def plot_bandwidths(bandwidths, experiments, spectra=[]):
'''
Plots the bandwidths of detection and pump pulses for multiple experiments .
If the EPR spectrum of the spin system is provided, the bandwidths are overlayed with the EPR spectrum.
'''
figsize = [10, 8]
num_subplots = len(experiments)
best_rcparams(num_subplots)
layout = best_layout(figsize[0], figsize[1], num_subplots)
fig = plt.figure(figsize=(figsize[0], figsize[1]),
facecolor='w',
edgecolor='w')
for i in range(num_subplots):
if num_subplots == 1:
axes = fig.gca()
else:
axes = fig.add_subplot(layout[0], layout[1], i + 1)
if (spectra != []) and (len(spectra) == num_subplots):
plot_bandwidths_single_experiment(axes, bandwidths[i],
experiments[i], spectra[i])
else:
plot_bandwidths_single_experiment(axes, bandwidths[i],
experiments[i])
plt.tight_layout()
plt.draw()
plt.pause(0.000001)
return fig
def plot_epr_spectrum(spectrum):
''' Plots a simulated EPR spectrum '''
best_rcparams(1)
fig = plt.figure(facecolor='w', edgecolor='w')
axes = fig.gca()
axes.plot(spectrum['f'], spectrum['p'] / np.amax(spectrum['p']), 'k-')
axes.set_xlabel(r'Frequency (GHz)')
axes.set_ylabel('Intensity (arb. u.)')
axes.set_xlim(np.amin(spectrum['f']), np.amax(spectrum['f']))
axes.set_ylim(0.0, 1.1)
plt.tight_layout()
plt.draw()
plt.pause(0.000001) # needed for displaying the plot
return fig
def plot_score(score, goodness_of_fit):
''' Plots the score as a function of optimization step '''
best_rcparams(1)
num_points = len(score)
x = np.linspace(1,num_points,num_points)
y = score
fig = plt.figure(facecolor='w', edgecolor='w')
axes = fig.gca()
axes.semilogy(x, y, linestyle='-', marker='o', color='k')
axes.set_xlim(0, x[-1] + 1)
plt.xlabel('No. iteration')
plt.ylabel(const['goodness_of_fit_axes_labels'][goodness_of_fit])
plt.grid(True)
plt.tight_layout()
plt.draw()
plt.pause(0.000001)
return fig
def update_score_plot(fig, score, goodness_of_fit):
''' Re-plots the score as a function of optimization step '''
best_rcparams(1)
num_points = len(score)
x = np.linspace(1,num_points,num_points)
y = score
axes = fig.gca()
axes.clear()
axes.semilogy(x, y, linestyle='-', marker='o', color='k')
axes.set_xlim(0, x[-1] + 1)
plt.xlabel('The number of optimization steps')
plt.xlabel('No. iteration')
plt.ylabel(const['goodness_of_fit_axes_labels'][goodness_of_fit])
plt.grid(True)
plt.tight_layout()
plt.draw()
plt.pause(0.000001)
def plot_simulated_time_traces(simulated_time_traces, experiments):
''' Plots simulated PDS time traces '''
figsize = [10, 8]
num_subplots = len(experiments)
best_rcparams(num_subplots)
layout = best_layout(figsize[0], figsize[1], num_subplots)
fig = plt.figure(figsize=(figsize[0], figsize[1]), facecolor='w', edgecolor='w')
for i in range(num_subplots):
if num_subplots == 1:
axes = fig.gca()
else:
axes = fig.add_subplot(layout[0], layout[1], i+1)
plot_simulated_time_trace(axes, simulated_time_traces[i], experiments[i])
plt.tight_layout()
plt.draw()
plt.pause(0.000001)
return fig
def plot_error_surfaces(score_vs_parameter_subsets, error_analysis_parameters,
fitting_parameters, optimized_parameters,
score_threshold):
''' Plots the score as a function of one or two fitting parameters '''
figsize = [10, 8]
num_subplots = len(error_analysis_parameters)
best_rcparams(num_subplots)
layout = best_layout(figsize[0], figsize[1], num_subplots)
fig = plt.figure(figsize=(figsize[0], figsize[1]),
facecolor='w',
edgecolor='w')
for i in range(num_subplots):
dim = len(error_analysis_parameters[i])
if num_subplots == 1:
axes = fig.gca()
else:
axes = fig.add_subplot(layout[0], layout[1], i + 1)
if (dim == 1):
im = plot_1d_error_surface(axes, score_vs_parameter_subsets[i],
error_analysis_parameters[i],
fitting_parameters,
optimized_parameters, score_threshold)
elif (dim == 2):
im = plot_2d_error_surface(axes, score_vs_parameter_subsets[i],
error_analysis_parameters[i],
fitting_parameters,
optimized_parameters, score_threshold)
else:
print(
'The score cannot be yet plotted as function of three or more parameters!'
)
left = 0
right = float(layout[1]) / float(layout[1] + 1)
bottom = 0.5 * (1 - right)
top = 1 - bottom
plt.tight_layout(rect=[left, bottom, right, top])
cax = plt.axes([right + 0.05, 0.3, 0.02, 0.4])
plt.colorbar(im, cax=cax, orientation='vertical')
plt.text(right + 0.1, 1.05, r'$\mathit{\chi^2}$', transform=cax.transAxes)
plt.draw()
plt.pause(0.000001)
return fig