#!/usr/bin/env python
#<examples/models_doc1.py>
from numpy import loadtxt
from lmfit import fit_report
from lmfit.models import GaussianModel, VoigtModel
import matplotlib.pyplot as plt
data = loadtxt('test_peak.dat')
x = data[:, 0]
y = data[:, 1]
gmodel = GaussianModel()
gmodel.guess_starting_values(y, x=x)
gresult = gmodel.fit(y, x=x)
print 'With Gaussian: '
print fit_report(gresult.params, min_correl=0.25)
print 'Chi-square = %.3f, Reduced Chi-square = %.3f' % (gresult.chisqr, gresult.redchi)
plt.plot(x, y, 'k')
plt.plot(x, 10*(y-gresult.best_fit), 'r-')
vmodel = VoigtModel()
vmodel.guess_starting_values(y, x=x)
vresult = vmodel.fit(y, x=x)
print 'With Voigt: '
print fit_report(vresult.params, min_correl=0.25)
print 'Chi-square = %.3f, Reduced Chi-square = %.3f' % (vresult.chisqr, vresult.redchi)
#!/usr/bin/env python
# <examples/models_doc2.py>
from numpy import loadtxt
from lmfit import fit_report
from lmfit.models import GaussianModel, LinearModel
import matplotlib.pyplot as plt
data = loadtxt("model1d_gauss.dat")
x = data[:, 0]
y = data[:, 1] + x * 0.1 - 3.0
gauss = GaussianModel()
gauss.guess_starting_values(y, x=x)
line = LinearModel()
line.params["slope"].value = 0
line.params["intercept"].value = -1.0
total = gauss + line
result = total.fit(y, x=x)
print fit_report(result.params, min_correl=0.25)
plt.plot(x, y, "bo")
plt.plot(x, result.init_fit, "k--")
plt.plot(x, result.best_fit, "r-")
plt.show()
# <end examples/models_doc2.py>
