unutbu

import numpy as np
import timeit

def random_data(N):
    # Generate some random data.
    return np.random.uniform(0., 10., N)

# Data lists.
array1 = np.array([random_data(4) for _ in range(1000)])
array2 = np.array([random_data(3) for _ in range(2000)])

unutbu

import numpy as np
import timeit

def random_data(N):
    # Generate some random data.
    return np.random.uniform(0., 10., N)

# Data lists.
array1 = np.array([random_data(4) for _ in range(10000)])   # pump up the number of iterations in optfunc
array2 = np.array([random_data(3) for _ in range(100)])

unutbu

import networkx as nx
import matplotlib.pyplot as plt
G = nx.Graph()
G.add_edges_from(
    [(7, 11), (7, 8), (5, 11), (3, 8), (3, 10), (11, 2), (11, 9),
     (11, 10), (8, 9)])

pos = nx.spring_layout(G, iterations=100)
for node, loc in pos.iteritems():
    print('{n}: {l}'.format(n=node, l=loc))

unutbu

 
def memo(f):
    """Decorator that caches the return value for each call to f(args).
    Then when called again with same args, we can just look it up."""
    cache = {}

    def _f(*args):
        try:
            return cache[args]
        except KeyError:

unutbu

import calendar
import pytz
import datetime as DT

tz1 = pytz.timezone('US/Eastern')
utc = pytz.timezone('UTC')
now = utc.localize(DT.datetime(2002, 10, 27, 7, 0, 0))
now_tz = now.astimezone(tz1)
now_epoch = calendar.timegm(now_tz.utctimetuple())

unutbu

import calendar
import time
import pytz
import datetime as DT

utc = pytz.timezone('UTC')

for tzname in [name for name in pytz.all_timezones if 'Brazil' in name]:
    tz1 = pytz.timezone(tzname)
    date = utc.localize(DT.datetime(2013, 1, 1))

unutbu

import matplotlib.pyplot as plt
import numpy as np
import timeit 

def naive_power(m, n):
    m = np.asarray(m)
    res = m.copy()
    for i in xrange(1,n):
        res *= m
    return res

unutbu

import warnings
import operator
import itertools as IT
import numpy as np
from numpy import nan
import pandas as pd
pd.options.display.width = 1000
pd.options.display.max_rows = 1000

def comparisons():

unutbu

import numpy as np
import pandas as pd
import timeit

def array_equivalent(a1, a2):
     try:
         a1, a2 = np.asarray(a1), np.asarray(a2)
     except (TypeError, ValueError):
         return False
     a1_mask = pd.isnull(a1)

unutbu

import numpy as np
import pandas as pd
nan = np.nan

def array_equivalent(a1, a2):
     try:
         a1, a2 = np.asarray(a1), np.asarray(a2)
     except (TypeError, ValueError):
         return False
     a1_mask = pd.isnull(a1)