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reBin: removed unnecessary whitespace
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@ibressler
ibressler committed Mar 11, 2021
1 parent 1c8244c commit 65e622b
Showing 1 changed file with 42 additions and 43 deletions.
85 changes: 42 additions & 43 deletions reBin.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,8 @@
"""
Overview
========
1D rebinning.
Should take input file, read, rebin and write.
1D rebinning.
Should take input file, read, rebin and write.
Rebins to log bins.
"""
Expand All @@ -23,7 +23,7 @@

def argparser():
parser = argparse.ArgumentParser(description = """
Re-binning function, reads three-column ASCII input files,
Re-binning function, reads three-column ASCII input files,
and outputs re-binned three-column ASCII files""")
# binning options
parser.add_argument("-n", "--numBins", type = int, default = 50,
Expand All @@ -34,29 +34,29 @@ def argparser():
help = "Minimum Q to clip from original data")
parser.add_argument("-e", "--minE", type = float, default = 0.01,
help = "Minimum error is at least this times intensity value.")
parser.add_argument("-s", "--scaling", type = str,
parser.add_argument("-s", "--scaling", type = str,
action = "store", default = 'logarithmic',
help = "q-axis scaling for binning, can be linear or logarithmic")
# csv / datafile options
parser.add_argument("-d", "--delimiter", type = str,
parser.add_argument("-d", "--delimiter", type = str,
action = "store", default = ',',
help = "Delimiter in original file. '\\t' is tab. (with quotes)")
parser.add_argument("-H", "--headerLines", type = int, default = 0,
help = "Number of header lines to skip")
parser.add_argument("-D", "--outputDelimiter", type = str,
parser.add_argument("-D", "--outputDelimiter", type = str,
action = "store", default = None,
help = "Delimiter in final file (defaults to input delimiter)")
parser.add_argument("-c", "--cleanEmpty", action = "store_true",
parser.add_argument("-c", "--cleanEmpty", action = "store_true",
default = True,
help = "Removes empty bins before writing")
parser.add_argument("-i", "--iScale", type = float, default = 1.,
help = "Intensity (and error) scaled by this factor on output.")
# program options
parser.add_argument("-v", "--verbose", action = "store_true",
help = "Be verbose about the steps")
parser.add_argument("-t", "--test", action = "store_true",
parser.add_argument("-t", "--test", action = "store_true",
help = "Do not save output files, test run only")
parser.add_argument("-N", "--noBin", action = "store_true",
parser.add_argument("-N", "--noBin", action = "store_true",
help = "Do not bin, just input -> output (for translation and scaling)")
parser.add_argument("fnames", nargs = "*", metavar = "FILENAME",
action = "store",
Expand All @@ -70,7 +70,7 @@ def argparser():

class reBin(object):
""" all kinds of binning-related functions """

# set defaults for file reading:
pandasArgs = {
"skipinitialspace": True,
Expand All @@ -79,17 +79,17 @@ class reBin(object):
"header": None}
# set defaults for kwargs, in case this is not called from command line:
reBinArgs = {
"delimiter" : ';',
"outputDelimiter" : ';',
"headerLines" : 0,
"fnames" : '',
"verbose" : False,
"qMin" : -np.inf,
"qMax" : np.inf,
"numBins" : 100,
"scaling" : 'logarithmic',
"cleanEmpty" : False,
"minE" : 0.01,
"delimiter" : ';',
"outputDelimiter" : ';',
"headerLines" : 0,
"fnames" : '',
"verbose" : False,
"qMin" : -np.inf,
"qMax" : np.inf,
"numBins" : 100,
"scaling" : 'logarithmic',
"cleanEmpty" : False,
"minE" : 0.01,
"noBin" : False
}

Expand Down Expand Up @@ -118,7 +118,7 @@ def __init__(self, **kwargs):

self.pandasArgs.update({
"delimiter": self.delimiter,
"skiprows": self.headerLines})
"skiprows": self.headerLines})
# process files individually:
for filename in self.fnames:
self.readFile(filename)
Expand Down Expand Up @@ -158,7 +158,7 @@ def outputFilename(self, filename):
if self.verbose:
print('output filename: {}'.format(ofname))
return ofname

def readFile(self, filename):
if self.verbose:
print('reading file: {} with settings: {}'
Expand All @@ -174,17 +174,17 @@ def readFile(self, filename):
numChanged = (self.minE * self.I > dval[:,2]).sum()
if self.verbose:
print('Minimum uncertainty set for {} out of {} ({} %) datapoints'
.format(numChanged, size(self.Q),
.format(numChanged, size(self.Q),
100. * numChanged / size(self.Q) ))

# writer modified from imp2/modules/Write1D
def writeFile(self, ofname, hstrs = None, append = False):
sep = self.outputDelimiter
sep = self.outputDelimiter
#scale if necessary
iterData = itertools.zip_longest(
self.QBin,
self.IBin * float(self.iScale),
self.EBin * float(self.iScale))
self.QBin,
self.IBin * float(self.iScale),
self.EBin * float(self.iScale))

def writeLine(filename, line = None, append = True):
if append:
Expand Down Expand Up @@ -227,11 +227,10 @@ def validate(self):
# appy integration limits:
iind = np.array((
(self.Q < self.qMin) + (self.Q > self.qMax) ),dtype=bool)
mask[iind] = True

mask[iind] = True

# define binning limits
(qmin, qmax) = (np.abs(self.Q[True ^ mask]).min(),
(qmin, qmax) = (np.abs(self.Q[True ^ mask]).min(),
np.abs(self.Q[True ^ mask]).max())
self.iqMin = np.maximum(qmin, self.qMin)
self.iqMax = np.minimum(qmax, self.qMax)
Expand All @@ -250,13 +249,13 @@ def defineBinEdges(self):
# define bin edges
if self.scaling.lower() in ("linear", "lin"):
qEdges = np.linspace(
self.iqMin,
self.iqMax,
self.iqMin,
self.iqMax,
self.numBins + 1)
else:
qEdges = np.logspace(
log10(self.iqMin),
log10(self.iqMax),
log10(self.iqMin),
log10(self.iqMax),
self.numBins + 1)
self.qEdges = qEdges
if self.verbose:
Expand Down Expand Up @@ -286,7 +285,7 @@ def binning1D(self, qError = None):
error = self.E.copy()
numBins = self.numBins
qEdges = self.qEdges

#flatten q, intensity and error
q = reshape(q, size(q))
intensity = reshape(intensity, size(intensity))
Expand All @@ -296,13 +295,13 @@ def binning1D(self, qError = None):
q = q[sortInd]
intensity = intensity[sortInd]

#initialise storage:
#initialise storage:
numBins = len(qEdges) - 1
ibin = zeros(numBins)
qbin = zeros(numBins)
sdbin = zeros(numBins)
sebin = zeros(numBins)
qebin = zeros(numBins)
sdbin = zeros(numBins)
sebin = zeros(numBins)
qebin = zeros(numBins)
binMask = zeros(numBins) # set one for masked bin values
if error is not None:
error = reshape(error, size(error))
Expand Down Expand Up @@ -343,11 +342,11 @@ def binning1D(self, qError = None):
#calculate the standard deviation of the intensity in the bin
# according to the definition of sample-standard deviation
sdbin = iToBin.std(ddof = 1)
#what we want is to have the "standard error of the mean":
#what we want is to have the "standard error of the mean":
sdbin = sdbin / sqrt(1.*np.size(iToBin))
# maximum between standard error and Poisson statistics
sebin[bini] = np.maximum(sebin[bini],sdbin)
#qebin is the mean error of the q-values in the bin, should
#qebin is the mean error of the q-values in the bin, should
#probably be superseded by the bin width
qe = 0.
if qError is not None:
Expand Down

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