shvechikov

#!/usr/bin/env python2
# -*- coding: utf-8 -*-

"""
Usage: %prog [width [columns]] < table.csv

Pretty-print CSV file with fixed width columns.

Arguments:

shvechikov

#!/usr/bin/env python2.7
# 
# Install this in your PATH as `pyhash`.
#
# curl https://gist.github.com/jbenet/6502583/raw/pyhash.py -o pyhash
# mv pyhash /usr/bin/local/pyhash
# chmod +x /usr/bin/local/pyhash
#
# If you want more cryptographic hashing functions, try the PassLib module.
#

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def my_local(init):
    key = object()
    def getter():
        t = _app_ctx_stack.top
        l = getattr(t, 'my_locals')
        if l is None:
            t.my_locals = l = {}
        if key not in l:
            l[key] = init()
        return l[key]

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from inspect import getattr_static


class dep:

    name = None

    def __init__(self, type):
        self.type = type

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__author__ = 'archeg'

import httplib
import urllib
import urllib2
import re


def URLRequest(url, params, headers, method="GET"):
    if method == "POST":

shvechikov

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

shvechikov

Concurrent HTTP Requests with Python3 and asyncio

http://geekgirl.io/concurrent-http-requests-with-python3-and-asyncio/

My friend who is a data scientist had wipped up a script that made lots (over 27K) of queries to the Google Places API. The problem was that it was synchronous and thus took over 2.5 hours to complete.

Given that I'm currently attending Hacker School and get to spend all day working on any coding problems that interests me, I decided to go about trying to optimise it.

I'm new to Python so had to do a bit of groundwork first to determine which course of action was best.

shvechikov

import logging
from contextlib import contextmanager
from timeit import default_timer

time_logger = logging.getLogger(__package__ + ".timer")

@contextmanager
def timed_code(name=None):
    next_unit = iter(("s", "ms", "ns", "us")).next
    msg = "section %s took" % (name,) if name else "section took"