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# Source code for samsifter.stats.summarize_stats

    #!/usr/bin/env python3
    # -*- coding: utf-8 -*-
    """Compilation of statistics for multiple files into a summary spreadsheet.

    Usually executed after batch processing of multiple SAM files with the same
    workflow. The script takes the final read count of each statistics file and
    adds it to a new spreadsheet using the input filename as header.

    All Bash scripts exported from the SamSifter GUI that are processing multiple
    files (sequential or parallel processing mode) will execute this script by
    default and remove all temporary statistics files when done.

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

    .. 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
    import re
    import pandas as pd
    import numpy as np

    def main():
        """Executes the summarization of multiple 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('-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 list of stats files in working dir
        files = []
        for entry in listdir():
            if isfile(entry):
                match = re.fullmatch('.*\.sifted\.csv', entry)
                if match:
                    files.append(match.group(0))
        log.info("Found %i statistic files." % len(files))

        if len(files) > 0:
            summary = pd.DataFrame(dtype=np.float)
            for filename in sorted(files):
                log.info("Gathering data from file %s" % filename)
                # read file
                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},
                                 quotechar="'",
                                 quoting=2)
                df = df.set_index('taxon_id', drop=False)
                # identify last column
                redux = df.ix[:, -1:]
                redux.columns = [filename]
                # filter taxa without reads
                filtered = redux[redux[filename] > 0]
                # merge it with summary file (outer join)
                summary = pd.concat([summary, filtered], axis=1)

            # save summary to CSV
            summary.to_csv(sys.stdout,
                           sep=',',
                           header=True,
                           # na_rep=0.0,
                           quotechar="'",
                           quoting=2)

        exit()

    if __name__ == "__main__":
        main()

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