pyodide/benchmark/benchmarks/hasting.py

# 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