Added minimal version of geoarray.

Signed-off-by: Daniel Scheffler <[email protected]>
GFZ · Nov 2, 2020 · 10ca3f5 · 10ca3f5
1 parent 27a1613
commit 10ca3f5
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Showing 5 changed files with 27 additions and 8 deletions.
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
@@ -12,6 +12,7 @@ test_spechomo:
   script:
     - source /root/miniconda3/bin/activate ci_env
     - conda install -y -c conda-forge "scikit-learn=0.23.2"  # FIXME remove as soon as docker container is rebuilt
+    - pip install 'geoarray>=0.10.4'  # FIXME remove as soon as docker container is rebuilt
 
     # run nosetests
     - make nosetests  # test are called here

diff --git a/HISTORY.rst b/HISTORY.rst
@@ -16,6 +16,7 @@ History
   (causes faulty predictions).
 * Bugfix for only choosing 25 spectra in classifier creation in case the maximum angle threshold is automatically
   set to 0 because there are many well matching spectra.
+* Added minimal version of geoarray.
 
 
 0.8.2 (2020-10-12)

diff --git a/setup.py b/setup.py
@@ -41,7 +41,7 @@
 
 req = [
     'dill',
-    'geoarray',
+    'geoarray>=0.10.4',
     'matplotlib',
     'natsort',
     'nested_dict',

diff --git a/spechomo/prediction.py b/spechomo/prediction.py
@@ -96,8 +96,11 @@ def interpolate_cube(self, arrcube, source_CWLs, target_CWLs, kind='linear'):
            np.max(outarr) <= np.iinfo(np.int16).max:
 
             outarr = outarr.astype(np.int16)
+
         elif np.min(outarr) >= np.iinfo(np.int32).min and np.max(outarr) <= np.iinfo(np.int32).max:
+
             outarr = outarr.astype(np.int32)
+
         else:
             raise TypeError('The interpolated data cube cannot be cast into a 16- or 32-bit integer array.')
 
@@ -405,7 +408,8 @@ def predict(self, image, classifier, in_nodataVal=None, out_nodataVal=None, cmap
                 if self.classif_alg == 'RF':
                     train_spectra = np.vstack([classifier.MLdict[clust].cluster_sample_spectra
                                                for clust in range(classifier.n_clusters)])
-                    train_labels = list(np.hstack([[i] * 100 for i in range(classifier.n_clusters)]))
+                    train_labels = list(np.hstack([[i] * 100
+                                                   for i in range(classifier.n_clusters)]))
                 else:
                     train_spectra = classifier.cluster_centers
                     train_labels = classifier.cluster_pixVals
@@ -424,15 +428,19 @@ def predict(self, image, classifier, in_nodataVal=None, out_nodataVal=None, cmap
                                  % time.strftime("%H:%M:%S", time.gmtime(time.time() - t0)))
 
             else:
-                self.classif_map = GeoArray(np.full((image.rows, image.cols), classifier.cluster_pixVals[0], np.int16),
+                self.classif_map = GeoArray(np.full((image.rows,
+                                                     image.cols),
+                                                    classifier.cluster_pixVals[0],
+                                                    np.int16),
                                             nodata=cmap_nodataVal)
 
                 # overwrite all pixels where the input image contains nodata in ANY band
                 # (would lead to faulty predictions due to multivariate prediction algorithms)
                 if in_nodataVal is not None and cmap_nodataVal is not None:
                     self.classif_map[np.any(image[:] == image.nodata, axis=2)] = cmap_nodataVal
 
-                self.distance_metrics = np.zeros_like(self.classif_map, np.float32)
+                self.distance_metrics = np.zeros_like(self.classif_map,
+                                                      np.float32)
 
         ####################
         # apply prediction #
@@ -446,9 +454,17 @@ def predict(self, image, classifier, in_nodataVal=None, out_nodataVal=None, cmap
         # NOTE: prediction is applied in 1000 x 1000 tiles to save memory (because classifier.predict returns float32)
         t0 = time.time()
         out_nodataVal = out_nodataVal if out_nodataVal is not None else image.nodata
-        image_predicted = GeoArray(np.empty((image.rows, image.cols, classifier.tgt_n_bands), dtype=image.dtype),
-                                   geotransform=image.gt, projection=image.prj, nodata=out_nodataVal,
-                                   bandnames=['B%s' % i if len(i) == 2 else 'B0%s' % i for i in classifier.tgt_LBA])
+        image_predicted = GeoArray(np.empty((image.rows,
+                                             image.cols,
+                                             classifier.tgt_n_bands),
+                                            dtype=image.dtype),
+                                   geotransform=image.gt,
+                                   projection=image.prj,
+                                   nodata=out_nodataVal,
+                                   bandnames=['B%s' % i
+                                              if len(i) == 2
+                                              else 'B0%s' % i
+                                              for i in classifier.tgt_LBA])
 
         if classifier.n_clusters > 1 and\
            self.classif_map.ndim > 2:
@@ -458,6 +474,7 @@ def predict(self, image, classifier, in_nodataVal=None, out_nodataVal=None, cmap
             dist_norm = (self.distance_metrics - dist_min) /\
                         (dist_max - dist_min)
             weights = 1 - dist_norm
+
         else:
             weights = None
 

diff --git a/tests/gitlab_CI_docker/context/environment_spechomo.yml b/tests/gitlab_CI_docker/context/environment_spechomo.yml
@@ -6,7 +6,7 @@ channels: &id1
 dependencies:
   - python>=3.7.*
   - pip
-  - geoarray>=0.9.0
+  - geoarray>=0.10.4
   - matplotlib
   - numpy
   - pandas