# Path: mitty/lib/bedfile.py
# def read_bed(bed_fname):
#   return list(map(lambda x: (x[0], int(x[1]), int(x[2])), map(lambda x: x.split(), open(bed_fname, 'r').readlines())))
# 
# Path: mitty/lib/sanitizeseq.py
# def sanitize(seq):
#   return seq.translate(sanitable)


import time
import pysam
import numpy as np
import logging
from itertools import chain
from mitty.lib.bedfile import read_bed
from mitty.lib.sanitizeseq import sanitize
      alt[n + 1] = dna[3]
  return ''.join(alt)


def ins_model(rng, region, seq, p, p_het, min_size, max_size):
  """Insertions uniformly spanning minimum and maximum lengths. Sequences are generated using a Markov chain
generator"""
  pos = place_poisson_seq(rng, p, seq)
  ref = [seq[x] for x in pos]
  alt = [markov_chain(r, rng, l) for r, l in zip(ref, rng.randint(min_size, max_size, size=pos.shape[0]))]
  gt = genotype(p_het, rng, pos.shape[0])
  return [pos + region[1] + 1, ref, alt, gt]


def del_model(rng, region, seq, p, p_het, min_size, max_size):
  """Deletions uniformly spanning minimum and maximum lengths"""
  pos = place_poisson_seq(rng, p, seq)
  ref = [seq[x:x + l + 1] for x, l in zip(pos, rng.randint(min_size, max_size, size=pos.shape[0]))]
  alt = [seq[x] for x in pos]
  gt = genotype(p_het, rng, pos.shape[0])
  return [pos + region[1] + 1, ref, alt, gt]


def copy_ins_model(rng, region, seq, p, p_het, min_size, max_size):
  """The `CINS` model works just like the `INS` model except the insertion sequences, instead of being
novel DNA sequences created with a Markov chain generator, are exact copies of random parts of
the input reference genome. This creates insertions that are more challenging to align to and
assemble, especially when their lengths start to exceed the template size of the sequencing
technology used.
"""

# Path: mitty/lib/bedfile.py
# def read_bed(bed_fname):
#   return list(map(lambda x: (x[0], int(x[1]), int(x[2])), map(lambda x: x.split(), open(bed_fname, 'r').readlines())))
# 
# Path: mitty/lib/sanitizeseq.py
# def sanitize(seq):
#   return seq.translate(sanitable)


# Path: mitty/simulation/genome/simulatevariants.py
import time
import pysam
import numpy as np
import logging
from itertools import chain
from mitty.lib.bedfile import read_bed
from mitty.lib.sanitizeseq import sanitize

      alt[n + 1] = dna[3]
  return ''.join(alt)


def ins_model(rng, region, seq, p, p_het, min_size, max_size):
  """Insertions uniformly spanning minimum and maximum lengths. Sequences are generated using a Markov chain
generator"""
  pos = place_poisson_seq(rng, p, seq)
  ref = [seq[x] for x in pos]
  alt = [markov_chain(r, rng, l) for r, l in zip(ref, rng.randint(min_size, max_size, size=pos.shape[0]))]
  gt = genotype(p_het, rng, pos.shape[0])
  return [pos + region[1] + 1, ref, alt, gt]


def del_model(rng, region, seq, p, p_het, min_size, max_size):
  """Deletions uniformly spanning minimum and maximum lengths"""
  pos = place_poisson_seq(rng, p, seq)
  ref = [seq[x:x + l + 1] for x, l in zip(pos, rng.randint(min_size, max_size, size=pos.shape[0]))]
  alt = [seq[x] for x in pos]
  gt = genotype(p_het, rng, pos.shape[0])
  return [pos + region[1] + 1, ref, alt, gt]


def copy_ins_model(rng, region, seq, p, p_het, min_size, max_size):
  """The `CINS` model works just like the `INS` model except the insertion sequences, instead of being
novel DNA sequences created with a Markov chain generator, are exact copies of random parts of
the input reference genome. This creates insertions that are more challenging to align to and
assemble, especially when their lengths start to exceed the template size of the sequencing
technology used.
"""

  seq = sanitize(seq)