File size: 7,653 Bytes
47d42b1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 | """Data loading utilities for train_xgboost: Excel files and API-based datasets."""
from __future__ import annotations
import json
import logging
import re
import urllib.parse
import urllib.request
import zipfile
from datetime import UTC, date, datetime, timedelta
from pathlib import Path
import pandas as pd
from config import CITY_COORDS, parse_iso_date
LOGGER = logging.getLogger("static_xgboost")
def load_api_test_dataset(
city: str,
test_year: int,
api_start_date: str | None,
api_end_date: str | None,
) -> pd.DataFrame:
"""Load test set from Open-Meteo archive API in canonical schema."""
lat, lon = CITY_COORDS[city]
start_date = parse_iso_date(api_start_date, "api-start-date") if api_start_date else date(test_year, 1, 1)
end_date = parse_iso_date(api_end_date, "api-end-date") if api_end_date else (datetime.now(UTC).date() - timedelta(days=2))
if end_date < start_date:
raise ValueError(f"api-end-date {end_date} is before api-start-date {start_date}.")
params = {
"latitude": lat,
"longitude": lon,
"hourly": (
"temperature_2m,relative_humidity_2m,wind_speed_10m,wind_gusts_10m,"
"surface_pressure,shortwave_radiation,direct_radiation,diffuse_radiation"
),
"timezone": "UTC",
"start_date": start_date.isoformat(),
"end_date": end_date.isoformat(),
}
url = "https://archive-api.open-meteo.com/v1/archive"
query = urllib.parse.urlencode(params)
request_url = f"{url}?{query}"
with urllib.request.urlopen(request_url, timeout=30) as response:
payload = json.loads(response.read().decode("utf-8"))
hourly = payload.get("hourly")
if not isinstance(hourly, dict):
raise RuntimeError("Open-Meteo response missing 'hourly' block.")
times = hourly.get("time") or []
if not times:
raise RuntimeError("Open-Meteo response contains no hourly time rows.")
size = len(times)
def col(name: str) -> list[float | None]:
values = hourly.get(name)
if values is None:
return [None] * size
return values
df = pd.DataFrame(
{
"timestamp": pd.to_datetime(times, errors="coerce", utc=True),
"temperature": col("temperature_2m"),
"humidity": col("relative_humidity_2m"),
"wind_speed": col("wind_speed_10m"),
"wind_gust": col("wind_gusts_10m"),
"pressure": col("surface_pressure"),
"ghi": col("shortwave_radiation"),
"dni": col("direct_radiation"),
"dhi": col("diffuse_radiation"),
"source_file": f"api:{city}:{start_date}->{end_date}",
}
)
df = df.dropna(subset=["timestamp", "ghi"]).reset_index(drop=True)
LOGGER.info(
"Loaded API test dataset for %s: rows=%d range=%s->%s",
city,
len(df),
start_date,
end_date,
)
return df
def _canonical_col_name(raw: str) -> str:
"""Normalize column name and apply known aliases."""
normalized = re.sub(r"[^a-z0-9]+", "_", raw.strip().lower()).strip("_")
aliases = {
"temp": "temperature",
"winddirection": "wind_direction",
"windspeed": "wind_speed",
"windgust": "wind_gust",
"tair_avg": "temperature",
"tamb": "temperature",
"rh_avg": "humidity",
"rh": "humidity",
"bp_cs100_avg": "pressure",
"bp": "pressure",
"ghi_corr_avg": "ghi",
"dni_corr_avg": "dni",
"dhi_corr_avg": "dhi",
"ws": "wind_speed",
"wsgust": "wind_gust",
"wd": "wind_direction",
"timestamp": "timestamp",
"ts": "timestamp",
"ws_wvc_1": "wind_speed",
}
return aliases.get(normalized, normalized)
def _read_with_strategies(file_path: Path, nrows: int | None) -> pd.DataFrame | None:
"""Read a file using format-specific sheet/header strategies."""
name_lower = file_path.name.lower()
if name_lower.startswith("pk_isb_") or name_lower.startswith("pk-isb_"):
strategies: list[tuple[str | int, int]] = [
("1h", 18),
("10min", 18),
("day", 18),
(0, 0),
]
else:
strategies = [
(0, 0),
("Sheet1", 0),
("data", 0),
("1h", 18),
]
for sheet_name, header_row in strategies:
try:
return pd.read_excel(file_path, sheet_name=sheet_name, header=header_row, nrows=nrows)
except Exception:
continue
return None
def _standardize_frame(df: pd.DataFrame, file_name: str) -> pd.DataFrame:
"""Normalize columns and produce canonical weather frame."""
work = df.copy()
work.columns = [_canonical_col_name(str(col)) for col in work.columns]
if "timestamp" in work.columns:
work["timestamp"] = pd.to_datetime(work["timestamp"], errors="coerce", utc=True)
elif "date" in work.columns and "time" in work.columns:
work["timestamp"] = pd.to_datetime(
work["date"].astype(str) + " " + work["time"].astype(str),
errors="coerce",
utc=True,
)
elif "date" in work.columns:
work["timestamp"] = pd.to_datetime(work["date"], errors="coerce", utc=True)
else:
return pd.DataFrame()
numeric_cols = [
"temperature",
"humidity",
"wind_direction",
"wind_speed",
"wind_gust",
"pressure",
"ghi",
"dni",
"dhi",
]
present_numeric = [col for col in numeric_cols if col in work.columns]
for col in present_numeric:
work[col] = pd.to_numeric(work[col], errors="coerce")
if "ghi" not in work.columns:
return pd.DataFrame()
keep_cols = ["timestamp", *present_numeric]
out = work[keep_cols].copy()
out = out.dropna(subset=["timestamp", "ghi"])
out["source_file"] = file_name
return out
def load_dataset(dataset_dir: Path, max_rows_per_file: int, max_files: int) -> pd.DataFrame:
"""Load and unify all Excel files from the dataset folder."""
files = sorted(dataset_dir.glob("*.xlsx"))
if max_files > 0:
files = files[:max_files]
if not files:
raise FileNotFoundError(f"No .xlsx files found in {dataset_dir}")
frames: list[pd.DataFrame] = []
for idx, file_path in enumerate(files, start=1):
LOGGER.info("Reading file %d/%d: %s", idx, len(files), file_path.name)
nrows = max_rows_per_file if max_rows_per_file > 0 else None
try:
if not zipfile.is_zipfile(file_path):
raise ValueError("File is not a zip file")
df = _read_with_strategies(file_path, nrows=nrows)
if df is None:
LOGGER.warning("Skipping %s (no readable sheet/header strategy)", file_path.name)
continue
except Exception as exc:
LOGGER.warning("Skipping %s (read failed: %s)", file_path.name, exc)
continue
if df.empty:
continue
standardized = _standardize_frame(df, file_path.name)
if standardized.empty:
LOGGER.warning("Skipping %s (required columns missing)", file_path.name)
continue
frames.append(standardized)
if not frames:
raise RuntimeError("No usable files found after schema normalization.")
merged = pd.concat(frames, ignore_index=True)
LOGGER.info(
"Loaded %d rows from %d files (max_rows_per_file=%d)",
len(merged),
len(frames),
max_rows_per_file,
)
return merged
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