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Merge pull request #158 from licode/lmfit_model
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Lmfit model for xrf
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@danielballan
danielballan committed Mar 30, 2015
2 parents 204d38e + 2e6492a commit 867a9af
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Showing 7 changed files with 1,561 additions and 74 deletions.
3 changes: 2 additions & 1 deletion skxray/constants/xrf.py
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Expand Up @@ -107,7 +107,8 @@ def __init__(self, caller, *args, **kwargs):


XRAYLIB_MAP = verbosedict({'lines': (line_dict, xraylib.LineEnergy),
'cs': (line_dict, xraylib.CS_FluorLine),
'cs': (line_dict, xraylib.CS_FluorLine_Kissel),
#'cs': (line_dict, xraylib.CS_FluorLine),
'binding_e': (shell_dict, xraylib.EdgeEnergy),
'jump': (shell_dict, xraylib.JumpFactor),
'yield': (shell_dict, xraylib.FluorYield),
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190 changes: 166 additions & 24 deletions skxray/fitting/base/parameter_data.py
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Expand Up @@ -11,7 +11,7 @@
# Copyright (c) 2013, Stefan Vogt, Argonne National Laboratory #
# All rights reserved. #
# #
# Redistribution and bound_type in source and binary forms, with or without #
# Redistribution and use in source and binary forms, with or without #
# modification, are permitted provided that the following conditions #
# are met: #
# #
Expand All @@ -24,7 +24,7 @@
# distribution. #
# #
# * Neither the name of the Brookhaven Science Associates, Brookhaven #
# National Laboratory nor the names of its contributors may be bound_typed #
# National Laboratory nor the names of its contributors may be used #
# to endorse or promote products derived from this software without #
# specific prior written permission. #
# #
Expand All @@ -35,14 +35,15 @@
# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, #
# INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES #
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR #
# SERVICES; LOSS OF bound_type, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) #
# HOWEVER CAbound_typeD AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, #
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) #
# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, #
# STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OTHERWISE) ARISING #
# IN ANY WAY OUT OF THE bound_type OF THIS SOFTWARE, EVEN IF ADVISED OF THE #
# IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE #
# POSSIBILITY OF SUCH DAMAGE. #
########################################################################

from __future__ import (absolute_import, division, unicode_literals, print_function)
from __future__ import (absolute_import, division,
unicode_literals, print_function)
import six


Expand All @@ -59,13 +60,13 @@
hi: with high boundary
none: no fitting boundary
Different fitting strategies are included to turn on or turn off some parameters.
Those strategies are default, linear, free_energy, free_all and e_calibration.
They are empirical experience from authors of the original code.
"""


# old param dict, keep it here for now.
para_dict = {'coherent_sct_amplitude': {'bound_type': 'none', 'min': 7.0, 'max': 8.0, 'value': 6.0},
'coherent_sct_energy': {'bound_type': 'none', 'min': 10.4, 'max': 12.4, 'value': 11.8},
'compton_amplitude': {'bound_type': 'none', 'min': 0.0, 'max': 10.0, 'value': 5.0},
Expand Down Expand Up @@ -109,25 +110,165 @@
}


# fitting strategy
linear = {'coherent_sct_amplitude': 'none', 'compton_amplitude': 'none'}
# fitting strategies
adjust_element = {'coherent_sct_amplitude': 'none',
'coherent_sct_energy': 'fixed',
'compton_amplitude': 'none',
'compton_angle': 'fixed',
'compton_f_step': 'fixed',
'compton_f_tail': 'fixed',
'compton_fwhm_corr': 'lohi',
'compton_gamma': 'fixed',
'compton_hi_f_tail': 'fixed',
'compton_hi_gamma': 'fixed',
'e_linear': 'fixed',
'e_offset': 'fixed',
'e_quadratic': 'fixed',
'fwhm_fanoprime': 'fixed',
'fwhm_offset': 'fixed',
'non_fitting_values': 'fixed'}

e_calibration = {'coherent_sct_amplitude': 'none',
'coherent_sct_energy': 'fixed',
'compton_amplitude': 'none',
'compton_angle': 'fixed',
'compton_f_step': 'fixed',
'compton_f_tail': 'fixed',
'compton_fwhm_corr': 'fixed',
'compton_gamma': 'fixed',
'compton_hi_f_tail': 'fixed',
'compton_hi_gamma': 'fixed',
'e_linear': 'lohi',
'e_offset': 'lohi',
'e_quadratic': 'fixed',
'fwhm_fanoprime': 'fixed',
'fwhm_offset': 'fixed',
'non_fitting_values': 'fixed'}

linear = {'coherent_sct_amplitude': 'none',
'coherent_sct_energy': 'fixed',
'compton_amplitude': 'none',
'compton_angle': 'fixed',
'compton_f_step': 'fixed',
'compton_f_tail': 'fixed',
'compton_fwhm_corr': 'fixed',
'compton_gamma': 'fixed',
'compton_hi_f_tail': 'fixed',
'compton_hi_gamma': 'fixed',
'e_linear': 'fixed',
'e_offset': 'fixed',
'e_quadratic': 'fixed',
'fwhm_fanoprime': 'fixed',
'fwhm_offset': 'fixed',
'non_fitting_values': 'fixed'}

free_more = {'coherent_sct_amplitude': 'none',
'coherent_sct_energy': 'lohi',
'compton_amplitude': 'none',
'compton_angle': 'lohi',
'compton_f_step': 'lohi',
'compton_f_tail': 'fixed',
'compton_fwhm_corr': 'lohi',
'compton_gamma': 'lohi',
'compton_hi_f_tail': 'fixed',
'compton_hi_gamma': 'fixed',
'e_linear': 'lohi',
'e_offset': 'lohi',
'e_quadratic': 'lohi',
'fwhm_fanoprime': 'lohi',
'fwhm_offset': 'lohi',
'non_fitting_values': 'fixed'}

e_calibration = {'coherent_sct_amplitude': 'none', 'compton_amplitude': 'none',
'e_linear': 'lohi', 'e_offset': 'lohi', 'e_quadratic': 'lohi'}
fit_with_tail = {'coherent_sct_amplitude': 'none',
'coherent_sct_energy': 'lohi',
'compton_amplitude': 'none',
'compton_angle': 'lohi',
'compton_f_step': 'fixed',
'compton_f_tail': 'lohi',
'compton_fwhm_corr': 'lohi',
'compton_gamma': 'fixed',
'compton_hi_f_tail': 'fixed',
'compton_hi_gamma': 'fixed',
'e_linear': 'lohi',
'e_offset': 'lohi',
'e_quadratic': 'lohi',
'fwhm_fanoprime': 'lohi',
'fwhm_offset': 'lohi',
'non_fitting_values': 'fixed'}

free_energy = {'coherent_sct_amplitude': 'none', 'coherent_sct_energy': 'lohi',
'compton_amplitude': 'none', 'compton_angle': 'lohi',
'compton_f_tail': 'lo', 'compton_fwhm_corr': 'lohi',
'e_linear': 'lohi', 'e_offset': 'lohi', 'e_quadratic': 'lohi',
'fwhm_fanoprime': 'lohi', 'fwhm_offset': 'lohi'}

free_all = {'coherent_sct_amplitude': 'none', 'coherent_sct_energy': 'lohi',
'compton_amplitude': 'none', 'compton_angle': 'lohi',
'compton_f_step': 'lohi', 'compton_fwhm_corr': 'lohi', 'compton_gamma': 'lohi',
'e_linear': 'lohi', 'e_offset': 'lohi', 'e_quadratic': 'lohi',
'f_tail_linear': 'lohi', 'f_tail_offset': 'lohi',
'fwhm_fanoprime': 'lohi', 'fwhm_offset': 'lohi',
'kb_f_tail_linear': 'lohi', 'kb_f_tail_offset': 'lohi'}
default_param = {'coherent_sct_amplitude': {'bound_type': 'none',
'max': 10000000.0,
'min': 0.10,
'value': 100000},
'coherent_sct_energy': {'bound_type': 'lohi',
'description': 'Incident E [keV]',
'max': 13.0,
'min': 9.0,
'value': 11.0},
'compton_amplitude': {'bound_type': 'none',
'max': 10000000.0,
'min': 0.10,
'value': 100000.0},
'compton_angle': {'bound_type': 'lohi',
'max': 100.0,
'min': 80.0,
'value': 90.0},
'compton_f_step': {'bound_type': 'fixed',
'max': 0.01,
'min': 0.0,
'value': 0.01},
'compton_f_tail': {'bound_type': 'fixed',
'max': 0.3,
'min': 0.0001,
'value': 0.05},
'compton_fwhm_corr': {'bound_type': 'lohi',
'description': 'fwhm Coef, Compton',
'max': 2.5,
'min': 0.5,
'value': 1.5},
'compton_gamma': {'bound_type': 'lohi', 'max': 4.2,
'min': 3.8, 'value': 4.0},
'compton_hi_f_tail': {'bound_type': 'fixed',
'max': 1.0,
'min': 1e-06,
'value': 0.1},
'compton_hi_gamma': {'bound_type': 'fixed',
'max': 3.0,
'min': 0.1,
'value': 2.0},
'e_linear': {'bound_type': 'lohi',
'description': 'E Calib. Coef, a1',
'max': 0.011,
'min': 0.009,
'tool_tip': 'E(channel) = a0 + a1*channel+ a2*channel**2',
'value': 0.009532},
'e_offset': {'bound_type': 'lohi',
'description': 'E Calib. Coef, a0',
'max': 0.015,
'min': 0.006,
'tool_tip': 'E(channel) = a0 + a1*channel+ a2*channel**2',
'value': 0.007826},
'e_quadratic': {'bound_type': 'lohi',
'description': 'E Calib. Coef, a2',
'max': 1e-06,
'min': -1e-06,
'tool_tip': 'E(channel) = a0 + a1*channel+ a2*channel**2',
'value': 0.0},
'fwhm_fanoprime': {'bound_type': 'fixed',
'description': 'fwhm Coef, b2',
'max': 0.0001,
'min': 1e-07,
'value': 1e-06},
'fwhm_offset': {'bound_type': 'lohi',
'description': 'fwhm Coef, b1 [keV]',
'max': 0.19,
'min': 0.16,
'tool_tip': 'width**2 = (b1/2.3548)**2 + 3.85*b2*E',
'value': 0.178},
'non_fitting_values': {'element_list': 'Ar, Fe, Ce_L, Pt_M',
'energy_bound_high': 12.0,
'energy_bound_low': 2.5}}


def get_para():
Expand All @@ -136,4 +277,5 @@ def get_para():
based on different algorithms.
Use copy for dict.
"""
return para_dict
#return para_dict
return default_param
13 changes: 7 additions & 6 deletions skxray/fitting/lineshapes.py
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Expand Up @@ -49,7 +49,7 @@
import numpy as np
import scipy.special
import six
from lmfit.lineshapes import gaussian
#from lmfit.lineshapes import gaussian


log2 = np.log(2)
Expand Down Expand Up @@ -260,7 +260,7 @@ def elastic(x, coherent_sct_amplitude,
coherent_sct_amplitude : float
area of elastic peak
coherent_sct_energy : float
incident energy
incident energy
fwhm_offset : float
global fitting parameter for peak width
fwhm_fanoprime : float
Expand Down Expand Up @@ -353,10 +353,11 @@ def compton(x, compton_amplitude, coherent_sct_energy,

x = e_offset + x * e_linear + x**2 * e_quadratic

compton_e = (coherent_sct_energy
/ (1 + (coherent_sct_energy / 511)
* (1 - np.cos(compton_angle * np.pi / 180))))

# the rest-mass energy of an electron (511 keV)
mc2 = 511
comp_denom = 1 + coherent_sct_energy / mc2 * (1 - np.cos(np.deg2rad(compton_angle)))
compton_e = coherent_sct_energy / comp_denom

temp_val = 2 * np.sqrt(2 * np.log(2))
sigma = np.sqrt((fwhm_offset / temp_val)**2 +
compton_e * epsilon * fwhm_fanoprime)
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28 changes: 18 additions & 10 deletions skxray/fitting/models.py
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Expand Up @@ -53,20 +53,19 @@
from lmfit.models import GaussianModel as LmGaussianModel
from lmfit.models import LorentzianModel as LmLorentzianModel
from .lineshapes import (elastic, compton, gaussian_tail, gausssian_step,
lorentzian2, gaussian, lorentzian
)

import logging
logger = logging.getLogger(__name__)
lorentzian2, gaussian, lorentzian)

from skxray.fitting.lineshapes import (elastic, compton, gaussian,
lorentzian, lorentzian2)

from skxray.fitting.base.parameter_data import get_para

import logging
logger = logging.getLogger(__name__)


def set_default(model_name, func_name):
"""set values and bounds to Model parameters in lmfit
"""
Set values and bounds to Model parameters in lmfit.
Parameters
----------
Expand Down Expand Up @@ -107,6 +106,17 @@ def set_default(model_name, func_name):


def _gen_class_docs(func):
"""
Parameters
----------
func : function
function of peak profile
Returns
-------
str :
documentation of the function
"""
return ("Wrap the {} function for fitting within lmfit framework\n".format(func.__name__) +
func.__doc__)

Expand All @@ -127,10 +137,10 @@ class ComptonModel(Model):
__doc__ = _gen_class_docs(compton)

def __init__(self, *args, **kwargs):

super(ComptonModel, self).__init__(compton, *args, **kwargs)
set_default(self, compton)
self.set_param_hint('epsilon', value=2.96, vary=False)
self.set_param_hint('matrix', value=False, vary=False)


class Lorentzian2Model(Model):
Expand All @@ -139,5 +149,3 @@ class Lorentzian2Model(Model):

def __init__(self, *args, **kwargs):
super(Lorentzian2Model, self).__init__(lorentzian2, *args, **kwargs)


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