'''
res_voigt:
submodule for residual function and dfient for fitting static spectrum with the
sum of voigt function, edge function and base function
:copyright: 2021-2022 by pistack (Junho Lee).
:license: LGPL3.
'''
from typing import Optional
import numpy as np
from numpy.polynomial.legendre import legval
from ..mathfun.A_matrix import fact_anal_A
from ..mathfun.peak_shape import voigt, edge_gaussian, edge_lorenzian
from ..mathfun.peak_shape import deriv_voigt, deriv_edge_gaussian, deriv_edge_lorenzian
[docs]def residual_voigt(x0: np.ndarray, num_voigt: int, edge: Optional[str] = None,
num_edge: Optional[int] = 0,
base_order: Optional[int] = None,
e: np.ndarray = None,
intensity: np.ndarray = None, eps: np.ndarray = None) -> np.ndarray:
'''
residual_voigt
scipy.optimize.least_squares compatible vector residual function for fitting static spectrum with the
sum of voigt function, edge function base function
Args:
x0: initial parameter
* i th: peak position e0_i for i th voigt component
* :math:`{num}_{voigt}+i` th: fwhm_G of i th voigt component
* :math:`2{num}_{voigt}+i` th: fwhm_L of i th voigt component
if edge is not None:
* :math:`3{num}_{voigt}+i` th: ith edge position
* :math:`3{num}_{voigt}+{num}_{edge}+i` th: fwhm of ith edge function
num_voigt: number of voigt component
edge ({'g', 'l'}): type of edge shape function
if edge is not set, it does not include edge function.
num_edge: number of edge component
base_order (int): polynomial order of baseline function
if base_order is not set, it does not include baseline function.
e: 1d array of energy points of data (n,)
intensity: intensity of static data (n,)
eps: estimated error of data (n,)
Returns:
Residucal vector
Note:
* If fwhm_G of ith voigt component is zero then it is treated as lorenzian function with fwhm_L
* If fwhm_L of ith voigt component is zero then it is treated as gaussian function with fwhm_G
'''
x0 = np.atleast_1d(x0)
tot_comp = num_voigt
e0 = x0[:num_voigt]
fwhm_G = x0[num_voigt:2*num_voigt]
fwhm_L = x0[2*num_voigt:3*num_voigt]
if edge is not None:
tot_comp = tot_comp+num_edge
if base_order is not None:
tot_comp = tot_comp+base_order+1
A = np.empty((tot_comp, e.size))
for i in range(num_voigt):
A[i, :] = voigt(e-e0[i], fwhm_G[i], fwhm_L[i])
base_start = num_voigt
if edge is not None:
base_start = base_start+num_edge
if edge == 'g':
for i in range(num_edge):
A[num_voigt+i, :] = edge_gaussian(e-x0[3*num_voigt+i],
x0[3*num_voigt+num_edge+i])
elif edge == 'l':
for i in range(num_edge):
A[num_voigt+i, :] = edge_lorenzian(e-x0[3*num_voigt+i],
x0[3*num_voigt+num_edge+i])
if base_order is not None:
e_max = np.max(e); e_min = np.min(e)
e_norm = 2*(e-(e_max+e_min)/2)/(e_max-e_min)
tmp = np.eye(base_order+1)
A[base_start:, :] = legval(e_norm, tmp, tensor=True)
c = fact_anal_A(A, intensity, eps)
chi = (c@A-intensity)/eps
return chi
[docs]def res_grad_voigt(x0: np.ndarray, num_voigt: int, edge: Optional[str] = None,
num_edge: Optional[int] = 0,
base_order: Optional[int] = None,
fix_param_idx: Optional[np.ndarray] = None,
e: np.ndarray = None,
intensity: np.ndarray = None, eps: np.ndarray = None) -> np.ndarray:
'''
res_grad_voigt
scipy.optimize.minimizer compatible scalar residual function and its gradient for fitting static spectrum with the
sum of voigt function, edge function base function
Args:
x0: initial parameter
* i th: peak position e0_i for i th voigt component
* :math:`{num}_{voigt}+i` th: fwhm_G of i th voigt component
* :math:`2{num}_{voigt}+i` th: fwhm_L of i th voigt component
if edge is not None:
* :math:`3{num}_{voigt}+i` th: ith edge position
* :math:`3{num}_{voigt}+{num}_{edge}+i` th: fwhm of ith edge function
num_voigt: number of voigt component
edge ({'g', 'l'}): type of edge shape function
if edge is not set, it does not include edge function.
num_edge: number of edge component
base_order (int): polynomial order of baseline function
if base_order is not set, it does not include baseline function.
fix_param_idx: idx for fixed parameter (masked array for `x0`)
e: 1d array of energy points of data (n,)
intensity: intensity of static data (n,)
eps: estimated error of data (n,)
Returns:
Tuple of scalar residual function :math:`(\\frac{1}{2}\\sum_i {res}^2_i)` and its gradient
Note:
* If fwhm_G of ith voigt component is zero then it is treated as lorenzian function with fwhm_L
* If fwhm_L of ith voigt component is zero then it is treated as gaussian function with fwhm_G
'''
x0 = np.atleast_1d(x0)
tot_comp = num_voigt
e0 = x0[:num_voigt]
fwhm_G = x0[num_voigt:2*num_voigt]
fwhm_L = x0[2*num_voigt:3*num_voigt]
if edge is not None:
tot_comp = tot_comp+num_edge
if base_order is not None:
tot_comp = tot_comp+base_order+1
A = np.empty((tot_comp, e.size))
for i in range(num_voigt):
A[i, :] = voigt(e-e0[i], fwhm_G[i], fwhm_L[i])
base_start = num_voigt
if edge is not None:
base_start = num_voigt+num_edge
if edge == 'g':
for i in range(num_edge):
A[num_voigt+i, :] = edge_gaussian(e-x0[3*num_voigt+i],
x0[3*num_voigt+num_edge+i])
elif edge == 'l':
for i in range(num_edge):
A[num_voigt,:] = edge_lorenzian(e-x0[3*num_voigt+i],
x0[3*num_voigt+num_edge+i])
if base_order is not None:
e_max = np.max(e); e_min = np.min(e)
e_norm = 2*(e-(e_max+e_min)/2)/(e_max-e_min)
tmp = np.eye(base_order+1)
A[base_start:, :] = legval(e_norm, tmp, tensor=True)
c = fact_anal_A(A, intensity, eps)
chi = (c@A-intensity)/eps
df = np.empty((intensity.size, x0.size))
for i in range(num_voigt):
df_tmp = c[i]*deriv_voigt(e-e0[i], fwhm_G[i], fwhm_L[i])
df[:, i] = -df_tmp[:, 0]
df[:, num_voigt+i] = df_tmp[:, 1]
df[:, 2*num_voigt+i] = df_tmp[:, 2]
if edge is not None:
if edge == 'g':
for i in range(num_edge):
df_edge = c[num_voigt+i]*deriv_edge_gaussian(e-x0[3*num_voigt+i],
x0[3*num_voigt+num_edge+i])
df[:, 3*num_voigt+i] = -df_edge[:, 0]
df[:, 3*num_voigt+num_edge+i] = df_edge[:, 1]
elif edge == 'l':
for i in range(num_edge):
df_edge = c[num_voigt+i]*deriv_edge_lorenzian(e-x0[3*num_voigt+i],
x0[3*num_voigt+num_edge+i])
df[:, 3*num_voigt+i] = -df_edge[:, 0]
df[:, 3*num_voigt+num_edge+i] = df_edge[:, 1]
df = np.einsum('i,ij->ij', 1/eps, df)
df[:, fix_param_idx] = 0
return np.sum(chi**2)/2, chi@df