group_name:
    
    group_description:
    
    evaluation_form:
    group_name: Simple_example  # custom analysis task name.

    group_description: xxxxxx # some simple description of the task.
    
    evaluation_form:
      # you should choose 0-3 based on your configurations.
      algorithm_dataset_type: 0 # 0: same_algorithm_same_dataset.          can choose: 1 2 3 4 6 7 8
                              # 1: different_algorithm_same dataset        can choose: 1 2 3 4 6 7
                              # 2: same_algorithm_different_dataset        can choose: 3 6 7
                              # 3: different_algorithm_different_dataset.  can choose: 3 6 7
      1_trajectory_comparison: # compare the trajectories.
        choose: 0 # choose this analysis (0) or not (1)
      
      2_accuracy_metrics_comparison: # compare the ATE and RPE metrics of trajectories
        choose: 0  
      
      3_accuracy_metrics_comparison: # generate a histogram of different (algorithm + dataset) group.
        choose: 0
        calculate_method: 1 # 0: average (because one algo + one dataset may include different configs)
                          # 1: choose the best one   
                          # then for each (algorithm, dataset), get one configuration, then calculate the average value
        metric: ate_rmse # eg: ATE-RMSE
        algorithm_id: [12] # choose the algorithms by ID
        dataset_id: [2] # choose the dataset sequences by ID
      
      4_usage_metrics_comparison: # compare the CPU and Memory usage.
        choose: 0  
    
      6_scatter_diagram: # generate a scatter to show the metrics of different configurations' task.
        choose: 1
        x-axis: general+image_frequency # X axis's metric
        y-axis: ate_mean # Y axis's metric
        # You can choose: 1) Evo metrics; 2) Resource Usage metrics; 3) Configuration parameters (provide the parameter template's name; and you should choose one that all configurations use this template).
        
      7_3d_scatter_diagram: # 3D version of the scatter
        choose: 1
        x-axis: general+image_width
        y-axis: general+imu_frequency
        z-axis: ate_mean
      # For the scatter and 3D scatter, when you create a analysis, you can also use this task to create more scatters with other axis's metrics; And you can also see the online dynamic scatter on the webpage.  
        
      8_repeatability_test: # Test one configuration's stability.
        choose: 0 # If you use this analysis, you should only choose one configuration.
        metric: ate_mean
     
    # 
    configuration_choose: # implicit limitation：chosen configs has running task and evaluation
      # three ways to choose confg
      configuration_id: [] # provide configurations id
      comb_configuration_id: [72] # provide combination configurations id (Equal to choose all configurations ID contained by these comb configs).
      limitation_rules:   # by a rule;
        algorithm_id: [12, 13]
        dataset_id: [2,3]
        parameters_value: ["nFeatures < 4000"]
        evaluation_value:
          ate_rmse_nolimitation: 1              # 0 or 1
          ate_rmse_minimum:                   # if just minimum and no maximum, can just fill the minimum
          ate_rmse_maximum:
          ate_mean_nolimitation:    1           # 0 or 1
          ate_mean_minimum:                  
          ate_mean_maximum:
          ate_median_nolimitation:   1           # 0 or 1
          ate_median_minimum:                  
          ate_median_maximum:
          ate_std_nolimitation:   1           # 0 or 1
          ate_std_minimum:                  
          ate_std_maximum:
          ate_min_nolimitation:    1          # 0 or 1
          ate_min_minimum:                  
          ate_min_maximum:
          ate_max_nolimitation:  1            # 0 or 1
          ate_max_minimum:                  
          ate_max_maximum:
          ate_sse_nolimitation:    1          # 0 or 1
          ate_sse_minimum:                  
          ate_sse_maximum:
          rpe_rmse_nolimitation:     1         # 0 or 1
          rpe_rmse_minimum:                   # if just minimum and no maximum, can just fill the minimum
          rpe_rmse_maximum:
          rpe_mean_nolimitation:    1          # 0 or 1
          rpe_mean_minimum:                  
          rpe_mean_maximum:
          rpe_median_nolimitation:    1          # 0 or 1
          rpe_median_minimum:                  
          rpe_median_maximum:
          rpe_std_nolimitation:    1          # 0 or 1
          rpe_std_minimum:                  
          rpe_std_maximum:
          rpe_min_nolimitation:    1          # 0 or 1
          rpe_min_minimum:                  
          rpe_min_maximum:
          rpe_max_nolimitation:   1           # 0 or 1
          rpe_max_minimum:                  
          rpe_max_maximum:
          rpe_sse_nolimitation:     1         # 0 or 1
          rpe_sse_minimum:                  
          rpe_sse_maximum:
          cpu_max_nolimitation:    1          # 0 or 1
          cpu_max_minimum:                  
          cpu_max_maximum:
          cpu_mean_nolimitation:    1          # 0 or 1
          cpu_mean_minimum:                  
          cpu_mean_maximum:
          ram_max_nolimitation:     1         # 0 or 1
          ram_max_minimum:                  
          ram_max_maximum:
      combination_rule:   # how to combine three ways：U - union set; I - intersection set; complement set;
        first_one: [1]
        first_rule: ["U"]
        second_one: [0,2]
        second_rule: ["I"] # (1) - (0 I 2)