# define the initial parameters
fit_params = Parameters()
if lineshape == 'pseudovoigt':
cen = newbin_axis[np.unravel_index(newhist.argmax(), newhist.shape)]
fit_params.add('amp_1', value=50000, min=100, max=1000000)
fit_params.add('cen_1', value=cen, min=cen - 0.2, max=cen + 0.2)
fit_params.add('sig_1', value=0.1, min=0.01, max=0.5)
fit_params.add('ratio_1', value=0.5, min=0, max=1)
# run the fit
result = minimize(util.objective_lmfit,
fit_params,
args=(fit_axis, fit_hist, lineshape))
report_fit(result.params)
y_fit = util.function_lmfit(params=result.params,
iterator=0,
x_axis=newbin_axis,
distribution=lineshape)
else:
y_fit = None
result = None
##################################
# plot the histogram and the fit #
##################################
fig, ax = plt.subplots(1, 1)
plt.plot(bin_axis, hist, 'o', newbin_axis, newhist, '-')
if histogram_Yaxis == 'log':
ax.set_yscale('log')
if fit:
if histogram_Yaxis == 'linear':
ax.plot(newbin_axis, y_fit, '-')
line, = axes_dict[idx][0].plot(
linecuts[idx, width_length // 2 - roi_width:width_length // 2 +
roi_width], # x axis
linecuts[idx + 3, width_length // 2 - roi_width:width_length // 2 +
roi_width], # cut
'ro',
fillstyle='none')
line.set_label(f'cut along {axes_dict[idx][1]}')
fit_x_axis = np.linspace(
linecuts[idx, width_length // 2 - roi_width:width_length // 2 +
roi_width].min(),
linecuts[idx, width_length // 2 - roi_width:width_length // 2 +
roi_width].max(),
num=200)
y_fit = util.function_lmfit(params=minimization.params,
iterator=idx,
x_axis=fit_x_axis,
distribution='gaussian')
line, = axes_dict[idx][0].plot(fit_x_axis, y_fit, 'b-')
sig_name = f'sig_{idx+1}'
line.set_label(
f'fit, FWHM={2*np.sqrt(2*np.log(2))*minimization.params[sig_name].value:.2f}nm'
)
gu.savefig(savedir=savedir,
figure=fig,
axes=(ax0, ax1, ax2),
tick_width=tick_width,
tick_length=tick_length,
tick_labelsize=16,
xlabels='width (nm)',
ylabels='psf (a.u.)',
fit_params,
args=(combined_xaxis, combined_data, lineshape))
report_fit(result.params)
#####################
# plot data and fit #
#####################
fig, ax = plt.subplots(1, 1)
if scale == 'linear':
ax.plot(distances, average, 'r')
else:
ax.plot(distances, np.log10(average), 'r')
plt.legend(['data'])
for idx in range(nb_ranges):
y_fit = util.function_lmfit(params=result.params,
iterator=idx,
x_axis=distances,
distribution=lineshape)
if scale == 'linear':
ax.plot(distances, y_fit, '-')
else:
ax.plot(distances, np.log10(y_fit), '-')
ax.set_xlim(xlim[0], xlim[1])
ax.set_ylim(ylim[0], ylim[1])
ax.set_xlabel('q (1/nm)')
ax.set_ylabel('Angular average (A.U.)')
try:
fig.text(0.15,
0.95,
'cen_1 = ' + str('{:.5f}'.format(result.params['cen_1'].value)) +
'+/-' + str('{:.5f}'.format(result.params['cen_1'].stderr)) +
' sig_1 = ' +
fit_params.add('loc_1', value=0, min=-0.1, max=0.1)
fit_params.add('sig_1', value=0.0005, min=0.0000001, max=0.1)
fit_params.add('alpha_1', value=0, min=-10, max=10)
else: # 'pseudovoigt'
fit_params.add('amp_1', value=0.0005, min=0.000001, max=10000)
fit_params.add('cen_1', value=0, min=-0.1, max=0.1)
fit_params.add('sig_1', value=0.0005, min=0.0000001, max=0.1)
fit_params.add('ratio_1', value=0.5, min=0, max=1)
# run the global fit to all the data sets
result = minimize(util.objective_lmfit,
fit_params,
args=(x_axis, hist, fit_pdf))
report_fit(result.params)
strain_fit = util.function_lmfit(params=result.params,
x_axis=x_axis,
distribution=fit_pdf)
if fit_pdf == 'skewed_gaussian': # find the position of the mode (maximum of the pdf)
x_mode = np.unravel_index(strain_fit.argmax(), x_axis.shape)
step = x_axis[x_mode] - x_axis[x_mode[0] - 1]
fine_x = np.copy(x_axis)
for idx in range(2):
fine_x = np.linspace(fine_x[x_mode] - step,
fine_x[x_mode] + step,
endpoint=True,
num=1000)
fine_y = util.function_lmfit(params=result.params,
x_axis=fine_x,
distribution=fit_pdf)
diff_fit = np.gradient(fine_y, fine_x[1] - fine_x[0])