refactor(scaffold): simplify full.py, fix dependency inversion

tests/test_scaffold_clean.py

@@ -10,12 +10,12 @@
 
 import pytest
 
+from toolkit.profile.raw import suggest_dateformat as _suggest_dateformat
 from toolkit.scaffold.clean import (
     _columns_spec,
     _find_anno_raw_column,
     _has_anno_column,
     _select_expr,
-    _suggest_dateformat,
     generate_clean_sql,
 )
 

tests/test_scaffold_full.py

@@ -0,0 +1,105 @@
+"""Tests for refactored scaffold/full.py — dict-based YAML generation."""
+
+from __future__ import annotations
+import pytest
+import yaml
+from toolkit.scaffold.full import generate_full_scaffold, suggest_mart_sql, suggest_validation
+
+pytestmark = pytest.mark.pure_unit
+
+
+class TestSuggestValidation:
+    def test_none(self) -> None:
+        assert suggest_validation(None) == {}
+
+    def test_with_columns(self) -> None:
+        v = suggest_validation({"columns_norm": ["Nome", "Importo"], "row_count": 100})
+        assert v["required_columns"] == ["nome", "importo"]
+        assert v["min_rows"] == 50
+
+
+class TestSuggestMartSql:
+    def test_empty(self) -> None:
+        assert "placeholder" in suggest_mart_sql([], {})
+
+    def test_with_columns(self) -> None:
+        p = {"mapping_suggestions": {"nome": {"type": "VARCHAR"}}}
+        sql = suggest_mart_sql(["nome"], p)
+        assert "nome: VARCHAR" in sql
+
+
+@pytest.mark.policy
+class TestGenerateFullScaffold:
+    def test_file_no_profile(self) -> None:
+        probe = {"final_url": "https://example.com/data.csv", "source_type": "file"}
+        files = generate_full_scaffold("my-ds", probe)
+        assert "dataset.yml" in files
+        cfg = yaml.safe_load(files["dataset.yml"])
+        assert cfg["raw"]["sources"][0]["type"] == "http_file"
+
+    def test_ckan(self) -> None:
+        probe = {
+            "final_url": "https://data.example.org/dataset/x",
+            "source_type": "ckan",
+            "ckan_resources": [
+                {
+                    "name": "r1",
+                    "url": "https://data.example.org/x.csv",
+                    "format": "CSV",
+                    "id": "123",
+                }
+            ],
+        }
+        files = generate_full_scaffold("my-ds", probe)
+        cfg = yaml.safe_load(files["dataset.yml"])
+        assert cfg["raw"]["sources"][0]["type"] == "ckan"
+
+    def test_sdmx_estat(self) -> None:
+        probe = {
+            "final_url": "https://ec.europa.eu/.../NAMA",
+            "source_type": "sdmx",
+            "sdmx_info": {"flow_id": "NAMA", "agency": "ESTAT"},
+        }
+        files = generate_full_scaffold("my-ds", probe, inferred_years=list(range(2010, 2024)))
+        cfg = yaml.safe_load(files["dataset.yml"])
+        assert cfg["raw"]["sources"][0]["args"]["agency"] == "ESTAT"
+
+    def test_sparql(self) -> None:
+        probe = {
+            "final_url": "https://ld.istat.it/sparql",
+            "source_type": "sparql",
+            "sparql_info": {"endpoint": "https://ld.istat.it/sparql"},
+        }
+        files = generate_full_scaffold("my-ds", probe)
+        cfg = yaml.safe_load(files["dataset.yml"])
+        assert cfg["raw"]["sources"][0]["type"] == "sparql"
+
+    def test_with_profile(self) -> None:
+        profile = {
+            "columns_norm": ["nome", "importo"],
+            "mapping_suggestions": {"nome": {"type": "VARCHAR"}, "importo": {"type": "DOUBLE"}},
+            "row_count": 200,
+            "encoding_suggested": "utf-8",
+            "delim_suggested": ";",
+        }
+        probe = {
+            "final_url": "https://example.com/data.csv",
+            "source_type": "file",
+            "encoding_suggested": "utf-8",
+            "delim_suggested": ";",
+        }
+        files = generate_full_scaffold("my-ds", probe, profile=profile, inferred_years=[2020])
+        cfg = yaml.safe_load(files["dataset.yml"])
+        assert "read" in cfg["clean"]
+        assert "normalize_string" in files["sql/clean.sql"]
+
+    def test_all_files(self) -> None:
+        probe = {"final_url": "https://example.com/data.csv", "source_type": "file"}
+        files = generate_full_scaffold("my-ds", probe)
+        assert set(files.keys()) == {
+            "dataset.yml",
+            "sql/clean.sql",
+            "sql/mart.sql",
+            "README.md",
+            "notes.md",
+        }

tests/test_scout_infer.py

@@ -269,7 +269,7 @@ def test_empty_fallback(self) -> None:
         cols: list[str] = []
         profile: dict[str, Any] = {"mapping_suggestions": {}}
         sql = suggest_mart_sql(cols, profile)
-        assert "Sostituisci" in sql
+        assert "sostituisci" in sql
         assert "SELECT * FROM clean_input" in sql
 
     @pytest.mark.pure_unit
@@ -303,9 +303,9 @@ def test_with_columns_and_rows(self) -> None:
             "row_count": 100,
         }
         val = suggest_validation(profile)
-        assert "clean" in val
-        assert val["clean"]["validate"]["required_columns"] == ["a", "b", "c"]
-        assert val["clean"]["validate"]["min_rows"] <= 100
+        assert "required_columns" in val
+        assert val["required_columns"] == ["a", "b", "c"]
+        assert val["min_rows"] <= 100
 
     @pytest.mark.pure_unit
     def test_empty_profile(self) -> None: