clevar.match_metrics.scaling.funcs module

@file clevar/match_metrics/scaling/funcs.py

Main scaling functions for mass and redshift plots, wrapper of catalog_funcs functions

clevar.match_metrics.scaling.funcs.mass(cat1, cat2, matching_type, log_mass=True, **kwargs)

Scatter plot with errorbars and color based on input

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• log_mass (bool) – Log scale for mass

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• ax (matplotlib.axes, None) – Ax to add plot. If equals None, one is created.

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• xlabel (str) – Label of x/y axis (default=cat1.labels[‘mass’]/cat2.labels[‘mass’]).

• ylabel (str) – Label of x/y axis (default=cat1.labels[‘mass’]/cat2.labels[‘mass’]).

• add_bindata (bool) – Plot binned data used for fit (default=False).

• add_fit (bool) – Fit and plot binned dat (default=False).

• add_fit_err (bool) – Use mass errors of catalog 2 in fit (default=True).

• fit_log (bool) – Bin and fit in log values (default=True).

• fit_statistics (str) –

  Statistics to be used in fit (default=mean). Options are:

  • individual - Use each point.

  • mode - Use mode of catalog 2 mass distribution in each catalog 1 mass bin, requires fit_bins2.

  • mean - Use mean of catalog 2 mass distribution in each catalog 1 mass bin, requires fit_bins2.

• fit_bins1 (array, None) – Bins for mass of catalog 1 (default=10).

• fit_bins2 (array, None) – Bins for mass of catalog 2 (default=30).

• fit_legend_kwargs (dict, None) – Additional arguments for plt.legend.

• fit_bindata_kwargs (dict, None) – Additional arguments for pylab.errorbar.

• fit_plt_kwargs (dict, None) – Additional arguments for plot of fit pylab.scatter.

• fit_label_components (tuple (of strings)) – Names of fitted components in fit line label, default=(xlabel, ylabel).

Returns

info –

Information of data in the plots, it contains the sections:

• ax: ax used in the plot.

• binned_data (optional): input data for fitting, with values:

  • x: x values in fit (log of values if log=True).

  • y: y values in fit (log of values if log=True).

  • y_err: errorbar on y values (error_log if log=True).

• fit (optional): fitting output dictionary, with values:

  • pars: fitted parameter.

  • cov: covariance of fitted parameters.

  • func: fitting function with fitted parameter.

  • func_plus: fitting function with fitted parameter plus 1x scatter.

  • func_minus: fitting function with fitted parameter minus 1x scatter.

  • func_scat: scatter of fited function.

  • func_dist: P(y|x) - Probability of having y given a value for x, assumes normal distribution and uses scatter of the fitted function.

  • func_scat_interp: interpolated scatter from data.

  • func_dist_interp: P(y|x) using interpolated scatter.

• plots (optional): additional plots:

  • fit: fitted data

  • errorbar: binned data

Return type

dict

clevar.match_metrics.scaling.funcs.mass_density(cat1, cat2, matching_type, log_mass=True, **kwargs)

Scatter plot with errorbars and color based on point density

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• log_mass (bool) – Log scale for mass

• bins1 (array, None) – Bins for mass of catalogs 1 and 2 (for density colors).

• bins2 (array, None) – Bins for mass of catalogs 1 and 2 (for density colors).

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• ax (matplotlib.axes, None) – Ax to add plot. If equals None, one is created.

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• add_cb (bool) – Plot colorbar

• cb_kwargs (dict, None) – Colorbar arguments

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• ax_rotation (float) – Angle (in degrees) for rotation of axis of binning. Overwrites use of (bins1, bins2)

• rotation_resolution (int) – Number of bins to be used when ax_rotation!=0.

• xlabel (str) – Label of x/y axis (default=cat1.labels[‘mass’]/cat2.labels[‘mass’]).

• ylabel (str) – Label of x/y axis (default=cat1.labels[‘mass’]/cat2.labels[‘mass’]).

• **fit_kwargs – Other fit arguments (see fit_* paramters in scaling.mass for more info).

Returns

info –

Information of data in the plots, it contains the sections:

• ax: ax used in the plot.

• ax_cb (optional): ax of colorbar

• binned_data (optional): input data for fitting (see scaling.mass for more info).

• fit (optional): fitting output dictionary (see scaling.mass for more info).

• plots (optional): fit and binning plots (see scaling.mass for more info).

Return type

dict

clevar.match_metrics.scaling.funcs.mass_density_dist(cat1, cat2, matching_type, log_mass=True, **kwargs)

Scatter plot with errorbars and color based on point density with scatter and bias panels

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• col (str) – Name of column to be plotted

• bins1 (array, None) – Bins for mass of catalogs 1 and 2 (for density colors).

• bins2 (array, None) – Bins for mass of catalogs 1 and 2 (for density colors).

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• log_mass (bool) – Log scale for mass

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• ax_rotation (float) – Angle (in degrees) for rotation of axis of binning. Overwrites use of (bins1, bins2) on main plot.

• rotation_resolution (int) – Number of bins to be used when ax_rotation!=0.

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• add_cb (bool) – Plot colorbar

• cb_kwargs (dict, None) – Colorbar arguments

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• metrics_kwargs (dict, None) – Dictionary of dictionary configs for each metric plots.

• fig_pos (tuple) – List with edges for the figure. Must be in format (left, bottom, right, top)

• fig_frac (tuple) – Sizes of each panel in the figure. Must be in the format (main_panel, gap, colorbar) and have values: [0, 1]. Colorbar is only used with add_cb key.

• **fit_kwargs – Other fit arguments (see fit_* paramters in scaling.mass for more info).

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: dictionary with each ax of the plot.

• binned_data (optional): input data for fitting (see scaling.mass for more info).

• fit (optional): fitting output dictionary (see scaling.mass for more info).

• plots (optional): fit and binning plots (see scaling.mass for more info).

Return type

dict

clevar.match_metrics.scaling.funcs.mass_density_metrics(cat1, cat2, matching_type, log_mass=True, **kwargs)

Scatter plot with errorbars and color based on point density with scatter and bias panels

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• log_mass (bool) – Log scale for mass

• bins1 (array, None) – Bins for mass of catalogs 1 and 2.

• bins2 (array, None) – Bins for mass of catalogs 1 and 2.

• metrics_mode (str) –

  Mode to run metrics (default=`log`). Options are:

  • simple : metrics for values2.

  • log : metrics for log10(values2).

  • diff : metrics for values2-values1.

  • diff_log : metrics for log10(values2)-log10(values1).

  • diff_z : metrics for (values2-values1)/(1+values1).

• metrics (list) –

  List of mettrics to be plotted (default=[std]). Possibilities are:

  • mean : compute the mean of values for points within each bin.

  • std : compute the standard deviation within each bin.

  • median : compute the median of values for points within each bin.

  • count : compute the count of points within each bin.

  • sum : compute the sum of values for points within each bin.

  • min : compute the minimum of values for points within each bin.

  • max : compute the maximum of values for point within each bin.

  • p_# : compute half the width where a percentile of data is found. Number must be between 0-100 (ex: p_68, p_95, p_99).

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• ax_rotation (float) – Angle (in degrees) for rotation of axis of binning. Overwrites use of (bins1, bins2) on main plot.

• rotation_resolution (int) – Number of bins to be used when ax_rotation!=0.

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• add_cb (bool) – Plot colorbar

• cb_kwargs (dict, None) – Colorbar arguments

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• metrics_kwargs (dict, None) – Dictionary of dictionary configs for each metric plots.

• fig_pos (tuple) – List with edges for the figure. Must be in format (left, bottom, right, top)

• fig_frac (tuple) – Sizes of each panel in the figure. Must be in the format (main_panel, gap, colorbar) and have values: [0, 1]. Colorbar is only used with add_cb key.

• **fit_kwargs – Other fit arguments (see fit_* paramters in scaling.mass for more info).

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: dictionary with each ax of the plot.

• metrics: dictionary with the plots for each metric.

Return type

dict

clevar.match_metrics.scaling.funcs.mass_density_zpanel(cat1, cat2, matching_type, redshift_bins=5, log_mass=True, **kwargs)

Scatter plot with errorbars and color based on point density with panels

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• redshift_bins (int, array, int) – Redshift bins to make panels

• log_mass (bool) – Log scale for mass

• panel_cat1 (bool) – Used catalog 1 for col_panel. If false uses catalog 2

• bins1 (array, None) – Bins for mass of catalogs 1 and 2 (for density colors).

• bins2 (array, None) – Bins for mass of catalogs 1 and 2 (for density colors).

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• ax (matplotlib.axes, None) – Ax to add plot. If equals None, one is created.

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• add_cb (bool) – Plot colorbar

• cb_kwargs (dict, None) – Colorbar arguments

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• ax_rotation (float) – Angle (in degrees) for rotation of axis of binning. Overwrites use of (bins1, bins2)

• rotation_resolution (int) – Number of bins to be used when ax_rotation!=0.

• panel_kwargs_list (list, None) – List of additional arguments for plotting each panel (using pylab.plot). Must have same size as len(bins2)-1

• panel_kwargs_errlist (list, None) – List of additional arguments for plotting each panel (using pylab.errorbar). Must have same size as len(bins2)-1

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• add_label (bool) – Add bin label to panel

• label_format (function) – Function to format the values of the bins

• label_fmt (str) – Format the values of binedges (ex: ‘.2f’)

• xlabel (str) – Label of x/y axis (default=cat1.labels[‘mass’]/cat2.labels[‘mass’]).

• ylabel (str) – Label of x/y axis (default=cat1.labels[‘mass’]/cat2.labels[‘mass’]).

• **fit_kwargs – Other fit arguments (see fit_* paramters in scaling.mass for more info).

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: matplotlib.axes used in the plot.

• ax_cb (optional): ax of colorbar

• binned_data (optional): input data for fitting (see scaling.mass for more info).

• fit (optional): fitting output dictionary (see scaling.mass for more info).

• plots (optional): fit and binning plots (see scaling.mass for more info).

Return type

dict

clevar.match_metrics.scaling.funcs.mass_dist(cat1, cat2, matching_type, mass_bins_dist=30, mass_bins=5, redshift_bins=5, log_mass=True, transpose=False, **kwargs)

Plot distribution of a cat1 mass, binned by the cat2 mass (in panels), with option for cat2 redshift bins (in lines).

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• mass_bins_dist (array, int) – Bins for distribution of the cat1 mass

• mass_bins (array, int) – Bins for cat2 mass

• redshift_bins (array, int) – Bins for cat2 redshift

• log_mass (bool) – Log scale for mass

• transpose (bool) – Invert lines and panels

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• shape (str) – Shape of the lines. Can be steps or line.

• plt_kwargs (dict, None) – Additional arguments for pylab.plot

• line_kwargs_list (list, None) – List of additional arguments for plotting each line (using pylab.plot). Must have same size as len(bins_aux)-1

• legend_kwargs (dict, None) – Additional arguments for plt.legend

• add_panel_label (bool) – Add bin label to panel

• panel_label_format (function) – Function to format the values of the bins

• add_line_label (bool) – Add bin label to line

• line_label_format (function) – Function to format the values of the bins

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: matplotlib.axes used in the plot.

Return type

dict

clevar.match_metrics.scaling.funcs.mass_dist_self(cat, mass_bins_dist=30, mass_bins=5, redshift_bins=5, log_mass=True, transpose=False, mask=None, **kwargs)

Plot distribution of a cat mass, binned by mass (in panels), with option for redshift bins (in lines). Is is useful to compare with mass_dist results.

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat (clevar.ClCatalog) – Input Catalog

• mass_bins_dist (array, int) – Bins for distribution of mass

• mass_bins (array, int) – Bins for mass panels

• redshift_bins (array, int) – Bins for redshift

• log_mass (bool) – Log scale for mass

• transpose (bool) – Invert lines and panels

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• shape (str) – Shape of the lines. Can be steps or line.

• plt_kwargs (dict, None) – Additional arguments for pylab.plot

• line_kwargs_list (list, None) – List of additional arguments for plotting each line (using pylab.plot). Must have same size as len(bins_aux)-1

• legend_kwargs (dict, None) – Additional arguments for plt.legend

• add_panel_label (bool) – Add bin label to panel

• panel_label_format (function) – Function to format the values of the bins

• add_line_label (bool) – Add bin label to line

• line_label_format (function) – Function to format the values of the bins

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: matplotlib.axes used in the plot.

Return type

dict

clevar.match_metrics.scaling.funcs.mass_metrics(cat1, cat2, matching_type, log_mass=True, **kwargs)

Plot metrics.

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• log_mass (bool) – Log scale for mass

• bins1 (array, None) – Bins for mass of catalogs 1 and 2.

• bins2 (array, None) – Bins for mass of catalogs 1 and 2.

• metrics_mode (str) –

  Mode to run metrics (default=`log`). Options are:

  • simple : metrics for values2.

  • log : metrics for log10(values2).

  • diff : metrics for values2-values1.

  • diff_log : metrics for log10(values2)-log10(values1).

  • diff_z : metrics for (values2-values1)/(1+values1).

• metrics (list) –

  List of mettrics to be plotted (default=[std]). Possibilities are:

  • mean : compute the mean of values for points within each bin.

  • std : compute the standard deviation within each bin.

  • median : compute the median of values for points within each bin.

  • count : compute the count of points within each bin.

  • sum : compute the sum of values for points within each bin.

  • min : compute the minimum of values for points within each bin.

  • max : compute the maximum of values for point within each bin.

  • p_# : compute half the width where a percentile of data is found. Number must be between 0-100 (ex: p_68, p_95, p_99).

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• metrics_kwargs (dict, None) – Dictionary of dictionary configs for each metric plots.

• legend_kwargs (dict, None) – Additional arguments for plt.legend

• label1 (str) – Label for catalog 1/2 masses.

• label2 (str) – Label for catalog 1/2 masses.

• scale1 (str) – Scale of catalog 1/2 masses.

• scale2 (str) – Scale of catalog 1/2 masses.

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: matplotlib.axes used in the plot.

Return type

dict

clevar.match_metrics.scaling.funcs.mass_zcolor(cat1, cat2, matching_type, log_mass=True, color1=True, **kwargs)

Scatter plot with errorbars and color based on input

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• log_mass (bool) – Log scale for mass

• color1 (bool) – Use catalog 1 for color. If false uses catalog 2

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• ax (matplotlib.axes, None) – Ax to add plot. If equals None, one is created.

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• add_cb (bool) – Plot colorbar

• cb_kwargs (dict, None) – Colorbar arguments

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• xlabel (str) – Label of x/y axis (default=cat1.labels[‘mass’]/cat2.labels[‘mass’]).

• ylabel (str) – Label of x/y axis (default=cat1.labels[‘mass’]/cat2.labels[‘mass’]).

• **fit_kwargs – Other fit arguments (see fit_* paramters in scaling.mass for more info).

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: matplotlib.axes used in the plot.

• ax_cb (optional): ax of colorbar

• binned_data (optional): input data for fitting (see scaling.mass for more info).

• fit (optional): fitting output dictionary (see scaling.mass for more info).

• plots (optional): fit and binning plots (see scaling.mass for more info).

Return type

dict

clevar.match_metrics.scaling.funcs.mass_zpanel(cat1, cat2, matching_type, redshift_bins=5, log_mass=True, **kwargs)

Scatter plot with errorbars and color based on input with panels

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• redshift_bins (int, array, int) – Redshift bins to make panels

• log_mass (bool) – Log scale for mass

• panel_cat1 (bool) – Used catalog 1 for col_panel. If false uses catalog 2

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• panel_kwargs_list (list, None) – List of additional arguments for plotting each panel (using pylab.plot). Must have same size as len(bins2)-1

• panel_kwargs_errlist (list, None) – List of additional arguments for plotting each panel (using pylab.errorbar). Must have same size as len(bins2)-1

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• add_label (bool) – Add bin label to panel

• label_format (function) – Function to format the values of the bins

• label_fmt (str) – Format the values of binedges (ex: ‘.2f’)

• xlabel (str) – Label of x/y axis (default=cat1.labels[‘mass’]/cat2.labels[‘mass’]).

• ylabel (str) – Label of x/y axis (default=cat1.labels[‘mass’]/cat2.labels[‘mass’]).

• **fit_kwargs – Other fit arguments (see fit_* paramters in scaling.mass for more info).

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: matplotlib.axes used in the plot.

• binned_data (optional): input data for fitting (see scaling.mass for more info).

• fit (optional): fitting output dictionary (see scaling.mass for more info).

• plots (optional): fit and binning plots (see scaling.mass for more info).

Return type

dict

clevar.match_metrics.scaling.funcs.redshift(cat1, cat2, matching_type, **kwargs)

Scatter plot with errorbars and color based on input

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• ax (matplotlib.axes, None) – Ax to add plot. If equals None, one is created.

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• xlabel (str) – Label of x/y axis (default=cat1.labels[‘z’]/cat2.labels[‘z’]).

• ylabel (str) – Label of x/y axis (default=cat1.labels[‘z’]/cat2.labels[‘z’]).

• add_bindata (bool) – Plot binned data used for fit (default=False).

• add_fit (bool) – Fit and plot binned dat (default=False).

• add_fit_err (bool) – Use redshift errors of catalog 2 in fit (default=True).

• fit_statistics (str) –

  Statistics to be used in fit (default=mean). Options are:

  • individual - Use each point.

  • mode - Use mode of catalog 2 redshift distribution in each catalog 1 redshift bin, requires fit_bins2.

  • mean - Use mean of catalog 2 redshift distribution in each catalog 1 redshift bin, requires fit_bins2.

• fit_bins1 (array, None) – Bins for redshift of catalog 1 (default=10).

• fit_bins2 (array, None) – Bins for redshift of catalog 2 (default=30).

• fit_legend_kwargs (dict, None) – Additional arguments for plt.legend.

• fit_bindata_kwargs (dict, None) – Additional arguments for pylab.errorbar.

• fit_plt_kwargs (dict, None) – Additional arguments for plot of fit pylab.scatter.

• fit_label_components (tuple (of strings)) – Names of fitted components in fit line label, default=(xlabel, ylabel).

Returns

info –

Information of data in the plots, it contains the sections:

• ax: ax used in the plot.

• binned_data (optional): input data for fitting, with values:

  • x: x values in fit (log of values if log=True).

  • y: y values in fit (log of values if log=True).

  • y_err: errorbar on y values (error_log if log=True).

• fit (optional): fitting output dictionary, with values:

  • pars: fitted parameter.

  • cov: covariance of fitted parameters.

  • func: fitting function with fitted parameter.

  • func_plus: fitting function with fitted parameter plus 1x scatter.

  • func_minus: fitting function with fitted parameter minus 1x scatter.

  • func_scat: scatter of fited function.

  • func_dist: P(y|x) - Probability of having y given a value for x, assumes normal distribution and uses scatter of the fitted function.

  • func_scat_interp: interpolated scatter from data.

  • func_dist_interp: P(y|x) using interpolated scatter.

• plots (optional): additional plots:

  • fit: fitted data

  • errorbar: binned data

Return type

dict

clevar.match_metrics.scaling.funcs.redshift_density(cat1, cat2, matching_type, **kwargs)

Scatter plot with errorbars and color based on point density

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• col (str) – Name of column to be plotted

• bins1 (array, None) – Bins for redshift of catalogs 1 and 2 (for density colors).

• bins2 (array, None) – Bins for redshift of catalogs 1 and 2 (for density colors).

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• ax (matplotlib.axes, None) – Ax to add plot. If equals None, one is created.

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• add_cb (bool) – Plot colorbar

• cb_kwargs (dict, None) – Colorbar arguments

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• ax_rotation (float) – Angle (in degrees) for rotation of axis of binning. Overwrites use of (bins1, bins2)

• rotation_resolution (int) – Number of bins to be used when ax_rotation!=0.

• xlabel (str) – Label of x/y axis (default=cat1.labels[‘z’]/cat2.labels[‘z’]).

• ylabel (str) – Label of x/y axis (default=cat1.labels[‘z’]/cat2.labels[‘z’]).

• xscale (str) – Scale for x/y axis.

• yscale (str) – Scale for x/y axis.

• **fit_kwargs – Other fit arguments (see fit_* paramters in scaling.redshift for more info).

Returns

info –

Information of data in the plots, it contains the sections:

• ax: ax used in the plot.

• ax_cb (optional): ax of colorbar

• binned_data (optional): input data for fitting (see scaling.redshift for more info).

• fit (optional): fitting output dictionary (see scaling.redshift for more info).

• plots (optional): fit and binning plots (see scaling.redshift for more info).

Return type

dict

clevar.match_metrics.scaling.funcs.redshift_density_dist(cat1, cat2, matching_type, **kwargs)

Scatter plot with errorbars and color based on point density with scatter and bias panels

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• col (str) – Name of column to be plotted

• bins1 (array, None) – Bins for redshift of catalogs 1 and 2 (for density colors).

• bins2 (array, None) – Bins for redshift of catalogs 1 and 2 (for density colors).

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• ax_rotation (float) – Angle (in degrees) for rotation of axis of binning. Overwrites use of (bins1, bins2) on main plot.

• rotation_resolution (int) – Number of bins to be used when ax_rotation!=0.

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• add_cb (bool) – Plot colorbar

• cb_kwargs (dict, None) – Colorbar arguments

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• metrics_kwargs (dict, None) – Dictionary of dictionary configs for each metric plots.

• xscale (str) – Scale for x/y axis.

• yscale (str) – Scale for x/y axis.

• fig_pos (tuple) – List with edges for the figure. Must be in format (left, bottom, right, top)

• fig_frac (tuple) – Sizes of each panel in the figure. Must be in the format (main_panel, gap, colorbar) and have values: [0, 1]. Colorbar is only used with add_cb key.

• **fit_kwargs – Other fit arguments (see fit_* paramters in scaling.redshift for more info).

• vline_kwargs (dict, None) – Arguments for vlines marking bins in main plot, used in plt.axvline.

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: dictionary with each ax of the plot.

• binned_data (optional): input data for fitting (see scaling.redshift for more info).

• fit (optional): fitting output dictionary (see scaling.redshift for more info).

• plots (optional): fit and binning plots (see scaling.redshift for more info).

Return type

dict

clevar.match_metrics.scaling.funcs.redshift_density_masspanel(cat1, cat2, matching_type, mass_bins=5, log_mass=True, **kwargs)

Scatter plot with errorbars and color based on point density with panels

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• mass_bins (int, array, int) – Mass bins to make panels

• log_mass (bool) – Log scale for mass

• panel_cat1 (bool) – Used catalog 1 for col_panel. If false uses catalog 2

• bins1 (array, None) – Bins for redshift of catalogs 1 and 2 (for density colors).

• bins2 (array, None) – Bins for redshift of catalogs 1 and 2 (for density colors).

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• ax (matplotlib.axes, None) – Ax to add plot. If equals None, one is created.

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• add_cb (bool) – Plot colorbar

• cb_kwargs (dict, None) – Colorbar arguments

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• ax_rotation (float) – Angle (in degrees) for rotation of axis of binning. Overwrites use of (bins1, bins2)

• rotation_resolution (int) – Number of bins to be used when ax_rotation!=0.

• panel_kwargs_list (list, None) – List of additional arguments for plotting each panel (using pylab.plot). Must have same size as len(bins2)-1

• panel_kwargs_errlist (list, None) – List of additional arguments for plotting each panel (using pylab.errorbar). Must have same size as len(bins2)-1

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• add_label (bool) – Add bin label to panel

• label_format (function) – Function to format the values of the bins

• label_fmt (str) – Format the values of binedges (ex: ‘.2f’)

• xlabel (str) – Label of x/y axis (default=cat1.labels[‘z’]/cat2.labels[‘z’]).

• ylabel (str) – Label of x/y axis (default=cat1.labels[‘z’]/cat2.labels[‘z’]).

• **fit_kwargs – Other fit arguments (see fit_* paramters in scaling.redshift for more info).

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: matplotlib.axes used in the plot.

• ax_cb (optional): ax of colorbar

• binned_data (optional): input data for fitting (see scaling.redshift for more info).

• fit (optional): fitting output dictionary (see scaling.redshift for more info).

• plots (optional): fit and binning plots (see scaling.redshift for more info).

Return type

dict

clevar.match_metrics.scaling.funcs.redshift_density_metrics(cat1, cat2, matching_type, **kwargs)

Scatter plot with errorbars and color based on point density with scatter and bias panels

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• col (str) – Name of column to be plotted

• bins1 (array, None) – Bins for redshift of catalog 1 and 2.

• bins2 (array, None) – Bins for redshift of catalog 1 and 2.

• metrics_mode (str) –

  Mode to run metrics (default=`diff_z`). Options are:

  • simple : metrics for values2.

  • log : metrics for log10(values2).

  • diff : metrics for values2-values1.

  • diff_log : metrics for log10(values2)-log10(values1).

  • diff_z : metrics for (values2-values1)/(1+values1).

• metrics (list) –

  List of mettrics to be plotted (default=[std.fill, mean]). Possibilities are:

  • mean : compute the mean of values for points within each bin.

  • std : compute the standard deviation within each bin.

  • median : compute the median of values for points within each bin.

  • count : compute the count of points within each bin.

  • sum : compute the sum of values for points within each bin.

  • min : compute the minimum of values for points within each bin.

  • max : compute the maximum of values for point within each bin.

  • p_# : compute half the width where a percentile of data is found. Number must be between 0-100 (ex: p_68, p_95, p_99).

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• ax_rotation (float) – Angle (in degrees) for rotation of axis of binning. Overwrites use of (bins1, bins2) on main plot.

• rotation_resolution (int) – Number of bins to be used when ax_rotation!=0.

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• add_cb (bool) – Plot colorbar

• cb_kwargs (dict, None) – Colorbar arguments

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• metrics_kwargs (dict, None) – Dictionary of dictionary configs for each metric plots.

• xscale (str) – Scale for x/y axis.

• yscale (str) – Scale for x/y axis.

• fig_pos (tuple) – List with edges for the figure. Must be in format (left, bottom, right, top)

• fig_frac (tuple) – Sizes of each panel in the figure. Must be in the format (main_panel, gap, colorbar) and have values: [0, 1]. Colorbar is only used with add_cb key.

• **fit_kwargs – Other fit arguments (see fit_* paramters in scaling.redshift for more info).

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: dictionary with each ax of the plot.

• metrics: dictionary with the plots for each metric.

Return type

dict

clevar.match_metrics.scaling.funcs.redshift_dist(cat1, cat2, matching_type, redshift_bins_dist=30, redshift_bins=5, mass_bins=5, log_mass=True, transpose=False, **kwargs)

Plot distribution of a cat1 redshift, binned by the cat2 redshift (in panels), with option for cat2 mass bins (in lines).

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• redshift_bins_dist (array, int) – Bins for distribution of the cat1 redshift

• redshift_bins (array, int) – Bins for cat2 redshift

• mass_bins (array, int) – Bins for cat2 mass

• log_mass (bool) – Log scale for mass

• transpose (bool) – Invert lines and panels

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• shape (str) – Shape of the lines. Can be steps or line.

• plt_kwargs (dict, None) – Additional arguments for pylab.plot

• line_kwargs_list (list, None) – List of additional arguments for plotting each line (using pylab.plot). Must have same size as len(bins_aux)-1

• legend_kwargs (dict, None) – Additional arguments for plt.legend

• add_panel_label (bool) – Add bin label to panel

• panel_label_format (function) – Function to format the values of the bins

• add_line_label (bool) – Add bin label to line

• line_label_format (function) – Function to format the values of the bins

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: matplotlib.axes used in the plot.

Return type

dict

clevar.match_metrics.scaling.funcs.redshift_dist_self(cat, redshift_bins_dist=30, redshift_bins=5, mass_bins=5, log_mass=True, transpose=False, mask=None, **kwargs)

Plot distribution of a cat redshift, binned by redshift (in panels), with option for mass bins (in lines). Is is useful to compare with redshift_dist results.

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat (clevar.ClCatalog) – Input Catalog

• redshift_bins_dist (array, int) – Bins for distribution of redshift

• redshift_bins (array, int) – Bins for redshift panels

• mass_bins (array, int) – Bins for mass

• log_mass (bool) – Log scale for mass

• transpose (bool) – Invert lines and panels

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• shape (str) – Shape of the lines. Can be steps or line.

• plt_kwargs (dict, None) – Additional arguments for pylab.plot

• line_kwargs_list (list, None) – List of additional arguments for plotting each line (using pylab.plot). Must have same size as len(bins_aux)-1

• legend_kwargs (dict, None) – Additional arguments for plt.legend

• add_panel_label (bool) – Add bin label to panel

• panel_label_format (function) – Function to format the values of the bins

• add_line_label (bool) – Add bin label to line

• line_label_format (function) – Function to format the values of the bins

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: matplotlib.axes used in the plot.

Return type

dict

clevar.match_metrics.scaling.funcs.redshift_masscolor(cat1, cat2, matching_type, log_mass=True, color1=True, **kwargs)

Scatter plot with errorbars and color based on input

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• log_mass (bool) – Log scale for mass

• color1 (bool) – Use catalog 1 for color. If false uses catalog 2

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• ax (matplotlib.axes, None) – Ax to add plot. If equals None, one is created.

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• add_cb (bool) – Plot colorbar

• cb_kwargs (dict, None) – Colorbar arguments

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• xlabel (str) – Label of x/y axis (default=cat1.labels[‘z’]/cat2.labels[‘z’]).

• ylabel (str) – Label of x/y axis (default=cat1.labels[‘z’]/cat2.labels[‘z’]).

• **fit_kwargs – Other fit arguments (see fit_* paramters in scaling.redshift for more info).

Returns

info –

Information of data in the plots, it contains the sections:

• ax: ax used in the plot.

• ax_cb (optional): ax of colorbar

• binned_data (optional): input data for fitting (see scaling.redshift for more info).

• fit (optional): fitting output dictionary (see scaling.redshift for more info).

• plots (optional): fit and binning plots (see scaling.redshift for more info).

Return type

dict

clevar.match_metrics.scaling.funcs.redshift_masspanel(cat1, cat2, matching_type, mass_bins=5, log_mass=True, **kwargs)

Scatter plot with errorbars and color based on input with panels

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• mass_bins (int, array, int) – Mass bins to make panels

• log_mass (bool) – Log scale for mass

• panel_cat1 (bool) – Used catalog 1 for col_panel. If false uses catalog 2

• add_err (bool) – Add errorbars

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• plt_kwargs (dict, None) – Additional arguments for pylab.scatter

• err_kwargs (dict, None) – Additional arguments for pylab.errorbar

• panel_kwargs_list (list, None) – List of additional arguments for plotting each panel (using pylab.plot). Must have same size as len(bins2)-1

• panel_kwargs_errlist (list, None) – List of additional arguments for plotting each panel (using pylab.errorbar). Must have same size as len(bins2)-1

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• add_label (bool) – Add bin label to panel

• label_format (function) – Function to format the values of the bins

• label_fmt (str) – Format the values of binedges (ex: ‘.2f’)

• xlabel (str) – Label of x/y axis (default=cat1.labels[‘z’]/cat2.labels[‘z’]).

• ylabel (str) – Label of x/y axis (default=cat1.labels[‘z’]/cat2.labels[‘z’]).

• **fit_kwargs – Other fit arguments (see fit_* paramters in scaling.redshift for more info).

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: matplotlib.axes used in the plot.

• binned_data (optional): input data for fitting (see scaling.redshift for more info).

• fit (optional): fitting output dictionary (see scaling.redshift for more info).

• plots (optional): fit and binning plots (see scaling.redshift for more info).

Return type

dict

clevar.match_metrics.scaling.funcs.redshift_metrics(cat1, cat2, matching_type, **kwargs)

Plot metrics.

Parameters

• cat1 (clevar.ClCatalog) – ClCatalogs with matching information.

• cat2 (clevar.ClCatalog) – ClCatalogs with matching information.

• matching_type (str) – Type of matching to be considered. Must be in: ‘cross’, ‘cat1’, ‘cat2’

• bins1 (array, None) – Bins for redshift of catalog 1 and 2.

• bins2 (array, None) – Bins for redshift of catalog 1 and 2.

• metrics_mode (str) –

  Mode to run metrics (default=`diff_z`). Options are:

  • simple : metrics for values2.

  • log : metrics for log10(values2).

  • diff : metrics for values2-values1.

  • diff_log : metrics for log10(values2)-log10(values1).

  • diff_z : metrics for (values2-values1)/(1+values1).

• metrics (list) –

  List of mettrics to be plotted (default=[std.fill, mean]). Possibilities are:

  • mean : compute the mean of values for points within each bin.

  • std : compute the standard deviation within each bin.

  • median : compute the median of values for points within each bin.

  • count : compute the count of points within each bin.

  • sum : compute the sum of values for points within each bin.

  • min : compute the minimum of values for points within each bin.

  • max : compute the maximum of values for point within each bin.

  • p_# : compute half the width where a percentile of data is found. Number must be between 0-100 (ex: p_68, p_95, p_99).

• mask1 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• mask2 (array, None) – Masks for clusters 1(2), must have size=cat1(2).size

• fig_kwargs (dict, None) – Additional arguments for plt.subplots

• metrics_kwargs (dict, None) – Dictionary of dictionary configs for each metric plots.

• legend_kwargs (dict, None) – Additional arguments for plt.legend

• label1 (str) – Label for catalog 1/2 redshifts.

• label2 (str) – Label for catalog 1/2 redshifts.

• scale1 (str) – Scale of catalog 1/2 redshifts.

• scale2 (str) – Scale of catalog 1/2 redshifts.

Returns

info –

Information of data in the plots, it contains the sections:

• fig: matplotlib.figure.Figure object.

• axes: matplotlib.axes used in the plot.

Return type

dict