fixing bugs (#26)

* fixing bugs

(1) adapted `step` and `reset` logic in BuildingEnv to align with new treatment of episode_len; (2) alerts user when `block_size` is too large following split into seasons

* R

* Removing unnecessary files

* Removing unnecessary files

* updating test file

examples/building/test_stochastic_generator.py → examples/building/test_building_stochastic.py

@@ -1,3 +1,5 @@
+import sys
+sys.path.append("/Users/ethanwilk/Documents/sustaingym/sustaingym/envs/building")
 from env import BuildingEnv
 from utils import ParameterGenerator
 
@@ -7,15 +9,15 @@
 
 from tqdm import tqdm
 
-block_size = 24  # always in hours
-episode_len = 365  # number of episodes in one year of data
-time_res = 3600  # always in seconds
+block_size = 12 # in hours
+episode_len = 24 # number of timesteps in each episode
+time_res = 3600 # resolution in seconds
 summer_params = ParameterGenerator(
     building="OfficeSmall",
     weather="Hot_Dry",
     location="Tucson",
-    stochastic_summer_percentage=1.0,
-    stochasic_generator_block_size=block_size,
+    stochastic_summer_percentage=1.,
+    block_size=block_size,
     episode_len=episode_len,
     time_res=time_res,
 )
@@ -24,7 +26,7 @@
     weather="Hot_Dry",
     location="Tucson",
     stochastic_summer_percentage=0.0,
-    stochasic_generator_block_size=block_size,
+    block_size=block_size,
     episode_len=episode_len,
     time_res=time_res,
 )
@@ -44,15 +46,15 @@
 winter_obs = []
 neutral_obs = []
 
-num_running_periods = episode_len
+num_running_periods = 365
 seed_start = None
 
 print("Collecting weather data...")
 for i in tqdm(range(num_running_periods)):
     done = False
-    this_summer_obs, _ = summer_env.reset(seed=seed_start)
-    this_winter_obs, _ = winter_env.reset(seed=seed_start)
-    this_neutral_obs, _ = neutral_env.reset(seed=seed_start)
+    this_summer_obs, _ = summer_env.reset(seed=None)
+    this_winter_obs, _ = winter_env.reset(seed=None)
+    this_neutral_obs, _ = neutral_env.reset(seed=None)
     if i == 0:
         summer_obs = this_summer_obs.copy()
         winter_obs = this_winter_obs.copy()
@@ -70,7 +72,7 @@
         winter_obs = np.vstack((winter_obs, this_winter_obs))
         neutral_obs = np.vstack((neutral_obs, this_neutral_obs))
 
-ewm_periods = 30  # num_periods
+ewm_periods = 100  # num_periods
 feature_mappings = {
     -2: "heat gain from irradiance",
     -3: "ground temperature",
@@ -83,9 +85,9 @@
     winter_series = pd.Series(winter_obs[:, ft_idx])
     neutral_series = pd.Series(neutral_obs[:, ft_idx])
 
-    summer_ewma = summer_series.ewm(ewm_periods).mean()  # [30:]
-    winter_ewma = winter_series.ewm(ewm_periods).mean()  # [30:]
-    neutral_ewma = neutral_series.ewm(ewm_periods).mean()  # [30:]
+    summer_ewma = summer_series.rolling(ewm_periods, center=True, min_periods=1).mean()
+    winter_ewma = winter_series.rolling(ewm_periods, center=True, min_periods=1).mean()
+    neutral_ewma = neutral_series.rolling(ewm_periods, center=True, min_periods=1).mean()
 
     plt.figure()
     plt.title(f"Summer vs. winter for {feature_mappings[ft_idx]}")
@@ -104,4 +106,4 @@
     plt.xlabel("Time")
     plt.ylabel("Feature value")
     plt.legend()
-    plt.show()
+    plt.show()

sustaingym/envs/building/env.py

@@ -187,6 +187,7 @@ def __init__(self, parameters: dict[str, Any]):
         self.statelist: list[np.ndarray] = []
         self.actionlist: list[np.ndarray] = []
         self.epoch = 0
+        self.num_epoch_runs = 0
 
         # Initialize zonal temperature
         self.X_new = self.target
@@ -299,16 +300,13 @@ def step(
 
         # Increment the epochs counter
         self.epoch += 1
+        self.num_epoch_runs += 1
 
         # Check if the environment has reached the end of the weather data
-        if (
-            self.epoch % (self.length_of_weather // self.episode_len) == 0
-            and self.epoch != 0
-        ):
-            done = True
-        if self.epoch >= self.length_of_weather - 1:
-            done = True
+        if self.epoch >= self.length_of_weather:
             self.epoch = 0
+        if self.num_epoch_runs >= self.episode_len:
+            done = True
 
         # Return the new state, reward, done flag, and info
         return self.state, reward, done, done, info
@@ -338,12 +336,14 @@ def reset(
         super().reset(seed=seed, options=options)
 
         # Initialize the episode counter
-        num_episodes = self.length_of_weather // self.episode_len
         if seed is None:
-            episode = self.np_random.integers(low=0, high=num_episodes)
+            self.epoch = self.np_random.integers(low=0, high=self.length_of_weather-1)
         else:
-            episode = seed % num_episodes
-        self.epoch = episode * self.episode_len
+            # uses seed to determine which observation of the year to map to
+            num_days_normalizer = ((self.episode_len * self.timestep) // 86_400) * 365
+            self.epoch = int((seed / num_days_normalizer) * self.length_of_weather)
+            self.epoch = min(self.epoch, self.length_of_weather-1)
+        self.num_epoch_runs = 0
 
         # Initialize state and action lists
         self.statelist = []

sustaingym/envs/building/stochastic_generator.py

@@ -95,6 +95,8 @@ def get_empirical_dist(
 
         if block_size is None:
             block_size = self.block_size
+
+        assert block_size < num_obs, "Block size should be less than number of obs"
 
         mu_vectors = []
         cov_matrices = []

sustaingym/envs/building/utils.py

@@ -451,11 +451,11 @@ def generate_stochastic_ambient_features(
             (block_size x num_rows, num_obs_features).
     """
     generator = StochasticUncontrollableGenerator(block_size=block_size)
-    generator.split_observations_into_seasons(observation_data=data)
-    generator.get_empirical_dist(season='summer', block_size=block_size)
-    generator.get_empirical_dist(season='winter', block_size=block_size)
+    generator.split_observations_into_seasons(data=data)
+    generator.get_empirical_dist(season='summer')
+    generator.get_empirical_dist(season='winter')
     samples = generator.draw_samples_from_dist(
-        num_samples=num_rows, summer_percentage=stochastic_summer_percentage)
+        num_samples=num_rows, summer_frac=stochastic_summer_percentage)
     return samples
 
 
@@ -481,6 +481,7 @@ def ParameterGenerator(
     root: str = "",
     stochastic_summer_percentage: float | None = None,
     episode_len: int = 288,
+    block_size: int = None
 ) -> dict[str, Any]:
     """Generates parameters from the selected building and temperature file for the env.
 
@@ -526,6 +527,9 @@ def ParameterGenerator(
             distribution. None if not using stochastic features
         episode_len: number of time steps in each episode (default: 288 steps at 5-min
             time_res is 1 day)
+        block_size: size (in hours) of blocks of data to fit distributions to (e.g., 
+            block_size=24 will sample daily blocks of 24 hourly observations to 
+            fit distributions to)
 
     Returns:
         parameters: Contains all parameters needed for environment initialization.
@@ -589,11 +593,12 @@ def ParameterGenerator(
 
     if stochastic_summer_percentage is not None:
         num_hours_per_episode = int(episode_len * time_res / 60 / 60)
+        this_block_size = block_size if block_size is not None else num_hours_per_episode
         samples = generate_stochastic_ambient_features(
             stochastic_summer_percentage,
             len(all_data),
             all_data,
-            block_size=num_hours_per_episode)
+            block_size=this_block_size)
         oneyear = samples[:, 0]
         oneyearrad = samples[:, 1]
         all_ground_temp = samples[:, 2]