def lastList(abar, bCtoS, alpha, beta, N, M, start, time, t_1, iter):
#runs app_sim 'iter' times and lists the last element per iteration
probVal = []
for i in range(iter):
x = app.app_sim(abar, bCtoS, alpha, beta, N, M, start, time, t_1)[-1]
if x == 0:
probVal.append(0.0)
else:
probVal.append(1.0)
return probVal
import applause_functions as app
import matplotlib.pyplot as plt
import numpy as np
N = 10
M = 10
population = N * M
time = 100
t_1 = 3
aStoC = 1.0
bCtoS = 0.5
alpha = 0.8
beta = 2
fig = plt.figure()
ax = fig.add_subplot(111)
plt.plot(app.app_sim(aStoC, bCtoS, alpha, beta, N, M, time, t_1))
plt.axhline(app.steady_nC(aStoC, bCtoS, alpha, beta, population, 0), color='r')
ax.text(0.5 * time,
app.steady_nC(aStoC, bCtoS, alpha, beta, population, -1) + 1,
'Steady-state: ' +
str(app.steady_nC(aStoC, bCtoS, alpha, beta, population, -1)),
fontsize=20)
ax.text(90, 100, 'N = ' + str(population))
plt.title('a = ' + str(aStoC) + ' b = ' + str(bCtoS) + ' alpha = ' +
str(alpha) + ' beta = ' + str(beta))
plt.xlabel('Time')
plt.ylabel('State C')
plt.savefig('a = ' + str(aStoC) + ' b = ' + str(bCtoS) + ' alpha = ' +
str(alpha) + ' beta = ' + str(beta) + '.png')
plt.show()
for alpha_iter in range(1, 3, 1):
for bCtoS_iter in range(1, 3, 1):
#lists to be graphed
duration = []
durationSTD = []
population = []
for pop in popSet: #goes thru popSet
appDurationTrials = [] #temporary holder of trials to be averaged
for j in range(
trials): #simulates parameters and population TRAIL times
N = pop
sim = app.app_sim(aStoC, bCtoS_iter * 0.1, alpha_iter * 0.1,
beta, N, N, C, t, t_1)
appDurationTrials.append(indexFilter(0, sim, 1, t))
duration.append(np.mean(appDurationTrials))
durationSTD.append(np.std(appDurationTrials))
population.append(N * N)
#scatter plot with error bars
#plt.subplot(111, xscale="log")
fig = plt.figure()
ax = fig.add_subplot(111, xscale="log")
x = population
y = duration
plt.errorbar(x, y, xerr=0, yerr=durationSTD, fmt='o')
plt.title('$a = $' + str(aStoC) + ' $b = $' + str(bCtoS_iter * 0.1) +
r' $\alpha=$' + str(alpha_iter * 0.1) + r' $\beta=$' +
N = 10
M = 10
population = N * M
t = 500
t_1 = 1
C = 0
aStoC = 0.7
bCtoS = 0.8
alpha = 0.5
beta = 4
start_time = time.time()
fig = plt.figure()
ax = fig.add_subplot(111)
sim1 = app.app_sim(aStoC, bCtoS, alpha, beta, N, M, C, t, t_1)
sim2 = app.feed_space(aStoC, bCtoS, alpha, beta, N, M, C, t, t_1,M,0)
sim3 = app.feed_space(aStoC, bCtoS, alpha, beta, N, M, C, t, t_1,0,1)
sim4 = app.feed_space(aStoC, bCtoS, alpha, beta, N, M, C, t, t_1,0,0)
plt.plot(sim1, color='b', label="FC")
plt.plot(sim2, color='r', label="180deg")
plt.plot(sim3, color='g', label="90deg")
plt.plot(sim4, color='y', label="0deg")
plt.legend(loc=4)
ax.text(1,5,'N = '+str(population), fontsize=15)
plt.title('$a = $'+str(aStoC)+' $b = $'+str(bCtoS)+r' $\alpha=$'+str(alpha)+r' $\beta=$'+str(beta),fontsize=20)
plt.xlabel('Time',fontsize=18)
plt.ylabel('State'+r' $n_{c}$',fontsize=18)
plt.xlim(0,30)
import numpy as np
N = 10
M = 10
population = N * M
time = 500
t_1 = 2
C = 0
aStoC = 1
bCtoS = 0.8
alpha = 0.6
beta = 1
fig = plt.figure()
ax = fig.add_subplot(111)
sim = app.app_sim(aStoC, bCtoS, alpha, beta, N, M, C, time, t_1)
plt.plot(sim)
ax.text(0.45 * time,
1,
'Steady-state:' +
str(round(app.steady_nC(aStoC, bCtoS, alpha, beta, population, 1), 0)),
fontsize=20)
ax.text(0.45 * time,
10,
'Mean: ' + str(round(np.mean(sim), 1)) + '$\pm$' +
str(round(np.std(sim), 2)),
fontsize=20)
ax.text(10, 100, 'N = ' + str(population), fontsize=15)
plt.axhline(np.mean(sim), color='r')
plt.axhline(app.steady_nC(aStoC, bCtoS, alpha, beta, population, 1), color='g')
plt.title('$a = $' + str(aStoC) + ' $b = $' + str(bCtoS) + r' $\alpha=$' +