/usr/lib/python2.7/dist-packages/PyMca/XASNormalization.py is in pymca 4.7.4+dfsg-1build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 | #/*##########################################################################
# Copyright (C) 2004-2012 European Synchrotron Radiation Facility
#
# This file is part of the PyMca X-ray Fluorescence Toolkit developed at
# the ESRF by the Software group.
#
# This toolkit is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the Free
# Software Foundation; either version 2 of the License, or (at your option)
# any later version.
#
# PyMca is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along with
# PyMca; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
# PyMca follows the dual licensing model of Riverbank's PyQt and cannot be
# used as a free plugin for a non-free program.
#
# Please contact the ESRF industrial unit (industry@esrf.fr) if this license
# is a problem for you.
#############################################################################*/
__author__ = "V.A. Sole - ESRF Software Group"
__doc__ = """This set of routines performs normalization of X-ray absorption
spectra for qualitative/preliminary analysis. For state-of-the-art XAS you
should take a look at dedicated and well-tested packages like IFEFFIT or
Viper/XANES dactyloscope """
import numpy
from PyMca import SpecfitFuns
from PyMca import SGModule
from PyMca.Gefit import LeastSquaresFit
DEBUG = 0
if DEBUG:
from pylab import *
def polynom(parameter_list, x):
if hasattr(x, 'shape'):
output = numpy.zeros(x.shape)
else:
output = 0.0
for i in range(len(parameter_list)):
output += parameter_list[i] * pow(x, i)
return output
def polynomDerivative(parameter_list, parameter_index, x):
return pow(x, parameter_index)
def victoreen(parameter_list, x):
return parameter_list[0] * pow(x, -3) + parameter_list[1] * pow(x, -4)
def victoreenDerivative(parameter_list, parameter_index, x):
if parameter_index == 0:
return pow(x, -3)
else:
return pow(x, -4)
def modifiedVictoreen(parameter_list, x):
return parameter_list[0] * pow(x, -3) + parameter_list[1]
def modifiedVictoreenDerivative(parameter_list, parameter_index, x):
if parameter_index == 0:
return pow(x, -3)
else:
return numpy.ones(x.shape, dtype=numpy.float)
def estimateXANESEdge(spectrum, energy=None, full=False):
if energy is None:
x = numpy.arange(len(spectrum)).astype(numpy.float)
else:
x = numpy.array(energy, dtype=numpy.float, copy=True)
y = numpy.array(spectrum, dtype=numpy.float, copy=True)
# make sure data are sorted
idx = energy.argsort(kind='mergesort')
x = numpy.take(energy, idx)
y = numpy.take(spectrum, idx)
# make sure data are strictly increasing
delta = x[1:] - x[:-1]
dmin = delta.min()
dmax = delta.max()
if delta.min() <= 1.0e-10:
# force data are strictly increasing
# although we do not consider last point
idx = numpy.nonzero(delta>0)[0]
x = numpy.take(x, idx)
y = numpy.take(y, idx)
delta = None
sortedX = x
sortedY = y
# use a regularly spaced spectrum
if dmax != dmin:
# choose the number of points or deduce it from
# the input data length?
nchannels = 2 * len(spectrum)
xi = numpy.linspace(x[1], x[-2], nchannels).reshape(-1, 1)
x.shape = -1
y.shape = -1
y = SpecfitFuns.interpol([x], y, xi, y.min())
x = xi
x.shape = -1
y.shape = -1
# take the first derivative
npoints = 7
xPrime = x[npoints:-npoints]
yPrime = SGModule.getSavitzkyGolay(y, npoints=npoints, degree=2, order=1)
# get the index at maximum value
iMax = numpy.argmax(yPrime)
# get the center of mass
w = 2 * npoints
selection = yPrime[iMax-w:iMax+w+1]
edge = (selection * xPrime[iMax-w:iMax+w+1]).sum(dtype=numpy.float)/\
selection.sum(dtype=numpy.float)
if full:
# return intermediate information
return edge, sortedX, sortedY, xPrime, yPrime
else:
# return the corresponding x value
return edge
def getRegionsData(x0, y0, regions, edge=0.0):
"""
x - 1D array
y - 1D array of the same dimension as x
regions - List of (xmin, xmax) values defining the regions.
edge - Supplied edge energy
The default is 0. That means regions are absolute energies.
The actual regions are defined as (xmin + edge, xmin + edge)
"""
# take a view of the data
x = x0[:]
y = y0[:]
x.shape = -1
y.shape = -1
i = 0
for region in regions:
xmin = region[0] + edge
xmax = region[1] + edge
toidx = numpy.nonzero((x >= xmin) & (x <= xmax))[0]
if i == 0:
i = 1
idx = toidx
else:
idx = numpy.concatenate((idx, toidx), axis=0)
xOut = numpy.take(x, idx)
yOut = numpy.take(y, idx)
if len(x0.shape) == 1:
xOut.shape = -1
yOut.shape = -1
elif x0.shape[0] == 1:
xOut.shape = 1, -1
yOut.shape = 1, -1
else:
xOut.shape = -1, 1
yOut.shape = -1, 1
return xOut, yOut
def XASNormalization(spectrum,
energy=None,
edge=None,
pre_edge_regions=None,
post_edge_regions=None,
algorithm='polynomial',
algorithm_parameters=None):
if algorithm not in SUPPORTED_ALGORITHMS:
raise ValueError("Unsupported algorithm %s" % algorithm)
if energy is None:
energy = numpy.arange(len(spectrum))
if edge in [None, 'Auto']:
edge = estimateXANESEdge(spectrum, energy=energy)
if pre_edge_regions is None:
# divide pre-edge zone in 4 regions and take the 3rd?
if edge < 200:
# data assumed to be in keV
pre_edge_regions = [[-0.4, -0.05]]
else:
# data assumend to be in eV
pre_edge_regions = [[-400., -50.]]
if post_edge_regions is None:
#divide post-edge by 20 and leave out the first one?
if edge < 200:
# data assumed to be in keV
post_edge_regions = [[0.020, energy.max()-edge]]
else:
# data assumend to be in eV
post_edge_regions = [[20., energy.max()-edge]]
return SUPPORTED_ALGORITHMS[algorithm](spectrum,
energy,
edge,
pre_edge_regions,
post_edge_regions,
parameters=algorithm_parameters)
def XASPolynomialNormalization(spectrum,
energy,
edge=None,
pre_edge_regions=None,
post_edge_regions=None,
parameters=None):
if edge in [None, 'Auto']:
edge = estimateXANESEdge(spectrum, energy=energy)
if parameters is None:
parameters = {}
pre_edge_order = parameters.get('pre_edge_order', 1)
post_edge_order = parameters.get('post_edge_order', 3)
xPre, yPre = getRegionsData(energy, spectrum, pre_edge_regions, edge=edge)
xPost, yPost = getRegionsData(energy, spectrum, post_edge_regions, edge=edge)
# get the proper pre-edge function to be used
pre_edge_function = polynom
if pre_edge_order in [0, 'Constant']:
pre_edge_order = 0
elif pre_edge_order in [1, 'Linear']:
pre_edge_order = 1
elif pre_edge_order in [2, 'Parabolic']:
pre_edge_order = 2
elif pre_edge_order in [3, 'Cubic']:
pre_edge_order = 3
elif pre_edge_order in [-1, 'Victoreen']:
pre_edge_order = -1
pre_edge_function = victoreen
elif pre_edge_order in [-2, 'Modif. Victoreen']:
pre_edge_order = -2
pre_edge_function = modifiedVictoreen
else:
# case of arriving with a 4th order polynom, for instance
pass
# calculate pre-edge
if pre_edge_order == 0:
prePol = [yPre.mean()]
elif pre_edge_order > 0:
p = numpy.arange(pre_edge_order + 1).astype(numpy.float)
prePol = LeastSquaresFit(pre_edge_function, p,
xdata=xPre, ydata=yPre,
model_deriv=polynomDerivative,
weightflag=0, linear=1)[0]
elif pre_edge_order == -1:
p = numpy.array([1.0, 1.0])
prePol = LeastSquaresFit(pre_edge_function, p,
xdata=xPre, ydata=yPre,
model_deriv=victoreenDerivative,
weightflag=0, linear=1)[0]
elif pre_edge_order == -2:
p = numpy.array([1.0, 1.0])
prePol = LeastSquaresFit(pre_edge_function, p,
xdata=xPre, ydata=yPre,
model_deriv=modifiedVictoreenDerivative,
weightflag=0, linear=1)[0]
# get the proper post-edge function to be used
post_edge_function = polynom
if post_edge_order in [0, 'Constant']:
post_edge_order = 0
elif post_edge_order in [1, 'Linear']:
post_edge_order = 1
elif post_edge_order in [2, 'Parabolic']:
post_edge_order = 2
elif post_edge_order in [3, 'Cubic']:
post_edge_order = 3
elif post_edge_order in [-1, 'Victoreen']:
post_edge_order = -1
post_edge_function = victoreen
elif post_edge_order in [-2, 'Modif. Victoreen']:
post_edge_order = -2
post_edge_function = modifiedVictoreen
else:
# case of arriving with a 4th order polynom, for instance
pass
# calculate post-edge
baseLine = pre_edge_function(prePol, xPost)
if post_edge_order == 0:
# just take the average
postPol = [(yPost-baseLine).mean()]
normalizedSpectrum = (spectrum - pre_edge_function(prePol, energy))/postPol[0]
elif post_edge_order > 0:
p = numpy.arange(post_edge_order + 1).astype(numpy.float)
postPol = LeastSquaresFit(post_edge_function, p,
xdata=xPost,
ydata=yPost-baseLine,
model_deriv=polynomDerivative,
weightflag=0, linear=1)[0]
normalizedSpectrum = (spectrum - pre_edge_function(prePol, energy))\
/post_edge_function(postPol, energy)
elif post_edge_order == -1:
p = numpy.array([1.0, 1.0])
postPol = LeastSquaresFit(post_edge_function, p,
xdata=xPost,
ydata=yPost-baseLine,
model_deriv=victoreenDerivative,
weightflag=0, linear=1)[0]
normalizedSpectrum = (spectrum - pre_edge_function(prePol, energy))\
/post_edge_function(postPol, energy)
elif post_edge_order == -2:
p = numpy.array([1.0, 1.0])
postPol = LeastSquaresFit(post_edge_function, p,
xdata=xPost,
ydata=yPost-baseLine,
model_deriv=modifiedVictoreenDerivative,
weightflag=0, linear=1)[0]
normalizedSpectrum = (spectrum - pre_edge_function(prePol, energy))\
/post_edge_function(postPol, energy)
jump = post_edge_function(postPol, edge)
if DEBUG:
plot(energy, spectrum, 'o')
plot(xPre, pre_edge_function(prePol, xPre), 'r')
plot(xPost, post_edge_function(postPol, xPost)+pre_edge_function(prePol, xPost), 'y')
show()
return energy, normalizedSpectrum, edge, jump, pre_edge_function, prePol, post_edge_function, postPol
def XASVictoreenNormalization(spectrum,
energy,
edge=None,
pre_edge_regions=None,
post_edge_regions=None,
parameters=None):
if edge in [None, 'Auto']:
edge = estimateXANESEdge(spectrum, energy=energy)
if parameters is None:
parameters = {}
xPre, yPre = getRegionsData(energy, spectrum, pre_edge_regions)
xPost, yPost = getRegionsData(energy, spectrum, post_edge_regions)
pre_edge_order = parameters.get('pre_edge_order', 1)
post_edge_order = parameters.get('post_edge_order', 1)
if pre_edge_order in [1, -1, 'Victoreen']:
pre_edge_function = victoreen
else:
pre_edge_function = modifiedVictoreen
if post_edge_order in [1, -1, 'Victoreen']:
post_edge_function = victoreen
else:
post_edge_function = modifiedVictoreen
p = numpy.array([1.0, 1.0])
prePol = LeastSquaresFit(pre_edge_function, p, xdata=xPre, ydata=yPre,
weightflag=0, linear=1)[0]
postPol = LeastSquaresFit(post_edge_function, p,
xdata=xPost,
ydata=yPost-pre_edge_function(prePol, xPost),
weightflag=0, linear=1)[0]
normalizedSpectrum = (spectrum - pre_edge_function(prePol, energy))\
/post_edge_function(postPol, energy)
if DEBUG:
print("VICTOREEN")
plot(energy, spectrum, 'o')
plot(xPre, pre_edge_function(prePol, xPre), 'r')
plot(xPost,
post_edge_function(postPol, xPost)+pre_edge_function(prePol, xPost), 'y')
show()
return energy, normalizedSpectrum, edge
SUPPORTED_ALGORITHMS = {"polynomial":XASPolynomialNormalization,
"victoreen": XASVictoreenNormalization}
if __name__ == "__main__":
import sys
from PyMca.PyMcaIO import specfilewrapper as specfile
import time
sf = specfile.Specfile(sys.argv[1])
scan = sf[0]
data = scan.data()
energy = data[0, :]
spectrum = data[1, :]
n = 100
t0 = time.time()
for i in range(n):
edge = estimateXANESEdge(spectrum+i, energy=energy)
print("EDGE ELAPSED = ", (time.time() - t0)/float(n))
print("EDGE = %f" % edge)
if DEBUG:
n = 1
else:
n = 100
t0 = time.time()
for i in range(n):
nEne0, nSpe0 = XASNormalization(spectrum+i, energy,
edge=edge,
algorithm='polynomial',
algorithm_parameters={'pre_edge_order':0,
'post_edge_order':0})[0:2]
print("ELAPSED 0 = ", (time.time() - t0)/float(n))
t0 = time.time()
for i in range(n):
nEneP, nSpeP = XASNormalization(spectrum+i,
energy,
edge=edge,
algorithm='polynomial',
algorithm_parameters={'pre_edge_order':1,
'post_edge_order':2})[0:2]
print("ELAPSED Poly = ", (time.time() - t0)/float(n))
t0 = time.time()
for i in range(n):
nEneV, nSpeV = XASNormalization(spectrum+i,
energy,
edge=edge,
algorithm='polynomial',
algorithm_parameters={'pre_edge_order':'Victoreen',
'post_edge_order':'Victoreen'})[0:2]
print("ELAPSED Victoreen = ", (time.time() - t0)/float(n))
if DEBUG:
#plot(energy, spectrum, 'b')
plot(nEne0, nSpe0, 'k', label='Polynomial')
plot(nEneP, nSpeP, 'b', label='Polynomial')
plot(nEneV, nSpeV, 'r', label='Victoreen')
show()
|