如何修复代码中‘索引976超出轴0大小976’的IndexError

运行代码时触发错误：IndexError: index 976 is out of bounds for axis 0 with size 976，该代码用于计算动能（输入能量减去摩擦等损耗），并绘制动能随移动距离变化的关系图。

原始代码如下：

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

g = 9.81
m_tot = 2
m_gewichtje = 0.5
ds = 0.001
s = np.arange(0, 9.76, ds)

A = 0.001 * 0.08 * 2 + 0.001 * 0.05 + 0.015 * 0.11 * 0.01* 32 *2
print(A)
rho = 1.29 #kg/m3
C_f = 0.5

mu = 0.005
Fn = m_tot *g

phi_max = 142 #degrtees
k = 0.02052 #Nm/degree
alpha = np.degrees(np.arccos((-0.5*(((0.008/0.11)**2)*(s**2))/(0.08**2))+1))+38
phi = 180 - alpha
df = pd.DataFrame({'s': s, 'phi' : phi})
E_k = np.zeros(976)
E_aandrijf = np.zeros(976)
E_luchtweerstand = np.zeros(976)
E_rolweerstand = np.zeros(976)
E_lagerwrijving = np.zeros(976)
F_veer = np.zeros(976)

for i, afstand in enumerate(s):
    if phi[i] >= 0 and np.isnan(phi[i]) == False :
        E_veer_max = 0.5 * k * phi_max ** 2
        E_veer = 0.5 * k * phi[i] ** 2
        E_aandrijf[i] = E_veer_max - E_veer
        F_veer[i] = E_aandrijf[i]/ds


    else:
        E_aandrijf = 0
        F_veer = 0
    F_rol = mu * Fn
    F_aandrijf = F_veer[i]- F_rol
    v = np.sqrt(E_k[i]/(0.5*m_tot))
    E_luchtweerstand[i] = 0.5 * rho * v**2 * C_f * A * ds
    E_rolweerstand[i] = F_rol * ds
    E_lagerwrijving[i] = F_aandrijf * ds



    E_k[i]= E_aandrijf[i] - E_lagerwrijving[i] - E_rolweerstand[i]- E_luchtweerstand[i]

fig, ax = plt.subplots()

ax.plot(s,E_k)
plt.show()

错误原因分析 #

1. 数组长度不匹配：s = np.arange(0, 9.76, ds)中，ds=0.001，所以s的实际长度是9760（9.76/0.001=9760），但所有能量、力数组（如E_k、E_aandrijf）都被硬编码初始化为长度976的数组。循环遍历s的9760个元素时，当索引i达到976，访问数组元素就会触发越界错误。
2. 分支赋值错误：else分支中E_aandrijf = 0和F_veer = 0会将整个数组变量覆盖为标量0，后续循环中访问E_aandrijf[i]会引发类型错误。

修正方案 #

• 所有数组初始化时，使用len(s)代替硬编码的976，保证数组长度与s一致。
• else分支中，将标量赋值改为对数组对应索引位置赋值，即E_aandrijf[i] = 0和F_veer[i] = 0。
• 可选：初始E_k为0，第一次计算v时是合法的（sqrt(0)），添加判断可让逻辑更严谨。

修正后的代码 #

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

g = 9.81
m_tot = 2
m_gewichtje = 0.5
ds = 0.001
s = np.arange(0, 9.76, ds)

A = 0.001 * 0.08 * 2 + 0.001 * 0.05 + 0.015 * 0.11 * 0.01* 32 *2
print(A)
rho = 1.29 #kg/m3
C_f = 0.5

mu = 0.005
Fn = m_tot *g

phi_max = 142 #degrtees
k = 0.02052 #Nm/degree
alpha = np.degrees(np.arccos((-0.5*(((0.008/0.11)**2)*(s**2))/(0.08**2))+1))+38
phi = 180 - alpha
df = pd.DataFrame({'s': s, 'phi' : phi})

# 用len(s)初始化数组，保证长度匹配
E_k = np.zeros(len(s))
E_aandrijf = np.zeros(len(s))
E_luchtweerstand = np.zeros(len(s))
E_rolweerstand = np.zeros(len(s))
E_lagerwrijving = np.zeros(len(s))
F_veer = np.zeros(len(s))

for i, afstand in enumerate(s):
    if phi[i] >= 0 and not np.isnan(phi[i]) :
        E_veer_max = 0.5 * k * phi_max ** 2
        E_veer = 0.5 * k * phi[i] ** 2
        E_aandrijf[i] = E_veer_max - E_veer
        F_veer[i] = E_aandrijf[i]/ds
    else:
        # 改为对对应索引赋值，不覆盖整个数组
        E_aandrijf[i] = 0
        F_veer[i] = 0
    
    F_rol = mu * Fn
    F_aandrijf_val = F_veer[i] - F_rol
    # 处理初始速度为0的情况
    if E_k[i] <= 0:
        v = 0
    else:
        v = np.sqrt(E_k[i]/(0.5*m_tot))
    
    E_luchtweerstand[i] = 0.5 * rho * v**2 * C_f * A * ds
    E_rolweerstand[i] = F_rol * ds
    E_lagerwrijving[i] = F_aandrijf_val * ds

    E_k[i] = E_aandrijf[i] - E_lagerwrijving[i] - E_rolweerstand[i] - E_luchtweerstand[i]

fig, ax = plt.subplots()
ax.plot(s, E_k)
plt.show()

内容的提问来源于stack exchange，提问作者Chris