Merge pull request #131 from pail23/master

Upgrade to python 3.11

.devcontainer/Dockerfile

@@ -1,4 +1,4 @@
-FROM mcr.microsoft.com/devcontainers/python:1-3.8-buster
+FROM mcr.microsoft.com/devcontainers/python:0-3.11
 
 COPY .devcontainer/setup.sh requirements.txt requirements_webserver.txt ./
 RUN ./setup.sh

.github/workflows/python-test.yml

@@ -32,8 +32,8 @@ jobs:
     - name: Special dependencies for macos
       run: |
         brew install hdf5
-        pip3 install numpy==1.22.2
-        pip3 install tables==3.7.0
+        pip3 install numpy==1.26.0
+        pip3 install tables==3.9.1
       if: ${{ matrix.os == 'macos-latest' }}
 
     - name: Install dependencies

docs/requirements.txt

@@ -1,15 +1,15 @@
 wheel
-numpy==1.22.2
-pandas==1.4.1
-scipy<1.9.0
+numpy==1.26.0
+pandas==2.0.3
+scipy==1.11.3
 pvlib>=0.10.1
 protobuf>=3.0.0
 pytz>=2021.1
 requests>=2.25.1
 beautifulsoup4>=4.9.3
 pulp>=2.4
 pyyaml>=5.4.1
-tables==3.7.0
+tables==3.9.1
 skforecast==0.11.0
 markupsafe==2.1.3
 Jinja2<3.2

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
 # These are the assumed default build requirements from pip:
 # https://pip.pypa.io/en/stable/reference/pip/#pep-517-and-518-support
-requires = ["setuptools==40.8.0", "wheel"]
+requires = ["setuptools>=62.0.0", "wheel"]
 build-backend = "setuptools.build_meta"

requirements.txt

@@ -1,13 +1,13 @@
 wheel
-numpy==1.22.2
-pandas==1.4.1
-scipy<1.9.0
+numpy==1.26.0
+pandas==2.0.3
+scipy==1.11.3
 pvlib>=0.10.1
 protobuf>=3.0.0
 pytz>=2021.1
 requests>=2.25.1
 beautifulsoup4>=4.9.3
 pulp>=2.4
 pyyaml>=5.4.1
-tables==3.7.0
-skforecast==0.11.0
+tables==3.9.1
+skforecast==0.11.0

setup.py

@@ -37,20 +37,20 @@
     keywords='energy, management, optimization, hass',  # Optional
     package_dir={'': 'src'},  # Optional
     packages=find_packages(where='src'),  # Required
-    python_requires='>=3.9, <=3.10',
+    python_requires='>=3.9, <3.12',
     install_requires=[
         'wheel', 
-        'numpy==1.22.2',
-        'scipy<1.9.0',
-        'pandas==1.4.1',
+        'numpy==1.26',
+        'scipy==1.11.3',
+        'pandas==2.0.3',
         'pvlib>=0.10.1',
         'protobuf>=3.0.0',
         'pytz>=2021.1',
         'requests>=2.25.1',
         'beautifulsoup4>=4.9.3',
         'pulp>=2.4',
         'pyyaml>=5.4.1',
-        'tables==3.7.0',
+        'tables==3.9.1',
         'skforecast==0.11.0',
     ],  # Optional
     entry_points={  # Optional

src/emhass/retrieve_hass.py

@@ -131,16 +131,16 @@ def get_data(self, days_list: pd.date_range, var_list: list, minimal_response: O
                     self.logger.error("Retrieved empty Dataframe, check that correct day or variable names are passed")
                     self.logger.error("Either the names of the passed variables are not correct or days_to_retrieve is larger than the recorded history of your sensor (check your recorder settings)")
                 if i == 0: # Defining the DataFrame container
-                    from_date = pd.to_datetime(df_raw['last_changed']).min()
-                    to_date = pd.to_datetime(df_raw['last_changed']).max()
+                    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)
                     df_day = pd.DataFrame(index = ts)
                 # Caution with undefined string data: unknown, unavailable, etc.
                 df_tp = df_raw.copy()[['state']].replace(
                     ['unknown', 'unavailable', ''], np.nan).astype(float).rename(columns={'state': var})
                 # Setting index, resampling and concatenation
-                df_tp.set_index(pd.to_datetime(df_raw['last_changed']), inplace=True)
+                df_tp.set_index(pd.to_datetime(df_raw['last_changed'], format="ISO8601"), inplace=True)
                 df_tp = df_tp.resample(self.freq).mean()
                 df_day = pd.concat([df_day, df_tp], axis=1)
 

tests/test_retrieve_hass.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+import datetime
 import unittest
 import requests_mock
 import numpy as np, pandas as pd
@@ -100,7 +101,7 @@ def test_get_data_mock(self):
             self.assertIsInstance(self.rh.df_final.index.dtype, pd.core.dtypes.dtypes.DatetimeTZDtype)
             self.assertEqual(len(self.rh.df_final.columns), len(var_list))
             self.assertEqual(self.rh.df_final.index.freq, self.retrieve_hass_conf['freq'])
-            self.assertEqual(self.rh.df_final.index.tz, pytz.UTC)
+            self.assertEqual(self.rh.df_final.index.tz, datetime.timezone.utc)
 
     def test_prepare_data(self):
         self.assertIsInstance(self.rh.df_final, type(pd.DataFrame()))