data_url = server_stem_url + file_stem + str(d.year) + str(d.month).zfill(2) + str(d.day).zfill(2) + '.nc'
# the utools class requires a mapping of specific model variable names (values)
# to common names (keys) so that the class methods can work with FVCOM, SELFE,
# and ADCIRC which have different variable names
# (This seemed easier than finding them by CF long_names etc)
var_map = { 'longitude':'lon', \
'latitude':'lat', \
'time':'time', \
'u_velocity':'u', \
'v_velocity':'v', \
'nodes_surrounding_ele':'nv',\
}
# class instantiation creates a netCDF Dataset object as an attribute
wfs = tri_grid.ugrid(data_url)
# get longitude, latitude, and time variables
print('Downloading data dimensions')
wfs.get_dimensions(var_map)
#display available time range for model output
nctools.show_tbounds(wfs.Dataset.variables['time'])
# get grid topo variables (nbe, nv)
print('Downloading grid topo variables')
wfs.get_grid_topo(var_map)
# find and order the boundary
print('Finding boundary segs')
bnd = wfs.find_bndry_segs()
# -*- coding: utf-8 -*-
from __future__ import print_function
from libgoods import tri_grid
from libgoods import data_files_dir
import os
import numpy as np
nc_file = 'http://geoport.whoi.edu/thredds/dodsC/usgs/vault0/models/tides/FLsab_adcirc54.nc'
var_map = {'latitude':'lat','longitude':'lon','nodes_surrounding_ele':'ele'}
adcirc = tri_grid.ugrid(nc_file)
adcirc.get_dimensions(var_map,get_time=False)
adcirc.get_grid_topo(var_map)
nl = 30.6; sl = 29
wl = -82; el = -80.6
adcirc.find_nodes_eles_in_ss(nl,sl,wl,el)
bnd = adcirc.find_bndry_segs(subset=True)
print('Size of boundary: ', len(bnd))
seg_types = [0] * len(bnd)
adcirc.order_boundary(bnd,seg_types)
#adcirc.update(os.path.join(data_files_dir,'vdatum','vdatum_fl_sab_adcirc54.nc'))
def parse_string(name):
lista = [e.decode().strip() for e in name.tolist()]
return ''.join(lista)
names = []
const = adcirc.Dataset.variables['tidenames'][:]
for name in const:
# and ADCIRC which have different variable names
# (This seemed easier than finding them by CF long_names etc)
#!!!!!!!!CREOFS output on server does not include eles_surrounding_ele info
#I have it saved as a netcdf file included in libgoods data_files directory
var_map = { 'longitude':'lon', \
'latitude':'lat', \
'time':'time', \
'u_velocity':'u', \
'v_velocity':'v', \
'nodes_surrounding_ele':'ele',\
'eles_surrounding_ele':'',\
}
# class instantiation creates a netCDF Dataset object as an attribute
# creofs = utools.ugrid(flist) #multiple files
creofs = tri_grid.ugrid(data_url) #single file output
# get longitude, latitude, and time variables
print 'Downloading data dimensions'
creofs.get_dimensions(var_map)
#display available time range for model output
nctools.show_tbounds(creofs.Dataset.variables['time'])
# get grid topo variables (nbe, nv)
print 'Downloading grid topo variables'
creofs.get_grid_topo(var_map)
creofs.atts['nbe']['order'] = 'ccw'
creofs.find_nodes_eles_in_ss(nl,sl,wl,el)
'u_velocity':'u', \
'v_velocity':'v', \
'nodes_surrounding_ele':'nv',\
'eles_surrounding_ele':'nbe',\
}
firsttime = 1
for f in flist:
if firsttime:
firsttime = 0
# class instantiation creates a netCDF Dataset object as an attribute --
# use the first file in the list only
ngofs = tri_grid.ugrid(f)
# # get longitude, latitude, and time variables
# print 'Downloading data dimensions'
# ngofs.get_dimensions(var_map)
# get grid topo variables (nbe, nv)
print 'Downloading grid topo variables'
ngofs.get_grid_topo(var_map)
# GNOME needs to know whether the elements are ordered clockwise (FVCOM) or counter-clockwise (SELFE)
ngofs.atts['nbe']['order'] = 'cw'
# find and order the boundary
print 'Finding boundary'
bnd = ngofs.find_bndry_segs()
print 'Ordering boundary'
# -*- coding: utf-8 -*-
from __future__ import print_function
from libgoods import tri_grid
from libgoods import data_files_dir
import os
ncfile = os.path.join(data_files_dir,'vdatum','vdatum_fl_sab_adcirc54.nc')
var_map = {'latitude':'lat','longitude':'lon','nodes_surrounding_ele':'ele'}
adcirc = tri_grid.ugrid(ncfile)
adcirc.get_dimensions(var_map,get_time=False)
adcirc.get_grid_topo(var_map)
# find and order the boundary
print('Finding boundary')
bnd = adcirc.find_bndry_segs()
seg_types = [0] * len(bnd)
print('Ordering boundary')
adcirc.order_boundary(bnd,seg_types)
adcirc.write_unstruc_grid_only(os.path.join(data_files_dir,'vdatum','fl_sab_grid.nc'))
'u_velocity':'u', \
'v_velocity':'v', \
'nodes_surrounding_ele':'ele',\
'eles_surrounding_ele':'',\
}
firsttime = 1
for f in flist:
if firsttime:
firsttime = 0
# class instantiation creates a netCDF Dataset object as an attribute --
# use the first file in the list only
creofs = tri_grid.ugrid(f)
# # get longitude, latitude, and time variables
# print 'Downloading data dimensions'
# creofs.get_dimensions(var_map)
# get grid topo variables (nbe, nv)
print('Downloading grid topo variables')
creofs.get_grid_topo(var_map)
creofs.get_dimensions(var_map,get_time=False)
# GNOME needs to know whether the elements are ordered clockwise (FVCOM) or counter-clockwise (SELFE)
creofs.atts['nbe']['order'] = 'ccw'
creofs.find_nodes_eles_in_ss(nl,sl,wl,el)
# find and order the boundary
# to common names (keys) so that the class methods can work with FVCOM, SELFE,
# and ADCIRC which have different variable names
# (This seemed easier than finding them by CF long_names etc)
var_map = { 'longitude':'lon', \
'latitude':'lat', \
'time':'time', \
'u_velocity':'u', \
'v_velocity':'v', \
'nodes_surrounding_ele':'nv',\
'eles_surrounding_ele':'nbe',\
'sigma':'siglay',\
'depth':'h',\
}
# class instantiation creates a netCDF Dataset object as an attribute
nwgofs = tri_grid.ugrid(flist[0])
# get longitude, latitude
print('Downloading data dimensions')
nwgofs.get_dimensions(var_map,get_time=False)
# get grid topo variables (nbe, nv)
print('Downloading grid topo variables')
nwgofs.get_grid_topo(var_map)
# GNOME needs to know whether the elements are ordered clockwise (FVCOM) or counter-clockwise (SELFE)
nwgofs.atts['nbe']['order'] = 'cw'
# find and order the boundary
print('Finding boundary')
bnd = nwgofs.find_bndry_segs()
print('Ordering boundary')
# to common names (keys) so that the class methods can work with FVCOM, SELFE,
# and ADCIRC which have different variable names
# (This seemed easier than finding them by CF long_names etc)
#!!!!!!!!txselfe output on server does not include eles_surrounding_ele info
#I have it saved as a netcdf file included in libgoods data_files directory
var_map = { 'longitude':'lon', \
'latitude':'lat', \
'time':'', \
'u_velocity':'u', \
'v_velocity':'v', \
'nodes_surrounding_ele':'ele',\
'eles_surrounding_ele':'',\
}
# class instantiation creates a netCDF Dataset object as an attribute
txselfe = tri_grid.ugrid(data_file)
# get longitude, latitude, and time variables
print 'Downloading data dimensions'
# Normally this would be a call to txself.get_dimensions(var_map) but
# this file has no time info, and only UTM coordinates -- so we do it
# manually
#Enter actual model time here for particular file
model_time = dt.datetime(2013,8,21,0,0,0) #I made this time up
t_units = 'hours since 2012-01-01 00:00:00'
txselfe.data['time'] = [date2num(model_time,t_units),]
txselfe.atts['time'] = {'units':t_units}
x = txselfe.Dataset.variables['x'][:]
y = txselfe.Dataset.variables['y'][:]
# to common names (keys) so that the class methods can work with FVCOM, SELFE,
# and ADCIRC which have different variable names
# (This seemed easier than finding them by CF long_names etc)
#!!!!!!!!txsuntans output on server does not include eles_surrounding_ele info
#I have it saved as a netcdf file included in libgoods data_files directory
var_map = {
'time':'time',\
'u_velocity':'uc', \
'v_velocity':'vc', \
'nodes_surrounding_ele':'cells',\
'eles_surrounding_ele':'nbe',\
'edge_node_connectivity':'edges',\
}
# class instantiation creates a netCDF Dataset object as an attribute
txsuntans = tri_grid.ugrid(data_file)
# get longitude, latitude, and time variables
print 'Downloading data dimensions'
txsuntans.get_dimensions(var_map)
# UTM coordinates -- calculate lat/lon
x = txsuntans.Dataset.variables['xp'][:]
y = txsuntans.Dataset.variables['yp'][:]
lon = np.ones_like(x); lat = np.ones_like(x)
for ii in range(len(x)):
lat[ii], lon[ii] = nctools.utmToLatLng(14,x[ii],y[ii])
txsuntans.data['lon'] = lon
txsuntans.data['lat'] = lat
txsuntans.atts['lon'] = {'long_name': 'longitude'}
txsuntans.atts['lat'] = {'long_name': 'latitude'}
grid = 'hecwfs'
# the utools class requires a mapping of specific model variable names (values)
# to common names (keys) so that the class methods can work with FVCOM, SELFE,
# and ADCIRC which have different variable names
# (This seemed easier than finding them by CF long_names etc)
var_map = { 'longitude':'lon', \
'latitude':'lat', \
'time':'time', \
'u_velocity':'u', \
'v_velocity':'v', \
'nodes_surrounding_ele':'nv',\
'eles_surrounding_ele':'nbe',\
}
# class instantiation creates a netCDF Dataset object as an attribute
stclair = tri_grid.ugrid(data_url)
# get longitude, latitude, and time variables
print('Downloading data dimensions')
stclair.get_dimensions(var_map)
#display available time range for model output
nctools.show_tbounds(stclair.Dataset.variables['time'])
# get grid topo variables (nbe, nv)
print('Downloading grid topo variables')
stclair.get_grid_topo(var_map)
# GNOME needs to know whether the elements are ordered clockwise (FVCOM) or counter-clockwise (SELFE)
stclair.atts['nbe']['order'] = 'cw'
# find and order the boundary
# the utools class requires a mapping of specific model variable names (values)
# to common names (keys) so that the class methods can work with FVCOM, SELFE,
# and ADCIRC which have different variable names
# (This seemed easier than finding them by CF long_names etc)
var_map = { 'longitude':'lon', \
'latitude':'lat', \
'time':'time', \
'u_velocity':'u', \
'v_velocity':'v', \
'nodes_surrounding_ele':'nv',\
'eles_surrounding_ele':'nbe',\
}
# class instantiation creates a netCDF Dataset object as an attribute --
# use the first file in the list only
ngofs = tri_grid.ugrid(flist[0])
#get longitude, latitude
print('Downloading data dimensions')
ngofs.get_dimensions(var_map, get_time=False)
# get grid topo variables (nbe, nv)
print('Downloading grid topo variables')
ngofs.get_grid_topo(var_map)
# subset bounding box
nl = 30.7
sl = 28.6
wl = -89.643
el = -87.391
ngofs.find_nodes_eles_in_ss(nl, sl, wl, el)
# the utools clasalish_sea requires a mapping of specific model variable names (values)
# to common names (keys) so that the clasalish_sea methods can work with FVCOM, SELFE,
# and ADCIRC which have different variable names
# (This seemed easier than finding them by CF long_names etc)
var_map = { 'longitude':'lon', \
'latitude':'lat', \
'time':'time', \
'u_velocity':'u', \
'v_velocity':'v', \
'nodes_surrounding_ele':'nv',\
'eles_surrounding_ele':'nbe',\
}
# clasalish_sea instantiation creates a netCDF Dataset object as an attribute
salish_sea = tri_grid.ugrid(filenames)
# get longitude, latitude, and time variables
print 'Downloading data dimensions'
salish_sea.get_dimensions(var_map)
#fix time units
salish_sea.atts['time']['units'] = 'seconds since 2006-01-01 00:00:00'
#display available time range for model output
#nctools.show_tbounds(salish_sea.Dataset.variables['time'])
# UTM coordinates -- calculate lat/lon
x = salish_sea.data['lon'] #these are actually UTM even though named lon/lat in file
y = salish_sea.data['lat']
lon = np.ones_like(x); lat = np.ones_like(x)
for ii in range(len(x)):
lat[ii], lon[ii] = nctools.utmToLatLng(10,x[ii],y[ii])
salish_sea.data['lon'] = lon
# the utools clasalish_sea requires a mapping of specific model variable names (values)
# to common names (keys) so that the clasalish_sea methods can work with FVCOM, SELFE,
# and ADCIRC which have different variable names
# (This seemed easier than finding them by CF long_names etc)
var_map = { 'longitude':'lon', \
'latitude':'lat', \
'time':'time', \
'u_velocity':'u', \
'v_velocity':'v', \
'nodes_surrounding_ele':'nv',\
'eles_surrounding_ele':'nbe',\
}
# clasalish_sea instantiation creates a netCDF Dataset object as an attribute
salish_sea = tri_grid.ugrid(filenames)
# get longitude, latitude, and time variables
print 'Downloading data dimensions'
salish_sea.get_dimensions(var_map)
#fix time units
salish_sea.atts['time']['units'] = 'seconds since 2006-01-01 00:00:00'
#display available time range for model output
#nctools.show_tbounds(salish_sea.Dataset.variables['time'])
# UTM coordinates -- calculate lat/lon
x = salish_sea.data[
'lon'] #these are actually UTM even though named lon/lat in file
y = salish_sea.data['lat']
lon = np.ones_like(x)
lat = np.ones_like(x)
for ii in range(len(x)):
grid = 'hecwfs'
# the utools class requires a mapping of specific model variable names (values)
# to common names (keys) so that the class methods can work with FVCOM, SELFE,
# and ADCIRC which have different variable names
# (This seemed easier than finding them by CF long_names etc)
var_map = { 'longitude':'lon', \
'latitude':'lat', \
'time':'time', \
'u_velocity':'u', \
'v_velocity':'v', \
'nodes_surrounding_ele':'nv',\
'eles_surrounding_ele':'nbe',\
}
# class instantiation creates a netCDF Dataset object as an attribute
stclair = tri_grid.ugrid(data_url)
# get longitude, latitude, and time variables
print 'Downloading data dimensions'
stclair.get_dimensions(var_map)
#display available time range for model output
nctools.show_tbounds(stclair.Dataset.variables['time'])
# get grid topo variables (nbe, nv)
print 'Downloading grid topo variables'
stclair.get_grid_topo(var_map)
# GNOME needs to know whether the elements are ordered clockwise (FVCOM) or counter-clockwise (SELFE)
stclair.atts['nbe']['order'] = 'cw'
# find and order the boundary
# to common names (keys) so that the class methods can work with FVCOM, SELFE,
# and ADCIRC which have different variable names
# (This seemed easier than finding them by CF long_names etc)
#!!!!!!!!txselfe output on server does not include eles_surrounding_ele info
#I have it saved as a netcdf file included in libgoods data_files directory
var_map = { 'longitude':'lon', \
'latitude':'lat', \
'time':'', \
'u_velocity':'u', \
'v_velocity':'v', \
'nodes_surrounding_ele':'ele',\
'eles_surrounding_ele':'',\
}
# class instantiation creates a netCDF Dataset object as an attribute
txselfe = tri_grid.ugrid(data_file)
# get longitude, latitude, and time variables
print 'Downloading data dimensions'
# Normally this would be a call to txself.get_dimensions(var_map) but
# this file has no time info, and only UTM coordinates -- so we do it
# manually
#Enter actual model time here for particular file
model_time = dt.datetime(2013, 8, 21, 0, 0, 0) #I made this time up
t_units = 'hours since 2012-01-01 00:00:00'
txselfe.data['time'] = [
date2num(model_time, t_units),
]
txselfe.atts['time'] = {'units': t_units}
# to common names (keys) so that the class methods can work with FVCOM, SELFE,
# and ADCIRC which have different variable names
# (This seemed easier than finding them by CF long_names etc)
#!!!!!!!!txsuntans output on server does not include eles_surrounding_ele info
#I have it saved as a netcdf file included in libgoods data_files directory
var_map = {
'time':'time',\
'u_velocity':'uc', \
'v_velocity':'vc', \
'nodes_surrounding_ele':'cells',\
'eles_surrounding_ele':'nbe',\
'edge_node_connectivity':'edges',\
}
# class instantiation creates a netCDF Dataset object as an attribute
txsuntans = tri_grid.ugrid(data_file)
# get longitude, latitude, and time variables
print 'Downloading data dimensions'
txsuntans.get_dimensions(var_map)
# UTM coordinates -- calculate lat/lon
x = txsuntans.Dataset.variables['xp'][:]
y = txsuntans.Dataset.variables['yp'][:]
lon = np.ones_like(x)
lat = np.ones_like(x)
for ii in range(len(x)):
lat[ii], lon[ii] = nctools.utmToLatLng(14, x[ii], y[ii])
txsuntans.data['lon'] = lon
txsuntans.data['lat'] = lat
txsuntans.atts['lon'] = {'long_name': 'longitude'}
