【发布时间】:2021-07-05 14:29:54
【问题描述】:
我有n_sample 脑信号,我想计算每个样本的功效。
这是我的代码:
def return_power_of_signal(input_signal):
#The power of a signal is the sum of the absolute squares of its time-domain samples divided
#by the signal length, or, equivalently, the square of its RMS level.
#my approach
#input: np.array of (n_sample, time_length)
n_sample = input_signal.shape[0]
n_time = input_signal.shape[1]
results_array = np.empty((n_sample, 1))
for i in range(n_sample):
sum_sample = 0
for j in range(n_time):
sum_sample += input_signal[i, j]*input_signal[i, j]
sum_sample = sum_sample/n_time
results_array[i] = sum_sample
return results_array
但是,我想知道有没有更好的方法(更有效/更少的编码?)计算这个的方法?
谢谢
【问题讨论】:
-
你能举个
input_signal的例子吗?
标签: python python-3.x performance multidimensional-array signal-processing