from __future__ import print_function, division
from numpy import arange, sqrt, exp, pi, random, ones_like
import matplotlib.pylab as plt
from PyAstronomy import funcFit2 as fuf2
import scipy.optimize as sco
random.seed(1234)
# Creating a Gaussian with some noise
# Choose some parameters...
gPar = {"A":1.0, "sig":10.0, "mu":10.0, "off":1.0, "lin":0.0}
# Calculate profile
x = arange(100) - 50.0
y = gPar["off"] + gPar["A"] / sqrt(2*pi*gPar["sig"]**2) \
* exp(-(x-gPar["mu"])**2/(2*gPar["sig"]**2))
# Add some noise...
y += random.normal(0.0, 0.002, x.size)
# ...and save the error bars
yerr = ones_like(x)*0.002
# Let us see what we have done...
plt.plot(x, y, 'bp')
# Create a model object
gf = fuf2.GaussFit()
# Set guess values for the parameters
gf.assignValues({"A":3, "sig":3.77, "off":0.96, "mu":10.5})
# 'Thaw' those (the order is irrelevant)
gf.thaw(["mu", "sig", "off", "A"])
# Restrict the valid range for sigma
gf.setRestriction({"sig":[0.01,5]})
# The convenience function 'fitfmin_l_bfgs_b1d' automatically channels
# the restrictions from the model to the algorithm.
fuf2.fitfmin_l_bfgs_b(gf, gf.chisqr, x, y, yerr=yerr)
gf.parameterSummary()
plt.title("Bad fit because of parameter restrictions")
plt.plot(x, gf.evaluate(x), 'r--')
plt.show()
