Improve outputs

dcm_check/cli.py

@@ -62,10 +62,16 @@ def main():
         sys.exit(1)
 
     in_dicom_values = load_dicom(args.in_file)
-    results = get_compliance_summary(reference_model, in_dicom_values)
+    results = get_compliance_summary(reference_model, in_dicom_values, args.scan, args.group)
 
     df = pd.DataFrame(results)
 
+    # remove "Acquisition" and/or "Group" columns if they are empty
+    if "Acquisition" in df.columns and df["Acquisition"].isnull().all():
+        df.drop(columns=["Acquisition"], inplace=True)
+    if "Group" in df.columns and df["Group"].isnull().all():
+        df.drop(columns=["Group"], inplace=True)
+
     if len(df) == 0:
         print("DICOM file is compliant with the reference model.")
     else:

dcm_check/dcm_check.py

@@ -192,7 +192,7 @@ def load_ref_pydantic(module_path: str, scan_type: str) -> BaseModel:
 
     return reference_model
 
-def get_compliance_summary(reference_model: BaseModel, dicom_values: Dict[str, Any], model_name: str = "Reference Model", raise_errors: bool = False) -> List[Dict[str, Any]]:
+def get_compliance_summary(reference_model: BaseModel, dicom_values: Dict[str, Any], acquisition: str = None, group: str = None, raise_errors: bool = False) -> List[Dict[str, Any]]:
     """Validate a DICOM file against the reference model."""
     compliance_summary = []
 
@@ -204,13 +204,15 @@ def get_compliance_summary(reference_model: BaseModel, dicom_values: Dict[str, A
         for error in e.errors():
             param = error['loc'][0] if error['loc'] else "Model-Level Error"
             expected = (error['ctx'].get('expected') if 'ctx' in error else None) or error['msg']
+            if isinstance(expected, str) and expected.startswith("'") and expected.endswith("'"):
+                expected = expected[1:-1]
             actual = dicom_values.get(param, "N/A") if param != "Model-Level Error" else "N/A"
             compliance_summary.append({
-                "Model_Name": model_name,
+                "Acquisition": acquisition,
+                "Group": group,
                 "Parameter": param,
-                "Expected": expected,
-                "Actual": actual,
-                "Pass": False
+                "Value": actual,
+                "Expected": expected
             })
 
     return compliance_summary

dcm_check/generate_json_ref.py

@@ -83,7 +83,7 @@ def generate_json_ref(in_session_dir, out_json_ref, acquisition_fields, referenc
         # Remove constant fields from the groups and only include changing fields
         groups = []
         group_number = 1
-        for group, example_path in unique_groups.items():
+        for group, ref_path in unique_groups.items():
             group_fields = [
                 {"field": field, "value": value}
                 for field, value in group if field not in constant_reference_fields
@@ -92,7 +92,7 @@ def generate_json_ref(in_session_dir, out_json_ref, acquisition_fields, referenc
                 groups.append({
                     "name": f"Group {group_number}",  # Assign default name
                     "fields": group_fields,
-                    "example": example_path
+                    "ref": ref_path
                 })
                 group_number += 1
 

dcm_check/session_check.py

@@ -4,7 +4,6 @@
 import argparse
 import pandas as pd
 from tabulate import tabulate
-
 from dcm_check import load_ref_json, load_dicom, get_compliance_summary, read_session
 
 def get_compliance_summaries_json(json_ref: str, in_session: str, output_json: str = "compliance_report.json") -> pd.DataFrame:
@@ -21,7 +20,7 @@ def get_compliance_summaries_json(json_ref: str, in_session: str, output_json: s
     """
     # Step 1: Identify matched acquisitions and groups in the session
     session_df = read_session(json_ref, in_session)
-    compliance_results = []
+    grouped_compliance = {}
 
     # Step 2: Iterate over each matched acquisition-group pair
     for _, row in session_df.dropna(subset=["Acquisition"]).iterrows():
@@ -37,17 +36,51 @@ def get_compliance_summaries_json(json_ref: str, in_session: str, output_json: s
             dicom_values = load_dicom(first_dicom_path)
 
             # Step 5: Run compliance check and gather results
-            compliance_summary = get_compliance_summary(reference_model, dicom_values, model_name=f"{acquisition}-{group}")
-            compliance_results.extend(compliance_summary)
+            compliance_summary = get_compliance_summary(reference_model, dicom_values, acquisition, group)
+            print(compliance_summary)
+
+            # Organize results in nested format without "Model_Name"
+            if acquisition not in grouped_compliance:
+                grouped_compliance[acquisition] = {"Acquisition": acquisition, "Groups": []}
+
+            if group:
+                group_entry = next((g for g in grouped_compliance[acquisition]["Groups"] if g["Name"] == group), None)
+                if not group_entry:
+                    group_entry = {"Name": group, "Parameters": []}
+                    grouped_compliance[acquisition]["Groups"].append(group_entry)
+                for entry in compliance_summary:
+                    entry.pop("Acquisition", None)
+                    entry.pop("Group", None)
+                group_entry["Parameters"].extend(compliance_summary)
+            else:
+                # If no group, add parameters directly under acquisition
+                for entry in compliance_summary:
+                    entry.pop("Acquisition", None)
+                    entry.pop("Group", None)
+                grouped_compliance[acquisition]["Parameters"] = compliance_summary
 
         except Exception as e:
             print(f"Error processing acquisition '{acquisition}' and group '{group}': {e}")
 
-    # Step 6: Save compliance summary to JSON and return as DataFrame
+    # Convert the grouped data to a list for JSON serialization
+    grouped_compliance_list = list(grouped_compliance.values())
+
+    # Save grouped compliance summary to JSON
     with open(output_json, "w") as json_file:
-        json.dump(compliance_results, json_file, indent=4)
+        json.dump(grouped_compliance_list, json_file, indent=4)
+
+    # Convert the compliance summary to a DataFrame for tabulated output
+    compliance_df = pd.json_normalize(
+        grouped_compliance_list,
+        record_path=["Groups", "Parameters"],
+        meta=["Acquisition", ["Groups", "Name"]],
+        errors="ignore"
+    )
+
+    # Rename "Groups.name" to "Group" and reorder columns
+    compliance_df.rename(columns={"Groups.Name": "Group"}, inplace=True)
+    compliance_df = compliance_df[["Acquisition", "Group", "Parameter", "Value", "Expected"]]
 
-    compliance_df = pd.DataFrame(compliance_results)
     return compliance_df
 
 def main():
@@ -65,4 +98,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-

dcm_check/tests/test_cli.py

@@ -92,14 +92,12 @@ def test_cli_output_file_not_compliant_with_group():
     result = subprocess.run(command, capture_output=True, text=True)
 
     expected_output = tabulate(pd.DataFrame({ # as above
-        "Model_Name": ["Reference Model"],
+        "Acquisition": ["T1"],
+        "Group": ["Group 1"],
         "Parameter": ["ImageType"],
-        "Expected": ["Value error, ImageType must contain 'M'"],
-        "Actual": [['ORIGINAL', 'PRIMARY', 'P', 'N']],
-        "Pass": [False]
+        "Value": [['ORIGINAL', 'PRIMARY', 'P', 'N']],
+        "Expected": ["Value error, ImageType must contain 'M'"]
     }), headers="keys", tablefmt="simple")
-
-    print(expected_output)
 
     assert result.returncode == 0
     assert expected_output in result.stdout
@@ -162,11 +160,9 @@ def test_cli_dicom_reference_non_compliant():
     result = subprocess.run(command, capture_output=True, text=True)
 
     expected_output = tabulate(pd.DataFrame({
-        "Model_Name": ["Reference Model"],
         "Parameter": ["FlipAngle"],
-        "Expected": [15],
-        "Actual": [45],
-        "Pass": [False]
+        "Value": [45],
+        "Expected": [15]
     }), headers="keys", tablefmt="simple")
 
     # delete the non-compliant DICOM file
@@ -181,11 +177,10 @@ def test_cli_pydantic_reference():
     result = subprocess.run(command, capture_output=True, text=True)
 
     expected_output = tabulate(pd.DataFrame({
-        "Model_Name": ["Reference Model", "Reference Model", "Reference Model", "Reference Model"],
+        "Acquisition": ["T1_MPR", "T1_MPR", "T1_MPR", "T1_MPR"],
         "Parameter": ["MagneticFieldStrength", "RepetitionTime", "PixelSpacing", "SliceThickness"],
-        "Expected": ["Field required", "Input should be greater than or equal to 2300", "Value error, Each value in PixelSpacing must be between 0.75 and 0.85", "Input should be less than or equal to 0.85"],
-        "Actual": ["N/A", 8.0, ['0.5', '0.5'], 1.0],
-        "Pass": [False, False, False, False]
+        "Value": ["N/A", 8.0, ['0.5', '0.5'], 1.0],
+        "Expected": ["Field required", "Input should be greater than or equal to 2300", "Value error, Each value in PixelSpacing must be between 0.75 and 0.85", "Input should be less than or equal to 0.85"]
     }), headers="keys", tablefmt="simple")
 
     assert result.returncode == 0
@@ -197,11 +192,10 @@ def test_cli_pydantic_reference_inferred_type():
     result = subprocess.run(command, capture_output=True, text=True)
 
     expected_output = tabulate(pd.DataFrame({
-        "Model_Name": ["Reference Model", "Reference Model", "Reference Model", "Reference Model"],
+        "Acquisition": ["T1_MPR", "T1_MPR", "T1_MPR", "T1_MPR"],
         "Parameter": ["MagneticFieldStrength", "RepetitionTime", "PixelSpacing", "SliceThickness"],
-        "Expected": ["Field required", "Input should be greater than or equal to 2300", "Value error, Each value in PixelSpacing must be between 0.75 and 0.85", "Input should be less than or equal to 0.85"],
-        "Actual": ["N/A", 8.0, ['0.5', '0.5'], 1.0],
-        "Pass": [False, False, False, False]
+        "Value": ["N/A", 8.0, ['0.5', '0.5'], 1.0],
+        "Expected": ["Field required", "Input should be greater than or equal to 2300", "Value error, Each value in PixelSpacing must be between 0.75 and 0.85", "Input should be less than or equal to 0.85"]
     }), headers="keys", tablefmt="simple")
 
     assert result.returncode == 0

dcm_check/tests/test_ref_dicom.py

@@ -87,8 +87,7 @@ def test_dicom_compliance_specific_fields_non_compliant(t1):
     assert len(compliance_summary) == 1
     assert compliance_summary[0]["Parameter"] == "RepetitionTime"
     assert compliance_summary[0]["Expected"] == "8.0"
-    assert compliance_summary[0]["Actual"] == 8.1
-    assert not compliance_summary[0]["Pass"]
+    assert compliance_summary[0]["Value"] == 8.1
 
 def test_dicom_compliance_small_change(t1):
     t1_values = dcm_check.get_dicom_values(t1)
@@ -102,8 +101,7 @@ def test_dicom_compliance_small_change(t1):
     assert len(compliance_summary) == 1
     assert compliance_summary[0]["Parameter"] == "RepetitionTime"
     assert compliance_summary[0]["Expected"] == "8.0"
-    assert compliance_summary[0]["Actual"] == 8.1
-    assert not compliance_summary[0]["Pass"]
+    assert compliance_summary[0]["Value"] == 8.1
 
 def test_dicom_compliance_num_errors(t1):
     t1_values = dcm_check.get_dicom_values(t1)
@@ -131,12 +129,9 @@ def test_dicom_compliance_error_message(t1):
     assert errors[0]["Expected"] == "8.0"
     assert errors[1]["Expected"] == "3.0"
     assert errors[2]["Expected"] == "400.0"
-    assert errors[0]["Actual"] == 8.1
-    assert errors[1]["Actual"] == 3.1
-    assert errors[2]["Actual"] == 400.1
-    assert not errors[0]["Pass"]
-    assert not errors[1]["Pass"]
-    assert not errors[2]["Pass"]
+    assert errors[0]["Value"] == 8.1
+    assert errors[1]["Value"] == 3.1
+    assert errors[2]["Value"] == 400.1
 
 def test_dicom_compliance_error_message_missing_field(t1):
     t1_values = dcm_check.get_dicom_values(t1)
@@ -151,8 +146,7 @@ def test_dicom_compliance_error_message_missing_field(t1):
     assert len(errors) == 1
     assert errors[0]["Parameter"] == "RepetitionTime"
     assert errors[0]["Expected"] == "Field required"
-    assert errors[0]["Actual"] == "N/A"
-    assert not errors[0]["Pass"]
+    assert errors[0]["Value"] == "N/A"
 
 def test_dicom_compliance_error_raise(t1):
     t1_values = dcm_check.get_dicom_values(t1)

dcm_check/tests/test_ref_json.py

@@ -127,8 +127,7 @@ def test_json_compliance_outside_tolerance_with_dcm(json_ref_with_dcm, dicom_tes
     assert len(compliance_summary) == 1
     assert compliance_summary[0]["Parameter"] == "EchoTime"
     assert compliance_summary[0]["Expected"] == "Input should be less than or equal to 3.1"
-    assert compliance_summary[0]["Actual"] == 3.2
-    assert not compliance_summary[0]["Pass"]
+    assert compliance_summary[0]["Value"] == 3.2
 
 def test_json_compliance_pattern_match(json_ref_no_dcm, dicom_test_file):
     """Test compliance with a pattern match for SeriesDescription within group."""

dcm_check/tests/test_ref_pydantic.py

@@ -101,8 +101,7 @@ def test_t1_mpr_repetition_vs_echo_rule(t1_mpr_dicom_values):
     assert len(compliance_summary) > 0
     assert compliance_summary[0]["Parameter"] == "Model-Level Error"
     assert compliance_summary[0]["Expected"] == "RepetitionTime must be at least 2x EchoTime"
-    assert compliance_summary[0]["Pass"] == False
-    assert compliance_summary[0]["Actual"] == "N/A"
+    assert compliance_summary[0]["Value"] == "N/A"
 
 def test_diffusion_config_compliance():
     """Test DiffusionConfig compliance for a sample diffusion scan."""