lstmcpipe.plots package

lstmcpipe.plots package#

Submodules#

lstmcpipe.plots.images_debug module#

lstmcpipe.plots.images_debug.get_cleaning_config(config_file=None)#
lstmcpipe.plots.images_debug.get_hillas_container(row)#
lstmcpipe.plots.images_debug.main(filename, config_file=None)#

lstmcpipe.plots.plot_irfs module#

lstmcpipe.plots.plot_irfs.main()#
lstmcpipe.plots.plot_irfs.plot_angular_resolution_from_file(filename, ax=None, **kwargs)#
lstmcpipe.plots.plot_irfs.plot_background_rate_from_file(filename, ax=None, **kwargs)#
lstmcpipe.plots.plot_irfs.plot_effective_area_from_file(file, all_cuts=False, ax=None, **kwargs)#
lstmcpipe.plots.plot_irfs.plot_energy_bias_from_file(filename, ax=None, **kwargs)#
lstmcpipe.plots.plot_irfs.plot_energy_dispersion_from_file(filename)#
lstmcpipe.plots.plot_irfs.plot_energy_resolution_from_file(filename, ax=None, **kwargs)#
lstmcpipe.plots.plot_irfs.plot_gh_cut_per_energy(filename, ax=None, **kwargs)#
lstmcpipe.plots.plot_irfs.plot_magic_2014(ax=None, **kwargs)#
lstmcpipe.plots.plot_irfs.plot_magic_bkg_rate(ax=None, **kwargs)#
lstmcpipe.plots.plot_irfs.plot_psf_from_file(filename)#
lstmcpipe.plots.plot_irfs.plot_sensitivity_from_file(irf_file, ax=None, **kwargs)#
lstmcpipe.plots.plot_irfs.plot_sensitivity_from_table(sens_table, ax=None, **kwargs)#
lstmcpipe.plots.plot_irfs.plot_sensitivity_ratio(sensitivity_tables, baseline_index=0, ax=None, labels=None, **kwargs)#

Plot the ratio of sensitivities as a function of the energy

Parameters:

  • sensitivity_tables (list) – list of sensitivity tables

  • baseline_index (int) – index of the baseline to use in the list

  • ax (pyplot.axis)

  • labels (list) – list of labels to use

  • kwargs (kwargs for the plot)

Returns:

ax

Return type:

pyplot.axis

lstmcpipe.plots.plot_irfs.plot_sensitivity_ratio_from_files(filelist, baseline_index=0, ax=None, **kwargs)#

Plot the ratio of sensitivities as a function of the energy

Parameters:

  • sensitivity_tables (list) – list of sensitivity tables

  • baseline_index (int) – index of the baseline to use in the list

  • ax (pyplot.axis)

  • kwargs (kwargs for the plot)

Returns:

ax

Return type:

pyplot.axis

lstmcpipe.plots.plot_irfs.plot_summary_from_file(filename, axes=None, **kwargs)#
lstmcpipe.plots.plot_irfs.plot_theta_cut_from_file(filename, ax=None, **kwargs)#
lstmcpipe.plots.plot_irfs.read_sensitivity_table(irf_file)#

lstmcpipe.plots.plot_models_importance module#

lstmcpipe.plots.plot_models_importance.main()#

lstmcpipe.plots.pointings module#

lstmcpipe.plots.pointings.plot_pointings(pointings, ax=None, projection='polar', add_grid3d=False, **kwargs)#

Produce a scatter plot of the pointings

Parameters:

  • pointings (2D array of astropy.quantities or numpy array in rad)

  • ax (matplotlib.pyplot.Axis)

  • projection (str or None) – ‘3d’ | ‘aitoff’ | ‘hammer’ | ‘lambert’ | ‘mollweide’ | ‘polar’ | ‘rectilinear’

  • add_grid3d (bool) – add a 3D grid in case of projection=’3d’

  • kwargs (dict) – kwargs for matplotlib.pyplot.scatter

Returns:

ax

Return type:

matplotlib.pyplot.axis

Module contents#