如何使用 NumPy.recarray 的两个视图修改它

发布于 2024-07-26 04:55:46 字数 630 浏览 1 评论 0原文

我是 Python 和 Numpy 的新手，我面临一个问题，即在应用于屏蔽视图时无法修改 numpy.recarray。我从文件中读取记录，然后创建两个屏蔽视图，然后尝试修改 for 循环中的值。这是一个示例代码。

import numpy as np
import matplotlib.mlab as mlab


dat = mlab.csv2rec(args[0], delimiter=' ')
m_Obsr = dat.is_observed == 1
m_ZeroScale = dat[m_Obsr].scale_mean < 0.01


for d in dat[m_Obsr][m_ZeroScale]:
    d.scale_mean = 1.0

但是当我打印结果时

newFile = args[0] + ".no-zero-scale"

mlab.rec2csv(dat[m_Obsr][m_ZeroScale], newFile, delimiter=' ')

，文件中的所有scale_means仍然为零。

我一定做错了什么。是否有修改值的正确方法看法？是因为我将两种观点一一应用吗？

谢谢。

原文

I am new to Python and Numpy, and I am facing a problem, that I can not modify a numpy.recarray, when applying to masked views. I read recarray from a file, then create two masked views, then try to modify the values in for loop. Here is an example code.

import numpy as np
import matplotlib.mlab as mlab


dat = mlab.csv2rec(args[0], delimiter=' ')
m_Obsr = dat.is_observed == 1
m_ZeroScale = dat[m_Obsr].scale_mean < 0.01


for d in dat[m_Obsr][m_ZeroScale]:
    d.scale_mean = 1.0

But when I print the result

newFile = args[0] + ".no-zero-scale"

mlab.rec2csv(dat[m_Obsr][m_ZeroScale], newFile, delimiter=' ')

All the scale_means in the files, are still zero.

I must be doing something wrong. Is there a proper way of modifying values of the
view? Is it because I am applying two views one by one?

Thank you.

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扬花落满肩 2024-08-02 04:55:46

我认为您对“屏蔽视图”这个术语有误解，应该（重新）阅读这本书（现在可免费下载）以澄清您的理解。

我引用3.4.2节：

高级选择在以下情况下触发
选择对象 obj 是
非元组序列对象，一个 ndarray
（数据类型为整数或布尔值），或
至少有一个序列的元组
对象或 ndarray（数据类型
整数或布尔值）。有两种类型
高级索引：整数和
布尔值。始终高级选择
返回数据的副本（对比
基本切片返回
视图）。

您在这里所做的是高级选择（布尔类型），因此您将获得一个副本，并且永远不会将其绑定到任何地方 - 您在副本上进行更改，然后让它消失，然后编写一个新的副本从原来的。

一旦您了解了问题，解决方案就应该很简单：制作一次副本，对该副本进行更改，然后编写相同的副本。 IE：

dat = mlab.csv2rec(args[0], delimiter=' ')
m_Obsr = dat.is_observed == 1
m_ZeroScale = dat[m_Obsr].scale_mean < 0.01
the_copy = dat[m_Obsr][m_ZeroScale]

for d in the_copy:
    d.scale_mean = 1.0

newFile = args[0] + ".no-zero-scale"
mlab.rec2csv(the_copy, newFile, delimiter=' ')

I think you have a misconception in this term "masked views" and should (re-)read The Book (now freely downloadable) to clarify your understanding.

I quote from section 3.4.2:

Advanced selection is triggered when
the selection object, obj, is a
non-tuple sequence object, an ndarray
(of data type integer or bool), or a
tuple with at least one sequence
object or ndarray (of data type
integer or bool). There are two types
of advanced indexing: integer and
Boolean. Advanced selection always
returns a copy of the data (contrast
with basic slicing that returns a
view).

What you're doing here is advanced selection (of the Boolean kind) so you're getting a copy and never binding it anywhere -- you make your changes on the copy and then just let it go away, then write a new fresh copy from the original.

Once you understand the issue the solution should be simple: make your copy once, make your changes on that copy, and write that same copy. I.e.:

dat = mlab.csv2rec(args[0], delimiter=' ')
m_Obsr = dat.is_observed == 1
m_ZeroScale = dat[m_Obsr].scale_mean < 0.01
the_copy = dat[m_Obsr][m_ZeroScale]

for d in the_copy:
    d.scale_mean = 1.0

newFile = args[0] + ".no-zero-scale"
mlab.rec2csv(the_copy, newFile, delimiter=' ')

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