如何优化这些循环？

Question

我是python的新手，但仍然想用最简单的方法来做事。你知道吗

下面我贴出我的代码，如果有人能查一查，找出这里出了什么问题，将是一个很大的帮助。你知道吗

问题：我必须解非线性方程组，我的代码在给定的例子中就是这样做的。但在解决我自己的练习时，它并不收敛，这意味着X(i-1)/x(1)->；1没有得到满足。你知道吗

另一件事是，它实际上停止了第5次迭代的计算，但我并没有指定任何关于第5次迭代的内容，当前3次迭代顺利进行时。我认为它储存了很多记忆…但这只是我的猜测。你知道吗

import matplotlib
import matplotlib.pyplot as plt
import numpy as np

#Needed variables and constants
n = 2
a1 = 0.04
a2 = 0.04
As1 = 19.64 * 10**(-4)
As2 = 12.64 * 10**(-4)
e0 = 0.07
b = 0.3
h = 0.5
Ned = 1990 * 10**3
fcd = 7.2 * 10**6
Ecm = 27 * 10**9
Es = 200 * 10**9
d = h - a1
e = (h/2) + e0
Eps2 = 0.002
Eps35 = 0.0035
d_x0 = 5 * 10**(-3)
d_r0 = 5 * 10**(-5)
fyd = 650 * 10**6

#Calculations
def x(i):
    if i == 1:
        return h
    return x(i - 1) + d_x(i-1)


def r(i):
    if i == 1:
        return (Ned * e) / ((Ecm * b * (h ** 3)) / 12)
    return r(i - 1) + d_r(i-1)


def d_x(i):
    A = np.array([[f1_x(i), f1_r(i)], [f2_x(i), f2_r(i)]])
    B = np.array([-f1(i), -f2(i)])
    C = np.linalg.solve(A, B)
    return C[0]


def d_r(i):
    A = np.array([[f1_x(i), f1_r(i)], [f2_x(i), f2_r(i)]])
    B = np.array([-f1(i), -f2(i)])
    C = np.linalg.solve(A, B)
    return C[1]


def Nb(i):
    return (b * fcd / r(i)) * (r(i) * h - (Eps2 / (n + 1)) * (1 - (r(i) * (x(i) - h)) / Eps2) ** (n + 1))


def Mb(i):
     return (b * fcd / (r(i) ** 2)) * (-(((Eps2 - r(i) * (x(i) - h)) ** (n + 1)) * (r(i) * (x(i) - h) * (n + 1) + Eps2) / ((n + 2) * (n + 1) * (Eps2 ** n))) + (r(i) ** 2) * h * (x(i) - h / 2))


def Ns1(i):
    return Es * r(i) * (x(i) - d) * As1


def Ns2(i):

    return Es * r(i) * (x(i) - a2) * As2


def Ms1(i):
    return Es * r(i) * ((x(i) - d) ** 2) * As1

def Ms2(i):
    return Es * r(i) * ((x(i) - a2) ** 2) * As2


def f1(i):
    return Nb(i) + Ns1(i) + Ns2(i) - Ned


def f2(i):
    return Mb(i) + Ms1(i) + Ms2(i) - (Ned * (x(i) - (h / 2) + e0))


# Derivatives of all the required elements

def Ns1_x(i):
    return Es * r(i) * As1


def Ns1_r(i):
    return Es * As1 * (x(i) - d)


def Ns2_x(i):
    return Es * r(i) * As2


def Ns2_r(i):
    return Es * As2 * (x(i) - a2)


def Ms1_x(i):
    return Es * r(i) * As1 * 2 * (x(i) - d)


def Ms1_r(i):
    return Es * As1 * (x(i) - d) ** 2


def Ms2_x(i):
    return Es * r(i) * As2 * 2 * (x(i) - a2)


def Ms2_r(i):
    return Es * As2 * (x(i) - a2) ** 2


def Nb_x(i):
    return (((b * fcd / r(i)) * (r(i) * h - (Eps2 / (n + 1)) * (1 - (r(i) * ((x(i) + d_x0) - h)) / Eps2) ** (n + 1))) -((b * fcd / r(i)) * (r(i) * h - (Eps2 / (n + 1)) * (1 - (r(i) * ((x(i) - d_x0) - h)) / Eps2) ** (n + 1)))) / (2 * d_x0)


def Nb_r(i):
    return (((b * fcd / (r(i) + d_r0)) * ((r(i) + d_r0) * h - (Eps2 / (n + 1)) * (1 - ((r(i) + d_r0) * (x(i) - h)) / Eps2) ** (n + 1))) -((b * fcd / (r(i) - d_r0)) * ((r(i) - d_r0) * h - (Eps2 / (n + 1)) * (1 - ((r(i) - d_r0) * (x(i) - h)) / Eps2) ** (n + 1)))) / (2 * d_r0)


def Mb_x(i):
    return ((b * fcd / (r(i) ** 2)) * (-(((Eps2 - r(i) * ((x(i) + d_x0) - h)) ** (n + 1)) * (r(i) * ((x(i) + d_x0) - h) * (n + 1) + Eps2) / ((n + 2) * (n + 1) * (Eps2 ** n))) + (r(i) ** 2) * h * ((x(i) + d_x0) - h / 2)) -(b * fcd / (r(i) ** 2)) * (-(((Eps2 - r(i) * ((x(i) - d_x0) - h)) ** (n + 1)) * (r(i) * ((x(i) - d_x0) - h) * (n + 1) + Eps2) / ((n + 2) * (n + 1) * (Eps2 ** n))) + (r(i) ** 2) * h * ((x(i) - d_x0) - h / 2))) / (2 * d_x0)


def Mb_r(i):
    return ((b * fcd / ((r(i) + d_r0) ** 2)) * (-(((Eps2 - (r(i) + d_r0) * (x(i) - h)) ** (n + 1)) * ((r(i) + d_r0) * (x(i) - h) * (n + 1) + Eps2) / ((n + 2) * (n + 1) * (Eps2 ** n))) + ((r(i) + d_r0) ** 2) * h * (x(i) - h / 2)) -(b * fcd / ((r(i) - d_r0) ** 2)) * (-(((Eps2 - (r(i) - d_r0) * (x(i) - h)) ** (n + 1)) * ((r(i) - d_r0) * (x(i) - h) * (n + 1) + Eps2) / ((n + 2) * (n + 1) * (Eps2 ** n))) + ((r(i) - d_r0) ** 2) * h * (x(i) - h / 2))) / (2 * d_r0)


def f1_x(i):
    return Nb_x(i) + Ns1_x(i) + Ns2_x(i)


def f1_r(i):
    return Nb_r(i) + Ns1_r(i) + Ns2_r(i)


def f2_x(i):
    return Mb_x(i) + Ms1_x(i) + Ms2_x(i) - Ned


def f2_r(i):
    return Mb_r(i) + Ms1_r(i) + Ms2_r(i)


# Results of iterations

def Eps_C2(i):
    return x(i) * r(i)


def Eps_C1(i):
    return (x(i) - h) * r(i)


def Sig_S1(i):
    return Es * r(i) * (x(i) - d)


def Sig_S2(i):
    return Es * r(i) * (x(i) - a2)

for i in range(1,10):
    print('Iteration', i)
    print(Mb(i))
    print(Mb_r(i))
    print(d_r(i))

Answer 1

t.y.所有评论。你知道吗

作为一个新手，我很抱歉再次发布这样的东西。你知道吗

所有的好主意都是一个上午产生的。你知道吗

问题是，代码甚至在没有FOR循环的情况下运行。问题是每次代码到达最后一次调用的迭代时。你知道吗

解决方案：我在计算后为新计算的值创建全局变量，并将其附加到新的全局空列表中。然后在第二次迭代中，它从全局变量中取出值，这意味着它不需要再次计算所有的内容。你知道吗

如果有人想计算一点非线性钢筋混凝土的工程，只要改变给定的全局变量。你知道吗

import matplotlib
import matplotlib.pyplot as plt
import numpy as np


#Needed variables and constants
n = 2   #Degree of nonlinearity(natural number ex.2,3,4 etc.)
a1 = 0.02   #Concrete cover of tensile reinf.[m]
a2 = 0.02   #Concrete cover of compressive reinf.[m]
As1 = 20 * 10**(-4)     #Area of tensile reinf.[m**2]
As2 = 20 * 10**(-4)     #Area of compressive reinf.[m**2]
e0 = 0.04       #Initial excetricity in [m]
b = 0.3         #Width of the columns cross-section.[m]
h = 0.3         #Height of the columns cross-section.[m]
Ned = 2600 * 10**3      #Axial Load.[N]
fcd = 8 * 10**6         #Concrete strength.[Pa]
Ecm = 27.6 * 10**9      #Concrete modulus of elasticity.[Pa]
Es = 200 * 10**9        #Steel modulus of elasticity. [Pa]
d = h - a1              #Effective height of tensile reinf. [m]
e = (h/2) + e0          #Excentricity of Ned[m]
Eps2 = 0.002            #Deformaton when stresses in concrete reaches fcd.
Eps35 = 0.0035          #Limit deformation for concrete.
d_x0 = 5 * 10**(-3)     #Increment for x. Needed when calculating derivatives of Mb and Nb.
d_r0 = 5 * 10**(-5)     #Increment fr r. Needed when calculating derivatives of Mb and Nb.
fyd = 600 * 10**6       #Yield stress for steel.[Pa]
d_x_n = []
d_r_n = []

#Calculation of nonlinear reinforced concrete.
#This code calculates 2 unknonws with system made out of 2 equations.
#Unknownws are physicaly connected in nonlinear dependecies.
#Solving this nonlinear system of equations is done by making linear iterative calculation, based on Taylor series.
#Program stops, when 2 adjacent iterations give same values.(at least 0.9999 accuracy)


def x(i):
    if i == 1:
        return h
    x_n = x(i-1) + d_x(i-1)
    return x_n

def r(i):
    if i == 1:
        return (Ned * e) / ((Ecm * b * (h ** 3)) / 12)
    r_n = r(i-1) + d_r(i-1)
    return r_n

def d_r(i):
    return d_r_n[i-1]

def d_x(i):
    return d_x_n[i-1]

def A(i):
    return np.array([[f1_x(i), f1_r(i)], [f2_x(i), f2_r(i)]])

def B(i):
    return np.array([-f1(i), -f2(i)])

def C(i):
    return np.linalg.solve(A(i), B(i))


def Nb(i):
    return (b * fcd / r(i)) * (r(i) * h - (Eps2 / (n + 1)) * (1 - (r(i) * (x(i) - h)) / Eps2) ** (n + 1))


def Mb(i):
     return (b * fcd / (r(i) ** 2)) * (-(((Eps2 - r(i) * (x(i) - h)) ** (n + 1)) * (r(i) * (x(i) - h) * (n + 1) + Eps2) / ((n + 2) * (n + 1) * (Eps2 ** n))) + (r(i) ** 2) * h * (x(i) - (h / 2)))


def Ns1(i):
    return Es * r(i) * (x(i) - d) * As1


def Ns2(i):

    return Es * r(i) * (x(i) - a2) * As2


def Ms1(i):
    return Es * r(i) * ((x(i) - d) ** 2) * As1

def Ms2(i):
    return Es * r(i) * ((x(i) - a2) ** 2) * As2


def f1(i):
    return Nb(i) + Ns1(i) + Ns2(i) - Ned


def f2(i):
    return Mb(i) + Ms1(i) + Ms2(i) - (Ned * (x(i) - (h / 2) + e0))


# Derivatives of all the required elements

def Ns1_x(i):
    return Es * r(i) * As1


def Ns1_r(i):
    return Es * As1 * (x(i) - d)


def Ns2_x(i):
    return Es * r(i) * As2


def Ns2_r(i):
    return Es * As2 * (x(i) - a2)


def Ms1_x(i):
    return Es * r(i) * As1 * 2 * (x(i) - d)


def Ms1_r(i):
    return Es * As1 * (x(i) - d) ** 2


def Ms2_x(i):
    return Es * r(i) * As2 * 2 * (x(i) - a2)


def Ms2_r(i):
    return Es * As2 * (x(i) - a2) ** 2


def Nb_x(i):
    return (((b * fcd / r(i)) * (r(i) * h - (Eps2 / (n + 1)) * (1 - (r(i) * ((x(i) + d_x0) - h)) / Eps2) ** (n + 1))) -((b * fcd / r(i)) * (r(i) * h - (Eps2 / (n + 1)) * (1 - (r(i) * ((x(i) - d_x0) - h)) / Eps2) ** (n + 1)))) / (2 * d_x0)


def Nb_r(i):
    return (((b * fcd / (r(i) + d_r0)) * ((r(i) + d_r0) * h - (Eps2 / (n + 1)) * (1 - ((r(i) + d_r0) * (x(i) - h)) / Eps2) ** (n + 1))) -((b * fcd / (r(i) - d_r0)) * ((r(i) - d_r0) * h - (Eps2 / (n + 1)) * (1 - ((r(i) - d_r0) * (x(i) - h)) / Eps2) ** (n + 1)))) / (2 * d_r0)


def Mb_x(i):
    return (((b * fcd / (r(i) ** 2)) * (-(((Eps2 - r(i) * ((x(i)+d_x0) - h)) ** (n + 1)) * (r(i) * ((x(i)+d_x0) - h) * (n + 1) + Eps2) / ((n + 2) * (n + 1) * (Eps2 ** n))) + (r(i) ** 2) * h * ((x(i)+d_x0) - (h / 2))))-((b * fcd / (r(i) ** 2)) * (-(((Eps2 - r(i) * ((x(i)-d_x0) - h)) ** (n + 1)) * (r(i) * ((x(i)-d_x0) - h) * (n + 1) + Eps2) / ((n + 2) * (n + 1) * (Eps2 ** n))) + (r(i) ** 2) * h * ((x(i)-d_x0) - (h / 2))))) / (2 * d_x0 )


def Mb_r(i):
    return (((b * fcd / ((r(i)+d_r0) ** 2)) * (-(((Eps2 - (r(i)+d_r0) * (x(i) - h)) ** (n + 1)) * ((r(i)+d_r0) * (x(i) - h) * (n + 1) + Eps2) / ((n + 2) * (n + 1) * (Eps2 ** n))) + ((r(i)+d_r0) ** 2) * h * (x(i) - (h / 2))))-((b * fcd / ((r(i)+d_r0) ** 2)) * (-(((Eps2 - (r(i)+d_r0) * (x(i) - h)) ** (n + 1)) * ((r(i)+d_r0) * (x(i) - h) * (n + 1) + Eps2) / ((n + 2) * (n + 1) * (Eps2 ** n))) + ((r(i)+d_r0) ** 2) * h * (x(i) - (h / 2))))) / (2 * d_r0)


def f1_x(i):
    return Nb_x(i) + Ns1_x(i) + Ns2_x(i)


def f1_r(i):
    return Nb_r(i) + Ns1_r(i) + Ns2_r(i)


def f2_x(i):
    return Mb_x(i) + Ms1_x(i) + Ms2_x(i) - Ned


def f2_r(i):
    return Mb_r(i) + Ms1_r(i) + Ms2_r(i)


# Results of iterations

def Eps_C2(i):
    return x(i) * r(i)


def Eps_C1(i):
    return (x(i) - h) * r(i)


def Sig_S1(i):
    return Es * r(i) * (x(i) - d)


def Sig_S2(i):
    return Es * r(i) * (x(i) - a2)

for i in range(1,20):
    d_x_n.append((C(i)[0]))
    d_r_n.append((C(i)[1]))
    print('Iteration', i)
    print(x(i))
    if round(x(i)/x(i+1), 4) == 1 and round(r(i)/r(i+1), 4) == 1:
        print('Ratio between two iterations ', round(x(i)/x(i+1), 4) )
        print('Convergence reached in ', i, 'Iterations')
        break
    else:
        print('Convergence was not reached in ', i, 'Iterations, calculation aborts')


print('Stress in tensile reinforcement,', Sig_S1(i))
print('Stress in compressive reinforcement,', Sig_S2(i))
print('Deformations in C1', Eps_C1(i))
print('Deformations in C2', Eps_C2(i))

Answer 2

我猜你是在浪费大量的时间重新评估相同的、不变的表达式。有些调用看起来是递归的，只是浪费了大量的计算工作。如果你想以自己的风格编写代码，Python并不是最好的语言——这可能是我在数学上的想法，但对于计算机来说，它是一种可怕的计算方式

作为黑客，您可以尝试在每个函数周围放置缓存，例如：

from functools import lru_cache

@lru_cache()
def x(i):
    if i == 1:
        return h
    return x(i - 1) + d_x(i-1)

（见https://stackoverflow.com/a/9674327/1358308）

如果可以的话，我建议重写你的代码来保存计算出的值。例如，类似于：

n = 10
x = np.zeros(n)
x[0] = h

for i in range(1, n):
  x[i] = x[i-1] + 1