Source code for pygmt.src.grdfilter

grdfilter - Filter a grid in the space (or time) domain.

from pygmt.clib import Session
from pygmt.helpers import (
from import load_dataarray

[docs]@fmt_docstring @use_alias( D="distance", F="filter", G="outgrid", I="spacing", N="nans", R="region", T="toggle", V="verbose", f="coltypes", r="registration", ) @kwargs_to_strings(I="sequence", R="sequence") def grdfilter(grid, **kwargs): r""" Filter a grid in the space (or time) domain. Filter a grid file in the 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 option list at :gmt-docs:`grdfilter.html` {aliases} Parameters ---------- grid : str or xarray.DataArray The file name of the input grid or the grid loaded as a DataArray. outgrid : str or None The name of the output netCDF file with extension .nc to store the grid in. filter : str **b**\|\ **c**\|\ **g**\|\ **o**\|\ **m**\|\ **p**\|\ **h**\ *xwidth*\ [/*width2*\][*modifiers*]. Name of filter type you which 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 Distance *flag* tells how grid (x,y) relates to filter width as follows: 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 cosine(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 : str or float **i**\|\ **p**\|\ **r**. Determine how NaN-values in the input grid affects the filtered output. {region} toggle : bool 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]. {verbose} {coltypes} {registration} Returns ------- ret: xarray.DataArray or None 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 file set by ``outgrid``) Example ------- >>> import os >>> import pygmt >>> # Apply a filter of 600km (full width) to the @earth_relief_30m file >>> # and return a filtered field (saved as netcdf) >>> pygmt.grdfilter( ... grid="@earth_relief_30m", ... filter="m600", ... distance="4", ... region=[150, 250, 10, 40], ... spacing=0.5, ... outgrid="", ... ) >>> os.remove("") # cleanup file >>> # Apply a gaussian smoothing filter of 600 km in the input data array, >>> # and returns a filtered data array with the smoothed field. >>> grid = pygmt.datasets.load_earth_relief() >>> smooth_field = pygmt.grdfilter(grid=grid, filter="g600", distance="4") """ with GMTTempFile(suffix=".nc") as tmpfile: with Session() as lib: file_context = lib.virtualfile_from_data(check_kind="raster", data=grid) with file_context as infile: if (outgrid := kwargs.get("G")) is None: kwargs["G"] = outgrid = # output to tmpfile lib.call_module( module="grdfilter", args=build_arg_string(kwargs, infile=infile) ) return load_dataarray(outgrid) if outgrid == else None