merged with main

.github/workflows/main.yml

@@ -7,7 +7,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.8, 3.9, '3.10', '3.11']
+        python-version: [3.9, '3.10', '3.11', '3.12']
     steps:
     - name: Checkout PyAutoConf
       uses: actions/checkout@v2

.gitignore

@@ -125,13 +125,13 @@ venv.bak/
 .idea
 workspace/output/
 output
-test/optimize/test_fit
+test/mle/test_fit
 test/test_files/text/
 test/
-test_autofit/optimize/test_fit/
+test_autofit/mle/test_fit/
 test_autofit/test_files/text/psycopg2-binary==2.8.1
 test_autofit/test_files/text/
-fit/test_autofit/optimize/test_fit
+fit/test_autofit/mle/test_fit
 *.DS_Store
 
 test_autofit/config/priors/old
@@ -157,7 +157,7 @@ test_autofit/samples.csv
 __MACOSX
 *.swp
 test/autofit/test_fit
-# Byte-compiled / optimized / DLL files
+# Byte-compiled / mled / DLL files
 __pycache__/
 *.py[cod]
 *$py.class
@@ -264,13 +264,13 @@ venv.bak/
 .idea
 workspace/output/
 output
-test/optimize/test_fit
+test/mle/test_fit
 test/test_files/text/
 test/
-test_autofit/optimize/test_fit/
+test_autofit/mle/test_fit/
 test_autofit/test_files/text/psycopg2-binary==2.8.1
 test_autofit/test_files/text/
-fit/test_autofit/optimize/test_fit
+fit/test_autofit/mle/test_fit
 *.DS_Store
 
 test_autofit/config/priors/old

README.rst

@@ -45,7 +45,7 @@ The following links are useful for new starters:
 
 - `The introduction Jupyter Notebook on Binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/release?filepath=notebooks/overview/overview_1_the_basics.ipynb>`_, where you can try **PyAutoFit** in a web browser (without installation).
 
-- `The autofit_workspace GitHub repository <https://github.com/Jammy2211/autofit_workspace>`_, which includes example scripts and the `HowToFit Jupyter notebook lectures <https://github.com/Jammy2211/autofit_workspace/tree/master/notebooks/howtofit>`_ which give new users a step-by-step introduction to **PyAutoFit**.
+- `The autofit_workspace GitHub repository <https://github.com/Jammy2211/autofit_workspace>`_, which includes example scripts and the `HowToFit Jupyter notebook lectures <https://github.com/Jammy2211/autofit_workspace/tree/main/notebooks/howtofit>`_ which give new users a step-by-step introduction to **PyAutoFit**.
 
 Support
 -------
@@ -72,7 +72,7 @@ API Overview
 To illustrate the **PyAutoFit** API, we use an illustrative toy model of fitting a one-dimensional Gaussian to
 noisy 1D data. Here's the ``data`` (black) and the model (red) we'll fit:
 
-.. image:: https://raw.github.com/rhayes777/PyAutoFit/master/files/toy_model_fit.png
+.. image:: https://raw.github.com/rhayes777/PyAutoFit/main/files/toy_model_fit.png
   :width: 400
 
 We define our model, a 1D Gaussian by writing a Python class using the format below:

autofit/__init__.py

@@ -57,7 +57,7 @@
 from .mapper.prior_model.annotation import AnnotationPriorModel
 from .mapper.prior_model.collection import Collection
 from .mapper.prior_model.prior_model import Model
-from .mapper.prior_model.prior_model import Model
+from .mapper.prior_model.array import Array
 from .non_linear.search.abstract_search import NonLinearSearch
 from .non_linear.analysis.visualize import Visualizer
 from .non_linear.analysis.analysis import Analysis
@@ -66,18 +66,20 @@
 from .non_linear.grid.sensitivity import Sensitivity
 from .non_linear.initializer import InitializerBall
 from .non_linear.initializer import InitializerPrior
-from .non_linear.initializer import SpecificRangeInitializer
+from .non_linear.initializer import InitializerParamBounds
+from .non_linear.initializer import InitializerParamStartPoints
 from .non_linear.search.mcmc.auto_correlations import AutoCorrelationsSettings
 from .non_linear.search.mcmc.emcee.search import Emcee
 from .non_linear.search.mcmc.zeus.search import Zeus
 from .non_linear.search.nest.nautilus.search import Nautilus
 from .non_linear.search.nest.dynesty.search.dynamic import DynestyDynamic
 from .non_linear.search.nest.dynesty.search.static import DynestyStatic
 from .non_linear.search.nest.ultranest.search import UltraNest
-from .non_linear.search.optimize.drawer.search import Drawer
-from .non_linear.search.optimize.lbfgs.search import LBFGS
-from .non_linear.search.optimize.pyswarms.search.globe import PySwarmsGlobal
-from .non_linear.search.optimize.pyswarms.search.local import PySwarmsLocal
+from .non_linear.search.mle.drawer.search import Drawer
+from .non_linear.search.mle.bfgs.search import BFGS
+from .non_linear.search.mle.bfgs.search import LBFGS
+from .non_linear.search.mle.pyswarms.search.globe import PySwarmsGlobal
+from .non_linear.search.mle.pyswarms.search.local import PySwarmsLocal
 from .non_linear.paths.abstract import AbstractPaths
 from .non_linear.paths import DirectoryPaths
 from .non_linear.paths import DatabasePaths
@@ -132,4 +134,4 @@ def save_abc(pickler, obj):
 
 conf.instance.register(__file__)
 
-__version__ = "2024.07.16.1"
+__version__ = "2024.9.21.2"

autofit/config/non_linear/README.rst

@@ -6,4 +6,4 @@ Files
 
 - ``mcmc.yaml``: Settings default behaviour of MCMC non-linear searches (e.g. Emcee).
 - ``nest.yaml``: Settings default behaviour of nested sampler non-linear searches (e.g. Dynesty).
-- ``optimizer.yaml``: Settings default behaviour of optimizer non-linear searches (e.g. PySwarms).
+- ``mle.yaml``: Settings default behaviour of maximum likelihood estimator (mle) searches (e.g. PySwarms).

autofit/config/non_linear/optimize.yaml → autofit/config/non_linear/mle.yaml

@@ -1,11 +1,11 @@
 # Configuration files that customize the default behaviour of non-linear searches.
 
-# **PyAutoFit** supports the following optimizer algorithms:
+# **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 optimizers method's own readthedocs.
+# determined by consulting that method's own readthedocs.
 
 PySwarmsGlobal:
   run:
@@ -49,6 +49,44 @@ PySwarmsLocal:
   updates:
     iterations_per_update: 500      # The number of iterations of the non-linear search performed between every 'update', where an update performs tasks like outputting model.results.
     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).
+BFGS:
+  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.
+  updates:
+    iterations_per_update: 500      # The number of iterations of the non-linear search performed between every 'update', where an update performs tasks like outputting model.results.
+    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.
+  updates:
+    iterations_per_update: 500      # The number of iterations of the non-linear search performed between every 'update', where an update performs tasks like outputting model.results.
+    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

autofit/config/visualize/plots_search.yaml

@@ -2,5 +2,6 @@ nest:
   corner_anesthetic: true   # Output corner figure (using anestetic) during a non-linear search fit?
 mcmc:
   corner_cornerpy: true     # Output corner figure (using corner.py) during a non-linear search fit?
-optimize:
-  corner_cornerpy: true     # Output corner figure (using corner.py) during a non-linear search fit?
+mle:
+  subplot_parameters: true   # Output a subplot of the best-fit parameters of the model?
+  log_likelihood_vs_iteration: true  # Output a plot of the log likelihood versus iteration number?

autofit/database/aggregator/aggregator.py

@@ -1,5 +1,6 @@
 import logging
 from abc import ABC, abstractmethod
+from sqlalchemy import text
 from typing import Optional, List, Union, cast
 
 from ..sqlalchemy_ import sa
@@ -370,7 +371,7 @@ def _fits_for_query(self, query: str) -> List[m.Fit]:
         query
         """
         logger.debug(f"Executing query: {query}")
-        fit_ids = {row[0] for row in self.session.execute(query)}
+        fit_ids = {row[0] for row in self.session.execute(text(query))}
 
         logger.info(f"{len(fit_ids)} fit(s) found matching query")
         query = self.session.query(m.Fit).filter(m.Fit.id.in_(fit_ids))

autofit/database/migration/migration.py

@@ -1,14 +1,13 @@
 import logging
 from abc import ABC, abstractmethod
 from hashlib import md5
+from sqlalchemy import text
 from typing import Union, Generator, Iterable, Optional
 
 from .session_wrapper import SessionWrapper
 from ..sqlalchemy_ import sa
 
-logger = logging.getLogger(
-    __name__
-)
+logger = logging.getLogger(__name__)
 
 
 class Identifiable(ABC):
@@ -19,22 +18,13 @@ def id(self) -> str:
         A unique identifier generated by hashing a string
         """
 
-    def __eq__(
-            self,
-            other: Union["Identifiable", str]
-    ) -> bool:
+    def __eq__(self, other: Union["Identifiable", str]) -> bool:
         """
         Compares ids
         """
-        if isinstance(
-                other,
-                Identifiable
-        ):
+        if isinstance(other, Identifiable):
             return self.id == other.id
-        if isinstance(
-                other,
-                str
-        ):
+        if isinstance(other, str):
             return self.id == other
         return False
 
@@ -57,13 +47,7 @@ def id(self) -> str:
         """
         Hash generated from underlying SQL statements
         """
-        return md5(
-            ":".join(
-                self.strings
-            ).encode(
-                "utf-8"
-            )
-        ).hexdigest()
+        return md5(":".join(self.strings).encode("utf-8")).hexdigest()
 
     def __str__(self):
         return "\n".join(self.strings)
@@ -72,10 +56,7 @@ def __str__(self):
 
 
 class Revision(Identifiable):
-    def __init__(
-            self,
-            steps: Iterable[Step]
-    ):
+    def __init__(self, steps: Iterable[Step]):
         """
         A specific revision of the database. This comprises
         a set of sequential steps and is uniquely identified
@@ -95,12 +76,7 @@ def id(self) -> str:
         A unique identifier created by joining and hashing the
         identifiers of comprised steps.
         """
-        return md5(
-            ":".join(
-                step.id for step
-                in self.steps
-            ).encode("utf-8")
-        ).hexdigest()
+        return md5(":".join(step.id for step in self.steps).encode("utf-8")).hexdigest()
 
     def __sub__(self, other: "Revision") -> "Revision":
         """
@@ -121,17 +97,11 @@ def __sub__(self, other: "Revision") -> "Revision":
         An object comprising steps required to move from the other
         revision to this revision.
         """
-        return Revision(tuple(
-            step for step in self.steps
-            if step not in other.steps
-        ))
+        return Revision(tuple(step for step in self.steps if step not in other.steps))
 
 
 class Migrator:
-    def __init__(
-            self,
-            *steps: Step
-    ):
+    def __init__(self, *steps: Step):
         """
         Manages migration of an old database.
 
@@ -153,14 +123,9 @@ def revisions(self) -> Generator[Revision, None, None]:
         starting on the first step and terminating on any step
         """
         for i in range(1, len(self._steps) + 1):
-            yield Revision(
-                self._steps[:i]
-            )
+            yield Revision(self._steps[:i])
 
-    def get_steps(
-            self,
-            revision_id: Optional[str] = None
-    ) -> Iterable[Step]:
+    def get_steps(self, revision_id: Optional[str] = None) -> Iterable[Step]:
         """
         Retrieve steps required to go from the specified
         revision to the latest revision.
@@ -188,9 +153,7 @@ def latest_revision(self) -> Revision:
         The latest revision according to the steps passed to the
         Migrator
         """
-        return Revision(
-            self._steps
-        )
+        return Revision(self._steps)
 
     def migrate(self, session: sa.orm.Session):
         """
@@ -207,19 +170,11 @@ def migrate(self, session: sa.orm.Session):
         session
             A session pointing at some database.
         """
-        wrapper = SessionWrapper(
-            session
-        )
+        wrapper = SessionWrapper(session)
         revision_id = wrapper.revision_id
-        steps = list(
-            self.get_steps(
-                revision_id
-            )
-        )
+        steps = list(self.get_steps(revision_id))
         if len(steps) == 0:
-            logger.info(
-                "Database already at latest revision"
-            )
+            logger.info("Database already at latest revision")
             return
 
         latest_revision_id = self.latest_revision.id
@@ -230,14 +185,10 @@ def migrate(self, session: sa.orm.Session):
         for step in steps:
             for string in step.strings:
                 try:
-                    session.execute(
-                        string
-                    )
+                    session.execute(text(string))
                 except sa.exc.OperationalError as e:
                     logger.debug(e)
 
         wrapper.revision_id = self.latest_revision.id
 
-        logger.info(
-            f"revision_id updated to {wrapper.revision_id}"
-        )
+        logger.info(f"revision_id updated to {wrapper.revision_id}")