"""
triangulate - Delaunay triangulation or Voronoi partitioning and gridding of
Cartesian data.
"""
import pandas as pd
from pygmt.clib import Session
from pygmt.exceptions import GMTInvalidInput
from pygmt.helpers import (
GMTTempFile,
build_arg_string,
fmt_docstring,
kwargs_to_strings,
use_alias,
validate_output_table_type,
)
from pygmt.io import load_dataarray
[docs]
class triangulate: # noqa: N801
"""
Delaunay triangulation or Voronoi partitioning and gridding of Cartesian data.
Triangulate reads in x,y[,z] data and performs Delaunay triangulation,
i.e., it finds how the points should be connected to give the most
equilateral triangulation possible. If a map projection (give ``region``
and ``projection``) is chosen then it is applied before the triangulation
is calculated. By default, the output is triplets of point id numbers that
make up each triangle. The id numbers refer to the points position (line
number, starting at 0 for the first line) in the input file. If ``outgrid``
and ``spacing`` are set a grid will be calculated based on the surface
defined by the planar triangles. The actual algorithm used in the
triangulations is either that of Watson [1982] or Shewchuk [1996] [Default
is Shewchuk if installed; type ``gmt get GMT_TRIANGULATE`` on the command
line to see which method is selected]. Furthermore, if the Shewchuk
algorithm is installed then you can also perform the calculation of Voronoi
polygons and optionally grid your data via the natural nearest neighbor
algorithm.
Note
----
For geographic data with global or very large extent you should consider
:gmt-docs:`sphtriangulate <sphtriangulate.html>` instead since
``triangulate`` is a Cartesian or small-geographic area operator and is
unaware of periodic or polar boundary conditions.
"""
@staticmethod
@fmt_docstring
@use_alias(
G="outgrid",
I="spacing",
J="projection",
R="region",
V="verbose",
b="binary",
d="nodata",
e="find",
f="coltypes",
h="header",
i="incols",
r="registration",
s="skiprows",
w="wrap",
)
@kwargs_to_strings(I="sequence", R="sequence", i="sequence_comma")
def _triangulate(
data=None, x=None, y=None, z=None, *, output_type, outfile=None, **kwargs
):
"""
Delaunay triangulation or Voronoi partitioning and gridding of Cartesian data.
Must provide ``outfile`` or ``outgrid``.
Full option list at :gmt-docs:`triangulate.html`
{aliases}
Parameters
----------
x/y/z : np.ndarray
Arrays of x and y coordinates and values z of the data points.
data : str, {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}.
{projection}
{region}
{spacing}
{outgrid}
The interpolation is performed in the original coordinates, so if
your triangles are close to the poles you are better off projecting
all data to a local coordinate system before using ``triangulate``
(this is true of all gridding routines) or instead select
:gmt-docs:`sphtriangulate <sphtriangulate.html>`.
outfile : str, bool or None
The name of the output ASCII file to store the results of the
histogram equalization in.
output_type: str
Determine the output type. Use "file", "xarray", "pandas", or
"numpy".
{verbose}
{binary}
{nodata}
{find}
{coltypes}
{header}
{incols}
{registration}
Only valid with ``outgrid``.
{skiprows}
{wrap}
Returns
-------
ret: numpy.ndarray or pandas.DataFrame or xarray.DataArray or None
Return type depends on the ``output_type`` parameter:
- numpy.ndarray if ``output_type`` is "numpy"
- pandas.DataFrame if ``output_type`` is "pandas"
- xarray.DataArray if ``output_type`` is "xarray""
- None if ``output_type`` is "file" (output is stored in
``outgrid`` or ``outfile``)
"""
with Session() as lib:
table_context = lib.virtualfile_from_data(
check_kind="vector", data=data, x=x, y=y, z=z, required_z=False
)
with table_context as infile:
# table output if outgrid is unset, else output to outgrid
if (outgrid := kwargs.get("G")) is None:
kwargs.update({">": outfile})
lib.call_module(
module="triangulate", args=build_arg_string(kwargs, infile=infile)
)
if output_type == "file":
return None
if output_type == "xarray":
return load_dataarray(outgrid)
result = pd.read_csv(outfile, sep="\t", header=None)
if output_type == "numpy":
return result.to_numpy()
return result
[docs]
@staticmethod
@fmt_docstring
def regular_grid( # noqa: PLR0913
data=None,
x=None,
y=None,
z=None,
outgrid=None,
spacing=None,
projection=None,
region=None,
verbose=None,
binary=None,
nodata=None,
find=None,
coltypes=None,
header=None,
incols=None,
registration=None,
skiprows=None,
wrap=None,
**kwargs,
):
"""
Delaunay triangle based gridding of Cartesian data.
Reads in x,y[,z] data and performs Delaunay triangulation, i.e., it
finds how the points should be connected to give the most equilateral
triangulation possible. If a map projection (give ``region`` and
``projection``) is chosen then it is applied before the triangulation
is calculated. By setting ``outgrid`` and ``spacing``, a grid will be
calculated based on the surface defined by the planar triangles. The
actual algorithm used in the triangulations is either that of Watson
[1982] or Shewchuk [1996] [Default is Shewchuk if installed; type
``gmt get GMT_TRIANGULATE`` on the command line to see which method is
selected]. This choice is made during the GMT installation.
Furthermore, if the Shewchuk algorithm is installed then you can also
perform the calculation of Voronoi polygons and optionally grid your
data via the natural nearest neighbor algorithm.
Must provide either ``data`` or ``x``, ``y``, and ``z``.
Must provide ``region`` and ``spacing``.
Full option list at :gmt-docs:`triangulate.html`
Parameters
----------
x/y/z : np.ndarray
Arrays of x and y coordinates and values z of the data points.
data : str, {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}.
{projection}
{region}
{spacing}
{outgrid}
The interpolation is performed in the original coordinates, so if
your triangles are close to the poles you are better off projecting
all data to a local coordinate system before using ``triangulate``
(this is true of all gridding routines) or instead select
:gmt-docs:`sphtriangulate <sphtriangulate.html>`.
{verbose}
{binary}
{nodata}
{find}
{coltypes}
{header}
{incols}
{registration}
{skiprows}
{wrap}
Returns
-------
ret: xarray.DataArray or None
Return type depends on whether the ``outgrid`` parameter is set:
- xarray.DataArray if ``outgrid`` is None (default)
- None if ``outgrid`` is a str (grid output is stored in
``outgrid``)
Note
----
For geographic data with global or very large extent you should
consider :gmt-docs:`sphtriangulate <sphtriangulate.html>` instead since
``triangulate`` is a Cartesian or small-geographic area operator and is
unaware of periodic or polar boundary conditions.
"""
# Return an xarray.DataArray if ``outgrid`` is not set
with GMTTempFile(suffix=".nc") as tmpfile:
if isinstance(outgrid, str):
output_type = "file"
elif outgrid is None:
output_type = "xarray"
outgrid = tmpfile.name
else:
raise GMTInvalidInput(
"'outgrid' should be a proper file name or `None`"
)
return triangulate._triangulate(
data=data,
x=x,
y=y,
z=z,
output_type=output_type,
outgrid=outgrid,
spacing=spacing,
projection=projection,
region=region,
verbose=verbose,
binary=binary,
nodata=nodata,
find=find,
coltypes=coltypes,
header=header,
incols=incols,
registration=registration,
skiprows=skiprows,
wrap=wrap,
**kwargs,
)
[docs]
@staticmethod
@fmt_docstring
def delaunay_triples( # noqa: PLR0913
data=None,
x=None,
y=None,
z=None,
output_type="pandas",
outfile=None,
projection=None,
verbose=None,
binary=None,
nodata=None,
find=None,
coltypes=None,
header=None,
incols=None,
skiprows=None,
wrap=None,
**kwargs,
):
"""
Delaunay triangle based gridding of Cartesian data.
Reads in x,y[,z] data and performs Delaunay triangulation, i.e., it
finds how the points should be connected to give the most equilateral
triangulation possible. If a map projection (give ``region`` and
``projection``) is chosen then it is applied before the triangulation
is calculated. The actual algorithm used in the triangulations is
either that of Watson [1982] or Shewchuk [1996] [Default if installed;
type ``gmt get GMT_TRIANGULATE`` on the command line to see which
method is selected).
Must provide either ``data`` or ``x``, ``y``, and ``z``.
Full option list at :gmt-docs:`triangulate.html`
Parameters
----------
x/y/z : np.ndarray
Arrays of x and y coordinates and values z of the data points.
data : str, {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}.
{projection}
{region}
outfile : str or bool or None
The name of the output ASCII file to store the results of the
histogram equalization in.
output_type : str
Determine the format the xyz data will be returned in [Default is
``pandas``]:
- ``numpy`` - :class:`numpy.ndarray`
- ``pandas``- :class:`pandas.DataFrame`
- ``file`` - ASCII file (requires ``outfile``)
{verbose}
{binary}
{nodata}
{find}
{coltypes}
{header}
{incols}
{skiprows}
{wrap}
Returns
-------
ret : pandas.DataFrame or numpy.ndarray or None
Return type depends on ``outfile`` and ``output_type``:
- None if ``outfile`` is set (output will be stored in file set by
``outfile``)
- :class:`pandas.DataFrame` or :class:`numpy.ndarray` if
``outfile`` is not set (depends on ``output_type``)
Note
----
For geographic data with global or very large extent you should
consider :gmt-docs:`sphtriangulate <sphtriangulate.html>` instead since
``triangulate`` is a Cartesian or small-geographic area operator and is
unaware of periodic or polar boundary conditions.
"""
output_type = validate_output_table_type(output_type, outfile)
# Return a pandas.DataFrame if ``outfile`` is not set
with GMTTempFile(suffix=".txt") as tmpfile:
if output_type != "file":
outfile = tmpfile.name
return triangulate._triangulate(
data=data,
x=x,
y=y,
z=z,
output_type=output_type,
outfile=outfile,
projection=projection,
verbose=verbose,
binary=binary,
nodata=nodata,
find=find,
coltypes=coltypes,
header=header,
incols=incols,
skiprows=skiprows,
wrap=wrap,
**kwargs,
)