87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
import logging
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log = logging.getLogger(__name__)
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import os
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import collections
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import numpy as np
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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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from . import storage
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default_extension = ".npz"
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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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files = data['fname'].astype(str)
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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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poni='pyfai.poni',storageFile='auto'):
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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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if storageFile == 'auto' : storageFile = folder + "/" + "pyfai_1d" + default_extension
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return azav.doFolder(folder,files="*.edf*",nQ=nQ,force=force,mask=mask,
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saveChi=saveChi,poni=poni,storageFile=storageFile,diagnostic=diag)
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def doFolder_dataRed(azavStorage,monitor=None,funcForAveraging=np.nanmean,
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errMask=5,chi2Mask=2,qlims=None,outStorageFile='auto'):
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""" azavStorage if a DataStorage instance or the filename to read """
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if isinstance(azavStorage,storage.DataStorage):
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data = azavStorage
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folder = os.path.dirname(data.filename) if data.filename is not None else "./"
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elif os.path.exists(azavStorage):
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folder = os.path.dirname(azavStorage)
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data = storage.DataStorage(azavStorage)
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else:
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# assume is just a folder name
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folder = azavStorage
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azavStorage = folder + "/pyfai_1d" + default_extension
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data = storage.DataStorage(azavStorage)
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if qlims is not None:
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idx = (data.q>qlims[0]) & (data.q<qlims[1])
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data.data = data.data[:,idx]
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data.q = data.q[idx]
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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,funcForAveraging=funcForAveraging)
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# mask if asked so
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if errMask>0:
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diffs = dataReduction.errorMask(diffs,threshold=errMask)
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if chi2Mask>0:
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diffs = dataReduction.chi2Mask(diffs,threshold=chi2Mask)
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diffs = dataReduction.applyMasks(diffs)
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# save txt and npz file
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dataReduction.saveTxt(folder,diffs,info=data.pyfai_info)
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if outStorageFile == 'auto':
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outStorageFile = folder + "/diffs" + default_extension
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diffs.save(outStorageFile)
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return data,diffs
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