pydase 0.10.21

A flexible and robust Python library for creating, managing, and interacting with data services, with built-in support for web and RPC servers, and customizable features for diverse use cases.

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 to provide the 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, web_port=8001).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, web_port=8001).run()

This will start the server, making your Device service accessible on http://localhost:8001. The port number for the web server can be customised in the server constructor or through environment variables and defaults to 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>:<web_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>:<web_port>/api/v1/get_value?access_path=<full_access_path>"
)
serialized_value = json.loads(response.text)

For more information, see here.

Configuring pydase via Environment Variables

pydase services work out of the box without requiring any configuration. However, you might want to change some options, such as the web server port or logging level. To accommodate such customizations, pydase allows configuration through environment variables, such as:

• ENVIRONMENT:
  Defines the operation mode ("development" or "production"), which influences behaviour such as logging (see Logging in pydase).

• SERVICE_CONFIG_DIR:
  Specifies the directory for configuration files (e.g., web_settings.json). Defaults to the config folder in the service root. Access this programmatically using:

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

• SERVICE_WEB_PORT:
  Defines the web server’s port. Ensure each service on the same host uses a unique port. Default: 8001.

• GENERATE_WEB_SETTINGS:
  When true, generates or updates the web_settings.json file. Existing entries are preserved, and new entries are appended.

For more information, see Configuring pydase.

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. a custom favicon image, and
3. 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 provides structured, per-module logging with support for log level configuration, rich formatting, and optional client identification in logs.

To configure logging in your own service, you can use:

from pydase.utils.logging import configure_logging_with_pydase_formatter

For more information, see the full guide.

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.

Acknowledgements

This work was funded by the ETH Zurich-PSI Quantum Computing Hub.

The main idea behind pydase is based on a previous project called tiqi-plugin, which was developed within the same research group. While the concept was inspired by that project, pydase was implemented from the ground up with a new architecture and design.

License

pydase is licensed under the MIT License.