Modules

plots.py

Functions to make IRF and other reconstruction quality-check plots

ctaplot.plots.plot_angles_distribution(RecoAlt, RecoAz, AltSource, AzSource, Outfile=None)

Plot the distribution of reconstructed angles. Save figure to Outfile in png format.

Parameters:
  • RecoAlt (numpy.ndarray) –
  • RecoAz (numpy.ndarray) –
  • AltSource (float) –
  • AzSource (float) –
  • Outfile (string) –
ctaplot.plots.plot_angles_map_distri(RecoAlt, RecoAz, AltSource, AzSource, E, Outfile=None)

Plot the angles map distribution

Parameters:
  • RecoAlt (numpy.ndarray) –
  • RecoAz (numpy.ndarray) –
  • AltSource (float) –
  • AzSource (float) –
  • E (numpy.ndarray) –
  • Outfile (str) –
Returns:

fig
Return type:

matplotlib.pyplot.figure
ctaplot.plots.plot_angular_res_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_performances) –
  • ax (matplotlib.pyplot.axes) –
  • kwargs (args for matplotlib.pyplot.plot) –
Returns:

ax
Return type:

matplotlib.pyplot.axes
ctaplot.plots.plot_angular_res_cta_requirements(cta_site, ax=None, **kwargs)

Plot the CTA requirement for the angular resolution :param cta_site: :type cta_site: string, see ana.cta_requirements :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.plot_angular_res_per_energy(RecoAlt, RecoAz, AltSource, AzSource, SimuE, percentile=68.27, confidence_level=0.95, bias_correction=False, ax=None, **kwargs)

Plot the angular resolution as a function of the energy

Parameters:
  • RecoAlt (numpy.ndarray) –
  • RecoAz (numpy.ndarray) –
  • AltSource (float) –
  • AzSource (float) –
  • SimuE (numpy.ndarray) –
  • ax (matplotlib.pyplot.axes) –
  • kwargs (args for matplotlib.pyplot.errorbar) –
Returns:

ax
Return type:

matplotlib.pyplot.axes
ctaplot.plots.plot_binned_stat(x, y, ax=None, errorbar=True, statistic='mean', bins=20, percentile=68, **kwargs)

Plot binned statistic with errorbars corresponding to the given percentile

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) –
Returns:
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)
ctaplot.plots.plot_dispersion(X_true, X_exp, x_log=False, ax=None, **kwargs)

Plot the dispersion around an expected value X_true

Parameters:
  • X_true (numpy.ndarray) –
  • X_exp (numpy.ndarray) –
  • ax (matplotlib.pyplot.axes) –
  • kwargs (args for matplotlib.pyplot.hist2d) –
Returns:
Return type:

maptlotlib.pyplot.axes
ctaplot.plots.plot_effective_area_cta_performances(cta_site, ax=None, **kwargs)

Plot the CTA performances for the effective area

Parameters:
  • cta_site (string - see hipectaold.ana.cta_requirements) –
  • ax (matplotlib.pyplot.axes, optional) –
Returns:

ax
Return type:

matplotlib.pyplot.axes
ctaplot.plots.plot_effective_area_cta_requirements(cta_site, ax=None, **kwargs)

Plot the CTA requirement for the effective area

Parameters:
  • cta_site (string - see hipectaold.ana.cta_requirements) –
  • ax (matplotlib.pyplot.axes, optional) –
Returns:

ax
Return type:

matplotlib.pyplot.axes
ctaplot.plots.plot_effective_area_per_energy(SimuE, RecoE, simuArea, ax=None, **kwargs)

Plot the effective area as a function of the energy

Parameters:
  • SimuE (numpy.ndarray - all simulated event energies) –
  • RecoE (numpy.ndarray - all reconstructed event energies) –
  • simuArea (float) –
  • ax (matplotlib.pyplot.axes) –
  • 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()
>>> simue = 10**(-2 + 4*np.random.rand(1000))
>>> recoe = 10**(-2 + 4*np.random.rand(100))
>>> ax = ctaplot.plots.plot_effective_area_per_energy(simue, recoe, irf.LaPalmaArea_prod3)
ctaplot.plots.plot_effective_area_per_energy_power_law(emin, emax, total_number_events, spectral_index, reco_energy, simu_area, ax=None, **kwargs)

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

Parameters:
  • emin (float) – min simulated energy
  • emax (float) – max simulated energy
  • total_number_events (int) – total number of simulated events
  • spectral_index (float) – spectral index of the simulated power-law
  • reco_energy (numpy.ndarray) – reconstructed energies
  • simu_area (float) – simulated core area
  • ax (matplotlib.pyplot.axes) –
  • kwargs (args for matplotlib.pyplot.errorbar) –
Returns:

ax
Return type:

matplotlib.pyplot.axes
ctaplot.plots.plot_energy_bias(SimuE, RecoE, ax=None, **kwargs)

Plot the energy bias

Parameters:
  • SimuE (numpy.ndarray) –
  • RecoE (numpy.ndarray) –
  • ax (matplotlib.pyplot.axes) –
  • kwargs (args for matplotlib.pyplot.plot) –
Returns:

ax
Return type:

matplotlib.pyplot.axes
ctaplot.plots.plot_energy_distribution(SimuE, RecoE, ax=None, outfile=None, maskSimuDetected=True)

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

Parameters:
  • SimuE (Numpy 1d array of simulated energies) –
  • RecoE (Numpy 1d array of reconstructed energies) –
  • ax (matplotlib.pyplot.axes) –
  • outfile (string - output file path) –
  • - Numpy 1d array - mask of detected particles for the SimuE array (maskSimuDetected) –
ctaplot.plots.plot_energy_resolution(SimuE, RecoE, percentile=68.27, confidence_level=0.95, bias_correction=False, ax=None, **kwargs)

Plot the enregy resolution as a function of the energy

Parameters:
  • SimuE (numpy.ndarray) –
  • RecoE (numpy.ndarray) –
  • ax (matplotlib.pyplot.axes) –
  • bias_correction (bool) –
  • kwargs (args for matplotlib.pyplot.plot) –
Returns:

ax
Return type:

matplotlib.pyplot.axes
ctaplot.plots.plot_energy_resolution_cta_performances(cta_site, ax=None, **kwargs)

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

Parameters:
  • cta_site (string, see ana.cta_performances) –
  • ax (matplotlib.pyplot.axes) –
  • kwargs (args for matplotlib.pyplot.plot) –
Returns:

ax
Return type:

matplotlib.pyplot.axes
ctaplot.plots.plot_energy_resolution_cta_requirements(cta_site, ax=None, **kwargs)

Plot the cta requirement for the energy resolution

Parameters:
  • cta_site (string, see ana.cta_requirements) –
  • ax (matplotlib.pyplot.axes) –
  • kwargs (args for matplotlib.pyplot.plot) –
Returns:

ax
Return type:

matplotlib.pyplot.axes
ctaplot.plots.plot_feature_importance(feature_keys, feature_importances, ax=None)

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

Parameters:
  • feature_keys (list of string) –
  • feature_importances (numpy.ndarray) –
  • ax (matplotlib.pyplot.axes) –
Returns:
Return type:

ax
ctaplot.plots.plot_field_of_view_map(RecoAlt, RecoAz, AltSource, AzSource, E=None, ax=None, Outfile=None)

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

Parameters:
  • RecoAlt (numpy.ndarray) –
  • RecoAz (numpy.ndarray) –
  • AltSource (float, source Altitude) –
  • AzSource (float, source Azimuth) –
  • E (numpy.ndarray) –
  • Outfile (string) –
ctaplot.plots.plot_impact_map(impactX, impactY, telX, telY, telTypes=None, Outfile='ImpactMap.png')

Map of the site with telescopes positions and impact points heatmap

Parameters:
  • impactX (numpy.ndarray) –
  • impactY (numpy.ndarray) –
  • telX (numpy.ndarray) –
  • telY (numpy.ndarray) –
  • telTypes (numpy.ndarray) –
  • Outfile (string - name of the output file) –
ctaplot.plots.plot_impact_parameter_error(RecoX, RecoY, SimuX, SimuY, ax=None, **kwargs)

plot impact parameter error distribution and save it under Outfile :param RecoX: :type RecoX: numpy.ndarray :param RecoY: :type RecoY: numpy.ndarray :param SimuX: :type SimuX: numpy.ndarray :param SimuY: :type SimuY: numpy.ndarray :param Outfile: :type Outfile: string

ctaplot.plots.plot_impact_parameter_error_per_energy(RecoX, RecoY, SimuX, SimuY, SimuE, ax=None, **kwargs)

plot the impact parameter error distance as a function of energy and save the plot as Outfile :param RecoX: :type RecoX: numpy.ndarray :param RecoY: :type RecoY: numpy.ndarray :param SimuX: :type SimuX: numpy.ndarray :param SimuY: :type SimuY: numpy.ndarray :param SimuE: :type SimuE: numpy.ndarray :param ax: :type ax: matplotlib.pyplot.axes :param kwargs: :type kwargs: args for matplotlib.pyplot.errorbar

Returns:E, err_mean
Return type:numpy arrays
ctaplot.plots.plot_impact_parameter_error_per_multiplicity(RecoX, RecoY, SimuX, SimuY, Multiplicity, max_mult=None, ax=None, **kwargs)

Plot the impact parameter error as a function of multiplicity TODO: refactor and clean code

Parameters:
  • RecoX (numpy.ndarray) –
  • RecoY (numpy.ndarray) –
  • SimuX (numpy.ndarray) –
  • SimuY (numpy.ndarray) –
  • Multiplicity (numpy.ndarray) –
  • max_mult (optional, max multiplicity - float) –
  • ax (matplotlib.pyplot.axes) –
ctaplot.plots.plot_impact_parameter_error_site_center(reco_x, reco_y, simu_x, simu_y, ax=None, **kwargs)

Plot the impact parameter error as a function of the distance to the site center. :param reco_x: :type reco_x: numpy.ndarray :param reco_y: :type reco_y: numpy.ndarray :param simu_x: :type simu_x: numpy.ndarray :param simu_y: :type simu_y: numpy.ndarray :param ax: :type ax: matplotlib.pyplot.axes :param kwargs: :type kwargs: kwargs for matplotlib.pyplot.hist2d

Returns:
Return type:ax
ctaplot.plots.plot_impact_point_heatmap(RecoX, RecoY, ax=None, Outfile=None)

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

Parameters:
  • RecoX (numpy.ndarray) –
  • RecoY (numpy.ndarray) –
  • ax (matplotlib.pyplot.axes) –
  • Outfile (string) –
ctaplot.plots.plot_impact_point_map_distri(RecoX, RecoY, telX, telY, **options)

Map and distributions of the reconstructed impact points.

Parameters:
  • RecoX (numpy.ndarray) –
  • RecoY (numpy.ndarray) –
  • telX (numpy.ndarray) –
  • telY (numpy.ndarray) –
  • options – kde=True : make a gaussian fit of the point density Outfile=’string’ : save a png image of the plot under ‘string.png’
Returns:

fig
Return type:

matplotlib.pyplot.figure
ctaplot.plots.plot_impact_resolution_per_energy(reco_x, reco_y, simu_x, simu_y, simu_energy, percentile=68.27, confidence_level=0.95, bias_correction=False, ax=None, **kwargs)

Plot the angular resolution as a function of the energy

Parameters:
  • reco_x (numpy.ndarray) –
  • reco_y (numpy.ndarray) –
  • simu_x (float) –
  • simu_y (float) –
  • simu_energy (numpy.ndarray) –
  • ax (matplotlib.pyplot.axes) –
  • kwargs (args for matplotlib.pyplot.errorbar) –
Returns:

ax
Return type:

matplotlib.pyplot.axes
ctaplot.plots.plot_layout_map(TelX, TelY, TelId, TelType, LayoutId, Outfile='LayoutMap')

Plot the layout map of telescopes positions - depreciated

Parameters:
  • TelX (numpy.ndarray) –
  • TelY (numpy.ndarray) –
  • TelId (numpy.ndarray) –
  • TelType (numpy.ndarray) –
  • LayoutId (numpy.ndarray) –
  • Outfile (string) –
ctaplot.plots.plot_migration_matrix(x, y, ax=None, colorbar=False, xy_line=False, hist2d_args={}, line_args={})

Make a simple plot of a migration matrix

Parameters:
  • x (list or numpy.ndarray) –
  • y (list or numpy.ndarray) –
  • ax (matplotlib.pyplot.axes) –
  • colorbar (matplotlib.colorbar) –
  • hist2d_args (dict, args for matplotlib.pyplot.hist2d) –
  • line_args (dict, args for matplotlib.pyplot.plot) –
Returns:
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.plot_multiplicity_hist(multiplicity, ax=None, Outfile=None, xmin=0, xmax=100)

Histogram of the telescopes multiplicity

Parameters:
  • multiplicity (numpy.ndarray) –
  • ax (matplotlib.pyplot.axes) –
  • Outfile (string) –
  • xmin (float) –
  • xmax (float) –
ctaplot.plots.plot_multiplicity_per_energy(Multiplicity, Energies, ax=None, outfile=None)

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

Parameters:
  • Multiplicity (numpy.ndarray) –
  • Energies (numpy.ndarray) –
  • ax (matplotlib.pyplot.axes) –
  • outfile (string) –
ctaplot.plots.plot_multiplicity_per_telescope_type(EventTup, Outfile=None)

Plot the multiplicity for each telescope type

Parameters:
  • EventTup –
  • Outfile –
ctaplot.plots.plot_reco_histo(y_true, y_reco)

plot the histogram of a reconstructed feature after prediction from a machine learning algorithm plt.show() to display :param y_true: :type y_true: real values of the feature to predict :param y_reco: :type y_reco: predicted values by the ML algo

ctaplot.plots.plot_resolution_per_energy(reco, simu, SimuE, ax=None, **kwargs)

Plot a variable resolution as a function of the energy

Parameters:
  • reco (numpy.ndarray) –
  • simu (numpy.ndarray) –
  • SimuE (numpy.ndarray) –
  • ax (matplotlib.pyplot.axes) –
  • kwargs (args for matplotlib.pyplot.errorbar) –
Returns:

ax
Return type:

matplotlib.pyplot.axes
ctaplot.plots.plot_sensitivity_cta_performances(cta_site, ax=None, **kwargs)

Plot the CTA performances for the sensitivity :param cta_site: :type cta_site: string - see ctaplot.ana.cta_requirements :param ax: :type ax: matplotlib.pyplot.axes, optional

Returns:ax
Return type:matplotlib.pyplot.axes
ctaplot.plots.plot_sensitivity_cta_requirements(cta_site, ax=None, **kwargs)

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

Returns:ax
Return type:matplotlib.pyplot.axes
ctaplot.plots.plot_site(tel_x, tel_y, ax=None, **kwargs)

Plot the telescopes positions :param tel_x: :type tel_x: 1D numpy array :param tel_y: :type tel_y: 1D numpy array :param ax: :type ax: ~matplotlib.axes.Axes or None :param **kwargs: :type **kwargs: Extra keyword arguments are passed to matplotlib.pyplot.scatter

Returns:ax
Return type:~matplotlib.axes.Axes
ctaplot.plots.plot_site_map(telX, telY, telTypes=None, Outfile='SiteMap.png')

Map of the site with telescopes positions

Parameters:
  • telX (numpy.ndarray) –
  • telY (numpy.ndarray) –
  • telTypes (numpy.ndarray) –
  • Outfile (string - name of the output file) –
ctaplot.plots.plot_theta2(RecoAlt, RecoAz, AltSource, AzSource, ax=None, **kwargs)

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

Parameters:
  • RecoAlt (numpy.ndarray - reconstructed altitude angle in radians) –
  • RecoAz (numpy.ndarray - reconstructed azimuth angle in radians) –
  • AltSource (numpy.ndarray - true altitude angle in radians) –
  • AzSource (numpy.ndarray - true azimuth angle in radians) –
  • ax (matplotlib.pyplot.axes) –
  • **kwargs (options for matplotlib.pyplot.hist) –
ctaplot.plots.saveplot_angular_res_per_energy(RecoAlt, RecoAz, AltSource, AzSource, SimuE, ax=None, Outfile='AngRes', cta_site=None, **kwargs)

Plot the angular resolution as a function of the energy and save the plot in png format

Parameters:
  • RecoAlt (numpy.ndarray) –
  • RecoAz (numpy.ndarray) –
  • AltSource (float) –
  • AzSource (float) –
  • SimuE (numpy.ndarray) –
  • ax (matplotlib.pyplot.axes) –
  • Outfile (string) –
  • cta_site (string) –
  • kwargs (args for hipectaold.plots.plot_angular_res_per_energy) –
Returns:

ax
Return type:

matplotlib.pyplot.axes
ctaplot.plots.saveplot_effective_area_per_energy(SimuE, RecoE, simuArea, ax=None, Outfile='AngRes', cta_site=None, **kwargs)

Plot the angular resolution as a function of the energy and save the plot in png format

Parameters:
  • RecoAlt (numpy.ndarray) –
  • RecoAz (numpy.ndarray) –
  • AltSource (float) –
  • AzSource (float) –
  • SimuE (numpy.ndarray) –
  • ax (matplotlib.pyplot.axes) –
  • Outfile (string) –
  • cta_site (string) –
  • kwargs (args for ctaplot.plots.plot_angular_res_per_energy) –
Returns:

ax
Return type:

matplotlib.pyplot.axes
ctaplot.plots.saveplot_energy_resolution(SimuE, RecoE, Outfile='EnergyResolution.png', cta_site=None)

plot the energy resolution of the reconstruction :param SimuE: :type SimuE: numpy.ndarray :param RecoE: :type RecoE: numpy.ndarray :param cta_goal: :type cta_goal: boolean - If True CTA energy resolution requirement is plotted

Returns:ax
Return type:matplotlib.pyplot.axes

ana.py

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

ctaplot.ana.angles_modulo_degrees(RecoAlt, RecoAz, SimuAlt, SimuAz)
ctaplot.ana.angular_resolution(reco_alt, reco_az, simu_alt, simu_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 (numpy.ndarray - reconstructed altitude angle in radians) –
  • reco_az (numpy.ndarray - reconstructed azimuth angle in radians) –
  • simu_alt (numpy.ndarray - true altitude angle in radians) –
  • simu_az (numpy.ndarray - true azimuth angle in radians) –
  • percentile (float - percentile, 68 corresponds to one sigma) –
  • confidence_level (float) –
Returns:
Return type:

numpy.array [angular_resolution, lower limit, upper limit]
ctaplot.ana.angular_resolution_per_energy(reco_alt, reco_az, simu_alt, simu_az, energy, percentile=68.27, confidence_level=0.95, bias_correction=False)

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

Parameters:
  • reco_alt (numpy.ndarray) –
  • reco_az (numpy.ndarray) –
  • simu_alt (numpy.ndarray) –
  • simu_az (numpy.ndarray) –
  • energy (numpy.ndarray) –
  • **kwargs (args for angular_resolution) –
Returns:

(E, RES)
Return type:

(1d numpy array, 1d numpy array) = Energies, Resolution
ctaplot.ana.angular_separation_altaz(alt1, az1, alt2, az2, unit='rad')

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

Parameters:
  • alt1 (1d numpy.ndarray, altitude of the first pointing direction) –
  • az1 (1d numpy.ndarray azimuth of the first pointing direction) –
  • alt2 (1d numpy.ndarray, altitude of the second pointing direction) –
  • az2 (1d numpy.ndarray, azimuth of the second pointing direction) –
  • unit ('deg' or 'rad') –
Returns:
Return type:

1d numpy.ndarray or float, angular separation
ctaplot.ana.bias(simu, reco)

Compute the bias of a reconstructed variable.

Parameters:
  • simu (numpy.ndarray) –
  • reco (numpy.ndarray) –
Returns:
Return type:

float
class ctaplot.ana.cta_performances

Bases: object

get_angular_resolution()
get_effective_area(observation_time=50)

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

Returns:
Return type:numpy.ndarray, numpy.ndarray
get_energy_resolution()
get_sensitivity(observation_time=50)
class ctaplot.ana.cta_requirements

Bases: object

get_angular_resolution()
get_effective_area(observation_time=50)

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

Returns:
Return type:numpy.ndarray, numpy.ndarray
get_energy_resolution()
get_sensitivity(observation_time=50)
ctaplot.ana.effective_area(SimuE, RecoE, simuArea)

Compute the effective area from a list of simulated energies and reconstructed energies :param SimuE: :type SimuE: 1d numpy array :param RecoE: :type RecoE: 1d numpy array :param simuArea: :type simuArea: float - area on which events are simulated

Returns:
Return type:float = effective area
ctaplot.ana.effective_area_per_energy(SimuE, RecoE, simuArea)

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

Parameters:
  • SimuE (1d numpy array) –
  • RecoE (1d numpy array) –
  • simuArea (float - area on which events are simulated) –
Returns:

(E, Seff)
Return type:

(1d numpy array, 1d numpy array)
ctaplot.ana.effective_area_per_energy_power_law(emin, emax, total_number_events, spectral_index, RecoE, simuArea)

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

Parameters:
  • SimuE (1d numpy array) –
  • RecoE (1d numpy array) –
  • simuArea (float - area on which events are simulated) –
Returns:

(E, Seff)
Return type:

(1d numpy array, 1d numpy array)
ctaplot.ana.energy_bias(SimuE, RecoE)

Compute the energy bias per energy bin. :param SimuE: :type SimuE: 1d numpy array of simulated energies :param RecoE: :type RecoE: 1d numpy array of reconstructed energies

Returns:(e, biasE)
Return type:tuple of 1d numpy arrays - energy, energy bias
ctaplot.ana.energy_resolution(true_energy, reco_energy, percentile=68.27, confidence_level=0.95, bias_correction=False)

Compute the energy resolution of reco_energy as the percentile (68 as standard) containment radius of DeltaE/E with the lower and upper confidence limits defined by the given confidence level

Parameters:
  • true_energy (1d numpy array of simulated energies) –
  • reco_energy (1d numpy array of reconstructed energies) –
  • percentile (float) – <= 100
Returns:
Return type:

numpy.array - [energy_resolution, lower_confidence_limit, upper_confidence_limit]
ctaplot.ana.energy_resolution_per_energy(simu_energy, reco_energy, percentile=68.27, confidence_level=0.95, bias_correction=False)
Parameters:
  • simu_energy (1d numpy array of simulated energies) –
  • reco_energy (1d numpy array of reconstructed energies) –
Returns:

(e, e_res)
Return type:

tuple of 1d numpy arrays - energy, resolution in energy
ctaplot.ana.get_angles_0pi(angles)

return angles modulo between 0 and +pi

Parameters:angles (numpy.ndarray) –
Returns:
Return type:numpy.ndarray
ctaplot.ana.get_angles_pipi(angles)

return angles modulo between -pi and +pi

Parameters:angles (numpy.ndarray) –
Returns:
Return type:numpy.ndarray
ctaplot.ana.impact_parameter_error(RecoX, RecoY, SimuX, SimuY)

compute the error distance between simulated and reconstructed impact parameters :param RecoX: :type RecoX: 1d numpy array :param RecoY: :param SimuX: :param SimuY:

Returns:1d numpy array
Return type:distances
ctaplot.ana.impact_resolution(reco_x, reco_y, simu_x, simu_y, percentile=68.27, confidence_level=0.95, bias_correction=False)

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:
  • RecoX (numpy.ndarray) –
  • RecoY (numpy.ndarray) –
  • SimuX (numpy.ndarray) –
  • SimuY (numpy.ndarray) –
  • confidence_level (float) –
Returns:
Return type:

numpy.array - [impact_resolution, lower_limit, upper_limit]
ctaplot.ana.impact_resolution_per_energy(reco_x, reco_y, simu_x, simu_y, energy, percentile=68.27, confidence_level=0.95, bias_correction=False)

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

Parameters:
  • reco_x (numpy.ndarray) –
  • reco_y (numpy.ndarray) –
  • simu_x (numpy.ndarray) –
  • simu_y (numpy.ndarray) –
  • energy (numpy.ndarray) –
Returns:

(E, RES)
Return type:

(1d numpy array, 1d numpy array) = Energies, Resolution
class ctaplot.ana.irf_cta

Bases: object

Class to handle Instrument Response Function data

set_E_bin(E_bin)
ctaplot.ana.logbin_mean(E_bin)

Function that gives back the mean of each bin in logscale

Parameters:E_bin (numpy.ndarray) –
Returns:
Return type:numpy.ndarray
ctaplot.ana.logspace_decades_nbin(Xmin, Xmax, n=5)

return an array with logspace and n bins / decade :param Xmin: :type Xmin: float :param Xmax: :type Xmax: float :param n: :type n: int - number of bins per decade

Returns:
Return type:1D Numpy array
ctaplot.ana.mask_range(X, Xmin=0, Xmax=inf)

create a mask for X to get values between Xmin and Xmax :param X: :type X: 1d numpy array :param Xmin: :type Xmin: float :param Xmax: :type Xmax: float

Returns:
Return type:1d numpy array of boolean
ctaplot.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.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.resolution(simu, reco, percentile=68.27, confidence_level=0.95, bias_correction=False)

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

Parameters:
  • simu (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 simu and reco
Returns:
Return type:

numpy.ndarray - [resolution, lower_confidence_limit, upper_confidence_limit]
ctaplot.ana.resolution_per_energy(simu, reco, simu_energy, bias_correction=False)
Parameters:
  • simu (1d numpy.ndarray of simulated energies) –
  • reco (1d numpy.ndarray of reconstructed energies) –
Returns:

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

(energy_bins, resolution)
ctaplot.ana.stat_per_energy(energy, y, statistic='mean')

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

Parameters:
  • energy (numpy.ndarray (1d)) – event energies
  • y (numpy.ndarray (1d)) –
  • statistic (string) – see scipy.stat.binned_statistic
Returns:

bin_stat, bin_edges, binnumber
Return type:

numpy.ndarray, numpy.ndarray, numpy.ndarray
ctaplot.ana.theta2(RecoAlt, RecoAz, AltSource, AzSource)

Compute the theta2 in radians

Parameters:
  • RecoAlt (1d numpy.ndarray - reconstructed Altitude in radians) –
  • RecoAz (1d numpy.ndarray - reconstructed Azimuth in radians) –
  • AltSource (1d numpy.ndarray - true Altitude in radians) –
  • AzSource (1d numpy.ndarray - true Azimuth in radians) –
Returns:
Return type:

1d numpy.ndarray