def plot_relevance_heatmap(self, onehot_seq, label, save_fig):
relevance = self.run_relevance(onehot_seq, label)
seq = dbt.onehot_to_nuc(onehot_seq.T)
fig, ax = nucheatmap.nuc_heatmap(seq,
relevance,
save_fig=save_fig,
clims=[0, np.max(relevance)],
cmap='Blues')
def plot_avg_alipanahi_mutmap_of_batcher(self, batcher, fsuffix=''):
amutmaps = self.avg_alipanahi_mutmap_of_batcher(batcher)
for i, amap in enumerate(amutmaps):
# Note: amax(arr, axis=1) give greatest val for each row (nuc for nx4)
max_avg_nuc = (amap == np.amax(amap, axis=1,
keepdims=True)).astype(np.float32)
seq = dbt.onehot_to_nuc(max_avg_nuc.T)
alipanahi_mutmap_dir = self.save_dir + os.sep + 'alipanahi_mutmap_dir'
if not os.path.exists(alipanahi_mutmap_dir):
os.makedirs(alipanahi_mutmap_dir)
save_fname = alipanahi_mutmap_dir+os.sep+'avg_batcher_mutmap_{}recs_class{}{}.png'.\
format(batcher.num_records,i,fsuffix)
nucheatmap.nuc_heatmap(seq, amap.T, save_fig=save_fname)
def plot_mutmap(self, onehot_seq, label, save_fig):
"""
:param onehot_seq: nx4 matrix
:param label:
:returns:
:rtype:
"""
seq = dbt.onehot_to_nuc(onehot_seq.T)
mut_onehot = self.mutmap(onehot_seq, label)
#print "mut_onehot",mut_onehot.shape
#print mut_onehot
return nucheatmap.nuc_heatmap(seq, mut_onehot, save_fig=save_fig)
def plot_alipanahi_mutmap(self, onehot_seq, label, save_fig):
seq = dbt.onehot_to_nuc(onehot_seq.T)
amut_onehot = self.alipanahi_mutmap(onehot_seq, label)
nucheatmap.nuc_heatmap(seq, amut_onehot.T, save_fig=save_fig)