# PAGA for hematopoiesis in mouse [(Paul *et al.*, 2015)](https://doi.org/10.1016/j.cell.2015.11.013)
# Hematopoiesis: trace myeloid and erythroid differentiation for data of [Paul *et al.* (2015)](http://doi.org/10.1016/j.cell.2015.11.013).
#
# This is the subsampled notebook for testing.

from pathlib import Path

import numpy as np
from matplotlib.testing import setup
setup()

import scanpy as sc


HERE: Path = Path(__file__).parent
ROOT = HERE / '_images_paga_paul15_subsampled'
FIGS = HERE / 'figures'


def test_paga_paul15_subsampled(image_comparer, plt):
    save_and_compare_images = image_comparer(ROOT, FIGS, tol=25)

    adata = sc.datasets.paul15()
    sc.pp.subsample(adata, n_obs=200)
    del adata.uns['iroot']
    adata.X = adata.X.astype('float64')

    # Preprocessing and Visualization
    sc.pp.recipe_zheng17(adata)
    sc.tl.pca(adata, svd_solver='arpack')
    sc.pp.neighbors(adata, n_neighbors=4, n_pcs=20)
    sc.tl.draw_graph(adata)
    sc.pl.draw_graph(adata, color='paul15_clusters', legend_loc='on data')

    sc.tl.diffmap(adata)
    sc.tl.diffmap(adata) # See #1262
    sc.pp.neighbors(adata, n_neighbors=10, use_rep='X_diffmap')
    sc.tl.draw_graph(adata)

    sc.pl.draw_graph(adata, color='paul15_clusters', legend_loc='on data')

    # Clustering and PAGA
    sc.tl.louvain(adata, resolution=1.0)
    sc.tl.paga(adata, groups='louvain')
    # sc.pl.paga(adata, color=['louvain', 'Hba-a2', 'Elane', 'Irf8'])
    # sc.pl.paga(adata, color=['louvain', 'Itga2b', 'Prss34'])

    adata.obs['louvain_anno'] = adata.obs['louvain']
    sc.tl.paga(adata, groups='louvain_anno')

    PAGA_CONNECTIVITIES = np.array([
        [0., 0.128553, 0., 0.07825,  0., 0., 0.238741, 0., 0., 0.657049],
        [0.128553, 0., 0.480676, 0.257505, 0.533036, 0.043871, 0., 0.032903, 0., 0.087743]])

    assert np.allclose(
        adata.uns['paga']['connectivities'].toarray()[:2],
        PAGA_CONNECTIVITIES, atol=1e-4)

    sc.pl.paga(adata, threshold=0.03)

    # !!!! no clue why it doesn't produce images with the same shape
    # save_and_compare_images('paga')

    sc.tl.draw_graph(adata, init_pos='paga')
    sc.pl.paga_compare(
        adata, threshold=0.03, title='', right_margin=0.2, size=10, edge_width_scale=0.5,
        legend_fontsize=12, fontsize=12, frameon=False, edges=True)

    # slight deviations because of graph drawing
    # save_and_compare_images('paga_compare')

    adata.uns['iroot'] = np.flatnonzero(adata.obs['louvain_anno']  == '3')[0]
    sc.tl.dpt(adata)
    gene_names = ['Gata2', 'Gata1', 'Klf1', 'Hba-a2',  # erythroid
                  'Elane', 'Cebpe',                    # neutrophil
                  'Irf8']                              # monocyte

    paths = [('erythrocytes', [3, 9, 0, 6]),
             ('neutrophils', [3, 1, 2]),
             ('monocytes', [3, 1, 4, 5])]

    adata.obs['distance'] = adata.obs['dpt_pseudotime']

    _, axs = plt.subplots(ncols=3, figsize=(6, 2.5), gridspec_kw={'wspace': 0.05, 'left': 0.12})
    plt.subplots_adjust(left=0.05, right=0.98, top=0.82, bottom=0.2)
    for ipath, (descr, path) in enumerate(paths):
        _, data = sc.pl.paga_path(
            adata, path, gene_names,
            show_node_names=False,
            ax=axs[ipath],
            ytick_fontsize=12,
            left_margin=0.15,
            n_avg=50,
            annotations=['distance'],
            show_yticks=True if ipath==0 else False,
            show_colorbar=False,
            color_map='Greys',
            color_maps_annotations={'distance': 'viridis'},
            title='{} path'.format(descr),
            return_data=True,
            show=False)
        # add a test for this at some point
        # data.to_csv('./write/paga_path_{}.csv'.format(descr))

    save_and_compare_images('paga_path')
