view profrep.py @ 0:a5f1638b73be draft

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#!/usr/bin/env python3

import subprocess
import csv
import time
import sys
import matplotlib
# matplotlib.use("PDF")
matplotlib.use("pdf")
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
import multiprocessing
import argparse
import os
from functools import partial
from multiprocessing import Pool
from tempfile import NamedTemporaryFile
from operator import itemgetter
from itertools import groupby
import gff
import configuration
import visualization
import distutils
from distutils import dir_util
import tempfile
import re
from Bio import SeqIO
import sys
import pickle
import shutil
import warnings
import random
import numpy as np
import dante_gff_output_filtering as domains_filtering
import dante as protein_domains

t_profrep = time.time()
np.set_printoptions(threshold=sys.maxsize)
warnings.filterwarnings("ignore", module="matplotlib")


class Range():
    '''
    This class is used to check float range in argparse
    '''

    def __init__(self, start, end):
        self.start = start
        self.end = end

    def __eq__(self, other):
        return self.start <= other <= self.end

    def __str__(self):
        return "float range {}..{}".format(self.start, self.end)

    def __repr__(self):
        return "float range {}..{}".format(self.start, self.end)


def get_version(path):
    branch = subprocess.check_output("git rev-parse --abbrev-ref HEAD",
                                     shell=True,
                                     cwd=path).decode('ascii').strip()
    shorthash = subprocess.check_output("git log --pretty=format:'%h' -n 1  ",
                                        shell=True,
                                        cwd=path).decode('ascii').strip()
    revcount = len(subprocess.check_output("git log --oneline",
                                           shell=True,
                                           cwd=path).decode('ascii').split())
    version_string = ("-------------------------------------"
                      "-------------------------------------\n"
                      "PIPELINE VERSION         : "
                      "{branch}-rv-{revcount}({shorthash})\n"
                      "-------------------------------------"
                      "-------------------------------------\n").format(
                          branch=branch,
                          shorthash=shorthash,
                          revcount=revcount, )
    return (version_string)


def str2bool(v):
    if v.lower() in ('yes', 'true', 't', 'y', '1'):
        return True
    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        raise argparse.ArgumentTypeError('Boolean value expected')


def check_fasta_id(QUERY):
    forbidden_ids = []
    headers = []
    for record in SeqIO.parse(QUERY, "fasta"):
        if any(x in record.id for x in configuration.FORBIDDEN_CHARS):
            forbidden_ids.append(record.id)
        headers.append(record.id)
    if len(headers) > len(set([header.split(" ")[0] for header in headers])):
        raise NameError(
            '''Sequences in multifasta format are not named correctly:
							seq IDs(before the first space) are the same''')
    return forbidden_ids, headers


def multifasta(QUERY):
    ''' Create single fasta temporary files to be processed sequentially '''
    PATTERN = ">"
    fasta_list = []
    with open(QUERY, "r") as fasta:
        reader = fasta.read()
        splitter = reader.split(PATTERN)[1:]
        for fasta_num, part in enumerate(splitter):
            ntf = NamedTemporaryFile(delete=False)
            ntf.write("{}{}".format(PATTERN, part).encode("utf-8"))
            fasta_list.append(ntf.name)
            ntf.close()
        return fasta_list


def fasta_read(subfasta):
    ''' Read fasta, gain header and sequence without gaps '''
    sequence_lines = []
    with open(subfasta, "r") as fasta:
        header = fasta.readline().strip().split(" ")[0][1:]
        for line in fasta:
            clean_line = line.strip()
            if clean_line:
                sequence_lines.append(clean_line)
    sequence = "".join(sequence_lines)
    return header, sequence


def cluster_annotation(CL_ANNOTATION_TBL):
    ''' Create dictionary of known annotations classes and related clusters '''
    cl_annotations = {}
    annot_table = np.genfromtxt(CL_ANNOTATION_TBL, dtype=str)
    for line in annot_table:
        if line[1] in cl_annotations:
            cl_annotations[line[1]].append(line[0])
        else:
            cl_annotations[line[1]] = [line[0]]
    return list(cl_annotations.items()), list(cl_annotations.keys())


def read_annotation(CLS, cl_annotations_items):
    ''' Dictionary of known repeat classes and related reads '''
    reads_annotations = {}
    with open(CLS, "r") as cls_file:
        count = 0
        for line in cls_file:
            line = line.rstrip()
            count += 1
            if count % 2 == 0:
                reads = re.split("\s+", line)
                for element in reads:
                    for key, value in cl_annotations_items:
                        if clust in value:
                            reads_annotations[element] = key
            else:
                clust = re.split("\s+", line)[0].split(">CL")[1]
    return reads_annotations


def annot_profile(annotation_keys, part):
    ''' Predefine dictionary of known annotations and partial sequence 
	repetitive profiles defined by parallel process '''
    subprofile = {}
    for key in annotation_keys:
        subprofile[key] = [np.zeros(part, dtype=int), np.zeros(part,
                                                               dtype=int)]
    subprofile["ALL"] = [np.zeros(part, dtype=int), np.zeros(part, dtype=int)]
    return subprofile


def parallel_process(WINDOW, OVERLAP, seq_length, annotation_keys,
                     reads_annotations, subfasta, BLAST_DB, E_VALUE, WORD_SIZE,
                     BLAST_TASK, MAX_ALIGNMENTS, BITSCORE, DUST_FILTER,
                     last_index, subsets_num, subset_index):
    ''' Run parallel function to process the input sequence in windows
		Run blast for subsequence defined by the input index and window size
 		Create and increment subprofile vector based on reads aligned within window '''
    loc_start = subset_index + 1
    loc_end = subset_index + WINDOW
    if loc_end >= seq_length:
        loc_end = seq_length
        subprofile = annot_profile(annotation_keys, seq_length - loc_start + 1)
    else:
        subprofile = annot_profile(annotation_keys, WINDOW + 1)

    # Find HSP records for every window defined by query location and parse the tabular stdout:
    # 1. query, 2. database read, 3. alignment start, 4. alignment end, 5. bitscore
    p = subprocess.Popen(
        "blastn -query {} -query_loc {}-{} -db {} -evalue {} -word_size {} -dust {} -task {} -num_alignments {} -outfmt '6 qseqid sseqid qstart qend bitscore pident'".format(
            subfasta, loc_start, loc_end, BLAST_DB, E_VALUE, WORD_SIZE,
            DUST_FILTER, BLAST_TASK, MAX_ALIGNMENTS),
        stdout=subprocess.PIPE,
        shell=True)
    count_hits = 0
    for line in p.stdout:
        column = line.decode("utf-8").rstrip().split("\t")
        if float(column[4]) >= BITSCORE:
            count_hits += 1
            read = column[1]  # ID of individual aligned read
            if "reduce" in read:
                reads_representation = int(read.split("reduce")[-1])
            else:
                reads_representation = 1
            qstart = int(column[2])  # starting position of alignment
            qend = int(column[3])  # ending position of alignemnt
            if read in reads_annotations:
                annotation = reads_annotations[read]
            else:
                annotation = "ALL"
            subprofile[annotation][0][
                qstart - subset_index - 1:qend - subset_index] = subprofile[
                    annotation][0][qstart - subset_index - 1:qend -
                                   subset_index] + reads_representation
            subprofile[annotation][1][qstart - subset_index - 1:qend -
                                      subset_index] = subprofile[annotation][
                                          1][qstart - subset_index - 1:qend -
                                             subset_index] + float(column[
                                                 5]) * reads_representation
    subprofile["ALL"][0] = sum([item[0] for item in subprofile.values()])
    subprofile["ALL"][1] = sum([item[1] for item in subprofile.values()])
    for repeat in subprofile.keys():
        subprofile[repeat][1] = [int(round(quality / hits_num))
                                 if hits_num != 0 else quality
                                 for hits_num, quality in zip(subprofile[
                                     repeat][0], subprofile[repeat][1])]
    if subset_index == 0:
        if subsets_num == 1:
            subprf_name = subprofile_single(subprofile, subset_index)
        else:
            subprf_name = subprofile_first(subprofile, subset_index, WINDOW,
                                           OVERLAP)
    elif subset_index == last_index:
        subprf_name = subprofile_last(subprofile, subset_index, OVERLAP)
    else:
        subprf_name = subprofiles_middle(subprofile, subset_index, WINDOW,
                                         OVERLAP)
    return subprf_name


def subprofile_single(subprofile, subset_index):
    subprofile['idx'] = list(range(1, len(subprofile["ALL"][0]) + 1))
    subprf_dict = NamedTemporaryFile(suffix='{}_.pickle'.format(subset_index),
                                     delete=False)
    with open(subprf_dict.name, 'wb') as handle:
        pickle.dump(subprofile, handle, protocol=pickle.HIGHEST_PROTOCOL)
    subprf_dict.close()
    return subprf_dict.name


def subprofile_first(subprofile, subset_index, WINDOW, OVERLAP):
    for key in subprofile.keys():
        subprofile[key][0] = subprofile[key][0][0:-OVERLAP // 2 - 1]
        subprofile[key][1] = subprofile[key][1][0:-OVERLAP // 2 - 1]
    subprofile['idx'] = list(range(subset_index + 1, subset_index + WINDOW -
                                   OVERLAP // 2 + 1))
    subprf_dict = NamedTemporaryFile(suffix='{}_.pickle'.format(subset_index),
                                     delete=False)
    with open(subprf_dict.name, 'wb') as handle:
        pickle.dump(subprofile, handle, protocol=pickle.HIGHEST_PROTOCOL)
    subprf_dict.close()
    return subprf_dict.name


def subprofiles_middle(subprofile, subset_index, WINDOW, OVERLAP):
    for key in subprofile.keys():
        subprofile[key][0] = subprofile[key][0][OVERLAP // 2:-OVERLAP // 2 - 1]
        subprofile[key][1] = subprofile[key][1][OVERLAP // 2:-OVERLAP // 2 - 1]
    subprofile['idx'] = list(range(subset_index + OVERLAP // 2 + 1,
                                   subset_index + WINDOW - OVERLAP // 2 + 1))
    subprf_dict = NamedTemporaryFile(suffix='{}_.pickle'.format(subset_index),
                                     delete=False)
    with open(subprf_dict.name, 'wb') as handle:
        pickle.dump(subprofile, handle, protocol=pickle.HIGHEST_PROTOCOL)
    subprf_dict.close()
    return subprf_dict.name


def subprofile_last(subprofile, subset_index, OVERLAP):
    len_subprofile = len(subprofile['ALL'][0])
    for key in subprofile.keys():
        subprofile[key][0] = subprofile[key][0][OVERLAP // 2:]
        subprofile[key][1] = subprofile[key][1][OVERLAP // 2:]
    subprofile['idx'] = list(range(subset_index + OVERLAP // 2 + 1,
                                   subset_index + len_subprofile + 1))
    subprf_dict = NamedTemporaryFile(suffix='{}_.pickle'.format(subset_index),
                                     delete=False)
    with open(subprf_dict.name, 'wb') as handle:
        pickle.dump(subprofile, handle, protocol=pickle.HIGHEST_PROTOCOL)
    subprf_dict.close()
    return subprf_dict.name


def concatenate_prof(subprofiles_all, files_dict, seq_id, HTML_DATA,
                     wig_files):
    for subprofile in subprofiles_all:
        with open(subprofile, 'rb') as handle:
            individual_dict = pickle.load(handle)
            exclude = set(["idx"])
            for key in set(individual_dict.keys()).difference(exclude):
                if any(individual_dict[key][0]):
                    indices = handle_zero_lines(individual_dict[key][0])
                    if key not in files_dict.keys():
                        prf_name = "{}/{}.wig".format(HTML_DATA, re.sub(
                            '[\/\|]', '_', key))
                        prf_qual_name = "{}/{}_qual.wig".format(
                            HTML_DATA, re.sub('[\/\|]', '_', key))
                        prf_file = open(prf_name, "w")
                        prf_q_file = open(prf_qual_name, "w")
                        prf_file.write("{}{}\n".format(
                            configuration.HEADER_WIG, seq_id))
                        prf_q_file.write("{}{}\n".format(
                            configuration.HEADER_WIG, seq_id))
                        for i in indices:
                            prf_file.write("{}\t{}\n".format(individual_dict[
                                'idx'][i], individual_dict[key][0][i]))
                            prf_q_file.write("{}\t{}\n".format(individual_dict[
                                'idx'][i], int(individual_dict[key][1][i])))
                        files_dict[key] = [prf_name, [seq_id], prf_qual_name]
                        wig_files.append(prf_file)
                        wig_files.append(prf_q_file)
                        prf_file.close()
                        prf_q_file.close()
                    else:
                        prf_name = files_dict[key][0]
                        prf_qual_name = files_dict[key][2]
                        with open(prf_name, "a") as prf_file, open(
                                prf_qual_name, "a") as prf_q_file:
                            if seq_id not in files_dict[key][1]:
                                prf_file.write("{}{}\n".format(
                                    configuration.HEADER_WIG, seq_id))
                                prf_q_file.write("{}{}\n".format(
                                    configuration.HEADER_WIG, seq_id))
                                files_dict[key][1].append(seq_id)
                            for i in indices:
                                prf_file.write("{}\t{}\n".format(
                                    individual_dict['idx'][i], individual_dict[
                                        key][0][i]))
                                prf_q_file.write("{}\t{}\n".format(
                                    individual_dict['idx'][i], int(
                                        individual_dict[key][1][i])))
    return files_dict, wig_files


def concatenate_prof_CN(CV, subprofiles_all, files_dict, seq_id, HTML_DATA,
                        wig_files):
    for subprofile in subprofiles_all:
        with open(subprofile, 'rb') as handle:
            individual_dict = pickle.load(handle)
            exclude = set(["idx"])
            for key in set(individual_dict.keys()).difference(exclude):
                if any(individual_dict[key][0]):
                    indices = handle_zero_lines(individual_dict[key][0])
                    if key not in files_dict.keys():
                        prf_name = "{}/{}.wig".format(HTML_DATA, re.sub(
                            '[\/\|]', '_', key))
                        prf_qual_name = "{}/{}_qual.wig".format(
                            HTML_DATA, re.sub('[\/\|]', '_', key))
                        prf_file = open(prf_name, "w")
                        prf_q_file = open(prf_qual_name, "w")
                        prf_file.write("{}{}\n".format(
                            configuration.HEADER_WIG, seq_id))
                        prf_q_file.write("{}{}\n".format(
                            configuration.HEADER_WIG, seq_id))
                        for i in indices:
                            prf_file.write("{}\t{}\n".format(individual_dict[
                                'idx'][i], int(individual_dict[key][0][i] /
                                               CV)))
                            prf_q_file.write("{}\t{}\n".format(individual_dict[
                                'idx'][i], int(individual_dict[key][1][i])))
                        files_dict[key] = [prf_name, [seq_id], prf_qual_name]
                        wig_files.append(prf_file)
                        wig_files.append(prf_q_file)
                        prf_file.close()
                        prf_q_file.close()
                    else:
                        prf_name = files_dict[key][0]
                        prf_qual_name = files_dict[key][2]
                        with open(prf_name, "a") as prf_file, open(
                                prf_qual_name, "a") as prf_q_file:
                            if seq_id not in files_dict[key][1]:
                                prf_file.write("{}{}\n".format(
                                    configuration.HEADER_WIG, seq_id))
                                prf_q_file.write("{}{}\n".format(
                                    configuration.HEADER_WIG, seq_id))
                                files_dict[key][1].append(seq_id)
                            for i in indices:
                                prf_file.write("{}\t{}\n".format(
                                    individual_dict['idx'][i], int(
                                        individual_dict[key][0][i] / CV)))
                                prf_q_file.write("{}\t{}\n".format(
                                    individual_dict['idx'][i], int(
                                        individual_dict[key][1][i])))
    return files_dict, wig_files


def handle_zero_lines(repeat_subhits):
    ''' Clean lines which contains only zeros, i.e. positons which do not contain any hit. However border zero positions need to be preserved due to correct graphs plotting '''
    zero_idx = [idx for idx, val in enumerate(repeat_subhits) if val == 0]
    indices = [idx for idx, val in enumerate(repeat_subhits) if val != 0]
    zero_breakpoints = []
    for key, group in groupby(
            enumerate(zero_idx),
            lambda index_item: index_item[0] - index_item[1]):
        group = list(map(itemgetter(1), group))
        zero_breakpoints.append(group[0])
        zero_breakpoints.append(group[-1])
    if indices:
        indices.extend(zero_breakpoints)
        indices = sorted(set(indices), key=int)
    else:
        indices = []
    return indices


def repeats_process_dom(OUTPUT_GFF, THRESHOLD, THRESHOLD_SEGMENT, HTML_DATA,
                        xminimal, xmaximal, domains, seq_ids_dom, CN,
                        seq_ids_all, seq_lengths_all, files_dict):
    ''' Process the hits table separately for each fasta, create gff file and profile picture '''
    if files_dict:
        gff.create_gff(THRESHOLD, THRESHOLD_SEGMENT, OUTPUT_GFF, files_dict,
                       seq_ids_all)
    else:
        with open(OUTPUT_GFF, "w") as gff_file:
            gff_file.write("{}\n".format(configuration.HEADER_GFF))

    # TODO remove plotting, keep only basic report
    return None
    seqs_all_part = seq_ids_all[0:configuration.MAX_PIC_NUM]
    graphs_dict = {}
    seqs_long = []
    if files_dict:
        [graphs_dict, seqs_long] = visualization.vis_profrep(
            seq_ids_all, files_dict, seq_lengths_all, CN, HTML_DATA,
            seqs_all_part)
    count_seq = 0
    for seq in seqs_all_part:
        if seq in graphs_dict.keys():
            fig = graphs_dict[seq][0]
            ax = graphs_dict[seq][1]
            art = []
            lgd = ax.legend(bbox_to_anchor=(0.5, -0.1), loc=9, ncol=3)
            art.append(lgd)
            if seq in seq_ids_dom:
                dom_idx = seq_ids_dom.index(seq)
                [fig, ax] = visualization.vis_domains(
                    fig, ax, seq, xminimal[dom_idx], xmaximal[dom_idx],
                    domains[dom_idx])
        elif seq in seqs_long:
            [fig, ax] = visualization.plot_figure(
                seq, seq_lengths_all[count_seq], CN)
            ax.text(
                0.3,
                0.5,
                "Graphs are only displayed if sequence is not longer than {} bp".format(
                    configuration.SEQ_LEN_VIZ),
                transform=ax.transAxes,
                fontsize=14,
                verticalalignment='center',
                color='blue')
        else:
            [fig, ax] = visualization.plot_figure(
                seq, seq_lengths_all[count_seq], CN)
            ax.hlines(0, 0, seq_lengths_all[count_seq], color="red", lw=4)
            if seq in seq_ids_dom:
                dom_idx = seq_ids_dom.index(seq)
                [fig, ax] = visualization.vis_domains(
                    fig, ax, seq, xminimal[dom_idx], xmaximal[dom_idx],
                    domains[dom_idx])
        output_pic_png = "{}/{}.png".format(HTML_DATA, count_seq)
        fig.savefig(output_pic_png,
                    bbox_inches="tight",
                    format="png",
                    dpi=configuration.IMAGE_RES)
        count_seq += 1
    return None


def repeats_process(OUTPUT_GFF, THRESHOLD, THRESHOLD_SEGMENT, HTML_DATA, CN,
                    seq_ids_all, seq_lengths_all, files_dict):
    ''' Process the hits table separately for each fasta, create gff file and profile picture '''
    if files_dict:
        gff.create_gff(THRESHOLD, THRESHOLD_SEGMENT, OUTPUT_GFF, files_dict,
                       seq_ids_all)
    else:
        with open(OUTPUT_GFF, "w") as gff_file:
            gff_file.write("{}\n".format(configuration.HEADER_GFF))

    # TODO remove plotting, keep only basic report
    return None
    seqs_all_part = seq_ids_all[0:configuration.MAX_PIC_NUM]
    graphs_dict = {}
    seqs_long = []
    if files_dict:
        [graphs_dict, seqs_long] = visualization.vis_profrep(
            seq_ids_all, files_dict, seq_lengths_all, CN, HTML_DATA,
            seqs_all_part)
    count_seq = 0
    for seq in seqs_all_part:
        if seq in graphs_dict.keys():
            fig = graphs_dict[seq][0]
            ax = graphs_dict[seq][1]
            art = []
            lgd = ax.legend(bbox_to_anchor=(0.5, -0.1), loc=9, ncol=3)
            art.append(lgd)
        elif seq in seqs_long:
            [fig, ax] = visualization.plot_figure(
                seq, seq_lengths_all[count_seq], CN)
            ax.text(
                0.3,
                0.5,
                "Graphs are only displayed if sequence is not longer than {} bp".format(
                    configuration.SEQ_LEN_VIZ),
                transform=ax.transAxes,
                fontsize=14,
                verticalalignment='center',
                color='blue')
        else:
            [fig, ax] = visualization.plot_figure(
                seq, seq_lengths_all[count_seq], CN)
            ax.hlines(0, 0, seq_lengths_all[count_seq], color="red", lw=4)
        output_pic_png = "{}/{}.png".format(HTML_DATA, count_seq)
        fig.savefig(output_pic_png,
                    bbox_inches="tight",
                    format="png",
                    dpi=configuration.IMAGE_RES)
        plt.close()
        count_seq += 1
    return None


def html_output(total_length, seq_lengths_all, seq_names, HTML, DB_NAME, REF,
                REF_LINK):
    ''' Define html output with limited number of output pictures and link to JBrowse '''
    info = "\t\t".join(['<pre> {} [{} bp]</pre>'.format(seq_name, seq_length)
                        for seq_name, seq_length in zip(seq_names,
                                                        seq_lengths_all)])
    if REF:
        ref_part_1 = REF.split("-")[0]
        ref_part_2 = "-".join(REF.split("-")[1:]).split(". ")[0]
        ref_part_3 = ". ".join("-".join(REF.split("-")[1:]).split(". ")[1:])
        ref_string = '''<h6> {} - <a href="{}" target="_blank" >{}</a>. {}'''.format(
            ref_part_1, REF_LINK, ref_part_2, ref_part_3)
    else:
        ref_string = "Custom Data"
    pictures = "\n\t\t".join(['<img src="{}.png" width=1800>'.format(
        pic) for pic in range(len(seq_names))[:configuration.MAX_PIC_NUM]])
    html_str = configuration.HTML_STR.format(info, total_length, DB_NAME,
                                             pictures, ref_string)
    with open(HTML, "w") as html_file:
        html_file.write(html_str)


def adjust_tracklist(jbrowse_data_path):
    starting_lines = []
    ending_lines = []
    end = False
    with open(
            os.path.join(jbrowse_data_path,
                         "trackList.json"), "r") as track_list:
        for line in track_list:
            if "]" not in line and not end:
                starting_lines.append(line)
            else:
                end = True
                ending_lines.append(line)
    with open(
            os.path.join(jbrowse_data_path,
                         "trackList.json"), "w") as track_list:
        for line in starting_lines:
            track_list.write(line)
    return ending_lines


def jbrowse_prep_dom(HTML_DATA, QUERY, OUT_DOMAIN_GFF, OUTPUT_GFF, N_GFF,
                     total_length, JBROWSE_BIN, files_dict):
    ''' Set up the paths, link and convert output data to be displayed as tracks in Jbrowse '''
    jbrowse_data_path = os.path.join(HTML_DATA, configuration.jbrowse_data_dir)
    with tempfile.TemporaryDirectory() as dirpath:
        subprocess.call(["{}/prepare-refseqs.pl".format(JBROWSE_BIN),
                         "--fasta", QUERY, "--out", jbrowse_data_path])
        subprocess.call(["{}/flatfile-to-json.pl".format(
            JBROWSE_BIN), "--gff", OUT_DOMAIN_GFF, "--trackLabel",
                         "GFF_domains", "--out", jbrowse_data_path])
        subprocess.call(["{}/flatfile-to-json.pl".format(
            JBROWSE_BIN), "--gff", OUTPUT_GFF, "--trackLabel", "GFF_repeats",
                         "--config", configuration.JSON_CONF_R, "--out",
                         jbrowse_data_path])
        subprocess.call(["{}/flatfile-to-json.pl".format(
            JBROWSE_BIN), "--gff", N_GFF, "--trackLabel", "N_regions",
                         "--config", configuration.JSON_CONF_N, "--out",
                         jbrowse_data_path])
        count = 0
        # Control the total length processed, if above threshold, dont create wig image tracks
        if files_dict:
            exclude = set(['ALL'])
            sorted_keys = sorted(set(files_dict.keys()).difference(exclude))
            sorted_keys.insert(0, "ALL")
            ending_lines = adjust_tracklist(jbrowse_data_path)
            track_list = open(
                os.path.join(jbrowse_data_path, "trackList.json"), "a")
            color_avail = len(configuration.COLORS_HEX)
            for repeat_id in sorted_keys:
                if count <= color_avail - 1:
                    color = configuration.COLORS_HEX[count]
                else:
                    r = lambda: random.randint(0, 255)
                    color = '#%02X%02X%02X' % (r(), r(), r())
                count += 1
                bw_name = "{}.bw".format(re.sub('[\/\|]', '_', repeat_id))
                subprocess.call(["wigToBigWig", files_dict[repeat_id][
                    0], os.path.join(HTML_DATA,
                                     configuration.CHROM_SIZES_FILE),
                                 os.path.join(jbrowse_data_path, bw_name)])
                track_list.write(configuration.TRACK_LIST.format(
                    "{", bw_name, repeat_id, repeat_id, "{", color, "}", "}"))
            for line in ending_lines:
                track_list.write(line)
        distutils.dir_util.copy_tree(dirpath, jbrowse_data_path)
    return None


def jbrowse_prep(HTML_DATA, QUERY, OUTPUT_GFF, N_GFF, total_length,
                 JBROWSE_BIN, files_dict):
    ''' Set up the paths, link and convert output data to be displayed as tracks in Jbrowse '''
    jbrowse_data_path = os.path.join(HTML_DATA, configuration.jbrowse_data_dir)
    with tempfile.TemporaryDirectory() as dirpath:
        subprocess.call(["{}/prepare-refseqs.pl".format(JBROWSE_BIN),
                         "--fasta", QUERY, "--out", jbrowse_data_path])
        subprocess.call(["{}/flatfile-to-json.pl".format(
            JBROWSE_BIN), "--gff", OUTPUT_GFF, "--trackLabel", "GFF_repeats",
                         "--config", configuration.JSON_CONF_R, "--out",
                         jbrowse_data_path])
        subprocess.call(["{}/flatfile-to-json.pl".format(
            JBROWSE_BIN), "--gff", N_GFF, "--trackLabel", "N_regions",
                         "--config", configuration.JSON_CONF_N, "--out",
                         jbrowse_data_path])
        count = 0
        ## Control the total length processed, if above threshold, dont create wig image tracks
        if files_dict:
            exclude = set(['ALL'])
            sorted_keys = sorted(set(files_dict.keys()).difference(exclude))
            sorted_keys.insert(0, "ALL")
            ending_lines = adjust_tracklist(jbrowse_data_path)
            track_list = open(
                os.path.join(jbrowse_data_path, "trackList.json"), "a")
            color_avail = len(configuration.COLORS_HEX)
            for repeat_id in sorted_keys:
                if count <= color_avail - 1:
                    color = configuration.COLORS_HEX[count]
                else:
                    r = lambda: random.randint(0, 255)
                    color = '#%02X%02X%02X' % (r(), r(), r())
                count += 1
                bw_name = "{}.bw".format(re.sub('[\/\|]', '_', repeat_id))
                subprocess.call(["wigToBigWig", files_dict[repeat_id][
                    0], os.path.join(HTML_DATA,
                                     configuration.CHROM_SIZES_FILE),
                                 os.path.join(jbrowse_data_path, bw_name)])
                track_list.write(configuration.TRACK_LIST.format(
                    "{", bw_name, repeat_id, repeat_id, "{", color, "}", "}"))
            for line in ending_lines:
                track_list.write(line)
            track_list.close()
        distutils.dir_util.copy_tree(dirpath, jbrowse_data_path)
    return None


def genome2coverage(GS, BLAST_DB):
    ''' Convert genome size to coverage '''
    num_of_reads = 0
    with open(BLAST_DB, "r") as reads_all:
        first_line = reads_all.readline()
        if first_line.startswith(">"):
            num_of_reads += 1
            first_seq = reads_all.readline().rstrip()
        for line in reads_all:
            if line.startswith(">"):
                num_of_reads += 1
    len_of_read = len(first_seq)
    CV = (num_of_reads * len_of_read) / (GS * 1000000)  # GS in Mb
    return CV


def prepared_data(DB_ID):
    ''' Get prepared rep. annotation data from the table based on the selected species ID '''
    tbl = os.path.join(
        os.path.dirname(os.path.realpath(__file__)),
        configuration.PROFREP_DATA, configuration.PROFREP_TBL)
    with open(tbl, "r") as datasets:
        for line in datasets:
            if line.split("\t")[0] == DB_ID:
                DB_NAME = line.split("\t")[1]
                CV = float(line.split("\t")[5])
                REF = line.split("\t")[6]
                REF_LINK = line.split("\t")[7]
    return DB_NAME, CV, REF, REF_LINK


def seq_sizes_file(seq_ids, seq_lengths_all, HTML_DATA):
    chrom_sizes = os.path.join(HTML_DATA, configuration.CHROM_SIZES_FILE)
    with open(chrom_sizes, "w") as chroms:
        for seq_id, seq_length in zip(seq_ids, seq_lengths_all):
            chroms.write("{}\t{}\n".format(seq_id, seq_length))


def main(args):
	## Command line arguments
    QUERY = args.query
    BLAST_DB = args.reads
    CL_ANNOTATION_TBL = args.ann_tbl 
    CLS = args.cls
    BITSCORE = args.bit_score
    E_VALUE = args.e_value
    WORD_SIZE = args.word_size
    WINDOW = args.window
    OVERLAP = args.overlap
    BLAST_TASK = args.task
    MAX_ALIGNMENTS = args.max_alignments
    NEW_DB = args.new_db
    THRESHOLD = args.threshold_repeat
    THRESHOLD_SEGMENT = args.threshold_segment
    TH_IDENTITY = args.th_identity
    TH_LENGTH = args.th_length 
    TH_INTERRUPT = args.interruptions
    TH_SIMILARITY = args.th_similarity
    TH_LEN_RATIO = args.max_len_proportion
    OUTPUT_GFF = args.output_gff
    DOMAINS = args.protein_domains
    LAST_DB = args.protein_database
    CLASSIFICATION = args.classification
    OUT_DOMAIN_GFF = args.domain_gff
    HTML = args.html_file
    HTML_DATA = args.html_path
    N_GFF = args.n_gff
    CN = args.copy_numbers
    GS = args.genome_size
    DB_ID = args.db_id
    THRESHOLD_SCORE = args.threshold_score
    WIN_DOM = args.win_dom
    OVERLAP_DOM = args.overlap_dom
    JBROWSE_BIN = args.jbrowse_bin
    DUST_FILTER = args.dust_filter
    LOG_FILE = args.log_file
    #JBROWSE_BIN = os.environ['JBROWSE_SOURCE_DIR']+"/bin"
	#if not JBROWSE_BIN: 
	#	try:
	#		JBROWSE_BIN = os.environ['JBROWSE_BIN']
	#	except KeyError:
	#		raise ValueError('There was no path to JBrowse bin found - set the enviroment variable JBROWSE_BIN or pass the argument explicitly')
    if CN and not DB_ID and not GS:
        raise ValueError("Genome size missing - if you want to convert hits to copy numbers please enter --genome_size parameter")


    ## Check if there are forbidden characters in fasta IDs
    [forbidden_ids, headers] = check_fasta_id(QUERY)
    if forbidden_ids:
        ##################### USER ERROR ###############################
        raise UserWarning(
            "The following IDs contain forbidden characters ('/' or '\\') - PLEASE REPLACE OR DELETE THEM:\n{}".format(
                "\n".join(forbidden_ids)))

    if len(headers) > len(set([header.split(" ")[0] for header in headers])):
        raise NameError(
            '''Sequences in multifasta format are not named correctly:
				seq IDs(before the first space) are the same''')

    ## Create new blast database of reads
    if NEW_DB:
        subprocess.call("makeblastdb -in {} -dbtype nucl".format(BLAST_DB),
                        shell=True)

    ## Parse prepared annotation data table
    if DB_ID:
        [DB_NAME, CV, REF, REF_LINK] = prepared_data(DB_ID)
    else:
        REF = None
        REF_LINK = None
        DB_NAME = "CUSTOM"

    ## Create dir to store outputs for html and JBROWSE
    if not os.path.exists(HTML_DATA):
        os.makedirs(HTML_DATA)

    if not os.path.isabs(HTML):
        HTML = os.path.join(HTML_DATA, HTML)

    if not os.path.isabs(OUT_DOMAIN_GFF):
        OUT_DOMAIN_GFF = os.path.join(HTML_DATA, OUT_DOMAIN_GFF)

    if not os.path.isabs(LOG_FILE):
        LOG_FILE = os.path.join(HTML_DATA, LOG_FILE)

    if not os.path.isabs(N_GFF):
        N_GFF = os.path.join(HTML_DATA, N_GFF)

    if not os.path.isabs(OUTPUT_GFF):
        OUTPUT_GFF = os.path.join(HTML_DATA, OUTPUT_GFF)

    path = os.path.dirname(os.path.realpath(__file__))
    version_string = get_version(path)

    log = os.open(LOG_FILE, os.O_RDWR | os.O_CREAT)

    os.write(log, version_string.encode("utf-8"))

    ## Define parameters for parallel process
    STEP = WINDOW - OVERLAP
    NUM_CORES = multiprocessing.cpu_count()
    os.write(log, "NUM_OF_CORES = {}\n".format(NUM_CORES).encode("utf-8"))

    ## Convert genome size to coverage
    if CN and GS:
        CV = genome2coverage(GS, BLAST_DB)
        os.write(log, "COVERAGE = {}\n".format(CV).encode("utf-8"))

    parallel_pool = Pool(NUM_CORES)

    ## Assign clusters to repetitive classes
    [cl_annotations_items, annotation_keys
     ] = cluster_annotation(CL_ANNOTATION_TBL)

    ## Assign reads to repetitive classes
    reads_annotations = read_annotation(CLS, cl_annotations_items)

    ## Detect all fasta sequences from input
    fasta_list = multifasta(QUERY)
    headers = []
    files_dict = {}
    wig_files = []
    seq_count = 1
    start = 1
    total_length = 0
    seq_lengths_all = []
    Ngff = open(N_GFF, "w")
    Ngff.write("{}\n".format(configuration.HEADER_GFF))

    ## Find hits for each fasta sequence separetely
    t_blast = time.time()
    for subfasta in fasta_list:
        [header, sequence] = fasta_read(subfasta)
        os.write(log, "Sequence {} is being processed...\n".format(
            header).encode("utf-8"))
        os.fsync(log)
        indices_N = [indices + 1
                     for indices, n in enumerate(sequence)
                     if n == "n" or n == "N"]
        if indices_N:
            gff.idx_ranges_N(indices_N, configuration.N_segment, header, Ngff,
                             configuration.N_NAME, configuration.N_FEATURE)
        seq_length = len(sequence)
        headers.append(header)
        ## Create parallel process
        subset_index = list(range(0, seq_length, STEP))
        ## Situation when penultimal window is not complete but it is following by another one
        if len(subset_index) > 1 and subset_index[-2] + WINDOW >= seq_length:
            subset_index = subset_index[:-1]
        last_index = subset_index[-1]
        index_range = range(len(subset_index))
        for chunk_index in index_range[0::configuration.MAX_FILES_SUBPROFILES]:
            multiple_param = partial(
                parallel_process, WINDOW, OVERLAP, seq_length, annotation_keys,
                reads_annotations, subfasta, BLAST_DB, E_VALUE, WORD_SIZE,
                BLAST_TASK, MAX_ALIGNMENTS, BITSCORE, DUST_FILTER, last_index,
                len(subset_index))
            subprofiles_all = parallel_pool.map(multiple_param, subset_index[
                chunk_index:chunk_index + configuration.MAX_FILES_SUBPROFILES])
            ## Join partial profiles to the final profile of the sequence
            if CN:
                [files_dict, wig_files
                 ] = concatenate_prof_CN(CV, subprofiles_all, files_dict,
                                         header, HTML_DATA, wig_files)
            else:
                [files_dict, wig_files] = concatenate_prof(
                    subprofiles_all, files_dict, header, HTML_DATA, wig_files)
            for subprofile in subprofiles_all:
                os.unlink(subprofile)
        total_length += seq_length
        seq_lengths_all.append(seq_length)

    os.write(log, "ELAPSED_TIME_BLAST = {} s\n".format(time.time(
    ) - t_blast).encode("utf-8"))
    os.write(
        log,
        "TOTAL_LENGHT_ANALYZED = {} bp\n".format(total_length).encode("utf-8"))

    ## Close opened files
    for opened_file in wig_files:
        opened_file.close()
    Ngff.close()

    ## Create file containing size of sequences to convert wig to bigwig
    seq_sizes_file(headers, seq_lengths_all, HTML_DATA)

    ## Protein domains module
    t_domains = time.time()
    if DOMAINS:
        os.write(log, "Domains module has started...\n".encode("utf-8"))
        os.fsync(log)
        domains_primary = NamedTemporaryFile(delete=False)
        protein_domains.domain_search(QUERY, LAST_DB, CLASSIFICATION,
                                      domains_primary.name, THRESHOLD_SCORE,
                                      WIN_DOM, OVERLAP_DOM)
        domains_primary.close()
        [xminimal, xmaximal, domains, seq_ids_dom
         ] = domains_filtering.filter_qual_dom(
             domains_primary.name, OUT_DOMAIN_GFF, TH_IDENTITY, TH_SIMILARITY,
             TH_LENGTH, TH_INTERRUPT, TH_LEN_RATIO, 'All', "")
        os.unlink(domains_primary.name)
        os.write(log, "ELAPSED_TIME_DOMAINS = {} s\n".format(time.time(
        ) - t_domains).encode("utf-8"))

        # Process individual sequences from the input file sequentially
        t_gff_vis = time.time()
        repeats_process_dom(OUTPUT_GFF, THRESHOLD, THRESHOLD_SEGMENT,
                            HTML_DATA, xminimal, xmaximal, domains,
                            seq_ids_dom, CN, headers, seq_lengths_all,
                            files_dict)
        os.write(log, "ELAPSED_TIME_GFF_VIS = {} s\n".format(time.time(
        ) - t_gff_vis).encode("utf-8"))
        os.fsync(log)
        # Prepare data for html output
        t_jbrowse = time.time()
        os.write(log, "JBrowse tracks are being prepared...\n".encode("utf-8"))
        os.fsync(log)
        jbrowse_prep_dom(HTML_DATA, QUERY, OUT_DOMAIN_GFF, OUTPUT_GFF, N_GFF,
                         total_length, JBROWSE_BIN, files_dict)
        os.write(log, "ELAPSED_TIME_JBROWSE_PREP = {} s\n".format(time.time(
        ) - t_jbrowse).encode("utf-8"))
    else:
        # Process individual sequences from the input file sequentially
        t_gff_vis = time.time()
        repeats_process(OUTPUT_GFF, THRESHOLD, THRESHOLD_SEGMENT, HTML_DATA,
                        CN, headers, seq_lengths_all, files_dict)
        os.write(log, "ELAPSED_TIME_GFF_VIS = {} s\n".format(time.time(
        ) - t_gff_vis).encode("utf-8"))

        # Prepare data for html output
        t_jbrowse = time.time()
        jbrowse_prep(HTML_DATA, QUERY, OUTPUT_GFF, N_GFF, total_length,
                     JBROWSE_BIN, files_dict)
        os.write(log, "ELAPSED_TIME_JBROWSE_PREP = {} s\n".format(time.time(
        ) - t_jbrowse).encode("utf-8"))

    # Create HTML output
    t_html = time.time()
    os.write(
        log,
        "HTML output and JBrowse data structure are being prepared...\n".encode(
            "utf-8"))
    os.fsync(log)
    html_output(total_length, seq_lengths_all, headers, HTML, DB_NAME, REF,
                REF_LINK)
    os.write(log, "ELAPSED_TIME_HTML = {} s\n".format(time.time() -
                                                      t_html).encode("utf-8"))
    os.write(log, "ELAPSED_TIME_PROFREP = {} s\n".format(time.time(
    ) - t_profrep).encode("utf-8"))
    os.close(log)

    ## Clean up the temporary fasta files
    for subfasta in fasta_list:
        os.unlink(subfasta)


if __name__ == "__main__":
    import argparse
    from argparse import RawDescriptionHelpFormatter

    class CustomFormatter(argparse.ArgumentDefaultsHelpFormatter,
                          argparse.RawDescriptionHelpFormatter):
        pass

    # Default paths (command line usage)
    HTML = configuration.HTML
    DOMAINS_GFF = configuration.DOMAINS_GFF
    REPEATS_GFF = configuration.REPEATS_GFF
    N_GFF = configuration.N_GFF
    LOG_FILE = configuration.LOG_FILE
    PROFREP_OUTPUT_DIR = configuration.PROFREP_OUTPUT_DIR

    # Command line arguments
    parser = argparse.ArgumentParser(
        description='''
		
	DEPENDENCIES:
		- python 3.4 or higher with packages:
			- numpy
			- matplotlib
			- biopython
 		- BLAST 2.2.28+ or higher
 		- LAST 744 or higher
 		- wigToBigWig
 		- cd-hit
 		- JBrowse - ! Only bin needed, does not have to be installed under a web server 
 		* ProfRep Modules:
			- gff.py
			- visualization.py
			- configuration.py 
			- protein_domains.py
			- domains_filtering.py

	EXAMPLE OF USAGE:
		
		./protein.py --query PATH_TO_DNA_SEQ --reads PATH_TO_READS --ann_tbl PATH_TO_CLUSTERS_CLASSIFICATION  --cls PATH_TO_hitsort.cls [--new_db True]
		''',
        epilog="""take a look at README for more detailed information""",
        formatter_class=CustomFormatter)

    Required = parser.add_argument_group('required arguments')
    altRequired = parser.add_argument_group(
        'alternative required arguments - prepared datasets')
    blastOpt = parser.add_argument_group('optional arguments - BLAST Search')
    parallelOpt = parser.add_argument_group(
        'optional arguments - Parallel Processing')
    protOpt = parser.add_argument_group('optional arguments - Protein Domains')
    outOpt = parser.add_argument_group('optional arguments - Output Paths')
    cnOpt = parser.add_argument_group(
        'optional arguments - Copy Numbers/Hits ')
    galaxyOpt = parser.add_argument_group(
        'optional arguments - Enviroment Variables')

    ################ INPUTS ############################################
    Required.add_argument('-q',
                          '--query',
                          type=str,
                          required=True,
                          help='input DNA sequence in (multi)fasta format')
    Required.add_argument('-rdb',
                          '--reads',
                          type=str,
                          required=True,
                          help='blast database of all sequencing reads')
    Required.add_argument(
        '-a',
        '--ann_tbl',
        type=str,
        required=True,
        help=
        'clusters annotation table, tab-separated number of cluster and its classification')
    Required.add_argument(
        '-c',
        '--cls',
        type=str,
        required=True,
        help='cls file containing reads assigned to clusters (hitsort.cls)')
    altRequired.add_argument(
        '-id',
        '--db_id',
        type=str,
        help='annotation dataset ID (first column of datasets table)')

    ################ BLAST parameters ##################################
    blastOpt.add_argument('-bs',
                          '--bit_score',
                          type=float,
                          default=50,
                          help='bitscore threshold')
    blastOpt.add_argument(
        '-m',
        '--max_alignments',
        type=int,
        default=10000000,
        help=
        'blast filtering option: maximal number of alignments in the output')
    blastOpt.add_argument('-e',
                          '--e_value',
                          type=str,
                          default=0.1,
                          help='blast setting option: e-value')
    blastOpt.add_argument(
        '-df',
        '--dust_filter',
        type=str,
        default="'20 64 1'",
        help='dust filters low-complexity regions during BLAST search')
    blastOpt.add_argument(
        '-ws',
        '--word_size',
        type=int,
        default=11,
        help='blast search option: initial word size for alignment')
    blastOpt.add_argument('-t',
                          '--task',
                          type=str,
                          default="blastn",
                          help='type of blast to be triggered')
    blastOpt.add_argument(
        '-n',
        '--new_db',
        type=str2bool,
        default=True,
        help=
        'create a new blast database, USE THIS OPTION IF YOU RUN PROFREP WITH NEW DATABASE FOR THE FIRST TIME')

    ############### PARALLEL PROCESSING ARGUMENTS ######################
    parallelOpt.add_argument(
        '-w',
        '--window',
        type=int,
        default=5000,
        help='sliding window size for parallel processing')
    parallelOpt.add_argument(
        '-o',
        '--overlap',
        type=int,
        default=150,
        help=
        'overlap for parallely processed regions, set greater than a read size')

    ################ PROTEIN DOMAINS PARAMETERS ########################
    protOpt.add_argument('-pd',
                         '--protein_domains',
                         type=str2bool,
                         default=False,
                         help='use module for protein domains')
    protOpt.add_argument('-pdb',
                         '--protein_database',
                         type=str,
                         help='protein domains database')
    protOpt.add_argument('-cs',
                         '--classification',
                         type=str,
                         help='protein domains classification file')
    protOpt.add_argument(
        '-wd',
        '--win_dom',
        type=int,
        default=10000000,
        help=
        'protein domains module: sliding window to process large input sequences sequentially')
    protOpt.add_argument(
        '-od',
        '--overlap_dom',
        type=int,
        default=10000,
        help=
        'protein domains module: overlap of sequences in two consecutive windows')
    protOpt.add_argument(
        '-thsc',
        '--threshold_score',
        type=int,
        default=80,
        help=
        'protein domains module: percentage of the best score within the cluster to  significant domains')
    protOpt.add_argument("-thl",
                         "--th_length",
                         type=float,
                         choices=[Range(0.0, 1.0)],
                         default=0.8,
                         help="proportion of alignment length threshold")
    protOpt.add_argument("-thi",
                         "--th_identity",
                         type=float,
                         choices=[Range(0.0, 1.0)],
                         default=0.35,
                         help="proportion of alignment identity threshold")
    protOpt.add_argument(
        "-ths",
        "--th_similarity",
        type=float,
        choices=[Range(0.0, 1.0)],
        default=0.45,
        help="threshold for alignment proportional similarity")
    protOpt.add_argument(
        "-ir",
        "--interruptions",
        type=int,
        default=3,
        help=
        "interruptions (frameshifts + stop codons) tolerance threshold per 100 AA")
    protOpt.add_argument(
        "-mlen",
        "--max_len_proportion",
        type=float,
        default=1.2,
        help=
        "maximal proportion of alignment length to the original length of protein domain from database")

    ################ OUTPUTS ###########################################
    outOpt.add_argument('-lg',
                        '--log_file',
                        type=str,
                        default=LOG_FILE,
                        help='path to log file')
    outOpt.add_argument('-ouf',
                        '--output_gff',
                        type=str,
                        default=REPEATS_GFF,
                        help='path to output gff of repetitive regions')
    outOpt.add_argument('-oug',
                        '--domain_gff',
                        type=str,
                        default=DOMAINS_GFF,
                        help='path to output gff of protein domains')
    outOpt.add_argument('-oun',
                        '--n_gff',
                        type=str,
                        default=N_GFF,
                        help='path to output gff of N regions')
    outOpt.add_argument('-hf',
                        '--html_file',
                        type=str,
                        default=HTML,
                        help='path to output html file')
    outOpt.add_argument('-hp',
                        '--html_path',
                        type=str,
                        default=PROFREP_OUTPUT_DIR,
                        help='path to html extra files')

    ################ HITS/COPY NUMBERS ####################################
    cnOpt.add_argument('-cn',
                       '--copy_numbers',
                       type=str2bool,
                       default=False,
                       help='convert hits to copy numbers')
    cnOpt.add_argument(
        '-gs',
        '--genome_size',
        type=float,
        help=
        'genome size is required when converting hits to copy numbers and you use custom data')
    cnOpt.add_argument(
        '-thr',
        '--threshold_repeat',
        type=int,
        default=3,
        help=
        'threshold for hits/copy numbers per position to be considered repetitive')
    cnOpt.add_argument(
        '-thsg',
        '--threshold_segment',
        type=int,
        default=80,
        help='threshold for the length of repetitive segment to be reported')

    ################ JBrowse ##########################
    galaxyOpt.add_argument('-jb',
                           '--jbrowse_bin',
                           type=str,
                           help='path to JBrowse bin directory')

    args = parser.parse_args()
    main(args)