import warnings
import itertools
from contextlib import contextmanager

import numpy as np
from matplotlib import transforms

from .. import utils
from .. import _py3k_compat as py3k


class Renderer(object):
    @staticmethod
    def ax_zoomable(ax):
        return bool(ax and ax.get_navigate())

    @staticmethod
    def ax_has_xgrid(ax):
        return bool(ax and ax.xaxis._gridOnMajor and ax.yaxis.get_gridlines())

    @staticmethod
    def ax_has_ygrid(ax):
        return bool(ax and ax.yaxis._gridOnMajor and ax.yaxis.get_gridlines())

    @property
    def current_ax_zoomable(self):
        return self.ax_zoomable(self._current_ax)

    @property
    def current_ax_has_xgrid(self):
        return self.ax_has_xgrid(self._current_ax)

    @property
    def current_ax_has_ygrid(self):
        return self.ax_has_ygrid(self._current_ax)

    @contextmanager
    def draw_figure(self, fig, props):
        if hasattr(self, "_current_fig") and self._current_fig is not None:
            warnings.warn("figure embedded in figure: something is wrong")
        self._current_fig = fig
        self._fig_props = props
        self.open_figure(fig=fig, props=props)
        yield
        self.close_figure(fig=fig)
        self._current_fig = None
        self._fig_props = {}

    @contextmanager
    def draw_axes(self, ax, props):
        if hasattr(self, "_current_ax") and self._current_ax is not None:
            warnings.warn("axes embedded in axes: something is wrong")
        self._current_ax = ax
        self._ax_props = props
        self.open_axes(ax=ax, props=props)
        yield
        self.close_axes(ax=ax)
        self._current_ax = None
        self._ax_props = {}

    @contextmanager
    def draw_legend(self, legend, props):
        self._current_legend = legend
        self._legend_props = props
        self.open_legend(legend=legend, props=props)
        yield
        self.close_legend(legend=legend)
        self._current_legend = None
        self._legend_props = {}

    # Following are the functions which should be overloaded in subclasses

    def open_figure(self, fig, props):
        """
        Begin commands for a particular figure.

        Parameters
        ----------
        fig : matplotlib.Figure
            The Figure which will contain the ensuing axes and elements
        props : dictionary
            The dictionary of figure properties
        """
        pass

    def close_figure(self, fig):
        """
        Finish commands for a particular figure.

        Parameters
        ----------
        fig : matplotlib.Figure
            The figure which is finished being drawn.
        """
        pass

    def open_axes(self, ax, props):
        """
        Begin commands for a particular axes.

        Parameters
        ----------
        ax : matplotlib.Axes
            The Axes which will contain the ensuing axes and elements
        props : dictionary
            The dictionary of axes properties
        """
        pass

    def close_axes(self, ax):
        """
        Finish commands for a particular axes.

        Parameters
        ----------
        ax : matplotlib.Axes
            The Axes which is finished being drawn.
        """
        pass

    def open_legend(self, legend, props):
        """
        Begin commands for a particular legend.

        Parameters
        ----------
        legend : matplotlib.legend.Legend
                The Legend that will contain the ensuing elements
        props : dictionary
                The dictionary of legend properties
        """
        pass

    def close_legend(self, legend):
        """
        Finish commands for a particular legend.

        Parameters
        ----------
        legend : matplotlib.legend.Legend
                The Legend which is finished being drawn
        """
        pass

    def draw_marked_line(self, data, coordinates, linestyle, markerstyle,
                         label, mplobj=None):
        """Draw a line that also has markers.

        If this isn't reimplemented by a renderer object, by default, it will
        make a call to BOTH draw_line and draw_markers when both markerstyle
        and linestyle are not None in the same Line2D object.

        """
        if linestyle is not None:
            self.draw_line(data, coordinates, linestyle, label, mplobj)
        if markerstyle is not None:
            self.draw_markers(data, coordinates, markerstyle, label, mplobj)

    def draw_line(self, data, coordinates, style, label, mplobj=None):
        """
        Draw a line. By default, draw the line via the draw_path() command.
        Some renderers might wish to override this and provide more
        fine-grained behavior.

        In matplotlib, lines are generally created via the plt.plot() command,
        though this command also can create marker collections.

        Parameters
        ----------
        data : array_like
            A shape (N, 2) array of datapoints.
        coordinates : string
            A string code, which should be either 'data' for data coordinates,
            or 'figure' for figure (pixel) coordinates.
        style : dictionary
            a dictionary specifying the appearance of the line.
        mplobj : matplotlib object
            the matplotlib plot element which generated this line
        """
        pathcodes = ['M'] + (data.shape[0] - 1) * ['L']
        pathstyle = dict(facecolor='none', **style)
        pathstyle['edgecolor'] = pathstyle.pop('color')
        pathstyle['edgewidth'] = pathstyle.pop('linewidth')
        self.draw_path(data=data, coordinates=coordinates,
                       pathcodes=pathcodes, style=pathstyle, mplobj=mplobj)

    @staticmethod
    def _iter_path_collection(paths, path_transforms, offsets, styles):
        """Build an iterator over the elements of the path collection"""
        N = max(len(paths), len(offsets))

        if len(path_transforms) == 0:
            path_transforms = [np.eye(3)]

        edgecolor = styles['edgecolor']
        if np.size(edgecolor) == 0:
            edgecolor = ['none']
        facecolor = styles['facecolor']
        if np.size(facecolor) == 0:
            facecolor = ['none']

        elements = [paths, path_transforms, offsets,
                    edgecolor, styles['linewidth'], facecolor]

        it = itertools
        return it.islice(py3k.zip(*py3k.map(it.cycle, elements)), N)

    def draw_path_collection(self, paths, path_coordinates, path_transforms,
                             offsets, offset_coordinates, offset_order,
                             styles, mplobj=None):
        """
        Draw a collection of paths. The paths, offsets, and styles are all
        iterables, and the number of paths is max(len(paths), len(offsets)).

        By default, this is implemented via multiple calls to the draw_path()
        function. For efficiency, Renderers may choose to customize this
        implementation.

        Examples of path collections created by matplotlib are scatter plots,
        histograms, contour plots, and many others.

        Parameters
        ----------
        paths : list
            list of tuples, where each tuple has two elements:
            (data, pathcodes).  See draw_path() for a description of these.
        path_coordinates: string
            the coordinates code for the paths, which should be either
            'data' for data coordinates, or 'figure' for figure (pixel)
            coordinates.
        path_transforms: array_like
            an array of shape (*, 3, 3), giving a series of 2D Affine
            transforms for the paths. These encode translations, rotations,
            and scalings in the standard way.
        offsets: array_like
            An array of offsets of shape (N, 2)
        offset_coordinates : string
            the coordinates code for the offsets, which should be either
            'data' for data coordinates, or 'figure' for figure (pixel)
            coordinates.
        offset_order : string
            either "before" or "after". This specifies whether the offset
            is applied before the path transform, or after.  The matplotlib
            backend equivalent is "before"->"data", "after"->"screen".
        styles: dictionary
            A dictionary in which each value is a list of length N, containing
            the style(s) for the paths.
        mplobj : matplotlib object
            the matplotlib plot element which generated this collection
        """
        if offset_order == "before":
            raise NotImplementedError("offset before transform")

        for tup in self._iter_path_collection(paths, path_transforms,
                                              offsets, styles):
            (path, path_transform, offset, ec, lw, fc) = tup
            vertices, pathcodes = path
            path_transform = transforms.Affine2D(path_transform)
            vertices = path_transform.transform(vertices)
            # This is a hack:
            if path_coordinates == "figure":
                path_coordinates = "points"
            style = {"edgecolor": utils.color_to_hex(ec),
                     "facecolor": utils.color_to_hex(fc),
                     "edgewidth": lw,
                     "dasharray": "10,0",
                     "alpha": styles['alpha'],
                     "zorder": styles['zorder']}
            self.draw_path(data=vertices, coordinates=path_coordinates,
                           pathcodes=pathcodes, style=style, offset=offset,
                           offset_coordinates=offset_coordinates,
                           mplobj=mplobj)

    def draw_markers(self, data, coordinates, style, label, mplobj=None):
        """
        Draw a set of markers. By default, this is done by repeatedly
        calling draw_path(), but renderers should generally overload
        this method to provide a more efficient implementation.

        In matplotlib, markers are created using the plt.plot() command.

        Parameters
        ----------
        data : array_like
            A shape (N, 2) array of datapoints.
        coordinates : string
            A string code, which should be either 'data' for data coordinates,
            or 'figure' for figure (pixel) coordinates.
        style : dictionary
            a dictionary specifying the appearance of the markers.
        mplobj : matplotlib object
            the matplotlib plot element which generated this marker collection
        """
        vertices, pathcodes = style['markerpath']
        pathstyle = dict((key, style[key]) for key in ['alpha', 'edgecolor',
                                                       'facecolor', 'zorder',
                                                       'edgewidth'])
        pathstyle['dasharray'] = "10,0"
        for vertex in data:
            self.draw_path(data=vertices, coordinates="points",
                           pathcodes=pathcodes, style=pathstyle,
                           offset=vertex, offset_coordinates=coordinates,
                           mplobj=mplobj)

    def draw_text(self, text, position, coordinates, style,
                  text_type=None, mplobj=None):