xroms.interp#

Interpolation functions.

Functions

interpll(var, lons, lats[, which])

Interpolate var to lons/lats positions.

isoslice(var, iso_values, xgrid[, ...])

Interpolate var to iso_values.
xroms.interp.interpll(var, lons, lats, which='pairs', **kwargs)[source]#

Interpolate var to lons/lats positions.

Wraps xESMF to perform proper horizontal interpolation on non-flat Earth.

Parameters:

  • var (DataArray) – Variable to operate on.

  • lons (list, ndarray) – Longitudes to interpolate to. Will be flattened upon input.

  • lats (list, ndarray) – Latitudes to interpolate to. Will be flattened upon input.

  • which (str, optional) –

    Which type of interpolation to do:

    • ”pairs”: lons/lats as unstructured coordinate pairs (in xESMF language, LocStream).

    • ”grid”: 2D array of points with 1 dimension the lons and the other dimension the lats.

  • **kwargs – passed on to xESMF Regridder class

Returns:

  • DataArray of var interpolated to lons/lats. Dimensionality will be the

  • same as var except the Y and X dimensions will be 1 dimension called

  • ”locations” that lons.size if which==’pairs’, or 2 dimensions called

  • ”lat” and “lon” if which==’grid’ that are of lats.size and lons.size,

  • respectively.

Notes

var cannot have chunks in the Y or X dimensions.

cf-xarray should still be usable after calling this function.

Examples

To return 1D pairs of points, in this case 3 points:

>>> xroms.interpll(var, [-96, -97, -96.5], [26.5, 27, 26.5], which='pairs')

To return 2D pairs of points, in this case a 3x3 array of points:

>>> xroms.interpll(var, [-96, -97, -96.5], [26.5, 27, 26.5], which='grid')
xroms.interp.isoslice(var, iso_values, xgrid, iso_array=None, axis='Z')[source]#

Interpolate var to iso_values.

This wraps xgcm transform function for slice interpolation, though transform has additional functionality.

Parameters:

  • var (DataArray) – Variable to operate on.

  • 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.

  • xgrid (xgcm.grid, optional) – Grid object associated with var.

  • 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.

Examples

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')