<reponame>matiscke/corner.py
# -*- coding: utf-8 -*-

from __future__ import print_function, absolute_import

import logging
import numpy as np
import matplotlib.pyplot as pl
from matplotlib.ticker import MaxNLocator, NullLocator
from matplotlib.colors import LinearSegmentedColormap, colorConverter
from matplotlib.ticker import ScalarFormatter

try:
    from scipy.ndimage import gaussian_filter
except ImportError:
    gaussian_filter = None

__all__ = ["corner", "hist2d", "quantile"]


def 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, axes=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.

    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.

    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.

    """
    if quantiles is None:
        quantiles = []
    if title_kwargs is None:
        title_kwargs = dict()
    if label_kwargs is None:
        label_kwargs = dict()

    # Try filling in labels from pandas.DataFrame columns.
    if labels is None:
        try:
            labels = xs.columns
        except AttributeError:
            pass

    # Deal with 1D sample lists.
    xs = np.atleast_1d(xs)
    if len(xs.shape) == 1:
        xs = np.atleast_2d(xs)
    else:
        assert len(xs.shape) == 2, "The input sample array must be 1- or 2-D."
        xs = xs.T
    assert xs.shape[0] <= xs.shape[1], "I don't believe that you want more " \
                                       "dimensions than samples!"

    # Parse the weight array.
    if weights is not None:
        weights = np.asarray(weights)
        if weights.ndim != 1:
            raise ValueError("Weights must be 1-D")
        if xs.shape[1] != weights.shape[0]:
            raise ValueError("Lengths of weights must match number of samples")

    # Parse the parameter ranges.
    if range is None:
        if "extents" in hist2d_kwargs:
            logging.warn("Deprecated keyword argument 'extents'. "
                         "Use 'range' instead.")
            range = hist2d_kwargs.pop("extents")
        else:
            range = [[x.min(), x.max()] for x in xs]
            # Check for parameters that never change.
            m = np.array([e[0] == e[1] for e in range], dtype=bool)
            if np.any(m):
                raise ValueError(("It looks like the parameter(s) in "
                                  "column(s) {0} have no dynamic range. "
                                  "Please provide a `range` argument.")
                                 .format(", ".join(map(
                                     "{0}".format, np.arange(len(m))[m]))))

    else:
        # If any of the extents are percentiles, convert them to ranges.
        # Also make sure it's a normal list.
        range = list(range)
        for i, _ in enumerate(range):
            try:
                emin, emax = range[i]
            except TypeError:
                q = [0.5 - 0.5*range[i], 0.5 + 0.5*range[i]]
                range[i] = quantile(xs[i], q, weights=weights)

    if len(range) != xs.shape[0]:
        raise ValueError("Dimension mismatch between samples and range")

    # Parse the bin specifications.
    try:
        bins = [int(bins) for _ in range]
    except TypeError:
        if len(bins) != len(range):
            raise ValueError("Dimension mismatch between bins and range")
    try:
        hist_bin_factor = [float(hist_bin_factor) for _ in range]
    except TypeError:
        if len(hist_bin_factor) != len(range):
            raise ValueError("Dimension mismatch between hist_bin_factor and "
                             "range")

    # Some magic numbers for pretty axis layout.
    K = len(xs)
    factor = 2.0           # size of one side of one panel
    if reverse:
        lbdim = 0.2 * factor   # size of left/bottom margin
        trdim = 0.5 * factor   # size of top/right margin
    else:
        lbdim = 0.5 * factor   # size of left/bottom margin
        trdim = 0.2 * factor   # size of top/right margin
    whspace = 0.05         # w/hspace size
    plotdim = factor * K + factor * (K - 1.) * whspace
    dim = lbdim + plotdim + trdim

    # Create a new figure if one wasn't provided.
    if fig is None:
        fig, axes = pl.subplots(K, K, figsize=(dim, dim))
    else:
        try:
            # axes = np.array(fig.axes).reshape((K, K))
            axes = np.array(axes).reshape((K, K))
        except:
            # raise ValueError("Provided figure has {0} axes, but data has "
            #                  "dimensions K={1}".format(len(fig.axes), K))
            pass

    # Format the figure.
    lb = lbdim / dim
    tr = (lbdim + plotdim) / dim
    fig.subplots_adjust(left=lb, bottom=lb, right=tr, top=tr,
                        wspace=whspace, hspace=whspace)

    # Set up the default histogram keywords.
    if hist_kwargs is None:
        hist_kwargs = dict()
    hist_kwargs["color"] = hist_kwargs.get("color", color)
    if smooth1d is None:
        hist_kwargs["histtype"] = hist_kwargs.get("histtype", "step")

    for i, x in enumerate(xs):
        # Deal with masked arrays.
        if hasattr(x, "compressed"):
            x = x.compressed()

        if np.shape(xs)[0] == 1:
            ax = axes
        else:
            if reverse:
                ax = axes[K-i-1, K-i-1]
            else:
                ax = axes[i, i]
        # Plot the histograms.
        if smooth1d is None:
            bins_1d = int(max(1, np.round(hist_bin_factor[i] * bins[i])))
            n, _, _ = ax.hist(x, bins=bins_1d, weights=weights,
                              range=np.sort(range[i]), **hist_kwargs)
        else:
            if gaussian_filter is None:
                raise ImportError("Please install scipy for smoothing")
            n, b = np.histogram(x, bins=bins[i], weights=weights,
                                range=np.sort(range[i]))
            n = gaussian_filter(n, smooth1d)
            x0 = np.array(list(zip(b[:-1], b[1:]))).flatten()
            y0 = np.array(list(zip(n, n))).flatten()
            ax.plot(x0, y0, **hist_kwargs)

        if truths is not None and truths[i] is not None:
            ax.axvline(truths[i], color=truth_color)

        # Plot quantiles if wanted.
        if len(quantiles) > 0:
            qvalues = quantile(x, quantiles, weights=weights)
            for q in qvalues:
                ax.axvline(q, ls="dashed", color=color)

            if verbose:
                print("Quantiles:")
                print([item for item in zip(quantiles, qvalues)])

        if show_titles:
            title = None
            if title_fmt is not None:
                # Compute the quantiles for the title. This might redo
                # unneeded computation but who cares.
                q_16, q_50, q_84 = quantile(x, [0.16, 0.5, 0.84],
                                            weights=weights)
                q_m, q_p = q_50-q_16, q_84-q_50

                # Format the quantile display.
                fmt = "{{0:{0}}}".format(title_fmt).format
                title = r"${{{0}}}_{{-{1}}}^{{+{2}}}$"
                title = title.format(fmt(q_50), fmt(q_m), fmt(q_p))

                # Add in the column name if it's given.
                if labels is not None:
                    title = "{0} = {1}".format(labels[i], title)

            elif labels is not None:
                title = "{0}".format(labels[i])

            if title is not None:
                if reverse:
                    ax.set_xlabel(title, **title_kwargs)
                else:
                    ax.set_title(title, **title_kwargs)

        # Set up the axes.
        ax.set_xlim(range[i])
        if scale_hist:
            maxn = np.max(n)
            ax.set_ylim(-0.1 * maxn, 1.1 * maxn)
        else:
            ax.set_ylim(0, 1.1 * np.max(n))
        ax.set_yticklabels([])
        if max_n_ticks == 0:
            ax.xaxis.set_major_locator(NullLocator())
            ax.yaxis.set_major_locator(NullLocator())
        else:
            ax.xaxis.set_major_locator(MaxNLocator(max_n_ticks, prune="lower"))
            ax.yaxis.set_major_locator(NullLocator())

        if i < K - 1:
            if top_ticks:
                ax.xaxis.set_ticks_position("top")
                [l.set_rotation(45) for l in ax.get_xticklabels()]
            else:
                ax.set_xticklabels([])
        else:
            if reverse:
                ax.xaxis.tick_top()
            [l.set_rotation(45) for l in ax.get_xticklabels()]
            if labels is not None:
                if reverse:
                    ax.set_title(labels[i], y=1.25, **label_kwargs)
                else:
                    ax.set_xlabel(labels[i], **label_kwargs)

            # use MathText for axes ticks
            ax.xaxis.set_major_formatter(
                ScalarFormatter(useMathText=use_math_text))

        for j, y in enumerate(xs):
            if np.shape(xs)[0] == 1:
                ax = axes
            else:
                if reverse:
                    ax = axes[K-i-1, K-j-1]
                else:
                    ax = axes[i, j]
            if j > i:
                ax.set_frame_on(False)
                ax.set_xticks([])
                ax.set_yticks([])
                continue
            elif j == i:
                continue

            # Deal with masked arrays.
            if hasattr(y, "compressed"):
                y = y.compressed()

            hist2d(y, x, ax=ax, range=[range[j], range[i]], weights=weights,
                   color=color, smooth=smooth, bins=[bins[j], bins[i]],
                   **hist2d_kwargs)

            if truths is not None:
                if truths[i] is not None and truths[j] is not None:
                    ax.plot(truths[j], truths[i], "s", color=truth_color)
                if truths[j] is not None:
                    ax.axvline(truths[j], color=truth_color)
                if truths[i] is not None:
                    ax.axhline(truths[i], color=truth_color)

            if max_n_ticks == 0:
                ax.xaxis.set_major_locator(NullLocator())
                ax.yaxis.set_major_locator(NullLocator())
            else:
                ax.xaxis.set_major_locator(MaxNLocator(max_n_ticks,
                                                       prune="lower"))
                ax.yaxis.set_major_locator(MaxNLocator(max_n_ticks,
                                                       prune="lower"))

            if i < K - 1:
                ax.set_xticklabels([])
            else:
                if reverse:
                    ax.xaxis.tick_top()
                [l.set_rotation(45) for l in ax.get_xticklabels()]
                if labels is not None:
                    ax.set_xlabel(labels[j], **label_kwargs)
                    if reverse:
                        ax.xaxis.set_label_coords(0.5, 1.4)
                    else:
                        ax.xaxis.set_label_coords(0.5, -0.3)

                # use MathText for axes ticks
                ax.xaxis.set_major_formatter(
                    ScalarFormatter(useMathText=use_math_text))

            if j > 0:
                ax.set_yticklabels([])
            else:
                if reverse:
                    ax.yaxis.tick_right()
                [l.set_rotation(45) for l in ax.get_yticklabels()]
                if labels is not None:
                    if reverse:
                        ax.set_ylabel(labels[i], rotation=-90, **label_kwargs)
                        ax.yaxis.set_label_coords(1.3, 0.5)
                    else:
                        ax.set_ylabel(labels[i], **label_kwargs)
                        ax.yaxis.set_label_coords(-0.3, 0.5)

                # use MathText for axes ticks
                ax.yaxis.set_major_formatter(
                    ScalarFormatter(useMathText=use_math_text))

    return fig


def 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

    Returns
    -------
    quantiles : array_like[nquantiles,]
        The sample quantiles computed at ``q``.

    Raises
    ------
    ValueError
        For invalid quantiles; ``q`` not in ``[0, 1]`` or dimension mismatch
        between ``x`` and ``weights``.

    """
    x = np.atleast_1d(x)
    q = np.atleast_1d(q)

    if np.any(q < 0.0) or np.any(q > 1.0):
        raise ValueError("Quantiles must be between 0 and 1")

    if weights is None:
        return np.percentile(x, list(100.0 * q))
    else:
        weights = np.atleast_1d(weights)
        if len(x) != len(weights):
            raise ValueError("Dimension mismatch: len(weights) != len(x)")
        idx = np.argsort(x)
        sw = weights[idx]
        cdf = np.cumsum(sw)[:-1]
        cdf /= cdf[-1]
        cdf = np.append(0, cdf)
        return np.interp(q, cdf, x[idx]).tolist()


def 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,
           pcolor_kwargs=None, **kwargs):
           
    """
    Plot a 2-D 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 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.

    pcolor_kwargs : dict
        Any additional keyword arguments to pass to the `pcolor` method when
        adding the density colormap.

    """
    if ax is None:
        ax = pl.gca()

    # Set the default range based on the data range if not provided.
    if range is None:
        if "extent" in kwargs:
            logging.warn("Deprecated keyword argument 'extent'. "
                         "Use 'range' instead.")
            range = kwargs["extent"]
        else:
            range = [[x.min(), x.max()], [y.min(), y.max()]]

    # Set up the default plotting arguments.
    if color is None:
        color = "k"

    # Choose the default "sigma" contour levels.
    if levels is None:
        levels = 1.0 - np.exp(-0.5 * np.arange(0.5, 2.1, 0.5) ** 2)

    # This is the color map for the density plot, over-plotted to indicate the
    # density of the points near the center.
    density_cmap = LinearSegmentedColormap.from_list(
        "density_cmap", [color, (1, 1, 1, 0)])

    # This color map is used to hide the points at the high density areas.
    white_cmap = LinearSegmentedColormap.from_list(
        "white_cmap", [(1, 1, 1), (1, 1, 1)], N=2)

    # This "color map" is the list of colors for the contour levels if the
    # contours are filled.
    rgba_color = colorConverter.to_rgba(color)
    contour_cmap = [list(rgba_color) for l in levels] + [rgba_color]
    for i, l in enumerate(levels):
        contour_cmap[i][-1] *= float(i) / (len(levels)+1)

    # We'll make the 2D histogram to directly estimate the density.
    try:
        H, X, Y = np.histogram2d(x.flatten(), y.flatten(), bins=bins,
                                 range=list(map(np.sort, range)),
                                 weights=weights)
    except ValueError:
        raise ValueError("It looks like at least one of your sample columns "
                         "have no dynamic range. You could try using the "
                         "'range' argument.")

    if smooth is not None:
        if gaussian_filter is None:
            raise ImportError("Please install scipy for smoothing")
        H = gaussian_filter(H, smooth)

    if plot_contours or plot_density:
        # Compute the density levels.
        Hflat = H.flatten()
        inds = np.argsort(Hflat)[::-1]
        Hflat = Hflat[inds]
        sm = np.cumsum(Hflat)
        sm /= sm[-1]
        V = np.empty(len(levels))
        for i, v0 in enumerate(levels):
            try:
                V[i] = Hflat[sm <= v0][-1]
            except:
                V[i] = Hflat[0]
        V.sort()
        m = np.diff(V) == 0
        if np.any(m) and not quiet:
            logging.warning("Too few points to create valid contours")
        while np.any(m):
            V[np.where(m)[0][0]] *= 1.0 - 1e-4
            m = np.diff(V) == 0
        V.sort()

        # Compute the bin centers.
        X1, Y1 = 0.5 * (X[1:] + X[:-1]), 0.5 * (Y[1:] + Y[:-1])

        # Extend the array for the sake of the contours at the plot edges.
        H2 = H.min() + np.zeros((H.shape[0] + 4, H.shape[1] + 4))
        H2[2:-2, 2:-2] = H
        H2[2:-2, 1] = H[:, 0]
        H2[2:-2, -2] = H[:, -1]
        H2[1, 2:-2] = H[0]
        H2[-2, 2:-2] = H[-1]
        H2[1, 1] = H[0, 0]
        H2[1, -2] = H[0, -1]
        H2[-2, 1] = H[-1, 0]
        H2[-2, -2] = H[-1, -1]
        X2 = np.concatenate([
            X1[0] + np.array([-2, -1]) * np.diff(X1[:2]),
            X1,
            X1[-1] + np.array([1, 2]) * np.diff(X1[-2:]),
        ])
        Y2 = np.concatenate([
            Y1[0] + np.array([-2, -1]) * np.diff(Y1[:2]),
            Y1,
            Y1[-1] + np.array([1, 2]) * np.diff(Y1[-2:]),
        ])

    if plot_datapoints:
        if data_kwargs is None:
            data_kwargs = dict()
        data_kwargs["color"] = data_kwargs.get("color", color)
        data_kwargs["ms"] = data_kwargs.get("ms", 2.0)
        data_kwargs["mec"] = data_kwargs.get("mec", "none")
        data_kwargs["alpha"] = data_kwargs.get("alpha", 0.1)
        ax.plot(x, y, "o", zorder=-1, rasterized=True, **data_kwargs)

    # Plot the base fill to hide the densest data points.
    if (plot_contours or plot_density) and not no_fill_contours:
        ax.contourf(X2, Y2, H2.T, [V.min(), H.max()],
                    cmap=white_cmap, antialiased=False)

    if plot_contours and fill_contours:
        if contourf_kwargs is None:
            contourf_kwargs = dict()
        contourf_kwargs["colors"] = contourf_kwargs.get("colors", contour_cmap)
        contourf_kwargs["antialiased"] = contourf_kwargs.get("antialiased",
                                                             False)
        ax.contourf(X2, Y2, H2.T, np.concatenate([[0], V, [H.max()*(1+1e-4)]]),
                    **contourf_kwargs)

    # Plot the density map. This can't be plotted at the same time as the
    # contour fills.
    elif plot_density:
        if pcolor_kwargs is None:
            pcolor_kwargs = dict()
        ax.pcolor(X, Y, H.max() - H.T, cmap=density_cmap, **pcolor_kwargs)

    # Plot the contour edge colors.
    if plot_contours:
        if contour_kwargs is None:
            contour_kwargs = dict()
        contour_kwargs["colors"] = contour_kwargs.get("colors", color)
        ax.contour(X2, Y2, H2.T, V, **contour_kwargs)

    ax.set_xlim(range[0])
    ax.set_ylim(range[1])
