Remove redundant return value from calculate_pattern

src/easydiffraction/analysis/analysis.py

@@ -5,7 +5,6 @@
 from typing import Optional
 from typing import Union
 
-import numpy as np
 import pandas as pd
 
 from easydiffraction.core.objects import Descriptor
@@ -362,20 +361,20 @@ def show_current_fit_mode(self) -> None:
         print(paragraph('Current fit mode'))
         print(self.fit_mode)
 
-    def calculate_pattern(self, expt_name: str) -> Optional[np.ndarray]:
+    def calculate_pattern(self, expt_name: str) -> None:
         """
         Calculate the diffraction pattern for a given experiment.
+        The calculated pattern is stored within the experiment's datastore.
 
         Args:
             expt_name: The name of the experiment.
 
         Returns:
-            The calculated pattern as a pandas DataFrame.
+            None.
         """
         experiment = self.project.experiments[expt_name]
         sample_models = self.project.sample_models
-        calculated_pattern = self.calculator.calculate_pattern(sample_models, experiment)
-        return calculated_pattern
+        self.calculator.calculate_pattern(sample_models, experiment)
 
     def show_constraints(self) -> None:
         constraints_dict = self.constraints._items

src/easydiffraction/analysis/calculation.py

@@ -5,8 +5,6 @@
 from typing import List
 from typing import Optional
 
-import numpy as np
-
 from easydiffraction.experiments.experiment import Experiment
 from easydiffraction.experiments.experiments import Experiments
 from easydiffraction.sample_models.sample_models import SampleModels
@@ -61,15 +59,16 @@ def calculate_pattern(
         self,
         sample_models: SampleModels,
         experiment: Experiment,
-    ) -> np.ndarray:
+    ) -> None:
         """
         Calculate diffraction pattern based on sample models and experiment.
+        The calculated pattern is stored within the experiment's datastore.
 
         Args:
             sample_models: Collection of sample models.
             experiment: A single experiment object.
 
         Returns:
-            Diffraction pattern calculated by the backend calculator.
+            None.
         """
-        return self._calculator.calculate_pattern(sample_models, experiment)
+        self._calculator.calculate_pattern(sample_models, experiment)

src/easydiffraction/analysis/calculators/calculator_base.py

@@ -45,17 +45,18 @@ def calculate_pattern(
         sample_models: SampleModels,
         experiment: Experiment,
         called_by_minimizer: bool = False,
-    ) -> np.ndarray:
+    ) -> None:
         """
         Calculate the diffraction pattern for multiple sample models and a single experiment.
+        The calculated pattern is stored within the experiment's datastore.
 
         Args:
             sample_models: Collection of sample models.
             experiment: The experiment object.
             called_by_minimizer: Whether the calculation is called by a minimizer.
 
         Returns:
-            The calculated diffraction pattern as a NumPy array.
+            None.
         """
         x_data = experiment.datastore.x
         y_calc_zeros = np.zeros_like(x_data)
@@ -95,8 +96,6 @@ def calculate_pattern(
         y_calc_total = y_calc_scaled + y_bkg
         experiment.datastore.calc = y_calc_total
 
-        return y_calc_total
-
     @abstractmethod
     def _calculate_single_model_pattern(
         self,

src/easydiffraction/analysis/minimization.py

@@ -168,11 +168,12 @@ def _residual_function(
 
         for (expt_id, experiment), weight in zip(experiments._items.items(), _weights):
             # Calculate the difference between measured and calculated patterns
-            y_calc: np.ndarray = calculator.calculate_pattern(
+            calculator.calculate_pattern(
                 sample_models,
                 experiment,
                 called_by_minimizer=True,
             )
+            y_calc: np.ndarray = experiment.datastore.calc
             y_meas: np.ndarray = experiment.datastore.meas
             y_meas_su: np.ndarray = experiment.datastore.meas_su
             diff = (y_meas - y_calc) / y_meas_su

src/easydiffraction/analysis/reliability_factors.py

@@ -142,7 +142,8 @@ def get_reliability_inputs(
     y_calc_all = []
     y_err_all = []
     for expt_name, experiment in experiments._items.items():
-        y_calc = calculator.calculate_pattern(sample_models, experiment)
+        calculator.calculate_pattern(sample_models, experiment)
+        y_calc = experiment.datastore.calc
         y_meas = experiment.datastore.meas
         y_meas_su = experiment.datastore.meas_su
 

tests/unit/analysis/calculators/test_calculator_base.py

@@ -52,7 +52,8 @@ def test_calculate_pattern(mock_constraints_handler, mock_sample_models, mock_ex
     mock_constraints_handler.return_value.apply = MagicMock()
 
     calculator = MockCalculator()
-    result = calculator.calculate_pattern(mock_sample_models, mock_experiment)
+    calculator.calculate_pattern(mock_sample_models, mock_experiment)
+    result = mock_experiment.datastore.calc
 
     # Assertions
     assert np.allclose(result, np.array([3.6, 7.2, 10.8]))

tests/unit/analysis/test_minimization.py

@@ -41,7 +41,11 @@ def mock_experiments():
 @pytest.fixture
 def mock_calculator():
     calculator = MagicMock()
-    calculator.calculate_pattern.return_value = np.array([9.0, 19.0, 29.0])
+
+    def mock_calculate_pattern(sample_models, experiment, **kwargs):
+        experiment.datastore.calc = np.array([9.0, 19.0, 29.0])
+
+    calculator.calculate_pattern.side_effect = mock_calculate_pattern
     return calculator
 
 

tests/unit/analysis/test_reliability_factors.py

@@ -96,7 +96,11 @@ def test_get_reliability_inputs():
             )
         )
     }
-    calculator.calculate_pattern.return_value = np.array([9.0, 19.0, 29.0])
+
+    def mock_calculate_pattern(sample_models, experiment, **kwargs):
+        experiment.datastore.calc = np.array([9.0, 19.0, 29.0])
+
+    calculator.calculate_pattern.side_effect = mock_calculate_pattern
 
     y_obs, y_calc, y_err = get_reliability_inputs(sample_models, experiments, calculator)