分解numpy代码[关闭]

Question

我一直在仔细阅读文档并重新阅读/运行以下代码，以便准确了解正在发生的事情。不过我的知识还是有差距的。我想用 cmets 向你展示代码，这表示我的知识空白，希望你们中的一些人愿意填补。

所以这是我的请求朋友：
1）帮助我填补我的知识空白
2）以非技术性和简单的格式逐步解释这里发生的事情。

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()

谢谢小伙伴们！

Answer 1

使用 Python 调试器在代码执行时单步执行代码总是很有用的。在您选择的任何地方写下以下内容：

import pdb; pdb.set_trace()

执行将停止，您可以检查任何变量，使用任何定义的函数，并逐行推进。

这里有您的代码的注释版本。函数的注释被转换成带有可执行文档测试的文档字符串。

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()