#!/usr/bin/env python

__author__ = 'peter hofmann'
__version__ = '0.0.6'

import sys
import os
import shutil
import traceback
import tempfile
from Bio import SeqIO
from fastaanonymizer import FastaAnonymizer
from scripts.Archive.compress import Compress
from scripts.argumenthandler import ArgumentHandler
from scripts.ComunityDesign.communitydesign import CommunityDesign
from scripts.ComunityDesign.taxonomicprofile import TaxonomicProfile
from scripts.GenomePreparation.genomepreparation import GenomePreparation
from scripts.GoldStandardAssembly.goldstandardassembly import GoldStandardAssembly
from scripts.GoldStandardAssembly.samtoolswrapper import SamtoolsWrapper
from scripts.GoldStandardFileFormat.goldstandardfileformat import GoldStandardFileFormat
from scripts.MetaDataTable.metadatatable import MetadataTable
from scripts.NcbiTaxonomy.ncbitaxonomy import NcbiTaxonomy
from scripts.ReadSimulationWrapper.readsimulationwrapper import dict_of_read_simulators


class MetagenomeSimulation(ArgumentHandler):
    """
    Pipeline for the generation of a simulated metagenome
    """

    _label = "MetagenomeSimulationPipeline"

    _list_tuple_archive_files = []

    def run_pipeline(self):
        """
        Run pipeline

        @rtype: None
        """
        if not self.is_valid():
            self._logger.info("Metagenome simulation aborted")
            return
        self._logger.info("Metagenome simulation starting")
        try:
            # Validate Genomes
            if self._phase_validate_raw_genomes:
                self._logger.info("Validating Genomes")
                self._validate_raw_genomes()

            # Design Communities
            if self._input_list_of_file_paths_distributions:
                assert len(self._input_list_of_file_paths_distributions) == self._number_of_samples
                
                meta_data_table = MetadataTable(separator=self._separator, logfile=self._logfile, verbose=self._verbose)
                file_path_genome_locations = self._project_file_folder_handler.get_genome_location_file_path()
                
                for community in self._list_of_communities:
                    meta_data_table.read(community.file_path_metadata_table, column_names=True)
                    file_path_metadata = self._project_file_folder_handler.get_genome_metadata_file_path()
                    meta_data_table.write(file_path_metadata, column_names=True)
                    out_locations = {}
                    # collect all paths
                    with open(community.file_path_genome_locations,'r') as in_locations: 
                        for line in in_locations:
                            genome, path = line.strip().split('\t')
                            out_locations[genome] = path 
                            # might overwrite path for genomes appearing multiple times and having been assigned different genomes
                # and write complete collection, so no genome appears multiple times
                with open(file_path_genome_locations, 'a') as locations:
                    for gen_id in out_locations:
                        locations.write("%s\t%s\n" % (gen_id, out_locations[gen_id]))
                
                genome_id_to_path_map = self.get_dict_gid_to_genome_file_path()
                directory_out_distributions = self._project_file_folder_handler.get_distribution_dir()
                list_of_file_paths_distributions = CommunityDesign.get_distribution_file_paths(
                    directory_out_distributions, self._number_of_samples)
                for file_path_src, file_path_dst in zip(self._input_list_of_file_paths_distributions, list_of_file_paths_distributions):
                    shutil.copy2(file_path_src, file_path_dst)
                self.write_profile_gold_standard(meta_data_table, list_of_file_paths_distributions)
            elif self._phase_design_community:
                self._logger.info("Design Communities")
                genome_id_to_path_map, list_of_file_paths_distributions = self._design_community()
            else:
                genome_id_to_path_map = self.get_dict_gid_to_genome_file_path()
                directory_out_distributions = self._project_file_folder_handler.get_distribution_dir()
                list_of_file_paths_distributions = CommunityDesign.get_distribution_file_paths(
                    directory_out_distributions, self._number_of_samples)

            # Move Genomes
            if self._phase_move_and_clean_genomes:
                self._logger.info("Move Genomes")
                self._move_and_cleanup_genomes(genome_id_to_path_map)

            # Read simulation (Art Illumina)
            if self._phase_simulate_reads:
                self._logger.info("Read simulation")
                for sample_index, file_path_distribution in enumerate(list_of_file_paths_distributions):
                    self._simulate_reads(file_path_distribution, sample_index)

            # Generate gold standard assembly
            list_of_output_gsa = None
            file_path_output_gsa_pooled = None
            if self._phase_pooled_gsa:
                self._logger.info("Generate gold standard assembly")
                list_of_output_gsa = self._generate_gsa()

            # Generate gold standard assembly from pooled reads of all samples
            if self._phase_pooled_gsa:
                self._logger.info("Generate pooled strains gold standard assembly")
                file_path_output_gsa_pooled = self._generate_gsa_pooled()

            # Anonymize Data (gsa)
            if self._phase_anonymize:
                self._logger.info("Anonymize Data")
                self._logger.debug(", ".join(list_of_output_gsa))
                self._anonymize_data(list_of_output_gsa, file_path_output_gsa_pooled)
            #elif self._phase_pooled_gsa: 
            else: # in any case create binning gold standard
                self._logger.info("Creating binning gold standard")
                self._logger.debug(", ".join(list_of_output_gsa))
                self._create_binning_gs(list_of_output_gsa)

            # Compress Data
            if self._phase_compress:
                self._logger.info("Compress Data")
                self._compress_data()

        except (KeyboardInterrupt, SystemExit, Exception, ValueError, RuntimeError) as e:
            self._logger.debug("\n{}\n".format(traceback.format_exc()))
            exc_tb = sys.exc_info()[-1]
            self._logger.error("%s in line %s" % (e, exc_tb.tb_lineno))
            self._logger.info("Metagenome simulation aborted")
        except AssertionError:
            self._logger.info("Metagenome simulation aborted, assertion %s failed" % e)
        else:
            self._logger.info("Metagenome simulation finished")

        if not self._debug:
            self._project_file_folder_handler.remove_directory_temp()
        else:
            self._logger.info("Temporary data stored at:\n{}".format(self._project_file_folder_handler.get_tmp_wd()))

    # #########################
    #
    # Validate Genomes
    #
    # #########################

    def _validate_raw_genomes(self):
        """
        Validate format raw genomes

        @return: True if all genomes valid
        @rtype: bool
        """
        prepare_genomes = GenomePreparation(
            logfile=self._logfile,
            verbose=self._verbose)

        meta_data_table = MetadataTable(
            separator=self._separator,
            logfile=self._logfile,
            verbose=self._verbose)

        are_valid = True
        for community in self._list_of_communities:
            meta_data_table.read(community.file_path_genome_locations)
            list_of_file_paths = meta_data_table.get_column(1)

            if not prepare_genomes.validate_format(
                list_of_file_paths,
                file_format="fasta",  # TODO: should be done dynamically
                sequence_type="dna",
                ambiguous=True):
                are_valid = False
        return are_valid

    # #########################
    #
    # Design Communities
    #
    # #########################

    def write_profile_gold_standard(self, meta_data_table, list_of_file_paths_distribution):
        taxonomy = NcbiTaxonomy(
            taxonomy_path=self._directory_ncbi_taxdump,
            build_node_tree=False,
            verbose=self._verbose,
            logfile=self._logfile
        )

        taxonomic_profile = TaxonomicProfile(
            taxonomy=taxonomy,
            logfile=self._logfile,
            verbose=self._verbose,
            debug=self._debug
        )
        taxonomic_profile.write_taxonomic_profile_from_abundance_files(
            metadata_table=meta_data_table,
            list_of_file_paths=list_of_file_paths_distribution,
            directory_output=self._directory_output,
            sample_id=""
        )

    def get_dict_gid_to_genome_file_path(self):
        """
        Get map genome id to genome file path

        @return: Genome id to geone file path
        @rtype: dict[str|unicode, str|unicode]
        """
        meta_data_table = MetadataTable(
            separator=self._separator,
            logfile=self._logfile,
            verbose=self._verbose)

        file_path_genome_locations = self._project_file_folder_handler.get_genome_location_file_path()
        if not self._validator.validate_file(file_path_genome_locations, silent=True):
            msg = "Required file not found! Was design of communities not completed?"
            raise RuntimeError(msg)
        meta_data_table.read(file_path_genome_locations)
        return meta_data_table.get_map(0, 1)

    def _design_community(self):
        """
        Start designing sample a community

        @return: map genome id to genome file path and list of distribution file paths
        @rtype: tuple[dict[str|unicode, str|unicode], list[str|unicode]]]
        """
        meta_data_table = MetadataTable(
            separator=self._separator,
            logfile=self._logfile,
            verbose=self._verbose)

        community_design = CommunityDesign(
            column_name_genome_id=self._column_name_genome_id,
            column_name_otu=self._column_name_otu,
            column_name_novelty_category=self._column_name_novelty_category,
            column_name_ncbi=self._column_name_ncbi,
            column_name_source=self._column_name_source,
            max_processors=self._max_processors,
            tmp_dir=self._project_file_folder_handler.get_tmp_wd(),
            logfile=self._logfile,
            verbose=self._verbose,
            debug=self._debug,
            seed=None
        )

        directory_out_distributions = self._project_file_folder_handler.get_distribution_dir()
        list_of_file_paths_distribution = community_design.get_distribution_file_paths(
            directory_out_distributions, self._number_of_samples)
        directory_out_metadata = self._project_file_folder_handler.get_meta_data_dir()
        directory_simulation_template = self._strain_simulation_template
        merged_genome_id_to_path_map = community_design.design_samples(
            list_of_communities=self._list_of_communities,
            metadata_table=meta_data_table,
            list_of_file_paths_distribution=list_of_file_paths_distribution,
            directory_out_metadata=directory_out_metadata,
            directory_in_template=directory_simulation_template)
        #     directory_out_distributions=directory_out_distributions,
        self.write_profile_gold_standard(meta_data_table, list_of_file_paths_distribution)

        file_path_metadata = self._project_file_folder_handler.get_genome_metadata_file_path()
        meta_data_table.write(file_path_metadata, column_names=True)
        return merged_genome_id_to_path_map, list_of_file_paths_distribution

    # #########################
    #
    # Move Genomes
    #
    # #########################

    def _move_and_cleanup_genomes(self, genome_id_to_path_map):
        """
        Move genomes, removing sequence descriptions and making sequence names unique

        @param genome_id_to_path_map: A map of genome id to genome file path
        @type genome_id_to_path_map: dict[str|unicode, str|unicode]

        @rtype: None
        """
        prepare_genomes = GenomePreparation(
            logfile=self._logfile,
            verbose=self._verbose)

        directory_output = self._project_file_folder_handler.get_genome_dir()
        prepare_genomes.move_genome_files(
            genome_id_to_path_map=genome_id_to_path_map,
            directory_output=directory_output
            # sequence_min_length=1000 TODO
            )

        file_path_genome_locations = self._project_file_folder_handler.get_genome_location_file_path()
        prepare_genomes.write_genome_id_to_path_map(genome_id_to_path_map, file_path_genome_locations)

    # #########################
    #
    # Read simulation (Art Illumina)
    #
    # #########################

    def _simulate_reads(self, file_path_distribution, sample_index):
        """
        Start the simulation of illumina reads

        @param file_path_distribution: File path to a distribution
        @type file_path_distribution: str | unicode
        @param sample_index: Sample index
        @type sample_index: int | long

        @rtype: None
        """
        self._project_file_folder_handler._location_reads = [True, True]  # TODO write public method for this
        
        sample_id = str(sample_index)
        directory_output_tmp = self._project_file_folder_handler.get_reads_dir(True, sample_id)
        directory_bam = self._project_file_folder_handler.get_bam_dir(sample_id)
        # directory_script = os.path.dirname(__file__)
        # file_path_executable = os.path.join(directory_script, "tools", "readsimulator", "art_illumina")
        # directory_error_profiles = os.path.join(directory_script, "tools", "readsimulator", "profile")

        if self._read_simulator_type not in dict_of_read_simulators:
            raise ValueError("Read simulator type '{}' not supported.".format(self._read_simulator_type))

        simulator = dict_of_read_simulators[self._read_simulator_type](
            file_path_executable=self._executable_readsim,
            directory_error_profiles=self._directory_error_profiles,
            separator=self._separator,
            max_processes=self._max_processors,
            logfile=self._logfile,
            verbose=self._verbose,
            debug=self._debug,
            seed=None,  # todo: setting seed here would cause the same seed used for every simulation
            tmp_dir=self._project_file_folder_handler.get_tmp_wd())

        file_path_genome_locations = self._project_file_folder_handler.get_genome_location_file_path()
        if self._read_simulator_type == "art":
            simulator.simulate(
                file_path_distribution=file_path_distribution,
                file_path_genome_locations=file_path_genome_locations,
                directory_output=directory_output_tmp,
                total_size=self._sample_size_in_base_pairs,
                profile=self._error_profile,
                fragment_size_mean=self._fragments_size_mean_in_bp,
                fragment_size_standard_deviation=self._fragment_size_standard_deviation_in_bp,
                profile_filename=self._custom_profile_filename,
                own_read_length=self._custom_readlength)
        else:
            simulator.simulate(
                file_path_distribution=file_path_distribution,
                file_path_genome_locations=file_path_genome_locations,
                directory_output=directory_output_tmp,
                total_size=self._sample_size_in_base_pairs,
                profile=self._error_profile,
                fragment_size_mean=self._fragments_size_mean_in_bp,
                fragment_size_standard_deviation=self._fragment_size_standard_deviation_in_bp)

        # convert sam to bam
        samtools = SamtoolsWrapper(
            file_path_samtools=self._executable_samtools,
            max_processes=self._max_processors,
            tmp_dir=self._project_file_folder_handler.get_tmp_wd(),
            logfile=self._logfile,
            verbose=self._verbose,
            debug=self._debug
        )

        directory_sam = directory_output_tmp
        samtools.convert_sam_to_bam(directory_sam, directory_bam)

        if not self._phase_anonymize:
            list_of_file_path = self._validator.get_files_in_directory(directory_output_tmp, extension="fq")
            directory_output_fastq = self._project_file_folder_handler.get_reads_dir(False, sample_id)
            if self._phase_compress:
                for file_path in list_of_file_path:
                    self._list_tuple_archive_files.append((file_path, directory_output_fastq))
            else:
                for file_path in list_of_file_path:
                    shutil.move(file_path, directory_output_fastq)

    # #########################
    #
    # Generate gold standard assembly
    #
    # #########################

    def _generate_gsa(self):
        """
        Create a perfect assembly of the reads of each sample.

        @return: List of file paths of assemblies
        @rtype: list[str|unicode]
        """
        dict_id_to_file_path_fasta = self.get_dict_gid_to_genome_file_path()

        list_of_directory_bam = [
            self._project_file_folder_handler.get_bam_dir(str(sample_index)) for sample_index in range(self._number_of_samples)]
        gs_handler = GoldStandardAssembly(
            file_path_samtools=self._executable_samtools,
            max_processes=self._max_processors,
            tmp_dir=self._project_file_folder_handler.get_tmp_wd(),
            logfile=self._logfile,
            verbose=self._verbose)

        list_of_output_gsa = []
        for directory_bam in list_of_directory_bam:
            dict_id_to_file_path_bam = gs_handler.get_dict_id_to_file_path_bam_from_dir(directory_bam)
            file_path_output_gs = gs_handler.gold_standard_assembly(
                dict_id_to_file_path_bam=dict_id_to_file_path_bam,
                dict_id_to_file_path_fasta=dict_id_to_file_path_fasta)
            list_of_output_gsa.append(file_path_output_gs)

        list_of_final_output_gsa = []
        if not self._phase_anonymize:
            for index, file_path in enumerate(list_of_output_gsa):
                file_path_output = self._project_file_folder_handler.get_gsa_file_path(str(index))
                if self._phase_compress:
                    self._list_tuple_archive_files.append((file_path, file_path_output+".gz"))
                else:
                    shutil.move(file_path, file_path_output)
                    list_of_final_output_gsa.append(file_path_output)
            if not self._phase_compress:
                list_of_output_gsa = list_of_final_output_gsa

        return list_of_output_gsa

    def _generate_gsa_pooled(self):
        """
        Create a perfect assembly of the reads of all samples.
            merge all sample bam files and create a assembly of all of them
            - create folder reads_on_genomes wherever you are
            - merge bamfiles from list_of_bamdirs into this dirs
            - run gsa for reads_on_genomes
            - create mapping

        @return: file paths of assembly
        @rtype: str|unicode
        """
        meta_data_table = MetadataTable(
            separator=self._separator,
            logfile=self._logfile,
            verbose=self._verbose)

        gs_handler = GoldStandardAssembly(
            file_path_samtools=self._executable_samtools,
            max_processes=self._max_processors,
            tmp_dir=self._project_file_folder_handler.get_tmp_wd(),
            logfile=self._logfile,
            verbose=self._verbose)

        file_path_genome_locations = self._project_file_folder_handler.get_genome_location_file_path()
        meta_data_table.read(file_path_genome_locations)
        dict_id_to_file_path_fasta = meta_data_table.get_map(0, 1)

        # list_of_directory_bam = [
        #     self._project_file_folder_handler.get_bam_dir(str(sample_index))
        #  for sample_index in range(self._number_of_samples)]
        list_of_directory_bam = self._project_file_folder_handler.get_bam_dirs()
        list_of_sample_folders = [os.path.basename(os.path.dirname(directory_bam)) for directory_bam in list_of_directory_bam]
        self._logger.info("Samples used for pooled assembly: '{}'".format("', '".join(list_of_sample_folders)))

        file_path_output_gsa_pooled = gs_handler.pooled_gold_standard_by_dir(
            list_of_directory_bam, dict_id_to_file_path_fasta)

        if not self._phase_anonymize:
            gsa_pooled_output = self._project_file_folder_handler.get_gsa_pooled_file_path()
            if self._phase_compress:
                self._list_tuple_archive_files.append((file_path_output_gsa_pooled, gsa_pooled_output+".gz"))
            else:
                shutil.move(file_path_output_gsa_pooled, gsa_pooled_output)

        return file_path_output_gsa_pooled

    def _create_binning_gs(self, list_of_output_gsa):
        """
        Create binning gold standard without anonymization first

        @param list_of_output_gsa: List of file paths of assemblies
        @type list_of_output_gsa: list[str|unicode]
        @param file_path_output_gsa_pooled: file paths of assembly from all samples
        @type file_path_output_gsa_pooled: str | unicode

        @rtype: None
        """
        gff = GoldStandardFileFormat(logfile = self._logfile, verbose = self._verbose)
        # read-based binning
        file_path_metadata = self._project_file_folder_handler.get_genome_metadata_file_path()
        file_path_genome_locations = self._project_file_folder_handler.get_genome_location_file_path()
        dict_sequence_to_genome_id = gff.get_dict_sequence_to_genome_id(file_path_genome_locations)
        dict_genome_id_to_tax_id = gff.get_dict_genome_id_to_tax_id(file_path_metadata)
        
        directories_fastq_dir_in = [
            self._project_file_folder_handler.get_reads_dir(True, str(sample_index))
            for sample_index in range(self._number_of_samples)]

        if (self._read_simulator_type == "art" or self._read_simulator_type == "wgsim"):
            paired_end = True
        else:
            paired_end = False
        
        for sample_index in range(self._number_of_samples):
            sample_id = str(sample_index)
            readfiles = directories_fastq_dir_in[sample_index]
            if self._phase_compress:
                file_path_gs_mapping = tempfile.mktemp(
                    dir=self._project_file_folder_handler.get_tmp_wd(),
                    prefix="gs_mapping")
            else:
                file_path_gs_mapping = self._project_file_folder_handler.get_anonymous_reads_map_file_path(sample_id)
            samtools = SamtoolsWrapper(
                file_path_samtools=self._executable_samtools,
                max_processes=self._max_processors,
                tmp_dir=self._project_file_folder_handler.get_tmp_wd(),
                logfile=self._logfile,
                verbose=self._verbose,
                debug=self._debug
                )
            list_file_paths_read_positions = [ 
                samtools.read_start_positions_from_dir_of_bam(self._project_file_folder_handler.get_bam_dir(sample_id))
                ]
            dict_original_seq_pos = gff.get_dict_sequence_name_to_positions(list_file_paths_read_positions)
            with open(file_path_gs_mapping, 'w') as stream_output:
                row_format = "{aid}\t{gid}\t{tid}\t{sid}\n"
                line = '#' + row_format.format(
                    aid="anonymous_read_id",
                    gid="genome_id",
                    tid="tax_id",
                    sid="read_id")
                stream_output.write(line)
                for read in dict_original_seq_pos:
                    seq_id = read.strip().split(' ')[0]
                    gen_id = read.strip().split('-')[0]
                    genome_id = dict_sequence_to_genome_id[gen_id]
                    tax_id = dict_genome_id_to_tax_id[genome_id]
                    line = row_format.format(
                        aid=seq_id,
                        gid=genome_id,
                        tid=tax_id,
                        sid=seq_id,
                    )
                    stream_output.write(line)
            if self._phase_compress:
                self._list_tuple_archive_files.append(
                    (file_path_gs_mapping, self._project_file_folder_handler.get_anonymous_reads_map_file_path(sample_id)+".gz"))
            
            if self._phase_compress:
                file_path_gsa_mapping = tempfile.mktemp(
                    dir=self._project_file_folder_handler.get_tmp_wd(),
                    prefix="anonymous_gsa_mapping")
            else:
                file_path_gsa_mapping = self._project_file_folder_handler.get_anonymous_gsa_map_file_path(sample_id)
            samtools = SamtoolsWrapper(
                file_path_samtools=self._executable_samtools,
                max_processes=self._max_processors,
                tmp_dir=self._project_file_folder_handler.get_tmp_wd(),
                logfile=self._logfile,
                verbose=self._verbose,
                debug=self._debug
                )
            list_file_paths_read_positions = [
                samtools.read_start_positions_from_dir_of_bam(self._project_file_folder_handler.get_bam_dir(sample_id))
                ]
            dict_original_seq_pos = gff.get_dict_sequence_name_to_positions(list_file_paths_read_positions)
            file_path_output_anonymous_gsa_out = self._project_file_folder_handler.get_anonymous_gsa_file_path(sample_id)
            
            gsa = list_of_output_gsa[sample_index]
            with open(gsa, 'r') as gs:
                with open(file_path_gsa_mapping, 'w') as stream_output:
                    row_format = "{name}\t{genome_id}\t{tax_id}\t{length}\n"
                    stream_output.write("@@SEQUENCEID\tBINID\tTAXID\t_LENGTH\n")
                    for seq_id in gs:
                        if not seq_id.startswith(">"):
                            continue
                        seq_id = seq_id[1:].strip()
                        seq_info = seq_id.rsplit("_from_", 1)
                        # print(seq_info)
                        sequence_id = seq_info[0]
                        # pos_start, pos_end = re.findall(r'\d+', seq_info[1])[:2]
                        pos_start = int(seq_info[1].split("_", 1)[0])
                        pos_end = int(seq_info[1].split("_to_", 1)[1].split("_", 1)[0])

                        genome_id = dict_sequence_to_genome_id[sequence_id]
                        tax_id = dict_genome_id_to_tax_id[genome_id]
                        stream_output.write(row_format.format(
                            name=seq_id,
                            genome_id=genome_id,
                            tax_id=tax_id,
                            length=str(pos_end-pos_start+1)
                            )
                        )
                if self._phase_compress:
                    self._list_tuple_archive_files.append(
                        (file_path_gsa_mapping, self._project_file_folder_handler.get_anonymous_gsa_map_file_path(sample_id)))
                else:
                    shutil.move(file_path_gsa_mapping, file_path_output_anonymous_gsa_out)

    # #########################
    #
    # Anonymize Data
    #
    # #########################

    def _anonymize_data(self, list_of_output_gsa, file_path_output_gsa_pooled):
        """
        Anonymize reads and assemblies.

        @param list_of_output_gsa: List of file paths of assemblies
        @type list_of_output_gsa: list[str|unicode]
        @param file_path_output_gsa_pooled: file paths of assembly from all samples
        @type file_path_output_gsa_pooled: str | unicode

        @rtype: None
        """
        gs_mapping = GoldStandardFileFormat(
            column_name_gid=self._column_name_genome_id,
            column_name_ncbi=self._column_name_ncbi,
            separator=self._separator,
            logfile=self._logfile,
            verbose=self._verbose
        )
        file_path_metadata = self._project_file_folder_handler.get_genome_metadata_file_path()

        directories_fastq_dir_in = [
            self._project_file_folder_handler.get_reads_dir(True, str(sample_index))
            for sample_index in range(self._number_of_samples)]

        if (self._read_simulator_type == "art" or self._read_simulator_type == "wgsim"):
            paired_end = True
        else:
            paired_end = False

        file_path_genome_locations = self._project_file_folder_handler.get_genome_location_file_path()
        for sample_index in range(self._number_of_samples):
            file_path_anonymous_reads_tmp, file_path_anonymous_mapping_tmp = self._anonymize_reads(
                directories_fastq_dir_in[sample_index],
                "S{}R".format(sample_index),
                paired_end)
            sample_id = str(sample_index)
            file_path_anonymous_reads_out = self._project_file_folder_handler.get_anonymous_reads_file_path(sample_id)
            file_path_anonymous_gs_mapping_out = self._project_file_folder_handler.get_anonymous_reads_map_file_path(sample_id)
            if self._phase_compress:
                file_path_anonymous_gs_mapping = tempfile.mktemp(
                    dir=self._project_file_folder_handler.get_tmp_wd(),
                    prefix="anonymous_gs_mapping")
            else:
                file_path_anonymous_gs_mapping = self._project_file_folder_handler.get_anonymous_reads_map_file_path(sample_id)
            with open(file_path_anonymous_gs_mapping, 'w') as stream_output:
                gs_mapping.gs_read_mapping(
                    file_path_genome_locations, file_path_metadata, file_path_anonymous_mapping_tmp, stream_output
                )
            if self._phase_compress:
                self._list_tuple_archive_files.append(
                    (file_path_anonymous_reads_tmp, file_path_anonymous_reads_out+".gz"))
                self._list_tuple_archive_files.append(
                    (file_path_anonymous_gs_mapping, file_path_anonymous_gs_mapping_out+".gz"))
            else:
                shutil.move(file_path_anonymous_reads_tmp, file_path_anonymous_reads_out)

        if not self._phase_gsa and not self._phase_pooled_gsa:
            return

        samtools = SamtoolsWrapper(
            file_path_samtools=self._executable_samtools,
            max_processes=self._max_processors,
            tmp_dir=self._project_file_folder_handler.get_tmp_wd(),
            logfile=self._logfile,
            verbose=self._verbose,
            debug=self._debug
            )

        if self._phase_gsa:
            for sample_index in range(self._number_of_samples):
                file_path_output_anonymous_gsa, file_path_anonymous_mapping_tmp = self._anonymize_gsa(
                    list_of_output_gsa[sample_index],
                    "S{}C".format(sample_index))
                sample_id = str(sample_index)
                file_path_output_anonymous_gsa_out = self._project_file_folder_handler.get_anonymous_gsa_file_path(sample_id)
                file_path_anonymous_gsa_mapping_out = self._project_file_folder_handler.get_anonymous_gsa_map_file_path(sample_id)
                if self._phase_compress:
                    file_path_anonymous_gsa_mapping = tempfile.mktemp(
                        dir=self._project_file_folder_handler.get_tmp_wd(),
                        prefix="anonymous_gsa_mapping")
                else:
                    file_path_anonymous_gsa_mapping = self._project_file_folder_handler.get_anonymous_gsa_map_file_path(sample_id)

                list_file_paths_read_positions = [
                    samtools.read_start_positions_from_dir_of_bam(self._project_file_folder_handler.get_bam_dir(sample_id))
                    ]
                with open(file_path_anonymous_gsa_mapping, 'w') as stream_output:
                    gs_mapping.gs_contig_mapping(
                        file_path_genome_locations, file_path_metadata, file_path_anonymous_mapping_tmp,
                        list_file_paths_read_positions, stream_output
                    )
                if self._phase_compress:
                    self._list_tuple_archive_files.append(
                        (file_path_output_anonymous_gsa, file_path_output_anonymous_gsa_out+".gz"))
                    self._list_tuple_archive_files.append(
                        (file_path_anonymous_gsa_mapping, file_path_anonymous_gsa_mapping_out+".gz"))
                else:
                    shutil.move(file_path_output_anonymous_gsa, file_path_output_anonymous_gsa_out)
        if self._phase_pooled_gsa:
            file_path_output_anonymous, file_path_anonymous_mapping_tmp = self._anonymize_pooled_gsa(
                file_path_output_gsa_pooled,
                "PC")
            file_path_output_anonymous_out = self._project_file_folder_handler.get_anonymous_gsa_pooled_file_path()
            file_path_anonymous_gsa_mapping_out = self._project_file_folder_handler.get_anonymous_gsa_pooled_map_file_path()
            if self._phase_compress:
                file_path_anonymous_gsa_mapping = tempfile.mktemp(
                    dir=self._project_file_folder_handler.get_tmp_wd(),
                    prefix="anonymous_gsa_pooled_mapping")
            else:
                file_path_anonymous_gsa_mapping = self._project_file_folder_handler.get_anonymous_gsa_pooled_map_file_path()

            list_file_paths_read_positions = [
                samtools.read_start_positions_from_dir_of_bam(self._project_file_folder_handler.get_bam_dir(str(sample_index)))
                for sample_index in range(self._number_of_samples)
                ]
            with open(file_path_anonymous_gsa_mapping, 'w') as stream_output:
                gs_mapping.gs_contig_mapping(
                    file_path_genome_locations, file_path_metadata, file_path_anonymous_mapping_tmp,
                    list_file_paths_read_positions, stream_output
                )
            if self._phase_compress:
                self._list_tuple_archive_files.append(
                    (file_path_output_anonymous, file_path_output_anonymous_out+".gz"))
                self._list_tuple_archive_files.append(
                    (file_path_anonymous_gsa_mapping, file_path_anonymous_gsa_mapping_out+".gz"))
            else:
                shutil.move(file_path_output_anonymous, file_path_output_anonymous_out)

    def _anonymize_reads(self, directory_fastq, sequence_prefix, paired_end):
        """
        Anonymize simulated reads.

        @param directory_fastq: fastq directory of a sample
        @type directory_fastq: str | unicode
        @param sequence_prefix: Prefix for anonymous sequence names
        @type sequence_prefix: str | unicode
        @param paired_end: True if reads are paired
        @type paired_end: bool

        @return: File path of anonymized reads and file path of a sequence name mapping
        @rtype: tuple[str|unicode, str|unicode]
        """
        fastaanonymizer = FastaAnonymizer(
            logfile=self._logfile,
            verbose=self._verbose,
            debug=self._debug,
            seed=None,
            tmp_dir=self._project_file_folder_handler.get_tmp_wd()
        )

        if paired_end:
            file_path_output_anonymous_reads, file_path_anonymous_mapping = fastaanonymizer.interweave_shuffle_anonymize(
                directory_fastq,
                prefix=sequence_prefix,
                file_format="fastq",
                file_extension="fq")
        else:
            file_path_output_anonymous_reads, file_path_anonymous_mapping = fastaanonymizer.shuffle_anonymize(
                directory_fastq,
                prefix=sequence_prefix,
                file_format="fastq",
                file_extension="fq")
        return file_path_output_anonymous_reads, file_path_anonymous_mapping

    def _anonymize_gsa(self, file_path_gsa, sequence_prefix):
        """
        Anonymize assembly of a sample.

        @param file_path_gsa: file paths of assembly from all samples
        @type file_path_gsa: str | unicode
        @param sequence_prefix: Prefix for anonymous sequence names
        @type sequence_prefix: str | unicode

        @return: File path of anonymized assembly and file path of a sequence name mapping
        @rtype: tuple[str|unicode, str|unicode]
        """
        fastaanonymizer = FastaAnonymizer(
            logfile=self._logfile,
            verbose=self._verbose,
            debug=self._debug,
            seed=None,
            tmp_dir=self._project_file_folder_handler.get_tmp_wd()
        )

        file_path_output_anonymous_gs, file_path_anonymous_mapping = fastaanonymizer.shuffle_anonymize(
            path_input=file_path_gsa,
            prefix=sequence_prefix,
            file_format="fasta")
        return file_path_output_anonymous_gs, file_path_anonymous_mapping

    def _anonymize_pooled_gsa(
        self, file_path_output_pooled_anonymous, sequence_prefix):
        """
        Anonymize assembly of a sample.

        @param file_path_output_pooled_anonymous: file paths of assembly from all samples
        @type file_path_output_pooled_anonymous: str | unicode
        @param sequence_prefix: Prefix for anonymous sequence names
        @type sequence_prefix: str | unicode

        @return: File path of anonymized assembly and file path of a sequence name mapping
        @rtype: tuple[str|unicode, str|unicode]
        """
        fastaanonymizer = FastaAnonymizer(
            logfile=self._logfile,
            verbose=self._verbose,
            debug=self._debug,
            seed=None,
            tmp_dir=self._project_file_folder_handler.get_tmp_wd()
        )

        file_path_output_anonymous, file_path_anonymous_mapping = fastaanonymizer.shuffle_anonymize(
            path_input=file_path_output_pooled_anonymous,
            prefix=sequence_prefix,
            file_format="fasta")

        return file_path_output_anonymous, file_path_anonymous_mapping

    # #########################
    #
    # Compress Data
    #
    # #########################

    def _compress_data(self):
        """
        Compress files

        @rtype: None
        """
        compressor = Compress(
            default_compression="gz",
            logfile=self._logfile,
            verbose=self._verbose,
            debug=self._debug)

        compressor.compress_list_tuples(
            self._list_tuple_archive_files,
            compresslevel=self._compresslevel,
            compression_type='gz',
            overwrite=False,
            max_processors=self._max_processors)


if __name__ == "__main__":
    pipeline = None
    try:
        pipeline = MetagenomeSimulation(
            args=None, separator="\t",
            column_name_genome_id="genome_ID", column_name_otu="OTU", column_name_novelty_category="novelty_category",
            column_name_ncbi="NCBI_ID", column_name_source="source")
    except (KeyboardInterrupt, SystemExit, Exception, ValueError, RuntimeError) as e:
        # if debug:
        #     sys.stderr.write("\n{}\n".format(traceback.format_exc()))
        if hasattr(e, 'args') and len(e.args) > 0:
            sys.stderr.write("ERROR: ")
            sys.stderr.write(str(e.args[0]))
            sys.stderr.write("\n")
            sys.exit(1)
        sys.stderr.write("Aborted\n")
    except AssertionError as e:
        if hasattr(e, 'args') and len(e.args) > 0:
            sys.stderr.write(e.args[0])
            sys.exit(1)
        sys.stderr.write("Aborted\n")
    if not pipeline:
        sys.exit(1)
    pipeline.run_pipeline()