import functools

import numpy as np
import pandas as pd

from ..core.alignment import broadcast
from .facetgrid import _easy_facetgrid
from .utils import (
    _add_colorbar,
    _is_numeric,
    _process_cmap_cbar_kwargs,
    get_axis,
    label_from_attrs,
)

# copied from seaborn
_MARKERSIZE_RANGE = np.array([18.0, 72.0])


def _infer_meta_data(ds, x, y, hue, hue_style, add_guide):
    dvars = set(ds.variables.keys())
    error_msg = " must be one of ({0:s})".format(", ".join(dvars))

    if x not in dvars:
        raise ValueError("x" + error_msg)

    if y not in dvars:
        raise ValueError("y" + error_msg)

    if hue is not None and hue not in dvars:
        raise ValueError("hue" + error_msg)

    if hue:
        hue_is_numeric = _is_numeric(ds[hue].values)

        if hue_style is None:
            hue_style = "continuous" if hue_is_numeric else "discrete"

        if not hue_is_numeric and (hue_style == "continuous"):
            raise ValueError(
                "Cannot create a colorbar for a non numeric" " coordinate: " + hue
            )

        if add_guide is None or add_guide is True:
            add_colorbar = True if hue_style == "continuous" else False
            add_legend = True if hue_style == "discrete" else False
        else:
            add_colorbar = False
            add_legend = False
    else:
        if add_guide is True:
            raise ValueError("Cannot set add_guide when hue is None.")
        add_legend = False
        add_colorbar = False

    if hue_style is not None and hue_style not in ["discrete", "continuous"]:
        raise ValueError(
            "hue_style must be either None, 'discrete' " "or 'continuous'."
        )

    if hue:
        hue_label = label_from_attrs(ds[hue])
        hue = ds[hue]
    else:
        hue_label = None
        hue = None

    return {
        "add_colorbar": add_colorbar,
        "add_legend": add_legend,
        "hue_label": hue_label,
        "hue_style": hue_style,
        "xlabel": label_from_attrs(ds[x]),
        "ylabel": label_from_attrs(ds[y]),
        "hue": hue,
    }


def _infer_scatter_data(ds, x, y, hue, markersize, size_norm, size_mapping=None):

    broadcast_keys = ["x", "y"]
    to_broadcast = [ds[x], ds[y]]
    if hue:
        to_broadcast.append(ds[hue])
        broadcast_keys.append("hue")
    if markersize:
        to_broadcast.append(ds[markersize])
        broadcast_keys.append("size")

    broadcasted = dict(zip(broadcast_keys, broadcast(*to_broadcast)))

    data = {"x": broadcasted["x"], "y": broadcasted["y"], "hue": None, "sizes": None}

    if hue:
        data["hue"] = broadcasted["hue"]

    if markersize:
        size = broadcasted["size"]

        if size_mapping is None:
            size_mapping = _parse_size(size, size_norm)

        data["sizes"] = size.copy(
            data=np.reshape(size_mapping.loc[size.values.ravel()].values, size.shape)
        )

    return data


# copied from seaborn
def _parse_size(data, norm):

    import matplotlib as mpl

    if data is None:
        return None

    data = data.values.flatten()

    if not _is_numeric(data):
        levels = np.unique(data)
        numbers = np.arange(1, 1 + len(levels))[::-1]
    else:
        levels = numbers = np.sort(np.unique(data))

    min_width, max_width = _MARKERSIZE_RANGE
    # width_range = min_width, max_width

    if norm is None:
        norm = mpl.colors.Normalize()
    elif isinstance(norm, tuple):
        norm = mpl.colors.Normalize(*norm)
    elif not isinstance(norm, mpl.colors.Normalize):
        err = "``size_norm`` must be None, tuple, " "or Normalize object."
        raise ValueError(err)

    norm.clip = True
    if not norm.scaled():
        norm(np.asarray(numbers))
    # limits = norm.vmin, norm.vmax

    scl = norm(numbers)
    widths = np.asarray(min_width + scl * (max_width - min_width))
    if scl.mask.any():
        widths[scl.mask] = 0
    sizes = dict(zip(levels, widths))

    return pd.Series(sizes)


class _Dataset_PlotMethods(object):
    """
    Enables use of xarray.plot functions as attributes on a Dataset.
    For example, Dataset.plot.scatter
    """

    def __init__(self, dataset):
        self._ds = dataset

    def __call__(self, *args, **kwargs):
        raise ValueError(
            "Dataset.plot cannot be called directly. Use "
            "an explicit plot method, e.g. ds.plot.scatter(...)"
        )


def _dsplot(plotfunc):
    commondoc = """
    Parameters
    ----------

    ds : Dataset
    x, y : string
        Variable names for x, y axis.
    hue: str, optional
        Variable by which to color scattered points
    hue_style: str, optional
        Can be either 'discrete' (legend) or 'continuous' (color bar).
    markersize: str, optional (scatter only)
        Variably by which to vary size of scattered points
    size_norm: optional
        Either None or 'Norm' instance to normalize the 'markersize' variable.
    add_guide: bool, optional
        Add a guide that depends on hue_style
            - for "discrete", build a legend.
              This is the default for non-numeric `hue` variables.
            - for "continuous",  build a colorbar
    row : string, optional
        If passed, make row faceted plots on this dimension name
    col : string, optional
        If passed, make column faceted plots on this dimension name
    col_wrap : integer, optional
        Use together with ``col`` to wrap faceted plots
    ax : matplotlib axes, optional
        If None, uses the current axis. Not applicable when using facets.
    subplot_kws : dict, optional
        Dictionary of keyword arguments for matplotlib subplots. Only applies
        to FacetGrid plotting.
    aspect : scalar, optional
        Aspect ratio of plot, so that ``aspect * size`` gives the width in
        inches. Only used if a ``size`` is provided.
    size : scalar, optional
        If provided, create a new figure for the plot with the given size.
        Height (in inches) of each plot. See also: ``aspect``.
    norm : ``matplotlib.colors.Normalize`` instance, optional
        If the ``norm`` has vmin or vmax specified, the corresponding kwarg
        must be None.
    vmin, vmax : floats, optional
        Values to anchor the colormap, otherwise they are inferred from the
        data and other keyword arguments. When a diverging dataset is inferred,
        setting one of these values will fix the other by symmetry around
        ``center``. Setting both values prevents use of a diverging colormap.
        If discrete levels are provided as an explicit list, both of these
        values are ignored.
    cmap : matplotlib colormap name or object, optional
        The mapping from data values to color space. If not provided, this
        will be either be ``viridis`` (if the function infers a sequential
        dataset) or ``RdBu_r`` (if the function infers a diverging dataset).
        When `Seaborn` is installed, ``cmap`` may also be a `seaborn`
        color palette. If ``cmap`` is seaborn color palette and the plot type
        is not ``contour`` or ``contourf``, ``levels`` must also be specified.
    colors : discrete colors to plot, optional
        A single color or a list of colors. If the plot type is not ``contour``
        or ``contourf``, the ``levels`` argument is required.
    center : float, optional
        The value at which to center the colormap. Passing this value implies
        use of a diverging colormap. Setting it to ``False`` prevents use of a
        diverging colormap.
    robust : bool, optional
        If True and ``vmin`` or ``vmax`` are absent, the colormap range is
        computed with 2nd and 98th percentiles instead of the extreme values.
    extend : {'neither', 'both', 'min', 'max'}, optional
        How to draw arrows extending the colorbar beyond its limits. If not
        provided, extend is inferred from vmin, vmax and the data limits.
    levels : int or list-like object, optional
        Split the colormap (cmap) into discrete color intervals. If an integer
        is provided, "nice" levels are chosen based on the data range: this can
        imply that the final number of levels is not exactly the expected one.
        Setting ``vmin`` and/or ``vmax`` with ``levels=N`` is equivalent to
        setting ``levels=np.linspace(vmin, vmax, N)``.
    **kwargs : optional
        Additional keyword arguments to matplotlib
    """

    # Build on the original docstring
    plotfunc.__doc__ = "%s\n%s" % (plotfunc.__doc__, commondoc)

    @functools.wraps(plotfunc)
    def newplotfunc(
        ds,
        x=None,
        y=None,
        hue=None,
        hue_style=None,
        col=None,
        row=None,
        ax=None,
        figsize=None,
        size=None,
        col_wrap=None,
        sharex=True,
        sharey=True,
        aspect=None,
        subplot_kws=None,
        add_guide=None,
        cbar_kwargs=None,
        cbar_ax=None,
        vmin=None,
        vmax=None,
        norm=None,
        infer_intervals=None,
        center=None,
        levels=None,
        robust=None,
        colors=None,
        extend=None,
        cmap=None,
        **kwargs
    ):

        _is_facetgrid = kwargs.pop("_is_facetgrid", False)
        if _is_facetgrid:  # facetgrid call
            meta_data = kwargs.pop("meta_data")
        else:
            meta_data = _infer_meta_data(ds, x, y, hue, hue_style, add_guide)

        hue_style = meta_data["hue_style"]

        # handle facetgrids first
        if col or row:
            allargs = locals().copy()
            allargs["plotfunc"] = globals()[plotfunc.__name__]
            allargs["data"] = ds
            # TODO dcherian: why do I need to remove kwargs?
            for arg in ["meta_data", "kwargs", "ds"]:
                del allargs[arg]

            return _easy_facetgrid(kind="dataset", **allargs, **kwargs)

        figsize = kwargs.pop("figsize", None)
        ax = get_axis(figsize, size, aspect, ax)

        if hue_style == "continuous" and hue is not None:
            if _is_facetgrid:
                cbar_kwargs = meta_data["cbar_kwargs"]
                cmap_params = meta_data["cmap_params"]
            else:
                cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs(
                    plotfunc, ds[hue].values, **locals()
                )

            # subset that can be passed to scatter, hist2d
            cmap_params_subset = dict(
                (vv, cmap_params[vv]) for vv in ["vmin", "vmax", "norm", "cmap"]
            )

        else:
            cmap_params_subset = {}

        primitive = plotfunc(
            ds=ds,
            x=x,
            y=y,
            hue=hue,
            hue_style=hue_style,
            ax=ax,
            cmap_params=cmap_params_subset,
            **kwargs
        )

        if _is_facetgrid:  # if this was called from Facetgrid.map_dataset,
            return primitive  # finish here. Else, make labels

        if meta_data.get("xlabel", None):
            ax.set_xlabel(meta_data.get("xlabel"))
        if meta_data.get("ylabel", None):
            ax.set_ylabel(meta_data.get("ylabel"))

        if meta_data["add_legend"]:
            ax.legend(
                handles=primitive,
                labels=list(meta_data["hue"].values),
                title=meta_data.get("hue_label", None),
            )
        if meta_data["add_colorbar"]:
            cbar_kwargs = {} if cbar_kwargs is None else cbar_kwargs
            if "label" not in cbar_kwargs:
                cbar_kwargs["label"] = meta_data.get("hue_label", None)
            _add_colorbar(primitive, ax, cbar_ax, cbar_kwargs, cmap_params)

        return primitive

    @functools.wraps(newplotfunc)
    def plotmethod(
        _PlotMethods_obj,
        x=None,
        y=None,
        hue=None,
        hue_style=None,
        col=None,
        row=None,
        ax=None,
        figsize=None,
        col_wrap=None,
        sharex=True,
        sharey=True,
        aspect=None,
        size=None,
        subplot_kws=None,
        add_guide=None,
        cbar_kwargs=None,
        cbar_ax=None,
        vmin=None,
        vmax=None,
        norm=None,
        infer_intervals=None,
        center=None,
        levels=None,
        robust=None,
        colors=None,
        extend=None,
        cmap=None,
        **kwargs
    ):
        """
        The method should have the same signature as the function.

        This just makes the method work on Plotmethods objects,
        and passes all the other arguments straight through.
        """
        allargs = locals()
        allargs["ds"] = _PlotMethods_obj._ds
        allargs.update(kwargs)
        for arg in ["_PlotMethods_obj", "newplotfunc", "kwargs"]:
            del allargs[arg]
        return newplotfunc(**allargs)

    # Add to class _PlotMethods
    setattr(_Dataset_PlotMethods, plotmethod.__name__, plotmethod)

    return newplotfunc


@_dsplot
def scatter(ds, x, y, ax, **kwargs):
    """
    Scatter Dataset data variables against each other.
    """

    if "add_colorbar" in kwargs or "add_legend" in kwargs:
        raise ValueError(
            "Dataset.plot.scatter does not accept "
            "'add_colorbar' or 'add_legend'. "
            "Use 'add_guide' instead."
        )

    cmap_params = kwargs.pop("cmap_params")
    hue = kwargs.pop("hue")
    hue_style = kwargs.pop("hue_style")
    markersize = kwargs.pop("markersize", None)
    size_norm = kwargs.pop("size_norm", None)
    size_mapping = kwargs.pop("size_mapping", None)  # set by facetgrid

    # need to infer size_mapping with full dataset
    data = _infer_scatter_data(ds, x, y, hue, markersize, size_norm, size_mapping)

    if hue_style == "discrete":
        primitive = []
        for label in np.unique(data["hue"].values):
            mask = data["hue"] == label
            if data["sizes"] is not None:
                kwargs.update(s=data["sizes"].where(mask, drop=True).values.flatten())

            primitive.append(
                ax.scatter(
                    data["x"].where(mask, drop=True).values.flatten(),
                    data["y"].where(mask, drop=True).values.flatten(),
                    label=label,
                    **kwargs
                )
            )

    elif hue is None or hue_style == "continuous":
        if data["sizes"] is not None:
            kwargs.update(s=data["sizes"].values.ravel())
        if data["hue"] is not None:
            kwargs.update(c=data["hue"].values.ravel())

        primitive = ax.scatter(
            data["x"].values.ravel(), data["y"].values.ravel(), **cmap_params, **kwargs
        )

    return primitive
