clevar.match_metrics.scaling.catalog_funcs module

@file clevar/match_metrics/scaling/catalog_funcs.py

Main scaling functions using catalogs, wrapper of array_funcs functions

clevar.match_metrics.scaling.catalog_funcs.plot(cat1, cat2, matching_type, col, col_color=None, color1=True, color_log=False, **kwargs)

Scatter plot with errorbars. Color can be based on input column.

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

• col_color (str, None) – Name of column for color

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

• color_log (bool) – Use log of col_color

• 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[col]/cat2.labels[col]).

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

• xscale (str) – Scale for x/y axis.

• yscale (str) – Scale for x/y axis.

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

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

• fit_err2 (array, None) – Error of catalog 2 component (set to err2 if not provided).

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

• fit_statistics (str) –

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

  • individual : Use each point

  • mode : Use mode of catalog 2 component distribution in each catalog 1 component bin, requires bins2.

  • mean : Use mean of catalog 2 component distribution in each catalog 1 component bin, requires bins2.

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

• fit_bins2 (array, None) – Bins for catalog 2 component (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.catalog_funcs.plot_density(cat1, cat2, matching_type, col, **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 component x and y (for density colors).

• bins2 (array, None) – Bins for component x and y (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[col]/cat2.labels[col]).

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

• xscale (str) – Scale for x/y axis.

• yscale (str) – Scale for x/y axis.

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

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

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

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

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

Return type

dict

clevar.match_metrics.scaling.catalog_funcs.plot_density_dist(cat1, cat2, matching_type, col, **kwargs)

Scatter plot with errorbars and color based on point density with distribution 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 component x and y (for density colors).

• bins2 (array, None) – Bins for component x and y (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.

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

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

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

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

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

Return type

dict

clevar.match_metrics.scaling.catalog_funcs.plot_density_metrics(cat1, cat2, matching_type, col, bins1=30, bins2=30, **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 component x and y.

• bins2 (array, None) – Bins for component x and y.

• metrics_mode (str) –

  Mode to run metrics (default=`simple`). 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=[mean, 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.

• 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.

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

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

• **fit_kwargs – Other fit arguments (see fit_* paramters in scaling.catalog_funcs.plot 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.

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

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

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

Return type

dict

clevar.match_metrics.scaling.catalog_funcs.plot_density_panel(cat1, cat2, matching_type, col, col_panel, bins_panel, panel_cat1=True, log_panel=False, **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’

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

• col_panel (str) – Name of column to make panels

• bins_panel (array, int) – Bins to make panels

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

• bins1 (array, None) – Bins for component x and y (for density colors).

• bins2 (array, None) – Bins for component x and y (for density colors).

• log_panel (bool) – Scale of the panel values

• 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

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

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

• xscale (str) – Scale for x/y axis.

• yscale (str) – Scale for x/y axis.

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

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

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

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

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

Return type

dict

clevar.match_metrics.scaling.catalog_funcs.plot_dist(cat1, cat2, matching_type, col, bins1=30, bins2=5, col_aux=None, bins_aux=5, log_vals=False, log_aux=False, transpose=False, **kwargs)

Plot distribution of a cat1 column, binned by the cat2 column in panels, with option for a second cat2 column 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’

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

• bins1 (array, int) – Bins for distribution of the cat1 column.

• bins2 (array, int) – Bins for cat2 column (for panels/lines).

• col_aux (array) – Auxiliary colum of cat2 (to bin data in lines/panels).

• bins_aux (array, int) – Bins for component aux

• log_vals (bool) – Log scale for values (and int bins)

• log_aux (bool) – Log scale for aux values (and int bins)

• 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.catalog_funcs.plot_dist_self(cat, col, bins1=30, bins2=5, col_aux=None, bins_aux=5, log_vals=False, log_aux=False, transpose=False, mask=None, **kwargs)

Plot distribution of a cat1 column, binned by the same column in panels, with option for a second column in lines. Is is useful to compare with plot_dist results.

Parameters

• cat (clevar.ClCatalog) – Input catalog

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

• bins1 (array, int) – Bins for distribution of the cat1 column.

• bins2 (array, int) – Bins for cat2 column (for panels/lines).

• col_aux (array) – Auxiliary colum of cat2 (to bin data in lines/panels).

• bins_aux (array, int) – Bins for component aux

• log_vals (bool) – Log scale for values (and int bins)

• log_aux (bool) – Log scale for aux values (and int bins)

• transpose (bool) – Invert lines and panels

• mask (ndarray) – Mask for catalog

• 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.catalog_funcs.plot_metrics(cat1, cat2, matching_type, col, bins1=30, bins2=30, **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’

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

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

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

• mode (str) –

  Mode to run (default=`simple`). 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=[mean, 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

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

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

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

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

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

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

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

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.catalog_funcs.plot_panel(cat1, cat2, matching_type, col, col_panel, bins_panel, panel_cat1=True, col_color=None, color1=True, log_panel=False, **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’

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

• col_panel (str) – Name of column to make panels

• bins_panel (array, int) – Bins to make panels

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

• col_color (str, None) – Name of column for color.

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

• log_panel (bool) – Scale of the panel values

• 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

• add_cb (bool) – Plot colorbar

• cb_kwargs (dict, None) – Colorbar arguments

• 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

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

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

• xscale (str) – Scale for x/y axis.

• yscale (str) – Scale for x/y axis.

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

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

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

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

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

Return type

dict