William Mayner wmayner

Resources for learning Python

Set up a new Mac

zsh

zsh is a more useful shell than bash, in my opinion, so we'll change your default shell from bash to zsh.

Follow instructions here: https://github.com/ohmyzsh/ohmyzsh/wiki/Installing-ZSH#macos

Note: zsh uses the ~/.zshrc file instead of ~/.bash_profile, so you should move anything that you need in your ~.bash_profile to the ~.zshrc.

Code style

Functions are preferred to scripts.

Nested structs are used to hold data, so that the data is labeled explicitly rather than relying on an indexing convention.

Fields in structs can be dynamically accessed with the someStruct.(fieldName) syntax; i.e.,

Slate bindings

Window focus

Pressing Ctrl + Esc (or Ctrl + Caps Lock if you have Caps Lock mapped to Esc) will put a “window hint” at the center of every currently visible window.
These will each have a unique letter in them.
Pressing a letter will focus that window.
Pressing Esc will cancel the window hints.

	import functools
	import time

	import ray


	ray.init()


	def as_completed(object_refs, num_returns=1):

	from IPython.display import Markdown, display

	colors = ['#018700', '#00acc6', '#e6a500']

	display(Markdown('<br>'.join(
	f'<span style="font-family: monospace">{color} <span style="color: {color}">████████</span></span>'
	for color in colors
	)))

	# Auto activate conda environments
	# - If an `environment.yml` or `.venv` file is in the current directory,
	# the environment specified by that file is activated
	# - `environment.yml` files are parsed
	# - `.venv` files must contain just the name of the environment
	# - If no such files are present and no environment is already activated, then
	# we try to activate an environment with the same name as the current directory
	# - Assumes that the conda installation is at $HOME/miniconda3
	# Modified from:
	# https://github.com/chdoig/conda-auto-env

	# -- coding: utf-8 --
	# state_pairs_to_tpm.py

	import numpy as np
	import pandas as pd


	def state_pairs_to_tpm(previous_states, next_states):
	"""Return a TPM from observed state transitions.

	# from
	# https://gist.githubusercontent.com/nils-werner/9d321441006b112a4b116a8387c2280c/raw/489137f314c022ca08f0c5089319ded2eb8c5ae1/sliding_window.py
	def sliding_window(size, data, step=1, padded=False, axis=-1, copy=True):
	"""Calculate a sliding window over an array.

	Note:
	If the array cannot be windowed evenly, it is truncated at the end.

	Args:
	data (np.ndarray): The array to be windowed.

	import json
	import os
	import pickle
	import random
	from time import time

	import numpy as np

	import pyphi
	from pyphi import Network, Subsystem