Source code for pygmt.src.grdgradient

grdgradient - Compute directional gradients from a grid.

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

[docs]@fmt_docstring @use_alias( A="azimuth", D="direction", E="radiance", G="outgrid", N="normalize", Q="tiles", R="region", S="slope_file", V="verbose", f="coltypes", n="interpolation", ) @kwargs_to_strings(A="sequence", E="sequence", R="sequence") def grdgradient(grid, **kwargs): r""" Compute the directional derivative of the vector gradient of the data. Can accept ``azimuth``, ``direction``, and ``radiance`` input to create the resulting gradient. Full option list at :gmt-docs:`grdgradient.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. azimuth : int or float or str or list *azim*\ [/*azim2*]. Azimuthal direction for a directional derivative; *azim* is the angle in the x,y plane measured in degrees positive clockwise from north (the +y direction) toward east (the +x direction). The negative of the directional derivative, :math:`-(\frac{{dz}}{{dx}}\sin(\mbox{{azim}}) + \ \frac{{dz}}{{dy}}\cos(\mbox{{azim}}))`, is found; negation yields positive values when the slope of :math:`z(x,y)` is downhill in the *azim* direction, the correct sense for shading the illumination of an image by a light source above the x,y plane shining from the *azim* direction. Optionally, supply two azimuths, *azim*/*azim2*, in which case the gradients in each of these directions are calculated and the one larger in magnitude is retained; this is useful for illuminating data with two directions of lineated structures, e.g., *0*/*270* illuminates from the north (top) and west (left). Finally, if *azim* is a file it must be a grid of the same domain, spacing and registration as *grid* that will update the azimuth at each output node when computing the directional derivatives. direction : str [**a**][**c**][**o**][**n**]. Find the direction of the positive (up-slope) gradient of the data. The following options are supported: - **a** - Find the aspect (i.e., the down-slope direction) - **c** - Use the conventional Cartesian angles measured counterclockwise from the positive x (east) direction. - **o** - Report orientations (0-180) rather than directions (0-360). - **n** - Add 90 degrees to all angles (e.g., to give local strikes of the surface). radiance : str or list [**m**\|\ **s**\|\ **p**]\ *azim/elev*\ [**+a**\ *ambient*][**+d**\ *diffuse*][**+p**\ *specular*][**+s**\ *shine*]. Compute Lambertian radiance appropriate to use with :doc:`pygmt.Figure.grdimage` and :doc:`pygmt.Figure.grdview`. The Lambertian Reflection assumes an ideal surface that reflects all the light that strikes it and the surface appears equally bright from all viewing directions. Here, *azim* and *elev* are the azimuth and elevation of the light vector. Optionally, supply *ambient* [0.55], *diffuse* [0.6], *specular* [0.4], or *shine* [10], which are parameters that control the reflectance properties of the surface. Default values are given in the brackets. Use **s** for a simpler Lambertian algorithm. Note that with this form you only have to provide azimuth and elevation. Alternatively, use **p** for the Peucker piecewise linear approximation (simpler but faster algorithm; in this case *azim* and *elev* are hardwired to 315 and 45 degrees. This means that even if you provide other values they will be ignored.). normalize : str or bool [**e**\|\ **t**][*amp*][**+a**\ *ambient*][**+s**\ *sigma*]\ [**+o**\ *offset*]. The actual gradients :math:`g` are offset and scaled to produce normalized gradients :math:`g_n` with a maximum output magnitude of *amp*. If *amp* is not given, default *amp* = 1. If *offset* is not given, it is set to the average of :math:`g`. The following forms are supported: - **True** - Normalize using :math:`g_n = \mbox{{amp}}\ (\frac{{g - \mbox{{offset}}}}{{max(|g - \mbox{{offset}}|)}})` - **e** - Normalize using a cumulative Laplace distribution yielding: :math:`g_n = \mbox{{amp}}(1 - \ \exp{{(\sqrt{{2}}\frac{{g - \mbox{{offset}}}}{{\sigma}}))}}`, where :math:`\sigma` is estimated using the L1 norm of :math:`(g - \mbox{{offset}})` if it is not given. - **t** - Normalize using a cumulative Cauchy distribution yielding: :math:`g_n = \ \frac{{2(\mbox{{amp}})}}{{\pi}}(\tan^{{-1}}(\frac{{g - \ \mbox{{offset}}}}{{\sigma}}))` where :math:`\sigma` is estimated using the L2 norm of :math:`(g - \mbox{{offset}})` if it is not given. As a final option, you may add **+a**\ *ambient* to add *ambient* to all nodes after gradient calculations are completed. tiles : str **c**\|\ **r**\|\ **R**. Controls how normalization via ``normalize`` is carried out. When multiple grids should be normalized the same way (i.e., with the same *offset* and/or *sigma*), we must pass these values via ``normalize``. However, this is inconvenient if we compute these values from a grid. Use **c** to save the results of *offset* and *sigma* to a statistics file; if grid output is not needed for this run then do not specify ``outgrid``. For subsequent runs, just use **r** to read these values. Using **R** will read then delete the statistics file. {R} slope_file : str Name of output grid file with scalar magnitudes of gradient vectors. Requires ``direction`` but makes ``outgrid`` optional. {V} {f} {n} 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``) """ with GMTTempFile(suffix=".nc") as tmpfile: if "Q" in kwargs and "N" not in kwargs: raise GMTInvalidInput("""Must specify normalize if tiles is specified.""") if not args_in_kwargs(args=["A", "D", "E"], kwargs=kwargs): raise GMTInvalidInput( """At least one of the following parameters must be specified: azimuth, direction, or radiance""" ) with Session() as lib: file_context = lib.virtualfile_from_data(check_kind="raster", data=grid) with file_context as infile: if "G" not in kwargs.keys(): # if outgrid is unset, output to tempfile kwargs.update({"G":}) outgrid = kwargs["G"] arg_str = " ".join([infile, build_arg_string(kwargs)]) lib.call_module("grdgradient", arg_str) return load_dataarray(outgrid) if outgrid == else None