from matplotlib import pyplot as pl
from pandas.api.types import is_categorical_dtype
import numpy as np
from matplotlib import rcParams
from matplotlib.colors import is_color_like
from .. import _utils as utils
from ...utils import sanitize_anndata, doc_params
from ... import settings
from .._docs import doc_adata_color_etc, doc_edges_arrows, doc_scatter_bulk, doc_show_save_ax
from ... import logging as logg


@doc_params(adata_color_etc=doc_adata_color_etc, edges_arrows=doc_edges_arrows, scatter_bulk=doc_scatter_bulk, show_save_ax=doc_show_save_ax)
def umap(adata, **kwargs):
    """\
    Scatter plot in UMAP basis.

    Parameters
    ----------
    {adata_color_etc}
    {edges_arrows}
    {scatter_bulk}
    {show_save_ax}

    Returns
    -------
    If `show==False` a `matplotlib.Axis` or a list of it.
    """
    return plot_scatter(adata, basis='umap', **kwargs)


@doc_params(adata_color_etc=doc_adata_color_etc, edges_arrows=doc_edges_arrows, scatter_bulk=doc_scatter_bulk, show_save_ax=doc_show_save_ax)
def tsne(adata, **kwargs):
    """\
    Scatter plot in tSNE basis.

    Parameters
    ----------
    {adata_color_etc}
    {edges_arrows}
    {scatter_bulk}
    {show_save_ax}

    Returns
    -------
    If `show==False` a `matplotlib.Axis` or a list of it.
    """
    return plot_scatter(adata, basis='tsne', **kwargs)


@doc_params(adata_color_etc=doc_adata_color_etc, edges_arrows=doc_edges_arrows, scatter_bulk=doc_scatter_bulk, show_save_ax=doc_show_save_ax)
def phate(adata, **kwargs):
    """\
    Scatter plot in PHATE basis.

    Parameters
    ----------
    {adata_color_etc}
    {edges_arrows}
    {scatter_bulk}
    {show_save_ax}

    Returns
    -------
    If `show==False`, a list of `matplotlib.Axis` objects. Every second element
    corresponds to the 'right margin' drawing area for color bars and legends.

    Examples
    --------
    >>> import scanpy.api as sc
    >>> import phate
    >>> data, branches = phate.tree.gen_dla(n_dim=100,
                                            n_branch=20,
                                            branch_length=100)
    >>> data.shape
    (2000, 100)
    >>> adata = sc.AnnData(data)
    >>> adata.obs['branches'] = branches
    >>> sc.tl.phate(adata, k=5, a=20, t=150)
    >>> adata.obsm['X_phate'].shape
    (2000, 2)
    >>> sc.pl.phate(adata,
                    color='branches',
                    color_map='tab20')
    """
    return plot_scatter(adata, basis='phate', **kwargs)


@doc_params(adata_color_etc=doc_adata_color_etc, scatter_bulk=doc_scatter_bulk, show_save_ax=doc_show_save_ax)
def diffmap(adata, **kwargs):
    """\
    Scatter plot in Diffusion Map basis.

    Parameters
    ----------
    {adata_color_etc}
    {scatter_bulk}
    {show_save_ax}

    Returns
    -------
    If `show==False` a `matplotlib.Axis` or a list of it.
    """
    return plot_scatter(adata, basis='diffmap', **kwargs)


@doc_params(adata_color_etc=doc_adata_color_etc, edges_arrows=doc_edges_arrows, scatter_bulk=doc_scatter_bulk, show_save_ax=doc_show_save_ax)
def draw_graph(adata, layout=None, **kwargs):
    """\
    Scatter plot in graph-drawing basis.

    Parameters
    ----------
    {adata_color_etc}
    layout : {{'fa', 'fr', 'drl', ...}}, optional (default: last computed)
        One of the `draw_graph` layouts, see
        :func:`~scanpy.api.tl.draw_graph`. By default, the last computed layout
        is used.
    {edges_arrows}
    {scatter_bulk}
    {show_save_ax}

    Returns
    -------
    If `show==False` a `matplotlib.Axis` or a list of it.
    """
    if layout is None:
        layout = str(adata.uns['draw_graph']['params']['layout'])
    basis = 'draw_graph_' + layout
    if 'X_' + basis not in adata.obsm_keys():
        raise ValueError('Did not find {} in adata.obs. Did you compute layout {}?'
                         .format('draw_graph_' + layout, layout))

    return plot_scatter(adata, basis=basis, **kwargs)


@doc_params(adata_color_etc=doc_adata_color_etc, scatter_bulk=doc_scatter_bulk, show_save_ax=doc_show_save_ax)
def pca(adata, **kwargs):
    """\
    Scatter plot in PCA coordinates.

    {adata_color_etc}
    {scatter_bulk}
    {show_save_ax}

    Returns
    -------
    If `show==False` a `matplotlib.Axis` or a list of it.
    """
    return plot_scatter(adata, basis='pca', **kwargs)


def plot_scatter(adata,
                 color=None,
                 use_raw=None,
                 sort_order=True,
                 edges=False,
                 edges_width=0.1,
                 edges_color='grey',
                 arrows=False,
                 arrows_kwds=None,
                 basis=None,
                 groups=None,
                 components=None,
                 projection='2d',
                 color_map=None,
                 palette=None,
                 size=None,
                 frameon=None,
                 legend_fontsize=None,
                 legend_fontweight='bold',
                 legend_loc='right margin',
                 ncols=4,
                 hspace=0.25,
                 wspace=None,
                 title=None,
                 show=None,
                 save=None,
                 ax=None, return_fig=None, **kwargs):

    sanitize_anndata(adata)
    if color_map is not None:
        kwargs['cmap'] = color_map
    if size is not None:
        kwargs['s'] = size
    if 'edgecolor' not in kwargs:
        # by default turn off edge color. Otherwise, for
        # very small sizes the edge will not reduce its size
        # (https://github.com/theislab/scanpy/issues/293)
        kwargs['edgecolor'] = 'none'

    if projection == '3d':
        from mpl_toolkits.mplot3d import Axes3D
        args_3d = {'projection': '3d'}
    else:
        args_3d = {}

    if use_raw is None:
        # check if adata.raw is set
        if adata.raw is None:
            use_raw = False
        else:
            use_raw = True

    if wspace is None:
        #  try to set a wspace that is not too large or too small given the
        #  current figure size
        wspace = 0.75 / rcParams['figure.figsize'][0] + 0.02
    if adata.raw is None and use_raw is True:
        raise ValueError("`use_raw` is set to True but annData object does not have raw. "
                         "Please check.")
    # turn color into a python list
    color = [color] if isinstance(color, str) or color is None else list(color)
    if title is not None:
        # turn title into a python list if not None
        title = [title] if isinstance(title, str) else list(title)

    ####
    # get the points position and the components list (only if components is not 'None)
    data_points, components_list = _get_data_points(adata, basis, projection, components)

    ###
    # setup layout. Most of the code is for the case when multiple plots are required
    # 'color' is a list of names that want to be plotted. Eg. ['Gene1', 'louvain', 'Gene2'].
    # component_list is a list of components [[0,1], [1,2]]
    if (isinstance(color, list) and len(color) > 1) or len(components_list) > 1:
        if ax is not None:
            raise ValueError("When plotting multiple panels (each for a given value of 'color' "
                             "a given ax can not be used")
        # change from None to empty list
        if isinstance(color, str) or color is None:
            color = [color]
        if len(components_list) == 0:
            components_list = [None]

        multi_panel = True
        # each plot needs to be its own panel
        from matplotlib import gridspec
        # set up the figure
        num_panels = len(color) * len(components_list)
        n_panels_x = min(ncols, num_panels)
        n_panels_y = np.ceil(num_panels / n_panels_x).astype(int)
        # each panel will have the size of rcParams['figure.figsize']
        fig = pl.figure(figsize=(n_panels_x * rcParams['figure.figsize'][0] * (1 + wspace),
                                 n_panels_y * rcParams['figure.figsize'][1]))
        left = 0.2 / n_panels_x
        bottom = 0.13 / n_panels_y
        gs = gridspec.GridSpec(nrows=n_panels_y,
                               ncols=n_panels_x,
                               left=left,
                               right=1-(n_panels_x-1)*left-0.01/n_panels_x,
                               bottom=bottom,
                               top=1-(n_panels_y-1)*bottom-0.1/n_panels_y,
                               hspace=hspace,
                               wspace=wspace)
    else:
        if len(components_list) == 0:
            components_list = [None]
        multi_panel = False
        if ax is None:
            fig = pl.figure()
            ax = fig.add_subplot(111, **args_3d)

    ###
    # make the plots
    axs = []
    import itertools
    idx_components = range(len(components_list))

    # use itertools.product to make a plot for each color and for each component
    # For example if color=[gene1, gene2] and components=['1,2, '2,3'].
    # The plots are: [color=gene1, components=[1,2], color=gene1, components=[2,3],
    #                 color=gene2, components = [1, 2], color=gene2, components=[2,3]]
    for count, (value_to_plot, component_idx) in enumerate(itertools.product(color, idx_components)):
        color_vector, categorical = _get_color_values(adata, value_to_plot,
                                                      groups=groups, palette=palette,
                                                      use_raw=use_raw)

        # check if higher value points should be plot on top
        if sort_order is True and value_to_plot is not None and categorical is False:
            order = np.argsort(color_vector)
            color_vector = color_vector[order]
            _data_points = data_points[component_idx][order, :]

        else:
            _data_points = data_points[component_idx]

        # if plotting multiple panels, get the ax from the grid spec
        # else use the ax value (either user given or created previously)
        if multi_panel is True:
            ax = pl.subplot(gs[count], **args_3d)
            axs.append(ax)
        if not (settings._frameon if frameon is None else frameon):
            ax.axis('off')
        if title is None:
            if value_to_plot is not None:
                ax.set_title(value_to_plot)
            else:
                ax.set_title('')
        else:

            try:
                ax.set_title(title[count])
            except IndexError:
                logg.warn("The title list is shorter than the number of panels. Using 'color' value instead for"
                          "some plots.")
                ax.set_title(value_to_plot)

        if 's' not in kwargs:
            kwargs['s'] = 120000 / _data_points.shape[0]

        # make the scatter plot
        if projection == '3d':
            cax= ax.scatter(_data_points[:, 0], _data_points[:, 1], _data_points[:, 2],
                            marker=".", c=color_vector, rasterized=settings._vector_friendly,
                            **kwargs)
        else:
            cax= ax.scatter(_data_points[:, 0], _data_points[:, 1],
                            marker=".", c=color_vector, rasterized=settings._vector_friendly,
                            **kwargs)

        # remove y and x ticks
        ax.set_yticks([])
        ax.set_xticks([])
        if projection == '3d':
            ax.set_zticks([])

        # set default axis_labels
        name = _basis2name(basis)
        if components is not None:
            axis_labels = [name + str(x + 1) for x in components_list[component_idx]]
        elif projection == '3d':
            axis_labels = [name + str(x + 1) for x in range(3)]

        else:
            axis_labels = [name + str(x + 1) for x in range(2)]

        ax.set_xlabel(axis_labels[0])
        ax.set_ylabel(axis_labels[1])
        if projection == '3d':
            # shift the label closer to the axis
            ax.set_zlabel(axis_labels[2], labelpad=-7)
        ax.autoscale_view()

        if edges:
            utils.plot_edges(ax, adata, basis, edges_width, edges_color)
        if arrows:
            utils.plot_arrows(ax, adata, basis, arrows_kwds)

        if value_to_plot is None:
            # if only dots were plotted without an associated value
            # there is not need to plot a legend or a colorbar
            continue

        _add_legend_or_colorbar(adata, ax, cax, categorical, value_to_plot, legend_loc,
                                _data_points, legend_fontweight, legend_fontsize, groups, multi_panel)

    if return_fig is True:
        return fig
    axs = axs if multi_panel else ax
    utils.savefig_or_show(basis, show=show, save=save)
    if show is False:
        return axs


def _get_data_points(adata, basis, projection, components):
    """
    Returns the data points corresponding to the selected basis, projection and/or components.

    Because multiple components are given (eg components=['1,2', '2,3'] the
    returned data are lists, containing each of the components. When only one component is plotted
    the list length is 1.

    Returns
    -------
    `tuple` of:
        data_points : `list`. Each list is a numpy array containing the data points
        components : `list` The cleaned list of components. Eg. [[0,1]] or [[0,1], [1,2]]
                    for components = [1,2] and components=['1,2', '2,3'] respectively
    """
    n_dims = 2
    if projection == '3d':
        # check if the data has a third dimension
        if adata.obsm['X_' + basis].shape[1] == 2:
            if settings._low_resolution_warning:
                logg.warn('Selected projections is "3d" but only two dimensions '
                          'are available. Only these two dimensions will be plotted')
        else:
            n_dims = 3

    if components == 'all':
        components = ['{},{}'.format(*((i, i+1) if i % 2 == 1 else (i+1, i)))
            for i in range(1, adata.obsm['X_{}'.format(basis)].shape[1])]

    components_list = []
    offset = 0
    if basis == 'diffmap': offset = 1
    if components is not None:
        # components have different formats, either a list with integers, a string
        # or a list of strings.

        if isinstance(components, str):
            # eg: components='1,2'
            components_list.append([int(x.strip()) - 1 + offset for x in components.split(',')])

        elif isinstance(components, list):
            if isinstance(components[0], int):
                # components=[1,2]
                components_list.append([int(x) - 1 + offset for x in components])
            else:
                # in this case, the components are str
                # eg: components=['1,2'] or components=['1,2', '2,3]
                # More than one component can be given and is stored
                # as a new item of components_list
                for comp in components:
                    components_list.append([int(x.strip()) - 1 + offset for x in comp.split(',')])

        else:
            raise ValueError("Given components: '{}' are not valid. Please check. "
                             "A valid example is `components='2,3'`")
        # check if the components are present in the data
        try:
            data_points = []
            for comp in components_list:
                data_points.append(adata.obsm['X_' + basis][:, comp])
        except:
            raise ValueError("Given components: '{}' are not valid. Please check. "
                             "A valid example is `components='2,3'`")

        if basis == 'diffmap':
            # remove the offset added in the case of diffmap, such that
            # plot_scatter can print the labels correctly.
            components_list = [[number-1 for number in comp] for comp in components_list]
    else:
        data_points = [adata.obsm['X_' + basis][:, offset:offset+n_dims]]
        components_list = []
    return data_points, components_list


def _add_legend_or_colorbar(adata, ax, cax, categorical, value_to_plot, legend_loc,
                            scatter_array, legend_fontweight, legend_fontsize,
                            groups, multi_panel):
    """
    Adds a color bar or a legend to the given ax. A legend is added when the
    data is categorical and a color bar is added when a continuous value was used.

    """
    # add legends or colorbars
    if categorical is True:
        # add legend to figure
        categories = list(adata.obs[value_to_plot].cat.categories)
        colors = adata.uns[value_to_plot + '_colors']

        if multi_panel is True:
            # Shrink current axis by 10% to fit legend and match
            # size of plots that are not categorical
            box = ax.get_position()
            ax.set_position([box.x0, box.y0, box.width * 0.91, box.height])

        if groups is not None:
            # only label groups with the respective color
            colors = [colors[categories.index(x)] for x in groups]
            categories = groups

        if legend_loc == 'right margin':
            for idx, label in enumerate(categories):
                color = colors[idx]
                # use empty scatter to set labels
                ax.scatter([], [], c=color, label=label)
            ax.legend(
                frameon=False, loc='center left',
                bbox_to_anchor=(1, 0.5),
                ncol=(1 if len(categories) <= 14
                      else 2 if len(categories) <= 30 else 3),
                fontsize=legend_fontsize)

        if legend_loc == 'on data':
            # identify centroids to put labels
            all_pos = np.zeros((len(categories), 2))
            for ilabel, label in enumerate(categories):
                _scatter = scatter_array[adata.obs[value_to_plot] == label, :]
                x_pos, y_pos = np.median(_scatter, axis=0)

                ax.text(x_pos, y_pos, label,
                        weight=legend_fontweight,
                        verticalalignment='center',
                        horizontalalignment='center',
                        fontsize=legend_fontsize)

                all_pos[ilabel] = [x_pos, y_pos]
            # this is temporary storage for access by other tools
            utils._tmp_cluster_pos = all_pos
    else:
        # add colorbar to figure
        pl.colorbar(cax, ax=ax, pad=0.01, fraction=0.08, aspect=30)


def _set_colors_for_categorical_obs(adata, value_to_plot, palette):
    """
    Sets the adata.uns[value_to_plot + '_colors'] according to the given palette

    Parameters
    ----------
    adata : annData object
    value_to_plot : name of a valid categorical observation
    palette : Palette should be either a valid `matplotlib.pyplot.colormaps()` string,
              a list of colors (in a format that can be understood by matplotlib,
              eg. RGB, RGBS, hex, or a cycler object with key='color'

    Returns
    -------
    None
    """
    from matplotlib.colors import to_hex
    from cycler import Cycler, cycler

    categories = adata.obs[value_to_plot].cat.categories
    # check is palette is a valid matplotlib colormap
    if isinstance(palette, str) and palette in pl.colormaps():
        # this creates a palette from a colormap. E.g. 'Accent, Dark2, tab20'
        cmap = pl.get_cmap(palette)
        colors_list = [to_hex(x) for x in cmap(np.linspace(0, 1, len(categories)))]

    else:
        # check if palette is a list and convert it to a cycler, thus
        # it doesnt matter if the list is shorter than the categories length:
        if isinstance(palette, list):
            if len(palette) < len(categories):
                logg.warn("Length of palette colors is smaller than the number of "
                          "categories (palette length: {}, categories length: {}. "
                          "Some categories will have the same color."
                          .format(len(palette), len(categories)))
            # check that colors are valid
            _color_list = []
            for color in palette:
                if not is_color_like(color):
                    # check if the color is a valid R color and translate it
                    # to a valid hex color value
                    if color in utils.additional_colors:
                        color = utils.additional_colors[color]
                    else:
                        raise ValueError("The following color value of the given palette is not valid: {}".format(color))
                _color_list.append(color)

            palette = cycler(color=_color_list)
        if not isinstance(palette, Cycler):
            raise ValueError("Please check that the value of 'palette' is a "
                             "valid matplotlib colormap string (eg. Set2), a "
                             "list of color names or a cycler with a 'color' key.")
        if 'color' not in palette.keys:
            raise ValueError("Please set the palette key 'color'.")

        cc = palette()
        colors_list = [to_hex(next(cc)['color']) for x in range(len(categories))]

    adata.uns[value_to_plot + '_colors'] = colors_list


def _set_default_colors_for_categorical_obs(adata, value_to_plot):
    """
    Sets the adata.uns[value_to_plot + '_colors'] using default color palettes

    Parameters
    ----------
    adata : annData object
    value_to_plot : name of a valid categorical observation

    Returns
    -------
    None
    """
    from .. import palettes

    categories = adata.obs[value_to_plot].cat.categories
    length = len(categories)

    # check if default matplotlib palette has enough colors
    if len(rcParams['axes.prop_cycle'].by_key()['color']) >= length:
        cc = rcParams['axes.prop_cycle']()
        palette = [next(cc)['color'] for _ in range(length)]

    else:
        if length <= 28:
            palette = palettes.default_26
        elif length <= len(palettes.default_64):  # 103 colors
            palette = palettes.default_64
        else:
            palette = ['grey' for i in range(length)]
            logg.info('the obs value: "{}" has more than 103 categories. Uniform '
                      '\'grey\' color will be used for all categories.')

    adata.uns[value_to_plot + '_colors'] = palette[:length]


def _get_color_values(adata, value_to_plot, groups=None, palette=None, use_raw=False):
    """
    Returns the value or color associated to each data point.
    For categorical data, the return value is list of colors taken
    from the category palette or from the given `palette` value.

    For non-categorical data, the values are returned
    """

    ###
    # when plotting, the color of the dots is determined for each plot
    # the data is either categorical or continuous and the data could be in
    # 'obs' or in 'var'
    categorical = False
    if value_to_plot is None:
        color_vector = 'lightgray'
    # check if value to plot is in obs
    elif value_to_plot in adata.obs.columns:
        if is_categorical_dtype(adata.obs[value_to_plot]):
            categorical = True

            if palette:
                # use category colors base on given palette
                _set_colors_for_categorical_obs(adata, value_to_plot, palette)
            else:
                if value_to_plot + '_colors' not in adata.uns or \
                    len(adata.uns[value_to_plot + '_colors']) < len(adata.obs[value_to_plot].cat.categories):
                    #  set a default palette in case that no colors or few colors are found
                    _set_default_colors_for_categorical_obs(adata, value_to_plot)
                else:
                    # check that the colors in 'uns' are valid
                    _palette = []
                    for color in adata.uns[value_to_plot + '_colors']:
                        if not is_color_like(color):
                            # check if the color is a valid R color and translate it
                            # to a valid hex color value
                            if color in utils.additional_colors:
                                color = utils.additional_colors[color]
                            else:
                                logg.warn("The following color value found in adata.uns['{}'] "
                                          " is not valid: '{}'. Default colors are used.".format(value_to_plot + '_colors', color))
                                _set_default_colors_for_categorical_obs(adata, value_to_plot)
                                _palette = None
                                break
                        _palette.append(color)
                    if _palette is not None:
                        adata.uns[value_to_plot + '_colors'] = _palette
            # for categorical data, colors should be
            # stored in adata.uns[value_to_plot + '_colors']
            # Obtain color vector by converting every category
            # into its respective color

            color_vector = [adata.uns[value_to_plot + '_colors'][x] for x in adata.obs[value_to_plot].cat.codes]
            if groups is not None:
                if isinstance(groups, str):
                    groups = [groups]
                color_vector = np.array(color_vector, dtype='<U15')
                # set color to 'light gray' for all values
                # that are not in the groups
                color_vector[~adata.obs[value_to_plot].isin(groups)] = "lightgray"
        else:
            color_vector = adata.obs[value_to_plot]

    # check if value to plot is in var
    elif use_raw is False and value_to_plot in adata.var_names:
        color_vector = adata[:, value_to_plot].X

    elif use_raw is True and value_to_plot in adata.raw.var_names:
        color_vector = adata.raw[:, value_to_plot].X
    else:
        raise ValueError("The passed `color` {} is not a valid observation annotation "
                         "or variable name. Valid observation annotation keys are: {}"
                         .format(value_to_plot, adata.obs.columns))

    return color_vector, categorical


def _basis2name(basis):
    """
    converts the 'basis' into the proper name.
    """

    component_name = (
        'DC' if basis == 'diffmap'
        else 'tSNE' if basis == 'tsne'
        else 'UMAP' if basis == 'umap'
        else 'PC' if basis == 'pca'
        else basis.replace('draw_graph_', '').upper() if 'draw_graph' in basis
        else basis)
    return component_name

matplotlib.pyplot.colorbar matplotlib.colors.to_hex matplotlib.colors.is_color_like matplotlib.pyplot.get_cmap matplotlib.pyplot.figure matplotlib.pyplot.colormaps matplotlib.gridspec.GridSpec matplotlib.pyplot.subplot