-
Notifications
You must be signed in to change notification settings - Fork 264
/
Copy pathsplit_ref_by_bai_datasize.py
168 lines (133 loc) · 6.26 KB
/
split_ref_by_bai_datasize.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
#!/usr/bin/env python3
"""
Create a list of regions by splitting a reference based on the amount of data in bam files.
Uses the `bai` index of the bam files. Useful for submitting jobs of equal size to a cluster.
"""
import sys
import os
import argparse
import time
import logging
import struct
import numpy as np
from scipy import interpolate
DEFAULT_LOGGING_LEVEL = logging.INFO
MAX_LOGGING_LEVEL = logging.CRITICAL
def setup_logger(verbose_level):
fmt=('%(levelname)s %(asctime)s [%(module)s:%(lineno)s %(funcName)s] :: '
'%(message)s')
logging.basicConfig(format=fmt, level=max((0, min((MAX_LOGGING_LEVEL,
DEFAULT_LOGGING_LEVEL-(verbose_level*10))))))
def Main(argv):
tic_total = time.time()
# parse arguments
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('bamfiles', metavar='BAMFILE', nargs='*')
parser.add_argument('-L', '--bam-list', nargs='*')
parser.add_argument('-r', '--reference-fai', help="reference fasta index file", required=True)
parser.add_argument('-s', '--target-data-size', default='100e6', help="target combined data size of bam files in each region (MB)")
parser.add_argument('--bai-interval-size', default=16384, type=int, help="Size in baseparis of each interval in the bam index (bai).")
parser.add_argument('-v', '--verbose', action='count', default=0,
help="increase logging verbosity")
parser.add_argument('-q', '--quiet', action='count', default=0,
help="decrease logging verbosity")
args = parser.parse_args(argv)
# setup logger
setup_logger(verbose_level=args.verbose-args.quiet)
if argv is not None:
logging.warning('Using passed arguments: '+str(argv))
logging.info('args: '+str(args))
# additional argument parsing and datatype handling
if not args.bamfiles and not args.bam_list:
logging.error("Must provide an BAMFILE and/or --bam-list argument")
sys.exit(2)
args.target_data_size = int(float(args.target_data_size))*1000000
logging.info('target-data-size: '+str(args.target_data_size)+' bytes')
# read bam-lists if provided
if args.bam_list:
for bamlistfile in args.bam_list:
with open(bamlistfile,'r') as fh:
for x in fh:
x = x.split('#')[0].strip()
if x:
args.bamfiles.append(x)
#logging.info('bam files: '+", ".join(args.bamfiles)) # output complete list of bam files being used
# read the reference fasta index
fai_chrom = []
fai_len = []
with open(args.reference_fai,'r') as fh:
for x in fh:
x = x.strip().split(sep='\t')
fai_chrom.append(str(x[0]))
fai_len.append(int(x[1]))
## read bai indexes, skipping bin info
# list by chrom of number of intervals
n_intvs = np.array([int(np.ceil(x/args.bai_interval_size)) for x in fai_len])
# list by chrom of lists of interval offsets
icumsz = [] # cumulative size of data by interval
for i,n in enumerate(n_intvs):
icumsz.append(np.zeros((n,), dtype=np.int64))
for bamfn in args.bamfiles:
baifn = bamfn+'.bai'
with open(baifn,'rb') as fh:
logging.info("processing: "+baifn)
# filetype magic check
assert struct.unpack('4s', fh.read(4))[0] == b'BAI\x01'
# number of reference sequences (chroms)
n_ref = struct.unpack('i', fh.read(4))[0]
assert n_ref == len(fai_len), "fasta index and bam index have must have same number of chroms"
for ci in range(n_ref):
# skip over the binning index
n_bin = struct.unpack('i', fh.read(4))[0]
for bini in range(n_bin):
bin_id = struct.unpack('I', fh.read(4))[0]
n_chunk = struct.unpack('i', fh.read(4))[0]
fh.seek(n_chunk*16, os.SEEK_CUR)
# read interval index
n_intv = struct.unpack('i', fh.read(4))[0]
if n_intv > 0:
ioff = np.array(struct.unpack(str(n_intv)+'Q', fh.read(n_intv*8)), dtype=np.int64)
while( len(ioff) < len(icumsz[ci]) ):
ioff = np.append(ioff, ioff[-1]+1)
icumsz[ci] += ioff-ioff[0]
## make the list of regions
regions = []
for ci,chrom in enumerate(fai_chrom):
# sanity check last point if there are more than one
if len(icumsz[ci]) > 1:
assert icumsz[ci][-1] >= icumsz[ci][-2]
# tiny chroms just get 1 region
if len(icumsz[ci]) < 2:
regions.extend([ (fai_chrom[ci], 0, fai_len[ci]) ])
continue
ds = icumsz[ci]
pos = np.arange(0, ds.shape[0])*args.bai_interval_size
# estimate total data size for the chrom
f = interpolate.interp1d(pos, ds, fill_value='extrapolate', kind='linear')
ds_total = f([fai_len[ci]])[0]
num_regions = int(np.ceil(ds_total/args.target_data_size))
# approx equal LENGTH regions
# tmp = np.linspace(0, fai_len[ci], num=num_regions+1, endpoint=True, dtype=int)
# approx equal DATA SIZE regions
f = interpolate.interp1d(ds, pos, fill_value='extrapolate', kind='linear')
dsx = np.linspace(0, ds_total, num=num_regions+1, endpoint=True, dtype=int)
tmp = f(dsx).astype(int)
# ensure we exactly hit the endpoints
tmp[0] = 0
tmp[-1] = fai_len[ci]
regions.extend([ (fai_chrom[ci], tmp[i], tmp[i+1]) for i in range(len(tmp)-1) ])
## Output regions file
for r in regions:
print(*r, sep='\t')
logging.info("Number of chroms: {}".format(len(fai_len)))
logging.info("Number of splits: {}".format(len(regions)-len(fai_len)))
logging.info("Number of regions: {}".format(len(regions)))
logging.info("Done: {:.2f} sec elapsed".format(time.time()-tic_total))
return 0
#########################################################################
# Main loop hook... if run as script run main, else this is just a module
if __name__ == '__main__':
if 'TESTING_ARGS' in globals():
sys.exit(Main(argv=TESTING_ARGS))
else:
sys.exit(Main(argv=None))