import click

from numpy import unique, ndarray
from scipy.sparse.csc import csc_matrix
from os.path import isfile, isdir, splitext, expanduser, sep


@click.command()
@click.argument('data', nargs=1, metavar='<dataset: file or path to data>', required=True)
@click.option('--layout', '-l', default=['umap', 'tsne'], multiple=True, type=click.Choice(['umap', 'tsne']),
              help='Layout algorithm', show_default=True)
@click.option('--recipe', '-r', default='none', type=click.Choice(['none', 'seurat', 'zheng17']),
              help='Preprocessing to run.', show_default=True)
@click.option('--output', '-o', default='', help='Save a new file to filename.', metavar='<filename>')
@click.option('--plotting', '-p', default=False, is_flag=True, help='Whether to generate plots.', show_default=True)
@click.option('--sparse', default=False, is_flag=True, help='Whether to force sparsity.', show_default=True)
@click.option('--overwrite', default=False, is_flag=True, help='Allow file overwriting.', show_default=True)
@click.option('--set-obs-names', default='', help='Named field to set as index for obs.', metavar='<name>')
@click.option('--set-var-names', default='', help='Named field to set as index for var.', metavar='<name>')
@click.option('--make-obs-names-unique', default=True, is_flag=True,
              help='Ensure obs index is unique.', show_default=True)
@click.option('--make-var-names-unique', default=True, is_flag=True,
              help='Ensure var index is unique.', show_default=True)
def prepare(data, layout, recipe, output, plotting, sparse, overwrite,
            set_obs_names, set_var_names, make_obs_names_unique, make_var_names_unique):
    """Preprocesses data for use with cellxgene.

    This tool runs a series of scanpy routines for preparing a dataset
    for use with cellxgene. It loads data from different formats
    (h5ad, loom, or a 10x directory), runs dimensionality reduction,
    computes nearest neighbors, computes a layout, performs clustering,
    and saves the results. Includes additional options for naming
    annotations, ensuring sparsity, and plotting results."""

    # collect slow imports here to make CLI startup more responsive
    click.echo('[cellxgene] Starting CLI...')
    import matplotlib
    matplotlib.use('Agg')
    import scanpy.api as sc

    # scanpy settings
    sc.settings.verbosity = 0
    sc.settings.autosave = True

    # check args
    if sparse and not recipe == 'none':
        raise click.UsageError('Cannot use a recipe when forcing sparsity')

    output = expanduser(output)
    if isfile(output) and not overwrite:
        raise click.UsageError(f'Cannot overwrite existing file {output}, try using the flag --overwrite')

    def load_data(data):
        if isfile(data):
            name, extension = splitext(data)
            if extension == '.h5ad':
                adata = sc.read_h5ad(data)
            elif extension == '.loom':
                adata = sc.read_loom(data)
            else:
                raise click.FileError(data, hint='does not have a valid extension [.h5ad | .loom]')
        elif isdir(data):
            if not data.endswith(sep):
                data += sep
            adata = sc.read_10x_mtx(data)
        else:
            raise click.FileError(data, hint='not a valid file or path')

        if not set_obs_names == '':
            if set_obs_names not in adata.obs_keys():
                raise click.UsageError(f'obs {set_obs_names} not found, options are: {adata.obs_keys()}')
            adata.obs_names = adata.obs[set_obs_names]
        if not set_var_names == '':
            if set_var_names not in adata.var_keys():
                raise click.UsageError(f'var {set_var_names} not found, options are: {adata.var_keys()}')
            adata.var_names = adata.var[set_var_names]
        if make_obs_names_unique:
            adata.obs_names_make_unique()
        if make_var_names_unique:
            adata.var_names_make_unique()
        if not adata._obs.index.is_unique:
            click.echo('Warning: obs index is not unique')
        if not adata._var.index.is_unique:
            click.echo('Warning: var index is not unique')

        return adata

    def make_sparse(adata):
        if (type(adata.X) is ndarray) and sparse:
            adata.X = csc_matrix(adata.X)

    def run_recipe(adata):
        if recipe == 'seurat':
            sc.pp.recipe_seurat(adata)
        elif recipe == 'zheng17':
            sc.pp.recipe_zheng17(adata)
        else:
            sc.pp.filter_cells(adata, min_genes=5)
            sc.pp.filter_genes(adata, min_cells=25)
            if sparse:
                sc.pp.scale(adata, zero_center=False)
            else:
                sc.pp.scale(adata)

    def run_pca(adata):
        if sparse:
            sc.pp.pca(adata, svd_solver='arpack', zero_center=False)
        else:
            sc.pp.pca(adata, svd_solver='arpack')

    def run_neighbors(adata):
        sc.pp.neighbors(adata)

    def run_louvain(adata):
        sc.tl.louvain(adata)

    def run_layout(adata):
        if len(unique(adata.obs['louvain'].values)) < 10:
            palette = 'tab10'
        else:
            palette = 'tab20'

        if 'umap' in layout:
            sc.tl.umap(adata)
            if plotting:
                sc.pl.umap(adata, color='louvain', palette=palette, save='_louvain')

        if 'tsne' in layout:
            sc.tl.tsne(adata)
            if plotting:
                sc.pl.tsne(adata, color='louvain', palette=palette, save='_louvain')

    def show_step(item):
        names = {
            'make_sparse': 'Ensuring sparsity',
            'run_recipe': 'Running preprocessing recipe "%s"' % recipe,
            'run_pca': 'Running PCA',
            'run_neighbors': 'Calculating neighbors',
            'run_louvain': 'Calculating clusters',
            'run_layout': 'Computing layout'
        }
        if item is not None:
            return names[item.__name__]

    steps = [make_sparse, run_recipe, run_pca, run_neighbors, run_louvain, run_layout]

    click.echo(f'[cellxgene] Loading data from {data}, please wait...')
    adata = load_data(data)

    click.echo('[cellxgene] Beginning preprocessing...')
    with click.progressbar(steps, label='[cellxgene] Progress', show_eta=False, item_show_func=show_step) as bar:
        for step in bar:
            step(adata)

    # saving
    if not output == '':
        click.echo(f'[cellxgene] Saving results to {output}...')
        adata.write(output)

    click.echo('[cellxgene] Success!')