purpleP

{-# LANGUAGE ConstraintKinds      #-}
{-# LANGUAGE DataKinds            #-}
{-# LANGUAGE FlexibleInstances    #-}
{-# LANGUAGE GADTs                #-}
{-# LANGUAGE ScopedTypeVariables  #-}
{-# LANGUAGE TypeApplications     #-}
{-# LANGUAGE TypeFamilies         #-}
{-# LANGUAGE TypeOperators        #-}
{-# LANGUAGE UndecidableInstances #-}

purpleP

from collections import Counter

import hypothesis.strategies as st
import hypothesis as hp
import pytest

def merge(xs, ys):                      
    xs, ys = iter(xs), iter(ys)
    none = object()
    y = next(ys, none)

purpleP

from bisect import bisect_left, bisect_right


def pairs_sums_to(k, xs):
    xs = [*sorted(xs)]
    lo, hi = 0, len(xs) - 1
    while True:
        left_pos = bisect_left(xs, k, lo, hi)
        if left_pos > hi or left_pos == lo:
            return

purpleP

class split:
    def __init__(self, delimiter, sequence):
        """
        Break a sequence on particular elements.
        Return an iterator over chunks.

        Arguments:

        delimiter   if a function, it returns True on chunk separators;
                    otherwise, it is the value of chunk separator.

purpleP

create table `values` (
     id integer unsigned primary key auto_increment,
     value double,
     `time` time
);

insert into `values` (time, value) values
  ('00:00:01', 3),
  ('00:00:02', 2),
  ('00:00:03', 1),

purpleP

import Data.Tuple (swap)
import System.Environment (getArgs)

minmax (a:b:xs) = foldl (minmax') (sort' (a, b)) (pairs xs)
    where minmax' (curmin, curmax) candidates = (newmin, newmax)
              where (cmin, cmax) = sort' candidates
                    newmin = if cmin < curmin then cmin else curmin
                    newmax = if cmax > curmax then cmax else curmin

sort' tup@(a, b) = if a <= b then tup else swap tup

purpleP

import Data.Function.Memoize (memoize)
import Data.List (maximumBy, group)
import qualified Data.Map as Map
import Data.Map.Lazy (empty, insertWith, size, Map)
import Data.Ord (comparing, compare, Down)
import Data.Monoid
import System.Environment (getArgs)


type Solution = (Map Integer Integer, Integer)

purpleP

If constructing an object is entagled with different cases and conditions it's better to use factory. That's not the case here.

Instead of this.

def make_board(mines, size):
    '''function that uses my created Board methods to create the playing board'''
    board = Board(tuple([tuple([Cell(True,True,True) for i in range(size+1)]) for j in range(size+1)]))
    open_pos = list(range(size-1)*(size-1))
    for i in range(mines):

purpleP

{-# LANGUAGE DeriveGeneric, DeriveAnyClass, StandaloneDeriving #-}

import Data.Map (empty, fromList, insert, delete, Map)
import GHC.Generics
import Data.Aeson
import qualified Data.Text.Lazy.IO as T
import qualified Data.Text.Lazy.Encoding as T

data Task = Task
    { id :: String

purpleP

Running tests with vim and tmux

After using vim and neovim for almost a year now I've come to the point when frustration I had with running test with vim made me think about ideal workflow for running tests (at least in command line, but some rules apply to GUI).

Below you can read my thoughts about ideal workflow, a summary of problems with current workflows (at least that I know of, but I think I've tried them all) and my own new (well, at least I think it's new) solution to them.