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fit_functions.py
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import numpy as np
from scipy import interpolate
import sys
import os
# from OMFITlib_fit import fit_rbf
from splines.pcs_fit_helpers import calculate_mhat, spline_eval
from mtanh_mpfit.mtanh_driver import mtanh_eval
from transport_helpers import my_interp
def linear_interp_1d(in_x, in_t, value, uncertainty, out_x):
final_sig=[]
for time_ind in range(len(in_t)):
excluded_inds=np.isnan(value[time_ind,:])
y=value[time_ind,~excluded_inds]
x=in_x[time_ind,~excluded_inds]
err=uncertainty[time_ind,~excluded_inds]
try:
get_value=my_interp(x,y,kind='linear')
final_sig.append(get_value(out_x))
except:
final_sig.append(np.zeros(len(out_x)))
final_sig=np.array(final_sig)
return final_sig
def csaps_1d(in_x, in_t, value, uncertainty, out_x):
from csaps import csaps
final_sig=[]
for time_ind in range(len(in_t)):
excluded_inds=np.isnan(value[time_ind,:])
y=value[time_ind,~excluded_inds]
x=in_x[time_ind,~excluded_inds]
err=uncertainty[time_ind,~excluded_inds]
inds=np.argsort(x)
x=x[inds]
y=y[inds]
try:
final_sig.append(csaps(x,y,out_x,smooth=0.99995)) #get_value(out_x))
except:
final_sig.append(np.zeros(len(out_x)))
final_sig=np.array(final_sig)
return final_sig
def spline_1d(in_x, in_t, value, uncertainty, out_x):
final_sig=[]
for time_ind in range(len(in_t)):
excluded_inds=np.isnan(value[time_ind,:])
y=value[time_ind,~excluded_inds]
x=in_x[time_ind,~excluded_inds]
err=uncertainty[time_ind,~excluded_inds]
ordered_inds=np.argsort(x)
x=x[ordered_inds]
y=y[ordered_inds]
try:
get_value=interpolate.UnivariateSpline(x,y)
final_sig.append(get_value(out_x))
except:
final_sig.append(np.zeros(len(out_x)))
final_sig=np.array(final_sig)
return final_sig
def pcs_spline_1d(in_x, in_t, value, uncertainty, out_x):
final_sig=[]
for time_ind in range(len(in_t)):
excluded_inds=np.isnan(value[time_ind,:])
y=value[time_ind,~excluded_inds]
x=in_x[time_ind,~excluded_inds]
err=uncertainty[time_ind,~excluded_inds]
(mPsi,mHat)=calculate_mhat(x,y,p=0.5,dxMin=0.01)
mPsi=mPsi[:-1] # still not sure why the last index is always 0, should talk to ricardo (TODO)
mHat=mHat[:-1]
splined_rot=spline_eval(mPsi,mHat,len(mHat))
get_rot=my_interp(np.linspace(0,1.2,121),splined_rot,kind='linear')
final_sig.append(get_rot(out_x))
final_sig=np.array(final_sig)
return final_sig
def pcs_mtanh_1d(in_x, in_t, value, uncertainty, out_x):
final_sig=[]
for time_ind in range(len(in_t)):
import pdb; pdb.set_trace()
excluded_inds=np.isnan(value[time_ind,:])
y=value[time_ind,~excluded_inds]
x=in_x[time_ind,~excluded_inds]
err=uncertainty[time_ind,~excluded_inds]
(mtanh_psi,mtanh_signal)=mtanh_eval(x,y,err)
get_signal=my_interp(mtanh_psi,mtanh_signal,kind='linear')
final_sig.append(get_signal(out_x))
final_sig=np.array(final_sig)
return final_sig
def nn_interp_2d(in_x, in_t, value, uncertainty, out_x, out_t):
final_sig=[]
excluded_inds=np.isnan(value)
y=value[~excluded_inds]
x=in_x[~excluded_inds]
t=in_t[~excluded_inds]
err=uncertainty[~excluded_inds]
def scale(signal, basis):
return (signal-min(basis))/(max(basis)-min(basis))
t_scaled=scale(t,out_t)
out_t_scaled=scale(out_t,out_t)
x_scaled=scale(x,[0,1]) #out_x)
out_x_scaled=scale(out_x,[0,1]) #out_x)
get_value=interpolate.NearestNDInterpolator(np.stack((t_scaled,x_scaled)).T,y)
import itertools
coords=np.array(list(itertools.product(out_t_scaled,out_x_scaled))).T
final_sig=get_value(coords[0],coords[1])
final_sig=final_sig.reshape((len(out_t_scaled),len(out_x_scaled)))
return final_sig
def linear_interp_2d(in_x, in_t, value, uncertainty, out_x, out_t):
final_sig=[]
excluded_inds=np.isnan(value)
y=value[~excluded_inds]
x=in_x[~excluded_inds]
t=in_t[~excluded_inds]
inds=np.random.permutation(list(range(len(y))))
y=y[inds[:100]]
x=x[inds[:100]]
t=t[inds[:100]]
err=uncertainty[~excluded_inds]
def scale(signal, basis):
return (signal-min(basis))/(max(basis)-min(basis))
t_scaled=scale(t,out_t)
out_t_scaled=scale(out_t,out_t)
x_scaled=scale(x,[0,1]) #out_x)
out_x_scaled=scale(out_x,[0,1]) #out_x)
get_value=interpolate.interp2d(t_scaled,x_scaled,y,
bounds_error=False,
kind='linear')
final_sig=get_value(out_t_scaled,out_x_scaled).T
return final_sig
# def rbf_interp_2d_new(in_x, in_t, value, uncertainty, out_x, out_t, debug=False):
# return fit_rbf(in_x, in_t, value, uncertainty, out_x, out_t)
def rbf_interp_2d(in_x, in_t, value, uncertainty, out_x, out_t, debug=False):
final_sig=[]
excluded_inds=np.isnan(value)
y=value[~excluded_inds]
x=in_x[~excluded_inds]
t=in_t[~excluded_inds]
err=uncertainty[~excluded_inds]
n=100
inds=np.random.permutation(list(range(len(y))))
y=y[inds[:n]]
x=x[inds[:n]]
t=t[inds[:n]]
def scale(signal, basis):
return (signal-min(basis))/(max(basis)-min(basis))
t_scaled=scale(t,out_t)
out_t_scaled=scale(out_t,out_t)
x_scaled=scale(x,[0,1]) #out_x)
out_x_scaled=scale(out_x,[0,1]) #out_x)
get_value=interpolate.Rbf(t_scaled,x_scaled,y, function='Gaussian', epsilon=.1) #,metric='seuclidean')
import itertools
coords=np.array(list(itertools.product(out_t_scaled,out_x_scaled))).T
final_sig=get_value(coords[0],coords[1])
final_sig=final_sig.reshape((len(out_t_scaled),len(out_x_scaled)))
return final_sig
def mtanh_1d(in_x, in_t, value, uncertainty, out_x):
sys.path.append(os.path.join(os.path.dirname(__file__),'astrolibpy/mpfit/'))
from mpfit import mpfit
p0=np.array([1.0, 3.0, 0.01, 1.0, 0.01],dtype='float64') #initial conditions
parinfo=[]
parinfo.append({'value':p0[0],
'fixed':0,
'limited':[1,0], #ped height positive
'limits':[0.1,0]}) #ped height positive
parinfo.append({'value':p0[1],
'fixed':0,
'limited':[1,0], #offset positive
'limits':[0.001,0]}) #offset positive
parinfo.append({'value':p0[2],
'fixed':0,
'limited':[1,0], #core slope positive
'limits':[0.0001,0]}) #core slope positive
parinfo.append({'value':p0[3],
'fixed':0,
'limited':[1,1], #sym point pos between rho=[0.85,1.15]
'limits':[.85,1.15]}) #sym point pos between rho=[0.85,1.15]
parinfo.append({'value':p0[4],
'fixed':0,
'limited':[1,0], #make width pos and >=0.01 to avoid inf from exp(z)
'limits':[0.01,0]}) #make width pos and >=0.01 to avoid inf from exp(z)
# if len(error)!=0:
# error=np.array(error)
# uncertainty=np.array(uncertainty)
# value=np.array(value)
# psi=np.array(psi)
# max_uncertainty=np.nanmax(uncertainty)
# uncertainty[np.isclose(value,0)]=1e30
# uncertainty[np.isclose(uncertainty,0)]=1e30
final_sig=[]
for ind in range(len(in_t)):
excluded_inds=np.isnan(value[ind,:])
fa = {'x': in_x[ind,~excluded_inds], 'y': value[ind,~excluded_inds], 'err': uncertainty[ind,~excluded_inds]}
m = mpfit(my_shifted_mtanh, p0, parinfo=parinfo, functkw=fa, quiet=1)
final_sig.append(shifted_mtanh(out_x,m.params))
return np.array(final_sig)
def mtanh(alpha,z):
return ((1+alpha*z)*np.exp(z) - np.exp(-z)) / (np.exp(z) + np.exp(-z))
def shifted_mtanh(x,p):
a=p[0]
b=p[1]
alpha=p[2]
xsym=p[3]
hwid=p[4]
z = (xsym-x)/hwid
y = a * mtanh(alpha,z) + b
return y
def my_shifted_mtanh(p, fjac=None, x=None, y=None, err=None):
# Parameter values are passed in "p"
# If fjac==None then partial derivatives should not be
# computed. It will always be None if MPFIT is called with default
# flag.
model = shifted_mtanh(x, p)
# Non-negative status value means MPFIT should continue, negative means
# stop the calculation.
status = 0
return [status, (y-model)/err]