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摘要: 点击查看代码 import numpy as np import matplotlib.pyplot as plt from scipy.stats import norm # 生成 x 值 x = np.linspace(-2, 2, 100) # 定义标准差列表 L = [1/2, 1, 2] 阅读全文
posted @ 2024-11-26 14:32 等我刷把宗师 阅读(6) 评论(0) 推荐(0) 编辑
摘要: 点击查看代码 from scipy.stats import expon, gamma import pylab as plt x = plt.linspace(0, 3, 100) L = [1/3, 1, 2] s1 = ['*-', '.-', 'o-'] s2 = ['$\\theta=\\ 阅读全文
posted @ 2024-11-26 14:28 等我刷把宗师 阅读(4) 评论(0) 推荐(0) 编辑
摘要: 点击查看代码 import numpy as np import pylab as plt a = 1 - 0.2**(1/12); m = 1.109 * 10 ** 5 w3 = 17.86; w4 = 22.99 X=[]; Z=[]; N=[] for k in np.arange(0, 1 阅读全文
posted @ 2024-11-21 22:59 等我刷把宗师 阅读(4) 评论(0) 推荐(0) 编辑
摘要: import numpy as np import pandas as pd import sympy as sp sp.init_printing(use_latex=True) from scipy.integrate import odeint import matplotlib.pyplot 阅读全文
posted @ 2024-11-19 18:36 等我刷把宗师 阅读(6) 评论(0) 推荐(0) 编辑
摘要: from scipy.integrate import odeint import numpy as np import pylab as plt mu = 1/82.45; lamda= 1-mu dz = lambda z, t: [z[1], 2*z[3]+z[0]-lamda*(z[0]+m 阅读全文
posted @ 2024-11-12 14:35 等我刷把宗师 阅读(5) 评论(0) 推荐(0) 编辑
摘要: from scipy.integrate import odeint import numpy as np import sympy as sp import pylab as plt df = lambda f, x: [f[1], 2*f[1]-f[0]+np.exp(x)] x1 = np.l 阅读全文
posted @ 2024-11-12 14:34 等我刷把宗师 阅读(4) 评论(0) 推荐(0) 编辑
摘要: from scipy.integrate import odeint import numpy as np import pylab as plt df = lambda f,t,w: [w/np.sqrt((10+20*np.cos(t)-f[0])**2+(20+ 15*np.sin(t)-f[ 阅读全文
posted @ 2024-11-12 14:33 等我刷把宗师 阅读(3) 评论(0) 推荐(0) 编辑
摘要: from scipy.integrate import odeint import numpy as np import pylab as plt np.random.seed(2) #为了进行一致性比较,每次运行取相同随机数 sigma=10; rho=28; beta=8/3; g=lambda 阅读全文
posted @ 2024-11-12 14:31 等我刷把宗师 阅读(4) 评论(0) 推荐(0) 编辑
摘要: from scipy.integrate import odeint import numpy as np import pylab as plt yx = lambda y,x: [y[1], np.sqrt(1+y[1]**2)/5/(1-x)] x0 = np.arange(0, 1, 0.0 阅读全文
posted @ 2024-11-12 14:28 等我刷把宗师 阅读(6) 评论(0) 推荐(0) 编辑
摘要: from scipy.integrate import odeint import numpy as np import pylab as plt import sympy as sp dy = lambda y, x: -2*y+2*x**2+2*x #自变量在后面 xx = np.linspac 阅读全文
posted @ 2024-11-12 14:27 等我刷把宗师 阅读(9) 评论(0) 推荐(0) 编辑
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