def teardown(self):
self.output["c216_filename"] = self.hdf5_filename
if self.group:
self.output["c216_path"] = self.group.name
self.group.parent["end_time"] = numpy.string_(get_isotime())
if self.hdf5:
self.hdf5.close()
Plugin.teardown(self)
def teardown(self):
self.output["c216_filename"] = self.hdf5_filename
if self.group:
self.output["c216_path"] = self.group.name
self.group.parent["end_time"] = numpy.string_(get_isotime())
if self.hdf5:
self.hdf5.close()
Plugin.teardown(self)
def save_result(self, out, I, sigma=None):
"""Save the result of the work as a HDF5 file
:param out: scattering result
:param I: Intensities as 2D array
:param sigma: standard deviation of I as 2D array, is possible
"""
logger.debug("IntegrateManyFrames.save_result")
isotime = numpy.string_(get_isotime())
try:
nxs = pyFAI.io.Nexus(self.output_file, "a")
except IOError as error:
self.log_warning(
"invalid HDF5 file %s: remove and re-create!\n%s" %
(self.output_file, error))
os.unlink(self.output_file)
nxs = pyFAI.io.Nexus(self.output_file)
entry = nxs.new_entry("entry",
program_name="dahu",
title="ID15.IntegrateManyFrames ")
entry["program_name"].attrs["version"] = dahu_version
entry["plugin_name"] = numpy.string_(".".join(
(os.path.splitext(os.path.basename(__file__))[0],
self.__class__.__name__)))
entry["plugin_name"].attrs["version"] = version
entry["input"] = numpy.string_(json.dumps(self.input))
entry["input"].attrs["format"] = 'json'
subentry = nxs.new_class(entry, "PyFAI", class_type="NXprocess")
subentry["program"] = numpy.string_("PyFAI")
subentry["version"] = numpy.string_(pyFAI.version)
subentry["date"] = isotime
subentry["processing_type"] = numpy.string_(self.integration_method)
coll = nxs.new_class(subentry,
"process_integrate1d",
class_type="NXdata")
metadata_grp = coll.require_group("parameters")
for key, value in self.ai.getPyFAI().items():
metadata_grp[key] = numpy.string_(value)
scale, unit = str(out.unit).split("_", 1)
coll[scale] = out.radial.astype("float32")
coll[scale].attrs["interpretation"] = "scalar"
coll[scale].attrs["unit"] = unit
coll["I"] = I.astype("float32")
coll["I"].attrs["interpretation"] = "spectrum"
coll["I"].attrs["axes"] = ["t", scale]
coll["I"].attrs["signal"] = "1"
if sigma is not None:
coll["sigma"] = sigma.astype("float32")
coll["sigma"].attrs["interpretation"] = "spectrum"
coll["sigma"].attrs["axes"] = ["t", scale]
nxs.close()
def save_result(self, out, I, sigma=None):
"""Save the result of the work as a HDF5 file
:param out: scattering result
:param I: Intensities as 2D array
:param sigma: standard deviation of I as 2D array, is possible
"""
logger.debug("IntegrateManyFrames.save_result")
isotime = numpy.string_(get_isotime())
try:
nxs = pyFAI.io.Nexus(self.output_file, "a")
except IOError as error:
self.log_warning("invalid HDF5 file %s: remove and re-create!\n%s" % (self.output_file, error))
os.unlink(self.output_file)
nxs = pyFAI.io.Nexus(self.output_file)
entry = nxs.new_entry("entry", program_name="dahu", title="ID15.IntegrateManyFrames ")
entry["program_name"].attrs["version"] = dahu_version
entry["plugin_name"] = numpy.string_(".".join((os.path.splitext(os.path.basename(__file__))[0], self.__class__.__name__)))
entry["plugin_name"].attrs["version"] = version
entry["input"] = numpy.string_(json.dumps(self.input))
entry["input"].attrs["format"] = 'json'
subentry = nxs.new_class(entry, "PyFAI", class_type="NXprocess")
subentry["program"] = numpy.string_("PyFAI")
subentry["version"] = numpy.string_(pyFAI.version)
subentry["date"] = isotime
subentry["processing_type"] = numpy.string_(self.integration_method)
coll = nxs.new_class(subentry, "process_%s" % self.integration_method,
class_type="NXdata")
metadata_grp = coll.require_group("parameters")
for key, value in self.ai.getPyFAI().items():
metadata_grp[key] = numpy.string_(value)
scale, unit = str(out.unit).split("_", 1)
coll[scale] = out.radial.astype("float32")
coll[scale].attrs["interpretation"] = "scalar"
coll[scale].attrs["unit"] = unit
coll["I"] = I.astype("float32")
coll["I"].attrs["interpretation"] = "spectrum"
coll["I"].attrs["signal"] = "1"
coll.attrs["signal"] = "I"
coll.attrs["axes"] = [".", scale]
if sigma is not None:
coll["errors"] = sigma.astype("float32")
coll["errors"].attrs["interpretation"] = "spectrum"
nxs.close()
def create_hdf5(self):
"""
Create one HDF5 file per output
Also initialize all workers
"""
basename = os.path.splitext(os.path.basename(self.image_file))[0]
if basename.endswith("_raw"):
basename = basename[:-4]
json_config = json.dumps(self.input)
isotime = numpy.string_(get_isotime())
detector_grp = self.input_nxs.find_detector(all=True)
detector_name = "undefined"
for grp in detector_grp:
if "detector_information/name" in grp:
detector_name = grp["detector_information/name"].value
md_entry = self.metadata_nxs.get_entries()[0]
instruments = self.metadata_nxs.get_class(md_entry, "NXinstrument")
if instruments:
collections = self.metadata_nxs.get_class(instruments[0], "NXcollection")
to_copy = collections + detector_grp
else:
to_copy = detector_grp
for ext in self.to_save:
if ext == "raw":
continue
outfile = os.path.join(self.dest, "%s_%s.h5" % (basename, ext))
self.output_hdf5[ext] = outfile
try:
nxs = pyFAI.io.Nexus(outfile, "a")
except IOError as error:
self.log_warning("invalid HDF5 file %s: remove and re-create!\n%s" % (outfile, error))
os.unlink(outfile)
nxs = pyFAI.io.Nexus(outfile)
entry = nxs.new_entry("entry", program_name="dahu", title=self.image_file + ":" + self.images_ds.name)
entry["program_name"].attrs["version"] = dahu.version
entry["plugin_name"] = numpy.string_(".".join((os.path.splitext(os.path.basename(__file__))[0], self.__class__.__name__)))
entry["plugin_name"].attrs["version"] = version
entry["input"] = numpy.string_(json_config)
entry["input"].attrs["format"] = 'json'
entry["detector_name"] = numpy.string_(detector_name)
subentry = nxs.new_class(entry, "PyFAI", class_type="NXprocess")
subentry["program"] = numpy.string_("PyFAI")
subentry["version"] = numpy.string_(pyFAI.version)
subentry["date"] = isotime
subentry["processing_type"] = numpy.string_(ext)
coll = nxs.new_class(subentry, "process_" + ext, class_type="NXdata")
metadata_grp = coll.require_group("parameters")
for key, val in self.metadata.iteritems():
if type(val) in [str, unicode]:
metadata_grp[key] = numpy.string_(val)
else:
metadata_grp[key] = val
# copy metadata from other files:
for grp in to_copy:
grp_name = posixpath.split(grp.name)[-1]
if grp_name not in coll:
toplevel = coll.require_group(grp_name)
for k, v in grp.attrs.items():
toplevel.attrs[k] = v
else:
toplevel = coll[grp_name]
def grpdeepcopy(name, obj):
nxs.deep_copy(name, obj, toplevel=toplevel, excluded=["data"])
grp.visititems(grpdeepcopy)
shape = self.in_shape[:]
if self.npt1_rad is None and "npt1_rad" in self.input:
self.npt1_rad = int(self.input["npt1_rad"])
else:
qmax = self.ai.qArray(self.in_shape[-2:]).max()
dqmin = self.ai.deltaQ(self.in_shape[-2:]).min() * 2.0
self.npt1_rad = int(qmax / dqmin)
if ext == "azim":
if "npt2_rad" in self.input:
self.npt2_rad = int(self.input["npt2_rad"])
else:
qmax = self.ai.qArray(self.in_shape[-2:]).max()
dqmin = self.ai.deltaQ(self.in_shape[-2:]).min() * 2.0
self.npt2_rad = int(qmax / dqmin)
if "npt2_azim" in self.input:
self.npt2_azim = int(self.input["npt2_azim"])
else:
chi = self.ai.chiArray(self.in_shape[-2:])
self.npt2_azim = int(numpy.degrees(chi.max() - chi.min()))
shape = (self.in_shape[0], self.npt2_azim, self.npt2_rad)
ai = self.ai.__deepcopy__()
worker = pyFAI.worker.Worker(ai, self.in_shape[-2:], (self.npt2_azim, self.npt2_rad), self.unit)
if self.flat is not None:
worker.ai.set_flatfield(self.flat)
if self.dark is not None:
worker.ai.set_darkcurrent(self.dark)
worker.output = "numpy"
if self.in_shape[0] < 5:
worker.method = "splitbbox"
else:
worker.method = "ocl_csr_gpu"
if self.correct_solid_angle:
worker.set_normalization_factor(self.ai.pixel1 * self.ai.pixel2 / self.ai.dist / self.ai.dist)
else:
worker.set_normalization_factor(1.0)
worker.correct_solid_angle = self.correct_solid_angle
self.log_warning("Normalization factor: %s" % worker.normalization_factor)
worker.dummy = self.dummy
worker.delta_dummy = self.delta_dummy
if self.input.get("do_polarization"):
worker.polarization_factor = self.input.get("polarization_factor")
self.workers[ext] = worker
elif ext.startswith("ave"):
if "_" in ext:
unit = ext.split("_", 1)[1]
npt1_rad = self.input.get("npt1_rad_"+unit, self.npt1_rad)
else:
unit = self.unit
npt1_rad = self.npt1_rad
shape = (self.in_shape[0], npt1_rad)
worker = pyFAI.worker.Worker(self.ai, self.in_shape[-2:], (1, npt1_rad), unit=unit)
worker.output = "numpy"
if self.in_shape[0] < 5:
worker.method = "splitbbox"
else:
worker.method = "ocl_csr_gpu"
if self.correct_solid_angle:
worker.set_normalization_factor(self.ai.pixel1 * self.ai.pixel2 / self.ai.dist / self.ai.dist)
else:
worker.set_normalization_factor(1.0)
worker.correct_solid_angle = self.correct_solid_angle
worker.dummy = self.dummy
worker.delta_dummy = self.delta_dummy
if self.input.get("do_polarization"):
worker.polarization_factor = True
self.workers[ext] = worker
elif ext == "sub":
worker = pyFAI.worker.PixelwiseWorker(dark=self.dark,
dummy=self.dummy, delta_dummy=self.delta_dummy,
)
self.workers[ext] = worker
elif ext == "flat":
worker = pyFAI.worker.PixelwiseWorker(dark=self.dark, flat=self.flat,
dummy=self.dummy, delta_dummy=self.delta_dummy,
)
self.workers[ext] = worker
elif ext == "solid":
worker = pyFAI.worker.PixelwiseWorker(dark=self.dark, flat=self.flat, solidangle=self.get_solid_angle(),
dummy=self.dummy, delta_dummy=self.delta_dummy, polarization=self.polarization
)
self.workers[ext] = worker
elif ext == "dist":
worker = pyFAI.worker.DistortionWorker(dark=self.dark, flat=self.flat, solidangle=self.get_solid_angle(),
dummy=self.dummy, delta_dummy=self.delta_dummy, polarization=self.polarization,
detector=self.ai.detector)
self.workers[ext] = worker
elif ext == "norm":
worker = pyFAI.worker.DistortionWorker(dark=self.dark, flat=self.flat, solidangle=self.get_solid_angle(),
dummy=self.dummy, delta_dummy=self.delta_dummy, polarization=self.polarization,
detector=self.ai.detector)
self.workers[ext] = worker
else:
self.log_warning("unknown treatment %s" % ext)
output_ds = coll.create_dataset("data", shape, "float32",
chunks=(1,) + shape[1:],
maxshape=(None,) + shape[1:])
if self.t is not None:
coll["t"] = self.t
coll["t"].attrs["axis"] = "1"
coll["t"].attrs["interpretation"] = "scalar"
coll["t"].attrs["unit"] = "s"
# output_ds.attrs["NX_class"] = "NXdata" -> see group
output_ds.attrs["signal"] = "1"
if ext == "azim":
output_ds.attrs["axes"] = ["t", "chi", "q"]
output_ds.attrs["interpretation"] = "image"
elif ext == "ave":
output_ds.attrs["axes"] = ["t", "q"]
output_ds.attrs["interpretation"] = "spectrum"
elif ext in ("sub", "flat", "solid", "dist"):
output_ds.attrs["axes"] = "t"
output_ds.attrs["interpretation"] = "image"
else:
output_ds.attrs["interpretation"] = "image"
self.output_ds[ext] = output_ds
def create_hdf5(self):
"""
Create a HDF5 file and datastructure
"""
try:
self.hdf5 = h5py.File(self.hdf5_filename, 'a')
except IOError as error:
os.unlink(self.hdf5_filename)
self.log_warning("Unable to open %s: %s. Removing file and starting from scratch" % (self.hdf5_filename, error))
self.hdf5 = h5py.File(self.hdf5_filename)
if not self.entry.endswith("_"):
self.entry += "_"
entries = len([i.startswith(self.entry) for i in self.hdf5])
self.entry = posixpath.join("", "%s%04d" % (self.entry, entries))
self.instrument = posixpath.join(self.entry, self.instrument)
self.group = self.hdf5.require_group(self.instrument)
self.group.parent.attrs["NX_class"] = "NXentry"
self.group.attrs["NX_class"] = "NXinstrument"
# TimeFrameGenerator
self.tfg_grp = self.hdf5.require_group(posixpath.join(self.instrument, "TFG"))
self.tfg_grp.attrs["NX_class"] = "NXcollection"
self.tfg_grp["device"] = numpy.string_(self.c216)
# MultiCounterScaler
self.mcs_grp = self.hdf5.require_group(posixpath.join(self.instrument, "MCS"))
self.mcs_grp.attrs["NX_class"] = "NXcollection"
self.mcs_grp["device"] = numpy.string_(self.c216)
# Static metadata
self.info_grp = self.hdf5.require_group(posixpath.join(self.instrument, "parameters"))
self.info_grp.attrs["NX_class"] = "NXcollection"
for field, value in self.input2.get("Info", {}).items():
if field not in self.TO_SKIP and not isinstance(value, dict):
try:
value.encode("ascii")
except UnicodeEncodeError:
self.log_warning("Unicode Error in field %s: %s, skipping" % (field, value))
except AttributeError as err:
self.log_warning("Attribute Error %s \n in field %s: %s, forcing to string." % (err, field, value))
self.info_grp[field] = numpy.string_(value)
else:
self.info_grp[field] = numpy.string_(value)
start_time = self.input2.get("HMStartTime", get_isotime())
# Factor
HS32F = self.input2.get("HS32F")
if HS32F is not None:
self.mcs_grp["HS32F"] = HS32F
# Zero
HS32Z = self.input2.get("HS32Z")
if HS32Z is not None:
self.mcs_grp["HS32Z"] = HS32Z
# Name
HS32N = self.input2.get("HS32N")
if HS32N is not None:
self.mcs_grp["HS32N"] = numpy.array([str(i) for i in HS32N])
# Mode
HS32M = self.input2.get("HS32M")
if HS32M is not None:
self.mcs_grp["HS32M"] = HS32M
if HS32N and HS32Z and HS32F:
self.mcs_grp.require_group("interpreted")
self.group.parent["title"] = numpy.string_("id02.metadata")
self.group.parent["program"] = numpy.string_("Dahu")
self.group.parent["start_time"] = numpy.string_(start_time)
def create_hdf5(self):
"""
Create one HDF5 file per output
Also initialize all workers
"""
basename = os.path.splitext(os.path.basename(self.image_file))[0]
if basename.endswith("_raw"):
basename = basename[:-4]
json_config = json.dumps(self.input)
isotime = numpy.string_(get_isotime())
detector_grp = self.input_nxs.find_detector(all=True)
detector_name = "undefined"
for grp in detector_grp:
if "detector_information/name" in grp:
detector_name = grp["detector_information/name"].value
md_entry = self.metadata_nxs.get_entries()[0]
instruments = self.metadata_nxs.get_class(md_entry, "NXinstrument")
if instruments:
collections = self.metadata_nxs.get_class(instruments[0],
"NXcollection")
to_copy = collections + detector_grp
else:
to_copy = detector_grp
for ext in self.to_save:
if ext == "raw":
continue
outfile = os.path.join(self.dest, "%s_%s.h5" % (basename, ext))
self.output_hdf5[ext] = outfile
try:
nxs = pyFAI.io.Nexus(outfile, "a")
except IOError as error:
self.log_warning(
"invalid HDF5 file %s: remove and re-create!\n%s" %
(outfile, error))
os.unlink(outfile)
nxs = pyFAI.io.Nexus(outfile)
entry = nxs.new_entry("entry",
program_name="dahu",
title=self.image_file + ":" +
self.images_ds.name)
entry["program_name"].attrs["version"] = dahu.version
entry["plugin_name"] = numpy.string_(".".join(
(os.path.splitext(os.path.basename(__file__))[0],
self.__class__.__name__)))
entry["plugin_name"].attrs["version"] = version
entry["input"] = numpy.string_(json_config)
entry["detector_name"] = numpy.string_(detector_name)
subentry = nxs.new_class(entry, "PyFAI", class_type="NXprocess")
subentry["program"] = numpy.string_("PyFAI")
subentry["version"] = numpy.string_(pyFAI.version)
subentry["date"] = isotime
subentry["processing_type"] = numpy.string_(ext)
coll = nxs.new_class(subentry,
"process_" + ext,
class_type="NXdata")
metadata_grp = coll.require_group("parameters")
for key, val in self.metadata.iteritems():
if type(val) in [str, unicode]:
metadata_grp[key] = numpy.string_(val)
else:
metadata_grp[key] = val
# copy metadata from other files:
for grp in to_copy:
grp_name = posixpath.split(grp.name)[-1]
if grp_name not in coll:
toplevel = coll.require_group(grp_name)
for k, v in grp.attrs.items():
toplevel.attrs[k] = v
else:
toplevel = coll[grp_name]
def grpdeepcopy(name, obj):
nxs.deep_copy(name,
obj,
toplevel=toplevel,
excluded=["data"])
grp.visititems(grpdeepcopy)
shape = self.in_shape[:]
if ext == "azim":
if "npt2_rad" in self.input:
self.npt2_rad = int(self.input["npt2_rad"])
else:
qmax = self.ai.qArray(self.in_shape[-2:]).max()
dqmin = self.ai.deltaQ(self.in_shape[-2:]).min() * 2.0
self.npt2_rad = int(qmax / dqmin)
if "npt2_azim" in self.input:
self.npt2_azim = int(self.input["npt2_azim"])
else:
chi = self.ai.chiArray(self.in_shape[-2:])
self.npt2_azim = int(numpy.degrees(chi.max() - chi.min()))
shape = (self.in_shape[0], self.npt2_azim, self.npt2_rad)
ai = self.ai.__deepcopy__()
worker = pyFAI.worker.Worker(ai, self.in_shape[-2:],
(self.npt2_azim, self.npt2_rad),
"q_nm^-1")
if self.flat is not None:
worker.ai.set_flatfield(self.flat)
if self.dark is not None:
worker.ai.set_darkcurrent(self.dark)
worker.output = "numpy"
if self.in_shape[0] < 5:
worker.method = "splitbbox"
else:
worker.method = "ocl_csr_gpu"
if self.correct_solid_angle:
worker.set_normalization_factor(
self.ai.pixel1 * self.ai.pixel2 / self.ai.dist /
self.ai.dist / self.scaling_factor)
else:
worker.set_normalization_factor(1.0 / self.scaling_factor)
worker.correct_solid_angle = self.correct_solid_angle
self.log_warning("Normalization factor: %s" %
worker.normalization_factor)
worker.dummy = self.dummy
worker.delta_dummy = self.delta_dummy
self.workers[ext] = worker
elif ext == "ave":
if "npt1_rad" in self.input:
self.npt1_rad = int(self.input["npt1_rad"])
else:
qmax = self.ai.qArray(self.in_shape[-2:]).max()
dqmin = self.ai.deltaQ(self.in_shape[-2:]).min() * 2.0
self.npt1_rad = int(qmax / dqmin)
shape = (self.in_shape[0], self.npt1_rad)
worker = pyFAI.worker.Worker(self.ai, self.in_shape[-2:],
(1, self.npt1_rad), "q_nm^-1")
worker.output = "numpy"
if self.in_shape[0] < 5:
worker.method = "splitbbox"
else:
worker.method = "ocl_csr_gpu"
if self.correct_solid_angle:
worker.set_normalization_factor(
self.ai.pixel1 * self.ai.pixel2 / self.ai.dist /
self.ai.dist / self.scaling_factor)
else:
worker.set_normalization_factor(1.0 / self.scaling_factor)
worker.correct_solid_angle = self.correct_solid_angle
worker.dummy = self.dummy
worker.delta_dummy = self.delta_dummy
self.workers[ext] = worker
elif ext == "sub":
worker = pyFAI.worker.PixelwiseWorker(dark=self.dark)
self.workers[ext] = worker
elif ext == "flat":
worker = pyFAI.worker.PixelwiseWorker(dark=self.dark,
flat=self.flat)
self.workers[ext] = worker
elif ext == "solid":
worker = pyFAI.worker.PixelwiseWorker(
dark=self.dark,
flat=self.flat,
solidangle=self.get_solid_angle())
self.workers[ext] = worker
elif ext == "dist":
worker = pyFAI.worker.DistortionWorker(
dark=self.dark,
flat=self.flat,
solidangle=self.get_solid_angle(),
detector=self.ai.detector)
self.workers[ext] = worker
elif ext == "norm":
worker = pyFAI.worker.DistortionWorker(
dark=self.dark,
flat=self.flat,
solidangle=self.get_solid_angle(),
detector=self.ai.detector)
self.workers[ext] = worker
else:
self.log_warning("unknown treatment %s" % ext)
output_ds = coll.create_dataset("data",
shape,
"float32",
chunks=(1, ) + shape[1:],
maxshape=(None, ) + shape[1:])
if self.t is not None:
coll["t"] = self.t
coll["t"].attrs["axis"] = "1"
coll["t"].attrs["interpretation"] = "scalar"
coll["t"].attrs["unit"] = "s"
# output_ds.attrs["NX_class"] = "NXdata" -> see group
output_ds.attrs["signal"] = "1"
if ext == "azim":
output_ds.attrs["axes"] = ["t", "chi", "q"]
output_ds.attrs["interpretation"] = "image"
elif ext == "ave":
output_ds.attrs["axes"] = ["t", "q"]
output_ds.attrs["interpretation"] = "spectrum"
elif ext in ("sub", "flat", "solid", "dist"):
output_ds.attrs["axes"] = "t"
output_ds.attrs["interpretation"] = "image"
else:
output_ds.attrs["interpretation"] = "image"
self.output_ds[ext] = output_ds
def create_hdf5(self):
"""
Create a HDF5 file and datastructure
"""
try:
self.hdf5 = h5py.File(self.hdf5_filename, 'a')
except IOError as error:
os.unlink(self.hdf5_filename)
self.log_warning(
"Unable to open %s: %s. Removing file and starting from scratch"
% (self.hdf5_filename, error))
self.hdf5 = h5py.File(self.hdf5_filename)
if not self.entry.endswith("_"):
self.entry += "_"
entries = len([i.startswith(self.entry) for i in self.hdf5])
self.entry = posixpath.join("", "%s%04d" % (self.entry, entries))
self.instrument = posixpath.join(self.entry, self.instrument)
self.group = self.hdf5.require_group(self.instrument)
self.group.parent.attrs["NX_class"] = "NXentry"
self.group.attrs["NX_class"] = "NXinstrument"
# TimeFrameGenerator
self.tfg_grp = self.hdf5.require_group(
posixpath.join(self.instrument, "TFG"))
self.tfg_grp.attrs["NX_class"] = "NXcollection"
self.tfg_grp["device"] = numpy.string_(self.c216)
# MultiCounterScaler
self.mcs_grp = self.hdf5.require_group(
posixpath.join(self.instrument, "MCS"))
self.mcs_grp.attrs["NX_class"] = "NXcollection"
self.mcs_grp["device"] = numpy.string_(self.c216)
# Static metadata
self.info_grp = self.hdf5.require_group(
posixpath.join(self.instrument, "parameters"))
self.info_grp.attrs["NX_class"] = "NXcollection"
for field, value in self.input2.get("Info", {}).items():
if field not in self.TO_SKIP and not isinstance(value, dict):
try:
value.encode("ascii")
except UnicodeEncodeError:
self.log_warning(
"Unicode Error in field %s: %s, skipping" %
(field, value))
except AttributeError as err:
self.log_warning(
"Attribute Error %s \n in field %s: %s, forcing to string."
% (err, field, value))
self.info_grp[field] = numpy.string_(value)
else:
self.info_grp[field] = numpy.string_(value)
start_time = self.input2.get("HMStartTime", get_isotime())
# Factor
HS32F = self.input2.get("HS32F")
if HS32F is not None:
self.mcs_grp["HS32F"] = HS32F
# Zero
HS32Z = self.input2.get("HS32Z")
if HS32Z is not None:
self.mcs_grp["HS32Z"] = HS32Z
# Name
HS32N = self.input2.get("HS32N")
if HS32N is not None:
self.mcs_grp["HS32N"] = numpy.array([str(i) for i in HS32N])
# Mode
HS32M = self.input2.get("HS32M")
if HS32M is not None:
self.mcs_grp["HS32M"] = HS32M
if HS32N and HS32Z and HS32F:
self.mcs_grp.require_group("interpreted")
self.group.parent["title"] = numpy.string_("id02.metadata")
self.group.parent["program"] = numpy.string_("Dahu")
self.group.parent["start_time"] = numpy.string_(start_time)
def create_hdf5(self):
"""
Create one HDF5 file per output
Also initialize all workers
"""
basename = os.path.splitext(os.path.basename(self.image_file))[0]
if basename.endswith("_raw"):
basename = basename[:-4]
isotime = str(get_isotime())
detector_grp = self.input_nxs.find_detector(all=True)
detector_name = "undefined"
for grp in detector_grp:
if "detector_information/name" in grp:
detector_name = grp["detector_information/name"][()]
md_entry = self.metadata_nxs.get_entries()[0]
instruments = self.metadata_nxs.get_class(md_entry, "NXinstrument")
if instruments:
collections = self.metadata_nxs.get_class(instruments[0],
"NXcollection")
to_copy = collections + detector_grp
else:
to_copy = detector_grp
for ext in self.to_save:
if ext == "raw":
continue
outfile = os.path.join(self.dest, "%s_%s.h5" % (basename, ext))
self.output_hdf5[ext] = outfile
try:
nxs = Nexus(outfile, mode="a", creator="dahu")
except IOError as error:
self.log_warning(
"invalid HDF5 file %s: remove and re-create!\n%s" %
(outfile, error))
os.unlink(outfile)
nxs = Nexus(outfile, mode="w", creator="dahu")
entry = nxs.new_entry("entry",
program_name=self.input.get(
"plugin_name",
fully_qualified_name(self.__class__)),
title=self.image_file + ":" +
self.images_ds.name)
entry["program_name"].attrs["version"] = __version__
#configuration
config_grp = nxs.new_class(entry, "configuration", "NXnote")
config_grp["type"] = "text/json"
config_grp["data"] = json.dumps(self.input,
indent=2,
separators=(",\r\n", ": "))
entry["detector_name"] = str(detector_name)
nxprocess = nxs.new_class(entry, "PyFAI", class_type="NXprocess")
nxprocess["program"] = str("PyFAI")
nxprocess["version"] = str(pyFAI.version)
nxprocess["date"] = isotime
nxprocess["processing_type"] = str(ext)
nxdata = nxs.new_class(nxprocess,
"result_" + ext,
class_type="NXdata")
entry.attrs["default"] = nxdata.name
metadata_grp = nxprocess.require_group("parameters")
for key, val in self.metadata.items():
if type(val) in StringTypes:
metadata_grp[key] = str(val)
else:
metadata_grp[key] = val
# copy metadata from other files:
for grp in to_copy:
grp_name = posixpath.split(grp.name)[-1]
if grp_name not in nxdata:
toplevel = nxprocess.require_group(grp_name)
for k, v in grp.attrs.items():
toplevel.attrs[k] = v
else:
toplevel = nxprocess[grp_name]
def grpdeepcopy(name, obj):
nxs.deep_copy(name,
obj,
toplevel=toplevel,
excluded=["data"])
grp.visititems(grpdeepcopy)
shape = self.in_shape[:]
if self.npt1_rad is None and "npt1_rad" in self.input:
self.npt1_rad = int(self.input["npt1_rad"])
else:
qmax = self.ai.qArray(self.in_shape[-2:]).max()
dqmin = self.ai.deltaQ(self.in_shape[-2:]).min() * 2.0
self.npt1_rad = int(qmax / dqmin)
if ext == "azim":
if "npt2_rad" in self.input:
self.npt2_rad = int(self.input["npt2_rad"])
else:
qmax = self.ai.qArray(self.in_shape[-2:]).max()
dqmin = self.ai.deltaQ(self.in_shape[-2:]).min() * 2.0
self.npt2_rad = int(qmax / dqmin)
if "npt2_azim" in self.input:
self.npt2_azim = int(self.input["npt2_azim"])
else:
chi = self.ai.chiArray(self.in_shape[-2:])
self.npt2_azim = int(numpy.degrees(chi.max() - chi.min()))
shape = (self.in_shape[0], self.npt2_azim, self.npt2_rad)
ai = self.ai.__copy__()
worker = Worker(ai, self.in_shape[-2:],
(self.npt2_azim, self.npt2_rad), self.unit)
if self.flat is not None:
worker.ai.set_flatfield(self.flat)
if self.dark is not None:
worker.ai.set_darkcurrent(self.dark)
worker.output = "numpy"
if self.in_shape[0] < 5:
worker.method = "splitbbox"
else:
worker.method = "ocl_csr_gpu"
if self.correct_solid_angle:
worker.set_normalization_factor(
self.ai.pixel1 * self.ai.pixel2 / self.ai.dist /
self.ai.dist)
else:
worker.set_normalization_factor(1.0)
worker.correct_solid_angle = self.correct_solid_angle
self.log_warning("Normalization factor: %s" %
worker.normalization_factor)
worker.dummy = self.dummy
worker.delta_dummy = self.delta_dummy
if self.input.get("do_polarization"):
worker.polarization_factor = self.input.get(
"polarization_factor")
self.workers[ext] = worker
elif ext.startswith("ave"):
if "_" in ext:
unit = ext.split("_", 1)[1]
npt1_rad = self.input.get("npt1_rad_" + unit,
self.npt1_rad)
ai = self.ai.__copy__()
else:
unit = self.unit
npt1_rad = self.npt1_rad
ai = self.ai
shape = (self.in_shape[0], npt1_rad)
worker = Worker(ai,
self.in_shape[-2:], (1, npt1_rad),
unit=unit)
worker.output = "numpy"
if self.in_shape[0] < 5:
worker.method = "splitbbox"
else:
worker.method = "ocl_csr_gpu"
if self.correct_solid_angle:
worker.set_normalization_factor(
self.ai.pixel1 * self.ai.pixel2 / self.ai.dist /
self.ai.dist)
else:
worker.set_normalization_factor(1.0)
worker.correct_solid_angle = self.correct_solid_angle
worker.dummy = self.dummy
worker.delta_dummy = self.delta_dummy
if self.input.get("do_polarization"):
worker.polarization_factor = True
self.workers[ext] = worker
elif ext == "sub":
worker = PixelwiseWorker(
dark=self.dark,
dummy=self.dummy,
delta_dummy=self.delta_dummy,
)
self.workers[ext] = worker
elif ext == "flat":
worker = PixelwiseWorker(
dark=self.dark,
flat=self.flat,
dummy=self.dummy,
delta_dummy=self.delta_dummy,
)
self.workers[ext] = worker
elif ext == "solid":
worker = PixelwiseWorker(
dark=self.dark,
flat=self.flat,
solidangle=self.get_solid_angle(),
dummy=self.dummy,
delta_dummy=self.delta_dummy,
polarization=self.polarization,
)
self.workers[ext] = worker
elif ext == "dist":
worker = DistortionWorker(
dark=self.dark,
flat=self.flat,
solidangle=self.get_solid_angle(),
dummy=self.dummy,
delta_dummy=self.delta_dummy,
polarization=self.polarization,
detector=self.ai.detector,
)
self.workers[ext] = worker
if self.distortion is None:
self.distortion = worker.distortion
self.cache_dis = str(self.ai.detector)
if self.cache_dis in self.cache:
self.distortion.lut = self.cache[self.cache_dis]
else:
self.distortion.calc_LUT()
self.cache[self.cache_dis] = self.distortion.lut
else:
worker.distortion = self.distortion
elif ext == "norm":
worker = DistortionWorker(
dark=self.dark,
flat=self.flat,
solidangle=self.get_solid_angle(),
dummy=self.dummy,
delta_dummy=self.delta_dummy,
polarization=self.polarization,
detector=self.ai.detector,
)
self.workers[ext] = worker
if self.distortion is None and worker.distortion is not None:
self.distortion = worker.distortion
self.cache_dis = str(self.ai.detector)
if self.cache_dis in self.cache:
self.distortion.lut = self.cache[self.cache_dis]
else:
self.distortion.calc_LUT()
self.cache[self.cache_dis] = self.distortion.lut
else:
worker.distortion = self.distortion
else:
self.log_warning("unknown treatment %s" % ext)
if (len(shape) >= 3):
compression = {k: v for k, v in COMPRESSION.items()}
else:
compression = {}
output_ds = nxdata.create_dataset("data",
shape,
dtype=numpy.float32,
chunks=(1, ) + shape[1:],
maxshape=(None, ) + shape[1:],
**compression)
nxdata.attrs["signal"] = "data"
# output_ds.attrs["signal"] = "1"
entry.attrs["default"] = nxdata.name
if self.variance_formula is not None:
error_ds = nxdata.create_dataset("data_errors",
shape,
dtype=numpy.float32,
chunks=(1, ) + shape[1:],
maxshape=(None, ) + shape[1:],
**compression)
# nxdata.attrs["uncertainties"] = "errors"
self.output_ds[ext + "_err"] = error_ds
if self.t is not None:
nxdata["t"] = self.t
nxdata["t"].attrs["interpretation"] = "scalar"
nxdata["t"].attrs["unit"] = "s"
if ext == "azim":
nxdata.attrs["axes"] = [".", "chi", "q"]
output_ds.attrs["interpretation"] = "image"
if self.variance_formula is not None:
error_ds.attrs["interpretation"] = "image"
elif ext == "ave":
nxdata.attrs["axes"] = [".", "q"]
output_ds.attrs["interpretation"] = "spectrum"
if self.variance_formula is not None:
error_ds.attrs["interpretation"] = "spectrum"
else:
output_ds.attrs["interpretation"] = "image"
if self.variance_formula is not None:
error_ds.attrs["interpretation"] = "image"
self.output_ds[ext] = output_ds
