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plotting.py
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plotting.py
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#!/usr/bin/env python
import numpy as np
import networkx as nx
import matplotlib as mpl
import matplotlib.pyplot as plt
from misc import zeros
from main import load_data
def main():
data = load_data()
hgraph = nx.Graph(data.humanppi)
fgraph = nx.Graph(data.functions)
#plot_cp_degree(hgraph)
#plot_fn_degree(hgraph, fgraph)
#plot_fn_cp_weight(hgraph, fgraph)
def plot_cp_degree(hgraph):
cps = [p for p in hgraph.nodes() if p.is_cancer_protein()]
ps = [p for p in hgraph.nodes() if not p.is_cancer_protein()]
cp_degrees_cp = [p.cp_degree(hgraph) for p in cps]
avg_cp = round(np.mean(cp_degrees_cp), 2)
std_cp = round(np.std(cp_degrees_cp), 2)
cp_degrees = [p.cp_degree(hgraph) for p in ps]
avg = round(np.mean(cp_degrees), 2)
std = round(np.std(cp_degrees), 2)
fig = plt.figure(figsize=(10,10))
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
kwargs = {'bins':50,
'normed':True,
'histtype':'step',
'color':'k'}
ax1.hist(cp_degrees_cp, **kwargs)
ax1.set_title('Cancer Proteins Only')
ax1.set_xlim(0,15)
ax1.set_ylabel('Normalized frequency')
ax1.text(5, 0.3, 'Avg: %s Std: %s'%(str(avg_cp), str(std_cp)))
ax2.hist(cp_degrees, **kwargs)
ax2.set_title('Non-Cancer Proteins Only')
ax2.set_xlim(0,15)
ax2.set_ylabel('Normalized frequency')
ax2.set_xlabel('Number of Cancer Protein Neighbors')
ax2.text(5, 0.3, 'Avg: %s Std: %s'%(str(avg), str(std)))
filepathname = 'report/img/cp_degrees.png'
plt.savefig(filepathname, bbox_inches='tight')
print 'Written to: '+filepathname
fig.clf()
plt.close()
del fig
def plot_fn_degree(hgraph, fgraph):
proteins_in_fgraph = [p for p in fgraph.nodes() \
if not p.is_function()]
cps = [p for p in proteins_in_fgraph if p.is_cancer_protein()]
ps = [p for p in proteins_in_fgraph if not p.is_cancer_protein()]
cps_fn_degrees = [p.degree(fgraph) for p in cps]
ps_fn_degrees = [p.degree(fgraph) for p in ps]
avg_cp = round(np.mean(cps_fn_degrees), 2)
std_cp = round(np.std(cps_fn_degrees), 2)
avg = round(np.mean(ps_fn_degrees), 2)
std = round(np.std(ps_fn_degrees), 2)
fig = plt.figure(figsize=(10,10))
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
kwargs = {'bins':50,
'normed':True,
'histtype':'step',
'color':'k'}
ax1.hist(cps_fn_degrees, **kwargs)
ax1.set_title('Cancer Proteins Only')
ax1.set_xlim(0,80)
ax1.set_ylabel('Normalized frequency')
ax1.text(50, 0.03, 'Avg: %s Std: %s'%(str(avg_cp), str(std_cp)))
ax2.hist(ps_fn_degrees, **kwargs)
ax2.set_title('Non-Cancer Proteins Only')
ax2.set_xlim(0,80)
ax2.set_ylabel('Normalized frequency')
ax2.set_xlabel('Number of Functions')
ax2.text(50, 0.03, 'Avg: %s Std: %s'%(str(avg), str(std)))
filepathname = 'report/img/fn_degrees.png'
plt.savefig(filepathname, bbox_inches='tight')
print 'Written to: '+filepathname
fig.clf()
plt.close()
del fig
def plot_fn_cp_weight(hgraph, fgraph):
proteins_in_fgraph = [p for p in fgraph.nodes() \
if not p.is_function()]
cps = [p for p in proteins_in_fgraph if p.is_cancer_protein()]
ps = [p for p in proteins_in_fgraph if not p.is_cancer_protein()]
cps_fn_cp_weight = [p.fn_cp_weight(hgraph, fgraph) for p in cps]
ps_fn_cp_weight = [p.fn_cp_weight(hgraph, fgraph) for p in ps]
avg_cp = round(np.mean(cps_fn_cp_weight), 2)
std_cp = round(np.std(cps_fn_cp_weight), 2)
avg = round(np.mean(ps_fn_cp_weight), 2)
std = round(np.std(ps_fn_cp_weight), 2)
fig = plt.figure(figsize=(10,10))
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
kwargs = {'bins':50,
'normed':True,
'histtype':'step',
'color':'k'}
ax1.hist(cps_fn_cp_weight, **kwargs)
ax1.set_title('Cancer Proteins Only')
ax1.set_ylabel('Normalized frequency')
#ax1.set_xlim(0,80)
#ax1.text(50, 0.03, 'Avg: %s Std: %s'%(str(avg_cp), str(std_cp)))
ax2.hist(ps_fn_cp_weight, **kwargs)
ax2.set_title('Non-Cancer Proteins Only')
ax2.set_ylabel('Normalized frequency')
ax2.set_xlabel('Number of Functions')
#ax2.set_xlim(0,80)
#ax2.text(50, 0.03, 'Avg: %s Std: %s'%(str(avg), str(std)))
filepathname = 'report/img/fn_cp_weights.png'
plt.savefig(filepathname, bbox_inches='tight')
print 'Written to: '+filepathname
fig.clf()
plt.close()
del fig
def make_function_graphs(data):
fgraph = nx.Graph(data.functions, name='Functions')
drawkwargs = {'font_size': 10,\
'linewidths': 1,\
'width': 2,\
'with_labels': True,\
'node_size': 700,\
'node_color':'w',\
'node_shape':'o',\
'style':'solid',\
'alpha':1,\
'cmap': mpl.cm.jet}
filepath = 'images'+'/functions/'
for function in data.functions_only:
g = nx.Graph()
nbrs = fgraph.neighbors(function)
edges_to_add = [tuple([function, i]) for i in nbrs]
g.add_edges_from(edges_to_add)
fig = plt.figure(figsize=(20,20))
ax = fig.add_subplot(111)
pos=nx.spring_layout(g, iterations=20)
nx.draw(g, pos, **drawkwargs )
ax.set_title(function)
filepathname = filepath+zeros(function, padlength=4)+'.jpg'
plt.savefig(filepathname, bbox_inches='tight')
print 'Written to: '+filepathname
fig.clf()
plt.close()
del fig
##Deprecated, don't call this function
def find_shortest_paths_to_cps(data):
"""
for every protein:
for every cp:
find shortest path from protein to cp.
add path to graph if path is shorter then x.
plot graph
"""
Tc, Tnonc = ppi.misc.get_Tc_Tnonc(data.cancer, data.test1)
ppigraph = nx.Graph(data.humanppi, name='HumanPPI')
for protein in Tc+Tnonc:
g = nx.Graph()
for cp in data.cancer:
path = nx.shortest_path(ppigraph, source=protein, target=cp)
if len(path) < 4:
g.add_path(path)
subst = (protein, cp, str(path))
nodecolor = ['r' if node in data.cancer else 'w' for node in\
g.nodes()]
nodesize = []
nodesymbol = ['^' if node in data.cancer else 'o' for node in\
g.nodes()]
plot_graph(g, protein, Tc, nodecolor, nodesymbol)
##Deprecated, don't call this function
def plot_graph(graph, protein, Tc, nodecolor, nodesymbol):
fig = plt.figure(figsize=(20,20))
ax = fig.add_subplot(111)
drawkwargs = {'font_size': 10,\
'linewidths': 1,\
'width': 2,\
'with_labels': True,\
'node_size': 700,\
'node_color':nodecolor,\
'node_shape':'o',\
'style':'solid',\
'alpha':1,\
'cmap': mpl.cm.jet}
pos=nx.spring_layout(graph, iterations=200)
nx.draw(graph, pos, **drawkwargs )
if protein in Tc:
string = "Protein: %i Cancer"
string_s = (protein)
title=string%string_s
else:
string = "Protein: %i Non-Cancer"
string_s = (protein)
title=string%string_s
ax.set_title(title)
filepath = 'images'+'/'+zeros(protein, padlength=4)+'.jpg'
plt.savefig(filepath, bbox_inches='tight')
print 'Written to: '+filepath
fig.clf()
plt.close()
del fig
##Deprecated, don't call this function
def plot_degrees_histogram(degree_sorted):
fig = plt.figure(figsize=(5,5))
ax1 = fig.add_subplot(111)
ax1.hist(degrees, bins=50, range=(0,50),\
normed=True, histtype='step', color='k')
ax1.set_xlabel('Degree')
ax1.set_ylabel('Normalized frequency')
plt.savefig('pictures/degrees_histogram.png', bbox_inches='tight')
fig.clf()
plt.close()
if __name__ == '__main__':
main()