目录

一、说明

二、函数和参数详解

2.1 scatter函数原型

2.2 参数详解

2.3 其中散点的形状参数marker如下：

2.4 其中颜色参数c如下:

三、画图示例

3.1 关于坐标x，y和s，c

3.2 多元高斯的情况

3.3  绘制例子

3.4 绘图例3

3.5  同心绘制

3.6 有标签绘制

3.7 直线划分

3.8 曲线划分

一、说明

       关于matplotlib的scatter函数有许多活动参数，如果不专门注解，是无法掌握精髓的，本文专门针对scatter的参数和调用说起，并配有若干案例。

二、函数和参数详解

2.1 scatter函数原型

matplotlib.pyplot.scatter(x, y, s=None, c=None, marker=None, cmap=None, nORM=None, vmin=None, vmax=None, alpha=None, linewidths=None, *, edgecolors=None, plotnonfinite=False, data=None, **kwargs)

2.2 参数详解

2.3 其中散点的形状参数marker如下：

2.4 其中颜色参数c如下:

三、画图示例

3.1 关于坐标x，y和s，c

import numpy as npimport matplotlib.pyplot as plt# Fixing random state for reproducibilitynp.random.seed(19680801)N = 50x = np.random.rand(N)y = np.random.rand(N)colors = np.random.rand(N)          # 颜色可以随机area = (30 * np.random.rand(N))**2  # 点的宽度30，半径15plt.scatter(x, y, s=area, c=colors, alpha=0.5)  plt.show()

        注意：以上核心语句是：

plt.scatter(x, y, s=area, c=colors, alpha=0.5, marker=",")

        其中：x，y，s，c维度一样就能成。

3.2 多元高斯的情况

​import numpy as npimport matplotlib.pyplot as pltfig=plt.figure(figsize=(8,6))#Generating a Gaussion dataset:#creating random vectors from the multivariate normal distribution#given mean and covariancemu_vec1=np.array([0,0])cov_mat1=np.array([[1,0],[0,1]])X=np.random.multivariate_normal(mu_vec1,cov_mat1,500)R=X**2R_sum=R.sum(axis=1)plt.scatter(X[:,0],X[:,1],color='green',marker='o', =32.*R_sum,edgecolor='black',alpha=0.5)plt.show()​

3.3  绘制例子

from matplotlib import pyplot as pltimport numpy as np# Generating a Gaussion dTset:#Creating random vectors from the multivaritate normal distribution#givem mean and covariancemu_vecl = np.array([0, 0])cov_matl = np.array([[2,0],[0,2]])x1_samples = np.random.multivariate_normal(mu_vecl, cov_matl,100)x2_samples = np.random.multivariate_normal(mu_vecl+0.2, cov_matl +0.2, 100)x3_samples = np.random.multivariate_normal(mu_vecl+0.4, cov_matl +0.4, 100)plt.figure(figsize = (8, 6))plt.scatter(x1_samples[:,0], x1_samples[:, 1], marker='x',           color = 'blue', alpha=0.7, label = 'x1 samples')plt.scatter(x2_samples[:,0], x1_samples[:,1], marker='o',           color ='green', alpha=0.7, label = 'x2 samples')plt.scatter(x3_samples[:,0], x1_samples[:,1], marker='^',           color ='red', alpha=0.7, label = 'x3 samples')plt.title('Basic scatter plot')plt.ylabel('variable X')plt.xlabel('Variable Y')plt.legend(loc = 'upper right')plt.show()    import matplotlib.pyplot as plt        fig,ax = plt.subplots()        ax.plot([0],[0], marker="o",  markersize=10)    ax.plot([0.07,0.93],[0,0],    linewidth=10)    ax.scatter([1],[0],           s=100)        ax.plot([0],[1], marker="o",  markersize=22)    ax.plot([0.14,0.86],[1,1],    linewidth=22)    ax.scatter([1],[1],           s=22**2)        plt.show()![image.png](Http://upload-images.jianshu.io/upload_images/8730384-8d27a5015b37ee97.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)    import matplotlib.pyplot as plt        for dpi in [72,100,144]:            fig,ax = plt.subplots(figsize=(1.5,2), dpi=dpi)        ax.set_title("fig.dpi={}".format(dpi))            ax.set_ylim(-3,3)        ax.set_xlim(-2,2)            ax.scatter([0],[1], s=10**2,                    marker="s", linewidth=0, label="100 points^2")        ax.scatter([1],[1], s=(10*72./fig.dpi)**2,                    marker="s", linewidth=0, label="100 pixels^2")            ax.legend(loc=8,framealpha=1, fontsize=8)            fig.savefig("fig{}.png".format(dpi), bbox_inches="tight")        plt.show() 

3.4 绘图例3

import matplotlib.pyplot as pltfor dpi in [72,100,144]:    fig,ax = plt.subplots(figsize=(1.5,2), dpi=dpi)    ax.set_title("fig.dpi={}".format(dpi))    ax.set_ylim(-3,3)    ax.set_xlim(-2,2)    ax.scatter([0],[1], s=10**2,                marker="s", linewidth=0, label="100 points^2")    ax.scatter([1],[1], s=(10*72./fig.dpi)**2,                marker="s", linewidth=0, label="100 pixels^2")    ax.legend(loc=8,framealpha=1, fontsize=8)    fig.savefig("fig{}.png".format(dpi), bbox_inches="tight")plt.show() 

3.5  同心绘制

plt.scatter(2, 1, s=4000, c='r')plt.scatter(2, 1, s=1000 ,c='b')plt.scatter(2, 1, s=10, c='g')

3.6 有标签绘制

import matplotlib.pyplot as pltx_coords = [0.13, 0.22, 0.39, 0.59, 0.68, 0.74,0.93]y_coords = [0.75, 0.34, 0.44, 0.52, 0.80, 0.25,0.55]fig = plt.figure(figsize = (8,5))plt.scatter(x_coords, y_coords, marker = 's', s = 50)for x, y in zip(x_coords, y_coords):    plt.annotate('(%s,%s)'%(x,y), xy=(x,y),xytext = (0, -10), textcoords = 'offset points',ha = 'center', va = 'top')plt.xlim([0,1])plt.ylim([0,1])plt.show()

3.7 直线划分

# 2-cateGory classfication with random 2D-sample data# from a multivariate normal distributionimport numpy as npfrom matplotlib import pyplot as pltdef decision_boundary(x_1):    """Calculates the x_2 value for plotting the decision boundary."""#    return 4 - np.sqrt(-x_1**2 + 4*x_1 + 6 + np.log(16))    return -x_1 + 1# Generating a gaussion dataset:# creating random vectors from the multivariate normal distribution# given mean and covariancemu_vec1 = np.array([0,0])cov_mat1 = np.array([[2,0],[0,2]])x1_samples = np.random.multivariate_normal(mu_vec1, cov_mat1,100)mu_vec1 = mu_vec1.reshape(1,2).T # TO 1-COL VECTORmu_vec2 = np.array([1,2])cov_mat2 = np.array([[1,0],[0,1]])x2_samples = np.random.multivariate_normal(mu_vec2, cov_mat2, 100)mu_vec2 = mu_vec2.reshape(1,2).T # to 2-col vector# Main scatter plot and plot annotationf, ax = plt.subplots(figsize = (7, 7))ax.scatter(x1_samples[:, 0], x1_samples[:,1], marker = 'o',color = 'green', s=40)ax.scatter(x2_samples[:, 0], x2_samples[:,1], marker = '^',color = 'blue', s =40)plt.legend(['Class1 (w1)', 'Class2 (w2)'], loc = 'upper right')plt.title('Densities of 2 classes with 25 bivariate random patterns each')plt.ylabel('x2')plt.xlabel('x1')ftext = 'p(x|w1) -N(mu1=(0,0)^t, cov1 = I)\np.(x|w2) -N(mu2 = (1, 1)^t), cov2 =I'plt.figtext(.15,.8, ftext, fontsize = 11, ha ='left')#Adding decision boundary to plotx_1 = np.arange(-5, 5, 0.1)bound = decision_boundary(x_1)plt.plot(x_1, bound, 'r--', lw = 3)x_vec = np.linspace(*ax.get_xlim())x_1 = np.arange(0, 100, 0.05)plt.show()

3.8 曲线划分

# 2-category classfication with random 2D-sample data# from a multivariate normal distributionimport numpy as npfrom matplotlib import pyplot as pltdef decision_boundary(x_1):    """Calculates the x_2 value for plotting the decision boundary."""    return 4 - np.sqrt(-x_1**2 + 4*x_1 + 6 + np.log(16))# Generating a gaussion dataset:# creating random vectors from the multivariate normal distribution# given mean and covariancemu_vec1 = np.array([0,0])cov_mat1 = np.array([[2,0],[0,2]])x1_samples = np.random.multivariate_normal(mu_vec1, cov_mat1,100)mu_vec1 = mu_vec1.reshape(1,2).T # TO 1-COL VECTORmu_vec2 = np.array([1,2])cov_mat2 = np.array([[1,0],[0,1]])x2_samples = np.random.multivariate_normal(mu_vec2, cov_mat2, 100)mu_vec2 = mu_vec2.reshape(1,2).T # to 2-col vector# Main scatter plot and plot annotationf, ax = plt.subplots(figsize = (7, 7))ax.scatter(x1_samples[:, 0], x1_samples[:,1], marker = 'o',color = 'green', s=40)ax.scatter(x2_samples[:, 0], x2_samples[:,1], marker = '^',color = 'blue', s =40)plt.legend(['Class1 (w1)', 'Class2 (w2)'], loc = 'upper right')plt.title('Densities of 2 classes with 25 bivariate random patterns each')plt.ylabel('x2')plt.xlabel('x1')ftext = 'p(x|w1) -N(mu1=(0,0)^t, cov1 = I)\np.(x|w2) -N(mu2 = (1, 1)^t), cov2 =I'plt.figtext(.15,.8, ftext, fontsize = 11, ha ='left')#Adding decision boundary to plotx_1 = np.arange(-5, 5, 0.1)bound = decision_boundary(x_1)plt.plot(x_1, bound, 'r--', lw = 3)x_vec = np.linspace(*ax.get_xlim())x_1 = np.arange(0, 100, 0.05)plt.show()

来源地址：https://blog.csdn.net/gongdiwudu/article/details/129947219