import sympy as smp from matplotlib import pyplot as plt import numpy as nmp from scipy.integrate import solve_ivp p = 1.225 # air density m = 0.003 # mass of the plane a = (9.3/180)*nmp.pi # angle of attack ar = 0.86 # aspect ratio s = 0.017 # wing area g = 9.807 # acceleration of free fall cla = (nmp.pi*ar)/1+nmp.sqrt(1+(ar/2)**2) # lift slope derivative cl = cla*a # lift coefficient e = 0.9 # oswald efficiency factor ε = 1/(nmp.pi*e*ar) # induced darg factor cd = 0.02 + ε*(cl**2) def airplane(t,f): v, γ, h, r = f return [-cd(p*v**2)*s/(2*m)-g*nmp.sin(γ), ((cl*(p*v**2/2))-g*nmp.cos(γ))/v, v*nmp.sin(γ), v*nmp.cos(γ)] solution = solve_ivp(airplane, (0,1000),[3.7,((-11/180)*nmp.pi),2.0,0.0]) print(solution) 哪里有 type error

from sympy import * from math import * import numpy as np from matplotlib import pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] #用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False #用来正常显示负号 import numpy as np import matplotlib.pyplot as plt from sympy import * # 用于求导积分等科学计算 # 一元一次函数图像 def fun_format(): plt.xlabel('x') plt.ylabel('y') plt.xlim((-10,10)) plt.ylim((-20,20)) plt.tight_layout() x,y = symbols('x y') # 引入x y变量 expr = log(x)# 计算表达式 x_value = [] # 用于保存x值 y_value = [] # 用于保存y值 y_value_dif = [] # 用于保存一阶导数值 expr_dif = diff(expr,x,1) for i in np.arange(-10,10,0.1): x_value.append(i) y_value.append(expr.subs('x',i)) # 将i值代入表达式 y_value_dif.append(expr_dif.subs('x',i)) # 将i值代入一阶求导表达式 fig=plt.figure() ax1=fig.add_subplot(2,1,1) # plt.title('f(x)='+str(expr)) fun_format() ax1.plot(x_value,y_value) # 画原函数图 ax2=fig.add_subplot(2,2,3) plt.title('f(x)_dot='+str(expr_dif)) fun_format() ax2.plot(x_value,y_value_dif) # 画一阶导数图

- 引入了 matplotlib 和 sympy 库。 - 定义了一个函数 fun_format()，用于设置坐标轴范围、标签等格式。 - 定义了变量 x 和 y。 - 定义了表达式 expr = log(x)，这是对数函数的表达式。 - 计算了 x 和 y 的值，...

import numpy as np import matplotlib.pyplot as plt import sympy from scipy.interpolate import interp1d gamma = 1.2 R = 8.314 T0 = 500 Q = 50 * R * T0 a0 = np.sqrt(gamma * R * T0) M0 = 6.216 P_P0 = sympy.symbols('P_P0') num = 81 x0 = np.linspace(0,1,num) t_t0 = np.linspace(0,15,num) x = x0[1:] T_T0 = t_t0[1:] h0 = [] h1 = []#创建拉姆达为1的空数组 r = [] t = [] c = [] s = [] i = 0 for V_V0 in x: n1 = sympy.solve(1 / (gamma-1) * (P_P0 * V_V0 - 1) - 0.5 * (P_P0 + 1) * (1 - V_V0)- gamma * 0 * Q / a0 ** 2,P_P0)#lamuda=0的Hugoniot曲线方程 n2 = sympy.solve(1 / (gamma-1) * (P_P0 * V_V0 - 1) - 0.5 * (P_P0 + 1) * (1 - V_V0)- gamma * 1 * Q / a0 ** 2,P_P0)#lamuda=1的Hugoniot曲线方程 n3 = sympy.solve(-1 * P_P0 + 1 - gamma * M0 ** 2 * (V_V0 - 1),P_P0)#Reyleigh曲线方程 n4 = 12.014556 / V_V0#等温线 n5 = sympy.solve((P_P0 - 1 / (gamma+1) )* (V_V0-gamma / (gamma + 1)) - gamma / ((gamma + 1) ** 2),P_P0)#声速线 n6 = 10.6677 / np.power(V_V0,1.2)#等熵线 h0.append(n1) h1.append(n2) r.append(n3) t.append(n4) c.append(n5) s.append(n6) i = i+1 h0 = np.array(h0) h1 = np.array(h1) r = np.array(r) t = np.array(t) c = np.array(c) s = np.array(s) plt.plot(x,r,label='Rayleigh') plt.plot(x,t,color='purple',label='isothermal') plt.plot(x,s,color='skyblue',label='isentropic') a = np.where(h0 < 0) b = np.where(c < 0) h0 = np.delete(h0,np.where(h0 < 0)[0],axis = 0)#去除解小于0的值 h1 = np.delete(h1,np.where(h1 < 0)[0],axis = 0)#去除解小于0的值 c = np.delete(c,np.where(c < 0)[0],axis = 0)#去除解小于0的值 x0 = np.delete(x,a,axis = 0)#对应去除x轴上错误值的坐标 x1 = np.delete(x,b,axis = 0) plt.plot(x0,h0,label='Hugoniot(lambda=0)') plt.plot(x0,h1,label='Hugoniot(lambda=1)') plt.plot(x1,c,color='yellow',label='soniclocus') plt.ylim((0,50)) plt.legend() # 显示图例 plt.xlabel('V/V0') plt.ylabel('P/P0') f1 = interp1d(x1, c.T, kind='cubic') f2 = interp1d(x,r.T,kind='cubic') f3 = interp1d(x, t.T, kind='cubic') epsilon = 0.0001 x0 = 0.56 y0 = f1(x0) - f2(x0) while abs(y0) > epsilon: df = (f1(x0 + epsilon) - f2(x0 + epsilon) - y0) / epsilon x0 -= y0 / df y0 = f1(x0) - f2(x0) plt.scatter(x0, y0, 50, color ='red') plt.show()

其中用到了一些科学计算库，比如 numpy、matplotlib、sympy 和 scipy.interpolate。这段代码还包括了一些数据处理的操作，比如删除数组中小于0的值、插值等等。最后，这段代码还用牛顿迭代法求解了两条曲线的交点。

#外点法(能运行出来) import math import sympy import numpy as np from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D plt.ion() fig = plt.figure() ax = Axes3D(fig) def draw(x,index,M): # F = f + MM * alpha # FF = sympy.lambdify((x1, x2), F, 'numpy') Z = FF((X, Y,M)) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='rainbow',alpha=0.5) ax.scatter(x[0], x[1], FF((x[0],x[1],M)), c='r',s=80) ax.text(x[0], x[1], FF((x[0],x[1],M)), 'here:(%0.3f,%0.3f)' % (x[0], x[1])) ax.set_zlabel('F') # 坐标轴 ax.set_ylabel('X2') ax.set_xlabel('X1') plt.pause(0.1) # plt.show() # plt.savefig('./image/%03d' % index) plt.cla() C = 10 # 放大系数 M = 1 # 惩罚因子 epsilon = 1e-5 # 终止限 x1, x2 = sympy.symbols('x1:3') MM=sympy.symbols('MM') f = -x1 + x2 h = x1 + x2 - 1 # g=sympy.log(x2) if sympy.log(x2)<0 else 0 g = sympy.Piecewise((x2-1, x2 < 1), (0, x2 >= 1)) # u=lambda x: alpha = h 2 + g 2 F = f + MM alpha # 梯度下降来最小化F def GD(x,M,n): # F = f + M * alpha # delta_x = 1e-11 # 数值求导 # t = 0.0001 # 步长 e = 0.001 # 极限 # my_print(e) np.array(x) for i in range(15): t = sympy.symbols('t') grad = np.asarray( [sympy.diff(F, x1).subs([(x1, x[0]), (x2, x[1]),(MM,M)]), sympy.diff(F, x2).subs([(x1, x[0]), (x2, x[1]),(MM,M)])]) # print('g',grad) # print((x-tgrad)) # print(F.subs([(x1,(x-tgrad)[0]),(x2,(x-tgrad)[1])])) t = sympy.solve(sympy.diff(F.subs([(x1, (x - t grad)[0]), (x2, (x - t * grad)[1]),(MM,M)]), t), t) print('t',t) x = x - t * grad print('x', x) # print('mmm',M) draw(x,n*10+i,M) # my_print(np.linalg.norm(grad)) # print(type(grad)) if (abs(grad[0]) < e and abs(grad[1]) < e): # print(np.linalg.norm(grad)) print('g', grad) break return list(x) pass x = [-0.5, 0.2] X = np.arange(0, 4, 0.25) Y = np.arange(0, 4,

0.25) # 定义网格点坐标 X, Y = np.meshgrid(X, Y) # 生成网格点 FF = sympy.lambdify((x1, x2,MM), F, 'numpy') # 将表达式转化为可计算的函数 draw(x,0,M) # 绘制函数图像 for i in range(5): # 迭代次数为5次 x =...

import sympy from scipy.integrate import quad y=xsympy.atan(x)sympy.ln(sympy.exp(1+x**2)) result,error=quad(y,0,1) print(result)出错怎么办

from scipy.integrate import quad x = sympy.symbols('x') y = x*sympy.atan(x)*sympy.ln(sympy.exp(1+x**2)) result, error = quad(sympy.lambdify(x, y), 0, 1) print(result) 在这里，我们使用 sympy....

from sympy import simplify import numpy as np import sympy as sp from sympy import symbols x, y, z = symbols('x y z') F = (x2 + y2 - 1)(x2 + z2 - 1)(y2 + z2 - 1) - 1 F_x = F.diff(x) F_y = F.diff(y) F_z = x**2 * F.diff(x) - y * F.diff(y) F_x = simplify(F_x) F_y = simplify(F_y) F_z = simplify(F_z) G_x = - F_x/F_z G_y = - F_y/F_z G_x = simplify(G_x) G_y = simplify(G_y) Fi = F_x.diff(x) Fj = F_x.diff(y) Fk = F_y.diff(y) N = G_x.cross(G_y) # normale N = simplify(N/N.norm()) E = simplify(G_x.norm()2) F = simplify(G_x.dot(G_y)) G = simplify(G_y.norm()2) I = simplify(N.dot(Fi)) J = simplify(N.dot(Fj)) K = simplify(N.dot(FK)) x = Symbol('x') factor(det(Matrix([[I - xE, J - xF], [J - xF, K - xG]])).subs({x:0, y:0}))跑不出来，

从代码上看，这是一个使用 SymPy 执行符号计算的程序。其中，定义了一些变量和表达式，对这些表达式求导，然后求取法向量、曲率等数据。最后一行代码是要计算一个行列式，并且把其中的 $x$ 和 $y$ 分别代入为 $0$，...

import sympy as sp import math a = int(input("请输入a：")) n0= float(input("请输入n0：")) d = 2 * n0 * a / (1 - a ** 2) p = int(input("请输入p：")) q_sym, nx = sp.symbols('q_sym nx') # 定义符号变量 q1 = a * a * sp.cos(math.radians(p)) * sp.cos(math.radians(p)) * sp.cos(math.radians(q_sym)) ** 2 q2 = (d - a * sp.sin(math.radians(p))) ** 2 q3 = q1 / q2 eq = sp.Eq(q3, 1 - q_sym) # 构造方程 sol = sp.solve(eq, [q_sym, nx]) # 求解 for s in sol: print(sp.N(s)) # 将表达式转换为浮点型以输出结果

这段代码的作用是解决一个三棱锥的问题，其中需要输入参数a、n0、p，然后通过计算得到q_sym和nx的值。具体来说，这段代码首先导入了sympy和math库，然后让用户输入a、n0和p的值。接着，根据这些输入的值，计算出d的...

import sympy x=sympy.Symbol('x') y=xsympy.atan(x)sympy.ln(sympy.exp(1+x**2)) dx=sympy.N(sympy.integrate(y,x)) print(dx)没有输出怎么改

y = x*sympy.atan(x)*sympy.ln(sympy.exp(1+x**2)) dx = sympy.integrate(y, x, show=True) print(dx) 如果还是没有输出，那可能是你的计算机处理时间太长了，你可以尝试将 show=True 参数去掉，这样就不会...

#导入sympy库 import sympy as sp #定义变量符号 x,y =sp.symbols('x y') #定义被积函数 f=(x2+2y) #定义积分区域 D = sp.Interval(x=y2)sp.Interval(y=x-2) #计算符号解 result = sp.integrate(f,(x,1,4),(y,-1,2)) print(result)

这段代码使用了Python的符号计算库sympy来进行多重积分的计算。具体来说，它定义了两个符号变量x和y，然后定义了被积函数f为x的平方加2乘以y。接着，它定义了积分区域D为y的取值范围在[x-2,x]之间，x的取值范围在[1,...

from sympy import symbols,diff import sympy as sp from sympy.abc import beta,omega,u import math k1 = 150.0 k2 = 100.0 a = 0.6 b = 0.77 L = 1.37 step_size = 0.01 time = 10 m = 256 vx = 30.0 Iz = 0.1 vx_plot = [] omega_plot = [] vx = vx sigma = 0.5 eq1 = sp.Eq((k1+k2)beta+omega/u(ak1-bk2)-k1sigma -m(0.09+uomega),0) eq2 = sp.Eq((ak1-bk2)beta+omega/u(a2k1-b2k2)-ak1sigma -Izomega,0) eq3 = sp.Eq((u-vx/math.tan(beta)),0) sol = sp.solve((eq1,eq2,eq3),(beta,omega,u)) print(sol)不能正常运行

1. 在第12行中，您缺少了一个乘号(*)，应该将(k1+k2)和beta相乘，正确的代码应该是： eq1 = sp.Eq((k1+k2)*beta+omega/u*(a*k1-b*k2)-k1*sigma -m*(0.09+u*omega),0) 2. 在第13行中，您也缺少了一个乘号(*...

#导入sympy库 import sympy as sp #定义变量符号 x,y =sp.symbols('x y') #定义被积函数 f=(x**2+2*y) #定义积分区域 D = sp.Interval(x=y**2)*sp.Interval(y=x-2) #计算符号解 result = sp.integrate(f,(x,1,4),(y,-1,2)) print(result)

这段代码使用了 Python 中的 sympy 库来进行符号计算，实现了对被积函数 f 在积分区域 D 上的重积分计算。具体来说，首先通过 sp.symbols() 函数定义了变量符号 x 和 y，然后定义了被积函数 f，即 x^2 + 2y。接...

下列代码在运行中有哪些错误，并改正：import numpy as np import sympy as sym def ff(a,b,n=3): h=(b-a)/(n-1) x_k=[a+kh for k in range(n)] #x_k随着k取值不同，有很多个值，所以是列表形式[]# x=sym.Symbol('x') def l(k): terms=[(x-x_k[j])/(x_k[k]-x_k[j]) for j in range(n) if j!=k] return sym.prod(terms) A_k=[sym.integrate(l(k),(x,a,b)) for k in range(n)] C_k=[sym.simplify(A_k[k]/(b-a)) for k in range(n)] def f(x): return sym.integrate(sym.sqrt(x-1),(x,a,b)) return (b-a)np.sum([f(x_k[k])*C_k[k] for k in range(n)]) ff(a=-1,b=1)

import numpy as np import sympy as sym def ff(a, b, n=3): h = (b - a) / (n - 1) x_k = [a + k * h for k in range(n)] x = sym.Symbol('x') def l(k): terms = [(x - x_k[j]) / (x_k[k] - x_k[j]) for ...

这段代码为什么报错： Invalid limits given: ((-1.0, -1, 1),) 怎样修改？代码：import numpy as np import sympy as sym def ff(a,b,n=3): h=(b-a)/(n-1) x_k=[a+kh for k in range(n)] #x_k随着k取值不同，有很多个值，所以是列表形式[]# x=sym.Symbol('x') def l(k): terms=[(x-x_k[j])/(x_k[k]-x_k[j]) for j in range(n) if j!=k] return sym.prod(terms) A_k=[sym.integrate(l(k),(x,a,b)) for k in range(n)] C_k=[sym.simplify(A_k[k]/(b-a)) for k in range(n)] def f(x): return sym.integrate(sym.sqrt(x+1),(x,a,b)) return (b-a)np.sum([f(x_k[k])*C_k[k] for k in range(n)]) ff(a=-1,b=1,n=3)

import numpy as np import sympy as sym def ff(a, b, n=3): h = (b - a) / (n - 1) x_k = [a + k * h for k in range(n)] x = sym.Symbol('x') def l(k): terms = [(x - x_k[j]) / (x_k[k] - x_k[j]) for ...

将以下代码转化为matlab代码表示：import xlrd import sympy import numpy as np from scipy import linalg #%% queue = [ 0, 29, 17, 2, 1, 20, 19, 26, 18, 25, 14, 6, 11, 7, 15, 9, 8, 12, 27, 16, 10, 13, 5, 4, 3, 22, 28, 24, 23, 21, 0] def read_data_model(): data = xlrd.open_workbook("/Users/lzs/Downloads/2020szcupc/data/C2.xlsx") table = data.sheet_by_name("Sheet1") rowNum = table.nrows colNum = table.ncols consumes = [] for i in range(1, rowNum): # 忽略DC的消耗 if i == 1: pass else: consumes.append(0 if table.cell_value(i, 3) == '/' else table.cell_value(i, 3)) return consumes #%% 获得矩阵A def get_A_matrix(data): A = np.ones([29,29], dtype = float) diagonal = np.eye(29) for i in range(29): for j in range(29): A[i][j] = data['consumes'][j] / data['r'] A = A - diagonal return A #%% def get_b_maatrix(data): b = np.ones([29,1], dtype=float) for i in range(29): b[i][0] = -data['dst']data['consumes'][i]/data['velocity']+data['f'] for j in range(29): b[i][0] = b[i][0] + data['f']data['consumes'][i]/data['r'] return b #%% 数值解 def numerical(data): data['velocity'] = 50 data['dst'] = 11469 data['r'] = 200 data['f'] = 10 A = get_A_matrix(data) b = get_b_maatrix(data) x = linalg.solve(A, b) return x #%% 符号解决方案 def symbolic(data): data['velocity'] = sympy.symbols("v", integer = True) data['dst'] = 12100 data['r'] = sympy.symbols("r", integer = True) data['f'] = sympy.symbols("f", integer = True) # 获取矩阵A并转移到符号矩阵M A = np.ones([29,29], dtype = float).tolist() diagonal = np.eye(29).tolist() for i in range(29): for j in range(29): A[i][j] = data['consumes'][j] / data['r'] - diagonal[i][j] M = sympy.Matrix(A) # 得到矩阵b并转移到符号矩阵b b = np.ones([29,1], dtype=float).tolist() for i in range(29): b[i][0] = -data['dst']data['consumes'][i]/data['velocity']+data['f'] for j in range(29): b[i][0] = b[i][0] + data['f']data['consumes'][i]/data['r'] b = sympy.Matrix(b) # LU solver x = M.LUsolve(b) return x #%% 主功能 if name == 'main': data = {} data['consumes'] = read_data_model() options = {"numerical":1, "symbolic":2} option = 1 if option == options['numerical']: x = numerical(data) print(x) elif option == options['symbolic']: x = symbolic(data) print(x) else: print("WARN!!!")

import numpy as np from scipy import linalg %% 读取数据模型 function consumes = read_data_model() data = xlsread('/Users/lzs/Downloads/2020szcupc/data/C2.xlsx'); consumes = []; for i = 2:size(data...

无需编写任何代码即可创建应用程序：Deepseek-R1 和 RooCode AI 编码代理.pdf

deepseek最新资讯、配置方法、使用技巧，持续更新中

Heric拓扑并网离网仿真模型：PR单环控制，SogIPLL锁相环及LCL滤波器共模电流抑制技术解析,基于Heric拓扑的离网并网仿真模型研究与应用分析：PR单环控制与Sogipll锁相环的共模电流抑

Heric拓扑并网离网仿真模型：PR单环控制，SogIPLL锁相环及LCL滤波器共模电流抑制技术解析,基于Heric拓扑的离网并网仿真模型研究与应用分析：PR单环控制与Sogipll锁相环的共模电流抑制效能,#Heric拓扑并离网仿真模型（plecs）逆变器拓扑为：heric拓扑。仿真说明： 1.离网时支持非单位功率因数负载。 2.并网时支持功率因数调节。 3.具有共模电流抑制能力（共模电压稳定在Udc 2）。此外，采用PR单环控制，具有sogipll锁相环，lcl滤波器。注：（V0004） Plecs版本4.7.3及以上 ,Heric拓扑; 离网仿真; 并网仿真; 非单位功率因数负载; 功率因数调节; 共模电流抑制; 共模电压稳定; PR单环控制; sogipll锁相环; lcl滤波器; Plecs版本4.7.3及以上,Heric拓扑：离网并网仿真模型，支持非单位功率因数与共模电流抑制

import matplotlib.pyplot as plt import matplotlib as mpl import numpy as np from scipy import integrate import sympy import mpmath

相关推荐

import matplotlib.pyplot as plt import matplotlib as mpl import numpy as np from scipy import integrate import sympy import mpmath

相关推荐

python调试文件时发生import requests报错.doc

Python科学计算-自动计算接触角.rar_Python科学计算-自动计算接触角_python科学计算_scipy_sympy

python使用pip安装SciPy、SymPy、matplotlib教程

import sympy from scipy.integrate import quad y=x*sympy.atan(x)*sympy.ln(sympy.exp(1+x**2)) result,error=quad(y,0,1) print(result)出错怎么办

import sympy x=sympy.Symbol('x') y=x*sympy.atan(x)*sympy.ln(sympy.exp(1+x**2)) dx=sympy.N(sympy.integrate(y,x)) print(dx)没有输出怎么改

#导入sympy库 import sympy as sp #定义变量符号 x,y =sp.symbols('x y') #定义被积函数 f=(x2+2*y) #定义积分区域 D = sp.Interval(x=y2)*sp.Interval(y=x-2) #计算符号解 result = sp.integrate(f,(x,1,4),(y,-1,2)) print(result)

#导入sympy库 import sympy as sp #定义变量符号 x,y =sp.symbols('x y') #定义被积函数 f=(x**2+2*y) #定义积分区域 D = sp.Interval(x=y**2)*sp.Interval(y=x-2) #计算符号解 result = sp.integrate(f,(x,1,4),(y,-1,2)) print(result)

无需编写任何代码即可创建应用程序：Deepseek-R1 和 RooCode AI 编码代理.pdf

Heric拓扑并网离网仿真模型：PR单环控制，SogIPLL锁相环及LCL滤波器共模电流抑制技术解析,基于Heric拓扑的离网并网仿真模型研究与应用分析：PR单环控制与Sogipll锁相环的共模电流抑

大家在看

基于springboot的智慧食堂系统源码.zip

C# 使用Selenium模拟浏览器获取CSDN博客内容

百度离线地图开发示例代码,示例含海量点图、热力图、自定义区域和实时运行轨迹查看功能

易语言-momo/陌陌/弹幕/优雅看直播

机器视觉选型计算概述-不错的总结

最新推荐

QML实现多功能虚拟键盘新功能介绍

揭秘交通灯控制系统：从电路到算法的革命性演进

rk3588 istore

React购物车项目入门及脚本使用指南

交通信号控制系统优化全解析：10大策略提升效率与安全性

pytorch 目标检测水果

Notepad++插件NppAStyle的使用与功能介绍

【Simulink振动模型构建全攻略】：一步步带你从零开始实现机械振动模型

fedora 41 安装百度网盘

Holberton学校推出高级WEB前端技术栈教育计划

import sympy from scipy.integrate import quad y=xsympy.atan(x)sympy.ln(sympy.exp(1+x**2)) result,error=quad(y,0,1) print(result)出错怎么办

import sympy x=sympy.Symbol('x') y=xsympy.atan(x)sympy.ln(sympy.exp(1+x**2)) dx=sympy.N(sympy.integrate(y,x)) print(dx)没有输出怎么改

#导入sympy库 import sympy as sp #定义变量符号 x,y =sp.symbols('x y') #定义被积函数 f=(x2+2y) #定义积分区域 D = sp.Interval(x=y2)sp.Interval(y=x-2) #计算符号解 result = sp.integrate(f,(x,1,4),(y,-1,2)) print(result)