In [1]:
import GenomicIntervals
import importlib
import pandas
import os, sys
In [2]:
import time
class Timer:
def __enter__(self):
self.start = time.clock()
return self
def __exit__(self, *args):
self.end = time.clock()
self.interval = self.end - self.start
print(self.interval)
In [3]:
import ipyparallel
from ipyparallel import depend, require
# create client & view
rc = ipyparallel.Client()
dview = rc[:]
bview = rc.load_balanced_view() # you can set block=True to have this apply globally
# scatter 'id', so id=0,1,2 on engines 0,1,2
dview.scatter('node_id', rc.ids, flatten=True)
print("Engine IDs: ", dview['node_id'])
# create a Reference to `id`. This will be a different value on each engine
ref = ipyparallel.Reference('node_id')
Engine IDs: [0, 1, 2, 3]
In [16]:
#importlib.reload(GenomicIntervals) ;
#annot = pd.DataFrame({'chrom': 'chr1', 'start': [0], 'end': [100]})
#query = pd.DataFrame({'chrom': 'chr1', 'start': [50], 'end': [150]})
annot = pandas.DataFrame({'chrom': 'chr1', 'start': range(0, 100, 4), 'end': range(2, 102, 4)})
query = pandas.DataFrame({'chrom': 'chr1', 'start': [1], 'end': [3]})
#print(GenomicIntervals.interval_jaccard(query, annot, samples=1000,
# chromosome_sizes={'chr1': 11}))
annot = pandas.DataFrame({'chrom': 'chr1',
'start': range(0, 10000, 4),
'end': range(2, 10002, 4)})
query = pandas.DataFrame({'chrom': 'chr1',
'start': range(1, 10001, 4),
'end': range(3, 10003, 4)})
#print(annot)
#print(query)
#interval_jaccard = GenomicIntervals.interval_jaccard
def scatter_func(f):
dview.scatter(f.__name__, [f]*len(bview), flatten=True)
with Timer():
print(interval_jaccard(query, annot, samples=100,# dview=dview,
chromosome_sizes={'chr1': 10010}))
10.672588999999988
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-16-0256f1e626ff> in <module>()
30 with Timer():
31 print(interval_jaccard(query, annot, samples=100,# dview=dview,
---> 32 chromosome_sizes={'chr1': 10010}))
<ipython-input-6-4600f01d91d4> in interval_jaccard(query, annot, samples, chromosome_sizes, dview)
280 null_distr = list(dview.map_sync(jaccard_stat_perm, [query] * samples, [annot] * samples))
281 else:
--> 282 null_distr = list(map(jaccard_stat_perm, [query] * samples, [annot] * samples))
283
284 # get p value
<ipython-input-6-4600f01d91d4> in jaccard_stat(a, b, chromosome_sizes, permute)
247 a = interval_permute(a, chromosome_sizes=chromosome_sizes)
248
--> 249 inter = interval_intersect(a, b)
250 union = interval_union(a, b)
251
<ipython-input-6-4600f01d91d4> in wrapper(*args)
60 for i, df in enumerate(data_frames):
61 func_args.append(df.loc[idx[i][chrom]])
---> 62 results.append(func(*func_args))
63
64 return pd.concat(results).reset_index()
<ipython-input-6-4600f01d91d4> in wrapper(*args)
79 assert len(chrom_set) == 1
80 chrom = chrom_set.pop()
---> 81 res_df = pd.DataFrame.from_records(func(*tps_list), columns = ['start', 'end'])
82 res_df['chrom'] = chrom
83 return res_df
<ipython-input-6-4600f01d91d4> in interval_intersect(a, b)
147 @with_chrom
148 def interval_intersect(a, b):
--> 149 return merge(a, b, lambda in_a, in_b: in_a and in_b)
150
151
<ipython-input-6-4600f01d91d4> in merge(a_tps, b_tps, op)
119 in_a = not ((scan < a_endpoints[a_index]) ^ (a_index % 2))
120 in_b = not ((scan < b_endpoints[b_index]) ^ (b_index % 2))
--> 121 in_res = op(in_a, in_b)
122
123 if in_res ^ (len(res) % 2):
<ipython-input-6-4600f01d91d4> in <lambda>(in_a, in_b)
147 @with_chrom
148 def interval_intersect(a, b):
--> 149 return merge(a, b, lambda in_a, in_b: in_a and in_b)
150
151
KeyboardInterrupt:
In [4]:
import cProfile
cProfile.run("GenomicIntervals.interval_jaccard(query, annot, samples=1000, chromosome_sizes={'chr1': 4})")
85593 function calls (85421 primitive calls) in 0.353 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
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---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-4-c5f0c30c6a74> in <module>()
1 import cProfile
----> 2 cProfile.run("GenomicIntervals.interval_jaccard(query, annot, samples=1000, chromosome_sizes={'chr1': 4})")
/Users/kasper/anaconda/envs/simons/lib/python3.4/cProfile.py in run(statement, filename, sort)
14
15 def run(statement, filename=None, sort=-1):
---> 16 return _pyprofile._Utils(Profile).run(statement, filename, sort)
17
18 def runctx(statement, globals, locals, filename=None, sort=-1):
/Users/kasper/anaconda/envs/simons/lib/python3.4/profile.py in run(self, statement, filename, sort)
53 prof = self.profiler()
54 try:
---> 55 prof.run(statement)
56 except SystemExit:
57 pass
/Users/kasper/anaconda/envs/simons/lib/python3.4/cProfile.py in run(self, cmd)
93 import __main__
94 dict = __main__.__dict__
---> 95 return self.runctx(cmd, dict, dict)
96
97 def runctx(self, cmd, globals, locals):
/Users/kasper/anaconda/envs/simons/lib/python3.4/cProfile.py in runctx(self, cmd, globals, locals)
98 self.enable()
99 try:
--> 100 exec(cmd, globals, locals)
101 finally:
102 self.disable()
<string> in <module>()
/Users/kasper/Dropbox/Code/GenomicIntervals/GenomicIntervals.py in interval_jaccard(query, annot, samples, chromosome_sizes, dview)
279 null_distr = list(dview.map(jaccard_stat_perm, [query] * samples, [annot] * samples))
280 else:
--> 281 null_distr = list(map(jaccard_stat_perm, [query] * samples, [annot] * samples))
282
283 # get p value
/Users/kasper/Dropbox/Code/GenomicIntervals/GenomicIntervals.py in jaccard_stat(a, b, chromosome_sizes, permute)
251
252 if permute:
--> 253 a = interval_permute(a, chromosome_sizes=chromosome_sizes)
254
255 inter = interval_intersect(a, b)
/Users/kasper/Dropbox/Code/GenomicIntervals/GenomicIntervals.py in interval_permute(df, chromosome_sizes)
225 for chrom, group in df.groupby('chrom'):
226
--> 227 assert group.end.max() <= chromosome_sizes[chrom]
228
229 segment_lengths = group.end - group.start
AssertionError:
In [42]:
#%load_ext rpy2.ipython
In [3]:
# source("http://bioconductor.org/biocLite.R")
# biocLite("IRanges")
# biocLite("GenomicRanges")
# biocLite("rtracklayer")
# biocLite("TxDb.Hsapiens.UCSC.hg19.knownGene")
# biocLite("gplots")
# biocLite("RCurl")
# install.packages('GenometriCorr',repos='http://genometricorr.sourceforge.net/R/',type='source')
In [4]:
#%%R
#library('GenometriCorr')
Content source: kaspermunch/GenomicIntervals
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