Merge pull request #213 from tiqi-group/release-v0.10.10

updates version to v0.10.10

README.md

@@ -226,45 +226,15 @@ For details, please see [here](https://pydase.readthedocs.io/en/stable/user-guid
 
 ## 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.
+The `pydase` library provides structured, per-module logging with support for log level configuration, rich formatting, and optional client identification in logs.
 
-### Changing the Log Level
+To configure logging in your own service, you can use:
 
-You have two primary ways to adjust the log levels in `pydase`:
+```python
+from pydase.utils.logging import configure_logging_with_pydase_formatter
+```
 
-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:
-
-   ```python
-   # <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
-   logging.basicConfig(level=logging.INFO)
-   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.
-
-   ```bash
-   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.
+For more information, see the [full guide](https://pydase.readthedocs.io/en/stable/user-guide/Logging/).
 
 ## Documentation
 

docs/dev-guide/api.md

@@ -1,6 +1,15 @@
 ::: pydase.data_service
     handler: python
 
+::: pydase.data_service.data_service_cache
+    handler: python
+
+::: pydase.data_service.data_service_observer
+    handler: python
+
+::: pydase.data_service.state_manager
+    handler: python
+
 ::: pydase.server.server
     handler: python
 
@@ -38,6 +47,9 @@
     options:
       filters: ["!render_in_frontend"]
 
+::: pydase.utils.logging
+    handler: python
+
 ::: pydase.units
     handler: python
 

docs/user-guide/Logging.md

@@ -1,44 +1,46 @@
-## Logging in pydase
+# Logging in pydase
 
 The `pydase` library organizes its loggers per module, mirroring the Python package hierarchy. This structured approach allows for granular control over logging levels and behaviour across different parts of the library. Logs can also include details about client identification based on headers sent by the client or proxy, providing additional context for debugging or auditing.
 
-### Changing the Log Level
+## Changing the pydase 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:
+     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:
 
-   ```python
-   # <your_script.py>
-   import logging
+    ```python
+    # <your_script.py>
+    import logging
 
-   # Set the log level for the main pydase logger
-   logging.getLogger("pydase").setLevel(logging.INFO)
+    # 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)
+    # Optionally, target a specific submodule logger
+    # logging.getLogger("pydase.data_service").setLevel(logging.DEBUG)
 
-   # Your logger for the current script
-   logging.basicConfig(level=logging.INFO)
-   logger = logging.getLogger(__name__)
-   logger.info("My info message.")
-   ```
+    # Your logger for the current script
+    from pydase.utils.logging import configure_logging_with_pydase_formatter
+    configure_logging_with_pydase_formatter(level=logging.DEBUG)
 
-   This approach allows for specific control over different parts of the `pydase` library, depending on your logging needs.
+    logger = logging.getLogger(__name__)
+    logger.debug("My debug 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.
+    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.
 
-   ```bash
-   ENVIRONMENT="production" python -m <module_using_pydase>
-   ```
+    ```bash
+    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.
+    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.
 
-### Client Identification in Logs
+## Client Identification in pydase Logs
 
 The logging system in `pydase` includes information about clients based on headers sent by the client or a proxy. The priority for identifying the client is fixed and as follows:
 
@@ -53,3 +55,37 @@ For example, a log entries might include the following details based on the avai
 
 2025-01-20 06:48:13.710 | INFO     | pydase.server.web_server.api.v1.application:_get_value:36 - Client [user=Max Muster] is getting the value of 'property_attr'
 ```
+
+## Configuring Logging in Services
+
+To configure logging in services built with `pydase`, use the helper function [`configure_logging_with_pydase_formatter`][pydase.utils.logging.configure_logging_with_pydase_formatter]. This function sets up a logger with the same formatting used internally by `pydase`, so your service logs match the style and structure of `pydase` logs.
+
+### Example
+
+If your service follows a typical layout like:
+
+```text
+└── src
+    └── my_service
+        ├── __init__.py
+        └── ...
+```
+
+you should call `configure_logging_with_pydase_formatter` inside `src/my_service/__init__.py`. This ensures the logger is configured as soon as your service is imported, and before any log messages are emitted.
+
+```python title="src/my_service/__init__.py"
+import sys
+from pydase.utils.logging import configure_logging_with_pydase_formatter
+
+configure_logging_with_pydase_formatter(
+    name="my_service",      # Use the package/module name or None for the root logger
+    level=logging.DEBUG,    # Set the desired logging level (defaults to INFO)
+    stream=sys.stderr       # Optional: set the output stream (stderr by default)
+)
+```
+
+### Notes
+
+- If you pass `name=None`, the root logger will be configured. This affects **all logs** that propagate to the root logger.
+- Passing a specific `name` like `"my_service"` allows you to scope the configuration to your service only, which is safer in multi-library environments.
+- You can use `sys.stdout` instead of `sys.stderr` if your logs are being captured or processed differently (e.g., in containers or logging systems).

docs/user-guide/Service_Persistence.md

@@ -2,29 +2,47 @@
 
 `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.
+To enable persistence, pass a `filename` keyword argument to the constructor of the [`pydase.Server`][pydase.Server] class. The `filename` specifies the file where the state will be saved:
 
-Here's an example:
+- If the file **does not exist**, it will be created and populated with the current state when the service shuts down or saves.
+- If the file **already exists**, the state manager will **load** the saved values into the service at startup.
+
+Here’s an example:
 
 ```python
 import pydase
 
 class Device(pydase.DataService):
-    # ... defining the Device class ...
-
+    # ... define your service class ...
 
 if __name__ == "__main__":
     service = Device()
     pydase.Server(service=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.
+In this example, the service state will be automatically loaded from `device_state.json` at startup (if it exists), and saved to the same file periodically and upon shutdown.
+
+## Automatic Periodic State Saving
+
+When a `filename` is provided, `pydase` automatically enables **periodic autosaving** of the service state to that file. This ensures that the current state is regularly persisted, reducing the risk of data loss during unexpected shutdowns.
+
+The autosave happens every 30 seconds by default. You can customize the interval using the `autosave_interval` argument (in seconds):
+
+```python
+pydase.Server(
+    service=service,
+    filename="device_state.json",
+    autosave_interval=10.0,  # save every 10 seconds
+).run()
+```
+
+To disable automatic saving, set `autosave_interval` to `None`.
 
 ## 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.
+By default, the state manager only restores values for public attributes of your service (i.e. *it does not restore property values*). If you have properties that you want to control the loading for, you can use the [`@load_state`][pydase.data_service.state_manager.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:
+Example:
 
 ```python
 import pydase
@@ -43,7 +61,6 @@ class Device(pydase.DataService):
         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.
+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 upon server startup.
 
+**Note**: If the structure of your service class changes between saves, only properties decorated with `@load_state` and unchanged public attributes will be restored safely.

docs/user-guide/interaction/Python Client.md

@@ -50,12 +50,14 @@ import pydase
 class MyService(pydase.DataService):
     proxy = pydase.Client(
         url="ws://<ip_addr>:<service_port>",
-        block_until_connected=False
+        block_until_connected=False,
+        client_id="my_pydase_client_id",
     ).proxy
     # For SSL-encrypted services, use the wss protocol
     # proxy = pydase.Client(
     #     url="wss://your-domain.ch",
-    #     block_until_connected=False
+    #     block_until_connected=False,
+    #     client_id="my_pydase_client_id",
     # ).proxy
 
 if __name__ == "__main__":
@@ -67,6 +69,7 @@ if __name__ == "__main__":
 In this example:
 - The `MyService` class has a `proxy` attribute that connects to a `pydase` service at `<ip_addr>:<service_port>`.
 - By setting `block_until_connected=False`, the service can start without waiting for the connection to succeed, which is particularly useful in distributed systems where services may initialize in any order.
+- By setting `client_id`, the server will provide more accurate logs of the connecting client. If set, this ID is sent as `X-Client-Id` header in the HTTP(s) request.
 
 ## Custom `socketio.AsyncClient` Connection Parameters
 

frontend/index.html

@@ -13,7 +13,7 @@
     // this will be set by the python backend if the service is behind a proxy which strips a prefix. The frontend can use this to build the paths to the resources.
     window.__FORWARDED_PREFIX__ = "";
     window.__FORWARDED_PROTO__ = "";
-</script>`
+</script>
 
 <body>
     <noscript>You need to enable JavaScript to run this app.</noscript>

frontend/package.json

@@ -10,31 +10,31 @@
         "preview": "vite preview"
     },
     "dependencies": {
-        "@emotion/styled": "^11.11.0",
-        "@mui/material": "^5.14.1",
+        "@emotion/styled": "^11.14.0",
+        "@mui/material": "^5.16.14",
         "bootstrap": "^5.3.3",
         "deep-equal": "^2.2.3",
-        "react": "^18.3.1",
-        "react-bootstrap": "^2.10.0",
-        "react-bootstrap-icons": "^1.11.4",
-        "socket.io-client": "^4.7.1"
+        "react": "^19.0.0",
+        "react-bootstrap": "^2.10.7",
+        "react-bootstrap-icons": "^1.11.5",
+        "socket.io-client": "^4.8.1"
     },
     "devDependencies": {
-        "@eslint/js": "^9.6.0",
+        "@eslint/js": "^9.18.0",
         "@types/deep-equal": "^1.0.4",
         "@types/eslint__js": "^8.42.3",
-        "@types/node": "^20.14.10",
-        "@types/react": "^18.3.3",
-        "@types/react-dom": "^18.3.0",
+        "@types/node": "^20.17.14",
+        "@types/react": "^19.0.7",
+        "@types/react-dom": "^19.0.3",
         "@typescript-eslint/eslint-plugin": "^7.15.0",
-        "@vitejs/plugin-react-swc": "^3.5.0",
-        "eslint": "^8.57.0",
+        "@vitejs/plugin-react-swc": "^3.7.2",
+        "eslint": "^8.57.1",
         "eslint-config-prettier": "^9.1.0",
-        "eslint-plugin-prettier": "^5.1.3",
-        "eslint-plugin-react": "^7.34.3",
+        "eslint-plugin-prettier": "^5.2.3",
+        "eslint-plugin-react": "^7.37.4",
         "prettier": "3.3.2",
-        "typescript": "^5.5.3",
-        "typescript-eslint": "^7.15.0",
-        "vite": "^5.3.1"
+        "typescript": "^5.7.3",
+        "typescript-eslint": "^7.18.0",
+        "vite": "^5.4.12"
     }
 }

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pydase"
-version = "0.10.8"
+version = "0.10.10"
 description = "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."
 authors = ["Mose Mueller <mosmuell@ethz.ch>"]
 readme = "README.md"

src/pydase/client/client.py

@@ -56,6 +56,9 @@ class Client:
             [`AsyncClient`][socketio.AsyncClient]. This allows fine-tuning of the
             client's behaviour (e.g., reconnection attempts or reconnection delay).
             Default is an empty dictionary.
+        client_id: Client identification that will be shown in the server logs this
+            client is connecting to. This ID is passed as a `X-Client-Id` header in the
+            HTTP(s) request. Defaults to None.
 
     Example:
         The following example demonstrates a `Client` instance that connects to another
@@ -84,6 +87,7 @@ class Client:
         url: str,
         block_until_connected: bool = True,
         sio_client_kwargs: dict[str, Any] = {},
+        client_id: str | None = None,
     ):
         # Parse the URL to separate base URL and path prefix
         parsed_url = urllib.parse.urlparse(url)
@@ -98,6 +102,7 @@ class Client:
         self._url = url
         self._sio = socketio.AsyncClient(**sio_client_kwargs)
         self._loop = asyncio.new_event_loop()
+        self._client_id = client_id
         self.proxy = ProxyClass(
             sio_client=self._sio, loop=self._loop, reconnect=self.connect
         )
@@ -136,8 +141,14 @@ class Client:
     async def _connect(self) -> None:
         logger.debug("Connecting to server '%s' ...", self._url)
         await self._setup_events()
+
+        headers = {}
+        if self._client_id is not None:
+            headers["X-Client-Id"] = self._client_id
+
         await self._sio.connect(
-            self._base_url,
+            url=self._base_url,
+            headers=headers,
             socketio_path=f"{self._path_prefix}/ws/socket.io",
             transports=["websocket"],
             retry=True,

src/pydase/data_service/data_service_cache.py

@@ -14,6 +14,22 @@ logger = logging.getLogger(__name__)
 
 
 class DataServiceCache:
+    """Maintains a serialized cache of the current state of a DataService instance.
+
+    This class is responsible for storing and updating a representation of the service's
+    public attributes and properties. It is primarily used by the StateManager and the
+    web server to serve consistent state to clients without accessing the DataService
+    attributes directly.
+
+    The cache is initialized once upon construction by serializing the full state of
+    the service. After that, it can be incrementally updated using attribute paths and
+    values as notified by the
+    [`DataServiceObserver`][pydase.data_service.data_service_observer.DataServiceObserver].
+
+    Args:
+        service: The DataService instance whose state should be cached.
+    """
+
     def __init__(self, service: "DataService") -> None:
         self._cache: SerializedObject
         self.service = service

src/pydase/data_service/state_manager.py

@@ -1,3 +1,4 @@
+import asyncio
 import contextlib
 import json
 import logging
@@ -66,43 +67,41 @@ def has_load_state_decorator(prop: property) -> bool:
 class StateManager:
     """
     Manages the state of a DataService instance, serving as both a cache and a
-    persistence layer. It is designed to provide quick access to the latest known state
-    for newly connecting web clients without the need for expensive property accesses
-    that may involve complex calculations or I/O operations.
+    persistence layer. It provides fast access to the most recently known state of the
+    service and ensures consistent state updates across connected clients and service
+    restarts.
 
-    The StateManager listens for state change notifications from the DataService's
-    callback manager and updates its cache accordingly. This cache does not always
-    reflect the most current complex property states but rather retains the value from
-    the last known state, optimizing for performance and reducing the load on the
-    system.
+    The StateManager is used by the web server to apply updates to service attributes
+    and to serve the current state to newly connected clients. Internally, it creates a
+    [`DataServiceCache`][pydase.data_service.data_service_cache.DataServiceCache]
+    instance to track the state of public attributes and properties.
 
-    While the StateManager ensures that the cached state is as up-to-date as possible,
-    it does not autonomously update complex properties of the DataService. Such
-    properties must be updated programmatically, for instance, by invoking specific
-    tasks or methods that trigger the necessary operations to refresh their state.
-
-    The cached state maintained by the StateManager is particularly useful for web
-    clients that connect to the system and need immediate access to the current state of
-    the DataService. By avoiding direct and potentially costly property accesses, the
-    StateManager provides a snapshot of the DataService's state that is sufficiently
-    accurate for initial rendering and interaction.
+    The StateManager also handles state persistence: it can load a previously saved
+    state from disk at startup and periodically autosave the current state to a file
+    during runtime.
 
     Args:
-        service:
-            The DataService instance whose state is being managed.
-        filename:
-            The file name used for storing the DataService's state.
+        service: The DataService instance whose state is being managed.
+        filename: The file name used for loading and storing the DataService's state.
+            If provided, the state is loaded from this file at startup and saved to it
+            on shutdown or at regular intervals.
+        autosave_interval: Interval in seconds between automatic state save events.
+            If set to `None`, automatic saving is disabled.
 
     Note:
-        The StateManager's cache updates are triggered by notifications and do not
-        include autonomous updates of complex DataService properties, which must be
-        managed programmatically. The cache serves the purpose of providing immediate
-        state information to web clients, reflecting the state after the last property
-        update.
+        The StateManager does not autonomously poll hardware state. It relies on the
+        service to perform such updates. The cache maintained by
+        [`DataServiceCache`][pydase.data_service.data_service_cache.DataServiceCache]
+        reflects the last known state as notified by the `DataServiceObserver`, and is
+        used by the web interface to provide fast and accurate state rendering for
+        connected clients.
     """
 
     def __init__(
-        self, service: "DataService", filename: str | Path | None = None
+        self,
+        service: "DataService",
+        filename: str | Path | None = None,
+        autosave_interval: float | None = None,
     ) -> None:
         self.filename = getattr(service, "_filename", None)
 
@@ -115,6 +114,29 @@ class StateManager:
 
         self.service = service
         self.cache_manager = DataServiceCache(self.service)
+        self.autosave_interval = autosave_interval
+
+    async def autosave(self) -> None:
+        """Periodically saves the current service state to the configured file.
+
+        This coroutine is automatically started by the [`pydase.Server`][pydase.Server]
+        when a filename is provided. It runs in the background and writes the latest
+        known state of the service to disk every `autosave_interval` seconds.
+
+        If `autosave_interval` is set to `None`, autosaving is disabled and this
+        coroutine exits immediately.
+        """
+
+        if self.autosave_interval is None:
+            return
+
+        while True:
+            try:
+                if self.filename is not None:
+                    self.save_state()
+                await asyncio.sleep(self.autosave_interval)
+            except Exception as e:
+                logger.exception(e)
 
     @property
     def cache_value(self) -> dict[str, SerializedObject]:
@@ -122,23 +144,21 @@ class StateManager:
         return cast(dict[str, SerializedObject], self.cache_manager.cache["value"])
 
     def save_state(self) -> None:
-        """
-        Saves the DataService's current state to a JSON file defined by `self.filename`.
-        Logs an error if `self.filename` is not set.
+        """Saves the DataService's current state to a JSON file defined by
+        `self.filename`.
         """
 
         if self.filename is not None:
             with open(self.filename, "w") as f:
                 json.dump(self.cache_value, f, indent=4)
         else:
-            logger.info(
+            logger.debug(
                 "State manager was not initialised with a filename. Skipping "
                 "'save_state'..."
             )
 
     def load_state(self) -> None:
-        """
-        Loads the DataService's state from a JSON file defined by `self.filename`.
+        """Loads the DataService's state from a JSON file defined by `self.filename`.
         Updates the service's attributes, respecting type and read-only constraints.
         """
 
@@ -191,8 +211,7 @@ class StateManager:
         path: str,
         serialized_value: SerializedObject,
     ) -> None:
-        """
-        Sets the value of an attribute in the service managed by the `StateManager`
+        """Sets the value of an attribute in the service managed by the `StateManager`
         given its path as a dot-separated string.
 
         This method updates the attribute specified by 'path' with 'value' only if the

src/pydase/frontend/index.html

@@ -7,15 +7,15 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <meta name="theme-color" content="#000000" />
     <meta name="description" content="Web site displaying a pydase UI." />
-  <script type="module" crossorigin src="/assets/index-B4RiTBEo.js"></script>
-  <link rel="stylesheet" crossorigin href="/assets/index-D2aktF3W.css">
+  <script type="module" crossorigin src="/assets/index-DpoEqi_N.js"></script>
+  <link rel="stylesheet" crossorigin href="/assets/index-DJzFvk4W.css">
 </head>
 
 <script>
     // this will be set by the python backend if the service is behind a proxy which strips a prefix. The frontend can use this to build the paths to the resources.
     window.__FORWARDED_PREFIX__ = "";
     window.__FORWARDED_PROTO__ = "";
-</script>`
+</script>
 
 <body>
     <noscript>You need to enable JavaScript to run this app.</noscript>

src/pydase/server/server.py

@@ -84,21 +84,15 @@ class Server:
     The `Server` class provides a flexible server implementation for the `DataService`.
 
     Args:
-        service:
-            The DataService instance that this server will manage.
-        host:
-            The host address for the server. Defaults to `'0.0.0.0'`, which means all
+        service: The DataService instance that this server will manage.
+        host: The host address for the server. Defaults to `'0.0.0.0'`, which means all
             available network interfaces.
-        web_port:
-            The port number for the web server. Defaults to
+        web_port: The port number for the web server. Defaults to
             [`ServiceConfig().web_port`][pydase.config.ServiceConfig.web_port].
-        enable_web:
-            Whether to enable the web server.
-        filename:
-            Filename of the file managing the service state persistence.
-        additional_servers:
-            A list of additional servers to run alongside the main server.
-
+        enable_web: Whether to enable the web server.
+        filename: Filename of the file managing the service state persistence.
+        additional_servers: A list of additional servers to run alongside the main
+            server.
             Here's an example of how you might define an additional server:
 
             ```python
@@ -137,8 +131,9 @@ class Server:
             )
             server.run()
             ```
-        **kwargs:
-            Additional keyword arguments.
+        autosave_interval: Interval in seconds between automatic state save events.
+            If set to `None`, automatic saving is disabled. Defaults to 30 seconds.
+        **kwargs: Additional keyword arguments.
     """
 
     def __init__(  # noqa: PLR0913
@@ -149,6 +144,7 @@ class Server:
         enable_web: bool = True,
         filename: str | Path | None = None,
         additional_servers: list[AdditionalServer] | None = None,
+        autosave_interval: float = 30.0,
         **kwargs: Any,
     ) -> None:
         if additional_servers is None:
@@ -161,7 +157,11 @@ class Server:
         self._additional_servers = additional_servers
         self.should_exit = False
         self.servers: dict[str, asyncio.Future[Any]] = {}
-        self._state_manager = StateManager(self._service, filename)
+        self._state_manager = StateManager(
+            service=self._service,
+            filename=filename,
+            autosave_interval=autosave_interval,
+        )
         self._observer = DataServiceObserver(self._state_manager)
         self._state_manager.load_state()
         autostart_service_tasks(self._service)
@@ -223,6 +223,8 @@ class Server:
             server_task.add_done_callback(self._handle_server_shutdown)
             self.servers["web"] = server_task
 
+        self._loop.create_task(self._state_manager.autosave())
+
     def _handle_server_shutdown(self, task: asyncio.Task[Any]) -> None:
         """Handle server shutdown. If the service should exit, do nothing. Else, make
         the service exit."""

src/pydase/server/web_server/sio_setup.py

@@ -202,7 +202,7 @@ def setup_sio_events(sio: socketio.AsyncServer, state_manager: StateManager) ->
     @sio.event
     async def trigger_method(sid: str, data: TriggerMethodDict) -> Any:
         async with sio.session(sid) as session:
-            logger.debug(
+            logger.info(
                 "Client [%s] is triggering the method '%s'",
                 session["client_id"],
                 data["access_path"],

src/pydase/server/web_server/web_server.py

@@ -150,10 +150,7 @@ class WebServer:
                     f"{escaped_prefix}/favicon.ico",
                 )
 
-                return aiohttp.web.Response(
-                    text=modified_html, content_type="text/html"
-                )
-            return aiohttp.web.FileResponse(self.frontend_src / "index.html")
+            return aiohttp.web.Response(text=modified_html, content_type="text/html")
 
         app = aiohttp.web.Application()
 

src/pydase/utils/logging.py

@@ -4,7 +4,7 @@ import logging.config
 import sys
 from collections.abc import Callable
 from copy import copy
-from typing import ClassVar, Literal
+from typing import ClassVar, Literal, TextIO
 
 import click
 import socketio  # type: ignore[import-untyped]
@@ -29,22 +29,44 @@ LOGGING_CONFIG = {
             "datefmt": "%Y-%m-%d %H:%M:%S",
         },
     },
+    "filters": {
+        "only_pydase_server": {
+            "()": "pydase.utils.logging.NameFilter",
+            "match": "pydase.server",
+        },
+        "exclude_pydase_server": {
+            "()": "pydase.utils.logging.NameFilter",
+            "match": "pydase.server",
+            "invert": True,
+        },
+    },
     "handlers": {
-        "default": {
+        "stdout_handler": {
             "formatter": "default",
             "class": "logging.StreamHandler",
             "stream": "ext://sys.stdout",
+            "filters": ["only_pydase_server"],
+        },
+        "stderr_handler": {
+            "formatter": "default",
+            "class": "logging.StreamHandler",
+            "stream": "ext://sys.stderr",
+            "filters": ["exclude_pydase_server"],
         },
     },
     "loggers": {
-        "pydase": {"handlers": ["default"], "level": LOG_LEVEL, "propagate": False},
+        "pydase": {
+            "handlers": ["stdout_handler", "stderr_handler"],
+            "level": LOG_LEVEL,
+            "propagate": False,
+        },
         "aiohttp_middlewares": {
-            "handlers": ["default"],
+            "handlers": ["stderr_handler"],
             "level": logging.WARNING,
             "propagate": False,
         },
         "aiohttp": {
-            "handlers": ["default"],
+            "handlers": ["stderr_handler"],
             "level": logging.INFO,
             "propagate": False,
         },
@@ -52,6 +74,23 @@ LOGGING_CONFIG = {
 }
 
 
+class NameFilter(logging.Filter):
+    """
+    Logging filter that allows filtering logs based on the logger name.
+    Can either include or exclude a specific logger.
+    """
+
+    def __init__(self, match: str, invert: bool = False):
+        super().__init__()
+        self.match = match
+        self.invert = invert
+
+    def filter(self, record: logging.LogRecord) -> bool:
+        if self.invert:
+            return not record.name.startswith(self.match)
+        return record.name.startswith(self.match)
+
+
 class DefaultFormatter(logging.Formatter):
     """
     A custom log formatter class that:
@@ -150,3 +189,51 @@ def setup_logging() -> None:
     logger.debug("Configuring pydase logging.")
 
     logging.config.dictConfig(LOGGING_CONFIG)
+
+
+def configure_logging_with_pydase_formatter(
+    name: str | None = None, level: int = logging.INFO, stream: TextIO | None = None
+) -> None:
+    """Configure a logger with the pydase `DefaultFormatter`.
+
+    This sets up a `StreamHandler` with the custom `DefaultFormatter`, which includes
+    timestamp, log level with color (if supported), logger name, function, and line
+    number. It can be used to configure the root logger or any named logger.
+
+    Args:
+        name: The name of the logger to configure. If None, the root logger is used.
+        level: The logging level to set on the logger (e.g., logging.DEBUG,
+            logging.INFO). Defaults to logging.INFO.
+        stream: The output stream for the log messages (e.g., sys.stdout or sys.stderr).
+            If None, defaults to sys.stderr.
+
+    Example:
+        Configure logging in your service:
+
+        ```python
+        import sys
+        from pydase.utils.logging import configure_logging_with_pydase_formatter
+
+        configure_logging_with_pydase_formatter(
+            name="my_service",      # Use the package/module name or None for the root logger
+            level=logging.DEBUG,    # Set the desired logging level (defaults to INFO)
+            stream=sys.stdout       # Set the output stream (stderr by default)
+        )
+        ```
+
+    Notes:
+        - This function adds a new handler each time it's called.
+          Use carefully to avoid duplicate logs.
+        - Colors are enabled if the stream supports TTY (e.g., in terminal).
+    """  # noqa: E501
+
+    logger = logging.getLogger(name=name)
+    handler = logging.StreamHandler(stream=stream)
+    formatter = DefaultFormatter(
+        fmt="%(asctime)s.%(msecs)03d | %(levelprefix)s | "
+        "%(name)s:%(funcName)s:%(lineno)d - %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+    logger.setLevel(level)

tests/client/test_client.py

@@ -161,3 +161,15 @@ def test_context_manager(pydase_client: pydase.Client) -> None:
         assert client.proxy.my_property == 1337.01
 
     assert not client.proxy.connected
+
+
+def test_client_id(
+    pydase_client: pydase.Client, caplog: pytest.LogCaptureFixture
+) -> None:
+    pydase.Client(url="ws://localhost:9999")
+
+    assert "Client [sid=" in caplog.text
+    caplog.clear()
+
+    pydase.Client(url="ws://localhost:9999", client_id="my_service")
+    assert "Client [id=my_service] connected" in caplog.text

tests/data_service/test_state_manager.py

@@ -1,10 +1,13 @@
+import asyncio
 import json
 from pathlib import Path
 from typing import Any
 
+import anyio
 import pydase
 import pydase.components
 import pydase.units as u
+import pytest
 from pydase.data_service.data_service_observer import DataServiceObserver
 from pydase.data_service.state_manager import (
     StateManager,
@@ -349,4 +352,24 @@ def test_property_load_state(tmp_path: Path) -> None:
 
     assert service_instance.name == "Some other name"
     assert service_instance.not_loadable_attr == "Not loadable"
-    assert not has_load_state_decorator(type(service_instance).property_without_setter)
+    assert not has_load_state_decorator(type(service_instance).property_without_setter)  # type: ignore
+
+
+@pytest.mark.asyncio()
+async def test_autosave(tmp_path: Path, caplog: LogCaptureFixture) -> None:
+    filename = tmp_path / "state.json"
+
+    service = Service()
+    manager = StateManager(service=service, filename=filename, autosave_interval=0.1)
+    DataServiceObserver(state_manager=manager)
+
+    task = asyncio.create_task(manager.autosave())
+    service.property_attr = 198.0
+    await asyncio.sleep(0.1)
+    task.cancel()
+
+    assert filename.exists(), "Autosave should write to the file"
+    async with await anyio.open_file(filename) as f:
+        data = json.loads(await f.read())
+
+    assert data["property_attr"]["value"] == service.property_attr

tests/utils/test_logging.py

@@ -1,9 +1,10 @@
 import logging
 
-from pytest import LogCaptureFixture
+import pytest
+from pydase.utils.logging import configure_logging_with_pydase_formatter
 
 
-def test_log_error(caplog: LogCaptureFixture):
+def test_log_error(caplog: pytest.LogCaptureFixture) -> None:
     logger = logging.getLogger("pydase")
     logger.setLevel(logging.ERROR)
 
@@ -20,7 +21,7 @@ def test_log_error(caplog: LogCaptureFixture):
     assert any(record.levelname == "ERROR" for record in caplog.records)
 
 
-def test_log_warning(caplog: LogCaptureFixture):
+def test_log_warning(caplog: pytest.LogCaptureFixture) -> None:
     logger = logging.getLogger("pydase")
     logger.setLevel(logging.WARNING)
 
@@ -37,7 +38,7 @@ def test_log_warning(caplog: LogCaptureFixture):
     assert any(record.levelname == "ERROR" for record in caplog.records)
 
 
-def test_log_debug(caplog: LogCaptureFixture):
+def test_log_debug(caplog: pytest.LogCaptureFixture) -> None:
     logger = logging.getLogger("pydase")
     logger.setLevel(logging.DEBUG)
 
@@ -53,7 +54,7 @@ def test_log_debug(caplog: LogCaptureFixture):
     assert "This is an error message" in caplog.text
 
 
-def test_log_info(caplog: LogCaptureFixture):
+def test_log_info(caplog: pytest.LogCaptureFixture) -> None:
     logger = logging.getLogger("pydase")
     logger.setLevel(logging.INFO)
 
@@ -67,3 +68,21 @@ def test_log_info(caplog: LogCaptureFixture):
     assert "This is an info message" in caplog.text
     assert "This is a warning message" in caplog.text
     assert "This is an error message" in caplog.text
+
+
+def test_before_configuring_root_logger(caplog: pytest.LogCaptureFixture) -> None:
+    logger = logging.getLogger(__name__)
+    logger.info("Hello world")
+
+    assert "Hello world" not in caplog.text
+
+
+def test_configure_root_logger(caplog: pytest.LogCaptureFixture) -> None:
+    configure_logging_with_pydase_formatter()
+    logger = logging.getLogger(__name__)
+    logger.info("Hello world")
+
+    assert (
+        "INFO     tests.utils.test_logging:test_logging.py:83 Hello world"
+        in caplog.text
+    )