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# -*- coding: utf8 -*-

def countdown(counter, s):
    """
    Print factorial numbers of emoji beasts
    Inspired by @direlog
    """
    if counter < 1:
        return
    else:

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#!/bin/bash
# original: http://julianoliver.com/output/log_2014-05-30_20-52
# GLASSHOLE.SH
#
# Find and kick Google Glass devices from your local wireless network.  Requires
# 'beep', 'arp-scan', 'aircrack-ng' and a GNU/Linux host.  Put on a BeagleBone
# black or Raspberry Pi. Plug in a good USB wireless NIC (like the TL-WN722N)
# and wear it, hide it in your workplace or your exhibition.
#
# Save as glasshole.sh, 'chmod +x glasshole.sh' and exec as follows:

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from pysal.esda.mapclassify import Natural_Breaks as nb

# Calculate Jenks natural breaks
# Assumes that there are no NaN values in Series
breaks = nb(
    df['column'].values,
    initial=300,
    k=5)

jb = pd.DataFrame({'jenks_bins': breaks.yb}, index=df['column'].index)

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from statsmodels.stats.outliers_influence import summary_table
from statsmodels.sandbox.regression.predstd import wls_prediction_std

# carry out yr fit

# ols cinv
st, data, ss2 = summary_table(ols_fit, alpha=0.05)

fittedvalues = data[:,2]
predict_mean_se  = data[:,3]

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import pandas as pd
import numpy as np
from pysal.esda.mapclassify import Natural_Breaks as nb

df = pd.DataFrame({'density': np.random.randint(0, 10, 500)})
# replace zero values with NaN
df.replace(to_replace={'density': {0: np.nan}}, inplace=True)

breaks = nb(df[df['density'].notnull()].density.values, k=5)
# this index will allow us to perform the join correctly

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import json
from pprint import pprint as pp

def jenks_matrices_init(data, n_classes):
    #fill the matrices with data+1 arrays of n_classes 0s
    lower_class_limits = []
    variance_combinations = []
    for i in xrange(0, len(data)+1):
        temp1 = []
        temp2 = []

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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'])

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""" 
required packages:
numpy
matplotlib
basemap: http://matplotlib.org/basemap/users/installing.html
shapely: https://pypi.python.org/pypi/Shapely
descartes: https://pypi.python.org/pypi/descartes
random

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