import numpy as np
import pyvinecopulib as pv


def random_data(d=5, n=1000):
  # Simulate some data
  np.random.seed(1234)  # seed for the random generator
  mean = np.random.normal(size=d)  # mean vector
  cov = np.random.normal(size=(d, d))  # covariance matrix
  cov = np.dot(cov.transpose(), cov)  # make it non-negative definite
  x = np.random.multivariate_normal(mean, cov, n)
  return x

d = 5
n = 1000
u = pv.to_pseudo_obs(random_data(d, n))

controls = pv.FitControlsVinecop(family_set=[pv.BicopFamily.gaussian])
cop = pv.Vinecop(u, controls=controls)
cop.plot()

var_names = [f"X{i}" for i in range(d)]
cop.plot(vars_names=var_names)