def pastMonthObs(): #gets the obs from Mesopy from the last month
d = datetime.now() - timedelta(
1
) #going back into last month, because we want to look back on the month that just passed
lastmonth = '%02d' % d.month #so the month is formatted correctly
year = '%04d' % d.year
lastday = '%02d' % d.day
start = str(year) + str(lastmonth) + str('01') + '0000' #start at 00 UTC
end = str(year) + str(lastmonth) + str(
lastday) + '2359' #end at end of month
m = Meso(token=MesoPy_token)
data = []
time = m.timeseries(stid='kmsn', start=start, end=end)
kmsn = time['STATION'][0] #KMSN station
rawtemps = kmsn['OBSERVATIONS']['air_temp_set_1']
rawobstime = kmsn['OBSERVATIONS']['date_time']
time = []
temps = [] #in fahrenheit
for i in range(len(rawobstime)):
t = str(rawobstime[i]).replace('T', ' ').replace('Z', '')
dt = datetime.strptime(t, "%Y-%m-%d %H:%M:%S")
time.append(mdates.date2num(dt))
temps.append('%.2f' % float(rawtemps[i] * 9 / 5 +
32)) #converts from celsius to fahrenheit
return temps, time
def retrieve_mesowest_observations(meso_token, tm_start, tm_end, glat, glon):
"""
Retrieve observation data from Mesowest and repackage them as a time-indexed
dictionary of lists of observations.
:param meso_token: the mesowest API access token
:param tm_start: the start of the observation window
:param tm_end: the end of the observation window
:param glat: the lattitudes of the grid points
:param glon: the longitudes of the grid points
"""
def decode_meso_time(t):
# example: '2016-03-30T00:30:00Z'
return datetime.strptime(t,
'%Y-%m-%dT%H:%M:%SZ').replace(tzinfo=pytz.UTC)
def meso_time(dt):
# example: 201603311600
return '%04d%02d%02d%02d%02d' % (dt.year, dt.month, dt.day, dt.hour,
dt.minute)
# the bbox for mesowest is: (min(lon), min(lat), max(lon), max(lat)).
min_lat, max_lat = np.amin(glat), np.amax(glat)
min_lon, max_lon = np.amin(glon), np.amax(glon)
# retrieve data from Mesowest API (http://mesowest.org/api/)
m = Meso(meso_token)
meso_obss = m.timeseries(meso_time(tm_start - timedelta(minutes=30)),
meso_time(tm_end + timedelta(minutes=30)),
showemptystations='0',
bbox='%g,%g,%g,%g' %
(min_lon, min_lat, max_lon, max_lat),
vars='fuel_moisture')
# repackage all the observations into a time-indexed structure which groups
# observations at the same time together
obs_data = {}
for stinfo in meso_obss['STATION']:
st_lat, st_lon = float(stinfo['LATITUDE']), float(stinfo['LONGITUDE'])
elev = float(stinfo['ELEVATION']) / 3.2808
ngp = find_closest_grid_point(st_lon, st_lat, glon, glat)
dts = [
decode_meso_time(x) for x in stinfo['OBSERVATIONS']['date_time']
]
if 'fuel_moisture_set_1' in stinfo['OBSERVATIONS']:
fms = stinfo['OBSERVATIONS']['fuel_moisture_set_1']
for ts, fm_obs in zip(dts, fms):
if fm_obs is not None:
o = FM10Observation(ts, st_lat, st_lon, elev,
float(fm_obs) / 100., ngp)
obs_t = obs_data.get(ts, [])
obs_t.append(o)
obs_data[ts] = obs_t
return obs_data
def retrieve_mesowest_observations(meso_token, tm_start, tm_end, glat, glon):
"""
Retrieve observation data from Mesowest and repackage them as a time-indexed
dictionary of lists of observations.
:param meso_token: the mesowest API access token
:param tm_start: the start of the observation window
:param tm_end: the end of the observation window
:param glat: the lattitudes of the grid points
:param glon: the longitudes of the grid points
"""
def decode_meso_time(t):
# example: '2016-03-30T00:30:00Z'
return datetime.strptime(t, '%Y-%m-%dT%H:%M:%SZ').replace(tzinfo=pytz.UTC)
def meso_time(dt):
# example: 201603311600
return '%04d%02d%02d%02d%02d' % (dt.year, dt.month, dt.day, dt.hour, dt.minute)
# the bbox for mesowest is: (min(lon), min(lat), max(lon), max(lat)).
min_lat, max_lat = np.amin(glat), np.amax(glat)
min_lon, max_lon = np.amin(glon), np.amax(glon)
# retrieve data from Mesowest API (http://mesowest.org/api/)
m = Meso(meso_token)
meso_obss = m.timeseries(meso_time(tm_start - timedelta(minutes=30)),
meso_time(tm_end + timedelta(minutes=30)),
showemptystations = '0', bbox='%g,%g,%g,%g' % (min_lon, min_lat, max_lon, max_lat),
vars='fuel_moisture')
# repackage all the observations into a time-indexed structure which groups
# observations at the same time together
obs_data = {}
for stinfo in meso_obss['STATION']:
st_lat, st_lon = float(stinfo['LATITUDE']), float(stinfo['LONGITUDE'])
elev = float(stinfo['ELEVATION']) / 3.2808
ngp = find_closest_grid_point(st_lon, st_lat, glon, glat)
dts = [decode_meso_time(x) for x in stinfo['OBSERVATIONS']['date_time']]
if 'fuel_moisture_set_1' in stinfo['OBSERVATIONS']:
fms = stinfo['OBSERVATIONS']['fuel_moisture_set_1']
for ts,fm_obs in zip(dts,fms):
if fm_obs is not None:
o = FM10Observation(ts,st_lat,st_lon,elev,float(fm_obs)/100.,ngp)
obs_t = obs_data.get(ts, [])
obs_t.append(o)
obs_data[ts] = obs_t
return obs_data
def build_pd(stn_id):
key = 'b1c91e501782441d97ac056e2501b5b0'
m = Meso(token=key)
time = m.timeseries(stid=stn_id, start='199801010000', end='201801010000')
ob = time['STATION'][0]
temp = np.expand_dims(np.array(ob['OBSERVATIONS']['air_temp_set_1'],
dtype=np.float),
axis=1)
wind_dir = np.expand_dims(np.array(
ob['OBSERVATIONS']['wind_direction_set_1'], dtype=np.float),
axis=1) * units.degrees
wind_spd = np.expand_dims(np.array(ob['OBSERVATIONS']['wind_speed_set_1'],
dtype=np.float),
axis=1) * 1.9438445
wind_max = np.expand_dims(np.array(
ob['OBSERVATIONS']['peak_wind_speed_set_1'], dtype=np.float),
axis=1) * 1.9438445
u, v = mpcalc.get_wind_components(wind_spd, wind_dir)
rel_hum = np.expand_dims(np.array(
ob['OBSERVATIONS']['relative_humidity_set_1'], dtype=np.float),
axis=1)
dates = ob['OBSERVATIONS']['date_time']
years = float(dates[0][0:4])
months = float(dates[0][5:7])
hours = float(dates[0][8:10])
days = float(dates[0][11:13])
for i in range(len(dates) - 1):
years = np.vstack((years, float(dates[i + 1][0:4])))
months = np.vstack((months, float(dates[i + 1][5:7])))
days = np.vstack((days, float(dates[i + 1][8:10])))
hours = np.vstack((hours, float(dates[i + 1][11:13])))
minutes = np.expand_dims(np.ones(len(hours)) * 55.0, axis=1)
cols = [
'Year', 'Month', 'Day', 'Hour', 'Minutes', 'Temp', 'RH', 'Dir', 'Spd',
'Max', 'U', 'V'
]
data = np.hstack((years, months, days, hours, minutes, temp, rel_hum,
wind_dir, wind_spd, wind_max, u, v))
data = pd.DataFrame(data=data, columns=cols)
pickle.dump(data, open('../Data/pickles/' + stn_id + '_20yr_RAWS.p', 'wb'))
return data
variables = m.variables()
# This returns a climatology for Denver from Apr 26 OOz to Apr 27 OOz
climate = m.climatology(stid='kden',
startclim='04260000',
endclim='04270000',
units='precip|in')
# Fetches the latest obs for Fort Collins airport within 30 min of Apr 26 18z
attime = m.attime(stid='kfnl', attime='201504261800', within='30')
# Or just get the latest observation within the last 15 minutes
latest = m.latest(stid='kfnl', within='15')
# Returns a time series from Fort Collins airport from Apr 26 18z to Apr 26 23z
time = m.timeseries(stid='kfnl', start='201504261800', end='201504262300')
# Returns the precip obs from Fort Collins airport from Apr 26 18z to Apr 27 12z
precip = m.precip(stid='kfnl',
start='201504261800',
end='201504271200',
units='precip|in')
# Learn more about all of the networks in MesoWest with the networks() func
networks = m.networks()
# Or explore the categories MesoWest networks belong to
nettypes = m.networktypes()
# You can obtain time series statistics for any station
stats = m.time_stats(stid='mtmet',
vname_1 = []
vname_2 = []
vname_3 = []
vlabel = []
mean_obs = []
mean1 = []
mean2 = []
mean3 = []
x_list = []
y_list = []
# Iterate over dictionary to make time series plots of each variable for each station
for i in stations:
# Return a time series from Aug 28 00z to Sep 9 23z for each station
mw_data = m.timeseries(stid=i,start=start_t, end=end_t,vars=var_list,obtimezone=tz)
# Skip station if the data is NoneType
if mw_data is None:
continue
# All data for a station
x = mw_data['STATION'][0]
# Only keep variables of interest
all_var = list(x['SENSOR_VARIABLES'])
new_list = []
for item in all_var:
if item in var_list:
new_list.append(item)
stations = m.metadata(state='CO', county='Larimer') # Calling variables() returns all possible sensor variables at stations variables = m.variables() # This returns a climatology for Denver from Apr 26 OOz to Apr 27 OOz climate = m.climatology(stid='kden', startclim='04260000', endclim='04270000', units='precip|in') # Fetches the latest obs for Fort Collins airport within 30 min of Apr 26 18z attime = m.attime(stid='kfnl', attime='201504261800', within='30') # Or just get the latest observation within the last 15 minutes latest = m.latest(stid='kfnl', within='15') # Returns a time series from Fort Collins airport from Apr 26 18z to Apr 26 23z time = m.timeseries(stid='kfnl', start='201504261800', end='201504262300') # Returns the precip obs from Fort Collins airport from Apr 26 18z to Apr 27 12z precip = m.precip(stid='kfnl', start='201504261800', end='201504271200', units='precip|in') # Learn more about all of the networks in MesoWest with the networks() func networks = m.networks() # Or explore the categories MesoWest networks belong to nettypes = m.networktypes() # You can obtain time series statistics for any station stats = m.time_stats(stid='mtmet', start='201403240000', end='201403280000', type='all') # Or get climatology stats for a station (remember to change the date!) clim_stats = m.climate_stats(stid='mtmet', startclim='03240000', endclim='03280000', type='all')
def retrieve_mesowest_observations(meso_token, tm_start, tm_end, glat, glon,
ghgt):
"""
Retrieve observation data from Mesowest and repackage them as a time-indexed
dictionary of lists of observations.
:param meso_token: the mesowest API access token or list of them
:param tm_start: the start of the observation window
:param tm_end: the end of the observation window
:param glat: the lattitudes of the grid points
:param glon: the longitudes of the grid points
:param ghgt: the elevation of the grid points
"""
def decode_meso_time(t):
# example: '2016-03-30T00:30:00Z'
return datetime.strptime(t,
'%Y-%m-%dT%H:%M:%SZ').replace(tzinfo=pytz.UTC)
def meso_time(dt):
# example: 201603311600
return '%04d%02d%02d%02d%02d' % (dt.year, dt.month, dt.day, dt.hour,
dt.minute)
# the bbox for mesowest is: (min(lon), min(lat), max(lon), max(lat)).
min_lat, max_lat = np.amin(glat), np.amax(glat)
min_lon, max_lon = np.amin(glon), np.amax(glon)
# retrieve data from Mesonet API (http://api.mesowest.net/)
try:
logging.info(
'retrieve_mesowest_observations: retrieving data using MesoDB')
db = mesoDB('ingest/MesoDB', meso_token)
db.update['startTime'] = tm_start - timedelta(minutes=30)
db.update['endTime'] = tm_end + timedelta(minutes=30)
db.params['startTime'] = tm_start - timedelta(minutes=30)
db.params['endTime'] = tm_end + timedelta(minutes=30)
db.params['longitude1'] = min_lon
db.params['longitude2'] = max_lon
db.params['latitude1'] = min_lat
db.params['latitude2'] = max_lat
df = db.get_DB().dropna(subset=['fm10'])
st = db.sites()
if not len(df):
logging.info(
'retrieve_mesowest_observations: no data for the query specified'
)
return {}
logging.info(
'retrieve_mesowest_observations: re-packaging the observations')
obs_data = {}
for stid, data in df.groupby('STID'):
if len(data):
st_data = st.loc[stid]
st_lat, st_lon = float(st_data['LATITUDE']), float(
st_data['LONGITUDE'])
ngp = find_closest_grid_point(st_lon, st_lat, glon, glat)
st_elev = st_data['ELEVATION']
if st_elev is None:
elev = ghgt[ngp]
else:
elev = float(st_data['ELEVATION']) / 3.2808
dts = data.datetime.dt.to_pydatetime()
fms = np.array(data.fm10)
for ts, fm_obs in zip(dts, fms):
if fm_obs is not None:
o = FM10Observation(ts, st_lat, st_lon, elev,
float(fm_obs) / 100., ngp)
obs_t = obs_data.get(ts, [])
obs_t.append(o)
obs_data[ts] = obs_t
return obs_data
except:
logging.warning(
'retrieve_mesowest_observations: failed with exception {}')
logging.info(
'retrieve_mesowest_observations: retrieving data using Meso instead'
)
if isinstance(meso_token, str):
meso_tokens = [meso_token]
n_tokens = 1
else:
meso_tokens = meso_token
n_tokens = len(meso_tokens)
for tn, meso_token in enumerate(meso_tokens):
m = Meso(meso_token)
logging.info("Retrieving fuel moisture from %s to %s" %
(meso_time(tm_start - timedelta(minutes=30)),
meso_time(tm_end + timedelta(minutes=30))))
logging.info("bbox=' %g,%g,%g,%g'" %
(min_lon, min_lat, max_lon, max_lat))
try:
meso_obss = m.timeseries(
meso_time(tm_start - timedelta(minutes=30)),
meso_time(tm_end + timedelta(minutes=30)),
showemptystations='0',
bbox='%g,%g,%g,%g' % (min_lon, min_lat, max_lon, max_lat),
vars='fuel_moisture')
break
except Exception as e:
if tn == n_tokens - 1:
raise MesoPyError(
'Could not connect to the API. Probably the token(s) usage for this month is full.'
)
else:
logging.warning(
'Could not connect to the API. Probably the token usage for this month is full. Trying next token...'
)
if meso_obss is None:
logging.info(
'retrieve_mesowest_observations: Meso.timeseries returned None'
)
return {}
logging.info(
'retrieve_mesowest_observations: re-packaging the observations')
# repackage all the observations into a time-indexed structure which groups
# observations at the same time together
obs_data = {}
for stinfo in meso_obss['STATION']:
st_lat, st_lon = float(stinfo['LATITUDE']), float(
stinfo['LONGITUDE'])
ngp = find_closest_grid_point(st_lon, st_lat, glon, glat)
st_elev = stinfo['ELEVATION']
if st_elev is None:
elev = ghgt[ngp]
else:
elev = float(stinfo['ELEVATION']) / 3.2808
dts = [
decode_meso_time(x)
for x in stinfo['OBSERVATIONS']['date_time']
]
if 'fuel_moisture_set_1' in stinfo['OBSERVATIONS']:
fms = stinfo['OBSERVATIONS']['fuel_moisture_set_1']
for ts, fm_obs in zip(dts, fms):
if fm_obs is not None:
o = FM10Observation(ts, st_lat, st_lon, elev,
float(fm_obs) / 100., ngp)
obs_t = obs_data.get(ts, [])
obs_t.append(o)
obs_data[ts] = obs_t
return obs_data
def retrieve_wxobs_synopticlabs(
api_key,
data_path,
station_id='knyc',
st_time='201801010000',
ed_time='201801020000',
vbl_list=['date_time', 'air_temp', 'relative_humidity'],
download_new=False):
"""
Function to retrieve timeseries weather observations from an observation site. Uses the
MesoWest/SynopticLabs API to retrieve the observations.
PARAMETERS:
***********
api_key: SynopticLabs api_key.
data_path: Path to directory in which we want to save the observations.
station_id: Four-letter station id.
st_time: Start time for observations, in format 'YYYYMMDDhhmm'.
ed_time: End time for observations, in format 'YYYYMMDDhhmm'.
vbl_list: List of variables to retrieve from SynopticLabs.
download_new: Boolean, re-download data from SynopticLabs if True.
OUTPUTS:
********
data_ts: Data structure returned from API request.
path_name: File path/name for data structure that was just retrieved.
"""
def get_synopticlabs_token(api_key):
request_generate_token = 'http://api.mesowest.net/v2/auth?apikey=' + api_key
api_out = requests.get(request_generate_token).text
token_dict = json.loads(api_out)
token_synopticlabs = token_dict['TOKEN']
return token_synopticlabs
file_name = 'wxobs_' + station_id + '_' + st_time + '_' + ed_time + '.pkl'
if download_new:
token_synopticlabs = get_synopticlabs_token(api_key)
m = Meso(token=token_synopticlabs)
data_ts = m.timeseries(stid=station_id,
start=st_time,
end=ed_time,
vars=vbl_list)
pickle.dump(data_ts, open(os.path.join(data_path, file_name), 'wb'))
else:
try:
data_ts = pickle.load(
open(os.path.join(data_path, file_name), 'rb'))
except:
raise (OSError('File not found: ' + file_name))
return data_ts, os.path.join(data_path, file_name)
m = Meso(token=MyToken)
#q = m.timeseries(start=StartTime,end=EndTime,stid='CNST2')#,vars='wind_speed,pressure')
#print(type(q['STATION']))
#print type(q['STATION'])
##for l in q['STATION']:
## for k,v in l.items():
## print k,v
#meso_df = pd.DataFrame.from_dict(q,orient='columns')
#meso_df
#print m.variables()
'''
SENSOR_VARIABLES {u'wind_speed': {u'wind_speed_set_1': {u'position': u''}},
u'date_time': {u'date_time': {}},
u'solar_radiation': {u'solar_radiation_set_1': {u'position': u''}},
u'wind_gust': {u'wind_gust_set_1': {u'position': u''}},
u'pressure': {u'pressure_set_1': {u'position': u''}},
u'precip_accum_one_minute': {u'precip_accum_one_minute_set_1': {u'position': u''}},
u'wind_direction': {u'wind_direction_set_1': {u'position': u''}},
u'wind_chill': {u'wind_chill_set_1d': {u'derived_from': [u'air_temp_set_1', u'wind_speed_set_1']}},
u'wind_cardinal_direction': {u'wind_cardinal_direction_set_1d': {u'derived_from': [u'wind_direction_set_1']}},
u'relative_humidity': {u'relative_humidity_set_1': {u'position': u''}},
u'sea_level_pressure': {u'sea_level_pressure_set_1d': {u'derived_from': [u'pressure_set_1', u'air_temp_set_1', u'relative_humidity_set_1']}},
u'air_temp': {u'air_temp_set_1': {u'position': u''}},
u'dew_point_temperature': {u'dew_point_temperature_set_1d': {u'derived_from': [u'air_temp_set_1', u'relative_humidity_set_1']}},
u'altimeter': {u'altimeter_set_1d': {u'derived_from': [u'pressure_set_1']}}}
'''
q_asos = m.timeseries(start=StartTime, end=EndTime,
stid='KCLL') #,vars='wind_speed,pressure')
#print type(q_asos['STATION'])
print q_asos['STATION'][0]['SENSOR_VARIABLES']
#varList = "precip_accum_one_hour_set_1, wind_direction_set_1, air_temp_high_6_hour_set_1, weather_cond_code_set_1, relative_humidity_set_1,precip_accum_three_hour_set_1,cloud_layer_1_code_set_1,air_temp_high_24_hour_set_1,precip_accum_six_hour_set_1,air_temp_low_24_hour_set_1,dew_point_temperature_set_1,date_time,peak_wind_direction_set_1,wind_speed_set_1,air_temp_set_1,wind_gust_set_1,cloud_layer_2_code_set_1,cloud_layer_3_code_set_1,peak_wind_speed_set_1,metar_set_1,precip_accum_24_hour_set_1,weather_condition_set_1d"
Units = 'precip|in,temp|F,speed|mph'
# #For FW13
# varList = ['wind_direction', 'weather_condition', 'wind_direction', 'precip_accum_one_hour', 'peak_wind_direction', 'wind_speed',
# 'relative_humidity', 'precip_accum_three_hour', 'dew_point_temperature', 'air_temp', 'wind_gust', 'peak_wind_speed', 'metar', 'precip_accum_24_hour']
#Just for FW9
#For historical it need f13 !(The peak wind and wind gust)
varList = [
'wind_direction', 'weather_condition', 'wind_direction',
'precip_accum_one_hour', 'peak_wind_direction', 'peak_wind_speed',
'wind_speed', 'relative_humidity', 'precip_accum_three_hour',
'dew_point_temperature', 'air_temp', 'metar', 'precip_accum_24_hour'
]
alldata = m.timeseries(stid=StationIDs,
start=StartTime,
end=EndTime,
vars=varList,
units=Units)
#alldata = json.dumps(alldata, sort_keys=True)
#print len(alldata['STATION'])
for stationobs in alldata['STATION']:
obs_df_dict = stationobs['OBSERVATIONS']
stnm = stationobs['STID']
stid = Stations[stnm]
print stnm, stid
df = pandas.DataFrame.from_dict(obs_df_dict)
#print df
variablelist = list(df.columns)
print variablelist
# precipitation query will return total precipitation over the last 24 hours
start, end = '201712111800', '201712121200'
mwprecipdata = m.precip(stid=kbdistations,
start=start,
end=end,
units='precip|in')
#precip = m.precip(stid='CDDT2', start='201709261800',end='201711271200', units='precip|in')
#print(mwprecipdata)
# temperature query will return all observations over the last 24 hours
# Add vars for air_temp_high_6_hour
mwtempdata = m.timeseries(stid=kbdistations,
start=start,
end=end,
vars='air_temp,air_temp_high_6_hour',
units='temp|F,precip|in')
for ob in mwmetadata['STATION']:
recentob = ob['PERIOD_OF_RECORD']['end'][:10]
obid = ob['STID']
stationsDict[obid]['RECENT_OBS'] = ob['PERIOD_OF_RECORD']['end']
stationsDict[obid]['LONGITUDE'] = ob['LONGITUDE']
stationsDict[obid]['LATITUDE'] = ob['LATITUDE']
if recentob == comparedate:
stationsDict[obid]['RECENT_OBS'] = "CURRENT"
#print obid + " is current"
#else:
def retr_wxobs_synopticlabs(api_key,
data_path,
station_id='knyc',
st_time='201801010000',
ed_time='201801020000',
download_new=False):
"""Function to retrieve timeseries weather observations from an observation site. Uses the
MesoWest/SynopticLabs API to retrieve the observations.
**********
PARAMETERS
api_key: SynopticLabs api_key.
data_path: Path to directory in which we want to save the observations.
station_id: Four-letter station id.
st_time: start time for observations, in format 'YYYYMMDDhhmm'
ed_time: end time for observations, in format 'YYYYMMDDhhmm'
OUTPUT:
obs_dict: Dictionary with station attributes, station observations, observation units, and
quality control summary.
path_name: File path/name for data that was just retrieved.
"""
def get_synopticlabs_token(api_key):
request_generate_token = 'http://api.mesowest.net/v2/auth?apikey=' + api_key
api_out = requests.get(request_generate_token).text
token_dict = json.loads(api_out)
token_synopticlabs = token_dict['TOKEN']
return token_synopticlabs
def get_station_attrs(data_ts, station_attrs):
station_info = {}
for attr in station_attrs:
station_info[attr] = data_ts['STATION'][0][attr]
return station_info
def get_station_obs(data_ts, vbl_list):
station_data = {}
station_data[vbl_list[0]] = data_ts['STATION'][0]['OBSERVATIONS'][
vbl_list[0]]
for vbl in vbl_list[1:]:
station_data[vbl] = data_ts['STATION'][0]['OBSERVATIONS'][list(
data_ts['STATION'][0]['SENSOR_VARIABLES'][vbl].keys())[0]]
return station_data
vbl_list = ['date_time', 'air_temp', 'relative_humidity']
station_attrs = ['STID', 'ELEVATION', 'NAME', 'LONGITUDE', 'LATITUDE']
file_name = 'wxobs_' + station_id + '_' + st_time + '_' + ed_time + '.npy'
token_synopticlabs = get_synopticlabs_token(api_key)
if download_new:
# Retrieve the station data from API.
m = Meso(token=token_synopticlabs)
data_ts = m.timeseries(stid=station_id,
start=st_time,
end=ed_time,
vars=vbl_list[1:])
# Put everything in a single dictionary.
obs_dict = {}
obs_dict['station_attrs'] = get_station_attrs(data_ts, station_attrs)
obs_dict['station_obs'] = get_station_obs(data_ts, vbl_list)
obs_dict['units'] = data_ts['UNITS']
obs_dict['qc_summary'] = data_ts['QC_SUMMARY']
# Save the dictionary
np.save(os.path.join('.', 'data', file_name), obs_dict)
else:
# Load the dictionary
try:
obs_dict = np.load(os.path.join('.', 'data', file_name)).item()
except:
raise (OSError('File not found: ' + filename))
return obs_dict, os.path.join(data_path, file_name)
