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#!/usr/bin/env python
"""
Predict background profiles of new alleles based on a model of stutter
occurrence obtained from stuttermodel.
"""
import argparse
import sys
#import numpy as np  # Only imported when actually running this tool.

from operator import mul

from ..lib import get_column_ids, reverse_complement, get_repeat_pattern,\
                  mutate_sequence,\
                  PAT_SEQ_RAW, ensure_sequence_format, add_sequence_format_args

__version__ = "0.1dev"


# Default values for parameters are specified below.

# Default name of the column that contains the marker name.
# This value can be overridden by the -m command line option.
_DEF_COLNAME_MARKER = "name"

# Default name of the column that contains the allele.
# This value can be overridden by the -a command line option.
_DEF_COLNAME_ALLELE = "allele"

# Default name of the column to write the output to.
# This value can be overridden by the -o command line option.
_DEF_COLNAME_ALLELE_OUT = "allele"

# Default minimum amount of background to consider, as a percentage of
# the highest allele.
# This value can be overridden by the -m command line option.
_DEF_THRESHOLD_PCT = 0.5

# Default minimum R2 score.
# This value can be overridden by the -t command line option.
_DEF_MIN_R2 = 0.8


def parse_stuttermodel(stuttermodel, min_r2=0):
    column_names = stuttermodel.readline().rstrip("\r\n").split("\t")
    (colid_unit, colid_marker, colid_stutter, colid_lbound, colid_direction,
     colid_r2) = get_column_ids(column_names, "unit", "marker", "stutter",
        "lbound", "direction", "r2")
    degree = ord('a')
    colids_coefs = []
    while True:
        try:
            colids_coefs.append(get_column_ids(column_names, chr(degree)))
            degree += 1
        except ValueError:
            break
    degree -= ord('b')
    if degree < 0:
        raise ValueError("Invalid stutter model file: Unable to determine "
                         "polynomial degree!")

    repeat_patterns = {}
    model = {}
    for line in stuttermodel:
        line = line.rstrip("\r\n").split("\t")
        marker = line[colid_marker]
        seq = line[colid_unit]
        stutter_fold = int(line[colid_stutter])
        direction = line[colid_direction]
        lbound = int(line[colid_lbound])
        r2 = float(line[colid_r2])
        if r2 < min_r2:
            continue
        coefs = [float(line[colid_coef]) for colid_coef in colids_coefs]
        if marker not in model:
            model[marker] = {}
        if not seq or not PAT_SEQ_RAW.match(seq):
            raise ValueError(
                "Invalid stutter model file: Encountered invalid repeat "
                "sequence '%s'!" % seq)
        if direction == "reverse":
            seq = reverse_complement(seq)
        elif direction != "forward":
            raise ValueError(
                "Invalid stutter model file: Unknown sequence strand '%s'!" %
                direction)
        if (seq, stutter_fold) in model[marker]:
            raise ValueError(
                "Invalid stutter model file: Encountered two models for %+i "
                "stutter of %s repeats in marker %s!" %
                (stutter_fold, seq, marker))
        if seq not in repeat_patterns:
            repeat_patterns[seq] = get_repeat_pattern(seq)
        model[marker][seq, stutter_fold] = {
            "lbound": lbound,
            "r2": r2,
            "pat": repeat_patterns[seq],
            "func": lambda x: 0. if x < lbound else max(0.,
                sum(coefs[i] * x**(degree-i) for i in range(len(coefs))))}
    return model
#parse_stuttermodel


def get_all_stutters(allele, flanks, model, min_pct):
    """Return a sorted list of all possible stutters."""
    # Include flanks in case the allele starts in a repeat.
    full_allele = flanks[0] + allele + flanks[1]

    stutters = []
    for seq, stutter_fold in model:
        stutlen = len(seq) * abs(stutter_fold)

        # Generate all stutter variants for this sequence.
        for m in model[seq, stutter_fold]["pat"].finditer(full_allele):
            start = m.start()
            end = m.end()
            length = end - start
            if length <= stutlen:
                continue  # Not repeated.
            if stutter_fold > 0:
                if (end < len(flanks[0]) or
                        start > len(full_allele)-len(flanks[1])):
                    continue  # Repeat is embedded in flank.
            elif (len(flanks[0]) > end-stutlen or
                    start+stutlen > len(full_allele)-len(flanks[1])):
                continue  # Shortening repeat disrupts flank.
            amount = model[seq, stutter_fold]["func"](length)
            if amount < min_pct:
                continue
            stutters.append({
                "seq": seq,
                "stutlen": stutlen,
                "start": min(0, start-len(flanks[0])),
                "end":min(len(full_allele)-len(flanks[1]), end)-len(flanks[0]),
                "fold": stutter_fold,
                "amount": amount/100.})
    return sorted(stutters, key=lambda x: (x["start"], x["end"]))
#get_all_stutters


def get_all_combinations(stutters, combinations=None, appendto=None, pos=0,
                         start=0):
    """Return a list of all non-overlapping combinations of stutters."""
    if combinations is None:
        combinations = []
    if appendto is None:
        appendto = []
    for i in range(start, len(stutters)):
        if stutters[i]["start"] < pos:
            continue
        withnewelement = [x for x in appendto]
        withnewelement.append(stutters[i])
        combinations.append(withnewelement)
        get_all_combinations(stutters, combinations, withnewelement,
                             stutters[i]["end"], i+1)
    return combinations
#get_all_combinations


def get_relative_frequencies(stutters, combinations):
    # Compute expected amount of each combination.
    A = np.array([reduce(mul, (s["amount"] for s in combo))
                  for combo in combinations])
    C = np.array([[s in c for c in combinations] for s in stutters])
    for iterations in xrange(1000): #TODO: get good stopcond
        for i in range(len(stutters)):
            A[C[i]] *= stutters[i]["amount"] / A[C[i]].sum()
    return A.tolist()
#get_relative_frequencies


def predict_profiles(stuttermodel, seqsfile, outfile,
                     marker_column, allele_column, default_marker,
                     crosstab, min_pct, min_r2, library):

    # Parse library and stutter model file.
    library = parse_library(library) if library is not None else None
    model = parse_stuttermodel(stuttermodel, min_r2)

    # Read list of sequences.
    seqlist = {}
    column_names = seqsfile.readline().rstrip("\r\n").split("\t")
    colid_allele = get_column_ids(column_names, "allele")
    try:
        colid_name = get_column_ids(column_names, "name")
    except:
        colid_name = None
    for line in seqsfile:
        line = line.rstrip("\r\n").split("\t")
        marker = line[colid_name] if colid_name is not None else default_marker
        if marker not in model:
            continue
        sequence = ensure_sequence_format(line[colid_allele], "raw", library)
        try:
            seqlist[marker].append(sequence)
        except KeyError:
            seqlist[marker] = [sequence]

    if not crosstab:
        outfile.write("\t".join(
            ["marker", "allele", "sequence", "fmean", "rmean"]) + "\n")
    for marker in seqlist:
        p = {
            "true alleles": len(seqlist[marker]),
            "alleles": seqlist[marker],
            "profiles_forward":
                [[100 if i == j else 0
                  for i in range(len(seqlist[marker]))]
                 for j in range(len(seqlist[marker]))],
            "profiles_reverse":
                [[100 if i == j else 0
                  for i in range(len(seqlist[marker]))]
                 for j in range(len(seqlist[marker]))]}
        if library and "flanks" in library and marker in library["flanks"]:
            flanks = library["flanks"][marker]
        else:
            flanks = ["", ""]
        for ai in range(len(seqlist[marker])):
            # TODO: repeat this part for reverse
            allele = seqlist[marker][ai]
            stutters = get_all_stutters(allele, flanks, model[marker], min_pct)
            combinations = get_all_combinations(stutters)
            frequencies = get_relative_frequencies(stutters, combinations)
            for i in range(len(combinations)):
                freq = frequencies[i] * 100.
                if freq < min_pct:
                    continue
                sequence = mutate_sequence(allele, [
                    "%+i.1->%s" %
                        (s["end"], allele[s["end"]-s["stutlen"]:s["end"]])
                    if s["fold"] > 0 else
                        "%+i%s>-" % (s["end"]-s["stutlen"]+1,
                            allele[s["end"]-s["stutlen"]:s["end"]])
                    for s in combinations[i]])
                try:
                    si = p["alleles"].index(sequence)
                except ValueError:
                    p["alleles"].append(sequence)
                    for profile in p["profiles_forward"]:
                        profile.append(0)
                    for profile in p["profiles_reverse"]:
                        profile.append(0)
                    si = -1
                p["profiles_forward"][ai][si] = freq

        if crosstab:
            # Cross-tabular output (profiles in rows)
            outfile.write("\t".join([marker, "0"] + p["alleles"]) + "\n")
            for i in range(p["true alleles"]):
                outfile.write("\t".join(
                    [marker, str(i+1)] + map(str, p["profiles_forward"][i])) +
                    "\n")
                outfile.write("\t".join(
                    [marker, str(-i-1)] + map(str, p["profiles_reverse"][i])) +
                    "\n")
        else:
            # Tab-separated columns format.
            for i in range(p["true alleles"]):
                for j in range(len(p["alleles"])):
                    if not (p["profiles_forward"][i][j] +
                            p["profiles_reverse"][i][j]):
                        continue
                    outfile.write("\t".join(
                        [marker, p["alleles"][i], p["alleles"][j]] +
                        map(str, [p["profiles_forward"][i][j],
                                  p["profiles_reverse"][i][j]])) + "\n")
#predict_profiles


def add_arguments(parser):
    # TODO: Add something to enable using the "All data" fits if needed.
    parser.add_argument('stuttermodel', metavar="STUT",
        type=argparse.FileType("r"),
        help="file containing a trained stutter model")
    parser.add_argument('seqs', metavar="SEQS",
        type=argparse.FileType("r"),
        help="file containing the sequences for which a profile should be "
             "predicted")
    parser.add_argument('outfile', metavar="OUT", nargs="?", default=sys.stdout,
        type=argparse.FileType("w"),
        help="the file to write the output to (default: write to stdout)")
    parser.add_argument('-m', '--marker-column', metavar="COLNAME",
        default=_DEF_COLNAME_MARKER,
        help="name of the column that contains the marker name "
             "(default: '%(default)s')")
    parser.add_argument('-a', '--allele-column', metavar="COLNAME",
        default=_DEF_COLNAME_ALLELE,
        help="name of the column that contains the allele "
             "(default: '%(default)s')")
    parser.add_argument('-M', '--marker', metavar="MARKER",
        help="assume the specified marker for all sequences")
    parser.add_argument('-C', '--cross-tabular', action="store_true",
        help="if specified, a space-efficient cross-tabular output format is "
             "used instead of the default tab-separated columns format")
    filtergroup = parser.add_argument_group("filtering options")
    filtergroup.add_argument('-n', '--min-pct', metavar="PCT", type=float,
        default=_DEF_THRESHOLD_PCT,
        help="minimum amount of background to consider, as a percentage "
             "of the highest allele (default: %4.2f)" % _DEF_THRESHOLD_PCT)
    filtergroup.add_argument('-t', '--min-r2', type=float,
        default=_DEF_MIN_R2, metavar="N",
        help="minimum required r-squared score (default: %(default)s)")
    add_sequence_format_args(parser, "raw", True)  # Force raw seqs.
#add_arguments


def run(args):
    # Import numpy now.
    import numpy as np
    global np

    predict_profiles(args.stuttermodel, args.seqs, args.outfile,
                     args.marker_column, args.allele_column, args.marker,
                     args.cross_tabular, args.min_pct, args.min_r2,
                     args.library)
#run


def main():
    """
    Main entry point.
    """
    parser = argparse.ArgumentParser(
        description=__doc__)
    try:
        add_arguments(parser)
        run(parser.parse_args())
    except OSError as error:
        parser.error(error)
#main


if __name__ == "__main__":
    main()