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#!/usr/bin/env python3
#######################################
import sys
import os
import os.path
import time
#import re
import logging
# ------------------------ logging levels ------------------------------------
logging.basicConfig(level= logging.INFO,
format='%(module)s.%(funcName)s: %(levelname)-7s: %(message)s')
config_log = logging.INFO
# ----------------------------------------------------------------------------
from optparse import OptionParser
import numpy
from scipy import optimize
import matplotlib.pyplot as plt
from extranormal3 import normal_xas
#---------- test -----------
E0 = 20003.9
PRE_EDGE_START = -200
PRE_EDGE_END = -60
PRE_FIT_DEGREE = 1
POST_EDGE_START = 100
POST_EDGE_END = 1200
POST_FIT_DEGREE = 2
#---------------------------
if __name__ == '__main__':
parser = OptionParser()
'''
parser.add_option("-p", "--parallel", dest="parallel",
action="store_true", default=False,
help="parallel execution (using mpirun)")
'''
(options, args) = parser.parse_args()
if len(args) == 0:
print ("Usage: "+ os.path.basename(__file__) +" data_file")
sys.exit()
if not os.path.exists(args[0]):
logging.error(" Data file "+ args[0] +" does not exist")
sys.exit()
data_file = args[0]
edge = E0
preedge_params = {}
preedge_params['from'] = edge + PRE_EDGE_START
preedge_params['to'] = edge + PRE_EDGE_END
preedge_params['deg'] = PRE_FIT_DEGREE
postedge_params = {}
postedge_params['from'] = edge + POST_EDGE_START
postedge_params['to'] = edge + POST_EDGE_END
postedge_params['deg'] = POST_FIT_DEGREE
spectrum = numpy.loadtxt(data_file, comments = '#', usecols=(0,2), unpack=True )
ref = numpy.loadtxt(data_file, comments = '#', usecols=(3,), unpack=True )
fig, ax = plt.subplots()
line1, = ax.plot(spectrum[0], spectrum[1], label='Measured spectrum')
norm_vals = normal_xas.normalize(spectrum, edge, preedge_params, postedge_params)[0]
'''
print (norm_vals)
print (norm_vals.shape)
norm_const = normal_xas.normalize(spectrum, edge, preedge_params, postedge_params)[1]
print (norm_const)
print (numpy.array([norm_const]).shape)
print (numpy.concatenate((norm_vals, numpy.array([norm_const])), axis=0))
'''
line2, = ax.plot(spectrum[0], norm_vals, label='Normalized spectrum')
ax.legend(loc='lower right')
plt.grid()
#plt.show()
norm_spectra, norm_consts = normal_xas.normalize_all(spectrum[0], [spectrum[1], ref],
edge, preedge_params, postedge_params)
print (norm_spectra.shape)
#print (numpy.ones((len(norm_consts), 1)).shape )
#print (numpy.hstack((norm_spectra, numpy.ones((len(norm_consts), 1)))) )
norm_consts = norm_consts.reshape((len(norm_consts),1))
print (norm_consts.shape)
print (norm_consts)
print (numpy.hstack((norm_spectra, norm_consts)) )
#print (normal_xas.normalize_all(spectrum[0], [spectrum[1], ref], edge, preedge_params, postedge_params))
#print (norm_params)
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