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#/*##########################################################################
#
# The PyMca X-Ray Fluorescence Toolkit
#
# Copyright (c) 2004-2022 European Synchrotron Radiation Facility
#
# This file is part of the PyMca X-ray Fluorescence Toolkit developed at
# the ESRF.
#
# 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"
__contact__ = "sole@esrf.fr"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
"""
Base class to handle stacks.
"""
from PyMca5.PyMcaCore import DataObject
import numpy
import time
import os
import sys
import glob
import logging
logger = logging.getLogger(__name__)
PLUGINS_DIR = None
try:
import PyMca5
if os.path.exists(os.path.join(os.path.dirname(__file__), "PyMcaPlugins")):
from PyMca5 import PyMcaPlugins
PLUGINS_DIR = os.path.dirname(PyMcaPlugins.__file__)
else:
directory = os.path.dirname(__file__)
while True:
if os.path.exists(os.path.join(directory, "PyMcaPlugins")):
PLUGINS_DIR = os.path.join(directory, "PyMcaPlugins")
break
directory = os.path.dirname(directory)
if len(directory) < 5:
break
userPluginsDirectory = PyMca5.getDefaultUserPluginsDirectory()
PYMCA_PLUGINS_DIR = PLUGINS_DIR
if userPluginsDirectory is not None:
if PLUGINS_DIR is None:
PLUGINS_DIR = userPluginsDirectory
else:
PLUGINS_DIR = [PLUGINS_DIR, userPluginsDirectory]
except:
PYMCA_PLUGINS_DIR = None
pass
class StackBase(object):
def __init__(self):
self._stack = DataObject.DataObject()
self._stack.x = None
self._stackImageData = None
self._selectionMask = None
self._finiteData = True
self._ROIDict = {'name': "ICR",
'type': "CHANNEL",
'calibration': [0, 1.0, 0.0],
'from': 0,
'to': -1}
self._ROIImageDict = {'ROI': None,
'Maximum': None,
'Minimum': None,
'Left': None,
'Middle': None,
'Right': None,
'Background': None}
self.__ROIImageCalculationIsUsingSuppliedEnergyAxis = False
self._ROIImageList = []
self._ROIImageNames = []
self.__pluginDirList = []
self.pluginList = []
self.pluginInstanceDict = {}
self.getPlugins()
# beyond 5 million elements, iterate to calculate the sums
# preventing huge intermediate use of memory when calculating
# the sums.
self._dynamicLimit = 5.0E6
self._tryNumpy = True
def setPluginDirectoryList(self, dirlist):
for directory in dirlist:
if not os.path.exists(directory):
raise IOError("Directory:\n%s\ndoes not exist." % directory)
self.__pluginDirList = dirlist
def getPluginDirectoryList(self):
return self.__pluginDirList
def getPlugins(self):
"""
Import or reloads all the available plugins.
It returns the number of plugins loaded.
"""
if PLUGINS_DIR is not None:
if self.__pluginDirList == []:
if type(PLUGINS_DIR) == type([]):
self.__pluginDirList = PLUGINS_DIR
else:
self.__pluginDirList = [PLUGINS_DIR]
self.pluginList = []
for directory in self.__pluginDirList:
if directory is None:
continue
if not os.path.exists(directory):
raise IOError("Directory:\n%s\ndoes not exist." % directory)
fileList = glob.glob(os.path.join(directory, "*.py"))
targetMethod = 'getStackPluginInstance'
# prevent unnecessary imports
moduleList = []
for fname in fileList:
# in Python 3, rb implies bytes and not strings
f = open(fname, 'r')
lines = f.readlines()
f.close()
f = None
for line in lines:
if line.startswith("def"):
if line.split(" ")[1].startswith(targetMethod):
moduleList.append(fname)
break
for module in moduleList:
try:
pluginName = os.path.basename(module)[:-3]
if directory == PYMCA_PLUGINS_DIR:
plugin = "PyMcaPlugins." + pluginName
else:
plugin = pluginName
if directory not in sys.path:
sys.path.insert(0, directory)
if pluginName in self.pluginList:
idx = self.pluginList.index(pluginName)
del self.pluginList[idx]
if plugin in self.pluginInstanceDict.keys():
del self.pluginInstanceDict[plugin]
if plugin in sys.modules:
if hasattr(sys.modules[plugin], targetMethod):
if sys.version < '3.0':
reload(sys.modules[plugin])
else:
import imp
imp.reload(sys.modules[plugin])
else:
try:
__import__(plugin)
except:
if directory == PYMCA_PLUGINS_DIR:
plugin = "PyMca5.PyMcaPlugins." + pluginName
__import__(plugin)
else:
raise
if hasattr(sys.modules[plugin], targetMethod):
self.pluginInstanceDict[plugin] = \
sys.modules[plugin].getStackPluginInstance(self)
self.pluginList.append(plugin)
except:
logger.debug("Problem importing module %s", plugin)
if logger.getEffectiveLevel() == logging.DEBUG:
raise
return len(self.pluginList)
def setStack(self, stack, mcaindex=2, fileindex=None):
#unfortunaly python 3 reports
#isinstance(stack, DataObject.DataObject) as false
#for DataObject derived classes like OmnicMap!!!!
if id(stack) == id(self._stack):
# just updated
pass
elif hasattr(stack, "shape") and\
hasattr(stack, "dtype"):
# array like
self._stack.x = None
self._stack.data = stack
self._stack.info['SourceName'] = "Data of unknown origin"
elif isinstance(stack, DataObject.DataObject) or\
("DataObject.DataObject" in ("%s" % type(stack))) or\
("QStack" in ("%s" % type(stack))) or\
("Map" in ("%s" % type(stack)))or\
("Stack" in ("%s" % type(stack))) or\
(hasattr(stack, "data") and hasattr(stack, "info")):
self._stack = stack
self._stack.info['SourceName'] = stack.info.get('SourceName',
"Data of unknown origin")
else:
self._stack.x = None
self._stack.data = stack
self._stack.info['SourceName'] = "Data of unknown origin"
info = self._stack.info
mcaIndex = info.get('McaIndex', mcaindex)
if (mcaIndex < 0) and (len(self._stack.data.shape) == 3):
mcaIndex = len(self._stack.data.shape) + mcaIndex
fileIndex = info.get('FileIndex', fileindex)
if fileIndex is None:
if mcaIndex == 2:
fileIndex = 0
elif mcaIndex == 0:
fileIndex = 1
else:
fileIndex = 0
for i in range(3):
if i not in [mcaIndex, fileIndex]:
otherIndex = i
break
self.mcaIndex = mcaIndex
self.fileIndex = fileIndex
self.otherIndex = otherIndex
self._stack.info['McaCalib'] = info.get('McaCalib', [0.0, 1.0, 0.0])
self._stack.info['Channel0'] = info.get('Channel0', 0.0)
self._stack.info['McaIndex'] = mcaIndex
self._stack.info['FileIndex'] = fileIndex
self._stack.info['OtherIndex'] = otherIndex
self.stackUpdated(info.get("positioners", None))
def stackUpdated(self, positioners=None):
"""
Recalculates the different images associated to the stack
"""
self._tryNumpy = True
if hasattr(self._stack.data, "size"):
if self._stack.data.size > self._dynamicLimit:
self._tryNumpy = False
else:
# is not a numpy ndarray in any case
self._tryNumpy = False
previousStackImageSize = None
if self._stackImageData is not None:
previousStackImageSize = self._stackImageData.size
mcaMax = None
if self._tryNumpy and isinstance(self._stack.data, numpy.ndarray):
self._stackImageData = numpy.sum(self._stack.data,
axis=self.mcaIndex,
dtype=numpy.float64)
#original ICR mca
logger.debug("(self.otherIndex, self.fileIndex) = (%d, %d)",
self.otherIndex, self.fileIndex)
i = max(self.otherIndex, self.fileIndex)
j = min(self.otherIndex, self.fileIndex)
mcaData0 = numpy.sum(numpy.sum(self._stack.data,
axis=i,
dtype=numpy.float64), j)
# max MCA
if self.mcaIndex == -1 or (self.mcaIndex == (len(self._stack.data.shape) - 1)):
mcaMax = numpy.nanmax(numpy.nanmax(self._stack.data, axis=0), axis=0)
elif self.mcaIndex == 0:
mcaMax = numpy.nanmax(numpy.nanmax(self._stack.data, axis=-1), axis=-1)
else:
_logger.info("Unsupported index for max spectrum calculation")
else:
t0 = time.time()
shape = self._stack.data.shape
if self.mcaIndex in [2, -1]:
self._stackImageData = numpy.zeros((shape[0], shape[1]),
dtype=numpy.float64)
mcaData0 = numpy.zeros((shape[2],), numpy.float64)
mcaMax = mcaData0 + numpy.NINF
step = 1
for i in range(shape[0]):
tmpData = self._stack.data[i:i+step,:,:]
numpy.add(self._stackImageData[i:i+step,:],
numpy.sum(tmpData, 2),
self._stackImageData[i:i+step,:])
tmpData.shape = step*shape[1], shape[2]
tmpMax = numpy.nanmax(tmpData, axis=0)
numpy.add(mcaData0, numpy.sum(tmpData, 0), mcaData0)
mcaMax =numpy.max([mcaMax, tmpMax], axis=0)
elif self.mcaIndex == 0:
self._stackImageData = numpy.zeros((shape[1], shape[2]),
dtype=numpy.float64)
mcaData0 = numpy.zeros((shape[0],), numpy.float64)
mcaMax = mcaData0 + numpy.NINF
step = 1
for i in range(shape[0]):
tmpData = self._stack.data[i:i+step,:,:]
tmpData.shape = tmpData.shape[1:]
numpy.add(self._stackImageData,
tmpData,
self._stackImageData)
mcaData0[i] = tmpData.sum()
mcaMax[i] = numpy.nanmax(tmpData)
else:
raise ValueError("Unhandled case 1D index = %d" % self.mcaIndex)
logger.debug("Print dynamic loading elapsed = %f", time.time() - t0)
logger.debug("__stackImageData.shape = %s", self._stackImageData.shape)
if previousStackImageSize:
if previousStackImageSize != self._stackImageData.size:
self._clearPositioners()
calib = self._stack.info.get('McaCalib', [0.0, 1.0, 0.0])
dataObject = DataObject.DataObject()
dataObject.info = {"McaCalib": calib,
"selectiontype": "1D",
"SourceName": "Stack",
"Key": "SUM"}
if "McaLiveTime" in self._stack.info:
if hasattr(self._stack.info["McaLiveTime"], "sum"):
dataObject.info["McaLiveTime"] = \
self._stack.info["McaLiveTime"].sum()
else:
print("Not an array. Skipping time information")
#del dataObject.info["McaLiveTime"]
if not hasattr(self._stack, 'x'):
self._stack.x = None
if self._stack.x in [None, []]:
self._stack.x = [numpy.arange(len(mcaData0)).astype(numpy.float64)+\
self._stack.info.get('Channel0', 0.0)]
# for the time being it can only contain one axis
dataObject.x = [self._stack.x[0]]
dataObject.y = [mcaData0]
#store the original spectrum
self._mcaData0 = dataObject
#store the original max spectrum
self._mcaMax = mcaMax
#add the original image
self.showOriginalImage()
#add the mca
goodData = numpy.isfinite(self._mcaData0.y[0].sum())
if goodData:
self._finiteData = True
self.showOriginalMca()
else:
self._finiteData = False
self.handleNonFiniteData()
#calculate the ROIs
self._ROIDict = {'name': "ICR",
'type': "CHANNEL",
'calibration': calib,
'from': dataObject.x[0][0],
'to': dataObject.x[0][-1]}
self.updateROIImages()
if positioners is not None:
try:
self.setPositioners(positioners)
except:
logging.error("Error setting positioners. Ignoring them")
self._clearPositioners()
for key in self.pluginInstanceDict.keys():
self.pluginInstanceDict[key].stackUpdated()
def getStackOriginalCurve(self):
# TODO: Make sure copies are returned
x = self._mcaData0.x[0]
y = self._mcaData0.y[0]
legend = "Stack SUM"
info = self._mcaData0.info
return [x, y, legend, info]
def isStackFinite(self):
"""
Returns True if stack does not contain inf or nans
Returns False if stack is not finite
"""
return self._finiteData
def handleNonFiniteData(self):
text = "Your data contain infinite values or nans.\n"
text += "Pixels containing those values will be ignored."
logger.info(text)
def updateROIImages(self, ddict=None):
if ddict is None:
updateROIDict = False
ddict = self._ROIDict
else:
updateROIDict = True
xw = ddict['calibration'][0] + \
ddict['calibration'][1] * self._mcaData0.x[0] + \
ddict['calibration'][2] * (self._mcaData0.x[0] ** 2)
if ddict["name"] == "ICR":
i1 = 0
i2 = self._stack.data.shape[self.mcaIndex]
imiddle = int(0.5 * (i1 + i2))
pos = 0.5 * (ddict['from'] + ddict['to'])
if ddict["type"].upper() != "CHANNEL":
imiddle = max(numpy.nonzero(xw <= pos)[0])
elif ddict["type"].upper() != "CHANNEL":
#energy like ROI
if xw[0] < xw[-1]:
i1 = numpy.nonzero(ddict['from'] <= xw)[0]
if len(i1):
i1 = min(i1)
else:
logger.debug("updateROIImages: nothing to be made")
return
i2 = numpy.nonzero(xw <= ddict['to'])[0]
if len(i2):
i2 = max(i2) + 1
else:
logger.debug("updateROIImages: nothing to be made")
return
pos = 0.5 * (ddict['from'] + ddict['to'])
imiddle = max(numpy.nonzero(xw <= pos)[0])
else:
i2 = numpy.nonzero(ddict['from'] <= xw)[0]
if len(i2):
i2 = max(i2)
else:
logger.debug("updateROIImages: nothing to be made")
return
i1 = numpy.nonzero(xw <= ddict['to'])[0]
if len(i1):
i1 = min(i1) + 1
else:
logger.debug("updateROIImages: nothing to be made")
return
pos = 0.5 * (ddict['from'] + ddict['to'])
imiddle = min(numpy.nonzero(xw <= pos)[0])
else:
i1 = numpy.nonzero(ddict['from'] <= self._mcaData0.x[0])[0]
if len(i1):
if self._mcaData0.x[0][0] > self._mcaData0.x[0][-1]:
i1 = max(i1)
else:
i1 = min(i1)
else:
i1 = 0
i1 = max(i1, 0)
i2 = numpy.nonzero(self._mcaData0.x[0] <= ddict['to'])[0]
if len(i2):
if self._mcaData0.x[0][0] > self._mcaData0.x[0][-1]:
i2 = min(i2)
else:
i2 = max(i2)
else:
i2 = 0
i2 = min(i2 + 1, self._stack.data.shape[self.mcaIndex])
pos = 0.5 * (ddict['from'] + ddict['to'])
if self._mcaData0.x[0][0] > self._mcaData0.x[0][-1]:
imiddle = min(numpy.nonzero(self._mcaData0.x[0] <= pos)[0])
else:
imiddle = max(numpy.nonzero(self._mcaData0.x[0] <= pos)[0])
xw = self._mcaData0.x[0]
self._ROIImageDict = self.calculateROIImages(i1, i2, imiddle, energy=xw)
if updateROIDict:
self._ROIDict.update(ddict)
roiKeys = ['ROI', 'Maximum', 'Minimum', 'Left', 'Middle', 'Right', 'Background']
nImages = len(roiKeys)
imageList = [None] * nImages
for i in range(nImages):
key = roiKeys[i]
imageList[i] = self._ROIImageDict[key]
title = "%s" % ddict["name"]
if ddict["name"] == "ICR":
cursor = "Energy"
if abs(ddict['calibration'][0]) < 1.0e-5:
if abs(ddict['calibration'][1] - 1) < 1.0e-5:
if abs(ddict['calibration'][2]) < 1.0e-5:
cursor = "Channel"
elif ddict["type"].upper() == "CHANNEL":
cursor = "Channel"
else:
cursor = ddict["type"]
imageNames = [title,
'%s Maximum' % title,
'%s Minimum' % title,
'%s %.6g' % (cursor, xw[i1]),
'%s %.6g' % (cursor, xw[imiddle]),
'%s %.6g' % (cursor, xw[(i2 - 1)]),
'%s Background' % title]
if self.__ROIImageCalculationIsUsingSuppliedEnergyAxis:
imageNames[1] = "%s %s at Max." % (title, cursor)
imageNames[2] = "%s %s at Min." % (title, cursor)
self.showROIImageList(imageList, image_names=imageNames)
def showOriginalImage(self):
logger.debug("showOriginalImage to be implemented")
def showOriginalMca(self):
logger.debug("showOriginalMca to be implemented")
def showROIImageList(self, imageList, image_names=None):
self._ROIImageList = imageList
self._ROIImageNames = image_names
self._stackROIImageListUpdated()
def _stackROIImageListUpdated(self):
for key in self.pluginInstanceDict.keys():
self.pluginInstanceDict[key].stackROIImageListUpdated()
def getStackROIImagesAndNames(self):
return self._ROIImageList, self._ROIImageNames
def getStackOriginalImage(self):
return self._stackImageData
def calculateMcaDataObject(self, normalize=False, mask=None):
#original ICR mca
if self._stackImageData is None:
return
if mask is None:
selectionMask = self._selectionMask
else:
selectionMask = mask
mcaData = None
goodData = numpy.isfinite(self._mcaData0.y[0].sum())
logger.debug("Stack data is not finite")
if (selectionMask is None) and goodData:
if normalize:
logger.debug("Case 1")
npixels = self._stackImageData.shape[0] *\
self._stackImageData.shape[1] * 1.0
dataObject = DataObject.DataObject()
dataObject.info.update(self._mcaData0.info)
dataObject.x = [self._mcaData0.x[0]]
dataObject.y = [self._mcaData0.y[0] / float(npixels)]
if "McaLiveTime" in dataObject.info:
dataObject.info["McaLiveTime"] /= float(npixels)
else:
logger.debug("Case 2")
dataObject = self._mcaData0
return dataObject
#deal with NaN and inf values
if selectionMask is None:
if (self._ROIImageDict["ROI"] is not None) and\
(self.mcaIndex != 0):
actualSelectionMask = numpy.isfinite(self._ROIImageDict["ROI"])
else:
actualSelectionMask = numpy.isfinite(self._stackImageData)
else:
if (self._ROIImageDict["ROI"] is not None) and\
(self.mcaIndex != 0):
actualSelectionMask = selectionMask * numpy.isfinite(self._ROIImageDict["ROI"])
else:
actualSelectionMask = selectionMask * numpy.isfinite(self._stackImageData)
npixels = actualSelectionMask.sum()
if (npixels == 0) and goodData:
if normalize:
logger.debug("Case 3")
npixels = self._stackImageData.shape[0] * self._stackImageData.shape[1] * 1.0
dataObject = DataObject.DataObject()
dataObject.info.update(self._mcaData0.info)
dataObject.x = [self._mcaData0.x[0]]
dataObject.y = [self._mcaData0.y[0] / float(npixels)]
if "McaLiveTime" in dataObject.info:
dataObject.info["McaLiveTime"] /= float(npixels)
else:
logger.debug("Case 4")
dataObject = self._mcaData0
return dataObject
mcaData = numpy.zeros(self._mcaData0.y[0].shape, numpy.float64)
n_nonselected = self._stackImageData.shape[0] *\
self._stackImageData.shape[1] - npixels
if goodData:
if n_nonselected < npixels:
arrayMask = (actualSelectionMask == 0)
else:
arrayMask = (actualSelectionMask > 0)
else:
arrayMask = (actualSelectionMask > 0)
logger.debug("Reached MCA calculation")
cleanMask = numpy.nonzero(arrayMask)
logger.debug("self.fileIndex, self.mcaIndex = %d , %d",
self.fileIndex, self.mcaIndex)
t0 = time.time()
if len(cleanMask[0]) and len(cleanMask[1]):
logger.debug("USING MASK")
cleanMask = numpy.array(cleanMask).transpose()
if self.fileIndex == 2:
if self.mcaIndex == 0:
if isinstance(self._stack.data, numpy.ndarray):
logger.debug("In memory case 0")
for r, c in cleanMask:
mcaData += self._stack.data[:, r, c]
else:
logger.debug("Dynamic loading case 0")
#no other choice than to read all images
#for the time being, one by one
rMin = cleanMask[0][0]
rMax = cleanMask[-1][0]
cMin = cleanMask[:, 1].min()
cMax = cleanMask[:, 1].max()
#rMin, cMin = cleanMask.min(axis=0)
#rMax, cMax = cleanMask.max(axis=0)
tmpMask = arrayMask[rMin:(rMax+1),cMin:(cMax+1)]
tmpData = numpy.zeros((1, rMax-rMin+1,cMax-cMin+1))
for i in range(self._stack.data.shape[0]):
tmpData[0:1,:,:] = self._stack.data[i:i+1,rMin:(rMax+1),cMin:(cMax+1)]
#multiplication is faster than selection
mcaData[i] = (tmpData[0]*tmpMask).sum(dtype=numpy.float64)
elif self.mcaIndex == 1:
if isinstance(self._stack.data, numpy.ndarray):
for r, c in cleanMask:
mcaData += self._stack.data[r,:,c]
else:
raise IndexError("Dynamic loading case 1")
else:
raise IndexError("Wrong combination of indices. Case 0")
elif self.fileIndex == 1:
if self.mcaIndex == 0:
if isinstance(self._stack.data, numpy.ndarray):
logger.debug("In memory case 2")
for r, c in cleanMask:
mcaData += self._stack.data[:, r, c]
else:
logger.debug("Dynamic loading case 2")
#no other choice than to read all images
#for the time being, one by one
if 1:
rMin = cleanMask[0][0]
rMax = cleanMask[-1][0]
cMin = cleanMask[:, 1].min()
cMax = cleanMask[:, 1].max()
#rMin, cMin = cleanMask.min(axis=0)
#rMax, cMax = cleanMask.max(axis=0)
tmpMask = arrayMask[rMin:(rMax + 1), cMin:(cMax + 1)]
tmpData = numpy.zeros((1, rMax - rMin + 1, cMax - cMin + 1))
for i in range(self._stack.data.shape[0]):
tmpData[0:1, :, :] = self._stack.data[i:i + 1, rMin:(rMax + 1), cMin:(cMax + 1)]
#multiplication is faster than selection
mcaData[i] = (tmpData[0] * tmpMask).sum(dtype=numpy.float64)
if 0:
tmpData = numpy.zeros((1, self._stack.data.shape[1], self._stack.data.shape[2]))
for i in range(self._stack.data.shape[0]):
tmpData[0:1, :, :] = self._stack.data[i:i + 1,:,:]
#multiplication is faster than selection
#tmpData[arrayMask].sum() in my machine
mcaData[i] = (tmpData[0] * arrayMask).sum(dtype=numpy.float64)
elif self.mcaIndex == 2:
if isinstance(self._stack.data, numpy.ndarray):
logger.debug("In memory case 3")
for r, c in cleanMask:
mcaData += self._stack.data[r, c, :]
else:
logger.debug("Dynamic loading case 3")
#try to minimize access to the file
row_list = []
row_dict = {}
for r, c in cleanMask:
if r not in row_list:
row_list.append(r)
row_dict[r] = []
row_dict[r].append(c)
for r in row_list:
tmpMcaData = self._stack.data[r:r + 1, row_dict[r], :]
tmpMcaData.shape = -1, mcaData.shape[0]
mcaData += numpy.sum(tmpMcaData, axis=0, dtype=numpy.float64)
else:
raise IndexError("Wrong combination of indices. Case 1")
elif self.fileIndex == 0:
if self.mcaIndex == 1:
if isinstance(self._stack.data, numpy.ndarray):
logger.debug("In memory case 4")
for r, c in cleanMask:
mcaData += self._stack.data[r, :, c]
else:
raise IndexError("Dynamic loading case 4")
elif self.mcaIndex in [2, -1]:
if isinstance(self._stack.data, numpy.ndarray):
logger.debug("In memory case 5")
for r, c in cleanMask:
mcaData += self._stack.data[r, c, :]
else:
logger.debug("Dynamic loading case 5")
#try to minimize access to the file
row_list = []
row_dict = {}
for r, c in cleanMask:
if r not in row_list:
row_list.append(r)
row_dict[r] = []
row_dict[r].append(c)
for r in row_list:
tmpMcaData = self._stack.data[r:r + 1, row_dict[r], :]
tmpMcaData.shape = -1, mcaData.shape[0]
mcaData += tmpMcaData.sum(axis=0, dtype=numpy.float64)
else:
raise IndexError("Wrong combination of indices. Case 2")
else:
raise IndexError("File index undefined")
else:
logger.debug("NOT USING MASK !")
logger.debug("Mca sum elapsed = %f", time.time() - t0)
if goodData:
if n_nonselected < npixels:
mcaData = self._mcaData0.y[0] - mcaData
if normalize:
mcaData = mcaData / float(npixels)
calib = self._stack.info['McaCalib']
dataObject = DataObject.DataObject()
dataObject.info = {"McaCalib": calib,
"selectiontype": "1D",
"SourceName": "Stack",
"Key": "Selection"}
if "McaLiveTime" in self._stack.info:
selectedPixels = actualSelectionMask > 0
liveTime = self._stack.info["McaLiveTime"][:]
liveTime.shape = actualSelectionMask.shape
liveTime = liveTime[selectedPixels].sum()
if normalize:
liveTime = liveTime / float(npixels)
dataObject.info["McaLiveTime"] = liveTime
dataObject.x = [self._mcaData0.x[0]]
dataObject.y = [mcaData]
return dataObject
def calculateROIImages(self, index1, index2, imiddle=None, energy=None):
logger.debug("Calculating ROI images")
i1 = min(index1, index2)
i2 = max(index1, index2)
if imiddle is None:
imiddle = int(0.5 * (i1 + i2))
if energy is None:
energy = self._mcaData0.x[0]
if i1 == i2:
dummy = numpy.zeros(self._stackImageData.shape, numpy.float64)
imageDict = {'ROI': dummy,
'Maximum': dummy,
'Minimum': dummy,
'Left': dummy,
'Middle': dummy,
'Right': dummy,
'Background': dummy}
return imageDict
isUsingSuppliedEnergyAxis = False
if self.fileIndex == 0:
if self.mcaIndex == 1:
leftImage = self._stack.data[:, i1, :]
middleImage = self._stack.data[:, imiddle, :]
rightImage = self._stack.data[:, i2 - 1, :]
dataImage = self._stack.data[:, i1:i2, :]
background = 0.5 * (i2 - i1) * (leftImage + rightImage)
roiImage = numpy.sum(dataImage, axis=1, dtype=numpy.float64)
maxImage = energy[(numpy.argmax(dataImage, axis=1) + i1)]
minImage = energy[(numpy.argmin(dataImage, axis=1) + i1)]
isUsingSuppliedEnergyAxis = True
else:
t0 = time.time()
if self._tryNumpy and\
isinstance(self._stack.data, numpy.ndarray):
leftImage = self._stack.data[:, :, i1]
middleImage = self._stack.data[:, :, imiddle]
rightImage = self._stack.data[:, :, i2 - 1]
dataImage = self._stack.data[:, :, i1:i2]
background = 0.5 * (i2 - i1) * (leftImage + rightImage)
roiImage = numpy.sum(dataImage, axis=2, dtype=numpy.float64)
maxImage = energy[numpy.argmax(dataImage, axis=2) + i1]
minImage = energy[numpy.argmin(dataImage, axis=2) + i1]
isUsingSuppliedEnergyAxis = True
logger.debug("Case 1 ROI image calculation elapsed = %f ",
time.time() - t0)
else:
shape = self._stack.data.shape
roiImage = numpy.zeros(self._stackImageData.shape,
numpy.float64)
background = roiImage * 1
leftImage = roiImage * 1
middleImage = roiImage * 1
rightImage = roiImage * 1
maxImage = numpy.zeros(self._stackImageData.shape,
numpy.uint)
minImage = numpy.zeros(self._stackImageData.shape,
numpy.uint)
step = 1
for i in range(shape[0]):
tmpData = self._stack.data[i:i+step,:, i1:i2] * 1
numpy.add(roiImage[i:i+step,:],
numpy.sum(tmpData, axis=2,dtype=numpy.float64),
roiImage[i:i+step,:])
minImage[i:i + step,:] = i1 + numpy.argmin(tmpData, axis=2)
maxImage[i:i + step, :] = i1 + numpy.argmax(tmpData, axis=2)
leftImage[i:i + step, :] += tmpData[:, :, 0]
middleImage[i:i + step, :] += tmpData[:, :, imiddle - i1]
rightImage[i:i + step, :] += tmpData[:, :, -1]
background = 0.5 * (i2 - i1) * (leftImage + rightImage)
isUsingSuppliedEnergyAxis = True
minImage = energy[minImage]
maxImage = energy[maxImage]
logger.debug("2 Dynamic ROI image calculation elapsed = %f ",
time.time() - t0)
elif self.fileIndex == 1:
if self.mcaIndex == 0:
t0 = time.time()
if isinstance(self._stack.data, numpy.ndarray) and\
self._tryNumpy:
leftImage = self._stack.data[i1, :, :]
middleImage= self._stack.data[imiddle, :, :]
rightImage = self._stack.data[i2 - 1, :, :]
dataImage = self._stack.data[i1:i2, :, :]
# this calculation is very slow but it is extremely useful
# for XANES studies
if 1:
maxImage = energy[numpy.argmax(dataImage, axis=0) + i1]
minImage = energy[numpy.argmin(dataImage, axis=0) + i1]
else:
# this is slower, but uses less memory
maxImage = numpy.zeros(leftImage.shape, numpy.int32)
minImage = numpy.zeros(leftImage.shape, numpy.int32)
for i in range(i1, i2):
tmpData = self._stack.data[i]
tmpData.shape = leftImage.shape
if i == i1:
minImageData = tmpData * 1.0
maxImageData = tmpData * 1.0
minImage[:,:] = i1
maxImage[:,:] = i1
else:
tmpIndex = numpy.where(tmpData < minImageData)
minImage[tmpIndex] = i
minImageData[tmpIndex] = tmpData[tmpIndex]
tmpIndex = numpy.where(tmpData > maxImageData)
maxImage[tmpIndex] = i
maxImageData[tmpIndex] = tmpData[tmpIndex]
minImage = energy[minImage]
maxImage = energy[maxImage]
isUsingSuppliedEnergyAxis = True
background = 0.5 * (i2 - i1) * (leftImage + rightImage)
roiImage = numpy.sum(dataImage, axis=0, dtype=numpy.float64)
logger.debug("Case 3 ROI image calculation elapsed = %f ",
time.time() - t0)
else:
shape = self._stack.data.shape
roiImage = numpy.zeros(self._stackImageData.shape,
numpy.float64)
background = roiImage * 1
leftImage = roiImage * 1
middleImage = roiImage * 1
rightImage = roiImage * 1
maxImage = numpy.zeros(roiImage.shape, numpy.int32)
minImage = numpy.zeros(roiImage.shape, numpy.int32)
istep = 1
for i in range(i1, i2):
tmpData = self._stack.data[i:i + istep]
tmpData.shape = roiImage.shape
if i == i1:
minImageData = tmpData * 1.0
maxImageData = tmpData * 1.0
minImage[:,:] = i1
maxImage[:,:] = i1
else:
tmpIndex = numpy.where(tmpData < minImageData)
minImage[tmpIndex] = i
minImageData[tmpIndex] = tmpData[tmpIndex]
tmpIndex = numpy.where(tmpData > maxImageData)
maxImage[tmpIndex] = i
maxImageData[tmpIndex] = tmpData[tmpIndex]
numpy.add(roiImage, tmpData, roiImage)
if (i == i1):
leftImage = tmpData
elif (i == imiddle):
middleImage = tmpData
elif i == (i2 - 1):
rightImage = tmpData
# the used approach is twice slower than argmax, but it
# requires much less memory
isUsingSuppliedEnergyAxis = True
minImage = energy[minImage]
maxImage = energy[maxImage]
if i2 > i1:
background = (leftImage + rightImage) * 0.5 * (i2 - i1)
logger.debug("Case 4 Dynamic ROI elapsed = %f",
time.time() - t0)
else:
t0 = time.time()
if self._tryNumpy and\
isinstance(self._stack.data, numpy.ndarray):
leftImage = self._stack.data[:, :, i1]
middleImage = self._stack.data[:, :, imiddle]
rightImage = self._stack.data[:, :, i2 - 1]
dataImage = self._stack.data[:, :, i1:i2]
background = 0.5 * (i2 - i1) * (leftImage + rightImage)
roiImage = numpy.sum(dataImage, axis=2, dtype=numpy.float64)
maxImage = energy[numpy.argmax(dataImage, axis=2) + i1]
minImage = energy[numpy.argmin(dataImage, axis=2) + i1]
isUsingSuppliedEnergyAxis = True
logger.debug("Case 5 ROI Image elapsed = %f",
time.time() - t0)
else:
shape = self._stack.data.shape
roiImage = numpy.zeros(self._stackImageData.shape,
numpy.float64)
background = roiImage * 1
leftImage = roiImage * 1
middleImage = roiImage * 1
rightImage = roiImage * 1
maxImage = roiImage * 1
minImage = roiImage * 1
step = 1
for i in range(shape[0]):
tmpData = self._stack.data[i:i+step,:, i1:i2] * 1
numpy.add(roiImage[i:i+step,:],
numpy.sum(tmpData, axis=2, dtype=numpy.float64),
roiImage[i:i+step,:])
numpy.add(minImage[i:i+step,:],
numpy.min(tmpData, 2),
minImage[i:i+step,:])
numpy.add(maxImage[i:i+step,:],
numpy.max(tmpData, 2),
maxImage[i:i+step,:])
leftImage[i:i+step, :] += tmpData[:, :, 0]
middleImage[i:i+step, :] += tmpData[:, :, imiddle-i1]
rightImage[i:i+step, :] += tmpData[:, :,-1]
background = 0.5*(i2-i1)*(leftImage+rightImage)
logger.debug("Case 6 Dynamic ROI image calculation elapsed = %f",
time.time() - t0)
else:
#self.fileIndex = 2
t0 = time.time()
if self.mcaIndex == 0:
leftImage = self._stack.data[i1]
middleImage = self._stack.data[imiddle]
rightImage = self._stack.data[i2 - 1]
background = 0.5 * (i2 - i1) * (leftImage + rightImage)
dataImage = self._stack.data[i1:i2]
roiImage = numpy.sum(dataImage, axis=0, dtype=numpy.float64)
minImage = energy[numpy.argmin(dataImage, axis=0) + i1]
maxImage = energy[numpy.argmax(dataImage, axis=0) + i1]
isUsingSuppliedEnergyAxis = True
logger.debug("Case 7 Default ROI image calculation elapsed = %f",
time.time() - t0)
else:
leftImage = self._stack.data[:, i1, :]
middleImage = self._stack.data[:, imiddle, :]
rightImage = self._stack.data[:, i2 - 1, :]
background = 0.5 * (i2 - i1) * (leftImage + rightImage)
dataImage = self._stack.data[:, i1:i2, :]
roiImage = numpy.sum(dataImage, axis=1, dtype=numpy.float64)
minImage = energy[numpy.argmin(dataImage, axis=1) + i1]
maxImage = energy[numpy.argmax(dataImage, axis=1) + i1]
isUsingSuppliedEnergyAxis = True
logger.debug("Case 8 Default ROI image calculation elapsed = %f",
time.time() - t0)
imageDict = {'ROI': roiImage,
'Maximum': maxImage,
'Minimum': minImage,
'Left': leftImage,
'Middle': middleImage,
'Right': rightImage,
'Background': background}
self.__ROIImageCalculationIsUsingSuppliedEnergyAxis = isUsingSuppliedEnergyAxis
logger.debug("ROI images calculated")
return imageDict
def setSelectionMask(self, mask):
logger.debug("setSelectionMask called")
goodData = numpy.isfinite(self._mcaData0.y[0].sum())
if goodData:
self._selectionMask = mask
else:
if (self._ROIImageDict["ROI"] is not None) and\
(self.mcaIndex != 0):
self._selectionMask = mask * numpy.isfinite(self._ROIImageDict["ROI"])
else:
self._selectionMask = mask * numpy.isfinite(self._stackImageData)
for key in self.pluginInstanceDict.keys():
self.pluginInstanceDict[key].selectionMaskUpdated()
def getSelectionMask(self):
logger.debug("getSelectionMask called")
return self._selectionMask
def addImage(self, image, name, info=None, replace=False, replot=True):
"""
Add image data to the RGB correlator
"""
print("Add image data not implemented")
def removeImage(self, name, replace=True):
"""
Remove image data from the RGB correlator
"""
print("Remove image data not implemented")
def addCurve(self, x, y, legend=None, info=None, replace=False, replot=True):
"""
Add the 1D curve given by x an y to the graph.
"""
print("addCurve not implemented")
return None
def removeCurve(self, legend, replot=True):
"""
Remove the curve associated to the supplied legend from the graph.
The graph will be updated if replot is true.
"""
print("removeCurve not implemented")
return None
def getGraphXLabel(self):
print("getGraphXLabel not implemented")
return None
def getGraphYLabel(self):
print("getGraphYLabel not implemented")
return None
def getActiveCurve(self):
"""
Function to access the currently active curve.
It returns None in case of not having an active curve.
Default output has the form:
xvalues, yvalues, legend, dict
where dict is a dictionary containing curve info.
For the time being, only the plot labels associated to the
curve are warranted to be present under the keys xlabel, ylabel.
If just_legend is True:
The legend of the active curve (or None) is returned.
"""
logger.debug("getActiveCurve default implementation")
info = {}
info['xlabel'] = 'Channel'
info['ylabel'] = 'Counts'
legend = 'ICR Spectrum'
return self._mcaData0.x[0], self._mcaData0.y[0], legend, info
def getGraphXLimits(self):
logger.debug("getGraphXLimits default implementation")
return self._mcaData0.x[0].min(), self._mcaData0.x[0].max()
def getGraphYLimits(self):
logger.debug("getGraphYLimits default implementation")
return self._mcaData0.y[0].min(), self._mcaData0.y[0].max()
def getStackDataObject(self):
return self._stack
def getStackData(self):
return self._stack.data
def getStackInfo(self):
return self._stack.info
def setPositioners(self, positioners, copy=True):
"""Updates the "positioners" field in the stack info, after
checking that the provided positioners have the proper number of
values.
:param dict positioners: Dictionary of positioners. The keys are
the motor names. The values should preferably be arrays with
the same number of values as there are stack pixels. Scalars
are accepted if the positioner has a constant value.
:param bool copy: If *True* (default), store a copy of the data.
If *False*, store the original data whenever it is possible.
If the dictionary contains lists, they need to be converted to
numpy arrays and a copy is mandatory.
:raise: TypeError if positioners is not a dict or if any positioner
is not a scalar, list or numpy array.
:raise: RuntimeError if any positioner is a list and copy=False
"""
if not hasattr(positioners, "items"):
raise TypeError("Dictionary expected for positioners")
npixels = self.getStackOriginalImage().size
stackPositioners = {}
for motorName, motorValues in positioners.items():
if numpy.isscalar(motorValues) or \
(hasattr(motorValues, "ndim") and
motorValues.ndim == 0):
stackPositioners[motorName] = motorValues
elif hasattr(motorValues, "size"):
# numpy array
numMotorValues = motorValues.size
if numMotorValues == npixels:
stackPositioners[motorName] = numpy.array(motorValues,
copy=copy)
elif hasattr(motorValues, "__len__") and numpy.isscalar(motorValues[0]):
# list: convert to numpy array before storing in info
numMotorValues = len(motorValues)
if numMotorValues == npixels:
stackPositioners[motorName] = numpy.array(motorValues)
else:
raise TypeError(
"Wrong type for positioner %s. " % motorName +
"Valid types are numpy arrays, scalars or list of scalars.")
if len(stackPositioners) != len(positioners):
ignored_motors = list(set(positioners.keys()) -
set(stackPositioners.keys()))
logger.debug("Ignored motors due to mismatch in number of values: %s",
', '.join(ignored_motors))
self._stack.info["positioners"] = stackPositioners
def _clearPositioners(self):
"""Removes the "positioners" field in the stack info"""
if "positioners" in self._stack.info:
self._stack.info["positioners"] = {}
def getPositionersFromIndex(self, index):
"""Return the value of all positioners for the spectrum identified by
its 1D index.
If positioners are stored as 1D arrays ``a``, the value returned
is simply ``a[index]``.
If positioners are stored as 2D arrays, the index is applied to the
flattened array.
:param int index: Index of spectrum for which the the positioners
value is to be returned.
:return: dictionary of positioners values whose keys are the motor
name
:rtype: dict
"""
positioners = self._stack.info.get("positioners", {})
positionersAtIdx = {}
if index >= self.getStackOriginalImage().size or index < 0:
raise IndexError("index out of bounds")
for motorName, motorValues in positioners.items():
# assert numpy.isscalar(motorValues) or hasattr(motorValues, "shape")
if numpy.isscalar(motorValues):
positionersAtIdx[motorName] = motorValues
elif len(motorValues.shape) == 1:
positionersAtIdx[motorName] = motorValues[index]
else:
positionersAtIdx[motorName] = motorValues.reshape((-1,))[index]
return positionersAtIdx
def test():
#create a dummy stack
nrows = 100
ncols = 200
nchannels = 1024
a = numpy.ones((nrows, ncols), numpy.float64)
stackData = numpy.zeros((nrows, ncols, nchannels), numpy.float64)
for i in range(nchannels):
stackData[:, :, i] = a * i
stack = StackBase()
stack.setStack(stackData, mcaindex=2)
print("This should be 0 = %f" % stack.calculateROIImages(0, 0)['ROI'].sum())
print("This should be 0 = %f" % stack.calculateROIImages(0, 1)['ROI'].sum())
print("%f should be = %f" %\
(stackData[:, :, 0:10].sum(),
stack.calculateROIImages(0, 10)['ROI'].sum()))
if __name__ == "__main__":
test()
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