from typing import Union, List, Optional, Any, Tuple, Collection

import numpy as np
import matplotlib.pyplot as plt
from anndata import AnnData
from matplotlib.axes import Axes

from .._utils import _doc_params
from ..plotting import embedding
from ..plotting._docs import (
    doc_adata_color_etc,
    doc_edges_arrows,
    doc_scatter_embedding,
    doc_show_save_ax,
)
from ..plotting._tools.scatterplots import _wraps_plot_scatter
from ..plotting import _utils
from .tl._wishbone import _anndata_to_wishbone


@_wraps_plot_scatter
@_doc_params(
    adata_color_etc=doc_adata_color_etc,
    edges_arrows=doc_edges_arrows,
    scatter_bulk=doc_scatter_embedding,
    show_save_ax=doc_show_save_ax,
)
def phate(adata, **kwargs) -> Union[List[Axes], None]:
    """\
    Scatter plot in PHATE basis.

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

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

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


@_wraps_plot_scatter
@_doc_params(
    adata_color_etc=doc_adata_color_etc,
    edges_arrows=doc_edges_arrows,
    scatter_bulk=doc_scatter_embedding,
    show_save_ax=doc_show_save_ax,
)
def trimap(adata, **kwargs) -> Union[Axes, List[Axes], None]:
    """\
    Scatter plot in TriMap basis.

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

    Returns
    -------
    If `show==False` a :class:`~matplotlib.axes.Axes` or a list of it.
    """
    return embedding(adata, 'trimap', **kwargs)


@_wraps_plot_scatter
@_doc_params(
    adata_color_etc=doc_adata_color_etc,
    edges_arrows=doc_edges_arrows,
    scatter_bulk=doc_scatter_embedding,
    show_save_ax=doc_show_save_ax,
)
def harmony_timeseries(
    adata, *, show: bool = True, return_fig: bool = False, **kwargs
) -> Union[Axes, List[Axes], None]:
    """\
    Scatter plot in Harmony force-directed layout basis.

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

    Returns
    -------
    If `return_fig` is True, a :class:`~matplotlib.figure.Figure`.
    If `show==False` a :class:`~matplotlib.axes.Axes` or a list of it.
    """

    tp_name = adata.uns["harmony_timepoint_var"]
    tps = adata.obs[tp_name].unique()

    fig, axes = plt.subplots(1, len(tps))
    for i, tp in enumerate(tps):
        p = embedding(
            adata,
            'harmony',
            color=tp_name,
            groups=tp,
            title=tp,
            show=False,
            ax=axes[i],
            legend_loc='none',
        )
        p.set_axis_off()
    if return_fig:
        return fig
    elif not show:
        return axes


def sam(
    adata: AnnData,
    projection: Union[str, np.ndarray] = 'X_umap',
    c: Optional[Union[str, np.ndarray]] = None,
    cmap: str = 'Spectral_r',
    linewidth: float = 0.0,
    edgecolor: str = 'k',
    axes: Optional[Axes] = None,
    colorbar: bool = True,
    s: float = 10.0,
    **kwargs: Any,
) -> Axes:
    """\
    Scatter plot using the SAM projection or another input projection.

    Parameters
    ----------
    projection
        A case-sensitive string indicating the projection to display (a key
        in adata.obsm) or a 2D numpy array with cell coordinates. If None,
        projection defaults to UMAP.
    c
        Cell color values overlaid on the projection. Can be a string from adata.obs
        to overlay cluster assignments / annotations or a 1D numpy array.
    axes
        Plot output to the specified, existing axes. If None, create new
        figure window.
    kwargs
        all keyword arguments in matplotlib.pyplot.scatter are eligible.
    """

    if isinstance(projection, str):
        try:
            dt = adata.obsm[projection]
        except KeyError:
            raise ValueError(
                'Please create a projection first using run_umap or run_tsne'
            )
    else:
        dt = projection

    if axes is None:
        plt.figure()
        axes = plt.gca()

    if c is None:
        axes.scatter(
            dt[:, 0], dt[:, 1], s=s, linewidth=linewidth, edgecolor=edgecolor, **kwargs
        )
        return axes

    if isinstance(c, str):
        try:
            c = np.array(list(adata.obs[c]))
        except KeyError:
            pass

    if isinstance(c[0], (str, np.str_)) and isinstance(c, (np.ndarray, list)):
        import samalg.utilities as ut

        i = ut.convert_annotations(c)
        ui, ai = np.unique(i, return_index=True)
        cax = axes.scatter(
            dt[:, 0],
            dt[:, 1],
            c=i,
            cmap=cmap,
            s=s,
            linewidth=linewidth,
            edgecolor=edgecolor,
            **kwargs,
        )

        if colorbar:
            cbar = plt.colorbar(cax, ax=axes, ticks=ui)
            cbar.ax.set_yticklabels(c[ai])
    else:
        if not isinstance(c, (np.ndarray, list)):
            colorbar = False
        i = c

        cax = axes.scatter(
            dt[:, 0],
            dt[:, 1],
            c=i,
            cmap=cmap,
            s=s,
            linewidth=linewidth,
            edgecolor=edgecolor,
            **kwargs,
        )

        if colorbar:
            plt.colorbar(cax, ax=axes)
    return axes


@_doc_params(show_save_ax=doc_show_save_ax)
def wishbone_marker_trajectory(
    adata: AnnData,
    markers: Collection[str],
    no_bins: int = 150,
    smoothing_factor: int = 1,
    min_delta: float = 0.1,
    show_variance: bool = False,
    figsize: Optional[Tuple[float, float]] = None,
    return_fig: bool = False,
    show: bool = True,
    save: Optional[Union[str, bool]] = None,
    ax: Optional[Axes] = None,
):
    """\
    Plot marker trends along trajectory, and return trajectory branches for further
    analysis and visualization (heatmap, etc..)

    Parameters
    ----------
    adata
        Annotated data matrix.
    markers
        Iterable of markers/genes to be plotted.
    show_variance
        Logical indicating if the trends should be accompanied with variance.
    no_bins
        Number of bins for calculating marker density.
    smoothing_factor
        Parameter controlling the degree of smoothing.
    min_delta
        Minimum difference in marker expression after normalization to show
        separate trends for the two branches.
    figsize
        width, height
    return_fig
        Return the matplotlib figure.
    {show_save_ax}

    Returns
    -------
    Updates `adata` with the following fields:

    `trunk_wishbone` : :class:`pandas.DataFrame` (`adata.uns`)
        Computed values before branching
    `branch1_wishbone` : :class:`pandas.DataFrame` (`adata.uns`)
        Computed values for the first branch
    `branch2_wishbone` : :class:`pandas.DataFrame` (`adata.uns`)
        Computed values for the second branch.
    """

    wb = _anndata_to_wishbone(adata)

    if figsize is None:
        width = 2 * len(markers)
        height = 0.75 * len(markers)
    else:
        width, height = figsize

    if ax:
        fig = ax.figure
    else:
        fig = plt.figure(figsize=(width, height))
        ax = plt.gca()

    ret_values, fig, ax = wb.plot_marker_trajectory(
        markers=markers,
        show_variance=show_variance,
        no_bins=no_bins,
        smoothing_factor=smoothing_factor,
        min_delta=min_delta,
        fig=fig,
        ax=ax,
    )

    adata.uns['trunk_wishbone'] = ret_values['Trunk']
    adata.uns['branch1_wishbone'] = ret_values['Branch1']
    adata.uns['branch2_wishbone'] = ret_values['Branch2']

    _utils.savefig_or_show('wishbone_trajectory', show=show, save=save)

    if return_fig:
        return fig
    elif not show:
        return ax


def scrublet_score_distribution(
    adata,
    scale_hist_obs: str = 'log',
    scale_hist_sim: str = 'linear',
    figsize: Optional[Tuple[float, float]] = (8, 3),
    return_fig: bool = False,
    show: bool = True,
    save: Optional[Union[str, bool]] = None,
):
    """\
    Plot histogram of doublet scores for observed transcriptomes and simulated doublets.

    The histogram for simulated doublets is useful for determining the correct doublet
    score threshold.

    Scrublet must have been run previously with the input object.

    Parameters
    ----------
    adata
        An annData object resulting from func:`~scanpy.external.scrublet`.
    scale_hist_obs
        Set y axis scale transformation in matplotlib for the plot of observed
        transcriptomes (e.g. "linear", "log", "symlog", "logit")
    scale_hist_sim
        Set y axis scale transformation in matplotlib for the plot of simulated
        doublets (e.g. "linear", "log", "symlog", "logit")
    figsize
        width, height
    show
         Show the plot, do not return axis.
    save
        If `True` or a `str`, save the figure.
        A string is appended to the default filename.
        Infer the filetype if ending on {`'.pdf'`, `'.png'`, `'.svg'`}.

    Returns
    -------
    If `return_fig` is True, a :class:`~matplotlib.figure.Figure`.
    If `show==False` a list of :class:`~matplotlib.axes.Axes`.

    See also
    --------
    :func:`~scanpy.external.pp.scrublet`: Main way of running Scrublet, runs
        preprocessing, doublet simulation and calling.
    :func:`~scanpy.external.pp.scrublet_simulate_doublets`: Run Scrublet's doublet
        simulation separately for advanced usage.
    """

    if 'scrublet' not in adata.uns:
        raise ValueError(
            'Please run scrublet before trying to generate the scrublet plot.'
        )

    # If batched_by is populated, then we know Scrublet was run over multiple batches

    if 'batched_by' in adata.uns['scrublet']:
        batch_key = adata.uns['scrublet']['batched_by']

        batches = np.unique(adata.obs[batch_key])
        adatas = [
            adata[
                adata.obs[batch_key] == batch,
            ]
            for batch in batches
        ]
        figsize = (figsize[0], figsize[1] * len(batches))

    else:
        adatas = [adata]

    fig, axs = plt.subplots(len(adatas), 2, figsize=figsize)

    for idx, ad in enumerate(adatas):

        # We'll need multiple rows if Scrublet was run in multiple batches

        if 'batched_by' in adata.uns['scrublet']:

            batch = batches[idx]

            threshold = adata.uns['scrublet']['batches'][batch]['threshold']
            doublet_scores_sim = adata.uns['scrublet']['batches'][batch][
                'doublet_scores_sim'
            ]
            axis_lab_suffix = " (%s)" % batch
            obs_ax = axs[idx][0]
            sim_ax = axs[idx][1]

        else:
            threshold = adata.uns['scrublet']['threshold']
            doublet_scores_sim = adata.uns['scrublet']['doublet_scores_sim']
            axis_lab_suffix = ''
            obs_ax = axs[0]
            sim_ax = axs[1]

        # Make the observed transcriptomes plot

        obs_ax.hist(
            ad.obs['doublet_score'],
            np.linspace(0, 1, 50),
            color='gray',
            linewidth=0,
            density=True,
        )
        obs_ax.set_yscale(scale_hist_obs)
        yl = obs_ax.get_ylim()
        obs_ax.set_ylim(yl)
        obs_ax.plot(threshold * np.ones(2), yl, c='black', linewidth=1)
        obs_ax.set_title('Observed transcriptomes%s' % axis_lab_suffix)
        obs_ax.set_xlabel('Doublet score')
        obs_ax.set_ylabel('Prob. density')

        # Make the simulated tranascriptomes plot

        sim_ax.hist(
            doublet_scores_sim,
            np.linspace(0, 1, 50),
            color='gray',
            linewidth=0,
            density=True,
        )
        sim_ax.set_yscale(scale_hist_sim)
        yl = sim_ax.get_ylim()
        sim_ax.set_ylim(yl)
        sim_ax.plot(threshold * np.ones(2), yl, c='black', linewidth=1)
        sim_ax.set_title('Simulated doublets%s' % axis_lab_suffix)
        sim_ax.set_xlabel('Doublet score')
        sim_ax.set_ylabel('Prob. density')

    fig.tight_layout()

    _utils.savefig_or_show('scrublet_score_distribution', show=show, save=save)
    if return_fig:
        return fig
    elif not show:
        return axs
