2017-01-05 19:22:37 +01:00
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import logging as log
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log.basicConfig(level=log.INFO)
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import os
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import collections
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import numpy as np
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2017-01-06 18:06:34 +01:00
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from . import azav
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from . import dataReduction
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from . import utils
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2017-01-06 15:40:26 +01:00
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storage_extension = ".npz"
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2017-01-05 19:22:37 +01:00
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def _conv(x):
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try:
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x = float(x)
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except:
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x = np.nan
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return x
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def readDelayFromDiagnostic(fname):
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""" return an ordered dict dictionary of filename; for each key a rounded
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value of delay is associated """
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if os.path.isdir(fname): fname += "/diagnostics.log"
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data = np.genfromtxt(fname,usecols=(2,3),\
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dtype=None,converters={3: lambda x: _conv(x)},
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names = ['fname','delay'])
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2017-01-06 15:40:26 +01:00
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files = data['fname'].astype(str)
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2017-01-05 19:22:37 +01:00
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delays = data['delay']
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# skip lines that cannot be interpreted as float (like done, etc)
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idx_ok = np.isfinite( delays )
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files = files[idx_ok]
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delays = delays[idx_ok]
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delays = np.round(delays.astype(float),12)
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return collections.OrderedDict( zip(files,delays) )
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def doFolder_azav(folder,nQ=1500,force=False,mask=None,saveChi=True,
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2017-01-06 15:40:26 +01:00
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poni='auto',storageFile='auto'):
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2017-01-05 19:22:37 +01:00
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""" very small wrapper around azav.doFolder, essentially just reading
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the diagnostics.log """
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diag = dict( delays = readDelayFromDiagnostic(folder) )
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2017-01-06 15:40:26 +01:00
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if storageFile == 'auto' : storageFile = folder + "/" + "pyfai_1d" + storage_extension
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2017-01-05 19:22:37 +01:00
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return azav.doFolder(folder,files="*.edf*",nQ=nQ,force=force,mask=mask,
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2017-01-06 15:40:26 +01:00
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saveChi=saveChi,poni=poni,storageFile=storageFile,diagnostic=diag)
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2017-01-06 18:06:34 +01:00
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def doFolder_dataRed(folder,storageFile='auto',monitor=None,
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funcForEveraging=np.nanmean,errFilter=True):
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if storageFile == 'auto' : storageFile = folder + "/" + "pyfai_1d" + storage_extension
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# read azimuthal averaged curves
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data = utils.data_storage(storageFile)
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# calculate differences
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diffs = dataReduction.calcTimeResolvedSignal(data.delays,data.data,q=data.q,\
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reference="min",monitor=monitor,funcForEveraging=funcForEveraging)
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# filter if asked so
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2017-01-06 15:40:26 +01:00
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if funcForEveraging == np.nanmean and errFilter:
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diffs = dataReduction.errorFilter(diffs)
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2017-01-06 18:06:34 +01:00
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# save txt and npz file
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2017-01-06 15:40:26 +01:00
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dataReduction.saveTxt(folder,diffs,info=data.pyfai_info)
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diffs.save(folder + "/" + "diffs" + storage_extension)
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2017-01-06 18:06:34 +01:00
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return data,diffs
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