python matplotlib绘制稀疏矩阵模式 [英] python matplotlib plot sparse matrix pattern

问题描述

给出一个稀疏的二进制矩阵A(csr，coo等)，我想绘制一个图，以便在A(i，j)= 1的情况下可以看到图中的位置(i，j)=白色. (i，j)=黑色，如果A(i，j)= 0;

Given a sparse binary matrix A (csr, coo, whatever) I want to make a plot such that I can see the position (i,j) = white in the figure if A(i,j) = 1, and (i,j) = black if A(i,j) = 0;

对于密集的numpy数组，matshow将完成这项工作.但是，我的稀疏矩阵的维数(例如100000 x 1000000)太大，无法转换为密集数组.我不知道如何在稀疏矩阵中绘制图案.

For a dense numpy array, matshow will do the job. However, the dimension of my sparse matrix (say 100000 x 1000000) is to big to be converted to a dense array. I wonder how could I plot the pattern in my sparse matrix.

谢谢

推荐答案

使用coo_matrix，plot()和一些调整，您可以得到不错的结果:

You can get a nice result using a coo_matrix, plot() and some adjustments:

import matplotlib.pyplot as plt
from scipy.sparse import coo_matrix

def plot_coo_matrix(m):
    if not isinstance(m, coo_matrix):
        m = coo_matrix(m)
    fig = plt.figure()
    ax = fig.add_subplot(111, facecolor='black')
    ax.plot(m.col, m.row, 's', color='white', ms=1)
    ax.set_xlim(0, m.shape[1])
    ax.set_ylim(0, m.shape[0])
    ax.set_aspect('equal')
    for spine in ax.spines.values():
        spine.set_visible(False)
    ax.invert_yaxis()
    ax.set_aspect('equal')
    ax.set_xticks([])
    ax.set_yticks([])
    return ax

请注意，将y轴反转以将第一行放在图的顶部.一个例子:

Note that the y axis is inverted to put the first row at the top of the figure. One example:

import numpy as np
from scipy.sparse import coo_matrix

shape = (100000, 100000)
rows = np.int_(np.round_(shape[0]*np.random.random(1000)))
cols = np.int_(np.round_(shape[1]*np.random.random(1000)))
vals = np.ones_like(rows)

m = coo_matrix((vals, (rows, cols)), shape=shape)
ax = plot_coo_matrix(m)
ax.figure.show()

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