Murgio

Overview of Python Virtual Environments

This guide is targetted at intermediate or expert users who want low-level control over their Python environments.

When you're working on multiple coding projects, you might want a couple different version of Python and/or modules installed. This helps keep each workflow in its own sandbox instead of trying to juggle multiple projects (each with different dependencies) on your system's version of Python. The guide here covers one way to handle multiple Python versions and Python environments on your own (i.e., without a package manager like conda). See the Using the workflow section to view the end result.

h/t @sharkinsspatial for linking me to the perfect cartoon

Murgio

int[][] result;
float t, c;

float ease(float p) {
  return 3*p*p - 2*p*p*p;
}

float ease(float p, float g) {
  if (p < 0.5) 
    return 0.5 * pow(2*p, g);

Murgio

int[][] result;
float t, c;

float ease(float p) {
  return 3*p*p - 2*p*p*p;
}

float ease(float p, float g) {
  if (p < 0.5) 
    return 0.5 * pow(2*p, g);

Murgio

do androids dream of cooking?

The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.

The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.