Expand SPSS conformance fixture coverage

Author: TonisOrmisson

tests/conformance.py

@@ -1,6 +1,5 @@
 """Reusable pyspssio fixtures and semantic comparators for SAV/ZSAV round trips."""
 
-import math
 from pathlib import Path
 from typing import Any
 
@@ -10,45 +9,79 @@
 
 
 def write_supported_semantics_fixture(destination: str | Path) -> dict[str, Any]:
-    """Write the metadata pyspssio demonstrably exposes and restores."""
+    """Write a real SAV/ZSAV fixture for every pyspssio-supported core feature.
+
+    SPSS time-like values are deliberately stored as numeric values. Their
+    interpretation belongs to the accompanying SPSS format, never to a SQL
+    temporal coercion. The fixture also covers every legal user-missing
+    shape: three discrete values, a range alone, and a range plus one value.
+    ``pyspssio`` exposes SPSS LOWEST/HIGHEST again as negative/positive
+    infinity, which is the representation the strict SQL catalog preserves.
+    """
     destination = Path(destination)
     long_text = "Õ🙂漢字" * 90
     frame = pd.DataFrame({
+        "discrete_missing": [1.0, 2.0, 3.0, 4.0],
+        "range_only": [-1.0, 0.0, 1.0, 2.0],
         "lowest_range": [-2.0, -1.0, 0.0, 7.0],
         "highest_range": [-99.0, 0.0, 1.0, 42.0],
         "code": [3.0, 1.0, 2.0, 1.0],
         "status": ["NA", "DK", "ok", ""],
         "comment": [long_text, "", "näide", long_text],
         "interview_date": [23123.0, 23124.0, 23125.0, 23126.0],
+        "interview_time": [3661.25, 0.0, 86399.5, 12.0],
+        "interview_datetime": [23123.5, 23124.0, 23125.75, 23126.125],
+        "interview_dtime": [90061.5, 0.0, 172800.25, 12.0],
+        "formatted_comma": [12345.67, -12345.67, 0.0, 1.25],
+        "formatted_dot": [12345.67, -12345.67, 0.0, 1.25],
+        "formatted_pct": [12.5, 0.0, 100.0, -2.5],
         "resp_a": [1.0, 0.0, 1.0, 0.0],
         "resp_b": [0.0, 1.0, 1.0, 0.0],
     })
     metadata = {
         "var_types": {"status": 8, "comment": 1024},
         "var_formats": {
+            "discrete_missing": "F8.0", "range_only": "F8.0",
             "lowest_range": "F12.1", "highest_range": "F12.1", "code": "F8.0",
             "status": "A8", "comment": "A1024", "interview_date": "DATE11",
+            "interview_time": "TIME8", "interview_datetime": "DATETIME20",
+            "interview_dtime": "DTIME10", "formatted_comma": "COMMA12.2",
+            "formatted_dot": "DOT12.2", "formatted_pct": "PCT8.1",
         },
         "var_labels": {
+            "discrete_missing": "Three numeric user-missing codes",
+            "range_only": "Range-only numeric user missing",
             "lowest_range": "LOWEST-style missing range", "highest_range": "HIGHEST-style missing range",
             "code": "Ordered numeric code", "status": "String missing code",
             "comment": "Long UTF-8 comment", "interview_date": "SPSS numeric date",
+            "interview_time": "SPSS numeric time", "interview_datetime": "SPSS numeric datetime",
+            "interview_dtime": "SPSS numeric duration", "formatted_comma": "SPSS comma format",
+            "formatted_dot": "SPSS dot format", "formatted_pct": "SPSS percent format",
         },
         "var_value_labels": {
+            "discrete_missing": {3.0: "third", 1.0: "first", 2.0: "second"},
             "code": {3.0: "third", 1.0: "first", 2.0: "second"},
             "status": {"DK": "don't know", "NA": "not answered", "ok": "valid"},
         },
         "var_missing_values": {
+            "discrete_missing": {"values": [1.0, 2.0, 3.0]},
+            "range_only": {"lo": -1.0, "hi": 1.0},
             "lowest_range": {"lo": -sys_float_max(), "hi": -1.0, "values": [7.0]},
             "highest_range": {"lo": 1.0, "hi": sys_float_max(), "values": [-99.0]},
             "status": {"values": ["NA", "DK"]},
         },
         "var_measure_levels": {
-            "lowest_range": "scale", "highest_range": "scale", "code": "scale",
+            "discrete_missing": "scale", "range_only": "scale", "lowest_range": "scale",
+            "highest_range": "scale", "code": "scale",
             "status": "nominal", "comment": "nominal", "interview_date": "scale",
         },
         "var_alignments": {column: "left" for column in frame.columns},
-        "var_column_widths": {"lowest_range": 12, "highest_range": 12, "code": 8, "status": 12, "comment": 48, "interview_date": 11},
+        "var_column_widths": {
+            "discrete_missing": 8, "range_only": 8, "lowest_range": 12, "highest_range": 12,
+            "code": 8, "status": 12, "comment": 48, "interview_date": 11,
+            "interview_time": 8, "interview_datetime": 20, "interview_dtime": 10,
+            "formatted_comma": 12, "formatted_dot": 12, "formatted_pct": 8,
+        },
         "var_roles": {"code": "target", "status": "input"},
         "var_attributes": {"code": {"Origin": "fixture"}},
         "mrsets": {"$responses": {"label": "Responses", "counted_value": 1, "variable_list": ["resp_a", "resp_b"]}},
@@ -74,6 +107,14 @@ def _canonical_missing(metadata: dict[str, Any]) -> dict[str, tuple[tuple[Any, A
     return result
 
 
+def _ordered_value_labels(metadata: dict[str, Any]) -> dict[str, tuple[tuple[Any, str], ...]]:
+    """Retain the label order exposed by the SPSS engine, not merely its mapping."""
+    return {
+        name: tuple((value, str(label)) for value, label in labels.items())
+        for name, labels in (metadata.get("var_value_labels") or {}).items()
+    }
+
+
 def compare_sav_semantics(source: str | Path, exported: str | Path) -> dict[str, Any]:
     source_frame, source_metadata = pyspssio.read_sav(str(source), convert_datetimes=False, include_user_missing=True)
     exported_frame, exported_metadata = pyspssio.read_sav(str(exported), convert_datetimes=False, include_user_missing=True)
@@ -93,4 +134,6 @@ def compare_sav_semantics(source: str | Path, exported: str | Path) -> dict[str,
             failures.append(attribute)
     if _canonical_missing(source_metadata) != _canonical_missing(exported_metadata):
         failures.append("var_missing_values")
-    return {"equivalent": not failures, "differences": failures}
+    if _ordered_value_labels(source_metadata) != _ordered_value_labels(exported_metadata):
+        failures.append("var_value_label_order")
+    return {"equivalent": not failures, "differences": failures}