#!/opt/conda/conda-bld/bx-python_1733802355882/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pla/bin/python

"""
Given a list of intervals in BED format (`interval_file`) and a set of scores
(`score_file`) print each interval plus the average, minimum, and maximum of
the scores that fall in that interval. Scores can either be wiggle format
data or a directory containing binned array files (named according to the
sequence source / chromosome of the intervals).

usage: %prog score_file interval_file [out_file] [options]
    -b, --binned: 'score_file' is actually a directory of binned array files
    -m, --mask=FILE: bed file containing regions not to consider valid
"""

import os
import os.path
import sys
from collections.abc import Mapping

import bx.wiggle
from bx import misc
from bx.binned_array import (
    BinnedArray,
    FileBinnedArray,
)
from bx.bitset_builders import binned_bitsets_from_file
from bx.cookbook import doc_optparse
from bx_extras.fpconst import isNaN


class FileBinnedArrayDir(Mapping):
    """
    Adapter that makes a directory of FileBinnedArray files look like
    a regular dict of BinnedArray objects.
    """

    def __init__(self, dir):
        self.dir = dir
        self.cache = {}

    def __getitem__(self, key):
        value = None
        if key in self.cache:
            value = self.cache[key]
        else:
            fname = os.path.join(self.dir, f"{key}.ba")
            if os.path.exists(fname):
                value = FileBinnedArray(open(fname))
                self.cache[key] = value
        if value is None:
            raise KeyError("File does not exist: " + fname)
        return value

    def __iter__(self):
        raise NotImplementedError()

    def __len__(self):
        raise NotImplementedError()


def load_scores_wiggle(fname):
    """
    Read a wiggle file and return a dict of BinnedArray objects keyed
    by chromosome.
    """
    scores_by_chrom = {}
    for chrom, pos, val in bx.wiggle.Reader(misc.open_compressed(fname)):
        if chrom not in scores_by_chrom:
            scores_by_chrom[chrom] = BinnedArray()
        scores_by_chrom[chrom][pos] = val
    return scores_by_chrom


def load_scores_ba_dir(dir):
    """
    Return a dict-like object (keyed by chromosome) that returns
    FileBinnedArray objects created from "key.ba" files in `dir`
    """
    return FileBinnedArrayDir(dir)


def main():
    # Parse command line
    options, args = doc_optparse.parse(__doc__)
    try:
        score_fname = args[0]
        interval_fname = args[1]
        if len(args) > 2:
            out_file = open(args[2], "w")
        else:
            out_file = sys.stdout
        binned = bool(options.binned)
        mask_fname = options.mask
    except Exception:
        doc_optparse.exit()

    if binned:
        scores_by_chrom = load_scores_ba_dir(score_fname)
    else:
        scores_by_chrom = load_scores_wiggle(score_fname)

    if mask_fname:
        masks = binned_bitsets_from_file(open(mask_fname))
    else:
        masks = None

    for line in open(interval_fname):
        fields = line.split()
        chrom, start, stop = fields[0], int(fields[1]), int(fields[2])
        total = 0
        count = 0
        min_score = 100000000
        max_score = -100000000
        for i in range(start, stop):
            if chrom in scores_by_chrom and scores_by_chrom[chrom][i]:
                # Skip if base is masked
                if masks and chrom in masks:
                    if masks[chrom][i]:
                        continue
                # Get the score, only count if not 'nan'
                score = scores_by_chrom[chrom][i]
                if not isNaN(score):
                    total += score
                    count += 1
                    max_score = max(score, max_score)
                    min_score = min(score, min_score)
        if count > 0:
            avg = total / count
        else:
            avg = "nan"
            min_score = "nan"
            max_score = "nan"

        print("\t".join(map(str, [chrom, start, stop, avg, min_score, max_score])), file=out_file)

    out_file.close()


if __name__ == "__main__":
    main()
