# 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(xs, bins=20, range=None, weights=None, color='k', smooth=None, smooth1d=None, labels=None, label_kwargs=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, hist_kwargs=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: xs (array_like[nsamples, ndim]) – The samples. This should be a 1- or 2-dimensional array. For a 1-D array this results in a simple histogram. For a 2-D array, the zeroth axis is the list of samples and the next axis are the dimensions of the space. 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. 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. smooth1d (smooth,) – 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. 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 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. 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, 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, **kwargs)

Plot a 2-D histogram of samples.

Parameters: x (array_like[nsamples,]) – The samples. y (array_like[nsamples,]) – The samples. 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.
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 quantiles – The sample quantiles computed at q. array_like[nquantiles,] ValueError – For invalid quantiles; q not in [0, 1] or dimension mismatch between x and weights.