def resample_to_latlon_and_save_to_nc(dir_data,dir_grid,dir_output,\
var_to_load,years_to_load, k_sub = [0], \
min_lat=-90, max_lat=90,min_lon=0, max_lon=360, dx = 0.25, dy = 0.25,\
mapping_method='nearest_neighbor',fill_value=np.nan):
import ecco_v4_py as ecco
import numpy as np
import xarray as xr
ds_grid = ecco.load_ecco_grid_nc(dir_grid)
mask_C = ds_grid.hFacC.isel(k=k_sub)
mask_C = mask_C.where(mask_C > 0, np.nan)
# resample
n_lon = len(np.arange(min_lon + dx, max_lon - dx, dx)) + 1
n_lat = len(np.arange(min_lat + dy, max_lat - dy, dy)) + 1
#%% load data
for year in list(years_to_load):
ds_data = ecco.load_ecco_var_from_years_nc(dir_data + '/' + str(year),
var_to_load=var_to_load,
years_to_load=[year],
k_subset=[0])
n_time = len(ds_data.time)
sst = np.full([n_time, n_lat, n_lon], np.nan)
for in_time in range(0, n_time):
print(year, in_time + 1)
lon,lat,sst[in_time,:,:] = ecco.resample_to_latlon(ds_data.XC,ds_data.YC,ds_data.THETA.isel(time=in_time, k=0)*mask_C,\
min_lat+dy, max_lat-dy, dy,\
min_lon+dx, max_lon-dx, dx,\
mapping_method=mapping_method,fill_value=fill_value)
ds_data_save = xr.Dataset({})
ds_data_save.coords['lon'] = lon[0, :]
ds_data_save.coords['lat'] = lat[:, 0]
ds_data_save.coords['time'] = ds_data.time
ds_data_save[var_to_load] = (('time', 'lat', 'lon'), sst)
# name
ds_data_save.lon['long_name'] = 'longitude (degree)'
ds_data_save.lat['long_name'] = 'latitude (degree)'
ds_data_save[var_to_load]['long_name'] = var_to_load
# output
file_output = dir_output + var_to_load + str(year) + '.nc'
print('Saving ' + file_output + '...')
ds_data_save.to_netcdf(path=file_output)
print('Finish ' + file_output + '.')
# Define main directory
base_dir = '/home/username/ECCOv4-release'
# Define ECCO version
ecco_version = 'v4r3'
# Define a high-level directory for ECCO fields
ECCO_dir = base_dir + '/Release3_alt'
# > **Note**: Change `base_dir` to your own directory path.
# In[7]:
# Load the model grid
grid_dir = ECCO_dir + '/nctiles_grid/'
ecco_grid = ecco.load_ecco_grid_nc(grid_dir, 'ECCOv4r3_grid.nc')
# ### Volume
# Calculate the volume of each grid cell. This is used when converting advective and diffusive flux convergences and calculating volume-weighted averages.
# In[8]:
# Volume (m^3)
vol = (ecco_grid.rA * ecco_grid.drF * ecco_grid.hFacC).transpose(
'tile', 'k', 'j', 'i')
# ### Load monthly snapshots
# In[9]:
data_dir = ECCO_dir + '/nctiles_monthly_snapshots'
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
import xarray as xr
import ecco_v4_py as ecco
dir_ecco = '/Volumes/ECCO/ecco.jpl.nasa.gov/drive/files/Version4/Release4/'
dir_monthly = dir_ecco + '/nctiles_monthly/'
dir_daily = dir_ecco + '/nctiles_daily/'
dir_grid = dir_ecco + '/nctiles_grid/'
fname_grid = 'ECCOv4r3_grid.nc'
## read grid
ds_grid = ecco.load_ecco_grid_nc(dir_grid,fname_grid, dask_chunk=True)
vars_ecco = ecco.recursive_load_ecco_var_from_years_nc(dir_monthly,\
vars_to_load=['ADVx_TH', 'ADVy_TH',\
'DFxE_TH', 'DFyE_TH',\
'ADVx_SLT','ADVy_SLT',\
'DFxE_SLT','DFyE_SLT',\
'UVELMASS','VVELMASS'],\
years_to_load='all',\
dask_chunk=True)
ds_ecco = xr.merge([vars_ecco,ds_grid])
del(ds_grid,vars_ecco)
lat = 26
ds_ecco['maskC'] = ds_ecco.hFacC
ds_ecco['maskW'] = ds_ecco.hFacW
ds_ecco['maskS'] = ds_ecco.hFacS
# else: # leap = False # else: # leap = True # else: # leap = False # return leap dir_ecco = '/Volumes/ECCO/ecco.jpl.nasa.gov/drive/files/Version4/Release4/' dir_monthly = dir_ecco + 'nctiles_monthly/' dir_grid = dir_ecco + '/nctiles_grid/' dir_daily = dir_ecco + 'nctiles_daily/' dir_output = '/Volumes/ECCO/ecco.jpl.nasa.gov/drive/files/Version4/Release4/interp_daily/SST/chinasea/' ds_grid = ecco.load_ecco_grid_nc(dir_grid, 'ECCOv4r3_grid.nc') mask_C = ds_grid.hFacC.isel(k=0) mask_C = mask_C.where(mask_C > 0, np.nan) # resample dx = 0.25 dy = 0.25 min_lat = 0 max_lat = 50 min_lon = 100 max_lon = 140 n_lon = len(np.arange(min_lon + dx, max_lon - dx, dx)) + 1 n_lat = len(np.arange(min_lat + dy, max_lat - dy, dy)) + 1 #%% load data for year in range(1992, 2018):
# In[5]: # Use plot_tiles to make a quick plot of the 13 tiles. See the tutorial on plotting for more examples. ecco.plot_tiles(bathy, layout='latlon',rotate_to_latlon=True,show_tile_labels=False, show_colorbar=True); # ## Load ecco-grid information to make a fancier lat-lon plot # # With the longitudes (XC) and latitudes (YC) and the 13 tile ndarray we can plot the field in different geographic projections. See the tutorial on plotting for more examples. # In[6]: ecco_grid_dir = '/Users/ifenty/tmp/nctiles_grid/' ecco_grid = ecco.load_ecco_grid_nc(input_dir, 'ECCO-GRID.nc') # In[7]: plt.figure(figsize=(15,6)); ecco.plot_proj_to_latlon_grid(ecco_grid.XC, ecco_grid.YC, bathy, show_colorbar=True, user_lon_0=-66); # In[8]: plt.figure(figsize=(12,8)); ecco.plot_proj_to_latlon_grid(ecco_grid.XC, ecco_grid.YC, bathy, show_colorbar=True, projection_type='stereo', lat_lim =-45, less_output=True,dx=.25,dy=.25);
# In[4]:
## Set top-level file directory for the ECCO NetCDF files
## =================================================================
# base_dir = '/home/username/'
base_dir = '/Users/ifenty/ECCOv4-release'
## define a high-level directory for ECCO fields
ECCO_dir = base_dir + '/Release3_alt'
# In[5]:
## Load the model grid
grid_dir = ECCO_dir + '/nctiles_grid/'
ecco_grid = ecco.load_ecco_grid_nc(grid_dir)
## Load one year of 2D daily data, SSH, SST, and SSS
data_dir = ECCO_dir + '/nctiles_monthly'
ecco_vars = ecco.recursive_load_ecco_var_from_years_nc(
data_dir,
vars_to_load=['ADVx_TH', 'ADVy_TH', 'DFxE_TH', 'DFyE_TH'],
years_to_load=range(2008, 2014))
## Merge the ecco_grid with the ecco_vars to make the ecco_ds
ecco_ds = xr.merge((ecco_grid, ecco_vars))
# ## Grab latitude band: 26$^\circ$N array as an example
#
# Here we want to grab the transport values which along the band closest represented in the model to 26$^\circ$N.
# In a latitude longitude grid this could simply be done by, e.g. `U.sel(lat=26)`.
# base_dir = '/home/username/'
base_dir = '/home/ifenty/ECCOv4-release'
## define a high-level directory for ECCO fields
ECCO_dir = base_dir + '/Release3_alt'
# Now load daily and monthly mean versions of SSH and THETA
# In[4]:
## Load the model grid
grid_dir= ECCO_dir + '/nctiles_grid/'
ecco_grid = ecco.load_ecco_grid_nc(grid_dir, 'ECCOv4r3_grid.nc', k_subset=[0])
# In[5]:
## Load 2D DAILY data, SSH, SST, and SSS
data_dir= ECCO_dir + '/nctiles_daily'
ecco_daily_vars = ecco.recursive_load_ecco_var_from_years_nc(data_dir, vars_to_load=['SSH','THETA'], years_to_load=range(1993,2018)).load()
## Merge the ecco_grid with the ecco_vars to make the ecco_ds
ecco_daily_ds = xr.merge((ecco_grid , ecco_daily_vars))
# In[7]:
import numpy as np
import xarray as xr
import sys
import matplotlib.pyplot as plt
import ecco_v4_py as ecco
## define a high-level directory for ECCO fields
dir_base = '/Volumes/ECCO/ecco.jpl.nasa.gov/drive/files/Version4'
dir_ECCO = dir_base + '/Release4'
dir_grid = dir_ECCO + '/nctiles_grid/'
## load the grid
grid = xr.open_dataset(dir_grid + '/ECCOv4r3_grid.nc')
# grid2 = ecco.load_ecco_grid_nc(grid_dir, 'ECCOv4r3_grid.nc')
## load the grid
grid = xr.open_dataset(dir_grid + '/ECCOv4r3_grid.nc')
# plot hFac
ecco.plot_tiles(grid.hFacC.sel(k=0),
show_colorbar=True,
cmap='gray',
figsize=(10, 9))
ecco.plot_tiles(grid.rA, show_colorbar=True)
## Method 2: Loading the model grid parameters using load_ecco_grid_nc
grid_subset = ecco.load_ecco_grid_nc(dir_grid,
'ECCOv4r3_grid.nc',
tiles_to_load='all',
k=0)
ecco.plot_tiles(grid_subset.hFacC.sel(k=0), show_colorbar=True, cmap='gray')
ecco.plot_tiles(grid.rA, show_colorbar=True); 'Model grid cell surface area [m^2]' # ### Method 2: Loading the model grid parameters using ``load_ecco_grid_nc`` # # A more advanced routine, ``load_ecco_grid_nc``, allows you to load only a subset of tiles and vertical levels. If no optional parameters are given, the entire grid object is loaded, just like ``open_dataset`` # In[7]: grid_dir = ECCO_dir + 'nctiles_grid' grid = ecco.load_ecco_grid_nc(grid_dir, 'ECCOv4r3_grid.nc') grid # Alternatively we can load just a subset of tiles and vertical levels. # In[8]: grid_subset = ecco.load_ecco_grid_nc(grid_dir, 'ECCOv4r3_grid.nc', tiles_to_load = [1, 10, 12], k_subset=[0,1,2,3]) grid_subset # notice that ``grid_subset`` only has 3 tiles (9,10, 11) and 4 depth levels (0, 1, 2, 3), as expected. # # Let's plot ``hFacC`` and ``rA`` again
