Initial conversion to Python 3

lib/audio_helpers.py

@@ -1,12 +1,12 @@
 import time
 import numpy
-from utils import note_name, percent_to_db
-from record import record
-from constants import CLIPPING_THRESHOLD, \
+from .utils import note_name, percent_to_db
+from .record import record
+from .constants import CLIPPING_THRESHOLD, \
     CLIPPING_CHECK_NOTE, \
     EXIT_ON_CLIPPING, \
     SAMPLE_RATE
-from midi_helpers import all_notes_off, CHANNEL_OFFSET
+from .midi_helpers import all_notes_off, CHANNEL_OFFSET
 
 
 def generate_sample(
@@ -46,7 +46,7 @@ def on_time_up():
 
 def sample_threshold_from_noise_floor(bit_depth, audio_interface_name):
     time.sleep(1)
-    print "Sampling noise floor..."
+    print("Sampling noise floor...")
     sample_width, data, release_time = record(
         limit=2.0,
         after_start=None,
@@ -60,9 +60,9 @@ def sample_threshold_from_noise_floor(bit_depth, audio_interface_name):
         numpy.amax(numpy.absolute(data)) /
         float(2 ** (bit_depth - 1))
     )
-    print "Noise floor has volume %8.8f dBFS" % percent_to_db(noise_floor)
+    print("Noise floor has volume %8.8f dBFS" % percent_to_db(noise_floor))
     threshold = noise_floor * 1.1
-    print "Setting threshold to %8.8f dBFS" % percent_to_db(threshold)
+    print("Setting threshold to %8.8f dBFS" % percent_to_db(threshold))
     return threshold
 
 
@@ -74,9 +74,9 @@ def check_for_clipping(
     audio_interface_name,
 ):
     time.sleep(1)
-    print "Checking for clipping and balance on note %s..." % (
+    print("Checking for clipping and balance on note %s..." % (
         note_name(CLIPPING_CHECK_NOTE)
-    )
+    ))
 
     sample_width, data, release_time = generate_sample(
         limit=2.0,
@@ -99,14 +99,14 @@ def check_for_clipping(
     )
 
     # All notes off, but like, a lot, again
-    for _ in xrange(0, 2):
+    for _ in range(0, 2):
         all_notes_off(midiout, midi_channel)
 
-    print "Maximum volume is around %8.8f dBFS" % percent_to_db(max_volume)
+    print("Maximum volume is around %8.8f dBFS" % percent_to_db(max_volume))
     if max_volume >= CLIPPING_THRESHOLD:
-        print "Clipping detected (%2.2f dBFS >= %2.2f dBFS) at max volume!" % (
+        print("Clipping detected (%2.2f dBFS >= %2.2f dBFS) at max volume!" % (
             percent_to_db(max_volume), percent_to_db(CLIPPING_THRESHOLD)
-        )
+        ))
         if EXIT_ON_CLIPPING:
             raise ValueError("Clipping detected at max volume!")
 

lib/compare.py

@@ -7,9 +7,9 @@
 from tqdm import tqdm
 from tabulate import tabulate
 
-from utils import normalized, trim_mono_data
-from audio_helpers import fundamental_frequency
-from wavio import read_wave_file
+from .utils import normalized, trim_mono_data
+from .audio_helpers import fundamental_frequency
+from .wavio import read_wave_file
 
 import matplotlib.pyplot as plt
 
@@ -106,21 +106,18 @@ def process_all(aifs):
     )
     results = sorted(
         results,
-        key=lambda (dl, dr,
-                    pl, pr,
-                    freqd,
-                    fa, fb): dl + dr + abs(freqd - 1))
+        key=lambda dl_dr_pl_pr_freqd_fa_fb: dl_dr_pl_pr_freqd_fa_fb[0] + dl_dr_pl_pr_freqd_fa_fb[1] + abs(dl_dr_pl_pr_freqd_fa_fb[4] - 1))
     with open('results.csv', 'wb') as f:
         writer = csv.writer(f)
         writer.writerows([headers])
         writer.writerows(results)
 
-    print "%d results" % len(results)
-    print tabulate(
+    print("%d results" % len(results))
+    print(tabulate(
         results,
         headers=headers,
         floatfmt='.4f'
-    )
+    ))
 
 
 def graph_ffts():
@@ -139,10 +136,9 @@ def graph_ffts():
         idx = numpy.argmax(numpy.abs(w))
         freq = freqs[idx]
         plt.plot(w)
-        print freq
-        print \
-            fundamental_frequency(normalized(list)), \
-            fundamental_frequency(normalized(left + right))
+        print(freq)
+        print(fundamental_frequency(normalized(list)), \
+            fundamental_frequency(normalized(left + right)))
     # plt.show()
 
 
@@ -189,11 +185,11 @@ def freq_shift():
     shifted_diffr = normalized_difference(*aligned_sublists(wavea[1],
                                                             waveb_shifted[1]))
 
-    print files
-    print 'diffs\t\t', diffl, diffr
-    print 'shifted diffs\t', shifted_diffl, shifted_diffr
-    print 'freqs', freqd
-    print 'shifted freqs', shifted_freqd
+    print(files)
+    print('diffs\t\t', diffl, diffr)
+    print('shifted diffs\t', shifted_diffl, shifted_diffr)
+    print('freqs', freqd)
+    print('shifted freqs', shifted_freqd)
 
 
 if __name__ == "__main__":

lib/deflac.py

@@ -5,11 +5,11 @@
 import argparse
 import subprocess
 from tqdm import tqdm
-from sfzparser import SFZFile
-from wavio import read_wave_file
-from utils import normalized
-from record import RATE, save_to_file
-from constants import bit_depth
+from .sfzparser import SFZFile
+from .wavio import read_wave_file
+from .utils import normalized
+from .record import RATE, save_to_file
+from .constants import bit_depth
 
 
 def full_path(sfzfile, filename):

lib/flacize.py

@@ -5,9 +5,9 @@
 import argparse
 import subprocess
 from tqdm import tqdm
-from sfzparser import SFZFile, Group
-from wavio import read_wave_file
-from utils import group_by_attr, note_name
+from .sfzparser import SFZFile, Group
+from .wavio import read_wave_file
+from .utils import group_by_attr, note_name
 
 
 def full_path(sfzfile, filename):
@@ -66,7 +66,7 @@ def flacize_after_sampling(
                       for key, key_regions in
                       group_by_attr(group.regions, [
                           'key', 'pitch_keycenter'
-                      ]).iteritems()], [])
+                      ]).items()], [])
         new_groups.append(Group(group.attributes, output))
 
     with open(sfzfile + '.flac.sfz', 'w') as file:
@@ -77,7 +77,7 @@ def flacize_after_sampling(
             try:
                 os.unlink(path)
             except OSError as e:
-                print "Could not unlink path: %s: %s" % (path, e)
+                print("Could not unlink path: %s: %s" % (path, e))
 
 
 ANTI_CLICK_OFFSET = 3
@@ -128,8 +128,8 @@ def concat_samples(regions, path, name=None):
                                               filename,
                                               note_name(key)))
                           for key, regions in
-                          tqdm(group_by_attr(group.regions,
-                                             'key').iteritems())], [])
-            print group.just_group()
+                          tqdm(iter(group_by_attr(group.regions,
+                                             'key').items()))], [])
+            print(group.just_group())
             for region in output:
-                print region
+                print(region)

lib/graph.py

@@ -7,7 +7,7 @@
 from tqdm import tqdm
 from tabulate import tabulate
 
-from wavio import read_wave_file
+from .wavio import read_wave_file
 
 import matplotlib.pyplot as plt
 

lib/group_velcurves.py

@@ -1,6 +1,6 @@
 import argparse
-from sfzparser import parse, Group
-from quantize import group_by_attr
+from .sfzparser import parse, Group
+from .quantize import group_by_attr
 
 parser = argparse.ArgumentParser(
     description='quantize and compress SFZ files'
@@ -18,14 +18,14 @@ def should_group_key(key):
 
 
 def group_by_pitch(regions):
-    for key, regions in group_by_attr(regions, 'key').iteritems():
+    for key, regions in group_by_attr(regions, 'key').items():
         # Group together all amp_velcurve_* and key params.
         yield Group(dict([
             (key, value)
             for region in regions
-            for key, value in region.attributes.iteritems()
+            for key, value in region.attributes.items()
             if should_group_key(key)
-        ] + DEFAULT_ATTRIBUTES.items()), [
+        ] + list(DEFAULT_ATTRIBUTES.items())), [
             region.without_attributes(should_group_key) for region in regions
         ])
 
@@ -41,4 +41,4 @@ def group_by_pitch(regions):
         groups = parse(open(filename).read())
         regions = sum([group.flattened_regions() for group in groups], [])
         for group in group_by_pitch(regions):
-            print group
+            print(group)

lib/loop.py

@@ -1,8 +1,8 @@
 import sys
 import numpy
 from tqdm import tqdm
-from truncate import read_wave_file
-from audio_helpers import fundamental_frequency
+from .truncate import read_wave_file
+from .audio_helpers import fundamental_frequency
 
 QUANTIZE_FACTOR = 8
 
@@ -12,20 +12,20 @@ def compare_windows(window_a, window_b):
 
 
 def slide_window(file, period, start_at=0, end_before=0):
-    for power in reversed(xrange(7, 10)):
+    for power in reversed(range(7, 10)):
         multiple = 2 ** power
         window_size = int(period * multiple)
         # Uncomment this to search from the start_at value to the end_before
         # rather than just through one window's length
         # end_range = len(file) - (window_size * 2) - end_before
         end_range = start_at + window_size
-        for i in xrange(start_at, end_range):
+        for i in range(start_at, end_range):
             yield power, i, window_size
 
 
 def window_match(file):
     period = (1.0 / fundamental_frequency(file, 1)) * 2
-    print period, 'period in samples'
+    print(period, 'period in samples')
 
     winner = None
 
@@ -48,14 +48,14 @@ def window_match(file):
                 i,
                 abs(file[i] - file[window_start])
             )
-            print 'new winner', winner
+            print('new winner', winner)
 
     lowest_difference, winning_window_size, winning_index, gap = winner
 
-    print "Best loop match:", lowest_difference
-    print "window size", winning_window_size
-    print "winning index", winning_index
-    print "winning gap", gap
+    print("Best loop match:", lowest_difference)
+    print("window size", winning_window_size)
+    print("winning index", winning_index)
+    print("winning gap", gap)
     return winning_index, winning_window_size
 
 
@@ -71,7 +71,7 @@ def find_similar_sample_index(
 ):
     reference_slope = slope_at_index(file, reference_index) > 0
     best_match = None
-    search_range = xrange(
+    search_range = range(
         search_around_index - search_size,
         search_around_index + search_size
     )
@@ -93,12 +93,12 @@ def find_similar_sample_index(
 
 def zero_crossing_match(file):
     period = (1.0 / fundamental_frequency(file, 1)) * 2
-    print period, 'period in samples'
+    print(period, 'period in samples')
 
     period_multiple = 64
     period = period * period_multiple
 
-    for i in reversed(xrange(2 * len(file) / 3, 5 * len(file) / 6)):
+    for i in reversed(range(2 * len(file) / 3, 5 * len(file) / 6)):
         if file[i] >= 0 and file[i + 1] < 0 and \
                 file[int(i + period)] >= 0 and \
                 file[int(i + 1 + period)] < 0 and \
@@ -234,7 +234,7 @@ def process(aif, sample_rate=48000):
 
     file = file[0]
 
-    print 'start, end', loop_start, loop_end
+    print('start, end', loop_start, loop_end)
 
     plt.plot(file[loop_start:loop_end])
     plt.plot(file[loop_end:loop_start + (2 * loop_size)])

lib/midi_helpers.py

@@ -49,7 +49,7 @@ def open_midi_port(midi_port_name):
 
 def set_program_number(midiout, midi_channel, program_number):
     if program_number is not None:
-        print "Sending program change to program %d..." % program_number
+        print("Sending program change to program %d..." % program_number)
         # Bank change (fine) to (program_number / 128)
         midiout.send_message([
             CC_CHANNEL_OFFSET + midi_channel,
@@ -64,7 +64,7 @@ def set_program_number(midiout, midi_channel, program_number):
         ])
 
     # All notes off, but like, a lot
-    for _ in xrange(0, 2):
+    for _ in range(0, 2):
         all_notes_off(midiout, midi_channel)
 
     time.sleep(0.5)