]
# A thread-safe task queue that stores actions to perform against Reddit
task_queue = SimpleQueue()
# Config the logger
logging.basicConfig(filename='weather_output.log',
filemode='w',
level=logging.INFO)
# Set up the weather API for later usage
config = configparser.ConfigParser()
config.read('weather_api.ini')
api_key = config['weatherbit.io']['api_key']
weather_api = Api(api_key)
weather_api.set_granularity('daily')
# Load locations to check from stored CSV file
locations = []
with open('top_150_cities.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile, fieldnames=['city', 'region', 'id'])
for row in reader:
locations.append(row)
# Setup Reddit Connection
reddit = praw.Reddit(
'niceweatherbot',
user_agent='python:niceweatherbot:v1.0.0 (by u/jakekarnes42)')
# Create a thread pool
pool = ThreadPoolExecutor()
from machine import Pin
from time import localtime, sleep
from weatherbit.api import Api
import threading
api_key = "key"
lat = 49.44684
lon = 8.357229999999959
api = Api(api_key)
api.set_granularity('daily')
class MQTT_subcriber(): #Class for controlling the ESP
pass
def auto_water(
): #This function looks what the forecast says and let flow enough water for everything
pass
def manuell(): #function for manuell control e.g. if it is not enough water
pass
threading.Thread(target=auto_water).start()
threading.Thread(target=manuell).start()
class weatherbit(awp):
def __init__(self, location, listOfTimePredictions) :
self.name = "weatherbit"
self.allowedTimePredictions = "N-N-0"
super(weatherbit, self).__init__(self.name, listOfTimePredictions)
self.login()
self.setLocation(location)
def login(self):
if self.login_data["apikey"] :
self.api = Api(self.login_data["apikey"])
else :
warnings.warn('MWP_WARNING. The "%s" Weather Package(WP) has not been \
properly activated due to the apikey' % (self.name.upper()))
def setLocation(self,location):
self.forecast = self.api.get_forecast(lat=location[0],lon=location[1])
def update(self,days=["ALL"]) :
listOfTimePredictions = self.WD.keys()
for api_periods in self.weather.keys() :
if api_periods == "daily" :
self.api.set_granularity('daily')
self.weather[api_periods] = self.forecast.get_series(['temp','precip','rh','wind_spd'])
elif api_periods == "hourly" :
self.api.set_granularity('hourly')
self.weather[api_periods] = self.forecast.get_series(['temp','precip','rh','wind_spd'])
elif api_periods == "currently" :
self.api.set_granularity('hourly')
self.weather[api_periods] = self.forecast.get_series(['temp','precip','rh','wind_spd'])[0]
for timePrediction in listOfTimePredictions :
self.setTemperature(timePrediction)
self.setWindVelocity(timePrediction)
self.setRainProbability(timePrediction)
self.setRainIntensity(timePrediction)
self.setRelativeHumidity(timePrediction)
def setTemperature(self,timePrediction="0-0-0"):
#Units: ºC
value = self.setWeatherProperty(timePrediction,'temp')
self.WD[timePrediction]["Temperature"] = value
def setWindVelocity(self,timePrediction="0-0-0"):
#Units: m/s
value = self.setWeatherProperty(timePrediction,'wind_spd')
self.WD[timePrediction]["WindVelocity"] = value
def setRainProbability(self,timePrediction="0-0-0"):
#Units: Percentatge [0-1]
#Warning: Weatherbit does not calculate Weather Prediction
self.WD[timePrediction]["RainProbability"] = None
def setRainIntensity(self,timePrediction="0-0-0"):
#Units: mm/hour
value = self.setWeatherProperty(timePrediction,'precip')
self.WD[timePrediction]["RainIntensity"] = value
def setRelativeHumidity(self,timePrediction="0-0-0"):
#Units: Percentatge [0-1]
value = self.setWeatherProperty(timePrediction,'rh')
self.WD[timePrediction]["RelativeHumidity"] = value/100.0 if value else value
def setWeatherProperty(self,timePrediction,WP) :
value = None
day, hour, minute, predictionType = decomposeTimePrediction(timePrediction)
try :
if day :
value = self.weather[predictionType][day][WP]
elif hour : # N hours
value = self.weather[predictionType][hour][WP]
elif not day and \
not hour and \
not minute :
value = self.weather[predictionType][WP]
except :
print("WP[%s]: \"%s\" is not available for \"%s\" prediction. " % (self.name,WP,predictionType))
return value
model_best_cv, [model_best_val, model_best_test]))
print('--Best model in CV not best in val and in test datasets/n')
# plot metrics
# df_metrics.pivot(index='df', columns='model', values='MSE').plot()
# df_metrics.dtypes
# get datetime column from date and period by mapping time and period with "df_map_HH_Period"
# plt.scatter(x = df_train['pred.GradBoostRegr'], y = df_train['dmd_coeff'])
###################################################################################################################################################
################
#### Step 5 ####
################
from weatherbit.api import Api # pip install pyweatherbit
api_call = Api(key_API_WeatherFct)
api_call.set_granularity('daily') # 'hourly', '3hourly'
df_weather_Fct = pd.DataFrame()
for iCity in list_cities:
print('{}: Downloading Weather Forecast for {}'.format(
ddt.now().strftime('%d%b%Y %H:%M:%S:'), iCity))
tmp_df_weather_fct = pd.DataFrame(
api_call.get_forecast(city=iCity, country='GB').get_series(
['temp', 'max_temp', 'min_temp', 'precip']))
tmp_df_weather_fct['City'] = iCity
df_weather_Fct = df_weather_Fct.append(tmp_df_weather_fct)
# simple daily average; more accurate would be to have a population- or other-weighted average temperature; other measure for wind speed
df_weather_Fct_avg = df_weather_Fct[[
'datetime', 'temp'
]].groupby('datetime').mean().reset_index()
df_weather_Fct_avg['date'], df_weather_Fct_avg['time'] = df_weather_Fct_avg[
'datetime'].dt.date, df_weather_Fct_avg['datetime'].dt.time
# Class for making Weatherbit API calls. This class will be instantiated as a
# singleton to gather all necessary information from the API.
from weatherbit.api import Api
import API_Key
import json
# Get the private key string from API_KEY.
api_key = API_Key.API_Key().Weatherbit_Key
# Authenticate our API Key. This variable will be used for all API calls.
weatherbit = Api(api_key)
# Set the API for retrieving the daily forecast.
weatherbit.set_granularity('daily')
# Gets the forecast for the current day.
# Params:
# lat: Float: The latitude of the city.
# long: Float: The longitude of the city.
# Return: List containing the precipitation, average temperature, and max
# temperature per hour from midnight to the current hour.
def get_daily_forecast(lat, long):
forecast = weatherbit.get_forecast(lat=lat, lon=long)
precip = forecast.json['data'][0]['precip']
precip = precip / 25.4 # Convert mm to inches
avg_temp = forecast.json['data'][0]['temp']
avg_temp = avg_temp * 1.8 + 32 # Convert Celsius to Fahrenheit
max_temp = forecast.json['data'][0]['max_temp']
max_temp = max_temp * 1.8 + 32
weather = [precip, avg_temp, max_temp]