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# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2016-2017 European Synchrotron Radiation Facility
#
# 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.
#
# ###########################################################################*/
__authors__ = ["H. Payno"]
__license__ = "MIT"
__date__ = "06/11/2019"
from silx.io.dictdump import dicttoh5, h5todict
from silx.io.url import DataUrl
_logger = logging.getLogger(__name__)
"""Base class of XAS
:param spectra: absorbed beam as a list of :class:`.Spectrum` or a
numpy.ndarray
:type: Union[numpy.ndarray, list]
:param energy: beam energy
:type: numpy.ndarray of one dimension
:param dict configuration: configuration of the different process
:param int dim1: first dimension of the spectra
:param int dim2: second dimension of the spectra
:param str name: name of the object. Will be used for the hdf5 entry
:param bool keep_process_flow: if True then will keep the trace of the set
of process applied to the XASObject into a
hdf5 file.
"""
def __init__(self, spectra=None, energy=None, configuration=None, dim1=None,
self.__channels = None
self.__spectra = []
self.__energy = None
self.__dim1 = 0
self.__dim2 = 0
self.__processing_index = 0
self.__h5_file = None
self.__entry_name = name
self.spectra = (energy, spectra, dim1, dim2)
self.configuration = configuration
@property
def entry(self):
return self.__entry_name
def spectra(self):
return self.__spectra
@spectra.setter
def spectra(self, energy_spectra):
energy, spectra, dim1, dim2 = energy_spectra
if spectra is None:
self.__spectra = []
self.__energy = energy
else:
assert energy is not None
self.__spectra.clear()
assert isinstance(spectra, (list, tuple, numpy.ndarray))
if isinstance(spectra, numpy.ndarray):
assert spectra.ndim is 3
self.__dim1 = spectra.shape[1]
self.__dim2 = spectra.shape[2]
for y_i_spectrum in range(spectra.shape[1]):
for x_i_spectrum in range(spectra.shape[2]):
self.addSpectrum(Spectrum(energy=energy,
mu=spectra[:, y_i_spectrum, x_i_spectrum]))
else:
if dim1 is None or dim2 is None:
raise ValueError(
'If you want to set spectra from a list/tuple '
'of Spectrum you should specify the spectra '
'dimensions')
self.__dim1 = dim1
self.__dim2 = dim2
for spectrum in spectra:
assert isinstance(spectrum, Spectrum)
self.addSpectrum(spectrum)
self.energy = energy
def _setSpectra(self, spectra):
self.__spectra = spectra
def getSpectrum(self, dim1_idx, dim2_idx):
"""Util function to access the spectrum at dim1_idx, dim2_idx"""
assert dim1_idx < self.dim1
assert dim2_idx < self.dim2
global_idx = dim1_idx * self.dim2 + dim2_idx
assert global_idx < len(self.spectra)
assert global_idx >= 0
return self.spectra[global_idx]
def addSpectrum(self, spectrum):
self.__spectra.append(spectrum)
@property
def dim1(self):
return self.__dim1
def forceDim1(self, value):
assert type(value) is int
self.__dim1 = value
def forceDim2(self, value):
assert type(value) is int
self.__dim2 = value
@property
def dim2(self):
return self.__dim2
@property
def energy(self):
return self.__energy
@energy.setter
def energy(self, energy):
self.__energy = energy
if len(self.__spectra) > 0:
if len(self.__spectra[0].energy) != len(energy):
_logger.warning('spectra and energy have incoherent dimension')
@property
def configuration(self):
return self.__configuration
@configuration.setter
def configuration(self, configuration):
assert configuration is None or isinstance(configuration, dict)
self.__configuration = configuration or {}
def to_dict(self, with_process_details=True):
"""convert the XAS object to a dict
By default made to simply import raw data.
:param with_process_details: used to embed a list of spectrum with
intermediary result instead of only raw mu.
This is needed especially for the
pushworkflow actors to keep a trace of the
processes.
:type: bool
"""
def get_list_spectra():
res = []
for spectrum in self.spectra:
res.append(spectrum.to_dict())
return res
res = {
'configuration': self.configuration,
'spectra': XASObject._spectra_volume(spectra=self.spectra,
key='Mu',
dim_1=self.dim1,
dim_2=self.dim2),
'energy': self.energy,
'dim1': self.dim1,
'dim2': self.dim2,
if with_process_details is True:
res['spectra'] = get_list_spectra()
res['linked_h5_file'] = self.linked_h5_file
res['current_processing_index'] = self.__processing_index
return res
def _spectra_to_dict(self):
spectra_dict = {}
for i_spectrum, spectrum in enumerate(self.spectra):
assert isinstance(spectrum, Spectrum)
spectra_dict[str(i_spectrum) + '_spectrum'] = spectrum.to_dict()
return spectra_dict
def absorbed_beam(self):
return XASObject._spectra_volume(spectra=self.spectra,
key='Mu',
dim_1=self.dim1,
dim_2=self.dim2)
def _spectra_volume(spectra, key, dim_1, dim_2):
"""Convert a list of spectra (mu) to a numpy array.
..note: only convert raw data for now"""
if len(spectra) is 0:
return None
else:
array = numpy.zeros((len(spectra[0].energy), dim_1 * dim_2))
for i_spectrum, spectrum in enumerate(spectra):
subkeys = key.split('/')
value = spectrum[subkeys[0]]
for subkey in subkeys[1:]:
value = value[subkey]
array[:, i_spectrum] = value
return array.reshape((len(spectra[0].energy), dim_1, dim_2))
def load_frm_dict(self, ddict):
"""load XAS values from a dict"""
contains_config_spectrum = 'configuration' in ddict or 'spectra' in ddict
"""The dict can be on the scheme of the to_dict function, containing
the spectra and the configuration. Otherwise we consider it is simply
the spectra"""
if 'configuration' in ddict:
self.configuration = ddict['configuration']
if 'spectra' in ddict:
spectra = ddict['spectra']
if not isinstance(spectra, numpy.ndarray):
new_spectra = []
for spectrum in spectra:
assert isinstance(spectrum, dict)
new_spectra.append(Spectrum.from_dict(spectrum))
spectra = new_spectra
else:
spectra = None
if 'energy' in ddict:
energy = ddict['energy']
else:
energy = None
if 'dim1' in ddict:
dim1 = ddict['dim1']
else:
dim1 = None
if 'dim2' in ddict:
dim2 = ddict['dim2']
else:
dim2 = None
if 'linked_h5_file' in ddict:
assert 'current_processing_index' in ddict
self.link_to_h5(ddict['linked_h5_file'])
self.__processing_index = ddict['current_processing_index']
self.spectra = (energy, spectra, dim1, dim2)
if not contains_config_spectrum:
self.spectrum = ddict
return self
@staticmethod
def from_dict(ddict):
return XASObject().load_frm_dict(ddict=ddict)
@staticmethod
def from_file(h5_file, entry='scan1', spectra_path='data/absorbed_beam',
energy_path='data/energy', configuration_path='configuration'):
# load only mu and energy from the file
import xas.io
spectra_url = DataUrl(file_path=h5_file,
data_path='/'.join((entry, spectra_path)),
scheme='silx')
energy_url = DataUrl(file_path=h5_file,
data_path='/'.join((entry, energy_path)),
scheme='silx')
if configuration_path is None:
config_url = None
else:
config_url = DataUrl(file_path=h5_file,
data_path='/'.join((entry, configuration_path)),
scheme='silx')
return xas.io.read_pymca_xas(spectra_url=spectra_url,
channel_url=energy_url,
config_url=config_url)
"""dump the XAS object to a file_path within the Nexus format"""
dicttoh5(treedict=self.to_dict(with_process_details=False),
h5file=h5_file)
def copy(self):
return copy.copy(self)
def __eq__(self, other):
return (isinstance(other, XASObject) and
numpy.array_equal(self.energy, other.energy) and
self.dim1 == other.dim1 and
self.dim2 == other.dim2 and
self.configuration == other.configuration and
self.spectra_equal(self.spectra, other.spectra))
@staticmethod
def spectra_equal(spectra1, spectra2):
if len(spectra1) != len(spectra2):
return False
else:
for i_spectrum, spectrum in enumerate(spectra1):
if not numpy.array_equal(spectrum.mu, spectra2[i_spectrum].mu):
return False
return True
@property
def n_spectrum(self):
"""return the number of spectra"""
if self.__spectra is None:
return 0
else:
return len(self.__spectra)
def spectra_keys(self):
"""keys contained by the spectrum object (energy, mu, normalizedmu...)
"""
if len(self.spectra) > 0:
assert isinstance(self.spectra[0], Spectrum)
return self.spectra[0].keys()
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@property
def linked_h5_file(self):
return self.__h5_file
def link_to_h5(self, h5_file):
"""
Associate a .h5 file to the XASObject. This can be used for storing
process flow.
:param h5_file:
:return:
"""
self.__h5_file = h5_file
def has_linked_file(self):
return self.__h5_file is not None
def get_next_processing_index(self):
self.__processing_index += 1
return self.__processing_index
def register_processing(self, process, data):
"""
Register one process for the current xas object. This require to having
link a h5file to this object
:param :class:`.Process` process:
:param data: result of the processing. If there is more than one
result then a dictionary with the key under which result
should be saved and a numpy.ndarray
:type: Union[numpy.ndarray, dict]
"""
import xas.io
xas.io.write_xas_proc(self.linked_h5_file, entry=self.__entry_name,
processing_order=self.get_next_processing_index(),
process=process, data=data)
def get_process_flow(self):
"""
:return: the dict of process information
:rtype: dict
"""
import xas.io
if not self.linked_h5_file:
_logger.warning('process flow is store in the linked .h5 file. If'
'no link is defined then this information is not'
'stored')
return {}
else:
recognized_process = xas.io.get_xasproc(self.linked_h5_file,
entry=self.__entry_name)
know_process = ('pymca_normalization', 'pymca_exafs', 'pymca_ft',
'pymca_k_weight')
def filter_recognized_process(process_list):
res = []
for process_ in process_list:
if 'program' in process_.keys() and process_['program'] in know_process:
res.append(process_)
return res
recognized_process = filter_recognized_process(recognized_process)
def get_ordered_process(process_list):
res = {}
for process_ in process_list:
if not 'processing_order' in process_:
_logger.warning('one processing has not processing order: ' + process_['program'])
else:
processing_order = int(process_['processing_order'])
res[processing_order] = process_
return res
return get_ordered_process(recognized_process)
def clean_process_flow(self):
"""
Remove existing process flow
"""
if not self.linked_h5_file:
_logger.warning('process flow is store in the linked .h5 file. If'
'no link is defined then this information is not'
'stored')
else:
process_flow = self.get_process_flow()
with h5py.File(self.linked_h5_file) as h5f:
for index, process_ in process_flow.items():
del h5f[process_['_h5py_path']]
def copy_process_flow_to(self, h5_file_target):
"""
copy all the recognized process from self.__h5_file to h5_file_target
:param str h5_file_target: path to the targeted file. Should be an
existing hdf5 file.
"""
assert os.path.exists(h5_file_target)
assert h5py.is_hdf5(h5_file_target)
flow = self.get_process_flow()
entry = self.entry
with h5py.File(self.__h5_file) as source_hdf:
with h5py.File(h5_file_target) as target_hdf:
target_entry = target_hdf.require_group(entry)
def remove_entry_prefix(name):
return name.replace('/'+entry+'/', '', 1)
for process_id, process in flow.items():
process_path = process['_h5py_path']
dst_path = remove_entry_prefix(name=process_path)
target_entry.copy(source=source_hdf[process_path],
dest=dst_path)
# TODO: add the spectra class. Would speed up and simplify stuff probably
class Spectra(object):
pass
"""
set of curve (one dimensional numpy.ndarray) to be pass to the different xas
treatment.
Can be accessed as a dictionnary for non standard parameters.
:param numpy.ndarray (1D) energy: beam energy
:param numpy.ndarray (1D) mu: beam absorption
"""
_MU_KEY = 'Mu'
_ENERGY_KEY = 'Energy'
_NORMALIZED_MU_KEY = 'NormalizedMu'
_NORMALIZED_ENERGY_KEY = 'NormalizedEnergy'
_NORMALIZED_SIGNAL_KEY = 'NormalizedSignal'
_FT_KEY = 'FT'
def __init__(self, energy=None, mu=None):
if energy is not None:
assert isinstance(energy, numpy.ndarray)
# properties
self.energy = energy
self.mu = mu
self.__normalized_mu = None
self.__normalized_energy = None
self.__normalized_signal = None
self.__other_parameters = {}
self.ft = {}
self.__key_mapper = {
self._MU_KEY: self.__class__.mu,
self._ENERGY_KEY: self.__class__.energy,
self._NORMALIZED_MU_KEY: self.__class__.normalized_mu,
self._NORMALIZED_ENERGY_KEY: self.__class__.normalized_energy,
self._NORMALIZED_SIGNAL_KEY: self.__class__.normalized_signal,
self._FT_KEY: self.__class__.ft
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}
@property
def energy(self):
return self.__energy
@energy.setter
def energy(self, energy):
assert isinstance(energy, numpy.ndarray) or energy is None
self.__energy = energy
@property
def mu(self):
return self.__mu
@mu.setter
def mu(self, mu):
assert isinstance(mu, numpy.ndarray) or mu is None
self.__mu = mu
@property
def normalized_mu(self):
return self.__normalized_mu
@normalized_mu.setter
def normalized_mu(self, mu):
assert isinstance(mu, numpy.ndarray) or mu is None
self.__normalized_mu = mu
@property
def normalized_energy(self):
return self.__normalized_energy
@normalized_energy.setter
def normalized_energy(self, energy):
assert isinstance(energy, numpy.ndarray) or energy is None
self.__normalized_energy = energy
@property
def normalized_signal(self):
return self.__normalized_signal
@normalized_signal.setter
def normalized_signal(self, signal):
assert isinstance(signal, numpy.ndarray) or signal is None
self.__normalized_signal = signal
@property
def ft(self):
return self.__ft
@ft.setter
def ft(self, ft):
if isinstance(ft, _FT):
self.__ft = ft
else:
self.__ft = _FT(ddict=ft)
@property
def shape(self):
_energy_len = 0
if self.__energy is not None:
_energy_len = len(self.__energy)
_mu_len = 0
if self.__mu is not None:
_mu_len = len(self.__mu)
return (_energy_len, _mu_len)
def extra_keys(self):
return self.__other_parameters.keys()
def __getitem__(self, key):
"""Need for pymca compatibility"""
if key in self.__key_mapper:
return self.__key_mapper[key].fget(self)
else:
return self.__other_parameters[key]
def __setitem__(self, key, value):
"""Need for pymca compatibility"""
if key in self.__key_mapper:
self.__key_mapper[key].fset(self, value)
else:
self.__other_parameters[key] = value
def __contains__(self, item):
return item in self.__key_mapper or item in self.__other_parameters
def load_frm_dict(self, ddict):
for key, value in ddict.items():
self[key] = value
return self
def update(self, spectrum):
assert isinstance(spectrum, Spectrum)
for key in spectrum:
self[key] = spectrum[key]
@staticmethod
def from_dict(ddict):
spectrum = Spectrum()
return spectrum.load_frm_dict(ddict=ddict)
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def to_dict(self):
res = {
self._MU_KEY: self.mu,
self._ENERGY_KEY: self.energy,
self._FT_KEY: self.ft.to_dict(),
self._NORMALIZED_MU_KEY: self.normalized_mu,
self._NORMALIZED_ENERGY_KEY: self.normalized_energy,
self._NORMALIZED_SIGNAL_KEY: self.normalized_signal,
}
res.update(self.__other_parameters)
return res
def __str__(self):
def add_info(str_, attr):
assert hasattr(self, attr)
sub_str = '- ' + attr + ': ' + str(getattr(self, attr)) + '\n'
return (str_ + sub_str)
main_info = ""
for info in ('energy', 'mu', 'normalized_mu', 'normalized_signal', 'normalized_energy'):
main_info = add_info(str_=main_info, attr=info)
def add_third_info(str_, key):
sub_str = ('- ' + key + ': ' + str(self[key])) + '\n'
return str_ + sub_str
for key in self.__other_parameters:
main_info = add_third_info(str_=main_info, key=key)
return main_info
def update(self, obj):
"""
Update the contained values from the given obj.
:param obj:
:type obj: Union[XASObject, dict]
"""
_obj = obj.to_dict()
else:
_obj = obj
assert isinstance(_obj, dict)
def get_missing_keys(self, keys):
"""Return missing keys on the spectrum"""
missing = []
for key in keys:
if key not in self:
missing.append(key)
if len(missing) is 0:
return None
else:
return missing
def keys(self):
keys = list(self.__other_parameters.keys())
keys += list(self.__key_mapper.keys())
return keys
class _FT(object):
_RADIUS_KEY = 'FTRadius'
_INTENSITY_KEY = 'FTIntensity'
def __init__(self, ddict):
self.__radius = None
self.__intensity = None
self.__imaginery = None
self.__other_parameters = {}
self.__key_mapper = {
self._RADIUS_KEY: self.__class__.radius,
self._INTENSITY_KEY: self.__class__.intensity,
self._IMAGINERY_KEY: self.__class__.imaginery,
}
if ddict is not None:
for key, values in ddict.items():
self[key] = values
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@property
def radius(self):
return self.__radius
@radius.setter
def radius(self, radius):
self.__radius = radius
@property
def intensity(self):
return self.__intensity
@intensity.setter
def intensity(self, intensity):
self.__intensity = intensity
@property
def imaginery(self):
return self.__imaginery
@imaginery.setter
def imaginery(self, imaginery):
self.__imaginery = imaginery
def __getitem__(self, key):
"""Need for pymca compatibility"""
if key in self.__key_mapper:
return self.__key_mapper[key].fget(self)
else:
return self.__other_parameters[key]
def __setitem__(self, key, value):
"""Need for pymca compatibility"""
if key in self.__key_mapper:
self.__key_mapper[key].fset(self, value)
else:
self.__other_parameters[key] = value
def __contains__(self, item):
return item in self.__key_mapper or item in self.__other_parameters
def to_dict(self):
res = {
self._RADIUS_KEY: self.radius,
self._INTENSITY_KEY: self.intensity,
self._IMAGINERY_KEY: self.imaginery,
}
res.update(self.__other_parameters)
return res
def get_missing_keys(self, keys):
"""Return missing keys on the spectrum"""
missing = []
for key in keys:
if key not in self:
missing.append(key)
if len(missing) is 0:
return None
else:
return missing