EELSFitter.plotting package
EELSFitter.plotting.hyperparameters module
- EELSFitter.plotting.hyperparameters.plot_figs(dpi=200, x=0, y=0, xlim=[0.4, 5], ylim=[-500, 500], yscale='linear', **kwargs)[source]
General parameters to plot figures
- Parameters:
dpi –
x –
y –
xlim –
ylim –
yscale –
kwargs –
- Returns:
fig
- Return type:
- EELSFitter.plotting.hyperparameters.plot_hp(eaxis, clusters_data, de1, de2, cmap='coolwarm', **kwargs)[source]
Plot with location of dE1 & dE2 shown on top of the clusters.
- Parameters:
eaxis (numpy.ndarray, shape=(M,)) – eaxis of the data
clusters_data (numpy.ndarray, shape=(M,N)) – Data per cluster
de1 (float) – Hyperparameter dE1
de2 (float) – Hyperparameter dE2
**kwargs (dictionary) – Additional keyword arguments.
- Returns:
fig
- Return type:
EELSFitter.plotting.zlp module
- EELSFitter.plotting.zlp.plot_figs(dpi=200, x=0, y=0, xlim=[0, 5], ylim=[-10, 3000], yscale='linear', **kwargs)[source]
General parameters to plot figures
- Parameters:
dpi –
x –
y –
xlim –
ylim –
yscale –
kwargs –
- Returns:
fig
- Return type:
- EELSFitter.plotting.zlp.plot_zlp_cluster_predictions(image, cmap='coolwarm', **kwargs)[source]
- Parameters:
image –
cmap –
kwargs –
- EELSFitter.plotting.zlp.plot_zlp_per_cluster(image, cluster, signal_type='EELS', zlp_gen=True, hyper_par=True, smooth=False, **kwargs)[source]
- Parameters:
image –
cluster –
signal_type –
zlp_gen –
hyper_par –
kwargs –
- EELSFitter.plotting.zlp.plot_zlp_per_pixel(image, pixx, pixy, signal_type='EELS', zlp_gen=False, zlp_match=True, subtract=False, deconv=True, hyper_par=True, random_zlp=None, **kwargs)[source]
Plots for
pixx,pixythe inelastic scattering distribution plus uncertainties
the ZLP plus uncertainties
the raw signal
- Parameters:
image –
pixx –
pixy –
signal –
zlp_gen –
zlp_match –
subtract –
deconv –
hyper_par –
random_zlp –
kwargs –
EELSFitter.plotting.training_perf module
- EELSFitter.plotting.training_perf.plot_cost_dist(cost_trains, cost_tests, cost_tests_std, title='$\\rm{Cost\\;distribution\\;}$')[source]
- Parameters:
cost_trains (numpy.ndarray, shape=(M,)) – Cost values of the training set with M trained NNs
cost_tests (numpy.ndarray, shape=(M,)) – Cost values of the test set with M trained NNs
cost_tests_std (float) – 68% CL of the test costs
title (str, optional) – Title of the plot
- Returns:
fig
- Return type:
EELSFitter.plotting.heatmaps module
- EELSFitter.plotting.heatmaps.get_ticks(image, sig_ticks=2, npix_xtick=10, npix_ytick=10, scale_ticks=1, tick_int=False)[source]
Sets the proper tick labels and tick positions for the heatmap plots.
- Parameters:
sig_ticks (int, optional) – Set the amount of significant numbers displayed in the ticks. The default is 2.
npix_xtick (float, optional) – Display a tick per n pixels in the x-axis. Note that this value can be a float. The default is 10.
npix_ytick (float, optional) – Display a tick per n pixels in the y-axis. Note that this value can be a float. The default is 10.
scale_ticks (float, optional) – Change the scaling of the numbers displayed in the ticks. Microns ([u03BCm]) are assumed as standard scale, adjust scaling from there. The default is 1.
tick_int (bool, optional) – Set whether you only want the ticks to display as integers instead of floats. The default is False.
- Returns:
xticks (numpy.ndarray, shape=(M,)) – Array of the xticks positions.
yticks (numpy.ndarray, shape=(M,)) – Array of the yticks positions.
xticks_labels (numpy.ndarray, shape=(M,)) – Array with strings of the xtick labels.
yticks_labels (numpy.ndarray, shape=(M,)) – Array with strings of the ytick labels.
- EELSFitter.plotting.heatmaps.plot_heatmap(image, data, dpi=200, title=None, xlabel='$\\rm{[nm]\\;}$', ylabel='$\\rm{[nm]\\;}$', cmap='coolwarm', discrete_colormap=False, sig_cbar=3, color_bin_size=None, sig_ticks=2, npix_xtick=10, npix_ytick=10, scale_ticks=1, tick_int=False, save_as=False, **kwargs)[source]
Plots a heatmap for given data input.
- Parameters:
image (SpectralImage) –
spectral_image.SpectralImageobjectdata (numpy.ndarray, shape=(M,N)) – Input data for heatmap, but be 2D.
dpi (int, optional) – Set the dpi of the heatmap. The default is 100
title (str, optional) – Set the title of the heatmap. The default is None.
xlabel (str, optional) – Set the label of the x-axis. Nanometer ([nm]) is assumed as standard scale. The default is ‘[nm]’.
ylabel (str, optional) – Set the label of the y-axis. Nanometer ([nm]) is assumed as standard scale. The default is ‘[nm]’.
cmap (str, optional) – Set the colormap of the heatmap. The default is ‘coolwarm’.
discrete_colormap (bool, optional) – Enables the heatmap values to be discretised. Best used in conjuction with color_bin_size. The default is False.
sig_cbar (int, optional) – Set the amount of significant numbers displayed in the colorbar. The default is 3.
color_bin_size (float, optional) – Set the size of the bins used for discretisation. Best used in conjuction discrete_colormap. The default is None.
sig_ticks (int, optional) – Set the amount of significant numbers displayed in the ticks. The default is 2.
npix_xtick (float, optional) – Display a tick per n pixels in the x-axis. Note that this value can be a float. The default is 10.
npix_ytick (float, optional) – Display a tick per n pixels in the y-axis. Note that this value can be a float. The default is 10.
scale_ticks (float, optional) – Change the scaling of the numbers displayed in the ticks. Nanometer ([nm]) is assumed as standard scale adjust scaling from there. The default is 1.
tick_int (bool, optional) – Set whether you only want the ticks to display as integers instead of floats. The default is False.
save_as (str, optional) – Set the location and name for the heatmap to be saved to. The default is False.
**kwargs (dictionary) – Additional keyword arguments.
- Returns:
fig
- Return type:
Seaborn.figure.Figure