Merge pull request #156 from AustralianCancerDataNetwork/add-missing-…

…unit-tests

Add missing unit tests

pydicer/dataset/nnunet.py

@@ -293,6 +293,7 @@ def check_structure_names(self) -> pd.DataFrame:
         """
 
         df = read_converted_data(self.working_directory, dataset_name=self.dataset_name)
+        df = df[df.patient_id.isin(self.testing_cases + self.training_cases)]
         df_structure_sets = df[df.modality == "RTSTRUCT"]
 
         # First get a set of all unique structure names available
@@ -369,6 +370,7 @@ def check_overlapping_structures(self):
                 "overlapping structures."
             )
         df = read_converted_data(self.working_directory, dataset_name=self.dataset_name)
+        df = df[df.patient_id.isin(self.testing_cases + self.training_cases)]
         df_structure_sets = df[df.modality == "RTSTRUCT"]
 
         has_overlapping_structures = False

tests/test_compare.py

@@ -0,0 +1,97 @@
+# pylint: disable=redefined-outer-name,missing-function-docstring
+
+import tempfile
+
+from pathlib import Path
+import pandas as pd
+
+from pydicer import PyDicer
+from pydicer.analyse.compare import (
+    compute_contour_similarity_metrics,
+    get_all_similarity_metrics_for_dataset,
+    prepare_similarity_metric_analysis,
+)
+from pydicer.utils import read_converted_data
+
+
+def test_compare_auto_segmentations(test_data_autoseg):
+    working_directory = test_data_autoseg
+    df = read_converted_data(working_directory=working_directory)
+
+    # We'll test this by comparing the structures against themselves,
+    # hence we expect perfect metrics
+    df_target = df[df.modality == "RTSTRUCT"]
+    df_reference = df[df.modality == "RTSTRUCT"]
+
+    PyDicer(working_directory)
+    segment_id = "test_seg"
+    compute_contour_similarity_metrics(df_target, df_reference, segment_id)
+
+    df_stats = get_all_similarity_metrics_for_dataset(working_directory)
+
+    assert len(df_stats) == 200
+
+    df_dsc = df_stats[df_stats["metric"] == "DSC"]
+    assert df_dsc.value.min() == 1.0
+    assert df_dsc.value.max() == 1.0
+
+
+def test_compaare_metrics_analysis(test_data_autoseg):
+    working_directory = test_data_autoseg
+    df = read_converted_data(working_directory=working_directory)
+
+    # We'll test this by comparing the structures against themselves,
+    # hence we expect perfect metrics
+    df_target = df[df.modality == "RTSTRUCT"]
+    df_reference = df[df.modality == "RTSTRUCT"]
+
+    PyDicer(working_directory)
+    segment_id = "test_seg"
+    compute_contour_similarity_metrics(df_target, df_reference, segment_id)
+
+    with tempfile.TemporaryDirectory() as analysis_dir:
+        analysis_dir = Path(analysis_dir)
+
+        prepare_similarity_metric_analysis(
+            working_directory=working_directory,
+            analysis_output_directory=analysis_dir,
+            segment_id=segment_id,
+        )
+
+        # Check that the output files exist
+        raw_metrics_file = analysis_dir.joinpath("raw_test_seg_default.csv")
+        assert raw_metrics_file.exists()
+        stats_metrics_file = analysis_dir.joinpath("stats_test_seg_default.csv")
+        assert stats_metrics_file.exists()
+        plot_dsc_file = analysis_dir.joinpath("plot_DSC_test_seg_default.png")
+        assert plot_dsc_file.exists()
+        plot_hd_file = analysis_dir.joinpath("plot_hausdorffDistance_test_seg_default.png")
+        assert plot_hd_file.exists()
+        plot_msd_file = analysis_dir.joinpath("plot_meanSurfaceDistance_test_seg_default.png")
+        assert plot_msd_file.exists()
+        plot_sdsc_file = analysis_dir.joinpath("plot_surfaceDSC_test_seg_default.png")
+        assert plot_sdsc_file.exists()
+
+        # Read in the raw metrics file and do some checks
+        df_raw = pd.read_csv(raw_metrics_file, index_col=0)
+        assert len(df_raw) == 200
+
+        # Since these structures compared against themselves, expect perfect metrics
+        assert df_raw[df_raw.metric == "DSC"].value.min() == 1.0
+        assert df_raw[df_raw.metric == "surfaceDSC"].value.min() == 1.0
+        assert df_raw[df_raw.metric == "hausdorffDistance"].value.max() == 0.0
+        assert df_raw[df_raw.metric == "meanSurfaceDistance"].value.max() == 0.0
+
+        # Read in the stats metrics file and do some checks
+        df_stats = pd.read_csv(stats_metrics_file, index_col=0)
+        assert len(df_stats) == 36
+
+        # Check one fo the rows
+        row_check = df_stats[
+            (df_stats.structure == "Esophagus") & (df_stats.metric == "surfaceDSC")
+        ].iloc[0]
+        assert row_check["mean"] == 1.0
+        assert row_check["std"] == 0.0
+        assert row_check["max"] == 1.0
+        assert row_check["min"] == 1.0
+        assert row_check["count"] == 10