# Copyright 2015 by Gert Hulselmans.  All rights reserved.
# This file is part of the Biopython distribution and governed by your
# choice of the "Biopython License Agreement" or the "BSD 3-Clause License".
# Please see the LICENSE file that should have been included as part of this
# package.

"""Parse various position frequency matrix format files."""

import re

from Bio import motifs


class Record(list):
    """Class to store the information in a position frequency matrix table.

    The record inherits from a list containing the individual motifs.
    """

    def __str__(self):
        return "\n".join(str(motif) for motif in self)


def read(handle, pfm_format):
    """Read motif(s) from a file in various position frequency matrix formats.

    Return the record of PFM(s).
    Call the appropriate routine based on the format passed.
    """
    # Supporting underscores here for backward compatibility
    pfm_format = pfm_format.lower().replace("_", "-")
    if pfm_format == "pfm-four-columns":
        record = _read_pfm_four_columns(handle)
        return record
    elif pfm_format == "pfm-four-rows":
        record = _read_pfm_four_rows(handle)
        return record
    else:
        raise ValueError("Unknown Position Frequency matrix format '%s'" % pfm_format)


def _read_pfm_four_columns(handle):
    """Read motifs in Cluster Buster position frequency matrix format from a file handle.

    Cluster Buster motif format: http://zlab.bu.edu/cluster-buster/help/cis-format.html

    #cisbp
    Pos A   C   G   T
    1   0.00961538461538462 0.00961538461538462 0.00961538461538462 0.971153846153846
    2   0.00961538461538462 0.00961538461538462 0.00961538461538462 0.971153846153846
    3   0.971153846153846   0.00961538461538462 0.00961538461538462 0.00961538461538462
    4   0.00961538461538462 0.00961538461538462 0.00961538461538462 0.971153846153846
    5   0.00961538461538462 0.971153846153846   0.00961538461538462 0.00961538461538462
    6   0.971153846153846   0.00961538461538462 0.00961538461538462 0.00961538461538462
    7   0.00961538461538462 0.971153846153846   0.00961538461538462 0.00961538461538462
    8   0.00961538461538462 0.00961538461538462 0.00961538461538462 0.971153846153846

    #c2h2 zfs
    Gene    ENSG00000197372
    Pos A   C   G   T
    1   0.341303    0.132427    0.117054    0.409215
    2   0.283785    0.077066    0.364552    0.274597
    3   0.491055    0.078208    0.310520    0.120217
    4   0.492621    0.076117    0.131007    0.300256
    5   0.250645    0.361464    0.176504    0.211387
    6   0.276694    0.498070    0.197793    0.027444
    7   0.056317    0.014631    0.926202    0.002850
    8   0.004470    0.007769    0.983797    0.003964
    9   0.936213    0.058787    0.002387    0.002613
    10  0.004352    0.004030    0.002418    0.989200
    11  0.013277    0.008165    0.001991    0.976567
    12  0.968132    0.002263    0.002868    0.026737
    13  0.397623    0.052017    0.350783    0.199577
    14  0.000000    0.000000    1.000000    0.000000
    15  1.000000    0.000000    0.000000    0.000000
    16  0.000000    0.000000    1.000000    0.000000
    17  0.000000    0.000000    1.000000    0.000000
    18  1.000000    0.000000    0.000000    0.000000
    19  0.000000    1.000000    0.000000    0.000000
    20  1.000000    0.000000    0.000000    0.000000

    #c2h2 zfs
    Gene    FBgn0000210
    Motif   M1734_0.90
    Pos A   C   G   T
    1   0.25    0.0833333   0.0833333   0.583333
    2   0.75    0.166667    0.0833333   0
    3   0.833333    0   0   0.166667
    4   1   0   0   0
    5   0   0.833333    0.0833333   0.0833333
    6   0.333333    0   0   0.666667
    7   0.833333    0   0   0.166667
    8   0.5 0   0.333333    0.166667
    9   0.5 0.0833333   0.166667    0.25
    10  0.333333    0.25    0.166667    0.25
    11  0.166667    0.25    0.416667    0.166667

    # flyfactorsurvey (cluster buster)
    >AbdA_Cell_FBgn0000014
    1   3   0   14
    0   0   0   18
    16  0   0   2
    18  0   0   0
    1   0   0   17
    0   0   6   12
    15  1   2   0

    # homer
    >ATGACTCATC AP-1(bZIP)/ThioMac-PU.1-ChIP-Seq(GSE21512)/Homer    6.049537    -1.782996e+03   0   9805.3,5781.0,3085.1,2715.0,0.00e+00
    0.419   0.275   0.277   0.028
    0.001   0.001   0.001   0.997
    0.010   0.002   0.965   0.023
    0.984   0.003   0.001   0.012
    0.062   0.579   0.305   0.054
    0.026   0.001   0.001   0.972
    0.043   0.943   0.001   0.012
    0.980   0.005   0.001   0.014
    0.050   0.172   0.307   0.471
    0.149   0.444   0.211   0.195

    # hocomoco
    > AHR_si
    40.51343240527031  18.259112547756697  56.41253757072521  38.77363485291994
    10.877470982533044  11.870876719950774  34.66312982331297  96.54723985087516
    21.7165707818416  43.883079837598544  20.706746561638717  67.6523201955933
    2.5465132509466635  1.3171620263517245  145.8637051322628  4.231336967110781
    0.0  150.35847450464382  1.4927836298652875  2.1074592421627525
    3.441039751299748  0.7902972158110341  149.37613720253387  0.3512432070271259
    0.0  3.441039751299748  0.7024864140542533  149.81519121131782
    0.0  0.0  153.95871737667187  0.0
    43.07922333291745  66.87558226865211  16.159862546986584  27.844049228115868

    # neph
    UW.Motif.0001   atgactca
    0.772949    0.089579    0.098612    0.038860
    0.026652    0.004653    0.025056    0.943639
    0.017663    0.023344    0.918728    0.040264
    0.919596    0.025414    0.029759    0.025231
    0.060312    0.772259    0.104968    0.062462
    0.037406    0.020643    0.006667    0.935284
    0.047316    0.899024    0.026928    0.026732
    0.948639    0.019497    0.005737    0.026128

    # tiffin
    T   A   G   C
    30  0   28  40
    0   0   0   99
    0   55  14  29
    0   99  0   0
    20  78  0   0
    0   52  7   39
    19  46  11  22
    0   60  38  0
    0   33  0   66
    73  0   25  0
    99  0   0   0
    """
    record = Record()

    motif_name = None
    motif_nbr = 0
    motif_nbr_added = 0

    default_nucleotide_order = ["A", "C", "G", "T"]
    nucleotide_order = default_nucleotide_order
    nucleotide_counts = {"A": [], "C": [], "G": [], "T": []}

    for line in handle:
        line = line.strip()

        if line:
            columns = line.split()
            nbr_columns = len(columns)

            if line.startswith("#"):
                # Skip comment lines.
                continue
            elif line.startswith(">"):
                # Parse ">AbdA_Cell_FBgn0000014" and "> AHR_si" like lines and put the part after ">" as motif name.
                if motif_nbr != 0 and motif_nbr_added != motif_nbr:
                    # Add the previous motif to the record.
                    motif = motifs.Motif(alphabet="GATC", counts=nucleotide_counts)
                    motif.name = motif_name
                    record.append(motif)
                    motif_nbr_added = motif_nbr

                # Reinitialize variables for the new motif.
                motif_name = line[1:].strip()
                nucleotide_order = default_nucleotide_order
            elif columns[0] == "Gene":
                # Parse "Gene   ENSG00000197372" like lines and put the gene name as motif name.
                if motif_nbr != 0 and motif_nbr_added != motif_nbr:
                    # Add the previous motif to the record.
                    motif = motifs.Motif(alphabet="GATC", counts=nucleotide_counts)
                    motif.name = motif_name
                    record.append(motif)
                    motif_nbr_added = motif_nbr

                # Reinitialize variables for the new motif.
                motif_name = columns[1]
                nucleotide_order = default_nucleotide_order
            elif columns[0] == "Motif":
                # Parse "Motif  M1734_0.90" like lines.
                if motif_nbr != 0 and motif_nbr_added != motif_nbr:
                    # Add the previous motif to the record.
                    motif = motifs.Motif(alphabet="GATC", counts=nucleotide_counts)
                    motif.name = motif_name
                    record.append(motif)
                    motif_nbr_added = motif_nbr

                # Reinitialize variables for the new motif.
                motif_name = columns[1]
                nucleotide_order = default_nucleotide_order
            elif columns[0] == "Pos":
                # Parse "Pos    A   C   G   T" like lines and change nucleotide order if necessary.
                if nbr_columns == 5:
                    # If the previous line was not a "Gene  ENSG00000197372" like line, a new motif starts here.
                    if motif_nbr != 0 and motif_nbr_added != motif_nbr:
                        # Add the previous motif to the record.
                        motif = motifs.Motif(alphabet="GATC", counts=nucleotide_counts)
                        motif.name = motif_name
                        record.append(motif)
                        motif_nbr_added = motif_nbr

                    nucleotide_order = default_nucleotide_order

                    if set(columns[1:]) == set(default_nucleotide_order):
                        nucleotide_order = columns[1:]
            elif columns[0] in default_nucleotide_order:
                # Parse "A  C   G   T" like lines and change nucleotide order if necessary.
                if nbr_columns == 4:
                    nucleotide_order = default_nucleotide_order
                    if set(columns) == set(default_nucleotide_order):
                        nucleotide_order = columns
            else:
                # Parse matrix columns lines and use the correct nucleotide order.
                if nbr_columns == 4:
                    matrix_columns = columns
                elif nbr_columns == 5:
                    matrix_columns = columns[1:]
                else:
                    continue

                if motif_nbr == motif_nbr_added:
                    # A new motif matrix starts here, so reinitialize variables for the new motif.
                    nucleotide_counts = {"A": [], "C": [], "G": [], "T": []}
                    motif_nbr += 1

                [
                    nucleotide_counts[nucleotide].append(float(nucleotide_count))
                    for nucleotide, nucleotide_count in zip(
                        nucleotide_order, matrix_columns
                    )
                ]
        else:
            # Empty lines can be separators between motifs.
            if motif_nbr != 0 and motif_nbr_added != motif_nbr:
                # Add the previous motif to the record.
                motif = motifs.Motif(alphabet="GATC", counts=nucleotide_counts)
                motif.name = motif_name
                record.append(motif)
                motif_nbr_added = motif_nbr

            # Reinitialize variables for the new motif.
            motif_name = None
            nucleotide_order = default_nucleotide_order
            # nucleotide_counts = {'A': [], 'C': [], 'G': [], 'T': []}

    if motif_nbr != 0 and motif_nbr_added != motif_nbr:
        motif = motifs.Motif(alphabet="GATC", counts=nucleotide_counts)
        motif.name = motif_name
        record.append(motif)

    return record


def _read_pfm_four_rows(handle):
    """Read motifs in position frequency matrix format from a file handle.

    Cluster Buster motif format: http://zlab.bu.edu/cluster-buster/help/cis-format.html

    #hdpi
    A   0   5   6   5   1   0
    C   1   1   0   0   0   4
    G   5   0   0   0   3   0
    T   0   0   0   1   2   2

    # yetfasco
    A   0.5 0.0 0.0 0.25    0.25    0.25    0.25    0.25    0.25    0.25    0.25    0.25    0.5 0.0 0.0833333334583333
    T   0.0 0.0 0.0 0.25    0.25    0.25    0.25    0.25    0.25    0.25    0.25    0.25    0.0 0.0 0.0833333334583333
    G   0.0 1.0 0.0 0.25    0.25    0.25    0.25    0.25    0.25    0.25    0.25    0.25    0.0 1.0 0.249999999875
    C   0.5 0.0 1.0 0.25    0.25    0.25    0.25    0.25    0.25    0.25    0.25    0.25    0.5 0.0 0.583333333208333

    #flyfactorsurvey ZFP finger
    A |     92    106    231    135      0      1    780     28      0    700    739     94     60    127    130
    C |    138     82    129     81    774      1      3      1      0      6     17     49    193    122    148
    G |    270    398     54    164      7    659      1    750    755     65      1     41    202    234    205
    T |    290    204    375    411      9    127      6     11     36     20     31    605    335    307    308

    # scertf pcm
    A | 9 1 1 97 1 94
    T | 80 1 97 1 1 2
    C | 9 97 1 1 1 2
    G | 2 1 1 1 97 2

    # scertf pfm
    A | 0.090 0.010 0.010 0.970 0.010 0.940
    C | 0.090 0.970 0.010 0.010 0.010 0.020
    G | 0.020 0.010 0.010 0.010 0.970 0.020
    T | 0.800 0.010 0.970 0.010 0.010 0.020

    #idmmpmm
    > abd-A
    0.218451749734889 0.0230646871686108 0.656680805938494 0.898197242841994 0.040694591728526 0.132953340402969 0.74907211028632 0.628313891834571
    0.0896076352067868 0.317338282078473 0.321580063626723 0.0461293743372216 0.0502386002120891 0.040694591728526 0.0284994697773065 0.0339342523860021
    0.455991516436904 0.0691940615058324 0.0108695652173913 0.0217391304347826 0.0284994697773065 0.0284994697773065 0.016304347826087 0.160127253446448
    0.235949098621421 0.590402969247084 0.0108695652173913 0.0339342523860021 0.880567338282079 0.797852598091198 0.206124072110286 0.17762460233298

    # JASPAR
        >MA0001.1 AGL3
        A  [ 0  3 79 40 66 48 65 11 65  0 ]
        C  [94 75  4  3  1  2  5  2  3  3 ]
        G  [ 1  0  3  4  1  0  5  3 28 88 ]
        T  [ 2 19 11 50 29 47 22 81  1  6 ]

    or::

        >MA0001.1 AGL3
        0  3 79 40 66 48 65 11 65  0
        94 75  4  3  1  2  5  2  3  3
        1  0  3  4  1  0  5  3 28 88
        2 19 11 50 29 47 22 81  1  6
    """
    record = Record()

    name_pattern = re.compile(r"^>\s*(.+)\s*")
    row_pattern_with_nucleotide_letter = re.compile(
        r"\s*([ACGT])\s*[[]*[|]*\s*([0-9.\s]+)\s*[]]*\s*"
    )
    row_pattern_without_nucleotide_letter = re.compile(r"\s*([0-9.\s]+)\s*")

    motif_name = None
    nucleotide_counts = {}
    row_count = 0
    nucleotides = ["A", "C", "G", "T"]

    for line in handle:
        line = line.strip()

        name_match = name_pattern.match(line)
        row_match_with_nucleotide_letter = row_pattern_with_nucleotide_letter.match(
            line
        )
        row_match_without_nucleotide_letter = row_pattern_without_nucleotide_letter.match(
            line
        )

        if name_match:
            motif_name = name_match.group(1)
        elif row_match_with_nucleotide_letter:
            (nucleotide, counts_str) = row_match_with_nucleotide_letter.group(1, 2)
            current_nucleotide_counts = counts_str.split()
            nucleotide_counts[nucleotide] = [
                float(current_nucleotide_count)
                for current_nucleotide_count in current_nucleotide_counts
            ]
            row_count += 1
            if row_count == 4:
                motif = motifs.Motif(alphabet="GATC", counts=nucleotide_counts)

                if motif_name:
                    motif.name = motif_name

                record.append(motif)

                motif_name = None
                nucleotide_counts = {}
                row_count = 0
        elif row_match_without_nucleotide_letter:
            current_nucleotide_counts = row_match_without_nucleotide_letter.group(
                1
            ).split()
            nucleotide_counts[nucleotides[row_count]] = [
                float(current_nucleotide_count)
                for current_nucleotide_count in current_nucleotide_counts
            ]
            row_count += 1
            if row_count == 4:
                motif = motifs.Motif(alphabet="GATC", counts=nucleotide_counts)

                if motif_name:
                    motif.name = motif_name

                record.append(motif)

                motif_name = None
                nucleotide_counts = {}
                row_count = 0

    return record


def write(motifs):
    """Return the representation of motifs in Cluster Buster position frequency matrix format."""
    lines = []
    for m in motifs:
        line = f">{m.name}\n"
        lines.append(line)
        for ACGT_counts in zip(
            m.counts["A"], m.counts["C"], m.counts["G"], m.counts["T"]
        ):
            lines.append(
                "{0:0.0f}\t{1:0.0f}\t{2:0.0f}\t{3:0.0f}\n".format(*ACGT_counts)
            )

    # Finished; glue the lines together.
    text = "".join(lines)

    return text
