Remove cache and other non-code files and add .gitignore

.gitignore

@@ -0,0 +1,178 @@
+# Allmende EMS specifics
+terminal_log
+modbus_log.csv
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc

.idea/.gitignore

@@ -1,3 +0,0 @@
-# Default ignored files
-/shelf/
-/workspace.xml

.idea/inspectionProfiles/Project_Default.xml

@@ -1,15 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <profile version="1.0">
-    <option name="myName" value="Project Default" />
-    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
-      <option name="ignoredPackages">
-        <value>
-          <list size="2">
-            <item index="0" class="java.lang.String" itemvalue="pandas" />
-            <item index="1" class="java.lang.String" itemvalue="Pyomo" />
-          </list>
-        </value>
-      </option>
-    </inspection_tool>
-  </profile>
-</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -1,6 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="USE_PROJECT_PROFILE" value="false" />
-    <version value="1.0" />
-  </settings>
-</component>

.idea/misc.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="Black">
-    <option name="sdkName" value="Python 3.12 (waermepumpen_logger)" />
-  </component>
-</project>

.idea/modules.xml

@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/waermepumpen_logger.iml" filepath="$PROJECT_DIR$/.idea/waermepumpen_logger.iml" />
-    </modules>
-  </component>
-</project>

.idea/vcs.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$" vcs="Git" />
-  </component>
-</project>

.idea/waermepumpen_logger.iml

@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 3.12 (waermepumpen_logger)" jdkType="Python SDK" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-</module>

README

@@ -11,42 +11,10 @@ Was needs to be done on the Raspberry pi before the tool can run.
     - pip install -r requirements.txt
 
 
-3) How to run the script for testing:
+How to run the script:
 
-    nohup python main.py > terminal_log 2>&1 &
+- nohup python main.py > terminal_log 2>&1 &
 
 For reading out the terminal_log while script is runing:
 
-    tail -f terminal_log
+- tail -f terminal_log
-
-
-4) Implement and run the ems as systemd service:
-create:
-    /etc/systemd/system/allmende_ems.service
-
-insert:
-    [Unit]
-    Description=Allmende EMS Python Script
-    After=network.target
-
-    [Service]
-    WorkingDirectory=/home/pi/projects/allmende_ems
-    ExecStart=/home/pi/allmende_ems/bin/python3.11 /home/pi/projects/allmende_ems/main.py
-    Restart=always
-    RestartSec=5
-    StandardOutput=journal
-    StandardError=journal
-
-    [Install]
-    WantedBy=multi-user.target
-
-manage the service with the following commands:
-Once:
-    sudo systemctl daemon-reload
-    sudo systemctl start allmende_ems.service
-    sudo systemctl enable allmende_ems.service
-While running:
-    sudo systemctl status allmende_ems.service
-    sudo systemctl restart allmende_ems.service
-    sudo systemctl stop allmende_ems.service
-    journalctl -u allmende_ems.service

data_base_csv.py

@@ -0,0 +1,46 @@
+import csv
+import os
+import tempfile
+import shutil
+
+class DataBaseCsv:
+    def __init__(self, filename: str):
+        self.filename = filename
+
+    def store_data(self, data: dict):
+        new_fields = list(data.keys())
+
+        # If file does not exist or is empty → create new file with header
+        if not os.path.exists(self.filename) or os.path.getsize(self.filename) == 0:
+            with open(self.filename, mode='w', newline='') as csv_file:
+                writer = csv.DictWriter(csv_file, fieldnames=new_fields)
+                writer.writeheader()
+                writer.writerow(data)
+            return
+
+        # If file exists → read existing header and data
+        with open(self.filename, mode='r', newline='') as csv_file:
+            reader = csv.DictReader(csv_file)
+            existing_fields = reader.fieldnames
+            existing_data = list(reader)
+
+        # Merge old and new fields (keep original order, add new ones)
+        all_fields = existing_fields.copy()
+        for field in new_fields:
+            if field not in all_fields:
+                all_fields.append(field)
+
+        # Write to a temporary file with updated header
+        with tempfile.NamedTemporaryFile(mode='w', delete=False, newline='', encoding='utf-8') as tmp_file:
+            writer = csv.DictWriter(tmp_file, fieldnames=all_fields)
+            writer.writeheader()
+
+            # Write old rows with updated field list
+            for row in existing_data:
+                writer.writerow({field: row.get(field, '') for field in all_fields})
+
+            # Write new data row
+            writer.writerow({field: data.get(field, '') for field in all_fields})
+
+        # Replace original file with updated temporary file
+        shutil.move(tmp_file.name, self.filename)

data_base_influx.py

@@ -1,48 +0,0 @@
-from influxdb_client import InfluxDBClient, Point, WritePrecision
-from datetime import datetime
-import datetime as dt
-import pandas as pd
-
-class DataBaseInflux:
-    def __init__(self, url: str, token: str, org: str, bucket: str):
-        self.url = url
-        self.token = token
-        self.org = org
-        self.bucket = bucket
-        self.client = InfluxDBClient(url=self.url, token=self.token, org=self.org)
-        self.write_api = self.client.write_api()
-
-    def store_data(self, device_name: str, data: dict):
-        measurement = device_name # Fest auf "messungen" gesetzt
-
-        point = Point(measurement)
-
-        # Alle Key/Value-Paare als Fields speichern
-        for key, value in data.items():
-            point = point.field(key, value)
-
-        # Zeitstempel automatisch auf jetzt setzen
-        point = point.time(datetime.utcnow(), WritePrecision.NS)
-
-        # Punkt in InfluxDB schreiben
-        self.write_api.write(bucket=self.bucket, org=self.org, record=point)
-
-    def store_forecasts(self, forecast_name: str, data: pd.Series):
-
-        measurement = forecast_name
-        run_tag = dt.datetime.now(dt.timezone.utc).replace(second=0, microsecond=0).isoformat(timespec="minutes")
-
-        pts = []
-
-        series = pd.to_numeric(data, errors="coerce").dropna()
-
-        for ts, val in series.items():
-            pts.append(
-                Point(measurement)
-                .tag("run", run_tag)
-                .field("value", float(val))
-                .time(ts.to_pydatetime(), WritePrecision.S)
-            )
-
-        self.write_api.write(bucket=self.bucket, org=self.org, record=pts)
-

energysystem.py

@@ -1,25 +0,0 @@
-
-
-
-class EnergySystem():
-    def __init__(self):
-        self.components = []
-
-    def add_components(self, *args):
-        for comp in args:
-            self.components.append(comp)
-
-    def get_state_and_store_to_database(self, db):
-        state = {}
-        for comp in self.components:
-            component_state = comp.get_state()
-            state[comp.device_name] = component_state
-            db.store_data(comp.device_name, component_state)
-
-        return state
-
-    def get_component_by_name(self, name):
-        for comp in self.components:
-            if comp.device_name == name:
-                return comp
-

forecaster/weather_forecaster.py

@@ -1,61 +0,0 @@
-#!/usr/bin/env python3
-import time
-import datetime as dt
-import requests
-from zoneinfo import ZoneInfo
-from matplotlib import pyplot as plt
-import pandas as pd
-
-TZ = "Europe/Berlin"
-DAYS = 2
-
-OPEN_METEO_URL = "https://api.open-meteo.com/v1/forecast"
-
-class WeatherForecaster:
-    def __init__(self, latitude, longitude):
-        self.lat = latitude
-        self.lon = longitude
-
-    def get_hourly_forecast(self, start_hour, days):
-        start_hour_local = start_hour
-        end_hour_local   = start_hour_local + dt.timedelta(days=days)
-
-        params = {
-            "latitude": self.lat,
-            "longitude": self.lon,
-            "hourly": ["temperature_2m", "shortwave_radiation", "wind_speed_10m"],
-            "timezone": TZ,
-            "start_hour": start_hour_local.strftime("%Y-%m-%dT%H:%M"),
-            "end_hour": end_hour_local.strftime("%Y-%m-%dT%H:%M")
-        }
-
-        h = requests.get(OPEN_METEO_URL, params=params).json()["hourly"]
-
-        time_stamps = h["time"]
-        time_stamps = [
-            dt.datetime.fromisoformat(t).replace(tzinfo=ZoneInfo(TZ))
-            for t in time_stamps
-        ]
-
-        weather = pd.DataFrame(index=time_stamps)
-        weather["ghi"] = h["shortwave_radiation"]
-        weather["temp_air"] = h["temperature_2m"]
-        weather["wind_speed"] = h["wind_speed_10m"]
-
-        return weather
-
-
-
-if __name__=='__main__':
-
-    weather_forecast = WeatherForecaster(latitude=48.041, longitude=7.862)
-    while True:
-        now = dt.datetime.now()
-        secs = 60 - now.second #(60 - now.minute) * 60 - now.second  # Sekunden bis volle Stunde
-        time.sleep(secs)
-
-        now_local = dt.datetime.now()
-        start_hour_local = (now_local + dt.timedelta(hours=1)).replace(minute=0, second=0, microsecond=0)
-        time_stamps, temps, ghi, wind_speed = weather_forecast.get_hourly_forecast(start_hour_local, DAYS)
-        plt.plot(time_stamps, temps)
-        plt.show()

heat_pump.py

@@ -3,17 +3,15 @@ import pandas as pd
 import time
 
 class HeatPump:
-    def __init__(self, device_name: str, ip_address: str, port: int=502):
+    def __init__(self, ip_address: str):
-        self.device_name = device_name
         self.ip = ip_address
-        self.port = port
         self.client = None
         self.connect_to_modbus()
         self.registers = None
         self.get_registers()
 
     def connect_to_modbus(self):
-        port = self.port
+        port = 502
         self.client = ModbusTcpClient(self.ip, port=port)
         try:
             if not self.client.connect():
@@ -27,7 +25,7 @@ class HeatPump:
 
     def get_registers(self):
         # Excel-Datei mit den Input-Registerinformationen
-        excel_path = "modbus_registers/heat_pump_registers.xlsx"
+        excel_path = "data/ModBus TCPIP 1.17(1).xlsx"
         xls = pd.ExcelFile(excel_path)
         df_input_registers = xls.parse('04 Input Register')
 
@@ -44,7 +42,7 @@ class HeatPump:
             for _, row in df_clean.iterrows()
         }
 
-    def get_state(self):
+    def get_data(self):
         data = {}
         data['Zeit'] = time.strftime('%Y-%m-%d %H:%M:%S')
         for address, info in self.registers.items():

main.py

@@ -1,82 +1,17 @@
 import time
 from datetime import datetime
-from data_base_influx import DataBaseInflux
+from data_base_csv import DataBaseCsv
-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
+interval = 10  # z.B. alle 10 Sekunden
-# For productive-System change IP-adress in heatpump to '10.0.0.10' and port to 502
 
-interval_seconds = 10
+db = DataBaseCsv('modbus_log.csv')
+hp = HeatPump(ip_address='10.0.0.10')
 
-es = EnergySystem()
-
-db = DataBaseInflux(
-    url="http://192.168.1.146:8086",
-    token="Cw_naEZyvJ3isiAh1P4Eq3TsjcHmzzDFS7SlbKDsS6ZWL04fMEYixWqtNxGThDdG27S9aW5g7FP9eiq5z1rsGA==",
-    org="allmende",
-    bucket="allmende_db"
-)
-
-hp_master = HeatPump(device_name='hp_master', ip_address='127.0.0.1', port=8111)
-hp_slave = HeatPump(device_name='hp_slave', ip_address='127.0.0.1', port=8111)
-shelly = ShellyPro3m(device_name='wohnung_2_6', ip_address='192.168.1.121')
-wr = PvInverter(device_name='solaredge_master', ip_address='192.168.1.112')
-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:
+    if now.second % interval == 0 and now.microsecond < 100_000:
-        state = es.get_state_and_store_to_database(db)
+       db.store_data(hp.get_data())
-        mode = controller.perform_action(heat_pump_name='hp_master', meter_name='solaredge_meter', state=state)
-
-        if mode == 'mode1':
-            mode_as_binary = 0
-        else:
-            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)
 

pv_inverter.py

@@ -1,139 +0,0 @@
-import time
-import struct
-import pandas as pd
-from typing import Dict, Any, List, Tuple, Optional
-from pymodbus.client import ModbusTcpClient
-
-EXCEL_PATH = "modbus_registers/pv_inverter_registers.xlsx"
-
-# Obergrenze: bis EXKLUSIVE 40206 (d.h. max. 40205)
-MAX_ADDR_EXCLUSIVE = 40121
-
-class PvInverter:
-    def __init__(self, device_name: str, ip_address: str, port: int = 502, unit: int = 1):
-        self.device_name = device_name
-        self.ip = ip_address
-        self.port = port
-        self.unit = unit
-        self.client: Optional[ModbusTcpClient] = None
-        self.registers: Dict[int, Dict[str, Any]] = {}  # addr -> {"desc":..., "type":...}
-        self.connect_to_modbus()
-        self.load_registers(EXCEL_PATH)
-
-    # ---------- Verbindung ----------
-    def connect_to_modbus(self):
-        self.client = ModbusTcpClient(self.ip, port=self.port, timeout=3.0, retries=3)
-        if not self.client.connect():
-            print("❌ Verbindung zu Wechselrichter fehlgeschlagen.")
-            raise SystemExit(1)
-        print("✅ Verbindung zu Wechselrichter hergestellt.")
-
-    def close(self):
-        if self.client:
-            self.client.close()
-            self.client = None
-
-    # ---------- Register-Liste ----------
-    def load_registers(self, excel_path: str):
-        xls = pd.ExcelFile(excel_path)
-        df = xls.parse()
-        # Passe Spaltennamen hier an, falls nötig:
-        cols = ["MB Adresse", "Beschreibung", "Variabel Typ"]
-        df = df[cols].dropna()
-        df["MB Adresse"] = df["MB Adresse"].astype(int)
-
-        # 1) Vorab-Filter: nur Adressen < 40206 übernehmen
-        df = df[df["MB Adresse"] < MAX_ADDR_EXCLUSIVE]
-
-        self.registers = {
-            int(row["MB Adresse"]): {
-                "desc": str(row["Beschreibung"]).strip(),
-                "type": str(row["Variabel Typ"]).strip()
-            }
-            for _, row in df.iterrows()
-        }
-
-
-    # ---------- Low-Level Lesen ----------
-    def _try_read(self, fn_name: str, address: int, count: int) -> Optional[List[int]]:
-        fn = getattr(self.client, fn_name)
-        # pymodbus 3.8.x hat 'slave='; Fallbacks schaden nicht
-        for kwargs in (dict(address=address, count=count, slave=self.unit),
-                       dict(address=address, count=count)):
-            try:
-                res = fn(**kwargs)
-                if res is None or (hasattr(res, "isError") and res.isError()):
-                    continue
-                return res.registers
-            except TypeError:
-                continue
-        return None
-
-    def _read_any(self, address: int, count: int) -> Optional[List[int]]:
-        regs = self._try_read("read_holding_registers", address, count)
-        if regs is None:
-            regs = self._try_read("read_input_registers", address, count)
-        return regs
-
-    # ---------- Decoding ----------
-    @staticmethod
-    def _to_i16(u16: int) -> int:
-        return struct.unpack(">h", struct.pack(">H", u16))[0]
-
-    @staticmethod
-    def _to_f32_from_two(u16_hi: int, u16_lo: int, msw_first: bool = True) -> float:
-        b = struct.pack(">HH", u16_hi, u16_lo) if msw_first else struct.pack(">HH", u16_lo, u16_hi)
-        return struct.unpack(">f", b)[0]
-
-    # Hilfsfunktion: wie viele 16-Bit-Register braucht dieser Typ?
-    @staticmethod
-    def _word_count_for_type(rtype: str) -> int:
-        rt = (rtype or "").lower()
-        # Passe hier an deine Excel-Typen an:
-        if "uint32" in rt or "real" in rt or "float" in rt or "string(32)" in rt:
-            return 2
-        # Default: 1 Wort (z.B. int16/uint16)
-        return 1
-
-    def read_one(self, address_excel: int, rtype: str) -> Optional[float]:
-        """
-        Liest einen Wert nach Typ ('INT' oder 'REAL' etc.).
-        Es werden ausschließlich Register < 40206 gelesen.
-        """
-        addr = int(address_excel)
-        words = self._word_count_for_type(rtype)
-
-        # 2) Harte Grenze prüfen: höchstes angefasstes Register muss < 40206 sein
-        if addr + words - 1 >= MAX_ADDR_EXCLUSIVE:
-            # Überspringen, da der Lesevorgang die Grenze >= 40206 berühren würde
-            return None
-
-        if words == 2:
-            regs = self._read_any(addr, 2)
-            if not regs or len(regs) < 2:
-                return None
-            # Deine bisherige Logik interpretiert 2 Worte als Float32:
-            return self._to_f32_from_two(regs[0], regs[1])
-        else:
-            regs = self._read_any(addr, 1)
-            if not regs:
-                return None
-            return float(self._to_i16(regs[0]))
-
-    def get_state(self) -> Dict[str, Any]:
-        """
-        Liest ALLE Register aus self.registers und gibt dict zurück.
-        Achtet darauf, dass keine Adresse (inkl. Mehrwort) >= 40206 gelesen wird.
-        """
-        data = {"Zeit": time.strftime("%Y-%m-%d %H:%M:%S")}
-        for address, meta in sorted(self.registers.items()):
-            words = self._word_count_for_type(meta["type"])
-            # 3) Nochmals Schutz auf Ebene der Iteration:
-            if address + words - 1 >= MAX_ADDR_EXCLUSIVE:
-                continue
-            val = self.read_one(address, meta["type"])
-            if val is None:
-                continue
-            key = f"{address} - {meta['desc']}"
-            data[key] = val
-        return data

requirements.txt

@@ -1,5 +1,3 @@
 pymodbus~=3.8.6
 pandas
-openpyxl
+openpyxl
-sshtunnel
-pvlib

sg_ready_controller.py

@@ -1,65 +0,0 @@
-from pymodbus.client import ModbusTcpClient
-
-class SgReadyController():
-    def __init__(self, es):
-        self.es = es
-
-    def perform_action(self, heat_pump_name, meter_name, state):
-        hp = self.es.get_component_by_name(heat_pump_name)
-        meter_values = state[meter_name]
-
-        power_to_grid = meter_values['40206 - M_AC_Power'] * 10 ** meter_values['40210 - M_AC_Power_SF']
-        mode = None
-        if power_to_grid > 10000:
-            mode = 'mode2'
-            self.switch_sg_ready_mode(hp.ip, hp.port, mode)
-        elif power_to_grid < 0:
-            mode = 'mode1'
-            self.switch_sg_ready_mode(hp.ip, hp.port, mode)
-
-        return mode
-
-    def switch_sg_ready_mode(self, 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()

shelly_pro_3m.py

@@ -1,64 +0,0 @@
-import struct
-
-from pymodbus.client import ModbusTcpClient
-import pandas as pd
-import time
-
-class ShellyPro3m:
-    def __init__(self, device_name: str, ip_address: str, port: int=502):
-        self.device_name = device_name
-        self.ip = ip_address
-        self.port = port
-        self.client = None
-        self.connect_to_modbus()
-        self.registers = None
-        self.get_registers()
-
-    def connect_to_modbus(self):
-        port = self.port
-        self.client = ModbusTcpClient(self.ip, port=port)
-        try:
-            if not self.client.connect():
-                print("Verbindung zum Shelly-Logger fehlgeschlagen.")
-                exit(1)
-            print("Verbindung zum Shelly-Logger erfolgreich.")
-        except KeyboardInterrupt:
-            print("Beendet durch Benutzer (Ctrl+C).")
-        finally:
-            self.client.close()
-
-    def get_registers(self):
-        # Excel-Datei mit den Input-Registerinformationen
-        excel_path = "modbus_registers/shelly_pro_3m_registers.xlsx"
-        xls = pd.ExcelFile(excel_path)
-        df_input_registers = xls.parse()
-
-        # Relevante Spalten bereinigen
-        df_clean = df_input_registers[['MB Adresse', 'Beschreibung', 'Variabel Typ']].dropna()
-        df_clean['MB Adresse'] = df_clean['MB Adresse'].astype(int)
-
-        # Dictionary aus Excel erzeugen
-        self.registers = {
-            row['MB Adresse']: {
-                'desc': row['Beschreibung'],
-                'type': 'REAL' if row['Variabel Typ'] == 'REAL' else 'INT'
-            }
-            for _, row in df_clean.iterrows()
-        }
-
-    def get_state(self):
-        data = {}
-        data['Zeit'] = time.strftime('%Y-%m-%d %H:%M:%S')
-        for address, info in self.registers.items():
-            reg_type = info['type']
-            result = self.client.read_input_registers(address, count=2 if reg_type == 'REAL' else 1)
-            if result.isError():
-                print(f"Fehler beim Lesen von Adresse {address}: {result}")
-                continue
-
-            packed = struct.pack(">HH", result.registers[1], result.registers[0])
-            value = round(struct.unpack(">f", packed)[0], 2)
-
-            print(f"Adresse {address} - {info['desc']}: {value}")
-            data[f"{address} - {info['desc']}"] = value
-        return data

simulators/pv_plant_simulator.py

@@ -1,210 +0,0 @@
-from __future__ import annotations
-from dataclasses import dataclass
-from typing import Optional, Dict, List, Literal, Tuple, Union
-
-import numpy as np
-import pandas as pd
-import pvlib
-import matplotlib.pyplot as plt
-from pvlib.location import Location
-from pvlib.pvsystem import PVSystem
-from pvlib.modelchain import ModelChain
-
-SeriesOrArray = Union[pd.Series, np.ndarray]
-
-# ----------------------------- Konfiguration -----------------------------
-
-@dataclass
-class PvWattsSubarrayConfig:
-    name: str
-    pdc0_w: float                   # STC-DC-Leistung [W]
-    tilt_deg: float                 # Neigung (0=horizontal)
-    azimuth_deg: float              # Azimut (180=Süd)
-    gamma_pdc: float = -0.004       # Tempkoeff. [1/K]
-    eta_inv_nom: float = 0.96       # WR-Wirkungsgrad (nominal)
-    albedo: float = 0.2             # Bodenreflexion
-
-    # Pauschale Verluste (PVWatts-Losses)
-    dc_loss: float = 0.0
-    ac_loss: float = 0.0
-    soiling: float = 0.0
-
-    # Modell
-    transposition_model: Literal["perez","haydavies","isotropic","klucher","reindl"] = "perez"
-
-
-# ------------------------------ Subarray ---------------------------------
-
-class PvWattsSubarray:
-    """
-    Ein Subarray mit pvlib.ModelChain (PVWatts).
-    Berechnet automatisch DNI/DHI aus GHI (ERBS-Methode)
-    und nutzt ein SAPM-Temperaturmodell.
-    """
-    def __init__(self, cfg: PvWattsSubarrayConfig, location: Location):
-        self.cfg = cfg
-        self.location = location
-        self._mc: Optional[ModelChain] = None
-
-    # ---------------------------------------------------------------------
-    def _create_modelchain(self) -> ModelChain:
-        """Erzeuge eine pvlib.ModelChain-Instanz mit PVWatts-Parametern."""
-        temp_params = pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS["sapm"]["open_rack_glass_polymer"]
-
-        system = PVSystem(
-            surface_tilt=self.cfg.tilt_deg,
-            surface_azimuth=self.cfg.azimuth_deg,
-            module_parameters={"pdc0": self.cfg.pdc0_w, "gamma_pdc": self.cfg.gamma_pdc},
-            inverter_parameters={"pdc0": self.cfg.pdc0_w, "eta_inv_nom": self.cfg.eta_inv_nom},
-            albedo=self.cfg.albedo,
-            temperature_model_parameters=temp_params,
-            module_type="glass_polymer",
-            racking_model="open_rack",
-        )
-
-        mc = ModelChain(
-            system, self.location,
-            transposition_model=self.cfg.transposition_model,
-            solar_position_method="nrel_numpy",
-            airmass_model="kastenyoung1989",
-            dc_model="pvwatts",
-            ac_model="pvwatts",
-            aoi_model="physical",
-            spectral_model=None,
-            losses_model="pvwatts",
-            temperature_model="sapm",
-        )
-
-        mc.losses_parameters = {
-            "dc_loss": float(self.cfg.dc_loss),
-            "ac_loss": float(self.cfg.ac_loss),
-            "soiling": float(self.cfg.soiling),
-        }
-
-        self._mc = mc
-        return mc
-
-    # ---------------------------------------------------------------------
-    def calc_dni_and_dhi(self, weather: pd.DataFrame) -> pd.DataFrame:
-        """
-        Berechnet DNI & DHI aus GHI über die ERBS-Methode.
-        Gibt ein neues DataFrame mit 'ghi', 'dni', 'dhi' zurück.
-        """
-        if "ghi" not in weather:
-            raise ValueError("Wetterdaten benötigen mindestens 'ghi'.")
-        # Sonnenstand bestimmen
-        sp = self.location.get_solarposition(weather.index)
-        erbs = pvlib.irradiance.erbs(weather["ghi"], sp["zenith"], weather.index)
-        out = weather.copy()
-        out["dni"] = erbs["dni"].clip(lower=0)
-        out["dhi"] = erbs["dhi"].clip(lower=0)
-        return out
-
-    # ---------------------------------------------------------------------
-    def _prepare_weather(self, weather: pd.DataFrame) -> pd.DataFrame:
-        """Sichert vollständige Spalten (ghi, dni, dhi, temp_air, wind_speed)."""
-        if "ghi" not in weather or "temp_air" not in weather:
-            raise ValueError("weather benötigt Spalten: 'ghi' und 'temp_air'.")
-
-        w = weather.copy()
-
-        # Zeitzone prüfen
-        if w.index.tz is None:
-            w.index = w.index.tz_localize(self.location.tz)
-        else:
-            if str(w.index.tz) != str(self.location.tz):
-                w = w.tz_convert(self.location.tz)
-
-        # Wind default
-        if "wind_speed" not in w:
-            w["wind_speed"] = 1.0
-
-        # DNI/DHI ergänzen (immer mit ERBS)
-        if "dni" not in w or "dhi" not in w:
-            w = self.calc_dni_and_dhi(w)
-
-        return w
-
-    # ---------------------------------------------------------------------
-    def get_power(self, weather: pd.DataFrame) -> pd.Series:
-        """
-        Berechnet AC-Leistung aus Wetterdaten.
-        """
-        w = self._prepare_weather(weather)
-        mc = self._create_modelchain()
-        mc.run_model(weather=w)
-        return mc.results.ac.rename(self.cfg.name)
-
-
-# ------------------------------- Anlage ----------------------------------
-
-class PvWattsPlant:
-    """
-    Eine PV-Anlage mit mehreren Subarrays, die ein gemeinsames Wetter-DataFrame nutzt.
-    """
-    def __init__(self, site: Location, subarray_cfgs: List[PvWattsSubarrayConfig]):
-        self.site = site
-        self.subs: Dict[str, PvWattsSubarray] = {c.name: PvWattsSubarray(c, site) for c in subarray_cfgs}
-
-    def get_power(
-        self,
-        weather: pd.DataFrame,
-        *,
-        return_breakdown: bool = False
-    ) -> pd.Series | Tuple[pd.Series, Dict[str, pd.Series]]:
-        """Berechne Gesamtleistung und optional Einzel-Subarrays."""
-        parts: Dict[str, pd.Series] = {name: sub.get_power(weather) for name, sub in self.subs.items()}
-
-        # gemeinsamen Index bilden
-        idx = list(parts.values())[0].index
-        for s in parts.values():
-            idx = idx.intersection(s.index)
-        parts = {k: v.reindex(idx).fillna(0.0) for k, v in parts.items()}
-
-        total = sum(parts.values())
-        total.name = "total_ac"
-
-        if return_breakdown:
-            return total, parts
-        return total
-
-
-# --------------------------- Beispielnutzung -----------------------------
-if __name__ == "__main__":
-    # Standort
-    site = Location(latitude=52.52, longitude=13.405, altitude=35, tz="Europe/Berlin", name="Berlin")
-
-    # Zeitachse: 1 Tag, 15-minütig
-    times = pd.date_range("2025-06-21 00:00", "2025-06-21 23:45", freq="15min", tz=site.tz)
-
-    # Dummy-Wetter
-    ghi = 1000 * np.clip(np.sin(np.linspace(0, np.pi, len(times)))**1.2, 0, None)
-    temp_air = 16 + 8 * np.clip(np.sin(np.linspace(-np.pi/2, np.pi/2, len(times))), 0, None)
-    wind = np.full(len(times), 1.0)
-    weather = pd.DataFrame(index=times)
-    weather["ghi"] = ghi
-    weather["temp_air"] = temp_air
-    weather["wind_speed"] = wind
-
-    # Zwei Subarrays
-    cfgs = [
-        PvWattsSubarrayConfig(name="Sued_30", pdc0_w=6000, tilt_deg=30, azimuth_deg=180, dc_loss=0.02, ac_loss=0.01),
-        PvWattsSubarrayConfig(name="West_20", pdc0_w=4000, tilt_deg=20, azimuth_deg=270, soiling=0.02),
-    ]
-    plant = PvWattsPlant(site, cfgs)
-
-    # Simulation
-    total, parts = plant.get_power(weather, return_breakdown=True)
-
-    # Plot
-    plt.figure(figsize=(10, 6))
-    plt.plot(total.index, total / 1000, label="Gesamtleistung (AC)", linewidth=2, color="black")
-    for name, s in parts.items():
-        plt.plot(s.index, s / 1000, label=name)
-    plt.title("PV-Leistung (PVWatts, ERBS-Methode für DNI/DHI)")
-    plt.ylabel("Leistung [kW]")
-    plt.xlabel("Zeit")
-    plt.legend()
-    plt.grid(True, linestyle="--", alpha=0.5)
-    plt.tight_layout()
-    plt.show()

solaredge_meter.py

@@ -1,134 +0,0 @@
-import time
-import struct
-import pandas as pd
-from typing import Dict, Any, List, Tuple, Optional
-from pymodbus.client import ModbusTcpClient
-
-EXCEL_PATH = "modbus_registers/pv_inverter_registers.xlsx"
-
-# Obergrenze: bis EXKLUSIVE 40206 (d.h. max. 40205)
-MIN_ADDR_INCLUSIVE = 40121
-ADDRESS_SHIFT = 50
-
-class SolaredgeMeter:
-    def __init__(self, device_name: str, ip_address: str, port: int = 502, unit: int = 1):
-        self.device_name = device_name
-        self.ip = ip_address
-        self.port = port
-        self.unit = unit
-        self.client: Optional[ModbusTcpClient] = None
-        self.registers: Dict[int, Dict[str, Any]] = {}  # addr -> {"desc":..., "type":...}
-        self.connect_to_modbus()
-        self.load_registers(EXCEL_PATH)
-
-    # ---------- Verbindung ----------
-    def connect_to_modbus(self):
-        self.client = ModbusTcpClient(self.ip, port=self.port, timeout=3.0, retries=3)
-        if not self.client.connect():
-            print("❌ Verbindung zu Zähler fehlgeschlagen.")
-            raise SystemExit(1)
-        print("✅ Verbindung zu Zähler hergestellt.")
-
-    def close(self):
-        if self.client:
-            self.client.close()
-            self.client = None
-
-    # ---------- Register-Liste ----------
-    def load_registers(self, excel_path: str):
-        xls = pd.ExcelFile(excel_path)
-        df = xls.parse()
-        # Passe Spaltennamen hier an, falls nötig:
-        cols = ["MB Adresse", "Beschreibung", "Variabel Typ"]
-        df = df[cols].dropna()
-        df["MB Adresse"] = df["MB Adresse"].astype(int)
-
-        # 1) Vorab-Filter: nur Adressen < 40206 übernehmen
-        df = df[df["MB Adresse"] >= MIN_ADDR_INCLUSIVE]
-
-        self.registers = {
-            int(row["MB Adresse"]): {
-                "desc": str(row["Beschreibung"]).strip(),
-                "type": str(row["Variabel Typ"]).strip()
-            }
-            for _, row in df.iterrows()
-        }
-
-
-    # ---------- Low-Level Lesen ----------
-    def _try_read(self, fn_name: str, address: int, count: int) -> Optional[List[int]]:
-        fn = getattr(self.client, fn_name)
-        # pymodbus 3.8.x hat 'slave='; Fallbacks schaden nicht
-        shifted_addr = address + ADDRESS_SHIFT
-        for kwargs in (dict(address=shifted_addr, count=count, slave=self.unit),
-                       dict(address=shifted_addr, count=count)):
-            try:
-                res = fn(**kwargs)
-                if res is None or (hasattr(res, "isError") and res.isError()):
-                    continue
-                return res.registers
-            except TypeError:
-                continue
-        return None
-
-    def _read_any(self, address: int, count: int) -> Optional[List[int]]:
-        regs = self._try_read("read_holding_registers", address, count)
-        if regs is None:
-            regs = self._try_read("read_input_registers", address, count)
-        return regs
-
-    # ---------- Decoding ----------
-    @staticmethod
-    def _to_i16(u16: int) -> int:
-        return struct.unpack(">h", struct.pack(">H", u16))[0]
-
-    @staticmethod
-    def _to_f32_from_two(u16_hi: int, u16_lo: int, msw_first: bool = True) -> float:
-        b = struct.pack(">HH", u16_hi, u16_lo) if msw_first else struct.pack(">HH", u16_lo, u16_hi)
-        return struct.unpack(">f", b)[0]
-
-    # Hilfsfunktion: wie viele 16-Bit-Register braucht dieser Typ?
-    @staticmethod
-    def _word_count_for_type(rtype: str) -> int:
-        rt = (rtype or "").lower()
-        # Passe hier an deine Excel-Typen an:
-        if "uint32" in rt or "real" in rt or "float" in rt or "string(32)" in rt:
-            return 2
-        # Default: 1 Wort (z.B. int16/uint16)
-        return 1
-
-    def read_one(self, address_excel: int, rtype: str) -> Optional[float]:
-        """
-        Liest einen Wert nach Typ ('INT' oder 'REAL' etc.).
-        Es werden ausschließlich Register < 40206 gelesen.
-        """
-        addr = int(address_excel)
-        words = self._word_count_for_type(rtype)
-
-        if words == 2:
-            regs = self._read_any(addr, 2)
-            if not regs or len(regs) < 2:
-                return None
-            # Deine bisherige Logik interpretiert 2 Worte als Float32:
-            return self._to_f32_from_two(regs[0], regs[1])
-        else:
-            regs = self._read_any(addr, 1)
-            if not regs:
-                return None
-            return float(self._to_i16(regs[0]))
-
-    def get_state(self) -> Dict[str, Any]:
-        """
-        Liest ALLE Register aus self.registers und gibt dict zurück.
-        Achtet darauf, dass keine Adresse (inkl. Mehrwort) >= 40206 gelesen wird.
-        """
-        data = {"Zeit": time.strftime("%Y-%m-%d %H:%M:%S")}
-        for address, meta in sorted(self.registers.items()):
-            words = self._word_count_for_type(meta["type"])
-
-            val = self.read_one(address, meta["type"])
-            if val is None:
-                continue
-            key = f"{address} - {meta['desc']}"
-            data[key] = val
-        return data

test.py

@@ -1,18 +0,0 @@
-from pymodbus.client import ModbusTcpClient
-import struct
-
-MODBUS_IP="10.0.0.40"
-client=ModbusTcpClient(MODBUS_IP, port=502)
-client.connect()
-
-try:
-    rr = client.read_input_registers(30, count=3, slave=1)
-    print("Raw 30..32:", rr.registers)
-
-    def as_int16(x):
-        return struct.unpack(">h", struct.pack(">H", x))[0]
-
-    for i, raw in enumerate(rr.registers, start=30):
-        print(i, "raw", raw, "int16", as_int16(raw), "scaled", as_int16(raw)/10.0)
-finally:
-    client.close()