text: import numpy as np
  import scipy.stats as stats
  import pandas as pd
  import banana_model
  import mmd
  import argparse
  import clipping
  
  parser = argparse.ArgumentParser()
  parser.add_argument("clip_bound", type=float)
  parser.add_argument("dim", type=int)
  parser.add_argument("index", type=int)
  parser.add_argument("output", type=str)
  args = parser.parse_args()
  
  problem = banana_model.get_problem(dim=args.dim, a=20, n0=None, n=100000)
  n, data_dim = problem.data.shape
  
  iters = 3000
  prop_sigma = 0.01 if args.dim == 2 else np.hstack(
      (np.array((0.015, 0.015)), np.repeat(0.002, args.dim - 2))
  )
  clip_bound = args.clip_bound
  dim = args.dim
  theta0 = np.zeros(dim)
  theta0[1] = 3
  
  # Set the seed for the starting points only based on index
  np.random.seed(4238946 + args.index)
  problem.theta0 += np.random.normal(scale=0.05, size=dim)
  
  # Set the seed for the algorithm to be different for each algorithm
  np.random.seed(
      (int(("rwmh" + str(args.clip_bound)).encode("utf8").hex(), 16) + args.index) % 2**32
  )
  
  res = clipping.rwmh(problem, iters, prop_sigma, clip_bound, theta0)
  
  acceptance = res.accepts / iters
  clipping = np.sum(res.clipped / iters / n)
  diff_decisions = np.sum(res.diff_accepts)
  
  posterior = problem.true_posterior
  mmd = mmd.mmd(res.final_chain, posterior)
  mean_error = np.sqrt(np.sum((np.mean(posterior, axis=0) - np.mean(res.final_chain, axis=0))**2))
  var_error = np.sqrt(np.sum((np.var(posterior, axis=0) - np.var(res.final_chain, axis=0))**2))
  
  result = pd.DataFrame({
      "clip bound": [clip_bound],
      "dim": [dim],
      "i": [args.index],
      "algo": ["RWMH"],
      "acceptance": [acceptance],
      "clipping": [clipping],
      "diff decisions": [diff_decisions],
      "mmd": [mmd],
      "mean error": [mean_error],
      "var error": [var_error]
  })
  result.to_csv(args.output, header=False)
  
  