pydase/README.md

pydase

pydase is a Python library that simplifies the creation of remote control interfaces for Python objects. It exposes the public attributes of a user-defined class via a Socket.IO web server, ensuring they are always in sync with the service state. You can interact with these attributes using an RPC client, a RESTful API, or a web browser. The web browser frontend is auto-generated, displaying components that correspond to each public attribute of the class for direct interaction. pydase implements an observer pattern and to provide real-time updates, ensuring that changes to the class attributes are reflected across all clients.

Whether you're managing lab sensors, network devices, or any abstract data entity, pydase facilitates service development and deployment.

Features

• Simple service definition through class-based interface
• Auto-generated web interface for interactive access and control of your service
• Python RPC client
• Customizable web interface
• Saving and restoring the service state
• Automated task management with built-in start/stop controls and optional autostart
• Support for units
• Validating Property Setters

Installation

Install pydase using poetry:

poetry add pydase

or pip:

pip install pydase

Usage

Using pydase involves three main steps: defining a pydase.DataService subclass, running the server, and then connecting to the service either programmatically using pydase.Client or through the web interface.

Defining a DataService

To use pydase, you'll first need to create a class that inherits from pydase.DataService. This class represents your custom service, which will be exposed via a web server.
Your class can implement synchronous and asynchronous methods, some built-in types (like int, float, str, bool, list or dict) and other components as attributes. For more information, please refer to the components guide.

Here's an example:

import pydase
from pydase.utils.decorators import frontend


class Device(pydase.DataService):
    _current = 0.0
    _voltage = 0.0
    _power = False

    @property
    def current(self) -> float:
        # run code to get current
        return self._current

    @current.setter
    def current(self, value: float) -> None:
        # run code to set current
        self._current = value

    @property
    def voltage(self) -> float:
        # run code to get voltage
        return self._voltage

    @voltage.setter
    def voltage(self, value: float) -> None:
        # run code to set voltage
        self._voltage = value

    @property
    def power(self) -> bool:
        # run code to get power state
        return self._power

    @power.setter
    def power(self, value: bool) -> None:
        # run code to set power state
        self._power = value

    @frontend
    def reset(self) -> None:
        self.current = 0.0
        self.voltage = 0.0


if __name__ == "__main__":
    service = Device()
    pydase.Server(service=service).run()

In the above example, we define a Device class that inherits from pydase.DataService. We define a few properties (current, voltage, power) and their getter and setter methods.

Running the Server

Once your service class is defined, you can create an instance of it and run the server:

import pydase

# ... defining the Device class ...

if __name__ == "__main__":
    service = Device()
    pydase.Server(service=service).run()

This will start the server, making your Device service accessible on http://localhost:8001.

Accessing the Web Interface

Once the server is running, you can access the web interface in a browser:

In this interface, you can interact with the properties of your Device service.

Connecting to the Service via Python RPC Client

You can connect to the service using the pydase.Client. Below is an example of how to establish a connection to a service and interact with it:

import pydase

# Replace the hostname and port with the IP address and the port of the machine where 
# the service is running, respectively
client_proxy = pydase.Client(url="ws://<ip_addr>:<service_port>").proxy
# client_proxy = pydase.Client(url="wss://your-domain.ch").proxy  # if your service uses ssl-encryption

# After the connection, interact with the service attributes as if they were local
client_proxy.voltage = 5.0
print(client_proxy.voltage)  # Expected output: 5.0

This example demonstrates setting and retrieving the voltage attribute through the client proxy. The proxy acts as a local representative of the remote service, enabling straightforward interaction.

The proxy class dynamically synchronizes with the server's exposed attributes. This synchronization allows the proxy to be automatically updated with any attributes or methods that the server exposes, essentially mirroring the server's API. This dynamic updating enables users to interact with the remote service as if they were working with a local object.

The RPC client also supports tab completion support in the interpreter, can be used as a context manager and integrates very well with other pydase services. For more information, please refer to the documentation.

RESTful API

The pydase RESTful API allows for standard HTTP-based interactions and provides access to various functionalities through specific routes.

For example, you can get a value like this:

import json

import requests

response = requests.get(
    "http://<hostname>:<port>/api/v1/get_value?access_path=<full_access_path>"
)
serialized_value = json.loads(response.text)

For more information, see here.

Understanding Tasks in pydase

In pydase, a task is defined as an asynchronous function without arguments contained in a class that inherits from DataService. These tasks usually contain a while loop and are designed to carry out periodic functions.

For example, a task might be used to periodically read sensor data, update a database, or perform any other recurring job. One core feature of pydase is its ability to automatically generate start and stop functions for these tasks. This allows you to control task execution via both the frontend and python clients, giving you flexible and powerful control over your service's operation.

Another powerful feature of pydase is its ability to automatically start tasks upon initialization of the service. By specifying the tasks and their arguments in the _autostart_tasks dictionary in your service class's __init__ method, pydase will automatically start these tasks when the server is started. Here's an example:

from pydase import DataService, Server

class SensorService(DataService):
    def __init__(self):
        super().__init__()
        self.readout_frequency = 1.0
        self._autostart_tasks["read_sensor_data"] = ()

    def _process_data(self, data: ...) -> None:
        ...

    def _read_from_sensor(self) -> Any:
        ...

    async def read_sensor_data(self):
        while True:
            data = self._read_from_sensor()
            self._process_data(data)  # Process the data as needed
            await asyncio.sleep(self.readout_frequency)


if __name__ == "__main__":
    service = SensorService()
    Server(service).run()

In this example, read_sensor_data is a task that continuously reads data from a sensor. By adding it to the _autostart_tasks dictionary, it will automatically start running when Server(service).run() is executed. As with all tasks, pydase will generate start_read_sensor_data and stop_read_sensor_data methods, which can be called to manually start and stop the data reading task. The readout frequency can be updated using the readout_frequency attribute.

Understanding Units in pydase

pydase integrates with the pint package to allow you to work with physical quantities within your service. This enables you to define attributes with units, making your service more expressive and ensuring consistency in the handling of physical quantities.

You can define quantities in your DataService subclass using pydase's units functionality.

Here's an example:

from typing import Any

import pydase.units as u
from pydase import DataService, Server


class ServiceClass(DataService):
    voltage = 1.0 * u.units.V
    _current: u.Quantity = 1.0 * u.units.mA

    @property
    def current(self) -> u.Quantity:
        return self._current

    @current.setter
    def current(self, value: u.Quantity) -> None:
        self._current = value


if __name__ == "__main__":
    service = ServiceClass()

    service.voltage = 10.0 * u.units.V
    service.current = 1.5 * u.units.mA

    Server(service).run()

In the frontend, quantities are rendered as floats, with the unit displayed as additional text. This allows you to maintain a clear and consistent representation of physical quantities across both the backend and frontend of your service.

Should you need to access the magnitude or the unit of a quantity, you can use the .m attribute or the .u attribute of the variable, respectively. For example, this could be necessary to set the periodicity of a task:

import asyncio
from pydase import DataService, Server
import pydase.units as u


class ServiceClass(DataService):
    readout_wait_time = 1.0 * u.units.ms

    async def read_sensor_data(self):
        while True:
            print("Reading out sensor ...")
            await asyncio.sleep(self.readout_wait_time.to("s").m)


if __name__ == "__main__":
    service = ServiceClass()

    Server(service).run()

For more information about what you can do with the units, please consult the documentation of pint.

Using validate_set to Validate Property Setters

The validate_set decorator ensures that a property setter reads back the set value using the property getter and checks it against the desired value. This decorator can be used to validate that a parameter has been correctly set on a device within a specified precision and timeout.

The decorator takes two keyword arguments: timeout and precision. The timeout argument specifies the maximum time (in seconds) to wait for the value to be within the precision boundary. If the value is not within the precision boundary after this time, an exception is raised. The precision argument defines the acceptable deviation from the desired value. If precision is None, the value must be exact. For example, if precision is set to 1e-5, the value read from the device must be within ±0.00001 of the desired value.

Here’s how to use the validate_set decorator in a DataService class:

import pydase
from pydase.observer_pattern.observable.decorators import validate_set


class Service(pydase.DataService):
    def __init__(self) -> None:
        super().__init__()
        self._device = RemoteDevice()  # dummy class

    @property
    def value(self) -> float:
        # Implement how to get the value from the remote device...
        return self._device.value

    @value.setter
    @validate_set(timeout=1.0, precision=1e-5)
    def value(self, value: float) -> None:
        # Implement how to set the value on the remote device...
        self._device.value = value


if __name__ == "__main__":
    pydase.Server(Service()).run()

Configuring pydase via Environment Variables

Configuring pydase through environment variables enhances flexibility, security, and reusability. This approach allows for easy adaptation of services across different environments without code changes, promoting scalability and maintainability. With that, it simplifies deployment processes and facilitates centralized configuration management. Moreover, environment variables enable separation of configuration from code, aiding in secure and collaborative development.

pydase offers various configurable options:

• ENVIRONMENT: Sets the operation mode to either "development" or "production". Affects logging behaviour (see logging section).

• SERVICE_CONFIG_DIR: Specifies the directory for service configuration files, like web_settings.json. This directory can also be used to hold user-defined configuration files. Default is the config folder in the service root folder. The variable can be accessed through:

  import pydase.config
  pydase.config.ServiceConfig().config_dir
  

• SERVICE_WEB_PORT: Defines the port number for the web server. This has to be different for each services running on the same host. Default is 8001.

• GENERATE_WEB_SETTINGS: When set to true, generates / updates the web_settings.json file. If the file already exists, only new entries are appended.

Some of those settings can also be altered directly in code when initializing the server:

import pathlib

from pydase import Server
from your_service_module import YourService


server = Server(
    YourService(),
    web_port=8080,
    config_dir=pathlib.Path("other_config_dir"),  # note that you need to provide an argument of type pathlib.Path
    generate_web_settings=True
).run()

Customizing the Web Interface

pydase allows you to enhance the user experience by customizing the web interface's appearance through

1. a custom CSS file, and
2. tailoring the frontend component layout and display style.

You can also provide a custom frontend source if you need even more flexibility.

For details, please see here.

Logging in pydase

The pydase library organizes its loggers on a per-module basis, mirroring the Python package hierarchy. This structured approach allows for granular control over logging levels and behaviour across different parts of the library.

Changing the Log Level

You have two primary ways to adjust the log levels in pydase:

1. directly targeting pydase loggers

   You can set the log level for any pydase logger directly in your code. This method is useful for fine-tuning logging levels for specific modules within pydase. For instance, if you want to change the log level of the main pydase logger or target a submodule like pydase.data_service, you can do so as follows:

   # <your_script.py>
   import logging
   
   # Set the log level for the main pydase logger
   logging.getLogger("pydase").setLevel(logging.INFO)
   
   # Optionally, target a specific submodule logger
   # logging.getLogger("pydase.data_service").setLevel(logging.DEBUG)
   
   # Your logger for the current script
   logger = logging.getLogger(__name__)
   logger.info("My info message.")
   

   This approach allows for specific control over different parts of the pydase library, depending on your logging needs.

2. using the ENVIRONMENT environment variable

   For a more global setting that affects the entire pydase library, you can utilize the ENVIRONMENT environment variable. Setting this variable to "production" will configure all pydase loggers to only log messages of level "INFO" and above, filtering out more verbose logging. This is particularly useful for production environments where excessive logging can be overwhelming or unnecessary.

   ENVIRONMENT="production" python -m <module_using_pydase>
   

   In the absence of this setting, the default behavior is to log everything of level "DEBUG" and above, suitable for development environments where more detailed logs are beneficial.

Note: It is recommended to avoid calling the pydase.utils.logging.setup_logging function directly, as this may result in duplicated logging messages.

Documentation

The full documentation provides more detailed information about pydase, including advanced usage examples, API references, and tips for troubleshooting common issues. See the full documentation for more information.

Contributing

We welcome contributions! Please see contributing.md for details on how to contribute.

License

pydase is licensed under the MIT License.