diff --git a/config/non_linear/mcmc.yaml b/config/non_linear/mcmc.yaml deleted file mode 100644 index 94459ee0..00000000 --- a/config/non_linear/mcmc.yaml +++ /dev/null @@ -1,63 +0,0 @@ -# Configuration files that customize the default behaviour of non-linear searches. - -# **PyAutoFit** supports the following MCMC algorithms: - -# - Emcee: https://github.com/dfm/emcee / https://emcee.readthedocs.io/en/stable/ -# - Zeus: https://github.com/minaskar/zeus / https://zeus-mcmc.readthedocs.io/en/latest/ - -# 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. - -Emcee: - run: - nsteps: 2000 - search: - nwalkers: 50 - auto_correlations: - change_threshold: 0.01 # The threshold value by which if the change in auto_correlations is below sampling will be terminated early. - check_for_convergence: true # Whether the auto-correlation lengths of the Emcee samples are checked to determine the stopping criteria. If `True`, Emcee may stop before nsteps are performed. - check_size: 100 # The length of the samples used to check the auto-correlation lengths (from the latest sample backwards). - required_length: 50 # The length an auto_correlation chain must be for it to be used to evaluate whether its change threshold is sufficiently small to terminate sampling early. - 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). -Zeus: - run: - check_walkers: true - light_mode: false - maxiter: 10000 - maxsteps: 10000 - mu: 1.0 - nsteps: 2000 - patience: 5 - shuffle_ensemble: true - tolerance: 0.05 - tune: true - vectorize: false - search: - nwalkers: 50 - auto_correlations: - change_threshold: 0.01 # The threshold value by which if the change in auto_correlations is below sampling will be terminated early. - check_for_convergence: true # Whether the auto-correlation lengths of the Emcee samples are checked to determine the stopping criteria. If `True`, Emcee may stop before nsteps are performed. - check_size: 100 # The length of the samples used to check the auto-correlation lengths (from the latest sample backwards). - required_length: 50 # The length an auto_correlation chain must be for it to be used to evaluate whether its change threshold is sufficiently small to terminate sampling early. - 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 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). \ No newline at end of file diff --git a/config/non_linear/mle.yaml b/config/non_linear/mle.yaml deleted file mode 100644 index 365b911f..00000000 --- a/config/non_linear/mle.yaml +++ /dev/null @@ -1,89 +0,0 @@ -# Configuration files that customize the default behaviour of non-linear searches. - -# **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 - options: - disp: false - eps: 1.0e-08 - ftol: 2.220446049250313e-09 - gtol: 1.0e-05 - iprint: -1.0 - maxcor: 10 - maxfun: 15000 - maxiter: 15000 - maxls: 20 - 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). -Drawer: - search: - total_draws: 50 - 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). \ No newline at end of file diff --git a/config/non_linear/nest.yaml b/config/non_linear/nest.yaml deleted file mode 100644 index c113fb30..00000000 --- a/config/non_linear/nest.yaml +++ /dev/null @@ -1,145 +0,0 @@ -# Configuration files that customize the default behaviour of non-linear searches. - -# **PyAutoFit** supports the following nested sampling algorithms: - -# - 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. - -DynestyStatic: - search: - bootstrap: null - bound: multi - enlarge: null - facc: 0.2 - first_update: null - fmove: 0.9 - max_move: 100 - nlive: 50 - sample: auto - slices: 5 - update_interval: null - walks: 5 - run: - dlogz: null - logl_max: .inf - maxcall: null - maxiter: null - n_effective: 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. - 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. - -DynestyDynamic: - search: - bootstrap: null - bound: multi - enlarge: null - facc: 0.2 - first_update: null - fmove: 0.9 - max_move: 100 - sample: auto - slices: 5 - update_interval: null - walks: 5 - run: - dlogz_init: 0.01 - logl_max_init: .inf - maxcall: null - maxcall_init: null - maxiter: null - maxiter_init: null - n_effective: .inf - n_effective_init: .inf - nlive_init: 500 - 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. - 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. - -Nautilus: - search: - n_live: 200 # Number of so-called live points. New bounds are constructed so that they encompass the live points. - n_update: # The maximum number of additions to the live set before a new bound is created - enlarge_per_dim: 1.1 # Along each dimension, outer ellipsoidal bounds are enlarged by this factor. - n_points_min: # The minimum number of points each ellipsoid should have. Effectively, ellipsoids with less than twice that number will not be split further. - split_threshold: 100 # Threshold used for splitting the multi-ellipsoidal bound used for sampling. - n_networks: 4 # Number of networks used in the estimator. - n_batch: 100 # Number of likelihood evaluations that are performed at each step. If likelihood evaluations are parallelized, should be multiple of the number of parallel processes. - n_like_new_bound: # The maximum number of likelihood calls before a new bounds is created. If None, use 10 times n_live. - vectorized: false # If True, the likelihood function can receive multiple input sets at once. - seed: # Seed for random number generation used for reproducible results accross different runs. - run: - f_live: 0.01 # Maximum fraction of the evidence contained in the live set before building the initial shells terminates. - n_shell: 1 # Minimum number of points in each shell. The algorithm will sample from the shells until this is reached. Default is 1. - n_eff: 500 # Minimum effective sample size. The algorithm will sample from the shells until this is reached. Default is 10000. - n_like_max: .inf # Maximum number of likelihood evaluations. Regardless of progress, the sampler will stop if this value is reached. Default is infinity. - discard_exploration: false # Whether to discard points drawn in the exploration phase. This is required for a fully unbiased posterior and evidence estimate. - verbose: true # Whether to print information about the run. - 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. - 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). \ No newline at end of file diff --git a/dataset/.gitignore b/dataset/.gitignore deleted file mode 100644 index d6b7ef32..00000000 --- a/dataset/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -* -!.gitignore diff --git a/dataset/example_1d/gaussian_x1/data.json b/dataset/example_1d/gaussian_x1/data.json index 386454bd..52d6c388 100644 --- a/dataset/example_1d/gaussian_x1/data.json +++ b/dataset/example_1d/gaussian_x1/data.json @@ -1 +1 @@ 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10.0 + "normalization": 25.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x1__exponential_x1/data.json b/dataset/example_1d/gaussian_x1__exponential_x1/data.json index de9a6c73..c2b7f86e 100644 --- a/dataset/example_1d/gaussian_x1__exponential_x1/data.json +++ b/dataset/example_1d/gaussian_x1__exponential_x1/data.json @@ -1 +1 @@ -[0.16798777464907544, 0.11753383536335323, 0.12298237901322544, 0.21216917770041052, 0.1963494219128968, 0.25718147770071415, 0.2567558825213089, 0.22484117193960879, 0.22348336250360482, 0.2714442241689342, 0.22266926956430594, 0.31686551126292267, 0.2540152300057976, 0.3854493370472456, 0.2655740148473867, 0.3107614760497517, 0.4337266946679159, 0.3867852766206659, 0.3990226472910345, 0.416300495038532, 0.4574406363926042, 0.4697637408736534, 0.5165298777003668, 0.4957649328192975, 0.5968561920622542, 0.6058978673199412, 0.6302984719425963, 0.7489306799046304, 0.7741025660896911, 0.7737238633099364, 0.8044442304603919, 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a/dataset/example_1d/gaussian_x1__exponential_x1/max_log_likelihood.json +++ b/dataset/example_1d/gaussian_x1__exponential_x1/max_log_likelihood.json @@ -1,3 +1,3 @@ { - "log_likelihood": 177.48500728143796 + "log_likelihood": 166.23640584798966 } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x1__exponential_x1/model_0.json b/dataset/example_1d/gaussian_x1__exponential_x1/model_0.json index e608850b..643b00c5 100644 --- a/dataset/example_1d/gaussian_x1__exponential_x1/model_0.json +++ b/dataset/example_1d/gaussian_x1__exponential_x1/model_0.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 10.0, "centre": 50.0, - "normalization": 25.0, - "sigma": 10.0 + "normalization": 25.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x1__exponential_x1/model_1.json b/dataset/example_1d/gaussian_x1__exponential_x1/model_1.json index bb77e6a8..141c8e03 100644 --- a/dataset/example_1d/gaussian_x1__exponential_x1/model_1.json +++ b/dataset/example_1d/gaussian_x1__exponential_x1/model_1.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Exponential", "arguments": { + "rate": 0.05, "centre": 50.0, - "normalization": 40.0, - "rate": 0.05 + "normalization": 40.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x1_convolved/data.json b/dataset/example_1d/gaussian_x1_convolved/data.json index 6f8c7b15..183bb824 100644 --- a/dataset/example_1d/gaussian_x1_convolved/data.json +++ b/dataset/example_1d/gaussian_x1_convolved/data.json @@ -1 +1 @@ -[0.03475075703124138, 0.0597776836228365, -0.0009594740351889157, 0.019481594183299897, 0.0627741763955863, 0.028782073965404123, -0.04878517315249458, 0.05954683006941188, 0.08625529823301693, -0.03538539043530023, -0.06924955745224852, -0.012492037557104978, -0.05702805150548129, 0.03963675197432516, 0.011823589474559146, 0.007506790747404016, 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No newline at end of file diff --git a/dataset/example_1d/gaussian_x1_convolved/image.png b/dataset/example_1d/gaussian_x1_convolved/image.png index 891ccb6f..dd21bc1f 100644 Binary files a/dataset/example_1d/gaussian_x1_convolved/image.png and b/dataset/example_1d/gaussian_x1_convolved/image.png differ diff --git a/dataset/example_1d/gaussian_x1_convolved/model.json b/dataset/example_1d/gaussian_x1_convolved/model.json index 3b66885d..3e8d675b 100644 --- a/dataset/example_1d/gaussian_x1_convolved/model.json +++ b/dataset/example_1d/gaussian_x1_convolved/model.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 3.0, "centre": 50.0, - "normalization": 25.0, - "sigma": 3.0 + "normalization": 25.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x1_identical_0/data.json b/dataset/example_1d/gaussian_x1_identical_0/data.json index e6aa5b34..2775c791 100644 --- 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"type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 20.0, "centre": 50.0, - "normalization": 50.0, - "sigma": 20.0 + "normalization": 50.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x1_variable/sigma_2/data.json b/dataset/example_1d/gaussian_x1_variable/sigma_2/data.json index 4cc8e061..8bf39507 100644 --- a/dataset/example_1d/gaussian_x1_variable/sigma_2/data.json +++ b/dataset/example_1d/gaussian_x1_variable/sigma_2/data.json @@ -1 +1 @@ -[0.15753544807677672, 0.18706482701706886, 0.22426950865851716, 0.14194396437490658, 0.16506018016336826, 0.17593852833090354, 0.20456717939659821, 0.2484364364910577, 0.23232432057766902, 0.29814148432876514, 0.33412884947286176, 0.2891549460614564, 0.24178467893928882, 0.35098189308431393, 0.21421328303333975, 0.3578571616789571, 0.33321103404093383, 0.4267838643566723, 0.3470068747482475, 0.351980449961298, 0.38112052994136664, 0.3978675594648143, 0.395432635612697, 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a/dataset/example_1d/gaussian_x1_variable/sigma_2/model.json b/dataset/example_1d/gaussian_x1_variable/sigma_2/model.json index 5d246c8f..7e92550b 100644 --- a/dataset/example_1d/gaussian_x1_variable/sigma_2/model.json +++ b/dataset/example_1d/gaussian_x1_variable/sigma_2/model.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 30.0, "centre": 50.0, - "normalization": 50.0, - "sigma": 30.0 + "normalization": 50.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x1_with_feature/data.json b/dataset/example_1d/gaussian_x1_with_feature/data.json index ef0600d1..7173ce4b 100644 --- a/dataset/example_1d/gaussian_x1_with_feature/data.json +++ b/dataset/example_1d/gaussian_x1_with_feature/data.json @@ -1 +1 @@ -[-0.021825749982356136, 0.0847814975197421, 0.026522427073386326, -0.03973787317351303, 0.03923743218758995, 0.019693539743596715, 0.015539652430629713, -0.05075961534977857, 0.0013814686009837875, 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e608850b..643b00c5 100644 --- a/dataset/example_1d/gaussian_x1_with_feature/model_0.json +++ b/dataset/example_1d/gaussian_x1_with_feature/model_0.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 10.0, "centre": 50.0, - "normalization": 25.0, - "sigma": 10.0 + "normalization": 25.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x1_with_feature/model_1.json b/dataset/example_1d/gaussian_x1_with_feature/model_1.json index f52421a8..c6c4a1a6 100644 --- a/dataset/example_1d/gaussian_x1_with_feature/model_1.json +++ b/dataset/example_1d/gaussian_x1_with_feature/model_1.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 0.5, "centre": 70.0, - "normalization": 0.3, - "sigma": 0.5 + "normalization": 0.3 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x2/data.json b/dataset/example_1d/gaussian_x2/data.json index 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b/dataset/example_1d/gaussian_x2/max_log_likelihood.json @@ -1,3 +1,3 @@ { - "log_likelihood": 181.27874504091736 + "log_likelihood": 161.57886725773147 } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x2/model_0.json b/dataset/example_1d/gaussian_x2/model_0.json index 620e5a19..fe008426 100644 --- a/dataset/example_1d/gaussian_x2/model_0.json +++ b/dataset/example_1d/gaussian_x2/model_0.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 1.0, "centre": 50.0, - "normalization": 20.0, - "sigma": 1.0 + "normalization": 20.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x2/model_1.json b/dataset/example_1d/gaussian_x2/model_1.json index e6ef6cea..7aeec10b 100644 --- a/dataset/example_1d/gaussian_x2/model_1.json +++ b/dataset/example_1d/gaussian_x2/model_1.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 5.0, "centre": 50.0, - "normalization": 40.0, - "sigma": 5.0 + "normalization": 40.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x2__exponential_x1/data.json b/dataset/example_1d/gaussian_x2__exponential_x1/data.json index 1f2e529a..0fce6897 100644 --- a/dataset/example_1d/gaussian_x2__exponential_x1/data.json +++ b/dataset/example_1d/gaussian_x2__exponential_x1/data.json @@ -1 +1 @@ -[0.10333000071163946, 0.14823080498154467, 0.14610036958109135, 0.17052302769297628, 0.12261105662163929, 0.21032522941135812, 0.15492766844371111, 0.2668619744339549, 0.2593741442692713, 0.3278380671718274, 0.4212037383781761, 0.6468337793101538, 0.8236300834502349, 1.0063417215372679, 1.256667107191035, 1.5271962757127484, 1.9402349854522738, 2.1239721700340954, 2.3344668410400855, 2.533913545679633, 2.5594038962179453, 2.4579067909942354, 2.4895401469095555, 2.192019576246093, 1.9431606946806257, 1.6707580233887152, 1.4373575963044063, 1.119189242587715, 0.9474126959109174, 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55d40091..5fe5654b 100644 --- a/dataset/example_1d/gaussian_x2__exponential_x1/max_log_likelihood.json +++ b/dataset/example_1d/gaussian_x2__exponential_x1/max_log_likelihood.json @@ -1,3 +1,3 @@ { - "log_likelihood": 179.0367571232497 + "log_likelihood": 185.25923049853168 } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x2__exponential_x1/model_0.json b/dataset/example_1d/gaussian_x2__exponential_x1/model_0.json index e608850b..643b00c5 100644 --- a/dataset/example_1d/gaussian_x2__exponential_x1/model_0.json +++ b/dataset/example_1d/gaussian_x2__exponential_x1/model_0.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 10.0, "centre": 50.0, - "normalization": 25.0, - "sigma": 10.0 + "normalization": 25.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x2__exponential_x1/model_1.json b/dataset/example_1d/gaussian_x2__exponential_x1/model_1.json index 9e109cf4..73215d1f 100644 --- a/dataset/example_1d/gaussian_x2__exponential_x1/model_1.json +++ b/dataset/example_1d/gaussian_x2__exponential_x1/model_1.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 5.0, "centre": 20.0, - "normalization": 30.0, - "sigma": 5.0 + "normalization": 30.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x2__exponential_x1/model_2.json b/dataset/example_1d/gaussian_x2__exponential_x1/model_2.json index f433e9b7..90db508b 100644 --- a/dataset/example_1d/gaussian_x2__exponential_x1/model_2.json +++ b/dataset/example_1d/gaussian_x2__exponential_x1/model_2.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Exponential", "arguments": { + "rate": 0.005, "centre": 70.0, - "normalization": 40.0, - "rate": 0.005 + "normalization": 40.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x2_split/data.json b/dataset/example_1d/gaussian_x2_split/data.json index f8c30147..1a7d4a77 100644 --- a/dataset/example_1d/gaussian_x2_split/data.json +++ b/dataset/example_1d/gaussian_x2_split/data.json @@ -1 +1 @@ -[0.23474096091743216, 0.3184065567361033, 0.287767625513894, 0.3324647908276505, 0.36173953022252553, 0.46065210247071336, 0.5201854192588766, 0.5900895808317916, 0.689907401537761, 0.7527646640617589, 0.786149964169985, 0.8831009045831971, 0.9614288993127809, 0.8776543052567161, 1.1126393576058538, 1.1301150396342807, 1.2261080435262792, 1.3155473710813093, 1.380590715251706, 1.4222407716391585, 1.4659118305959247, 1.4765580320073248, 1.581095004111149, 1.5271627996462107, 1.5666513110345714, 1.6166577947810068, 1.5537352035464553, 1.5670910414763999, 1.595212014155501, 1.3761569801378903, 1.5139054362129332, 1.41909277168745, 1.396656478001046, 1.2818890403551129, 1.3020470192894362, 1.1479338720004255, 1.0734025357761539, 1.0226328736668402, 0.9413699493562452, 0.8601941437885535, 0.7859658821330595, 0.7705959977009783, 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0.7551275742630009, 0.6875614485511091, 0.6755465601506001, 0.652546217059414, 0.5183571759421755, 0.4523826077826506, 0.41048353045970537, 0.2887382392347546, 0.19831198648388534, 0.22198081416084103] \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x2_split/image.png b/dataset/example_1d/gaussian_x2_split/image.png index 26e39d26..721c57e8 100644 Binary files a/dataset/example_1d/gaussian_x2_split/image.png and b/dataset/example_1d/gaussian_x2_split/image.png differ diff --git a/dataset/example_1d/gaussian_x2_split/max_log_likelihood.json b/dataset/example_1d/gaussian_x2_split/max_log_likelihood.json index 8c2d0813..93a6f3de 100644 --- a/dataset/example_1d/gaussian_x2_split/max_log_likelihood.json +++ b/dataset/example_1d/gaussian_x2_split/max_log_likelihood.json @@ -1,3 +1,3 @@ { - "log_likelihood": 179.30070656665868 + "log_likelihood": 193.21442074359857 } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x2_split/model_0.json b/dataset/example_1d/gaussian_x2_split/model_0.json index 5765d05b..6749c289 100644 --- a/dataset/example_1d/gaussian_x2_split/model_0.json +++ b/dataset/example_1d/gaussian_x2_split/model_0.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 12.5, "centre": 25.0, - "normalization": 50.0, - "sigma": 12.5 + "normalization": 50.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x2_split/model_1.json b/dataset/example_1d/gaussian_x2_split/model_1.json index e6f4d505..8c1a8281 100644 --- a/dataset/example_1d/gaussian_x2_split/model_1.json +++ b/dataset/example_1d/gaussian_x2_split/model_1.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 12.5, "centre": 75.0, - "normalization": 50.0, - "sigma": 12.5 + "normalization": 50.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x3/data.json b/dataset/example_1d/gaussian_x3/data.json index 7e2066a1..c6d10eba 100644 --- a/dataset/example_1d/gaussian_x3/data.json +++ b/dataset/example_1d/gaussian_x3/data.json @@ -1 +1 @@ -[0.008272631524964599, 0.018098287263209126, 0.0022552703940882223, -0.01263049594537083, -0.010438720476828667, 0.0165408508776962, -0.027913177442482683, 0.007102114390872443, 0.011744616926230432, 0.006750313239519462, 0.04798110931851585, -0.004453264299231908, 0.018514652942898502, -0.05999148820433705, 0.03486965050549621, -0.03233485248938313, 0.005471765109382, -0.05079705705405258, -0.017112840905990925, -0.02365918406785286, -0.009757059120723655, 0.03845920361548741, 0.07797724359460356, 0.10641446970577245, 0.12009711076889693, 0.1057625170128619, 0.13947012071396195, 0.15329958199923657, 0.15839029028307267, 0.2572800643343683, 0.3941622111435423, 0.41270513954739, 0.5329752383674176, 0.6389230291731277, 0.7209352001203652, 0.8000905250100101, 0.9029549135905311, 1.1571510754994243, 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"log_likelihood": 183.62191657023908 + "log_likelihood": 172.69529018638957 } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x3/model_0.json b/dataset/example_1d/gaussian_x3/model_0.json index 620e5a19..fe008426 100644 --- a/dataset/example_1d/gaussian_x3/model_0.json +++ b/dataset/example_1d/gaussian_x3/model_0.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 1.0, "centre": 50.0, - "normalization": 20.0, - "sigma": 1.0 + "normalization": 20.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x3/model_1.json b/dataset/example_1d/gaussian_x3/model_1.json index e6ef6cea..7aeec10b 100644 --- a/dataset/example_1d/gaussian_x3/model_1.json +++ b/dataset/example_1d/gaussian_x3/model_1.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 5.0, "centre": 50.0, - "normalization": 40.0, - "sigma": 5.0 + "normalization": 40.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x3/model_2.json b/dataset/example_1d/gaussian_x3/model_2.json index 0035610f..4422fe22 100644 --- a/dataset/example_1d/gaussian_x3/model_2.json +++ b/dataset/example_1d/gaussian_x3/model_2.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 10.0, "centre": 50.0, - "normalization": 60.0, - "sigma": 10.0 + "normalization": 60.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x5/data.json b/dataset/example_1d/gaussian_x5/data.json index e85be1b4..287b63d2 100644 --- a/dataset/example_1d/gaussian_x5/data.json +++ b/dataset/example_1d/gaussian_x5/data.json @@ -1 +1 @@ -[0.07713568130255428, 0.08709603291090812, 0.15049208274270442, 0.16988183462789308, 0.106173992600397, 0.17542790722871668, 0.23158412714006377, 0.3310938945586806, 0.3482240179530948, 0.2241645606940056, 0.29989611011448075, 0.3586923893396994, 0.4029463515430942, 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4.191978218259282, 3.8706276862756335, 3.595314776103047, 3.3403304014006707, 3.137795257699385, 2.9482946786589173, 2.6520537845431553, 2.347277916527376, 2.1689562344202638, 2.0557214934901165, 1.8370600700608863, 1.7207482991451164, 1.6084124397851034, 1.4676102450983566, 1.2221128618007984, 1.1580058306336665, 1.0601717398591326, 0.9593390135991439, 0.9288607735498047, 0.8185647823752465, 0.7329509212426185, 0.6463941189051616, 0.6318840637645242, 0.5037458294307304, 0.48865857736130724, 0.4282200520194125, 0.35249047660089217, 0.3600128886903534, 0.2784327571487065, 0.3005144817247989, 0.18397222127211718, 0.24877257660433083, 0.21381399919214056, 0.1758928966272406, 0.06517935120146084, 0.13777693994216253, 0.08223843192643633] \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x5/image.png b/dataset/example_1d/gaussian_x5/image.png index 0390f896..050b2732 100644 Binary files a/dataset/example_1d/gaussian_x5/image.png and b/dataset/example_1d/gaussian_x5/image.png differ diff --git a/dataset/example_1d/gaussian_x5/max_log_likelihood.json b/dataset/example_1d/gaussian_x5/max_log_likelihood.json index 63114aae..4b6d6b10 100644 --- a/dataset/example_1d/gaussian_x5/max_log_likelihood.json +++ b/dataset/example_1d/gaussian_x5/max_log_likelihood.json @@ -1,3 +1,3 @@ { - "log_likelihood": 173.49856483876357 + "log_likelihood": 175.53079405573214 } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x5/model_0.json b/dataset/example_1d/gaussian_x5/model_0.json index 620e5a19..fe008426 100644 --- a/dataset/example_1d/gaussian_x5/model_0.json +++ b/dataset/example_1d/gaussian_x5/model_0.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 1.0, "centre": 50.0, - "normalization": 20.0, - "sigma": 1.0 + "normalization": 20.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x5/model_1.json b/dataset/example_1d/gaussian_x5/model_1.json index e6ef6cea..7aeec10b 100644 --- a/dataset/example_1d/gaussian_x5/model_1.json +++ b/dataset/example_1d/gaussian_x5/model_1.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 5.0, "centre": 50.0, - "normalization": 40.0, - "sigma": 5.0 + "normalization": 40.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x5/model_2.json b/dataset/example_1d/gaussian_x5/model_2.json index 0035610f..4422fe22 100644 --- a/dataset/example_1d/gaussian_x5/model_2.json +++ b/dataset/example_1d/gaussian_x5/model_2.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 10.0, "centre": 50.0, - "normalization": 60.0, - "sigma": 10.0 + "normalization": 60.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x5/model_3.json b/dataset/example_1d/gaussian_x5/model_3.json index 4252e4c7..98bab1f1 100644 --- a/dataset/example_1d/gaussian_x5/model_3.json +++ b/dataset/example_1d/gaussian_x5/model_3.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 15.0, "centre": 50.0, - "normalization": 80.0, - "sigma": 15.0 + "normalization": 80.0 } } \ No newline at end of file diff --git a/dataset/example_1d/gaussian_x5/model_4.json b/dataset/example_1d/gaussian_x5/model_4.json index d716526a..5026e364 100644 --- a/dataset/example_1d/gaussian_x5/model_4.json +++ b/dataset/example_1d/gaussian_x5/model_4.json @@ -2,8 +2,8 @@ "type": "instance", "class_path": "autofit.example.model.Gaussian", "arguments": { + "sigma": 20.0, "centre": 50.0, - "normalization": 100.0, - "sigma": 20.0 + "normalization": 100.0 } } \ No newline at end of file diff --git a/output/.gitignore b/output/.gitignore deleted file mode 100644 index d6b7ef32..00000000 --- a/output/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -* -!.gitignore diff --git a/projects/cosmology/config/non_linear/mcmc.yaml b/projects/cosmology/config/non_linear/mcmc.yaml deleted file mode 100644 index 94459ee0..00000000 --- a/projects/cosmology/config/non_linear/mcmc.yaml +++ /dev/null @@ -1,63 +0,0 @@ -# Configuration files that customize the default behaviour of non-linear searches. - -# **PyAutoFit** supports the following MCMC algorithms: - -# - Emcee: https://github.com/dfm/emcee / https://emcee.readthedocs.io/en/stable/ -# - Zeus: https://github.com/minaskar/zeus / https://zeus-mcmc.readthedocs.io/en/latest/ - -# 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. - -Emcee: - run: - nsteps: 2000 - search: - nwalkers: 50 - auto_correlations: - change_threshold: 0.01 # The threshold value by which if the change in auto_correlations is below sampling will be terminated early. - check_for_convergence: true # Whether the auto-correlation lengths of the Emcee samples are checked to determine the stopping criteria. If `True`, Emcee may stop before nsteps are performed. - check_size: 100 # The length of the samples used to check the auto-correlation lengths (from the latest sample backwards). - required_length: 50 # The length an auto_correlation chain must be for it to be used to evaluate whether its change threshold is sufficiently small to terminate sampling early. - 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). -Zeus: - run: - check_walkers: true - light_mode: false - maxiter: 10000 - maxsteps: 10000 - mu: 1.0 - nsteps: 2000 - patience: 5 - shuffle_ensemble: true - tolerance: 0.05 - tune: true - vectorize: false - search: - nwalkers: 50 - auto_correlations: - change_threshold: 0.01 # The threshold value by which if the change in auto_correlations is below sampling will be terminated early. - check_for_convergence: true # Whether the auto-correlation lengths of the Emcee samples are checked to determine the stopping criteria. If `True`, Emcee may stop before nsteps are performed. - check_size: 100 # The length of the samples used to check the auto-correlation lengths (from the latest sample backwards). - required_length: 50 # The length an auto_correlation chain must be for it to be used to evaluate whether its change threshold is sufficiently small to terminate sampling early. - 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 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). \ No newline at end of file diff --git a/projects/cosmology/config/non_linear/nest.yaml b/projects/cosmology/config/non_linear/nest.yaml deleted file mode 100644 index 90016114..00000000 --- a/projects/cosmology/config/non_linear/nest.yaml +++ /dev/null @@ -1,122 +0,0 @@ -# Configuration files that customize the default behaviour of non-linear searches. - -# - 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. - -DynestyStatic: - search: - bootstrap: null - bound: multi - enlarge: null - facc: 0.2 - first_update: null - fmove: 0.9 - max_move: 100 - nlive: 50 - sample: auto - slices: 5 - update_interval: null - walks: 5 - run: - dlogz: null - logl_max: .inf - maxcall: null - maxiter: null - n_effective: 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. - 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. - - 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). -DynestyDynamic: - search: - bootstrap: null - bound: multi - enlarge: null - facc: 0.2 - first_update: null - fmove: 0.9 - max_move: 100 - sample: auto - slices: 5 - update_interval: null - walks: 5 - run: - dlogz_init: 0.01 - logl_max_init: .inf - maxcall: null - maxcall_init: null - maxiter: null - maxiter_init: null - n_effective: .inf - n_effective_init: .inf - nlive_init: 500 - 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. - 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. - - 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). -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). \ No newline at end of file diff --git a/projects/cosmology/example_1_intro.ipynb b/projects/cosmology/example_1_intro.ipynb index 98d3b5f2..eec0de0c 100644 --- a/projects/cosmology/example_1_intro.ipynb +++ b/projects/cosmology/example_1_intro.ipynb @@ -79,11 +79,11 @@ "metadata": {}, "source": [ "\n", - "%matplotlib inline\n", - "from pyprojroot import here\n", - "workspace_path = str(here())\n", - "%cd $workspace_path\n", - "print(f\"Working Directory has been set to `{workspace_path}`\")\n", + "# %matplotlib inline\n", + "# from pyprojroot import here\n", + "# workspace_path = str(here())\n", + "# %cd $workspace_path\n", + "# print(f\"Working Directory has been set to `{workspace_path}`\")\n", "\n", "import src as cosmo\n", "import matplotlib.pyplot as plt\n", diff --git a/projects/cosmology/example_2_multi_level_model.ipynb b/projects/cosmology/example_2_multi_level_model.ipynb index 92135287..39dc8e33 100644 --- a/projects/cosmology/example_2_multi_level_model.ipynb +++ b/projects/cosmology/example_2_multi_level_model.ipynb @@ -34,11 +34,11 @@ "config_path = path.join(cwd, \"projects\", \"cosmology\", \"config\")\n", "conf.instance.push(new_path=config_path)\n", "\n", - "%matplotlib inline\n", - "from pyprojroot import here\n", - "workspace_path = str(here())\n", - "%cd $workspace_path\n", - "print(f\"Working Directory has been set to `{workspace_path}`\")\n", + "# %matplotlib inline\n", + "# from pyprojroot import here\n", + "# workspace_path = str(here())\n", + "# %cd $workspace_path\n", + "# print(f\"Working Directory has been set to `{workspace_path}`\")\n", "\n", "import autofit as af\n", "import src as cosmo\n",