def get_argo_data(extent, t0, t1):
# Written by Mike Smith
Argo = namedtuple('Argo', ['name', 'time', 'lon', 'lat'])
argo_floats = []
data = active_argo_floats(extent, t0, t1)
if not data.empty:
most_recent = data.loc[data.groupby('platform_number')
['time (UTC)'].idxmax()]
for float in most_recent.itertuples():
A = Argo(float.platform_number,
pd.to_datetime(float._2).strftime('%Y-%m-%dT%H:%M:%S'),
float._4, float._5)
argo_floats.append(A)
return argo_floats
rtofs = xr.open_mfdataset(rtofs_files)
rtofs = rtofs.rename({
'Longitude': 'lon',
'Latitude': 'lat',
'MT': 'time',
'Depth': 'depth'
})
# for r in regions:
# extent = regions[r]['lonlat']
floats = ['4903354', '4902350']
extent = regions['Gulf of Mexico']['lonlat']
floats = active_argo_floats(bbox=extent,
time_start=argo_search_start,
time_end=argo_search_end,
floats=floats[1])
for argo in floats.platform_number.unique():
temp = floats[floats['platform_number'] == argo]
for t_float in temp['time (UTC)'].unique():
temp_float_time = temp[temp['time (UTC)'] == t_float]
filtered = temp_float_time[(np.abs(
stats.zscore(temp_float_time['psal (PSU)'])) <
3)] # filter salinity
x = filtered['longitude (degrees_east)'].unique()[-1]
y = filtered['latitude (degrees_north)'].unique()[-1]
t0 = dt.datetime(
columns=['lonmin', 'lonmax', 'latmin', 'latmax'])
global_extent = [
extent_df.lonmin.min(),
extent_df.lonmax.max(),
extent_df.latmin.min(),
extent_df.latmax.max()
]
search_start = ranges[0] - dt.timedelta(hours=search_hours)
search_end = ranges[-1]
if argo:
print(
f'Download ARGO Data within {global_extent} between {search_start} and {search_end}'
)
argo_data = active_argo_floats(global_extent, search_start, search_end)
else:
argo_data = pd.DataFrame()
if gliders:
print(
f'Downloading glider data within {global_extent} between {search_start} and {search_end}'
)
glider_data = active_gliders(global_extent, search_start, search_end)
else:
glider_data = pd.DataFrame()
# Loop through regions
for region in regions.items():
extent = region[1]['lonlat']
print(f'Region: {region[0]}, Extent: {extent}')
'Depth': 'depth'
})
lat = ds.lat.data
lon = ds.lon.data
t0 = pd.to_datetime(ds.time.data[0] -
np.timedelta64(search_hours, 'h'))
t1 = pd.to_datetime(ds.time.data[0])
kwargs['t0'] = t0
# Loop through regions
for region in regions.items():
extent = region[1]['lonlat']
if argo:
kwargs['argo'] = active_argo_floats(extent, t0, t1)
if gliders:
kwargs['gliders'] = active_gliders(extent, t0, t1)
if bathy:
kwargs['bathy'] = bathy.sel(
lon=slice(extent[0] - 1, extent[1] + 1),
lat=slice(extent[2] - 1, extent[3] + 1))
if region[1]['currents']['bool']:
kwargs['currents'] = region[1]['currents']
# kwargs['transform'] = projection
extent = np.add(extent, [-1, 1, -1, 1]).tolist()
t1 = pd.to_datetime(now)
# cmems_salinity = grab_cmems('global-analysis-forecast-phy-001-024-3dinst-so', user, pw)
# cmems_temp = grab_cmems('global-analysis-forecast-phy-001-024-3dinst-thetao', user, pw)
# cmems = xr.merge([cmems_salinity, cmems_temp])
# cmems = cmems.sel(depth=slice(0, 400), time=slice(t0, t1),)
# Loop through regions
for region, values in regions.items():
extent = values['lonlat']
print(f'Region: {region}, Extent: {extent}')
temp_save_dir = os.path.join(save_dir, '_'.join(region.split(' ')).lower())
os.makedirs(temp_save_dir, exist_ok=True)
floats = active_argo_floats(extent, t0, t1)
temp_df = floats.loc[floats.groupby('platform_number')
['time (UTC)'].idxmax()]
region_limits = regions[region]
vargs = {}
vargs['vmin'] = values['temperature'][0]['limits'][0]
vargs['vmax'] = values['temperature'][0]['limits'][1]
vargs['transform'] = ccrs.PlateCarree()
vargs['cmap'] = cmocean.cm.thermal
vargs['levels'] = np.arange(vargs['vmin'], vargs['vmax'],
values['temperature'][0]['limits'][2])
vargs['argo'] = temp_df
})
rtofs = rtofs.sel(depth=slice(0, depth))
# cmems_salinity = grab_cmems('global-analysis-forecast-phy-001-024-3dinst-so', user, pw)
# cmems_temp = grab_cmems('global-analysis-forecast-phy-001-024-3dinst-thetao', user, pw)
# cmems = xr.merge([cmems_salinity, cmems_temp])
# cmems = cmems.sel(depth=slice(0, 400))
# floats = ['4902350', '6902851', '4902915', '4901719', '4903356', '4903238', '4903238', '4901719']
# floats = ['4901719']
# floats = ['4901716', '6902851', '4902351', '4902915', '4902915', '4902114', '4902441', '4902928']
# floats = ['4903248', '4903253', '4903259', '6902855', '4902916']
for f in floats:
argos = active_argo_floats(time_start=argo_search_start,
time_end=argo_search_end,
floats=f)
if not argos.empty:
for argo in argos.platform_number.unique():
temp = argos[argos['platform_number'] == argo]
for t_float in temp['time (UTC)'].unique():
temp_float_time = temp[temp['time (UTC)'] == t_float]
filtered = temp_float_time[(np.abs(
stats.zscore(temp_float_time['psal (PSU)'])) <
3)] # filter salinity
filtered = filtered[filtered['pres (decibar)'] <= 400]
x = filtered['longitude (degrees_east)'].unique()[-1]
y = filtered['latitude (degrees_north)'].unique()[-1]