Matplotlib：如何强制整数刻度标签？

Question

我的 python 脚本使用 matplotlib 绘制 x、y、z 数据集的二维“热图”。我的 x 和 y 值代表蛋白质中的氨基酸残基，因此只能是整数。当我放大情节时，它看起来像这样：

正如我所说，x-y 轴上的浮点值对我的数据没有意义，因此我希望它看起来像这样：

任何想法如何实现这一目标？ 这是生成情节的代码：

def plotDistanceMap(self):
    # Read on x,y,z
    x = self.currentGraph['xData']
    y = self.currentGraph['yData']
    X, Y = numpy.meshgrid(x, y)
    Z = self.currentGraph['zData']
    # Define colormap
    cmap = colors.ListedColormap(['blue', 'green', 'orange', 'red'])
    cmap.set_under('white')
    cmap.set_over('white')
    bounds = [1,15,50,80,100]
    norm = colors.BoundaryNorm(bounds, cmap.N)
    # Draw surface plot
    img = self.axes.pcolor(X, Y, Z, cmap=cmap, norm=norm)
    self.axes.set_xlim(x.min(), x.max())
    self.axes.set_ylim(y.min(), y.max())
    self.axes.set_xlabel(self.currentGraph['xTitle'])
    self.axes.set_ylabel(self.currentGraph['yTitle'])
    # Cosmetics
    #matplotlib.rcParams.update({'font.size': 12})
    xminorLocator = MultipleLocator(10)
    yminorLocator = MultipleLocator(10)
    self.axes.xaxis.set_minor_locator(xminorLocator)
    self.axes.yaxis.set_minor_locator(yminorLocator)
    self.axes.tick_params(direction='out', length=6, width=1)
    self.axes.tick_params(which='minor', direction='out', length=3, width=1)
    self.axes.xaxis.labelpad = 15
    self.axes.yaxis.labelpad = 15
    # Draw colorbar
    colorbar = self.figure.colorbar(img, boundaries = [0,1,15,50,80,100], 
                                    spacing = 'proportional',
                                    ticks = [15,50,80,100], 
                                    extend = 'both')
    colorbar.ax.set_xlabel('Angstrom')
    colorbar.ax.xaxis.set_label_position('top')
    colorbar.ax.xaxis.labelpad = 20
    self.figure.tight_layout()      
    self.canvas.draw()

Answer 1

根据modifying tick labels 的答案，我想出了一个解决方案，不知道它是否适用于您的情况，因为您的代码 sn-p 无法自行执行。

这个想法是强制刻度标签的间距为 0.5，然后将每个 0.5 刻度替换为其整数对应物，并将其他标记替换为空字符串。

import numpy
import matplotlib.pyplot as plt

fig, (ax1, ax2) = plt.subplots(1,2)

x1, x2 = 1, 5
y1, y2 = 3, 7

# first axis: ticks spaced at 0.5
ax1.plot([x1, x2], [y1, y2])
ax1.set_xticks(numpy.arange(x1-1, x2+1, 0.5))
ax1.set_yticks(numpy.arange(y1-1, y2+1, 0.5))

# second axis: tick labels will be replaced
ax2.plot([x1, x2], [y1, y2])
ax2.set_xticks(numpy.arange(x1-1, x2+1, 0.5))
ax2.set_yticks(numpy.arange(y1-1, y2+1, 0.5))

# We need to draw the canvas, otherwise the labels won't be positioned and 
# won't have values yet.
fig.canvas.draw()

# new x ticks  '1'->'', '1.5'->'1', '2'->'', '2.5'->'2' etc.
labels = [item.get_text() for item in ax2.get_xticklabels()]
new_labels = [ "%d" % int(float(l)) if '.5' in l else '' for l in labels]
ax2.set_xticklabels(new_labels)

# new y ticks
labels = [item.get_text() for item in ax2.get_yticklabels()]
new_labels = [ "%d" % int(float(l)) if '.5' in l else '' for l in labels]
ax2.set_yticklabels(new_labels)

fig.canvas.draw()
plt.show()

如果您想缩小很多，则需要格外小心，因为这会产生一组非常密集的刻度标签。

Answer 2

这应该更简单：

（来自https://scivision.co/matplotlib-force-integer-labeling-of-axis/）

import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
#...
ax = plt.figure().gca()
#...
ax.xaxis.set_major_locator(MaxNLocator(integer=True))

Answer 3

通过简单地将索引i 转换为字符串的以下解决方案对我有用：

    import matplotlib.pyplot as plt
    import time

    datay = [1,6,8,4] # Just an example
    datax = []
    
    # In the following for loop datax in the end will have the same size of datay, 
    # can be changed by replacing the range with wathever you need
    for i in range(len(datay)):
        # In the following assignment statement every value in the datax 
        # list will be set as a string, this solves the floating point issue
        datax += [str(1 + i)]

    a = plt

    # The plot function sets the datax content as the x ticks, the datay values
    # are used as the actual values to plot
    a.plot(datax, datay)

    a.show()

Answer 4

ax.set_xticks([2,3])
ax.set_yticks([2,3])