alexrutherford

bigSmallDict = (df['crime_category'].value_counts(normalize=True) > 0.05).to_dict()
# Make a dictionary saying if a category is > 5% of total or not

def assignOther(t):
    if bigSmallDict[t]: 
        return t
    else:
        return 'other'
# Define a mini function that looks in the dictionary

alexrutherford

# Grab data from https://drive.google.com/open?id=18IVEIp5qn4OnoWerC4f3e9MUwcBnM5U-

df = pd.read_excel('all_data_M_2018.xlsx')

df = df[df['o_group']=='detailed']
# Drop broad occupation groups

df = df[df['area']==99]
# Drop occupations for states, keep only those for the entire US

alexrutherford

def rescale(p,rho = 0.001):
    
    rhoAbs = abs(rho)
    
    if abs(rho) > 10.001:
        print('Error in rho ')
        sys.exit(1)
    
    out = np.zeros_like(p)
    

alexrutherford

import sklearn.model_selection
import pandas as pd

df = pd.DataFrame(data={'A':[1,2,3],'B':[4,5,6],'C':[1,0,1]})

res = sklearn.model_selection.train_test_split(df[['A','B']],df['C'])
# Returns 4 things

res[0].shape
# (2,2)

alexrutherford

# coding: utf-8

from __future__ import print_function
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import LSTM
from keras.optimizers import RMSprop
from keras.utils.data_utils import get_file
import numpy as np
import random

alexrutherford

import shapefile
import seaborn as sns


sf = shapefile.Reader("voronoi/voronoi.shp")
shapes = sf.shapes()

def getMid(bbox):
    '''
    Helper function to get midpoint of a bounding box

alexrutherford

'''
Script to map points to administrative units defined by shapefiles

Requires shapefiles of administrative units from GADM (http://gadm.org/).

Alex Rutherford 2016
'''

import scipy.spatial
import shapefile,pickle,random,re,collections,csv

alexrutherford

import requests
import json,re,csv,re,sys
from secrets import *
import datetime,time
import logging


def makeFileName(n,log=False):
    now=datetime.datetime.now()
    timeStem='%d_%d_%d_%d_%d' % (now.year,now.month,now.day,now.hour,now.minute)

alexrutherford

import pandas as pd

pandas.date_range("11:00", "21:30", freq="30min")

alexrutherford

import requests
from secrets import key

def getCountry(s):
    '''
    Takes address string as input, queries Google geocoding API and returns country as ISO code
    '''
    tempUrl='http://maps.googleapis.com/maps/api/geocode/json?address=%s' % (s)
    res=requests.get(tempUrl)