# -*- coding: utf-8 -*- __all__ = "corner" import logging import numpy as np from .core import corner_impl try: from .arviz_corner import arviz_corner except ImportError: arviz_corner = None def corner( data, bins=20, *, # Original corner parameters 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, # Arviz parameters 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, 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 :func:`corner.hist2d` to generate the 2-D histogram plots. """ if arviz_corner is None: if not ( isinstance(data, np.ndarray) or data.__class__.__name__ == "DataFrame" ): raise ImportError( "Please install arviz or use a numpy array as input" ) if ( var_names is not None or filter_vars is not None or coords is not None or divergences or divergences_kwargs is not None or labeller is not None ): logging.warning( "Please install arviz to use the advanced features of corner" ) return corner_impl( data, bins=bins, range=range, weights=weights, color=color, hist_bin_factor=hist_bin_factor, smooth=smooth, smooth1d=smooth1d, labels=labels, label_kwargs=label_kwargs, titles=titles, show_titles=show_titles, title_fmt=title_fmt, title_kwargs=title_kwargs, truths=truths, truth_color=truth_color, scale_hist=scale_hist, quantiles=quantiles, verbose=verbose, fig=fig, max_n_ticks=max_n_ticks, top_ticks=top_ticks, use_math_text=use_math_text, reverse=reverse, labelpad=labelpad, hist_kwargs=hist_kwargs, **hist2d_kwargs, ) return arviz_corner( data, bins=bins, range=range, weights=weights, color=color, hist_bin_factor=hist_bin_factor, smooth=smooth, smooth1d=smooth1d, labels=labels, label_kwargs=label_kwargs, titles=titles, show_titles=show_titles, title_fmt=title_fmt, title_kwargs=title_kwargs, truths=truths, truth_color=truth_color, scale_hist=scale_hist, quantiles=quantiles, verbose=verbose, fig=fig, max_n_ticks=max_n_ticks, top_ticks=top_ticks, use_math_text=use_math_text, reverse=reverse, labelpad=labelpad, hist_kwargs=hist_kwargs, group=group, var_names=var_names, filter_vars=filter_vars, coords=coords, divergences=divergences, divergences_kwargs=divergences_kwargs, labeller=labeller, **hist2d_kwargs, )