# numpy.fft¶

Functions related to Fourier transforms can be called by prepending them
with `numpy.fft.`

. The module defines the following two functions:

`numpy`

:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.fft.ifft.html

## fft¶

Since `ulab`

’s `ndarray`

does not support complex numbers, the
invocation of the Fourier transform differs from that in `numpy`

. In
`numpy`

, you can simply pass an array or iterable to the function, and
it will be treated as a complex array:

```
# code to be run in CPython
fft.fft([1, 2, 3, 4, 1, 2, 3, 4])
```

```
array([20.+0.j, 0.+0.j, -4.+4.j, 0.+0.j, -4.+0.j, 0.+0.j, -4.-4.j,
0.+0.j])
```

**WARNING:** The array returned is also complex, i.e., the real and
imaginary components are cast together. In `ulab`

, the real and
imaginary parts are treated separately: you have to pass two
`ndarray`

s to the function, although, the second argument is
optional, in which case the imaginary part is assumed to be zero.

**WARNING:** The function, as opposed to `numpy`

, returns a 2-tuple,
whose elements are two `ndarray`

s, holding the real and imaginary
parts of the transform separately.

```
# code to be run in micropython
from ulab import numpy as np
x = np.linspace(0, 10, num=1024)
y = np.sin(x)
z = np.zeros(len(x))
a, b = np.fft.fft(x)
print('real part:\t', a)
print('\nimaginary part:\t', b)
c, d = np.fft.fft(x, z)
print('\nreal part:\t', c)
print('\nimaginary part:\t', d)
```

```
real part: array([5119.996, -5.004663, -5.004798, ..., -5.005482, -5.005643, -5.006577], dtype=float)
imaginary part: array([0.0, 1631.333, 815.659, ..., -543.764, -815.6588, -1631.333], dtype=float)
real part: array([5119.996, -5.004663, -5.004798, ..., -5.005482, -5.005643, -5.006577], dtype=float)
imaginary part: array([0.0, 1631.333, 815.659, ..., -543.764, -815.6588, -1631.333], dtype=float)
```

### ulab with complex support¶

If the `ULAB_SUPPORTS_COMPLEX`

, and `ULAB_FFT_IS_NUMPY_COMPATIBLE`

pre-processor constants are set to 1 in
ulab.h
as

```
// Adds support for complex ndarrays
#ifndef ULAB_SUPPORTS_COMPLEX
#define ULAB_SUPPORTS_COMPLEX (1)
#endif
```

```
#ifndef ULAB_FFT_IS_NUMPY_COMPATIBLE
#define ULAB_FFT_IS_NUMPY_COMPATIBLE (1)
#endif
```

then the FFT routine will behave in a `numpy`

-compatible way: the
single input array can either be real, in which case the imaginary part
is assumed to be zero, or complex. The output is also complex.

While `numpy`

-compatibility might be a desired feature, it has one
side effect, namely, the FFT routine consumes approx. 50% more RAM. The
reason for this lies in the implementation details.

## ifft¶

The above-mentioned rules apply to the inverse Fourier transform. The
inverse is also normalised by `N`

, the number of elements, as is
customary in `numpy`

. With the normalisation, we can ascertain that
the inverse of the transform is equal to the original array.

```
# code to be run in micropython
from ulab import numpy as np
x = np.linspace(0, 10, num=1024)
y = np.sin(x)
a, b = np.fft.fft(y)
print('original vector:\t', y)
y, z = np.fft.ifft(a, b)
# the real part should be equal to y
print('\nreal part of inverse:\t', y)
# the imaginary part should be equal to zero
print('\nimaginary part of inverse:\t', z)
```

```
original vector: array([0.0, 0.009775016, 0.0195491, ..., -0.5275068, -0.5357859, -0.5440139], dtype=float)
real part of inverse: array([-2.980232e-08, 0.0097754, 0.0195494, ..., -0.5275064, -0.5357857, -0.5440133], dtype=float)
imaginary part of inverse: array([-2.980232e-08, -1.451171e-07, 3.693752e-08, ..., 6.44871e-08, 9.34986e-08, 2.18336e-07], dtype=float)
```

Note that unlike in `numpy`

, the length of the array on which the
Fourier transform is carried out must be a power of 2. If this is not
the case, the function raises a `ValueError`

exception.

### ulab with complex support¶

The `fft.ifft`

function can also be made `numpy`

-compatible by
setting the `ULAB_SUPPORTS_COMPLEX`

, and
`ULAB_FFT_IS_NUMPY_COMPATIBLE`

pre-processor constants to 1.

## Computation and storage costs¶

### RAM¶

The FFT routine of `ulab`

calculates the transform in place. This
means that beyond reserving space for the two `ndarray`

s that will
be returned (the computation uses these two as intermediate storage
space), only a handful of temporary variables, all floats or 32-bit
integers, are required.

### Speed of FFTs¶

A comment on the speed: a 1024-point transform implemented in python would cost around 90 ms, and 13 ms in assembly, if the code runs on the pyboard, v.1.1. You can gain a factor of four by moving to the D series https://github.com/peterhinch/micropython-fourier/blob/master/README.md#8-performance.

```
# code to be run in micropython
from ulab import numpy as np
x = np.linspace(0, 10, num=1024)
y = np.sin(x)
@timeit
def np_fft(y):
return np.fft.fft(y)
a, b = np_fft(y)
```

```
execution time: 1985 us
```

The C implementation runs in less than 2 ms on the pyboard (we have just measured that), and has been reported to run in under 0.8 ms on the D series board. That is an improvement of at least a factor of four.