docstr: Compare bootstrap distribution of the mean to sampling distribution
  of the mean.
code: def test_calc_bootstrap(self, true_mean, true_sd, n):
      """Compare bootstrap distribution of the mean to sampling distribution
      of the mean.
  
      """
      np.random.seed(123)
      func = np.mean
      returns = pd.Series((np.random.randn(n) * true_sd) + true_mean)
  
      samples = timeseries.calc_bootstrap(
          func, returns, n_samples=10000
      )
  
      # Calculate statistics of sampling distribution of the mean
      mean_of_mean = np.mean(returns)
      sd_of_mean = np.std(returns) / np.sqrt(n)
  
      assert_almost_equal(
          np.mean(samples),
          mean_of_mean,
          3,
          "Mean of bootstrap does not match theoretical mean of"
          "sampling distribution",
      )
  
      assert_almost_equal(
          np.std(samples),
          sd_of_mean,
          3,
          "SD of bootstrap does not match theoretical SD of"
          "sampling distribution",
      )
  
imports: from __future__ import division
  import os
  from unittest import TestCase
  from nose_parameterized import parameterized
  from numpy.testing import assert_allclose, assert_almost_equal
  from pandas.util.testing import assert_series_equal
  import numpy
  import pandas
  from ..None import timeseries
  from pyfolio.utils import to_utc, to_series
  import gzip
label_desc: Use image node_id 1 for calling the TestBootstrap obj's underlying member method code with example usage: obj.test_calc_bootstrap(true_mean, true_sd, n) without return types