created base structure
This commit is contained in:
Martin
@@ -0,0 +1,3 @@
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from pkgutil import extend_path
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__path__ = extend_path(__path__, __name__)
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@@ -0,0 +1,12 @@
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# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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__version__ = "0.1.0"
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__author__ = "Martin Schröder"
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__email__ = "team@pyscada.org"
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__description__ = (
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"Influxdb extension for PyScada a Python and Django based Open Source SCADA System"
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)
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__app_name__ = "influxdb"
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PROTOCOL_ID = 21
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import logging
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import traceback
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from django.contrib import admin
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from pyscada.admin import admin_site
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from pyscada.influxdb.models import (
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InfluxDatabase,
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)
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logger = logging.getLogger(__name__)
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admin_site.register(InfluxDatabase, InfluxDatabaseAdmin)
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@@ -0,0 +1,14 @@
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# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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from django.apps import AppConfig
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from django.utils.translation import gettext_lazy as _
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class PyScadaEMSConfig(AppConfig):
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name = "pyscada.influxdb"
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verbose_name = _("PyScada Influx Database")
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default_auto_field = "django.db.models.AutoField"
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def ready(self):
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pass
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@@ -0,0 +1,98 @@
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# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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try:
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driver_ok = True
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except ImportError:
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driver_ok = False
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import logging
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from pyscada.ems.models import MeteringPoint, VirtualMeteringPoint
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from pyscada.models import BackgroundProcess
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from pyscada.utils.scheduler import Process
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logger = logging.getLogger(__name__)
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class Device:
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"""
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EMS Background class
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"""
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def __init__(self, device):
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self.device = device
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self._device_not_accessible = 0
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self.variables = {}
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def request_data(self):
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"""process the data that was added to pyscada db"""
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output = []
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return output
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class PreCalculationBackgroundTask(Process):
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def init_process(self):
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if hasattr(self, "mp_ids_to_calculate") or hasattr(
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self, "vmp_ids_to_calculate"
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):
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self.mp_to_calculate = []
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else:
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self.mp_to_calculate = [mp for mp in MeteringPoint.objects.all()]
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self.calculate_type = "mp"
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if hasattr(self, "mp_ids_to_calculate"):
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self.calculate_type = "mp"
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if self.mp_ids_to_calculate == "all":
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self.mp_to_calculate += [mp for mp in MeteringPoint.objects.all()]
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elif self.mp_ids_to_calculate == "none":
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pass
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else:
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self.mp_to_calculate += [
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mp
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for mp in MeteringPoint.objects.filter(
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pk__in=self.mp_ids_to_calculate
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)
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]
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elif hasattr(self, "vmp_ids_to_calculate"):
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self.calculate_type = "vmp"
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if self.vmp_ids_to_calculate == "all":
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self.mp_to_calculate += [
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mp for mp in VirtualMeteringPoint.objects.all()
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]
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elif self.vmp_ids_to_calculate == "none":
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pass
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else:
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self.mp_to_calculate += [
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mp
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for mp in VirtualMeteringPoint.objects.filter(
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pk__in=self.vmp_ids_to_calculate
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)
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]
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self.dt_set = 0.1
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return True
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def loop(self):
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if len(self.mp_to_calculate) == 0:
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self.next_message = "done"
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return 0, None # done
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if self.calculate_type == "vmp" and BackgroundProcess.objects.filter(
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process_class="pyscada.ems.daemon.PreCalculationBackgroundTask",
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label="update precalculated mp",
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done=False,
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):
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self.dt_set = 30
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self.next_message = "wait for running update mp processes to finish"
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return 1, None
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mp = self.mp_to_calculate.pop(0)
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self.dt_set = 0.1
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mp.update_calculated_energy_deltas()
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self.next_message = (
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f"Calculated {mp.name}, {len(self.mp_to_calculate)} to calculate"
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)
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return 1, None
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@@ -0,0 +1,99 @@
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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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import json
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from django.core.management.base import BaseCommand
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from pyscada.ems.models import MeteringPoint, VirtualMeteringPoint
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from pyscada.models import BackgroundProcess
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from time import time
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class Command(BaseCommand):
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help = "Run Maintenance Jobs for PyScada-EMS"
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def add_arguments(self, parser):
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parser.add_argument(
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"type", choices=["mp", "vmp", "all"], type=str, default="all"
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)
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parser.add_argument(
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"id", type=int, default=-1, nargs="?",
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)
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def handle(self, *args, **options):
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workers = 10
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if options["id"] >= 0:
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tic = time()
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if options["type"] == "all":
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print("argument id not supported for type == 'all'")
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if options["type"] == "mp":
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mp = MeteringPoint.objects.filter(pk=options["id"]).first()
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if mp is None:
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print(f"MeteringPoint with id {options['id']} not found!")
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mp.update_calculated_energy_deltas()
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print(f"{(time()-tic):1.2f}")
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return
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if options["type"] == "vmp":
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tic = time()
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vmp = VirtualMeteringPoint.objects.filter(pk=options["id"]).first()
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if vmp is None:
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print(f"VirtualMeteringPoint with id {options['id']} not found!")
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vmp.update_calculated_energy_deltas()
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print(f"{(time()-tic):1.2f}")
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return
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if options["type"] in ["mp", "all"] and options["id"] == -1:
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nb_mp = MeteringPoint.objects.count()
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mp_i = 1
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mp_ids_to_calculate = []
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for worker_id in range(workers):
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mp_ids_to_calculate.append([])
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worker = 0
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for mp in MeteringPoint.objects.all():
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print(f"add mp {mp_i}/{nb_mp}: {mp.name} ", end="", flush=True)
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# mp.update_calculated_energy_deltas()
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mp_ids_to_calculate[worker % workers].append(mp.pk)
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worker += 1
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mp_i += 1
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print(" done")
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for worker_id in range(workers):
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process_class_kwargs = json.dumps(
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{"mp_ids_to_calculate": mp_ids_to_calculate[worker_id]}
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)
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if len(process_class_kwargs) > 400:
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print(
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f"process_class_kwargs to long \
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({len(process_class_kwargs)}>400)"
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)
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continue
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bp = BackgroundProcess(
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pid=0,
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label="update precalculated mp",
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enabled=True,
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parent_process_id=1,
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process_class="pyscada.ems.daemon.PreCalculationBackgroundTask",
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process_class_kwargs=process_class_kwargs,
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)
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bp.save()
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if options["type"] in ["vmp", "all"] and options["id"] == -1:
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process_class_kwargs = json.dumps({"vmp_ids_to_calculate": "all"})
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bp = BackgroundProcess(
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pid=0,
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label="update precalculated vmp",
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enabled=True,
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parent_process_id=1,
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process_class="pyscada.ems.daemon.PreCalculationBackgroundTask",
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process_class_kwargs=process_class_kwargs,
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)
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bp.save()
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@@ -0,0 +1,226 @@
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# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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import os
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import re
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import traceback
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from datetime import datetime
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import pytz
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from django.conf import settings
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from django.db import models
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from influxdb_client import InfluxDBClient, Point, WritePrecision
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from influxdb_client.client.write_api import SYNCHRONOUS
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from pyscada.models import DataSource
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tz_local = pytz.timezone(settings.TIME_ZONE)
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class InfluxDatabase(DjangoDatabase):
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bucket = models.CharField(max_length=255)
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api_key = models.CharField(max_length=255)
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organisation = models.CharField(max_length=255)
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write_precision = models.CharField(default="ms", max_length=2)
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url = models.CharField(default="127.0.0.1:8086")
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measurement_name = models.CharField(default="pyscada.models.RecordedData", max_length=255)
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only_write_to_influxdb = models.BooleanField(default=True, help_text="when selected only a copy of the data is written to the InfluxDB and the SQL Database is used for everything else")
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def connect(self):
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self.client = InfluxDBClient(url=self.url, token=self.token, org=self.organisation)
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return self.client
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def get_write_api(self):
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if self.client is None:
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self.connect()
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return self.client.write_api(write_options=SYNCHRONOUS)
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def get_query_api(self):
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if self.client is None:
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self.connect()
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return self.client.query_api()
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def create_data_element_from_variable(self, variable, value, timestamp, **kwargs):
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if value is not None:
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date_saved = (
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kwargs["date_saved"]
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if "date_saved" in kwargs
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else variable.date_saved
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if hasattr(variable, "date_saved")
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else now()
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)
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return Point(self.measurement_name)
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#.field("id", int(int(int(timestamp) * 2097152) + variable.pk))
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.tag("variable_id", variable.pk) # has to be a tag to be easily filtered by, even if it is an numeric value
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.tag("device_protocol", variable.device.protocol.protocol)
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.tag"device_id", variable.device_id) # has to be a tag to be easily filtered by, even if it is an numeric value
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.tag("value_class", variable.value_class)
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.tag("unit", str(variable.unit))
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.field("value", value)
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.field("date_saved", date_saved)
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.timestamp(timestamp / 1000)
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return None
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def last_value(self, **kwargs):
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if self.only_write_to_influxdb:
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return super(self).last_value(**kwargs)
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variable = kwargs.pop("variable") if "variable" in kwargs else None
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use_date_saved = (
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kwargs.pop("use_date_saved") if "use_date_saved" in kwargs else False
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)
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query = f'from(bucket: "{self.bucket}") |> range(start: {time_min}) |> filter(fn:(r) => r._measurement == "{self.measurement_name}" ) |> filter(fn:(r) => r.variable_id == "{variable_id}") |> filter(fn:(r) => r._field == "value") |> keep(columns: ["_time","_value"]) |> last()'
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r = query_api.query(query)
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r = r.to_values(columns=['_time','_value'])[0]
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if len(r) == 0:
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return None
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return [r[-1][0].timestamp(), r[-1][1]]
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def read_multiple(self, **kwargs):
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if self.only_write_to_influxdb:
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return super(self).read_multiple(**kwargs)
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variable_ids = kwargs.pop("variable_ids") if "variable_ids" in kwargs else []
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time_min = kwargs.pop("time_min") if "time_min" in kwargs else 0
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time_max = kwargs.pop("time_max") if "time_max" in kwargs else time.time()
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time_in_ms = kwargs.pop("time_in_ms") if "time_in_ms" in kwargs else True
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query_first_value = (
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kwargs.pop("query_first_value") if "query_first_value" in kwargs else False
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)
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variable_ids = self.datasource.datasource_check(
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variable_ids, items_as_id=True, ids_model=Variable
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)
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if kwargs.get("time_min_excluded", False):
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time_min = time_min + 0.001
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if kwargs.get("time_max_excluded", False):
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time_max = time_max - 0.001
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if time_in_ms:
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f_time_scale = 1000
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else:
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f_time_scale = 1
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values = {}
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query_api = self.get_query_api()
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tmp_time_max = time_min
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date_saved_max = time_min
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for variable_id in variable_ids:
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query = f'from(bucket: "{self.bucket}") |> range(start: {time_min}, stop: {time_max}) |> filter(fn:(r) => r._measurement == "{self.measurement_name}" ) |> filter(fn:(r) => r.variable_id == "{variable_id}") |> filter(fn:(r) => r._field == "value") |> keep(columns: ["_time","_value"])'
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r = query_api.query(query)
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values[variable_id] = [ [i_time.timestamp(), i_value] for i_time, i_value in r.to_values(["_time","_value"])]
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tmp_time_max = max(tmp_time_max, max([i_time for i_time, i_value in values[variable_id]])
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query = f'from(bucket: "{self.bucket}") |> range(start: {time_min}, stop: {time_max}) |> filter(fn:(r) => r._measurement == "{self.measurement_name}" ) |> filter(fn:(r) => r.variable_id == "{variable_id}") |> filter(fn:(r) => r._field == "date_saved") |> max()'
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r = query_api.query(query)
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date_saved_max = max(date_saved_max, r.to_values(["_value"])[0][0])
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values["timestamp"] = tmp_time_max * f_time_scale
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values["date_saved_max"] = date_saved_max * f_time_scale
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return values
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def write_multiple(self, **kwargs):
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if self.only_write_to_influxdb:
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data_model = self._import_model()
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items = kwargs.pop("items") if "items" in kwargs else []
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items = self.datasource.datasource_check(items) # FIXME what is happening here?
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points = []
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recordings = []
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date_saved = kwargs.pop("date_saved") if "date_saved" in kwargs else now()
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write_api = self.get_write_api()
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batch_size = kwargs.pop("batch_size") if "batch_size" in kwargs else 5000
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i = 0
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for item in items:
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logger.debug(f"{item} has {len(item.cached_values_to_write)} to write.")
|
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if len(item.cached_values_to_write):
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for cached_value in item.cached_values_to_write:
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# add date saved if not exist in variable object, if date_saved is in kwargs it will be used instead of the variable.date_saved (see the create_data_element_from_variable function)
|
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if not hasattr(item, "date_saved") or item.date_saved is None:
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item.date_saved = date_saved
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# create the recorded data object
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point = self.create_data_element_from_variable(
|
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item, cached_value[1], cached_value[0], **kwargs
|
||||
)
|
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if self.only_write_to_influxdb:
|
||||
rc = data_model.objects.create_data_element_from_variable(
|
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item, cached_value[1], cached_value[0], **kwargs
|
||||
)
|
||||
if rc is not None:
|
||||
recordings.append(rc)
|
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|
||||
# append the object to the elements to save
|
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if point is not None:
|
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points.append(point)
|
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i += 1
|
||||
|
||||
|
||||
|
||||
if i%batch_size == 0:
|
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write_api.write(bucket=bucket, org="tub", record=points, write_precision=self.write_precision)
|
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points = []
|
||||
|
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if self.only_write_to_influxdb:
|
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try:
|
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data_model.objects.bulk_create(recorded_datas, batch_size=batch_size, **kwargs)
|
||||
except IntegrityError:
|
||||
logger.debug(
|
||||
f'{data_model._meta.object_name} objects already exists, retrying ignoring conflicts for : {", ".join(str(i.id) + " " + str(i.variable.id) for i in recorded_datas)}'
|
||||
)
|
||||
data_model.objects.bulk_create(
|
||||
recorded_datas, ignore_conflicts=True, batch_size=batch_size, **kwargs
|
||||
)
|
||||
|
||||
if len(points) > 0:
|
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write_api.write(bucket=bucket, org="tub", record=points, write_precision=self.write_precision)
|
||||
|
||||
for item in items:
|
||||
item.date_saved = None
|
||||
|
||||
def get_first_element_timestamp(self, **kwargs):
|
||||
"""this will likly time out and should be considert non functioning!
|
||||
"""
|
||||
if self.only_write_to_influxdb:
|
||||
return super(self).get_first_element_timestamp(**kwargs)
|
||||
return self.get_edge_element_timestamp(first_last="first", **kwargs)
|
||||
|
||||
def get_last_element_timestamp(self, **kwargs):
|
||||
if self.only_write_to_influxdb:
|
||||
return super(self).get_last_element_timestamp(**kwargs)
|
||||
return self.get_edge_element_timestamp(first_last="last", **kwargs)
|
||||
|
||||
def get_edge_element_timestamp(self, first_last="last", **kwargs):
|
||||
if "variables" in kwargs:
|
||||
variable_ids = [ v.pk for v in kwargs["variables"]]
|
||||
elif "variable" in kwargs:
|
||||
variable_ids = [ kwargs["variable"].pk ]
|
||||
elif "variable_ids" in kwargs:
|
||||
variable_ids = kwargs["variable_ids"]
|
||||
elif "variable_id" in kwargs:
|
||||
variable_ids = [ kwargs["variable_id"]]
|
||||
else:
|
||||
return None
|
||||
|
||||
query_api = self.get_query_api()
|
||||
start_time = "-24h"
|
||||
query = f'from(bucket: "{self.bucket}") |> range(start: {start_time}) |> filter(fn:(r) => r._measurement == "{self.measurement_name}" ) |> filter(fn:(r) =>'
|
||||
for variable_id in variable_ids:
|
||||
query += ' r.variable_id == "{variable_id}" or '
|
||||
query = query[:-3]
|
||||
|
||||
if first_last == "last":
|
||||
query += ') |> keep(columns: ["_time"]) |> sort(columns: ["_time"], desc: false) |> last(column: "_time")'
|
||||
elif first_last == "first":
|
||||
query += ') |> keep(columns: ["_time"]) |> sort(columns: ["_time"], desc: false) |> first(column: "_time")'
|
||||
else:
|
||||
return None
|
||||
|
||||
r = query_api.query(query)
|
||||
r = r.to_values(columns=['_time'])[0]
|
||||
if len(r) == 0:
|
||||
return None
|
||||
if first_last == "last":
|
||||
return r[-1].timestamp()
|
||||
elif first_last == "first":
|
||||
return r[0].timestamp()
|
||||
@@ -0,0 +1,9 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
from __future__ import unicode_literals
|
||||
|
||||
import logging
|
||||
|
||||
# from django.db.models.signals import post_save, pre_delete
|
||||
# from django.dispatch import receiver
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -0,0 +1,5 @@
|
||||
from datetime import datetime
|
||||
|
||||
from django.test import TestCase
|
||||
|
||||
# Create your tests here.
|
||||
@@ -0,0 +1,6 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
from __future__ import unicode_literals
|
||||
|
||||
from django.urls import path
|
||||
|
||||
urlpatterns = []
|
||||
Reference in New Issue
Block a user