def test_getncmodeldata_timeid():
from dfm_tools.get_nc import get_ncmodeldata
file_map1 = os.path.join(dir_testinput,r'DFM_sigma_curved_bend\DFM_OUTPUT_cb_3d\cb_3d_map.nc')
data_frommap = get_ncmodeldata(file_nc=file_map1, varname='mesh2d_sa1', timestep=1, layer=5)#, multipart=False)
assert (data_frommap.data[0,0,0] - 31. ) < 1E-9
def test_gettimesfromnc():
from dfm_tools.get_nc import get_ncmodeldata
#this file seems to have a reconstructable array, but contains gaps, so the time reader falls back to reading all
file_nc = os.path.join(dir_testinput,'id1-LICHTELGRE.nc')
data_fromhis_time = get_ncmodeldata(file_nc,varname='time',timestep='all')
def test_gethisbottomdata():
"""
checks whether time dimension is indexed once, resulted in shape of (10,10,1)
"""
from dfm_tools.get_nc import get_ncmodeldata
file_nc_his = os.path.join(dir_testinput,'MWRA','MB_02_0000_his.nc')
moddata_dfm = get_ncmodeldata(file_nc_his,varname='salinity',station='MWRA_F22',timestep=range(10),layer='bottom')
assert moddata_dfm.shape == (10, 1, 1)
def test_getmapdata(file_nc, expected_size):
"""
Checks whether ghost cells are properly taken care of.
file_nc = os.path.join(dir_testinput,'DFM_3D_z_Grevelingen','computations','run01','DFM_OUTPUT_Grevelingen-FM','Grevelingen-FM_0000_map.nc')
expected_size = (1, 44796)
"""
from dfm_tools.get_nc import get_ncmodeldata
data_nc = get_ncmodeldata(file_nc=file_nc, varname='mesh2d_s1',timestep=2)
assert data_nc.shape == expected_size
def test_getncmodeldata_indexcountmetadata():
from dfm_tools.get_nc import get_ncmodeldata
#check if retrieving 1 index of data from 1 dimensional variable works (does not work if indices are np.arrays, so conversion to list in get_nc.py)
file_his = os.path.join(dir_testinput,r'DFM_fou_RMM\RMM_dflowfm_0000_his.nc')
data_statcoord = get_ncmodeldata(file_nc=file_his, varname='station_x_coordinate',station='NM_1005.26_R_HBR-Cl_VCS-Nieuwe-Maas-85m')
assert len(data_statcoord.var_stations) == 1
file_his = os.path.join(dir_testinput,r'DFM_sigma_curved_bend\DFM_OUTPUT_cb_3d\cb_3d_his.nc')
data_fromhis = get_ncmodeldata(file_nc=file_his, varname='x_velocity', timestep=1, layer=5, station='Innersouth boundary')#, multipart=False)
assert len(data_fromhis.var_times) == 1
assert len(data_fromhis.var_layers) == 1
assert len(data_fromhis.var_stations) == 1
#data_fromhis_all = get_ncmodeldata(file_nc=file_his, varname='x_velocity', timestep='all', layer='all', station='all')
data_fromhis = get_ncmodeldata(file_nc=file_his, varname='x_velocity', timestep=[1,0,-5,1,4,3,2,0,1,-1], layer=[5,-2,3,0,5], station=[4,-1,0,0])
assert len(data_fromhis.var_times) == 7
assert data_fromhis.var_times.index.tolist() == [0,1,2,3,4,2156,2160]
assert len(data_fromhis.var_layers) == 4
assert data_fromhis.var_layers == [0,3,5,8]
assert len(data_fromhis.var_stations) == 3
assert data_fromhis.var_stations.index.tolist() == [0,4,5]
def test_getncmodeldata_datetime():
import numpy as np
import datetime as dt
import pandas as pd
from dfm_tools.get_nc import get_ncmodeldata
#retrieve all
file_nc = os.path.join(dir_testinput,r'DFM_sigma_curved_bend\DFM_OUTPUT_cb_3d\cb_3d_map.nc')
data_frommap = get_ncmodeldata(file_nc=file_nc, varname='mesh2d_sa1', timestep='all', layer=5)#, multipart=False)
assert data_frommap.shape[0] == len(data_frommap.var_times)
#retrieve with numpy datetime array
data_frommap = get_ncmodeldata(file_nc=file_nc, varname='mesh2d_sa1',
timestep=np.arange(dt.datetime(2001,1,1),dt.datetime(2001,1,2),dt.timedelta(hours=1)), layer=5)#, multipart=False)
assert (data_frommap.data[0,0,0] - 31. ) < 1E-9
assert data_frommap.shape[0] == len(data_frommap.var_times)
#retrieve with pandas date_range
data_frommap = get_ncmodeldata(file_nc=file_nc, varname='mesh2d_sa1',
timestep=pd.date_range(dt.datetime(2001,1,1),dt.datetime(2001,1,2),freq='30min'), layer=5)#, multipart=False)
assert (data_frommap.data[0,0,0] - 31. ) < 1E-9
assert data_frommap.shape[0] == len(data_frommap.var_times)
def test_getmapbottomdata(file_nc, varname, expected_size):
"""
Checks whether ghost cells are properly taken care and if the mask comes trough (needed for selecting the bottom layer).
It will fail on get_ncmodeldata, the assertions are just extras
file_nc = os.path.join(dir_testinput,'DFM_3D_z_Grevelingen','computations','run01','DFM_OUTPUT_Grevelingen-FM','Grevelingen-FM_0000_map.nc')
varname = 'mesh2d_sa1'
expected_size = (1, 44796, 1)
file_nc = os.path.join('p:\\11203850-coastserv\\06-Model\\waq_model\\simulations\\run0_20200319\\DFM_OUTPUT_kzn_waq', 'kzn_waq_0000_map.nc')
varname = 'Chlfa'
expected_size = (1, 17385, 1)
"""
from dfm_tools.get_nc import get_ncmodeldata
data_fromnc_bot = get_ncmodeldata(file_nc=file_nc, varname=varname, timestep=3, layer='bottom')
assert data_fromnc_bot.shape == expected_size
assert data_fromnc_bot.mask.shape == expected_size
#
# import libraries
#
from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
from dfm_tools.get_nc_helpers import get_ncvardimlist, get_timesfromnc, get_hisstationlist
import matplotlib.pyplot as plt
import xarray as xr
import numpy as np
fname = 'C:\\Users\\backeber\\OneDrive - Stichting Deltares\\Desktop\\Project-D-HYDRO-Phase-4\\dflowfm\\dflowfm_serial\\DFM_OUTPUT_ocean_eddy_' + expt + '\\ocean_eddy_' + expt + '_map.nc'
ugrid_all = get_netdata(file_nc=fname)
ds = xr.open_dataset(fname)
fig, axs = plt.subplots(1, 1, figsize=(5, 5))
var = get_ncmodeldata(file_nc=fname, varname=varname, timestep=tstep2plot)
pc = plot_netmapdata(ugrid_all.verts,
values=var[0, :],
ax=axs,
linewidth=0.5,
cmap="jet")
#pc.set_clim([0, 0.25])
#axs.plot(x[:i],y[:i],'r.', markersize = 2)
axs.set_title(np.datetime_as_string(ds.time.data[tstep2plot], unit='h'))
#axs.set_title('t = '+str(tstep2plot)+' hours')
axs.set_aspect('equal')
xticks = np.linspace(np.min(ds.mesh2d_face_x.data),
np.max(ds.mesh2d_face_x.data),
num=5,
endpoint=True)
plt.close('all')
from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
from dfm_tools.get_nc_helpers import get_ncvardimlist
from dfm_tools.regulargrid import scatter_to_regulargrid
dir_testinput = r'c:\DATA\dfm_tools_testdata'
dir_output = '.'
#RMM foufile met quivers
file_nc = os.path.join(dir_testinput, r'DFM_fou_RMM\RMM_dflowfm_0000_fou.nc')
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc)
#stations_pd = get_hisstationlist(file_nc,varname='waterlevel')
ugrid_all = get_netdata(file_nc=file_nc)
ux_mean = get_ncmodeldata(file_nc=file_nc, varname='mesh2d_fourier001_mean')
uy_mean = get_ncmodeldata(file_nc=file_nc, varname='mesh2d_fourier002_mean')
magn_mean = np.sqrt(ux_mean**2 + uy_mean**2)
#uc_mean = get_ncmodeldata(file_nc=file_nc, varname='mesh2d_fourier003_mean')
#uc_max = get_ncmodeldata(file_nc=file_nc, varname='mesh2d_fourier004_max')
facex = get_ncmodeldata(file_nc=file_nc, varname='mesh2d_face_x')
facey = get_ncmodeldata(file_nc=file_nc, varname='mesh2d_face_y')
X, Y, U = scatter_to_regulargrid(xcoords=facex,
ycoords=facey,
ncellx=60,
ncelly=35,
values=ux_mean)
X, Y, V = scatter_to_regulargrid(xcoords=facex,
ycoords=facey,
ncellx=60,
import os
import matplotlib.pyplot as plt
plt.close('all')
from dfm_tools.get_nc import get_ncmodeldata
#from dfm_tools.get_nc_helpers import get_hisstationlist
dir_testinput = r'c:\DATA\dfm_tools_testdata'
dir_output = '.'
file_nc = os.path.join(dir_testinput, 'KenmerkendeWaarden', 'observations.nc')
#station_names = get_hisstationlist(file_nc=file_nc, varname='observation_id')
#station_names = get_hisstationlist(file_nc=file_nc, varname='water_level')
data_fromsobek = get_ncmodeldata(file_nc=file_nc,
varname='water_level',
station=['Maasmond', 'HKVHLD', 'MO_1035.00'],
timestep='all')
fig, ax = plt.subplots()
for iL in range(data_fromsobek.shape[1]):
ax.plot(data_fromsobek.var_times,
data_fromsobek[:, iL],
'-',
label=data_fromsobek.var_stations.iloc[iL, 0])
ax.legend()
plt.savefig(
os.path.join(
dir_output,
'%s_waterlevel' % (os.path.basename(file_nc).replace('.', ''))))
#
from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
from dfm_tools.get_nc_helpers import get_ncvardimlist, get_timesfromnc, get_hisstationlist
import matplotlib.pyplot as plt
import xarray as xr
import numpy as np
fname = 'C:\\Users\\backeber\\OneDrive - Stichting Deltares\\Desktop\\Project-D-HYDRO-Phase-4\\dflowfm\\dflowfm_serial\\DFM_OUTPUT_ocean_eddy_' + expt + '\\ocean_eddy_' + expt + '_map.nc'
ugrid_all = get_netdata(file_nc=fname)
ds = xr.open_dataset(fname)
for i in np.arange(0, len(ds.time.data), 1):
fig, axs = plt.subplots(1, 1, figsize=(5, 5))
ssh = get_ncmodeldata(file_nc=fname, varname=var2plot, timestep=i)
pc = plot_netmapdata(ugrid_all.verts,
values=ssh[0, :],
ax=axs,
linewidth=0.5,
cmap="jet")
pc.set_clim(cmaplims)
#axs.plot(x[:i],y[:i],'r.', markersize = 2)
axs.set_title(np.datetime_as_string(ds.time.data[i], unit='h'))
#axs.set_title('t = '+str(i)+' hours')
axs.set_aspect('equal')
xticks = np.linspace(np.min(ds.mesh2d_face_x.data),
np.max(ds.mesh2d_face_x.data),
num=5,
endpoint=True)
from dfm_tools.get_nc import get_ncmodeldata #, get_netdata, plot_netmapdata
from dfm_tools.get_nc_helpers import get_ncvardimlist, get_hisstationlist #, get_varname_fromnc
from dfm_tools.regulargrid import uva2xymagdeg
from dfm_tools.io.polygon import Polygon
dir_testinput = r'c:\DATA\dfm_tools_testdata'
dir_output = '.'
file_ldb = r'p:\archivedprojects\1220688-lake-kivu\3_modelling\1_FLOW\4_CH4_CO2_included\008\lake_kivu_geo.ldb'
data_ldb = Polygon.fromfile(file_ldb, pd_output=True)
file_nc = r'p:\archivedprojects\1220688-lake-kivu\3_modelling\1_FLOW\7_heatfluxinhis\062_netcdf\trim-thiery_002_coarse.nc'
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc)
data_nc_XZ = get_ncmodeldata(file_nc=file_nc, varname='XZ')
data_nc_YZ = get_ncmodeldata(file_nc=file_nc, varname='YZ')
data_nc_XCOR = get_ncmodeldata(file_nc=file_nc, varname='XCOR')
data_nc_YCOR = get_ncmodeldata(file_nc=file_nc, varname='YCOR')
data_nc_ALFAS = get_ncmodeldata(
file_nc=file_nc,
varname='ALFAS') #contains rotation of all cells wrt real world
#data_nc_S1 = get_ncmodeldata(file_nc=file_nc, varname='S1',timestep='all')
data_nc_QNET = get_ncmodeldata(file_nc=file_nc, varname='QNET', timestep='all')
data_nc_DPV0 = get_ncmodeldata(file_nc=file_nc, varname='DPV0')
#data_nc_QEVA = get_ncmodeldata(file_nc=file_nc, varname='QEVA',timestep='all')
data_nc_KCU = get_ncmodeldata(file_nc=file_nc, varname='KCU')
data_nc_KCV = get_ncmodeldata(file_nc=file_nc, varname='KCV')
layno = -2
data_nc_U1 = get_ncmodeldata(file_nc=file_nc,
pol_shp = Polygon(pol_data_nonan)
pol_shp_list.append(pol_shp)
print('creating geodataframe with cells')
newdata = gpd.GeoDataFrame(crs="EPSG:4326")
newdata[
'geometry'] = pol_shp_list #way more time efficient than doing it the loop
for iV, varname in enumerate(varlist):
newdata[varname] = None
for timestep in [6]: #[0,10,20,30]:
for iV, varname in enumerate(varlist):
try:
data_fromnc_all = get_ncmodeldata(file_nc=file_nc,
varname=varname,
timestep=timestep,
layer='all')
data_fromnc_bot = get_ncmodeldata(file_nc=file_nc,
varname=varname,
timestep=timestep,
layer='bottom')
data_fromnc_top = get_ncmodeldata(file_nc=file_nc,
varname=varname,
timestep=timestep,
layer='top')
except:
data_fromnc_top = get_ncmodeldata(file_nc=file_nc,
varname=varname,
timestep=timestep)
data_fromnc_nonan = data_fromnc_top[:]
import datetime as dt
import numpy as np
import matplotlib.pyplot as plt
plt.close('all')
from dfm_tools.get_nc import get_ncmodeldata #, get_netdata, plot_netmapdata
from dfm_tools.get_nc_helpers import get_ncvardimlist, get_hisstationlist #, get_varname_fromnc
dir_testinput = r'c:\DATA\dfm_tools_testdata'
dir_output = '.'
#MAP ZUNO
file_nc = r'p:\1204257-dcsmzuno\2019\DCSMv6\A01\SDS-A01_map.nc'
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc)
data_nc_x = get_ncmodeldata(file_nc=file_nc, varname='grid_x')
data_nc_y = get_ncmodeldata(file_nc=file_nc, varname='grid_y')
data_nc_SEP = get_ncmodeldata(file_nc=file_nc, varname='SEP', timestep='all')
#data_nc_VELU = get_ncmodeldata(file_nc=file_nc, varname='VELU',timestep='all')
#data_nc_VELV = get_ncmodeldata(file_nc=file_nc, varname='VELV',timestep='all')
data_nc_xcen = np.mean(data_nc_x, axis=2)
data_nc_ycen = np.mean(data_nc_y, axis=2)
fig, ax = plt.subplots()
#vel_x, vel_y, vel_magn, direction_naut_deg = uva2xymagdeg(u=data_nc_U1[timestep,90,:,:],v=data_nc_V1[timestep,90,:,:],alpha=data_nc_ALFAS)
#pc = ax.pcolor(data_nc_XZ,data_nc_YZ,direction_naut_deg,cmap='jet')
#pc.set_clim([0,360])
timestep = 0
pc = ax.pcolor(data_nc_xcen,
data_nc_ycen,
data_nc_SEP[timestep, :, :],
#calcdist_fromlatlon = None
multipart = None
#line_array = np.array([[ 104.15421399, 2042.7077107 ],
# [2913.47878063, 2102.48057382]])
#val_ylim = None
#clim_bl = None
#optimize_dist = None
#ugrid = get_netdata(file_nc=file_nc, multipart=multipart)
#intersect_gridnos, intersect_coords = ugrid.polygon_intersect(line_array, optimize_dist=None)
#code from get_xzcoords_onintersection
data_nc = Dataset(file_nc)
varn_mesh2d_s1 = get_varname_fromnc(data_nc, 'mesh2d_s1', vardim='var')
data_frommap_wl3 = get_ncmodeldata(file_nc,
varname=varn_mesh2d_s1,
timestep=timestep,
multipart=multipart)
data_frommap_wl3 = data_frommap_wl3[0, :]
#data_frommap_wl3_sel = data_frommap_wl3[0,intersect_gridnos]
varn_mesh2d_flowelem_bl = get_varname_fromnc(data_nc,
'mesh2d_flowelem_bl',
vardim='var')
data_frommap_bl = get_ncmodeldata(file_nc,
varname=varn_mesh2d_flowelem_bl,
multipart=multipart)
#data_frommap_bl_sel = data_frommap_bl[intersect_gridnos]
dimn_layer = get_varname_fromnc(data_nc, 'nmesh2d_layer', vardim='dim')
if dimn_layer is None: #no layers, 2D model
nlay = 1
else:
return time_cor
if 'Grevelingen-FM_0000' in file_nc:
#file_nc = os.path.join(dir_testinput,r'DFM_3D_z_Grevelingen\computations\run01\DFM_OUTPUT_Grevelingen-FM\Grevelingen-FM_0000_his.nc')
station = 'all'
station_zt = 'GTSO-02'
elif 'tttz' in file_nc: #NITHIN
#file_nc = os.path.join(dir_testinput,'vanNithin','tttz_0000_his.nc')
station = ['Peiraias', 'Ovrios_2','Ovrios','Ovrios','Ortholithi']
station_zt = 'Ortholithi'
elif 'impaqt' in file_nc:
station = ['MO_TS_MO_ATHOS','MO_TS_MO_LESVO','MO_TS_MO_SKYRO','IOC_thes','farm_impaqt']
station_zt = 'MO_TS_MO_ATHOS'
print('plot bedlevel from his')
data_fromhis = get_ncmodeldata(file_nc=file_nc, varname='bedlevel', station=station)#, multipart=False)
fig, ax = plt.subplots()
ax.plot(data_fromhis.var_stations.iloc[:,0],data_fromhis,'-')
ax.tick_params('x',rotation=90)
fig.savefig(os.path.join(dir_output,'%s_bedlevel'%(os.path.basename(file_nc).replace('.',''))))
print('plot waterlevel from his')
data_fromhis = get_ncmodeldata(file_nc=file_nc, varname='waterlevel', timestep='all', station=station)#, multipart=False)
fig, ax = plt.subplots()
ax.plot(data_fromhis.var_times,data_fromhis,'-')
fig.savefig(os.path.join(dir_output,'%s_waterlevel'%(os.path.basename(file_nc).replace('.',''))))
print('plot salinity from his')
data_fromhis = get_ncmodeldata(file_nc=file_nc, varname='salinity', timestep='all', layer=5, station=station)#, multipart=False)
data_fromhis_flat = data_fromhis[:,:,0]
fig, ax = plt.subplots()
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()
dir_testinput = r'c:\DATA\dfm_tools_testdata'
dir_output = '.'
file_nc = os.path.join(dir_testinput, 'DFM_3D_z_Grevelingen', 'computations',
'run01', 'DFM_OUTPUT_Grevelingen-FM',
'Grevelingen-FM_0000_map.nc')
#file_nc = os.path.join(dir_testinput,'DFM_sigma_curved_bend\\DFM_OUTPUT_cb_3d\\cb_3d_map.nc')
#file_nc = 'p:\\1204257-dcsmzuno\\2013-2017\\3D-DCSM-FM\\A17b\\DFM_OUTPUT_DCSM-FM_0_5nm\\DCSM-FM_0_5nm_0000_map.nc'
#file_nc = 'p:\\11205258-006-kpp2020_rmm-g6\\C_Work\\08_RMM_FMmodel\\computations\\run_156\\DFM_OUTPUT_RMM_dflowfm\\RMM_dflowfm_0000_map.nc'
clim_bl = [-40, 10]
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc)
ugrid = get_netdata(file_nc=file_nc)
#get bed layer
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')
data_frommap_bl = get_ncmodeldata(file_nc=file_nc,
varname='mesh2d_flowelem_bl')
for maskland_dist in [None, 100]:
#interpolate to regular grid
x_grid, y_grid, val_grid = scatter_to_regulargrid(
xcoords=data_frommap_x,
ycoords=data_frommap_y,
ncellx=100,
ncelly=80,
values=data_frommap_bl,
method='linear',
maskland_dist=maskland_dist)
from dfm_tools.testutils import try_importmodule
try_importmodule(
modulename='contextily'
) #check if contextily was installed since it is an optional module, also happens in plot_cartopybasemap()
import contextily as ctx
from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
dir_testinput = r'c:\DATA\dfm_tools_testdata'
dir_output = '.'
file_nc_map = os.path.join(
dir_testinput,
'DFM_3D_z_Grevelingen\\computations\\run01\\DFM_OUTPUT_Grevelingen-FM\\Grevelingen-FM_0000_map.nc'
)
ugrid = get_netdata(file_nc=file_nc_map)
data_frommap_bl = get_ncmodeldata(file_nc=file_nc_map,
varname='mesh2d_flowelem_bl')
source_list = [
ctx.providers.Stamen.Terrain, #default source
ctx.providers.Esri.WorldImagery,
ctx.providers.CartoDB.Voyager,
#ctx.providers.NASAGIBS.ViirsEarthAtNight2012,
ctx.providers.Stamen.Watercolor
]
for source_ctx in source_list:
source_name = source_ctx['name'].replace('.', '_')
fig, ax = plt.subplots(1, 1, figsize=(10, 6))
pc = plot_netmapdata(ugrid.verts,
values=data_frommap_bl,
ax=ax,
import numpy as np
import matplotlib.pyplot as plt
# set filename
fname = 'C:\\Users\\backeber\\OneDrive - Stichting Deltares\\Desktop\\Project-D-HYDRO-Phase-4\\dflowfm\\dflowfm_serial\\oceaneddy_expt00_20010101_000000_rst.nc'
ds = xr.open_dataset(fname)
# plt.scatter(ds.FlowElem_xzw.data,ds.FlowElem_yzw.data,1,ds.s1.data[0,:])
ugrid_all = get_netdata(
file_nc=
'C:\\Users\\backeber\\OneDrive - Stichting Deltares\\Desktop\\Project-D-HYDRO-Phase-4\\dflowfm\\dflowfm_serial\\DFM_OUTPUT_oceaneddy_expt00\\oceaneddy_expt00_map.nc'
)
#ugrid_all = get_netdata(file_nc=fname)
ds = xr.open_dataset(fname)
ssh = get_ncmodeldata(file_nc=fname, varname='s1', timestep=0)
# find location of max ssh to add to plot
maxi = np.argmax(ssh)
fig, ax = plt.subplots() #(figsize=(15, 10))
pc = plot_netmapdata(ugrid_all.verts,
values=ssh[0, :],
ax=ax,
linewidth=0.5,
cmap="jet")
#pc.set_clim([0, 0.05])
x, y = ugrid_all.verts[maxi, :, :].mean(axis=0)
ax.plot(x, y, 'wx')
ax.set_title('%s (%s)' % (ds.s1.long_name, ds.s1.units))
color="crimson",
facecolor="None")
ax.set_xlabel('x-direction')
ax.set_ylabel('y-direction')
ax.set_aspect('equal')
fig.tight_layout()
fig.savefig(
os.path.join(dir_output,
'%s_grid' % (os.path.basename(file_nc).replace('.', ''))))
#PLOT bedlevel
if not 'cb_3d_map' in file_nc:
print('plot grid and values from mapdata (constantvalue, 1 dim)')
ugrid = get_netdata(file_nc=file_nc) #,multipart=False)
#iT = 3 #for iT in range(10):
data_frommap = get_ncmodeldata(
file_nc=file_nc, varname='mesh2d_flowelem_bl') #, multipart=False)
data_frommap_flat = data_frommap.flatten()
fig, ax = plt.subplots()
pc = plot_netmapdata(ugrid.verts,
values=data_frommap_flat,
ax=None,
linewidth=0.5,
cmap="jet")
pc.set_clim(clim_bl)
cbar = fig.colorbar(pc, ax=ax)
cbar.set_label('%s [%s]' % (data_frommap.var_ncattrs['long_name'],
data_frommap.var_ncattrs['units']))
coordnames_xy = data_frommap.var_ncattrs['coordinates'].split()
varcoords_x = get_ncmodeldata(file_nc=file_nc,
varname=coordnames_xy[0])
varcoords_y = get_ncmodeldata(file_nc=file_nc,
#uncomment the line below, copy data locally and change this path to increase performance
dir_testinput = os.path.join(r'E:\proj\Pescadero\Model_Runs\Testing')
file_nc_map = os.path.join(dir_testinput, 'run_tide_test-v12A',
'DFM_OUTPUT_flowfm', 'flowfm_map.nc')
file_nc_his = os.path.join(dir_testinput, 'run_tide_test-v12A',
'DFM_OUTPUT_flowfm', 'flowfm_his.nc')
#get lists with vars/dims, times, station/crs/structures
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc_map)
times_pd = get_timesfromnc(file_nc=file_nc_map)
statlist_pd = get_hisstationlist(file_nc=file_nc_his, varname='station_name')
#retrieve his data
data_fromhis_wl = get_ncmodeldata(file_nc=file_nc_his,
varname='waterlevel',
station='all',
timestep='all')
fig, ax = plt.subplots(1, 1, figsize=(10, 5))
for iP, station in enumerate(data_fromhis_wl.var_stations['station_name']):
ax.plot(data_fromhis_wl.var_times,
data_fromhis_wl[:, iP],
'-',
label=station)
ax.legend()
ax.set_ylabel(
'%s (%s)' %
(data_fromhis_wl.var_varname, data_fromhis_wl.var_ncvarobject.units))
#plot net/grid
ugrid_all = get_netdata(file_nc=file_nc_map) #,multipart=False)
fig, ax = plt.subplots()
import matplotlib.pyplot as plt
plt.close('all')
from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
dir_testinput = r'c:\DATA\dfm_tools_testdata'
file_nc = os.path.join(dir_testinput,'oost_tracer_2_map.nc')
ugrid = get_netdata(file_nc=file_nc)
print('plot grid and values from mapdata (constantvalue, 1 dim)')
if 'oost_tracer_map' in file_nc:
var_names = ['FColi1','HIWAI1','mspaf1','Pharma1'] #nieuwe file, te veel dimensies
var_clims = [None,[0,100000000000],None,[0,10000]]
elif 'oost_tracer_2_map' in file_nc:
var_names = ['mesh2d_FColi','mesh2d_HIWAI','mesh2d_Pharma'] #oude file
var_clims = [None,[0,100000000000],[0,10000]]
else:
raise Exception('ERROR: no settings provided for this mapfile')
for var_name, var_clim in zip(var_names, var_clims):
fig, ax = plt.subplots()
data_frommap = get_ncmodeldata(file_nc=file_nc, varname=var_name)#, multipart=False)
pc = plot_netmapdata(ugrid.verts, values=data_frommap, ax=None, linewidth=0.5, cmap="jet")
if var_clim != None:
pc.set_clim(var_clim)
fig.colorbar(pc, ax=ax)
ax.set_aspect('equal')
ax.set_xlabel(var_name)
plt.savefig('%s_%s'%(os.path.basename(file_nc).replace('.',''),var_name))
from dfm_tools.get_nc_helpers import get_ncvardimlist
dir_testinput = r'c:\DATA\dfm_tools_testdata'
dir_output = '.'
file_nc_list = [r'p:\11203869-morwaqeco3d\05-Tidal_inlet\02_FM_201910\FM_MF10_Max_30s\wave\wavm-inlet.nc',
r'p:\11200665-c3s-codec\2_Hydro\ECWMF_meteo\meteo\ERA-5\2000\ERA5_metOcean_atm_19991201_19991231.nc',
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')
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc)
vars_pd.to_csv(os.path.join(dir_output, 'vars_pd.csv'))
vars_pd_sel = vars_pd[vars_pd['long_name'].str.contains('transport')]
#vars_pd_sel = vars_pd[vars_pd['dimensions'].str.contains('mesh2d_nFaces') & vars_pd['long_name'].str.contains('wave')]
ugrid = get_netdata(file_nc=file_nc)
varname = 'mesh2d_mor_bl'
var_clims = [-50, 0]
var_longname = vars_pd['long_name'][vars_pd['nc_varkeys'] == varname].iloc[0]
fig, axs = plt.subplots(3, 1, figsize=(6, 9))
fig.suptitle('%s (%s)' % (varname, var_longname))
ax = axs[0]
data_frommap_0 = get_ncmodeldata(file_nc=file_nc,
varname=varname,
timestep=0,
get_linkedgridinfo=True)
pc = plot_netmapdata(ugrid.verts,
values=data_frommap_0.flatten(),
ax=ax,
linewidth=0.5,
cmap='jet',
clim=var_clims)
cbar = fig.colorbar(pc, ax=ax)
cbar.set_label(
'%s (%s)' %
(data_frommap_0.var_varname, data_frommap_0.var_ncattrs['units']))
ax.set_title('t=0 (%s)' % (data_frommap_0.var_times.iloc[0]))
ax = axs[1]
data_frommap_end = get_ncmodeldata(file_nc=file_nc,
[86606.61594052, 434685.69068637],
[88689.74425466, 435355.26764449],
[90772.8725688, 434983.28044554],
[91926.03288556, 435132.07532512]
])
val_ylim = None
clim_bl = [-10, 10]
#optimize_dist = 150
else:
raise Exception('ERROR: no settings provided for this mapfile')
ugrid = get_netdata(file_nc=file_nc, multipart=multipart)
#get bedlevel and create plot with ugrid and cross section line
data_frommap_bl = get_ncmodeldata(file_nc=file_nc,
varname='mesh2d_flowelem_bl',
multipart=multipart)
fig, ax_input = plt.subplots()
pc = plot_netmapdata(ugrid.verts,
values=data_frommap_bl,
ax=ax_input,
linewidth=0.5,
edgecolors='face',
cmap='jet') #, color='crimson', facecolor="None")
pc.set_clim(clim_bl)
fig.colorbar(pc, ax=ax_input)
ax_input.set_aspect('equal')
if 0: #click interactive polygon
#pol_frominput = Polygon.frominteractive(ax) #this is old, does not work, use code below
line, = ax_input.plot([], [], 'o-') # empty line
linebuilder = LineBuilder(
import numpy as np
import matplotlib.pyplot as plt
plt.close('all')
from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
from dfm_tools.get_nc_helpers import get_ncvardimlist, get_hisstationlist #, get_varname_fromnc
from dfm_tools.io.polygon import Polygon
dir_testinput = r'c:\DATA\dfm_tools_testdata'
dir_output = '.'
#print gridinfo of several files to compare
file_nc = r'p:\1204257-dcsmzuno\2014\data\meteo\HIRLAM72_2018\h72_201803.nc'
print('\nfile = %s' % (file_nc))
data_dummy = get_ncmodeldata(file_nc=file_nc,
varname='northward_wind',
timestep=0,
get_linkedgridinfo=True)
file_nc = r'p:\archivedprojects\1220688-lake-kivu\2_data\COSMO\COSMOCLM_2012_out02_merged_4Wouter.nc'
print('\nfile = %s' % (file_nc))
data_dummy = get_ncmodeldata(file_nc=file_nc,
varname='U_10M',
timestep=0,
get_linkedgridinfo=True)
file_nc = r'p:\11200665-c3s-codec\2_Hydro\ECWMF_meteo\meteo\ERA-5\2000\ERA5_metOcean_atm_19991201_19991231.nc'
print('\nfile = %s' % (file_nc))
data_dummy = get_ncmodeldata(file_nc=file_nc,
varname='msl',
timestep=0,
get_linkedgridinfo=True)
file_nc = r'p:\11202255-sfincs\Testbed\Original_tests\01_Implementation\08_restartfile\sfincs_map.nc'
print('\nfile = %s' % (file_nc))
