original_docstring: 
      Takes a list of disorders, subset_size, and function values determined 
      for different subsets of r
      Returns the standard deviation of the function value for overlapping
      r values
      
docstring: Takes a list of disorders, subset_size, and function values determined
  for different subsets of r
  Returns the standard deviation of the function value for overlapping
  r values
docstring_tokens: Takes a list of disorders subset_size and function values determined for different subsets of r Returns the standard deviation of the function value for overlapping r values
code: def std_funcs(r, subset_size, func_values):
      """
      Takes a list of disorders, subset_size, and function values determined 
      for different subsets of r
      Returns the standard deviation of the function value for overlapping
      r values
      """
      # Set up empty dataframe
      columns = [str(r_val) for r_val in r]
      index = [x for x in range(len(r)-subset_size+1)]
      df =  pd.DataFrame(columns=columns, index=index)
      # Fill in dataframe 
      for i in range(len(func_values)):
           r_vals = [ str(r_val) for r_val in r[i:i+subset_size]]
           func_values_dict = dict(zip(r_vals, func_values[i]))
           df.loc[i] = pd.Series(func_values_dict)
      # Create error dict
      border_len = len(r)-subset_size
      subset_r = r[border_len:-border_len]
      errors = np.std(np.asarray(df.dropna(axis=1).values,dtype=np.float32), axis=0)
      error_dict = dict(zip(subset_r, errors))
      return error_dict
code_tokens: def std_funcs ( r , subset_size , func_values ) : columns = [ str ( r_val ) for r_val in r ] index = [ x for x in range ( len ( r ) - subset_size + 1 ) ] df = pd . DataFrame ( columns = columns , index = index ) for i in range ( len ( func_values ) ) : r_vals = [ str ( r_val ) for r_val in r [ i : i + subset_size ] ] func_values_dict = dict ( zip ( r_vals , func_values [ i ] ) ) df . loc [ i ] = pd . Series ( func_values_dict ) border_len = len ( r ) - subset_size subset_r = r [ border_len : - border_len ] errors = np . std ( np . asarray ( df . dropna ( axis = 1 ) . values , dtype = np . float32 ) , axis = 0 ) error_dict = dict ( zip ( subset_r , errors ) ) return error_dict
short_docstring: Takes a list of disorders, subset_size, and function values determined
  for different subsets of r
  Returns the standard deviation of the function value for overlapping
  r values
short_docstring_tokens: Takes a list of disorders subset_size and function values determined for different subsets of r Returns the standard deviation of the function value for overlapping r values
comment: """
      Takes a list of disorders, subset_size, and function values determined 
      for different subsets of r
      Returns the standard deviation of the function value for overlapping
      r values
      """$SEP$# Set up empty dataframe$SEP$# Fill in dataframe $SEP$# Create error dict