Update README.md (#27)

Author: dannys4

README.md

@@ -23,3 +23,15 @@ Interface:
 - `bfft(x::AbstractMatrix{<:Union{Real,Complex}})`-- Backward FFT (unscaled inverse FFT)
 
 NOTE: Currently, my C++ code is actually faster than this, so "Fastest Fourier Transform in my Apartment" is a bit of a misnomer.
+
+
+## Why use this?
+There's a lot of FFT packages out there, no doubt. Many are great. Some, like mine, are "good enough". Many aren't so great. As far as I know, though, very few are as generic as FFTA. Does that matter? Yes. One of the main draws of Julia is the fact that a lot of functions "just work" with types from other packages. FFTA aims to abide by this philosophy. For example, have you ever wanted to generate what an FFT looks like symbolically? Well, now you can.
+```julia
+using FFTA, Symbolics
+N = 16
+@variables x_a[1:N]::Complex
+x = collect(x_a)
+y = simplify.(fft(x))
+```
+Now, if you have a signal afterward that you want to substitute in, you can call `map(y_el -> substitute(y_el, Dict(x .=> signal)), y)`. Make no mistake-- it's almost certainly more efficient to just plug your type into `FFTA.fft` than using substitution. But this is an example of how `FFTA` integrates wonderfully and gracefully with the Julia ecosystem. If you want high precision FFTs, use `Complex{BigFloat}`. If you want to use an `SVector` from `StaticArrays` because your data is small, then use that! If you want to use `SVector{Complex{BigFloat}}`, be my guest. These are opportunities that won't be provided to you in almost any other package out there.