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
属性 | 参数 | 意义 |
坐标 | x,y | 输入点列的数组,长度都是size |
点大小 | s | 点的直径数组,默认直径20,长度最大size |
点颜色 | c | 点的颜色,默认蓝色 'b',也可以javascript是个 RGB 或 RGBA 二维行数组。 |
点形状 | marker | 点的样式,默认小圆圈 'o'。 |
调色板 | cmap | Colormap,默认 None,标量或者是一个 colormap 的名字,只有 c 是一个浮点数数组时才使用。如果没有申明就是 image.cmap。 |
亮度(1) | norm | Normalize,默认 None,数据亮度在 0-1 之间,只有 c 是一个浮点数的数组的时才使用。 |
亮度(2) | vmin,vmax | 亮度设置,在 norm 参数存在时会忽略。 |
透明度 | alpha | 透明度设置,0-1 之间,默认 None,即不透明 |
线 | linewidths | 标记点的长度 |
颜色 | edgecolors | 颜色或颜色序列,默认为 'face',可选值有 'face', 'none', None。 |
plotnonfinite | 布尔值,设置是否使用非限定的 c ( inf, -inf 或 nan) 绘制点。 | |
**kwargs | 其他参数。 |
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 np import matplotlib.pyplot as plt # Fixing random state for reproducibility np.random.seed(19680801) N = 50 x = 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)http://www.devze.com)**2 # 点的宽度30,半径15 plt.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 np import matplotlib.pyplot as plt fig=plt.figure(figsize=(8,6)) #Generating a Gaussion dataset: #creating random vectors from the multivariate normal distribution #given mean and covariance mu_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**2 R_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 plt import numpy as np # Generating a Gaussion dTset: #Creating random vectors from the multivaritate normal distribution #givem mean and covariance mu_vecl = np.array([0, 0]) cov_matl = np.array([[2,0],[0,2]]) x1_samplandroides = 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], m开发者_开发培训arker='^', 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, http://www.devze.com 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 plt for dpi in [72,100,144]: fig,ax = plt.subplots(figsize=(1.5,2), dpi=dpi) ax.set_title("fig.dpi={}".format(dpi)) axhttp://www.devze.com.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 plt x_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 distribution import numpy as np from matplotlib import pyplot as plt def 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 covariance mu_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 VECTOR mu_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 annotation f, 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 plot x_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 distribution import numpy as np from matplotlib import pyplot as plt def 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 covariance mu_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 VECTOR mu_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 annotation f, 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 plot x_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()
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