Remove UltraNest and PySwarms references

config/build/no_run.yaml

@@ -7,11 +7,6 @@
 - GetDist # Cant get it to install, even in optional requirements.
 - Zeus # Test Model Iniitalization no good.
 - ZeusPlotter # Test Model Iniitalization no good.
-- UltraNestPlotter # Test Model Iniitalization no good.
 - DynestyPlotter # Test Model Iniitalization no good.
 - start_point # bug https://github.com/rhayes777/PyAutoFit/issues/1017
 - tutorial_8_astronomy_example # Requires dataset/howtofit/chapter_1/astro/simple/data.npy (not auto-generated)
-- searches/mle/PySwarmsGlobal # PySwarms does not support JAX.
-- searches/mle/PySwarmsLocal # PySwarms does not support JAX.
-- searches/nest/UltraNest # UltraNest does not support JAX.
-- plot/PySwarmsPlotter # PySwarms does not support JAX.

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.
@@ -94,52 +93,3 @@ Nautilus:
     force_x1_cpu: false             # Force Dynesty to not use Python multiprocessing Pool, which can fix issues on certain operating systems.
   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).

notebooks/plot/README.rst

@@ -5,8 +5,7 @@ Files
 
 - ``DynestyPlotter.py``: Plots results of a Dynesty fit (e.g. corner).
 - ``MCMCPlotter.py``: Plots results of an Emcee fit (e.g. corner).
-- ``MLEPlotter.py``: Plots results of a PySwarms fit (e.g. contour).
-- ``UltraNest.py``: Plots results of an UltraNest fit (e.g. corner).
+- ``MLEPlotter.py``: Plots results of an MLE fit (e.g. contour).
 - ``ZeusPlotter.py``: Plots results of a Zeus fit (e.g. corner).
 
 - ``GetDist.py``: Plot results of any MCMC / nested sampler non-linear search using the library GetDist.