def test_stochastic():
# Case 1
# ------
noise = 1.
tau = 10.
def int_m(m, t, V):
alpha = 0.1 * (V + 40) / (1 - np.exp(-(V + 40) / 10))
beta = 4.0 * np.exp(-(V + 65) / 18)
return alpha * (1 - m) - beta * m, noise / beta
diff_eq = DiffEquation(int_m)
assert diff_eq.is_stochastic is True
# Case 2
# ------
noise = 0.
tau = 10.
def int_m(m, t, V):
alpha = 0.1 * (V + 40) / (1 - np.exp(-(V + 40) / 10))
beta = 4.0 * np.exp(-(V + 65) / 18)
return alpha * (1 - m) - beta * m, noise / beta
diff_eq = DiffEquation(int_m)
assert diff_eq.is_stochastic is True
# Case 3
# ------
noise = 0.
tau = 10.
def int_m(m, t, V):
alpha = 0.1 * (V + 40) / (1 - np.exp(-(V + 40) / 10))
beta = 4.0 * np.exp(-(V + 65) / 18)
return alpha * (1 - m) - beta * m, noise / tau
diff_eq = DiffEquation(int_m)
assert diff_eq.is_stochastic is False
# Case 4
# ------
noise = 1.
tau = 10.
def int_m(m, t, V):
alpha = 0.1 * (V + 40) / (1 - np.exp(-(V + 40) / 10))
beta = 4.0 * np.exp(-(V + 65) / 18)
return alpha * (1 - m) - beta * m, noise / tau
diff_eq = DiffEquation(int_m)
assert diff_eq.is_stochastic is True
def try_analyse_func():
import numpy as np
def int_m(m, t, V):
alpha = 0.1 * (V + 40) / (1 - np.exp(-(V + 40) / 10))
beta = 4.0 * np.exp(-(V + 65) / 18)
return alpha * (1 - m) - beta * m, (alpha, beta)
from pprint import pprint
df = DiffEquation(int_m)
pprint(df.expressions)
pprint(df.returns)
pprint(df.get_f_expressions())
pprint(df.get_g_expressions())
print('-'* 30)
def try_analyse_func3():
def g_func(m, t):
a = t + 2
b = m + 6
c = a + b
d = m * 2 + c
return d
from pprint import pprint
df = DiffEquation(func=None, g=g_func)
pprint(df.expressions)
pprint(df.returns)
pprint(df.get_f_expressions())
pprint(df.get_g_expressions())
print('-' * 30)
def try_analyse_func2():
def func(m, t):
a = t + 2
b = m + 6
c = a + b
d = m * 2 + c
return d, c
from pprint import pprint
df = DiffEquation(func=func, g=np.zeros(10))
pprint(df.expressions)
pprint(df.returns)
pprint(df.get_f_expressions())
pprint(df.get_g_expressions())
print('-' * 30)