"""
grdfilter - Filter a grid in the space (or time) domain.
"""
from collections.abc import Sequence
from typing import Literal
import xarray as xr
from pygmt._typing import PathLike
from pygmt.alias import Alias, AliasSystem
from pygmt.clib import Session
from pygmt.helpers import build_arg_list, fmt_docstring, use_alias
__doctest_skip__ = ["grdfilter"]
[docs]
@fmt_docstring
@use_alias(D="distance", F="filter", f="coltypes")
def grdfilter(
grid: PathLike | xr.DataArray,
outgrid: PathLike | None = None,
spacing: Sequence[float | str] | None = None,
nans: Literal["ignore", "replace", "preserve"] | None = None,
toggle: bool = False,
region: Sequence[float | str] | str | None = None,
verbose: Literal["quiet", "error", "warning", "timing", "info", "compat", "debug"]
| bool = False,
registration: Literal["gridline", "pixel"] | bool = False,
cores: int | bool = False,
**kwargs,
) -> xr.DataArray | None:
r"""
Filter a grid in the space (or time) domain.
Filter a grid file in the space (or time) domain using one of the selected
convolution or non-convolution isotropic or rectangular filters and compute
distances using Cartesian or Spherical geometries. The output grid file
can optionally be generated as a sub-region of the input (via ``region``)
and/or with new increment (via ``spacing``) or registration
(via ``toggle``). In this way, one may have "extra space" in the input
data so that the edges will not be used and the output can be within one
half-width of the input edges. If the filter is low-pass, then the output
may be less frequently sampled than the input.
Full GMT docs at :gmt-docs:`grdfilter.html`.
$aliases
- I = spacing
- N = nans
- R = region
- T = toggle
- V = verbose
- r = registration
- x = cores
Parameters
----------
$grid
$outgrid
filter : str
**b**\|\ **c**\|\ **g**\|\ **o**\|\ **m**\|\ **p**\|\ **h**\ *width*\
[/*width2*\][*modifiers*].
Name of the filter type you wish to apply, followed by the *width*:
- **b**: Box Car
- **c**: Cosine Arch
- **g**: Gaussian
- **o**: Operator
- **m**: Median
- **p**: Maximum Likelihood probability
- **h**: Histogram
distance : str
State how the grid (x,y) relates to the filter *width*:
- ``"p"``: grid (px,py) with *width* an odd number of pixels,
Cartesian distances.
- ``"0"``: grid (x,y) same units as *width*, Cartesian distances.
- ``"1"``: grid (x,y) in degrees, *width* in kilometers, Cartesian
distances.
- ``"2"``: grid (x,y) in degrees, *width* in km, dx scaled by
cos(middle y), Cartesian distances.
The above options are fastest because they allow weight matrix to be
computed only once. The next three options are slower because they
recompute weights for each latitude.
- ``"3"``: grid (x,y) in degrees, *width* in km, dx scaled by cos(y),
Cartesian distance calculation.
- ``"4"``: grid (x,y) in degrees, *width* in km, Spherical distance
calculation.
- ``"5"``: grid (x,y) in Mercator ``projection="m1"`` img units,
*width* in km, Spherical distance calculation.
$spacing
nans
Determine how NaN-values in the input grid affect the filtered output grid.
Choose one of:
- ``"ignore"``: Ignore all NaNs in the calculation of filtered value [Default].
- ``"replace"``: Similar to ``"ignore"`` except if the input node was NaN then
the output node will be set to NaN (only applied if both grids are
co-registered).
- ``"preserve"``: Force the filtered value to be NaN if any grid nodes with
NaN-values are found inside the filter circle.
toggle
Toggle the node registration for the output grid so as to become the opposite of
the input grid [Default gives the same registration as the input grid].
Alternatively, use ``registration`` to set the registration explicitly.
$region
$verbose
$coltypes
$registration
$cores
Returns
-------
ret
Return type depends on whether the ``outgrid`` parameter is set:
- :class:`xarray.DataArray` if ``outgrid`` is not set
- ``None`` if ``outgrid`` is set (grid output will be stored in the file set by
``outgrid``)
Examples
--------
>>> from pathlib import Path
>>> import pygmt
>>> # Apply a median filter of 600 km (full width) to the @earth_relief_30m_g grid
>>> # and return a filtered grid (saved as netCDF file).
>>> pygmt.grdfilter(
... grid="@earth_relief_30m_g",
... filter="m600",
... distance="4",
... region=[150, 250, 10, 40],
... spacing=0.5,
... outgrid="filtered_pacific.nc",
... )
>>> Path("filtered_pacific.nc").unlink() # Cleanup file
>>> # Apply a Gaussian smoothing filter of 600 km to the input DataArray and return
>>> # a filtered DataArray with the smoothed grid.
>>> grid = pygmt.datasets.load_earth_relief()
>>> smooth_field = pygmt.grdfilter(grid=grid, filter="g600", distance="4")
"""
aliasdict = AliasSystem(
I=Alias(spacing, name="spacing", sep="/", size=2),
N=Alias(
nans, name="nans", mapping={"ignore": "i", "replace": "r", "preserve": "p"}
),
T=Alias(toggle, name="toggle"),
).add_common(
R=region,
V=verbose,
r=registration,
x=cores,
)
aliasdict.merge(kwargs)
with Session() as lib:
with (
lib.virtualfile_in(check_kind="raster", data=grid) as vingrd,
lib.virtualfile_out(kind="grid", fname=outgrid) as voutgrd,
):
aliasdict["G"] = voutgrd
lib.call_module(
module="grdfilter", args=build_arg_list(aliasdict, infile=vingrd)
)
return lib.virtualfile_to_raster(vfname=voutgrd, outgrid=outgrid)