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_func5():
def g_func(m, t):
a = t + 2
b = m + 6
c = a + b
d = m * 2 + c
return (c, d), a, b
from pprint import pprint
df = DiffEquation(func=g_func)
pprint(df.expressions)
pprint(df.return_intermediates)
pprint(df.get_f_expressions())
pprint(df.get_g_expressions())
print('-' * 30)
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.return_intermediates)
pprint(df.get_f_expressions())
pprint(df.get_g_expressions())
print('-' * 30)
def test_multi_system():
def int_func(array, t, V):
alpha = 0.1 * (V + 40) / (1 - np.exp(-(V + 40) / 10))
beta = 4.0 * np.exp(-(V + 65) / 18)
m = alpha * (1 - array[0]) - beta * array[0]
alpha2 = 0.07 * np.exp(-(V + 65) / 20.)
beta2 = 1 / (1 + np.exp(-(V + 35) / 10))
h = alpha2 * (1 - array[1]) - beta2 * array[1]
return np.array([m, h])
from pprint import pprint
df = DiffEquation(func=int_func)
pprint(df.expressions)
pprint(df.return_intermediates)
pprint(df.get_f_expressions())
pprint(df.get_g_expressions())
print('-' * 30)
def test_assignment():
def int_func(array, t, V):
res = np.zeros_like(array)
alpha = 0.1 * (V + 40) / (1 - np.exp(-(V + 40) / 10))
beta = 4.0 * np.exp(-(V + 65) / 18)
res[0] = alpha * (1 - array[0]) - beta * array[0]
alpha2 = 0.07 * np.exp(-(V + 65) / 20.)
beta2 = 1 / (1 + np.exp(-(V + 35) / 10))
res[1] = alpha2 * (1 - array[1]) - beta2 * array[1]
return res
from pprint import pprint
df = DiffEquation(func=int_func)
pprint(df.expressions)
pprint(df.return_intermediates)
pprint(df.get_f_expressions())
pprint(df.get_g_expressions())
print('-' * 30)