def __init__(self, fire_data):
username = USERNAME
password = PASSWORD
lat = fire_data[0]
lon = fire_data[1]
startdate = dt.datetime(fire_data[2], fire_data[3],
fire_data[4]) - dt.timedelta(days=3)
enddate = dt.datetime(fire_data[5], fire_data[6],
fire_data[7]) + dt.timedelta(days=3)
interval = dt.timedelta(days=1)
parameters = ["t_2m:C", "relative_humidity_2m:p"]
self.dataframe = api.query_time_series([(lat, lon)], startdate,
enddate, interval, parameters,
username, password)
def getData(d):
now = dt.datetime.utcnow().replace(hour=12,
minute=12,
second=0,
microsecond=0)
startdate_ts = d
startdate_ts.replace(hour=12, minute=12, second=0, microsecond=0)
enddate_ts = startdate_ts + dt.timedelta(days=1)
interval_ts = dt.timedelta(hours=1)
coordinates_ts = [(51.507351, -0.127758), (50., 10.)] # London
parameters_ts = ['t_2m:C']
model = 'mix'
ens_select = None # e.g. 'median'
cluster_select = None # e.g. "cluster:1", see http://api.meteomatics.com/API-Request.html#cluster-selection
interp_select = 'gradient_interpolation'
limits = api.query_user_features(username, password)
try:
df_ts = api.query_time_series(coordinates_ts,
startdate_ts,
enddate_ts,
interval_ts,
parameters_ts,
username,
password,
model,
ens_select,
interp_select,
cluster_select=cluster_select)
print(df_ts.head(13))
line = str(df_ts.head(13)).replace(
'lat lon validdate ', '')
line = line.replace(
' t_2m:C', '')
line = line.replace('51.507351 -0.127758 ', '')
line = line.replace('+00:00 ', ',')
line = line.replace('+00:00 ', ',')
line = line.replace(' ', '')
line = str(line).replace('\n\n', '')
if line != '':
SaveLine(line)
except Exception as e:
print("Failed, the exception is {}".format(e))
def example():
###Input timeseries:
now = dt.datetime.utcnow().replace(hour=0,
minute=0,
second=0,
microsecond=0)
startdate_ts = now
enddate_ts = startdate_ts + dt.timedelta(days=1)
interval_ts = dt.timedelta(hours=1)
coordinates_ts = [(47.249297, 9.342854), (50., 10.)]
parameters_ts = ['t_2m:C', 'rr_1h:mm']
model = 'mix'
ens_select = None # e.g. 'median'
cluster_select = None # e.g. "cluster:1", see http://api.meteomatics.com/API-Request.html#cluster-selection
interp_select = 'gradient_interpolation'
###Input grid / grid unpivoted:
lat_N = 50
lon_W = -15
lat_S = 20
lon_E = 10
res_lat = 3
res_lon = 3
startdate_grid = dt.datetime.utcnow().replace(hour=0,
minute=0,
second=0,
microsecond=0)
parameter_grid = 'evapotranspiration_1h:mm' # 't_2m:C'
parameters_grid_unpiv = ['t_2m:C', 'rr_1h:mm']
valid_dates_unpiv = [
dt.datetime.utcnow(),
dt.datetime.utcnow() + dt.timedelta(days=1)
]
###input grid png
filename_png = "grid_target.png"
startdate_png = dt.datetime.utcnow().replace(hour=0,
minute=0,
second=0,
microsecond=0)
parameter_png = 't_2m:C'
###input lightning
startdate_l = dt.datetime(2017, 8, 6, 00, 00, 00)
enddate_l = dt.datetime(2017, 8, 6, 00, 20, 00)
lat_N_l = 50
lon_W_l = 10
lat_S_l = 40
lon_E_l = 20
###input grads
filename_grads = "path_grads/grads_target.png"
startdate_grads = now
parameters_grads = ['wind_speed_u_100m:ms',
'wind_speed_v_100m:ms'] # ['t_500hPa:C','gh_500hPa:m']
model_grads = 'ecmwf-ifs'
area_grads = 'europe'
###input netcdf
filename_nc = "path_netcdf/netcdf_target.nc"
startdate_nc = now
enddate_nc = startdate_nc + dt.timedelta(days=1)
interval_nc = dt.timedelta(days=1)
parameter_nc = 't_2m:C'
###input png timeseries
# prefixpath_png_ts = 'path/to/directory' #TODO
prefixpath_png_ts = '' # TODO
startdate_png_ts = now
enddate_png_ts = startdate_png_ts + dt.timedelta(days=2)
interval_png_ts = dt.timedelta(hours=12)
parameter_png_ts = 't_2m:C'
###input grads timeseries
# prefixpath_grads_ts = 'path/to/directory' #TODO
prefixpath_grads_ts = '' # TODO
startdate_grads_ts = now
enddate_grads_ts = startdate_grads_ts + dt.timedelta(days=2)
interval_grads_ts = dt.timedelta(hours=24)
parameters_grads_ts = ['t_500hPa:C', 'gh_500hPa:m']
model_grads_ts = 'ecmwf-ifs'
area_grads_ts = 'australia' # For Lat/Lon setting: None
###input station data timeseries
startdate_station_ts = startdate_grads_ts - dt.timedelta(days=2)
enddate_station_ts = startdate_grads_ts - dt.timedelta(hours=3)
interval_station_ts = dt.timedelta(hours=1)
parameters_station_ts = ['t_2m:C', 'wind_speed_10m:ms', 'precip_1h:mm']
model_station_ts = 'mix-obs'
coordinates_station_ts = [(47.43, 9.4),
(50.03, 8.52)] # St. Gallen / Frankfurt/Main
wmo_stations = ['066810'] # St. Gallen
metar_stations = ['EDDF'] # Frankfurt/Main
mch_stations = ['STG'] # MeteoSchweiz Station St. Gallen
limits = api.query_user_features(username, password)
print("\ntime series:")
try:
df_ts = api.query_time_series(coordinates_ts,
startdate_ts,
enddate_ts,
interval_ts,
parameters_ts,
username,
password,
model,
ens_select,
interp_select,
cluster_select=cluster_select)
print(df_ts.head())
except Exception as e:
print("Failed, the exception is {}".format(e))
print("\npng timeseries:")
try:
api.query_png_timeseries(prefixpath_png_ts, startdate_png_ts,
enddate_png_ts, interval_png_ts,
parameter_png_ts, lat_N, lon_W, lat_S, lon_E,
res_lat, res_lon, username, password)
except Exception as e:
print("Failed, the exception is {}".format(e))
if limits['area request option']:
print("\ngrid:")
try:
df_grid = api.query_grid(startdate_grid, parameter_grid, lat_N,
lon_W, lat_S, lon_E, res_lat, res_lon,
username, password)
print(df_grid.head())
except Exception as e:
print("Failed, the exception is {}".format(e))
print("\nunpivoted grid:")
try:
df_grid_unpivoted = api.query_grid_unpivoted(
valid_dates_unpiv, parameters_grid_unpiv, lat_N, lon_W, lat_S,
lon_E, res_lat, res_lon, username, password)
print(df_grid_unpivoted.head())
except Exception as e:
print("Failed, the exception is {}".format(e))
print("\ngrid timeseries:")
try:
df_grid_timeseries = api.query_grid_timeseries(
startdate_ts, enddate_ts, interval_ts, parameters_ts, lat_N,
lon_W, lat_S, lon_E, res_lat, res_lon, username, password)
print(df_grid_timeseries.head())
except Exception as e:
print("Failed, the exception is {}".format(e))
print("\ngrid as a png:")
try:
api.query_grid_png(filename_png, startdate_png, parameter_png,
lat_N, lon_W, lat_S, lon_E, res_lat, res_lon,
username, password)
print("filename = {}".format(filename_png))
except Exception as e:
print("Failed, the exception is {}".format(e))
else:
print("""
Your account '{}' does not include area requests.
With the corresponding upgrade you could query whole grids of data at once or even time series of grids.
Please check http://shop.meteomatics.com or contact us at [email protected] for an individual offer.
""".format(username))
if limits['historic request option'] and limits['area request option']:
print("\nlighning strokes as csv:")
try:
df_lightning = api.query_lightnings(startdate_l, enddate_l,
lat_N_l, lon_W_l, lat_S_l,
lon_E_l, username, password)
print(df_lightning.head())
except Exception as e:
print("Failed, the exception is {}".format(e))
else:
print("""
Your account '{}' does not include historic requests.
With the corresponding upgrade you could query data from the past as well as forecasts.
Please check http://shop.meteomatics.com or contact us at [email protected] for an individual offer.
""".format(username))
if limits['model select option']:
print("\nnetCDF file:")
try:
api.query_netcdf(filename_nc, startdate_nc, enddate_nc,
interval_nc, parameter_nc, lat_N, lon_W, lat_S,
lon_E, res_lat, res_lon, username, password)
print("filename = {}".format(filename_nc))
except Exception as e:
print("Failed, the exception is {}".format(e))
print("\nGrads plot:")
try:
api.query_grads(filename_grads,
startdate_grads,
parameters_grads,
lat_N,
lon_W,
lat_S,
lon_E,
res_lat,
res_lon,
username,
password,
model_grads,
area=area_grads)
print("filename = {}".format(filename_grads))
except Exception as e:
print("Failed, the exception is {}".format(e))
print("\ngrads timeseries:")
try:
api.query_grads_timeseries(prefixpath_grads_ts,
startdate_grads_ts,
enddate_grads_ts,
interval_grads_ts,
parameters_grads_ts,
lat_N,
lon_W,
lat_S,
lon_E,
res_lat,
res_lon,
username,
password,
model=model_grads_ts,
area=area_grads_ts)
print("prefix = {}".format(prefixpath_grads_ts))
except Exception as e:
print("Failed, the exception is {}".format(e))
print("\nfind stations:")
try:
met = api.query_station_list(username,
password,
startdate=startdate_station_ts,
enddate=enddate_station_ts,
parameters=parameters_station_ts)
print(met.head())
except Exception as e:
print("Failed, the exception is {}".format(e))
print("\nstation coordinates timeseries:")
try:
df_sd_coord = api.query_station_timeseries(
startdate_station_ts,
enddate_station_ts,
interval_station_ts,
parameters_station_ts,
username,
password,
model=model_station_ts,
latlon_tuple_list=coordinates_station_ts,
on_invalid='fill_with_invalid',
request_type="POST",
temporal_interpolation='none')
print(df_sd_coord.head())
except Exception as e:
print("Failed, the exception is {}".format(e))
print("\nstation wmo + metar ids timeseries:")
try:
df_sd_ids = api.query_station_timeseries(
startdate_station_ts,
enddate_station_ts,
interval_station_ts,
parameters_station_ts,
username,
password,
model=model_station_ts,
wmo_ids=wmo_stations,
metar_ids=metar_stations,
mch_ids=mch_stations,
on_invalid='fill_with_invalid',
request_type="POST",
temporal_interpolation='none')
print(df_sd_ids.head())
except Exception as e:
print("Failed, the exception is {}".format(e))
print("\nget init dates:")
try:
df_init_dates = api.query_init_date(now,
now + dt.timedelta(days=2),
dt.timedelta(hours=3),
't_2m:C', username, password,
'ecmwf-ens')
print(df_init_dates.head())
except Exception as e:
print("Failed, the exception is {}".format(e))
print("\nget available time ranges:")
try:
df_time_ranges = api.query_available_time_ranges(
['t_2m:C', 'precip_6h:mm'], username, password, 'ukmo-euro4')
print(df_time_ranges.head())
except Exception as e:
print("Failed, the exception is {}".format(e))
else:
print("""
Your account '{}' does not include model selection.
With the corresponding upgrade you could query data from stations and request your data in netcdf or grads format.
Please check http://shop.meteomatics.com or contact us at [email protected] for an individual offer.
""".format(username))