gz'), 'wb')
             else:
                 self.fq1_file = open(fq1, 'w')
 
             self.fq2_file = None
             if paired:
                 if gzipped:
-                    self.fq2_file = gzip.open(fq2 + '.gz', 'wb')
+                    self.fq2_file = gzip.open(fq2.with_suffix(fq2.suffix + '.gz'), 'wb')
                 else:
                     self.fq2_file = open(fq2, 'w')
 
-        #
-        #	VCF OUTPUT
-        #
+        # VCF OUTPUT
         self.vcf_file = None
         if vcf_header is not None:
             if gzipped:
-                self.vcf_file = gzip.open(vcf + '.gz', 'wb')
+                self.vcf_file = gzip.open(vcf.with_suffix(vcf.suffix + '.gz'), 'wb')
             else:
                 self.vcf_file = open(vcf, 'wb')
 
@@ -141,7 +134,6 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
             self.vcf_file.write('##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">\n'.encode('utf-8'))
             self.vcf_file.write(
                 '##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">\n'.encode('utf-8'))
-            # self.vcf_file.write('##INFO=<ID=READS,Number=1,Type=String,Description="Names of Reads Covering this Variant">\n')
             self.vcf_file.write(
                 '##INFO=<ID=VMX,Number=1,Type=String,Description="SNP is Missense in these Read Frames">\n'.encode(
                     'utf-8'))
@@ -160,11 +152,10 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
             self.vcf_file.write('##ALT=<ID=CNV,Description="Copy number variable region">\n'.encode('utf-8'))
             self.vcf_file.write('##ALT=<ID=TRANS,Description="Translocation">\n'.encode('utf-8'))
             self.vcf_file.write('##ALT=<ID=INV-TRANS,Description="Inverted translocation">\n'.encode('utf-8'))
+            # TODO add sample to vcf output
             self.vcf_file.write('#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\n'.encode('utf-8'))
 
-        #
-        #	BAM OUTPUT
-        #
+        # BAM OUTPUT
         self.bam_file = None
         if bam_header is not None:
             self.bam_file = BgzfWriter(bam, 'w', compresslevel=BAM_COMPRESSION_LEVEL)
@@ -192,6 +183,8 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
         self.fq2_buffer = []
         self.bam_buffer = []
 
+    # TODO add write_fasta_record
+
     def write_fastq_record(self, read_name, read1, qual1, read2=None, qual2=None, orientation=None):
         (r1, q1) = (read1, qual1)
         if read2 is not None and orientation is True:
@@ -226,13 +219,13 @@ def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag,
         next_ref_id = ref_id
         if mate_pos is None:
             next_pos = 0
-            my_tlen = 0
+            my_t_len = 0
         else:
             next_pos = mate_pos
             if pos_0 < next_pos:
-                my_tlen = next_pos + len(seq) - pos_0
+                my_t_len = next_pos + len(seq) - pos_0
             else:
-                my_tlen = -pos_0 - len(seq) + next_pos
+                my_t_len = -pos_0 - len(seq) + next_pos
 
         encoded_cig = bytearray()
         for i in range(cig_ops):
@@ -285,7 +278,7 @@ def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag,
         self.bam_buffer.append((ref_id, pos_0, pack('<i', block_size) + pack('<i', ref_id) + pack('<i', pos_0) +
                                 pack('<I', (my_bin << 16) + (my_map_quality << 8) + len(read_name) + 1) +
                                 pack('<I', (sam_flag << 16) + cig_ops) + pack('<i', seq_len) + pack('<i', next_ref_id) +
-                                pack('<i', next_pos) + pack('<i', my_tlen) + read_name.encode('utf-8') +
+                                pack('<i', next_pos) + pack('<i', my_t_len) + read_name.encode('utf-8') +
                                 b'\0' + encoded_cig + encoded_seq + encoded_qual.encode('utf-8')))
 
     def flush_buffers(self, bam_max=None, last_time=False):
diff --git a/py/ref_func.py b/py/ref_func.py
index 6ff8c8a..870a60c 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -1,24 +1,38 @@
 import sys
 import time
 import os
+import pathlib
 import random
 from Bio.Seq import Seq
 from Bio.Alphabet import IUPAC
 
 
-#	Index reference fasta
-def index_ref(reference_path):
+def index_ref(reference_path: str) -> list:
+    """
+    Index reference fasta
+    :param reference_path: string path to the reference
+    :return: reference index in list from
+    """
     tt = time.time()
 
+    absolute_reference_location = pathlib.Path(reference_path)
+
+    # sanity check
+    if not absolute_reference_location.is_file():
+        print("\nProblem reading the reference fasta file.\n")
+        exit(1)
+
     filename = None
-    if os.path.isfile(reference_path + 'i'):
-        print('found index ' + reference_path + 'i')
-        filename = reference_path + 'i'
-    if os.path.isfile(reference_path + '.fai'):
-        print('found index ' + reference_path + '.fai')
-        filename = reference_path + '.fai'
-
-    ref_inds = []
+
+    # check if the reference file already exists
+    if absolute_reference_location.with_suffix('.fai').is_file():
+        print('found index ' + absolute_reference_location.with_suffix('.fai'))
+        filename = absolute_reference_location.with_suffix('.fai')
+    elif absolute_reference_location.with_suffix(absolute_reference_location.suffix + '.fai').is_file():
+        print('found index ' + absolute_reference_location.with_suffix(absolute_reference_location.suffix + '.fai'))
+        filename = absolute_reference_location.with_suffix(absolute_reference_location.suffix + '.fai')
+
+    ref_indices = []
     if filename is not None:
         fai = open(filename, 'r')
         for line in fai:
@@ -29,24 +43,22 @@ def index_ref(reference_path):
             n_lines = seq_len // line_ln
             if seq_len % line_ln != 0:
                 n_lines += 1
-            ref_inds.append((splt[0], offset, offset + seq_len + n_lines, seq_len))
+            ref_indices.append((splt[0], offset, offset + seq_len + n_lines, seq_len))
         fai.close()
-        return ref_inds
+        return ref_indices
 
-    sys.stdout.write('index not found, creating one... ')
-    sys.stdout.flush()
-    ref_file = open(reference_path, 'r')
+    print('Index not found, creating one... ')
+    ref_file = open(absolute_reference_location, 'r')
     prev_r = None
     prev_p = None
     seq_len = 0
-    while 1:
-        data = ref_file.readline()
+    for data in ref_file:
         if not data:
-            ref_inds.append((prev_r, prev_p, ref_file.tell() - len(data), seq_len))
+            ref_indices.append((prev_r, prev_p, ref_file.tell() - len(data), seq_len))
             break
-        if data[0] == '>':
+        elif data[0] == '>':
             if prev_p is not None:
-                ref_inds.append((prev_r, prev_p, ref_file.tell() - len(data), seq_len))
+                ref_indices.append((prev_r, prev_p, ref_file.tell() - len(data), seq_len))
             seq_len = 0
             prev_p = ref_file.tell()
             prev_r = data[1:-1]
@@ -55,7 +67,7 @@ def index_ref(reference_path):
     ref_file.close()
 
     print('{0:.3f} (sec)'.format(time.time() - tt))
-    return ref_inds
+    return ref_indices
 
 
 def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
@@ -134,8 +146,16 @@ def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
     return my_dat, n_info
 
 
-#	find all non-N regions in reference sequence ahead of time, for computing jobs in parallel
 def get_all_ref_regions(ref_path, ref_inds, n_handling, save_output=False):
+    """
+    Find all non-N regions in reference sequence ahead of time, for computing jobs in parallel
+
+    :param ref_path:
+    :param ref_inds:
+    :param n_handling:
+    :param save_output:
+    :return:
+    """
     out_regions = {}
     fn = ref_path + '.nnr'
     if os.path.isfile(fn) and not (save_output):
@@ -163,8 +183,16 @@ def get_all_ref_regions(ref_path, ref_inds, n_handling, save_output=False):
         return out_regions
 
 
-#	find which of the non-N regions are going to be used for this job
 def partition_ref_regions(in_regions, ref_inds, my_job, n_jobs):
+    """
+    Find which of the non-N regions are going to be used for this job
+
+    :param in_regions:
+    :param ref_inds:
+    :param my_job:
+    :param n_jobs:
+    :return:
+    """
     tot_size = 0
     for RI in range(len(ref_inds)):
         ref_name = ref_inds[RI][0]

From bb1d349a752ecf2d1fde9f2c73e1ff7e2ead5588 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 14:00:17 -0500
Subject: [PATCH 149/323] Smoothing out genreads and adding todos

---
 py/ref_func.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/py/ref_func.py b/py/ref_func.py
index 870a60c..682aec3 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -52,7 +52,10 @@ def index_ref(reference_path: str) -> list:
     prev_r = None
     prev_p = None
     seq_len = 0
-    for data in ref_file:
+
+    # TODO I really don't like this while true statement
+    while True:
+        data = ref_file.readline()
         if not data:
             ref_indices.append((prev_r, prev_p, ref_file.tell() - len(data), seq_len))
             break

From c937c7d8d46df7c5dff5c3e703ac7164462ddf1c Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 14:04:18 -0500
Subject: [PATCH 150/323] debugging sequence container

---
 gen_reads.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index 72399fd..7ad355c 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -400,7 +400,7 @@ def main(raw_args=None):
     read_name_count = 1
     unmapped_records = []
 
-    print(ref_index)
+    print('ref_index = ' + str(ref_index))
 
     for RI in range(len(ref_index)):
 

From ef7cbff2ce95711d81a6fe3462a5398252488e6c Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 14:07:56 -0500
Subject: [PATCH 151/323] debugging sequence container

---
 gen_reads.py   | 10 +++++-----
 py/ref_func.py |  3 +--
 2 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 7ad355c..60d3bdf 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -405,7 +405,7 @@ def main(raw_args=None):
     for RI in range(len(ref_index)):
 
         # read in reference sequence and notate blocks of Ns
-        (refSequence, n_regions) = read_ref(reference, ref_index[RI], n_handling)
+        (ref_sequence, n_regions) = read_ref(reference, ref_index[RI], n_handling)
 
         # count total bp we'll be spanning so we can get an idea of how far along we are
         # (for printing progress indicators)
@@ -423,7 +423,7 @@ def main(raw_args=None):
         if ref_index[RI][0] in input_variants:
             for n in input_variants[ref_index[RI][0]]:
                 span = (n[0], n[0] + len(n[1]))
-                r_seq = str(refSequence[span[0] - 1:span[1] - 1])  # -1 because going from VCF coords to array coords
+                r_seq = str(ref_sequence[span[0] - 1:span[1] - 1])  # -1 because going from VCF coords to array coords
                 any_bad_chr = any((nn not in ALLOWED_NUCL) for nn in [item for sublist in n[2] for item in sublist])
                 if r_seq != n[1]:
                     n_skipped[0] += 1
@@ -597,10 +597,10 @@ def main(raw_args=None):
 
                 # construct sequence data that we will sample reads from
                 if sequences is None:
-                    sequences = SequenceContainer(start, refSequence[start:end], ploids, overlap, read_len,
+                    sequences = SequenceContainer(start, ref_sequence[start:end], ploids, overlap, read_len,
                                                   [mut_model] * ploids, mut_rate, only_vcf=only_vcf)
                 else:
-                    sequences.update(start, refSequence[start:end], ploids, overlap, read_len, [mut_model] * ploids,
+                    sequences.update(start, ref_sequence[start:end], ploids, overlap, read_len, [mut_model] * ploids,
                                      mut_rate)
 
                 # insert variants
@@ -617,7 +617,7 @@ def main(raw_args=None):
 
                 # unused cancer stuff
                 if cancer:
-                    tumor_sequences = SequenceContainer(start, refSequence[start:end], ploids, overlap, read_len,
+                    tumor_sequences = SequenceContainer(start, ref_sequence[start:end], ploids, overlap, read_len,
                                                         [cancer_model] * ploids, mut_rate, coverage_dat)
                     tumor_sequences.insert_mutations(vars_cancer_from_prev_overlap + all_inserted_variants)
                     all_cancer_variants = tumor_sequences.random_mutations()
diff --git a/py/ref_func.py b/py/ref_func.py
index 682aec3..4e5e7a8 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -78,8 +78,7 @@ def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
     ALLOWED_NUCL = ['A', 'C', 'G', 'T']
     tt = time.time()
     if not quiet:
-        sys.stdout.write('reading ' + ref_inds_i[0] + '... ')
-        sys.stdout.flush()
+        print('reading ' + ref_inds_i[0] + '... ')
 
     ref_file = open(ref_path, 'r')
     ref_file.seek(ref_inds_i[1])

From 88254d2bddf36957c84b2bbed723f2159cd3a021 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 14:58:46 -0500
Subject: [PATCH 152/323] debugging sequence container

---
 gen_reads.py   | 33 ++++++++++++++++-----------------
 py/ref_func.py | 12 +++++++++++-
 2 files changed, 27 insertions(+), 18 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 60d3bdf..18b9a21 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -402,10 +402,11 @@ def main(raw_args=None):
 
     print('ref_index = ' + str(ref_index))
 
-    for RI in range(len(ref_index)):
+    for chrom in range(len(ref_index)):
 
         # read in reference sequence and notate blocks of Ns
-        (ref_sequence, n_regions) = read_ref(reference, ref_index[RI], n_handling)
+        # TODO convert this line to SeqIO
+        (ref_sequence, n_regions) = read_ref(reference, ref_index[chrom], n_handling)
 
         # count total bp we'll be spanning so we can get an idea of how far along we are
         # (for printing progress indicators)
@@ -420,8 +421,9 @@ def main(raw_args=None):
                 - any alt allele contains anything other than allowed characters"""
         valid_variants = []
         n_skipped = [0, 0, 0]
-        if ref_index[RI][0] in input_variants:
-            for n in input_variants[ref_index[RI][0]]:
+        if ref_index[chrom][0] in input_variants:
+            for n in input_variants[ref_index[chrom][0]]:
+                print('n = ' + str(n))
                 span = (n[0], n[0] + len(n[1]))
                 r_seq = str(ref_sequence[span[0] - 1:span[1] - 1])  # -1 because going from VCF coords to array coords
                 any_bad_chr = any((nn not in ALLOWED_NUCL) for nn in [item for sublist in n[2] for item in sublist])
@@ -434,10 +436,8 @@ def main(raw_args=None):
                 elif any_bad_chr:
                     n_skipped[2] += 1
                     continue
-                # if bisect.bisect(n_regions['big'],span[0])%2 or bisect.bisect(n_regions['big'],span[1])%2:
-                # continue
                 valid_variants.append(n)
-            print('found', len(valid_variants), 'valid variants for ' + ref_index[RI][0] + ' in input VCF...')
+            print('found', len(valid_variants), 'valid variants for ' + ref_index[chrom][0] + ' in input VCF...')
             if any(n_skipped):
                 print(sum(n_skipped), 'variants skipped...')
                 print(' - [' + str(n_skipped[0]) + '] ref allele does not match reference')
@@ -452,7 +452,6 @@ def main(raw_args=None):
         #		- fragment_size (mean), if paired-end reads
         # ploidy is fixed per large sampling window,
         # coverage distributions due to GC% and targeted regions are specified within these windows
-        sampling_windows = []
         all_variants_out = {}
         sequences = None
         if paired_end:
@@ -560,11 +559,11 @@ def main(raw_args=None):
                 if input_bed is None:
                     coverage_dat[2] = [1.0] * (end - start)
                 else:
-                    if ref_index[RI][0] not in input_regions:
+                    if ref_index[chrom][0] not in input_regions:
                         coverage_dat[2] = [off_target_scalar] * (end - start)
                     else:
                         for j in range(start, end):
-                            if not (bisect.bisect(input_regions[ref_index[RI][0]], j) % 2):
+                            if not (bisect.bisect(input_regions[ref_index[chrom][0]], j) % 2):
                                 coverage_dat[2].append(1.0)
                                 target_hits += 1
                             else:
@@ -687,21 +686,21 @@ def main(raw_args=None):
                         # are we discarding offtargets?
                         outside_boundaries = []
                         if off_target_discard and input_bed is not None:
-                            outside_boundaries += [bisect.bisect(input_regions[ref_index[RI][0]], n[0]) % 2 for n
+                            outside_boundaries += [bisect.bisect(input_regions[ref_index[chrom][0]], n[0]) % 2 for n
                                                    in my_read_data]
                             outside_boundaries += [
-                                bisect.bisect(input_regions[ref_index[RI][0]], n[0] + len(n[2])) % 2 for n in
+                                bisect.bisect(input_regions[ref_index[chrom][0]], n[0] + len(n[2])) % 2 for n in
                                 my_read_data]
                         if discard_bed is not None:
-                            outside_boundaries += [bisect.bisect(discard_regions[ref_index[RI][0]], n[0]) % 2 for
+                            outside_boundaries += [bisect.bisect(discard_regions[ref_index[chrom][0]], n[0]) % 2 for
                                                    n in my_read_data]
                             outside_boundaries += [
-                                bisect.bisect(discard_regions[ref_index[RI][0]], n[0] + len(n[2])) % 2 for n in
+                                bisect.bisect(discard_regions[ref_index[chrom][0]], n[0] + len(n[2])) % 2 for n in
                                 my_read_data]
                         if len(outside_boundaries) and any(outside_boundaries):
                             continue
 
-                        my_read_name = out_prefix_name + '-' + ref_index[RI][0] + '-' + str(read_name_count)
+                        my_read_name = out_prefix_name + '-' + ref_index[chrom][0] + '-' + str(read_name_count)
                         read_name_count += len(my_read_data)
 
                         # if desired, replace all low-quality bases with Ns
@@ -732,7 +731,7 @@ def main(raw_args=None):
                                 flag1 = sam_flag(['unmapped'])
                                 unmapped_records.append((my_read_name + '/1', my_read_data[0], flag1))
 
-                        my_ref_index = indices_by_ref_name[ref_index[RI][0]]
+                        my_ref_index = indices_by_ref_name[ref_index[chrom][0]]
 
                         # write SE output
                         if len(my_read_data) == 1:
@@ -853,7 +852,7 @@ def main(raw_args=None):
         if save_vcf:
             print('Writing output VCF...')
             for k in sorted(all_variants_out.keys()):
-                current_ref = ref_index[RI][0]
+                current_ref = ref_index[chrom][0]
                 my_id = '.'
                 my_quality = '.'
                 my_filter = 'PASS'
diff --git a/py/ref_func.py b/py/ref_func.py
index 4e5e7a8..7560552 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -80,7 +80,14 @@ def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
     if not quiet:
         print('reading ' + ref_inds_i[0] + '... ')
 
-    ref_file = open(ref_path, 'r')
+    absolute_reference_path = pathlib.Path(ref_path)
+    try:
+        ref_file = open(absolute_reference_path, 'r')
+    except IOError:
+        print('\nProblem reading reference file.\n')
+        exit(1)
+
+    # TODO convert to SeqIO containers
     ref_file.seek(ref_inds_i[1])
     my_dat = ''.join(ref_file.read(ref_inds_i[2] - ref_inds_i[1]).split('\n'))
     my_dat = Seq(my_dat.upper(), IUPAC.unambiguous_dna)
@@ -143,8 +150,11 @@ def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
         if n[0] != n[1]:
             n_info['non_N'].append(n)
 
+    ref_file.close()
+
     if not quiet:
         print('{0:.3f} (sec)'.format(time.time() - tt))
+
     return my_dat, n_info
 
 

From f285088b1fedd99401985ca7dc921d8498f2120b Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 15:05:31 -0500
Subject: [PATCH 153/323] Figuring out genreads

---
 gen_reads.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 18b9a21..d3259ae 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -400,8 +400,6 @@ def main(raw_args=None):
     read_name_count = 1
     unmapped_records = []
 
-    print('ref_index = ' + str(ref_index))
-
     for chrom in range(len(ref_index)):
 
         # read in reference sequence and notate blocks of Ns
@@ -423,8 +421,8 @@ def main(raw_args=None):
         n_skipped = [0, 0, 0]
         if ref_index[chrom][0] in input_variants:
             for n in input_variants[ref_index[chrom][0]]:
-                print('n = ' + str(n))
                 span = (n[0], n[0] + len(n[1]))
+                print(ref_sequence)
                 r_seq = str(ref_sequence[span[0] - 1:span[1] - 1])  # -1 because going from VCF coords to array coords
                 any_bad_chr = any((nn not in ALLOWED_NUCL) for nn in [item for sublist in n[2] for item in sublist])
                 if r_seq != n[1]:

From 769511b3245eda10cacd0d05542fd5c8026d9663 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 15:07:41 -0500
Subject: [PATCH 154/323] Figuring out genreads

---
 gen_reads.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index d3259ae..2a4d18e 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -422,8 +422,8 @@ def main(raw_args=None):
         if ref_index[chrom][0] in input_variants:
             for n in input_variants[ref_index[chrom][0]]:
                 span = (n[0], n[0] + len(n[1]))
-                print(ref_sequence)
                 r_seq = str(ref_sequence[span[0] - 1:span[1] - 1])  # -1 because going from VCF coords to array coords
+                print('r_seq = ' + r_seq)
                 any_bad_chr = any((nn not in ALLOWED_NUCL) for nn in [item for sublist in n[2] for item in sublist])
                 if r_seq != n[1]:
                     n_skipped[0] += 1

From ecec09086ab92e1586d092ae719677607e0dc150 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 15:12:24 -0500
Subject: [PATCH 155/323] debugging sequence container

---
 gen_reads.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index 2a4d18e..00d6d85 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -421,9 +421,9 @@ def main(raw_args=None):
         n_skipped = [0, 0, 0]
         if ref_index[chrom][0] in input_variants:
             for n in input_variants[ref_index[chrom][0]]:
+                print('n = ' + str(n))
                 span = (n[0], n[0] + len(n[1]))
                 r_seq = str(ref_sequence[span[0] - 1:span[1] - 1])  # -1 because going from VCF coords to array coords
-                print('r_seq = ' + r_seq)
                 any_bad_chr = any((nn not in ALLOWED_NUCL) for nn in [item for sublist in n[2] for item in sublist])
                 if r_seq != n[1]:
                     n_skipped[0] += 1

From b8ee89bb679c504e213173e1ef3134a39e286c5e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 15:26:01 -0500
Subject: [PATCH 156/323] debugging sequence container

---
 gen_reads.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 00d6d85..3786e10 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -248,7 +248,7 @@ def main(raw_args=None):
 
         # Using artificial fragment length distribution, if the parameters were specified
         # fragment length distribution: normal distribution that goes out to +- 6 standard deviations
-        if fragment_size is not None and fragment_std is not None:
+        elif fragment_size is not None and fragment_std is not None:
             print(
                 'Using artificial fragment length distribution. mean=' + str(fragment_size) + ', std=' + str(
                     fragment_std))
@@ -424,17 +424,23 @@ def main(raw_args=None):
                 print('n = ' + str(n))
                 span = (n[0], n[0] + len(n[1]))
                 r_seq = str(ref_sequence[span[0] - 1:span[1] - 1])  # -1 because going from VCF coords to array coords
-                any_bad_chr = any((nn not in ALLOWED_NUCL) for nn in [item for sublist in n[2] for item in sublist])
+                # Checks if there are any invalid nucleotides in the vcf items
+                any_bad_nucl = any((nn not in ALLOWED_NUCL) for nn in [item for sublist in n[2] for item in sublist])
+                # Ensure reference sequence matches the nucleotide in the vcf
                 if r_seq != n[1]:
                     n_skipped[0] += 1
                     continue
+                # Ensure that we aren't trying to insert into an N region
                 elif 'N' in r_seq:
                     n_skipped[1] += 1
                     continue
-                elif any_bad_chr:
+                # Ensure that we don't insert any disallowed characters
+                elif any_bad_nucl:
                     n_skipped[2] += 1
                     continue
+                # If it passes the above tests, append to valid variants list
                 valid_variants.append(n)
+
             print('found', len(valid_variants), 'valid variants for ' + ref_index[chrom][0] + ' in input VCF...')
             if any(n_skipped):
                 print(sum(n_skipped), 'variants skipped...')
@@ -453,6 +459,7 @@ def main(raw_args=None):
         all_variants_out = {}
         sequences = None
         if paired_end:
+            print('fragment size = ' + fragment_size)
             target_size = WINDOW_TARGET_SCALE * fragment_size
             overlap = fragment_size
             overlap_min_window_size = max(fraglen_distribution.values) + 10

From 21122dfc9cd7a6b785c5591add25d2ec6361b00c Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 15:26:19 -0500
Subject: [PATCH 157/323] debugging sequence container

---
 gen_reads.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index 3786e10..ca78807 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -421,7 +421,6 @@ def main(raw_args=None):
         n_skipped = [0, 0, 0]
         if ref_index[chrom][0] in input_variants:
             for n in input_variants[ref_index[chrom][0]]:
-                print('n = ' + str(n))
                 span = (n[0], n[0] + len(n[1]))
                 r_seq = str(ref_sequence[span[0] - 1:span[1] - 1])  # -1 because going from VCF coords to array coords
                 # Checks if there are any invalid nucleotides in the vcf items

From 2ecd4ab8a2cc9705d796419103ae855b2357afbc Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 15:28:55 -0500
Subject: [PATCH 158/323] debugging sequence container

---
 gen_reads.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index ca78807..3253841 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -458,7 +458,7 @@ def main(raw_args=None):
         all_variants_out = {}
         sequences = None
         if paired_end:
-            print('fragment size = ' + fragment_size)
+            print('fragment size = ' + str(fragment_size))
             target_size = WINDOW_TARGET_SCALE * fragment_size
             overlap = fragment_size
             overlap_min_window_size = max(fraglen_distribution.values) + 10

From 48af5b7805fb3e20b44306bf2ee8b809b9d3a513 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 15:39:32 -0500
Subject: [PATCH 159/323] Smoothing out genreads and adding todos

---
 gen_reads.py                              |   4 +++-
 py/__pycache__/probability.cpython-37.pyc | Bin 4082 -> 4135 bytes
 py/ref_func.py                            |   7 +++++--
 3 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 3253841..e032c7b 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -458,7 +458,6 @@ def main(raw_args=None):
         all_variants_out = {}
         sequences = None
         if paired_end:
-            print('fragment size = ' + str(fragment_size))
             target_size = WINDOW_TARGET_SCALE * fragment_size
             overlap = fragment_size
             overlap_min_window_size = max(fraglen_distribution.values) + 10
@@ -470,11 +469,14 @@ def main(raw_args=None):
         print('--------------------------------')
         if only_vcf:
             print('generating vcf...')
+        elif fasta_instead:
+            print('generating mutated fasta...')
         else:
             print('sampling reads...')
         tt = time.time()
 
         for i in range(len(n_regions['non_N'])):
+            print('pi, pf = ' + str(n_regions['non_N'][i]))
             (pi, pf) = n_regions['non_N'][i]
             n_target_windows = max([1, (pf - pi) // target_size])
             bpd = int((pf - pi) / float(n_target_windows))
diff --git a/py/__pycache__/probability.cpython-37.pyc b/py/__pycache__/probability.cpython-37.pyc
index e59de6b00ff33dfb009c597b6e564cd1ee7552a3..cbb55aefc3adffea0f39a7f42bdf2ca3dc1a2485 100644
GIT binary patch
delta 401
zcmXAlze~eV5XW;bHEGi%br406wjg3NSVYC5(5kg4O0kM`=~7w~G)+zIYigm0i;JUr
z;^ZheOBNCT1aUXBgM))OI{BX7@VOt~@4<23iTk9U*EB_7(HJRIUxps^G=VS7l1;et
z&xr+E?nG#ars9wbpzru2rUu=x2c~ugAeh3gVCa4-<bctiRLZX7wt8;Tv^#s$vOHdR
z)UJ0uyX7ViE$^UZrWGFtqA(W7ox@tQY0*Gx*62RV#BnO2BigD5_#{oi7Jf)^ASlbz
zWKhJ6d;=T)t6TyoVm#7_&a#{7JjWtpBn9*SbL1Mp#+~S8yus!j!XOETM5Ej_&XM3)
zx>83cR(Q3{P?398T65V2Vb&QU{ZsN>Ud5a`40SB42`Kno^$nnlCH)ox{L~XOfpkQz
z<EkByy8XXQ=o;T!=ZG)_TDx0wJ-csJo9q>B%*GbNMJ5<3&ERQlPAYI~5g%h2aXTFQ
E1sBU|R{#J2

delta 372
zcmZ3k@JXJ}iI<m)0SFxa*vIFKZRFEqVth8)mdT58*5(eTR7PHt8ip+96s~Nh35-RL
zCjVmAU`t^LX3*r_Y{ZhyxcL|>H=|4xdvR`JPEKl36kASWQ96)j%Fe#UQc#qi6gBxR
zn=YfzWNCIeM()X0>;_C#vXl8a#3#41*E0HVX6Eo`WDJ@d%$db)0CYo<F^Djq{FYyQ
z@_kN0Mw`taIj1l(=1*?pp2%1*S%OC<%m$=F0z|L@2~CzL_U!ng#5|y-b|67pAQ8o0
z4i(}6DPe}l*n^}UKm;d{xW!YDpIKa-pBJB#SzMAaxr;}h(Q)%Co|}w})stKJ<}gN0
zR^*pAief7$N-c}ejABa4ECQRxROAdY!v$^-Z((U-UP)$ED%hm*$wmB@9Bx2aO`al?
V$(#92*c?Gh-GF{FXAa}w2LPMoVXpuH

diff --git a/py/ref_func.py b/py/ref_func.py
index 7560552..95e3c96 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -7,6 +7,10 @@
 from Bio.Alphabet import IUPAC
 
 
+OK_CHR_ORD = {'A': True, 'C': True, 'G': True, 'T': True, 'U': True}
+ALLOWED_NUCL = ['A', 'C', 'G', 'T']
+
+
 def index_ref(reference_path: str) -> list:
     """
     Index reference fasta
@@ -74,8 +78,7 @@ def index_ref(reference_path: str) -> list:
 
 
 def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
-    OK_CHR_ORD = {'A': True, 'C': True, 'G': True, 'T': True, 'U': True}
-    ALLOWED_NUCL = ['A', 'C', 'G', 'T']
+
     tt = time.time()
     if not quiet:
         print('reading ' + ref_inds_i[0] + '... ')

From 2f1832b0687525be37f98bd474091043cf684258 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 15:41:02 -0500
Subject: [PATCH 160/323] Smoothing out genreads and adding todos

---
 .gitignore | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..1377b78
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+# Ignore filetypes
+*.pyc
\ No newline at end of file

From 748d11f2245b49b24cc761c6eebc2c5b6df41f2f Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 15:50:42 -0500
Subject: [PATCH 161/323] debugging sequence container

---
 gen_reads.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index e032c7b..06abff2 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -476,17 +476,19 @@ def main(raw_args=None):
         tt = time.time()
 
         for i in range(len(n_regions['non_N'])):
-            print('pi, pf = ' + str(n_regions['non_N'][i]))
             (pi, pf) = n_regions['non_N'][i]
-            n_target_windows = max([1, (pf - pi) // target_size])
-            bpd = int((pf - pi) / float(n_target_windows))
+            number_target_windows = max([1, (pf - pi) // target_size])
+            bpd = int((pf - pi) / float(number_target_windows))
+
+            # TODO figure out what was being attempted with the below three lines
+            print(gc_window_size)
             # bpd += gc_window_size - bpd%gc_window_size
 
             # print len(refSequence), (pi,pf), n_target_windows
             # print structural_vars
 
             # if for some reason our region is too small to process, skip it! (sorry)
-            if n_target_windows == 1 and (pf - pi) < overlap_min_window_size:
+            if number_target_windows == 1 and (pf - pi) < overlap_min_window_size:
                 # print 'Does this ever happen?'
                 continue
 

From 84035f5f074f6885e05e12762dd0ce365d3eb376 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 16:13:47 -0500
Subject: [PATCH 162/323] debugging sequence container

---
 gen_reads.py | 64 ++++++++++++++++++++++++----------------------------
 1 file changed, 29 insertions(+), 35 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 06abff2..891ce22 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -417,7 +417,7 @@ def main(raw_args=None):
                 - try to delete or alter any N characters
                 - don't match the reference base at their specified position
                 - any alt allele contains anything other than allowed characters"""
-        valid_variants = []
+        valid_variants_from_vcf = []
         n_skipped = [0, 0, 0]
         if ref_index[chrom][0] in input_variants:
             for n in input_variants[ref_index[chrom][0]]:
@@ -438,9 +438,9 @@ def main(raw_args=None):
                     n_skipped[2] += 1
                     continue
                 # If it passes the above tests, append to valid variants list
-                valid_variants.append(n)
+                valid_variants_from_vcf.append(n)
 
-            print('found', len(valid_variants), 'valid variants for ' + ref_index[chrom][0] + ' in input VCF...')
+            print('found', len(valid_variants_from_vcf), 'valid variants for ' + ref_index[chrom][0] + ' in input VCF...')
             if any(n_skipped):
                 print(sum(n_skipped), 'variants skipped...')
                 print(' - [' + str(n_skipped[0]) + '] ref allele does not match reference')
@@ -475,57 +475,52 @@ def main(raw_args=None):
             print('sampling reads...')
         tt = time.time()
 
+        # Applying variants to non-N regions
         for i in range(len(n_regions['non_N'])):
-            (pi, pf) = n_regions['non_N'][i]
-            number_target_windows = max([1, (pf - pi) // target_size])
-            bpd = int((pf - pi) / float(number_target_windows))
-
-            # TODO figure out what was being attempted with the below three lines
-            print(gc_window_size)
-            # bpd += gc_window_size - bpd%gc_window_size
-
-            # print len(refSequence), (pi,pf), n_target_windows
-            # print structural_vars
+            (initial_position, final_position) = n_regions['non_N'][i]
+            number_target_windows = max([1, (final_position - initial_position) // target_size])
+            base_pair_distance = int((final_position - initial_position) / float(number_target_windows))
 
             # if for some reason our region is too small to process, skip it! (sorry)
-            if number_target_windows == 1 and (pf - pi) < overlap_min_window_size:
-                # print 'Does this ever happen?'
+            if number_target_windows == 1 and (final_position - initial_position) < overlap_min_window_size:
                 continue
 
-            start = pi
-            end = min([start + bpd, pf])
-            # print '------------------RAWR:', (pi,pf), n_target_windows, bpd
+            start = initial_position
+            end = min([start + base_pair_distance, final_position])
             vars_from_prev_overlap = []
             vars_cancer_from_prev_overlap = []
-            vind_from_prev = 0
+            v_index_from_prev = 0
             is_last_time = False
             have_printed100 = False
 
             while True:
-
                 # which inserted variants are in this window?
                 vars_in_window = []
                 updated = False
-                for j in range(vind_from_prev, len(valid_variants)):
-                    v_pos = valid_variants[j][0]
-                    if start < v_pos < end:  # update: changed >= to >, so variant cannot be inserted in first position
-                        vars_in_window.append(tuple([v_pos - 1] + list(valid_variants[j][1:])))  # vcf --> array coords
-                    if v_pos >= end - overlap - 1 and updated is False:
+                for j in range(v_index_from_prev, len(valid_variants_from_vcf)):
+                    variants_position = valid_variants_from_vcf[j][0]
+                    # update: changed >= to >, so variant cannot be inserted in first position
+                    if start < variants_position < end:
+                        # vcf --> array coords
+                        vars_in_window.append(tuple([variants_position - 1] + list(valid_variants_from_vcf[j][1:])))
+                    if variants_position >= end - overlap - 1 and updated is False:
                         updated = True
-                        vind_from_prev = j
-                    if v_pos >= end:
+                        v_index_from_prev = j
+                    if variants_position >= end:
                         break
 
                 # determine which structural variants will affect our sampling window positions
                 structural_vars = []
                 for n in vars_in_window:
+                    print('variant in window = ' + str(n))
+                    # change: added abs() so that insertions are also buffered.
                     buffer_needed = max([max([abs(len(n[1]) - len(alt_allele)), 1]) for alt_allele in
-                                         n[2]])  # change: added abs() so that insertions are also buffered.
+                                         n[2]])
+                    # -1 because going from VCF coords to array coords
                     structural_vars.append(
-                        (n[0] - 1, buffer_needed))  # -1 because going from VCF coords to array coords
+                        (n[0] - 1, buffer_needed))
 
                 # adjust end-position of window based on inserted structural mutations
-                buffer_added = 0
                 keep_going = True
                 while keep_going:
                     keep_going = False
@@ -534,15 +529,14 @@ def main(raw_args=None):
                         # (which can cause problems if random mutations from the previous window land on top of them)
                         delta = (end - 1) - (n[0] + n[1]) - 2 - overlap
                         if delta < 0:
-                            # print 'DELTA:', delta, 'END:', end, '-->',
                             buffer_added = -delta
                             end += buffer_added
                             ####print end
                             keep_going = True
                             break
                 next_start = end - overlap
-                next_end = min([next_start + bpd, pf])
-                if next_end - next_start < bpd:
+                next_end = min([next_start + base_pair_distance, final_position])
+                if next_end - next_start < base_pair_distance:
                     end = next_end
                     is_last_time = True
 
@@ -597,7 +591,7 @@ def main(raw_args=None):
                     end = next_end
                     if is_last_time:
                         break
-                    if end >= pf:
+                    if end >= final_position:
                         is_last_time = True
                     vars_from_prev_overlap = []
                     continue
@@ -843,7 +837,7 @@ def main(raw_args=None):
                 end = next_end
                 if is_last_time:
                     break
-                if end >= pf:
+                if end >= final_position:
                     is_last_time = True
 
         if current_percent != 100 and not have_printed100:

From 794dca01219c07e432c935eadfcdf31cf27253eb Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 17:43:12 -0500
Subject: [PATCH 163/323] debugging sequence container

---
 gen_reads.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 891ce22..e240ae3 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -427,6 +427,7 @@ def main(raw_args=None):
                 any_bad_nucl = any((nn not in ALLOWED_NUCL) for nn in [item for sublist in n[2] for item in sublist])
                 # Ensure reference sequence matches the nucleotide in the vcf
                 if r_seq != n[1]:
+                    print(str(r_seq) + ' and ' + str(n[1]))
                     n_skipped[0] += 1
                     continue
                 # Ensure that we aren't trying to insert into an N region
@@ -514,8 +515,7 @@ def main(raw_args=None):
                 for n in vars_in_window:
                     print('variant in window = ' + str(n))
                     # change: added abs() so that insertions are also buffered.
-                    buffer_needed = max([max([abs(len(n[1]) - len(alt_allele)), 1]) for alt_allele in
-                                         n[2]])
+                    buffer_needed = max([max([abs(len(n[1]) - len(alt_allele)), 1]) for alt_allele in n[2]])
                     # -1 because going from VCF coords to array coords
                     structural_vars.append(
                         (n[0] - 1, buffer_needed))

From 5690c2a7e3e70bac4f73b1f023889d482b91595f Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 17:45:07 -0500
Subject: [PATCH 164/323] debugging sequence container

---
 gen_reads.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index e240ae3..074af47 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -427,7 +427,6 @@ def main(raw_args=None):
                 any_bad_nucl = any((nn not in ALLOWED_NUCL) for nn in [item for sublist in n[2] for item in sublist])
                 # Ensure reference sequence matches the nucleotide in the vcf
                 if r_seq != n[1]:
-                    print(str(r_seq) + ' and ' + str(n[1]))
                     n_skipped[0] += 1
                     continue
                 # Ensure that we aren't trying to insert into an N region
@@ -495,6 +494,7 @@ def main(raw_args=None):
             have_printed100 = False
 
             while True:
+                print('\n\nstarting ...\n\n')
                 # which inserted variants are in this window?
                 vars_in_window = []
                 updated = False

From a832112d8bdabf9f1305124dca0fe6db65c8c61c Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 17:54:45 -0500
Subject: [PATCH 165/323] debugging sequence container

---
 gen_reads.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index 074af47..2c07c50 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -494,7 +494,6 @@ def main(raw_args=None):
             have_printed100 = False
 
             while True:
-                print('\n\nstarting ...\n\n')
                 # which inserted variants are in this window?
                 vars_in_window = []
                 updated = False

From 3fe2a25ab4b869be3058eaed9eb1995ddb5f6876 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 18:35:36 -0500
Subject: [PATCH 166/323] debugging sequence container

---
 gen_reads.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 2c07c50..4111216 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -512,7 +512,6 @@ def main(raw_args=None):
                 # determine which structural variants will affect our sampling window positions
                 structural_vars = []
                 for n in vars_in_window:
-                    print('variant in window = ' + str(n))
                     # change: added abs() so that insertions are also buffered.
                     buffer_needed = max([max([abs(len(n[1]) - len(alt_allele)), 1]) for alt_allele in n[2]])
                     # -1 because going from VCF coords to array coords
@@ -530,7 +529,6 @@ def main(raw_args=None):
                         if delta < 0:
                             buffer_added = -delta
                             end += buffer_added
-                            ####print end
                             keep_going = True
                             break
                 next_start = end - overlap
@@ -540,15 +538,19 @@ def main(raw_args=None):
                     is_last_time = True
 
                 # print progress indicator
-                ####print 'PROCESSING WINDOW:',(start,end), [buffer_added], 'next:', (next_start,next_end)
+                start_progress_bar = True
                 current_progress += end - start
                 new_percent = int((current_progress * 100) / float(total_bp_span))
                 if new_percent > current_percent:
+                    if start_progress_bar:
+                        print("[", end='')
+                        start_progress_bar = False
                     if new_percent <= 99 or (new_percent == 100 and not have_printed100):
-                        sys.stdout.write(str(new_percent) + '% ')
-                        sys.stdout.flush()
+                        if new_percent % 20 == 0:
+                            print('-', end='')
                     current_percent = new_percent
                     if current_percent == 100:
+                        print(']')
                         have_printed100 = True
 
                 skip_this_window = False

From a74a9e67215dbd71557c8c02fbf5c3d3af6e25f7 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 18:40:23 -0500
Subject: [PATCH 167/323] debugging sequence container

---
 gen_reads.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index 4111216..d15633c 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -492,6 +492,7 @@ def main(raw_args=None):
             v_index_from_prev = 0
             is_last_time = False
             have_printed100 = False
+            start_progress_bar = True
 
             while True:
                 # which inserted variants are in this window?
@@ -538,7 +539,6 @@ def main(raw_args=None):
                     is_last_time = True
 
                 # print progress indicator
-                start_progress_bar = True
                 current_progress += end - start
                 new_percent = int((current_progress * 100) / float(total_bp_span))
                 if new_percent > current_percent:

From ab04730a12aa72066e47c18d003c8cec94a9b002 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 18:41:11 -0500
Subject: [PATCH 168/323] debugging sequence container

---
 gen_reads.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index d15633c..eb6d1f7 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -495,6 +495,9 @@ def main(raw_args=None):
             start_progress_bar = True
 
             while True:
+                if start_progress_bar:
+                    print("[", end='')
+                    start_progress_bar = False
                 # which inserted variants are in this window?
                 vars_in_window = []
                 updated = False
@@ -542,9 +545,6 @@ def main(raw_args=None):
                 current_progress += end - start
                 new_percent = int((current_progress * 100) / float(total_bp_span))
                 if new_percent > current_percent:
-                    if start_progress_bar:
-                        print("[", end='')
-                        start_progress_bar = False
                     if new_percent <= 99 or (new_percent == 100 and not have_printed100):
                         if new_percent % 20 == 0:
                             print('-', end='')

From 468d86320310ab171d5bc44b0cac8d316931c413 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 25 Aug 2020 18:47:09 -0500
Subject: [PATCH 169/323] debugging sequence container

---
 gen_reads.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/gen_reads.py b/gen_reads.py
index eb6d1f7..536967f 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -495,9 +495,11 @@ def main(raw_args=None):
             start_progress_bar = True
 
             while True:
+                # start the progress bar
                 if start_progress_bar:
                     print("[", end='')
                     start_progress_bar = False
+
                 # which inserted variants are in this window?
                 vars_in_window = []
                 updated = False

From f349c424bd14115f983977c2ab9311c9dc0e35ec Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 28 Aug 2020 12:40:27 -0500
Subject: [PATCH 170/323] Minor changes to several functions

---
 gen_reads.py                  |  63 ++--
 py/SequenceContainer.py       | 557 ++++++++++++++++++++--------------
 py/biopython_modified_bgzf.py |  10 +-
 py/input_checking.py          |  12 +-
 py/neat_cigar.py              |  48 ++-
 py/output_file_writer.py      |   2 +-
 py/probability.py             |   9 +-
 7 files changed, 414 insertions(+), 287 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 536967f..e5340cb 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -286,6 +286,7 @@ def main(raw_args=None):
     if input_vcf is not None:
         if cancer:
             (sample_names, input_variants) = parse_vcf(input_vcf, tumor_normal=True, ploidy=ploids)
+            # TODO figure out what Zach was trying to do with these
             tumor_ind = sample_names.index('TUMOR')
             normal_ind = sample_names.index('NORMAL')
         else:
@@ -310,6 +311,7 @@ def main(raw_args=None):
             exit(1)
         finally:
             f.close()
+
         # some validation
         n_in_bed_only = 0
         n_in_ref_only = 0
@@ -317,7 +319,7 @@ def main(raw_args=None):
             if k not in input_regions:
                 n_in_ref_only += 1
         for k in input_regions.keys():
-            if not k in ref_list:
+            if k not in ref_list:
                 n_in_bed_only += 1
                 del input_regions[k]
         if n_in_ref_only > 0:
@@ -333,10 +335,10 @@ def main(raw_args=None):
         try:
             f = open(discard_bed, 'r')
             for line in f:
-                [myChr, pos1, pos2] = line.strip().split('\t')[:3]
-                if myChr not in discard_regions:
-                    discard_regions[myChr] = [-1]
-                discard_regions[myChr].extend([int(pos1), int(pos2)])
+                [my_chr, pos1, pos2] = line.strip().split('\t')[:3]
+                if my_chr not in discard_regions:
+                    discard_regions[my_chr] = [-1]
+                discard_regions[my_chr].extend([int(pos1), int(pos2)])
         except IOError:
             print("\nProblem reading discard BED file.\n")
         finally:
@@ -403,7 +405,6 @@ def main(raw_args=None):
     for chrom in range(len(ref_index)):
 
         # read in reference sequence and notate blocks of Ns
-        # TODO convert this line to SeqIO
         (ref_sequence, n_regions) = read_ref(reference, ref_index[chrom], n_handling)
 
         # count total bp we'll be spanning so we can get an idea of how far along we are
@@ -474,6 +475,8 @@ def main(raw_args=None):
         else:
             print('sampling reads...')
         tt = time.time()
+        # start the progress bar
+        print("[", end='')
 
         # Applying variants to non-N regions
         for i in range(len(n_regions['non_N'])):
@@ -491,21 +494,14 @@ def main(raw_args=None):
             vars_cancer_from_prev_overlap = []
             v_index_from_prev = 0
             is_last_time = False
-            have_printed100 = False
-            start_progress_bar = True
 
             while True:
-                # start the progress bar
-                if start_progress_bar:
-                    print("[", end='')
-                    start_progress_bar = False
-
                 # which inserted variants are in this window?
                 vars_in_window = []
                 updated = False
                 for j in range(v_index_from_prev, len(valid_variants_from_vcf)):
                     variants_position = valid_variants_from_vcf[j][0]
-                    # update: changed >= to >, so variant cannot be inserted in first position
+                    # update: changed <= to <, so variant cannot be inserted in first position
                     if start < variants_position < end:
                         # vcf --> array coords
                         vars_in_window.append(tuple([variants_position - 1] + list(valid_variants_from_vcf[j][1:])))
@@ -548,11 +544,10 @@ def main(raw_args=None):
                 new_percent = int((current_progress * 100) / float(total_bp_span))
                 if new_percent > current_percent:
                     if new_percent <= 99 or (new_percent == 100 and not have_printed100):
-                        if new_percent % 20 == 0:
+                        if new_percent % 10 == 1:
                             print('-', end='')
                     current_percent = new_percent
                     if current_percent == 100:
-                        print(']')
                         have_printed100 = True
 
                 skip_this_window = False
@@ -574,7 +569,7 @@ def main(raw_args=None):
                             else:
                                 coverage_dat[2].append(off_target_scalar)
 
-                # offtarget and we're not interested?
+                # off-target and we're not interested?
                 if off_target_discard and target_hits <= read_len:
                     coverage_avg = 0.0
                     skip_this_window = True
@@ -642,6 +637,11 @@ def main(raw_args=None):
                     pass
                 else:
                     window_span = end - start
+                    print('window_span = ' + str(window_span))
+                    print('coverage = ' + str(coverage))
+                    print('read_len = ' + str(read_len))
+                    print('coverage_avg = ' + str(coverage_avg))
+
                     if paired_end:
                         if force_coverage:
                             reads_to_sample = int((window_span * float(coverage)) / (2 * read_len)) + 1
@@ -659,20 +659,21 @@ def main(raw_args=None):
                         reads_to_sample = 0
 
                     # sample reads
-                    ASDF2_TT = time.time()
-                    for i in range(reads_to_sample):
+                    for k in range(reads_to_sample):
 
                         is_unmapped = []
                         if paired_end:
                             my_fraglen = fraglen_distribution.sample()
                             my_read_data = sequences.sample_read(se_class, my_fraglen)
-                            if my_read_data is None:  # skip if we failed to find a valid position to sample read
+                            # skip if we failed to find a valid position to sample read
+                            if my_read_data is None:
                                 continue
                             if my_read_data[0][0] is None:
                                 is_unmapped.append(True)
                             else:
                                 is_unmapped.append(False)
-                                my_read_data[0][0] += start  # adjust mapping position based on window start
+                                # adjust mapping position based on window start
+                                my_read_data[0][0] += start
                             if my_read_data[1][0] is None:
                                 is_unmapped.append(True)
                             else:
@@ -680,13 +681,16 @@ def main(raw_args=None):
                                 my_read_data[1][0] += start
                         else:
                             my_read_data = sequences.sample_read(se_class)
-                            if my_read_data is None:  # skip if we failed to find a valid position to sample read
+                            # skip if we failed to find a valid position to sample read
+                            if my_read_data is None:
                                 continue
-                            if my_read_data[0][0] is None:  # unmapped read (lives in large insertion)
+                            # unmapped read (lives in large insertion)
+                            if my_read_data[0][0] is None:
                                 is_unmapped = [True]
                             else:
                                 is_unmapped = [False]
-                                my_read_data[0][0] += start  # adjust mapping position based on window start
+                                # adjust mapping position based on window start
+                                my_read_data[0][0] += start
 
                         # are we discarding offtargets?
                         outside_boundaries = []
@@ -712,10 +716,10 @@ def main(raw_args=None):
                         if n_max_qual > -1:
                             for j in range(len(my_read_data)):
                                 my_read_string = [n for n in my_read_data[j][2]]
-                                for k in range(len(my_read_data[j][3])):
-                                    adjusted_qual = ord(my_read_data[j][3][k]) - se_class.off_q
+                                for m in range(len(my_read_data[j][3])):
+                                    adjusted_qual = ord(my_read_data[j][3][m]) - se_class.off_q
                                     if adjusted_qual <= n_max_qual:
-                                        my_read_string[k] = 'N'
+                                        my_read_string[m] = 'N'
                                 my_read_data[j][2] = ''.join(my_read_string)
 
                         # flip a coin, are we forward or reverse strand?
@@ -824,7 +828,6 @@ def main(raw_args=None):
                         else:
                             print('\nError: Unexpected number of reads generated...\n')
                             exit(1)
-                    # print 'READS:',time.time()-ASDF2_TT
 
                     if not is_last_time:
                         output_file_writer.flush_buffers(bam_max=next_start)
@@ -844,7 +847,7 @@ def main(raw_args=None):
                     is_last_time = True
 
         if current_percent != 100 and not have_printed100:
-            print('100%')
+            print(']')
         else:
             print('')
         if only_vcf:
@@ -865,8 +868,6 @@ def main(raw_args=None):
                 output_file_writer.write_vcf_record(current_ref, str(int(k[0]) + 1), my_id, k[1], k[2], my_quality,
                                                     my_filter, k[4])
 
-    # break
-
     # write unmapped reads to bam file
     if save_bam and len(unmapped_records):
         print('writing unmapped reads to bam file...')
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 211dce7..60c9564 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -1,6 +1,5 @@
 import random
 import copy
-import os
 import bisect
 import pickle
 import numpy as np
@@ -46,8 +45,8 @@
                     [0.15, 0.0, 0.15, 0.7],
                     [0.7, 0.15, 0.0, 0.15],
                     [0.15, 0.7, 0.15, 0.0]]
-DEFAULT_1_TRI_FREQS = [copy.deepcopy(example_matrix_1) for n in range(16)]
-DEFAULT_1_TRINUC_BIAS = [1. / float(len(ALL_TRI)) for n in ALL_TRI]
+DEFAULT_1_TRI_FREQS = [copy.deepcopy(example_matrix_1) for _ in range(16)]
+DEFAULT_1_TRINUC_BIAS = [1. / float(len(ALL_TRI)) for _ in ALL_TRI]
 DEFAULT_MODEL_1 = [DEFAULT_1_OVERALL_MUT_RATE,
                    DEFAULT_1_HOMOZYGOUS_FREQ,
                    DEFAULT_1_INDEL_FRACTION,
@@ -65,14 +64,15 @@
 DEFAULT_2_INS_VS_DEL = 0.3
 DEFAULT_2_INS_LENGTH_VALUES = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
 DEFAULT_2_INS_LENGTH_WEIGHTS = [0.1, 0.1, 0.2, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]
+# noinspection DuplicatedCode
 DEFAULT_2_DEL_LENGTH_VALUES = [1, 2, 3, 4, 5]
 DEFAULT_2_DEL_LENGTH_WEIGHTS = [0.3, 0.2, 0.2, 0.2, 0.1]
 example_matrix_2 = [[0.0, 0.15, 0.7, 0.15],
                     [0.15, 0.0, 0.15, 0.7],
                     [0.7, 0.15, 0.0, 0.15],
                     [0.15, 0.7, 0.15, 0.0]]
-DEFAULT_2_TRI_FREQS = [copy.deepcopy(example_matrix_2) for n in range(16)]
-DEFAULT_2_TRINUC_BIAS = [1. / float(len(ALL_TRI)) for n in ALL_TRI]
+DEFAULT_2_TRI_FREQS = [copy.deepcopy(example_matrix_2) for _ in range(16)]
+DEFAULT_2_TRINUC_BIAS = [1. / float(len(ALL_TRI)) for _ in ALL_TRI]
 DEFAULT_MODEL_2 = [DEFAULT_2_OVERALL_MUT_RATE,
                    DEFAULT_2_HOMOZYGOUS_FREQ,
                    DEFAULT_2_INDEL_FRACTION,
@@ -85,40 +85,119 @@
                    DEFAULT_2_TRINUC_BIAS]
 
 
-#
-#	Container for reference sequences, applies mutations
-#
 class SequenceContainer:
-    def __init__(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_models=[], mut_rate=None,
+    """
+    Container for reference sequences, applies mutations
+    """
+
+    def __init__(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_models=None, mut_rate=None,
                  only_vcf=False):
+
         # initialize basic variables
         self.only_vcf = only_vcf
-        self.init_basic_vars(x_offset, sequence, ploidy, window_overlap, read_len)
+        self.x = x_offset
+        self.ploidy = ploidy
+        self.read_len = read_len
+        self.sequences = [Seq(str(sequence), IUPAC.unambiguous_dna).tomutable() for _ in range(self.ploidy)]
+        self.seq_len = len(sequence)
+        self.indel_list = [[] for _ in range(self.ploidy)]
+        self.snp_list = [[] for _ in range(self.ploidy)]
+        self.all_cigar = [[] for _ in range(self.ploidy)]
+        self.fm_pos = [[] for _ in range(self.ploidy)]
+        self.fm_span = [[] for _ in range(self.ploidy)]
+
+        # Blacklist explanation:
+        # black_list[ploid][pos] = 0		safe to insert variant here
+        # black_list[ploid][pos] = 1		indel inserted here
+        # black_list[ploid][pos] = 2		snp inserted here
+        # black_list[ploid][pos] = 3		invalid position for various processing reasons
+        self.black_list = [np.zeros(self.seq_len, dtype='<i4') for _ in range(self.ploidy)]
+
+        # disallow mutations to occur on window overlap points
+        self.win_buffer = window_overlap
+        for p in range(self.ploidy):
+            self.black_list[p][-self.win_buffer] = 3
+            self.black_list[p][-self.win_buffer - 1] = 3
+
         # initialize mutation models
-        self.init_mut_models(mut_models, mut_rate)
+        if not mut_models:
+            default_model = [copy.deepcopy(DEFAULT_MODEL_1) for _ in range(self.ploidy)]
+            self.model_data = default_model[:self.ploidy]
+        else:
+            if len(mut_models) != self.ploidy:
+                print('\nError: Number of mutation models received is not equal to specified ploidy\n')
+                exit(1)
+            self.model_data = copy.deepcopy(mut_models)
+
+        # do we need to rescale mutation frequencies?
+        mut_rate_sum = sum([n[0] for n in self.model_data])
+        self.mut_rescale = mut_rate
+        if self.mut_rescale is None:
+            self.mut_scalar = 1.0
+        else:
+            self.mut_scalar = float(self.mut_rescale) // (mut_rate_sum / float(len(self.model_data)))
+
+        # how are mutations spread to each ploid, based on their specified mut rates?
+        self.ploid_mut_frac = [float(n[0]) / mut_rate_sum for n in self.model_data]
+        self.ploid_mut_prior = DiscreteDistribution(self.ploid_mut_frac, range(self.ploidy))
+
+        # init mutation models
+        #
+        # self.models[ploid][0] = average mutation rate
+        # self.models[ploid][1] = p(mut is homozygous | mutation occurs)
+        # self.models[ploid][2] = p(mut is indel | mut occurs)
+        # self.models[ploid][3] = p(insertion | indel occurs)
+        # self.models[ploid][4] = distribution of insertion lengths
+        # self.models[ploid][5] = distribution of deletion lengths
+        # self.models[ploid][6] = distribution of trinucleotide SNP transitions
+        # self.models[ploid][7] = p(trinuc mutates)
+        self.models = []
+        for n in self.model_data:
+            self.models.append([self.mut_scalar * n[0], n[1], n[2], n[3], DiscreteDistribution(n[5], n[4]),
+                                DiscreteDistribution(n[7], n[6]), []])
+            for m in n[8]:
+                # noinspection PyTypeChecker
+                self.models[-1][6].append([DiscreteDistribution(m[0], NUCL), DiscreteDistribution(m[1], NUCL),
+                                           DiscreteDistribution(m[2], NUCL), DiscreteDistribution(m[3], NUCL)])
+            self.models[-1].append([m for m in n[9]])
+
+        # initialize poisson attributes
+        self.indel_poisson, self.snp_poisson = self.init_poisson()
+
         # sample the number of variants that will be inserted into each ploid
-        self.init_poisson()
-        self.indels_to_add = [n.sample() for n in self.ind_pois]
-        self.snps_to_add = [n.sample() for n in self.snp_pois]
+        self.indels_to_add = [n.sample() for n in self.indel_poisson]
+        self.snps_to_add = [n.sample() for n in self.snp_poisson]
+
         # initialize trinuc snp bias
-        self.init_trinuc_bias()
+        # compute mutation positional bias given trinucleotide strings of the sequence (ONLY AFFECTS SNPs)
+        #
+        # note: since indels are added before snps, it's possible these positional biases aren't correctly utilized
+        #       at positions affected by indels. At the moment I'm going to consider this negligible.
+        trinuc_snp_bias = [[0. for _ in range(self.seq_len)] for _ in range(self.ploidy)]
+        self.trinuc_bias = [None for _ in range(self.ploidy)]
+        for p in range(self.ploidy):
+            for i in range(self.win_buffer + 1, self.seq_len - 1):
+                trinuc_snp_bias[p][i] = self.models[p][7][ALL_IND[str(self.sequences[p][i - 1:i + 2])]]
+            self.trinuc_bias[p] = DiscreteDistribution(trinuc_snp_bias[p][self.win_buffer + 1:self.seq_len - 1],
+                                                       range(self.win_buffer + 1, self.seq_len - 1))
+
+        # initialize coverage attributes
+        self.window_size = None
+        self.coverage_distribution = None
+        self.fraglen_ind_map = None
 
-    def init_basic_vars(self, x_offset, sequence, ploidy, window_overlap, read_len):
+    def update_basic_vars(self, x_offset, sequence, ploidy, window_overlap, read_len):
         self.x = x_offset
         self.ploidy = ploidy
         self.read_len = read_len
-        self.sequences = [Seq(str(sequence), IUPAC.unambiguous_dna).tomutable() for n in range(self.ploidy)]
+        self.sequences = [Seq(str(sequence), IUPAC.unambiguous_dna).tomutable() for _ in range(self.ploidy)]
         self.seq_len = len(sequence)
-        self.indel_list = [[] for n in range(self.ploidy)]
-        self.snp_list = [[] for n in range(self.ploidy)]
-        self.all_cigar = [[] for n in range(self.ploidy)]
-        self.fm_pos = [[] for n in range(self.ploidy)]
-        self.fm_span = [[] for n in range(self.ploidy)]
-        # blackList[ploid][pos] = 0		safe to insert variant here
-        # blackList[ploid][pos] = 1		indel inserted here
-        # blackList[ploid][pos] = 2		snp inserted here
-        # blackList[ploid][pos] = 3		invalid position for various processing reasons
-        self.black_list = [np.zeros(self.seq_len, dtype='<i4') for n in range(self.ploidy)]
+        self.indel_list = [[] for _ in range(self.ploidy)]
+        self.snp_list = [[] for _ in range(self.ploidy)]
+        self.all_cigar = [[] for _ in range(self.ploidy)]
+        self.fm_pos = [[] for _ in range(self.ploidy)]
+        self.fm_span = [[] for _ in range(self.ploidy)]
+        self.black_list = [np.zeros(self.seq_len, dtype='<i4') for _ in range(self.ploidy)]
 
         # disallow mutations to occur on window overlap points
         self.win_buffer = window_overlap
@@ -126,14 +205,62 @@ def init_basic_vars(self, x_offset, sequence, ploidy, window_overlap, read_len):
             self.black_list[p][-self.win_buffer] = 3
             self.black_list[p][-self.win_buffer - 1] = 3
 
+    def update_mut_models(self, mut_models, mut_rate):
+        if not mut_models:
+            default_model = [copy.deepcopy(DEFAULT_MODEL_1) for _ in range(self.ploidy)]
+            self.model_data = default_model[:self.ploidy]
+        else:
+            if len(mut_models) != self.ploidy:
+                print('\nError: Number of mutation models received is not equal to specified ploidy\n')
+                exit(1)
+            self.model_data = copy.deepcopy(mut_models)
+
+        # do we need to rescale mutation frequencies?
+        mut_rate_sum = sum([n[0] for n in self.model_data])
+        self.mut_rescale = mut_rate
+        if self.mut_rescale is None:
+            self.mut_scalar = 1.0
+        else:
+            self.mut_scalar = float(self.mut_rescale) // (mut_rate_sum / float(len(self.model_data)))
+
+        # how are mutations spread to each ploid, based on their specified mut rates?
+        self.ploid_mut_frac = [float(n[0]) / mut_rate_sum for n in self.model_data]
+        self.ploid_mut_prior = DiscreteDistribution(self.ploid_mut_frac, range(self.ploidy))
+        self.models = []
+        for n in self.model_data:
+            self.models.append([self.mut_scalar * n[0], n[1], n[2], n[3], DiscreteDistribution(n[5], n[4]),
+                                DiscreteDistribution(n[7], n[6]), []])
+            for m in n[8]:
+                # noinspection PyTypeChecker
+                self.models[-1][6].append([DiscreteDistribution(m[0], NUCL), DiscreteDistribution(m[1], NUCL),
+                                           DiscreteDistribution(m[2], NUCL), DiscreteDistribution(m[3], NUCL)])
+            self.models[-1].append([m for m in n[9]])
+
+    def update_trinuc_bias(self):
+        trinuc_snp_bias = [[0. for _ in range(self.seq_len)] for _ in range(self.ploidy)]
+        self.trinuc_bias = [None for _ in range(self.ploidy)]
+        for p in range(self.ploidy):
+            for i in range(self.win_buffer + 1, self.seq_len - 1):
+                trinuc_snp_bias[p][i] = self.models[p][7][ALL_IND[str(self.sequences[p][i - 1:i + 2])]]
+            self.trinuc_bias[p] = DiscreteDistribution(trinuc_snp_bias[p][self.win_buffer + 1:self.seq_len - 1],
+                                                       range(self.win_buffer + 1, self.seq_len - 1))
+
     def init_coverage(self, coverage_data, frag_dist=None):
-        # if we're only creating a vcf, skip some expensive initialization related to coverage depth
+        """
+        Initializes coverage for the sequence container. Only makes changes if we are not in vcf-only mode.
+
+        :param coverage_data: A tuple containing the window size, gc scalars and target coverage values.
+        :param frag_dist: A probability distribution of the fragment size.
+        :return: Mean coverage value
+        """
+
+        # If we're only creating a vcf, skip some expensive initialization related to coverage depth
         if not self.only_vcf:
             (self.window_size, gc_scalars, target_cov_vals) = coverage_data
-            gc_cov_vals = [[] for n in self.sequences]
-            tr_cov_vals = [[] for n in self.sequences]
-            self.coverage_distribution = []
+            gc_cov_vals = [[] for _ in self.sequences]
+            tr_cov_vals = [[] for _ in self.sequences]
             avg_out = []
+            self.coverage_distribution = []
             for i in range(len(self.sequences)):
                 max_coord = min([len(self.sequences[i]) - self.read_len, len(self.all_cigar[i]) - self.read_len])
                 # Trying to fix a problem wherein the above line gives a negative answer
@@ -142,12 +269,12 @@ def init_coverage(self, coverage_data, frag_dist=None):
                 # compute gc-bias
                 j = 0
                 while j + self.window_size < len(self.sequences[i]):
-                    gc_c = self.sequences[i][j:j + self.window_size].count('G') + self.sequences[i][
-                                                                                  j:j + self.window_size].count('C')
+                    gc_c = self.sequences[i][j:j + self.window_size].count('G') + \
+                           self.sequences[i][j:j + self.window_size].count('C')
                     gc_cov_vals[i].extend([gc_scalars[gc_c]] * self.window_size)
                     j += self.window_size
-                gc_c = self.sequences[i][-self.window_size:].count('G') + self.sequences[i][-self.window_size:].count(
-                    'C')
+                gc_c = self.sequences[i][-self.window_size:].count('G') + \
+                       self.sequences[i][-self.window_size:].count('C')
                 gc_cov_vals[i].extend([gc_scalars[gc_c]] * (len(self.sequences[i]) - len(gc_cov_vals[i])))
                 #
                 tr_cov_vals[i].append(target_cov_vals[0])
@@ -161,7 +288,7 @@ def init_coverage(self, coverage_data, frag_dist=None):
                         tr_cov_vals[i].append(sum(target_cov_vals[self.fm_pos[i][j]:self.fm_span[i][j]]) / float(
                             self.fm_span[i][j] - self.fm_pos[i][j]))
                         prev_val = self.fm_pos[i][j]
-                    # print (i,j), self.adj[i][j], self.all_cigar[i][j], self.FM_pos[i][j], self.FM_span[i][j]
+
                 # shift by half of read length
                 if len(tr_cov_vals[i]) > int(self.read_len / 2.):
                     tr_cov_vals[i] = [0.0] * int(self.read_len // 2) + tr_cov_vals[i][:-int(self.read_len / 2.)]
@@ -192,19 +319,19 @@ def init_coverage(self, coverage_data, frag_dist=None):
                         flq.append(frag_dist.values[-1])
                     flq.append(LARGE_NUMBER)
 
-                    self.fraglens_ind_map = {}
+                    self.fraglen_ind_map = {}
                     for j in frag_dist.values:
-                        bInd = bisect.bisect(flq, j)
-                        if abs(flq[bInd - 1] - j) <= abs(flq[bInd] - j):
-                            self.fraglens_ind_map[j] = flq[bInd - 1]
+                        b_ind = bisect.bisect(flq, j)
+                        if abs(flq[b_ind - 1] - j) <= abs(flq[b_ind] - j):
+                            self.fraglen_ind_map[j] = flq[b_ind - 1]
                         else:
-                            self.fraglens_ind_map[j] = flq[bInd]
+                            self.fraglen_ind_map[j] = flq[b_ind]
 
                     self.coverage_distribution.append({})
-                    for flv in sorted(list(set(self.fraglens_ind_map.values()))):
+                    for flv in sorted(list(set(self.fraglen_ind_map.values()))):
                         buffer_val = self.read_len
                         for j in frag_dist.values:
-                            if self.fraglens_ind_map[j] == flv and j > buffer_val:
+                            if self.fraglen_ind_map[j] == flv and j > buffer_val:
                                 buffer_val = j
                         max_coord = min([len(self.sequences[i]) - buffer_val - 1,
                                          len(self.all_cigar[i]) - buffer_val + self.read_len - 2])
@@ -237,92 +364,41 @@ def init_coverage(self, coverage_data, frag_dist=None):
 
             return np.mean(avg_out)
 
-    def init_mut_models(self, mut_models, mut_rate):
-        if not mut_models:
-            default_model = [copy.deepcopy(DEFAULT_MODEL_1) for n in range(self.ploidy)]
-            self.model_data = default_model[:self.ploidy]
-        else:
-            if len(mut_models) != self.ploidy:
-                print('\nError: Number of mutation models recieved is not equal to specified ploidy\n')
-                exit(1)
-            self.model_data = copy.deepcopy(mut_models)
-
-        # do we need to rescale mutation frequencies?
-        mut_rateSum = sum([n[0] for n in self.model_data])
-        self.mut_rescale = mut_rate
-        if self.mut_rescale == None:
-            self.mut_scalar = 1.0
-        else:
-            self.mut_scalar = float(self.mut_rescale) // (mut_rateSum / float(len(self.model_data)))
-
-        # how are mutations spread to each ploid, based on their specified mut rates?
-        self.ploid_mut_frac = [float(n[0]) / mut_rateSum for n in self.model_data]
-        self.ploid_mut_prior = DiscreteDistribution(self.ploid_mut_frac, range(self.ploidy))
-
-        # init mutation models
-        #
-        # self.models[ploid][0] = average mutation rate
-        # self.models[ploid][1] = p(mut is homozygous | mutation occurs)
-        # self.models[ploid][2] = p(mut is indel | mut occurs)
-        # self.models[ploid][3] = p(insertion | indel occurs)
-        # self.models[ploid][4] = distribution of insertion lengths
-        # self.models[ploid][5] = distribution of deletion lengths
-        # self.models[ploid][6] = distribution of trinucleotide SNP transitions
-        # self.models[ploid][7] = p(trinuc mutates)
-        self.models = []
-        for n in self.model_data:
-            self.models.append([self.mut_scalar * n[0], n[1], n[2], n[3], DiscreteDistribution(n[5], n[4]),
-                                DiscreteDistribution(n[7], n[6]), []])
-            for m in n[8]:
-                self.models[-1][6].append([DiscreteDistribution(m[0], NUCL), DiscreteDistribution(m[1], NUCL),
-                                           DiscreteDistribution(m[2], NUCL), DiscreteDistribution(m[3], NUCL)])
-            self.models[-1].append([m for m in n[9]])
-
     def init_poisson(self):
         ind_l_list = [self.seq_len * self.models[i][0] * self.models[i][2] * self.ploid_mut_frac[i] for i in
                       range(len(self.models))]
         snp_l_list = [self.seq_len * self.models[i][0] * (1. - self.models[i][2]) * self.ploid_mut_frac[i] for i in
                       range(len(self.models))]
         k_range = range(int(self.seq_len * MAX_MUTFRAC))
-        self.ind_pois = [poisson_list(k_range, ind_l_list[n]) for n in range(len(self.models))]
-        self.snp_pois = [poisson_list(k_range, snp_l_list[n]) for n in range(len(self.models))]
-
-    def init_trinuc_bias(self):
-        # compute mutation positional bias given trinucleotide strings of the sequence (ONLY AFFECTS SNPs)
-        #
-        # note: since indels are added before snps, it's possible these positional biases aren't correctly utilized
-        #       at positions affected by indels. At the moment I'm going to consider this negligible.
-        trinuc_snp_bias = [[0. for n in range(self.seq_len)] for m in range(self.ploidy)]
-        self.trinuc_bias = [None for n in range(self.ploidy)]
-        for p in range(self.ploidy):
-            for i in range(self.win_buffer + 1, self.seq_len - 1):
-                trinuc_snp_bias[p][i] = self.models[p][7][ALL_IND[str(self.sequences[p][i - 1:i + 2])]]
-            self.trinuc_bias[p] = DiscreteDistribution(trinuc_snp_bias[p][self.win_buffer + 1:self.seq_len - 1],
-                                                       range(self.win_buffer + 1, self.seq_len - 1))
+        # return (indel_poisson, snp_poisson)
+        return [poisson_list(k_range, ind_l_list[n]) for n in range(len(self.models))], \
+               [poisson_list(k_range, snp_l_list[n]) for n in range(len(self.models))]
 
-    def update(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_models=[], mut_rate=None):
+    def update(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_models=None, mut_rate=None):
         # if mutation model is changed, we have to reinitialize it...
-        if ploidy != self.ploidy or mut_rate != self.mut_rescale or mut_models != []:
+        if ploidy != self.ploidy or mut_rate != self.mut_rescale or mut_models is not None:
             self.ploidy = ploidy
             self.mut_rescale = mut_rate
-            self.init_mut_models(mut_models, mut_rate)
+            self.update_mut_models(mut_models, mut_rate)
         # if sequence length is different than previous window, we have to redo snp/indel poissons
         if len(sequence) != self.seq_len:
             self.seq_len = len(sequence)
-            self.init_poisson()
+            self.indel_poisson, self.snp_poisson = self.init_poisson()
         # basic vars
-        self.init_basic_vars(x_offset, sequence, ploidy, window_overlap, read_len)
-        self.indels_to_add = [n.sample() for n in self.ind_pois]
-        self.snps_to_add = [n.sample() for n in self.snp_pois]
+        self.update_basic_vars(x_offset, sequence, ploidy, window_overlap, read_len)
+        self.indels_to_add = [n.sample() for n in self.indel_poisson]
+        self.snps_to_add = [n.sample() for n in self.snp_poisson]
         # initialize trinuc snp bias
         if not IGNORE_TRINUC:
-            self.init_trinuc_bias()
+            self.update_trinuc_bias()
 
     def insert_mutations(self, input_list):
-        for inpV in input_list:
+        for input_variable in input_list:
             which_ploid = []
-            wps = inpV[4][0]
-            if wps is None:  # if no genotype given, assume heterozygous and choose a single ploid based on their mut rates
+            wps = input_variable[4][0]
+
+            # if no genotype given, assume heterozygous and choose a single ploid based on their mut rates
+            if wps is None:
                 which_ploid.append(self.ploid_mut_prior.sample())
                 which_alt = [0]
             else:
@@ -332,13 +408,13 @@ def insert_mutations(self, input_list):
                     else:
                         splt = wps.split('|')
                     which_ploid = []
-                    which_alt = []
                     for i in range(len(splt)):
                         if splt[i] == '1':
                             which_ploid.append(i)
                     # assume we're just using first alt for inserted variants?
-                    which_alt = [0 for n in which_ploid]
-                else:  # otherwise assume monoploidy
+                    which_alt = [0 for _ in which_ploid]
+                # otherwise assume monoploidy
+                else:
                     which_ploid = [0]
                     which_alt = [0]
 
@@ -349,22 +425,22 @@ def insert_mutations(self, input_list):
 
             for i in range(len(which_ploid)):
                 p = which_ploid[i]
-                myAlt = inpV[2][which_alt[i]]
-                myVar = (inpV[0] - self.x, inpV[1], myAlt)
-                inLen = max([len(inpV[1]), len(myAlt)])
+                my_alt = input_variable[2][which_alt[i]]
+                my_var = (input_variable[0] - self.x, input_variable[1], my_alt)
+                in_len = max([len(input_variable[1]), len(my_alt)])
 
-                if myVar[0] < 0 or myVar[0] >= len(self.black_list[p]):
+                if my_var[0] < 0 or my_var[0] >= len(self.black_list[p]):
                     print('\nError: Attempting to insert variant out of window bounds:')
-                    print(myVar, '--> blackList[0:' + str(len(self.black_list[p])) + ']\n')
+                    print(my_var, '--> blackList[0:' + str(len(self.black_list[p])) + ']\n')
                     exit(1)
-                if len(inpV[1]) == 1 and len(myAlt) == 1:
-                    if self.black_list[p][myVar[0]]:
+                if len(input_variable[1]) == 1 and len(my_alt) == 1:
+                    if self.black_list[p][my_var[0]]:
                         continue
-                    self.snp_list[p].append(myVar)
-                    self.black_list[p][myVar[0]] = 2
+                    self.snp_list[p].append(my_var)
+                    self.black_list[p][my_var[0]] = 2
                 else:
                     indel_failed = False
-                    for k in range(myVar[0], myVar[0] + inLen + 1):
+                    for k in range(my_var[0], my_var[0] + in_len + 1):
                         if k >= len(self.black_list[p]):
                             indel_failed = True
                             continue
@@ -373,104 +449,111 @@ def insert_mutations(self, input_list):
                             continue
                     if indel_failed:
                         continue
-                    for k in range(myVar[0], myVar[0] + inLen + 1):
+                    for k in range(my_var[0], my_var[0] + in_len + 1):
                         self.black_list[p][k] = 1
-                    self.indel_list[p].append(myVar)
+                    self.indel_list[p].append(my_var)
 
     def random_mutations(self):
 
-        #	add random indels
-        all_indels = [[] for n in self.sequences]
+        # add random indels
+        all_indels = [[] for _ in self.sequences]
         for i in range(self.ploidy):
             for j in range(self.indels_to_add[i]):
-                if random.random() <= self.models[i][1]:  # insert homozygous indel
-                    whichPloid = range(self.ploidy)
-                else:  # insert heterozygous indel
-                    whichPloid = [self.ploid_mut_prior.sample()]
+                # insert homozygous indel
+                if random.random() <= self.models[i][1]:
+                    which_ploid = range(self.ploidy)
+                # insert heterozygous indel
+                else:
+                    which_ploid = [self.ploid_mut_prior.sample()]
 
                 # try to find suitable places to insert indels
-                eventPos = -1
+                event_pos = -1
                 for attempt in range(MAX_ATTEMPTS):
-                    eventPos = random.randint(self.win_buffer, self.seq_len - 1)
-                    for p in whichPloid:
-                        if self.black_list[p][eventPos]:
-                            eventPos = -1
-                    if eventPos != -1:
+                    event_pos = random.randint(self.win_buffer, self.seq_len - 1)
+                    for p in which_ploid:
+                        if self.black_list[p][event_pos]:
+                            event_pos = -1
+                    if event_pos != -1:
                         break
-                if eventPos == -1:
+                if event_pos == -1:
                     continue
 
-                if random.random() <= self.models[i][3]:  # insertion
-                    inLen = self.models[i][4].sample()
+                # insertion
+                if random.random() <= self.models[i][3]:
+                    in_len = self.models[i][4].sample()
                     # sequence content of random insertions is uniformly random (change this later, maybe)
-                    inSeq = ''.join([random.choice(NUCL) for n in range(inLen)])
-                    ref_nucl = self.sequences[i][eventPos]
-                    myIndel = (eventPos, ref_nucl, ref_nucl + inSeq)
-                else:  # deletion
-                    inLen = self.models[i][5].sample()
-                    if eventPos + inLen + 1 >= len(self.sequences[i]):  # skip if deletion too close to boundary
+                    in_seq = ''.join([random.choice(NUCL) for _ in range(in_len)])
+                    ref_nucl = self.sequences[i][event_pos]
+                    my_indel = (event_pos, ref_nucl, ref_nucl + in_seq)
+                # deletion
+                else:
+                    in_len = self.models[i][5].sample()
+                    # skip if deletion too close to boundary
+                    if event_pos + in_len + 1 >= len(self.sequences[i]):
                         continue
-                    if inLen == 1:
-                        inSeq = self.sequences[i][eventPos + 1]
+                    if in_len == 1:
+                        in_seq = self.sequences[i][event_pos + 1]
                     else:
-                        inSeq = str(self.sequences[i][eventPos + 1:eventPos + inLen + 1])
-                    ref_nucl = self.sequences[i][eventPos]
-                    myIndel = (eventPos, ref_nucl + inSeq, ref_nucl)
+                        in_seq = str(self.sequences[i][event_pos + 1:event_pos + in_len + 1])
+                    ref_nucl = self.sequences[i][event_pos]
+                    my_indel = (event_pos, ref_nucl + in_seq, ref_nucl)
 
                 # if event too close to boundary, skip. if event conflicts with other indel, skip.
-                skipEvent = False
-                if eventPos + len(myIndel[1]) >= self.seq_len - self.win_buffer - 1:
-                    skipEvent = True
-                if skipEvent:
+                skip_event = False
+                if event_pos + len(my_indel[1]) >= self.seq_len - self.win_buffer - 1:
+                    skip_event = True
+                if skip_event:
                     continue
-                for p in whichPloid:
-                    for k in range(eventPos, eventPos + inLen + 1):
+                for p in which_ploid:
+                    for k in range(event_pos, event_pos + in_len + 1):
                         if self.black_list[p][k]:
-                            skipEvent = True
-                if skipEvent:
+                            skip_event = True
+                if skip_event:
                     continue
 
-                for p in whichPloid:
-                    for k in range(eventPos, eventPos + inLen + 1):
+                for p in which_ploid:
+                    for k in range(event_pos, event_pos + in_len + 1):
                         self.black_list[p][k] = 1
-                    all_indels[p].append(myIndel)
+                    all_indels[p].append(my_indel)
 
-        #	add random snps
-        all_snps = [[] for n in self.sequences]
+        # add random snps
+        all_snps = [[] for _ in self.sequences]
         for i in range(self.ploidy):
             for j in range(self.snps_to_add[i]):
-                if random.random() <= self.models[i][1]:  # insert homozygous SNP
-                    whichPloid = range(self.ploidy)
-                else:  # insert heterozygous SNP
-                    whichPloid = [self.ploid_mut_prior.sample()]
+                # insert homozygous SNP
+                if random.random() <= self.models[i][1]:
+                    which_ploid = range(self.ploidy)
+                # insert heterozygous SNP
+                else:
+                    which_ploid = [self.ploid_mut_prior.sample()]
 
                 # try to find suitable places to insert snps
-                eventPos = -1
+                event_pos = -1
                 for attempt in range(MAX_ATTEMPTS):
                     # based on the mutation model for the specified ploid, choose a SNP location based on trinuc bias
                     # (if there are multiple ploids, choose one at random)
                     if IGNORE_TRINUC:
-                        eventPos = random.randint(self.win_buffer + 1, self.seq_len - 2)
+                        event_pos = random.randint(self.win_buffer + 1, self.seq_len - 2)
                     else:
-                        ploid_to_use = whichPloid[random.randint(0, len(whichPloid) - 1)]
-                        eventPos = self.trinuc_bias[ploid_to_use].sample()
-                    for p in whichPloid:
-                        if self.black_list[p][eventPos]:
-                            eventPos = -1
-                    if eventPos != -1:
+                        ploid_to_use = which_ploid[random.randint(0, len(which_ploid) - 1)]
+                        event_pos = self.trinuc_bias[ploid_to_use].sample()
+                    for p in which_ploid:
+                        if self.black_list[p][event_pos]:
+                            event_pos = -1
+                    if event_pos != -1:
                         break
-                if eventPos == -1:
+                if event_pos == -1:
                     continue
 
-                ref_nucl = self.sequences[i][eventPos]
-                context = str(self.sequences[i][eventPos - 1]) + str(self.sequences[i][eventPos + 1])
+                ref_nucl = self.sequences[i][event_pos]
+                context = str(self.sequences[i][event_pos - 1]) + str(self.sequences[i][event_pos + 1])
                 # sample from tri-nucleotide substitution matrices to get SNP alt allele
                 new_nucl = self.models[i][6][TRI_IND[context]][NUC_IND[ref_nucl]].sample()
-                mySNP = (eventPos, ref_nucl, new_nucl)
+                my_snp = (event_pos, ref_nucl, new_nucl)
 
-                for p in whichPloid:
-                    all_snps[p].append(mySNP)
-                    self.black_list[p][mySNP[0]] = 2
+                for p in which_ploid:
+                    all_snps[p].append(my_snp)
+                    self.black_list[p][my_snp[0]] = 2
 
         # combine random snps with inserted snps, remove any snps that overlap indels
         for p in range(len(all_snps)):
@@ -496,10 +579,9 @@ def random_mutations(self):
         all_indels_ins = [sorted([list(m) for m in n]) for n in all_indels]
 
         # MODIFY REFERENCE STRING: INDELS
-        adj_to_add = [[] for n in range(self.ploidy)]
         for i in range(len(all_indels_ins)):
             rolling_adj = 0
-            temp_symbol_string = ['M' for n in self.sequences[i]]
+            temp_symbol_string = ['M' for _ in self.sequences[i]]
             # there's an off-by-one error somewhere in the position sampling routines.. this might fix it
             # temp_symbol_string.append('M')
             for j in range(len(all_indels_ins[i])):
@@ -524,23 +606,25 @@ def random_mutations(self):
                     elif d < 0:
                         temp_symbol_string[v_pos + 1] = 'D' * abs(d) + 'M'
 
-            # precompute cigar strings
+            # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
-                self.all_cigar[i].append(CigarString(listIn=temp_symbol_string[j:j + self.read_len]).get_string())
+                self.all_cigar[i].append(CigarString(list_in=temp_symbol_string[j:j + self.read_len]).get_string())
 
             # create some data structures we will need later:
-            # --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e. corresponding to the unmodified reference genome)
-            # --- self.FM_span[ploid][pos]: number of reference positions spanned by a read originating from this coordinate
-            MD_so_far = 0
+            # --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e.
+            #       corresponding to the unmodified reference genome)
+            # --- self.FM_span[ploid][pos]: number of reference positions spanned by a read originating from
+            #       this coordinate
+            md_so_far = 0
             for j in range(len(temp_symbol_string)):
-                self.fm_pos[i].append(MD_so_far)
+                self.fm_pos[i].append(md_so_far)
                 # fix an edge case with deletions
                 if 'D' in temp_symbol_string[j]:
                     self.fm_pos[i][-1] += temp_symbol_string[j].count('D')
                 # compute number of ref matches for each read
                 span_dif = len([n for n in temp_symbol_string[j:j + self.read_len] if 'M' in n])
                 self.fm_span[i].append(self.fm_pos[i][-1] + span_dif)
-                MD_so_far += temp_symbol_string[j].count('M') + temp_symbol_string[j].count('D')
+                md_so_far += temp_symbol_string[j].count('M') + temp_symbol_string[j].count('D')
 
         # tally up all the variants we handled...
         count_dict = {}
@@ -553,20 +637,18 @@ def random_mutations(self):
                     count_dict[t] = []
                 count_dict[t].append(i)
 
-        #
-        #	TODO: combine multiple variants that happened to occur at same position into single vcf entry?
-        #
+        # TODO: combine multiple variants that happened to occur at same position into single vcf entry?
 
         output_variants = []
         for k in sorted(count_dict.keys()):
             output_variants.append(k + tuple([len(count_dict[k]) / float(self.ploidy)]))
-            ploid_string = ['0' for n in range(self.ploidy)]
+            ploid_string = ['0' for _ in range(self.ploidy)]
             for k2 in [n for n in count_dict[k]]:
                 ploid_string[k2] = '1'
             output_variants[-1] += tuple(['WP=' + '/'.join(ploid_string)])
         return output_variants
 
-    def sample_read(self, sequencingModel, frag_len=None):
+    def sample_read(self, sequencing_model, frag_len=None):
 
         # choose a ploid
         my_ploid = random.randint(0, self.ploidy - 1)
@@ -576,17 +658,17 @@ def sample_read(self, sequencingModel, frag_len=None):
         attempts_thus_far = 0
 
         # choose a random position within the ploid, and generate quality scores / sequencing errors
-        readsToSample = []
-        if frag_len == None:
+        reads_to_sample = []
+        if frag_len is None:
             r_pos = self.coverage_distribution[my_ploid].sample()
 
             # sample read position and call function to compute quality scores / sequencing errors
             r_dat = self.sequences[my_ploid][r_pos:r_pos + self.read_len]
-            (myQual, myErrors) = sequencingModel.get_sequencing_errors(r_dat)
-            readsToSample.append([r_pos, myQual, myErrors, r_dat])
+            (my_qual, my_errors) = sequencing_model.get_sequencing_errors(r_dat)
+            reads_to_sample.append([r_pos, my_qual, my_errors, r_dat])
 
         else:
-            r_pos1 = self.coverage_distribution[my_ploid][self.fraglens_ind_map[frag_len]].sample()
+            r_pos1 = self.coverage_distribution[my_ploid][self.fraglen_ind_map[frag_len]].sample()
 
             # EXPERIMENTAL
             # coords_to_select_from = self.coverage_distribution[my_ploid][self.fraglens_ind_map[frag_len]].sample()
@@ -595,76 +677,82 @@ def sample_read(self, sequencingModel, frag_len=None):
             r_pos2 = r_pos1 + frag_len - self.read_len
             r_dat1 = self.sequences[my_ploid][r_pos1:r_pos1 + self.read_len]
             r_dat2 = self.sequences[my_ploid][r_pos2:r_pos2 + self.read_len]
-            (myQual1, myErrors1) = sequencingModel.get_sequencing_errors(r_dat1)
-            (myQual2, myErrors2) = sequencingModel.get_sequencing_errors(r_dat2, is_reverse_strand=True)
-            readsToSample.append([r_pos1, myQual1, myErrors1, r_dat1])
-            readsToSample.append([r_pos2, myQual2, myErrors2, r_dat2])
+            (my_qual1, my_errors1) = sequencing_model.get_sequencing_errors(r_dat1)
+            (my_qual2, my_errors2) = sequencing_model.get_sequencing_errors(r_dat2, is_reverse_strand=True)
+            reads_to_sample.append([r_pos1, my_qual1, my_errors1, r_dat1])
+            reads_to_sample.append([r_pos2, my_qual2, my_errors2, r_dat2])
 
         # error format:
         # myError[i] = (type, len, pos, ref, alt)
 
-        # examine sequencing errors to-be-inserted.
-        #	- remove deletions that don't have enough bordering sequence content to "fill in"
-        # if error is valid, make the changes to the read data
+        """
+        examine sequencing errors to-be-inserted.
+            - remove deletions that don't have enough bordering sequence content to "fill in"
+            if error is valid, make the changes to the read data
+        """
         r_out = []
-        for r in readsToSample:
+        for r in reads_to_sample:
             try:
-                myCigar = self.all_cigar[my_ploid][r[0]]
+                my_cigar = self.all_cigar[my_ploid][r[0]]
             except IndexError:
                 print('Index error when attempting to find cigar string.')
                 print(my_ploid, len(self.all_cigar[my_ploid]), r[0])
                 if frag_len is not None:
                     print((r_pos1, r_pos2))
-                    print(frag_len, self.fraglens_ind_map[frag_len])
+                    print(frag_len, self.fraglen_ind_map[frag_len])
                 exit(1)
             total_d = sum([error[1] for error in r[2] if error[0] == 'D'])
             total_i = sum([error[1] for error in r[2] if error[0] == 'I'])
-            availB = len(self.sequences[my_ploid]) - r[0] - self.read_len - 1
+            avail_b = len(self.sequences[my_ploid]) - r[0] - self.read_len - 1
             # add buffer sequence to fill in positions that get deleted
             r[3] += self.sequences[my_ploid][r[0] + self.read_len:r[0] + self.read_len + total_d]
             expanded_cigar = []
-            extraCigar = []
+            extra_cigar = []
             adj = 0
-            sse_adj = [0 for n in range(self.read_len + max(sequencingModel.err_p[3]))]
+            sse_adj = [0 for n in range(self.read_len + max(sequencing_model.err_p[3]))]
             any_indel_err = False
 
             # sort by letter (D > I > S) such that we introduce all indel errors before substitution errors
             # secondarily, sort by index
-            arrangedErrors = {'D': [], 'I': [], 'S': []}
+            arranged_errors = {'D': [], 'I': [], 'S': []}
             for error in r[2]:
-                arrangedErrors[error[0]].append((error[2], error))
-            sortedErrors = []
-            for k in sorted(arrangedErrors.keys()):
-                sortedErrors.extend([n[1] for n in sorted(arrangedErrors[k])])
+                arranged_errors[error[0]].append((error[2], error))
+            sorted_errors = []
+            for k in sorted(arranged_errors.keys()):
+                sorted_errors.extend([n[1] for n in sorted(arranged_errors[k])])
 
             skip_indels = False
 
             # FIXED TdB 05JUN2018
-            # Moved this outside the for error loop, since it messes up the CIGAR string when more than one deletion is in the same read
+            # Moved this outside the for error loop, since it messes up the CIGAR string when
+            # more than one deletion is in the same read
             extra_cigar_val = []
             # END FIXED TdB
 
-            for error in sortedErrors:
+            for error in sorted_errors:
                 e_len = error[1]
                 e_pos = error[2]
                 if error[0] == 'D' or error[0] == 'I':
                     any_indel_err = True
 
                     # FIXED TdB 05JUN2018
-                    # Moved this OUTSIDE the for error loop, since it messes up the CIGAR string when more than one deletion is in the same read
+                    # Moved this OUTSIDE the for error loop, since it messes up the CIGAR string
+                    # when more than one deletion is in the same read
                     # extra_cigar_val = []
                     # END FIXED TdB
 
-                    if total_d > availB:  # if not enough bases to fill-in deletions, skip all indel erors
+                    if total_d > avail_b:  # if not enough bases to fill-in deletions, skip all indel erors
                         continue
-                    if expanded_cigar == []:
-                        expanded_cigar = CigarString(stringIn=myCigar).get_list()
+                    if not expanded_cigar:
+                        expanded_cigar = CigarString(string_in=my_cigar).get_list()
                         fill_to_go = total_d - total_i + 1
                         if fill_to_go > 0:
                             try:
                                 extra_cigar_val = CigarString(
-                                    stringIn=self.all_cigar[my_ploid][r[0] + fill_to_go]).get_list()[-fill_to_go:]
-                            except IndexError:  # applying the deletions we want requires going beyond region boundaries. skip all indel errors
+                                    string_in=self.all_cigar[my_ploid][r[0] + fill_to_go]).get_list()[-fill_to_go:]
+                            except IndexError:
+                                # Applying the deletions we want requires going beyond region boundaries.
+                                # Skip all indel errors
                                 skip_indels = True
 
                     if skip_indels:
@@ -678,8 +766,8 @@ def sample_read(self, sequencingModel, frag_len=None):
                         if str(r[3][pi:pf]) == str(error[3]):
                             r[3] = r[3][:pi + 1] + r[3][pf:]
                             expanded_cigar = expanded_cigar[:pi + 1] + expanded_cigar[pf:]
-                            if pi + 1 == len(
-                                    expanded_cigar):  # weird edge case with del at very end of region. Make a guess and add a "M"
+                            # weird edge case with del at very end of region. Make a guess and add a "M"
+                            if pi + 1 == len(expanded_cigar):
                                 expanded_cigar.append('M')
                             expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
                         else:
@@ -713,12 +801,12 @@ def sample_read(self, sequencingModel, frag_len=None):
 
             if any_indel_err:
                 if len(expanded_cigar):
-                    relevantCigar = (expanded_cigar + extra_cigar_val)[:self.read_len]
-                    myCigar = CigarString(listIn=relevantCigar).get_string()
+                    relevant_cigar = (expanded_cigar + extra_cigar_val)[:self.read_len]
+                    my_cigar = CigarString(list_in=relevant_cigar).get_string()
 
                 r[3] = r[3][:self.read_len]
 
-            r_out.append([self.fm_pos[my_ploid][r[0]], myCigar, str(r[3]), str(r[1])])
+            r_out.append([self.fm_pos[my_ploid][r[0]], my_cigar, str(r[3]), str(r[1])])
 
         # r_out[i] = (pos, cigar, read_string, qual_string)
         return r_out
@@ -728,6 +816,7 @@ class SequencingError:
     """
     Container to model sequencing errors: computes quality scores and positions to insert errors
     """
+
     def __init__(self, read_len, error_model, rescaled_error):
 
         self.read_len = read_len
@@ -918,7 +1007,6 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
 
 # parse mutation model pickle file
 def parse_input_mutation_model(model=None, which_default=1):
-
     if which_default == 1:
         out_model = [copy.deepcopy(n) for n in DEFAULT_MODEL_1]
     elif which_default == 2:
@@ -978,4 +1066,3 @@ def parse_input_mutation_model(model=None, which_default=1):
                 out_model[9][ALL_IND[trinuc]] = trinuc_mean
 
     return out_model
-
diff --git a/py/biopython_modified_bgzf.py b/py/biopython_modified_bgzf.py
index 2177a01..ee701f2 100755
--- a/py/biopython_modified_bgzf.py
+++ b/py/biopython_modified_bgzf.py
@@ -21,10 +21,10 @@
 
 class BgzfWriter:
 
-    def __init__(self, filename=None, mode="w", fileobj=None, compresslevel=6):
-        if fileobj:
+    def __init__(self, filename=None, mode="w", file_obj=None, compress_level=6):
+        if file_obj:
             assert filename is None
-            handle = fileobj
+            handle = file_obj
         else:
             if "w" not in mode.lower() and "a" not in mode.lower():
                 raise ValueError("Must use write or append mode, not %r" % mode)
@@ -35,14 +35,14 @@ def __init__(self, filename=None, mode="w", fileobj=None, compresslevel=6):
         self._text = "b" not in mode.lower()
         self._handle = handle
         self._buffer = b""
-        self.compresslevel = compresslevel
+        self.compress_level = compress_level
 
     def _write_block(self, block):
         _bgzf_header = b"\x1f\x8b\x08\x04\x00\x00\x00\x00\x00\xff\x06\x00\x42\x43\x02\x00"
         start_offset = self._handle.tell()
         assert len(block) <= 65536
         # Giving a negative window bits means no gzip/zlib headers, -15 used in samtools
-        c = zlib.compressobj(self.compresslevel,
+        c = zlib.compressobj(self.compress_level,
                              zlib.DEFLATED,
                              -15,
                              zlib.DEF_MEM_LEVEL,
diff --git a/py/input_checking.py b/py/input_checking.py
index 25c4885..a4bea65 100644
--- a/py/input_checking.py
+++ b/py/input_checking.py
@@ -16,12 +16,13 @@ def required_field(variable_to_test: any, err_string: str) -> None:
     """
     if variable_to_test is None:
         print('\n' + err_string + '\n')
-        raise ValueError
+        exit(1)
 
 
 def check_file_open(filename: str, err_string: str, required: bool = False) -> None:
     """
     Checks that the filename is not empty and that it is indeed a  file
+
     :param filename: file name, string
     :param err_string: string of the error if it is not a file
     :param required: If not required, skips the check
@@ -30,7 +31,7 @@ def check_file_open(filename: str, err_string: str, required: bool = False) -> N
     if required or filename is not None:
         if filename is None:
             print('\n' + err_string + '\n')
-            raise ValueError
+            exit(1)
         else:
             try:
                 pathlib.Path(filename).resolve(strict=True)
@@ -53,8 +54,9 @@ def check_dir(directory: str, err_string: str) -> None:
 
 def is_in_range(value: float, lower_bound: float, upper_bound: float, err_string: str) -> None:
     """
-    Checks that value (val) is between the lower bound (lb) and upper bound (ub), and if not prints an error message
-    (errString) and exits the program.
+    Checks that value is between the lower bound and upper bound, and if not prints an error message
+    (err_string) and exits the program.
+
     :param value: float for the value
     :param lower_bound: float for the upper bound
     :param upper_bound: float for the lower bound
@@ -63,4 +65,4 @@ def is_in_range(value: float, lower_bound: float, upper_bound: float, err_string
     """
     if value < lower_bound or value > upper_bound:
         print('\n' + err_string + '\n')
-        raise ValueError
+        exit(1)
diff --git a/py/neat_cigar.py b/py/neat_cigar.py
index e6f821e..f6202fe 100644
--- a/py/neat_cigar.py
+++ b/py/neat_cigar.py
@@ -2,22 +2,31 @@
 
 
 class CigarString:
-    def __init__(self, stringIn=None, listIn=None):
+    """
+    Creates and modifies CigarString for output
+    """
+    def __init__(self, string_in=None, list_in=None):
 
-        if stringIn is None and listIn is None:
+        if string_in is None and list_in is None:
             print('\nError: CigarString object not initialized.\n')
             exit(1)
 
         self.cigar_data = []
 
-        if stringIn is not None:
-            self.join_cigar(j_string_in=stringIn)
+        if string_in is not None:
+            self.join_cigar(j_string_in=string_in)
 
-        if listIn is not None:
-            self.join_cigar(j_list_in=listIn)
+        if list_in is not None:
+            self.join_cigar(j_list_in=list_in)
 
     @staticmethod
     def string_to_list(string_to_covert: str) -> list:
+        """
+        Converts cigar string to a full list
+
+        :param string_to_covert: Cigar string to convert to list
+        :return: cigar string in list format
+        """
 
         cigar_dat = []
         letters = re.split(r"\d+", string_to_covert)[1:]
@@ -36,6 +45,12 @@ def string_to_list(string_to_covert: str) -> list:
 
     @staticmethod
     def list_to_string(list_to_convert: list) -> str:
+        """
+        Convert a cigar string in list format to a standard cigar string
+
+        :param list_to_convert: Cigar string in list format
+        :return: cigar string in string format
+        """
 
         symbols = ''
         current_sym = list_to_convert[0]
@@ -65,7 +80,14 @@ def get_string(self):
 
         return self.list_to_string(self.cigar_data)
 
-    def join_cigar(self, j_string_in=None, j_list_in=None):
+    def join_cigar(self, j_string_in: str = None, j_list_in: list = None):
+        """
+        Adds an inputted cigar string in string or list format to the existing cigar string
+
+        :param j_string_in: cigar string in string format
+        :param j_list_in: cigar string in list format
+        :return: None
+        """
 
         if j_string_in is None and j_list_in is None:
             print('\nError: Invalid join operation in CigarString\n')
@@ -77,7 +99,15 @@ def join_cigar(self, j_string_in=None, j_list_in=None):
         if j_list_in is not None:
             self.cigar_data += j_list_in
 
-    def insert_cigar_element(self, pos, i_string_in=None, i_list_in=None):
+    def insert_cigar_element(self, pos: int, i_string_in: str = None, i_list_in: list = None):
+        """
+        Inserts a cigar string in either string or list format to the existing cigar string at position pos.
+
+        :param pos: integer position where to insert the input cigar string
+        :param i_string_in: input cigar string in string format
+        :param i_list_in: input cigar string in list format
+        :return:
+        """
 
         if i_string_in is None and i_list_in is None:
             print('\nError: Invalid insertion operation in CigarString\n')
@@ -100,7 +130,7 @@ def insert_cigar_element(self, pos, i_string_in=None, i_list_in=None):
     str1 = '50M10D7I23M'
     str2 = '10I25M'
     iPos = 20
-    my_cigar = CigarString(stringIn=str1)
+    my_cigar = CigarString(string_in=str1)
     my_cigar.insert_cigar_element(iPos, i_string_in=str2)
     assert(my_cigar.get_string() == "20M10I55M10D7I23M")
     print("passed")
diff --git a/py/output_file_writer.py b/py/output_file_writer.py
index 906e65b..de1b97f 100644
--- a/py/output_file_writer.py
+++ b/py/output_file_writer.py
@@ -158,7 +158,7 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
         # BAM OUTPUT
         self.bam_file = None
         if bam_header is not None:
-            self.bam_file = BgzfWriter(bam, 'w', compresslevel=BAM_COMPRESSION_LEVEL)
+            self.bam_file = BgzfWriter(bam, 'w', compress_level=BAM_COMPRESSION_LEVEL)
 
             # WRITE BAM HEADER
             self.bam_file.write("BAM\1")
diff --git a/py/probability.py b/py/probability.py
index 2f34fa3..9dcd44d 100644
--- a/py/probability.py
+++ b/py/probability.py
@@ -7,6 +7,12 @@
 
 
 def mean_ind_of_weighted_list(candidate_list: list) -> int:
+    """
+    Returns the index of the mean of a weighted list
+
+    :param candidate_list: weighted list
+    :return: index of mean
+    """
     my_mid = sum(candidate_list) / 2.0
     my_sum = 0.0
     for i in range(len(candidate_list)):
@@ -93,7 +99,8 @@ def sample(self):
                 return self.values[bisect.bisect(self.cum_prob, r) - 1]
 
 
-# takes k_range, lambda, [0,1,2,..], returns a DiscreteDistribution object with the corresponding to a poisson distribution
+# takes k_range, lambda, [0,1,2,..], returns a DiscreteDistribution object
+# with the corresponding to a poisson distribution
 
 def poisson_list(k_range, input_lambda):
     min_weight = 1e-12

From ec95331f967afcefdde1ffe1abcdfc16fd9c750f Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 31 Aug 2020 11:36:02 -0500
Subject: [PATCH 171/323] minor changes to genreads and sequencecontainer

---
 gen_reads.py            | 8 +++-----
 py/SequenceContainer.py | 5 +++--
 2 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 536967f..8a08258 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -521,8 +521,7 @@ def main(raw_args=None):
                     # change: added abs() so that insertions are also buffered.
                     buffer_needed = max([max([abs(len(n[1]) - len(alt_allele)), 1]) for alt_allele in n[2]])
                     # -1 because going from VCF coords to array coords
-                    structural_vars.append(
-                        (n[0] - 1, buffer_needed))
+                    structural_vars.append((n[0] - 1, buffer_needed))
 
                 # adjust end-position of window based on inserted structural mutations
                 keep_going = True
@@ -547,9 +546,8 @@ def main(raw_args=None):
                 current_progress += end - start
                 new_percent = int((current_progress * 100) / float(total_bp_span))
                 if new_percent > current_percent:
-                    if new_percent <= 99 or (new_percent == 100 and not have_printed100):
-                        if new_percent % 20 == 0:
-                            print('-', end='')
+                    if new_percent <= 99 or (new_percent == 100 and not have_printed100) and new_percent % 20 == 0:
+                        print('-', end='')
                     current_percent = new_percent
                     if current_percent == 100:
                         print(']')
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 211dce7..a50acf1 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -1,6 +1,6 @@
 import random
 import copy
-import os
+import pathlib
 import bisect
 import pickle
 import numpy as np
@@ -732,8 +732,9 @@ def __init__(self, read_len, error_model, rescaled_error):
 
         self.read_len = read_len
 
+        model_path = pathlib.Path(error_model)
         try:
-            error_dat = pickle.load(open(error_model, 'rb'), encoding="bytes")
+            error_dat = pickle.load(open(model_path, 'rb'), encoding="bytes")
         except IOError:
             print("\nProblem opening the sequencing error model.\n")
             exit(1)

From 6334b33d17cdbd9dbb3c8c316d113fcf45c05ffb Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 4 Sep 2020 11:10:58 -0500
Subject: [PATCH 172/323] Updates to the bacterial wrapper

---
 bacterial_genreads_wrapper.py | 21 +++++++++++++--------
 gen_reads.py                  |  8 ++------
 py/output_file_writer.py      |  4 ++--
 3 files changed, 17 insertions(+), 16 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 6de014c..de6877c 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -32,7 +32,7 @@ def analyze(self):
         manipulation to unzip the file and fix genreads horribly formatted fasta file.
         :return: None
         """
-        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa']
+        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa', '-c', '1']
         gen_reads.main(args)
 
         # The following workaround is due to the fact that genReads writes out a compressed
@@ -68,8 +68,8 @@ def analyze(self):
         # re-write file with just the chrom name and sequence
         self.file.open('w').write(chromosome_name + sequence)
 
-    def sample(self):
-        args = ['-r', str(self.reference), '-R', '101', '-o', self.name]
+    def sample(self, coverage_value: int):
+        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '-c', coverage_value]
         gen_reads.main(args)
 
     def remove(self):
@@ -142,14 +142,15 @@ def evolve_population(population: list, generation: int) -> list:
     return new_population
 
 
-def sample_population(population: list):
+def sample_population(population: list, target_coverage: int):
     """
     This will create a fastq based on each member of the population.
+    :param target_coverage: The target coverage value for the sample.
     :param population: a list of bacteria
     :return: None
     """
     for bacterium in population:
-        bacterium.sample()
+        bacterium.sample(target_coverage)
 
 
 def main():
@@ -158,13 +159,17 @@ def main():
                         help="Reference file for organism in fasta format")
     parser.add_argument('-g', type=int, required=True, metavar='generations', help="Number of generations to run")
     parser.add_argument('-i', type=int, required=True, metavar='initial population', help="Initial population size")
-    parser.add_argument('-c', type=float, required=False, metavar='cull pct',
+    parser.add_argument('-k', type=float, required=False, metavar='cull pct',
                         help="Percentage of population to cull each cycle (0.5 will keep population relatively stable)",
                         default=0.5)
+    parser.add_argument('-c', type=int, required=False, default=10, metavar='coverage value',
+                        help='Target coverage value for final set of sampled fastqs')
     args = parser.parse_args()
+    print(args)
 
     (ref_fasta, init_population_size, generations) = (args.r, args.i, args.g)
-    cull_percentage = args.c
+    cull_percentage = args.k
+    coverage = args.c
 
     population = initialize_population(ref_fasta, init_population_size)
 
@@ -179,7 +184,7 @@ def main():
 
         population = new_population
 
-    sample_population(population)
+    sample_population(population, coverage)
 
 
 if __name__ == '__main__':
diff --git a/gen_reads.py b/gen_reads.py
index 5b9d847..fa08a3a 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -25,8 +25,8 @@
 import argparse
 import pathlib
 
-from py.input_checking import required_field, check_file_open, is_in_range
-from py.ref_func import index_ref, read_ref, get_all_ref_regions, partition_ref_regions
+from py.input_checking import check_file_open, is_in_range
+from py.ref_func import index_ref, read_ref
 from py.vcf_func import parse_vcf
 from py.output_file_writer import OutputFileWriter, reverse_complement, sam_flag
 from py.probability import DiscreteDistribution, mean_ind_of_weighted_list
@@ -636,10 +636,6 @@ def main(raw_args=None):
                     pass
                 else:
                     window_span = end - start
-                    print('window_span = ' + str(window_span))
-                    print('coverage = ' + str(coverage))
-                    print('read_len = ' + str(read_len))
-                    print('coverage_avg = ' + str(coverage_avg))
 
                     if paired_end:
                         if force_coverage:
diff --git a/py/output_file_writer.py b/py/output_file_writer.py
index de1b97f..f18664b 100644
--- a/py/output_file_writer.py
+++ b/py/output_file_writer.py
@@ -10,7 +10,7 @@
 from Bio.bgzf import *
 import pathlib
 
-from py.biopython_modified_bgzf import BgzfWriter
+# from py.biopython_modified_bgzf import BgzfWriter
 
 BAM_COMPRESSION_LEVEL = 6
 
@@ -158,7 +158,7 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
         # BAM OUTPUT
         self.bam_file = None
         if bam_header is not None:
-            self.bam_file = BgzfWriter(bam, 'w', compress_level=BAM_COMPRESSION_LEVEL)
+            self.bam_file = BgzfWriter(bam, 'w', compresslevel=BAM_COMPRESSION_LEVEL)
 
             # WRITE BAM HEADER
             self.bam_file.write("BAM\1")

From 2d2118064e6a4134ae6dcba6c05f3a271ce6a797 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 4 Sep 2020 11:34:57 -0500
Subject: [PATCH 173/323] Changes to the bacterial wrapper

---
 bacterial_genreads_wrapper.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index de6877c..2462c80 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -38,13 +38,13 @@ def analyze(self):
         # The following workaround is due to the fact that genReads writes out a compressed
         # fasta but does not put the .gz extension on it. Also, genReads cannot handle gzipped
         # fasta files, so we further have to unzip it for it to actually work.
-        self.file = pathlib.Path().absolute() / (self.name + "_read1.fa")
-        new_name = self.name + "_read1.fa.gz"
+        self.file = pathlib.Path().absolute() / (self.name + ".fasta")
+        new_name = self.name + ".fasta.gz"
         self.file.rename(pathlib.Path(pathlib.Path().absolute(), new_name))
-        self.file = pathlib.Path().absolute() / (self.name + "_read1.fa.gz")
-        true_path = pathlib.Path().absolute() / (self.name + "_read1.fa")
+        self.file = pathlib.Path().absolute() / (self.name + ".fasta.gz")
+        true_path = pathlib.Path().absolute() / (self.name + ".fasta")
         unzip_file(self.file, true_path)
-        pathlib.Path.unlink(pathlib.Path().absolute() / (self.name + "_read1.fa.gz"))  # deletes unused zip file
+        pathlib.Path.unlink(pathlib.Path().absolute() / (self.name + ".fasta.gz"))  # deletes unused zip file
         self.file = true_path
         # end workaround
 

From adb90972a369305e695bff47f2972a63bff6d0c8 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 4 Sep 2020 12:03:28 -0500
Subject: [PATCH 174/323] updates to the bacterial wrapper and changing exit(1)
 to sys.exit(1)

---
 bacterial_genreads_wrapper.py |  5 ++---
 gen_reads.py                  | 20 ++++++++++----------
 py/SequenceContainer.py       | 28 ++++++++++++++--------------
 py/input_checking.py          |  9 +++++----
 py/ref_func.py                |  6 +++---
 5 files changed, 34 insertions(+), 34 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 2462c80..7d95b9e 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -6,6 +6,7 @@
 import pathlib
 import gzip
 import shutil
+import sys
 # from Bio import SeqIO
 
 
@@ -58,7 +59,7 @@ def analyze(self):
                 chromosome_name = first_line
             else:
                 print("Something went wrong with the fasta file")
-                exit(1)
+                sys.exit(1)
             for line in f:
                 if line.startswith(">"):
                     continue
@@ -136,7 +137,6 @@ def evolve_population(population: list, generation: int) -> list:
     for j in range(len(children_population)):
         names.append("bacterium_{}_{}".format(generation, j+1))
     for i in range(len(children_population)):
-        print(children_population[i].get_file())
         child = Bacterium(children_population[i].get_file(), names[i])
         new_population.append(child)
     return new_population
@@ -165,7 +165,6 @@ def main():
     parser.add_argument('-c', type=int, required=False, default=10, metavar='coverage value',
                         help='Target coverage value for final set of sampled fastqs')
     args = parser.parse_args()
-    print(args)
 
     (ref_fasta, init_population_size, generations) = (args.r, args.i, args.g)
     cull_percentage = args.k
diff --git a/gen_reads.py b/gen_reads.py
index fa08a3a..8998186 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -146,7 +146,7 @@ def main(raw_args=None):
     # if user specified no fastq, not fasta only, and no bam and no vcf, then print error and exit.
     if no_fastq and not fasta_instead and not save_bam and not save_vcf:
         print('\nERROR: No files would be written.\n')
-        exit(1)
+        sys.exit(1)
 
     if no_fastq:
         print('Bypassing FASTQ generation...')
@@ -157,7 +157,7 @@ def main(raw_args=None):
 
     if (fragment_size is None and fragment_std is not None) or (fragment_size is not None and fragment_std is None):
         print('\nERROR: --pe argument takes 2 space-separated arguments.\n')
-        exit(1)
+        sys.exit(1)
 
     # If user specified mean/std, or specified an empirical model, then the reads will be paired_ended
     # If not, then we're doing single-end reads.
@@ -214,14 +214,14 @@ def main(raw_args=None):
             [gc_scale_count, gc_scale_val] = pickle.load(open(gc_bias_model, 'rb'))
         except IOError:
             print("\nProblem reading the default gc-bias model.\n")
-            exit(1)
+            sys.exit(1)
         gc_window_size = gc_scale_count[-1]
     else:
         try:
             [gc_scale_count, gc_scale_val] = pickle.load(open(gc_bias_model, 'rb'))
         except IOError:
             print("\nProblem reading the gc-bias model.\n")
-            exit(1)
+            sys.exit(1)
         gc_window_size = gc_scale_count[-1]
 
     # Assign appropriate values to the needed variables if we're dealing with paired-ended data
@@ -233,7 +233,7 @@ def main(raw_args=None):
                 [potential_values, potential_prob] = pickle.load(open(fraglen_model, 'rb'))
             except IOError:
                 print('\nProblem loading the empirical fragment length model.\n')
-                exit(1)
+                sys.exit(1)
 
             fraglen_values = []
             fraglen_probability = []
@@ -308,7 +308,7 @@ def main(raw_args=None):
                     input_regions[my_chr].extend([int(pos1), int(pos2)])
         except IOError:
             print("\nProblem reading input target BED file.\n")
-            exit(1)
+            sys.exit(1)
         finally:
             f.close()
 
@@ -366,7 +366,7 @@ def main(raw_args=None):
                         mut_rate_values.extend([mut_rate * (pos2 - pos1)] * 2)
         except IOError:
             print("\nProblem reading mutational BED file.\n")
-            exit(1)
+            sys.exit(1)
         finally:
             f.close()
 
@@ -476,7 +476,7 @@ def main(raw_args=None):
             print('sampling reads...')
         tt = time.time()
         # start the progress bar
-        print("[", end='')
+        print("[", end='', flush=True)
 
         # Applying variants to non-N regions
         for i in range(len(n_regions['non_N'])):
@@ -544,7 +544,7 @@ def main(raw_args=None):
                 if new_percent > current_percent:
                     if new_percent <= 99 or (new_percent == 100 and not have_printed100):
                         if new_percent % 10 == 1:
-                            print('-', end='')
+                            print('-', end='', flush=True)
                     current_percent = new_percent
                     if current_percent == 100:
                         have_printed100 = True
@@ -822,7 +822,7 @@ def main(raw_args=None):
                                                                         sam_flag=flag2, mate_pos=my_read_data[1][0])
                         else:
                             print('\nError: Unexpected number of reads generated...\n')
-                            exit(1)
+                            sys.exit(1)
 
                     if not is_last_time:
                         output_file_writer.flush_buffers(bam_max=next_start)
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 047e81e..4456a94 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -127,7 +127,7 @@ def __init__(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_mod
         else:
             if len(mut_models) != self.ploidy:
                 print('\nError: Number of mutation models received is not equal to specified ploidy\n')
-                exit(1)
+                sys.exit(1)
             self.model_data = copy.deepcopy(mut_models)
 
         # do we need to rescale mutation frequencies?
@@ -213,7 +213,7 @@ def update_mut_models(self, mut_models, mut_rate):
         else:
             if len(mut_models) != self.ploidy:
                 print('\nError: Number of mutation models received is not equal to specified ploidy\n')
-                exit(1)
+                sys.exit(1)
             self.model_data = copy.deepcopy(mut_models)
 
         # do we need to rescale mutation frequencies?
@@ -358,7 +358,7 @@ def init_coverage(self, coverage_data, frag_dist=None):
                         # for qcv in quantized_covVals:
                         #	mpl.plot([qcv[0], qcv[1]+1], [qcv[2],qcv[2]], 'r')
                         # mpl.show()
-                        # exit(1)
+                        # sys.exit(1)
 
                         self.coverage_distribution[i][flv] = DiscreteDistribution(coverage_vals,
                                                                                   range(len(coverage_vals)))
@@ -433,7 +433,7 @@ def insert_mutations(self, input_list):
                 if my_var[0] < 0 or my_var[0] >= len(self.black_list[p]):
                     print('\nError: Attempting to insert variant out of window bounds:')
                     print(my_var, '--> blackList[0:' + str(len(self.black_list[p])) + ']\n')
-                    exit(1)
+                    sys.exit(1)
                 if len(input_variable[1]) == 1 and len(my_alt) == 1:
                     if self.black_list[p][my_var[0]]:
                         continue
@@ -570,7 +570,7 @@ def random_mutations(self):
                     print('\nError: Something went wrong!\n', all_snps[i][j], self.sequences[i][v_pos], '\n')
                     print(all_snps[i][j])
                     print(self.sequences[i][v_pos])
-                    exit(1)
+                    sys.exit(1)
                 else:
                     self.sequences[i][v_pos] = all_snps[i][j][2]
 
@@ -593,7 +593,7 @@ def random_mutations(self):
                 if all_indels_ins[i][j][1] != str(self.sequences[i][v_pos:v_pos2]):
                     print('\nError: Something went wrong!\n', all_indels_ins[i][j], [v_pos, v_pos2],
                           str(self.sequences[i][v_pos:v_pos2]), '\n')
-                    exit(1)
+                    sys.exit(1)
                 else:
                     # alter reference sequence
                     self.sequences[i] = self.sequences[i][:v_pos] + Seq(all_indels_ins[i][j][2],
@@ -701,7 +701,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                 if frag_len is not None:
                     print((r_pos1, r_pos2))
                     print(frag_len, self.fraglen_ind_map[frag_len])
-                exit(1)
+                sys.exit(1)
             total_d = sum([error[1] for error in r[2] if error[0] == 'D'])
             total_i = sum([error[1] for error in r[2] if error[0] == 'I'])
             avail_b = len(self.sequences[my_ploid]) - r[0] - self.read_len - 1
@@ -773,7 +773,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                             expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
                         else:
                             print('\nError, ref does not match alt while attempting to insert deletion error!\n')
-                            exit(1)
+                            sys.exit(1)
                         adj -= e_len
                         for i in range(e_pos, len(sse_adj)):
                             sse_adj[i] -= e_len
@@ -788,7 +788,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                         else:
                             print('\nError, ref does not match alt while attempting to insert insertion error!\n')
                             print('---', chr(r[3][e_pos + my_adj]), '!=', error[3])
-                            exit(1)
+                            sys.exit(1)
                         adj += e_len
                         for i in range(e_pos, len(sse_adj)):
                             sse_adj[i] += e_len
@@ -798,7 +798,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                         r[3][e_pos + sse_adj[e_pos]] = error[4]
                     else:
                         print('\nError, ref does not match alt while attempting to insert substitution error!\n')
-                        exit(1)
+                        sys.exit(1)
 
             if any_indel_err:
                 if len(expanded_cigar):
@@ -827,7 +827,7 @@ def __init__(self, read_len, error_model, rescaled_error):
             error_dat = pickle.load(open(model_path, 'rb'), encoding="bytes")
         except IOError:
             print("\nProblem opening the sequencing error model.\n")
-            exit(1)
+            sys.exit(1)
 
         self.uniform = False
 
@@ -850,12 +850,12 @@ def __init__(self, read_len, error_model, rescaled_error):
             self.pe_models = True
             if len(init_q1) != len(init_q2) or len(prob_q1) != len(prob_q2):
                 print('\nError: R1 and R2 quality score models are of different length.\n')
-                exit(1)
+                sys.exit(1)
 
         # This serves as a sanity check for the input model
         else:
             print('\nError: Something wrong with error model.\n')
-            exit(1)
+            sys.exit(1)
 
         self.q_err_rate = [0.] * (max(q_scores) + 1)
         for q in q_scores:
@@ -1015,7 +1015,7 @@ def parse_input_mutation_model(model=None, which_default=1):
         out_model = [copy.deepcopy(n) for n in DEFAULT_MODEL_2]
     else:
         print('\nError: Unknown default mutation model specified\n')
-        exit(1)
+        sys.exit(1)
 
     if model is not None:
         pickle_dict = pickle.load(open(model, "rb"))
diff --git a/py/input_checking.py b/py/input_checking.py
index a4bea65..dc27fff 100644
--- a/py/input_checking.py
+++ b/py/input_checking.py
@@ -4,6 +4,7 @@
 """
 
 import pathlib
+import sys
 
 
 def required_field(variable_to_test: any, err_string: str) -> None:
@@ -16,7 +17,7 @@ def required_field(variable_to_test: any, err_string: str) -> None:
     """
     if variable_to_test is None:
         print('\n' + err_string + '\n')
-        exit(1)
+        sys.exit(1)
 
 
 def check_file_open(filename: str, err_string: str, required: bool = False) -> None:
@@ -31,13 +32,13 @@ def check_file_open(filename: str, err_string: str, required: bool = False) -> N
     if required or filename is not None:
         if filename is None:
             print('\n' + err_string + '\n')
-            exit(1)
+            sys.exit(1)
         else:
             try:
                 pathlib.Path(filename).resolve(strict=True)
             except FileNotFoundError:
                 print('\n' + err_string + '\n')
-                exit(1)
+                sys.exit(1)
 
 
 def check_dir(directory: str, err_string: str) -> None:
@@ -65,4 +66,4 @@ def is_in_range(value: float, lower_bound: float, upper_bound: float, err_string
     """
     if value < lower_bound or value > upper_bound:
         print('\n' + err_string + '\n')
-        exit(1)
+        sys.exit(1)
diff --git a/py/ref_func.py b/py/ref_func.py
index 95e3c96..aebc76c 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -24,7 +24,7 @@ def index_ref(reference_path: str) -> list:
     # sanity check
     if not absolute_reference_location.is_file():
         print("\nProblem reading the reference fasta file.\n")
-        exit(1)
+        sys.exit(1)
 
     filename = None
 
@@ -88,7 +88,7 @@ def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
         ref_file = open(absolute_reference_path, 'r')
     except IOError:
         print('\nProblem reading reference file.\n')
-        exit(1)
+        sys.exit(1)
 
     # TODO convert to SeqIO containers
     ref_file.seek(ref_inds_i[1])
@@ -137,7 +137,7 @@ def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
             n_info['big'].extend(region)
     else:
         print('\nERROR: UNKNOWN N_HANDLING MODE\n')
-        exit(1)
+        sys.exit(1)
 
     habitable_regions = []
     if not n_info['big']:

From 19445aa3cc532b1c5089e97ba271f36f3e18db20 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 8 Sep 2020 10:59:40 -0500
Subject: [PATCH 175/323] Update to bacterial wrapper

---
 bacterial_genreads_wrapper.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 7d95b9e..a75c268 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -70,7 +70,7 @@ def analyze(self):
         self.file.open('w').write(chromosome_name + sequence)
 
     def sample(self, coverage_value: int):
-        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '-c', coverage_value]
+        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '-c', str(coverage_value)]
         gen_reads.main(args)
 
     def remove(self):

From 640dfc31dd9516e5bbee03ead489f3ebdd36f3ad Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 8 Sep 2020 12:08:42 -0500
Subject: [PATCH 176/323] Update to bacterial wrapper

---
 bacterial_genreads_wrapper.py | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index a75c268..6470414 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -70,9 +70,23 @@ def analyze(self):
         self.file.open('w').write(chromosome_name + sequence)
 
     def sample(self, coverage_value: int):
-        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '-c', str(coverage_value)]
+        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '-c', str(coverage_value), '--pe', '300 30']
         gen_reads.main(args)
 
+        # The following workaround is due to the fact that genReads writes out a compressed
+        # fasta but does not put the .gz extension on it. Also, genReads cannot handle gzipped
+        # fasta files, so we further have to unzip it for it to actually work.
+        filename1 = pathlib.Path().absolute() / (self.name + "_read1.fq")
+        filename2 = pathlib.Path().absolute() / (self.name + "_read2.fq")
+        new_name1 = self.name + "_read1.fq.gz"
+        new_name2 = self.name + "_read2.fq.gz"
+
+        filename1.rename(pathlib.Path(pathlib.Path().absolute(), new_name1))
+        if filename2.is_file():
+            filename2.rename(pathlib.Path(pathlib.Path().absolute(), new_name2))
+
+        # end workaround
+
     def remove(self):
         pathlib.Path.unlink(self.file)
 

From 2edc2850adfc8282e102ad260b95b3a1721ae0f8 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 8 Sep 2020 12:29:49 -0500
Subject: [PATCH 177/323] Update to bacterial wrapper

---
 gen_reads.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 8998186..a221b1b 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -841,10 +841,8 @@ def main(raw_args=None):
                 if end >= final_position:
                     is_last_time = True
 
-        if current_percent != 100 and not have_printed100:
-            print(']')
-        else:
-            print('')
+        print(']', flush=True)
+
         if only_vcf:
             print('VCF generation completed in ', end='')
         else:

From 9bb0b467c7f54409b2210eb93d3ef1bde2921a5f Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 8 Sep 2020 13:14:27 -0500
Subject: [PATCH 178/323] Fixed a bug in bacterial wrapper

---
 bacterial_genreads_wrapper.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 6470414..549bfe5 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -70,7 +70,7 @@ def analyze(self):
         self.file.open('w').write(chromosome_name + sequence)
 
     def sample(self, coverage_value: int):
-        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '-c', str(coverage_value), '--pe', '300 30']
+        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '-c', str(coverage_value), '--pe', '300', '30']
         gen_reads.main(args)
 
         # The following workaround is due to the fact that genReads writes out a compressed

From 524ed6b8d1023236be94e9e3e7ddc5c814a21d5e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 8 Sep 2020 13:16:52 -0500
Subject: [PATCH 179/323] Fixed a bug in bacterial wrapper

---
 bacterial_genreads_wrapper.py                 |   3 ++-
 .../SequenceContainer.cpython-37.pyc          | Bin 29131 -> 32804 bytes
 py/__pycache__/neat_cigar.cpython-37.pyc      | Bin 3114 -> 4175 bytes
 py/__pycache__/probability.cpython-37.pyc     | Bin 4135 -> 4264 bytes
 py/__pycache__/ref_func.cpython-37.pyc        | Bin 4837 -> 5682 bytes
 5 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 549bfe5..509cc02 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -70,7 +70,8 @@ def analyze(self):
         self.file.open('w').write(chromosome_name + sequence)
 
     def sample(self, coverage_value: int):
-        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '-c', str(coverage_value), '--pe', '300', '30']
+        args = ['-r', str(self.reference), '-R', '101', '-o', self.name,
+                '-c', str(coverage_value), '--pe', '300', '30']
         gen_reads.main(args)
 
         # The following workaround is due to the fact that genReads writes out a compressed
diff --git a/py/__pycache__/SequenceContainer.cpython-37.pyc b/py/__pycache__/SequenceContainer.cpython-37.pyc
index 40c7ad84da8dc41dbad4cba5097f905ccd03b4e7..13a3ddedb4daff8f5fb3aac6f7cc131c17b02214 100644
GIT binary patch
delta 13763
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diff --git a/py/__pycache__/neat_cigar.cpython-37.pyc b/py/__pycache__/neat_cigar.cpython-37.pyc
index 8ff80b40fd3e959f25df2eac9b717fc96fded530..48c18cd8a3092324e0c422fa9073dc049fce5830 100644
GIT binary patch
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diff --git a/py/__pycache__/probability.cpython-37.pyc b/py/__pycache__/probability.cpython-37.pyc
index cbb55aefc3adffea0f39a7f42bdf2ca3dc1a2485..c496961c7b7d0019b2674d47a3eaabcb6d1ac49b 100644
GIT binary patch
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delta 384
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diff --git a/py/__pycache__/ref_func.cpython-37.pyc b/py/__pycache__/ref_func.cpython-37.pyc
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GIT binary patch
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delta 2796
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From 22f1cc791762ae19f814e4977299410076423ecf Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 9 Sep 2020 16:04:30 -0500
Subject: [PATCH 180/323] working on genreads, minor changes only, debugging

---
 bacterial_genreads_wrapper.py |  3 +--
 gen_reads.py                  | 12 +++++++-----
 py/ref_func.py                | 15 ++++++++-------
 3 files changed, 16 insertions(+), 14 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 509cc02..89de7d2 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -75,8 +75,7 @@ def sample(self, coverage_value: int):
         gen_reads.main(args)
 
         # The following workaround is due to the fact that genReads writes out a compressed
-        # fasta but does not put the .gz extension on it. Also, genReads cannot handle gzipped
-        # fasta files, so we further have to unzip it for it to actually work.
+        # fasta but does not put the .gz extension on it.
         filename1 = pathlib.Path().absolute() / (self.name + "_read1.fq")
         filename2 = pathlib.Path().absolute() / (self.name + "_read2.fq")
         new_name1 = self.name + "_read1.fq.gz"
diff --git a/gen_reads.py b/gen_reads.py
index a221b1b..2d3b82e 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -24,6 +24,7 @@
 import numpy as np
 import argparse
 import pathlib
+from Bio import SeqIO
 
 from py.input_checking import check_file_open, is_in_range
 from py.ref_func import index_ref, read_ref
@@ -78,7 +79,7 @@ def main(raw_args=None):
     parser.add_argument('-v', type=str, required=False, metavar='vcf.file', default=None,
                         help="Input VCF file of variants to include")
     parser.add_argument('--pe', nargs=2, type=int, required=False, metavar=('<int>', '<int>'), default=(None, None),
-                        help='paired-end fragment length mean and std')
+                        help='Paired-end fragment length mean and std')
     parser.add_argument('--pe-model', type=str, required=False, metavar='<str>', default=None,
                         help='empirical fragment length distribution')
     parser.add_argument('--gc-model', type=str, required=False, metavar='<str>', default=None,
@@ -268,8 +269,7 @@ def main(raw_args=None):
     Process Inputs
     """
 
-    # index reference
-    # TODO change reference processing to Biopython
+    # index reference: [("chromosome_name", # bases in the contig, byte index where the seq begins,
     ref_index = index_ref(reference)
 
     if paired_end:
@@ -277,8 +277,8 @@ def main(raw_args=None):
     else:
         n_handling = ('ignore', read_len)
 
-    # TODO pretty sure all this can be handled by SeqIO
     indices_by_ref_name = {ref_index[n][0]: n for n in range(len(ref_index))}
+    ref_list = [n[0] for n in ref_index]
 
     # parse input variants, if present
     # TODO read this in as a pandas dataframe
@@ -297,7 +297,6 @@ def main(raw_args=None):
     # parse input targeted regions, if present
     # TODO convert bed to pandas dataframe
     input_regions = {}
-    ref_list = [n[0] for n in ref_index]
     if input_bed is not None:
         try:
             with open(input_bed, 'r') as f:
@@ -406,6 +405,9 @@ def main(raw_args=None):
 
         # read in reference sequence and notate blocks of Ns
         (ref_sequence, n_regions) = read_ref(reference, ref_index[chrom], n_handling)
+        print(ref_sequence)
+
+        sys.exit(1)
 
         # count total bp we'll be spanning so we can get an idea of how far along we are
         # (for printing progress indicators)
diff --git a/py/ref_func.py b/py/ref_func.py
index aebc76c..1e46a5f 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -26,19 +26,21 @@ def index_ref(reference_path: str) -> list:
         print("\nProblem reading the reference fasta file.\n")
         sys.exit(1)
 
-    filename = None
+    index_filename = None
 
     # check if the reference file already exists
     if absolute_reference_location.with_suffix('.fai').is_file():
         print('found index ' + absolute_reference_location.with_suffix('.fai'))
-        filename = absolute_reference_location.with_suffix('.fai')
+        index_filename = absolute_reference_location.with_suffix('.fai')
     elif absolute_reference_location.with_suffix(absolute_reference_location.suffix + '.fai').is_file():
         print('found index ' + absolute_reference_location.with_suffix(absolute_reference_location.suffix + '.fai'))
-        filename = absolute_reference_location.with_suffix(absolute_reference_location.suffix + '.fai')
+        index_filename = absolute_reference_location.with_suffix(absolute_reference_location.suffix + '.fai')
+    else:
+        pass
 
     ref_indices = []
-    if filename is not None:
-        fai = open(filename, 'r')
+    if index_filename is not None:
+        fai = open(index_filename, 'r')
         for line in fai:
             splt = line[:-1].split('\t')
             seq_len = int(splt[1])
@@ -57,7 +59,6 @@ def index_ref(reference_path: str) -> list:
     prev_p = None
     seq_len = 0
 
-    # TODO I really don't like this while true statement
     while True:
         data = ref_file.readline()
         if not data:
@@ -186,7 +187,7 @@ def get_all_ref_regions(ref_path, ref_inds, n_handling, save_output=False):
     else:
         print('enumerating all non-N regions in reference sequence...')
         for RI in range(len(ref_inds)):
-            (refSequence, N_regions) = read_ref(ref_path, ref_inds[RI], n_handling, quiet=True)
+            (ref_sequence, N_regions) = read_ref(ref_path, ref_inds[RI], n_handling, quiet=True)
             ref_name = ref_inds[RI][0]
             out_regions[ref_name] = [n for n in N_regions['non_N']]
         if save_output:

From 8986b13d057c2201a1dcbfbe083ece949f207bd1 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 14 Sep 2020 11:25:02 -0500
Subject: [PATCH 181/323] appended 'gz' at the end of the filenames

---
 py/output_file_writer.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/py/output_file_writer.py b/py/output_file_writer.py
index f18664b..08a1051 100644
--- a/py/output_file_writer.py
+++ b/py/output_file_writer.py
@@ -97,13 +97,13 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
         self.fasta_instead = fasta_instead
         # TODO Eliminate paired end as an option for fastas
         if self.fasta_instead:
-            fq1 = pathlib.Path(out_prefix + '.fasta')
+            fq1 = pathlib.Path(out_prefix + '.fasta.gz')
             fq2 = None
         else:
-            fq1 = pathlib.Path(out_prefix + '_read1.fq')
-            fq2 = pathlib.Path(out_prefix + '_read2.fq')
+            fq1 = pathlib.Path(out_prefix + '_read1.fq.gz')
+            fq2 = pathlib.Path(out_prefix + '_read2.fq.gz')
         bam = pathlib.Path(out_prefix + '_golden.bam')
-        vcf = pathlib.Path(out_prefix + '_golden.vcf')
+        vcf = pathlib.Path(out_prefix + '_golden.vcf.gz')
 
         self.no_fastq = no_fastq
         if not self.no_fastq:

From 6ecfed3532438a90e39ef78c07eda96415fe2e92 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 14 Sep 2020 11:49:18 -0500
Subject: [PATCH 182/323] eliminated a debugging line

---
 bacterial_genreads_wrapper.py | 45 ++++++++++++++++++-----------------
 gen_reads.py                  | 10 ++++----
 py/ref_func.py                | 10 ++++++--
 py/vcf_func.py                |  3 +--
 4 files changed, 37 insertions(+), 31 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 89de7d2..efd706c 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -36,18 +36,18 @@ def analyze(self):
         args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa', '-c', '1']
         gen_reads.main(args)
 
-        # The following workaround is due to the fact that genReads writes out a compressed
-        # fasta but does not put the .gz extension on it. Also, genReads cannot handle gzipped
-        # fasta files, so we further have to unzip it for it to actually work.
-        self.file = pathlib.Path().absolute() / (self.name + ".fasta")
-        new_name = self.name + ".fasta.gz"
-        self.file.rename(pathlib.Path(pathlib.Path().absolute(), new_name))
-        self.file = pathlib.Path().absolute() / (self.name + ".fasta.gz")
-        true_path = pathlib.Path().absolute() / (self.name + ".fasta")
-        unzip_file(self.file, true_path)
-        pathlib.Path.unlink(pathlib.Path().absolute() / (self.name + ".fasta.gz"))  # deletes unused zip file
-        self.file = true_path
-        # end workaround
+        # # The following workaround is due to the fact that genReads writes out a compressed
+        # # fasta but does not put the .gz extension on it. Also, genReads cannot handle gzipped
+        # # fasta files, so we further have to unzip it for it to actually work.
+        # self.file = pathlib.Path().absolute() / (self.name + ".fasta")
+        # new_name = self.name + ".fasta.gz"
+        # self.file.rename(pathlib.Path(pathlib.Path().absolute(), new_name))
+        # self.file = pathlib.Path().absolute() / (self.name + ".fasta.gz")
+        # true_path = pathlib.Path().absolute() / (self.name + ".fasta")
+        # unzip_file(self.file, true_path)
+        # pathlib.Path.unlink(pathlib.Path().absolute() / (self.name + ".fasta.gz"))  # deletes unused zip file
+        # self.file = true_path
+        # # end workaround
 
         # Now we further have to fix the fasta file, which outputs in a form that doesn't make much sense,
         # so that it can be properly analyzed in the next generation by genreads.
@@ -74,16 +74,16 @@ def sample(self, coverage_value: int):
                 '-c', str(coverage_value), '--pe', '300', '30']
         gen_reads.main(args)
 
-        # The following workaround is due to the fact that genReads writes out a compressed
-        # fasta but does not put the .gz extension on it.
-        filename1 = pathlib.Path().absolute() / (self.name + "_read1.fq")
-        filename2 = pathlib.Path().absolute() / (self.name + "_read2.fq")
-        new_name1 = self.name + "_read1.fq.gz"
-        new_name2 = self.name + "_read2.fq.gz"
+        # # The following workaround is due to the fact that genReads writes out a compressed
+        # # fasta but does not put the .gz extension on it.
+        # filename1 = pathlib.Path().absolute() / (self.name + "_read1.fq")
+        # filename2 = pathlib.Path().absolute() / (self.name + "_read2.fq")
+        # new_name1 = self.name + "_read1.fq.gz"
+        # new_name2 = self.name + "_read2.fq.gz"
 
-        filename1.rename(pathlib.Path(pathlib.Path().absolute(), new_name1))
-        if filename2.is_file():
-            filename2.rename(pathlib.Path(pathlib.Path().absolute(), new_name2))
+        # filename1.rename(pathlib.Path(pathlib.Path().absolute(), new_name1))
+        # if filename2.is_file():
+        #     filename2.rename(pathlib.Path(pathlib.Path().absolute(), new_name2))
 
         # end workaround
 
@@ -174,7 +174,8 @@ def main():
     parser.add_argument('-g', type=int, required=True, metavar='generations', help="Number of generations to run")
     parser.add_argument('-i', type=int, required=True, metavar='initial population', help="Initial population size")
     parser.add_argument('-k', type=float, required=False, metavar='cull pct',
-                        help="Percentage of population to cull each cycle (0.5 will keep population relatively stable)",
+                        help="Percentage of population to cull each cycle "
+                             "(The default of 0.5 will keep population relatively stable)",
                         default=0.5)
     parser.add_argument('-c', type=int, required=False, default=10, metavar='coverage value',
                         help='Target coverage value for final set of sampled fastqs')
diff --git a/gen_reads.py b/gen_reads.py
index 2d3b82e..b4b378c 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -269,7 +269,10 @@ def main(raw_args=None):
     Process Inputs
     """
 
-    # index reference: [("chromosome_name", # bases in the contig, byte index where the seq begins,
+    # index reference: [(0: chromosome name, 1: byte index where the contig seq begins,
+    #                    2: byte index where the next contig begins, 3: contig seq length),
+    #                    (repeat for every chrom)]
+    # TODO check to see if this might work better as a dataframe
     ref_index = index_ref(reference)
 
     if paired_end:
@@ -286,7 +289,7 @@ def main(raw_args=None):
     if input_vcf is not None:
         if cancer:
             (sample_names, input_variants) = parse_vcf(input_vcf, tumor_normal=True, ploidy=ploids)
-            # TODO figure out what Zach was trying to do with these
+            # TODO figure out what these were going to be used for
             tumor_ind = sample_names.index('TUMOR')
             normal_ind = sample_names.index('NORMAL')
         else:
@@ -405,9 +408,6 @@ def main(raw_args=None):
 
         # read in reference sequence and notate blocks of Ns
         (ref_sequence, n_regions) = read_ref(reference, ref_index[chrom], n_handling)
-        print(ref_sequence)
-
-        sys.exit(1)
 
         # count total bp we'll be spanning so we can get an idea of how far along we are
         # (for printing progress indicators)
diff --git a/py/ref_func.py b/py/ref_func.py
index 1e46a5f..f40b697 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -6,11 +6,14 @@
 from Bio.Seq import Seq
 from Bio.Alphabet import IUPAC
 
-
 OK_CHR_ORD = {'A': True, 'C': True, 'G': True, 'T': True, 'U': True}
 ALLOWED_NUCL = ['A', 'C', 'G', 'T']
 
 
+class Dog:
+    pass
+
+
 def index_ref(reference_path: str) -> list:
     """
     Index reference fasta
@@ -43,12 +46,16 @@ def index_ref(reference_path: str) -> list:
         fai = open(index_filename, 'r')
         for line in fai:
             splt = line[:-1].split('\t')
+            # Defined as the number of bases in the contig
             seq_len = int(splt[1])
+            # Defined as the byte index where the contig sequence begins
             offset = int(splt[2])
+            # Defined as bases per line in the Fasta file
             line_ln = int(splt[3])
             n_lines = seq_len // line_ln
             if seq_len % line_ln != 0:
                 n_lines += 1
+            # Item 3 in this gives you the byte position of the next contig, I believe
             ref_indices.append((splt[0], offset, offset + seq_len + n_lines, seq_len))
         fai.close()
         return ref_indices
@@ -79,7 +86,6 @@ def index_ref(reference_path: str) -> list:
 
 
 def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
-
     tt = time.time()
     if not quiet:
         print('reading ' + ref_inds_i[0] + '... ')
diff --git a/py/vcf_func.py b/py/vcf_func.py
index 55170ff..2a173d3 100644
--- a/py/vcf_func.py
+++ b/py/vcf_func.py
@@ -70,8 +70,7 @@ def parse_line(vcf_line, col_dict, col_samp):
 def parse_vcf(vcf_path, tumor_normal=False, ploidy=2):
     tt = time.time()
     print('--------------------------------')
-    sys.stdout.write('reading input VCF...\n')
-    sys.stdout.flush()
+    print('reading input VCF...\n', flush=True)
 
     col_dict = {}
     col_samp = []

From 60a5b8c5684552cba6ffe57a674ee1d9f8f07ed9 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 21 Sep 2020 10:25:32 -0500
Subject: [PATCH 183/323] Several improvements to bacterial wrapper

---
 bacterial_genreads_wrapper.py | 96 ++++++++++++++++++++---------------
 gen_reads.py                  |  2 +-
 py/output_file_writer.py      |  1 +
 3 files changed, 57 insertions(+), 42 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index efd706c..4007073 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -7,13 +7,15 @@
 import gzip
 import shutil
 import sys
+import copy
 # from Bio import SeqIO
 
 
 class Bacterium:
-    def __init__(self, reference: str, name: str):
+    def __init__(self, reference: str, name: str, chrom_names: list):
         self.reference = pathlib.Path(reference)
         self.name = name
+        self.chroms = chrom_names
         # Temporarily set the reference as the bacterium's file, until it is analyzed
         self.file = pathlib.Path(reference)
         self.analyze()
@@ -27,6 +29,9 @@ def __str__(self):
     def get_file(self):
         return self.file
 
+    def get_chroms(self):
+        return self.chroms
+
     def analyze(self):
         """
         This function is supposed to just run genreads for the bacterium, but doing so requires some file
@@ -35,58 +40,50 @@ def analyze(self):
         """
         args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa', '-c', '1']
         gen_reads.main(args)
-
-        # # The following workaround is due to the fact that genReads writes out a compressed
-        # # fasta but does not put the .gz extension on it. Also, genReads cannot handle gzipped
-        # # fasta files, so we further have to unzip it for it to actually work.
-        # self.file = pathlib.Path().absolute() / (self.name + ".fasta")
-        # new_name = self.name + ".fasta.gz"
-        # self.file.rename(pathlib.Path(pathlib.Path().absolute(), new_name))
-        # self.file = pathlib.Path().absolute() / (self.name + ".fasta.gz")
-        # true_path = pathlib.Path().absolute() / (self.name + ".fasta")
-        # unzip_file(self.file, true_path)
-        # pathlib.Path.unlink(pathlib.Path().absolute() / (self.name + ".fasta.gz"))  # deletes unused zip file
-        # self.file = true_path
-        # # end workaround
+        self.file = pathlib.Path().absolute() / (self.name + ".fasta.gz")
+
+        # The following workaround is due to the fact that genReads cannot handle gzipped
+        # fasta files, so we have to unzip it for it to actually work.
+        unzipped_path = pathlib.Path().absolute() / (self.name + ".fasta")
+        unzip_file(self.file, unzipped_path)
+        pathlib.Path.unlink(pathlib.Path().absolute() / (self.name + ".fasta.gz"))  # deletes unused zip file
+        self.file = unzipped_path
+        # end workaround
 
         # Now we further have to fix the fasta file, which outputs in a form that doesn't make much sense,
         # so that it can be properly analyzed in the next generation by genreads.
+        temp_name_list = copy.deepcopy(self.chroms)
+        temp_file = self.file.parents[0] / 'neat_temporary_fasta_file.fa'
+        temp_file.touch()
         chromosome_name = ""
         sequence = ""
         with self.file.open() as f:
-            first_line = f.readline()
-            if first_line.startswith(">"):
-                chromosome_name = first_line
-            else:
-                print("Something went wrong with the fasta file")
-                sys.exit(1)
             for line in f:
                 if line.startswith(">"):
-                    continue
+                    for name in temp_name_list:
+                        if name in line:
+                            if chromosome_name != ">" + name + "\n":
+                                if sequence:
+                                    temp_file.open('a').write(chromosome_name + sequence)
+                                    sequence = ""
+                                chromosome_name = ">" + name + "\n"
+                                temp_name_list.remove(name)
+                            else:
+                                continue
+                    if not chromosome_name:
+                        print("Something went wrong with the generated fasta file.\n")
+                        sys.exit(1)
                 else:
                     sequence = sequence + line
-            f.close()
-        # re-write file with just the chrom name and sequence
-        self.file.open('w').write(chromosome_name + sequence)
+        temp_file.open('a').write(chromosome_name + sequence)
+        shutil.copy(temp_file, self.file)
+        pathlib.Path.unlink(temp_file)
 
     def sample(self, coverage_value: int):
         args = ['-r', str(self.reference), '-R', '101', '-o', self.name,
                 '-c', str(coverage_value), '--pe', '300', '30']
         gen_reads.main(args)
 
-        # # The following workaround is due to the fact that genReads writes out a compressed
-        # # fasta but does not put the .gz extension on it.
-        # filename1 = pathlib.Path().absolute() / (self.name + "_read1.fq")
-        # filename2 = pathlib.Path().absolute() / (self.name + "_read2.fq")
-        # new_name1 = self.name + "_read1.fq.gz"
-        # new_name2 = self.name + "_read2.fq.gz"
-
-        # filename1.rename(pathlib.Path(pathlib.Path().absolute(), new_name1))
-        # if filename2.is_file():
-        #     filename2.rename(pathlib.Path(pathlib.Path().absolute(), new_name2))
-
-        # end workaround
-
     def remove(self):
         pathlib.Path.unlink(self.file)
 
@@ -119,10 +116,11 @@ def cull(population: list, percentage: float = 0.5) -> list:
     return population
 
 
-def initialize_population(reference: str, pop_size) -> list:
+def initialize_population(reference: str, pop_size: int, chrom_names: list) -> list:
     """
     The purpose of this function is to evolve the initial population of bacteria. All bacteria are stored as
     Bacterium objects.
+    :param chrom_names: A list of contigs from the original fasta
     :param reference: string path to the reference fasta file
     :param pop_size: size of the population to initialize.
     :return population: returns a list of bacterium objects.
@@ -132,7 +130,7 @@ def initialize_population(reference: str, pop_size) -> list:
         names.append("bacterium_0_{}".format(j+1))
     population = []
     for i in range(pop_size):
-        new_member = Bacterium(reference, names[i])
+        new_member = Bacterium(reference, names[i], chrom_names)
         population.append(new_member)
     return population
 
@@ -151,7 +149,7 @@ def evolve_population(population: list, generation: int) -> list:
     for j in range(len(children_population)):
         names.append("bacterium_{}_{}".format(generation, j+1))
     for i in range(len(children_population)):
-        child = Bacterium(children_population[i].get_file(), names[i])
+        child = Bacterium(children_population[i].get_file(), names[i], children_population[i].get_chroms())
         new_population.append(child)
     return new_population
 
@@ -167,6 +165,20 @@ def sample_population(population: list, target_coverage: int):
         bacterium.sample(target_coverage)
 
 
+def extract_names(reference: str) -> list:
+    ref_names = []
+    absolute_reference_path = pathlib.Path(reference)
+    with open(absolute_reference_path, 'r') as ref:
+        for line in ref:
+            if line.startswith(">"):
+                ref_names.append(line[1:].rstrip())
+    if not ref_names:
+        print("Malformed fasta file. Missing properly formatted chromosome names.\n")
+        sys.exit(1)
+
+    return ref_names
+
+
 def main():
     parser = argparse.ArgumentParser(description='bacterial_genreads_wrapper.py')
     parser.add_argument('-r', type=str, required=True, metavar='reference fasta',
@@ -185,7 +197,9 @@ def main():
     cull_percentage = args.k
     coverage = args.c
 
-    population = initialize_population(ref_fasta, init_population_size)
+    chrom_names = extract_names(ref_fasta)
+
+    population = initialize_population(ref_fasta, init_population_size, chrom_names)
 
     for i in range(generations):
         new_population = evolve_population(population, i+1)
diff --git a/gen_reads.py b/gen_reads.py
index b4b378c..5a02a16 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -741,7 +741,7 @@ def main(raw_args=None):
 
                         # write SE output
                         if len(my_read_data) == 1:
-                            if no_fastq is not True:
+                            if not no_fastq:
                                 if is_forward:
                                     output_file_writer.write_fastq_record(my_read_name, my_read_data[0][2],
                                                                           my_read_data[0][3])
diff --git a/py/output_file_writer.py b/py/output_file_writer.py
index 08a1051..9300bb6 100644
--- a/py/output_file_writer.py
+++ b/py/output_file_writer.py
@@ -105,6 +105,7 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
         bam = pathlib.Path(out_prefix + '_golden.bam')
         vcf = pathlib.Path(out_prefix + '_golden.vcf.gz')
 
+        # TODO Make a fasta-specific method
         self.no_fastq = no_fastq
         if not self.no_fastq:
             if gzipped:

From e2ec3928d4a4a3635dbe2e27152cb8eb8615409b Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 21 Sep 2020 10:27:36 -0500
Subject: [PATCH 184/323] minor changes to bacterial wrapper

---
 bacterial_genreads_wrapper.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 4007073..1845db3 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -181,10 +181,10 @@ def extract_names(reference: str) -> list:
 
 def main():
     parser = argparse.ArgumentParser(description='bacterial_genreads_wrapper.py')
-    parser.add_argument('-r', type=str, required=True, metavar='reference fasta',
+    parser.add_argument('-r', type=str, required=True, metavar='reference.fasta',
                         help="Reference file for organism in fasta format")
     parser.add_argument('-g', type=int, required=True, metavar='generations', help="Number of generations to run")
-    parser.add_argument('-i', type=int, required=True, metavar='initial population', help="Initial population size")
+    parser.add_argument('-i', type=int, required=True, metavar='initial pop', help="Initial population size")
     parser.add_argument('-k', type=float, required=False, metavar='cull pct',
                         help="Percentage of population to cull each cycle "
                              "(The default of 0.5 will keep population relatively stable)",

From fbc06f5a9105cdcf5b68d83f7a03f5b902c3bfd0 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 21 Sep 2020 11:38:51 -0500
Subject: [PATCH 185/323] fixed a parameter

---
 bacterial_genreads_wrapper.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 1845db3..46eb54f 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -81,7 +81,7 @@ def analyze(self):
 
     def sample(self, coverage_value: int):
         args = ['-r', str(self.reference), '-R', '101', '-o', self.name,
-                '-c', str(coverage_value), '--pe', '300', '30']
+                '-c', str(coverage_value), '--pe', '500', '50']
         gen_reads.main(args)
 
     def remove(self):

From bcdac56f6b34244ec492b778f78981ac3a6580b2 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 21 Sep 2020 14:46:59 -0500
Subject: [PATCH 186/323] Added inputs for paired-ended variables

---
 bacterial_genreads_wrapper.py | 13 ++++++++-----
 gen_reads.py                  |  2 +-
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 4007073..0c470a5 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -79,9 +79,9 @@ def analyze(self):
         shutil.copy(temp_file, self.file)
         pathlib.Path.unlink(temp_file)
 
-    def sample(self, coverage_value: int):
+    def sample(self, coverage_value: int, fragment_size: int, fragment_std: int):
         args = ['-r', str(self.reference), '-R', '101', '-o', self.name,
-                '-c', str(coverage_value), '--pe', '300', '30']
+                '-c', str(coverage_value), '--pe', str(fragment_size), str(fragment_std)]
         gen_reads.main(args)
 
     def remove(self):
@@ -154,7 +154,7 @@ def evolve_population(population: list, generation: int) -> list:
     return new_population
 
 
-def sample_population(population: list, target_coverage: int):
+def sample_population(population: list, target_coverage: int, fragment_size: int, fragment_std: int):
     """
     This will create a fastq based on each member of the population.
     :param target_coverage: The target coverage value for the sample.
@@ -162,7 +162,7 @@ def sample_population(population: list, target_coverage: int):
     :return: None
     """
     for bacterium in population:
-        bacterium.sample(target_coverage)
+        bacterium.sample(target_coverage, fragment_size, fragment_std)
 
 
 def extract_names(reference: str) -> list:
@@ -191,11 +191,14 @@ def main():
                         default=0.5)
     parser.add_argument('-c', type=int, required=False, default=10, metavar='coverage value',
                         help='Target coverage value for final set of sampled fastqs')
+    parser.add_argument('--pe', nargs=2, type=int, required=False, metavar=('<int>', '<int>'), default=(500, 50),
+                        help='Paired-end fragment length mean and std')
     args = parser.parse_args()
 
     (ref_fasta, init_population_size, generations) = (args.r, args.i, args.g)
     cull_percentage = args.k
     coverage = args.c
+    (fragment_size, fragment_std) = args.pe
 
     chrom_names = extract_names(ref_fasta)
 
@@ -212,7 +215,7 @@ def main():
 
         population = new_population
 
-    sample_population(population, coverage)
+    sample_population(population, coverage, fragment_size, fragment_std)
 
 
 if __name__ == '__main__':
diff --git a/gen_reads.py b/gen_reads.py
index 5a02a16..51f38c7 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -123,7 +123,7 @@ def main(raw_args=None):
     # (cancer, cancer_model, cancer_purity) = (args.cancer, args.cm, args.cp)
     (cancer, cancer_model, cancer_purity) = (False, None, 0.8)
     (off_target_scalar, off_target_discard, force_coverage, rescale_qual) = (args.to, args.discard_offtarget,
-                                                                             args.force_coverage, args.rescale_qual)
+                          args.force_coverage, args.rescale_qual)
     # important flags
     (save_bam, save_vcf, fasta_instead, gzipped_out, no_fastq) = \
         (args.bam, args.vcf, args.fa, args.gz, args.no_fastq)

From 6270cc21dedd7d23076192fbec5bcfa0f810c77a Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 21 Sep 2020 19:35:31 -0500
Subject: [PATCH 187/323] Added some documentation and made minor changes to
 the file.

---
 bacterial_genreads_wrapper.py | 33 +++++++++++++++++++++++++++++----
 1 file changed, 29 insertions(+), 4 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 77b0952..d3c9892 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -13,6 +13,12 @@
 
 class Bacterium:
     def __init__(self, reference: str, name: str, chrom_names: list):
+        """
+        Class Bacterium for keeping track of all the elements of a bacterium for the purposes of this simulation
+        :param reference: The str representing the location of the reference file
+        :param name: The name of this particular bacterium.
+        :param chrom_names: The list of chromosome names from the progenitor bacterium
+        """
         self.reference = pathlib.Path(reference)
         self.name = name
         self.chroms = chrom_names
@@ -80,21 +86,35 @@ def analyze(self):
         pathlib.Path.unlink(temp_file)
 
     def sample(self, coverage_value: int, fragment_size: int, fragment_std: int):
+        """
+        This function simple runs genreads on the file associated with this bacterium
+        :param coverage_value: What depth of coverage to sample the reads at.
+        :param fragment_size: The mean insert size for the resultant fastqs.
+        :param fragment_std: The standard deviation of the insert size
+        :return: None
+        """
         args = ['-r', str(self.reference), '-R', '101', '-o', self.name,
                 '-c', str(coverage_value), '--pe', str(fragment_size), str(fragment_std)]
 
         gen_reads.main(args)
 
     def remove(self):
-        pathlib.Path.unlink(self.file)
-
+        """
+        This function simple deletes the file associated with this bacterium, or raises an error if there is a problem
+        :return: None
+        """
+        try:
+            pathlib.Path.unlink(self.file)
+        except FileExistsError:
+            print('\nThere was a problem deleting a file\n')
+            raise FileExistsError()
 
 def unzip_file(zipped_file: pathlib, unzipped_file: pathlib):
     """
     This unzips a gzipped file, then saves the unzipped file as a new file.
     :param zipped_file: pathlib object that points to the zipped file
     :param unzipped_file: pathlib object that points to the unzipped file
-    :return:
+    :return: None
     """
     with gzip.open(zipped_file, 'rb') as f_in:
         with open(unzipped_file, 'wb') as f_out:
@@ -142,7 +162,7 @@ def evolve_population(population: list, generation: int) -> list:
     each member of the population.
     :param generation: Helps determine the starting point of the numbering system so the bacteria have unique names
     :param population: A list of fasta files representing the bacteria.
-    :return:
+    :return: None
     """
     children_population = population + population
     names = []
@@ -167,6 +187,11 @@ def sample_population(population: list, target_coverage: int, fragment_size: int
 
 
 def extract_names(reference: str) -> list:
+    """
+    This function attempts to extract the chromosome names from a fasta file
+    :param reference: The fasta file to analyze
+    :return: A list of chromosome names
+    """
     ref_names = []
     absolute_reference_path = pathlib.Path(reference)
     with open(absolute_reference_path, 'r') as ref:

From 600d01601b92e9896d3e723c0e09b4df69a81a88 Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Mon, 28 Sep 2020 15:40:22 -0500
Subject: [PATCH 188/323] Update README.md

Added list of dependencies
---
 README.md | 27 +++++++++++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 35fb7ee..5e09213 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,6 @@ To cite this work, please use:
 > Stephens, Zachary D., Matthew E. Hudson, Liudmila S. Mainzer, Morgan Taschuk, Matthew R. Weber, and Ravishankar K. Iyer. "Simulating next-generation sequencing datasets from empirical mutation and sequencing models." PloS one 11, no. 11 (2016): e0167047.
 
 
-
 Table of Contents
 =================
 
@@ -42,7 +41,31 @@ Table of Contents
 ## Requirements
 
 * Python 2.7
-* Numpy 1.9.1+
+
+* List of Required Packages for each file. The main function is bolded, and the first instance of any package in the table is *italicized*.
+
+| File		| Packages Needed |
+| ----		| ------------    |
+| **gen_reads** | *sys* \| *copy* \| *random* \| *re* \| *time* \| *bisect* \| *pickle* \| *numpy* \| *argparse* \| *pathlib* \| *SeqIO* 	|
+|bacterial_genreads_wrapper | argparse \| random \| pathlib \| sys \| copy \| *gzip* \| *shutil*	|
+|vcf_compare_OLD | sys \| copy \| time \| bisect \| re \| numpy \| *optparse* \| *IUPAC* \| *Seq*	|
+|genSeqErrorModel | gzip \| numpy \| argparse \| sys \| pickle \| *os* \| *matplotlib.pyplot*	|
+|compute_gc | time \| argparse \| numpy \| pickle \| SeqIO |
+|compute_fraglen | pickle \| argparse \| *platform* \| *doctest* \| *pysam*	|
+|gen_mut_model | os \| re \| pickle \| argparse \| numpy \| SeqIO \| *pandas*	|
+|plotMutModel | argparse \| sys \| pickle \| bisect \| numpy \| matplotlib.pylot \| *matplotlib.colors* \| *matplotlib.cm*	|
+|validateBam | sys \| os \| gzip \| *unpack*	|
+|validateFQ | sys |
+|merge_jobs | os \| argparse |
+|neat_cigar | re |
+|vcf_func | sys \| time \| os \| re \| random |
+|ref_func | sys \| time \| os \| pathlib \| random \| Seq \| IUPAC |
+|SequenceContainer | random \| copy \| pathlib \| bisect \| pickle \| numpy \| Seq \| IUPAC |
+|biopython_modified_bgzf | *zlib* \| *struct* |
+|output_file_writer | sys \| os \| re \| gzip \| Seq \| IUPAC \| SeqIO \| pathlib \| \* \| *pack* |
+|probability | random \| bisect \| copy \| numpy |
+|input_checking | pathlib \| sys |
+
 
 ## Usage
 Here's the simplest invocation of genReads using default parameters. This command produces a single ended fastq file with reads of length 101, ploidy 2, coverage 10X, using the default sequencing substitution, GC% bias, and mutation rate models.

From f2eb0235af9cded74e9c0b444c23681711e678f4 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 5 Oct 2020 11:25:25 -0500
Subject: [PATCH 189/323] Remove Bio.Alphabet references

---
 bacterial_genreads_wrapper.py | 1 +
 py/SequenceContainer.py       | 9 ++++-----
 py/output_file_writer.py      | 1 -
 py/ref_func.py                | 3 +--
 4 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index d3c9892..07e237b 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -109,6 +109,7 @@ def remove(self):
             print('\nThere was a problem deleting a file\n')
             raise FileExistsError()
 
+
 def unzip_file(zipped_file: pathlib, unzipped_file: pathlib):
     """
     This unzips a gzipped file, then saves the unzipped file as a new file.
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 4456a94..aa19303 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -3,9 +3,9 @@
 import pathlib
 import bisect
 import pickle
+import sys
 import numpy as np
 from Bio.Seq import Seq
-from Bio.Alphabet import IUPAC
 
 from py.probability import DiscreteDistribution, poisson_list, quantize_list
 from py.neat_cigar import CigarString
@@ -99,7 +99,7 @@ def __init__(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_mod
         self.x = x_offset
         self.ploidy = ploidy
         self.read_len = read_len
-        self.sequences = [Seq(str(sequence), IUPAC.unambiguous_dna).tomutable() for _ in range(self.ploidy)]
+        self.sequences = [Seq(str(sequence)).tomutable() for _ in range(self.ploidy)]
         self.seq_len = len(sequence)
         self.indel_list = [[] for _ in range(self.ploidy)]
         self.snp_list = [[] for _ in range(self.ploidy)]
@@ -191,7 +191,7 @@ def update_basic_vars(self, x_offset, sequence, ploidy, window_overlap, read_len
         self.x = x_offset
         self.ploidy = ploidy
         self.read_len = read_len
-        self.sequences = [Seq(str(sequence), IUPAC.unambiguous_dna).tomutable() for _ in range(self.ploidy)]
+        self.sequences = [Seq(str(sequence)).tomutable() for _ in range(self.ploidy)]
         self.seq_len = len(sequence)
         self.indel_list = [[] for _ in range(self.ploidy)]
         self.snp_list = [[] for _ in range(self.ploidy)]
@@ -596,8 +596,7 @@ def random_mutations(self):
                     sys.exit(1)
                 else:
                     # alter reference sequence
-                    self.sequences[i] = self.sequences[i][:v_pos] + Seq(all_indels_ins[i][j][2],
-                                                                        IUPAC.unambiguous_dna).tomutable() + \
+                    self.sequences[i] = self.sequences[i][:v_pos] + Seq(all_indels_ins[i][j][2],).tomutable() + \
                                         self.sequences[i][v_pos2:]
                     # notate indel positions for cigar computation
                     d = len(all_indels_ins[i][j][2]) - len(all_indels_ins[i][j][1])
diff --git a/py/output_file_writer.py b/py/output_file_writer.py
index 9300bb6..2d34284 100644
--- a/py/output_file_writer.py
+++ b/py/output_file_writer.py
@@ -4,7 +4,6 @@
 import gzip
 from struct import pack
 from Bio.Seq import Seq
-from Bio.Alphabet import IUPAC
 from Bio import SeqIO
 import gzip
 from Bio.bgzf import *
diff --git a/py/ref_func.py b/py/ref_func.py
index f40b697..35fa917 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -4,7 +4,6 @@
 import pathlib
 import random
 from Bio.Seq import Seq
-from Bio.Alphabet import IUPAC
 
 OK_CHR_ORD = {'A': True, 'C': True, 'G': True, 'T': True, 'U': True}
 ALLOWED_NUCL = ['A', 'C', 'G', 'T']
@@ -100,7 +99,7 @@ def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
     # TODO convert to SeqIO containers
     ref_file.seek(ref_inds_i[1])
     my_dat = ''.join(ref_file.read(ref_inds_i[2] - ref_inds_i[1]).split('\n'))
-    my_dat = Seq(my_dat.upper(), IUPAC.unambiguous_dna)
+    my_dat = Seq(my_dat.upper())
     my_dat = my_dat.tomutable()
 
     # find N regions

From 191a6e58cbbe79b4d028f061027a4eaeac51ecf6 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 5 Oct 2020 15:07:09 -0500
Subject: [PATCH 190/323] Change to final sampling in bacterial wrapper (set
 random mutation rate to zero)

---
 bacterial_genreads_wrapper.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 07e237b..96408c1 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -93,7 +93,7 @@ def sample(self, coverage_value: int, fragment_size: int, fragment_std: int):
         :param fragment_std: The standard deviation of the insert size
         :return: None
         """
-        args = ['-r', str(self.reference), '-R', '101', '-o', self.name,
+        args = ['-r', str(self.file), '-M', '0', '-R', '101', '-o', self.name,
                 '-c', str(coverage_value), '--pe', str(fragment_size), str(fragment_std)]
 
         gen_reads.main(args)

From ae34b790ea7a1da760301b5e42e377d5d145b393 Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Wed, 7 Oct 2020 14:03:41 -0500
Subject: [PATCH 191/323] updated requirements

---
 README.md | 29 ++++-------------------------
 1 file changed, 4 insertions(+), 25 deletions(-)

diff --git a/README.md b/README.md
index 5e09213..6668a5e 100644
--- a/README.md
+++ b/README.md
@@ -40,31 +40,10 @@ Table of Contents
 
 ## Requirements
 
-* Python 2.7
-
-* List of Required Packages for each file. The main function is bolded, and the first instance of any package in the table is *italicized*.
-
-| File		| Packages Needed |
-| ----		| ------------    |
-| **gen_reads** | *sys* \| *copy* \| *random* \| *re* \| *time* \| *bisect* \| *pickle* \| *numpy* \| *argparse* \| *pathlib* \| *SeqIO* 	|
-|bacterial_genreads_wrapper | argparse \| random \| pathlib \| sys \| copy \| *gzip* \| *shutil*	|
-|vcf_compare_OLD | sys \| copy \| time \| bisect \| re \| numpy \| *optparse* \| *IUPAC* \| *Seq*	|
-|genSeqErrorModel | gzip \| numpy \| argparse \| sys \| pickle \| *os* \| *matplotlib.pyplot*	|
-|compute_gc | time \| argparse \| numpy \| pickle \| SeqIO |
-|compute_fraglen | pickle \| argparse \| *platform* \| *doctest* \| *pysam*	|
-|gen_mut_model | os \| re \| pickle \| argparse \| numpy \| SeqIO \| *pandas*	|
-|plotMutModel | argparse \| sys \| pickle \| bisect \| numpy \| matplotlib.pylot \| *matplotlib.colors* \| *matplotlib.cm*	|
-|validateBam | sys \| os \| gzip \| *unpack*	|
-|validateFQ | sys |
-|merge_jobs | os \| argparse |
-|neat_cigar | re |
-|vcf_func | sys \| time \| os \| re \| random |
-|ref_func | sys \| time \| os \| pathlib \| random \| Seq \| IUPAC |
-|SequenceContainer | random \| copy \| pathlib \| bisect \| pickle \| numpy \| Seq \| IUPAC |
-|biopython_modified_bgzf | *zlib* \| *struct* |
-|output_file_writer | sys \| os \| re \| gzip \| Seq \| IUPAC \| SeqIO \| pathlib \| \* \| *pack* |
-|probability | random \| bisect \| copy \| numpy |
-|input_checking | pathlib \| sys |
+* Python 3.6+
+* Biopython 1.77+
+* Matplotlib 3.2.2+
+* Numpy 1.19.0+
 
 
 ## Usage

From 9d78ec11c5f692916b164c930c3e8b039651846e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 12 Oct 2020 10:36:17 -0500
Subject: [PATCH 192/323] fixed str concatenation error in print statement in
 ref_func.py

---
 py/ref_func.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/py/ref_func.py b/py/ref_func.py
index 35fa917..7c64b6b 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -32,10 +32,11 @@ def index_ref(reference_path: str) -> list:
 
     # check if the reference file already exists
     if absolute_reference_location.with_suffix('.fai').is_file():
-        print('found index ' + absolute_reference_location.with_suffix('.fai'))
+        print('found index ' + str(absolute_reference_location.with_suffix('.fai')))
         index_filename = absolute_reference_location.with_suffix('.fai')
     elif absolute_reference_location.with_suffix(absolute_reference_location.suffix + '.fai').is_file():
-        print('found index ' + absolute_reference_location.with_suffix(absolute_reference_location.suffix + '.fai'))
+        print('found index ' +
+              str(absolute_reference_location.with_suffix(absolute_reference_location.suffix + '.fai')))
         index_filename = absolute_reference_location.with_suffix(absolute_reference_location.suffix + '.fai')
     else:
         pass

From 174f76e87f0fe90fbddc2f2537f7c99c92f5396b Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 14 Oct 2020 12:33:03 -0500
Subject: [PATCH 193/323] Removed IUPAC references from Biopython imports

---
 py/SequenceContainer.py                |   6 ++----
 py/__pycache__/__init__.cpython-37.pyc | Bin 146 -> 146 bytes
 py/__pycache__/ref_func.cpython-37.pyc | Bin 4837 -> 4769 bytes
 py/output_file_writer.py               |   1 -
 py/ref_func.py                         |   4 +---
 5 files changed, 3 insertions(+), 8 deletions(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 89e0154..633c42f 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -5,7 +5,6 @@
 import pickle
 import numpy as np
 from Bio.Seq import Seq
-from Bio.Alphabet import IUPAC
 
 from py.probability import DiscreteDistribution, poisson_list, quantize_list
 from py.neat_cigar import CigarString
@@ -50,7 +49,7 @@ def init_basic_vars(self, x_offset, sequence, ploidy, window_overlap, read_len):
         self.x = x_offset
         self.ploidy = ploidy
         self.read_len = read_len
-        self.sequences = [Seq(str(sequence), IUPAC.unambiguous_dna).tomutable() for n in range(self.ploidy)]
+        self.sequences = [Seq(str(sequence)).tomutable() for n in range(self.ploidy)]
         self.seq_len = len(sequence)
         self.indel_list = [[] for n in range(self.ploidy)]
         self.snp_list = [[] for n in range(self.ploidy)]
@@ -453,8 +452,7 @@ def random_mutations(self):
                     exit(1)
                 else:
                     # alter reference sequence
-                    self.sequences[i] = self.sequences[i][:v_pos] + Seq(all_indels_ins[i][j][2],
-                                                                        IUPAC.unambiguous_dna).tomutable() + \
+                    self.sequences[i] = self.sequences[i][:v_pos] + Seq(all_indels_ins[i][j][2]).tomutable() + \
                                         self.sequences[i][v_pos2:]
                     # notate indel positions for cigar computation
                     d = len(all_indels_ins[i][j][2]) - len(all_indels_ins[i][j][1])
diff --git a/py/__pycache__/__init__.cpython-37.pyc b/py/__pycache__/__init__.cpython-37.pyc
index d4c91fd49ae6f04e83c9aa419deb0d8ce40a9c4e..693fb8059cd93cd4ba44f2a2db43a537eccac44d 100644
GIT binary patch
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delta 19
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diff --git a/py/output_file_writer.py b/py/output_file_writer.py
index a84a34c..384649a 100644
--- a/py/output_file_writer.py
+++ b/py/output_file_writer.py
@@ -4,7 +4,6 @@
 import gzip
 from struct import pack
 from Bio.Seq import Seq
-from Bio.Alphabet import IUPAC
 from Bio import SeqIO
 import gzip
 from Bio.bgzf import *
diff --git a/py/ref_func.py b/py/ref_func.py
index 6ff8c8a..9170759 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -3,8 +3,6 @@
 import os
 import random
 from Bio.Seq import Seq
-from Bio.Alphabet import IUPAC
-
 
 #	Index reference fasta
 def index_ref(reference_path):
@@ -69,7 +67,7 @@ def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
     ref_file = open(ref_path, 'r')
     ref_file.seek(ref_inds_i[1])
     my_dat = ''.join(ref_file.read(ref_inds_i[2] - ref_inds_i[1]).split('\n'))
-    my_dat = Seq(my_dat.upper(), IUPAC.unambiguous_dna)
+    my_dat = Seq(my_dat.upper())
     my_dat = my_dat.tomutable()
 
     # find N regions

From 5ebd20e0b855d69d6f1390e8d28861a0c607b6aa Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 14 Oct 2020 13:13:58 -0500
Subject: [PATCH 194/323] added a function to try to repickle the pickle files

---
 bacterial_genreads_wrapper.py                 |  14 +++++++---
 .../SequenceContainer.cpython-37.pyc          | Bin 32804 -> 32723 bytes
 py/__pycache__/neat_cigar.cpython-37.pyc      | Bin 4175 -> 4175 bytes
 py/__pycache__/probability.cpython-37.pyc     | Bin 4264 -> 4264 bytes
 py/__pycache__/ref_func.cpython-37.pyc        | Bin 5682 -> 5784 bytes
 utilities/repickle.py                         |  25 ++++++++++++++++++
 6 files changed, 35 insertions(+), 4 deletions(-)
 create mode 100644 utilities/repickle.py

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 96408c1..e251888 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -195,10 +195,16 @@ def extract_names(reference: str) -> list:
     """
     ref_names = []
     absolute_reference_path = pathlib.Path(reference)
-    with open(absolute_reference_path, 'r') as ref:
-        for line in ref:
-            if line.startswith(">"):
-                ref_names.append(line[1:].rstrip())
+    if absolute_reference_path.suffix == '.gz':
+        with gzip.open(absolute_reference_path, 'rt') as ref:
+            for line in ref:
+                if line.startswith(">"):
+                    ref_names.append(line[1:].rstrip())
+    else:
+        with open(absolute_reference_path, 'r') as ref:
+            for line in ref:
+                if line.startswith(">"):
+                    ref_names.append(line[1:].rstrip())
     if not ref_names:
         print("Malformed fasta file. Missing properly formatted chromosome names.\n")
         sys.exit(1)
diff --git a/py/__pycache__/SequenceContainer.cpython-37.pyc b/py/__pycache__/SequenceContainer.cpython-37.pyc
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diff --git a/py/__pycache__/neat_cigar.cpython-37.pyc b/py/__pycache__/neat_cigar.cpython-37.pyc
index 48c18cd8a3092324e0c422fa9073dc049fce5830..1d9329700f767335f219708097173a3aa77ba761 100644
GIT binary patch
delta 104
zcmX@Fa9)AiiI<m)0SKzh+Bb4vWoL}q{E%Ibkx_HA0H+Eow+>LINEbxtO}6JdCZ!2t
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delta 104
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diff --git a/py/__pycache__/probability.cpython-37.pyc b/py/__pycache__/probability.cpython-37.pyc
index c496961c7b7d0019b2674d47a3eaabcb6d1ac49b..ee6ff072f3d04a8aeef5b8d697697f40c0022518 100644
GIT binary patch
delta 175
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delta 175
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diff --git a/py/__pycache__/ref_func.cpython-37.pyc b/py/__pycache__/ref_func.cpython-37.pyc
index 9e51f0f471b0f68a6a7084f259e6f3176bedc232..7b0e10c2b5542bba36174378588a14a18808e7e6 100644
GIT binary patch
delta 2358
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diff --git a/utilities/repickle.py b/utilities/repickle.py
new file mode 100644
index 0000000..44c7929
--- /dev/null
+++ b/utilities/repickle.py
@@ -0,0 +1,25 @@
+import sys
+import os
+import pathlib
+import glob
+try:
+    import cPickle as pickle
+finally:
+    import pickle
+
+
+def main():
+    dir_to_repickle = pathlib.Path(sys.argv[1])
+
+    if not dir_to_repickle.is_dir():
+        print("Input is not a directory.")
+        sys.exit(1)
+
+    os.chdir(dir_to_repickle)
+    for file in glob.glob("*.p"):
+        data = pickle.load(open(file, 'rb'), encoding="bytes")
+        pickle.dump(data, open(file, "wb"), pickle.HIGHEST_PROTOCOL)
+
+
+if __name__ == "__main__":
+    main()

From a9c59e55c6d8f4ed938bd31994c6fea99b8013ae Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 14 Oct 2020 13:17:14 -0500
Subject: [PATCH 195/323] fixed bug in repickle

---
 utilities/repickle.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/utilities/repickle.py b/utilities/repickle.py
index 44c7929..f845a50 100644
--- a/utilities/repickle.py
+++ b/utilities/repickle.py
@@ -2,10 +2,7 @@
 import os
 import pathlib
 import glob
-try:
-    import cPickle as pickle
-finally:
-    import pickle
+import pickle
 
 
 def main():

From 2d764ce40cc08218d4559addd7e23fb82c2b522f Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 14 Oct 2020 13:18:42 -0500
Subject: [PATCH 196/323] fixed bug in repickle

---
 utilities/repickle.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/utilities/repickle.py b/utilities/repickle.py
index 44c7929..f845a50 100644
--- a/utilities/repickle.py
+++ b/utilities/repickle.py
@@ -2,10 +2,7 @@
 import os
 import pathlib
 import glob
-try:
-    import cPickle as pickle
-finally:
-    import pickle
+import pickle
 
 
 def main():

From d5fe9c0e3b61908e1cdd2080444ff54052bab0aa Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 14 Oct 2020 13:19:52 -0500
Subject: [PATCH 197/323] Repickled files

---
 models/MutModel_BRCA_US_ICGC.p | Bin 404224 -> 193920 bytes
 models/MutModel_CLLE-ES_ICGC.p | Bin 2003913 -> 950391 bytes
 models/MutModel_SKCM-US_ICGC.p | Bin 993782 -> 458729 bytes
 models/errorModel_pacbio_toy.p | Bin 776 -> 426 bytes
 models/errorModel_toy.p        | Bin 7791940 -> 3282719 bytes
 models/fraglenModel_toy.p      | Bin 23396 -> 8991 bytes
 models/gcBias_toy.p            | Bin 1355 -> 585 bytes
 models/gcBias_uniform.p        | Bin 569 -> 585 bytes
 8 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/models/MutModel_BRCA_US_ICGC.p b/models/MutModel_BRCA_US_ICGC.p
index 0450b94f8c0b1f9678924fbf151aadf47299769f..0f946fc869afe09b2adbb7b041ae3782af69ea69 100644
GIT binary patch
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diff --git a/models/MutModel_SKCM-US_ICGC.p b/models/MutModel_SKCM-US_ICGC.p
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diff --git a/models/errorModel_pacbio_toy.p b/models/errorModel_pacbio_toy.p
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diff --git a/models/errorModel_toy.p b/models/errorModel_toy.p
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diff --git a/models/fraglenModel_toy.p b/models/fraglenModel_toy.p
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diff --git a/models/gcBias_uniform.p b/models/gcBias_uniform.p
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From 194ca56cdb3cb075d72aa470f98d5a6848730b05 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 14 Oct 2020 13:22:58 -0500
Subject: [PATCH 198/323] repickled the files

---
 models/MutModel_BRCA_US_ICGC.p | Bin 404224 -> 193920 bytes
 models/MutModel_CLLE-ES_ICGC.p | Bin 2003913 -> 950391 bytes
 models/MutModel_SKCM-US_ICGC.p | Bin 993782 -> 458729 bytes
 models/errorModel_pacbio_toy.p | Bin 776 -> 426 bytes
 models/errorModel_toy.p        | Bin 7791940 -> 3282719 bytes
 models/fraglenModel_toy.p      | Bin 23396 -> 8991 bytes
 models/gcBias_toy.p            | Bin 1355 -> 585 bytes
 models/gcBias_uniform.p        | Bin 569 -> 585 bytes
 8 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/models/MutModel_BRCA_US_ICGC.p b/models/MutModel_BRCA_US_ICGC.p
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diff --git a/models/MutModel_SKCM-US_ICGC.p b/models/MutModel_SKCM-US_ICGC.p
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diff --git a/models/errorModel_pacbio_toy.p b/models/errorModel_pacbio_toy.p
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diff --git a/models/errorModel_toy.p b/models/errorModel_toy.p
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diff --git a/models/fraglenModel_toy.p b/models/fraglenModel_toy.p
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diff --git a/models/gcBias_toy.p b/models/gcBias_toy.p
index f4630a513136a66e36752f57c564846909793a5c..8a36dc60fcc4e1dbc145d180638952709f393c40 100644
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V&l@pG>01L8M(cijzVDah=Rb>89U1@t

diff --git a/models/gcBias_uniform.p b/models/gcBias_uniform.p
index 52c5b860179d5f35a4df049e3111779ce6b8b1d7..547084a0173b2855baddad2cbdb8c18fdb4d09de 100644
GIT binary patch
literal 585
zcmeIu$8ACZ00dETGTCHfGMJnSFh>rr29CcE7Xf{`1iqjOBvy0S+eq`5y#2-^k6o_w
z5D`%^aS2H&X&G5LAMy%{O3Es#KGoDUG_|yKeChhu(>E|QGBz<a^J8vdX=QC=YiIA^
U7=)(t>HjHypRmJAM+k!L9X{}6aR2}S

literal 569
zcmeIuyA6Xt6hzT{D<MM6yoWyxSfUJa0{V|UcPnrK4lW{?gV<fJdmGDjYaH7+4<)Bc
zo%Zxd&-6;~q&Z|MreZ6`VlC!kFMtq6czzpUgb_v<VT2Jz6rvC{e+N;BLKLD9g(yTL
g8qtWJCqOi!5shd>Bld_hNB8(>&HMlP`wg7y7anDL)Bpeg


From c9eab808c254ae98da481bc98f9050e8a81c46d7 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 14 Oct 2020 13:25:14 -0500
Subject: [PATCH 199/323] Another attempt at repickling

---
 models/MutModel_BRCA_US_ICGC.p | Bin 193920 -> 193920 bytes
 models/MutModel_CLLE-ES_ICGC.p | Bin 950391 -> 950391 bytes
 models/MutModel_SKCM-US_ICGC.p | Bin 458729 -> 458729 bytes
 models/errorModel_pacbio_toy.p | Bin 426 -> 426 bytes
 models/errorModel_toy.p        | Bin 3282719 -> 3282719 bytes
 models/fraglenModel_toy.p      | Bin 8991 -> 8991 bytes
 models/gcBias_toy.p            | Bin 585 -> 585 bytes
 models/gcBias_uniform.p        | Bin 585 -> 585 bytes
 utilities/repickle.py          |   2 +-
 9 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/models/MutModel_BRCA_US_ICGC.p b/models/MutModel_BRCA_US_ICGC.p
index f86c49a1ee8298ead1959c0cb7c5e5353c55c750..0f946fc869afe09b2adbb7b041ae3782af69ea69 100644
GIT binary patch
delta 19
acmZp8&E4>to2h}Nk*Sr5aVry3-B$ocSq7>A

delta 19
acmZp8&E4>to2h}dk*Sr5aVry3-B$ocWCp7M

diff --git a/models/MutModel_CLLE-ES_ICGC.p b/models/MutModel_CLLE-ES_ICGC.p
index d2cf0ec744967017b2357d1fa12aea1ddd5e5f01..b9dc37fcf0b25a41957a44606ad80cb2a13994f4 100644
GIT binary patch
delta 55
zcmWN>u@QhE5CFmP1Vu#)qA&6%xRR`mR5#ceZAK%~Ko<F5B}tY|b}4emDVJ2aU!EQx
Dp{@;F

delta 55
zcmWN>yA^;S5CFmFD*iwVqA&6@*pjG?R3?}abwnl7hb%H*Ws@Yk9CFGfw`3{ublw&}
Dq1_E#

diff --git a/models/MutModel_SKCM-US_ICGC.p b/models/MutModel_SKCM-US_ICGC.p
index f05e98de1f53a2ced826cf43f4b4b34f0b0177db..9bcac55609000d0c59526b7505a952323f3fb153 100644
GIT binary patch
delta 31
lcmaF)U;5>LX{H92My6IK##SaE$=u4s(#piTm5J^7e*m}|3S|HQ

delta 31
lcmaF)U;5>LX{H9&My6IK##SaE$=u4s(#piTm5J^7e*m~K3T6NR

diff --git a/models/errorModel_pacbio_toy.p b/models/errorModel_pacbio_toy.p
index 38f61416163af0aff94b6e02174d3100ac82eeb3..ba376c09cb542c9233b2c608682fd3f7624ef12e 100644
GIT binary patch
delta 11
ScmZ3*yo#Brfn_7p3Pu1F#sfV7

delta 11
ScmZ3*yo#BrfpsI(3Pu1F%L6_D

diff --git a/models/errorModel_toy.p b/models/errorModel_toy.p
index 02304514b7fa34c9cc642e431bc23cc8c24ae18f..5975f30bba48356a09036cd28c5bc56a7b22d7cc 100644
GIT binary patch
delta 165
zcmWN>#Sy{)006<s5!^LcAh>IV@VGkC0Y7@6Eb`b*KffT&@Cx5ZAlW$yB`*ajN=eF6
zk*d_BE)8i)OWM+ruJoiY1BqlPBN@v?rV`6c=90)lma>wyY-B4t*~>wWa+0%L<SIA0
J%j3&4j{aWxB=Z0O

delta 165
zcmWN>#Sy{)006<sL2$QVf#9wc!sF^l2mI)PvdCjM{rrM7!z+9vfn?_-FQF8qC?zRN
zMXFMhx-_IIEon<fy3&)r3?!1FjASemnMy1(nM)!IS;|V*vXQOqWG@Ff%1O?0k*nO~
KE{`wIIQj!$lqB>3

diff --git a/models/fraglenModel_toy.p b/models/fraglenModel_toy.p
index 8db64cfe5eebef3f55defca3a4c7e3fbcaffcc95..02883c1778d29cb34fba730961d8670c5c074480 100644
GIT binary patch
delta 11
ScmbR5Hs6h@fn_6;tTF%>tOG;<

delta 11
ScmbR5Hs6h@fpsI3tTF%>u>(Z_

diff --git a/models/gcBias_toy.p b/models/gcBias_toy.p
index 8a36dc60fcc4e1dbc145d180638952709f393c40..627497d1227f8196296d5c3871357e633cc06f64 100644
GIT binary patch
delta 11
ScmX@fa*~Cqfn_6;I}-pFg99A^

delta 11
ScmX@fa*~CqfpsI3I}-pFhyxw~

diff --git a/models/gcBias_uniform.p b/models/gcBias_uniform.p
index 547084a0173b2855baddad2cbdb8c18fdb4d09de..a8ad93dc97e0859272460912c57bfe82f76f97ec 100644
GIT binary patch
delta 11
ScmX@fa*~Cqfn_6;I}-pFg99A^

delta 11
ScmX@fa*~CqfpsI3I}-pFhyxw~

diff --git a/utilities/repickle.py b/utilities/repickle.py
index f845a50..461eb06 100644
--- a/utilities/repickle.py
+++ b/utilities/repickle.py
@@ -15,7 +15,7 @@ def main():
     os.chdir(dir_to_repickle)
     for file in glob.glob("*.p"):
         data = pickle.load(open(file, 'rb'), encoding="bytes")
-        pickle.dump(data, open(file, "wb"), pickle.HIGHEST_PROTOCOL)
+        pickle.dump(data, open(file, "wb"))
 
 
 if __name__ == "__main__":

From e4971dbd3ee40595610dbce825ec71649df2958f Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 14 Oct 2020 14:36:05 -0500
Subject: [PATCH 200/323] A few small changes to make genreads more robust

---
 py/__pycache__/ref_func.cpython-37.pyc | Bin 5784 -> 5796 bytes
 py/ref_func.py                         |  14 +++++++++-----
 2 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/py/__pycache__/ref_func.cpython-37.pyc b/py/__pycache__/ref_func.cpython-37.pyc
index 7b0e10c2b5542bba36174378588a14a18808e7e6..7829ca5f55ed63678835188d8d63a2996e131078 100644
GIT binary patch
delta 2050
zcma)7U5ryj6rP#;+uQrsZo6z3S(k+cs#5=ue}OEU1!dXgPast+EYp^5moA;&gtAVl
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k{uV+BSx#6p{>lCgL!xbTDB$0ZNnC77w(K4<jvJ$Y0Y+lfIRF3v

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gkIjhLoIAU2bH8w-KuLq45d&+XxMUnr&#FUz0jKNY4*&oF

diff --git a/py/ref_func.py b/py/ref_func.py
index 7c64b6b..7791644 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -1,6 +1,7 @@
 import sys
 import time
 import os
+import gzip
 import pathlib
 import random
 from Bio.Seq import Seq
@@ -61,7 +62,10 @@ def index_ref(reference_path: str) -> list:
         return ref_indices
 
     print('Index not found, creating one... ')
-    ref_file = open(absolute_reference_location, 'r')
+    if absolute_reference_location.suffix == ".gz":
+        ref_file = gzip.open(absolute_reference_location, 'rt')
+    else:
+        ref_file = open(absolute_reference_location, 'r')
     prev_r = None
     prev_p = None
     seq_len = 0
@@ -91,11 +95,11 @@ def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
         print('reading ' + ref_inds_i[0] + '... ')
 
     absolute_reference_path = pathlib.Path(ref_path)
-    try:
+    if absolute_reference_path.suffix == '.gz':
+        ref_file = gzip.open(absolute_reference_path, 'rt')
+    else:
         ref_file = open(absolute_reference_path, 'r')
-    except IOError:
-        print('\nProblem reading reference file.\n')
-        sys.exit(1)
+
 
     # TODO convert to SeqIO containers
     ref_file.seek(ref_inds_i[1])

From b6d9b3f9bbb3e203d63936ee31a6c3332070e36e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 15 Oct 2020 14:27:33 -0500
Subject: [PATCH 201/323] Eliminated some stray code in ref_func.py

---
 py/ref_func.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/py/ref_func.py b/py/ref_func.py
index 7791644..546155c 100644
--- a/py/ref_func.py
+++ b/py/ref_func.py
@@ -10,10 +10,6 @@
 ALLOWED_NUCL = ['A', 'C', 'G', 'T']
 
 
-class Dog:
-    pass
-
-
 def index_ref(reference_path: str) -> list:
     """
     Index reference fasta

From 713669ca794821272b77fe7b304ec33ce99028c6 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 19 Oct 2020 11:31:13 -0500
Subject: [PATCH 202/323] set mutation rate to between 0 and 1 rather than 0
 and 0.3

---
 bacterial_genreads_wrapper.py          |   2 +-
 gen_reads.py                           |   2 +-
 py/__pycache__/ref_func.cpython-37.pyc | Bin 5796 -> 5650 bytes
 3 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index e251888..ea538f6 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -94,7 +94,7 @@ def sample(self, coverage_value: int, fragment_size: int, fragment_std: int):
         :return: None
         """
         args = ['-r', str(self.file), '-M', '0', '-R', '101', '-o', self.name,
-                '-c', str(coverage_value), '--pe', str(fragment_size), str(fragment_std)]
+                '-c', str(coverage_value), '--pe', str(fragment_size), str(fragment_std), '--vcf', '--bam']
 
         gen_reads.main(args)
 
diff --git a/gen_reads.py b/gen_reads.py
index 51f38c7..56223fe 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -196,7 +196,7 @@ def main(raw_args=None):
         mut_rate = None
 
     if mut_rate != -1 and mut_rate is not None:
-        is_in_range(mut_rate, 0, 0.3, 'Error: -M must be between 0 and 0.3')
+        is_in_range(mut_rate, 0.0, 1.0, 'Error: -M must be between 0 and 0.3')
 
     # sequencing error model
     if se_model is None:
diff --git a/py/__pycache__/ref_func.cpython-37.pyc b/py/__pycache__/ref_func.cpython-37.pyc
index 7829ca5f55ed63678835188d8d63a2996e131078..f163e2e1d0de671b75a0e4097130cb9c90dfe938 100644
GIT binary patch
delta 1028
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From f10d6f99923859fc3e2d78fc7c192b7d16e09ebb Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 20 Oct 2020 16:20:19 -0500
Subject: [PATCH 203/323] Fixed error with path to import probability

---
 .../SequenceContainer.cpython-37.pyc          | Bin 32723 -> 32723 bytes
 py/__pycache__/__init__.cpython-37.pyc        | Bin 146 -> 146 bytes
 py/__pycache__/neat_cigar.cpython-37.pyc      | Bin 4175 -> 4175 bytes
 py/__pycache__/probability.cpython-37.pyc     | Bin 4264 -> 4264 bytes
 py/__pycache__/ref_func.cpython-37.pyc        | Bin 5650 -> 5650 bytes
 utilities/compute_fraglen.py                  |   2 +-
 utilities/genSeqErrorModel.py                 |   6 +++---
 utilities/vcf_compare_OLD.py                  |   1 -
 8 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/py/__pycache__/SequenceContainer.cpython-37.pyc b/py/__pycache__/SequenceContainer.cpython-37.pyc
index 949a42371068e9b5a4e5adf6e2026dc7a7e88722..9fccf9830e89a9af47f2e44abf2b51ca851751d1 100644
GIT binary patch
delta 22
ccmccopYig4Ms6owUM>b8_^i^uk^4kF0A7Fx!2kdN

delta 22
ccmccopYig4Ms6owUM>b8s4{Ec$bF(709$njQUCw|

diff --git a/py/__pycache__/__init__.cpython-37.pyc b/py/__pycache__/__init__.cpython-37.pyc
index 693fb8059cd93cd4ba44f2a2db43a537eccac44d..38eacc9cb4c82242ca58224d052d210dc94e6ebf 100644
GIT binary patch
delta 19
ZcmbQlIEj(liI<m)0SG>;^iSk&2LL6b1YrOG

delta 19
ZcmbQlIEj(liI<m)0SFxa*iYnc2LL2#1U3Kw

diff --git a/py/__pycache__/neat_cigar.cpython-37.pyc b/py/__pycache__/neat_cigar.cpython-37.pyc
index 1d9329700f767335f219708097173a3aa77ba761..2848402061ee52f5d071044691e3cc8bb07e33aa 100644
GIT binary patch
delta 20
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delta 20
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index ee6ff072f3d04a8aeef5b8d697697f40c0022518..5f0a0cc2c2f740d7786b821b63ff2bb12e2166bd 100644
GIT binary patch
delta 20
acmZ3XxI&TJiI<m)0SG>;^l#*zD*ym8gas1-

delta 20
acmZ3XxI&TJiI<m)0SKzh+Bb5~6#xJ)c?6^Y

diff --git a/py/__pycache__/ref_func.cpython-37.pyc b/py/__pycache__/ref_func.cpython-37.pyc
index f163e2e1d0de671b75a0e4097130cb9c90dfe938..6967a2f79c46f00228e11d1b769ff04ca032ac1d 100644
GIT binary patch
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delta 233
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diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index 1abda2d..950f6d8 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -12,7 +12,7 @@
 import pickle
 import argparse
 import platform
-import doctest
+
 os = platform.system()
 if os !='Windows':
     import pysam
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 1dfe7fc..15d2d7f 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -16,13 +16,13 @@
 import gzip
 import numpy as np
 import argparse
+import pathlib
 import sys
 import pickle
 import matplotlib.pyplot as mpl
 
-# # absolute path to this script
-# sim_path = '/'.join(os.path.realpath(__file__).split('/')[:-2]) + '/py/'
-# sys.path.append(sim_path)
+# enables import from neighboring package
+sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
 
 from py.probability import DiscreteDistribution
 
diff --git a/utilities/vcf_compare_OLD.py b/utilities/vcf_compare_OLD.py
index d2d16e3..d3c4af4 100644
--- a/utilities/vcf_compare_OLD.py
+++ b/utilities/vcf_compare_OLD.py
@@ -24,7 +24,6 @@
 import optparse
 
 from Bio.Seq import Seq
-from Bio.Alphabet import IUPAC
 
 EV_BPRANGE = 50  # how far to either side of a particular variant location do we want to check for equivalents?
 

From 465eb3f4b3b07ad56e2153aa617d830650c1ee0a Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 20 Oct 2020 17:03:01 -0500
Subject: [PATCH 204/323] Trying a different approach in genSeqErrorModel.py

---
 gen_reads.py                                  |   1 -
 py/SequenceContainer.py                       |   4 ++--
 .../SequenceContainer.cpython-37.pyc          | Bin 32723 -> 0 bytes
 .../biopython_modified_bgzf.cpython-37.pyc    | Bin 2668 -> 0 bytes
 py/__pycache__/neat_cigar.cpython-37.pyc      | Bin 4175 -> 0 bytes
 py/__pycache__/probability.cpython-37.pyc     | Bin 4264 -> 4264 bytes
 py/__pycache__/ref_func.cpython-37.pyc        | Bin 5650 -> 0 bytes
 py/biopython_modified_bgzf.py                 |   0
 utilities/genSeqErrorModel.py                 |   5 +++--
 9 files changed, 5 insertions(+), 5 deletions(-)
 delete mode 100644 py/__pycache__/SequenceContainer.cpython-37.pyc
 delete mode 100644 py/__pycache__/biopython_modified_bgzf.cpython-37.pyc
 delete mode 100644 py/__pycache__/neat_cigar.cpython-37.pyc
 delete mode 100644 py/__pycache__/ref_func.cpython-37.pyc
 mode change 100755 => 100644 py/biopython_modified_bgzf.py

diff --git a/gen_reads.py b/gen_reads.py
index 56223fe..9065a14 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -24,7 +24,6 @@
 import numpy as np
 import argparse
 import pathlib
-from Bio import SeqIO
 
 from py.input_checking import check_file_open, is_in_range
 from py.ref_func import index_ref, read_ref
diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index aa19303..3d2b1a8 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -7,8 +7,8 @@
 import numpy as np
 from Bio.Seq import Seq
 
-from py.probability import DiscreteDistribution, poisson_list, quantize_list
-from py.neat_cigar import CigarString
+from py.probability import DiscreteDistribution, poisson_list
+from py import CigarString
 
 """
 Constants needed for analysis
diff --git a/py/__pycache__/SequenceContainer.cpython-37.pyc b/py/__pycache__/SequenceContainer.cpython-37.pyc
deleted file mode 100644
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diff --git a/py/biopython_modified_bgzf.py b/py/biopython_modified_bgzf.py
old mode 100755
new mode 100644
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 15d2d7f..4c8ae12 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -16,13 +16,14 @@
 import gzip
 import numpy as np
 import argparse
-import pathlib
 import sys
+import pathlib
 import pickle
 import matplotlib.pyplot as mpl
 
 # enables import from neighboring package
-sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
+print(pathlib.Path(__file__).resolve().parents[1])
+sys.path.append(pathlib.Path(__file__).resolve().parents[1])
 
 from py.probability import DiscreteDistribution
 

From 95cc09f7969f88cfed36e73703daf66b0c617f7a Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 20 Oct 2020 17:33:35 -0500
Subject: [PATCH 205/323] May have finally fixed import issue

---
 utilities/genSeqErrorModel.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 4c8ae12..7b37331 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -22,12 +22,14 @@
 import matplotlib.pyplot as mpl
 
 # enables import from neighboring package
-print(pathlib.Path(__file__).resolve().parents[1])
-sys.path.append(pathlib.Path(__file__).resolve().parents[1])
+# print(pathlib.Path(__file__).resolve().parents[1] / 'py')
+sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
+print(sys.path)
 
 from py.probability import DiscreteDistribution
 
 
+
 def parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff):
     init_smooth = 0.
     prob_smooth = 0.

From 63e24fa8f6071dcdb3913808ac959e275cb4f93e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 20 Oct 2020 17:44:43 -0500
Subject: [PATCH 206/323] May have finally fixed import issue

---
 utilities/genSeqErrorModel.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 7b37331..a69986d 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -17,14 +17,12 @@
 import numpy as np
 import argparse
 import sys
-import pathlib
 import pickle
 import matplotlib.pyplot as mpl
 
 # enables import from neighboring package
 # print(pathlib.Path(__file__).resolve().parents[1] / 'py')
-sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
-print(sys.path)
+sys.path.append("..")
 
 from py.probability import DiscreteDistribution
 

From b4138c6100cc30af8c5ef5637e8b21171ab4e685 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 20 Oct 2020 17:55:42 -0500
Subject: [PATCH 207/323] Trying yet another fix

---
 py/__init__.py                         |   0
 py/__pycache__/__init__.cpython-37.pyc | Bin 146 -> 0 bytes
 utilities/__init__.py                  |   0
 3 files changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 py/__init__.py
 delete mode 100644 py/__pycache__/__init__.cpython-37.pyc
 delete mode 100644 utilities/__init__.py

diff --git a/py/__init__.py b/py/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/py/__pycache__/__init__.cpython-37.pyc b/py/__pycache__/__init__.cpython-37.pyc
deleted file mode 100644
index 38eacc9cb4c82242ca58224d052d210dc94e6ebf..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 146
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T!zMRBr8Fni4rJ(OAZ7pnPSGTQ

diff --git a/utilities/__init__.py b/utilities/__init__.py
deleted file mode 100644
index e69de29..0000000

From 78754d1360e3d159361b061ba01f35310242293d Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 20 Oct 2020 19:21:35 -0500
Subject: [PATCH 208/323] Trying yet another fix, again

---
 utilities/genSeqErrorModel.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index a69986d..aa8f197 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -21,10 +21,10 @@
 import matplotlib.pyplot as mpl
 
 # enables import from neighboring package
-# print(pathlib.Path(__file__).resolve().parents[1] / 'py')
-sys.path.append("..")
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
 
 from py.probability import DiscreteDistribution
+print(DiscreteDistribution)
 
 
 

From 8bac60857cb80c84d71328165d490178fd823f80 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 20 Oct 2020 19:35:41 -0500
Subject: [PATCH 209/323] Trying yet another fix, again, again

---
 py/SequenceContainer.py       | 2 +-
 utilities/genSeqErrorModel.py | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 3d2b1a8..2ae0714 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -8,7 +8,7 @@
 from Bio.Seq import Seq
 
 from py.probability import DiscreteDistribution, poisson_list
-from py import CigarString
+from py.neat_cigar import CigarString
 
 """
 Constants needed for analysis
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index aa8f197..225b8f5 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -22,6 +22,7 @@
 
 # enables import from neighboring package
 sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+print("path {}".format(os.path.dirname(os.path.abspath(__file__))))
 
 from py.probability import DiscreteDistribution
 print(DiscreteDistribution)

From e633979603a4d246aa187e2b91819bcb7242e3b2 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 20 Oct 2020 19:55:25 -0500
Subject: [PATCH 210/323] Another another attempt to resolve import issue

---
 py/__pycache__/probability.cpython-37.pyc | Bin 4264 -> 0 bytes
 utilities/genSeqErrorModel.py             |   6 +++---
 2 files changed, 3 insertions(+), 3 deletions(-)
 delete mode 100644 py/__pycache__/probability.cpython-37.pyc

diff --git a/py/__pycache__/probability.cpython-37.pyc b/py/__pycache__/probability.cpython-37.pyc
deleted file mode 100644
index dff090d204ac2287ef5662123b1a1c2971cd1323..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 225b8f5..0c3bd15 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -17,18 +17,18 @@
 import numpy as np
 import argparse
 import sys
+import pathlib
 import pickle
 import matplotlib.pyplot as mpl
 
 # enables import from neighboring package
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-print("path {}".format(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
+print("path {}".format(str(pathlib.Path(__file__).resolve().parents[1])))
 
 from py.probability import DiscreteDistribution
 print(DiscreteDistribution)
 
 
-
 def parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff):
     init_smooth = 0.
     prob_smooth = 0.

From e8234ff01e51a15527804b2b16652d513a91ce1b Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 21 Oct 2020 11:02:29 -0500
Subject: [PATCH 211/323] Updated genSeqErrorModel.py to fix import

---
 utilities/genSeqErrorModel.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 0c3bd15..f62965a 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -23,10 +23,8 @@
 
 # enables import from neighboring package
 sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
-print("path {}".format(str(pathlib.Path(__file__).resolve().parents[1])))
 
 from py.probability import DiscreteDistribution
-print(DiscreteDistribution)
 
 
 def parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff):

From f5d6a02bbf191c36350ec328440ab8f8e9cf43b9 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 14 Oct 2020 12:33:03 -0500
Subject: [PATCH 212/323] A couple of small changes to some files

---
 py/SequenceContainer.py                |   2 +-
 py/__pycache__/__init__.cpython-37.pyc | Bin 0 -> 146 bytes
 py/__pycache__/ref_func.cpython-37.pyc | Bin 0 -> 4769 bytes
 3 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 py/__pycache__/__init__.cpython-37.pyc
 create mode 100644 py/__pycache__/ref_func.cpython-37.pyc

diff --git a/py/SequenceContainer.py b/py/SequenceContainer.py
index 2ae0714..4119064 100644
--- a/py/SequenceContainer.py
+++ b/py/SequenceContainer.py
@@ -596,7 +596,7 @@ def random_mutations(self):
                     sys.exit(1)
                 else:
                     # alter reference sequence
-                    self.sequences[i] = self.sequences[i][:v_pos] + Seq(all_indels_ins[i][j][2],).tomutable() + \
+                    self.sequences[i] = self.sequences[i][:v_pos] + Seq(all_indels_ins[i][j][2]).tomutable() + \
                                         self.sequences[i][v_pos2:]
                     # notate indel positions for cigar computation
                     d = len(all_indels_ins[i][j][2]) - len(all_indels_ins[i][j][1])
diff --git a/py/__pycache__/__init__.cpython-37.pyc b/py/__pycache__/__init__.cpython-37.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..693fb8059cd93cd4ba44f2a2db43a537eccac44d
GIT binary patch
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From 3ea84e66b18d655729099704d16fe766f71a7426 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 27 Oct 2020 13:16:01 -0500
Subject: [PATCH 213/323] Update README.md

---
 README.md | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/README.md b/README.md
index 6668a5e..891d36b 100644
--- a/README.md
+++ b/README.md
@@ -3,9 +3,6 @@ NEAT-genReads is a fine-grained read simulator. GenReads simulates real-looking
 
 This is an in-progress v2.0 of the software. For a previous stable release please see: [genReads1](https://github.com/zstephens/genReads1)
 
-
-***PLEASE NOTE: The module "pysam" does not work on Windows OS. If you are using a windows system and wish to input a BAM file, you will first have to use samtools to convert the BAM to a SAM file.****
-
 To cite this work, please use:
 
 > Stephens, Zachary D., Matthew E. Hudson, Liudmila S. Mainzer, Morgan Taschuk, Matthew R. Weber, and Ravishankar K. Iyer. "Simulating next-generation sequencing datasets from empirical mutation and sequencing models." PloS one 11, no. 11 (2016): e0167047.

From 201eb7f865691fd1910fa817e54acb8026fbc8e1 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 27 Oct 2020 13:29:54 -0500
Subject: [PATCH 214/323] Update README.md

---
 utilities/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utilities/README.md b/utilities/README.md
index d5f425c..590bfe1 100644
--- a/utilities/README.md
+++ b/utilities/README.md
@@ -33,7 +33,7 @@ python computeGC.py			\
 
 The main function in this file will save a pickle (.p) in the location and with the name from the path the user provides with the -o command.
 
-**Please be aware that pysam is not usable on windows, so any BAM file will need to be turned into a SAM file using samtools beforehand.
+**Please be aware that pysam is not usable on Windows, so any BAM file will need to be turned into a SAM file using samtools beforehand.**
 
 To convert a BAM file to a SAM file using samtools, use the following command:
 

From 49e6574e4405272b6789f2b5ee7483fb2a36e789 Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Mon, 9 Nov 2020 10:18:10 -0600
Subject: [PATCH 215/323] Create __init__.py

---
 py/__init__.py | 7 +++++++
 1 file changed, 7 insertions(+)
 create mode 100644 py/__init__.py

diff --git a/py/__init__.py b/py/__init__.py
new file mode 100644
index 0000000..9107771
--- /dev/null
+++ b/py/__init__.py
@@ -0,0 +1,7 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov  9 09:52:13 2020
+
+@author: membry2
+"""
+

From be4351c3e032ebeffe4187b69965f62077742524 Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Mon, 9 Nov 2020 10:28:25 -0600
Subject: [PATCH 216/323] added init files

---
 py/__init__.py        | 2 +-
 utilities/__init__.py | 7 +++++++
 2 files changed, 8 insertions(+), 1 deletion(-)
 create mode 100644 utilities/__init__.py

diff --git a/py/__init__.py b/py/__init__.py
index 9107771..af1027a 100644
--- a/py/__init__.py
+++ b/py/__init__.py
@@ -2,6 +2,6 @@
 """
 Created on Mon Nov  9 09:52:13 2020
 
-@author: membry2
+@author: membry
 """
 
diff --git a/utilities/__init__.py b/utilities/__init__.py
new file mode 100644
index 0000000..1d51685
--- /dev/null
+++ b/utilities/__init__.py
@@ -0,0 +1,7 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov  9 10:27:36 2020
+
+@author: membry
+"""
+

From 6d6b0b9f9b55584c48c83dd7607610681d1bce9b Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Mon, 9 Nov 2020 10:42:17 -0600
Subject: [PATCH 217/323] Create __init__.py

---
 py/__init__.py | 7 +++++++
 1 file changed, 7 insertions(+)
 create mode 100644 py/__init__.py

diff --git a/py/__init__.py b/py/__init__.py
new file mode 100644
index 0000000..69cb74e
--- /dev/null
+++ b/py/__init__.py
@@ -0,0 +1,7 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov  9 10:41:07 2020
+
+@author: membry2
+"""
+

From 6b767418dabb7ad4e97c1e98b2a7e579a94b6629 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 9 Nov 2020 10:52:00 -0600
Subject: [PATCH 218/323] renamed folder from py to python

---
 bacterial_genreads_wrapper.py             |  2 +-
 gen_reads.py                              | 14 +++++++-------
 merge_jobs.py                             |  2 +-
 {py => python}/SequenceContainer.py       |  4 ++--
 {py => python}/__init__.py                |  0
 {py => python}/biopython_modified_bgzf.py |  0
 {py => python}/input_checking.py          |  0
 {py => python}/neat_cigar.py              |  0
 {py => python}/output_file_writer.py      |  2 +-
 {py => python}/probability.py             |  0
 {py => python}/ref_func.py                |  0
 {py => python}/vcf_func.py                |  0
 utilities/compute_fraglen.py              |  8 ++++----
 utilities/compute_gc.py                   |  8 ++++----
 utilities/genSeqErrorModel.py             | 10 +++++-----
 utilities/gen_mut_model.py                |  2 +-
 utilities/plotMutModel.py                 |  4 ++--
 utilities/validateFQ.py                   |  2 +-
 utilities/vcf_compare_OLD.py              |  2 +-
 19 files changed, 30 insertions(+), 30 deletions(-)
 rename {py => python}/SequenceContainer.py (99%)
 rename {py => python}/__init__.py (100%)
 rename {py => python}/biopython_modified_bgzf.py (100%)
 rename {py => python}/input_checking.py (100%)
 rename {py => python}/neat_cigar.py (100%)
 rename {py => python}/output_file_writer.py (99%)
 rename {py => python}/probability.py (100%)
 rename {py => python}/ref_func.py (100%)
 rename {py => python}/vcf_func.py (100%)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index ea538f6..e48f5b4 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -213,7 +213,7 @@ def extract_names(reference: str) -> list:
 
 
 def main():
-    parser = argparse.ArgumentParser(description='bacterial_genreads_wrapper.py')
+    parser = argparse.ArgumentParser(description='bacterial_genreads_wrapper.python')
     parser.add_argument('-r', type=str, required=True, metavar='reference.fasta',
                         help="Reference file for organism in fasta format")
     parser.add_argument('-g', type=int, required=True, metavar='generations', help="Number of generations to run")
diff --git a/gen_reads.py b/gen_reads.py
index 9065a14..44ed525 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -2,7 +2,7 @@
 # encoding: utf-8
 """ ////////////////////////////////////////////////////////////////////////////////
    ///                                                                          ///
-  ///       gen_reads.py                                                        ///
+  ///       gen_reads.python                                                        ///
  ///        VERSION 2.0: HARDER, BETTER, FASTER, STRONGER!                    ///
 ///////                                                                      //////
    ///      Variant and read simulator for benchmarking NGS workflows          ///
@@ -25,12 +25,12 @@
 import argparse
 import pathlib
 
-from py.input_checking import check_file_open, is_in_range
-from py.ref_func import index_ref, read_ref
-from py.vcf_func import parse_vcf
-from py.output_file_writer import OutputFileWriter, reverse_complement, sam_flag
-from py.probability import DiscreteDistribution, mean_ind_of_weighted_list
-from py.SequenceContainer import SequenceContainer, SequencingError, parse_input_mutation_model
+from python.input_checking import check_file_open, is_in_range
+from python.ref_func import index_ref, read_ref
+from python.vcf_func import parse_vcf
+from python.output_file_writer import OutputFileWriter, reverse_complement, sam_flag
+from python.probability import DiscreteDistribution, mean_ind_of_weighted_list
+from python.SequenceContainer import SequenceContainer, SequencingError, parse_input_mutation_model
 
 """
 Some constants needed for analysis
diff --git a/merge_jobs.py b/merge_jobs.py
index 32d400e..1d50734 100644
--- a/merge_jobs.py
+++ b/merge_jobs.py
@@ -71,7 +71,7 @@ def catBams(l,outName,samtools_exe):
 #####################################
 
 def main():
-	parser = argparse.ArgumentParser(description='merge_jobs.py')
+	parser = argparse.ArgumentParser(description='merge_jobs.python')
 	parser.add_argument('-i', type=str, required=True, metavar='<str>', nargs='+', help="* input prefix: [prefix_1] [prefix_2] ...")
 	parser.add_argument('-o', type=str, required=True, metavar='<str>',            help="* output prefix")
 	parser.add_argument('-s', type=str, required=True, metavar='<str>',            help="* /path/to/samtools")
diff --git a/py/SequenceContainer.py b/python/SequenceContainer.py
similarity index 99%
rename from py/SequenceContainer.py
rename to python/SequenceContainer.py
index 4119064..1f4d044 100644
--- a/py/SequenceContainer.py
+++ b/python/SequenceContainer.py
@@ -7,8 +7,8 @@
 import numpy as np
 from Bio.Seq import Seq
 
-from py.probability import DiscreteDistribution, poisson_list
-from py.neat_cigar import CigarString
+from python.probability import DiscreteDistribution, poisson_list
+from python.neat_cigar import CigarString
 
 """
 Constants needed for analysis
diff --git a/py/__init__.py b/python/__init__.py
similarity index 100%
rename from py/__init__.py
rename to python/__init__.py
diff --git a/py/biopython_modified_bgzf.py b/python/biopython_modified_bgzf.py
similarity index 100%
rename from py/biopython_modified_bgzf.py
rename to python/biopython_modified_bgzf.py
diff --git a/py/input_checking.py b/python/input_checking.py
similarity index 100%
rename from py/input_checking.py
rename to python/input_checking.py
diff --git a/py/neat_cigar.py b/python/neat_cigar.py
similarity index 100%
rename from py/neat_cigar.py
rename to python/neat_cigar.py
diff --git a/py/output_file_writer.py b/python/output_file_writer.py
similarity index 99%
rename from py/output_file_writer.py
rename to python/output_file_writer.py
index 2d34284..0c80d9d 100644
--- a/py/output_file_writer.py
+++ b/python/output_file_writer.py
@@ -9,7 +9,7 @@
 from Bio.bgzf import *
 import pathlib
 
-# from py.biopython_modified_bgzf import BgzfWriter
+# from python.biopython_modified_bgzf import BgzfWriter
 
 BAM_COMPRESSION_LEVEL = 6
 
diff --git a/py/probability.py b/python/probability.py
similarity index 100%
rename from py/probability.py
rename to python/probability.py
diff --git a/py/ref_func.py b/python/ref_func.py
similarity index 100%
rename from py/ref_func.py
rename to python/ref_func.py
diff --git a/py/vcf_func.py b/python/vcf_func.py
similarity index 100%
rename from py/vcf_func.py
rename to python/vcf_func.py
diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index 950f6d8..f1807fe 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -1,10 +1,10 @@
 #
 #
-#      Compute Fragment Length Model for gen_reads.py
-#                  compute_fraglen.py
+#      Compute Fragment Length Model for gen_reads.python
+#                  compute_fraglen.python
 #
 #
-#      Usage: samtools view normal.bam | python compute_fraglen.py
+#      Usage: samtools view normal.bam | python compute_fraglen.python
 #
 #
 # Upgraded 5/6/2020 to match Python 3 standards and refactored for easier reading
@@ -149,7 +149,7 @@ def main():
 
     :return: None
     """
-    parser = argparse.ArgumentParser(description="compute_fraglen.py")
+    parser = argparse.ArgumentParser(description="compute_fraglen.python")
     parser.add_argument('-i', type=str, metavar="input", required=True, default=None,
                         help="Sam file input (samtools view name.bam > name.sam)")
     parser.add_argument('-o', type=str, metavar="output", required=True, default=None, help="Prefix for output")
diff --git a/utilities/compute_gc.py b/utilities/compute_gc.py
index 895b780..97b9210 100644
--- a/utilities/compute_gc.py
+++ b/utilities/compute_gc.py
@@ -1,12 +1,12 @@
 #
 #
-#   compute_gc.py
-#   Compute GC and coverage model for gen_reads.py
+#   compute_gc.python
+#   Compute GC and coverage model for gen_reads.python
 #
 #   Takes output file from bedtools genomecov to generate GC/coverage model
 #
 #   Usage: bedtools genomecov -d -ibam input.bam -g reference.fa > genomeCov.dat
-#          python compute_gc.py -r reference.fa -i genomeCov.dat -w [sliding window length] -o output_name.p
+#          python compute_gc.python -r reference.fa -i genomeCov.dat -w [sliding window length] -o output_name.p
 #
 #
 # Updated to Python 3 standards
@@ -122,7 +122,7 @@ def main():
         -w is the sliding window length. The default is 50, but you can declare any reasonable integer
     :return: None
     """
-    parser = argparse.ArgumentParser(description='compute_gc.py')
+    parser = argparse.ArgumentParser(description='compute_gc.python')
     parser.add_argument('-i', type=str, required=True, metavar='input', help="input.genomecov")
     parser.add_argument('-r', type=str, required=True, metavar='reference', help="reference.fasta")
     parser.add_argument('-o', type=str, required=True, metavar='output prefix',
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index f62965a..c1bf13f 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -2,11 +2,11 @@
 
 #
 #
-#          genSeqErrorModel.py
-#          Computes sequencing error model for gen_reads.py
+#          genSeqErrorModel.python
+#          Computes sequencing error model for gen_reads.python
 #
 #         
-#          Usage: python genSeqErrorModel.py -i input_reads.fq -o path/to/output_name.p
+#          Usage: python genSeqErrorModel.python -i input_reads.fq -o path/to/output_name.p
 #
 #
 # Python 3 ready
@@ -24,7 +24,7 @@
 # enables import from neighboring package
 sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
 
-from py.probability import DiscreteDistribution
+from python.probability import DiscreteDistribution
 
 
 def parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff):
@@ -231,7 +231,7 @@ def parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff):
 
 
 def main():
-    parser = argparse.ArgumentParser(description='genSeqErrorModel.py')
+    parser = argparse.ArgumentParser(description='genSeqErrorModel.python')
     parser.add_argument('-i', type=str, required=True, metavar='<str>', help="* input_read1.fq (.gz) / input_read1.sam")
     parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output.p")
     parser.add_argument('-i2', type=str, required=False, metavar='<str>', default=None,
diff --git a/utilities/gen_mut_model.py b/utilities/gen_mut_model.py
index 948ff18..820dc4b 100644
--- a/utilities/gen_mut_model.py
+++ b/utilities/gen_mut_model.py
@@ -52,7 +52,7 @@ def main():
     # if parsing a dbsnp vcf, and no CAF= is found in info tag, use this as default val for population freq
     VCF_DEFAULT_POP_FREQ = 0.00001
 
-    parser = argparse.ArgumentParser(description='gen_mut_model.py')
+    parser = argparse.ArgumentParser(description='gen_mut_model.python')
     parser.add_argument('-r', type=str, required=True, metavar='/path/to/reference.fasta',
                         help="Reference file for organism in fasta format")
     parser.add_argument('-m', type=str, required=True, metavar='/path/to/mutations.vcf',
diff --git a/utilities/plotMutModel.py b/utilities/plotMutModel.py
index 159b532..74cce25 100644
--- a/utilities/plotMutModel.py
+++ b/utilities/plotMutModel.py
@@ -3,7 +3,7 @@
 #
 #	a quick script for comparing mutation models
 #
-#	python plotMutModel.py -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix 
+#	python plotMutModel.python -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix
 #
 # Python 3 ready
 
@@ -20,7 +20,7 @@
 # mpl.rcParams['text.latex.preamble']=[r"\usepackage{amsmath}"]
 
 parser = argparse.ArgumentParser(
-    description='Plot and compare mutation models from gen_mut_model.py Usage: python plotMutModel.py -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix')
+    description='Plot and compare mutation models from gen_mut_model.python Usage: python plotMutModel.python -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix')
 parser.add_argument('-i', type=str, required=True, metavar='<str>', nargs='+',
                     help="* mutation_model_1.p [mutation_model_2.p] [mutation_model_3] ...")
 parser.add_argument('-l', type=str, required=True, metavar='<str>', nargs='+',
diff --git a/utilities/validateFQ.py b/utilities/validateFQ.py
index 20d0a94..e813666 100644
--- a/utilities/validateFQ.py
+++ b/utilities/validateFQ.py
@@ -3,7 +3,7 @@
 #
 #	A quickie tool for validating the correctness of a FASTQ file
 #
-#	python validateFQ.py read1.fq [read2.fq]
+#	python validateFQ.python read1.fq [read2.fq]
 #
 # Python 3 ready
 
diff --git a/utilities/vcf_compare_OLD.py b/utilities/vcf_compare_OLD.py
index d3c4af4..4af0d69 100644
--- a/utilities/vcf_compare_OLD.py
+++ b/utilities/vcf_compare_OLD.py
@@ -5,7 +5,7 @@
 
 """ **************************************************
 
-vcf_compare.py
+vcf_compare.python
 
 - compare vcf file produced by workflow to golden vcf produced by simulator
 

From 23482948042c236f57ada2c95a6b575c328bc17f Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 9 Nov 2020 14:46:50 -0600
Subject: [PATCH 219/323] Messed with files and stuff but no major changes

---
 bacterial_genreads_wrapper.py                 |  4 ++--
 gen_reads.py                                  | 16 ++++++++--------
 merge_jobs.py                                 |  4 ++--
 {python => source}/SequenceContainer.py       |  4 ++--
 {python => source}/__init__.py                |  1 +
 {python => source}/biopython_modified_bgzf.py |  4 ++--
 {python => source}/input_checking.py          |  0
 {python => source}/neat_cigar.py              |  0
 {python => source}/output_file_writer.py      |  2 +-
 {python => source}/probability.py             |  0
 {python => source}/ref_func.py                |  0
 {python => source}/vcf_func.py                |  0
 utilities/__init__.py                         |  7 -------
 utilities/compute_fraglen.py                  |  8 ++++----
 utilities/compute_gc.py                       |  8 ++++----
 utilities/genSeqErrorModel.py                 | 12 ++++++------
 utilities/gen_mut_model.py                    |  6 +++---
 utilities/plotMutModel.py                     |  6 +++---
 utilities/validateBam.py                      |  2 +-
 utilities/validateFQ.py                       |  4 ++--
 utilities/vcf_compare_OLD.py                  |  8 ++++----
 21 files changed, 45 insertions(+), 51 deletions(-)
 rename {python => source}/SequenceContainer.py (99%)
 rename {python => source}/__init__.py (73%)
 rename {python => source}/biopython_modified_bgzf.py (97%)
 rename {python => source}/input_checking.py (100%)
 rename {python => source}/neat_cigar.py (100%)
 rename {python => source}/output_file_writer.py (99%)
 rename {python => source}/probability.py (100%)
 rename {python => source}/ref_func.py (100%)
 rename {python => source}/vcf_func.py (100%)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index e48f5b4..8ec1b4c 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env source
 
 import gen_reads
 import argparse
@@ -213,7 +213,7 @@ def extract_names(reference: str) -> list:
 
 
 def main():
-    parser = argparse.ArgumentParser(description='bacterial_genreads_wrapper.python')
+    parser = argparse.ArgumentParser(description='bacterial_genreads_wrapper.source')
     parser.add_argument('-r', type=str, required=True, metavar='reference.fasta',
                         help="Reference file for organism in fasta format")
     parser.add_argument('-g', type=int, required=True, metavar='generations', help="Number of generations to run")
diff --git a/gen_reads.py b/gen_reads.py
index 44ed525..a5e1002 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -1,8 +1,8 @@
-#!/usr/bin/env python
+#!/usr/bin/env source
 # encoding: utf-8
 """ ////////////////////////////////////////////////////////////////////////////////
    ///                                                                          ///
-  ///       gen_reads.python                                                        ///
+  ///       gen_reads.source                                                        ///
  ///        VERSION 2.0: HARDER, BETTER, FASTER, STRONGER!                    ///
 ///////                                                                      //////
    ///      Variant and read simulator for benchmarking NGS workflows          ///
@@ -25,12 +25,12 @@
 import argparse
 import pathlib
 
-from python.input_checking import check_file_open, is_in_range
-from python.ref_func import index_ref, read_ref
-from python.vcf_func import parse_vcf
-from python.output_file_writer import OutputFileWriter, reverse_complement, sam_flag
-from python.probability import DiscreteDistribution, mean_ind_of_weighted_list
-from python.SequenceContainer import SequenceContainer, SequencingError, parse_input_mutation_model
+from source.input_checking import check_file_open, is_in_range
+from source.ref_func import index_ref, read_ref
+from source.vcf_func import parse_vcf
+from source.output_file_writer import OutputFileWriter, reverse_complement, sam_flag
+from source.probability import DiscreteDistribution, mean_ind_of_weighted_list
+from source.SequenceContainer import SequenceContainer, SequencingError, parse_input_mutation_model
 
 """
 Some constants needed for analysis
diff --git a/merge_jobs.py b/merge_jobs.py
index 1d50734..7b98acb 100644
--- a/merge_jobs.py
+++ b/merge_jobs.py
@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env source
 import os
 import argparse
 
@@ -71,7 +71,7 @@ def catBams(l,outName,samtools_exe):
 #####################################
 
 def main():
-	parser = argparse.ArgumentParser(description='merge_jobs.python')
+	parser = argparse.ArgumentParser(description='merge_jobs.source')
 	parser.add_argument('-i', type=str, required=True, metavar='<str>', nargs='+', help="* input prefix: [prefix_1] [prefix_2] ...")
 	parser.add_argument('-o', type=str, required=True, metavar='<str>',            help="* output prefix")
 	parser.add_argument('-s', type=str, required=True, metavar='<str>',            help="* /path/to/samtools")
diff --git a/python/SequenceContainer.py b/source/SequenceContainer.py
similarity index 99%
rename from python/SequenceContainer.py
rename to source/SequenceContainer.py
index 1f4d044..e7a4ffd 100644
--- a/python/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -7,8 +7,8 @@
 import numpy as np
 from Bio.Seq import Seq
 
-from python.probability import DiscreteDistribution, poisson_list
-from python.neat_cigar import CigarString
+from source.probability import DiscreteDistribution, poisson_list
+from source.neat_cigar import CigarString
 
 """
 Constants needed for analysis
diff --git a/python/__init__.py b/source/__init__.py
similarity index 73%
rename from python/__init__.py
rename to source/__init__.py
index 69cb74e..d42441e 100644
--- a/python/__init__.py
+++ b/source/__init__.py
@@ -5,3 +5,4 @@
 @author: membry2
 """
 
+from source.probability import *
\ No newline at end of file
diff --git a/python/biopython_modified_bgzf.py b/source/biopython_modified_bgzf.py
similarity index 97%
rename from python/biopython_modified_bgzf.py
rename to source/biopython_modified_bgzf.py
index ee701f2..d7faf90 100644
--- a/python/biopython_modified_bgzf.py
+++ b/source/biopython_modified_bgzf.py
@@ -1,4 +1,4 @@
-#!/user/env/bin python
+#!/user/env/bin source
 
 # Copyright 2010-2013 by Peter Cock.
 # All rights reserved.
@@ -11,7 +11,7 @@
 #                                                                  #######
 #    06/02/2015:                                                   #######
 #        - I picked out the bits and pieces of code needed         #######
-#          to write BAM files, removed python 3.0 compatibility    #######
+#          to write BAM files, removed source 3.0 compatibility    #######
 #                                                                  #######
 ##########################################################################
 
diff --git a/python/input_checking.py b/source/input_checking.py
similarity index 100%
rename from python/input_checking.py
rename to source/input_checking.py
diff --git a/python/neat_cigar.py b/source/neat_cigar.py
similarity index 100%
rename from python/neat_cigar.py
rename to source/neat_cigar.py
diff --git a/python/output_file_writer.py b/source/output_file_writer.py
similarity index 99%
rename from python/output_file_writer.py
rename to source/output_file_writer.py
index 0c80d9d..15192ee 100644
--- a/python/output_file_writer.py
+++ b/source/output_file_writer.py
@@ -9,7 +9,7 @@
 from Bio.bgzf import *
 import pathlib
 
-# from python.biopython_modified_bgzf import BgzfWriter
+# from source.biopython_modified_bgzf import BgzfWriter
 
 BAM_COMPRESSION_LEVEL = 6
 
diff --git a/python/probability.py b/source/probability.py
similarity index 100%
rename from python/probability.py
rename to source/probability.py
diff --git a/python/ref_func.py b/source/ref_func.py
similarity index 100%
rename from python/ref_func.py
rename to source/ref_func.py
diff --git a/python/vcf_func.py b/source/vcf_func.py
similarity index 100%
rename from python/vcf_func.py
rename to source/vcf_func.py
diff --git a/utilities/__init__.py b/utilities/__init__.py
index 1d51685..e69de29 100644
--- a/utilities/__init__.py
+++ b/utilities/__init__.py
@@ -1,7 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Nov  9 10:27:36 2020
-
-@author: membry
-"""
-
diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index f1807fe..f61ab7f 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -1,10 +1,10 @@
 #
 #
-#      Compute Fragment Length Model for gen_reads.python
-#                  compute_fraglen.python
+#      Compute Fragment Length Model for gen_reads.source
+#                  compute_fraglen.source
 #
 #
-#      Usage: samtools view normal.bam | python compute_fraglen.python
+#      Usage: samtools view normal.bam | source compute_fraglen.source
 #
 #
 # Upgraded 5/6/2020 to match Python 3 standards and refactored for easier reading
@@ -149,7 +149,7 @@ def main():
 
     :return: None
     """
-    parser = argparse.ArgumentParser(description="compute_fraglen.python")
+    parser = argparse.ArgumentParser(description="compute_fraglen.source")
     parser.add_argument('-i', type=str, metavar="input", required=True, default=None,
                         help="Sam file input (samtools view name.bam > name.sam)")
     parser.add_argument('-o', type=str, metavar="output", required=True, default=None, help="Prefix for output")
diff --git a/utilities/compute_gc.py b/utilities/compute_gc.py
index 97b9210..8f086e0 100644
--- a/utilities/compute_gc.py
+++ b/utilities/compute_gc.py
@@ -1,12 +1,12 @@
 #
 #
-#   compute_gc.python
-#   Compute GC and coverage model for gen_reads.python
+#   compute_gc.source
+#   Compute GC and coverage model for gen_reads.source
 #
 #   Takes output file from bedtools genomecov to generate GC/coverage model
 #
 #   Usage: bedtools genomecov -d -ibam input.bam -g reference.fa > genomeCov.dat
-#          python compute_gc.python -r reference.fa -i genomeCov.dat -w [sliding window length] -o output_name.p
+#          source compute_gc.source -r reference.fa -i genomeCov.dat -w [sliding window length] -o output_name.p
 #
 #
 # Updated to Python 3 standards
@@ -122,7 +122,7 @@ def main():
         -w is the sliding window length. The default is 50, but you can declare any reasonable integer
     :return: None
     """
-    parser = argparse.ArgumentParser(description='compute_gc.python')
+    parser = argparse.ArgumentParser(description='compute_gc.source')
     parser.add_argument('-i', type=str, required=True, metavar='input', help="input.genomecov")
     parser.add_argument('-r', type=str, required=True, metavar='reference', help="reference.fasta")
     parser.add_argument('-o', type=str, required=True, metavar='output prefix',
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index c1bf13f..6021cf2 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -1,12 +1,12 @@
-#!/usr/bin/env python
+#!/usr/bin/env source
 
 #
 #
-#          genSeqErrorModel.python
-#          Computes sequencing error model for gen_reads.python
+#          genSeqErrorModel.source
+#          Computes sequencing error model for gen_reads.source
 #
 #         
-#          Usage: python genSeqErrorModel.python -i input_reads.fq -o path/to/output_name.p
+#          Usage: source genSeqErrorModel.source -i input_reads.fq -o path/to/output_name.p
 #
 #
 # Python 3 ready
@@ -24,7 +24,7 @@
 # enables import from neighboring package
 sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
 
-from python.probability import DiscreteDistribution
+from source.probability import DiscreteDistribution
 
 
 def parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff):
@@ -231,7 +231,7 @@ def parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff):
 
 
 def main():
-    parser = argparse.ArgumentParser(description='genSeqErrorModel.python')
+    parser = argparse.ArgumentParser(description='genSeqErrorModel.source')
     parser.add_argument('-i', type=str, required=True, metavar='<str>', help="* input_read1.fq (.gz) / input_read1.sam")
     parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output.p")
     parser.add_argument('-i2', type=str, required=False, metavar='<str>', default=None,
diff --git a/utilities/gen_mut_model.py b/utilities/gen_mut_model.py
index 820dc4b..23d7894 100644
--- a/utilities/gen_mut_model.py
+++ b/utilities/gen_mut_model.py
@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env source
 
 # Python 3 ready
 
@@ -52,7 +52,7 @@ def main():
     # if parsing a dbsnp vcf, and no CAF= is found in info tag, use this as default val for population freq
     VCF_DEFAULT_POP_FREQ = 0.00001
 
-    parser = argparse.ArgumentParser(description='gen_mut_model.python')
+    parser = argparse.ArgumentParser(description='gen_mut_model.source')
     parser.add_argument('-r', type=str, required=True, metavar='/path/to/reference.fasta',
                         help="Reference file for organism in fasta format")
     parser.add_argument('-m', type=str, required=True, metavar='/path/to/mutations.vcf',
@@ -174,7 +174,7 @@ def main():
     matching_variants = matching_variants.rename(columns={0: "CHROM", 1: 'chr_start', 2: 'ID', 3: 'REF', 4: 'ALT',
                                                           5: 'QUAL', 6: 'FILTER', 7: 'INFO'})
 
-    # Change the indexing by -1 to match python format indexing (0-based)
+    # Change the indexing by -1 to match source format indexing (0-based)
     matching_variants['chr_start'] = matching_variants['chr_start'] - 1
     matching_variants['chr_end'] = matching_variants['chr_start']
 
diff --git a/utilities/plotMutModel.py b/utilities/plotMutModel.py
index 74cce25..0053500 100644
--- a/utilities/plotMutModel.py
+++ b/utilities/plotMutModel.py
@@ -1,9 +1,9 @@
-#!/usr/bin/env python
+#!/usr/bin/env source
 
 #
 #	a quick script for comparing mutation models
 #
-#	python plotMutModel.python -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix
+#	source plotMutModel.source -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix
 #
 # Python 3 ready
 
@@ -20,7 +20,7 @@
 # mpl.rcParams['text.latex.preamble']=[r"\usepackage{amsmath}"]
 
 parser = argparse.ArgumentParser(
-    description='Plot and compare mutation models from gen_mut_model.python Usage: python plotMutModel.python -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix')
+    description='Plot and compare mutation models from gen_mut_model.source Usage: source plotMutModel.source -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix')
 parser.add_argument('-i', type=str, required=True, metavar='<str>', nargs='+',
                     help="* mutation_model_1.p [mutation_model_2.p] [mutation_model_3] ...")
 parser.add_argument('-l', type=str, required=True, metavar='<str>', nargs='+',
diff --git a/utilities/validateBam.py b/utilities/validateBam.py
index f4d85fb..aff1ada 100644
--- a/utilities/validateBam.py
+++ b/utilities/validateBam.py
@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env source
 
 # Python 3 ready
 
diff --git a/utilities/validateFQ.py b/utilities/validateFQ.py
index e813666..3cc00ee 100644
--- a/utilities/validateFQ.py
+++ b/utilities/validateFQ.py
@@ -1,9 +1,9 @@
-#!/usr/bin/env python
+#!/usr/bin/env source
 
 #
 #	A quickie tool for validating the correctness of a FASTQ file
 #
-#	python validateFQ.python read1.fq [read2.fq]
+#	source validateFQ.source read1.fq [read2.fq]
 #
 # Python 3 ready
 
diff --git a/utilities/vcf_compare_OLD.py b/utilities/vcf_compare_OLD.py
index 4af0d69..9efa24d 100644
--- a/utilities/vcf_compare_OLD.py
+++ b/utilities/vcf_compare_OLD.py
@@ -1,11 +1,11 @@
-#!/usr/bin/env python
+#!/usr/bin/env source
 # encoding: utf-8
 
 # Python 3 ready
 
 """ **************************************************
 
-vcf_compare.python
+vcf_compare.source
 
 - compare vcf file produced by workflow to golden vcf produced by simulator
 
@@ -34,7 +34,7 @@
 DESC = """%prog: vcf comparison script."""
 VERS = 0.1
 
-PARSER = optparse.OptionParser('python %prog [options] -r <ref.fa> -g <golden.vcf> -w <workflow.vcf>', description=DESC,
+PARSER = optparse.OptionParser('source %prog [options] -r <ref.fa> -g <golden.vcf> -w <workflow.vcf>', description=DESC,
                                version="%prog v" + str(VERS))
 
 PARSER.add_option('-r', help='* Reference Fasta', dest='REFF', action='store', metavar='<ref.fa>')
@@ -291,7 +291,7 @@ def condense_by_pos(list_in):
         if c not in ind_count:
             ind_count[c] = 0
         ind_count[c] += 1
-    # non_unique_dict = {n:[] for n in sorted(ind_count.keys()) if ind_count[n] > 1}		# the python 2.7 way
+    # non_unique_dict = {n:[] for n in sorted(ind_count.keys()) if ind_count[n] > 1}		# the source 2.7 way
     non_unique_dict = {}
     for n in sorted(ind_count.keys()):
         if ind_count[n] > 1:

From 1ffdd44046fc00692fc235169978942b24779ec3 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 23 Nov 2020 15:20:42 -0600
Subject: [PATCH 220/323] working out genSeqErrorModel.py

---
 py/test_write.py              |  97 ++++++++++++++++++++
 utilities/genSeqErrorModel.py | 168 ++++++++++++++++++++++------------
 2 files changed, 205 insertions(+), 60 deletions(-)
 create mode 100644 py/test_write.py

diff --git a/py/test_write.py b/py/test_write.py
new file mode 100644
index 0000000..72a353c
--- /dev/null
+++ b/py/test_write.py
@@ -0,0 +1,97 @@
+#!/bin/bash/python
+
+import pathlib
+import gzip
+from timeit import default_timer as timer
+
+
+class OutputFileWriter1:
+    def __init__(self, out_prefix, gzipped, fasta):
+        start = timer()
+
+        if gzipped:
+            file = pathlib.Path(out_prefix + '.fasta.gz')
+        else:
+            file = pathlib.Path(out_prefix + '.fasta')
+
+        if gzipped:
+            self.file = gzip.open(file, 'wb')
+        else:
+            self.file = open(file, 'w')
+
+        fasta_path = pathlib.Path(fasta)
+
+        for i in range(100):
+            with open(fasta_path, 'r') as file:
+                for line in file:
+                    self.file.write(line)
+
+        self.file.close()
+
+        end = timer()
+
+        print("It took {} seconds!".format(end-start))
+
+
+class OutputFileWriter2:
+    def __init__(self, out_prefix, gzipped, fasta):
+        start = timer()
+
+        if gzipped:
+            self.file = pathlib.Path(out_prefix + '.fasta.gz')
+        else:
+            self.file = pathlib.Path(out_prefix + '.fasta')
+
+        lines = []
+        fasta_path = pathlib.Path(fasta)
+        for i in range(100):
+            with open(fasta_path, 'r') as file:
+                for line in file:
+                    lines.append(line)
+                if i % 10 == 0:
+                    if gzipped:
+                        with gzip.open(self.file, 'wb') as f:
+                            f.write("\n".join(lines))
+                            lines = []
+                    else:
+                        with open(self.file, 'w') as f:
+                            f.write("\n".join(lines))
+                            lines = []
+
+        end = timer()
+
+        print("It took {} seconds!".format(end - start))
+
+
+class OutputFileWriter3:
+    def __init__(self, out_prefix, gzipped, fasta):
+        start = timer()
+
+        if gzipped:
+            self.file = pathlib.Path(out_prefix + '.fasta.gz')
+        else:
+            self.file = pathlib.Path(out_prefix + '.fasta')
+
+        lines = []
+        fasta_path = pathlib.Path(fasta)
+        for i in range(100):
+            with open(fasta_path, 'r') as file:
+                for line in file:
+                    lines.append(line)
+
+        if gzipped:
+            with gzip.open(self.file, 'wb') as f:
+                f.write("\n".join(lines))
+        else:
+            with open(self.file, 'w') as f:
+                f.write("\n".join(lines))
+
+        end = timer()
+
+        print("It took {} seconds!".format(end - start))
+
+
+fasta = '/home/joshfactorial/Documents/neat_data/chr21.fasta'
+OutputFileWriter1('test1', False, fasta)
+OutputFileWriter2('test2', False, fasta)
+# OutputFileWriter3('test3', False, fasta)
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index f62965a..4024d22 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -20,6 +20,7 @@
 import pathlib
 import pickle
 import matplotlib.pyplot as mpl
+import pysam
 
 # enables import from neighboring package
 sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
@@ -27,80 +28,127 @@
 from py.probability import DiscreteDistribution
 
 
-def parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff):
+def parse_fq(input_file, real_q, off_q, max_reads, n_samp, plot_stuff):
     init_smooth = 0.
     prob_smooth = 0.
     print_every = 10000
 
     #Takes a gzip or sam file and returns the simulation's average error rate,
-    print('reading ' + inf + '...')
-    if inf[-3:] == '.gz':
-        print('detected gzip suffix...')
-        f = gzip.open(inf, 'r')
-    else:
-        f = open(inf, 'r')
-
+    print('reading ' + input_file + '...')
     is_sam = False
-    if inf[-4:] == '.sam':
-        print('detected sam input...')
-        is_sam = True
+    is_bam = False
+    is_fq = False
+    try:
+        if input_file[-3:] == '.gz':
+            print('detected gzip suffix...')
+            f = gzip.open(input_file, 'r')
+            is_fq = True
+        elif input_file[-4:] == '.bam':
+            print('detected bam file....')
+            f = pysam.AlignmentFile(input_file, 'rb')
+            is_bam = True
+        elif input_file[-4:] == '.sam':
+            print('detected sam input....')
+            f = open(input_file, 'r')
+            is_sam = True
+        else:
+            f = open(input_file, 'r')
+            is_fq = True
+    except FileNotFoundError:
+        print("Something wrong with input file.")
+        sys.exit(1)
 
     r_read = 0
     actual_readlen = 0
     q_dict = {}
-    while True:
+    if is_bam:
+        for read in f.fetch():
+            data4 = read
+            data4 = data4.query_alignment_sequence
+            if actual_readlen == 0:
+                actual_readlen = len(data4) - 1
+                print('assuming read length is uniform...')
+                print('detected read length (from first read found):', actual_readlen)
+                prior_q = np.zeros([actual_readlen, real_q])
+                total_q = [None] + [np.zeros([real_q, real_q]) for n in range(actual_readlen - 1)]
+
+            # sanity-check readlengths
+            if len(data4) - 1 != actual_readlen:
+                print('skipping read with unexpected length...')
+                continue
 
-        if is_sam:
-            data4 = f.readline()
-            if not len(data4):
-                break
-            try:
-                data4 = data4.split('\t')[10]
-            except IndexError:
+            for i in range(len(data4) - 1):
+                q = data4[i] - off_q
+                q_dict[q] = True
+                prev_q = q
+                if i == 0:
+                    prior_q[i][q] += 1
+                else:
+                    total_q[i][prev_q, q] += 1
+                    prior_q[i][q] += 1
+
+            r_read += 1
+            if r_read % print_every == 0:
+                print(r_read)
+            if max_reads > 0 and r_read >= max_reads:
                 break
-        # need to add some input checking here? Yup, probably.
-        else:
-            data1 = f.readline()
-            data2 = f.readline()
-            data3 = f.readline()
-            data4 = f.readline()
-            if not all([data1, data2, data3, data4]):
-                break
-
-        if actual_readlen == 0:
-            if inf[-3:] != '.gz' and not is_sam:
-                total_size = os.path.getsize(inf)
-                entry_size = sum([len(n) for n in [data1, data2, data3, data4]])
-                print('estimated number of reads in file:', int(float(total_size) / entry_size))
-            actual_readlen = len(data4) - 1
-            print('assuming read length is uniform...')
-            print('detected read length (from first read found):', actual_readlen)
-            prior_q = np.zeros([actual_readlen, real_q])
-            total_q = [None] + [np.zeros([real_q, real_q]) for n in range(actual_readlen - 1)]
-
-        # sanity-check readlengths
-        if len(data4) - 1 != actual_readlen:
-            print('skipping read with unexpected length...')
-            continue
-
-        for i in range(len(data4) - 1):
-            q = data4[i] - off_q
-            q_dict[q] = True
-            prev_q = q
-            if i == 0:
-                prior_q[i][q] += 1
-            else:
-                total_q[i][prev_q, q] += 1
-                prior_q[i][q] += 1
-
-
-        r_read += 1
-        if r_read % print_every == 0:
-            print(r_read)
-        if max_reads > 0 and r_read >= max_reads:
-            break
     f.close()
 
+    # while True:
+    #     if is_bam:
+    #         for read in f.fetch():
+    #             print(read)
+    #     elif is_sam:
+    #         data4 = f.readline()
+    #         if not len(data4):
+    #             break
+    #         try:
+    #             data4 = data4.split('\t')[10]
+    #         except IndexError:
+    #             break
+    #     # need to add some input checking here? Yup, probably.
+    #     else:
+    #         data1 = f.readline()
+    #         data2 = f.readline()
+    #         data3 = f.readline()
+    #         data4 = f.readline()
+    #         if not all([data1, data2, data3, data4]):
+    #             break
+    #
+    #     if actual_readlen == 0:
+    #         if is_fq:
+    #             total_size = os.path.getsize(input_file)
+    #             entry_size = sum([len(n) for n in [data1, data2, data3, data4]])
+    #             print('estimated number of reads in file:', int(float(total_size) / entry_size))
+    #         actual_readlen = len(data4) - 1
+    #         print('assuming read length is uniform...')
+    #         print('detected read length (from first read found):', actual_readlen)
+    #         prior_q = np.zeros([actual_readlen, real_q])
+    #         total_q = [None] + [np.zeros([real_q, real_q]) for n in range(actual_readlen - 1)]
+    #
+    #     # sanity-check readlengths
+    #     if len(data4) - 1 != actual_readlen:
+    #         print('skipping read with unexpected length...')
+    #         continue
+    #
+    #     for i in range(len(data4) - 1):
+    #         q = data4[i] - off_q
+    #         q_dict[q] = True
+    #         prev_q = q
+    #         if i == 0:
+    #             prior_q[i][q] += 1
+    #         else:
+    #             total_q[i][prev_q, q] += 1
+    #             prior_q[i][q] += 1
+    #
+    #
+    #     r_read += 1
+    #     if r_read % print_every == 0:
+    #         print(r_read)
+    #     if max_reads > 0 and r_read >= max_reads:
+    #         break
+    # f.close()
+
     # some sanity checking again...
     q_range = [min(q_dict.keys()), max(q_dict.keys())]
     if q_range[0] < 0:

From 0594d2ab58a6ca2ed3f334f6b5d9c3aeab44fae9 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 24 Nov 2020 09:43:16 -0600
Subject: [PATCH 221/323] Reworked genSeqErrorModel.py

---
 .gitignore                    |   3 +-
 utilities/genSeqErrorModel.py | 165 ++++++++++++----------------------
 2 files changed, 60 insertions(+), 108 deletions(-)

diff --git a/.gitignore b/.gitignore
index 1377b78..15d3cec 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 # Ignore filetypes
-*.pyc
\ No newline at end of file
+*.pyc
+/python2env/
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 8e80af5..50f69f7 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -21,6 +21,7 @@
 import pickle
 import matplotlib.pyplot as mpl
 import pysam
+from functools import reduce
 
 # enables import from neighboring package
 sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
@@ -31,123 +32,72 @@
 def parse_fq(input_file, real_q, off_q, max_reads, n_samp, plot_stuff):
     init_smooth = 0.
     prob_smooth = 0.
-    print_every = 10000
 
     #Takes a gzip or sam file and returns the simulation's average error rate,
     print('reading ' + input_file + '...')
-    is_sam = False
-    is_bam = False
-    is_fq = False
+    is_aligned = False
+    lines_to_read = 0
     try:
-        if input_file[-3:] == '.gz':
-            print('detected gzip suffix...')
-            f = gzip.open(input_file, 'r')
-            is_fq = True
-        elif input_file[-4:] == '.bam':
-            print('detected bam file....')
-            f = pysam.AlignmentFile(input_file, 'rb')
-            is_bam = True
-        elif input_file[-4:] == '.sam':
-            print('detected sam input....')
-            f = open(input_file, 'r')
-            is_sam = True
+        if input_file[-4:] == '.bam' or input_file[-4:] == '.sam':
+            print('detected aligned file....')
+            stats = pysam.idxstats(input_file).strip().split('\n')
+            lines_to_read = reduce(lambda x, y: x + y, [eval('+'.join(l.rstrip('\n').split('\t')[2:])) for l in stats])
+            f = pysam.AlignmentFile(input_file)
+            is_aligned = True
         else:
-            f = open(input_file, 'r')
-            is_fq = True
+            print('detected fastq file....')
+            with pysam.FastxFile(input_file) as f:
+                for _ in f:
+                    lines_to_read += 1
+            f = pysam.FastxFile(input_file)
     except FileNotFoundError:
-        print("Something wrong with input file.")
+        print("Check input file. Must be fastq, gzipped fastq, or bam/sam file.")
         sys.exit(1)
 
-    r_read = 0
     actual_readlen = 0
     q_dict = {}
-    if is_bam:
-        for read in f.fetch():
-            data4 = read
-            data4 = data4.query_alignment_sequence
-            if actual_readlen == 0:
-                actual_readlen = len(data4) - 1
-                print('assuming read length is uniform...')
-                print('detected read length (from first read found):', actual_readlen)
-                prior_q = np.zeros([actual_readlen, real_q])
-                total_q = [None] + [np.zeros([real_q, real_q]) for n in range(actual_readlen - 1)]
-
-            # sanity-check readlengths
-            if len(data4) - 1 != actual_readlen:
-                print('skipping read with unexpected length...')
-                continue
+    current_line = 0
+    quarters = lines_to_read // 4
 
-            for i in range(len(data4) - 1):
-                q = data4[i] - off_q
-                q_dict[q] = True
-                prev_q = q
-                if i == 0:
-                    prior_q[i][q] += 1
-                else:
-                    total_q[i][prev_q, q] += 1
-                    prior_q[i][q] += 1
-
-            r_read += 1
-            if r_read % print_every == 0:
-                print(r_read)
-            if max_reads > 0 and r_read >= max_reads:
-                break
-    f.close()
+    if is_aligned:
+        g = f.fetch()
+    else:
+        g = f
 
-    # while True:
-    #     if is_bam:
-    #         for read in f.fetch():
-    #             print(read)
-    #     elif is_sam:
-    #         data4 = f.readline()
-    #         if not len(data4):
-    #             break
-    #         try:
-    #             data4 = data4.split('\t')[10]
-    #         except IndexError:
-    #             break
-    #     # need to add some input checking here? Yup, probably.
-    #     else:
-    #         data1 = f.readline()
-    #         data2 = f.readline()
-    #         data3 = f.readline()
-    #         data4 = f.readline()
-    #         if not all([data1, data2, data3, data4]):
-    #             break
-    #
-    #     if actual_readlen == 0:
-    #         if is_fq:
-    #             total_size = os.path.getsize(input_file)
-    #             entry_size = sum([len(n) for n in [data1, data2, data3, data4]])
-    #             print('estimated number of reads in file:', int(float(total_size) / entry_size))
-    #         actual_readlen = len(data4) - 1
-    #         print('assuming read length is uniform...')
-    #         print('detected read length (from first read found):', actual_readlen)
-    #         prior_q = np.zeros([actual_readlen, real_q])
-    #         total_q = [None] + [np.zeros([real_q, real_q]) for n in range(actual_readlen - 1)]
-    #
-    #     # sanity-check readlengths
-    #     if len(data4) - 1 != actual_readlen:
-    #         print('skipping read with unexpected length...')
-    #         continue
-    #
-    #     for i in range(len(data4) - 1):
-    #         q = data4[i] - off_q
-    #         q_dict[q] = True
-    #         prev_q = q
-    #         if i == 0:
-    #             prior_q[i][q] += 1
-    #         else:
-    #             total_q[i][prev_q, q] += 1
-    #             prior_q[i][q] += 1
-    #
-    #
-    #     r_read += 1
-    #     if r_read % print_every == 0:
-    #         print(r_read)
-    #     if max_reads > 0 and r_read >= max_reads:
-    #         break
-    # f.close()
+    for read in g:
+        if is_aligned:
+            qualities_to_check = read.query_alignment_qualities
+        else:
+            qualities_to_check = read.get_quality_array()
+        if actual_readlen == 0:
+            actual_readlen = len(qualities_to_check) - 1
+            print('assuming read length is uniform...')
+            print('detected read length (from first read found):', actual_readlen)
+            prior_q = np.zeros([actual_readlen, real_q])
+            total_q = [None] + [np.zeros([real_q, real_q]) for n in range(actual_readlen - 1)]
+
+        # sanity-check readlengths
+        if len(qualities_to_check) - 1 != actual_readlen:
+            print('skipping read with unexpected length...')
+            continue
+
+        for i in range(actual_readlen):
+            q = qualities_to_check[i]
+            q_dict[q] = True
+            prev_q = q
+            if i == 0:
+                prior_q[i][q] += 1
+            else:
+                total_q[i][prev_q, q] += 1
+                prior_q[i][q] += 1
+
+        current_line += 1
+        if current_line % quarters == 0:
+            print(f'{(current_line/lines_to_read)*100:.0f}%')
+        if max_reads > 0 and current_line >= max_reads:
+            break
+
+    f.close()
 
     # some sanity checking again...
     q_range = [min(q_dict.keys()), max(q_dict.keys())]
@@ -258,9 +208,10 @@ def parse_fq(input_file, real_q, off_q, max_reads, n_samp, plot_stuff):
     count_dict = {}
     for q in q_scores:
         count_dict[q] = 0
+    samp_quarters = len(range(1, n_samp + 1)) // 4
     for samp in range(1, n_samp + 1):
-        if samp % print_every == 0:
-            print(samp)
+        if samp % samp_quarters == 0:
+            print(f'{(samp/samp_quarters)*100:.0f}%')
         my_q = init_dist_by_pos[0].sample()
         count_dict[my_q] += 1
         for i in range(1, len(init_q)):

From bd0bf2fca4b4dba5c67fd8a4e79943cf568314d3 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 4 Dec 2020 16:23:02 -0600
Subject: [PATCH 222/323] Minor changes

---
 .gitignore                        | 1 +
 source/SequenceContainer.py       | 1 +
 source/biopython_modified_bgzf.py | 1 +
 source/output_file_writer.py      | 5 ++++-
 source/test_write.py              | 1 +
 utilities/compute_gc.py           | 2 +-
 utilities/genSeqErrorModel.py     | 7 ++-----
 7 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/.gitignore b/.gitignore
index 15d3cec..de8c783 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
 # Ignore filetypes
 *.pyc
 /python2env/
+/.ipynb_checkpoints/
\ No newline at end of file
diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index e7a4ffd..43eb41a 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -10,6 +10,7 @@
 from source.probability import DiscreteDistribution, poisson_list
 from source.neat_cigar import CigarString
 
+
 """
 Constants needed for analysis
 """
diff --git a/source/biopython_modified_bgzf.py b/source/biopython_modified_bgzf.py
index d7faf90..f3fc6ac 100644
--- a/source/biopython_modified_bgzf.py
+++ b/source/biopython_modified_bgzf.py
@@ -29,6 +29,7 @@ def __init__(self, filename=None, mode="w", file_obj=None, compress_level=6):
             if "w" not in mode.lower() and "a" not in mode.lower():
                 raise ValueError("Must use write or append mode, not %r" % mode)
             if "a" in mode.lower():
+                raise NotImplementedError("Append mode is not implemented yet")
                 handle = open(filename, "ab")
             else:
                 handle = open(filename, "wb")
diff --git a/source/output_file_writer.py b/source/output_file_writer.py
index 15192ee..de8a5c8 100644
--- a/source/output_file_writer.py
+++ b/source/output_file_writer.py
@@ -85,7 +85,8 @@ def sam_flag(string_list: list) -> int:
 SEQ_PACKED = {'=': 0, 'A': 1, 'C': 2, 'M': 3, 'G': 4, 'R': 5, 'S': 6, 'V': 7,
               'T': 8, 'W': 9, 'Y': 10, 'H': 11, 'K': 12, 'D': 13, 'B': 14, 'N': 15}
 
-BUFFER_BATCH_SIZE = 1000  # write out to file after this many reads
+# TODO figure out an optimum batch size
+BUFFER_BATCH_SIZE = 8000  # write out to file after this many reads
 
 
 # TODO find a better way to write output files
@@ -104,6 +105,8 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
         bam = pathlib.Path(out_prefix + '_golden.bam')
         vcf = pathlib.Path(out_prefix + '_golden.vcf.gz')
 
+        print(fq1)
+        print(fq2)
         # TODO Make a fasta-specific method
         self.no_fastq = no_fastq
         if not self.no_fastq:
diff --git a/source/test_write.py b/source/test_write.py
index 72a353c..aaabdf7 100644
--- a/source/test_write.py
+++ b/source/test_write.py
@@ -8,6 +8,7 @@
 class OutputFileWriter1:
     def __init__(self, out_prefix, gzipped, fasta):
         start = timer()
+        self.buffer = []
 
         if gzipped:
             file = pathlib.Path(out_prefix + '.fasta.gz')
diff --git a/utilities/compute_gc.py b/utilities/compute_gc.py
index 8f086e0..d2ef0bc 100644
--- a/utilities/compute_gc.py
+++ b/utilities/compute_gc.py
@@ -157,4 +157,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 50f69f7..109b598 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -12,19 +12,16 @@
 # Python 3 ready
 
 
-import os
-import gzip
 import numpy as np
 import argparse
 import sys
-import pathlib
 import pickle
 import matplotlib.pyplot as mpl
 import pysam
 from functools import reduce
 
 # enables import from neighboring package
-sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
+# sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
 
 from source.probability import DiscreteDistribution
 
@@ -94,7 +91,7 @@ def parse_fq(input_file, real_q, off_q, max_reads, n_samp, plot_stuff):
         current_line += 1
         if current_line % quarters == 0:
             print(f'{(current_line/lines_to_read)*100:.0f}%')
-        if max_reads > 0 and current_line >= max_reads:
+        if 0 < max_reads <= current_line:
             break
 
     f.close()

From d217b49454d9affefbd6aef036127f53ab19b092 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 7 Dec 2020 19:48:12 -0600
Subject: [PATCH 223/323] Wrote a new script to handle cigar strings. Still
 need to incorporate it into the main code, but I got the functions working

---
 bacterial_genreads_wrapper.py |   2 +-
 source/SequenceContainer.py   |  29 ++-
 source/neat_cigar_rework.py   | 358 ++++++++++++++++++++++++++++++++++
 3 files changed, 381 insertions(+), 8 deletions(-)
 create mode 100644 source/neat_cigar_rework.py

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 8ec1b4c..24d69b1 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -44,7 +44,7 @@ def analyze(self):
         manipulation to unzip the file and fix genreads horribly formatted fasta file.
         :return: None
         """
-        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa', '-c', '1']
+        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa', '-c', '1', '--rng', '1']
         gen_reads.main(args)
         self.file = pathlib.Path().absolute() / (self.name + ".fasta.gz")
 
diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 43eb41a..78711cb 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -7,6 +7,7 @@
 import numpy as np
 from Bio.Seq import Seq
 
+from source.neat_cigar_rework import CigarString as CigarStringNew
 from source.probability import DiscreteDistribution, poisson_list
 from source.neat_cigar import CigarString
 
@@ -105,6 +106,7 @@ def __init__(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_mod
         self.indel_list = [[] for _ in range(self.ploidy)]
         self.snp_list = [[] for _ in range(self.ploidy)]
         self.all_cigar = [[] for _ in range(self.ploidy)]
+        self.test = [[] for _ in range(self.ploidy)]
         self.fm_pos = [[] for _ in range(self.ploidy)]
         self.fm_span = [[] for _ in range(self.ploidy)]
 
@@ -583,13 +585,17 @@ def random_mutations(self):
         # MODIFY REFERENCE STRING: INDELS
         for i in range(len(all_indels_ins)):
             rolling_adj = 0
+            # TODO rework this section so that we use the new method
+            temp_symbol_string2 = CigarStringNew(str(len(self.sequences[i])) + "M")
             temp_symbol_string = ['M' for _ in self.sequences[i]]
-            # there's an off-by-one error somewhere in the position sampling routines.. this might fix it
+            # Zach's comment: there's an off-by-one error somewhere in the position sampling routines..
+            # this might fix it
             # temp_symbol_string.append('M')
             for j in range(len(all_indels_ins[i])):
                 v_pos = all_indels_ins[i][j][0] + rolling_adj
                 v_pos2 = v_pos + len(all_indels_ins[i][j][1])
-                rolling_adj += len(all_indels_ins[i][j][2]) - len(all_indels_ins[i][j][1])
+                indel_length = len(all_indels_ins[i][j][2]) - len(all_indels_ins[i][j][1])
+                rolling_adj += indel_length
 
                 if all_indels_ins[i][j][1] != str(self.sequences[i][v_pos:v_pos2]):
                     print('\nError: Something went wrong!\n', all_indels_ins[i][j], [v_pos, v_pos2],
@@ -597,18 +603,27 @@ def random_mutations(self):
                     sys.exit(1)
                 else:
                     # alter reference sequence
+                    # TODO need to fix this line too
                     self.sequences[i] = self.sequences[i][:v_pos] + Seq(all_indels_ins[i][j][2]).tomutable() + \
                                         self.sequences[i][v_pos2:]
                     # notate indel positions for cigar computation
-                    d = len(all_indels_ins[i][j][2]) - len(all_indels_ins[i][j][1])
-                    if d > 0:
-                        temp_symbol_string = temp_symbol_string[:v_pos + 1] + ['I'] * d + temp_symbol_string[
+                    if indel_length > 0:
+                        cigar_to_insert = CigarStringNew(str(indel_length) + 'I')
+                        print(list(temp_symbol_string2.items()))
+                        temp_symbol_string2.insert_cigar_element(v_pos+1, cigar_to_insert, len(all_indels_ins[i][j][1]))
+                        print(list(temp_symbol_string2.items()))
+                        temp_symbol_string = temp_symbol_string[:v_pos + 1] + ['I'] * indel_length + temp_symbol_string[
                                                                                           v_pos2 + 1:]
-                    elif d < 0:
-                        temp_symbol_string[v_pos + 1] = 'D' * abs(d) + 'M'
+                    elif indel_length < 0:
+                        cigar_to_insert = CigarStringNew(str(abs(indel_length)) + 'D1M')
+                        print(list(temp_symbol_string2.items()))
+                        temp_symbol_string2.insert_cigar_element(v_pos+1, cigar_to_insert, indel_length)
+                        print(list(temp_symbol_string2.items()))
+                        temp_symbol_string[v_pos + 1] = 'D' * abs(indel_length) + 'M'
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
+                self.test[i].append(CigarStringNew(temp_symbol_string2[j:j + self.read_len].cigar))
                 self.all_cigar[i].append(CigarString(list_in=temp_symbol_string[j:j + self.read_len]).get_string())
 
             # create some data structures we will need later:
diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
new file mode 100644
index 0000000..d70b5df
--- /dev/null
+++ b/source/neat_cigar_rework.py
@@ -0,0 +1,358 @@
+import sys
+from itertools import groupby
+from operator import itemgetter
+
+class Cigar(object):
+    """
+    This code borrowed in whole from https://github.com/brentp/cigar
+
+    cigar is a simple library for dealing with cigar strings. the most useful
+    feature now is soft-masking from left or right. This allows one to adjust
+    a SAM record only by changing the cigar string to soft-mask a number of bases
+    such that the rest of the SAM record (pos, tlen, etc.) remain valid, but
+    downstream tools will not consider the soft-masked bases in further analysis.
+    >>> c = Cigar('100M')
+    >>> len(c)
+    100
+    >>> str(c)
+    '100M'
+    >>> list(c.items())
+    [(100, 'M')]
+    >>> c = Cigar('20H20M20S')
+    >>> len(c)
+    40
+    >>> str(c)
+    '20H20M20S'
+    >>> list(c.items())
+    [(20, 'H'), (20, 'M'), (20, 'S')]
+    >>> c.mask_left(29).cigar, c.cigar
+    ('20H9S11M20S', '20H20M20S')
+    >>> c = Cigar('10M20S10M')
+    >>> c.mask_left(10).cigar
+    '30S10M'
+    >>> c.mask_left(9).cigar
+    '9S1M20S10M'
+    >>> Cigar('10S').mask_left(10).cigar
+    '10S'
+    >>> Cigar('10H').mask_left(10).cigar
+    '10H'
+    >>> Cigar('10H').mask_left(11).cigar
+    '10H'
+    >>> Cigar('10H').mask_left(9).cigar
+    '10H'
+    >>> Cigar('1M10H').mask_left(9).cigar
+    '1S10H'
+    >>> Cigar('5M10H').mask_left(9).cigar
+    '5S10H'
+    >>> c = Cigar('1S1H1S5H1S5M10H')
+    >>> c.mask_left(9).cigar == c.cigar
+    True
+    >>> c = Cigar('1S1H1S5H1S5M10H')
+    >>> c.mask_right(9).cigar == c.cigar
+    True
+    >>> c.mask_right(11).cigar
+    '1S1H1S5H1S4M1S10H'
+    """
+    read_consuming_ops = ("M", "I", "S", "=", "X")
+    ref_consuming_ops = ("M", "D", "N", "=", "X")
+
+    def __init__(self, cigar_string):
+        self.cigar = cigar_string
+
+    def items(self):
+        if self.cigar == "*":
+            yield (0, None)
+            raise StopIteration
+        cig_iter = groupby(self.cigar, lambda c: c.isdigit())
+        for g, n in cig_iter:
+            yield int("".join(n)), "".join(next(cig_iter)[1])
+
+    def __str__(self):
+        return self.cigar
+
+    def __repr__(self):
+        return "Cigar('%s')" % self
+
+    def __len__(self):
+        """
+        sum of MIS=X ops shall equal the sequence length.
+        """
+        return sum(l for l, op,in self.items() \
+                               if op in Cigar.read_consuming_ops)
+
+    def reference_length(self):
+        return sum(l for l, op in self.items() \
+                               if op in Cigar.ref_consuming_ops)
+
+    def mask_left(self, n_seq_bases, mask="S"):
+        """
+        Return a new cigar with cigar string where the first `n_seq_bases` are
+        soft-masked unless they are already hard-masked.
+        """
+        cigs = list(self.items())
+        new_cigs = []
+
+        c, cum_len  = self.cigar, 0
+        for i, (l, op) in enumerate(cigs):
+            if op in Cigar.read_consuming_ops:
+                cum_len += l
+            if op == "H":
+                cum_len += l
+                new_cigs.append(cigs[i])
+            elif cum_len < n_seq_bases:
+                new_cigs.append(cigs[i])
+            else:
+                # the current cigar element is split by the masking.
+                right_extra = cum_len - n_seq_bases
+                new_cigs.append((l - right_extra, 'S'))
+                if right_extra != 0:
+                    new_cigs.append((right_extra, cigs[i][1]))
+            if cum_len >= n_seq_bases: break
+        else:
+            pass
+
+        new_cigs[:i] = [(l, op if op in "HS" else "S") for l, op in
+                new_cigs[:i]]
+        new_cigs.extend(cigs[i + 1:])
+        return Cigar(Cigar.string_from_elements(new_cigs)).merge_like_ops()
+
+
+    @classmethod
+    def string_from_elements(self, elements):
+        return "".join("%i%s" % (l, op) for l, op in elements if l !=0)
+
+
+    def mask_right(self, n_seq_bases, mask="S"):
+        """
+        Return a new cigar with cigar string where the last `n_seq_bases` are
+        soft-masked unless they are already hard-masked.
+        """
+        return Cigar(Cigar(self._reverse_cigar()).mask_left(n_seq_bases, mask)._reverse_cigar())
+
+
+    def _reverse_cigar(self):
+        return Cigar.string_from_elements(list(self.items())[::-1])
+
+    def merge_like_ops(self):
+        """
+        >>> Cigar("1S20M").merge_like_ops()
+        Cigar('1S20M')
+        >>> Cigar("1S1S20M").merge_like_ops()
+        Cigar('2S20M')
+        >>> Cigar("1S1S1S20M").merge_like_ops()
+        Cigar('3S20M')
+        >>> Cigar("1S1S1S20M1S1S").merge_like_ops()
+        Cigar('3S20M2S')
+        """
+
+        cigs = []
+        for op, grps in groupby(self.items(), itemgetter(1)):
+            cigs.append((sum(g[0] for g in grps), op))
+
+        return Cigar(self.string_from_elements(cigs))
+
+
+class CigarString(Cigar):
+    """
+    This adds odditional functionality to the cigar module above, to make it compatible with
+    the rest of the NEAT codebase
+    """
+
+    def insert_cigar_element(self, pos: int,
+                             insertion_cigar: 'CigarString',
+                             length: int = 0) -> None:
+        """
+        Inserts a cigar string in either string or list format to the existing cigar string at position pos.
+
+        :param length: If consuming spaces, length of cigar to be added
+        :param insertion_cigar: A cigar to insert into current cigar string
+        :param pos: integer position where to insert the input cigar string
+        :return:
+        """
+
+        if insertion_cigar is None:
+            print('\nError: Invalid insertion operation in CigarString\n')
+            sys.exit(1)
+
+        if pos < 0 or pos >= len(self):
+            print('\nError: Invalid insertion position in CigarString\n')
+            sys.exit(1)
+
+        try:
+            current_pos = 0
+            previous_pos = 0
+            new_element = []
+            found = False
+            for item in self.items():
+                current_pos += item[0]
+                if current_pos > pos and not found:
+                    current_block = item[0]
+                    current_index = pos - previous_pos
+                    new_element.append((current_index, item[1]))
+                    for insert in insertion_cigar.items():
+                        new_element.append(insert)
+                    if length != 0:
+                        new_element.append((current_block-current_index-abs(length), item[1]))
+                    else:
+                        new_element.append((current_block-current_index, item[1]))
+                    found = True
+                else:
+                    new_element.append(item)
+                previous_pos = current_pos
+            new_string = self.string_from_elements(new_element)
+            self.cigar = CigarString(new_string).merge_like_ops().cigar
+        except ValueError:
+            print('\nBug: Problem with insertion.\n')
+            sys.exit(1)
+
+    def get_cigar_fragment(self, start: int, end: int) -> str:
+        """
+        Take a slice of a cigar string. E.g., if we have a cigar string that is "1000M" and we want a slice of the
+        first 101 characters, then the return should be '101M'. If we had "20M100I20M" and want the
+        first 101 characters, it would return '20M81I'.
+
+        :param start: start point of sequence to retrieve
+        :param end: end point of sequence to retrieve
+        :return: The sequence that spans start to end
+        """
+        # Minus 1 because python slices don't include the end coordinate
+        window_size = end - start
+        if window_size < 0:
+            print(f'\nError: start and end coordinates for get_cigar_fragment '
+                  f'are wrong: start: {start}, end: {end}')
+        if start is None or end is None:
+            print('\nError: Invalid coordinates in get_cigar_fragment\n')
+            sys.exit(1)
+
+        if start < 0 or start >= len(self):
+            print('\nError: Invalid start point in get_cigar_fragment\n')
+            sys.exit(1)
+
+        if end > len(self) or end < 0:
+            print('\nError: Invalid end point in get_cigar_fragment')
+            sys.exit(1)
+
+        try:
+            # Minus 1 because of indexing
+            current_pos = -1
+            previous_pos = -1
+            current_index = 0
+            start_found = False
+            ret = []
+            for item in self.items():
+                current_pos += item[0]
+                current_block_size = item[0]
+                current_symbol = item[1]
+                if current_pos >= start and not start_found:
+                    start_found = True
+                    current_index = previous_pos + 1
+                    # start - current index = The index where we start relative to the start of the block
+                    # if there are not enough items in the current block, we will consume what is left
+                    # and move to the next block
+                    if current_block_size - (start - current_index) >= window_size:
+                        # If from where we start to the end of the current block is sufficient for our window,
+                        # then the return is just everything of that symbol
+                        ret.append((window_size, current_symbol))
+                        break
+                    else:
+                        # if there are not enough items in the current block, we will consume what is left
+                        # and move to the next block
+                        ret.append((current_block_size - (start - current_index), current_symbol))
+                        # shrink the window to account for the items we've found
+                        window_size -= current_block_size - (start - current_index)
+                elif start_found:
+                    if current_block_size >= window_size:
+                        # If this block can cover the remaining slice, then append the end and break
+                        ret.append((window_size, current_symbol))
+                        break
+                    else:
+                        # If we still need more, then consume this entire block
+                        ret.append((current_block_size, current_symbol))
+                        # shrink the window to account for the items we've found
+                        window_size -= current_block_size
+                else:
+                    pass
+
+                previous_pos = current_pos
+
+        except ValueError:
+            print('\nBug: Problem retrieving fragment.\n')
+            sys.exit(1)
+        return Cigar.string_from_elements(ret)
+
+
+if __name__ == '__main__':
+    print('testing CigarString class...')
+
+    str1 = CigarString('50M10D7I23M')
+    str2 = CigarString('10I25M')
+    iPos = 55
+
+    str1.insert_cigar_element(iPos, str2)
+    print(str1.cigar)
+    assert(str1.cigar == "50M5D10I25M5D7I23M")
+    print("passed")
+
+    str1 = CigarString('50M10D7I23M')
+    iPos = 20
+    str1.insert_cigar_element(iPos, str2)
+    print(str1.cigar)
+    assert(str1.cigar == "20M10I55M10D7I23M")
+    print("passed")
+
+    str1 = CigarString('11100M')
+    str2 = CigarString('2I')
+    iPos = 5785 + 1
+    str1.insert_cigar_element(iPos, str2, 1)
+    print(str1.cigar)
+    assert (str1.cigar == "5786M2I5313M")
+    print("passed")
+
+    str1 = CigarString('11100M')
+    str2 = CigarString('1D1M')
+    iPos = 6610 + 1
+    str1.insert_cigar_element(iPos, str2, -1)
+    print(str1.cigar)
+    assert (str1.cigar == "6611M1D4489M")
+    print("passed")
+
+    str1 = CigarString('10M5D10M')
+    # [('10', 'M'), ('5', 'D'), ('10', 'M')]
+    start = 0
+    end = 14
+    frag = str1.get_cigar_fragment(start, end)
+    print(frag)
+    assert(frag == "10M4D")
+
+    str1 = CigarString('10M2D10M')
+    # [(10, 'M'), (2, 'D'), (10, 'M')]
+    start = 10
+    end = 12
+    frag = str1.get_cigar_fragment(start, end)
+    print(frag)
+    assert(frag == "2D")
+    print("passed")
+
+    str1 = CigarString('10M1D10M')
+    # [(10, 'M'), (1, 'D'), (10, 'M')]
+    start = 10
+    end = 12
+    frag = str1.get_cigar_fragment(start, end)
+    print(frag)
+    assert(frag == "1D1M")
+    print("passed")
+
+    str1 = CigarString('102M2I10000M')
+    start1 = 1
+    end1 = 102
+    frag1 = str1.get_cigar_fragment(start1, end1)
+    start2 = 102
+    end2 = 203
+    frag2 = str1.get_cigar_fragment(start2, end2)
+    print(frag1)
+    print(frag2)
+    assert(frag1 == "101M")
+    assert(frag2 == "2I99M")
+
+
+

From 5a3abcba1a9cb688bfc311324da920164cb864de Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 7 Dec 2020 19:57:49 -0600
Subject: [PATCH 224/323] Ran a couple quick tests on the new cigar string
 function and it works, so I commented out the old function call in
 SequenceContainer.

---
 bacterial_genreads_wrapper.py |  2 +-
 source/SequenceContainer.py   | 27 +++++++++++++--------------
 2 files changed, 14 insertions(+), 15 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 24d69b1..8ec1b4c 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -44,7 +44,7 @@ def analyze(self):
         manipulation to unzip the file and fix genreads horribly formatted fasta file.
         :return: None
         """
-        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa', '-c', '1', '--rng', '1']
+        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa', '-c', '1']
         gen_reads.main(args)
         self.file = pathlib.Path().absolute() / (self.name + ".fasta.gz")
 
diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 78711cb..c887dff 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -585,9 +585,9 @@ def random_mutations(self):
         # MODIFY REFERENCE STRING: INDELS
         for i in range(len(all_indels_ins)):
             rolling_adj = 0
-            # TODO rework this section so that we use the new method
-            temp_symbol_string2 = CigarStringNew(str(len(self.sequences[i])) + "M")
-            temp_symbol_string = ['M' for _ in self.sequences[i]]
+            temp_symbol_string = CigarStringNew(str(len(self.sequences[i])) + "M")
+            # TODO Delete commented out lines once CigarString works 100%
+            # temp_symbol_string = ['M' for _ in self.sequences[i]]
             # Zach's comment: there's an off-by-one error somewhere in the position sampling routines..
             # this might fix it
             # temp_symbol_string.append('M')
@@ -609,22 +609,21 @@ def random_mutations(self):
                     # notate indel positions for cigar computation
                     if indel_length > 0:
                         cigar_to_insert = CigarStringNew(str(indel_length) + 'I')
-                        print(list(temp_symbol_string2.items()))
-                        temp_symbol_string2.insert_cigar_element(v_pos+1, cigar_to_insert, len(all_indels_ins[i][j][1]))
-                        print(list(temp_symbol_string2.items()))
-                        temp_symbol_string = temp_symbol_string[:v_pos + 1] + ['I'] * indel_length + temp_symbol_string[
-                                                                                          v_pos2 + 1:]
+                        temp_symbol_string.insert_cigar_element(v_pos+1, cigar_to_insert, len(all_indels_ins[i][j][1]))
+                        # TODO Delete commented out lines once CigarString works 100%
+                        # temp_symbol_string = temp_symbol_string[:v_pos + 1] + ['I'] * indel_length + temp_symbol_string[
+                        #                                                                   v_pos2 + 1:]
                     elif indel_length < 0:
                         cigar_to_insert = CigarStringNew(str(abs(indel_length)) + 'D1M')
-                        print(list(temp_symbol_string2.items()))
-                        temp_symbol_string2.insert_cigar_element(v_pos+1, cigar_to_insert, indel_length)
-                        print(list(temp_symbol_string2.items()))
-                        temp_symbol_string[v_pos + 1] = 'D' * abs(indel_length) + 'M'
+                        temp_symbol_string.insert_cigar_element(v_pos+1, cigar_to_insert, indel_length)
+                        # TODO Delete commented out lines once CigarString works 100%
+                        # temp_symbol_string[v_pos + 1] = 'D' * abs(indel_length) + 'M'
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
-                self.test[i].append(CigarStringNew(temp_symbol_string2[j:j + self.read_len].cigar))
-                self.all_cigar[i].append(CigarString(list_in=temp_symbol_string[j:j + self.read_len]).get_string())
+                self.test[i].append(temp_symbol_string.get_cigar_fragment(j, j+self.read_len))
+                # TODO Delete commented out lines once CigarString works 100%
+                # self.all_cigar[i].append(CigarString(list_in=temp_symbol_string[j:j + self.read_len]).get_string())
 
             # create some data structures we will need later:
             # --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e.

From 2ec7604197016162e8473129530ed4455f49f036 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 8 Dec 2020 14:25:31 -0600
Subject: [PATCH 225/323] Some testing with the fm_pos feature

---
 bacterial_genreads_wrapper.py |  2 +-
 source/SequenceContainer.py   | 33 +++++++++++++----------
 source/fm_pos_test.py         | 50 +++++++++++++++++++++++++++++++++++
 source/neat_cigar_rework.py   |  7 +++++
 4 files changed, 77 insertions(+), 15 deletions(-)
 create mode 100644 source/fm_pos_test.py

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 8ec1b4c..24d69b1 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -44,7 +44,7 @@ def analyze(self):
         manipulation to unzip the file and fix genreads horribly formatted fasta file.
         :return: None
         """
-        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa', '-c', '1']
+        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa', '-c', '1', '--rng', '1']
         gen_reads.main(args)
         self.file = pathlib.Path().absolute() / (self.name + ".fasta.gz")
 
diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index c887dff..d140edf 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -587,10 +587,8 @@ def random_mutations(self):
             rolling_adj = 0
             temp_symbol_string = CigarStringNew(str(len(self.sequences[i])) + "M")
             # TODO Delete commented out lines once CigarString works 100%
-            # temp_symbol_string = ['M' for _ in self.sequences[i]]
-            # Zach's comment: there's an off-by-one error somewhere in the position sampling routines..
-            # this might fix it
-            # temp_symbol_string.append('M')
+            temp_symbol_string2 = ['M' for _ in self.sequences[i]]
+
             for j in range(len(all_indels_ins[i])):
                 v_pos = all_indels_ins[i][j][0] + rolling_adj
                 v_pos2 = v_pos + len(all_indels_ins[i][j][1])
@@ -603,7 +601,6 @@ def random_mutations(self):
                     sys.exit(1)
                 else:
                     # alter reference sequence
-                    # TODO need to fix this line too
                     self.sequences[i] = self.sequences[i][:v_pos] + Seq(all_indels_ins[i][j][2]).tomutable() + \
                                         self.sequences[i][v_pos2:]
                     # notate indel positions for cigar computation
@@ -611,19 +608,19 @@ def random_mutations(self):
                         cigar_to_insert = CigarStringNew(str(indel_length) + 'I')
                         temp_symbol_string.insert_cigar_element(v_pos+1, cigar_to_insert, len(all_indels_ins[i][j][1]))
                         # TODO Delete commented out lines once CigarString works 100%
-                        # temp_symbol_string = temp_symbol_string[:v_pos + 1] + ['I'] * indel_length + temp_symbol_string[
-                        #                                                                   v_pos2 + 1:]
+                        temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
+                                              ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
                     elif indel_length < 0:
                         cigar_to_insert = CigarStringNew(str(abs(indel_length)) + 'D1M')
                         temp_symbol_string.insert_cigar_element(v_pos+1, cigar_to_insert, indel_length)
                         # TODO Delete commented out lines once CigarString works 100%
-                        # temp_symbol_string[v_pos + 1] = 'D' * abs(indel_length) + 'M'
+                        temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
                 self.test[i].append(temp_symbol_string.get_cigar_fragment(j, j+self.read_len))
                 # TODO Delete commented out lines once CigarString works 100%
-                # self.all_cigar[i].append(CigarString(list_in=temp_symbol_string[j:j + self.read_len]).get_string())
+                self.all_cigar[i].append(CigarString(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
 
             # create some data structures we will need later:
             # --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e.
@@ -631,15 +628,22 @@ def random_mutations(self):
             # --- self.FM_span[ploid][pos]: number of reference positions spanned by a read originating from
             #       this coordinate
             md_so_far = 0
-            for j in range(len(temp_symbol_string)):
+            for item in temp_symbol_string.items():
+                if item[1] != 'I':
+                    for j in range(item[0]):
+                        self.fm_pos[i].append(md_so_far)
+                        md_so_far += 1
+                else:
+                   pass
+            for j in range(len(temp_symbol_string2)):
                 self.fm_pos[i].append(md_so_far)
                 # fix an edge case with deletions
-                if 'D' in temp_symbol_string[j]:
-                    self.fm_pos[i][-1] += temp_symbol_string[j].count('D')
+                if temp_symbol_string2[j] == 'D':
+                    self.fm_pos[i][-1] += temp_symbol_string2[j].count('D')
                 # compute number of ref matches for each read
-                span_dif = len([n for n in temp_symbol_string[j:j + self.read_len] if 'M' in n])
+                span_dif = len([n for n in temp_symbol_string2[j:j + self.read_len] if 'M' in n])
                 self.fm_span[i].append(self.fm_pos[i][-1] + span_dif)
-                md_so_far += temp_symbol_string[j].count('M') + temp_symbol_string[j].count('D')
+                md_so_far += temp_symbol_string2[j].count('M') + temp_symbol_string2[j].count('D')
 
         # tally up all the variants we handled...
         count_dict = {}
@@ -759,6 +763,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                     if total_d > avail_b:  # if not enough bases to fill-in deletions, skip all indel erors
                         continue
                     if not expanded_cigar:
+                        expanded_cigar2 = CigarStringNew(my_cigar)
                         expanded_cigar = CigarString(string_in=my_cigar).get_list()
                         fill_to_go = total_d - total_i + 1
                         if fill_to_go > 0:
diff --git a/source/fm_pos_test.py b/source/fm_pos_test.py
new file mode 100644
index 0000000..74a36cb
--- /dev/null
+++ b/source/fm_pos_test.py
@@ -0,0 +1,50 @@
+from source.neat_cigar_rework import CigarString as CigarStringNew
+from source.neat_cigar import CigarString
+from time import time
+
+ploidy = 2
+start_time = time()
+temp_symbol_string = CigarStringNew('10777M1I322M')
+cigar_string_new_time = time() - start_time
+print(f'CigarStringNew time = {cigar_string_new_time}')
+
+start_time = time()
+temp_symbol_string2 = CigarString.string_to_list('10777M1I322M')
+old_method_time = time() - start_time
+print(f'String-to-list time = {old_method_time}')
+fm_pos1 = [[] for _ in range(ploidy)]
+fm_span1 = [[] for _ in range(ploidy)]
+fm_pos2 = [[] for _ in range(ploidy)]
+fm_span2 = [[] for _ in range(ploidy)]
+span_dif = 0
+read_len = 101
+
+for i in range(ploidy):
+    md_so_far = 0
+    start_time = time()
+    for item in temp_symbol_string.items():
+        if item[1] in ['M', 'D']:
+            for j in range(item[0]):
+                fm_pos1[i].append(md_so_far)
+                span_dif = CigarStringNew(temp_symbol_string.get_cigar_fragment(j, j + read_len)).count_elements('M')
+                fm_span1[i].append(fm_pos1[i][-1] + span_dif)
+                md_so_far += 1
+        else:
+            pass
+    print(f'For new method, ploid {i}, time = {time()-start_time + cigar_string_new_time}')
+
+    start_time = time()
+    for j in range(len(temp_symbol_string2)):
+        fm_pos2[i].append(md_so_far)
+        # fix an edge case with deletions
+        if temp_symbol_string2[j] == 'D':
+            fm_pos2[i][-1] += temp_symbol_string2[j].count('D')
+        # compute number of ref matches for each read
+        span_dif = len([n for n in temp_symbol_string2[j:j + read_len] if n == 'M'])
+        fm_span2[i].append(fm_pos2[i][-1] + span_dif)
+        md_so_far += temp_symbol_string2[j].count('M') + temp_symbol_string2[j].count('D')
+    print(f'For old method, ploid {i}, time = {time() - start_time + old_method_time}')
+
+
+assert(fm_pos1 == fm_pos2)
+assert(fm_span1 == fm_span2)
diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index d70b5df..dbe96e1 100644
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -280,6 +280,13 @@ def get_cigar_fragment(self, start: int, end: int) -> str:
             sys.exit(1)
         return Cigar.string_from_elements(ret)
 
+    def count_elements(self, element: str) -> int:
+        count = 0
+        for item in self.items():
+            if item[1] == element:
+                count += item[0]
+        return count
+
 
 if __name__ == '__main__':
     print('testing CigarString class...')

From b8d1449077d9897c2cf168f87a4bb381721c76b4 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 9 Dec 2020 14:07:57 -0600
Subject: [PATCH 226/323] Made significant changes to the cigar handling. May
 implement some more.

---
 bacterial_genreads_wrapper.py |   3 +
 source/SequenceContainer.py   |  90 +++++++++--------
 source/fm_pos_test.py         |  39 +++++--
 source/neat_cigar_rework.py   | 185 ++++++++++++++++++----------------
 source/probability.py         |   4 +-
 5 files changed, 181 insertions(+), 140 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 24d69b1..864fab5 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -8,6 +8,7 @@
 import shutil
 import sys
 import copy
+from time import time
 # from Bio import SeqIO
 
 
@@ -252,4 +253,6 @@ def main():
 
 
 if __name__ == '__main__':
+    start_time = time()
     main()
+    print(f'Finished bacterial wrapper in {time() - start_time} seconds.')
\ No newline at end of file
diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index d140edf..9340d55 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -4,6 +4,8 @@
 import bisect
 import pickle
 import sys
+from time import time
+
 import numpy as np
 from Bio.Seq import Seq
 
@@ -106,7 +108,6 @@ def __init__(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_mod
         self.indel_list = [[] for _ in range(self.ploidy)]
         self.snp_list = [[] for _ in range(self.ploidy)]
         self.all_cigar = [[] for _ in range(self.ploidy)]
-        self.test = [[] for _ in range(self.ploidy)]
         self.fm_pos = [[] for _ in range(self.ploidy)]
         self.fm_span = [[] for _ in range(self.ploidy)]
 
@@ -587,7 +588,7 @@ def random_mutations(self):
             rolling_adj = 0
             temp_symbol_string = CigarStringNew(str(len(self.sequences[i])) + "M")
             # TODO Delete commented out lines once CigarString works 100%
-            temp_symbol_string2 = ['M' for _ in self.sequences[i]]
+            # temp_symbol_string2 = ['M' for _ in self.sequences[i]]
 
             for j in range(len(all_indels_ins[i])):
                 v_pos = all_indels_ins[i][j][0] + rolling_adj
@@ -608,19 +609,19 @@ def random_mutations(self):
                         cigar_to_insert = CigarStringNew(str(indel_length) + 'I')
                         temp_symbol_string.insert_cigar_element(v_pos+1, cigar_to_insert, len(all_indels_ins[i][j][1]))
                         # TODO Delete commented out lines once CigarString works 100%
-                        temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
-                                              ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
+                        # temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
+                        #                       ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
                     elif indel_length < 0:
                         cigar_to_insert = CigarStringNew(str(abs(indel_length)) + 'D1M')
                         temp_symbol_string.insert_cigar_element(v_pos+1, cigar_to_insert, indel_length)
                         # TODO Delete commented out lines once CigarString works 100%
-                        temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
+                        # temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
-                self.test[i].append(temp_symbol_string.get_cigar_fragment(j, j+self.read_len))
+                self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j+self.read_len))
                 # TODO Delete commented out lines once CigarString works 100%
-                self.all_cigar[i].append(CigarString(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
+                # self.all_cigar[i].append(CigarString(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
 
             # create some data structures we will need later:
             # --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e.
@@ -628,22 +629,16 @@ def random_mutations(self):
             # --- self.FM_span[ploid][pos]: number of reference positions spanned by a read originating from
             #       this coordinate
             md_so_far = 0
-            for item in temp_symbol_string.items():
-                if item[1] != 'I':
-                    for j in range(item[0]):
-                        self.fm_pos[i].append(md_so_far)
-                        md_so_far += 1
-                else:
-                   pass
-            for j in range(len(temp_symbol_string2)):
+            temp_symbol_string_list = temp_symbol_string.string_to_list()
+            for j in range(len(temp_symbol_string_list)):
                 self.fm_pos[i].append(md_so_far)
                 # fix an edge case with deletions
-                if temp_symbol_string2[j] == 'D':
-                    self.fm_pos[i][-1] += temp_symbol_string2[j].count('D')
+                if temp_symbol_string_list[j] == 'D':
+                    self.fm_pos[i][-1] += temp_symbol_string_list[j].count('D')
                 # compute number of ref matches for each read
-                span_dif = len([n for n in temp_symbol_string2[j:j + self.read_len] if 'M' in n])
+                span_dif = len([n for n in temp_symbol_string_list[j:j + self.read_len] if 'M' in n])
                 self.fm_span[i].append(self.fm_pos[i][-1] + span_dif)
-                md_so_far += temp_symbol_string2[j].count('M') + temp_symbol_string2[j].count('D')
+                md_so_far += temp_symbol_string_list[j].count('M') + temp_symbol_string_list.count('D')
 
         # tally up all the variants we handled...
         count_dict = {}
@@ -709,22 +704,22 @@ def sample_read(self, sequencing_model, frag_len=None):
             - remove deletions that don't have enough bordering sequence content to "fill in"
             if error is valid, make the changes to the read data
         """
-        r_out = []
-        for r in reads_to_sample:
+        read_out = []
+        for read in reads_to_sample:
             try:
-                my_cigar = self.all_cigar[my_ploid][r[0]]
+                my_cigar = self.all_cigar[my_ploid][read[0]]
             except IndexError:
                 print('Index error when attempting to find cigar string.')
-                print(my_ploid, len(self.all_cigar[my_ploid]), r[0])
+                print(my_ploid, len(self.all_cigar[my_ploid]), read[0])
                 if frag_len is not None:
                     print((r_pos1, r_pos2))
                     print(frag_len, self.fraglen_ind_map[frag_len])
                 sys.exit(1)
-            total_d = sum([error[1] for error in r[2] if error[0] == 'D'])
-            total_i = sum([error[1] for error in r[2] if error[0] == 'I'])
-            avail_b = len(self.sequences[my_ploid]) - r[0] - self.read_len - 1
+            total_d = sum([error[1] for error in read[2] if error[0] == 'D'])
+            total_i = sum([error[1] for error in read[2] if error[0] == 'I'])
+            avail_b = len(self.sequences[my_ploid]) - read[0] - self.read_len - 1
             # add buffer sequence to fill in positions that get deleted
-            r[3] += self.sequences[my_ploid][r[0] + self.read_len:r[0] + self.read_len + total_d]
+            read[3] += self.sequences[my_ploid][read[0] + self.read_len:read[0] + self.read_len + total_d]
             expanded_cigar = []
             extra_cigar = []
             adj = 0
@@ -734,7 +729,7 @@ def sample_read(self, sequencing_model, frag_len=None):
             # sort by letter (D > I > S) such that we introduce all indel errors before substitution errors
             # secondarily, sort by index
             arranged_errors = {'D': [], 'I': [], 'S': []}
-            for error in r[2]:
+            for error in read[2]:
                 arranged_errors[error[0]].append((error[2], error))
             sorted_errors = []
             for k in sorted(arranged_errors.keys()):
@@ -763,13 +758,18 @@ def sample_read(self, sequencing_model, frag_len=None):
                     if total_d > avail_b:  # if not enough bases to fill-in deletions, skip all indel erors
                         continue
                     if not expanded_cigar:
-                        expanded_cigar2 = CigarStringNew(my_cigar)
-                        expanded_cigar = CigarString(string_in=my_cigar).get_list()
+                        expanded_cigar = CigarStringNew(my_cigar).string_to_list()
+                        # TODO delete these lines using old CigarString once it is working 100%
+                        # expanded_cigar = CigarString(string_in=my_cigar).get_list()
                         fill_to_go = total_d - total_i + 1
                         if fill_to_go > 0:
                             try:
-                                extra_cigar_val = CigarString(
-                                    string_in=self.all_cigar[my_ploid][r[0] + fill_to_go]).get_list()[-fill_to_go:]
+                                # TODO delete these lines using old CigarString once it is working 100%
+                                # extra_cigar_val = CigarString(string_in=self.all_cigar[my_ploid][read[0]
+                                #                                + fill_to_go]).get_list()[-fill_to_go:]
+                                extra_cigar_val = CigarStringNew(self.all_cigar[my_ploid][read[0]
+                                                                 + fill_to_go]).string_to_list()[-fill_to_go:]
+
                             except IndexError:
                                 # Applying the deletions we want requires going beyond region boundaries.
                                 # Skip all indel errors
@@ -783,8 +783,8 @@ def sample_read(self, sequencing_model, frag_len=None):
                         my_adj = sse_adj[e_pos]
                         pi = e_pos + my_adj
                         pf = e_pos + my_adj + e_len + 1
-                        if str(r[3][pi:pf]) == str(error[3]):
-                            r[3] = r[3][:pi + 1] + r[3][pf:]
+                        if str(read[3][pi:pf]) == str(error[3]):
+                            read[3] = read[3][:pi + 1] + read[3][pf:]
                             expanded_cigar = expanded_cigar[:pi + 1] + expanded_cigar[pf:]
                             # weird edge case with del at very end of region. Make a guess and add a "M"
                             if pi + 1 == len(expanded_cigar):
@@ -800,21 +800,21 @@ def sample_read(self, sequencing_model, frag_len=None):
                     # insert insertion error into read and update cigar string accordingly
                     else:
                         my_adj = sse_adj[e_pos]
-                        if str(r[3][e_pos + my_adj]) == error[3]:
-                            r[3] = r[3][:e_pos + my_adj] + error[4] + r[3][e_pos + my_adj + 1:]
+                        if str(read[3][e_pos + my_adj]) == error[3]:
+                            read[3] = read[3][:e_pos + my_adj] + error[4] + read[3][e_pos + my_adj + 1:]
                             expanded_cigar = expanded_cigar[:e_pos + my_adj] + ['I'] * e_len + expanded_cigar[
                                                                                                e_pos + my_adj:]
                         else:
                             print('\nError, ref does not match alt while attempting to insert insertion error!\n')
-                            print('---', chr(r[3][e_pos + my_adj]), '!=', error[3])
+                            print('---', chr(read[3][e_pos + my_adj]), '!=', error[3])
                             sys.exit(1)
                         adj += e_len
                         for i in range(e_pos, len(sse_adj)):
                             sse_adj[i] += e_len
 
                 else:  # substitution errors, much easier by comparison...
-                    if str(r[3][e_pos + sse_adj[e_pos]]) == error[3]:
-                        r[3][e_pos + sse_adj[e_pos]] = error[4]
+                    if str(read[3][e_pos + sse_adj[e_pos]]) == error[3]:
+                        read[3][e_pos + sse_adj[e_pos]] = error[4]
                     else:
                         print('\nError, ref does not match alt while attempting to insert substitution error!\n')
                         sys.exit(1)
@@ -822,14 +822,16 @@ def sample_read(self, sequencing_model, frag_len=None):
             if any_indel_err:
                 if len(expanded_cigar):
                     relevant_cigar = (expanded_cigar + extra_cigar_val)[:self.read_len]
-                    my_cigar = CigarString(list_in=relevant_cigar).get_string()
+                    my_cigar2 = CigarStringNew.list_to_string(relevant_cigar)
+                    # TODO delete this line once new cigar is 100% working
+                    # my_cigar = CigarString(list_in=relevant_cigar).get_string()
 
-                r[3] = r[3][:self.read_len]
+                read[3] = read[3][:self.read_len]
 
-            r_out.append([self.fm_pos[my_ploid][r[0]], my_cigar, str(r[3]), str(r[1])])
+            read_out.append([self.fm_pos[my_ploid][read[0]], my_cigar, str(read[3]), str(read[1])])
 
-        # r_out[i] = (pos, cigar, read_string, qual_string)
-        return r_out
+        # read_out[i] = (pos, cigar, read_string, qual_string)
+        return read_out
 
 
 class SequencingError:
diff --git a/source/fm_pos_test.py b/source/fm_pos_test.py
index 74a36cb..9569f6a 100644
--- a/source/fm_pos_test.py
+++ b/source/fm_pos_test.py
@@ -4,7 +4,8 @@
 
 ploidy = 2
 start_time = time()
-temp_symbol_string = CigarStringNew('10777M1I322M')
+temp_symbol_string = CigarStringNew('10777M1I322M').string_to_list()
+# temp_symbol_string = CigarStringNew('10777M1I322M')
 cigar_string_new_time = time() - start_time
 print(f'CigarStringNew time = {cigar_string_new_time}')
 
@@ -12,6 +13,14 @@
 temp_symbol_string2 = CigarString.string_to_list('10777M1I322M')
 old_method_time = time() - start_time
 print(f'String-to-list time = {old_method_time}')
+
+print(f'temp_symbol_string = {temp_symbol_string}')
+print(f'temp_symbol_string2 = {temp_symbol_string2}')
+assert(len(temp_symbol_string) == len(temp_symbol_string2))
+print('passed')
+# assert(temp_symbol_string == temp_symbol_string2)
+# print('passed')
+
 fm_pos1 = [[] for _ in range(ploidy)]
 fm_span1 = [[] for _ in range(ploidy)]
 fm_pos2 = [[] for _ in range(ploidy)]
@@ -22,18 +31,28 @@
 for i in range(ploidy):
     md_so_far = 0
     start_time = time()
-    for item in temp_symbol_string.items():
-        if item[1] in ['M', 'D']:
-            for j in range(item[0]):
-                fm_pos1[i].append(md_so_far)
-                span_dif = CigarStringNew(temp_symbol_string.get_cigar_fragment(j, j + read_len)).count_elements('M')
-                fm_span1[i].append(fm_pos1[i][-1] + span_dif)
-                md_so_far += 1
-        else:
-            pass
+    # for item in temp_symbol_string.items():
+    #     if item[1] in ['M', 'D']:
+    #         for j in range(item[0]):
+    #             fm_pos1[i].append(md_so_far)
+    #             span_dif = CigarStringNew(temp_symbol_string.get_cigar_fragment(j, j + read_len)).count_elements('M')
+    #             fm_span1[i].append(fm_pos1[i][-1] + span_dif)
+    #             md_so_far += 1
+    #     else:
+    #         pass
+    for j in range(len(temp_symbol_string)):
+        fm_pos2[i].append(md_so_far)
+        # fix an edge case with deletions
+        if temp_symbol_string[j] == 'D':
+            fm_pos2[i][-1] += temp_symbol_string[j].count('D')
+        # compute number of ref matches for each read
+        span_dif = len([n for n in temp_symbol_string[j:j + read_len] if n == 'M'])
+        fm_span2[i].append(fm_pos2[i][-1] + span_dif)
+        md_so_far += temp_symbol_string[j].count('M') + temp_symbol_string[j].count('D')
     print(f'For new method, ploid {i}, time = {time()-start_time + cigar_string_new_time}')
 
     start_time = time()
+    md_so_far = 0
     for j in range(len(temp_symbol_string2)):
         fm_pos2[i].append(md_so_far)
         # fix an edge case with deletions
diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index dbe96e1..9bea656 100644
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -2,6 +2,7 @@
 from itertools import groupby
 from operator import itemgetter
 
+
 class Cigar(object):
     """
     This code borrowed in whole from https://github.com/brentp/cigar
@@ -61,7 +62,7 @@ def __init__(self, cigar_string):
 
     def items(self):
         if self.cigar == "*":
-            yield (0, None)
+            yield 0, None
             raise StopIteration
         cig_iter = groupby(self.cigar, lambda c: c.isdigit())
         for g, n in cig_iter:
@@ -77,12 +78,10 @@ def __len__(self):
         """
         sum of MIS=X ops shall equal the sequence length.
         """
-        return sum(l for l, op,in self.items() \
-                               if op in Cigar.read_consuming_ops)
+        return sum(l for l, op in self.items() if op in Cigar.read_consuming_ops)
 
     def reference_length(self):
-        return sum(l for l, op in self.items() \
-                               if op in Cigar.ref_consuming_ops)
+        return sum(l for l, op in self.items() if op in Cigar.ref_consuming_ops)
 
     def mask_left(self, n_seq_bases, mask="S"):
         """
@@ -92,7 +91,7 @@ def mask_left(self, n_seq_bases, mask="S"):
         cigs = list(self.items())
         new_cigs = []
 
-        c, cum_len  = self.cigar, 0
+        c, cum_len = self.cigar, 0
         for i, (l, op) in enumerate(cigs):
             if op in Cigar.read_consuming_ops:
                 cum_len += l
@@ -111,17 +110,14 @@ def mask_left(self, n_seq_bases, mask="S"):
         else:
             pass
 
-        new_cigs[:i] = [(l, op if op in "HS" else "S") for l, op in
-                new_cigs[:i]]
+        new_cigs[:i] = [(l, op if op in "HS" else "S") for l, op in new_cigs[:i]]
         new_cigs.extend(cigs[i + 1:])
         return Cigar(Cigar.string_from_elements(new_cigs)).merge_like_ops()
 
-
     @classmethod
     def string_from_elements(self, elements):
         return "".join("%i%s" % (l, op) for l, op in elements if l !=0)
 
-
     def mask_right(self, n_seq_bases, mask="S"):
         """
         Return a new cigar with cigar string where the last `n_seq_bases` are
@@ -129,7 +125,6 @@ def mask_right(self, n_seq_bases, mask="S"):
         """
         return Cigar(Cigar(self._reverse_cigar()).mask_left(n_seq_bases, mask)._reverse_cigar())
 
-
     def _reverse_cigar(self):
         return Cigar.string_from_elements(list(self.items())[::-1])
 
@@ -167,7 +162,29 @@ def insert_cigar_element(self, pos: int,
         :param length: If consuming spaces, length of cigar to be added
         :param insertion_cigar: A cigar to insert into current cigar string
         :param pos: integer position where to insert the input cigar string
-        :return:
+        :return: None
+        str1 = CigarString('50M10D7I23M')
+        >>>str2 = CigarString('10I25M')
+        >>>iPos = 55
+        >>>str1.insert_cigar_element(iPos, str2)
+        >>>assert(str1.cigar == "50M5D10I25M5D7I23M")
+
+        >>>str1 = CigarString('50M10D7I23M')
+        >>>iPos = 20
+        >>>str1.insert_cigar_element(iPos, str2)
+        >>>assert(str1.cigar == "20M10I55M10D7I23M")
+
+        >>>str1 = CigarString('11100M')
+        >>>str2 = CigarString('2I')
+        >>>iPos = 5785 + 1
+        >>>str1.insert_cigar_element(iPos, str2, 1)
+        >>>assert (str1.cigar == "5786M2I5313M")
+
+        >>>str1 = CigarString('11100M')
+        >>>str2 = CigarString('1D1M')
+        >>>iPos = 6610 + 1
+        >>>str1.insert_cigar_element(iPos, str2, -1)
+        >>>assert (str1.cigar == "6611M1D4489M")
         """
 
         if insertion_cigar is None:
@@ -214,6 +231,33 @@ def get_cigar_fragment(self, start: int, end: int) -> str:
         :param start: start point of sequence to retrieve
         :param end: end point of sequence to retrieve
         :return: The sequence that spans start to end
+        >>>str1 = CigarString('10M5D10M')
+        >>>start = 0
+        >>>end = 14
+        >>>frag = str1.get_cigar_fragment(start, end)
+        >>>assert(frag == "10M4D")
+
+        >>>str1 = CigarString('10M2D10M')
+        >>>start = 10
+        >>>end = 12
+        >>>frag = str1.get_cigar_fragment(start, end)
+        >>>assert(frag == "2D")
+
+        >>>str1 = CigarString('10M1D10M')
+        >>>start = 10
+        >>>end = 12
+        >>>frag = str1.get_cigar_fragment(start, end)
+        >>>assert(frag == "1D1M")
+
+        >>>str1 = CigarString('102M2I10000M')
+        >>>start1 = 1
+        >>>end1 = 102
+        >>>frag1 = str1.get_cigar_fragment(start1, end1)
+        >>>start2 = 102
+        >>>end2 = 203
+        >>>frag2 = str1.get_cigar_fragment(start2, end2)
+        >>>assert(frag1 == "101M")
+        >>>assert(frag2 == "2I99M")
         """
         # Minus 1 because python slices don't include the end coordinate
         window_size = end - start
@@ -236,7 +280,6 @@ def get_cigar_fragment(self, start: int, end: int) -> str:
             # Minus 1 because of indexing
             current_pos = -1
             previous_pos = -1
-            current_index = 0
             start_found = False
             ret = []
             for item in self.items():
@@ -280,6 +323,7 @@ def get_cigar_fragment(self, start: int, end: int) -> str:
             sys.exit(1)
         return Cigar.string_from_elements(ret)
 
+    # TODO use this method or delete it.
     def count_elements(self, element: str) -> int:
         count = 0
         for item in self.items():
@@ -287,79 +331,50 @@ def count_elements(self, element: str) -> int:
                 count += item[0]
         return count
 
+    def string_to_list(self) -> list:
+        """
+        Converts the cigar string to a full list.
 
-if __name__ == '__main__':
-    print('testing CigarString class...')
-
-    str1 = CigarString('50M10D7I23M')
-    str2 = CigarString('10I25M')
-    iPos = 55
-
-    str1.insert_cigar_element(iPos, str2)
-    print(str1.cigar)
-    assert(str1.cigar == "50M5D10I25M5D7I23M")
-    print("passed")
-
-    str1 = CigarString('50M10D7I23M')
-    iPos = 20
-    str1.insert_cigar_element(iPos, str2)
-    print(str1.cigar)
-    assert(str1.cigar == "20M10I55M10D7I23M")
-    print("passed")
-
-    str1 = CigarString('11100M')
-    str2 = CigarString('2I')
-    iPos = 5785 + 1
-    str1.insert_cigar_element(iPos, str2, 1)
-    print(str1.cigar)
-    assert (str1.cigar == "5786M2I5313M")
-    print("passed")
-
-    str1 = CigarString('11100M')
-    str2 = CigarString('1D1M')
-    iPos = 6610 + 1
-    str1.insert_cigar_element(iPos, str2, -1)
-    print(str1.cigar)
-    assert (str1.cigar == "6611M1D4489M")
-    print("passed")
-
-    str1 = CigarString('10M5D10M')
-    # [('10', 'M'), ('5', 'D'), ('10', 'M')]
-    start = 0
-    end = 14
-    frag = str1.get_cigar_fragment(start, end)
-    print(frag)
-    assert(frag == "10M4D")
-
-    str1 = CigarString('10M2D10M')
-    # [(10, 'M'), (2, 'D'), (10, 'M')]
-    start = 10
-    end = 12
-    frag = str1.get_cigar_fragment(start, end)
-    print(frag)
-    assert(frag == "2D")
-    print("passed")
-
-    str1 = CigarString('10M1D10M')
-    # [(10, 'M'), (1, 'D'), (10, 'M')]
-    start = 10
-    end = 12
-    frag = str1.get_cigar_fragment(start, end)
-    print(frag)
-    assert(frag == "1D1M")
-    print("passed")
-
-    str1 = CigarString('102M2I10000M')
-    start1 = 1
-    end1 = 102
-    frag1 = str1.get_cigar_fragment(start1, end1)
-    start2 = 102
-    end2 = 203
-    frag2 = str1.get_cigar_fragment(start2, end2)
-    print(frag1)
-    print(frag2)
-    assert(frag1 == "101M")
-    assert(frag2 == "2I99M")
+        :return: cigar string in list format.
+        """
+        cigar_dat = []
+        d_reserve = 0
+        for item in self.items():
+            if item[1] == 'D':
+                d_reserve = item[0]
+            if item[1] in ['M', 'I']:
+                if d_reserve:
+                    cigar_dat += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
+                else:
+                    cigar_dat += [item[1]] * item[0]
+                d_reserve = 0
+        return cigar_dat
 
+    @staticmethod
+    def list_to_string(input_list: list) -> str:
+        """
+        Convert a cigar string in list format to a standard cigar string
 
+        :param input_list: Cigar string in list format
+        :return: cigar string in string format
+        """
 
+        symbols = ''
+        current_sym = input_list[0]
+        current_count = 1
+        if 'D' in current_sym:
+            current_sym = current_sym[-1]
+        for k in range(1, len(input_list)):
+            next_sym = input_list[k]
+            if len(next_sym) == 1 and next_sym == current_sym:
+                current_count += 1
+            else:
+                symbols += str(current_count) + current_sym
+                if 'D' in next_sym:
+                    symbols += str(next_sym.count('D')) + 'D'
+                    current_sym = next_sym[-1]
+                else:
+                    current_sym = next_sym
+                current_count = 1
+        symbols += str(current_count) + current_sym
+        return symbols
diff --git a/source/probability.py b/source/probability.py
index 9dcd44d..1cb7bb4 100644
--- a/source/probability.py
+++ b/source/probability.py
@@ -1,6 +1,8 @@
 import random
 import bisect
 import copy
+import sys
+
 import numpy as np
 
 LOW_PROB_THRESH = 1e-12
@@ -27,7 +29,7 @@ def __init__(self, weights, values, degenerate_val=None, method='bisect'):
         # some sanity checking
         if not len(weights) or not len(values):
             print('\nError: weight or value vector given to DiscreteDistribution() are 0-length.\n')
-            exit(1)
+            sys.exit(1)
 
         self.method = method
         sum_weight = float(sum(weights))

From 02442fadb2be0e9c7438e839856a750c55f509f5 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 9 Dec 2020 14:33:08 -0600
Subject: [PATCH 227/323] Fixed a bug in SequenceContainer. Runs 10x faster
 now,

---
 bacterial_genreads_wrapper.py |  2 +-
 source/SequenceContainer.py   |  2 +-
 source/neat_cigar.py          | 18 +++++++-----------
 3 files changed, 9 insertions(+), 13 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 864fab5..d428996 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -45,7 +45,7 @@ def analyze(self):
         manipulation to unzip the file and fix genreads horribly formatted fasta file.
         :return: None
         """
-        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa', '-c', '1', '--rng', '1']
+        args = ['-r', str(self.reference), '-R', '101', '-o', self.name, '--fa', '-c', '1']
         gen_reads.main(args)
         self.file = pathlib.Path().absolute() / (self.name + ".fasta.gz")
 
diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 9340d55..c150423 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -638,7 +638,7 @@ def random_mutations(self):
                 # compute number of ref matches for each read
                 span_dif = len([n for n in temp_symbol_string_list[j:j + self.read_len] if 'M' in n])
                 self.fm_span[i].append(self.fm_pos[i][-1] + span_dif)
-                md_so_far += temp_symbol_string_list[j].count('M') + temp_symbol_string_list.count('D')
+                md_so_far += temp_symbol_string_list[j].count('M') + temp_symbol_string_list[j].count('D')
 
         # tally up all the variants we handled...
         count_dict = {}
diff --git a/source/neat_cigar.py b/source/neat_cigar.py
index f6202fe..3b747f2 100644
--- a/source/neat_cigar.py
+++ b/source/neat_cigar.py
@@ -107,6 +107,13 @@ def insert_cigar_element(self, pos: int, i_string_in: str = None, i_list_in: lis
         :param i_string_in: input cigar string in string format
         :param i_list_in: input cigar string in list format
         :return:
+
+        >>>str1 = '50M10D7I23M'
+        >>>str2 = '10I25M'
+        >>>iPos = 20
+        >>>my_cigar = CigarString(string_in=str1)
+        >>>my_cigar.insert_cigar_element(iPos, i_string_in=str2)
+        >>>assert(my_cigar.get_string() == "20M10I55M10D7I23M")
         """
 
         if i_string_in is None and i_list_in is None:
@@ -123,14 +130,3 @@ def insert_cigar_element(self, pos: int, i_string_in: str = None, i_list_in: lis
         if i_list_in is not None:
             self.cigar_data = self.cigar_data[:pos] + i_list_in + self.cigar_data[pos:]
 
-
-if __name__ == '__main__':
-    print('testing CigarString class...')
-
-    str1 = '50M10D7I23M'
-    str2 = '10I25M'
-    iPos = 20
-    my_cigar = CigarString(string_in=str1)
-    my_cigar.insert_cigar_element(iPos, i_string_in=str2)
-    assert(my_cigar.get_string() == "20M10I55M10D7I23M")
-    print("passed")

From 4b35f1b8f933f89d2c08966a8ccbfb191c6c7c2a Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 10 Dec 2020 09:08:52 -0600
Subject: [PATCH 228/323] A few small updates, removing some debugging
 statements

---
 source/SequenceContainer.py  | 11 ++++++-----
 source/output_file_writer.py |  2 --
 2 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index c150423..4fecf2c 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -13,7 +13,6 @@
 from source.probability import DiscreteDistribution, poisson_list
 from source.neat_cigar import CigarString
 
-
 """
 Constants needed for analysis
 """
@@ -607,19 +606,20 @@ def random_mutations(self):
                     # notate indel positions for cigar computation
                     if indel_length > 0:
                         cigar_to_insert = CigarStringNew(str(indel_length) + 'I')
-                        temp_symbol_string.insert_cigar_element(v_pos+1, cigar_to_insert, len(all_indels_ins[i][j][1]))
+                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert,
+                                                                len(all_indels_ins[i][j][1]))
                         # TODO Delete commented out lines once CigarString works 100%
                         # temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
                         #                       ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
                     elif indel_length < 0:
                         cigar_to_insert = CigarStringNew(str(abs(indel_length)) + 'D1M')
-                        temp_symbol_string.insert_cigar_element(v_pos+1, cigar_to_insert, indel_length)
+                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert, indel_length)
                         # TODO Delete commented out lines once CigarString works 100%
                         # temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
-                self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j+self.read_len))
+                self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j + self.read_len))
                 # TODO Delete commented out lines once CigarString works 100%
                 # self.all_cigar[i].append(CigarString(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
 
@@ -768,7 +768,8 @@ def sample_read(self, sequencing_model, frag_len=None):
                                 # extra_cigar_val = CigarString(string_in=self.all_cigar[my_ploid][read[0]
                                 #                                + fill_to_go]).get_list()[-fill_to_go:]
                                 extra_cigar_val = CigarStringNew(self.all_cigar[my_ploid][read[0]
-                                                                 + fill_to_go]).string_to_list()[-fill_to_go:]
+                                                                                          + fill_to_go]).string_to_list()[
+                                                  -fill_to_go:]
 
                             except IndexError:
                                 # Applying the deletions we want requires going beyond region boundaries.
diff --git a/source/output_file_writer.py b/source/output_file_writer.py
index de8a5c8..cdf2985 100644
--- a/source/output_file_writer.py
+++ b/source/output_file_writer.py
@@ -105,8 +105,6 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
         bam = pathlib.Path(out_prefix + '_golden.bam')
         vcf = pathlib.Path(out_prefix + '_golden.vcf.gz')
 
-        print(fq1)
-        print(fq2)
         # TODO Make a fasta-specific method
         self.no_fastq = no_fastq
         if not self.no_fastq:

From 507df7b8672ae3f2765ff5e99a1a7d056e41ea8e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 11 Dec 2020 15:46:46 -0600
Subject: [PATCH 229/323] added some debugging around problem code

---
 source/SequenceContainer.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 4fecf2c..8b108bb 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -790,7 +790,14 @@ def sample_read(self, sequencing_model, frag_len=None):
                             # weird edge case with del at very end of region. Make a guess and add a "M"
                             if pi + 1 == len(expanded_cigar):
                                 expanded_cigar.append('M')
-                            expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
+                            try:
+                                expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
+                            except ValueError as e:
+                                print(f'pi = {pi}')
+                                print(f'expanded_cigar = {expanded_cigar}')
+                                print("Debug")
+                                sys.exit(1)
+
                         else:
                             print('\nError, ref does not match alt while attempting to insert deletion error!\n')
                             sys.exit(1)

From d52d1203304120e86afbb4dd7eb32ec3c085a30e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 11 Dec 2020 21:23:06 -0600
Subject: [PATCH 230/323] changed except type, trying to debug

---
 source/SequenceContainer.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 8b108bb..83d8f8b 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -768,8 +768,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                                 # extra_cigar_val = CigarString(string_in=self.all_cigar[my_ploid][read[0]
                                 #                                + fill_to_go]).get_list()[-fill_to_go:]
                                 extra_cigar_val = CigarStringNew(self.all_cigar[my_ploid][read[0]
-                                                                                          + fill_to_go]).string_to_list()[
-                                                  -fill_to_go:]
+                                                                 + fill_to_go]).string_to_list()[-fill_to_go:]
 
                             except IndexError:
                                 # Applying the deletions we want requires going beyond region boundaries.
@@ -792,7 +791,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                                 expanded_cigar.append('M')
                             try:
                                 expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
-                            except ValueError as e:
+                            except IndexError:
                                 print(f'pi = {pi}')
                                 print(f'expanded_cigar = {expanded_cigar}')
                                 print("Debug")

From 2ff8631c124082d0abd6a52229ecc2e16e54abae Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Sat, 12 Dec 2020 10:33:40 -0600
Subject: [PATCH 231/323] further debugging

---
 source/SequenceContainer.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 83d8f8b..2cc4779 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -792,8 +792,14 @@ def sample_read(self, sequencing_model, frag_len=None):
                             try:
                                 expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
                             except IndexError:
+                                print(f'error = {error}')
+                                print(f'e_pos = {e_pos}')
                                 print(f'pi = {pi}')
-                                print(f'expanded_cigar = {expanded_cigar}')
+                                print(f'my_adj = {my_adj}')
+                                print(f'total_d = {total_d}')
+                                print(f'total_i = {total_i}')
+                                print(f'expanded_cigar = {CigarStringNew.list_to_string(expanded_cigar)}')
+                                print(f'sorted_errors = {sorted_errors}')
                                 print("Debug")
                                 sys.exit(1)
 

From 23f67d337994cb6eda7f2164fee5638df04b8e32 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 14 Dec 2020 09:38:27 -0600
Subject: [PATCH 232/323] pulling in changes to genSeq from py3 branch

---
 utilities/genSeqErrorModel.py | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 109b598..941a6e2 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -17,16 +17,17 @@
 import sys
 import pickle
 import matplotlib.pyplot as mpl
+import pathlib
 import pysam
 from functools import reduce
 
 # enables import from neighboring package
-# sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
+sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
 
 from source.probability import DiscreteDistribution
 
 
-def parse_fq(input_file, real_q, off_q, max_reads, n_samp, plot_stuff):
+def parse_file(input_file, real_q, off_q, max_reads, n_samp, plot_stuff):
     init_smooth = 0.
     prob_smooth = 0.
 
@@ -205,10 +206,11 @@ def parse_fq(input_file, real_q, off_q, max_reads, n_samp, plot_stuff):
     count_dict = {}
     for q in q_scores:
         count_dict[q] = 0
-    samp_quarters = len(range(1, n_samp + 1)) // 4
+    lines_to_sample = len(range(1, n_samp + 1))
+    samp_quarters = lines_to_sample // 4
     for samp in range(1, n_samp + 1):
         if samp % samp_quarters == 0:
-            print(f'{(samp/samp_quarters)*100:.0f}%')
+            print(f'{(samp/lines_to_sample)*100:.0f}%')
         my_q = init_dist_by_pos[0].sample()
         count_dict[my_q] += 1
         for i in range(1, len(init_q)):
@@ -243,19 +245,19 @@ def main():
                         help='perform some optional plotting')
     args = parser.parse_args()
 
-    (inf, ouf, off_q, max_q, max_reads, n_samp) = (args.i, args.o, args.q, args.Q, args.n, args.s)
-    (inf2, pile_up) = (args.i2, args.p)
+    (infile, outfile, off_q, max_q, max_reads, n_samp) = (args.i, args.o, args.q, args.Q, args.n, args.s)
+    (infile2, pile_up) = (args.i2, args.p)
 
     real_q = max_q + 1
 
     plot_stuff = args.plot
 
     q_scores = range(real_q)
-    if inf2 == None:
-        (init_q, prob_q, avg_err) = parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff)
+    if infile2 is None:
+        (init_q, prob_q, avg_err) = parse_file(infile, real_q, off_q, max_reads, n_samp, plot_stuff)
     else:
-        (init_q, prob_q, avg_err1) = parse_fq(inf, real_q, off_q, max_reads, n_samp, plot_stuff)
-        (init_q2, prob_q2, avg_err2) = parse_fq(inf2, real_q, off_q, max_reads, n_samp, plot_stuff)
+        (init_q, prob_q, avg_err1) = parse_file(infile, real_q, off_q, max_reads, n_samp, plot_stuff)
+        (init_q2, prob_q2, avg_err2) = parse_file(infile2, real_q, off_q, max_reads, n_samp, plot_stuff)
         avg_err = (avg_err1 + avg_err2) / 2.
 
     #
@@ -292,11 +294,12 @@ def main():
     #
     #	finally, let's save our output model
     #
+    outfile = pathlib.Path(outfile).with_suffix(".p")
     print('saving model...')
-    if inf2 == None:
-        pickle.dump([init_q, prob_q, q_scores, off_q, avg_err, err_params], open(ouf, 'wb'))
+    if infile2 is None:
+        pickle.dump([init_q, prob_q, q_scores, off_q, avg_err, err_params], open(outfile, 'wb'))
     else:
-        pickle.dump([init_q, prob_q, init_q2, prob_q2, q_scores, off_q, avg_err, err_params], open(ouf, 'wb'))
+        pickle.dump([init_q, prob_q, init_q2, prob_q2, q_scores, off_q, avg_err, err_params], open(outfile, 'wb'))
 
 
 if __name__ == '__main__':

From 374f7367fce8b8fbbc8cc47b17a9f11208cac393 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 14 Dec 2020 12:06:31 -0600
Subject: [PATCH 233/323] Added help formatter to argparse in all programs.

---
 bacterial_genreads_wrapper.py | 6 ++++--
 gen_reads.py                  | 3 ++-
 utilities/compute_fraglen.py  | 3 ++-
 utilities/compute_gc.py       | 3 ++-
 utilities/genSeqErrorModel.py | 4 ++--
 utilities/gen_mut_model.py    | 3 ++-
 utilities/plotMutModel.py     | 7 +++++--
 utilities/validateFQ.py       | 2 +-
 utilities/vcf_compare_OLD.py  | 3 ++-
 9 files changed, 22 insertions(+), 12 deletions(-)

diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index d428996..0afcdf6 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -214,7 +214,9 @@ def extract_names(reference: str) -> list:
 
 
 def main():
-    parser = argparse.ArgumentParser(description='bacterial_genreads_wrapper.source')
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+                                     description="Wrapper for gen_reads.py that simulates multiple generations"
+                                                 "of bacteria.")
     parser.add_argument('-r', type=str, required=True, metavar='reference.fasta',
                         help="Reference file for organism in fasta format")
     parser.add_argument('-g', type=int, required=True, metavar='generations', help="Number of generations to run")
@@ -226,7 +228,7 @@ def main():
     parser.add_argument('-c', type=int, required=False, default=10, metavar='coverage value',
                         help='Target coverage value for final set of sampled fastqs')
     parser.add_argument('--pe', nargs=2, type=int, required=False, metavar=('<int>', '<int>'), default=(500, 50),
-                        help='Paired-end fragment length mean and std')
+                        help='Paired-end fragment length mean and std.')
     args = parser.parse_args()
 
     (ref_fasta, init_population_size, generations) = (args.r, args.i, args.g)
diff --git a/gen_reads.py b/gen_reads.py
index a5e1002..49e5621 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -48,7 +48,8 @@ def main(raw_args=None):
     ////////////    PARSE INPUT ARGUMENTS    ////////////
     //////////////////////////////////////////////////"""
 
-    parser = argparse.ArgumentParser(description='NEAT-genReads V2.0')
+    parser = argparse.ArgumentParser(description='NEAT-genReads V3.0',
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter,)
     parser.add_argument('-r', type=str, required=True, metavar='reference', help="Path to reference fasta")
     parser.add_argument('-R', type=int, required=True, metavar='read length', help="The desired read length")
     parser.add_argument('-o', type=str, required=True, metavar='output_prefix',
diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
index f61ab7f..5eb6b9c 100644
--- a/utilities/compute_fraglen.py
+++ b/utilities/compute_fraglen.py
@@ -149,7 +149,8 @@ def main():
 
     :return: None
     """
-    parser = argparse.ArgumentParser(description="compute_fraglen.source")
+    parser = argparse.ArgumentParser(description="compute_fraglen.source",
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter,)
     parser.add_argument('-i', type=str, metavar="input", required=True, default=None,
                         help="Sam file input (samtools view name.bam > name.sam)")
     parser.add_argument('-o', type=str, metavar="output", required=True, default=None, help="Prefix for output")
diff --git a/utilities/compute_gc.py b/utilities/compute_gc.py
index d2ef0bc..697cdec 100644
--- a/utilities/compute_gc.py
+++ b/utilities/compute_gc.py
@@ -122,7 +122,8 @@ def main():
         -w is the sliding window length. The default is 50, but you can declare any reasonable integer
     :return: None
     """
-    parser = argparse.ArgumentParser(description='compute_gc.source')
+    parser = argparse.ArgumentParser(description='compute_gc.source',
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter,)
     parser.add_argument('-i', type=str, required=True, metavar='input', help="input.genomecov")
     parser.add_argument('-r', type=str, required=True, metavar='reference', help="reference.fasta")
     parser.add_argument('-o', type=str, required=True, metavar='output prefix',
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
index 941a6e2..85a6a59 100644
--- a/utilities/genSeqErrorModel.py
+++ b/utilities/genSeqErrorModel.py
@@ -31,7 +31,7 @@ def parse_file(input_file, real_q, off_q, max_reads, n_samp, plot_stuff):
     init_smooth = 0.
     prob_smooth = 0.
 
-    #Takes a gzip or sam file and returns the simulation's average error rate,
+    # Takes a gzip or sam file and returns the simulation's average error rate,
     print('reading ' + input_file + '...')
     is_aligned = False
     lines_to_read = 0
@@ -229,7 +229,7 @@ def parse_file(input_file, real_q, off_q, max_reads, n_samp, plot_stuff):
 
 
 def main():
-    parser = argparse.ArgumentParser(description='genSeqErrorModel.source')
+    parser = argparse.ArgumentParser(description='genSeqErrorModel.py')
     parser.add_argument('-i', type=str, required=True, metavar='<str>', help="* input_read1.fq (.gz) / input_read1.sam")
     parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output.p")
     parser.add_argument('-i2', type=str, required=False, metavar='<str>', default=None,
diff --git a/utilities/gen_mut_model.py b/utilities/gen_mut_model.py
index 23d7894..905863a 100644
--- a/utilities/gen_mut_model.py
+++ b/utilities/gen_mut_model.py
@@ -52,7 +52,8 @@ def main():
     # if parsing a dbsnp vcf, and no CAF= is found in info tag, use this as default val for population freq
     VCF_DEFAULT_POP_FREQ = 0.00001
 
-    parser = argparse.ArgumentParser(description='gen_mut_model.source')
+    parser = argparse.ArgumentParser(description='gen_mut_model.source',
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter,)
     parser.add_argument('-r', type=str, required=True, metavar='/path/to/reference.fasta',
                         help="Reference file for organism in fasta format")
     parser.add_argument('-m', type=str, required=True, metavar='/path/to/mutations.vcf',
diff --git a/utilities/plotMutModel.py b/utilities/plotMutModel.py
index 0053500..e94207f 100644
--- a/utilities/plotMutModel.py
+++ b/utilities/plotMutModel.py
@@ -19,8 +19,11 @@
 # mpl.rc('text',usetex=True)
 # mpl.rcParams['text.latex.preamble']=[r"\usepackage{amsmath}"]
 
-parser = argparse.ArgumentParser(
-    description='Plot and compare mutation models from gen_mut_model.source Usage: source plotMutModel.source -i model1.p [model2.p] [model3.p]... -l legend_label1 [legend_label2] [legend_label3]... -o path/to/pdf_plot_prefix')
+parser = argparse.ArgumentParser(description='Plot and compare mutation models from gen_mut_model.source Usage: '
+                                             'source plotMutModel.source -i model1.p [model2.p] [model3.p]... '
+                                             '-l legend_label1 [legend_label2] [legend_label3]... '
+                                             '-o path/to/pdf_plot_prefix',
+                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter,)
 parser.add_argument('-i', type=str, required=True, metavar='<str>', nargs='+',
                     help="* mutation_model_1.p [mutation_model_2.p] [mutation_model_3] ...")
 parser.add_argument('-l', type=str, required=True, metavar='<str>', nargs='+',
diff --git a/utilities/validateFQ.py b/utilities/validateFQ.py
index 3cc00ee..7ce02ea 100644
--- a/utilities/validateFQ.py
+++ b/utilities/validateFQ.py
@@ -3,7 +3,7 @@
 #
 #	A quickie tool for validating the correctness of a FASTQ file
 #
-#	source validateFQ.source read1.fq [read2.fq]
+#	python validateFQ.py read1.fq [read2.fq]
 #
 # Python 3 ready
 
diff --git a/utilities/vcf_compare_OLD.py b/utilities/vcf_compare_OLD.py
index 9efa24d..082db97 100644
--- a/utilities/vcf_compare_OLD.py
+++ b/utilities/vcf_compare_OLD.py
@@ -35,7 +35,8 @@
 VERS = 0.1
 
 PARSER = optparse.OptionParser('source %prog [options] -r <ref.fa> -g <golden.vcf> -w <workflow.vcf>', description=DESC,
-                               version="%prog v" + str(VERS))
+                               version="%prog v" + str(VERS),
+                               formatter_class=argparse.ArgumentDefaultsHelpFormatter,)
 
 PARSER.add_option('-r', help='* Reference Fasta', dest='REFF', action='store', metavar='<ref.fa>')
 PARSER.add_option('-g', help='* Golden VCF', dest='GVCF', action='store', metavar='<golden.vcf>')

From c93ee565b4aaec1e9556ef3ed7647b23e3277038 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 14 Dec 2020 12:59:32 -0600
Subject: [PATCH 234/323] Made some changes to argparse across the spectrum and
 refactored vcf_compare somewhat

---
 utilities/vcf_compare_OLD.py | 367 ++++++++++++++++++-----------------
 1 file changed, 187 insertions(+), 180 deletions(-)

diff --git a/utilities/vcf_compare_OLD.py b/utilities/vcf_compare_OLD.py
index 082db97..910c5ce 100644
--- a/utilities/vcf_compare_OLD.py
+++ b/utilities/vcf_compare_OLD.py
@@ -21,7 +21,7 @@
 import bisect
 import re
 import numpy as np
-import optparse
+import argparse
 
 from Bio.Seq import Seq
 
@@ -34,82 +34,84 @@
 DESC = """%prog: vcf comparison script."""
 VERS = 0.1
 
-PARSER = optparse.OptionParser('source %prog [options] -r <ref.fa> -g <golden.vcf> -w <workflow.vcf>', description=DESC,
-                               version="%prog v" + str(VERS),
-                               formatter_class=argparse.ArgumentDefaultsHelpFormatter,)
-
-PARSER.add_option('-r', help='* Reference Fasta', dest='REFF', action='store', metavar='<ref.fa>')
-PARSER.add_option('-g', help='* Golden VCF', dest='GVCF', action='store', metavar='<golden.vcf>')
-PARSER.add_option('-w', help='* Workflow VCF', dest='WVCF', action='store', metavar='<workflow.vcf>')
-PARSER.add_option('-o', help='* Output Prefix', dest='OUTF', action='store', metavar='<prefix>')
-PARSER.add_option('-m', help='Mappability Track', dest='MTRK', action='store', metavar='<track.bed>')
-PARSER.add_option('-M', help='Maptrack Min Len', dest='MTMM', action='store', metavar='<int>')
-PARSER.add_option('-t', help='Targetted Regions', dest='TREG', action='store', metavar='<regions.bed>')
-PARSER.add_option('-T', help='Min Region Len', dest='MTRL', action='store', metavar='<int>')
-PARSER.add_option('-c', help='Coverage Filter Threshold [%default]', dest='DP_THRESH', default=15, action='store',
-                  metavar='<int>')
-PARSER.add_option('-a', help='Allele Freq Filter Threshold [%default]', dest='AF_THRESH', default=0.3, action='store',
-                  metavar='<float>')
-
-PARSER.add_option('--vcf-out', help="Output Match/FN/FP variants [%default]", dest='VCF_OUT', default=False,
-                  action='store_true')
-PARSER.add_option('--no-plot', help="No plotting [%default]", dest='NO_PLOT', default=False, action='store_true')
-PARSER.add_option('--incl-homs', help="Include homozygous ref calls [%default]", dest='INCL_H', default=False,
-                  action='store_true')
-PARSER.add_option('--incl-fail', help="Include calls that failed filters [%default]", dest='INCL_F', default=False,
-                  action='store_true')
-PARSER.add_option('--fast', help="No equivalent variant detection [%default]", dest='FAST', default=False,
-                  action='store_true')
-
-(OPTS, ARGS) = PARSER.parse_args()
-
-REFERENCE = OPTS.REFF
-GOLDEN_VCF = OPTS.GVCF
-WORKFLOW_VCF = OPTS.WVCF
-OUT_PREFIX = OPTS.OUTF
-MAPTRACK = OPTS.MTRK
-MIN_READ_LEN = OPTS.MTMM
-BEDFILE = OPTS.TREG
-DP_THRESH = int(OPTS.DP_THRESH)
-AF_THRESH = float(OPTS.AF_THRESH)
-
-VCF_OUT = OPTS.VCF_OUT
-NO_PLOT = OPTS.NO_PLOT
-INCLUDE_HOMS = OPTS.INCL_H
-INCLUDE_FAIL = OPTS.INCL_F
-FAST = OPTS.FAST
+parser = argparse.ArgumentParser('source %prog [options] -r <ref.fa> -g <golden.vcf> -w <workflow.vcf>',
+                                 description=DESC,
+                                 version="%prog v" + str(VERS),
+                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter, )
+
+parser.add_argument('-r', help='* Reference Fasta', dest='reff', action='store', metavar='<ref.fa>')
+parser.add_argument('-g', help='* Golden VCF', dest='golden_vcf', action='store', metavar='<golden.vcf>')
+parser.add_argument('-w', help='* Workflow VCF', dest='workflow_vcf', action='store', metavar='<workflow.vcf>')
+parser.add_argument('-o', help='* Output Prefix', dest='outfile', action='store', metavar='<prefix>')
+parser.add_argument('-m', help='Mappability Track', dest='map_track', action='store', metavar='<track.bed>')
+parser.add_argument('-M', help='Maptrack Min Len', dest='map_track_min_len', action='store', metavar='<int>')
+parser.add_argument('-t', help='Targetted Regions', dest='target_reg', action='store', metavar='<regions.bed>')
+parser.add_argument('-T', help='Min Region Len', dest='min_reg_len', action='store', metavar='<int>')
+parser.add_argument('-c', help='Coverage Filter Threshold [%default]', dest='dp_thresh', default=15, action='store',
+                    metavar='<int>')
+parser.add_argument('-a', help='Allele Freq Filter Threshold [%default]', dest='af_thresh', default=0.3, action='store',
+                    metavar='<float>')
+
+parser.add_argument('--vcf-out', help="Output Match/FN/FP variants [%default]", dest='vcf_out', default=False,
+                    action='store_true')
+parser.add_argument('--no-plot', help="No plotting [%default]", dest='no_plot', default=False, action='store_true')
+parser.add_argument('--incl-homs', help="Include homozygous ref calls [%default]", dest='include_homs', default=False,
+                    action='store_true')
+parser.add_argument('--incl-fail', help="Include calls that failed filters [%default]", dest='include_fail',
+                    default=False,
+                    action='store_true')
+parser.add_argument('--fast', help="No equivalent variant detection [%default]", dest='fast', default=False,
+                    action='store_true')
+
+(opts, args) = parser.parse_args()
+
+reference = opts.reff
+golden_vcf = opts.golden_vcf
+workflow_vcf = opts.workflow_vcf
+out_prefix = opts.outfile
+maptrack = opts.map_track
+min_read_len = opts.map_track_min_len
+bedfile = opts.target_reg
+dp_thresh = int(opts.dp_thresh)
+af_thresh = float(opts.af_thresh)
+
+vcf_out = opts.vcf_out
+no_plot = opts.no_plot
+include_homs = opts.include_homs
+include_fail = opts.include_fail
+fast = opts.fast
 
 if len(sys.argv[1:]) == 0:
-    PARSER.print_help()
+    parser.print_help()
     exit(1)
 
-if OPTS.MTRL is not None:
-    MIN_REGION_LEN = int(OPTS.MTRL)
+if opts.MTRL is not None:
+    min_region_len = int(opts.min_reg_len)
 else:
-    MIN_REGION_LEN = None
+    min_region_len = None
 
-if MIN_READ_LEN is None:
-    MIN_READ_LEN = 0
+if min_read_len is None:
+    min_read_len = 0
 else:
-    MIN_READ_LEN = int(MIN_READ_LEN)
+    min_read_len = int(min_read_len)
 
-if REFERENCE is None:
+if reference is None:
     print('Error: No reference provided.')
-    exit(1)
-if GOLDEN_VCF is None:
+    sys.exit(1)
+if golden_vcf is None:
     print('Error: No golden VCF provided.')
-    exit(1)
-if WORKFLOW_VCF is None:
+    sys.exit(1)
+if workflow_vcf is None:
     print('Error: No workflow VCF provided.')
-    exit(1)
-if OUT_PREFIX is None:
+    sys.exit(1)
+if out_prefix is None:
     print('Error: No output prefix provided.')
-    exit(1)
-if (BEDFILE is not None and MIN_REGION_LEN is None) or (BEDFILE is None and MIN_REGION_LEN is not None):
+    sys.exit(1)
+if (bedfile is not None and min_region_len is None) or (bedfile is None and min_region_len is not None):
     print('Error: Both -t and -T must be specified')
-    exit(1)
+    sys.exit(1)
 
-if NO_PLOT is False:
+if no_plot is False:
     import matplotlib
 
     matplotlib.use('Agg')
@@ -132,7 +134,7 @@ def quantize_AF(af):
         return int(af * AF_STEPS)
 
 
-VCF_HEADER = '##fileformat=VCFv4.1\n##reference=' + REFERENCE + '##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">\n##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">\n'
+VCF_HEADER = '##fileformat=VCFv4.1\n##reference=' + reference + '##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">\n##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">\n'
 
 DP_TOKENS = ['DP', 'DPU', 'DPI']  # in the order that we'll look for them
 
@@ -141,9 +143,9 @@ def parse_line(splt, col_dict, col_samp):
     #	check if we want to proceed..
     ra = splt[col_dict['REF']]
     aa = splt[col_dict['ALT']]
-    if not (INCLUDE_HOMS) and (aa == '.' or aa == '' or aa == ra):
+    if not (include_homs) and (aa == '.' or aa == '' or aa == ra):
         return None
-    if not (INCLUDE_FAIL) and (splt[col_dict['FILTER']] != 'PASS' and splt[col_dict['FILTER']] != '.'):
+    if not (include_fail) and (splt[col_dict['FILTER']] != 'PASS' and splt[col_dict['FILTER']] != '.'):
         return None
 
     #	default vals
@@ -199,25 +201,25 @@ def parse_line(splt, col_dict, col_samp):
     return (cov, qual, alt_alleles, alt_freqs)
 
 
-def parse_vcf(VCF_FILENAME, ref_name, targ_regions_FL, out_file, out_bool):
-    v_Hashed = {}
-    v_pos_Hash = {}
-    v_Alts = {}
-    v_Cov = {}
-    v_AF = {}
-    v_Qual = {}
-    v_Targ_Len = {}
-    n_below_min_R_Len = 0
+def parse_vcf(vcf_filename, ref_name, targ_regions_FL, out_file, out_bool):
+    v_hashed = {}
+    v_pos_hash = {}
+    v_alts = {}
+    v_cov = {}
+    v_af = {}
+    v_qual = {}
+    v_targ_len = {}
+    n_below_min_r_len = 0
     line_unique = 0  # number of lines in vcf file containing unique variant
     hash_coll = 0  # number of times we saw a hash collision ("per line" so non-unique alt alleles don't get counted multiple times)
     var_filtered = 0  # number of variants excluded due to filters (e.g. hom-refs, qual)
     var_merged = 0  # number of variants we merged into another due to having the same position specified
     col_dict = {}
     col_samp = []
-    for line in open(VCF_FILENAME, 'r'):
+    for line in open(vcf_filename, 'r'):
         if line[0] != '#':
             if len(col_dict) == 0:
-                print('\n\nError: VCF has no header?\n' + VCF_FILENAME + '\n\n')
+                print('\n\nError: VCF has no header?\n' + vcf_filename + '\n\n')
                 exit(1)
             splt = line[:-1].split('\t')
             if splt[0] == ref_name:
@@ -227,7 +229,7 @@ def parse_vcf(VCF_FILENAME, ref_name, targ_regions_FL, out_file, out_bool):
 
                 if targ_ind % 2 == 1:
                     targ_Len = targ_regions_FL[targ_ind] - targ_regions_FL[targ_ind - 1]
-                    if (BEDFILE is not None and targ_Len >= MIN_REGION_LEN) or BEDFILE is None:
+                    if (bedfile is not None and targ_Len >= min_region_len) or bedfile is None:
 
                         pl_out = parse_line(splt, col_dict, col_samp)
                         if pl_out is None:
@@ -235,39 +237,39 @@ def parse_vcf(VCF_FILENAME, ref_name, targ_regions_FL, out_file, out_bool):
                             continue
                         (cov, qual, aa, af) = pl_out
 
-                        if var not in v_Hashed:
+                        if var not in v_hashed:
 
                             v_pos = var[0]
-                            if v_pos in v_pos_Hash:
+                            if v_pos in v_pos_hash:
                                 if len(aa) == 0:
                                     aa = [var[2]]
-                                aa.extend([n[2] for n in v_Hashed.keys() if n[0] == v_pos])
+                                aa.extend([n[2] for n in v_hashed.keys() if n[0] == v_pos])
                                 var_merged += 1
-                            v_pos_Hash[v_pos] = 1
+                            v_pos_hash[v_pos] = 1
 
                             if len(aa):
                                 all_vars = [(var[0], var[1], n) for n in aa]
                                 for i in range(len(all_vars)):
-                                    v_Hashed[all_vars[i]] = 1
-                                    # if all_vars[i] not in v_Alts:
-                                    #	v_Alts[all_vars[i]] = []
-                                    # v_Alts[all_vars[i]].extend(all_vars)
-                                    v_Alts[all_vars[i]] = all_vars
+                                    v_hashed[all_vars[i]] = 1
+                                    # if all_vars[i] not in v_alts:
+                                    #	v_alts[all_vars[i]] = []
+                                    # v_alts[all_vars[i]].extend(all_vars)
+                                    v_alts[all_vars[i]] = all_vars
                             else:
-                                v_Hashed[var] = 1
+                                v_hashed[var] = 1
 
                             if cov is not None:
-                                v_Cov[var] = cov
-                            v_AF[var] = af[0]  # only use first AF, even if multiple. fix this later?
-                            v_Qual[var] = qual
-                            v_Targ_Len[var] = targ_Len
+                                v_cov[var] = cov
+                            v_af[var] = af[0]  # only use first AF, even if multiple. fix this later?
+                            v_qual[var] = qual
+                            v_targ_len[var] = targ_Len
                             line_unique += 1
 
                         else:
                             hash_coll += 1
 
                     else:
-                        n_below_min_R_Len += 1
+                        n_below_min_r_len += 1
         else:
             if line[1] != '#':
                 cols = line[1:-1].split('\t')
@@ -275,12 +277,12 @@ def parse_vcf(VCF_FILENAME, ref_name, targ_regions_FL, out_file, out_bool):
                     if 'FORMAT' in col_dict:
                         col_samp.append(i)
                     col_dict[cols[i]] = i
-                if VCF_OUT and out_bool:
+                if vcf_out and out_bool:
                     out_bool = False
                     out_file.write(line)
 
     return (
-        v_Hashed, v_Alts, v_Cov, v_AF, v_Qual, v_Targ_Len, n_below_min_R_Len, line_unique, var_filtered, var_merged,
+        v_hashed, v_alts, v_cov, v_af, v_qual, v_targ_len, n_below_min_r_len, line_unique, var_filtered, var_merged,
         hash_coll)
 
 
@@ -312,11 +314,12 @@ def condense_by_pos(list_in):
 
 
 def main():
+    global bedfile
     ref = []
-    f = open(REFERENCE, 'r')
+    f = open(reference, 'r')
     n_lines = 0
-    prev_R = None
-    prev_P = None
+    prev_r = None
+    prev_p = None
     ref_inds = []
     sys.stdout.write('\nindexing reference fasta... ')
     sys.stdout.flush()
@@ -325,30 +328,30 @@ def main():
         n_lines += 1
         data = f.readline()
         if not data:
-            ref_inds.append((prev_R, prev_P, f.tell() - len(data)))
+            ref_inds.append((prev_r, prev_p, f.tell() - len(data)))
             break
         if data[0] == '>':
-            if prev_P is not None:
-                ref_inds.append((prev_R, prev_P, f.tell() - len(data)))
-            prev_P = f.tell()
-            prev_R = data[1:-1]
+            if prev_p is not None:
+                ref_inds.append((prev_r, prev_p, f.tell() - len(data)))
+            prev_p = f.tell()
+            prev_r = data[1:-1]
     print('{0:.3f} (sec)'.format(time.time() - tt))
     # ref_inds = [('chrM', 6, 16909), ('chr1', 16915, 254252549), ('chr2', 254252555, 502315916), ('chr3', 502315922, 704298801), ('chr4', 704298807, 899276169), ('chr5', 899276175, 1083809741), ('chr6', 1083809747, 1258347116), ('chr7', 1258347122, 1420668559), ('chr8', 1420668565, 1569959868), ('chr9', 1569959874, 1713997574), ('chr10', 1713997581, 1852243023), ('chr11', 1852243030, 1989949677), ('chr12', 1989949684, 2126478617), ('chr13', 2126478624, 2243951900), ('chr14', 2243951907, 2353448438), ('chr15', 2353448445, 2458030465), ('chr16', 2458030472, 2550192321), ('chr17', 2550192328, 2633011443), ('chr18', 2633011450, 2712650243), ('chr19', 2712650250, 2772961813), ('chr20', 2772961820, 2837247851), ('chr21', 2837247858, 2886340351), ('chr22', 2886340358, 2938671016), ('chrX', 2938671022, 3097046994), ('chrY', 3097047000, 3157608038)]
 
-    ztV = 0  # total golden variants
-    ztW = 0  # total workflow variants
-    znP = 0  # total perfect matches
-    zfP = 0  # total false positives
-    znF = 0  # total false negatives
-    znE = 0  # total equivalent variants detected
+    zt_v = 0  # total golden variants
+    zt_w = 0  # total workflow variants
+    zn_p = 0  # total perfect matches
+    zf_p = 0  # total false positives
+    zn_f = 0  # total false negatives
+    zn_e = 0  # total equivalent variants detected
     zgF = 0  # total golden variants that were filtered and excluded
     zgR = 0  # total golden variants that were excluded for being redundant
     zgM = 0  # total golden variants that were merged into a single position
     zwF = 0  # total workflow variants that were filtered and excluded
     zwR = 0  # total workflow variants that were excluded for being redundant
     zwM = 0  # total workflow variants that were merged into a single position
-    if BEDFILE is not None:
-        zbM = 0
+    if bedfile is not None:
+        zb_m = 0
 
     mappability_vs_FN = {0: 0,
                          1: 0}  # [0] = # of FNs that were in mappable regions, [1] = # of FNs that were in unmappable regions
@@ -363,8 +366,8 @@ def main():
     mappability_tracks = {}  # indexed by chr string (e.g. 'chr1'), has boolean array
     prev_Ref = ''
     relevant_regions = []
-    if MAPTRACK is not None:
-        mtf = open(MAPTRACK, 'r')
+    if maptrack is not None:
+        mtf = open(maptrack, 'r')
         for line in mtf:
             splt = line.strip().split('\t')
             if prev_Ref != '' and splt[0] != prev_Ref:
@@ -377,7 +380,7 @@ def main():
                     mappability_tracks[prev_Ref] = [n for n in my_track]
                 #
                 relevant_regions = []
-            if int(splt[3]) >= MIN_READ_LEN:
+            if int(splt[3]) >= min_read_len:
                 relevant_regions.append((int(splt[1]), int(splt[2])))
             prev_Ref = splt[0]
         mtf.close()
@@ -399,9 +402,9 @@ def main():
     global vcfo3_first_time
     vcfo2_first_time = False
     vcfo3_first_time = False
-    if VCF_OUT:
-        vcfo2 = open(OUT_PREFIX + '_FN.vcf', 'w')
-        vcfo3 = open(OUT_PREFIX + '_FP.vcf', 'w')
+    if vcf_out:
+        vcfo2 = open(out_prefix + '_FN.vcf', 'w')
+        vcfo3 = open(out_prefix + '_FP.vcf', 'w')
         vcfo2_first_time = True
         vcfo3_first_time = True
 
@@ -421,7 +424,7 @@ def main():
     for n_RI in ref_inds:
 
         ref_name = n_RI[0]
-        if FAST == False:
+        if not fast:
             f.seek(n_RI[1])
             print('reading ' + ref_name + '...', end=' ')
             my_dat = f.read(n_RI[2] - n_RI[1]).split('\n')
@@ -447,16 +450,16 @@ def main():
         #
         #	Parse relevant targeted regions
         #
-        targ_regions_FL = []
-        if BEDFILE is not None:
-            bedfile = open(BEDFILE, 'r')
+        targ_regions_fl = []
+        if bedfile is not None:
+            bedfile = open(bedfile, 'r')
             for line in bedfile:
                 splt = line.split('\t')
                 if splt[0] == ref_name:
-                    targ_regions_FL.extend((int(splt[1]), int(splt[2])))
+                    targ_regions_fl.extend((int(splt[1]), int(splt[2])))
             bedfile.close()
         else:
-            targ_regions_FL = [-1, MAX_VAL + 1]
+            targ_regions_fl = [-1, MAX_VAL + 1]
 
         #
         #	Parse vcf files
@@ -466,11 +469,12 @@ def main():
         tt = time.time()
 
         (correct_hashed, correct_alts, correct_cov, correct_AF, correct_qual, correct_targ_len, correct_below_min_R_len,
-         correct_unique, g_filtered, g_merged, g_redundant) = parse_vcf(GOLDEN_VCF, ref_name, targ_regions_FL, vcfo2,
-                                                                   vcfo2_first_time)
-        (workflow_hashed, workflow_alts, workflow_COV, workflow_AF, workflow_qual, workflow_tar_len, workflow_below_min_R_len,
-         workflow_unique, w_filtered, w_merged, w_redundant) = parse_vcf(WORKFLOW_VCF, ref_name, targ_regions_FL, vcfo3,
-                                                                    vcfo3_first_time)
+         correct_unique, g_filtered, g_merged, g_redundant) = parse_vcf(golden_vcf, ref_name, targ_regions_fl, vcfo2,
+                                                                        vcfo2_first_time)
+        (workflow_hashed, workflow_alts, workflow_COV, workflow_AF, workflow_qual, workflow_tar_len,
+         workflow_below_min_R_len,
+         workflow_unique, w_filtered, w_merged, w_redundant) = parse_vcf(workflow_vcf, ref_name, targ_regions_fl, vcfo3,
+                                                                         vcfo3_first_time)
         zgF += g_filtered
         zgR += g_redundant
         zgM += g_merged
@@ -497,44 +501,44 @@ def main():
         #                    = 2: should be discluded because we were found
         #                    = 3: should be discluded because an alt was found
         not_found = [n for n in sorted(correct_hashed.keys()) if correct_hashed[n] == 1]
-        FP_variants = [n for n in sorted(workflow_hashed.keys()) if workflow_hashed[n] == 1]
+        fp_variants = [n for n in sorted(workflow_hashed.keys()) if workflow_hashed[n] == 1]
 
         #
         #	condense all variants who have alternate alleles and were *not* found to have perfect matches
         #	into a single variant again. These will not be included in the candidates for equivalency checking. Sorry!
         #
         not_found = condense_by_pos(not_found)
-        FP_variants = condense_by_pos(FP_variants)
+        fp_variants = condense_by_pos(fp_variants)
 
         #
         #	tally up some values, if there are no golden variants lets save some CPU cycles and move to the next ref
         #
         tot_golden_variants = n_perfect + len(not_found)
-        totalWorkflowVariants = n_perfect + len(FP_variants)
+        total_workflow_variants = n_perfect + len(fp_variants)
         if tot_golden_variants == 0:
-            zfP += len(FP_variants)
-            ztW += totalWorkflowVariants
+            zf_p += len(fp_variants)
+            zt_w += total_workflow_variants
             print('{0:.3f} (sec)'.format(time.time() - tt))
             continue
 
         #
         #	let's check for equivalent variants
         #
-        if FAST == False:
+        if fast == False:
             del_list_i = []
             del_list_j = []
             regions_to_check = []
-            for i in range(len(FP_variants)):
-                pos = FP_variants[i][0]
+            for i in range(len(fp_variants)):
+                pos = fp_variants[i][0]
                 regions_to_check.append((max([pos - EV_BPRANGE - 1, 0]), min([pos + EV_BPRANGE, len(my_dat) - 1])))
 
             for n in regions_to_check:
                 ref_section = my_dat[n[0]:n[1]]
 
                 FP_within = []
-                for i in range(len(FP_variants)):
-                    m = FP_variants[i]
-                    if (m[0] > n[0] and m[0] < n[1]):
+                for i in range(len(fp_variants)):
+                    m = fp_variants[i]
+                    if n[0] < m[0] < n[1]:
                         FP_within.append((m, i))
                 FP_within = sorted(FP_within)
                 adj = 0
@@ -549,7 +553,7 @@ def main():
                 nf_within = []
                 for j in range(len(not_found)):
                     m = not_found[j]
-                    if (m[0] > n[0] and m[0] < n[1]):
+                    if n[0] < m[0] < n[1]:
                         nf_within.append((m, j))
                 nf_within = sorted(nf_within)
                 adj = 0
@@ -571,7 +575,7 @@ def main():
 
             n_equiv = 0
             for i in sorted(list(set(del_list_i)), reverse=True):
-                del FP_variants[i]
+                del fp_variants[i]
             for j in sorted(list(set(del_list_j)), reverse=True):
                 del not_found[j]
                 n_equiv += 1
@@ -580,15 +584,15 @@ def main():
         #
         #	Tally up errors and whatnot
         #
-        ztV += tot_golden_variants
-        ztW += totalWorkflowVariants
-        znP += n_perfect
-        zfP += len(FP_variants)
-        znF += len(not_found)
-        if FAST is False:
-            znE += n_equiv
-        if BEDFILE is not None:
-            zbM += correct_below_min_R_len
+        zt_v += tot_golden_variants
+        zt_w += total_workflow_variants
+        zn_p += n_perfect
+        zf_p += len(fp_variants)
+        zn_f += len(not_found)
+        if fast is False:
+            zn_e += n_equiv
+        if bedfile is not None:
+            zb_m += correct_below_min_R_len
 
         #
         #	try to identify a reason for FN variants:
@@ -601,7 +605,7 @@ def main():
             no_reason = True
 
             #	mappability?
-            if MAPTRACK is not None:
+            if maptrack is not None:
                 if ref_name in mappability_tracks and var[0] < len(mappability_tracks[ref_name]):
                     if mappability_tracks[ref_name][var[0]]:
                         mappability_vs_FN[1] += 1
@@ -617,7 +621,7 @@ def main():
                     if c not in coverage_vs_FN:
                         coverage_vs_FN[c] = 0
                     coverage_vs_FN[c] += 1
-                    if c < DP_THRESH:
+                    if c < dp_thresh:
                         venn_data[i][1] = 1
                         no_reason = False
 
@@ -637,30 +641,33 @@ def main():
                 venn_data[i][2] += 1
 
         for i in range(len(not_found)):
-            if venn_data[i][0]: set1.append(i + var_adj)
-            if venn_data[i][1]: set2.append(i + var_adj)
-            if venn_data[i][2]: set3.append(i + var_adj)
+            if venn_data[i][0]:
+                set1.append(i + var_adj)
+            if venn_data[i][1]:
+                set2.append(i + var_adj)
+            if venn_data[i][2]:
+                set3.append(i + var_adj)
         var_adj += len(not_found)
 
         #
         #	if desired, write out vcf files.
         #
         not_found = sorted(not_found)
-        FP_variants = sorted(FP_variants)
-        if VCF_OUT:
-            for line in open(GOLDEN_VCF, 'r'):
+        fp_variants = sorted(fp_variants)
+        if vcf_out:
+            for line in open(golden_vcf, 'r'):
                 if line[0] != '#':
                     splt = line.split('\t')
                     if splt[0] == ref_name:
                         var = (int(splt[1]), splt[3], splt[4])
                         if var in not_found:
                             vcfo2.write(line)
-            for line in open(WORKFLOW_VCF, 'r'):
+            for line in open(workflow_vcf, 'r'):
                 if line[0] != '#':
                     splt = line.split('\t')
                     if splt[0] == ref_name:
                         var = (int(splt[1]), splt[3], splt[4])
-                        if var in FP_variants:
+                        if var in fp_variants:
                             vcfo3.write(line)
 
         print('{0:.3f} (sec)'.format(time.time() - tt))
@@ -669,16 +676,16 @@ def main():
     #	close vcf output
     #
     print('')
-    if VCF_OUT:
-        print(OUT_PREFIX + '_FN.vcf')
-        print(OUT_PREFIX + '_FP.vcf')
+    if vcf_out:
+        print(out_prefix + '_FN.vcf')
+        print(out_prefix + '_FP.vcf')
         vcfo2.close()
         vcfo3.close()
 
     #
     #	plot some FN stuff
     #
-    if NO_PLOT == False:
+    if no_plot == False:
         n_detected = len(set(set1 + set2 + set3))
         set1 = set(set1)
         set2 = set(set2)
@@ -689,7 +696,7 @@ def main():
         else:
             s1 = ''
         if len(set2):
-            s2 = 'DP < ' + str(DP_THRESH)
+            s2 = 'DP < ' + str(dp_thresh)
         else:
             s2 = ''
         # if len(set3): s3 = 'AF < '+str(AF_THRESH)
@@ -700,16 +707,16 @@ def main():
 
         mpl.figure(0)
         tstr1 = 'False Negative Variants (Missed Detections)'
-        # tstr2 = str(n_detected)+' / '+str(znF)+' FN variants categorized'
+        # tstr2 = str(n_detected)+' / '+str(zn_f)+' FN variants categorized'
         tstr2 = ''
-        if MAPTRACK is not None:
+        if maptrack is not None:
             v = venn3([set1, set2, set3], (s1, s2, s3))
         else:
             v = venn2([set2, set3], (s2, s3))
         mpl.figtext(0.5, 0.95, tstr1, fontdict={'size': 14, 'weight': 'bold'}, horizontalalignment='center')
         mpl.figtext(0.5, 0.03, tstr2, fontdict={'size': 14, 'weight': 'bold'}, horizontalalignment='center')
 
-        ouf = OUT_PREFIX + '_FNvenn.pdf'
+        ouf = out_prefix + '_FNvenn.pdf'
         print(ouf)
         mpl.savefig(ouf)
 
@@ -717,20 +724,20 @@ def main():
     #	spit out results to console
     #
     print('\n**********************************\n')
-    if BEDFILE is not None:
+    if bedfile is not None:
         print('ONLY CONSIDERING VARIANTS FOUND WITHIN TARGETED REGIONS\n\n')
-    print('Total Golden Variants:  ', ztV, '\t[', zgF, 'filtered,', zgM, 'merged,', zgR, 'redundant ]')
-    print('Total Workflow Variants:', ztW, '\t[', zwF, 'filtered,', zwM, 'merged,', zwR, 'redundant ]')
+    print('Total Golden Variants:  ', zt_v, '\t[', zgF, 'filtered,', zgM, 'merged,', zgR, 'redundant ]')
+    print('Total Workflow Variants:', zt_w, '\t[', zwF, 'filtered,', zwM, 'merged,', zwR, 'redundant ]')
     print('')
-    if ztV > 0 and ztW > 0:
-        print('Perfect Matches:', znP, '({0:.2f}%)'.format(100. * float(znP) / ztV))
-        print('FN variants:    ', znF, '({0:.2f}%)'.format(100. * float(znF) / ztV))
-        print('FP variants:    ', zfP)  # ,'({0:.2f}%)'.format(100.*float(zfP)/ztW)
-    if FAST == False:
-        print('\nNumber of equivalent variants denoted differently between the two vcfs:', znE)
-    if BEDFILE is not None:
-        print('\nNumber of golden variants located in targeted regions that were too small to be sampled from:', zbM)
-    if FAST:
+    if zt_v > 0 and zt_w > 0:
+        print('Perfect Matches:', zn_p, '({0:.2f}%)'.format(100. * float(zn_p) / zt_v))
+        print('FN variants:    ', zn_f, '({0:.2f}%)'.format(100. * float(zn_f) / zt_v))
+        print('FP variants:    ', zf_p)  # ,'({0:.2f}%)'.format(100.*float(zf_p)/zt_w)
+    if not fast:
+        print('\nNumber of equivalent variants denoted differently between the two vcfs:', zn_e)
+    if bedfile is not None:
+        print('\nNumber of golden variants located in targeted regions that were too small to be sampled from:', zb_m)
+    if fast:
         print(
             "\nWarning! Running with '--fast' means that identical variants denoted differently between the two vcfs will not be detected! The values above may be lower than the true accuracy.")
     # if NO_PLOT:

From f63a8be32e618ed0f5af0c6c242e7dba3d43ba66 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 4 Jan 2021 15:53:38 -0600
Subject: [PATCH 235/323] Minor changes to gitignore etc

---
 .gitignore                    | 3 ++-
 bacterial_genreads_wrapper.py | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/.gitignore b/.gitignore
index de8c783..bf24818 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
 # Ignore filetypes
 *.pyc
 /python2env/
-/.ipynb_checkpoints/
\ No newline at end of file
+/.ipynb_checkpoints/
+/.vscode/
\ No newline at end of file
diff --git a/bacterial_genreads_wrapper.py b/bacterial_genreads_wrapper.py
index 0afcdf6..5d31aa2 100644
--- a/bacterial_genreads_wrapper.py
+++ b/bacterial_genreads_wrapper.py
@@ -132,7 +132,7 @@ def cull(population: list, percentage: float = 0.5) -> list:
     """
     cull_amount = round(len(population) * percentage)
     print("Culling {} members from population".format(cull_amount))
-    for i in range(cull_amount):
+    for _ in range(cull_amount):
         selection = random.choice(population)
         population.remove(selection)
         selection.remove()

From 46aa8c4c22ccb7c7f7a2d0e3baa99a8323322725 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 5 Jan 2021 16:53:17 -0600
Subject: [PATCH 236/323] Might have found the bug

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 2cc4779..1b2865a 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -835,7 +835,7 @@ def sample_read(self, sequencing_model, frag_len=None):
             if any_indel_err:
                 if len(expanded_cigar):
                     relevant_cigar = (expanded_cigar + extra_cigar_val)[:self.read_len]
-                    my_cigar2 = CigarStringNew.list_to_string(relevant_cigar)
+                    my_cigar = CigarStringNew.list_to_string(relevant_cigar)
                     # TODO delete this line once new cigar is 100% working
                     # my_cigar = CigarString(list_in=relevant_cigar).get_string()
 

From 55d2a6d84bcc61dc0b0689ded88622da04d3c909 Mon Sep 17 00:00:00 2001
From: jallen17 <jallen17@illinois.edu>
Date: Tue, 5 Jan 2021 16:54:48 -0600
Subject: [PATCH 237/323] Debugging changes

---
 source/SequenceContainer.py | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 2cc4779..8ddf541 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -792,15 +792,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                             try:
                                 expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
                             except IndexError:
-                                print(f'error = {error}')
-                                print(f'e_pos = {e_pos}')
-                                print(f'pi = {pi}')
-                                print(f'my_adj = {my_adj}')
-                                print(f'total_d = {total_d}')
-                                print(f'total_i = {total_i}')
-                                print(f'expanded_cigar = {CigarStringNew.list_to_string(expanded_cigar)}')
-                                print(f'sorted_errors = {sorted_errors}')
-                                print("Debug")
+                                pdb.set_trace()
                                 sys.exit(1)
 
                         else:

From f01743a59854b16cb0232a9a52d5bfd91336c16c Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 5 Jan 2021 20:34:28 -0600
Subject: [PATCH 238/323] Further debugging

---
 source/SequenceContainer.py | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 1b2865a..6518b44 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -5,13 +5,14 @@
 import pickle
 import sys
 from time import time
+import pdb
 
 import numpy as np
 from Bio.Seq import Seq
 
-from source.neat_cigar_rework import CigarString as CigarStringNew
+from source.neat_cigar_rework import CigarString
 from source.probability import DiscreteDistribution, poisson_list
-from source.neat_cigar import CigarString
+# from source.neat_cigar import CigarString
 
 """
 Constants needed for analysis
@@ -585,7 +586,7 @@ def random_mutations(self):
         # MODIFY REFERENCE STRING: INDELS
         for i in range(len(all_indels_ins)):
             rolling_adj = 0
-            temp_symbol_string = CigarStringNew(str(len(self.sequences[i])) + "M")
+            temp_symbol_string = CigarString(str(len(self.sequences[i])) + "M")
             # TODO Delete commented out lines once CigarString works 100%
             # temp_symbol_string2 = ['M' for _ in self.sequences[i]]
 
@@ -605,14 +606,14 @@ def random_mutations(self):
                                         self.sequences[i][v_pos2:]
                     # notate indel positions for cigar computation
                     if indel_length > 0:
-                        cigar_to_insert = CigarStringNew(str(indel_length) + 'I')
+                        cigar_to_insert = CigarString(str(indel_length) + 'I')
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert,
                                                                 len(all_indels_ins[i][j][1]))
                         # TODO Delete commented out lines once CigarString works 100%
                         # temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
                         #                       ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
                     elif indel_length < 0:
-                        cigar_to_insert = CigarStringNew(str(abs(indel_length)) + 'D1M')
+                        cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert, indel_length)
                         # TODO Delete commented out lines once CigarString works 100%
                         # temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
@@ -723,7 +724,7 @@ def sample_read(self, sequencing_model, frag_len=None):
             expanded_cigar = []
             extra_cigar = []
             adj = 0
-            sse_adj = [0 for n in range(self.read_len + max(sequencing_model.err_p[3]))]
+            sse_adj = [0 for _ in range(self.read_len + max(sequencing_model.err_p[3]))]
             any_indel_err = False
 
             # sort by letter (D > I > S) such that we introduce all indel errors before substitution errors
@@ -758,7 +759,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                     if total_d > avail_b:  # if not enough bases to fill-in deletions, skip all indel erors
                         continue
                     if not expanded_cigar:
-                        expanded_cigar = CigarStringNew(my_cigar).string_to_list()
+                        expanded_cigar = CigarString(my_cigar).string_to_list()
                         # TODO delete these lines using old CigarString once it is working 100%
                         # expanded_cigar = CigarString(string_in=my_cigar).get_list()
                         fill_to_go = total_d - total_i + 1
@@ -767,7 +768,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                                 # TODO delete these lines using old CigarString once it is working 100%
                                 # extra_cigar_val = CigarString(string_in=self.all_cigar[my_ploid][read[0]
                                 #                                + fill_to_go]).get_list()[-fill_to_go:]
-                                extra_cigar_val = CigarStringNew(self.all_cigar[my_ploid][read[0]
+                                extra_cigar_val = CigarString(self.all_cigar[my_ploid][read[0]
                                                                  + fill_to_go]).string_to_list()[-fill_to_go:]
 
                             except IndexError:
@@ -798,7 +799,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                                 print(f'my_adj = {my_adj}')
                                 print(f'total_d = {total_d}')
                                 print(f'total_i = {total_i}')
-                                print(f'expanded_cigar = {CigarStringNew.list_to_string(expanded_cigar)}')
+                                print(f'expanded_cigar = {CigarString.list_to_string(expanded_cigar)}')
                                 print(f'sorted_errors = {sorted_errors}')
                                 print("Debug")
                                 sys.exit(1)
@@ -835,7 +836,7 @@ def sample_read(self, sequencing_model, frag_len=None):
             if any_indel_err:
                 if len(expanded_cigar):
                     relevant_cigar = (expanded_cigar + extra_cigar_val)[:self.read_len]
-                    my_cigar = CigarStringNew.list_to_string(relevant_cigar)
+                    my_cigar = CigarString.list_to_string(relevant_cigar)
                     # TODO delete this line once new cigar is 100% working
                     # my_cigar = CigarString(list_in=relevant_cigar).get_string()
 

From d1c11f2c0de6efd170a1769e41301f4291b8a4d5 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 5 Jan 2021 21:25:44 -0600
Subject: [PATCH 239/323] More debugging

---
 source/SequenceContainer.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 5c26e7c..c966c75 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -722,7 +722,6 @@ def sample_read(self, sequencing_model, frag_len=None):
             # add buffer sequence to fill in positions that get deleted
             read[3] += self.sequences[my_ploid][read[0] + self.read_len:read[0] + self.read_len + total_d]
             expanded_cigar = []
-            extra_cigar = []
             adj = 0
             sse_adj = [0 for _ in range(self.read_len + max(sequencing_model.err_p[3]))]
             any_indel_err = False

From e8f3452277b390b30b3a3ff3997ea667cbe57223 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 5 Jan 2021 23:52:50 -0600
Subject: [PATCH 240/323] Further debugging

---
 gen_reads.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/gen_reads.py b/gen_reads.py
index 49e5621..59e6c43 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -15,6 +15,7 @@
 /////////////////////////////////////////////////////////////////////////////// """
 
 import sys
+import pdb
 import copy
 import random
 import re
@@ -781,6 +782,7 @@ def main(raw_args=None):
                                     else:
                                         flag1 = sam_flag(['paired', 'proper', 'second', 'mate_reverse'])
                                         flag2 = sam_flag(['paired', 'proper', 'first', 'reverse'])
+                                    pdb.set_trace()
                                     output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
                                                                         my_read_data[0][1], my_read_data[0][2],
                                                                         my_read_data[0][3],

From f10d38f21e49e94e8c1273027d30738e591fff90 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 5 Jan 2021 23:54:10 -0600
Subject: [PATCH 241/323] Further debugging

---
 source/SequenceContainer.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index c966c75..16c5c23 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -5,7 +5,6 @@
 import pickle
 import sys
 from time import time
-import pdb
 
 import numpy as np
 from Bio.Seq import Seq
@@ -792,7 +791,6 @@ def sample_read(self, sequencing_model, frag_len=None):
                             try:
                                 expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
                             except IndexError:
-                                pdb.set_trace()
                                 sys.exit(1)
 
                         else:

From eb7b4b14cd0183b15698b62a36c230b5f1a8ef12 Mon Sep 17 00:00:00 2001
From: jallen17 <jallen17@illinois.edu>
Date: Tue, 5 Jan 2021 23:54:30 -0600
Subject: [PATCH 242/323] And even more debugging

---
 source/SequenceContainer.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index c966c75..00c738a 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -827,10 +827,12 @@ def sample_read(self, sequencing_model, frag_len=None):
             if any_indel_err:
                 if len(expanded_cigar):
                     relevant_cigar = (expanded_cigar + extra_cigar_val)[:self.read_len]
-                    my_cigar = CigarString.list_to_string(relevant_cigar)
+                    my_cigar = CigarString(CigarString.list_to_string(relevant_cigar))
                     # TODO delete this line once new cigar is 100% working
                     # my_cigar = CigarString(list_in=relevant_cigar).get_string()
-
+                    if len(my_cigar) < self.read_len:
+                        pdb.set_trace()
+                        sys.exit(1)
                 read[3] = read[3][:self.read_len]
 
             read_out.append([self.fm_pos[my_ploid][read[0]], my_cigar, str(read[3]), str(read[1])])

From 2b63f3c504a5238f557dc2cf7668eb8d1cb5379e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 5 Jan 2021 23:56:05 -0600
Subject: [PATCH 243/323] Further debugging

---
 source/SequenceContainer.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index dd825ab..a6efc4c 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -828,9 +828,6 @@ def sample_read(self, sequencing_model, frag_len=None):
                     my_cigar = CigarString(CigarString.list_to_string(relevant_cigar))
                     # TODO delete this line once new cigar is 100% working
                     # my_cigar = CigarString(list_in=relevant_cigar).get_string()
-                    if len(my_cigar) < self.read_len:
-                        pdb.set_trace()
-                        sys.exit(1)
                 read[3] = read[3][:self.read_len]
 
             read_out.append([self.fm_pos[my_ploid][read[0]], my_cigar, str(read[3]), str(read[1])])

From 2a4918e357748906fd9ef831b408c81658a627e2 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 6 Jan 2021 02:16:45 -0600
Subject: [PATCH 244/323] I think I fixed the bug

---
 gen_reads.py                 |   2 -
 source/neat_cigar_rework.py  | 106 ++++++++++++++++++-----------------
 source/output_file_writer.py |  11 +++-
 3 files changed, 63 insertions(+), 56 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 59e6c43..49e5621 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -15,7 +15,6 @@
 /////////////////////////////////////////////////////////////////////////////// """
 
 import sys
-import pdb
 import copy
 import random
 import re
@@ -782,7 +781,6 @@ def main(raw_args=None):
                                     else:
                                         flag1 = sam_flag(['paired', 'proper', 'second', 'mate_reverse'])
                                         flag2 = sam_flag(['paired', 'proper', 'first', 'reverse'])
-                                    pdb.set_trace()
                                     output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
                                                                         my_read_data[0][1], my_read_data[0][2],
                                                                         my_read_data[0][3],
diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index 9bea656..2837418 100644
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -3,6 +3,7 @@
 from operator import itemgetter
 
 
+
 class Cigar(object):
     """
     This code borrowed in whole from https://github.com/brentp/cigar
@@ -115,8 +116,8 @@ def mask_left(self, n_seq_bases, mask="S"):
         return Cigar(Cigar.string_from_elements(new_cigs)).merge_like_ops()
 
     @classmethod
-    def string_from_elements(self, elements):
-        return "".join("%i%s" % (l, op) for l, op in elements if l !=0)
+    def string_from_elements(cls, elements):
+        return "".join("%i%s" % (l, op) for l, op in elements if l != 0)
 
     def mask_right(self, n_seq_bases, mask="S"):
         """
@@ -163,28 +164,29 @@ def insert_cigar_element(self, pos: int,
         :param insertion_cigar: A cigar to insert into current cigar string
         :param pos: integer position where to insert the input cigar string
         :return: None
-        str1 = CigarString('50M10D7I23M')
-        >>>str2 = CigarString('10I25M')
-        >>>iPos = 55
-        >>>str1.insert_cigar_element(iPos, str2)
-        >>>assert(str1.cigar == "50M5D10I25M5D7I23M")
-
-        >>>str1 = CigarString('50M10D7I23M')
-        >>>iPos = 20
-        >>>str1.insert_cigar_element(iPos, str2)
-        >>>assert(str1.cigar == "20M10I55M10D7I23M")
-
-        >>>str1 = CigarString('11100M')
-        >>>str2 = CigarString('2I')
-        >>>iPos = 5785 + 1
-        >>>str1.insert_cigar_element(iPos, str2, 1)
-        >>>assert (str1.cigar == "5786M2I5313M")
-
-        >>>str1 = CigarString('11100M')
-        >>>str2 = CigarString('1D1M')
-        >>>iPos = 6610 + 1
-        >>>str1.insert_cigar_element(iPos, str2, -1)
-        >>>assert (str1.cigar == "6611M1D4489M")
+
+        >>> str1 = CigarString('50M10D7I23M')
+        >>> str2 = CigarString('10I25M')
+        >>> iPos = 55
+        >>> str1.insert_cigar_element(iPos, str2)
+        >>> assert(str1.cigar == "50M5D10I25M5D7I23M")
+
+        >>> str1 = CigarString('50M10D7I23M')
+        >>> iPos = 20
+        >>> str1.insert_cigar_element(iPos, str2)
+        >>> assert(str1.cigar == "20M10I55M10D7I23M")
+
+        >>> str1 = CigarString('11100M')
+        >>> str2 = CigarString('2I')
+        >>> iPos = 5785 + 1
+        >>> str1.insert_cigar_element(iPos, str2, 1)
+        >>> assert (str1.cigar == "5786M2I5313M")
+
+        >>> str1 = CigarString('11100M')
+        >>> str2 = CigarString('1D1M')
+        >>> iPos = 6610 + 1
+        >>> str1.insert_cigar_element(iPos, str2, -1)
+        >>> assert (str1.cigar == "6611M1D4489M")
         """
 
         if insertion_cigar is None:
@@ -231,33 +233,33 @@ def get_cigar_fragment(self, start: int, end: int) -> str:
         :param start: start point of sequence to retrieve
         :param end: end point of sequence to retrieve
         :return: The sequence that spans start to end
-        >>>str1 = CigarString('10M5D10M')
-        >>>start = 0
-        >>>end = 14
-        >>>frag = str1.get_cigar_fragment(start, end)
-        >>>assert(frag == "10M4D")
-
-        >>>str1 = CigarString('10M2D10M')
-        >>>start = 10
-        >>>end = 12
-        >>>frag = str1.get_cigar_fragment(start, end)
-        >>>assert(frag == "2D")
-
-        >>>str1 = CigarString('10M1D10M')
-        >>>start = 10
-        >>>end = 12
-        >>>frag = str1.get_cigar_fragment(start, end)
-        >>>assert(frag == "1D1M")
-
-        >>>str1 = CigarString('102M2I10000M')
-        >>>start1 = 1
-        >>>end1 = 102
-        >>>frag1 = str1.get_cigar_fragment(start1, end1)
-        >>>start2 = 102
-        >>>end2 = 203
-        >>>frag2 = str1.get_cigar_fragment(start2, end2)
-        >>>assert(frag1 == "101M")
-        >>>assert(frag2 == "2I99M")
+        >>> str1 = CigarString('10M5D10M')
+        >>> start = 0
+        >>> end = 14
+        >>> frag = str1.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == "10M4D")
+
+        >>> str1 = CigarString('10M2D10M')
+        >>> start = 10
+        >>> end = 12
+        >>> frag = str1.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == "2D")
+
+        >>> str1 = CigarString('10M1D10M')
+        >>> start = 10
+        >>> end = 12
+        >>> frag = str1.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == "1D1M")
+
+        >>> str1 = CigarString('102M2I10000M')
+        >>> start1 = 1
+        >>> end1 = 102
+        >>> frag1 = str1.get_cigar_fragment(start1, end1)
+        >>> start2 = 102
+        >>> end2 = 203
+        >>> frag2 = str1.get_cigar_fragment(start2, end2)
+        >>> assert(frag1.cigar == "101M")
+        >>> assert(frag2.cigar == "2I99M")
         """
         # Minus 1 because python slices don't include the end coordinate
         window_size = end - start
@@ -321,7 +323,7 @@ def get_cigar_fragment(self, start: int, end: int) -> str:
         except ValueError:
             print('\nBug: Problem retrieving fragment.\n')
             sys.exit(1)
-        return Cigar.string_from_elements(ret)
+        return CigarString(Cigar.string_from_elements(ret))
 
     # TODO use this method or delete it.
     def count_elements(self, element: str) -> int:
diff --git a/source/output_file_writer.py b/source/output_file_writer.py
index cdf2985..c3de5d5 100644
--- a/source/output_file_writer.py
+++ b/source/output_file_writer.py
@@ -8,6 +8,7 @@
 import gzip
 from Bio.bgzf import *
 import pathlib
+from source.neat_cigar_rework import CigarString
 
 # from source.biopython_modified_bgzf import BgzfWriter
 
@@ -214,8 +215,14 @@ def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag,
         # my_bin     = 0	# or just use a dummy value, does this actually matter?
 
         my_map_quality = aln_map_quality
-        cig_letters = re.split(r"\d+", cigar)[1:]
-        cig_numbers = [int(n) for n in re.findall(r"\d+", cigar)]
+        cig_letters = []
+        cig_numbers = []
+        for item in cigar.items():
+            cig_numbers.append(item[0])
+            cig_letters.append(item[1])
+        # TODO delete the following two lines once the new cigar rework is 100% working
+        # cig_letters = re.split(r"\d+", cigar)[1:]
+        # cig_numbers = [int(n) for n in re.findall(r"\d+", cigar)]
         cig_ops = len(cig_letters)
         next_ref_id = ref_id
         if mate_pos is None:

From 3da025d65fb758e85bb09ff3da64cb56566a6bab Mon Sep 17 00:00:00 2001
From: jallen17 <jallen17@illinois.edu>
Date: Wed, 6 Jan 2021 02:17:53 -0600
Subject: [PATCH 245/323] Not sure

---
 gen_reads.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index 59e6c43..8d07d44 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -782,7 +782,8 @@ def main(raw_args=None):
                                     else:
                                         flag1 = sam_flag(['paired', 'proper', 'second', 'mate_reverse'])
                                         flag2 = sam_flag(['paired', 'proper', 'first', 'reverse'])
-                                    pdb.set_trace()
+                                    if not isinstance(my_read_data[0][1], str):
+                                        pdb.set_trace()
                                     output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
                                                                         my_read_data[0][1], my_read_data[0][2],
                                                                         my_read_data[0][3],

From bba7025c870951e54051db239c096c667b2b026c Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 6 Jan 2021 13:17:43 -0600
Subject: [PATCH 246/323] Debugging gen_reads

---
 source/SequenceContainer.py |  7 ++++---
 source/neat_cigar_rework.py | 28 +++++++++++++++-------------
 2 files changed, 19 insertions(+), 16 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index a6efc4c..8107408 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -619,6 +619,7 @@ def random_mutations(self):
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
+                print(type(temp_symbol_string.get_cigar_fragment(j, j + self.read_len)))
                 self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j + self.read_len))
                 # TODO Delete commented out lines once CigarString works 100%
                 # self.all_cigar[i].append(CigarString(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
@@ -757,7 +758,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                     if total_d > avail_b:  # if not enough bases to fill-in deletions, skip all indel erors
                         continue
                     if not expanded_cigar:
-                        expanded_cigar = CigarString(my_cigar).string_to_list()
+                        expanded_cigar = my_cigar.string_to_list()
                         # TODO delete these lines using old CigarString once it is working 100%
                         # expanded_cigar = CigarString(string_in=my_cigar).get_list()
                         fill_to_go = total_d - total_i + 1
@@ -766,8 +767,8 @@ def sample_read(self, sequencing_model, frag_len=None):
                                 # TODO delete these lines using old CigarString once it is working 100%
                                 # extra_cigar_val = CigarString(string_in=self.all_cigar[my_ploid][read[0]
                                 #                                + fill_to_go]).get_list()[-fill_to_go:]
-                                extra_cigar_val = CigarString(self.all_cigar[my_ploid][read[0]
-                                                                 + fill_to_go]).string_to_list()[-fill_to_go:]
+                                extra_cigar_val = CigarString(
+                                    self.all_cigar[my_ploid][read[0] + fill_to_go]).string_to_list()[-fill_to_go:]
 
                             except IndexError:
                                 # Applying the deletions we want requires going beyond region boundaries.
diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index 2837418..06fe7f0 100644
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -3,7 +3,6 @@
 from operator import itemgetter
 
 
-
 class Cigar(object):
     """
     This code borrowed in whole from https://github.com/brentp/cigar
@@ -150,7 +149,7 @@ def merge_like_ops(self):
 
 class CigarString(Cigar):
     """
-    This adds odditional functionality to the cigar module above, to make it compatible with
+    This adds additional functionality to the cigar module above, to make it compatible with
     the rest of the NEAT codebase
     """
 
@@ -224,7 +223,7 @@ def insert_cigar_element(self, pos: int,
             print('\nBug: Problem with insertion.\n')
             sys.exit(1)
 
-    def get_cigar_fragment(self, start: int, end: int) -> str:
+    def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
         """
         Take a slice of a cigar string. E.g., if we have a cigar string that is "1000M" and we want a slice of the
         first 101 characters, then the return should be '101M'. If we had "20M100I20M" and want the
@@ -341,16 +340,19 @@ def string_to_list(self) -> list:
         """
         cigar_dat = []
         d_reserve = 0
-        for item in self.items():
-            if item[1] == 'D':
-                d_reserve = item[0]
-            if item[1] in ['M', 'I']:
-                if d_reserve:
-                    cigar_dat += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
-                else:
-                    cigar_dat += [item[1]] * item[0]
-                d_reserve = 0
-        return cigar_dat
+        try:
+            for item in self.items():
+                if item[1] == 'D':
+                    d_reserve = item[0]
+                if item[1] in ['M', 'I']:
+                    if d_reserve:
+                        cigar_dat += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
+                    else:
+                        cigar_dat += [item[1]] * item[0]
+                    d_reserve = 0
+            return cigar_dat
+        except:
+            sys.exit(1)
 
     @staticmethod
     def list_to_string(input_list: list) -> str:

From fb9dcddbe48862fa854467961389bb051c3bb2a4 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 6 Jan 2021 13:22:50 -0600
Subject: [PATCH 247/323] removing debug print

---
 source/SequenceContainer.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 8107408..a32fffe 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -619,7 +619,6 @@ def random_mutations(self):
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
-                print(type(temp_symbol_string.get_cigar_fragment(j, j + self.read_len)))
                 self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j + self.read_len))
                 # TODO Delete commented out lines once CigarString works 100%
                 # self.all_cigar[i].append(CigarString(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())

From 33e73436535356d8874759d9bf6c159f5bf30bfa Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 6 Jan 2021 13:51:45 -0600
Subject: [PATCH 248/323] Further debugging

---
 source/SequenceContainer.py |  5 +++--
 source/neat_cigar_rework.py | 23 ++++++++++-------------
 2 files changed, 13 insertions(+), 15 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index a32fffe..f8e985e 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -766,8 +766,8 @@ def sample_read(self, sequencing_model, frag_len=None):
                                 # TODO delete these lines using old CigarString once it is working 100%
                                 # extra_cigar_val = CigarString(string_in=self.all_cigar[my_ploid][read[0]
                                 #                                + fill_to_go]).get_list()[-fill_to_go:]
-                                extra_cigar_val = CigarString(
-                                    self.all_cigar[my_ploid][read[0] + fill_to_go]).string_to_list()[-fill_to_go:]
+                                extra_cigar_val = self.all_cigar[my_ploid][read[0]
+                                                                           + fill_to_go].string_to_list()[-fill_to_go:]
 
                             except IndexError:
                                 # Applying the deletions we want requires going beyond region boundaries.
@@ -791,6 +791,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                             try:
                                 expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
                             except IndexError:
+                                print("Bug!! Index error on expanded cigar")
                                 sys.exit(1)
 
                         else:
diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index 06fe7f0..4c87566 100644
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -340,19 +340,16 @@ def string_to_list(self) -> list:
         """
         cigar_dat = []
         d_reserve = 0
-        try:
-            for item in self.items():
-                if item[1] == 'D':
-                    d_reserve = item[0]
-                if item[1] in ['M', 'I']:
-                    if d_reserve:
-                        cigar_dat += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
-                    else:
-                        cigar_dat += [item[1]] * item[0]
-                    d_reserve = 0
-            return cigar_dat
-        except:
-            sys.exit(1)
+        for item in self.items():
+            if item[1] == 'D':
+                d_reserve = item[0]
+            if item[1] in ['M', 'I']:
+                if d_reserve:
+                    cigar_dat += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
+                else:
+                    cigar_dat += [item[1]] * item[0]
+                d_reserve = 0
+        return cigar_dat
 
     @staticmethod
     def list_to_string(input_list: list) -> str:

From 7464f6444b60d948e50f1423a150858c50f827f3 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 6 Jan 2021 17:09:17 -0600
Subject: [PATCH 249/323] debugging still

---
 source/SequenceContainer.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index f8e985e..ffababa 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -1,3 +1,4 @@
+import pdb
 import random
 import copy
 import pathlib
@@ -792,6 +793,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                                 expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
                             except IndexError:
                                 print("Bug!! Index error on expanded cigar")
+                                pdb.set_trace()
                                 sys.exit(1)
 
                         else:

From 8784e88647995a47fe6ae54c5a091092dbaa1c54 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 6 Jan 2021 19:05:38 -0600
Subject: [PATCH 250/323] Nope, still debugging

---
 source/SequenceContainer.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index ffababa..b312937 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -709,6 +709,7 @@ def sample_read(self, sequencing_model, frag_len=None):
         for read in reads_to_sample:
             try:
                 my_cigar = self.all_cigar[my_ploid][read[0]]
+                pdb.set_trace()
             except IndexError:
                 print('Index error when attempting to find cigar string.')
                 print(my_ploid, len(self.all_cigar[my_ploid]), read[0])

From 57139e3b49df59ad7c20774538e5884825f522c2 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 6 Jan 2021 19:20:16 -0600
Subject: [PATCH 251/323] Nope, still debugging

---
 source/SequenceContainer.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index b312937..2beed8b 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -709,7 +709,9 @@ def sample_read(self, sequencing_model, frag_len=None):
         for read in reads_to_sample:
             try:
                 my_cigar = self.all_cigar[my_ploid][read[0]]
-                pdb.set_trace()
+                if my_cigar.cigar != "100M":
+                    print(my_cigar)
+                    pdb.set_trace()
             except IndexError:
                 print('Index error when attempting to find cigar string.')
                 print(my_ploid, len(self.all_cigar[my_ploid]), read[0])

From 41d54a18c5b0983ffc570efa0a2694c555d70eda Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 6 Jan 2021 20:00:58 -0600
Subject: [PATCH 252/323] Nope, still debugging

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 2beed8b..44816bb 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -709,7 +709,7 @@ def sample_read(self, sequencing_model, frag_len=None):
         for read in reads_to_sample:
             try:
                 my_cigar = self.all_cigar[my_ploid][read[0]]
-                if my_cigar.cigar != "100M":
+                if my_cigar.cigar != "100M" and read[2][0][0] == 'D':
                     print(my_cigar)
                     pdb.set_trace()
             except IndexError:

From 242d568dc3f039c5556f6b21f2af3969fc034a7a Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 6 Jan 2021 20:46:31 -0600
Subject: [PATCH 253/323] Nope, still debugging

---
 source/SequenceContainer.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 44816bb..2109f7e 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -709,9 +709,6 @@ def sample_read(self, sequencing_model, frag_len=None):
         for read in reads_to_sample:
             try:
                 my_cigar = self.all_cigar[my_ploid][read[0]]
-                if my_cigar.cigar != "100M" and read[2][0][0] == 'D':
-                    print(my_cigar)
-                    pdb.set_trace()
             except IndexError:
                 print('Index error when attempting to find cigar string.')
                 print(my_ploid, len(self.all_cigar[my_ploid]), read[0])
@@ -719,6 +716,9 @@ def sample_read(self, sequencing_model, frag_len=None):
                     print((r_pos1, r_pos2))
                     print(frag_len, self.fraglen_ind_map[frag_len])
                 sys.exit(1)
+            if my_cigar.cigar != "100M" and read[2][0][0] == 'D':
+                print(my_cigar)
+                pdb.set_trace()
             total_d = sum([error[1] for error in read[2] if error[0] == 'D'])
             total_i = sum([error[1] for error in read[2] if error[0] == 'I'])
             avail_b = len(self.sequences[my_ploid]) - read[0] - self.read_len - 1

From c64e65b7325b0ebf1d4fe8d307ecd3526f7a9854 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 6 Jan 2021 21:07:27 -0600
Subject: [PATCH 254/323] debugging

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 2109f7e..d3d4361 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -716,7 +716,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                     print((r_pos1, r_pos2))
                     print(frag_len, self.fraglen_ind_map[frag_len])
                 sys.exit(1)
-            if my_cigar.cigar != "100M" and read[2][0][0] == 'D':
+            if my_cigar.cigar != "100M" and 'D' in my_cigar.cigar:
                 print(my_cigar)
                 pdb.set_trace()
             total_d = sum([error[1] for error in read[2] if error[0] == 'D'])

From cb80514c27a43c9e947511f35121aed9ea400bf7 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 6 Jan 2021 21:13:57 -0600
Subject: [PATCH 255/323] debugging

---
 source/SequenceContainer.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index d3d4361..85c7797 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -716,13 +716,13 @@ def sample_read(self, sequencing_model, frag_len=None):
                     print((r_pos1, r_pos2))
                     print(frag_len, self.fraglen_ind_map[frag_len])
                 sys.exit(1)
-            if my_cigar.cigar != "100M" and 'D' in my_cigar.cigar:
-                print(my_cigar)
-                pdb.set_trace()
             total_d = sum([error[1] for error in read[2] if error[0] == 'D'])
             total_i = sum([error[1] for error in read[2] if error[0] == 'I'])
             avail_b = len(self.sequences[my_ploid]) - read[0] - self.read_len - 1
             # add buffer sequence to fill in positions that get deleted
+            if my_cigar.cigar != "100M" and read[2][0][0] == 'D':
+                print(my_cigar)
+                pdb.set_trace()
             read[3] += self.sequences[my_ploid][read[0] + self.read_len:read[0] + self.read_len + total_d]
             expanded_cigar = []
             adj = 0

From 7791bccc067b9e762d9bd2233acb3ea4542500c3 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 6 Jan 2021 21:25:54 -0600
Subject: [PATCH 256/323] Nope, still debugging

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 85c7797..592ab54 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -720,7 +720,7 @@ def sample_read(self, sequencing_model, frag_len=None):
             total_i = sum([error[1] for error in read[2] if error[0] == 'I'])
             avail_b = len(self.sequences[my_ploid]) - read[0] - self.read_len - 1
             # add buffer sequence to fill in positions that get deleted
-            if my_cigar.cigar != "100M" and read[2][0][0] == 'D':
+            if my_cigar.cigar != "100M" and [error[1] for error in read[2] if error[0] == 'D']:
                 print(my_cigar)
                 pdb.set_trace()
             read[3] += self.sequences[my_ploid][read[0] + self.read_len:read[0] + self.read_len + total_d]

From afb57f4ab13d0a99d8c3be3adc8227e4ebe94a9b Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 7 Jan 2021 10:38:23 -0600
Subject: [PATCH 257/323] Debugging neat still

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 592ab54..b3173c1 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -836,7 +836,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                     # my_cigar = CigarString(list_in=relevant_cigar).get_string()
                 read[3] = read[3][:self.read_len]
 
-            read_out.append([self.fm_pos[my_ploid][read[0]], my_cigar, str(read[3]), str(read[1])])
+            read_out.append([self.fm_pos[my_ploid][read[0]], my_cigar, read[3], str(read[1])])
 
         # read_out[i] = (pos, cigar, read_string, qual_string)
         return read_out

From 27d41f7ef72b6f7f85929d3296e768f6815859ff Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 7 Jan 2021 10:43:58 -0600
Subject: [PATCH 258/323] Debugging neat still

---
 source/SequenceContainer.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index b3173c1..e68f1f1 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -719,10 +719,13 @@ def sample_read(self, sequencing_model, frag_len=None):
             total_d = sum([error[1] for error in read[2] if error[0] == 'D'])
             total_i = sum([error[1] for error in read[2] if error[0] == 'I'])
             avail_b = len(self.sequences[my_ploid]) - read[0] - self.read_len - 1
+
+            # debugging
+            # if my_cigar.cigar != "100M" and [error[1] for error in read[2] if error[0] == 'D']:
+            #     print(my_cigar)
+            #     pdb.set_trace()
+
             # add buffer sequence to fill in positions that get deleted
-            if my_cigar.cigar != "100M" and [error[1] for error in read[2] if error[0] == 'D']:
-                print(my_cigar)
-                pdb.set_trace()
             read[3] += self.sequences[my_ploid][read[0] + self.read_len:read[0] + self.read_len + total_d]
             expanded_cigar = []
             adj = 0

From 2faff6939a5920625130f31c756cbf3699bb1837 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 7 Jan 2021 11:10:19 -0600
Subject: [PATCH 259/323] Hopefully fixing bugs, along with some refactoring

---
 source/output_file_writer.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/source/output_file_writer.py b/source/output_file_writer.py
index c3de5d5..3123d9c 100644
--- a/source/output_file_writer.py
+++ b/source/output_file_writer.py
@@ -188,21 +188,21 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
     # TODO add write_fasta_record
 
     def write_fastq_record(self, read_name, read1, qual1, read2=None, qual2=None, orientation=None):
-        (r1, q1) = (read1, qual1)
+        (read1, quality1) = (str(read1), qual1)
         if read2 is not None and orientation is True:
-            (r2, q2) = (reverse_complement(read2), qual2[::-1])
+            (read2, quality2) = (str(reverse_complement(read2)), qual2[::-1])
         elif read2 is not None and orientation is False:
-            (r1, q1) = (reverse_complement(read2), qual2[::-1])
-            (r2, q2) = (read1, qual1)
+            (read1, quality1) = (str(reverse_complement(read2)), qual2[::-1])
+            (read2, quality2) = (str(read1), qual1)
 
         if self.fasta_instead:
-            self.fq1_buffer.append('>' + read_name + '/1\n' + r1 + '\n')
+            self.fq1_buffer.append('>' + read_name + '/1\n' + read1 + '\n')
             if read2 is not None:
-                self.fq2_buffer.append('>' + read_name + '/2\n' + r2 + '\n')
+                self.fq2_buffer.append('>' + read_name + '/2\n' + read2 + '\n')
         else:
-            self.fq1_buffer.append('@' + read_name + '/1\n' + r1 + '\n+\n' + q1 + '\n')
+            self.fq1_buffer.append('@' + read_name + '/1\n' + read1 + '\n+\n' + quality1 + '\n')
             if read2 is not None:
-                self.fq2_buffer.append('@' + read_name + '/2\n' + r2 + '\n+\n' + q2 + '\n')
+                self.fq2_buffer.append('@' + read_name + '/2\n' + read2 + '\n+\n' + quality2 + '\n')
 
     def write_vcf_record(self, chrom, pos, id_str, ref, alt, qual, filt, info):
         self.vcf_file.write(

From d59fdc29481a48bacd6e7fe71557c14e6decd8e6 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 7 Jan 2021 13:01:27 -0600
Subject: [PATCH 260/323] still tracking down this bug

---
 source/SequenceContainer.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index e68f1f1..d53d140 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -721,9 +721,9 @@ def sample_read(self, sequencing_model, frag_len=None):
             avail_b = len(self.sequences[my_ploid]) - read[0] - self.read_len - 1
 
             # debugging
-            # if my_cigar.cigar != "100M" and [error[1] for error in read[2] if error[0] == 'D']:
-            #     print(my_cigar)
-            #     pdb.set_trace()
+            if my_cigar.cigar != "100M" and [error[1] for error in read[2] if error[0] == 'D']:
+                print(my_cigar)
+                pdb.set_trace()
 
             # add buffer sequence to fill in positions that get deleted
             read[3] += self.sequences[my_ploid][read[0] + self.read_len:read[0] + self.read_len + total_d]

From 4c21d8630ac44897bb3ba3b4012d9a1b9b2e6bbe Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 7 Jan 2021 13:45:16 -0600
Subject: [PATCH 261/323] still tracking down this bug

---
 gen_reads.py                | 3 +++
 source/SequenceContainer.py | 8 +++++++-
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index 49e5621..cd53a2c 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -660,6 +660,9 @@ def main(raw_args=None):
 
                         is_unmapped = []
                         if paired_end:
+                            if len(any(sequences.all_cigar)) != 100:
+                                print("There's a cigar that's off.")
+                                breakpoint()
                             my_fraglen = fraglen_distribution.sample()
                             my_read_data = sequences.sample_read(se_class, my_fraglen)
                             # skip if we failed to find a valid position to sample read
diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index d53d140..edd327c 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -721,7 +721,7 @@ def sample_read(self, sequencing_model, frag_len=None):
             avail_b = len(self.sequences[my_ploid]) - read[0] - self.read_len - 1
 
             # debugging
-            if my_cigar.cigar != "100M" and [error[1] for error in read[2] if error[0] == 'D']:
+            if len(my_cigar) != 100:
                 print(my_cigar)
                 pdb.set_trace()
 
@@ -835,6 +835,12 @@ def sample_read(self, sequencing_model, frag_len=None):
                 if len(expanded_cigar):
                     relevant_cigar = (expanded_cigar + extra_cigar_val)[:self.read_len]
                     my_cigar = CigarString(CigarString.list_to_string(relevant_cigar))
+
+                    # debugging
+                    if len(my_cigar) != 100:
+                        print(my_cigar)
+                        pdb.set_trace()
+
                     # TODO delete this line once new cigar is 100% working
                     # my_cigar = CigarString(list_in=relevant_cigar).get_string()
                 read[3] = read[3][:self.read_len]

From af096b90f9c27026474a7da79d3de59c9ce2a7ab Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 7 Jan 2021 13:48:11 -0600
Subject: [PATCH 262/323] still tracking down this bug

---
 gen_reads.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index cd53a2c..1a6242c 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -660,7 +660,7 @@ def main(raw_args=None):
 
                         is_unmapped = []
                         if paired_end:
-                            if len(any(sequences.all_cigar)) != 100:
+                            if [cigar for cigar in sequences.all_cigar if len(cigar) != 100]:
                                 print("There's a cigar that's off.")
                                 breakpoint()
                             my_fraglen = fraglen_distribution.sample()

From 3b229ea9a879e3501ff9f8b5ccb308e9fe583702 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 7 Jan 2021 13:54:45 -0600
Subject: [PATCH 263/323] still tracking down this bug

---
 gen_reads.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index 1a6242c..7f243b5 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -24,6 +24,7 @@
 import numpy as np
 import argparse
 import pathlib
+import pdb
 
 from source.input_checking import check_file_open, is_in_range
 from source.ref_func import index_ref, read_ref
@@ -662,7 +663,7 @@ def main(raw_args=None):
                         if paired_end:
                             if [cigar for cigar in sequences.all_cigar if len(cigar) != 100]:
                                 print("There's a cigar that's off.")
-                                breakpoint()
+                                pdb.set_trace()
                             my_fraglen = fraglen_distribution.sample()
                             my_read_data = sequences.sample_read(se_class, my_fraglen)
                             # skip if we failed to find a valid position to sample read

From 2d1d5de89f4718416001042d3b62151f689a0b24 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 7 Jan 2021 13:56:49 -0600
Subject: [PATCH 264/323] Hopefully fixing bugs, along with some refactoring

---
 gen_reads.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index 7f243b5..11d0695 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -661,7 +661,8 @@ def main(raw_args=None):
 
                         is_unmapped = []
                         if paired_end:
-                            if [cigar for cigar in sequences.all_cigar if len(cigar) != 100]:
+                            if [cigar for cigar in sequences.all_cigar[0] if len(cigar) != 100] or \
+                                    [cig for cig in sequences.all_cigar[1] if len(cig) != 100]:
                                 print("There's a cigar that's off.")
                                 pdb.set_trace()
                             my_fraglen = fraglen_distribution.sample()

From 75f26ca5bfc6e9b3627c513ab191b6620000405e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 7 Jan 2021 14:19:25 -0600
Subject: [PATCH 265/323] Debugging neat still

---
 gen_reads.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index 11d0695..c64a4a1 100644
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -600,9 +600,17 @@ def main(raw_args=None):
                 if sequences is None:
                     sequences = SequenceContainer(start, ref_sequence[start:end], ploids, overlap, read_len,
                                                   [mut_model] * ploids, mut_rate, only_vcf=only_vcf)
+                    if [cigar for cigar in sequences.all_cigar[0] if len(cigar) != 100] or \
+                            [cig for cig in sequences.all_cigar[1] if len(cig) != 100]:
+                        print("There's a cigar that's off.")
+                        pdb.set_trace()
                 else:
                     sequences.update(start, ref_sequence[start:end], ploids, overlap, read_len, [mut_model] * ploids,
                                      mut_rate)
+                    if [cigar for cigar in sequences.all_cigar[0] if len(cigar) != 100] or \
+                            [cig for cig in sequences.all_cigar[1] if len(cig) != 100]:
+                        print("There's a cigar that's off.")
+                        pdb.set_trace()
 
                 # insert variants
                 sequences.insert_mutations(vars_from_prev_overlap + vars_in_window)
@@ -661,10 +669,6 @@ def main(raw_args=None):
 
                         is_unmapped = []
                         if paired_end:
-                            if [cigar for cigar in sequences.all_cigar[0] if len(cigar) != 100] or \
-                                    [cig for cig in sequences.all_cigar[1] if len(cig) != 100]:
-                                print("There's a cigar that's off.")
-                                pdb.set_trace()
                             my_fraglen = fraglen_distribution.sample()
                             my_read_data = sequences.sample_read(se_class, my_fraglen)
                             # skip if we failed to find a valid position to sample read

From 86b7a55e1187077eff80889b95017b0cc2a88a8e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 11:15:56 -0600
Subject: [PATCH 266/323] Debugging neat still

---
 source/SequenceContainer.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index edd327c..47a7158 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -831,6 +831,9 @@ def sample_read(self, sequencing_model, frag_len=None):
                         print('\nError, ref does not match alt while attempting to insert substitution error!\n')
                         sys.exit(1)
 
+            if len(my_cigar) != 100:
+                print(str(my_cigar) + "is not equal to 100.")
+                pdb.set_trace()
             if any_indel_err:
                 if len(expanded_cigar):
                     relevant_cigar = (expanded_cigar + extra_cigar_val)[:self.read_len]

From a5540d3c37388c4c4119a332b234425d0a09afa2 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 11:29:28 -0600
Subject: [PATCH 267/323] Debugging neat still

---
 source/SequenceContainer.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 47a7158..602d85e 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -720,11 +720,9 @@ def sample_read(self, sequencing_model, frag_len=None):
             total_i = sum([error[1] for error in read[2] if error[0] == 'I'])
             avail_b = len(self.sequences[my_ploid]) - read[0] - self.read_len - 1
 
-            # debugging
-            if len(my_cigar) != 100:
-                print(my_cigar)
+            if total_d:
+                print("there is a deletion error")
                 pdb.set_trace()
-
             # add buffer sequence to fill in positions that get deleted
             read[3] += self.sequences[my_ploid][read[0] + self.read_len:read[0] + self.read_len + total_d]
             expanded_cigar = []
@@ -832,7 +830,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                         sys.exit(1)
 
             if len(my_cigar) != 100:
-                print(str(my_cigar) + "is not equal to 100.")
+                print(str(my_cigar) + " is not equal to 100.")
                 pdb.set_trace()
             if any_indel_err:
                 if len(expanded_cigar):

From 9ad8fb9cd5c9a2196329264bf5d9c7c3dd27f2a4 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 11:46:44 -0600
Subject: [PATCH 268/323] Debugging neat still

---
 source/SequenceContainer.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 602d85e..ec23976 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -618,6 +618,9 @@ def random_mutations(self):
                         # TODO Delete commented out lines once CigarString works 100%
                         # temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
 
+            if len(CigarString(temp_symbol_string)) != 100:
+                print("Something wrong with temp symbol string")
+                pdb.set_trace()
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
                 self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j + self.read_len))

From 7e6a2c4780b1ac26845e8fb757bd1e87e5e811ea Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 11:52:41 -0600
Subject: [PATCH 269/323] Hopefully fixing bugs, along with some refactoring

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index ec23976..2d6920f 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -618,7 +618,7 @@ def random_mutations(self):
                         # TODO Delete commented out lines once CigarString works 100%
                         # temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
 
-            if len(CigarString(temp_symbol_string)) != 100:
+            if len(temp_symbol_string) != 100:
                 print("Something wrong with temp symbol string")
                 pdb.set_trace()
             # pre-compute cigar strings

From 3dba28a390f32e5b146d001e3fc6f3de2d4c17f4 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 12:00:51 -0600
Subject: [PATCH 270/323] still tracking down this bug

---
 source/SequenceContainer.py | 6 +++---
 source/neat_cigar_rework.py | 3 +++
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 2d6920f..d767f25 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -521,6 +521,9 @@ def random_mutations(self):
                         self.black_list[p][k] = 1
                     all_indels[p].append(my_indel)
 
+        if my_indel:
+            print("Tracing indels")
+            pdb.set_trace()
         # add random snps
         all_snps = [[] for _ in self.sequences]
         for i in range(self.ploidy):
@@ -618,9 +621,6 @@ def random_mutations(self):
                         # TODO Delete commented out lines once CigarString works 100%
                         # temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
 
-            if len(temp_symbol_string) != 100:
-                print("Something wrong with temp symbol string")
-                pdb.set_trace()
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
                 self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j + self.read_len))
diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index 4c87566..1c5b67b 100644
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -1,4 +1,5 @@
 import sys
+import pdb
 from itertools import groupby
 from operator import itemgetter
 
@@ -322,6 +323,8 @@ def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
         except ValueError:
             print('\nBug: Problem retrieving fragment.\n')
             sys.exit(1)
+        print("checking cigar string creation")
+        pdb.set_trace()
         return CigarString(Cigar.string_from_elements(ret))
 
     # TODO use this method or delete it.

From c33e8053d5c7ab4fca8a7b2f3d9893b12c73fce3 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 12:47:05 -0600
Subject: [PATCH 271/323] still tracking down this bug

---
 source/SequenceContainer.py | 29 +++++++++++++++++++----------
 source/neat_cigar_rework.py |  3 +--
 2 files changed, 20 insertions(+), 12 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index d767f25..8ddf62c 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -12,7 +12,7 @@
 
 from source.neat_cigar_rework import CigarString
 from source.probability import DiscreteDistribution, poisson_list
-# from source.neat_cigar import CigarString
+from source.neat_cigar import CigarString as CigarStringOld
 
 """
 Constants needed for analysis
@@ -591,7 +591,8 @@ def random_mutations(self):
             rolling_adj = 0
             temp_symbol_string = CigarString(str(len(self.sequences[i])) + "M")
             # TODO Delete commented out lines once CigarString works 100%
-            # temp_symbol_string2 = ['M' for _ in self.sequences[i]]
+            temp_symbol_string2 = ['M' for _ in self.sequences[i]]
+            assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
 
             for j in range(len(all_indels_ins[i])):
                 v_pos = all_indels_ins[i][j][0] + rolling_adj
@@ -613,19 +614,22 @@ def random_mutations(self):
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert,
                                                                 len(all_indels_ins[i][j][1]))
                         # TODO Delete commented out lines once CigarString works 100%
-                        # temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
-                        #                       ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
+                        temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
+                                              ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
+                        assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
                     elif indel_length < 0:
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert, indel_length)
                         # TODO Delete commented out lines once CigarString works 100%
-                        # temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
+                        temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
+                        assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
                 self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j + self.read_len))
                 # TODO Delete commented out lines once CigarString works 100%
-                # self.all_cigar[i].append(CigarString(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
+                self.all_cigar2[i].append(CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
+            assert(self.all_cigar[i] == self.all_cigar2[i])
 
             # create some data structures we will need later:
             # --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e.
@@ -767,13 +771,14 @@ def sample_read(self, sequencing_model, frag_len=None):
                     if not expanded_cigar:
                         expanded_cigar = my_cigar.string_to_list()
                         # TODO delete these lines using old CigarString once it is working 100%
-                        # expanded_cigar = CigarString(string_in=my_cigar).get_list()
+                        expanded_cigar2 = CigarStringOld(string_in=my_cigar).get_list()
+                        assert(expanded_cigar == expanded_cigar2)
                         fill_to_go = total_d - total_i + 1
                         if fill_to_go > 0:
                             try:
                                 # TODO delete these lines using old CigarString once it is working 100%
-                                # extra_cigar_val = CigarString(string_in=self.all_cigar[my_ploid][read[0]
-                                #                                + fill_to_go]).get_list()[-fill_to_go:]
+                                extra_cigar_val2 = CigarStringOld(string_in=self.all_cigar2[my_ploid][read[0]
+                                                               + fill_to_go]).get_list()[-fill_to_go:]
                                 extra_cigar_val = self.all_cigar[my_ploid][read[0]
                                                                            + fill_to_go].string_to_list()[-fill_to_go:]
 
@@ -782,6 +787,8 @@ def sample_read(self, sequencing_model, frag_len=None):
                                 # Skip all indel errors
                                 skip_indels = True
 
+                            assert(extra_cigar_val == extra_cigar_val2)
+
                     if skip_indels:
                         continue
 
@@ -846,7 +853,9 @@ def sample_read(self, sequencing_model, frag_len=None):
                         pdb.set_trace()
 
                     # TODO delete this line once new cigar is 100% working
-                    # my_cigar = CigarString(list_in=relevant_cigar).get_string()
+                    my_cigar2 = CigarStringOld(list_in=relevant_cigar).get_string()
+
+                assert(my_cigar == my_cigar2)
                 read[3] = read[3][:self.read_len]
 
             read_out.append([self.fm_pos[my_ploid][read[0]], my_cigar, read[3], str(read[1])])
diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index 1c5b67b..215c05a 100644
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -186,6 +186,7 @@ def insert_cigar_element(self, pos: int,
         >>> str2 = CigarString('1D1M')
         >>> iPos = 6610 + 1
         >>> str1.insert_cigar_element(iPos, str2, -1)
+        >>> assert(len(str1) == 11100)
         >>> assert (str1.cigar == "6611M1D4489M")
         """
 
@@ -323,8 +324,6 @@ def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
         except ValueError:
             print('\nBug: Problem retrieving fragment.\n')
             sys.exit(1)
-        print("checking cigar string creation")
-        pdb.set_trace()
         return CigarString(Cigar.string_from_elements(ret))
 
     # TODO use this method or delete it.

From 8f9e9ba01b796a0765348eaeed0cfd79e0552527 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 12:53:01 -0600
Subject: [PATCH 272/323] still tracking down this bug

---
 source/SequenceContainer.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 8ddf62c..27935a6 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -521,9 +521,6 @@ def random_mutations(self):
                         self.black_list[p][k] = 1
                     all_indels[p].append(my_indel)
 
-        if my_indel:
-            print("Tracing indels")
-            pdb.set_trace()
         # add random snps
         all_snps = [[] for _ in self.sequences]
         for i in range(self.ploidy):

From c113b367c41470d1a747523699d17c83f0ffa917 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 12:57:48 -0600
Subject: [PATCH 273/323] still tracking down this bug

---
 source/SequenceContainer.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 27935a6..dd82a66 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -108,6 +108,7 @@ def __init__(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_mod
         self.indel_list = [[] for _ in range(self.ploidy)]
         self.snp_list = [[] for _ in range(self.ploidy)]
         self.all_cigar = [[] for _ in range(self.ploidy)]
+        self.all_cigar2 = [[] for _ in range(self.ploidy)]
         self.fm_pos = [[] for _ in range(self.ploidy)]
         self.fm_span = [[] for _ in range(self.ploidy)]
 
@@ -200,6 +201,7 @@ def update_basic_vars(self, x_offset, sequence, ploidy, window_overlap, read_len
         self.indel_list = [[] for _ in range(self.ploidy)]
         self.snp_list = [[] for _ in range(self.ploidy)]
         self.all_cigar = [[] for _ in range(self.ploidy)]
+        self.all_cigar2 = [[] for _ in range(self.ploidy)]
         self.fm_pos = [[] for _ in range(self.ploidy)]
         self.fm_span = [[] for _ in range(self.ploidy)]
         self.black_list = [np.zeros(self.seq_len, dtype='<i4') for _ in range(self.ploidy)]

From 2f9482431d4b887464c55abe5009855f72250d92 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 13:14:49 -0600
Subject: [PATCH 274/323] still tracking down this bug

---
 source/SequenceContainer.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index dd82a66..e6ad2b6 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -628,7 +628,11 @@ def random_mutations(self):
                 self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j + self.read_len))
                 # TODO Delete commented out lines once CigarString works 100%
                 self.all_cigar2[i].append(CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
-            assert(self.all_cigar[i] == self.all_cigar2[i])
+
+            for j in range(len(self.all_cigar[i])):
+                if self.all_cigar[i][j] != self.all_cigar2[i][j]:
+                    print("mismatch in the cigars")
+                    pdb.set_trace()
 
             # create some data structures we will need later:
             # --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e.

From 57b1804fadef3cd594ec4edc4e1b5a8306bd9dec Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 13:19:51 -0600
Subject: [PATCH 275/323] still tracking down this bug

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index e6ad2b6..abebd79 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -630,7 +630,7 @@ def random_mutations(self):
                 self.all_cigar2[i].append(CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
 
             for j in range(len(self.all_cigar[i])):
-                if self.all_cigar[i][j] != self.all_cigar2[i][j]:
+                if self.all_cigar[i][j].cigar != self.all_cigar2[i][j]:
                     print("mismatch in the cigars")
                     pdb.set_trace()
 

From 51696a3f63a4ad62290674960e0917fa86e0c34c Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 13:27:15 -0600
Subject: [PATCH 276/323] still tracking down this bug

---
 source/SequenceContainer.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index abebd79..95d419c 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -628,10 +628,9 @@ def random_mutations(self):
                 self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j + self.read_len))
                 # TODO Delete commented out lines once CigarString works 100%
                 self.all_cigar2[i].append(CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
-
-            for j in range(len(self.all_cigar[i])):
-                if self.all_cigar[i][j].cigar != self.all_cigar2[i][j]:
-                    print("mismatch in the cigars")
+                if temp_symbol_string.get_cigar_fragment(j, j + self.read_len) != CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string():
+                    print("new " + str(temp_symbol_string.get_cigar_fragment(j, j + self.read_len)))
+                    print("old " + CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
                     pdb.set_trace()
 
             # create some data structures we will need later:

From 4f93a57c7c760a195bfdf92425f4782ad3a4827c Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 13:32:35 -0600
Subject: [PATCH 277/323] still tracking down this bug

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 95d419c..bd79945 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -628,7 +628,7 @@ def random_mutations(self):
                 self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j + self.read_len))
                 # TODO Delete commented out lines once CigarString works 100%
                 self.all_cigar2[i].append(CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
-                if temp_symbol_string.get_cigar_fragment(j, j + self.read_len) != CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string():
+                if temp_symbol_string.get_cigar_fragment(j, j + self.read_len).cigar != CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string():
                     print("new " + str(temp_symbol_string.get_cigar_fragment(j, j + self.read_len)))
                     print("old " + CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
                     pdb.set_trace()

From 5384c899a669781156cf39d8d735a7429bb0f44d Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 15:47:19 -0600
Subject: [PATCH 278/323] May have found the bug

---
 source/neat_cigar_rework.py | 85 +++++++++++++++++++++++++++++++++++--
 1 file changed, 81 insertions(+), 4 deletions(-)

diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index 215c05a..e8320d8 100644
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -238,19 +238,26 @@ def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
         >>> start = 0
         >>> end = 14
         >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "10M4D")
+        >>> assert(len(frag) == 14)
+        >>> assert(frag.cigar == "10M5D4M")
+
+        >>> str1 = CigarString('90M1D10M')
+        >>> start = 0
+        >>> end = 100
+        >>> frag = str1.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == "90M1D10M")
 
         >>> str1 = CigarString('10M2D10M')
         >>> start = 10
         >>> end = 12
         >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "2D")
+        >>> assert(frag.cigar == "2M")
 
         >>> str1 = CigarString('10M1D10M')
         >>> start = 10
         >>> end = 12
         >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "1D1M")
+        >>> assert(frag.cigar == "2M")
 
         >>> str1 = CigarString('102M2I10000M')
         >>> start1 = 1
@@ -261,6 +268,12 @@ def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
         >>> frag2 = str1.get_cigar_fragment(start2, end2)
         >>> assert(frag1.cigar == "101M")
         >>> assert(frag2.cigar == "2I99M")
+
+        >>> temp_symbol_string = CigarString('25179M1D8304M')
+        >>> start = 25080
+        >>> end = 25180
+        >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == '99M1D1M')
         """
         # Minus 1 because python slices don't include the end coordinate
         window_size = end - start
@@ -286,9 +299,18 @@ def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
             start_found = False
             ret = []
             for item in self.items():
-                current_pos += item[0]
                 current_block_size = item[0]
                 current_symbol = item[1]
+                if current_symbol == 'D' and not start_found:
+                    continue
+                elif current_symbol == 'D' and start_found:
+                    ret.append(item)
+                    continue
+                elif current_symbol in Cigar.read_consuming_ops:
+                    current_pos += current_block_size
+                else:
+                    print("Bug: Unknown symbol!")
+                    sys.exit(1)
                 if current_pos >= start and not start_found:
                     start_found = True
                     current_index = previous_pos + 1
@@ -381,3 +403,58 @@ def list_to_string(input_list: list) -> str:
                 current_count = 1
         symbols += str(current_count) + current_sym
         return symbols
+
+
+if __name__ == "__main__":
+    str1 = CigarString('10M5D10M')
+    start = 0
+    end = 14
+    frag = str1.get_cigar_fragment(start, end)
+    assert (len(frag) == 14)
+    assert (frag.cigar == "10M5D4M")
+    print(frag.cigar + " == 10M5D4M")
+
+    str1 = CigarString('90M1D10M')
+    start = 0
+    end = 100
+    frag = str1.get_cigar_fragment(start, end)
+    assert (frag.cigar == "90M1D10M")
+    print(frag.cigar + " == 90M1D10M")
+
+    str1 = CigarString('10M2D10M')
+    start = 10
+    end = 12
+    frag = str1.get_cigar_fragment(start, end)
+    assert (frag.cigar == "2M")
+    print(frag.cigar + " == 2M")
+
+    str1 = CigarString('10M1D10M')
+    start = 10
+    end = 12
+    frag = str1.get_cigar_fragment(start, end)
+    assert (frag.cigar == "2M")
+    print(frag.cigar + " == 2M")
+
+    str1 = CigarString('102M2I10000M')
+    start1 = 1
+    end1 = 102
+    frag1 = str1.get_cigar_fragment(start1, end1)
+    start2 = 102
+    end2 = 203
+    frag2 = str1.get_cigar_fragment(start2, end2)
+    assert (frag1.cigar == "101M")
+
+    print(frag1.cigar + " == 101M")
+    assert (frag2.cigar == "2I99M")
+    print(frag2.cigar + " == 2I99M")
+
+    temp_symbol_string = CigarString('25179M1D8304M')
+    print(temp_symbol_string.get_cigar_fragment(25080, 25180))
+
+    str1 = CigarString('2I101M')
+    start = 0
+    end = 100
+    frag = str1.get_cigar_fragment(start, end)
+    assert(len(frag) == 100)
+    assert(frag.cigar == "2I98M")
+    print(frag.cigar + " == 2I98M")

From 5a820d5ca749edb8eebbda1174ebb126b0539ac8 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 15:47:42 -0600
Subject: [PATCH 279/323] May have found the bug

---
 source/neat_cigar_rework.py | 55 -------------------------------------
 1 file changed, 55 deletions(-)

diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index e8320d8..f987174 100644
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -403,58 +403,3 @@ def list_to_string(input_list: list) -> str:
                 current_count = 1
         symbols += str(current_count) + current_sym
         return symbols
-
-
-if __name__ == "__main__":
-    str1 = CigarString('10M5D10M')
-    start = 0
-    end = 14
-    frag = str1.get_cigar_fragment(start, end)
-    assert (len(frag) == 14)
-    assert (frag.cigar == "10M5D4M")
-    print(frag.cigar + " == 10M5D4M")
-
-    str1 = CigarString('90M1D10M')
-    start = 0
-    end = 100
-    frag = str1.get_cigar_fragment(start, end)
-    assert (frag.cigar == "90M1D10M")
-    print(frag.cigar + " == 90M1D10M")
-
-    str1 = CigarString('10M2D10M')
-    start = 10
-    end = 12
-    frag = str1.get_cigar_fragment(start, end)
-    assert (frag.cigar == "2M")
-    print(frag.cigar + " == 2M")
-
-    str1 = CigarString('10M1D10M')
-    start = 10
-    end = 12
-    frag = str1.get_cigar_fragment(start, end)
-    assert (frag.cigar == "2M")
-    print(frag.cigar + " == 2M")
-
-    str1 = CigarString('102M2I10000M')
-    start1 = 1
-    end1 = 102
-    frag1 = str1.get_cigar_fragment(start1, end1)
-    start2 = 102
-    end2 = 203
-    frag2 = str1.get_cigar_fragment(start2, end2)
-    assert (frag1.cigar == "101M")
-
-    print(frag1.cigar + " == 101M")
-    assert (frag2.cigar == "2I99M")
-    print(frag2.cigar + " == 2I99M")
-
-    temp_symbol_string = CigarString('25179M1D8304M')
-    print(temp_symbol_string.get_cigar_fragment(25080, 25180))
-
-    str1 = CigarString('2I101M')
-    start = 0
-    end = 100
-    frag = str1.get_cigar_fragment(start, end)
-    assert(len(frag) == 100)
-    assert(frag.cigar == "2I98M")
-    print(frag.cigar + " == 2I98M")

From 7aa276e00e4ef78d4e4f52cd022df4dcd1aef1f8 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 15:56:46 -0600
Subject: [PATCH 280/323] found another bug

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index bd79945..f9abf74 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -618,7 +618,7 @@ def random_mutations(self):
                         assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
                     elif indel_length < 0:
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
-                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert, indel_length)
+                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert)
                         # TODO Delete commented out lines once CigarString works 100%
                         temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
                         assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())

From 3c331ae2636e55e8ff69561aa752402fd09c4d22 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 16:37:45 -0600
Subject: [PATCH 281/323] found another bug

---
 source/SequenceContainer.py | 1 +
 source/neat_cigar_rework.py | 2 ++
 2 files changed, 3 insertions(+)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index f9abf74..2be1ca2 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -617,6 +617,7 @@ def random_mutations(self):
                                               ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
                         assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
                     elif indel_length < 0:
+                        pdb.set_trace()
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert)
                         # TODO Delete commented out lines once CigarString works 100%
diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index f987174..3ece12d 100644
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -188,6 +188,8 @@ def insert_cigar_element(self, pos: int,
         >>> str1.insert_cigar_element(iPos, str2, -1)
         >>> assert(len(str1) == 11100)
         >>> assert (str1.cigar == "6611M1D4489M")
+
+        >>> str1 = CigarString('')
         """
 
         if insertion_cigar is None:

From 959d1e9b09e0af71777c2008db84ae643de37725 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 16:49:26 -0600
Subject: [PATCH 282/323] found another bug

---
 source/SequenceContainer.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 2be1ca2..f9abf74 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -617,7 +617,6 @@ def random_mutations(self):
                                               ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
                         assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
                     elif indel_length < 0:
-                        pdb.set_trace()
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert)
                         # TODO Delete commented out lines once CigarString works 100%

From 0615c46bd36b86151473bd977aced3ab2b289baa Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 16:53:07 -0600
Subject: [PATCH 283/323] found another bug

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index f9abf74..d8dfa0f 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -618,7 +618,7 @@ def random_mutations(self):
                         assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
                     elif indel_length < 0:
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
-                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert)
+                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert, abs(indel_length))
                         # TODO Delete commented out lines once CigarString works 100%
                         temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
                         assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())

From 2f7e3632d4e6c9678c04ebe906f1a8fb3f059465 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 16:57:02 -0600
Subject: [PATCH 284/323] found another bug

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index d8dfa0f..3dee975 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -620,8 +620,8 @@ def random_mutations(self):
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert, abs(indel_length))
                         # TODO Delete commented out lines once CigarString works 100%
+                        assert (temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
                         temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
-                        assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):

From a4cf3eb694ce880f93d3d9e6fbd3ab41ab863a16 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 18:03:10 -0600
Subject: [PATCH 285/323] found another bug

---
 source/SequenceContainer.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 3dee975..167a4ae 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -620,8 +620,10 @@ def random_mutations(self):
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert, abs(indel_length))
                         # TODO Delete commented out lines once CigarString works 100%
-                        assert (temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
+                        check = temp_symbol_string2[v_pos + 1]
                         temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
+                        assert (temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
+
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):

From a86dbe56f0a0bc7c7e45b39711460132e9bf5267 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 18:09:35 -0600
Subject: [PATCH 286/323] found another bug

---
 source/SequenceContainer.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 167a4ae..1fee3af 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -613,16 +613,20 @@ def random_mutations(self):
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert,
                                                                 len(all_indels_ins[i][j][1]))
                         # TODO Delete commented out lines once CigarString works 100%
+                        check = temp_symbol_string2[:v_pos + 1]
                         temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
                                               ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
                         assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
                     elif indel_length < 0:
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
-                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert, abs(indel_length))
+                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert)
                         # TODO Delete commented out lines once CigarString works 100%
                         check = temp_symbol_string2[v_pos + 1]
                         temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
                         assert (temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
+                        if temp_symbol_string.cigar == '28662M4D4818M':
+                            print("check this one")
+                            pdb.set_trace()
 
 
             # pre-compute cigar strings

From a881cc49bf2d38bfef9cfa93eb9cd0c48b729b8a Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 18:11:45 -0600
Subject: [PATCH 287/323] found another bug

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 1fee3af..394556c 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -624,7 +624,7 @@ def random_mutations(self):
                         check = temp_symbol_string2[v_pos + 1]
                         temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
                         assert (temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
-                        if temp_symbol_string.cigar == '28662M4D4818M':
+                        if temp_symbol_string.cigar == '28662M4D4821M':
                             print("check this one")
                             pdb.set_trace()
 

From 962e3110e07e047fde06c0bec07be3d285018996 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 19:06:18 -0600
Subject: [PATCH 288/323] found another bug

---
 source/SequenceContainer.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 394556c..0892f74 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -619,9 +619,10 @@ def random_mutations(self):
                         assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
                     elif indel_length < 0:
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
+                        original_string = copy.deepcopy(temp_symbol_string)
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert)
                         # TODO Delete commented out lines once CigarString works 100%
-                        check = temp_symbol_string2[v_pos + 1]
+                        original_list = copy.deepcopy(temp_symbol_string2)
                         temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
                         assert (temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
                         if temp_symbol_string.cigar == '28662M4D4821M':

From 0edbd0fa93a030909879c32dee3deb43596d9589 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 19:12:22 -0600
Subject: [PATCH 289/323] found another bug

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 0892f74..d48b279 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -620,7 +620,7 @@ def random_mutations(self):
                     elif indel_length < 0:
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
                         original_string = copy.deepcopy(temp_symbol_string)
-                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert)
+                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert, 1)
                         # TODO Delete commented out lines once CigarString works 100%
                         original_list = copy.deepcopy(temp_symbol_string2)
                         temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'

From bb3eea6d653b2a56141b937c04e07a43bdb061d6 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 19:12:45 -0600
Subject: [PATCH 290/323] found another bug

---
 source/SequenceContainer.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index d48b279..a419c46 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -625,9 +625,7 @@ def random_mutations(self):
                         original_list = copy.deepcopy(temp_symbol_string2)
                         temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
                         assert (temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
-                        if temp_symbol_string.cigar == '28662M4D4821M':
-                            print("check this one")
-                            pdb.set_trace()
+                        pdb.set_trace()
 
 
             # pre-compute cigar strings

From 053b833d5f01cd0e5a0e5506d19af914b5def3f2 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 19:37:18 -0600
Subject: [PATCH 291/323] found another bug

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index a419c46..a46a3f8 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -778,7 +778,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                     if not expanded_cigar:
                         expanded_cigar = my_cigar.string_to_list()
                         # TODO delete these lines using old CigarString once it is working 100%
-                        expanded_cigar2 = CigarStringOld(string_in=my_cigar).get_list()
+                        expanded_cigar2 = CigarStringOld(string_in=my_cigar.cigar).get_list()
                         assert(expanded_cigar == expanded_cigar2)
                         fill_to_go = total_d - total_i + 1
                         if fill_to_go > 0:

From a650437a01862f1b03fde2ca996da004a990e98e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 19:45:05 -0600
Subject: [PATCH 292/323] found another bug

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index a46a3f8..bc7af60 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -862,7 +862,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                     # TODO delete this line once new cigar is 100% working
                     my_cigar2 = CigarStringOld(list_in=relevant_cigar).get_string()
 
-                assert(my_cigar == my_cigar2)
+                assert(my_cigar.cigar == my_cigar2)
                 read[3] = read[3][:self.read_len]
 
             read_out.append([self.fm_pos[my_ploid][read[0]], my_cigar, read[3], str(read[1])])

From 920beb1c6ff6f0411ccd72fbb2a4918bb95fd104 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 19:50:19 -0600
Subject: [PATCH 293/323] found another bug

---
 source/SequenceContainer.py | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index bc7af60..a09d833 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -734,9 +734,6 @@ def sample_read(self, sequencing_model, frag_len=None):
             total_i = sum([error[1] for error in read[2] if error[0] == 'I'])
             avail_b = len(self.sequences[my_ploid]) - read[0] - self.read_len - 1
 
-            if total_d:
-                print("there is a deletion error")
-                pdb.set_trace()
             # add buffer sequence to fill in positions that get deleted
             read[3] += self.sequences[my_ploid][read[0] + self.read_len:read[0] + self.read_len + total_d]
             expanded_cigar = []
@@ -846,19 +843,10 @@ def sample_read(self, sequencing_model, frag_len=None):
                         print('\nError, ref does not match alt while attempting to insert substitution error!\n')
                         sys.exit(1)
 
-            if len(my_cigar) != 100:
-                print(str(my_cigar) + " is not equal to 100.")
-                pdb.set_trace()
             if any_indel_err:
                 if len(expanded_cigar):
                     relevant_cigar = (expanded_cigar + extra_cigar_val)[:self.read_len]
                     my_cigar = CigarString(CigarString.list_to_string(relevant_cigar))
-
-                    # debugging
-                    if len(my_cigar) != 100:
-                        print(my_cigar)
-                        pdb.set_trace()
-
                     # TODO delete this line once new cigar is 100% working
                     my_cigar2 = CigarStringOld(list_in=relevant_cigar).get_string()
 

From 40bda9eb88c8feb5ce19f926d08132ab530e1497 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 20:09:27 -0600
Subject: [PATCH 294/323] found another bug

---
 source/SequenceContainer.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index a09d833..75a8371 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -610,13 +610,16 @@ def random_mutations(self):
                     # notate indel positions for cigar computation
                     if indel_length > 0:
                         cigar_to_insert = CigarString(str(indel_length) + 'I')
+                        original_string = copy.deepcopy(temp_symbol_string)
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert,
                                                                 len(all_indels_ins[i][j][1]))
                         # TODO Delete commented out lines once CigarString works 100%
-                        check = temp_symbol_string2[:v_pos + 1]
+                        original_list = copy.deepcopy(temp_symbol_string2)
                         temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
                                               ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
-                        assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
+                        if temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string():
+                            print('something wrong with inserting')
+                            pdb.set_trace()
                     elif indel_length < 0:
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
                         original_string = copy.deepcopy(temp_symbol_string)
@@ -624,9 +627,9 @@ def random_mutations(self):
                         # TODO Delete commented out lines once CigarString works 100%
                         original_list = copy.deepcopy(temp_symbol_string2)
                         temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
-                        assert (temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
-                        pdb.set_trace()
-
+                        if temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string():
+                            print('something wrong with deletion')
+                            pdb.set_trace()
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):

From 9b4f154593309fb490d8677770edac79b911e38d Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 11 Jan 2021 20:13:54 -0600
Subject: [PATCH 295/323] found another bug

---
 source/SequenceContainer.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 75a8371..5091fe2 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -591,7 +591,9 @@ def random_mutations(self):
             temp_symbol_string = CigarString(str(len(self.sequences[i])) + "M")
             # TODO Delete commented out lines once CigarString works 100%
             temp_symbol_string2 = ['M' for _ in self.sequences[i]]
-            assert(temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string())
+            if not temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string():
+                print('something wrong with temp symbol creation')
+                pdb.set_trace()
 
             for j in range(len(all_indels_ins[i])):
                 v_pos = all_indels_ins[i][j][0] + rolling_adj
@@ -617,7 +619,7 @@ def random_mutations(self):
                         original_list = copy.deepcopy(temp_symbol_string2)
                         temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
                                               ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
-                        if temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string():
+                        if not temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string():
                             print('something wrong with inserting')
                             pdb.set_trace()
                     elif indel_length < 0:
@@ -627,7 +629,7 @@ def random_mutations(self):
                         # TODO Delete commented out lines once CigarString works 100%
                         original_list = copy.deepcopy(temp_symbol_string2)
                         temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
-                        if temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string():
+                        if not temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string():
                             print('something wrong with deletion')
                             pdb.set_trace()
 

From 10cf69f15c776f23860bd73bc0612ce2396750fb Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 12 Jan 2021 01:30:13 -0600
Subject: [PATCH 296/323] found another bug

---
 source/neat_cigar_rework.py | 210 +++++++++++++++++++++++-------------
 1 file changed, 134 insertions(+), 76 deletions(-)

diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index 3ece12d..4a4b872 100644
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -2,6 +2,7 @@
 import pdb
 from itertools import groupby
 from operator import itemgetter
+from source.neat_cigar import CigarString as CigarStringOld
 
 
 class Cigar(object):
@@ -154,13 +155,44 @@ class CigarString(Cigar):
     the rest of the NEAT codebase
     """
 
-    def insert_cigar_element(self, pos: int,
-                             insertion_cigar: 'CigarString',
-                             length: int = 0) -> None:
+    def __str__(self):
+        return self.cigar
+
+    def __repr__(self):
+        return "CigarString('%s')" % self
+
+    def find_position(self, position: int) -> int:
+        """
+        This finds the index of the element of list(self.items()) that contains the starting position given.
+
+        :param position: The position to find of the CigarString
+        :return: the index of that in terms of the elements of list(self.items())
+        >>> cigar = CigarString('2064M1I12979M3D18439M')
+        >>> position = 15414
+        >>> index, bases = cigar.find_position(position)
+        >>> assert(index == 4)
+        >>> assert(bases == 370)
+        """
+        consumed_bases = 0
+        bases_left = position
+        index = 0
+        for item in self.items():
+            if item[1] == 'D':
+                index += 1
+                continue
+            consumed_bases += item[0]
+            if consumed_bases > position:
+                return index, bases_left
+            else:
+                bases_left -= item[0]
+                index += 1
+        return index, bases_left
+
+    def insert_cigar_element(self, pos: int, insertion_cigar: 'CigarString', length: int = 0) -> None:
         """
         Inserts a cigar string in either string or list format to the existing cigar string at position pos.
 
-        :param length: If consuming spaces, length of cigar to be added
+        :param length: Length of the items to be consumed by the insertion
         :param insertion_cigar: A cigar to insert into current cigar string
         :param pos: integer position where to insert the input cigar string
         :return: None
@@ -169,7 +201,7 @@ def insert_cigar_element(self, pos: int,
         >>> str2 = CigarString('10I25M')
         >>> iPos = 55
         >>> str1.insert_cigar_element(iPos, str2)
-        >>> assert(str1.cigar == "50M5D10I25M5D7I23M")
+        >>> assert(str1.cigar == "50M10D15I25M2I23M")
 
         >>> str1 = CigarString('50M10D7I23M')
         >>> iPos = 20
@@ -179,13 +211,13 @@ def insert_cigar_element(self, pos: int,
         >>> str1 = CigarString('11100M')
         >>> str2 = CigarString('2I')
         >>> iPos = 5785 + 1
-        >>> str1.insert_cigar_element(iPos, str2, 1)
-        >>> assert (str1.cigar == "5786M2I5313M")
+        >>> str1.insert_cigar_element(iPos, str2)
+        >>> assert (str1.cigar == "5786M2I5314M")
 
         >>> str1 = CigarString('11100M')
-        >>> str2 = CigarString('1D1M')
+        >>> str2 = CigarString('1D')
         >>> iPos = 6610 + 1
-        >>> str1.insert_cigar_element(iPos, str2, -1)
+        >>> str1.insert_cigar_element(iPos, str2)
         >>> assert(len(str1) == 11100)
         >>> assert (str1.cigar == "6611M1D4489M")
 
@@ -200,27 +232,37 @@ def insert_cigar_element(self, pos: int,
             print('\nError: Invalid insertion position in CigarString\n')
             sys.exit(1)
 
+        if length < 0:
+            print('\nError: Did not understand the indel length to be inserted\n')
+            sys.exit(1)
+
         try:
-            current_pos = 0
-            previous_pos = 0
-            new_element = []
             found = False
-            for item in self.items():
-                current_pos += item[0]
-                if current_pos > pos and not found:
-                    current_block = item[0]
-                    current_index = pos - previous_pos
-                    new_element.append((current_index, item[1]))
-                    for insert in insertion_cigar.items():
-                        new_element.append(insert)
-                    if length != 0:
-                        new_element.append((current_block-current_index-abs(length), item[1]))
-                    else:
-                        new_element.append((current_block-current_index, item[1]))
-                    found = True
-                else:
-                    new_element.append(item)
-                previous_pos = current_pos
+            index, bases_remain = self.find_position(pos)
+            new_element = list(self.items())[:index]
+            list_of_items = list(self.items())[index:]
+            if len(list_of_items) == 1:
+                new_element.append((bases_remain, list_of_items[0][1]))
+                new_element += list(insertion_cigar.items())
+                new_element.append((list_of_items[0][0] - bases_remain - length, list_of_items[0][1]))
+            else:
+                for item in list_of_items:
+                    if item[1] == 'D':
+                        new_element.append(item)
+                    elif found:
+                        new_element.append(item)
+                    elif bases_remain > item[0]:
+                        new_element.append(item)
+                        bases_remain -= item[0]
+                    elif bases_remain == item[0]:
+                        new_element.append(item)
+                        new_element += list(insertion_cigar.items())
+                        found = True
+                    elif bases_remain < item[0]:
+                        new_element.append((bases_remain, item[1]))
+                        new_element += list(insertion_cigar.items())
+                        new_element.append((item[0]-bases_remain-length, item[1]))
+                        found = True
             new_string = self.string_from_elements(new_element)
             self.cigar = CigarString(new_string).merge_like_ops().cigar
         except ValueError:
@@ -295,60 +337,38 @@ def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
             sys.exit(1)
 
         try:
-            # Minus 1 because of indexing
-            current_pos = -1
-            previous_pos = -1
-            start_found = False
-            ret = []
-            for item in self.items():
-                current_block_size = item[0]
-                current_symbol = item[1]
-                if current_symbol == 'D' and not start_found:
-                    continue
-                elif current_symbol == 'D' and start_found:
-                    ret.append(item)
-                    continue
-                elif current_symbol in Cigar.read_consuming_ops:
-                    current_pos += current_block_size
-                else:
-                    print("Bug: Unknown symbol!")
-                    sys.exit(1)
-                if current_pos >= start and not start_found:
-                    start_found = True
-                    current_index = previous_pos + 1
-                    # start - current index = The index where we start relative to the start of the block
-                    # if there are not enough items in the current block, we will consume what is left
-                    # and move to the next block
-                    if current_block_size - (start - current_index) >= window_size:
-                        # If from where we start to the end of the current block is sufficient for our window,
-                        # then the return is just everything of that symbol
-                        ret.append((window_size, current_symbol))
-                        break
-                    else:
-                        # if there are not enough items in the current block, we will consume what is left
-                        # and move to the next block
-                        ret.append((current_block_size - (start - current_index), current_symbol))
-                        # shrink the window to account for the items we've found
-                        window_size -= current_block_size - (start - current_index)
-                elif start_found:
-                    if current_block_size >= window_size:
-                        # If this block can cover the remaining slice, then append the end and break
-                        ret.append((window_size, current_symbol))
+            start_index, bases_remain = self.find_position(start)
+            end_index = self.find_position(end)[0]
+            new_element = []
+            # +1 here to make it inclusive
+            list_of_items = list(self.items())[start_index:end_index+1]
+            if len(list_of_items) == 1:
+                new_element.append((window_size, list_of_items[0][1]))
+            else:
+                for item in list_of_items:
+                    if item[1] == 'D':
+                        new_element.append(item)
+                    elif bases_remain > 0:
+                        bases = item[0] - bases_remain
+                        if bases <= 0:
+                            print("Something went wrong retrieving fragment")
+                            sys.exit(1)
+                        bases_remain = 0
+                        window_size -= bases
+                        new_element.append((bases, item[1]))
+                    elif window_size > item[0]:
+                        new_element.append(item)
+                        window_size -= item[0]
+                    elif window_size <= item[0]:
+                        new_element.append((window_size, item[1]))
                         break
-                    else:
-                        # If we still need more, then consume this entire block
-                        ret.append((current_block_size, current_symbol))
-                        # shrink the window to account for the items we've found
-                        window_size -= current_block_size
-                else:
-                    pass
 
-                previous_pos = current_pos
+            new_string = self.string_from_elements(new_element)
+            return CigarString(new_string).merge_like_ops()
 
         except ValueError:
             print('\nBug: Problem retrieving fragment.\n')
             sys.exit(1)
-        return CigarString(Cigar.string_from_elements(ret))
 
     # TODO use this method or delete it.
     def count_elements(self, element: str) -> int:
@@ -405,3 +425,41 @@ def list_to_string(input_list: list) -> str:
                 current_count = 1
         symbols += str(current_count) + current_sym
         return symbols
+
+
+if __name__ == "__main__":
+    original_string = '2064M1I12979M3D18439M'
+    original_cigar = CigarString(original_string)
+    v_pos = 15414
+    cigar_to_insert = CigarString('6I')
+    original_cigar.insert_cigar_element(v_pos, cigar_to_insert, 1)
+    if original_cigar.cigar == '2064M1I12979M3D370M6I18068M':
+        print("OKAY")
+    else:
+        print("NOT OKKKAAAAAY")
+
+    str1 = CigarString('50M10D7I23M')
+    str2 = CigarString('10I25M')
+    iPos = 55
+    str1.insert_cigar_element(iPos, str2)
+    assert (str1.cigar == "50M10D15I25M2I23M")
+
+    str1 = CigarString('50M10D7I23M')
+    iPos = 20
+    str1.insert_cigar_element(iPos, str2)
+    assert (str1.cigar == "20M10I55M10D7I23M")
+
+    str1 = CigarString('11100M')
+    str2 = CigarString('2I')
+    iPos = 5785 + 1
+    str1.insert_cigar_element(iPos, str2)
+    assert (str1.cigar == "5786M2I5314M")
+
+    str1 = CigarString('11100M')
+    str2 = CigarString('1D')
+    iPos = 6610 + 1
+    str1.insert_cigar_element(iPos, str2)
+    assert (len(str1) == 11100)
+    assert (str1.cigar == "6611M1D4489M")
+
+    str1 = CigarString('')
\ No newline at end of file

From 42f8dce92217d5bf96243150f9cafb555be13500 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 12 Jan 2021 01:38:43 -0600
Subject: [PATCH 297/323] found another bug

---
 source/neat_cigar_rework.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index 4a4b872..8803eed 100644
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -220,8 +220,6 @@ def insert_cigar_element(self, pos: int, insertion_cigar: 'CigarString', length:
         >>> str1.insert_cigar_element(iPos, str2)
         >>> assert(len(str1) == 11100)
         >>> assert (str1.cigar == "6611M1D4489M")
-
-        >>> str1 = CigarString('')
         """
 
         if insertion_cigar is None:
@@ -244,7 +242,10 @@ def insert_cigar_element(self, pos: int, insertion_cigar: 'CigarString', length:
             if len(list_of_items) == 1:
                 new_element.append((bases_remain, list_of_items[0][1]))
                 new_element += list(insertion_cigar.items())
-                new_element.append((list_of_items[0][0] - bases_remain - length, list_of_items[0][1]))
+                if list_of_items[0][0] - bases_remain >= length:
+                    new_element.append((list_of_items[0][0] - bases_remain - length, list_of_items[0][1]))
+                else:
+                    new_element.append((list_of_items[0][0] - bases_remain, list_of_items[0][1]))
             else:
                 for item in list_of_items:
                     if item[1] == 'D':
@@ -261,7 +262,10 @@ def insert_cigar_element(self, pos: int, insertion_cigar: 'CigarString', length:
                     elif bases_remain < item[0]:
                         new_element.append((bases_remain, item[1]))
                         new_element += list(insertion_cigar.items())
-                        new_element.append((item[0]-bases_remain-length, item[1]))
+                        if item[0] - bases_remain >= length:
+                            new_element.append((item[0] - bases_remain - length, item[1]))
+                        else:
+                            new_element.append((list_of_items[0][0] - bases_remain, item[1]))
                         found = True
             new_string = self.string_from_elements(new_element)
             self.cigar = CigarString(new_string).merge_like_ops().cigar

From a3dd14a0959ee8ceb6f68181c4935a45833325ee Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 19 Jan 2021 15:58:27 -0600
Subject: [PATCH 298/323] Rewrote how the insert element function works in
 cigars.

---
 source/SequenceContainer.py |   4 +-
 source/neat_cigar_rework.py | 128 ++++++++++++++++++++++--------------
 2 files changed, 81 insertions(+), 51 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 5091fe2..372fc0d 100644
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -614,7 +614,7 @@ def random_mutations(self):
                         cigar_to_insert = CigarString(str(indel_length) + 'I')
                         original_string = copy.deepcopy(temp_symbol_string)
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert,
-                                                                len(all_indels_ins[i][j][1]))
+                                                                v_pos2 + 1)
                         # TODO Delete commented out lines once CigarString works 100%
                         original_list = copy.deepcopy(temp_symbol_string2)
                         temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
@@ -625,7 +625,7 @@ def random_mutations(self):
                     elif indel_length < 0:
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
                         original_string = copy.deepcopy(temp_symbol_string)
-                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert, 1)
+                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert)
                         # TODO Delete commented out lines once CigarString works 100%
                         original_list = copy.deepcopy(temp_symbol_string2)
                         temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index 8803eed..435d46a 100644
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -188,13 +188,13 @@ def find_position(self, position: int) -> int:
                 index += 1
         return index, bases_left
 
-    def insert_cigar_element(self, pos: int, insertion_cigar: 'CigarString', length: int = 0) -> None:
+    def insert_cigar_element(self, pos1: int, insertion_cigar: 'CigarString', pos2: int = None) -> None:
         """
         Inserts a cigar string in either string or list format to the existing cigar string at position pos.
 
-        :param length: Length of the items to be consumed by the insertion
         :param insertion_cigar: A cigar to insert into current cigar string
-        :param pos: integer position where to insert the input cigar string
+        :param pos1: integer position where to insert the input cigar string
+        :param pos2: integer position where to pick up the remainder
         :return: None
 
         >>> str1 = CigarString('50M10D7I23M')
@@ -220,54 +220,71 @@ def insert_cigar_element(self, pos: int, insertion_cigar: 'CigarString', length:
         >>> str1.insert_cigar_element(iPos, str2)
         >>> assert(len(str1) == 11100)
         >>> assert (str1.cigar == "6611M1D4489M")
+
+        >>> str1 = CigarString('10M1I2D10M')
+        >>> str2 = CigarString('1D')
+        >>> pos1 = 10
+        >>> pos2 = pos1 + 2
+        >>> str1.insert_cigar_element(pos1, str2, pos2)
+        >>> cigar_to_insert = '1D1M'
+        >>> str1_list[pos1] = cigar_to_insert
+        >>> test_string = CigarStringOld.list_to_string(str1_list)
+        >>> assert(str1.cigar == test_string)
+
+        >>> original_string = '100M1I10M'
+        >>> original_cigar = CigarString(original_string)
+        >>> v_pos = 9
+        >>> v_pos2 = 9 + 6
+        >>> cigar_to_insert = CigarString('6I')
+        >>> original_cigar.insert_cigar_element(v_pos, cigar_to_insert, v_pos2)
+        >>> cigar_list = CigarStringOld.string_to_list('100M1I10M')
+        >>> indel_length = 6
+        >>> test_string = cigar_list[:v_pos] + ['I'] * indel_length + cigar_list[v_pos2:]
+        >>> test_string = CigarStringOld.list_to_string(test_string)
+        >>> assert (original_cigar.cigar == test_string)
         """
 
         if insertion_cigar is None:
             print('\nError: Invalid insertion operation in CigarString\n')
             sys.exit(1)
 
-        if pos < 0 or pos >= len(self):
+        if pos1 < 0 or pos1 >= len(self):
             print('\nError: Invalid insertion position in CigarString\n')
             sys.exit(1)
 
-        if length < 0:
-            print('\nError: Did not understand the indel length to be inserted\n')
+        if pos2 and (pos2 < pos1 or pos2 >= len(self)):
+            print('\nError: Invalid second position\n')
             sys.exit(1)
 
         try:
             found = False
-            index, bases_remain = self.find_position(pos)
-            new_element = list(self.items())[:index]
-            list_of_items = list(self.items())[index:]
-            if len(list_of_items) == 1:
-                new_element.append((bases_remain, list_of_items[0][1]))
-                new_element += list(insertion_cigar.items())
-                if list_of_items[0][0] - bases_remain >= length:
-                    new_element.append((list_of_items[0][0] - bases_remain - length, list_of_items[0][1]))
-                else:
-                    new_element.append((list_of_items[0][0] - bases_remain, list_of_items[0][1]))
-            else:
-                for item in list_of_items:
-                    if item[1] == 'D':
-                        new_element.append(item)
-                    elif found:
-                        new_element.append(item)
-                    elif bases_remain > item[0]:
-                        new_element.append(item)
-                        bases_remain -= item[0]
-                    elif bases_remain == item[0]:
-                        new_element.append(item)
-                        new_element += list(insertion_cigar.items())
-                        found = True
-                    elif bases_remain < item[0]:
-                        new_element.append((bases_remain, item[1]))
-                        new_element += list(insertion_cigar.items())
-                        if item[0] - bases_remain >= length:
-                            new_element.append((item[0] - bases_remain - length, item[1]))
-                        else:
-                            new_element.append((list_of_items[0][0] - bases_remain, item[1]))
-                        found = True
-            new_string = self.string_from_elements(new_element)
+            start_index, bases_remain = self.find_position(pos1)
+            extra_bases_to_discard = 0
+            if pos2:
+                extra_bases_to_discard = pos2 - pos1
+            # TODO search for all instances of insert and get fragment and make sure they all work
+            ret = list(self.items())[:start_index]
+            items_to_iterate = list(self.items())[start_index:]
+            new_item = ((bases_remain, items_to_iterate[0][1]), (items_to_iterate[0][0]
+                                                                 - bases_remain, items_to_iterate[0][1]))
+            ret.append(new_item[0])
+            items_to_iterate[0:1] = new_item[1:]
+            ret += [item for item in insertion_cigar.items()]
+            for item in items_to_iterate:
+                if item[1] == 'D':
+                    ret.append(item)
+                elif found:
+                    ret.append(item)
+                elif extra_bases_to_discard > item[0]:
+                    extra_bases_to_discard -= item[0]
+                elif extra_bases_to_discard == item[0]:
+                    extra_bases_to_discard = 0
+                    found = True
+                elif extra_bases_to_discard < item[0]:
+                    ret.append((item[0] - extra_bases_to_discard, item[1]))
+                    extra_bases_to_discard = 0
+                    found = True
+            new_string = self.string_from_elements(ret)
             self.cigar = CigarString(new_string).merge_like_ops().cigar
         except ValueError:
             print('\nBug: Problem with insertion.\n')
@@ -432,16 +449,6 @@ def list_to_string(input_list: list) -> str:
 
 
 if __name__ == "__main__":
-    original_string = '2064M1I12979M3D18439M'
-    original_cigar = CigarString(original_string)
-    v_pos = 15414
-    cigar_to_insert = CigarString('6I')
-    original_cigar.insert_cigar_element(v_pos, cigar_to_insert, 1)
-    if original_cigar.cigar == '2064M1I12979M3D370M6I18068M':
-        print("OKAY")
-    else:
-        print("NOT OKKKAAAAAY")
-
     str1 = CigarString('50M10D7I23M')
     str2 = CigarString('10I25M')
     iPos = 55
@@ -466,4 +473,27 @@ def list_to_string(input_list: list) -> str:
     assert (len(str1) == 11100)
     assert (str1.cigar == "6611M1D4489M")
 
-    str1 = CigarString('')
\ No newline at end of file
+    str1 = CigarString('10M1I2D10M')
+    str2 = CigarString('1D1M')
+    pos1 = 10
+    pos2 = pos1 + 2
+    str1_list = str1.string_to_list()
+    cigar_to_insert = '1D1M'
+    str1_list[pos1] = cigar_to_insert
+    test_string = CigarStringOld.list_to_string(str1_list)
+    str1.insert_cigar_element(pos1, str2, pos1+1)
+    assert (str1.cigar == test_string)
+
+    original_string = '100M1I10M'
+    cig2 = CigarStringOld(string_in=original_string).get_list()
+    original_cigar = CigarString(original_string)
+    v_pos = 9
+    v_pos2 = 9 + 6
+    cigar_to_insert = CigarString('6I')
+    original_cigar.insert_cigar_element(v_pos, cigar_to_insert, v_pos2)
+    cigar_list = CigarStringOld.string_to_list('100M1I10M')
+    indel_length = 6
+    test_string = cigar_list[:v_pos] + ['I'] * indel_length + cigar_list[v_pos2:]
+    test_string = CigarStringOld.list_to_string(test_string)
+    assert (original_cigar.cigar == test_string)
+

From d87bb198a4fc6c785ce886baa2c2e1958f2d42e0 Mon Sep 17 00:00:00 2001
From: jallen17 <jallen17@illinois.edu>
Date: Tue, 19 Jan 2021 16:55:31 -0600
Subject: [PATCH 299/323] minor bug fixes

---
 .gitattributes                            |   0
 .github/ISSUE_TEMPLATE/bug_report.md      |   0
 .github/ISSUE_TEMPLATE/feature_request.md |   0
 .gitignore                                |   0
 CODE_OF_CONDUCT.md                        |   0
 CONTRIBUTING.md                           |   0
 LICENSE.md                                |   0
 README.md                                 |   0
 bacterial_genreads_wrapper.py             |   0
 docs/PE_SE_reads.png                      | Bin
 docs/flow_new.png                         | Bin
 gen_reads.py                              |   0
 merge_jobs.py                             |   0
 models/MutModel_BRCA_US_ICGC.p            | Bin
 models/MutModel_CLLE-ES_ICGC.p            | Bin
 models/MutModel_NA12878.p.gz              | Bin
 models/MutModel_SKCM-US_ICGC.p            | Bin
 models/README.md                          |   0
 models/errorModel_pacbio_toy.p            | Bin
 models/errorModel_toy.p                   | Bin
 models/fraglenModel_toy.p                 | Bin
 models/gcBias_toy.p                       | Bin
 models/gcBias_uniform.p                   | Bin
 models/genReadsTumorTutorial.zip          | Bin
 source/SequenceContainer.py               |   0
 source/__init__.py                        |   0
 source/biopython_modified_bgzf.py         |   0
 source/fm_pos_test.py                     |   0
 source/input_checking.py                  |   0
 source/neat_cigar.py                      |   0
 source/neat_cigar_rework.py               |   0
 source/output_file_writer.py              |   0
 source/probability.py                     |   0
 source/ref_func.py                        |   0
 source/test_write.py                      |   0
 source/vcf_func.py                        |   0
 utilities/README.md                       |   0
 utilities/__init__.py                     |   0
 utilities/compute_fraglen.py              |   0
 utilities/compute_gc.py                   |   0
 utilities/genSeqErrorModel.py             |   0
 utilities/gen_mut_model.py                |   0
 utilities/plotMutModel.py                 |   0
 utilities/repickle.py                     |   0
 utilities/validateBam.py                  |   0
 utilities/validateFQ.py                   |   0
 utilities/vcf_compare_OLD.py              |   0
 47 files changed, 0 insertions(+), 0 deletions(-)
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diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
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old mode 100644
new mode 100755
diff --git a/source/ref_func.py b/source/ref_func.py
old mode 100644
new mode 100755
diff --git a/source/test_write.py b/source/test_write.py
old mode 100644
new mode 100755
diff --git a/source/vcf_func.py b/source/vcf_func.py
old mode 100644
new mode 100755
diff --git a/utilities/README.md b/utilities/README.md
old mode 100644
new mode 100755
diff --git a/utilities/__init__.py b/utilities/__init__.py
old mode 100644
new mode 100755
diff --git a/utilities/compute_fraglen.py b/utilities/compute_fraglen.py
old mode 100644
new mode 100755
diff --git a/utilities/compute_gc.py b/utilities/compute_gc.py
old mode 100644
new mode 100755
diff --git a/utilities/genSeqErrorModel.py b/utilities/genSeqErrorModel.py
old mode 100644
new mode 100755
diff --git a/utilities/gen_mut_model.py b/utilities/gen_mut_model.py
old mode 100644
new mode 100755
diff --git a/utilities/plotMutModel.py b/utilities/plotMutModel.py
old mode 100644
new mode 100755
diff --git a/utilities/repickle.py b/utilities/repickle.py
old mode 100644
new mode 100755
diff --git a/utilities/validateBam.py b/utilities/validateBam.py
old mode 100644
new mode 100755
diff --git a/utilities/validateFQ.py b/utilities/validateFQ.py
old mode 100644
new mode 100755
diff --git a/utilities/vcf_compare_OLD.py b/utilities/vcf_compare_OLD.py
old mode 100644
new mode 100755

From 21b89654a863927ba362944decb2ba7e3975e9c5 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 19 Jan 2021 17:20:48 -0600
Subject: [PATCH 300/323] Bug fixin'

---
 source/SequenceContainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 372fc0d..cc96d17 100755
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -623,7 +623,7 @@ def random_mutations(self):
                             print('something wrong with inserting')
                             pdb.set_trace()
                     elif indel_length < 0:
-                        cigar_to_insert = CigarString(str(abs(indel_length)) + 'D1M')
+                        cigar_to_insert = CigarString(str(abs(indel_length)) + 'D')
                         original_string = copy.deepcopy(temp_symbol_string)
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert)
                         # TODO Delete commented out lines once CigarString works 100%

From 55fd67b0f306a2b7b9fbcffe6d2ff241e0722ee5 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 19 Jan 2021 17:32:38 -0600
Subject: [PATCH 301/323] Still trying to fix this bug

---
 source/neat_cigar_rework.py | 60 ++++++++-----------------------------
 1 file changed, 12 insertions(+), 48 deletions(-)

diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index 435d46a..f5971f0 100755
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -181,7 +181,7 @@ def find_position(self, position: int) -> int:
                 index += 1
                 continue
             consumed_bases += item[0]
-            if consumed_bases > position:
+            if consumed_bases >= position:
                 return index, bases_left
             else:
                 bases_left -= item[0]
@@ -339,6 +339,12 @@ def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
         >>> end = 25180
         >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
         >>> assert(frag.cigar == '99M1D1M')
+
+        >>> temp_symbol_string = CigarString('25179M1D8304M')
+        >>> start = 25079
+        >>> end = start + 100
+        >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar = "100M")
         """
         # Minus 1 because python slices don't include the end coordinate
         window_size = end - start
@@ -449,51 +455,9 @@ def list_to_string(input_list: list) -> str:
 
 
 if __name__ == "__main__":
-    str1 = CigarString('50M10D7I23M')
-    str2 = CigarString('10I25M')
-    iPos = 55
-    str1.insert_cigar_element(iPos, str2)
-    assert (str1.cigar == "50M10D15I25M2I23M")
-
-    str1 = CigarString('50M10D7I23M')
-    iPos = 20
-    str1.insert_cigar_element(iPos, str2)
-    assert (str1.cigar == "20M10I55M10D7I23M")
-
-    str1 = CigarString('11100M')
-    str2 = CigarString('2I')
-    iPos = 5785 + 1
-    str1.insert_cigar_element(iPos, str2)
-    assert (str1.cigar == "5786M2I5314M")
-
-    str1 = CigarString('11100M')
-    str2 = CigarString('1D')
-    iPos = 6610 + 1
-    str1.insert_cigar_element(iPos, str2)
-    assert (len(str1) == 11100)
-    assert (str1.cigar == "6611M1D4489M")
-
-    str1 = CigarString('10M1I2D10M')
-    str2 = CigarString('1D1M')
-    pos1 = 10
-    pos2 = pos1 + 2
-    str1_list = str1.string_to_list()
-    cigar_to_insert = '1D1M'
-    str1_list[pos1] = cigar_to_insert
-    test_string = CigarStringOld.list_to_string(str1_list)
-    str1.insert_cigar_element(pos1, str2, pos1+1)
-    assert (str1.cigar == test_string)
-
-    original_string = '100M1I10M'
-    cig2 = CigarStringOld(string_in=original_string).get_list()
-    original_cigar = CigarString(original_string)
-    v_pos = 9
-    v_pos2 = 9 + 6
-    cigar_to_insert = CigarString('6I')
-    original_cigar.insert_cigar_element(v_pos, cigar_to_insert, v_pos2)
-    cigar_list = CigarStringOld.string_to_list('100M1I10M')
-    indel_length = 6
-    test_string = cigar_list[:v_pos] + ['I'] * indel_length + cigar_list[v_pos2:]
-    test_string = CigarStringOld.list_to_string(test_string)
-    assert (original_cigar.cigar == test_string)
+    temp_symbol_string = CigarString('25179M1D8304M')
+    start = 25079
+    end = start + 100
+    frag = temp_symbol_string.get_cigar_fragment(start, end)
+    assert (frag.cigar == "100M")
 

From ce67b8088f8c8f8bc3836fe75138fd6f8a0f34a2 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 20 Jan 2021 19:37:11 -0600
Subject: [PATCH 302/323] added some set_traces

---
 source/neat_cigar_rework.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index f5971f0..b20fc01 100755
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -379,6 +379,7 @@ def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
                         bases = item[0] - bases_remain
                         if bases <= 0:
                             print("Something went wrong retrieving fragment")
+                            pdb.set_trace()
                             sys.exit(1)
                         bases_remain = 0
                         window_size -= bases
@@ -395,6 +396,7 @@ def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
 
         except ValueError:
             print('\nBug: Problem retrieving fragment.\n')
+            pdb.set_trace()
             sys.exit(1)
 
     # TODO use this method or delete it.
@@ -459,5 +461,6 @@ def list_to_string(input_list: list) -> str:
     start = 25079
     end = start + 100
     frag = temp_symbol_string.get_cigar_fragment(start, end)
+    print(frag)
     assert (frag.cigar == "100M")
 

From ccd372cf4c6838627516a59d5506e9fd62707bbd Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 21 Jan 2021 12:21:10 -0600
Subject: [PATCH 303/323] cleaning up insert cigar and get cigar fragment

---
 source/neat_cigar_rework.py | 81 ++++++++++++++++++++++++++-----------
 1 file changed, 57 insertions(+), 24 deletions(-)

diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index b20fc01..000eb0e 100755
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -161,7 +161,7 @@ def __str__(self):
     def __repr__(self):
         return "CigarString('%s')" % self
 
-    def find_position(self, position: int) -> int:
+    def find_position(self, position: int) -> (int,int):
         """
         This finds the index of the element of list(self.items()) that contains the starting position given.
 
@@ -227,6 +227,7 @@ def insert_cigar_element(self, pos1: int, insertion_cigar: 'CigarString', pos2:
         >>> pos2 = pos1 + 2
         >>> str1.insert_cigar_element(pos1, str2, pos2)
         >>> cigar_to_insert = '1D1M'
+        >>> str1_list = str1.string_to_list()
         >>> str1_list[pos1] = cigar_to_insert
         >>> test_string = CigarStringOld.list_to_string(str1_list)
         >>> assert(str1.cigar == test_string)
@@ -263,27 +264,40 @@ def insert_cigar_element(self, pos1: int, insertion_cigar: 'CigarString', pos2:
             if pos2:
                 extra_bases_to_discard = pos2 - pos1
             # TODO search for all instances of insert and get fragment and make sure they all work
-            ret = list(self.items())[:start_index]
+            new_element = list(self.items())[:start_index]
             items_to_iterate = list(self.items())[start_index:]
             new_item = ((bases_remain, items_to_iterate[0][1]), (items_to_iterate[0][0]
                                                                  - bases_remain, items_to_iterate[0][1]))
-            ret.append(new_item[0])
+            new_element.append(new_item[0])
             items_to_iterate[0:1] = new_item[1:]
-            ret += [item for item in insertion_cigar.items()]
+            new_element += [item for item in insertion_cigar.items()]
             for item in items_to_iterate:
-                if item[1] == 'D':
-                    ret.append(item)
+                if item[0] == 0:
+                    continue
+                if extra_bases_to_discard <= 0 and item[1] == 'D':
+                    new_element.append(item)
+                elif extra_bases_to_discard > 0 and item[1] == 'D':
+                    continue
                 elif found:
-                    ret.append(item)
+                    new_element.append(item)
                 elif extra_bases_to_discard > item[0]:
                     extra_bases_to_discard -= item[0]
                 elif extra_bases_to_discard == item[0]:
                     extra_bases_to_discard = 0
                     found = True
                 elif extra_bases_to_discard < item[0]:
-                    ret.append((item[0] - extra_bases_to_discard, item[1]))
+                    new_element.append((item[0] - extra_bases_to_discard, item[1]))
                     extra_bases_to_discard = 0
                     found = True
+
+            # The point of this block of code is just to trim off any starting or ending "D"s, so we don't have
+            # something like "100M1D" as the return.
+            ret = new_element
+            for i in range(len(new_element)):
+                if new_element[0][1] == 'D':
+                    ret = new_element[1:]
+                if new_element[-1][1] == 'D':
+                    ret = new_element[:-1]
             new_string = self.string_from_elements(ret)
             self.cigar = CigarString(new_string).merge_like_ops().cigar
         except ValueError:
@@ -344,10 +358,11 @@ def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
         >>> start = 25079
         >>> end = start + 100
         >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar = "100M")
+        >>> assert(frag.cigar == "100M")
         """
         # Minus 1 because python slices don't include the end coordinate
         window_size = end - start
+        remaining_window = window_size
         if window_size < 0:
             print(f'\nError: start and end coordinates for get_cigar_fragment '
                   f'are wrong: start: {start}, end: {end}')
@@ -373,25 +388,37 @@ def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
                 new_element.append((window_size, list_of_items[0][1]))
             else:
                 for item in list_of_items:
+                    if sum([item[0] for item in new_element if item[1] != 'D']) == window_size:
+                        break
                     if item[1] == 'D':
                         new_element.append(item)
                     elif bases_remain > 0:
-                        bases = item[0] - bases_remain
-                        if bases <= 0:
+                        bases_remain = item[0] - bases_remain
+                        if bases_remain < 0:
                             print("Something went wrong retrieving fragment")
                             pdb.set_trace()
                             sys.exit(1)
+                        elif bases_remain == 0:
+                            continue
+                        new_element.append((bases_remain, item[1]))
+                        remaining_window -= bases_remain
                         bases_remain = 0
-                        window_size -= bases
-                        new_element.append((bases, item[1]))
-                    elif window_size > item[0]:
+                    elif remaining_window > item[0]:
                         new_element.append(item)
-                        window_size -= item[0]
-                    elif window_size <= item[0]:
-                        new_element.append((window_size, item[1]))
+                        remaining_window -= item[0]
+                    elif remaining_window <= item[0]:
+                        new_element.append((remaining_window, item[1]))
                         break
 
-            new_string = self.string_from_elements(new_element)
+            # The point of this block of code is just to trim off any starting or ending "D"s, so we don't have
+            # something like "100M1D" as the return.
+            ret = new_element
+            for i in range(len(new_element)):
+                if new_element[0][1] == 'D':
+                    ret = new_element[1:]
+                if new_element[-1][1] == 'D':
+                    ret = new_element[:-1]
+            new_string = self.string_from_elements(ret)
             return CigarString(new_string).merge_like_ops()
 
         except ValueError:
@@ -457,10 +484,16 @@ def list_to_string(input_list: list) -> str:
 
 
 if __name__ == "__main__":
-    temp_symbol_string = CigarString('25179M1D8304M')
-    start = 25079
-    end = start + 100
-    frag = temp_symbol_string.get_cigar_fragment(start, end)
-    print(frag)
-    assert (frag.cigar == "100M")
+    str1 = CigarString('10M1I2D10M')
+    str2 = CigarString('1D')
+    pos1 = 10
+    pos2 = pos1 + 2
+    str1.insert_cigar_element(pos1, str2, pos2)
+    cigar_to_insert = '1D1M'
+    str1_list = str1.string_to_list()
+    str1_list[pos1] = cigar_to_insert
+    test_string = CigarStringOld.list_to_string(str1_list)
+    assert (str1.cigar == test_string)
+
+
 

From 17d6fe0d829c4b12d15d49411871057e6e381579 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 21 Jan 2021 13:03:30 -0600
Subject: [PATCH 304/323] ran into some problems with the mixture of Cigar and
 CigarString so I got rid of the Cigar class and moved relevant methods into
 CigarString

---
 source/neat_cigar_rework.py | 127 +++++-------------------------------
 1 file changed, 17 insertions(+), 110 deletions(-)

diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index 000eb0e..4119b90 100755
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -5,7 +5,7 @@
 from source.neat_cigar import CigarString as CigarStringOld
 
 
-class Cigar(object):
+class CigarString():
     """
     This code borrowed in whole from https://github.com/brentp/cigar
 
@@ -14,48 +14,11 @@ class Cigar(object):
     a SAM record only by changing the cigar string to soft-mask a number of bases
     such that the rest of the SAM record (pos, tlen, etc.) remain valid, but
     downstream tools will not consider the soft-masked bases in further analysis.
-    >>> c = Cigar('100M')
-    >>> len(c)
-    100
-    >>> str(c)
-    '100M'
-    >>> list(c.items())
-    [(100, 'M')]
-    >>> c = Cigar('20H20M20S')
-    >>> len(c)
-    40
-    >>> str(c)
-    '20H20M20S'
-    >>> list(c.items())
-    [(20, 'H'), (20, 'M'), (20, 'S')]
-    >>> c.mask_left(29).cigar, c.cigar
-    ('20H9S11M20S', '20H20M20S')
-    >>> c = Cigar('10M20S10M')
-    >>> c.mask_left(10).cigar
-    '30S10M'
-    >>> c.mask_left(9).cigar
-    '9S1M20S10M'
-    >>> Cigar('10S').mask_left(10).cigar
-    '10S'
-    >>> Cigar('10H').mask_left(10).cigar
-    '10H'
-    >>> Cigar('10H').mask_left(11).cigar
-    '10H'
-    >>> Cigar('10H').mask_left(9).cigar
-    '10H'
-    >>> Cigar('1M10H').mask_left(9).cigar
-    '1S10H'
-    >>> Cigar('5M10H').mask_left(9).cigar
-    '5S10H'
-    >>> c = Cigar('1S1H1S5H1S5M10H')
-    >>> c.mask_left(9).cigar == c.cigar
-    True
-    >>> c = Cigar('1S1H1S5H1S5M10H')
-    >>> c.mask_right(9).cigar == c.cigar
-    True
-    >>> c.mask_right(11).cigar
-    '1S1H1S5H1S4M1S10H'
+
+    CigarString adds additional functionality to the cigar module, to make it compatible with
+    the rest of the NEAT codebase. Many functions are lifted directly from brentp's repo
     """
+
     read_consuming_ops = ("M", "I", "S", "=", "X")
     ref_consuming_ops = ("M", "D", "N", "=", "X")
 
@@ -74,92 +37,36 @@ def __str__(self):
         return self.cigar
 
     def __repr__(self):
-        return "Cigar('%s')" % self
+        return "CigarString('%s')" % self
 
     def __len__(self):
         """
         sum of MIS=X ops shall equal the sequence length.
         """
-        return sum(l for l, op in self.items() if op in Cigar.read_consuming_ops)
-
-    def reference_length(self):
-        return sum(l for l, op in self.items() if op in Cigar.ref_consuming_ops)
-
-    def mask_left(self, n_seq_bases, mask="S"):
-        """
-        Return a new cigar with cigar string where the first `n_seq_bases` are
-        soft-masked unless they are already hard-masked.
-        """
-        cigs = list(self.items())
-        new_cigs = []
-
-        c, cum_len = self.cigar, 0
-        for i, (l, op) in enumerate(cigs):
-            if op in Cigar.read_consuming_ops:
-                cum_len += l
-            if op == "H":
-                cum_len += l
-                new_cigs.append(cigs[i])
-            elif cum_len < n_seq_bases:
-                new_cigs.append(cigs[i])
-            else:
-                # the current cigar element is split by the masking.
-                right_extra = cum_len - n_seq_bases
-                new_cigs.append((l - right_extra, 'S'))
-                if right_extra != 0:
-                    new_cigs.append((right_extra, cigs[i][1]))
-            if cum_len >= n_seq_bases: break
-        else:
-            pass
-
-        new_cigs[:i] = [(l, op if op in "HS" else "S") for l, op in new_cigs[:i]]
-        new_cigs.extend(cigs[i + 1:])
-        return Cigar(Cigar.string_from_elements(new_cigs)).merge_like_ops()
+        return sum(l for l, op in self.items() if op in CigarString.read_consuming_ops)
 
     @classmethod
     def string_from_elements(cls, elements):
         return "".join("%i%s" % (l, op) for l, op in elements if l != 0)
 
-    def mask_right(self, n_seq_bases, mask="S"):
-        """
-        Return a new cigar with cigar string where the last `n_seq_bases` are
-        soft-masked unless they are already hard-masked.
-        """
-        return Cigar(Cigar(self._reverse_cigar()).mask_left(n_seq_bases, mask)._reverse_cigar())
-
-    def _reverse_cigar(self):
-        return Cigar.string_from_elements(list(self.items())[::-1])
-
+    # I moved this from the main Cigar class in order to make the code more consistent
     def merge_like_ops(self):
         """
-        >>> Cigar("1S20M").merge_like_ops()
-        Cigar('1S20M')
-        >>> Cigar("1S1S20M").merge_like_ops()
-        Cigar('2S20M')
-        >>> Cigar("1S1S1S20M").merge_like_ops()
-        Cigar('3S20M')
-        >>> Cigar("1S1S1S20M1S1S").merge_like_ops()
-        Cigar('3S20M2S')
+        >>> CigarString("1S20M").merge_like_ops()
+        CigarString('1S20M')
+        >>> CigarString("1S1S20M").merge_like_ops()
+        CigarString('2S20M')
+        >>> CigarString("1S1S1S20M").merge_like_ops()
+        CigarString('3S20M')
+        >>> CigarString("1S1S1S20M1S1S").merge_like_ops()
+        CigarString('3S20M2S')
         """
 
         cigs = []
         for op, grps in groupby(self.items(), itemgetter(1)):
             cigs.append((sum(g[0] for g in grps), op))
 
-        return Cigar(self.string_from_elements(cigs))
-
-
-class CigarString(Cigar):
-    """
-    This adds additional functionality to the cigar module above, to make it compatible with
-    the rest of the NEAT codebase
-    """
-
-    def __str__(self):
-        return self.cigar
-
-    def __repr__(self):
-        return "CigarString('%s')" % self
+        return CigarString(self.string_from_elements(cigs))
 
     def find_position(self, position: int) -> (int,int):
         """

From 80d330c91ad5a89b9defed7730baceb5d092b816 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 21 Jan 2021 18:14:55 -0600
Subject: [PATCH 305/323] eliminated some extraneous characters

---
 source/neat_cigar_rework.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index 4119b90..7b4aa46 100755
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -5,7 +5,7 @@
 from source.neat_cigar import CigarString as CigarStringOld
 
 
-class CigarString():
+class CigarString:
     """
     This code borrowed in whole from https://github.com/brentp/cigar
 

From 3c6ed5701d5b5ff7f42ef80d6427c2f0f50b1def Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 21 Jan 2021 21:36:22 -0600
Subject: [PATCH 306/323] Removed debugging commands for a full run on iforge

---
 gen_reads.py                |  6 ++++--
 source/SequenceContainer.py | 14 +++++++++-----
 source/neat_cigar_rework.py |  4 ++--
 3 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index c64a4a1..c949501 100755
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -603,14 +603,16 @@ def main(raw_args=None):
                     if [cigar for cigar in sequences.all_cigar[0] if len(cigar) != 100] or \
                             [cig for cig in sequences.all_cigar[1] if len(cig) != 100]:
                         print("There's a cigar that's off.")
-                        pdb.set_trace()
+                        # pdb.set_trace()
+                        sys.exit(1)
                 else:
                     sequences.update(start, ref_sequence[start:end], ploids, overlap, read_len, [mut_model] * ploids,
                                      mut_rate)
                     if [cigar for cigar in sequences.all_cigar[0] if len(cigar) != 100] or \
                             [cig for cig in sequences.all_cigar[1] if len(cig) != 100]:
                         print("There's a cigar that's off.")
-                        pdb.set_trace()
+                        # pdb.set_trace()
+                        sys.exit(1)
 
                 # insert variants
                 sequences.insert_mutations(vars_from_prev_overlap + vars_in_window)
diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index cc96d17..90854b9 100755
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -593,7 +593,8 @@ def random_mutations(self):
             temp_symbol_string2 = ['M' for _ in self.sequences[i]]
             if not temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string():
                 print('something wrong with temp symbol creation')
-                pdb.set_trace()
+                # pdb.set_trace()
+                sys.exit(1)
 
             for j in range(len(all_indels_ins[i])):
                 v_pos = all_indels_ins[i][j][0] + rolling_adj
@@ -621,7 +622,8 @@ def random_mutations(self):
                                               ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
                         if not temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string():
                             print('something wrong with inserting')
-                            pdb.set_trace()
+                            # pdb.set_trace()
+                            sys.exit(1)
                     elif indel_length < 0:
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D')
                         original_string = copy.deepcopy(temp_symbol_string)
@@ -631,7 +633,8 @@ def random_mutations(self):
                         temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
                         if not temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string():
                             print('something wrong with deletion')
-                            pdb.set_trace()
+                            # pdb.set_trace()
+                            sys.exit(1)
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
@@ -641,7 +644,8 @@ def random_mutations(self):
                 if temp_symbol_string.get_cigar_fragment(j, j + self.read_len).cigar != CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string():
                     print("new " + str(temp_symbol_string.get_cigar_fragment(j, j + self.read_len)))
                     print("old " + CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
-                    pdb.set_trace()
+                    # pdb.set_trace()
+                    sys.exit(1)
 
             # create some data structures we will need later:
             # --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e.
@@ -816,7 +820,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                                 expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
                             except IndexError:
                                 print("Bug!! Index error on expanded cigar")
-                                pdb.set_trace()
+                                # pdb.set_trace()
                                 sys.exit(1)
 
                         else:
diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
index 7b4aa46..bf6daa7 100755
--- a/source/neat_cigar_rework.py
+++ b/source/neat_cigar_rework.py
@@ -303,7 +303,7 @@ def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
                         bases_remain = item[0] - bases_remain
                         if bases_remain < 0:
                             print("Something went wrong retrieving fragment")
-                            pdb.set_trace()
+                            # pdb.set_trace()
                             sys.exit(1)
                         elif bases_remain == 0:
                             continue
@@ -330,7 +330,7 @@ def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
 
         except ValueError:
             print('\nBug: Problem retrieving fragment.\n')
-            pdb.set_trace()
+            # pdb.set_trace()
             sys.exit(1)
 
     # TODO use this method or delete it.

From f1cd19fcda44f89470be322016f752a195a2deb7 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 25 Jan 2021 12:58:03 -0600
Subject: [PATCH 307/323] Code now working. Removed some checks and will now
 run speedtest.

---
 gen_reads.py                 |   3 +-
 source/SequenceContainer.py  |  48 +---
 source/fm_pos_test.py        |   4 +-
 source/neat_cigar.py         | 436 ++++++++++++++++++++++++++++-------
 source/neat_cigar_old.py     | 132 +++++++++++
 source/neat_cigar_rework.py  | 406 --------------------------------
 source/output_file_writer.py |  11 +-
 source/ref_func.py           |   1 -
 8 files changed, 490 insertions(+), 551 deletions(-)
 mode change 100755 => 100644 source/neat_cigar.py
 create mode 100644 source/neat_cigar_old.py
 delete mode 100755 source/neat_cigar_rework.py

diff --git a/gen_reads.py b/gen_reads.py
index c949501..2694ef5 100755
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -24,7 +24,6 @@
 import numpy as np
 import argparse
 import pathlib
-import pdb
 
 from source.input_checking import check_file_open, is_in_range
 from source.ref_func import index_ref, read_ref
@@ -94,7 +93,7 @@ def main(raw_args=None):
                              'things like read locations, variant positions, error positions, etc, '
                              'should all be the same.')
     # TODO check if this argument does anything at all. Near as I can tell the results are ALWAYS gzipped.
-    parser.add_argument('--gz', required=False, action='store_true', default=False, help='gzip output FQ and VCF')
+    # parser.add_argument('--gz', required=False, action='store_true', default=False, help='gzip output FQ and VCF')
     parser.add_argument('--no-fastq', required=False, action='store_true', default=False,
                         help='bypass fastq generation')
     parser.add_argument('--discard-offtarget', required=False, action='store_true', default=False,
diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 90854b9..ee0f01d 100755
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -1,18 +1,15 @@
-import pdb
 import random
 import copy
 import pathlib
 import bisect
 import pickle
 import sys
-from time import time
 
 import numpy as np
 from Bio.Seq import Seq
 
-from source.neat_cigar_rework import CigarString
+from source.neat_cigar import CigarString
 from source.probability import DiscreteDistribution, poisson_list
-from source.neat_cigar import CigarString as CigarStringOld
 
 """
 Constants needed for analysis
@@ -589,12 +586,6 @@ def random_mutations(self):
         for i in range(len(all_indels_ins)):
             rolling_adj = 0
             temp_symbol_string = CigarString(str(len(self.sequences[i])) + "M")
-            # TODO Delete commented out lines once CigarString works 100%
-            temp_symbol_string2 = ['M' for _ in self.sequences[i]]
-            if not temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string():
-                print('something wrong with temp symbol creation')
-                # pdb.set_trace()
-                sys.exit(1)
 
             for j in range(len(all_indels_ins[i])):
                 v_pos = all_indels_ins[i][j][0] + rolling_adj
@@ -613,39 +604,15 @@ def random_mutations(self):
                     # notate indel positions for cigar computation
                     if indel_length > 0:
                         cigar_to_insert = CigarString(str(indel_length) + 'I')
-                        original_string = copy.deepcopy(temp_symbol_string)
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert,
                                                                 v_pos2 + 1)
-                        # TODO Delete commented out lines once CigarString works 100%
-                        original_list = copy.deepcopy(temp_symbol_string2)
-                        temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
-                                              ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
-                        if not temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string():
-                            print('something wrong with inserting')
-                            # pdb.set_trace()
-                            sys.exit(1)
                     elif indel_length < 0:
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D')
-                        original_string = copy.deepcopy(temp_symbol_string)
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert)
-                        # TODO Delete commented out lines once CigarString works 100%
-                        original_list = copy.deepcopy(temp_symbol_string2)
-                        temp_symbol_string2[v_pos + 1] = 'D' * abs(indel_length) + 'M'
-                        if not temp_symbol_string.cigar == CigarStringOld(list_in=temp_symbol_string2).get_string():
-                            print('something wrong with deletion')
-                            # pdb.set_trace()
-                            sys.exit(1)
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
                 self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j + self.read_len))
-                # TODO Delete commented out lines once CigarString works 100%
-                self.all_cigar2[i].append(CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
-                if temp_symbol_string.get_cigar_fragment(j, j + self.read_len).cigar != CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string():
-                    print("new " + str(temp_symbol_string.get_cigar_fragment(j, j + self.read_len)))
-                    print("old " + CigarStringOld(list_in=temp_symbol_string2[j:j + self.read_len]).get_string())
-                    # pdb.set_trace()
-                    sys.exit(1)
 
             # create some data structures we will need later:
             # --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e.
@@ -783,15 +750,9 @@ def sample_read(self, sequencing_model, frag_len=None):
                         continue
                     if not expanded_cigar:
                         expanded_cigar = my_cigar.string_to_list()
-                        # TODO delete these lines using old CigarString once it is working 100%
-                        expanded_cigar2 = CigarStringOld(string_in=my_cigar.cigar).get_list()
-                        assert(expanded_cigar == expanded_cigar2)
                         fill_to_go = total_d - total_i + 1
                         if fill_to_go > 0:
                             try:
-                                # TODO delete these lines using old CigarString once it is working 100%
-                                extra_cigar_val2 = CigarStringOld(string_in=self.all_cigar2[my_ploid][read[0]
-                                                               + fill_to_go]).get_list()[-fill_to_go:]
                                 extra_cigar_val = self.all_cigar[my_ploid][read[0]
                                                                            + fill_to_go].string_to_list()[-fill_to_go:]
 
@@ -800,8 +761,6 @@ def sample_read(self, sequencing_model, frag_len=None):
                                 # Skip all indel errors
                                 skip_indels = True
 
-                            assert(extra_cigar_val == extra_cigar_val2)
-
                     if skip_indels:
                         continue
 
@@ -820,7 +779,6 @@ def sample_read(self, sequencing_model, frag_len=None):
                                 expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
                             except IndexError:
                                 print("Bug!! Index error on expanded cigar")
-                                # pdb.set_trace()
                                 sys.exit(1)
 
                         else:
@@ -856,10 +814,6 @@ def sample_read(self, sequencing_model, frag_len=None):
                 if len(expanded_cigar):
                     relevant_cigar = (expanded_cigar + extra_cigar_val)[:self.read_len]
                     my_cigar = CigarString(CigarString.list_to_string(relevant_cigar))
-                    # TODO delete this line once new cigar is 100% working
-                    my_cigar2 = CigarStringOld(list_in=relevant_cigar).get_string()
-
-                assert(my_cigar.cigar == my_cigar2)
                 read[3] = read[3][:self.read_len]
 
             read_out.append([self.fm_pos[my_ploid][read[0]], my_cigar, read[3], str(read[1])])
diff --git a/source/fm_pos_test.py b/source/fm_pos_test.py
index 9569f6a..67ff4da 100755
--- a/source/fm_pos_test.py
+++ b/source/fm_pos_test.py
@@ -1,5 +1,5 @@
-from source.neat_cigar_rework import CigarString as CigarStringNew
-from source.neat_cigar import CigarString
+from source.neat_cigar import CigarString as CigarStringNew
+from source.neat_cigar_old import CigarString
 from time import time
 
 ploidy = 2
diff --git a/source/neat_cigar.py b/source/neat_cigar.py
old mode 100755
new mode 100644
index 3b747f2..134161a
--- a/source/neat_cigar.py
+++ b/source/neat_cigar.py
@@ -1,64 +1,377 @@
-import re
+import sys
+from itertools import groupby
+from operator import itemgetter
+from source.neat_cigar_old import CigarString as CigarStringOld
 
 
 class CigarString:
     """
-    Creates and modifies CigarString for output
+    This code borrowed in whole from https://github.com/brentp/cigar
+
+    cigar is a simple library for dealing with cigar strings. the most useful
+    feature now is soft-masking from left or right. This allows one to adjust
+    a SAM record only by changing the cigar string to soft-mask a number of bases
+    such that the rest of the SAM record (pos, tlen, etc.) remain valid, but
+    downstream tools will not consider the soft-masked bases in further analysis.
+
+    CigarString adds additional functionality to the cigar module, to make it compatible with
+    the rest of the NEAT codebase. Many functions are lifted directly from brentp's repo
     """
-    def __init__(self, string_in=None, list_in=None):
 
-        if string_in is None and list_in is None:
-            print('\nError: CigarString object not initialized.\n')
-            exit(1)
+    read_consuming_ops = ("M", "I", "S", "=", "X")
+    ref_consuming_ops = ("M", "D", "N", "=", "X")
 
-        self.cigar_data = []
+    def __init__(self, cigar_string):
+        self.cigar = cigar_string
 
-        if string_in is not None:
-            self.join_cigar(j_string_in=string_in)
+    def items(self):
+        if self.cigar == "*":
+            yield 0, None
+            raise StopIteration
+        cig_iter = groupby(self.cigar, lambda c: c.isdigit())
+        for g, n in cig_iter:
+            yield int("".join(n)), "".join(next(cig_iter)[1])
 
-        if list_in is not None:
-            self.join_cigar(j_list_in=list_in)
+    def __str__(self):
+        return self.cigar
 
-    @staticmethod
-    def string_to_list(string_to_covert: str) -> list:
+    def __repr__(self):
+        return "CigarString('%s')" % self
+
+    def __len__(self):
+        """
+        sum of MIS=X ops shall equal the sequence length.
+        """
+        return sum(l for l, op in self.items() if op in CigarString.read_consuming_ops)
+
+    @classmethod
+    def string_from_elements(cls, elements):
+        return "".join("%i%s" % (l, op) for l, op in elements if l != 0)
+
+    # I moved this from the main Cigar class in order to make the code more consistent
+    def merge_like_ops(self):
+        """
+        >>> CigarString("1S20M").merge_like_ops()
+        CigarString('1S20M')
+        >>> CigarString("1S1S20M").merge_like_ops()
+        CigarString('2S20M')
+        >>> CigarString("1S1S1S20M").merge_like_ops()
+        CigarString('3S20M')
+        >>> CigarString("1S1S1S20M1S1S").merge_like_ops()
+        CigarString('3S20M2S')
+        """
+
+        cigs = []
+        for op, grps in groupby(self.items(), itemgetter(1)):
+            cigs.append((sum(g[0] for g in grps), op))
+
+        return CigarString(self.string_from_elements(cigs))
+
+    def find_position(self, position: int) -> (int,int):
+        """
+        This finds the index of the element of list(self.items()) that contains the starting position given.
+
+        :param position: The position to find of the CigarString
+        :return: the index of that in terms of the elements of list(self.items())
+        >>> cigar = CigarString('2064M1I12979M3D18439M')
+        >>> position = 15414
+        >>> index, bases = cigar.find_position(position)
+        >>> assert(index == 4)
+        >>> assert(bases == 370)
+        """
+        consumed_bases = 0
+        bases_left = position
+        index = 0
+        for item in self.items():
+            if item[1] == 'D':
+                index += 1
+                continue
+            consumed_bases += item[0]
+            if consumed_bases >= position:
+                return index, bases_left
+            else:
+                bases_left -= item[0]
+                index += 1
+        return index, bases_left
+
+    def insert_cigar_element(self, pos1: int, insertion_cigar: 'CigarString', pos2: int = None) -> None:
+        """
+        Inserts a cigar string in either string or list format to the existing cigar string at position pos.
+
+        :param insertion_cigar: A cigar to insert into current cigar string
+        :param pos1: integer position where to insert the input cigar string
+        :param pos2: integer position where to pick up the remainder
+        :return: None
+
+        >>> str1 = CigarString('50M10D7I23M')
+        >>> str2 = CigarString('10I25M')
+        >>> iPos = 55
+        >>> str1.insert_cigar_element(iPos, str2)
+        >>> assert(str1.cigar == "50M10D15I25M2I23M")
+
+        >>> str1 = CigarString('50M10D7I23M')
+        >>> iPos = 20
+        >>> str1.insert_cigar_element(iPos, str2)
+        >>> assert(str1.cigar == "20M10I55M10D7I23M")
+
+        >>> str1 = CigarString('11100M')
+        >>> str2 = CigarString('2I')
+        >>> iPos = 5785 + 1
+        >>> str1.insert_cigar_element(iPos, str2)
+        >>> assert (str1.cigar == "5786M2I5314M")
+
+        >>> str1 = CigarString('11100M')
+        >>> str2 = CigarString('1D')
+        >>> iPos = 6610 + 1
+        >>> str1.insert_cigar_element(iPos, str2)
+        >>> assert(len(str1) == 11100)
+        >>> assert (str1.cigar == "6611M1D4489M")
+
+        >>> str1 = CigarString('10M1I2D10M')
+        >>> str2 = CigarString('1D')
+        >>> pos1 = 10
+        >>> pos2 = pos1 + 2
+        >>> str1.insert_cigar_element(pos1, str2, pos2)
+        >>> cigar_to_insert = '1D1M'
+        >>> str1_list = str1.string_to_list()
+        >>> str1_list[pos1] = cigar_to_insert
+        >>> test_string = CigarStringOld.list_to_string(str1_list)
+        >>> assert(str1.cigar == test_string)
+
+        >>> original_string = '100M1I10M'
+        >>> original_cigar = CigarString(original_string)
+        >>> v_pos = 9
+        >>> v_pos2 = 9 + 6
+        >>> cigar_to_insert = CigarString('6I')
+        >>> original_cigar.insert_cigar_element(v_pos, cigar_to_insert, v_pos2)
+        >>> cigar_list = CigarStringOld.string_to_list('100M1I10M')
+        >>> indel_length = 6
+        >>> test_string = cigar_list[:v_pos] + ['I'] * indel_length + cigar_list[v_pos2:]
+        >>> test_string = CigarStringOld.list_to_string(test_string)
+        >>> assert (original_cigar.cigar == test_string)
+        """
+
+        if insertion_cigar is None:
+            print('\nError: Invalid insertion operation in CigarString\n')
+            sys.exit(1)
+
+        if pos1 < 0 or pos1 >= len(self):
+            print('\nError: Invalid insertion position in CigarString\n')
+            sys.exit(1)
+
+        if pos2 and (pos2 < pos1 or pos2 >= len(self)):
+            print('\nError: Invalid second position\n')
+            sys.exit(1)
+
+        try:
+            found = False
+            start_index, bases_remain = self.find_position(pos1)
+            extra_bases_to_discard = 0
+            if pos2:
+                extra_bases_to_discard = pos2 - pos1
+            new_element = list(self.items())[:start_index]
+            items_to_iterate = list(self.items())[start_index:]
+            new_item = ((bases_remain, items_to_iterate[0][1]), (items_to_iterate[0][0]
+                                                                 - bases_remain, items_to_iterate[0][1]))
+            new_element.append(new_item[0])
+            items_to_iterate[0:1] = new_item[1:]
+            new_element += [item for item in insertion_cigar.items()]
+            for item in items_to_iterate:
+                if item[0] == 0:
+                    continue
+                if extra_bases_to_discard <= 0 and item[1] == 'D':
+                    new_element.append(item)
+                elif extra_bases_to_discard > 0 and item[1] == 'D':
+                    continue
+                elif found:
+                    new_element.append(item)
+                elif extra_bases_to_discard > item[0]:
+                    extra_bases_to_discard -= item[0]
+                elif extra_bases_to_discard == item[0]:
+                    extra_bases_to_discard = 0
+                    found = True
+                elif extra_bases_to_discard < item[0]:
+                    new_element.append((item[0] - extra_bases_to_discard, item[1]))
+                    extra_bases_to_discard = 0
+                    found = True
+
+            # The point of this block of code is just to trim off any starting or ending "D"s, so we don't have
+            # something like "100M1D" as the return.
+            ret = new_element
+            for i in range(len(new_element)):
+                if new_element[0][1] == 'D':
+                    ret = new_element[1:]
+                if new_element[-1][1] == 'D':
+                    ret = new_element[:-1]
+            new_string = self.string_from_elements(ret)
+            self.cigar = CigarString(new_string).merge_like_ops().cigar
+        except ValueError:
+            print('\nBug: Problem with insertion.\n')
+            sys.exit(1)
+
+    def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
+        """
+        Take a slice of a cigar string. E.g., if we have a cigar string that is "1000M" and we want a slice of the
+        first 101 characters, then the return should be '101M'. If we had "20M100I20M" and want the
+        first 101 characters, it would return '20M81I'.
+
+        :param start: start point of sequence to retrieve
+        :param end: end point of sequence to retrieve
+        :return: The sequence that spans start to end
+        >>> str1 = CigarString('10M5D10M')
+        >>> start = 0
+        >>> end = 14
+        >>> frag = str1.get_cigar_fragment(start, end)
+        >>> assert(len(frag) == 14)
+        >>> assert(frag.cigar == "10M5D4M")
+
+        >>> str1 = CigarString('90M1D10M')
+        >>> start = 0
+        >>> end = 100
+        >>> frag = str1.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == "90M1D10M")
+
+        >>> str1 = CigarString('10M2D10M')
+        >>> start = 10
+        >>> end = 12
+        >>> frag = str1.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == "2M")
+
+        >>> str1 = CigarString('10M1D10M')
+        >>> start = 10
+        >>> end = 12
+        >>> frag = str1.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == "2M")
+
+        >>> str1 = CigarString('102M2I10000M')
+        >>> start1 = 1
+        >>> end1 = 102
+        >>> frag1 = str1.get_cigar_fragment(start1, end1)
+        >>> start2 = 102
+        >>> end2 = 203
+        >>> frag2 = str1.get_cigar_fragment(start2, end2)
+        >>> assert(frag1.cigar == "101M")
+        >>> assert(frag2.cigar == "2I99M")
+
+        >>> temp_symbol_string = CigarString('25179M1D8304M')
+        >>> start = 25080
+        >>> end = 25180
+        >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == '99M1D1M')
+
+        >>> temp_symbol_string = CigarString('25179M1D8304M')
+        >>> start = 25079
+        >>> end = start + 100
+        >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == "100M")
         """
-        Converts cigar string to a full list
+        # Minus 1 because python slices don't include the end coordinate
+        window_size = end - start
+        remaining_window = window_size
+        if window_size < 0:
+            print(f'\nError: start and end coordinates for get_cigar_fragment '
+                  f'are wrong: start: {start}, end: {end}')
+        if start is None or end is None:
+            print('\nError: Invalid coordinates in get_cigar_fragment\n')
+            sys.exit(1)
 
-        :param string_to_covert: Cigar string to convert to list
-        :return: cigar string in list format
+        if start < 0 or start >= len(self):
+            print('\nError: Invalid start point in get_cigar_fragment\n')
+            sys.exit(1)
+
+        if end > len(self) or end < 0:
+            print('\nError: Invalid end point in get_cigar_fragment')
+            sys.exit(1)
+
+        try:
+            start_index, bases_remain = self.find_position(start)
+            end_index = self.find_position(end)[0]
+            new_element = []
+            # +1 here to make it inclusive
+            list_of_items = list(self.items())[start_index:end_index+1]
+            if len(list_of_items) == 1:
+                new_element.append((window_size, list_of_items[0][1]))
+            else:
+                for item in list_of_items:
+                    if sum([item[0] for item in new_element if item[1] != 'D']) == window_size:
+                        break
+                    if item[1] == 'D':
+                        new_element.append(item)
+                    elif bases_remain > 0:
+                        bases_remain = item[0] - bases_remain
+                        if bases_remain < 0:
+                            print("Something went wrong retrieving fragment")
+                            sys.exit(1)
+                        elif bases_remain == 0:
+                            continue
+                        new_element.append((bases_remain, item[1]))
+                        remaining_window -= bases_remain
+                        bases_remain = 0
+                    elif remaining_window > item[0]:
+                        new_element.append(item)
+                        remaining_window -= item[0]
+                    elif remaining_window <= item[0]:
+                        new_element.append((remaining_window, item[1]))
+                        break
+
+            # The point of this block of code is just to trim off any starting or ending "D"s, so we don't have
+            # something like "100M1D" as the return.
+            ret = new_element
+            for i in range(len(new_element)):
+                if new_element[0][1] == 'D':
+                    ret = new_element[1:]
+                if new_element[-1][1] == 'D':
+                    ret = new_element[:-1]
+            new_string = self.string_from_elements(ret)
+            return CigarString(new_string).merge_like_ops()
+
+        except ValueError:
+            print('\nBug: Problem retrieving fragment.\n')
+            sys.exit(1)
+
+    # TODO use this method or delete it.
+    def count_elements(self, element: str) -> int:
+        count = 0
+        for item in self.items():
+            if item[1] == element:
+                count += item[0]
+        return count
+
+    def string_to_list(self) -> list:
         """
+        Converts the cigar string to a full list.
 
+        :return: cigar string in list format.
+        """
         cigar_dat = []
-        letters = re.split(r"\d+", string_to_covert)[1:]
-        numbers = [int(n) for n in re.findall(r"\d+", string_to_covert)]
         d_reserve = 0
-        for i in range(len(letters)):
-            if letters[i] == 'D':
-                d_reserve = numbers[i]
-            if letters[i] == 'M' or letters[i] == 'I':
+        for item in self.items():
+            if item[1] == 'D':
+                d_reserve = item[0]
+            if item[1] in ['M', 'I']:
                 if d_reserve:
-                    cigar_dat += ['D' * d_reserve + letters[i]] + [letters[i]] * (int(numbers[i]) - 1)
+                    cigar_dat += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
                 else:
-                    cigar_dat += [letters[i]] * int(numbers[i])
+                    cigar_dat += [item[1]] * item[0]
                 d_reserve = 0
         return cigar_dat
 
     @staticmethod
-    def list_to_string(list_to_convert: list) -> str:
+    def list_to_string(input_list: list) -> str:
         """
         Convert a cigar string in list format to a standard cigar string
 
-        :param list_to_convert: Cigar string in list format
+        :param input_list: Cigar string in list format
         :return: cigar string in string format
         """
 
         symbols = ''
-        current_sym = list_to_convert[0]
+        current_sym = input_list[0]
         current_count = 1
         if 'D' in current_sym:
             current_sym = current_sym[-1]
-        for k in range(1, len(list_to_convert)):
-            next_sym = list_to_convert[k]
+        for k in range(1, len(input_list)):
+            next_sym = input_list[k]
             if len(next_sym) == 1 and next_sym == current_sym:
                 current_count += 1
             else:
@@ -72,61 +385,18 @@ def list_to_string(list_to_convert: list) -> str:
         symbols += str(current_count) + current_sym
         return symbols
 
-    def get_list(self):
-
-        return self.cigar_data
 
-    def get_string(self):
-
-        return self.list_to_string(self.cigar_data)
-
-    def join_cigar(self, j_string_in: str = None, j_list_in: list = None):
-        """
-        Adds an inputted cigar string in string or list format to the existing cigar string
-
-        :param j_string_in: cigar string in string format
-        :param j_list_in: cigar string in list format
-        :return: None
-        """
-
-        if j_string_in is None and j_list_in is None:
-            print('\nError: Invalid join operation in CigarString\n')
-            exit(1)
-
-        if j_string_in is not None:
-            self.cigar_data += self.string_to_list(j_string_in)
-
-        if j_list_in is not None:
-            self.cigar_data += j_list_in
-
-    def insert_cigar_element(self, pos: int, i_string_in: str = None, i_list_in: list = None):
-        """
-        Inserts a cigar string in either string or list format to the existing cigar string at position pos.
-
-        :param pos: integer position where to insert the input cigar string
-        :param i_string_in: input cigar string in string format
-        :param i_list_in: input cigar string in list format
-        :return:
-
-        >>>str1 = '50M10D7I23M'
-        >>>str2 = '10I25M'
-        >>>iPos = 20
-        >>>my_cigar = CigarString(string_in=str1)
-        >>>my_cigar.insert_cigar_element(iPos, i_string_in=str2)
-        >>>assert(my_cigar.get_string() == "20M10I55M10D7I23M")
-        """
-
-        if i_string_in is None and i_list_in is None:
-            print('\nError: Invalid insertion operation in CigarString\n')
-            exit(1)
-
-        if pos < 0 or pos >= len(self.cigar_data):
-            print('\nError: Invalid insertion position in CigarString\n')
-            exit(1)
+if __name__ == "__main__":
+    str1 = CigarString('10M1I2D10M')
+    str2 = CigarString('1D')
+    pos1 = 10
+    pos2 = pos1 + 2
+    str1.insert_cigar_element(pos1, str2, pos2)
+    cigar_to_insert = '1D1M'
+    str1_list = str1.string_to_list()
+    str1_list[pos1] = cigar_to_insert
+    test_string = CigarStringOld.list_to_string(str1_list)
+    assert (str1.cigar == test_string)
 
-        if i_string_in is not None:
-            self.cigar_data = self.cigar_data[:pos] + self.string_to_list(i_string_in) + self.cigar_data[pos:]
 
-        if i_list_in is not None:
-            self.cigar_data = self.cigar_data[:pos] + i_list_in + self.cigar_data[pos:]
 
diff --git a/source/neat_cigar_old.py b/source/neat_cigar_old.py
new file mode 100644
index 0000000..3b747f2
--- /dev/null
+++ b/source/neat_cigar_old.py
@@ -0,0 +1,132 @@
+import re
+
+
+class CigarString:
+    """
+    Creates and modifies CigarString for output
+    """
+    def __init__(self, string_in=None, list_in=None):
+
+        if string_in is None and list_in is None:
+            print('\nError: CigarString object not initialized.\n')
+            exit(1)
+
+        self.cigar_data = []
+
+        if string_in is not None:
+            self.join_cigar(j_string_in=string_in)
+
+        if list_in is not None:
+            self.join_cigar(j_list_in=list_in)
+
+    @staticmethod
+    def string_to_list(string_to_covert: str) -> list:
+        """
+        Converts cigar string to a full list
+
+        :param string_to_covert: Cigar string to convert to list
+        :return: cigar string in list format
+        """
+
+        cigar_dat = []
+        letters = re.split(r"\d+", string_to_covert)[1:]
+        numbers = [int(n) for n in re.findall(r"\d+", string_to_covert)]
+        d_reserve = 0
+        for i in range(len(letters)):
+            if letters[i] == 'D':
+                d_reserve = numbers[i]
+            if letters[i] == 'M' or letters[i] == 'I':
+                if d_reserve:
+                    cigar_dat += ['D' * d_reserve + letters[i]] + [letters[i]] * (int(numbers[i]) - 1)
+                else:
+                    cigar_dat += [letters[i]] * int(numbers[i])
+                d_reserve = 0
+        return cigar_dat
+
+    @staticmethod
+    def list_to_string(list_to_convert: list) -> str:
+        """
+        Convert a cigar string in list format to a standard cigar string
+
+        :param list_to_convert: Cigar string in list format
+        :return: cigar string in string format
+        """
+
+        symbols = ''
+        current_sym = list_to_convert[0]
+        current_count = 1
+        if 'D' in current_sym:
+            current_sym = current_sym[-1]
+        for k in range(1, len(list_to_convert)):
+            next_sym = list_to_convert[k]
+            if len(next_sym) == 1 and next_sym == current_sym:
+                current_count += 1
+            else:
+                symbols += str(current_count) + current_sym
+                if 'D' in next_sym:
+                    symbols += str(next_sym.count('D')) + 'D'
+                    current_sym = next_sym[-1]
+                else:
+                    current_sym = next_sym
+                current_count = 1
+        symbols += str(current_count) + current_sym
+        return symbols
+
+    def get_list(self):
+
+        return self.cigar_data
+
+    def get_string(self):
+
+        return self.list_to_string(self.cigar_data)
+
+    def join_cigar(self, j_string_in: str = None, j_list_in: list = None):
+        """
+        Adds an inputted cigar string in string or list format to the existing cigar string
+
+        :param j_string_in: cigar string in string format
+        :param j_list_in: cigar string in list format
+        :return: None
+        """
+
+        if j_string_in is None and j_list_in is None:
+            print('\nError: Invalid join operation in CigarString\n')
+            exit(1)
+
+        if j_string_in is not None:
+            self.cigar_data += self.string_to_list(j_string_in)
+
+        if j_list_in is not None:
+            self.cigar_data += j_list_in
+
+    def insert_cigar_element(self, pos: int, i_string_in: str = None, i_list_in: list = None):
+        """
+        Inserts a cigar string in either string or list format to the existing cigar string at position pos.
+
+        :param pos: integer position where to insert the input cigar string
+        :param i_string_in: input cigar string in string format
+        :param i_list_in: input cigar string in list format
+        :return:
+
+        >>>str1 = '50M10D7I23M'
+        >>>str2 = '10I25M'
+        >>>iPos = 20
+        >>>my_cigar = CigarString(string_in=str1)
+        >>>my_cigar.insert_cigar_element(iPos, i_string_in=str2)
+        >>>assert(my_cigar.get_string() == "20M10I55M10D7I23M")
+        """
+
+        if i_string_in is None and i_list_in is None:
+            print('\nError: Invalid insertion operation in CigarString\n')
+            exit(1)
+
+        if pos < 0 or pos >= len(self.cigar_data):
+            print('\nError: Invalid insertion position in CigarString\n')
+            exit(1)
+
+        if i_string_in is not None:
+            self.cigar_data = self.cigar_data[:pos] + self.string_to_list(i_string_in) + self.cigar_data[pos:]
+
+        if i_list_in is not None:
+            self.cigar_data = self.cigar_data[:pos] + i_list_in + self.cigar_data[pos:]
+
diff --git a/source/neat_cigar_rework.py b/source/neat_cigar_rework.py
deleted file mode 100755
index bf6daa7..0000000
--- a/source/neat_cigar_rework.py
+++ /dev/null
@@ -1,406 +0,0 @@
-import sys
-import pdb
-from itertools import groupby
-from operator import itemgetter
-from source.neat_cigar import CigarString as CigarStringOld
-
-
-class CigarString:
-    """
-    This code borrowed in whole from https://github.com/brentp/cigar
-
-    cigar is a simple library for dealing with cigar strings. the most useful
-    feature now is soft-masking from left or right. This allows one to adjust
-    a SAM record only by changing the cigar string to soft-mask a number of bases
-    such that the rest of the SAM record (pos, tlen, etc.) remain valid, but
-    downstream tools will not consider the soft-masked bases in further analysis.
-
-    CigarString adds additional functionality to the cigar module, to make it compatible with
-    the rest of the NEAT codebase. Many functions are lifted directly from brentp's repo
-    """
-
-    read_consuming_ops = ("M", "I", "S", "=", "X")
-    ref_consuming_ops = ("M", "D", "N", "=", "X")
-
-    def __init__(self, cigar_string):
-        self.cigar = cigar_string
-
-    def items(self):
-        if self.cigar == "*":
-            yield 0, None
-            raise StopIteration
-        cig_iter = groupby(self.cigar, lambda c: c.isdigit())
-        for g, n in cig_iter:
-            yield int("".join(n)), "".join(next(cig_iter)[1])
-
-    def __str__(self):
-        return self.cigar
-
-    def __repr__(self):
-        return "CigarString('%s')" % self
-
-    def __len__(self):
-        """
-        sum of MIS=X ops shall equal the sequence length.
-        """
-        return sum(l for l, op in self.items() if op in CigarString.read_consuming_ops)
-
-    @classmethod
-    def string_from_elements(cls, elements):
-        return "".join("%i%s" % (l, op) for l, op in elements if l != 0)
-
-    # I moved this from the main Cigar class in order to make the code more consistent
-    def merge_like_ops(self):
-        """
-        >>> CigarString("1S20M").merge_like_ops()
-        CigarString('1S20M')
-        >>> CigarString("1S1S20M").merge_like_ops()
-        CigarString('2S20M')
-        >>> CigarString("1S1S1S20M").merge_like_ops()
-        CigarString('3S20M')
-        >>> CigarString("1S1S1S20M1S1S").merge_like_ops()
-        CigarString('3S20M2S')
-        """
-
-        cigs = []
-        for op, grps in groupby(self.items(), itemgetter(1)):
-            cigs.append((sum(g[0] for g in grps), op))
-
-        return CigarString(self.string_from_elements(cigs))
-
-    def find_position(self, position: int) -> (int,int):
-        """
-        This finds the index of the element of list(self.items()) that contains the starting position given.
-
-        :param position: The position to find of the CigarString
-        :return: the index of that in terms of the elements of list(self.items())
-        >>> cigar = CigarString('2064M1I12979M3D18439M')
-        >>> position = 15414
-        >>> index, bases = cigar.find_position(position)
-        >>> assert(index == 4)
-        >>> assert(bases == 370)
-        """
-        consumed_bases = 0
-        bases_left = position
-        index = 0
-        for item in self.items():
-            if item[1] == 'D':
-                index += 1
-                continue
-            consumed_bases += item[0]
-            if consumed_bases >= position:
-                return index, bases_left
-            else:
-                bases_left -= item[0]
-                index += 1
-        return index, bases_left
-
-    def insert_cigar_element(self, pos1: int, insertion_cigar: 'CigarString', pos2: int = None) -> None:
-        """
-        Inserts a cigar string in either string or list format to the existing cigar string at position pos.
-
-        :param insertion_cigar: A cigar to insert into current cigar string
-        :param pos1: integer position where to insert the input cigar string
-        :param pos2: integer position where to pick up the remainder
-        :return: None
-
-        >>> str1 = CigarString('50M10D7I23M')
-        >>> str2 = CigarString('10I25M')
-        >>> iPos = 55
-        >>> str1.insert_cigar_element(iPos, str2)
-        >>> assert(str1.cigar == "50M10D15I25M2I23M")
-
-        >>> str1 = CigarString('50M10D7I23M')
-        >>> iPos = 20
-        >>> str1.insert_cigar_element(iPos, str2)
-        >>> assert(str1.cigar == "20M10I55M10D7I23M")
-
-        >>> str1 = CigarString('11100M')
-        >>> str2 = CigarString('2I')
-        >>> iPos = 5785 + 1
-        >>> str1.insert_cigar_element(iPos, str2)
-        >>> assert (str1.cigar == "5786M2I5314M")
-
-        >>> str1 = CigarString('11100M')
-        >>> str2 = CigarString('1D')
-        >>> iPos = 6610 + 1
-        >>> str1.insert_cigar_element(iPos, str2)
-        >>> assert(len(str1) == 11100)
-        >>> assert (str1.cigar == "6611M1D4489M")
-
-        >>> str1 = CigarString('10M1I2D10M')
-        >>> str2 = CigarString('1D')
-        >>> pos1 = 10
-        >>> pos2 = pos1 + 2
-        >>> str1.insert_cigar_element(pos1, str2, pos2)
-        >>> cigar_to_insert = '1D1M'
-        >>> str1_list = str1.string_to_list()
-        >>> str1_list[pos1] = cigar_to_insert
-        >>> test_string = CigarStringOld.list_to_string(str1_list)
-        >>> assert(str1.cigar == test_string)
-
-        >>> original_string = '100M1I10M'
-        >>> original_cigar = CigarString(original_string)
-        >>> v_pos = 9
-        >>> v_pos2 = 9 + 6
-        >>> cigar_to_insert = CigarString('6I')
-        >>> original_cigar.insert_cigar_element(v_pos, cigar_to_insert, v_pos2)
-        >>> cigar_list = CigarStringOld.string_to_list('100M1I10M')
-        >>> indel_length = 6
-        >>> test_string = cigar_list[:v_pos] + ['I'] * indel_length + cigar_list[v_pos2:]
-        >>> test_string = CigarStringOld.list_to_string(test_string)
-        >>> assert (original_cigar.cigar == test_string)
-        """
-
-        if insertion_cigar is None:
-            print('\nError: Invalid insertion operation in CigarString\n')
-            sys.exit(1)
-
-        if pos1 < 0 or pos1 >= len(self):
-            print('\nError: Invalid insertion position in CigarString\n')
-            sys.exit(1)
-
-        if pos2 and (pos2 < pos1 or pos2 >= len(self)):
-            print('\nError: Invalid second position\n')
-            sys.exit(1)
-
-        try:
-            found = False
-            start_index, bases_remain = self.find_position(pos1)
-            extra_bases_to_discard = 0
-            if pos2:
-                extra_bases_to_discard = pos2 - pos1
-            # TODO search for all instances of insert and get fragment and make sure they all work
-            new_element = list(self.items())[:start_index]
-            items_to_iterate = list(self.items())[start_index:]
-            new_item = ((bases_remain, items_to_iterate[0][1]), (items_to_iterate[0][0]
-                                                                 - bases_remain, items_to_iterate[0][1]))
-            new_element.append(new_item[0])
-            items_to_iterate[0:1] = new_item[1:]
-            new_element += [item for item in insertion_cigar.items()]
-            for item in items_to_iterate:
-                if item[0] == 0:
-                    continue
-                if extra_bases_to_discard <= 0 and item[1] == 'D':
-                    new_element.append(item)
-                elif extra_bases_to_discard > 0 and item[1] == 'D':
-                    continue
-                elif found:
-                    new_element.append(item)
-                elif extra_bases_to_discard > item[0]:
-                    extra_bases_to_discard -= item[0]
-                elif extra_bases_to_discard == item[0]:
-                    extra_bases_to_discard = 0
-                    found = True
-                elif extra_bases_to_discard < item[0]:
-                    new_element.append((item[0] - extra_bases_to_discard, item[1]))
-                    extra_bases_to_discard = 0
-                    found = True
-
-            # The point of this block of code is just to trim off any starting or ending "D"s, so we don't have
-            # something like "100M1D" as the return.
-            ret = new_element
-            for i in range(len(new_element)):
-                if new_element[0][1] == 'D':
-                    ret = new_element[1:]
-                if new_element[-1][1] == 'D':
-                    ret = new_element[:-1]
-            new_string = self.string_from_elements(ret)
-            self.cigar = CigarString(new_string).merge_like_ops().cigar
-        except ValueError:
-            print('\nBug: Problem with insertion.\n')
-            sys.exit(1)
-
-    def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
-        """
-        Take a slice of a cigar string. E.g., if we have a cigar string that is "1000M" and we want a slice of the
-        first 101 characters, then the return should be '101M'. If we had "20M100I20M" and want the
-        first 101 characters, it would return '20M81I'.
-
-        :param start: start point of sequence to retrieve
-        :param end: end point of sequence to retrieve
-        :return: The sequence that spans start to end
-        >>> str1 = CigarString('10M5D10M')
-        >>> start = 0
-        >>> end = 14
-        >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(len(frag) == 14)
-        >>> assert(frag.cigar == "10M5D4M")
-
-        >>> str1 = CigarString('90M1D10M')
-        >>> start = 0
-        >>> end = 100
-        >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "90M1D10M")
-
-        >>> str1 = CigarString('10M2D10M')
-        >>> start = 10
-        >>> end = 12
-        >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "2M")
-
-        >>> str1 = CigarString('10M1D10M')
-        >>> start = 10
-        >>> end = 12
-        >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "2M")
-
-        >>> str1 = CigarString('102M2I10000M')
-        >>> start1 = 1
-        >>> end1 = 102
-        >>> frag1 = str1.get_cigar_fragment(start1, end1)
-        >>> start2 = 102
-        >>> end2 = 203
-        >>> frag2 = str1.get_cigar_fragment(start2, end2)
-        >>> assert(frag1.cigar == "101M")
-        >>> assert(frag2.cigar == "2I99M")
-
-        >>> temp_symbol_string = CigarString('25179M1D8304M')
-        >>> start = 25080
-        >>> end = 25180
-        >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == '99M1D1M')
-
-        >>> temp_symbol_string = CigarString('25179M1D8304M')
-        >>> start = 25079
-        >>> end = start + 100
-        >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "100M")
-        """
-        # Minus 1 because python slices don't include the end coordinate
-        window_size = end - start
-        remaining_window = window_size
-        if window_size < 0:
-            print(f'\nError: start and end coordinates for get_cigar_fragment '
-                  f'are wrong: start: {start}, end: {end}')
-        if start is None or end is None:
-            print('\nError: Invalid coordinates in get_cigar_fragment\n')
-            sys.exit(1)
-
-        if start < 0 or start >= len(self):
-            print('\nError: Invalid start point in get_cigar_fragment\n')
-            sys.exit(1)
-
-        if end > len(self) or end < 0:
-            print('\nError: Invalid end point in get_cigar_fragment')
-            sys.exit(1)
-
-        try:
-            start_index, bases_remain = self.find_position(start)
-            end_index = self.find_position(end)[0]
-            new_element = []
-            # +1 here to make it inclusive
-            list_of_items = list(self.items())[start_index:end_index+1]
-            if len(list_of_items) == 1:
-                new_element.append((window_size, list_of_items[0][1]))
-            else:
-                for item in list_of_items:
-                    if sum([item[0] for item in new_element if item[1] != 'D']) == window_size:
-                        break
-                    if item[1] == 'D':
-                        new_element.append(item)
-                    elif bases_remain > 0:
-                        bases_remain = item[0] - bases_remain
-                        if bases_remain < 0:
-                            print("Something went wrong retrieving fragment")
-                            # pdb.set_trace()
-                            sys.exit(1)
-                        elif bases_remain == 0:
-                            continue
-                        new_element.append((bases_remain, item[1]))
-                        remaining_window -= bases_remain
-                        bases_remain = 0
-                    elif remaining_window > item[0]:
-                        new_element.append(item)
-                        remaining_window -= item[0]
-                    elif remaining_window <= item[0]:
-                        new_element.append((remaining_window, item[1]))
-                        break
-
-            # The point of this block of code is just to trim off any starting or ending "D"s, so we don't have
-            # something like "100M1D" as the return.
-            ret = new_element
-            for i in range(len(new_element)):
-                if new_element[0][1] == 'D':
-                    ret = new_element[1:]
-                if new_element[-1][1] == 'D':
-                    ret = new_element[:-1]
-            new_string = self.string_from_elements(ret)
-            return CigarString(new_string).merge_like_ops()
-
-        except ValueError:
-            print('\nBug: Problem retrieving fragment.\n')
-            # pdb.set_trace()
-            sys.exit(1)
-
-    # TODO use this method or delete it.
-    def count_elements(self, element: str) -> int:
-        count = 0
-        for item in self.items():
-            if item[1] == element:
-                count += item[0]
-        return count
-
-    def string_to_list(self) -> list:
-        """
-        Converts the cigar string to a full list.
-
-        :return: cigar string in list format.
-        """
-        cigar_dat = []
-        d_reserve = 0
-        for item in self.items():
-            if item[1] == 'D':
-                d_reserve = item[0]
-            if item[1] in ['M', 'I']:
-                if d_reserve:
-                    cigar_dat += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
-                else:
-                    cigar_dat += [item[1]] * item[0]
-                d_reserve = 0
-        return cigar_dat
-
-    @staticmethod
-    def list_to_string(input_list: list) -> str:
-        """
-        Convert a cigar string in list format to a standard cigar string
-
-        :param input_list: Cigar string in list format
-        :return: cigar string in string format
-        """
-
-        symbols = ''
-        current_sym = input_list[0]
-        current_count = 1
-        if 'D' in current_sym:
-            current_sym = current_sym[-1]
-        for k in range(1, len(input_list)):
-            next_sym = input_list[k]
-            if len(next_sym) == 1 and next_sym == current_sym:
-                current_count += 1
-            else:
-                symbols += str(current_count) + current_sym
-                if 'D' in next_sym:
-                    symbols += str(next_sym.count('D')) + 'D'
-                    current_sym = next_sym[-1]
-                else:
-                    current_sym = next_sym
-                current_count = 1
-        symbols += str(current_count) + current_sym
-        return symbols
-
-
-if __name__ == "__main__":
-    str1 = CigarString('10M1I2D10M')
-    str2 = CigarString('1D')
-    pos1 = 10
-    pos2 = pos1 + 2
-    str1.insert_cigar_element(pos1, str2, pos2)
-    cigar_to_insert = '1D1M'
-    str1_list = str1.string_to_list()
-    str1_list[pos1] = cigar_to_insert
-    test_string = CigarStringOld.list_to_string(str1_list)
-    assert (str1.cigar == test_string)
-
-
-
diff --git a/source/output_file_writer.py b/source/output_file_writer.py
index 3123d9c..713ce4d 100755
--- a/source/output_file_writer.py
+++ b/source/output_file_writer.py
@@ -1,14 +1,8 @@
-import sys
-import os
-import re
-import gzip
 from struct import pack
-from Bio.Seq import Seq
-from Bio import SeqIO
 import gzip
 from Bio.bgzf import *
 import pathlib
-from source.neat_cigar_rework import CigarString
+from source.neat_cigar import CigarString
 
 # from source.biopython_modified_bgzf import BgzfWriter
 
@@ -220,9 +214,6 @@ def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag,
         for item in cigar.items():
             cig_numbers.append(item[0])
             cig_letters.append(item[1])
-        # TODO delete the following two lines once the new cigar rework is 100% working
-        # cig_letters = re.split(r"\d+", cigar)[1:]
-        # cig_numbers = [int(n) for n in re.findall(r"\d+", cigar)]
         cig_ops = len(cig_letters)
         next_ref_id = ref_id
         if mate_pos is None:
diff --git a/source/ref_func.py b/source/ref_func.py
index 546155c..239ef40 100755
--- a/source/ref_func.py
+++ b/source/ref_func.py
@@ -96,7 +96,6 @@ def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
     else:
         ref_file = open(absolute_reference_path, 'r')
 
-
     # TODO convert to SeqIO containers
     ref_file.seek(ref_inds_i[1])
     my_dat = ''.join(ref_file.read(ref_inds_i[2] - ref_inds_i[1]).split('\n'))

From bf78644e1ad1c91bed0d5f6198b9cd629118937a Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 25 Jan 2021 13:06:05 -0600
Subject: [PATCH 308/323] Got a little too aggressive with eliminating lines

---
 gen_reads.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index 2694ef5..ba08b94 100755
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -93,7 +93,7 @@ def main(raw_args=None):
                              'things like read locations, variant positions, error positions, etc, '
                              'should all be the same.')
     # TODO check if this argument does anything at all. Near as I can tell the results are ALWAYS gzipped.
-    # parser.add_argument('--gz', required=False, action='store_true', default=False, help='gzip output FQ and VCF')
+    parser.add_argument('--gz', required=False, action='store_true', default=False, help='gzip output FQ and VCF')
     parser.add_argument('--no-fastq', required=False, action='store_true', default=False,
                         help='bypass fastq generation')
     parser.add_argument('--discard-offtarget', required=False, action='store_true', default=False,

From 5d3be4e09dd80677e8ecee767c4e96a9cfb007f0 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Mon, 1 Feb 2021 10:09:22 -0600
Subject: [PATCH 309/323] notes

---
 source/SequenceContainer.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index ee0f01d..aed7e13 100755
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -303,6 +303,7 @@ def init_coverage(self, coverage_data, frag_dist=None):
                 coverage_vector = np.cumsum([tr_cov_vals[i][nnn] *
                                              gc_cov_vals[i][nnn] for nnn in range(len(tr_cov_vals[i]))])
                 coverage_vals = []
+                # TODO if max_coord is <=0, this is a problem
                 for j in range(0, max_coord):
                     coverage_vals.append(coverage_vector[j + self.read_len] - coverage_vector[j])
                 avg_out.append(np.mean(coverage_vals) / float(self.read_len))

From b93ee0f331a19c0957686b458754d04a36886ffa Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Mon, 1 Feb 2021 10:42:06 -0600
Subject: [PATCH 310/323] Update README.md

---
 README.md | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 891d36b..c8aee7e 100755
--- a/README.md
+++ b/README.md
@@ -62,12 +62,14 @@ Option           |  Description
 -c <float>       |  Average coverage across the entire dataset. Default: 10
 -e <str>         |  Sequencing error model pickle file
 -E <float>       |  Average sequencing error rate. The sequencing error rate model is rescaled to make this the average value. 
--p <int>         |  ploidy [2]
--t <str>         |  bed file containing targeted regions; default coverage for targeted regions is 98% of -c option; default coverage outside targeted regions is 2% of -c option
+-p <int>         |  Sample Ploidy, default 2
+-t <str>         |  Bed file containing targeted regions; default coverage for targeted regions is 98% of -c option; default coverage outside targeted regions is 2% of -c option
+-d <str>	 |  Bed file with sample regions to discard.
 -to <float>      |  off-target coverage scalar [0.02]
 -m <str>         |  mutation model pickle file
 -M <float>       |  Average mutation rate. The mutation rate model is rescaled to make this the average value. Must be between 0 and 0.3. These random mutations are inserted in addition to the once specified in the -v option.
--s <str>         |  input sample model
+-Mb <str>	 |  Bed file containing positional mutation rates
+-N <int>	 |  Below this quality score, base-call's will be replaced with N's
 -v <str>         |  Input VCF file. Variants from this VCF will be inserted into the simulated sequence with 100% certainty.
 --pe <int> <int> |  Paired-end fragment length mean and standard deviation. To produce paired end data, one of --pe or --pe-model must be specified.
 --pe-model <str> |  Empirical fragment length distribution. Can be generated using [computeFraglen.py](#computefraglenpy). To produce paired end data, one of --pe or --pe-model must be specified.
@@ -76,9 +78,12 @@ Option           |  Description
 --nnr            |  save non-N ref regions (for parallel jobs)
 --bam            |  Output golden BAM file
 --vcf            |  Output golden VCF file
+--fa		 |  Output FASTA instead of FASTQ
 --rng <int>      |  rng seed value; identical RNG value should produce identical runs of the program, so things like read locations, variant positions, error positions, etc, should all be the same.
 --gz             |  Gzip output FQ and VCF
 --no-fastq       |  Bypass generation of FASTQ read files
+--discard-offtarget |  Discard reads outside of targeted regions
+--rescale-qual   |  Rescale Quality scores to match -E input
 
 
 ## Functionality

From 9d5594719fa721580a568486b8ca4d095b2345a6 Mon Sep 17 00:00:00 2001
From: meridith-e <66380709+meridith-e@users.noreply.github.com>
Date: Mon, 1 Feb 2021 11:02:18 -0600
Subject: [PATCH 311/323] Update README.md

---
 README.md | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index c8aee7e..04a68d2 100755
--- a/README.md
+++ b/README.md
@@ -200,7 +200,7 @@ To simulate human WGS 50X, try 50 chunks or less.
 # Utilities	
 Several scripts are distributed with genReads that are used to generate the models used for simulation.
 
-## computeGC.py
+## compute_gc.py
 
 Computes GC% coverage bias distribution from sample (bedrolls genomecov) data.
 Takes .genomecov files produced by BEDtools genomeCov (with -d option).
@@ -220,7 +220,7 @@ python computeGC.py                 \
         -o /path/to/model.p
 ```
 
-## computeFraglen.py
+## compute_fraglen.py
 
 Computes empirical fragment length distribution from sample data.
 Takes SAM file via stdin:
@@ -229,7 +229,7 @@ Takes SAM file via stdin:
 
 and creates fraglen.p model in working directory.
 
-## genMutModel.py
+## gen_mut_model.py
 
 Takes references genome and TSV file to generate mutation models:
 
@@ -242,6 +242,17 @@ python genMutModel.py               \
 
 Trinucleotides are identified in the reference genome and the variant file. Frequencies of each trinucleotide transition are calculated and output as a pickle (.p) file.
 
+Option           |  Description
+------           |:----------
+-r <str>         |  Reference file for organism in FASTA format. Required
+-m <str>         |  Mutation file for organism in VCF format. Required
+-o <str>         |  Path to output file and prefix. Required. 
+-b <str>         |  BED file of regions to include
+--save-trinuc    |  Save trinucleotide counts for reference
+--human-sample   |  Use to skip unnumbered scaffolds in human references
+--skip-common    |  Do not save common snps or high mutation areas
+	
+
 ## genSeqErrorModel.py
 
 Generates sequence error model for genReads.py -e option.
@@ -259,6 +270,7 @@ python genSeqErrorModel.py                            \
         -s number of simulation iterations [1000000]  \
         --plot perform some optional plotting
 ```
+
 ## plotMutModel.py
 
 Performs plotting and comparison of mutation models generated from genMutModel.py.
@@ -276,8 +288,6 @@ Tool for comparing VCF files.
 
 ```
 python vcf_compare_OLD.py
-        --version          show program's version number and exit      \
-        -h, --help         show this help message and exit             \
         -r <ref.fa>        * Reference Fasta                           \
         -g <golden.vcf>    * Golden VCF                                \
         -w <workflow.vcf>  * Workflow VCF                              \

From 6ca633549d74b6f6cb7f2e5508338dfd05f6be56 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 2 Feb 2021 01:39:20 -0600
Subject: [PATCH 312/323] Still debugging. Compare two loops above line 664 in
 SequenceContainer

---
 gen_reads.py                      |   8 +-
 source/SequenceContainer.py       |  45 ++++++--
 source/biopython_modified_bgzf.py | 104 ------------------
 source/fm_pos_test.py             |  69 ------------
 source/neat_cigar.py              |  36 +++++--
 source/neat_cigar_new.py          | 170 ++++++++++++++++++++++++++++++
 source/output_file_writer.py      |  48 ++++++---
 source/ref_func.py                |  17 ++-
 utilities/generate_random_dna.py  |  25 +++++
 9 files changed, 311 insertions(+), 211 deletions(-)
 delete mode 100755 source/biopython_modified_bgzf.py
 delete mode 100755 source/fm_pos_test.py
 create mode 100644 source/neat_cigar_new.py
 create mode 100644 utilities/generate_random_dna.py

diff --git a/gen_reads.py b/gen_reads.py
index ba08b94..fea4ff3 100755
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -24,6 +24,7 @@
 import numpy as np
 import argparse
 import pathlib
+# from Bio import SeqIO
 
 from source.input_checking import check_file_open, is_in_range
 from source.ref_func import index_ref, read_ref
@@ -272,14 +273,18 @@ def main(raw_args=None):
     # index reference: [(0: chromosome name, 1: byte index where the contig seq begins,
     #                    2: byte index where the next contig begins, 3: contig seq length),
     #                    (repeat for every chrom)]
-    # TODO check to see if this might work better as a dataframe
+    # TODO check to see if this might work better as a dataframe or biopython object
     ref_index = index_ref(reference)
 
+    # TODO check if this index can work, maybe it's faster
+    # ref_index2 = SeqIO.index(reference, 'fasta')
+
     if paired_end:
         n_handling = ('random', fragment_size)
     else:
         n_handling = ('ignore', read_len)
 
+
     indices_by_ref_name = {ref_index[n][0]: n for n in range(len(ref_index))}
     ref_list = [n[0] for n in ref_index]
 
@@ -375,6 +380,7 @@ def main(raw_args=None):
     # initialize output files (part I)
     bam_header = None
     if save_bam:
+        # TODO wondering if this is actually needed in the bam_header
         bam_header = [copy.deepcopy(ref_index)]
     vcf_header = None
     if save_vcf:
diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index ee0f01d..cfa850a 100755
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -9,6 +9,7 @@
 from Bio.Seq import Seq
 
 from source.neat_cigar import CigarString
+from source.neat_cigar_new import CigarString as CigarStringNew
 from source.probability import DiscreteDistribution, poisson_list
 
 """
@@ -100,13 +101,14 @@ def __init__(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_mod
         self.x = x_offset
         self.ploidy = ploidy
         self.read_len = read_len
-        self.sequences = [Seq(str(sequence)).tomutable() for _ in range(self.ploidy)]
+        self.sequences = [Seq(str(sequence)) for _ in range(self.ploidy)]
         self.seq_len = len(sequence)
         self.indel_list = [[] for _ in range(self.ploidy)]
         self.snp_list = [[] for _ in range(self.ploidy)]
         self.all_cigar = [[] for _ in range(self.ploidy)]
         self.all_cigar2 = [[] for _ in range(self.ploidy)]
         self.fm_pos = [[] for _ in range(self.ploidy)]
+        self.fm_pos2 = [[] for _ in range(self.ploidy)]
         self.fm_span = [[] for _ in range(self.ploidy)]
 
         # Blacklist explanation:
@@ -193,14 +195,16 @@ def update_basic_vars(self, x_offset, sequence, ploidy, window_overlap, read_len
         self.x = x_offset
         self.ploidy = ploidy
         self.read_len = read_len
-        self.sequences = [Seq(str(sequence)).tomutable() for _ in range(self.ploidy)]
+        self.sequences = [Seq(str(sequence)) for _ in range(self.ploidy)]
         self.seq_len = len(sequence)
         self.indel_list = [[] for _ in range(self.ploidy)]
         self.snp_list = [[] for _ in range(self.ploidy)]
         self.all_cigar = [[] for _ in range(self.ploidy)]
         self.all_cigar2 = [[] for _ in range(self.ploidy)]
         self.fm_pos = [[] for _ in range(self.ploidy)]
+        self.fm_pos2 = [[] for _ in range(self.ploidy)]
         self.fm_span = [[] for _ in range(self.ploidy)]
+        self.fm_span2 = [[] for _ in range(self.ploidy)]
         self.black_list = [np.zeros(self.seq_len, dtype='<i4') for _ in range(self.ploidy)]
 
         # disallow mutations to occur on window overlap points
@@ -566,16 +570,18 @@ def random_mutations(self):
 
         # MODIFY REFERENCE STRING: SNPS
         for i in range(len(all_snps)):
+            temp = self.sequences[i].tomutable()
             for j in range(len(all_snps[i])):
                 v_pos = all_snps[i][j][0]
 
-                if all_snps[i][j][1] != self.sequences[i][v_pos]:
-                    print('\nError: Something went wrong!\n', all_snps[i][j], self.sequences[i][v_pos], '\n')
+                if all_snps[i][j][1] != temp[v_pos]:
+                    print('\nError: Something went wrong!\n', all_snps[i][j], temp[v_pos], '\n')
                     print(all_snps[i][j])
                     print(self.sequences[i][v_pos])
                     sys.exit(1)
                 else:
-                    self.sequences[i][v_pos] = all_snps[i][j][2]
+                    temp[v_pos] = all_snps[i][j][2]
+            self.sequences[i] = temp.toseq()
 
         # organize the indels we want to insert
         for i in range(len(all_indels)):
@@ -585,7 +591,11 @@ def random_mutations(self):
         # MODIFY REFERENCE STRING: INDELS
         for i in range(len(all_indels_ins)):
             rolling_adj = 0
+            # TODO CigarString might be too slow. Convert these to lists
             temp_symbol_string = CigarString(str(len(self.sequences[i])) + "M")
+            temp_symbol_string2 = CigarStringNew(str(len(self.sequences[i])) + "M")
+
+            assert (temp_symbol_string.string_to_list() == temp_symbol_string2.cigar)
 
             for j in range(len(all_indels_ins[i])):
                 v_pos = all_indels_ins[i][j][0] + rolling_adj
@@ -599,38 +609,57 @@ def random_mutations(self):
                     sys.exit(1)
                 else:
                     # alter reference sequence
-                    self.sequences[i] = self.sequences[i][:v_pos] + Seq(all_indels_ins[i][j][2]).tomutable() + \
+                    self.sequences[i] = self.sequences[i][:v_pos] + Seq(all_indels_ins[i][j][2]) + \
                                         self.sequences[i][v_pos2:]
                     # notate indel positions for cigar computation
                     if indel_length > 0:
                         cigar_to_insert = CigarString(str(indel_length) + 'I')
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert,
                                                                 v_pos2 + 1)
+                        temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
+                                              ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
                     elif indel_length < 0:
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D')
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert)
+                        temp_symbol_string2[v_pos + 1] = "D" * abs(indel_length) + "M"
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
+                # TODO see if there is a way to do this more efficiently
                 self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j + self.read_len))
+                self.all_cigar2[i].append(temp_symbol_string2[j:j + self.read_len])
 
             # create some data structures we will need later:
-            # --- self.FM_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e.
+            # --- self.fm_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e.
             #       corresponding to the unmodified reference genome)
-            # --- self.FM_span[ploid][pos]: number of reference positions spanned by a read originating from
+            # --- self.fm_span[ploid][pos]: number of reference positions spanned by a read originating from
             #       this coordinate
             md_so_far = 0
+            # TODO the following will replace the block below if everything goes well
+            for j in range(len(temp_symbol_string2)):
+                self.fm_pos2[i].append(md_so_far)
+                # fix an edge case with deletions
+                if 'D' in temp_symbol_string2[j]:
+                    self.fm_pos2[i][-1] += temp_symbol_string2[j].count('D')
+                # compute number of ref matches for each read
+                span_dif = len([n for n in temp_symbol_string2[j: j+self.read_len] if 'M' in n])
+                self.fm_span2[i].append(self.fm_pos2[i][-1] + span_dif)
+                md_so_far += temp_symbol_string2[j].count('M') + temp_symbol_string2[j].count('D')
+            # TODO can this be done with new cigar string method?
             temp_symbol_string_list = temp_symbol_string.string_to_list()
+            md_so_far = 0
             for j in range(len(temp_symbol_string_list)):
                 self.fm_pos[i].append(md_so_far)
                 # fix an edge case with deletions
                 if temp_symbol_string_list[j] == 'D':
                     self.fm_pos[i][-1] += temp_symbol_string_list[j].count('D')
                 # compute number of ref matches for each read
+                # TODO convert to cigar string
                 span_dif = len([n for n in temp_symbol_string_list[j:j + self.read_len] if 'M' in n])
                 self.fm_span[i].append(self.fm_pos[i][-1] + span_dif)
                 md_so_far += temp_symbol_string_list[j].count('M') + temp_symbol_string_list[j].count('D')
 
+        # TODO compare the above two methods
         # tally up all the variants we handled...
         count_dict = {}
         all_variants = [sorted(all_snps[i] + all_indels[i]) for i in range(self.ploidy)]
diff --git a/source/biopython_modified_bgzf.py b/source/biopython_modified_bgzf.py
deleted file mode 100755
index f3fc6ac..0000000
--- a/source/biopython_modified_bgzf.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/user/env/bin source
-
-# Copyright 2010-2013 by Peter Cock.
-# All rights reserved.
-# This code is part of the Biopython distribution and governed by its
-# license.  Please see the LICENSE file that should have been included
-# as part of this package.
-
-
-##########################################################################
-#                                                                  #######
-#    06/02/2015:                                                   #######
-#        - I picked out the bits and pieces of code needed         #######
-#          to write BAM files, removed source 3.0 compatibility    #######
-#                                                                  #######
-##########################################################################
-
-import zlib
-import struct
-
-
-class BgzfWriter:
-
-    def __init__(self, filename=None, mode="w", file_obj=None, compress_level=6):
-        if file_obj:
-            assert filename is None
-            handle = file_obj
-        else:
-            if "w" not in mode.lower() and "a" not in mode.lower():
-                raise ValueError("Must use write or append mode, not %r" % mode)
-            if "a" in mode.lower():
-                raise NotImplementedError("Append mode is not implemented yet")
-                handle = open(filename, "ab")
-            else:
-                handle = open(filename, "wb")
-        self._text = "b" not in mode.lower()
-        self._handle = handle
-        self._buffer = b""
-        self.compress_level = compress_level
-
-    def _write_block(self, block):
-        _bgzf_header = b"\x1f\x8b\x08\x04\x00\x00\x00\x00\x00\xff\x06\x00\x42\x43\x02\x00"
-        start_offset = self._handle.tell()
-        assert len(block) <= 65536
-        # Giving a negative window bits means no gzip/zlib headers, -15 used in samtools
-        c = zlib.compressobj(self.compress_level,
-                             zlib.DEFLATED,
-                             -15,
-                             zlib.DEF_MEM_LEVEL,
-                             0)
-        compressed = c.compress(block) + c.flush()
-        del c
-        assert len(compressed) < 65536, "TODO - Didn't compress enough, try less data in this block"
-        crc = zlib.crc32(block)
-        # Should cope with a mix of Python platforms...
-        if crc < 0:
-            crc = struct.pack("<i", crc)
-        else:
-            crc = struct.pack("<I", crc)
-        bsize = struct.pack("<H", len(compressed) + 25)  # includes -1
-        crc = struct.pack("<I", zlib.crc32(block) & 0xffffffff)
-        uncompressed_length = struct.pack("<I", len(block))
-        data = _bgzf_header + bsize + compressed + crc + uncompressed_length
-        self._handle.write(data)
-
-    def write(self, data):
-        if type(data) is str:
-            data = data.encode('utf-8')
-        data_len = len(data)
-        if len(self._buffer) + data_len < 65536:
-            self._buffer += data
-            return
-        else:
-            self._buffer += data
-            while len(self._buffer) >= 65536:
-                self._write_block(self._buffer[:65536])
-                self._buffer = self._buffer[65536:]
-
-    def flush(self):
-        while len(self._buffer) >= 65536:
-            self._write_block(self._buffer[:65535])
-            self._buffer = self._buffer[65535:]
-        self._write_block(self._buffer)
-        self._buffer = b""
-        self._handle.flush()
-
-    def close(self):
-        """Flush data, write 28 bytes empty BGZF EOF marker, and close the BGZF file."""
-        _bgzf_eof = b"\x1f\x8b\x08\x04\x00\x00\x00\x00\x00\xff\x06\x00\x42\x43\x02\x00\x1b\x00\x03\x00\x00\x00\x00\x00" \
-                    b"\x00\x00\x00\x00"
-        if self._buffer:
-            self.flush()
-        # samtools will look for a magic EOF marker, just a 28 byte empty BGZF block,
-        # and if it is missing warns the BAM file may be truncated. In addition to
-        # samtools writing this block, so too does bgzip - so we should too.
-        self._handle.write(_bgzf_eof)
-        self._handle.flush()
-        self._handle.close()
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, type, value, traceback):
-        self.close()
diff --git a/source/fm_pos_test.py b/source/fm_pos_test.py
deleted file mode 100755
index 67ff4da..0000000
--- a/source/fm_pos_test.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from source.neat_cigar import CigarString as CigarStringNew
-from source.neat_cigar_old import CigarString
-from time import time
-
-ploidy = 2
-start_time = time()
-temp_symbol_string = CigarStringNew('10777M1I322M').string_to_list()
-# temp_symbol_string = CigarStringNew('10777M1I322M')
-cigar_string_new_time = time() - start_time
-print(f'CigarStringNew time = {cigar_string_new_time}')
-
-start_time = time()
-temp_symbol_string2 = CigarString.string_to_list('10777M1I322M')
-old_method_time = time() - start_time
-print(f'String-to-list time = {old_method_time}')
-
-print(f'temp_symbol_string = {temp_symbol_string}')
-print(f'temp_symbol_string2 = {temp_symbol_string2}')
-assert(len(temp_symbol_string) == len(temp_symbol_string2))
-print('passed')
-# assert(temp_symbol_string == temp_symbol_string2)
-# print('passed')
-
-fm_pos1 = [[] for _ in range(ploidy)]
-fm_span1 = [[] for _ in range(ploidy)]
-fm_pos2 = [[] for _ in range(ploidy)]
-fm_span2 = [[] for _ in range(ploidy)]
-span_dif = 0
-read_len = 101
-
-for i in range(ploidy):
-    md_so_far = 0
-    start_time = time()
-    # for item in temp_symbol_string.items():
-    #     if item[1] in ['M', 'D']:
-    #         for j in range(item[0]):
-    #             fm_pos1[i].append(md_so_far)
-    #             span_dif = CigarStringNew(temp_symbol_string.get_cigar_fragment(j, j + read_len)).count_elements('M')
-    #             fm_span1[i].append(fm_pos1[i][-1] + span_dif)
-    #             md_so_far += 1
-    #     else:
-    #         pass
-    for j in range(len(temp_symbol_string)):
-        fm_pos2[i].append(md_so_far)
-        # fix an edge case with deletions
-        if temp_symbol_string[j] == 'D':
-            fm_pos2[i][-1] += temp_symbol_string[j].count('D')
-        # compute number of ref matches for each read
-        span_dif = len([n for n in temp_symbol_string[j:j + read_len] if n == 'M'])
-        fm_span2[i].append(fm_pos2[i][-1] + span_dif)
-        md_so_far += temp_symbol_string[j].count('M') + temp_symbol_string[j].count('D')
-    print(f'For new method, ploid {i}, time = {time()-start_time + cigar_string_new_time}')
-
-    start_time = time()
-    md_so_far = 0
-    for j in range(len(temp_symbol_string2)):
-        fm_pos2[i].append(md_so_far)
-        # fix an edge case with deletions
-        if temp_symbol_string2[j] == 'D':
-            fm_pos2[i][-1] += temp_symbol_string2[j].count('D')
-        # compute number of ref matches for each read
-        span_dif = len([n for n in temp_symbol_string2[j:j + read_len] if n == 'M'])
-        fm_span2[i].append(fm_pos2[i][-1] + span_dif)
-        md_so_far += temp_symbol_string2[j].count('M') + temp_symbol_string2[j].count('D')
-    print(f'For old method, ploid {i}, time = {time() - start_time + old_method_time}')
-
-
-assert(fm_pos1 == fm_pos2)
-assert(fm_span1 == fm_span2)
diff --git a/source/neat_cigar.py b/source/neat_cigar.py
index 134161a..5b1f033 100644
--- a/source/neat_cigar.py
+++ b/source/neat_cigar.py
@@ -2,6 +2,7 @@
 from itertools import groupby
 from operator import itemgetter
 from source.neat_cigar_old import CigarString as CigarStringOld
+import timeit
 
 
 class CigarString:
@@ -23,6 +24,8 @@ class CigarString:
 
     def __init__(self, cigar_string):
         self.cigar = cigar_string
+        # storing length as an attribute to see if that speeds anything up.
+        self.length = sum(l for l, op in self.items() if op in CigarString.read_consuming_ops)
 
     def items(self):
         if self.cigar == "*":
@@ -42,7 +45,7 @@ def __len__(self):
         """
         sum of MIS=X ops shall equal the sequence length.
         """
-        return sum(l for l, op in self.items() if op in CigarString.read_consuming_ops)
+        return self.length
 
     @classmethod
     def string_from_elements(cls, elements):
@@ -387,16 +390,27 @@ def list_to_string(input_list: list) -> str:
 
 
 if __name__ == "__main__":
-    str1 = CigarString('10M1I2D10M')
-    str2 = CigarString('1D')
-    pos1 = 10
-    pos2 = pos1 + 2
-    str1.insert_cigar_element(pos1, str2, pos2)
-    cigar_to_insert = '1D1M'
-    str1_list = str1.string_to_list()
-    str1_list[pos1] = cigar_to_insert
-    test_string = CigarStringOld.list_to_string(str1_list)
-    assert (str1.cigar == test_string)
+    string = '25179M1D8304M'
+    start = 25079
+    end = start + 100
+
+    temp_symbol_string = CigarString('25179M1D8304M')
+    eval_list = temp_symbol_string.string_to_list()
+    frag = temp_symbol_string.get_cigar_fragment(start, end)
+
+    def eval1():
+        temp_symbol_string = CigarString('25179M1D8304M')
+        temp_symbol_string.get_cigar_fragment(start, end)
+
+    def eval2():
+        temp_symbol_string2 = CigarStringOld(string_in='25179M1D8304M').get_list()
+        temp_symbol_string2[start:end]
+
+    print("new method: " + str(timeit.timeit('eval1()', number=10000, globals=globals())))
+    print("original method: " + str(timeit.timeit('eval2()', number=10000, globals=globals())))
+    print(timeit.timeit('eval_list[start:end]', number=10000, globals=globals()))
+
+
 
 
 
diff --git a/source/neat_cigar_new.py b/source/neat_cigar_new.py
new file mode 100644
index 0000000..c6e4cdd
--- /dev/null
+++ b/source/neat_cigar_new.py
@@ -0,0 +1,170 @@
+import sys
+from itertools import groupby
+from operator import itemgetter
+from source.neat_cigar_old import CigarString as CigarStringOld
+from source.neat_cigar import CigarString as CigarStringIntermediate
+import timeit
+
+
+class CigarString:
+    """"
+    Now we're testing out a list method of CigarString that we're hoping is faster
+    """
+    _read_consuming_ops = ("M", "I", "S", "=", "X")
+    _ref_consuming_ops = ("M", "D", "N", "=", "X")
+
+    def __init__(self, cigar_string: str = None, list_in: list = None):
+        """
+        Converting the cigar string to a list type method thingy as a test
+        :param cigar_string: An actual string version of the cigar
+        """
+        self._needs_update = False
+        self._cigar_string = "*"
+        if cigar_string:
+            self._cigar_string = cigar_string
+        self._cigar = []
+        if list_in:
+            self._cigar = list_in
+            self._needs_update = True
+        else:
+            d_reserve = 0
+            for item in self.items():
+                if item[1] == 'D':
+                    d_reserve = item[0]
+                if item[1] in ['M', 'I']:
+                    if d_reserve:
+                        self._cigar += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
+                    else:
+                        self._cigar += [item[1]] * item[0]
+                    d_reserve = 0
+            self.update_cig_string()
+
+    def __str__(self):
+        if self._needs_update:
+            return "Needs Update, old string: " + self._cigar_string
+        else:
+            return self._cigar_string
+
+    def __repr__(self):
+        if self._needs_update:
+            return "Needs update, old: CigarString('%s')" % self._cigar_string
+        else:
+            return "CigarString('%s')" % self._cigar_string
+
+    def __len__(self):
+        return self._cigar.__len__()
+
+    def __getitem__(self, item):
+        if type(item) == slice:
+            ret = self._cigar.__getitem__(item)
+            return CigarString(list_in=ret)
+        return self._cigar.__getitem__(item)
+
+    def __setitem__(self, key, value):
+        self._needs_update = True
+        self._cigar[key] = value
+
+    def __getslice__(self, i, j):
+        ret = self._cigar.__getslice__(i, j)
+        print(type(ret))
+        ret2 = CigarString(list_in=ret)
+        return ret2
+
+    def append(self, item):
+        self._needs_update = True
+        self._cigar.append(item)
+
+    def __add__(self, other):
+        self._needs_update = True
+        result = self._cigar.__add__(other)
+        try:
+            return CigarString(list_in=result)
+        except TypeError:
+            return result
+
+    def __mul__(self, other):
+        return CigarString(list_in=self._cigar.__mul__(self, other))
+
+    def items(self):
+        """
+        iterator for cigar string items
+        :return: Creates an iterator object
+        """
+        if self._cigar_string == "*" or self._needs_update:
+            yield 0, None
+            raise StopIteration
+        cig_iter = groupby(self._cigar_string, lambda c: c.isdigit())
+        for g, n in cig_iter:
+            yield int("".join(n)), "".join(next(cig_iter)[1])
+
+    @property
+    def cigar(self):
+        """
+        getter for cigar item
+        :return: None
+        """
+        return self._cigar
+
+    # setter for cigar item
+    @cigar.setter
+    def cigar(self, new):
+        """
+        Setter that updates cigar list to new list
+        :param new: new cigar list
+        :return: None
+        """
+        # TODO this validation isn't correct and may not be needed
+        # if type(input) is not list or [l for l in input if l not in self._read_consuming_ops]:
+        #     raise ValueError("Invalid input string to cigar")
+        #     sys.exit(1)
+        self._cigar = new
+        self._needs_update = True
+
+    def update_cig_string(self):
+        """
+        If the list has changed, this will update the corresponding string.
+        Due to speed problems with converting list to string, we won't do this unless
+        instructed, so note that that cigar_string is only valid if needs_update is also false
+        :return: None
+
+        >>> test_cig = CigarString("100M")
+        >>> test_cig[10] = "I"
+        >>> test_cig.update_cig_string()
+        >>> assert(test_cig._cigar_string == "10M1I89M")
+        """
+        if self._needs_update:
+            symbols = ''
+            current_sym = self._cigar[0]
+            current_count = 1
+            if 'D' in current_sym:
+                current_sym = current_sym[-1]
+            for k in range(1, len(self._cigar)):
+                next_sym = self._cigar[k]
+                if len(next_sym) == 1 and next_sym == current_sym:
+                    current_count += 1
+                else:
+                    symbols += str(current_count) + current_sym
+                    if 'D' in next_sym:
+                        symbols += str(next_sym.count('D')) + "D"
+                        current_sym = next_sym[-1]
+                    else:
+                        current_sym = next_sym
+                    current_count = 1
+            symbols += str(current_count) + current_sym
+            self._cigar_string = symbols
+            self._needs_update = False
+        else:
+            pass
+
+
+if __name__ == '__main__':
+    cigar = CigarString('20M')
+    new_thing = cigar[1:11]
+    assert(type(cigar[1:11]) == CigarString)
+    print(cigar.cigar)
+    cigar.append("I")
+    print(cigar.cigar)
+    cigar[3] = "I"
+    cigar.update_cig_string()
+    print(cigar.cigar)
+    print(cigar)
diff --git a/source/output_file_writer.py b/source/output_file_writer.py
index 713ce4d..4ec8b4a 100755
--- a/source/output_file_writer.py
+++ b/source/output_file_writer.py
@@ -2,22 +2,40 @@
 import gzip
 from Bio.bgzf import *
 import pathlib
-from source.neat_cigar import CigarString
+import timeit
+from utilities.generate_random_dna import generate_random_dna
+from Bio.Seq import Seq
 
 # from source.biopython_modified_bgzf import BgzfWriter
 
 BAM_COMPRESSION_LEVEL = 6
 
 
-# TODO figure out why these functions are in this file in the first place
+# def reverse_complement(dna_string: str) -> str:
+#     """
+#     Return the reverse complement of a string from a DNA strand
+#     :param dna_string: string of DNA
+#     :return: the reverse compliment of the above string
+#     """
+#     rc_dict = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'N': 'N'}
+#     return ''.join(rc_dict[n] for n in dna_string[::-1])
+
+
 def reverse_complement(dna_string: str) -> str:
     """
-    Return the reverse complement of a string from a DNA strand
-    :param dna_string: string of DNA
-    :return: the reverse compliment of the above string
+    Return the reverse complement of a string from a DNA strand. Found this method that is slightly faster than
+    biopython. Thanks to this stack exchange post:
+    https://bioinformatics.stackexchange.com/questions/3583/what-is-the-fastest-way-to-get-the-reverse-complement-of-a-dna-sequence-in-pytho #noqa
+    :param dna_string: string of DNA, either in string or MutableSeq format
+    :return: the reverse complement of the above string in either string or MutableSeq format
     """
-    rc_dict = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'N': 'N'}
-    return ''.join(rc_dict[n] for n in dna_string[::-1])
+    if type(dna_string) != str:
+        dna_string.reverse_complement()
+        return dna_string
+    else:
+        tab = str.maketrans("ACTGN", "TGACN")
+
+        return dna_string.translate(tab)[::-1]
 
 
 # SAMtools reg2bin function
@@ -182,21 +200,21 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
     # TODO add write_fasta_record
 
     def write_fastq_record(self, read_name, read1, qual1, read2=None, qual2=None, orientation=None):
-        (read1, quality1) = (str(read1), qual1)
+        (read1, quality1) = (read1, qual1)
         if read2 is not None and orientation is True:
-            (read2, quality2) = (str(reverse_complement(read2)), qual2[::-1])
+            (read2, quality2) = (read2.reverse_complement(), qual2[::-1])
         elif read2 is not None and orientation is False:
-            (read1, quality1) = (str(reverse_complement(read2)), qual2[::-1])
-            (read2, quality2) = (str(read1), qual1)
+            (read1, quality1) = (read2.reverse_complement(), qual2[::-1])
+            (read2, quality2) = (read1, qual1)
 
         if self.fasta_instead:
-            self.fq1_buffer.append('>' + read_name + '/1\n' + read1 + '\n')
+            self.fq1_buffer.append('>' + read_name + '/1\n' + read1.seq + '\n')
             if read2 is not None:
-                self.fq2_buffer.append('>' + read_name + '/2\n' + read2 + '\n')
+                self.fq2_buffer.append('>' + read_name + '/2\n' + read2.seq + '\n')
         else:
-            self.fq1_buffer.append('@' + read_name + '/1\n' + read1 + '\n+\n' + quality1 + '\n')
+            self.fq1_buffer.append('@' + read_name + '/1\n' + read1.seq + '\n+\n' + quality1 + '\n')
             if read2 is not None:
-                self.fq2_buffer.append('@' + read_name + '/2\n' + read2 + '\n+\n' + quality2 + '\n')
+                self.fq2_buffer.append('@' + read_name + '/2\n' + read2.sq + '\n+\n' + quality2 + '\n')
 
     def write_vcf_record(self, chrom, pos, id_str, ref, alt, qual, filt, info):
         self.vcf_file.write(
diff --git a/source/ref_func.py b/source/ref_func.py
index 239ef40..1c817d2 100755
--- a/source/ref_func.py
+++ b/source/ref_func.py
@@ -5,6 +5,7 @@
 import pathlib
 import random
 from Bio.Seq import Seq
+from Bio import SeqIO
 
 OK_CHR_ORD = {'A': True, 'C': True, 'G': True, 'T': True, 'U': True}
 ALLOWED_NUCL = ['A', 'C', 'G', 'T']
@@ -97,10 +98,15 @@ def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
         ref_file = open(absolute_reference_path, 'r')
 
     # TODO convert to SeqIO containers
+    # for seq_record in SeqIO.parse(ref_file, "fasta"):
+    #     pass
+
+
     ref_file.seek(ref_inds_i[1])
     my_dat = ''.join(ref_file.read(ref_inds_i[2] - ref_inds_i[1]).split('\n'))
     my_dat = Seq(my_dat.upper())
-    my_dat = my_dat.tomutable()
+    # Mutable seqs have a number of disadvantages. I'm going to try making them immutable and see if that helps
+    # my_dat = my_dat.tomutable()
 
     # find N regions
     # data explanation: my_dat[n_atlas[0][0]:n_atlas[0][1]] = solid block of Ns
@@ -120,13 +126,16 @@ def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
             n_count = 0
 
     # handle N base-calls as desired
+    # TODO this seems to randomly replace an N with a base. Is this necessary? How to do this in an immutable seq?
     n_info = {'all': [], 'big': [], 'non_N': []}
     if n_handling[0] == 'random':
         for region in n_atlas:
             n_info['all'].extend(region)
             if region[1] - region[0] <= n_handling[1]:
                 for i in range(region[0], region[1]):
-                    my_dat[i] = random.choice(ALLOWED_NUCL)
+                    temp = my_dat.tomutable()
+                    temp[i] = random.choice(ALLOWED_NUCL)
+                    my_dat = temp.toseq()
             else:
                 n_info['big'].extend(region)
     elif n_handling[0] == 'allChr' and n_handling[2] in OK_CHR_ORD:
@@ -134,7 +143,9 @@ def read_ref(ref_path, ref_inds_i, n_handling, n_unknowns=True, quiet=False):
             n_info['all'].extend(region)
             if region[1] - region[0] <= n_handling[1]:
                 for i in range(region[0], region[1]):
-                    my_dat[i] = n_handling[2]
+                    temp = my_dat.tomutable()
+                    temp[i] = n_handling[2]
+                    my_dat = temp.toseq()
             else:
                 n_info['big'].extend(region)
     elif n_handling[0] == 'ignore':
diff --git a/utilities/generate_random_dna.py b/utilities/generate_random_dna.py
new file mode 100644
index 0000000..39e176a
--- /dev/null
+++ b/utilities/generate_random_dna.py
@@ -0,0 +1,25 @@
+import random
+
+
+def generate_random_dna(lnth: int, seed: int = None) -> str:
+    """
+    Takes a parameter length and returns a randomly generated DNA string of that length
+    :param lnth: how long of a string to generate
+    :param seed: Optional seed to produce reproducibly random results
+    :return: randomly generated string
+    """
+    set = ["A", "G", "C", "T"]
+    if seed:
+        random.seed(seed)
+    else:
+        random.seed()
+    ret = ""
+    for i in range(lnth):
+        ret += random.choice(set)
+    return ret
+
+
+if __name__ == '__main__':
+    print(generate_random_dna(10))
+    print(generate_random_dna(10, 1))
+    print(generate_random_dna(10, 1))

From dd1d4d77b7d632ca0cc0ef6275fb783bc1658616 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 2 Feb 2021 02:09:11 -0600
Subject: [PATCH 313/323] Working on converting cigar strings to all list
 methods still. SC line 628 to start

---
 source/SequenceContainer.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index cb64a75..6370cca 100755
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -655,7 +655,6 @@ def random_mutations(self):
                 if temp_symbol_string_list[j] == 'D':
                     self.fm_pos[i][-1] += temp_symbol_string_list[j].count('D')
                 # compute number of ref matches for each read
-                # TODO convert to cigar string
                 span_dif = len([n for n in temp_symbol_string_list[j:j + self.read_len] if 'M' in n])
                 self.fm_span[i].append(self.fm_pos[i][-1] + span_dif)
                 md_so_far += temp_symbol_string_list[j].count('M') + temp_symbol_string_list[j].count('D')

From 541d7637efa6c6500a3b3d7a4f4964d84d3ee302 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Tue, 2 Feb 2021 12:24:02 -0600
Subject: [PATCH 314/323] Currently broken but working on it

---
 source/SequenceContainer.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 6370cca..b2d8305 100755
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -110,6 +110,7 @@ def __init__(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_mod
         self.fm_pos = [[] for _ in range(self.ploidy)]
         self.fm_pos2 = [[] for _ in range(self.ploidy)]
         self.fm_span = [[] for _ in range(self.ploidy)]
+        self.fm_span2 = [[] for _ in range(self.ploidy)]
 
         # Blacklist explanation:
         # black_list[ploid][pos] = 0		safe to insert variant here
@@ -592,7 +593,7 @@ def random_mutations(self):
         # MODIFY REFERENCE STRING: INDELS
         for i in range(len(all_indels_ins)):
             rolling_adj = 0
-            # TODO CigarString might be too slow. Convert these to lists
+            # TODO Check that converting to list-based cigar string solves the problems, then remove
             temp_symbol_string = CigarString(str(len(self.sequences[i])) + "M")
             temp_symbol_string2 = CigarStringNew(str(len(self.sequences[i])) + "M")
 
@@ -619,10 +620,13 @@ def random_mutations(self):
                                                                 v_pos2 + 1)
                         temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
                                               ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
+                        assert(temp_symbol_string.string_to_list() == temp_symbol_string2.cigar)
                     elif indel_length < 0:
                         cigar_to_insert = CigarString(str(abs(indel_length)) + 'D')
                         temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert)
                         temp_symbol_string2[v_pos + 1] = "D" * abs(indel_length) + "M"
+                        assert(temp_symbol_string.string_to_list() == temp_symbol_string2.cigar)
+
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
@@ -635,17 +639,17 @@ def random_mutations(self):
             #       corresponding to the unmodified reference genome)
             # --- self.fm_span[ploid][pos]: number of reference positions spanned by a read originating from
             #       this coordinate
-            md_so_far = 0
+            md_so_far2 = 0
             # TODO the following will replace the block below if everything goes well
             for j in range(len(temp_symbol_string2)):
-                self.fm_pos2[i].append(md_so_far)
+                self.fm_pos2[i].append(md_so_far2)
                 # fix an edge case with deletions
                 if 'D' in temp_symbol_string2[j]:
                     self.fm_pos2[i][-1] += temp_symbol_string2[j].count('D')
                 # compute number of ref matches for each read
                 span_dif = len([n for n in temp_symbol_string2[j: j+self.read_len] if 'M' in n])
                 self.fm_span2[i].append(self.fm_pos2[i][-1] + span_dif)
-                md_so_far += temp_symbol_string2[j].count('M') + temp_symbol_string2[j].count('D')
+                md_so_far2 += temp_symbol_string2[j].count('M') + temp_symbol_string2[j].count('D')
             # TODO can this be done with new cigar string method?
             temp_symbol_string_list = temp_symbol_string.string_to_list()
             md_so_far = 0
@@ -659,6 +663,7 @@ def random_mutations(self):
                 self.fm_span[i].append(self.fm_pos[i][-1] + span_dif)
                 md_so_far += temp_symbol_string_list[j].count('M') + temp_symbol_string_list[j].count('D')
 
+            assert(md_so_far2 == md_so_far)
         # TODO compare the above two methods
         # tally up all the variants we handled...
         count_dict = {}

From bef2b0fd5b2ba6d90a059d8414c783baa60c0c23 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 3 Feb 2021 13:57:16 -0600
Subject: [PATCH 315/323] Changed cigar string to a primarily list based
 method. All three versions of neat_cigar are present in this commit, in case
 I need to walk back

---
 source/SequenceContainer.py       |  91 ++---
 source/neat_cigar.py              | 529 +++++++++---------------------
 source/neat_cigar_intermediate.py | 416 +++++++++++++++++++++++
 source/neat_cigar_new.py          | 170 ----------
 source/output_file_writer.py      |  18 +-
 5 files changed, 601 insertions(+), 623 deletions(-)
 create mode 100644 source/neat_cigar_intermediate.py
 delete mode 100644 source/neat_cigar_new.py

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index b2d8305..b2b48d1 100755
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -9,7 +9,6 @@
 from Bio.Seq import Seq
 
 from source.neat_cigar import CigarString
-from source.neat_cigar_new import CigarString as CigarStringNew
 from source.probability import DiscreteDistribution, poisson_list
 
 """
@@ -106,11 +105,8 @@ def __init__(self, x_offset, sequence, ploidy, window_overlap, read_len, mut_mod
         self.indel_list = [[] for _ in range(self.ploidy)]
         self.snp_list = [[] for _ in range(self.ploidy)]
         self.all_cigar = [[] for _ in range(self.ploidy)]
-        self.all_cigar2 = [[] for _ in range(self.ploidy)]
         self.fm_pos = [[] for _ in range(self.ploidy)]
-        self.fm_pos2 = [[] for _ in range(self.ploidy)]
         self.fm_span = [[] for _ in range(self.ploidy)]
-        self.fm_span2 = [[] for _ in range(self.ploidy)]
 
         # Blacklist explanation:
         # black_list[ploid][pos] = 0		safe to insert variant here
@@ -201,11 +197,8 @@ def update_basic_vars(self, x_offset, sequence, ploidy, window_overlap, read_len
         self.indel_list = [[] for _ in range(self.ploidy)]
         self.snp_list = [[] for _ in range(self.ploidy)]
         self.all_cigar = [[] for _ in range(self.ploidy)]
-        self.all_cigar2 = [[] for _ in range(self.ploidy)]
         self.fm_pos = [[] for _ in range(self.ploidy)]
-        self.fm_pos2 = [[] for _ in range(self.ploidy)]
         self.fm_span = [[] for _ in range(self.ploidy)]
-        self.fm_span2 = [[] for _ in range(self.ploidy)]
         self.black_list = [np.zeros(self.seq_len, dtype='<i4') for _ in range(self.ploidy)]
 
         # disallow mutations to occur on window overlap points
@@ -593,11 +586,7 @@ def random_mutations(self):
         # MODIFY REFERENCE STRING: INDELS
         for i in range(len(all_indels_ins)):
             rolling_adj = 0
-            # TODO Check that converting to list-based cigar string solves the problems, then remove
             temp_symbol_string = CigarString(str(len(self.sequences[i])) + "M")
-            temp_symbol_string2 = CigarStringNew(str(len(self.sequences[i])) + "M")
-
-            assert (temp_symbol_string.string_to_list() == temp_symbol_string2.cigar)
 
             for j in range(len(all_indels_ins[i])):
                 v_pos = all_indels_ins[i][j][0] + rolling_adj
@@ -615,56 +604,32 @@ def random_mutations(self):
                                         self.sequences[i][v_pos2:]
                     # notate indel positions for cigar computation
                     if indel_length > 0:
-                        cigar_to_insert = CigarString(str(indel_length) + 'I')
-                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert,
-                                                                v_pos2 + 1)
-                        temp_symbol_string2 = temp_symbol_string2[:v_pos + 1] + \
-                                              ['I'] * indel_length + temp_symbol_string2[v_pos2 + 1:]
-                        assert(temp_symbol_string.string_to_list() == temp_symbol_string2.cigar)
+                        temp_symbol_string = temp_symbol_string[:v_pos + 1] + ['I'] * indel_length \
+                                              + temp_symbol_string[v_pos2 + 1:]
                     elif indel_length < 0:
-                        cigar_to_insert = CigarString(str(abs(indel_length)) + 'D')
-                        temp_symbol_string.insert_cigar_element(v_pos + 1, cigar_to_insert)
-                        temp_symbol_string2[v_pos + 1] = "D" * abs(indel_length) + "M"
-                        assert(temp_symbol_string.string_to_list() == temp_symbol_string2.cigar)
-
+                        temp_symbol_string[v_pos + 1] = "D" * abs(indel_length) + "M"
 
             # pre-compute cigar strings
             for j in range(len(temp_symbol_string) - self.read_len):
-                # TODO see if there is a way to do this more efficiently
-                self.all_cigar[i].append(temp_symbol_string.get_cigar_fragment(j, j + self.read_len))
-                self.all_cigar2[i].append(temp_symbol_string2[j:j + self.read_len])
+                self.all_cigar[i].append(temp_symbol_string[j:j + self.read_len])
 
             # create some data structures we will need later:
             # --- self.fm_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e.
             #       corresponding to the unmodified reference genome)
             # --- self.fm_span[ploid][pos]: number of reference positions spanned by a read originating from
             #       this coordinate
-            md_so_far2 = 0
-            # TODO the following will replace the block below if everything goes well
-            for j in range(len(temp_symbol_string2)):
-                self.fm_pos2[i].append(md_so_far2)
-                # fix an edge case with deletions
-                if 'D' in temp_symbol_string2[j]:
-                    self.fm_pos2[i][-1] += temp_symbol_string2[j].count('D')
-                # compute number of ref matches for each read
-                span_dif = len([n for n in temp_symbol_string2[j: j+self.read_len] if 'M' in n])
-                self.fm_span2[i].append(self.fm_pos2[i][-1] + span_dif)
-                md_so_far2 += temp_symbol_string2[j].count('M') + temp_symbol_string2[j].count('D')
-            # TODO can this be done with new cigar string method?
-            temp_symbol_string_list = temp_symbol_string.string_to_list()
             md_so_far = 0
-            for j in range(len(temp_symbol_string_list)):
+            for j in range(len(temp_symbol_string)):
                 self.fm_pos[i].append(md_so_far)
                 # fix an edge case with deletions
-                if temp_symbol_string_list[j] == 'D':
-                    self.fm_pos[i][-1] += temp_symbol_string_list[j].count('D')
+                if 'D' in temp_symbol_string[j]:
+                    self.fm_pos[i][-1] += temp_symbol_string[j].count('D')
                 # compute number of ref matches for each read
-                span_dif = len([n for n in temp_symbol_string_list[j:j + self.read_len] if 'M' in n])
+                # This line gets hit a lot and is relatively slow. Might look for an improvement
+                span_dif = len([n for n in temp_symbol_string[j: j + self.read_len] if 'M' in n])
                 self.fm_span[i].append(self.fm_pos[i][-1] + span_dif)
-                md_so_far += temp_symbol_string_list[j].count('M') + temp_symbol_string_list[j].count('D')
+                md_so_far += temp_symbol_string[j].count('M') + temp_symbol_string[j].count('D')
 
-            assert(md_so_far2 == md_so_far)
-        # TODO compare the above two methods
         # tally up all the variants we handled...
         count_dict = {}
         all_variants = [sorted(all_snps[i] + all_indels[i]) for i in range(self.ploidy)]
@@ -746,7 +711,9 @@ def sample_read(self, sequencing_model, frag_len=None):
 
             # add buffer sequence to fill in positions that get deleted
             read[3] += self.sequences[my_ploid][read[0] + self.read_len:read[0] + self.read_len + total_d]
-            expanded_cigar = []
+            # this is leftover code and a patch for a method that isn't used. There is probably a better
+            # way to structure this than with a boolean
+            first_time = True
             adj = 0
             sse_adj = [0 for _ in range(self.read_len + max(sequencing_model.err_p[3]))]
             any_indel_err = False
@@ -782,14 +749,14 @@ def sample_read(self, sequencing_model, frag_len=None):
 
                     if total_d > avail_b:  # if not enough bases to fill-in deletions, skip all indel erors
                         continue
-                    if not expanded_cigar:
-                        expanded_cigar = my_cigar.string_to_list()
+                    if first_time:
+                        # Again, this whole first time thing is a workaround for the previous
+                        # code, which is simplified. May need to fix this all at some point
+                        first_time = False
                         fill_to_go = total_d - total_i + 1
                         if fill_to_go > 0:
                             try:
-                                extra_cigar_val = self.all_cigar[my_ploid][read[0]
-                                                                           + fill_to_go].string_to_list()[-fill_to_go:]
-
+                                extra_cigar_val = self.all_cigar[my_ploid][read[0] + fill_to_go][-fill_to_go:]
                             except IndexError:
                                 # Applying the deletions we want requires going beyond region boundaries.
                                 # Skip all indel errors
@@ -805,12 +772,13 @@ def sample_read(self, sequencing_model, frag_len=None):
                         pf = e_pos + my_adj + e_len + 1
                         if str(read[3][pi:pf]) == str(error[3]):
                             read[3] = read[3][:pi + 1] + read[3][pf:]
-                            expanded_cigar = expanded_cigar[:pi + 1] + expanded_cigar[pf:]
+                            my_cigar = my_cigar[:pi + 1] + my_cigar[pf:]
                             # weird edge case with del at very end of region. Make a guess and add a "M"
-                            if pi + 1 == len(expanded_cigar):
-                                expanded_cigar.append('M')
+                            if pi + 1 == len(my_cigar):
+                                my_cigar.append('M')
+
                             try:
-                                expanded_cigar[pi + 1] = 'D' * e_len + expanded_cigar[pi + 1]
+                                my_cigar[pi + 1] = 'D' * e_len + my_cigar[pi + 1]
                             except IndexError:
                                 print("Bug!! Index error on expanded cigar")
                                 sys.exit(1)
@@ -827,8 +795,7 @@ def sample_read(self, sequencing_model, frag_len=None):
                         my_adj = sse_adj[e_pos]
                         if str(read[3][e_pos + my_adj]) == error[3]:
                             read[3] = read[3][:e_pos + my_adj] + error[4] + read[3][e_pos + my_adj + 1:]
-                            expanded_cigar = expanded_cigar[:e_pos + my_adj] + ['I'] * e_len + expanded_cigar[
-                                                                                               e_pos + my_adj:]
+                            my_cigar = my_cigar[:e_pos + my_adj] + ['I'] * e_len + my_cigar[e_pos + my_adj:]
                         else:
                             print('\nError, ref does not match alt while attempting to insert insertion error!\n')
                             print('---', chr(read[3][e_pos + my_adj]), '!=', error[3])
@@ -839,15 +806,17 @@ def sample_read(self, sequencing_model, frag_len=None):
 
                 else:  # substitution errors, much easier by comparison...
                     if str(read[3][e_pos + sse_adj[e_pos]]) == error[3]:
-                        read[3][e_pos + sse_adj[e_pos]] = error[4]
+                        temp = read[3].tomutable()
+                        temp[e_pos + sse_adj[e_pos]] = error[4]
+                        read[3] = temp.toseq()
                     else:
                         print('\nError, ref does not match alt while attempting to insert substitution error!\n')
                         sys.exit(1)
 
             if any_indel_err:
-                if len(expanded_cigar):
-                    relevant_cigar = (expanded_cigar + extra_cigar_val)[:self.read_len]
-                    my_cigar = CigarString(CigarString.list_to_string(relevant_cigar))
+                if len(my_cigar):
+                    my_cigar = (my_cigar + extra_cigar_val)[:self.read_len]
+
                 read[3] = read[3][:self.read_len]
 
             read_out.append([self.fm_pos[my_ploid][read[0]], my_cigar, read[3], str(read[1])])
diff --git a/source/neat_cigar.py b/source/neat_cigar.py
index 5b1f033..a88b3ab 100644
--- a/source/neat_cigar.py
+++ b/source/neat_cigar.py
@@ -1,416 +1,179 @@
-import sys
 from itertools import groupby
-from operator import itemgetter
-from source.neat_cigar_old import CigarString as CigarStringOld
-import timeit
 
 
 class CigarString:
+    """"
+    Now we're testing out a list method of CigarString that we're hoping is faster
     """
-    This code borrowed in whole from https://github.com/brentp/cigar
+    _read_consuming_ops = ("M", "I", "S", "=", "X")
+    _ref_consuming_ops = ("M", "D", "N", "=", "X")
 
-    cigar is a simple library for dealing with cigar strings. the most useful
-    feature now is soft-masking from left or right. This allows one to adjust
-    a SAM record only by changing the cigar string to soft-mask a number of bases
-    such that the rest of the SAM record (pos, tlen, etc.) remain valid, but
-    downstream tools will not consider the soft-masked bases in further analysis.
-
-    CigarString adds additional functionality to the cigar module, to make it compatible with
-    the rest of the NEAT codebase. Many functions are lifted directly from brentp's repo
-    """
-
-    read_consuming_ops = ("M", "I", "S", "=", "X")
-    ref_consuming_ops = ("M", "D", "N", "=", "X")
-
-    def __init__(self, cigar_string):
-        self.cigar = cigar_string
-        # storing length as an attribute to see if that speeds anything up.
-        self.length = sum(l for l, op in self.items() if op in CigarString.read_consuming_ops)
-
-    def items(self):
-        if self.cigar == "*":
-            yield 0, None
-            raise StopIteration
-        cig_iter = groupby(self.cigar, lambda c: c.isdigit())
-        for g, n in cig_iter:
-            yield int("".join(n)), "".join(next(cig_iter)[1])
+    def __init__(self, cigar_string: str = None, list_in: list = None):
+        """
+        Converting the cigar string to a list type method thingy as a test
+        :param cigar_string: An actual string version of the cigar
+        """
+        self._needs_update = False
+        self._cigar_string = "*"
+        if cigar_string:
+            self._cigar_string = cigar_string
+        self._cigar = []
+        if list_in:
+            self._cigar = list_in
+            self._needs_update = True
+        else:
+            d_reserve = 0
+            for item in self.items():
+                if item[1] == 'D':
+                    d_reserve = item[0]
+                if item[1] in ['M', 'I']:
+                    if d_reserve:
+                        self._cigar += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
+                    else:
+                        self._cigar += [item[1]] * item[0]
+                    d_reserve = 0
+            self.update_cig_string()
 
     def __str__(self):
-        return self.cigar
+        if self._needs_update:
+            return "Needs Update, old string: " + self._cigar_string
+        else:
+            return self._cigar_string
 
     def __repr__(self):
-        return "CigarString('%s')" % self
+        if self._needs_update:
+            return "Needs update, old: CigarString('%s')" % self._cigar_string
+        else:
+            return "CigarString('%s')" % self._cigar_string
 
     def __len__(self):
-        """
-        sum of MIS=X ops shall equal the sequence length.
-        """
-        return self.length
+        return self._cigar.__len__()
 
-    @classmethod
-    def string_from_elements(cls, elements):
-        return "".join("%i%s" % (l, op) for l, op in elements if l != 0)
+    def __getitem__(self, item):
 
-    # I moved this from the main Cigar class in order to make the code more consistent
-    def merge_like_ops(self):
-        """
-        >>> CigarString("1S20M").merge_like_ops()
-        CigarString('1S20M')
-        >>> CigarString("1S1S20M").merge_like_ops()
-        CigarString('2S20M')
-        >>> CigarString("1S1S1S20M").merge_like_ops()
-        CigarString('3S20M')
-        >>> CigarString("1S1S1S20M1S1S").merge_like_ops()
-        CigarString('3S20M2S')
-        """
+        if type(item) == slice:
+            ret = self._cigar.__getitem__(item)
+            return CigarString(list_in=ret)
+        return self._cigar.__getitem__(item)
 
-        cigs = []
-        for op, grps in groupby(self.items(), itemgetter(1)):
-            cigs.append((sum(g[0] for g in grps), op))
+    def __setitem__(self, key, value):
+        self._needs_update = True
+        self._cigar[key] = value
 
-        return CigarString(self.string_from_elements(cigs))
+    def __getslice__(self, i, j):
+        ret = self._cigar.__getslice__(i, j)
+        print(type(ret))
+        ret2 = CigarString(list_in=ret)
+        return ret2
 
-    def find_position(self, position: int) -> (int,int):
-        """
-        This finds the index of the element of list(self.items()) that contains the starting position given.
+    def append(self, item):
+        self._needs_update = True
+        self._cigar.append(item)
 
-        :param position: The position to find of the CigarString
-        :return: the index of that in terms of the elements of list(self.items())
-        >>> cigar = CigarString('2064M1I12979M3D18439M')
-        >>> position = 15414
-        >>> index, bases = cigar.find_position(position)
-        >>> assert(index == 4)
-        >>> assert(bases == 370)
+    def __add__(self, other):
         """
-        consumed_bases = 0
-        bases_left = position
-        index = 0
-        for item in self.items():
-            if item[1] == 'D':
-                index += 1
-                continue
-            consumed_bases += item[0]
-            if consumed_bases >= position:
-                return index, bases_left
-            else:
-                bases_left -= item[0]
-                index += 1
-        return index, bases_left
-
-    def insert_cigar_element(self, pos1: int, insertion_cigar: 'CigarString', pos2: int = None) -> None:
+        This adds functionality to the "+" operation that makes sure it returns a CigarString object.
+        Now you can add two CigarStrings together and get a CigarString back in return.
+        :param other: Another object
+        :return: CigarString or a list
+
+        >>>my_cigar = CigarString('100M')
+        >>>pi = 30
+        >>>pf = 32
+        >>>new_cig = my_cigar[:pi + 1] + my_cigar[pf:]
+        >>>assert(len(new_cig) == 99)
         """
-        Inserts a cigar string in either string or list format to the existing cigar string at position pos.
-
-        :param insertion_cigar: A cigar to insert into current cigar string
-        :param pos1: integer position where to insert the input cigar string
-        :param pos2: integer position where to pick up the remainder
-        :return: None
-
-        >>> str1 = CigarString('50M10D7I23M')
-        >>> str2 = CigarString('10I25M')
-        >>> iPos = 55
-        >>> str1.insert_cigar_element(iPos, str2)
-        >>> assert(str1.cigar == "50M10D15I25M2I23M")
-
-        >>> str1 = CigarString('50M10D7I23M')
-        >>> iPos = 20
-        >>> str1.insert_cigar_element(iPos, str2)
-        >>> assert(str1.cigar == "20M10I55M10D7I23M")
-
-        >>> str1 = CigarString('11100M')
-        >>> str2 = CigarString('2I')
-        >>> iPos = 5785 + 1
-        >>> str1.insert_cigar_element(iPos, str2)
-        >>> assert (str1.cigar == "5786M2I5314M")
-
-        >>> str1 = CigarString('11100M')
-        >>> str2 = CigarString('1D')
-        >>> iPos = 6610 + 1
-        >>> str1.insert_cigar_element(iPos, str2)
-        >>> assert(len(str1) == 11100)
-        >>> assert (str1.cigar == "6611M1D4489M")
-
-        >>> str1 = CigarString('10M1I2D10M')
-        >>> str2 = CigarString('1D')
-        >>> pos1 = 10
-        >>> pos2 = pos1 + 2
-        >>> str1.insert_cigar_element(pos1, str2, pos2)
-        >>> cigar_to_insert = '1D1M'
-        >>> str1_list = str1.string_to_list()
-        >>> str1_list[pos1] = cigar_to_insert
-        >>> test_string = CigarStringOld.list_to_string(str1_list)
-        >>> assert(str1.cigar == test_string)
-
-        >>> original_string = '100M1I10M'
-        >>> original_cigar = CigarString(original_string)
-        >>> v_pos = 9
-        >>> v_pos2 = 9 + 6
-        >>> cigar_to_insert = CigarString('6I')
-        >>> original_cigar.insert_cigar_element(v_pos, cigar_to_insert, v_pos2)
-        >>> cigar_list = CigarStringOld.string_to_list('100M1I10M')
-        >>> indel_length = 6
-        >>> test_string = cigar_list[:v_pos] + ['I'] * indel_length + cigar_list[v_pos2:]
-        >>> test_string = CigarStringOld.list_to_string(test_string)
-        >>> assert (original_cigar.cigar == test_string)
-        """
-
-        if insertion_cigar is None:
-            print('\nError: Invalid insertion operation in CigarString\n')
-            sys.exit(1)
-
-        if pos1 < 0 or pos1 >= len(self):
-            print('\nError: Invalid insertion position in CigarString\n')
-            sys.exit(1)
-
-        if pos2 and (pos2 < pos1 or pos2 >= len(self)):
-            print('\nError: Invalid second position\n')
-            sys.exit(1)
-
+        self._needs_update = True
+        if isinstance(other, CigarString):
+            result = self._cigar.__add__(other.cigar)
+        else:
+            result = self._cigar.__add__(other)
         try:
-            found = False
-            start_index, bases_remain = self.find_position(pos1)
-            extra_bases_to_discard = 0
-            if pos2:
-                extra_bases_to_discard = pos2 - pos1
-            new_element = list(self.items())[:start_index]
-            items_to_iterate = list(self.items())[start_index:]
-            new_item = ((bases_remain, items_to_iterate[0][1]), (items_to_iterate[0][0]
-                                                                 - bases_remain, items_to_iterate[0][1]))
-            new_element.append(new_item[0])
-            items_to_iterate[0:1] = new_item[1:]
-            new_element += [item for item in insertion_cigar.items()]
-            for item in items_to_iterate:
-                if item[0] == 0:
-                    continue
-                if extra_bases_to_discard <= 0 and item[1] == 'D':
-                    new_element.append(item)
-                elif extra_bases_to_discard > 0 and item[1] == 'D':
-                    continue
-                elif found:
-                    new_element.append(item)
-                elif extra_bases_to_discard > item[0]:
-                    extra_bases_to_discard -= item[0]
-                elif extra_bases_to_discard == item[0]:
-                    extra_bases_to_discard = 0
-                    found = True
-                elif extra_bases_to_discard < item[0]:
-                    new_element.append((item[0] - extra_bases_to_discard, item[1]))
-                    extra_bases_to_discard = 0
-                    found = True
+            return CigarString(list_in=result)
+        except TypeError:
+            return result
 
-            # The point of this block of code is just to trim off any starting or ending "D"s, so we don't have
-            # something like "100M1D" as the return.
-            ret = new_element
-            for i in range(len(new_element)):
-                if new_element[0][1] == 'D':
-                    ret = new_element[1:]
-                if new_element[-1][1] == 'D':
-                    ret = new_element[:-1]
-            new_string = self.string_from_elements(ret)
-            self.cigar = CigarString(new_string).merge_like_ops().cigar
-        except ValueError:
-            print('\nBug: Problem with insertion.\n')
-            sys.exit(1)
+    def __mul__(self, other):
+        return CigarString(list_in=self._cigar.__mul__(self, other))
 
-    def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
+    def items(self):
         """
-        Take a slice of a cigar string. E.g., if we have a cigar string that is "1000M" and we want a slice of the
-        first 101 characters, then the return should be '101M'. If we had "20M100I20M" and want the
-        first 101 characters, it would return '20M81I'.
-
-        :param start: start point of sequence to retrieve
-        :param end: end point of sequence to retrieve
-        :return: The sequence that spans start to end
-        >>> str1 = CigarString('10M5D10M')
-        >>> start = 0
-        >>> end = 14
-        >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(len(frag) == 14)
-        >>> assert(frag.cigar == "10M5D4M")
-
-        >>> str1 = CigarString('90M1D10M')
-        >>> start = 0
-        >>> end = 100
-        >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "90M1D10M")
-
-        >>> str1 = CigarString('10M2D10M')
-        >>> start = 10
-        >>> end = 12
-        >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "2M")
-
-        >>> str1 = CigarString('10M1D10M')
-        >>> start = 10
-        >>> end = 12
-        >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "2M")
-
-        >>> str1 = CigarString('102M2I10000M')
-        >>> start1 = 1
-        >>> end1 = 102
-        >>> frag1 = str1.get_cigar_fragment(start1, end1)
-        >>> start2 = 102
-        >>> end2 = 203
-        >>> frag2 = str1.get_cigar_fragment(start2, end2)
-        >>> assert(frag1.cigar == "101M")
-        >>> assert(frag2.cigar == "2I99M")
-
-        >>> temp_symbol_string = CigarString('25179M1D8304M')
-        >>> start = 25080
-        >>> end = 25180
-        >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == '99M1D1M')
-
-        >>> temp_symbol_string = CigarString('25179M1D8304M')
-        >>> start = 25079
-        >>> end = start + 100
-        >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "100M")
+        iterator for cigar string items
+        :return: Creates an iterator object
         """
-        # Minus 1 because python slices don't include the end coordinate
-        window_size = end - start
-        remaining_window = window_size
-        if window_size < 0:
-            print(f'\nError: start and end coordinates for get_cigar_fragment '
-                  f'are wrong: start: {start}, end: {end}')
-        if start is None or end is None:
-            print('\nError: Invalid coordinates in get_cigar_fragment\n')
-            sys.exit(1)
-
-        if start < 0 or start >= len(self):
-            print('\nError: Invalid start point in get_cigar_fragment\n')
-            sys.exit(1)
-
-        if end > len(self) or end < 0:
-            print('\nError: Invalid end point in get_cigar_fragment')
-            sys.exit(1)
-
-        try:
-            start_index, bases_remain = self.find_position(start)
-            end_index = self.find_position(end)[0]
-            new_element = []
-            # +1 here to make it inclusive
-            list_of_items = list(self.items())[start_index:end_index+1]
-            if len(list_of_items) == 1:
-                new_element.append((window_size, list_of_items[0][1]))
-            else:
-                for item in list_of_items:
-                    if sum([item[0] for item in new_element if item[1] != 'D']) == window_size:
-                        break
-                    if item[1] == 'D':
-                        new_element.append(item)
-                    elif bases_remain > 0:
-                        bases_remain = item[0] - bases_remain
-                        if bases_remain < 0:
-                            print("Something went wrong retrieving fragment")
-                            sys.exit(1)
-                        elif bases_remain == 0:
-                            continue
-                        new_element.append((bases_remain, item[1]))
-                        remaining_window -= bases_remain
-                        bases_remain = 0
-                    elif remaining_window > item[0]:
-                        new_element.append(item)
-                        remaining_window -= item[0]
-                    elif remaining_window <= item[0]:
-                        new_element.append((remaining_window, item[1]))
-                        break
-
-            # The point of this block of code is just to trim off any starting or ending "D"s, so we don't have
-            # something like "100M1D" as the return.
-            ret = new_element
-            for i in range(len(new_element)):
-                if new_element[0][1] == 'D':
-                    ret = new_element[1:]
-                if new_element[-1][1] == 'D':
-                    ret = new_element[:-1]
-            new_string = self.string_from_elements(ret)
-            return CigarString(new_string).merge_like_ops()
-
-        except ValueError:
-            print('\nBug: Problem retrieving fragment.\n')
-            sys.exit(1)
-
-    # TODO use this method or delete it.
-    def count_elements(self, element: str) -> int:
-        count = 0
-        for item in self.items():
-            if item[1] == element:
-                count += item[0]
-        return count
+        if self._cigar_string == "*" or self._needs_update:
+            yield 0, None
+            raise StopIteration
+        cig_iter = groupby(self._cigar_string, lambda c: c.isdigit())
+        for g, n in cig_iter:
+            yield int("".join(n)), "".join(next(cig_iter)[1])
 
-    def string_to_list(self) -> list:
+    @property
+    def cigar(self):
         """
-        Converts the cigar string to a full list.
-
-        :return: cigar string in list format.
+        getter for cigar item
+        :return: None
         """
-        cigar_dat = []
-        d_reserve = 0
-        for item in self.items():
-            if item[1] == 'D':
-                d_reserve = item[0]
-            if item[1] in ['M', 'I']:
-                if d_reserve:
-                    cigar_dat += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
-                else:
-                    cigar_dat += [item[1]] * item[0]
-                d_reserve = 0
-        return cigar_dat
+        return self._cigar
 
-    @staticmethod
-    def list_to_string(input_list: list) -> str:
+    # setter for cigar item
+    @cigar.setter
+    def cigar(self, new):
         """
-        Convert a cigar string in list format to a standard cigar string
-
-        :param input_list: Cigar string in list format
-        :return: cigar string in string format
+        Setter that updates cigar list to new list
+        :param new: new cigar list
+        :return: None
         """
+        # TODO this validation isn't correct and may not be needed
+        # if type(input) is not list or [l for l in input if l not in self._read_consuming_ops]:
+        #     raise ValueError("Invalid input string to cigar")
+        #     sys.exit(1)
+        self._cigar = new
+        self._needs_update = True
+
+    def update_cig_string(self):
+        """
+        If the list has changed, this will update the corresponding string.
+        Due to speed problems with converting list to string, we won't do this unless
+        instructed, so note that that cigar_string is only valid if needs_update is also false
+        :return: None
 
-        symbols = ''
-        current_sym = input_list[0]
-        current_count = 1
-        if 'D' in current_sym:
-            current_sym = current_sym[-1]
-        for k in range(1, len(input_list)):
-            next_sym = input_list[k]
-            if len(next_sym) == 1 and next_sym == current_sym:
-                current_count += 1
-            else:
-                symbols += str(current_count) + current_sym
-                if 'D' in next_sym:
-                    symbols += str(next_sym.count('D')) + 'D'
-                    current_sym = next_sym[-1]
+        >>> test_cig = CigarString("100M")
+        >>> test_cig[10] = "I"
+        >>> test_cig.update_cig_string()
+        >>> assert(test_cig._cigar_string == "10M1I89M")
+        """
+        if self._needs_update:
+            symbols = ''
+            current_sym = self._cigar[0]
+            current_count = 1
+            if 'D' in current_sym:
+                current_sym = current_sym[-1]
+            for k in range(1, len(self._cigar)):
+                next_sym = self._cigar[k]
+                if len(next_sym) == 1 and next_sym == current_sym:
+                    current_count += 1
                 else:
-                    current_sym = next_sym
-                current_count = 1
-        symbols += str(current_count) + current_sym
-        return symbols
-
-
-if __name__ == "__main__":
-    string = '25179M1D8304M'
-    start = 25079
-    end = start + 100
-
-    temp_symbol_string = CigarString('25179M1D8304M')
-    eval_list = temp_symbol_string.string_to_list()
-    frag = temp_symbol_string.get_cigar_fragment(start, end)
-
-    def eval1():
-        temp_symbol_string = CigarString('25179M1D8304M')
-        temp_symbol_string.get_cigar_fragment(start, end)
-
-    def eval2():
-        temp_symbol_string2 = CigarStringOld(string_in='25179M1D8304M').get_list()
-        temp_symbol_string2[start:end]
-
-    print("new method: " + str(timeit.timeit('eval1()', number=10000, globals=globals())))
-    print("original method: " + str(timeit.timeit('eval2()', number=10000, globals=globals())))
-    print(timeit.timeit('eval_list[start:end]', number=10000, globals=globals()))
-
-
-
-
-
+                    symbols += str(current_count) + current_sym
+                    if 'D' in next_sym:
+                        symbols += str(next_sym.count('D')) + "D"
+                        current_sym = next_sym[-1]
+                    else:
+                        current_sym = next_sym
+                    current_count = 1
+            symbols += str(current_count) + current_sym
+            self._cigar_string = symbols
+            self._needs_update = False
+        else:
+            pass
+
+
+if __name__ == '__main__':
+    my_cigar = CigarString('100M')
+    pi = 30
+    pf = 32
+    new_cig = my_cigar[:pi + 1] + my_cigar[pf:]
+    assert(len(new_cig) == 99)
+
+    new_cig = my_cigar[:5] + ["I"] * 5 + my_cigar[5:]
+    print(new_cig.cigar)
diff --git a/source/neat_cigar_intermediate.py b/source/neat_cigar_intermediate.py
new file mode 100644
index 0000000..5b1f033
--- /dev/null
+++ b/source/neat_cigar_intermediate.py
@@ -0,0 +1,416 @@
+import sys
+from itertools import groupby
+from operator import itemgetter
+from source.neat_cigar_old import CigarString as CigarStringOld
+import timeit
+
+
+class CigarString:
+    """
+    This code borrowed in whole from https://github.com/brentp/cigar
+
+    cigar is a simple library for dealing with cigar strings. the most useful
+    feature now is soft-masking from left or right. This allows one to adjust
+    a SAM record only by changing the cigar string to soft-mask a number of bases
+    such that the rest of the SAM record (pos, tlen, etc.) remain valid, but
+    downstream tools will not consider the soft-masked bases in further analysis.
+
+    CigarString adds additional functionality to the cigar module, to make it compatible with
+    the rest of the NEAT codebase. Many functions are lifted directly from brentp's repo
+    """
+
+    read_consuming_ops = ("M", "I", "S", "=", "X")
+    ref_consuming_ops = ("M", "D", "N", "=", "X")
+
+    def __init__(self, cigar_string):
+        self.cigar = cigar_string
+        # storing length as an attribute to see if that speeds anything up.
+        self.length = sum(l for l, op in self.items() if op in CigarString.read_consuming_ops)
+
+    def items(self):
+        if self.cigar == "*":
+            yield 0, None
+            raise StopIteration
+        cig_iter = groupby(self.cigar, lambda c: c.isdigit())
+        for g, n in cig_iter:
+            yield int("".join(n)), "".join(next(cig_iter)[1])
+
+    def __str__(self):
+        return self.cigar
+
+    def __repr__(self):
+        return "CigarString('%s')" % self
+
+    def __len__(self):
+        """
+        sum of MIS=X ops shall equal the sequence length.
+        """
+        return self.length
+
+    @classmethod
+    def string_from_elements(cls, elements):
+        return "".join("%i%s" % (l, op) for l, op in elements if l != 0)
+
+    # I moved this from the main Cigar class in order to make the code more consistent
+    def merge_like_ops(self):
+        """
+        >>> CigarString("1S20M").merge_like_ops()
+        CigarString('1S20M')
+        >>> CigarString("1S1S20M").merge_like_ops()
+        CigarString('2S20M')
+        >>> CigarString("1S1S1S20M").merge_like_ops()
+        CigarString('3S20M')
+        >>> CigarString("1S1S1S20M1S1S").merge_like_ops()
+        CigarString('3S20M2S')
+        """
+
+        cigs = []
+        for op, grps in groupby(self.items(), itemgetter(1)):
+            cigs.append((sum(g[0] for g in grps), op))
+
+        return CigarString(self.string_from_elements(cigs))
+
+    def find_position(self, position: int) -> (int,int):
+        """
+        This finds the index of the element of list(self.items()) that contains the starting position given.
+
+        :param position: The position to find of the CigarString
+        :return: the index of that in terms of the elements of list(self.items())
+        >>> cigar = CigarString('2064M1I12979M3D18439M')
+        >>> position = 15414
+        >>> index, bases = cigar.find_position(position)
+        >>> assert(index == 4)
+        >>> assert(bases == 370)
+        """
+        consumed_bases = 0
+        bases_left = position
+        index = 0
+        for item in self.items():
+            if item[1] == 'D':
+                index += 1
+                continue
+            consumed_bases += item[0]
+            if consumed_bases >= position:
+                return index, bases_left
+            else:
+                bases_left -= item[0]
+                index += 1
+        return index, bases_left
+
+    def insert_cigar_element(self, pos1: int, insertion_cigar: 'CigarString', pos2: int = None) -> None:
+        """
+        Inserts a cigar string in either string or list format to the existing cigar string at position pos.
+
+        :param insertion_cigar: A cigar to insert into current cigar string
+        :param pos1: integer position where to insert the input cigar string
+        :param pos2: integer position where to pick up the remainder
+        :return: None
+
+        >>> str1 = CigarString('50M10D7I23M')
+        >>> str2 = CigarString('10I25M')
+        >>> iPos = 55
+        >>> str1.insert_cigar_element(iPos, str2)
+        >>> assert(str1.cigar == "50M10D15I25M2I23M")
+
+        >>> str1 = CigarString('50M10D7I23M')
+        >>> iPos = 20
+        >>> str1.insert_cigar_element(iPos, str2)
+        >>> assert(str1.cigar == "20M10I55M10D7I23M")
+
+        >>> str1 = CigarString('11100M')
+        >>> str2 = CigarString('2I')
+        >>> iPos = 5785 + 1
+        >>> str1.insert_cigar_element(iPos, str2)
+        >>> assert (str1.cigar == "5786M2I5314M")
+
+        >>> str1 = CigarString('11100M')
+        >>> str2 = CigarString('1D')
+        >>> iPos = 6610 + 1
+        >>> str1.insert_cigar_element(iPos, str2)
+        >>> assert(len(str1) == 11100)
+        >>> assert (str1.cigar == "6611M1D4489M")
+
+        >>> str1 = CigarString('10M1I2D10M')
+        >>> str2 = CigarString('1D')
+        >>> pos1 = 10
+        >>> pos2 = pos1 + 2
+        >>> str1.insert_cigar_element(pos1, str2, pos2)
+        >>> cigar_to_insert = '1D1M'
+        >>> str1_list = str1.string_to_list()
+        >>> str1_list[pos1] = cigar_to_insert
+        >>> test_string = CigarStringOld.list_to_string(str1_list)
+        >>> assert(str1.cigar == test_string)
+
+        >>> original_string = '100M1I10M'
+        >>> original_cigar = CigarString(original_string)
+        >>> v_pos = 9
+        >>> v_pos2 = 9 + 6
+        >>> cigar_to_insert = CigarString('6I')
+        >>> original_cigar.insert_cigar_element(v_pos, cigar_to_insert, v_pos2)
+        >>> cigar_list = CigarStringOld.string_to_list('100M1I10M')
+        >>> indel_length = 6
+        >>> test_string = cigar_list[:v_pos] + ['I'] * indel_length + cigar_list[v_pos2:]
+        >>> test_string = CigarStringOld.list_to_string(test_string)
+        >>> assert (original_cigar.cigar == test_string)
+        """
+
+        if insertion_cigar is None:
+            print('\nError: Invalid insertion operation in CigarString\n')
+            sys.exit(1)
+
+        if pos1 < 0 or pos1 >= len(self):
+            print('\nError: Invalid insertion position in CigarString\n')
+            sys.exit(1)
+
+        if pos2 and (pos2 < pos1 or pos2 >= len(self)):
+            print('\nError: Invalid second position\n')
+            sys.exit(1)
+
+        try:
+            found = False
+            start_index, bases_remain = self.find_position(pos1)
+            extra_bases_to_discard = 0
+            if pos2:
+                extra_bases_to_discard = pos2 - pos1
+            new_element = list(self.items())[:start_index]
+            items_to_iterate = list(self.items())[start_index:]
+            new_item = ((bases_remain, items_to_iterate[0][1]), (items_to_iterate[0][0]
+                                                                 - bases_remain, items_to_iterate[0][1]))
+            new_element.append(new_item[0])
+            items_to_iterate[0:1] = new_item[1:]
+            new_element += [item for item in insertion_cigar.items()]
+            for item in items_to_iterate:
+                if item[0] == 0:
+                    continue
+                if extra_bases_to_discard <= 0 and item[1] == 'D':
+                    new_element.append(item)
+                elif extra_bases_to_discard > 0 and item[1] == 'D':
+                    continue
+                elif found:
+                    new_element.append(item)
+                elif extra_bases_to_discard > item[0]:
+                    extra_bases_to_discard -= item[0]
+                elif extra_bases_to_discard == item[0]:
+                    extra_bases_to_discard = 0
+                    found = True
+                elif extra_bases_to_discard < item[0]:
+                    new_element.append((item[0] - extra_bases_to_discard, item[1]))
+                    extra_bases_to_discard = 0
+                    found = True
+
+            # The point of this block of code is just to trim off any starting or ending "D"s, so we don't have
+            # something like "100M1D" as the return.
+            ret = new_element
+            for i in range(len(new_element)):
+                if new_element[0][1] == 'D':
+                    ret = new_element[1:]
+                if new_element[-1][1] == 'D':
+                    ret = new_element[:-1]
+            new_string = self.string_from_elements(ret)
+            self.cigar = CigarString(new_string).merge_like_ops().cigar
+        except ValueError:
+            print('\nBug: Problem with insertion.\n')
+            sys.exit(1)
+
+    def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
+        """
+        Take a slice of a cigar string. E.g., if we have a cigar string that is "1000M" and we want a slice of the
+        first 101 characters, then the return should be '101M'. If we had "20M100I20M" and want the
+        first 101 characters, it would return '20M81I'.
+
+        :param start: start point of sequence to retrieve
+        :param end: end point of sequence to retrieve
+        :return: The sequence that spans start to end
+        >>> str1 = CigarString('10M5D10M')
+        >>> start = 0
+        >>> end = 14
+        >>> frag = str1.get_cigar_fragment(start, end)
+        >>> assert(len(frag) == 14)
+        >>> assert(frag.cigar == "10M5D4M")
+
+        >>> str1 = CigarString('90M1D10M')
+        >>> start = 0
+        >>> end = 100
+        >>> frag = str1.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == "90M1D10M")
+
+        >>> str1 = CigarString('10M2D10M')
+        >>> start = 10
+        >>> end = 12
+        >>> frag = str1.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == "2M")
+
+        >>> str1 = CigarString('10M1D10M')
+        >>> start = 10
+        >>> end = 12
+        >>> frag = str1.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == "2M")
+
+        >>> str1 = CigarString('102M2I10000M')
+        >>> start1 = 1
+        >>> end1 = 102
+        >>> frag1 = str1.get_cigar_fragment(start1, end1)
+        >>> start2 = 102
+        >>> end2 = 203
+        >>> frag2 = str1.get_cigar_fragment(start2, end2)
+        >>> assert(frag1.cigar == "101M")
+        >>> assert(frag2.cigar == "2I99M")
+
+        >>> temp_symbol_string = CigarString('25179M1D8304M')
+        >>> start = 25080
+        >>> end = 25180
+        >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == '99M1D1M')
+
+        >>> temp_symbol_string = CigarString('25179M1D8304M')
+        >>> start = 25079
+        >>> end = start + 100
+        >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
+        >>> assert(frag.cigar == "100M")
+        """
+        # Minus 1 because python slices don't include the end coordinate
+        window_size = end - start
+        remaining_window = window_size
+        if window_size < 0:
+            print(f'\nError: start and end coordinates for get_cigar_fragment '
+                  f'are wrong: start: {start}, end: {end}')
+        if start is None or end is None:
+            print('\nError: Invalid coordinates in get_cigar_fragment\n')
+            sys.exit(1)
+
+        if start < 0 or start >= len(self):
+            print('\nError: Invalid start point in get_cigar_fragment\n')
+            sys.exit(1)
+
+        if end > len(self) or end < 0:
+            print('\nError: Invalid end point in get_cigar_fragment')
+            sys.exit(1)
+
+        try:
+            start_index, bases_remain = self.find_position(start)
+            end_index = self.find_position(end)[0]
+            new_element = []
+            # +1 here to make it inclusive
+            list_of_items = list(self.items())[start_index:end_index+1]
+            if len(list_of_items) == 1:
+                new_element.append((window_size, list_of_items[0][1]))
+            else:
+                for item in list_of_items:
+                    if sum([item[0] for item in new_element if item[1] != 'D']) == window_size:
+                        break
+                    if item[1] == 'D':
+                        new_element.append(item)
+                    elif bases_remain > 0:
+                        bases_remain = item[0] - bases_remain
+                        if bases_remain < 0:
+                            print("Something went wrong retrieving fragment")
+                            sys.exit(1)
+                        elif bases_remain == 0:
+                            continue
+                        new_element.append((bases_remain, item[1]))
+                        remaining_window -= bases_remain
+                        bases_remain = 0
+                    elif remaining_window > item[0]:
+                        new_element.append(item)
+                        remaining_window -= item[0]
+                    elif remaining_window <= item[0]:
+                        new_element.append((remaining_window, item[1]))
+                        break
+
+            # The point of this block of code is just to trim off any starting or ending "D"s, so we don't have
+            # something like "100M1D" as the return.
+            ret = new_element
+            for i in range(len(new_element)):
+                if new_element[0][1] == 'D':
+                    ret = new_element[1:]
+                if new_element[-1][1] == 'D':
+                    ret = new_element[:-1]
+            new_string = self.string_from_elements(ret)
+            return CigarString(new_string).merge_like_ops()
+
+        except ValueError:
+            print('\nBug: Problem retrieving fragment.\n')
+            sys.exit(1)
+
+    # TODO use this method or delete it.
+    def count_elements(self, element: str) -> int:
+        count = 0
+        for item in self.items():
+            if item[1] == element:
+                count += item[0]
+        return count
+
+    def string_to_list(self) -> list:
+        """
+        Converts the cigar string to a full list.
+
+        :return: cigar string in list format.
+        """
+        cigar_dat = []
+        d_reserve = 0
+        for item in self.items():
+            if item[1] == 'D':
+                d_reserve = item[0]
+            if item[1] in ['M', 'I']:
+                if d_reserve:
+                    cigar_dat += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
+                else:
+                    cigar_dat += [item[1]] * item[0]
+                d_reserve = 0
+        return cigar_dat
+
+    @staticmethod
+    def list_to_string(input_list: list) -> str:
+        """
+        Convert a cigar string in list format to a standard cigar string
+
+        :param input_list: Cigar string in list format
+        :return: cigar string in string format
+        """
+
+        symbols = ''
+        current_sym = input_list[0]
+        current_count = 1
+        if 'D' in current_sym:
+            current_sym = current_sym[-1]
+        for k in range(1, len(input_list)):
+            next_sym = input_list[k]
+            if len(next_sym) == 1 and next_sym == current_sym:
+                current_count += 1
+            else:
+                symbols += str(current_count) + current_sym
+                if 'D' in next_sym:
+                    symbols += str(next_sym.count('D')) + 'D'
+                    current_sym = next_sym[-1]
+                else:
+                    current_sym = next_sym
+                current_count = 1
+        symbols += str(current_count) + current_sym
+        return symbols
+
+
+if __name__ == "__main__":
+    string = '25179M1D8304M'
+    start = 25079
+    end = start + 100
+
+    temp_symbol_string = CigarString('25179M1D8304M')
+    eval_list = temp_symbol_string.string_to_list()
+    frag = temp_symbol_string.get_cigar_fragment(start, end)
+
+    def eval1():
+        temp_symbol_string = CigarString('25179M1D8304M')
+        temp_symbol_string.get_cigar_fragment(start, end)
+
+    def eval2():
+        temp_symbol_string2 = CigarStringOld(string_in='25179M1D8304M').get_list()
+        temp_symbol_string2[start:end]
+
+    print("new method: " + str(timeit.timeit('eval1()', number=10000, globals=globals())))
+    print("original method: " + str(timeit.timeit('eval2()', number=10000, globals=globals())))
+    print(timeit.timeit('eval_list[start:end]', number=10000, globals=globals()))
+
+
+
+
+
diff --git a/source/neat_cigar_new.py b/source/neat_cigar_new.py
deleted file mode 100644
index c6e4cdd..0000000
--- a/source/neat_cigar_new.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import sys
-from itertools import groupby
-from operator import itemgetter
-from source.neat_cigar_old import CigarString as CigarStringOld
-from source.neat_cigar import CigarString as CigarStringIntermediate
-import timeit
-
-
-class CigarString:
-    """"
-    Now we're testing out a list method of CigarString that we're hoping is faster
-    """
-    _read_consuming_ops = ("M", "I", "S", "=", "X")
-    _ref_consuming_ops = ("M", "D", "N", "=", "X")
-
-    def __init__(self, cigar_string: str = None, list_in: list = None):
-        """
-        Converting the cigar string to a list type method thingy as a test
-        :param cigar_string: An actual string version of the cigar
-        """
-        self._needs_update = False
-        self._cigar_string = "*"
-        if cigar_string:
-            self._cigar_string = cigar_string
-        self._cigar = []
-        if list_in:
-            self._cigar = list_in
-            self._needs_update = True
-        else:
-            d_reserve = 0
-            for item in self.items():
-                if item[1] == 'D':
-                    d_reserve = item[0]
-                if item[1] in ['M', 'I']:
-                    if d_reserve:
-                        self._cigar += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
-                    else:
-                        self._cigar += [item[1]] * item[0]
-                    d_reserve = 0
-            self.update_cig_string()
-
-    def __str__(self):
-        if self._needs_update:
-            return "Needs Update, old string: " + self._cigar_string
-        else:
-            return self._cigar_string
-
-    def __repr__(self):
-        if self._needs_update:
-            return "Needs update, old: CigarString('%s')" % self._cigar_string
-        else:
-            return "CigarString('%s')" % self._cigar_string
-
-    def __len__(self):
-        return self._cigar.__len__()
-
-    def __getitem__(self, item):
-        if type(item) == slice:
-            ret = self._cigar.__getitem__(item)
-            return CigarString(list_in=ret)
-        return self._cigar.__getitem__(item)
-
-    def __setitem__(self, key, value):
-        self._needs_update = True
-        self._cigar[key] = value
-
-    def __getslice__(self, i, j):
-        ret = self._cigar.__getslice__(i, j)
-        print(type(ret))
-        ret2 = CigarString(list_in=ret)
-        return ret2
-
-    def append(self, item):
-        self._needs_update = True
-        self._cigar.append(item)
-
-    def __add__(self, other):
-        self._needs_update = True
-        result = self._cigar.__add__(other)
-        try:
-            return CigarString(list_in=result)
-        except TypeError:
-            return result
-
-    def __mul__(self, other):
-        return CigarString(list_in=self._cigar.__mul__(self, other))
-
-    def items(self):
-        """
-        iterator for cigar string items
-        :return: Creates an iterator object
-        """
-        if self._cigar_string == "*" or self._needs_update:
-            yield 0, None
-            raise StopIteration
-        cig_iter = groupby(self._cigar_string, lambda c: c.isdigit())
-        for g, n in cig_iter:
-            yield int("".join(n)), "".join(next(cig_iter)[1])
-
-    @property
-    def cigar(self):
-        """
-        getter for cigar item
-        :return: None
-        """
-        return self._cigar
-
-    # setter for cigar item
-    @cigar.setter
-    def cigar(self, new):
-        """
-        Setter that updates cigar list to new list
-        :param new: new cigar list
-        :return: None
-        """
-        # TODO this validation isn't correct and may not be needed
-        # if type(input) is not list or [l for l in input if l not in self._read_consuming_ops]:
-        #     raise ValueError("Invalid input string to cigar")
-        #     sys.exit(1)
-        self._cigar = new
-        self._needs_update = True
-
-    def update_cig_string(self):
-        """
-        If the list has changed, this will update the corresponding string.
-        Due to speed problems with converting list to string, we won't do this unless
-        instructed, so note that that cigar_string is only valid if needs_update is also false
-        :return: None
-
-        >>> test_cig = CigarString("100M")
-        >>> test_cig[10] = "I"
-        >>> test_cig.update_cig_string()
-        >>> assert(test_cig._cigar_string == "10M1I89M")
-        """
-        if self._needs_update:
-            symbols = ''
-            current_sym = self._cigar[0]
-            current_count = 1
-            if 'D' in current_sym:
-                current_sym = current_sym[-1]
-            for k in range(1, len(self._cigar)):
-                next_sym = self._cigar[k]
-                if len(next_sym) == 1 and next_sym == current_sym:
-                    current_count += 1
-                else:
-                    symbols += str(current_count) + current_sym
-                    if 'D' in next_sym:
-                        symbols += str(next_sym.count('D')) + "D"
-                        current_sym = next_sym[-1]
-                    else:
-                        current_sym = next_sym
-                    current_count = 1
-            symbols += str(current_count) + current_sym
-            self._cigar_string = symbols
-            self._needs_update = False
-        else:
-            pass
-
-
-if __name__ == '__main__':
-    cigar = CigarString('20M')
-    new_thing = cigar[1:11]
-    assert(type(cigar[1:11]) == CigarString)
-    print(cigar.cigar)
-    cigar.append("I")
-    print(cigar.cigar)
-    cigar[3] = "I"
-    cigar.update_cig_string()
-    print(cigar.cigar)
-    print(cigar)
diff --git a/source/output_file_writer.py b/source/output_file_writer.py
index 4ec8b4a..789ea8f 100755
--- a/source/output_file_writer.py
+++ b/source/output_file_writer.py
@@ -2,6 +2,7 @@
 import gzip
 from Bio.bgzf import *
 import pathlib
+import re
 import timeit
 from utilities.generate_random_dna import generate_random_dna
 from Bio.Seq import Seq
@@ -208,13 +209,13 @@ def write_fastq_record(self, read_name, read1, qual1, read2=None, qual2=None, or
             (read2, quality2) = (read1, qual1)
 
         if self.fasta_instead:
-            self.fq1_buffer.append('>' + read_name + '/1\n' + read1.seq + '\n')
+            self.fq1_buffer.append('>' + read_name + '/1\n' + str(read1) + '\n')
             if read2 is not None:
-                self.fq2_buffer.append('>' + read_name + '/2\n' + read2.seq + '\n')
+                self.fq2_buffer.append('>' + read_name + '/2\n' + str(read2) + '\n')
         else:
-            self.fq1_buffer.append('@' + read_name + '/1\n' + read1.seq + '\n+\n' + quality1 + '\n')
+            self.fq1_buffer.append('@' + read_name + '/1\n' + str(read1) + '\n+\n' + quality1 + '\n')
             if read2 is not None:
-                self.fq2_buffer.append('@' + read_name + '/2\n' + read2.sq + '\n+\n' + quality2 + '\n')
+                self.fq2_buffer.append('@' + read_name + '/2\n' + str(read2) + '\n+\n' + quality2 + '\n')
 
     def write_vcf_record(self, chrom, pos, id_str, ref, alt, qual, filt, info):
         self.vcf_file.write(
@@ -227,11 +228,10 @@ def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag,
         # my_bin     = 0	# or just use a dummy value, does this actually matter?
 
         my_map_quality = aln_map_quality
-        cig_letters = []
-        cig_numbers = []
-        for item in cigar.items():
-            cig_numbers.append(item[0])
-            cig_letters.append(item[1])
+        cigar.update_cig_string()
+        cigar_string = str(cigar)
+        cig_letters = re.split(r"\d+", cigar_string)[1:]
+        cig_numbers = [int(n) for n in re.findall(r"\d+", cigar_string)]
         cig_ops = len(cig_letters)
         next_ref_id = ref_id
         if mate_pos is None:

From b44eb8bf65bb7254ff5314cb5051949db9324b0b Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Wed, 3 Feb 2021 13:57:57 -0600
Subject: [PATCH 316/323] Deleted the two extraneous neat_cigar files to clean
 up repo

---
 source/neat_cigar_intermediate.py | 416 ------------------------------
 source/neat_cigar_old.py          | 132 ----------
 2 files changed, 548 deletions(-)
 delete mode 100644 source/neat_cigar_intermediate.py
 delete mode 100644 source/neat_cigar_old.py

diff --git a/source/neat_cigar_intermediate.py b/source/neat_cigar_intermediate.py
deleted file mode 100644
index 5b1f033..0000000
--- a/source/neat_cigar_intermediate.py
+++ /dev/null
@@ -1,416 +0,0 @@
-import sys
-from itertools import groupby
-from operator import itemgetter
-from source.neat_cigar_old import CigarString as CigarStringOld
-import timeit
-
-
-class CigarString:
-    """
-    This code borrowed in whole from https://github.com/brentp/cigar
-
-    cigar is a simple library for dealing with cigar strings. the most useful
-    feature now is soft-masking from left or right. This allows one to adjust
-    a SAM record only by changing the cigar string to soft-mask a number of bases
-    such that the rest of the SAM record (pos, tlen, etc.) remain valid, but
-    downstream tools will not consider the soft-masked bases in further analysis.
-
-    CigarString adds additional functionality to the cigar module, to make it compatible with
-    the rest of the NEAT codebase. Many functions are lifted directly from brentp's repo
-    """
-
-    read_consuming_ops = ("M", "I", "S", "=", "X")
-    ref_consuming_ops = ("M", "D", "N", "=", "X")
-
-    def __init__(self, cigar_string):
-        self.cigar = cigar_string
-        # storing length as an attribute to see if that speeds anything up.
-        self.length = sum(l for l, op in self.items() if op in CigarString.read_consuming_ops)
-
-    def items(self):
-        if self.cigar == "*":
-            yield 0, None
-            raise StopIteration
-        cig_iter = groupby(self.cigar, lambda c: c.isdigit())
-        for g, n in cig_iter:
-            yield int("".join(n)), "".join(next(cig_iter)[1])
-
-    def __str__(self):
-        return self.cigar
-
-    def __repr__(self):
-        return "CigarString('%s')" % self
-
-    def __len__(self):
-        """
-        sum of MIS=X ops shall equal the sequence length.
-        """
-        return self.length
-
-    @classmethod
-    def string_from_elements(cls, elements):
-        return "".join("%i%s" % (l, op) for l, op in elements if l != 0)
-
-    # I moved this from the main Cigar class in order to make the code more consistent
-    def merge_like_ops(self):
-        """
-        >>> CigarString("1S20M").merge_like_ops()
-        CigarString('1S20M')
-        >>> CigarString("1S1S20M").merge_like_ops()
-        CigarString('2S20M')
-        >>> CigarString("1S1S1S20M").merge_like_ops()
-        CigarString('3S20M')
-        >>> CigarString("1S1S1S20M1S1S").merge_like_ops()
-        CigarString('3S20M2S')
-        """
-
-        cigs = []
-        for op, grps in groupby(self.items(), itemgetter(1)):
-            cigs.append((sum(g[0] for g in grps), op))
-
-        return CigarString(self.string_from_elements(cigs))
-
-    def find_position(self, position: int) -> (int,int):
-        """
-        This finds the index of the element of list(self.items()) that contains the starting position given.
-
-        :param position: The position to find of the CigarString
-        :return: the index of that in terms of the elements of list(self.items())
-        >>> cigar = CigarString('2064M1I12979M3D18439M')
-        >>> position = 15414
-        >>> index, bases = cigar.find_position(position)
-        >>> assert(index == 4)
-        >>> assert(bases == 370)
-        """
-        consumed_bases = 0
-        bases_left = position
-        index = 0
-        for item in self.items():
-            if item[1] == 'D':
-                index += 1
-                continue
-            consumed_bases += item[0]
-            if consumed_bases >= position:
-                return index, bases_left
-            else:
-                bases_left -= item[0]
-                index += 1
-        return index, bases_left
-
-    def insert_cigar_element(self, pos1: int, insertion_cigar: 'CigarString', pos2: int = None) -> None:
-        """
-        Inserts a cigar string in either string or list format to the existing cigar string at position pos.
-
-        :param insertion_cigar: A cigar to insert into current cigar string
-        :param pos1: integer position where to insert the input cigar string
-        :param pos2: integer position where to pick up the remainder
-        :return: None
-
-        >>> str1 = CigarString('50M10D7I23M')
-        >>> str2 = CigarString('10I25M')
-        >>> iPos = 55
-        >>> str1.insert_cigar_element(iPos, str2)
-        >>> assert(str1.cigar == "50M10D15I25M2I23M")
-
-        >>> str1 = CigarString('50M10D7I23M')
-        >>> iPos = 20
-        >>> str1.insert_cigar_element(iPos, str2)
-        >>> assert(str1.cigar == "20M10I55M10D7I23M")
-
-        >>> str1 = CigarString('11100M')
-        >>> str2 = CigarString('2I')
-        >>> iPos = 5785 + 1
-        >>> str1.insert_cigar_element(iPos, str2)
-        >>> assert (str1.cigar == "5786M2I5314M")
-
-        >>> str1 = CigarString('11100M')
-        >>> str2 = CigarString('1D')
-        >>> iPos = 6610 + 1
-        >>> str1.insert_cigar_element(iPos, str2)
-        >>> assert(len(str1) == 11100)
-        >>> assert (str1.cigar == "6611M1D4489M")
-
-        >>> str1 = CigarString('10M1I2D10M')
-        >>> str2 = CigarString('1D')
-        >>> pos1 = 10
-        >>> pos2 = pos1 + 2
-        >>> str1.insert_cigar_element(pos1, str2, pos2)
-        >>> cigar_to_insert = '1D1M'
-        >>> str1_list = str1.string_to_list()
-        >>> str1_list[pos1] = cigar_to_insert
-        >>> test_string = CigarStringOld.list_to_string(str1_list)
-        >>> assert(str1.cigar == test_string)
-
-        >>> original_string = '100M1I10M'
-        >>> original_cigar = CigarString(original_string)
-        >>> v_pos = 9
-        >>> v_pos2 = 9 + 6
-        >>> cigar_to_insert = CigarString('6I')
-        >>> original_cigar.insert_cigar_element(v_pos, cigar_to_insert, v_pos2)
-        >>> cigar_list = CigarStringOld.string_to_list('100M1I10M')
-        >>> indel_length = 6
-        >>> test_string = cigar_list[:v_pos] + ['I'] * indel_length + cigar_list[v_pos2:]
-        >>> test_string = CigarStringOld.list_to_string(test_string)
-        >>> assert (original_cigar.cigar == test_string)
-        """
-
-        if insertion_cigar is None:
-            print('\nError: Invalid insertion operation in CigarString\n')
-            sys.exit(1)
-
-        if pos1 < 0 or pos1 >= len(self):
-            print('\nError: Invalid insertion position in CigarString\n')
-            sys.exit(1)
-
-        if pos2 and (pos2 < pos1 or pos2 >= len(self)):
-            print('\nError: Invalid second position\n')
-            sys.exit(1)
-
-        try:
-            found = False
-            start_index, bases_remain = self.find_position(pos1)
-            extra_bases_to_discard = 0
-            if pos2:
-                extra_bases_to_discard = pos2 - pos1
-            new_element = list(self.items())[:start_index]
-            items_to_iterate = list(self.items())[start_index:]
-            new_item = ((bases_remain, items_to_iterate[0][1]), (items_to_iterate[0][0]
-                                                                 - bases_remain, items_to_iterate[0][1]))
-            new_element.append(new_item[0])
-            items_to_iterate[0:1] = new_item[1:]
-            new_element += [item for item in insertion_cigar.items()]
-            for item in items_to_iterate:
-                if item[0] == 0:
-                    continue
-                if extra_bases_to_discard <= 0 and item[1] == 'D':
-                    new_element.append(item)
-                elif extra_bases_to_discard > 0 and item[1] == 'D':
-                    continue
-                elif found:
-                    new_element.append(item)
-                elif extra_bases_to_discard > item[0]:
-                    extra_bases_to_discard -= item[0]
-                elif extra_bases_to_discard == item[0]:
-                    extra_bases_to_discard = 0
-                    found = True
-                elif extra_bases_to_discard < item[0]:
-                    new_element.append((item[0] - extra_bases_to_discard, item[1]))
-                    extra_bases_to_discard = 0
-                    found = True
-
-            # The point of this block of code is just to trim off any starting or ending "D"s, so we don't have
-            # something like "100M1D" as the return.
-            ret = new_element
-            for i in range(len(new_element)):
-                if new_element[0][1] == 'D':
-                    ret = new_element[1:]
-                if new_element[-1][1] == 'D':
-                    ret = new_element[:-1]
-            new_string = self.string_from_elements(ret)
-            self.cigar = CigarString(new_string).merge_like_ops().cigar
-        except ValueError:
-            print('\nBug: Problem with insertion.\n')
-            sys.exit(1)
-
-    def get_cigar_fragment(self, start: int, end: int) -> 'CigarString':
-        """
-        Take a slice of a cigar string. E.g., if we have a cigar string that is "1000M" and we want a slice of the
-        first 101 characters, then the return should be '101M'. If we had "20M100I20M" and want the
-        first 101 characters, it would return '20M81I'.
-
-        :param start: start point of sequence to retrieve
-        :param end: end point of sequence to retrieve
-        :return: The sequence that spans start to end
-        >>> str1 = CigarString('10M5D10M')
-        >>> start = 0
-        >>> end = 14
-        >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(len(frag) == 14)
-        >>> assert(frag.cigar == "10M5D4M")
-
-        >>> str1 = CigarString('90M1D10M')
-        >>> start = 0
-        >>> end = 100
-        >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "90M1D10M")
-
-        >>> str1 = CigarString('10M2D10M')
-        >>> start = 10
-        >>> end = 12
-        >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "2M")
-
-        >>> str1 = CigarString('10M1D10M')
-        >>> start = 10
-        >>> end = 12
-        >>> frag = str1.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "2M")
-
-        >>> str1 = CigarString('102M2I10000M')
-        >>> start1 = 1
-        >>> end1 = 102
-        >>> frag1 = str1.get_cigar_fragment(start1, end1)
-        >>> start2 = 102
-        >>> end2 = 203
-        >>> frag2 = str1.get_cigar_fragment(start2, end2)
-        >>> assert(frag1.cigar == "101M")
-        >>> assert(frag2.cigar == "2I99M")
-
-        >>> temp_symbol_string = CigarString('25179M1D8304M')
-        >>> start = 25080
-        >>> end = 25180
-        >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == '99M1D1M')
-
-        >>> temp_symbol_string = CigarString('25179M1D8304M')
-        >>> start = 25079
-        >>> end = start + 100
-        >>> frag = temp_symbol_string.get_cigar_fragment(start, end)
-        >>> assert(frag.cigar == "100M")
-        """
-        # Minus 1 because python slices don't include the end coordinate
-        window_size = end - start
-        remaining_window = window_size
-        if window_size < 0:
-            print(f'\nError: start and end coordinates for get_cigar_fragment '
-                  f'are wrong: start: {start}, end: {end}')
-        if start is None or end is None:
-            print('\nError: Invalid coordinates in get_cigar_fragment\n')
-            sys.exit(1)
-
-        if start < 0 or start >= len(self):
-            print('\nError: Invalid start point in get_cigar_fragment\n')
-            sys.exit(1)
-
-        if end > len(self) or end < 0:
-            print('\nError: Invalid end point in get_cigar_fragment')
-            sys.exit(1)
-
-        try:
-            start_index, bases_remain = self.find_position(start)
-            end_index = self.find_position(end)[0]
-            new_element = []
-            # +1 here to make it inclusive
-            list_of_items = list(self.items())[start_index:end_index+1]
-            if len(list_of_items) == 1:
-                new_element.append((window_size, list_of_items[0][1]))
-            else:
-                for item in list_of_items:
-                    if sum([item[0] for item in new_element if item[1] != 'D']) == window_size:
-                        break
-                    if item[1] == 'D':
-                        new_element.append(item)
-                    elif bases_remain > 0:
-                        bases_remain = item[0] - bases_remain
-                        if bases_remain < 0:
-                            print("Something went wrong retrieving fragment")
-                            sys.exit(1)
-                        elif bases_remain == 0:
-                            continue
-                        new_element.append((bases_remain, item[1]))
-                        remaining_window -= bases_remain
-                        bases_remain = 0
-                    elif remaining_window > item[0]:
-                        new_element.append(item)
-                        remaining_window -= item[0]
-                    elif remaining_window <= item[0]:
-                        new_element.append((remaining_window, item[1]))
-                        break
-
-            # The point of this block of code is just to trim off any starting or ending "D"s, so we don't have
-            # something like "100M1D" as the return.
-            ret = new_element
-            for i in range(len(new_element)):
-                if new_element[0][1] == 'D':
-                    ret = new_element[1:]
-                if new_element[-1][1] == 'D':
-                    ret = new_element[:-1]
-            new_string = self.string_from_elements(ret)
-            return CigarString(new_string).merge_like_ops()
-
-        except ValueError:
-            print('\nBug: Problem retrieving fragment.\n')
-            sys.exit(1)
-
-    # TODO use this method or delete it.
-    def count_elements(self, element: str) -> int:
-        count = 0
-        for item in self.items():
-            if item[1] == element:
-                count += item[0]
-        return count
-
-    def string_to_list(self) -> list:
-        """
-        Converts the cigar string to a full list.
-
-        :return: cigar string in list format.
-        """
-        cigar_dat = []
-        d_reserve = 0
-        for item in self.items():
-            if item[1] == 'D':
-                d_reserve = item[0]
-            if item[1] in ['M', 'I']:
-                if d_reserve:
-                    cigar_dat += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
-                else:
-                    cigar_dat += [item[1]] * item[0]
-                d_reserve = 0
-        return cigar_dat
-
-    @staticmethod
-    def list_to_string(input_list: list) -> str:
-        """
-        Convert a cigar string in list format to a standard cigar string
-
-        :param input_list: Cigar string in list format
-        :return: cigar string in string format
-        """
-
-        symbols = ''
-        current_sym = input_list[0]
-        current_count = 1
-        if 'D' in current_sym:
-            current_sym = current_sym[-1]
-        for k in range(1, len(input_list)):
-            next_sym = input_list[k]
-            if len(next_sym) == 1 and next_sym == current_sym:
-                current_count += 1
-            else:
-                symbols += str(current_count) + current_sym
-                if 'D' in next_sym:
-                    symbols += str(next_sym.count('D')) + 'D'
-                    current_sym = next_sym[-1]
-                else:
-                    current_sym = next_sym
-                current_count = 1
-        symbols += str(current_count) + current_sym
-        return symbols
-
-
-if __name__ == "__main__":
-    string = '25179M1D8304M'
-    start = 25079
-    end = start + 100
-
-    temp_symbol_string = CigarString('25179M1D8304M')
-    eval_list = temp_symbol_string.string_to_list()
-    frag = temp_symbol_string.get_cigar_fragment(start, end)
-
-    def eval1():
-        temp_symbol_string = CigarString('25179M1D8304M')
-        temp_symbol_string.get_cigar_fragment(start, end)
-
-    def eval2():
-        temp_symbol_string2 = CigarStringOld(string_in='25179M1D8304M').get_list()
-        temp_symbol_string2[start:end]
-
-    print("new method: " + str(timeit.timeit('eval1()', number=10000, globals=globals())))
-    print("original method: " + str(timeit.timeit('eval2()', number=10000, globals=globals())))
-    print(timeit.timeit('eval_list[start:end]', number=10000, globals=globals()))
-
-
-
-
-
diff --git a/source/neat_cigar_old.py b/source/neat_cigar_old.py
deleted file mode 100644
index 3b747f2..0000000
--- a/source/neat_cigar_old.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import re
-
-
-class CigarString:
-    """
-    Creates and modifies CigarString for output
-    """
-    def __init__(self, string_in=None, list_in=None):
-
-        if string_in is None and list_in is None:
-            print('\nError: CigarString object not initialized.\n')
-            exit(1)
-
-        self.cigar_data = []
-
-        if string_in is not None:
-            self.join_cigar(j_string_in=string_in)
-
-        if list_in is not None:
-            self.join_cigar(j_list_in=list_in)
-
-    @staticmethod
-    def string_to_list(string_to_covert: str) -> list:
-        """
-        Converts cigar string to a full list
-
-        :param string_to_covert: Cigar string to convert to list
-        :return: cigar string in list format
-        """
-
-        cigar_dat = []
-        letters = re.split(r"\d+", string_to_covert)[1:]
-        numbers = [int(n) for n in re.findall(r"\d+", string_to_covert)]
-        d_reserve = 0
-        for i in range(len(letters)):
-            if letters[i] == 'D':
-                d_reserve = numbers[i]
-            if letters[i] == 'M' or letters[i] == 'I':
-                if d_reserve:
-                    cigar_dat += ['D' * d_reserve + letters[i]] + [letters[i]] * (int(numbers[i]) - 1)
-                else:
-                    cigar_dat += [letters[i]] * int(numbers[i])
-                d_reserve = 0
-        return cigar_dat
-
-    @staticmethod
-    def list_to_string(list_to_convert: list) -> str:
-        """
-        Convert a cigar string in list format to a standard cigar string
-
-        :param list_to_convert: Cigar string in list format
-        :return: cigar string in string format
-        """
-
-        symbols = ''
-        current_sym = list_to_convert[0]
-        current_count = 1
-        if 'D' in current_sym:
-            current_sym = current_sym[-1]
-        for k in range(1, len(list_to_convert)):
-            next_sym = list_to_convert[k]
-            if len(next_sym) == 1 and next_sym == current_sym:
-                current_count += 1
-            else:
-                symbols += str(current_count) + current_sym
-                if 'D' in next_sym:
-                    symbols += str(next_sym.count('D')) + 'D'
-                    current_sym = next_sym[-1]
-                else:
-                    current_sym = next_sym
-                current_count = 1
-        symbols += str(current_count) + current_sym
-        return symbols
-
-    def get_list(self):
-
-        return self.cigar_data
-
-    def get_string(self):
-
-        return self.list_to_string(self.cigar_data)
-
-    def join_cigar(self, j_string_in: str = None, j_list_in: list = None):
-        """
-        Adds an inputted cigar string in string or list format to the existing cigar string
-
-        :param j_string_in: cigar string in string format
-        :param j_list_in: cigar string in list format
-        :return: None
-        """
-
-        if j_string_in is None and j_list_in is None:
-            print('\nError: Invalid join operation in CigarString\n')
-            exit(1)
-
-        if j_string_in is not None:
-            self.cigar_data += self.string_to_list(j_string_in)
-
-        if j_list_in is not None:
-            self.cigar_data += j_list_in
-
-    def insert_cigar_element(self, pos: int, i_string_in: str = None, i_list_in: list = None):
-        """
-        Inserts a cigar string in either string or list format to the existing cigar string at position pos.
-
-        :param pos: integer position where to insert the input cigar string
-        :param i_string_in: input cigar string in string format
-        :param i_list_in: input cigar string in list format
-        :return:
-
-        >>>str1 = '50M10D7I23M'
-        >>>str2 = '10I25M'
-        >>>iPos = 20
-        >>>my_cigar = CigarString(string_in=str1)
-        >>>my_cigar.insert_cigar_element(iPos, i_string_in=str2)
-        >>>assert(my_cigar.get_string() == "20M10I55M10D7I23M")
-        """
-
-        if i_string_in is None and i_list_in is None:
-            print('\nError: Invalid insertion operation in CigarString\n')
-            exit(1)
-
-        if pos < 0 or pos >= len(self.cigar_data):
-            print('\nError: Invalid insertion position in CigarString\n')
-            exit(1)
-
-        if i_string_in is not None:
-            self.cigar_data = self.cigar_data[:pos] + self.string_to_list(i_string_in) + self.cigar_data[pos:]
-
-        if i_list_in is not None:
-            self.cigar_data = self.cigar_data[:pos] + i_list_in + self.cigar_data[pos:]
-

From b5dd05f5829e2fdad44ac40f901343ffebf0e1a1 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Thu, 4 Feb 2021 16:27:15 -0600
Subject: [PATCH 317/323] Fix to CigarString. Stripped it down to its base
 elements to speed up the code.

---
 gen_reads.py                 |  13 +--
 source/SequenceContainer.py  |  22 ++--
 source/neat_cigar.py         | 200 +++++++++--------------------------
 source/output_file_writer.py |  24 +----
 4 files changed, 73 insertions(+), 186 deletions(-)

diff --git a/gen_reads.py b/gen_reads.py
index fea4ff3..33be104 100755
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -123,8 +123,9 @@ def main(raw_args=None):
     # cancer params (disabled currently)
     # (cancer, cancer_model, cancer_purity) = (args.cancer, args.cm, args.cp)
     (cancer, cancer_model, cancer_purity) = (False, None, 0.8)
-    (off_target_scalar, off_target_discard, force_coverage, rescale_qual) = (args.to, args.discard_offtarget,
-                          args.force_coverage, args.rescale_qual)
+    (off_target_scalar, off_target_discard, force_coverage, rescale_qual) = (args.to,
+                                                                             args.discard_offtarget,
+                                                                             args.force_coverage, args.rescale_qual)
     # important flags
     (save_bam, save_vcf, fasta_instead, gzipped_out, no_fastq) = \
         (args.bam, args.vcf, args.fa, args.gz, args.no_fastq)
@@ -284,7 +285,6 @@ def main(raw_args=None):
     else:
         n_handling = ('ignore', read_len)
 
-
     indices_by_ref_name = {ref_index[n][0]: n for n in range(len(ref_index))}
     ref_list = [n[0] for n in ref_index]
 
@@ -449,7 +449,8 @@ def main(raw_args=None):
                 # If it passes the above tests, append to valid variants list
                 valid_variants_from_vcf.append(n)
 
-            print('found', len(valid_variants_from_vcf), 'valid variants for ' + ref_index[chrom][0] + ' in input VCF...')
+            print('found', len(valid_variants_from_vcf), 'valid variants for ' +
+                  ref_index[chrom][0] + ' in input VCF...')
             if any(n_skipped):
                 print(sum(n_skipped), 'variants skipped...')
                 print(' - [' + str(n_skipped[0]) + '] ref allele does not match reference')
@@ -460,8 +461,8 @@ def main(raw_args=None):
 
         # determine sampling windows based on read length, large N regions, and structural mutations.
         # in order to obtain uniform coverage, windows should overlap by:
-        #		- read_len, if single-end reads
-        #		- fragment_size (mean), if paired-end reads
+        # - read_len, if single-end reads
+        # - fragment_size (mean), if paired-end reads
         # ploidy is fixed per large sampling window,
         # coverage distributions due to GC% and targeted regions are specified within these windows
         all_variants_out = {}
diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index b2b48d1..280051b 100755
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -586,7 +586,7 @@ def random_mutations(self):
         # MODIFY REFERENCE STRING: INDELS
         for i in range(len(all_indels_ins)):
             rolling_adj = 0
-            temp_symbol_string = CigarString(str(len(self.sequences[i])) + "M")
+            temp_symbol_list = CigarString.string_to_list(str(len(self.sequences[i])) + "M")
 
             for j in range(len(all_indels_ins[i])):
                 v_pos = all_indels_ins[i][j][0] + rolling_adj
@@ -604,14 +604,14 @@ def random_mutations(self):
                                         self.sequences[i][v_pos2:]
                     # notate indel positions for cigar computation
                     if indel_length > 0:
-                        temp_symbol_string = temp_symbol_string[:v_pos + 1] + ['I'] * indel_length \
-                                              + temp_symbol_string[v_pos2 + 1:]
+                        temp_symbol_list = temp_symbol_list[:v_pos + 1] + ['I'] * indel_length \
+                                              + temp_symbol_list[v_pos2 + 1:]
                     elif indel_length < 0:
-                        temp_symbol_string[v_pos + 1] = "D" * abs(indel_length) + "M"
+                        temp_symbol_list[v_pos + 1] = "D" * abs(indel_length) + "M"
 
             # pre-compute cigar strings
-            for j in range(len(temp_symbol_string) - self.read_len):
-                self.all_cigar[i].append(temp_symbol_string[j:j + self.read_len])
+            for j in range(len(temp_symbol_list) - self.read_len):
+                self.all_cigar[i].append(temp_symbol_list[j:j + self.read_len])
 
             # create some data structures we will need later:
             # --- self.fm_pos[ploid][pos]: position of the left-most matching base (IN REFERENCE COORDINATES, i.e.
@@ -619,16 +619,16 @@ def random_mutations(self):
             # --- self.fm_span[ploid][pos]: number of reference positions spanned by a read originating from
             #       this coordinate
             md_so_far = 0
-            for j in range(len(temp_symbol_string)):
+            for j in range(len(temp_symbol_list)):
                 self.fm_pos[i].append(md_so_far)
                 # fix an edge case with deletions
-                if 'D' in temp_symbol_string[j]:
-                    self.fm_pos[i][-1] += temp_symbol_string[j].count('D')
+                if 'D' in temp_symbol_list[j]:
+                    self.fm_pos[i][-1] += temp_symbol_list[j].count('D')
                 # compute number of ref matches for each read
                 # This line gets hit a lot and is relatively slow. Might look for an improvement
-                span_dif = len([n for n in temp_symbol_string[j: j + self.read_len] if 'M' in n])
+                span_dif = len([n for n in temp_symbol_list[j: j + self.read_len] if 'M' in n])
                 self.fm_span[i].append(self.fm_pos[i][-1] + span_dif)
-                md_so_far += temp_symbol_string[j].count('M') + temp_symbol_string[j].count('D')
+                md_so_far += temp_symbol_list[j].count('M') + temp_symbol_list[j].count('D')
 
         # tally up all the variants we handled...
         count_dict = {}
diff --git a/source/neat_cigar.py b/source/neat_cigar.py
index a88b3ab..e1d1ef2 100644
--- a/source/neat_cigar.py
+++ b/source/neat_cigar.py
@@ -8,172 +8,72 @@ class CigarString:
     _read_consuming_ops = ("M", "I", "S", "=", "X")
     _ref_consuming_ops = ("M", "D", "N", "=", "X")
 
-    def __init__(self, cigar_string: str = None, list_in: list = None):
-        """
-        Converting the cigar string to a list type method thingy as a test
-        :param cigar_string: An actual string version of the cigar
-        """
-        self._needs_update = False
-        self._cigar_string = "*"
-        if cigar_string:
-            self._cigar_string = cigar_string
-        self._cigar = []
-        if list_in:
-            self._cigar = list_in
-            self._needs_update = True
-        else:
-            d_reserve = 0
-            for item in self.items():
-                if item[1] == 'D':
-                    d_reserve = item[0]
-                if item[1] in ['M', 'I']:
-                    if d_reserve:
-                        self._cigar += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
-                    else:
-                        self._cigar += [item[1]] * item[0]
-                    d_reserve = 0
-            self.update_cig_string()
-
-    def __str__(self):
-        if self._needs_update:
-            return "Needs Update, old string: " + self._cigar_string
-        else:
-            return self._cigar_string
-
-    def __repr__(self):
-        if self._needs_update:
-            return "Needs update, old: CigarString('%s')" % self._cigar_string
-        else:
-            return "CigarString('%s')" % self._cigar_string
-
-    def __len__(self):
-        return self._cigar.__len__()
-
-    def __getitem__(self, item):
-
-        if type(item) == slice:
-            ret = self._cigar.__getitem__(item)
-            return CigarString(list_in=ret)
-        return self._cigar.__getitem__(item)
-
-    def __setitem__(self, key, value):
-        self._needs_update = True
-        self._cigar[key] = value
-
-    def __getslice__(self, i, j):
-        ret = self._cigar.__getslice__(i, j)
-        print(type(ret))
-        ret2 = CigarString(list_in=ret)
-        return ret2
-
-    def append(self, item):
-        self._needs_update = True
-        self._cigar.append(item)
-
-    def __add__(self, other):
-        """
-        This adds functionality to the "+" operation that makes sure it returns a CigarString object.
-        Now you can add two CigarStrings together and get a CigarString back in return.
-        :param other: Another object
-        :return: CigarString or a list
-
-        >>>my_cigar = CigarString('100M')
-        >>>pi = 30
-        >>>pf = 32
-        >>>new_cig = my_cigar[:pi + 1] + my_cigar[pf:]
-        >>>assert(len(new_cig) == 99)
-        """
-        self._needs_update = True
-        if isinstance(other, CigarString):
-            result = self._cigar.__add__(other.cigar)
-        else:
-            result = self._cigar.__add__(other)
-        try:
-            return CigarString(list_in=result)
-        except TypeError:
-            return result
-
-    def __mul__(self, other):
-        return CigarString(list_in=self._cigar.__mul__(self, other))
-
-    def items(self):
+    @staticmethod
+    def items(string_in: str) -> iter:
         """
         iterator for cigar string items
         :return: Creates an iterator object
         """
-        if self._cigar_string == "*" or self._needs_update:
+        if string_in == "*":
             yield 0, None
             raise StopIteration
-        cig_iter = groupby(self._cigar_string, lambda c: c.isdigit())
+        cig_iter = groupby(string_in, lambda c: c.isdigit())
         for g, n in cig_iter:
             yield int("".join(n)), "".join(next(cig_iter)[1])
 
-    @property
-    def cigar(self):
+    @staticmethod
+    def string_to_list(string_in: str) -> list:
         """
-        getter for cigar item
-        :return: None
+        This will convert a cigar string into a list of elements
+        :param string_in: a valid cigar string.
+        :return: a list version of that string.
         """
-        return self._cigar
+        cigar_dat = []
+        d_reserve = 0
+        for item in CigarString.items(string_in):
+            if item[1] == 'D':
+                d_reserve = item[0]
+            if item[1] in ['M', 'I']:
+                if d_reserve:
+                    cigar_dat += ['D' * d_reserve + item[1]] + [item[1]] * (item[0] - 1)
+                else:
+                    cigar_dat += [item[1]] * item[0]
+                d_reserve = 0
+        return cigar_dat
 
-    # setter for cigar item
-    @cigar.setter
-    def cigar(self, new):
-        """
-        Setter that updates cigar list to new list
-        :param new: new cigar list
-        :return: None
+    @staticmethod
+    def list_to_string(input_list: list) -> str:
         """
-        # TODO this validation isn't correct and may not be needed
-        # if type(input) is not list or [l for l in input if l not in self._read_consuming_ops]:
-        #     raise ValueError("Invalid input string to cigar")
-        #     sys.exit(1)
-        self._cigar = new
-        self._needs_update = True
-
-    def update_cig_string(self):
+        Convert a cigar string in list format to a standard cigar string
+        :param input_list: Cigar string in list format
+        :return: cigar string in string format
         """
-        If the list has changed, this will update the corresponding string.
-        Due to speed problems with converting list to string, we won't do this unless
-        instructed, so note that that cigar_string is only valid if needs_update is also false
-        :return: None
 
-        >>> test_cig = CigarString("100M")
-        >>> test_cig[10] = "I"
-        >>> test_cig.update_cig_string()
-        >>> assert(test_cig._cigar_string == "10M1I89M")
-        """
-        if self._needs_update:
-            symbols = ''
-            current_sym = self._cigar[0]
-            current_count = 1
-            if 'D' in current_sym:
-                current_sym = current_sym[-1]
-            for k in range(1, len(self._cigar)):
-                next_sym = self._cigar[k]
-                if len(next_sym) == 1 and next_sym == current_sym:
-                    current_count += 1
+        symbols = ''
+        current_sym = input_list[0]
+        current_count = 1
+        if 'D' in current_sym:
+            current_sym = current_sym[-1]
+        for k in range(1, len(input_list)):
+            next_sym = input_list[k]
+            if len(next_sym) == 1 and next_sym == current_sym:
+                current_count += 1
+            else:
+                symbols += str(current_count) + current_sym
+                if 'D' in next_sym:
+                    symbols += str(next_sym.count('D')) + 'D'
+                    current_sym = next_sym[-1]
                 else:
-                    symbols += str(current_count) + current_sym
-                    if 'D' in next_sym:
-                        symbols += str(next_sym.count('D')) + "D"
-                        current_sym = next_sym[-1]
-                    else:
-                        current_sym = next_sym
-                    current_count = 1
-            symbols += str(current_count) + current_sym
-            self._cigar_string = symbols
-            self._needs_update = False
-        else:
-            pass
+                    current_sym = next_sym
+                current_count = 1
+        symbols += str(current_count) + current_sym
+        return symbols
 
 
 if __name__ == '__main__':
-    my_cigar = CigarString('100M')
-    pi = 30
-    pf = 32
-    new_cig = my_cigar[:pi + 1] + my_cigar[pf:]
-    assert(len(new_cig) == 99)
-
-    new_cig = my_cigar[:5] + ["I"] * 5 + my_cigar[5:]
-    print(new_cig.cigar)
+    cigar = "10M1I3D1M"
+    lst = CigarString.string_to_list(cigar)
+    print(lst)
+    st = CigarString.list_to_string(lst)
+    print(st)
+    
\ No newline at end of file
diff --git a/source/output_file_writer.py b/source/output_file_writer.py
index 789ea8f..69c6c04 100755
--- a/source/output_file_writer.py
+++ b/source/output_file_writer.py
@@ -3,31 +3,18 @@
 from Bio.bgzf import *
 import pathlib
 import re
-import timeit
-from utilities.generate_random_dna import generate_random_dna
-from Bio.Seq import Seq
 
-# from source.biopython_modified_bgzf import BgzfWriter
+from source.neat_cigar import CigarString
 
 BAM_COMPRESSION_LEVEL = 6
 
 
-# def reverse_complement(dna_string: str) -> str:
-#     """
-#     Return the reverse complement of a string from a DNA strand
-#     :param dna_string: string of DNA
-#     :return: the reverse compliment of the above string
-#     """
-#     rc_dict = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'N': 'N'}
-#     return ''.join(rc_dict[n] for n in dna_string[::-1])
-
-
-def reverse_complement(dna_string: str) -> str:
+def reverse_complement(dna_string) -> str:
     """
     Return the reverse complement of a string from a DNA strand. Found this method that is slightly faster than
     biopython. Thanks to this stack exchange post:
-    https://bioinformatics.stackexchange.com/questions/3583/what-is-the-fastest-way-to-get-the-reverse-complement-of-a-dna-sequence-in-pytho #noqa
-    :param dna_string: string of DNA, either in string or MutableSeq format
+    https://bioinformatics.stackexchange.com/questions/3583/what-is-the-fastest-way-to-get-the-reverse-complement-of-a-dna-sequence-in-pytho
+    :param dna_string: string of DNA, either in string or Seq format
     :return: the reverse complement of the above string in either string or MutableSeq format
     """
     if type(dna_string) != str:
@@ -228,8 +215,7 @@ def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag,
         # my_bin     = 0	# or just use a dummy value, does this actually matter?
 
         my_map_quality = aln_map_quality
-        cigar.update_cig_string()
-        cigar_string = str(cigar)
+        cigar_string = CigarString.list_to_string(cigar)
         cig_letters = re.split(r"\d+", cigar_string)[1:]
         cig_numbers = [int(n) for n in re.findall(r"\d+", cigar_string)]
         cig_ops = len(cig_letters)

From 62a6746ea6dc98accc04be68f9637b82be378dce Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 5 Feb 2021 12:23:02 -0600
Subject: [PATCH 318/323] Tested and ready to pull to master. Made some notes
 for future improvements.

---
 source/SequenceContainer.py |  7 +++++++
 source/probability.py       | 12 +++++++++++-
 2 files changed, 18 insertions(+), 1 deletion(-)

diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 280051b..4690c40 100755
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -256,6 +256,7 @@ def init_coverage(self, coverage_data, frag_dist=None):
         :return: Mean coverage value
         """
 
+        # TODO this section is also quite slow and will need further investigation
         # If we're only creating a vcf, skip some expensive initialization related to coverage depth
         if not self.only_vcf:
             (self.window_size, gc_scalars, target_cov_vals) = coverage_data
@@ -374,6 +375,7 @@ def init_poisson(self):
                       range(len(self.models))]
         k_range = range(int(self.seq_len * MAX_MUTFRAC))
         # return (indel_poisson, snp_poisson)
+        # TODO These next two lines are really slow. Maybe there's a better way
         return [poisson_list(k_range, ind_l_list[n]) for n in range(len(self.models))], \
                [poisson_list(k_range, snp_l_list[n]) for n in range(len(self.models))]
 
@@ -935,6 +937,7 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
         :return: modified sequence and associate quality scores
         """
 
+        # TODO this is one of the slowest methods in the code. Need to investigate how to speed this up.
         q_out = [0] * self.read_len
         s_err = []
 
@@ -951,6 +954,8 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
                 my_q = self.init_dist_by_pos_1[0].sample()
             q_out[0] = my_q
 
+            # Every time this is hit, we loop the entire read length twice. I feel like these two loops
+            # Could be combined into one fairly easily. The culprit seems to bee too many hits to the sample() method.
             for i in range(1, self.read_len):
                 if self.pe_models and is_reverse_strand:
                     my_q = self.prob_dist_by_pos_by_prev_q2[self.q_ind_remap[i]][my_q].sample()
@@ -977,6 +982,8 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
         # don't allow other sequencing errors to occur on bases removed by deletion errors
         del_blacklist = []
 
+        # Need to check into this loop, to make sure it isn't slowing us down.
+        # The culprit seems to bee too many hits to the sample() method. This has a few of those calls.
         for ind in s_err[::-1]:  # for each error that we're going to insert...
 
             # determine error type
diff --git a/source/probability.py b/source/probability.py
index 1cb7bb4..12cc076 100755
--- a/source/probability.py
+++ b/source/probability.py
@@ -2,6 +2,7 @@
 import bisect
 import copy
 import sys
+from typing import Union
 
 import numpy as np
 
@@ -39,6 +40,8 @@ def __init__(self, weights, values, degenerate_val=None, method='bisect'):
             self.degenerate = values[0]
         else:
             self.weights = [n / sum_weight for n in weights]
+            # TODO This line is slowing things down and seems unnecessary. Are these "values
+            # possibly some thing from another class?
             self.values = copy.deepcopy(values)
             if len(self.values) != len(self.weights):
                 print('\nError: length and weights and values vectors must be the same.\n')
@@ -81,7 +84,14 @@ def __init__(self, weights, values, degenerate_val=None, method='bisect'):
     def __str__(self):
         return str(self.weights) + ' ' + str(self.values) + ' ' + self.method
 
-    def sample(self):
+    def sample(self) -> Union[int, float]:
+        """
+        This is one of the slowest parts of the code. Or it just gets hit the most times. Will need
+        to investigate at some point.
+        :return: Since this function is selecting an item from a list, and the list could theoretically be anything,
+        then in a broad sense this function returns a list item or a generic object. But I'm fairly confident that most
+        of these uses will be lists of ints or floats, but will investigate further
+        """
 
         if self.degenerate is not None:
             return self.degenerate

From 8b45613c60ef659329ef1c538b40240f2c2bf175 Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 5 Feb 2021 12:43:03 -0600
Subject: [PATCH 319/323] Changed gen_reads to match master

---
 gen_reads.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gen_reads.py b/gen_reads.py
index 33be104..d69cc36 100755
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -72,7 +72,7 @@ def main(raw_args=None):
     parser.add_argument('-m', type=str, required=False, metavar='model.p', default=None,
                         help="Mutation model pickle file")
     parser.add_argument('-M', type=float, required=False, metavar='avg mut rate', default=-1,
-                        help="Rescale avg mutation rate to this, must be between 0 and 0.3")
+                        help="Rescale avg mutation rate to this (1/bp), must be between 0 and 0.3")
     parser.add_argument('-Mb', type=str, required=False, metavar='mut_rates.bed', default=None,
                         help="Bed file containing positional mut rates")
     parser.add_argument('-N', type=int, required=False, metavar='min qual score', default=-1,

From 80a7b63c408aa03a0242f15b3c3c7768a3bc876d Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 5 Feb 2021 12:48:59 -0600
Subject: [PATCH 320/323] Pulled in the no indel model and repickled to python
 3 format

---
 models/MutModel_BRCA_US_ICGC.p    | Bin
 models/MutModel_CLLE-ES_ICGC.p    | Bin
 models/MutModel_NA12878.p.gz      | Bin
 models/MutModel_NA12878_noIndel.p | Bin 0 -> 7487 bytes
 models/MutModel_SKCM-US_ICGC.p    | Bin
 models/README.md                  |   0
 models/errorModel_pacbio_toy.p    | Bin
 models/errorModel_toy.p           | Bin 3282719 -> 3282717 bytes
 models/fraglenModel_toy.p         | Bin
 models/gcBias_toy.p               | Bin
 models/gcBias_uniform.p           | Bin
 models/genReadsTumorTutorial.zip  | Bin
 12 files changed, 0 insertions(+), 0 deletions(-)
 mode change 100755 => 100644 models/MutModel_BRCA_US_ICGC.p
 mode change 100755 => 100644 models/MutModel_CLLE-ES_ICGC.p
 mode change 100755 => 100644 models/MutModel_NA12878.p.gz
 create mode 100644 models/MutModel_NA12878_noIndel.p
 mode change 100755 => 100644 models/MutModel_SKCM-US_ICGC.p
 mode change 100755 => 100644 models/README.md
 mode change 100755 => 100644 models/errorModel_pacbio_toy.p
 mode change 100755 => 100644 models/errorModel_toy.p
 mode change 100755 => 100644 models/fraglenModel_toy.p
 mode change 100755 => 100644 models/gcBias_toy.p
 mode change 100755 => 100644 models/gcBias_uniform.p
 mode change 100755 => 100644 models/genReadsTumorTutorial.zip

diff --git a/models/MutModel_BRCA_US_ICGC.p b/models/MutModel_BRCA_US_ICGC.p
old mode 100755
new mode 100644
diff --git a/models/MutModel_CLLE-ES_ICGC.p b/models/MutModel_CLLE-ES_ICGC.p
old mode 100755
new mode 100644
diff --git a/models/MutModel_NA12878.p.gz b/models/MutModel_NA12878.p.gz
old mode 100755
new mode 100644
diff --git a/models/MutModel_NA12878_noIndel.p b/models/MutModel_NA12878_noIndel.p
new file mode 100644
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diff --git a/models/MutModel_SKCM-US_ICGC.p b/models/MutModel_SKCM-US_ICGC.p
old mode 100755
new mode 100644
diff --git a/models/README.md b/models/README.md
old mode 100755
new mode 100644
diff --git a/models/errorModel_pacbio_toy.p b/models/errorModel_pacbio_toy.p
old mode 100755
new mode 100644
diff --git a/models/errorModel_toy.p b/models/errorModel_toy.p
old mode 100755
new mode 100644
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diff --git a/models/fraglenModel_toy.p b/models/fraglenModel_toy.p
old mode 100755
new mode 100644
diff --git a/models/gcBias_toy.p b/models/gcBias_toy.p
old mode 100755
new mode 100644
diff --git a/models/gcBias_uniform.p b/models/gcBias_uniform.p
old mode 100755
new mode 100644
diff --git a/models/genReadsTumorTutorial.zip b/models/genReadsTumorTutorial.zip
old mode 100755
new mode 100644

From bf74db0dbeacbb5c4278b20fe02225938ab6724c Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 5 Feb 2021 18:25:28 -0600
Subject: [PATCH 321/323] Added in some features from main branch.

---
 ChangeLog.md                 |  24 +++++++
 README.md                    |  50 +++++++------
 gen_reads.py                 |  72 +++++++++----------
 merge_jobs.py                | 133 -----------------------------------
 requirements.txt             |   6 ++
 source/SequenceContainer.py  |  85 +++++++++++++++-------
 source/output_file_writer.py |  95 ++++++++++++-------------
 source/vcf_func.py           |  40 +++++++----
 8 files changed, 226 insertions(+), 279 deletions(-)
 create mode 100644 ChangeLog.md
 delete mode 100755 merge_jobs.py
 create mode 100644 requirements.txt

diff --git a/ChangeLog.md b/ChangeLog.md
new file mode 100644
index 0000000..bc48ba7
--- /dev/null
+++ b/ChangeLog.md
@@ -0,0 +1,24 @@
+#NEAT v3.0
+- NEAT gen_reads now runs in Python 3 exclusively. The previous, Python 2 version is stored in the repo as v2.0, but will not be undergoing active development.
+- Converted sequence objects to Biopython Sequence objects to take advantage of the Biopython library
+- Converted cigar strings to lists. Now there are simply a functions that convert a generic cigar string to a list and vice versa.
+- Tried to take better advantage of some Biopython libraries, such as for parsing fastas.
+- For now, we've eliminated the "Jobs" option and the merge jobs function. We plan to implement multi-threading instead as a way to speed up NEAT's simulation process.
+- Added a basic bacterial wrapper that will simulate multiple generations of bacteria based on an input fasta, mutate them and then produce the fastqs, bams, and vcfs for the resultant bacterial population.
+
+
+##TODOs for v3.1
+NEAT is still undergoing active development with many exciting upgrades planned. We also plan to bring the code up to full production scale and will continue to improve the following features (if you would like to [Contribute](CONTRIBUTING.md))
+- Using Python's multithreading libraries, speed up NEAT's gen_reads tool significantly.
+- Take advantage of pandas library for reading in bed files and other files.
+- Code optimization for all gen_reads files (in source folder)
+- Further cleanup to PEP8 standards
+- Refactor the code to integrate NEAT's utilities into the package
+- Improvements and standardization for the utilities, across the board
+- VCF compare has some nice features and output, but is very slow to use. Can we improve this utility?
+
+For improvements, we have a lot of great ideas for general improvements aimed at better simulating bacteria, but we believe this same improvements will have applications in other species as well. 
+- Multiploidy - all right this has nothing to do with bacteria specifically, but it is a feature we would like to implement into gen_reads.
+- Structural Variants - model large scale structural variants with an eye toward intergenic SVs.
+- Transposable Elements - model transposons within the sequence
+- Repeat regions - will bring a variety of interesting applications
diff --git a/README.md b/README.md
index 04a68d2..535e5f3 100755
--- a/README.md
+++ b/README.md
@@ -1,4 +1,8 @@
-# neat-genreads
+# The NEAT Project v3.0
+Welcome to the NEAT project, the NExt-generation sequencing Analysis Toolkit, version 3.0. Neat has now been updated with Python 3, and is moving toward PEP8 standards. There is still lots of work to be done. See the [ChangeLog](ChangeLog.md) for notes.
+
+Stay tuned over the coming weeks for exciting updates to NEAT, and learn how to [contribute](CONTRIBUTING.md) yourself. If you'd like to use some of our code, no problem! Just review the [license](LICENSE.md), first.
+
 NEAT-genReads is a fine-grained read simulator. GenReads simulates real-looking data using models learned from specific datasets. There are several supporting utilities for generating models used for simulation.
 
 This is an in-progress v2.0 of the software. For a previous stable release please see: [genReads1](https://github.com/zstephens/genReads1)
@@ -37,17 +41,20 @@ Table of Contents
 
 ## Requirements
 
-* Python 3.6+
-* Biopython 1.77+
-* Matplotlib 3.2.2+
-* Numpy 1.19.0+
+* Python >= 3.6
+* biopython >= 1.78
+* matplotlib >= 3.3.4 (optional, for plotting utilities)
+* matplotlib_venn >= 0.11.6 (optional, for plotting utilities)
+* pandas >= 1.2.1
+* numpy >= 1.19.5
+* pysam >= 0.16.0.1
 
 
 ## Usage
 Here's the simplest invocation of genReads using default parameters. This command produces a single ended fastq file with reads of length 101, ploidy 2, coverage 10X, using the default sequencing substitution, GC% bias, and mutation rate models.
 
 ```
-python genReads.py -r ref.fa -R 101 -o simulated_data
+python gen_reads.py -r ref.fa -R 101 -o simulated_data
 ``` 
 
 The most commonly added options are --pe, --bam, --vcf, and -c. 
@@ -63,8 +70,8 @@ Option           |  Description
 -e <str>         |  Sequencing error model pickle file
 -E <float>       |  Average sequencing error rate. The sequencing error rate model is rescaled to make this the average value. 
 -p <int>         |  Sample Ploidy, default 2
--t <str>         |  Bed file containing targeted regions; default coverage for targeted regions is 98% of -c option; default coverage outside targeted regions is 2% of -c option
--d <str>	 |  Bed file with sample regions to discard.
+-tr <str>        |  Bed file containing targeted regions; default coverage for targeted regions is 98% of -c option; default coverage outside targeted regions is 2% of -c option
+-dr <str>	     |  Bed file with sample regions to discard.
 -to <float>      |  off-target coverage scalar [0.02]
 -m <str>         |  mutation model pickle file
 -M <float>       |  Average mutation rate. The mutation rate model is rescaled to make this the average value. Must be between 0 and 0.3. These random mutations are inserted in addition to the once specified in the -v option.
@@ -74,8 +81,6 @@ Option           |  Description
 --pe <int> <int> |  Paired-end fragment length mean and standard deviation. To produce paired end data, one of --pe or --pe-model must be specified.
 --pe-model <str> |  Empirical fragment length distribution. Can be generated using [computeFraglen.py](#computefraglenpy). To produce paired end data, one of --pe or --pe-model must be specified.
 --gc-model <str> |  Empirical GC coverage bias distribution.  Can be generated using [computeGC.py](#computegcpy)
---job <int> <int>|  Jobs IDs for generating reads in parallel
---nnr            |  save non-N ref regions (for parallel jobs)
 --bam            |  Output golden BAM file
 --vcf            |  Output golden VCF file
 --fa		 |  Output FASTA instead of FASTQ
@@ -84,13 +89,14 @@ Option           |  Description
 --no-fastq       |  Bypass generation of FASTQ read files
 --discard-offtarget |  Discard reads outside of targeted regions
 --rescale-qual   |  Rescale Quality scores to match -E input
+-d  |   Turn on debugging mode (useful for development)
 
 
 ## Functionality
 
 ![Diagram describing the way that genReads simulates datasets](docs/flow_new.png "Diagram describing the way that genReads simulates datasets")
 
-NEAT genReads produces simulated sequencing datasets. It creates FASTQ files with reads sampled from a provided reference genome, using sequencing error rates and mutation rates learned from real sequencing data. The strength of genReads lies in the ability for the user to customize many sequencing parameters, produce 'golden', true positive datasets, and produce types of data that other simulators cannot (e.g. tumour/normal data).
+NEAT gen_reads produces simulated sequencing datasets. It creates FASTQ files with reads sampled from a provided reference genome, using sequencing error rates and mutation rates learned from real sequencing data. The strength of genReads lies in the ability for the user to customize many sequencing parameters, produce 'golden', true positive datasets, and produce types of data that other simulators cannot (e.g. tumour/normal data).
 
 Features:
 
@@ -119,7 +125,7 @@ The following commands are examples for common types of data to be generated. Th
 Simulate whole genome dataset with random variants inserted according to the default model. 
 
 ```
-python genReads.py                  \
+python gen_reads.py                  \
         -r hg19.fa                  \
         -R 126                      \
         -o /home/me/simulated_reads \
@@ -132,7 +138,7 @@ python genReads.py                  \
 Simulate a targeted region of a genome, e.g. exome, with -t.
 
 ```
-python genReads.py                  \
+python gen_reads.py                  \
         -r hg19.fa                  \
         -R 126                      \
         -o /home/me/simulated_reads \
@@ -146,7 +152,7 @@ python genReads.py                  \
 Simulate a whole genome dataset with only the variants in the provided VCF file using -v and -M.
 
 ```
-python genReads.py                  \
+python gen_reads.py                  \
         -r hg19.fa                  \
         -R 126                      \
         -o /home/me/simulated_reads \
@@ -161,7 +167,7 @@ python genReads.py                  \
 Simulate single-end reads by omitting the --pe option.
 
 ```
-python genReads.py                  \
+python gen_reads.py                  \
         -r hg19.fa                  \
         -R 126                      \
         -o /home/me/simulated_reads \
@@ -173,7 +179,7 @@ python genReads.py                  \
 Simulate PacBio-like reads by providing an error model.
 
 ```
-python genReads.py                         \
+python gen_reads.py                         \
 	-r hg19.fa                         \
 	-R 5000                            \
 	-e models/errorModel_pacbio_toy.p  \
@@ -185,10 +191,10 @@ python genReads.py                         \
 When possible, simulation can be done in parallel via multiple executions with different --job options. The resultant files will then need to be merged using utilities/mergeJobs.py. The following example shows splitting a simulation into 4 separate jobs (which can be run independently):
 
 ```
-python genReads.py  -r hg19.fa -R 126 -o /home/me/simulated_reads --bam --vcf --job 1 4
-python genReads.py  -r hg19.fa -R 126 -o /home/me/simulated_reads --bam --vcf --job 2 4
-python genReads.py  -r hg19.fa -R 126 -o /home/me/simulated_reads --bam --vcf --job 3 4
-python genReads.py  -r hg19.fa -R 126 -o /home/me/simulated_reads --bam --vcf --job 4 4
+python gen_reads.py  -r hg19.fa -R 126 -o /home/me/simulated_reads --bam --vcf --job 1 4
+python gen_reads.py  -r hg19.fa -R 126 -o /home/me/simulated_reads --bam --vcf --job 2 4
+python gen_reads.py  -r hg19.fa -R 126 -o /home/me/simulated_reads --bam --vcf --job 3 4
+python gen_reads.py  -r hg19.fa -R 126 -o /home/me/simulated_reads --bam --vcf --job 4 4
 
 python mergeJobs.py -i /home/me/simulated_reads -o /home/me/simulated_reads_merged -s /path/to/samtools
 ```
@@ -198,7 +204,7 @@ In future revisions the dependence on SAMtools will be removed.
 To simulate human WGS 50X, try 50 chunks or less.
 
 # Utilities	
-Several scripts are distributed with genReads that are used to generate the models used for simulation.
+Several scripts are distributed with gen_reads that are used to generate the models used for simulation.
 
 ## compute_gc.py
 
@@ -255,7 +261,7 @@ Option           |  Description
 
 ## genSeqErrorModel.py
 
-Generates sequence error model for genReads.py -e option.
+Generates sequence error model for gen_reads.py -e option.
 This script needs revision, to improve the quality-score model eventually, and to include code to learn sequencing errors from pileup data.
 
 ```
diff --git a/gen_reads.py b/gen_reads.py
index d69cc36..1fb5649 100755
--- a/gen_reads.py
+++ b/gen_reads.py
@@ -31,7 +31,7 @@
 from source.vcf_func import parse_vcf
 from source.output_file_writer import OutputFileWriter, reverse_complement, sam_flag
 from source.probability import DiscreteDistribution, mean_ind_of_weighted_list
-from source.SequenceContainer import SequenceContainer, SequencingError, parse_input_mutation_model
+from source.SequenceContainer import SequenceContainer, ReadContainer, parse_input_mutation_model
 
 """
 Some constants needed for analysis
@@ -63,9 +63,9 @@ def main(raw_args=None):
                         help="Rescale avg sequencing error rate to this, must be between 0.0 and 0.3")
     parser.add_argument('-p', type=int, required=False, metavar='ploidy', default=2,
                         help="Desired ploidy, default = 2")
-    parser.add_argument('-t', type=str, required=False, metavar='target.bed', default=None,
+    parser.add_argument('-tr', type=str, required=False, metavar='target.bed', default=None,
                         help="Bed file containing targeted regions")
-    parser.add_argument('-d', type=str, required=False, metavar='discard_regions.bed', default=None,
+    parser.add_argument('-dr', type=str, required=False, metavar='discard_regions.bed', default=None,
                         help="Bed file with regions to discard")
     parser.add_argument('-to', type=float, required=False, metavar='off-target coverage scalar', default=0.00,
                         help="off-target coverage scalar")
@@ -93,8 +93,6 @@ def main(raw_args=None):
                         help='rng seed value; identical RNG value should produce identical runs of the program, so '
                              'things like read locations, variant positions, error positions, etc, '
                              'should all be the same.')
-    # TODO check if this argument does anything at all. Near as I can tell the results are ALWAYS gzipped.
-    parser.add_argument('--gz', required=False, action='store_true', default=False, help='gzip output FQ and VCF')
     parser.add_argument('--no-fastq', required=False, action='store_true', default=False,
                         help='bypass fastq generation')
     parser.add_argument('--discard-offtarget', required=False, action='store_true', default=False,
@@ -102,7 +100,9 @@ def main(raw_args=None):
     parser.add_argument('--force-coverage', required=False, action='store_true', default=False,
                         help='[debug] ignore fancy models, force coverage to be constant')
     parser.add_argument('--rescale-qual', required=False, action='store_true', default=False,
-                        help='rescale quality scores to match -E input')
+                        help='Rescale quality scores to match -E input')
+    # TODO implement a broader debugging scheme for subclasses.
+    parser.add_argument('-d', required=False, action='store_true', default=False, help='Activate Debug Mode')
     args = parser.parse_args(raw_args)
 
     """
@@ -119,7 +119,7 @@ def main(raw_args=None):
     (reference, read_len, out_prefix) = (args.r, args.R, args.o)
     # various dataset parameters
     (coverage, ploids, input_bed, discard_bed, se_model, se_rate, mut_model, mut_rate, mut_bed, input_vcf) = \
-        (args.c, args.p, args.t, args.d, args.e, args.E, args.m, args.M, args.Mb, args.v)
+        (args.c, args.p, args.tr, args.dr, args.e, args.E, args.m, args.M, args.Mb, args.v)
     # cancer params (disabled currently)
     # (cancer, cancer_model, cancer_purity) = (args.cancer, args.cm, args.cp)
     (cancer, cancer_model, cancer_purity) = (False, None, 0.8)
@@ -127,8 +127,8 @@ def main(raw_args=None):
                                                                              args.discard_offtarget,
                                                                              args.force_coverage, args.rescale_qual)
     # important flags
-    (save_bam, save_vcf, fasta_instead, gzipped_out, no_fastq) = \
-        (args.bam, args.vcf, args.fa, args.gz, args.no_fastq)
+    (save_bam, save_vcf, fasta_instead, no_fastq) = \
+        (args.bam, args.vcf, args.fa, args.no_fastq)
 
     # sequencing model parameters
     (fragment_size, fragment_std) = args.pe
@@ -137,6 +137,8 @@ def main(raw_args=None):
 
     rng_seed = args.rng
 
+    debug = args.d
+
     """
     INPUT ERROR CHECKING
     """
@@ -204,10 +206,10 @@ def main(raw_args=None):
     if se_model is None:
         print('Using default sequencing error model.')
         se_model = sim_path / 'models/errorModel_toy.p'
-        se_class = SequencingError(read_len, se_model, se_rate)
+        se_class = ReadContainer(read_len, se_model, se_rate, rescale_qual)
     else:
         # probably need to do some sanity checking
-        se_class = SequencingError(read_len, se_model, se_rate)
+        se_class = ReadContainer(read_len, se_model, se_rate, rescale_qual)
 
     # GC-bias model
     if gc_bias_model is None:
@@ -316,8 +318,6 @@ def main(raw_args=None):
         except IOError:
             print("\nProblem reading input target BED file.\n")
             sys.exit(1)
-        finally:
-            f.close()
 
         # some validation
         n_in_bed_only = 0
@@ -340,16 +340,15 @@ def main(raw_args=None):
     discard_regions = {}
     if discard_bed is not None:
         try:
-            f = open(discard_bed, 'r')
-            for line in f:
-                [my_chr, pos1, pos2] = line.strip().split('\t')[:3]
-                if my_chr not in discard_regions:
-                    discard_regions[my_chr] = [-1]
-                discard_regions[my_chr].extend([int(pos1), int(pos2)])
+            with open(discard_bed, 'r') as f:
+                for line in f:
+                    [my_chr, pos1, pos2] = line.strip().split('\t')[:3]
+                    if my_chr not in discard_regions:
+                        discard_regions[my_chr] = [-1]
+                    discard_regions[my_chr].extend([int(pos1), int(pos2)])
         except IOError:
             print("\nProblem reading discard BED file.\n")
-        finally:
-            f.close()
+            sys.exit(1)
 
     # parse input mutation rate rescaling regions, if present
     # TODO convert to pandas dataframe
@@ -374,8 +373,6 @@ def main(raw_args=None):
         except IOError:
             print("\nProblem reading mutational BED file.\n")
             sys.exit(1)
-        finally:
-            f.close()
 
     # initialize output files (part I)
     bam_header = None
@@ -391,14 +388,14 @@ def main(raw_args=None):
     if cancer:
         output_file_writer = OutputFileWriter(out_prefix + '_normal', paired=paired_end, bam_header=bam_header,
                                               vcf_header=vcf_header,
-                                              gzipped=gzipped_out, no_fastq=no_fastq, fasta_instead=fasta_instead)
+                                              no_fastq=no_fastq, fasta_instead=fasta_instead)
         output_file_writer_cancer = OutputFileWriter(out_prefix + '_tumor', paired=paired_end, bam_header=bam_header,
-                                                     vcf_header=vcf_header, gzipped=gzipped_out,
+                                                     vcf_header=vcf_header,
                                                      no_fastq=no_fastq, fasta_instead=fasta_instead)
     else:
         output_file_writer = OutputFileWriter(out_prefix, paired=paired_end, bam_header=bam_header,
                                               vcf_header=vcf_header,
-                                              gzipped=gzipped_out, no_fastq=no_fastq,
+                                              no_fastq=no_fastq,
                                               fasta_instead=fasta_instead)
     # Using pathlib to make this more machine agnostic
     out_prefix_name = pathlib.Path(out_prefix).name
@@ -548,6 +545,9 @@ def main(raw_args=None):
                     is_last_time = True
 
                 # print progress indicator
+                if debug:
+                    print(f'PROCESSING WINDOW: {(start, end), [buffer_added]}, '
+                          f'next: {(next_start, next_end)}, isLastTime: {is_last_time}')
                 current_progress += end - start
                 new_percent = int((current_progress * 100) / float(total_bp_span))
                 if new_percent > current_percent:
@@ -774,14 +774,14 @@ def main(raw_args=None):
                                                                             my_read_data[0][0],
                                                                             my_read_data[0][1], my_read_data[0][2],
                                                                             my_read_data[0][3],
-                                                                            sam_flag=flag1)
+                                                                            output_sam_flag=flag1)
                                     else:
                                         flag1 = sam_flag(['reverse'])
                                         output_file_writer.write_bam_record(my_ref_index, my_read_name,
                                                                             my_read_data[0][0],
                                                                             my_read_data[0][1], my_read_data[0][2],
                                                                             my_read_data[0][3],
-                                                                            sam_flag=flag1)
+                                                                            output_sam_flag=flag1)
                         # write PE output
                         elif len(my_read_data) == 2:
                             if no_fastq is not True:
@@ -801,12 +801,12 @@ def main(raw_args=None):
                                     output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
                                                                         my_read_data[0][1], my_read_data[0][2],
                                                                         my_read_data[0][3],
-                                                                        sam_flag=flag1,
+                                                                        output_sam_flag=flag1,
                                                                         mate_pos=my_read_data[1][0])
                                     output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[1][0],
                                                                         my_read_data[1][1], my_read_data[1][2],
                                                                         my_read_data[1][3],
-                                                                        sam_flag=flag2, mate_pos=my_read_data[0][0])
+                                                                        output_sam_flag=flag2, mate_pos=my_read_data[0][0])
                                 elif is_unmapped[0] is False and is_unmapped[1] is True:
                                     if is_forward:
                                         flag1 = sam_flag(['paired', 'first', 'mate_unmapped', 'mate_reverse'])
@@ -817,11 +817,11 @@ def main(raw_args=None):
                                     output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
                                                                         my_read_data[0][1], my_read_data[0][2],
                                                                         my_read_data[0][3],
-                                                                        sam_flag=flag1, mate_pos=my_read_data[0][0])
+                                                                        output_sam_flag=flag1, mate_pos=my_read_data[0][0])
                                     output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[0][0],
                                                                         my_read_data[1][1], my_read_data[1][2],
                                                                         my_read_data[1][3],
-                                                                        sam_flag=flag2, mate_pos=my_read_data[0][0],
+                                                                        output_sam_flag=flag2, mate_pos=my_read_data[0][0],
                                                                         aln_map_quality=0)
                                 elif is_unmapped[0] is True and is_unmapped[1] is False:
                                     if is_forward:
@@ -833,12 +833,12 @@ def main(raw_args=None):
                                     output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[1][0],
                                                                         my_read_data[0][1], my_read_data[0][2],
                                                                         my_read_data[0][3],
-                                                                        sam_flag=flag1, mate_pos=my_read_data[1][0],
+                                                                        output_sam_flag=flag1, mate_pos=my_read_data[1][0],
                                                                         aln_map_quality=0)
                                     output_file_writer.write_bam_record(my_ref_index, my_read_name, my_read_data[1][0],
                                                                         my_read_data[1][1], my_read_data[1][2],
                                                                         my_read_data[1][3],
-                                                                        sam_flag=flag2, mate_pos=my_read_data[1][0])
+                                                                        output_sam_flag=flag2, mate_pos=my_read_data[1][0])
                         else:
                             print('\nError: Unexpected number of reads generated...\n')
                             sys.exit(1)
@@ -885,11 +885,11 @@ def main(raw_args=None):
         print('writing unmapped reads to bam file...')
         for umr in unmapped_records:
             if paired_end:
-                output_file_writer.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], sam_flag=umr[2],
+                output_file_writer.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], output_sam_flag=umr[2],
                                                     mate_pos=0,
                                                     aln_map_quality=0)
             else:
-                output_file_writer.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], sam_flag=umr[2],
+                output_file_writer.write_bam_record(-1, umr[0], 0, umr[1][1], umr[1][2], umr[1][3], output_sam_flag=umr[2],
                                                     aln_map_quality=0)
 
     # close output files
diff --git a/merge_jobs.py b/merge_jobs.py
deleted file mode 100755
index 7b98acb..0000000
--- a/merge_jobs.py
+++ /dev/null
@@ -1,133 +0,0 @@
-#!/usr/bin/env source
-import os
-import argparse
-
-
-def getListOfFiles(inDir, pattern):
-	return [inDir + n for n in os.listdir(inDir) if (pattern in n and os.path.getsize(inDir + n))]
-
-
-TEMP_IND = 0
-
-
-def stripVCF_header(fn):
-
-	global TEMP_IND
-	f = open(fn,'r')
-	ftn = fn+'_temp'+str(TEMP_IND)
-	f_t = open(ftn,'w')
-	hasHeader = False
-	for line in f:
-		if line[0] == '#':
-			if not hasHeader:
-				TEMP_IND += 1
-			hasHeader = True
-		elif hasHeader:
-			f_t.write(line)
-		else:
-			break
-	f_t.close()
-	f.close()
-	if hasHeader:
-		return ftn
-	else:
-		os.system('rm '+ftn)
-		return fn
-
-def catListOfFiles(l,outName,gzipped=False):
-	for n in l:
-		if n[-3:] == '.gz' or n[-5:] == '.gzip':
-			gzipped = True
-	if gzipped:
-		for n in l:
-			if not n[-3:] == '.gz' and not n[-5:] == '.gzip':
-				print(
-					'\nError: Found a mixture of compressed and decompressed files with the specified prefix. Abandoning ship...\n')
-				for m in l:
-					print(m)
-				print('')
-				exit(1)
-		cmd = 'cat '+' '.join(sorted(l))+' > '+outName+'.gz'
-	else:
-		cmd = 'cat '+' '.join(sorted(l))+' > '+outName
-	print(cmd)
-	os.system(cmd)
-
-def catBams(l,outName,samtools_exe):
-	l_sort = sorted(l)
-	tmp = outName+'.tempHeader.sam'
-	os.system(samtools_exe+' view -H '+l_sort[0]+' > '+tmp)
-	cmd = samtools_exe+' cat -h '+tmp+' '+' '.join(l_sort)+' | '+samtools_exe+' sort - > '+outName
-	print(cmd)
-	os.system(cmd)
-	cmd = samtools_exe+' index '+outName
-	print(cmd)
-	os.system(cmd)
-	os.system('rm '+tmp)
-
-
-#####################################
-#				main()				#
-#####################################
-
-def main():
-	parser = argparse.ArgumentParser(description='merge_jobs.source')
-	parser.add_argument('-i', type=str, required=True, metavar='<str>', nargs='+', help="* input prefix: [prefix_1] [prefix_2] ...")
-	parser.add_argument('-o', type=str, required=True, metavar='<str>',            help="* output prefix")
-	parser.add_argument('-s', type=str, required=True, metavar='<str>',            help="* /path/to/samtools")
-	parser.add_argument('--no-job',     required=False, action='store_true',       help='files do not have .job suffix', default=False)
-
-	args = parser.parse_args()
-	(INP, OUP, SAMTOOLS, NO_JOB) = (args.i, args.o, args.s, args.no_job)
-
-	inDir = '/'.join(INP[0].split('/')[:-1])+'/'
-	if inDir == '/':
-		inDir = './'
-	#print inDir
-
-	INP_LIST = []
-	for n in INP:
-		if n[-1] == '/':
-			n = n[:-1]
-		INP_LIST.append(n.split('/')[-1])
-	listing_r1 = []
-	listing_r2 = []
-	listing_b  = []
-	listing_v  = []
-	pat_r1 = '_read1.fq'   + (NO_JOB == False)*'.job'
-	pat_r2 = '_read2.fq'   + (NO_JOB == False)*'.job'
-	pat_gb = '_golden.bam' + (NO_JOB == False)*'.job'
-	pat_gv = '_golden.vcf' + (NO_JOB == False)*'.job'
-	for n in INP_LIST:
-		listing_r1 += getListOfFiles(inDir,n+pat_r1)
-		listing_r2 += getListOfFiles(inDir,n+pat_r2)
-		listing_b  += getListOfFiles(inDir,n+pat_gb)
-		if len(listing_v):	# remove headers from vcf files that aren't the first being processed
-			initList   = getListOfFiles(inDir,n+pat_gv)
-			listing_v += [stripVCF_header(n) for n in initList]
-		else:
-			listing_v  += getListOfFiles(inDir,n+pat_gv)
-	
-	#
-	#	merge fq files
-	#
-	if len(listing_r1):
-		catListOfFiles(listing_r1,OUP+'_read1.fq')
-	if len(listing_r2):
-		catListOfFiles(listing_r2,OUP+'_read2.fq')
-
-	#
-	#	merge golden alignments, if present
-	#
-	if len(listing_b):
-		catBams(listing_b,OUP+'_golden.bam',SAMTOOLS)
-
-	#
-	#	merge golden vcfs, if present
-	#
-	if len(listing_v):
-		catListOfFiles(listing_v,OUP+'_golden.vcf')
-
-
-if __name__ == "__main__":
-	main()
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..0836577
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,6 @@
+numpy==1.19.5
+biopython==1.78
+matplotlib==3.3.4
+matplotlib_venn==0.11.6
+pandas==1.2.1
+pysam==0.16.0.1
\ No newline at end of file
diff --git a/source/SequenceContainer.py b/source/SequenceContainer.py
index 4690c40..fe9fc0d 100755
--- a/source/SequenceContainer.py
+++ b/source/SequenceContainer.py
@@ -11,6 +11,8 @@
 from source.neat_cigar import CigarString
 from source.probability import DiscreteDistribution, poisson_list
 
+# TODO This whole file is in desperate need of refactoring
+
 """
 Constants needed for analysis
 """
@@ -265,10 +267,15 @@ def init_coverage(self, coverage_data, frag_dist=None):
             avg_out = []
             self.coverage_distribution = []
             for i in range(len(self.sequences)):
-                max_coord = min([len(self.sequences[i]) - self.read_len, len(self.all_cigar[i]) - self.read_len])
+                # Zach implemented a change here but I can't remember if I changed it back for some reason.
+                # If second line below doesn't work, reactivate the first line.
+                # max_coord = min([len(self.sequences[i]) - self.read_len, len(self.all_cigar[i]) - self.read_len])
+                max_coord = min([len(self.sequences[i]) - self.read_len, len(self.all_cigar[i]) - 1])
+
                 # Trying to fix a problem wherein the above line gives a negative answer
                 if max_coord <= 0:
                     max_coord = min([len(self.sequences[i]), len(self.all_cigar[i])])
+
                 # compute gc-bias
                 j = 0
                 while j + self.window_size < len(self.sequences[i]):
@@ -279,7 +286,8 @@ def init_coverage(self, coverage_data, frag_dist=None):
                 gc_c = self.sequences[i][-self.window_size:].count('G') + \
                        self.sequences[i][-self.window_size:].count('C')
                 gc_cov_vals[i].extend([gc_scalars[gc_c]] * (len(self.sequences[i]) - len(gc_cov_vals[i])))
-                #
+
+                # Targeted values
                 tr_cov_vals[i].append(target_cov_vals[0])
                 prev_val = self.fm_pos[i][0]
                 for j in range(1, max_coord):
@@ -291,6 +299,8 @@ def init_coverage(self, coverage_data, frag_dist=None):
                         tr_cov_vals[i].append(sum(target_cov_vals[self.fm_pos[i][j]:self.fm_span[i][j]]) / float(
                             self.fm_span[i][j] - self.fm_pos[i][j]))
                         prev_val = self.fm_pos[i][j]
+                    # Debug statement
+                    # print(f'({i, j}), {self.all_cigar[i][j]}, {self.fm_pos[i][j]}, {self.fm_span[i][j]}')
 
                 # shift by half of read length
                 if len(tr_cov_vals[i]) > int(self.read_len / 2.):
@@ -305,9 +315,16 @@ def init_coverage(self, coverage_data, frag_dist=None):
                 # TODO if max_coord is <=0, this is a problem
                 for j in range(0, max_coord):
                     coverage_vals.append(coverage_vector[j + self.read_len] - coverage_vector[j])
-                avg_out.append(np.mean(coverage_vals) / float(self.read_len))
+                # Below is Zach's attempt to fix this. The commented out line is the original
+                # avg_out.append(np.mean(coverage_vals) / float(self.read_len))
+                avg_out.append(np.mean(coverage_vals)/float(min([self.read_len, max_coord])))
+                # Debug statement
+                # print(f'{avg_out}, {np.mean(avg_out)}')
 
                 if frag_dist is None:
+                    # Debug statement
+                    # print(f'++++, {max_coord}, {len(self.sequences[i])}, '
+                    #       f'{len(self.all_cigar[i])}, {len(coverage_vals)}')
                     self.coverage_distribution.append(DiscreteDistribution(coverage_vals, range(len(coverage_vals))))
 
                 # fragment length nightmare
@@ -350,16 +367,18 @@ def init_coverage(self, coverage_data, frag_dist=None):
                                     j + flv - self.read_len])
 
                         # EXPERIMENTAL
-                        # quantized_covVals = quantize_list(coverage_vals)
-                        # self.coverage_distribution[i][flv] = DiscreteDistribution([n[2] for n in quantized_covVals],[(n[0],n[1]) for n in quantized_covVals])
+                        # quantized_cov_vals = quantize_list(coverage_vals)
+                        # self.coverage_distribution[i][flv] = \
+                        #     DiscreteDistribution([n[2] for n in quantized_cov_vals],
+                        #                          [(n[0], n[1]) for n in quantized_cov_vals])
 
                         # TESTING
                         # import matplotlib.pyplot as mpl
-                        # print len(coverage_vals),'-->',len(quantized_covVals)
+                        # print len(coverage_vals),'-->',len(quantized_cov_vals)
                         # mpl.figure(0)
                         # mpl.plot(range(len(coverage_vals)), coverage_vals)
-                        # for qcv in quantized_covVals:
-                        #	mpl.plot([qcv[0], qcv[1]+1], [qcv[2],qcv[2]], 'r')
+                        # for qcv in quantized_cov_vals:
+                        # mpl.plot([qcv[0], qcv[1]+1], [qcv[2],qcv[2]], 'r')
                         # mpl.show()
                         # sys.exit(1)
 
@@ -432,7 +451,9 @@ def insert_mutations(self, input_list):
                 p = which_ploid[i]
                 my_alt = input_variable[2][which_alt[i]]
                 my_var = (input_variable[0] - self.x, input_variable[1], my_alt)
-                in_len = max([len(input_variable[1]), len(my_alt)])
+                # This is a potential fix implemented by Zach in a previous commit. He left the next line in.
+                # in_len = max([len(input_variable[1]), len(my_alt)])
+                in_len = len(input_variable[1])
 
                 if my_var[0] < 0 or my_var[0] >= len(self.black_list[p]):
                     print('\nError: Attempting to insert variant out of window bounds:')
@@ -445,7 +466,7 @@ def insert_mutations(self, input_list):
                     self.black_list[p][my_var[0]] = 2
                 else:
                     indel_failed = False
-                    for k in range(my_var[0], my_var[0] + in_len + 1):
+                    for k in range(my_var[0], my_var[0] + in_len):
                         if k >= len(self.black_list[p]):
                             indel_failed = True
                             continue
@@ -454,7 +475,7 @@ def insert_mutations(self, input_list):
                             continue
                     if indel_failed:
                         continue
-                    for k in range(my_var[0], my_var[0] + in_len + 1):
+                    for k in range(my_var[0], my_var[0] + in_len):
                         self.black_list[p][k] = 1
                     self.indel_list[p].append(my_var)
 
@@ -827,14 +848,15 @@ def sample_read(self, sequencing_model, frag_len=None):
         return read_out
 
 
-class SequencingError:
+class ReadContainer:
     """
-    Container to model sequencing errors: computes quality scores and positions to insert errors
+    Container for read data: computes quality scores and positions to insert errors
     """
 
-    def __init__(self, read_len, error_model, rescaled_error):
+    def __init__(self, read_len, error_model, rescaled_error, rescale_qual=False):
 
         self.read_len = read_len
+        self.rescale_qual = rescale_qual
 
         model_path = pathlib.Path(error_model)
         try:
@@ -849,8 +871,8 @@ def __init__(self, read_len, error_model, rescaled_error):
         if len(error_dat) == 4:
             self.uniform = True
             [q_scores, off_q, avg_error, error_params] = error_dat
-            self.uniform_q_score = int(-10. * np.log10(avg_error) + 0.5)
-            print('Using uniform sequencing error model. (q=' + str(self.uniform_q_score) + '+' + str(
+            self.uniform_q_score = min([max(q_scores), int(-10. * np.log10(avg_error) + 0.5)])
+            print('Reading in uniform sequencing error model... (q=' + str(self.uniform_q_score) + '+' + str(
                 off_q) + ', p(err)={0:0.2f}%)'.format(100. * avg_error))
 
         # only 1 q-score model present, use same model for both strands
@@ -888,8 +910,16 @@ def __init__(self, read_len, error_model, rescaled_error):
             self.error_scale = 1.0
         else:
             self.error_scale = rescaled_error / avg_error
-            print('Warning: Quality scores no longer exactly representative of error probability. '
-                  'Error model scaled by {0:.3f} to match desired rate...'.format(self.error_scale))
+            if not self.rescale_qual:
+                print('Warning: Quality scores no longer exactly representative of error probability. '
+                      'Error model scaled by {0:.3f} to match desired rate...'.format(self.error_scale))
+            if self.uniform:
+                if rescaled_error <= 0.:
+                    self.uniform_q_score = max(q_scores)
+                else:
+                    self.uniform_q_score = min([max(q_scores), int(-10. * np.log10(rescaled_error) + 0.5)])
+                print(' - Uniform quality score scaled to match specified error rate (q=' + str(
+                    self.uniform_qscore) + '+' + str(self.off_q) + ', p(err)={0:0.2f}%)'.format(100. * rescaled_error))
 
         if not self.uniform:
             # adjust length to match desired read length
@@ -970,7 +1000,12 @@ def get_sequencing_errors(self, read_data, is_reverse_strand=False):
                 if random.random() < self.error_scale * self.q_err_rate[q_out[i]]:
                     s_err.append(i)
 
-            q_out = ''.join([chr(n + self.off_q) for n in q_out])
+            if self.rescale_qual:  # do we want to rescale qual scores to match rescaled error?
+                q_out = [max([0, int(-10. * np.log10(self.error_scale * self.q_err_rate[n]) + 0.5)]) for n in q_out]
+                q_out = [min([int(self.q_err_rate[-1]), n]) for n in q_out]
+                q_out = ''.join([chr(n + self.off_q) for n in q_out])
+            else:
+                q_out = ''.join([chr(n + self.off_q) for n in q_out])
 
         if self.error_scale == 0.0:
             return q_out, []
@@ -1046,21 +1081,21 @@ def parse_input_mutation_model(model=None, which_default=1):
             ins_count = sum([ins_list[k] for k in ins_list.keys() if k >= 1])
             del_count = sum([ins_list[k] for k in ins_list.keys() if k <= -1])
             ins_vals = [k for k in sorted(ins_list.keys()) if k >= 1]
-            ins_wght = [ins_list[k] / float(ins_count) for k in ins_vals]
+            ins_weight = [ins_list[k] / float(ins_count) for k in ins_vals]
             del_vals = [k for k in sorted([abs(k) for k in ins_list.keys() if k <= -1])]
-            del_wght = [ins_list[-k] / float(del_count) for k in del_vals]
+            del_weight = [ins_list[-k] / float(del_count) for k in del_vals]
         else:  # degenerate case where no indel stats are provided
             ins_count = 1
             del_count = 1
             ins_vals = [1]
-            ins_wght = [1.0]
+            ins_weight = [1.0]
             del_vals = [1]
-            del_wght = [1.0]
+            del_weight = [1.0]
         out_model[3] = ins_count / float(ins_count + del_count)
         out_model[4] = ins_vals
-        out_model[5] = ins_wght
+        out_model[5] = ins_weight
         out_model[6] = del_vals
-        out_model[7] = del_wght
+        out_model[7] = del_weight
 
         trinuc_trans_prob = pickle_dict['TRINUC_TRANS_PROBS']
         for k in sorted(trinuc_trans_prob.keys()):
diff --git a/source/output_file_writer.py b/source/output_file_writer.py
index 69c6c04..87e1347 100755
--- a/source/output_file_writer.py
+++ b/source/output_file_writer.py
@@ -1,6 +1,5 @@
 from struct import pack
-import gzip
-from Bio.bgzf import *
+import Bio.bgzf as bgzf
 import pathlib
 import re
 
@@ -92,11 +91,11 @@ def sam_flag(string_list: list) -> int:
 
 # TODO find a better way to write output files
 class OutputFileWriter:
-    def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, gzipped=False,
+    def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None,
                  no_fastq=False, fasta_instead=False):
 
         self.fasta_instead = fasta_instead
-        # TODO Eliminate paired end as an option for fastas
+        # TODO Eliminate paired end as an option for fastas. Plan is to create a write fasta method.
         if self.fasta_instead:
             fq1 = pathlib.Path(out_prefix + '.fasta.gz')
             fq2 = None
@@ -109,25 +108,16 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
         # TODO Make a fasta-specific method
         self.no_fastq = no_fastq
         if not self.no_fastq:
-            if gzipped:
-                self.fq1_file = gzip.open(fq1.with_suffix(fq1.suffix + '.gz'), 'wb')
-            else:
-                self.fq1_file = open(fq1, 'w')
+            self.fq1_file = bgzf.open(fq1, 'w')
 
             self.fq2_file = None
             if paired:
-                if gzipped:
-                    self.fq2_file = gzip.open(fq2.with_suffix(fq2.suffix + '.gz'), 'wb')
-                else:
-                    self.fq2_file = open(fq2, 'w')
+                self.fq2_file = bgzf.open(fq2, 'w')
 
         # VCF OUTPUT
         self.vcf_file = None
         if vcf_header is not None:
-            if gzipped:
-                self.vcf_file = gzip.open(vcf.with_suffix(vcf.suffix + '.gz'), 'wb')
-            else:
-                self.vcf_file = open(vcf, 'wb')
+            self.vcf_file = bgzf.open(vcf, 'wb')
 
             # WRITE VCF HEADER
             self.vcf_file.write('##fileformat=VCFv4.1\n'.encode('utf-8'))
@@ -160,7 +150,7 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
         # BAM OUTPUT
         self.bam_file = None
         if bam_header is not None:
-            self.bam_file = BgzfWriter(bam, 'w', compresslevel=BAM_COMPRESSION_LEVEL)
+            self.bam_file = bgzf.BgzfWriter(bam, 'w', compresslevel=BAM_COMPRESSION_LEVEL)
 
             # WRITE BAM HEADER
             self.bam_file.write("BAM\1")
@@ -188,6 +178,7 @@ def __init__(self, out_prefix, paired=False, bam_header=None, vcf_header=None, g
     # TODO add write_fasta_record
 
     def write_fastq_record(self, read_name, read1, qual1, read2=None, qual2=None, orientation=None):
+        # Since read1 and read2 are Seq objects from Biopython, they have reverse_complement methods built-in
         (read1, quality1) = (read1, qual1)
         if read2 is not None and orientation is True:
             (read2, quality2) = (read2.reverse_complement(), qual2[::-1])
@@ -209,7 +200,8 @@ def write_vcf_record(self, chrom, pos, id_str, ref, alt, qual, filt, info):
             str(chrom) + '\t' + str(pos) + '\t' + str(id_str) + '\t' + str(ref) + '\t' + str(alt) + '\t' + str(
                 qual) + '\t' + str(filt) + '\t' + str(info) + '\n')
 
-    def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag, mate_pos=None, aln_map_quality=70):
+    def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, output_sam_flag,
+                         mate_pos=None, aln_map_quality=70):
 
         my_bin = reg2bin(pos_0, pos_0 + len(seq))
         # my_bin     = 0	# or just use a dummy value, does this actually matter?
@@ -225,10 +217,10 @@ def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag,
             my_t_len = 0
         else:
             next_pos = mate_pos
-            if pos_0 < next_pos:
-                my_t_len = next_pos + len(seq) - pos_0
+            if next_pos > pos_0:
+                my_t_len = next_pos - pos_0 + len(seq)
             else:
-                my_t_len = -pos_0 - len(seq) + next_pos
+                my_t_len = next_pos - pos_0 - len(seq)
 
         encoded_cig = bytearray()
         for i in range(cig_ops):
@@ -246,41 +238,45 @@ def write_bam_record(self, ref_id, read_name, pos_0, cigar, seq, qual, sam_flag,
         # apparently samtools automatically adds 33 to the quality score string...
         encoded_qual = ''.join([chr(ord(n) - 33) for n in qual])
 
-        # block_size = 4 +		# refID 		int32
-        #            4 +		# pos			int32
-        #            4 +		# bin_mq_nl		uint32
-        #            4 +		# flag_nc		uint32
-        #            4 +		# l_seq			int32
-        #            4 +		# next_ref_id	int32
-        #            4 +		# next_pos		int32
-        #            4 +		# tlen			int32
-        #            len(readName)+1 +
-        #            4*cig_ops +
-        #            encoded_len +
-        #            len(seq)
-
+        """
+        block_size = 4 +		# refID 		int32
+                     4 +		# pos			int32
+                     4 +		# bin_mq_nl		uint32
+                     4 +		# flag_nc		uint32
+                     4 +		# l_seq			int32
+                     4 +		# next_ref_id	int32
+                     4 +		# next_pos		int32
+                     4 +		# tlen			int32
+                     len(readName)+1 +
+                     4*cig_ops +
+                     encoded_len +
+                     len(seq)
+        """
         # block_size = 32 + len(readName)+1 + 4*cig_ops + encoded_len + len(seq)
         block_size = 32 + len(read_name) + 1 + len(encoded_cig) + len(encoded_seq) + len(encoded_qual)
 
-        ####self.bam_file.write(pack('<i',block_size))
-        ####self.bam_file.write(pack('<i',refID))
-        ####self.bam_file.write(pack('<i',pos_0))
-        ####self.bam_file.write(pack('<I',(my_bin<<16) + (my_map_quality<<8) + len(readName)+1))
-        ####self.bam_file.write(pack('<I',(samFlag<<16) + cig_ops))
-        ####self.bam_file.write(pack('<i',seq_len))
-        ####self.bam_file.write(pack('<i',next_ref_id))
-        ####self.bam_file.write(pack('<i',next_pos))
-        ####self.bam_file.write(pack('<i',my_tlen))
-        ####self.bam_file.write(readName+'\0')
-        ####self.bam_file.write(encoded_cig)
-        ####self.bam_file.write(encoded_seq)
-        ####self.bam_file.write(encoded_qual)
+        """
+        Not sure what the point of the following lines are
+        # self.bam_file.write(pack('<i',block_size))
+        # self.bam_file.write(pack('<i',refID))
+        # self.bam_file.write(pack('<i',pos_0))
+        # self.bam_file.write(pack('<I',(my_bin<<16) + (my_map_quality<<8) + len(readName)+1))
+        # self.bam_file.write(pack('<I',(samFlag<<16) + cig_ops))
+        # self.bam_file.write(pack('<i',seq_len))
+        # self.bam_file.write(pack('<i',next_ref_id))
+        # self.bam_file.write(pack('<i',next_pos))
+        # self.bam_file.write(pack('<i',my_tlen))
+        # self.bam_file.write(readName+'\0')
+        # self.bam_file.write(encoded_cig)
+        # self.bam_file.write(encoded_seq)
+        # self.bam_file.write(encoded_qual)
+        """
 
         # a horribly compressed line, I'm sorry.
         # (ref_index, position, data)
         self.bam_buffer.append((ref_id, pos_0, pack('<i', block_size) + pack('<i', ref_id) + pack('<i', pos_0) +
                                 pack('<I', (my_bin << 16) + (my_map_quality << 8) + len(read_name) + 1) +
-                                pack('<I', (sam_flag << 16) + cig_ops) + pack('<i', seq_len) + pack('<i', next_ref_id) +
+                                pack('<I', (output_sam_flag << 16) + cig_ops) + pack('<i', seq_len) + pack('<i', next_ref_id) +
                                 pack('<i', next_pos) + pack('<i', my_t_len) + read_name.encode('utf-8') +
                                 b'\0' + encoded_cig + encoded_seq + encoded_qual.encode('utf-8')))
 
@@ -308,7 +304,8 @@ def flush_buffers(self, bam_max=None, last_time=False):
                         else:
                             break
                     self.bam_file.write(b''.join([n[2] for n in bam_data[:ind_to_stop_at]]))
-                    ####print 'BAM WRITING:',ind_to_stop_at,'/',len(bam_data)
+                    # Debug statement
+                    # print(f'BAM WRITING: {ind_to_stop_at}/{len(bam_data)}')
                     if ind_to_stop_at >= len(bam_data):
                         self.bam_buffer = []
                     else:
diff --git a/source/vcf_func.py b/source/vcf_func.py
index 2a173d3..ba233b2 100755
--- a/source/vcf_func.py
+++ b/source/vcf_func.py
@@ -4,19 +4,21 @@
 import re
 import random
 
-
 def parse_line(vcf_line, col_dict, col_samp):
-    #	check if we want to proceed..
+    # these were in the original. Not sure the point other than debugging.
+    include_homs = False
+    include_fail = False
+
+    # check if we want to proceed...
     reference_allele = vcf_line[col_dict['REF']]
     alternate_allele = vcf_line[col_dict['ALT']]
     # enough columns?
     if len(vcf_line) != len(col_dict):
         return None
     # exclude homs / filtered?
-    # There was an attempt I think to make these optional that was never implemented.
-    if alternate_allele == '.' or alternate_allele == '' or alternate_allele == reference_allele:
+    if not include_homs and alternate_allele == '.' or alternate_allele == '' or alternate_allele == reference_allele:
         return None
-    if vcf_line[col_dict['FILTER']] != 'PASS' and vcf_line[col_dict['FILTER']] != '.':
+    if not include_fail and vcf_line[col_dict['FILTER']] != 'PASS' and vcf_line[col_dict['FILTER']] != '.':
         return None
 
     #	default vals
@@ -68,6 +70,10 @@ def parse_line(vcf_line, col_dict, col_samp):
 
 
 def parse_vcf(vcf_path, tumor_normal=False, ploidy=2):
+    # this var was in the orig. May have just been a debugging thing.
+    # I think this is trying to implement a check on GT
+    choose_random_ploid_if_no_gt_found = True
+
     tt = time.time()
     print('--------------------------------')
     print('reading input VCF...\n', flush=True)
@@ -87,7 +93,7 @@ def parse_vcf(vcf_path, tumor_normal=False, ploidy=2):
                 print('\n\nERROR: VCF has no header?\n' + vcf_path + '\n\n')
                 f.close()
                 exit(1)
-            splt = line[:-1].split('\t')
+            splt = line.strip().split('\t')
             pl_out = parse_line(splt, col_dict, col_samp)
             if pl_out is None:
                 n_skipped += 1
@@ -102,13 +108,18 @@ def parse_vcf(vcf_path, tumor_normal=False, ploidy=2):
                 # For some reason this had an additional "if True" inserted. I guess it was supposed to be an option
                 # the user could set but was never implemented.
                 if None in gt_eval:
-                    if not printed_warning:
-                        print('Warning: Found variants without a GT field, assuming heterozygous...')
-                        printed_warning = True
-                    for i in range(len(gt_eval)):
-                        tmp = ['0'] * ploidy
-                        tmp[random.randint(0, ploidy - 1)] = '1'
-                        gt_eval[i] = '/'.join(tmp)
+                    if choose_random_ploid_if_no_gt_found:
+                        if not printed_warning:
+                            print('Warning: Found variants without a GT field, assuming heterozygous...')
+                            printed_warning = True
+                        for i in range(len(gt_eval)):
+                            tmp = ['0'] * ploidy
+                            tmp[random.randint(0, ploidy - 1)] = '1'
+                            gt_eval[i] = '/'.join(tmp)
+                    else:
+                        # skip because no GT field was found
+                        n_skipped += 1
+                        continue
                 non_reference = False
                 for gtVal in gt_eval:
                     if gtVal is not None:
@@ -166,7 +177,8 @@ def parse_vcf(vcf_path, tumor_normal=False, ploidy=2):
         vars_out[r] = [list(all_vars[r][k]) for k in sorted(all_vars[r].keys())]
         # prune unnecessary sequence from ref/alt alleles
         for i in range(len(vars_out[r])):
-            while len(vars_out[r][i][1]) > 1 and all([n[-1] == vars_out[r][i][1][-1] for n in vars_out[r][i][2]]):
+            while len(vars_out[r][i][1]) > 1 and all([n[-1] == vars_out[r][i][1][-1] for n in vars_out[r][i][2]]) \
+                    and all([len(n) > 1 for n in vars_out[r][i][2]]):
                 vars_out[r][i][1] = vars_out[r][i][1][:-1]
                 vars_out[r][i][2] = [n[:-1] for n in vars_out[r][i][2]]
             vars_out[r][i] = tuple(vars_out[r][i])

From d11363d4c8f3708bdaff1e6d072474ad1940988b Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 5 Feb 2021 19:34:54 -0600
Subject: [PATCH 322/323] Update .gitignore

---
 .gitignore | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index bf24818..fa00a23 100755
--- a/.gitignore
+++ b/.gitignore
@@ -2,4 +2,4 @@
 *.pyc
 /python2env/
 /.ipynb_checkpoints/
-/.vscode/
\ No newline at end of file
+/.vscode/

From 231f78992cfdbe97cb6b4f385bac5309ebfe5a5e Mon Sep 17 00:00:00 2001
From: Joshua Allen <jallen17@illinois.edu>
Date: Fri, 5 Feb 2021 20:00:28 -0600
Subject: [PATCH 323/323] Update ChangeLog.md

---
 ChangeLog.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ChangeLog.md b/ChangeLog.md
index bc48ba7..6caaed0 100644
--- a/ChangeLog.md
+++ b/ChangeLog.md
@@ -1,4 +1,4 @@
-#NEAT v3.0
+# NEAT v3.0
 - NEAT gen_reads now runs in Python 3 exclusively. The previous, Python 2 version is stored in the repo as v2.0, but will not be undergoing active development.
 - Converted sequence objects to Biopython Sequence objects to take advantage of the Biopython library
 - Converted cigar strings to lists. Now there are simply a functions that convert a generic cigar string to a list and vice versa.
@@ -7,7 +7,7 @@
 - Added a basic bacterial wrapper that will simulate multiple generations of bacteria based on an input fasta, mutate them and then produce the fastqs, bams, and vcfs for the resultant bacterial population.
 
 
-##TODOs for v3.1
+## TODOs for v3.1
 NEAT is still undergoing active development with many exciting upgrades planned. We also plan to bring the code up to full production scale and will continue to improve the following features (if you would like to [Contribute](CONTRIBUTING.md))
 - Using Python's multithreading libraries, speed up NEAT's gen_reads tool significantly.
 - Take advantage of pandas library for reading in bed files and other files.