taichi.lang.matrix#
- class taichi.lang.matrix.Matrix(arr, dt=None, is_ref=False, ndim=None)#
Bases:
taichi.lang.common_ops.TaichiOperationsThe matrix class.
A matrix is a 2-D rectangular array with scalar entries, it’s row-majored, and is aligned continuously. We recommend only use matrix with no more than 32 elements for efficiency considerations.
Note: in taichi a matrix is strictly two-dimensional and only stores scalars.
- Parameters:
arr (Union[list, tuple, np.ndarray]) – the initial values of a matrix.
dt (
primitive_types) – the element data type.ndim (int optional) – the number of dimensions of the matrix; forced reshape if given.
Example:
use a 2d list to initialize a matrix >>> @ti.kernel >>> def test(): >>> n = 5 >>> M = ti.Matrix([[0] * n for _ in range(n)], ti.i32) >>> print(M) # a 5x5 matrix with integer elements get the number of rows and columns via the `n`, `m` property: >>> M = ti.Matrix([[0, 1], [2, 3], [4, 5]], ti.i32) >>> M.n # number of rows 3 >>> M.m # number of cols >>> 2 you can even initialize a matrix with an empty list: >>> M = ti.Matrix([[], []], ti.i32) >>> M.n 2 >>> M.m 0
- all(self)#
Test whether all element not equal zero.
- Returns:
True if all elements are not equal zero, False otherwise.
- Return type:
bool
Example:
>>> v = ti.Vector([0, 0, 1]) >>> v.all() False
- any(self)#
Test whether any element not equal zero.
- Returns:
True if any element is not equal zero, False otherwise.
- Return type:
bool
Example:
>>> v = ti.Vector([0, 0, 1]) >>> v.any() True
- cast(self, dtype)#
Cast the matrix elements to a specified data type.
- Parameters:
dtype (
primitive_types) – data type of the returned matrix.- Returns:
A new matrix with the specified data dtype.
- Return type:
Example:
>>> A = ti.Matrix([0, 1, 2], ti.i32) >>> B = A.cast(ti.f32) >>> B [0.0, 1.0, 2.0]
- static cols(cols)#
Constructs a Matrix instance by concatenating Vectors/lists column by column.
- Parameters:
cols (List) – A list of Vector (1-D Matrix) or a list of list.
- Returns:
A matrix.
- Return type:
Example:
>>> @ti.kernel >>> def test(): >>> v1 = ti.Vector([1, 2, 3]) >>> v2 = ti.Vector([4, 5, 6]) >>> m = ti.Matrix.cols([v1, v2]) >>> print(m) >>> >>> test() [[1, 4], [2, 5], [3, 6]]
- cross(self, other)#
Performs the cross product with the input vector (1-D Matrix).
Both two vectors must have the same dimension <= 3.
For two 2d vectors (x1, y1) and (x2, y2), the return value is the scalar x1*y2 - x2*y1.
For two 3d vectors v and w, the return value is the 3d vector v x w.
- determinant(a)#
Returns the determinant of this matrix.
Note
The matrix dimension should be less than or equal to 4.
- Returns:
The determinant of this matrix.
- Return type:
dtype
- Raises:
Exception – Determinants of matrices with sizes >= 5 are not supported.
- static diag(dim, val)#
Returns a diagonal square matrix with the diagonals filled with val.
- Parameters:
dim (int) – the dimension of the wanted square matrix.
val (TypeVar) – value for the diagonal elements.
- Returns:
The wanted diagonal matrix.
- Return type:
Example:
>>> m = ti.Matrix.diag(3, 1) [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
- dot(self, other)#
Performs the dot product of two vectors.
To call this method, both multiplicatives must be vectors.
- Parameters:
other (
Matrix) – The input Vector.- Returns:
The dot product result (scalar) of the two Vectors.
- Return type:
DataType
Example:
>>> v1 = ti.Vector([1, 2, 3]) >>> v2 = ti.Vector([3, 4, 5]) >>> v1.dot(v2) 26
- element_type(self)#
- classmethod field(cls, n, m, dtype, shape=None, order=None, name='', offset=None, needs_grad=False, needs_dual=False, layout=Layout.AOS, ndim=None)#
Construct a data container to hold all elements of the Matrix.
- Parameters:
n (int) – The desired number of rows of the Matrix.
m (int) – The desired number of columns of the Matrix.
dtype (DataType, optional) – The desired data type of the Matrix.
shape (Union[int, tuple of int], optional) – The desired shape of the Matrix.
order (str, optional) – order of the shape laid out in memory.
name (string, optional) – The custom name of the field.
offset (Union[int, tuple of int], optional) – The coordinate offset of all elements in a field.
needs_grad (bool, optional) – Whether the Matrix need grad field (reverse mode autodiff).
needs_dual (bool, optional) – Whether the Matrix need dual field (forward mode autodiff).
layout (Layout, optional) – The field layout, either Array Of Structure (AOS) or Structure Of Array (SOA).
- Returns:
A matrix.
- Return type:
- fill(self, val)#
Fills the matrix with a specified value.
- Parameters:
val (Union[int, float]) – Value to fill.
Example:
>>> A = ti.Matrix([0, 1, 2, 3]) >>> A.fill(-1) >>> A [-1, -1, -1, -1]
- get_shape(self)#
- static identity(dt, n)#
Constructs an identity Matrix with shape (n, n).
- Parameters:
dt (DataType) – The desired data type.
n (int) – The number of rows/columns.
- Returns:
An n x n identity matrix.
- Return type:
- inverse(self)#
Returns the inverse of this matrix.
Note
The matrix dimension should be less than or equal to 4.
- Returns:
The inverse of a matrix.
- Return type:
- Raises:
Exception – Inversions of matrices with sizes >= 5 are not supported.
- max(self)#
Returns the maximum element value.
- min(self)#
Returns the minimum element value.
- classmethod ndarray(cls, n, m, dtype, shape)#
Defines a Taichi ndarray with matrix elements. This function must be called in Python scope, and after ti.init is called.
- Parameters:
n (int) – Number of rows of the matrix.
m (int) – Number of columns of the matrix.
dtype (DataType) – Data type of each value.
shape (Union[int, tuple[int]]) – Shape of the ndarray.
Example:
The code below shows how a Taichi ndarray with matrix elements can be declared and defined:: >>> x = ti.Matrix.ndarray(4, 5, ti.f32, shape=(16, 8))
- norm(self, eps=0)#
Returns the square root of the sum of the absolute squares of its elements.
- Parameters:
eps (Number) – a safe-guard value for sqrt, usually 0.
Example:
>>> a = ti.Vector([3, 4]) >>> a.norm() 5
- Returns:
The square root of the sum of the absolute squares of its elements.
- norm_inv(self, eps=0)#
The inverse of the matrix
norm().- Parameters:
eps (float) – a safe-guard value for sqrt, usually 0.
- Returns:
The inverse of the matrix/vector norm.
- norm_sqr(self)#
Returns the sum of the absolute squares of its elements.
- normalized(self, eps=0)#
Normalize a vector, i.e. matrices with the second dimension being equal to one.
The normalization of a vector v is a vector of length 1 and has the same direction with v. It’s equal to v/|v|.
- Parameters:
eps (float) – a safe-guard value for sqrt, usually 0.
Example:
>>> a = ti.Vector([3, 4], ti.f32) >>> a.normalized() [0.6, 0.8]
- static one(dt, n, m=None)#
Constructs a Matrix filled with ones.
- outer_product(self, other)#
Performs the outer product with the input Vector (1-D Matrix).
The outer_product of two vectors v = (x1, x2, …, xn), w = (y1, y2, …, yn) is a n times n square matrix, and its (i, j) entry is equal to xi*yj.
- static rotation2d(alpha)#
Returns the matrix representation of the 2D anti-clockwise rotation of angle alpha. The angle alpha is in radians.
Example:
>>> import math >>> ti.Matrix.rotation2d(math.pi/4) [[ 0.70710678 -0.70710678] [ 0.70710678 0.70710678]]
- static rows(rows)#
Constructs a matrix by concatenating a list of vectors/lists row by row. Must be called in Taichi scope.
- Parameters:
rows (List) – A list of Vector (1-D Matrix) or a list of list.
- Returns:
A matrix.
- Return type:
Example:
>>> @ti.kernel >>> def test(): >>> v1 = ti.Vector([1, 2, 3]) >>> v2 = ti.Vector([4, 5, 6]) >>> m = ti.Matrix.rows([v1, v2]) >>> print(m) >>> >>> test() [[1, 2, 3], [4, 5, 6]]
- sum(self)#
Return the sum of all elements.
Example:
>>> m = ti.Matrix([[1, 2], [3, 4]]) >>> m.sum() 10
- to_list(self)#
Return this matrix as a 1D list.
This is similar to numpy.ndarray’s flatten and ravel methods, the difference is that this function always returns a new list.
- to_numpy(self, keep_dims=False)#
Converts this matrix to a numpy array.
- Parameters:
keep_dims (bool, optional) – Whether to keep the dimension after conversion. If set to False, the resulting numpy array will discard the axis of length one.
- Returns:
The result numpy array.
- Return type:
numpy.ndarray
Example:
>>> A = ti.Matrix([[0], [1], [2], [3]]) >>> A.to_numpy(keep_dims=False) >>> A array([0, 1, 2, 3])
- trace(self)#
The sum of a matrix diagonal elements.
To call this method the matrix must be square-like.
- Returns:
The sum of a matrix diagonal elements.
Example:
>>> m = ti.Matrix([[1, 2], [3, 4]]) >>> m.trace() 5
- transpose(self)#
Returns the transpose of a matrix.
- Returns:
The transpose of this matrix.
- Return type:
Example:
>>> A = ti.Matrix([[0, 1], [2, 3]]) >>> A.transpose() [[0, 2], [1, 3]]
- static unit(n, i, dt=None)#
Constructs a n-D vector with the i-th entry being equal to one and the remaining entries are all zeros.
- Parameters:
n (int) – The length of the vector.
i (int) – The index of the entry that will be filled with one.
dt (
primitive_types, optional) – The desired data type.
- Returns:
The returned vector.
- Return type:
Example:
>>> A = ti.Matrix.unit(3, 1) >>> A [0, 1, 0]
- class taichi.lang.matrix.MatrixField(_vars, n, m, ndim=2)#
Bases:
taichi.lang.field.FieldTaichi matrix field with SNode implementation.
- Parameters:
vars (List[Expr]) – Field members.
n (Int) – Number of rows.
m (Int) – Number of columns.
ndim (Int) – Number of dimensions; forced reshape if given.
- copy_from(self, other)#
Copies all elements from another field.
The shape of the other field needs to be the same as self.
- Parameters:
other (Field) – The source field.
- fill(self, val)#
Fills this matrix field with specified values.
- Parameters:
val (Union[Number, Expr, List, Tuple, Matrix]) – Values to fill, should have consistent dimension consistent with self.
- from_numpy(self, arr)#
Copies an numpy.ndarray into this field.
Example:
>>> m = ti.Matrix.field(2, 2, ti.f32, shape=(3, 3)) >>> arr = numpy.ones((3, 3, 2, 2)) >>> m.from_numpy(arr)
- from_paddle(self, arr)#
Loads all elements from a paddle tensor.
The shape of the paddle tensor needs to be the same as self.
- Parameters:
arr (paddle.Tensor) – The source paddle tensor.
- from_torch(self, arr)#
Loads all elements from a torch tensor.
The shape of the torch tensor needs to be the same as self.
- Parameters:
arr (torch.tensor) – The source torch tensor.
- get_scalar_field(self, *indices)#
Creates a ScalarField using a specific field member.
- Parameters:
indices (Tuple[Int]) – Specified indices of the field member.
- Returns:
The result ScalarField.
- Return type:
- parent(self, n=1)#
Gets an ancestor of the representative SNode in the SNode tree.
- Parameters:
n (int) – the number of levels going up from the representative SNode.
- Returns:
The n-th parent of the representative SNode.
- Return type:
- to_numpy(self, keep_dims=False, dtype=None)#
Converts the field instance to a NumPy array.
- Parameters:
keep_dims (bool, optional) – Whether to keep the dimension after conversion. When keep_dims=True, on an n-D matrix field, the numpy array always has n+2 dims, even for 1x1, 1xn, nx1 matrix fields. When keep_dims=False, the resulting numpy array should skip the matrix dims with size 1. For example, a 4x1 or 1x4 matrix field with 5x6x7 elements results in an array of shape 5x6x7x4.
dtype (DataType, optional) – The desired data type of returned numpy array.
- Returns:
The result NumPy array.
- Return type:
numpy.ndarray
- to_paddle(self, place=None, keep_dims=False)#
Converts the field instance to a Paddle tensor.
- Parameters:
place (paddle.CPUPlace()/CUDAPlace(n), optional) – The desired place of returned tensor.
keep_dims (bool, optional) – Whether to keep the dimension after conversion. See
to_numpy()for more detailed explanation.
- Returns:
The result paddle tensor.
- Return type:
paddle.Tensor
- to_torch(self, device=None, keep_dims=False)#
Converts the field instance to a PyTorch tensor.
- Parameters:
device (torch.device, optional) – The desired device of returned tensor.
keep_dims (bool, optional) – Whether to keep the dimension after conversion. See
to_numpy()for more detailed explanation.
- Returns:
The result torch tensor.
- Return type:
torch.tensor
- class taichi.lang.matrix.MatrixNdarray(n, m, dtype, shape)#
Bases:
taichi.lang._ndarray.NdarrayTaichi ndarray with matrix elements.
- Parameters:
n (int) – Number of rows of the matrix.
m (int) – Number of columns of the matrix.
dtype (DataType) – Data type of each value.
shape (Union[int, tuple[int]]) – Shape of the ndarray.
Example:
>>> arr = ti.MatrixNdarray(2, 2, ti.f32, shape=(3, 3))
- copy_from(self, other)#
Copies all elements from another ndarray.
The shape of the other ndarray needs to be the same as self.
- Parameters:
other (Ndarray) – The source ndarray.
- fill(self, val)#
Fills ndarray with a specific scalar value.
- Parameters:
val (Union[int, float]) – Value to fill.
- from_numpy(self, arr)#
Copies the data of a numpy.ndarray into this array.
Example:
>>> m = ti.MatrixNdarray(2, 2, ti.f32, shape=(2, 1), layout=0) >>> arr = np.ones((2, 1, 2, 2)) >>> m.from_numpy(arr)
- get_type(self)#
- to_numpy(self)#
Converts this ndarray to a numpy.ndarray.
Example:
>>> arr = ti.MatrixNdarray(2, 2, ti.f32, shape=(2, 1)) >>> arr.to_numpy() [[[[0. 0.] [0. 0.]]] [[[0. 0.] [0. 0.]]]]
- class taichi.lang.matrix.Vector(arr, dt=None, **kwargs)#
Bases:
MatrixThe matrix class.
A matrix is a 2-D rectangular array with scalar entries, it’s row-majored, and is aligned continuously. We recommend only use matrix with no more than 32 elements for efficiency considerations.
Note: in taichi a matrix is strictly two-dimensional and only stores scalars.
- Parameters:
arr (Union[list, tuple, np.ndarray]) – the initial values of a matrix.
dt (
primitive_types) – the element data type.ndim (int optional) – the number of dimensions of the matrix; forced reshape if given.
Example:
use a 2d list to initialize a matrix >>> @ti.kernel >>> def test(): >>> n = 5 >>> M = ti.Matrix([[0] * n for _ in range(n)], ti.i32) >>> print(M) # a 5x5 matrix with integer elements get the number of rows and columns via the `n`, `m` property: >>> M = ti.Matrix([[0, 1], [2, 3], [4, 5]], ti.i32) >>> M.n # number of rows 3 >>> M.m # number of cols >>> 2 you can even initialize a matrix with an empty list: >>> M = ti.Matrix([[], []], ti.i32) >>> M.n 2 >>> M.m 0
- all(self)#
Test whether all element not equal zero.
- Returns:
True if all elements are not equal zero, False otherwise.
- Return type:
bool
Example:
>>> v = ti.Vector([0, 0, 1]) >>> v.all() False
- any(self)#
Test whether any element not equal zero.
- Returns:
True if any element is not equal zero, False otherwise.
- Return type:
bool
Example:
>>> v = ti.Vector([0, 0, 1]) >>> v.any() True
- cast(self, dtype)#
Cast the matrix elements to a specified data type.
- Parameters:
dtype (
primitive_types) – data type of the returned matrix.- Returns:
A new matrix with the specified data dtype.
- Return type:
Example:
>>> A = ti.Matrix([0, 1, 2], ti.i32) >>> B = A.cast(ti.f32) >>> B [0.0, 1.0, 2.0]
- static cols(cols)#
Constructs a Matrix instance by concatenating Vectors/lists column by column.
- Parameters:
cols (List) – A list of Vector (1-D Matrix) or a list of list.
- Returns:
A matrix.
- Return type:
Example:
>>> @ti.kernel >>> def test(): >>> v1 = ti.Vector([1, 2, 3]) >>> v2 = ti.Vector([4, 5, 6]) >>> m = ti.Matrix.cols([v1, v2]) >>> print(m) >>> >>> test() [[1, 4], [2, 5], [3, 6]]
- cross(self, other)#
Performs the cross product with the input vector (1-D Matrix).
Both two vectors must have the same dimension <= 3.
For two 2d vectors (x1, y1) and (x2, y2), the return value is the scalar x1*y2 - x2*y1.
For two 3d vectors v and w, the return value is the 3d vector v x w.
- determinant(a)#
Returns the determinant of this matrix.
Note
The matrix dimension should be less than or equal to 4.
- Returns:
The determinant of this matrix.
- Return type:
dtype
- Raises:
Exception – Determinants of matrices with sizes >= 5 are not supported.
- static diag(dim, val)#
Returns a diagonal square matrix with the diagonals filled with val.
- Parameters:
dim (int) – the dimension of the wanted square matrix.
val (TypeVar) – value for the diagonal elements.
- Returns:
The wanted diagonal matrix.
- Return type:
Example:
>>> m = ti.Matrix.diag(3, 1) [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
- dot(self, other)#
Performs the dot product of two vectors.
To call this method, both multiplicatives must be vectors.
- Parameters:
other (
Matrix) – The input Vector.- Returns:
The dot product result (scalar) of the two Vectors.
- Return type:
DataType
Example:
>>> v1 = ti.Vector([1, 2, 3]) >>> v2 = ti.Vector([3, 4, 5]) >>> v1.dot(v2) 26
- element_type(self)#
- classmethod field(cls, n, dtype, *args, **kwargs)#
ti.Vector.field
- fill(self, val)#
Fills the matrix with a specified value.
- Parameters:
val (Union[int, float]) – Value to fill.
Example:
>>> A = ti.Matrix([0, 1, 2, 3]) >>> A.fill(-1) >>> A [-1, -1, -1, -1]
- get_shape(self)#
- static identity(dt, n)#
Constructs an identity Matrix with shape (n, n).
- Parameters:
dt (DataType) – The desired data type.
n (int) – The number of rows/columns.
- Returns:
An n x n identity matrix.
- Return type:
- inverse(self)#
Returns the inverse of this matrix.
Note
The matrix dimension should be less than or equal to 4.
- Returns:
The inverse of a matrix.
- Return type:
- Raises:
Exception – Inversions of matrices with sizes >= 5 are not supported.
- max(self)#
Returns the maximum element value.
- min(self)#
Returns the minimum element value.
- classmethod ndarray(cls, n, dtype, shape)#
Defines a Taichi ndarray with vector elements.
- Parameters:
n (int) – Size of the vector.
dtype (DataType) – Data type of each value.
shape (Union[int, tuple[int]]) – Shape of the ndarray.
layout (Layout, optional) – Memory layout, AOS by default.
Example
The code below shows how a Taichi ndarray with vector elements can be declared and defined:
>>> x = ti.Vector.ndarray(3, ti.f32, shape=(16, 8))
- norm(self, eps=0)#
Returns the square root of the sum of the absolute squares of its elements.
- Parameters:
eps (Number) – a safe-guard value for sqrt, usually 0.
Example:
>>> a = ti.Vector([3, 4]) >>> a.norm() 5
- Returns:
The square root of the sum of the absolute squares of its elements.
- norm_inv(self, eps=0)#
The inverse of the matrix
norm().- Parameters:
eps (float) – a safe-guard value for sqrt, usually 0.
- Returns:
The inverse of the matrix/vector norm.
- norm_sqr(self)#
Returns the sum of the absolute squares of its elements.
- normalized(self, eps=0)#
Normalize a vector, i.e. matrices with the second dimension being equal to one.
The normalization of a vector v is a vector of length 1 and has the same direction with v. It’s equal to v/|v|.
- Parameters:
eps (float) – a safe-guard value for sqrt, usually 0.
Example:
>>> a = ti.Vector([3, 4], ti.f32) >>> a.normalized() [0.6, 0.8]
- static one(dt, n, m=None)#
Constructs a Matrix filled with ones.
- outer_product(self, other)#
Performs the outer product with the input Vector (1-D Matrix).
The outer_product of two vectors v = (x1, x2, …, xn), w = (y1, y2, …, yn) is a n times n square matrix, and its (i, j) entry is equal to xi*yj.
- static rotation2d(alpha)#
Returns the matrix representation of the 2D anti-clockwise rotation of angle alpha. The angle alpha is in radians.
Example:
>>> import math >>> ti.Matrix.rotation2d(math.pi/4) [[ 0.70710678 -0.70710678] [ 0.70710678 0.70710678]]
- static rows(rows)#
Constructs a matrix by concatenating a list of vectors/lists row by row. Must be called in Taichi scope.
- Parameters:
rows (List) – A list of Vector (1-D Matrix) or a list of list.
- Returns:
A matrix.
- Return type:
Example:
>>> @ti.kernel >>> def test(): >>> v1 = ti.Vector([1, 2, 3]) >>> v2 = ti.Vector([4, 5, 6]) >>> m = ti.Matrix.rows([v1, v2]) >>> print(m) >>> >>> test() [[1, 2, 3], [4, 5, 6]]
- sum(self)#
Return the sum of all elements.
Example:
>>> m = ti.Matrix([[1, 2], [3, 4]]) >>> m.sum() 10
- to_list(self)#
Return this matrix as a 1D list.
This is similar to numpy.ndarray’s flatten and ravel methods, the difference is that this function always returns a new list.
- to_numpy(self, keep_dims=False)#
Converts this matrix to a numpy array.
- Parameters:
keep_dims (bool, optional) – Whether to keep the dimension after conversion. If set to False, the resulting numpy array will discard the axis of length one.
- Returns:
The result numpy array.
- Return type:
numpy.ndarray
Example:
>>> A = ti.Matrix([[0], [1], [2], [3]]) >>> A.to_numpy(keep_dims=False) >>> A array([0, 1, 2, 3])
- trace(self)#
The sum of a matrix diagonal elements.
To call this method the matrix must be square-like.
- Returns:
The sum of a matrix diagonal elements.
Example:
>>> m = ti.Matrix([[1, 2], [3, 4]]) >>> m.trace() 5
- transpose(self)#
Returns the transpose of a matrix.
- Returns:
The transpose of this matrix.
- Return type:
Example:
>>> A = ti.Matrix([[0, 1], [2, 3]]) >>> A.transpose() [[0, 2], [1, 3]]
- static unit(n, i, dt=None)#
Constructs a n-D vector with the i-th entry being equal to one and the remaining entries are all zeros.
- Parameters:
n (int) – The length of the vector.
i (int) – The index of the entry that will be filled with one.
dt (
primitive_types, optional) – The desired data type.
- Returns:
The returned vector.
- Return type:
Example:
>>> A = ti.Matrix.unit(3, 1) >>> A [0, 1, 0]
- class taichi.lang.matrix.VectorNdarray(n, dtype, shape)#
Bases:
taichi.lang._ndarray.NdarrayTaichi ndarray with vector elements.
- Parameters:
n (int) – Size of the vector.
dtype (DataType) – Data type of each value.
shape (Tuple[int]) – Shape of the ndarray.
layout (Layout) – Memory layout.
Example:
>>> a = ti.VectorNdarray(3, ti.f32, (3, 3))
- copy_from(self, other)#
Copies all elements from another ndarray.
The shape of the other ndarray needs to be the same as self.
- Parameters:
other (Ndarray) – The source ndarray.
- fill(self, val)#
Fills ndarray with a specific scalar value.
- Parameters:
val (Union[int, float]) – Value to fill.
- from_numpy(self, arr)#
Copies the data from a numpy.ndarray into this ndarray.
The shape and data type of arr must match this ndarray.
Example:
>>> import numpy as np >>> a = ti.VectorNdarray(3, ti.f32, (2, 2), 0) >>> b = np.ones((2, 2, 3), dtype=np.float32) >>> a.from_numpy(b)
- get_type(self)#
- to_numpy(self)#
Converts this vector ndarray to a numpy.ndarray.
Example:
>>> a = ti.VectorNdarray(3, ti.f32, (2, 2)) >>> a.to_numpy() array([[[0., 0., 0.], [0., 0., 0.]], [[0., 0., 0.], [0., 0., 0.]]], dtype=float32)