Esempi in Python per mean_ind_of_weighted_list

Esempio n. 1

File: genReads.py Progetto: meridith-e/neat-genreads

	GC_WINDOW_SIZE = GC_SCALE_COUNT[-1]

#	fragment length distribution
#
if PAIRED_END and not(PAIRED_END_ARTIFICIAL):
	print 'Using empirical fragment length distribution.'
	[potential_vals, potential_prob] = pickle.load(open(FRAGLEN_MODEL,'rb'))
	FRAGLEN_VALS = []
	FRAGLEN_PROB = []
	for i in xrange(len(potential_vals)):
		if potential_vals[i] > READLEN:
			FRAGLEN_VALS.append(potential_vals[i])
			FRAGLEN_PROB.append(potential_prob[i])
	# should probably add some validation and sanity-checking code here...
	FRAGLEN_DISTRIBUTION = DiscreteDistribution(FRAGLEN_PROB,FRAGLEN_VALS)
	FRAGMENT_SIZE = FRAGLEN_VALS[mean_ind_of_weighted_list(FRAGLEN_PROB)]

#	Indicate not writing FASTQ reads
#
if NO_FASTQ:
	print 'Bypassing FASTQ generation...'

"""************************************************
****            HARD-CODED CONSTANTS
************************************************"""


# target window size for read sampling. how many times bigger than read/frag length
WINDOW_TARGET_SCALE = 100
# sub-window size for read sampling windows. this is basically the finest resolution
# that can be obtained for targeted region boundaries and GC% bias

Esempio n. 2

File: genReads.py Progetto: zstephens/neat-genreads

	GC_WINDOW_SIZE = GC_SCALE_COUNT[-1]

#	fragment length distribution
#
if PAIRED_END and not(PAIRED_END_ARTIFICIAL):
	print 'Using empirical fragment length distribution.'
	[potential_vals, potential_prob] = pickle.load(open(FRAGLEN_MODEL,'rb'))
	FRAGLEN_VALS = []
	FRAGLEN_PROB = []
	for i in xrange(len(potential_vals)):
		if potential_vals[i] > READLEN:
			FRAGLEN_VALS.append(potential_vals[i])
			FRAGLEN_PROB.append(potential_prob[i])
	# should probably add some validation and sanity-checking code here...
	FRAGLEN_DISTRIBUTION = DiscreteDistribution(FRAGLEN_PROB,FRAGLEN_VALS)
	FRAGMENT_SIZE = FRAGLEN_VALS[mean_ind_of_weighted_list(FRAGLEN_PROB)]

#	Indicate not writing FASTQ reads
#
if NO_FASTQ:
	print 'Bypassing FASTQ generation...'

"""************************************************
****            HARD-CODED CONSTANTS
************************************************"""


# target window size for read sampling. how many times bigger than read/frag length
WINDOW_TARGET_SCALE = 100
# sub-window size for read sampling windows. this is basically the finest resolution
# that can be obtained for targeted region boundaries and GC% bias