Platform independent abstraction of the idea of a "device".
The intent is that you can make plugins for automation frameworks that can also be trivially used as a standalone library.
The API is basically: Get a class based on a data dict, and make it.
It's designed so you can create a generic mixin that applies to any device matching the spec, and build your own API that can be used to access any device using this spec.
It also aims to include a library of commonly used devices.
It can be installed with "pip3 install iot_devices".
To use it, edit the tui-dash.conf file and run the tui-dash command after installing the app. You'll get a nice text UI with all the devices in your file.
You can either put the file in ~/.config/tui-dash/tui-dash.conf, or pass the filename as the first argument.
Each section represents a device that will be added. For example, here is the configuration to access YoLink's home automation devices via the TUI.
Only one device is needed here, the top level service, all other devices are autodiscovered, but you can add extra configuration on a per-device basis.
[YoLinkService]
device.key= Your UAC key here
device.user_id=Your UAC password here
type = YoLinkService
# Hide this, it just creates the subdevices
hidden = True
[YoLinkService.Bedroom]
title = Bedroom Sensor
Note: All devices must NOT do anything destructive when created with default arguments.
import iot_devices.device as device
import random
class RandomDevice(device.Device):
device_type = "RandomDevice"
def __init__(self,name, data, **kw):
device.Device.__init__(self,name, data, **kw)
# Push type data point set by the device
self.numeric_data_point("random")
self.set_data_point("random",random.random())
# On demand requestable data point pulled by application.
# All you have to do is set the val to a callable.
self.numeric_data_point("dyn_random")
self.set_data_point_getter("dyn_random", random.random)This tells the host what module you would need to import to get a device having a certain name.
This would go in a devices_manifest.json file in the root folder of any module.
Note that the name RandomDevice matches the name of the class.
The system will effectively do from your_module.devices.random import RandomDevice to find the device class.
{
"devices":{
"RandomDevice": {
"submodule":"devices.random"
}
}
}Note: We never have to import the module ourselves. It is imported on demand based on the data! We automatically search sys.path.
from iot_devices.host import get_class, create_device
data = {
"type": "DemoDevice"
}
# Get the class that would be able to construct a matching device given the data
c = get_class(data)
# Make an instance of that device.
# Create device is very simple, it just calls cls(name, data),
# But you can override it to add hooks whenever a device is created.
device = create_device(c ,"Random Device", data)
#One of the values this class exposes.
# Note that values here can be "None" if there is no data yet.
print(device.datapoints['random'])
# This is an on-demand getter.
# This explicitly calls the getter we set.
# It also sets the key in device.datapoints
print(device.request_data_point('dyn_random'))
# clean up
device.close()See host_demo.py
See devicedocs.md for a code example of each one. Note these are generated with iot_devices_scan.py. This script searches all of the python paths for any folder that contains devices, creates an instance of each one, and inspects the object to generate report, including a usable code example.
For example, here's an auto-generated example of using a GPIO input, powered by the GPIOZero library.
from iot_devices.host import create_device
from iot_devices.devices.GPIODevice import GPIOInput
dev = create_device(GPIOInput, "name", {
'device.active_high': 'true',
'device.pull_up': 'false',
'device.pull_down': 'false',
'device.pin': 'MOCK1',
'device.debounce_time_ms': '0'
})
# boolean
print(dev.datapoints['value'])
# >>> 0Full signature of data point functions:
def numeric_data_point(
self,
name: str,
*,
min: float | None = None,
max: float | None = None,
hi: float | None = None, # pylint: disable=unused-argument
lo: float | None = None, # pylint: disable=unused-argument
default: float | None = None,
description: str = "", # pylint: disable=unused-argument
unit: str = "", # pylint: disable=unused-argument
handler: Callable[[float, float, Any], Any] | None = None,
interval: float = 0, # pylint: disable=unused-argument
subtype: str = "", # pylint: disable=unused-argument
writable=True, # pylint: disable=unused-argument
dashboard=True, # pylint: disable=unused-argument
**kwargs, # pylint: disable=unused-argument
):
"""Register a new numeric data point with the given properties.
Handler will be called when it changes.
self.datapoints[name] will start out with tha value of None
The intent is that you can subclass this and have your own implementation of data points,
such as exposing an MQTT api or whatever else.
Most fields are just extra annotations to the host.
Args:
min: The min value the point can take on
max: The max value the point can take on
hi: A value the point can take on that would be
considered excessive
lo: A value the point can take on that would be
considered excessively low
description: Free text
unit: A unit of measure, such as "degC" or "MPH"
default: If unset default value is None,
or may be framework defined. Default does not trigger handler.
handler: A function taking the value,timestamp,
and annotation on changes.
interval :annotates the default data rate the point
will produce, for use in setting default poll
rates by the host, if the host wants to poll.
It does not mean the host SHOULD poll this,
it only suggest a rate to poll at if the host
has an interest in this data.
writable: is purely for a host that might subclass
this, to determine if it should allow writing to the point.
subtype: A string further describing the data
type of this value, as a hint to UI generation.
dashboard: Whether to show this data point in overview displays.
"""
def string_data_point(
self,
name: str,
*,
description: str = "", # pylint: disable=unused-argument
unit: str = "", # pylint: disable=unused-argument
handler: Callable[[str, float, Any], Any] | None = None,
default: str | None = None,
interval: float = 0, # pylint: disable=unused-argument
writable=True, # pylint: disable=unused-argument
subtype: str = "", # pylint: disable=unused-argument
dashboard=True, # pylint: disable=unused-argument
**kwargs, # pylint: disable=unused-argument
):
"""Register a new string data point with the given properties.
Handler will be called when it changes.
self.datapoints[name] will start out with tha value of None
Interval annotates the default data rate the point will produce, for use in setting default poll
rates by the host, if the host wants to poll.
Most fields are just extra annotations to the host.
Args:
description: Free text
default: If unset default value is None, or may be framework defined. Default does not trigger handler.
handler: A function taking the value,timestamp, and annotation on changes.
interval: annotates the default data rate the point will produce, for use in setting default poll
rates by the host if the host wants to poll.
It does not mean the host SHOULD poll this,
it only suggest a rate to poll at if the host has an interest in this data.
writable: is purely for a host that might subclass this, to determine if it should allow writing to the point.
subtype: A string further describing the data type of this value, as a hint to UI generation.
dashboard: Whether to show this data point in overview displays.
"""
def object_data_point(
self,
name: str,
*,
description: str = "", # pylint: disable=unused-argument
unit: str = "", # pylint: disable=unused-argument
handler: Callable[[dict, float, Any], Any] | None = None,
interval: float = 0, # pylint: disable=unused-argument
writable=True, # pylint: disable=unused-argument
subtype: str = "", # pylint: disable=unused-argument
dashboard=True, # pylint: disable=unused-argument
**kwargs, # pylint: disable=unused-argument
):
"""Register a new object data point with the given properties. Here "object"
means a JSON-like object.
Handler will be called when it changes.
self.datapoints[name] will start out with tha value of None
Interval annotates the default data rate the point will produce, for use in setting default poll
rates by the host, if the host wants to poll.
Most fields are just extra annotations to the host.
Args:
description: Free text
handler: A function taking the value,timestamp, and annotation on changes
interval :annotates the default data rate the point will produce, for use in setting default poll
rates by the host, if the host wants to poll. It does not mean the host SHOULD poll this,
it only suggest a rate to poll at if the host has an interest in this data.
writable: is purely for a host that might subclass this, to determine if it should allow writing to the point.
subtype: A string further describing the data type of this value, as a hint to UI generation.
dashboard: Whether to show this data point in overview displays.
"""
def bytestream_data_point(
self,
name: str,
*,
description: str = "", # pylint: disable=unused-argument
unit: str = "", # pylint: disable=unused-argument
handler: Callable[[bytes, float, Any], Any] | None = None,
writable=True, # pylint: disable=unused-argument
dashboard=True, # pylint: disable=unused-argument
**kwargs, # pylint: disable=unused-argument
):
"""register a new bytestream data point with the
given properties. handler will be called when it changes.
only meant to be called from within __init__.
Bytestream data points do not store data,
they only push it through.
Despite the name, buffers of bytes may not be broken up or combined, this is buffer oriented,
"""