# This code is part of the Biopython distribution and governed by its
# license.  Please see the LICENSE file that should have been included
# as part of this package.
#

"""Command line wrapper for bwa."""

from __future__ import print_function
from Bio._py3k import basestring

from Bio.Application import _Option, _Argument, _Switch, AbstractCommandline
from Bio.Application import _StaticArgument


class BwaIndexCommandline(AbstractCommandline):
    """Command line wrapper for Burrows Wheeler Aligner (BWA) index.

    Index database sequences in the FASTA format, equivalent to::

        $ bwa index [-p prefix] [-a algoType] [-c] <in.db.fasta>

    See http://bio-bwa.sourceforge.net/bwa.shtml for details.

    Examples
    --------
    >>> from Bio.Sequencing.Applications import BwaIndexCommandline
    >>> reference_genome = "/path/to/reference_genome.fasta"
    >>> index_cmd = BwaIndexCommandline(infile=reference_genome, algorithm="bwtsw")
    >>> print(index_cmd)
    bwa index -a bwtsw /path/to/reference_genome.fasta

    You would typically run the command using index_cmd() or via the
    Python subprocess module, as described in the Biopython tutorial.

    """

    def __init__(self, cmd="bwa", **kwargs):
        """Initialize the class."""
        self.program_name = cmd
        self.parameters = [
            _StaticArgument("index"),
            _Option(["-a", "a", "algorithm"],
                    """Algorithm for constructing BWT index.

                    Available options are:
                        - is:    IS linear-time algorithm for constructing suffix array.
                          It requires 5.37N memory where N is the size of the database.
                          IS is moderately fast, but does not work with database larger
                          than 2GB. IS is the default algorithm due to its simplicity.
                        - bwtsw: Algorithm implemented in BWT-SW. This method works with the
                          whole human genome, but it does not work with database
                          smaller than 10MB and it is usually slower than IS.""",
                    checker_function=lambda x: x in ["is", "bwtsw"],
                    equate=False, is_required=True),
            _Option(["-p", "p", "prefix"],
                    "Prefix of the output database [same as db filename]",
                    equate=False, is_required=False),
            _Argument(["infile"], "Input file name", filename=True, is_required=True),
            _Switch(["-c", "c"],
                    "Build color-space index. The input fasta should be in nucleotide space.")
        ]
        AbstractCommandline.__init__(self, cmd, **kwargs)


class BwaAlignCommandline(AbstractCommandline):
    """Command line wrapper for Burrows Wheeler Aligner (BWA) aln.

    Run a BWA alignment, equivalent to::

        $ bwa aln [...] <in.db.fasta> <in.query.fq> > <out.sai>

    See http://bio-bwa.sourceforge.net/bwa.shtml for details.

    Examples
    --------
    >>> from Bio.Sequencing.Applications import BwaAlignCommandline
    >>> reference_genome = "/path/to/reference_genome.fasta"
    >>> read_file = "/path/to/read_1.fq"
    >>> output_sai_file = "/path/to/read_1.sai"
    >>> align_cmd = BwaAlignCommandline(reference=reference_genome, read_file=read_file)
    >>> print(align_cmd)
    bwa aln /path/to/reference_genome.fasta /path/to/read_1.fq

    You would typically run the command line using align_cmd(stdout=output_sai_file)
    or via the Python subprocess module, as described in the Biopython tutorial.

    """

    def __init__(self, cmd="bwa", **kwargs):
        """Initialize the class."""
        self.program_name = cmd
        self.parameters = [
            _StaticArgument("aln"),
            _Argument(["reference"], "Reference file name",
                      filename=True, is_required=True),
            _Argument(["read_file"], "Read file name",
                      filename=True, is_required=True),
            _Option(["-n", "n"],
                    "Maximum edit distance if the value is INT, or the fraction of missing alignments given 2% uniform base error rate if FLOAT. In the latter case, the maximum edit distance is automatically chosen for different read lengths. [0.04]",
                    checker_function=lambda x: isinstance(x, (int, float)),
                    equate=False),
            _Option(["-o", "o"],
                    "Maximum edit distance if the value is INT, or the fraction of missing alignments given 2% uniform base error rate if FLOAT. In the latter case, the maximum edit distance is automatically chosen for different read lengths. [0.04]",
                    checker_function=lambda x: isinstance(x, (int, float)),
                    equate=False),
            _Option(["-e", "e"],
                    "Maximum number of gap extensions, -1 for k-difference mode (disallowing long gaps) [-1]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-d", "d"],
                    "Disallow a long deletion within INT bp towards the 3-end [16]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-i", "i"],
                    "Disallow an indel within INT bp towards the ends [5]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-l", "l"],
                    """Take the first INT subsequence as seed.

                    If INT is larger than the query sequence, seeding will be disabled.
                    For long reads, this option is typically ranged from 25 to 35 for
                    -k 2. [inf]""",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-k", "k"], "Maximum edit distance in the seed [2]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-t", "t"], "Number of threads (multi-threading mode) [1]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-M", "M"],
                    "Mismatch penalty. BWA will not search for suboptimal hits with a score lower than (bestScore-misMsc). [3]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-O", "O"], "Gap open penalty [11]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-E", "E"], "Gap extension penalty [4]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-R", "R"],
                    """Proceed with suboptimal alignments if there are no more than INT equally best hits.

                    This option only affects paired-end mapping. Increasing this threshold helps
                    to improve the pairing accuracy at the cost of speed, especially for short
                    reads (~32bp).""",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-q", "q"],
                    r"""Parameter for read trimming [0].

                    BWA trims a read down to argmax_x{\sum_{i=x+1}^l(INT-q_i)} if q_l<INT
                    where l is the original read length.""",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-B", "B"],
                    "Length of barcode starting from the 5-end. When INT is positive, the barcode of each read will be trimmed before mapping and will be written at the BC SAM tag. For paired-end reads, the barcode from both ends are concatenated. [0]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Switch(["-c", "c"],
                    "Reverse query but not complement it, which is required for alignment in the color space."),
            _Switch(["-N", "N"],
                    "Disable iterative search. All hits with no more than maxDiff differences will be found. This mode is much slower than the default."),
            _Switch(["-I", "I"],
                    "The input is in the Illumina 1.3+ read format (quality equals ASCII-64)."),
            _Switch(["-b", "b"],
                    "Specify the input read sequence file is the BAM format"),
            _Switch(["-b1", "b1"],
                    "When -b is specified, only use the first read in a read pair in mapping (skip single-end reads and the second reads)."),
            _Switch(["-b2", "b2"],
                    "When -b is specified, only use the second read in a read pair in mapping.")
        ]
        AbstractCommandline.__init__(self, cmd, **kwargs)


class BwaSamseCommandline(AbstractCommandline):
    """Command line wrapper for Burrows Wheeler Aligner (BWA) samse.

    Generate alignments in the SAM format given single-end reads.
    Equvialent to::

        $ bwa samse [-n maxOcc] <in.db.fasta> <in.sai> <in.fq> > <out.sam>

    See http://bio-bwa.sourceforge.net/bwa.shtml for details.

    Examples
    --------
    >>> from Bio.Sequencing.Applications import BwaSamseCommandline
    >>> reference_genome = "/path/to/reference_genome.fasta"
    >>> read_file = "/path/to/read_1.fq"
    >>> sai_file = "/path/to/read_1.sai"
    >>> output_sam_file = "/path/to/read_1.sam"
    >>> samse_cmd = BwaSamseCommandline(reference=reference_genome,
    ...                                 read_file=read_file, sai_file=sai_file)
    >>> print(samse_cmd)
    bwa samse /path/to/reference_genome.fasta /path/to/read_1.sai /path/to/read_1.fq

    You would typically run the command line using samse_cmd(stdout=output_sam_file)
    or via the Python subprocess module, as described in the Biopython tutorial.

    """

    def __init__(self, cmd="bwa", **kwargs):
        """Initialize the class."""
        self.program_name = cmd
        self.parameters = [
            _StaticArgument("samse"),
            _Argument(["reference"], "Reference file name", filename=True, is_required=True),
            _Argument(["sai_file"], "Sai file name", filename=True, is_required=True),
            _Argument(["read_file"], "Read  file name", filename=True, is_required=True),
            _Option(["-n", "n"],
                    """Maximum number of alignments to output in the XA tag for reads paired properly.

                    If a read has more than INT hits, the XA tag will not be written. [3]""",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-r", "r"],
                    "Specify the read group in a format like '@RG\tID:foo\tSM:bar'. [null]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
        ]
        AbstractCommandline.__init__(self, cmd, **kwargs)


class BwaSampeCommandline(AbstractCommandline):
    r"""Command line wrapper for Burrows Wheeler Aligner (BWA) sampe.

    Generate alignments in the SAM format given paired-end reads.
    Equivalent to::

        $ bwa sampe [...] <in.db.fasta> <in1.sai> <in2.sai> <in1.fq> <in2.fq> > <out.sam>

    See http://bio-bwa.sourceforge.net/bwa.shtml for details.

    Examples
    --------
    >>> from Bio.Sequencing.Applications import BwaSampeCommandline
    >>> reference_genome = "/path/to/reference_genome.fasta"
    >>> read_file1 = "/path/to/read_1.fq"
    >>> read_file2 = "/path/to/read_2.fq"
    >>> sai_file1 = "/path/to/read_1.sai"
    >>> sai_file2 = "/path/to/read_2.sai"
    >>> output_sam_file = "/path/to/output.sam"
    >>> read_group = r"@RG\tID:foo\tSM:bar"  # BWA will turn backslash-t into tab
    >>> sampe_cmd = BwaSampeCommandline(reference=reference_genome,
    ...                                 sai_file1=sai_file1, sai_file2=sai_file2,
    ...                                 read_file1=read_file1, read_file2=read_file2,
    ...                                 r=read_group)
    >>> print(sampe_cmd)
    bwa sampe /path/to/reference_genome.fasta /path/to/read_1.sai /path/to/read_2.sai /path/to/read_1.fq /path/to/read_2.fq -r @RG\tID:foo\tSM:bar

    You would typically run the command line using sampe_cmd(stdout=output_sam_file)
    or via the Python subprocess module, as described in the Biopython tutorial.

    """

    # TODO - Should the read group have a raw tab in it, or \t?

    def __init__(self, cmd="bwa", **kwargs):
        """Initialize the class."""
        self.program_name = cmd
        self.parameters = [
            _StaticArgument("sampe"),
            _Argument(["reference"], "Reference file name", filename=True, is_required=True),
            _Argument(["sai_file1"], "Sai file 1", filename=True, is_required=True),
            _Argument(["sai_file2"], "Sai file 2", filename=True, is_required=True),
            _Argument(["read_file1"], "Read  file 1", filename=True, is_required=True),
            _Argument(["read_file2"], "Read  file 2", filename=True, is_required=True),
            _Option(["-a", "a"],
                    """Maximum insert size for a read pair to be considered being mapped properly [500].

                    Since 0.4.5, this option is only used when there are not enough
                    good alignments to infer the distribution of insert sizes.""",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-o", "o"],
                    """Maximum occurrences of a read for pairing [100000].

                        A read with more occurrences will be treated as a single-end read.
                        Reducing this parameter helps faster pairing.""",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-n", "n"],
                    """Maximum number of alignments to output in the XA tag for reads paired properly [3].

                    If a read has more than INT hits, the XA tag will not be written.""",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-N", "N"],
                    """Maximum number of alignments to output in the XA tag for disconcordant read pairs (excluding singletons) [10].

                    If a read has more than INT hits, the XA tag will not be written.""",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-r", "r"], "Specify the read group in a format like '@RG\tID:foo\tSM:bar'. [null]",
                    checker_function=lambda x: isinstance(x, basestring),
                    equate=False),
        ]
        AbstractCommandline.__init__(self, cmd, **kwargs)


class BwaBwaswCommandline(AbstractCommandline):
    """Command line wrapper for Burrows Wheeler Aligner (BWA) bwasw.

    Align query sequences from FASTQ files. Equivalent to::

        $ bwa bwasw [...] <in.db.fasta> <in.fq>

    See http://bio-bwa.sourceforge.net/bwa.shtml for details.

    Examples
    --------
    >>> from Bio.Sequencing.Applications import BwaBwaswCommandline
    >>> reference_genome = "/path/to/reference_genome.fasta"
    >>> read_file = "/path/to/read_1.fq"
    >>> bwasw_cmd = BwaBwaswCommandline(reference=reference_genome, read_file=read_file)
    >>> print(bwasw_cmd)
    bwa bwasw /path/to/reference_genome.fasta /path/to/read_1.fq

    You would typically run the command line using bwasw_cmd() or via the
    Python subprocess module, as described in the Biopython tutorial.

    """

    def __init__(self, cmd="bwa", **kwargs):
        """Initialize the class."""
        self.program_name = cmd
        self.parameters = [
            _StaticArgument("bwasw"),
            _Argument(["reference"], "Reference file name", filename=True, is_required=True),
            _Argument(["read_file"], "Read file", filename=True, is_required=True),
            _Argument(["mate_file"], "Mate file", filename=True, is_required=False),
            _Option(["-a", "a"],
                    "Score of a match [1]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-b", "b"],
                    "Mismatch penalty [3]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-q", "q"],
                    "Gap open penalty [5]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-r", "r"],
                    "Gap extension penalty. The penalty for a contiguous gap of size k is q+k*r. [2]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-t", "t"],
                    "Number of threads in the multi-threading mode [1]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-w", "w"],
                    "Band width in the banded alignment [33]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-T", "T"],
                    "Minimum score threshold divided by a [37]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-c", "c"],
                    """Coefficient for threshold adjustment according to query length [5.5].

                    Given an l-long query, the threshold for a hit to be retained is
                    a*max{T,c*log(l)}.""",
                    checker_function=lambda x: isinstance(x, float),
                    equate=False),
            _Option(["-z", "z"],
                    "Z-best heuristics. Higher -z increases accuracy at the cost of speed. [1]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-s", "s"],
                    """Maximum SA interval size for initiating a seed [3].

                    Higher -s increases accuracy at the cost of speed.""",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-N", "N"],
                    "Minimum number of seeds supporting the resultant alignment to skip reverse alignment. [5]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
        ]
        AbstractCommandline.__init__(self, cmd, **kwargs)


class BwaMemCommandline(AbstractCommandline):
    """Command line wrapper for Burrows Wheeler Aligner (BWA) mem.

    Run a BWA-MEM alignment, with single- or paired-end reads, equivalent to::

        $ bwa mem [...] <in.db.fasta> <in1.fq> <in2.fq> > <out.sam>

    See http://bio-bwa.sourceforge.net/bwa.shtml for details.

    Examples
    --------
    >>> from Bio.Sequencing.Applications import BwaMemCommandline
    >>> reference_genome = "/path/to/reference_genome.fasta"
    >>> read_file = "/path/to/read_1.fq"
    >>> output_sam_file = "/path/to/output.sam"
    >>> align_cmd = BwaMemCommandline(reference=reference_genome, read_file1=read_file)
    >>> print(align_cmd)
    bwa mem /path/to/reference_genome.fasta /path/to/read_1.fq

    You would typically run the command line using align_cmd(stdout=output_sam_file)
    or via the Python subprocess module, as described in the Biopython tutorial.

    """

    def __init__(self, cmd="bwa", **kwargs):
        """Initialize the class."""
        self.program_name = cmd
        self.parameters = [
            _StaticArgument("mem"),
            _Argument(["reference"], "Reference file name",
                      filename=True, is_required=True),
            _Argument(["read_file1"], "Read 1 file name",
                      filename=True, is_required=True),
            _Argument(["read_file2"], "Read 2 file name",
                      filename=True, is_required=False),
            _Option(["-t", "t"], "Number of threads [1]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-k", "k"],
                    "Minimum seed length. Matches shorter than INT will be missed. The alignment speed is usually insensitive to this value unless it significantly deviates 20. [19]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-w", "w"],
                    "Band width. Essentially, gaps longer than INT will not be found. Note that the maximum gap length is also affected by the scoring matrix and the hit length, not solely determined by this option. [100]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-d", "d"],
                    "Off-diagonal X-dropoff (Z-dropoff). Stop extension when the difference between the best and the current extension score is above |i-j|*A+INT, where i and j are the current positions of the query and reference, respectively, and A is the matching score. Z-dropoff is similar to BLAST\'s X-dropoff except that it doesn\'t penalize gaps in one of the sequences in the alignment. Z-dropoff not only avoids unnecessary extension, but also reduces poor alignments inside a long good alignment. [100]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-r", "r"],
                    "Trigger re-seeding for a MEM longer than minSeedLen*FLOAT. This is a key heuristic parameter for tuning the performance. Larger value yields fewer seeds, which leads to faster alignment speed but lower accuracy. [1.5]",
                    checker_function=lambda x: isinstance(x, (int, float)),
                    equate=False),
            _Option(["-c", "c"],
                    "Discard a MEM if it has more than INT occurence in the genome. This is an insensitive parameter. [10000]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-A", "A"],
                    "Matching score. [1]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-B", "B"],
                    "Mismatch penalty. The sequence error rate is approximately: {.75 * exp[-log(4) * B/A]}. [4]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-O", "O"],
                    "Gap open penalty. [6]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-E", "E"],
                    "Gap extension penalty. A gap of length k costs O + k*E (i.e. -O is for opening a zero-length gap). [1]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-L", "L"],
                    "Clipping penalty. When performing SW extension, BWA-MEM keeps track of the best score reaching the end of query. If this score is larger than the best SW score minus the clipping penalty, clipping will not be applied. Note that in this case, the SAM AS tag reports the best SW score; clipping penalty is not deducted. [5]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-U", "U"],
                    "Penalty for an unpaired read pair. BWA-MEM scores an unpaired read pair as scoreRead1+scoreRead2-INT and scores a paired as scoreRead1+scoreRead2-insertPenalty. It compares these two scores to determine whether we should force pairing. [9] ",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-R", "R"],
                    "Complete read group header line. \'\\t\' can be used in STR and will be converted to a TAB in the output SAM. The read group ID will be attached to every read in the output. An example is \'@RG\tID:foo\tSM:bar\'. [null]",
                    checker_function=lambda x: isinstance(x, basestring),
                    equate=False),
            _Option(["-T", "T"],
                    "Don\'t output alignment with score lower than INT. This option only affects output. [30]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),
            _Option(["-v", "v"],
                    "Control the verbose level of the output. This option has not been fully supported throughout BWA. Ideally, a value 0 for disabling all the output to stderr; 1 for outputting errors only; 2 for warnings and errors; 3 for all normal messages; 4 or higher for debugging. When this option takes value 4, the output is not SAM. [3]",
                    checker_function=lambda x: isinstance(x, int),
                    equate=False),

            _Switch(["-P", "P"],
                    "In the paired-end mode, perform SW to rescue missing hits only but do not try to find hits that fit a proper pair."),
            _Switch(["-p", "p"],
                    "Assume the first input query file is interleaved paired-end FASTA/Q. See the command description for details."),
            _Switch(["-a", "a"],
                    "Output all found alignments for single-end or unpaired paired-end reads. These alignments will be flagged as secondary alignments."),
            _Switch(["-C", "C"],
                    "Append FASTA/Q comment to SAM output. This option can be used to transfer read meta information (e.g. barcode) to the SAM output. Note that the FASTA/Q comment (the string after a space in the header line) must conform the SAM spec (e.g. BC:Z:CGTAC). Malformated comments lead to incorrect SAM output."),
            _Switch(["-H", "H"],
                    "Use hard clipping \'H\' in the SAM output. This option may dramatically reduce the redundancy of output when mapping long contig or BAC sequences."),
            _Switch(["-M", "M"],
                    "Mark shorter split hits as secondary (for Picard compatibility).")
        ]
        AbstractCommandline.__init__(self, cmd, **kwargs)


if __name__ == "__main__":
    from Bio._utils import run_doctest
    run_doctest()
