jason-curtis

jason-curtis

from components.cards import Color
from components.style import redify, blueify

trash_talked = set()

def _trash_talk(color, *messages):
    colorize = redify if color == Color.red else blueify
    full_message = ' '.join(map(str, messages))
    if full_message in trash_talked:
        return

jason-curtis

# couple things I'd throw in there:
# moar whitespace
res = [
    [
        [
            kriger.predict(x,y,z)
            for x in grid[0]
        ]
        for y in grid[1]
    ]

jason-curtis

extends:
  - eslint-config-airbnb
  - plugin:import/errors
  - plugin:import/warnings

parser:
  babel-eslint

settings:
  ecmascript: 6

jason-curtis

jason-curtis

__author__ = 'VladimirDel'

import roman
import unittest


class KnownValues(unittest.TestCase):
    known_values = ( (1, 'I'),
                     (2, 'II'),
                     (3, 'III'),

jason-curtis

These are standards that we came up with at Osmo Systems to allow technicians to use Jupyter notebooks to run experiments, while maintaining quality standards to avoid bugs and promote readability and reusability.

Each experiment we did was saved in a shared Google Drive folder, which contained a README entry point and any supporting notebooks and other files.

Standard Notebook Practices

• In general, see Code Style Manifesto
• Write code conforming to Pep8, e.g. imports, top-level constants
• Black code formatting is preferred. You can add a black formatting button to your notebook with this tool.
• Self-documentation: Someone else (or yourself in 3 months) should be able to open the notebook and know wtf is going on and why. Note: your README is the standard entry point for understanding the experiment at a high level (so that you don’t need jupyter to understand the purpose and basi

jason-curtis

import bisect

def value_is_present(list_, value):
    insertion_point = bisect.bisect_left(list_, value)
    return (
        # If value is larger than all values in list_, it will be len(list_)
        insertion_point < len(list_)
        and arr[insertion_point] == value
    )

jason-curtis

import hypothesis

@hypotheis.given(hypothesis.strategies.text())
def test_my_function_returns_string_input(s):
    assert my_function(s) == s

# Tests your “hypothesis” with a whole bunch of different types of garbage.
my_function()

jason-curtis

import heapq

# you have to create the list yourself
heap = [some list]

# heapq will modify your list in place!

# Organize the heap as a heap.
# Worst case time complexity: O(n log(n))
# Average time complexity: O(1)