Source code for est.core.process.pymca.normalization
"""wrapper to pymca `normalization` process"""
import logging
from PyMca5.PyMcaPhysics.xas.XASClass import XASClass
from est.core.process.process import Process
from est.core.types import Spectrum, XASObject
_logger = logging.getLogger(__name__)
[docs]
def process_spectr_norm(
spectrum: Spectrum,
configuration,
overwrite=True,
callbacks=None,
output=None,
output_dict=None,
):
"""
:param spectrum: spectrum to process
:type: :class:`.Spectrum`
:param configuration: configuration of the pymca normalization
:type: dict
:param overwrite: False if we want to return a new Spectrum instance
:type: bool
:param callbacks: callback to execute.
:param output: list to store the result, needed for pool processing
:type: multiprocessing.manager.list
:param output_dict: key is input spectrum, value is index in the output
list.
:type: dict
:return: processed spectrum
:rtype: tuple (configuration, spectrum)
"""
assert isinstance(spectrum, Spectrum)
_logger.debug("start normalization on spectrum (%s, %s)" % (spectrum.x, spectrum.y))
if spectrum.energy is None or spectrum.mu is None:
_logger.error(
"Energy and or Mu is/are not specified, unable to " "compute exafs"
)
return None, None
pymca_xas = XASClass()
pymca_xas.setSpectrum(energy=spectrum.energy, mu=spectrum.mu)
if configuration is not None:
if "e0" in configuration:
configuration["E0Value"] = configuration["e0"]
configuration["E0Method"] = "Manual"
pymca_xas.setConfiguration(configuration)
configuration = pymca_xas.getConfiguration()
res = pymca_xas.normalize()
if not overwrite:
spectrum = Spectrum.from_dict(spectrum.to_dict())
# set output we want to keep
spectrum.normalized_energy = res.get("NormalizedEnergy", None)
spectrum.normalized_mu = res.get("NormalizedMu", None)
spectrum.e0 = res.get("Edge", None)
spectrum.pre_edge = res.get("NormalizedBackground", None)
spectrum.post_edge = res.get("NormalizedSignal", None)
if callbacks:
for callback in callbacks:
callback()
if output is not None:
assert output_dict is not None
output[output_dict[spectrum]] = spectrum
return configuration, spectrum
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def pymca_normalization(xas_obj, **optional_inputs):
"""
:param xas_obj: object containing the configuration and spectra to process
:type: Union[XASObject, dict]. If is a dict, should contain configuration or
spectra keys. Otherwise is simply the spectra
:return: spectra dict
:rtype: dict
"""
process = PyMca_normalization(inputs={"xas_obj": xas_obj, **optional_inputs})
process.run()
return process.get_output_value("xas_obj", None)
[docs]
class PyMca_normalization(
Process,
name="normalization",
input_names=["xas_obj"],
output_names=["xas_obj"],
optional_input_names=["normalization"],
):
[docs]
def set_properties(self, properties):
if "_pymcaSettings" in properties:
self._settings = properties["_pymcaSettings"]
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def run(self):
"""
:param xas_obj: object containing the configuration and spectra to process
:type: Union[XASObject, dict]. If is a dict, should contain configuration or
spectra keys. Otherwise is simply the spectra
:return: updated XASObject
:rtype: :class:`.XASObject`
"""
xas_obj = self.inputs.xas_obj
if xas_obj is None:
raise ValueError("xas_obj should be provided")
xas_obj = self.getXasObject(xas_obj)
if xas_obj.energy is None:
_logger.error("Energy not specified, unable to normalize spectra")
return
if self.inputs.normalization:
self.setConfiguration(self.inputs.normalization)
xas_obj.configuration["Normalization"] = self.inputs.normalization
self.progress = 0.0
self._pool_process(xas_obj=xas_obj)
self.progress = 100.0
if xas_obj.normalized_energy is None:
raise ValueError("Fail to compute normalize energy")
self.outputs.xas_obj = xas_obj
def _pool_process(self, xas_obj):
"""process normalization from a pool"""
assert isinstance(xas_obj, XASObject)
n_s = len(xas_obj.spectra.data.flat)
for i_s, spectrum in enumerate(xas_obj.spectra.data.flat):
process_spectr_norm(
spectrum=spectrum,
configuration=xas_obj.configuration,
callbacks=self.callbacks,
overwrite=True,
)
self.progress = i_s / n_s * 100.0
[docs]
def definition(self):
return "Normalization of the spectrum"
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def program_version(self):
import PyMca5
return PyMca5.version()
[docs]
@staticmethod
def program_name():
return "pymca_normalization"