def downloadData(self,nlat_start,nlat_end):
'''downloadData(self,nlat_start,nlat_end)
routine to download data by latitude grid point.
nlat_start, nlat_end are the indexes of the desired
latitude points (starting from 0 and ending in self.nlat)
Done this way because takes a long time to download data, so
in case some part of the data are missing, can add...
saves downloaded data into DarkSky.csv'''
import pandas as pd
from forecastiopy import ForecastIO,FIODaily
times = self.dates["time"].to_list()
key = "43b5a6b6b264933c0cac096ee0900db4"
df = pd.DataFrame()
for lat in range(nlat_start,nlat_end):
for lon in self.lons:
for t in range(0, len(times)):
OKflag = True
try:
forecast = ForecastIO.ForecastIO(
key,
latitude=lat,
longitude=lon,
time=times[t],
exclude=["currently", "hourly"],
)
daily = FIODaily.FIODaily(forecast)
new_line_dict = daily.get()
except:
OKflag=False
if OKflag:
if df.empty:
df = pd.DataFrame.from_dict(new_line_dict["data"])
df["time"] = times[t][0:10]
df["lat"] = lat
df["lon"] = lon
else:
df1 = pd.DataFrame.from_dict(new_line_dict["data"])
df1["time"] = times[t][0:10]
df1["lat"] = lat
df1["lon"] = lon
df = pd.concat([df, df1], sort=True)
print(lon, "out of ", self.nlons)
print(lat,'out of ',nlat_start,'-',nlat_end)
# if wants to save from scratch each iteration (recommended..):
df.to_csv('DarkSky.csv')
def TomorrowWeather(apikey, nowLatitude, nowLongitude, selCity):
global tomorrowList
tomorrowList = []
# Start from tomorrow 00:00
selDate = datetime.datetime.now() + datetime.timedelta(days=1)
startTime = datetime.datetime(selDate.year, selDate.month, selDate.day,
0).isoformat()
fio = CallFIO(apikey, nowLatitude, nowLongitude, startTime)
if fio.has_daily() is True:
daily = FIODaily.FIODaily(fio)
tomorrowList = daily.get()['data']
for num in range(len(tomorrowList)):
for idx, item in enumerate(tomorrowList[num].items()):
# Change timestamp to a readable time format
if bool(re.findall('time', item[0], re.IGNORECASE)) == True:
tomorrowList[num][
item[0]] = datetime.datetime.fromtimestamp(
item[1]).strftime('%Y-%m-%d %H:%M:%S')
index_value = tomorrowList[0].values()
index_name = tomorrowList[0].keys()
report = {'': list(index_name), 'Forecast': list(index_value)}
df = pd.DataFrame(report, columns=['', 'Forecast'])
outdir_LOG = "./Files/" + selCity + "/"
if not os.path.exists(outdir_LOG):
os.makedirs(outdir_LOG, exist_ok=True)
nowTime = selDate.strftime('%Y%m%d')
filename = outdir_LOG + nowTime + '.csv'
if not os.path.exists(filename):
df.to_csv(filename, index=False, encoding='utf-8-sig')
else:
df = pd.read_csv(filename)
df['Forecast'] = list(index_value)
df = df.rename(columns={'Unnamed: 0': ''})
df.to_csv(filename, index=False, encoding='utf-8-sig')
return True
else:
return False
def YesterdayWeather(apikey, nowLatitude, nowLongitude, selCity):
global yesterdayList
yesterdayList = []
# Start from yesterday 00:00
selDate = datetime.datetime.now() - datetime.timedelta(days=1)
startTime = datetime.datetime(selDate.year, selDate.month, selDate.day,
0).isoformat()
fio = CallFIO(apikey, nowLatitude, nowLongitude, startTime)
if fio.has_daily() is True:
daily = FIODaily.FIODaily(fio)
yesterdayList = daily.get()['data']
for num in range(len(yesterdayList)):
for idx, item in enumerate(yesterdayList[num].items()):
# Change timestamp to a readable time format
if bool(re.findall('time', item[0], re.IGNORECASE)) == True:
yesterdayList[num][
item[0]] = datetime.datetime.fromtimestamp(
item[1]).strftime('%Y-%m-%d %H:%M:%S')
index_value = yesterdayList[0].values()
outdir_LOG = "./Files/" + selCity + "/"
if not os.path.exists(outdir_LOG):
os.makedirs(outdir_LOG, exist_ok=True)
nowTime = selDate.strftime('%Y%m%d')
filename = outdir_LOG + nowTime + '.csv'
if not os.path.exists(filename):
ctypes.windll.user32.MessageBoxW(
0, "No past forecast record for yesterday", "Warning!", 0)
return False
else:
df = pd.read_csv(filename)
df['Reality'] = list(index_value)
df = df.rename(columns={'Unnamed: 0': ''})
df.to_csv(filename, index=False, encoding='utf-8-sig')
return True
else:
return False
def check_for_freeze(self):
if self._fio.has_daily():
daily = FIODaily.FIODaily(self._fio)
for x in range(0, 14):
try:
tempMin = daily.get_day(x)["temperatureMin"]
if float(tempMin) <= 32:
fmt = "%m-%d-%Y"
now_time = datetime.now(
timezone('US/Eastern')) + timedelta(days=x)
time = now_time.strftime(
fmt) # + datetime.timedelta(days=x)
#print(time)
TEXT = "Hello GardeNet User,\n\nThe forecast for " + time + " shows that the temperature" \
" may reach " + str(tempMin) + " degrees. We suggest preparing your system for the " \
"freeze. \n\nRegards,\nGardeNet"
Alert(TEXT)
break
except:
pass
def __init__(self, *args, **kwargs):
super(Forecast, self).__init__(*args, **kwargs)
self.current_forecast = FIOCurrently.FIOCurrently(self)
self.hourly_forecast = FIOHourly.FIOHourly(self)
self.daily_forecast = FIODaily.FIODaily(self)
def get_max_temp(self):
if self._fio.has_daily():
daily = FIODaily.FIODaily(self._fio)
return daily.get_day(0)["temperatureMax"]
col_names.append("Max_5")
col_names.append("Min_avg")
col_names.append("Max_max")
#Obtain and read in weather data through Python3 wrapper for Dark Sky API
for i in range(len(cities)):
fio = ForecastIO.ForecastIO(key, latitude = cities[i].get_lat(),
longitude = cities[i].get_lon(), units= 'si' )
daily = FIODaily.FIODaily( fio )
for day in range(2, 7):
val = daily.get( day )
cities[i].add_minmax( str(val[ "temperatureMin" ]),
str(val[ "temperatureMax" ]))
#Write to CSV
util.print_as_csv(out_file_name, cities, col_names)