merge main

README.rst

@@ -97,7 +97,7 @@ We define our model, a 1D Gaussian by writing a Python class using the format be
         This method will be used to fit the model to data and compute a likelihood.
         """
 
-        def model_data_1d_via_xvalues_from(self, xvalues):
+        def model_data_from(self, xvalues):
 
             transformed_xvalues = xvalues - self.centre
 
@@ -133,12 +133,12 @@ To fit this Gaussian to the ``data`` we create an Analysis object, which gives *
 
             """
             We fit the ``data`` with the Gaussian instance, using its
-            "model_data_1d_via_xvalues_from" function to create the model data.
+            "model_data_from" function to create the model data.
             """
 
             xvalues = np.arange(self.data.shape[0])
 
-            model_data = instance.model_data_1d_via_xvalues_from(xvalues=xvalues)
+            model_data = instance.model_data_from(xvalues=xvalues)
             residual_map = self.data - model_data
             chi_squared_map = (residual_map / self.noise_map) ** 2.0
             log_likelihood = -0.5 * sum(chi_squared_map)

autofit/config/priors/Gaussian.yaml

@@ -1,10 +1,3 @@
-GaussianPrior:
-  lower_limit:
-    type: Constant
-    value: -inf
-  upper_limit:
-    type: Constant
-    value: inf
 centre:
   gaussian_limits:
     lower: -inf

autofit/config/priors/Gaussian2D.yaml

@@ -0,0 +1,40 @@
+centre_0:
+  gaussian_limits:
+    lower: -inf
+    upper: inf
+  lower_limit: 0.0
+  type: Uniform
+  upper_limit: 100.0
+  width_modifier:
+    type: Absolute
+    value: 20.0
+centre_1:
+  gaussian_limits:
+    lower: -inf
+    upper: inf
+  lower_limit: 0.0
+  type: Uniform
+  upper_limit: 100.0
+  width_modifier:
+    type: Absolute
+    value: 20.0
+normalization:
+  gaussian_limits:
+    lower: 0.0
+    upper: inf
+  lower_limit: 1.0e-06
+  type: LogUniform
+  upper_limit: 1000000.0
+  width_modifier:
+    type: Relative
+    value: 0.5
+sigma:
+  gaussian_limits:
+    lower: 0.0
+    upper: inf
+  lower_limit: 0.0
+  type: Uniform
+  upper_limit: 25.0
+  width_modifier:
+    type: Relative
+    value: 0.5

autofit/database/model/fit.py

@@ -332,7 +332,10 @@ def __getitem__(self, item: str):
         """
         for p in self.jsons + self.arrays + self.hdus + self.pickles:
             if p.name == item:
-                return p.value
+                value = p.value
+                if item == "samples_summary":
+                    value.model = self.model
+                return value
 
         return getattr(self, item)
 

autofit/example/analysis.py

@@ -83,8 +83,8 @@ def model_data_1d_from(self, instance: af.ModelInstance) -> np.ndarray:
         The way this is generated changes depending on if the model is a `Model` (therefore having only one profile)
         or a `Collection` (therefore having multiple profiles).
 
-        If its a model, the model component's `model_data_1d_via_xvalues_from` is called and the output returned.
-        For a collection, each components `model_data_1d_via_xvalues_from` is called, iterated through and summed
+        If its a model, the model component's `model_data_from` is called and the output returned.
+        For a collection, each components `model_data_from` is called, iterated through and summed
         to return the combined model data.
 
         Parameters
@@ -103,13 +103,13 @@ def model_data_1d_from(self, instance: af.ModelInstance) -> np.ndarray:
         try:
             for profile in instance:
                 try:
-                    model_data_1d += profile.model_data_1d_via_xvalues_from(
+                    model_data_1d += profile.model_data_from(
                         xvalues=xvalues
                     )
                 except AttributeError:
                     pass
         except TypeError:
-            model_data_1d += instance.model_data_1d_via_xvalues_from(xvalues=xvalues)
+            model_data_1d += instance.model_data_from(xvalues=xvalues)
 
         return model_data_1d
 

autofit/example/model.py

@@ -9,7 +9,7 @@
 
 The log_likelihood_function in the Analysis class receives an instance of this classes where the values of its
 parameters have been set up according to the non-linear search. Because instances of the classes are used, this means
-their methods (e.g. model_data_1d_via_xvalues_from) can be used in the log likelihood function.
+their methods (e.g. model_data_from) can be used in the log likelihood function.
 """
 
 
@@ -56,7 +56,7 @@ def __eq__(self, other):
             and self.sigma == other.sigma
         )
 
-    def model_data_1d_via_xvalues_from(self, xvalues: np.ndarray) -> np.ndarray:
+    def model_data_from(self, xvalues: np.ndarray) -> np.ndarray:
         """
         Calculate the normalization of the profile on a 1D grid of Cartesian x coordinates.
 
@@ -91,7 +91,7 @@ def __call__(self, xvalues: np.ndarray) -> np.ndarray:
         xvalues
             The x coordinates in the original reference frame of the grid.
         """
-        return self.model_data_1d_via_xvalues_from(xvalues=xvalues)
+        return self.model_data_from(xvalues=xvalues)
 
     def inverse(self, y):
         """
@@ -129,7 +129,7 @@ def __init__(
         self.normalization = normalization
         self.rate = rate
 
-    def model_data_1d_via_xvalues_from(self, xvalues: np.ndarray) -> np.ndarray:
+    def model_data_from(self, xvalues: np.ndarray) -> np.ndarray:
         """
         Calculate the 1D Gaussian profile on a 1D grid of Cartesian x coordinates.
 
@@ -156,7 +156,7 @@ def __call__(self, xvalues: np.ndarray) -> np.ndarray:
         values
             The x coordinates in the original reference frame of the grid.
         """
-        return self.model_data_1d_via_xvalues_from(xvalues=xvalues)
+        return self.model_data_from(xvalues=xvalues)
 
 
 class PhysicalNFW:

autofit/example/visualize.py

@@ -95,16 +95,18 @@ def visualize(
         """
 
         xvalues = np.arange(analysis.data.shape[0])
-        model_data_1d = np.zeros(analysis.data.shape[0])
+        model_data_1d_list = []
 
         try:
             for profile in instance:
                 try:
-                    model_data_1d += profile.model_data_1d_via_xvalues_from(xvalues=xvalues)
+                    model_data_1d_list.append(profile.model_data_from(xvalues=xvalues))
                 except AttributeError:
                     pass
         except TypeError:
-            model_data_1d += instance.model_data_1d_via_xvalues_from(xvalues=xvalues)
+            model_data_1d_list.append(instance.model_data_from(xvalues=xvalues))
+
+        model_data_1d = sum(model_data_1d_list)
 
         plt.errorbar(
             x=xvalues,
@@ -116,7 +118,12 @@ def visualize(
             capsize=2,
         )
         plt.plot(xvalues, model_data_1d, color="r")
-        plt.title("Model fit to 1D Gaussian + Exponential dataset.")
+
+        for model_data_1d_profile in model_data_1d_list:
+
+            plt.plot(xvalues, model_data_1d_profile, color="b", linestyle="--")
+
+        plt.title("Model fit to multiple 1D profiles dataset.")
         plt.xlabel("x values of profile")
         plt.ylabel("Profile normalization")
 

autofit/non_linear/fitness.py

@@ -195,7 +195,10 @@ def check_log_likelihood(self, fitness):
         except FileNotFoundError:
             return
 
-        max_log_likelihood_sample = samples_summary.max_log_likelihood_sample
+        try:
+            max_log_likelihood_sample = samples_summary.max_log_likelihood_sample
+        except AttributeError:
+            return
         log_likelihood_old = samples_summary.max_log_likelihood_sample.log_likelihood
 
         parameters = max_log_likelihood_sample.parameter_lists_for_model(model=self.model)

autofit/non_linear/initializer.py

@@ -64,17 +64,14 @@ def samples_from_model(
         if os.environ.get("PYAUTOFIT_TEST_MODE") == "1" and test_mode_samples:
             return self.samples_in_test_mode(total_points=total_points, model=model)
 
-        logger.info(
-            f"Generating initial samples of model, which are subject to prior limits and other constraints. "
-            f"Using {n_cores} cores."
-        )
-
         unit_parameter_lists = []
         parameter_lists = []
         figures_of_merit_list = []
 
         sneaky_pool = SneakyPool(n_cores, fitness, paths)
 
+        logger.info(f"Generating initial samples of model using {n_cores} cores")
+
         while len(figures_of_merit_list) < total_points:
             remaining_points = total_points - len(figures_of_merit_list)
             batch_size = min(remaining_points, n_cores)
@@ -96,8 +93,9 @@ def samples_from_model(
 
             for figure_of_merit, unit_parameter_list, parameter_list in zip(
                 sneaky_pool.map(
-                    self.figure_of_metric,
-                    [(fitness, parameter_list) for parameter_list in parameter_lists_],
+                    function=self.figure_of_metric,
+                    args_list=[(fitness, parameter_list) for parameter_list in parameter_lists_],
+                    log_info=False
                 ),
                 unit_parameter_lists_,
                 parameter_lists_,
@@ -124,6 +122,8 @@ def samples_from_model(
                 """
             )
 
+        logger.info(f"Initial samples generated, starting non-linear search")
+
         return unit_parameter_lists, parameter_lists, figures_of_merit_list
 
     def samples_in_test_mode(self, total_points: int, model: AbstractPriorModel):

autofit/non_linear/parallel/sneaky.py

@@ -234,7 +234,7 @@ def __init__(
     def processes(self):
         return self._processes
 
-    def map(self, function, args_list):
+    def map(self, function, args_list, log_info : bool = True):
         """
         Execute the function with the given arguments across all of the
         processes. The likelihood  argument is removed from each args in
@@ -258,7 +258,8 @@ def map(self, function, args_list):
             for args in args_list
         ]
 
-        logger.info(f"Running {len(jobs)} jobs across {self.processes} processes")
+        if log_info:
+            logger.info(f"Running {len(jobs)} jobs across {self.processes} processes")
 
         for i, job in enumerate(jobs):
             process = self.processes[i % len(self.processes)]

autofit/non_linear/paths/abstract.py

@@ -210,6 +210,10 @@ def image_path(self) -> Path:
         """
         The path to the image folder.
         """
+
+        if not os.path.exists(self.output_path / "image"):
+            os.makedirs(self.output_path / "image")
+
         return self.output_path / "image"
 
     @property

autofit/non_linear/search/abstract_search.py

@@ -98,6 +98,8 @@ def configure_handler(func):
     def decorated(self, *args, **kwargs):
         if not should_output("search_log"):
             return func(self, *args, **kwargs)
+        if self.disable_output:
+            return func(self, *args, **kwargs)
         try:
             os.makedirs(
                 self.paths.output_path,
@@ -151,6 +153,11 @@ def __init__(
         """
         super().__init__()
 
+        if name is None and path_prefix is None:
+            self.disable_output = True
+        else:
+            self.disable_output = False
+
         from autofit.non_linear.paths.database import DatabasePaths
 
         try:
@@ -606,6 +613,8 @@ class represented by model M and gives a score for their fitness.
                 search_internal=result.search_internal,
             )
 
+        self.logger.info("Search complete, returning result")
+
         return result
 
     def pre_fit_output(
@@ -640,12 +649,17 @@ def pre_fit_output(
             (e.g. as `files/info.json`) and can be loaded via the database.
         """
 
-        self.logger.info(f"The output path of this fit is {self.paths.output_path}")
+        if not self.disable_output:
+            self.logger.info(f"The output path of this fit is {self.paths.output_path}")
+        else:
+            self.logger.info("Output to hard-disk disabled, input a search name to enable.")
 
         if not self.paths.is_complete or self.force_pickle_overwrite:
-            self.logger.info(
-                f"Outputting pre-fit files (e.g. model.info, visualization)."
-            )
+
+            if not self.disable_output:
+                self.logger.info(
+                    f"Outputting pre-fit files (e.g. model.info, visualization)."
+                )
 
             self.paths.save_all(
                 search_config_dict=self.config_dict_search,
@@ -827,7 +841,9 @@ def post_fit_output(self, search_internal, bypass_nuclear_if_on: bool):
         else:
             self.output_search_internal(search_internal=search_internal)
 
-        self.logger.info("Removing all files except for .zip file")
+        if not self.disable_output:
+            self.logger.info("Removing all files except for .zip file")
+
         self.paths.zip_remove()
 
         if not bypass_nuclear_if_on:
@@ -940,14 +956,17 @@ def perform_update(
         """
 
         self.iterations += self.iterations_per_update
-        if during_analysis:
-            self.logger.info(
-                f"""Fit Running: Updating results after {self.iterations} iterations (see output folder)."""
-            )
-        else:
-            self.logger.info(
-                "Fit Complete: Updating final results (see output folder)."
-            )
+
+        if not self.disable_output:
+
+            if during_analysis:
+                self.logger.info(
+                    f"""Fit Running: Updating results after {self.iterations} iterations (see output folder)."""
+                )
+            else:
+                self.logger.info(
+                    "Fit Complete: Updating final results (see output folder)."
+                )
 
         if not isinstance(self.paths, DatabasePaths) and not isinstance(
             self.paths, NullPaths
@@ -966,8 +985,16 @@ def perform_update(
             self.paths.save_samples_summary(samples_summary=samples_summary)
 
             samples_save = samples
+
+            log_message = True
+
+            if during_analysis:
+                log_message = False
+            elif self.disable_output:
+                log_message = False
+
             samples_save = samples_save.samples_above_weight_threshold_from(
-                log_message=not during_analysis
+                log_message=log_message
             )
             self.paths.save_samples(samples=samples_save)
 

autofit/non_linear/search/nest/dynesty/search/static.py

@@ -98,8 +98,6 @@ def __init__(
 
         self.use_gradient = use_gradient
 
-        self.logger.debug("Creating DynestyStatic Search")
-
     @property
     def search_internal(self):
         return StaticSampler.restore(self.checkpoint_file)