Modules

plots.py

Functions to make IRF and other reconstruction quality-check plots

ctaplot.plots.plots.plot_angular_resolution_cta_performance(cta_site, ax=None, **kwargs)

Plot the official CTA performances (June 2018) for the angular resolution

Parameters:

• cta_site (string) – see ana.cta_performance

• ax (matplotlib.pyplot.axes) –

• kwargs (args for matplotlib.pyplot.plot) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_angular_resolution_cta_requirement(cta_site, ax=None, **kwargs)

Plot the CTA requirement for the angular resolution :param cta_site: see ctaplot.ana.cta_requirement :type cta_site: string :param ax: :type ax: matplotlib.pyplot.axes :param kwargs: :type kwargs: args for matplotlib.pyplot.plot

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_angular_resolution_per_energy(true_alt, reco_alt, true_az, reco_az, true_energy, percentile=68.27, confidence_level=0.95, bias_correction=False, ax=None, bins=None, **kwargs)

Plot the angular resolution as a function of the reconstructed true_energy

Parameters:

• reco_alt (astropy.Quantity) – array of reconstructed altitudes in radians

• reco_az (astropy.Quantity) – array of reconstructed azimuths in radians

• true_alt (astropy.Quantity) – array of true altitudes in radians

• true_az (astropy.Quantity) – array of true azimuths in radians

• reco_energy (astropy.Quantity) – array of energy in TeV

• ax (matplotlib.pyplot.axes) –

• bins (numpy.ndarray) –

• kwargs (args for matplotlib.pyplot.errorbar) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_angular_resolution_per_off_pointing_angle(true_alt, reco_alt, true_az, reco_az, alt_pointing, az_pointing, res_unit=Unit('deg'), bins=10, ax=None, **kwargs)

Plot the angular resolution as a function of the angular separation between events true position and the pointing direction. Angles must be given in radians.

Parameters:

• true_alt (numpy.ndarray) –

• true_az (numpy.ndarray) –

• reco_alt (numpy.ndarray) –

• reco_az (numpy.ndarray) –

• alt_pointing (numpy.ndarray) –

• az_pointing (numpy.ndarray) –

• res_degree (bool) – if True, the angular resolution is computed in degrees.

• bins (int or numpy.ndarray) –

• ax (matplotlib.pyplot.axes) –

• kwargs (kwargs for matplotlib.pyplot.errorbar) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_background_rate(e_min, e_max, background_rate, background_rate_err=None, ax=None, **kwargs)

Plot the background rate [Hz] as a function of the true_energy [TeV]

Parameters:

• e_min (numpy.ndarray) – Reconstructed true_energy in TeV

• e_max (numpy.ndarray) – Reconstructed true_energy in TeV

• background_rate (astropy.Quantity) – Background rate in Hz

• background_rate_err (astropy.Quantity) – error bar on the rate, either either 1D (symmetrical) or 2D

• ax (matplotlib.pyplot.axis) –

• kwargs (kwargs for matplotlib.pyplot.errobar) –

Return type:

matplotlib.pyplot.axis

ctaplot.plots.plots.plot_background_rate_magic(ax=None, **kwargs)

Plot the MAGIC background rate from Aleksić, Jelena, et al. 2016, DOI: 10.1016/j.astropartphys.2015.02.005

Parameters:

• ax (matplotlib.pyplot.axis or None) –

• kwargs (kwargs for ctaplot.plots.plot_background_rate) –

Return type:

matplotlib.pyplot.axis

ctaplot.plots.plots.plot_bias_per_energy(simu, reco, energy, relative_scaling_method=None, ax=None, bins=None, **kwargs)

Plot the bias per bins of true_energy

Parameters:

• simu (numpy.ndarray) –

• reco (numpy.ndarray) –

• energy (astropy.Quantity) –

• relative_scaling_method (str) – see ctaplot.ana.relative_scaling

• ax (matplotlib.pyplot.axis) –

• bins (astropy.Quantity) –

• kwargs (args for matplotlib.pyplot.errorbar) –

Returns:

ax

Return type:

matplotlib.pyplot.axis

ctaplot.plots.plots.plot_binned_bias(simu, reco, x, relative_scaling_method=None, ax=None, bins=10, log=False, **kwargs)

Plot the bias between true and reco as a function of bins of x

Parameters:

• simu (numpy.ndarray) –

• reco (numpy.ndarray) –

• x (numpy.ndarray) –

• relative_scaling_method (str) – see ctaplot.ana.relative_scaling

• ax (matplotlib.pyplot.axis) –

• bins (bins for numpy.histogram) –

• log (bool) – if True, logscale is applied to the x axis

• kwargs (args for matplotlib.pyplot.errorbar) –

Returns:

ax

Return type:

matplotlib.pyplot.axis

ctaplot.plots.plots.plot_binned_stat(x, y, statistic='mean', bins=20, errorbar=False, percentile=68.27, line=True, ax=None, **kwargs)

Plot statistics on the quantity y binned following the quantity x. The statistic can be given by a string (‘mean’, ‘sum’, ‘max’…) or a function. See scipy.stats.binned_statistic. Errorbars may be added and represents the dispersion (given by the percentile option) of the y distribution around the measured value in a bin. These error bars might not make sense for some statistics, it is left to the user to use the function responsibly.

Parameters:

• x (numpy.ndarray) –

• y (numpy.ndarray) –

• ax (matplotlib.pyplot.axes) –

• errorbar (bool) –

• statistic (string or callable - see scipy.stats.binned_statistic) –

• bins (bins for scipy.stats.binned_statistic) –

• kwargs (if errorbar: kwargs for matplotlib.pyplot.hlines else: kwargs for matplotlib.pyplot.plot) –

Return type:

matplotlib.pyplot.axes

Examples

>>> from ctaplot.plots import plot_binned_stat
>>> import numpy as np
>>> x = np.random.rand(1000)
>>> y = x**2
>>> plot_binned_stat(x, y, statistic='median', bins=40, percentile=95, line=False, color='red', errorbar=True, s=0)

ctaplot.plots.plots.plot_dispersion(true_x, reco_x, x_log=False, ax=None, **kwargs)

Plot the dispersion around an expected value X_true: (true_x-reco_x) as a function of true_x

Parameters:

• true_x (numpy.ndarray) – true value of a variable x

• reco_x (numpy.ndarray) – reconstructed value of a variable x

• x_log (bool) – if True, the dispersion is plotted as a function of log10(true_x)

• ax (matplotlib.pyplot.axes) –

• kwargs (args for matplotlib.pyplot.hist2d) –

Returns:

ax

Return type:

maptlotlib.pyplot.axes

ctaplot.plots.plots.plot_effective_area_cta_performance(cta_site, ax=None, **kwargs)

Plot the CTA performances for the effective area as a function of the true true_energy

Parameters:

• cta_site (string) – see ctaplot.ana.cta_requirement

• ax (matplotlib.pyplot.axes) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_effective_area_cta_requirement(cta_site, ax=None, **kwargs)

Plot the CTA requirement for the effective area as a function of the true true_energy

Parameters:

• cta_site (string) – see ctaplot.ana.cta_requirement

• ax (matplotlib.pyplot.axes) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_effective_area_per_energy(true_energy, reco_energy, simulated_area, ax=None, bins=None, **kwargs)

Plot the effective area as a function of the true true_energy

Parameters:

• true_energy (astropy.Quantity) – all simulated event energy

• reco_energy (astropy.Quantity) – all reconstructed event energy

• simulated_area (astropy.Quantity) –

• ax (matplotlib.pyplot.axes) –

• bins (numpy.ndarray) –

• kwargs (options for maplotlib.pyplot.errorbar) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

Example

>>> import numpy as np
>>> import ctaplot
>>> irf = ctaplot.ana.irf_cta()
>>> true_e = 10**(-2 + 4*np.random.rand(1000)) * u.TeV
>>> reco_e = 10**(-2 + 4*np.random.rand(100)) * u.TeV
>>> ax = ctaplot.plots.plot_effective_area_per_energy(true_e, reco_e, irf.LaPalmaArea_prod3)

ctaplot.plots.plots.plot_effective_area_per_energy_power_law(emin, emax, total_number_events, spectral_index, true_energy, simu_area, ax=None, bins=None, **kwargs)

Plot the effective area as a function of the true true_energy. The effective area is computed using the ctaplot.ana.effective_area_per_energy_power_law.

Parameters:

• emin (astropy.Quantity) – min simulated true_energy

• emax (astropy.Quantity) – max simulated true_energy

• total_number_events (int) – total number of simulated events

• spectral_index (float) – spectral index of the simulated power-law

• true_energy (astropy.Quantity) – array of reconstructed events’ true energy

• simu_area (astropy.Quantity) – simulated core area

• ax (matplotlib.pyplot.axes) –

• bins (numpy.ndarray) –

• kwargs (args for matplotlib.pyplot.errorbar) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_energy_bias(true_energy, reco_energy, ax=None, bins=None, **kwargs)

Plot the true_energy bias

Parameters:

• true_energy (astropy.Quantity) –

• reco_energy (astropy.Quantity) –

• ax (matplotlib.pyplot.axes) –

• bins (numpy.ndarray) –

• kwargs (args for matplotlib.pyplot.plot) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_energy_distribution(true_energy, reco_energy, bins=10, ax=None, outfile=None, mask_mc_detected=True)

Plot the true_energy distribution of the simulated particles, detected particles and reconstructed particles The plot might be saved automatically if outfile is provided.

Parameters:

• true_energy (astropy.Quantity) – array of simulated energy

• reco_energy (astropy.Quantity) – array of reconstructed energy

• bins (int or numpy.ndarray) –

• ax (matplotlib.pyplot.axes) –

• outfile (string) – output file path

• mask_mc_detected (numpy.ndarray) – mask of detected particles for the SimuE array if True (default), no mask is applied

ctaplot.plots.plots.plot_energy_resolution(true_energy, reco_energy, percentile=68.27, confidence_level=0.95, bias_correction=False, ax=None, bins=None, **kwargs)

Plot the enregy resolution as a function of the true_energy

Parameters:

• true_energy (astropy.Quantity) –

• reco_energy (astropy.Quantity) –

• ax (matplotlib.pyplot.axes) –

• bias_correction (bool) –

• bins (numpy.ndarray) –

• kwargs (args for matplotlib.pyplot.plot) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_energy_resolution_cta_performance(cta_site, ax=None, **kwargs)

Plot the cta performances (June 2018) for the true_energy resolution

Parameters:

• cta_site (string) – see ctaplot.ana.cta_performance

• ax (matplotlib.pyplot.axes) –

• kwargs (args for matplotlib.pyplot.plot) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_energy_resolution_cta_requirement(cta_site, ax=None, **kwargs)

Plot the cta requirement for the true_energy resolution

Parameters:

• cta_site (string) – see ana.cta_requirement

• ax (matplotlib.pyplot.axes) –

• kwargs (args for matplotlib.pyplot.plot) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_feature_importance(feature_keys, feature_importances, ax=None, **kwargs)

Plot features importance after model training (typically from scikit-learn)

Parameters:

• feature_keys (list of string) –

• feature_importances (numpy.ndarray or list) –

• ax (matplotlib.pyplot.axes) –

• kwargs (kwargs for matplotlib.pyplot.bar) –

Return type:

ax

ctaplot.plots.plots.plot_gamma_rate(e_min, e_max, gamma_rate, gamma_rate_err=None, ax=None, **kwargs)

Plot the gamma rate [Hz] as a function of the true_energy [TeV]

Parameters:

• e_min (numpy.ndarray) – Reconstructed true_energy in TeV

• e_max (numpy.ndarray) – Reconstructed true_energy in TeV

• gamma_rate (astropy.Quantity) – gamma rate in Hz

• gamma_rate_err (astropy.Quantity) – error bar on the rate, either either 1D (symmetrical) or 2D

• ax (matplotlib.pyplot.axis) –

• kwargs (kwargs for matplotlib.pyplot.errobar) –

Return type:

matplotlib.pyplot.axis

ctaplot.plots.plots.plot_gamma_rate_magic(ax=None, **kwargs)

Plot the MAGIC gamma rate from Aleksić, Jelena, et al. 2016, DOI: 10.1016/j.astropartphys.2015.02.005

Parameters:

• ax (matplotlib.pyplot.axis or None) –

• kwargs (kwargs for ctaplot.plots.plot_gamma_rate) –

Return type:

matplotlib.pyplot.axis

ctaplot.plots.plots.plot_gammaness_distribution(mc_type, gammaness, ax=None, **kwargs)

Plot the distribution of gammaness based on mc_type

Parameters:

• mc_type (numpy.ndarray) – true labeling

• gammaness (numpy.ndarray) – reconstructed gammaness

• ax (matplotlib.pyplot.axes) –

• kwargs (args for matplotlib.pyplot.hist) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_gammaness_threshold_efficiency(gammaness, efficiency, ax=None, **kwargs)

Plot the cumulative histogram of the gammaness with the threshold to obtain a give efficiency. See also ctaplot.ana.gammaness_threshold_efficiency.

Parameters:

• gammaness (numpy.ndarray) – gammaness of true events (e.g. gammas)

• efficiency (float) – between 0 and 1

• ax (matplotlib.pyplot.axes) –

• kwargs (kwargs for matplotlib.pyplot.hist) –

Returns:

ax: matplotlib.pyplot.axes threshold: float

Return type:

ax, threshold

ctaplot.plots.plots.plot_impact_map(impact_x, impact_y, tel_x, tel_y, tel_types=None, ax=None, outfile=None, hist_kwargs=None, scatter_kwargs=None)

Map of the site with telescopes positions and impact points heatmap

Parameters:

• impact_x (astropy.Quantity) –

• impact_y (astropy.Quantity) –

• tel_x (astropy.Quantity) –

• tel_y (astropy.Quantity) –

• tel_types (numpy.ndarray) –

• ax (matplotlib.pyplot.axes) –

• hist_kwargs (kwargs for matplotlib.pyplot.hist) –

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

• (optional) (outfile) –

ctaplot.plots.plots.plot_impact_parameter_error_site_center(true_x, reco_x, true_y, reco_y, ax=None, **kwargs)

Plot the impact parameter error as a function of the distance to the site center.

Parameters:

• reco_x (astropy.Quantity) –

• reco_y (astropy.Quantity) –

• true_x (astropy.Quantity) –

• true_y (astropy.Quantity) –

• ax (matplotlib.pyplot.axes) –

• kwargs (kwargs for matplotlib.pyplot.hist2d) –

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_impact_parameter_resolution_per_bin(x, true_x, reco_x, true_y, reco_y, bins=10, ax=None, **kwargs)

Plot the impact parameter error per bin

Parameters:

• x (numpy.ndarray) –

• reco_x (astropy.Quantity) –

• reco_y (astropy.Quantity) –

• true_x (astropy.Quantity) –

• true_y (astropy.Quantity) –

• bins (arg for np.histogram) –

• ax (matplotlib.pyplot.axes) –

• kwargs (args for plot_resolution) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_impact_parameter_resolution_per_energy(true_x, reco_x, true_y, reco_y, true_energy, ax=None, bins=None, **kwargs)

Parameters:

• true_x (astropy.Quantity) –

• reco_x (astropy.Quantity) –

• true_y (astropy.Quantity) –

• reco_y (astropy.Quantity) –

• true_energy (astropy.Quantity) –

• ax (matplotlib.pyplot.axes) –

• bins (astropy.Quantity) –

• kwargs (args for ctaplot.plots.plot_resolution) –

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_impact_point_heatmap(reco_x, reco_y, ax=None, outfile=None, **kwargs)

Plot the heatmap of the impact points on the site ground and save it under outfile

Parameters:

• reco_x (astropy.Quantity) – reconstructed x positions

• reco_y (astropy.Quantity) – reconstructed y positions

• ax (matplotlib.pyplot.axes) –

• outfile (string) – path to the output file. If None, the figure is not saved.

ctaplot.plots.plots.plot_impact_resolution_per_energy(true_x, reco_x, true_y, reco_y, true_energy, percentile=68.27, confidence_level=0.95, bias_correction=False, ax=None, bins=None, **kwargs)

Plot the impact resolution as a function of the true_energy

Parameters:

• reco_x (astropy.Quantity) –

• reco_y (astropy.Quantity) –

• true_x (astropy.Quantity) –

• true_y (astropy.Quantity) –

• true_energy (numpy.ndarray) –

• ax (matplotlib.pyplot.axes) –

• bins (numpy.ndarray) –

• kwargs (args for matplotlib.pyplot.errorbar) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_layout_map(tel_x, tel_y, tel_type=None, ax=None, **kwargs)

Plot the layout map of telescopes positions

Parameters:

• tel_x (astropy.Quantity) – telescopes x positions

• tel_y (astropy.Quantity) – telescopes y positions

• tel_type (numpy.ndarray) – telescopes types

• ax (matplotlib.pyplot.axes) – optional

• kwargs – options for matplotlib.pyplot.scatter

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_migration_matrix(x, y, ax=None, colorbar=False, xy_line=False, hist2d_args=None, line_args=None)

Make a simple plot of a migration matrix

Parameters:

• x (list or numpy.ndarray) –

• y (list or numpy.ndarray) –

• ax (matplotlib.pyplot.axes) –

• colorbar (bool) –

• hist2d_args (dict, args for matplotlib.pyplot.hist2d) –

• line_args (dict, args for matplotlib.pyplot.plot) –

Return type:

matplotlib.pyplot.axes

Examples

>>> from ctaplot.plots import plot_migration_matrix
>>> import matplotlib
>>> x = np.random.rand(10000)
>>> y = x**2
>>> plot_migration_matrix(x, y, colorbar=True, hist2d_args=dict(norm=matplotlib.colors.LogNorm()))
In this example, the colorbar will be log normed

ctaplot.plots.plots.plot_multiplicity_hist(multiplicity, ax=None, outfile=None, quartils=False, **kwargs)

Histogram of the telescopes multiplicity

Parameters:

• multiplicity (numpy.ndarray) –

• ax (matplotlib.pyplot.axes) –

• outfile (string) –

• **kwargs (args for matplotlib.pyplot.bar) –

ctaplot.plots.plots.plot_multiplicity_per_energy(energy, multiplicity, bins=10, ax=None, outfile=None, **kwargs)

Plot the telescope multiplicity as a function of the true_energy The plot might be saved automatically if outfile is provided.

Parameters:

• multiplicity (numpy.ndarray) – telescope multiplcity

• energy (numpy.ndarray) – event energies

• ax (matplotlib.pyplot.axes) –

• outfile (string) – path to the output file to save the figure

ctaplot.plots.plots.plot_precision_recall(y_true, proba_pred, pos_label=0, sample_weigth=None, threshold=None, ax=None, **kwargs)

Precision as a function of recall.

Parameters:

• y_true (ndarray of shape (n_samples,)) – True binary labels. If labels are not either {-1, 1} or {0, 1}, then pos_label should be explicitly given.

• proba_pred (ndarray of shape (n_samples,)) – Estimated probabilities or output of a decision function.

• pos_label (int or str, default=0) – The label of the positive class. The default is 0 for gammas’. When pos_label=None, if y_true is in {-1, 1} or {0, 1}, pos_label is set to 1, otherwise an error will be raised.

• sample_weigth (array-like of shape (n_samples,), default=None) – Sample weights.

• threshold (float) – between 0 and 1. Add a point on the curve corresponding to the given threshold.

• ax (matplotlib.pyplot.axes) –

• kwargs (kwargs for sklearn.metrics.PrecisionRecallDisplay) –

Returns:

display

Return type:

sklearn.metrics.PrecisionRecallDisplay

ctaplot.plots.plots.plot_rate(e_min, e_max, rate, rate_err=None, ax=None, **kwargs)

Plot the background rate [Hz] as a function of the true_energy [TeV]

Parameters:

• e_min (astropy.Quantity) – Reconstructed true_energy in TeV

• e_max (astropy.Quantity) – Reconstructed true_energy in TeV

• rate (astropy.Quantity) – rate in Hz

• rate_err (astropy.Quantity) – error bar on the rate, either 1D (symmetrical) or 2D

• ax (matplotlib.pyplot.axis) –

• kwargs (kwargs for matplotlib.pyplot.errobar) –

Return type:

matplotlib.pyplot.axis

ctaplot.plots.plots.plot_resolution(bins, res, log=False, ax=None, **kwargs)

Plot the passed resolution.

Parameters:

• bins (1D numpy.ndarray) –

• res (2D numpy.ndarray - output from ctpalot.ana.resolution) – res[:,0]: resolution res[:,1]: lower confidence limit res[:,2]: upper confidence limit

• log (bool) – if true, x is logscaled

• ax (matplotlib.pyplot.axes) –

• kwargs (kwargs for matplotlib.pyplot.errorbar) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_resolution_difference(bins, reference_resolution, new_resolution, ax=None, **kwargs)

Plot the algebric difference between a new resolution and reference resolution.

Parameters:

• bins (numpy.ndarray) –

• reference_resolution (numpy.ndarray) – output from ctaplot.ana.resolution

• new_resolution (numpy.ndarray) – output from ctaplot.ana.resolution

• ax (matplotlib.pyplot.axis) –

• kwargs (args for ctaplot.plots.plot_resolution) –

Returns:

ax

Return type:

matplotlib.pyplot.axis

ctaplot.plots.plots.plot_resolution_per_energy(true, reco, energy, ax=None, bins=None, **kwargs)

Plot a variable resolution as a function of the true_energy

Parameters:

• reco (numpy.ndarray) – reconstructed values of a variable

• true (numpy.ndarray) – true values of the variable

• energy (astropy.Quantity) – event energy in TeV

• ax (matplotlib.pyplot.axes) –

• bins (numpy.ndarray) –

• kwargs (args for matplotlib.pyplot.errorbar) –

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_roc_auc_per_energy(energy_bins, auc_scores, ax=None, **kwargs)

Plot AUC scores as a function of the energy. These can be computed thanks to ctaplot.ana.auc_per_energy

Parameters:

• energy_bins (numpy.ndarray) –

• auc_scores (numpy.ndarray) –

• ax (matplotlib.pyplot.axes or None) –

• kwargs (options for matplotlib.pyplot.errorbar) –

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_roc_curve(true_type, reco_proba, pos_label=None, sample_weight=None, drop_intermediate=True, ax=None, **kwargs)

Parameters:

• true_type (numpy.ndarray) – true labels: must contain only two labels of type int, float or str

• reco_proba (numpy.ndarray) – reconstruction probability, values must be between 0 and 1

• pos_label (int or str, default=None) – The label of the positive class. When pos_label=None, if y_true is in {-1, 1} or {0, 1}, pos_label is set to 1, otherwise an error will be raised.

• sample_weight (array-like of shape = [n_samples], optional) – Sample weights.

• drop_intermediate (boolean, optional (default=True)) – Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves.

• ax (matplotlib.pyplot.axis) –

• kwargs (args for matplotlib.pyplot.plot) –

Returns:

ax

Return type:

matplotlib.pyplot.axis

ctaplot.plots.plots.plot_roc_curve_gammaness(true_type, gammaness, gamma_label=0, sample_weight=None, drop_intermediate=True, ax=None, **kwargs)

Parameters:

• true_type (numpy.ndarray) – true labels: int, float or str

• gammaness (numpy.ndarray) – probability of each event to be a gamma, values must be between 0 and 1

• gamma_label (the label of the gamma class in true_type.) –

• sample_weight (array-like of shape = [n_samples], optional) – Sample weights.

• drop_intermediate (boolean, optional (default=True)) – Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves.

• ax (matplotlib.pyplot.axis) –

• kwargs (args for matplotlib.pyplot.plot) –

Returns:

ax

Return type:

matplotlib.pyplot.axis

ctaplot.plots.plots.plot_roc_curve_gammaness_per_energy(true_type, gammaness, true_energy, gamma_label=0, energy_bins=None, ax=None, **kwargs)

Plot a gamma ROC curve per gamma true_energy bin.

Parameters:

• true_type (numpy.ndarray) – true labels: int, float or str

• gammaness (numpy.ndarray) – probability of each event to be a gamma, values must be between 0 and 1

• true_energy (astropy.Quantity) – true_energy of the gamma events in TeV true_energy.shape == true_type.shape (but energy for events that are not gammas are not considered)

• gamma_label (the label of the gamma class in true_type.) –

• energy_bins (None or int or numpy.ndarray) – bins in true_energy. If energy_bins is None, the default binning given by ctaplot.ana.irf_cta().energy_bin if used. If energy_bins is an int, it defines the number of equal-width energy_bins in the given range. If energy_bins is a sequence, it defines a monotonically increasing array of bin edges, including the rightmost edge, allowing for non-uniform bin widths.

• sample_weight (array-like of shape = [n_samples], optional) – Sample weights.

• drop_intermediate (boolean, optional (default=True)) – Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves.

• ax (matplotlib.pyplot.axis) –

• kwargs (args for matplotlib.pyplot.plot) –

Returns:

ax

Return type:

matplotlib.pyplot.axis

ctaplot.plots.plots.plot_roc_curve_multiclass(true_type, reco_proba, pos_label=None, sample_weight=None, drop_intermediate=True, ax=None, **kwargs)

Plot a ROC curve for a multiclass classification.

Parameters:

• true_type (numpy.ndarray) – true labels: int, float or str

• reco_proba (dict of numpy.ndarray of shape (len(true_type), len(set(true_type))) – reconstruction probability for each class in true_type, values must be between 0 and 1

• pos_label (int or str, default=None) – The label of the positive class. When pos_label=None, the ROC curve of each class is ploted. If pos_label is not None, only the ROC curve of this class is ploted.

• sample_weight (array-like of shape = [n_samples], optional) – Sample weights.

• drop_intermediate (boolean, optional (default=True)) – Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves.

• ax (matplotlib.pyplot.axis) –

• kwargs (args for matplotlib.pyplot.plot) –

Returns:

ax

Return type:

matplotlib.pyplot.axis

ctaplot.plots.plots.plot_sensitivity_cta_performance(cta_site, ax=None, **kwargs)

Plot the CTA performances for the sensitivity

Parameters:

• cta_site (string) – see ctaplot.ana.cta_requirement

• ax (matplotlib.pyplot.axes) – optional

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_sensitivity_cta_requirement(cta_site, ax=None, **kwargs)

Plot the CTA requirement for the sensitivity :param cta_site: see ctaplot.ana.cta_requirement :type cta_site: string :param ax: optional :type ax: matplotlib.pyplot.axes

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.plot_sensitivity_magic_performance(key='lima_5off', ax=None, **kwargs)

Plot the MAGIC sensitivity from Aleksić, Jelena, et al. 2016, DOI: 10.1016/j.astropartphys.2015.02.005

Parameters:

• key (string) – ‘lima_1off’: LiMa 1 off position ‘lima_3off’: LiMa 3 off positions ‘lima_5off’: LiMa 5 off positions ‘snr’: Nex/sqrt(Nbkg)

• ax (matplotlib.pyplot.axis) –

• kwargs (kwargs for matplotlib.pyplot.errorbar) –

Return type:

matplotlib.pyplot.axis

ctaplot.plots.plots.plot_theta2(true_alt, reco_alt, true_az, reco_az, bias_correction=False, ax=None, **kwargs)

Plot the theta2 distribution and display the corresponding angular resolution in degrees. The input must be given in radians.

Parameters:

• reco_alt (astropy.Quantity) – reconstructed altitude angle in radians

• reco_az (astropy.Quantity) – reconstructed azimuth angle in radians

• true_alt (astropy.Quantity) – true altitude angle in radians

• true_az (astropy.Quantity) – true azimuth angle in radians

• ax (matplotlib.pyplot.axes) –

• **kwargs – options for matplotlib.pyplot.hist

Returns:

ax

Return type:

matplotlib.pyplot.axes

ctaplot.plots.plots.scatter_events_field_of_view(reco_alt, reco_az, source_alt, source_az, color_scale=None, ax=None)

Plot a map in angles [in degrees] of the photons seen by the telescope (after reconstruction)

Parameters:

• reco_alt (astropy.Quantity) – array of reconstructed altitudes

• reco_az (astropy.Quantity) – array of reconstructed azimuths

• source_alt (astropy.Quantity) – single altitude of the source

• source_az (astropy.Quantity) – single azimuth of the source

• color_scale (numpy.ndarray) – if given, set the colorbar

• ax (matplotlib.pyplot.axes) –

• outfile (string) – path to the output figure file. if None, the plot is not saved

Returns:

ax

Return type:

matplitlib.pyplot.axes

ctaplot.plots.calib.plot_photoelectron_true_reco(true_pe, reco_pe, bins=200, stat='median', errorbar=True, percentile=68.27, ax=None, hist_args={}, stat_args={}, xy_args={})

Plot the number of reconstructed photo-electrons as a function of the number of true photo-electron

Parameters:

• true_pe (numpy.ndarray) – shape: (n_pixels, )

• reco_pe (numpy.ndarray) – shape: (n_pixels, )

• bins (int or numpy.ndarray) –

• stat (str or None) – ‘mean’, ‘median’, ‘min’, ‘max’. if None, not plotted.

• errorbar (bool) – plot the errorbar corresponding to the percentile as colored area around the stat line

• percentile (float) – between 0 and 100 percentile for the errorbars

• ax (matplotlib.pyplot.axis or None) –

• hist_args (args for pyplot.hist2d) –

• stat_args (args for ctaplot.plots.plot_binned_stat) –

• xy_args (args for pyplot.plot) –

Returns:

ax

Return type:

matplotlib.pyplot.axis

Gridded plots

ctaplot.plots.grid.plot_binned_stat_grid(data, x_col, n_cols=4, **binned_stat_args)

Make a figure with a grid of binned stat plots. All variable in data are plotted versus the x_col variable.

Parameters:

• data (pandas.dataframe) –

• x_col (str) – name of the column in the data to consider as X variable.

• n_col (int) – number of columns in the plot grid. The number of rows in determined automatically.

• binned_stat_args (args for ctaplot.plot.plot_binned_stat) –

Return type:

matplotlib.figure.Figure

ana.py

Contain mathematical functions to make results analysis (compute angular resolution, effective surface, true_energy resolution… )

ctaplot.ana.ana.angular_resolution(true_alt, reco_alt, true_az, reco_az, percentile=68.27, confidence_level=0.95, bias_correction=False)

Compute the angular resolution as the Qth (standard being 68) containment radius of theta2 with lower and upper limits on this value corresponding to the confidence value required (1.645 for 95% confidence)

Parameters:

• reco_alt (astropy.Quantity) – reconstructed altitude angle in radians

• reco_az (astropy.Quantity) – reconstructed azimuth angle in radians

• true_alt (astropy.Quantity) – true altitude angle in radians

• true_az (astropy.Quantity) – true azimuth angle in radians

• percentile (float - percentile, 68 corresponds to one sigma) –

• confidence_level (float) –

Return type:

numpy.array [angular_resolution, lower limit, upper limit]

ctaplot.ana.ana.angular_resolution_per_bin(true_alt, reco_alt, true_az, reco_az, x, percentile=68.27, confidence_level=0.95, bias_correction=False, bins=10)

Compute the angular resolution per binning of x

Parameters:

• true_alt (astropy.Quantity) –

• true_az (astropy.Quantity) –

• reco_alt (astropy.Quantity) –

• reco_az (astropy.Quantity) –

• x (numpy.ndarray) –

• percentile (float) – 0 < percentile < 100

• confidence_level (float) – 0 < confidence_level < 1

• bias_correction (bool) –

• bins (int or numpy.ndarray) –

Returns:

bins, ang_res

Return type:

(numpy.ndarray, numpy.ndarray)

ctaplot.ana.ana.angular_resolution_per_energy(true_alt, reco_alt, true_az, reco_az, energy, percentile=68.27, confidence_level=0.95, bias_correction=False, bins=None)

Plot the angular resolution as a function of the event simulated true_energy

Parameters:

• reco_alt (astropy.Quantity) –

• reco_az (astropy.Quantity) –

• true_alt (astropy.Quantity) –

• true_az (astropy.Quantity) –

• energy (astropy.Quantity) –

• bins (astropy.Quantity) –

• **kwargs (args for angular_resolution) –

Returns:

(energy, RES)

Return type:

(astropy.Quantity, numpy.array)

ctaplot.ana.ana.angular_resolution_per_off_pointing_angle(true_alt, reco_alt, true_az, reco_az, alt_pointing, az_pointing, bins=10)

Compute the angular resolution as a function of separation angle for the pointing direction

Parameters:

• true_alt (astropy.Quantity) –

• true_az (astropy.Quantity) –

• reco_alt (astropy.Quantity) –

• reco_az (astropy.Quantity) –

• alt_pointing (astropy.Quantity) –

• az_pointing (astropy.Quantity) –

• bins (int or astropy.Quantity) –

Returns:

bins: 1D astropy.Quantity res: 2D numpy.ndarray - resolutions with confidence intervals (output from ctaplot.ana.resolution)

Return type:

(bins, res)

ctaplot.ana.ana.angular_separation_altaz(alt1, az1, alt2, az2)

Compute the angular separation in radians or degrees between two pointing direction given with alt-az

Parameters:

• alt1 (1d astropy.Quantity, altitude of the first pointing direction) –

• az1 (1d astropy.Quantity azimuth of the first pointing direction) –

• alt2 (1d astropy.Quantity, altitude of the second pointing direction) –

• az2 (1d astropy.Quantity, azimuth of the second pointing direction) –

Return type:

1d numpy.ndarray or float, angular separation

ctaplot.ana.ana.bias(true, reco)

Compute the bias of a reconstructed variable as median(reco-true)

Parameters:

• true (numpy.ndarray) –

• reco (numpy.ndarray) –

Return type:

float

ctaplot.ana.ana.bias_per_bin(true, reco, x, relative_scaling_method=None, bins=10)

Bias between true and reco per bin of x.

Parameters:

• true (numpy.ndarray) –

• reco (numpy.ndarray) –

• x (: numpy.ndarray) –

• relative_scaling_method (str) – see ctaplot.ana.relative_scaling

• bins (bins for numpy.histogram) –

Returns:

bins, bias

Return type:

numpy.ndarray, numpy.ndarray

ctaplot.ana.ana.bias_per_energy(true, reco, energy, relative_scaling_method=None, energy_bins=None)

Bias between true and reco per bins of true_energy

Parameters:

• true (numpy.ndarray) –

• reco (numpy.ndarray) –

• energy (: astropy.Quantity) –

• relative_scaling_method (str) – see ctaplot.ana.relative_scaling

• bins (astropy.Quantity) –

Returns:

bins, bias

Return type:

astropy.Quantity, numpy.ndarray

class ctaplot.ana.ana.cta_performance(site)

Bases: object

get_angular_resolution()

get_effective_area(observation_time=<Quantity 50. h>)

Return the effective area at the given observation time in hours. NB: Only 50h supported Returns the true_energy array and the effective area array :param observation_time: :type observation_time: optional

Return type:

numpy.ndarray, numpy.ndarray

get_energy_resolution()

get_sensitivity(observation_time=<Quantity 50. h>)

class ctaplot.ana.ana.cta_requirement(site)

Bases: object

get_angular_resolution()

get_effective_area(observation_time=<Quantity 50. h>)

Return the effective area at the given observation time in hours. NB: Only 0.5h supported Returns the true_energy array and the effective area array :param observation_time: :type observation_time: optional

Return type:

numpy.ndarray, numpy.ndarray

get_energy_resolution()

get_sensitivity(observation_time=<Quantity 50. h>)

ctaplot.ana.ana.distance2d_resolution(true_x, reco_x, true_y, reco_y, percentile=68.27, confidence_level=0.95, bias_correction=False, relative_scaling_method=None)

Compute the 2D distance resolution as the Qth (standard being 68) containment radius of the relative distance with lower and upper limits on this value corresponding to the confidence value required (1.645 for 95% confidence)

Parameters:

• true_x (numpy.ndarray or astropy.units.Quantity) –

• reco_x (numpy.ndarray or astropy.units.Quantity) –

• true_y (numpy.ndarray or astropy.units.Quantity) –

• reco_y (numpy.ndarray`or `astropy.units.Quantity) –

• percentile (float - percentile, 68.27 corresponds to one sigma) –

• confidence_level (float) –

• bias_correction (bool) –

• relative_scaling_method (str) –

  • see ctaplot.ana.relative_scaling

Return type:

numpy.array [resolution, lower limit, upper limit]

ctaplot.ana.ana.distance2d_resolution_per_bin(x, true_x, reco_x, true_y, reco_y, bins=10, percentile=68.27, confidence_level=0.95, bias_correction=False, relative_scaling_method=None)

Compute the 2D distance per bin of x

Parameters:

• x (numpy.ndarray) –

• reco_x (numpy.ndarray) –

• reco_y (numpy.ndarray) –

• true_x (numpy.ndarray) –

• true_y (numpy.ndarray) –

• bins (bins args of np.histogram) –

• percentile (float - percentile, 68.27 corresponds to one sigma) –

• confidence_level (float) –

• bias_correction (bool) –

• relative_scaling_method (str) – see ctaplot.ana.relative_scaling

Return type:

x_bins, distance_res

ctaplot.ana.ana.effective_area(true_energy, reco_energy, simu_area)

Compute the effective area from a list of simulated energy and reconstructed energy :param true_energy: :type true_energy: 1d numpy array :param reco_energy: :type reco_energy: 1d numpy array :param simu_area: :type simu_area: float - area on which events are simulated

Return type:

float = effective area

ctaplot.ana.ana.effective_area_per_energy(true_energy, reco_energy, simu_area, bins=None)

Compute the effective area per true_energy bins from a list of simulated energy and reconstructed energy

Parameters:

• true_energy (astropy.Quantity) –

• reco_energy (astropy.Quantity) –

• simu_area (astropy.Quantity) – area on which events are simulated

• bins (astropy.Quantity) –

Returns:

(energy, Seff)

Return type:

(1d numpy array, 1d numpy array)

ctaplot.ana.ana.effective_area_per_energy_power_law(emin, emax, total_number_events, spectral_index, true_energy, simu_area, bins=None)

Compute the effective area per true_energy bins from a list of simulated energy and reconstructed energy

Parameters:

• emin (astropy.Quantity) –

• emax (astropy.Quantity) –

• total_number_events (int) –

• spectral_index (float) –

• true_energy (1d astropy.Quantity) –

• simu_area (astropy.Quantity) – area on which events are simulated

• bins (astropy.Quantity) –

Returns:

(true_energy bins, effective_area)

Return type:

(astropy.Quantity array, 1d numpy array)

ctaplot.ana.ana.energy_bias(true_energy, reco_energy, bins=None)

Compute the true_energy relative bias per true_energy bin.

Parameters:

• true_energy (astropy.Quantity (1d array)) – simulated energies

• reco_energy (astropy.Quantity (1d array)) – reconstructed energies

• bins (astropy.Quantity (1d array)) – energy bins - if None, standard CTA binning is used

Returns:

(energy_bins, bias) – true_energy, true_energy bias

Return type:

(astropy.Quantity, numpy.array)

ctaplot.ana.ana.energy_resolution(true_energy, reco_energy, percentile=68.27, confidence_level=0.95, bias_correction=False)

Compute the true_energy resolution of true_energy as the percentile (68 as standard) containment radius of true_energy-true_energy)/true_energy with the lower and upper confidence limits defined by the given confidence level

Parameters:

• true_energy (1d numpy array of simulated energy) –

• reco_energy (1d numpy array of reconstructed energy) –

• percentile (float) – <= 100

Return type:

numpy.array - [energy_resolution, lower_confidence_limit, upper_confidence_limit]

ctaplot.ana.ana.energy_resolution_per_energy(true_energy, reco_energy, percentile=68.27, confidence_level=0.95, bias_correction=False, bins=None)

The true_energy resolution ΔE / energy is obtained from the distribution of (ER – ET) / ET, where R and T refer to the reconstructed and true energy of gamma-ray events. ΔE/energy is the half-width of the interval around 0 which contains given percentile of the distribution.

Parameters:

• true_energy (astropy.Quantity) – 1d array of simulated energy

• reco_energy (astropy.Quantity) – 1d array of reconstructed energy

• percentile (float) – between 0 and 100

• confidence_level (float) – between 0 and 1

• bias_correction (bool) –

• bins (int | astropy.Quantity) –

Returns:

(e, e_res) – true_energy, resolution in true_energy

Return type:

(astropy.Quantity, numpy.array)

ctaplot.ana.ana.get_magic_sensitivity()

Load MAGIC differential sensitivity data from file magic_sensitivity_2014.ecsv. Extracted from table A.7 in Aleksić, Jelena, et al. “The major upgrade of the MAGIC telescopes, Part II: A performance study using observations of the Crab Nebula.” Astroparticle Physics 72 (2016): 76-94., DOI: 10.1016/j.astropartphys.2015.02.005’

Return type:

astropy.table.table.QTable

ctaplot.ana.ana.impact_parameter_error(true_x, reco_x, true_y, reco_y)

compute the error distance between true and reconstructed impact parameters :param reco_x: :type reco_x: astropy.Quantity :param reco_y: :type reco_y: astropy.Quantity :param true_x: :type true_x: astropy.Quantity :param true_y: :type true_y: astropy.Quantity

Returns:

1d numpy array

Return type:

distances

ctaplot.ana.ana.impact_resolution(true_x, reco_x, true_y, reco_y, percentile=68.27, confidence_level=0.95, bias_correction=False, relative_scaling_method=None)

Compute the shower impact parameter resolution as the Qth (68 as standard) containment radius of the square distance to the simulated one with the lower and upper limits corresponding to the required confidence level

Parameters:

• reco_x (astropy.Quantity) –

• reco_y (astropy.Quantity) –

• true_x (astropy.Quantity) –

• true_y (astropy.Quantity) –

• percentile (float) – see ctaplot.ana.resolution

• confidence_level (float) – see ctaplot.ana.resolution

• bias_correction (bool) – see ctaplot.ana.resolution

• relative_scaling_method (str) – see ctaplot.ana.relative_scaling

Returns:

(impact_resolution, lower_confidence_level, upper_confidence_level)

Return type:

(numpy.array, numpy.array, numpy.array)

ctaplot.ana.ana.impact_resolution_per_energy(true_x, reco_x, true_y, reco_y, true_energy, percentile=68.27, confidence_level=0.95, bias_correction=False, relative_scaling_method=None, bins=None)

Plot the angular resolution as a function of the event simulated true_energy

Parameters:

• reco_x (astropy.Quantity) –

• reco_y (astropy.Quantity) –

• true_x (astropy.Quantity) –

• true_y (astropy.Quantity) –

• true_energy (astropy.Quantity) –

• percentile (float) – see ctaplot.ana.resolution

• confidence_level (float) – see ctaplot.ana.resolution

• bias_correction (bool) – see ctaplot.ana.resolution

• relative_scaling_method (str) – see ctaplot.ana.relative_scaling

• bins (astropy.Quantity) –

Returns:

(true_energy, resolution)

Return type:

(astropy.Quantity, 1d numpy array)

class ctaplot.ana.ana.irf_cta

Bases: object

Class to handle Instrument Response Function data

set_ebin(energy_bins)

ctaplot.ana.ana.logbin_mean(x_bin)

Function that gives back the mean of each bin in logscale

Parameters:

x_bin (numpy.ndarray) –

Return type:

numpy.ndarray

ctaplot.ana.ana.logspace_decades_nbin(x_min, x_max, n=5)

return an array with logspace and n bins / decade

Parameters:

• x_min (float) –

• x_max (float) –

• n (int - number of bins per decade) –

Returns:

bins

Return type:

1D Numpy array

ctaplot.ana.ana.percentile_confidence_interval(x, percentile=68, confidence_level=0.95)

Return the confidence interval for the qth percentile of x for a given confidence level

REF: http://people.stat.sfu.ca/~cschwarz/Stat-650/Notes/PDF/ChapterPercentiles.pdf S. Chakraborti and J. Li, Confidence Interval Estimation of a Normal Percentile, doi:10.1198/000313007X244457

Parameters:

• x (numpy.ndarray) –

• percentile (float) – 0 < percentile < 100

• confidence_level (float) – 0 < confidence level (by default 95%) < 1

ctaplot.ana.ana.power_law_integrated_distribution(xmin, xmax, total_number_events, spectral_index, bins)

For each bin, return the expected number of events for a power-law distribution. bins: numpy.ndarray, e.g. np.logspace(np.log10(emin), np.logspace(xmax))

Parameters:

• xmin (float, min of the simulated power-law) –

• xmax (float, max of the simulated power-law) –

• total_number_events (int) –

• spectral_index (float) –

• bins (numpy.ndarray) –

Returns:

y

Return type:

numpy.ndarray, len(y) = len(bins) - 1

ctaplot.ana.ana.relative_bias(true, reco, relative_scaling_method='s1')

Compute the relative bias of a reconstructed variable as median(reco-true)/relative_scaling(true, reco)

Parameters:

• true (numpy.ndarray) –

• reco (numpy.ndarray) –

• relative_scaling_method (str) – see ctaplot.ana.relative_scaling

ctaplot.ana.ana.relative_scaling(true, reco, method='s0')

Define the relative scaling for the relative error calculation. There are different ways to calculate this scaling factor. The easiest and most spread one is simply np.abs(true). However this is possible only when true != 0. Possible methods: - None or ‘s0’: scale = 1 - ‘s1’: scale = np.abs(true) - ‘s2’: scale = np.abs(reco) - ‘s3’: scale = (np.abs(true) + np.abs(reco))/2. - ‘s4’: scale = np.max([np.abs(reco), np.abs(true)], axis=0)

This method is not exposed but kept for tests and future reference. The s1 method is used in all ctaplot functions.

Parameters:

• true (numpy.ndarray) –

• reco (numpy.ndarray) –

Return type:

numpy.ndarray

ctaplot.ana.ana.resolution(true, reco, percentile=68.27, confidence_level=0.95, bias_correction=False, relative_scaling_method='s1')

Compute the resolution of reco as the Qth (68.27 as standard = 1 sigma) containment radius of (true-reco)/relative_scaling with the lower and upper confidence limits defined the values inside the error_percentile

Parameters:

• true (numpy.ndarray (1d)) – simulated quantity

• reco (numpy.ndarray (1d)) – reconstructed quantity

• percentile (float) – percentile for the resolution containment radius

• error_percentile (float) – percentile for the confidence limits

• bias_correction (bool) – if True, the resolution is corrected with the bias computed on true and reco

• relative_scaling (str) – see ctaplot.ana.relative_scaling

Return type:

numpy.ndarray - [resolution, lower_confidence_limit, upper_confidence_limit]

ctaplot.ana.ana.resolution_per_bin(x, y_true, y_reco, percentile=68.27, confidence_level=0.95, bias_correction=False, relative_scaling_method=None, bins=10)

Resolution of y as a function of binned x.

Parameters:

• x (numpy.ndarray) –

• y_true (numpy.ndarray) –

• y_reco (numpy.ndarray) –

• percentile (float) –

• confidence_level (float) –

• bias_correction (bool) –

• relative_scaling_method (see ctaplot.ana.relative_scaling) –

• bins (int or numpy.ndarray (see numpy.histogram)) –

Returns:

(x_bins, res) – x_bins: bins for x res: resolutions with confidence level intervals for each bin

Return type:

(numpy.ndarray, numpy.ndarray)

ctaplot.ana.ana.resolution_per_energy(true, reco, true_energy, percentile=68.27, confidence_level=0.95, bias_correction=False, bins=None)

Parameters:

• true (1d numpy.ndarray of simulated quantity) –

• reco (1d numpy.ndarray of reconstructed quantity) –

• true_energy (astropy.Quantity (1d array)) – len(true_energy) == len(true) == len(reco)

• bins (astropy.Quantity (1d array)) –

Returns:

energy_bins - 1D numpy.ndarray resolution: - 3D numpy.ndarray see ctaplot.ana.resolution

Return type:

(energy_bins, resolution)

ctaplot.ana.ana.roc_auc_per_energy(true_type, gammaness, true_energy, energy_bins=None, gamma_label=0, **roc_auc_score_opt)

Compute AUC score as a function of the true gamma energy. The AUC score is calculated in a gamma versus all fashion.

Parameters:

• true_type (numpy.ndarray) – labels

• gammaness (numpy.ndarray) – likeliness of a particle to be a gamma

• true_energy (numpy.ndarray) – particles true energy

• energy_bins (astropy.Quantity) –

• gamma_label (label of gammas in true_type array) –

• roc_auc_score_opt (see sklearn.metrics.roc_auc_score options) –

Returns:

energy_bins, auc_scores

Return type:

numpy.ndarray, numpy.ndarray

ctaplot.ana.ana.stat_per_energy(energy, y, statistic='mean', bins=None)

Return statistic for the given quantity per energy bins. The binning is given by irf_cta

Parameters:

• energy (astropy.Quantity (1d array)) – event energy

• y (astropy.Quantity or numpy.ndarray (1d array)) – len(y) == len(energy)

• statistic (string) – see scipy.stat.binned_statistic

• bins (astropy.Quantity (1d array)) –

Returns:

bin_stat, bin_edges, binnumber

Return type:

astropy.Quantity or numpy.ndarray, astropy.Quantity, numpy.ndarray

ctaplot.io.dataset.get(resource_name)

get the filename for a resource