matshow()的替代方法是什么？

Question

matshow()的替代方法是什么？

4

我有一个 101x101 的矩阵，我想以图形方式进行可视化。到目前为止，我使用了如下的函数matshow来自matplotlib.pyplot：

import numpy as np
import random
import matplotlib.pyplot as plt

A = np.zeros((101,101))
# do stuff to randomly change some values of A
plt.ion()
plt.matshow(A,cmap='PuBuGn')
plt.colorbar()
plt.show()

输出结果如下：

你应该将其视为物种之间的交互矩阵，正如你所看到的只有三个物种相互作用强烈。这就是为什么要考虑像我在论文中发现的以下图形一样的可视化方式，但我不知道这是否可以在Python中实现：

- Zdenko Heyvaert

1个回答

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原文链接

- berna1111 · Accepted Answer

这应该能解决问题：

import numpy as np
import matplotlib.pyplot as plt

def old_graph(A):
    plt.matshow(A,cmap='PuBuGn')
    plt.colorbar()
    plt.title(r"abs$\left(\left[\mathbf{A}_{ij} \right ] \right )$ ; SIS=%d"%(sis,), va='bottom')
    plt.show()

def new_graph(A, sis_list=np.zeros(0,int), symmetric=True, fig=None, pos=111):
    #create and edit figure:
    if fig is None:
        fig = plt.figure()
    ax = fig.add_subplot(pos, projection='polar')
    ax.set_rgrids([1],[' '])
    ax.set_rmax(1)
    ax.set_thetagrids([])
    ax.set_title(r"abs$\left(\left[\mathbf{A}_{ij} \right ] \right )$ ; SIS=%d"%(sis,), va='bottom')
    colormap = plt.get_cmap('PuBuGn')

    # make each species an angle value:
    n_species = A.shape[0]
    angles = np.linspace(0, 2*np.pi, n_species+1)
    # the radius will always be r_max, and each line
    # will always unite two points:
    r = np.ones((2,))
    # prepare list of lines to sort:
    unordered_pairs_and_values = []
    for index_line in xrange(n_species):
        for index_column in xrange(index_line):
            if symmetric:
                value = A[index_line,index_column]
            else: # not symmetric
                value= abs(A[index_line,index_column]- A[index_column,index_line])
            unordered_pairs_and_values.append([[angles[index_line],angles[index_column]], value])
    # sort the lines (otherwise white lines would cover the 'important' ones):
    ordered_pairs_and_values = sorted(unordered_pairs_and_values, key=lambda pair: pair[1])
    # get the maximum value for scaling:
    I_max = ordered_pairs_and_values[-1][1]
    # plot every line in order:
    for pair in ordered_pairs_and_values:
        ax.plot(pair[0], r, color=colormap(pair[1]/I_max), linewidth=2, alpha=0.8)
    # don't know how to add the colorbar:
    #fig.colorbar(orientation='horizontal')
    # mark the angles:
    ax.plot(angles, np.ones(angles.shape), 'ko')
    # mark the important angles (comment if you don't know which ones are these):
    ax.plot(angles[sis_list],  np.ones(sis_list.shape), 'ro')
    fig.show()

if __name__ == '__main__':
    n_species = 51
    sis = 3 # strongly interacting species
    sis_factor = 4.
    A = np.zeros((n_species,n_species))
    # do stuff to randomly change some values of A:
    for index_line in xrange(n_species):
        for index_column in xrange(index_line+1):
            A[index_line,index_column] = np.random.random()
            A[index_column,index_line] = A[index_line,index_column]

    sis_list = np.random.randint(0,n_species,sis)
    for species in sis_list:
        A[species,:] *= sis_factor
        A[:,species] *= sis_factor
        for species2 in sis_list: # correct crossings
            A[species,species2] /= sis_factor
    # stuff to randomly change some values of A done
    old_graph(A=A)
    new_graph(A=A, sis_list=sis_list, symmetric=True)

旧图：

新图：我仍然不知道：

如何添加颜色条，因为它需要使用了一种颜色映射的绘图。
如何在不改变图形比例的情况下删除极轴刻度（您可以尝试取消注释#ax.set_rticks([])）。

去除互动物种后，图像看起来更好：