#!/opt/anaconda1anaconda2anaconda3/bin/python
# -*- coding: utf-8

import sys
import numpy as numpy

import anvio
import anvio.utils as utils
import anvio.terminal as terminal
import anvio.summarizer as summarizer
import anvio.ccollections as ccollections

from anvio.errors import ConfigError, FilesNPathsError


__author__ = "A. Murat Eren"
__copyright__ = "Copyright 2016, The anvio Project"
__license__ = "GPL 3.0"
__version__ = anvio.__version__
__maintainer__ = "A. Murat Eren"
__email__ = "a.murat.eren@gmail.com"


run = terminal.Run()
progress = terminal.Progress()
pp = terminal.pretty_print


def main(args):
    fraction_of_median_coverage = float(args.fraction_of_median_coverage)
    min_detection = float(args.min_detection)

    run.info('Fraction of median coverage for core genes', fraction_of_median_coverage)
    run.info('Min detection in at last one metagenome', min_detection)

    progress.new('Initializing internal genomes')

    profile_db_path = args.profile_db
    contigs_db_path = args.contigs_db
    collection_name = args.collection_name
    genome_name = args.bin_id

    progress.update('Computing gene presence data ...')
    gene_presence_in_the_environment_dict = {}

    ARGS = summarizer.ArgsTemplateForSummarizerClass()
    ARGS.profile_db = profile_db_path
    ARGS.contigs_db = contigs_db_path
    ARGS.collection_name = collection_name

    summary = summarizer.ProfileSummarizer(ARGS)

    # recover the detection statistic
    summary.init_collection_profile(collection_name)
    detection_across_metagenomes = summary.collection_profile[genome_name]['detection']

    num_metagenomes_above_min_detection = [m for m in detection_across_metagenomes if detection_across_metagenomes[m] > min_detection]

    num_metagenomes = len(detection_across_metagenomes)
    average_detection_of_genome = sum(detection_across_metagenomes.values()) / num_metagenomes
    max_detection_of_genome = max(detection_across_metagenomes.values())
    if not len(num_metagenomes_above_min_detection):
        not_enough_detection = True
        run.warning("This genome is not detected in any of the %d metgenomes you have more than %.2f of its fraction. In fact, its\
                     average detection is %.2f across all metagenomes, %.2f being the maximum. Well, you can take a look at the\
                     anvi-summarize output if you would like to learn more. The bottom line is this: the metagenomes you have in this\
                     project are not the best ones to investigate the occurrence of the genes in this genome in the environment. So.\
                     This program will give you quite useless output files, but anvi'o hopes that you will enjoy them anyway :(" %\
                            (num_metagenomes, min_detection, average_detection_of_genome, max_detection_of_genome))
    else:
        not_enough_detection = False

    split_names_of_interest = ccollections.GetSplitNamesInBins(args).get_dict()[genome_name]

    gene_presence_in_the_environment_dict = {}

    genome_bin_summary = summarizer.Bin(summary, genome_name, split_names_of_interest)
    gene_coverages_across_samples = genome_bin_summary.get_gene_coverages_across_samples_dict()

    # at this point we have all the genes in the genome bin. what we need is to characterize their detection. first,
    # summarize the coverage of each gene in all samples:
    sum_gene_coverages_across_samples = dict([(gene_callers_id, sum(gene_coverages_across_samples[gene_callers_id].values())) for gene_callers_id in gene_coverages_across_samples])

    # now we will identify the median coverage
    median_coverage_across_samples = numpy.median(list(sum_gene_coverages_across_samples.values()))

    # now we will store decide whether a gene found in this genome is also found in the environment, and store that
    # information into `gene_presence_in_the_environment_dict`, and move on to the next stage.
    for gene_caller_id in sum_gene_coverages_across_samples:
        if not_enough_detection:
            gene_presence_in_the_environment_dict[gene_caller_id] = 'UNKNOWN'
        elif sum_gene_coverages_across_samples[gene_caller_id] < median_coverage_across_samples * fraction_of_median_coverage:
            gene_presence_in_the_environment_dict[gene_caller_id] = 'NOT DETECTED'
        else:
            gene_presence_in_the_environment_dict[gene_caller_id] = 'DETECTED'

    progress.update('Storing the output for gene presence data')

    utils.store_dict_as_TAB_delimited_file(gene_coverages_across_samples, '%s-GENE-COVs.txt' % genome_name)

    output = open(genome_name + '-ENV-DETECTION.txt', 'w')
    output.write('gene_callers_id\tdetection\n')
    for gene_callers_id in gene_presence_in_the_environment_dict:
        output.write('%s\t%s\n' % (gene_callers_id, gene_presence_in_the_environment_dict[gene_callers_id]))
    output.close()

    progress.end()


if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser(description="Quantify the detection of genes in genomes in metagenomes to identify the environmental\
                                                  core. This is a helper script for anvi'o metapangenomic workflow.")
    parser.add_argument(*anvio.A('contigs-db'), **anvio.K('contigs-db'))
    parser.add_argument(*anvio.A('profile-db'), **anvio.K('profile-db', {'required': False}))
    parser.add_argument(*anvio.A('collection-name'), **anvio.K('collection-name'))
    parser.add_argument(*anvio.A('bin-id'), **anvio.K('bin-id'))
    parser.add_argument('--min-detection', metavar="FLOAT", default=0.50, type=float, help="For this entire thing to work, the\
                        genome you are focusing on should be detected in at least one metagenome. If that is not the case, it would\
                        mean that you do not have any sample that represents the niche for this organism (or you do not have enough\
                        depth of coverage) to investigate the detection of genes in the environment. By default, this script requires\
                        at least '0.5' of the genome to be detected in at least one metagenome. This parameter allows you to change\
                        that. 0 would mean no detection test required, 1 would mean the entire genome must be detected.")
    parser.add_argument('--fraction-of-median-coverage', metavar="FLOAT", default=0.25, type=float, help="The value set here\
                        will be used to remove a gene if its total coverage across environments is less than the median coverage\
                        of all genes multiplied by this value. The default is 0.25, which means, if the median total coverage of\
                        all genes across all samples is 100X, then, a gene with a total coverage of less than 25X across all\
                        samples will be assumed not a part of the 'environmental core'.")


    args = parser.parse_args()

    try:
        main(args)
    except ConfigError as e:
        print(e)
        sys.exit(-1)
    except FilesNPathsError as e:
        print(e)
        sys.exit(-1)
