Skip to main content
Version: v1.1.3


The term field is borrowed from mathematics and physics. If you already know scalar field (for example heat field), or vector field (for example gravitational field), then it is straightforward for you to understand fields in Taichi.

Fields in Taichi are the global data containers that can be accessed from both the Python scope and the Taichi scope. Just like an ndarray in NumPy or a tensor in PyTorch, a field in Taichi is defined as a multi-dimensional array of elements, and elements in a field can be a scalar, a vector, a matrix, or a struct.


A 0D (zero-dimensional) field contains only one element.

Scalar fields

Scalar fields refer to the fields that store scalars and are the most basic fields. A 0D scalar field is a single scalar; a 1D scalar field is a 1D array of scalars; a 2D scalar field is a 2D array of scalars, and so on and so forth.


The simplest way to declare a scalar field is to call ti.field(dtype, shape), where dtype is a primitive data type as explained in type system and shape is a tuple of integers.

  • To declare a 0D scalar field, set its shape to the empty tuple ():

    # Declare a 0D scalar field whose data type is f32
    f_0d = ti.field(ti.f32, shape=()) # 0D field

    An illustration of f_0d is shown below:

    │ │
  • To declare a 1D scalar field of length n, set its shape to n or (n,):

    f_1d = ti.field(ti.i32, shape=9)  # 1D field

    An illustration of f_1d is shown below:

    │ │ │ │ │ │ │ │ │ │
    f_1d.shape = (9,)
  • To declare a 2D scalar field, set the sizes of its first two dimensions, i.e., the number of rows and columns, respectively. For example, the following code defines a 2D scalar field of shape (3, 6), which has 3 rows and 6 columns:

    f_2d = ti.field(int, shape=(3, 6))  # 2D field

    An illustration of f_2d is shown below:


    ┌ ┌───┬───┬───┬───┬───┬───┐ ┐
    │ │ │ │ │ │ │ │ │
    │ ├───┼───┼───┼───┼───┼───┤ │
    f_2d.shape[0] │ │ │ │ │ │ │ │ │
    (=3) │ ├───┼───┼───┼───┼───┼───┤ │
    │ │ │ │ │ │ │ │ │
    └ └───┴───┴───┴───┴───┴───┘ ┘

Scalar fields of higher dimensions can be similarily defined.


Taichi only supports fields of dimensions <= 8.

Access elements in a scalar field

Once a field is declared, Taichi automatically assigns an initial value of zero to its elements.

To access an element in a scalar field, you need to explicitly use the index of the element.


When accessing a 0D field x, use x[None] = 0, not x = 0.

  • To access the element in a 0D field, you are required to use the index None even though it has only one element:

    f_0d[None] = 10.0

    The value in f_0d will be like:

  • To access an element in a 1D field, you are required to use its index i, with i being an integer in the range [0, f_1d.shape[0]):

    for i in range(9):
    f_1d[i] = i

    The elements in f_1d will be like:

  • To access an element in a 2D field, you are required to use its index (i, j), which is a pair of integers with i in the range [0, f_2d.shape[0] - 1) and j in the range [0, f_2d.shape[1] - 1):

    for i, j in f_2d:
    f_2d[i, j] = i

    The elements in f_2d will be like:

  • Similarily, an element in an n-dimensional field is indexed by a n-tuple of integers, and you will need n integers (i, j, k, ...) to access it.

As illustrated above, you can use a 2D scalar field to represent a 2D grid of values. The following code snippet creates and displays a 640×480 image with randomly-generated gray scales:

import taichi as ti

width, height = 640,480
# Create a 640x480 scalar field, each of its elements representing a pixel value (f32)
gray_scale_image = ti.field(dtype=ti.f32, shape=(width, height))

def fill_image():
# Fill the image with random gray
for i,j in gray_scale_image:
gray_scale_image[i,j] = ti.random()

# Create a GUI of same size as the gray-scale image
gui = ti.GUI('gray-scale image with random values', (width, height))
while gui.running:

Taichi does not support slicing on a Taichi field. You should always use n integers as indices to access an element, and n equals the number of dimensions of the field. For example, with the 2D scalar field f_2d above, you may try to use f_2d[0] to access its first row:

for x in f_2d[0]:  # Error!

Or you may want to access a slice of the first row:

f_2d[0][3:] = [4, 5, 6]  # Error!

Either way, you will see the error raised: "Slicing is not supported on ti.field".

Fill a scalar field with a given value

You can call the field.fill() method to set all elements in a scalar field to a given value. For example:

x = ti.field(int, shape=(5, 5))
x.fill(1) # set all elements in x to 1

def test():
x.fill(-1) # set all elements in x to -1


Metadata provides the basic information of a scalar field. You can retrieve the data type and shape of a scalar field via its shape and dtype properties:

f_1d.shape  # (9,)
f_3d.dtype # f32

Vector fields

As the name suggests, vector fields are the fields whose elements are vectors. What a vector represents depends on the scenario of your program. For example, a vector may stand for the (R, G, B) triple of a pixel, the position of a particle, or the gravitational field in space.


Declaring a vector field where each element is an N-dimensional vector is similar to the way you declare a scalar field, except that you need to call the function ti.Vector.field instead of ti.field and specify N as the first positional argument.

For example, the following code snippet declares a 2D field of 2D vectors:

# Declare a 3x3 vector field comprising 2D vectors
f = ti.Vector.field(n=2, dtype=float, shape=(3, 3))

The memory layout of f will be like:


┌ ┌──────┬──────┬──────┐ ┐
│ │[*, *][*, *][*, *]│ │
│ ├──────┼──────┼──────┤ │
f.shape[0] │ │[*, *][*, *][*, *]│ │ [*, *]
(=3) │ ├──────┼──────┼──────┤ │ └─────┘
│ │[*, *][*, *][*, *]│ │ n=2
└ └──────┴──────┴──────┘ ┘

The following code snippet declares a 300x300x300 vector field volumetric_field, whose vector dimension is 3:

box_size = (300, 300, 300)  # A 300x300x300 grid in a 3D space
# Declare a 300x300x300 vector field, whose vector dimension is n=3
volumetric_field = ti.Vector.field(n=3, dtype=ti.f32, shape=box_size)

Access elements in a vector field

Accessing a vector field is similar to accessing a multi-dimensional array: You use an index operator [] to access an element in the field. The only difference is that, to access a specific component of an element (vector in this case), you need an extra index operator []:

  • To access the velocity vector at a specific position of the volumetric field above:

    volumetric_field[i, j, k]

  • To access the l-th component of the velocity vector:

    volumetric_field[i, j, k][l]

  • Alternatively, you can use swizzling with the indices xyzw or rgba to access the components of a vector, provided that the dimension of the vector is no more than four:

    volumetric_field[i, j, k].x = 1  # equivalent to volumetric_field[i, j, k][0] = 1
    volumetric_field[i, j, k].y = 2 # equivalent to volumetric_field[i, j, k][1] = 2
    volumetric_field[i, j, k].z = 3 # equivalent to volumetric_field[i, j, k][2] = 3
    volumetric_field[i, j, k].w = 4 # equivalent to volumetric_field[i, j, k][3] = 4
    volumetric_field[i, j, k].xyz = 1, 2, 3 # assign 1, 2, 3 to the first three components
    volumetric_field[i, j, k].rgb = 1, 2, 3 # equivalent to the above

The following code snippet generates and prints a random vector field:

# n: vector dimension; w: width; h: height
n, w, h = 3, 128, 64
vec_field = ti.Vector.field(n, dtype=float, shape=(w,h))

def fill_vector():
for i,j in vec_field:
for k in ti.static(range(n)):
#ti.static unrolls the inner loops
vec_field[i,j][k] = ti.random()


To access the p-th component of the 0D vector field x = ti.Vector.field(n=3, dtype=ti.f32, shape=()):

x[None][p] (0 p < n).

Matrix fields

As the name suggests, matrix fields are the fields whose elements are matrices. In continuum mechanics, at each infinitesimal point in a 3D material exists a strain and stress tensor. The strain and stress tensor is a 3 x 2 matrix. Then, you can use a matrix field to represent such a tensor field.


The following code snippet declares a tensor field:

# Declare a 300x400x500 matrix field, each of its elements being a 3x2 matrix
tensor_field = ti.Matrix.field(n=3, m=2, dtype=ti.f32, shape=(300, 400, 500))

Access elements in a matrix field

Accessing a matrix field is similar to accessing a vector field: You use an index operator [] for field indexing and a second [] for matrix indexing.

  • To retrieve the i, j element of the matrix field tensor_field:

    mat = tensor_field[i, j]

  • To retrieve the member on the first row and second column of the element mat:

    mat[0, 1] or tensor_field[i, j][0, 1]


To access the 0D matrix field x = ti.Matrix.field(n=3, m=4, dtype=ti.f32, shape=()):

x[None][p, q] (0 p < n, 0 q < m)

Considerations: Matrix size

Matrix operations are unrolled during compile time. Take a look at the following example:

import taichi as ti

a = ti.Matrix.field(n=2, m=3, dtype=ti.f32, shape=(2, 2))
def test():
for i in ti.grouped(a):
# a[i] is a 2x3 matrix
a[i] = [[1, 1, 1], [1, 1, 1]]
# The assignment is unrolled to the following during compile time:
# a[i][0, 0] = 1
# a[i][0, 1] = 1
# a[i][0, 2] = 1
# a[i][1, 0] = 1
# a[i][1, 1] = 1
# a[i][1, 2] = 1

Operating on large matrices (for example 32x128) can lead to long compilation time and poor performance. For performance reasons, it is recommended that you keep your matrices small:

  • 2x1, 3x3, and 4x4 matrices work fine.
  • 32x6 is a bit too large.


When declaring the matrix field, leave large dimensions to the fields, rather than to the matrices. If you have a 3x2 field of 64x32 matrices:

  • Not recommended: ti.Matrix.field(64, 32, dtype=ti.f32, shape=(3, 2))
  • Recommended: ti.Matrix.field(3, 2, dtype=ti.f32, shape=(64, 32))

Struct fields

Struct fields are fields that store user-defined structs. Members of a struct element can be:

  • Scalars
  • Vectors
  • Matrices
  • Other struct fields.


The following code snippet declares a 1D field of particle information (position, velocity, acceleration, and mass) using ti.Struct.field(). Note that:

  • Member variables pos, vel, acc, and mass are provided in the dictionary format.
  • Compound types, such as ti.types.vector, ti.types.matrix, and ti.types.struct, can be used to declare vectors, matrices, or structs as struct members.
# Declare a 1D struct field using the ti.Struct.field() method
particle_field = ti.Struct.field({
"pos": ti.math.vec3,
"vel": ti.math.vec3,
"acc": ti.math.vec3),
"mass": float,
}, shape=(n,))

Alternatively, instead of directly using ti.Struct.field(), you can first declare a compound type particle and then create a field of it:

# vec3 is a built-in vector type suppied in the `taichi.math` module
vec3 = ti.math.vec3
# Declare a struct composed of three vectors and one floating-point number
particle = ti.types.struct(
pos=vec3, vel=vec3, acc=vec3, mass=float,
# Declare a 1D field of the struct particle using field()
particle_field = particle.field(shape=(n,))

Access elements in a struct field

You can access a member of an element in a struct field in either of the following ways: "index-first" or "name-first".

  • The index-first approach locates a certain element with its index before specifying the name of the target member:
# Set the position of the first particle in the field to origin [0.0, 0.0, 0.0]
particle_field[0].pos = vec3(0) # pos is a 3D vector

The name-first approach, in contrast, first creates the sub-field that gathers all the mass members in the struct field and then uses the index to access a specific one:

particle_field.mass[0] = 1.0  # Set the mass of the first particle in the field to 1.0

Considering that paticle_field.mass is a field consisting of all the mass members of the structs in paticle_field, we can also call its fill() method to set the members to a specific value all at once:

particle_field.mass.fill(1.0)  # Set all mass of the particles in the struct field to 1.0