first draft for unit tests

tests/test_clustering.py

+import os
+import hashlib
+import unittest
+from qgis.core import QgsProcessingContext, QgsProcessingFeedback
+
+from ..clustering import ClusterAlgorithm
+
+## for hashing without using to much memory
+BUF_SIZE = 65536
+
+
+class TestClusteringAlgorithm(unittest.TestCase):
+
+    def setUp(self):
+        self.context = QgsProcessingContext()
+        self.feedback = QgsProcessingFeedback()
+        self.algorithm = ClusterAlgorithm()
+
+    def test_valid_parameters(self):
+        self.algorithm.initAlgorithm()
+        parameters = {}
+        result = self.algorithm.processAlgorithm(parameters, self.context, self.feedback)
+        expected_result_path = os.path.join(self.algorithm.output_dir,'cluster.tif')
+        md5 = hashlib.md5()
+        with open(expected_result_path, 'rb') as f:
+            while True:
+                data = f.read(BUF_SIZE)
+                if not data:
+                    break
+                md5.update(data)
+        result_file_hash = md5.hexdigest()
+        assert result_file_hash == 'd316fa71b627ee361786154118101f0e'
+
+
+if __name__ == "__main__":
+
+    test_algo = TestClusteringAlgorithm()
+    test_algo.setUp()
+    test_algo.test_valid_parameters()
+
+

tests/test_encoder.py

+import os
+import hashlib
+import unittest
+from qgis.core import QgsProcessingContext, QgsProcessingFeedback
+
+from ..encoder import EncoderAlgorithm
+
+## for hashing without using to much memory
+BUF_SIZE = 65536
+
+
+class TestEncoderAlgorithm(unittest.TestCase):
+
+    def setUp(self):
+        self.context = QgsProcessingContext()
+        self.feedback = QgsProcessingFeedback()
+        self.algorithm = EncoderAlgorithm()
+
+    def test_valid_parameters(self):
+        self.algorithm.initAlgorithm()
+        parameters = {}
+        result = self.algorithm.processAlgorithm(parameters, self.context, self.feedback)
+        expected_result_path = os.path.join(self.algorithm.output_subdir,'merged.tif')
+        md5 = hashlib.md5()
+        with open(expected_result_path, 'rb') as f:
+            while True:
+                data = f.read(BUF_SIZE)
+                if not data:
+                    break
+                md5.update(data)
+        result_file_hash = md5.hexdigest()
+        assert result_file_hash == '018b6fc5d88014a7e515824d95ca8686'
+
+
+if __name__ == "__main__":
+
+    test_encoder = TestEncoderAlgorithm()
+    test_encoder.setUp()
+    test_encoder.test_valid_parameters()

tests/test_reduction.py

+import os
+import hashlib
+import unittest
+from qgis.core import QgsProcessingContext, QgsProcessingFeedback
+
+from ..reduction import ReductionAlgorithm
+
+## for hashing without using to much memory
+BUF_SIZE = 65536
+
+
+class TestReductionAlgorithm(unittest.TestCase):
+
+    def setUp(self):
+        self.context = QgsProcessingContext()
+        self.feedback = QgsProcessingFeedback()
+        self.algorithm = ReductionAlgorithm()
+
+    def test_valid_parameters(self):
+        self.algorithm.initAlgorithm()
+        parameters = {}
+        result = self.algorithm.processAlgorithm(parameters, self.context, self.feedback)
+        expected_result_path = os.path.join(self.algorithm.output_dir,'proj.tif')
+        md5 = hashlib.md5()
+        with open(expected_result_path, 'rb') as f:
+            while True:
+                data = f.read(BUF_SIZE)
+                if not data:
+                    break
+                md5.update(data)
+        result_file_hash = md5.hexdigest()
+        assert result_file_hash == 'e47ca7b39f252506526a1ebfa1f33f6c'
+
+
+if __name__ == "__main__":
+
+    test_algo = TestReductionAlgorithm()
+    test_algo.setUp()
+    test_algo.test_valid_parameters()
+

tests/test_rf.py

+import os
+import hashlib
+import unittest
+from qgis.core import QgsProcessingContext, QgsProcessingFeedback
+
+from ..random_forest import RFAlgorithm
+
+## for hashing without using to much memory
+BUF_SIZE = 65536
+
+
+class TestRFAlgorithm(unittest.TestCase):
+
+    def setUp(self):
+        self.context = QgsProcessingContext()
+        self.feedback = QgsProcessingFeedback()
+        self.algorithm = RFAlgorithm()
+
+    def test_valid_parameters(self):
+        self.algorithm.initAlgorithm()
+        parameters = {}
+        result = self.algorithm.processAlgorithm(parameters, self.context, self.feedback)
+        expected_result_path = os.path.join(self.algorithm.output_dir,'random_forest.tif')
+        md5 = hashlib.md5()
+        with open(expected_result_path, 'rb') as f:
+            while True:
+                data = f.read(BUF_SIZE)
+                if not data:
+                    break
+                md5.update(data)
+        result_file_hash = md5.hexdigest()
+        assert result_file_hash == '80b7dd5b5ad5a4c0ad1d637fa20cf8b0'
+
+
+if __name__ == "__main__":
+
+    test_algo = TestRFAlgorithm()
+    test_algo.setUp()
+    test_algo.test_valid_parameters()
+
+
+
+

tests/test_similarity.py

+import os
+import hashlib
+import unittest
+from qgis.core import QgsProcessingContext, QgsProcessingFeedback
+
+from ..similarity import SimilarityAlgorithm
+
+## for hashing without using to much memory
+BUF_SIZE = 65536
+
+
+class TestSimilarityAlgorithm(unittest.TestCase):
+
+    def setUp(self):
+        self.context = QgsProcessingContext()
+        self.feedback = QgsProcessingFeedback()
+        self.algorithm = SimilarityAlgorithm()
+
+    def test_valid_parameters(self):
+        self.algorithm.initAlgorithm()
+        parameters = {}
+        result = self.algorithm.processAlgorithm(parameters, self.context, self.feedback)
+        expected_result_path = os.path.join(self.algorithm.output_dir,'similarity.tif')
+        md5 = hashlib.md5()
+        with open(expected_result_path, 'rb') as f:
+            while True:
+                data = f.read(BUF_SIZE)
+                if not data:
+                    break
+                md5.update(data)
+        result_file_hash = md5.hexdigest()
+        assert result_file_hash == 'f76eb1f0469725b49fe0252cfe86829a'
+
+
+if __name__ == "__main__":
+
+    test_algo = TestSimilarityAlgorithm()
+    test_algo.setUp()
+    test_algo.test_valid_parameters()
+
+
+