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#!/usr/bin/env python3
#######################################
import sys
import os
import os.path
import logging
# ------------------------ logging levels ------------------------------------
logging.basicConfig(level= logging.INFO,
format='%(module)s.%(funcName)s: %(levelname)-7s: %(message)s')
config_log = logging.INFO
# ----------------------------------------------------------------------------
import numpy
from extranormal3 import curved_tools
class InvalidInput(Exception): pass
def process_params(energies, edge, preedge_params, postedge_params):
if not (energies[0] < edge and edge < energies[-1]):
sys.tracebacklimit = -1
raise Exception('Edge value does not belong to the data energy range')
if preedge_params['from']<0 and preedge_params['to']<0:
# relative pre-edge fit bounds
preedge_params['from'] += edge
preedge_params['to'] += edge
postedge_params['from'] += edge
postedge_params['to'] += edge
if preedge_params['from'] < energies[0]:
preedge_params['from'] = energies[0]
if postedge_params['to'] > energies[-1]:
postedge_params['to'] = energies[-1]
def flatten(spectrum, edge, preedge_fit, postedge_fit):
'''
spectrum is supposed to be shifted to the edge by now
preedge_fit = self.preedge_params['fit_func'] from normal_gui.NormalWindow
postedge_fit = self.postedge_params['fit_func'] from normal_gui.NormalWindow
'''
flattening_params = list(postedge_fit)[:-1]+[0.]
flattening_params[-2] -= preedge_fit[-2]
#print flattening_params
beforeE0_inxs, = numpy.where(spectrum[0] < edge)
afterE0_inxs, = numpy.where(spectrum[0] >= edge)
flattening = numpy.zeros(len(spectrum[0]))
flattening[afterE0_inxs] = numpy.polyval(flattening_params,
spectrum[0][afterE0_inxs])
flattening[afterE0_inxs] = (flattening[afterE0_inxs] -
numpy.polyval(flattening_params, edge))
return flattening
def normalize(spectrum, edge, preedge_params, postedge_params, checked_params=False):
if not checked_params:
process_params(spectrum[0], edge, preedge_params, postedge_params)
try:
preedge_fitcurve, preedge_fitcoeffs = curved_tools.poly_fit(spectrum, preedge_params)
postedge_fitcurve, postedge_fitcoeffs = curved_tools.poly_fit(spectrum, postedge_params)
except:
#sys.tracebacklimit = -1
raise Exception('Invalid fit parameters')
# http://cars9.uchicago.edu/~ravel/software/doc/Athena/html/bkg/norm.html
norm_const = (numpy.polyval(postedge_fitcoeffs, edge)-
numpy.polyval(preedge_fitcoeffs, edge))
flattening = flatten(spectrum, edge, preedge_fitcoeffs, postedge_fitcoeffs)
#return numpy.array([spectrum[0], (spectrum[1]-preedge_fitcurve[1]-flattening)/norm_const])
return numpy.array((spectrum[1]-preedge_fitcurve[1]-flattening)/norm_const), norm_const
def normalize_all(energies, abs_data, edge, preedge_params, postedge_params):
'''
*** This function is only applicable to the interpolated data ***
'''
#print(energies, abs_data)
if any([len(energies) != len(vals) for vals in abs_data]):
sys.tracebacklimit = -1
raise InvalidInput(' Invalid data format')
#raise Exception(' Invalid data format')
try:
process_params(energies, edge, preedge_params, postedge_params)
except:
sys.tracebacklimit = -1
raise InvalidInput('Invalid or inconsistent normalization parameters')
'''
otvet = numpy.asarray([normalize(numpy.array([energies, abs_vals]), edge, preedge_params, postedge_params)
for abs_vals in abs_data])
'''
otvet = [normalize(numpy.array([energies, abs_vals]), edge, preedge_params, postedge_params)
for abs_vals in abs_data]
norm_spectra_list = []
norm_consts_list = []
for tuple_instance in otvet:
norm_spectra_list.append(tuple_instance[0])
norm_consts_list.append(tuple_instance[1])
norm_spectra = numpy.array(norm_spectra_list)
norm_consts = numpy.array(norm_consts_list)
return norm_spectra, norm_consts
'''
return numpy.array([normalize(numpy.array([energies, abs_vals]),
edge, preedge_params, postedge_params,
True)
for abs_vals in abs_data])
'''
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