Merge pull request #25 from davidusb-geek/dev/mockgetrequest

Dev/mockgetrequest

CHANGELOG.md

@@ -5,6 +5,8 @@
 - Added more detailed examples to the forecast module documentation.
 - Improved handling of datatime indexes in DataFrames on forecast module.
 - Added warning messages if passed list values contains non numeric items.
+- Added missing unittests for forecast module with request.get dependencies using MagicMock.
+- Added the Solar.Forecast method.
 
 ## [0.3.19] - 2022-09-14
 ### Fix

README.md

@@ -308,6 +308,8 @@ Here is the list of the other additional dictionnary keys that can be passed at
 
 - `solcast_rooftop_id` for the ID of your rooftop for the SolCast service implementation.
 
+- `solar_forecast_kwp` for the PV peak installed power in kW used for the solar.forecast API call. 
+
 ### A naive Model Predictive Controller
 
 A MPC controller was introduced in v0.3.0. This is an informal/naive representation of a MPC controller. 

docs/forecasts.md

@@ -27,6 +27,13 @@ For example:
 curl -i -H 'Content-Type:application/json' -X POST -d '{"solcast_rooftop_id":"<your_system_id>","solcast_api_key":"<your_secret_api_key>"}' http://localhost:5000/action/dayahead-optim
 ```
 
+A thrd method uses the Solar.Forecast service. You will need to set `method=solar.forecast` and use just one parameter `solar_forecast_kwp` (the PV peak installed power in kW) that should be passed at runtime.
+
+For example, for a 5 kWp installation:
+```
+curl -i -H 'Content-Type:application/json' -X POST -d '{"solar_forecast_kwp":5}' http://localhost:5000/action/dayahead-optim
+```
+
 ## Load power forecast
 
 The default method for load forecast is a naive method, also called persistence. This is obtained using `method=naive`. This method simply assumes that the forecast for a future period will be equal to the observed values in a past period. The past period is controlled using parameter `delta_forecast` and the default value for this is 24h.

secrets_emhass(example).yaml

@@ -8,4 +8,5 @@ lat: 45.83
 lon: 6.86
 alt: 4807.8
 solcast_api_key: yoursecretsolcastapikey
-solcast_rooftop_id: yourrooftopid
+solcast_rooftop_id: yourrooftopid
+solar_forecast_kwp: 5

src/emhass/forecast.py

@@ -197,6 +197,10 @@ def get_weather_forecast(self, method: Optional[str] = 'scrapper',
             data['relative_humidity'] = raw_data['Relative Humidity (%)']
             data['precipitable_water'] = pvlib.atmosphere.gueymard94_pw(
                 data['temp_air'], data['relative_humidity'])
+            '''import bz2 # Uncomment to save a serialized data for tests
+            import _pickle as cPickle
+            with bz2.BZ2File("test_response_scrapper_method.pbz2", "w") as f: 
+                cPickle.dump(data, f)'''
         elif method == 'solcast': # using solcast API
             # Retrieve data from the solcast API
             headers = {
@@ -220,6 +224,36 @@ def get_weather_forecast(self, method: Optional[str] = 'scrapper',
                 data = pd.DataFrame.from_dict(data_dict)
                 # Define index
                 data.set_index('ts', inplace=True)
+            '''import bz2 # Uncomment to save a serialized data for tests
+            import _pickle as cPickle
+            with bz2.BZ2File("test_response_solcast_method.pbz2", "w") as f: 
+                cPickle.dump(data, f)'''
+        elif method == 'solar.forecast': # using the solar.forecast API
+            # Retrieve data from the solar.forecast API
+            headers = {
+                "Accept": "application/json"
+                }
+            url = "https://api.forecast.solar/estimate/"+str(round(self.lat, 2))+"/"+str(round(self.lon, 2))+\
+                "/"+str(self.plant_conf["surface_tilt"])+"/"+str(self.plant_conf["surface_azimuth"]-180)+\
+                "/"+str(self.retrieve_hass_conf["solar_forecast_kwp"])
+            response = get(url, headers=headers)
+            data_raw = response.json()
+            data_dict = {'ts':list(data_raw['result']['watts'].keys()), 'yhat':list(data_raw['result']['watts'].values())}
+            # Form the final DataFrame
+            data = pd.DataFrame.from_dict(data_dict)
+            data.set_index('ts', inplace=True)
+            data.index = pd.to_datetime(data.index)
+            data = data.tz_localize(self.forecast_dates.tz)
+            data = data.reindex(index=self.forecast_dates)
+            mask_up_data_df = data.copy(deep=True).fillna(method = "ffill").isnull()
+            mask_down_data_df = data.copy(deep=True).fillna(method = "bfill").isnull()
+            data.interpolate(inplace=True)
+            data.loc[data.index[mask_up_data_df['yhat']==True],:] = 0.0
+            data.loc[data.index[mask_down_data_df['yhat']==True],:] = 0.0
+            '''import bz2 # Uncomment to save a serialized data for tests
+            import _pickle as cPickle
+            with bz2.BZ2File("test_response_solarforecast_method.pbz2", "w") as f: 
+                cPickle.dump(data, f)'''
         elif method == 'csv': # reading from a csv file
             weather_csv_file_path = self.root + csv_path
             # Loading the csv file, we will consider that this is the PV power in W
@@ -313,7 +347,8 @@ def get_power_from_weather(self, df_weather: pd.DataFrame,
 
         """
         # If using csv method we consider that yhat is the PV power in W
-        if self.weather_forecast_method == 'solcast' or self.weather_forecast_method == 'csv' or self.weather_forecast_method == 'list':
+        if self.weather_forecast_method == 'solcast' or self.weather_forecast_method == 'solar.forecast' or \
+            self.weather_forecast_method == 'csv' or self.weather_forecast_method == 'list':
             P_PV_forecast = df_weather['yhat']
             P_PV_forecast.name = None
         else: # We will transform the weather data into electrical power

src/emhass/utils.py

@@ -197,8 +197,13 @@ def treat_runtimeparams(runtimeparams: str, params:str, retrieve_hass_conf: dict
             optim_conf['set_def_constant'] = runtimeparams['set_def_constant']
         if 'solcast_api_key' in runtimeparams.keys():
             retrieve_hass_conf['solcast_api_key'] = runtimeparams['solcast_api_key']
+            optim_conf['weather_forecast_method'] = 'solcast'
         if 'solcast_rooftop_id' in runtimeparams.keys():
             retrieve_hass_conf['solcast_rooftop_id'] = runtimeparams['solcast_rooftop_id']
+            optim_conf['weather_forecast_method'] = 'solcast'
+        if 'solar_forecast_kwp' in runtimeparams.keys():
+            retrieve_hass_conf['solar_forecast_kwp'] = runtimeparams['solar_forecast_kwp']
+            optim_conf['weather_forecast_method'] = 'solar.forecast'
         params = json.dumps(params)
     return params, retrieve_hass_conf, optim_conf
 

tests/test_forecast.py

@@ -2,8 +2,11 @@
 # -*- coding: utf-8 -*-
 
 import unittest
+from unittest.mock import MagicMock
 import pandas as pd
 import pathlib, pickle, json, copy, yaml
+import bz2
+import _pickle as cPickle
 
 from emhass.retrieve_hass import retrieve_hass
 from emhass.forecast import forecast
@@ -44,7 +47,7 @@ def setUp(self):
         self.fcst = forecast(self.retrieve_hass_conf, self.optim_conf, self.plant_conf, 
                              params, root, logger, get_data_from_file=self.get_data_from_file)
         # The default for test is csv read
-        self.df_weather_scrap = self.fcst.get_weather_forecast(method='csv') # Still need to unittest these methods: 'scrapper','solcast','forecast.solar'
+        self.df_weather_scrap = self.fcst.get_weather_forecast(method='csv')
         self.P_PV_forecast = self.fcst.get_power_from_weather(self.df_weather_scrap)
         self.P_load_forecast = self.fcst.get_load_forecast(method=optim_conf['load_forecast_method'])
         self.df_input_data_dayahead = pd.concat([self.P_PV_forecast, self.P_load_forecast], axis=1)
@@ -66,15 +69,6 @@ def setUp(self):
         }
 
     def test_get_weather_forecast(self):
-        # self.assertTrue(self.df_input_data.isnull().sum().sum()==0)
-        # self.assertIsInstance(self.df_weather_scrap, type(pd.DataFrame()))
-        # self.assertTrue(col in self.df_weather_scrap.columns for col in ['ghi', 'dni', 'dhi', 'temp_air'])
-        # self.assertIsInstance(self.df_weather_scrap.index, pd.core.indexes.datetimes.DatetimeIndex)
-        # self.assertIsInstance(self.df_weather_scrap.index.dtype, pd.core.dtypes.dtypes.DatetimeTZDtype)
-        # self.assertEqual(self.df_weather_scrap.index.tz, self.fcst.time_zone)
-        # self.assertTrue(self.fcst.start_forecast < ts for ts in self.df_weather_scrap.index)
-        # self.assertEqual(len(self.df_weather_scrap), 
-        #                  int(self.optim_conf['delta_forecast'].total_seconds()/3600/self.fcst.timeStep))
         self.df_weather_csv = self.fcst.get_weather_forecast(method='csv')
         self.assertEqual(self.fcst.weather_forecast_method, 'csv')
         self.assertIsInstance(self.df_weather_csv, type(pd.DataFrame()))
@@ -91,6 +85,51 @@ def test_get_weather_forecast(self):
         self.assertEqual(P_PV_forecast.index.tz, self.fcst.time_zone)
         self.assertEqual(len(self.df_weather_csv), len(P_PV_forecast))
 
+    def test_get_weather_forecast_scrapper_method(self):
+        data = bz2.BZ2File(str(pathlib.Path(root+'/data/test_response_scrapper_method.pbz2')), "rb")
+        data = cPickle.load(data)
+        self.fcst.get_weather_forecast = MagicMock(return_value=data)
+        df_weather_scrap = self.fcst.get_weather_forecast(method='scrapper')
+        self.fcst.get_weather_forecast.assert_called_with(method='scrapper')
+        self.fcst.get_weather_forecast.assert_called_once()
+        self.assertIsInstance(df_weather_scrap, type(pd.DataFrame()))
+        self.assertIsInstance(df_weather_scrap.index, pd.core.indexes.datetimes.DatetimeIndex)
+        self.assertIsInstance(df_weather_scrap.index.dtype, pd.core.dtypes.dtypes.DatetimeTZDtype)
+        self.assertEqual(df_weather_scrap.index.tz, self.fcst.time_zone)
+        self.assertTrue(self.fcst.start_forecast < ts for ts in df_weather_scrap.index)
+        self.assertEqual(len(df_weather_scrap), 
+                         int(self.optim_conf['delta_forecast'].total_seconds()/3600/self.fcst.timeStep))
+
+    def test_get_weather_forecast_solcast_method(self):
+        data = bz2.BZ2File(str(pathlib.Path(root+'/data/test_response_solcast_method.pbz2')), "rb")
+        data = cPickle.load(data)
+        self.fcst.get_weather_forecast = MagicMock(return_value=data)
+        df_weather_solcast = self.fcst.get_weather_forecast(method='solcast')
+        self.fcst.get_weather_forecast.assert_called_with(method='solcast')
+        self.fcst.get_weather_forecast.assert_called_once()
+        self.assertIsInstance(df_weather_solcast, type(pd.DataFrame()))
+        self.assertIsInstance(df_weather_solcast.index, pd.core.indexes.datetimes.DatetimeIndex)
+        self.assertIsInstance(df_weather_solcast.index.dtype, pd.core.dtypes.dtypes.DatetimeTZDtype)
+        self.assertEqual(df_weather_solcast.index.tz, self.fcst.time_zone)
+        self.assertTrue(self.fcst.start_forecast < ts for ts in df_weather_solcast.index)
+        self.assertEqual(len(df_weather_solcast), 
+                         int(self.optim_conf['delta_forecast'].total_seconds()/3600/self.fcst.timeStep))
+
+    def test_get_weather_forecast_solcast_method(self):
+        data = bz2.BZ2File(str(pathlib.Path(root+'/data/test_response_solarforecast_method.pbz2')), "rb")
+        data = cPickle.load(data)
+        self.fcst.get_weather_forecast = MagicMock(return_value=data)
+        df_weather_solarforecast = self.fcst.get_weather_forecast(method='solar.forecast')
+        self.fcst.get_weather_forecast.assert_called_with(method='solar.forecast')
+        self.fcst.get_weather_forecast.assert_called_once()
+        self.assertIsInstance(df_weather_solarforecast, type(pd.DataFrame()))
+        self.assertIsInstance(df_weather_solarforecast.index, pd.core.indexes.datetimes.DatetimeIndex)
+        self.assertIsInstance(df_weather_solarforecast.index.dtype, pd.core.dtypes.dtypes.DatetimeTZDtype)
+        self.assertEqual(df_weather_solarforecast.index.tz, self.fcst.time_zone)
+        self.assertTrue(self.fcst.start_forecast < ts for ts in df_weather_solarforecast.index)
+        self.assertEqual(len(df_weather_solarforecast), 
+                         int(self.optim_conf['delta_forecast'].total_seconds()/3600/self.fcst.timeStep))
+
     def test_get_forecasts_with_lists(self):
         with open(root+'/config_emhass.yaml', 'r') as file:
             params = yaml.load(file, Loader=yaml.FullLoader)
@@ -181,7 +220,7 @@ def test_get_power_from_weather(self):
         self.plant_conf['strings_per_inverter'] = [1, 1]
         self.fcst = forecast(self.retrieve_hass_conf, self.optim_conf, self.plant_conf, 
                              None, root, logger, get_data_from_file=self.get_data_from_file)
-        df_weather_scrap = self.fcst.get_weather_forecast(method='scrapper')
+        df_weather_scrap = self.fcst.get_weather_forecast(method='csv')
         P_PV_forecast = self.fcst.get_power_from_weather(df_weather_scrap)
         self.assertIsInstance(P_PV_forecast, pd.core.series.Series)
         self.assertIsInstance(P_PV_forecast.index, pd.core.indexes.datetimes.DatetimeIndex)
@@ -193,7 +232,7 @@ def test_get_power_from_weather(self):
         df_input_data = self.input_data_dict['rh'].df_final.copy()
         self.fcst = forecast(self.retrieve_hass_conf, self.optim_conf, self.plant_conf, 
                              params, root, logger, get_data_from_file=self.get_data_from_file)
-        df_weather_scrap = self.fcst.get_weather_forecast(method='scrapper')
+        df_weather_scrap = self.fcst.get_weather_forecast(method='csv')
         P_PV_forecast = self.fcst.get_power_from_weather(df_weather_scrap, set_mix_forecast=True, df_now=df_input_data)
         self.assertIsInstance(P_PV_forecast, pd.core.series.Series)
         self.assertIsInstance(P_PV_forecast.index, pd.core.indexes.datetimes.DatetimeIndex)