How To Visualize Sparse Matrix in Python using Matplotlib?

Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.

Visualize Sparse Matrix using Matplotlib

Spy is a function used to visualize the array as an image similar to matplotlib imshow function, but it is used in case of sparse matrix instead of dense matrix. A sparse matrix is a matrix where most of the elements are zero.

Sparse matrix and its representation

Spy function uses two plotting styles to visualize the array, these are:

• Image style
• Marker style

Both the styles can be used for full arrays but in case of spmatrix instances only the marker style works. If marker or markersize is None then imshow function is used, and all the remaining keyword arguments are passed to this function; else, a Line2D object will be returned with the value of marker determining the marker type, and any remaining keyword arguments passed to plot function.

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.

Parameters:

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”]

Other parameters: **kwargs

These are the additional parameters that helps to get different plotting styles.

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

Example 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)

Output:

Example 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)

Output: