y,linestyle='--',color='gray',label='Line - how2matplotlib.com')# 绘制线plt.scatter([x[2]],[y[2]],color='red',s=100,label='Highlight Point (3,5) - how2matplotlib.com')# 突出显示中间点plt.legend()plt.show()
4, endpoint=False)yy = np.transpose([np.sin(x + phi) for phi in offsets])fig, ax = plt.subplots(figsize=(8, 4))ax.set_prop_cycle(line_prop_cycler) # Set propcycle before plottingax.plot(x, yy)plt.show();rcParams
ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5), arrowprops=dict(facecolor='black', shrink=0.05), ) ax.set_ylim(-2,2) plt.show() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 在上面的例子中,两个左边使用的都是原始数据的坐标系,不过我们还可以通过xycoords和textc...
2)values=np.sin(5*points[:,0])+np.cos(5*points[:,1])# 创建网格grid_x,grid_y=np.mgrid[0:1:100j,0:1:100j]# 插值grid_z=griddata(points,values,(grid_x,grid_y),method='cubic')# 绘制等高线图plt.figure(figsize=(10,8))contour=plt...
IPython的魔法命令%matplotlib inline用于配置matplotlib使用inline后端,这可以让生成的图形直接在Jupyter终端显示。如果需要显示交互式动态数据,则需要使用魔法命令%matplotlib widget指定ipympl后端。 2. 入门 matplotlib中的图形是由一个Figure(画布)实例和若干个Axes(轴)实例构建而成。如果要在一个画布中显示多个数据面板...
linebuilder=LineBuilder(line)plt.show() 我们刚刚使用的MouseEvent是一个LocationEvent,因此我们可以访问event.x和event.xdata中的数据和像素坐标。 除了LocationEvent属性,它拥有: button 按下的按钮,None、1、2、3、'up'、'down'('up'、'down'用于滚动事件) ...
show() from matplotlib.ticker import MultipleLocator, FormatStrFormatter # 自定义大刻度线的位置 ax.xaxis.set_major_locator(MultipleLocator(100)) # 自定义大刻度线的标签格式 ax.xaxis.set_major_formatter(FormatStrFormatter('%d')) # 启用小刻度线 ax.minorticks_on() # 自定义小刻度线的位置 ax.x...
plt.plot(y1) plt.plot(y2) plt.show() Result: Try it Yourself » You can also plot many lines by adding the points for the x- and y-axis for each line in the same plt.plot() function.(In the examples above we only specified the points on the y-axis, meaning that the points...
self.line.figure.canvas.draw() fig = plt.figure() ax = fig.add_subplot(111) ax.set_title('click to build line segments') line, = ax.plot([0], [0]) # empty line linebuilder = LineBuilder(line) plt.show() 1. 2. 3.
# Draw bars, position them in the center of the tick mark on the x-axis ax.bar(x_data, y_data, color = '#539caf', align = 'center') # Draw error bars to show standard deviation, set ls to 'none' # to remove line between points ...