ANSA二次开发_Python基础-基于matplotlib的模型厚度分布图

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import matplotlib.pyplot as pltx = range(1, 6)y = [1, 4, 6, 8, 4]plt.plot(x, y)plt.title('Line Plot')plt.xlabel('X Axis')plt.ylabel('Y Axis')plt.show()

import matplotlib.pyplot as pltx = [5, 7, 8, 5, 6, 7, 9, 2, 3, 4, 4, 4, 2, 6, 3, 6, 8, 6]y = [7, 4, 3, 9, 1, 3, 2, 5, 2, 4, 8, 7, 1, 6, 4, 9, 7, 8]plt.scatter(x, y)plt.title('Scatter Plot')plt.xlabel('X Axis')plt.ylabel('Y Axis')plt.show()

import matplotlib.pyplot as pltcategories = ['A', 'B', 'C', 'D']values = [23, 45, 56, 78]plt.bar(categories, values)plt.title('Bar Chart')plt.xlabel('Categories')plt.ylabel('Values')plt.show()

import matplotlib.pyplot as pltimport numpy as npdata = np.random.randn(1000)plt.hist(data, bins=30)plt.title('Histogram')plt.xlabel('Value')plt.ylabel('Frequency')plt.show()

import matplotlib.pyplot as pltsizes = [215, 130, 245, 210]labels = ['A', 'B', 'C', 'D']plt.pie(sizes, labels=labels, autopct='%1.1f%%')plt.title('Pie Chart')plt.show()

import matplotlib.pyplot as pltimport numpy as npdata = np.random.rand(10, 4)plt.boxplot(data)plt.title('Box Plot')plt.show()

import matplotlib.pyplot as pltimport numpy as npx = np.linspace(-3.0, 3.0, 100)y = np.linspace(-3.0, 3.0, 100)X, Y = np.meshgrid(x, y)Z = np.sin(X**2 + Y**2)plt.contour(X, Y, Z)plt.title('Contour Plot')plt.xlabel('X Axis')plt.ylabel('Y Axis')plt.show()

from mpl_toolkits.mplot3d import Axes3Dimport matplotlib.pyplot as pltimport numpy as npfig = plt.figure()ax = fig.add_subplot(111, projection='3d')x = np.random.standard_normal(100)y = np.random.standard_normal(100)z = np.random.standard_normal(100)ax.scatter(x, y, z)ax.set_title('3D Scatter Plot')plt.show()

import numpy as npimport matplotlib.pyplot as pltimport seaborn as sns; sns.set()  # 使用seaborn样式# 生成数据data = np.random.rand(10, 12)# 绘制热图plt.figure(figsize=(8, 6))sns.heatmap(data, annot=True, fmt=".1f", linewidths=.5)plt.title('Heatmap')plt.show()

import numpy as npimport matplotlib.pyplot as plt# 生成数据r = np.arange(0, 2, 0.01)theta = 2 * np.pi * rplt.subplot(111, polar=True)plt.plot(theta, r)plt.title('Polar Plot')plt.show()

import matplotlib.pyplot as pltimport numpy as npx = np.arange(0.1, 4, 0.5)y = np.exp(-x)# 定义误差error = 0.1 + 0.2 * np.sqrt(x)plt.errorbar(x, y, yerr=error, fmt='-o')plt.title('Error Bar Plot')plt.show()

import matplotlib.pyplot as pltimport numpy as npN = 5menMeans = (20, 35, 30, 35, 27)womenMeans = (25, 32, 34, 20, 25)ind = np.arange(N)    width = 0.35       plt.bar(ind, menMeans, width, label='Men')plt.bar(ind, womenMeans, width, bottom=menMeans, label='Women')plt.ylabel('Scores')plt.title('Scores by group and gender')plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))plt.yticks(np.arange(0, 81, 10))plt.legend()plt.show()

import matplotlib.pyplot as pltx = range(1, 6)y = [1, 4, 6, 8, 4]plt.fill_between(x, y)plt.title('Area Plot')plt.xlabel('X Axis')plt.ylabel('Y Axis')plt.show()

import matplotlib.pyplot as pltx = range(1, 6)y = [1, 4, 6, 8, 4]plt.step(x, y)plt.title('Step Plot')plt.xlabel('X Axis')plt.ylabel('Y Axis')plt.show()

import matplotlib.pyplot as pltimport numpy as npx = np.linspace(0.1, 2 * np.pi, 10)y = np.cos(x)plt.stem(x, y)plt.title('Stem Plot')plt.xlabel('X Axis')plt.ylabel('Y Axis')plt.show()

import numpy as npimport matplotlib.pyplot as pltfrom math import pi# 设置数据categories = ['A', 'B', 'C', 'D', 'E']values = [4, 3, 2, 5, 4]values += values[:1]  # 闭合图形# 计算角度angles = [n / float(len(categories)) * 2 * pi for n in range(len(categories))]angles += angles[:1]# 绘图ax = plt.subplot(111, polar=True)plt.xticks(angles[:-1], categories)ax.plot(angles, values)ax.fill(angles, values, 'teal', alpha=0.1)plt.show()