import anndata

import scanpy as sc

import scipy as s

from scipy.sparse import csr_matrix, issparse

def load_adata(adata_file, metadata_file = None, normalise = False, cells = None, cell_column = "cell", features = None, filter_lowly_expressed_genes = False, set_colors = False, keep_counts=False):

    adata = sc.read(adata_file)

    # Convert to sparse matrices

    if not s.sparse.issparse(adata.X):

        adata.X = csr_matrix(adata.X)

    if len(adata.layers.keys())>0:

        for i in list(adata.layers.keys()):

            if not issparse(adata.layers[i]):

                adata.layers[i] = csr_matrix(adata.layers[i])

    if cells is not None:

        tmp = np.mean(np.isin(cells,adata.obs.index.values)==False)

        if tmp<1: print("%.2f%% of cells provided are not observed in the adata, taking the intersect..." % (100*tmp))

        cells = np.intersect1d(cells,adata.obs.index.values)

        adata = adata[cells,:]

    if features is not None:

        adata = adata[:,features]

    if metadata_file is not None:

        metadata = pd.read_table(metadata_file, delimiter="\t", header=0).set_index(cell_column, drop=False)

        metadata = metadata.loc[cells]

        assert np.all(adata.obs.index.isin(metadata[cell_column]))

        # assert np.all(metadata.cell.isin(adata.obs.index))

        assert metadata.shape[0] == adata.shape[0]

        adata.obs = metadata#.reindex(adata.obs.index)

    if filter_lowly_expressed_genes:

        sc.pp.filter_genes(adata, min_counts=10)

    if keep_counts:

        adata.layers["raw"] = adata.X.copy()

    if normalise:

        sc.pp.normalize_total(adata, target_sum=None, exclude_highly_expressed=False)

        sc.pp.log1p(adata)

    if set_colors:

        colPalette_celltypes = [opts["celltype_colors"][i.replace(" ","_").replace("/","_")] for i in sorted(np.unique(adata.obs['celltype']))]

        adata.uns['celltype_colors'] = colPalette_celltypes

        colPalette_stages = [opts["stage_colors"][i.replace(" ","_").replace("/","_")] for i in sorted(np.unique(adata.obs['stage']))]

        adata.uns['stage_colors'] = colPalette_stages

    return adata

def scale(X, x_min, x_max):

    nom = (X - X.min(axis=0)) * (x_max - x_min)

    denom = X.max(axis=0) - X.min(axis=0)

    denom[denom == 0] = 1

    return x_min + nom / denom

# cmap = custom_div_cmap(11, mincol='g', midcol='0.9' ,maxcol='CornflowerBlue')

def custom_div_cmap(numcolors=11, name='custom_div_cmap',

                    mincol='blue', midcol='white', maxcol='red'):

    """ 

    Default is blue to white to red with 11 colors.  

    Colors can be specified in any way understandable by matplotlib.colors.ColorConverter.to_rgb()

    """

    from matplotlib.colors import LinearSegmentedColormap 

    cmap = LinearSegmentedColormap.from_list(name=name, colors =[mincol, midcol, maxcol], N=numcolors)

    return cmap