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', 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, 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.  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.
 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, 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.
 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
.