如何使用带注释框的 Matplotlib imshow() 生成带标签的二维图 [英] How to use Matplotlib imshow() with annotated boxes to produce a labeled 2d plot

问题描述

我正在尝试使用 imshow()生成稀疏数组的二维图，并使用 plt.text()将其与文本框重叠.我想出了一个额外的选择，使用 plt.scatter().在第二种情况下，彩色图块和文本框太小，无法缩放.在第一种情况下，由 imshow() 生成的彩色图块的大小和文本框具有退相干大小，并且只有在使用对话框窗口的缩放功能时才能使绘图看起来很好.下面的代码说明了这一点.

 将matplotlib.pyplot导入为plt导入 matplotlib.colors 作为颜色将numpy导入为np#https://matplotlib.org/gallery/images_contours_and_fields/image_annotated_heatmap.htmlP=[1, 4, 11, 18, 20, 39, 40, 41, 41, 71, 71, 71, 71, 71, 71, 72, 72, 72, 72, 72, 72, 73, 73, 73，74，74，74，74，74，74，75，75，75，71]N = [2、3、11、19、25、49、48、50、54、101、102、103、103、106、106、100、103、106、106、107、109、105、106、109，104、107、109、110、111、112、108、109、109、101]B=np.random.rand(34)# 创建要与 imshow() 一起使用的数组A=np.zeros((max(N)+1,max(N)+1))对于zip(N，P，B)中的i，j，k:A[i,j]=kdef plot_map(N，P，A):无花果，ax = plt.subplots()plt.imshow(A，norm = colors.LogNorm()，cmap ='jet'，origin ='lower')plt.colorbar()对于zip(N，P)中的n，p:ax.text(p,n, "\n%s\n%s\n%5.1E"%(p,n,A[n,p]),ha ="center"，va ="center"，bbox=dict(fc="none",boxstyle = "square"))plt.tight_layout()plt.show()# 调用绘图函数plot_map(N,P,A)# 尝试使用 plt.scatter() 拖拽plt.scatter(N，P，c = B，marker ='s'，s = 70，norm = colors.LogNorm()，cmap ='jet')对于zip(N，P)中的n，p:plt.text(n，p，"\ n％s \ n％s"％(p，n)，大小= 3，va ="center"，ha ="center"，multialignment ="left"，bbox=dict(fc="none",boxstyle = "square"))plt.colorbar()plt.show()

理想情况下，我想制作这样的东西

我的绘图程序产生的效果看起来不太好，彩色图块和注释框都不连续.
因此，感谢您的帮助.

解决方案

以下方法使用

要获取放大后的文本，需要 TextPath ，如

I am trying to produce a 2d plot of a sparse array with imshow() and use plt.text() to overlay it with text boxes. I came up with an additional option, using plt.scatter(). In the second case, the colored tiles and the text boxes are too small and can not be zoomed. In the first case, the size of the colored tiles, produced by imshow() and the text boxes have decoherent size and the plot looks fine only if the zoom function of the dialog window is used. This is illustrated by the code below.

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

#https://matplotlib.org/gallery/images_contours_and_fields/image_annotated_heatmap.html

P=[1, 4, 11, 18, 20, 39, 40, 41, 41, 71, 71, 71, 71, 71, 71, 72, 72, 72, 72, 72, 72, 73, 73, 73, 74, 74, 74, 74, 74, 74, 75, 75, 75, 71]
N=[2, 3, 11, 19, 25, 49, 48, 50, 54, 101, 102, 103, 103, 106, 106, 100, 103, 106, 106, 107, 109, 105, 106, 109, 104, 107, 109, 110, 111, 112, 108, 109, 109, 101]
B=np.random.rand(34)

# crate the array to use with imshow()
A=np.zeros((max(N)+1,max(N)+1))
for i,j,k in zip(N,P,B):
     A[i,j]=k

def plot_map(N,P,A):     
    fig, ax = plt.subplots()     
    plt.imshow(A,norm=colors.LogNorm(),cmap='jet',origin='lower')
    plt.colorbar()
    for n,p in zip(N,P):
            ax.text(p,n, "\n%s\n%s\n%5.1E"%(p,n,A[n,p]),
                ha="center", va="center",
            bbox=dict(fc="none",boxstyle = "square"))
    plt.tight_layout()
    plt.show()

# call the plot function
plot_map(N,P,A)    

# attempt tow using plt.scatter() 
plt.scatter(N,P,c=B,marker='s',s=70,norm=colors.LogNorm(),cmap='jet')
for n,p in zip(N,P):
         plt.text(n,p, "\n%s\n%s"%(p,n), size=3,
             va="center", ha="center", multialignment="left",
             bbox=dict(fc="none",boxstyle = "square"))
plt.colorbar()
plt.show()

I ideally, I would like to produce something like this

What my plot routines produce does not look so good and both the colored tiles and the annotation boxes are discontinuous.
Therefore, I would appreciate your help.

解决方案

The following approach uses mplcursors to display the information on screen, and also saves an image file that could be printed.

When printed on A4 paper, each little square would be about 2x2 mm, so a good printer and a looking glass can be helpful. You might want to experiment with the fontsize.

On screen, mplcursors displays a popup annotation when clicking on a little square. While zoomed in, a double click is needed in order not to interfere with the zooming UI. mplcursors also has a 'hover' mode, but then no information is displayed while zoomed in.

Some code to demonstrate how it might work:

import matplotlib.pyplot as plt
import matplotlib.colors as colors
import mplcursors
import numpy as np

P = [1, 4, 11, 18, 20, 39, 40, 41, 41, 71, 71, 71, 71, 71, 71, 72, 72, 72, 72, 72, 72, 73, 73, 73, 74, 74, 74, 74, 74, 74, 75, 75, 75, 71]
N = [2, 3, 11, 19, 25, 49, 48, 50, 54, 101, 102, 103, 103, 106, 106, 100, 103, 106, 106, 107, 109, 105, 106, 109, 104,  107, 109, 110, 111, 112, 108, 109, 109, 101]
B = np.random.rand(34)

# create the array to use with imshow()
A = np.zeros((max(N) + 1, max(N) + 1))
for i, j, k in zip(N, P, B):
    A[i, j] = k

fig, ax = plt.subplots(figsize=(21, 15))
img = ax.imshow(A, norm=colors.LogNorm(), cmap='jet', origin='lower')
plt.colorbar(img)
for n, p in zip(N, P):
    plt.text(p, n, "%s\n%s\n%5.1E"%(p,n,A[n,p]), size=2,
             va="center", ha="center", multialignment="left")

cursor = mplcursors.cursor(img, hover=False)
@cursor.connect("add")
def on_add(sel):
    i,j = sel.target.index
    if A[i][j] == 0:
        sel.annotation.set_visible(False)
    else:
        sel.annotation.set_text(f'P: {j}\nN: {i}\n{A[i][j]:.3f}')

plt.tight_layout()
plt.savefig('test.png', dpi=300)
plt.show()

At the left is how it would look on screen when zoomed in and double-clicking on a square. At the right how the image-to-be-printed would look when zoomed in.

To get text that gets bigger when zoomed in, TextPath is needed, as explained in this post. As TextPath doesn't really deal with multiple lines and alignments, the code calculates the positions. Also, depending on the color of the box, the text is easier to read when white. You'll need to test which values are good cut-offs in your situation and colormap.

To cope with the empty space, you could zoom in to the 3 places with data. The code below creates a subplot for each of these areas.

import matplotlib.pyplot as plt
import matplotlib.colors as colors
from matplotlib.textpath import TextPath
from matplotlib.patches import PathPatch
from matplotlib.ticker import MaxNLocator
import numpy as np

P = [1, 4, 11, 18, 20, 39, 40, 41, 41, 71, 71, 71, 71, 71, 71, 72, 72, 72, 72, 72, 72, 73, 73, 73, 74, 74, 74, 74, 74, 74, 75, 75, 75, 71]
N = [2, 3, 11, 19, 25, 49, 48, 50, 54, 101, 102, 103, 103, 106, 106, 100, 103, 106, 106, 107, 109, 105, 106, 109, 104,  107, 109, 110, 111, 112, 108, 109, 109, 101]
B = np.random.rand(34)

# create the array to use with imshow()
A = np.zeros((max(N) + 1, max(N) + 1))
for i, j, k in zip(N, P, B):
    A[i, j] = k

plot_limits = [[[0, 19], [1, 20]],
               [[38, 42], [47 - 1, 55 + 2]],  # second subplot with higher y-range to better fit with the rest
               [[70, 76], [99, 113]],
               [[0, 0.05], [0, 1]]]  # separate subplot for the colorbar

width_ratios = [(lim[0][1] - lim[0][0] ) / (lim[1][1] - lim[1][0]) for lim in plot_limits]

fig, ax = plt.subplots(figsize=(16, 8), ncols=4, gridspec_kw={'width_ratios': width_ratios})
for i in range(3):
    img = ax[i].imshow(A, norm=colors.LogNorm(), cmap='jet', origin='lower')
    for n, p in zip(N, P):
        textsize = 0.3
        for line, label in zip((n + 0.2, n - 0.1, n - 0.4), (f"{p}", f"{n}", f"{A[n, p]:.3f}")):
            tp = TextPath((p - 0.4, line), label, size=0.3)
            ax[i].add_patch(PathPatch(tp, color="black" if 0.08 < A[n, p] < 0.7 else "white"))
    ax[i].xaxis.set_major_locator(MaxNLocator(integer=True))
    ax[i].yaxis.set_major_locator(MaxNLocator(integer=True))
    ax[i].set_xlim(plot_limits[i][0])
    ax[i].set_ylim(plot_limits[i][1])

plt.colorbar(img, cax=ax[3])
plt.tight_layout()
plt.show()

This is how it looks like:

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