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":-5.0, "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, 0.01, x.size)
# ...and save the error bars
yerr = ones_like(x)*0.01
# 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":10.77, "off":0.96, "mu":10.5})
# 'Thaw' those (the order is irrelevant)
gf.thaw(["mu", "sig", "off", "A"])
# We need the order to get the order of bounds right
# This is not necessarily the order in which they are thawed!
print("Free parameter names and their order: ", gf.freeParamNames())
# Use fmin_l_bfgs_b with area restricted to the (-2,0) interval
fr = sco.fmin_l_bfgs_b(gf.chisqr, gf.freeParamVals(), args=(x,y,yerr), \
bounds=((-2.,0), (None,None), (None,None), (None,None)), \
approx_grad=True)
# Set the parameter values to best-fit
gf.setFreeParamVals(fr[0])
gf.parameterSummary()
plt.title("Bad fit because of restricted parameter range")
plt.plot(x, gf.evaluate(x), 'r--')
plt.show()
