Example Configuration File

Table of Examples

• Execute Genetic Algorithm Fitting and MCMC With a Age-Depth Model and a Eccentricity Solution

• Execute Genetic Algorithm Fitting Without a Age-Depth Model and a Eccentricity Solution

• Execute Significance Test

• Example MCMC and Weights (Only using eccentricity and precession. Using uniform distributions for prior values )

---

Execute Genetic Algorithm Fitting and MCMC With a Age-Depth Model and a Eccentricity Solution

data_set:
    data_path:  path/dataset/ODP926.txt
    depth_column_name: depth
    proxy_column_name: proxy
    skiprows: 0
    delimiter: " "
    header: True

age_depth_model_data:
    data_path: path/model/U926_AgeModel_Wilkens_etal_2017.txt
    depth_column_name: Depth
    age_column_name: age
    skiprows: 0
    delimiter: ","
    header: True

eccentricity_solution_data:
    data_path: path/solution/La2010d_ecc3L.dat
    age_column_name:
    eccentricity_column_name:
    skiprows: 0
    delimiter: \s+
    header: False
    start_time: 0e3
    final_time: 20e3

data_model_parameters:
    sedimentation_rate_min: 0.4
    sedimentation_rate_max: 5

    #unit: arcsec/yr
    ### gi frequencies should be strictly in the order of g1, g2, g3, g4.
    frequency_values:
        use_precession: True
        use_eccentricity: True
        use_tilt: True

        p0_values: [50,55]
        gi_values: [[5.45, 5.75], [7.43, 7.48], [17.1, 17.4], [17.7, 18.0]]
        si_values: [[-19., -18.8], [-17.85, -17.7]]

        g5_value: default
        s6_value: default

genetic_algorithm_parameters:
    seed:
    interpolator: linear
    metric_type: r2
    population_size: 300
    number_generations: 300
    number_processors_used: 10
    number_algorithm_solutions: 30
    list_number_genes: [2,  4,  8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96]

mcmc_parameters:
    length_mcmc_chains: 5000
    discard: 2000
    thin: 50
    number_processors_used_mcmc: 15
    number_mcmc_solutions: 15
    list_of_genes_mcmc: [ 2, 12, 24, 36, 48, 60, 72, 84, 96]
    prior_distributions:
        p0_distribution: gaussian
        gi_distribution: gaussian
        si_distribution: gaussian
    prior_frequencies:
        p0_prior: [50.6443465,0.15]
        gi_prior: [[5.579378, 0.055], [7.456665, 0.004], [17.366595, 0.030], [17.910194, 0.032]]
        si_prior: [[-18.845166, 0.047], [-17.758310, 0.023]]

# ------------------- OUTPUT FILE TO GET THE RESULTS ------------------------ #
output_folder:  path/to/the/output/folder
output_file_name: name_of_the_result_files_ODP_926

---

Execute Genetic Algorithm Fitting Without a Age-Depth Model and a Eccentricity Solution

data_set:
    data_path:  path/dataset/ODP926.txt
    depth_column_name: depth
    proxy_column_name: proxy
    skiprows: 0
    delimiter: " "
    header: True

data_model_parameters:
    sedimentation_rate_min: 0.4
    sedimentation_rate_max: 5

    #unit: arcsec/yr
    ### gi frequencies should be strictly in the order of g1, g2, g3, g4.
    frequency_values:
        use_precession: True
        use_eccentricity: True
        use_tilt: True

        p0_values: [50,55]
        gi_values: [[5.45, 5.75], [7.43, 7.48], [17.1, 17.4], [17.7, 18.0]]
        si_values: [[-19., -18.8], [-17.85, -17.7]]

        g5_value: default
        s6_value: default

genetic_algorithm_parameters:
    seed:
    interpolator: linear
    metric_type: r2
    population_size: 300
    number_generations: 300
    number_processors_used: 10
    number_algorithm_solutions: 30
    list_number_genes: [2,  4,  8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96]

# ------------------- OUTPUT FILE TO GET THE RESULTS ------------------------ #
output_folder:  path/to/the/output/folder
output_file_name: name_of_the_result_files_ODP_926

---

Execute Significance Test

data_set:
    data_path:  path/dataset/ODP926.txt
    depth_column_name: depth
    proxy_column_name: proxy
    skiprows: 0
    delimiter: " "
    header: True

data_model_parameters:
    sedimentation_rate_min: 0.4
    sedimentation_rate_max: 5

    #unit: arcsec/yr
    ### gi frequencies should be strictly in the order of g1, g2, g3, g4.
    frequency_values:
        use_precession: True
        use_eccentricity: True
        use_tilt: True

        p0_values: [50,55]
        gi_values: [[5.45, 5.75], [7.43, 7.48], [17.1, 17.4], [17.7, 18.0]]
        si_values: [[-19., -18.8], [-17.85, -17.7]]

        g5_value: default
        s6_value: default

significance_test_parameters:
    seed:
    interpolator: linear
    metric_type: loglike
    population_size: 300
    number_generations: 300
    number_processors_used: 10
    number_algorithm_executions: 50
    number_noise_executions: 50
    list_number_genes: [2, 12, 24, 36]

# ------------------- OUTPUT FILE TO GET THE RESULTS ------------------------ #
output_folder:  path/to/the/output/folder
output_file_name: name_of_the_result_files_ODP_926

---

Example MCMC and Weights (Only using eccentricity and precession. Using uniform distributions for prior values )

data_set:
    data_path:  path/dataset/ODP926.txt
    depth_column_name: depth
    proxy_column_name: proxy
    skiprows: 0
    delimiter: " "
    header: True

data_model_parameters:
    sedimentation_rate_min: 0.4
    sedimentation_rate_max: 5

    #unit: arcsec/yr
    ### gi frequencies should be strictly in the order of g1, g2, g3, g4.
    frequency_values:
        use_precession: True
        use_eccentricity: True
        use_tilt: False

        p0_values: [50,55]
        gi_values: [[5.45, 5.75], [7.43, 7.48], [17.1, 17.4], [17.7, 18.0]]
        si_values:

        g5_value: default
        s6_value: default

mcmc_parameters:
    length_mcmc_chains: 5000
    discard: 2000
    thin: 50
    number_processors_used_mcmc: 15
    number_mcmc_solutions: 15
    list_of_genes_mcmc: [ 2, 12, 24, 36, 48, 60, 72, 84, 96]
    prior_distributions:
        p0_distribution: uniform
        gi_distribution: uniform
        si_distribution: uniform
    prior_frequencies:
        p0_prior: [50.6443465,0.15]
        gi_prior: [[5.579378, 0.055], [7.456665, 0.004], [17.366595, 0.030], [17.910194, 0.032]]
        si_prior:

weight_calcula_configuration:
    number_weight_evaluation_per_chain: 5
    number_processors_used_weights: 5
    stability_factor: 1e-8
    pareto_smoothing: True

# ------------------- OUTPUT FILE TO GET THE RESULTS ------------------------ #
output_folder:  path/to/the/output/folder
output_file_name: name_of_the_result_files_ODP_926

---

Execute Genetic Algorithm Fitting with fixed frequencies (only using eccentricity and precession)

data_set:
    data_path:  path/dataset/ODP926.txt
    depth_column_name: depth
    proxy_column_name: proxy
    skiprows: 0
    delimiter: " "
    header: True

data_model_parameters:
    sedimentation_rate_min: 0.4
    sedimentation_rate_max: 5

    #unit: arcsec/yr
    ### gi frequencies should be strictly in the order of g1, g2, g3, g4.
    frequency_values:
        use_precession: True
        use_eccentricity: True
        use_tilt:

        p0_values: 55
        gi_values: [5.75, 7.48, 17.4, 18.0]
        si_values:

        g5_value: default
        s6_value: default

genetic_algorithm_parameters:
    seed:
    interpolator: linear
    metric_type: r2
    population_size: 300
    number_generations: 300
    number_processors_used: 10
    number_algorithm_solutions: 30
    list_number_genes: [2,  4,  8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96]

# ------------------- OUTPUT FILE TO GET THE RESULTS ------------------------ #
output_folder:  path/to/the/output/folder
output_file_name: name_of_the_result_files_ODP_926

---