def get_adapterstype(flowcell,lane,index,table='DB_library_data'):
if index:
td = preprocess_radtag_lane.get_table_as_dict(table,sq='flowcell="%s" and lane="%s" and index="%s"' % (flowcell,lane,index))
else:
td = preprocess_radtag_lane.get_table_as_dict(table,sq='flowcell="%s" and lane="%s"' % (flowcell,lane))
adapterstypes = list(set([d.get('adapterstype','') for d in td]))
if len(adapterstypes) == 1:
return adapterstypes[0]
else:
errstr = 'invalid number of matches: %s' % adapterstypes
raise ValueError, errstr
def get_adapterstype(flowcell,lane,index,table='DB_library_data'):
if index:
td = [d for d in preprocess_radtag_lane.get_table_as_dict(table) if d.get('flowcell','') == flowcell and d.get('lane','') == lane and d.get('index','') == index]
else:
td = [d for d in preprocess_radtag_lane.get_table_as_dict(table) if d.get('flowcell','') == flowcell and d.get('lane','') == lane]
adapterstypes = list(set([d.get('adapterstype','') for d in td]))
if len(adapterstypes) == 1:
return adapterstypes[0]
else:
errstr = 'invalid number of matches: %s' % adapterstypes
raise ValueError, errstr
def get_adapterstype(flowcell, lane, index, table='DB_library_data'):
if index:
td = [
d for d in preprocess_radtag_lane.get_table_as_dict(table)
if d.get('flowcell', '') == flowcell and d.get('lane', '') == lane
and d.get('index', '') == index
]
else:
td = [
d for d in preprocess_radtag_lane.get_table_as_dict(table)
if d.get('flowcell', '') == flowcell and d.get('lane', '') == lane
]
adapterstypes = list(set([d.get('adapterstype', '') for d in td]))
if len(adapterstypes) == 1:
return adapterstypes[0]
else:
errstr = 'invalid number of matches: %s' % adapterstypes
raise ValueError, errstr
counts_by_pool[pool][ind] += ct
return counts_by_pool
def get_uniqued_info(uniqued):
if 'index' in uniqued:
ufields = os.path.splitext(os.path.basename(uniqued))[0].rsplit('_',3)
ufields[3] = ufields[3][5:]
else:
ufields = os.path.splitext(os.path.basename(uniqued))[0].rsplit('_',2)
ufields.append(None)
ufields[1] = ufields[1][4:]
return ufields
if __name__ == "__main__":
db = preprocess_radtag_lane.get_table_as_dict('DB_library_data',suppress_fc_check=True)
uniqued = sys.argv[1]
ufields = get_uniqued_info(uniqued)
counts_by_pool = get_counts_by_pool(uniqued,db)
for k,v in counts_by_pool.items():
print '%s\t%s\t%s\t%s\t%s\t%s\t%s\t%0.1f\t%d' % (ufields[0],ufields[1],ufields[2],ufields[3],k,sum(v.values()),len(v),numpy.mean(v.values()),numpy.median(v.values()))
def get_idxseq(table='DB_multiplex_indices'):
return dict([(d['idx'],d['seq']) for d in preprocess_radtag_lane.get_table_as_dict(table,suppress_fc_check=True)])
def get_adaptseq(table='DB_adapt_trim_seqs'):
return dict([(d['adapterstype'],{'r1':d['r1'],'r2':d['r2']}) for d in preprocess_radtag_lane.get_table_as_dict(table,suppress_fc_check=True)])
return counts_by_pool
def get_uniqued_info(uniqued):
if 'index' in uniqued:
ufields = os.path.splitext(os.path.basename(uniqued))[0].rsplit('_', 3)
ufields[3] = ufields[3][5:]
else:
ufields = os.path.splitext(os.path.basename(uniqued))[0].rsplit('_', 2)
ufields.append(None)
ufields[1] = ufields[1][4:]
return ufields
if __name__ == "__main__":
db = preprocess_radtag_lane.get_table_as_dict('DB_library_data',
suppress_fc_check=True)
uniqued = sys.argv[1]
ufields = get_uniqued_info(uniqued)
counts_by_pool = get_counts_by_pool(uniqued, db)
for k, v in counts_by_pool.items():
print '%s\t%s\t%s\t%s\t%s\t%s\t%s\t%0.1f\t%d' % (
ufields[0], ufields[1], ufields[2], ufields[3], k, sum(v.values()),
len(v), numpy.mean(v.values()), numpy.median(v.values()))
def get_adaptseq(table='DB_adapt_trim_seqs'):
return dict([(d['adapterstype'], {
'r1': d['r1'],
'r2': d['r2']
}) for d in preprocess_radtag_lane.get_table_as_dict(
table, suppress_fc_check=True)])
def get_idxseq(table='DB_multiplex_indices'):
return dict([(d['idx'], d['seq'])
for d in preprocess_radtag_lane.get_table_as_dict(
table, suppress_fc_check=True)])
