Replaced deprecated numpy data types. Bumped version.

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

specclassify/classifiers/euclidian.py

@@ -66,7 +66,7 @@ class MinimumDistance_Classifier(_ImageClassifier):
             if nodataVal_cmap is not None and lbl == nodataVal_cmap:
                 continue
             mask = labels == lbl
-            centroid = self.class_centroids[list(self.train_labels).index(lbl), :].reshape(1, -1).astype(np.float)
+            centroid = self.class_centroids[list(self.train_labels).index(lbl), :].reshape(1, -1).astype(float)
             diff = spectra[mask, :] - centroid
             distances[mask] = np.sqrt((diff ** 2).sum(axis=1))
 
@@ -114,7 +114,7 @@ class kNN_MinimumDistance_Classifier(MinimumDistance_Classifier, _kNN_ImageClass
 
         # loop over all training spectra and compute spectral angle for each pixel
         for n_sample in range(n_samples):
-            train_spectrum = endmembers[n_sample, :].reshape(1, 1, n_features).astype(np.float)
+            train_spectrum = endmembers[n_sample, :].reshape(1, 1, n_features).astype(float)
             diff = image - train_spectrum
             dists[:, :, n_sample] = np.sqrt((diff ** 2).sum(axis=2))
 

specclassify/classifiers/hybrid.py

@@ -53,8 +53,8 @@ class FEDSA_Classifier(_ImageClassifier):
         # 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)
-        ed = np.zeros((image.shape[0], image.shape[1], self.n_samples), np.float)
+        angles = np.zeros((image.shape[0], image.shape[1], self.n_samples), float)
+        ed = np.zeros((image.shape[0], image.shape[1], self.n_samples), float)
         tileimspectra = im2spectra(image)
         # if np.std(tileimdata) == 0:  # skip tiles that only contain the same value
 
@@ -62,8 +62,8 @@ class FEDSA_Classifier(_ImageClassifier):
         for n_sample in range(self.n_samples):
             train_spectrum = train_spectra_norm[n_sample, :].reshape(1, 1, self.n_features)
             angles[:, :, n_sample] = calc_sam(tileimdata_norm, train_spectrum, axis=2)
-            ed[:, :, n_sample] = np.sqrt(np.sum((tileimspectra.astype(np.float) -
-                                                 train_spectrum.flatten().astype(np.float)) ** 2, axis=1))\
+            ed[:, :, n_sample] = np.sqrt(np.sum((tileimspectra.astype(float) -
+                                                 train_spectrum.flatten().astype(float)) ** 2, axis=1))\
                 .reshape(image.shape[:2])
 
         angles_norm = angles / angles.max()

specclassify/classifiers/sid.py

@@ -54,7 +54,7 @@ class SID_Classifier(_ImageClassifier):
         # normalize input data because SID asserts only data between -1 and 1
         train_spectra_norm, tileimdata_norm = normalize_endmembers_image(endmembers, imdata[:])
 
-        sid = np.zeros((imdata.shape[0], imdata.shape[1], self.n_samples), np.float)
+        sid = np.zeros((imdata.shape[0], imdata.shape[1], self.n_samples), float)
         # if np.std(tileimdata) == 0:  # skip tiles that only contain the same value
 
         # loop over all training spectra and compute spectral information divergence for each pixel

specclassify/misc.py

@@ -34,8 +34,8 @@ def normalize_endmembers_image(endmembers, image):
     # type: (np.ndarray, np.ndarray) -> Tuple[np.ndarray, np.ndarray]
     from sklearn.preprocessing import MaxAbsScaler  # avoids static TLS errors here
 
-    em = endmembers.astype(np.float)
-    im = image.astype(np.float)
+    em = endmembers.astype(float)
+    im = image.astype(float)
 
     # provide training values as 2D ROW (n samples x 1 feature),
     # because normalization should be applied globally, not band-by-band

specclassify/version.py

@@ -24,5 +24,5 @@
 # limitations under the License.
 
 
-__version__ = '0.3.1'
-__versionalias__ = '20211215.01'
+__version__ = '0.3.2'
+__versionalias__ = '20211215.02'