Remove UltraNest and PySwarms config blocks

config/non_linear/mle.yaml

@@ -2,53 +2,10 @@
 
 # **PyAutoFit** supports the following maximum likelihood estimator (MLE) algorithms:
 
-# - PySwarms: https://github.com/ljvmiranda921/pyswarms / https://pyswarms.readthedocs.io/en/latest/index.html
 
 # Settings in the [search], [run] and [options] entries are specific to each nested algorithm and should be
 # determined by consulting that method's own readthedocs.
 
-PySwarmsGlobal:
-  run:
-    iters: 2000
-  search:
-    cognitive: 0.5
-    ftol: -.inf
-    inertia: 0.9
-    n_particles: 50
-    social: 0.3
-  initialize:                       # The method used to generate where walkers are initialized in parameter space {prior | ball}.
-    method: ball                    # priors: samples are initialized by randomly drawing from each parameter's prior. ball: samples are initialized by randomly drawing unit values from a narrow uniform distribution.
-    ball_lower_limit: 0.49          # The lower limit of the uniform distribution unit values are drawn from when initializing walkers using the ball method.
-    ball_upper_limit: 0.51          # The upper limit of the uniform distribution unit values are drawn from when initializing walkers using the ball method.
-  parallel:
-    number_of_cores: 1              # The number of cores the search is parallelized over by default, using Python multiprocessing.
-  printing:
-    silence: false                  # If True, the default print output of the non-linear search is silcened and not printed by the Python interpreter.
-    iterations_per_full_update: 500      # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.
-    iterations_per_quick_update: 500 #  Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.
-    remove_state_files_at_end: true # Whether to remove the savestate of the seach (e.g. the Emcee hdf5 file) at the end to save hard-disk space (results are still stored as PyAutoFit pickles and loadable).
-PySwarmsLocal:
-  run:
-    iters: 2000
-  search:
-    cognitive: 0.5
-    ftol: -.inf
-    inertia: 0.9
-    minkowski_p_norm: 2
-    n_particles: 50
-    number_of_k_neighbors: 3
-    social: 0.3
-  initialize:                       # The method used to generate where walkers are initialized in parameter space {prior | ball}.
-    method: ball                    # priors: samples are initialized by randomly drawing from each parameter's prior. ball: samples are initialized by randomly drawing unit values from a narrow uniform distribution.
-    ball_lower_limit: 0.49          # The lower limit of the uniform distribution unit values are drawn from when initializing walkers using the ball method.
-    ball_upper_limit: 0.51          # The upper limit of the uniform distribution unit values are drawn from when initializing walkers using the ball method.
-  parallel:
-    number_of_cores: 1              # The number of cores the search is parallelized over by default, using Python multiprocessing.
-  printing:
-    silence: false                  # If True, the default print output of the non-linear search is silcened and not printed by the Python interpreter.
-    iterations_per_full_update: 500      # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.
-    iterations_per_quick_update: 500 #  Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.
-    remove_state_files_at_end: true # Whether to remove the savestate of the seach (e.g. the Emcee hdf5 file) at the end to save hard-disk space (results are still stored as PyAutoFit pickles and loadable).
 LBFGS:
   search:
     tol: null

config/non_linear/nest.yaml

@@ -2,7 +2,6 @@
 
 # - Nautilus https://https://github.com/johannesulf/nautilus / https://nautilus-sampler.readthedocs.io/en/stable/index.html
 # - Dynesty: https://github.com/joshspeagle/dynesty / https://dynesty.readthedocs.io/en/latest/index.html
-# - UltraNest: https://github.com/JohannesBuchner/UltraNest / https://johannesbuchner.github.io/UltraNest/readme.html
 # Settings in the [search] and [run] entries are specific to each nested algorithm and should be determined by
 # consulting that MCMC method's own readthedocs.
 Nautilus:
@@ -90,51 +89,3 @@ DynestyDynamic:
   printing:
     silence: false                  # If True, the default print output of the non-linear search is silenced and not printed by the Python interpreter.
 
-UltraNest:
-  search:
-    draw_multiple: true
-    ndraw_max: 65536
-    ndraw_min: 128
-    num_bootstraps: 30
-    num_test_samples: 2
-    resume: true
-    run_num: null
-    storage_backend: hdf5
-    vectorized: false
-    warmstart_max_tau: -1.0
-  run:
-    cluster_num_live_points: 40
-    dkl: 0.5
-    dlogz: 0.5
-    frac_remain: 0.01
-    insertion_test_window: 10
-    insertion_test_zscore_threshold: 2
-    lepsilon: 0.001
-    log_interval: null
-    max_iters: null
-    max_ncalls: null
-    max_num_improvement_loops: -1.0
-    min_ess: 400
-    min_num_live_points: 400
-    show_status: true
-    update_interval_ncall: null
-    update_interval_volume_fraction: 0.8
-    viz_callback: auto
-  stepsampler:
-    adaptive_nsteps: false
-    log: false
-    max_nsteps: 1000
-    nsteps: 25
-    region_filter: false
-    scale: 1.0
-    stepsampler_cls: null
-  initialize:                       # The method used to generate where walkers are initialized in parameter space {prior}.
-    method: prior                   # priors: samples are initialized by randomly drawing from each parameter's prior.
-  parallel:
-    number_of_cores: 1              # The number of cores the search is parallelized over by default, using Python multiprocessing.
-  printing:
-    silence: false                  # If True, the default print output of the non-linear search is silenced and not printed by the Python interpreter.
-
-    iterations_per_full_update: 1000      # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.
-    iterations_per_quick_update: 500 #  Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.
-    remove_state_files_at_end: true # Whether to remove the savestate of the seach (e.g. the Emcee hdf5 file) at the end to save hard-disk space (results are still stored as PyAutoFit pickles and loadable).

config/visualize/plots_search.yaml

@@ -10,15 +10,6 @@ emcee:
   trajectories: true       # Output Emcee trajectories figure during a non-linear search fit?
 nautilus:
   cornerplot: true         # Output Nautilus cornerplot figure during a non-linear search fit?
-pyswarms:
-  contour: true            # Output PySwarms contour figure during a non-linear search fit?
-  cost_history: true       # Output PySwarms cost_history figure during a non-linear search fit?
-  time_series: true        # Output PySwarms time_series figure during a non-linear search fit?
-  trajectories: true       # Output PySwarms trajectories figure during a non-linear search fit?
-ultranest:
-  cornerplot: true         # Output Ultranest cornerplot figure during a non-linear search fit?
-  runplot: true            # Output Ultranest runplot figure during a non-linear search fit?
-  traceplot: true          # Output Ultranest traceplot figure during a non-linear search fit?
 zeus:
   corner: true             # Output Zeus corner figure during a non-linear search fit?
   likelihood_series: true  # Output Zeus likelihood series figure during a non-linear search fit?