Detailed API documentation

The main user-facing function is corner.corner() but the lower level functions corner.hist2d() and corner.quantile() are also documented here.

corner.corner(data, bins=20, *, range=None, weights=None, color='k', hist_bin_factor=1, smooth=None, smooth1d=None, labels=None, label_kwargs=None, titles=None, show_titles=False, title_fmt='.2f', title_kwargs=None, truths=None, truth_color='#4682b4', scale_hist=False, quantiles=None, verbose=False, fig=None, max_n_ticks=5, top_ticks=False, use_math_text=False, reverse=False, labelpad=0.0, hist_kwargs=None, group='posterior', var_names=None, filter_vars=None, coords=None, divergences=False, divergences_kwargs=None, labeller=None, **hist2d_kwargs)

Make a sick corner plot showing the projections of a data set in a multi-dimensional space. kwargs are passed to hist2d() or used for matplotlib styling.

Parameters
  • data (obj) – Any object that can be converted to an arviz.InferenceData object. Refer to documentation of arviz.convert_to_dataset for details.

  • bins (int or array_like[ndim,]) – The number of bins to use in histograms, either as a fixed value for all dimensions, or as a list of integers for each dimension.

  • group (str) – Specifies which InferenceData group should be plotted. Defaults to 'posterior'.

  • var_names (list) – Variables to be plotted, if None all variable are plotted. Prefix the variables by ~ when you want to exclude them from the plot.

  • filter_vars ({None, "like", "regex"}) – If None (default), interpret var_names as the real variables names. If "like", interpret var_names as substrings of the real variables names. If "regex", interpret var_names as regular expressions on the real variables names. A la pandas.filter.

  • coords (mapping) – Coordinates of var_names to be plotted. Passed to arviz.Dataset.sel.

  • divergences (bool) – If True divergences will be plotted in a different color, only if group is either 'prior' or 'posterior'.

  • divergences_kwargs (dict) – Any extra keyword arguments to send to the overplot_points when plotting the divergences.

  • labeller (arviz.Labeller) – Class providing the method make_label_vert to generate the labels in the plot. Read the ArviZ label guide for more details and usage examples.

  • weights (array_like[nsamples,]) – The weight of each sample. If None (default), samples are given equal weight.

  • color (str) – A matplotlib style color for all histograms.

  • hist_bin_factor (float or array_like[ndim,]) – This is a factor (or list of factors, one for each dimension) that will multiply the bin specifications when making the 1-D histograms. This is generally used to increase the number of bins in the 1-D plots to provide more resolution.

  • smooth (float) – The standard deviation for Gaussian kernel passed to scipy.ndimage.gaussian_filter to smooth the 2-D and 1-D histograms respectively. If None (default), no smoothing is applied.

  • smooth1d (float) – The standard deviation for Gaussian kernel passed to scipy.ndimage.gaussian_filter to smooth the 2-D and 1-D histograms respectively. If None (default), no smoothing is applied.

  • labels (iterable (ndim,)) – A list of names for the dimensions. If a xs is a pandas.DataFrame, labels will default to column names.

  • label_kwargs (dict) – Any extra keyword arguments to send to the set_xlabel and set_ylabel methods. Note that passing the labelpad keyword in this dictionary will not have the desired effect. Use the labelpad keyword in this function instead.

  • titles (iterable (ndim,)) – A list of titles for the dimensions. If None (default), uses labels as titles.

  • show_titles (bool) – Displays a title above each 1-D histogram showing the 0.5 quantile with the upper and lower errors supplied by the quantiles argument.

  • title_fmt (string) – The format string for the quantiles given in titles. If you explicitly set show_titles=True and title_fmt=None, the labels will be shown as the titles. (default: .2f)

  • title_kwargs (dict) – Any extra keyword arguments to send to the set_title command.

  • range (iterable (ndim,)) – A list where each element is either a length 2 tuple containing lower and upper bounds or a float in range (0., 1.) giving the fraction of samples to include in bounds, e.g., [(0.,10.), (1.,5), 0.999, etc.]. If a fraction, the bounds are chosen to be equal-tailed.

  • truths (iterable (ndim,)) – A list of reference values to indicate on the plots. Individual values can be omitted by using None.

  • truth_color (str) – A matplotlib style color for the truths makers.

  • scale_hist (bool) – Should the 1-D histograms be scaled in such a way that the zero line is visible?

  • quantiles (iterable) – A list of fractional quantiles to show on the 1-D histograms as vertical dashed lines.

  • verbose (bool) – If true, print the values of the computed quantiles.

  • plot_contours (bool) – Draw contours for dense regions of the plot.

  • use_math_text (bool) – If true, then axis tick labels for very large or small exponents will be displayed as powers of 10 rather than using e.

  • reverse (bool) – If true, plot the corner plot starting in the upper-right corner instead of the usual bottom-left corner

  • labelpad (float) – Padding between the axis and the x- and y-labels in units of the fraction of the axis from the lower left

  • max_n_ticks (int) – Maximum number of ticks to try to use

  • top_ticks (bool) – If true, label the top ticks of each axis

  • fig (matplotlib.Figure) – Overplot onto the provided figure object, which must either have no axes yet, or ndim * ndim axes already present. If not set, the plot will be drawn on a newly created figure.

  • hist_kwargs (dict) – Any extra keyword arguments to send to the 1-D histogram plots.

  • **hist2d_kwargs – Any remaining keyword arguments are sent to corner.hist2d() to generate the 2-D histogram plots.

corner.hist2d(x, y, bins=20, range=None, weights=None, levels=None, smooth=None, ax=None, color=None, quiet=False, plot_datapoints=True, plot_density=True, plot_contours=True, no_fill_contours=False, fill_contours=False, contour_kwargs=None, contourf_kwargs=None, data_kwargs=None, pcolor_kwargs=None, new_fig=True, **kwargs)

Plot a 2-D histogram of samples.

Parameters
  • x (array_like[nsamples,]) – The samples.

  • y (array_like[nsamples,]) – The samples.

  • quiet (bool) – If true, suppress warnings for small datasets.

  • levels (array_like) – The contour levels to draw.

  • ax (matplotlib.Axes) – A axes instance on which to add the 2-D histogram.

  • plot_datapoints (bool) – Draw the individual data points.

  • plot_density (bool) – Draw the density colormap.

  • plot_contours (bool) – Draw the contours.

  • no_fill_contours (bool) – Add no filling at all to the contours (unlike setting fill_contours=False, which still adds a white fill at the densest points).

  • fill_contours (bool) – Fill the contours.

  • contour_kwargs (dict) – Any additional keyword arguments to pass to the contour method.

  • contourf_kwargs (dict) – Any additional keyword arguments to pass to the contourf method.

  • data_kwargs (dict) – Any additional keyword arguments to pass to the plot method when adding the individual data points.

  • pcolor_kwargs (dict) – Any additional keyword arguments to pass to the pcolor method when adding the density colormap.

corner.quantile(x, q, weights=None)

Compute sample quantiles with support for weighted samples.

Note

When weights is None, this method simply calls numpy’s percentile function with the values of q multiplied by 100.

Parameters
  • x (array_like[nsamples,]) – The samples.

  • q (array_like[nquantiles,]) – The list of quantiles to compute. These should all be in the range [0, 1].

  • weights (Optional[array_like[nsamples,]]) – An optional weight corresponding to each sample. These

Returns

quantiles – The sample quantiles computed at q.

Return type

array_like[nquantiles,]

Raises

ValueError – For invalid quantiles; q not in [0, 1] or dimension mismatch between x and weights.

corner.overplot_lines(fig, xs, **kwargs)

Overplot lines on a figure generated by corner.corner

Parameters
  • fig (Figure) – The figure generated by a call to corner.corner().

  • xs (array_like[ndim]) – The values where the lines should be plotted. This must have ndim entries, where ndim is compatible with the corner.corner() call that originally generated the figure. The entries can optionally be None to omit the line in that axis.

  • **kwargs – Any remaining keyword arguments are passed to the ax.axvline method.

corner.overplot_points(fig, xs, **kwargs)

Overplot points on a figure generated by corner.corner

Parameters
  • fig (Figure) – The figure generated by a call to corner.corner().

  • xs (array_like[nsamples, ndim]) – The coordinates of the points to be plotted. This must have an ndim that is compatible with the corner.corner() call that originally generated the figure.

  • **kwargs – Any remaining keyword arguments are passed to the ax.plot method.