minor formatting

src/depiction/calibration/spectrum/calibration_method_mcc.py

@@ -16,31 +16,32 @@ def __init__(
         model_smoothing_activated: bool = True,
         model_smoothing_kernel_size: int = 27,
         model_smoothing_kernel_std: float = 10.0,
+        max_pairwise_distance: float = 500,
     ) -> None:
         self._model_smoothing_activated = model_smoothing_activated
         self._model_smoothing_kernel_size = model_smoothing_kernel_size
         self._model_smoothing_kernel_std = model_smoothing_kernel_std
+        self._max_pairwise_distance = max_pairwise_distance
 
     def extract_spectrum_features(self, peak_mz_arr: NDArray[float], peak_int_arr: NDArray[float]) -> DataArray:
         l_none = 1.000482
         # c_0 = 0.029
         # c_1 = 4.98*10e-4
 
-        # compute all differences for elements in peak_mz_arr amd store in a DataArray
-        # delta = scipy.spatial.distance_matrix(np.expand_dims(peak_mz_arr,1), np.expand_dims(peak_mz_arr,1), p = 1)
+        # Compute all differences for elements in peak_mz_arr amd store in a DataArray
         delta = scipy.spatial.distance.pdist(np.expand_dims(peak_mz_arr, 1), metric="cityblock")
-        # get all distances smaller then 500
-        delta = delta[delta < 500]
-        # for each x compute
+
+        # Get all distances smaller than the max pairwise distance
+        delta = delta[delta < self._max_pairwise_distance]
+
         # Compute delta_lambda for each x
         delta_lambda = np.zeros_like(delta)
         for i, mi in enumerate(delta):
-            term1 = (mi) % l_none
+            term1 = mi % l_none
             if term1 < 0.5:
                 delta_lambda[i] = term1
             else:
                 delta_lambda[i] = -1 + term1
-        # create a scatterplot of delte_lambda vs x
 
         sorted_indices = np.argsort(delta)
         delta = delta[sorted_indices]
@@ -57,11 +58,12 @@ def extract_spectrum_features(self, peak_mz_arr: NDArray[float], peak_int_arr: N
 
         delta_intercept = np.zeros_like(peak_mz_corrected)
         for i, mi in enumerate(peak_mz_corrected):
-            term1 = (mi) % l_none
+            term1 = mi % l_none
             if term1 < 0.5:
                 delta_intercept[i] = term1
             else:
                 delta_intercept[i] = -1 + term1
+
         intercept_coef = stats.trim_mean(delta_intercept, 0.3)
         return DataArray([intercept_coef, slope], dims=["c"])