Update fitters and add test that weights behave as expected

src/easyscience/fitting/minimizers/minimizer_bumps.py

@@ -98,6 +98,11 @@ def fit(
         :type method: str
         :return: Fit results
         :rtype: ModelResult
+
+        For standard least squares, the weights should be 1/sigma, where
+        sigma is the standard deviation of the measurement. For
+        unweighted least squares, these should be 1.
+
         """
         method_dict = self._get_method_kwargs(method)
 
@@ -191,6 +196,8 @@ def _make_model(self, parameters: Optional[List[BumpsParameter]] = None) -> Call
         Generate a bumps model from the supplied `fit_function` and parameters in the base object.
         Note that this makes a callable as it needs to be initialized with *x*, *y*, *weights*
 
+        Weights are converted to dy (standard deviation of y).
+
         :return: Callable to make a bumps Curve model
         :rtype: Callable
         """
@@ -205,7 +212,7 @@ def _make_func(x, y, weights):
                 else:
                     for par in parameters:
                         bumps_pars[MINIMIZER_PARAMETER_PREFIX + par.unique_name] = obj.convert_to_par_object(par)
-                return Curve(fit_func, x, y, dy=weights, **bumps_pars)
+                return Curve(fit_func, x, y, dy=1 / weights, **bumps_pars)
 
             return _make_func
 

src/easyscience/fitting/minimizers/minimizer_dfo.py

@@ -74,7 +74,7 @@ def fit(
         :type x: np.ndarray
         :param y: measured points
         :type y: np.ndarray
-        :param weights: Weights for supplied measured points
+        :param weights: Weights for supplied measured points.
         :type weights: np.ndarray
         :param model: Optional Model which is being fitted to
         :type model: lmModel
@@ -85,6 +85,10 @@ def fit(
         :type method: str
         :return: Fit results
         :rtype: ModelResult
+
+        For standard least squares, the weights should be 1/sigma, where
+        sigma is the standard deviation of the measurement. For
+        unweighted least squares, these should be 1.
         """
         x, y, weights = np.asarray(x), np.asarray(y), np.asarray(weights)
 
@@ -163,7 +167,7 @@ def _make_func(x, y, weights):
                 def _residuals(pars_values: List[float]) -> np.ndarray:
                     for idx, par_name in enumerate(dfo_pars.keys()):
                         dfo_pars[par_name] = pars_values[idx]
-                    return (y - fit_func(x, **dfo_pars)) / weights
+                    return (y - fit_func(x, **dfo_pars)) * weights
 
                 return _residuals
 
@@ -267,7 +271,11 @@ def _dfo_fit(
         return results
 
     @staticmethod
-    def _prepare_kwargs(tolerance: Optional[float] = None, max_evaluations: Optional[int] = None, **kwargs) -> dict[str:str]:
+    def _prepare_kwargs(
+        tolerance: Optional[float] = None,
+        max_evaluations: Optional[int] = None,
+        **kwargs,
+    ) -> dict[str:str]:
         if max_evaluations is not None:
             kwargs['maxfun'] = max_evaluations  # max number of function evaluations
         if tolerance is not None:

src/easyscience/fitting/minimizers/minimizer_lmfit.py

@@ -111,6 +111,11 @@ def fit(
         :param kwargs: Additional arguments for the fitting function.
         :return: Fit results
         :rtype: ModelResult
+
+        For standard least squares, the weights should be 1/sigma, where
+        sigma is the standard deviation of the measurement. For
+        unweighted least squares, these should be 1.
+
         """
         x, y, weights = np.asarray(x), np.asarray(y), np.asarray(weights)
 
@@ -230,7 +235,12 @@ def _make_model(self, pars: Optional[LMParameters] = None) -> LMModel:
             else:
                 value = item.value
 
-            model.set_param_hint(MINIMIZER_PARAMETER_PREFIX + str(name), value=value, min=item.min, max=item.max)
+            model.set_param_hint(
+                MINIMIZER_PARAMETER_PREFIX + str(name),
+                value=value,
+                min=item.min,
+                max=item.max,
+            )
 
         # Cache the model for later reference
         self._cached_model = model