# Basic Usage

The main transform functions are `stransform` and `istransform`.  They can be
used simply, as in the following example.

## Forward transform

```python
import numpy as np
from scipy.signal import chirp
from particleman import stransform

sample_rate = 40.0  #[Hz]
total_sec = 30.0

# make a linear chirp
t = np.arange(0.,total_sec,1./sample_rate)
c = chirp(t, 0.2, 20.0, 10.0, method='linear', phi=0, vertex_zero=True)

S, T, F = stransform(c, Fs=sample_rate, return_time_freq=True)

```

`S` is the time-frequency Stockwell tile, a 2D `numpy.ndarray`.  `T` and `F` are
the time and frequency domain grids for plotting `S`.  As these can sometimes
be large, you may use the `return_time_freq=False` keyword.

This example shows that a time-integration of the the Stockwell transform is 
equivalent to the traditional FFT.

![chirp](data/chirp.png "chirp")


Optionally, only certain rows of the S-transform can be returned (**filtered**),
using the `hp` (high-pass) and `lp` (low-pass) keywords, which are in Hertz.
This is useful if you know the frequency band of interest, or the return tile(s)
are unmanageably large.


## Inverse transform

The inverse transform has very similar syntax:

```python
ctr = istransform(S, Fs=sample_rate)

np.allclose(c, ctr)
```

```
True
```