Topbar & Events¶
This section gives an overview of how events are handled across the library.
How to use events¶
Take a look at this minimal example, which uses the search
event:
from lightweight_charts import Chart
def on_search(chart, string):
print(f'Search Text: "{string}" | Chart/SubChart ID: "{chart.id}"')
if __name__ == '__main__':
chart = Chart()
# Subscribe the function above to search event
chart.events.search += on_search
chart.show(block=True)
Upon searching in a pane, the expected output would be akin to:
Search Text: "AAPL" | Chart/SubChart ID: "window.blyjagcr"
The ID shown above will change depending upon which pane was used to search, allowing for access to the object in question.
Important
When using
show
rather thanshow_async
, block should be set toTrue
(chart.show(block=True)
).Event callables can be either coroutines, methods, or functions.
Topbar events¶
Events can also be emitted from the topbar:
from lightweight_charts import Chart
def on_button_press(chart):
new_button_value = 'On' if chart.topbar['my_button'].value == 'Off' else 'Off'
chart.topbar['my_button'].set(new_button_value)
print(f'Turned something {new_button_value.lower()}.')
if __name__ == '__main__':
chart = Chart()
chart.topbar.button('my_button', 'Off', func=on_button_press)
chart.show(block=True)
In this example, we are passing on_button_press
to the func
parameter.
When the button is pressed, the function will be emitted the chart
object as with the previous example, allowing access to the topbar dictionary.
The switcher
is typically used for timeframe selection:
from lightweight_charts import Chart
def on_timeframe_selection(chart):
print(f'Getting data with a {chart.topbar["my_switcher"].value} timeframe.')
if __name__ == '__main__':
chart = Chart()
chart.topbar.switcher(
name='my_switcher',
options=('1min', '5min', '30min'),
default='5min',
func=on_timeframe_selection)
chart.show(block=True)
Async clock¶
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:
import asyncio
from datetime import datetime
from lightweight_charts import Chart
async def update_clock(chart):
while chart.is_alive:
await asyncio.sleep(1-(datetime.now().microsecond/1_000_000))
chart.topbar['clock'].set(datetime.now().strftime('%H:%M:%S'))
async def main():
chart = Chart()
chart.topbar.textbox('clock')
await asyncio.gather(chart.show_async(block=True), update_clock(chart))
if __name__ == '__main__':
asyncio.run(main())
This is how the library is intended to be used with live data (option #2 described here).
Live data, topbar & events¶
Now we can create an asyncio program which updates chart data whilst allowing the GUI loop to continue processing events, based the Live data example:
import asyncio
import pandas as pd
from lightweight_charts import Chart
async def data_loop(chart):
ticks = pd.read_csv('ticks.csv')
for i, tick in ticks.iterrows():
if not chart.is_alive:
return
chart.update_from_tick(ticks.iloc[i])
await asyncio.sleep(0.03)
i += 1
def on_new_bar(chart):
print('New bar event!')
def on_timeframe_selection(chart):
print(f'Selected timeframe of {chart.topbar["timeframe"].value}')
async def main():
chart = Chart()
chart.events.new_bar += on_new_bar
chart.topbar.switcher('timeframe', ('1min', '5min'), func=on_timeframe_selection)
df = pd.read_csv('ohlc.csv')
chart.set(df)
await asyncio.gather(chart.show_async(block=True), data_loop(chart))
if __name__ == '__main__':
asyncio.run(main())