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基于 SOC 职业分类
| name | numpy-numerical-analysis-6-numerical-integration |
| description | Sub-skill of numpy-numerical-analysis: 6. Numerical Integration. |
| version | 1.0.0 |
| category | data |
| type | reference |
| scripts_exempt | true |
Time-Stepping Integration:
def runge_kutta_4th_order(
derivative_func,
y0: np.ndarray,
t: np.ndarray
) -> np.ndarray:
"""
4th-order Runge-Kutta integration.
Args:
derivative_func: Function dy/dt = f(t, y)
y0: Initial conditions
t: Time array
Returns:
Solution array
"""
n = len(t)
y = np.zeros((n, len(y0)))
y[0] = y0
for i in range(n - 1):
dt = t[i+1] - t[i]
k1 = derivative_func(t[i], y[i])
k2 = derivative_func(t[i] + dt/2, y[i] + k1*dt/2)
k3 = derivative_func(t[i] + dt/2, y[i] + k2*dt/2)
k4 = derivative_func(t[i] + dt, y[i] + k3*dt)
y[i+1] = y[i] + (dt/6) * (k1 + 2*k2 + 2*k3 + k4)
return y
# Example: Simple harmonic oscillator
# m*x'' + k*x = 0
# y = [x, v], dy/dt = [v, -k/m*x]
def oscillator_derivatives(t, y):
"""Simple harmonic oscillator."""
m = 1.0 # Mass
k = 4.0 # Stiffness (omega = 2 rad/s, T = pi s)
x, v = y
dxdt = v
dvdt = -k/m * x
return np.array([dxdt, dvdt])
# Initial conditions
y0 = np.array([1.0, 0.0]) # x = 1, v = 0
# Time array
t = np.linspace(0, 10, 1000)
# Solve
solution = runge_kutta_4th_order(oscillator_derivatives, y0, t)
print(f"Final position: {solution[-1, 0]:.4f}")
print(f"Final velocity: {solution[-1, 1]:.4f}")
Trapezoidal Integration:
def integrate_spectrum(
frequencies: np.ndarray,
spectral_density: np.ndarray
) -> float:
"""
Integrate spectral density to get variance.
m_0 = ∫ S(f) df
Args:
frequencies: Frequency array
spectral_density: Spectral density array
Returns:
Integral (variance)
"""
variance = np.trapz(spectral_density, frequencies)
return variance
# Example: Calculate significant wave height from spectrum
freq = np.linspace(0.05, 0.5, 100) # Hz
S = 10 * freq**(-5) # Simplified spectrum
m0 = integrate_spectrum(freq, S)
Hs = 4 * np.sqrt(m0)
print(f"Significant wave height: {Hs:.2f} m")