199 make fitter a part of project

pyproject.toml

@@ -29,7 +29,7 @@ classifiers = [
 ]
 requires-python = ">=3.9,<3.13"
 dependencies = [
-    'easyscience @ git+https://github.com/EasyScience/EasyScience.git@develop',
+    "easyscience>=1.2.0",
     "scipp>=23.12.0",
     "refnx>=0.1.15",
     "refl1d>=0.8.14",

src/easyreflectometry/data/__init__.py

@@ -1,9 +1,11 @@
 from .data_store import DataSet1D
 from .data_store import ProjectData
 from .measurement import load
+from .measurement import load_as_dataset
 
 __all__ = [
     load,
+    load_as_dataset,
     ProjectData,
     DataSet1D,
 ]

src/easyreflectometry/data/measurement.py

@@ -8,6 +8,19 @@
 from orsopy.fileio import Header
 from orsopy.fileio import orso
 
+from easyreflectometry.data import DataSet1D
+
+
+def load_as_dataset(fname: Union[TextIO, str]) -> DataSet1D:
+    """Load data from an ORSO .ort file as a DataSet1D."""
+    data_group = load(fname)
+    return DataSet1D(
+        x=data_group['coords']['Qz_0'].values,
+        y=data_group['data']['R_0'].values,
+        ye=data_group['data']['R_0'].variances,
+        xe=data_group['coords']['Qz_0'].variances,
+    )
+
 
 def load(fname: Union[TextIO, str]) -> sc.DataGroup:
     """Load data from an ORSO .ort file.

src/easyreflectometry/fitting.py

@@ -3,12 +3,14 @@
 import numpy as np
 import scipp as sc
 from easyscience.fitting import AvailableMinimizers
-from easyscience.fitting.multi_fitter import MultiFitter as easyFitter
+from easyscience.fitting import FitResults
+from easyscience.fitting.multi_fitter import MultiFitter as EasyScienceMultiFitter
 
+from easyreflectometry.data import DataSet1D
 from easyreflectometry.model import Model
 
 
-class Fitter:
+class MultiFitter:
     def __init__(self, *args: Model):
         r"""A convinence class for the :py:class:`easyscience.Fitting.Fitting`
         which will populate the :py:class:`sc.DataGroup` appropriately
@@ -26,7 +28,7 @@ def wrapped(*args, **kwargs):
 
         self._fit_func = [func_wrapper(m.interface.fit_func, m.unique_name) for m in args]
         self._models = args
-        self.easy_f = easyFitter(args, self._fit_func)
+        self.easy_science_multi_fitter = EasyScienceMultiFitter(args, self._fit_func)
 
     def fit(self, data: sc.DataGroup, id: int = 0) -> sc.DataGroup:
         """
@@ -39,28 +41,37 @@ def fit(self, data: sc.DataGroup, id: int = 0) -> sc.DataGroup:
         x = [data['coords'][f'Qz_{i}'].values for i in refl_nums]
         y = [data['data'][f'R_{i}'].values for i in refl_nums]
         dy = [1 / np.sqrt(data['data'][f'R_{i}'].variances) for i in refl_nums]
-        result = self.easy_f.fit(x, y, weights=dy)
+        result = self.easy_science_multi_fitter.fit(x, y, weights=dy)
         new_data = data.copy()
         for i, _ in enumerate(result):
             id = refl_nums[i]
             new_data[f'R_{id}_model'] = sc.array(
                 dims=[f'Qz_{id}'], values=self._fit_func[i](data['coords'][f'Qz_{id}'].values)
             )
-            sld_profile = self.easy_f._fit_objects[i].interface.sld_profile(self._models[i].unique_name)
+            sld_profile = self.easy_science_multi_fitter._fit_objects[i].interface.sld_profile(self._models[i].unique_name)
             new_data[f'SLD_{id}'] = sc.array(dims=[f'z_{id}'], values=sld_profile[1] * 1e-6, unit=sc.Unit('1/angstrom') ** 2)
             new_data['attrs'][f'R_{id}_model'] = {'model': sc.scalar(self._models[i].as_dict())}
             new_data['coords'][f'z_{id}'] = sc.array(
                 dims=[f'z_{id}'], values=sld_profile[0], unit=(1 / new_data['coords'][f'Qz_{id}'].unit).unit
             )
         return new_data
 
+    def fit_single_data_set_1d(self, data: DataSet1D) -> FitResults:
+        """
+        Perform the fitting and populate the DataGroups with the result.
+
+        :param data: DataGroup to be fitted to and populated
+        :param method: Optimisation method
+        """
+        return self.easy_science_multi_fitter.fit(x=[data.x], y=[data.y], weights=[data.ye])[0]
+
     def switch_minimizer(self, minimizer: AvailableMinimizers) -> None:
         """
         Switch the minimizer for the fitting.
 
         :param minimizer: Minimizer to be switched to
         """
-        self.easy_f.switch_minimizer(minimizer)
+        self.easy_science_multi_fitter.switch_minimizer(minimizer)
 
 
 def _flatten_list(this_list: list) -> list:

src/easyreflectometry/model/model.py

@@ -12,10 +12,10 @@
 from easyscience.Objects.new_variable import Parameter
 from easyscience.Objects.ObjectClasses import BaseObj
 
-from easyreflectometry.parameter_utils import get_as_parameter
-from easyreflectometry.parameter_utils import yaml_dump
 from easyreflectometry.sample import BaseAssembly
 from easyreflectometry.sample import Sample
+from easyreflectometry.utils import get_as_parameter
+from easyreflectometry.utils import yaml_dump
 
 from .resolution_functions import PercentageFhwm
 from .resolution_functions import ResolutionFunction

src/easyreflectometry/model/model_collection.py

@@ -41,7 +41,7 @@ def add_model(self, model: Optional[Model] = None):
         :param model: Model to add.
         """
         if model is None:
-            model = Model(name='Model new', interface=self.interface)
+            model = Model(name='EasyModel added', interface=self.interface)
         self.append(model)
 
     def duplicate_model(self, index: int):

src/easyreflectometry/model/resolution_functions.py

@@ -59,4 +59,4 @@ def smearing(self, q: Union[np.array, float]) -> np.array:
     def as_dict(
         self, skip: Optional[List[str]] = None
     ) -> dict[str, str]:  # skip is kept for consistency of the as_dict signature
-        return {'smearing': 'LinearSpline', 'q_data_points': self.q_data_points, 'fwhm_values': self.fwhm_values}
+        return {'smearing': 'LinearSpline', 'q_data_points': list(self.q_data_points), 'fwhm_values': list(self.fwhm_values)}