def testSimpleSpecfileVersusPyMca(self):
import glob
import PyMca5
from PyMca5 import PyMcaDataDir
from PyMca5.PyMcaIO import specfilewrapper as Specfile
fileList = glob.glob(os.path.join(PyMcaDataDir.PYMCA_DATA_DIR, '*.dat'))
self.testSimpleSpecfileImport()
for filename in fileList:
self._sf = self.specfileClass(filename)
a = self._sf
nScans = a.getNumberOfScans()
sf = Specfile.Specfile(filename)
for i in range(nScans):
scan = sf[i]
if PyMca5.version() > "5.1.1":
# previous versions of PyMca5 could segfault here
labels0 = a.getScanLabels(i)
labels1 = scan.alllabels()
for j in range(len(labels0)):
self.assertEqual(labels0[j], labels1[j],
"Read <%s> instead of <%s>" %\
(labels0[j], labels1[j]))
data0 = a.getScanData(i)
data1 = scan.data().T
M = abs(data0 - data1).max()
if M > 1.0e-8:
raise ValueError("Error reading data")
def testSimpleSpecfileVersusPyMca(self):
import glob
import PyMca5
from PyMca5 import PyMcaDataDir
from PyMca5.PyMcaIO import specfilewrapper as Specfile
fileList = glob.glob(
os.path.join(PyMcaDataDir.PYMCA_DATA_DIR, '*.dat'))
self.testSimpleSpecfileImport()
for filename in fileList:
self._sf = self.specfileClass(filename)
a = self._sf
nScans = a.getNumberOfScans()
sf = Specfile.Specfile(filename)
for i in range(nScans):
scan = sf[i]
if PyMca5.version() > "5.1.1":
# previous versions of PyMca5 could segfault here
labels0 = a.getScanLabels(i)
labels1 = scan.alllabels()
for j in range(len(labels0)):
self.assertEqual(labels0[j], labels1[j],
"Read <%s> instead of <%s>" %\
(labels0[j], labels1[j]))
data0 = a.getScanData(i)
data1 = scan.data().T
M = abs(data0 - data1).max()
if M > 1.0e-8:
raise ValueError("Error reading data")
for f in ['qt', 'qttable', 'qtcanvas', 'Qwt5']:
excludes.append(f)
buildOptions = dict(
compressed = True,
include_files = include_files,
excludes = excludes,
includes = includes,
#includes = ["scipy.interpolate", "scipy.linalg"]
#optimize=2,
#packages = packages,
#includes = ["Object3D"],
#path = [PyMcaDir] + sys.path
)
install_dir = PyMcaDir + " " + PyMca5.version()
if not sys.platform.startswith('win'):
install_dir = install_dir.replace(" ","")
if os.path.exists(install_dir):
try:
def dir_cleaner(directory):
for f in glob.glob(os.path.join(directory,'*')):
if os.path.isfile(f):
try:
os.remove(f)
except:
print("file <%s> not deleted" % f)
if os.path.isdir(f):
dir_cleaner(f)
try:
os.rmdir(directory)
packages = ["PyMca5"] # ["silx"]
includes = []
# modules excluded from library.zip
excludes = ["collections.abc", "tcl", "tk", "OpenGL", "scipy", ]
build_options = {
"packages": packages,
"includes": includes,
"include_files": include_files,
"excludes": excludes, }
#"compressed": True, }
install_options = {}
executables = [
Executable('scripts/pymca_launcher',
# base="Win32GUI" if sys.platform == 'win32' else None,
base="Console" if sys.platform == 'win32' else None,)
# compress=True)
]
setup(name='pymca',
version=PyMca5.version(),
description="PyMca %s" % PyMca5.version(),
options=dict(build_exe=build_options,
install_exe=install_options),
executables=executables)
for f in ['qt', 'qttable', 'qtcanvas', 'Qwt5']:
excludes.append(f)
buildOptions = dict(
compressed = True,
include_files = include_files,
excludes = excludes,
includes = includes,
#includes = ["scipy.interpolate", "scipy.linalg"]
#optimize=2,
#packages = packages,
#includes = ["Object3D"],
#path = [PyMcaDir] + sys.path
)
install_dir = PyMcaDir + " " + PyMca5.version()
if not sys.platform.startswith('win'):
install_dir = install_dir.replace(" ","")
if os.path.exists(install_dir):
try:
def dir_cleaner(directory):
for f in glob.glob(os.path.join(directory,'*')):
if os.path.isfile(f):
try:
os.remove(f)
except:
print("file <%s> not deleted" % f)
if os.path.isdir(f):
dir_cleaner(f)
try:
os.rmdir(directory)
def _appendOneResultToHdf5(self, resultDict):
# Get all the necessary data (TODO: pass it to method as attrs)
idx = self._currentFitIndex
end_time = self._currentFitEndTime
start_time = self._currentFitStartTime
sigma = self._currentSigma
legend = self.legends[idx]
xlabel = self.xlabels[idx]
ylabel = self.ylabels[idx]
x, y, _inSigma, xMin, xMax = self.getFitInputValues(idx)
fitted_data = self.fit.evaluateDefinedFunction(x)
configIni = ConfigDict.ConfigDict(self.fit.getConfiguration()).tostring()
fit_paramlist = self.fit.paramlist
filename = self.getOutputFileName()
# Write the data to file (append)
with h5py.File(filename, mode="r+") as h5f:
entry = h5f.create_group("fit_curve_%d" % idx)
entry.attrs["NX_class"] = to_h5py_utf8("NXentry")
entry.attrs["default"] = to_h5py_utf8("fit_process/results/plot")
entry.create_dataset("start_time",
data=to_h5py_utf8(start_time))
entry.create_dataset("end_time", data=to_h5py_utf8(end_time))
entry.create_dataset("title",
data=to_h5py_utf8("Fit of '%s'" % legend))
process = entry.create_group("fit_process")
process.attrs["NX_class"] = to_h5py_utf8("NXprocess")
process.create_dataset("program", data=to_h5py_utf8("pymca"))
process.create_dataset("version", data=to_h5py_utf8(PyMca5.version()))
process.create_dataset("date", data=to_h5py_utf8(end_time))
configuration = process.create_group("configuration")
configuration.attrs["NX_class"] = to_h5py_utf8("NXnote")
configuration.create_dataset("type", data=to_h5py_utf8("text/plain"))
configuration.create_dataset("data", data=to_h5py_utf8(configIni))
configuration.create_dataset("file_name", data=to_h5py_utf8("SimpleFit.ini"))
configuration.create_dataset("description",
data=to_h5py_utf8("Fit configuration"))
results = process.create_group("results")
results.attrs["NX_class"] = to_h5py_utf8("NXcollection")
estimation = results.create_group("estimation")
estimation.attrs["NX_class"] = to_h5py_utf8("NXcollection")
for p in fit_paramlist:
pgroup = estimation.create_group(p["name"])
# constraint code can be an int, convert to str
if numpy.issubdtype(numpy.array(p['code']).dtype,
numpy.integer):
pgroup.create_dataset('code', data=to_h5py_utf8(CONS[p['code']]))
else:
pgroup.create_dataset('code', data=to_h5py_utf8(p['code']))
pgroup.create_dataset('cons1', data=p['cons1'])
pgroup.create_dataset('cons2', data=p['cons2'])
pgroup.create_dataset('estimation', data=p['estimation'])
for key, value in resultDict.items():
if not numpy.issubdtype(type(key), numpy.character):
_logger.debug("skipping key %s (not a text string)", key)
continue
if key == "fittedvalues":
output_key = "parameter_values"
elif key == "parameters":
output_key = "parameter_names"
elif key == "sigma_values":
output_key = "parameter_sigmas"
else:
output_key = key
value_dtype = numpy.array(value).dtype
if numpy.issubdtype(value_dtype, numpy.number) or\
numpy.issubdtype(value_dtype, numpy.bool_):
# straightforward conversion to HDF5
results.create_dataset(output_key,
data=value)
elif numpy.issubdtype(value_dtype, numpy.character):
# ensure utf-8 output
results.create_dataset(output_key,
data=to_h5py_utf8(value))
plot = results.create_group("plot")
plot.attrs["NX_class"] = to_h5py_utf8("NXdata")
plot.attrs["signal"] = to_h5py_utf8("raw_data")
plot.attrs["auxiliary_signals"] = to_h5py_utf8(["fitted_data"])
plot.attrs["axes"] = to_h5py_utf8(["x"])
plot.attrs["title"] = to_h5py_utf8("Fit of '%s'" % legend)
signal = plot.create_dataset("raw_data", data=y)
if ylabel is not None:
signal.attrs["long_name"] = to_h5py_utf8(ylabel)
axis = plot.create_dataset("x", data=x)
if xlabel is not None:
axis.attrs["long_name"] = to_h5py_utf8(xlabel)
if sigma is not None:
plot.create_dataset("errors", data=sigma)
plot.create_dataset("fitted_data", data=fitted_data)
for f in ['qt', 'qttable', 'qtcanvas', 'Qwt5']:
excludes.append(f)
buildOptions = dict(
compressed=True,
include_files=include_files,
excludes=excludes,
includes=includes,
#includes = ["scipy.interpolate", "scipy.linalg"]
#optimize=2,
#packages = packages,
#includes = ["Object3D"],
#path = [PyMcaDir] + sys.path
)
install_dir = PyMcaDir + " " + PyMca5.version()
if not sys.platform.startswith('win'):
install_dir = install_dir.replace(" ", "")
if os.path.exists(install_dir):
try:
def dir_cleaner(directory):
for f in glob.glob(os.path.join(directory, '*')):
if os.path.isfile(f):
try:
os.remove(f)
except:
print("file <%s> not deleted" % f)
if os.path.isdir(f):
dir_cleaner(f)
try: