* Bug fixes and documentation update.
This commit is contained in:
@@ -490,7 +490,7 @@ class fit_func_base(object):
|
||||
leastsq=dict(xtol=1e-8, epsfcn=1e-6),
|
||||
)
|
||||
fit_default_opts["lmfit:leastsq"] = dict(xtol=1e-8, epsfcn=1e-6)
|
||||
debug = 1
|
||||
debug = 0
|
||||
dbg_params = 1
|
||||
fit_method = 'fmin'
|
||||
fit_opts = fit_default_opts
|
||||
|
||||
@@ -11,10 +11,13 @@
|
||||
wpylib.math.fitting.funcs_pec module
|
||||
A library of simple f(x) functions for PEC fitting
|
||||
|
||||
For use with OO-style x-y curve fitting interface.
|
||||
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):
|
||||
@@ -99,7 +102,7 @@ class morse2_fit_func(fit_func_base):
|
||||
imin = numpy.argmin(y)
|
||||
harm_params = fit_harm(x[0], y)
|
||||
if self.debug >= 10:
|
||||
print "Initial guess by fit_harm gives: ", harm_params
|
||||
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):
|
||||
@@ -134,7 +137,7 @@ class ext3Bmorse2_fit_func(fit_func_base):
|
||||
imin = numpy.argmin(y)
|
||||
harm_params = fit_harm(x[0], y)
|
||||
if self.debug >= 10:
|
||||
print "Initial guess by fit_harm gives: ", harm_params
|
||||
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
|
||||
|
||||
|
||||
@@ -15,6 +15,7 @@ For use with OO-style x-y curve fitting interface.
|
||||
"""
|
||||
|
||||
import numpy
|
||||
from wpylib.math.fitting import fit_func_base
|
||||
|
||||
|
||||
class FermiDirac_fit_func(fit_func_base):
|
||||
|
||||
@@ -15,6 +15,7 @@ For use with OO-style x-y curve fitting interface.
|
||||
"""
|
||||
|
||||
import numpy
|
||||
from wpylib.math.fitting import fit_func_base
|
||||
|
||||
|
||||
# Some simple function fitting--to aid fitting the complex ones later
|
||||
@@ -114,7 +115,7 @@ class linear_leastsq_fit_func(linear_fit_func):
|
||||
# Changed from:
|
||||
# rslt = fit_linear_weighted(x,y,dy)
|
||||
# to:
|
||||
rslt = (x, y, sigma=None)
|
||||
rslt = linregr2d_SZ(x, y, sigma=dy)
|
||||
|
||||
self.last_fit = rslt[1]
|
||||
# Retrofit for API compatibility: not necessarily meaningful
|
||||
@@ -137,6 +138,7 @@ class exp_fit_func(fit_func_base):
|
||||
"""
|
||||
dim = 1 # a function with 1-D domain
|
||||
param_names = ['A', 'B', 'x0']
|
||||
# FIXME: AD HOC PARAMETERS!
|
||||
A_guess = -2.62681
|
||||
B_guess = -9.05046
|
||||
x0_guess = 1.57327
|
||||
@@ -182,6 +184,7 @@ class powx_fit_func(fit_func_base):
|
||||
"""
|
||||
dim = 1 # a function with 1-D domain
|
||||
param_names = ['A', 'B', 'x0']
|
||||
# FIXME: AD HOC PARAMETERS!
|
||||
A_guess = -2.62681
|
||||
B_guess = -9.05046
|
||||
x0_guess = 1.57327
|
||||
|
||||
Reference in New Issue
Block a user