几个常用的 Matplotlib 绘图案例: line_plot():多条曲线 multiyy_plot():双 y 轴 time_series_with_peaks():时序曲线带有峰点标记 scatter_plot():散点图 marginal_histogram():散点图+直方图 correllogram(…
line, = ax.plot(x,np.sin(x)) def animate(i): line.set_ydata(np.sin(x + i/10.0))#updata the data return line, def init(): line.set_ydata(np.sin(x)) return line, # call the animator. blit=True means only re-draw the parts that have changed. # blit=True dose not work ...
→ ax.fill_between(X, Y+error, Y‐error) … draw a rectangle? → ax.add_patch(plt.Rectangle((0, 0),1,1) … draw a vertical line? → ax.axvline(x=0.5) … draw outside frame? → ax.plot(…, clip_on=False) … use transparency? → ax.plot(…, alpha=0.25) … convert an ...
**2. Labeling existing plot elements** To make a legend for lines which already exist on the axes (via plot for instance), simply call this function with an iterable of strings, one for each legend item. For example:: ax.plot([1, 2, 3]) ax.legend(['A simple line'])Note:This w...
折线图(Line Plot):用于显示数据随时间或其他连续变量的变化趋势。在实际项目中,可以用于可视化模型性能随着训练迭代次数的变化。 下面的示例中,我们将绘制一个包含多个数据系列的折线图。 首先,确保已经安装了Matplotlib库。(pip install matplotlib) 代码语言:javascript ...
(categories))]# Step 2: Draw Scatterplot with unique color for each categoryfig=plt.figure(figsize=(16,10),dpi=80,facecolor='w',edgecolor='k')fori,categoryinenumerate(categories):plt.scatter('area','poptotal',data=midwest.loc[midwest.category==category,:],s='dot_size',c=colors[i],...
(X)) # redraw canvas while idle fig.canvas.draw_idle() def reset(event): global yvals global spline #reset the values yvals = func(x) if cache_file is not None: load_cache_weight(cache_file) for i in np.arange(N): sliders[i].reset() spline = inter.InterpolatedUnivariateSpline(x,...
line,=plt.plot(x,y) # 动画函数 def animate(i): line.set_ydata(np.sin(x+i/100)) return line, # 动画初始函数 def init(): line.set_ydata(np.sin(x)) return line, """ fig:figure对象 animate:动画函数,不断更新图像的函数,生成新的xdata和ydata ...
折线图(Line Plot):用于显示数据随时间或其他连续变量的变化趋势。在实际项目中,可以用于可视化模型性能随着训练迭代次数的变化。 下面的示例中,我们将绘制一个包含多个数据系列的折线图。 首先,确保已经安装了Matplotlib库。(pip install matplotlib) import matplotlib.pyplot as pltimport numpy as np# 创建示例数据集...
def btnDrawImg(self): x = eval(f"np.linspace({self.xEntry.get()})") self.ys = eval(self.yEntry.get()) self.xs = x self.drawPlot() #vself.drawPlot就是核心的绘图函数,主要流程与命令行调用plt如出一辙, # 首先创建一个坐标轴,然后在坐标轴上绘图,区别是最后需要调用self.canvas中的引擎...