替代matshow（）

Question

我有一个101x101矩阵，我希望以图形方式显示它。到目前为止，我使用matshow中的matplotlib.pyplot函数，如下所示：

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()

输出如下：

您应该将其视为物种之间的相互作用矩阵，正如您所看到的，只有三种物种强烈相互作用。这就是考虑像我在paper中找到的下图所示的可视化的原因，但我不知道是否以及如何在Python中实现这一点：

Answer 1

这应该可以解决问题：

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：

新图表： 我还是不知道：

• 如何输入颜色条，因为它需要使用颜色图映射的图。
• 如何在不重新缩放图表的情况下删除r-ticks（您可以尝试取消注释#ax.set_rticks([])）

随着相互作用较少的物种，情节看起来更好：