import warnings
from pathlib import Path
from os import PathLike, fspath

import pandas as pd
import math
import numpy as np
from scipy.sparse import issparse

from .. import AnnData
from ..logging import get_logger
from . import WriteWarning

# Exports
from .h5ad import write_h5ad as _write_h5ad

try:
    from .zarr import write_zarr
except ImportError as e:

    def write_zarr(*_, **__):
        raise e


logger = get_logger(__name__)


def write_csvs(
    dirname: PathLike, adata: AnnData, skip_data: bool = True, sep: str = ","
):
    """See :meth:`~anndata.AnnData.write_csvs`."""
    dirname = Path(dirname)
    if dirname.suffix == ".csv":
        dirname = dirname.with_suffix("")
    logger.info(f"writing .csv files to {dirname}")
    if not dirname.is_dir():
        dirname.mkdir(parents=True, exist_ok=True)
    dir_uns = dirname / "uns"
    if not dir_uns.is_dir():
        dir_uns.mkdir(parents=True, exist_ok=True)
    d = dict(
        obs=adata._obs,
        var=adata._var,
        obsm=adata._obsm.to_df(),
        varm=adata._varm.to_df(),
    )
    if not skip_data:
        d["X"] = pd.DataFrame(adata._X.toarray() if issparse(adata._X) else adata._X)
    d_write = {**d, **adata._uns}
    not_yet_raised_sparse_warning = True
    for key, value in d_write.items():
        if issparse(value):
            if not_yet_raised_sparse_warning:
                warnings.warn("Omitting to write sparse annotation.", WriteWarning)
                not_yet_raised_sparse_warning = False
            continue
        filename = dirname
        if key not in {"X", "var", "obs", "obsm", "varm"}:
            filename = dir_uns
        filename /= f"{key}.csv"
        df = value
        if not isinstance(value, pd.DataFrame):
            value = np.array(value)
            if np.ndim(value) == 0:
                value = value[None]
            try:
                df = pd.DataFrame(value)
            except Exception as e:
                warnings.warn(
                    f"Omitting to write {key!r} of type {type(e)}.",
                    WriteWarning,
                )
                continue
        df.to_csv(
            filename,
            sep=sep,
            header=key in {"obs", "var", "obsm", "varm"},
            index=key in {"obs", "var"},
        )


def write_loom(filename: PathLike, adata: AnnData, write_obsm_varm: bool = False):
    filename = Path(filename)
    row_attrs = {k: np.array(v) for k, v in adata.var.to_dict("list").items()}
    row_attrs["var_names"] = adata.var_names.values
    col_attrs = {k: np.array(v) for k, v in adata.obs.to_dict("list").items()}
    col_attrs["obs_names"] = adata.obs_names.values

    if adata.X is None:
        raise ValueError("loompy does not accept empty matrices as data")

    if write_obsm_varm:
        for key in adata.obsm.keys():
            col_attrs[key] = adata.obsm[key]
        for key in adata.varm.keys():
            row_attrs[key] = adata.varm[key]
    elif len(adata.obsm.keys()) > 0 or len(adata.varm.keys()) > 0:
        logger.warning(
            f"The loom file will lack these fields:\n"
            f"{adata.obsm.keys() | adata.varm.keys()}\n"
            f"Use write_obsm_varm=True to export multi-dimensional annotations"
        )

    layers = {"": adata.X.T}
    for key in adata.layers.keys():
        layers[key] = adata.layers[key].T

    from loompy import create

    if filename.exists():
        filename.unlink()
    create(fspath(filename), layers, row_attrs=row_attrs, col_attrs=col_attrs)


def _get_chunk_indices(za):
    # TODO: does zarr provide code for this?
    """\
    Return all the indices (coordinates) for the chunks in a zarr array,
    even empty ones.
    """
    return [
        (i, j)
        for i in range(int(math.ceil(float(za.shape[0]) / za.chunks[0])))
        for j in range(int(math.ceil(float(za.shape[1]) / za.chunks[1])))
    ]


def _write_in_zarr_chunks(za, key, value):
    if key != "X":
        za[:] = value  # don’t chunk metadata
    else:
        for ci in _get_chunk_indices(za):
            s0, e0 = za.chunks[0] * ci[0], za.chunks[0] * (ci[0] + 1)
            s1, e1 = za.chunks[1] * ci[1], za.chunks[1] * (ci[1] + 1)
            print(ci, s0, e1, s1, e1)
            if issparse(value):
                za[s0:e0, s1:e1] = value[s0:e0, s1:e1].todense()
            else:
                za[s0:e0, s1:e1] = value[s0:e0, s1:e1]