class_docstr: Main class producing plots. Each create_*() method reads group data from base_path and creates a plot at the
  save_path. create_all_plots() runs all these functions at once.
class_docstr_tok: ["Main","class","producing","plots",".","Each","create_","*","(",")","method","reads","group","data","from","base_path","and","creates","a","plot","at","the","save_path",".","create_all_plots","(",")","runs","all","these","functions","at","once","."]
docstr: For each scenario, plot the best model performance for each access level (e.g., closed). We plot both the
  performance of the best model chosen for a particular scenario (transparent, in the back) as well as the best
  overall model at that access level.
docstr_tok: ["For","each","scenario",",","plot","the","best","model","performance","for","each","access","level","(","e.g.",",","closed",")",".","We","plot","both","the","performance","of","the","best","model","chosen","for","a","particular","scenario","(","transparent",",","in","the","back",")","as","well","as","the","best","overall","model","at","that","access","level","."]
code: def create_accuracy_v_access_bar_plot(self):
      """
      For each scenario, plot the best model performance for each access level (e.g., closed). We plot both the
      performance of the best model chosen for a particular scenario (transparent, in the back) as well as the best
      overall model at that access level.
      """
      table = self.get_group_tables("core_scenarios")["Accuracy"]
  
      all_groups = [column.group for column in table.columns]
      fig, ax = plt.subplots(1, 1, figsize=(9, 3))
      palette = get_color_palette(n_colors=3)
      access_levels = ["open", "limited", "closed"]
  
      for i, access_level in enumerate(access_levels):
          model_indices: List[int] = [
              idx
              for idx, model in enumerate(table.adapters)
              if self.model_metadata[model].access == access_level
          ]
          best_model_index = model_indices[
              table.mean_win_rates[model_indices].argmax()
          ]
  
          xs = np.arange(len(all_groups))
          width = 0.25
          ys, ys_single = [], []
          for column in table.columns:
              ys.append(column.values[model_indices].max())
              ys_single.append(column.values[best_model_index])
          ax.bar(
              xs + (i - 1) * width,
              ys,
              width,
              color=palette[i],
              alpha=0.5,
          )
          ax.bar(
              xs + (i - 1) * width,
              ys_single,
              width,
              label=access_level,
              color=palette[i],
          )
  
      ax.set_ylabel("Accuracy")
      ax.set_xticks(xs, all_groups, rotation=-20, ha="left")
      ax.legend(loc="upper left", bbox_to_anchor=(0.61, 0.99))
      self.save_figure(fig, "accuracy_v_access")
  
imports: import argparse
  from collections import defaultdict
  from dataclasses import dataclass
  from datetime import date
  import json
  import os
  from typing import List, Dict, Optional, Any, Callable, Union, Mapping, Tuple, Set
  import colorcet
  import matplotlib
  import matplotlib.pyplot
  import numpy
  from scipy.stats import pearsonr
  import seaborn
  from helm.common.hierarchical_logger import hlog
  from helm.benchmark.presentation.schema import read_schema
  from helm.benchmark.presentation.summarize import AGGREGATE_WIN_RATE_COLUMN
label_desc: Use image node_id 9 for calling the Plotter obj's underlying member method code with example usage: obj.create_accuracy_v_access_bar_plot() without return types