def test_main():
text = """
A = True
B = Random
C = Random
D = Random
B* = A or C
C* = A and not D
D* = B and C
"""
repeat = 5000
coll = util.Collector()
for i in range(repeat):
update_progress(i, repeat)
model = boolean2.Model(text, mode='async')
model.initialize()
model.iterate(steps=15)
# in this case we take all nodes
# one could just list a few nodes such as [ 'A', 'B', 'C' ]
nodes = model.nodes
# this collects states for each run
coll.collect(states=model.states, nodes=nodes)
# this step averages the values for each node
# returns a dictionary keyed by nodes and a list of values
# with the average state for in each timestep
avgs = coll.get_averages(normalize=True)
# make some shortcut to data to for easier plotting
valueB = avgs["B"]
valueC = avgs["C"]
valueD = avgs["D"]
#
# plot the state of the nodes
#
p1, = plt.plot(valueB, 'ob-')
p2, = plt.plot(valueC, 'sr-')
p3, = plt.plot(valueD, '^g-')
plt.legend([p1, p2, p3], ["B", "C", "D"])
plt.show()
plt.savefig('t-05_figure.png')
import pandas as pd
from pyhet.dataset import ccle
from pyhet import util
# Bring data from filelist
mutcna = ccle.mutcna()
therapy = ccle.therapy()
mutcna_col = mutcna.filter(regex='LARGE_INTESTINE')
therapy_col = therapy[therapy['CCLE Cell Line Name'].str.contains(
"LARGE_INTESTINE")]
gene = pd.read_csv('node_name.csv')
# Select data that has 'LARGE INTESTINE' and same gene name with network
i = 0
for i in gene.index:
util.update_progress(i, gene.shape[0])
gene_index = gene.loc[i, 'node_name']
data_mut = mutcna_col[mutcna_col.index.str.contains(gene_index)]
data_therapy = therapy_col[therapy_col['Target'].str.contains(gene_index)]
if i == 0:
total_mut = data_mut
total_therapy = data_therapy
else:
if gene_index == 'AMP':
j = 0
for j in range(len(data_mut.index)):
gene_name = data_mut.index[j]
if gene_name.find(gene_index) < gene_name.find('_'):
temp = data_mut.ix[[j], :]
if j == 0:
data_mut_cor = temp