def test_zslice():
testvars = ["salt", "u", "v", "z_w"]
for testvar in testvars:
varin = ds[testvar]
depths = np.asarray(ds[testvar].cf["vertical"][0, :, 0, 0].values)
varout = xroms.isoslice(varin, depths, grid, axis="Z")
assert np.allclose(varout.cf.isel(T=0, Y=0, X=0),
varin.cf.isel(T=0, Y=0, X=0))
def isoslice(self, iso_values, iso_array=None, axis="Z"):
"""Interpolate var to iso_values.
This wraps `xgcm` `transform` function for slice interpolation,
though `transform` has additional functionality.
Inputs
------
iso_values: list, ndarray
Values to interpolate to. If calculating var at fixed depths,
iso_values are the fixed depths, which should be negative if
below mean sea level. If input as array, should be 1D.
iso_array: DataArray, optional
Array that var is interpolated onto (e.g., z coordinates or
density). If calculating var on fixed depth slices, iso_array
contains the depths [m] associated with var. In that case and
if None, will use z coordinate attached to var. Also use this
option if you want to interpolate with z depths constant in
time and input the appropriate z coordinate.
dim: str, optional
Dimension over which to calculate isoslice. If calculating var
onto fixed depths, `dim='Z'`. Options are 'Z', 'Y', and 'X'.
Returns
-------
DataArray of var interpolated to iso_values. Dimensionality will be the
same as var except with dim dimension of size of iso_values.
Notes
-----
var cannot have chunks in the dimension dim.
cf-xarray should still be usable after calling this function.
Example usage
-------------
To calculate temperature onto fixed depths:
>>> xroms.isoslice(ds.temp, np.linspace(0, -30, 50))
To calculate temperature onto salinity:
>>> xroms.isoslice(ds.temp, np.arange(0, 36), iso_array=ds.salt, axis='Z')
Calculate lat-z slice of salinity along a constant longitude value (-91.5):
>>> xroms.isoslice(ds.salt, -91.5, iso_array=ds.lon_rho, axis='X')
Calculate slice of salt at 28 deg latitude
>>> xroms.isoslice(ds.salt, 28, iso_array=ds.lat_rho, axis='Y')
Interpolate temp to salinity values between 0 and 36 in the X direction
>>> xroms.isoslice(ds.temp, np.linspace(0, 36, 50), iso_array=ds.salt, axis='X')
Interpolate temp to salinity values between 0 and 36 in the Z direction
>>> xroms.isoslice(ds.temp, np.linspace(0, 36, 50), iso_array=ds.salt, axis='Z')
Calculate the depth of a specific isohaline (33):
>>> xroms.isoslice(ds.salt, 33, iso_array=ds.z_rho, axis='Z')
Calculate dye 10 meters above seabed. Either do this on the vertical
rho grid, or first change to the w grid and then use `isoslice`. You may prefer
to do the latter if there is a possibility that the distance above the seabed you are
interpolating to (10 m) could be below the deepest rho grid depth.
* on rho grid directly:
>>> height_from_seabed = ds.z_rho + ds.h
>>> height_from_seabed.name = 'z_rho'
>>> xroms.isoslice(ds.dye_01, 10, iso_array=height_from_seabed, axis='Z')
* on w grid:
>>> var_w = ds.dye_01.xroms.to_grid(scoord='w').chunk({'s_w': -1})
>>> ds['dye_01_w'] = var_w # currently this is the easiest way to reattached coords xgcm variables
>>> height_from_seabed = ds.z_w + ds.h
>>> height_from_seabed.name = 'z_w'
>>> xroms.isoslice(ds['dye_01_w'], 10, iso_array=height_from_seabed, axis='Z')
"""
return xroms.isoslice(
self.da,
iso_values,
grid=self.da.attrs["grid"],
iso_array=iso_array,
axis=axis,
)
def mld(sig0, h, mask, z=None, thresh=0.03):
"""Calculate the mixed layer depth [m].
Inputs
------
sig0: DataArray
Potential density [kg/m^3]
h: DataArray, ndarray
Depths [m].
mask: DataArray, ndarray
mask to match sig0
z: DataArray, ndarray, optional
The vertical depths associated with sig0. Should be on 'rho'
grid horizontally and vertically. Use z coords associated with DataArray sig0
if not input.
thresh: float, optional
For detection of mixed layer [kg/m^3]
Returns
-------
DataArray of mixed layer depth on rho horizontal grid.
Output is `[T,Y,X]`.
Notes
-----
Mixed layer depth is based on the fixed Potential Density (PD) threshold.
Converted to xroms by K. Thyng Aug 2020 from:
Update history:
v1.0 DL 2020Jun07
References:
ncl mixed_layer_depth function at https://github.com/NCAR/ncl/blob/ed6016bf579f8c8e8f77341503daef3c532f1069/ni/src/lib/nfpfort/ocean.f
de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A., & Iudicone, D. (2004). Mixed layer depth over the global ocean: An examination of profile data and a profile‐based climatology. Journal of Geophysical Research: Oceans, 109(C12).
Example usage
-------------
>>> xroms.mld(sig0, h, mask)
"""
if h.mean() > 0: # if depths are positive, change to negative
h = -h
# xisoslice will operate over the relevant s dimension
skey = sig0.dims[[dim[:2] == "s_" for dim in sig0.dims].index(True)]
if z is None:
z = sig0.z_rho
# the mixed layer depth is the isosurface of depth where the potential density equals the surface - a threshold
mld = xroms.isoslice(
z,
0.0,
sig0.attrs["grid"],
iso_array=sig0 - sig0.isel(s_rho=-1) - thresh,
axis="Z",
)
# mld = xroms.xisoslice(sig0 - sig0.isel(s_rho=-1) - thresh, 0.0, z, skey)
# Replace nan's that are not masked with the depth of the water column.
cond = (mld.isnull()) & (mask == 1)
mld = mld.where(~cond, h)
mld.attrs["name"] = "mld"
mld.attrs["long_name"] = "mixed layer depth"
mld.attrs["units"] = "m"
if "grid" in sig0.attrs:
mld.attrs["grid"] = sig0.attrs["grid"]
mld.name = mld.attrs["name"]
return mld.squeeze()