minor changes

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()

heat_pump.py

@@ -7,7 +7,7 @@ import math
 
 class HeatPump:
     def __init__(self, device_name: str, ip_address: str, port: int = 502,
-                 excel_path: str = "modbus_registers/heat_pump_registers_modbus.xlsx",
+                 excel_path: str = "modbus_registers/heat_pump_registers.xlsx",
                  sheet_name: str = "Register_Map"):
         self.device_name = device_name
         self.ip = ip_address
@@ -160,14 +160,10 @@ class HeatPump:
                 # Optional filter
                 # if self._is_invalid_sentinel(value):
                 #     continue
-
+                value = float(value)
                 desc = meta.get("desc") or ""
-                label = f"{address} - {desc}".strip(" -")
+                field_name = f"{address} - {desc}".strip(" -")
-
+                data[field_name] = float(value)
-                data[label] = value
-                tag = meta.get("tag")
-                if tag:
-                    data[tag] = value
 
                 print(f"Adresse {address} - {desc}: {value}")
 

main.py

@@ -1,19 +1,12 @@
 import time
 from datetime import datetime
 from data_base_influx import DataBaseInflux
-from forecaster.weather_forecaster import WeatherForecaster
 from heat_pump import HeatPump
 from pv_inverter import PvInverter
-from simulators.pv_plant_simulator import PvWattsSubarrayConfig, PvWattsPlant
 from solaredge_meter import SolaredgeMeter
 from shelly_pro_3m import ShellyPro3m
 from energysystem import EnergySystem
 from sg_ready_controller import SgReadyController
-from pvlib.location import Location
-import datetime as dt
-
-# For dev-System run in terminal: ssh -N -L 127.0.0.1:8111:10.0.0.10:502 pi@192.168.1.146
-# For productive-System change IP-adress in heatpump to '10.0.0.10' and port to 502
 
 interval_seconds = 10
 
@@ -23,7 +16,7 @@ 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)
@@ -35,24 +28,7 @@ meter = SolaredgeMeter(device_name='solaredge_meter', ip_address='192.168.1.112'
 es.add_components(hp_master, hp_slave, shelly, wr, meter)
 controller = SgReadyController(es)
 
-# FORECASTING
-latitude = 48.041
-longitude = 7.862
-TZ = "Europe/Berlin"
-HORIZON_DAYS = 2
-weather_forecaster = WeatherForecaster(latitude=latitude, longitude=longitude)
-site = Location(latitude=latitude, longitude=longitude, altitude=35, tz=TZ, name="Gundelfingen")
-
-p_module = 435
-upper_roof_north = PvWattsSubarrayConfig(name="north", pdc0_w=(29+29+21)*p_module, tilt_deg=10, azimuth_deg=20, dc_loss=0.02, ac_loss=0.01)
-upper_roof_south = PvWattsSubarrayConfig(name="south", pdc0_w=(29+21+20)*p_module, tilt_deg=10, azimuth_deg=200, dc_loss=0.02, ac_loss=0.01)
-upper_roof_east = PvWattsSubarrayConfig(name="east", pdc0_w=7*p_module, tilt_deg=10, azimuth_deg=110, dc_loss=0.02, ac_loss=0.01)
-upper_roof_west = PvWattsSubarrayConfig(name="west", pdc0_w=7*p_module, tilt_deg=10, azimuth_deg=290, dc_loss=0.02, ac_loss=0.01)
-cfgs = [upper_roof_north, upper_roof_south, upper_roof_east, upper_roof_west]
-pv_plant = PvWattsPlant(site, cfgs)
-
 now = datetime.now()
-next_forecast_at = (now + dt.timedelta(hours=1)).replace(minute=0, second=0, microsecond=0)
 while True:
     now = datetime.now()
     if now.second % interval_seconds == 0 and now.microsecond < 100_000:
@@ -65,18 +41,5 @@ while True:
             mode_as_binary = 1
         db.store_data('sg_ready', {'mode': mode_as_binary})
 
-    if now >= next_forecast_at:
-        # Start der Prognose: ab der kommenden vollen Stunde
-        start_hour_local = (now + dt.timedelta(hours=1)).replace(minute=0, second=0, microsecond=0)
-        weather = weather_forecaster.get_hourly_forecast(start_hour_local, HORIZON_DAYS)
-        total = pv_plant.get_power(weather)
-        db.store_forecasts('pv_forecast', total)
-
-        # Nächste geplante Ausführung definieren (immer volle Stunde)
-        # Falls wir durch Delay mehrere Stunden verpasst haben, hole auf:
-        while next_forecast_at <= now:
-            next_forecast_at = (next_forecast_at + dt.timedelta(hours=1)).replace(minute=0, second=0, microsecond=0)
-
-
     time.sleep(0.1)