def run_methods(bins, count, cscore):
count[cscore] += 1
## Single Read Methods
fchunk, fcall = Methods.first_chunk( contexts, labels, cscore )
lchunk, lcall = Methods.last_chunk( contexts, labels, cscore )
rchunk, rcall = Methods.random_chunk( contexts, labels, cscore )
## Multi-Read Methods
bchunk, bcall = Methods.best_chunk( contexts, labels )
ichunk, icall = Methods.ind_consensus( contexts, labels, cscore )
hchunk, hcall = Methods.hmm_consensus( indices, ems, len(means), chunk_vector )
# First Chunk
if barcode == fcall:
bins['f'].append( 1 )
else:
bins['f'].append( 0 )
# Last Chunk
if barcode == lcall:
bins['l'].append( 1 )
else:
bins['l'].append( 0 )
# Random Chunk
if barcode == rcall:
bins['r'].append( 1 )
else:
bins['r'].append( 0 )
# Best Chunk
if barcode == bcall:
bins['b'].append( 1 )
else:
bins['b'].append( 0 )
#HMM Consensus
if barcode == hcall:
bins['h'].append( 1 )
else:
bins['h'].append( 0 )
# Ind Consensus
if barcode == icall:
bins['i'].append( 1 )
else:
bins['i'].append( 0 )
def run_methods(bins, cm, cm_counter ):
## Single Read Methods
fchunk, fcall = Methods.first_chunk( contexts, labels, cscore )
lchunk, lcall = Methods.last_chunk( contexts, labels, cscore )
rchunk, rcall = Methods.random_chunk( contexts, labels, cscore )
## Multi-Read Methods
bchunk, bcall = Methods.best_chunk( contexts, labels )
ichunk, icall = Methods.ind_consensus( contexts, labels, cscore )
hchunk, hcall = Methods.hmm_consensus( indices, ems, len(means), chunk_vector )
# First Chunk
#soft_calls['f'].append( fchunk )
if barcode == fcall:
bins['f'] += 1
# Last Chunk
#soft_calls['l'].append( lchunk )
if barcode == lcall:
bins['l'] += 1
# Random Chunk
#soft_calls['r'].append( rchunk )
if barcode == rcall:
bins['r'] += 1
# Best Chunk
#soft_calls['b'].append( bchunk )
if barcode == bcall:
bins['b'] += 1
#HMM Consensus
#soft_calls['h'].append( hchunk )
if barcode == hcall:
bins['h'] += 1
# Ind Consensus
#soft_calls['i'].append( ichunk )
if barcode == icall:
bins['i'] += 1
## Confusion Matrix
cm_counter[ cm_choice.index( barcode) ] += 1 # Update appropriate counter
cm[ cm_choice.index( barcode ), cm_choice.index( icall ) ] += 1 # Updates correct row given bar code
## For a given cscore, group and iterate through.
event_sum = 0
for i in xrange(9, int(cscore*10-1), -1):
event_sum += len(ranked_events[i])
for event in ranked_events[i]:
# Unpack Variables
event_name = event[0]
contexts = event[1]
labels = event[2]
ems = event[3]
means = event[4]
barcode = event_name.split('-')[0]
# Counter for keeping track of number of events
counter += 1
## Single Read Methods
fchunk, fcall = Methods.first_chunk( contexts, labels, cscore )
lchunk, lcall = Methods.last_chunk( contexts, labels, cscore )
rchunk, rcall = Methods.random_chunk( contexts, labels, cscore )
## Multi-Read Methods
bchunk, bcall = Methods.best_chunk( contexts, labels )
ichunk, icall = Methods.ind_consensus( contexts, labels, cscore )
hchunk, hcall = Methods.hmm_consensus( indices, ems, len(means), chunk_vector )
#-=Single Read Methods=-
# First Chunk
soft_calls['f'].append( fchunk )
if fcall[0] == barcode:
bins['f'] += 1
# Last Chunk