self._lastWidgetId = wid
self._widgetDict[wid] = widget
widget.show()
if __name__ == "__main__":
try:
#this is to add the 3D buttons ...
from PyMca5 import Object3D
except:
#not a big deal for this tests
pass
app = qt.QApplication(sys.argv)
w = PyMcaNexusWidget()
if 0:
w.setFile(sys.argv[1])
else:
from PyMca5.PyMcaCore import NexusDataSource
dataSource = NexusDataSource.NexusDataSource(sys.argv[1:])
w.setDataSource(dataSource)
def addSelection(sel):
print(sel)
def removeSelection(sel):
print(sel)
def replaceSelection(sel):
print(sel)
w.show()
w.sigAddSelection.connect(addSelection)
w.sigRemoveSelection.connect(removeSelection)
w.sigReplaceSelection.connect(replaceSelection)
sys.exit(app.exec_())
def loadFileList(self, filelist, selection, scanlist=None):
"""
loadFileList(self, filelist, y, scanlist=None, monitor=None, x=None)
filelist is the list of file names belonging to the stack
selection is a dictionary with the keys x, y, m.
x is the path to the x data (the channels) in the spectrum,
without the first level "directory". It is unused (for now).
y is the path to the 1D data (the counts) in the spectrum,
without the first level "directory"
m is the path to the normalizing data (I0 or whatever)
without the first level "directory".
scanlist is the list of first level "directories" containing the 1D data
Example: The actual path has the form:
/whatever1/whatever2/counts
That means scanlist = ["/whatever1"]
and selection['y'] = "/whatever2/counts"
"""
_logger.info("filelist = %s", filelist)
_logger.info("selection = %s", selection)
_logger.info("scanlist = %s", scanlist)
# all the files in the same source
hdfStack = NexusDataSource.NexusDataSource(filelist)
# if there is more than one file, it is assumed all the files have
# the same structure.
tmpHdf = hdfStack._sourceObjectList[0]
entryNames = []
for key in tmpHdf["/"].keys():
try:
if isinstance(tmpHdf["/" + key], h5py.Group):
entryNames.append(key)
except KeyError:
_logger.info("Broken link with key? <%s>" % key)
# built the selection in terms of HDF terms
# for the time being
xSelectionList = selection.get('x', None)
if xSelectionList == []:
xSelectionList = None
if xSelectionList is not None:
if type(xSelectionList) != type([]):
xSelectionList = [xSelectionList]
if len(xSelectionList):
xSelection = xSelectionList[0]
else:
xSelection = None
else:
xSelection = None
# only one y is taken
ySelection = selection['y']
if type(ySelection) == type([]):
ySelectionList = list(ySelection)
ySelection = ySelection[0]
else:
ySelectionList = [ySelection]
# monitor selection
mSelection = selection.get('m', None)
if mSelection not in [None, []]:
if type(mSelection) != type([]):
mSelection = [mSelection]
if type(mSelection) == type([]):
if len(mSelection):
mSelection = mSelection[0]
else:
mSelection = None
else:
mSelection = None
USE_JUST_KEYS = False
# deal with the pathological case where the scanlist corresponds
# to a selected top level dataset
if len(entryNames) == 0:
if scanlist is not None:
if (ySelection in scanlist) or \
(xSelection in scanlist) or \
(mSelection in scanlist):
scanlist = None
USE_JUST_KEYS = True
else:
USE_JUST_KEYS = True
elif len(entryNames) == 1:
# deal with the SOLEIL case of one entry but with different name
# in different files
USE_JUST_KEYS = True
elif scanlist in [None, []]:
USE_JUST_KEYS = True
if USE_JUST_KEYS:
# if the scanlist is None, it is assumed we are interested on all
# the scans containing the selection, not that all the scans
# contain the selection.
scanlist = []
if 0:
JUST_KEYS = False
#expect same entry names in the files
#Unfortunately this does not work for SOLEIL
for entry in entryNames:
path = "/" + entry + ySelection
dirname = posixpath.dirname(path)
base = posixpath.basename(path)
try:
file_entry = tmpHdf[dirname]
if base in file_entry.keys():
scanlist.append(entry)
except:
pass
else:
JUST_KEYS = True
#expect same structure in the files even if the
#names are different (SOLEIL ...)
if len(entryNames):
i = 0
for entry in entryNames:
i += 1
path = "/" + entry + ySelection
dirname = posixpath.dirname(path)
base = posixpath.basename(path)
try:
file_entry = tmpHdf[dirname]
if hasattr(file_entry, "keys"):
if base in file_entry.keys():
# this is the case of a selection inside a group
scanlist.append("1.%d" % i)
except KeyError:
_logger.warning("%s not in file, ignoring.",
dirname)
if not len(scanlist):
if not ySelection.startswith("/"):
path = "/" + ySelection
else:
path = ySelection
dirname = posixpath.dirname(path)
base = posixpath.basename(path)
try:
if dirname in tmpHdf["/"]:
# this is the case of a dataset at top plevel
# or having given the complete path
if base in tmpHdf[dirname]:
JUST_KEYS = False
scanlist.append("")
elif base in file_entry.keys():
JUST_KEYS = False
scanlist.append("")
except:
#it will crash later on
pass
else:
JUST_KEYS = False
scanlist.append("")
else:
try:
number, order = [int(x) for x in scanlist[0].split(".")]
JUST_KEYS = True
except:
JUST_KEYS = False
if not JUST_KEYS:
for scan in scanlist:
if scan.startswith("/"):
t = scan[1:]
else:
t = scan
if t not in entryNames:
raise ValueError("Entry %s not in file" % scan)
nFiles = len(filelist)
nScans = len(scanlist)
if JUST_KEYS:
if not nScans:
raise IOError("No entry contains the required data")
_logger.debug("Retained number of files = %d", nFiles)
_logger.debug("Retained number of scans = %d", nScans)
# Now is to decide the number of mca ...
# I assume all the scans contain the same number of mca
if JUST_KEYS:
path = "/" + entryNames[int(scanlist[0].split(".")[-1]) -
1] + ySelection
if mSelection is not None:
mpath = "/" + entryNames[int(scanlist[0].split(".")[-1]) -
1] + mSelection
if xSelectionList is not None:
xpathList = []
for xSelection in xSelectionList:
xpath = "/" + entryNames[int(scanlist[0].split(".")[-1]) -
1] + xSelection
xpathList.append(xpath)
else:
path = scanlist[0] + ySelection
if mSelection is not None:
mpath = scanlist[0] + mSelection
if xSelectionList is not None:
xpathList = []
for xSelection in xSelectionList:
xpath = scanlist[0] + xSelection
xpathList.append(xpath)
yDataset = tmpHdf[path]
if (self.__dtype is None) or (mSelection is not None):
self.__dtype = yDataset.dtype
if self.__dtype in [numpy.int16, numpy.uint16]:
self.__dtype = numpy.float32
elif self.__dtype in [numpy.int32, numpy.uint32]:
if mSelection:
self.__dtype = numpy.float32
else:
self.__dtype = numpy.float64
elif self.__dtype not in [
numpy.float16, numpy.float32, numpy.float64
]:
# Some datasets form CLS (origin APS?) arrive as data format
# equal to ">u2" and are not triggered as integer types
_logger.debug("Not basic dataset type %s", self.__dtype)
if ("%s" % self.__dtype).endswith("2"):
self.__dtype = numpy.float32
else:
if mSelection:
self.__dtype = numpy.float32
else:
self.__dtype = numpy.float64
# figure out the shape of the stack
shape = yDataset.shape
mcaIndex = selection.get('index', len(shape) - 1)
if mcaIndex == -1:
mcaIndex = len(shape) - 1
_logger.debug("mcaIndex = %d", mcaIndex)
considerAsImages = False
dim0, dim1, mcaDim = self.getDimensions(nFiles,
nScans,
shape,
index=mcaIndex)
try:
if self.__dtype in [numpy.float32, numpy.int32]:
bytefactor = 4
elif self.__dtype in [numpy.int16, numpy.uint16]:
bytefactor = 2
elif self.__dtype in [numpy.int8, numpy.uint8]:
bytefactor = 1
else:
bytefactor = 8
neededMegaBytes = nFiles * dim0 * dim1 * (mcaDim * bytefactor /
(1024 * 1024.))
_logger.info("Using %d bytes per item" % bytefactor)
_logger.info("Needed %d Megabytes" % neededMegaBytes)
physicalMemory = None
if hasattr(PhysicalMemory, "getAvailablePhysicalMemoryOrNone"):
physicalMemory = PhysicalMemory.getAvailablePhysicalMemoryOrNone(
)
if not physicalMemory:
physicalMemory = PhysicalMemory.getPhysicalMemoryOrNone()
else:
_logger.info("Available physical memory %.1f GBytes" % \
(physicalMemory/(1024*1024*1024.)))
if physicalMemory is None:
# 6 Gigabytes of available memory
# should be a good compromise in 2018
physicalMemory = 6000
_logger.info("Assumed physical memory %.1f MBytes" %
physicalMemory)
else:
physicalMemory /= (1024 * 1024.)
_logger.info("Using physical memory %.1f GBytes" %
(physicalMemory / 1024))
if (neededMegaBytes > (0.95*physicalMemory))\
and (nFiles == 1) and (len(shape) == 3):
if self.__dtype0 is None:
if (bytefactor == 8) and (neededMegaBytes <
(2 * physicalMemory)):
# try reading as float32
print("Forcing the use of float32 data")
self.__dtype = numpy.float32
else:
raise MemoryError("Force dynamic loading")
else:
raise MemoryError("Force dynamic loading")
if (mcaIndex == 0) and (nFiles == 1) and (nScans == 1):
#keep the original arrangement but in memory
self.data = numpy.zeros(yDataset.shape, self.__dtype)
considerAsImages = True
else:
# force arrangement as spectra
self.data = numpy.zeros((dim0, dim1, mcaDim), self.__dtype)
DONE = False
except (MemoryError, ValueError):
# some versions report ValueError instead of MemoryError
if (nFiles == 1) and (len(shape) == 3):
_logger.warning("Attempting dynamic loading")
if mSelection is not None:
_logger.warning("Ignoring monitor")
self.data = yDataset
if mSelection is not None:
mdtype = tmpHdf[mpath].dtype
if mdtype not in [numpy.float64, numpy.float32]:
mdtype = numpy.float64
mDataset = numpy.asarray(tmpHdf[mpath], dtype=mdtype)
self.monitor = [mDataset]
if xSelectionList is not None:
if len(xpathList) == 1:
xpath = xpathList[0]
xDataset = tmpHdf[xpath][()]
self.x = [xDataset]
if h5py.version.version < '2.0':
#prevent automatic closing keeping a reference
#to the open file
self._fileReference = hdfStack
DONE = True
else:
# what to do if the number of dimensions is only 2?
raise
# get the positioners information associated to the path
positioners = {}
try:
positionersGroup = NexusTools.getPositionersGroup(tmpHdf, path)
for motorName, motorValues in positionersGroup.items():
positioners[motorName] = motorValues[()]
except:
positionersGroup = None
positioners = {}
# get the mca information associated to the path
mcaObjectPaths = NexusTools.getMcaObjectPaths(tmpHdf, path)
_time = None
_calibration = None
_channels = None
if considerAsImages:
self._pathHasRelevantInfo = False
else:
numberOfRelevantInfoKeys = 0
for objectPath in mcaObjectPaths:
if objectPath not in ["counts", "target"]:
numberOfRelevantInfoKeys += 1
if numberOfRelevantInfoKeys: # not just "counts" or "target"
self._pathHasRelevantInfo = True
if "live_time" in mcaObjectPaths:
if DONE:
# hopefully it will fit into memory
if mcaObjectPaths["live_time"] in tmpHdf:
_time = tmpHdf[mcaObjectPaths["live_time"]][()]
elif "::" in mcaObjectPaths["live_time"]:
tmpFileName, tmpDatasetPath = \
mcaObjectPaths["live_time"].split("::")
with h5py.File(tmpFileName, "r") as tmpH5:
_time = tmpH5[tmpDatasetPath][()]
else:
del mcaObjectPaths["live_time"]
else:
# we have to have as many live times as MCA spectra
_time = numpy.zeros( \
(self.data.shape[0] * self.data.shape[1]),
dtype=numpy.float64)
elif "elapsed_time" in mcaObjectPaths:
if DONE:
# hopefully it will fit into memory
if mcaObjectPaths["elapsed_time"] in tmpHdf:
_time = \
tmpHdf[mcaObjectPaths["elapsed_time"]][()]
elif "::" in mcaObjectPaths["elapsed_time"]:
tmpFileName, tmpDatasetPath = \
mcaObjectPaths["elapsed_time"].split("::")
with h5py.File(tmpFileName, "r") as tmpH5:
_time = tmpH5[tmpDatasetPath][()]
else:
del mcaObjectPaths["elapsed_time"]
else:
# we have to have as many elpased times as MCA spectra
_time = numpy.zeros(
(self.data.shape[0] * self.data.shape[1]),
numpy.float32)
if "calibration" in mcaObjectPaths:
if mcaObjectPaths["calibration"] in tmpHdf:
_calibration = \
tmpHdf[mcaObjectPaths["calibration"]][()]
elif "::" in mcaObjectPaths["calibration"]:
tmpFileName, tmpDatasetPath = \
mcaObjectPaths["calibration"].split("::")
with h5py.File(tmpFileName, "r") as tmpH5:
_calibration = tmpH5[tmpDatasetPath][()]
else:
del mcaObjectPaths["calibration"]
if "channels" in mcaObjectPaths:
if mcaObjectPaths["channels"] in tmpHdf:
_channels = \
tmpHdf[mcaObjectPaths["channels"]][()]
elif "::" in mcaObjectPaths["channels"]:
tmpFileName, tmpDatasetPath = \
mcaObjectPaths["channels"].split("::")
with h5py.File(tmpFileName, "r") as tmpH5:
_channels = tmpH5[tmpDatasetPath][()]
else:
del mcaObjectPaths["channels"]
else:
self._pathHasRelevantInfo = False
if (not DONE) and (not considerAsImages):
_logger.info("Data in memory as spectra")
self.info["McaIndex"] = 2
n = 0
if dim0 == 1:
self.onBegin(dim1)
else:
self.onBegin(dim0)
self.incrProgressBar = 0
for hdf in hdfStack._sourceObjectList:
entryNames = list(hdf["/"].keys())
goodEntryNames = []
for entry in entryNames:
tmpPath = "/" + entry
try:
if hasattr(hdf[tmpPath], "keys"):
goodEntryNames.append(entry)
except KeyError:
_logger.info("Broken link with key? <%s>" % tmpPath)
for scan in scanlist:
IN_MEMORY = None
nStart = n
for ySelection in ySelectionList:
n = nStart
if JUST_KEYS:
entryName = goodEntryNames[
int(scan.split(".")[-1]) - 1]
path = entryName + ySelection
if mSelection is not None:
mpath = entryName + mSelection
mdtype = hdf[mpath].dtype
if mdtype not in [
numpy.float64, numpy.float32
]:
mdtype = numpy.float64
mDataset = numpy.asarray(hdf[mpath],
dtype=mdtype)
if xSelectionList is not None:
xDatasetList = []
for xSelection in xSelectionList:
xpath = entryName + xSelection
xDataset = hdf[xpath][()]
xDatasetList.append(xDataset)
else:
path = scan + ySelection
if mSelection is not None:
mpath = scan + mSelection
mdtype = hdf[mpath].dtype
if mdtype not in [
numpy.float64, numpy.float32
]:
mdtype = numpy.float64
mDataset = numpy.asarray(hdf[mpath],
dtype=mdtype)
if xSelectionList is not None:
xDatasetList = []
for xSelection in xSelectionList:
xpath = scan + xSelection
xDataset = hdf[xpath][()]
xDatasetList.append(xDataset)
try:
yDataset = hdf[path]
tmpShape = yDataset.shape
totalBytes = numpy.ones((1, ),
yDataset.dtype).itemsize
for nItems in tmpShape:
totalBytes *= nItems
# should one be conservative or just try?
if (totalBytes /
(1024. * 1024.)) > (0.4 * physicalMemory):
_logger.info(
"Force dynamic loading of spectra")
#read from disk
IN_MEMORY = False
else:
#read the data into memory
_logger.info(
"Attempt to load whole map into memory")
yDataset = hdf[path][()]
IN_MEMORY = True
except (MemoryError, ValueError):
_logger.info("Dynamic loading of spectra")
yDataset = hdf[path]
IN_MEMORY = False
nMcaInYDataset = 1
for dim in yDataset.shape:
nMcaInYDataset *= dim
nMcaInYDataset = int(nMcaInYDataset / mcaDim)
timeData = None
if _time is not None:
if "live_time" in mcaObjectPaths:
# it is assumed that all have the same structure!!!
timePath = NexusTools.getMcaObjectPaths(
hdf, path)["live_time"]
elif "elapsed_time" in mcaObjectPaths:
timePath = NexusTools.getMcaObjectPaths(
hdf, path)["elapsed_time"]
if timePath in hdf:
timeData = hdf[timePath][()]
elif "::" in timePath:
externalFile, externalPath = timePath.split(
"::")
with h5py.File(externalFile, "r") as timeHdf:
timeData = timeHdf[externalPath][()]
if mcaIndex != 0:
if IN_MEMORY:
yDataset.shape = -1, mcaDim
if mSelection is not None:
case = -1
nMonitorData = 1
for v in mDataset.shape:
nMonitorData *= v
if nMonitorData == nMcaInYDataset:
mDataset.shape = nMcaInYDataset
case = 0
elif nMonitorData == (nMcaInYDataset * mcaDim):
case = 1
mDataset.shape = nMcaInYDataset, mcaDim
if case == -1:
raise ValueError(\
"I do not know how to handle this monitor data")
if timeData is not None:
case = -1
nTimeData = 1
for v in timeData.shape:
nTimeData *= v
if nTimeData == nMcaInYDataset:
timeData.shape = nMcaInYDataset
case = 0
_time[nStart:nStart +
nMcaInYDataset] += timeData
if case == -1:
_logger.warning(
"I do not know how to handle this time data"
)
_logger.warning(
"Ignoring time information")
_time = None
if (len(yDataset.shape) == 3) and\
(dim1 == yDataset.shape[1]):
mca = 0
deltaI = int(yDataset.shape[1] / dim1)
for ii in range(yDataset.shape[0]):
i = int(n / dim1)
yData = yDataset[ii:(ii + 1)]
yData.shape = -1, mcaDim
if mSelection is not None:
if case == 0:
mData = numpy.outer(
mDataset[mca:(mca + dim1)],
numpy.ones((mcaDim)))
self.data[i, :, :] += yData / mData
elif case == 1:
mData = mDataset[mca:(mca +
dim1), :]
mData.shape = -1, mcaDim
self.data[i, :, :] += yData / mData
else:
self.data[i:(i + deltaI), :] += yData
n += yDataset.shape[1]
mca += dim1
else:
for mca in range(nMcaInYDataset):
i = int(n / dim1)
j = n % dim1
if len(yDataset.shape) == 3:
ii = int(mca / yDataset.shape[1])
jj = mca % yDataset.shape[1]
yData = yDataset[ii, jj]
elif len(yDataset.shape) == 2:
yData = yDataset[mca, :]
elif len(yDataset.shape) == 1:
yData = yDataset
if mSelection is not None:
if case == 0:
self.data[
i,
j, :] += yData / mDataset[mca]
elif case == 1:
self.data[
i, j, :] += yData / mDataset[
mca, :]
else:
self.data[i, j, :] += yData
n += 1
def loadFileList(self, filelist, selection, scanlist=None):
"""
loadFileList(self, filelist, y, scanlist=None, monitor=None, x=None)
filelist is the list of file names belonging to the stack
selection is a dictionary with the keys x, y, m.
x is the path to the x data (the channels) in the spectrum,
without the first level "directory". It is unused (for now).
y is the path to the 1D data (the counts) in the spectrum,
without the first level "directory"
m is the path to the normalizing data (I0 or whatever)
without the first level "directory".
scanlist is the list of first level "directories" containing the 1D data
Example: The actual path has the form:
/whatever1/whatever2/counts
That means scanlist = ["/whatever1"]
and selection['y'] = "/whatever2/counts"
"""
if DEBUG:
print("filelist = ", filelist)
print("selection = ", selection)
print("scanlist = ", scanlist)
# all the files in the same source
hdfStack = NexusDataSource.NexusDataSource(filelist)
#if there is more than one file, it is assumed all the files have
#the same structure.
tmpHdf = hdfStack._sourceObjectList[0]
entryNames = []
for key in tmpHdf["/"].keys():
if isinstance(tmpHdf["/" + key], h5py.Group):
entryNames.append(key)
# built the selection in terms of HDF terms
# for the time being, only the first item in x selection used
xSelection = selection['x']
if xSelection is not None:
if type(xSelection) != type([]):
xSelection = [xSelection]
if type(xSelection) == type([]):
if len(xSelection):
xSelection = xSelection[0]
else:
xSelection = None
else:
xSelection = None
# only one y is taken
ySelection = selection['y']
if type(ySelection) == type([]):
ySelection = ySelection[0]
# monitor selection
mSelection = selection['m']
if mSelection not in [None, []]:
if type(mSelection) != type([]):
mSelection = [mSelection]
if type(mSelection) == type([]):
if len(mSelection):
mSelection = mSelection[0]
else:
mSelection = None
else:
mSelection = None
USE_JUST_KEYS = False
# deal with the pathological case where the scanlist corresponds
# to a selected top level dataset
if len(entryNames) == 0:
if scanlist is not None:
if len(scanlist) == 1:
if scanlist[0] == ySelection:
scanlist = None
USE_JUST_KEYS = True
elif len(entryNames) == 1:
# deal with the SOLEIL case of one entry but with different name
# in different files
USE_JUST_KEYS = True
elif scanlist in [None, []]:
USE_JUST_KEYS = True
if USE_JUST_KEYS:
#if the scanlist is None, it is assumed we are interested on all
#the scans containing the selection, not that all the scans
#contain the selection.
scanlist = []
if 0:
JUST_KEYS = False
#expect same entry names in the files
#Unfortunately this does not work for SOLEIL
for entry in entryNames:
path = "/" + entry + ySelection
dirname = posixpath.dirname(path)
base = posixpath.basename(path)
try:
if base in tmpHdf[dirname].keys():
scanlist.append(entry)
except:
pass
else:
JUST_KEYS = True
#expect same structure in the files even if the
#names are different (SOLEIL ...)
if len(entryNames):
i = 0
for entry in entryNames:
path = "/" + entry + ySelection
dirname = posixpath.dirname(path)
base = posixpath.basename(path)
if hasattr(tmpHdf[dirname], "keys"):
i += 1
if base in tmpHdf[dirname].keys():
scanlist.append("1.%d" % i)
if not len(scanlist):
path = "/" + ySelection
dirname = posixpath.dirname(path)
base = posixpath.basename(path)
try:
if base in tmpHdf[dirname].keys():
JUST_KEYS = False
scanlist.append("")
except:
#it will crash later on
pass
else:
JUST_KEYS = False
scanlist.append("")
else:
try:
number, order = [int(x) for x in scanlist[0].split(".")]
JUST_KEYS = True
except:
JUST_KEYS = False
if not JUST_KEYS:
for scan in scanlist:
if scan.startswith("/"):
t = scan[1:]
else:
t = scan
if t not in entryNames:
raise ValueError("Entry %s not in file" % scan)
nFiles = len(filelist)
nScans = len(scanlist)
if JUST_KEYS:
if not nScans:
raise IOError("No entry contains the required data")
if DEBUG:
print("Retained number of files = %d" % nFiles)
print("Retained number of scans = %d" % nScans)
#Now is to decide the number of mca ...
#I assume all the scans contain the same number of mca
if JUST_KEYS:
path = "/" + entryNames[int(scanlist[0].split(".")[-1]) -
1] + ySelection
if mSelection is not None:
mpath = "/" + entryNames[int(scanlist[0].split(".")[-1]) -
1] + mSelection
if xSelection is not None:
xpath = "/" + entryNames[int(scanlist[0].split(".")[-1]) -
1] + xSelection
else:
path = scanlist[0] + ySelection
if mSelection is not None:
mpath = scanlist[0] + mSelection
if xSelection is not None:
xpath = scanlist[0] + xSelection
yDataset = tmpHdf[path]
if self.__dtype is None:
self.__dtype = yDataset.dtype
if self.__dtype in [numpy.int16, numpy.uint16]:
self.__dtype = numpy.float32
elif self.__dtype in [numpy.int32, numpy.uint32]:
self.__dtype = numpy.float64
#figure out the shape of the stack
shape = yDataset.shape
mcaIndex = selection.get('index', len(shape) - 1)
if mcaIndex == -1:
mcaIndex = len(shape) - 1
if DEBUG:
print("mcaIndex = %d" % mcaIndex)
considerAsImages = False
dim0, dim1, mcaDim = self.getDimensions(nFiles,
nScans,
shape,
index=mcaIndex)
try:
if self.__dtype in [numpy.float32, numpy.int32]:
bytefactor = 4
elif self.__dtype in [numpy.int16, numpy.uint16]:
bytefactor = 2
elif self.__dtype in [numpy.int8, numpy.uint8]:
bytefactor = 1
else:
bytefactor = 8
neededMegaBytes = nFiles * dim0 * dim1 * (mcaDim * bytefactor /
(1024 * 1024.))
physicalMemory = PhysicalMemory.getPhysicalMemoryOrNone()
if physicalMemory is None:
# 5 Gigabytes should be a good compromise
physicalMemory = 6000
else:
physicalMemory /= (1024 * 1024.)
if (neededMegaBytes > (0.95*physicalMemory))\
and (nFiles == 1) and (len(shape) == 3):
if self.__dtype0 is None:
if (bytefactor == 8) and (neededMegaBytes <
(2 * physicalMemory)):
#try reading as float32
self.__dtype = numpy.float32
else:
raise MemoryError("Force dynamic loading")
else:
raise MemoryError("Force dynamic loading")
if (mcaIndex == 0) and (nFiles == 1) and (nScans == 1):
#keep the original arrangement but in memory
self.data = numpy.zeros(yDataset.shape, self.__dtype)
considerAsImages = True
else:
# force arrangement as spectra
self.data = numpy.zeros((dim0, dim1, mcaDim), self.__dtype)
DONE = False
except (MemoryError, ValueError):
#some versions report ValueError instead of MemoryError
if (nFiles == 1) and (len(shape) == 3):
print("Attempting dynamic loading")
self.data = yDataset
if mSelection is not None:
mDataset = tmpHdf[mpath].value
self.monitor = [mDataset]
if xSelection is not None:
xDataset = tmpHdf[xpath].value
self.x = [xDataset]
if h5py.version.version < '2.0':
#prevent automatic closing keeping a reference
#to the open file
self._fileReference = hdfStack
DONE = True
else:
#what to do if the number of dimensions is only 2?
raise
if (not DONE) and (not considerAsImages):
self.info["McaIndex"] = 2
n = 0
if dim0 == 1:
self.onBegin(dim1)
else:
self.onBegin(dim0)
self.incrProgressBar = 0
for hdf in hdfStack._sourceObjectList:
entryNames = list(hdf["/"].keys())
goodEntryNames = []
for entry in entryNames:
tmpPath = "/" + entry
if hasattr(hdf[tmpPath], "keys"):
goodEntryNames.append(entry)
for scan in scanlist:
if JUST_KEYS:
entryName = goodEntryNames[int(scan.split(".")[-1]) -
1]
path = entryName + ySelection
if mSelection is not None:
mpath = entryName + mSelection
mDataset = hdf[mpath].value
if xSelection is not None:
xpath = entryName + xSelection
xDataset = hdf[xpath].value
else:
path = scan + ySelection
if mSelection is not None:
mpath = scan + mSelection
mDataset = hdf[mpath].value
if xSelection is not None:
xpath = scan + xSelection
xDataset = hdf[xpath].value
try:
yDataset = hdf[path]
tmpShape = yDataset.shape
totalBytes = numpy.ones((1, ), yDataset.dtype).itemsize
for nItems in tmpShape:
totalBytes *= nItems
if (totalBytes / (1024. * 1024.)) > 500:
#read from disk
IN_MEMORY = False
else:
#read the data into memory
yDataset = hdf[path].value
IN_MEMORY = True
except (MemoryError, ValueError):
yDataset = hdf[path]
IN_MEMORY = False
nMcaInYDataset = 1
for dim in yDataset.shape:
nMcaInYDataset *= dim
nMcaInYDataset = int(nMcaInYDataset / mcaDim)
if mcaIndex != 0:
if IN_MEMORY:
yDataset.shape = -1, mcaDim
if mSelection is not None:
case = -1
nMonitorData = 1
for v in mDataset.shape:
nMonitorData *= v
if nMonitorData == nMcaInYDataset:
mDataset.shape = nMcaInYDataset
case = 0
elif nMonitorData == (nMcaInYDataset * mcaDim):
case = 1
mDataset.shape = nMcaInYDataset, mcaDim
if case == -1:
raise ValueError(\
"I do not know how to handle this monitor data")
if (len(yDataset.shape) == 3) and\
(dim1 == yDataset.shape[1]):
mca = 0
deltaI = int(yDataset.shape[1] / dim1)
for ii in range(yDataset.shape[0]):
i = int(n / dim1)
yData = yDataset[ii:(ii + 1)]
yData.shape = -1, mcaDim
if mSelection is not None:
if case == 0:
mData = numpy.outer(
mDataset[mca:(mca + dim1)],
numpy.ones((mcaDim)))
self.data[i, :, :] = yData / mData
elif case == 1:
mData = mDataset[mca:(mca + dim1), :]
mData.shape = -1, mcaDim
self.data[i, :, :] = yData / mData
else:
self.data[i:(i + deltaI), :] = yData
n += yDataset.shape[1]
mca += dim1
else:
for mca in range(nMcaInYDataset):
i = int(n / dim1)
j = n % dim1
if len(yDataset.shape) == 3:
ii = int(mca / yDataset.shape[1])
jj = mca % yDataset.shape[1]
yData = yDataset[ii, jj]
elif len(yDataset.shape) == 2:
yData = yDataset[mca, :]
elif len(yDataset.shape) == 1:
yData = yDataset
if mSelection is not None:
if case == 0:
self.data[i,
j, :] = yData / mDataset[mca]
elif case == 1:
self.data[
i, j, :] = yData / mDataset[mca, :]
else:
self.data[i, j, :] = yData
n += 1
else:
if mSelection is not None:
case = -1
nMonitorData = 1
for v in mDataset.shape:
nMonitorData *= v
if nMonitorData == yDataset.shape[0]:
case = 3
mDataset.shape = yDataset.shape[0]
elif nMonitorData == nMcaInYDataset:
mDataset.shape = nMcaInYDataset
case = 0
def setFileList(self, filelist):
self.dataSource = NexusDataSource.NexusDataSource(filelist[0])
self.nexusWidget.setDataSource(self.dataSource)
phynxFile = self.dataSource._sourceObjectList[0]
keys = list(phynxFile.keys())
if len(keys) != 1:
return
#check if it is an NXentry
entry = phynxFile[keys[0]]
attrs = list(entry.attrs)
if 'NX_class' in attrs:
attr = entry.attrs['NX_class']
if sys.version > '2.9':
try:
attr = attr.decode('utf-8')
except:
print("WARNING: Cannot decode NX_class attribute")
attr = None
else:
attr = None
if attr is None:
return
if attr != 'NXentry':
return
#check if there is only one NXdata
nxDataList = []
for key in entry.keys():
attr = entry[key].attrs.get('NX_class', None)
if attr is None:
continue
if sys.version > '2.9':
try:
attr = attr.decode('utf-8')
except:
print("WARNING: Cannot decode NX_class attribute")
continue
if attr in ['NXdata']:
nxDataList.append(key)
if len(nxDataList) != 1:
return
nxData = entry[nxDataList[0]]
#try to get the signals
signalList = []
axesList = []
interpretation = ""
for key in nxData.keys():
if 'signal' in nxData[key].attrs.keys():
if int(nxData[key].attrs['signal']) == 1:
signalList.append(key)
if len(signalList) == 1:
if 'interpretation' in nxData[key].attrs.keys():
interpretation = nxData[key].attrs['interpretation']
if sys.version > '2.9':
try:
interpretation = interpretation.decode('utf-8')
except:
print("WARNING: Cannot decode interpretation")
if interpretation == "image":
self.stackIndexWidget.setIndex(0)
if 'axes' in nxData[key].attrs.keys():
axes = nxData[key].attrs['axes']
if sys.version > '2.9':
try:
axes = axes.decode('utf-8')
except:
print("WARNING: Cannot decode axes")
axes = axes.split(":")
for axis in axes:
if axis in nxData.keys():
axesList.append(axis)
if not len(signalList):
return
ddict = {}
ddict['counters'] = []
ddict['aliases'] = []
for signal in signalList:
path = posixpath.join("/",nxDataList[0], signal)
ddict['counters'].append(path)
ddict['aliases'].append(posixpath.basename(signal))
for axis in axesList:
path = posixpath.join("/",nxDataList[0], axis)
ddict['counters'].append(path)
ddict['aliases'].append(posixpath.basename(axis))
if sys.platform == "darwin" and\
len(ddict['counters']) > 3 and\
qt.qVersion().startswith('4.8'):
# workaround a strange bug on Mac:
# when the counter list has to be scrolled
# the selected button also changes!!!!
return
self.nexusWidget.setWidgetConfiguration(ddict)
if len(signalList):
if len(axesList) == 0:
self.nexusWidget.cntTable.setCounterSelection({'y':[0]})
elif interpretation == "image":
self.nexusWidget.cntTable.setCounterSelection({'y':[0], 'x':[1]})
elif interpretation == "spectrum":
self.nexusWidget.cntTable.setCounterSelection({'y':[0], 'x':[len(axesList)]})
else:
self.nexusWidget.cntTable.setCounterSelection({'y':[0]})
def setFileList(self, filelist):
self.dataSource = NexusDataSource.NexusDataSource(filelist[0])
self.nexusWidget.setDataSource(self.dataSource)
phynxFile = self.dataSource._sourceObjectList[0]
keys = list(phynxFile.keys())
if len(keys) != 1:
return
#check if it is an NXentry
entry = phynxFile[keys[0]]
attrs = list(entry.attrs)
if 'NX_class' in attrs:
attr = entry.attrs['NX_class']
if hasattr(attr, "decode"):
try:
attr = attr.decode('utf-8')
except:
print("WARNING: Cannot decode NX_class attribute")
attr = None
else:
attr = None
if attr is None:
return
if attr not in ['NXentry', b'NXentry']:
return
#check if there is only one NXdata
nxDataList = []
for key in entry.keys():
attr = entry[key].attrs.get('NX_class', None)
if attr is None:
continue
if hasattr(attr, "decode"):
try:
attr = attr.decode('utf-8')
except:
print("WARNING: Cannot decode NX_class attribute")
continue
if attr in ['NXdata', b'NXdata']:
nxDataList.append(key)
if len(nxDataList) != 1:
return
nxData = entry[nxDataList[0]]
ddict = {'counters': [], 'aliases': []}
signalList = []
axesList = []
interpretation = ""
signal_key = nxData.attrs.get("signal")
if signal_key is not None:
# recent NXdata specification
if hasattr(signal_key, "decode"):
try:
signal_key = signal_key.decode('utf-8')
except AttributeError:
print("WARNING: Cannot decode NX_class attribute")
signal_dataset = nxData.get(signal_key)
if signal_dataset is None:
return
interpretation = signal_dataset.attrs.get("interpretation", "")
if hasattr(interpretation, "decode"):
try:
interpretation = interpretation.decode('utf-8')
except AttributeError:
print("WARNING: Cannot decode interpretation")
axesList = list(nxData.attrs.get("axes", []))
if not axesList:
# try the old method, still documented on nexusformat.org:
# colon-delimited "array" of dataset names as a signal attr
axes = signal_dataset.attrs.get('axes')
if axes is not None:
if hasattr(axes, "decode"):
try:
axes = axes.decode('utf-8')
except AttributeError:
print("WARNING: Cannot decode axes")
axes = axes.split(":")
axesList = [ax for ax in axes if ax in nxData]
signalList.append(signal_key)
else:
# old specification
for key in nxData.keys():
if 'signal' in nxData[key].attrs.keys():
if int(nxData[key].attrs['signal']) == 1:
signalList.append(key)
if len(signalList) == 1:
if 'interpretation' in nxData[key].attrs.keys():
interpretation = nxData[key].attrs[
'interpretation']
if sys.version > '2.9':
try:
interpretation = interpretation.decode(
'utf-8')
except:
print(
"WARNING: Cannot decode interpretation"
)
if 'axes' in nxData[key].attrs.keys():
axes = nxData[key].attrs['axes']
if sys.version > '2.9':
try:
axes = axes.decode('utf-8')
except:
print("WARNING: Cannot decode axes")
axes = axes.split(":")
for axis in axes:
if axis in nxData.keys():
axesList.append(axis)
if not len(signalList):
return
if interpretation in ["image", b"image"]:
self.stackIndexWidget.setIndex(0)
for signal_key in signalList:
path = posixpath.join("/", nxDataList[0], signal_key)
ddict['counters'].append(path)
ddict['aliases'].append(posixpath.basename(signal_key))
for axis in axesList:
path = posixpath.join("/", nxDataList[0], axis)
ddict['counters'].append(path)
ddict['aliases'].append(posixpath.basename(axis))
if sys.platform == "darwin" and\
len(ddict['counters']) > 3 and\
qt.qVersion().startswith('4.8'):
# workaround a strange bug on Mac:
# when the counter list has to be scrolled
# the selected button also changes!!!!
return
self.nexusWidget.setWidgetConfiguration(ddict)
if axesList and (interpretation in ["image", b"image"]):
self.nexusWidget.cntTable.setCounterSelection({'y': [0], 'x': [1]})
elif axesList and (interpretation in ["spectrum", b"spectrum"]):
self.nexusWidget.cntTable.setCounterSelection({
'y': [0],
'x': [len(axesList)]
})
else:
self.nexusWidget.cntTable.setCounterSelection({'y': [0]})
