def query(self,sqls = [],description = False,compare = False, percent = False,sep = ' ',std_out = False,csv = ""): search_hits = [] for sql in sqls: sql = self.evaluate_sql(sql) try: self.cursor.execute(sql) except Exception as e: import sys self.cursor.close() self.conn.close() print e sys.exit(0) results = self.cursor.fetchall() if results: variables = [] for key in results[0].keys(): variables.append(key) if csv: print_variables = self.extract_variables(variables) if std_out: rg.search((0,0)) print sql print sep.join(variables) for rank,result in enumerate(results): print rank, for var in variables: print result[var],sep, if 'trans_lat' in variables and 'trans_lon' in variables: lat = result['trans_lat'] lon = result['trans_lon'] location = rg.search((lat,lon))[0] print location['name'],',',location['admin1'],sep elif 'INTPTLAT10' in variables and 'INTPTLON10' in variables: lat = result['INTPTLAT10'] lon = result['INTPTLON10'] location = rg.search((lat,lon))[0] print location['name'],',',location['admin1'],sep else: print '' if csv: self.print_csv(variables,results,csv) if description: self.description(variables,results,sql) if compare: self.compare_to_secondary(results,sql,percent = percent) search_hits += results return search_hits
def getUserLocationNewModel(userId):
"""
:param userId:
:return: userCoords [lat, lon]
Function to triangular the location of a user based on his reviewed business locations.
"""
max = -9999
userCountryKey = -1
reviews = findReviewUserBusinessByUserId(userId)
# print(reviews)
bCountry = {}
latlondeg = []
#print reviews.count()
reviewIndex = 0
# reviewsCount = reviews.count()
for review in reviews:
# reviewIndex += 1
#print "Processing Review ", reviewIndex, " of ", reviewsCount, " of user: "******"name"]
#print businessCityId
try:
bCountry[businessCityId].addCoord((latBus, lonBus))
except KeyError:
bCountry[businessCityId] = businessCountryObj
bCountry[businessCityId].addCoord((latBus, lonBus))
for k, v in bCountry.iteritems():
if v.getCount() > max:
max = v.getCount()
userCountryKey = k
#userCountryKey = max(bCountry.iteritems(), key=operator.itemgetter(1))[0]
for k, v in bCountry.items():
#print v
#print v.getCount()
#print v.getCoords()
if k == userCountryKey:
#print v[1]
for loc in v.getCoords():
latlondeg.append({"LATIDX": loc[0], "LNGIDX": loc[1]})
#print latlondeg
# Triangularing user coords.
userCoords = getLatLngCenter(latlondeg)
userCountry = rg.search((userCoords[0], userCoords[1]))[0]
userCoords.append(userCountry)
#print userCountry
return userCountry
def add_geo_assets(self):
map(
lambda way, geoass: way.add_geo_asset(geoass), self.waypoints,
rg.search([(waypoint.latitude, waypoint.longitude)
for waypoint in self.waypoints]))
route_start_geoassets = rg.search([(route.start_latitude,
route.start_longitude)
for route in self.metadata])
route_end_geoassets = rg.search([(route.end_latitude,
route.end_longitude)
for route in self.metadata])
map(
lambda rte, start_geoass, end_geoass: rte.add_geo_asset(
start_geoass, end_geoass), self.metadata,
route_start_geoassets, route_end_geoassets)
def main(argv):
try:
input_data = sys.stdin
csv.field_size_limit(sys.maxsize)
csv_in = csv.DictReader(fixNulls(input_data),
delimiter=delimiter,
fieldnames=headers,
quoting=csv.QUOTE_MINIMAL)
""" load formatted geocoded file (preventing it to print <Loading formatted geocoded file...> to stdout) """
sys.stdout = open(os.devnull, "w")
rg.search((0, 0))
sys.stdout = sys.__stdout__
previousLat = 0
previousLng = 0
for line in csv_in:
try:
""" CUSTOMIZE HERE """
lat = str(line['lat'])
lng = str(line['lng'])
if lat != previousLat or lng != previousLng:
r = rg.search((lat, lng))
latlng2country = r[0]['cc']
""" CUSTOMIZE HERE """
outline = '{}{}{}{}{}'.format( \
lat, \
delimiter, \
lng, \
delimiter, \
latlng2country \
)
print(outline)
previousLat = lat
previousLng = lng
except:
latlng2country = ''
continue
except EOFError:
return None
except Exception as e:
print(e)
sys.exit(1)
def update_name_from_location(self):
"""In the given list resolve the city and append the location city to the filename"""
for entry in self.file_list:
if entry.location:
lat, lon = entry.location.split(',')
default_city = Folder.clean_name(
reverse_geocoder.search([(float(lat), float(lon))
])[0]['name'])
alt_city = []
try:
alt_city = Folder.geo.reverse(
entry.location, timeout=60).address.split(', ')
except geopy.exc.GeocoderTimedOut as e:
print(
f"Timeout on GeoLookup for {entry.location} of {entry.name}. Found {default_city} already."
)
raise FactoryError("Exiting.")
name, ext = os.path.splitext(entry.name)
known_city = ""
if len(alt_city) > 4:
known_city = Folder.get_known_city(alt_city[-5])
if not known_city and len(alt_city) > 5:
known_city = Folder.get_known_city(alt_city[-6])
if not known_city:
known_city = Folder.get_known_city(default_city)
if known_city:
entry.name = name + ' ' + known_city + ext
continue
entry.name = name + ' ' + default_city + ext
def reverseGeocode(self, latlon):
# call using geocoder, biggest con is performance for this option
#geo = self.geocoder.reverse_geocode(latlon[0], latlon[1], no_annotations = '1', pretty = '1', language='pt')
#reversed = self.removeStopWords(geo[0]['formatted'])
# call using reverse_geocoder, nice thing about this: performance
aux = (latlon[0], latlon[1])
data = rg.search(aux)
city = data[0]["name"]
province = data[0]["admin1"] + " " + data[0]["admin2"]
dictionary = {'AC': 'Acre','AL': 'Alagoas','AP': 'Amapá','AM': 'Amazonas','BA': 'Bahia',
'CE': 'Ceará','DF': 'Distrito Federal','ES': 'Espírito Santo','GO': 'Goiás',
'MA': 'Maranhão','MT': 'Mato Grosso','MS': 'Mato Grosso do Sul',
'MG': 'Minas Gerais','PA': 'Pará','PB': 'Paraíba','PR': 'Paraná',
'PE': 'Pernambuco','PI': 'Piauí','RJ': 'Rio de Janeiro',
'RN': 'Rio Grande do Norte','RS': 'Rio Grande do Sul','RO': 'Rondônia',
'RR': 'Roraima','SC': 'Santa Catarina','SP': 'São Paulo','SE': 'Sergipe',
'TO': 'Tocantins'}
country = data[0]["cc"].replace("BR","Brasil")
reversed = city + " " + province + " " + country
for key in dictionary.keys():
latlon[3] = latlon[3].upper().replace(key, dictionary[key]) if len(latlon[3]) == 2 else latlon[3]
reported = ' '.join(latlon[2:5])
reversed = self.removeStopWords(reversed)
reported = self.removeStopWords(reported)
reversed = self.removeNonAscii(reversed)
reported = self.removeNonAscii(reported)
similarity = 100 * textdistance.Cosine(qval=None).similarity(reported , reversed)
return pd.Series((reported, reversed, similarity))
def update_filter_data(country, n):
df = pd.DataFrame(requests.get(url).json()['data'])
station = json_normalize(df['station'])
df.drop(['station'], axis=1, inplace=True)
df = pd.concat([df, station], axis=1)
df.drop(['time'], axis=1, inplace=True)
location = rg.search(list(zip(df.lat, df.lon)))
df['coordinates'] = location
df['location'] = df['coordinates'].apply(lambda x: x['name'])
df['cc'] = df['coordinates'].apply(lambda x: x['cc'])
df.drop(['coordinates', 'name', 'uid'], axis=1, inplace=True)
df = df.merge(countries, how="inner", left_on="cc", right_on="value")
df.rename(columns={'label': 'country'}, inplace=True)
df.drop(['value'], axis=1, inplace=True)
df.aqi.replace('-', np.NaN, inplace=True)
df.dropna(axis=0, inplace=True)
df['aqi'] = pd.to_numeric(df['aqi'])
df['marker_color'] = pd.cut(
df['aqi'], [0, 50, 100, 150, 200, 300, df['aqi'].max()],
labels=['green', 'yellow', 'orange', 'red', 'purple', 'brown'])
df['country'] = df['country'].apply(
lambda x: 'USA' if (x == 'United States of America') else x)
df = df[df['cc'] == country]
df.reset_index(drop=True, inplace=True)
return df.to_dict('records')
def build_geocodes(self):
gps_tuples = [tuple(xy) for xy in self.centroids]
geocodes = reverse_geocoder.search(gps_tuples)
columns= ['latitude','longitude', 'city', 'state', 'region', 'country']
geocodes = pd.DataFrame(geocodes)
geocodes.columns = columns
self.geocodes = geocodes
def add_country_codes(df):
'''
Use latitude and longitude coordinates to determine the country of a project.
:param df: dataframe with coordinates
:returns: dataframe with project country codes appended
'''
latitude = df.Latitude.values
longitude = df.Longitude.values
coordinates = []
for lat, lng in zip(latitude, longitude):
coordinates.append((lat, lng))
codes = []
results = rg.search(coordinates)
for r in results:
try:
code = r['cc']
codes.append(code)
except:
codes.append('NA')
df['Country'] = codes
return df
def home(request):
# Current Location
g = geocoder.ip('me')
latlon = g.latlng
result = rg.search((latlon[0], latlon[1]))
# Weather
url = "https://api.darksky.net/forecast/9229258e1491d592cda918516dd382f9/{},{}?exclude=minutely,hourly,daily,alerts,flags&units=auto".format(
latlon[0], latlon[1])
response = urllib.request.urlopen(url)
data = json.loads(response.read())
# Compliments
compliment = [
'You Look Stunning!', 'Hey, Gorgeous!', 'You look Sexy!',
'Synonym for beauty is you!', 'Fair and Lovely!'
]
chooser = random.randint(0, len(compliment) - 1)
final_data = {
'temperature': round(int(data['currently']['temperature'])),
'summary': data['currently']['summary'],
'place': result[0]['name'],
'compliment': compliment[chooser]
}
return render(request, 'base.html', context=final_data)
def transformToCountry(self, x: float, y: float) -> str:
geoms = fiona.open(
shpreader.natural_earth(resolution='50m',
category='physical',
name='land'))
land_geom = sgeom.MultiPolygon(
[sgeom.shape(geom['geometry']) for geom in geoms])
land = prep(land_geom)
if x is not None and y is not None:
on = land.contains(sgeom.Point(x, y))
if on:
result = rg.search((y, x))
country_full = self.cc.convert(names=result[0]["cc"],
to="name_short")
country = self.cc.convert(names=result[0]["cc"], to="ISO3")
print("Starting in", country_full)
plt.title("Starting in " + str(country_full), fontsize=50)
plt.scatter(x,
y,
color="black",
marker=".",
transform=ccrs.Geodetic())
self.go = True
return country
else:
print("Mer")
plt.title("Please choose a location on land !", fontsize=50)
return ""
def add_missing_countries(tweets_df):
'''
Note: this functions work 'inplace'
'''
print('{} tweets do not have country information!'.format(
tweets_df['Location'].isna().sum()))
# get tweets without a country info
no_country = tweets_df[tweets_df['Location'].isna()][['Lat', 'Long'
]].drop_duplicates()
# extract coordinates
coordinates = list(no_country.itertuples(index=False, name=None))
# map coordinates with countries using an external package
results = rg.search(coordinates)
no_country['found_countries'] = [i['cc'] for i in results]
no_country['found_countries'] = no_country['found_countries'].map(
cf.country_abbr)
no_country['Lat_Long'] = no_country[['Lat', 'Long']].apply(
lambda x: '_'.join(x.map(str)), axis=1)
# add mapped countries to the original tweets data
tweets_df.loc[tweets_df['Location'].isna(), 'Location'] = list(
tweets_df[tweets_df['Location'].isna()]['Lat_Long'].map(
dict(zip(no_country['Lat_Long'], no_country['found_countries']))))
tweets_df.drop(['Lat_Long'], axis=1, inplace=True)
print('{} tweets that do not have country information will be discarded!'.
format(tweets_df['Location'].isna().sum()))
tweets_df = tweets_df[~tweets_df['Location'].isna()]
return None
def _get_state_and_county(self, lon, lat):
try:
#result = None
coordinates = (lat,lon)
#print coordinates
results = rg.search(coordinates)
try:
county = results[0]['admin2'].split()[:1][0]
state = results[0]['admin1']
except:
state = None
county = None
#state = results[0]['admin1']
if (lat>33 and lat <38 and lon>-103.5 and lon<-94):
result = True
else:
print "NOT AOI event"
result = None
raise TypeError("Not in AOI")
#print "We are in the AOI"
#print result, Lat, Lon
except pgdb.Error as error:
raise pgdb.Error(error)
except Exception as error:
print error
if result is None:
# print "Not in the AOI!!!!!!"
raise TypeError("Not in AOI")
#print "We are NOT in the AOI"
return result,county,state
def gzworker(fullpath):
"""Worker will open the .csv file and process the information inside"""
print('Processing {}'.format(fullpath))
# try:
with open(fullpath, 'r+') as f:
reader = csv.reader(f)
for row in reader:
geoloc = row[3]
geoloc = geoloc.split(',')
lon = geoloc[0].replace('[', '')
lat = geoloc[1].replace(']', '').replace(' ', '')
# print('Longitude: {} \nLatitude: {}'.format(lon, lat))
# m_obj = re.search(r"(\d+)", geoloc)
# print(m_obj)
coordinates = (lat,lon)
results = rg.search(coordinates) # default mode = 2
print(results)
state_num = mx_ca_us_state_abbrev.get(results[0].get('admin1'))
print(state_num)
# state_num = us_state_abbrev.results['admin1']
# print(state_num)
# [('lat', '29.23329'), ('lon', '-98.79641'), ('name', 'Lytle'), ('admin1', 'Texas'), ('admin2', 'Atascosa County'), ('cc', 'US')]
# except:
# print("Error in {}".format(fullpath))
# pass
print('Finished {}'.format(fullpath))
def do_print(lat, lon):
print(
lat, lon,
reverse_geocode.search([lat, lon])[0], ":",
TestLocations.a.reverse(f"{lat},{lon}").address.split(', ')[-5:-4],
":",
reverse_geocoder.search([(lat, lon)])[0])
def reverse_geocode(lat, lng):
res = rg.search((lat, lng))[0]
res = list(res.items())
name = res[2][1]
admin1 = res[3][1]
cc = res[5][1]
return (name, admin1, cc)
def get(self, name):
phq = Client(access_token="")
retJ = []
if name == '':
name = "eminem"
for event in phq.events.search(q=name, limit=5, sort='rank', category='concerts'):
try:
cood = event.location
local = (rg.search([cood[1],cood[0]]))[0]['name']
except IndexError as e:
cood = [0,0]
local = 'USA'
# this = stateplane.identify(cood[0], cood[1])
resp = {
"eTitle" : event.title,
"eDate" : event.start.strftime('%Y-%m-%d'),
"eCountry" : event.country,
"eRank" : event.rank,
"eLocation" : local,
}
# print (event.scope, event.description, event.start.strftime
# ('%Y-%m-%d'), event.category, event.country, event.rank, event.location, event.labels, event.title)
# print(event.rank, event.category, event.title, event.start.strftime('%Y-%m-%d'))
retJ.append(resp)
return retJ, 200
def predicton(image_url):
# path to the image or video
imagename = "static/" + image_url + ".jpg"
print(imagename)
# read the image data using PIL
image = Image.open(imagename)
exifdata = image.getexif()
tags = {}
# Put tags into dict
for tag_id in exifdata:
tag = TAGS.get(tag_id, tag_id)
data = exifdata.get(tag_id)
if isinstance(data, bytes):
data = data.decode()
tags[tag] = data
# extract gps coordinates
# if statements account for W or S gps values (which should be negative)
lat = float(
tags['GPSInfo'].get(2)[0]) + float(tags['GPSInfo'].get(2)[1]) / 60
if tags['GPSInfo'].get(1)[0] == 'S':
lat = lat * -1
lng = float(
tags['GPSInfo'].get(4)[0]) + float(tags['GPSInfo'].get(4)[1]) / 60
if tags['GPSInfo'].get(3)[0] == 'W':
lng = lng * -1
locationDict = rg.search((lat, lng))[0]
location = locationDict.get('name') + ', ' + locationDict.get('admin1')
return render_template('image.html',
tags=tags,
image_url="../" + imagename,
location=location)
def main():
# print(rg.search(()))
coordinates = (40.73971301, -73.99456405)
pprint.pprint(rg.search(coordinates))
"""
def getlocation_drop(x):
point_coord = (x[3] , x[2])
this_location = rg.search(point_coord, mode = 1)
if ( this_location[0]['admin2'] == 'Queens County'):
return ('Queens',x[0],x[1])
else :
return (this_location[0]['name'],x[0],x[1])
def map_filter(country):
user_loc = country
# Retrieve Device GPS locations
GPS = get_GPS()
map = {}
for device in GPS:
coordinates = (GPS[device]['lat'], GPS[device]['lon'])
lat = GPS[device]['lat']
lon = GPS[device]['lon']
loc = rg.search(coordinates)
country = loc[0]['cc']
if country == user_loc:
map.update({device: {'Country': country, 'lat': lat, 'lon': lon}})
#print("Map:")
#print(map)
json_map = json.dumps(map)
return (json_map)
def getlocation2(x):
point_coord_pickup = (float(x[1]) , float(x[0]))
point_coord_drop = (float(x[3]) , float(x[2]))
pickup_location = rg.search(point_coord_pickup, mode = 1)
drop_location = rg.search(point_coord_drop , mode = 1)
if ( pickup_location[0]['admin2'] == 'Queens County'):
pickup_name = 'Queens'
else :
pickup_name = pickup_location[0]['name']
if ( drop_location[0]['admin2'] == 'Queens County'):
drop_name = 'Queens'
else :
drop_name = drop_location[0]['name']
names = (pickup_name , drop_name)
return names
def convert_geo(self):
self._df['lat_long'] = list(
zip(self._df['latitude'], self._df['longitude']))
rg_result = rg.search(self._df['lat_long'].tolist())
df_geo = json_normalize(rg_result)
self._df['area'] = df_geo['name']
self._df['location'] = df_geo['admin2']
def reverseGeocode(coordinates):
result = rg.search(coordinates)
#dict=json.loads(result)
# result is a list containing ordered dictionary.
#pprint.pprint(dict)
dict = result[0]
return dict
def fetchsamples(db):
query_coords_string = '-155.6811,18.91,-66.9470,44.81'
url = 'https://stream.twitter.com/1.1/statuses/filter.json?lang=en&locations=' + query_coords_string
response = twitterreq(url, "POST", [])
for line in response:
tweet_json = ujson.loads(line.strip().decode('utf8'))
# We only process geolocated tweets and tweets in English
if tweet_json['lang'] == 'en':
if 'geo' in tweet_json and tweet_json['geo'] is not None:
latitude = tweet_json['geo']['coordinates'][0]
longitude = tweet_json['geo']['coordinates'][1]
result_json = rg.search((latitude, longitude), verbose=False)[0]
if result_json is not None:
if 'cc' in result_json and result_json['cc'] == 'US':
tweet_json['usa_state'] = result_json['admin1']
print_tweet(remove_fields(tweet_json), db)
elif 'place' in tweet_json and tweet_json['place'] == 'United States':
full_name = tweet_json['place']['full_name']
matches = re.findall('([\w+\s]+)', full_name)
if len(matches) is 2:
if matches[1] == 'USA':
tweet_json['usa_state'] = matches[0]
else:
tweet_json['usa_state'] = str(us.states.lookup(matches[1][1:])) # Find state full name for abbr
print_tweet(remove_fields(tweet_json), db)
def reverseGeocode(a,b):
coordinates = (a,b)
result = rg.search(coordinates,verbose=False)
if len(result)>0:
return (result[0].get("cc"))
else:
return "ASSENTE"
def filter_US_locations(collection):
cursor = collection.find(
{
"location_dict": {
"$exists": False
},
"latitude": {
"$exists": True
}
},
no_cursor_timeout=True)
added_counter = 0
deleted_counter = 0
for record in cursor:
location_data = reverse_geocoder.search(
(record['latitude'], record['longitude']))
if location_data[0]['cc'] == 'US':
collection.update_one(
{"_id": record["_id"]},
{"$set": {
'location_dict': location_data[0]
}})
added_counter += 1
else:
collection.delete_one({"_id": record["_id"]})
deleted_counter += 1
string_report = "added {} location dicts and deleted {} records".format(
added_counter, deleted_counter)
print(string_report)
time.sleep(1.5)
cursor.close()
def reverseGeocode(coordinates):
result = rg.search(coordinates)
pprint.pprint(result)
pprint.pprint(result[0]["name"])
address = result[0]["name"]
print(address)
return address
def get_coords(x, y):
if pd.isnull(x) | pd.isnull(y):
return 'nan'
else:
coordinates = (x, y)
results = rg.search(coordinates)
return results
def fetch_information(lat, long):
final_info = []
st.text(
"Note : Radius used for feature collection and model building is 1km")
st.warning("Fetching features......This might take a few minutes")
country_code_info = rg.search((lat, long))
country_code = country_code_info[0]['cc']
continent = country_to_continent(country_code)
final_info.append(continent)
dist_to_road_collected = getDist(lat, long)
final_info.append(dist_to_road_collected)
population_info = getPopulationInfo(lat, long, population)
final_info.extend(population_info)
# test_information_model['Continent'] = country_to_continent(country_code)
venues = getVenueInfo(lat, long)
final_info.extend(venues)
st.text(f"fetching population information ={population_info}")
st.text(f"fetching nearby venue information ={venues}")
st.text(f"fetching nearest distance to roads ={dist_to_road_collected}")
st.success("Process is complete")
st.text(final_info)
final_info_numpy = np.array(final_info).reshape(1, 17)
return final_info_numpy
def which_state(self, tweet):
box = tweet.place.bounding_box.coordinates[0]
longs = [x[0] for x in box]
lats = [x[1] for x in box]
coords = (np.mean(longs), np.mean(lats))
results = rg.search(tuple(reversed(box[0])))[0]
if results['cc'] == 'US':
return results['admin1']
else:
return 'Non-US'
def get_city_and_state(lat, lon): cityNum, stateNum = -1, -1 result = rg.search((lat, lon))[0] if 'name' in result: city = result['name'] if city in cityMap: cityNum = cityMap[city] if 'admin1' in result: state = result['admin1'] if state in stateMap: stateNum = stateMap[stateToAbbr[state]] return cityNum, stateNum
def get_location():
API_KEY = "7efb02dc0ba8ee84f6f63945578cdcca"
request_url = "http://api.ipstack.com/check?access_key={}".format(API_KEY)
r = requests.get(request_url)
j = json.loads(r.text)
# print j
lat = j['latitude']
lon = j['longitude']
print lat
print lon
results = rg.search((lat,lon))
return results[0]["name"]
def count_single_field(field, data):
if field == 'hashtag':
out = [i[1] for i in data if i[1] != 'null']
out = Counter(list(itertools.chain.from_iterable(out)))
if field == 'coordinates':
coord = [i[3] for i in data if i[3] != 'null']
result = rg.search(coord)
out = Counter([i['admin1'] for i in result])
if field == 'mention':
out = Counter([i[4] for i in data if i[4] != 'null'])
if field == 'source':
out = Counter([i[5] for i in data if i[5] != 'null'])
if field == 'tweet':
out = Counter([i[6] for i in data if i[6] != 'null'])
if field == 'user':
out = Counter([i[7] for i in data if i[7] != 'null'])
return out
def process():
#global scoreOfStates
x = 0
for i in states:
scoreOfStates[states[i]]=0
tweetofStates[states[i]]=0
#print scoreOfStates
while True:
# print line
try:
x+=1
if x==100:
break
TweetInfo = consumer.recv_json()
#print(TweetInfo)
print x
if (TweetInfo.has_key("place")):
if TweetInfo["place"].has_key("bounding_box"):
if TweetInfo["place"]["bounding_box"]["coordinates"]:
lis = TweetInfo["place"]["bounding_box"]["coordinates"]
'''taking the average of the coordinates as the assumed
location of the tweet and finding the corresponding state
using reverse geo lookup'''
a=0
b=0
for i in lis[0]:
a += i[0]
b += i[1]
a /= 4.0
b /= 4.0
state = rg.search((b,a))[0]["admin1"]
score = 0
if (TweetInfo.has_key("text")):
#print(TweetInfo['text'])
listOfTokens = normalize(TweetInfo['text'])
text = " ".join(listOfTokens)
score = TextBlob(text).sentiment.polarity
if state in states.values():
scoreOfStates[state]+=score
tweetofStates[state]+=1
#print score,state
except:
print "Exception found"
pass
def findLocation(hashtag, coordinates):
print 'Finding corresponding locations...'
results_ = rg.search(coordinates)
# print results_
results = [i['admin1'] for i in results_]
total_result = []
for i in zip(results, hashtag):
if len(i[1]) != 0:
temp = []
for j in i[1]:
temp.append(j)
total_result.append((i[0], temp))
else:
total_result.append((i[0],i[1]))
# placeFinal = [i for i in total_result if i[0] in listStates]
placeFinal = [i for i in total_result]
print 'Done'
return placeFinal
my_postpaid = int(math.ceil(float(my_stat) * .66))
my_prepaid = int(math.ceil(float(my_stat) * .16))
my_other = int(math.ceil(float(my_stat) * .18))
#use yearly data to find hourly amount, create that number of points
count = 0;
while (count < my_postpaid):
if ((my_state =='HI' or my_state =='AK')):
x,y = create_random_point(my_lat,my_lon ,100000)
elif (my_state=='CA'):
x,y = create_random_point(my_lat,my_lon ,500000)
else:
x,y = create_random_point(my_lat,my_lon ,1000000)
coordinates = (x,y)
results = rg.search(coordinates,mode=1)
#used to verify if point in US- do we need this?
if (results[0]['cc']=='US'):
my_list = {'name':my_name,'lat':my_lat, 'lon':my_lon, 'newlat':x, 'newlon':y, 'type': 'postpaid'}
data[my_state].append(my_list)
count += 1
count = 0;
while (count < my_prepaid):
if ((my_state =='HI' or my_state =='AK')):
x,y = create_random_point(my_lat,my_lon ,100000)
elif (my_state=='CA'):
x,y = create_random_point(my_lat,my_lon ,500000)
else:
x,y = create_random_point(my_lat,my_lon ,1000000)
coordinates = (x,y)
results = rg.search(coordinates,mode=1)
import string
flist=str(sys.argv)
tfile= flist[12:len(flist)-2]
#f = open(tfile, 'r')
f = open("sent9.txt", 'r')
g = open("t9.txt", 'w')
zcdb = ZipCodeDatabase()
c=0
for line in f:
fcoords=()
tweet = line.split("|")
coords = re.search(r"\[(.*)\]", tweet[0]).group(1)
x, y = map(float, re.findall(r'[+-]?[0-9.]+', coords))
location = rg.search([x,y])
if location[0]['cc'] == "US":
state = location[0]['admin1']
city = location[0]['name']
zlist=zcdb.find_zip(city=city)
if zlist>0:
zipcode = random.choice(zlist)
s = tweet[-1].strip('\n')+","+zipcode.zip+","+state+"\n"
# print s.encode("UTF-8")
g.write(s.encode('utf8'))
c+=1
if c>100:
g.flush()
c=0
f.close()
g.close()
for row in fin:
row = row.strip()
if not row:
continue
try:
first = row.split("\t")
_hash = int(first[0])
rows.append(row)
points.append(from_ord(_hash))
except IndexError, e:
print "IndexError: %s" % row
sys.sdout.flush()
if rows:
ret_vals = search(points, max_distance=1)
file_name = "%s/%s" %(out_dir, file_name)
fout = open(file_name, "w")
for i, row in enumerate(rows):
ret_val = ret_vals[i] or {}
first = row.split("\t")
_hash = first[0]
content = first[1].split(chr(0001))
postal_code = ret_val.get("pc", "") or content[3]
country_code = ret_val.get("cc", "").upper() or content[4]
if len(country_code) > 2:
cc = mappings.get(country_code)
if cc:
country_code = cc
content[3] = postal_code
def analyze_clusters():
# if not request.json:
# abort(400)
clusters = json.loads(request.data)["ids"]
q = {
"size": 5000,
"query": {"terms": {"_id": clusters}},
"aggregations": {
"forces": {"terms": {"field": "city"}, "aggregations": {"prices": {"terms": {"field": "rate60"}}}}
},
}
es = Elasticsearch(["es_url"])
res = es.search(body=q, index="memex_ht", doc_type="ad")
geo = filter(lambda x: "latitude" in x["_source"].keys(), res["hits"]["hits"])
geopts = map(lambda x: (float(x["_source"]["latitude"]), float(x["_source"]["longitude"])), geo)
ethnicity = filter(lambda x: "ethnicity" in x["_source"].keys(), res["hits"]["hits"])
ethnicity = map(lambda x: str(x["_source"]["ethnicity"]), ethnicity)
city = filter(lambda x: "city" in x["_source"].keys(), res["hits"]["hits"])
city = map(lambda x: str(x["_source"]["city"]), city)
ethnicity_all = dict(Counter(ethnicity))
prices = filter(lambda x: "rate60" in x["_source"].keys() and "city" in x["_source"].keys(), res["hits"]["hits"])
prices = filter(lambda x: x["_source"]["rate60"] != "", prices)
time = filter(lambda x: "posttime" in x["_source"].keys(), geo)
time_dist = map(
lambda x: (
x["_source"]["latitude"],
x["_source"]["longitude"],
datetime.datetime.strptime(x["_source"]["posttime"], "%Y-%m-%dT%H:%M:%S").date(),
),
time,
)
imps = [] # implied travel speed
imps2 = [] # average distance between multiple posts at exact timestamp
for item in window(sorted(time_dist, key=lambda item: item[2]), 2):
dist = vincenty((item[0][0], item[0][1]), (item[1][0], item[1][1])).miles
time = abs(item[1][2] - item[0][2]).total_seconds() / 3600.00
try:
imps.append(dist / time)
except ZeroDivisionError:
if dist != 0:
imps2.append(dist)
else:
pass
if len(ethnicity_all) > 1:
eth = "More than one"
else:
eth = "One"
if len(geopts) > 0:
results = rg.search(geopts) # default mode = 2
countries = set(map(lambda x: x["cc"], results))
states = set(map(lambda x: x["admin1"], results))
cities = set(map(lambda x: x["name"], results))
if len(countries) > 1:
location = "International"
elif len(countries) == 1 and len(states) > 1:
location = "National"
else:
location = "Local"
else:
location = "No information"
q2 = {
"size": 5000,
"query": {"terms": {"city": list(set(city))}},
"aggregations": {
"forces": {"terms": {"field": "city"}, "aggregations": {"prices": {"terms": {"field": "rate60"}}}}
},
}
pres = es.search(body=q2, index="memex_ht", doc_type="ad")
quantiles = pres["aggregations"]["forces"]["buckets"]
df2 = pd.DataFrame(quantiles)
hist = []
for i, city in enumerate(df2["key"]):
df = pd.DataFrame(dict(df2["prices"][df2["key"] == city]).values()[0]["buckets"])
df[["key", "doc_count"]] = df[["key", "doc_count"]].astype(float)
df.sort("key", inplace=True)
df["doc_count"] = df["doc_count"] / df["doc_count"].sum()
norm_cumul = 1.0 * np.array(df["doc_count"]).cumsum()
df["quantile"] = norm_cumul
hist.append({"city": city, "data": df})
pq = []
raw = []
for item in map(lambda x: (x["_source"]["city"], x["_source"]["rate60"]), prices):
try:
pq.append(price_quantile(hist, item[0], float(item[1])))
raw.append(float(item[1]))
except:
pass
return json.dumps(
{
"avg_price_quantile": np.mean(pq),
"loc": location,
"ethnicity": eth,
"price_var": np.std(raw),
"mean_price": np.mean(raw),
"implied_speed": np.mean(imps),
"avg_dist_sim_posts": np.mean(imps2),
}
)
lat = 28.7041
lon = 77.1025
x = webMercX(lon, zoom) - cx
y = webMercY(lat, zoom) - cy
#i = 1
# while i < len(data):
# stuff = data[i].split(' ')
# print stuff[1], stuff[2]
# lat = float(stuff[1])
# lon = float(stuff[2])
# x = webMercX(lon, zoom) - cx
# y = webMercY(lat, zoom) - cy
# i += 1
data = cTA.csv_to_array(
"http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_hour.csv")
for row in data: # don't use indexes, just iterate over the data
# you already split on commas.
print(row[1], row[2]) # again, better names
lat = float(row[1])
lon = float(row[2])
x = webMercX(lon, zoom) - cx
y = webMercY(lat, zoom) - cy
coords = (lat, lon)
results = rg.search(coords)
print results
def compare(points, should_filter=False, max_distance=DISTANCE_THRESHOLD):
ret_val = rg.search([p[0] for p in points], max_distance=max_distance)
counts = {
"country_mismatch": 0
, "postal_code_mismatch": 0
, "country_new": 0
, "postal_code_new": 0
, "filtered": 0
, "postal_code_match": 0
, "internal_filtered": 0
, "country_match": 0
}
for i, point in enumerate(points):
if chr(0001) in point[1] and "|" in point[1]:
expected = point[1].split(chr(0001))[0].split("|")
elif chr(0001) in point[1]:
expected = point[1].split(chr(0001))
else:
expected = point[1].split('|')
expected_postal_code = expected[3].upper()
expected_country = expected[4].upper()
if len(expected_country) > 2:
temp = expected_country
expected_country = expected_postal_code
expected_postal_code = temp
if not ret_val[i]:
counts["filtered"] += 1
elif should_filter and LatLon(point[0][0], point[0][1]).distance(LatLon(ret_val[i]["lat"], ret_val[i]["lon"])) > MAX_DISTANCE:
counts["internal_filtered"] += 1
else:
result_postal_code = ret_val[i].get("pc", "").upper()
result_country = ret_val[i].get("cc", "").upper()
result_point = LatLon(ret_val[i]["lat"], ret_val[i]["lon"])
def print_mismatch(mismatch_type=""):
if random() <= sampling_rate:
print ",".join([str(k) for k in [mismatch_type, expected_country, result_country, expected_postal_code, result_postal_code, point[0][0], point[0][1], ret_val[i]["lat"], ret_val[i]["lon"], LatLon(point[0][0], point[0][1]).distance(LatLon(ret_val[i]["lat"], ret_val[i]["lon"])), point[1]]])
sys.stdout.flush()
if not expected_country and result_country:
counts["country_new"] += 1
elif result_country != expected_country:
print_mismatch("c")
counts["country_mismatch"] += 1
elif result_country == expected_country:
counts["country_match"] += 1
if not expected_postal_code and result_postal_code:
counts["postal_code_new"] += 1
elif result_postal_code != expected_postal_code:
# print result_postal_code, expected_postal_code, expected_country
counts["postal_code_mismatch"] += 1
print_mismatch("p")
elif result_postal_code == expected_postal_code:
counts["postal_code_match"] += 1
# else:
# counts["filtered"] += 1
counts["total"] = i+1
return counts
df = s.tweets['geo.coordinates']
bad = df.apply(lambda x: x is None)
df = df[~bad]
s.tweets = s.tweets[~bad]
lat = df.apply(lambda x: x[0])
lon = df.apply(lambda x: x[1])
# lat, lon = zip(*df) # Alternate
# Remove Alaska and Hawaii
del states["HI"]
del states["AK"]
# Match tweets by state
coordinates = zip(lat, lon)
results = rg.search(coordinates) # default mode = 2
# print results
state_match = [results[row]['admin1'] for row,_ in enumerate(results)]
s.tweets['state'] = state_match
cc = pd.Series([results[row]['cc'] for row,_ in enumerate(results)])
# Check if not in US:
good = cc == 'US'
if len(cc)>0:
s.tweets.index = range(len(s.tweets))
lon.index = range(len(lon))
lat.index = range(len(lat))
s.tweets = s.tweets[good]
lon = lon[good]
lat = lat[good]
# Coordinate DataFrame
from geo_hash import from_ord from reverse_geocoder import search points = [1028940903367840592, 1028940741691456133, 1028940714896802299, 1028937300827959104, 1028936919208416560, 1028936496796163181, 1028936321951596184] for point in points: coords = from_ord(point) print point, coords result = search(coords, max_distance=1) print coords, result coords = (43.646964, -79.455647) result = search(coords, max_distance=1) print coords, result coords = (43.6469, -79.4521) result = search(coords, max_distance=1) print coords, result
def get_country_code(coordinate_string): # put method here AIzaSyDuihORQ83RaQ-bkxJz-PirUUoi0YHR_x4 coordinates = coordinate_string.split() location = rg.search([(coordinates[0],coordinates[1])],mode=2) return location[0]
def threadRun():
Boston = (42.359502, -71.062282)
result = rg.search(Boston)
return result
def political_boundary(self,type,latitude,longitude):
assert type == 'name' or type == 'admin1' or type == 'admin2' or type == 'cc'
data = rg.search((latitude,longitude))[0]
return data[type.encode("ASCII")]
os.system("rm -r " + path)
rdd.saveAsTextFile(path)
sc = SparkContext(appName="SentimentAnalysis")
rawTweets = sc.textFile("./tweets.json", 100)
# first thing to do is extract the information we need from the tweets, i.e. the coordinates and the text
parsedTweets = (rawTweets.map(lambda tweet: json.loads(tweet))
.filter(lambda tweet: tweet["text"] != "" and tweet["coordinates"] is not None) # filter early
.map(lambda tweet: (tweet["coordinates"]["coordinates"], tweet["text"])) # project early
.map(lambda t: ((t[0][1], t[0][0]), t[1]))) # putting coordinates in usual lat - lon format
# extract state from coordinates using geopy
state_text = (parsedTweets.map(lambda t: (geo.search(t[0])[0], t[1]))
.map(lambda t: ( (t[0]["cc"], t[0]["admin1"]), t[1]) )
.filter(lambda t: t[0][0] == "US")
.map(lambda t: (t[0][1], t[1]) ) )
# at this point data is like (u'state', u'tweet text') for each tweet
# compute sentiment for each tweet and return a list of (state, sentiment) tuples
state_sent = state_text.map(lambda t: (t[0].upper(), TextBlob(t[1]).sentiment.polarity))
# and I simply save one file
# all the state, sentiment entries, for detailed statistical analysis in R
saveAsTextFile(state_sent.map(lambda t: ",".join(map(str, (t[0], t[1])))) , # turn into nice output to store as csv
"./sentiments_states.csv", overwrite = True)
from timeit import timeit
import csv
import reverse_geocoder as rg
if __name__ == '__main__':
setup = "import csv;import reverse_geocoder as rg;print('Loading coordinates...');" + \
"cities = [(row[0],row[1]) for row in csv.reader(open('../test/coordinates_10000000.csv','rt'),delimiter='\t')];"
num = 3
t = timeit(stmt="rg.search(cities,mode=1)",setup=setup,number=num)
print('Running time: %.2f secs' % (t / num))
print('\nLoading coordinates to compare mode 1 and mode 2...')
cities = [(row[0],row[1]) for row in csv.reader(open('../test/coordinates_1000.csv','rt'),delimiter='\t')]
result1 = rg.search(cities,mode=1)
result2 = rg.search(cities,mode=2)
if result1 != result2:
print('Results do not match!')
else:
print('Both results match!')
