pygmt.grdfilter

pygmt.grdfilter(grid, *, distance=None, filter=None, outgrid=None, spacing=None, nans=None, region=None, toggle=None, verbose=None, coltypes=None, registration=None, **kwargs)[source]

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 https://docs.generic-mapping-tools.org/latest/grdfilter.html

Aliases:

  • D = distance

  • F = filter

  • G = outgrid

  • I = spacing

  • N = nans

  • R = region

  • T = toggle

  • V = verbose

  • f = coltypes

  • r = registration

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|hxwidth[/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 (str) – xinc[unit][+e|n] [/yinc[unit][+e|n]]. xinc [and optionally yinc] is the grid spacing.

  • nans (str or float) – i|p|r. Determine how NaN-values in the input grid affects the filtered output.

  • region (str or list) – Required if this is the first plot command. xmin/xmax/ymin/ymax[+r][+uunit]. Specify the region of interest.

  • 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 (bool or str) –

    Select verbosity level [Default is w], which modulates the messages written to stderr. Choose among 7 levels of verbosity:

    • q - Quiet, not even fatal error messages are produced

    • e - Error messages only

    • w - Warnings [Default]

    • t - Timings (report runtimes for time-intensive algorithms);

    • i - Informational messages (same as verbose=True)

    • c - Compatibility warnings

    • d - Debugging messages

  • coltypes (str) – [i|o]colinfo. Specify data types of input and/or output columns (time or geographical data). Full documentation is at https://docs.generic-mapping-tools.org/latest/gmt.html#f-full.

  • registration (str) – g|p. Force gridline (g) or pixel (p) node registration. [Default is g(ridline)].

Returns

ret (xarray.DataArray or None) – Return type depends on whether the outgrid parameter is set:

  • xarray.DataArray if outgrid is not set

  • None if outgrid is set (grid output will be stored in file set by outgrid)

Examples

>>> 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="filtered_pacific.nc",
... )
>>> os.remove("filtered_pacific.nc")  # 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")