# Path: mirtop/mirna/fasta.py
# def read_precursor(precursor, sps=None):
# 
# Path: mirtop/mirna/mapper.py
# def guess_database(args):
# def _guess_database_file(gff, database=None):
# def get_primary_transcript(database):
# def read_gtf_to_mirna(gtf):
# def read_gtf_chr2mirna(gtf):  # read from read_gtf_to_precursor
# def read_gtf_chr2mirna2(gtf):  # to remove
# def read_gtf_to_precursor(gtf):
# def _parse_db_mir_chrom(db_mir, db, id_dict):
# def _parse_db_mir(db_mir, db, id_dict):
# def _parse_db_mir_genomic(db_mir, db, id_dict):
# def read_gtf_to_precursor_mirgenedb(gtf, format="precursor"):
# def read_gtf_to_precursor_mirbase(gtf, format="precursor"):
# def liftover_genomic_precursor(read, genome, hairpin, expected=None):
# 
# Path: mirtop/mirna/realign.py
# class hits:
# class isomir:
#     def __init__(self):
#     def set_sequence(self, seq):
#     def set_precursor(self, precursor, isomir):
#     def remove_precursor(self, precursor):
#     def __init__(self):
#     def set_pos(self, start, l, strand="+"):
#     def formatGFF(self):
#     def format(self, sep="\t"):
#     def format_id(self, sep="\t"):
#     def get_score(self, sc):
#     def is_iso(self):
# def read_id(idu):
# def make_id(seq):
# def is_sequence(seq):
# def align(x, y, local=False):
# def _add_cigar_char(counter, cache):
# def make_cigar(seq, mature):
# def cigar_correction(cigarLine, query, target):
# def expand_cigar(cigar):
# def cigar2snp(cigar, reference):
# def reverse_complement(seq):
# def get_mature_sequence(precursor, mature, exact=False, nt = 5):
# def align_from_variants(sequence, mature, variants):
# def variant_to_5p(hairpin, pos, variant):
# def variant_to_3p(hairpin, pos, variant):
# def variant_to_add(read, variant):
# 
# Path: mirtop/gff/body.py
# def read(fn, args):
# def write_body_on_handle(lines, out_handle):
# def create(reads, database, sample, args, quiet=False):
# def lift_to_genome(line, mapper):
# def create_line(read, name, database, args):
# def guess_format(line):
# def paste_columns(line, sep=" "):
# def read_variant(attrb, sep=" "):
# def read_attributes(gff_line, sep=" "):
# def read_gff_line(line):
# def variant_with_nt(line, precursors, matures):
# 
# Path: mirtop/gff/header.py
# def create(samples, database, custom, filter=None):
# def get_gff_version():
# def make_tools(tools):
# def _get_samples(samples):
# def _get_database(database):
# def _filter(filters):
# def read_version(fn):
# def read_samples(fn):


from optparse import OptionParser
from mirtop.mirna import fasta
from mirtop.mirna import mapper
from mirtop.mirna import realign
from mirtop.gff import body, header
import os
import random
import numpy
import mirtop.libs.logger as mylog
    clean_read = list()
    seen = set()
    for mirna in mir[name]:
        info = mir[name][mirna]
        for rand in range(int(numsim)):
            e = 1
            if exp:
                trial = random.randint(1, 100)
                p = random.randint(1, 50) / 50.0
                e = numpy.random.negative_binomial(trial, p, 1)[0]
            iso = realign.isomir()
            randSeq, iso.start, iso.t5, iso.t3, iso.subs, iso.add = variation(info, seq)
            if randSeq in seen:
                continue
            seen.add(randSeq)
            iso.end = iso.start + len(randSeq)
            aln = realign.align(randSeq, seq[iso.start:iso.end])
            iso.cigar = realign.make_cigar(aln[0], aln[1])
            iso.mirna = mirna
            query_name = "%s.%s.%s" % (mirna, iso.format_id("."), randSeq)
            reads[query_name] = realign.hits()
            reads[query_name].set_sequence(randSeq)
            reads[query_name].counts = e
            reads[query_name].set_precursor(name, iso)
            full_read.extend(create_read(randSeq, e))
            clean_read.append([randSeq, e])
            # print([randSeq, mutLab, addTag, t5Lab, t3Lab, mirSeq])
            # data[randSeq] = [exp, iso] # create real object used in code to generate GFF
    write_fastq(full_read, full_fq)
    write_collapse_fastq(clean_read, clean_fq)

# Path: mirtop/mirna/fasta.py
# def read_precursor(precursor, sps=None):
# 
# Path: mirtop/mirna/mapper.py
# def guess_database(args):
# def _guess_database_file(gff, database=None):
# def get_primary_transcript(database):
# def read_gtf_to_mirna(gtf):
# def read_gtf_chr2mirna(gtf):  # read from read_gtf_to_precursor
# def read_gtf_chr2mirna2(gtf):  # to remove
# def read_gtf_to_precursor(gtf):
# def _parse_db_mir_chrom(db_mir, db, id_dict):
# def _parse_db_mir(db_mir, db, id_dict):
# def _parse_db_mir_genomic(db_mir, db, id_dict):
# def read_gtf_to_precursor_mirgenedb(gtf, format="precursor"):
# def read_gtf_to_precursor_mirbase(gtf, format="precursor"):
# def liftover_genomic_precursor(read, genome, hairpin, expected=None):
# 
# Path: mirtop/mirna/realign.py
# class hits:
# class isomir:
#     def __init__(self):
#     def set_sequence(self, seq):
#     def set_precursor(self, precursor, isomir):
#     def remove_precursor(self, precursor):
#     def __init__(self):
#     def set_pos(self, start, l, strand="+"):
#     def formatGFF(self):
#     def format(self, sep="\t"):
#     def format_id(self, sep="\t"):
#     def get_score(self, sc):
#     def is_iso(self):
# def read_id(idu):
# def make_id(seq):
# def is_sequence(seq):
# def align(x, y, local=False):
# def _add_cigar_char(counter, cache):
# def make_cigar(seq, mature):
# def cigar_correction(cigarLine, query, target):
# def expand_cigar(cigar):
# def cigar2snp(cigar, reference):
# def reverse_complement(seq):
# def get_mature_sequence(precursor, mature, exact=False, nt = 5):
# def align_from_variants(sequence, mature, variants):
# def variant_to_5p(hairpin, pos, variant):
# def variant_to_3p(hairpin, pos, variant):
# def variant_to_add(read, variant):
# 
# Path: mirtop/gff/body.py
# def read(fn, args):
# def write_body_on_handle(lines, out_handle):
# def create(reads, database, sample, args, quiet=False):
# def lift_to_genome(line, mapper):
# def create_line(read, name, database, args):
# def guess_format(line):
# def paste_columns(line, sep=" "):
# def read_variant(attrb, sep=" "):
# def read_attributes(gff_line, sep=" "):
# def read_gff_line(line):
# def variant_with_nt(line, precursors, matures):
# 
# Path: mirtop/gff/header.py
# def create(samples, database, custom, filter=None):
# def get_gff_version():
# def make_tools(tools):
# def _get_samples(samples):
# def _get_database(database):
# def _filter(filters):
# def read_version(fn):
# def read_samples(fn):


# Path: scripts/miRNA.simulator.py
from optparse import OptionParser
from mirtop.mirna import fasta
from mirtop.mirna import mapper
from mirtop.mirna import realign
from mirtop.gff import body, header
import os
import random
import numpy
import mirtop.libs.logger as mylog

    clean_read = list()
    seen = set()
    for mirna in mir[name]:
        info = mir[name][mirna]
        for rand in range(int(numsim)):
            e = 1
            if exp:
                trial = random.randint(1, 100)
                p = random.randint(1, 50) / 50.0
                e = numpy.random.negative_binomial(trial, p, 1)[0]
            iso = realign.isomir()
            randSeq, iso.start, iso.t5, iso.t3, iso.subs, iso.add = variation(info, seq)
            if randSeq in seen:
                continue
            seen.add(randSeq)
            iso.end = iso.start + len(randSeq)
            aln = realign.align(randSeq, seq[iso.start:iso.end])
            iso.cigar = realign.make_cigar(aln[0], aln[1])
            iso.mirna = mirna
            query_name = "%s.%s.%s" % (mirna, iso.format_id("."), randSeq)
            reads[query_name] = realign.hits()
            reads[query_name].set_sequence(randSeq)
            reads[query_name].counts = e
            reads[query_name].set_precursor(name, iso)
            full_read.extend(create_read(randSeq, e))
            clean_read.append([randSeq, e])
            # print([randSeq, mutLab, addTag, t5Lab, t3Lab, mirSeq])
            # data[randSeq] = [exp, iso] # create real object used in code to generate GFF
    write_fastq(full_read, full_fq)
    write_collapse_fastq(clean_read, clean_fq)

    gff = body.create(reads, "miRBase21", "sim1")