test different time step sizes and fix test failing due to api consumption

Author: calofonseca

tests/conftest.py

@@ -1,15 +1,64 @@
 """
-Pytest-wide configuration: make the repository importable without installation.
+Pytest-wide configuration: make the repository importable without installation and
+avoid external downloads during test runs.
 
 By pushing the project root onto ``sys.path`` we allow ``import citylearn`` to
-work even when the package has not been installed into the active environment.
-This mirrors the behaviour of several legacy scripts and keeps local runs
-simple.
+work even when the package has not been installed. This mirrors legacy scripts
+and keeps local runs simple. Additionally, we make ``DataSet`` prefer the
+bundled datasets so tests don't hit the GitHub API.
 """
 
+import shutil
 import sys
 from pathlib import Path
 
+import pytest
+from typing import Union
+
 ROOT = Path(__file__).resolve().parents[1]
 if str(ROOT) not in sys.path:
     sys.path.insert(0, str(ROOT))
+
+
+@pytest.fixture(autouse=True)
+def _prefer_local_datasets(monkeypatch):
+    """Ensure tests use the repo's datasets without hitting the network."""
+
+    from citylearn.data import DataSet
+
+    local_root = ROOT / "data" / "datasets"
+    original_get_dataset = DataSet.get_dataset
+    original_get_dataset_names = DataSet.get_dataset_names
+
+    def _copy_dataset(src: Path, dest: Path) -> None:
+        if dest.exists():
+            shutil.rmtree(dest)
+
+        dest.parent.mkdir(parents=True, exist_ok=True)
+        shutil.copytree(src, dest)
+
+    def patched_get_dataset(self, name: str, directory: Union[Path, str, None] = None):
+        src = local_root / name
+
+        if src.is_dir():
+            if directory is not None:
+                directory = Path(directory)
+                directory.mkdir(parents=True, exist_ok=True)
+                dest = directory / name
+                _copy_dataset(src, dest)
+                return str(dest / "schema.json")
+
+            cache_dir = Path(self.cache_directory) / "datasets" / name
+            _copy_dataset(src, cache_dir)
+            return str(cache_dir / "schema.json")
+
+        return original_get_dataset(self, name, directory)
+
+    def patched_get_dataset_names(self):
+        if local_root.is_dir():
+            return sorted(p.name for p in local_root.iterdir() if p.is_dir())
+
+        return original_get_dataset_names(self)
+
+    monkeypatch.setattr(DataSet, "get_dataset", patched_get_dataset)
+    monkeypatch.setattr(DataSet, "get_dataset_names", patched_get_dataset_names)

tests/data/minute_ev_demo/Building_1.csv

@@ -0,0 +1,9 @@
+month,hour,minutes,day_type,daylight_savings_status,indoor_dry_bulb_temperature,average_unmet_cooling_setpoint_difference,indoor_relative_humidity,non_shiftable_load,dhw_demand,cooling_demand,heating_demand,solar_generation
+1,0,0,1,0,21.0,0.0,45.0,1.2,0.0,0.0,0.0,0.0
+1,0,15,1,0,21.0,0.0,45.0,1.1,0.0,0.0,0.0,0.0
+1,0,30,1,0,20.9,0.0,45.0,1.0,0.0,0.0,0.0,0.0
+1,0,45,1,0,21.1,0.0,45.0,0.9,0.0,0.0,0.0,0.1
+1,1,0,1,0,21.2,0.0,45.0,0.8,0.0,0.0,0.0,0.2
+1,1,15,1,0,21.2,0.0,45.0,0.9,0.0,0.0,0.0,0.3
+1,1,30,1,0,21.3,0.0,45.0,1.0,0.0,0.0,0.0,0.4
+1,1,45,1,0,21.3,0.0,45.0,1.1,0.0,0.0,0.0,0.2

tests/data/minute_ev_demo/carbon_intensity.csv

@@ -0,0 +1,9 @@
+carbon_intensity
+0.20
+0.20
+0.18
+0.16
+0.15
+0.17
+0.19
+0.20

tests/data/minute_ev_demo/charger_1_1.csv

@@ -0,0 +1,9 @@
+electric_vehicle_charger_state,electric_vehicle_id,electric_vehicle_battery_capacity_khw,current_soc,electric_vehicle_departure_time,electric_vehicle_required_soc_departure,electric_vehicle_estimated_arrival_time,electric_vehicle_estimated_soc_arrival
+1,Electric_Vehicle_1,20,8.0,4,80,,
+1,Electric_Vehicle_1,20,8.5,3,80,,
+1,Electric_Vehicle_1,20,9.0,2,80,,
+1,Electric_Vehicle_1,20,10.0,1,80,,
+3,Electric_Vehicle_1,20,,,,,
+3,Electric_Vehicle_1,20,,,,,
+2,Electric_Vehicle_1,20,,,,3,45
+2,Electric_Vehicle_1,20,,,,2,45

tests/data/minute_ev_demo/pricing.csv

@@ -0,0 +1,9 @@
+electricity_pricing,electricity_pricing_predicted_1,electricity_pricing_predicted_2,electricity_pricing_predicted_3
+0.10,0.11,0.12,0.13
+0.10,0.11,0.12,0.13
+0.11,0.12,0.13,0.14
+0.12,0.13,0.14,0.15
+0.13,0.14,0.15,0.16
+0.12,0.13,0.14,0.15
+0.11,0.12,0.13,0.14
+0.10,0.11,0.12,0.13

tests/data/minute_ev_demo/schema.json

@@ -0,0 +1,106 @@
+{
+  "random_seed": 0,
+  "root_directory": null,
+  "central_agent": true,
+  "simulation_start_time_step": 0,
+  "simulation_end_time_step": 7,
+  "episode_time_steps": 8,
+  "rolling_episode_split": false,
+  "random_episode_split": false,
+  "seconds_per_time_step": 900,
+  "start_date": "2024-01-01",
+  "observations": {
+    "month": { "active": true, "shared_in_central_agent": true },
+    "day_type": { "active": true, "shared_in_central_agent": true },
+    "hour": { "active": true, "shared_in_central_agent": true },
+    "minutes": { "active": true, "shared_in_central_agent": true },
+    "outdoor_dry_bulb_temperature": { "active": true, "shared_in_central_agent": true },
+    "outdoor_dry_bulb_temperature_predicted_1": { "active": true, "shared_in_central_agent": true },
+    "outdoor_relative_humidity": { "active": true, "shared_in_central_agent": true },
+    "outdoor_relative_humidity_predicted_1": { "active": true, "shared_in_central_agent": true },
+    "diffuse_solar_irradiance": { "active": true, "shared_in_central_agent": true },
+    "diffuse_solar_irradiance_predicted_1": { "active": true, "shared_in_central_agent": true },
+    "direct_solar_irradiance": { "active": true, "shared_in_central_agent": true },
+    "direct_solar_irradiance_predicted_1": { "active": true, "shared_in_central_agent": true },
+    "carbon_intensity": { "active": true, "shared_in_central_agent": true },
+    "non_shiftable_load": { "active": true, "shared_in_central_agent": false },
+    "solar_generation": { "active": true, "shared_in_central_agent": false },
+    "electrical_storage_soc": { "active": true, "shared_in_central_agent": false },
+    "net_electricity_consumption": { "active": true, "shared_in_central_agent": false },
+    "electricity_pricing": { "active": true, "shared_in_central_agent": true },
+    "electricity_pricing_predicted_1": { "active": true, "shared_in_central_agent": true },
+    "electricity_pricing_predicted_2": { "active": true, "shared_in_central_agent": true },
+    "electricity_pricing_predicted_3": { "active": true, "shared_in_central_agent": true },
+    "electric_vehicle_charger_connected_state": { "active": true, "shared_in_central_agent": true },
+    "connected_electric_vehicle_at_charger_battery_capacity": { "active": true, "shared_in_central_agent": true },
+    "connected_electric_vehicle_at_charger_departure_time": { "active": true, "shared_in_central_agent": true },
+    "connected_electric_vehicle_at_charger_required_soc_departure": { "active": true, "shared_in_central_agent": true },
+    "connected_electric_vehicle_at_charger_soc": { "active": true, "shared_in_central_agent": true },
+    "electric_vehicle_charger_incoming_state": { "active": true, "shared_in_central_agent": true },
+    "incoming_electric_vehicle_at_charger_estimated_arrival_time": { "active": true, "shared_in_central_agent": true }
+  },
+  "actions": {
+    "electrical_storage": { "active": true },
+    "electric_vehicle_storage": { "active": true }
+  },
+  "agent": {
+    "type": "citylearn.agents.rbc.BasicElectricVehicleRBC_ReferenceController",
+    "attributes": {}
+  },
+  "reward_function": {
+    "type": "citylearn.reward_function.RewardFunction",
+    "attributes": {}
+  },
+  "electric_vehicles_def": {
+    "Electric_Vehicle_1": {
+      "include": true,
+      "battery": {
+        "type": "citylearn.energy_model.Battery",
+        "autosize": false,
+        "attributes": {
+          "capacity": 20.0,
+          "nominal_power": 7.2,
+          "initial_soc": 0.4,
+          "efficiency": 0.95
+        }
+      }
+    }
+  },
+  "buildings": {
+    "Building_1": {
+      "include": true,
+      "energy_simulation": "Building_1.csv",
+      "weather": "weather.csv",
+      "carbon_intensity": "carbon_intensity.csv",
+      "pricing": "pricing.csv",
+      "inactive_observations": [],
+      "inactive_actions": [],
+      "electrical_storage": {
+        "type": "citylearn.energy_model.Battery",
+        "autosize": false,
+        "attributes": {
+          "capacity": 5.0,
+          "nominal_power": 5.0,
+          "initial_soc": 0.5,
+          "efficiency": 0.9
+        }
+      },
+      "chargers": {
+        "charger_1_1": {
+          "type": "citylearn.electric_vehicle_charger.Charger",
+          "charger_simulation": "charger_1_1.csv",
+          "autosize": false,
+          "attributes": {
+            "nominal_power": 7.2,
+            "efficiency": 0.95,
+            "charger_type": 0,
+            "max_charging_power": 7.2,
+            "min_charging_power": 0.0,
+            "max_discharging_power": 7.2,
+            "min_discharging_power": 0.0
+          }
+        }
+      }
+    }
+  }
+}

tests/data/minute_ev_demo/weather.csv

@@ -0,0 +1,9 @@
+outdoor_dry_bulb_temperature,outdoor_relative_humidity,diffuse_solar_irradiance,direct_solar_irradiance,outdoor_dry_bulb_temperature_predicted_1,outdoor_dry_bulb_temperature_predicted_2,outdoor_dry_bulb_temperature_predicted_3,outdoor_relative_humidity_predicted_1,outdoor_relative_humidity_predicted_2,outdoor_relative_humidity_predicted_3,diffuse_solar_irradiance_predicted_1,diffuse_solar_irradiance_predicted_2,diffuse_solar_irradiance_predicted_3,direct_solar_irradiance_predicted_1,direct_solar_irradiance_predicted_2,direct_solar_irradiance_predicted_3
+5.0,60.0,0.0,0.0,5.0,5.0,5.0,60.0,60.0,60.0,0.0,0.0,0.0,0.0,0.0,0.0
+5.2,59.5,0.0,0.0,5.0,5.1,5.2,60.0,59.8,59.5,0.0,0.0,0.0,0.0,0.0,0.0
+5.5,59.0,0.0,0.0,5.3,5.2,5.0,59.7,59.5,59.0,0.0,0.0,0.0,0.0,0.0,0.0
+6.0,58.5,50.0,30.0,5.8,5.5,5.2,59.3,59.0,58.5,40.0,30.0,20.0,20.0,10.0,0.0
+6.5,58.0,90.0,60.0,6.2,5.8,5.5,58.8,58.5,58.0,60.0,50.0,40.0,40.0,20.0,10.0
+6.3,57.5,120.0,80.0,6.0,5.6,5.3,58.0,57.8,57.5,70.0,60.0,50.0,50.0,40.0,20.0
+6.1,57.0,70.0,40.0,5.8,5.4,5.2,57.5,57.3,57.0,50.0,40.0,30.0,30.0,20.0,10.0
+5.8,56.5,20.0,10.0,5.5,5.2,5.0,57.0,56.8,56.5,20.0,10.0,0.0,15.0,10.0,5.0

tests/scripts/run_ev_rbc_export_minutes.py

@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+"""EV reference controller run for the minute-level dataset with export enabled."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+
+ROOT = Path(__file__).resolve().parents[2]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from citylearn.agents.rbc import BasicElectricVehicleRBC_ReferenceController as Agent  # noqa: E402
+from citylearn.citylearn import CityLearnEnv  # noqa: E402
+
+SCHEMA = ROOT / "tests/data/minute_ev_demo/schema.json"
+
+
+def main() -> None:
+    render_root = ROOT / "SimulationData"
+    env = CityLearnEnv(
+        str(SCHEMA),
+        central_agent=True,
+        render_mode="end",
+        render_directory=render_root,
+        random_seed=0,
+    )
+
+    try:
+        controller = Agent(env)
+        observations, _ = env.reset()
+
+        for _ in range(env.episode_tracker.episode_time_steps):
+            actions = controller.predict(observations, deterministic=True)
+            observations, _, terminated, truncated, _ = env.step(actions)
+
+            if terminated or truncated:
+                break
+
+        outputs_path = Path(env.new_folder_path)
+        print(f"Exports written to: {outputs_path}")
+    finally:
+        env.close()
+
+
+if __name__ == "__main__":
+    main()

tests/unit/test_minute_time_step_export.py

@@ -0,0 +1,54 @@
+from __future__ import annotations
+
+from datetime import datetime
+import shutil
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+pytest.importorskip("gymnasium")
+
+from citylearn.citylearn import CityLearnEnv
+
+
+DATASET = Path(__file__).resolve().parents[2] / "tests/data/minute_ev_demo/schema.json"
+
+
+def test_minute_level_export(tmp_path):
+    env = CityLearnEnv(
+        str(DATASET),
+        central_agent=True,
+        render_mode="during",
+        render_directory=tmp_path,
+        random_seed=0,
+    )
+
+    try:
+        env.reset()
+        zeros = [np.zeros(env.action_space[0].shape[0], dtype="float32")]
+
+        while not (env.terminated or env.truncated):
+            _, _, terminated, truncated, _ = env.step(zeros)
+            if terminated or truncated:
+                break
+
+        outputs_path = Path(env.new_folder_path)
+        community_export = outputs_path / "exported_data_community_ep0.csv"
+        assert community_export.exists()
+
+        with community_export.open() as f:
+            header = next(f)
+            first_row = next(f).strip()
+            second_row = next(f).strip()
+
+        first_ts = first_row.split(",")[0]
+        second_ts = second_row.split(",")[0]
+
+        delta = datetime.fromisoformat(second_ts) - datetime.fromisoformat(first_ts)
+        assert delta.total_seconds() == pytest.approx(env.seconds_per_time_step)
+    finally:
+        outputs_root = getattr(env, "new_folder_path", None)
+        env.close()
+        if outputs_root is not None:
+            shutil.rmtree(outputs_root, ignore_errors=True)