print 'cython rc: %f sec to rc one dna seq of length %d'%(c_time,len(seq))
print '%.1f-fold speedup.'%(p_time/c_time)
print '-----------------------------'
# Test seq2sitelist
site_length = 20
t = time.time()
x = [seq[i:(i+site_length)] for i in range(len(seq)-site_length+1)]
p_time = time.time()-t
print 'python seq2sitelist: %f sec to chop a seq into %d sites'%\
(p_time,len(x))
t = time.time()
x = fast.seq2sitelist(seq,site_length, safe=False)
c_time = time.time()-t
print 'cython seq2sitelist: %f sec to chop a seq into %d sites'%\
(c_time,len(x))
print '%.1f-fold speedup.'%(p_time/c_time)
print '-----------------------------'
# Test seq2sitelist rc feature
site_length = 20
t = time.time()
x = fast.seq2sitelist(seq,site_length, safe=False)
y = [fast.reverse_complement(s, safe=False) for s in x]
p_time = time.time()-t
print 'python, seq2sitelist w/ rc: %f sec to chop seq into %d rc sites'%\
def main(model_df, contig_list, numsites=10, verbose=False):
# Determine type of string from model
qc.validate_model(model_df)
seqtype, modeltype = qc.get_model_type(model_df)
seq_dict,inv_dict = utils.choose_dict(seqtype,modeltype=modeltype)
# Check that all characters are from the correct alphabet
alphabet = qc.seqtype_to_alphabet_dict[seqtype]
search_string = r"[^%s]"%alphabet
for contig_str, contig_name, pos_offset in contig_list:
if re.search(search_string,contig_str):
raise SortSeqError(\
'Invalid character for seqtype %s found in %s.'%\
(seqtype,contig_name))
# Create model object to evaluate on seqs
if modeltype == 'MAT':
model_obj = Models.LinearModel(model_df)
elif modeltype == 'NBR':
model_obj = Models.NeighborModel(model_df)
# Create list of dataframes, one for each contig
seq_col = qc.seqtype_to_seqcolname_dict[seqtype]
L = model_obj.length
sitelist_df = pd.DataFrame(\
columns=['val',seq_col,'left','right','ori','contig'])
for contig_str, contig_name, pos_offset in contig_list:
if len(contig_str) < L:
continue
this_df = pd.DataFrame(\
columns=['val',seq_col,'left','right','ori','contig'])
num_sites = len(contig_str) - L + 1
poss = np.arange(num_sites).astype(int)
this_df['left'] = poss + pos_offset
this_df['right'] = poss + pos_offset + L - 1
#this_df[seq_col] = [contig_str[i:(i+L)] for i in poss]
this_df[seq_col] = fast.seq2sitelist(contig_str,L) #Cython
this_df['ori'] = '+'
this_df['contig'] = contig_name
this_df['val'] = model_obj.evaluate(this_df[seq_col])
sitelist_df = pd.concat([sitelist_df,this_df], ignore_index=True)
# If scanning DNA, scan reverse-complement as well
if seqtype=='dna':
#this_df[seq_col] = [qc.rc(s) for s in this_df[seq_col]]
this_df[seq_col] = fast.seq2sitelist(contig_str,L,rc=True) #Cython
this_df['ori'] = '-'
this_df['val'] = model_obj.evaluate(this_df[seq_col])
sitelist_df = pd.concat([sitelist_df,this_df], ignore_index=True)
# Sort by value and reindex
sitelist_df.sort_values(by='val', ascending=False, inplace=True)
sitelist_df.reset_index(drop=True,inplace=True)
# Crop list at numsites
if sitelist_df.shape[0]>numsites:
sitelist_df.drop(sitelist_df.index[numsites:], inplace=True)
if verbose:
print '.',
sys.stdout.flush()
if verbose:
print ''
sys.stdout.flush()
# If no sites were found, raise error
if sitelist_df.shape[0]==0:
raise SortSeqError(\
'No full-length sites found within provided contigs.')
sitelist_df = qc.validate_sitelist(sitelist_df,fix=True)
return sitelist_df