SciPy 和 NumPy 之间的关系 [英] Relationship between SciPy and NumPy

问题描述

SciPy 似乎在其自己的命名空间中提供了大部分(但不是全部 [1])NumPy 的函数.换句话说，如果有一个名为 numpy.foo 的函数，那么几乎可以肯定有一个 scipy.foo.大多数时候，两者看起来完全一样，甚至经常指向同一个函数对象.

SciPy appears to provide most (but not all [1]) of NumPy's functions in its own namespace. In other words, if there's a function named numpy.foo, there's almost certainly a scipy.foo. Most of the time, the two appear to be exactly the same, oftentimes even pointing to the same function object.

有时，它们是不同的.举一个最近出现的例子:

Sometimes, they're different. To give an example that came up recently:

• numpy.log10 是一个 ufunc 为否定参数返回 NaN；
• scipy.log10 返回负参数的复数值并且看起来不是 ufunc.

• numpy.log10 is a ufunc that returns NaNs for negative arguments;
• scipy.log10 returns complex values for negative arguments and doesn't appear to be a ufunc.

对于log、log2 和logn 也可以这样说，但对于log1p 则不然[2].

The same can be said about log, log2 and logn, but not about log1p [2].

另一方面，对于同一个 ufunc，numpy.exp 和 scipy.exp 似乎是不同的名称.scipy.log1p 和 numpy.log1p 也是如此.

On the other hand, numpy.exp and scipy.exp appear to be different names for the same ufunc. This is also true of scipy.log1p and numpy.log1p.

另一个例子是 numpy.linalg.solve 与 scipy.linalg.solve.它们很相似，但后者比前者提供了一些额外的功能.

Another example is numpy.linalg.solve vs scipy.linalg.solve. They're similar, but the latter offers some additional features over the former.

为什么明显重复?如果这意味着将 numpy 批量导入到 scipy 命名空间中，为什么在行为和缺少函数方面存在细微差别?是否有一些总体逻辑可以帮助消除混淆?

Why the apparent duplication? If this is meant to be a wholesale import of numpy into the scipy namespace, why the subtle differences in behaviour and the missing functions? Is there some overarching logic that would help clear up the confusion?

[1] numpy.min、numpy.max、numpy.abs 和其他一些在 中没有对应项scipy 命名空间.

[1] numpy.min, numpy.max, numpy.abs and a few others have no counterparts in the scipy namespace.

[2] 使用 NumPy 1.5.1 和 SciPy 0.9.0rc2 进行测试.

[2] Tested using NumPy 1.5.1 and SciPy 0.9.0rc2.

推荐答案

上次查了一下，scipy的__init__方法执行了一个

Last time I checked it, the scipy __init__ method executes a

from numpy import *

以便在导入 scipy 模块时将整个 numpy 命名空间包含到 scipy 中.

so that the whole numpy namespace is included into scipy when the scipy module is imported.

您描述的 log10 行为很有趣，因为 两个 版本都来自 numpy.一个是ufunc，另一个是numpy.lib 函数.为什么 scipy 比 ufunc 更喜欢库函数，我不知道.

The log10 behavior you are describing is interesting, because both versions are coming from numpy. One is a ufunc, the other is a numpy.lib function. Why scipy is preferring the library function over the ufunc, I don't know off the top of my head.

事实上，我可以回答 log10 问题.查看 scipy __init__ 方法我看到了这个:

In fact, I can answer the log10 question. Looking in the scipy __init__ method I see this:

# Import numpy symbols to scipy name space
import numpy as _num
from numpy import oldnumeric
from numpy import *
from numpy.random import rand, randn
from numpy.fft import fft, ifft
from numpy.lib.scimath import *

您在 scipy 中获得的 log10 函数来自 numpy.lib.scimath.查看该代码，它说:

The log10 function you get in scipy comes from numpy.lib.scimath. Looking at that code, it says:

"""
Wrapper functions to more user-friendly calling of certain math functions
whose output data-type is different than the input data-type in certain
domains of the input.

For example, for functions like log() with branch cuts, the versions in this
module provide the mathematically valid answers in the complex plane:

>>> import math
>>> from numpy.lib import scimath
>>> scimath.log(-math.exp(1)) == (1+1j*math.pi)
True

Similarly, sqrt(), other base logarithms, power() and trig functions are
correctly handled.  See their respective docstrings for specific examples.
"""

似乎该模块覆盖了 sqrt、log、log2、logn、log10、power、arccos、arcsin 和 arctanh.这解释了您所看到的行为.这样做的底层设计原因可能隐藏在某处的邮件列表帖子中.

It seems that module overlays the base numpy ufuncs for sqrt, log, log2, logn, log10, power, arccos, arcsin, and arctanh. That explains the behavior you are seeing. The underlying design reason why it is done like that is probably buried in a mailing list post somewhere.

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