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|
#/*##########################################################################
#
# The PyMca X-Ray Fluorescence Toolkit
#
# Copyright (c) 2004-2018 European Synchrotron Radiation Facility
#
# This file is part of the PyMca X-ray Fluorescence Toolkit developed at
# the ESRF by the Software group.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
#############################################################################*/
__author__ = "V.A. Sole - ESRF Data Analysis"
__contact__ = "sole@esrf.fr"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
import posixpath
import numpy
import h5py
import logging
_logger = logging.getLogger(__name__)
try:
from PyMca5.PyMcaCore import DataObject
from PyMca5.PyMcaMisc import PhysicalMemory
except ImportError:
_logger.info("HDF5Stack1D importing DataObject from local directory!")
import DataObject
import PhysicalMemory
try:
from PyMca5.PyMcaCore import NexusDataSource
from PyMca5.PyMcaCore import NexusTools
except ImportError:
_logger.info("HDF5Stack1D importing NexusDataSource from local directory!")
import NexusDataSource
import NexusDataSource
SOURCE_TYPE = "HDF5Stack1D"
class HDF5Stack1D(DataObject.DataObject):
def __init__(self, filelist, selection,
scanlist=None,
dtype=None):
DataObject.DataObject.__init__(self)
#the data type of the generated stack
self.__dtype0 = dtype
self.__dtype = dtype
if filelist is not None:
if selection is not None:
self.loadFileList(filelist, selection, scanlist)
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.debug("filelist = %s", filelist)
_logger.debug("selection = %s", selection)
_logger.debug("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():
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.get('x', None)
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([]):
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 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) 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.))
physicalMemory = None
if hasattr(PhysicalMemory, "getAvailablePhysicalMemoryOrNone"):
physicalMemory = PhysicalMemory.getAvailablePhysicalMemoryOrNone()
if not physicalMemory:
physicalMemory = PhysicalMemory.getPhysicalMemoryOrNone()
if physicalMemory is None:
# 6 Gigabytes of available memory
# should be a good compromise in 2018
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
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 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
# 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:
if len(list(mcaObjectPaths.keys())) > 1: # not just "counts"
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"]].value
elif "::" in mcaObjectPaths["live_time"]:
tmpFileName, tmpDatasetPath = \
mcaObjectPaths["live_time"].split("::")
with h5py.File(tmpFileName, "r") as tmpH5:
_time = tmpH5[tmpDatasetPath].value
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"]].value
elif "::" in mcaObjectPaths["elapsed_time"]:
tmpFileName, tmpDatasetPath = \
mcaObjectPaths["elapsed_time"].split("::")
with h5py.File(tmpFileName, "r") as tmpH5:
_time = tmpH5[tmpDatasetPath].value
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"]].value
elif "::" in mcaObjectPaths["calibration"]:
tmpFileName, tmpDatasetPath = \
mcaObjectPaths["calibration"].split("::")
with h5py.File(tmpFileName, "r") as tmpH5:
_calibration = tmpH5[tmpDatasetPath].value
else:
del mcaObjectPaths["calibration"]
if "channels" in mcaObjectPaths:
if mcaObjectPaths["channels"] in tmpHdf:
_channels = \
tmpHdf[mcaObjectPaths["channels"]].value
elif "::" in mcaObjectPaths["channels"]:
tmpFileName, tmpDatasetPath = \
mcaObjectPaths["channels"].split("::")
with h5py.File(tmpFileName, "r") as tmpH5:
_channels = tmpH5[tmpDatasetPath].value
else:
del mcaObjectPaths["channels"]
else:
self._pathHasRelevantInfo = False
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:
IN_MEMORY = None
nStart = n
for ySelection in ySelectionList:
n = nStart
if IN_MEMORY == False:
# We can only deal with one dynamic dataset
_logger.warning("Selection %s ignored", ySelection)
continue
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 xSelection is not None:
xpath = entryName + xSelection
xDataset = hdf[xpath].value
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 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)
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].value
elif "::" in timePath:
externalFile, externalPath = timePath.split("::")
with h5py.File(externalFile, "r") as timeHdf:
timeData = timeHdf[externalPath].value
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
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
#elif nMonitorData == (yDataset.shape[1] * yDataset.shape[2]):
# case = 1
# mDataset.shape = yDataset.shape[1], yDataset.shape[2]
if case == -1:
raise ValueError(\
"I do not know how to handle this monitor data")
if IN_MEMORY:
yDataset.shape = mcaDim, -1
if len(yDataset.shape) != 3:
for mca in range(nMcaInYDataset):
i = int(n/dim1)
j = n % dim1
if len(yDataset.shape) == 3:
ii = int(mca/yDataset.shape[2])
jj = mca % yDataset.shape[2]
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]
elif case == 3:
self.data[i, j, :] += yData / mDataset
else:
self.data[i, j, :] += yData
n += 1
else:
#stack of images to be read as MCA
for nImage in range(yDataset.shape[0]):
tmp = yDataset[nImage:(nImage+1)]
if len(tmp.shape) == 3:
i = int(n/dim1)
j = n % dim1
if 0:
#this loop is extremely SLOW!!!(and useless)
for ii in range(tmp.shape[1]):
for jj in range(tmp.shape[2]):
self.data[i+ii, j+jj, nImage] += tmp[0, ii, jj]
else:
self.data[i:i+tmp.shape[1],
j:j+tmp.shape[2], nImage] += tmp[0]
if mSelection is not None:
for mca in range(yDataset.shape[0]):
i = int(n/dim1)
j = n % dim1
yData = self.data[i, j, :]
if case == 0:
self.data[i, j, :] += yData / mDataset[mca]
elif case == 1:
self.data[i, j, :] += yData / mDataset[:, mca]
n += 1
else:
n += tmp.shape[1] * tmp.shape[2]
if dim0 == 1:
self.onProgress(j)
if dim0 != 1:
self.onProgress(i)
self.onEnd()
elif not DONE:
# data into memory but as images
self.info["McaIndex"] = mcaIndex
for hdf in hdfStack._sourceObjectList:
entryNames = list(hdf["/"].keys())
for scan in scanlist:
for ySelection in ySelectionList:
if JUST_KEYS:
entryName = entryNames[int(scan.split(".")[-1])-1]
path = entryName + ySelection
if mSelection is not None:
mpath = entryName + mSelection
mDataset.shape
if xSelection is not None:
xpath = entryName + xSelection
xDataset = hdf[xpath].value
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 xSelection is not None:
xpath = scan + xSelection
xDataset = hdf[xpath].value
if mSelection is not None:
nMonitorData = mDataset.size
case = -1
yDatasetShape = yDataset.shape
if nMonitorData == yDatasetShape[0]:
#as many monitor data as images
mDataset.shape = yDatasetShape[0]
case = 0
elif nMonitorData == (yDatasetShape[1] * yDatasetShape[2]):
#as many monitorData as pixels
case = 1
mDataset.shape = yDatasetShape[1], yDatasetShape[2]
if case == -1:
raise ValueError(\
"I do not know how to handle this monitor data")
if case == 0:
for i in range(yDatasetShape[0]):
self.data[i] += yDataset[i].value / mDataset[i]
elif case == 1:
for i in range(yDataset.shape[0]):
self.data[i] += yDataset[i] / mDataset
else:
for i in range(yDataset.shape[0]):
self.data[i:i+1] += yDataset[i:i+1]
else:
self.info["McaIndex"] = mcaIndex
if _time:
nRequiredValues = 1
for i in range(len(self.data.shape)):
if i != mcaIndex:
nRequiredValues *= self.data.shape[i]
if _time.size != nRequiredValues:
_logger.warning("I do not know how to interpret the time information")
_logger.warning("Ignoring time information")
_time = None
else:
_time.shape = -1
self.info["SourceType"] = SOURCE_TYPE
self.info["SourceName"] = filelist
self.info["Size"] = 1
self.info["NumberOfFiles"] = 1
if mcaIndex == 0:
self.info["FileIndex"] = 1
else:
self.info["FileIndex"] = 0
if _calibration is not None:
self.info['McaCalib'] = _calibration
else:
self.info['McaCalib'] = [ 0.0, 1.0, 0.0]
shape = self.data.shape
for i in range(len(shape)):
key = 'Dim_%d' % (i+1,)
self.info[key] = shape[i]
self.info['Channel0'] = 0
if xSelection is not None:
if xDataset.size == shape[self.info['McaIndex']]:
self.x = [xDataset.reshape(-1)]
else:
_logger.warning("Ignoring xSelection")
elif _channels is not None:
_channels.shape = -1
self.x = [_channels]
if _time is not None:
self.info["McaLiveTime"] = _time
def getDimensions(self, nFiles, nScans, shape, index=None):
#some body may want to overwrite this
"""
Returns the shape of the final stack as (Dim0, Dim1, Nchannels)
"""
if index is None:
index = -1
if index == -1:
index = len(shape) - 1
_logger.debug("INDEX = %d", index)
#figure out the shape of the stack
if len(shape) == 0:
#a scalar?
raise ValueError("Selection corresponds to a scalar")
elif len(shape) == 1:
#nchannels
nMca = 1
elif len(shape) == 2:
if index == 0:
#npoints x nchannels
nMca = shape[1]
else:
#npoints x nchannels
nMca = shape[0]
elif len(shape) == 3:
if index in [2, -1]:
#dim1 x dim2 x nchannels
nMca = shape[0] * shape[1]
elif index == 0:
nMca = shape[1] * shape[2]
else:
raise IndexError("Only first and last dimensions handled")
else:
nMca = 1
for i in range(len(shape)):
if i == index:
continue
nMca *= shape[i]
mcaDim = shape[index]
_logger.debug("nMca = %d", nMca)
_logger.debug("mcaDim = %s", mcaDim)
# HDF allows to work directly from the files without loading
# them into memory.
if (nScans == 1) and (nFiles > 1):
if nMca == 1:
#specfile like case
dim0 = nFiles
dim1 = nMca * nScans # nScans is 1
else:
#ESRF EDF like case
dim0 = nFiles
dim1 = nMca * nScans # nScans is 1
elif (nScans == 1) and (nFiles == 1):
if nMca == 1:
#specfile like single mca
dim0 = nFiles # it is 1
dim1 = nMca * nScans # nScans is 1
elif len(shape) == 2:
dim0 = nFiles # it is 1
dim1 = nMca * nScans # nScans is 1
elif len(shape) == 3:
if index == 0:
dim0 = shape[1]
dim1 = shape[2]
else:
dim0 = shape[0]
dim1 = shape[1]
else:
#specfile like multiple mca
dim0 = nFiles # it is 1
dim1 = nMca * nScans # nScans is 1
elif (nScans > 1) and (nFiles == 1):
if nMca == 1:
#specfile like case
dim0 = nFiles
dim1 = nMca * nScans
elif nMca > 1:
if len(shape) == 1:
#specfile like case
dim0 = nFiles
dim1 = nMca * nScans
elif len(shape) == 2:
dim0 = nScans
dim1 = nMca #shape[0]
elif len(shape) == 3:
if (shape[0] == 1) or (shape[1] == 1):
dim0 = nScans
dim1 = nMca
else:
#The user will have to decide the shape
dim0 = 1
dim1 = nScans * nMca
else:
#The user will have to decide the shape
dim0 = 1
dim1 = nScans * nMca
elif (nScans > 1) and (nFiles > 1):
dim0 = nFiles
dim1 = nMca * nScans
else:
#I should not reach this point
raise ValueError("Unhandled case")
return dim0, dim1, shape[index]
def onBegin(self, n):
pass
def onProgress(self, n):
pass
def onEnd(self):
pass
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