B* = A or C
C* = A and not D
D* = B and C
"""
model = Model(text=text, mode='sync')
model.initialize()
model.iterate(steps=15)
# the model data attribute holds the states keyed by nodes
for node in model.data:
print node, model.data[node]
# this is a helper function that reports the cycle lengths
# and the index at wich the cycle started
model.report_cycles()
#
# the same thing as above but
# will not print only return the two parameters
#
print model.detect_cycles()
#
# this is how one plots the values, delete this below
# if matplotlib is not installed
#
p1 = pylab.plot(model.data["B"], 'ob-')
p2 = pylab.plot(model.data["C"], 'sr-')
pylab.legend([p1, p2], ["B", "C"])
pylab.ylim((-0.1, 1.1))
B* = A or C
C* = A and not D
D* = B and C
"""
model = Model(text=text, mode="sync")
model.initialize()
model.iterate(steps=15)
# the model data attribute holds the states keyed by nodes
for node in model.data:
print node, model.data[node]
# this is a helper function that reports the cycle lengths
# and the index at wich the cycle started
model.report_cycles()
#
# the same thing as above but
# will not print only return the two parameters
#
print model.detect_cycles()
#
# this is how one plots the values, delete this below
# if matplotlib is not installed
#
p1 = pylab.plot(model.data["B"], "ob-")
p2 = pylab.plot(model.data["C"], "sr-")
pylab.legend([p1, p2], ["B", "C"])
pylab.ylim((-0.1, 1.1))
def BoolMod(timecourse, booldata, str_mode):
coll = util.Collector()
model = Model(text=booldata, mode=str_mode)
initializer = state.all_initial_states(model.nodes, limit=None)
# the data is the inital data, the func is the initializer
n = 0
for data, initfunc in initializer:
# shows the initial values
print((n) / float(32768)) * 100, "% done."
n = n + 1
model.initialize(missing=initfunc)
model.iterate(steps=timecourse)
# takes all nodes
nodes = model.nodes
coll.collect(states=model.states, nodes=nodes)
# --------------- Detect Cycles ------------------#
print model.report_cycles()
# Return results
avgs = coll.get_averages(normalize=True)
data = pd.DataFrame(avgs)
pd.DataFrame.to_csv(data, "test.csv")
print avgs
print model.fp()
# ------------- Retrieve the Data -------------#
# 6-gene input signature
#ALK = avgs.get('ALK')
#MDK = avgs.get('MDK')
#TrkA = avgs.get('TrkA')
#NGF = avgs.get('NGF')
#TrkB = avgs.get('TrkB')
#BDNF = avgs.get('BDNF')
# Model outcome states
Differentiation = avgs.get('Differentiation')
Apoptosis = avgs.get('Apoptosis')
Proliferation = avgs.get('Proliferation')
Angiogenesis = avgs.get('Angiogenesis')
# Nodes at Issue
#DNADamage = avgs.get('DNADamage')
#p53 = avgs.get('p53')
# Other Nodes
#MDM2 = avgs.get('MDM2')
#MAPK = avgs.get('MAPK')
#p27 = avgs.get('p27')
#FoxO = avgs.get('FoxO')
#AKT = avgs.get('AKT')
#Ras = avgs.get('Ras')
#MYCN = avgs.get('MYCN')
#MTOR = avgs.get('MTOR')
#IP3 = avgs.get('IP3')
### Time axis (x)
t = range(0, timecourse + 1)
# Create plots with pre-defined labels. Try to make this a for loop
fig, ax = plt.subplots()
ax.plot(t, Differentiation, label='Differentiation')
ax.plot(t, Apoptosis, label='Apoptosis')
ax.plot(t, Angiogenesis, label='Angiogenesis')
ax.plot(t, Proliferation, label='Proliferation')
# ax.plot(t, TrkA, label='TrkA')
# ax.plot(t, TrkB, label = 'TrkB')
# ax.plot(t, MYCN, label = 'MYCN')
# ax.plot(t, NGF, label = 'NGF')
# ax.plot(t, MDK, label = 'MDK')
# ax.plot(t, ALK, label = 'ALK')
# ax.plot(t, Ras, label = 'Ras')
# x.plot(t, AKT, label = 'AKT')
# ax.plot(t, FoxO, label = 'FoxO')
# ax.plot(t, p27, label = 'P27')
# ax.plot(t, p53, label = 'P53')
# Legends
legend = ax.legend(loc=0, shadow=True, fontsize='medium')
plt.xlabel('Iterations')
plt.ylabel('On Proportion')
plt.title("Asynchronous Updating Model")
# beautify
legend.get_frame().set_facecolor('#C0C0C0')
# View Plot
plt.show()
return avgs
def BoolModPlot(timecourse, samples, booldata, str_mode):
coll = util.Collector()
for i in range(samples):
model = Model(text=booldata, mode=str_mode)
model.initialize()
model.iterate(steps=timecourse)
print model.states
# takes all nodes
nodes = model.nodes
coll.collect(states=model.states, nodes=nodes)
# --------------- Detect Cycles ------------------#
print model.report_cycles()
# Return results
avgs = coll.get_averages(normalize=True)
data = pd.DataFrame(avgs)
pd.DataFrame.to_csv(data, "test.csv")
# ------------- Retrieve the Data -------------#
# 6-gene input signature
ALK = avgs.get('ALK')
MDK = avgs.get('MDK')
TrkA = avgs.get('TrkA')
NGF = avgs.get('NGF')
TrkB = avgs.get('TrkB')
BDNF = avgs.get('BDNF')
# Model outcome states
Differentiation = avgs.get('Differentiation')
Apoptosis = avgs.get('Apoptosis')
Proliferation = avgs.get('Proliferation')
Angiogenesis = avgs.get('Angiogenesis')
# Nodes at Issue
DNADamage = avgs.get('DNADamage')
p53 = avgs.get('p53')
# Other Nodes
MDM2 = avgs.get('MDM2')
MAPK = avgs.get('MAPK')
p27 = avgs.get('p27')
FoxO = avgs.get('FoxO')
AKT = avgs.get('AKT')
Ras = avgs.get('Ras')
MYCN = avgs.get('MYCN')
MTOR = avgs.get('MTOR')
IP3 = avgs.get('IP3')
### Time axis (x)
t = range(0, timecourse + 1)
# Create plots with pre-defined labels. Try to make this a for loop
fig, ax = plt.subplots()
ax.plot(t, Differentiation, label='Differentiation')
ax.plot(t, Apoptosis, label='Apoptosis')
ax.plot(t, Angiogenesis, label='Angiogenesis')
ax.plot(t, Proliferation, label='Proliferation')
# ax.plot(t, TrkA, label='TrkA')
# ax.plot(t, TrkB, label = 'TrkB')
# ax.plot(t, MYCN, label = 'MYCN')
# ax.plot(t, NGF, label = 'NGF')
# ax.plot(t, MDK, label = 'MDK')
# ax.plot(t, ALK, label = 'ALK')
# ax.plot(t, Ras, label = 'Ras')
# ax.plot(t, AKT, label = 'AKT')
# ax.plot(t, FoxO, label = 'FoxO')
# ax.plot(t, p27, label = 'P27')
# ax.plot(t, p53, label = 'P53')
# Legends
legend = ax.legend(loc=0, shadow=True, fontsize='medium')
plt.xlabel('Iterations')
plt.ylabel('On Proportion')
plt.title("Asynchronous Updating Model")
# beautify
legend.get_frame().set_facecolor('#C0C0C0')
# View Plot
plt.show()
return avgs
