visualization_utils/plotting.py [361:395]:
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                            delta_improvement = (float(last_row[dis_to_goal_column_number]) - float(row[dis_to_goal_column_number]))/(float(last_row[dis_to_goal_column_number])*len(col_values))


                            if not col_val == last_col_val and i > 1:
                                if not last_row_change == "":
                                    distance_from_last_change =  float(last_row[x_column_number]) - float(last_row_change[x_column_number]) + idx * delta_x_column
                                    column_co_design_dist[y_column_name].append(distance_from_last_change)
                                    improvement_from_last_change =  (float(last_row[dis_to_goal_column_number]) - float(row[dis_to_goal_column_number]))/float(last_row[dis_to_goal_column_number])  + idx *delta_improvement
                                    column_co_design_improvement[y_column_name].append(improvement_from_last_change)

                                last_row_change = copy.deepcopy(last_row)


                            last_col_val = col_val



            # co_des cnt
            # we ignore the first element as the first element distance is always zero
            co_design_dist_sum = 0
            co_design_efficacy_sum = 0
            avg_ctr = 1
            co_design_dist_selected = column_co_design_dist[y_column_name]
            co_design_improvement_selected = column_co_design_improvement[y_column_name]
            for idx,el in enumerate(column_co_design_dist[y_column_name]):
                if idx == len(co_design_dist_selected) - 1:
                    break
                co_design_dist_sum += 1/(column_co_design_dist[y_column_name][idx] + column_co_design_dist[y_column_name][idx+1])
                co_design_efficacy_sum += (column_co_design_improvement[y_column_name][idx] + column_co_design_improvement[y_column_name][idx+1])
                #/(column_co_design_dist[y_column_name][idx] + column_co_design_dist[y_column_name][idx+1])
                avg_ctr+=1

            column_co_design_improvement = {}
            column_co_design_dist_avg[experiment_name][y_column_name]= co_design_dist_sum/avg_ctr
            column_co_design_efficacy_avg[experiment_name][y_column_name] = co_design_efficacy_sum/avg_ctr
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visualization_utils/plotting.py [521:555]:
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                            delta_improvement = (float(last_row[dis_to_goal_column_number]) - float(row[dis_to_goal_column_number]))/(float(last_row[dis_to_goal_column_number])*len(col_values))


                            if not col_val == last_col_val and i > 1:
                                if not last_row_change == "":
                                    distance_from_last_change =  float(last_row[x_column_number]) - float(last_row_change[x_column_number]) + idx * delta_x_column
                                    column_co_design_dist[y_column_name].append(distance_from_last_change)
                                    improvement_from_last_change =  (float(last_row[dis_to_goal_column_number]) - float(row[dis_to_goal_column_number]))/float(last_row[dis_to_goal_column_number])  + idx *delta_improvement
                                    column_co_design_improvement[y_column_name].append(improvement_from_last_change)

                                last_row_change = copy.deepcopy(last_row)


                            last_col_val = col_val



            # co_des cnt
            # we ignore the first element as the first element distance is always zero
            co_design_dist_sum = 0
            co_design_efficacy_sum = 0
            avg_ctr = 1
            co_design_dist_selected = column_co_design_dist[y_column_name]
            co_design_improvement_selected = column_co_design_improvement[y_column_name]
            for idx,el in enumerate(column_co_design_dist[y_column_name]):
                if idx == len(co_design_dist_selected) - 1:
                    break
                co_design_dist_sum += 1/(column_co_design_dist[y_column_name][idx] + column_co_design_dist[y_column_name][idx+1])
                co_design_efficacy_sum += (column_co_design_improvement[y_column_name][idx] + column_co_design_improvement[y_column_name][idx+1])
                #/(column_co_design_dist[y_column_name][idx] + column_co_design_dist[y_column_name][idx+1])
                avg_ctr+=1

            column_co_design_improvement = {}
            column_co_design_dist_avg[experiment_name][y_column_name]= co_design_dist_sum/avg_ctr
            column_co_design_efficacy_avg[experiment_name][y_column_name] = co_design_efficacy_sum/avg_ctr
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