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|
#/*##########################################################################
# Copyright (C) 2004-2020 V.A. Sole, 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.
#
#############################################################################*/
"""This plugin provides normalisation methods.
Two methods can be applied to normalize the stack based on the
active curve (I0):
- I/I0 Normalization: divide all spectra by the active curve
- -log(I/I0) Normalization
- -log10(I) Particular case not needing an active curve, for FTIR for instance
- -log10(I/100) Same as above for data expressed in percentage.
Three methods are provided to normalize the stack images based on
an external image (I0) read from a file:
- Image I/I0 Normalization
- Image I * (max(I0)/I0) Scaling
- Image -log(I/I0) Normalization
External images can be read from following file formats:
- EDF
- HDF5
- ASCII
If a multiframe EDF file is opened, the first frame is used. In case
a HDF5 file is selected, a browser is used to select a 2D dataset.
"""
__author__ = "V.A. Sole - ESRF Data Analysis"
__contact__ = "sole@esrf.fr"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
import numpy
import logging
from PyMca5 import StackPluginBase
# Add support for normalization by data
from PyMca5.PyMcaGui.io import PyMcaFileDialogs
from PyMca5.PyMca import EdfFile
from PyMca5.PyMca import specfilewrapper
from PyMca5.PyMca import HDF5Widget
try:
import h5py
HDF5 = True
except:
HDF5 = False
_logger = logging.getLogger(__name__)
class StackNormalizationPlugin(StackPluginBase.StackPluginBase):
def __init__(self, stackWindow, **kw):
if _logger.getEffectiveLevel() == logging.DEBUG:
StackPluginBase.pluginBaseLogger.setLevel(logging.DEBUG)
StackPluginBase.StackPluginBase.__init__(self, stackWindow, **kw)
self.methodDict = {}
text = "Stack/I0 where I0 is the active curve\n"
function = self.divideByCurve
info = text
icon = None
self.methodDict["I/I0 Normalization"] =[function,
info,
icon]
text = "-log(Stack/I0) Normalization where I0 is the active curve\n"
function = self.logNormalizeByCurve
info = text
icon = None
self.methodDict["-log(I/I0) Normalization"] =[function,
info,
icon]
text = "-log10(Stack) Convert from transmission to absorption\n"
function = self.logNormalizeByOne
info = text
icon = None
self.methodDict["-log10(I) Normalization"] =[function,
info,
icon]
text = "-log10(Stack) Convert from percentual transmission to absorption\n"
function = self.logNormalizeByHundred
info = text
icon = None
self.methodDict["-log10(I/100) Normalization"] =[function,
info,
icon]
text = "External Image I/I0 Normalization where\n"
text += "I0 is an image read from file\n"
function = self.divideByExternalImage
info = text
icon = None
self.methodDict["Image I/I0 Normalization"] =[function,
info,
icon]
text = "External Image (I/I0) * max(I0) Normalization where\n"
text += "I0 is an image read from file\n"
function = self.scaleByExternalImage
info = text
icon = None
self.methodDict["Image I * (max(I0)/I0) Scaling"] =[function,
info,
icon]
text = "External Image -log(Stack/I0) Normalization\n"
text += "where I0 is an image read from file\n"
function = self.logNormalizeByExternalImage
info = text
icon = None
self.methodDict["Image -log(I/I0) Normalization"] =[function,
info,
icon]
self.__methodKeys = ["I/I0 Normalization",
"-log(I/I0) Normalization",
"-log10(I) Normalization",
"-log10(I/100) Normalization",
"Image I/I0 Normalization",
"Image I * (max(I0)/I0) Scaling",
"Image -log(I/I0) Normalization"]
#Methods implemented by the plugin
def getMethods(self):
return self.__methodKeys
def getMethodToolTip(self, name):
return self.methodDict[name][1]
def getMethodPixmap(self, name):
return self.methodDict[name][2]
def applyMethod(self, name):
return self.methodDict[name][0]()
def _loadExternalData(self):
getfilter = True
fileTypeList = ["EDF Files (*edf *ccd *tif)"]
if HDF5:
fileTypeList.append('HDF5 Files (*.h5 *.nxs *.hdf *.hdf5)')
fileTypeList.append('ASCII Files (*)')
fileTypeList.append("EDF Files (*)")
message = "Open data file"
filenamelist, ffilter = PyMcaFileDialogs.getFileList(parent=None,
filetypelist=fileTypeList,
message=message,
getfilter=getfilter,
single=True,
currentfilter=None)
if len(filenamelist) < 1:
return
filename = filenamelist[0]
if ffilter.startswith('HDF5'):
data = HDF5Widget.getDatasetValueDialog(
filename=filename,
message='Select your data set by a double click')
elif ffilter.startswith("EDF"):
edf = EdfFile.EdfFile(filename, "rb")
if edf.GetNumImages() > 1:
# TODO: A dialog showing the different images
# based on the external images browser needed
_logger.warning("WARNING: Taking first image")
data = edf.GetData(0)
edf = None
elif ffilter.startswith("ASCII"):
#data=numpy.loadtxt(filename)
sf = specfilewrapper.Specfile(filename)
targetScan = sf[0]
data = numpy.array(targetScan.data().T, copy=True)
targetScan = None
sf = None
return data
def scaleByExternalImage(self):
self._externalImageOperation("scale")
def divideByExternalImage(self):
self._externalImageOperation("divide")
def logNormalizeByExternalImage(self):
self._externalImageOperation("log")
def _externalImageOperation(self, operation="divide"):
if operation == "log":
operator = numpy.log
stack = self.getStackDataObject()
if stack is None:
return
if not isinstance(stack.data, numpy.ndarray):
text = "This method does not work with dynamically loaded stacks yet"
raise TypeError(text)
normalizationData = self._loadExternalData()
if normalizationData is None:
return
mcaIndex = stack.info.get('McaIndex', -1)
stackShape = stack.data.shape
if mcaIndex < 0:
mcaIndex = len(stackShape) - mcaIndex
imageSize = stack.data.size / stackShape[mcaIndex]
if normalizationData.size != imageSize:
if normalizationData.shape[0] == imageSize:
if len(normalizationData.shape) == 2:
# assume the last column are the normalization data
normalizationData = normalizationData[:, -1]
if normalizationData.size != imageSize:
raise ValueError("Loaded data size does not match required size")
if normalizationData.dtype not in [numpy.float32,
numpy.float64]:
normalizationData = normalizationData.astype(numpy.float32)
if operation == "scale":
normalizationData /= normalizationData.max()
# TODO: Use an intermediate array and set divisions 0/0 to 0.
if stack.data.dtype in [numpy.int32, numpy.uint32]:
view = stack.data.view(numpy.float32)
elif stack.data.dtype in [numpy.int64, numpy.uint64]:
view = stack.data.view(numpy.float64)
else:
view = stack.data
if mcaIndex == 0:
normalizationData.shape = stackShape[1:]
if operation in ["divide", "scale"]:
for i in range(stackShape[mcaIndex]):
view[i] = stack.data[i] / normalizationData
elif operation == "log":
for i in range(stackShape[mcaIndex]):
view[i] = -operator(stack.data[i]/normalizationData)
elif mcaIndex == 2:
normalizationData.shape = stackShape[:2]
if operation in ["divide", "scale"]:
for i in range(stackShape[mcaIndex]):
view[:, :, i] = stack.data[:, :, i] / normalizationData
else:
for i in range(stackShape[mcaIndex]):
view[:, :, i] = -operator(stack.data[:, :, i]/ \
normalizationData)
elif mcaIndex == 1:
normalizationData.shape = stackShape[0], stackShape[2]
if operation in ["divide", "scale"]:
for i in range(stackShape[mcaIndex]):
view[:, i, :] = stack.data[:, i, :] / normalizationData
else:
for i in range(stackShape[mcaIndex]):
view[:, i, :] = -operator(stack.data[:, i, :]/ \
normalizationData)
else:
raise ValueError("Unsupported 1D index %d" % mcaIndex)
self.setStack(view)
def divideByExternalCurve(self):
stack = self.getStackDataObject()
if stack is None:
return
if not isinstance(stack.data, numpy.ndarray):
text = "This method does not work with dynamically loaded stacks yet"
raise TypeError(text)
def divideByCurve(self):
stack = self.getStackDataObject()
if not isinstance(stack.data, numpy.ndarray):
text = "This method does not work with dynamically loaded stacks"
raise TypeError(text)
curve = self.getActiveCurve()
if curve is None:
text = "Please make sure to have an active curve"
raise TypeError(text)
x, y, legend, info = self.getActiveCurve()
yWork = y[y!=0].astype(numpy.float64)
mcaIndex = stack.info.get('McaIndex', -1)
if mcaIndex in [-1, 2]:
for i, value in enumerate(yWork):
stack.data[:, :, i] = stack.data[:,:,i]/value
elif mcaIndex == 0:
for i, value in enumerate(yWork):
stack.data[i, :, :] = stack.data[i,:,:]/value
elif mcaIndex == 1:
for i, value in enumerate(yWork):
stack.data[:, i, :] = stack.data[:,i,:]/value
else:
raise ValueError("Invalid 1D index %d" % mcaIndex)
self.setStack(stack)
def logNormalizeByOne(self):
return self.logNormalizeByCurve(divider=1.0)
def logNormalizeByHundred(self):
return self.logNormalizeByCurve(divider=100.)
def logNormalizeByCurve(self, divider=None):
stack = self.getStackDataObject()
if not isinstance(stack.data, numpy.ndarray):
text = "This method does not work with dynamically loaded stacks"
raise TypeError(text)
if divider is None:
curve = self.getActiveCurve()
if curve is None:
text = "Please make sure to have an active curve"
raise TypeError(text)
x, y, legend, info = self.getActiveCurve()
if divider is None:
yWork = y[y>0].astype(numpy.float64)
mcaIndex = stack.info.get('McaIndex', -1)
if mcaIndex in [-1, 2]:
for i, value in enumerate(yWork):
stack.data[:, :, i] = -numpy.log(stack.data[:,:,i] / value)
elif mcaIndex == 0:
for i, value in enumerate(yWork):
stack.data[i, :, :] = -numpy.log(stack.data[i,:,:] / value)
elif mcaIndex == 1:
for i, value in enumerate(yWork):
stack.data[:, i, :] = -numpy.log(stack.data[:,i,:] / value)
else:
raise ValueError("Invalid 1D index %d" % mcaIndex)
else:
# this loop is to try to avoid avoid huge temporary arrays
if stack.data.shape[0] > 1:
for i in range(stack.data.shape[0]):
stack.data[i] = -numpy.log10(stack.data[i] / divider)
else:
stack.data[:] = -numpy.log10(stack.data[:] / divider)
self.setStack(stack)
MENU_TEXT = "Stack Normalization"
def getStackPluginInstance(stackWindow, **kw):
ob = StackNormalizationPlugin(stackWindow)
return ob
|
