yticks([]) #不显示所有坐标轴刻度 plt.axis('off') #不显示刻度线 plt.tick_params(bottom=False,top=False,left=False,right=False) 4、刻度范围设置 plt.xlim(1950,2030)#x轴范围设置 plt.ylim(0,40)#y轴范围设置 x_major_locator=MultipleLocator(10)#x轴刻度线间隔 y_major_locator=MultipleLocator(...
ax.axis((0, 100, 0, 100)) # 坐标轴区间: x 为 10 , y 为 20 ax.set_xticks([i * 10 for i in range(11)]) ax.set_yticks([i * 20 for i in range(6)]) # 坐标原点在左上角 ax.xaxis.set_ticks_position("top") ax.invert_yaxis() # 显示 plt.tight_layout() plt.show() ...
ax.xaxis.set_major_formatter(daysFmt) ax.xaxis.set_ticks(pd.date_range(x.iloc[1,0], x.iloc[-1,0], freq='7d'))#ax.set_xlabel('Date')ax.set_ylabel('Errors')#ax.set_xticklabels('off')#ax.set_yticklabels('off')ax.grid() plt.xticks(rotation=30, fontsize='8') plt.yticks...
plt.plot(x,y) plt.grid() ax.set(xlim=(xmin, xmax), ylim=(ymin, ymax), yticks=range(-12,1,3), xlabel='X/μm', ylabel='Y/μm', title='X vs Y, 1 cm on plot equals 5 μm') fig.suptitle('Figure dimensions: w = %.2f cm, h = %.2f cm.'%( left+xlen+right, bot...
Matplotlib - Axis Ticks Matplotlib - Formatting Axes Matplotlib - Axes Class Matplotlib - Twin Axes Matplotlib - Figure Class Matplotlib - Multiplots Matplotlib - Grids Matplotlib - Object-oriented Interface Matplotlib - PyLab module Matplotlib - Subplots() Function ...
# 设置默认子区域的x轴和y轴的刻度的标签 plt.xticks([0, 0.5 * np.pi, 1.0 * np.pi, 1.5 * np.pi, 2 * np.pi], [r'0', r'0.5π', r'π', r'1.5π', r'2π']) plt.yticks([-1.0, 0.0, 1.0], [r'min -1.0', r'0.0', r'max 1.0']) ...
plt.axes().get_xaxis().set_major_locator(plt.MaxNLocator(integer=True)) plt.axes().get_yaxis().set_major_locator(plt.MaxNLocator(integer=True)) cb = plt.colorbar(ticks=np.arange(-1,1.01,0.25).tolist()) cb.set_clim(-1,1) ...
# format the ticks ax.xaxis.set_major_locator(years) ax.xaxis.set_major_formatter(yearsFmt) ax.xaxis.set_minor_locator(months) datemin = datetime.date(r.date.min().year, 1, 1) datemax = datetime.date(r.date.max().year + 1, 1, 1) ...
imshow(X)pyplot.set_cmap('hot')pyplot.set_cmap('jet') In interactive mode,set_cmap()will update the colormap post-hoc, allowing you to see which one works best for your data. All built-in colormaps can be reversed by appending_r: For instance,gray_ris the reverse ofgray. ...
fig.colorbar(image, ticks=[-0.5, 1], fraction=0.061, orientation='horizontal', pad=0.04) cbar.set_label('Temperatures(C)') cbar.ax.set_xticklabels(self.crange) self.ax.set_title(self.title) self.ax.set_axis_off() self.elements = [] for d in self.draw: self.elements.append(...