下采样一维numpy数组 [英] Downsample a 1D numpy array

问题描述

我有一个一维的numpy数组，我想对其进行下采样.如果下采样栅格不能完全适合数据，则可以使用以下任何一种方法:

I have a 1-d numpy array which I would like to downsample. Any of the following methods are acceptable if the downsampling raster doesn't perfectly fit the data:

• 重叠下采样间隔
• 将结尾处剩余的任意数量的值转换为单独的下采样值
• 插值以适合栅格

如果有的话，基本上是

1 2 6 2 1

我将采样降低3倍，以下所有内容都可以:

and I am downsampling by a factor of 3, all of the following are ok:

3 3

3 1.5

或任何插值给我带来的效果.

or whatever an interpolation would give me here.

我只是在寻找最快/最简单的方法.

I'm just looking for the fastest/easiest way to do this.

我找到了 scipy.signal.decimate ，但这听起来像是 decimates 值(根据需要将其取出，只在X中留一个). scipy.signal.resample 似乎具有正确的名称，但我不了解说明中的傅立叶处理方法.我的信号不是特别周期性.

I found scipy.signal.decimate, but that sounds like it decimates the values (takes them out as needed and only leaves one in X). scipy.signal.resample seems to have the right name, but I do not understand where they are going with the whole fourier thing in the description. My signal is not particularly periodic.

你能帮我一下吗?这似乎很简单，但是所有这些功能都很复杂...

Could you give me a hand here? This seems like a really simple task to do, but all these functions are quite intricate...

推荐答案

在简单的情况下，如果数组的大小可被下采样因子(R)整除，则可以reshape数组，并取平均值新轴:

In the simple case where your array's size is divisible by the downsampling factor (R), you can reshape your array, and take the mean along the new axis:

import numpy as np
a = np.array([1.,2,6,2,1,7])
R = 3
a.reshape(-1, R)
=> array([[ 1.,  2.,  6.],
         [ 2.,  1.,  7.]])

a.reshape(-1, R).mean(axis=1)
=> array([ 3.        ,  3.33333333])

通常情况下，您可以使用NaN填充数组，使其大小可以被R整除，然后使用scipy.nanmean取平均值.

In the general case, you can pad your array with NaNs to a size divisible by R, and take the mean using scipy.nanmean.

import math, scipy
b = np.append(a, [ 4 ])
b.shape
=> (7,)
pad_size = math.ceil(float(b.size)/R)*R - b.size
b_padded = np.append(b, np.zeros(pad_size)*np.NaN)
b_padded.shape
=> (9,)
scipy.nanmean(b_padded.reshape(-1,R), axis=1)
=> array([ 3.        ,  3.33333333,  4.])

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