You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
144 lines
3.8 KiB
144 lines
3.8 KiB
#
|
|
# wpylib.math.fitting.funcs_pec module
|
|
# Created: 20150521
|
|
# Wirawan Purwanto
|
|
#
|
|
# Imported 20150521 from Cr2_analysis_cbs.py
|
|
# (dated 20141017, CVS rev 1.143).
|
|
#
|
|
|
|
"""
|
|
wpylib.math.fitting.funcs_pec module
|
|
A library of simple f(x) functions for PEC fitting
|
|
|
|
For use with the OO-style x-y curve fitting interface
|
|
(fit_func_base).
|
|
"""
|
|
|
|
import numpy
|
|
from wpylib.math.fitting import fit_func_base
|
|
from wpylib.math.fitting.funcs_simple import fit_harm
|
|
|
|
|
|
class harm_fit_func(fit_func_base):
|
|
"""Harmonic function object.
|
|
For use with fit_func function on a PEC.
|
|
|
|
Functional form:
|
|
|
|
E0 + 0.5 * k * (x - re)**2
|
|
|
|
Coefficients:
|
|
* C[0] = energy minimum
|
|
* C[1] = spring constant
|
|
* C[2] = equilibrium distance
|
|
"""
|
|
dim = 1 # a function with 1-D domain
|
|
param_names = ('E0', 'k', 'r0')
|
|
def __call__(self, C, x):
|
|
xdisp = (x[0] - C[2])
|
|
y = C[0] + 0.5 * C[1] * xdisp**2
|
|
self.func_call_hook(C, x, y)
|
|
return y
|
|
def Guess_xy(self, x, y):
|
|
fit_rslt = fit_harm(x[0], y)
|
|
self.guess_params = tuple(fit_rslt[0])
|
|
return self.guess_params
|
|
|
|
|
|
class harmcube_fit_func(fit_func_base):
|
|
"""Harmonic + cubic term function object.
|
|
For use with fit_func function on a PEC.
|
|
|
|
Functional form:
|
|
|
|
E0 + 0.5 * k * (x - re)**2 + cub * (x - re)**3;
|
|
|
|
Coefficients:
|
|
* C[0] = energy minimum
|
|
* C[1] = spring constant
|
|
* C[2] = equilibrium distance
|
|
* C[3] = nonlinear (cubic) constant
|
|
"""
|
|
dim = 1 # a function with 1-D domain
|
|
param_names = ('E0', 'k', 'r0', 'c3')
|
|
def __call__(self, C, x):
|
|
xdisp = (x[0] - C[2])
|
|
y = C[0] + 0.5 * C[1] * xdisp**2 + C[3] * xdisp**3
|
|
self.func_call_hook(C, x, y)
|
|
return y
|
|
def Guess_xy(self, x, y):
|
|
fit_rslt = fit_harm(x[0], y)
|
|
self.guess_params = tuple(fit_rslt[0]) + (0,)
|
|
return self.guess_params
|
|
def Guess_xy_old(self, x, y):
|
|
imin = numpy.argmin(y)
|
|
return (y[imin], 2, x[0][imin], 0.00001)
|
|
|
|
|
|
class morse2_fit_func(fit_func_base):
|
|
"""Morse2 function object.
|
|
For use with fit_func function.
|
|
|
|
Functional form:
|
|
|
|
E0 + 0.5 * k / a**2 * (1 - exp(-a * (x - re)))**2
|
|
|
|
Coefficients:
|
|
* C[0] = energy minimum
|
|
* C[1] = spring constant
|
|
* C[2] = equilibrium distance
|
|
* C[3] = nonlinear constant
|
|
"""
|
|
dim = 1 # a function with 1-D domain
|
|
param_names = ('E0', 'k', 'r0', 'a')
|
|
def __call__(self, C, x):
|
|
from numpy import exp
|
|
E0, k, r0, a = self.get_params(C, *(self.param_names))
|
|
y = E0 + 0.5 * k / a**2 * (1 - exp(-a * (x[0] - r0)))**2
|
|
self.func_call_hook(C, x, y)
|
|
return y
|
|
def Guess_xy(self, x, y):
|
|
imin = numpy.argmin(y)
|
|
harm_params = fit_harm(x[0], y)
|
|
if self.debug >= 10:
|
|
print("Initial guess by fit_harm gives: %s" % (harm_params,))
|
|
self.guess_params = (y[imin], harm_params[0][1], x[0][imin], 0.01 * harm_params[0][1])
|
|
return self.guess_params
|
|
def Guess_xy_old(self, x, y):
|
|
imin = numpy.argmin(y)
|
|
return (y[imin], 2, x[0][imin], 0.01)
|
|
|
|
|
|
class ext3Bmorse2_fit_func(fit_func_base):
|
|
"""ext3Bmorse2 function object.
|
|
For use with fit_func function.
|
|
|
|
Functional form:
|
|
|
|
E0 + 0.5 * k / a**2 * (1 - exp(-a * (x - re)))**2
|
|
+ C3 * (1 - exp(-a * (x - re)))**3
|
|
|
|
Coefficients:
|
|
* C[0] = energy minimum
|
|
* C[1] = spring constant
|
|
* C[2] = equilibrium distance
|
|
* C[3] = nonlinear constant
|
|
* C[4] = coefficient of cubic term
|
|
"""
|
|
dim = 1 # a function with 1-D domain
|
|
def __call__(self, C, x):
|
|
from numpy import exp
|
|
E = 1 - exp(-C[3] * (x[0] - C[2]))
|
|
y = C[0] + 0.5 * C[1] / C[3]**2 * E**2 + C[4] * E**3
|
|
self.func_call_hook(C, x, y)
|
|
return y
|
|
def Guess_xy(self, x, y):
|
|
imin = numpy.argmin(y)
|
|
harm_params = fit_harm(x[0], y)
|
|
if self.debug >= 10:
|
|
print("Initial guess by fit_harm gives: %s " % (harm_params,))
|
|
self.guess_params = (y[imin], harm_params[0][1], x[0][imin], 0.01 * harm_params[0][1], 0)
|
|
return self.guess_params
|
|
|
|
|
|
|