pydase 0.8.5

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

pydase is a Python library designed to streamline the creation of services that interface with devices and data. It offers a unified API, simplifying the process of data querying and device interaction. Whether you're managing lab sensors, network devices, or any abstract data entity, pydase facilitates rapid service development and deployment.

• Features
• Installation
• Usage
  • Defining a DataService
  • Running the Server
  • Accessing the Web Interface
  • Connecting to the Service via Python Client
    • Tab Completion Support
    • Integration within Another Service
  • RESTful API
• Understanding the Component System
  • Built-in Type and Enum Components
  • Method Components
  • DataService Instances (Nested Classes)
  • Custom Components (pydase.components)
    • DeviceConnection
      • Customizing Connection Logic
      • Reconnection Interval
    • 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
• Using validate_set to Validate Property Setters
• Configuring pydase via Environment Variables
• Customizing the Web Interface
  • Enhancing the Web Interface Style with Custom CSS
  • Tailoring Frontend Component Layout
  • Specifying a Custom Frontend Source
• Logging in pydase
  • Changing the Log Level
• Documentation
• Contributing
• License

Features

• Simple service definition through class-based interface
• Integrated web interface for interactive access and control of your service
• Support for programmatic control and interaction with your service
• Component system bridging Python backend with frontend visual representation
• Customizable styling for the web interface
• Saving and restoring the service state for service persistence
• 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 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 DataService. This class represents your custom data service, which will be exposed via a web server. Your class can implement class / instance attributes and synchronous and asynchronous tasks.

Here's an example:

from pydase import DataService, Server
from pydase.utils.decorators import frontend


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

    @frontend
    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 via Python 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(hostname="<ip_addr>", port=8001).proxy

# 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.

Tab Completion Support

In interactive environments such as Python interpreters and Jupyter notebooks, the proxy class supports tab completion, which allows users to explore available methods and attributes.

Integration within Another Service

You can also integrate a client proxy within another service. Here's how you can set it up:

import pydase

class MyService(pydase.DataService):
    # Initialize the client without blocking the constructor
    proxy = pydase.Client(hostname="<ip_addr>", port=8001, block_until_connected=False).proxy

if __name__ == "__main__":
    service = MyService()
    # Create a server that exposes this service; adjust the web_port as needed
    server = pydase.Server(service, web_port=8002). run()

In this setup, the MyService class has a proxy attribute that connects to a pydase service located at <ip_addr>:8001. The block_until_connected=False argument allows the service to start up even if the initial connection attempt fails. This configuration is particularly useful in distributed systems where services may start in any order.

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 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.
• dict: Each key-value pair displayed individually, named after the dictionary attribute and its key. Note that the dictionary keys must be strings.
• enum.Enum: Presented as an EnumComponent, facilitating dropdown selection.

Method Components

Within the DataService class of pydase, only methods devoid of arguments can be represented in the frontend, classified into two distinct categories

1. Tasks: Argument-free asynchronous functions, identified within pydase as tasks, are inherently designed for frontend interaction. These tasks are automatically rendered with a start/stop button, allowing users to initiate or halt the task execution directly from the web interface.
2. Synchronous Methods with @frontend Decorator: Synchronous methods without arguments can also be presented in the frontend. For this, they have to be decorated with the @frontend decorator.

import pydase
import pydase.components
import pydase.units as u
from pydase.utils.decorators import frontend


class MyService(pydase.DataService):
    @frontend
    def exposed_method(self) -> None:
        ...

    async def my_task(self) -> None:
        while True:
            # ...

You can still define synchronous tasks with arguments and call them using a python client. However, decorating them with the @frontend decorator will raise a FunctionDefinitionError. Defining a task with arguments will raise a TaskDefinitionError. I decided against supporting function arguments for functions rendered in the frontend due to the following reasons:

1. Feature Request Pitfall: supporting function arguments create a bottomless pit of feature requests. As users encounter the limitations of supported types, demands for extending support to more complex types would grow.
2. Complexity in Supported Argument Types: while simple types like int, float, bool and str could be easily supported, more complicated types are not (representation, (de-)serialization).

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:
        super().__init__()
        self._channel_id = channel_id
        self._current = 0.0

    @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:
        super().__init__()
        self.channels = [Channel(i) for i in range(2)]


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:

DeviceConnection

The DeviceConnection component acts as a base class within the pydase framework for managing device connections. It provides a structured approach to handle connections by offering a customizable connect method and a connected property. This setup facilitates the implementation of automatic reconnection logic, which periodically attempts reconnection whenever the connection is lost.

In the frontend, this class abstracts away the direct interaction with the connect method and the connected property. Instead, it showcases user-defined attributes, methods, and properties. When the connected status is False, the frontend displays an overlay that prompts manual reconnection through the connect() method. Successful reconnection removes the overlay.

import pydase.components
import pydase.units as u


class Device(pydase.components.DeviceConnection):
    def __init__(self) -> None:
        super().__init__()
        self._voltage = 10 * u.units.V

    def connect(self) -> None:
        if not self._connected:
            self._connected = True

    @property
    def voltage(self) -> float:
        return self._voltage


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


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

Customizing Connection Logic

Users are encouraged to primarily override the connect method to tailor the connection process to their specific device. This method should adjust the self._connected attribute based on the outcome of the connection attempt:

import pydase.components


class MyDeviceConnection(pydase.components.DeviceConnection):
    def __init__(self) -> None:
        super().__init__()
        # Add any necessary initialization code here

    def connect(self) -> None:
        # Implement device-specific connection logic here
        # Update self._connected to `True` if the connection is successful,
        # or `False` if unsuccessful
        ...

Moreover, if the connection status requires additional logic, users can override the connected property:

import pydase.components

class MyDeviceConnection(pydase.components.DeviceConnection):
    def __init__(self) -> None:
        super().__init__()
        # Add any necessary initialization code here

    def connect(self) -> None:
        # Implement device-specific connection logic here
        # Ensure self._connected reflects the connection status accurately
        ...

    @property
    def connected(self) -> bool:
        # Implement custom logic to accurately report connection status
        return self._connected

Reconnection Interval

The DeviceConnection component automatically executes a task that checks for device availability at a default interval of 10 seconds. This interval is adjustable by modifying the _reconnection_wait_time attribute on the class instance.

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:
        """Slider value."""
        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

Note that each enumeration name and value must be unique. This means that you should use different colour formats when you want to use a colour multiple times.

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 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.

• 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.
• Control Component Visibility: Utilize the "display" key-value pair to control whether a component is rendered in the frontend. Set the value to true to make the component visible or false to hide it.
• Adjustable Component Order: The "displayOrder" values determine the order of components. Alter these values to rearrange the components as desired. The value defaults to Number.MAX_SAFE_INTEGER.

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.

For example, styling the following service

import pydase


class Device(pydase.DataService):
    name = "My Device"
    temperature = 1.0
    power = 1


class Service(pydase.DataService):
    device = Device()
    state = "RUNNING"


service_instance = Service()
pydase.Server(service_instance).run()

with the following web_settings.json

{
    "device": {
        "displayName": "My Device",
        "displayOrder": 1
    },
    "device.name": {
        "display": false
    },
    "device.power": {
        "displayName": "Power",
        "displayOrder": 1
    },
    "device.temperature": {
      "displayName": "Temperature",
      "displayOrder": 0
    },
    "state": {
        "displayOrder": 0
    }
}

looks like this:

Specifying a Custom Frontend Source

To further personalize your web interface, you can provide pydase with a custom frontend GUI. To do so, you can use the frontend_src keyword in the pydase.Server:

from pathlib import Path

import pydase


class MyService(pydase.DataService):
    # Service definition


if __name__ == "__main__":
    service = MyService()
    pydase.Server(
        service,
        frontend_src=Path("path/to/your/frontend/directory"),
    ).run()

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.