normalization.py 8.22 KB
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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"


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import functools
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import logging
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import multiprocessing
from PyMca5.PyMcaPhysics.xas.XASClass import XASClass
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from est.core.process.process import _input_desc
from est.core.process.process import _output_desc
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from est.core.process.process import Process, Progress
from est.core.types import Spectrum, XASObject
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_logger = logging.getLogger(__name__)


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def process_spectr_norm(
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    spectrum: Spectrum,
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    configuration,
    overwrite=True,
    callbacks=None,
    output=None,
    output_dict=None,
):
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    """

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    :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
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    :param callbacks: callback to execute.
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    :param output: list to store the result, needed for pool processing
    :type: multiprocessing.manager.list
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    :param output_dict: key is input spectrum, value is index in the output
                        list.
    :type: dict
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    :return: processed spectrum
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    :rtype: tuple (configuration, spectrum)
    """
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    assert isinstance(spectrum, Spectrum)
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    _logger.debug("start normalization on spectrum (%s, %s)" % (spectrum.x, spectrum.y))
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    if spectrum.energy is None or spectrum.mu is None:
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        _logger.error(
            "Energy and or Mu is/are not specified, unable to " "compute exafs"
        )
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        return None, None
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    pymca_xas = XASClass()
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    pymca_xas.setSpectrum(energy=spectrum.energy, mu=spectrum.mu)
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    if configuration is not None:
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        if "e0" in configuration:
            configuration["E0Value"] = configuration["e0"]
            configuration["E0Method"] = "Manual"
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        pymca_xas.setConfiguration(configuration)
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    configuration = pymca_xas.getConfiguration()
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    try:
        res = pymca_xas.normalize()
        ddict = spectrum.to_dict()
        ddict.update(res)
        spectrum_ = Spectrum.from_dict(ddict)
    except (IndexError, ValueError) as e:
        _logger.error(e)
        return None, None
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    if callbacks:
        for callback in callbacks:
            callback()
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    def get_output(orignal_spec, res_spec):
        if overwrite:
            orignal_spec.update(res_spec)
            return orignal_spec
        else:
            return res_spec

    if output is not None:
        assert output_dict is not None
        output[output_dict[spectrum]] = get_output(spectrum, spectrum_)

    return configuration, get_output(spectrum, spectrum_)
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def pymca_normalization(xas_obj):
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    """
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    :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
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    :rtype: dict
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    """
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    normalization_obj = PyMca_normalization()
    return normalization_obj.process(xas_obj=xas_obj)
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_USE_MULTIPROCESSING_POOL = False
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# note: we cannot use multiprocessing pool with pypushflow for now.
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class PyMca_normalization(Process):
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    inputs = [
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        _input_desc(name="xas_obj", type=XASObject, handler="process", doc=""),
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    ]

    outputs = [
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        _output_desc(name="xas_obj", type=XASObject, doc=""),
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    ]

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    def __init__(self):
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        Process.__init__(self, "normalization")
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        self._advancement = Progress(self.name)
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    def set_properties(self, properties):
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        if "_pymcaSettings" in properties:
            self._settings = properties["_pymcaSettings"]
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    def process(self, xas_obj):
        """

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        :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
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        :return: updated XASObject
        :rtype: :class:`.XASObject`
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        """
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        _xas_obj = self.getXasObject(xas_obj)
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        if _xas_obj.energy is None:
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            _logger.error("Energy not specified, unable to normalize spectra")
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            return

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        if self._settings:
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            _xas_obj.configuration["Normalization"] = self._settings
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        self._advancement.reset(max_=_xas_obj.n_spectrum)
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        self._advancement.startProcess()
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        self._pool_process(xas_obj=_xas_obj)
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        self._advancement.endProcess()
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        self.register_process(
            _xas_obj, data_keys=("NormalizedEnergy", "NormalizedMu", "NormalizedSignal")
        )
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        return _xas_obj

    def _pool_process(self, xas_obj):
        """process normalization from a pool"""
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        assert isinstance(xas_obj, XASObject)
        if not _USE_MULTIPROCESSING_POOL:
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            for spectrum in xas_obj.spectra.data.flat:
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                process_spectr_norm(
                    spectrum=spectrum,
                    configuration=xas_obj.configuration,
                    callbacks=self.callbacks,
                    overwrite=True,
                )
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        else:
            from multiprocessing import Manager
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            manager = Manager()
            output_dict = {}
            res_list = manager.list()
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            for i_spect, spect in enumerate(xas_obj.spectra.data.flat):
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                res_list.append(None)
                output_dict[spect] = i_spect

            with multiprocessing.Pool(5) as p:
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                partial_ = functools.partial(
                    process_spectr_norm,
                    configuration=xas_obj.configuration,
                    callbacks=self.callbacks,
                    overwrite=False,
                    output=res_list,
                    output_dict=output_dict,
                )
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                p.map(partial_, xas_obj.spectra)

            # then update local spectrum
            for spectrum, res in zip(xas_obj.spectra, res_list):
                spectrum.update(res)

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    def definition(self):
        return "Normalization of the spectrum"

    def program_version(self):
        import PyMca5
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        return PyMca5.version()

    def program_name(self):
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        return "pymca_normalization"
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    __call__ = process
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if __name__ == "__main__":
    import sys
    import yaml
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    import os

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    xas_object_yaml_file = sys.argv[1]
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    working_dir = sys.argv[2]
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    _logger.debug("Load xas object from {}".format(xas_object_yaml_file))
    with open(xas_object_yaml_file, "r") as file:
        ddict = yaml.load(file)["input_data"]
        xas_object = XASObject.from_dict(ddict)
    print("******* do normalization ********")
    res_xas_object = pymca_normalization(xas_obj=xas_object)
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    res_xas_object._create_saving_pt()
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    # dump resulting object in input file
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    with open(xas_object_yaml_file, "w") as file:
        yaml.dump({"input_data": res_xas_object.to_dict()}, file)
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    output_dest = os.path.join(os.getcwd(), "output_normalization.yaml")
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    # dump resulting object into output_normalization file
    with open(output_dest, "w") as file:
        yaml.dump({"input_data": res_xas_object.to_dict()}, file)