以编程方式查找数据集中的“峰值"或下降 [英] Finding a 'spike' or drop in a dataset programatically

问题描述

如果我有一个看起来像这样的数据集

If I have a dataset that looks like this

[0.523,0.445,0.558,0.492,0.440,0.502,0.742,0.802,0.821,0.811,0.804,0.860]

如您所见，在 0.502 之后的值中有一个尖峰".有没有办法在 Python 中以编程方式找到它?我已经在使用 Numpy 和 Scipy；我确信那些库包含这样的东西.我只是不知道这个程序叫什么.

As you can see, there is a 'spike' in the values after 0.502. Is there a way to find this programmatically in Python? I'm already using Numpy and Scipy; I'm sure those libraries contain something like this. I just don't know what this procedure is called.

一个额外的好处是调整检测尖峰或下降的灵敏度"，因为数据集可能非常嘈杂.峰值意味着值的移动平均值持续增加，下降意味着值的持续下降.

An added bonus would be to adjust the 'sensitivity' of detecting a spike or drop, since the dataset can be quite noise. A spike would mean a sustained increase in the moving averages of the values, and a drop would mean a sustained decrease values.

每个值的范围是 [-1,1].数组中值的数量为 50-100.

The range of each value is [-1,1]. The number of values in the array would be 50-100.

推荐答案

我建议使用 numpy 的 diff 函数:

I would recommend using the diff function of numpy:

import numpy    
a = [0.523,0.445,0.558,0.492,0.440,0.502,0.742,0.802,0.821,0.811,0.804,0.860]
numpy.diff(a)

这会给你:

array([-0.078,  0.113, -0.066, -0.052,  0.062,  0.24 ,  0.06 ,  0.019,
       -0.01 , -0.007,  0.056])

如果数字是正数，则上升，如果是负数，则下降.

If the number is positive, then it's a jump up, if it's negative, then it's a jump down.

如果你只是想找出哪里有尖峰，向上或向下试试这个:

If you just want to find where there are spikes, up or down try this:

abs(numpy.diff(a)) > 0.2

向上或向下调整 0.2 会分别降低或提高敏感度.这会给:

Adjusting the 0.2 up or down would make it less or more sensitive, respectively. This would give:

array([False, False, False, False, False,  True, False, False, False,
       False, False], dtype=bool)

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