Source code for pygmt.src.grdtrack

grdtrack - Sample grids at specified (x,y) locations.

from typing import Literal

import numpy as np
import pandas as pd
from pygmt.clib import Session
from pygmt.exceptions import GMTInvalidInput
from pygmt.helpers import (

__doctest_skip__ = ["grdtrack"]

[docs] @fmt_docstring @use_alias( A="resample", C="crossprofile", D="dfile", E="profile", F="critical", R="region", N="no_skip", S="stack", T="radius", V="verbose", Z="z_only", a="aspatial", b="binary", d="nodata", e="find", f="coltypes", g="gap", h="header", i="incols", j="distcalc", n="interpolation", o="outcols", s="skiprows", w="wrap", ) @kwargs_to_strings(R="sequence", S="sequence", i="sequence_comma", o="sequence_comma") def grdtrack( grid, points=None, output_type: Literal["pandas", "numpy", "file"] = "pandas", outfile: str | None = None, newcolname=None, **kwargs, ) -> pd.DataFrame | np.ndarray | None: r""" Sample grids at specified (x,y) locations. Reads one or more grid files and a table (from file or an array input; but see ``profile`` for exception) with (x,y) [or (lon,lat)] positions in the first two columns (more columns may be present). It interpolates the grid(s) at the positions in the table and writes out the table with the interpolated values added as (one or more) new columns. Alternatively (``crossprofile``), the input is considered to be line-segments and we create orthogonal cross-profiles at each data point or with an equidistant separation and sample the grid(s) along these profiles. A bicubic [Default], bilinear, B-spline or nearest-neighbor interpolation is used, requiring boundary conditions at the limits of the region (see ``interpolation``; Default uses "natural" conditions (second partial derivative normal to edge is zero) unless the grid is automatically recognized as periodic.) Full option list at :gmt-docs:`grdtrack.html` {aliases} Parameters ---------- {grid} points : str, {table-like} Pass in either a file name to an ASCII data table, a 2-D {table-classes}. {output_type} {outfile} newcolname : str Required if ``points`` is a :class:`pandas.DataFrame`. The name for the new column in the track :class:`pandas.DataFrame` table where the sampled values will be placed. resample : str **f**\|\ **p**\|\ **m**\|\ **r**\|\ **R**\ [**+l**] For track resampling (if ``crossprofile`` or ``profile`` are set) we can select how this is to be performed. Append **f** to keep original points, but add intermediate points if needed [Default], **m** as **f**, but first follow meridian (along y) then parallel (along x), **p** as **f**, but first follow parallel (along y) then meridian (along x), **r** to resample at equidistant locations; input points are not necessarily included in the output, and **R** as **r**, but adjust given spacing to fit the track length exactly. Finally, append **+l** if geographic distances should be measured along rhumb lines (loxodromes) instead of great circles. Ignored unless ``crossprofile`` is used. crossprofile : str *length*/\ *ds*\ [*/spacing*][**+a**\|\ **+v**][**l**\|\ **r**]. Use input line segments to create an equidistant and (optionally) equally-spaced set of crossing profiles along which we sample the grid(s) [Default simply samples the grid(s) at the input locations]. Specify two length scales that control how the sampling is done: *length* sets the full length of each cross-profile, while *ds* is the sampling spacing along each cross-profile. Optionally, append **/**\ *spacing* for an equidistant spacing between cross-profiles [Default erects cross-profiles at the input coordinates]; see ``resample`` for how resampling the input track is controlled. By default, all cross-profiles have the same direction (left to right as we look in the direction of the input line segment). Append **+a** to alternate the direction of cross-profiles, or **v** to enforce either a "west-to-east" or "south-to-north" view. By default the entire profiles are output. Choose to only output the left or right halves of the profiles by appending **+l** or **+r**, respectively. Append suitable units to *length*; it sets the unit used for *ds* [and *spacing*] (See :gmt-docs:`Units <grdtrack.html#units>`). The default unit for geographic grids is meters while Cartesian grids implies the user unit. The output columns will be *lon*, *lat*, *dist*, *azimuth*, *z1*, *z2*, ..., *zn* (The *zi* are the sampled values for each of the *n* grids). dfile : str In concert with ``crossprofile`` we can save the (possibly resampled) original lines to *dfile* [Default only saves the cross-profiles]. The columns will be *lon*, *lat*, *dist*, *azimuth*, *z1*, *z2*, ... (sampled value for each grid). profile : str *line*\ [,\ *line*,...][**+a**\ *az*][**+c**][**+d**][**+g**]\ [**+i**\ *inc*][**+l**\ *length*][**+n**\ *np*][**+o**\ *az*]\ [**+r**\ *radius*]. Instead of reading input track coordinates, specify profiles via coordinates and modifiers. The format of each *line* is *start*/*stop*, where *start* or *stop* are either *lon*/*lat* (*x*/*y* for Cartesian data) or a 2-character XY key that uses the :gmt-docs:`text <text.html>`-style justification format to specify a point on the map as [LCR][BMT]. Each line will be a separate segment unless **+c** is used which will connect segments with shared joints into a single segment. In addition to line coordinates, you can use Z-, Z+ to mean the global minimum and maximum locations in the grid (only available if a single grid is given via **outfile**). You may append **+i**\ *inc* to set the sampling interval; if not given then we default to half the minimum grid interval. For a *line* along parallels or meridians you can add **+g** to report degrees of longitude or latitude instead of great circle distances starting at zero. Instead of two coordinates you can specify an origin and one of **+a**, **+o**, or **+r**. The **+a** sets the azimuth of a profile of given length starting at the given origin, while **+o** centers the profile on the origin; both require **+l**. For circular sampling specify **+r** to define a circle of given radius centered on the origin; this option requires either **+n** or **+i**. The **+n**\ *np* modifier sets the desired number of points, while **+l**\ *length* gives the total length of the profile. Append **+d** to output the along-track distances after the coordinates. **Note**: No track file will be read. Also note that only one distance unit can be chosen. Giving different units will result in an error. If no units are specified we default to great circle distances in km (if geographic). If working with geographic data you can use ``distcalc`` to control distance calculation mode [Default is Great Circle]. **Note**: If ``crossprofile`` is set and *spacing* is given then that sampling scheme overrules any modifier set in ``profile``. critical : str [**+b**][**+n**][**+r**][**+z**\ *z0*]. Find critical points along each cross-profile as a function of along-track distance. Requires ``crossprofile`` and a single input grid (*z*). We examine each cross-profile generated and report (*dist*, *lonc*, *latc*, *distc*, *azimuthc*, *zc*) at the center peak of maximum *z* value, (*lonl*, *latl*, *distl*) and (*lonr*, *latr*, *distr*) at the first and last non-NaN point whose *z*-value exceeds *z0*, respectively, and the *width* based on the two extreme points found. Here, *dist* is the distance along the original input ``points`` and the other 12 output columns are a function of that distance. When searching for the center peak and the extreme first and last values that exceed the threshold we assume the profile is positive up. If we instead are looking for a trough then you must use **+n** to temporarily flip the profile to positive. The threshold *z0* value is always given as >= 0; use **+z** to change it [Default is 0]. Alternatively, use **+b** to determine the balance point and standard deviation of the profile; this is the weighted mean and weighted standard deviation of the distances, with *z* acting as the weight. Finally, use **+r** to obtain the weighted rms about the cross-track center (*distc* == 0). **Note**: We round the exact results to the nearest distance nodes along the cross-profiles. We write 13 output columns per track: *dist, lonc, latc, distc, azimuthc, zc, lonl, latl, distl, lonr, latr, distr, width*. {region} no_skip : bool Do *not* skip points that fall outside the domain of the grid(s) [Default only output points within grid domain]. stack : str or list *method*/*modifiers*. In conjunction with ``crossprofile``, compute a single stacked profile from all profiles across each segment. Choose how stacking should be computed [Default method is **a**]: - **a** = mean (average) - **m** = median - **p** = mode (maximum likelihood) - **l** = lower - **L** = lower but only consider positive values - **u** = upper - **U** = upper but only consider negative values. The *modifiers* control the output; choose one or more among these choices: - **+a** : Append stacked values to all cross-profiles. - **+d** : Append stack deviations to all cross-profiles. - **+r** : Append data residuals (data - stack) to all cross-profiles. - **+s**\ [*file*] : Save stacked profile to *file* [Default file name is grdtrack_stacked_profile.txt]. - **+c**\ *fact* : Compute envelope on stacked profile as ±\ *fact* \*\ *deviation* [Default fact value is 2]. Here are some notes: 1. Deviations depend on *method* and are (**a**), L1 scale, i.e., 1.4826 \* median absolute deviation (MAD) (for **m** and **p**), or half-range (upper-lower)/2. 2. The stacked profile file contains a leading column plus groups of 4-6 columns, with one group for each sampled grid. The leading column holds cross distance, while the first four columns in a group hold stacked value, deviation, min value, and max value, respectively. If *method* is one of **a**\|\ **m**\|\ **p** then we also write the lower and upper confidence bounds (see **+c**). When one or more of **+a**, **+d**, and **+r** are used then we also append the stacking results to the end of each row, for all cross-profiles. The order is always stacked value (**+a**), followed by deviations (**+d**) and finally residuals (**+r**). When more than one grid is sampled this sequence of 1-3 columns is repeated for each grid. radius : bool, float, or str [*radius*][**+e**\|\ **p**]. To be used with normal grid sampling, and limited to a single, non-IMG grid. If the nearest node to the input point is NaN, search outwards until we find the nearest non-NaN node and report that value instead. Optionally specify a search radius which limits the consideration to points within this distance from the input point. To report the location of the nearest node and its distance from the input point, append **+e**. The default unit for geographic grid distances is spherical degrees. Use *radius* to change the unit and give *radius* = 0 if you do not want to limit the radius search. To instead replace the input point with the coordinates of the nearest node, append **+p**. {verbose} z_only : bool Only write out the sampled z-values [Default writes all columns]. {aspatial} {binary} {nodata} {find} {coltypes} {gap} {header} {incols} {distcalc} {interpolation} {outcols} {skiprows} {wrap} Returns ------- ret 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``) Example ------- >>> import pygmt >>> # Load a grid of @earth_relief_30m data, with a longitude range of >>> # -118° E to -107° E, and a latitude range of -49° N to -42° N >>> grid = pygmt.datasets.load_earth_relief( ... resolution="30m", region=[-118, -107, -49, -42] ... ) >>> # Load a pandas dataframe with ocean ridge points >>> points = pygmt.datasets.load_sample_data(name="ocean_ridge_points") >>> # Create a pandas dataframe from an input grid and set of points >>> # The output dataframe adds a column named "bathymetry" >>> output_dataframe = pygmt.grdtrack( ... points=points, grid=grid, newcolname="bathymetry" ... ) """ if points is not None and kwargs.get("E") is not None: raise GMTInvalidInput("Can't set both 'points' and 'profile'.") if points is None and kwargs.get("E") is None: raise GMTInvalidInput("Must give 'points' or set 'profile'.") if hasattr(points, "columns") and newcolname is None: raise GMTInvalidInput("Please pass in a str to 'newcolname'") output_type = validate_output_table_type(output_type, outfile=outfile) column_names = None if output_type == "pandas" and isinstance(points, pd.DataFrame): column_names = [*points.columns.to_list(), newcolname] with Session() as lib: with ( lib.virtualfile_in(check_kind="raster", data=grid) as vingrd, lib.virtualfile_in( check_kind="vector", data=points, required_data=False ) as vintbl, lib.virtualfile_out(kind="dataset", fname=outfile) as vouttbl, ): kwargs["G"] = vingrd lib.call_module( module="grdtrack", args=build_arg_list(kwargs, infile=vintbl, outfile=vouttbl), ) return lib.virtualfile_to_dataset( vfname=vouttbl, output_type=output_type, column_names=column_names, )