import functools
import itertools
import warnings
from inspect import getfullargspec

import numpy as np

from ..core.formatting import format_item
from .utils import (
    _infer_xy_labels, _process_cmap_cbar_kwargs,
    import_matplotlib_pyplot, label_from_attrs)

# Overrides axes.labelsize, xtick.major.size, ytick.major.size
# from mpl.rcParams
_FONTSIZE = 'small'
# For major ticks on x, y axes
_NTICKS = 5


def _nicetitle(coord, value, maxchar, template):
    """
    Put coord, value in template and truncate at maxchar
    """
    prettyvalue = format_item(value, quote_strings=False)
    title = template.format(coord=coord, value=prettyvalue)

    if len(title) > maxchar:
        title = title[:(maxchar - 3)] + '...'

    return title


class FacetGrid(object):
    """
    Initialize the matplotlib figure and FacetGrid object.

    The :class:`FacetGrid` is an object that links a xarray DataArray to
    a matplotlib figure with a particular structure.

    In particular, :class:`FacetGrid` is used to draw plots with multiple
    Axes where each Axes shows the same relationship conditioned on
    different levels of some dimension. It's possible to condition on up to
    two variables by assigning variables to the rows and columns of the
    grid.

    The general approach to plotting here is called "small multiples",
    where the same kind of plot is repeated multiple times, and the
    specific use of small multiples to display the same relationship
    conditioned on one ore more other variables is often called a "trellis
    plot".

    The basic workflow is to initialize the :class:`FacetGrid` object with
    the DataArray and the variable names that are used to structure the grid.
    Then plotting functions can be applied to each subset by calling
    :meth:`FacetGrid.map_dataarray` or :meth:`FacetGrid.map`.

    Attributes
    ----------
    axes : numpy object array
        Contains axes in corresponding position, as returned from
        plt.subplots
    fig : matplotlib.Figure
        The figure containing all the axes
    name_dicts : numpy object array
        Contains dictionaries mapping coordinate names to values. None is
        used as a sentinel value for axes which should remain empty, ie.
        sometimes the bottom right grid

    """

    def __init__(self, data, col=None, row=None, col_wrap=None,
                 sharex=True, sharey=True, figsize=None, aspect=1, size=3,
                 subplot_kws=None):
        """
        Parameters
        ----------
        data : DataArray
            xarray DataArray to be plotted
        row, col : strings
            Dimesion names that define subsets of the data, which will be drawn
            on separate facets in the grid.
        col_wrap : int, optional
            "Wrap" the column variable at this width, so that the column facets
        sharex : bool, optional
            If true, the facets will share x axes
        sharey : bool, optional
            If true, the facets will share y axes
        figsize : tuple, optional
            A tuple (width, height) of the figure in inches.
            If set, overrides ``size`` and ``aspect``.
        aspect : scalar, optional
            Aspect ratio of each facet, so that ``aspect * size`` gives the
            width of each facet in inches
        size : scalar, optional
            Height (in inches) of each facet. See also: ``aspect``
        subplot_kws : dict, optional
            Dictionary of keyword arguments for matplotlib subplots

        """

        plt = import_matplotlib_pyplot()

        # Handle corner case of nonunique coordinates
        rep_col = col is not None and not data[col].to_index().is_unique
        rep_row = row is not None and not data[row].to_index().is_unique
        if rep_col or rep_row:
            raise ValueError('Coordinates used for faceting cannot '
                             'contain repeated (nonunique) values.')

        # single_group is the grouping variable, if there is exactly one
        if col and row:
            single_group = False
            nrow = len(data[row])
            ncol = len(data[col])
            nfacet = nrow * ncol
            if col_wrap is not None:
                warnings.warn('Ignoring col_wrap since both col and row '
                              'were passed')
        elif row and not col:
            single_group = row
        elif not row and col:
            single_group = col
        else:
            raise ValueError(
                'Pass a coordinate name as an argument for row or col')

        # Compute grid shape
        if single_group:
            nfacet = len(data[single_group])
            if col:
                # idea - could add heuristic for nice shapes like 3x4
                ncol = nfacet
            if row:
                ncol = 1
            if col_wrap is not None:
                # Overrides previous settings
                ncol = col_wrap
            nrow = int(np.ceil(nfacet / ncol))

        # Set the subplot kwargs
        subplot_kws = {} if subplot_kws is None else subplot_kws

        if figsize is None:
            # Calculate the base figure size with extra horizontal space for a
            # colorbar
            cbar_space = 1
            figsize = (ncol * size * aspect + cbar_space, nrow * size)

        fig, axes = plt.subplots(nrow, ncol,
                                 sharex=sharex, sharey=sharey, squeeze=False,
                                 figsize=figsize, subplot_kw=subplot_kws)

        # Set up the lists of names for the row and column facet variables
        col_names = list(data[col].values) if col else []
        row_names = list(data[row].values) if row else []

        if single_group:
            full = [{single_group: x} for x in
                    data[single_group].values]
            empty = [None for x in range(nrow * ncol - len(full))]
            name_dicts = full + empty
        else:
            rowcols = itertools.product(row_names, col_names)
            name_dicts = [{row: r, col: c} for r, c in rowcols]

        name_dicts = np.array(name_dicts).reshape(nrow, ncol)

        # Set up the class attributes
        # ---------------------------

        # First the public API
        self.data = data
        self.name_dicts = name_dicts
        self.fig = fig
        self.axes = axes
        self.row_names = row_names
        self.col_names = col_names

        # Next the private variables
        self._single_group = single_group
        self._nrow = nrow
        self._row_var = row
        self._ncol = ncol
        self._col_var = col
        self._col_wrap = col_wrap
        self._x_var = None
        self._y_var = None
        self._cmap_extend = None
        self._mappables = []
        self._finalized = False

    @property
    def _left_axes(self):
        return self.axes[:, 0]

    @property
    def _bottom_axes(self):