def test_height_fwhm_calculation(peakdata):
"""Test for correctness of height and FWHM calculation."""
# mu = 0
# variance = 1.0
# sigma = np.sqrt(variance)
# x = np.linspace(mu - 20*sigma, mu + 20*sigma, 100.0)
# y = norm.pdf(x, mu, 1)
x = peakdata[0]
y = peakdata[1]
check_height_fwhm(x, y, lineshapes.voigt, models.VoigtModel())
check_height_fwhm(x, y, lineshapes.pvoigt, models.PseudoVoigtModel())
check_height_fwhm(x, y, lineshapes.pearson7, models.Pearson7Model())
check_height_fwhm(x, y, lineshapes.moffat, models.MoffatModel())
check_height_fwhm(x, y, lineshapes.students_t, models.StudentsTModel())
check_height_fwhm(x, y, lineshapes.breit_wigner, models.BreitWignerModel())
check_height_fwhm(x, y, lineshapes.damped_oscillator,
models.DampedOscillatorModel())
check_height_fwhm(x, y, lineshapes.dho,
models.DampedHarmonicOscillatorModel())
check_height_fwhm(x, y, lineshapes.expgaussian,
models.ExponentialGaussianModel())
check_height_fwhm(x, y, lineshapes.skewed_gaussian,
models.SkewedGaussianModel())
check_height_fwhm(x, y, lineshapes.doniach, models.DoniachModel())
x = x-9 # Lognormal will only fit peaks with centers < 1
check_height_fwhm(x, y, lineshapes.lognormal, models.LognormalModel())
def test_peak_like():
# mu = 0
# variance = 1.0
# sigma = np.sqrt(variance)
# x = np.linspace(mu - 20*sigma, mu + 20*sigma, 100.0)
# y = norm.pdf(x, mu, 1)
data = np.loadtxt('../examples/test_peak.dat')
x = data[:, 0]
y = data[:, 1]
check_height_fwhm(x, y, lineshapes.voigt, models.VoigtModel())
check_height_fwhm(x, y, lineshapes.pvoigt, models.PseudoVoigtModel())
check_height_fwhm(x, y, lineshapes.pearson7, models.Pearson7Model())
check_height_fwhm(x, y, lineshapes.moffat, models.MoffatModel())
check_height_fwhm(x, y, lineshapes.students_t, models.StudentsTModel())
check_height_fwhm(x, y, lineshapes.breit_wigner, models.BreitWignerModel())
check_height_fwhm(x, y, lineshapes.damped_oscillator,
models.DampedOscillatorModel())
check_height_fwhm(x, y, lineshapes.dho,
models.DampedHarmonicOscillatorModel())
check_height_fwhm(x, y, lineshapes.expgaussian,
models.ExponentialGaussianModel())
check_height_fwhm(x, y, lineshapes.skewed_gaussian,
models.SkewedGaussianModel())
check_height_fwhm(x, y, lineshapes.donaich, models.DonaichModel())
x = x - 9 # Lognormal will only fit peaks with centers < 1
check_height_fwhm(x, y, lineshapes.lognormal, models.LognormalModel())
def make_peak_model(settings):
if settings.peak_model == "GaussDerivative":
return lmfit_custom_models.GaussDerivativeModel()
elif settings.peak_model == "Voigt":
return lmfit_models.PseudoVoigtModel()
else:
return lmfit_models.GaussianModel()
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 pseudovoigt(self,
oversample_multiplier=1,
delta_rp=0,
mz_overlay=1,
fraction=0.5):
'''
Legacy pseudovoigt lineshape function
'''
if self.resolving_power:
# full width half maximum distance
self.fwhm = (self.mz_exp / (self.resolving_power + delta_rp)
) #self.resolving_power)
# stardart deviation
sigma = self.fwhm / 2
# half width baseline distance
#mz_domain = linspace(self.mz_exp - hw_base_distance,
# self.mz_exp + hw_base_distance, datapoint)
mz_domain = self.get_mz_domain(oversample_multiplier, mz_overlay)
# gaussian_pdf = lambda x0, x, s: (1/ math.sqrt(2*math.pi*math.pow(s,2))) * math.exp(-1 * math.pow(x-x0,2) / 2*math.pow(s,2) )
model = models.PseudoVoigtModel()
# TODO derive amplitude
gamma = sigma
amplitude = (sqrt(2 * pi) * sigma) * self.abundance
amplitude = (sqrt(pi / log(2)) * (pi * sigma * self.abundance)) / (
(pi * (1 - gamma)) + (sqrt(pi * log(2)) * gamma))
params = model.make_params(center=self.mz_exp, sigma=sigma)
calc_abundance = model.eval(params=params, x=mz_domain)
return mz_domain, calc_abundance
else:
raise LookupError(
'resolving power is not defined, try to use set_max_resolving_power()'
)
def test_height_and_fwhm_expression_evalution_in_builtin_models():
"""Assert models do not throw an ZeroDivisionError."""
mod = models.GaussianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.LorentzianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.SplitLorentzianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, sigma_r=1.0)
params.update_constraints()
mod = models.VoigtModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=1.0)
params.update_constraints()
mod = models.PseudoVoigtModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, fraction=0.5)
params.update_constraints()
mod = models.MoffatModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, beta=0.0)
params.update_constraints()
mod = models.Pearson7Model()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, expon=1.0)
params.update_constraints()
mod = models.StudentsTModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.BreitWignerModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, q=0.0)
params.update_constraints()
mod = models.LognormalModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.DampedOscillatorModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.DampedHarmonicOscillatorModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0)
params.update_constraints()
mod = models.ExponentialGaussianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0)
params.update_constraints()
mod = models.SkewedGaussianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0)
params.update_constraints()
mod = models.SkewedVoigtModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0,
skew=0.0)
params.update_constraints()
mod = models.DoniachModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0)
params.update_constraints()
mod = models.StepModel()
for f in ('linear', 'arctan', 'erf', 'logistic'):
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, form=f)
params.update_constraints()
mod = models.RectangleModel()
for f in ('linear', 'arctan', 'erf', 'logistic'):
params = mod.make_params(amplitude=1.0, center1=0.0, sigma1=0.0,
center2=0.0, sigma2=0.0, form=f)
params.update_constraints()
def model_gen(V_series, dQdV_series, cd, i, cyc, v_toappend, thresh):
"""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.
v_toappend is the list of voltages to append to the peak indices
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)
if len(sigx_bot)>5 and sigx_bot[5]>sigx_bot[1]:
# check if the voltage values are increasing - the right order for gaussian
sigx_bot_new = sigx_bot
sigy_bot_new = sigy_bot
newi = i
else:
sigx_bot_new = sigx_bot[::-1] # reverses the order of elements in the array
sigy_bot_new = sigy_bot[::-1]
newi = np.array([], dtype = int)
for elem in i:
# append a new index, because now everything is backwards
newi = np.append(newi, int(len(sigx_bot_new) - elem - 1))
# creates a polynomial fitting object
# prints a notice if no peaks are found
if all(newi) is False or len(newi) < 1:
notice = 'Cycle ' + str(cyc) + cd + \
' in battery ' + ' has no peaks.'
print(notice)
base_mod = models.GaussianModel(prefix = 'base_')
mod = base_mod
# changed from PolynomialModel to Gaussian on 10-10-18
# Gaussian params are A, mew, and sigma
# sets polynomial parameters based on a
# guess of a polynomial fit to the data with no peaks
#mod.set_param_hint('base_amplitude', min = 0)
#mod.set_param_hint('base_sigma', min = 0.001)
par = mod.make_params()
# iterates over all peak indices
else:
# have to convert from inputted voltages to indices of peaks within sigx_bot
user_appended_ind = []
rev_user_append = []
if len(v_toappend) > 0:
for vapp in v_toappend:
if sigx_bot.min()<=vapp<=sigx_bot.max():
#check if voltage given is valid
ind_app= np.where(np.isclose(sigx_bot, float(vapp), atol = 0.1))[0][0]
user_appended_ind.append(ind_app)
rev_user_app = np.where(np.isclose(sigx_bot_new, float(vapp), atol = 0.1))[0][0]
rev_user_append.append(rev_user_app)
# this gives a final list of user appended indices
i = i.tolist() + user_appended_ind
newi = newi.tolist() + rev_user_append # combine the two lists of indices to get the final set of peak locations
else:
i = i.tolist()
newi = newi.tolist()
count = 0
for index in newi:
# 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)
if count == 0:
mod = gaus_loop
#mod.set_param_hint(amplitude, min = 0.001)
par = mod.make_params()
#par = mod.guess(sigy_bot_new, x=sigx_bot_new)
count = count + 1
else:
mod = mod + gaus_loop
#gaus_loop.set_param_hint(amplitude, min = 0.001)
par.update(gaus_loop.make_params())
count = count + 1
# 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_new[index], vary=False)
# don't allow the centers of the peaks found by peakutsils to vary
par[sigma].set((np.max(sigx_bot_new)-np.min(sigx_bot_new))/100)
par[amplitude].set((np.mean(sigy_bot_new))/50, min=0)
par[fraction].set(.5, min=0, max=1)
# then add the gaussian after the peaks
base_mod = models.GaussianModel(prefix = 'base_')
mod = mod + base_mod
base_par = base_mod.make_params()
base_par['base_amplitude'].set(np.mean(sigy_bot_new))
# these are initial guesses for the base
base_par['base_center'].set(np.mean(sigx_bot_new))
base_par['base_sigma'].set((np.max(sigx_bot_new)-np.min(sigx_bot_new))/2)
# changed from PolynomialModel to Gaussian on 10-10-18
# Gaussian params are A, mew, and sigma
# sets polynomial parameters based on a
# guess of a polynomial fit to the data with no peaks
#base_mod.set_param_hint('base_amplitude', min = 0)
#base_mod.set_param_hint('base_sigma', min = 0.001)
par.update(base_par)
#mod.set_param_hint('base_height', min = 0, max = 0.01)
#par = mod.guess(sigy_bot_new, x=sigx_bot_new)
#print(cyc)
return par, mod, i
def model_gen(V_series, dQdV_series, cd, i, cyc, thresh):
"""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.
i = list of peak indices found by peak finder
Output:
par = lmfit parameters object
mod = lmfit model object"""
# generates numpy arrays for use in fitting
sigx_bot, sigy_bot = cd_dataframe(V_series, dQdV_series, cd)
if len(sigx_bot) > 5 and sigx_bot[5] > sigx_bot[1]:
# check if the voltage values are increasing - the right order for
# gaussian
sigx_bot_new = sigx_bot
sigy_bot_new = sigy_bot
newi = i
else:
sigx_bot_new = sigx_bot[::-1]
# reverses the order of elements in the array
sigy_bot_new = sigy_bot[::-1]
newi = np.array([], dtype=int)
for elem in i:
# append a new index, because now everything is backwards
newi = np.append(newi, int(len(sigx_bot_new) - elem - 1))
if all(newi) is False or len(newi) < 1:
base_mod = models.GaussianModel(prefix='base_')
mod = base_mod
par = mod.make_params()
else:
# have to convert from inputted voltages to indices of peaks within
# sigx_bot
user_appended_ind = []
rev_user_append = []
if not isinstance(i, list):
i = i.tolist()
if not isinstance(newi, list):
newi = newi.tolist()
count = 0
for index in newi:
# generates unique parameter strings based on index of peak
center, sigma, amplitude, fraction, comb = label_gen(index)
# generates a pseudo voigt fitting model
gaus_loop = models.PseudoVoigtModel(prefix=comb)
if count == 0:
mod = gaus_loop
par = mod.make_params()
count = count + 1
else:
mod = mod + gaus_loop
par.update(gaus_loop.make_params())
count = count + 1
par[center].set(sigx_bot_new[index], vary=False)
par[sigma].set((np.max(sigx_bot_new) - np.min(sigx_bot_new)) / 100)
par[amplitude].set((np.mean(sigy_bot_new)) / 50, min=0)
par[fraction].set(.5, min=0, max=1)
# then add the gaussian after the peaks
base_mod = models.GaussianModel(prefix='base_')
mod = mod + base_mod
base_par = base_mod.make_params()
base_par['base_amplitude'].set(np.mean(sigy_bot_new))
# these are initial guesses for the base
base_par['base_center'].set(np.mean(sigx_bot_new))
base_par['base_sigma'].set(
(np.max(sigx_bot_new) - np.min(sigx_bot_new)) / 2)
par.update(base_par)
return par, mod, i
