python - 如何在python中的时间序列之间有效地映射数据

Question

我正在尝试创建一个有效的函数来对时间序列数据进行重新采样。

假设:两组时间序列数据具有相同的开始和结束时间。 (我在单独的步骤中执行此操作。)

重采样函数(低效)

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

速度测试

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 

有兴趣听取任何建议。

Answer 1

替换

downsampled_array = [data_sequence[ind] for ind in downsampling_indices]

与

downsampled_array = data_sequence[downsampling_indices]

为我的测试数据提供了 7 倍的加速。

用于测量加速比的代码:

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