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#/*##########################################################################
#
# The PyMca X-Ray Fluorescence Toolkit
#
# Copyright (c) 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. Armando Sole - ESRF Data Analysis"
__contact__ = "sole@esrf.fr"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
import unittest
import os
import sys
import numpy
import gc
import shutil
try:
import h5py
HAS_H5PY = True
except:
HAS_H5PY = None
if sys.version_info < (3,):
from StringIO import StringIO
else:
from io import StringIO
DEBUG = 0
class testStackInfo(unittest.TestCase):
def setUp(self):
"""
Get the data directory
"""
self._importSuccess = False
self._outputDir = None
self._h5File = None
try:
from PyMca5 import PyMcaDataDir
self._importSuccess = True
self.dataDir = PyMcaDataDir.PYMCA_DATA_DIR
except:
self.dataDir = None
def tearDown(self):
gc.collect()
if self._outputDir is not None:
shutil.rmtree(self._outputDir, ignore_errors=True)
if os.path.exists(self._outputDir):
raise IOError("Directory <%s> not deleted" % self._outputDir)
if self._h5File is not None:
fileName = self._h5File
if os.path.exists(fileName):
os.remove(fileName)
fileName = self._h5File + "external.h5"
if os.path.exists(fileName):
os.remove(fileName)
def testDataDirectoryPresence(self):
self.assertTrue(self._importSuccess,
'Unsuccessful PyMca5.PyMcaDataDir import')
self.assertTrue(self.dataDir is not None,
'Unassigned PyMca5.PyMcaDataDir.PYMCA_DATA_DIR')
self.assertTrue(os.path.exists(self.dataDir),
'Directory "%s" does not exist' % self.dataDir)
self.assertTrue(os.path.isdir(self.dataDir),
'"%s" expected to be a directory' % self.dataDir)
def testDataFilePresence(self):
for fileName in ["Steel.spe", "Steel.cfg"]:
dataFile = os.path.join(self.dataDir, fileName)
self.assertTrue(os.path.exists(dataFile),
"File %s does not exists" % dataFile)
self.assertTrue(os.path.isfile(dataFile),
"File %s is not an actual file" % dataFile)
def testStackBaseAverageAndSum(self):
from PyMca5.PyMcaIO import specfilewrapper as specfile
from PyMca5.PyMcaIO import ConfigDict
from PyMca5.PyMcaCore import DataObject
from PyMca5.PyMcaCore import StackBase
from PyMca5.PyMcaPhysics.xrf import FastXRFLinearFit
spe = os.path.join(self.dataDir, "Steel.spe")
cfg = os.path.join(self.dataDir, "Steel.cfg")
sf = specfile.Specfile(spe)
self.assertTrue(len(sf) == 1, "File %s cannot be read" % spe)
self.assertTrue(sf[0].nbmca() == 1,
"Spe file should contain MCA data")
y = counts = sf[0].mca(1)
x = channels = numpy.arange(y.size).astype(numpy.float64)
sf = None
configuration = ConfigDict.ConfigDict()
configuration.read(cfg)
calibration = configuration["detector"]["zero"], \
configuration["detector"]["gain"], 0.0
initialTime = configuration["concentrations"]["time"]
# create the data
nRows = 5
nColumns = 10
nTimes = 3
data = numpy.zeros((nRows, nColumns, counts.size), dtype = numpy.float64)
live_time = numpy.zeros((nRows * nColumns), dtype=numpy.float64)
mcaIndex = 0
for i in range(nRows):
for j in range(nColumns):
data[i, j] = counts
live_time[i * nColumns + j] = initialTime * \
(1 + mcaIndex % nTimes)
mcaIndex += 1
# create the stack data object
stack = DataObject.DataObject()
stack.data = data
stack.info = {}
stack.info["McaCalib"] = calibration
stack.info["McaLiveTime"] = live_time
stack.x = [channels]
# let's play
sb = StackBase.StackBase()
sb.setStack(stack)
x, y, legend, info = sb.getStackOriginalCurve()
readCalib = info["McaCalib"]
readLiveTime = info["McaLiveTime"]
self.assertTrue(abs(readCalib[0] - calibration[0]) < 1.0e-10,
"Calibration zero. Expected %f got %f" % \
(calibration[0], readCalib[0]))
self.assertTrue(abs(readCalib[1] - calibration[1]) < 1.0e-10,
"Calibration gain. Expected %f got %f" % \
(calibration[1], readCalib[0]))
self.assertTrue(abs(readCalib[2] - calibration[2]) < 1.0e-10,
"Calibration 2nd order. Expected %f got %f" % \
(calibration[2], readCalib[2]))
self.assertTrue(abs(live_time.sum() - readLiveTime) < 1.0e-5,
"Incorrect sum of live time data")
mask = sb.getSelectionMask()
if mask is None:
mask = numpy.zeros((nRows, nColumns), dtype=numpy.uint8)
mask[2, :] = 1
mask[0, 0:2] = 1
live_time.shape = mask.shape
sb.setSelectionMask(mask)
mcaObject = sb.calculateMcaDataObject(normalize=False)
live_time.shape = mask.shape
readLiveTime = mcaObject.info["McaLiveTime"]
self.assertTrue(abs(live_time[mask > 0].sum() - readLiveTime) < 1.0e-5,
"Incorrect sum of masked live time data")
mcaObject = sb.calculateMcaDataObject(normalize=True)
live_time.shape = mask.shape
tmpBuffer = numpy.zeros(mask.shape, dtype=numpy.int32)
tmpBuffer[mask > 0] = 1
nSelectedPixels = float(tmpBuffer.sum())
readLiveTime = mcaObject.info["McaLiveTime"]
self.assertTrue( \
abs((live_time[mask > 0].sum() / nSelectedPixels) - readLiveTime) < 1.0e-5,
"Incorrect average of masked live time data")
def testStackFastFit(self):
# TODO: this is done in PyMcaBatchTest on multiple input formats
# so not needed here
return
from PyMca5.PyMcaIO import specfilewrapper as specfile
from PyMca5.PyMcaIO import ConfigDict
from PyMca5.PyMcaCore import DataObject
spe = os.path.join(self.dataDir, "Steel.spe")
cfg = os.path.join(self.dataDir, "Steel.cfg")
sf = specfile.Specfile(spe)
self.assertTrue(len(sf) == 1, "File %s cannot be read" % spe)
self.assertTrue(sf[0].nbmca() == 1,
"Spe file should contain MCA data")
counts = sf[0].mca(1)
channels = numpy.arange(counts.size)
sf = None
configuration = ConfigDict.ConfigDict()
configuration.read(cfg)
calibration = configuration["detector"]["zero"], \
configuration["detector"]["gain"], 0.0
initialTime = configuration["concentrations"]["time"]
# Fit MCA data with different dimensions: vector, image, stack
for ndim in [1, 2, 3]:
# create the data
imgShape = tuple(range(3, 3+ndim))
data = numpy.tile(counts, imgShape+(1,))
nTimes = 3
live_time = numpy.arange(numpy.prod(imgShape), dtype=int)
live_time = initialTime + (live_time % nTimes)*initialTime
# create the stack data object
stack = DataObject.DataObject()
stack.data = data
stack.info = {}
stack.info["McaCalib"] = calibration
stack.info["McaLiveTime"] = live_time
stack.x = [channels]
# Test the fast XRF
# we need to make sure we use fundamental parameters and
# the time read from the file
configuration["concentrations"]["usematrix"] = 0
configuration["concentrations"]["useautotime"] = 1
# make sure we use the SNIP background
configuration['fit']['stripalgorithm'] = 1
self._verifyFastFit(stack, configuration, live_time, nTimes)
def _verifyFastFit(self, stack, configuration, live_time, nTimes):
from PyMca5.PyMcaPhysics.xrf import FastXRFLinearFit
ffit = FastXRFLinearFit.FastXRFLinearFit()
firstIndex = tuple([0]*(stack.data.ndim-1))
for refit in [0, 1]:
outputDict = ffit.fitMultipleSpectra(y=stack,
weight=0,
configuration=configuration,
concentrations=True,
refit=refit)
parameter_names = outputDict.labels('parameters')
parameters = outputDict["parameters"].astype(numpy.float32)
uncertainties = outputDict["uncertainties"].astype(numpy.float32)
for i, (name, values, uvalues) in enumerate(zip(parameter_names, parameters, uncertainties)):
if DEBUG:
print(name, values[firstIndex])
delta = (values - values[firstIndex])
self.assertTrue(delta.max() == 0,
"Different fit value for parameter %s delta %f" % \
(name, delta.max()))
self.assertTrue(delta.min() == 0,
"Different fit value for parameter %s delta %f" % \
(name, delta.min()))
delta = (uvalues - uvalues[firstIndex])
self.assertTrue(delta.max() == 0,
"Different sigma value for parameter %s delta %f" % \
(name, delta.max()))
self.assertTrue(delta.min() == 0,
"Different sigma value for parameter %s delta %f" % \
(name, delta.min()))
massfraction_names = outputDict.labels('massfractions')
massfractions = outputDict["massfractions"]
for i, (name, fractions) in enumerate(zip(massfraction_names, massfractions)):
# verify that massfractions took into account the time
reference = fractions[firstIndex]
cTime = configuration['concentrations']['time']
values = fractions.flatten()
for point in range(live_time.size):
current = values[point]
if DEBUG:
print(name, point, reference, current, point % nTimes)
if (point % nTimes) and (abs(reference) > 1.0e-10):
self.assertTrue(reference != current,
"Incorrect concentration for point %d" % point)
corrected = current * live_time[point] / cTime
if abs(reference) > 1.0e-10:
delta = 100 * abs((reference - corrected) / reference)
self.assertTrue(delta < 0.01,
"Incorrect concentration(t) for point %d" % point)
else:
self.assertTrue(abs(reference - corrected) < 1.0e-5,
"Incorrect concentration(t) for point %d" % point)
@unittest.skipIf(not HAS_H5PY, "skipped h5py missing")
def testFitHdf5Stack(self):
import tempfile
from PyMca5.PyMcaIO import specfilewrapper as specfile
from PyMca5.PyMcaIO import ConfigDict
from PyMca5.PyMcaIO import HDF5Stack1D
from PyMca5.PyMcaPhysics.xrf import McaAdvancedFitBatch
from PyMca5.PyMcaPhysics.xrf import LegacyMcaAdvancedFitBatch
spe = os.path.join(self.dataDir, "Steel.spe")
cfg = os.path.join(self.dataDir, "Steel.cfg")
sf = specfile.Specfile(spe)
self.assertTrue(len(sf) == 1, "File %s cannot be read" % spe)
self.assertTrue(sf[0].nbmca() == 1,
"Spe file should contain MCA data")
y = counts = sf[0].mca(1)
x = channels = numpy.arange(y.size).astype(numpy.float64)
sf = None
configuration = ConfigDict.ConfigDict()
configuration.read(cfg)
calibration = configuration["detector"]["zero"], \
configuration["detector"]["gain"], 0.0
initialTime = configuration["concentrations"]["time"]
# create the data
nRows = 5
nColumns = 10
nTimes = 3
data = numpy.zeros((nRows, nColumns, counts.size), dtype = numpy.float64)
live_time = numpy.zeros((nRows * nColumns), dtype=numpy.float64)
mcaIndex = 0
for i in range(nRows):
for j in range(nColumns):
data[i, j] = counts
live_time[i * nColumns + j] = initialTime * \
(1 + mcaIndex % nTimes)
mcaIndex += 1
self._h5File = os.path.join(tempfile.gettempdir(), "Steel.h5")
# write the stack to an HDF5 file
if os.path.exists(self._h5File):
os.remove(self._h5File)
h5 = h5py.File(self._h5File, "w")
h5["/entry/instrument/detector/calibration"] = calibration
h5["/entry/instrument/detector/channels"] = channels
h5["/entry/instrument/detector/data"] = data
h5["/entry/instrument/detector/live_time"] = live_time
# add nexus conventions
h5["/entry"].attrs["NX_class"] = u"NXentry"
h5["/entry/instrument"].attrs["NX_class"] = u"NXinstrument"
h5["/entry/instrument/detector/"].attrs["NX_class"] = u"NXdetector"
h5["/entry/instrument/detector/data"].attrs["interpretation"] = \
u"spectrum"
# case with softlink
h5["/entry/measurement/mca_soft/data"] = \
h5py.SoftLink("/entry/instrument/detector/data")
# case with info
h5["/entry/measurement/mca_with_info/data"] = \
h5["/entry/instrument/detector/data"]
h5["/entry/measurement/mca_with_info/info"] = \
h5["/entry/instrument/detector"]
h5.flush()
h5.close()
h5 = None
# check that the data can be read as a stack as
# single top level dataset (issue #226)
external = self._h5File + "external.h5"
if os.path.exists(external):
os.remove(external)
h5 = h5py.File(external, "w")
h5["/data_at_top"] = h5py.ExternalLink(self._h5File,
"/entry/measurement/mca_soft/data")
h5.flush()
h5.close()
h5 = None
stack = HDF5Stack1D.HDF5Stack1D([external], {"y":"/data_at_top"})
# check that the data can be read as a stack through a external link
external = self._h5File + "external.h5"
if os.path.exists(external):
os.remove(external)
h5 = h5py.File(external, "w")
h5["/data_at_top"] = h5py.ExternalLink(self._h5File,
"/entry/measurement/mca_soft/data")
h5["/entry/data"] = h5py.ExternalLink(self._h5File,
"/entry/measurement/mca_soft/data")
h5.flush()
h5.close()
h5 = None
fileList = [external]
for selection in [{"y":"/data_at_top"}, # dataset at top level
{"y":"/data"}, # GOOD: selection inside /entry
{"y":"/entry/data"}]: # WRONG: complete path
stack = HDF5Stack1D.HDF5Stack1D(fileList, selection)
info = stack.info
for key in ["McaCalib", "McaLiveTime"]:
self.assertTrue(key in info,
"Key <%s> not present but it should be there")
readCalib = info["McaCalib"]
readLiveTime = info["McaLiveTime"]
self.assertTrue(abs(readCalib[0] - calibration[0]) < 1.0e-10,
"Calibration zero. Expected %f got %f" % \
(calibration[0], readCalib[0]))
self.assertTrue(abs(readCalib[1] - calibration[1]) < 1.0e-10,
"Calibration gain. Expected %f got %f" % \
(calibration[1], readCalib[0]))
self.assertTrue(abs(readCalib[2] - calibration[2]) < 1.0e-10,
"Calibration 2nd order. Expected %f got %f" % \
(calibration[2], readCalib[2]))
self.assertTrue(live_time.size == readLiveTime.size,
"Incorrect size of live time data")
self.assertTrue(numpy.allclose(live_time, readLiveTime),
"Incorrect live time read")
self.assertTrue(numpy.allclose(stack.x, channels),
"Incorrect channels read")
self.assertTrue(numpy.allclose(stack.data, data),
"Incorrect data read")
# check that the data can be read as a stack
fileList = [self._h5File]
for selection in [{"y":"/measurement/mca_with_info/data"},
{"y":"/measurement/mca_soft/data"},
{"y":"/instrument/detector/data"}]:
stack = HDF5Stack1D.HDF5Stack1D(fileList, selection)
info = stack.info
for key in ["McaCalib", "McaLiveTime"]:
self.assertTrue(key in info,
"Key <%s> not present but it should be there")
readCalib = info["McaCalib"]
readLiveTime = info["McaLiveTime"]
self.assertTrue(abs(readCalib[0] - calibration[0]) < 1.0e-10,
"Calibration zero. Expected %f got %f" % \
(calibration[0], readCalib[0]))
self.assertTrue(abs(readCalib[1] - calibration[1]) < 1.0e-10,
"Calibration gain. Expected %f got %f" % \
(calibration[1], readCalib[0]))
self.assertTrue(abs(readCalib[2] - calibration[2]) < 1.0e-10,
"Calibration 2nd order. Expected %f got %f" % \
(calibration[2], readCalib[2]))
self.assertTrue(live_time.size == readLiveTime.size,
"Incorrect size of live time data")
self.assertTrue(numpy.allclose(live_time, readLiveTime),
"Incorrect live time read")
self.assertTrue(numpy.allclose(stack.x, channels),
"Incorrect channels read")
self.assertTrue(numpy.allclose(stack.data, data),
"Incorrect data read")
# TODO: this is done in PyMcaBatchTest on multiple input formats
# so not needed here
return
# perform the batch fit
self._outputDir = os.path.join(tempfile.gettempdir(), "SteelTestDir")
if not os.path.exists(self._outputDir):
os.mkdir(self._outputDir)
cfgFile = os.path.join(tempfile.gettempdir(), "SteelNew.cfg")
if os.path.exists(cfgFile):
try:
os.remove(cfgFile)
except:
print("Cannot remove file %s" % cfgFile)
# we need to make sure we use fundamental parameters and
# the time read from the file
configuration["concentrations"]["usematrix"] = 0
configuration["concentrations"]["useautotime"] = 1
if not os.path.exists(cfgFile):
configuration.write(cfgFile)
os.chmod(cfgFile, 0o777)
# Test batch fitting (legacy)
batch = LegacyMcaAdvancedFitBatch.McaAdvancedFitBatch(cfgFile,
filelist=[self._h5File],
outputdir=self._outputDir,
concentrations=True,
selection=selection,
quiet=True)
batch.processList()
imageFile = os.path.join(self._outputDir, "IMAGES", "Steel.dat")
self._verifyBatchFitResult(imageFile, nRows, nColumns, live_time, nTimes, legacy=True)
# Test batch fitting
batch = McaAdvancedFitBatch.McaAdvancedFitBatch(cfgFile,
filelist=[self._h5File],
outputdir=self._outputDir,
concentrations=True,
selection=selection,
quiet=True)
batch.outbuffer.extensions = ['.dat']
batch.processList()
imageFile = batch.outbuffer.filename('.dat')
self._verifyBatchFitResult(imageFile, nRows, nColumns, live_time, nTimes)
# Batch fitting went well
# Test the fast XRF
configuration["concentrations"]["usematrix"] = 0
configuration["concentrations"]["useautotime"] = 1
configuration['fit']['stripalgorithm'] = 1
self._verifyFastFit(stack, configuration, live_time, nTimes)
def _verifyBatchFitResult(self, imageFile, nRows, nColumns, live_time, nTimes, legacy=False):
from PyMca5.PyMcaIO import specfilewrapper as specfile
# recover the results
self.assertTrue(os.path.isfile(imageFile),
"Batch fit result file <%s> not present" % imageFile)
sf = specfile.Specfile(imageFile)
labels = sf[0].alllabels()
scanData = sf[0].data()
sf = None
self.assertTrue(scanData.shape[-1] == (nRows * nColumns),
"Expected %d values got %d" % (nRows * nColumns, scanData.shape[-1]))
if legacy:
ismassfrac = lambda label: label.endswith("-mass-fraction")
else:
ismassfrac = lambda label: label.startswith("w(")
referenceResult = {}
for point in range(scanData.shape[-1]):
for label in labels:
idx = labels.index(label)
if label in ["Point", "row", "column"]:
continue
elif point == 0:
referenceResult[label] = scanData[idx, point]
elif ismassfrac(label):
#print("label = ", label)
#print("reference = ", referenceResult[label])
#print("current = ", scanData[idx, point])
reference = referenceResult[label]
current = scanData[idx, point]
#print("ratio = ", current / reference)
#print("time ratio = ", live_time[point] / live_time[0])
if point % nTimes:
if abs(reference) > 1.0e-10:
self.assertNotEqual(reference, current,
"Incorrect concentration for point %d" % point)
corrected = current * \
(live_time[point] / live_time[0])
if abs(reference) > 1.0e-10:
delta = \
100 * abs((reference - corrected) / reference)
self.assertTrue(delta < 0.01,
"Incorrect concentration(t) for point %d" % point)
else:
self.assertTrue(abs(reference - corrected) < 1.0e-5,
"Incorrect concentration(t) for point %d" % point)
else:
self.assertEqual(reference, current,
"Incorrect concentration for point %d" % point)
else:
reference = referenceResult[label]
current = scanData[idx, point]
self.assertEqual(reference, current,
"Incorrect value for point %d" % point)
def getSuite(auto=True):
testSuite = unittest.TestSuite()
if auto:
testSuite.addTest(unittest.TestLoader().loadTestsFromTestCase(testStackInfo))
else:
# use a predefined order
testSuite.addTest(testStackInfo("testDataDirectoryPresence"))
testSuite.addTest(testStackInfo("testStackBaseAverageAndSum"))
testSuite.addTest(testStackInfo("testDataFilePresence"))
testSuite.addTest(testStackInfo("testStackFastFit"))
testSuite.addTest(testStackInfo("testFitHdf5Stack"))
return testSuite
def test(auto=False):
return unittest.TextTestRunner(verbosity=2).run(getSuite(auto=auto))
if __name__ == '__main__':
if len(sys.argv) > 1:
auto = False
else:
auto = True
result = test(auto)
sys.exit(not result.wasSuccessful())
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