Detailed API documentation¶
The main userfacing 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', hist_bin_factor=1, 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 multidimensional 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 2dimensional array. For a 1D array this results in a simple histogram. For a 2D 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.  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 1D histograms. This is generally used to increase the number of bins in the 1D plots to provide more resolution.
 smooth1d (smooth,) – The standard deviation for Gaussian kernel passed to scipy.ndimage.gaussian_filter to smooth the 2D and 1D histograms respectively. If None (default), no smoothing is applied.
 labels (iterable (ndim,)) – A list of names for the dimensions. If a
xs
is apandas.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 1D 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
andtitle_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 equaltailed.
 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 thetruths
makers.  scale_hist (bool) – Should the 1D histograms be scaled in such a way that the zero line is visible?
 quantiles (iterable) – A list of fractional quantiles to show on the 1D 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 upperright corner instead of the usual bottomleft 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 1D histogram plots.
 **hist2d_kwargs – Any remaining keyword arguments are sent to corner.hist2d to generate the 2D 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, **kwargs)¶ Plot a 2D 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 2D 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
isNone
, this method simply calls numpy’s percentile function with the values ofq
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 betweenx
andweights
.