打破numpy代码

我一直在认真阅读文档和重读/运行下面的代码，以便准确理解发生了什么。尽管我的知识仍然存在差距。我希望向您提供代码，并提供意见，这些意见表示我希望有些人愿意填补的知识空白。打破numpy代码

因此，这里有我的要求的朋友：
1）帮我填空白我所知
2）解释什么是在非技术和简单的格式怎么回事一步一步来。

import numpy 
import scipy.misc 
import matplotlib.pyplot 

lena = scipy.misc.lena() 


''' Generates an artificial range within the framework of the original array (Which is an image) 
This artificial range will be paired with another one and used to 'climb' 
Through the original array and make changes''' 

def get_indices(size): 
    arr = numpy.arange(size) 
    #This sets every fourth element to False? How? 
    return arr % 4 == 0 

lena1 = lena.copy() 
xindices = get_indices(lena.shape[0]) 
yindices = get_indices(lena.shape[1]) 




'''I am unsure of HOW the below code is executing. I know something is being 
Set to zero, but what? And how can I verify it?''' 

lena[xindices, yindices] = 0 

#What does the argument 211 do exactly? 
matplotlib.pyplot.subplot(211) 
matplotlib.pyplot.imshow(lena1) 


matplotlib.pyplot.show()

谢谢配偶！

来源

2014-02-24 chopper draw lion4

您是否尝试了解scipy.misc.lena是否返回？你在哪里或为什么卡住了？ –

是的，我明白这一点。对我来说，最令人困惑的部分是“lena [xindices，yindices] = 0” –

'a [i，j]'只是对'a [i] [j]'说的一种粗糙的方式，即索引多维数组。 http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html –

使用Python调试器对于在代码执行过程中执行代码时非常有用。写在你选择的任何地方如下：

import pdb; pdb.set_trace()

执行将停止，您可以通过线检查任何变量，使用任何定义的函数，并提前线。

这里有一个注释版本的代码。该函数的评论被转换为一个文档字符串，并且可以执行doctest。

import numpy 
import scipy.misc 
import matplotlib.pyplot 

# Get classic image processing example image, Lena, at 8-bit grayscale 
# bit-depth, 512 x 512 size. 
lena = scipy.misc.lena() 
# lena is now a Numpy array of integers, between 245 and 25, of 512 rows and 
# 512 columns. 


def get_indices(size): 
    """ 
    Returns each fourth index in a Numpy vector of the passed in size. 
    Specifically, return a vector of booleans, where all indices are set to 
    False except those of every fourth element. This vector can be used to 
    index another Numpy array and select *only* those elements. Example use: 

     >>> import numpy as np 
     >>> vector = np.array([0, 1, 2, 3, 4]) 
     >>> get_indices(vector.size) 
     array([ True, False, False, False, True], ...) 

    """ 
    arr = numpy.arange(size) 
    return arr % 4 == 0 

# Keep a copy of the original image 
lena1 = lena.copy() 

# Use the defined function to get every fourth index, first in the x direction, 
# then in the y direction 
xindices = get_indices(lena.shape[0]) 
yindices = get_indices(lena.shape[1]) 


# Set every pixel that equals true in the vectors further up to 0. This 
# selects **each fourth pixel on the diagonal** (from up left to bottom right). 
lena[xindices, yindices] = 0 

# Create a Matplotlib plot, with 2 subplots, and selects the one on the 1st 
# colum, 1st row. The layout for all subplots is determined from all calls to 
# subplot, i.e. if you later call `subplot(212)` you will get a vertical layout 
# in one column and two rows; but if you call `subplot(221)` you will get a 
# horizontal layout in two columns and one row. 
matplotlib.pyplot.subplot(211) 
# Show the unaltered image on the first subplot 
matplotlib.pyplot.imshow(lena1) 
# You could plot the modified original image in the second subplot, and compare 
# to the unmodified copy by issuing: 
#matplotlib.pyplot.subplot(212) 
#matplotlib.pyplot.imshow(lena) 

matplotlib.pyplot.show()

来源

2014-02-24 21:29:48 logc

打破numpy代码

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