def makeboxplot(filteredclusts, dblibrary, figname, pool=False):
'''takes a filtered dict of clusts worth keeping and creates a boxplot of either by lane (default) or pool'''
indiv_cluster_count = defaultdict(int)
for clust, inddict in filteredclusts.items():
for ind, reads in inddict.items():
if ind in indiv_cluster_count.keys():
indiv_cluster_count[ind]+=1
else:
indiv_cluster_count[ind]+=1
t = gdata_tools.get_table_as_dict(dblibrary)
db_ind_countd = Util.countdict([d['sampleid'] for d in t if d['sampleid'] in indiv_cluster_count.keys()[3]]) #creates a table of individual dicts from google spreadsheet
indiv_by_group = defaultdict(list)
for d in t:
if 'pool' in d:
indkey = (d.get('flowcell',None),d.get('lane',None),d.get('index',None),d.get('sampleid',None))
if indkey in indiv_cluster_count:
if pool == True:
indiv_by_group[(d['flowcell'],d['lane'],d.get('index',None),d['pool'])].append(indiv_cluster_count[indkey])
else:
indiv_by_group[(d['flowcell'],d['lane'],d.get('index',None))].append(indiv_cluster_count[indkey])
boxes = []
labels = []
for group,indcounts in indiv_by_group.items():
boxes.append(indcounts)
labels.append(group)
boxplt = pylab.figure(1)
pylab.boxplot(boxes)
pylab.xticks(arange(1,(len(labels)+1)),labels,fontsize='small') #legend with best location (0) if pools
boxplt.savefig(figname)
def makeboxplot(filteredclusts, dblibrary, figname, pool=False):
'''takes a filtered dict of clusts worth keeping and creates a boxplot of either by lane (default) or pool'''
indiv_cluster_count = defaultdict(int)
for clust, inddict in filteredclusts.items():
for ind, reads in inddict.items():
if ind in indiv_cluster_count.keys():
indiv_cluster_count[ind] += 1
else:
indiv_cluster_count[ind] += 1
t = gdata_tools.get_table_as_dict(dblibrary)
db_ind_countd = Util.countdict([
d['sampleid'] for d in t
if d['sampleid'] in indiv_cluster_count.keys()[3]
]) #creates a table of individual dicts from google spreadsheet
indiv_by_group = defaultdict(list)
for d in t:
if 'pool' in d:
indkey = (d.get('flowcell', None), d.get('lane', None),
d.get('index', None), d.get('sampleid', None))
if indkey in indiv_cluster_count:
if pool == True:
indiv_by_group[(d['flowcell'], d['lane'],
d.get('index', None), d['pool'])].append(
indiv_cluster_count[indkey])
else:
indiv_by_group[(d['flowcell'], d['lane'],
d.get('index', None))].append(
indiv_cluster_count[indkey])
boxes = []
labels = []
for group, indcounts in indiv_by_group.items():
boxes.append(indcounts)
labels.append(group)
boxplt = pylab.figure(1)
pylab.boxplot(boxes)
pylab.xticks(arange(1, (len(labels) + 1)), labels,
fontsize='small') #legend with best location (0) if pools
boxplt.savefig(figname)
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 = gdata_tools.get_table_as_dict('DB_library_data')
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()))
