Merge pull request #29 from slaclab/apply_formatting_linting

Apply formatting with auto-formatter

calibrationSuite/Stats.py

@@ -1,45 +1,47 @@
 import numpy
 import logging
+
 logger = logging.getLogger(__name__)
 
+
 class Stats(object):
     def __init__(self, shape):
         self._n = 0
-        self._x = numpy.zeros(shape)##, dtype=double)
-        self._xx = numpy.zeros(shape)##, dtype=double)
-        self._xy = numpy.zeros(shape)##, dtype=double)
+        self._x = numpy.zeros(shape)  ##, dtype=double)
+        self._xx = numpy.zeros(shape)  ##, dtype=double)
+        self._xy = numpy.zeros(shape)  ##, dtype=double)
 
     def mean(self):
-        return self._x/self._n
+        return self._x / self._n
 
     def rms(self):
-        return ((self._xx/self._n - (self._x/self._n)**2)).clip(0)**0.5
+        return ((self._xx / self._n - (self._x / self._n) ** 2)).clip(0) ** 0.5
 
     def corr(self, yMean, ySigma):
-##        return (self._xy -self._x*yMean)/self._n
-        if ySigma >0:
+        ##        return (self._xy -self._x*yMean)/self._n
+        if ySigma > 0:
             rmsPosDef = self.rms().clip(0.000001, self.rms().max())
-            return numpy.double((self._xy -self._x*yMean)/(self._n*ySigma*rmsPosDef))
+            return numpy.double((self._xy - self._x * yMean) / (self._n * ySigma * rmsPosDef))
         else:
             return None
 
     def accumulate(self, x, y=0):
         self._n += 1
         self._x += x
-        self._xx += x*x
-        self._xy += x*y
+        self._xx += x * x
+        self._xy += x * y
+
 
-
-if __name__ == '__main__':
+if __name__ == "__main__":
     a = numpy.zeros([10])
     s = Stats(a.shape)
     for i in range(10000):
-        d = numpy.sin((numpy.array(list(range(10)))+i)/3.14159)
+        d = numpy.sin((numpy.array(list(range(10))) + i) / 3.14159)
         s.accumulate(d, d[7])
 
     print("mean: " + str(s.mean()))
     print("rms: " + str(s.rms()))
     print("s.corr(s.mean()[7], s.rms()[7]): " + str(s.corr(s.mean()[7], s.rms()[7])))
     logger.info("mean: " + str(s.mean()))
     logger.info("rms: " + str(s.rms()))
-    logger.info("s.corr(s.mean()[7], s.rms()[7]): " + str(s.corr(s.mean()[7], s.rms()[7])))
+    logger.info("s.corr(s.mean()[7], s.rms()[7]): " + str(s.corr(s.mean()[7], s.rms()[7])))

calibrationSuite/ancillaryMethods.py

@@ -1,54 +1,59 @@
 import numpy as np
 from scipy.stats import binned_statistic
 import logging
+
 logger = logging.getLogger(__name__)
 
+
 def makeProfile(x, y, bins, range=None, spread=False):
     ## NaN for empty bins are suppressed
     ## using mean root(N) for non-empty bins to calculate 0 var weights
     ##
     ## spread=True to return standard deviation instead of standard error
 
-    meansObj = binned_statistic(x, [y, y**2], bins=bins, range=range, statistic='mean')
+    meansObj = binned_statistic(x, [y, y**2], bins=bins, range=range, statistic="mean")
     means, means2 = meansObj.statistic
-    countsObj = binned_statistic(x, y, bins=bins, range=range, statistic='count')
-    stdObj = binned_statistic(x, y, bins=bins, range=range, statistic='std')
+    countsObj = binned_statistic(x, y, bins=bins, range=range, statistic="count")
+    stdObj = binned_statistic(x, y, bins=bins, range=range, statistic="std")
     bin_N = countsObj.statistic
-    usefulBins = np.bitwise_and(bin_N>0, ~np.isnan(means))
-    if bin_N.sum()==0:
+    usefulBins = np.bitwise_and(bin_N > 0, ~np.isnan(means))
+    if bin_N.sum() == 0:
         ##no data
         print("no data in profile")
         logger.error("no data in profile")
         return None, None, None
-    
+
     ##yErr = np.sqrt(means2 - means**2)
     yErr = stdObj.statistic
     if not spread:
         root_N = np.sqrt(bin_N)
-        root_N[root_N==0] = root_N[usefulBins].mean()
-        yErr = yErr/root_N
+        root_N[root_N == 0] = root_N[usefulBins].mean()
+        yErr = yErr / root_N
         ##yErr = yErr.clip(0, 6666666.)
 
     bin_edges = meansObj.bin_edges
-    bin_centers = (bin_edges[:-1] + bin_edges[1:])/2.
+    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2.0
     return bin_centers[usefulBins], means[usefulBins], yErr[usefulBins]
 
+
 def plotProfile(x, y, yErr):
-    plt.errorbar(x=x, y=y, yerr=yErr, linestyle='none', marker='.')
+    plt.errorbar(x=x, y=y, yerr=yErr, linestyle="none", marker=".")
+
 
 def selectedClusters(clusters, row, col, lowEnerygCut, highEnergyCut, nPixelCut=4, isSquare=1):
     pass
 
+
 def goodClusters(clusters, row, col, nPixelCut=4, isSquare=None):
     ##print(clusters)
-    pixelRowCol = np.bitwise_and((clusters[:,:,1] == row),
-                                 (clusters[:,:,2] == col))
+    pixelRowCol = np.bitwise_and((clusters[:, :, 1] == row), (clusters[:, :, 2] == col))
     if isSquare is None:
-        small = clusters[:,:,3]<nPixelCut
+        small = clusters[:, :, 3] < nPixelCut
     else:
-        small = np.bitwise_and((clusters[:,:,3]<nPixelCut), (clusters[:,:,4]==isSquare))
+        small = np.bitwise_and((clusters[:, :, 3] < nPixelCut), (clusters[:, :, 4] == isSquare))
     return clusters[np.bitwise_and(small, pixelRowCol)]
 
+
 def getClusterEnergies(clusters):
     ##print(clusters)
     return clusters[:, 0]

calibrationSuite/argumentParser.py

@@ -1,31 +1,47 @@
 import argparse
 
+
 class ArgumentParser:
     def __init__(self):
         self.parser = argparse.ArgumentParser(
-            description='Configures calibration suite, overriding experimentHash',
-            formatter_class=argparse.ArgumentDefaultsHelpFormatter
+            description="Configures calibration suite, overriding experimentHash",
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+        )
+        self.parser.add_argument("-e", "--exp", help="experiment")
+        self.parser.add_argument("-l", "--location", help="hutch location, e.g. MfxEndstation or DetLab")
+        self.parser.add_argument("-r", "--run", type=int, help="run")
+        self.parser.add_argument("-R", "--runRange", help="run range, format ...")
+        self.parser.add_argument("--fivePedestalRun", type=int, help="5 pedestal run")
+        self.parser.add_argument("--fakePedestal", type=str, help="fake pedestal file")
+        self.parser.add_argument("-c", "--camera", type=int, help="camera.n")
+        self.parser.add_argument("-p", "--path", type=str, help="the base path to the output directory")
+        self.parser.add_argument("-n", "--nModules", type=int, help="nModules")
+        self.parser.add_argument(
+            "-d", "--detType", type=str, default="", help="Epix100, Epix10ka, Epix10kaQuad, Epix10ka2M, ..."
+        )
+        self.parser.add_argument("--maxNevents", type=int, default="666666", help="max number of events to analyze")
+        self.parser.add_argument(
+            "--skipNevents", type=int, default=0, help="max number of events to skip at the start of each step"
+        )
+        self.parser.add_argument(
+            "--configScript",
+            type=str,
+            default="experimentSuiteConfig.py",
+            help="name of python config file to load if any",
         )
-        self.parser.add_argument('-e', '--exp', help='experiment')
-        self.parser.add_argument('-l', '--location', help='hutch location, e.g. MfxEndstation or DetLab')
-        self.parser.add_argument('-r', '--run', type=int, help='run')
-        self.parser.add_argument('-R', '--runRange', help='run range, format ...')
-        self.parser.add_argument('--fivePedestalRun', type=int, help='5 pedestal run')
-        self.parser.add_argument('--fakePedestal', type=str, help='fake pedestal file')
-        self.parser.add_argument('-c', '--camera', type=int, help='camera.n')
-        self.parser.add_argument('-p', '--path', type=str, help='the base path to the output directory')
-        self.parser.add_argument('-n', '--nModules', type=int, help='nModules')
-        self.parser.add_argument('-d', '--detType', type=str, default='', help='Epix100, Epix10ka, Epix10kaQuad, Epix10ka2M, ...')
-        self.parser.add_argument('--maxNevents', type=int, default='666666', help='max number of events to analyze')
-        self.parser.add_argument('--skipNevents', type=int, default=0, help='max number of events to skip at the start of each step')
-        self.parser.add_argument('--configScript', type=str, default='experimentSuiteConfig.py', help='name of python config file to load if any')
-        self.parser.add_argument('--detObj', help='"raw", "calib", "image"')
-        self.parser.add_argument('-f','--files', type=str, default=None, help='run analysis on file or comma-separated files')
-        self.parser.add_argument('-cf', '--configFile', type=str, help='config file path, can be relative or full path')
-        self.parser.add_argument('-L','--label', type=str, help='analysis label')
-        self.parser.add_argument('-t', '--threshold', help="threshold (ADU or keV or wave8) depending on --detObj")
-        self.parser.add_argument('--fluxCut', type=float, help="minimum flux to be included in analysis")
-        self.parser.add_argument('--special', type=str, help='comma-separated list of special behaviors - maybe this is too lazy.  E.g. positiveParity,doKazEvents,...')
-
+        self.parser.add_argument("--detObj", help='"raw", "calib", "image"')
+        self.parser.add_argument(
+            "-f", "--files", type=str, default=None, help="run analysis on file or comma-separated files"
+        )
+        self.parser.add_argument("-cf", "--configFile", type=str, help="config file path, can be relative or full path")
+        self.parser.add_argument("-L", "--label", type=str, help="analysis label")
+        self.parser.add_argument("-t", "--threshold", help="threshold (ADU or keV or wave8) depending on --detObj")
+        self.parser.add_argument("--fluxCut", type=float, help="minimum flux to be included in analysis")
+        self.parser.add_argument(
+            "--special",
+            type=str,
+            help="comma-separated list of special behaviors - maybe this is too lazy.  E.g. positiveParity,doKazEvents,...",
+        )
+
     def parse_args(self):
-        return self.parser.parse_args()
+        return self.parser.parse_args()