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visualization.py
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visualization.py
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import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.text import Text
from matplotlib.patches import FancyArrow, Rectangle
import matplotlib
import argparse
import os
ip=1
parser = argparse.ArgumentParser(description='PiPred')
parser.add_argument('-i',
help='Path to output file from PiPred',
required=True,
metavar='DIR',
default = '.')
parser.add_argument('-m',
help='Plot mode: heatmap, bar, curve',
default = 'curve')
args = parser.parse_args()
def read_output(out_file):
data = pd.read_csv(out_file,sep ='\s+',index_col=0)
return data,list(data.index.values),out_file.split('.')[0]
def gen_heatmap(data,savefig=False):
heatmap = sns.heatmap(data)
plt.show()
def gen_probability_graph(prob,seq,savefig=False):
def pi_plot(ind,value):
ax=fig.add_subplot(1,1,1)
ax.set_ylim([0,1])
ax.tick_params(axis='x',
which='major', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='on')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
for cv in range(ind):
if np.argmax(prob[cv])==2:
pi = ax.bar(cv,I[cv+value*ind],w,color=(0,0,1),picker=True)
else:
pi = ax.bar(cv,I[cv+value*ind],w,color=(0.6,0.6,1),picker=True)
ax.set_xticklabels(seq[value*ind:(value+1)*ind])
ax.set_xticks(range(value*ind,(value+1)*ind))
for label in ax.get_xticklabels(): # make the xtick labels pickable
label.set_picker(True)
art = ax.arrow(ind-2,0.96,1.5,0,width=0.016,head_width=0.034,head_length=0.24,picker=True,label='next')
art1 = art = ax.arrow(ind-2,0.96,-1.5,0,width=0.016,head_width=0.034,head_length=0.24,picker=True,label='back')
fig.text(0.08, 0.5, 'probability', ha='center', va='center', rotation='vertical')
fig.text(0.5, 0.9, 'residues {} - {}'.format(value*ind+1,(value+1)*ind), ha='center', va='center', rotation='horizontal')
ax1 = fig.add_axes((0,0,1,0.06))
ax1.spines['top'].set_visible(False)
ax.set_picker(True)
nb = 0
w=0.8
seq_len = len(seq)
ind=100
tot = seq_len//ind+1
rest = tot*ind
print(prob.shape,len(seq))
prob = np.concatenate((prob,np.zeros(((tot)*ind-seq_len,4))))
for i in range((tot)*ind-seq_len):
seq.append(' ')
prt =seq[:]
E,H,I,C=prob[:,0],prob[:,1],prob[:,2],prob[:,3]
fig = plt.figure(figsize=(30,15))
pi_plot(ind,0)
def onpick(event):
global ip
patch =event.artist
if patch:
pop=0
ax1 = fig.add_axes((0,0,1,0.06))
ax1.spines['top'].set_visible(False)
if isinstance(patch,Text):
ax1.clear()
ax1.spines['top'].set_visible(False)
wt = patch.get_position()[0]
wr= patch.get_text()
pb,ph,pi,pc=round(prob[(ip-1)*ind+wt][0],4),round(prob[(ip-1)*ind+wt][1],4),round(prob[(ip-1)*ind+wt][2],4),round(prob[(ip-1)*ind+wt][3],4)
ax1.patch.set_alpha(0.1)
ax1.patch.set_color('b')
nap = 'residue nr {},aa_name {}, beta: {} alpha: {} pi:{} coil: {}'.format((ip-1)*ind+wt+1,wr,str(pb),str(ph),str(pi),str(pc))
ax1.text(0.5, 0.2, nap, ha='center', va='center')
elif isinstance(patch,FancyArrow) and patch._label == 'next':
if ip<tot:
fig.clear()
ax1 = fig.add_axes((0,0,1,0.06))
ax1.spines['top'].set_visible(False)
ax = fig.add_subplot(1,1,1)
ax.set_picker(True)
ax.set_ylim([0,1])
ax.tick_params(axis='x',
which='major', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='on')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
for cv in range(ind):
if np.argmax(prob[ip*ind+cv])==2:
pi = ax.bar(cv,I[ip*ind+cv],w,color=(0,0,1),picker=True)
else:
pi = ax.bar(cv,I[ip*ind+cv],w,color=(0.6,0.6,1),picker=True)
ax.set_xticklabels(seq[ip*ind:ip*ind+ind])
ax.set_xticks(range(ind))
for label in ax.get_xticklabels(): # make the xtick labels pickable
label.set_picker(True)
#ax.clear()
art = ax.arrow(ind-2,0.96,1.5,0,width=0.016,head_width=0.034,head_length=0.24,picker=True,label='next')
art1 = art = ax.arrow(ind-2,0.96,-1.5,0,width=0.016,head_width=0.034,head_length=0.24,picker=True,label='back')
fig.text(0.08, 0.5, 'probability', ha='center', va='center', rotation='vertical')
if ip+1 == tot:
fig.text(0.5, 0.9, 'residues {} - {}'.format(ip*ind+1,seq_len), ha='center', va='center', rotation='horizontal')
else:
fig.text(0.5, 0.9, 'residues {} - {}'.format(ip*ind+1,ip*ind+ind), ha='center', va='center', rotation='horizontal')
ip+=1
elif isinstance(patch, FancyArrow) and patch._label == 'back':
if ip>1:
ip-=1
fig.clear()
ax1 = fig.add_axes((0,0,1,0.06))
ax1.spines['top'].set_visible(False)
ax = fig.add_subplot(1,1,1)
ax.set_picker(True)
ax.set_ylim([0,1])
ax.tick_params(axis='x',
which='major',
bottom='off',
top='off',
labelbottom='on')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
for cv in range(ind):
if np.argmax(prob[(ip-1)*ind+cv])==2:
pi = ax.bar(cv,I[(ip-1)*ind+cv],w,color=(0,0,1),picker=True)
else:
pi = ax.bar(cv,I[(ip-1)*ind+cv],w,color=(0.6,0.6,1),picker=True)
ax.set_xticklabels(seq[(ip-1)*ind:(ip)*ind])
ax.set_xticks(range(ind))
for label in ax.get_xticklabels():
label.set_picker(True)
art = ax.arrow(ind-2,0.96,1.5,0,width=0.016,head_width=0.034,head_length=0.24,picker=True,label='next')
art1 = art = ax.arrow(ind-2,0.96,-1.5,0,width=0.016,head_width=0.034,head_length=0.24,picker=True,label='back')
fig.text(0.08, 0.5, 'probability', ha='center', va='center', rotation='vertical')
fig.text(0.5, 0.9, 'residues {} - {}'.format((ip-1)*ind+1,(ip)*ind), ha='center', va='center', rotation='horizontal')
#ax.add_patch(rect)
else:
ax1.clear()
fig.canvas.draw_idle()
fig.canvas.mpl_connect('pick_event',onpick)
plt.show()
def plotCurve(prob,title,savefig=False,wyb = ['I'],figsize = (10,5)):
fig = plt.figure(figsize=figsize)
dl = len(prob)
lab = {'E':0,'H':1,'I':2,'C':3}
ax = fig.add_subplot(1,1,1)
ax.set_ylim([0,1])
ax.set_title(title)
ax.set_xlabel('residue_number')
ax.set_ylabel('pi_probability')
for i in wyb:
ax.plot(range(dl),prob[:,lab[i]],label=i)
ax.legend()
if savefig:
plt.savefig('prob_dist1',dpi=300)
plt.show()
if __name__ == '__main__':
file_name = args.i
mode = args.m
prob,seq,title = read_output(file_name)
#prob = np.array(prob)
if mode == 'curve':
plotCurve(np.array(prob),title,savefig = True)
elif mode == 'heatmap':
gen_heatmap(prob)
elif mode =='bar':
gen_probability_graph(prob,seq)