fix(scada): use project-scoped metadata

tests/unit/test_pressure_cleaning.py

@@ -1,47 +1,25 @@
-import importlib.util
-import sys
-import types
 from pathlib import Path
 
 import numpy as np
 import pandas as pd
+import pytest
+
+from app.algorithms.cleaning import pressure as pressure_cleaning
 
 
-def _load_pressure_cleaning_module():
-    project_root = Path(__file__).resolve().parents[2]
-    utils_path = project_root / "app" / "algorithms" / "_utils.py"
-    pressure_path = project_root / "app" / "algorithms" / "cleaning" / "pressure.py"
-
-    app_module = sys.modules.setdefault("app", types.ModuleType("app"))
-    algorithms_module = sys.modules.setdefault(
-        "app.algorithms",
-        types.ModuleType("app.algorithms"),
-    )
-    setattr(app_module, "algorithms", algorithms_module)
-
-    utils_spec = importlib.util.spec_from_file_location("app.algorithms._utils", utils_path)
-    assert utils_spec and utils_spec.loader
-    utils_module = importlib.util.module_from_spec(utils_spec)
-    sys.modules["app.algorithms._utils"] = utils_module
-    utils_spec.loader.exec_module(utils_module)
-
-    pressure_spec = importlib.util.spec_from_file_location(
-        "tests_pressure_under_test",
-        pressure_path,
-    )
-    assert pressure_spec and pressure_spec.loader
-    pressure_module = importlib.util.module_from_spec(pressure_spec)
-    pressure_spec.loader.exec_module(pressure_module)
-    return pressure_module
-
+DATA_DIR = Path(__file__).resolve().parents[3] / "data"
+RAW_DATA_PATH = DATA_DIR / "node_simulation.csv"
+NOISY_DATA_PATH = DATA_DIR / "node_simulation_noisy.csv"
+REQUIRES_PRESSURE_SAMPLES = pytest.mark.skipif(
+    not RAW_DATA_PATH.exists() or not NOISY_DATA_PATH.exists(),
+    reason="pressure cleaning sample CSV files are not available",
+)
 
+@REQUIRES_PRESSURE_SAMPLES
 def test_clean_pressure_data_df_km_repairs_long_form_pressure_series():
-    module = _load_pressure_cleaning_module()
-    repo_root = Path(__file__).resolve().parents[3]
-
-    raw_df = pd.read_csv(repo_root / "data" / "node_simulation.csv")
-    noisy_df = pd.read_csv(repo_root / "data" / "node_simulation_noisy.csv")
-    cleaned_df = module.clean_pressure_data_df_km(noisy_df)
+    raw_df = pd.read_csv(RAW_DATA_PATH)
+    noisy_df = pd.read_csv(NOISY_DATA_PATH)
+    cleaned_df = pressure_cleaning.clean_pressure_data_df_km(noisy_df)
 
     for df in (raw_df, noisy_df, cleaned_df):
         df["time"] = pd.to_datetime(df["time"])
@@ -50,7 +28,12 @@ def test_clean_pressure_data_df_km_repairs_long_form_pressure_series():
     assert set(cleaned_df.columns) == {"time", "id", "pressure"}
     assert cleaned_df["pressure"].isna().sum() == 0
 
-    noisy_joined = raw_df.merge(noisy_df, on=["time", "id"], how="inner", suffixes=("_raw", "_noisy"))
+    noisy_joined = raw_df.merge(
+        noisy_df,
+        on=["time", "id"],
+        how="inner",
+        suffixes=("_raw", "_noisy"),
+    )
     cleaned_joined = raw_df.merge(
         cleaned_df,
         on=["time", "id"],
@@ -59,10 +42,20 @@ def test_clean_pressure_data_df_km_repairs_long_form_pressure_series():
     )
 
     noisy_rmse = float(
-        np.sqrt(np.mean((noisy_joined["pressure_raw"] - noisy_joined["pressure_noisy"]) ** 2))
+        np.sqrt(
+            np.mean(
+                (noisy_joined["pressure_raw"] - noisy_joined["pressure_noisy"])
+                ** 2
+            )
+        )
     )
     cleaned_rmse = float(
-        np.sqrt(np.mean((cleaned_joined["pressure_raw"] - cleaned_joined["pressure_clean"]) ** 2))
+        np.sqrt(
+            np.mean(
+                (cleaned_joined["pressure_raw"] - cleaned_joined["pressure_clean"])
+                ** 2
+            )
+        )
     )
     noisy_mae = float(
         np.mean(np.abs(noisy_joined["pressure_raw"] - noisy_joined["pressure_noisy"]))
@@ -88,11 +81,9 @@ def test_clean_pressure_data_df_km_repairs_long_form_pressure_series():
     assert abs(spike_row - 28.018701553344727) < 2.0
 
 
+@REQUIRES_PRESSURE_SAMPLES
 def test_clean_pressure_data_df_km_accepts_single_sensor_wide_frame_with_utc_strings():
-    module = _load_pressure_cleaning_module()
-    repo_root = Path(__file__).resolve().parents[3]
-
-    noisy_df = pd.read_csv(repo_root / "data" / "node_simulation_noisy.csv")
+    noisy_df = pd.read_csv(NOISY_DATA_PATH)
     single_sensor = (
         noisy_df[noisy_df["id"] == 170490][["time", "pressure"]]
         .rename(columns={"pressure": "170490"})
@@ -102,7 +93,7 @@ def test_clean_pressure_data_df_km_accepts_single_sensor_wide_frame_with_utc_str
         pd.to_datetime(single_sensor["time"], utc=True).dt.strftime("%Y-%m-%dT%H:%M:%SZ")
     )
 
-    cleaned_df = module.clean_pressure_data_df_km(single_sensor)
+    cleaned_df = pressure_cleaning.clean_pressure_data_df_km(single_sensor)
 
     assert len(cleaned_df) == 192
     assert cleaned_df["170490"].isna().sum() == 0