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

@@ -4,7 +4,6 @@
 
 # - Dynesty: https://github.com/joshspeagle/dynesty / https://dynesty.readthedocs.io/en/latest/index.html
 # - Nautilus https://https://github.com/johannesulf/nautilus / https://nautilus-sampler.readthedocs.io/en/stable/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.
@@ -95,51 +94,3 @@ Nautilus:
   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: 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).

scripts/features/config/non_linear/nest.yaml

@@ -55,47 +55,3 @@ DynestyDynamic:
     force_x1_cpu: false
   printing:
     silence: false
-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:
-    method: prior
-  parallel:
-    number_of_cores: 1
-  printing:
-    silence: false

scripts/features/config/non_linear/optimize.yaml

@@ -1,39 +1,3 @@
-PySwarmsGlobal:
-  run:
-    iters: 2000
-  search:
-    cognitive: 0.5
-    ftol: -.inf
-    inertia: 0.9
-    n_particles: 50
-    social: 0.3
-  initialize:
-    method: ball
-    ball_lower_limit: 0.49
-    ball_upper_limit: 0.51
-  parallel:
-    number_of_cores: 1
-  printing:
-    silence: false
-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:
-    method: ball
-    ball_lower_limit: 0.49
-    ball_upper_limit: 0.51
-  parallel:
-    number_of_cores: 1
-  printing:
-    silence: false
 LBFGS:
   search:
     tol: null

scripts/features/config/visualize.yaml

@@ -12,15 +12,6 @@ plots_search:
     likelihood_series: true
     time_series: true
     trajectories: true
-  pyswarms:
-    contour: true
-    cost_history: true
-    time_series: true
-    trajectories: true
-  ultranest:
-    corner: true
-    runplot: true
-    traceplot: true
   zeus:
     corner: true
     likelihood_series: true

scripts/searches/LBFGS.py

@@ -120,7 +120,7 @@
     capsize=2,
 )
 plt.plot(range(data.shape[0]), model_data, color="r")
-plt.title("PySwarmsLocal model fit to 1D Gaussian dataset.")
+plt.title("LBFGS model fit to 1D Gaussian dataset.")
 plt.xlabel("x values of profile")
 plt.ylabel("Profile normalization")
 plt.show()