def loadZarrRun(filename, chunks='auto'):
dsWC = xr.open_zarr(filename, chunks=chunks)
ds = xr.open_dataset('../data/model/jet_his.nc')
# For some reason I have to correct the grid metrics that aren't being output with this CROCO run.
dsWC['s_rho'] = ('s_rho', ds.s_rho)
dsWC['s_w'] = ('s_w', ds.s_w)
dsWC['Cs_r'] = ds.Cs_r
dsWC['Cs_w'] = ds.Cs_w
dsWC['h'] = (('eta_rho', 'xi_rho'), 4000 + 0 * dsWC.hbl.isel(time=0))
dsWC['pm'] = (('eta_rho', 'xi_rho'), 1 / 500 + 0 * dsWC.hbl.isel(time=0))
dsWC['pn'] = (('eta_rho', 'xi_rho'), 1 / 500 + 0 * dsWC.hbl.isel(time=0))
dsWC['pm_u'] = (('eta_rho', 'xi_u'),
1 / 500 + 0 * dsWC.u.isel(time=0, s_rho=0))
dsWC['pn_v'] = (('eta_v', 'xi_rho'),
1 / 500 + 0 * dsWC.v.isel(time=0, s_rho=0))
#dsWC = dsWC.rename(time='ocean_time')
dsWC, grid = xroms.roms_dataset(dsWC, Vtransform=1)
dsWC['rho'] = dsWC.temp
dsWC = dsWC.swap_dims({'time': 'ocean_time'})
dsWC['temp'] = (-(1000 + dsWC.rho) /
(1000 + dsWC.rho.isel(ocean_time=1, s_rho=-1).mean()) +
1) / 2e-4 + 20
return dsWC, grid
def loadNewRun(filename, chunks):
# note here that filename should be the directory of history files
files = sorted(glob(filename + 'jet_his.*.nc'))
dsWC = xr.open_mfdataset(files,
combine='nested',
concat_dim='time',
data_vars='minimal',
chunks=chunks)
ds = xr.open_dataset('../data/model/jet_his.nc')
# For some reason I have to correct the grid metrics that aren't being output with this CROCO run.
dsWC['s_rho'] = ('s_rho', ds.s_rho)
dsWC['s_w'] = ('s_w', ds.s_w)
dsWC['Cs_r'] = ds.Cs_r
dsWC['Cs_w'] = ds.Cs_w
dsWC['h'] = (('eta_rho', 'xi_rho'), 4000 + 0 * dsWC.hbl.isel(time=0))
dsWC['pm'] = (('eta_rho', 'xi_rho'), 1 / 500 + 0 * dsWC.hbl.isel(time=0))
dsWC['pn'] = (('eta_rho', 'xi_rho'), 1 / 500 + 0 * dsWC.hbl.isel(time=0))
dsWC['pm_u'] = (('eta_rho', 'xi_u'),
1 / 500 + 0 * dsWC.u.isel(time=0, s_rho=0))
dsWC['pn_v'] = (('eta_v', 'xi_rho'),
1 / 500 + 0 * dsWC.v.isel(time=0, s_rho=0))
#dsWC['f'] = dsNC.f ## XXX THIS IS A HACK THAT WON"T ALWAYS WORK
#dsWC = dsWC.rename(time='ocean_time')
dsWC, grid = xroms.roms_dataset(dsWC, Vtransform=1)
dsWC['rho'] = dsWC.temp
dsWC = dsWC.swap_dims({'time': 'ocean_time'})
dsWC['temp'] = (-(1000 + dsWC.rho) /
(1000 + dsWC.rho.isel(ocean_time=1, s_rho=-1).mean()) +
1) / 2e-4 + 20
return dsWC, grid
def __init__(self, ds):
self.ds = ds
# if ds wasn't read in with an xroms load function, it probably doesn't have a grid object
if "grid" not in ds.attrs:
self.ds, grid = xroms.roms_dataset(self.ds)
self.grid = grid
def loadOldRun(filename, chunks):
ds = xr.open_dataset(filename, chunks=chunks)
ds['zeta'] = 0 * ds.hbl # didn't save zeta...oops.
ds = ds.rename(time='ocean_time')
ds, grid = xroms.roms_dataset(ds)
ds['temp'] = (-(1000 + ds.rho) /
(1000 + ds.rho.isel(ocean_time=0, s_rho=-1).mean()) +
1) / 2e-4 + 20
return ds, grid
def __init__(self, ds):
self.ds = ds
# extra for getting coordinates but changes variables
self._ds = ds.copy(deep=True)
# if ds wasn't read in with an xroms load function, it probably doesn't have a grid object
if "grid" not in ds.attrs:
self.ds, grid = xroms.roms_dataset(self.ds)
self.grid = grid
"""Test utilities in xroms"""
import cartopy
import numpy as np
import xarray as xr
import xroms
grid1 = xr.open_dataset("xroms/tests/input/grid.nc")
# ds = xroms.open_netcdf('xroms/tests/input/ocean_his_0001.nc')
ds = xr.open_dataset("xroms/tests/input/ocean_his_0001.nc")
# combine the two:
ds = ds.merge(grid1, overwrite_vars=True, compat="override")
ds, grid = xroms.roms_dataset(ds)
# # missing psi grid in variables
# ds = ds.assign_coords({'lon_psi': ds.lon_psi, 'lat_psi': ds.lat_psi})
xl, yl, N = 14, 9, 3
z_rho = np.array([-97.5025, -50.05, -2.5975]) # eta_rho=0, xi_rho=0
dz = np.array([33.3, 33.3, 33.3]) # eta_rho=0, xi_rho=0
dx = 14168.78734979 # when eta_rho=0
dy = 18467.47219268 # when eta_rho=0
u = np.linspace(0, 1.2, xl - 1)
v = np.linspace(-0.1, 0.1, yl - 1)[:, np.newaxis]
temp = np.linspace(15, 20, N)
salt = np.linspace(25, 15, N)
zeta = np.linspace(-0.1, 0.1, xl)
g = 9.81
rho0 = 1025
from xhistogram.xarray import histogram
import xarray as xr
import xroms
import h5netcdf
from glob import glob
#Need to subset the netcdf files because xroms will crash if I open them all.
years = np.arange(1994, 2017)
for y in years:
paths = glob('/d1/shared/TXLA_ROMS/output_20yr_obc/%i/ocean_his_00*.nc' %
y)
print('Loading Data')
ds = xroms.open_mfnetcdf(paths, chunks={'ocean_time': 1})
ds, grid = xroms.roms_dataset(ds, Vtransform=None)
print('Data Loaded')
#This will yield a box in the SUNRISE area of interest: see histogram_demonstration for examples.
etaslice = slice(30, 150)
xislice = slice(270, 405)
# dsdx = grid.derivative(ds.salt, 'X')
# dsdy = grid.derivative(ds.salt, 'Y')
dsdx = grid.interp(grid.diff(ds.salt, 'X'), 'X', boundary='extend') / ds.dx
dsdy = grid.interp(grid.diff(ds.salt, 'Y'), 'Y', boundary='extend') / ds.dy
dsdx.attrs = ''
dsdy.attrs = ''
dsdx_slice = dsdx.isel(
eta_rho=etaslice, xi_rho=xislice,
from pytest import approx
import numpy as np
import xroms
from xroms import ll2xy, xy2ll
# A = xroms.roms_dataset('../examples/ocean_avg_0014.nc')
A = xroms.roms_dataset('/home/bjorn/python/xroms/examples/ocean_avg_0014.nc')
A = xroms.roms_dataset('examples/ocean_avg_0014.nc')
def test_xy2ll2xy():
"""grid to geography to grid should be identity"""
x, y = 100, 110
lon, lat = xy2ll(A, x, y)
x1, y1 = ll2xy(A, lon, lat)
assert (x1 == approx(x))
assert (y1 == approx(y))
def test_ll2xy2ll():
"""geography to grid to geography should be identity"""
lon, lat = 5, 60
x, y = ll2xy(A, lon, lat)
lon1, lat1 = xy2ll(A, x, y)
print(float(lon1), float(lat1))
assert (lon1 == approx(lon, abs=1e-4))
assert (lat1 == approx(lat))
def test_ll2xy_edge():
"""Testing edge cases, literally"""
