transformation of data from old database to new was updated.

component_test_connectors/heat_pump_connection_read_data.py

@@ -0,0 +1,7 @@
+from heat_pump import HeatPump
+
+hp_master = HeatPump(device_name='hp_master', ip_address='10.0.0.10', port=502, excel_path="../modbus_registers/heat_pump_registers.xlsx")
+
+state = hp_master.get_state()
+
+print(state)

component_test_connectors/heat_pump_connection_sg_ready.py

@@ -0,0 +1,49 @@
+from pymodbus.client import ModbusTcpClient
+
+def switch_sg_ready_mode(ip, port, mode):
+    """
+    Register 300: 1=BUS 0= Hardware Kontakte
+    Register 301 & 302:
+        0-0= Kein Offset
+        0-1 Boiler und Heizung Offset
+        1-1 Boiler Offset + E-Einsatz Sollwert Erhöht
+        1-0 SG EVU Sperre
+    :param ip:
+    :param mode:
+    'mode1' = [True, False, False] => SG Ready deactivated
+    'mode2' = [True, False, True] => SG ready activated for heatpump only
+    'mode3' = [True, True, True] => SG ready activated for heatpump and heat rod
+    :return:
+    """
+    client = ModbusTcpClient(ip, port=port)
+    if not client.connect():
+        print("Verbindung zur Wärmepumpe fehlgeschlagen.")
+        return
+
+    mode_code = None
+    if mode == 'mode1':
+        mode_code = [True, False, False]
+    elif mode == 'mode2':
+        mode_code = [True, False, True]
+    elif mode == 'mode3':
+        mode_code = [True, True, True]
+    else:
+        print('Uncorrect or no string for mode!')
+
+    try:
+        response_300 = client.write_coil(300, mode_code[0])
+        response_301 = client.write_coil(301, mode_code[1])
+        response_302 = client.write_coil(302, mode_code[2])
+
+        # Optional: Rückmeldungen prüfen
+        for addr, resp in zip([300, 301, 302], [response_300, response_301, response_302]):
+            if resp.isError():
+                print(f"Fehler beim Schreiben von Coil {addr}: {resp}")
+            else:
+                print(f"Coil {addr} erfolgreich geschrieben.")
+
+    finally:
+        client.close()
+
+if '__name__' == '__main__':
+    switch_sg_ready_mode(ip='10.0.0.10', port=502, mode='mode2')

data_base_operations/transform_old_db_to_new.py

@@ -0,0 +1,213 @@
+import os, re, math, time
+from datetime import datetime, timezone, timedelta
+
+import pandas as pd
+from influxdb_client import InfluxDBClient, Point, WritePrecision
+from influxdb_client.client.write_api import SYNCHRONOUS
+from influxdb_client.rest import ApiException
+
+
+# -----------------------
+# CONFIG
+# -----------------------
+INFLUX_URL = "http://192.168.1.146:8086"
+INFLUX_ORG = "allmende"
+INFLUX_TOKEN = os.environ.get("INFLUX_TOKEN", "Cw_naEZyvJ3isiAh1P4Eq3TsjcHmzzDFS7SlbKDsS6ZWL04fMEYixWqtNxGThDdG27S9aW5g7FP9eiq5z1rsGA==")
+
+SOURCE_BUCKET = "allmende_db"
+TARGET_BUCKET = "allmende_db_v2"
+
+MEASUREMENTS = [
+    "hp_master", "hp_slave", "pv_forecast", "sg_ready",
+    "solaredge_master", "solaredge_meter", "solaredge_slave", "wohnung_2_6"
+]
+
+START_DT = datetime(2025, 6, 1, tzinfo=timezone.utc)
+STOP_DT = datetime.now(timezone.utc)
+WINDOW = timedelta(days=1)
+
+EXCEL_PATH = "../modbus_registers/heat_pump_registers.xlsx"
+EXCEL_SHEET = "Register_Map"
+
+BATCH_SIZE = 1000
+MAX_RETRIES = 8
+
+
+# -----------------------
+# Helpers
+# -----------------------
+def normalize(s) -> str:
+    s = "" if s is None else str(s).strip()
+    return re.sub(r"\s+", " ", s)
+
+def is_invalid_sentinel(v: float) -> bool:
+    return v in (-999.9, -999.0, 30000.0, 32767.0, 65535.0)
+
+def ensure_bucket(client: InfluxDBClient, name: str):
+    bapi = client.buckets_api()
+    if bapi.find_bucket_by_name(name):
+        return
+    bapi.create_bucket(bucket_name=name, org=INFLUX_ORG, retention_rules=None)
+
+def build_field_type_map_from_excel(path: str) -> dict[str, str]:
+    df = pd.read_excel(path, sheet_name=EXCEL_SHEET)
+    df = df[df["Register_Type"].astype(str).str.upper() == "IR"].copy()
+    df["Address"] = df["Address"].astype(int)
+    df["Description"] = df["Description"].fillna("").astype(str)
+    df["Tag_Name"] = df["Tag_Name"].fillna("").astype(str)
+    df["Data_Type"] = df["Data_Type"].fillna("").astype(str)
+
+    m: dict[str, str] = {}
+    for _, r in df.iterrows():
+        addr = int(r["Address"])
+        desc = normalize(r["Description"])
+        tag  = normalize(r["Tag_Name"])
+        dtp  = normalize(r["Data_Type"]).upper()
+        if tag:
+            m[tag] = dtp
+        old_key = normalize(f"{addr} - {desc}".strip(" -"))
+        if old_key:
+            m[old_key] = dtp
+    return m
+
+def coerce_value_to_dtype(v, dtype: str):
+    if v is None:
+        return None
+    dtp = (dtype or "").upper()
+
+    if isinstance(v, (int, float)):
+        fv = float(v)
+        if math.isnan(fv) or math.isinf(fv):
+            return None
+
+    if dtp in ("BOOL", "BOOLEAN"):
+        if isinstance(v, bool): return v
+        if isinstance(v, (int, float)): return bool(int(v))
+        return None
+
+    if dtp.startswith("INT") or dtp.startswith("UINT"):
+        if isinstance(v, bool): return int(v)
+        if isinstance(v, (int, float)): return int(float(v))
+        return None
+
+    if dtp.startswith("FLOAT") or dtp in ("DOUBLE",):
+        if isinstance(v, bool): return float(int(v))
+        if isinstance(v, (int, float)): return float(v)
+        return None
+
+    return None
+
+def write_with_retry(write_api, batch):
+    delay = 1.0
+    last_msg = ""
+    for _ in range(MAX_RETRIES):
+        try:
+            write_api.write(bucket=TARGET_BUCKET, org=INFLUX_ORG, record=batch)
+            return
+        except ApiException as e:
+            last_msg = getattr(e, "body", "") or str(e)
+            status = getattr(e, "status", None)
+            if "timeout" in last_msg.lower() or status in (429, 500, 502, 503, 504):
+                time.sleep(delay)
+                delay = min(delay * 2, 30)
+                continue
+            raise
+    raise RuntimeError(f"Write failed after {MAX_RETRIES} retries: {last_msg}")
+
+def window_already_migrated(query_api, measurement: str, start: datetime, stop: datetime) -> bool:
+    # Prüft: gibt es im Zielbucket im Fenster mindestens 1 Punkt?
+    flux = f'''
+from(bucket: "{TARGET_BUCKET}")
+  |> range(start: time(v: "{start.isoformat()}"), stop: time(v: "{stop.isoformat()}"))
+  |> filter(fn: (r) => r._measurement == "{measurement}")
+  |> limit(n: 1)
+'''
+    tables = query_api.query(flux, org=INFLUX_ORG)
+    for t in tables:
+        if t.records:
+            return True
+    return False
+
+def migrate_window(query_api, write_api, measurement: str,
+                   start: datetime, stop: datetime,
+                   type_map: dict[str, str],
+                   do_type_cast: bool) -> int:
+    flux = f'''
+from(bucket: "{SOURCE_BUCKET}")
+  |> range(start: time(v: "{start.isoformat()}"), stop: time(v: "{stop.isoformat()}"))
+  |> filter(fn: (r) => r._measurement == "{measurement}")
+  |> keep(columns: ["_time","_measurement","_field","_value"])
+'''
+    tables = query_api.query(flux, org=INFLUX_ORG)
+
+    batch, written = [], 0
+    for table in tables:
+        for rec in table.records:
+            t = rec.get_time()
+            field = normalize(rec.get_field())
+            value = rec.get_value()
+            if value is None:
+                continue
+
+            if do_type_cast:
+                dtp = type_map.get(field)
+                if dtp:
+                    cv = coerce_value_to_dtype(value, dtp)
+                    if cv is None:
+                        continue
+                    if isinstance(cv, (int, float)) and is_invalid_sentinel(float(cv)):
+                        continue
+                    value = cv
+                # kein Mapping -> unverändert schreiben
+
+            batch.append(Point(measurement).field(field, value).time(t, WritePrecision.NS))
+
+            if len(batch) >= BATCH_SIZE:
+                write_with_retry(write_api, batch)
+                written += len(batch)
+                batch = []
+
+    if batch:
+        write_with_retry(write_api, batch)
+        written += len(batch)
+
+    return written
+
+
+# -----------------------
+# Main
+# -----------------------
+def main():
+    if not INFLUX_TOKEN:
+        raise RuntimeError("INFLUX_TOKEN fehlt (Env-Var INFLUX_TOKEN setzen).")
+
+    with InfluxDBClient(url=INFLUX_URL, token=INFLUX_TOKEN, org=INFLUX_ORG, timeout=900_000) as client:
+        ensure_bucket(client, TARGET_BUCKET)
+
+        type_map = build_field_type_map_from_excel(EXCEL_PATH)
+        query_api = client.query_api()
+        write_api = client.write_api(write_options=SYNCHRONOUS)
+
+        for meas in MEASUREMENTS:
+            do_cast = meas in ("hp_master", "hp_slave")
+            cur, total = START_DT, 0
+            print(f"\n== {meas}  (cast={'ON' if do_cast else 'OFF'}) ==")
+
+            while cur < STOP_DT:
+                nxt = min(cur + WINDOW, STOP_DT)
+
+                if window_already_migrated(query_api, meas, cur, nxt):
+                    print(f"{cur.isoformat()} -> {nxt.isoformat()} : SKIP (existiert schon)")
+                    cur = nxt
+                    continue
+
+                n = migrate_window(query_api, write_api, meas, cur, nxt, type_map, do_cast)
+                total += n
+                print(f"{cur.isoformat()} -> {nxt.isoformat()} : {n} (gesamt {total})")
+                cur = nxt
+
+            print(f"== Fertig {meas}: {total} Punkte ==")
+
+
+if __name__ == "__main__":
+    main()

forecaster/load_forecaster.py

@@ -1,349 +0,0 @@
-# load_forecaster.py
-# -*- coding: utf-8 -*-
-"""
-LoadForecaster: builds a 36-hour forecast at 15-min resolution from InfluxDB data.
-
-- Data source: InfluxDB (Flux query provided by user)
-- Target: House load = M_AC_real - I_AC_real
-- Frequency: 15 minutes (changeable via init)
-- Model: Keras (LSTM by default, pluggable)
-- Persistence: Saves model (H5) and scaler (joblib)
-
-Usage (example):
-
-    from load_forecaster import LoadForecaster
-    import tensorflow as tf
-
-    lf = LoadForecaster(
-        url="http://localhost:8086",
-        token="<YOUR_TOKEN>",
-        org="<YOUR_ORG>",
-        bucket="allmende_db",
-        agg_every="15m",
-        input_hours=72,
-        output_hours=36,
-        model_path="model/load_forecaster.h5",
-        scaler_path="model/scaler.joblib",
-    )
-
-    # Train or retrain
-    lf.train_and_save(train_days=90, epochs=60)
-
-    # Load model and forecast
-    model = lf.load_model()
-    forecast_df = lf.get_15min_forecast(model)
-    print(forecast_df.head())
-
-"""
-from __future__ import annotations
-import os
-import math
-import json
-import warnings
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import numpy as np
-import pandas as pd
-from influxdb_client import InfluxDBClient
-from influxdb_client.client.warnings import MissingPivotFunction
-from sklearn.preprocessing import StandardScaler
-from sklearn.exceptions import NotFittedError
-import joblib
-
-# TensorFlow / Keras
-import tensorflow as tf
-from tensorflow.keras.models import Sequential, load_model
-from tensorflow.keras.layers import LSTM, Dense, Dropout
-from tensorflow.keras.callbacks import EarlyStopping
-
-warnings.filterwarnings("ignore", category=MissingPivotFunction)
-
-@dataclass
-class InfluxParams:
-    url: str
-    token: str
-    org: str
-    bucket: str = "allmende_db"
-
-class LoadForecaster:
-    def __init__(
-        self,
-        url: str,
-        token: str,
-        org: str,
-        bucket: str = "allmende_db",
-        agg_every: str = "15m",
-        input_hours: int = 72,
-        output_hours: int = 36,
-        model_path: str = "model/load_forecaster.h5",
-        scaler_path: str = "model/scaler.joblib",
-        feature_config: Optional[dict] = None,
-    ) -> None:
-        self.influx = InfluxParams(url=url, token=token, org=org, bucket=bucket)
-        self.agg_every = agg_every
-        self.input_steps = int((input_hours * 60) / self._freq_minutes(agg_every))
-        self.output_steps = int((output_hours * 60) / self._freq_minutes(agg_every))
-        self.model_path = model_path
-        self.scaler_path = scaler_path
-        self.feature_config = feature_config or {"use_temp": True, "use_time_cyc": True}
-        self._scaler: Optional[StandardScaler] = None
-
-        # Ensure model dir exists
-        os.makedirs(os.path.dirname(model_path), exist_ok=True)
-
-    # ---------------------------- Public API ---------------------------- #
-    def get_15min_forecast(self, model: tf.keras.Model) -> pd.DataFrame:
-        """Create a 36-hour forecast at 15-min resolution using the latest data.
-        Assumes a StandardScaler has been fitted during training and saved.
-        The method uses the most recent input window from InfluxDB.
-        """
-        # Pull just enough history for one input window
-        history_hours = math.ceil(self.input_steps * self._freq_minutes(self.agg_every) / 60)
-        df = self._query_and_prepare(range_hours=history_hours)
-        if len(df) < self.input_steps:
-            raise RuntimeError(f"Not enough data: need {self.input_steps} steps, got {len(df)}")
-
-        # Build features for the latest window
-        feats = self._build_features(df)
-        X_window = feats[-self.input_steps :]
-
-        # Load scaler
-        scaler = self._load_or_get_scaler()
-        X_scaled = scaler.transform(X_window)
-
-        # Predict
-        pred_scaled = model.predict(X_scaled[np.newaxis, ...], verbose=0)[0]
-
-        # Inverse transform only the target column (index 0 is Load)
-        # Reconstruct a full array to inverse_transform
-        inv = np.zeros((self.output_steps, X_scaled.shape[1]))
-        inv[:, 0] = pred_scaled
-        inv_full = scaler.inverse_transform(inv)
-        y_pred = inv_full[:, 0]
-
-        # Build forecast index
-        last_ts = df.index[-1]
-        freq = pd.tseries.frequencies.to_offset(self.agg_every)
-        idx = pd.date_range(last_ts + freq, periods=self.output_steps, freq=freq)
-        out = pd.DataFrame({"Forecast_Load": y_pred}, index=idx)
-        out.index.name = "timestamp"
-        return out
-
-    def train_and_save(
-        self,
-        train_days: int = 90,
-        epochs: int = 80,
-        batch_size: int = 128,
-        validation_split: float = 0.2,
-        learning_rate: float = 1e-3,
-        fine_tune: bool = False,
-    ) -> tf.keras.Model:
-        """Train (or fine-tune) a model from recent history and persist model + scaler."""
-        df = self._query_and_prepare(range_hours=24 * train_days)
-        feats = self._build_features(df)
-
-        # Prepare windows
-        X, y = self._make_windows(feats)
-        if len(X) < 10:
-            raise RuntimeError("Not enough windowed samples to train.")
-
-        # Fit scaler on full X
-        scaler = StandardScaler()
-        X_scaled = scaler.fit_transform(X)
-        self._scaler = scaler
-        joblib.dump(scaler, self.scaler_path)
-
-        # Build model (or load existing for fine-tune)
-        if fine_tune and os.path.exists(self.model_path):
-            model = load_model(self.model_path)
-        else:
-            model = self._build_default_model(input_dim=X.shape[1], output_dim=self.output_steps, lr=learning_rate)
-
-        # Train
-        es = EarlyStopping(monitor="val_loss", patience=10, restore_best_weights=True)
-        model.fit(
-            X_scaled.reshape((-1, self.input_steps, X.shape[1] // self.input_steps)),
-            y,
-            epochs=epochs,
-            batch_size=batch_size,
-            validation_split=validation_split,
-            callbacks=[es],
-            verbose=1,
-        )
-
-        model.save(self.model_path)
-        return model
-
-    # A convenience wrapper to be called from an external script once per day
-    def retrain_daily(self, train_days: int = 90, epochs: int = 40, fine_tune: bool = True) -> None:
-        self.train_and_save(train_days=train_days, epochs=epochs, fine_tune=fine_tune)
-
-    def load_model(self) -> tf.keras.Model:
-        if not os.path.exists(self.model_path):
-            raise FileNotFoundError(f"Model not found at {self.model_path}")
-        return load_model(self.model_path)
-
-    # ------------------------- Internals: Data ------------------------- #
-    def _query_and_prepare(self, range_hours: int) -> pd.DataFrame:
-        """Query InfluxDB for the last `range_hours` and construct the Load series.
-        Expected fields (exactly as in DB):
-          - "40206 - M_AC_Power"
-          - "40210 - M_AC_Power_SF"
-          - "40083 - I_AC_Power"
-          - "40084 - I_AC_Power_SF"
-          - "300 - Aussentemperatur"
-        """
-        start_str = f"-{range_hours}h"
-        flux = f'''
-from(bucket: "{self.influx.bucket}")
-  |> range(start: {start_str})
-  |> filter(fn: (r) => r["_measurement"] == "solaredge_meter" or r["_measurement"] == "solaredge_master" or r["_measurement"] == "hp_master")
-  |> filter(fn: (r) => r["_field"] == "40206 - M_AC_Power" or r["_field"] == "40210 - M_AC_Power_SF" or r["_field"] == "40083 - I_AC_Power" or r["_field"] == "40084 - I_AC_Power_SF" or r["_field"] == "300 - Aussentemperatur")
-  |> aggregateWindow(every: {self.agg_every}, fn: mean, createEmpty: false)
-  |> yield(name: "mean")
-'''
-        with InfluxDBClient(url=self.influx.url, token=self.influx.token, org=self.influx.org) as client:
-            tables = client.query_api().query_data_frame(flux)
-
-        # Concatenate if list of frames is returned
-        if isinstance(tables, list):
-            df = pd.concat(tables, ignore_index=True)
-        else:
-            df = tables
-
-        # Keep relevant columns and pivot
-        df = df[["_time", "_field", "_value"]]
-        df = df.pivot(index="_time", columns="_field", values="_value").reset_index()
-        df = df.rename(
-            columns={
-                "_time": "timestamp",
-                "40206 - M_AC_Power": "M_AC",
-                "40210 - M_AC_Power_SF": "M_SF",
-                "40083 - I_AC_Power": "I_AC",
-                "40084 - I_AC_Power_SF": "I_SF",
-                "300 - Aussentemperatur": "Temp",
-            }
-        )
-        df = df.sort_values("timestamp").set_index("timestamp")
-
-        # Forward-fill reasonable gaps (e.g., scaler factors and temp)
-        df[["M_SF", "I_SF", "Temp"]] = df[["M_SF", "I_SF", "Temp"]].ffill()
-
-        # Apply scaling: real = value * 10^sf
-        df["I_AC_real"] = df["I_AC"] * np.power(10.0, df["I_SF"]).astype(float)
-        df["M_AC_real"] = df["M_AC"] * np.power(10.0, df["M_SF"]).astype(float)
-
-        # Compute load
-        df["Load"] = df["M_AC_real"] - df["I_AC_real"]
-
-        # Ensure regular 15-min grid
-        df = df.asfreq(self.agg_every)
-        df[["Load", "Temp"]] = df[["Load", "Temp"]].interpolate(limit_direction="both")
-
-        return df[["Load", "Temp"]]
-
-    def _build_features(self, df: pd.DataFrame) -> np.ndarray:
-        """Create feature matrix: [Load, Temp?, sin/cos day, sin/cos dow]."""
-        feats = [df["Load"].values.reshape(-1, 1)]
-
-        if self.feature_config.get("use_temp", True):
-            feats.append(df["Temp"].values.reshape(-1, 1))
-
-        if self.feature_config.get("use_time_cyc", True):
-            idx = df.index
-            minute_of_day = (idx.hour * 60 + idx.minute).values.astype(float)
-            sod = 2 * np.pi * minute_of_day / (24 * 60)
-            dow = 2 * np.pi * idx.dayofweek.values.astype(float) / 7.0
-            feats.append(np.sin(sod).reshape(-1, 1))
-            feats.append(np.cos(sod).reshape(-1, 1))
-            feats.append(np.sin(dow).reshape(-1, 1))
-            feats.append(np.cos(dow).reshape(-1, 1))
-
-        X = np.hstack(feats)  # shape: (T, n_features)
-
-        # Flatten windows to 2D for scaler fitting, but model expects 3D; we reshape later
-        return X
-
-    def _make_windows(self, X_2d: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
-        """Create sliding windows: returns (X_flat, y) where X_flat stacks the windowed features.
-        For Keras we later reshape X_flat -> (N, input_steps, n_features).
-        """
-        n = X_2d.shape[0]
-        n_features = X_2d.shape[1]
-        X_list, y_list = [], []
-        for i in range(n - self.input_steps - self.output_steps):
-            xw = X_2d[i : i + self.input_steps, :]
-            yw = X_2d[i + self.input_steps : i + self.input_steps + self.output_steps, 0]  # target: Load
-            X_list.append(xw.reshape(-1))  # flatten
-            y_list.append(yw)
-        X_flat = np.stack(X_list)
-        y = np.stack(y_list)
-        return X_flat, y
-
-    # ----------------------- Internals: Modeling ----------------------- #
-    def _build_default_model(self, input_dim: int, output_dim: int, lr: float = 1e-3) -> tf.keras.Model:
-        n_features = input_dim // self.input_steps
-        model = Sequential([
-            LSTM(96, input_shape=(self.input_steps, n_features), return_sequences=False),
-            Dropout(0.1),
-            Dense(output_dim)
-        ])
-        model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr), loss="mse")
-        return model
-
-    def _load_or_get_scaler(self) -> StandardScaler:
-        if self._scaler is not None:
-            return self._scaler
-        if not os.path.exists(self.scaler_path):
-            raise NotFittedError("Scaler not found. Train the model first to create scaler.")
-        self._scaler = joblib.load(self.scaler_path)
-        return self._scaler
-
-    @staticmethod
-    def _freq_minutes(spec: str) -> int:
-        # supports formats like "15m", "1h"
-        if spec.endswith("m"):
-            return int(spec[:-1])
-        if spec.endswith("h"):
-            return int(spec[:-1]) * 60
-        raise ValueError(f"Unsupported frequency spec: {spec}")
-
-
-# ----------------------------- retrain_daily.py -----------------------------
-# A tiny script you can run once per day (e.g., via cron/systemd) to retrain the model.
-# It delegates the work to LoadForecaster.retrain_daily().
-
-if __name__ == "__main__":
-    # Read credentials/config from env vars or fill here
-    URL = os.getenv("INFLUX_URL", "http://localhost:8086")
-    TOKEN = os.getenv("INFLUX_TOKEN", "<YOUR_TOKEN>")
-    ORG = os.getenv("INFLUX_ORG", "<YOUR_ORG>")
-    BUCKET = os.getenv("INFLUX_BUCKET", "allmende_db")
-
-    lf = LoadForecaster(
-        url=URL,
-        token=TOKEN,
-        org=ORG,
-        bucket=BUCKET,
-        agg_every="15m",
-        input_hours=72,
-        output_hours=36,
-        model_path=os.getenv("FORECASTER_MODEL", "model/load_forecaster.h5"),
-        scaler_path=os.getenv("FORECASTER_SCALER", "model/scaler.joblib"),
-    )
-
-    # One call per day is enough; decrease epochs for faster daily updates
-    lf.retrain_daily(train_days=int(os.getenv("TRAIN_DAYS", "120")), epochs=int(os.getenv("EPOCHS", "30")), fine_tune=True)
-
-    # Optionally, produce a fresh forecast right after training
-    try:
-        model = lf.load_model()
-        fc = lf.get_15min_forecast(model)
-        # Save latest forecast to CSV for dashboards/consumers
-        out_path = os.getenv("FORECAST_OUT", "model/latest_forecast_15min.csv")
-        os.makedirs(os.path.dirname(out_path), exist_ok=True)
-        fc.to_csv(out_path)
-        print(f"Saved forecast: {out_path}")
-    except Exception as e:
-        print(f"Forecast generation failed: {e}")

heat_pump.py

@@ -1,64 +1,173 @@
 from pymodbus.client import ModbusTcpClient
 import pandas as pd
 import time
+import struct
+import math
+
 
 class HeatPump:
-    def __init__(self, device_name: str, ip_address: str, port: int=502):
+    def __init__(self, device_name: str, ip_address: str, port: int = 502,
+                 excel_path: str = "modbus_registers/heat_pump_registers.xlsx",
+                 sheet_name: str = "Register_Map"):
         self.device_name = device_name
         self.ip = ip_address
         self.port = port
-        self.client = None
+        self.client = ModbusTcpClient(self.ip, port=self.port)
-        self.connect_to_modbus()
-        self.registers = None
-        self.get_registers()
 
-    def connect_to_modbus(self):
+        self.excel_path = excel_path
-        port = self.port
+        self.sheet_name = sheet_name
-        self.client = ModbusTcpClient(self.ip, port=port)
+        self.registers = self.get_registers()
+
+    # -------------
+    # Connection
+    # -------------
+    def connect(self) -> bool:
+        ok = self.client.connect()
+        if not ok:
+            print("Verbindung zur Wärmepumpe fehlgeschlagen.")
+        return ok
+
+    def close(self):
         try:
-            if not self.client.connect():
-                print("Verbindung zur Wärmepumpe fehlgeschlagen.")
-                exit(1)
-            print("Verbindung zur Wärmepumpe erfolgreich.")
-        except KeyboardInterrupt:
-            print("Beendet durch Benutzer (Ctrl+C).")
-        finally:
             self.client.close()
+        except Exception:
+            pass
 
-    def get_registers(self):
+    # -------------
-        # Excel-Datei mit den Input-Registerinformationen
+    # Excel parsing
-        excel_path = "modbus_registers/heat_pump_registers.xlsx"
+    # -------------
-        xls = pd.ExcelFile(excel_path)
+    def get_registers(self) -> dict:
-        df_input_registers = xls.parse('04 Input Register')
+        df = pd.read_excel(self.excel_path, sheet_name=self.sheet_name)
+        df = df[df["Register_Type"].astype(str).str.upper() == "IR"].copy()
 
-        # Relevante Spalten bereinigen
+        df["Address"] = df["Address"].astype(int)
-        df_clean = df_input_registers[['MB Adresse', 'Variable', 'Beschreibung', 'Variabel Typ']].dropna()
+        df["Length"] = df["Length"].astype(int)
-        df_clean['MB Adresse'] = df_clean['MB Adresse'].astype(int)
+        df["Data_Type"] = df["Data_Type"].astype(str).str.upper()
+        df["Byteorder"] = df["Byteorder"].astype(str).str.upper()
 
-        # Dictionary aus Excel erzeugen
+        df["Scaling"] = df.get("Scaling", 1.0)
-        self.registers = {
+        df["Scaling"] = df["Scaling"].fillna(1.0).astype(float)
-            row['MB Adresse']: {
+
-                'desc': row['Beschreibung'],
+        df["Offset"] = df.get("Offset", 0.0)
-                'type': 'REAL' if row['Variabel Typ'] == 'REAL' else 'INT'
+        df["Offset"] = df["Offset"].fillna(0.0).astype(float)
+
+        regs = {}
+        for _, row in df.iterrows():
+            regs[int(row["Address"])] = {
+                "length": int(row["Length"]),
+                "data_type": row["Data_Type"],
+                "byteorder": row["Byteorder"],
+                "scaling": float(row["Scaling"]),
+                "offset": float(row["Offset"]),
+                "tag": str(row.get("Tag_Name", "")).strip(),
+                "desc": "" if pd.isna(row.get("Description")) else str(row.get("Description")).strip(),
             }
-            for _, row in df_clean.iterrows()
+        return regs
-        }
 
-    def get_state(self):
+    # -------------
-        data = {}
+    # Byteorder handling
-        data['Zeit'] = time.strftime('%Y-%m-%d %H:%M:%S')
+    # -------------
-        for address, info in self.registers.items():
+    @staticmethod
-            reg_type = info['type']
+    def _registers_to_bytes(registers: list[int], byteorder_code: str) -> bytes:
-            result = self.client.read_input_registers(address, count=2 if reg_type == 'REAL' else 1)
+        """
-            if result.isError():
+        registers: Liste von uint16 (0..65535), wie pymodbus sie liefert.
-                print(f"Fehler beim Lesen von Adresse {address}: {result}")
+        byteorder_code: AB, ABCD, CDAB, BADC, DCBA (gemäß Template)
-                continue
+        Rückgabe: bytes in der Reihenfolge, wie sie für struct.unpack benötigt werden.
+        """
+        code = (byteorder_code or "ABCD").upper()
 
-            if reg_type == 'REAL':
+        # Pro Register: 16-bit => zwei Bytes (MSB, LSB)
-                value = result.registers[0] / 10.0
+        words = [struct.pack(">H", r & 0xFFFF) for r in registers]  # big endian pro Wort
-            else:
+
-                value = result.registers[0]
+        if len(words) == 1:
+            w = words[0]  # b'\xAA\xBB'
+            if code in ("AB", "ABCD", "CDAB"):
+                return w
+            if code == "BADC":  # byte swap
+                return w[::-1]
+            if code == "DCBA":  # byte swap (bei 16-bit identisch zu BADC)
+                return w[::-1]
+            return w
+
+        # 32-bit (2 words) oder 64-bit (4 words): Word/Byte swaps abbilden
+        # words[0] = high word bytes, words[1] = low word bytes (in Modbus-Reihenfolge gelesen)
+        if code == "ABCD":
+            ordered = words
+        elif code == "CDAB":
+            # word swap
+            ordered = words[1:] + words[:1]
+        elif code == "BADC":
+            # byte swap innerhalb jedes Words
+            ordered = [w[::-1] for w in words]
+        elif code == "DCBA":
+            # word + byte swap
+            ordered = [w[::-1] for w in (words[1:] + words[:1])]
+        else:
+            ordered = words
+
+        return b"".join(ordered)
+
+    @staticmethod
+    def _decode_by_type(raw_bytes: bytes, data_type: str):
+        dt = (data_type or "").upper()
+
+        # struct: > = big endian, < = little endian
+        # Wir liefern raw_bytes bereits in der richtigen Reihenfolge; daher nutzen wir ">" konsistent.
+        if dt == "UINT16":
+            return struct.unpack(">H", raw_bytes[:2])[0]
+        if dt == "INT16":
+            return struct.unpack(">h", raw_bytes[:2])[0]
+        if dt == "UINT32":
+            return struct.unpack(">I", raw_bytes[:4])[0]
+        if dt == "INT32":
+            return struct.unpack(">i", raw_bytes[:4])[0]
+        if dt == "FLOAT32":
+            return struct.unpack(">f", raw_bytes[:4])[0]
+        if dt == "FLOAT64":
+            return struct.unpack(">d", raw_bytes[:8])[0]
+
+        raise ValueError(f"Unbekannter Data_Type: {dt}")
+
+    def _decode_value(self, registers: list[int], meta: dict):
+        raw = self._registers_to_bytes(registers, meta["byteorder"])
+        val = self._decode_by_type(raw, meta["data_type"])
+        return (val * meta["scaling"]) + meta["offset"]
+
+    # -------------
+    # Reading
+    # -------------
+    def get_state(self) -> dict:
+        data = {"Zeit": time.strftime("%Y-%m-%d %H:%M:%S")}
+
+        if not self.connect():
+            data["error"] = "connect_failed"
+            return data
+
+        try:
+            for address, meta in self.registers.items():
+                count = int(meta["length"])
+                result = self.client.read_input_registers(address, count=count)
+                if result.isError():
+                    print(f"Fehler beim Lesen von Adresse {address}: {result}")
+                    continue
+
+                try:
+                    value = self._decode_value(result.registers, meta)
+                except Exception as e:
+                    print(f"Decode-Fehler an Adresse {address} ({meta.get('tag','')}): {e}")
+                    continue
+
+                # Optional filter
+                # if self._is_invalid_sentinel(value):
+                #     continue
+                value = float(value)
+                desc = meta.get("desc") or ""
+                field_name = f"{address} - {desc}".strip(" -")
+                data[field_name] = float(value)
+
+                print(f"Adresse {address} - {desc}: {value}")
+
+        finally:
+            self.close()
 
-            print(f"Adresse {address} - {info['desc']}: {value}")
-            data[f"{address} - {info['desc']}"] = value
         return data

main.py

@@ -23,7 +23,7 @@ db = DataBaseInflux(
     url="http://192.168.1.146:8086",
     token="Cw_naEZyvJ3isiAh1P4Eq3TsjcHmzzDFS7SlbKDsS6ZWL04fMEYixWqtNxGThDdG27S9aW5g7FP9eiq5z1rsGA==",
     org="allmende",
-    bucket="allmende_db"
+    bucket="allmende_db_v3"
 )
 
 hp_master = HeatPump(device_name='hp_master', ip_address='10.0.0.10', port=502)