Polygon:
- Added async methods to polygon. - The `requests` library is no longer required, with `urllib` being used instead. - Added the `get_bar_data` function, which returns a dataframe of aggregate data from polygon. - Opened up the `subscribe` and `unsubscribe` functions Enhancements: - Tables will now scroll when the rows exceed table height. Bugs: - Fixed a bug preventing async functions being used with horizontal line event. - Fixed a bug causing the legend to show duplicate lines if the line was created after the legend. - Fixed a bug causing the line hide icon to persist within the legend after deletion (#75) - Fixed a bug causing the search box to be unfocused when the chart is loaded.
This commit is contained in:
louisnw
160
docs/source/tutorials/events.md
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docs/source/tutorials/events.md
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# Topbar & Events
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This section gives an overview of how events are handled across the library.
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## How to use events
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Take a look at this minimal example, which uses the [`search`](#AbstractChart.Events) event:
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```python
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from lightweight_charts import Chart
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def on_search(chart, string):
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print(f'Search Text: "{string}" | Chart/SubChart ID: "{chart.id}"')
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if __name__ == '__main__':
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chart = Chart()
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# Subscribe the function above to search event
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chart.events.search += on_search
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chart.show(block=True)
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```
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Upon searching in a pane, the expected output would be akin to:
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```
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Search Text: "AAPL" | Chart/SubChart ID: "window.blyjagcr"
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```
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The ID shown above will change depending upon which pane was used to search, allowing for access to the object in question.
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```{important}
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* When using `show` rather than `show_async`, block should be set to `True` (`chart.show(block=True)`).
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* Event callables can be either coroutines, methods, or functions.
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```
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___
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## Topbar events
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Events can also be emitted from the topbar:
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```python
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from lightweight_charts import Chart
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def on_button_press(chart):
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new_button_value = 'On' if chart.topbar['my_button'].value == 'Off' else 'Off'
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chart.topbar['my_button'].set(new_button_value)
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print(f'Turned something {new_button_value.lower()}.')
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if __name__ == '__main__':
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chart = Chart()
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chart.topbar.button('my_button', 'Off', func=on_button_press)
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chart.show(block=True)
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```
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In this example, we are passing `on_button_press` to the `func` parameter.
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When the button is pressed, the function will be emitted the `chart` object as with the previous example, allowing access to the topbar dictionary.
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The `switcher` is typically used for timeframe selection:
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```python
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from lightweight_charts import Chart
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def on_timeframe_selection(chart):
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print(f'Getting data with a {chart.topbar["my_switcher"].value} timeframe.')
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if __name__ == '__main__':
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chart = Chart()
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chart.topbar.switcher(
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name='my_switcher',
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options=('1min', '5min', '30min'),
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default='5min',
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func=on_timeframe_selection)
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chart.show(block=True)
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```
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___
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## Async clock
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There are many use cases where we will need to run our own code whilst the GUI loop continues to listen for events. Let's demonstrate this by using the `textbox` widget to display a clock:
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```python
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import asyncio
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from datetime import datetime
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from lightweight_charts import Chart
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async def update_clock(chart):
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while chart.is_alive:
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await asyncio.sleep(1-(datetime.now().microsecond/1_000_000))
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chart.topbar['clock'].set(datetime.now().strftime('%H:%M:%S'))
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async def main():
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chart = Chart()
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chart.topbar.textbox('clock')
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await asyncio.gather(chart.show_async(block=True), update_clock(chart))
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if __name__ == '__main__':
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asyncio.run(main())
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```
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This is how the library is intended to be used with live data (option #2 [described here]()).
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___
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## Live data, topbar & events
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Now we can create an asyncio program which updates chart data whilst allowing the GUI loop to continue processing events, based the [Live data](live_chart.md) example:
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```python
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import asyncio
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import pandas as pd
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from lightweight_charts import Chart
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async def data_loop(chart):
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ticks = pd.read_csv('ticks.csv')
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for i, tick in ticks.iterrows():
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if not chart.is_alive:
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return
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chart.update_from_tick(ticks.iloc[i])
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await asyncio.sleep(0.03)
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i += 1
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def on_new_bar(chart):
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print('New bar event!')
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def on_timeframe_selection(chart):
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print(f'Selected timeframe of {chart.topbar["timeframe"].value}')
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async def main():
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chart = Chart()
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chart.events.new_bar += on_new_bar
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chart.topbar.switcher('timeframe', ('1min', '5min'), func=on_timeframe_selection)
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df = pd.read_csv('ohlc.csv')
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chart.set(df)
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await asyncio.gather(chart.show_async(block=True), data_loop(chart))
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if __name__ == '__main__':
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asyncio.run(main())
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```
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docs/source/tutorials/getting_started.md
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docs/source/tutorials/getting_started.md
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# Getting Started
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## Installation
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To install the library, use pip:
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```text
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pip install lightweight-charts
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```
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Pywebview's installation can differ depending on OS. Please refer to their [documentation](https://pywebview.flowrl.com/guide/installation.html#installation).
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___
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## A simple static chart
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```python
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import pandas as pd
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from lightweight_charts import Chart
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```
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Download this
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[`ohlcv.csv`](../../../examples/1_setting_data/ohlcv.csv)
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file for this tutorial.
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In this example, we are reading a csv file using pandas:
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```text
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date open high low close volume
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0 2010-06-29 1.2667 1.6667 1.1693 1.5927 277519500.0
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1 2010-06-30 1.6713 2.0280 1.5533 1.5887 253039500.0
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2 2010-07-01 1.6627 1.7280 1.3513 1.4640 121461000.0
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3 2010-07-02 1.4700 1.5500 1.2473 1.2800 75871500.0
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4..
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```
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..which can be used as data for the `Chart` object:
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```python
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if __name__ == '__main__':
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chart = Chart()
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df = pd.read_csv('ohlcv.csv')
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chart.set(df)
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chart.show(block=True)
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```
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The `block` parameter is set to `True` in this case, as we do not want the program to exit.
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```{warning}
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Due to the library's use of multiprocessing, instantiations of `Chart` should be encapsulated within an `if __name__ == '__main__'` block.
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```
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## Adding a line
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Now lets add a moving average to the chart using the following function:
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```python
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def calculate_sma(df, period: int = 50):
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return pd.DataFrame({
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'time': df['date'],
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f'SMA {period}': df['close'].rolling(window=period).mean()
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}).dropna()
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```
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`calculate_sma` derives the data column from `f'SMA {period}'`, which we will use as the name of our line:
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```python
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if __name__ == '__main__':
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chart = Chart()
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line = chart.create_line(name='SMA 50')
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df = pd.read_csv('ohlcv.csv')
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sma_df = calculate_sma(df, period=50)
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chart.set(df)
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line.set(sma_df)
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chart.show(block=True)
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```
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