Source code for pygmt.gridops

"""
GMT modules for grid operations.
"""

import xarray as xr
from pygmt.clib import Session
from pygmt.exceptions import GMTInvalidInput
from pygmt.helpers import (
    GMTTempFile,
    build_arg_string,
    data_kind,
    dummy_context,
    fmt_docstring,
    kwargs_to_strings,
    use_alias,
)


[docs]@fmt_docstring @use_alias( G="outgrid", R="region", J="projection", N="extend", S="circ_subregion", V="verbose", Z="z_subregion", ) @kwargs_to_strings(R="sequence") def grdcut(grid, **kwargs): """ Extract subregion from a grid. Produce a new *outgrid* file which is a subregion of *grid*. The subregion is specified with *region*; the specified range must not exceed the range of *grid* (but see *extend*). If in doubt, run :meth:`pygmt.grdinfo` to check range. Alternatively, define the subregion indirectly via a range check on the node values or via distances from a given point. Finally, you can give *projection* for oblique projections to determine the corresponding rectangular *region* setting that will give a grid that fully covers the oblique domain. Full option list at :gmt-docs:`grdcut.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. {J} {R} extend : bool or int or float Allow grid to be extended if new *region* exceeds existing boundaries. Give a value to initialize nodes outside current region. circ_subregion : str ``'lon/lat/radius[unit][+n]'``. Specify an origin (*lon* and *lat*) and *radius*; append a distance *unit* and we determine the corresponding rectangular region so that all grid nodes on or inside the circle are contained in the subset. If **+n** is appended we set all nodes outside the circle to NaN. z_subregion : str ``'[min/max][+n|N|r]'``. Determine a new rectangular region so that all nodes outside this region are also outside the given z-range [-inf/+inf]. To indicate no limit on *min* or *max* only, specify a hyphen (-). Normally, any NaNs encountered are simply skipped and not considered in the range-decision. Append **+n** to consider a NaN to be outside the given z-range. This means the new subset will be NaN-free. Alternatively, append **+r** to consider NaNs to be within the data range. In this case we stop shrinking the boundaries once a NaN is found [Default simply skips NaNs when making the range decision]. Finally, if your core subset grid is surrounded by rows and/or columns that are all NaNs, append **+N** to strip off such columns before (optionally) considering the range of the core subset for further reduction of the area. {V} 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 *outgrid*) """ kind = data_kind(grid) with GMTTempFile(suffix=".nc") as tmpfile: with Session() as lib: if kind == "file": file_context = dummy_context(grid) elif kind == "grid": file_context = lib.virtualfile_from_grid(grid) else: raise GMTInvalidInput("Unrecognized data type: {}".format(type(grid))) with file_context as infile: if "G" not in kwargs.keys(): # if outgrid is unset, output to tempfile kwargs.update({"G": tmpfile.name}) outgrid = kwargs["G"] arg_str = " ".join([infile, build_arg_string(kwargs)]) lib.call_module("grdcut", arg_str) if outgrid == tmpfile.name: # if user did not set outgrid, return DataArray with xr.open_dataarray(outgrid) as dataarray: result = dataarray.load() _ = result.gmt # load GMTDataArray accessor information else: result = None # if user sets an outgrid, return None return result
[docs]@fmt_docstring @use_alias( D="distance", F="filter", G="outgrid", I="spacing", N="nans", R="region", T="toggle", V="verbose", ) @kwargs_to_strings(R="sequence") def grdfilter(grid, **kwargs): """ 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 ``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 Example: F='m600' for a median filter with width of 600 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[+e|n][/yinc[+e|n]]``. x_inc [and optionally y_inc] is the grid spacing. nans : str or float ``i|p|r``. Determine how NaN-values in the input grid affects the filtered output. {R} 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]. {V} 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 *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") """ kind = data_kind(grid) with GMTTempFile(suffix=".nc") as tmpfile: with Session() as lib: if kind == "file": file_context = dummy_context(grid) elif kind == "grid": file_context = lib.virtualfile_from_grid(grid) else: raise GMTInvalidInput("Unrecognized data type: {}".format(type(grid))) with file_context as infile: if "G" not in kwargs.keys(): # if outgrid is unset, output to tempfile kwargs.update({"G": tmpfile.name}) outgrid = kwargs["G"] arg_str = " ".join([infile, build_arg_string(kwargs)]) lib.call_module("grdfilter", arg_str) if outgrid == tmpfile.name: # if user did not set outgrid, return DataArray with xr.open_dataarray(outgrid) as dataarray: result = dataarray.load() _ = result.gmt # load GMTDataArray accessor information else: result = None # if user sets an outgrid, return None return result