pos += v * dt numpy.core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'add' output from dtype('float64') to dtype('int32') with casting rule 'same_kind'

这个错误是因为您正在使用NumPy中的ufunc，将浮点数与整数相加，导致输出的结果类型与输入类型不匹配。默认情况下，NumPy不会执行此类类型转换，因为它可能会导致精度丢失或不一致。您可以通过将输出类型显式转换为整数来解决此问题，例如：

pos = pos.astype(int) + (v * dt).astype(int)

或者您可以使用向下取整运算符“//”而不是除法运算符“/”，这样输出将始终是整数类型，例如：

pos += (v * dt) // 1 # // 1 确保结果是整数类型

另一种解决方法是在计算之前将dt转换为整数类型，例如：

pos += v * dt.astype(int)

这些解决方案中的任何一个都应该可以解决您遇到的问题。

相关问题

Traceback (most recent call last): File "C:\Users\liang\Desktop\天体物理\天体力学期中作业3.py", line 27, in <module> positions, velocities = simulate(m, r0, v0, dt, tmax) File "C:\Users\liang\Desktop\天体物理\天体力学期中作业3.py", line 14, in simulate v += a * dt numpy.core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'add' output from dtype('float64') to dtype('int32') with casting rule 'same_kind'

这个错误是因为在你的代码中，你正在尝试将一个浮点数和一个整数相加，然后将结果转换为整数。但是，浮点数不能直接转换为整数类型，因此会引发这个错误。

你可以通过将结果转换为整数类型来解决这个问题。例如，你可以使用`int()`函数将结果转换为整数类型。另外，建议使用`np.int32`来指定整数类型。

示例代码如下：

import numpy as np

# 定义变量
m = np.array([1, 1])  # 质量
r0 = np.array([[0, 0], [1, 0]])  # 初始位置
v0 = np.array([[0, 0], [0, 1]])  # 初始速度
dt = 0.01  # 时间步长
tmax = 10  # 模拟时间

def simulate(m, r0, v0, dt, tmax):
    # 初始化
    positions = [r0]
    velocities = [v0]
    
    # 计算加速度
    def acceleration(r, m):
        G = 1  # 引力常数
        n = len(m)
        a = np.zeros_like(r)
        for i in range(n):
            for j in range(n):
                if i != j:
                    rij = r[j] - r[i]
                    a[i] += G * m[j] / np.linalg.norm(rij)**3 * rij
        return a
    
    # 模拟
    t = 0
    while t < tmax:
        a = acceleration(positions[-1], m)
        v = velocities[-1] + a * dt
        r = positions[-1] + v * dt
        
        # 将结果添加到列表中
        velocities.append(v)
        positions.append(r)
        
        t += dt
    
    return np.array(positions), np.array(velocities)

positions, velocities = simulate(m, r0, v0, dt, tmax)

print(positions)

希望能帮到你！

numpy.core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'subtract' output from dtype('float64') to dtype('int32') with casting rule 'same_kind'

The error message indicates that you are trying to perform a subtraction operation in numpy that results in a type casting error. Specifically, you are trying to cast the output of the subtraction operation from a float64 type to an int32 type, which is not allowed with the "same_kind" casting rule.

One way to solve this issue is to explicitly specify the output data type of the operation to be float64 or int32, depending on your needs. Here is an example:

import numpy as np

# create two arrays with different data types
a = np.array([1, 2, 3], dtype=np.int32)
b = np.array([0.5, 1.5, 2.5], dtype=np.float64)

# perform subtraction operation with explicit output data type
c = np.subtract(a, b, dtype=np.float64)

# print result
print(c)

In this example, we explicitly specify the output data type of the subtraction operation to be float64 using the `dtype` parameter. This allows the operation to complete without any casting errors.

Alternatively, you can also use the `astype()` method to convert the data type of the array after the operation has completed. For example:

import numpy as np

# create two arrays with different data types
a = np.array([1, 2, 3], dtype=np.int32)
b = np.array([0.5, 1.5, 2.5], dtype=np.float64)

# perform subtraction operation and convert result to int32
c = np.subtract(a, b).astype(np.int32)

# print result
print(c)

In this example, we perform the subtraction operation first and then convert the data type of the result to int32 using the `astype()` method. This allows us to perform the operation without any casting errors and then convert the result to the desired data type.