spitz-dan-l

## demo_parser_styles.ts
// This is what imperative style looks like when the parser is
// implemented with exceptions for control flow
function imperative_style_with_exceptions(p: Parser) {
    p.consume('look at'); // could throw NoMatch

    let who = p.split([ // could throw Split or NoMatch
        () => p.consume('me', 'me'),
        () => p.consume('mewtwo', 'mewtwo'),
        () => p.consume('steven', 'steven'),
        () => p.consume('martha', 'martha')

## update.ts
/*
update.ts - Concise, typed immutable updates to deeply-nested objects

Daniel Spitz

Use it like this:

    import {update} from 'update';
    let obj = { a: 3, b: 'horse', c: { d: [1,2,3] }};
    let obj2 = update(obj, { a: 0, c: { d: _ => [..._, 4] } });

## matching.py
import sys
import functools

"""
Python Atrocity: Structural Pattern Matching built on Exception Handling Syntax
Daniel Spitz

(Python 3.7+ only)

The goal of this Python Atrocity is to implement extensible, composable pattern

## graph_transform_pickle.py
import pickle
import importlib
import pkg_resources
from operator import attrgetter
from functools import reduce
import io
from contextlib import ExitStack
from collections import defaultdict

"""

## distributed_count_aggregation.py
from distributed import Client, Queue, Variable, get_client, secede, rejoin, wait, fire_and_forget

from time import sleep
import gc
from functools import partial
from collections import Counter, defaultdict, namedtuple

from tornado import gen
import pandas as pd

## bad_monads.py
from functools import wraps

def do(lift_func=iter):
    """
    Decorator that allows python generators to behave a bit like Haskell's do blocks.

    The basic intuition to follow is that the Haskell "x <- y" syntax is mimicked in python by "x = yield y",
    if it occurs inside a generator function decorated by @do().

    By passing different arguments to @do(), you can change which monad type is mimicked.

## psql_binary_parser.pyx
from chatto_transform.schema.schema_base import cat

from libc.string cimport memcpy
from cpython cimport array
import array

import numpy as np
cimport numpy as np
import pandas as pd
from libc.stdlib cimport malloc, free

## multiple_dispatch.py
import random #definitely a good thing to see at the top of a multiple-dispatch implementation!
import abc
import itertools

class ArgValidator:
    @classmethod
    def validate(cls, *args, mro_distances=None):
        if mro_distances is None:
            mro_distances = []
        #disqualify validation rules with the wrong number of arguments

## horse.html
<html>
    <style>
        //put all styles here
        body {
            background: brown;
        }
    </style>
    <body>
        <!-- Put all html here -->
        horse

## gist:5cfdf625ede28d66f568
public boolean compare(Grid<T> grid2) {
	if(this.row != grid2.row || this.col != grid2.col) {
		return false;
	}

	for(Grid<T>.Element tmpElem : this.elements()) {
		Grid<T>.Element tmpElem2 = grid2.getElement(tmpElem.pos());

		T v1 = tmpElem.value();
		T v2 = tmpElem2.value();
	// This is what imperative style looks like when the parser is
	// implemented with exceptions for control flow
	function imperative_style_with_exceptions(p: Parser) {
	p.consume('look at'); // could throw NoMatch

	let who = p.split([ // could throw Split or NoMatch
	() => p.consume('me', 'me'),
	() => p.consume('mewtwo', 'mewtwo'),
	() => p.consume('steven', 'steven'),
	() => p.consume('martha', 'martha')
	/*
	update.ts - Concise, typed immutable updates to deeply-nested objects

	Daniel Spitz

	Use it like this:

	import {update} from 'update';
	let obj = { a: 3, b: 'horse', c: { d: [1,2,3] }};
	let obj2 = update(obj, { a: 0, c: { d: _ => [..._, 4] } });
	import sys
	import functools

	"""
	Python Atrocity: Structural Pattern Matching built on Exception Handling Syntax
	Daniel Spitz

	(Python 3.7+ only)

	The goal of this Python Atrocity is to implement extensible, composable pattern
	import pickle
	import importlib
	import pkg_resources
	from operator import attrgetter
	from functools import reduce
	import io
	from contextlib import ExitStack
	from collections import defaultdict

	"""
	from distributed import Client, Queue, Variable, get_client, secede, rejoin, wait, fire_and_forget

	from time import sleep
	import gc
	from functools import partial
	from collections import Counter, defaultdict, namedtuple

	from tornado import gen
	import pandas as pd
	from functools import wraps

	def do(lift_func=iter):
	"""
	Decorator that allows python generators to behave a bit like Haskell's do blocks.

	The basic intuition to follow is that the Haskell "x <- y" syntax is mimicked in python by "x = yield y",
	if it occurs inside a generator function decorated by @do().

	By passing different arguments to @do(), you can change which monad type is mimicked.
	from chatto_transform.schema.schema_base import cat

	from libc.string cimport memcpy
	from cpython cimport array
	import array

	import numpy as np
	cimport numpy as np
	import pandas as pd
	from libc.stdlib cimport malloc, free
	import random #definitely a good thing to see at the top of a multiple-dispatch implementation!
	import abc
	import itertools

	class ArgValidator:
	@classmethod
	def validate(cls, *args, mro_distances=None):
	if mro_distances is None:
	mro_distances = []
	#disqualify validation rules with the wrong number of arguments
	<html>
	<style>
	//put all styles here
	body {
	background: brown;
	}
	</style>
	<body>
	<!-- Put all html here -->
	horse
	public boolean compare(Grid<T> grid2) {
	if(this.row != grid2.row \|\| this.col != grid2.col) {
	return false;
	}

	for(Grid<T>.Element tmpElem : this.elements()) {
	Grid<T>.Element tmpElem2 = grid2.getElement(tmpElem.pos());

	T v1 = tmpElem.value();
	T v2 = tmpElem2.value();