# jax.scipy.fft.dctn#

jax.scipy.fft.dctn(x, type=2, s=None, axes=None, norm=None)[source]#

Computes the multidimensional discrete cosine transform of the input

JAX implementation of `scipy.fft.dctn()`.

Parameters:
• x (Array) â€“ array

• type (int) â€“ integer, default = 2. Currently only type 2 is supported.

• s (Sequence[int] | None) â€“ integer or sequence of integers. Specifies the shape of the result. If not specified, it will default to the shape of `x` along the specified `axes`.

• axes (Sequence[int] | None) â€“ integer or sequence of integers. Specifies the axes along which the transform will be computed.

• norm (str | None) â€“ string. The normalization mode. Currently only `"ortho"` is supported.

Returns:

array containing the discrete cosine transform of x

Return type:

Array

Example

`jax.scipy.fft.dctn` computes the transform along both the axes by default when `axes` argument is `None`.

```>>> x = jax.random.normal(jax.random.key(0), (3, 3))
>>> with jnp.printoptions(precision=2, suppress=True):
...   print(jax.scipy.fft.dctn(x))
[[-5.04 -7.54 -3.26]
[ 0.83  3.64 -4.03]
[ 0.12 -0.73  3.74]]
```

When `s=[2]`, dimension of the transform along `axis 0` will be `2` and dimension along `axis 1` will be same as that of input.

```>>> with jnp.printoptions(precision=2, suppress=True):
...   print(jax.scipy.fft.dctn(x, s=[2]))
[[-2.92 -2.68 -5.74]
[ 0.42  0.97  1.  ]]
```

When `s=[2]` and `axes=[1]`, dimension of the transform along `axis 1` will be `2` and dimension along `axis 0` will be same as that of input. Also when `axes=[1]`, transform will be computed only along `axis 1`.

```>>> with jnp.printoptions(precision=2, suppress=True):
...   print(jax.scipy.fft.dctn(x, s=[2], axes=[1]))
[[-0.22 -0.9 ]
[-0.57 -1.68]
[-2.52 -0.11]]
```

When `s=[2, 4]`, shape of the transform will be `(2, 4)`.

```>>> with jnp.printoptions(precision=2, suppress=True):
...   print(jax.scipy.fft.dctn(x, s=[2, 4]))
[[-2.92 -2.49 -4.21 -5.57]
[ 0.42  0.79  1.16  0.8 ]]
```