81 lines
3.5 KiB
Python
81 lines
3.5 KiB
Python
from __future__ import annotations
|
|
from datetime import datetime, timedelta, date
|
|
from typing import List, Tuple, Dict, Any
|
|
import pandas as pd
|
|
from EnergyPriceScraper import EnergyPriceScraperBase
|
|
from utils.time_helpers import WARSAW_TZ, UTC
|
|
|
|
# CSV pobrane z https://energy.instrat.pl/ceny/energia-rdn-godzinowe/
|
|
class InstratRDN_CSVScraper(EnergyPriceScraperBase):
|
|
"""
|
|
Przykładowy scraper RDN z CSV/JSON (public HTTP).
|
|
Oczekuje CSV z kolumnami: 'date', 'fixing_i_price' (PLN/MWh) lub już PLN/kWh.
|
|
"""
|
|
url: str
|
|
|
|
def __init__(self, path: str, **kwargs):
|
|
super().__init__(**kwargs)
|
|
|
|
self.PROVIDER = "instrat"
|
|
self.KIND = "fixing_I"
|
|
self.SIDE = "buy"
|
|
self.BUYER = "end_user" # sprzedawca rozliczajacy prosumenta
|
|
self.SELLER = "market_index"
|
|
|
|
self.data = self.load_instrat_csv(path)
|
|
|
|
def load_instrat_csv(self, path: str) -> pd.DataFrame:
|
|
"""
|
|
Wczytuje CSV Instrat z format
|
|
date,fixing_i_price,fixing_i_volume,fixing_ii_price,fixing_ii_volume
|
|
01.01.2016 00:00,108.27,2565.10,108.55,89.10
|
|
"""
|
|
# 1) Wczytanie z autodetekcją polskiego formatu
|
|
dateparse = lambda x: datetime.strptime(x, '%d.%m.%Y %H:%M').replace(tzinfo=WARSAW_TZ)
|
|
df = pd.read_csv(path, parse_dates=['date'], date_parser=dateparse)
|
|
|
|
fi_pln_kwh = (df["fixing_i_price"] / 1000.0).round(4)
|
|
fii_pln_kwh = (df["fixing_ii_price"] / 1000.0).round(4)
|
|
|
|
self.out = pd.DataFrame({
|
|
"fixing_i_pln_kwh": fi_pln_kwh.values,
|
|
"fixing_ii_pln_kwh": fii_pln_kwh.values,
|
|
"fixing_i_volume": pd.to_numeric(df.get("fixing_i_volume"), errors="coerce").values,
|
|
"fixing_ii_volume": pd.to_numeric(df.get("fixing_ii_volume"), errors="coerce").values,
|
|
}, index=df["date"]).sort_index()
|
|
|
|
# sanity check — nie wyszło pusto
|
|
if self.out[["fixing_i_pln_kwh", "fixing_ii_pln_kwh"]].notna().sum().sum() == 0:
|
|
raise RuntimeError("Brak cen po przeliczeniu — sprawdź separator/format liczb w CSV.")
|
|
|
|
def fetch_day(self, business_day: date) -> List[Tuple[datetime, datetime, float, Dict[str, Any]]]:
|
|
if not hasattr(self, "out"):
|
|
raise RuntimeError("Brak danych: najpierw wczytaj CSV i zbuduj self.out")
|
|
|
|
# wybór kolumny wg KIND (domyślnie Fixing I)
|
|
kind = getattr(self, "KIND", "fixing_I")
|
|
kind_norm = str(kind).replace(" ", "_").lower()
|
|
if "fixing_ii" in kind_norm:
|
|
col = "fixing_ii_pln_kwh"
|
|
fixing_tag = "II"
|
|
else:
|
|
col = "fixing_i_pln_kwh"
|
|
fixing_tag = "I"
|
|
|
|
# zakres doby lokalnej
|
|
day_start = datetime(business_day.year, business_day.month, business_day.day, 0, 0, tzinfo=self.tz)
|
|
day_end = day_start + timedelta(days=1)
|
|
|
|
# filtr i emisja punktów
|
|
df_day = self.out.loc[(self.out.index >= day_start) & (self.out.index < day_end)]
|
|
if col not in df_day.columns:
|
|
raise KeyError(f"Kolumna '{col}' nie istnieje w self.out")
|
|
|
|
points: List[Tuple[datetime, datetime, float, Dict[str, Any]]] = []
|
|
for ts, price in df_day[col].dropna().items():
|
|
ts_end = ts + getattr(self, "period", timedelta(hours=1))
|
|
points.append((ts.to_pydatetime().astimezone(UTC), ts_end.to_pydatetime().astimezone(UTC), float(price),
|
|
{"source": "instrat_csv", "unit": "PLN/kWh", "fixing": fixing_tag, "taxes_included": False}))
|
|
return points
|
|
|