madminer.plotting package

Submodules

madminer.plotting.distributions module

madminer.plotting.distributions.plot_distributions(filename, observables=None, parameter_points=None, uncertainties='nuisance', nuisance_parameters=None, draw_nuisance_toys=None, normalize=False, log=False, observable_labels=None, n_bins=50, line_labels=None, colors=None, linestyles=None, linewidths=1.5, toy_linewidths=0.5, alpha=0.15, toy_alpha=0.75, n_events=None, n_toys=100, n_cols=3, quantiles_for_range=(0.025, 0.975), sample_only_from_closest_benchmark=True)

Plots one-dimensional histograms of observables in a MadMiner file for a given set of benchmarks.

Parameters

filenamestr

Filename of a MadMiner HDF5 file.

observableslist of str or None, optional

Which observables to plot, given by a list of their names. If None, all observables in the file are plotted. Default value: None.

parameter_pointslist of (str or ndarray) or None, optional

Which parameter points to use for histogramming the data. Given by a list, each element can either be the name of a benchmark in the MadMiner file, or an ndarray specifying any parameter point in a morphing setup. If None, all physics (non-nuisance) benchmarks defined in the MadMiner file are plotted. Default value: None.

uncertainties{“nuisance”, “none”}, optional

Defines how uncertainty bands are drawn. With “nuisance”, the variation in cross section from all nuisance parameters is added in quadrature. With “none”, no error bands are drawn.

nuisance_parametersNone or list of int, optional

If uncertainties is “nuisance”, this can restrict which nuisance parameters are used to draw the uncertainty bands. Each entry of this list is the index of one nuisance parameter (same order as in the MadMiner file).

draw_nuisance_toysNone or int, optional

If not None and uncertainties is “nuisance”, sets the number of nuisance toy distributions that are drawn (in addition to the error bands).

normalizebool, optional

Whether the distribution is normalized to the total cross section. Default value: False.

logbool, optional

Whether to draw the y axes on a logarithmic scale. Default value: False.

observable_labelsNone or list of (str or None), optional

x-axis labels naming the observables. If None, the observable names from the MadMiner file are used. Default value: None.

n_binsint, optional

Number of histogram bins. Default value: 50.

line_labelsNone or list of (str or None), optional

Labels for the different parameter points. If None and if parameter_points is None, the benchmark names from the MadMiner file are used. Default value: None.

colorsNone or str or list of str, optional

Matplotlib line (and error band) colors for the distributions. If None, uses default colors. Default value: None.

linestylesNone or str or list of str, optional

Matplotlib line styles for the distributions. If None, uses default linestyles. Default value: None.

linewidthsfloat or list of float, optional

Line widths for the contours. Default value: 1.5.

toy_linewidthsfloat or list of float or None, optional

Line widths for the toy replicas, if uncertainties is “nuisance” and draw_nuisance_toys is not None. If None, linewidths is used. Default value: 1.

alphafloat, optional

alpha value for the uncertainty bands. Default value: 0.25.

toy_alphafloat, optional

alpha value for the toy replicas, if uncertainties is “nuisance” and draw_nuisance_toys is not None. Default value: 0.75.

n_eventsNone or int, optional

If not None, sets the number of events from the MadMiner file that will be analyzed and plotted. Default value: None.

n_toysint, optional

Number of toy nuisance parameter vectors used to estimate the systematic uncertainties. Default value: 100.

n_colsint, optional

Number of columns of subfigures in the plot. Default value: 3.

quantiles_for_rangetuple of two float, optional

Tuple (min_quantile, max_quantile) that defines how the observable range is determined for each panel. Default: (0.025, 0.075).

sample_only_from_closest_benchmarkbool, optional

If True, only weighted events originally generated from the closest benchmarks are used. Default value: True.

Returns

figureFigure

Plot as Matplotlib Figure instance.

madminer.plotting.distributions.plot_histograms(histos, observed=None, observed_weights=None, xrange=None, yrange=None, zrange=None, log=False, histo_labels=None, observed_label='Data', xlabel=None, ylabel=None, zlabel=None, colors=None, linestyles=None, linewidths=1.5, markercolor='black', markersize=20.0, cmap='viridis', n_cols=2)

madminer.plotting.fisherinformation module

madminer.plotting.fisherinformation.plot_distribution_of_information(xbins, xsecs, fisher_information_matrices, fisher_information_matrices_aux=None, xlabel=None, xmin=None, xmax=None, log_xsec=False, norm_xsec=True, epsilon=1e-09, figsize=(5.4, 4.5), fontsize=None)

Plots the distribution of the cross section together with the distribution of the Fisher information.

Parameters

xbinslist of float

Bin boundaries.

xsecslist of float

Cross sections (in pb) per bin.

fisher_information_matriceslist of ndarray

Fisher information matrices for each bin.

fisher_information_matrices_auxlist of ndarray or None, optional

Additional Fisher information matrices for each bin (will be plotted with a dashed line).

xlabelstr or None, optional

Label for the x axis.

xminfloat or None, optional

Minimum value for the x axis.

xmaxfloat or None, optional

Maximum value for the x axis.

log_xsecbool, optional

Whether to plot the cross section on a logarithmic y axis.

norm_xsecbool, optional

Whether the cross sections are normalized to 1.

epsilonfloat, optional

Numerical factor.

figsizetuple of float, optional

Figure size, default: (5.4, 4.5)

fontsize: float, optional

Fontsize, default None

Returns

figureFigure

Plot as Matplotlib Figure instance.

madminer.plotting.fisherinformation.plot_fisher_information_contours_2d(fisher_information_matrices, fisher_information_covariances=None, reference_thetas=None, contour_distance=1.0, xlabel='$\\theta_0$', ylabel='$\\theta_1$', xrange=(-1.0, 1.0), yrange=(-1.0, 1.0), labels=None, inline_labels=None, resolution=500, colors=None, linestyles=None, linewidths=1.5, alphas=1.0, alphas_uncertainties=0.25, sigma_uncertainties=1, ax=None)

Visualizes 2x2 Fisher information matrices as contours of constant Fisher distance from a reference point theta0.

The local (tangent-space) approximation is used: distances d(theta) are given by d(theta)^2 = (theta - theta0)_i I_ij (theta - theta0)_j, summing over i and j.

Parameters

fisher_information_matriceslist of ndarray

Fisher information matrices, each with shape (2,2).

fisher_information_covariancesNone or list of (ndarray or None), optional

Covariance matrices for the Fisher information matrices. Has to have the same length as fisher_information_matrices, and each entry has to be None (no uncertainty) or a tensor with shape (2,2,2,2). Default value: None.

reference_thetasNone or list of (ndarray or None), optional

Reference points from which the distances are calculated. If None, the origin (0,0) is used. Default value: None.

contour_distancefloat, optional.

Distance threshold at which the contours are drawn. Default value: 1.

xlabelstr, optional

Label for the x axis. Default value: r’$ heta_0$’.

ylabelstr, optional

Label for the y axis. Default value: r’$ heta_1$’.

xrangetuple of float, optional

Range (min, max) for the x axis. Default value: (-1., 1.).

yrangetuple of float, optional

Range (min, max) for the y axis. Default value: (-1., 1.).

labelsNone or list of (str or None), optional

Legend labels for the contours. Default value: None.

inline_labelsNone or list of (str or None), optional

Inline labels for the contours. Default value: None.

resolutionint

Number of points per axis for the calculation of the distances. Default value: 500.

colorsNone or str or list of str, optional

Matplotlib line (and error band) colors for the contours. If None, uses default colors. Default value: None.

linestylesNone or str or list of str, optional

Matploitlib line styles for the contours. If None, uses default linestyles. Default value: None.

linewidthsfloat or list of float, optional

Line widths for the contours. Default value: 1.5.

alphasfloat or list of float, optional

Opacities for the contours. Default value: 1.

alphas_uncertaintiesfloat or list of float, optional

Opacities for the error bands. Default value: 0.25.

sigma_uncertaintiesfloat, optional

Number of gaussian sigmas used when presenting uncertainty bands. Default value: 1.

ax: axes or None, optional

Predefined axes as part of figure instead of standalone figure. Default: None

Returns

figureFigure

Plot as Matplotlib Figure instance.

madminer.plotting.fisherinformation.plot_fisherinfo_barplot(fisher_information_matrices, labels, determinant_indices=None, eigenvalue_colors=None, bar_colors=None)

Parameters

fisher_information_matriceslist of ndarray

Fisher information matrices

labelslist of str

Labels for the x axis

determinant_indiceslist of int or None, optional

If not None, the determinants will be based only on the indices given here. Default value: None.

eigenvalue_colorsNone or list of str

Colors for the eigenvalue decomposition. If None, default colors are used. Default value: None.

bar_colorsNone or list of str

Colors for the determinant bars. If None, default colors are used. Default value: None.

Returns

figureFigure

Plot as Matplotlib Figure instance.

madminer.plotting.limits module

madminer.plotting.limits.plot_pvalue_limits(p_values, best_fits, labels, grid_ranges, grid_resolutions, levels=[0.32], single_plot=True, show_index=None, xlabel='$\\theta_0$', ylabel='$\\theta_1$', p_val_min=0.001, p_val_max=1)

Function that plots the limits obtained from the AsymptoticLimits, Likelihood, FisherInformation and Information Geometry class. Note that only 2 dimensional grids are supported.

Parameters

p_valueslist of ndarray or dict

List/dictionary of p-values with shape (nmethods, ngridpoints)

best_fitslist of int or dict

List/dictionary of best fit points for each method with shape (nmethods)

labelslist of string or None

List/dictionary of best labels for each method with shape (nmethods). If None, it is assumed that dictionaries are provided and all entries will be used.

grid_rangeslist of (tuple of float) or None, optional

Specifies the boundaries of the parameter grid on which the p-values are evaluated. It should be [(min, max), (min, max), …, (min, max)], where the list goes over all parameters and min and max are float. If None, thetas_eval has to be given. Default: None.

grid_resolutionsint or list of int, optional

Resolution of the parameter space grid on which the p-values are evaluated. If int, the resolution is the same along every dimension of the hypercube. If list of int, the individual entries specify the number of points along each parameter individually. Doesn’t have any effect if grid_ranges is None. Default value: 25.

levelslist of float, optional

list of p-values used to draw contour lines. Default: [0.32]

single_plotbool, optional

If True, only one summary plot is shown which contains confidence contours and best fit points for all methods, and the p-value grid for a selected method (if show_index is not None). If False, additional plots with the p-value grid, confidence contours and best fit points for all methods are provided. Default: True

show_indexint, optional

If None, no p-value grid is shown in summary plot. If show_index=n, the p-value grid of the nth method is shown in the summary plot. Default is None.

xlabel,ylabelstring, optional

Labels for the x and y axis. Default: xlabel=r’$ heta_0$’ and ylabel=r’$ heta_1$’.

p_val_min,p_val_maxfloat, optional

Plot range for p-values. Default: p_val_min=0.001 and p_val_max=1.

madminer.plotting.morphing module

madminer.plotting.morphing.plot_1d_morphing_basis(morpher, xlabel='$\\theta$', xrange=(-1.0, 1.0), resolution=100)

Visualizes a morphing basis and morphing errors for problems with a two-dimensional parameter space.

Parameters

morpherPhysicsMorpher

PhysicsMorpher instance with defined basis.

xlabelstr, optional

Label for the x axis. Default value: r’$ heta$’.

xrangetuple of float, optional

Range (min, max) for the x axis. Default value: (-1., 1.).

resolutionint, optional

Number of points per axis for the rendering of the squared morphing weights. Default value: 100.

Returns

figureFigure

Plot as Matplotlib Figure instance.

madminer.plotting.morphing.plot_2d_morphing_basis(morpher, xlabel='$\\theta_0$', ylabel='$\\theta_1$', xrange=(-1.0, 1.0), yrange=(-1.0, 1.0), crange=(1.0, 100.0), resolution=100)

Visualizes a morphing basis and morphing errors for problems with a two-dimensional parameter space.

Parameters

morpherPhysicsMorpher

PhysicsMorpher instance with defined basis.

xlabelstr, optional

Label for the x axis. Default value: r’$ heta_0$’.

ylabelstr, optional

Label for the y axis. Default value: r’$ heta_1$’.

xrangetuple of float, optional

Range (min, max) for the x axis. Default value: (-1., 1.).

yrangetuple of float, optional

Range (min, max) for the y axis. Default value: (-1., 1.).

crangetuple of float, optional

Range (min, max) for the color map. Default value: (1., 100.).

resolutionint, optional

Number of points per axis for the rendering of the squared morphing weights. Default value: 100.

Returns

figureFigure

Plot as Matplotlib Figure instance.

madminer.plotting.morphing.plot_nd_morphing_basis_scatter(morpher, crange=(1.0, 100.0), n_test_thetas=1000)

Visualizes a morphing basis and morphing errors with scatter plots between each pair of operators.

Parameters

morpherPhysicsMorpher

PhysicsMorpher instance with defined basis.

crangetuple of float, optional

Range (min, max) for the color map. Default value: (1. 100.).

n_test_thetasint, optional

Number of random points evaluated. Default value: 1000.

Returns

figureFigure

Plot as Matplotlib Figure instance.

madminer.plotting.morphing.plot_nd_morphing_basis_slices(morpher, crange=(1.0, 100.0), resolution=50)

Visualizes a morphing basis and morphing errors with two-dimensional slices through parameter space.

Parameters

morpherPhysicsMorpher

PhysicsMorpher instance with defined basis.

crangetuple of float, optional

Range (min, max) for the color map.

resolutionint, optional

Number of points per panel and axis for the rendering of the squared morphing weights. Default value: 50.

Returns

figureFigure

Plot as Matplotlib Figure instance.

madminer.plotting.uncertainties module

madminer.plotting.uncertainties.plot_systematics(filename, theta, observable, obs_label, obs_range, n_bins=50, n_events=None, n_toys=100, linecolor='black', bandcolors=None, band_alpha=0.2, ratio_range=(0.8, 1.2))

Plots absolute and relative uncertainty bands for all systematic uncertainties in a histogram of one observable in a MadMiner file.

Parameters

filenamestr

Filename of a MadMiner HDF5 file.

thetandarray, optional

Which parameter points to use for histogramming the data.

observablestr

Which observable to plot, given by its name in the MadMiner file.

obs_labelstr

x-axis label naming the observable.

obs_rangetuple of two float

Range to be plotted for the observable.

n_binsint

Number of bins. Default value: 50.

n_eventsNone or int, optional

If not None, sets the number of events from the MadMiner file that will be analyzed and plotted. Default value: None.

n_toysint, optional

Number of toy nuisance parameter vectors used to estimate the systematic uncertainties. Default value: 100.

linecolorstr, optional

Line color for central prediction. Default value: “black”.

bandcolorsNone or list of str, optional

Error band colors. Default value: None.

ratio_rangetuple of two float

y-axis range for the plots of the ratio to the central prediction. Default value: (0.8, 1.2).

Returns

figureFigure

Plot as Matplotlib Figure instance.

madminer.plotting.uncertainties.plot_uncertainty(filename, theta, observable, obs_label, obs_range, n_bins=50, systematics=None, n_events=None, n_toys=100, linecolor='black', bandcolor1='#CC002E', bandcolor2='orange', ratio_range=(0.8, 1.2))

Plots absolute and relative uncertainty bands in a histogram of one observable in a MadMiner file.

Parameters

filenamestr

Filename of a MadMiner HDF5 file.

thetandarray, optional

Which parameter points to use for histogramming the data.

observablestr

Which observable to plot, given by its name in the MadMiner file.

obs_labelstr

x-axis label naming the observable.

obs_rangetuple of two float

Range to be plotted for the observable.

n_binsint

Number of bins. Default value: 50.

systematicsNone or list of str, optional

This can restrict which nuisance parameters are used to draw the uncertainty bands. Each entry of this list is the name of a systematic uncertainty (see MadMiner.add_systematics()).

n_eventsNone or int, optional

If not None, sets the number of events from the MadMiner file that will be analyzed and plotted. Default value: None.

n_toysint, optional

Number of toy nuisance parameter vectors used to estimate the systematic uncertainties. Default value: 100.

linecolorstr, optional

Line color for central prediction. Default value: “black”.

bandcolor1str, optional

Error band color for 1 sigma uncertainty. Default value: “#CC002E”.

bandcolor2str, optional

Error band color for 2 sigma uncertainty. Default value: “orange”.

ratio_rangetuple of two floar

y-axis range for the plots of the ratio to the central prediction. Default value: (0.8, 1.2).

Returns

figureFigure

Plot as Matplotlib Figure instance.

Module contents