hughdbrown

from itertools import groupby

def cumulative(arr):
    return [arr[i] - arr[i - 1] for i in range(1, len(arr))]


def stable_particle(arr):
    """
    >>> stable_particle([-1, 1, 3, 3, 3, 2, 3, 2, 1, 0])
    5

hughdbrown

from typing import List
from random import randint


def findMedianSortedArrays(nums1: List[int], nums2: List[int]) -> float:
    n1: int = len(nums1)
    n2: int = len(nums2)

    if n1 > n2:
        return findMedianSortedArrays(nums2, nums1)

hughdbrown

# Reimplementation of tfidf.py here:
#     https://gist.github.com/sloria/6407257
from math import log
from collections import Counter


class TFIDF(object):
    def __init__(self, corpus):
        self.corpus = corpus
        self.ndocs = len(corpus)

hughdbrown

# Based on this repository of coronavirus data:
#   git clone git@github.com:nytimes/covid-19-data.git

from csv import DictReader
from collections import defaultdict
from pprint import pprint

# fields = ['date', 'state', 'cases', 'deaths']
# Selecting on 'cases' gives results that appear influenced by testing.
# Using 'deaths' appears less influenced by noise and human factors, in my opinion.

hughdbrown

def load():
    with open("config/create_index.js") as handle:
        data = [line.rstrip() for line in handle]
        coll_iter = (line.split('.') for line in data)
        return [
            (i, coll[0] + '.' + coll[1])
            for i, coll in enumerate(coll_iter)
            if len(coll) >= 2 and coll[0] == 'db'
        ]

hughdbrown

import os.path
from csv import DictReader

datafile = os.path.normpath(os.path.expanduser("~/workspace/DataRobot/tests/testdata/10k_diabetes.csv"))

with open(datafile) as handle:
    reader = DictReader(handle)
    fields = reader.fieldnames
    rows = list(reader)

hughdbrown

from queue import Queue
from pprint import pprint

def neighbors(coord):
    x, y = coord
    return {
        (x - 1, y),
        (x + 1, y),
        (x, y - 1),
        (x, y + 1),

hughdbrown

#!/usr/bin/env python
import os


class SedFile(object):
    def __init__(self, filename):
        self.filename = filename
        self.modified = False
        with open(self.filename, "r") as handle:
            self.data = [line.rstrip() for line in handle]

hughdbrown

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Resolve where some test fixture lives

Provides CLI:

* start -- location in tree of directories to start
* resolve  -- resource to resolve
"""
from glob import glob

hughdbrown

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Resolve where some test fixture lives

Provides CLI:

* start -- location in tree of directories to start
* resolve  -- resource to resolve
"""
from glob import glob