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import Util
from collections import defaultdict
import gdata_tools
import matplotlib
import pylab
import re
'''build two dictionaries from one cluni file, 1 for pickcluststokeep fn and 1 for scoring for boxplot construction'''
def clunidict(clunifile):
'''creates two dictionaries, indbyclust of form {clust1: {i1:r1, i2:r2}, clust2: {i1:r1, i5:r5}}, and fc_ln_idx_indiv_byclust of form {clust1: {(flowcell, lane, index, ind): reads} etc for each cluster in a .cluni file'''
fh = open(clunifile)
indbyclust = {}
fc_ln_idx_indiv_byclust = {}
for line in fh:
clustID = line.strip().split()[0]
if not clustID in indbyclust:
indbyclust[clustID] = defaultdict(int)
fc_ln_idx_indiv_byclust[clustID] = defaultdict(int)
mtch ='^.+?\.(.+?)_lane(\d+).+?(?:(?:_index(\d+))|$)',line.strip().split()[1]) #splits out fc, ln, idx info per indiv
flowcell, lane, index = mtch.groups()
inds = line.strip().split()[5].split(',') #add parse for flowcell
reads = line.strip().split()[6].split(',')
coupled = zip(inds, reads)
for ind,reads in coupled:
indbyclust[clustID][ind] += int(reads)
fc_ln_idx_indiv_byclust[clustID][(flowcell, lane, index, ind)] += int(reads)
return (indbyclust, fc_ln_idx_indiv_byclust)
def pickcluststokeep(indbyclust, fcbyclust, reqreads=4):
'''takes an indbyclust dictionary and a fcbyclust dictionary and filters the fcbyclust to remove inds with reads < thresh, and clusts with fewer than half the max # of inds seen in a cluster'''
numinds = []
for clustID, indcts in indbyclust.items(): #first remove inds that don't have a requisite no. of reads
for ind, reads in indcts.items():
if reads < reqreads:
thresh = max(map(len, indbyclust.values()))*0.5
for clustID, indcts in indbyclust.items():
if len(indcts) < thresh:
dropk = []
for k in fcbyclust[clustID]:
if not k[3] in indbyclust[clustID]: #k[3] designates ind from the key of (flowcell, lane, index, ind)
for k in dropk:
return fcbyclust
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():
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:
boxes = []
labels = []
for group,indcounts in indiv_by_group.items():
boxplt = pylab.figure(1)
pylab.xticks(arange(1,(len(labels)+1)),labels,fontsize='small') #legend with best location (0) if pools
#####FROM VCF FILE#####
#def getindsfromvcf(vcffilename):
# vcf = variant_detection.load_vcf('vcffilename')
# for sd in vcf.values():
# for ind in sd['indiv_gt']:
# indivs.append(ind)
# indivs = set(indivs)
# return indivs
#indivs = getindsfromvcf(vcffilename)