neilalbrock

## Solve Every Sudoku Puzzle

## See http://norvig.com/sudoku.html

## Throughout this program we have:
##   r is a row,    e.g. 'A'
##   c is a column, e.g. '3'
##   s is a square, e.g. 'A3'
##   d is a digit,  e.g. '9'
##   u is a unit,   e.g. ['A1','B1','C1','D1','E1','F1','G1','H1','I1']

erans

from PIL import Image
from PIL.ExifTags import TAGS, GPSTAGS

def get_exif_data(image):
    """Returns a dictionary from the exif data of an PIL Image item. Also converts the GPS Tags"""
    exif_data = {}
    info = image._getexif()
    if info:
        for tag, value in info.items():
            decoded = TAGS.get(tag, tag)

MercuryRising

from bs4 import BeautifulSoup as bs
from pyquery import PyQuery as pq
from lxml.html import fromstring
import re

import requests

import time

def Timer():

techniq

from datetime import datetime
from sqlalchemy import Column, Integer, DateTime, ForeignKey
from sqlalchemy.orm import relationship
from sqlalchemy.ext.declarative import declared_attr
from flask_security import current_user


class AuditMixin(object):
    created_at = Column(DateTime, default=datetime.now)
    updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)

kachayev

# Simple:
# a --> b
#   --> c --> d
#   --> d 
graph1 = {
    "a": ["b", "c", "d"],
    "b": [],
    "c": ["d"],
    "d": []
}

urschrei

import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from descartes import PolygonPatch
import fiona
from shapely.geometry import Polygon, MultiPolygon, shape

# We can extract the London Borough boundaries by filtering on the AREA_CODE key
mp = MultiPolygon(
    [shape(pol['geometry']) for pol in fiona.open('data/boroughs/boroughs.shp')
    if pol['properties']['AREA_CODE'] == 'LBO'])

gear11

"""
A simple proxy server. Usage:

http://hostname:port/p/(URL to be proxied, minus protocol)

For example:

http://localhost:8080/p/www.google.com

"""

devdattaT

import math
import os
import urllib
from shapely.geometry import Polygon

def deg2num(lat_deg, lon_deg, zoom):
	lat_rad = math.radians(lat_deg)
	n = 2.0 ** zoom
	xtile = int((lon_deg + 180.0) / 360.0 * n)
	ytile = int((1.0 - math.log(math.tan(lat_rad) + (1 / math.cos(lat_rad))) / math.pi) / 2.0 * n)

satra

from scipy.spatial.distance import pdist, squareform
import numpy as np

from numbapro import jit, float32

def distcorr(X, Y):
    """ Compute the distance correlation function
    
    >>> a = [1,2,3,4,5]
    >>> b = np.array([1,2,9,4,4])

rickyah

Getting Started with Git SVN

• Getting Started with Git SVN
• Introduction
  • Cloning the SVN repository
  • Getting the latest changes from SVN
  • Local work
  • Pushing local changes to SVN
• Caveats
• Local merges