"""
import cartopy.crs as ccrs
import matplotlib as mpl
mpl.rcParams['mathtext.default'] = 'regular'
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
from windspharm.xarray import VectorWind
from windspharm.examples import example_data_path
# Read zonal and meridional wind components from file using the xarray module.
# The components are in separate files.
ds = xr.open_mfdataset(
[example_data_path(f) for f in ('uwnd_mean.nc', 'vwnd_mean.nc')])
uwnd = ds['uwnd']
vwnd = ds['vwnd']
# Create a VectorWind instance to handle the computation of streamfunction and
# velocity potential.
w = VectorWind(uwnd, vwnd)
# Compute the streamfunction and velocity potential.
sf, vp = w.sfvp()
# Pick out the field for December.
sf_dec = sf[sf['time.month'] == 12]
vp_dec = vp[vp['time.month'] == 12]
# Plot streamfunction.
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from cartopy.util import add_cyclic_point
import matplotlib as mpl
import matplotlib.pyplot as plt
from netCDF4 import Dataset
from windspharm.standard import VectorWind
from windspharm.tools import prep_data, recover_data, order_latdim
from windspharm.examples import example_data_path
mpl.rcParams['mathtext.default'] = 'regular'
# Read zonal and meridional wind components from file using the netCDF4
# module. The components are defined on pressure levels and are in separate
# files.
ncu = Dataset(example_data_path('uwnd_mean.nc'), 'r')
uwnd = ncu.variables['uwnd'][:]
lons = ncu.variables['longitude'][:]
lats = ncu.variables['latitude'][:]
ncu.close()
ncv = Dataset(example_data_path('vwnd_mean.nc'), 'r')
vwnd = ncv.variables['vwnd'][:]
ncv.close()
# The standard interface requires that latitude and longitude be the leading
# dimensions of the input wind components, and that wind components must be
# either 2D or 3D arrays. The data read in is 3D and has latitude and
# longitude as the last dimensions. The bundled tools can make the process of
# re-shaping the data a lot easier to manage.
uwnd, uwnd_info = prep_data(uwnd, 'tyx')
vwnd, vwnd_info = prep_data(vwnd, 'tyx')
"""
import numpy as np
import matplotlib as mpl
mpl.rcParams['mathtext.default'] = 'regular'
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import cdms2
from windspharm.cdms import VectorWind
from windspharm.examples import example_data_path
# Read zonal and meridional wind components from file using the cdms2 module
# from CDAT. The components are in separate files.
ncu = cdms2.open(example_data_path('uwnd_mean.nc'), 'r')
uwnd = ncu('uwnd')
ncu.close()
ncv = cdms2.open(example_data_path('vwnd_mean.nc'), 'r')
vwnd = ncv('vwnd')
ncv.close()
# Create a VectorWind instance to handle the computation of streamfunction and
# velocity potential.
w = VectorWind(uwnd, vwnd)
# Compute the streamfunction and velocity potential.
sf, vp = w.sfvp()
# Pick out the field for December and add a cyclic point (the cyclic point is
# for plotting purposes).
from iris.coord_categorisation import add_month
import matplotlib as mpl
import matplotlib.pyplot as plt
from windspharm.iris import VectorWind
from windspharm.examples import example_data_path
mpl.rcParams['mathtext.default'] = 'regular'
# Read zonal and meridional wind components from file using the iris module.
# The components are in separate files. We catch warnings here because the
# files are not completely CF compliant.
with warnings.catch_warnings():
warnings.simplefilter('ignore', UserWarning)
uwnd = iris.load_cube(example_data_path('uwnd_mean.nc'))
vwnd = iris.load_cube(example_data_path('vwnd_mean.nc'))
uwnd.coord('longitude').circular = True
vwnd.coord('longitude').circular = True
# Create a VectorWind instance to handle the computations.
w = VectorWind(uwnd, vwnd)
# Compute components of rossby wave source: absolute vorticity, divergence,
# irrotational (divergent) wind components, gradients of absolute vorticity.
eta = w.absolutevorticity()
div = w.divergence()
uchi, vchi = w.irrotationalcomponent()
etax, etay = w.gradient(eta)
etax.units = 'm**-1 s**-1'
etay.units = 'm**-1 s**-1'
import matplotlib as mpl
mpl.rcParams["mathtext.default"] = "regular"
import matplotlib.pyplot as plt
from netCDF4 import Dataset
import numpy as np
from windspharm.standard import VectorWind
from windspharm.tools import prep_data, recover_data, order_latdim
from windspharm.examples import example_data_path
# Read zonal and meridional wind components from file using the cdms2 module
# from CDAT. The components are defined on pressure levels and are in separate
# files.
ncu = Dataset(example_data_path("uwnd_mean.nc"), "r")
uwnd = ncu.variables["uwnd"][:]
lons = ncu.variables["longitude"][:]
lats = ncu.variables["latitude"][:]
ncu.close()
ncv = Dataset(example_data_path("vwnd_mean.nc"), "r")
vwnd = ncv.variables["vwnd"][:]
ncv.close()
# The standard interface requires that latitude and longitude be the leading
# dimensions of the input wind components, and that wind components must be
# either 2D or 3D arrays. The data read in is 3D and has latitude and
# longitude as the last dimensions. The bundled tools can make the process of
# re-shaping the data a lot easier to manage.
uwnd, uwnd_info = prep_data(uwnd, "tyx")
vwnd, vwnd_info = prep_data(vwnd, "tyx")
import numpy as np
import matplotlib as mpl
mpl.rcParams['mathtext.default'] = 'regular'
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap, addcyclic
from netCDF4 import Dataset
from windspharm.standard import VectorWind
from windspharm.tools import prep_data, recover_data, order_latdim
from windspharm.examples import example_data_path
# Read zonal and meridional wind components from file using the netCDF4
# module. The components are defined on pressure levels and are in separate
# files.
ncu = Dataset(example_data_path('uwnd_mean.nc'), 'r')
uwnd = ncu.variables['uwnd'][:]
lons = ncu.variables['longitude'][:]
lats = ncu.variables['latitude'][:]
ncu.close()
ncv = Dataset(example_data_path('vwnd_mean.nc'), 'r')
vwnd = ncv.variables['vwnd'][:]
ncv.close()
# The standard interface requires that latitude and longitude be the leading
# dimensions of the input wind components, and that wind components must be
# either 2D or 3D arrays. The data read in is 3D and has latitude and
# longitude as the last dimensions. The bundled tools can make the process of
# re-shaping the data a lot easier to manage.
uwnd, uwnd_info = prep_data(uwnd, 'tyx')
vwnd, vwnd_info = prep_data(vwnd, 'tyx')
"""
import cartopy.crs as ccrs
import cdms2
import matplotlib as mpl
import matplotlib.pyplot as plt
from windspharm.cdms import VectorWind
from windspharm.examples import example_data_path
mpl.rcParams['mathtext.default'] = 'regular'
# Read zonal and meridional wind components from file using the cdms2 module
# from CDAT. The components are in separate files.
ncu = cdms2.open(example_data_path('uwnd_mean.nc'), 'r')
uwnd = ncu('uwnd')
ncu.close()
ncv = cdms2.open(example_data_path('vwnd_mean.nc'), 'r')
vwnd = ncv('vwnd')
ncv.close()
# Create a VectorWind instance to handle the computation of streamfunction and
# velocity potential.
w = VectorWind(uwnd, vwnd)
# Compute the streamfunction and velocity potential.
sf, vp = w.sfvp()
# Pick out the field for December and add a cyclic point (the cyclic point is
# for plotting purposes).
from iris.coord_categorisation import add_month
import matplotlib as mpl
mpl.rcParams['mathtext.default'] = 'regular'
import matplotlib.pyplot as plt
import numpy as np
from windspharm.iris import VectorWind
from windspharm.examples import example_data_path
# Read zonal and meridional wind components from file using the iris module.
# The components are in separate files. We catch warnings here because the
# files are not completely CF compliant.
with warnings.catch_warnings():
warnings.simplefilter('ignore', UserWarning)
uwnd = iris.load_cube(example_data_path('uwnd_mean.nc'))
vwnd = iris.load_cube(example_data_path('vwnd_mean.nc'))
uwnd.coord('longitude').circular = True
vwnd.coord('longitude').circular = True
# Create a VectorWind instance to handle the computations.
w = VectorWind(uwnd, vwnd)
# Compute components of rossby wave source: absolute vorticity, divergence,
# irrotational (divergent) wind components, gradients of absolute vorticity.
eta = w.absolutevorticity()
div = w.divergence()
uchi, vchi = w.irrotationalcomponent()
etax, etay = w.gradient(eta)
etax.units = 'm**-1 s**-1'
etay.units = 'm**-1 s**-1'
import matplotlib as mpl
mpl.rcParams["mathtext.default"] = "regular"
import matplotlib.pyplot as plt
import numpy as np
from windspharm.iris import VectorWind
from windspharm.examples import example_data_path
# Read zonal and meridional wind components from file using the iris module.
# The components are in separate files. We catch warnings here because the
# files are not completely CF compliant.
with warnings.catch_warnings():
warnings.simplefilter("ignore", UserWarning)
uwnd = iris.load_cube(example_data_path("uwnd_mean.nc"))
vwnd = iris.load_cube(example_data_path("vwnd_mean.nc"))
uwnd.coord("longitude").circular = True
vwnd.coord("longitude").circular = True
# Create a VectorWind instance to handle the computations.
w = VectorWind(uwnd, vwnd)
# Compute components of rossby wave source: absolute vorticity, divergence,
# irrotational (divergent) wind components, gradients of absolute vorticity.
eta = w.absolutevorticity()
div = w.divergence()
uchi, vchi = w.irrotationalcomponent()
etax, etay = w.gradient(eta)
etax.units = "m**-1 s**-1"
etay.units = "m**-1 s**-1"
"""
import cartopy.crs as ccrs
import matplotlib as mpl
import matplotlib.pyplot as plt
import xarray as xr
from windspharm.xarray import VectorWind
from windspharm.examples import example_data_path
mpl.rcParams['mathtext.default'] = 'regular'
# Read zonal and meridional wind components from file using the xarray module.
# The components are in separate files.
ds = xr.open_mfdataset([example_data_path(f)
for f in ('uwnd_mean.nc', 'vwnd_mean.nc')])
uwnd = ds['uwnd']
vwnd = ds['vwnd']
# Create a VectorWind instance to handle the computations.
w = VectorWind(uwnd, vwnd)
# Compute components of rossby wave source: absolute vorticity, divergence,
# irrotational (divergent) wind components, gradients of absolute vorticity.
eta = w.absolutevorticity()
div = w.divergence()
uchi, vchi = w.irrotationalcomponent()
etax, etay = w.gradient(eta)
etax.attrs['units'] = 'm**-1 s**-1'
etay.attrs['units'] = 'm**-1 s**-1'
