Fix - Preparing version 0.6.0, see CHANGELOG

CHANGELOG.md

@@ -1,5 +1,21 @@
 # Changelog
 
+## [0.6.0] - 2023-12-16
+### Improvement
+- Now Python 3.11 is fully supported, thanks to @pail23
+- Bumped setuptools, skforecast, numpy, scipy, pandas
+- A good bunch of documentation improvements thanks to @g1za
+### Fix
+- Some fixes managing time zones, thanks to @pail23
+- Bug fix on grid cost function equation, thanks to @michaelpiron
+- Applying a first set of fixes proposed by @smurfix:
+  - Don't ignore HTTP errors
+  - Handle missing variable correctly
+  - Slight error message improvement
+  - Just use the default solver
+  - Get locations from environment in non-app mode
+  - Tolerate running directly from source
+
 ## [0.5.1] - 2023-10-19
 ### Improvement
 - Improved documentation, thanks to @g1za
@@ -471,6 +487,9 @@
 [0.4.13]: https://github.com/davidusb-geek/emhass/releases/tag/v0.4.13
 [0.4.14]: https://github.com/davidusb-geek/emhass/releases/tag/v0.4.14
 [0.4.15]: https://github.com/davidusb-geek/emhass/releases/tag/v0.4.15
+[0.5.0]: https://github.com/davidusb-geek/emhass/releases/tag/v0.5.0
+[0.5.1]: https://github.com/davidusb-geek/emhass/releases/tag/v0.5.1
+[0.6.0]: https://github.com/davidusb-geek/emhass/releases/tag/v0.6.0
 
 # Notes
 All notable changes to this project will be documented in this file.

config_emhass.yaml

@@ -45,7 +45,7 @@ optim_conf:
   - prod_price_forecast_method: 'constant' # options are 'constant' for constant fixed value or 'csv' to load custom price forecast from a CSV file
   - prod_sell_price: 0.065 # power production selling price in €/kWh (only needed if prod_price_forecast_method='constant')
   - set_total_pv_sell: False # consider that all PV power is injected to the grid (self-consumption with total sell)
-  - lp_solver: 'PULP_CBC_CMD' # set the name of the linear programming solver that will be used
+  - lp_solver: 'default' # set the name of the linear programming solver that will be used
   - lp_solver_path: 'empty' # set the path to the LP solver
   - set_nocharge_from_grid: False # avoid battery charging from the grid
   - set_nodischarge_to_grid: True # avoid battery discharging to the grid

scripts/load_clustering_stumpy.py

@@ -93,7 +93,7 @@
     # fig = (px.line(df, x='Clusters', y='Distortions', template=template)).update_traces(mode='lines+markers')
     # fig.show()
 
-    # The silouhette metod
+    # The silouhette method
     silhouette_scores = []
     K = range(2,12)
 
@@ -111,7 +111,7 @@
     # The clustering
     kmeans = KMeans(n_clusters=6, init='k-means++')
     kmeans = kmeans.fit(data_lag)
-    data['cluster_group'] = kmeans.labels_
+    data_lag['cluster_group'] = kmeans.labels_
 
-    fig = px.scatter(data, x='power_load y(t)', y='power_load y(t+1)', color='cluster_group', template=template)
+    fig = px.scatter(data_lag, x='power_load y(t)', y='power_load y(t+1)', color='cluster_group', template=template)
     fig.show()

src/emhass/forecast.py

@@ -315,7 +315,7 @@ def get_weather_forecast(self, method: Optional[str] = 'scrapper',
                 # Define index
                 data.set_index('ts', inplace=True)
         else:
-            self.logger.error("Passed method is not valid")
+            self.logger.error("Method %r is not valid", method)
         return data
 
     def cloud_cover_to_irradiance(self, cloud_cover: pd.Series, 

src/emhass/optimization.py

@@ -387,25 +387,24 @@ def perform_optimization(self, data_opt: pd.DataFrame, P_PV: np.array, P_load: n
 
         ## Finally, we call the solver to solve our optimization model:
         # solving with default solver CBC
-        try:
-            if self.lp_solver == 'PULP_CBC_CMD':
-                opt_model.solve(PULP_CBC_CMD(msg=0))
-            elif self.lp_solver == 'GLPK_CMD':
-                opt_model.solve(GLPK_CMD(msg=0))
-            elif self.lp_solver == 'COIN_CMD':
-                opt_model.solve(COIN_CMD(msg=0, path=self.lp_solver_path))
-            else:
-                self.logger.error("Invalid solver name passed")
-        except Exception:
-            self.logger.error("It was not possible to find a valid solver for Pulp package")
+        if self.lp_solver == 'PULP_CBC_CMD':
+            opt_model.solve(PULP_CBC_CMD(msg=0))
+        elif self.lp_solver == 'GLPK_CMD':
+            opt_model.solve(GLPK_CMD(msg=0))
+        elif self.lp_solver == 'COIN_CMD':
+            opt_model.solve(COIN_CMD(msg=0, path=self.lp_solver_path))
+        else:
+            self.logger.warning("Solver %s unknown, using default", self.lp_solver)
+            opt_model.solve()
 
         # The status of the solution is printed to the screen
         self.optim_status = plp.LpStatus[opt_model.status]
         self.logger.info("Status: " + self.optim_status)
         if plp.value(opt_model.objective) is None:
-            self.logger.warning("Cost function cannot be evaluated, probably None")
+            self.logger.warning("Cost function cannot be evaluated")
+            return
         else:
-            self.logger.info("Total value of the Cost function = " + str(round(plp.value(opt_model.objective),2)))
+            self.logger.info("Total value of the Cost function = %.02f", plp.value(opt_model.objective))
 
         # Build results Dataframe
         opt_tp = pd.DataFrame()

src/emhass/retrieve_hass.py

@@ -116,6 +116,9 @@ def get_data(self, days_list: pd.date_range, var_list: list, minimal_response: O
                     response = get(url, headers=headers)
                 except Exception:
                     return "Request Get Error"
+                else:
+                    if response.status_code > 299:
+                        return f"Request Get Error: {response.status_code}"
                 '''import bz2 # Uncomment to save a serialized data for tests
                 import _pickle as cPickle
                 with bz2.BZ2File("data/test_response_get_data_get_method.pbz2", "w") as f: 
@@ -174,17 +177,14 @@ def prepare_data(self, var_load: str, load_negative: Optional[bool] = False, set
         :rtype: pandas.DataFrame
         
         """
-        if load_negative: # Apply the correct sign to load power
-            try:
+        try:
+            if load_negative: # Apply the correct sign to load power
                 self.df_final[var_load+'_positive'] = -self.df_final[var_load]
-            except KeyError:
-                self.logger.error("Variable "+var_load+" was not found. This is typically because no data could be retrieved from Home Assistant")
-        else:
-            try:
+            else:
                 self.df_final[var_load+'_positive'] = self.df_final[var_load]
-            except KeyError:
-                self.logger.error("Variable "+var_load+" was not found. This is typically because no data could be retrieved from Home Assistant")
-        self.df_final.drop([var_load], inplace=True, axis=1)
+            self.df_final.drop([var_load], inplace=True, axis=1)
+        except KeyError:
+            self.logger.error("Variable "+var_load+" was not found. This is typically because no data could be retrieved from Home Assistant")
         if set_zero_min: # Apply minimum values
             self.df_final.clip(lower=0.0, inplace=True, axis=1)
             self.df_final.replace(to_replace=0.0, value=np.nan, inplace=True)

src/emhass/web_server.py

@@ -5,7 +5,7 @@
 from jinja2 import Environment, PackageLoader
 from requests import get
 from waitress import serve
-from importlib.metadata import version
+from importlib.metadata import version, PackageNotFoundError
 from pathlib import Path
 import os, json, argparse, pickle, yaml, logging
 from distutils.util import strtobool
@@ -265,9 +265,9 @@ def action_call(action_name):
             app.logger.error("options.json does not exists")
         DATA_PATH = "/share/" #"/data/"
     else:
-        CONFIG_PATH = "/app/config_emhass.yaml"
+        CONFIG_PATH = os.getenv("CONFIG_PATH", default="/app/config_emhass.yaml")
         options = None
-        DATA_PATH = "/app/data/"
+        DATA_PATH = os.getenv("DATA_PATH", default="/app/data/")
     config_path = Path(CONFIG_PATH)
     data_path = Path(DATA_PATH)
 
@@ -328,7 +328,7 @@ def action_call(action_name):
     else:
         costfun = os.getenv('LOCAL_COSTFUN', default='profit')
         logging_level = os.getenv('LOGGING_LEVEL', default='INFO')
-        with open('/app/secrets_emhass.yaml', 'r') as file:
+        with open(os.getenv('SECRETS_PATH', default='/app/secrets_emhass.yaml'), 'r') as file:
             params_secrets = yaml.load(file, Loader=yaml.FullLoader)
         hass_url = params_secrets['hass_url']
 
@@ -364,5 +364,8 @@ def action_call(action_name):
     app.logger.info("Launching the emhass webserver at: http://"+web_ui_url+":"+str(port))
     app.logger.info("Home Assistant data fetch will be performed using url: "+hass_url)
     app.logger.info("The data path is: "+str(data_path))
-    app.logger.info("Using core emhass version: "+version('emhass'))
+    try:
+        app.logger.info("Using core emhass version: "+version('emhass'))
+    except PackageNotFoundError:
+        app.logger.info("Using development emhass version")
     serve(app, host=web_ui_url, port=port, threads=8)

tests/test_retrieve_hass.py

@@ -7,7 +7,7 @@
 import numpy as np, pandas as pd
 import pytz, pathlib, pickle, json, yaml, copy
 import bz2
-import _pickle as cPickle
+import pickle as cPickle
 
 from emhass.retrieve_hass import retrieve_hass
 from emhass.utils import get_root, get_yaml_parse, get_days_list, get_logger