Fix - Fixed get_loc deprecation warning using now get_indexer pandas …

…method

CHANGELOG.md

@@ -4,6 +4,10 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.2.9] - 2022-04-21
+### Fix
+- Fixed get_loc deprecation warning using now get_indexer pandas method. Improved selection of closest index.
+
 ## [0.2.8] - 2022-04-18
 ### Fix
 - Fixed if sentence to correctly use the supervisor API for publish data.

docs/conf.py

@@ -22,7 +22,7 @@
 author = 'David HERNANDEZ'
 
 # The full version, including alpha/beta/rc tags
-release = '0.2.8'
+release = '0.2.9'
 
 # -- General configuration ---------------------------------------------------
 

setup.py

@@ -19,7 +19,7 @@
 
 setup(
  name='emhass', # Required
- version='0.2.8', # Required
+ version='0.2.9', # 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/command_line.py

@@ -163,11 +163,9 @@ def publish_data(input_data_dict: dict, logger: logging.Logger,
  opt_res_dayahead.index.freq = input_data_dict['retrieve_hass_conf']['freq']
  # Estimate the current index
  now_precise = datetime.now(input_data_dict['retrieve_hass_conf']['time_zone']).replace(second=0, microsecond=0)
- try:
- idx_closest = opt_res_dayahead.index.get_loc(now_precise, method='ffill')
- except Exception:
- logger.warning("Problem finding DataFrame index, using nearest option as a backup")
- idx_closest = opt_res_dayahead.index.get_loc(now_precise, method='nearest')
+ idx_closest = opt_res_dayahead.index.get_indexer([now_precise], method='ffill')[0]
+ if idx_closest == -1:
+ idx_closest = opt_res_dayahead.index.get_indexer([now_precise], method='nearest')[0]
  # Publish PV forecast
  input_data_dict['rh'].post_data(opt_res_dayahead['P_PV'], idx_closest, 
  'sensor.p_pv_forecast', "W", "PV Power Forecast")

tests/test_optimization.py

@@ -5,6 +5,7 @@
 import pandas as pd
 import pathlib
 import pickle
+from datetime import datetime, timezone
 
 from emhass.retrieve_hass import retrieve_hass
 from emhass.optimization import optimization
@@ -55,6 +56,9 @@ def setUp(self):
  self.days_list, self.costfun, root, logger)
  self.df_input_data = self.fcst.get_load_cost_forecast(self.df_input_data)
  self.df_input_data = self.fcst.get_prod_price_forecast(self.df_input_data)
+ self.input_data_dict = {
+ 'retrieve_hass_conf': retrieve_hass_conf,
+ }
 
  def test_perform_perfect_forecast_optim(self):
  self.opt_res = self.opt.perform_perfect_forecast_optim(self.df_input_data)
@@ -72,6 +76,12 @@ def test_perform_dayahead_forecast_optim(self):
  self.assertIsInstance(self.opt_res_dayahead.index, pd.core.indexes.datetimes.DatetimeIndex)
  self.assertIsInstance(self.opt_res_dayahead.index.dtype, pd.core.dtypes.dtypes.DatetimeTZDtype)
  self.assertTrue('cost_fun_'+self.costfun in self.opt_res_dayahead.columns)
+ # Testing estimation of the current index
+ now_precise = datetime.now(self.input_data_dict['retrieve_hass_conf']['time_zone']).replace(second=0, microsecond=0)
+ idx_closest = self.opt_res_dayahead.index.get_indexer([now_precise], method='ffill')[0]
+ self.assertTrue(idx_closest == -1)
+ idx_closest = self.opt_res_dayahead.index.get_indexer([now_precise], method='nearest')[0]
+ self.assertTrue(idx_closest == 0)
 
 if __name__ == '__main__':
  unittest.main()