using suggestion from @noopurintel

Author: scap3yvt

testing/test_full.py

@@ -152,77 +152,76 @@ def prerequisites_constructTrainingCSV():
             os.remove(os.path.join(inputDir, item))
 
     for application_data in os.listdir(inputDir):
-        if "segmentation" in application_data:
-            # this is to ensure other types of unit-testing data do not inadvertently get pulled in during testing
-            currentApplicationDir = os.path.join(inputDir, application_data)
-
-            if "2d_rad_segmentation" in application_data:
-                channelsID = "image.png"
-                labelID = "mask.png"
-            elif "3d_rad_segmentation" in application_data:
-                channelsID = "image"
-                labelID = "mask"
-            elif "2d_histo_segmentation" in application_data:
-                channelsID = "image"
-                labelID = "mask"
-            # else:
-            #     continue
-            outputFile = inputDir + "/train_" + application_data + ".csv"
-            outputFile_rel = inputDir + "/train_" + application_data + "_relative.csv"
-            # Test with various combinations of relative/absolute paths
-            # Absolute input/output
-            writeTrainingCSV(
-                currentApplicationDir,
-                channelsID,
-                labelID,
-                outputFile,
-                relativizePathsToOutput=False,
-            )
-            writeTrainingCSV(
-                currentApplicationDir,
-                channelsID,
-                labelID,
-                outputFile_rel,
-                relativizePathsToOutput=True,
-            )
+        if "segmentation" not in application_data:
+            continue
+        # this is to ensure other types of unit-testing data do not inadvertently get pulled in during testing
+        currentApplicationDir = os.path.join(inputDir, application_data)
+
+        if "2d_rad_segmentation" in application_data:
+            channelsID = "image.png"
+            labelID = "mask.png"
+        elif "3d_rad_segmentation" in application_data:
+            channelsID = "image"
+            labelID = "mask"
+        elif "2d_histo_segmentation" in application_data:
+            channelsID = "image"
+            labelID = "mask"
+        # else:
+        #     continue
+        outputFile = inputDir + "/train_" + application_data + ".csv"
+        outputFile_rel = inputDir + "/train_" + application_data + "_relative.csv"
+        # Test with various combinations of relative/absolute paths
+        # Absolute input/output
+        writeTrainingCSV(
+            currentApplicationDir,
+            channelsID,
+            labelID,
+            outputFile,
+            relativizePathsToOutput=False,
+        )
+        writeTrainingCSV(
+            currentApplicationDir,
+            channelsID,
+            labelID,
+            outputFile_rel,
+            relativizePathsToOutput=True,
+        )
 
-            # write regression and classification files
-            application_data_regression = application_data.replace(
-                "segmentation", "regression"
-            )
-            application_data_classification = application_data.replace(
-                "segmentation", "classification"
-            )
-            with open(
-                inputDir + "/train_" + application_data + ".csv", "r"
-            ) as read_f, open(
-                inputDir + "/train_" + application_data_regression + ".csv",
-                "w",
-                newline="",
-            ) as write_reg, open(
-                inputDir + "/train_" + application_data_classification + ".csv",
-                "w",
-                newline="",
-            ) as write_class:
-                csv_reader = csv.reader(read_f)
-                csv_writer_1 = csv.writer(write_reg)
-                csv_writer_2 = csv.writer(write_class)
-                i = 0
-                for row in csv_reader:
-                    if i == 0:
-                        row.append("ValueToPredict")
-                        csv_writer_2.writerow(row)
-                        # row.append('ValueToPredict_2')
-                        csv_writer_1.writerow(row)
-                    else:
-                        row_regression = copy.deepcopy(row)
-                        row_classification = copy.deepcopy(row)
-                        row_regression.append(str(random.uniform(0, 1)))
-                        # row_regression.append(str(random.uniform(0, 1)))
-                        row_classification.append(str(random.randint(0, 2)))
-                        csv_writer_1.writerow(row_regression)
-                        csv_writer_2.writerow(row_classification)
-                    i += 1
+        # write regression and classification files
+        application_data_regression = application_data.replace(
+            "segmentation", "regression"
+        )
+        application_data_classification = application_data.replace(
+            "segmentation", "classification"
+        )
+        with open(
+            inputDir + "/train_" + application_data + ".csv", "r"
+        ) as read_f, open(
+            inputDir + "/train_" + application_data_regression + ".csv", "w", newline=""
+        ) as write_reg, open(
+            inputDir + "/train_" + application_data_classification + ".csv",
+            "w",
+            newline="",
+        ) as write_class:
+            csv_reader = csv.reader(read_f)
+            csv_writer_1 = csv.writer(write_reg)
+            csv_writer_2 = csv.writer(write_class)
+            i = 0
+            for row in csv_reader:
+                if i == 0:
+                    row.append("ValueToPredict")
+                    csv_writer_2.writerow(row)
+                    # row.append('ValueToPredict_2')
+                    csv_writer_1.writerow(row)
+                else:
+                    row_regression = copy.deepcopy(row)
+                    row_classification = copy.deepcopy(row)
+                    row_regression.append(str(random.uniform(0, 1)))
+                    # row_regression.append(str(random.uniform(0, 1)))
+                    row_classification.append(str(random.randint(0, 2)))
+                    csv_writer_1.writerow(row_regression)
+                    csv_writer_2.writerow(row_classification)
+                i += 1
 
 
 def test_prepare_data_for_ci():