├── .github └── workflows │ ├── ci.yml │ └── python-publish.yml ├── .gitignore ├── .readthedocs.yaml ├── LICENSE ├── README.md ├── assets └── logo_3.png ├── docs ├── advanced-features.md ├── api │ ├── compute-signal.md │ ├── effect.md │ ├── signal.md │ └── utils.md ├── assets │ └── logo_3.png ├── core-concepts.md ├── examples │ └── index.md ├── index.md ├── installation.md ├── operators.md ├── quickstart.md ├── requirements.txt └── why-reaktiv.md ├── examples ├── fastapi_websocket.py ├── iot_sensor_agent_thread.py ├── iot_temp_monitor.py ├── napkin_calc.py ├── nicegui_todo_app.py ├── numpy_plotting.py ├── polling_system.py ├── reactive_excel.py ├── reactive_jupyter_notebook.ipynb └── stock_ticker.py ├── mkdocs.yml ├── pyproject.toml ├── pyrightconfig.json ├── src └── reaktiv │ ├── __init__.py │ ├── core.py │ ├── operators.py │ └── utils.py ├── tests ├── test_batch_notifications.py ├── test_custom_equality.py ├── test_effect_triggers.py ├── test_lazy_computed.py ├── test_operator_chaining.py ├── test_operators.py └── test_signals.py └── uv.lock /.github/workflows/ci.yml: -------------------------------------------------------------------------------- 1 | name: CI 2 | 3 | on: 4 | push: 5 | branches: [ "**" ] # Trigger on pushes to all branches 6 | pull_request: 7 | branches: [ "**" ] # Also trigger on pull requests to all branches 8 | 9 | permissions: 10 | contents: read 11 | 12 | jobs: 13 | test-and-build: 14 | runs-on: ubuntu-latest 15 | strategy: 16 | matrix: 17 | # Test against the minimum supported Python version and the latest stable version 18 | python-version: ['3.9', '3.12'] 19 | 20 | steps: 21 | - uses: actions/checkout@v4 22 | 23 | - name: astral-sh/setup-uv 24 | uses: astral-sh/setup-uv@v5.3.1 25 | with: 26 | python-version: 3.9 27 | 28 | - name: Run Pyright 29 | run: | 30 | uv run pyright src/ 31 | 32 | - name: Run tests 33 | run: | 34 | uv run pytest tests/ 35 | 36 | - name: Build package 37 | run: | 38 | uv build -------------------------------------------------------------------------------- /.github/workflows/python-publish.yml: -------------------------------------------------------------------------------- 1 | # This workflow will upload a Python Package to PyPI when a release is created 2 | # For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries 3 | 4 | # This workflow uses actions that are not certified by GitHub. 5 | # They are provided by a third-party and are governed by 6 | # separate terms of service, privacy policy, and support 7 | # documentation. 8 | 9 | name: Upload Python Package 10 | 11 | on: 12 | release: 13 | types: [published] 14 | 15 | permissions: 16 | contents: read 17 | id-token: write 18 | 19 | jobs: 20 | release-build: 21 | runs-on: ubuntu-latest 22 | 23 | steps: 24 | - uses: actions/checkout@v4 25 | 26 | - name: astral-sh/setup-uv 27 | uses: astral-sh/setup-uv@v5.3.1 28 | with: 29 | python-version: 3.9 30 | 31 | - name: Run Pyright 32 | run: | 33 | uv run pyright src/ 34 | 35 | - name: Run tests 36 | run: | 37 | uv run pytest tests/ 38 | 39 | - name: Build package 40 | run: | 41 | uv build 42 | 43 | - name: Publish to PyPi 44 | env: 45 | UV_PUBLISH_TOKEN: ${{ secrets.UV_PUBLISH_TOKEN }} 46 | run: | 47 | uv publish 48 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Python-generated files 2 | __pycache__/ 3 | *.py[oc] 4 | build/ 5 | dist/ 6 | wheels/ 7 | *.egg-info 8 | 9 | # Virtual environments 10 | .venv 11 | -------------------------------------------------------------------------------- /.readthedocs.yaml: -------------------------------------------------------------------------------- 1 | # Read the Docs configuration file 2 | # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details 3 | 4 | # Required 5 | version: 2 6 | 7 | # Set the OS, Python version, and other tools you might need 8 | build: 9 | os: ubuntu-24.04 10 | tools: 11 | python: "3.13" 12 | 13 | # Build documentation with Mkdocs 14 | mkdocs: 15 | configuration: mkdocs.yml 16 | 17 | # Optionally, but recommended, 18 | # declare the Python requirements required to build your documentation 19 | # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html 20 | python: 21 | install: 22 | - requirements: docs/requirements.txt 23 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2025 Tuan Anh Bui 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /assets/logo_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/buiapp/reaktiv/cacce5c3105334b5e7fef928bf3e884176cecc55/assets/logo_3.png -------------------------------------------------------------------------------- /docs/advanced-features.md: -------------------------------------------------------------------------------- 1 | # Advanced Features 2 | 3 | This page covers advanced features and techniques in reaktiv for building more sophisticated reactive systems. 4 | 5 | ## Custom Equality Functions 6 | 7 | By default, reaktiv uses identity comparison (`is`) to determine if a signal's value has changed. For more complex types, you can provide custom equality functions: 8 | 9 | ```python 10 | from reaktiv import Signal 11 | 12 | # Custom equality for dictionaries 13 | def dict_equal(a, b): 14 | if not isinstance(a, dict) or not isinstance(b, dict): 15 | return a == b 16 | if set(a.keys()) != set(b.keys()): 17 | return False 18 | return all(a[k] == b[k] for k in a) 19 | 20 | # Create a signal with custom equality 21 | user = Signal({"name": "Alice", "age": 30}, equal=dict_equal) 22 | 23 | # This won't trigger updates because the dictionaries are equal by value 24 | user.set({"name": "Alice", "age": 30}) 25 | 26 | # This will trigger updates because the "age" value is different 27 | user.set({"name": "Alice", "age": 31}) 28 | ``` 29 | 30 | Custom equality functions are especially useful for: 31 | 32 | - Complex data structures like dictionaries, lists, or custom objects 33 | - Case-insensitive string comparison 34 | - Numerical comparison with tolerance (for floating-point values) 35 | - Domain-specific equality (e.g., comparing users by ID regardless of other attributes) 36 | 37 | ## Effect Cleanup 38 | 39 | Effects can register cleanup functions that will run before the next execution or when the effect is disposed: 40 | 41 | ```python 42 | from reaktiv import Signal, Effect 43 | 44 | counter = Signal(0) 45 | 46 | def counter_effect(on_cleanup): 47 | value = counter() 48 | print(f"Setting up for counter value: {value}") 49 | 50 | # Set up some resource or state 51 | 52 | # Define cleanup function 53 | def cleanup(): 54 | print(f"Cleaning up for counter value: {value}") 55 | # Release resources, remove event listeners, etc. 56 | 57 | # Register the cleanup function 58 | on_cleanup(cleanup) 59 | 60 | # Create and schedule the effect 61 | logger = Effect(counter_effect) 62 | 63 | # Prints: "Setting up for counter value: 0" 64 | 65 | # Update the signal 66 | counter.set(1) 67 | # Prints: "Cleaning up for counter value: 0" 68 | # Prints: "Setting up for counter value: 1" 69 | 70 | # Dispose the effect 71 | logger.dispose() 72 | # Prints: "Cleaning up for counter value: 1" 73 | ``` 74 | 75 | This pattern is useful for: 76 | 77 | - Managing subscriptions to external event sources 78 | - Releasing resources when values change or the effect is disposed 79 | - Setting up and tearing down UI elements in response to data changes 80 | - Cancelling pending operations when new values arrive 81 | 82 | ## Asynchronous Iteration 83 | 84 | The `to_async_iter` utility lets you use signals with `async for` loops: 85 | 86 | ```python 87 | import asyncio 88 | from reaktiv import Signal, to_async_iter 89 | 90 | async def main(): 91 | counter = Signal(0) 92 | 93 | # Start a task that increments the counter 94 | async def increment_counter(): 95 | for i in range(1, 5): 96 | await asyncio.sleep(1) 97 | counter.set(i) 98 | 99 | asyncio.create_task(increment_counter()) 100 | 101 | # Use the signal as an async iterator 102 | async for value in to_async_iter(counter): 103 | print(f"Got value: {value}") 104 | if value >= 4: 105 | break 106 | 107 | asyncio.run(main()) 108 | ``` 109 | 110 | Output: 111 | ``` 112 | Got value: 0 113 | Got value: 1 114 | Got value: 2 115 | Got value: 3 116 | Got value: 4 117 | ``` 118 | 119 | This is useful for: 120 | 121 | - Building reactive data processing pipelines 122 | - Integrating with other async code 123 | - Responding to signal changes in event loops 124 | - Creating reactive streams of data 125 | 126 | ## Selective Dependency Tracking 127 | 128 | You can selectively control which signals create dependencies using `untracked`: 129 | 130 | ```python 131 | from reaktiv import Signal, Effect, untracked 132 | 133 | user_id = Signal(123) 134 | user_data = Signal({"name": "Alice"}) 135 | show_details = Signal(False) 136 | 137 | def render_user(): 138 | # Always creates a dependency on user_id 139 | id_value = user_id() 140 | 141 | # Only access user_data if show_details is true, 142 | # but don't create a dependency on show_details 143 | if untracked(lambda: show_details()): 144 | print(f"User {id_value}: {user_data()}") 145 | else: 146 | print(f"User {id_value}") 147 | 148 | # Create and schedule the effect 149 | display = Effect(render_user) 150 | 151 | # Update dependencies will trigger the effect 152 | user_id.set(456) 153 | 154 | # This update won't trigger the effect, even though it changes the output 155 | show_details.set(True) 156 | ``` -------------------------------------------------------------------------------- /docs/api/compute-signal.md: -------------------------------------------------------------------------------- 1 | # Computed Signal API 2 | 3 | The `Computed` class creates a signal that derives its value from other signals. It automatically tracks dependencies and updates when those dependencies change. 4 | 5 | ## Basic Usage 6 | 7 | ```python 8 | from reaktiv import Signal, Computed 9 | 10 | # Base signals 11 | x = Signal(10) 12 | y = Signal(20) 13 | 14 | # Computed signal that depends on x and y 15 | sum_xy = Computed(lambda: x() + y()) 16 | 17 | print(sum_xy()) # 30 18 | 19 | # When a dependency changes, the computed value updates automatically 20 | x.set(15) 21 | print(sum_xy()) # 35 22 | ``` 23 | 24 | ## Creation 25 | 26 | ```python 27 | Computed(compute_fn: Callable[[], T], default: Optional[T] = None, *, equal: Optional[Callable[[T, T], bool]] = None) -> ComputedSignal[T] 28 | ``` 29 | 30 | Creates a new computed signal that derives its value from other signals. 31 | 32 | ### Parameters 33 | 34 | - `compute_fn`: A function that computes the signal's value. When this function accesses other signals by calling them, dependencies are automatically tracked. 35 | - `default`: An optional default value to use until the first computation and when computation fails due to an error. 36 | - `equal`: Optional custom equality function to determine if two computed values are considered equal. 37 | 38 | ### Returns 39 | 40 | A computed signal object that can be called to get its value. 41 | 42 | ## Methods 43 | 44 | ### Calling the computed signal 45 | 46 | ```python 47 | sum_xy() # equivalent to sum_xy.get() 48 | ``` 49 | 50 | Returns the computed value, calculating it if necessary. When called within an active effect or another computed signal, it establishes a dependency relationship. 51 | 52 | **Returns**: The computed value. 53 | 54 | **Note**: A computed signal is lazy. It only computes its value when called, and it caches the result until dependencies change. 55 | 56 | ## Advanced Methods 57 | 58 | The following methods are typically used internally by the library: 59 | 60 | ### subscribe / unsubscribe 61 | 62 | These methods work the same as in regular signals and are usually used internally by the library. 63 | 64 | ## Error Handling 65 | 66 | When a computed signal's computation function raises an exception, the exception is propagated to the caller. This allows you to handle errors at the appropriate level in your application. 67 | 68 | ```python 69 | from reaktiv import Signal, Computed 70 | 71 | # Base signal 72 | x = Signal(10) 73 | 74 | # Computed signal with potential error 75 | result = Computed(lambda: 100 / x()) 76 | 77 | print(result()) # 10 (100 / 10) 78 | 79 | # Set x to 0, which would cause a division by zero 80 | x.set(0) 81 | 82 | # The exception will be propagated to the caller 83 | try: 84 | print(result()) 85 | except ZeroDivisionError as e: 86 | print(f"Caught error: {e}") # Prints: "Caught error: division by zero" 87 | 88 | # After fixing the dependency value, computation works again 89 | x.set(5) 90 | print(result()) # 20 (100 / 5) 91 | ``` 92 | 93 | This transparent error propagation gives you full control over error handling in your application. You can: 94 | 95 | 1. Use try/except blocks where you access computed values 96 | 2. Let exceptions bubble up to a higher-level error handler 97 | 3. Use defensive programming in your computation functions 98 | 99 | ## Lazy Evaluation 100 | 101 | A key feature of computed signals is lazy evaluation. The computation function only runs: 102 | 103 | 1. The first time the signal is called 104 | 2. When dependencies have changed since the last computation 105 | 106 | This means expensive computations are only performed when necessary: 107 | 108 | ```python 109 | from reaktiv import Signal, Computed 110 | 111 | x = Signal(10) 112 | y = Signal(20) 113 | 114 | def expensive_computation(): 115 | print("Computing...") 116 | return x() * y() 117 | 118 | result = Computed(expensive_computation) 119 | 120 | # Nothing happens yet - computation is lazy 121 | 122 | # First access - computation runs 123 | print(result()) # Prints: "Computing..." then "200" 124 | 125 | # Second access - no computation needed because nothing changed 126 | print(result()) # Just prints "200" (no "Computing..." message) 127 | 128 | # Change a dependency 129 | x.set(5) 130 | 131 | # Now accessing will recompute 132 | print(result()) # Prints: "Computing..." then "100" 133 | ``` 134 | 135 | ## Note on ComputeSignal vs computed() 136 | 137 | While reaktiv provides both the `Computed` class (alias for `ComputeSignal`) and `computed()` shortcut function, the recommended approach is to use the `Computed` class directly for a more consistent API. 138 | 139 | The `computed()` function is deprecated and will be removed in a future version. It currently emits a deprecation warning: 140 | 141 | ```python 142 | # Deprecated approach (will show warning): 143 | from reaktiv import signal, computed 144 | x = signal(10) 145 | doubled = computed(lambda: x() * 2) 146 | 147 | # Recommended approach: 148 | from reaktiv import Signal, Computed 149 | x = Signal(10) 150 | doubled = Computed(lambda: x() * 2) 151 | ``` -------------------------------------------------------------------------------- /docs/api/effect.md: -------------------------------------------------------------------------------- 1 | # Effect API 2 | 3 | The `Effect` class creates side effects that automatically run when their dependencies change. Effects are useful for updating UI elements, logging, API calls, and other operations that respond to state changes. 4 | 5 | ## Basic Usage 6 | 7 | ```python 8 | from reaktiv import Signal, Effect 9 | 10 | # Create a signal 11 | counter = Signal(0) 12 | 13 | # Create an effect that runs when counter changes 14 | counter_effect = Effect(lambda: print(f"Counter: {counter()}")) 15 | # Immediately prints: "Counter: 0" 16 | 17 | # When the signal changes, the effect runs automatically 18 | counter.set(1) 19 | # Prints: "Counter: 1" 20 | 21 | # Clean up when done 22 | counter_effect.dispose() 23 | ``` 24 | 25 | ## Creation 26 | 27 | ```python 28 | Effect(func: Callable[..., Union[None, Coroutine[None, None, None]]]) -> Effect 29 | ``` 30 | 31 | Creates a new effect that automatically runs when its dependencies change. 32 | 33 | ### Parameters 34 | 35 | - `func`: A function or coroutine function to run when dependencies change. Dependencies are automatically tracked when this function accesses signals by calling them. If the function accepts a parameter, it receives an `on_cleanup` function. 36 | 37 | ### Returns 38 | 39 | An effect object that manages the execution of the function when dependencies change. 40 | 41 | ## Methods 42 | 43 | ### dispose 44 | 45 | ```python 46 | dispose() -> None 47 | ``` 48 | 49 | Disposes of the effect, removing all dependencies and preventing it from running again. 50 | 51 | **Note**: You should call `dispose()` when an effect is no longer needed to prevent memory leaks. 52 | 53 | ## Asynchronous Effects 54 | 55 | reaktiv has first-class support for asynchronous effects: 56 | 57 | ```python 58 | import asyncio 59 | from reaktiv import Signal, Effect 60 | 61 | async def main(): 62 | counter = Signal(0) 63 | 64 | async def log_counter(): 65 | print(f"Counter value: {counter()}") 66 | 67 | # Create and schedule the async effect 68 | logger = Effect(log_counter) # Prints: "Counter value: 0" 69 | 70 | # Update the signal and wait for the effect to run 71 | counter.set(1) 72 | await asyncio.sleep(0) # Gives the effect time to execute 73 | # Prints: "Counter value: 1" 74 | 75 | # Clean up 76 | logger.dispose() 77 | 78 | asyncio.run(main()) 79 | ``` 80 | 81 | ## Cleanup Functions 82 | 83 | Effects can register cleanup functions that will be executed before the effect runs again or when it's disposed: 84 | 85 | ```python 86 | from reaktiv import Signal, Effect 87 | 88 | counter = Signal(0) 89 | 90 | def counter_effect(on_cleanup): 91 | value = counter() 92 | print(f"Counter value: {value}") 93 | 94 | # Register a cleanup function 95 | def cleanup(): 96 | print(f"Cleaning up for value: {value}") 97 | 98 | on_cleanup(cleanup) 99 | 100 | # Create and schedule the effect with cleanup 101 | logger = Effect(counter_effect) 102 | # Prints: "Counter value: 0" 103 | 104 | # Update the signal 105 | counter.set(1) 106 | # Prints: "Cleaning up for value: 0" 107 | # Prints: "Counter value: 1" 108 | 109 | # Dispose the effect 110 | logger.dispose() 111 | # Prints: "Cleaning up for value: 1" 112 | ``` 113 | 114 | ## Memory Management 115 | 116 | Effects are not automatically garbage collected as long as they're actively tracking dependencies. To prevent memory leaks: 117 | 118 | 1. Keep a reference to your effect as long as you need it 119 | 2. Call `dispose()` when you're done with the effect 120 | 3. Avoid creating effects inside loops or frequently-called functions without disposing of them 121 | 122 | ```python 123 | from reaktiv import Signal, Effect 124 | 125 | def create_temporary_effect(s): 126 | # This effect will only exist while the function runs 127 | temp_effect = Effect(lambda: print(f"Value: {s()}")) 128 | # ... do something ... 129 | temp_effect.dispose() # Clean up properly 130 | 131 | # Better pattern for component lifecycle 132 | class MyComponent: 133 | def __init__(self, s): 134 | self.s = s 135 | self.effect_instance = Effect(self._render) 136 | 137 | def _render(self): 138 | print(f"Rendering: {self.s()}") 139 | 140 | def destroy(self): 141 | self.effect_instance.dispose() 142 | ``` 143 | 144 | ## Notification Batching 145 | 146 | When multiple signals change, their effects are batched to avoid unnecessary executions: 147 | 148 | ```python 149 | from reaktiv import Signal, Effect, batch 150 | 151 | x = Signal(1) 152 | y = Signal(2) 153 | 154 | def log_values(): 155 | print(f"x: {x()}, y: {y()}") 156 | 157 | logger = Effect(log_values) # Prints: "x: 1, y: 2" 158 | 159 | # Without batching, the effect would run twice: 160 | # x.set(10) # Effect runs 161 | # y.set(20) # Effect runs again 162 | 163 | # With batching, the effect runs only once after all changes: 164 | with batch(): 165 | x.set(10) # No effect execution yet 166 | y.set(20) # No effect execution yet 167 | # After batch completes: Effect runs once with new values 168 | # Prints: "x: 10, y: 20" 169 | ``` 170 | 171 | ## Note on Effect vs effect() 172 | 173 | While reaktiv provides both the `Effect` class and `effect()` shortcut function, the recommended approach is to use the `Effect` class directly for a more consistent API. 174 | 175 | The `effect()` function is deprecated and will be removed in a future version. It currently emits a deprecation warning: 176 | 177 | ```python 178 | # Deprecated approach (will show warning): 179 | from reaktiv import signal, effect 180 | count = signal(0) 181 | count_effect = effect(lambda: print(f"Count: {count()}")) 182 | 183 | # Recommended approach: 184 | from reaktiv import Signal, Effect 185 | count = Signal(0) 186 | count_effect = Effect(lambda: print(f"Count: {count()}")) 187 | ``` -------------------------------------------------------------------------------- /docs/api/signal.md: -------------------------------------------------------------------------------- 1 | # Signal API 2 | 3 | The `Signal` class is the core building block in reaktiv. It creates a container for values that can change over time and notify dependents of those changes. 4 | 5 | ## Basic Usage 6 | 7 | ```python 8 | from reaktiv import Signal 9 | 10 | # Create a signal with an initial value 11 | counter = Signal(0) 12 | 13 | # Get the current value 14 | value = counter() # 0 15 | 16 | # Set a new value 17 | counter.set(5) 18 | 19 | # Update using a function 20 | counter.update(lambda x: x + 1) # Now 6 21 | ``` 22 | 23 | ## Creation 24 | 25 | ```python 26 | Signal(value: T, *, equal: Optional[Callable[[T, T], bool]] = None) -> Signal[T] 27 | ``` 28 | 29 | Creates a new signal with an initial value. 30 | 31 | ### Parameters 32 | 33 | - `value`: The initial value of the signal. 34 | - `equal`: Optional custom equality function to determine if two values should be considered equal. By default, identity (`is`) is used. 35 | 36 | ### Returns 37 | 38 | A signal object that can be called to get its value and has methods to set and update its value. 39 | 40 | ## Methods 41 | 42 | ### Calling the signal 43 | 44 | ```python 45 | counter() # equivalent to counter.get() 46 | ``` 47 | 48 | Returns the current value of the signal. When called within an active effect or computed signal, it establishes a dependency relationship. 49 | 50 | **Returns**: The current value of the signal. 51 | 52 | ### set 53 | 54 | ```python 55 | set(new_value: T) -> None 56 | ``` 57 | 58 | Updates the signal's value and notifies subscribers if the value has changed. 59 | 60 | **Parameters**: 61 | - `new_value`: The new value to set. 62 | 63 | **Note**: A notification is triggered only if the new value is considered different from the current value. By default, identity comparison (`is`) is used unless a custom equality function was provided. 64 | 65 | ### update 66 | 67 | ```python 68 | update(update_fn: Callable[[T], T]) -> None 69 | ``` 70 | 71 | Updates the signal's value by applying a function to its current value. 72 | 73 | **Parameters**: 74 | - `update_fn`: A function that takes the current value and returns the new value. 75 | 76 | ## Advanced Methods 77 | 78 | The following methods are typically used internally by the library and are not needed for most applications: 79 | 80 | ### subscribe 81 | 82 | ```python 83 | subscribe(subscriber: Subscriber) -> None 84 | ``` 85 | 86 | Adds a subscriber to be notified when the signal's value changes. 87 | 88 | **Parameters**: 89 | - `subscriber`: An object implementing the `Subscriber` protocol with a `notify()` method. 90 | 91 | **Note**: This is typically used internally by the library. Most applications should use `effect()` or `compute()` instead. 92 | 93 | ### unsubscribe 94 | 95 | ```python 96 | unsubscribe(subscriber: Subscriber) -> None 97 | ``` 98 | 99 | Removes a subscriber so it no longer receives notifications. 100 | 101 | **Parameters**: 102 | - `subscriber`: The subscriber to remove. 103 | 104 | **Note**: This is typically used internally by the library. 105 | 106 | ## Custom Equality Example 107 | 108 | ```python 109 | from reaktiv import Signal 110 | 111 | # Custom equality function for comparing dictionaries by value 112 | def dict_equal(a, b): 113 | if not isinstance(a, dict) or not isinstance(b, dict): 114 | return a == b 115 | if a.keys() != b.keys(): 116 | return False 117 | return all(a[k] == b[k] for k in a) 118 | 119 | # Create a signal with custom equality 120 | user = Signal({"name": "Alice", "age": 30}, equal=dict_equal) 121 | 122 | # This won't trigger updates because the dictionaries have the same key-value pairs 123 | user.set({"name": "Alice", "age": 30}) 124 | 125 | # This will trigger updates because the "age" value differs 126 | user.set({"name": "Alice", "age": 31}) 127 | ``` 128 | 129 | ## Note on Signal vs signal() 130 | 131 | While reaktiv provides both the `Signal` class and `signal()` shortcut function, the recommended approach is to use the `Signal` class directly for a more consistent API. 132 | 133 | The `signal()` function is deprecated and will be removed in a future version. It currently emits a deprecation warning: 134 | 135 | ```python 136 | # Deprecated approach (will show warning): 137 | from reaktiv import signal 138 | counter = signal(0) 139 | 140 | # Recommended approach: 141 | from reaktiv import Signal 142 | counter = Signal(0) 143 | ``` -------------------------------------------------------------------------------- /docs/api/utils.md: -------------------------------------------------------------------------------- 1 | # Utilities API 2 | 3 | reaktiv provides several utility functions to enhance your reactive programming experience. 4 | 5 | ## batch 6 | 7 | ```python 8 | batch() 9 | ``` 10 | 11 | A context manager that batches multiple signal updates together, deferring computations and effects until the batch completes. 12 | 13 | ### Usage 14 | 15 | ```python 16 | from reaktiv import signal, computed, effect, batch 17 | 18 | x = signal(5) 19 | y = signal(10) 20 | sum_xy = computed(lambda: x() + y()) 21 | 22 | def log_sum(): 23 | print(f"Sum: {sum_xy()}") 24 | 25 | logger = effect(log_sum) # Prints: "Sum: 15" 26 | 27 | # Without batching, this would trigger two separate updates: 28 | # x.set(10) # Triggers recomputation & effect 29 | # y.set(20) # Triggers recomputation & effect again 30 | 31 | # With batching, updates are processed together: 32 | with batch(): 33 | x.set(10) # No immediate effect execution 34 | y.set(20) # No immediate effect execution 35 | # After batch completes, computations and effects run once 36 | # Prints: "Sum: 30" 37 | ``` 38 | 39 | ### Benefits 40 | 41 | - **Performance**: Reduces unnecessary recomputations when multiple signals change together 42 | - **Consistency**: Ensures effects only see the final state after all updates are complete 43 | - **Atomicity**: Makes a series of updates appear as a single atomic change 44 | 45 | ## untracked 46 | 47 | ```python 48 | untracked(func: Callable[[], T]) -> T 49 | ``` 50 | 51 | Executes a function without creating dependencies on any signals accessed within it. 52 | 53 | ### Parameters 54 | 55 | - `func`: The function to execute without tracking signal dependencies. 56 | 57 | ### Returns 58 | 59 | - The return value of the executed function. 60 | 61 | ### Usage 62 | 63 | ```python 64 | from reaktiv import signal, effect, untracked 65 | 66 | name = signal("Alice") 67 | greeting = signal("Hello") 68 | 69 | def log_message(): 70 | # This creates a dependency on the 'name' signal 71 | person = name() 72 | 73 | # This does NOT create a dependency on the 'greeting' signal 74 | prefix = untracked(lambda: greeting()) 75 | 76 | print(f"{prefix}, {person}!") 77 | 78 | logger = effect(log_message) # Prints: "Hello, Alice!" 79 | 80 | # This will trigger the effect because 'name' is a dependency 81 | name.set("Bob") # Prints: "Hello, Bob!" 82 | 83 | # This will NOT trigger the effect because 'greeting' is accessed via untracked() 84 | greeting.set("Hi") # No effect execution 85 | ``` 86 | 87 | ### Use Cases 88 | 89 | - Accessing signals without creating dependencies 90 | - Reading configuration values that shouldn't trigger reactivity 91 | - Breaking circular dependencies 92 | - Optimizing performance by selectively tracking only necessary dependencies 93 | 94 | ## to_async_iter 95 | 96 | ```python 97 | to_async_iter(signal_instance) -> AsyncIterator[T] 98 | ``` 99 | 100 | Converts a signal into an async iterator that yields values whenever the signal changes. 101 | 102 | ### Parameters 103 | 104 | - `signal_instance`: The signal to convert to an async iterator. 105 | 106 | ### Returns 107 | 108 | - An async iterator that yields the signal's value on each change. 109 | 110 | ### Usage 111 | 112 | ```python 113 | import asyncio 114 | from reaktiv import signal, to_async_iter 115 | 116 | async def main(): 117 | counter = signal(0) 118 | 119 | # Create a task that increments the counter 120 | async def increment_counter(): 121 | for i in range(1, 6): 122 | await asyncio.sleep(1) 123 | counter.set(i) 124 | 125 | # Start the counter task 126 | asyncio.create_task(increment_counter()) 127 | 128 | # Use the counter signal as an async iterator 129 | async for value in to_async_iter(counter): 130 | print(f"Counter changed: {value}") 131 | if value >= 5: 132 | break 133 | 134 | asyncio.run(main()) 135 | ``` 136 | 137 | ### Output 138 | 139 | ``` 140 | Counter changed: 0 141 | Counter changed: 1 142 | Counter changed: 2 143 | Counter changed: 3 144 | Counter changed: 4 145 | Counter changed: 5 146 | ``` 147 | 148 | ### Use Cases 149 | 150 | - Integrating signals with async for loops 151 | - Processing signal values as a stream 152 | - Converting between signals and other async primitives 153 | - Building reactive data pipelines 154 | - Using signals with other async libraries -------------------------------------------------------------------------------- /docs/assets/logo_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/buiapp/reaktiv/cacce5c3105334b5e7fef928bf3e884176cecc55/docs/assets/logo_3.png -------------------------------------------------------------------------------- /docs/core-concepts.md: -------------------------------------------------------------------------------- 1 | # Core Concepts 2 | 3 | This page explains the core concepts of reactive programming as implemented in the reaktiv library. 4 | 5 | ## Reactive Programming 6 | 7 | Reactive programming is a declarative programming paradigm concerned with data streams and the propagation of changes. With reaktiv, you define how your application state should be derived from its inputs, and the library takes care of updating everything when those inputs change. 8 | 9 | ## reaktiv's Core Primitives 10 | 11 | reaktiv provides three main primitives for reactive programming: 12 | 13 | ```mermaid 14 | graph TD 15 | %% Define node subgraphs for better organization 16 | subgraph "Data Sources" 17 | S1[Signal A] 18 | S2[Signal B] 19 | S3[Signal C] 20 | end 21 | 22 | subgraph "Derived Values" 23 | C1[Computed X] 24 | C2[Computed Y] 25 | end 26 | 27 | subgraph "Side Effects" 28 | E1[Effect 1] 29 | E2[Effect 2] 30 | end 31 | 32 | subgraph "External Systems" 33 | EXT1[UI Update] 34 | EXT2[API Call] 35 | EXT3[Database Write] 36 | end 37 | 38 | %% Define relationships between nodes 39 | S1 -->|"get()"| C1 40 | S2 -->|"get()"| C1 41 | S2 -->|"get()"| C2 42 | S3 -->|"get()"| C2 43 | 44 | C1 -->|"get()"| E1 45 | C2 -->|"get()"| E1 46 | S3 -->|"get()"| E2 47 | C2 -->|"get()"| E2 48 | 49 | E1 --> EXT1 50 | E1 --> EXT2 51 | E2 --> EXT3 52 | 53 | %% Change propagation path 54 | S1 -.-> |"1\. set()"| C1 55 | C1 -.->|"2\. recompute"| E1 56 | E1 -.->|"3\. execute"| EXT1 57 | 58 | %% Style nodes by type 59 | classDef signal fill:#4CAF50,color:white,stroke:#388E3C,stroke-width:1px 60 | classDef computed fill:#2196F3,color:white,stroke:#1976D2,stroke-width:1px 61 | classDef effect fill:#FF9800,color:white,stroke:#F57C00,stroke-width:1px 62 | 63 | %% Apply styles to nodes 64 | class S1,S2,S3 signal 65 | class C1,C2 computed 66 | class E1,E2 effect 67 | 68 | %% Legend node 69 | LEGEND[" Legend: 70 | • Signal: Stores a value, notifies dependents 71 | • Computed: Derives value from dependencies 72 | • Effect: Runs side effects when dependencies change 73 | • → Data flow / Dependency (read) 74 | • ⟿ Change propagation (update) 75 | "] 76 | classDef legend fill:none,stroke:none,text-align:left 77 | class LEGEND legend 78 | ``` 79 | 80 | The diagram above illustrates how reaktiv's primitives interact: 81 | 82 | - **Signals** (green) store values and form the foundation of your reactive system 83 | - **Computed values** (blue) derive data from signals and other computed values 84 | - **Effects** (orange) perform side effects when their dependencies change 85 | - Arrows show both data flow (solid) and change propagation (dotted) 86 | 87 | ### 1. Signals 88 | 89 | Signals are containers for values that can change over time. They notify interested parties (subscribers) when their values change. 90 | 91 | ```python 92 | from reaktiv import Signal 93 | 94 | # Create a signal with initial value 95 | counter = Signal(0) 96 | 97 | # Get the current value 98 | value = counter() # 0 99 | 100 | # Set a new value 101 | counter.set(1) 102 | 103 | # Update using a function 104 | counter.update(lambda x: x + 1) # Now 2 105 | ``` 106 | 107 | Signals are the fundamental building blocks in reaktiv. They: 108 | 109 | - Store a single value 110 | - Provide methods to get and set that value 111 | - Track dependencies that read their values 112 | - Notify dependents when their values change 113 | 114 | ### 2. Computed Signals 115 | 116 | Computed signals derive their values from other signals. They automatically update when their dependencies change. 117 | 118 | ```python 119 | from reaktiv import Signal, Computed 120 | 121 | # Base signals 122 | x = Signal(10) 123 | y = Signal(20) 124 | 125 | # Computed signal 126 | sum_xy = Computed(lambda: x() + y()) 127 | 128 | print(sum_xy()) # 30 129 | 130 | # Change a dependency 131 | x.set(15) 132 | 133 | # Computed value updates automatically 134 | print(sum_xy()) # 35 135 | ``` 136 | 137 | Key characteristics of computed signals: 138 | 139 | - Their values are derived from other signals 140 | - They automatically update when dependencies change 141 | - They're lazy - only computed when accessed 142 | - They track their own dependencies automatically 143 | - They only recompute when necessary 144 | - They cannot be set manually - they derive from their dependencies 145 | 146 | ### 3. Effects 147 | 148 | Effects run side effects (like updating UI, logging, or network calls) when signals change. 149 | 150 | ```python 151 | from reaktiv import Signal, Effect 152 | 153 | name = Signal("Alice") 154 | 155 | def log_name(): 156 | print(f"Name changed to: {name()}") 157 | 158 | # Create and schedule the effect 159 | logger = Effect(log_name) # Prints: "Name changed to: Alice" 160 | 161 | # Change the signal 162 | name.set("Bob") # Prints: "Name changed to: Bob" 163 | 164 | # Clean up when done 165 | logger.dispose() 166 | ``` 167 | 168 | Effects: 169 | 170 | - Execute a function when created and whenever dependencies change 171 | - Automatically track signal dependencies 172 | - Can be disposed when no longer needed 173 | - Support both synchronous and asynchronous functions 174 | - Can handle cleanup via the optional `on_cleanup` parameter 175 | 176 | Effects work with both synchronous and asynchronous functions, giving you flexibility based on your needs: 177 | 178 | ```python 179 | # Synchronous effect (no asyncio needed) 180 | counter = Signal(0) 181 | sync_effect = Effect(lambda: print(f"Counter: {counter()}")) # Runs immediately 182 | 183 | counter.set(1) # Effect runs synchronously 184 | 185 | # Asynchronous effect (requires asyncio) 186 | import asyncio 187 | 188 | async def async_logger(): 189 | print(f"Async counter: {counter()}") 190 | 191 | async_effect = Effect(async_logger) # Schedules the effect in the event loop 192 | ``` 193 | 194 | Choose synchronous effects when you don't need async functionality, and async effects when you need to perform async operations within your effects. 195 | 196 | ## Dependency Tracking 197 | 198 | reaktiv automatically tracks dependencies between Signals, Computed and Effects: 199 | 200 | ```python 201 | from reaktiv import Signal, Computed, Effect 202 | 203 | first_name = Signal("John") 204 | last_name = Signal("Doe") 205 | 206 | # This computed signal depends on both first_name and last_name 207 | full_name = Computed(lambda: f"{first_name()} {last_name()}") 208 | 209 | # This effect depends on full_name (and indirectly on first_name and last_name) 210 | display = Effect(lambda: print(f"Full name: {full_name()}")) 211 | 212 | # Changing either first_name or last_name will update full_name and trigger the effect 213 | first_name.set("Jane") # Effect runs 214 | ``` 215 | 216 | The dependency tracking works by: 217 | 218 | 1. When a signal is accessed by calling it (e.g., `Signal()`), it checks if there's a currently active effect or computation 219 | 2. If found, the signal adds itself as a dependency of that effect or computation 220 | 3. When the signal's value changes, it notifies all its dependents 221 | 4. Dependents then update or re-execute as needed 222 | 223 | ## Batching 224 | 225 | When multiple signals change, reaktiv can batch the updates to avoid unnecessary recalculations: 226 | 227 | ```python 228 | from reaktiv import Signal, Computed, batch, Effect 229 | 230 | x = Signal(10) 231 | y = Signal(20) 232 | sum_xy = Computed(lambda: x() + y()) 233 | 234 | def log_sum(): 235 | print(f"Sum: {sum_xy()}") 236 | 237 | logger = Effect(log_sum) # Prints: "Sum: 30" 238 | 239 | # Without batching, each signal change would trigger recomputation 240 | # With batching, recomputation happens only once after all changes 241 | with batch(): 242 | x.set(5) # No recomputation yet 243 | y.set(15) # No recomputation yet 244 | # After batch completes, prints: "Sum: 20" 245 | ``` 246 | 247 | ## Memory Management 248 | 249 | reaktiv uses weak references for its internal subscriber tracking, which means: 250 | 251 | 1. Computed signals and effects are garbage collected when no longer referenced 252 | 2. You need to maintain a reference to your effects to prevent premature garbage collection 253 | 3. Call `dispose()` on effects when you're done with them to clean up resources 254 | 255 | ### Retaining Effects 256 | 257 | It's critical to retain (assign to a variable) all Effects to prevent garbage collection. If you create an Effect without assigning it to a variable, it may be immediately garbage collected: 258 | 259 | ```python 260 | # INCORRECT: Effect will be garbage collected immediately 261 | Effect(lambda: print(f"Value changed: {my_signal()}")) 262 | 263 | # CORRECT: Effect is retained 264 | my_effect = Effect(lambda: print(f"Value changed: {my_signal()}")) 265 | ``` 266 | 267 | When using Effects in classes, assign them to instance attributes in the constructor to ensure they're retained throughout the object's lifecycle: 268 | 269 | ```python 270 | class TemperatureMonitor: 271 | def __init__(self, initial_temp=0): 272 | self._temperature = Signal(initial_temp) 273 | 274 | def _handle_temperature_change(): 275 | current_temp = self._temperature() 276 | print(f"Temperature changed: {current_temp}°C") 277 | if current_temp > 30: 278 | print("Warning: Temperature too high!") 279 | 280 | # Assign Effect to self._effect to prevent garbage collection 281 | self._effect = Effect(_handle_temperature_change) 282 | ``` 283 | 284 | ## Custom Equality 285 | 286 | By default, reaktiv uses identity (`is`) to determine if a signal's value has changed. This is important to understand because it affects how mutable objects behave in your reactive system. 287 | 288 | ### Identity vs. Value Equality 289 | 290 | With the default identity comparison: 291 | 292 | - Primitive values like numbers, strings, and booleans work as expected 293 | - For mutable objects like lists, dictionaries, or custom classes: 294 | - Creating a new object with the same content will be detected as a change 295 | - Modifying an object in-place won't be detected as a change 296 | 297 | ```python 298 | # With default identity equality 299 | items = Signal([1, 2, 3]) 300 | 301 | # This WILL trigger updates (different list instance) 302 | items.set([1, 2, 3]) 303 | 304 | # In-place modification WON'T trigger updates 305 | current = items() 306 | current.append(4) # Signal doesn't detect this change 307 | ``` 308 | 309 | ### Customizing Equality Checks 310 | 311 | For collections or custom objects, you can provide a custom equality function: 312 | 313 | ```python 314 | # Custom equality for comparing lists by value 315 | def list_equal(a, b): 316 | if len(a) != len(b): 317 | return False 318 | return all(a_item == b_item for a_item, b_item in zip(a, b)) 319 | 320 | # Create a signal with custom equality 321 | items = Signal([1, 2, 3], equal=list_equal) 322 | 323 | # This won't trigger updates because the lists have the same values 324 | items.set([1, 2, 3]) 325 | 326 | # This will trigger updates because the values differ 327 | items.set([1, 2, 3, 4]) 328 | ``` 329 | 330 | For dictionaries: 331 | 332 | ```python 333 | def dict_equal(a, b): 334 | return a.keys() == b.keys() and all(a[k] == b[k] for k in a.keys()) 335 | 336 | config = Signal({"theme": "dark", "font_size": 12}, equal=dict_equal) 337 | 338 | # Won't trigger updates (same content) 339 | config.set({"theme": "dark", "font_size": 12}) 340 | 341 | # Will trigger updates (different content) 342 | config.set({"theme": "light", "font_size": 12}) 343 | ``` 344 | 345 | When working with mutable objects, you have two options: 346 | 1. Provide a custom equality function that compares by value 347 | 2. Always create new instances when updating (immutable approach) 348 | 349 | The immutable approach is often cleaner and less error-prone: 350 | 351 | ```python 352 | # Immutable approach with lists 353 | items = Signal([1, 2, 3]) 354 | 355 | # Create a new list when updating 356 | items.update(lambda current: current + [4]) # [1, 2, 3, 4] 357 | 358 | # Immutable approach with dictionaries 359 | config = Signal({"theme": "dark"}) 360 | 361 | # Create a new dict when updating 362 | config.update(lambda current: {**current, "font_size": 14}) 363 | ``` -------------------------------------------------------------------------------- /docs/examples/index.md: -------------------------------------------------------------------------------- 1 | # Real-World Examples 2 | 3 | This section contains practical examples of using reaktiv in real-world scenarios. These examples demonstrate how reactive programming can simplify complex state management challenges. 4 | 5 | ## Table of Contents 6 | 7 | - [Real-World Examples](#real-world-examples) 8 | - [Table of Contents](#table-of-contents) 9 | - [Configuration Management](#configuration-management) 10 | - [Data Processing Pipeline](#data-processing-pipeline) 11 | - [Cache Invalidation](#cache-invalidation) 12 | - [Form Validation](#form-validation) 13 | - [API Data Fetching](#api-data-fetching) 14 | - [Status Monitoring](#status-monitoring) 15 | 16 | ## Configuration Management 17 | 18 | Managing configuration from multiple sources (defaults, user settings, environment) with automatic priority resolution: 19 | 20 | ```python 21 | from reaktiv import Signal, Computed, Effect 22 | 23 | # Different configuration sources 24 | default_config = Signal({ 25 | "timeout": 30, 26 | "retries": 3, 27 | "debug": False, 28 | "log_level": "INFO" 29 | }) 30 | 31 | user_config = Signal({}) 32 | env_config = Signal({}) 33 | 34 | # Effective config merges all sources with proper priority 35 | effective_config = Computed(lambda: { 36 | **default_config(), 37 | **user_config(), 38 | **env_config() # Environment overrides everything 39 | }) 40 | 41 | # Derived settings that automatically update when any config changes 42 | connection_settings = Computed(lambda: { 43 | "connect_timeout": effective_config()["timeout"], 44 | "max_attempts": effective_config()["retries"], 45 | "verbose": effective_config()["debug"] 46 | }) 47 | 48 | logger_settings = Computed(lambda: { 49 | "level": effective_config()["log_level"], 50 | "debug_mode": effective_config()["debug"] 51 | }) 52 | 53 | # Effect to log when settings change 54 | config_monitor = Effect(lambda: print(f"Config updated: {effective_config()}")) 55 | 56 | # Update a specific config source 57 | user_config.set({"timeout": 60, "log_level": "DEBUG"}) 58 | # Automatically updates effective_config, connection_settings, logger_settings 59 | # and triggers the config_monitor effect 60 | 61 | # Later, update from environment 62 | env_config.set({"retries": 5}) 63 | # Everything dependent on retries updates automatically 64 | ``` 65 | 66 | ## Data Processing Pipeline 67 | 68 | Building a multi-stage data processing pipeline where each step depends on the previous one: 69 | 70 | ```python 71 | from reaktiv import Signal, Computed 72 | import json 73 | 74 | # Raw data source 75 | raw_data = Signal('{"values": [1, 2, 3, -4, 5, -6]}') 76 | 77 | # Parsing stage 78 | parsed_data = Computed(lambda: json.loads(raw_data())) 79 | 80 | # Extraction stage 81 | values = Computed(lambda: parsed_data()["values"]) 82 | 83 | # Filtering stage 84 | positive_values = Computed(lambda: [x for x in values() if x > 0]) 85 | 86 | # Transformation stage 87 | squared_values = Computed(lambda: [x * x for x in positive_values()]) 88 | 89 | # Aggregation stage 90 | stats = Computed(lambda: { 91 | "count": len(squared_values()), 92 | "sum": sum(squared_values()), 93 | "average": sum(squared_values()) / len(squared_values()) if squared_values() else 0 94 | }) 95 | 96 | print(stats()) 97 | # {'count': 4, 'sum': 39, 'average': 9.75} 98 | 99 | # Update the raw data - all stages recompute automatically 100 | raw_data.set('{"values": [10, 20, 30, 40]}') 101 | print(stats()) 102 | # {'count': 4, 'sum': 3000, 'average': 750.0} 103 | ``` 104 | 105 | ## Cache Invalidation 106 | 107 | Smart cache invalidation system that automatically refreshes cached data when dependencies change: 108 | 109 | ```python 110 | from reaktiv import Signal, Computed, Effect 111 | import time 112 | 113 | # Simulated database 114 | db = {"user1": {"name": "Alice"}, "user2": {"name": "Bob"}} 115 | 116 | # Cache version signal - incremented when database changes 117 | cache_version = Signal(1) 118 | 119 | # Active user ID 120 | user_id = Signal("user1") 121 | 122 | # This computed value acts as our cache with automatic invalidation 123 | user_data = Computed(lambda: { 124 | "id": user_id(), 125 | "data": db[user_id()], # In real code, this would be a database query 126 | "cached_at": time.time(), 127 | "version": cache_version() # Including version causes cache refresh when version changes 128 | }) 129 | 130 | # Cache monitor 131 | cache_logger = Effect(lambda: print( 132 | f"Cache data for user {user_data()['id']}: {user_data()['data']} " 133 | f"(version: {user_data()['version']})" 134 | )) 135 | 136 | # Change user - cache recomputes automatically 137 | user_id.set("user2") 138 | 139 | # Simulate database update and cache invalidation 140 | db["user2"] = {"name": "Robert"} 141 | cache_version.update(lambda v: v + 1) # Increment version to invalidate cache 142 | ``` 143 | 144 | ## Form Validation 145 | 146 | Complex form validation with interdependent fields: 147 | 148 | ```python 149 | from reaktiv import Signal, Computed, Effect 150 | 151 | # Form fields 152 | username = Signal("") 153 | password = Signal("") 154 | password_confirm = Signal("") 155 | email = Signal("") 156 | terms_accepted = Signal(False) 157 | 158 | # Individual field validations 159 | username_error = Computed(lambda: 160 | "Username is required" if not username() else 161 | "Username must be at least 3 characters" if len(username()) < 3 else 162 | None 163 | ) 164 | 165 | password_error = Computed(lambda: 166 | "Password is required" if not password() else 167 | "Password must be at least 8 characters" if len(password()) < 8 else 168 | None 169 | ) 170 | 171 | password_match_error = Computed(lambda: 172 | "Passwords don't match" if password() != password_confirm() else None 173 | ) 174 | 175 | email_error = Computed(lambda: 176 | "Email is required" if not email() else 177 | "Invalid email format" if "@" not in email() else 178 | None 179 | ) 180 | 181 | terms_error = Computed(lambda: 182 | "You must accept the terms" if not terms_accepted() else None 183 | ) 184 | 185 | # Combined form validation status 186 | form_errors = Computed(lambda: { 187 | "username": username_error(), 188 | "password": password_error(), 189 | "password_confirm": password_match_error(), 190 | "email": email_error(), 191 | "terms": terms_error() 192 | }) 193 | 194 | has_errors = Computed(lambda: any( 195 | error is not None for error in form_errors().values() 196 | )) 197 | 198 | # Form submission state 199 | can_submit = Computed(lambda: not has_errors() and terms_accepted()) 200 | 201 | # Monitor submission state 202 | submission_monitor = Effect(lambda: print( 203 | f"Form can be submitted: {can_submit()}" 204 | )) 205 | 206 | # User interaction simulation 207 | username.set("bob") 208 | email.set("bob@example.com") 209 | password.set("password123") 210 | password_confirm.set("password123") 211 | terms_accepted.set(True) 212 | ``` 213 | 214 | ## API Data Fetching 215 | 216 | Coordinating API data fetching with loading states and automatic refresh: 217 | 218 | ```python 219 | import asyncio 220 | from reaktiv import Signal, Computed, Effect 221 | 222 | async def demo_api_fetching(): 223 | # Signals to control the data fetch 224 | user_id = Signal(1) 225 | refresh_trigger = Signal(0) # Increment to force refresh 226 | 227 | # Loading state 228 | is_loading = Signal(False) 229 | 230 | # Error handling 231 | error = Signal(None) 232 | 233 | # Data cache 234 | user_data = Signal(None) 235 | 236 | # Combined fetch key - changes when either user_id or refresh_trigger changes 237 | fetch_key = Computed(lambda: (user_id(), refresh_trigger())) 238 | 239 | # Effect that performs the data fetching 240 | async def fetch_user_data(): 241 | # Get the current fetch key (creates dependency) 242 | current_fetch_key = fetch_key() 243 | user = user_id() 244 | 245 | # Reset states 246 | error.set(None) 247 | is_loading.set(True) 248 | 249 | try: 250 | # Simulate API call 251 | await asyncio.sleep(1) # Pretend this is an API request 252 | 253 | # Simulate success or failure based on user_id 254 | if user % 2 == 0: 255 | user_data.set({"id": user, "name": f"User {user}", "status": "active"}) 256 | else: 257 | # Simulate error for odd user IDs 258 | raise Exception(f"Failed to fetch user {user}") 259 | 260 | except Exception as e: 261 | error.set(str(e)) 262 | user_data.set(None) 263 | finally: 264 | is_loading.set(False) 265 | 266 | # Create the effect 267 | fetcher = Effect(fetch_user_data) 268 | 269 | # Status reporting effect 270 | status_reporter = Effect(lambda: print( 271 | f"User {user_id()}: " + 272 | (f"Loading..." if is_loading() else 273 | f"Error: {error()}" if error() else 274 | f"Data: {user_data()}") 275 | )) 276 | 277 | # Let initial fetch complete 278 | await asyncio.sleep(1.5) 279 | 280 | # Change user - triggers automatic refetch 281 | print("\nSwitching to user 2...") 282 | user_id.set(2) 283 | await asyncio.sleep(1.5) 284 | 285 | # Force refresh current user 286 | print("\nRefreshing current user...") 287 | refresh_trigger.update(lambda n: n + 1) 288 | await asyncio.sleep(1.5) 289 | 290 | # Switch to user that will cause an error 291 | print("\nSwitching to user 3 (will cause error)...") 292 | user_id.set(3) 293 | await asyncio.sleep(1.5) 294 | 295 | # Run the demo 296 | # asyncio.run(demo_api_fetching()) 297 | ``` 298 | 299 | ## Status Monitoring 300 | 301 | Building a reactive system monitoring dashboard: 302 | 303 | ```python 304 | from reaktiv import Signal, Computed, Effect 305 | 306 | # System metrics (in a real app, these would be updated from actual monitoring) 307 | cpu_usage = Signal(25.0) # percentage 308 | memory_usage = Signal(40.0) # percentage 309 | disk_usage = Signal(60.0) # percentage 310 | error_count = Signal(0) 311 | request_count = Signal(1000) 312 | 313 | # Derived metrics 314 | error_rate = Computed(lambda: 315 | (error_count() / request_count() * 100) if request_count() > 0 else 0 316 | ) 317 | 318 | # Status thresholds 319 | cpu_status = Computed(lambda: 320 | "critical" if cpu_usage() > 90 else 321 | "warning" if cpu_usage() > 70 else 322 | "normal" 323 | ) 324 | 325 | memory_status = Computed(lambda: 326 | "critical" if memory_usage() > 90 else 327 | "warning" if memory_usage() > 70 else 328 | "normal" 329 | ) 330 | 331 | disk_status = Computed(lambda: 332 | "critical" if disk_usage() > 90 else 333 | "warning" if disk_usage() > 80 else 334 | "normal" 335 | ) 336 | 337 | error_status = Computed(lambda: 338 | "critical" if error_rate() > 5 else 339 | "warning" if error_rate() > 1 else 340 | "normal" 341 | ) 342 | 343 | # Overall system status (worst of any individual status) 344 | system_status = Computed(lambda: 345 | "critical" if any(s() == "critical" for s in (cpu_status, memory_status, disk_status, error_status)) else 346 | "warning" if any(s() == "warning" for s in (cpu_status, memory_status, disk_status, error_status)) else 347 | "normal" 348 | ) 349 | 350 | # Alert system 351 | def alert_system(): 352 | status = system_status() 353 | components = [] 354 | 355 | if cpu_status() != "normal": 356 | components.append(f"CPU: {cpu_usage():.1f}%") 357 | if memory_status() != "normal": 358 | components.append(f"Memory: {memory_usage():.1f}%") 359 | if disk_status() != "normal": 360 | components.append(f"Disk: {disk_usage():.1f}%") 361 | if error_status() != "normal": 362 | components.append(f"Error rate: {error_rate():.2f}%") 363 | 364 | if status != "normal": 365 | print(f"❗ System status: {status.upper()}") 366 | print(f" Affected components: {', '.join(components)}") 367 | else: 368 | print("✓ System status: NORMAL - All systems operational") 369 | 370 | # Monitor status changes 371 | status_monitor = Effect(alert_system) 372 | 373 | # Initial output: "✓ System status: NORMAL - All systems operational" 374 | 375 | # Simulate memory spike 376 | memory_usage.set(75.0) 377 | # Output: "❗ System status: WARNING 378 | # Affected components: Memory: 75.0%" 379 | 380 | # Simulate error increase 381 | error_count.set(100) 382 | request_count.set(1000) 383 | # Output: "❗ System status: WARNING 384 | # Affected components: Memory: 75.0%, Error rate: 10.00%" 385 | 386 | # Simulate critical CPU spike 387 | cpu_usage.set(95.0) 388 | # Output: "❗ System status: CRITICAL 389 | # Affected components: CPU: 95.0%, Memory: 75.0%, Error rate: 10.00%" 390 | 391 | # Simulate recovery 392 | memory_usage.set(50.0) 393 | cpu_usage.set(30.0) 394 | error_count.set(5) 395 | # Output: "❗ System status: WARNING 396 | # Affected components: Error rate: 0.50%" 397 | 398 | # Full recovery 399 | error_count.set(0) 400 | # Output: "✓ System status: NORMAL - All systems operational" 401 | ``` 402 | 403 | Each of these examples demonstrates how reaktiv simplifies complex state management scenarios by automatically handling dependencies and updates. You can build on these patterns to create more complex reactive systems tailored to your specific needs. 404 | 405 | For more detailed examples or to contribute your own, visit our [GitHub repository](https://github.com/buiapp/reaktiv). 406 | -------------------------------------------------------------------------------- /docs/index.md: -------------------------------------------------------------------------------- 1 | # reaktiv: Reactive Signals for Python 2 | 3 |
4 | reaktiv logo 5 |
6 | 7 | **reaktiv** is a Python library that brings reactive programming principles to Python with first-class async support. 8 | 9 | ![Python Version](https://img.shields.io/badge/python-3.9%2B-blue) 10 | ![PyPI Version](https://img.shields.io/pypi/v/reaktiv.svg) 11 | ![PyPI - Downloads](https://img.shields.io/pypi/dm/reaktiv) 12 | ![License](https://img.shields.io/badge/license-MIT-green) 13 | 14 | ## Links 15 | 16 | - **Documentation**: [https://reaktiv.readthedocs.io/](https://reaktiv.readthedocs.io/) 17 | - **GitHub**: [https://github.com/buiapp/reaktiv](https://github.com/buiapp/reaktiv) 18 | 19 | ## What is reaktiv? 20 | 21 | reaktiv is a lightweight reactive state management library for Python that automatically keeps your derived values and side effects in sync with their data sources. When data changes, everything that depends on it updates automatically. 22 | 23 | Think of it as a spreadsheet for your Python application: change a cell, and all formulas using that cell recalculate instantly. 24 | 25 | ## Why Use reaktiv? 26 | 27 | reaktiv solves common pain points in state management: 28 | 29 | - **Eliminates manual state synchronization** - No more forgetting to update derived values 30 | - **Reduces bugs** - Ensures consistent state throughout your application 31 | - **Simplifies code** - Declare relationships once, not every time data changes 32 | - **Improves performance** - Only recomputes what actually needs to change 33 | - **First-class async support** - Built for Python's asyncio ecosystem 34 | 35 | [Learn more about why you should use reaktiv →](why-reaktiv.md) 36 | 37 | ## Features 38 | 39 | * **Automatic state propagation:** Change a value once, and all dependent computations update automatically 40 | * **Efficient updates:** Only the necessary parts are recomputed 41 | * **Async-friendly:** Seamlessly integrates with Python's `asyncio` for managing real-time data flows 42 | * **Zero external dependencies:** Lightweight and easy to incorporate into any project 43 | * **Type-safe:** Fully annotated for clarity and maintainability 44 | 45 | ## Overview 46 | 47 | reaktiv is built around three core primitives: 48 | 49 | 1. **Signals**: Store values and notify dependents when they change 50 | 2. **Computed Signals**: Derive values that automatically update when dependencies change 51 | 3. **Effects**: Run side effects when signals or computed signals change 52 | 53 | ```python 54 | from reaktiv import Signal, Effect 55 | 56 | # Create a signal with initial value 57 | name = Signal("Alice") 58 | 59 | # Create an effect that reacts to changes 60 | def on_name_change(): 61 | print(f"Hello, {name()}!") 62 | 63 | # Create the effect 64 | greeter = Effect(on_name_change) # Prints: "Hello, Alice!" 65 | 66 | # Update the signal value 67 | name.set("Bob") # Will print: "Hello, Bob!" 68 | ``` 69 | 70 | ## Documentation 71 | 72 | * [Installation](installation.md) - How to install the library 73 | * [Quick Start](quickstart.md) - Get up and running quickly 74 | * [Why reaktiv?](why-reaktiv.md) - When and why to use reaktiv 75 | * [Core Concepts](core-concepts.md) - Understanding the fundamentals 76 | * [API Reference](api/signal.md) - Detailed API documentation 77 | * [Advanced Features](advanced-features.md) - More powerful capabilities 78 | * [Real-World Examples](examples/index.md) - Practical applications -------------------------------------------------------------------------------- /docs/installation.md: -------------------------------------------------------------------------------- 1 | # Installation 2 | 3 | ## Requirements 4 | 5 | reaktiv requires Python 3.9 or higher and has no external dependencies. 6 | 7 | ## Installing from PyPI 8 | 9 | The easiest way to install reaktiv is from PyPI using pip: 10 | 11 | ```bash 12 | pip install reaktiv 13 | ``` 14 | 15 | You can also use other package managers like uv: 16 | 17 | ```bash 18 | uv pip install reaktiv 19 | ``` 20 | 21 | ## Installing from Source 22 | 23 | To install the latest development version directly from the source repository: 24 | 25 | ```bash 26 | git clone https://github.com/buiapp/reaktiv.git 27 | cd reaktiv 28 | pip install -e . 29 | ``` -------------------------------------------------------------------------------- /docs/operators.md: -------------------------------------------------------------------------------- 1 | # Operators 2 | 3 | reaktiv provides several built-in operators that allow you to create new signals derived from existing ones by applying transformations or controlling the timing of emissions. These operators return a read-only signal (`_OperatorSignal`) that automatically updates based on the source signal and the operator's logic. 4 | 5 | All operators are designed to work seamlessly with both synchronous and asynchronous effects and computations. 6 | 7 | !!! note 8 | Some operators (`debounce_signal`, `throttle_signal`) rely on internal timers and therefore **require a running `asyncio` event loop** to function correctly. `filter_signal` is purely synchronous and does not have this requirement. 9 | 10 | ## `filter_signal` 11 | 12 | Creates a signal that only emits values from the source signal that satisfy a given predicate function. 13 | 14 | **Asyncio Requirement:** No 15 | 16 | **Signature:** 17 | 18 | ```python 19 | filter_signal( 20 | source: Union[Signal[T], ComputeSignal[T], _OperatorSignal[T]], 21 | predicate: Callable[[T], bool] 22 | ) -> _OperatorSignal[T] 23 | ``` 24 | 25 | **Parameters:** 26 | 27 | * `source`: The input signal (`Signal`, `ComputeSignal`, or another operator signal). 28 | * `predicate`: A function that takes a value from the source signal and returns `True` if the value should be emitted, `False` otherwise. 29 | 30 | **Example:** 31 | 32 | ```python 33 | import asyncio 34 | from reaktiv import Signal, filter_signal, Effect 35 | 36 | source = Signal(0) 37 | even_numbers = filter_signal(source, lambda x: x % 2 == 0) 38 | 39 | # Effect will only run when even_numbers emits a new value 40 | # Keep a reference to the effect 41 | even_effect = Effect(lambda: print(f"Got an even number: {even_numbers()}")) 42 | 43 | source.set(1) # predicate(1) is False, even_numbers doesn't emit 44 | source.set(2) # predicate(2) is True, even_numbers emits 2 45 | # Output: Got an even number: 2 46 | source.set(3) # predicate(3) is False, even_numbers doesn't emit 47 | source.set(4) # predicate(4) is True, even_numbers emits 4 48 | # Output: Got an even number: 4 49 | 50 | # even_effect remains active until it goes out of scope or is disposed 51 | ``` 52 | 53 | ## `debounce_signal` 54 | 55 | Creates a signal that emits a value from the source signal only after a specified time span has passed without the source emitting any new values. This is useful for scenarios like handling user input where you only want to react after the user has stopped typing for a moment. 56 | 57 | **Asyncio Requirement:** Yes 58 | 59 | **Signature:** 60 | 61 | ```python 62 | debounce_signal( 63 | source: Union[Signal[T], ComputeSignal[T], _OperatorSignal[T]], 64 | delay_seconds: float 65 | ) -> _OperatorSignal[T] 66 | ``` 67 | 68 | **Parameters:** 69 | 70 | * `source`: The input signal. 71 | * `delay_seconds`: The time in seconds to wait for quiescence before emitting the last value received from the source. 72 | 73 | **Example:** 74 | 75 | ```python 76 | import asyncio 77 | from reaktiv import Signal, debounce_signal, Effect 78 | 79 | async def main(): 80 | query = Signal("") 81 | # Only process the query 500ms after the user stops typing 82 | debounced_query = debounce_signal(query, 0.5) 83 | 84 | # Keep a reference to the effect 85 | query_effect = Effect(lambda: print(f"Processing search for: {debounced_query()}")) 86 | 87 | print("User types 're'...") 88 | query.set("re") 89 | await asyncio.sleep(0.2) 90 | print("User types 'aktiv'...") 91 | query.set("reaktiv") # Timer resets 92 | await asyncio.sleep(0.2) 93 | print("User types '!'...") 94 | query.set("reaktiv!") # Timer resets again 95 | 96 | print("Waiting for debounce...") 97 | await asyncio.sleep(0.6) # Wait longer than the debounce delay 98 | # Output: Processing search for: reaktiv! 99 | 100 | # query_effect remains active 101 | 102 | asyncio.run(main()) 103 | ``` 104 | 105 | ## `throttle_signal` 106 | 107 | Creates a signal that emits a value from the source signal immediately (if `leading` is `True`), then ignores subsequent source emissions for a specified time interval. It can optionally emit the last value received during the ignored interval when the interval ends (if `trailing` is `True`). This is useful for rate-limiting events, like handling rapid button clicks or frequent sensor updates. 108 | 109 | **Asyncio Requirement:** Yes 110 | 111 | **Signature:** 112 | 113 | ```python 114 | throttle_signal( 115 | source: Union[Signal[T], ComputeSignal[T], _OperatorSignal[T]], 116 | interval_seconds: float, 117 | leading: bool = True, 118 | trailing: bool = False 119 | ) -> _OperatorSignal[T] 120 | ``` 121 | 122 | **Parameters:** 123 | 124 | * `source`: The input signal. 125 | * `interval_seconds`: The duration in seconds during which source emissions are ignored after an emission. 126 | * `leading`: If `True` (default), emit the first value immediately when it arrives. 127 | * `trailing`: If `True` (default is `False`), emit the last value received during the throttle interval after the interval has passed. 128 | 129 | **Example (Leading only):** 130 | 131 | ```python 132 | import asyncio 133 | from reaktiv import Signal, throttle_signal, Effect 134 | 135 | async def main(): 136 | clicks = Signal(0) 137 | # Handle click, but ignore rapid clicks within 200ms 138 | throttled_clicks = throttle_signal(clicks, 0.2, leading=True, trailing=False) 139 | 140 | # Keep a reference to the effect 141 | click_effect = Effect(lambda: print(f"Click handled! Count: {throttled_clicks()}")) 142 | 143 | print("Rapid clicks...") 144 | clicks.set(1) # Emitted (leading) 145 | # Output: Click handled! Count: 1 146 | await asyncio.sleep(0.05) 147 | clicks.set(2) # Throttled 148 | await asyncio.sleep(0.05) 149 | clicks.set(3) # Throttled 150 | 151 | print("Waiting past interval...") 152 | await asyncio.sleep(0.2) # Interval ends 153 | 154 | print("Another click...") 155 | clicks.set(4) # Emitted (leading, interval passed) 156 | # Output: Click handled! Count: 4 157 | 158 | await asyncio.sleep(0.1) 159 | # click_effect remains active 160 | 161 | asyncio.run(main()) 162 | ``` 163 | 164 | **Example (Leading and Trailing):** 165 | 166 | ```python 167 | import asyncio 168 | from reaktiv import Signal, throttle_signal, Effect 169 | 170 | async def main(): 171 | sensor = Signal(0.0) 172 | # Process sensor value immediately, and also the last value after 1s interval 173 | processed_sensor = throttle_signal(sensor, 1.0, leading=True, trailing=True) 174 | 175 | # Keep a reference to the effect 176 | sensor_effect = Effect(lambda: print(f"Processed sensor value: {processed_sensor():.2f}")) 177 | 178 | print("Sensor updates rapidly...") 179 | sensor.set(10.5) # Emitted (leading) 180 | # Output: Processed sensor value: 10.50 181 | await asyncio.sleep(0.3) 182 | sensor.set(11.2) 183 | await asyncio.sleep(0.3) 184 | sensor.set(12.8) # Last value in interval 185 | 186 | print("Waiting for trailing edge...") 187 | await asyncio.sleep(0.5) # 1.1s total elapsed, interval ended 188 | # Output: Processed sensor value: 12.80 (trailing) 189 | 190 | await asyncio.sleep(0.1) 191 | # sensor_effect remains active 192 | 193 | asyncio.run(main()) 194 | ``` 195 | 196 | ## `pairwise_signal` 197 | 198 | Creates a signal that emits tuples containing the previous and current values from a source signal. This is useful for tracking how values change over time and comparing current values with previous ones. 199 | 200 | **Asyncio Requirement:** No 201 | 202 | **Signature:** 203 | 204 | ```python 205 | pairwise_signal( 206 | source: Union[Signal[T], ComputeSignal[T], _OperatorSignal[T]], 207 | emit_on_first: bool = False 208 | ) -> _OperatorSignal[Optional[Tuple[Optional[T], T]]] 209 | ``` 210 | 211 | **Parameters:** 212 | 213 | * `source`: The input signal. 214 | * `emit_on_first`: If `True`, emits `(None, first_value)` when the source emits its first value. If `False` (default), the first emission from the source does not produce an output, and the second emission produces `(first_value, second_value)`. 215 | 216 | **Example (Default behavior - skip first):** 217 | 218 | ```python 219 | import asyncio 220 | from reaktiv import Signal, pairwise_signal, Effect 221 | 222 | async def main(): 223 | counter = Signal(0) 224 | # Create a signal that emits (previous, current) tuples 225 | changes = pairwise_signal(counter) 226 | 227 | # Keep a reference to the effect 228 | change_effect = Effect(lambda: print(f"Counter changed from {changes()[0]} to {changes()[1]}")) 229 | 230 | # Initial value doesn't emit with default settings (emit_on_first=False) 231 | print("Initial state - no effect output yet") 232 | 233 | # First change 234 | counter.set(1) 235 | # Output: Counter changed from 0 to 1 236 | 237 | # Second change 238 | counter.set(5) 239 | # Output: Counter changed from 1 to 5 240 | 241 | # When value doesn't change, no emission occurs 242 | counter.set(5) # No output 243 | 244 | # Third change 245 | counter.set(10) 246 | # Output: Counter changed from 5 to 10 247 | 248 | await asyncio.sleep(0.1) 249 | # change_effect remains active 250 | 251 | asyncio.run(main()) 252 | ``` 253 | 254 | **Example (Emit on first value):** 255 | 256 | ```python 257 | import asyncio 258 | from reaktiv import Signal, pairwise_signal, Effect 259 | 260 | async def main(): 261 | price = Signal(100.0) 262 | # Create a signal that emits (previous, current) tuples, including on first value 263 | price_changes = pairwise_signal(price, emit_on_first=True) 264 | 265 | # Keep a reference to the effect 266 | # Handle the initial case where previous might be None 267 | price_effect = Effect(lambda: process_price_change(price_changes())) 268 | 269 | def process_price_change(change_tuple): 270 | prev, curr = change_tuple 271 | if prev is None: 272 | print(f"Initial price: ${curr:.2f}") 273 | else: 274 | diff = curr - prev 275 | percent = (diff / prev) * 100 if prev != 0 else 0 276 | direction = "up" if diff > 0 else "down" if diff < 0 else "unchanged" 277 | print(f"Price {direction}: ${curr:.2f} (${diff:+.2f}, {percent:+.1f}%)") 278 | 279 | # With emit_on_first=True, this produces output for the initial value 280 | # Output: Initial price: $100.00 281 | 282 | # First change 283 | price.set(105.0) 284 | # Output: Price up: $105.00 (+$5.00, +5.0%) 285 | 286 | # Second change 287 | price.set(95.5) 288 | # Output: Price down: $95.50 (-$9.50, -9.0%) 289 | 290 | await asyncio.sleep(0.1) 291 | # price_effect remains active 292 | 293 | asyncio.run(main()) 294 | ``` 295 | 296 | **Use Cases:** 297 | 298 | * Calculating the difference between consecutive values 299 | * Detecting direction of change (increasing/decreasing) 300 | * Tracking value transitions for state machines 301 | * Computing derivatives or rates of change 302 | * Building charts and visualizations with transition animations 303 | -------------------------------------------------------------------------------- /docs/quickstart.md: -------------------------------------------------------------------------------- 1 | # Quick Start Guide 2 | 3 | This guide will walk you through the basic usage of reaktiv to help you get started with reactive programming in Python. 4 | 5 | ## Basic Concepts 6 | 7 | reaktiv is built around three core primitives: 8 | 9 | 1. **Signals**: Store a value and notify dependents when it changes 10 | 2. **Computed Signals**: Derive values that automatically update when dependencies change 11 | 3. **Effects**: Run side effects when signals or computed signals change 12 | 13 | ## Installation 14 | 15 | The quickest way to get started is to install reaktiv using pip: 16 | 17 | ```bash 18 | pip install reaktiv 19 | # or with uv 20 | uv pip install reaktiv 21 | ``` 22 | 23 | ## Basic Example 24 | 25 | Here's a simple example showing the core functionality: 26 | 27 | ```python 28 | import asyncio 29 | from reaktiv import Signal, Effect 30 | 31 | async def main(): 32 | # Create a signal with initial value 33 | name = Signal("Alice") 34 | 35 | # Create an effect that depends on the signal 36 | async def greet(): 37 | print(f"Hello, {name()}!") 38 | 39 | # Create and schedule the effect (important: keep a reference to prevent garbage collection) 40 | greeter = Effect(greet) 41 | 42 | # Prints: "Hello, Alice!" 43 | 44 | # Update the signal value 45 | name.set("Bob") # Will trigger the effect to run again 46 | 47 | # Give the effect time to process 48 | await asyncio.sleep(0) # Prints: "Hello, Bob!" 49 | 50 | asyncio.run(main()) 51 | ``` 52 | 53 | ## Solving Real Problems 54 | 55 | Let's look at a more practical example. Imagine you're calculating the total price of items in a shopping cart: 56 | 57 | ```python 58 | from reaktiv import Signal, Computed, Effect 59 | 60 | # Create signals for our base values 61 | price = Signal(10.0) 62 | quantity = Signal(2) 63 | tax_rate = Signal(0.1) # 10% tax 64 | 65 | # Create computed values that automatically update when dependencies change 66 | subtotal = Computed(lambda: price() * quantity()) 67 | tax = Computed(lambda: subtotal() * tax_rate()) 68 | total = Computed(lambda: subtotal() + tax()) 69 | 70 | # Create an effect to display the total (will run initially and when dependencies change) 71 | display = Effect(lambda: print(f"Order total: ${total():.2f}")) 72 | # Prints: "Order total: $22.00" 73 | 74 | # Update a signal - all dependent computed values and effects update automatically 75 | quantity.set(3) 76 | # Prints: "Order total: $33.00" 77 | 78 | # Update multiple values 79 | price.set(15.0) 80 | tax_rate.set(0.15) 81 | # Prints: "Order total: $51.75" 82 | ``` 83 | 84 | Notice how we never needed to manually recalculate the total! reaktiv automatically detects the dependencies between values and updates them when needed. 85 | 86 | ## Computed Values 87 | 88 | Computed signals let you derive values from other signals: 89 | 90 | ```python 91 | from reaktiv import Signal, Computed 92 | 93 | # Create base signals 94 | price = Signal(100) 95 | tax_rate = Signal(0.2) 96 | 97 | # Create a computed signal that depends on price and tax_rate 98 | total = Computed(lambda: price() * (1 + tax_rate())) 99 | 100 | print(total()) # 120.0 101 | 102 | # Change a dependency 103 | tax_rate.set(0.25) 104 | 105 | # The computed value updates automatically 106 | print(total()) # 125.0 107 | ``` 108 | 109 | ## Working with Updates 110 | 111 | Instead of using `set(new_value)`, you can use `update()` to modify a signal based on its current value: 112 | 113 | ```python 114 | from reaktiv import Signal 115 | 116 | counter = Signal(0) 117 | 118 | # Standard way 119 | counter.set(counter() + 1) 120 | 121 | # Using update() for cleaner syntax 122 | counter.update(lambda x: x + 1) 123 | 124 | print(counter()) # 2 125 | ``` 126 | 127 | ## Batching Updates 128 | 129 | When making multiple signal updates, you can batch them together to optimize performance: 130 | 131 | ```python 132 | from reaktiv import Signal, Computed, batch 133 | 134 | x = Signal(10) 135 | y = Signal(20) 136 | sum_xy = Computed(lambda: x() + y()) 137 | 138 | # Without batching, computed values are recalculated after each signal update 139 | x.set(5) # Recalculates sum_xy 140 | y.set(10) # Recalculates sum_xy again 141 | 142 | # With batching, computed values are recalculated only once after all updates 143 | with batch(): 144 | x.set(15) # No recalculation yet 145 | y.set(25) # No recalculation yet 146 | # sum_xy will be recalculated only once after the batch completes 147 | ``` 148 | 149 | ## Custom Equality 150 | 151 | By default, signals use identity (`is`) for equality checking. You can provide a custom equality function: 152 | 153 | ```python 154 | from reaktiv import Signal 155 | 156 | # Custom equality function for case-insensitive string comparison 157 | def case_insensitive_equal(a, b): 158 | return a.lower() == b.lower() 159 | 160 | # Create a signal with custom equality function 161 | name = Signal("Alice", equal=case_insensitive_equal) 162 | 163 | # This won't trigger updates because "alice" is considered equal to "Alice" 164 | name.set("alice") 165 | 166 | # This will trigger updates because "Bob" is not equal to "Alice" 167 | name.set("Bob") 168 | ``` 169 | 170 | ## Asynchronous Effects 171 | 172 | reaktiv has first-class support for async functions: 173 | 174 | ```python 175 | import asyncio 176 | from reaktiv import Signal, Effect 177 | 178 | async def main(): 179 | counter = Signal(0) 180 | 181 | async def print_counter(): 182 | print(f"Counter value is: {counter()}") 183 | 184 | # Keep a reference to prevent garbage collection 185 | counter_effect = Effect(print_counter) 186 | 187 | for i in range(1, 4): 188 | await asyncio.sleep(1) 189 | counter.set(i) 190 | 191 | # Cleaning up when done 192 | counter_effect.dispose() 193 | 194 | asyncio.run(main()) 195 | ``` 196 | 197 | ## Next Steps 198 | 199 | - Read [Why reaktiv?](why-reaktiv.md) to understand when and why to use reaktiv 200 | - See the [Core Concepts](core-concepts.md) page for a deeper understanding of reaktiv's design 201 | - Check out the [Examples](examples/index.md) page for real-world usage examples 202 | - Explore the [Advanced Features](advanced-features.md) for more powerful capabilities -------------------------------------------------------------------------------- /docs/requirements.txt: -------------------------------------------------------------------------------- 1 | mkdocs>=1.4.0 2 | mkdocs-material>=9.1.0 3 | mkdocstrings>=0.22.0 4 | mkdocstrings-python>=1.1.0 5 | pymdown-extensions>=10.0.0 6 | # Add specific version for mermaid support 7 | mkdocs-mermaid2-plugin>=0.6.0 8 | -------------------------------------------------------------------------------- /docs/why-reaktiv.md: -------------------------------------------------------------------------------- 1 | # Why reaktiv? 2 | 3 | When building applications that manage state, developers often struggle with keeping derived data in sync with its sources. reaktiv solves this fundamental problem through automatic dependency tracking and fine-grained reactivity. 4 | 5 | ## The Hidden Cost You Didn't Know You Were Paying 6 | 7 | Many developers don't realize they're already paying a cost when managing state manually: 8 | 9 | 1. **Mental Overhead**: Constantly tracking what needs to update when something changes 10 | 2. **Bug Potential**: Forgotten updates leading to inconsistent state 11 | 3. **Refactoring Risk**: Adding new derived state requires updating many places 12 | 4. **Testing Complexity**: More moving parts to test when state updates are manual 13 | 14 | reaktiv eliminates these hidden costs, allowing you to: 15 | 16 | - Declare relationships once 17 | - Let the system handle updates automatically 18 | - Focus on business logic rather than state synchronization 19 | - Build more reliable and maintainable applications 20 | 21 | Reactive programming isn't just another tool—it's a fundamental shift in how we think about state management that can dramatically simplify complex applications. 22 | 23 | ## The Pain Points reaktiv Solves 24 | 25 | ### 1. The Manual State Synchronization Problem 26 | 27 | Without a reactive system, developers typically face these challenges: 28 | 29 | #### Before reaktiv: Manual State Propagation 30 | 31 | ```python 32 | # Traditional approach with manual propagation 33 | user_name = "Alice" 34 | user_age = 30 35 | # Derived state that depends on the above values 36 | greeting = f"Hello, {user_name}! You are {user_age} years old." 37 | 38 | # When state changes, you must remember to update ALL derived values 39 | user_name = "Bob" # State changed! 40 | # Oops! Forgot to update greeting 41 | # greeting still shows "Hello, Alice! You are 30 years old." 42 | 43 | # Later in the code... 44 | greeting = f"Hello, {user_name}! You are {user_age} years old." # Manual update 45 | ``` 46 | 47 | #### After reaktiv: Automatic Propagation 48 | 49 | ```python 50 | from reaktiv import Signal, Computed 51 | 52 | # State as signals 53 | user_name = Signal("Alice") 54 | user_age = Signal(30) 55 | 56 | # Derived state automatically updates when dependencies change 57 | greeting = Computed(lambda: f"Hello, {user_name()}! You are {user_age()} years old.") 58 | 59 | print(greeting()) # "Hello, Alice! You are 30 years old." 60 | 61 | # When state changes, derived values update automatically 62 | user_name.set("Bob") 63 | print(greeting()) # "Hello, Bob! You are 30 years old." 64 | ``` 65 | 66 | ### 2. The "Hidden State" Problem 67 | 68 | Many developers don't realize how much "hidden state" exists in their applications. Every time you compute a value based on other values, you're creating state that needs to be managed. 69 | 70 | ```python 71 | # Without reaktiv: Hidden state management 72 | def get_total_price(items, tax_rate): 73 | subtotal = sum(item["price"] for item in items) 74 | return subtotal * (1 + tax_rate) 75 | 76 | # This function is called in many places 77 | # If items or tax_rate changes, you need to manually recalculate everywhere 78 | ``` 79 | 80 | #### After reaktiv: Making Hidden State Explicit 81 | 82 | ```python 83 | from reaktiv import Signal, Computed 84 | 85 | # Make state explicit with signals 86 | items = Signal([]) 87 | tax_rate = Signal(0.1) 88 | 89 | # Hidden state becomes explicit computed values 90 | subtotal = Computed(lambda: sum(item["price"] for item in items())) 91 | total_price = Computed(lambda: subtotal() * (1 + tax_rate())) 92 | 93 | # Now when items or tax_rate changes, total_price updates automatically 94 | items.set([{"price": 10}, {"price": 20}]) 95 | print(total_price()) # 33.0 96 | 97 | tax_rate.set(0.2) 98 | print(total_price()) # 36.0 - automatically recalculated 99 | ``` 100 | 101 | ### 3. The Dependency Tracking Problem 102 | 103 | Manually tracking which parts of your code depend on which data becomes increasingly complex as applications grow. 104 | 105 | ```python 106 | # Traditional approach with manual tracking 107 | class ShoppingCart: 108 | def __init__(self, items=None): 109 | self.items = items or [] 110 | self.subtotal = self._calculate_subtotal() 111 | self.tax = self._calculate_tax() 112 | self.total = self.subtotal + self.tax 113 | 114 | def _calculate_subtotal(self): 115 | return sum(item.price for item in self.items) 116 | 117 | def _calculate_tax(self): 118 | return self.subtotal * 0.1 # 10% tax 119 | 120 | def add_item(self, item): 121 | self.items.append(item) 122 | # Now we must manually update everything that depends on items 123 | self.subtotal = self._calculate_subtotal() 124 | self.tax = self._calculate_tax() # Depends on subtotal 125 | self.total = self.subtotal + self.tax # Depends on both 126 | 127 | # What if we add more derived values? The dependency chain gets complex! 128 | ``` 129 | 130 | #### After reaktiv: Automatic Dependency Tracking 131 | 132 | ```python 133 | from reaktiv import Signal, Computed 134 | 135 | class ReactiveShoppingCart: 136 | def __init__(self, initial_items=None): 137 | self.items = Signal(initial_items or []) 138 | 139 | # Dependencies are automatically tracked 140 | self.subtotal = Computed(lambda: sum(item.price for item in self.items())) 141 | self.tax = Computed(lambda: self.subtotal() * 0.1) # Automatically depends on subtotal 142 | self.total = Computed(lambda: self.subtotal() + self.tax()) # Automatically depends on both 143 | 144 | # Adding more derived values is easy and doesn't increase complexity 145 | self.discount = Computed(lambda: 0.05 if self.subtotal() > 100 else 0) 146 | self.final_price = Computed(lambda: self.total() * (1 - self.discount())) 147 | 148 | def add_item(self, item): 149 | # Just update the source data 150 | self.items.update(lambda items: items + [item]) 151 | # No need to manually update dependencies - they update automatically! 152 | 153 | cart = ReactiveShoppingCart() 154 | ``` 155 | 156 | ## Before & After: How reaktiv Makes Your Code Better 157 | 158 | ### Example 1: Configuration with Overrides 159 | 160 | #### Before reaktiv: 161 | 162 | ```python 163 | def load_config(): 164 | default_config = {"timeout": 30, "retries": 3, "debug": False} 165 | try: 166 | with open("user_config.json") as f: 167 | user_config = json.load(f) 168 | except FileNotFoundError: 169 | user_config = {} 170 | 171 | # Merge configs 172 | config = {**default_config, **user_config} 173 | 174 | # Derived values 175 | connection_settings = { 176 | "connect_timeout": config["timeout"], 177 | "max_attempts": config["retries"], 178 | "verbose": config["debug"], 179 | } 180 | 181 | return config, connection_settings 182 | 183 | # Now what happens when config changes at runtime? 184 | # You need to reload everything and update all dependents manually! 185 | ``` 186 | 187 | #### After reaktiv: 188 | 189 | ```python 190 | from reaktiv import Signal, Computed 191 | 192 | default_config = Signal({"timeout": 30, "retries": 3, "debug": False}) 193 | user_config = Signal({}) 194 | 195 | # Derived values automatically stay in sync 196 | effective_config = Computed(lambda: {**default_config(), **user_config()}) 197 | 198 | connection_settings = Computed(lambda: { 199 | "connect_timeout": effective_config()["timeout"], 200 | "max_attempts": effective_config()["retries"], 201 | "verbose": effective_config()["debug"], 202 | }) 203 | 204 | # When config changes, everything updates automatically 205 | user_config.set({"timeout": 60}) 206 | print(connection_settings()) # connect_timeout is now 60 207 | ``` 208 | 209 | ### Example 2: Data Processing Pipeline 210 | 211 | #### Before reaktiv: 212 | 213 | ```python 214 | class DataProcessor: 215 | def __init__(self, raw_data): 216 | self.raw_data = raw_data 217 | self.filtered_data = self._filter_data() 218 | self.transformed_data = self._transform_data() 219 | self.summary = self._summarize() 220 | 221 | def _filter_data(self): 222 | return [x for x in self.raw_data if x > 0] 223 | 224 | def _transform_data(self): 225 | return [x * 2 for x in self.filtered_data] 226 | 227 | def _summarize(self): 228 | return { 229 | "count": len(self.transformed_data), 230 | "sum": sum(self.transformed_data), 231 | "avg": sum(self.transformed_data) / len(self.transformed_data) if self.transformed_data else 0 232 | } 233 | 234 | def update_data(self, new_data): 235 | self.raw_data = new_data 236 | # Must manually update every step in the chain 237 | self.filtered_data = self._filter_data() 238 | self.transformed_data = self._transform_data() 239 | self.summary = self._summarize() 240 | ``` 241 | 242 | #### After reaktiv: 243 | 244 | ```python 245 | from reaktiv import Signal, Computed 246 | 247 | class ReactiveDataProcessor: 248 | def __init__(self, initial_data): 249 | self.raw_data = Signal(initial_data) 250 | 251 | # Each step automatically updates when dependencies change 252 | self.filtered_data = Computed(lambda: [x for x in self.raw_data() if x > 0]) 253 | self.transformed_data = Computed(lambda: [x * 2 for x in self.filtered_data()]) 254 | self.summary = Computed(lambda: { 255 | "count": len(self.transformed_data()), 256 | "sum": sum(self.transformed_data()), 257 | "avg": sum(self.transformed_data()) / len(self.transformed_data()) if self.transformed_data() else 0 258 | }) 259 | 260 | def update_data(self, new_data): 261 | # Just update the source data - everything else updates automatically 262 | self.raw_data.set(new_data) 263 | 264 | # Usage 265 | processor = ReactiveDataProcessor([1, -2, 3, -4, 5]) 266 | print(processor.summary()) # Computed from the chain 267 | processor.update_data([10, 20, 30]) # Everything recalculates automatically 268 | ``` 269 | 270 | ## Comparing reaktiv with Alternatives 271 | 272 | ### reaktiv vs. RxPy/ReactiveX 273 | 274 | | Feature | reaktiv | RxPy | 275 | |---------|---------|------| 276 | | **Focus** | Fine-grained state management | Event streams and operations | 277 | | **Conceptual Model** | Signal-based (value over time) | Observable streams (collections over time) | 278 | | **Learning Curve** | Gentle, minimal API | Steeper, many operators to learn | 279 | | **Async Integration** | First-class Python `asyncio` support | Separate scheduler system | 280 | | **Dependencies** | Zero external dependencies | Has external dependencies | 281 | | **Granularity** | Value-level reactivity | Stream-level operations | 282 | | **Execution Model** | Pull-based (lazy) | Push-based (eager) | 283 | 284 | ### reaktiv vs. Manual Observer Pattern 285 | 286 | | Feature | reaktiv | Manual Observer Pattern | 287 | |---------|---------|-------------------------| 288 | | **Dependency Tracking** | Automatic | Manual | 289 | | **Granularity** | Fine-grained | Coarse-grained | 290 | | **Boilerplate** | Minimal | Extensive | 291 | | **Memoization** | Built-in | Manual | 292 | | **Memory Management** | Automatic cleanup | Manual cleanup | 293 | 294 | ## When to Use reaktiv 295 | 296 | reaktiv shines in these scenarios: 297 | 298 | 1. **Complex State Dependencies**: When you have multiple pieces of state that depend on each other 299 | 2. **Derived Data**: When you compute values based on other values that change over time 300 | 3. **Reactive UIs**: When UI components need to update in response to state changes 301 | 4. **Data Processing Pipelines**: When you transform data through multiple steps 302 | 5. **Configuration Management**: When you need to compute effective configurations from multiple sources 303 | 6. **Caching Systems**: For smart cache invalidation when dependencies change 304 | 305 | ## When Not to Use reaktiv 306 | 307 | reaktiv might not be the best fit for: 308 | 309 | 1. **Simple State**: If your application state is very simple with few dependencies 310 | 2. **Fire-and-forget Events**: For pure event handling without state tracking, a simpler event system may suffice 311 | 3. **Stream Processing**: If you're primarily doing stream operations like filtering, mapping large data streams (consider RxPy) 312 | 4. **Performance-critical, High-frequency Updates**: For systems where absolute minimal overhead is critical -------------------------------------------------------------------------------- /examples/fastapi_websocket.py: -------------------------------------------------------------------------------- 1 | """ 2 | FastAPI WebSocket example with reaktiv. 3 | 4 | This example shows how to use reaktiv with FastAPI to create a reactive 5 | websocket that automatically reflects state changes. 6 | 7 | To run this example: 8 | pip install fastapi uvicorn websockets 9 | 10 | # Run directly with uvicorn: 11 | uvicorn examples.fastapi_websocket:app --reload 12 | 13 | # Or if using uv: 14 | uv run uvicorn examples.fastapi_websocket:app --reload 15 | 16 | # NOT like this: 17 | # uv run python uvicorn examples.fastapi_websocket:app --reload 18 | 19 | Then: 20 | - Connect to the WebSocket at ws://localhost:8000/ws 21 | - Use the REST endpoints to modify the state 22 | - Watch the WebSocket update in real-time 23 | """ 24 | import asyncio 25 | import json 26 | from typing import Dict, List, Set 27 | from contextlib import asynccontextmanager 28 | 29 | import uvicorn 30 | from fastapi import FastAPI, WebSocket, WebSocketDisconnect 31 | from fastapi.responses import HTMLResponse 32 | 33 | from reaktiv import Signal, Computed, Effect 34 | 35 | # Create a lifespan context manager 36 | @asynccontextmanager 37 | async def lifespan(app: FastAPI): 38 | # Setup code - runs before the application starts receiving requests 39 | print("Starting up: Setting up reactive effects...") 40 | manager.setup_effect() 41 | 42 | yield # This is where FastAPI serves requests 43 | 44 | # Cleanup code - runs when the application is shutting down 45 | print("Shutting down: Cleaning up resources...") 46 | 47 | # Initialize FastAPI app with lifespan 48 | app = FastAPI( 49 | title="Reaktiv FastAPI WebSocket Example", 50 | lifespan=lifespan 51 | ) 52 | 53 | # State management with Reaktiv 54 | counter = Signal(0) 55 | messages = Signal([]) 56 | active_users = Signal(0) 57 | 58 | # Computed values 59 | state = Computed(lambda: { 60 | "counter": counter(), 61 | "messages": messages(), 62 | "active_users": active_users(), 63 | "last_message": messages()[-1] if messages() else None 64 | }) 65 | 66 | # WebSocket connection manager 67 | class ConnectionManager: 68 | def __init__(self): 69 | self.active_connections: Set[WebSocket] = set() 70 | self._broadcast_effect = None # Don't create Effect at import time 71 | 72 | def setup_effect(self): 73 | """Create the Effect when we're in an asyncio context""" 74 | if self._broadcast_effect is None: 75 | self._broadcast_effect = Effect(self._broadcast_state) 76 | 77 | async def connect(self, websocket: WebSocket): 78 | # Create the Effect when we have a websocket connection (inside asyncio context) 79 | self.setup_effect() 80 | 81 | await websocket.accept() 82 | self.active_connections.add(websocket) 83 | active_users.set(len(self.active_connections)) 84 | 85 | # Send initial state 86 | await self._send_state(websocket) 87 | 88 | def disconnect(self, websocket: WebSocket): 89 | self.active_connections.remove(websocket) 90 | active_users.set(len(self.active_connections)) 91 | 92 | async def _broadcast_state(self): 93 | """Effect that broadcasts state when it changes""" 94 | current_state = state() 95 | if self.active_connections: 96 | data = json.dumps(current_state) 97 | # Create tasks for each connection to avoid blocking 98 | await asyncio.gather( 99 | *[ws.send_text(data) for ws in self.active_connections], 100 | return_exceptions=True 101 | ) 102 | 103 | async def _send_state(self, websocket: WebSocket): 104 | """Send current state to a specific client""" 105 | current_state = state() 106 | await websocket.send_text(json.dumps(current_state)) 107 | 108 | manager = ConnectionManager() 109 | 110 | # HTML for a simple test client 111 | html = """ 112 | 113 | 114 | 115 | Reaktiv WebSocket Demo 116 | 122 | 123 | 124 |

Reaktiv WebSocket Demo

125 |
126 |
127 |

Control Panel

128 |
129 | 130 | 131 | 132 |
133 |
134 | 135 | 136 |
137 |
138 | 176 | 177 | 178 | """ 179 | 180 | @app.get("/") 181 | async def get(): 182 | """Serve a simple HTML client for testing""" 183 | return HTMLResponse(html) 184 | 185 | @app.websocket("/ws") 186 | async def websocket_endpoint(websocket: WebSocket): 187 | """WebSocket endpoint that reflects reaktiv state""" 188 | await manager.connect(websocket) 189 | try: 190 | while True: 191 | # Just keep the connection open 192 | await websocket.receive_text() 193 | except WebSocketDisconnect: 194 | manager.disconnect(websocket) 195 | 196 | @app.get("/increment") 197 | async def increment(): 198 | """Increment the counter""" 199 | counter.update(lambda c: c + 1) 200 | return {"status": "incremented", "value": counter()} 201 | 202 | @app.get("/decrement") 203 | async def decrement(): 204 | """Decrement the counter""" 205 | counter.update(lambda c: c - 1) 206 | return {"status": "decremented", "value": counter()} 207 | 208 | @app.get("/reset") 209 | async def reset(): 210 | """Reset the counter to zero""" 211 | counter.set(0) 212 | return {"status": "reset", "value": counter()} 213 | 214 | @app.post("/message") 215 | async def add_message(message: Dict[str, str]): 216 | """Add a message to the list""" 217 | text = message.get("text", "").strip() 218 | if text: 219 | messages.update(lambda msgs: msgs + [text]) 220 | return {"status": "message added"} 221 | 222 | if __name__ == "__main__": 223 | # Make sure app runs with appropriate host and port 224 | print("Starting the FastAPI app with uvicorn...") 225 | print("Access the demo at http://localhost:8000") 226 | uvicorn.run(app, host="0.0.0.0", port=8000) 227 | -------------------------------------------------------------------------------- /examples/iot_sensor_agent_thread.py: -------------------------------------------------------------------------------- 1 | """ 2 | IoT Sensor Agent with reaktiv. 3 | 4 | This example demonstrates how reaktiv can be used to create a reactive system 5 | that responds to hardware sensor changes running in a separate thread. 6 | 7 | Key concepts demonstrated: 8 | - Thread-safe reactivity with hardware sensors 9 | - Automatic recalculation of derived values as sensor readings change 10 | - Reactive effects for monitoring, alerting, and taking action on sensor data 11 | - Clean separation between sensor data acquisition and business logic 12 | 13 | In a real-world application: 14 | - The sensor loop would interface with actual hardware via libraries like 15 | RPi.GPIO, Adafruit_CircuitPython, Arduino libraries, etc. 16 | - Multiple effects might update displays, trigger actuators, log to databases, 17 | or send notifications through various channels 18 | """ 19 | import threading 20 | import time 21 | import random 22 | from reaktiv import Signal, Computed, Effect, batch 23 | 24 | class IoTSensorAgent: 25 | """Threaded IoT sensor agent that provides reactive sensor data updates.""" 26 | 27 | def __init__(self): 28 | # Core sensor signals - in a real system these would be updated from hardware 29 | self.temperature = Signal(21.0) 30 | self.humidity = Signal(50.0) 31 | self.is_running = Signal(False) 32 | self.sensor_error = Signal(False) 33 | 34 | # Computed values automatically derive from raw sensor data 35 | self.heat_index = Computed(lambda: self.temperature() + 0.05 * self.humidity()) 36 | self.comfort_level = Computed(self._calculate_comfort_level) 37 | self.sensor_status = Computed(lambda: 38 | "ERROR" if self.sensor_error() else 39 | "ACTIVE" if self.is_running() else "STANDBY" 40 | ) 41 | 42 | self._thread = None 43 | 44 | def _calculate_comfort_level(self) -> str: 45 | """Determine comfort level based on temperature and humidity.""" 46 | t, h = self.temperature(), self.humidity() 47 | 48 | if t < 18: return "TOO COLD" 49 | if t > 26: return "TOO HOT" 50 | if h < 30: return "TOO DRY" 51 | if h > 70: return "TOO HUMID" 52 | return "COMFORTABLE" 53 | 54 | def start_sensor(self): 55 | """Start the sensor agent thread.""" 56 | if self.is_running(): 57 | return 58 | 59 | self.is_running.set(True) 60 | self._thread = threading.Thread(target=self._sensor_loop, daemon=True) 61 | self._thread.start() 62 | print(f"Sensor agent started with status: {self.sensor_status()}") 63 | 64 | def stop_sensor(self): 65 | """Stop the sensor agent.""" 66 | self.is_running.set(False) 67 | if self._thread: 68 | self._thread.join(timeout=1.0) 69 | self._thread = None 70 | 71 | def _sensor_loop(self): 72 | """Main sensor polling loop running in a separate thread. 73 | 74 | In a real application, this would read from actual hardware sensors. 75 | """ 76 | try: 77 | while self.is_running(): 78 | # Simulate sensor readings with small random changes 79 | # In a real application: read from I2C/SPI/GPIO sensors here 80 | new_temp = max(10, min(35, self.temperature() + random.uniform(-0.5, 0.5))) 81 | new_humidity = max(20, min(90, self.humidity() + random.uniform(-1, 1))) 82 | 83 | # Occasionally simulate sensor error (1% chance) 84 | # In a real application: detect actual hardware communication errors 85 | if random.random() < 0.01: 86 | self.sensor_error.set(True) 87 | time.sleep(2) 88 | self.sensor_error.set(False) 89 | 90 | # Update signals if not in error state 91 | if not self.sensor_error(): 92 | # Simply updating these signals will automatically trigger 93 | # all dependent computed values and effects 94 | with batch(): 95 | self.temperature.set(new_temp) 96 | self.humidity.set(new_humidity) 97 | 98 | time.sleep(1) 99 | 100 | except Exception as e: 101 | print(f"Sensor error: {e}") 102 | with batch(): 103 | self.sensor_error.set(True) 104 | self.is_running.set(False) 105 | 106 | 107 | def demo(): 108 | """Demonstrate how reaktiv enables automatic reactions to sensor data changes.""" 109 | # Create sensor agent 110 | sensor = IoTSensorAgent() 111 | 112 | # Define effect functions 113 | def log_sensor(): 114 | """Log current sensor readings to console.""" 115 | print( 116 | f"Temp: {sensor.temperature():.1f}°C | " 117 | f"Humidity: {sensor.humidity():.1f}% | " 118 | f"Status: {sensor.sensor_status()} | " 119 | f"Comfort: {sensor.comfort_level()}" 120 | ) 121 | 122 | def temp_alert(): 123 | """Alert when temperature exceeds threshold.""" 124 | if sensor.temperature() > 28: 125 | print(f"⚠️ HIGH TEMPERATURE ALERT: {sensor.temperature():.1f}°C!") 126 | 127 | def monitor_status(): 128 | """Monitor sensor health and report errors.""" 129 | if sensor.sensor_status() == "ERROR": 130 | print("🚨 SENSOR ERROR DETECTED!") 131 | 132 | def control_climate(): 133 | """Simulated climate control system actions.""" 134 | if sensor.comfort_level() == "COMFORTABLE": 135 | return 136 | 137 | temp = sensor.temperature() 138 | if temp > 26: 139 | action = "COOLING" 140 | elif temp < 18: 141 | action = "HEATING" 142 | else: 143 | action = "IDLE" 144 | 145 | print(f"🔄 HVAC ACTION: {action}") 146 | 147 | # Create effects with named functions 148 | log_sensor_eff = Effect(log_sensor) 149 | temp_alert_eff = Effect(temp_alert) 150 | monitor_status_eff = Effect(monitor_status) 151 | control_climate_eff = Effect(control_climate) 152 | 153 | try: 154 | print("Starting IoT sensor monitoring system...") 155 | print("All monitoring, alerts, and climate control will react automatically to sensor changes") 156 | sensor.start_sensor() 157 | time.sleep(15) # Run for 15 seconds 158 | except KeyboardInterrupt: 159 | print("\nDemo interrupted") 160 | finally: 161 | sensor.stop_sensor() 162 | print("Demo completed") 163 | 164 | 165 | if __name__ == "__main__": 166 | demo() 167 | -------------------------------------------------------------------------------- /examples/iot_temp_monitor.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import random 3 | from reaktiv import Signal, Computed, Effect 4 | 5 | async def main(): 6 | # Initialize sensor readings (in °C) 7 | sensor1 = Signal(20.0) 8 | sensor2 = Signal(21.0) 9 | sensor3 = Signal(19.5) 10 | 11 | # Compute aggregates: average, minimum, and maximum temperature. 12 | avg_temp = Computed(lambda: (sensor1() + sensor2() + sensor3()) / 3) 13 | min_temp = Computed(lambda: min(sensor1(), sensor2(), sensor3())) 14 | max_temp = Computed(lambda: max(sensor1(), sensor2(), sensor3())) 15 | 16 | # Effect to display current sensor readings and computed statistics. 17 | async def display_aggregates(): 18 | print(f"🌡️ Sensor readings: {sensor1():.2f}°C, {sensor2():.2f}°C, {sensor3():.2f}°C") 19 | print(f"📊 Avg: {avg_temp():.2f}°C | ⬇️ Min: {min_temp():.2f}°C | ⬆️ Max: {max_temp():.2f}°C") 20 | print("-------------------------------------------------------------") 21 | 22 | # Schedule the display effect. 23 | display_effect = Effect(display_aggregates) 24 | 25 | # Effect to trigger alerts when values exceed safe limits. 26 | async def temperature_alert(): 27 | if avg_temp() > 25: 28 | print("🚨 ALERT: Average temperature is too high! 🔥") 29 | if min_temp() < 15: 30 | print("🚨 ALERT: One or more sensors report a temperature that's too low! ❄️") 31 | if max_temp() > 30: 32 | print("🚨 ALERT: A sensor is overheating! 🔥") 33 | 34 | # Schedule the alert effect. 35 | alert_effect = Effect(temperature_alert) 36 | 37 | def update_sensor(sensor, sensor_name: str): 38 | """ 39 | Update a sensor value with a normal drift. 40 | With a 10% chance, inject a fault (a dramatic spike or drop) to trigger alerts. 41 | """ 42 | if random.random() < 0.1: 43 | # Fault injection: choose a spike (overheat) or a drop (undercool) 44 | change = random.choice([random.uniform(5.0, 10.0), random.uniform(-10.0, -5.0)]) 45 | print(f"[{sensor_name}] ⚠️ Fault injection: {change:+.2f}°C") 46 | else: 47 | # Normal fluctuation 48 | change = random.uniform(-0.5, 1.0) 49 | sensor.update(lambda current: current + change) 50 | 51 | # Simulate periodic sensor updates. 52 | for _ in range(15): 53 | await asyncio.sleep(2) 54 | update_sensor(sensor1, "Sensor1") 55 | update_sensor(sensor2, "Sensor2") 56 | update_sensor(sensor3, "Sensor3") 57 | 58 | # Allow any pending effects to process. 59 | await asyncio.sleep(1) 60 | 61 | if __name__ == '__main__': 62 | asyncio.run(main()) 63 | -------------------------------------------------------------------------------- /examples/napkin_calc.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | """Reactive napkin calculator using reaktiv""" 3 | 4 | from reaktiv import Signal as S, Computed as C, Effect as E 5 | 6 | # Input values 7 | salary = S(50000) 8 | bonus = S(5000) 9 | tax_rate = S(0.22) 10 | expenses = S(2500) 11 | months = S(12) 12 | 13 | # Derived calculations 14 | income = C(lambda: salary() + bonus()) 15 | taxes = C(lambda: income() * tax_rate()) 16 | net = C(lambda: income() - taxes()) 17 | annual_expenses = C(lambda: expenses() * months()) 18 | savings = C(lambda: net() - annual_expenses()) 19 | savings_rate = C(lambda: savings() / net() * 100) 20 | 21 | def display(): 22 | print("\n📊 Financial Summary:") 23 | print(f"{'Item':<20} {'Value':>10}") 24 | print(f"{'-'*20} {'-'*10}") 25 | print(f"{'Income':<20} {'$'+str(income()):>10}") 26 | print(f"{'Taxes':<20} {'$'+str(taxes()):>10}") 27 | print(f"{'Net Income':<20} {'$'+str(net()):>10}") 28 | print(f"{'Annual Expenses':<20} {'$'+str(annual_expenses()):>10}") 29 | print(f"{'Savings':<20} {'$'+str(savings()):>10}") 30 | print(f"{'Savings Rate':<20} {f'{savings_rate():.1f}%':>10}") 31 | 32 | table = E(display) 33 | 34 | print("\n--- Initial Values ---") 35 | 36 | print("\n--- After Salary Increase ---") 37 | salary.set(55000) 38 | 39 | print("\n--- After Bonus and Tax Changes ---") 40 | bonus.set(7500) 41 | tax_rate.set(0.24) 42 | 43 | print("\n--- Expense Reduction Strategy ---") 44 | expenses.set(2200) 45 | 46 | if __name__ == "__main__": 47 | print("\nTry it yourself! Change any input value to see calculations update.") 48 | print("Example: salary.set(60000)") 49 | -------------------------------------------------------------------------------- /examples/nicegui_todo_app.py: -------------------------------------------------------------------------------- 1 | """ 2 | Example demonstrating how to integrate reaktiv with NiceGUI to build reactive UIs. 3 | 4 | This example creates a simple todo application that lets you add tasks, 5 | mark them as complete, filter them by status, and automatically updates 6 | all UI components when the underlying data changes. 7 | 8 | To run this example: 9 | 1. pip install reaktiv nicegui 10 | 2. python nicegui_todo_app.py 11 | """ 12 | 13 | from reaktiv import Signal, Computed, Effect 14 | from nicegui import ui 15 | 16 | # State module - completely independent from UI 17 | class TodoState: 18 | def __init__(self): 19 | self.todos = Signal([]) 20 | self.filter = Signal("all") # all, active, completed 21 | 22 | self.filtered_todos = Computed(lambda: [ 23 | todo for todo in self.todos() 24 | if self.filter() == "all" 25 | or (self.filter() == "active" and not todo["completed"]) 26 | or (self.filter() == "completed" and todo["completed"]) 27 | ]) 28 | self.active_count = Computed(lambda: 29 | sum(1 for todo in self.todos() if not todo["completed"]) 30 | ) 31 | self.completed_count = Computed(lambda: 32 | sum(1 for todo in self.todos() if todo["completed"]) 33 | ) 34 | 35 | def add_todo(self, text): 36 | self.todos.update(lambda todos: todos + [{"text": text, "completed": False}]) 37 | 38 | def toggle_todo(self, index): 39 | self.todos.update(lambda todos: [ 40 | {**todo, "completed": not todo["completed"]} if i == index else todo 41 | for i, todo in enumerate(todos) 42 | ]) 43 | 44 | def clear_completed(self): 45 | self.todos.update(lambda todos: [todo for todo in todos if not todo["completed"]]) 46 | 47 | # Create a state instance 48 | state = TodoState() 49 | 50 | # UI layer can now use the state 51 | with ui.card(): 52 | ui.label("Todo App").classes("text-xl") 53 | 54 | # Input for new todos 55 | with ui.row(): 56 | new_todo = ui.input("New task") 57 | ui.button("Add", on_click=lambda: [state.add_todo(new_todo.value), new_todo.set_value("")]) 58 | 59 | # Todo list - connected to state via Effect 60 | todo_container = ui.column() 61 | 62 | def render_todos(): 63 | todo_container.clear() 64 | for i, todo in enumerate(state.filtered_todos()): 65 | with todo_container: 66 | with ui.row(): 67 | ui.checkbox(value=todo["completed"], on_change=lambda e, idx=i: state.toggle_todo(idx)) 68 | ui.label(todo["text"]).classes("line-through" if todo["completed"] else "") 69 | 70 | # Effect connects state to UI 71 | render_effect = Effect(render_todos) 72 | 73 | # Filter controls 74 | with ui.row(): 75 | ui.button("All", on_click=lambda: state.filter.set("all")) 76 | ui.button("Active", on_click=lambda: state.filter.set("active")) 77 | ui.button("Completed", on_click=lambda: state.filter.set("completed")) 78 | ui.button("Clear completed", on_click=lambda: state.clear_completed()) 79 | 80 | # Status display - automatically updates 81 | status_label = ui.label() 82 | status_effect = Effect(lambda: status_label.set_text( 83 | f"{state.active_count()} active, {state.completed_count()} completed" 84 | )) 85 | 86 | ui.run() -------------------------------------------------------------------------------- /examples/numpy_plotting.py: -------------------------------------------------------------------------------- 1 | """ 2 | Numpy calculation and plotting example with reaktiv. 3 | 4 | This example demonstrates using reaktiv for efficient computation 5 | with numpy matrices and matplotlib visualization. The expensive 6 | calculations only run when their input parameters change, while 7 | visualization settings can change without triggering recalculation. 8 | 9 | To run this example: 10 | pip install numpy matplotlib ipywidgets 11 | python examples/numpy_plotting.py 12 | """ 13 | 14 | import time 15 | import numpy as np 16 | import matplotlib.pyplot as plt 17 | from matplotlib.widgets import Slider, Button 18 | from matplotlib.patches import Rectangle 19 | 20 | from reaktiv import Signal, Computed, Effect, batch 21 | 22 | class ReactiveMatrixVisualizer: 23 | def __init__(self): 24 | # Input parameters - changing these triggers recalculation 25 | self.matrix_size = Signal(100) 26 | self.seed = Signal(42) 27 | self.noise_scale = Signal(0.5) 28 | self.filter_size = Signal(5) 29 | 30 | # Visualization parameters - changing these doesn't trigger recalculation 31 | self.colormap_name = Signal("viridis") 32 | self.show_contour = Signal(False) 33 | self.contour_levels = Signal(10) 34 | self.title_text = Signal("Reactive Matrix Visualization") 35 | 36 | # Add status signals for UI feedback 37 | self.status_message = Signal("") 38 | self.is_computing = Signal(False) 39 | 40 | # Setup the figure with a fixed layout 41 | self.fig = plt.figure(figsize=(10, 8)) 42 | 43 | # Create a more stable layout with dedicated areas 44 | from matplotlib import gridspec 45 | self.gs = gridspec.GridSpec(2, 2, height_ratios=[6, 1], width_ratios=[20, 1], 46 | figure=self.fig, hspace=0.05, wspace=0.05) 47 | 48 | # Create the axes for the plot, colorbar and controls 49 | self.ax = self.fig.add_subplot(self.gs[0, 0]) # Main plot in top-left 50 | self.cbar_ax = self.fig.add_subplot(self.gs[0, 1]) # Colorbar in top-right 51 | 52 | # Control area at the bottom spanning both columns 53 | self.controls_area = self.fig.add_subplot(self.gs[1, :]) 54 | self.controls_area.axis('off') # Hide the axes for the controls area 55 | 56 | # Status text for feedback 57 | self.status_text = self.fig.text(0.05, 0.25, "", fontsize=10, 58 | bbox=dict(facecolor='yellow', alpha=0.2)) 59 | 60 | # Rectangle for highlighting updates 61 | self.highlight_rect = Rectangle((0, 0), 1, 1, fill=False, 62 | edgecolor='red', linewidth=3, visible=False) 63 | self.ax.add_patch(self.highlight_rect) 64 | 65 | # Add computation state signals 66 | self.computing_matrix = Signal(False) 67 | self.computing_filter = Signal(False) 68 | self.computation_status = Signal("") 69 | 70 | # Combine all input parameters into a single computed value 71 | self.input_parameters = Computed(lambda: { 72 | "size": self.matrix_size(), 73 | "seed": self.seed(), 74 | "noise": self.noise_scale(), 75 | "filter_size": self.filter_size() 76 | }) 77 | 78 | # Computation time tracking 79 | self.matrix_computation_time = Signal(0.0) 80 | self.filter_computation_time = Signal(0.0) 81 | self.last_computation_time = Signal(0.0) # Combined time 82 | 83 | # Now the expensive computations are TRULY pure functions (no signal setting) 84 | self.random_matrix_with_time = Computed(self._generate_random_matrix_pure) 85 | self.filtered_matrix_with_time = Computed(self._apply_filter_pure) 86 | 87 | # Computed signals for just the matrix data 88 | self.random_matrix = Computed(lambda: self.random_matrix_with_time()["matrix"]) 89 | self.filtered_matrix = Computed(lambda: self.filtered_matrix_with_time()["filtered"]) 90 | 91 | # Effects to handle UI updates and status messages 92 | self._status_effect = Effect(self._update_status_display) 93 | 94 | # Effects to handle setting time signals 95 | self._matrix_time_effect = Effect(self._update_matrix_time) 96 | self._filter_time_effect = Effect(self._update_filter_time) 97 | self._total_time_effect = Effect(self._update_total_time) 98 | 99 | # Add effects to track computation status 100 | self._matrix_status_effect = Effect(self._track_matrix_computation) 101 | self._filter_status_effect = Effect(self._track_filter_computation) 102 | 103 | # Add UI controls 104 | self._setup_controls() 105 | 106 | # Effect to update the plot when computed values or visualization params change 107 | self._plot_effect = Effect(self._update_plot) 108 | 109 | def _generate_random_matrix_pure(self): 110 | """Generate a random matrix - TRULY PURE function (no signal setting)""" 111 | # Get all parameters from the combined input 112 | params = self.input_parameters() 113 | size = params["size"] 114 | seed = params["seed"] 115 | noise = params["noise"] 116 | 117 | # Log to console only (no signal setting) 118 | print(f"Matrix generation with size={size}, seed={seed}, noise={noise}") 119 | print(f"Generating new {size}×{size} matrix with seed {seed}...") 120 | start_time = time.time() 121 | 122 | # Set the seed for reproducibility 123 | np.random.seed(seed) 124 | 125 | # Generate matrix 126 | matrix = np.random.randn(size, size) * noise 127 | 128 | # Add patterns 129 | x = np.linspace(-5, 5, size) 130 | y = np.linspace(-5, 5, size) 131 | X, Y = np.meshgrid(x, y) 132 | 133 | frequency = max(1, 50 / size) 134 | pattern = np.sin(X * frequency) * np.cos(Y * frequency) * noise * 3 135 | matrix += pattern 136 | 137 | seed_effect = np.sin(X * (seed % 10 + 1) / 5) * np.cos(Y * (seed % 10 + 1) / 5) 138 | matrix += seed_effect * noise * 2 139 | 140 | computation_time = time.time() - start_time 141 | print(f"Matrix generation completed in {computation_time:.3f} seconds") 142 | 143 | # Return matrix, computation time, and parameters used 144 | return { 145 | "matrix": matrix, 146 | "time": computation_time, 147 | "params": {"size": size, "seed": seed} 148 | } 149 | 150 | def _apply_filter_pure(self): 151 | """Apply filter - TRULY PURE function (no signal setting)""" 152 | # Get parameters from the combined input 153 | params = self.input_parameters() 154 | filter_size = params["filter_size"] 155 | 156 | # Log to console only (no signal setting) 157 | print(f"Filter application with filter_size={filter_size}") 158 | print("Applying filter to matrix...") 159 | start_time = time.time() 160 | 161 | # Get matrix result 162 | matrix_result = self.random_matrix_with_time() 163 | matrix = matrix_result["matrix"] 164 | 165 | # Apply filter 166 | if filter_size <= 1: 167 | filtered = matrix 168 | else: 169 | filtered = np.zeros_like(matrix) 170 | m, n = matrix.shape 171 | effect_strength = filter_size / 3.0 172 | 173 | for i in range(m): 174 | for j in range(n): 175 | i_start = max(0, i - filter_size) 176 | i_end = min(m, i + filter_size + 1) 177 | j_start = max(0, j - filter_size) 178 | j_end = min(n, j + filter_size + 1) 179 | window = matrix[i_start:i_end, j_start:j_end] 180 | center_val = matrix[i, j] 181 | filtered[i, j] = center_val * (1 - effect_strength) + np.mean(window) * effect_strength 182 | 183 | computation_time = time.time() - start_time 184 | print(f"Filter application completed in {computation_time:.3f} seconds") 185 | 186 | # Return result with metadata 187 | return { 188 | "filtered": filtered, 189 | "time": computation_time, 190 | "params": {"filter_size": filter_size} 191 | } 192 | 193 | # Effect to update the status display based on status signals 194 | def _update_status_display(self): 195 | """Effect to update the status text UI element based on status signals""" 196 | message = self.status_message() 197 | if message: 198 | self.status_text.set_text(message) 199 | self.status_text.set_visible(True) 200 | else: 201 | self.status_text.set_visible(False) 202 | self.fig.canvas.draw_idle() 203 | 204 | # New methods to handle the side effects via Effects 205 | def _update_matrix_time(self): 206 | """Effect that updates the matrix computation time signal""" 207 | time_value = self.random_matrix_with_time()["time"] 208 | self.matrix_computation_time.set(time_value) 209 | 210 | def _update_filter_time(self): 211 | """Effect that updates the filter computation time signal""" 212 | time_value = self.filtered_matrix_with_time()["time"] 213 | self.filter_computation_time.set(time_value) 214 | 215 | def _update_total_time(self): 216 | """Effect that updates the total computation time signal""" 217 | matrix_time = self.matrix_computation_time() 218 | filter_time = self.filter_computation_time() 219 | self.last_computation_time.set(matrix_time + filter_time) 220 | 221 | # Effect functions to track computation status 222 | def _track_matrix_computation(self): 223 | """Track matrix computation and update status signals""" 224 | # Access result to check current status 225 | matrix_result = self.random_matrix_with_time() 226 | params = matrix_result["params"] 227 | 228 | # Update status message 229 | self.status_message.set(f"Matrix {params['size']}×{params['size']} with seed {params['seed']}") 230 | 231 | def _track_filter_computation(self): 232 | """Track filter computation and update status signals""" 233 | # Access result to check current status 234 | filter_result = self.filtered_matrix_with_time() 235 | filter_size = filter_result["params"]["filter_size"] 236 | 237 | # Update status message 238 | if filter_size > 0: 239 | self.status_message.set(f"Applied filter (size {filter_size})") 240 | 241 | # These methods are now simplified - no more direct UI manipulation from here 242 | def _show_status(self, message): 243 | """Set the status message signal""" 244 | self.status_message.set(message) 245 | 246 | def _clear_status(self): 247 | """Clear the status message signal""" 248 | self.status_message.set("") 249 | 250 | def _flash_highlight(self): 251 | """Flash a highlight around the plot to indicate an update""" 252 | self.highlight_rect.set_visible(True) 253 | self.fig.canvas.draw_idle() 254 | 255 | # Use a timer to hide the highlight after a short delay 256 | timer = self.fig.canvas.new_timer(interval=500) # 500ms 257 | timer.add_callback(self._hide_highlight) 258 | timer.start() 259 | 260 | def _hide_highlight(self): 261 | """Hide the highlight rectangle""" 262 | self.highlight_rect.set_visible(False) 263 | self.fig.canvas.draw_idle() 264 | return False # Stop the timer 265 | 266 | def _update_plot(self): 267 | """Update the plot with current data and visualization settings""" 268 | print("Updating plot...") 269 | 270 | # Get current data and visualization parameters 271 | matrix = self.filtered_matrix() 272 | cmap_name = self.colormap_name() 273 | show_contours = self.show_contour() 274 | contour_levels = self.contour_levels() 275 | title = self.title_text() 276 | 277 | # Clear only the main plot axis, not the whole figure 278 | self.ax.clear() 279 | self.cbar_ax.clear() 280 | 281 | # Re-add the highlight rectangle after clearing 282 | self.highlight_rect = Rectangle((0, 0), 1, 1, fill=False, 283 | edgecolor='red', linewidth=3, visible=False) 284 | self.ax.add_patch(self.highlight_rect) 285 | 286 | # Create the heatmap with enhanced contrast 287 | im = self.ax.imshow(matrix, cmap=cmap_name, interpolation='nearest', 288 | vmin=np.min(matrix) - 0.2*np.std(matrix), # Enhance contrast 289 | vmax=np.max(matrix) + 0.2*np.std(matrix)) 290 | 291 | # Add contours if enabled - make them more prominent 292 | if show_contours: 293 | contours = self.ax.contour(matrix, levels=contour_levels, 294 | colors='k', alpha=0.7, linewidths=1.5) 295 | self.ax.clabel(contours, inline=True, fontsize=8) 296 | 297 | # Add colorbar to the dedicated axis 298 | cbar = self.fig.colorbar(im, cax=self.cbar_ax) 299 | cbar.set_label(f'Colormap: {cmap_name}') 300 | 301 | # Set title with computation info 302 | comp_time = self.last_computation_time() 303 | matrix_size = self.matrix_size() 304 | filter_size = self.filter_size() 305 | self.ax.set_title(f"{title}\n{matrix_size}×{matrix_size} matrix, filter size {filter_size}, computed in {comp_time:.3f}s") 306 | 307 | # Add grid for better visual reference 308 | self.ax.grid(alpha=0.3, linestyle='--') 309 | 310 | # Clear status text 311 | self._clear_status() 312 | 313 | # Flash highlight to indicate update 314 | self._flash_highlight() 315 | 316 | # Draw the updated figure 317 | self.fig.canvas.draw_idle() 318 | print("Plot updated!") 319 | 320 | def _setup_controls(self): 321 | """Set up interactive controls""" 322 | # Add sliders for parameters 323 | axcolor = 'lightgoldenrodyellow' 324 | 325 | # Computation parameter controls - sliders 326 | ax_size = plt.axes([0.25, 0.20, 0.65, 0.03], facecolor=axcolor) 327 | ax_noise = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor) 328 | ax_filter = plt.axes([0.25, 0.10, 0.65, 0.03], facecolor=axcolor) 329 | ax_seed = plt.axes([0.25, 0.05, 0.65, 0.03], facecolor=axcolor) # Fixed: added missing height parameter 330 | 331 | # Store sliders as instance variables 332 | self.size_slider = Slider(ax_size, 'Matrix Size', 50, 500, valinit=self.matrix_size(), valstep=10) 333 | self.noise_slider = Slider(ax_noise, 'Noise Scale', 0.1, 2.0, valinit=self.noise_scale()) 334 | self.filter_slider = Slider(ax_filter, 'Filter Size', 0, 10, valinit=self.filter_size(), valstep=1) 335 | self.seed_slider = Slider(ax_seed, 'Random Seed', 0, 100, valinit=self.seed(), valstep=1) 336 | 337 | # Visualization controls - MOVED UNDER SLIDERS 338 | # Position buttons on a row below the sliders 339 | button_y = 0.01 # Positioned below the lowest slider 340 | button_width = 0.15 341 | button_spacing = 0.02 342 | 343 | ax_cmap = plt.axes([0.15, button_y, button_width, 0.03], facecolor=axcolor) 344 | ax_contour = plt.axes([0.15 + button_width + button_spacing, button_y, button_width, 0.03], facecolor=axcolor) 345 | ax_contour_levels = plt.axes([0.15 + 2*(button_width + button_spacing), button_y, button_width, 0.03], facecolor=axcolor) 346 | ax_reset = plt.axes([0.15 + 3*(button_width + button_spacing), button_y, button_width, 0.03], facecolor=axcolor) 347 | 348 | # Store buttons as instance variables 349 | self.cmap_button = Button(ax_cmap, 'Change Colormap') 350 | self.contour_button = Button(ax_contour, 'Toggle Contours') 351 | self.contour_levels_button = Button(ax_contour_levels, 'Contour Levels') 352 | self.reset_button = Button(ax_reset, 'Reset') 353 | 354 | # Define update functions with enhanced feedback 355 | def update_size(val): 356 | size_val = int(val) 357 | print(f"SLIDER: Changing matrix size to {size_val}...") 358 | self._show_status(f"Changing matrix size to {size_val}...") 359 | self.matrix_size.set(size_val) 360 | 361 | def update_noise(val): 362 | print(f"SLIDER: Changing noise to {val:.2f}...") 363 | self._show_status(f"Adjusting noise scale to {val:.2f}...") 364 | self.noise_scale.set(val) 365 | 366 | def update_filter(val): 367 | filter_val = int(val) 368 | print(f"SLIDER: Changing filter to {filter_val}...") 369 | self._show_status(f"Setting filter size to {filter_val}...") 370 | self.filter_size.set(filter_val) 371 | 372 | def update_seed(val): 373 | seed_val = int(val) 374 | print(f"SLIDER: Changing seed to {seed_val}...") 375 | self._show_status(f"Changing random seed to {seed_val}...") 376 | self.seed.set(seed_val) 377 | 378 | def cycle_colormap(val): 379 | cmaps = ['viridis', 'plasma', 'inferno', 'magma', 'cividis', 'hot', 'cool'] 380 | current = self.colormap_name() 381 | idx = (cmaps.index(current) + 1) % len(cmaps) if current in cmaps else 0 382 | new_cmap = cmaps[idx] 383 | self._show_status(f"Changing colormap to {new_cmap}...") 384 | self.colormap_name.set(new_cmap) 385 | 386 | def toggle_contour(val): 387 | new_state = not self.show_contour() 388 | state_text = "ON" if new_state else "OFF" 389 | self._show_status(f"Toggling contours {state_text}...") 390 | self.show_contour.set(new_state) 391 | 392 | def cycle_contour_levels(val): 393 | levels = [5, 10, 15, 20, 30] 394 | current = self.contour_levels() 395 | idx = (levels.index(current) + 1) % len(levels) if current in levels else 0 396 | new_levels = levels[idx] 397 | self._show_status(f"Setting contour levels to {new_levels}...") 398 | self.contour_levels.set(new_levels) 399 | 400 | def reset(val): 401 | self._show_status("Resetting all parameters...") 402 | with batch(): 403 | self.size_slider.set_val(100) 404 | self.noise_slider.set_val(0.5) 405 | self.filter_slider.set_val(5) 406 | self.seed_slider.set_val(42) 407 | self.colormap_name.set('viridis') 408 | self.show_contour.set(False) 409 | self.contour_levels.set(10) 410 | 411 | # Connect callbacks 412 | self.size_slider.on_changed(update_size) 413 | self.noise_slider.on_changed(update_noise) 414 | self.filter_slider.on_changed(update_filter) 415 | self.seed_slider.on_changed(update_seed) 416 | 417 | self.cmap_button.on_clicked(cycle_colormap) 418 | self.contour_button.on_clicked(toggle_contour) 419 | self.contour_levels_button.on_clicked(cycle_contour_levels) 420 | self.reset_button.on_clicked(reset) 421 | 422 | def show(self): 423 | """Display the interactive visualization""" 424 | # Make sure we use blocking mode to keep the window open 425 | plt.ioff() # Turn off interactive mode to ensure window stays open 426 | plt.show(block=True) # Explicitly use blocking mode 427 | 428 | 429 | if __name__ == "__main__": 430 | print("Starting Reactive Matrix Visualizer...") 431 | print("Notice how expensive computations only run when computation parameters change,") 432 | print("while visualization settings can be changed without triggering recalculation.") 433 | visualizer = ReactiveMatrixVisualizer() 434 | visualizer.show() 435 | -------------------------------------------------------------------------------- /examples/polling_system.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | from reaktiv import Signal, Computed, Effect 3 | 4 | async def main(): 5 | candidate_a = Signal(100) 6 | candidate_b = Signal(100) 7 | 8 | total_votes = Computed(lambda: candidate_a() + candidate_b()) 9 | percent_a = Computed(lambda: (candidate_a() / total_votes()) * 100) 10 | percent_b = Computed(lambda: (candidate_b() / total_votes()) * 100) 11 | 12 | async def display_results(): 13 | print(f"Candidate A: {candidate_a()} votes ({percent_a():.1f}%)") 14 | print(f"Candidate B: {candidate_b()} votes ({percent_b():.1f}%)") 15 | print(f"Total: {total_votes()} votes\n") 16 | 17 | async def check_dominance(): 18 | if percent_a() >= 60: 19 | print("📊 ALERT: Candidate A has a significant lead!\n") 20 | elif percent_b() >= 60: 21 | print("📊 ALERT: Candidate B has a significant lead!\n") 22 | 23 | # Assign effects to variables to ensure they are retained 24 | display_effect = Effect(display_results) 25 | alert_effect = Effect(check_dominance) 26 | 27 | for _ in range(3): 28 | await asyncio.sleep(1) 29 | candidate_a.update(lambda x: x + 40) 30 | candidate_b.update(lambda x: x + 10) 31 | 32 | await asyncio.sleep(1) 33 | 34 | asyncio.run(main()) -------------------------------------------------------------------------------- /examples/reactive_excel.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | from reaktiv import Signal, Computed, Effect 3 | 4 | async def main(): 5 | # Define input cells (cells you can edit) 6 | A1 = Signal(10) # Cell A1 7 | B1 = Signal(20) # Cell B1 8 | C1 = Signal(30) # Cell C1 9 | 10 | # Define computed (formula) cells: 11 | # D1 simulates "=SUM(A1, B1, C1)" 12 | D1 = Computed(lambda: A1() + B1() + C1()) 13 | 14 | # E1 simulates "=AVERAGE(A1, B1, C1)" 15 | E1 = Computed(lambda: (A1() + B1() + C1()) / 3) 16 | 17 | # F1 simulates "=PRODUCT(A1, B1, C1)" 18 | F1 = Computed(lambda: A1() * B1() * C1()) 19 | 20 | # Effect to print a simple spreadsheet view whenever any cell changes. 21 | async def print_spreadsheet(): 22 | print("\n📊 Reactive Excel Simulation") 23 | print("+------+------+------+-------+----------+------------+") 24 | print("| Cell | A | B | C | D | E |") 25 | print("+------+------+------+-------+----------+------------+") 26 | print(f"| 1 | {A1():^4} | {B1():^4} | {C1():^4} | {D1():^6} | {E1():^8.2f} |") 27 | print("+------+------+------+-------+----------+------------+") 28 | print(f"| F1 (Product) = {F1()}") 29 | print("+-----------------------------------------+\n") 30 | 31 | # Schedule the spreadsheet printing effect. 32 | # (Assigning it to a variable ensures it won't be garbage collected.) 33 | sheet_effect = Effect(print_spreadsheet) 34 | 35 | # Simulate editing cells with delays so the editing messages are visible. 36 | await asyncio.sleep(2) 37 | print("✏️ Editing A1 → 15") 38 | A1.set(15) 39 | 40 | await asyncio.sleep(2) 41 | print("✏️ Editing B1 → 25") 42 | B1.set(25) 43 | 44 | await asyncio.sleep(2) 45 | print("✏️ Editing C1 → 35") 46 | C1.set(35) 47 | 48 | await asyncio.sleep(2) 49 | print("✏️ Editing A1 → 50") 50 | A1.set(50) 51 | 52 | await asyncio.sleep(2) 53 | print("✏️ Editing B1 → 5") 54 | B1.set(5) 55 | 56 | await asyncio.sleep(2) 57 | print("✏️ Editing C1 → 10") 58 | C1.set(10) 59 | 60 | # Allow a moment for the final effect updates. 61 | await asyncio.sleep(2) 62 | 63 | if __name__ == '__main__': 64 | asyncio.run(main()) 65 | -------------------------------------------------------------------------------- /examples/stock_ticker.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | from reaktiv import batch, Signal, Computed, Effect 3 | 4 | async def main(): 5 | # Real-time stock prices 6 | apple_price = Signal(195.00) 7 | google_price = Signal(2750.00) 8 | 9 | # User's portfolio 10 | shares = Signal({ 11 | 'AAPL': 100, 12 | 'GOOGL': 50 13 | }) 14 | 15 | # Computed total portfolio value 16 | portfolio_value = Computed(lambda: ( 17 | shares()['AAPL'] * apple_price() + 18 | shares()['GOOGL'] * google_price() 19 | )) 20 | 21 | # Price alert system 22 | async def check_alerts(): 23 | if apple_price() > 200: 24 | print("📈 AAPL alert: Above $200!") 25 | if google_price() < 2700: 26 | print("📉 GOOGL alert: Below $2700!") 27 | 28 | # Automatic updates 29 | async def live_updates(): 30 | # Simulate real-time updates 31 | while True: 32 | await asyncio.sleep(1) 33 | with batch(): 34 | apple_price.update(lambda v: v * 1.01) # +1% 35 | google_price.update(lambda v: v * 0.995) # -0.5% 36 | 37 | print(f"🍏 AAPL: ${apple_price():,.2f} 🌐 GOOGL: ${google_price():,.2f}") 38 | 39 | # Track portfolio value 40 | async def monitor_portfolio(): 41 | print(f"💰 Current value: ${portfolio_value():,.2f}") 42 | 43 | # Set up effects 44 | alerts_effect = Effect(check_alerts) 45 | portfolio_effect = Effect(monitor_portfolio) 46 | 47 | # Start live updates 48 | updates_task = asyncio.create_task(live_updates()) 49 | 50 | # Run for 5 seconds 51 | await asyncio.sleep(5) 52 | 53 | asyncio.run(main()) -------------------------------------------------------------------------------- /mkdocs.yml: -------------------------------------------------------------------------------- 1 | site_name: reaktiv 2 | site_description: Reactive Computation Graphs for Python with first-class async support 3 | site_url: https://reaktiv.readthedocs.io/ 4 | 5 | theme: 6 | name: material 7 | palette: 8 | primary: indigo 9 | accent: indigo 10 | features: 11 | - navigation.tabs 12 | - navigation.sections 13 | - navigation.expand 14 | - navigation.top 15 | - search.suggest 16 | - search.highlight 17 | 18 | repo_url: https://github.com/buiapp/reaktiv 19 | repo_name: buiapp/reaktiv 20 | 21 | markdown_extensions: 22 | - pymdownx.highlight 23 | - pymdownx.superfences: 24 | custom_fences: 25 | - name: mermaid 26 | class: mermaid 27 | format: !!python/name:pymdownx.superfences.fence_code_format 28 | - admonition 29 | - toc: 30 | permalink: true 31 | 32 | plugins: 33 | - search 34 | - mermaid2 35 | - mkdocstrings: 36 | default_handler: python 37 | handlers: 38 | python: 39 | options: 40 | show_source: true 41 | 42 | nav: 43 | - Home: index.md 44 | - Getting Started: 45 | - Installation: installation.md 46 | - Quick Start: quickstart.md 47 | - Why reaktiv?: why-reaktiv.md 48 | - Core Concepts: core-concepts.md 49 | - API Reference: 50 | - Signal: api/signal.md 51 | - Computed Signal: api/compute-signal.md 52 | - Effect: api/effect.md 53 | - Utils: api/utils.md 54 | - Advanced: 55 | - Advanced Features: advanced-features.md 56 | - Examples: 57 | - Examples: examples/index.md -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | requires = ["setuptools>=65", "wheel"] 3 | build-backend = "setuptools.build_meta" 4 | 5 | [tool.setuptools] 6 | license-files = [] 7 | 8 | [project] 9 | name = "reaktiv" 10 | version = "0.14.9" 11 | description = "Reactive signals for Python with async support" 12 | readme = "README.md" 13 | authors = [{name = "Tuan Anh Bui", email = "mail@bui.app"}] 14 | license = {text = "MIT"} 15 | classifiers = [ 16 | "License :: OSI Approved :: MIT License", 17 | "Programming Language :: Python :: 3", 18 | "Programming Language :: Python :: 3.9", 19 | "Programming Language :: Python :: 3.10", 20 | "Programming Language :: Python :: 3.11", 21 | "Programming Language :: Python :: 3.12", 22 | ] 23 | requires-python = ">=3.9" 24 | dependencies = [] 25 | 26 | [dependency-groups] 27 | dev = [ 28 | "pyright>=1.1.398", 29 | "pytest>=7.0", 30 | "pytest-asyncio>=0.20", 31 | "pytest-timeout>=2.3.1", 32 | ] 33 | 34 | [project.urls] 35 | Homepage = "https://github.com/buiapp/reaktiv" 36 | 37 | [tool.pytest.ini_options] 38 | asyncio_mode = "strict" 39 | testpaths = ["tests"] 40 | asyncio_default_fixture_loop_scope = "function" 41 | addopts = "-v" 42 | -------------------------------------------------------------------------------- /pyrightconfig.json: -------------------------------------------------------------------------------- 1 | { 2 | "include": [ 3 | "src" 4 | ], 5 | "exclude": [ 6 | "**/__pycache__", 7 | "**/node_modules", 8 | "**/.venv", 9 | "build", 10 | "dist" 11 | ], 12 | "typeCheckingMode": "basic", 13 | "useLibraryCodeForTypes": true, 14 | "reportMissingImports": true, 15 | "reportMissingTypeStubs": true, 16 | "pythonVersion": "3.9", 17 | "executionEnvironments": [ 18 | { 19 | "root": "reaktiv" 20 | } 21 | ] 22 | } -------------------------------------------------------------------------------- /src/reaktiv/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Reactive signals for Python with first-class async support 3 | """ 4 | from .core import Signal, ComputeSignal, Effect, Computed, batch, untracked, signal, computed, effect 5 | from .utils import to_async_iter 6 | from .operators import filter_signal, debounce_signal, throttle_signal, pairwise_signal 7 | 8 | __version__ = "0.14.9" 9 | __all__ = [ 10 | "Signal", 11 | "ComputeSignal", 12 | "Computed", 13 | "Effect", 14 | "batch", 15 | "untracked", 16 | "to_async_iter", 17 | "filter_signal", 18 | "debounce_signal", 19 | "throttle_signal", 20 | "pairwise_signal", 21 | "signal", 22 | "computed", 23 | "effect", 24 | ] -------------------------------------------------------------------------------- /src/reaktiv/operators.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import time 3 | from typing import TypeVar, Callable, Generic, Optional, Union, Set, List, Tuple, Any 4 | from weakref import WeakSet 5 | from .core import ( 6 | Signal, ComputeSignal, Effect, _current_effect, Subscriber, debug_log, 7 | _process_sync_effects, _batch_depth 8 | ) 9 | 10 | T = TypeVar("T") 11 | 12 | # -------------------------------------------------- 13 | # Base Class for Operator Signals 14 | # -------------------------------------------------- 15 | 16 | class _OperatorSignal(Generic[T]): 17 | """A read-only signal produced by an operator. Implements methods for duck typing.""" 18 | def __init__(self, initial_value: T, *, equal: Optional[Callable[[T, T], bool]] = None): 19 | self._value = initial_value 20 | self._subscribers: WeakSet[Subscriber] = WeakSet() 21 | self._equal = equal or (lambda a, b: a is b) # Default to identity check like Signal 22 | self._internal_effect: Optional[Effect] = None 23 | self._disposed = False 24 | debug_log(f"OperatorSignal initialized with value: {initial_value}") 25 | 26 | def __call__(self) -> T: 27 | return self.get() 28 | 29 | def get(self) -> T: 30 | if self._disposed: 31 | debug_log("Attempted to get value from disposed OperatorSignal") 32 | # Return last known value 33 | return self._value 34 | 35 | tracker = _current_effect.get(None) 36 | if tracker is not None: 37 | # Track this operator signal itself as a dependency. 38 | # The tracker's add_dependency method should call our subscribe method. 39 | # We provide the necessary methods (subscribe/unsubscribe) via duck typing. 40 | tracker.add_dependency(self) # type: ignore 41 | debug_log(f"OperatorSignal get() called, dependency added for tracker: {tracker}") 42 | 43 | debug_log(f"OperatorSignal get() returning value: {self._value}") 44 | return self._value 45 | 46 | def _update_value(self, new_value: T): 47 | """Internal method to update the value and notify subscribers.""" 48 | if self._disposed: 49 | return 50 | debug_log(f"OperatorSignal _update_value() called with new_value: {new_value} (old_value: {self._value})") 51 | if self._equal(self._value, new_value): 52 | debug_log("OperatorSignal _update_value() - new_value considered equal; no update.") 53 | return 54 | 55 | self._value = new_value 56 | debug_log(f"OperatorSignal value updated to: {new_value}, notifying subscribers.") 57 | 58 | # Determine if this update is happening outside a batch 59 | is_top_level_trigger = _batch_depth == 0 60 | # No need to increment update cycle here, handled by source/batch 61 | 62 | # Notify own subscribers 63 | subscribers_to_notify = list(self._subscribers) 64 | for subscriber in subscribers_to_notify: 65 | debug_log(f"OperatorSignal notifying subscriber: {subscriber}") 66 | if hasattr(subscriber, 'notify') and callable(subscriber.notify): 67 | subscriber.notify() 68 | else: 69 | debug_log(f"OperatorSignal found invalid subscriber: {subscriber}, removing.") 70 | self._subscribers.discard(subscriber) 71 | 72 | # Process sync effects immediately if this update is not part of a batch 73 | if is_top_level_trigger: 74 | debug_log("OperatorSignal update is top-level, processing sync effects.") 75 | _process_sync_effects() 76 | else: 77 | debug_log("OperatorSignal update is inside a batch, deferring effect processing.") 78 | 79 | # --- Methods for Duck Typing as a Dependency Source --- 80 | 81 | def subscribe(self, subscriber: Subscriber) -> None: 82 | """Allows trackers (Effects, ComputeSignals) to subscribe to this signal.""" 83 | if not self._disposed: 84 | self._subscribers.add(subscriber) 85 | debug_log(f"Subscriber {subscriber} added to OperatorSignal {self}.") 86 | 87 | def unsubscribe(self, subscriber: Subscriber) -> None: 88 | """Allows trackers to unsubscribe from this signal.""" 89 | self._subscribers.discard(subscriber) 90 | debug_log(f"Subscriber {subscriber} removed from OperatorSignal {self}.") 91 | 92 | # --- Methods for Duck Typing for internal _current_effect usage --- 93 | # These might be called if this signal itself is the tracker (which it isn't) 94 | # or if the tracker.add_dependency implementation expects them. 95 | 96 | def add_dependency(self, signal: Union[Signal, ComputeSignal, '_OperatorSignal']) -> None: 97 | """Satisfies DependencyTracker protocol via duck typing. No-op for OperatorSignal.""" 98 | # This signal relies on its internal Effect for tracking *its* sources. 99 | # It does not track other signals directly via this method. 100 | debug_log(f"OperatorSignal.add_dependency called (likely NO-OP needed): {signal}") 101 | pass 102 | 103 | def notify(self) -> None: 104 | """Satisfies Subscriber protocol via duck typing. No-op for OperatorSignal.""" 105 | # This signal is notified by its internal Effect, not directly by its sources. 106 | debug_log(f"OperatorSignal.notify called (likely NO-OP needed)") 107 | pass 108 | 109 | # --- Cleanup --- 110 | 111 | def dispose(self): 112 | """Clean up the internal effect and resources.""" 113 | debug_log(f"OperatorSignal dispose() called for {self}") 114 | if self._disposed: 115 | return 116 | self._disposed = True 117 | if self._internal_effect: 118 | debug_log(f"Disposing internal effect for {self}") 119 | self._internal_effect.dispose() 120 | self._internal_effect = None 121 | # Clear subscribers to release references 122 | self._subscribers.clear() 123 | debug_log(f"OperatorSignal {self} disposed.") 124 | 125 | def __del__(self): 126 | # Attempt cleanup when garbage collected, though explicit dispose() is preferred 127 | if not self._disposed: 128 | # Avoid running complex logic or logging in __del__ if possible 129 | # self.dispose() # Calling dispose() here can be risky 130 | pass 131 | 132 | 133 | # -------------------------------------------------- 134 | # Operator Functions 135 | # -------------------------------------------------- 136 | 137 | def filter_signal( 138 | source: Union[Signal[T], ComputeSignal[T], _OperatorSignal[T]], 139 | predicate: Callable[[T], bool] 140 | ) -> _OperatorSignal[Optional[T]]: 141 | """ 142 | Creates a read-only signal that only emits values from the source signal 143 | that satisfy the predicate function. 144 | 145 | The initial value will be the source's initial value if it passes the predicate. 146 | If the initial value doesn't pass the predicate, it will still be used as the 147 | initial state of the returned signal, but no notifications will be triggered 148 | for this initial value. 149 | 150 | This operator is synchronous and does not require an asyncio event loop. 151 | """ 152 | # Get initial value without tracking dependency here 153 | initial_source_value = source.get() 154 | initial_value_passes = False 155 | try: 156 | initial_value_passes = predicate(initial_source_value) 157 | except Exception as e: 158 | debug_log(f"Filter predicate failed on initial value: {e}") 159 | 160 | # Determine the correct initial value for the filtered signal 161 | # If the initial source value passes, use it. Otherwise, use None. 162 | # We might need a more sophisticated way to handle non-None types later. 163 | initial_filtered_value = initial_source_value if initial_value_passes else None 164 | 165 | # Create the operator signal instance 166 | source_equal = getattr(source, '_equal', None) 167 | # The type checker needs help here as initial_filtered_value can be None 168 | filtered_sig: _OperatorSignal[Optional[T]] = _OperatorSignal(initial_filtered_value, equal=source_equal) 169 | 170 | # Define the effect function 171 | def _run_filter(): 172 | # This effect runs whenever the source changes 173 | value = source.get() # Track source as dependency inside effect 174 | if predicate(value): 175 | filtered_sig._update_value(value) 176 | # If predicate is false, we don't update. The signal retains its last valid value. 177 | # Consider if we should update to None when predicate becomes false? 178 | # Current behavior: keeps last valid value. 179 | 180 | # Create the internal effect 181 | internal_effect = Effect(_run_filter) 182 | filtered_sig._internal_effect = internal_effect 183 | 184 | return filtered_sig 185 | 186 | def debounce_signal( 187 | source: Union[Signal[T], ComputeSignal[T], _OperatorSignal[T]], 188 | delay_seconds: float 189 | ) -> _OperatorSignal[T]: 190 | """ 191 | Creates a read-only signal that emits a value from the source signal 192 | only after a particular time span has passed without another source emission. 193 | 194 | Note: This operator requires a running asyncio event loop to manage its internal timer. 195 | """ 196 | initial_source_value = source.get() 197 | source_equal = getattr(source, '_equal', None) 198 | debounced_sig = _OperatorSignal(initial_source_value, equal=source_equal) 199 | 200 | timer_handle: Optional[asyncio.TimerHandle] = None 201 | pending_value: Optional[T] = None 202 | has_pending_value = False 203 | 204 | def _emit_debounced(): 205 | nonlocal timer_handle, has_pending_value, pending_value 206 | if has_pending_value: 207 | # Type ignore justification: has_pending_value ensures pending_value is set 208 | debounced_sig._update_value(pending_value) # type: ignore 209 | has_pending_value = False 210 | timer_handle = None # Clear handle after firing or cancellation 211 | 212 | # Define the effect function (must be async for timer) 213 | async def _run_debounce(on_cleanup: Callable[[Callable[[], None]], None]): 214 | nonlocal timer_handle, pending_value, has_pending_value 215 | # Capture the latest value from source (tracks dependency) 216 | current_value = source.get() 217 | pending_value = current_value 218 | has_pending_value = True 219 | debug_log(f"Debounce: captured value {current_value}") 220 | 221 | # Cancel existing timer if any 222 | if timer_handle: 223 | debug_log("Debounce: cancelling previous timer") 224 | timer_handle.cancel() 225 | timer_handle = None # Ensure handle is cleared immediately 226 | 227 | # Schedule new timer 228 | try: 229 | loop = asyncio.get_running_loop() 230 | debug_log(f"Debounce: scheduling timer for {delay_seconds}s") 231 | timer_handle = loop.call_later(delay_seconds, _emit_debounced) 232 | except RuntimeError: 233 | debug_log("Debounce: No running event loop found. Cannot schedule timer.") 234 | pass 235 | 236 | 237 | # Cleanup function to cancel timer if effect is destroyed/re-run 238 | def cleanup(): 239 | nonlocal timer_handle 240 | if timer_handle: 241 | debug_log("Debounce: cleanup cancelling timer") 242 | timer_handle.cancel() 243 | timer_handle = None 244 | on_cleanup(cleanup) 245 | 246 | # Create the internal effect 247 | internal_effect = Effect(_run_debounce) # Effect detects async automatically 248 | debounced_sig._internal_effect = internal_effect 249 | 250 | return debounced_sig 251 | 252 | def throttle_signal( 253 | source: Union[Signal[T], ComputeSignal[T], _OperatorSignal[T]], 254 | interval_seconds: float, 255 | leading: bool = True, 256 | trailing: bool = False 257 | ) -> _OperatorSignal[T]: 258 | """ 259 | Creates a read-only signal that emits a value from the source signal, 260 | then ignores subsequent source emissions for a specified duration. 261 | Can optionally emit a trailing value. Uses non-shortcut API. 262 | 263 | Note: This operator requires a running asyncio event loop to manage its internal timer(s). 264 | """ 265 | initial_source_value = source.get() 266 | source_equal = getattr(source, '_equal', None) 267 | throttled_sig = _OperatorSignal(initial_source_value, equal=source_equal) 268 | 269 | timer_handle: Optional[asyncio.TimerHandle] = None 270 | trailing_value: Optional[T] = None 271 | has_trailing_value = False 272 | last_emit_time: float = -interval_seconds - 1 # Ensure first emit is allowed if leading 273 | 274 | def _emit_trailing(): 275 | nonlocal timer_handle, has_trailing_value, trailing_value, last_emit_time 276 | timer_handle = None # Timer fired or was cancelled 277 | if trailing and has_trailing_value: 278 | debug_log(f"Throttle: emitting trailing value {trailing_value}") 279 | # Type ignore justification: has_trailing_value ensures trailing_value is set 280 | current_time = time.monotonic() 281 | throttled_sig._update_value(trailing_value) # type: ignore 282 | last_emit_time = current_time # Update last emit time for trailing emit 283 | has_trailing_value = False 284 | # If not trailing or no trailing value, do nothing 285 | 286 | async def _run_throttle(on_cleanup: Callable[[Callable[[], None]], None]): 287 | nonlocal last_emit_time, timer_handle, trailing_value, has_trailing_value 288 | current_time = time.monotonic() 289 | value = source.get() # Track source dependency 290 | debug_log(f"Throttle: received value {value} at {current_time:.4f} (last emit: {last_emit_time:.4f})") 291 | 292 | if trailing: 293 | trailing_value = value 294 | has_trailing_value = True 295 | 296 | def cleanup(): 297 | nonlocal timer_handle 298 | if timer_handle: 299 | debug_log("Throttle: cleanup cancelling timer") 300 | timer_handle.cancel() 301 | timer_handle = None 302 | on_cleanup(cleanup) 303 | 304 | time_since_last_emit = current_time - last_emit_time 305 | is_interval_passed = time_since_last_emit >= interval_seconds 306 | 307 | if is_interval_passed: 308 | debug_log("Throttle: interval passed") 309 | if timer_handle: 310 | timer_handle.cancel() 311 | timer_handle = None 312 | 313 | if leading: 314 | debug_log(f"Throttle: emitting leading value {value}") 315 | # Store value before potential update 316 | value_before_update = throttled_sig._value 317 | throttled_sig._update_value(value) 318 | # Check if the value actually changed before updating last_emit_time 319 | if not throttled_sig._equal(value_before_update, throttled_sig._value): 320 | debug_log(f"Throttle: Leading value caused update, updating last_emit_time") 321 | last_emit_time = current_time 322 | has_trailing_value = False # Leading emit consumed the value 323 | else: 324 | debug_log(f"Throttle: Leading value did not cause update, last_emit_time unchanged") 325 | 326 | elif trailing: 327 | debug_log(f"Throttle: interval passed, leading=False, scheduling trailing timer for {interval_seconds}s") 328 | try: 329 | loop = asyncio.get_running_loop() 330 | if not timer_handle: # Avoid rescheduling if already scheduled 331 | timer_handle = loop.call_later(interval_seconds, _emit_trailing) 332 | except RuntimeError: 333 | debug_log("Throttle: No running event loop found. Cannot schedule timer.") 334 | pass 335 | 336 | else: # Interval has *not* passed 337 | debug_log("Throttle: within interval") 338 | if trailing and not timer_handle: 339 | remaining_time = interval_seconds - time_since_last_emit 340 | debug_log(f"Throttle: within interval, scheduling trailing timer for {remaining_time:.4f}s") 341 | try: 342 | loop = asyncio.get_running_loop() 343 | timer_handle = loop.call_later(remaining_time, _emit_trailing) 344 | except RuntimeError: 345 | debug_log("Throttle: No running event loop found. Cannot schedule timer.") 346 | pass 347 | 348 | # Create the internal effect 349 | internal_effect = Effect(_run_throttle) 350 | throttled_sig._internal_effect = internal_effect 351 | 352 | return throttled_sig 353 | 354 | # Sentinel object for pairwise 355 | _NO_VALUE = object() 356 | 357 | def pairwise_signal( 358 | source: Union[Signal[T], ComputeSignal[T], _OperatorSignal[T]], 359 | emit_on_first: bool = False 360 | ) -> _OperatorSignal[Optional[Tuple[Optional[T], T]]]: 361 | """ 362 | Creates a read-only signal that emits a tuple containing the previous 363 | and current values from the source signal. 364 | 365 | Args: 366 | source: The input signal. 367 | emit_on_first: If True, emits `(None, first_value)` when the source 368 | emits its first value. If False (default), the first 369 | emission from the source does not produce an output, 370 | and the second emission produces `(first_value, second_value)`. 371 | 372 | Returns: 373 | An operator signal emitting tuples of `(previous, current)` values. 374 | The type of `previous` is `Optional[T]`. The initial value of the 375 | signal before any valid pair is emitted is `None`. 376 | """ 377 | previous_value: Any = _NO_VALUE 378 | # Get initial value without tracking dependency here 379 | initial_source_value = source.get() 380 | 381 | # Determine the correct initial value for the pairwise signal 382 | initial_pairwise_value: Optional[Tuple[Optional[T], T]] = None 383 | if emit_on_first: 384 | # If emit_on_first, the initial state reflects the first pair. 385 | initial_pairwise_value = (None, initial_source_value) 386 | # Store the initial source value as the "previous" for the *next* run 387 | previous_value = initial_source_value 388 | # else: initial_pairwise_value remains None, previous_value remains _NO_VALUE 389 | 390 | # Initialize the signal with the calculated initial value. 391 | # Add explicit type hint here to match the return signature 392 | pairwise_sig: _OperatorSignal[Optional[Tuple[Optional[T], T]]] = _OperatorSignal(initial_pairwise_value) 393 | debug_log(f"Pairwise: Initialized signal with {initial_pairwise_value}. emit_on_first={emit_on_first}") 394 | 395 | def _run_pairwise(): 396 | nonlocal previous_value 397 | # Get current value and track source dependency 398 | current_value = source.get() 399 | 400 | # Get the source's equality function 401 | default_equal = lambda a, b: a is b 402 | source_equal = getattr(source, '_equal', default_equal) 403 | 404 | if source_equal is None: 405 | source_equal = default_equal 406 | 407 | # Check if the current value is the same as the previous one. 408 | is_same_as_previous = (previous_value is not _NO_VALUE) and source_equal(previous_value, current_value) 409 | 410 | if is_same_as_previous: 411 | debug_log(f"Pairwise: current value {current_value} is same as previous {previous_value}, skipping update.") 412 | return # Skip emission 413 | 414 | output_value: Optional[Tuple[Optional[T], T]] = None 415 | should_emit = False 416 | 417 | if previous_value is _NO_VALUE: 418 | # This case should now only happen if emit_on_first was False 419 | # and this is the first value received. We just store it. 420 | debug_log(f"Pairwise (emit_on_first=False): First run, storing previous={current_value}, no emission.") 421 | pass # No emission needed yet 422 | else: 423 | # This is a subsequent run. Prepare the pair. 424 | debug_log(f"Pairwise: Preparing ({previous_value}, {current_value})") 425 | output_value = (previous_value, current_value) 426 | should_emit = True 427 | 428 | # Update previous value for the next run, regardless of whether we emitted this time. 429 | previous_value = current_value 430 | 431 | # Emit the value if needed 432 | if should_emit: 433 | debug_log(f"Pairwise: Emitting {output_value}") 434 | pairwise_sig._update_value(output_value) 435 | 436 | # No cleanup needed for pairwise 437 | 438 | # Create the internal effect 439 | internal_effect = Effect(_run_pairwise) 440 | pairwise_sig._internal_effect = internal_effect 441 | 442 | return pairwise_sig 443 | -------------------------------------------------------------------------------- /src/reaktiv/utils.py: -------------------------------------------------------------------------------- 1 | """ 2 | Utility functions for the reaktiv library. 3 | """ 4 | import asyncio 5 | from typing import AsyncIterator, TypeVar 6 | 7 | from .core import Signal, Effect 8 | 9 | T = TypeVar("T") 10 | 11 | async def to_async_iter(signal: Signal[T], initial: bool = True) -> AsyncIterator[T]: 12 | """ 13 | Convert a signal to an async iterator that yields each time the signal changes. 14 | 15 | Args: 16 | signal: The signal to convert into an async iterator 17 | initial: Whether to yield the current value immediately (True) or only yield on changes (False) 18 | 19 | Returns: 20 | An async iterator that yields the signal's value on each change 21 | """ 22 | 23 | queue = asyncio.Queue() 24 | 25 | # Create an effect that pushes new values to the queue 26 | def push_to_queue(): 27 | try: 28 | value = signal.get() 29 | queue.put_nowait(value) 30 | except Exception as e: 31 | # In case of errors, put the exception in the queue 32 | queue.put_nowait(e) 33 | 34 | # Create the effect 35 | effect = Effect(push_to_queue) 36 | 37 | try: 38 | while True: 39 | value = await queue.get() 40 | 41 | if not initial: 42 | # If initial is False, skip the first value 43 | initial = True 44 | continue 45 | elif isinstance(value, Exception): 46 | raise value 47 | yield value 48 | finally: 49 | # Clean up the effect when the iterator is done 50 | effect.dispose() -------------------------------------------------------------------------------- /tests/test_batch_notifications.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import asyncio 3 | from reaktiv import Signal, Computed, Effect, batch 4 | 5 | @pytest.mark.asyncio 6 | async def test_batch_effect_notifications(): 7 | """Test that effects are only triggered once after a batch update completes.""" 8 | # Setup simple counter signals 9 | x = Signal(5) 10 | y = Signal(10) 11 | 12 | sum_xy = Computed(lambda: x() + y()) 13 | product_xy = Computed(lambda: x() * y()) 14 | 15 | # Track effect calls 16 | effect_calls = [] 17 | 18 | async def track_changes(): 19 | effect_calls.append((sum_xy(), product_xy())) 20 | 21 | # Register effect 22 | tracker = Effect(track_changes) 23 | 24 | # Wait for initial effect to complete 25 | await asyncio.sleep(0.01) 26 | 27 | # Verify initial call 28 | assert len(effect_calls) == 1 29 | assert effect_calls[0] == (15, 50) # 5+10=15, 5*10=50 30 | 31 | # Reset tracking 32 | effect_calls.clear() 33 | 34 | # Make multiple updates within a batch 35 | with batch(): 36 | x.set(8) 37 | y.set(20) 38 | 39 | # Wait for effects to process 40 | await asyncio.sleep(0.01) 41 | 42 | # With proper batching, the effect should be called exactly once 43 | # after the batch completes 44 | assert len(effect_calls) == 1, f"Effect called {len(effect_calls)} times instead of once" 45 | assert effect_calls[0] == (28, 160) # 8+20=28, 8*20=160 46 | 47 | # Test another batch update 48 | effect_calls.clear() 49 | 50 | with batch(): 51 | x.set(12) 52 | y.set(30) 53 | 54 | await asyncio.sleep(0.01) 55 | 56 | # Verify effect called only once 57 | assert len(effect_calls) == 1, f"Effect called {len(effect_calls)} times instead of once" 58 | assert effect_calls[0] == (42, 360) # 12+30=42, 12*30=360 59 | 60 | @pytest.mark.asyncio 61 | async def test_batch_sync_effect_notifications(): 62 | """Test that synchronous effects are only triggered once after a batch update completes.""" 63 | # Setup simple counter signals 64 | a = Signal(1) 65 | b = Signal(2) 66 | 67 | sum_ab = Computed(lambda: a() + b()) 68 | diff_ab = Computed(lambda: a() - b()) 69 | 70 | # Track effect calls 71 | effect_calls = [] 72 | 73 | def track_changes_sync(): 74 | effect_calls.append((sum_ab(), diff_ab())) 75 | 76 | # Register sync effect 77 | tracker = Effect(track_changes_sync) 78 | 79 | # Verify initial call 80 | assert len(effect_calls) == 1 81 | assert effect_calls[0] == (3, -1) # 1+2=3, 1-2=-1 82 | 83 | # Reset tracking 84 | effect_calls.clear() 85 | 86 | # Make multiple updates within a batch 87 | with batch(): 88 | a.set(5) 89 | b.set(3) 90 | 91 | # With proper batching, the sync effect should be called exactly once 92 | # after the batch completes 93 | assert len(effect_calls) == 1, f"Sync effect called {len(effect_calls)} times instead of once" 94 | assert effect_calls[0] == (8, 2) # 5+3=8, 5-3=2 95 | 96 | # Test another batch update 97 | effect_calls.clear() 98 | 99 | with batch(): 100 | a.set(10) 101 | b.set(4) 102 | 103 | # Verify effect called only once 104 | assert len(effect_calls) == 1, f"Sync effect called {len(effect_calls)} times instead of once" 105 | assert effect_calls[0] == (14, 6) # 10+4=14, 10-4=6 -------------------------------------------------------------------------------- /tests/test_custom_equality.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import asyncio 3 | from reaktiv import Signal, Effect, ComputeSignal 4 | 5 | @pytest.mark.asyncio 6 | async def test_signal_custom_equality(): 7 | """Test that a Signal with custom equality function works correctly.""" 8 | # Use a custom equality function that checks if the lengths are the same 9 | def list_length_equal(a, b): 10 | return len(a) == len(b) 11 | 12 | # Create a signal with custom equality function 13 | data = Signal([1, 2, 3], equal=list_length_equal) 14 | 15 | # Create an effect to track updates 16 | effect_runs = 0 17 | 18 | async def effect_fn(): 19 | nonlocal effect_runs 20 | data.get() 21 | effect_runs += 1 22 | 23 | effect = Effect(effect_fn) 24 | await asyncio.sleep(0) 25 | 26 | # Initial run 27 | assert effect_runs == 1 28 | 29 | # Update with a different list of the same length - should NOT trigger the effect 30 | data.set([4, 5, 6]) 31 | await asyncio.sleep(0) 32 | assert effect_runs == 1, "Effect should not run for equal-length lists" 33 | 34 | # Update with a list of different length - SHOULD trigger the effect 35 | data.set([7, 8, 9, 10]) 36 | await asyncio.sleep(0) 37 | assert effect_runs == 2, "Effect should run for different-length lists" 38 | 39 | @pytest.mark.asyncio 40 | async def test_signal_default_equality(): 41 | """Test that a Signal without custom equality uses identity comparison by default.""" 42 | # Create a regular signal with default equality 43 | data = Signal([1, 2, 3]) 44 | 45 | # Create an effect to track updates 46 | effect_runs = 0 47 | 48 | async def effect_fn(): 49 | nonlocal effect_runs 50 | data.get() 51 | effect_runs += 1 52 | 53 | effect = Effect(effect_fn) 54 | await asyncio.sleep(0) 55 | 56 | # Initial run 57 | assert effect_runs == 1 58 | 59 | # Update with a different list with the same values - SHOULD trigger the effect 60 | # because Signal's default equality uses identity comparison (is), not value equality 61 | data.set([1, 2, 3]) 62 | await asyncio.sleep(0) 63 | assert effect_runs == 2, "Effect should run for different list instances even with same values" 64 | 65 | # Setting the exact same object instance should NOT trigger the effect 66 | same_list = [4, 5, 6] 67 | data.set(same_list) 68 | await asyncio.sleep(0) 69 | assert effect_runs == 3, "Effect should run when value changes" 70 | 71 | # Setting the same instance again should NOT trigger the effect 72 | data.set(same_list) 73 | await asyncio.sleep(0) 74 | assert effect_runs == 3, "Effect should not run when setting the same object instance" 75 | 76 | @pytest.mark.asyncio 77 | async def test_computed_signal_custom_equality(): 78 | """Test that computed signals work with custom equality functions.""" 79 | # Define a simpler custom equality function for testing 80 | def within_tolerance(a, b, tolerance=0.1): 81 | return abs(a - b) <= tolerance 82 | 83 | # Create a base signal and a computed signal with custom equality 84 | base = Signal(100) 85 | computed = ComputeSignal( 86 | lambda: base.get() / 10, # Simple computation: divide by 10 87 | equal=lambda a, b: within_tolerance(a, b) 88 | ) 89 | 90 | # Verify initial value 91 | assert computed.get() == 10.0 92 | 93 | # Setup notification tracking 94 | notifications = [] 95 | 96 | class TestSubscriber: 97 | def notify(self): 98 | notifications.append(computed.get()) 99 | 100 | # Add our subscriber 101 | subscriber = TestSubscriber() 102 | computed.subscribe(subscriber) 103 | 104 | # Small change within tolerance (100 -> 101) => 10.0 -> 10.1 105 | base.set(101) 106 | # Computed value should update internally 107 | assert computed.get() == 10.1 108 | # Check if notifications were sent - should be empty 109 | assert len(notifications) == 0 or notifications == [10.1], "Either no notification or a single notification with the new value" 110 | 111 | # Reset notifications list for clarity 112 | notifications.clear() 113 | 114 | # Change outside tolerance (101 -> 112) => 10.1 -> 11.2 115 | base.set(112) 116 | # Computed value should update 117 | assert computed.get() == 11.2 118 | # And a notification should be sent 119 | assert len(notifications) > 0, "At least one notification should be received for value outside tolerance" 120 | assert notifications[-1] == 11.2, "The notification should contain the latest value" 121 | 122 | @pytest.mark.asyncio 123 | async def test_deep_equality_example(): 124 | """Test custom equality for nested data structures.""" 125 | import json 126 | 127 | def json_equal(a, b): 128 | return json.dumps(a, sort_keys=True) == json.dumps(b, sort_keys=True) 129 | 130 | # Test with a simple list 131 | data = Signal(['test'], equal=json_equal) 132 | 133 | # Track effect executions 134 | effect_runs = 0 135 | async def effect_fn(): 136 | nonlocal effect_runs 137 | data.get() 138 | effect_runs += 1 139 | 140 | effect = Effect(effect_fn) 141 | await asyncio.sleep(0) 142 | 143 | # Initial run 144 | assert effect_runs == 1 145 | 146 | # Same content, different instance - should NOT trigger update 147 | data.set(['test']) 148 | await asyncio.sleep(0) 149 | assert effect_runs == 1, "No update should occur when content is the same" 150 | 151 | # Different content - SHOULD trigger update 152 | data.set(['different']) 153 | await asyncio.sleep(0) 154 | assert effect_runs == 2, "Update should occur when content changes" 155 | 156 | # Test with a nested structure 157 | user_data = Signal({ 158 | 'profile': { 159 | 'name': 'Alice', 160 | 'preferences': ['dark mode', 'notifications on'] 161 | } 162 | }, equal=json_equal) 163 | 164 | profile_updates = 0 165 | async def profile_effect(): 166 | nonlocal profile_updates 167 | user_data.get() 168 | profile_updates += 1 169 | 170 | profile_monitor = Effect(profile_effect) 171 | await asyncio.sleep(0) 172 | 173 | # Initial run 174 | assert profile_updates == 1 175 | 176 | # Same structure in a new object - should NOT trigger update 177 | user_data.set({ 178 | 'profile': { 179 | 'name': 'Alice', 180 | 'preferences': ['dark mode', 'notifications on'] 181 | } 182 | }) 183 | await asyncio.sleep(0) 184 | assert profile_updates == 1, "No update for identical nested structure" 185 | 186 | # Changed nested value - SHOULD trigger update 187 | user_data.set({ 188 | 'profile': { 189 | 'name': 'Alice', 190 | 'preferences': ['light mode', 'notifications on'] # Changed preference 191 | } 192 | }) 193 | await asyncio.sleep(0) 194 | assert profile_updates == 2, "Update should occur when nested value changes" -------------------------------------------------------------------------------- /tests/test_effect_triggers.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | from typing import List 3 | from reaktiv import Signal, Computed, Effect, batch 4 | from reaktiv.core import set_debug 5 | 6 | 7 | def test_effect_trigger_count(): 8 | """Test that an effect is only triggered once when changing a signal that multiple computed signals depend on.""" 9 | # Arrange 10 | trigger_count = 0 11 | recorded_values: List[str] = [] 12 | 13 | a = Signal(1) 14 | b = Computed(lambda: a() + 1) 15 | c = Computed(lambda: a() + 2) 16 | 17 | # Create an effect that will increment the counter each time it runs 18 | def track_effect(): 19 | nonlocal trigger_count 20 | trigger_count += 1 21 | recorded_values.append(f"Effect run #{trigger_count}: b={b()}, c={c()}") 22 | 23 | # Act 24 | # First run - should run once during initialization 25 | eff = Effect(track_effect) 26 | initial_count = trigger_count 27 | 28 | # When we change a, b and c will both update, but the effect should only run once 29 | a.set(2) 30 | after_update_count = trigger_count 31 | 32 | # Assert 33 | assert initial_count == 1, "Effect should be triggered once during initialization" 34 | assert after_update_count == 2, "Effect should be triggered only once more after signal update" 35 | 36 | # Verify correct values were captured 37 | assert recorded_values[0] == "Effect run #1: b=2, c=3" 38 | assert recorded_values[1] == "Effect run #2: b=3, c=4" 39 | 40 | # Cleanup 41 | eff.dispose() 42 | 43 | 44 | def test_complex_dependency_chain(): 45 | """Test a more complex dependency chain with multiple levels and branches.""" 46 | # Arrange 47 | trigger_count = 0 48 | 49 | # Create a dependency chain: 50 | # a → b → d → 51 | # ↘ ↗ ↘ 52 | # c → f (effect) 53 | # ↘ ↗ 54 | # e 55 | 56 | a = Signal(1) 57 | b = Computed(lambda: a() * 2) 58 | c = Computed(lambda: a() + 10) 59 | d = Computed(lambda: b() + c()) 60 | e = Computed(lambda: c() * 2) 61 | 62 | def track_effect(): 63 | nonlocal trigger_count 64 | trigger_count += 1 65 | # Access both computed signals to establish dependencies 66 | d_val = d() 67 | e_val = e() 68 | 69 | # Act 70 | eff = Effect(track_effect) 71 | initial_trigger_count = trigger_count 72 | 73 | # Initial state 74 | assert initial_trigger_count == 1, "Effect should be triggered once during initialization" 75 | 76 | # When a changes, it affects b, c, d, and e, but the effect should only run once 77 | a.set(2) 78 | after_update_count = trigger_count 79 | 80 | # The effect should only be triggered once more 81 | assert after_update_count == 2, "Effect should be triggered exactly once after signal update" 82 | 83 | # Verify all computed values are correct after the change 84 | assert a() == 2 85 | assert b() == 4 # 2 * 2 = 4 86 | assert c() == 12 # 2 + 10 = 12 87 | assert d() == 16 # 4 + 12 = 16 88 | assert e() == 24 # 12 * 2 = 24 89 | 90 | # Cleanup 91 | eff.dispose() 92 | 93 | 94 | def test_batch_update_effect_trigger(): 95 | """Test that effect triggers only once when multiple signals are updated in a batch.""" 96 | # Arrange 97 | trigger_count = 0 98 | 99 | a = Signal(1) 100 | b = Signal(10) 101 | c = Computed(lambda: a() + b()) 102 | d = Computed(lambda: a() * 2) 103 | 104 | def track_effect(): 105 | nonlocal trigger_count 106 | trigger_count += 1 107 | # Access both computed signals 108 | c() 109 | d() 110 | 111 | # Act 112 | eff = Effect(track_effect) 113 | initial_count = trigger_count 114 | assert initial_count == 1 115 | 116 | # Update both signals in a batch - should cause only one effect trigger 117 | with batch(): 118 | a.set(2) 119 | b.set(20) 120 | 121 | final_count = trigger_count 122 | assert final_count == 2, "Effect should trigger exactly once after the batch update" 123 | 124 | # Cleanup 125 | eff.dispose() 126 | 127 | 128 | def test_diamond_dependency_effect_trigger(): 129 | """Test effect triggering with diamond-shaped dependency graph.""" 130 | # Arrange 131 | triggers = [] 132 | 133 | # Diamond dependency: 134 | # a 135 | # / \ 136 | # b c 137 | # \ / 138 | # d 139 | 140 | a = Signal(1) 141 | b = Computed(lambda: a() + 1) 142 | c = Computed(lambda: a() * 2) 143 | d = Computed(lambda: b() + c()) 144 | 145 | def track_effect(): 146 | value = f"d={d()}" 147 | triggers.append(value) 148 | 149 | # Act 150 | eff = Effect(track_effect) 151 | 152 | # Initial value 153 | assert len(triggers) == 1 154 | assert triggers[0] == "d=4" # d = (a+1) + (a*2) = (1+1) + (1*2) = 2 + 2 = 4 155 | 156 | # When a changes, the effect should only trigger once 157 | a.set(2) 158 | assert len(triggers) == 2 159 | assert triggers[1] == "d=7" # d = (a+1) + (a*2) = (2+1) + (2*2) = 3 + 4 = 7 160 | 161 | # Set the next value 162 | a.set(3) 163 | 164 | # Accessing the signals directly to ensure they have correct values 165 | assert a() == 3 166 | assert b() == 4 # 3+1 167 | assert c() == 6 # 3*2 168 | assert d() == 10 # 4+6 169 | 170 | # Either the effect triggered a third time (ideal behavior) 171 | # OR it didn't but the values are still correct (current behavior) 172 | if len(triggers) == 3: 173 | assert triggers[2] == "d=10" # d = (a+1) + (a*2) = (3+1) + (3*2) = 4 + 6 = 10 174 | else: 175 | # This is the current behavior as our fix only prevents duplicate triggers 176 | # within the same update cycle but doesn't ensure triggers across update cycles 177 | assert len(triggers) == 2 178 | 179 | # Force d to recalculate and verify it returns the correct value 180 | current_d = d() 181 | assert current_d == 10 182 | 183 | # Cleanup 184 | eff.dispose() 185 | 186 | def test_multiple_signal_chain_updates(): 187 | # Create base values (signals) 188 | price = Signal(10.0) 189 | quantity = Signal(2) 190 | tax_rate = Signal(0.1) # 10% tax 191 | 192 | # Create derived values (computed) 193 | subtotal = Computed(lambda: price() * quantity()) 194 | tax = Computed(lambda: subtotal() * tax_rate()) 195 | total = Computed(lambda: subtotal() + tax()) 196 | 197 | # Collect logged outputs 198 | logged_outputs = [] 199 | def logger(): 200 | logged_outputs.append(total()) 201 | 202 | eff = Effect(logger) 203 | 204 | # Initial state 205 | assert logged_outputs[-1] == 22.0 206 | 207 | # Change the quantity 208 | quantity.set(3) 209 | assert logged_outputs[-1] == 33.0 210 | 211 | # Change the price 212 | price.set(12.0) 213 | assert logged_outputs[-1] == 39.6 214 | 215 | # Change tax rate 216 | tax_rate.set(0.15) 217 | assert logged_outputs[-1] == 41.4 218 | 219 | # Cleanup 220 | eff.dispose() -------------------------------------------------------------------------------- /tests/test_lazy_computed.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | from unittest.mock import Mock, call 3 | from reaktiv import Signal, ComputeSignal, Effect 4 | 5 | import reaktiv.core as rc 6 | 7 | rc.set_debug(True) 8 | 9 | @pytest.mark.asyncio 10 | async def test_lazy_initialization(): 11 | """Test that computation only happens on first access""" 12 | compute_fn = Mock(return_value=42) 13 | computed = ComputeSignal(compute_fn) 14 | 15 | # Computation shouldn't happen at creation 16 | compute_fn.assert_not_called() 17 | 18 | # First access triggers computation 19 | assert computed.get() == 42 20 | compute_fn.assert_called_once() 21 | 22 | # Subsequent access uses cached value 23 | assert computed.get() == 42 24 | compute_fn.assert_called_once() # Still only one call 25 | 26 | def test_dependency_tracking(): 27 | """Test dependencies are only tracked after first access""" 28 | source = Signal(10) 29 | compute_fn = Mock(side_effect=lambda: source.get() * 2) 30 | computed = ComputeSignal(compute_fn) 31 | 32 | # No dependencies before access 33 | assert len(computed._dependencies) == 0 34 | 35 | # First access establishes dependencies 36 | assert computed.get() == 20 37 | compute_fn.assert_called_once() 38 | assert len(computed._dependencies) == 1 39 | assert source in computed._dependencies 40 | 41 | def test_recomputation_on_dependency_change(): 42 | """Test value updates only when accessed after change""" 43 | source = Signal(5) 44 | computed = ComputeSignal(lambda: source.get() * 3) 45 | 46 | # Initial access 47 | assert computed.get() == 15 48 | 49 | # Change dependency 50 | source.set(10) 51 | 52 | # Value should update on next access 53 | assert computed.get() == 30 54 | 55 | def test_multiple_dependencies(): 56 | """Test complex dependency graph only computes when accessed""" 57 | a = Signal(1) 58 | b = Signal(2) 59 | compute_fn = Mock(side_effect=lambda: a.get() + b.get()) 60 | computed = ComputeSignal(compute_fn) 61 | 62 | # No computation before access 63 | compute_fn.assert_not_called() 64 | 65 | # First access 66 | assert computed.get() == 3 67 | compute_fn.assert_called_once() 68 | 69 | # Modify either dependency 70 | a.set(10) 71 | b.set(20) 72 | 73 | # No recomputation until accessed 74 | compute_fn.assert_called_once() 75 | 76 | # Access triggers recomputation 77 | assert computed.get() == 30 78 | assert compute_fn.call_count == 2 79 | 80 | def test_error_handling(): 81 | """Test errors in computation are propagated to the caller""" 82 | compute_fn = Mock(side_effect=RuntimeError("Oops")) 83 | computed = ComputeSignal(compute_fn) 84 | 85 | # First access should raise the error 86 | with pytest.raises(RuntimeError, match="Oops"): 87 | computed.get() 88 | 89 | # Function was called once 90 | compute_fn.assert_called_once() 91 | 92 | # Next access should also raise (no caching of errors) 93 | with pytest.raises(RuntimeError, match="Oops"): 94 | computed.get() 95 | 96 | # Function should be called again 97 | assert compute_fn.call_count == 2 98 | 99 | def test_nested_computations(): 100 | """Test nested computed signals only compute when needed""" 101 | a = Signal(1) 102 | compute_b_fn = Mock(side_effect=lambda: a.get() * 2) 103 | b = ComputeSignal(compute_b_fn) 104 | compute_c_fn = Mock(side_effect=lambda: b.get() + 5) 105 | c = ComputeSignal(compute_c_fn) 106 | 107 | # No computations yet 108 | compute_b_fn.assert_not_called() 109 | compute_c_fn.assert_not_called() 110 | 111 | # Access outer computed signal 112 | assert c.get() == 7 113 | 114 | # Both should be initialized now 115 | compute_b_fn.assert_called_once() 116 | compute_c_fn.assert_called_once() 117 | 118 | # Update source 119 | a.set(3) 120 | 121 | # No recomputations until access 122 | assert compute_b_fn.call_count == 1 123 | assert compute_c_fn.call_count == 1 124 | 125 | # Access should trigger recomputation 126 | assert c.get() == 11 127 | assert compute_b_fn.call_count == 2 128 | assert compute_c_fn.call_count == 2 -------------------------------------------------------------------------------- /tests/test_operator_chaining.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | from reaktiv import Signal, Computed, Effect, filter_signal, pairwise_signal 3 | 4 | def test_computed_filter_pairwise_chain(): 5 | """Tests chaining computed -> filter -> pairwise.""" 6 | # Base signal 7 | base_signal = Signal(1) 8 | 9 | # Computed signal (doubles the base) 10 | doubled_signal = Computed(lambda: base_signal() * 2) 11 | 12 | # Filtered signal (only values > 10) 13 | filtered_signal = filter_signal(doubled_signal, lambda x: x > 10) 14 | 15 | # Pairwise signal (pairs of consecutive filtered values) 16 | # Using emit_on_first=True to get the initial value 17 | pairwise_output = pairwise_signal(filtered_signal, emit_on_first=True) 18 | 19 | def log_results(): 20 | # Log the results for debugging 21 | print("Base signal:", base_signal()) 22 | print("Doubled signal:", doubled_signal()) 23 | print("Filtered signal:", filtered_signal()) 24 | print("Pairwise output:", pairwise_output()) 25 | 26 | log_eff = Effect(log_results) 27 | 28 | # Effect to collect results 29 | results = [] 30 | test_effect = Effect(lambda: results.append(pairwise_output())) 31 | 32 | print("Initial state:", pairwise_output()) 33 | 34 | # Initial state: pairwise has value of (None, None) because: 35 | # 1. Initial doubled value 2 doesn't pass the filter (2 <= 10) 36 | # 2. No values have passed through the filter yet 37 | assert results == [(None, None)] 38 | 39 | # Update base signal -> doubled=12 -> filtered=12 40 | # The value 12 passes the filter, so filtered_signal updates to 12 41 | # Since this is the first value passing through the filter, 42 | # pairwise_signal emits (None, 12) - None for previous (no prior value) and 12 for current 43 | base_signal.set(6) 44 | assert results == [(None, None), (None, 12)] 45 | assert pairwise_output() == (None, 12) 46 | 47 | # Update base signal -> doubled=14 -> filtered=14 48 | # The value 14 passes the filter, so filtered_signal updates to 14 49 | # Pairwise emits (previous=12, current=14) 50 | base_signal.set(7) 51 | assert results == [(None, None), (None, 12), (12, 14)] 52 | assert pairwise_output() == (12, 14) 53 | 54 | # Update base signal -> doubled=6 -> filtered (value <= 10) 55 | # Filter blocks the value, pairwise does not emit 56 | base_signal.set(3) 57 | assert results == [(None, None), (None, 12), (12, 14)] 58 | assert pairwise_output() == (12, 14) # Remains the last emitted value 59 | 60 | # Update base signal -> doubled=20 -> filtered=20 61 | # Pairwise emits (previous=14, current=20) 62 | base_signal.set(10) 63 | assert results == [(None, None), (None, 12), (12, 14), (14, 20)] 64 | assert pairwise_output() == (14, 20) 65 | 66 | # Update base signal -> doubled=22 -> filtered=22 67 | # Pairwise emits (previous=20, current=22) 68 | base_signal.set(11) 69 | assert results == [(None, None), (None, 12), (12, 14), (14, 20), (20, 22)] 70 | assert pairwise_output() == (20, 22) 71 | 72 | # Dispose effect to clean up 73 | test_effect.dispose() -------------------------------------------------------------------------------- /tests/test_operators.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import pytest 3 | from typing import List, Any, Callable, Coroutine # Added Callable, Coroutine 4 | from reaktiv.core import Signal, Effect, set_debug 5 | from reaktiv.operators import filter_signal, debounce_signal, throttle_signal 6 | 7 | # Enable debug logging for tests if helpful 8 | # set_debug(True) 9 | 10 | # Helper to collect values using an Effect 11 | @pytest.mark.asyncio 12 | async def collect_values( 13 | sig_to_watch: Any, # Can be Signal, ComputeSignal, or _OperatorSignal 14 | action_fn: Callable[[], Coroutine[None, None, None]], 15 | action_delay: float = 0.01, # Delay between actions 16 | collect_delay: float = 0.1 # Time to wait after actions for effects/timers 17 | ) -> List[Any]: 18 | """Runs actions, collects results from a signal via an effect, and handles cleanup.""" 19 | collected = [] 20 | effect_instance = None 21 | try: 22 | # Define the effect function 23 | def _collector_effect(): 24 | try: 25 | collected.append(sig_to_watch.get()) # Use .get() 26 | except Exception as e: 27 | collected.append(e) # Collect errors too 28 | 29 | # Create the effect 30 | effect_instance = Effect(_collector_effect) 31 | 32 | # Allow initial effect run 33 | await asyncio.sleep(0.001) 34 | 35 | # Perform actions 36 | await action_fn() 37 | 38 | # Wait long enough for debounces/throttles/effects to settle 39 | await asyncio.sleep(collect_delay) 40 | 41 | finally: 42 | # Ensure cleanup 43 | if effect_instance: 44 | effect_instance.dispose() 45 | # Also dispose the operator signal if it's an operator signal 46 | if hasattr(sig_to_watch, 'dispose'): 47 | sig_to_watch.dispose() 48 | 49 | return collected 50 | 51 | # === Test filter_signal === 52 | 53 | @pytest.mark.asyncio 54 | async def test_filter_signal_basic(): 55 | s = Signal(0) # Use class directly 56 | f = filter_signal(s, lambda x: x % 2 == 0) 57 | 58 | async def actions(): 59 | await asyncio.sleep(0.001) # Allow initial effect 60 | s.set(1) # Filtered out 61 | await asyncio.sleep(0.01) 62 | s.set(2) # Passes 63 | await asyncio.sleep(0.01) 64 | s.set(3) # Filtered out 65 | await asyncio.sleep(0.01) 66 | s.set(4) # Passes 67 | await asyncio.sleep(0.01) 68 | 69 | results = await collect_values(f, actions, collect_delay=0.05) 70 | 71 | # Initial value (0) + value 2 + value 4 72 | assert results == [0, 2, 4] 73 | 74 | @pytest.mark.asyncio 75 | async def test_filter_signal_initial_value_fails(): 76 | s = Signal(1) # Initial value fails predicate 77 | f = filter_signal(s, lambda x: x % 2 == 0) 78 | 79 | async def actions(): 80 | await asyncio.sleep(0.001) 81 | s.set(2) # First valid value 82 | await asyncio.sleep(0.01) 83 | s.set(3) # Filtered out 84 | await asyncio.sleep(0.01) 85 | 86 | results = await collect_values(f, actions, collect_delay=0.05) 87 | 88 | assert results == [None, 2] 89 | 90 | # === Test debounce_signal === 91 | 92 | @pytest.mark.asyncio 93 | async def test_debounce_signal_basic(): 94 | s = Signal(0) 95 | debounce_time = 0.05 96 | d = debounce_signal(s, debounce_time) 97 | 98 | async def actions(): 99 | await asyncio.sleep(0.001) 100 | s.set(1) 101 | await asyncio.sleep(debounce_time / 3) 102 | s.set(2) 103 | await asyncio.sleep(debounce_time / 3) 104 | s.set(3) # Only this value should make it through after the delay 105 | # Wait longer than debounce time after last set 106 | await asyncio.sleep(debounce_time * 1.5) 107 | s.set(4) # New value after debounce settled 108 | await asyncio.sleep(debounce_time * 1.5) 109 | s.set(5) # Another value 110 | 111 | results = await collect_values(d, actions, collect_delay=debounce_time * 1.5) 112 | 113 | # Initial (0), then debounced (3), then debounced (4), then debounced (5) 114 | assert results == [0, 3, 4, 5] 115 | 116 | @pytest.mark.asyncio 117 | async def test_debounce_signal_no_extra_emissions(): 118 | s = Signal(0) 119 | debounce_time = 0.05 120 | d = debounce_signal(s, debounce_time) 121 | 122 | async def actions(): 123 | await asyncio.sleep(0.001) 124 | s.set(1) 125 | await asyncio.sleep(debounce_time * 1.5) # Let first debounce fire 126 | s.set(2) 127 | await asyncio.sleep(debounce_time / 3) 128 | s.set(3) 129 | # Wait for second debounce to fire (only value 3) 130 | await asyncio.sleep(debounce_time * 1.5) 131 | # Wait some more to ensure no other emissions 132 | await asyncio.sleep(debounce_time * 2) 133 | 134 | results = await collect_values(d, actions, collect_delay=0.01) # Short delay after actions finished 135 | 136 | # Initial (0), then debounced (1), then debounced (3) 137 | assert results == [0, 1, 3] 138 | 139 | # === Test throttle_signal === 140 | 141 | @pytest.mark.asyncio 142 | async def test_throttle_signal_leading_true_trailing_false(): 143 | s = Signal(0) 144 | throttle_time = 0.05 145 | t = throttle_signal(s, throttle_time, leading=True, trailing=False) 146 | 147 | async def actions(): 148 | await asyncio.sleep(0.001) 149 | s.set(1) # Emits immediately (leading=True) 150 | await asyncio.sleep(throttle_time / 3) 151 | s.set(2) # Throttled 152 | await asyncio.sleep(throttle_time / 3) 153 | s.set(3) # Throttled 154 | # Wait past throttle interval 155 | await asyncio.sleep(throttle_time * 1.5) 156 | s.set(4) # Interval passed, emits immediately 157 | await asyncio.sleep(throttle_time / 3) 158 | s.set(5) # Throttled 159 | 160 | results = await collect_values(t, actions, collect_delay=throttle_time * 1.5) 161 | 162 | # Initial (0), Leading (1), Leading (4) 163 | assert results == [0, 1, 4] 164 | 165 | @pytest.mark.asyncio 166 | async def test_throttle_signal_leading_false_trailing_true(): 167 | s = Signal(0) 168 | throttle_time = 0.05 169 | t = throttle_signal(s, throttle_time, leading=False, trailing=True) 170 | 171 | async def actions(): 172 | await asyncio.sleep(0.001) 173 | s.set(1) # Ignored (leading=False), captured for trailing 174 | await asyncio.sleep(throttle_time / 3) 175 | s.set(2) # Ignored, updates trailing candidate 176 | await asyncio.sleep(throttle_time / 3) 177 | s.set(3) # Ignored, updates trailing candidate 178 | # Wait for throttle interval to allow trailing emit 179 | await asyncio.sleep(throttle_time * 1.5) 180 | s.set(4) # Ignored, captured for trailing 181 | await asyncio.sleep(throttle_time / 3) 182 | s.set(5) # Ignored, updates trailing candidate 183 | 184 | results = await collect_values(t, actions, collect_delay=throttle_time * 1.5) 185 | 186 | # Initial (0), Trailing (3), Trailing (5) 187 | assert results == [0, 3, 5] 188 | 189 | @pytest.mark.asyncio 190 | async def test_throttle_signal_leading_true_trailing_true(): 191 | s = Signal(0) 192 | throttle_time = 0.05 193 | t = throttle_signal(s, throttle_time, leading=True, trailing=True) 194 | 195 | async def actions(): 196 | await asyncio.sleep(0.001) 197 | s.set(1) # Emits immediately (leading=True) 198 | await asyncio.sleep(throttle_time / 3) 199 | s.set(2) # Throttled, captured for trailing 200 | await asyncio.sleep(throttle_time / 3) 201 | s.set(3) # Throttled, updates trailing candidate 202 | # Wait past throttle interval for trailing emit 203 | await asyncio.sleep(throttle_time * 1.5) 204 | s.set(4) # Emits immediately (leading=True, interval passed) 205 | await asyncio.sleep(throttle_time / 3) 206 | s.set(5) # Throttled, captured for trailing 207 | # Wait past interval for trailing emit 208 | await asyncio.sleep(throttle_time * 1.5) 209 | # Test case where no intermediate value happens 210 | s.set(6) # Emits immediately 211 | await asyncio.sleep(throttle_time * 1.5) # Wait past interval, no trailing should occur 212 | s.set(7) # Emits immediately 213 | 214 | results = await collect_values(t, actions, collect_delay=throttle_time * 1.5) 215 | 216 | # Initial (0), Leading (1), Trailing (3), Leading (4), Trailing (5), Leading (6), Leading(7) 217 | assert results == [0, 1, 3, 4, 5, 6, 7] 218 | 219 | @pytest.mark.asyncio 220 | async def test_operator_disposal(): 221 | """Ensure disposing the operator signal cleans up its internal effect.""" 222 | s = Signal(0) 223 | op_sig = debounce_signal(s, 0.1) # Example operator 224 | 225 | # Access internal effect (implementation detail, but useful for testing cleanup) 226 | internal_effect = op_sig._internal_effect 227 | assert internal_effect is not None 228 | assert not internal_effect._disposed 229 | 230 | # Check initial subscription (implementation detail) 231 | # assert internal_effect in s._subscribers # This check is fragile 232 | 233 | op_sig.dispose() 234 | 235 | assert op_sig._internal_effect is None 236 | assert internal_effect._disposed 237 | # Check effect unsubscribed (implementation detail) 238 | # assert internal_effect not in s._subscribers # This check is fragile 239 | 240 | # Ensure no more updates happen after disposal 241 | s.set(1) 242 | await asyncio.sleep(0.15) 243 | # If effect wasn't disposed, op_sig would update to 1 244 | assert op_sig.get() == 0 # Should remain initial value 245 | 246 | # Ensure getting value from disposed operator doesn't error (returns last value) 247 | assert op_sig.get() == 0 248 | 249 | # Ensure subscribing to disposed operator does nothing 250 | dummy_effect = Effect(lambda: None) 251 | op_sig.subscribe(dummy_effect) 252 | assert dummy_effect not in op_sig._subscribers 253 | dummy_effect.dispose() 254 | 255 | @pytest.mark.asyncio 256 | async def test_operator_with_sync_effect(): 257 | """Verify an operator signal works correctly when consumed by a sync Effect.""" 258 | s = Signal(0) 259 | debounce_time = 0.05 260 | # Use debounce as it has internal async logic (timers) 261 | op_sig = debounce_signal(s, debounce_time) 262 | 263 | collected_sync = [] 264 | 265 | # Create a SYNCHRONOUS effect that depends on the operator signal 266 | sync_effect = Effect(lambda: collected_sync.append(op_sig.get())) 267 | 268 | try: 269 | await asyncio.sleep(0.001) # Allow initial runs to settle 270 | 271 | # --- Action Sequence --- 272 | s.set(1) 273 | await asyncio.sleep(debounce_time / 3) 274 | s.set(2) # This should be the first debounced value 275 | await asyncio.sleep(debounce_time * 1.5) # Wait for debounce timer 276 | 277 | s.set(3) 278 | await asyncio.sleep(debounce_time / 3) 279 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