def onProcessStackFinished(self):
_logger.debug("Stack proccessed")
self._status = "Stack Fitting finished"
if self.fixedLenghtOutput:
self._status = "Writing output files"
nParameters = len(self._parameters)
datalist = [None] * (2 * len(self._sigmas.keys()) + 1)
labels = []
for i in range(nParameters):
parameter = self._parameters[i]
datalist[2 * i] = self._images[parameter]
datalist[2 * i + 1] = self._sigmas[parameter]
labels.append(parameter)
labels.append('s(%s)' % parameter)
datalist[-1] = self._images['chisq']
labels.append('chisq')
filenames = self.getOutputFileNames()
csvName = filenames['csv']
edfName = filenames['edf']
ArraySave.save2DArrayListAsASCII(datalist,
csvName,
labels=labels,
csv=True,
csvseparator=";")
ArraySave.save2DArrayListAsEDF(datalist,
edfName,
labels=labels,
dtype=numpy.float32)
def saveStackAsSimplestHDF5(self):
filename = self._getOutputHDF5Filename()
if not len(filename):
return
view = self._getCroppedView()
ArraySave.save3DArrayAsHDF5(view, filename,
labels = None, dtype=None, mode='simplest')
def onProcessStackFinished(self):
_logger.debug("Stack proccessed")
self._status = "Stack Fitting finished"
if self.fixedLenghtOutput:
self._status = "Writing output files"
nParameters = len(self._parameters)
datalist = [None] * (2*len(self._sigmas.keys())+1)
labels = []
for i in range(nParameters):
parameter = self._parameters[i]
datalist[2*i] = self._images[parameter]
datalist[2*i + 1] = self._sigmas[parameter]
labels.append(parameter)
labels.append('s(%s)' % parameter)
datalist[-1] = self._images['chisq']
labels.append('chisq')
filenames = self.getOutputFileNames()
csvName = filenames['csv']
edfName = filenames['edf']
ArraySave.save2DArrayListAsASCII(datalist,
csvName,
labels=labels,
csv=True,
csvseparator=";")
ArraySave.save2DArrayListAsEDF(datalist,
edfName,
labels = labels,
dtype=numpy.float32)
def saveAllSpectra(self):
fltrs = ['Raw ASCII *.txt',
'","-separated CSV *.csv',
'";"-separated CSV *.csv',
'"tab"-separated CSV *.csv',
'OMNIC CSV *.csv']
message = "Enter file name to be used as root"
fileList, fileFilter = PyMcaFileDialogs.getFileList(parent=self,
filetypelist=fltrs,
message=message,
currentdir=None,
mode="SAVE",
getfilter=True,
single=True,
currentfilter=None,
native=None)
if not len(fileList):
return
fileroot = fileList[0]
dirname = os.path.dirname(fileroot)
root, ext = os.path.splitext(os.path.basename(fileroot))
if ext not in [".txt", ".csv"]:
root = root + ext
ext = ""
# get appropriate extensions and separators
filterused = fileFilter.split()
if filterused[0].startswith("Raw"):
csv = False
ext = "txt"
csvseparator = " "
elif filterused[0].startswith("OMNIC"):
# extension is csv but saved as ASCII
csv = False
ext = "csv"
csvseparator = ","
else:
csv = True
ext = "csv"
if "," in filterused[0]:
csvseparator = ","
elif ";" in filterused[0]:
csvseparator = ";"
elif "OMNIC" in filterused[0]:
csvseparator = ","
else:
csvseparator = "\t"
nSpectra = len(self.spectrumList)
n = int(numpy.log10(nSpectra)) + 1
fmt = "_%" + "0%dd" % n + ".%s"
for index in range(nSpectra):
legend = self.spectrumNames[index]
x = self.xValues[index]
y = self.spectrumList[index]
filename = os.path.join(dirname, root + fmt % (index, ext))
ArraySave.saveXY(x, y, filename, ylabel=legend,
csv=csv, csvseparator=csvseparator)
def saveStackAsSimpleHDF5(self):
filename = self._getOutputHDF5Filename()
if not len(filename):
return
ArraySave.save3DArrayAsHDF5(self._stack.data,
filename,
labels=None,
dtype=None,
mode='simple')
def saveStackAsNeXusPlus(self):
filename = self._getOutputHDF5Filename()
if not len(filename):
return
ArraySave.save3DArrayAsHDF5(self._stack.data,
filename,
labels=None,
dtype=None,
mode='nexus+')
def saveStackAsSimplestHDF5(self):
filename = self._getOutputHDF5Filename()
if not len(filename):
return
view = self._getCroppedView()
ArraySave.save3DArrayAsHDF5(view,
filename,
labels=None,
dtype=None,
mode='simplest')
def saveStackAsNeXus(self,
dtype=None,
interpretation=None,
compression=False):
mcaIndex = self._stack.info.get('McaIndex', -1)
if interpretation is None:
if mcaIndex in [0]:
interpretation = "image"
else:
interpretation = "spectrum"
if interpretation not in ["spectrum", "image"]:
raise ValueError("Unknown data interpretation %s" % interpretation)
filename = self._getOutputHDF5Filename()
if not len(filename):
return
# get only the seen stack portion
view = self._getCroppedView()
# the current graph axis is saved
axes = [None] * len(self._stack.data.shape)
labels = [None] * len(self._stack.data.shape)
try:
xLabel = qt.safe_str(self.mcaWidget.graph.getGraphXLabel())
except:
xLabel = None
try:
xData, y, legend, info = self.mcaWidget.getActiveCurve()[:4]
except:
xData = self._mcaData0.x[0]
xLabel = 'Channels'
if interpretation == 'image':
labels[0] = xLabel
axes[0] = xData
else:
labels[-1] = xLabel
axes[-1] = xData
try:
ArraySave.save3DArrayAsHDF5(view,
filename,
axes=axes,
labels=labels,
dtype=dtype,
mode='nexus',
mcaindex=mcaIndex,
interpretation=interpretation,
compression=compression)
except:
msg = qt.QMessageBox(self)
msg.setIcon(qt.QMessageBox.Critical)
msg.setWindowTitle("Save error")
msg.setText("An error has occured while saving the data:")
msg.setInformativeText(qt.safe_str(sys.exc_info()[1]))
msg.setDetailedText(traceback.format_exc())
msg.exec_()
def _saveSlot(self):
if self.outputDir is None:
if self.inputDir is not None:
self.outputDir = self.inputDir
else:
self.outputDir = PyMcaDirs.outputDir
wdir = self.outputDir
format_list = ['";"-separated CSV *.csv',
'","-separated CSV *.csv',
'"tab"-separated CSV *.csv']
if self.outputFilter is None:
self.outputFilter = format_list[0]
outfile, filterused = PyMcaFileDialogs.getFileList(self,
filetypelist=format_list,
mode="SAVE",
message="Output File Selection",
currentdir=wdir,
currentfilter=self.outputFilter,
getfilter=True,
single=True)
if len(outfile):
outputFile = qt.safe_str(outfile[0])
else:
return
self.outputFilter = qt.safe_str(filterused)
filterused = self.outputFilter.split()
try:
self.outputDir = os.path.dirname(outputFile)
PyMcaDirs.outputDir = os.path.dirname(outputFile)
except:
self.outputDir = "."
if not outputFile.endswith('.csv'):
outputFile += '.csv'
#always overwrite
if "," in filterused[0]:
csv = ","
elif ";" in filterused[0]:
csv = ";"
else:
csv = "\t"
ddict = self.getTransmissionTable()
x = ddict["energy"]
y = ddict["transmission"]
try:
ArraySave.saveXY(x, y, outputFile,
xlabel="Energy", ylabel="Transmission",
csv=True, csvseparator=csv)
except IOError:
msg = qt.QMessageBox(self)
msg.setIcon(qt.QMessageBox.Critical)
msg.setText("Input Output Error: %s" % (sys.exc_info()[1]))
msg.exec()
return
def saveStackAsNeXus(self, dtype=None, interpretation=None, compression=False):
mcaIndex = self._stack.info.get('McaIndex', -1)
if interpretation is None:
if mcaIndex in [0]:
interpretation = "image"
else:
interpretation = "spectrum"
if interpretation not in ["spectrum", "image"]:
raise ValueError("Unknown data interpretation %s" % interpretation)
filename = self._getOutputHDF5Filename()
if not len(filename):
return
# get only the seen stack portion
view = self._getCroppedView()
# the current graph axis is saved
axes = [None] * len(self._stack.data.shape)
labels = [None] * len(self._stack.data.shape)
try:
xLabel = qt.safe_str(self.mcaWidget.graph.getGraphXLabel())
except:
xLabel = None
try:
xData, y, legend, info = self.mcaWidget.getActiveCurve()[:4]
except:
xData = self._mcaData0.x[0]
xLabel = 'Channels'
if interpretation == 'image':
labels[0] = xLabel
axes[0] = xData
else:
labels[-1] = xLabel
axes[-1] = xData
try:
ArraySave.save3DArrayAsHDF5(view,
filename,
axes=axes,
labels=labels,
dtype=dtype,
mode='nexus',
mcaindex=mcaIndex,
interpretation=interpretation,
compression=compression)
except:
msg = qt.QMessageBox(self)
msg.setIcon(qt.QMessageBox.Critical)
msg.setWindowTitle("Save error")
msg.setText("An error has occured while saving the data:")
msg.setInformativeText(qt.safe_str(sys.exc_info()[1]))
msg.setDetailedText(traceback.format_exc())
msg.exec_()
def finishHDF5File(self, hdf):
#add final date
toplevelEntry = hdf["entry_000"]
toplevelEntry['end_time'] = numpy.string_(
ArraySave.getDate().encode('utf-8'))
hdf.flush()
hdf.close()
def saveStackAsMonochromaticTiffImages(self, dtype=None):
if dtype is None:
dtype = self._stack.data.dtype
if dtype in [numpy.uint32, numpy.uint64]:
dtype = numpy.float32
elif dtype in [numpy.int32, numpy.int64]:
dtype = numpy.float32
mcaIndex = self._stack.info.get('McaIndex', -1)
filename = self._getOutputTiffFilename()
if not len(filename):
return
ArraySave.save3DArrayAsMonochromaticTiff(self._stack.data,
filename,
labels = None,
dtype=dtype,
mcaindex=mcaIndex)
def saveStackAsMonochromaticTiffImages(self, dtype=None):
if dtype is None:
dtype = self._stack.data.dtype
if dtype in [numpy.uint32, numpy.uint64]:
dtype = numpy.float32
elif dtype in [numpy.int32, numpy.int64]:
dtype = numpy.float32
mcaIndex = self._stack.info.get('McaIndex', -1)
filename = self._getOutputTiffFilename()
if not len(filename):
return
ArraySave.save3DArrayAsMonochromaticTiff(self._stack.data,
filename,
labels = None,
dtype=dtype,
mcaindex=mcaIndex)
def initializeHDF5File(self, fname):
# for the time being overwriting
if os.path.exists(fname):
os.remove(fname)
hdf = h5py.File(fname, "w")
entryName = "entry_000"
nxEntry = hdf.require_group(entryName)
if "NX_class" not in nxEntry.attrs:
nxEntry.attrs["NX_class"] = "NXentry".encode("utf-8")
nxEntry["title"] = numpy.string_("PyMca saved 3D Array".encode("utf-8"))
nxEntry["start_time"] = numpy.string_(ArraySave.getDate().encode("utf-8"))
alignmentGroup = nxEntry.require_group("Alignment")
dataGroup = nxEntry.require_group("Data")
dataGroup.attrs["NX_class"] = "NXdata".encode("utf-8")
return hdf
def initializeHDF5File(self, fname):
#for the time being overwriting
if os.path.exists(fname):
os.remove(fname)
hdf = h5py.File(fname, 'w')
entryName = "entry_000"
nxEntry = hdf.require_group(entryName)
if 'NX_class' not in nxEntry.attrs:
nxEntry.attrs['NX_class'] = 'NXentry'.encode('utf-8')
nxEntry['title'] = numpy.string_("PyMca saved 3D Array".encode('utf-8'))
nxEntry['start_time'] = numpy.string_(ArraySave.getDate().encode('utf-8'))
alignmentGroup = nxEntry.require_group('Alignment')
dataGroup = nxEntry.require_group('Data')
dataGroup.attrs['NX_class'] = 'NXdata'.encode('utf-8')
return hdf
def _handleSaveIcon(self, event):
"""Handle save icon events.
Get current active image and save it as a file.
"""
if event['event'] == 'iconClicked' and event['key'] == 'save':
imageData = self.getActiveImage()
if imageData is None:
qt.QMessageBox.information(self, "No Data",
"No image to be saved")
return
data, legend, info, pixmap = imageData
imageList = [data]
labels = ['value']
# Copied from MaskImageWidget.saveImageList
filename = self.getOutputFileName()
if not len(filename):
return
# Add PNG and JPG adapted from PlotWindow.defaultSaveAction
if 'WIDGET' in self._saveFilter.upper():
fformat = self._saveFilter[-3:].upper()
pixmap = qt.QPixmap.grabWidget(self._imagePlot)
# Use the following instead to grab the image + histograms
# pixmap = qt.QPixmap.grabWidget(self)
if not pixmap.save(filename, fformat):
msg = qt.QMessageBox(self)
msg.setIcon(qt.QMessageBox.Critical)
msg.setInformativeText(str(sys.exc_info()[1]))
msg.setDetailedText(traceback.format_exc())
msg.exec_()
return
if filename.lower().endswith(".edf"):
ArraySave.save2DArrayListAsEDF(imageList, filename, labels)
elif filename.lower().endswith(".csv"):
if "," in self._saveFilter:
csvseparator = ","
elif ";" in self._saveFilter:
csvseparator = ";"
else:
csvseparator = "\t"
ArraySave.save2DArrayListAsASCII(imageList, filename, labels,
csv=True,
csvseparator=csvseparator)
else:
ArraySave.save2DArrayListAsASCII(imageList, filename, labels,
csv=False)
def saveImageList(self, filename, imageList, labels,
csvseparator=None):
if not imageList:
qt.QMessageBox.information(
self,
"No Data",
"Image list is empty.\nNothing to be saved")
return
if filename.lower().endswith(".edf"):
ArraySave.save2DArrayListAsEDF(imageList, filename, labels)
elif filename.lower().endswith(".tif"):
ArraySave.save2DArrayListAsMonochromaticTiff(imageList,
filename,
labels)
elif filename.lower().endswith(".csv"):
assert csvseparator is not None
ArraySave.save2DArrayListAsASCII(imageList, filename, labels,
csv=True,
csvseparator=csvseparator)
else:
ArraySave.save2DArrayListAsASCII(imageList, filename, labels,
csv=False)
def saveImageList(self, filename, imageList, labels, csvseparator=None):
if not imageList:
qt.QMessageBox.information(
self, "No Data", "Image list is empty.\nNothing to be saved")
return
if filename.lower().endswith(".edf"):
ArraySave.save2DArrayListAsEDF(imageList, filename, labels)
elif filename.lower().endswith(".tif"):
ArraySave.save2DArrayListAsMonochromaticTiff(
imageList, filename, labels)
elif filename.lower().endswith(".csv"):
assert csvseparator is not None
ArraySave.save2DArrayListAsASCII(imageList,
filename,
labels,
csv=True,
csvseparator=csvseparator)
else:
ArraySave.save2DArrayListAsASCII(imageList,
filename,
labels,
csv=False)
def _threadFinished(self):
result = self.thread.result
self.thread = None
if type(result) == type((1, )):
#if we receive a tuple there was an error
if len(result):
if result[0] == "Exception":
# somehow this exception is not caught
raise Exception(result[1], result[2]) #, result[3])
return
if 'concentrations' in result:
imageNames = result['names']
images = numpy.concatenate(
(result['parameters'], result['concentrations']), axis=0)
else:
images = result['parameters']
imageNames = result['names']
nImages = images.shape[0]
self._widget = StackPluginResultsWindow.StackPluginResultsWindow(\
usetab=False)
self._widget.buildAndConnectImageButtonBox(replace=True, multiple=True)
qt = StackPluginResultsWindow.qt
self._widget.sigMaskImageWidgetSignal.connect(self.mySlot)
self._widget.setStackPluginResults(images, image_names=imageNames)
self._showWidget()
# save to output directory
parameters = self.configurationWidget.getParameters()
outputDir = parameters["output_dir"]
if outputDir in [None, ""]:
if DEBUG:
print("Nothing to be saved")
return
if parameters["file_root"] is None:
fileRoot = ""
else:
fileRoot = parameters["file_root"].replace(" ", "")
if fileRoot in [None, ""]:
fileRoot = "images"
if not os.path.exists(outputDir):
os.mkdir(outputDir)
imagesDir = os.path.join(outputDir, "IMAGES")
if not os.path.exists(imagesDir):
os.mkdir(imagesDir)
imageList = [None] * (nImages + len(result['uncertainties']))
fileImageNames = [None] * (nImages + len(result['uncertainties']))
j = 0
for i in range(nImages):
name = imageNames[i].replace(" ", "-")
fileImageNames[j] = name
imageList[j] = images[i]
j += 1
if not imageNames[i].startswith("C("):
# fitted parameter
fileImageNames[j] = "s(%s)" % name
imageList[j] = result['uncertainties'][i]
j += 1
fileName = os.path.join(imagesDir, fileRoot + ".edf")
ArraySave.save2DArrayListAsEDF(imageList,
fileName,
labels=fileImageNames)
fileName = os.path.join(imagesDir, fileRoot + ".csv")
ArraySave.save2DArrayListAsASCII(imageList,
fileName,
csv=True,
labels=fileImageNames)
if parameters["tiff"]:
i = 0
for i in range(len(fileImageNames)):
label = fileImageNames[i]
if label.startswith("s("):
continue
elif label.startswith("C("):
mass_fraction = "_" + label[2:-1] + "_mass_fraction"
else:
mass_fraction = "_" + label
fileName = os.path.join(imagesDir,
fileRoot + mass_fraction + ".tif")
ArraySave.save2DArrayListAsMonochromaticTiff(
[imageList[i]],
fileName,
labels=[label],
dtype=numpy.float32)
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
def finishHDF5File(self, hdf):
#add final date
toplevelEntry = hdf["entry_000"]
toplevelEntry['end_time'] = numpy.string_(ArraySave.getDate().encode('utf-8'))
hdf.flush()
hdf.close()
def _threadFinished(self):
result = self.thread.result
self.thread = None
if type(result) == type((1,)):
#if we receive a tuple there was an error
if len(result):
if result[0] == "Exception":
# somehow this exception is not caught
raise Exception(result[1], result[2])#, result[3])
return
imageNames = result['names']
images = result["images"]
nImages = images.shape[0]
self._widget = StackPluginResultsWindow.StackPluginResultsWindow(\
usetab=False)
self._widget.buildAndConnectImageButtonBox(replace=True,
multiple=True)
qt = StackPluginResultsWindow.qt
self._widget.sigMaskImageWidgetSignal.connect(self.mySlot)
self._widget.setStackPluginResults(images,
image_names=imageNames)
self._showWidget()
# save to output directory
parameters = self.configurationWidget.getParameters()
outputDir = parameters["output_dir"]
if outputDir in [None, ""]:
if DEBUG:
print("Nothing to be saved")
return
if parameters["file_root"] is None:
fileRoot = ""
else:
fileRoot = parameters["file_root"].replace(" ","")
if fileRoot in [None, ""]:
fileRoot = "images"
if not os.path.exists(outputDir):
os.mkdir(outputDir)
imagesDir = os.path.join(outputDir, "IMAGES")
if not os.path.exists(imagesDir):
os.mkdir(imagesDir)
imageList = [None] * (nImages)
fileImageNames = [None] * (nImages)
j = 0
for i in range(nImages):
name = imageNames[i].replace(" ","-")
fileImageNames[j] = name
imageList[j] = images[i]
j += 1
fileName = os.path.join(imagesDir, fileRoot+".edf")
ArraySave.save2DArrayListAsEDF(imageList, fileName,
labels=fileImageNames)
fileName = os.path.join(imagesDir, fileRoot+".csv")
ArraySave.save2DArrayListAsASCII(imageList, fileName, csv=True,
labels=fileImageNames)
if parameters["tiff"]:
i = 0
for i in range(len(fileImageNames)):
label = fileImageNames[i]
mass_fraction = "_" + label
fileName = os.path.join(imagesDir,
fileRoot + mass_fraction + ".tif")
ArraySave.save2DArrayListAsMonochromaticTiff([imageList[i]],
fileName,
labels=[label],
dtype=numpy.float32)
def saveStackAsSimplestHDF5(self):
filename = self._getOutputHDF5Filename()
if not len(filename):
return
ArraySave.save3DArrayAsHDF5(self._stack.data, filename,
labels = None, dtype=None, mode='simplest')
def saveStackAsNeXusPlus(self):
filename = self._getOutputHDF5Filename()
if not len(filename):
return
ArraySave.save3DArrayAsHDF5(self._stack.data, filename,
labels = None, dtype=None, mode='nexus+')
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)
filecounter = 0
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
key = "%s.1.%s" % (keyindex, numberofmca)
dataObject = tempInstance._getMcaData(key)
arrRet = dataObject.data
mca_number += 1
for i in iterlist:
#mcadata = scan_obj.mca(i)
self.data[filecounter,
mca_number,
:] = arrRet[:]
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
filecounter += 1
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)
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
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:
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
print("**************************************************")
print(" Memory error!, attempting %dx%d sub-sampling " %\
(sampling_order, sampling_order))
print("**************************************************")
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):
print("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
def threadFinished(self):
result = self.thread.result
self.thread = None
if type(result) == type((1,)):
#if we receive a tuple there was an error
if len(result):
if result[0] == "Exception":
# somehow this exception is not caught
raise Exception(result[1], result[2])
return
if 'concentrations' in result:
imageNames = result['names']
images = numpy.concatenate((result['parameters'],
result['concentrations']), axis=0)
else:
images = result['parameters']
imageNames = result['names']
nImages = images.shape[0]
self._widget = StackPluginResultsWindow.StackPluginResultsWindow(\
usetab=False)
self._widget.buildAndConnectImageButtonBox()
qt = StackPluginResultsWindow.qt
self._widget.sigMaskImageWidgetSignal.connect(self.mySlot)
self._widget.setStackPluginResults(images,
image_names=imageNames)
self._showWidget()
# save to output directory
parameters = self.configurationWidget.getParameters()
outputDir = parameters["output_dir"]
if outputDir in [None, ""]:
if DEBUG:
print("Nothing to be saved")
return
if parameters["file_root"] is None:
fileRoot = ""
else:
fileRoot = parameters["file_root"].replace(" ","")
if fileRoot in [None, ""]:
fileRoot = "images"
if not os.path.exists(outputDir):
os.mkdir(outputDir)
imagesDir = os.path.join(outputDir, "IMAGES")
if not os.path.exists(imagesDir):
os.mkdir(imagesDir)
imageList = [None] * (nImages + len(result['uncertainties']))
fileImageNames = [None] * (nImages + len(result['uncertainties']))
j = 0
for i in range(nImages):
name = imageNames[i].replace(" ","-")
fileImageNames[j] = name
imageList[j] = images[i]
j += 1
if not imageNames[i].startswith("C("):
# fitted parameter
fileImageNames[j] = "s(%s)" % name
imageList[j] = result['uncertainties'][i]
j += 1
fileName = os.path.join(imagesDir, fileRoot+".edf")
ArraySave.save2DArrayListAsEDF(imageList, fileName,
labels=fileImageNames)
fileName = os.path.join(imagesDir, fileRoot+".csv")
ArraySave.save2DArrayListAsASCII(imageList, fileName, csv=True,
labels=fileImageNames)