def __init__(self, filename):
"""Initialize class
Parameters
----------
filename : string
Path to the spec file
"""
self.filename = filename
self.source = SpecFileDataSource.SpecFileDataSource(filename)
def loadFileList(self, filelist, fileindex=0, shape=None):
if type(filelist) == type(''):
filelist = [filelist]
self.__keyList = []
self.sourceName = filelist
self.__indexedStack = True
self.sourceType = SOURCE_TYPE
self.info = {}
self.nbFiles = len(filelist)
# read first file
# get information
tempInstance = SpecFileDataSource.SpecFileDataSource(filelist[0])
keylist = tempInstance.getSourceInfo()['KeyList']
nscans = len(keylist) # that is the number of scans
nmca = 0
numberofdetectors = 0
for key in keylist:
info = tempInstance.getKeyInfo(key)
numberofmca = info['NbMca']
if numberofmca > 0:
numberofdetectors = info['NbMcaDet']
scantype = info["ScanType"]
if numberofmca:
nmca += numberofmca
if numberofdetectors == 0:
raise ValueError("No MCA found in file %s" % filelist[0])
if (nscans > 1) and ((nmca // numberofdetectors) == nscans):
SLOW_METHOD = True
else:
SLOW_METHOD = False
# get last mca of first point
key = "%s.1.%s" % (keylist[-1], numberofmca)
dataObject = tempInstance._getMcaData(key)
self.info.update(dataObject.info)
arrRet = dataObject.data
self.onBegin(self.nbFiles * nmca // numberofdetectors)
self.incrProgressBar = 0
if info['NbMcaDet'] > 1:
# Should I generate a map for each mca and not just for the last one as I am doing?
iterlist = range(info['NbMcaDet'], info['NbMca'] + 1,
info['NbMcaDet'])
else:
iterlist = [1]
if SLOW_METHOD and shape is None:
self.data = numpy.zeros(
(self.nbFiles, nmca // numberofdetectors, arrRet.shape[0]),
arrRet.dtype.char)
nTimes = self.nbFiles * (nmca // numberofdetectors)
filecounter = 0
for key in ["McaLiveTime", "McaElapsedTime"]:
if key in dataObject.info:
self.info[key] = numpy.zeros((nTimes, ), numpy.float32)
# positioners
key = "MotorNames"
positioners = None
if key in dataObject.info:
positioners = {}
for mne in dataObject.info[key]:
positioners[mne] = numpy.zeros((nTimes, ), numpy.float32)
nTimes = -1
for tempFileName in filelist:
tempInstance = SpecFileDataSource.SpecFileDataSource(
tempFileName)
mca_number = -1
for keyindex in keylist:
info = tempInstance.getKeyInfo(keyindex)
numberofmca = info['NbMca']
if numberofmca <= 0:
continue
# the positioners are for all the mca in the scan
# only the last mca is read
key = "%s.1.%s" % (keyindex, numberofmca)
dataObject = tempInstance._getMcaData(key)
arrRet = dataObject.data
mca_number += 1
nTimes += 1
for i in iterlist:
# mcadata = scan_obj.mca(i)
self.data[filecounter, mca_number, :] = arrRet[:]
self.incrProgressBar += 1
for timeKey in ["McaElapsedTime", "McaLiveTime"]:
if timeKey in dataObject.info:
self.info[timeKey][nTimes] = \
dataObject.info[timeKey]
if positioners and "MotorNames" in dataObject.info:
for mne in positioners:
if mne in dataObject.info["MotorNames"]:
mneIdx = \
dataObject.info["MotorNames"].index(mne)
positioners[mne][nTimes] = \
dataObject.info["MotorValues"][mneIdx]
self.onProgress(self.incrProgressBar)
filecounter += 1
if positioners:
self.info["positioners"] = positioners
elif shape is None and (self.nbFiles == 1) and (iterlist == [1]):
# it can only be here if there is one file
# it can only be here if there is only one scan
# it can only be here if there is only one detector
self.data = numpy.zeros((1, numberofmca, arrRet.shape[0]),
arrRet.dtype.char)
# when reading fast we do not read the time information
# therefore we have to remove it from the info
self._cleanupTimeInfo()
for tempFileName in filelist:
tempInstance = specfile.Specfile(tempFileName)
# it can only be here if there is one scan per file
# prevent problems if the scan number is different
# scan = tempInstance.select(keylist[-1])
scan = tempInstance[-1]
iterationList = range(scan.nbmca())
for i in iterationList:
# mcadata = scan_obj.mca(i)
self.data[0, i, :] = scan.mca(i + 1)[:]
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
filecounter = 1
elif shape is None:
# it can only be here if there is one scan per file
# when reading fast we do not read the time information
# therefore we have to remove it from the info
self._cleanupTimeInfo()
try:
self.data = numpy.zeros(
(self.nbFiles, numberofmca // numberofdetectors,
arrRet.shape[0]), arrRet.dtype.char)
filecounter = 0
for tempFileName in filelist:
tempInstance = specfile.Specfile(tempFileName)
# it can only be here if there is one scan per file
# prevent problems if the scan number is different
# scan = tempInstance.select(keylist[-1])
scan = tempInstance[-1]
for i in iterlist:
# mcadata = scan_obj.mca(i)
self.data[filecounter, 0, :] = scan.mca(i)[:]
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
filecounter += 1
except MemoryError:
qtflag = False
if ('PyQt4.QtCore' in sys.modules) or \
('PySide' in sys.modules) or \
('PyMca5.PyMcaGui.PyMcaQt' in sys.modules):
qtflag = True
hdf5done = False
if HDF5 and qtflag:
from PyMca5.PyMcaGui import PyMcaQt as qt
from PyMca5.PyMcaIO import ArraySave
msg = qt.QMessageBox.information( \
None,
"Memory error\n",
"Do you want to convert your data to HDF5?\n",
qt.QMessageBox.Yes,qt.QMessageBox.No)
if msg != qt.QMessageBox.No:
hdf5file = qt.QFileDialog.getSaveFileName( \
None,
"Please select output file name",
os.path.dirname(filelist[0]),
"HDF5 files *.h5")
if not len(hdf5file):
raise IOError("Invalid output file")
hdf5file = qt.safe_str(hdf5file)
if not hdf5file.endswith(".h5"):
hdf5file += ".h5"
# get the final shape
from PyMca5.RGBCorrelatorWidget import ImageShapeDialog
stackImageShape = self.nbFiles,\
int(numberofmca/numberofdetectors)
dialog = ImageShapeDialog(None, shape=stackImageShape)
dialog.setModal(True)
ret = dialog.exec()
if ret:
stackImageShape = dialog.getImageShape()
dialog.close()
del dialog
hdf, self.data = ArraySave.getHDF5FileInstanceAndBuffer( \
hdf5file,
(stackImageShape[0],
stackImageShape[1],
arrRet.shape[0]),
compression=None,
interpretation="spectrum")
nRow = 0
nCol = 0
for tempFileName in filelist:
tempInstance = specfile.Specfile(tempFileName)
# it can only be here if there is one scan per file
# prevent problems if the scan number is different
# scan = tempInstance.select(keylist[-1])
scan = tempInstance[-1]
nRow = int(self.incrProgressBar /
stackImageShape[1])
nCol = self.incrProgressBar % stackImageShape[1]
for i in iterlist:
# mcadata = scan_obj.mca(i)
self.data[nRow, nCol, :] = scan.mca(i)[:]
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
hdf5done = True
hdf.flush()
self.onEnd()
self.info["SourceType"] = "HDF5Stack1D"
self.info["McaIndex"] = 2
self.info["FileIndex"] = 0
self.info["SourceName"] = [hdf5file]
self.info["NumberOfFiles"] = 1
self.info["Size"] = 1
return
else:
raise
else:
# time information not read
self._cleanupTimeInfo()
sampling_order = 1
s0 = shape[0]
s1 = shape[1]
MEMORY_ERROR = False
try:
self.data = numpy.zeros((shape[0], shape[1], arrRet.shape[0]),
arrRet.dtype.char)
except MemoryError:
try:
self.data = numpy.zeros(
(shape[0], shape[1], arrRet.shape[0]), numpy.float32)
except MemoryError:
MEMORY_ERROR = True
while MEMORY_ERROR:
try:
for i in range(5):
print("\7")
sampling_order += 1
_logger.warning(
"**************************************************")
_logger.warning(
" Memory error!, attempting %dx%d sub-sampling ",
sampling_order, sampling_order)
_logger.warning(
"**************************************************")
s0 = int(shape[0] / sampling_order)
s1 = int(shape[1] / sampling_order)
#if shape[0] % sampling_order:
# s0 = s0 + 1
#if shape[1] % sampling_order:
# s1 = s1 + 1
self.data = numpy.zeros((s0, s1, arrRet.shape[0]),
numpy.float32)
MEMORY_ERROR = False
except MemoryError:
pass
filecounter = 0
for j in range(s0):
filecounter = (j * sampling_order) * shape[1]
for k in range(s1):
tempFileName = filelist[filecounter]
tempInstance = specfile.Specfile(tempFileName)
if tempInstance is None:
if not os.path.exists(tempFileName):
_logger.error("File %s does not exists",
tempFileName)
raise IOError("File %s does not exists" %
tempFileName)
scan = tempInstance.select(keylist[-1])
for i in iterlist:
# sum the present mcas
self.data[j, k, :] += scan.mca(i)[:]
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
filecounter += sampling_order
self.nbFiles = s0 * s1
self.onEnd()
"""
# Scan types
# ----------
#SF_EMPTY = 0 # empty scan
#SF_SCAN = 1 # non-empty scan
#SF_MESH = 2 # mesh scan
#SF_MCA = 4 # single mca
#SF_NMCA = 8 # multi mca (more than 1 mca per acq)
case = None
if scantype == (SpecFileDataSource.SF_MESH + \
SpecFileDataSource.SF_MCA):
# SINGLE MESH + SINGLE MCA
# nfiles = 1
# nscans = 1
# nmca = 1
# there is a danger if it can be considered an indexed file ...
pass
elif scantype == (SpecFileDataSource.SF_MESH + \
SpecFileDataSource.SF_NMCA):
# SINGLE MESH + MULTIPLE MCA
# nfiles = 1
# nscans = 1
# nmca > 1
# there is a danger if it can be considered an indexed file ...
#for the time being I take last mca
pass
elif scantype == (SpecFileDataSource.SF_SCAN+ \
SpecFileDataSource.SF_MCA):
#Assumed scans containing always 1 detector
pass
elif scantype == (SpecFileDataSource.SF_MCA):
#Assumed scans containing always 1 detector
pass
elif scantype == (SpecFileDataSource.SF_SCAN+ \
SpecFileDataSource.SF_NMCA):
#Assumed scans containing the same number of detectors
#for the time being I take last mca
pass
elif scantype == (SpecFileDataSource.SF_NMCA):
#Assumed scans containing the same number of detectors
#for the time being I take last mca
pass
else:
raise ValueError, "Unhandled scan type = %s" % scantype
"""
self.__nFiles = self.nbFiles
self.__nImagesPerFile = 1
shape = self.data.shape
for i in range(len(shape)):
key = 'Dim_%d' % (i + 1, )
self.info[key] = shape[i]
self.info["SourceType"] = SOURCE_TYPE
self.info["SourceName"] = self.sourceName
self.info["Size"] = self.__nFiles * self.__nImagesPerFile
self.info["NumberOfFiles"] = self.__nFiles * 1
self.info["FileIndex"] = fileindex