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* Change to new way to obtain the 'C' fitting parameters.

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Wirawan Purwanto
2015-05-28 17:21:24 -04:00
parent 78c638a371
commit 64691c0425
1 changed files with 28 additions and 24 deletions
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52
math/fitting/funcs_pec.py
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@@ -26,18 +26,19 @@ class harm_fit_func(fit_func_base):
Functional form:
E0 + 0.5 * k * (x - re)**2
E0 + 0.5 * k * (x - r0)**2
Coefficients:
* C[0] = energy minimum
* C[1] = spring constant
* C[2] = equilibrium distance
Fitting parameters:
* C[0] = E0 = energy minimum
* C[1] = k = spring constant
* C[2] = r0 = 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
E0, k, r0 = self.get_params(C, *(self.param_names))
xdisp = (x[0] - r0)
y = E0 + 0.5 * k * xdisp**2
self.func_call_hook(C, x, y)
return y
def Guess_xy(self, x, y):
@@ -52,7 +53,7 @@ class harmcube_fit_func(fit_func_base):
Functional form:
E0 + 0.5 * k * (x - re)**2 + cub * (x - re)**3;
E0 + 0.5 * k * (x - re)**2 + c3 * (x - re)**3;
Coefficients:
* C[0] = energy minimum
@@ -63,8 +64,9 @@ class harmcube_fit_func(fit_func_base):
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
E0, k, r0, c3 = self.get_params(C, *(self.param_names))
xdisp = (x[0] - r0)
y = E0 + 0.5 * k * xdisp**2 + c3 * xdisp**3
self.func_call_hook(C, x, y)
return y
def Guess_xy(self, x, y):
@@ -82,13 +84,13 @@ class morse2_fit_func(fit_func_base):
Functional form:
E0 + 0.5 * k / a**2 * (1 - exp(-a * (x - re)))**2
E0 + 0.5 * k / a**2 * (1 - exp(-a * (x - r0)))**2
Coefficients:
* C[0] = energy minimum
* C[1] = spring constant
* C[2] = equilibrium distance
* C[3] = nonlinear constant
* C[0] = E0 = energy minimum
* C[1] = k = spring constant
* C[2] = r0 = equilibrium distance
* C[3] = a = nonlinear constant
"""
dim = 1 # a function with 1-D domain
param_names = ('E0', 'k', 'r0', 'a')
@@ -116,21 +118,23 @@ class ext3Bmorse2_fit_func(fit_func_base):
Functional form:
E0 + 0.5 * k / a**2 * (1 - exp(-a * (x - re)))**2
+ C3 * (1 - exp(-a * (x - re)))**3
E0 + 0.5 * k / a**2 * (1 - exp(-a * (x - r0)))**2
+ C3 * (1 - exp(-a * (x - r0)))**3
Coefficients:
* C[0] = energy minimum
* C[1] = spring constant
* C[2] = equilibrium distance
* C[3] = nonlinear constant
* C[4] = coefficient of cubic term
* C[0] = E0 = energy minimum
* C[1] = k = spring constant
* C[2] = r0 = equilibrium distance
* C[3] = a = nonlinear constant
* C[4] = C3 = coefficient of cubic term
"""
dim = 1 # a function with 1-D domain
param_names = ('E0', 'k', 'r0', 'a', 'C3')
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
E0, k, r0, a, C3 = self.get_params(C, *(self.param_names))
E = 1 - exp(-a * (x[0] - r0))
y = E0 + 0.5 * k / a**2 * E**2 + C3 * E**3
self.func_call_hook(C, x, y)
return y
def Guess_xy(self, x, y):