def test_constraints1():
def residual(pars, x, sigma=None, data=None):
yg = gaussian(x, pars['amp_g'], pars['cen_g'], pars['wid_g'])
yl = lorentzian(x, pars['amp_l'], pars['cen_l'], pars['wid_l'])
model = yg + yl + pars['line_off'] + x * pars['line_slope']
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data)/sigma
n = 601
xmin = 0.
xmax = 20.0
x = linspace(xmin, xmax, n)
data = (gaussian(x, 21, 8.1, 1.2) +
lorentzian(x, 10, 9.6, 2.4) +
random.normal(scale=0.23, size=n) +
x*0.5)
pfit = Parameters()
pfit.add(name='amp_g', value=10)
pfit.add(name='cen_g', value=9)
pfit.add(name='wid_g', value=1)
pfit.add(name='amp_tot', value=20)
pfit.add(name='amp_l', expr='amp_tot - amp_g')
pfit.add(name='cen_l', expr='1.5+cen_g')
pfit.add(name='wid_l', expr='2*wid_g')
pfit.add(name='line_slope', value=0.0)
pfit.add(name='line_off', value=0.0)
sigma = 0.021 # estimate of data error (for all data points)
myfit = Minimizer(residual, pfit,
fcn_args=(x,), fcn_kws={'sigma':sigma, 'data':data},
scale_covar=True)
myfit.prepare_fit()
init = residual(myfit.params, x)
result = myfit.leastsq()
print(' Nfev = ', result.nfev)
print( result.chisqr, result.redchi, result.nfree)
report_fit(result.params)
pfit= result.params
fit = residual(result.params, x)
assert(pfit['cen_l'].value == 1.5 + pfit['cen_g'].value)
assert(pfit['amp_l'].value == pfit['amp_tot'].value - pfit['amp_g'].value)
assert(pfit['wid_l'].value == 2 * pfit['wid_g'].value)
def residual(pars, x, sigma=None, data=None):
yg = gaussian(x, pars['amp_g'], pars['cen_g'], pars['wid_g'])
yl = lorentzian(x, pars['amp_l'], pars['cen_l'], pars['wid_l'])
model = yg + yl + pars['line_off'] + x * pars['line_slope']
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data) / sigma
def residual(pars, x, sigma=None, data=None):
yg = gaussian(x, pars['amp_g'], pars['cen_g'], pars['wid_g'])
yl = lorentzian(x, pars['amp_l'], pars['cen_l'], pars['wid_l'])
model = yg + yl + pars['line_off'] + x * pars['line_slope']
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data) / sigma
def residual(pars, x, data):
model = gaussian(x,
pars['amp_g'].value,
pars['cen_g'].value,
pars['wid_g'].value)
model += lorentzian(x,
pars['amp_l'].value,
pars['cen_l'].value,
pars['wid_l'].value)
return (model - data)
def residual_GC(pars, x, sigma=None, data=None):
yg = gaussian(x) #, pars['amp_g'], pars['cen_g'], pars['wid_g'])
yl = lorentzian(x) #, pars['amp_l'], pars['cen_l'], pars['wid_l'])
slope = pars['line_slope']
offset = pars['line_off']
model = yg + yl + offset + x * slope
if data is None:
return model
if sigma is None:
return (model - data / sigma)
def conv_rotations_pvoigt(left, right, params, **kwargs):
"""Convolution between the rotation model and a pseudo-Voigt profile."""
# set left to rotations component if not so
if left.func.__name__ == "pvoigt":
left, right = (right, left)
lp, rp, x, q, out, lglob, rglob = _getVariables(
left, right, params, **kwargs
)
for qId, qVal in enumerate(q):
amplitude = (
lp["amplitude"]
if "amplitude" in lglob
else lp["amplitude_%i" % qId]
)
amplitude *= (
rp["amplitude"]
if "amplitude" in rglob
else rp["amplitude_%i" % qId]
)
center = lp["center_%i" % qId] + rp["center_%i" % qId]
frac = rp["fraction_%i" % qId]
lsigma = lp["sigma"] if "sigma" in lglob else lp["sigma_%i" % qId]
rsigma = rp["sigma_%i" % qId]
sigma_v = rsigma / np.sqrt(2 * np.log(2))
bondDist = lp["bondDist"]
eisf = spherical_jn(0, bondDist * qVal) ** 2
eisf *= pvoigt(x, amplitude, center, rsigma, frac)
qisf = np.sum(
[
spherical_jn(i, bondDist * qVal) ** 2
* (2 * i + 1)
* (
frac
* voigt(
x, amplitude, center, sigma_v, i * (i + 1) * lsigma
)
* (1 - frac)
* lorentzian(
amplitude, center, rsigma + i * (i + 1) * lsigma
)
)
for i in range(1, 5)
],
axis=0,
)
out.append(eisf + qisf)
return np.array(out)
def residual(pars, x, sigma=None, data=None):
yg = gaussian(x, pars['amp_g'].value, pars['cen_g'].value,
pars['wid_g'].value)
yl = lorentzian(x, pars['amp_l'].value, pars['cen_l'].value,
pars['wid_l'].value)
slope = pars['line_slope'].value
offset = pars['line_off'].value
model = yg + yl + offset + x * slope
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data) / sigma
def residual(pars, x, sigma=None, data=None):
yg = gaussian(x, pars['amp_g'].value,
pars['cen_g'].value, pars['wid_g'].value)
yl = lorentzian(x, pars['amp_l'].value,
pars['cen_l'].value, pars['wid_l'].value)
slope = pars['line_slope'].value
offset = pars['line_off'].value
model = yg + yl + offset + x * slope
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data)/sigma
def test_constraints1():
def residual(pars, x, sigma=None, data=None):
yg = gaussian(x, pars['amp_g'], pars['cen_g'], pars['wid_g'])
yl = lorentzian(x, pars['amp_l'], pars['cen_l'], pars['wid_l'])
model = yg + yl + pars['line_off'] + x * pars['line_slope']
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data)/sigma
n = 601
xmin = 0.
xmax = 20.0
x = linspace(xmin, xmax, n)
data = (gaussian(x, 21, 8.1, 1.2) +
lorentzian(x, 10, 9.6, 2.4) +
random.normal(scale=0.23, size=n) +
x*0.5)
pfit = Parameters()
pfit.add(name='amp_g', value=10)
pfit.add(name='cen_g', value=9)
pfit.add(name='wid_g', value=1)
pfit.add(name='amp_tot', value=20)
pfit.add(name='amp_l', expr='amp_tot - amp_g')
pfit.add(name='cen_l', expr='1.5+cen_g')
pfit.add(name='wid_l', expr='2*wid_g')
pfit.add(name='line_slope', value=0.0)
pfit.add(name='line_off', value=0.0)
sigma = 0.021 # estimate of data error (for all data points)
myfit = Minimizer(residual, pfit,
fcn_args=(x,), fcn_kws={'sigma': sigma, 'data': data},
scale_covar=True)
myfit.prepare_fit()
result = myfit.leastsq()
pfit = result.params
assert(pfit['cen_l'].value == 1.5 + pfit['cen_g'].value)
assert(pfit['amp_l'].value == pfit['amp_tot'].value - pfit['amp_g'].value)
assert(pfit['wid_l'].value == 2 * pfit['wid_g'].value)
def test_guess_from_peak():
"""Regression test for guess_from_peak function (see GH #627)."""
x = np.linspace(-5, 5)
amplitude = 0.8
center = 1.7
sigma = 0.3
y = lineshapes.lorentzian(x, amplitude=amplitude, center=center, sigma=sigma)
model = models.LorentzianModel()
guess_increasing_x = model.guess(y, x=x)
guess_decreasing_x = model.guess(y[::-1], x=x[::-1])
assert guess_increasing_x == guess_decreasing_x
for param, value in zip(['amplitude', 'center', 'sigma'],
[amplitude, center, sigma]):
assert np.abs((guess_increasing_x[param].value - value)/value) < 0.5
def minimizer():
"""Return the Minimizer object."""
def residual(pars, x, sigma=None, data=None):
"""Define objective function."""
yg = gaussian(x, pars['amp_g'], pars['cen_g'], pars['wid_g'])
yl = lorentzian(x, pars['amp_l'], pars['cen_l'], pars['wid_l'])
model = yg + yl + pars['line_off'] + x * pars['line_slope']
if data is None:
return model
if sigma is None:
return model - data
return (model-data) / sigma
# generate synthetic data
n = 601
xmin = 0.
xmax = 20.0
x = np.linspace(xmin, xmax, n)
data = (gaussian(x, 21, 8.1, 1.2) + lorentzian(x, 10, 9.6, 2.4) +
np.random.normal(scale=0.23, size=n) + x*0.5)
# create initial Parameters
pars = Parameters()
pars.add(name='amp_g', value=10)
pars.add(name='cen_g', value=9)
pars.add(name='wid_g', value=1)
pars.add(name='amp_tot', value=20)
pars.add(name='amp_l', expr='amp_tot - amp_g')
pars.add(name='cen_l', expr='1.5+cen_g')
pars.add(name='wid_l', expr='2*wid_g')
pars.add(name='line_slope', value=0.0)
pars.add(name='line_off', value=0.0)
sigma = 0.021 # estimate of data error (for all data points)
mini = Minimizer(residual, pars, fcn_args=(x,), fcn_kws={'sigma': sigma,
'data': data})
return mini
def lorentzian2d(x,
y,
amplitude=1.,
centerx=0.,
centery=0.,
sigmax=1.,
sigmay=1.,
rotation=0):
"""Return a two dimensional lorentzian.
The maximum of the peak occurs at ``centerx`` and ``centery``
with widths ``sigmax`` and ``sigmay`` in the x and y directions
respectively. The peak can be rotated by choosing the value of ``rotation``
in radians.
"""
xp = (x - centerx) * np.cos(rotation) - (y - centery) * np.sin(rotation)
yp = (x - centerx) * np.sin(rotation) + (y - centery) * np.cos(rotation)
R = (xp / sigmax)**2 + (yp / sigmay)**2
return 2 * amplitude * lorentzian(R) / (np.pi * sigmax * sigmay)
def conv_lorentzian_lorentzian(left, right, params, **kwargs):
r"""Convolution between two Lorentzians.
.. math::
a_1 \mathcal{L}_{\sigma_1, center_1}
\otimes a_2 \mathcal{L}_{sigma_2, center_2} =
a_1 a_2 . \mathcal{L}_{\sigma_1 + \sigma_2, center_1 + center_2}
"""
lp, rp, x, q, out, lglob, rglob = _getVariables(
left, right, params, **kwargs
)
for qId, qVal in enumerate(q):
amplitude = (
lp["amplitude"]
if "amplitude" in lglob
else lp["amplitude_%i" % qId]
)
amplitude *= (
rp["amplitude"]
if "amplitude" in rglob
else rp["amplitude_%i" % qId]
)
center = lp["center_%i" % qId] + rp["center_%i" % qId]
lsigma = (
lp["sigma"] * qVal ** 2
if "sigma" in lglob
else lp["sigma_%i" % qId]
)
rsigma = (
rp["sigma"] * qVal ** 2
if "sigma" in rglob
else lp["sigma_%i" % qId]
)
out.append(lorentzian(x, amplitude, center, lsigma + rsigma))
return np.array(out)
def conv_jumpdiff_pvoigt(left, right, params, **kwargs):
"""Convolution between the jump diffusion model and a
pseudo-Voigt profile.
"""
# set left to jump_diff component if not so
if left.func.__name__ == "pvoigt":
left, right = (right, left)
lp, rp, x, q, out, lglob, rglob = _getVariables(
left, right, params, **kwargs
)
for qId, qVal in enumerate(q):
amplitude = (
lp["amplitude"]
if "amplitude" in lglob
else lp["amplitude_%i" % qId]
)
amplitude *= (
rp["amplitude"]
if "amplitude" in rglob
else rp["amplitude_%i" % qId]
)
center = lp["center_%i" % qId] + rp["center_%i" % qId]
frac = rp["fraction_%i" % qId]
lsigma = (
lp["sigma"] * qVal ** 2 / (1 + lp["sigma"] * qVal ** 2 * lp["tau"])
if "sigma" in lglob
else lp["sigma_%i" % qId]
)
rsigma = rp["sigma_%i" % qId]
sigma_v = rsigma / np.sqrt(2 * np.log(2))
out.append(
frac * voigt(x, amplitude, center, sigma_v, lsigma)
+ (1 - frac) * lorentzian(x, amplitude, center, lsigma + rsigma)
)
return np.array(out)
def plot_fit(out):
fig, ax = plt.subplots(
2, 1, sharex=True, figsize=(12, 6), gridspec_kw={"height_ratios": [1, 4]}
)
xd = out.userkws[out.model.independent_vars[0]]
xnew = np.linspace(xd[0], xd[-1], 1000)
ax[0].plot(xd, out.residual, "k-", ms=3, lw=1, alpha=0.5)
ax[1].plot(xd, out.data, "b+-", ms=3, lw=1, alpha=0.8)
ax[1].plot(xnew, out.eval(x=xnew), "k--", lw=1.5)
for i, p in enumerate(out.peak_pos):
# print(out.params['p%s_'])
ax[1].plot(
xnew,
lorentzian(
xnew,
amplitude=out.params["p%s_amplitude" % i],
center=out.params["p%s_center" % i],
sigma=out.params["p%s_sigma" % i],
),
lw=0.5,
)
return fig
import matplotlib.pyplot as plt
import numpy as np
from lmfit import Minimizer, Parameters, report_fit
from lmfit.lineshapes import gaussian, lorentzian
def residual(pars, x, data):
model = (gaussian(x, pars['amp_g'], pars['cen_g'], pars['wid_g']) +
lorentzian(x, pars['amp_l'], pars['cen_l'], pars['wid_l']))
return model - data
np.random.seed(0)
x = np.linspace(0, 20.0, 601)
data = (gaussian(x, 21, 6.1, 1.0) + lorentzian(x, 10, 9.6, 1.3) +
np.random.normal(scale=0.1, size=x.size))
pfit = Parameters()
# pfit.add(name='amp_g', value=10)
# pfit.add(name='amp_l', value=10)
# pfit.add(name='cen_g', value=5)
# pfit.add(name='peak_split', value=2.5, min=0, max=5, vary=True)
# pfit.add(name='cen_l', expr='peak_split+cen_g')
# pfit.add(name='wid_g', value=1)
# pfit.add(name='wid_l', expr='wid_g')
pfit.add(name='amp_g', value=10)
pfit.add(name='amp_l', value=10)
pfit.add(name='cen_g', value=5)
pfit.add(name='cen_l', value=10)
def test_constraints2():
"""add a user-defined function to symbol table"""
def residual(pars, x, sigma=None, data=None):
yg = gaussian(x, pars['amp_g'].value,
pars['cen_g'].value, pars['wid_g'].value)
yl = lorentzian(x, pars['amp_l'].value,
pars['cen_l'].value, pars['wid_l'].value)
slope = pars['line_slope'].value
offset = pars['line_off'].value
model = yg + yl + offset + x * slope
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data)/sigma
n = 601
xmin = 0.
xmax = 20.0
x = linspace(xmin, xmax, n)
data = (gaussian(x, 21, 8.1, 1.2) +
lorentzian(x, 10, 9.6, 2.4) +
random.normal(scale=0.23, size=n) +
x*0.5)
pfit = Parameters()
pfit.add(name='amp_g', value=10)
pfit.add(name='cen_g', value=9)
pfit.add(name='wid_g', value=1)
pfit.add(name='amp_tot', value=20)
pfit.add(name='amp_l', expr='amp_tot - amp_g')
pfit.add(name='cen_l', expr='1.5+cen_g')
pfit.add(name='line_slope', value=0.0)
pfit.add(name='line_off', value=0.0)
sigma = 0.021 # estimate of data error (for all data points)
myfit = Minimizer(residual, pfit,
fcn_args=(x,), fcn_kws={'sigma':sigma, 'data':data},
scale_covar=True)
def width_func(wpar):
""" """
return 2*wpar
myfit.params._asteval.symtable['wfun'] = width_func
try:
myfit.params.add(name='wid_l', expr='wfun(wid_g)')
except:
assert(False)
result = myfit.leastsq()
print(' Nfev = ', result.nfev)
print( result.chisqr, result.redchi, result.nfree)
report_fit(result.params)
pfit= result.params
fit = residual(result.params, x)
assert(pfit['cen_l'].value == 1.5 + pfit['cen_g'].value)
assert(pfit['amp_l'].value == pfit['amp_tot'].value - pfit['amp_g'].value)
assert(pfit['wid_l'].value == 2 * pfit['wid_g'].value)
offset = pars['line_off'].value
model = yg + yl + offset + x * slope
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data)/sigma
n = 601
xmin = 0.
xmax = 20.0
x = linspace(xmin, xmax, n)
data = (gaussian(x, 21, 8.1, 1.2) +
lorentzian(x, 10, 9.6, 2.4) +
random.normal(scale=0.23, size=n) +
x*0.5)
if HASPYLAB:
pylab.plot(x, data, 'r+')
pfit = [Parameter(name='amp_g', value=10),
Parameter(name='cen_g', value=9),
Parameter(name='wid_g', value=1),
Parameter(name='amp_tot', value=20),
Parameter(name='amp_l', expr='amp_tot - amp_g'),
Parameter(name='cen_l', expr='1.5+cen_g'),
Parameter(name='wid_l', expr='2*wid_g'),
def mypvoigt(x, amp, center, fwhm, frac):
sig = fwhm * 0.424660900144
return ((1 - frac) * gaussian(x, amp, center, sig) +
frac * lorentzian(x, amp, center, sig))
# Test that FT{Gaussian} = Gaussian, and FT{exponential} = Lorentzian
x = np.arange(-0.1, 0.5, 0.0004) # energy domain, in meV
# items are (tau, beta, intensities). Assumed that tau unit is picoseconds
trios = [
(20.0, 2.0,
gaussian(x,
amplitude=1.0,
center=0.0,
sigma=planck_constant / (np.sqrt(2.0) * 20.0 * np.pi))),
(
100.0,
1.0,
# sigma below is actually the HWHM
lorentzian(x,
amplitude=1.0,
center=0.0,
sigma=planck_constant / (2 * np.pi * 100.0)))
]
@pytest.mark.parametrize('tau, beta, y', trios)
def test_lineshapes(tau, beta, y):
model = StretchedExponentialFTModel()
params = model.guess(y, x=None)
# Stray away from optimal parameters
[
params[name].set(value=val) for name, val in dict(
amplitude=3.0, center=0.0002, tau=50.0, beta=1.5).items()
]
r = model.fit(y, params, x=x)
all_params = 'tau beta amplitude center'.split()
pars['amp_g'].value,
pars['cen_g'].value,
pars['wid_g'].value)
model += lorentzian(x,
pars['amp_l'].value,
pars['cen_l'].value,
pars['wid_l'].value)
return (model - data)
n = 601
random.seed(0)
x = linspace(0, 20.0, n)
data = (gaussian(x, 21, 6.1, 1.2) +
lorentzian(x, 10, 9.6, 1.3) +
random.normal(scale=0.1, size=n))
pfit = Parameters()
pfit.add(name='amp_g', value=10)
pfit.add(name='amp_l', value=10)
pfit.add(name='cen_g', value=5)
pfit.add(name='peak_split', value=2.5, min=0, max=5, vary=True)
pfit.add(name='cen_l', expr='peak_split+cen_g')
pfit.add(name='wid_g', value=1)
pfit.add(name='wid_l', expr='wid_g')
mini = Minimizer(residual, pfit, fcn_args=(x, data))
out = mini.leastsq()
report_fit(out.params)
def test_constraints(with_plot=True):
with_plot = with_plot and WITHPLOT
def residual(pars, x, sigma=None, data=None):
yg = gaussian(x, pars['amp_g'].value, pars['cen_g'].value,
pars['wid_g'].value)
yl = lorentzian(x, pars['amp_l'].value, pars['cen_l'].value,
pars['wid_l'].value)
slope = pars['line_slope'].value
offset = pars['line_off'].value
model = yg + yl + offset + x * slope
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data) / sigma
n = 201
xmin = 0.
xmax = 20.0
x = linspace(xmin, xmax, n)
data = (gaussian(x, 21, 8.1, 1.2) + lorentzian(x, 10, 9.6, 2.4) +
random.normal(scale=0.23, size=n) + x * 0.5)
if with_plot:
pylab.plot(x, data, 'r+')
pfit = Parameters()
pfit.add(name='amp_g', value=10)
pfit.add(name='cen_g', value=9)
pfit.add(name='wid_g', value=1)
pfit.add(name='amp_tot', value=20)
pfit.add(name='amp_l', expr='amp_tot - amp_g')
pfit.add(name='cen_l', expr='1.5+cen_g')
pfit.add(name='wid_l', expr='2*wid_g')
pfit.add(name='line_slope', value=0.0)
pfit.add(name='line_off', value=0.0)
sigma = 0.021 # estimate of data error (for all data points)
myfit = Minimizer(residual,
pfit,
fcn_args=(x, ),
fcn_kws={
'sigma': sigma,
'data': data
},
scale_covar=True)
myfit.prepare_fit()
init = residual(myfit.params, x)
myfit.leastsq()
print(' Nfev = ', myfit.nfev)
print(myfit.chisqr, myfit.redchi, myfit.nfree)
report_fit(myfit.params, min_correl=0.3)
fit = residual(myfit.params, x)
if with_plot:
pylab.plot(x, fit, 'b-')
assert (myfit.params['cen_l'].value == 1.5 + myfit.params['cen_g'].value)
assert (myfit.params['amp_l'].value == myfit.params['amp_tot'].value -
myfit.params['amp_g'].value)
assert (myfit.params['wid_l'].value == 2 * myfit.params['wid_g'].value)
# now, change fit slightly and re-run
myfit.params['wid_l'].expr = '1.25*wid_g'
myfit.leastsq()
report_fit(myfit.params, min_correl=0.4)
fit2 = residual(myfit.params, x)
if with_plot:
pylab.plot(x, fit2, 'k')
pylab.show()
assert (myfit.params['cen_l'].value == 1.5 + myfit.params['cen_g'].value)
assert (myfit.params['amp_l'].value == myfit.params['amp_tot'].value -
myfit.params['amp_g'].value)
assert (myfit.params['wid_l'].value == 1.25 * myfit.params['wid_g'].value)
def test_constraints2():
"""add a user-defined function to symbol table"""
def residual(pars, x, sigma=None, data=None):
yg = gaussian(x, pars['amp_g'].value, pars['cen_g'].value,
pars['wid_g'].value)
yl = lorentzian(x, pars['amp_l'].value, pars['cen_l'].value,
pars['wid_l'].value)
slope = pars['line_slope'].value
offset = pars['line_off'].value
model = yg + yl + offset + x * slope
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data) / sigma
n = 601
xmin = 0.
xmax = 20.0
x = linspace(xmin, xmax, n)
data = (gaussian(x, 21, 8.1, 1.2) + lorentzian(x, 10, 9.6, 2.4) +
random.normal(scale=0.23, size=n) + x * 0.5)
pfit = [
Parameter(name='amp_g', value=10),
Parameter(name='cen_g', value=9),
Parameter(name='wid_g', value=1),
Parameter(name='amp_tot', value=20),
Parameter(name='amp_l', expr='amp_tot - amp_g'),
Parameter(name='cen_l', expr='1.5+cen_g'),
Parameter(name='line_slope', value=0.0),
Parameter(name='line_off', value=0.0)
]
sigma = 0.021 # estimate of data error (for all data points)
myfit = Minimizer(residual,
pfit,
fcn_args=(x, ),
fcn_kws={
'sigma': sigma,
'data': data
},
scale_covar=True)
def width_func(wpar):
""" """
return 2 * wpar
myfit.params._asteval.symtable['wfun'] = width_func
myfit.params.add(name='wid_l', expr='wfun(wid_g)')
myfit.leastsq()
print(' Nfev = ', myfit.nfev)
print(myfit.chisqr, myfit.redchi, myfit.nfree)
report_fit(myfit.params)
pfit = myfit.params
fit = residual(myfit.params, x)
assert (pfit['cen_l'].value == 1.5 + pfit['cen_g'].value)
assert (pfit['amp_l'].value == pfit['amp_tot'].value - pfit['amp_g'].value)
assert (pfit['wid_l'].value == 2 * pfit['wid_g'].value)
def residual(pars, x, data):
model = (gaussian(x, pars['amp_g'], pars['cen_g'], pars['wid_g']) +
lorentzian(x, pars['amp_l'], pars['cen_l'], pars['wid_l']))
return model - data
out = mod.fit(yn, pars, x=xs)
out2 = mod.fit(yn, pars, x=xs, fit_kws={'Dfun': dfunc_gaussian,
'col_deriv': 1})
print('lmfit without dfunc **************')
print('number of function calls: ', out.nfev)
print('params', out.best_values)
print('lmfit with dfunc *****************')
print('number of function calls: ', out2.nfev)
print('params', out2.best_values)
print('\n \n')
out2.plot(datafmt='.')
print('**********************************')
print('***** Test Lorentzian ************')
print('**********************************')
ys = lorentzian(xs, 2.5, 0, 0.5)
yn = ys + 0.1*np.random.normal(size=len(xs))
mod = LorentzianModel()
pars = mod.guess(yn, xs)
out = mod.fit(yn, pars, x=xs)
out2 = mod.fit(yn, pars, x=xs, fit_kws={'Dfun': dfunc_lorentzian, 'col_deriv': 1})
print('lmfit without dfunc **************')
print('number of function calls: ', out.nfev)
print('params', out.best_values)
print('lmfit with dfunc *****************')
print('number of function calls: ', out2.nfev)
print('params', out2.best_values)
print('\n \n')
out2.plot(datafmt='.')
def teixeira_water(x, amplitude=1.0, center=1.0, tau=1.0, dcf=1.0, q=1.0):
dq2 = dcf * q * q
hwhm = hbar * dq2 / (1 + tau * dq2)
return lorentzian(x, amplitude=amplitude, center=center, sigma=hwhm)
def residual(pars, x, data):
model = gaussian(x, pars['amp_g'].value, pars['cen_g'].value,
pars['wid_g'].value)
model += lorentzian(x, pars['amp_l'].value, pars['cen_l'].value,
pars['wid_l'].value)
return (model - data)
tau = max(y) / amplitude # assumed beta==1.0
return self.make_params(amplitude=amplitude,
center=center,
tau=tau,
beta=beta)
# Test that FT{Gaussian} = Gaussian, and FT{exponential} = Lorentzian
x = np.arange(-0.1, 0.5, 0.0004) # energy domain, in meV
# items are (tau, beta, intensities). Assumed that tau unit is picoseconds
trios = [(20.0, 2.0,
gaussian(x, amplitude=1.0, center=0.0,
sigma=planck_constant/(np.sqrt(2.0)*20.0*np.pi))),
(100.0, 1.0,
# sigma below is actually the HWHM
lorentzian(x, amplitude=1.0, center=0.0,
sigma=planck_constant / (2 * np.pi * 100.0)))
]
@pytest.mark.parametrize('tau, beta, y', trios)
def test_lineshapes(tau, beta, y):
model = StretchedExponentialFTModel()
params = model.guess(y, x=None)
# Stray away from optimal parameters
[params[name].set(value=val) for name, val in
dict(amplitude=3.0, center=0.0002, tau=50.0, beta=1.5).items()]
r = model.fit(y, params, x=x)
all_params = 'tau beta amplitude center'.split()
assert_allclose([tau, beta, 1.0, 0.0],
[r.params[p].value for p in all_params],
rtol=0.1, atol=0.000001)
def test_constraints():
def residual(pars, x, sigma=None, data=None):
yg = gaussian(x, pars['amp_g'], pars['cen_g'], pars['wid_g'])
yl = lorentzian(x, pars['amp_l'], pars['cen_l'], pars['wid_l'])
model = yg + yl + pars['line_off'] + x * pars['line_slope']
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data) / sigma
n = 201
xmin = 0.
xmax = 20.0
x = linspace(xmin, xmax, n)
data = (gaussian(x, 21, 8.1, 1.2) + lorentzian(x, 10, 9.6, 2.4) +
random.normal(scale=0.23, size=n) + x * 0.5)
pfit = Parameters()
pfit.add(name='amp_g', value=10)
pfit.add(name='cen_g', value=9)
pfit.add(name='wid_g', value=1)
pfit.add(name='amp_tot', value=20)
pfit.add(name='amp_l', expr='amp_tot - amp_g')
pfit.add(name='cen_l', expr='1.5+cen_g')
pfit.add(name='wid_l', expr='2*wid_g')
pfit.add(name='line_slope', value=0.0)
pfit.add(name='line_off', value=0.0)
sigma = 0.021 # estimate of data error (for all data points)
myfit = Minimizer(residual,
pfit,
fcn_args=(x, ),
fcn_kws={
'sigma': sigma,
'data': data
},
scale_covar=True)
myfit.prepare_fit()
init = residual(myfit.params, x)
result = myfit.leastsq()
print(' Nfev = ', result.nfev)
print(result.chisqr, result.redchi, result.nfree)
report_fit(result.params, min_correl=0.3)
fit = residual(result.params, x)
assert (result.params['cen_l'].value == 1.5 + result.params['cen_g'].value)
assert (result.params['amp_l'].value == result.params['amp_tot'].value -
result.params['amp_g'].value)
assert (result.params['wid_l'].value == 2 * result.params['wid_g'].value)
# now, change fit slightly and re-run
myfit.params['wid_l'].expr = '1.25*wid_g'
result = myfit.leastsq()
report_fit(result.params, min_correl=0.4)
fit2 = residual(result.params, x)
assert (result.params['cen_l'].value == 1.5 + result.params['cen_g'].value)
assert (result.params['amp_l'].value == result.params['amp_tot'].value -
result.params['amp_g'].value)
assert (result.params['wid_l'].value == 1.25 *
result.params['wid_g'].value)
def residual(pars, x, data):
model = (gaussian(x, pars['amp_g'], pars['cen_g'], pars['wid_g']) +
lorentzian(x, pars['amp_l'], pars['cen_l'], pars['wid_l']))
return model - data
def test_constraints(with_plot=True):
with_plot = with_plot and WITHPLOT
def residual(pars, x, sigma=None, data=None):
yg = gaussian(x, pars['amp_g'], pars['cen_g'], pars['wid_g'])
yl = lorentzian(x, pars['amp_l'], pars['cen_l'], pars['wid_l'])
model = yg + yl + pars['line_off'] + x * pars['line_slope']
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data) / sigma
n = 201
xmin = 0.
xmax = 20.0
x = linspace(xmin, xmax, n)
data = (gaussian(x, 21, 8.1, 1.2) +
lorentzian(x, 10, 9.6, 2.4) +
random.normal(scale=0.23, size=n) +
x*0.5)
if with_plot:
pylab.plot(x, data, 'r+')
pfit = Parameters()
pfit.add(name='amp_g', value=10)
pfit.add(name='cen_g', value=9)
pfit.add(name='wid_g', value=1)
pfit.add(name='amp_tot', value=20)
pfit.add(name='amp_l', expr='amp_tot - amp_g')
pfit.add(name='cen_l', expr='1.5+cen_g')
pfit.add(name='wid_l', expr='2*wid_g')
pfit.add(name='line_slope', value=0.0)
pfit.add(name='line_off', value=0.0)
sigma = 0.021 # estimate of data error (for all data points)
myfit = Minimizer(residual, pfit,
fcn_args=(x,), fcn_kws={'sigma':sigma, 'data':data},
scale_covar=True)
myfit.prepare_fit()
init = residual(myfit.params, x)
result = myfit.leastsq()
print(' Nfev = ', result.nfev)
print( result.chisqr, result.redchi, result.nfree)
report_fit(result.params, min_correl=0.3)
fit = residual(result.params, x)
if with_plot:
pylab.plot(x, fit, 'b-')
assert(result.params['cen_l'].value == 1.5 + result.params['cen_g'].value)
assert(result.params['amp_l'].value == result.params['amp_tot'].value - result.params['amp_g'].value)
assert(result.params['wid_l'].value == 2 * result.params['wid_g'].value)
# now, change fit slightly and re-run
myfit.params['wid_l'].expr = '1.25*wid_g'
result = myfit.leastsq()
report_fit(result.params, min_correl=0.4)
fit2 = residual(result.params, x)
if with_plot:
pylab.plot(x, fit2, 'k')
pylab.show()
assert(result.params['cen_l'].value == 1.5 + result.params['cen_g'].value)
assert(result.params['amp_l'].value == result.params['amp_tot'].value - result.params['amp_g'].value)
assert(result.params['wid_l'].value == 1.25 * result.params['wid_g'].value)
def ltz():
r"""Sample data is a Lorentzian"""
x = np.arange(-0.1, 0.5, 0.0004) # energy domain, in meV
params = dict(amplitude=1.0, center=0.0, sigma=0.025)
y = lorentzian(x, **params)
return dict(x=x, y=y, p=params)
def find_grain_diameter(X, Y, min_size, max_size, size_step, return_figure,
peaks_params, is_bg_active):
"""
New parameters are stored in peaks_params. The old ones are still present to provide compatibility with old code.
:param X:
:param Y:
:param min_size:
:param max_size:
:param size_step:
:param center:
:param gamma_0:
:param peak_height:
:param return_figure:
:param sample_type:
:param peaks_params: [[center, gamma_0, amplitude, from, to, is_visible, is_lorentz, is fano], ...]
:return:
"""
best_d = -1
best_sigma = -1
best_center = -1
best_amplitude = -1
best_q = 5
X_ex = X
max_y = max(Y)
x_range = (X > 400) * (X < 600)
lin_mod = LinearModel(prefix="background_", min=0, max=0.5 * max_y)
pars = lin_mod.guess(Y, x=X)
final_mod = lin_mod
for ii in range(0, len(peaks_params)):
if peaks_params[ii][6] == True:
mod = LorentzianModel(prefix="l" + str(ii) + '_')
pars.update(mod.make_params())
pars['l' + str(ii) + '_center'].set(peaks_params[ii][0],
min=peaks_params[ii][3],
max=peaks_params[ii][4])
pars['l' + str(ii) + '_sigma'].set(peaks_params[ii][1])
pars['l' + str(ii) + '_amplitude'].set(peaks_params[ii][2],
min=0) #, max=max_y)
elif peaks_params[ii][7] == True:
mod = Model(fano_function)
pars.update(mod.make_params())
pars['q'].set(
-4, min=-20, max=0
) #q may be negative as well as positive, the most common values for q: [-1,5]
pars['center'].set(peaks_params[ii][0], vary=True)
pars['sigma'].set(peaks_params[ii][1], min=0.1, vary=True)
pars['amplitude'].set(peaks_params[ii][2], min=0)
else:
mod = Model(
total_intensity_fit_func
) #total_intensity_fit_func(omega, d, N, center, gamma_0, amplitude)
pars.update(mod.make_params())
pars['d'].set((max_size - min_size), min=min_size, max=max_size)
pars['N'].set(100, vary=False)
pars['center'].set(peaks_params[ii][0], vary=False)
pars['sigma'].set(peaks_params[ii][1], vary=False)
pars['amplitude'].set(peaks_params[ii][2], min=0, max=max_y)
final_mod = final_mod + mod
###########PLOT & RETURN######################################
if return_figure:
my_figure = plt.Figure()
my_plot = my_figure.add_subplot(111)
legend_labels = []
for ax in my_figure.axes:
ax.set_xlabel(u'\u03C9' + ' [cm^-1]')
ax.set_ylabel('Intensity [arb.]')
my_plot.plot(X[x_range], Y[x_range], "ro")
legend_labels.append('Data')
out = final_mod.fit(Y[x_range], pars, x=X[x_range])
if not out.success:
print("!!!!!!!!!!!!!FIT HAVE FAILED!!!!!!!!!!!")
legend_labels = ['FIT FAILED!']
if return_figure:
my_plot.legend(legend_labels)
return best_d, best_sigma, best_center, best_amplitude, my_figure
else:
return best_d, best_sigma, best_center, best_amplitude
elif not return_figure:
if (peaks_params[0][6] == True):
best_sigma = out.best_values['l' + str(0) + '_sigma']
best_center = out.best_values['l' + str(0) + '_center']
best_amplitude = out.best_values['l' + str(0) + '_amplitude']
return 0, best_sigma, best_center, best_amplitude
elif (peaks_params[0][7] == True):
best_sigma = out.best_values['sigma']
best_center = out.best_values['center']
best_amplitude = out.best_values['amplitude']
best_q = out.best_values['q']
print(best_q)
return best_q, best_sigma, best_center, best_amplitude
else:
best_sigma = out.best_values['sigma']
best_center = out.best_values['center']
best_amplitude = out.best_values['amplitude']
best_d = out.best_values['d']
print(best_d)
return best_d, best_sigma, best_center, best_amplitude
else:
print(out.fit_report(min_correl=0.5))
legend_labels.append('Best fit')
my_plot.plot(X[x_range], out.best_fit, "b-")
my_plot.plot(
X[x_range], out.best_values['background_slope'] * X[x_range] +
out.best_values['background_intercept'], '--')
legend_labels.append('background')
for jj in range(0, len(peaks_params)):
if (peaks_params[jj][6] == True):
sigma = out.best_values['l' + str(jj) + '_sigma']
center = out.best_values['l' + str(jj) + '_center']
amplitude = out.best_values['l' + str(jj) + '_amplitude']
legend_labels.append('l' + str(jj))
my_plot.plot(
X[x_range],
lorentzian(X[x_range],
center=center,
sigma=sigma,
amplitude=amplitude), '--')
elif (peaks_params[jj][7] == True):
sigma = out.best_values['sigma']
center = out.best_values['center']
amplitude = out.best_values['amplitude']
q = out.best_values['q']
legend_labels.append('fano; q=' + str(q))
my_plot.plot(
X[x_range],
fano_function(X[x_range], q, center, sigma, amplitude),
'--')
else:
sigma = out.best_values['sigma']
center = out.best_values['center']
amplitude = out.best_values['amplitude']
d = out.best_values['d']
N = out.best_values['N']
legend_labels.append('fc; d=' + str(d))
my_plot.plot(
X[x_range],
total_intensity_fit_func(X[x_range], d, N, center, sigma,
amplitude), '--')
if (peaks_params[0][7] == True):
my_figure.suptitle(
"q:{0:.1f}[nm] omega:{1:.4f}[cm^-1] sigma:{2:.4f}[cm^-1] inten:{3:.4}[arb]"
.format(float(best_q), float(best_center), float(best_sigma),
float(best_amplitude)))
else:
my_figure.suptitle(
"d:{0:.1f}[nm] omega:{1:.4f}[cm^-1] sigma:{2:.4f}[cm^-1] inten:{3:.4}[arb]"
.format(float(best_d), float(best_center), float(best_sigma),
float(best_amplitude)))
my_plot.legend(legend_labels, loc=2)
return best_d, best_sigma, best_center, best_amplitude, my_figure
offset = pars['line_off']
model = yg + yl + offset + x * slope
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data) / sigma
n = 601
xmin = 0.
xmax = 20.0
random.seed(0)
x = linspace(xmin, xmax, n)
data = (gaussian(x, 21, 8.1, 1.2) + lorentzian(x, 10, 9.6, 2.4) +
random.normal(scale=0.23, size=n) + x * 0.5)
if HASPYLAB:
pylab.plot(x, data, 'r+')
pfit = Parameters()
pfit.add(name='amp_g', value=10)
pfit.add(name='cen_g', value=9)
pfit.add(name='wid_g', value=1)
pfit.add(name='amp_tot', value=20)
pfit.add(name='amp_l', expr='amp_tot - amp_g')
pfit.add(name='cen_l', expr='1.5+cen_g')
pfit.add(name='wid_l', expr='2*wid_g')
pfit.add(name='line_slope', value=0.0)
pfit.add(name='line_off', value=0.0)
model = yg + yl + offset + x * slope
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data)/sigma
n = 601
xmin = 0.
xmax = 20.0
random.seed(0)
x = linspace(xmin, xmax, n)
data = (gaussian(x, 21, 8.1, 1.2) +
lorentzian(x, 10, 9.6, 2.4) +
random.normal(scale=0.23, size=n) +
x*0.5)
if HASPYLAB:
pylab.plot(x, data, 'r+')
pfit = [Parameter(name='amp_g', value=10),
Parameter(name='cen_g', value=9),
Parameter(name='wid_g', value=1),
Parameter(name='amp_tot', value=20),
Parameter(name='amp_l', expr='amp_tot - amp_g'),
Parameter(name='cen_l', expr='1.5+cen_g'),
Parameter(name='wid_l', expr='2*wid_g'),
