Feature/truncated gaussian

autofit/__init__.py

@@ -1,13 +1,15 @@
+from autoconf.dictable import register_parser
+from . import conf
+
+conf.instance.register(__file__)
+
 import abc
 import pickle
-
 from dill import register
 
-from autoconf.dictable import register_parser
-from .non_linear.grid.grid_search import GridSearch as SearchGridSearch
-from . import conf
 from . import exc
 from . import mock as m
+from .non_linear.grid.grid_search import GridSearch as SearchGridSearch
 from .aggregator.base import AggBase
 from .database.aggregator.aggregator import GridSearchAggregator
 from .graphical.expectation_propagation.history import EPHistory
@@ -54,6 +56,7 @@
 from .mapper.prior import GaussianPrior
 from .mapper.prior import LogGaussianPrior
 from .mapper.prior import LogUniformPrior
+from .mapper.prior import TruncatedGaussianPrior
 from .mapper.prior.abstract import Prior
 from .mapper.prior.tuple_prior import TuplePrior
 from .mapper.prior import UniformPrior
@@ -136,6 +139,6 @@ def save_abc(pickler, obj):
     pickle._Pickler.save_type(pickler, obj)
 
 
-conf.instance.register(__file__)
+
 
 __version__ = "2025.5.10.1"

autofit/config/general.yaml

@@ -1,13 +1,13 @@
+jax:
+  use_jax: false                    # If True, PyAutoFit uses JAX internally, whereas False uses normal Numpy.
 analysis:
   n_cores: 1                        # The number of cores a parallelized sum of Analysis classes uses by default.
 hpc:
   hpc_mode: false                   # If True, use HPC mode, which disables GUI visualization, logging to screen and other settings which are not suited to running on a super computer.
   iterations_per_update: 5000       # The number of iterations between every update (visualization, results output, etc) in HPC mode.
 inversion:
   check_reconstruction: true        # If True, the inversion's reconstruction is checked to ensure the solution of a meshs's mapper is not an invalid solution where the values are all the same.
-  reconstruction_vmax_factor: 0.5   # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.  
-model:
-  ignore_prior_limits: false        # If ``True`` the limits applied to priors will be ignored, where limits set upper / lower limits. This stops PriorLimitException's from being raised.
+  reconstruction_vmax_factor: 0.5   # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.
 output:
   force_pickle_overwrite: false     # If True pickle files output by a search (e.g. samples.pickle) are recreated when a new model-fit is performed.
   force_visualize_overwrite: false  # If True, visualization images output by a search (e.g. subplots of the fit) are recreated when a new model-fit is performed.

autofit/config/priors/Exponential.yaml

@@ -1,5 +1,5 @@
 centre:
-  gaussian_limits:
+  limits:
     lower: -inf
     upper: inf
   lower_limit: 0.0
@@ -9,7 +9,7 @@ centre:
     type: Absolute
     value: 20.0
 normalization:
-  gaussian_limits:
+  limits:
     lower: 0.0
     upper: inf
   lower_limit: 1.0e-06
@@ -19,7 +19,7 @@ normalization:
     type: Relative
     value: 0.5
 rate:
-  gaussian_limits:
+  limits:
     lower: 0.0
     upper: inf
   lower_limit: 0.0

autofit/config/priors/Gaussian.yaml

@@ -1,5 +1,5 @@
 centre:
-  gaussian_limits:
+  limits:
     lower: -inf
     upper: inf
   lower_limit: 0.0
@@ -9,7 +9,7 @@ centre:
     type: Absolute
     value: 20.0
 normalization:
-  gaussian_limits:
+  limits:
     lower: 0.0
     upper: inf
   lower_limit: 1.0e-06
@@ -19,7 +19,7 @@ normalization:
     type: Relative
     value: 0.5
 sigma:
-  gaussian_limits:
+  limits:
     lower: 0.0
     upper: inf
   lower_limit: 0.0

autofit/config/priors/Gaussian2D.yaml

@@ -1,5 +1,5 @@
 centre_0:
-  gaussian_limits:
+  limits:
     lower: -inf
     upper: inf
   lower_limit: 0.0
@@ -9,7 +9,7 @@ centre_0:
     type: Absolute
     value: 20.0
 centre_1:
-  gaussian_limits:
+  limits:
     lower: -inf
     upper: inf
   lower_limit: 0.0
@@ -19,7 +19,7 @@ centre_1:
     type: Absolute
     value: 20.0
 normalization:
-  gaussian_limits:
+  limits:
     lower: 0.0
     upper: inf
   lower_limit: 1.0e-06
@@ -29,7 +29,7 @@ normalization:
     type: Relative
     value: 0.5
 sigma:
-  gaussian_limits:
+  limits:
     lower: 0.0
     upper: inf
   lower_limit: 0.0

autofit/config/priors/GaussianKurtosis.yaml

@@ -1,12 +1,5 @@
-GaussianPrior:
-  lower_limit:
-    type: Constant
-    value: -inf
-  upper_limit:
-    type: Constant
-    value: inf
 centre:
-  gaussian_limits:
+  limits:
     lower: -inf
     upper: inf
   lower_limit: 0.0
@@ -16,7 +9,7 @@ centre:
     type: Absolute
     value: 20.0
 kurtosis:
-  gaussian_limits:
+  limits:
     lower: -inf
     upper: inf
   lower_limit: 0.0
@@ -26,7 +19,7 @@ kurtosis:
     type: Absolute
     value: 20.0
 normalization:
-  gaussian_limits:
+  limits:
     lower: 0.0
     upper: inf
   lower_limit: 1.0e-06
@@ -36,7 +29,7 @@ normalization:
     type: Relative
     value: 0.5
 sigma:
-  gaussian_limits:
+  limits:
     lower: 0.0
     upper: inf
   lower_limit: 0.0

autofit/config/priors/MultiLevelGaussians.yaml

@@ -1,5 +1,5 @@
 higher_level_centre:
-  gaussian_limits:
+  limits:
     lower: -inf
     upper: inf
   lower_limit: 0.0

autofit/config/priors/README.rst

@@ -13,7 +13,7 @@ They appear as follows:
         width_modifier:
           type: Absolute
           value: 20.0
-        gaussian_limits:
+        limits:
           lower: -inf
           upper: inf
 
@@ -28,9 +28,9 @@ The sections of this example config set the following:
         When the results of a search are passed to a subsequent search to set up the priors of its non-linear search,
         this entry describes how the Prior is passed. For a full description of prior passing, checkout the examples
         in 'autolens_workspace/examples/complex/linking'.
-    gaussian_limits
+    limits
         When the results of a search are passed to a subsequent search, they are passed using a GaussianPrior. The
-        gaussian_limits set the physical lower and upper limits of this GaussianPrior, such that parameter samples
+        limits set the physical lower and upper limits of this GaussianPrior, such that parameter samples
         can not go beyond these limits.
 
 The files ``template_module.yaml`` and ``TemplateObject.yaml`` give templates one can use to set up prior default

autofit/config/priors/model.yaml

@@ -1,6 +1,6 @@
 Exponential:
   centre:
-    gaussian_limits:
+    limits:
       lower: -inf
       upper: inf
     lower_limit: 0.0
@@ -10,7 +10,7 @@ Exponential:
       type: Absolute
       value: 20.0
   normalization:
-    gaussian_limits:
+    limits:
       lower: 0.0
       upper: inf
     lower_limit: 1.0e-06
@@ -20,7 +20,7 @@ Exponential:
       type: Relative
       value: 0.5
   rate:
-    gaussian_limits:
+    limits:
       lower: 0.0
       upper: inf
     lower_limit: 0.0
@@ -31,7 +31,7 @@ Exponential:
       value: 0.5
 Gaussian:
   centre:
-    gaussian_limits:
+    limits:
       lower: -inf
       upper: inf
     lower_limit: 0.0
@@ -41,7 +41,7 @@ Gaussian:
       type: Absolute
       value: 20.0
   normalization:
-    gaussian_limits:
+    limits:
       lower: 0.0
       upper: inf
     lower_limit: 1.0e-06
@@ -51,7 +51,7 @@ Gaussian:
       type: Relative
       value: 0.5
   sigma:
-    gaussian_limits:
+    limits:
       lower: 0.0
       upper: inf
     lower_limit: 0.0

autofit/config/priors/profiles.yaml

@@ -1,6 +1,6 @@
 Exponential:
   centre:
-    gaussian_limits:
+    limits:
       lower: -inf
       upper: inf
     lower_limit: 0.0
@@ -10,7 +10,7 @@ Exponential:
       type: Absolute
       value: 20.0
   normalization:
-    gaussian_limits:
+    limits:
       lower: 0.0
       upper: inf
     lower_limit: 1.0e-06
@@ -20,7 +20,7 @@ Exponential:
       type: Relative
       value: 0.5
   rate:
-    gaussian_limits:
+    limits:
       lower: 0.0
       upper: inf
     lower_limit: 0.0
@@ -31,7 +31,7 @@ Exponential:
       value: 0.5
 Gaussian:
   centre:
-    gaussian_limits:
+    limits:
       lower: -inf
       upper: inf
     lower_limit: 0.0
@@ -41,7 +41,7 @@ Gaussian:
       type: Absolute
       value: 20.0
   normalization:
-    gaussian_limits:
+    limits:
       lower: 0.0
       upper: inf
     lower_limit: 1.0e-06
@@ -51,7 +51,7 @@ Gaussian:
       type: Relative
       value: 0.5
   sigma:
-    gaussian_limits:
+    limits:
       lower: 0.0
       upper: inf
     lower_limit: 0.0

autofit/config/priors/template.yaml

@@ -1,6 +1,6 @@
 ModelComponent0:
   parameter0:
-    gaussian_limits:
+    limits:
       lower: -inf
       upper: inf
     lower_limit: 0.0
@@ -10,7 +10,7 @@ ModelComponent0:
       type: Absolute
       value: 20.0
   parameter1:
-    gaussian_limits:
+    limits:
       lower: 0.0
       upper: inf
     lower_limit: 1.0e-06
@@ -20,7 +20,7 @@ ModelComponent0:
       type: Relative
       value: 0.5
   parameter2:
-    gaussian_limits:
+    limits:
       lower: 0.0
       upper: inf
     lower_limit: 0.0
@@ -31,7 +31,7 @@ ModelComponent0:
       value: 0.5
 ModelComponent1:
   parameter0:
-    gaussian_limits:
+    limits:
       lower: -inf
       upper: inf
     lower_limit: 0.0
@@ -41,7 +41,7 @@ ModelComponent1:
       type: Absolute
       value: 20.0
   parameter1:
-    gaussian_limits:
+    limits:
       lower: 0.0
       upper: inf
     lower_limit: 1.0e-06
@@ -51,7 +51,7 @@ ModelComponent1:
       type: Relative
       value: 0.5
   parameter2:
-    gaussian_limits:
+    limits:
       lower: 0.0
       upper: inf
     lower_limit: 0.0

autofit/database/model/prior.py

@@ -100,6 +100,7 @@ class Prior(Object):
     def _from_object(cls, model: abstract.Prior):
         instance = cls()
         instance.cls = type(model)
+        print(model.__database_args__)
         instance._add_children(
             [(key, getattr(model, key)) for key in model.__database_args__]
         )

autofit/exc.py

@@ -19,10 +19,6 @@ class FitException(Exception):
     pass
 
 
-class PriorLimitException(FitException, PriorException):
-    pass
-
-
 class PipelineException(Exception):
     pass