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, cores=None, **kwargs)[source]
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 (viaspacing
) or registration (viatoggle
). 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
x = cores
- Parameters
grid (str or xarray.DataArray) – The file name of the input grid or the grid loaded as a
xarray.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|hwidth[/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 Mercatorprojection="m1"
img units, width in km, Spherical distance calculation.
spacing (int or float or str or list or tuple) –
x_inc[+e|n][/y_inc[+e|n]]. x_inc [and optionally y_inc] is the grid spacing.
Geographical (degrees) coordinates: Optionally, append an increment unit. Choose among m to indicate arc-minutes or s to indicate arc-seconds. If one of the units e, f, k, M, n or u is appended instead, the increment is assumed to be given in meter, foot, km, mile, nautical mile or US survey foot, respectively, and will be converted to the equivalent degrees longitude at the middle latitude of the region (the conversion depends on PROJ_ELLIPSOID). If y_inc is given but set to 0 it will be reset equal to x_inc; otherwise it will be converted to degrees latitude.
All coordinates: If +e is appended then the corresponding max x (east) or y (north) may be slightly adjusted to fit exactly the given increment [by default the increment may be adjusted slightly to fit the given domain]. Finally, instead of giving an increment you may specify the number of nodes desired by appending +n to the supplied integer argument; the increment is then recalculated from the number of nodes, the
registration
, and the domain. The resulting increment value depends on whether you have selected a gridline-registered or pixel-registered grid; see GMT File Formats for details.
Note: If
region=grdfile
is used then the grid spacing and the registration have already been initialized; usespacing
andregistration
to override these values.nans (str or float) – i|p|r. Determine how NaN-values in the input grid affect the filtered output. Use i to ignore all NaNs in the calculation of the filtered value [Default]. r is same as i except if the input node was NaN then the output node will be set to NaN (only applies if both grids are co-registered). p will force the filtered value to be NaN if any grid nodes with NaN-values are found inside the filter circle.
region (str or list) – xmin/xmax/ymin/ymax[+r][+uunit]. Specify the region of interest.
toggle (bool) – Toggle the node registration for the output grid to get the opposite of the input grid [Default gives the same registration as the input grid].
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)].
cores (bool or int) – [[-]n]. Limit the number of cores to be used in any OpenMP-enabled multi-threaded algorithms. By default we try to use all available cores. Set a number n to only use n cores (if too large it will be truncated to the maximum cores available). Finally, give a negative number -n to select (all - n) cores (or at least 1 if n equals or exceeds all).
- Returns
ret (xarray.DataArray or None) – Return type depends on whether the
outgrid
parameter is set:xarray.DataArray
ifoutgrid
is not setNone if
outgrid
is set (grid output will be stored in file set byoutgrid
)
Examples
>>> import os >>> import pygmt >>> # Apply a filter of 600 km (full width) to the @earth_relief_30m_g file >>> # and return a filtered field (saved as netCDF) >>> pygmt.grdfilter( ... grid="@earth_relief_30m_g", ... 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 to the input DataArray >>> # and return a filtered DataArray with the smoothed field >>> grid = pygmt.datasets.load_earth_relief() >>> smooth_field = pygmt.grdfilter(grid=grid, filter="g600", distance="4")