mirror of https://github.com/pyodide/pyodide.git
20 lines
656 B
Python
20 lines
656 B
Python
# from: http://wiki.scipy.org/Cookbook/Theoretical_Ecology/Hastings_and_Powell
|
|
# setup: import numpy as np ; y = np.random.rand(3) ; args = np.random.rand(7)
|
|
# run: hasting(y, *args)
|
|
|
|
# pythran export hasting(float [], float, float, float, float, float,
|
|
# float, float)
|
|
import numpy as np
|
|
|
|
|
|
def hasting(y, t, a1, a2, b1, b2, d1, d2):
|
|
yprime = np.empty((3,))
|
|
yprime[0] = y[0] * (1.0 - y[0]) - a1 * y[0] * y[1] / (1.0 + b1 * y[0])
|
|
yprime[1] = (
|
|
a1 * y[0] * y[1] / (1.0 + b1 * y[0])
|
|
- a2 * y[1] * y[2] / (1.0 + b2 * y[1])
|
|
- d1 * y[1]
|
|
)
|
|
yprime[2] = a2 * y[1] * y[2] / (1.0 + b2 * y[1]) - d2 * y[2]
|
|
return yprime
|