pydase 0.5.0

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 (Python Data Service)

pydase is a Python library for creating data service servers with integrated web and RPC servers. It's designed to handle the management of data structures, automated tasks, and callbacks, and provides built-in functionality for serving data over different protocols.

• Features
• Installation
• Usage
  • Defining a DataService
  • Running the Server
  • Accessing the Web Interface
  • Connecting to the Service using rpyc
• Understanding the Component System
  • Built-in Type and Enum Components
  • Method Components
  • DataService Instances (Nested Classes)
  • Custom Components (pydase.components)
    • Image
    • NumberSlider
    • ColouredEnum
    • Extending with New Components
• Understanding Service Persistence
  • Controlling Property State Loading with @load_state
• Understanding Tasks in pydase
• Understanding Units in pydase
• Configuring pydase via Environment Variables
• Customizing the Web Interface
  • Enhancing the Web Interface Style with Custom CSS
  • Tailoring Frontend Component Layout
• Logging in pydase
  • Changing the Log Level
• Documentation
• Contributing
• License

Features

• Simple data service definition through class-based interface
• Integrated web interface for interactive access and control of your data service
• Support for rpyc connections, allowing for programmatic control and interaction with your service
• Component system bridging Python backend with frontend visual representation
• Customizable styling for the web interface through user-defined CSS
• Saving and restoring the service state for service persistence
• Automated task management with built-in start/stop controls and optional autostart
• Support for units

Installation

Install pydase using poetry:

poetry add pydase

or pip:

pip install pydase

Usage

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

Defining a DataService

To use pydase, you'll first need to create a class that inherits from DataService. This class represents your custom data service, which will be exposed via RPC (using rpyc) and a web server. Your class can implement class / instance attributes and synchronous and asynchronous tasks.

Here's an example:

from pydase import DataService, Server


class Device(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

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


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

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

Running the Server

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

from pydase import Server

# ... defining the Device class ...

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

This will start the server, making your Device service accessible via RPC and a web server at 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 using rpyc

You can also connect to the service using rpyc. Here's an example on how to establish a connection and interact with the service:

import rpyc

# Connect to the service
conn = rpyc.connect("<ip_addr>", 18871)
client = conn.root

# Interact with the service
client.voltage = 5.0
print(client.voltage)  # prints 5.0

In this example, replace <ip_addr> with the IP address of the machine where the service is running. After establishing a connection, you can interact with the service attributes as if they were local attributes.

Understanding the Component System

In pydase, components are fundamental building blocks that bridge the Python backend logic with frontend visual representation and interactions. This system can be understood based on the following categories:

Built-in Type and Enum Components

pydase automatically maps standard Python data types to their corresponding frontend components:

• str: Translated into a StringComponent on the frontend.
• int and float: Manifested as the NumberComponent.
• bool: Rendered as a ButtonComponent.
• list: Each item displayed individually, named after the list attribute and its index.
• enum.Enum: Presented as an EnumComponent, facilitating dropdown selection.

Method Components

Methods within the DataService class have frontend representations:

• Regular Methods: These are rendered as a MethodComponent in the frontend, allowing users to execute the method via an "execute" button.
• Asynchronous Methods: These are manifested as the AsyncMethodComponent with "start"/"stop" buttons to manage the execution of tasks.

DataService Instances (Nested Classes)

Nested DataService instances offer an organized hierarchy for components, enabling richer applications. Each nested class might have its own attributes and methods, each mapped to a frontend component.

Here is an example:

from pydase import DataService, Server


class Channel(DataService):
    def __init__(self, channel_id: int) -> None:
        self._channel_id = channel_id
        self._current = 0.0
        super().__init__()

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

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


class Device(DataService):
    def __init__(self) -> None:
        self.channels = [Channel(i) for i in range(2)]

        super().__init__()


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

Note that defining classes within DataService classes is not supported (see this issue).

Custom Components (pydase.components)

The custom components in pydase have two main parts:

• A Python Component Class in the backend, implementing the logic needed to set, update, and manage the component's state and data.
• A Frontend React Component that renders and manages user interaction in the browser.

Below are the components available in the pydase.components module, accompanied by their Python usage:

Image

This component provides a versatile interface for displaying images within the application. Users can update and manage images from various sources, including local paths, URLs, and even matplotlib figures.

The component offers methods to load images seamlessly, ensuring that visual content is easily integrated and displayed within the data service.

import matplotlib.pyplot as plt
import numpy as np

import pydase
from pydase.components.image import Image


class MyDataService(pydase.DataService):
    my_image = Image()


if __name__ == "__main__":
    service = MyDataService()
    # loading from local path
    service.my_image.load_from_path("/your/image/path/")

    # loading from a URL
    service.my_image.load_from_url("https://cataas.com/cat")

    # loading a matplotlib figure
    fig = plt.figure()
    x = np.linspace(0, 2 * np.pi)
    plt.plot(x, np.sin(x))
    plt.grid()
    service.my_image.load_from_matplotlib_figure(fig)

    pydase.Server(service).run()

NumberSlider

The NumberSlider component in the pydase package provides an interactive slider interface for adjusting numerical values on the frontend. It is designed to support both numbers and quantities and ensures that values adjusted on the frontend are synchronized with the backend.

To utilize the NumberSlider, users should implement a class that derives from NumberSlider. This class can then define the initial values, minimum and maximum limits, step sizes, and additional logic as needed.

Here's an example of how to implement and use a custom slider:

import pydase
import pydase.components


class MySlider(pydase.components.NumberSlider):
    def __init__(
        self,
        value: float = 0.0,
        min_: float = 0.0,
        max_: float = 100.0,
        step_size: float = 1.0,
    ) -> None:
        super().__init__(value, min_, max_, step_size)

    @property
    def min(self) -> float:
        return self._min

    @min.setter
    def min(self, value: float) -> None:
        self._min = value

    @property
    def max(self) -> float:
        return self._max

    @max.setter
    def max(self, value: float) -> None:
        self._max = value

    @property
    def step_size(self) -> float:
        return self._step_size

    @step_size.setter
    def step_size(self, value: float) -> None:
        self._step_size = value

    @property
    def value(self) -> float:
        return self._value

    @value.setter
    def value(self, value: float) -> None:
        if value < self._min or value > self._max:
            raise ValueError("Value is either below allowed min or above max value.")
        self._value = value


class MyService(pydase.DataService):
    def __init__(self) -> None:
        super().__init__()
        self.voltage = MySlider()


if __name__ == "__main__":
    service_instance = MyService()
    service_instance.voltage.value = 5
    print(service_instance.voltage.value)  # Output: 5
    pydase.Server(service_instance).run()

In this example, MySlider overrides the min, max, step_size, and value properties. Users can make any of these properties read-only by omitting the corresponding setter method.

• Accessing parent class resources in NumberSlider

  In scenarios where you need the slider component to interact with or access resources from its parent class, you can achieve this by passing a callback function to it. This method avoids directly passing the entire parent class instance (self) and offers a more encapsulated approach. The callback function can be designed to utilize specific attributes or methods of the parent class, allowing the slider to perform actions or retrieve data in response to slider events.

  Here's an illustrative example:

  from collections.abc import Callable
  
  import pydase
  import pydase.components
  
  
  class MySlider(pydase.components.NumberSlider):
      def __init__(
          self,
          value: float,
          on_change: Callable[[float], None],
      ) -> None:
          super().__init__(value=value)
          self._on_change = on_change
  
      # ... other properties ...
  
      @property
      def value(self) -> float:
          return self._value
  
      @value.setter
      def value(self, new_value: float) -> None:
          if new_value < self._min or new_value > self._max:
              raise ValueError("Value is either below allowed min or above max value.")
          self._value = new_value
          self._on_change(new_value)
  
  
  class MyService(pydase.DataService):
      def __init__(self) -> None:
          self.voltage = MySlider(
              5,
              on_change=self.handle_voltage_change,
          )
  
      def handle_voltage_change(self, new_voltage: float) -> None:
          print(f"Voltage changed to: {new_voltage}")
          # Additional logic here
  
  if __name__ == "__main__":
     service_instance = MyService()
     my_service.voltage.value = 7  # Output: "Voltage changed to: 7"
     pydase.Server(service_instance).run()
  

• Incorporating units in NumberSlider

  The NumberSlider is capable of displaying units alongside values, enhancing its usability in contexts where unit representation is crucial. When utilizing pydase.units, you can specify units for the slider's value, allowing the component to reflect these units in the frontend.

  Here's how to implement a NumberSlider with unit display:

  import pydase
  import pydase.components
  import pydase.units as u
  
  class MySlider(pydase.components.NumberSlider):
      def __init__(
          self,
          value: u.Quantity = 0.0 * u.units.V,
      ) -> None:
          super().__init__(value)
  
      @property
      def value(self) -> u.Quantity:
          return self._value
  
      @value.setter
      def value(self, value: u.Quantity) -> None:
          if value.m < self._min or value.m > self._max:
              raise ValueError("Value is either below allowed min or above max value.")
          self._value = value
  
  class MyService(pydase.DataService):
      def __init__(self) -> None:
          super().__init__()
          self.voltage = MySlider()
  
  if __name__ == "__main__":
      service_instance = MyService()
      service_instance.voltage.value = 5 * u.units.V
      print(service_instance.voltage.value)  # Output: 5 V
      pydase.Server(service_instance).run()
  

ColouredEnum

This component provides a way to visually represent different states or categories in a data service using colour-coded options. It behaves similarly to a standard Enum, but the values encode colours in a format understood by CSS. The colours can be defined using various methods like Hexadecimal, RGB, HSL, and more.

If the property associated with the ColouredEnum has a setter function, the keys of the enum will be rendered as a dropdown menu, allowing users to interact and select different options. Without a setter function, the selected key will simply be displayed as a coloured box with text inside, serving as a visual indicator.

import pydase
import pydase.components as pyc


class MyStatus(pyc.ColouredEnum):
    PENDING = "#FFA500"  # Hexadecimal colour (Orange)
    RUNNING = "#0000FF80"  # Hexadecimal colour with transparency (Blue)
    PAUSED = "rgb(169, 169, 169)"  # RGB colour (Dark Gray)
    RETRYING = "rgba(255, 255, 0, 0.3)"  # RGB colour with transparency (Yellow)
    COMPLETED = "hsl(120, 100%, 50%)"  # HSL colour (Green)
    FAILED = "hsla(0, 100%, 50%, 0.7)"  # HSL colour with transparency (Red)
    CANCELLED = "SlateGray"  # Cross-browser colour name (Slate Gray)


class StatusTest(pydase.DataService):
    _status = MyStatus.RUNNING

    @property
    def status(self) -> MyStatus:
        return self._status

    @status.setter
    def status(self, value: MyStatus) -> None:
        # do something ...
        self._status = value

# Modifying or accessing the status value:
my_service = StatusExample()
my_service.status = MyStatus.FAILED

Extending with New Components

Users can also extend the library by creating custom components. This involves defining the behavior on the Python backend and the visual representation on the frontend. For those looking to introduce new components, the guide on adding components provides detailed steps on achieving this.

Understanding Service Persistence

pydase allows you to easily persist the state of your service by saving it to a file. This is especially useful when you want to maintain the service's state across different runs.

To save the state of your service, pass a filename keyword argument to the constructor of the pydase.Server class. If the file specified by filename does not exist, the state manager will create this file and store its state in it when the service is shut down. If the file already exists, the state manager will load the state from this file, setting the values of its attributes to the values stored in the file.

Here's an example:

from pydase import DataService, Server

class Device(DataService):
    # ... defining the Device class ...


if __name__ == "__main__":
    service = Device()
    Server(service, filename="device_state.json").run()

In this example, the state of the Device service will be saved to device_state.json when the service is shut down. If device_state.json exists when the server is started, the state manager will restore the state of the service from this file.

Controlling Property State Loading with @load_state

By default, the state manager only restores values for public attributes of your service. If you have properties that you want to control the loading for, you can use the @load_state decorator on your property setters. This indicates to the state manager that the value of the property should be loaded from the state file.

Here is how you can apply the @load_state decorator:

from pydase import DataService
from pydase.data_service.state_manager import load_state

class Device(DataService):
    _name = "Default Device Name"

    @property
    def name(self) -> str:
        return self._name

    @name.setter
    @load_state
    def name(self, value: str) -> None:
        self._name = value

With the @load_state decorator applied to the name property setter, the state manager will load and apply the name property's value from the file storing the state upon server startup, assuming it exists.

Note: If the service class structure has changed since the last time its state was saved, only the attributes and properties decorated with @load_state that have remained the same will be restored from the settings file.

Understanding Tasks in pydase

In pydase, a task is defined as an asynchronous function 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. The 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 an rpyc client, 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):
        self.readout_frequency = 1.0
        self._autostart_tasks = {"read_sensor_data": ()}  # args passed to the function go there
        super().__init__()

    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. The readout frequency can be updated using the readout_frequency attribute. By listing it in the _autostart_tasks dictionary, it will automatically start running when Server(service).run() is executed. As with all tasks, pydase will also generate start_read_sensor_data and stop_read_sensor_data methods, which can be called to manually start and stop the data reading task.

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. These quantities can be set and accessed like regular attributes, and pydase will automatically handle the conversion between floats and quantities with units.

Here's an example:

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


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: Any) -> None:
        self._current = value


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

    # You can just set floats to the Quantity objects. The DataService __setattr__ will
    # automatically convert this
    service.voltage = 10.0
    service.current = 1.5

    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.

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.

• SERVICE_RPC_PORT: Defines the port number for the rpc server. This has to be different for each services running on the same host. Default is 18871.

• 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,
    rpc_port=18880,
    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

Enhancing the Web Interface Style with Custom CSS

pydase allows you to enhance the user experience by customizing the web interface's appearance. You can apply your own styles globally across the web interface by passing a custom CSS file to the server during initialization.

Here's how you can use this feature:

1. Prepare your custom CSS file with the desired styles.

2. When initializing your server, use the css parameter of the Server class to specify the path to your custom CSS file.

from pydase import Server, DataService

class MyService(DataService):
    # ... your service definition ...

if __name__ == "__main__":
    service = MyService()
    server = Server(service, css="path/to/your/custom.css").run()

This will apply the styles defined in custom.css to the web interface, allowing you to maintain branding consistency or improve visual accessibility.

Please ensure that the CSS file path is accessible from the server's running location. Relative or absolute paths can be used depending on your setup.

Tailoring Frontend Component Layout

pydase enables users to customize the frontend layout via the web_settings.json file. Each key in the file corresponds to the full access path of public attributes, properties, and methods of the exposed service, using dot-notation.

• Custom Display Names: Modify the "displayName" value in the file to change how each component appears in the frontend.

The web_settings.json file will be stored in the directory specified by SERVICE_CONFIG_DIR. You can generate a web_settings.json file by setting the GENERATE_WEB_SETTINGS to True. For more information, see the configuration section.

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.