code: import itertools
  import time
  import warnings
  
  import numpy as np
  import matplotlib.colors
  import matplotlib.pyplot as plt
  
  import thalesians.tsa.checks as checks
  import thalesians.tsa.numpyutils as npu
  import thalesians.tsa.utils as utils
  
  def _aggregate(aggregate_func, data, empty_aggregate):
      if empty_aggregate != 'none':
          return npu.apply(lambda x: empty_aggregate if len(x) == 0 else aggregate_func(x), data)
      else:
          return npu.apply(aggregate_func, data)
  
  def visualize_grid_search(grid_search_result,
          aggregate_func=np.nanmean, empty_aggregate='none',
          fig=None, title=None,
          refresh_until_ready=False):
      if fig is None: fig = plt.figure()
  
      if title is None: title = grid_search_result.optimization_id
      fig.suptitle(title)
  
      param_names = list(grid_search_result.param_ranges.keys())
  
      subplots = {}
      heatmaps = {}
      datas = {}
  
      for i1 in range(len(param_names)):
          param_name1 = param_names[i1]
          param_values1 = grid_search_result.param_ranges[param_name1]
          for i2 in range(i1):
              param_name2 = param_names[i2]
              param_values2 = grid_search_result.param_ranges[param_name2]
              data = np.empty((len(param_values1), len(param_values2)), dtype=object)
              for i in range(np.size(data)): data.flat[i] = []
              datas[(i1, i2)] = data
  
              ax = fig.add_subplot(len(param_names) - 1, len(param_names) - 1, (i1 - 1) * (len(param_names) - 1) + i2 + 1)
              subplots[(i1, i2)] = ax
              
              initial_data = _aggregate(aggregate_func, datas[(i1, i2)], empty_aggregate)
  
              heatmaps[(i1, i2)] = ax.matshow(npu.apply(aggregate_func, initial_data), cmap='coolwarm')
  
              if i2 == i1 - 1:
                  ax.set_xticklabels([np.nan] + [0. if x == 1e-06 else x for x in param_values2], fontsize=6, rotation='vertical', verticalalignment='bottom')
                  ax.xaxis.set_ticks_position('top')
                  ax.set_yticklabels([np.nan] + [0. if x == 1e-06 else x for x in param_values1], fontsize=6)
                  ax.yaxis.set_ticks_position('right')
              else:
                  ax.set_xticks([])
                  ax.set_yticks([])
              if i1 == len(param_names) - 1: ax.set_xlabel(param_name2)
              if i2 == 0: ax.set_ylabel(param_name1)
  
      while True:
          all_ready = True
          for status in grid_search_result.evaluation_statuses:
              if not status.ready: all_ready = False
              else:
                  checks.check(utils.sequence_eq(param_names, status.work.info['param_names']))
                  param_value_index_combinations = itertools.combinations(range(len(param_names)), 2)
                  param_value_index_combinations = [(i2, i1) for (i1, i2) in param_value_index_combinations if i1 != i2]
                  for i1, i2 in param_value_index_combinations:
                      param_value_index1 = status.work.info['param_value_indices'][i1]
                      param_value_index2 = status.work.info['param_value_indices'][i2]
                      if status.result.exception is not None:
                          result = np.nan
                      elif status.result.result is None:
                          result = np.nan
                      else:
                          result = status.result.result
                      datas[(i1, i2)][param_value_index1, param_value_index2].append(result)
          for i1 in range(len(param_names)):
              for i2 in range(i1):
                  new_data = _aggregate(aggregate_func, datas[(i1, i2)], empty_aggregate)
                  heatmaps[(i1, i2)].set_data(new_data)
                  heatmaps[(i1, i2)].autoscale()
          if (not refresh_until_ready) or all_ready: break
          else:
              fig.canvas.draw()
              time.sleep(1)
  
      return fig
  
apis: matplotlib.pyplot.figure numpy.size