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teste_6079_01.py
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teste_6079_01.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Jul 4 11:14:02 2016
@author: camacho
"""
import numpy as np
from lmfit.models import GaussianModel, ExponentialModel
import sys
import matplotlib.pyplot as plt
from numpy import sqrt, pi, exp, linspace, loadtxt
from astropy.io import fits
#------------------------------------------------------------------------------
def gaussian(x, amp, cen, wid):
"1-d gaussian: gaussian(x, amp, cen, wid)"
return (amp/(sqrt(2*pi)*wid)) * exp(-(x-cen)**2 /(2*wid**2))
#------------------------------------------------------------------------------
def lmfit_ngauss(x,y, *params):
params = params[0]
mods = []
prefixes = []
for i in range(0, len(params), 3):
pref = "g%02i_" % (i/3)
gauss_i = GaussianModel(prefix=pref)
if i == 0:
pars = gauss_i.guess(y, x=x)
else:
pars.update(gauss_i.make_params())
A = params[i]
l_cen = params[i+1]
sigma = params[i+2]
pars[pref+'amplitude'].set(A)
pars[pref+'center'].set(l_cen)
pars[pref+'sigma'].set(sigma)
mods.append(gauss_i)
prefixes.append(pref)
mod = mods[0]
if len(mods) > 1:
for m in mods[1:]:
mod += m
print mod
init = mod.eval(pars, x=x)
out = mod.fit(y, pars, x=x)
return mod, out, init
#------------------------------------------------------------------------------
def lmfit_ngauss_constrains(x,y, params, constrains):
#params = params[0]
#constrains = constrains[0]
mods = []
prefixes = []
for i in range(0, len(params), 3):
pref = "g%02i_" % (i/3)
gauss_i = GaussianModel(prefix=pref)
if i == 0:
pars = gauss_i.guess(y, x=x)
else:
pars.update(gauss_i.make_params())
A = params[i]
limA = constrains[i]
l_cen = params[i+1]
limL = constrains[i+1]
sigma = params[i+2]
limS = constrains[i+2]
pars[pref+'amplitude'].set(A, min=limA[0], max=limA[1])
pars[pref+'center'].set(l_cen, min=limL[0], max=limL[1])
pars[pref+'sigma'].set(sigma, min=limS[0], max=limS[1])
mods.append(gauss_i)
prefixes.append(pref)
mod = mods[0]
if len(mods) > 1:
for m in mods[1:]:
mod += m
init = mod.eval(pars, x=x)
out = mod.fit(y, pars, x=x)
return mod, out, init
#------------------------------------------------------------------------------
import pylab as pl
data= np.loadtxt('tmp_6079_01.txt',dtype=None)
y=data[:,1]
x=data[:,0]
#figure()
#pl.plot(data[:,0],data[:,1])
#pl.plot(x,y)
#ver onde fazemos corte
#LIMITS SPECTRA FOR FIT: lli: 4537.560 -- llf: 4538.260 <-do ARES
a0=np.where(x<6078.760)
a1=list(a0[-1])
a2=a1[-1]
b0=np.where(x<6079.150)
b1=list(b0[-1])
b2=b1[-1]
#corte
xfinal=x ;yfinal=y
for i in range(0,a2+1):
yfinal[i]=1
for j in range(b2+1,len(y)):
yfinal[j]=1
#figure()
#pl.plot(xfinal,yfinal)
### TENTATIVA DE FIT
#gaussian(x,amplitude,centro,width/largura)
params = [-0.06, 6079.03, 0.07]
constrains = [(-0.8,-0.05), (6079.00,6079.05), (0.08,0.05)]
mod, out, init = lmfit_ngauss_constrains(xfinal,yfinal, params, constrains)
#figure()
pl.plot(xfinal, yfinal)
pl.plot(xfinal, init+1, 'k--')
print(out.fit_report(min_correl=0.9))
pl.plot(xfinal, out.best_fit+1, 'r-')
pl.show()