pygmt.GMTDataArrayAccessor

class pygmt.GMTDataArrayAccessor(xarray_obj)[source]

GMT accessor for xarray.DataArray.

The accessor extends xarray.DataArray to store GMT-specific properties about grids, which are important for PyGMT to correctly process and plot the grids.

Notes

Due to the limitations of xarray accessors, the GMT accessors are created once per xarray.DataArray instance. You may lose these GMT-specific properties when manipulating grids (e.g., arithmetic and slice operations) or when accessing a xarray.DataArray from a xarray.Dataset. In these cases, you need to manually set these properties before passing the grid to PyGMT.

Examples

For GMT’s built-in remote datasets, these GMT-specific properties are automatically determined and you can access them as follows:

>>> from pygmt.datasets import load_earth_relief
>>> # Use the global Earth relief grid with 1 degree spacing
>>> grid = load_earth_relief(resolution="01d", registration="pixel")
>>> # See if grid uses Gridline (0) or Pixel (1) registration
>>> grid.gmt.registration
1
>>> # See if grid uses Cartesian (0) or Geographic (1) coordinate system
>>> grid.gmt.gtype
1

For xarray.DataArray grids created by yourself, grid properties registration and gtype default to 0 (i.e., a gridline-registered, Cartesian grid). You need to set the correct properties before passing it to PyGMT functions:

>>> import numpy as np
>>> import pygmt
>>> import xarray as xr
>>> # create a DataArray in gridline coordinates of sin(lon) * cos(lat)
>>> interval = 2.5
>>> lat = np.arange(90, -90 - interval, -interval)
>>> lon = np.arange(0, 360 + interval, interval)
>>> longrid, latgrid = np.meshgrid(lon, lat)
>>> data = np.sin(np.deg2rad(longrid)) * np.cos(np.deg2rad(latgrid))
>>> grid = xr.DataArray(
...     data, coords=[("latitude", lat), ("longitude", lon)]
... )
>>> # default to a gridline-registrated Cartesian grid
>>> grid.gmt.registration, grid.gmt.gtype
(0, 0)
>>> # set it to a gridline-registered geographic grid
>>> grid.gmt.registration = 0
>>> grid.gmt.gtype = 1
>>> grid.gmt.registration, grid.gmt.gtype
(0, 1)

Note that the accessors are created once per xarray.DataArray instance, so you may lose these GMT-specific properties after manipulating your grid.

Inplace assignment operators like *= don’t create new instances, so the properties are still kept:

>>> grid *= 2.0
>>> grid.gmt.registration, grid.gmt.gtype
(0, 1)

Other grid operations (e.g., arithmetic or slice operations) create new instances, so the properties will be lost:

>>> # grid2 is a slice of the original grid
>>> grid2 = grid[0:30, 50:80]
>>> # properties are reset to the default values for new instance
>>> grid2.gmt.registration, grid2.gmt.gtype
(0, 0)
>>> # need to set these properties before passing the grid to PyGMT
>>> grid2.gmt.registration = grid.gmt.registration
>>> grid2.gmt.gtype = grid.gmt.gtype
>>> grid2.gmt.registration, grid2.gmt.gtype
(0, 1)

Accesing a xarray.DataArray from a xarray.Dataset always creates new instances, so these properties are always lost. The workaround is to assign the xarray.DataArray into a variable:

>>> ds = xr.Dataset({"zval": grid})
>>> ds.zval.gmt.registration, ds.zval.gmt.gtype
(0, 0)
>>> # manually set these properties won't work as expected
>>> ds.zval.gmt.registration, ds.zval.gmt.gtype = 0, 1
>>> ds.zval.gmt.registration, ds.zval.gmt.gtype
(0, 0)
>>> # workaround: assign the DataArray into a variable
>>> zval = ds.zval
>>> zval.gmt.registration, zval.gmt.gtype
(0, 0)
>>> zval.gmt.registration, zval.gmt.gtype = 0, 1
>>> zval.gmt.registration, zval.gmt.gtype
(0, 1)

Attributes

property GMTDataArrayAccessor.gtype: Coordinate system type of the grid, either 0 (Cartesian) or 1 (Geographic).

property GMTDataArrayAccessor.registration: Registration type of the grid, either 0 (Gridline) or 1 (Pixel).