如何使用 Matplotlib 在Python中可视化稀疏矩阵？

Matplotlib是Python中用于数组二维图的惊人可视化库。 Matplotlib 是一个基于 NumPy 数组构建的多平台数据可视化库，旨在与更广泛的 SciPy 堆栈配合使用。

使用 Matplotlib 可视化稀疏矩阵

Spy是一个用于将数组可视化为图像的函数，类似于 matplotlib imshow函数，但它用于稀疏矩阵而不是密集矩阵的情况。稀疏矩阵是大多数元素为零的矩阵。

稀疏矩阵及其表示

Spy函数使用两种绘图样式来可视化数组，它们是：

• 图片风格
• 标记样式

两种样式都可用于完整数组，但在 spmatrix 实例的情况下，只有标记样式有效。如果marker或markersize为None ，则使用imshow函数，并将所有剩余的关键字参数传递给该函数；否则，将返回一个 Line2D 对象，其中 marker 的值确定标记类型，并将任何剩余的关键字参数传递给 plot函数。

Syntax: matplotlib.pyplot.spy(Z, precision=0, marker=None, markersize=None, aspect=’equal’, origin=’upper’, \*\*kwargs)

Return value:

The return type depends on the plotting style, i.e. AxesImage or Line2D.

参数：

Parameter	Value	Use
Z	array-like (M, N)	The array to be plotted
Precision	float or ‘present’, optional default:zero	If precision is 0, any non-zero value will be plotted; else, values of |Z| > precision will be plotted. For spmatrix instances, there is a special case: if precision is ‘present’, any value present in the array will be plotted, even if it is identically zero.
Origin	{‘upper’, ‘lower’}, optional default:’upper’	Place the [0, 0] index of the array in the upper left or lower left corner of the axes.
Aspect	{‘equal’, ‘auto’, None} or float, optional default:’equal’	It controls the aspect ratio of the axes. The aspect is of particular relevance for images since it may distort the image, i.e. pixel will not be square. ‘equal’: Ensures an aspect ratio of 1. Pixels will be square. ‘auto’: The axes is kept fixed and the aspect is adjusted so that the data fit in the axes. In general, this will result in non-square pixels. None: Use rcParams[“image.aspect”]

其他参数：**kwargs

这些是有助于获得不同绘图样式的附加参数。

Property	Description
agg_filter	a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha	float or None
animated	bool
antialiased	bool
clip_box	Bbox
clip_on	bool
clip_path	Patch or (Path, Transform) or None
color	color
contains	callable
dash_capstyle	{‘butt’, ’round’, ‘projecting’}
dash_joinstyle	{‘miter’, ’round’, ‘bevel’}
dashes	sequence of floats (on/off ink in points) or (None, None)
data	(2, N) array or two 1D arrays
drawstyle	{‘default’, ‘steps’, ‘steps-pre’, ‘steps-mid’, ‘steps-post’}
figure	figure
fillstyle	{‘full’, ‘left’, ‘right’, ‘bottom’, ‘top’, ‘none’}
grid	str
in_layout	bool
label	object
linestyle	{‘-‘, ‘–‘, ‘-.’, ‘:’, ”, (offset, on-off-seq), …}
linewidth	float
marker	marker style
markeredgecolor	color
markeredgewidth	float
markerfacecolor	color
markerfacecoloralt	color
markersize	float
markevery	None or int or (int, int) or slice or List[int] or float or (float, float)
path_effects	Abstract path effects
picker	float or callable[[Artist, Event], Tuple[bool, dict]]
pickradius	float
rasterized	bool or None
sketch_params	(scale: float, length: float, randomness: float)
snap	bool or None
solid_capstyle	{‘butt’, ’round’, ‘projecting’}
solid_joinstyle	{‘miter’, ’round’, ‘bevel’}
transform	matplotlib.transforms.Transform
url	str
visible	bool
xdata	1D array
ydata	1D array
zorder	float

示例 1：

# Implementation of matplotlib spy function
import matplotlib.pyplot as plt
import numpy as np
  
  
x = np.random.randn(50, 50)
  
x[15, :] = 0.
x[:, 40] = 0.
  
plt.spy(x)

输出：

示例 2：

# Implementation of matplotlib spy function
import matplotlib.pyplot as plt
import numpy as np
  
x = np.random.randn(50, 50)
x[15, :] = 0.
x[:, 40] = 0.
  
plt.spy(x, precision = 0.1, markersize = 5)

输出：