Getting Visual With It

Time to Get Vizzy With It!

Like Will says:

Gettin vizzy wit it Na na na na na na na nana Na na na na nana Gettin vizzy wit it…

Plot thickens

Time to turn on the lights and see what our data objects look like when we turn them into visualized plots. Sure, we can use the print function to see the numbers within our numpy objects. Better yet, let’s turn our objects into pretty graphical images. To do so, we turn to the matplotlib library. An extensive library of plotting routines that easily allows us to render image plots into many different formats (png, jpg, pdf, etc…) - both onto the screen for quick visual inspection, or written to a file for general use.

Do the Numpy

Do the Numpy Dance, Is your Chance to do the Nump!

Sir Numpy says:

The Numpy Dance is your chance to do the nump Do the Numpy Nump, come on and do the Numpty Nump Do the Numpy Nump, just watch me do the Numpty Nump Do ya know what I’m doin’, doin’ the Numpty Nump Do the Numpy Nump, do the Numpty Nump

Start at the beginning: NumPy

The first library that we will investigate is numpy. Simply put, numpy allows us to represent mathematical objects like arrays and matricies of different datatypes and performs operations on those objects thereby easing the burden of writing the tedious code within your applications to do the mundane representations and operations.

Data Science with Python: Let there be Light

Data-Science - Clueless? No prob. We got you dude.

So you’ve likely heard all the hype about “data-science” - and if you’re not among the cool kids, it might be a wee-bit overwhelming to you. Where to even start? What does it all mean? How do you even begin to understand what you need to know to begin learning more and making progress in the field.

Let’s start off with a few definitions:

Conway's Game of Life

Conway’s Game of Life

In 1970, British mathematician John Horton Conway invented a cellular automaton called The Game of Life. This simple game is governed by its initial cellular state and four simple rules:

• Any live cell with fewer than two live neighbors dies, as if caused by underpopulation.
• Any live cell with two or three live neighbors lives on to the next generation.
• Any live cell with more than three live neighbors dies, as if by overpopulation.
• Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.

The rules are applied to all the cells in the grid, starting with the initial grid populated with the starting live cells. After many iterations, the grid comes to an end-state which can be unexpected given the initial seed state.

Happy Halloween!

Happy Halloween!

Some music to go along with the spooky holiday!

Here’s some more halloween midi music to entertain you.

Remember you can convert or play it directly with fluidsynth or mplay, respectively.

— Enjoy!

Maestro Cue the Music!

Now it’s time to get conducting.

We’ve learned all about L-systems, midi magic, tools like MISEP that generates musical patterns. Now we can take it all together and generate some algorithmically inspired music!

To do this, I’mg going to reach for the isobar kit I mentioned in the first post. Rather than spend time writing the tedious algorithms to perform the mappings, this fine python library offers a rich collection of algorithmic generators and mappings - for chromatic, velocity, and rhythmic elements.

Listen to my MISPELling

expremigen - a lesson in MISPEL

Expremigen is an expressive midi generation library written in python by shimpe. It’s an exceptional tool to create musical compositions which can be rendered into MIDI and played out to an audio device.

In this post, we’re going to cover how to setup your OSX-based computer to play a midi file and then dive into using the python library, expremigen and it’s MIDI Specification Language (MISPEL) to generate some midi compositions.