class_docstr: Statistical Power calculations for one sample or paired sample t-test
  
      
class_docstr_tok: ["Statistical","Power","calculations","for","one","sample","or","paired","sample","t-test"]
docstr: Calculate the power of a t-test for one sample or paired samples.
  
  Parameters
  ----------
  effect_size : float
      standardized effect size, mean divided by the standard deviation.
      effect size has to be positive.
  nobs : int or float
      sample size, number of observations.
  alpha : float in interval (0,1)
      significance level, e.g. 0.05, is the probability of a type I
      error, that is wrong rejections if the Null Hypothesis is true.
  df : int or float
      degrees of freedom. By default this is None, and the df from the
      one sample or paired ttest is used, ``df = nobs1 - 1``
  alternative : str, 'two-sided' (default), 'larger', 'smaller'
      extra argument to choose whether the power is calculated for a
      two-sided (default) or one sided test. The one-sided test can be
      either 'larger', 'smaller'.
      .
  
  Returns
  -------
  power : float
      Power of the test, e.g. 0.8, is one minus the probability of a
      type II error. Power is the probability that the test correctly
      rejects the Null Hypothesis if the Alternative Hypothesis is true.
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code: def power(
      self, effect_size, nobs, alpha, df=None, alternative="two-sided"
  ):
      """Calculate the power of a t-test for one sample or paired samples.
  
      Parameters
      ----------
      effect_size : float
          standardized effect size, mean divided by the standard deviation.
          effect size has to be positive.
      nobs : int or float
          sample size, number of observations.
      alpha : float in interval (0,1)
          significance level, e.g. 0.05, is the probability of a type I
          error, that is wrong rejections if the Null Hypothesis is true.
      df : int or float
          degrees of freedom. By default this is None, and the df from the
          one sample or paired ttest is used, ``df = nobs1 - 1``
      alternative : str, 'two-sided' (default), 'larger', 'smaller'
          extra argument to choose whether the power is calculated for a
          two-sided (default) or one sided test. The one-sided test can be
          either 'larger', 'smaller'.
          .
  
      Returns
      -------
      power : float
          Power of the test, e.g. 0.8, is one minus the probability of a
          type II error. Power is the probability that the test correctly
          rejects the Null Hypothesis if the Alternative Hypothesis is true.
  
      """
      # for debugging
      # print 'calling ttest power with', (effect_size, nobs, alpha, df, alternative)
      return ttest_power(
          effect_size, nobs, alpha, df=df, alternative=alternative
      )
  
imports: import warnings
  import numpy
  from scipy import stats, optimize, special
  from statsmodels.tools.rootfinding import brentq_expanding
label_desc: Use image node_id 1 for calling the TTestPower obj's underlying member method code with example usage: obj.power(effect_size, nobs, alpha, df, alternative) and returns: ttest_power