如何在Python中集成两个一维数据数组? [英] How do I integrate two 1-D data arrays in Python?
问题描述
我有两个列表数据数组，x和y，但我不知道生成数据的函数.我希望能够评估数据在x轴上任何点处生成的线的积分.
I have two tabulated data arrays, x and y, and I don't know the function that generated the data. I want to be able to evaluate the integral of the line produced by the data at any point along the x-axis.
不是将分段函数插值到数据上，然后尝试将其集成到我遇到的麻烦中，有什么我可以使用的东西，可以通过评估数组来简单地提供积分?
Rather than interpolating a piecewise function to the data and then attempting to integrate that, which I am having trouble with, is there something I can use that will simply provide the integral by evaluating the arrays?
在寻找解决方案时，我看到了对iPython和Pandas的引用，但是我无法找到这些软件包中有助于该任务的部分.
When searching for solutions, I have seen references to iPython and Pandas, but I haven't been able to find the parts of those packages that will aid in this task.
如果没有一种方法可以简单地集成阵列，您能提供一些建议以最佳方式来完成此任务吗?
If there isn't a way to simply integrate the arrays, could you provide some advice on the best way to handle this task?
推荐答案
例如，您可以使用scipy.integrate.simps执行辛普森法则，并可以将其传递给以下内容:
For example, you can use scipy.integrate.simps to perform simpson's Rule, and you can pass it the following:
scipy.integrate.simps(y，x = None，dx = 1，axis = -1，even ='avg')
scipy.integrate.simps(y, x=None, dx=1, axis=-1, even='avg')
参数:
y:类似于数组
要集成的数组.
Parameters :
y : array_like
Array to be integrated.
x:类似array_，可选 如果给出，则采样y的点.
x : array_like, optional If given, the points at which y is sampled.
dx:int，可选 积分点沿y轴的间距.仅在x为None时使用.默认值为1.
dx : int, optional Spacing of integration points along axis of y. Only used when x is None. Default is 1.
axis:int，可选 整合所沿的轴.默认为最后一个轴.
axis : int, optional Axis along which to integrate. Default is the last axis.
偶数:{'avg'，'first'，'str'}，可选
even : {‘avg’, ‘first’, ‘str’}, optional
"avg":平均两个结果:1)使用前N-2个间隔 最后一个区间是梯形法则，2)使用最近的N-2个区间，第一个区间是梯形法则.
‘avg’ : Average two results:1) use the first N-2 intervals with a trapezoidal rule on the last interval and 2) use the last N-2 intervals with a trapezoidal rule on the first interval.
第一":对第N-2个间隔使用辛普森规则， 最后间隔上的梯形规则.
‘first’ : Use Simpson’s rule for the first N-2 intervals with a trapezoidal rule on the last interval.
'last':对最后N-2个间隔使用Simpson规则， 梯形规则在第一个间隔上.
‘last’ : Use Simpson’s rule for the last N-2 intervals with a trapezoidal rule on the first interval.
因此您可以使用两个数组进行数值积分.
So you can use your two arrays to do numerical integration.
这篇关于如何在Python中集成两个一维数据数组?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
