11 poc minimizer factory

docs/src/fitting/constraints.rst

@@ -19,7 +19,7 @@ Constraints on Parameters
 Constraints on Fitting
 ^^^^^^^^^^^^^^^^^^^^^^
 
-:class:`easyscience.Fitting.Fitting.Fitter` has the ability to evaluate user supplied constraints which effect the value of both fixed and non-fixed parameters. A good example of one such use case would be the ratio between two parameters, where you would create a :class:`easyscience.Fitting.Constraints.ObjConstraint`.
+:class:`easyscience.fitting.Fitter` has the ability to evaluate user supplied constraints which effect the value of both fixed and non-fixed parameters. A good example of one such use case would be the ratio between two parameters, where you would create a :class:`easyscience.fitting.Constraints.ObjConstraint`.
 
 Using constraints
 -----------------
@@ -28,7 +28,7 @@ A constraint can be used in one of three ways; Assignment to a parameter, assign
 
 .. code-block:: python
 
-     from easyscience.Fitting.Constraints import NumericConstraint
+     from easyscience.fitting.Constraints import NumericConstraint
      from easyscience.Objects.Base import Parameter
      # Create an `a < 1` constraint
      a = Parameter('a', 0.5)
@@ -41,35 +41,35 @@ A constraint can be used in one of three ways; Assignment to a parameter, assign
 Constraint Reference
 --------------------
 
-.. minigallery:: easyscience.Fitting.Constraints.NumericConstraint
+.. minigallery:: easyscience.fitting.Constraints.NumericConstraint
     :add-heading: Examples using `Constraints`
 
 Built-in constraints
 ^^^^^^^^^^^^^^^^^^^^
 
 These are the built in constraints which you can use
 
-.. autoclass:: easyscience.Fitting.Constraints.SelfConstraint
+.. autoclass:: easyscience.fitting.Constraints.SelfConstraint
    :members: +enabled
 
-.. autoclass:: easyscience.Fitting.Constraints.NumericConstraint
+.. autoclass:: easyscience.fitting.Constraints.NumericConstraint
   :members: +enabled
 
-.. autoclass:: easyscience.Fitting.Constraints.ObjConstraint
+.. autoclass:: easyscience.fitting.Constraints.ObjConstraint
   :members: +enabled
 
-.. autoclass:: easyscience.Fitting.Constraints.FunctionalConstraint
+.. autoclass:: easyscience.fitting.Constraints.FunctionalConstraint
   :members: +enabled
 
-.. autoclass:: easyscience.Fitting.Constraints.MultiObjConstraint
+.. autoclass:: easyscience.fitting.Constraints.MultiObjConstraint
   :members: +enabled
 
 User created constraints
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
-You can also make your own constraints by subclassing the :class:`easyscience.Fitting.Constraints.ConstraintBase` class. For this at a minimum the abstract methods ``_parse_operator`` and ``__repr__`` need to be written.
+You can also make your own constraints by subclassing the :class:`easyscience.fitting.Constraints.ConstraintBase` class. For this at a minimum the abstract methods ``_parse_operator`` and ``__repr__`` need to be written.
 
-.. autoclass:: easyscience.Fitting.Constraints.ConstraintBase
+.. autoclass:: easyscience.fitting.Constraints.ConstraintBase
   :members:
   :private-members:
   :special-members: __repr__

examples_old/dataset_examples.ipynb

@@ -9,7 +9,7 @@
     "import numpy as np\n",
     "from easyscience.Datasets.xarray import xr\n",
     "from easyscience.Objects.Base import Parameter, BaseObj\n",
-    "from easyscience.Fitting.Fitting import Fitter"
+    "from easyscience.fitting import Fitter"
    ]
   },
   {

examples_old/dimer_example.ipynb

@@ -18,7 +18,7 @@
     "import numpy as np\n",
     "from easyscience.Datasets.xarray import xr\n",
     "from easyscience.Objects.Base import Parameter, BaseObj\n",
-    "from easyscience.Fitting.Fitting import Fitter"
+    "from easyscience.fitting import Fitter"
    ]
   },
   {
@@ -914,7 +914,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from easyscience.Fitting.Fitting import Fitter\n",
+    "from easyscience.fitting import Fitter\n",
     "f = Fitter()\n",
     "f.initialize(sl, sl.dispersion)"
    ]

examples_old/example1_dream.py

@@ -19,7 +19,7 @@ def fit_fun(x):
 
 f = Fitter()
 f.initialize(b, fit_fun)
-f.switch_engine("bumps")
+f.switch_minimizer("bumps")
 
 x = np.array([1, 2, 3])
 y = np.array([2, 4, 6]) - 1

examples_old/example5_broken.py

@@ -357,7 +357,7 @@ def __repr__(self):
 a = line.c
 
 # Now lets change fitting engine
-f.switch_engine("bumps")
+f.switch_minimizer("bumps")
 # Reset the values so we don't cheat
 line.m = 1
 line.c = 0

examples_old/example6_broken.py

@@ -436,7 +436,7 @@ def __repr__(self):
 print(hybrid)
 
 # Now lets change fitting engine
-f.switch_engine("bumps")
+f.switch_minimizer("bumps")
 # Reset the values so we don't cheat
 hybrid.m = 1
 hybrid.c = 0

examples_old/example_dataset2pt2.py

@@ -43,7 +43,7 @@ def fit_fun(x, *args, **kwargs):
 
     b.m = m_starting_point
     b.c = c_starting_point
-    f.switch_engine(minimizer)
+    f.switch_minimizer(minimizer)
 
     f_res = d['y'].easyscience.fit(f, vectorize=True)
     print(f_res.p)

examples_old/example_dataset2pt2_broken.py

@@ -43,7 +43,7 @@ def fit_fun(x, *args, **kwargs):
 
     b.m = m_starting_point
     b.c = c_starting_point
-    f.switch_engine(minimizer)
+    f.switch_minimizer(minimizer)
 
     f_res = d['y'].easyscience.fit(f, vectorize=True)
     print(f_res.p)

pyproject.toml

@@ -34,10 +34,11 @@ dependencies = [
     "bumps",
     "DFO-LS",
     "lmfit",
-    "numpy",
+    "numpy==1.26",  # Should be updated to numpy 2.0
     "pint",
     "uncertainties",
-    "xarray"
+    "xarray",
+    "pint==0.23"  # Only to ensure that unit is reported as dimensionless rather than empty string
 ]
 
 [project.optional-dependencies]

src/easyscience/Datasets/xarray.py

@@ -20,7 +20,7 @@
 import xarray as xr
 
 from easyscience import ureg
-from easyscience.Fitting import FitResults
+from easyscience.fitting import FitResults
 
 T_ = TypeVar('T_')
 
@@ -371,13 +371,13 @@ def fit(
         **kwargs,
     ) -> List[FitResults]:
         """
-        Perform a fit on one or more DataArrays. This fit utilises a given fitter from `EasyScience.Fitting.Fitter`, though
+        Perform a fit on one or more DataArrays. This fit utilises a given fitter from `EasyScience.fitting.Fitter`, though
         there are a few differences to a standard EasyScience fit. In particular, key-word arguments to control the
         optimisation algorithm go in the `fit_kwargs` dictionary, fit function key-word arguments go in the `fn_kwargs`
         and given key-word arguments control the `xarray.apply_ufunc` function.
 
         :param fitter: Fitting object which controls the fitting
-        :type fitter: EasyScience.Fitting.Fitter
+        :type fitter: EasyScience.fitting.Fitter
         :param args: Arguments to go to the fit function
         :type args: Any
         :param dask: Dask control string. See `xarray.apply_ufunc` documentation
@@ -681,13 +681,13 @@ def fit(
         **kwargs,
     ) -> FitResults:
         """
-        Perform a fit on the given DataArray. This fit utilises a given fitter from `EasyScience.Fitting.Fitter`, though
+        Perform a fit on the given DataArray. This fit utilises a given fitter from `EasyScience.fitting.Fitter`, though
         there are a few differences to a standard EasyScience fit. In particular, key-word arguments to control the
         optimisation algorithm go in the `fit_kwargs` dictionary, fit function key-word arguments go in the `fn_kwargs`
         and given key-word arguments control the `xarray.apply_ufunc` function.
 
         :param fitter: Fitting object which controls the fitting
-        :type fitter: EasyScience.Fitting.Fitter
+        :type fitter: EasyScience.fitting.Fitter
         :param args: Arguments to go to the fit function
         :type args: Any
         :param dask: Dask control string. See `xarray.apply_ufunc` documentation
@@ -732,7 +732,7 @@ def local_fit_func(x, *args, **kwargs):
 
         # Set the new callable to the fitter and initialize
         fitter.initialize(fitter.fit_object, local_fit_func)
-        # Make EasyScience.Fitting.Fitter compatible `x`
+        # Make EasyScience.fitting.Fitter compatible `x`
         x_for_fit = xr.concat(bdims, dim='fit_dim')
         x_for_fit = x_for_fit.stack(all_x=[d.name for d in bdims])
         try:
@@ -803,7 +803,7 @@ def check_sanity_multiple(fit_results: FitResults, originals: List[xr.DataArray]
         current_results = fit_results.__class__()
         # Fill out the basic stuff....
         current_results.engine_result = fit_results.engine_result
-        current_results.fitting_engine = fit_results.fitting_engine
+        current_results.minimizer_engine = fit_results.minimizer_engine
         current_results.success = fit_results.success
         current_results.p = fit_results.p
         current_results.p0 = fit_results.p0

src/easyscience/Objects/Inferface.py

@@ -19,7 +19,7 @@
 _C = TypeVar('_C', bound=ABCMeta)
 _M = TypeVar('_M')
 if TYPE_CHECKING:
-    from easyscience.Fitting import Fitter
+    from easyscience.fitting import Fitter
 
 
 class InterfaceFactoryTemplate:
@@ -65,7 +65,7 @@ def switch(self, new_interface: str, fitter: Optional[Type[Fitter]] = None):
         :param new_interface: name of new interface to be created
         :type new_interface: str
         :param fitter: Fitting interface which contains the fitting object which may have bindings which will be updated.
-        :type fitter: EasyScience.Fitting.Fitting.Fitter
+        :type fitter: EasyScience.fitting.Fitter
         :return: None
         :rtype: noneType
         """

src/easyscience/Objects/Variable.py

@@ -30,7 +30,7 @@
 from easyscience import borg
 from easyscience import pint
 from easyscience import ureg
-from easyscience.Fitting.Constraints import SelfConstraint
+from easyscience.fitting.Constraints import SelfConstraint
 from easyscience.Objects.core import ComponentSerializer
 from easyscience.Utils.classTools import addProp
 from easyscience.Utils.Exceptions import CoreSetException