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.
smooth, 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.
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.
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.
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