code: def ftest_anova_power(effect_size, nobs, alpha, k_groups=2, df=None):
      """power for ftest for one way anova with k equal sized groups
  
      nobs total sample size, sum over all groups
  
      should be general nobs observations, k_groups restrictions ???
      """
      df_num = k_groups - 1
      df_denom = nobs - k_groups
      crit = stats.f.isf(alpha, df_num, df_denom)
      pow_ = ncf_sf(crit, df_num, df_denom, effect_size**2 * nobs)
      return pow_
  
imports: import warnings
  import numpy
  from scipy import stats, optimize, special
  from statsmodels.tools.rootfinding import brentq_expanding
label_desc: Use image node_id 10 for calling a global function with example usage: ftest_anova_power(effect_size, nobs, alpha, k_groups, df) and returns: pow_