def get_barometer(API_KEY,myPostCode,sensor_locations):
opensensor = London(API_KEY)
weight = opensensor.get_weights(myPostCode,sensor_locations)
#the first date we try and get data
start_date = (datetime.datetime.now() + datetime.timedelta(hours=-8)).strftime("%Y-%m-%dT%H:00:00.000Z")
#the last date defaults to today
end_date = (datetime.datetime.now() + datetime.timedelta(days=1)).strftime("%Y-%m-%d")
NO2 = pandas.DataFrame()
for row in weight.index:
mtx = opensensor.getAirQuality(start=start_date,end=end_date,location = row).get('NO2')
if mtx is not None:
NO2[row] = mtx[row]
#grab the NO2 data where we bhave the intersect of weights and NO2
weight = weight[list(NO2.columns)]/weight[list(NO2.columns)].sum()
#sort the matrices
NO2 = NO2.reindex_axis(sorted(NO2.columns), axis=1)
weight = weight.reindex(sorted(weight.index))
#ffill and bfill NA's
NO2 = NO2.ffill().bfill()
#the AQ vector for my post code
myAQ = (NO2 * weight).sum(axis = 1)
#set the decay parameters for the exp moving avgs
nslow = 6
nfast = 3
#get the MACD and exp moving avgs
NO2_emafast,NO2_emaslow,NO2_macd = opensensor.getMovingAverageConvergence(myAQ, nslow=nslow, nfast=nfast)
#make into 1/0 signal and get the last reading
signal = (-NO2_macd/abs(NO2_macd))[len(NO2_macd) - 1]
return signal
#how many days we want to look back over time
lookback = 20
#the file that holds all the meta data for LAQN sensors
LAQN_file = '/home/troups/osio/AQ/Data/LAQN.csv'
Barometer_file = 'Barometer.csv'
#API Key comes from your account on the opensensors platform
API_KEY = ''
#the first date we try and get data
start_date = (datetime.datetime.now() + datetime.timedelta(days=-lookback)).strftime("%Y%m%d")
#the last date defaults to today
end_date =datetime.datetime.now().strftime("%Y%m%d")
#create instance of Air Quality Object
opensensor = London(API_KEY)
#Get the meta data not on opensensors at the moment
with open(LAQN_file) as f:
reader = csv.reader(f)
LAQN_locations = dict((rows[0],[float(rows[1]),float(rows[2])]) for rows in reader)
def moving_average_convergence(x, nslow=8, nfast=4):
"""
wrap the method in the London object so we can use apply
"""
NO2_emafast,NO2_emaslow,NO2_macd = opensensor.getMovingAverageConvergence(x, nslow, nfast)
return pandas.Series(NO2_macd)
#get the list of LAQN measures
LAQN_mtx = opensensor.getAirQuality(start=start_date,end=end_date)
import pandas
import numpy as np
import datetime
import matplotlib.pyplot as plt
import scipy
lookback = 2
#API Key comes from your account on the opensensors platform
API_KEY = ''
#the first date we try and get data
start_date = (datetime.datetime.now() + datetime.timedelta(days=-lookback)).strftime("%Y%m%d")
#the last date defaults to today
end_date =datetime.datetime.now().strftime("%Y%m%d")
#create instance of Air Quality Object
opensensor = London(API_KEY)
#get the tube data
mtx = opensensor.getTFLTube(start_date,end_date)
#add the index as a column so we can pivot
mtx['datetime'] = mtx.index
#pivot the data
mtx = mtx.pivot(index='datetime', columns='name', values='severity')
#reset the index
mtx.set_index(pandas.DatetimeIndex(mtx.index),inplace=True)
#make sure everything is numeric
mtx = mtx.convert_objects(convert_numeric=True)
from London import *
import datetime
#API Key comes from your account on the opensensors platform
API_KEY = ''
#the days to look back
lookback = 2
#the first date we try and get data
start_date = (datetime.datetime.now() + datetime.timedelta(days=-lookback)).strftime("%Y%m%d")
#the last date defaults to today
end_date =datetime.datetime.now().strftime("%Y%m%d")
#create instance of Air Quality Object
opensensor = London(API_KEY)
AQ_mtx = opensensor.getAirQuality(start=start_date,end=end_date)
Bike_mtx, Bike_meta_data = opensensor.getTFLBikes(start_date,end_date)
Roads_mtx, Roads_meta_data = opensensor.getTFLRoads(start_date,end_date)
Tubes_mtx = opensensor.getTFLTube(start_date,end_date)
from London import *
import datetime
#API Key comes from your account on the opensensors platform
API_KEY = '36a1dd80-b5f4-48ce-8dce-50b50905f0fd'
#the days to look back
lookback = 20
#the first date we try and get data
start_date = (datetime.datetime.now() + datetime.timedelta(days=-lookback)).strftime("%Y%m%d")
#the last date defaults to today
end_date =datetime.datetime.now().strftime("%Y%m%d")
#create instance of Air Quality Object
opensensor = London(API_KEY)
Bike_mtx, Bike_meta_data = opensensor.getTFLBikes(start_date,end_date)
Tubes_mtx = opensensor.getTFLTube(start_date,end_date)
Biketopics = json_normalize(opensensor.getTopicsForOrg('Tfl'))
Biketopics = Biketopics[Biketopics['topic'].str.contains("/orgs/tfl/bikes/")]
from London import *
import pandas
import datetime
lookback = 2
#API Key comes from your account on the opensensors platform
API_KEY = ''
#the first date we try and get data
start_date = (datetime.datetime.now() + datetime.timedelta(days=-lookback)).strftime("%Y%m%d")
#the last date defaults to today
end_date =datetime.datetime.now().strftime("%Y%m%d")
#create instance of Air Quality Object
opensensor = London(API_KEY)
roads_mtx_raw, roads_meta_data = opensensor.getTFLRoads(start_date,end_date)
roads_mtx = roads_mtx_raw
roads_mtx['datetime'] = roads_mtx.index
roads_mtx['severity'][roads_mtx['severity'] == 'Closure'] = 3
roads_mtx['severity'][roads_mtx['severity'] == 'Severe'] = 2
roads_mtx['severity'][roads_mtx['severity'] == 'Serious'] = 1
roads_mtx['severity'][roads_mtx['severity'] == 'Good'] = 0
roads_mtx = roads_mtx.convert_objects(convert_numeric=True)
roads_mtx = roads_mtx[~roads_mtx.duplicated(['name', 'datetime', 'severity'])]
LAQN_file = '\Data\LAQN.csv'
#API Key comes from your account on the opensensors platform
API_KEY = ''
#the first date we try and get data
start_date = (datetime.datetime.now() + datetime.timedelta(days=-lookback)).strftime("%Y%m%d")
#the last date defaults to today
end_date =datetime.datetime.now().strftime("%Y%m%d")
#Get the meta data not on opensensors at the moment
with open(LAQN_file) as f:
reader = csv.reader(f)
LAQN_locations = dict((rows[0],[float(rows[1]),float(rows[2])]) for rows in reader)
#create instance of Air Quality Object
opensensor = London(API_KEY)
#set my postcode
myPostCode = 'E1W 2PA'
#get the geo data for my postcode
geo_meta_data=opensensor.getPostcode(myPostCode)
#get the distance from all the LAQN sensors from my postcode
distance = pandas.Series(dict((k, opensensor.getDistance(v,[geo_meta_data['geo']['lng'], geo_meta_data['geo']['lat']])) for k, v in LAQN_locations.items()))
#work out the weight for each LAQN sensor
weight = np.exp(-2*distance)/sum(np.exp(-2*distance))
distance = pandas.concat([distance, weight], axis=1)
distance.columns = ['distance','weight']
#get the list of LAQN measures
LAQN_mtx = opensensor.getAirQuality(start=start_date,end=end_date)
from London import *
import pandas
import datetime
lookback = 2
#API Key comes from your account on the opensensors platform
API_KEY = ''
#the first date we try and get data
start_date = (datetime.datetime.now() + datetime.timedelta(days=-lookback)).strftime("%Y%m%d")
#the last date defaults to today
end_date =datetime.datetime.now().strftime("%Y%m%d")
#create instance of Air Quality Object
opensensor = London(API_KEY)
#initiated the list of days for which well have a list of measures
BikeData = pandas.DataFrame(columns = ['name', 'level_1', 'value'])
BikeMetaData = {}
todays_matrix, todays_metadata = opensensor.getTFLBikes(start_date,end_date)
#lose dupes
todays_matrix = todays_matrix[~todays_matrix.duplicated(['name', 'level_1', 'value'])]
#lose NA
todays_matrix = todays_matrix.dropna(subset=['name', 'level_1', 'value'], how='any')
#reset the index
todays_matrix = todays_matrix.reset_index(drop=True)
#pivot into all the locations
bike_mtx = todays_matrix.pivot(index='level_1', columns='name', values='value')
How
Use the London city class
Get the data from Opensensors.io API
Plot
"""
#API Key comes from your account on the opensensors platform
API_KEY = ''
#the first date we try and get data
start_date = (datetime.datetime.now() + datetime.timedelta(days=-lookback)).strftime("%Y%m%d")
#the last date defaults to today
end_date =datetime.datetime.now().strftime("%Y%m%d")
#create instance of Air Quality Object
opensensor = London(API_KEY)
#get the raw roads data
roads_mtx_raw, roads_meta_data = opensensor.getTFLRoads(start_date,end_date)
roads_mtx = roads_mtx_raw
#Add a datatime column needed to pivot
roads_mtx['datetime'] = roads_mtx.index
#Change text columns to numeric grade
roads_mtx['severity'][roads_mtx['severity'] == 'Closure'] = 3
roads_mtx['severity'][roads_mtx['severity'] == 'Severe'] = 2
roads_mtx['severity'][roads_mtx['severity'] == 'Serious'] = 1
roads_mtx['severity'][roads_mtx['severity'] == 'Good'] = 0
#make into 1/0 signal and get the last reading
signal = (-NO2_macd/abs(NO2_macd))[len(NO2_macd) - 1]
return signal
#Get the meta data not on opensensors at the moment
#this is the list of locations for the London Air Quality Network
with open(LAQN_file) as f:
reader = csv.reader(f)
LAQN_locations = dict((rows[0],[float(rows[1]),float(rows[2])]) for rows in reader)
#Create a dictionary to put the barometer readings into
barometer = {}
opensensor = London(API_KEY)
myPostCode = 'E14 8EJ'
#get the index of locations that are important for our post code
weights = opensensor.getWeights(myPostCode,LAQN_locations).index
#set the org since we need to get all mesages (easier than creating more than one SSE stream)
org ='london-air-quality-network'
#make sure the messages stream is dead
messages = None
#set the SSE stream in motion
messages = SSEClient('https://realtime.opensensors.io/v1/public/events/orgs/'+org+'/topics?api-key='+ API_KEY)
for msg in messages:
#get the data for the message
outputMsg = msg.data
#sometimes we seem to get empty messages to check there is indeed something there
