def printLocHist(leftPoses, rightPoses,singlePoses, motifs, translate, savefig = None):
'''
Generate a plot of the distribution of the left and right motifs within each sequence.
'''
import pylab as pl
import numpy as np
from plotDistCat import getHistBinCenters, saveOrPrint
for i,m in enumerate( motifs ):
temp = [ (a,b) for a,b in zip(leftPoses[i], rightPoses[i])]
temp.sort(key=lambda k:(k[0],k[1]))
fig = pl.figure()
subp = fig.add_subplot(211)
if translate != None and m in translate:
m = translate[m]
subp.set_title(m)
subp.plot([k[0] for k in temp], [k[1] for k in temp],'.')
ly, binCenters = getHistBinCenters( leftPoses[i], 40, normed=1)
lr, binCenters = getHistBinCenters( rightPoses[i], 40, normed=1)
ls, binCenters = getHistBinCenters( singlePoses[i], 40, normed=1)
subp.set_xlabel("Left position")
subp.set_ylabel("Right position")
subp2 = fig.add_subplot(212)
subp2.plot( binCenters, ly, '-', label="Left (%d)"%len(leftPoses[i]))
subp2.plot( binCenters, lr, '-', label="Right (%d)"%len(rightPoses[i]))
subp2.plot( binCenters, ls, '-', label="Single (%d)"%len(singlePoses[i]))
subp2.legend()
subp2.set_xlabel("Position")
subp2.set_ylabel("Frequency")
saveOrPrint(fig, "LocHist_"+ m, '', savefig)
def plotPieChart(counts, fastaCount, savefig):
import numpy as np
import pylab as pl
from plotDistCat import saveOrPrint, getIntervalIdx
from collections import defaultdict
inter_names = counts[0]
NUMPLOTS = len(inter_names) + 1
colors = ['b','g','r','c','m','y','k','w']
for c in counts[1:]:
print c
totals = defaultdict(int)
motif = c[0]
nrows = (NUMPLOTS+1)/2
pl.close("all")
fig = pl.figure(figsize=(10,14))
for i in range(len(inter_names)):
ax = fig.add_subplot(nrows, 2, i+1)
curr = np.array(c[i+1])
for k in c[i+1]:
totals[k[0]] += k[1]
fracs = list(curr[:,1])
labels = list(curr[:,0])
if fastaCount is not None:
fracs.insert(0,fastaCount[i] - curr[:,1].sum())
labels.insert(0,0)
print fracs , ' ', labels
ax.pie(fracs, autopct='%1.1f%%',colors=colors,labels=labels)
ax.set_title('%d,%s'%(sum(fracs),inter_names[i]))
labels = totals.keys()
labels.sort()
fracs = []
for f in labels:
fracs.append(totals[f])
if fastaCount is not None:
fracs.insert(0,sum(fastaCount) - sum(fracs))
labels.insert(0,0)
ax = fig.add_subplot(nrows, 2, i+2)
ax.pie(fracs, labels=labels, autopct='%1.1f%%' ,colors=colors)
ax.set_title('%d, All'%sum(fracs))
fig.tight_layout()
saveOrPrint(fig, motif, "pie", savefig)
def generateHeatMatrix( motifs, details, records, seq_length, intervals , savefig=None):
import numpy as np
import pylab as pl
from plotDistCat import saveOrPrint, getIntervalIdx
data = {}
motif2value = {}
for i,m in enumerate(motifs):
print m
motif2value[ m ] = i
print motifs
for r in records:
if r[-1] not in motif2value:
continue
if r[0] not in data:
data[r[0]] = [r[2],]
for i in range(len(motifs)):
data[r[0]].append([])
for p in details[r[-1]][r[0]]:
data[r[0]][motif2value[r[-1]]+1].append(( p[0],p[1]))
matrix_all = []
matrix_exist = []
ticks = [0, seq_length/2, seq_length]
labels = [-(seq_length/2),0,seq_length/2]
count_diff = 0
count_all = 0
count_all_by_dist = []
count_exist_by_dist = []
#the following are used to store the indexes of the rows in
# the complete set
matrix_by_dist = []
matrix_exist_by_dist = []
for i in range(len(intervals) - 1):
matrix_by_dist.append([])
matrix_exist_by_dist.append([])
count_all_by_dist.append(0)
count_exist_by_dist.append(0)
NUMPLOTS = len(intervals)
for seq in data:
exist = True
temp = np.zeros(seq_length) - 1
for i,p in enumerate(data[seq][1:]):
for t in p:
temp[(t[0]+t[1])/2] = i
if len(p) == 0:
exist = False
temp = list(temp)
if sum(temp) == -1*seq_length:
continue
interIdx = getIntervalIdx(intervals, data[seq][0])
matrix_all.append(temp)
if interIdx != None:
matrix_by_dist[interIdx].append(len(matrix_all)-1)
count_all_by_dist[interIdx] += 1
count_all += 1
if exist:
matrix_exist.append(temp)
if interIdx != None:
matrix_exist_by_dist[interIdx].append(len(matrix_exist)-1)
count_exist_by_dist[interIdx] += 1
else:
count_diff += 1
print count_diff, ' ', count_all, ' ', len(matrix_all),' ', len(matrix_exist)
print count_all_by_dist
print count_exist_by_dist
pl.close("all")
fig_all = pl.figure(1)
fig_exist = pl.figure(2)
sub_count = 1
matrix_all_idx = range(len(matrix_all))
matrix_exist_idx = range(len(matrix_exist))
matrix_all_idx.sort(key=lambda k:(np.average(matrix_all[k]),))
matrix_exist_idx.sort(key=lambda k:(np.average(matrix_exist[k]),))
for i in range(len(intervals) - 1):
matrix_by_dist[i].sort(key=lambda k:(np.average(matrix_all[k]),))
matrix_exist_by_dist[i].sort(key=lambda k:(np.average(matrix_exist[k]),))
subTitles = []
for i in range(len(intervals) - 1):
t = '[%d, %d'%(intervals[i], intervals[i+1])
if i != len(intervals) - 2:
t += ')'
else:
t += ']'
subTitles.append(t)
#matrix_all.sort(key=lambda k:(np.average(k),)) # np.average(k[0:60]), np.average(k[60:140]),np.average(k[140:])))
#matrix_exist.sort(key=lambda k:(np.average(k),)) # np.average(k[0:60]), np.average(k[60:140]),np.average(k[140:])))
matrix_all = np.array(matrix_all)
matrix_exist = np.array(matrix_exist)
#matrix_all[matrix_all<0] = None
#matrix_exist[matrix_exist<0] = None
matrix_all += 1
matrix_exist += 1
ncolumns = 2
def setTickAndLabel(ax, ticks_a, labels_a):
ax.set_xticks(ticks_a)
ax.set_xticklabels(labels_a)
if len(intervals) > 2:
for i in range(len(intervals) - 1):
fas = fig_all.add_subplot((NUMPLOTS+1)/2, 2, sub_count)
fes = fig_exist.add_subplot((NUMPLOTS+1)/2, 2, sub_count)
#fas.imshow(matrix_all[matrix_by_dist[i]], aspect='auto')
fas.plot(matrix_all[matrix_by_dist[i]].sum(axis=0)/matrix_all[matrix_by_dist[i]].shape[0])
fas.set_title(subTitles[ sub_count - 1] + ' %d'%len(matrix_by_dist[i]))
setTickAndLabel(fas, ticks, labels)
#fes.imshow(matrix_exist[matrix_exist_by_dist[i]], aspect='auto')
fes.plot(matrix_exist[matrix_exist_by_dist[i]].sum(axis=0)/matrix_exist[matrix_exist_by_dist[i]].shape[0])
fes.set_title(subTitles[ sub_count - 1] + ' %d'%len(matrix_exist_by_dist[i]))
setTickAndLabel(fes, ticks, labels)
sub_count += 1
else:
ncolumns = 1
NUMPLOTS=1
fas = fig_all.add_subplot((NUMPLOTS+1)/2, ncolumns, sub_count)
fes = fig_exist.add_subplot((NUMPLOTS+1)/2, ncolumns, sub_count)
matrix_all_sorted = matrix_all[matrix_all_idx]
matrix_exist_sorted = matrix_exist[matrix_exist_idx]
fas.plot(matrix_all_sorted.sum(axis=0)/matrix_all_sorted.shape[0])
fas.set_title("All dist %d" % len(matrix_all_idx))
setTickAndLabel(fas, ticks, labels)
fig_all.suptitle("Heatmap all for %s"%motifs[0])
#pl.colorbar()
#fig_all.close()
#pl.close()
fes.plot(matrix_exist_sorted.sum(axis=0)/matrix_exist_sorted.shape[0])
fig_exist.suptitle("Heatmap exist for %s"%motifs[0])
fes.set_title("All dist %d" % len(matrix_exist_idx))
setTickAndLabel(fes, ticks, labels)
fig_all.subplots_adjust(right=0.8)
fig_exist.subplots_adjust(right=0.8)
#cbar_ax = fig_all.add_axes([0.85,0.15,0.05,0.7])
#cbar_ax_e = fig_exist.add_axes([0.85,0.15,0.05,0.7])
#fig_all.colorbar(ima, cax=cbar_ax)
#fig_all.show()
#pl.show()
fig_all.tight_layout()
fig_exist.tight_layout()
saveOrPrint(fig_all, "Heatmap_all", '', savefig)
#fig_exist.colorbar(ime, cax=cbar_ax_e)
#fig_exist.show()
saveOrPrint(fig_exist, "Heatmap_exist", '', savefig)
