def getDetailsForDate(location, m, d):
# Connects to weather.com and downloads data for a given day of the year,
# for each year we've specified. From the data we download, extract the
# bits we're interested in
data = {}
data["Date"] = "/".join([str(m).zfill(2), str(d).zfill(2)]) # MM/DD
data["Results"] = []
for y in range(years[first], years[last] + 1):
# weather.com is missing random data entries all over the place; when
# we hit one, handle it gracefully rather than crashing
try: # data found!
results = json.loads(
weathercom.getCityWeatherDetails(
location,
queryType="particular-date-data",
date={
"year": "{:04}".format(y),
"month": "{:02}".format(m),
"date": "{:02}".format(d)
}))
data["Results"].append(extractInterestingData(results))
except: # data missing OR invalid date
print("MISSING DATA:", location, data["Date"])
data["Results"].append({
"LowTempC": "n/a",
"HighTempC": "n/a",
"Weather": "n/a"
})
data = consolidateWeatherData(data)
return data
def weatherReport(city):
weatherDetails = weathercom.getCityWeatherDetails(city)
print(weatherDetails)
humidity = json.loads(weatherDetails)["vt1observation"]["humidity"]
temp = json.loads(weatherDetails)["vt1observation"]["temperature"]
phrase = json.loads(weatherDetails)["vt1observation"]["phrase"]
return humidity, temp, phrase
def predict(self):
model = load('./package/model.joblib')
self.rain_json = json.loads(
weathercom.getCityWeatherDetails(city="besiktas",
queryType=self.query_type))
# hour ve isRainy dinamik.
for hour, is_rainy in self.find_rain():
ex = [[
self.date.month, self.date.day, hour, self.is_holiday,
self.is_weekend, self.is_school_holiday, is_rainy
]]
label = model.predict(ex)[0]
self.will_be_returned['result'].append({
'hour':
hour,
'rain':
is_rainy,
'speed':
SPEED_CLASS[str(label)]
})
return self.will_be_returned
def PrevisaoDoTempo(cidade):
weatherDetails = weathercom.getCityWeatherDetails(
city=cidade, queryType="daily-data")
print(
f'Cidade: {weatherDetails[weatherDetails.find("city")+8:weatherDetails.find("longitude")-4]}'
)
temperatura = weatherDetails[weatherDetails.find("temperature") +
14:weatherDetails.find("temperatureMax") -
3]
print(f'Temperatura: {temperatura}ºC')
temperaturamax = weatherDetails[weatherDetails.find("temperatureMax") +
25:weatherDetails.find("uvIndex") - 3]
print(f'Temperatura máxima desde as 7 da manhã: {temperaturamax}ºC')
sensacao = weatherDetails[weatherDetails.find("feelsLike") +
12:weatherDetails.find("gust") - 3]
print(f'Sensação térmica: {sensacao}ºC')
umidade = weatherDetails[weatherDetails.find("humidity") +
11:weatherDetails.find("icon") - 3]
print(f'Umidade relativa do ar: {umidade}%')
precip = weatherDetails[weatherDetails.find("precip24") +
15:weatherDetails.find("snowD") - 3]
print(f'Precipitação do dia: {precip}mm')
vento = weatherDetails[weatherDetails.find("windSpeed") +
11:weatherDetails.find("windDir") - 3]
print(f'Velocidade do vento: {vento}km/h')
return (temperaturamax, temperatura, precip)
def weathercomapi():
# check for single or double quotes
# https://pypi.org/project/weathercom/
tenday = weathercom.getCityWeatherDetails(city="bs273al", queryType="ten-days-data")
forcast = json.loads(tenday)
day = forcast['vt1dailyForecast']['dayOfWeek'][0]
tempnow = forcast['vt1dailyForecast']['day']['temperature'][0]
iconnow = forcast['vt1dailyForecast']['day']['icon'][0]
context = {
'today': day,
'todaytemp': tempnow,
'todayicon': iconnow,
# 'day1pop': day1pop,
# 'day1rain': day1rain,
# 'day1snow': day1snow,
# 'day2min': "%.0f" % day2min,
# 'day2max': "%.0f" % day2max,
# 'day2desc': day2desc,
# 'icon2': icon2,
# 'day2pop': day2pop,
# 'day2rain': day2rain,
# 'day2snow': day2snow,
}
return render_template("weathercom.html", page_title="Nested dictionary", weather=context)
def precip(num=False, city='moscow'):
weatherDetails = weathercom.getCityWeatherDetails(city)
weatherDetails = eval(weatherDetails.replace("null", "None"))
s = weatherDetails['vt1observation']['precip24Hour']
if num:
return s
else:
return '%s мм' % s
def pressure(num=False, city='moscow'):
weatherDetails = weathercom.getCityWeatherDetails(city)
weatherDetails = eval(weatherDetails.replace("null", "None"))
s = weatherDetails['vt1observation']['altimeter']
if num:
return s
else:
return '%s мбар' % s
def snow(num=False, city='moscow'):
weatherDetails = weathercom.getCityWeatherDetails(city)
weatherDetails = eval(weatherDetails.replace("null", "None"))
s = weatherDetails['vt1observation']['snowDepth']
if num:
return s
else:
return '%s м' % s
def wind_speed(num=False, city='moscow'):
weatherDetails = weathercom.getCityWeatherDetails(city)
weatherDetails = eval(weatherDetails.replace("null", "None"))
s = weatherDetails['vt1observation']['windSpeed']
if num:
return s
else:
return '%s км/ч' % s
def max_temp(num=False, city='moscow'):
weatherDetails = weathercom.getCityWeatherDetails(city)
weatherDetails = eval(weatherDetails.replace("null", "None"))
s = weatherDetails['vt1observation']['temperatureMaxSince7am']
if num:
return s
else:
return '%s °C' % s
def temp_feel(num=False, city='moscow'):
weatherDetails = weathercom.getCityWeatherDetails(city)
weatherDetails = eval(weatherDetails.replace("null", "None"))
s = weatherDetails['vt1observation']['feelsLike']
if num:
return s
else:
return '%s °C' % s
def getAirTemp(loadingWhat): #NOT DONE
loading(loadingWhat)
query = "\"temperature\""
weatherDetailsJSON = weathercom.getCityWeatherDetails("Gwynn, Virginia")
jsonData = json.loads(weatherDetailsJSON)
temp = int(jsonData["vt1observation"]["temperature"])
temp = (temp * 9 / 5 + 32)
print("AIR TEMP(at gwynn island, virginia) : {0}".format(temp) + "\n")
def weather():
speak('which city, sir.')
city = takeCommand()
weatherDetails = weathercom.getCityWeatherDetails(city)
humidity = json.loads(weatherDetails)["vt1observation"]["humidity"]
temp = json.loads(weatherDetails)["vt1observation"]["temperature"]
phrase = json.loads(weatherDetails)["vt1observation"]["phrase"]
print('humidity =', humidity)
print('temperature =', temp, 'c')
print('climate', phrase)
speak('report from' + str(city) + 'sir')
speak('humidity' + str(humidity))
speak('temperature' + str(temp) + 'degree celsius')
speak('climate' + str(phrase))
import weathercom
import requests
import pandas as pd
import matplotlib as plt
weatherDetails = weathercom.getCityWeatherDetails("san fransisco",
queryType="ten-days-data")
print(weatherDetails)
df = pd.read_json(weatherDetails, convert_axes=True)
print(df)
#plt.show()
async def __weather(city):
data = weathercom.getCityWeatherDetails(city)
return loads(data)
def printit():
threading.Timer(60.0, printit).start() # Run o código a cada 1h (3600 s)
cidade = "Novo Hamburgo"
weatherDetails = weathercom.getCityWeatherDetails(city=cidade,
queryType="daily-data")
print(
f'Cidade: {weatherDetails[weatherDetails.find("city")+8:weatherDetails.find("longitude")-4]}'
)
temperatura = weatherDetails[weatherDetails.find("temperature") +
14:weatherDetails.find("temperatureMax") - 3]
print(f'Temperatura: {temperatura}ºC')
temperaturamax = weatherDetails[weatherDetails.find("temperatureMax") +
25:weatherDetails.find("uvIndex") - 3]
print(f'Temperatura máxima desde as 7 da manhã: {temperaturamax}ºC')
sensacao = weatherDetails[weatherDetails.find("feelsLike") +
12:weatherDetails.find("gust") - 3]
print(f'Sensação térmica: {sensacao}ºC')
umidade = weatherDetails[weatherDetails.find("humidity") +
11:weatherDetails.find("icon") - 3]
print(f'Umidade relativa do ar: {umidade}%')
precip = weatherDetails[weatherDetails.find("precip24") +
15:weatherDetails.find("snowD") - 3]
print(f'Precipitação do dia: {precip}mm')
vento = weatherDetails[weatherDetails.find("windSpeed") +
11:weatherDetails.find("windDir") - 3]
print(f'Velocidade do vento: {vento}km/h')
print('\nConexão por protocolo MQTT')
def envia_relatorio(cliente):
previsao = [{
'variable': 'temperaturamin',
'value': temperatura
}, {
'variable': 'temperaturamax',
'value': temperaturamax
}, {
'variable': 'chuva',
'value': precip
}]
json_file = json.dumps(previsao)
cliente.publish(topico1, payload=json_file, qos=1,
retain=True) # Publica os dados no broker com retenção
# Método que exibe o registro da comunicacao por protocolo mqtt no terminal
def on_log_esdra(esdra, userdata, level, buf):
print("log: ", buf)
# Rotina que trata o evento de conexao, exibindo o return code e subscrevendo o cliente aos topicos de interesse
def on_connect_esdra(esdra, userdata, flags, rc):
print("[STATUS] Conectado ao Broker " + broker +
" Resultado de conexao: " + str(rc))
print("subscrevendo ao topico", topico1)
# Método que exibe o registro da comunicacao por protocolo mqtt no terminal
def on_log_carlos(esdra, userdata, level, buf):
print("log: ", buf)
# Rotina que trata o evento de conexao, exibindo o return code e subscrevendo o cliente aos topicos de interesse
def on_connect_carlos(esdra, userdata, flags, rc):
print("[STATUS] Conectado ao Broker " + broker +
" Resultado de conexao: " + str(rc))
print("subscrevendo ao topico", topico1)
def configura_cliente(cliente, id):
cliente.loop_start()
print("Conectando ao broker...")
cliente.connect(broker, porta)
if id == 1:
cliente.on_connect = on_connect_esdra
cliente.on_log = on_log_esdra
if id == 2:
cliente.on_connect = on_connect_carlos
cliente.on_log = on_log_carlos
envia_relatorio(cliente)
time.sleep(5)
cliente.loop_stop()
# Definindo os objetos
broker = "mqtt.tago.io" # Endereço do broker
porta = 1883 # Porta sem segurança para testes
#keepAlive = 60 # Tempo em segundos para o envio de uma requisicao ping
# Topicos para publicar os dados no tago.io
topico1 = "tago/data/previsao"
print("Criando nova instancia")
esdra = mqtt.Client()
esdra.username_pw_set('', "e35c4944-06a4-46f1-be9d-243af76bd4a0")
print("Configurando o cliente")
configura_cliente(esdra, 1)
print("Criando nova instancia")
carlos = mqtt.Client()
carlos.username_pw_set('', '7f1d7f85-761e-4b98-92b4-7bab3f528b82')
print("Configurando o cliente")
configura_cliente(carlos, 2)
datetime.date(2020, 4, 19),
datetime.date(2021, 1, 1),
datetime.date(2021, 3, 3),
datetime.date(2020, 5, 1),
datetime.date(2020, 5, 6),
datetime.date(2020, 5, 24),
datetime.date(2020, 9, 6),
datetime.date(2020, 9, 22),
datetime.date(2020, 12, 24),
datetime.date(2020, 12, 25),
datetime.date(2020, 12, 26)
]
sc_regr = joblib.load('std_scaler.bin')
data = weathercom.getCityWeatherDetails(city='Sofia',
queryType="ten-days-data")
# This would be an input to the ANN to estimate the air quality for the next few days
df = pd.read_json(data)
weather = pd.DataFrame()
date = pd.DataFrame()
weather['Temperature'] = df['vt1dailyForecast']['day']['temperature']
weather['Humidity'] = df['vt1dailyForecast']['day']['humidityPct']
date['Date'] = pd.to_datetime(df['vt1dailyForecast']['validDate'])
weather['IsHoliday'] = date['Date'].apply(is_holiday)
weather['Weekday'] = date['Date'].dt.dayofweek
weather['Season'] = date['Date'].apply(season)
def get_weather():
# This would be an input to the ANN to estimate the air quality for the next few days
data = weathercom.getCityWeatherDetails(city='Sofia',
queryType="ten-days-data")
return data
