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Shared data-loading stubs for all four Smart Grid MCP servers.
Each server imports from here to get a consistent view of the processed
datasets. Fill in each function once the corresponding Kaggle CSV(s) have
been downloaded to data/processed/.
Dataset → server mapping:
Power Transformers FDD & RUL → IoT, TSFM
DGA Fault Classification → FMSR
Smart Grid Fault Records → WO
Transformer Health Index → FMSR (supplemental)
Current & Voltage Monitoring → IoT, TSFM (supplemental)
"""
from __future__ import annotations
from pathlib import Path
import pandas as pd
# Root of the repository — resolved relative to this file so imports work
# from any working directory.
REPO_ROOT = Path(__file__).resolve().parents[1]
DATA_DIR = REPO_ROOT / "data" / "processed"
# ---------------------------------------------------------------------------
# IoT domain
# ---------------------------------------------------------------------------
def load_asset_metadata() -> pd.DataFrame:
"""
Load static asset metadata (transformer ID, location, manufacturer,
installation date, rated capacity, etc.).
Source CSV: data/processed/asset_metadata.csv
Synthesized from: Power Transformers FDD & RUL dataset.
"""
path = DATA_DIR / "asset_metadata.csv"
_require(path)
return pd.read_csv(path)
def load_sensor_readings() -> pd.DataFrame:
"""
Load time-series sensor readings indexed by (transformer_id, timestamp).
Source CSV: data/processed/sensor_readings.csv
Synthesized from: Power Transformers FDD & RUL + Current & Voltage
Monitoring datasets.
Expected columns:
transformer_id, timestamp, sensor_id, value, unit, source
"""
path = DATA_DIR / "sensor_readings.csv"
_require(path)
df = pd.read_csv(path, parse_dates=["timestamp"])
return df
# ---------------------------------------------------------------------------
# FMSR domain
# ---------------------------------------------------------------------------
def load_failure_modes() -> pd.DataFrame:
"""
Load failure mode descriptions and their associated sensor signatures.
Source CSV: data/processed/failure_modes.csv
Synthesized from: DGA Fault Classification + Transformer Health Index.
Expected columns:
failure_mode_id, name, dga_label, description, severity, iec_code,
key_gases, recommended_action
"""
path = DATA_DIR / "failure_modes.csv"
_require(path)
return pd.read_csv(path)
def load_dga_records() -> pd.DataFrame:
"""
Load dissolved gas analysis (DGA) records used for fault classification.
Source CSV: data/processed/dga_records.csv
Synthesized from: DGA Fault Classification dataset.
Expected columns:
transformer_id, sample_date, dissolved_h2_ppm, dissolved_ch4_ppm,
dissolved_c2h2_ppm, dissolved_c2h4_ppm, dissolved_c2h6_ppm,
dissolved_co_ppm, dissolved_co2_ppm, fault_label, source_dataset
"""
path = DATA_DIR / "dga_records.csv"
_require(path)
return pd.read_csv(path, parse_dates=["sample_date"])
# ---------------------------------------------------------------------------
# TSFM domain
# ---------------------------------------------------------------------------
def load_rul_labels() -> pd.DataFrame:
"""
Load remaining-useful-life (RUL) ground-truth labels per transformer.
Source CSV: data/processed/rul_labels.csv
Synthesized from: Power Transformers FDD & RUL dataset.
Expected columns:
transformer_id, timestamp, rul_days, health_index, fdd_category
"""
path = DATA_DIR / "rul_labels.csv"
_require(path)
return pd.read_csv(path, parse_dates=["timestamp"])
# ---------------------------------------------------------------------------
# WO domain
# ---------------------------------------------------------------------------
def load_fault_records() -> pd.DataFrame:
"""
Load historical fault / maintenance event records.
Source CSV: data/processed/fault_records.csv
Synthesized from: Smart Grid Fault Records dataset.
Expected columns:
transformer_id, fault_id, fault_type, location, voltage_v, current_a,
power_load_mw, temperature_c, wind_speed_kmh, weather_condition,
maintenance_status, component_health, duration_hrs, downtime_hrs
"""
path = DATA_DIR / "fault_records.csv"
_require(path)
return pd.read_csv(path)
# ---------------------------------------------------------------------------
# Internal helpers
# ---------------------------------------------------------------------------
def _require(path: Path) -> None:
"""Raise a clear error if a processed data file hasn't been created yet."""
if not path.exists():
raise FileNotFoundError(
f"Processed data file not found: {path}\n"
"Run the data pipeline (data/processed/) to generate it first."
)
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