def getAggregateForecast(allForecasts, allWeights, cities_location,
sample_date):
cityDict = getForecasts.getCities(cities_location)
return {
cityState: getAggregateForecastByCityDay(allForecasts, allWeights,
cityState, sample_date)
for cityState in cityDict
}
def getAllForecasts(data_directory, cities_location):
file_dictionary = getFiles(data_directory)
cityDict = getForecasts.getCities(cities_location)
cities = cityDict.keys()
services = file_dictionary.keys()
cities.sort()
services.sort()
allResults = {"AccuWeather" : {}, "DarkSky": {}, "NWS": {}, "WUnderground": {}}
for service in file_dictionary:
for file_name in file_dictionary[service]:
cityState = whichCity(cities, file_name)
allResults[service][cityState] = useService(service, data_directory, file_name)
return allResults
def getPosteriors():
date_time_string = "2017-07-22_11" #Date time to get forecast data from. Name of the directory.
date_of_forecast = datetime.date(
2017, 07, 22) #The date that the forecasts were sampled from.
date_to_predict = datetime.date(2017, 07, 23) #The date to predict
past_days = 5 #The number of prior days to use
#Likelihood
weight_directory = "G:\School\VA Tech Courses\ZhangLab\DataIncubator\ForecastAdvisor\LastMonth"
cities_location = 'G:\School\VA Tech Courses\ZhangLab\DataIncubator\LatLongCities.csv'
data_directory = os.path.join("C:\Users\jsporter\Downloads\Data",
date_time_string)
output_directory = os.path.join("C:\Users\jsporter\Downloads\Temp",
date_time_string)
if not os.path.isdir(os.path.dirname(output_directory)):
os.makedirs(os.path.dirname(output_directory))
likelihood_location = os.path.join(
output_directory, "Likelihoods_" + date_time_string + ".csv")
all_forecasts_location = os.path.join(output_directory,
"All_Forecasts_" + date_time_string)
likelihoods = extractInfo.runExtractInfo(weight_directory, data_directory,
cities_location,
likelihood_location,
all_forecasts_location,
date_of_forecast)
#Prior
api_key_location = 'G:\School\VA Tech Courses\ZhangLab\DataIncubator\Weather_API_Keys.csv'
prior_location = os.path.join(output_directory,
"Priors_" + date_time_string + ".csv")
history_directory = os.path.join(output_directory,
"DarkSky_PriorData_" + date_time_string)
priors = getHistory.runGetHistory(api_key_location,
cities_location,
prior_location,
history_directory,
date_to_predict,
past_days=past_days)
#Posterior
posterior_file = os.path.join(output_directory,
"Posteriors_" + date_time_string + '.csv')
cityDict = getForecasts.getCities(cities_location)
#date_to_predict = datetime.date_to_predict(2017, 07, 21)
cityStates = cityDict.keys()
cityStates.sort()
posteriors = {}
for cityState in cityStates:
posteriors[cityState] = computePosteriorArguments(
likelihoods[cityState][date_to_predict], priors[cityState])
pprint(posteriors)
writePosteriorArguments(posteriors, posterior_file, date_to_predict)
return posteriors
def getPosteriors():
cityDict = getForecasts.getCities('G:\School\VA Tech Courses\ZhangLab\DataIncubator\LatLongCities.csv')
outfile = 'C:\Users\jsporter\Downloads\Temp\Posteriors_2017_07_21.csv'
date = datetime.date(2017, 07, 21)
likelihoods = extractInfo.main()
priors = getHistory.parseArgs()
cityStates = cityDict.keys()
cityStates.sort()
posteriors = {}
for cityState in cityStates:
posteriors[cityState] = computePosteriorArguments(likelihoods[cityState][date], priors[cityState])
pprint(posteriors)
writePosteriorArguments(posteriors, outfile, date)
return posteriors
def parseArgs():
DarkSkyKey = getForecasts.getAPIKeys(
'G:\Weather\Weather_API_Keys.csv')["DarkSky"]
cityDict = getForecasts.getCities('G:\Cities\LatLongCities.csv')
outfile = 'G:\Temp\Priors_2017_07_21.csv'
priorDataLocation = 'G:Temp\DarkSky_PriorData_2017_07_21'
allHistoriesFile = os.path.join(priorDataLocation, "allHistories.pickle")
booleanDarkSkyHistory = False
thisDay = False
date = datetime.date(2017, 07, 21)
past_days = 20
cityStateKeys = cityDict.keys()
cityStateKeys.sort()
priorWriter = setupPriorWriter(outfile)
allPriorArguments = {}
counter = 0
allHistories = {}
if not booleanDarkSkyHistory:
allHistories = pickle.load(open(allHistoriesFile, 'rb'))
for cityState in cityStateKeys:
# if counter >= 2:
# break
counter += 1
if booleanDarkSkyHistory:
latlon = cityDict[cityState]
history = getHistory(DarkSkyKey,
latlon[0],
latlon[1],
date,
past_days,
priorDataLocation,
thisDay=thisDay)
allHistories[cityState] = history
else:
history = allHistories[cityState]
if not thisDay:
priorArguments = getPriorArguments(history)
allPriorArguments[cityState] = priorArguments
writePriorArguments(priorArguments, cityState, date, priorWriter)
if booleanDarkSkyHistory:
pickle.dump(allHistories, open(allHistoriesFile, 'wb'))
pprint(allHistories)
pprint(allPriorArguments)
return allPriorArguments
def getAllWeights(cities_location, weight_directory):
cities = getForecasts.getCities(cities_location).keys()
return {cityState : getWeights(cityState, weight_directory) for cityState in cities}