mirror of
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341 lines
5.3 KiB
Markdown
341 lines
5.3 KiB
Markdown
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---
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title: mbcp.mp\nmath.vector
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order: 1
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icon: laptop-code
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category: API
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---
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### ***class*** `Vector3`
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###   ***def*** `__init__(self, x: float, y: float, z: float) -> None`
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 3维向量
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Args:
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x: x轴分量
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y: y轴分量
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z: z轴分量
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<details>
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<summary>源代码</summary>
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```python
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def __init__(self, x: float, y: float, z: float):
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"""
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3维向量
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Args:
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x: x轴分量
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y: y轴分量
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z: z轴分量
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"""
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self.x = x
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self.y = y
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self.z = z
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```
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</details>
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###   ***def*** `approx(self, other: 'Vector3', epsilon: float) -> bool`
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 判断两个向量是否近似相等。
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Args:
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other:
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epsilon:
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Returns:
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是否近似相等
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<details>
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<summary>源代码</summary>
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```python
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def approx(self, other: 'Vector3', epsilon: float=APPROX) -> bool:
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"""
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判断两个向量是否近似相等。
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Args:
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other:
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epsilon:
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Returns:
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是否近似相等
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"""
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return all([abs(self.x - other.x) < epsilon, abs(self.y - other.y) < epsilon, abs(self.z - other.z) < epsilon])
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```
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</details>
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###   ***def*** `cal_angle(self, other: 'Vector3') -> 'AnyAngle'`
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 计算两个向量之间的夹角。
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Args:
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other: 另一个向量
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Returns:
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夹角
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<details>
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<summary>源代码</summary>
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```python
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def cal_angle(self, other: 'Vector3') -> 'AnyAngle':
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"""
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计算两个向量之间的夹角。
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Args:
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other: 另一个向量
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Returns:
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夹角
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"""
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return AnyAngle(math.acos(self @ other / (self.length * other.length)), is_radian=True)
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```
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</details>
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###   ***def*** `cross(self, other: 'Vector3') -> 'Vector3'`
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 向量积 叉乘:v1 cross v2 -> v3
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叉乘为0,则两向量平行。
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其余结果的模为平行四边形的面积。
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返回如下行列式的结果:
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``i j k``
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``x1 y1 z1``
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``x2 y2 z2``
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Args:
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other:
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Returns:
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行列式的结果
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<details>
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<summary>源代码</summary>
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```python
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def cross(self, other: 'Vector3') -> 'Vector3':
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"""
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向量积 叉乘:v1 cross v2 -> v3
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叉乘为0,则两向量平行。
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其余结果的模为平行四边形的面积。
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返回如下行列式的结果:
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``i j k``
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``x1 y1 z1``
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``x2 y2 z2``
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Args:
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other:
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Returns:
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行列式的结果
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"""
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return Vector3(self.y * other.z - self.z * other.y, self.z * other.x - self.x * other.z, self.x * other.y - self.y * other.x)
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```
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</details>
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###   ***def*** `is_approx_parallel(self, other: 'Vector3', epsilon: float) -> bool`
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 判断两个向量是否近似平行。
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Args:
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other: 另一个向量
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epsilon: 允许的误差
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Returns:
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是否近似平行
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<details>
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<summary>源代码</summary>
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```python
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def is_approx_parallel(self, other: 'Vector3', epsilon: float=APPROX) -> bool:
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"""
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判断两个向量是否近似平行。
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Args:
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other: 另一个向量
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epsilon: 允许的误差
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Returns:
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是否近似平行
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"""
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return self.cross(other).length < epsilon
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```
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</details>
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###   ***def*** `is_parallel(self, other: 'Vector3') -> bool`
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 判断两个向量是否平行。
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Args:
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other: 另一个向量
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Returns:
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是否平行
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<details>
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<summary>源代码</summary>
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```python
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def is_parallel(self, other: 'Vector3') -> bool:
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"""
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判断两个向量是否平行。
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Args:
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other: 另一个向量
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Returns:
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是否平行
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"""
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return self.cross(other).approx(zero_vector3)
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```
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</details>
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###   ***def*** `normalize(self) -> None`
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 将向量归一化。
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自体归一化,不返回值。
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<details>
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<summary>源代码</summary>
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```python
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def normalize(self):
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"""
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将向量归一化。
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自体归一化,不返回值。
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"""
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length = self.length
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self.x /= length
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self.y /= length
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self.z /= length
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```
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</details>
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###   ***@property***
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###   ***def*** `np_array(self: Any) -> 'np.ndarray'`
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 返回numpy数组
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Returns:
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<details>
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<summary>源代码</summary>
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```python
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@property
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def np_array(self) -> 'np.ndarray':
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"""
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返回numpy数组
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Returns:
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"""
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return np.array([self.x, self.y, self.z])
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```
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</details>
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###   ***@property***
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###   ***def*** `length(self: Any) -> float`
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 向量的模。
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Returns:
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模
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<details>
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<summary>源代码</summary>
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```python
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@property
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def length(self) -> float:
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"""
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向量的模。
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Returns:
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模
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"""
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return math.sqrt(self.x ** 2 + self.y ** 2 + self.z ** 2)
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```
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</details>
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###   ***@property***
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###   ***def*** `unit(self: Any) -> 'Vector3'`
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 获取该向量的单位向量。
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Returns:
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单位向量
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<details>
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<summary>源代码</summary>
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```python
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@property
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def unit(self) -> 'Vector3':
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"""
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获取该向量的单位向量。
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Returns:
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单位向量
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"""
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return self / self.length
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```
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</details>
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### ***var*** `zero_vector3 = Vector3(0, 0, 0)`
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### ***var*** `x_axis = Vector3(1, 0, 0)`
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### ***var*** `y_axis = Vector3(0, 1, 0)`
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### ***var*** `z_axis = Vector3(0, 0, 1)`
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### ***var*** `length = self.length`
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