def model_gen(V_series, dQdV_series, cd, i, cyc, battery):
"""Develops initial model and parameters for battery data fitting.
V_series = Pandas series of voltage data
dQdV_series = Pandas series of differential capacity data
cd = either 'c' for charge and 'd' for discharge.
Output:
par = lmfit parameters object
mod = lmfit model object"""
# generates numpy arrays for use in fitting
sigx_bot, sigy_bot = fitters.cd_dataframe(V_series, dQdV_series, cd)
# creates a polynomial fitting object
mod = models.PolynomialModel(4)
# sets polynomial parameters based on a
# guess of a polynomial fit to the data with no peaks
par = mod.guess(sigy_bot, x=sigx_bot)
# prints a notice if no peaks are found
if all(i) is False:
notice = 'Cycle ' + str(cyc) + cd + \
' in battery ' + battery + ' has no peaks.'
print(notice)
# iterates over all peak indices
else:
for index in i:
# generates unique parameter strings based on index of peak
center, sigma, amplitude, fraction, comb = fitters.label_gen(
index)
# generates a pseudo voigt fitting model
gaus_loop = models.PseudoVoigtModel(prefix=comb)
par.update(gaus_loop.make_params())
# uses unique parameter strings to generate parameters
# with initial guesses
# in this model, the center of the peak is locked at the
# peak location determined from PeakUtils
par[center].set(sigx_bot[index], vary=False)
par[sigma].set(0.01)
par[amplitude].set(.05, min=0)
par[fraction].set(.5, min=0, max=1)
mod = mod + gaus_loop
return par, mod
def test_guess_modelparams():
"""Tests for the 'guess' function of built-in models."""
x = np.linspace(-10, 10, 501)
mod = models.ConstantModel()
y = 6.0 + x*0.005
pars = mod.guess(y)
assert_allclose(pars['c'].value, 6.0, rtol=0.01)
mod = models.ComplexConstantModel(prefix='f_')
y = 6.0 + x*0.005 + (4.0 - 0.02*x)*1j
pars = mod.guess(y)
assert_allclose(pars['f_re'].value, 6.0, rtol=0.01)
assert_allclose(pars['f_im'].value, 4.0, rtol=0.01)
mod = models.QuadraticModel(prefix='g_')
y = -0.2 + 3.0*x + 0.005*x**2
pars = mod.guess(y, x=x)
assert_allclose(pars['g_a'].value, 0.005, rtol=0.01)
assert_allclose(pars['g_b'].value, 3.0, rtol=0.01)
assert_allclose(pars['g_c'].value, -0.2, rtol=0.01)
mod = models.PolynomialModel(4, prefix='g_')
y = -0.2 + 3.0*x + 0.005*x**2 - 3.3e-6*x**3 + 1.e-9*x**4
pars = mod.guess(y, x=x)
assert_allclose(pars['g_c0'].value, -0.2, rtol=0.01)
assert_allclose(pars['g_c1'].value, 3.0, rtol=0.01)
assert_allclose(pars['g_c2'].value, 0.005, rtol=0.1)
assert_allclose(pars['g_c3'].value, -3.3e-6, rtol=0.1)
assert_allclose(pars['g_c4'].value, 1.e-9, rtol=0.1)
mod = models.GaussianModel(prefix='g_')
y = lineshapes.gaussian(x, amplitude=2.2, center=0.25, sigma=1.3)
y += np.random.normal(size=len(x), scale=0.004)
pars = mod.guess(y, x=x)
assert_allclose(pars['g_amplitude'].value, 3, rtol=2)
assert_allclose(pars['g_center'].value, 0.25, rtol=1)
assert_allclose(pars['g_sigma'].value, 1.3, rtol=1)
mod = models.LorentzianModel(prefix='l_')
pars = mod.guess(y, x=x)
assert_allclose(pars['l_amplitude'].value, 3, rtol=2)
assert_allclose(pars['l_center'].value, 0.25, rtol=1)
assert_allclose(pars['l_sigma'].value, 1.3, rtol=1)
mod = models.SplitLorentzianModel(prefix='s_')
pars = mod.guess(y, x=x)
assert_allclose(pars['s_amplitude'].value, 3, rtol=2)
assert_allclose(pars['s_center'].value, 0.25, rtol=1)
assert_allclose(pars['s_sigma'].value, 1.3, rtol=1)
assert_allclose(pars['s_sigma_r'].value, 1.3, rtol=1)
mod = models.VoigtModel(prefix='l_')
pars = mod.guess(y, x=x)
assert_allclose(pars['l_amplitude'].value, 3, rtol=2)
assert_allclose(pars['l_center'].value, 0.25, rtol=1)
assert_allclose(pars['l_sigma'].value, 1.3, rtol=1)
mod = models.SkewedVoigtModel(prefix='l_')
pars = mod.guess(y, x=x)
assert_allclose(pars['l_amplitude'].value, 3, rtol=2)
assert_allclose(pars['l_center'].value, 0.25, rtol=1)
assert_allclose(pars['l_sigma'].value, 1.3, rtol=1)