def _get_devices(self):
for device in driver.find_liquidctl_devices():
device.connect()
device_info = device.initialize()
device_name = device.description.replace(" (experimental)", "") #As of liquidctl:1.4.2, this no longer seems to contain "experimental" but I'll leave for compatibility
self.devices[device_name] = {"device": device,
"version": device_info[0][1],
"channels": {}}
self.data[device_name] = {}
device_channels = self.devices[device_name]["channels"]
for line in device_info[1:]:
channel = line[0][:5].lower().replace(" ","")
if channel not in device_channels:
device_channels[channel] = []
self.data[device_name][channel] = {
"per_led": {
"colours":[[255]*3]*40,
"group_size":1,
"speed":3.0,
"breathing":0},
"animations": {
"colours":[],
"mode":"fading",
"length":3.0,
"backwards":0}}
length = line[1].value * 2
device_channels[channel].append(length)
for ind in range(len(device_channels)):
prev = list(device_channels.keys())[ind]
while len(device_channels[prev])!=4:
device_channels[prev].append(0)
def _get_smart_driver(self):
return next((dev for dev in find_liquidctl_devices()
if isinstance(dev, SmartDeviceV2Driver)), None)
def list_devices():
devices = []
for dev in find_liquidctl_devices():
devices.append(dev.description)
return devices
def provide_kraken_two_driver(self) -> Optional[KrakenTwoDriver]:
_LOG.debug("provide KrakenTwoDriver")
return next((dev for dev in find_liquidctl_devices()
if isinstance(dev, KrakenTwoDriver)), None)
exit()
set_colours(colours)
print(f"Running gradient with {delay * 1000}ms delay")
self._thread_run = Thread(target=run_)
self._thread_run.start()
input("Enter to stop")
self.run = 0
if __name__ == "__main__":
from liquidctl import driver
from time import sleep
from threading import Thread
devices = []
for device in driver.find_liquidctl_devices():
device.connect()
devices.append(device)
def ambi():
amb = Ambient(10, 16, sampling=True, sampling_weighted=True)
amb.run(devices[0])
# all_colours = Marquee(26, 4, [0,0,125], [[255,0,0]], number_of_marquees=3, spacing=10)# .06
def gradi():
grad = Gradient(26, cross_channels=0)
grad.generate(
[[255, 0, 50], [255, 0, 255], [50, 0, 255], [0, 200, 255]],
mode="wave",
step=int(input("Step: ")))
grad.run(devices[0], delay=float(input("Delay: ")) / 1000)