Converted _calc _sam to staticmethod.

Signed-off-by: Daniel Scheffler <danschef@gfz-potsdam.de>

gms_preprocessing/algorithms/classification.py

@@ -332,20 +332,23 @@ class SAM_Classifier(_ImageClassifier):
     def angles_deg(self):
         return np.rad2deg(self._distance_metrics) if self._distance_metrics is not None else None
 
-    def _calc_sam(self, image, endmembers):
-        if not image.shape[2] == self.train_spectra.shape[1]:
+    @staticmethod
+    def calc_sam(image, endmembers):
+        n_samples, n_features = endmembers.shape
+
+        if not image.shape[2] == endmembers.shape[1]:
             raise RuntimeError('Matrix dimensions are not aligned. Input image has %d bands but input spectra '
-                               'have %d.' % (image.shape[2], self.train_spectra.shape[1]))
+                               'have %d.' % (image.shape[2], endmembers.shape[1]))
 
         # normalize input data because SAM asserts only data between -1 and 1
         train_spectra_norm, tileimdata_norm = normalize_endmembers_image(endmembers, image)
 
-        angles = np.zeros((image.shape[0], image.shape[1], self.n_samples), np.float)
+        angles = np.zeros((image.shape[0], image.shape[1], n_samples), np.float)
         # if np.std(tileimdata) == 0:  # skip tiles that only contain the same value
 
         # loop over all training spectra and compute spectral angle for each pixel
-        for n_sample in range(self.n_samples):
-            train_spectrum = train_spectra_norm[n_sample, :].reshape(1, 1, self.n_features)
+        for n_sample in range(n_samples):
+            train_spectrum = train_spectra_norm[n_sample, :].reshape(1, 1, n_features)
             angles[:, :, n_sample] = calc_sam(tileimdata_norm, train_spectrum, axis=2)
 
         return angles
@@ -356,7 +359,7 @@ class SAM_Classifier(_ImageClassifier):
         (rS, rE), (cS, cE) = tilepos
         tileimdata = global_shared_im2classify[rS: rE + 1, cS: cE + 1, :]
 
-        angles = self._calc_sam(tileimdata, global_shared_endmembers)
+        angles = self.calc_sam(tileimdata, global_shared_endmembers)
         angles_min = np.min(angles, axis=2).astype(np.float32)
         cmap = np.argmin(angles, axis=2).astype(np.int16)
 
@@ -393,7 +396,7 @@ class kNN_SAM_Classifier(SAM_Classifier):
         (rS, rE), (cS, cE) = tilepos
         tileimdata = global_shared_im2classify[rS: rE + 1, cS: cE + 1, :]
 
-        angles = self._calc_sam(tileimdata, global_shared_endmembers)
+        angles = self.calc_sam(tileimdata, global_shared_endmembers)
         k = self.n_neighbors if self.n_neighbors <= angles.shape[2] else angles.shape[2]
 
         if self.n_neighbors < angles.shape[2]: