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# Source code for samsifter.stats.compile_stats

    #!/usr/bin/env python3
    # -*- coding: utf-8 -*-
    """Compilation of statistics for a SAM file.

    Usually executed after processing a SAM file with a workflow containing steps
    that produce temporary statistics files, eg. the
    :py:mod:`samsifter.tools.count_taxon_reads` module. The script takes all
    temporary files in the working directory, sorts them by their filenames and
    appends their values as new columns to statistics spreadsheet named after the
    original input file.

    Both the SamSifter GUI as well as the Bash scripts exported from it will
    execute this script by default and remove all temporary statistics files when
    done.

    Note
    ----
    See the script ``enrich_summary`` for details on how to enrich the resulting
    file with data from external databases like IMG/M.

    See the script ``summarize_stats`` for details on how to summarize multiple
    statistics files to quickly inspect the results of a batch run.

    .. moduleauthor:: Florian Aldehoff <faldehoff@student.uni-tuebingen.de>
    """
    import sys
    if not (sys.version_info[0] >= 3):
        print("Error, I need python 3.x or newer")
        exit(1)

    import argparse
    import logging as log
    from os.path import isfile
    from os import listdir, remove
    import re
    import pandas as pd
    import numpy as np
    pd.set_option('max_columns', 50)

    def main():
        """Executes the compilation of temporary statistics files.

        See ``--help`` for details on expected arguments.
        """
        # parse arguments
        parser = argparse.ArgumentParser(description="compile statistics from "
                                         "temporary files")
        parser.add_argument('-v', '--verbose',
                            required=False,
                            action='store_true',
                            help='print additional information to STDERR')
        parser.add_argument('-d', '--debug',
                            required=False,
                            action='store_true',
                            help='print debug messages to STDERR')
        parser.add_argument('-r', '--remove',
                            required=False,
                            action='store_true',
                            help='remove temporary statistics files after use')
        parser.add_argument('-p', '--prefix',
                            required=False,
                            default='reads_per_taxon',
                            help='prefix of temporary statistics files')
        (args, remainArgs) = parser.parse_known_args()

        # configure logging
        if args.verbose:
            log.basicConfig(format="%(levelname)s: %(message)s", level=log.DEBUG)
        else:
            log.basicConfig(format="%(levelname)s: %(message)s")

        # create dict of stats files in working dir
        # using dict instead of list as steps may be omitted
        steps = {}
        for entry in listdir():
            if isfile(entry):
                match = re.match('%s\.(\d{3})\.csv$' % args.prefix, entry)
    #            match = re.fullmatch('%s\.(\d{3})\.csv' % args.prefix, entry)   # new in Python 3.4
                if match:
                    index = int(match.group(1))
                    steps[index] = entry
        log.info("Found %i temporary statistic files with prefix '%s'."
                 % (len(steps), args.prefix))

        if len(steps) > 0:
            readcounts = pd.DataFrame(dtype=np.float)
            # sort them by consecutive number
            for idx, filename in sorted(steps.items()):
                log.info("Gathering data from step %i (%s)" % (idx, filename))
                df = pd.read_csv(filename,
                                 sep=',',
                                 # index_col=0,      # better set index explicitly
                                 # engine='python',  # C is faster, supports dtype
                                 engine='c',
                                 dtype={'taxon_id': str, 'read_count': np.float},
                                 quotechar="'",
                                 quoting=2)
                df = df.set_index('taxon_id', drop=False)
                # merge stats into sparse array, assumes we never gain new taxa
                readcounts['read_count_%i' % idx] = df['read_count']
            # save array to CSV
            readcounts.to_csv(sys.stdout,
                              sep=',',
                              header=True,
                              na_rep=0.0,
                              quotechar="'",
                              quoting=2)
        # remove stats files
        if args.remove:
            for filename in steps.values():
                remove(filename)
        exit()

    if __name__ == "__main__":
        main()

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