def hdfgroup2signaldict(group):
exp = {}
exp['data'] = group['data'][:]
axes = []
for i in xrange(len(exp['data'].shape)):
try:
axes.append(dict(group['axis-%i' % i].attrs))
except KeyError:
raise IOError(not_valid_format)
for axis in axes:
for key, item in axis.iteritems():
axis[key] = ensure_unicode(item)
exp['mapped_parameters'] = hdfgroup2dict(
group['mapped_parameters'], {})
exp['original_parameters'] = hdfgroup2dict(
group['original_parameters'], {})
exp['axes'] = axes
exp['attributes']={}
if 'learning_results' in group.keys():
exp['attributes']['learning_results'] = \
hdfgroup2dict(group['learning_results'],{})
if 'peak_learning_results' in group.keys():
exp['attributes']['peak_learning_results'] = \
hdfgroup2dict(group['peak_learning_results'],{})
# Load the decomposition results written with the old name,
# mva_results
if 'mva_results' in group.keys():
exp['attributes']['learning_results'] = hdfgroup2dict(
group['mva_results'],{})
if 'peak_mva_results' in group.keys():
exp['attributes']['peak_learning_results']=hdfgroup2dict(
group['peak_mva_results'],{})
# Replace the old signal and name keys with their current names
if 'signal' in exp['mapped_parameters']:
exp['mapped_parameters']['signal_type'] = \
exp['mapped_parameters']['signal']
del exp['mapped_parameters']['signal']
if 'name' in exp['mapped_parameters']:
exp['mapped_parameters']['title'] = \
exp['mapped_parameters']['name']
del exp['mapped_parameters']['name']
# If the title was not defined on writing the Experiment is
# then called __unnamed__. The next "if" simply sets the title
# back to the empty string
if '__unnamed__' == exp['mapped_parameters']['title']:
exp['mapped_parameters']['title'] = ''
return exp
def hdfgroup2signaldict(group):
exp = {}
exp['data'] = group['data'][:]
axes = []
for i in xrange(len(exp['data'].shape)):
try:
axes.append(dict(group['axis-%i' % i].attrs))
except KeyError:
raise IOError(not_valid_format)
for axis in axes:
for key, item in axis.iteritems():
axis[key] = ensure_unicode(item)
exp['mapped_parameters'] = hdfgroup2dict(group['mapped_parameters'], {})
exp['original_parameters'] = hdfgroup2dict(group['original_parameters'],
{})
exp['axes'] = axes
exp['attributes'] = {}
if 'learning_results' in group.keys():
exp['attributes']['learning_results'] = \
hdfgroup2dict(group['learning_results'],{})
if 'peak_learning_results' in group.keys():
exp['attributes']['peak_learning_results'] = \
hdfgroup2dict(group['peak_learning_results'],{})
# Load the decomposition results written with the old name,
# mva_results
if 'mva_results' in group.keys():
exp['attributes']['learning_results'] = hdfgroup2dict(
group['mva_results'], {})
if 'peak_mva_results' in group.keys():
exp['attributes']['peak_learning_results'] = hdfgroup2dict(
group['peak_mva_results'], {})
# Replace the old signal and name keys with their current names
if 'signal' in exp['mapped_parameters']:
exp['mapped_parameters']['signal_type'] = \
exp['mapped_parameters']['signal']
del exp['mapped_parameters']['signal']
if 'name' in exp['mapped_parameters']:
exp['mapped_parameters']['title'] = \
exp['mapped_parameters']['name']
del exp['mapped_parameters']['name']
# If the title was not defined on writing the Experiment is
# then called __unnamed__. The next "if" simply sets the title
# back to the empty string
if '__unnamed__' == exp['mapped_parameters']['title']:
exp['mapped_parameters']['title'] = ''
return exp
def hdfgroup2signaldict(group, load_to_memory=True):
global current_file_version
global default_version
if current_file_version < LooseVersion("1.2"):
metadata = "mapped_parameters"
original_metadata = "original_parameters"
else:
metadata = "metadata"
original_metadata = "original_metadata"
exp = {
'metadata':
hdfgroup2dict(group[metadata], load_to_memory=load_to_memory),
'original_metadata':
hdfgroup2dict(group[original_metadata], load_to_memory=load_to_memory)
}
data = group['data']
if load_to_memory:
data = np.asanyarray(data)
exp['data'] = data
axes = []
for i in range(len(exp['data'].shape)):
try:
axes.append(dict(group['axis-%i' % i].attrs))
axis = axes[-1]
for key, item in axis.items():
if isinstance(item, np.bool_):
axis[key] = bool(item)
else:
axis[key] = ensure_unicode(item)
except KeyError:
break
if len(axes) != len(exp['data'].shape): # broke from the previous loop
try:
axes = [
i for k, i in sorted(
iter(
hdfgroup2dict(group['_list_' +
str(len(exp['data'].shape)) +
'_axes'],
load_to_memory=load_to_memory).items()))
]
except KeyError:
raise IOError(not_valid_format)
exp['axes'] = axes
exp['attributes'] = {}
if 'learning_results' in group.keys():
exp['attributes']['learning_results'] = \
hdfgroup2dict(
group['learning_results'],
load_to_memory=load_to_memory)
if 'peak_learning_results' in group.keys():
exp['attributes']['peak_learning_results'] = \
hdfgroup2dict(
group['peak_learning_results'],
load_to_memory=load_to_memory)
# If the title was not defined on writing the Experiment is
# then called __unnamed__. The next "if" simply sets the title
# back to the empty string
if "General" in exp["metadata"] and "title" in exp["metadata"]["General"]:
if '__unnamed__' == exp['metadata']['General']['title']:
exp['metadata']["General"]['title'] = ''
if current_file_version < LooseVersion("1.1"):
# Load the decomposition results written with the old name,
# mva_results
if 'mva_results' in group.keys():
exp['attributes']['learning_results'] = hdfgroup2dict(
group['mva_results'], load_to_memory=load_to_memory)
if 'peak_mva_results' in group.keys():
exp['attributes']['peak_learning_results'] = hdfgroup2dict(
group['peak_mva_results'], load_to_memory=load_to_memory)
# Replace the old signal and name keys with their current names
if 'signal' in exp['metadata']:
if "Signal" not in exp["metadata"]:
exp["metadata"]["Signal"] = {}
exp['metadata']["Signal"]['signal_type'] = \
exp['metadata']['signal']
del exp['metadata']['signal']
if 'name' in exp['metadata']:
if "General" not in exp["metadata"]:
exp["metadata"]["General"] = {}
exp['metadata']['General']['title'] = \
exp['metadata']['name']
del exp['metadata']['name']
if current_file_version < LooseVersion("1.2"):
if '_internal_parameters' in exp['metadata']:
exp['metadata']['_HyperSpy'] = \
exp['metadata']['_internal_parameters']
del exp['metadata']['_internal_parameters']
if 'stacking_history' in exp['metadata']['_HyperSpy']:
exp['metadata']['_HyperSpy']["Stacking_history"] = \
exp['metadata']['_HyperSpy']['stacking_history']
del exp['metadata']['_HyperSpy']["stacking_history"]
if 'folding' in exp['metadata']['_HyperSpy']:
exp['metadata']['_HyperSpy']["Folding"] = \
exp['metadata']['_HyperSpy']['folding']
del exp['metadata']['_HyperSpy']["folding"]
if 'Variance_estimation' in exp['metadata']:
if "Noise_properties" not in exp["metadata"]:
exp["metadata"]["Noise_properties"] = {}
exp['metadata']['Noise_properties']["Variance_linear_model"] = \
exp['metadata']['Variance_estimation']
del exp['metadata']['Variance_estimation']
if "TEM" in exp["metadata"]:
if "Acquisition_instrument" not in exp["metadata"]:
exp["metadata"]["Acquisition_instrument"] = {}
exp["metadata"]["Acquisition_instrument"]["TEM"] = \
exp["metadata"]["TEM"]
del exp["metadata"]["TEM"]
tem = exp["metadata"]["Acquisition_instrument"]["TEM"]
if "EELS" in tem:
if "dwell_time" in tem:
tem["EELS"]["dwell_time"] = tem["dwell_time"]
del tem["dwell_time"]
if "dwell_time_units" in tem:
tem["EELS"]["dwell_time_units"] = tem["dwell_time_units"]
del tem["dwell_time_units"]
if "exposure" in tem:
tem["EELS"]["exposure"] = tem["exposure"]
del tem["exposure"]
if "exposure_units" in tem:
tem["EELS"]["exposure_units"] = tem["exposure_units"]
del tem["exposure_units"]
if "Detector" not in tem:
tem["Detector"] = {}
tem["Detector"] = tem["EELS"]
del tem["EELS"]
if "EDS" in tem:
if "Detector" not in tem:
tem["Detector"] = {}
if "EDS" not in tem["Detector"]:
tem["Detector"]["EDS"] = {}
tem["Detector"]["EDS"] = tem["EDS"]
del tem["EDS"]
del tem
if "SEM" in exp["metadata"]:
if "Acquisition_instrument" not in exp["metadata"]:
exp["metadata"]["Acquisition_instrument"] = {}
exp["metadata"]["Acquisition_instrument"]["SEM"] = \
exp["metadata"]["SEM"]
del exp["metadata"]["SEM"]
sem = exp["metadata"]["Acquisition_instrument"]["SEM"]
if "EDS" in sem:
if "Detector" not in sem:
sem["Detector"] = {}
if "EDS" not in sem["Detector"]:
sem["Detector"]["EDS"] = {}
sem["Detector"]["EDS"] = sem["EDS"]
del sem["EDS"]
del sem
if "Sample" in exp["metadata"] and "Xray_lines" in exp["metadata"][
"Sample"]:
exp["metadata"]["Sample"]["xray_lines"] = exp["metadata"][
"Sample"]["Xray_lines"]
del exp["metadata"]["Sample"]["Xray_lines"]
for key in ["title", "date", "time", "original_filename"]:
if key in exp["metadata"]:
if "General" not in exp["metadata"]:
exp["metadata"]["General"] = {}
exp["metadata"]["General"][key] = exp["metadata"][key]
del exp["metadata"][key]
for key in ["record_by", "signal_origin", "signal_type"]:
if key in exp["metadata"]:
if "Signal" not in exp["metadata"]:
exp["metadata"]["Signal"] = {}
exp["metadata"]["Signal"][key] = exp["metadata"][key]
del exp["metadata"][key]
return exp
def hdfgroup2signaldict(group, load_to_memory=True):
global current_file_version
global default_version
if current_file_version < StrictVersion("1.2"):
metadata = "mapped_parameters"
original_metadata = "original_parameters"
else:
metadata = "metadata"
original_metadata = "original_metadata"
exp = {'metadata': hdfgroup2dict(
group[metadata], load_to_memory=load_to_memory),
'original_metadata': hdfgroup2dict(
group[original_metadata], load_to_memory=load_to_memory)
}
data = group['data']
if load_to_memory:
data = np.asanyarray(data)
exp['data'] = data
axes = []
for i in range(len(exp['data'].shape)):
try:
axes.append(dict(group['axis-%i' % i].attrs))
axis = axes[-1]
for key, item in axis.items():
if isinstance(item, np.bool_):
axis[key] = bool(item)
else:
axis[key] = ensure_unicode(item)
except KeyError:
break
if len(axes) != len(exp['data'].shape): # broke from the previous loop
try:
axes = [i for k, i in sorted(iter(hdfgroup2dict(
group['_list_' + str(len(exp['data'].shape)) + '_axes'],
load_to_memory=load_to_memory).items()))]
except KeyError:
raise IOError(not_valid_format)
exp['axes'] = axes
exp['attributes'] = {}
if 'learning_results' in group.keys():
exp['attributes']['learning_results'] = \
hdfgroup2dict(
group['learning_results'],
load_to_memory=load_to_memory)
if 'peak_learning_results' in group.keys():
exp['attributes']['peak_learning_results'] = \
hdfgroup2dict(
group['peak_learning_results'],
load_to_memory=load_to_memory)
# If the title was not defined on writing the Experiment is
# then called __unnamed__. The next "if" simply sets the title
# back to the empty string
if "General" in exp["metadata"] and "title" in exp["metadata"]["General"]:
if '__unnamed__' == exp['metadata']['General']['title']:
exp['metadata']["General"]['title'] = ''
if current_file_version < StrictVersion("1.1"):
# Load the decomposition results written with the old name,
# mva_results
if 'mva_results' in group.keys():
exp['attributes']['learning_results'] = hdfgroup2dict(
group['mva_results'], load_to_memory=load_to_memory)
if 'peak_mva_results' in group.keys():
exp['attributes']['peak_learning_results'] = hdfgroup2dict(
group['peak_mva_results'], load_to_memory=load_to_memory)
# Replace the old signal and name keys with their current names
if 'signal' in exp['metadata']:
if "Signal" not in exp["metadata"]:
exp["metadata"]["Signal"] = {}
exp['metadata']["Signal"]['signal_type'] = \
exp['metadata']['signal']
del exp['metadata']['signal']
if 'name' in exp['metadata']:
if "General" not in exp["metadata"]:
exp["metadata"]["General"] = {}
exp['metadata']['General']['title'] = \
exp['metadata']['name']
del exp['metadata']['name']
if current_file_version < StrictVersion("1.2"):
if '_internal_parameters' in exp['metadata']:
exp['metadata']['_HyperSpy'] = \
exp['metadata']['_internal_parameters']
del exp['metadata']['_internal_parameters']
if 'stacking_history' in exp['metadata']['_HyperSpy']:
exp['metadata']['_HyperSpy']["Stacking_history"] = \
exp['metadata']['_HyperSpy']['stacking_history']
del exp['metadata']['_HyperSpy']["stacking_history"]
if 'folding' in exp['metadata']['_HyperSpy']:
exp['metadata']['_HyperSpy']["Folding"] = \
exp['metadata']['_HyperSpy']['folding']
del exp['metadata']['_HyperSpy']["folding"]
if 'Variance_estimation' in exp['metadata']:
if "Noise_properties" not in exp["metadata"]:
exp["metadata"]["Noise_properties"] = {}
exp['metadata']['Noise_properties']["Variance_linear_model"] = \
exp['metadata']['Variance_estimation']
del exp['metadata']['Variance_estimation']
if "TEM" in exp["metadata"]:
if "Acquisition_instrument" not in exp["metadata"]:
exp["metadata"]["Acquisition_instrument"] = {}
exp["metadata"]["Acquisition_instrument"]["TEM"] = \
exp["metadata"]["TEM"]
del exp["metadata"]["TEM"]
tem = exp["metadata"]["Acquisition_instrument"]["TEM"]
if "EELS" in tem:
if "dwell_time" in tem:
tem["EELS"]["dwell_time"] = tem["dwell_time"]
del tem["dwell_time"]
if "dwell_time_units" in tem:
tem["EELS"]["dwell_time_units"] = tem["dwell_time_units"]
del tem["dwell_time_units"]
if "exposure" in tem:
tem["EELS"]["exposure"] = tem["exposure"]
del tem["exposure"]
if "exposure_units" in tem:
tem["EELS"]["exposure_units"] = tem["exposure_units"]
del tem["exposure_units"]
if "Detector" not in tem:
tem["Detector"] = {}
tem["Detector"] = tem["EELS"]
del tem["EELS"]
if "EDS" in tem:
if "Detector" not in tem:
tem["Detector"] = {}
if "EDS" not in tem["Detector"]:
tem["Detector"]["EDS"] = {}
tem["Detector"]["EDS"] = tem["EDS"]
del tem["EDS"]
del tem
if "SEM" in exp["metadata"]:
if "Acquisition_instrument" not in exp["metadata"]:
exp["metadata"]["Acquisition_instrument"] = {}
exp["metadata"]["Acquisition_instrument"]["SEM"] = \
exp["metadata"]["SEM"]
del exp["metadata"]["SEM"]
sem = exp["metadata"]["Acquisition_instrument"]["SEM"]
if "EDS" in sem:
if "Detector" not in sem:
sem["Detector"] = {}
if "EDS" not in sem["Detector"]:
sem["Detector"]["EDS"] = {}
sem["Detector"]["EDS"] = sem["EDS"]
del sem["EDS"]
del sem
if "Sample" in exp["metadata"] and "Xray_lines" in exp[
"metadata"]["Sample"]:
exp["metadata"]["Sample"]["xray_lines"] = exp[
"metadata"]["Sample"]["Xray_lines"]
del exp["metadata"]["Sample"]["Xray_lines"]
for key in ["title", "date", "time", "original_filename"]:
if key in exp["metadata"]:
if "General" not in exp["metadata"]:
exp["metadata"]["General"] = {}
exp["metadata"]["General"][key] = exp["metadata"][key]
del exp["metadata"][key]
for key in ["record_by", "signal_origin", "signal_type"]:
if key in exp["metadata"]:
if "Signal" not in exp["metadata"]:
exp["metadata"]["Signal"] = {}
exp["metadata"]["Signal"][key] = exp["metadata"][key]
del exp["metadata"][key]
return exp
def hdfgroup2signaldict(group, lazy=False):
global current_file_version
global default_version
if current_file_version < LooseVersion("1.2"):
metadata = "mapped_parameters"
original_metadata = "original_parameters"
else:
metadata = "metadata"
original_metadata = "original_metadata"
exp = {
'metadata': hdfgroup2dict(group[metadata], lazy=lazy),
'original_metadata': hdfgroup2dict(group[original_metadata],
lazy=lazy),
'attributes': {}
}
if "package" in group.attrs:
# HyperSpy version is >= 1.5
exp["package"] = group.attrs["package"]
exp["package_version"] = group.attrs["package_version"]
else:
# Prior to v1.4 we didn't store the package information. Since there
# were already external package we cannot assume any package provider so
# we leave this empty.
exp["package"] = ""
exp["package_version"] = ""
data = group['data']
if lazy:
data = da.from_array(data, chunks=data.chunks)
exp['attributes']['_lazy'] = True
else:
data = np.asanyarray(data)
exp['data'] = data
axes = []
for i in range(len(exp['data'].shape)):
try:
axes.append(dict(group['axis-%i' % i].attrs))
axis = axes[-1]
for key, item in axis.items():
if isinstance(item, np.bool_):
axis[key] = bool(item)
else:
axis[key] = ensure_unicode(item)
except KeyError:
break
if len(axes) != len(exp['data'].shape): # broke from the previous loop
try:
axes = [
i for k, i in sorted(
iter(
hdfgroup2dict(group['_list_' +
str(len(exp['data'].shape)) +
'_axes'],
lazy=lazy).items()))
]
except KeyError:
raise IOError(not_valid_format)
exp['axes'] = axes
if 'learning_results' in group.keys():
exp['attributes']['learning_results'] = \
hdfgroup2dict(
group['learning_results'],
lazy=lazy)
if 'peak_learning_results' in group.keys():
exp['attributes']['peak_learning_results'] = \
hdfgroup2dict(
group['peak_learning_results'],
lazy=lazy)
# If the title was not defined on writing the Experiment is
# then called __unnamed__. The next "if" simply sets the title
# back to the empty string
if "General" in exp["metadata"] and "title" in exp["metadata"]["General"]:
if '__unnamed__' == exp['metadata']['General']['title']:
exp['metadata']["General"]['title'] = ''
if current_file_version < LooseVersion("1.1"):
# Load the decomposition results written with the old name,
# mva_results
if 'mva_results' in group.keys():
exp['attributes']['learning_results'] = hdfgroup2dict(
group['mva_results'], lazy=lazy)
if 'peak_mva_results' in group.keys():
exp['attributes']['peak_learning_results'] = hdfgroup2dict(
group['peak_mva_results'], lazy=lazy)
# Replace the old signal and name keys with their current names
if 'signal' in exp['metadata']:
if "Signal" not in exp["metadata"]:
exp["metadata"]["Signal"] = {}
exp['metadata']["Signal"]['signal_type'] = \
exp['metadata']['signal']
del exp['metadata']['signal']
if 'name' in exp['metadata']:
if "General" not in exp["metadata"]:
exp["metadata"]["General"] = {}
exp['metadata']['General']['title'] = \
exp['metadata']['name']
del exp['metadata']['name']
if current_file_version < LooseVersion("1.2"):
if '_internal_parameters' in exp['metadata']:
exp['metadata']['_HyperSpy'] = \
exp['metadata']['_internal_parameters']
del exp['metadata']['_internal_parameters']
if 'stacking_history' in exp['metadata']['_HyperSpy']:
exp['metadata']['_HyperSpy']["Stacking_history"] = \
exp['metadata']['_HyperSpy']['stacking_history']
del exp['metadata']['_HyperSpy']["stacking_history"]
if 'folding' in exp['metadata']['_HyperSpy']:
exp['metadata']['_HyperSpy']["Folding"] = \
exp['metadata']['_HyperSpy']['folding']
del exp['metadata']['_HyperSpy']["folding"]
if 'Variance_estimation' in exp['metadata']:
if "Noise_properties" not in exp["metadata"]:
exp["metadata"]["Noise_properties"] = {}
exp['metadata']['Noise_properties']["Variance_linear_model"] = \
exp['metadata']['Variance_estimation']
del exp['metadata']['Variance_estimation']
if "TEM" in exp["metadata"]:
if "Acquisition_instrument" not in exp["metadata"]:
exp["metadata"]["Acquisition_instrument"] = {}
exp["metadata"]["Acquisition_instrument"]["TEM"] = \
exp["metadata"]["TEM"]
del exp["metadata"]["TEM"]
tem = exp["metadata"]["Acquisition_instrument"]["TEM"]
if "EELS" in tem:
if "dwell_time" in tem:
tem["EELS"]["dwell_time"] = tem["dwell_time"]
del tem["dwell_time"]
if "dwell_time_units" in tem:
tem["EELS"]["dwell_time_units"] = tem["dwell_time_units"]
del tem["dwell_time_units"]
if "exposure" in tem:
tem["EELS"]["exposure"] = tem["exposure"]
del tem["exposure"]
if "exposure_units" in tem:
tem["EELS"]["exposure_units"] = tem["exposure_units"]
del tem["exposure_units"]
if "Detector" not in tem:
tem["Detector"] = {}
tem["Detector"] = tem["EELS"]
del tem["EELS"]
if "EDS" in tem:
if "Detector" not in tem:
tem["Detector"] = {}
if "EDS" not in tem["Detector"]:
tem["Detector"]["EDS"] = {}
tem["Detector"]["EDS"] = tem["EDS"]
del tem["EDS"]
del tem
if "SEM" in exp["metadata"]:
if "Acquisition_instrument" not in exp["metadata"]:
exp["metadata"]["Acquisition_instrument"] = {}
exp["metadata"]["Acquisition_instrument"]["SEM"] = \
exp["metadata"]["SEM"]
del exp["metadata"]["SEM"]
sem = exp["metadata"]["Acquisition_instrument"]["SEM"]
if "EDS" in sem:
if "Detector" not in sem:
sem["Detector"] = {}
if "EDS" not in sem["Detector"]:
sem["Detector"]["EDS"] = {}
sem["Detector"]["EDS"] = sem["EDS"]
del sem["EDS"]
del sem
if "Sample" in exp["metadata"] and "Xray_lines" in exp["metadata"][
"Sample"]:
exp["metadata"]["Sample"]["xray_lines"] = exp["metadata"][
"Sample"]["Xray_lines"]
del exp["metadata"]["Sample"]["Xray_lines"]
for key in ["title", "date", "time", "original_filename"]:
if key in exp["metadata"]:
if "General" not in exp["metadata"]:
exp["metadata"]["General"] = {}
exp["metadata"]["General"][key] = exp["metadata"][key]
del exp["metadata"][key]
for key in ["record_by", "signal_origin", "signal_type"]:
if key in exp["metadata"]:
if "Signal" not in exp["metadata"]:
exp["metadata"]["Signal"] = {}
exp["metadata"]["Signal"][key] = exp["metadata"][key]
del exp["metadata"][key]
if current_file_version < LooseVersion("3.0"):
if "Acquisition_instrument" in exp["metadata"]:
# Move tilt_stage to Stage.tilt_alpha
# Move exposure time to Detector.Camera.exposure_time
if "TEM" in exp["metadata"]["Acquisition_instrument"]:
tem = exp["metadata"]["Acquisition_instrument"]["TEM"]
exposure = None
if "tilt_stage" in tem:
tem["Stage"] = {"tilt_alpha": tem["tilt_stage"]}
del tem["tilt_stage"]
if "exposure" in tem:
exposure = "exposure"
# Digital_micrograph plugin was parsing to 'exposure_time'
# instead of 'exposure': need this to be compatible with
# previous behaviour
if "exposure_time" in tem:
exposure = "exposure_time"
if exposure is not None:
if "Detector" not in tem:
tem["Detector"] = {
"Camera": {
"exposure": tem[exposure]
}
}
tem["Detector"]["Camera"] = {"exposure": tem[exposure]}
del tem[exposure]
# Move tilt_stage to Stage.tilt_alpha
if "SEM" in exp["metadata"]["Acquisition_instrument"]:
sem = exp["metadata"]["Acquisition_instrument"]["SEM"]
if "tilt_stage" in sem:
sem["Stage"] = {"tilt_alpha": sem["tilt_stage"]}
del sem["tilt_stage"]
return exp
def group2signaldict(self, group, lazy=False):
"""
Reads a h5py/zarr group and returns a signal dictionary.
Parameters
----------
group : :py:class:`h5py.Group` or :py:class:`zarr.hierarchy.Group`
A group following hspy specification.
lazy : bool, optional
Return the data as dask array. The default is False.
Raises
------
IOError
Raise an IOError when the group can't be read, if the group
doesn't follow hspy format specification, etc.
"""
if self.version < Version("1.2"):
metadata = "mapped_parameters"
original_metadata = "original_parameters"
else:
metadata = "metadata"
original_metadata = "original_metadata"
exp = {'metadata': self._group2dict(
group[metadata], lazy=lazy),
'original_metadata': self._group2dict(
group[original_metadata], lazy=lazy),
'attributes': {}
}
if "package" in group.attrs:
# HyperSpy version is >= 1.5
exp["package"] = group.attrs["package"]
exp["package_version"] = group.attrs["package_version"]
else:
# Prior to v1.4 we didn't store the package information. Since there
# were already external package we cannot assume any package provider so
# we leave this empty.
exp["package"] = ""
exp["package_version"] = ""
data = group['data']
try:
ragged_shape = group["ragged_shapes"]
new_data = np.empty(shape=data.shape, dtype=object)
for i in np.ndindex(data.shape):
new_data[i] = np.reshape(data[i], ragged_shape[i])
data = new_data
except KeyError:
pass
if lazy:
data = da.from_array(data, chunks=data.chunks)
exp['attributes']['_lazy'] = True
else:
data = np.asanyarray(data)
exp['data'] = data
axes = []
for i in range(len(exp['data'].shape)):
try:
axes.append(self._group2dict(group[f'axis-{i}']))
axis = axes[-1]
for key, item in axis.items():
if isinstance(item, np.bool_):
axis[key] = bool(item)
else:
axis[key] = ensure_unicode(item)
except KeyError:
break
if len(axes) != len(exp['data'].shape): # broke from the previous loop
try:
axes = [i for k, i in sorted(iter(self._group2dict(
group['_list_' + str(len(exp['data'].shape)) + '_axes'],
lazy=lazy).items()))]
except KeyError:
raise IOError(not_valid_format)
exp['axes'] = axes
if 'learning_results' in group.keys():
exp['attributes']['learning_results'] = \
self._group2dict(
group['learning_results'],
lazy=lazy)
if 'peak_learning_results' in group.keys():
exp['attributes']['peak_learning_results'] = \
self._group2dict(
group['peak_learning_results'],
lazy=lazy)
# If the title was not defined on writing the Experiment is
# then called __unnamed__. The next "if" simply sets the title
# back to the empty string
if "General" in exp["metadata"] and "title" in exp["metadata"]["General"]:
if '__unnamed__' == exp['metadata']['General']['title']:
exp['metadata']["General"]['title'] = ''
if self.version < Version("1.1"):
# Load the decomposition results written with the old name,
# mva_results
if 'mva_results' in group.keys():
exp['attributes']['learning_results'] = self._group2dict(
group['mva_results'], lazy=lazy)
if 'peak_mva_results' in group.keys():
exp['attributes']['peak_learning_results'] = self._group2dict(
group['peak_mva_results'], lazy=lazy)
# Replace the old signal and name keys with their current names
if 'signal' in exp['metadata']:
if "Signal" not in exp["metadata"]:
exp["metadata"]["Signal"] = {}
exp['metadata']["Signal"]['signal_type'] = \
exp['metadata']['signal']
del exp['metadata']['signal']
if 'name' in exp['metadata']:
if "General" not in exp["metadata"]:
exp["metadata"]["General"] = {}
exp['metadata']['General']['title'] = \
exp['metadata']['name']
del exp['metadata']['name']
if self.version < Version("1.2"):
if '_internal_parameters' in exp['metadata']:
exp['metadata']['_HyperSpy'] = \
exp['metadata']['_internal_parameters']
del exp['metadata']['_internal_parameters']
if 'stacking_history' in exp['metadata']['_HyperSpy']:
exp['metadata']['_HyperSpy']["Stacking_history"] = \
exp['metadata']['_HyperSpy']['stacking_history']
del exp['metadata']['_HyperSpy']["stacking_history"]
if 'folding' in exp['metadata']['_HyperSpy']:
exp['metadata']['_HyperSpy']["Folding"] = \
exp['metadata']['_HyperSpy']['folding']
del exp['metadata']['_HyperSpy']["folding"]
if 'Variance_estimation' in exp['metadata']:
if "Noise_properties" not in exp["metadata"]:
exp["metadata"]["Noise_properties"] = {}
exp['metadata']['Noise_properties']["Variance_linear_model"] = \
exp['metadata']['Variance_estimation']
del exp['metadata']['Variance_estimation']
if "TEM" in exp["metadata"]:
if "Acquisition_instrument" not in exp["metadata"]:
exp["metadata"]["Acquisition_instrument"] = {}
exp["metadata"]["Acquisition_instrument"]["TEM"] = \
exp["metadata"]["TEM"]
del exp["metadata"]["TEM"]
tem = exp["metadata"]["Acquisition_instrument"]["TEM"]
if "EELS" in tem:
if "dwell_time" in tem:
tem["EELS"]["dwell_time"] = tem["dwell_time"]
del tem["dwell_time"]
if "dwell_time_units" in tem:
tem["EELS"]["dwell_time_units"] = tem["dwell_time_units"]
del tem["dwell_time_units"]
if "exposure" in tem:
tem["EELS"]["exposure"] = tem["exposure"]
del tem["exposure"]
if "exposure_units" in tem:
tem["EELS"]["exposure_units"] = tem["exposure_units"]
del tem["exposure_units"]
if "Detector" not in tem:
tem["Detector"] = {}
tem["Detector"] = tem["EELS"]
del tem["EELS"]
if "EDS" in tem:
if "Detector" not in tem:
tem["Detector"] = {}
if "EDS" not in tem["Detector"]:
tem["Detector"]["EDS"] = {}
tem["Detector"]["EDS"] = tem["EDS"]
del tem["EDS"]
del tem
if "SEM" in exp["metadata"]:
if "Acquisition_instrument" not in exp["metadata"]:
exp["metadata"]["Acquisition_instrument"] = {}
exp["metadata"]["Acquisition_instrument"]["SEM"] = \
exp["metadata"]["SEM"]
del exp["metadata"]["SEM"]
sem = exp["metadata"]["Acquisition_instrument"]["SEM"]
if "EDS" in sem:
if "Detector" not in sem:
sem["Detector"] = {}
if "EDS" not in sem["Detector"]:
sem["Detector"]["EDS"] = {}
sem["Detector"]["EDS"] = sem["EDS"]
del sem["EDS"]
del sem
if "Sample" in exp["metadata"] and "Xray_lines" in exp[
"metadata"]["Sample"]:
exp["metadata"]["Sample"]["xray_lines"] = exp[
"metadata"]["Sample"]["Xray_lines"]
del exp["metadata"]["Sample"]["Xray_lines"]
for key in ["title", "date", "time", "original_filename"]:
if key in exp["metadata"]:
if "General" not in exp["metadata"]:
exp["metadata"]["General"] = {}
exp["metadata"]["General"][key] = exp["metadata"][key]
del exp["metadata"][key]
for key in ["record_by", "signal_origin", "signal_type"]:
if key in exp["metadata"]:
if "Signal" not in exp["metadata"]:
exp["metadata"]["Signal"] = {}
exp["metadata"]["Signal"][key] = exp["metadata"][key]
del exp["metadata"][key]
if self.version < Version("3.0"):
if "Acquisition_instrument" in exp["metadata"]:
# Move tilt_stage to Stage.tilt_alpha
# Move exposure time to Detector.Camera.exposure_time
if "TEM" in exp["metadata"]["Acquisition_instrument"]:
tem = exp["metadata"]["Acquisition_instrument"]["TEM"]
exposure = None
if "tilt_stage" in tem:
tem["Stage"] = {"tilt_alpha": tem["tilt_stage"]}
del tem["tilt_stage"]
if "exposure" in tem:
exposure = "exposure"
# Digital_micrograph plugin was parsing to 'exposure_time'
# instead of 'exposure': need this to be compatible with
# previous behaviour
if "exposure_time" in tem:
exposure = "exposure_time"
if exposure is not None:
if "Detector" not in tem:
tem["Detector"] = {"Camera": {
"exposure": tem[exposure]}}
tem["Detector"]["Camera"] = {"exposure": tem[exposure]}
del tem[exposure]
# Move tilt_stage to Stage.tilt_alpha
if "SEM" in exp["metadata"]["Acquisition_instrument"]:
sem = exp["metadata"]["Acquisition_instrument"]["SEM"]
if "tilt_stage" in sem:
sem["Stage"] = {"tilt_alpha": sem["tilt_stage"]}
del sem["tilt_stage"]
return exp
def hdfgroup2signaldict(group):
global current_file_version
global default_version
if current_file_version < StrictVersion("1.2"):
metadata = "mapped_parameters"
original_metadata = "original_parameters"
else:
metadata = "metadata"
original_metadata = "original_metadata"
exp = {}
exp['data'] = group['data'][:]
axes = []
for i in xrange(len(exp['data'].shape)):
try:
axes.append(dict(group['axis-%i' % i].attrs))
except KeyError:
raise IOError(not_valid_format)
for axis in axes:
for key, item in axis.iteritems():
axis[key] = ensure_unicode(item)
exp['metadata'] = hdfgroup2dict(
group[metadata], {})
exp['original_metadata'] = hdfgroup2dict(
group[original_metadata], {})
exp['axes'] = axes
exp['attributes'] = {}
if 'learning_results' in group.keys():
exp['attributes']['learning_results'] = \
hdfgroup2dict(group['learning_results'], {})
if 'peak_learning_results' in group.keys():
exp['attributes']['peak_learning_results'] = \
hdfgroup2dict(group['peak_learning_results'], {})
# If the title was not defined on writing the Experiment is
# then called __unnamed__. The next "if" simply sets the title
# back to the empty string
if "General" in exp["metadata"] and "title" in exp["metadata"]["General"]:
if '__unnamed__' == exp['metadata']['General']['title']:
exp['metadata']["General"]['title'] = ''
if current_file_version < StrictVersion("1.1"):
# Load the decomposition results written with the old name,
# mva_results
if 'mva_results' in group.keys():
exp['attributes']['learning_results'] = hdfgroup2dict(
group['mva_results'], {})
if 'peak_mva_results' in group.keys():
exp['attributes']['peak_learning_results'] = hdfgroup2dict(
group['peak_mva_results'], {})
# Replace the old signal and name keys with their current names
if 'signal' in exp['metadata']:
if not "Signal" in exp["metadata"]:
exp["metadata"]["Signal"] = {}
exp['metadata']["Signal"]['signal_type'] = \
exp['metadata']['signal']
del exp['metadata']['signal']
if 'name' in exp['metadata']:
if "General" not in exp["metadata"]:
exp["metadata"]["General"] = {}
exp['metadata']['General']['title'] = \
exp['metadata']['name']
del exp['metadata']['name']
if current_file_version < StrictVersion("1.2"):
if '_internal_parameters' in exp['metadata']:
exp['metadata']['_HyperSpy'] = \
exp['metadata']['_internal_parameters']
del exp['metadata']['_internal_parameters']
if 'stacking_history' in exp['metadata']['_HyperSpy']:
exp['metadata']['_HyperSpy']["Stacking_history"] = \
exp['metadata']['_HyperSpy']['stacking_history']
del exp['metadata']['_HyperSpy']["stacking_history"]
if 'folding' in exp['metadata']['_HyperSpy']:
exp['metadata']['_HyperSpy']["Folding"] = \
exp['metadata']['_HyperSpy']['folding']
del exp['metadata']['_HyperSpy']["folding"]
if 'Variance_estimation' in exp['metadata']:
if "Noise_properties" not in exp["metadata"]:
exp["metadata"]["Noise_properties"] = {}
exp['metadata']['Noise_properties']["Variance_linear_model"] = \
exp['metadata']['Variance_estimation']
del exp['metadata']['Variance_estimation']
if "TEM" in exp["metadata"]:
if not "Acquisition_instrument" in exp["metadata"]:
exp["metadata"]["Acquisition_instrument"] = {}
exp["metadata"]["Acquisition_instrument"][
"TEM"] = exp["metadata"]["TEM"]
del exp["metadata"]["TEM"]
if "EELS" in exp["metadata"]["Acquisition_instrument"]["TEM"]:
if "dwell_time" in exp["metadata"]["Acquisition_instrument"]["TEM"]:
exp["metadata"]["Acquisition_instrument"]["TEM"]["EELS"]["dwell_time"] =\
exp["metadata"]["Acquisition_instrument"][
"TEM"]["dwell_time"]
del exp["metadata"]["Acquisition_instrument"][
"TEM"]["dwell_time"]
if "dwell_time_units" in exp["metadata"]["Acquisition_instrument"]["TEM"]:
exp["metadata"]["Acquisition_instrument"]["TEM"]["EELS"]["dwell_time_units"] =\
exp["metadata"]["Acquisition_instrument"][
"TEM"]["dwell_time_units"]
del exp["metadata"]["Acquisition_instrument"][
"TEM"]["dwell_time_units"]
if "exposure" in exp["metadata"]["Acquisition_instrument"]["TEM"]:
exp["metadata"]["Acquisition_instrument"]["TEM"]["EELS"]["exposure"] =\
exp["metadata"]["Acquisition_instrument"][
"TEM"]["exposure"]
del exp["metadata"]["Acquisition_instrument"][
"TEM"]["exposure"]
if "exposure_units" in exp["metadata"]["Acquisition_instrument"]["TEM"]:
exp["metadata"]["Acquisition_instrument"]["TEM"]["EELS"]["exposure_units"] =\
exp["metadata"]["Acquisition_instrument"][
"TEM"]["exposure_units"]
del exp["metadata"]["Acquisition_instrument"][
"TEM"]["exposure_units"]
if "Detector" not in exp["metadata"]["Acquisition_instrument"]["TEM"]:
exp["metadata"]["Acquisition_instrument"][
"TEM"]["Detector"] = {}
exp["metadata"]["Acquisition_instrument"]["TEM"]["Detector"] = \
exp["metadata"]["Acquisition_instrument"]["TEM"]["EELS"]
del exp["metadata"]["Acquisition_instrument"]["TEM"]["EELS"]
if "EDS" in exp["metadata"]["Acquisition_instrument"]["TEM"]:
if "Detector" not in exp["metadata"]["Acquisition_instrument"]["TEM"]:
exp["metadata"]["Acquisition_instrument"][
"TEM"]["Detector"] = {}
if "EDS" not in exp["metadata"]["Acquisition_instrument"]["TEM"]["Detector"]:
exp["metadata"]["Acquisition_instrument"][
"TEM"]["Detector"]["EDS"] = {}
exp["metadata"]["Acquisition_instrument"]["TEM"]["Detector"]["EDS"] = \
exp["metadata"]["Acquisition_instrument"]["TEM"]["EDS"]
del exp["metadata"]["Acquisition_instrument"]["TEM"]["EDS"]
if "SEM" in exp["metadata"]:
if not "Acquisition_instrument" in exp["metadata"]:
exp["metadata"]["Acquisition_instrument"] = {}
exp["metadata"]["Acquisition_instrument"][
"SEM"] = exp["metadata"]["SEM"]
del exp["metadata"]["SEM"]
if "EDS" in exp["metadata"]["Acquisition_instrument"]["SEM"]:
if "Detector" not in exp["metadata"]["Acquisition_instrument"]["SEM"]:
exp["metadata"]["Acquisition_instrument"][
"SEM"]["Detector"] = {}
if "EDS" not in exp["metadata"]["Acquisition_instrument"]["SEM"]["Detector"]:
exp["metadata"]["Acquisition_instrument"][
"SEM"]["Detector"]["EDS"] = {}
exp["metadata"]["Acquisition_instrument"]["SEM"]["Detector"]["EDS"] = \
exp["metadata"]["Acquisition_instrument"]["SEM"]["EDS"]
del exp["metadata"]["Acquisition_instrument"]["SEM"]["EDS"]
if "Sample" in exp["metadata"] and "Xray_lines" in exp["metadata"]["Sample"]:
exp["metadata"]["Sample"]["xray_lines"] = exp[
"metadata"]["Sample"]["Xray_lines"]
del exp["metadata"]["Sample"]["Xray_lines"]
for key in ["title", "date", "time", "original_filename"]:
if key in exp["metadata"]:
if "General" not in exp["metadata"]:
exp["metadata"]["General"] = {}
exp["metadata"]["General"][key] = exp["metadata"][key]
del exp["metadata"][key]
for key in ["record_by", "signal_origin", "signal_type"]:
if key in exp["metadata"]:
if "Signal" not in exp["metadata"]:
exp["metadata"]["Signal"] = {}
exp["metadata"]["Signal"][key] = exp["metadata"][key]
del exp["metadata"][key]
return exp
def hdfgroup2signaldict(group):
global current_file_version
global default_version
if current_file_version < StrictVersion("1.2"):
metadata = "mapped_parameters"
original_metadata = "original_parameters"
else:
metadata = "metadata"
original_metadata = "original_metadata"
exp = {}
exp['data'] = group['data'][:]
axes = []
for i in xrange(len(exp['data'].shape)):
try:
axes.append(dict(group['axis-%i' % i].attrs))
except KeyError:
raise IOError(not_valid_format)
for axis in axes:
for key, item in axis.iteritems():
axis[key] = ensure_unicode(item)
exp['metadata'] = hdfgroup2dict(group[metadata], {})
exp['original_metadata'] = hdfgroup2dict(group[original_metadata], {})
exp['axes'] = axes
exp['attributes'] = {}
if 'learning_results' in group.keys():
exp['attributes']['learning_results'] = \
hdfgroup2dict(group['learning_results'], {})
if 'peak_learning_results' in group.keys():
exp['attributes']['peak_learning_results'] = \
hdfgroup2dict(group['peak_learning_results'], {})
# If the title was not defined on writing the Experiment is
# then called __unnamed__. The next "if" simply sets the title
# back to the empty string
if "General" in exp["metadata"] and "title" in exp["metadata"]["General"]:
if '__unnamed__' == exp['metadata']['General']['title']:
exp['metadata']["General"]['title'] = ''
if current_file_version < StrictVersion("1.1"):
# Load the decomposition results written with the old name,
# mva_results
if 'mva_results' in group.keys():
exp['attributes']['learning_results'] = hdfgroup2dict(
group['mva_results'], {})
if 'peak_mva_results' in group.keys():
exp['attributes']['peak_learning_results'] = hdfgroup2dict(
group['peak_mva_results'], {})
# Replace the old signal and name keys with their current names
if 'signal' in exp['metadata']:
if not "Signal" in exp["metadata"]:
exp["metadata"]["Signal"] = {}
exp['metadata']["Signal"]['signal_type'] = \
exp['metadata']['signal']
del exp['metadata']['signal']
if 'name' in exp['metadata']:
if "General" not in exp["metadata"]:
exp["metadata"]["General"] = {}
exp['metadata']['General']['title'] = \
exp['metadata']['name']
del exp['metadata']['name']
if current_file_version < StrictVersion("1.2"):
if '_internal_parameters' in exp['metadata']:
exp['metadata']['_HyperSpy'] = \
exp['metadata']['_internal_parameters']
del exp['metadata']['_internal_parameters']
if 'stacking_history' in exp['metadata']['_HyperSpy']:
exp['metadata']['_HyperSpy']["Stacking_history"] = \
exp['metadata']['_HyperSpy']['stacking_history']
del exp['metadata']['_HyperSpy']["stacking_history"]
if 'folding' in exp['metadata']['_HyperSpy']:
exp['metadata']['_HyperSpy']["Folding"] = \
exp['metadata']['_HyperSpy']['folding']
del exp['metadata']['_HyperSpy']["folding"]
if 'Variance_estimation' in exp['metadata']:
if "Noise_properties" not in exp["metadata"]:
exp["metadata"]["Noise_properties"] = {}
exp['metadata']['Noise_properties']["Variance_linear_model"] = \
exp['metadata']['Variance_estimation']
del exp['metadata']['Variance_estimation']
if "TEM" in exp["metadata"]:
if not "Acquisition_instrument" in exp["metadata"]:
exp["metadata"]["Acquisition_instrument"] = {}
exp["metadata"]["Acquisition_instrument"]["TEM"] = exp["metadata"][
"TEM"]
del exp["metadata"]["TEM"]
if "EELS" in exp["metadata"]["Acquisition_instrument"]["TEM"]:
if "dwell_time" in exp["metadata"]["Acquisition_instrument"][
"TEM"]:
exp["metadata"]["Acquisition_instrument"]["TEM"]["EELS"]["dwell_time"] =\
exp["metadata"]["Acquisition_instrument"][
"TEM"]["dwell_time"]
del exp["metadata"]["Acquisition_instrument"]["TEM"][
"dwell_time"]
if "dwell_time_units" in exp["metadata"][
"Acquisition_instrument"]["TEM"]:
exp["metadata"]["Acquisition_instrument"]["TEM"]["EELS"]["dwell_time_units"] =\
exp["metadata"]["Acquisition_instrument"][
"TEM"]["dwell_time_units"]
del exp["metadata"]["Acquisition_instrument"]["TEM"][
"dwell_time_units"]
if "exposure" in exp["metadata"]["Acquisition_instrument"][
"TEM"]:
exp["metadata"]["Acquisition_instrument"]["TEM"]["EELS"]["exposure"] =\
exp["metadata"]["Acquisition_instrument"][
"TEM"]["exposure"]
del exp["metadata"]["Acquisition_instrument"]["TEM"][
"exposure"]
if "exposure_units" in exp["metadata"][
"Acquisition_instrument"]["TEM"]:
exp["metadata"]["Acquisition_instrument"]["TEM"]["EELS"]["exposure_units"] =\
exp["metadata"]["Acquisition_instrument"][
"TEM"]["exposure_units"]
del exp["metadata"]["Acquisition_instrument"]["TEM"][
"exposure_units"]
if "Detector" not in exp["metadata"]["Acquisition_instrument"][
"TEM"]:
exp["metadata"]["Acquisition_instrument"]["TEM"][
"Detector"] = {}
exp["metadata"]["Acquisition_instrument"]["TEM"]["Detector"] = \
exp["metadata"]["Acquisition_instrument"]["TEM"]["EELS"]
del exp["metadata"]["Acquisition_instrument"]["TEM"]["EELS"]
if "EDS" in exp["metadata"]["Acquisition_instrument"]["TEM"]:
if "Detector" not in exp["metadata"]["Acquisition_instrument"][
"TEM"]:
exp["metadata"]["Acquisition_instrument"]["TEM"][
"Detector"] = {}
if "EDS" not in exp["metadata"]["Acquisition_instrument"][
"TEM"]["Detector"]:
exp["metadata"]["Acquisition_instrument"]["TEM"][
"Detector"]["EDS"] = {}
exp["metadata"]["Acquisition_instrument"]["TEM"]["Detector"]["EDS"] = \
exp["metadata"]["Acquisition_instrument"]["TEM"]["EDS"]
del exp["metadata"]["Acquisition_instrument"]["TEM"]["EDS"]
if "SEM" in exp["metadata"]:
if not "Acquisition_instrument" in exp["metadata"]:
exp["metadata"]["Acquisition_instrument"] = {}
exp["metadata"]["Acquisition_instrument"]["SEM"] = exp["metadata"][
"SEM"]
del exp["metadata"]["SEM"]
if "EDS" in exp["metadata"]["Acquisition_instrument"]["SEM"]:
if "Detector" not in exp["metadata"]["Acquisition_instrument"][
"SEM"]:
exp["metadata"]["Acquisition_instrument"]["SEM"][
"Detector"] = {}
if "EDS" not in exp["metadata"]["Acquisition_instrument"][
"SEM"]["Detector"]:
exp["metadata"]["Acquisition_instrument"]["SEM"][
"Detector"]["EDS"] = {}
exp["metadata"]["Acquisition_instrument"]["SEM"]["Detector"]["EDS"] = \
exp["metadata"]["Acquisition_instrument"]["SEM"]["EDS"]
del exp["metadata"]["Acquisition_instrument"]["SEM"]["EDS"]
if "Sample" in exp["metadata"] and "Xray_lines" in exp["metadata"][
"Sample"]:
exp["metadata"]["Sample"]["xray_lines"] = exp["metadata"][
"Sample"]["Xray_lines"]
del exp["metadata"]["Sample"]["Xray_lines"]
for key in ["title", "date", "time", "original_filename"]:
if key in exp["metadata"]:
if "General" not in exp["metadata"]:
exp["metadata"]["General"] = {}
exp["metadata"]["General"][key] = exp["metadata"][key]
del exp["metadata"][key]
for key in ["record_by", "signal_origin", "signal_type"]:
if key in exp["metadata"]:
if "Signal" not in exp["metadata"]:
exp["metadata"]["Signal"] = {}
exp["metadata"]["Signal"][key] = exp["metadata"][key]
del exp["metadata"][key]
return exp
def storeTag( tagName, tagValue ): global storedTags, tagHash storedTags.append( str(tagName) + " = " + str(tagValue) ) tagHash[ ensure_unicode(tagName, 'utf8', 'latin-1')] = ensure_unicode(tagValue, 'utf16')
