NumPy中的模型测量和误差 [英] Model measurement and error in NumPy

问题描述

我想在实验室实验中尝试使用SciPy套件而不是Octave进行统计. 在这里，回答了我的大多数问题，只剩下另一件事了:

I'd like to try the SciPy suite instead of Octave for doing the statistics in my lab experiments. Most of my questions were answered here, there is just another thing left:

我通常在测量中有一个错误，在八度中，我做了以下事情:

I usually have an error attached to the measurements, in Octave I just did the following:

R.val = 10;
R.err = 0.1;

U.val = 4;
U.err = 0.1;

然后我将像这样计算I:

I.val = U.val / R.val;
I.err = sqrt(
    (1 / R.val * U.err)^2
    + (U.val / R.val^2 * R.err)^2
);

当我进行大量测量时，我通常使用一个结构数组，如下所示:

When I had a bunch of measurements, I usually used a structure array, like this:

R(0).val = 1;
R(0).err = 0.1;
…
R(15).val = 100;
R(15).err = 9;

然后我可以执行R(0).val或使用R.val直接访问所有它们，并且我有一个包含所有值的列向量，例如mean(R.val).

Then I could do R(0).val or directly access all of them using R.val and I had a column vector with all the values, for mean(R.val) for instance.

我如何使用SciPy/NumPy/Python来表示这一点?

How could I represent this using SciPy/NumPy/Python?

推荐答案

最简单的方法实际上是使用NumPy

The easiest is indeed to use NumPy structured arrays, that give you the possibility to define arrays of homogeneous elements (a record) composed of other homogeneous elements (fields).

例如，您可以定义

R = np.empty(15, dtype=[('val',float),('err',float)])

，然后填写相应的列:

R['val'] = ...
R['err'] = ...

或者，如果您将val和err放在两个列表中，则可以一次定义数组:

Alternatively, you could define the array at once if you have your val and err in two lists:

R = np.array(zip(val_list, err_list), dtype=[('val',float),('err',float)])

在两种情况下，您都可以按索引访问单个元素，如R[0](这将为您提供特定的对象，np.void，但仍使您可以单独访问字段)，或按切片R[1:-1] ...

In both cases, you can access individual elements by indices, like R[0] (which would give you a specific object, a np.void, that still gives you the possibility to access the fields separately), or by slices R[1:-1]...

以您的示例为例，您可以这样做:

With your example, you could do:

I = np.empty_like(R)
I['val'] = U['val'] / R['val']
I['err'] = np.sqrt((1 / R['val'] * U['err'])**2 + (U['val'] / R['val']**2 * R['err'])**2)

您还可以使用 record array ，它们是基本的结构化数组，其中的__getattr__和__setattr__方法被重载，以便您可以将字段作为属性来访问(例如在R.val中)以及索引(例如标准R['val']).当然，由于这些基本方法很重载，记录数组的效率不如结构化数组.

You could also use record array, which are basic structured array with the __getattr__ and __setattr__ methods overloaded in such way that you can access the fields as attributes (like in R.val) as well as indices (like the standard R['val']). Of course, as these basic methods are overloaded, record arrays are not as efficient as structured arrays.

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