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Bartosz Wieczorek
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aadcd4a193
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a5b16136f0
185
.gitignore
vendored
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185
.gitignore
vendored
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@ -0,0 +1,185 @@
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# Created by .ignore support plugin (hsz.mobi)
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### Python template
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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env/
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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*.egg-info/
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.installed.cfg
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*.egg
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*,cover
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.hypothesis/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# IPython Notebook
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.ipynb_checkpoints
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# pyenv
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.python-version
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# celery beat schedule file
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celerybeat-schedule
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# dotenv
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.env
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# virtualenv
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venv/
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ENV/
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# Spyder project settings
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.spyderproject
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# Rope project settings
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.ropeproject
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### VirtualEnv template
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# Virtualenv
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# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
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[Bb]in
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[Ii]nclude
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[Ll]ib
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[Ll]ib64
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[Ll]ocal
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[Ss]cripts
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pyvenv.cfg
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.venv
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pip-selfcheck.json
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### JetBrains template
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# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
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# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
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# User-specific stuff
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.idea/**/workspace.xml
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.idea/**/tasks.xml
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.idea/**/usage.statistics.xml
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.idea/**/dictionaries
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.idea/**/shelf
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# AWS User-specific
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.idea/**/aws.xml
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# Generated files
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.idea/**/contentModel.xml
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# Sensitive or high-churn files
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.idea/**/dataSources/
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.idea/**/dataSources.ids
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.idea/**/dataSources.local.xml
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.idea/**/sqlDataSources.xml
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.idea/**/dynamic.xml
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.idea/**/uiDesigner.xml
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.idea/**/dbnavigator.xml
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# Gradle
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.idea/**/gradle.xml
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.idea/**/libraries
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# Gradle and Maven with auto-import
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# When using Gradle or Maven with auto-import, you should exclude module files,
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# since they will be recreated, and may cause churn. Uncomment if using
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# auto-import.
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# .idea/artifacts
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# .idea/compiler.xml
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# .idea/jarRepositories.xml
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# .idea/modules.xml
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# .idea/*.iml
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# .idea/modules
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# *.iml
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# *.ipr
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# CMake
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cmake-build-*/
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# Mongo Explorer plugin
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.idea/**/mongoSettings.xml
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# File-based project format
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*.iws
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# IntelliJ
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out/
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# mpeltonen/sbt-idea plugin
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.idea_modules/
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# JIRA plugin
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atlassian-ide-plugin.xml
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# Cursive Clojure plugin
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.idea/replstate.xml
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# SonarLint plugin
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.idea/sonarlint/
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# Crashlytics plugin (for Android Studio and IntelliJ)
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com_crashlytics_export_strings.xml
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crashlytics.properties
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crashlytics-build.properties
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fabric.properties
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# Editor-based Rest Client
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.idea/httpRequests
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# Android studio 3.1+ serialized cache file
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.idea/caches/build_file_checksums.ser
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# idea folder, uncomment if you don't need it
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# .idea
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0
EnergyPriceProvider/PstrykBuyProvider.py
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0
EnergyPriceProvider/PstrykBuyProvider.py
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@ -1,178 +0,0 @@
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from __future__ import annotations
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from dataclasses import dataclass, field
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from datetime import datetime, timedelta, timezone, date
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from typing import Dict, List, Tuple, Optional
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import psycopg
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from psycopg.rows import dict_row
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from EnergyPrice import EnergyPriceBase
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from utils.time import WARSAW_TZ
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@dataclass
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class RDNProviderPG(EnergyPriceBase):
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"""
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Odczyt stawek RDN z tabeli pricing.energy_prices w Postgresie.
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- rate(ts) -> PLN/kWh (netto) dla znacznika czasu.
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- Korzysta z kolumny ts_range (tstzrange, '[)'), więc obsługuje dowolne podziały doby.
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- Caching per-dzień (w czasie lokalnym Europe/Warsaw) dla wydajności.
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"""
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dsn: str
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provider: str
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kind: str
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side: str # 'buy' | 'sell'
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table: str = "pricing.energy_prices"
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_conn: Optional[psycopg.Connection] = field(default=None, init=False, repr=False)
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_day_cache: Dict[date, List[Tuple[datetime, datetime, float]]] = field(default_factory=dict, init=False, repr=False)
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# --- Połączenie ----------------------------------------------------------
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def _connect(self) -> psycopg.Connection:
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if self._conn is None or self._conn.closed:
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self._conn = psycopg.connect(self.dsn, row_factory=dict_row)
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return self._conn
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def close(self) -> None:
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if self._conn and not self._conn.closed:
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self._conn.close()
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self._conn = None
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self._day_cache.clear()
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# --- Pobranie i cache jednego dnia --------------------------------------
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def _day_bounds_local(self, d: date) -> Tuple[datetime, datetime]:
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start_local = datetime(d.year, d.month, d.day, 0, 0, tzinfo=self.tz)
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end_local = start_local + timedelta(days=1)
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return start_local, end_local
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def _load_day(self, d: date) -> List[Tuple[datetime, datetime, float]]:
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"""
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Zwraca listę interwałów [(start_utc, end_utc, price_net), ...] dla dnia d (lokalnie).
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Wynik sortowany po starcie; przycina dobie lokalnej po stronie SQL.
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"""
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if d in self._day_cache:
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return self._day_cache[d]
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start_local, end_local = self._day_bounds_local(d)
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start_utc = start_local.astimezone(timezone.utc)
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end_utc = end_local.astimezone(timezone.utc)
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sql = f"""
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SELECT ts_start, ts_end, price_pln_net
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FROM {self.table}
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WHERE provider = %s AND kind = %s AND side = %s
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AND ts_range && tstzrange(%s, %s, '[)')
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ORDER BY ts_start;
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"""
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with self._connect().cursor() as cur:
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cur.execute(sql, (self.provider, self.kind, self.side, start_utc, end_utc))
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rows = cur.fetchall()
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# Clip do granic doby lokalnej i zapisz do cache
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out: List[Tuple[datetime, datetime, float]] = []
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for r in rows:
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s = max(r["ts_start"], start_utc)
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e = min(r["ts_end"], end_utc)
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if e > s:
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out.append((s, e, float(r["price_pln_net"])))
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# Porządek i scalanie nakładających się fragmentów o tej samej cenie
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out.sort(key=lambda t: t[0])
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merged: List[Tuple[datetime, datetime, float]] = []
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for s, e, p in out:
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if not merged:
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merged.append((s, e, p))
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continue
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ps, pe, pp = merged[-1]
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# jeśli overlap i ta sama cena -> łączymy
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if s <= pe and p == pp:
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merged[-1] = (ps, max(pe, e), pp)
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else:
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# jeśli overlap, ale inna cena — rozcinamy granicę (zachowujemy kolejność)
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if s < pe and p != pp:
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s = pe
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if s >= e:
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continue
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merged.append((s, e, p))
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self._day_cache[d] = merged
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return merged
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# --- Public API: jedna stawka w danym ts --------------------------------
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def rate(self, ts: datetime) -> float:
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"""
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Zwraca PLN/kWh (netto) dla timestampa ts.
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Rzuca KeyError, jeśli w dobie brakuje pokrycia interwałami lub ts wpada w lukę.
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"""
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ts_local = self.to_local_dt(ts)
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d = ts_local.date()
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intervals = self._load_day(d)
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if not intervals:
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raise KeyError(f"Brak danych cenowych dla {d.isoformat()} ({self.provider}/{self.kind}/{self.side})")
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ts_utc = ts_local.astimezone(timezone.utc)
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# znajdź interwał ts_start <= ts < ts_end
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# (lista jest posortowana)
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lo, hi = 0, len(intervals) - 1
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while lo <= hi:
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mid = (lo + hi) // 2
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s, e, p = intervals[mid]
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if ts_utc < s:
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hi = mid - 1
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elif ts_utc >= e:
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lo = mid + 1
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else:
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return p
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raise KeyError(f"Brak ceny dla {ts_local.isoformat()} (luka w dobie {d.isoformat()})")
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# --- Dodatkowe: eksport do HH:MM->price (np. do debug/raportów) ----------
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def day_schedule_local(self, d: date) -> List[Tuple[str, str, float]]:
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"""
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Zwraca listę [(from_HHMM, to_HHMM, price)] w CZASIE LOKALNYM,
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przyciętą do pełnej doby lokalnej. Ostatni 'to' = '00:00'.
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"""
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start_local, end_local = self._day_bounds_local(d)
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intervals = self._load_day(d)
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if not intervals:
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return []
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# przekładamy na lokalny czas
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parts: List[Tuple[datetime, datetime, float]] = []
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for s, e, p in intervals:
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sl = s.astimezone(self.tz)
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el = e.astimezone(self.tz)
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# przycięcie na wszelki wypadek
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sl = max(sl, start_local)
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el = min(el, end_local)
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if el > sl:
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parts.append((sl, el, p))
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# łączenie sąsiadów z tą samą ceną
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parts.sort(key=lambda x: x[0])
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merged: List[Tuple[datetime, datetime, float]] = []
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for s, e, p in parts:
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if merged and merged[-1][1] == s and merged[-1][2] == p:
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merged[-1] = (merged[-1][0], e, p)
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else:
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merged.append((s, e, p))
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# upewnij się, że start=00:00, koniec=00:00
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if merged and merged[0][0] != start_local:
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if merged[0][0] > start_local:
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# luka na początku doby
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raise KeyError(f"Luka na początku doby {d}")
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merged[0] = (start_local, merged[0][1], merged[0][2])
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if merged and merged[-1][1] != end_local:
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if merged[-1][1] < end_local:
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# luka na końcu doby
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raise KeyError(f"Luka na końcu doby {d}")
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merged[-1] = (merged[-1][0], end_local, merged[-1][2])
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def hhmm(x: datetime) -> str:
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return x.strftime("%H:%M")
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out: List[Tuple[str, str, float]] = []
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for i, (s, e, p) in enumerate(merged):
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out.append((hhmm(s), "00:00" if i == len(merged) - 1 else hhmm(e), p))
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return out
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6
requirements.txt
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6
requirements.txt
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@ -0,0 +1,6 @@
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psycopg[binary]>=3.1,<3.3
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pandas~=2.3.2
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requests>=2.32,<3.0
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apscheduler>=3.10,<4.0
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systemd-python>=235; sys_platform != "win32"
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