我正在尝试创建一个有效的功能来重新采样时间序列数据。
假设:两组时间序列数据都具有相同的开始和结束时间。(我在另一个步骤中执行此操作。)
import numpy as np
def resample(desired_time_sequence, data_sequence):
downsampling_indices = np.linspace(0, len(data_sequence)-1, len(desired_time_sequence)).round().astype(int)
downsampled_array = [data_sequence[ind] for ind in downsampling_indices]
return downsampled_array
import timeit
def test_speed(): resample([1,2,3], [.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6])
print(timeit.timeit(test_speed, number=100000))
# 1.5003695999998854
有兴趣听到任何建议。
更换
downsampled_array = [data_sequence[ind] for ind in downsampling_indices]
与
downsampled_array = data_sequence[downsampling_indices]
在我的测试数据上提供了7倍的加速。
用于测量加速的代码:
import timeit
f1 = """
def resample(output_len, data_sequence):
downsampling_indices = np.linspace(0, len(data_sequence)-1, output_len).round().astype(int)
downsampled_array = [data_sequence[ind] for ind in downsampling_indices]
return downsampled_array
resample(output_len, data_sequence)
"""
f2 = """
def resample_fast(output_len, data_sequence):
downsampling_indices = np.linspace(0, len(data_sequence)-1, output_len).round().astype(int)
downsampled_array = data_sequence[downsampling_indices]
return downsampled_array
resample_fast(output_len, data_sequence)
"""
setup="""
import numpy as np
data_sequence = np.random.randn(10000)
output_len = 752
"""
print(timeit.timeit(f1, setup, number=1000))
print(timeit.timeit(f2, setup, number=1000))
# prints:
# 0.30194038699846715
# 0.041797632933594286
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我来说两句