def fit_gaussian_peak_step_background_2(x, y):
# create data from broadened step
npts = 201
x = np.linspace(0, 10, npts)
y = step(x, amplitude=12.5, center=4.5, sigma=0.88, form='erf')
y = y + np.random.normal(size=npts, scale=0.35)
# create Composite Model using the custom convolution operator
mod = CompositeModel(Model(jump), Model(gaussian), convolve)
pars = mod.make_params(amplitude=1, center=3.5, sigma=1.5, mid=5.0)
# 'mid' and 'center' should be completely correlated, and 'mid' is
# used as an integer index, so a very poor fit variable:
pars['mid'].vary = False
# fit this model to data array y
result = mod.fit(y, params=pars, x=x)
print(result.fit_report())
plot_components = True
# plot results
plt.plot(x, y, 'bo')
if plot_components:
# generate components
comps = result.eval_components(x=x)
plt.plot(x, 10 * comps['jump'], 'k--')
plt.plot(x, 10 * comps['gaussian'], 'r-')
else:
plt.plot(x, result.init_fit, 'k--')
plt.plot(x, result.best_fit, 'r-')
plt.show()
# #<end examples/model_doc3.py>
return fit_fwhm, fit_center, fwhm_err
#!/usr/bin/env python
#<examples/model_doc3.py>
import numpy as np
from lmfit import Model, CompositeModel
from lmfit.lineshapes import step, gaussian
import matplotlib.pyplot as plt
# create data from broadened step
npts = 201
x = np.linspace(0, 10, npts)
y = step(x, amplitude=12.5, center=4.5, sigma=0.88, form='erf')
y = y + np.random.normal(size=npts, scale=0.35)
def jump(x, mid):
"heaviside step function"
o = np.zeros(len(x))
imid = max(np.where(x<=mid)[0])
o[imid:] = 1.0
return o
def convolve(arr, kernel):
# simple convolution of two arrays
npts = min(len(arr), len(kernel))
pad = np.ones(npts)
tmp = np.concatenate((pad*arr[0], arr, pad*arr[-1]))
out = np.convolve(tmp, kernel, mode='valid')
noff = int((len(out) - npts)/2)
return out[noff:noff+npts]
#
#!/usr/bin/env python
#<examples/model_doc3.py>
import numpy as np
from lmfit import Model, CompositeModel
from lmfit.lineshapes import step, gaussian
import matplotlib.pyplot as plt
# create data from broadened step
npts = 201
x = np.linspace(0, 10, npts)
y = step(x, amplitude=12.5, center=4.5, sigma=0.88, form='erf')
y = y + np.random.normal(size=npts, scale=0.35)
def jump(x, mid):
"heaviside step function"
o = np.zeros(len(x))
imid = max(np.where(x <= mid)[0])
o[imid:] = 1.0
return o
def convolve(arr, kernel):
# simple convolution of two arrays
npts = min(len(arr), len(kernel))
pad = np.ones(npts)
tmp = np.concatenate((pad * arr[0], arr, pad * arr[-1]))
out = np.convolve(tmp, kernel, mode='valid')
noff = int((len(out) - npts) / 2)