opensemantic.base 0.42.8.post1000002004000

Library with Python models derived from the page package world.opensemantic.base

opensemantic.base

Python models and controllers for the world.opensemantic.base page package.

Builds on oold-python (BaseController, LinkedBaseModel, cast(), _types registry) and opensemantic (OswBaseModel, compute_scoped_uuid).

Overview

• Auto-generated Pydantic models (v1 and v2): Database, WebService, DataTool, DataChannel, Person, Organization, etc.
• DataToolController - generic controller for any DataTool
• TimeSeriesDatabaseController - SQLite and PostgREST backends for time series storage

Architecture

opensemantic.base/
  _model.py            # auto-generated v2 Pydantic models (DO NOT EDIT)
  _controller_mixin.py # DataToolMixin, TimeSeriesDatabaseController mixins
  _controller.py       # v2 controllers
  __init__.py           # re-exports model + controller classes
  v1/                   # same structure for Pydantic v1

DataToolController

Extends DataTool with channel management, subdevice traversal, and data archiving.

from opensemantic.base import DataToolController

tool = DataToolController(
    name="sensor",
    label=[...],
    data_channels=[ch1, ch2],
    storage_locations=[db],
    auto_archive=True,  # auto-creates archive DB from storage_locations[0]
)

tool.get_all_channels()       # recursive across subdevices
tool.get_channel_owner(ch)    # find which device owns a channel
tool.to_json()                # only model fields (controller fields stripped)

Auto-archive from storage_locations

When auto_archive=True and no explicit archive_database is set, the controller auto-creates a LocalTimeSeriesDatabaseController from the first storage_locations entry (resolved via oold backend).

Typed read/write

Channels with a characteristic IRI enable typed serialization:

# Write: converts to base unit, strips defaults for compact storage
await tool.store_typed_data(DataToolMixin.StoreTypedDataParams(
    tool_osw_id=tool.get_osw_id(),
    rows=[DataToolMixin.TypedDataRow(ts=now, channel=ch, value=Temperature(value=300.0))],
))

# Read: resolves characteristic class via oold's _types registry
results = await tool.read_typed_data(DataToolMixin.ReadTypedDataParams(
    tool_osw_id=tool.get_osw_id(), channel=ch,
))
# results[0] is a Temperature instance with defaults restored

Subobject ID auto-computation

Inline subobject osw_id fields are auto-prefixed with the parent's osw_id:

Parent:  OSW<parent_uuid>
Channel: OSW<parent_uuid>#OSW<channel_uuid>

Fields with range in json_schema_extra are references to separate entities and are not prefixed.

Unloaded characteristic warning

On init, DataToolController checks if channel characteristic IRIs are present in oold's _types registry. Missing entries produce a warning with guidance to import the corresponding package.

TimeSeriesDatabaseController

Abstract base for time series storage, with SQLite and PostgREST implementations.

from opensemantic.base import LocalTimeSeriesDatabaseController

db = LocalTimeSeriesDatabaseController(name="archive", label=[...], db_path="./data.sqlite")
await db.create_tool(params)
await db.write_tool_channel_raw(params)
await db.read_tool_channel_raw(params)

DataToolView (Dashboard UI)

Interactive dashboard for visualizing archived time series data from DataToolControllers.

Features:

• Wunderbaum TreeGrid for channel selection with characteristic metadata
• Stacked Bokeh plots grouped by characteristic (temperature, pressure, etc.)
• Unit conversion via dropdown (e.g. K to C, Pa to hPa)
• Composite channel splitting (e.g. AirQuality into temperature + pressure sub-plots)
• Text channel log console with timestamped entries
• Configurable via JsonEditor (grouping, auto-fetch, row limit, cache)

Interactive demo: channel selection, plotting, unit switching

Channel selection with stacked plots grouped by characteristic

Unit conversion via dropdown (K to C)

Text channel log console with timestamped entries

from opensemantic.base.view import DataToolView
from opensemantic.base.view._config import DashboardConfig, PlotConfig

view = DataToolView(
    controllers=[ctrl],
    config=DashboardConfig(lang="en", plot=PlotConfig(auto_fetch=True)),
    title="My Dashboard",
)
view.servable()  # for panel serve

See examples/datatool_dashboard.py for a full working example.

To regenerate the screenshots after UI changes, see docs/generate_screenshots.py.

Installation

pip install opensemantic.base            # models only
pip install opensemantic.base[controller] # + aiosqlite, postgrest
pip install opensemantic.base[view]       # + panel, bokeh, panelini, pint

Testing

pytest tests/test_controller.py

PostgREST integration tests require a running pgstack instance. To enable them:

1. Start pgstack: docker compose -f docker-compose.yml -f docker-compose.example-tsdb.override.yml up -d
2. Copy tests/.env.example to tests/.env and fill in TEST_PGRST_URL and TEST_PGRST_JWT_SECRET
3. Run tests - PostgREST tests are skipped unless both env vars are set