imshow(X,cmap=None,norm=None,aspect=None,interpolation=None,alpha=None,vmin=None,vmax=None,origin=None,extent=None,shape=None,filternorm=1,filterrad=4.0,imlim=None,resample=None,url=None,*,data=None,**kwargs) 参数说明: X:输入数据。可以是二维数组、三维数组、PIL图像对象、matplotlib路径对象等。
1,(10,10))# 创建图形fig,(ax1,ax2)=plt.subplots(1,2,figsize=(12,5))# 自动范围im1=ax1.imshow(data,cmap='viridis')fig.colorbar(im1,ax=ax1,label='Value')ax1.set_title('Auto Range - how2matplotlib.com')# 手动设置范围im2=ax2.imshow(data,cmap='viridis',vmin=-2,vmax...
plt.imshow(data, cmap='coolwarm') for i in range(data.shape[0]): for j in range(data.shape[1]): plt.text(j, i, data[i, j]) plt.xticks(range(data.shape[1])) plt.yticks(range(data.shape[0])) plt.colorbar() plt.show() A选项:左上角 B选项:左下角 C选项:右下角 D选项:...
data=np.random.rand(10,10)plt.imshow(data,cmap='hot')plt.xticks(ticks=np.arange(0,10,1),labels=[f"{i}unit"foriinrange(10)])plt.yticks(ticks=np.arange(0,10,1),labels=[f"{i}unit"foriinrange(10)])plt.title("Example 6: Custom Ticks - how2matplotlib.com")plt.show() Python ...
数据本身是imshow使用渲染的。 code.reshape(1, -1)将数据转换为一行的二维数组。 imshow(..., aspect='auto')允许非方形像素。 imshow(..., interpolation='nearest')以防止边缘模糊。 代码语言:javascript 复制 code = np.array([1,0,1,0... 0, 1]) ax.imshow(code.reshape(1, -1), cmap='binar...
matplotlib.pyplot.imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, shape=None, filternorm=1, filterrad=4.0, imlim=None, resample=None, url=None, *, data=None, **kwargs) ...
y,i%(x*y)+1)plt.imshow(dt.images[i],cmap=cmaps[i%(x*y)])ax[int(i/x)][i%x].set_...
() ms = plt.imshow(z.T, cmap='plasma', vmin=-2.5, vmax=0, origin='lower', interpolation='none', extent=[-0.5,2.0,-0.5,2.0]) ax.set_xlabel('x', fontsize=16, fontname = "Helvetica") ax.set_ylabel('y', fontsize=16, fontname = "Helvetica") cbar = plt.colorbar(ms) c...
sm = plt.cm.ScalarMappable(norm=norm, cmap=cmap) # 创建标量映射对象 # 生成测试数据 data = [0.25, 0.75] # 根据数据值获取相应的颜色 color_values = sm.to_rgba(data) print("Color values:", color_values) # 显示结果 plt.imshow([[i for i in range(len(data))]], aspect='auto') ...
plt.imshow(np.random.random((5, 5)), cmap="winter") plt.subplots_adjust(bottom=0.09, right=0.5, top=0.9) cax = plt.axes([0.75, 0.1, 0.065, 0.8]) plt.colorbar(cax=cax) plt.show() 1. 2. 3. 4. 5. 6. 7. 8. 9.