def get_twc_forecast(stid, api_key, forecast_date):
# retrieve api json data
api_url = 'https://api.weather.com/v3/wx/forecast/daily/5day'
api_options = {
'language': 'en-US',
'format': 'json',
'units': 'e',
'apiKey': api_key,
'icaoCode': stid
}
response = requests.get(api_url, params=api_options)
twc_data = response.json()
# Raise error for invalid HTTP response
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
print('twc: got HTTP error when querying API')
raise
# The data has a 'daypart' section which has a time series of day/night pairs. This is useful for wind and
# precipitation information, but we have to make some assumptions about the datetime to use it.
twc_df = pd.DataFrame(twc_data['daypart'][0])
valid_days = [epoch_time_to_datetime(d) for d in twc_data['validTimeUtc'] for _ in range(2)]
twc_df['DateTime'] = pd.Series(valid_days) + twc_df['dayOrNight'].apply(dn_to_timedelta)
if twc_df['dayOrNight'][0] is None:
twc_df.drop(0, axis=0, inplace=True)
# Add dew point, fix units, and rename
twc_df['dewpoint'] = dewpoint_from_t_rh(twc_df['temperature'], twc_df['relativeHumidity'])
twc_df['windSpeed'] = twc_df['windSpeed'].apply(mph_to_kt)
column_names_dict = {
'cloudCover': 'cloud',
'qpf': 'rain',
'wxPhraseLong': 'condition'
}
twc_df = twc_df.rename(columns=column_names_dict)
twc_df.set_index('DateTime', inplace=True)
# Resample to 3-hourly. Carefully consider the QPF.
offset = twc_df.index[0].hour % 3
twc_hourly = twc_df.resample('3H', base=offset).interpolate()
twc_hourly['rain'] = twc_hourly['rain'].apply(lambda x: x / 4.)
twc_hourly['qpfSnow'] = twc_hourly['qpfSnow'].apply(lambda x: x / 4.)
twc_hourly['windDirection'] = twc_hourly['windDirection'].round()
# calculate daily values
forecast_start = forecast_date.replace(hour=6)
forecast_end = forecast_start + timedelta(days=1)
daily_high = twc_hourly.loc[forecast_start:forecast_end, 'temperature'].max()
daily_low = twc_hourly.loc[forecast_start:forecast_end, 'temperature'].min()
daily_wind = twc_hourly.loc[forecast_start:forecast_end, 'windSpeed'].max()
daily_rain = twc_hourly.loc[forecast_start:forecast_end - timedelta(hours=1), 'rain'].sum()
# create Forecast object
forecast = Forecast(stid, default_model_name, forecast_date)
forecast.daily.set_values(daily_high, daily_low, daily_wind, daily_rain)
forecast.timeseries.data = twc_hourly.reset_index()
return forecast
def get_accuwx_forecast(config, stid, location_key, api_key, forecast_date):
"""
Get a Forecast from the AccuWeather API or the cache file.
:param config:
:param stid:
:param location_key:
:param api_key:
:param forecast_date:
:return:
"""
# Check if we have a cached file and if it is recent enough
site_directory = '%s/site_data' % config['THETAE_ROOT']
cache_file = '%s/%s_accuwx.txt' % (site_directory, stid)
cache_ok = check_cache_file(config, cache_file)
# Retrieve data. Looks like only daily temperatures will be of any use right now.
if not cache_ok:
api_url = 'http://dataservice.accuweather.com/forecasts/v1/daily/5day/%s' % location_key
api_options = {'apikey': api_key}
response = requests.get(api_url, params=api_options)
accuwx_data = response.json()
# Raise error if we have invalid HTTP response
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
print('accuweather: got HTTP error when querying API')
raise
# Cache the response
with open(cache_file, 'w') as f:
f.write(response.text)
else:
accuwx_data = json.load(open(cache_file))
# Convert to pandas DataFrame, fix time, and get high and low
accuwx_df = pd.DataFrame(accuwx_data['DailyForecasts'])
accuwx_df['DateTime'] = np.nan
for idx in accuwx_df.index:
accuwx_df.loc[idx, 'DateTime'] = localized_date_to_utc(
parse_iso(accuwx_df.loc[idx, 'Date'])).replace(hour=0)
accuwx_df.set_index('DateTime', inplace=True)
high = float(accuwx_df.loc[forecast_date,
'Temperature']['Maximum']['Value'])
# Low should be for night before. We can also 'guess' that the low could be non-diurnal and halfway between the
# max and next min.
low = float(accuwx_df.loc[forecast_date - timedelta(days=1),
'Temperature']['Minimum']['Value'])
alt_low = 0.5 * (high + float(
accuwx_df.loc[forecast_date, 'Temperature']['Minimum']['Value']))
if low - alt_low > 3:
if config['debug'] > 9:
print(
'accuweather: warning: setting low down from %0.0f to %0.0f' %
(low, alt_low))
low = alt_low
# Create Forecast object
forecast = Forecast(stid, default_model_name, forecast_date)
forecast.daily.set_values(high, low, None, None)
return forecast
def get_yrno_forecast(stid, state, city, forecast_date):
"""
Retrieve yr.no forecast for a city, state
:param stid:
:param state:
:param city:
:param forecast_date:
:return:
"""
yrno_url = 'https://www.yr.no/place/United_States/%s/%s/forecast_hour_by_hour.xml' % (state, city)
response = requests.get(yrno_url)
# Raise error for invalid HTTP response
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
print('nws: got HTTP error when querying for XML file from %s' % yrno_url)
raise
# Get the XML tree into dictionary form
hourly_xml = eTree.fromstring(response.text)
hourly_dict = etree_to_dict(hourly_xml)
hourly_list = hourly_dict['weatherdata']['forecast']['tabular']['time']
timezone = hourly_dict['weatherdata']['location']['timezone']['@id']
# Create a DataFrame for hourly data
hourly = pd.DataFrame()
hourly['DateTime'] = [v['@from'] for v in hourly_list]
hourly['DateTime'] = hourly['DateTime'].apply(parse_iso).apply(lambda x: x.tz_localize(timezone))
hourly['DateTime'] = hourly['DateTime'].apply(lambda x: x.astimezone('UTC').replace(tzinfo=None))
hourly['datetime_index'] = hourly['DateTime']
hourly.set_index('datetime_index', inplace=True)
# Add in the other parameters
hourly['temperature'] = [c_to_f(to_float(v['temperature']['@value'])) for v in hourly_list]
hourly['windSpeed'] = [ms_to_kt(to_float(v['windSpeed']['@mps'])) for v in hourly_list]
hourly['windDirection'] = [to_float(v['windDirection']['@deg']) for v in hourly_list]
hourly['pressure'] = [to_float(v['pressure']['@value']) for v in hourly_list]
hourly['condition'] = [v['symbol']['@name'] for v in hourly_list]
hourly['rain'] = [mm_to_in(to_float(v['precipitation']['@value'])) for v in hourly_list]
# Aggregate daily values from hourly series
forecast_start = forecast_date.replace(hour=6)
forecast_end = forecast_start + timedelta(days=1)
hourly_high = hourly.loc[forecast_start:forecast_end, 'temperature'].max()
hourly_low = hourly.loc[forecast_start:forecast_end, 'temperature'].min()
hourly_wind = hourly.loc[forecast_start:forecast_end, 'windSpeed'].max()
hourly_rain = hourly.loc[forecast_start:forecast_end - timedelta(hours=1), 'rain'].sum()
# Create the Forecast object
forecast = Forecast(stid, default_model_name, forecast_date)
forecast.daily.set_values(hourly_high, hourly_low, hourly_wind, hourly_rain)
forecast.timeseries.data = hourly
return forecast
def get_darksky_forecast(stid, lat, lon, api_key, forecast_date):
# Retrieve data
api_url = 'https://api.darksky.net/forecast/%s/%s'
point = '%0.3f,%0.3f' % (lat, lon)
api_options = {'exclude': 'currently,minutely,daily,alerts,flags'}
json_url = api_url % (api_key, point)
response = requests.get(json_url, params=api_options)
darksky_data = response.json()
# Raise error for invalid HTTP response
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
print('darksky: got HTTP error when querying API')
raise
# Convert to pandas DataFrame and fix time, units, and columns
darksky_df = pd.DataFrame(darksky_data['hourly']['data'])
darksky_df['DateTime'] = np.nan
for idx in darksky_df.index:
darksky_df.loc[idx, 'DateTime'] = epoch_time_to_datetime(
darksky_df.loc[idx, 'time']) # already UTC
darksky_df.set_index('DateTime', inplace=True)
column_names_dict = {
'cloudCover': 'cloud',
'dewPoint': 'dewpoint',
'precipIntensity': 'rain',
'windBearing': 'windDirection',
'summary': 'condition'
}
darksky_df = darksky_df.rename(columns=column_names_dict)
darksky_df.loc[:, 'cloud'] = 100. * darksky_df.loc[:, 'cloud']
darksky_df.loc[:, 'windSpeed'] = mph_to_kt(darksky_df.loc[:, 'windSpeed'])
darksky_df.loc[:, 'windGust'] = mph_to_kt(darksky_df.loc[:, 'windGust'])
# Calculate daily values
forecast_start = forecast_date.replace(hour=6)
forecast_end = forecast_start + timedelta(days=1)
daily_high = darksky_df.loc[forecast_start:forecast_end,
'temperature'].max()
daily_low = darksky_df.loc[forecast_start:forecast_end,
'temperature'].min()
daily_wind = darksky_df.loc[forecast_start:forecast_end, 'windSpeed'].max()
daily_rain = darksky_df.loc[forecast_start:forecast_end -
timedelta(hours=1), 'rain'].sum()
# Create Forecast object
forecast = Forecast(stid, default_model_name, forecast_date)
forecast.daily.set_values(daily_high, daily_low, daily_wind, daily_rain)
forecast.timeseries.data = darksky_df.reset_index()
return forecast
def get_mosx_forecast(stid, mosx_dir, forecast_date):
# Retrieve data
mosx_file = '%s/MOSX_%s_%s.json' % (
mosx_dir, stid.upper(), datetime.strftime(forecast_date, '%Y%m%d'))
with open(mosx_file, 'r') as f:
data = json.load(f)
# Create a Forecast object and add daily values
forecast = Forecast(stid, default_model_name, forecast_date)
forecast.daily.set_values(data['daily']['high'], data['daily']['low'],
data['daily']['wind'], data['daily']['precip'])
# Set the hourly data if it is present. Column names are already set!
if 'hourly' in data:
hourly_ds = pd.DataFrame(data['hourly'])
hourly_ds['DateTime'] = hourly_ds['DateTime'].apply(date_to_datetime)
forecast.timeseries.data = hourly_ds
return forecast
def gfs_mos_forecast(stid, forecast_date):
"""
Do the data retrieval.
"""
# Generate a Forecast object
forecast = Forecast(stid, default_model_name, forecast_date)
import numpy as np
forecast.daily.high = np.round(np.random.rand() * 100.)
forecast.daily.low = np.round(np.random.rand() * 100.)
forecast.daily.wind = np.round(np.random.rand() * 40.)
forecast.daily.rain = np.round(np.random.rand() * 3., 2)
# Create a dummy pd dataframe to test
forecast.timeseries.data['DateTime'] = [forecast_date, forecast_date +
timedelta(hours=3)]
forecast.timeseries.data['temperature'] = [56., 55.]
forecast.timeseries.data['dewpoint'] = [51., 51.]
return forecast
def get_aeris_forecast(stid, lat, lon, api_id, api_secret, forecast_date):
# Retrieve data
api_url = 'https://api.aerisapi.com/forecasts/%s'
point = '%0.3f,%0.3f' % (lat, lon)
api_options = {
'client_id': api_id,
'client_secret': api_secret,
'filter': '1hr',
'plimit': '60',
}
json_url = api_url % point
response = requests.get(json_url, params=api_options)
aeris_data = response.json()
# Raise error for invalid HTTP response
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
print('aeris: got HTTP error when querying API')
raise
# Convert to pandas DataFrame and fix time, units, and columns
aeris_df = pd.DataFrame(aeris_data['response'][0]['periods'])
aeris_df['DateTime'] = np.nan
for idx in aeris_df.index:
aeris_df.loc[idx, 'DateTime'] = localized_date_to_utc(
parse_iso(aeris_df.loc[idx, 'dateTimeISO']))
aeris_df.set_index('DateTime', inplace=True)
column_names_dict = {
'avgTempF': 'temperature',
'avgDewpointF': 'dewpoint',
'sky': 'cloud',
'windSpeedMaxKTS': 'windSpeed',
'windGustKTS': 'windGust',
'windDirDEG': 'windDirection',
'precipIN': 'rain',
'pressureMB': 'pressure',
'weatherPrimary': 'condition'
}
aeris_df = aeris_df.rename(columns=column_names_dict)
# Calculate daily values. Aeris includes period maxima and minima, although they appear just to be hourly values.
forecast_start = forecast_date.replace(hour=6)
forecast_end = forecast_start + timedelta(days=1)
try:
daily_high = aeris_df.loc[forecast_start:forecast_end,
'maxTempF'].max()
except KeyError:
daily_high = aeris_df.loc[forecast_start:forecast_end,
'temperature'].max()
try:
daily_low = aeris_df.loc[forecast_start:forecast_end, 'minTempF'].min()
except KeyError:
daily_low = aeris_df.loc[forecast_start:forecast_end,
'temperature'].min()
try:
daily_wind = aeris_df.loc[forecast_start:forecast_end,
'windSpeedMaxKTS'].max()
except KeyError:
daily_wind = aeris_df.loc[forecast_start:forecast_end,
'windSpeed'].max()
daily_rain = aeris_df.loc[forecast_start:forecast_end - timedelta(hours=1),
'rain'].sum()
# Create Forecast object
forecast = Forecast(stid, default_model_name, forecast_date)
forecast.daily.set_values(daily_high, daily_low, daily_wind, daily_rain)
forecast.timeseries.data = aeris_df.reset_index()
return forecast
def get_wunderground_forecast(stid, api_key, forecast_date):
# retrieve api json data
api_url = 'https://api.wunderground.com/api/%s/hourly/forecast/q/%s.json'
api_options = {'features': 'hourly,forecast'}
json_url = api_url % (api_key, stid)
response = requests.get(json_url, params=api_options)
wunderground_data = response.json()
# Raise error for invalid HTTP response
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
print('wunderground: got HTTP error when querying API')
raise
# Convert to DataFrame, fix time
wunderground_df = pd.DataFrame(wunderground_data['hourly_forecast'])
timezone_df = pd.DataFrame(wunderground_data['forecast']['simpleforecast'])
timezone = get_timezone(timezone_df['forecastday'])
time_series = convert_fcttime(wunderground_df['FCTTIME']) # already UTC
for column in wunderground_df.columns.values:
wunderground_df[column] = wunderground_df[column].apply(
get_english_units)
wunderground_df['mslp'] = inhg_to_mb(wunderground_df['mslp'])
wunderground_df['wspd'] = mph_to_kt(wunderground_df['wspd'])
wunderground_df['wdir'] = wunderground_df['wdir'].apply(get_wind_degrees)
column_names_dict = {
'FCTTIME': 'DateTime',
'temp': 'temperature',
'wspd': 'windSpeed',
'mslp': 'pressure',
'sky': 'cloud',
'dewpoint': 'dewpoint',
'qpf': 'rain',
'wdir': 'windDirection',
'wx': 'condition'
}
wunderground_df.drop('condition', axis=1, inplace=True)
wunderground_df = wunderground_df.rename(columns=column_names_dict)
wunderground_df['DateTime'] = time_series
wunderground_df.set_index('DateTime', inplace=True)
# calculate daily values
forecast_start = forecast_date.replace(hour=6)
forecast_end = forecast_start + timedelta(days=1)
daily_high = wunderground_df.loc[forecast_start:forecast_end,
'temperature'].max()
daily_low = wunderground_df.loc[forecast_start:forecast_end,
'temperature'].min()
daily_wind = wunderground_df.loc[forecast_start:forecast_end,
'windSpeed'].max()
daily_rain = wunderground_df.loc[forecast_start:forecast_end -
timedelta(hours=1), 'rain'].sum()
# create Forecast object
forecast = Forecast(stid, default_model_name, forecast_date)
forecast.daily.set_values(daily_high, daily_low, daily_wind, daily_rain)
forecast.timeseries.data = wunderground_df.reset_index()
return forecast
def get_gefs_mos_forecast(stid, forecast_date):
"""
Retrieve GEFS MOS data.
:param stid: station ID
:param forecast_date: datetime of day to forecast
:return: Forecast object for high, low, precip for next day. No wind.
"""
# Retrieve the model data
url = 'http://www.nws.noaa.gov/cgi-bin/mos/getens.pl?sta=%s' % stid
response = requests.get(url)
page = response.text
soup = BeautifulSoup(page, 'html.parser')
# Lists for tomorrow's ensemble data
ens_highs = []
ens_lows = []
ens_precips = []
dailys = []
# 22 total model runs
pars = soup.find_all('pre')
for ii in range(0,
len(pars) -
1): # last one is operational run... don't use that
text = pars[ii].text.split()
# control run
if ii == 0:
# get model time
dates = text[5].split('/')
hour = int(text[6])
model_time = datetime(int(dates[2]), int(dates[0]), int(dates[1]),
hour)
# find all of the forecast hours (every 12 hr)
forecast_hours_tmp = pars[ii].text.split('FHR')[1].split('\n')[0][:-6]
forecast_hours = list(map(int, re.findall(r'\d+', forecast_hours_tmp)))
# find all of the temperatures that match the forecast hours
forecast_temps_tmp = pars[ii].text.split('X/N')[1].split('\n')[0][:-6]
forecast_temps = list(
map(int, re.findall(r'-?\d+', forecast_temps_tmp)))
# find all of the 24 hour precips
forecast_precip_tmp = pars[ii].text.split('Q24')[1].split('|')[1]
forecast_precip = list(
map(int, re.findall(r'\d+', forecast_precip_tmp)))[0:5]
forecast_dates_utc = []
temps = []
for f, forecast_hour in enumerate(forecast_hours):
# append forecast time, but subtract 1 hour so the 00Z time is for the correct date
forecast_dates_utc.append(
(model_time + timedelta(hours=forecast_hour - 1)).date())
temps.append(forecast_temps[f])
forecast_dates_utc = np.array(forecast_dates_utc)
temps = np.array(temps)
valid_dates = np.where((forecast_dates_utc == forecast_date.date()))[0]
ens_highs.append(np.max(temps[valid_dates]))
ens_lows.append(np.min(temps[valid_dates]))
# the 24 hour precip for the next day is always the first value
ens_precips.append(qpf_interpreter(forecast_precip[0]))
# Add each member to the list of Daily objects, for writing to a file
daily = Daily(stid, forecast_date)
daily.model = 'GEFS MOS %d' % ii
daily.set_values(ens_highs[-1], ens_lows[-1], None, ens_precips[-1])
dailys.append(daily)
# Get ensemble mean
high_mean = np.round(np.mean(ens_highs))
low_mean = np.round(np.mean(ens_lows))
precip_mean = np.round(np.mean(ens_precips), 2)
# Create ensemble mean Forecast object
mean_forecast = Forecast(stid, default_model_name, forecast_date)
mean_forecast.daily.set_values(high_mean, low_mean, None, precip_mean)
return mean_forecast, dailys
def bufr_surface_parser(config, model, stid, forecast_date, bufr_file_name):
"""
By Luke Madaus. Modified by jweyn and joejoezz.
Parse surface data from a bufkit file.
"""
# Open bufkit file
infile = open(bufr_file_name, 'r', newline='')
# define variables
dateTime = []
temperature = []
dewpoint = []
windSpeed = []
windDirection = []
rain = []
pressure = []
block_lines = []
inblock = False
for line in infile:
if re.search(r'SELV', line):
try: # jweyn
elev = re.search(r'SELV = -?(\d{1,4})', line).groups()[0] # jweyn: -?
elev = float(elev)
except:
elev = 0.0
if line.startswith('STN YY'):
# We've found the line that starts the header info
inblock = True
block_lines.append(line)
elif inblock:
# Keep appending lines until we start hitting numbers
if re.search(r'\d{6}', line):
inblock = False
else:
block_lines.append(line)
# Build an re search pattern based on this
# We know the first two parts of the section are station id num and date
re_string = r"(\d{6}|\w{4}) (\d{6})/(\d{4})"
# Now compute the remaining number of variables
dum_num = len(block_lines[0].split()) - 2
for n in range(dum_num):
re_string = re_string + r" (-?\d{1,4}.\d{2})"
re_string = re_string + '\r\n'
for line in block_lines[1:]:
dum_num = len(line.split())
for n in range(dum_num):
re_string = re_string + r'(-?\d{1,4}.\d{2}) '
re_string = re_string[:-1] # Get rid of the trailing space
re_string = re_string + '\r\n'
# Compile this re_string for more efficient re searches
block_expr = re.compile(re_string)
# Now get corresponding indices of the variables we need
full_line = ''
for r in block_lines:
full_line = full_line + r[:-2] + ' '
# Now split it
varlist = re.split(r'[ /]', full_line)
# Now loop through all blocks that match the search pattern we defined above
blocknum = -1
infile.seek(0)
for block_match in block_expr.finditer(infile.read()):
blocknum += 1
# Split out the match into each component number
vals = list(block_match.groups())
# Check for missing values
for v in range(len(vals)):
if vals[v] == -9999.:
vals[v] = np.nan
# Set the time
dt = '20' + vals[varlist.index('YYMMDD')] + vals[varlist.index('HHMM')]
validtime = datetime.strptime(dt, '%Y%m%d%H%M')
# End loop if we are more than 60 hours past the start of the forecast date
if validtime > forecast_date + timedelta(hours=60):
break
# Append values at this time step
dateTime.append(validtime)
pressure.append(vals[varlist.index('PMSL')])
temperature.append(c_to_f(vals[varlist.index('T2MS')]))
dewpoint.append(c_to_f(vals[varlist.index('TD2M')]))
uwind = ms_to_kt(vals[varlist.index('UWND')])
vwind = ms_to_kt(vals[varlist.index('VWND')])
speed, dir = wind_uv_to_speed_dir(uwind, vwind)
windSpeed.append(speed)
windDirection.append(dir)
if 'P01M' in varlist:
rain.append(mm_to_in(vals[varlist.index('P01M')]))
else:
# This condition only applies to FV3 model: save 3 hr precipitation instead of 1 hour
rain.append(mm_to_in(vals[varlist.index('P03M')]))
infile.close()
# first element of rain should be zero (sometimes it is -9999.99)
rain[0] = '0.0'
# Make into dataframe
df = pd.DataFrame({
'temperature': temperature,
'dewpoint': dewpoint,
'windSpeed': windSpeed,
'windDirection': windDirection,
'rain': rain,
'pressure': pressure,
'dateTime': dateTime
}, index=dateTime)
# Convert to forecast object
forecast_start = forecast_date.replace(hour=6)
forecast_end = forecast_start + timedelta(days=1)
# Find forecast start location in timeseries
try:
# unlike the mos code, we always use the 'include'
iloc_start_include = df.index.get_loc(forecast_start)
except BaseException as e:
print('bufkit: error getting start time index for %s; check data' % model)
raise
# Create forecast object and save timeseries
forecast = Forecast(stid, model, forecast_date)
forecast.timeseries.data = df
# Find forecast end location in time series and save daily values if it exists
if df.index[-1] >= forecast_end:
iloc_end = df.index.get_loc(forecast_end)
high = int(np.round(df.iloc[iloc_start_include:iloc_end]['temperature'].max()))
low = int(np.round(df.iloc[iloc_start_include:iloc_end]['temperature'].min()))
max_wind = int(np.round(df.iloc[iloc_start_include:iloc_end]['windSpeed'].max()))
total_rain = np.sum(df.iloc[iloc_start_include + 1:iloc_end]['rain'])
forecast.daily.set_values(high, low, max_wind, total_rain)
else:
if config['debug'] > 9:
print('bufkit warning: model %s does not extend to end of forecast period; omitting daily values' % model)
return forecast
def get_ukmet_forecast(stid, ukmet_code, forecast_date):
"""
Retrieve UKMET data.
:param stid: station ID
:param ukmet_code: site-specific URL code from ukmet.codes
:param forecast_date: datetime of day to forecast
:return: Forecast object for high, low, max wind for next 6Z--6Z. No precip.
"""
# Retrieve the model data
url = 'https://www.metoffice.gov.uk/public/weather/forecast/%s' % ukmet_code
req = Request(url, headers=hdr)
response = urlopen(req)
page = response.read().decode('utf-8', 'ignore')
soup = BeautifulSoup(page, 'lxml')
# Find UTC offset and current time in HTML
utcoffset = int(soup.find(id='country').text.split('-')[1][0:2])
epoch = float(soup.find("td", {"id": "firstTimeStep"})['data-epoch'])
utcnow = datetime.utcfromtimestamp(epoch)
# Store daily variables
days = []
highs = [] # this can be overwritten by hourly
lows = [] # this can be overwritten by hourly
winds = [] # this comes from hourly
# Pull in daily data using li tabs
tabids = ['tabDay1', 'tabDay2', 'tabDay3']
for ids in tabids:
pars = soup.find(id=ids)
days.append(datetime.strptime(pars['data-date'], '%Y-%m-%d'))
highs.append(
c_to_f(
pars.findAll("span", {"title": "Maximum daytime temperature"
})[0]['data-value-raw']))
lows.append(
c_to_f(
pars.findAll("span",
{"title": "Minimum nighttime temperature"
})[0]['data-value-raw']))
# Pull in hourly data
# This requires PhantomJS to pull out additional HTML code
driver = webdriver.PhantomJS(
executable_path='/home/disk/p/wxchallenge/bin/phantomjs')
driver.get(url + '#?date=2017-09-21')
source = driver.page_source
soup = BeautifulSoup(source, 'html.parser')
dateTime = []
temperature = []
temperature_c = []
dewpoint = []
windSpeed = []
windGust = []
windDirection = []
humidity = [] # this is temporary--converted to dew point below
divids = [
'divDayModule0', 'divDayModule1', 'divDayModule2', 'divDayModule3'
]
for i, divs in enumerate(divids):
day0 = datetime.strptime(
soup.find("div", {"id": "divDayModule0"})['data-content-id'],
'%Y-%m-%d')
day1 = (day0 + timedelta(days=1)).strftime('%Y-%m-%d')
pars = soup.find(id=divs)
divdate = datetime.strptime(pars['data-content-id'], '%Y-%m-%d').date()
hourels = pars.findAll("tr",
{"class": "weatherTime"})[0].find_all('td')
for ii, ele in enumerate(hourels):
if ele.text == 'Now':
dateTime.append(utcnow)
else:
dtmp = datetime(divdate.year, divdate.month, divdate.day,
int(ele.text.split(':')[0]),
int(ele.text.split(':')[1]))
dateTime.append(dtmp + timedelta(hours=utcoffset))
tempels = pars.findAll("tr", {"class": "weatherTemp"})[0].findAll(
"i", {"class": "icon icon-animated"})
for ele in tempels:
temperature_c.append(float(ele['data-value-raw']))
temperature.append(c_to_f(ele['data-value-raw']))
# relative humidity for conversion to dew point
humels = pars.findAll("tr",
{"class": "weatherHumidity"})[0].text.split()
for ele in humels:
humidity.append(float(ele.split('%')[0]))
# add wind
speedels = pars.findAll("i", {"data-type": "windSpeed"})
for ele in speedels:
windSpeed.append(np.round(mph_to_kt(ele['data-value-raw']), 2))
gustels = pars.findAll("span", {"class": "gust"})
for ele in gustels:
windGust.append(mph_to_kt(ele['data-value-raw']))
direls = pars.findAll("span", {"class": "direction"})
for ele in direls:
windDirection.append(wind_dir_to_deg(ele.text))
# Convert T and humidity to dewpt
for ii, rh in enumerate(humidity):
td_tmp = dewpoint_from_t_rh(temperature_c[ii], rh)
dewpoint.append(c_to_f(td_tmp))
# Make into dataframe
df = pd.DataFrame(
{
'temperature': temperature,
'dewpoint': dewpoint,
'windSpeed': windSpeed,
'windGust': windGust,
'windDirection': windDirection,
'dateTime': dateTime
},
index=dateTime)
# Correct the highs and lows with the hourly data, find max wind speed
forecast_start = forecast_date.replace(hour=6)
forecast_end = forecast_start + timedelta(days=1)
for d in range(0, len(days)):
try:
# unlike the mos code, we always use the 'include'
iloc_start_include = df.index.get_loc(forecast_start)
except BaseException:
print('ukmet: error getting start time index in db; check data.')
break
try:
iloc_end = df.index.get_loc(forecast_end)
except BaseException:
print('ukmet: error getting end time index in db; check data.')
break
raw_high = df.iloc[iloc_start_include:iloc_end]['temperature'].max()
raw_low = df.iloc[iloc_start_include:iloc_end]['temperature'].min()
winds.append(
int(
np.round(
df.iloc[iloc_start_include:iloc_end]['windSpeed'].max())))
if raw_high > highs[d]:
highs[d] = raw_high
if raw_low < lows[d]:
lows[d] = raw_low
forecast_start = forecast_start + timedelta(days=1)
forecast_end = forecast_end + timedelta(days=1)
forecast = Forecast(stid, default_model_name, days[0])
forecast.timeseries.data = df
forecast.daily.set_values(highs[0], lows[0], winds[0], None)
# # Make list of forecast objects for future days--currently not implemented
#
# forecast = []
#
# for i in range(0,len(days)):
# forecast_tmp = Forecast(stid, default_model_name, days[i])
# forecast_tmp.daily.date = days[i]
# forecast_tmp.daily.high = highs[i]
# forecast_tmp.daily.low = lows[i]
# forecast.append(forecast_tmp)
return forecast
def get_usl_forecast(config, stid, run, forecast_date):
# Retrieve data
api_url = 'http://www.microclimates.org/forecast/%s/%s.html'
run_date = (forecast_date - timedelta(days=1)).replace(hour=int(run))
get_url = api_url % (stid, datetime.strftime(run_date, '%Y%m%d_%H'))
try:
response = urlopen(get_url)
except HTTPError:
if config['debug'] > 9:
print("usl: forecast for %s at run time %s doesn't exist" %
(stid, run_date))
raise
usl_data = response.read().decode('utf-8')
# Create a DataFrame
forecast_start = forecast_date.replace(hour=6)
forecast_end = forecast_start + timedelta(days=1)
usl_df = pd.DataFrame(
index=pd.date_range(forecast_start, forecast_end, freq='1H'))
columns = [
'temperature', 'dewpoint', 'humidity', 'soilTemperature',
'windDirection', 'windSpeed', 'cloud', 'netRadiation', 'rain'
]
for column in columns:
usl_df[column] = np.nan
# Parse the values
info = usl_data.split('<tr>')
date_index = 0
for block in info:
# Daily values, if that's the appropriate block
if re.search('°F</td>', block):
split_block = block.split('<td>')
try:
high = int(
re.search('(-?\d{1,3})', split_block[1]).groups()[0])
low = int(re.search('(-?\d{1,3})', split_block[2]).groups()[0])
max_wind = int(
re.search('(\d{1,3})', split_block[3]).groups()[0])
precip = float(
re.search('(\d{1,3}.\d{2})', split_block[4]).groups()[0])
continue
except:
pass
# Hourly values
block = re.sub('<th scope="row" class="nobg3">', '', block)
block = re.sub('<th scope="row" class="nobg">', '', block)
block = re.sub('</th>', ',', block)
block = re.sub('</td>', ',', block)
block = re.sub('</tr>', '', block)
block = re.sub('<td>', '', block)
block = re.sub('<td class="hr3">', '', block)
block = re.sub('\n', '', block)
if re.search('Time', block):
continue
values = block.split(',')[
1:-1] # Omit time and an extra space at the end
values = [v.strip() for v in values] # Remove white space
for v in range(len(values)): # Convert numbers to float
try:
values[v] = float(values[v])
except (TypeError, ValueError):
pass
if values[v] == '':
values[v] = np.nan
try:
usl_df.loc[usl_df.index[date_index], :] = values
date_index += 1
except (IndexError, ValueError):
pass
# Fix a couple of things
usl_df['DateTime'] = usl_df.index
for index in usl_df.index:
usl_df.loc[index, 'windDirection'] = wind_dir_to_deg(
usl_df.loc[index, 'windDirection'])
usl_df['humidity'] = usl_df['humidity'].apply(remove_last_char)
usl_df['cloud'] = usl_df['cloud'].apply(remove_last_char)
# Create Forecast object
forecast = Forecast(stid, default_model_name, forecast_date)
forecast.daily.set_values(high, low, max_wind, precip)
forecast.timeseries.data = usl_df
return forecast
def get_owm_forecast(stid, lat, lon, api_key, forecast_date):
# Retrieve data
api_url = 'http://api.openweathermap.org/data/2.5/forecast'
api_options = {
'APPID': api_key,
'lat': lat,
'lon': lon,
'units': 'imperial',
}
response = requests.get(api_url, params=api_options)
owm_data = response.json()
# Raise error for invalid HTTP response
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
print('openweathermap: got HTTP error when querying API')
raise
# Convert to pandas DataFrame and fix time
owm_df = pd.DataFrame(owm_data['list'])
owm_df['DateTime'] = np.nan
for idx in owm_df.index:
owm_df.loc[idx, 'DateTime'] = date_to_datetime(owm_df.loc[idx,
'dt_txt'])
owm_df.set_index('DateTime', inplace=True)
# OWM has a column 'main' which contains some parameters at all times. Get all of those.
for parameter in owm_df.loc[owm_df.index[0], 'main'].keys():
owm_df[parameter] = owm_df['main'].apply(get_parameter,
args=(parameter, ))
# Get some other special parameters
# Make sure the 'rain' parameter exists (if no rain in forecast, the column is missing)
if 'rain' not in owm_df:
owm_df = owm_df.assign(**{'rain': 0.0})
else:
owm_df.loc[:, 'rain'] = mm_to_in(owm_df['rain'].apply(get_parameter,
args=('3h', )))
owm_df['condition'] = owm_df['weather'].apply(get_parameter,
args=('description', ),
is_list=True)
owm_df['windSpeed'] = mph_to_kt(owm_df['wind'].apply(get_parameter,
args=('speed', )))
owm_df['windDirection'] = owm_df['wind'].apply(get_parameter,
args=('deg', ))
owm_df['cloud'] = owm_df['clouds'].apply(get_parameter, args=('all', ))
owm_df['dewpoint'] = np.nan
for idx in owm_df.index:
owm_df.loc[idx, 'dewpoint'] = dewpoint_from_t_rh(
owm_df.loc[idx, 'temp'], owm_df.loc[idx, 'humidity'])
# Rename remaining columns for default schema
column_names_dict = {
'temp': 'temperature',
}
owm_df = owm_df.rename(columns=column_names_dict)
# Calculate daily values. OWM includes period maxima and minima. Note that rain in OWM is cumulative for the LAST
# 3 hours.
forecast_start = forecast_date.replace(hour=6)
forecast_end = forecast_start + timedelta(days=1)
try:
daily_high = owm_df.loc[forecast_start:forecast_end, 'temp_max'].max()
except KeyError:
daily_high = owm_df.loc[forecast_start:forecast_end,
'temperature'].max()
try:
daily_low = owm_df.loc[forecast_start:forecast_end, 'temp_min'].min()
except KeyError:
daily_low = owm_df.loc[forecast_start:forecast_end,
'temperature'].min()
daily_wind = owm_df.loc[forecast_start:forecast_end, 'windSpeed'].max()
daily_rain = np.nanmax([
owm_df.loc[forecast_start + timedelta(hours=3):forecast_end,
'rain'].sum(), 0.0
])
# Create Forecast object
forecast = Forecast(stid, default_model_name, forecast_date)
forecast.daily.set_values(daily_high, daily_low, daily_wind, daily_rain)
forecast.timeseries.data = owm_df.reset_index()
return forecast
def get_mos_forecast(stid, mos_model, init_date, forecast_date):
"""
Retrieve MOS data. No unit conversions, yay!
:param stid: station ID
:param mos_model: model name ('GFS' or 'NAM')
:param init_date: datetime of model initialization
:param forecast_date: datetime of day to forecast
:return: Forecast object for forecast_date
"""
# Create forecast object
forecast = Forecast(stid, default_model_name, forecast_date)
# Retrieve the model data
base_url = 'http://mesonet.agron.iastate.edu/mos/csv.php?station=%s&runtime=%s&model=%s'
formatted_date = init_date.strftime('%Y-%m-%d%%20%H:00')
url = base_url % (stid, formatted_date, mos_model)
response = requests.get(url, stream=True)
# Create pandas DataFrame
df = pd.read_csv(response.raw, index_col=False)
# Raise exception if DataFrame is empty
if len(df.index) == 0:
raise ValueError('mos: error: empty DataFrame; data missing.')
date_index = pd.Index(pd.to_datetime(df['ftime'])).tz_localize(None)
df['datetime'] = date_index
# Remove duplicate rows
df = df.drop_duplicates()
# Fix rain
df['q06'] = df['q06'].apply(qpf_interpreter)
# Format the DataFrame for the default schema
# Dictionary for renaming columns
ts = df.copy()
names_dict = {
'datetime': 'DateTime',
'tmp': 'temperature',
'dpt': 'dewpoint',
'wsp': 'windSpeed',
'wdr': 'windDirection',
'q06': 'rain'
}
col_names = list(map(''.join, ts.columns.values))
for col in col_names:
if col not in names_dict.keys():
ts = ts.drop(col, axis=1)
# Set the timeseries
forecast.timeseries.data = ts.rename(columns=names_dict)
# Now do the daily forecast part
df = df.set_index('datetime')
forecast_start = forecast_date.replace(hour=6)
forecast_end = forecast_start + timedelta(days=1)
# Some parameters need to include the forecast start; others, like total rain and 6-hour maxes, don't
try:
iloc_start_include = df.index.get_loc(forecast_start)
iloc_start_exclude = iloc_start_include + 1
except BaseException:
print('mos.py: error getting start time index in db; check data.')
raise
try:
iloc_end = df.index.get_loc(forecast_end) + 1
except BaseException:
print('mos.py: error getting end time index in db; check data.')
raise
raw_high = df.iloc[iloc_start_include:iloc_end]['tmp'].max()
raw_low = df.iloc[iloc_start_include:iloc_end]['tmp'].min()
nx_high = df.iloc[iloc_start_exclude:iloc_end]['n_x'].max()
nx_low = df.iloc[iloc_start_exclude:iloc_end]['n_x'].min()
# Set the daily
forecast.daily.set_values(np.nanmax([raw_high, nx_high]), np.nanmin([raw_low, nx_low]),
df.iloc[iloc_start_include:iloc_end]['wsp'].max(),
df.iloc[iloc_start_exclude:iloc_end]['q06'].sum())
return forecast
def get_climacell_forecast(stid, lat, lon, api_key, forecast_date):
# Retrieve data
api_url = 'https://api.climacell.co/v3/weather/forecast/hourly'
api_options = {
'apikey':
api_key,
'lat':
lat,
'lon':
lon,
'unit_system':
'us',
'fields':
'precipitation,temp,dewpoint,wind_speed:knots,wind_gust:knots,baro_pressure:hPa,'
'wind_direction:degrees,cloud_cover:%,weather_code'
}
response = requests.get(api_url, params=api_options)
# Raise error for invalid HTTP response
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
print('climacell: got HTTP error when querying API')
raise
clima_data = response.json()
# Convert to pandas DataFrame and fix time, units, and columns
clima_df = pd.DataFrame(clima_data)
# Drop lat, lon and get values
clima_df.drop(['lat', 'lon'], axis=1, inplace=True)
clima_df = clima_df.apply(lambda y: y.apply(lambda x: x['value']))
column_names_dict = {
'observation_time': 'DateTime',
'temp': 'temperature',
'cloud_cover': 'cloud',
'precipitation': 'rain',
'baro_pressure': 'pressure',
'wind_speed': 'windSpeed',
'wind_gust': 'windGust',
'wind_direction': 'windDirection',
'weather_code': 'condition'
}
clima_df = clima_df.rename(columns=column_names_dict)
clima_df['DateTime'] = clima_df['DateTime'].apply(
lambda x: localized_date_to_utc(pd.Timestamp(x)))
clima_df.set_index('DateTime', inplace=True)
# Calculate daily values
forecast_start = forecast_date.replace(hour=6)
forecast_end = forecast_start + timedelta(days=1)
daily_high = clima_df.loc[forecast_start:forecast_end, 'temperature'].max()
daily_low = clima_df.loc[forecast_start:forecast_end, 'temperature'].min()
daily_wind = clima_df.loc[forecast_start:forecast_end, 'windSpeed'].max()
daily_rain = clima_df.loc[forecast_start:forecast_end - timedelta(hours=1),
'rain'].sum()
# Create Forecast object
forecast = Forecast(stid, default_model_name, forecast_date)
forecast.daily.set_values(daily_high, daily_low, daily_wind, daily_rain)
forecast.timeseries.data = clima_df.reset_index()
return forecast
def get_nws_forecast(config, stid, lat, lon, forecast_date):
"""
Retrieve current NWS forecast for a point location.
:param config:
:param stid: str: station ID
:param lat: float: latitude
:param lon: float: longitude
:param forecast_date: datetime:
:return:
"""
hourly_url = 'http://forecast.weather.gov/MapClick.php?lat=%f&lon=%f&FcstType=digitalDWML'
response = requests.get(hourly_url % (lat, lon))
# Raise error for invalid HTTP response
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
print('nws: got HTTP error when querying for XML file from %s' %
(hourly_url % (lat, lon)))
raise
hourly_xml = eTree.fromstring(response.text)
hourly_dict = etree_to_dict(hourly_xml)
# Create a DataFrame for hourly data
hourly = pd.DataFrame()
hourly['DateTime'] = hourly_dict['dwml']['data']['time-layout'][
'start-valid-time']
# De-localize the starting time so we can do an explicit datetime comparison
hourly['DateTime'] = [
localized_date_to_utc(parse_iso(hourly['DateTime'].iloc[j]))
for j in range(len(hourly['DateTime']))
]
hourly['DateTime'] = [
hourly['DateTime'].iloc[j].to_pydatetime().replace(tzinfo=None)
for j in range(len(hourly['DateTime']))
]
hourly['datetime_index'] = hourly['DateTime']
hourly.set_index('datetime_index', inplace=True)
parameters = hourly_dict['dwml']['data']['parameters']
# Get the temperatures
for element in parameters['temperature']:
if element['@type'] == 'hourly':
hourly['temperature'] = xml_to_values(element['value'])
elif element['@type'] == 'dew point':
hourly['dewPoint'] = xml_to_values(element['value'])
# Get the winds
for element in parameters['wind-speed']:
if element['@type'] == 'sustained':
hourly['windSpeed'] = xml_to_values(element['value'])
hourly['windSpeed'] = mph_to_kt(hourly['windSpeed'])
elif element['@type'] == 'gust':
hourly['windGust'] = xml_to_values(element['value'])
hourly['windGust'] = mph_to_kt(hourly['windGust'])
# Get other parameters
hourly['cloud'] = xml_to_values(parameters['cloud-amount']['value'])
hourly['windDirection'] = xml_to_values(parameters['direction']['value'])
hourly['rain'] = xml_to_values(parameters['hourly-qpf']['value'])
try:
hourly['condition'] = xml_to_condition(
parameters['weather']['weather-conditions'])
except:
pass
# Aggregate daily values from hourly series
forecast_start = forecast_date.replace(hour=6)
forecast_end = forecast_start + timedelta(days=1)
hourly_high = hourly.loc[forecast_start:forecast_end, 'temperature'].max()
hourly_low = hourly.loc[forecast_start:forecast_end, 'temperature'].min()
hourly_wind = hourly.loc[forecast_start:forecast_end, 'windSpeed'].max()
hourly_rain = hourly.loc[forecast_start:forecast_end - timedelta(hours=1),
'rain'].sum()
# Create the Forecast object
forecast = Forecast(stid, default_model_name, forecast_date)
forecast.daily.set_values(hourly_high, hourly_low, hourly_wind,
hourly_rain)
forecast.timeseries.data = hourly
# Now do the daily data from the Forecast API
api_url = 'https://api.weather.gov/points'
point = '%0.3f,%0.3f' % (lat, lon)
# Retrieve daily forecast
daily_url = '%s/%s/forecast' % (api_url, point)
response = requests.get(daily_url)
# Test for an error HTTP response. If there is an error response, omit the daily part.
try:
response.raise_for_status()
daily_data = response.json()
except BaseException as e:
if config['debug'] > 0:
print("nws: warning: no daily values used for %s ('%s')" %
(stid, str(e)))
return forecast
# Daily values: convert to DataFrame
daily = pd.DataFrame.from_dict(daily_data['properties']['periods'])
# Change the wind to its max value
daily['windSpeed'] = daily['windSpeed'].apply(wind_speed_interpreter)
# De-localize the starting time so we can do an explicit datetime comparison
daily['startTime'] = [
parse_iso(daily['startTime'].iloc[j])
for j in range(len(daily['startTime']))
]
daily['startTime'] = [
daily['startTime'].iloc[j].replace(tzinfo=None)
for j in range(len(daily['startTime']))
]
daily.set_index('startTime', inplace=True)
try:
daily_high = daily.loc[forecast_date + timedelta(hours=6),
'temperature']
except KeyError:
daily_high = np.nan
try:
daily_low = daily.loc[forecast_date - timedelta(hours=6),
'temperature']
except KeyError:
daily_low = np.nan
daily_wind = mph_to_kt(
np.max(daily.loc[forecast_start:forecast_end]['windSpeed']))
# Update the Forecast object
forecast.daily.set_values(np.nanmax([hourly_high, daily_high]),
np.nanmin([hourly_low, daily_low]),
np.nanmax([hourly_wind, daily_wind]),
hourly_rain)
return forecast
def get_ukmet_forecast(config, stid, lat, lon, api_id, api_secret,
forecast_date):
json_url = 'https://api-metoffice.apiconnect.ibmcloud.com/metoffice/production/v0/forecasts/point'
headers = {
'x-ibm-client-id': api_id,
'x-ibm-client-secret': api_secret,
'accept': "application/json"
}
api_options = {
'excludeParameterMetaData': 'false',
'includeLocationName': 'false',
'latitude': lat,
'longitude': lon,
}
# Get hourly forecast data
# Check if we have a cached hourly file and if it is recent enough
site_directory = '%s/site_data' % config['THETAE_ROOT']
cache_file = '%s/%s_ukmet_hourly.txt' % (site_directory, stid)
cache_ok = check_cache_file(config, cache_file, interval=4)
if not cache_ok:
response = requests.get('%s/hourly' % json_url,
params=api_options,
headers=headers)
ukmet_data_hourly = response.json()
# Raise error for invalid HTTP response
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
print('ukmet: got HTTP error when querying API for hourly data')
raise
# Cache the response
with open(cache_file, 'w') as f:
f.write(response.text)
else:
ukmet_data_hourly = json.load(open(cache_file))
# model run date--currently not using this but might be of interest later
model_run_date = ukmet_data_hourly['features'][0]['properties'][
'modelRunDate']
ukmet_df = pd.DataFrame(
ukmet_data_hourly['features'][0]['properties']['timeSeries'])
ukmet_df['DateTime'] = ukmet_df['time'].apply(
pd.to_datetime).apply(lambda x: x.replace(tzinfo=None))
ukmet_df.set_index('DateTime', inplace=True)
# rename columns
column_names_dict = {
'screenTemperature': 'temperature',
'screenDewPointTemperature': 'dewpoint',
'windSpeed10m': 'windSpeed',
'windGustSpeed10m': 'windGust',
'windDirectionFrom10m': 'windDirection',
'precipitationRate':
'rain', # Assume constant in hour. parameter totalPrecipAmount no longer exists.
'mslp': 'pressure',
}
ukmet_df = ukmet_df.rename(columns=column_names_dict)
# drop columns that we are not using
ukmet_df.drop([
'feelsLikeTemperature', 'probOfPrecipitation',
'screenRelativeHumidity', 'significantWeatherCode', 'totalSnowAmount',
'uvIndex', 'visibility'
],
inplace=True,
axis=1)
# correct units
ukmet_df['pressure'] /= 100.
ukmet_df['temperature'] = c_to_f(ukmet_df['temperature'])
ukmet_df['dewpoint'] = c_to_f(ukmet_df['dewpoint'])
ukmet_df['windSpeed'] = ms_to_kt(ukmet_df['windSpeed'])
ukmet_df['windGust'] = ms_to_kt(ukmet_df['windGust'])
ukmet_df['rain'] = mm_to_in(ukmet_df['rain'])
# Create Forecast object, save timeseries
forecast = Forecast(stid, default_model_name, forecast_date)
forecast.timeseries.data = ukmet_df.reset_index()
forecast_start = forecast_date.replace(hour=6)
forecast_end = forecast_start + timedelta(days=1)
# Now use the daily API to find daily values
# Check if we have a cached daily file and if it is recent enough
site_directory = '%s/site_data' % config['THETAE_ROOT']
cache_file = '%s/%s_ukmet_daily.txt' % (site_directory, stid)
cache_ok = check_cache_file(config, cache_file, interval=4)
have_daily_values = True
if not cache_ok:
response = requests.get('%s/daily' % json_url,
params=api_options,
headers=headers)
ukmet_data_daily = response.json()
# Raise error for invalid HTTP response
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
print(
'ukmet warning: got HTTP error when querying API for daily data; using hourly values'
)
have_daily_values = False
# Cache the response
with open(cache_file, 'w') as f:
f.write(response.text)
else:
ukmet_data_daily = json.load(open(cache_file))
# extract daily values for the forecast date
if have_daily_values:
ukmet_df_daily = pd.DataFrame(
ukmet_data_daily['features'][0]['properties']['timeSeries'])
ukmet_df_daily.set_index('time', inplace=True)
ukmet_df_daily.index = pd.to_datetime(ukmet_df_daily.index)
daily_forecast = ukmet_df_daily.loc[forecast_date]
daytime_max = c_to_f(daily_forecast['dayMaxScreenTemperature'])
nighttime_min = c_to_f(daily_forecast['nightMinScreenTemperature'])
else:
daytime_max = -1000.
nighttime_min = 1000.
# compare hourly temperature to daily--update if needed
daily_high = ukmet_df.loc[forecast_start:forecast_end, 'temperature'].max()
if daytime_max > daily_high:
daily_high = daytime_max
daily_low = ukmet_df.loc[forecast_start:forecast_end, 'temperature'].min()
if nighttime_min < daily_low:
daily_low = nighttime_min
daily_wind = ukmet_df.loc[forecast_start:forecast_end, 'windSpeed'].max()
daily_rain = ukmet_df.loc[forecast_start:forecast_end - timedelta(hours=1),
'rain'].sum()
forecast.daily.set_values(daily_high, daily_low, daily_wind, daily_rain)
return forecast