def test_convert_compact_to_tiles(llc_mds_datadirs, mydir, fname, nk, nl,
skip):
"""Read in compact data, convert to tiles.
Verify this equals reading in face data
"""
if mydir == 'xmitgcm':
mydir, _ = llc_mds_datadirs
data_compact = ecco.read_llc_to_compact(fdir=mydir,
fname=fname,
llc=90,
nk=nk,
nl=nl,
filetype='>f4',
skip=skip,
less_output=False)
data_tiles = ecco.read_llc_to_tiles(fdir=mydir,
fname=fname,
llc=90,
nk=nk,
nl=nl,
filetype='>f4',
skip=skip,
less_output=False)
data_converted = ecco.llc_compact_to_tiles(data_compact)
assert np.all(np.equal(data_converted, data_tiles))
def read_single_mds(fnamebase, xdalike, **kwargs):
"""Read meta/data that are not grid files but do not have time stamps.
If coords, dims are provided, create xarray DataArray.
Otherwise, return data as numpy ndarray
Parameters
----------
fnamebase : string
the base filename <fname>.meta/data
coords : dictionary, optional
specify coordinates of the data in the file
Returns
-------
data : numpy ndarray or xarray DataArray
"""
# Read in as n_timesteps x n_vertical_levels x 1170 x 90 numpy.ndarray
data = mut.rdmds(fnamebase, **kwargs)
# Convert to the 13 tile format
data = llc_compact_to_tiles(data, less_output=True)
# Potentially convert to xarray.DataArray
data = xr.DataArray(data, coords=xdalike.coords, dims=xdalike.dims)
return data
def test_convert_compact_to_tiles():
"""Read in compact data, convert to tiles.
Verify this equals reading in face data
"""
# Loop through 2D, 3D, 4D cases
for fname, nk, nl in zip(_TEST_FILES, _TEST_NK, _TEST_RECS):
data_compact = ecco.read_llc_to_compact(fdir=_DATA_DIR,
fname=fname,
llc=90,
nk=nk,
nl=nl,
filetype='>f4',
less_output=False)
data_tiles = ecco.read_llc_to_tiles(fdir=_DATA_DIR,
fname=fname,
llc=90,
nk=nk,
nl=nl,
filetype='>f4',
less_output=False)
data_converted = ecco.llc_compact_to_tiles(data_compact)
assert np.all(np.equal(data_converted, data_tiles))
def interploate_one3d(infile, outfile, varnm, unitnm):
# ## Load the Grid Data
# In[2]:
fdir = '/Volumes/data01/forum/user/owang/junk/nonblank/'
fname = 'XC.data'
# in compact format
XC = ecco.load_llc_compact(fdir, fname)
# in 13 tiles format
XC_t = ecco.load_llc_compact_to_tiles(fdir, fname, less_output=True)
fname = 'YC.data'
YC = ecco.load_llc_compact(fdir, fname)
YC_t = ecco.load_llc_compact_to_tiles(fdir, fname, less_output=True)
fname = 'maskCtrlC.data'
maskc = ecco.load_llc_compact(fdir, fname, nk=50)
maskc_t = ecco.load_llc_compact_to_tiles(fdir,
fname,
less_output=True,
nk=50)
# This is a 1D array.
RC = np.squeeze(-1 * ecco.load_binary_array(fdir, 'RC.data', 1, 50))
# In[3]:
fname = infile
fdir = '/Volumes/data01/forum/user/owang/junk/h8i_i48/diags/STATE/'
# as compact
data = ecco.load_llc_compact(fdir, fname, nk=50)
# as tiles
data_t = ecco.llc_compact_to_tiles(data, less_output=True)
# In[4]:
# print(data_t.shape)
# print(data.shape)
# # Plot level 11 of the data
# In[5]:
ecco.plot_tiles(data_t[:, 10, :],
layout='latlon',
rotate_to_latlon=True,
cmin=-.2,
cmax=.2)
# # Resample to nearest latlon point
# In[6]:
#help(ecco.resample_to_latlon)
# In[7]:
new_grid_delta_deg = 0.25
new_grid_min_lat = -90 + new_grid_delta_deg
new_grid_max_lat = 90 - new_grid_delta_deg
new_grid_min_lon = -180
new_grid_max_lon = 180 - new_grid_delta_deg
radius_of_influence = 120000 #m
# In[8]:
RC.shape
# In[9]:
for num, name in np.ndenumerate(RC):
print num, name
print type(num)
# In[10]:
data_latlon_projection = []
for num, name in np.ndenumerate(RC):
print(num[0])
# create mask
masktmp = 1. - maskc[num, :]
datamsked = ma.array(data[num, :], mask=masktmp)
new_grid_lon, new_grid_lat, tmp = ecco.resample_to_latlon(
XC,
YC,
datamsked,
new_grid_min_lat,
new_grid_max_lat,
new_grid_delta_deg,
new_grid_min_lon,
new_grid_max_lon,
new_grid_delta_deg,
nprocs_user=1,
mapping_method='nearest_neighbor',
radius_of_influence=radius_of_influence)
print(tmp.shape)
tmp = np.expand_dims(tmp, 0)
if num[0] == 0:
data_latlon_projection = tmp
else:
data_latlon_projection = np.append(data_latlon_projection, tmp, 0)
print data_latlon_projection.shape
# In[11]:
lons_1d = new_grid_lon[0, :]
lats_1d = new_grid_lat[:, 0]
# In[12]:
nz, ny, nx = np.shape(data_latlon_projection)
print(lons_1d[0])
print(lons_1d[-1])
print(nx, ny, nz)
# In[13]:
plt.imshow(data_latlon_projection[0, :],
origin='lower',
vmin=0,
vmax=.2,
cmap='jet')
# In[14]:
plt.imshow(data_latlon_projection[25, :],
origin='lower',
vmin=0,
vmax=.2,
cmap='jet')
# In[16]:
foo = xr.DataArray(data_latlon_projection,
coords=[RC, lats_1d, lons_1d],
dims=['depth', 'latitude', 'longitude'])
foo.isel(depth=1).plot()
# In[17]:
foo.attrs['xmin'] = np.min(new_grid_lon)
foo.attrs['ymax'] = np.max(new_grid_lat)
foo.attrs['spacing'] = new_grid_delta_deg
foo.attrs['no_data'] = -9999.0
foo.attrs['proj4'] = '+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs'
foo.attrs['Projection'] = 'Lat Lon EPSG:4326'
foo.attrs['Insitution'] = 'JPL'
foo.attrs['original_filename'] = fname
foo.attrs['author'] = 'Ou Wang'
foo.attrs['product_version'] = '0.1'
foo.attrs['nx'] = nx
foo.attrs['ny'] = ny
foo.attrs['nz'] = nz
foo.attrs['units'] = unitnm
foo.attrs['Conventions'] = 'CF-1.6'
foo.attrs[
'Metadata_Conventions'] = 'CF-1.6, Unidata Dataset Discovery v1.0, GDS v2.0'
foo.attrs['cdm_data_type'] = 'Grid'
foo.attrs['grid_method'] = 'bin average neighbor'
foo.attrs['radius_of_influence'] = radius_of_influence
foo.attrs['depth_units'] = 'meters'
foo.name = varnm
# In[18]:
#foo
# In[19]:
foo.to_netcdf('/Volumes/data01/forum/user/owang/junk/output/' + varnm +
'/' + outfile + '.nc')