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