code: def setup1(n, get_model, noise):
      mn_par, sc_par, sm_par, no_par = get_model(noise)
  
      groups = np.kron(np.arange(n // 5), np.ones(5))
      time = np.kron(np.ones(n // 5), np.arange(5))
      time_z = (time - time.mean()) / time.std()
  
      x_mean = np.random.normal(size=(n, len(mn_par)))
      x_sc = np.random.normal(size=(n, len(sc_par)))
      x_sc[:, 0] = 1
      x_sc[:, 1] = time_z
      x_sm = np.random.normal(size=(n, len(sm_par)))
      x_sm[:, 0] = 1
      x_sm[:, 1] = time_z
  
      mn = np.dot(x_mean, mn_par)
      sc = np.exp(np.dot(x_sc, sc_par))
      sm = np.exp(np.dot(x_sm, sm_par))
  
      if noise:
          x_no = np.random.normal(size=(n, len(no_par)))
          x_no[:, 0] = 1
          x_no[:, 1] = time_z
          no = np.exp(np.dot(x_no, no_par))
      else:
          x_no = None
  
      y = mn.copy()
  
      gc = GaussianCovariance()
  
      ix = collections.defaultdict(list)
      for i, g in enumerate(groups):
          ix[g].append(i)
  
      for g, ii in ix.items():
          c = gc.get_cov(time[ii], sc[ii], sm[ii])
          r = np.linalg.cholesky(c)
          y[ii] += np.dot(r, np.random.normal(size=len(ii)))
  
      # Additive white noise
      if noise:
          y += no * np.random.normal(size=y.shape)
  
      return y, x_mean, x_sc, x_sm, x_no, time, groups
  
file_path: statsmodels/statsmodels/regression/tests/test_processreg.py
imports: from statsmodels.compat.platform import PLATFORM_OSX
  from statsmodels.regression.process_regression import ProcessMLE, GaussianCovariance
  import numpy
  import pandas
  import pytest
  import collections
  import statsmodels.tools.numdiff
  from numpy.testing import assert_allclose, assert_equal
label_desc: Use image node_id 2 for calling a global function with example usage: setup1(n, get_model, noise) and returns: y,  x_mean,  x_sc,  x_sm,  x_no,  time,  groups