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