jo32

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jo32

import twitter

#gist https://gist.github.com/1592859
twitter_api=twitter.Twitter(domain="api.twitter.com", api_version='1')
WORLD_WOE_ID = 1 # The Yahoo! Where On Earth ID for the entire world
world_trends = twitter_api.trends._(WORLD_WOE_ID) # get back a callable
print [ trend for trend in world_trends()[0]['trends'] ] # call the callable and iterate through the trends returned

twitter_search = twitter.Twitter(domain="search.twitter.com")
search_results = []

jo32

# implemented according to the psudocode from:
# http://en.wikipedia.org/wiki/Heapsort (2013/03/05)

def heapSort(unsortedList):
    heapify(unsortedList)
    end = len(unsortedList) - 1
    while end > 0:
        unsortedList[end], unsortedList[0] = unsortedList[0], unsortedList[end]
        end -= 1
        shiftDown(unsortedList, 0, end)

jo32

# Given a balanced binary tree like this:
#         1
#        / \
#       2   3
#      / \ / \
#     4  5 6  7
#
# Sample Output:
# 1. preorder traversal: 1 2 4 5 3 6 7
# 2. inorder traversal: 4 2 5 1 6 3 7

jo32

class KdTree(object):

    # the KdTree must know the domains (numerical) and ensure the
    # consistence of domains of all the points in the tree
    def __init__(self, domains, root=None):
        self.domains = domains
        self.root = root
        if not root is None:
            for domain in domains:
                setattr(self, domain, getattr(root.point, domain))

jo32

#!/usr/bin/python
"""
This program is used for face detection and save the faces it detects.
"""

import sys
import re
import cv2
import cv2.cv as cv
import numpy

jo32

# sample input: [1, -1, 0, -1, -1]
# means one sailor on the first plank, the third plank
# is the one with nothing and the left are pirates.

# sample output:
# 00 [1, -1, 0, -1, -1]
# 01 [0, 1, 1, -1, -1]
# 02 [-1, -1, -1, 0, -1]
# 03 [-1, 0, 1, -1, -1]
# 04 [-1, -1, -1, 1, 0]

jo32

from collections import deque
from functools import reduce

a = deque([9, 6, 6, 6])
b = deque([7, 1, 5, 3])
c = deque([4, 3, 2, 8])


def is_satisfied(a, b, c):
    avg = (sum(a) + sum(b) + sum(c)) / 4

jo32

from itertools import combinations
from functools import reduce
from operator import xor

a = [1, 2, 4, 7, 15, 31]
s = set([])
for i in range(1, len(a) + 1):
    for e in combinations(a, i):
        s.add(sum(e))

jo32

import json

f = open('all.json')
json = json.loads(f.read())
f.close()

predicts = []
results = []
for i in json:
    bodys = i['data']['body']