'''
import numpy as np
import tables
import logging
import pysam
from track import Track, TrackError, parse_interval
from intervaltrack import IntervalTrack, get_base_dtype_fields, \
REF_COL_NAME, START_COL_NAME, END_COL_NAME
from vectortrack import VectorTrack
from io.sam import BamCoverageIterator, BamCoverageStatistics
JUNCTION_GROUP = "junctions"
COVERAGE_GROUP = "coverage"
junction_dtype = (get_base_dtype_fields() +
[('id', '<u4'),
('seqdata_name', 'a36'),
('strand', 'a4'),
('reads', '<i4'),
('left_coverage', '<u4'),
('right_coverage', '<u4')])
junction_expectedrows = 1e5
def tophat_bed_to_juncs(source, line_iter):
'''
chr5 180666535 180668532 JUNC00002848 36 - 180666535 180668532 255,0,0 2 47,41 0,1956
'''
juncarr = np.empty(1, dtype=junction_dtype)
junc = juncarr[0]
for line in line_iter:
from track import TrackError
from intervaltrack import IntervalTrack, get_base_dtype_fields, \
REF_COL_NAME, START_COL_NAME, END_COL_NAME
EXON_TYPE = "exon"
GENE_TYPE = "gene"
TRANSCRIPT_TYPE = "transcript"
FEATURE_TABLE = "features"
FEATURE_ALIAS_TABLE = "aliases"
FEATURE_ASSOC_TABLE = "feature_assoc"
ALIAS_ASSOC_TABLE = "alias_assoc"
# the numpy dtype used to represent features
feature_dtype = (get_base_dtype_fields() +
[('id', '<u4'),
('name', 'a32'),
('source', 'a24'),
('feature_type', 'a23'),
('strand', 'a1'),
('score', '<i4')])
feature_expectedrows = 1e5
feature_assoc_dtype = [('parent_id', '<u4'),
('child_id', '<u4'),
('value', '<u4')]
feature_alias_dtype = [('id', '<u4'),
('source', 'a16'),
('name', 'a128')]
alias_assoc_dtype = [('alias_id', '<u4'),
