(v3.7.3) - Weather variability config logging in CSVLogger wrapper (#466

)

* Modeling: Delete OU configuration in episode folder, save episode OU parameters as attribute

* CSVLogger: Save OU parameters in CSV for each episode

* Tests fixed

* Documentation: Update Sinergym output graph image and documentation section

* Update Sinergym version from 3.7.2 to 3.7.3

docs/source/pages/output.rst

@@ -46,6 +46,8 @@ The contents of this root output directory include the results of the simulation
 
 - ``progress.csv``. This file contains information about general simulation results. Each row contains episode information registering relevant data such as mean power consumption, rewards or comfort penalties. This file is only available when the environment has been wrapped with a ``LoggerWrapper`` and ``CSVLogger`` (see :ref:`Logger Wrappers` for more information). The structure of this file is defined by the ``LoggerWrapper`` class.
 
+- ``weather_variability_config.json``. This file contains the configuration of the weather variability for each episode. It is only created when the environment has been wrapped with ``LoggerWrapper`` and ``CSVLogger``. It is very useful when you are using ranges in weather variability paramters (more information in :ref:`Weather variability`)
+
 - ``data_available.txt``. It is generated when the *EnergyPlus* API initializes all callbacks and handlers for the simulation. In this file, you can find all the available components of the building model, such as actuators, schedulers, meters, variables,  internal variables, etc.
 
 - ``mean.txt`` and ``var.txt``. These files contain the mean and variation values for calibration of normalization in observation space if wrapper ``NormalizeObservation`` is used (see :ref:`NormalizeObservation`).

pyproject.toml

@@ -6,7 +6,7 @@
 package-mode = true
 name = "sinergym"
 
-version = "3.7.2"
+version = "3.7.3"
 description = "Sinergym provides a Gymnasium-based interface to interact with building simulations. This allows control in simulation time through custom controllers, including reinforcement learning agents"
 license = "MIT"
 

sinergym/config/modeling.py

@@ -103,6 +103,8 @@ def __init__(
         # Weather data (epw.weather object)
         self.weather_data = Weather()
         self.weather_data.read(self._weather_path)
+        # Weather variability if exists
+        self.weather_variability_config = None
 
         # ----------------------------- Other attributes ----------------------------- #
 
@@ -340,29 +342,23 @@ def apply_weather_variability(
 
             # Check if there are ranges specified in params and get a random
             # value
-            variability_config = {
+            self.weather_variability_config = {
                 weather_var: tuple(
                     np.random.uniform(param[0], param[1]) if isinstance(param, tuple) else param
                     for param in params
                 )
                 for weather_var, params in weather_variability.items()
             }
 
-            # Write variability_config to a JSON file for episode
-            config_path = f"{
-                self.episode_path}/weather_variability_config.json"
-            with open(config_path, 'w') as f:
-                json.dump(variability_config, f)
-
             # Apply Ornstein-Uhlenbeck process to weather data
             weather_data_mod.dataframe = ornstein_uhlenbeck_process(
                 data=self.weather_data.dataframe,
-                variability_config=variability_config)
+                variability_config=self.weather_variability_config)
 
             self.logger.info(
                 'Weather noise applied in columns: {}'.format(
                     list(
-                        variability_config.keys())))
+                        self.weather_variability_config.keys())))
 
             # Modify filename to reflect noise addition
             filename = f"{filename.split('.epw')[0]}_OU_Noise.epw"

sinergym/utils/wrappers.py

@@ -1478,6 +1478,9 @@ def __init__(
         self.progress_file_path = self.get_wrapper_attr(
             'workspace_path') + '/progress.csv'
 
+        self.weather_variability_config_path = self.get_wrapper_attr(
+            'workspace_path') + '/weather_variability_config.csv'
+
         self.logger.info('Wrapper initialized.')
 
     def reset(self,
@@ -1602,6 +1605,31 @@ def dump_log_files(self) -> None:
                     writer.writerow(list(episode_summary.keys()))
                 writer.writerow(list(episode_summary.values()))
 
+            # Update weather_variability_config if exists
+            modeling = self.get_wrapper_attr('model')
+            config_path = self.get_wrapper_attr(
+                'weather_variability_config_path')
+
+            if modeling.weather_variability_config is not None:
+                with open(config_path, 'a+') as f:
+                    writer = csv.writer(f)
+
+                    # If first episode, write header
+                    if self.get_wrapper_attr('episode') == 1:
+                        header = ['episode_num'] + [
+                            f"{var_name}_{var_param}"
+                            for var_name in list(modeling.weather_variability_config.keys())
+                            for var_param in ['sigma', 'mu', 'tau']
+                        ]
+                        writer.writerow(header)
+
+                    # Write OU params for each weather variable
+                    var_values = list()
+                    var_values = [
+                        self.get_wrapper_attr('episode')] + [
+                        value for params in modeling.weather_variability_config.values() for value in params]
+                    writer.writerow(var_values)
+
 
 # ---------------------------------------------------------------------------- #
 

sinergym/version.txt

@@ -1 +1 @@
-3.7.2
+3.7.3

tests/test_modeling.py

@@ -200,23 +200,13 @@ def test_apply_weather_variability(model_5zone):
     original_filename = model_5zone._weather_path.split('/')[-1]
     path_filename = path_result.split('/')[-1]
     assert original_filename == path_filename
-    # It shouldn't generate variability config
-    # It should generate a json file
-    assert not os.path.exists(
-        model_5zone.episode_path +
-        '/weather_variability_config.json')
-
     # Check with a variation
     weather_variability = {
         'Dry Bulb Temperature': (1.0, 0.0, 24.0),
         'Wind Speed': (3.0, 0.0, 35.0)
     }
     path_result = model_5zone.apply_weather_variability(
         weather_variability=weather_variability)
-    # It should generate weather variability config file
-    assert os.path.exists(
-        model_5zone.episode_path +
-        '/weather_variability_config.json')
     filename = model_5zone._weather_path.split('/')[-1]
     filename = filename.split('.epw')[0]
     filename += '_OU_Noise.epw'

tests/test_wrapper.py

@@ -850,9 +850,14 @@ def test_custom_loggers(env_demo, custom_logger_wrapper):
     assert logger.interactions == 0
 
 
-def test_CSVlogger_wrapper(env_demo):
-
-    env = CSVLogger(env=LoggerWrapper(env=NormalizeObservation(env=env_demo)))
+@pytest.mark.parametrize('env_name',
+                         [('env_demo'),
+                          ('env_5zone_stochastic')
+                          ])
+def test_CSVlogger_wrapper(env_name, request):
+    env = request.getfixturevalue(env_name)
+
+    env = CSVLogger(env=LoggerWrapper(env=NormalizeObservation(env=env)))
     # Check progress CSV path
     assert env.get_wrapper_attr('progress_file_path') == env.get_wrapper_attr(
         'workspace_path') + '/progress.csv'
@@ -862,6 +867,8 @@ def test_CSVlogger_wrapper(env_demo):
 
     # Assert logger files are not created
     assert not os.path.isfile(env.get_wrapper_attr('progress_file_path'))
+    assert not os.path.isfile(env.get_wrapper_attr(
+        'weather_variability_config_path'))
     assert not os.path.isdir(env.get_wrapper_attr('episode_path') + '/monitor')
 
     # simulating short episode
@@ -881,6 +888,15 @@ def test_CSVlogger_wrapper(env_demo):
         reader = csv.reader(csvfile, delimiter=',')
         # Header row and episode summary
         assert len(list(reader)) == 2
+    if env_name == 'env_demo':
+        # File not exists
+        assert not os.path.isfile(env.get_wrapper_attr(
+            'weather_variability_config_path'))
+    else:
+        with open(env.get_wrapper_attr('weather_variability_config_path'), mode='r', newline='') as csvfile:
+            reader = csv.reader(csvfile, delimiter=',')
+            # Header row and episode config
+            assert len(list(reader)) == 2
     # Check csv in monitor is created correctly (only check with observations)
     with open(episode_path + '/monitor/observations.csv', mode='r', newline='') as csvfile:
         reader = csv.reader(csvfile, delimiter=',')