Normalized power
Information about normalized power can be found at the TrainingPeaks website.
from collections import deque
from fittie import decode
def main(filename: str) -> None:
fitfile = decode(filename)
power_data: list[int] = []
for data in fitfile(message_type="record", fields=["power"]):
# Add only non-zero values to power_data
if power := data.get("power"):
power_data.append(power)
print(get_normalized_power(power_data))
def get_moving_averages(data: list[int]) -> list[int]:
"""
Get the moving / rolling averages of the provided data with a window size
of 30 seconds.
"""
window_size = 30
moving_averages = []
sample_size = deque(data[:window_size], maxlen=window_size)
moving_averages.append(round(sum(sample_size) / window_size))
for n in data[30:]:
sample_size.append(n)
moving_averages.append(round(sum(sample_size) / window_size))
return moving_averages
def get_normalized_power(power_data: list[int]) -> int:
"""
Get normalized power of the provided power data
"""
data = [pow(n, 4) for n in get_moving_averages(power_data)]
average = sum(data) / len(data)
normalized_power = pow(average, 0.25)
return round(normalized_power)
if __name__ == "__main__":
main("/path/to/fit/file.fit")