"""
grdtrack - Sample grids at specified (x,y) locations.
"""
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,
)
[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(points, grid, newcolname=None, outfile=None, **kwargs):
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
----------
points : str or {table-like}
Pass in either a file name to an ASCII data table, a 2D
{table-classes}.
grid : xarray.DataArray or str
Gridded array from which to sample values from, or a filename (netcdf
format).
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.
outfile : str
The file name for the output ASCII file.
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 meter 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*.
{R}
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 filename
is grdtrack_stacked_profile.txt].
- **+c**\ *fact* : Compute envelope on stacked profile as
±\ *fact* \*\ *deviation* [Default fact value is 2].
Notes:
1. Deviations depend on *method* and are st.dev (**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 or int or 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**.
{V}
z_only : bool
Only write out the sampled z-values [Default writes all columns].
{a}
{b}
{d}
{e}
{f}
{g}
{h}
{i}
{j}
{n}
{o}
{s}
{w}
Returns
-------
track: pandas.DataFrame or None
Return type depends on whether the ``outfile`` parameter is set:
- :class:`pandas.DataFrame` table with (x, y, ..., newcolname) if
``outfile`` is not set
- None if ``outfile`` is set (track output will be stored in file set
by ``outfile``)
"""
if hasattr(points, "columns") and newcolname is None:
raise GMTInvalidInput("Please pass in a str to 'newcolname'")
with GMTTempFile(suffix=".csv") 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=points)
# Store the xarray.DataArray grid in virtualfile
grid_context = lib.virtualfile_from_data(check_kind="raster", data=grid)
# Run grdtrack on the temporary (csv) points table
# and (netcdf) grid virtualfile
with table_context as csvfile:
with grid_context as grdfile:
kwargs.update({"G": grdfile})
if outfile is None: # Output to tmpfile if outfile is not set
outfile = tmpfile.name
arg_str = " ".join(
[csvfile, build_arg_string(kwargs), "->" + outfile]
)
lib.call_module(module="grdtrack", args=arg_str)
# Read temporary csv output to a pandas table
if outfile == tmpfile.name: # if user did not set outfile, return pd.DataFrame
try:
column_names = points.columns.to_list() + [newcolname]
result = pd.read_csv(tmpfile.name, sep="\t", names=column_names)
except AttributeError: # 'str' object has no attribute 'columns'
result = pd.read_csv(tmpfile.name, sep="\t", header=None, comment=">")
elif outfile != tmpfile.name: # return None if outfile set, output in outfile
result = None
return result