Source code for pygmt.src.nearneighbor

nearneighbor - Grid table data using a "Nearest neighbor" algorithm.

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

__doctest_skip__ = ["nearneighbor"]

[docs]@fmt_docstring @use_alias( E="empty", G="outgrid", I="spacing", N="sectors", R="region", S="search_radius", V="verbose", a="aspatial", b="binary", d="nodata", e="find", f="coltypes", g="gap", h="header", i="incols", r="registration", w="wrap", ) @kwargs_to_strings(I="sequence", R="sequence", i="sequence_comma") def nearneighbor(data=None, x=None, y=None, z=None, **kwargs): r""" Grid table data using a "Nearest neighbor" algorithm. **nearneighbor** reads arbitrarily located (*x*, *y*, *z*\ [, *w*]) triplets [quadruplets] and uses a nearest neighbor algorithm to assign a weighted average value to each node that has one or more data points within a search radius centered on the node with adequate coverage across a subset of the chosen sectors. The node value is computed as a weighted mean of the nearest point from each sector inside the search radius. The weighting function and the averaging used is given by: .. math:: w(r_i) = \frac{{w_i}}{{1 + d(r_i) ^ 2}}, \quad d(r) = \frac {{3r}}{{R}}, \quad \bar{{z}} = \frac{{\sum_i^n w(r_i) z_i}}{{\sum_i^n w(r_i)}} where :math:`n` is the number of data points that satisfy the selection criteria and :math:`r_i` is the distance from the node to the *i*'th data point. If no data weights are supplied then :math:`w_i = 1`. .. figure:: # noqa: W505 :width: 300 px :align: center Search geometry includes the search radius (R) which limits the points considered and the number of sectors (here 4), which restricts how points inside the search radius contribute to the value at the node. Only the closest point in each sector (red circles) contribute to the weighted estimate. Takes a matrix, (x, y, z) triplets, or a file name as input. Must provide either ``data`` or ``x``, ``y``, and ``z``. Full option list at :gmt-docs:`nearneighbor.html` {aliases} Parameters ---------- data : str or {table-like} Pass in (x, y, z) or (longitude, latitude, elevation) values by providing a file name to an ASCII data table, a 2-D {table-classes}. x/y/z : 1-D arrays Arrays of x and y coordinates and values z of the data points. {spacing} {region} search_radius : str Set the search radius that determines which data points are considered close to a node. outgrid : str Optional. The file name for the output netcdf file with extension .nc to store the grid in. empty : str Optional. Set the value assigned to empty nodes. Defaults to NaN. sectors : str *sectors*\ [**+m**\ *min_sectors*]\|\ **n**. Optional. The circular search area centered on each node is divided into *sectors* sectors. Average values will only be computed if there is *at least* one value inside each of at least *min_sectors* of the sectors for a given node. Nodes that fail this test are assigned the value NaN (but see ``empty``). If **+m** is omitted then *min_sectors* is set to be at least 50% of *sectors* (i.e., rounded up to next integer) [Default is a quadrant search with 100% coverage, i.e., *sectors* = *min_sectors* = 4]. Note that only the nearest value per sector enters into the averaging; the more distant points are ignored. Alternatively, use ``sectors="n"`` to call GDAL's nearest neighbor algorithm instead. {verbose} {aspatial} {binary} {nodata} {find} {coltypes} {gap} {header} {incols} {registration} {wrap} 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 pygmt >>> # Load a sample dataset of bathymetric x, y, and z values >>> data = pygmt.datasets.load_sample_data(name="bathymetry") >>> # Create a new grid with 5 arc-minutes spacing in the designated region >>> # Set search_radius to only consider points within 10 arc-minutes of a node >>> output = pygmt.nearneighbor( ... data=data, ... spacing="5m", ... region=[245, 255, 20, 30], ... search_radius="10m", ... ) """ with GMTTempFile(suffix=".nc") as tmpfile: with Session() as lib: # Choose how data will be passed into the module table_context = lib.virtualfile_from_data( check_kind="vector", data=data, x=x, y=y, z=z, required_z=True ) with table_context as infile: if (outgrid := kwargs.get("G")) is None: kwargs["G"] = outgrid = # output to tmpfile lib.call_module( module="nearneighbor", args=build_arg_string(kwargs, infile=infile) ) return load_dataarray(outgrid) if outgrid == else None