from numba import float64
from numba.experimental import jitclass

opt_growth_data = [
    ('α', float64),          # Production parameter
    ('β', float64),          # Discount factor
    ('μ', float64),          # Shock location parameter
    ('γ', float64),          # Preference parameter
    ('s', float64),          # Shock scale parameter
    ('grid', float64[:]),    # Grid (array)
    ('shocks', float64[:])   # Shock draws (array)
]

@jitclass(opt_growth_data)
class OptimalGrowthModel_CRRA:

    def __init__(self,
                α=0.4,
                β=0.96,
                μ=0,
                s=0.1,
                γ=1.5,
                grid_max=4,
                grid_size=120,
                shock_size=250,
                seed=1234):

        self.α, self.β, self.γ, self.μ, self.s = α, β, γ, μ, s

        # Set up grid
        self.grid = np.linspace(1e-5, grid_max, grid_size)

        # Store shocks (with a seed, so results are reproducible)
        np.random.seed(seed)
        self.shocks = np.exp(μ + s * np.random.randn(shock_size))


    def f(self, k):
        "The production function."
        return k**self.α

    def u(self, c):
        "The utility function."
        return c**(1 - self.γ) / (1 - self.γ)

    def f_prime(self, k):
        "Derivative of f."
        return self.α * (k**(self.α - 1))

    def u_prime(self, c):
        "Derivative of u."
        return c**(-self.γ)

    def u_prime_inv(c):
        return c**(-1 / self.γ)