def test_getncmatchingvarlist():
"""
this test tests retrieves a pandas list of variable long names in a netcdf that match the pattern, useful for waq variables.
"""
from dfm_tools.get_nc_helpers import get_ncvardimlist
file_nc = os.path.join(dir_testinput,r'DFM_3D_z_Grevelingen\computations\run01\DFM_OUTPUT_Grevelingen-FM\Grevelingen-FM_0000_map.nc')
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc)
pattern = 'Flow .*component'
vars_pd_matching = vars_pd[vars_pd.loc[:,'long_name'].str.match(pattern)] #does not have to stop after pattern
#vars_pd_matching = vars_pd[vars_pd.loc[:,'long_name'].str.startswith('Flow') & vars_pd.loc[:,'long_name'].str.endswith('component')]
varkeys_list_matching = list(vars_pd_matching['nc_varkeys'])
assert varkeys_list_matching == ['mesh2d_ucx', 'mesh2d_ucy', 'mesh2d_ucz', 'mesh2d_ucxa', 'mesh2d_ucya']
r'p:\1204257-dcsmzuno\2014\data\meteo\HIRLAM72_2018\h72_201803.nc',
r'p:\11202255-sfincs\Testbed\Original_tests\01_Implementation\08_restartfile\sfincs_map.nc', #not available anymore
]
for file_nc in file_nc_list:
#get cell center coordinates from regular grid
if 'ERA5_metOcean_atm' in file_nc:
data_fromnc_x_1D = get_ncmodeldata(file_nc=file_nc, varname='longitude')
data_fromnc_y_1D = get_ncmodeldata(file_nc=file_nc, varname='latitude')
data_fromnc_x, data_fromnc_y = np.meshgrid(data_fromnc_x_1D, data_fromnc_y_1D)
else:
data_fromnc_x = get_ncmodeldata(file_nc=file_nc, varname='x')
data_fromnc_y = get_ncmodeldata(file_nc=file_nc, varname='y')
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc)
x_cen_withbnd = center2corner(data_fromnc_x)
y_cen_withbnd = center2corner(data_fromnc_y)
grid_verts = meshgridxy2verts(x_cen_withbnd, y_cen_withbnd)
fig, axs = plt.subplots(2,1, figsize=(10,9))
ax = axs[0]
ax.set_title('xy center data converted to xy corners')
ax.plot(data_fromnc_x,data_fromnc_y, linewidth=0.5, color='blue')
ax.plot(data_fromnc_x.T,data_fromnc_y.T, linewidth=0.5, color='blue')
ax.plot(x_cen_withbnd,y_cen_withbnd, linewidth=0.5, color='crimson')
ax.plot(x_cen_withbnd.T,y_cen_withbnd.T, linewidth=0.5, color='crimson')
ax.set_aspect('equal')
ax = axs[1]
ax.set_title('xy corner data converted to vertices (useful for map plotting)')
plot_netmapdata(grid_verts, values=None, ax=ax, linewidth=0.5, color='crimson', facecolor='None')
def regularGrid_to_netcdf(fp_in, nx, ny, treg, lreg):
dir_output = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', 'output'))
if not os.path.exists(dir_output):
os.makedirs(dir_output)
file_nc = fp_in
input_nc = Dataset(file_nc, 'r', format='NetCDF4')
time_old = input_nc.variables['time'][:]
if treg != 'all':
time_old = np.take(time_old, treg)
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc)
df = vars_pd
key_values = [
'mesh2d_tem1', 'time', 'mesh2d_s1', 'mesh2d_ucx', 'mesh2d_ucy',
'mesh2d_tem1', 'mesh2d_sa1', 'mesh2d_water_quality_output_17',
'mesh2d_OXY', 'mesh2d_face_x', 'mesh2d_face_y'
]
df = df.loc[df['nc_varkeys'].isin(key_values)]
"""
####################################################################################################################
# Regularise all files with 3 dimensions (time, nFaces, layers).
# This will be equal to four dimensions in the regular grid format since nFaces is the x- and y- dimension.
####################################################################################################################
"""
df2 = df.loc[df['ndims'] == 3]
data_frommap_x = get_ncmodeldata(file_nc=file_nc, varname='mesh2d_face_x')
data_frommap_y = get_ncmodeldata(file_nc=file_nc, varname='mesh2d_face_y')
time = get_ncmodeldata(file_nc=file_nc, varname='time', timestep=treg)
outname = '%s_regular.nc' % os.path.split(fileNC)[1][0:-3]
file_nc_reg = os.path.join(dir_output, outname)
root_grp = Dataset(file_nc_reg, 'w', format='NETCDF4')
root_grp.description = 'Example simulation data'
first_read = True
i = 0
for index, row in df2.iterrows():
if row['dimensions'][1] == 'mesh2d_nEdges':
continue
data_frommap_var = get_ncmodeldata(file_nc=file_nc,
varname=row['nc_varkeys'],
timestep=treg,
layer=lreg)
data_frommap_var = data_frommap_var.filled(np.nan)
field_array = np.empty(
(data_frommap_var.shape[0], ny, nx, data_frommap_var.shape[-1]))
tms = data_frommap_var.shape[0]
lrs = data_frommap_var.shape[-1]
trange = range(0, tms)
lrange = range(0, lrs)
A = np.array([
scatter_to_regulargrid(xcoords=data_frommap_x,
ycoords=data_frommap_y,
ncellx=nx,
ncelly=ny,
values=data_frommap_var[t, :, l].flatten(),
method='linear') for t in trange
for l in lrange
])
x_grid = A[0][0]
y_grid = A[0][1]
A = A[:, 2, :, :]
A = np.moveaxis(A, [0], [2])
subs = np.split(A, tms, axis=2)
field_array[:, :, :, 0:lrs] = [subs[tn] for tn in trange]
field_array = np.ma.masked_invalid(field_array)
print('done with variable %s' % row['nc_varkeys'])
if first_read:
unout = 'seconds since 2015-01-01 00:00:00'
lon = x_grid[0, :]
lat = y_grid[:, 0]
# create dimensions
root_grp.createDimension('time', None)
root_grp.createDimension('lon', lon.shape[0])
root_grp.createDimension('lat', lat.shape[0])
root_grp.createDimension('layer', lrs)
lonvar = root_grp.createVariable('lon', 'float32', 'lon')
lonvar.setncattr('axis', 'X')
lonvar.setncattr('reference',
'geographical coordinates, WGS84 projection')
lonvar.setncattr('units', 'degrees_east')
lonvar.setncattr('_CoordinateAxisType', 'Lon')
lonvar.setncattr('long_name', 'longitude')
lonvar.setncattr('valid_max', '180')
lonvar.setncattr('valid_min', '-180')
lonvar[:] = lon
latvar = root_grp.createVariable('lat', 'float32', 'lat')
latvar.setncattr('axis', 'Y')
latvar.setncattr('reference',
'geographical coordinates, WGS84 projection')
latvar.setncattr('units', 'degrees_north')
latvar.setncattr('_CoordinateAxisType', 'Lat')
latvar.setncattr('long_name', 'latitude')
latvar.setncattr('valid_max', '90')
latvar.setncattr('valid_min', '-90')
latvar[:] = lat
layervar = root_grp.createVariable('layer', 'float32', 'layer')
layervar.setncattr('axis', 'Z')
layervar.setncattr('reference',
'geographical coordinates, WGS84 projection')
layervar.setncattr('units', 'm')
layervar.setncattr('_CoordinateZisPositive', 'down')
layervar.setncattr('_CoordinateAxisType', 'Height')
layervar.setncattr('long_name', 'Depth')
layervar[:] = range(0, lrs)
timevar = root_grp.createVariable('time', 'float64', 'time')
timevar.setncattr('units', unout)
timevar.setncattr('calendar', 'standard')
timevar.setncattr('long_name', 'time')
timevar.setncattr('_CoordinateAxisType', 'Time')
timevar[:] = time_old
fieldName = row['nc_varkeys']
fieldvar = root_grp.createVariable(fieldName,
'float32',
('time', 'lat', 'lon', 'layer'),
fill_value=-999)
key = fieldName
for ncattr in input_nc.variables[key].ncattrs():
if ncattr != "_FillValue":
root_grp.variables[fieldName].setncattr(
ncattr, input_nc.variables[key].getncattr(ncattr))
fieldvar[:] = field_array
first_read = False
i += 1
"""
####################################################################################################################
# Regularise all files with 2 dimensions (time, nFaces, layers).
# This will be equal to 3 dimensions in the regular grid format since nFaces is the x- and y- dimension.
####################################################################################################################
"""
print('STARTING 2D')
df2 = df.loc[df['ndims'] == 2]
excludeList = ['edge', 'face', 'x', 'y']
for index, row in df2.iterrows():
test = any(n in str(row['nc_varkeys']) for n in excludeList)
if not test:
if row['dimensions'][1] == 'mesh2d_nEdges':
continue
ntimes = row['shape'][0]
data_frommap_var = get_ncmodeldata(file_nc=file_nc,
varname=row['nc_varkeys'],
timestep=treg)
data_frommap_var = data_frommap_var.filled(np.nan)
field_array = np.empty((data_frommap_var.shape[0], ny, nx))
trange = range(0, data_frommap_var.shape[0])
tms = data_frommap_var.shape[0]
A = np.array([
scatter_to_regulargrid(xcoords=data_frommap_x,
ycoords=data_frommap_y,
ncellx=nx,
ncelly=ny,
values=data_frommap_var[t, :].flatten(),
method='linear') for t in trange
])
A = A[:, 2, :, :]
field_array[:, :, :] = A
field_array = np.ma.masked_invalid(field_array)
"""write data to new netcdf"""
fieldName = row['nc_varkeys']
fieldvar = root_grp.createVariable(fieldName,
'float32',
('time', 'lat', 'lon'),
fill_value=-999)
key = fieldName
for ncattr in input_nc.variables[key].ncattrs():
if ncattr != "_FillValue":
root_grp.variables[fieldName].setncattr(
ncattr, input_nc.variables[key].getncattr(ncattr))
fieldvar[:] = field_array
root_grp.close()
