Feat - Added new solution to DST survive and prepared new version

CHANGELOG.md

@@ -1,5 +1,14 @@
 # Changelog
 
+## [0.8.2] - 2024-03-10
+### Improvement
+- Proposed a new solution to survive DST using special option of Pandas `round` method
+- Added option to `web_server` to init `data_path` as an options param
+- Styling docs and html files on webui
+- Advanced and basic pages improvements on webui
+### Fix
+- Fixed support for ARM achitectures
+
 ## [0.8.1] - 2024-02-28
 ### Improvement
 - Improved documentation
@@ -587,6 +596,9 @@ Thanks to @GeoDerp for this great work!
 [0.7.6]: https://github.com/davidusb-geek/emhass/releases/tag/v0.7.6
 [0.7.7]: https://github.com/davidusb-geek/emhass/releases/tag/v0.7.7
 [0.7.8]: https://github.com/davidusb-geek/emhass/releases/tag/v0.7.8
+[0.8.0]: https://github.com/davidusb-geek/emhass/releases/tag/v0.8.0
+[0.8.1]: https://github.com/davidusb-geek/emhass/releases/tag/v0.8.1
+[0.8.2]: https://github.com/davidusb-geek/emhass/releases/tag/v0.8.2
 
 # Notes
 All notable changes to this project will be documented in this file.

docs/conf.py

@@ -22,7 +22,7 @@
 author = 'David HERNANDEZ'
 
 # The full version, including alpha/beta/rc tags
-release = '0.8.1'
+release = '0.8.2'
 
 # -- General configuration ---------------------------------------------------
 

docs/develop.md

@@ -222,7 +222,7 @@ docker run ... -v $(pwd)/data/:/app/data ...
 ```
 
 #### Issue with TARGETARCH
-If your docker build fails with an error related to `TARGETARCH`. It may be best to add your devices artitecture this in manually:
+If your docker build fails with an error related to `TARGETARCH`. It may be best to add your devices architecture manually:
 
 Example with armhf architecture 
 ```bash

setup.py

@@ -19,7 +19,7 @@
 
 setup(
     name='emhass',  # Required
-    version='0.8.1',  # Required
+    version='0.8.2',  # Required
     description='An Energy Management System for Home Assistant',  # Optional
     long_description=long_description,  # Optional
     long_description_content_type='text/markdown',  # Optional (see note above)

src/emhass/forecast.py

@@ -159,7 +159,7 @@ def __init__(self, retrieve_hass_conf: dict, optim_conf: dict, plant_conf: dict,
         self.end_forecast = (self.start_forecast + self.optim_conf['delta_forecast']).replace(microsecond=0)
         self.forecast_dates = pd.date_range(start=self.start_forecast, 
                                             end=self.end_forecast-self.freq, 
-                                            freq=self.freq).round(self.freq)
+                                            freq=self.freq).round(self.freq, ambiguous='infer', nonexistent=self.freq)
         if params is not None:
             if 'prediction_horizon' in list(self.params['passed_data'].keys()):
                 if self.params['passed_data']['prediction_horizon'] is not None:
@@ -184,7 +184,7 @@ def get_weather_forecast(self, method: Optional[str] = 'scrapper',
             freq_scrap = pd.to_timedelta(60, "minutes") # The scrapping time step is 60min
             forecast_dates_scrap = pd.date_range(start=self.start_forecast,
                                                  end=self.end_forecast-freq_scrap, 
-                                                 freq=freq_scrap).round(freq_scrap)
+                                                 freq=freq_scrap).round(freq_scrap, ambiguous='infer', nonexistent=freq_scrap)
             # Using the clearoutside webpage
             response = get("https://clearoutside.com/forecast/"+str(round(self.lat, 2))+"/"+str(round(self.lon, 2))+"?desktop=true")
             '''import bz2 # Uncomment to save a serialized data for tests
@@ -476,7 +476,7 @@ def get_forecast_days_csv(self, timedelta_days: Optional[int] = 1) -> pd.date_ra
         end_forecast_csv = (start_forecast_csv + self.optim_conf['delta_forecast']).replace(microsecond=0)
         forecast_dates_csv = pd.date_range(start=start_forecast_csv, 
                                            end=end_forecast_csv+timedelta(days=timedelta_days)-self.freq, 
-                                           freq=self.freq).round(self.freq)
+                                           freq=self.freq).round(self.freq, ambiguous='infer', nonexistent=self.freq)
         if self.params is not None:
             if 'prediction_horizon' in list(self.params['passed_data'].keys()):
                 if self.params['passed_data']['prediction_horizon'] is not None:

src/emhass/retrieve_hass.py

@@ -149,7 +149,7 @@ def get_data(self, days_list: pd.date_range, var_list: list, minimal_response: O
                     from_date = pd.to_datetime(df_raw['last_changed'], format="ISO8601").min()
                     to_date = pd.to_datetime(df_raw['last_changed'], format="ISO8601").max()
                     ts = pd.to_datetime(pd.date_range(start=from_date, end=to_date, freq=self.freq), 
-                                        format='%Y-%d-%m %H:%M').round(self.freq)
+                                        format='%Y-%d-%m %H:%M').round(self.freq, ambiguous='infer', nonexistent=self.freq)
                     df_day = pd.DataFrame(index = ts)
                 # Caution with undefined string data: unknown, unavailable, etc.
                 df_tp = df_raw.copy()[['state']].replace(