importmatplotlib.pyplotaspltimportnumpyasnp# 创建初始数据x=np.linspace(0,10,100)y=np.sin(x)# 创建图表fig,ax=plt.subplots()line,=ax.plot(x,y,label='sin(x)')# 获取线条对象lines=ax.get_lines()sin_line=lines[0]# 定义更新函数defupdate_line(phase):new_y=np.sin(x+phase)sin_line.set...
Matplotlib 是一个Python的 2D绘图库。通过 Matplotlib,开发者可以仅需要几行代码,便可以生成绘图,直方图,功率谱,条形图,错误图,散点图等。 通过学习Matplotlib,可让数据可视化,更直观的真实给用户。使数据更加客观、更具有说服力。 Matplotlib是Python的库,又是开发中常用的库。 2. Matplotlib的安装 Windows系统安装 ...
ax.plot(X, Y+i, linestyle=linestyle, linewidth=1.5, color='black') yticklabels.append(name) ax.set(xticks=[], ylim=(-0.5, len(linestyles)-0.5), yticks=np.arange(len(linestyles)), yticklabels=yticklabels)#For each line style, add a text annotation with a small offset from#the r...
fig,ax=plt.subplots()ax.set_title('Colormap axvline - how2matplotlib.com')# 创建一个颜色映射cmap=plt.get_cmap('viridis')# 绘制多条垂直线,颜色根据位置变化foriinrange(10):x=i/10color=cmap(x)ax.axvline(x=x,color=color,label=f'Line{i+1}')ax.legend()plt.show() Python Copy Outp...
line.get_linewidth() 1.0 plt.getp(lines[0], "color") # 返回color属性 'r' plt.getp(lines[1]) # 输出全部属性 alpha = 1.0 animated = False antialiased or aa = True axes = Axes(0.125,0.1;0.775x0.8) ... ... 注意getp函数只能对一个对象进行操作,它有两种用法: ...
# bottomplt.plot(x, np.sin(x + 0.5), label="zorder=3", zorder=3)plt.axhline(0, label="zorder=2.5", color="lightgrey", zorder=2.5)plt.title("Custom order of elements")l = plt.legend(loc="upper right")l.set_zorder(2.5) # legend between blue and orange lineplt.show...
line= plt.plot(x, x * x)line.set_antialiased(False) lines= plt.plot(x, np.sin(x), x,cos(x)) plt.setp(lines, color ="r", linewidth =2.0) xlabel(), ylabel(), title() legend() pyplot.plot()和pyplot.legend()搭配使用
linefmt,线条的线型和颜色,'[line][color]',类似于 plot 的 fmt 参数。 markerfmt,标记点的形状,类似于 plot 的 fmt 参数 basefmt,定义基线属性的格式字符串。 orientation,方向,{'vertical','horizontal'},默认'vertical' bottom,条形基底部的 y 坐标,float, default: 0 ...
Set the line color to red: importmatplotlib.pyplotasplt importnumpyasnp ypoints = np.array([3,8,1,10]) plt.plot(ypoints, color ='r') plt.show() Result: Try it Yourself » You can also useHexadecimal color values: Example
# adjust from https://stackoverflow.com/questions/38051922/how-to-get-differents-colors-in-a-single-line-in-a-matplotlib-figure import numpy as np, matplotlib.pyplot as plt from matplotlib.collections import LineCollection from matplotlib.colors import ListedColormap, BoundaryNorm # my func x = ...