在Matlab中,“del2”函数用于计算离散拉普拉斯算子。我正在 Python 中寻找类似的函数或方法来执行相同的任务。谁能建议Python中的等效函数或方法来计算离散拉普拉斯算子?任何指导或代码示例将不胜感激。谢谢!
import deepxde
def pde_1D(x, y):
V, W = y[:, 0:1], y[:, 1:2]
dv_dt = dde.grad.jacobian(y, x, i=0, j=1)
dv_dxx = dde.grad.hessian(y, x, component=0, i=0, j=0)
dw_dt = dde.grad.jacobian(y, x, i=1, j=1)
## Coupled PDE+ODE Equations
eq_a = dv_dt - D*dv_dxx + k*V*(V-a)*(V-1) +W*V
eq_b = dw_dt - (epsilon + (mu_1*W)/(mu_2+V))*(-W -k*V*(V-b-1))
return [eq_a, eq_b]
Scipy 有一个 scipy.stats.dlaplace 可用于计算 dlaplace 概率质量函数:
import numpy as np
from scipy.stats import dlaplace
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
a = 0.8
mean, var, skew, kurt = dlaplace.stats(a, moments='mvsk')
x = np.arange(dlaplace.ppf(0.01, a),
dlaplace.ppf(0.99, a))
ax.plot(x, dlaplace.pmf(x, a), 'bo', ms=8, label='dlaplace pmf')
ax.vlines(x, 0, dlaplace.pmf(x, a), colors='b', lw=5, alpha=0.5)