%matplotlib inline
import matplotlib.pyplot as plt
import datetime
import urllib2, urllib
import pandas as pd
from StringIO import StringIO

# Trick from http://stackoverflow.com/questions/7800213/can-i-use-pythons-csv-reader-with-google-fusion-tables

request_url = 'https://www.googleapis.com/fusiontables/v1/query'
query = 'SELECT * FROM 1KlX4PFF81wlx_TJ4zGudN_NoV_gq_GwrxuVau_M'

url = "%s?%s" % (request_url, urllib.urlencode({'sql': query}))
serv_req = urllib2.Request(url=url)
serv_resp = urllib2.urlopen(serv_req)
table = serv_resp.read()
print '\nLast pull of data from the Google FusionTable: ', datetime.datetime.now()

def parse_loc(loc, ret_lon=True):
    try:
        lon, lat = loc.split(',')
        lon, lat = lon.strip(' '), lat.strip(' ')
        lon, lat = map(float, [lon, lat])
        if ret_lon:
            return lon
        else:
            return lat
    except:
        return None

db = pd.read_csv(StringIO(table))

db = pd.read_csv('/Users/dani/Desktop/Gaza and Israel incidents mapped.csv')

db['lon'] = db['Location (approximate)'].apply(lambda x: parse_loc(x))
db['lat'] = db['Location (approximate)'].apply(lambda x: parse_loc(x, ret_lon=False))
db['Date'] = db['Date'].apply(pd.to_datetime)
db.head()

t = db['Date']
t = t.reindex(t)
by_day = t.groupby(lambda x: x.day).size()
by_day.plot(kind='bar')
plt.title('Number of events by day')
plt.show()

f = plt.figure(figsize=(10, 6))
ax = f.add_subplot(111)
x, y = db['lon'], db['lat']
s = plt.scatter(x, y, marker='.', color='k')
for d, day in db.set_index('Date').groupby(lambda x: x.day):
    x, y = day['lon'], day['lat']
    c = cm.Set1(d/30.)
    s = plt.scatter(x, y, marker='^', color=c, label=str(d), s=20)
ax.get_yaxis().set_visible(False)
ax.get_xaxis().set_visible(False)
plt.legend(loc=2)
plt.title('Spatial distribution of events by day')
ax.set_axis_bgcolor("0.2") 

# You'll need cartopy for a pretty map
import cartopy.crs as ccrs
import cartopy.io.img_tiles as cimgt
import matplotlib.cm as cm

bg = cimgt.OSM()
src = ccrs.PlateCarree()

f = plt.figure(figsize=(20, 30))
ax = plt.axes(projection=bg.crs)
ax.add_image(bg, 9, alpha=0.5)

x, y = db['lon'], db['lat']
extent = [y.min(), y.max(), x.min(), 34]
extent = [34, 36, x.min(), x.max()] #Manually tweaked
for d, day in db.set_index('Date').groupby(lambda x: x.day):
    y, x = day['lon'], day['lat']
    c = cm.Set1(d/30.)
    s = plt.scatter(x, y, marker='^', color=c, label=str(d), s=40, \
                    transform=src)
ax.set_extent(extent, crs=src)
plt.legend(loc=2)
plt.title('Spatial distribution of events by day')
plt.show()