[GRASS-SVN] r71850 - in grass/trunk/raster: . r.fill.stats r.fill.stats/testsuite r.fill.stats/testsuite/data
svn_grass at osgeo.org
svn_grass at osgeo.org
Sun Nov 26 14:30:03 PST 2017
Author: annakrat
Date: 2017-11-26 14:30:03 -0800 (Sun, 26 Nov 2017)
New Revision: 71850
Added:
grass/trunk/raster/r.fill.stats/
grass/trunk/raster/r.fill.stats/r.fill.stats.html
grass/trunk/raster/r.fill.stats/r_fill_stats_01.png
grass/trunk/raster/r.fill.stats/r_fill_stats_02.png
grass/trunk/raster/r.fill.stats/r_fill_stats_lidar.png
grass/trunk/raster/r.fill.stats/r_fill_stats_smoothing.png
grass/trunk/raster/r.fill.stats/testsuite/
grass/trunk/raster/r.fill.stats/testsuite/data/
grass/trunk/raster/r.fill.stats/testsuite/data/input_ascii.txt
grass/trunk/raster/r.fill.stats/testsuite/data/output_mean.txt
grass/trunk/raster/r.fill.stats/testsuite/data/output_median.txt
grass/trunk/raster/r.fill.stats/testsuite/data/output_mode.txt
grass/trunk/raster/r.fill.stats/testsuite/data/output_wmean.txt
grass/trunk/raster/r.fill.stats/testsuite/test_r_fill_stats.py
Removed:
grass/trunk/raster/r.fill.stats/r.fill.gaps.html
grass/trunk/raster/r.fill.stats/r_fill_gaps_01.png
grass/trunk/raster/r.fill.stats/r_fill_gaps_02.png
grass/trunk/raster/r.fill.stats/r_fill_gaps_lidar.png
grass/trunk/raster/r.fill.stats/r_fill_gaps_smoothing.png
Modified:
grass/trunk/raster/Makefile
grass/trunk/raster/r.fill.stats/Makefile
grass/trunk/raster/r.fill.stats/cell_funcs.c
grass/trunk/raster/r.fill.stats/cell_funcs.h
grass/trunk/raster/r.fill.stats/main.c
Log:
r.fill.stats: move r.fill.gaps from addons to core, added test
Modified: grass/trunk/raster/Makefile
===================================================================
--- grass/trunk/raster/Makefile 2017-11-26 21:39:24 UTC (rev 71849)
+++ grass/trunk/raster/Makefile 2017-11-26 22:30:03 UTC (rev 71850)
@@ -22,6 +22,7 @@
r.external \
r.external.out \
r.fill.dir \
+ r.fill.stats \
r.flow \
r.geomorphon \
r.grow.distance \
Modified: grass/trunk/raster/r.fill.stats/Makefile
===================================================================
--- grass-addons/grass7/raster/r.fill.gaps/Makefile 2017-11-26 21:39:24 UTC (rev 71849)
+++ grass/trunk/raster/r.fill.stats/Makefile 2017-11-26 22:30:03 UTC (rev 71850)
@@ -1,6 +1,6 @@
MODULE_TOPDIR = ../..
-PGM = r.fill.gaps
+PGM = r.fill.stats
LIBES = $(GISLIB) $(RASTERLIB)
DEPENDENCIES = $(GISDEP) $(RASTERDEP)
Modified: grass/trunk/raster/r.fill.stats/cell_funcs.c
===================================================================
--- grass-addons/grass7/raster/r.fill.gaps/cell_funcs.c 2017-11-26 21:39:24 UTC (rev 71849)
+++ grass/trunk/raster/r.fill.stats/cell_funcs.c 2017-11-26 22:30:03 UTC (rev 71850)
@@ -1,7 +1,7 @@
/***************************************************************************
*
- * MODULE: r.fill.gaps (commandline)
+ * MODULE: r.fill.stats
* FILE: cell_funcs.c
* AUTHOR(S): Benjamin Ducke
* PURPOSE: Provide cell type dependent functions for cell data operations.
Modified: grass/trunk/raster/r.fill.stats/cell_funcs.h
===================================================================
--- grass-addons/grass7/raster/r.fill.gaps/cell_funcs.h 2017-11-26 21:39:24 UTC (rev 71849)
+++ grass/trunk/raster/r.fill.stats/cell_funcs.h 2017-11-26 22:30:03 UTC (rev 71850)
@@ -1,7 +1,7 @@
/***************************************************************************
*
- * MODULE: r.fill.gaps (commandline)
+ * MODULE: r.fill.stats
* FILE: cell_funcs.h
* AUTHOR(S): Benjamin Ducke
* PURPOSE: See cell_funcs.c
Modified: grass/trunk/raster/r.fill.stats/main.c
===================================================================
--- grass-addons/grass7/raster/r.fill.gaps/main.c 2017-11-26 21:39:24 UTC (rev 71849)
+++ grass/trunk/raster/r.fill.stats/main.c 2017-11-26 22:30:03 UTC (rev 71850)
@@ -1,12 +1,13 @@
/***************************************************************************
*
- * MODULE: r.fill.gaps (commandline)
+ * MODULE: r.fill.stats
* AUTHOR(S): Benjamin Ducke
- * PURPOSE: Rapidly fill "no data" cells in a raster map by simple interpolation.
- *
- * COPYRIGHT: (C) 2011-2015 by the GRASS Development Team
+ * Anna Petrasova (speedup with -p, fix of median, mode computation)
+ * PURPOSE: Rapidly fill "no data" cells in a raster map by simple interpolation.
*
+ * COPYRIGHT: (C) 2011-2018 by the GRASS Development Team
+ *
* This program is free software under the GPL (>=v2)
* Read the file COPYING that comes with GRASS for details.
*
Deleted: grass/trunk/raster/r.fill.stats/r.fill.gaps.html
===================================================================
--- grass-addons/grass7/raster/r.fill.gaps/r.fill.gaps.html 2017-11-26 21:39:24 UTC (rev 71849)
+++ grass/trunk/raster/r.fill.stats/r.fill.gaps.html 2017-11-26 22:30:03 UTC (rev 71850)
@@ -1,485 +0,0 @@
-<h2>DESCRIPTION</h2>
-
-
-<em><b>r.fill.gaps</b></em> is a module for fast gap filling and
-interpolation (with smoothing) of dense raster data.
-
-<p>
-
-The <em>r.fill.gaps</em> module is capable of quickly filling small
-data gaps in large and high-resolution raster maps. It's primary purpose
-is to improve high-resolution, rasterized sensor data (such as Lidar
-data). As a rule of thumb, there
-should be at least as many data cells as there are "no data" (NULL) cells in
-the input raster map. Several interpolation modes are available. By
-default, all values of the input raster map will be replaced with
-locally interpolated values in the output raster map. This is the
-equivalent of running a low-pass smoothing filter on the interpolated
-data and is often desirable, owing to noisy nature of high-resolution
-sensor data. With dense data and small gaps, this should result in only slight
-deviations from the original data and produce smooth output. Alternatively,
-setting the <b>-p</b> flag will disable the low-pass filter and preserve
-the original cell values.
-
-<p>
-
-Available gap filling modes:
-<ul>
-<li>spatially weighted mean (default)</li>
-<li>simple mean</li>
-<li>simple median</li>
-<li>simple mode</li>
-</ul>
-
-<p>
-
-The spatially weighted mean is equivalent to an Inverse Distance
-Weighting (IDW;
-see also <em><a href="r.surf.idw.html">r.surf.idw</a></em>)
-interpolation. The simple mean is equivalent to a simple low-pass filter.
-Median and mode replacements can also be achieved using
-<em><a href="r.neighbors.html">r.neighbors</a></em>.
-
-<p>
-
-Note that <em>r.fill.gaps</em> is highly optimized for fast processing
-of large raster datasets with a small interpolation distance threshold
-(i.e. closing small gaps). As a trade-off for speed and a small memory
-foot print, some spatial accuracy is lost due to the rounding of the
-distance threshold to the closest approximation in input raster cells
-and the use of a matrix of precomputed weights at cell resolution (see
-further down for details). Note also that processing time will increase
-exponentially with higher distance settings. Cells outside the distance
-threshold will not be interpolated, preserving the edges of the original data
-extent.
-
-<p>
-
-This module is not well suited for interpolating sparse input data and
-closing large gaps. For such purposes more appropriate
-methods are available, such as
-<em><a href="v.surf.idw.html">v.surf.idw</a></em> or
-<em><a href="v.surf.rst.html">v.surf.rst</a></em>.
-
-<p>
-
-Applications where the properties of <em>r.fill.gaps</em> are
-advantageous include the processing of high resolution, close range
-scanning and remote sensing datasets. Such datasets commonly feature
-densely spaced measurements that have some gaps after rasterization,
-due to blind spots, instrument failures, and misalignments between the
-GIS' raster cell grids and the original measurement locations. In these
-cases, <em>r.fill.gaps</em> should typically be run using the "weighted
-mean" (default) method and with a small distance setting (the default
-is to use a search radius of just three cells).
-
-<p>
-
-The images below show a gradiometer dataset with gaps and its
-interpolated equivalent, produced using the spatially weighted mean
-operator (<tt>mode="wmean"</tt>).
-
-<p>
-
-<img src="r_fill_gaps_01.png" alt="Raw data"> <img src="r_fill_gaps_02.png" alt="Gaps filled">
-
-<p>
-
-In addition, <em>r.fill.gaps</em> can be useful for raster map
-generalization. Often, this involves removing small clumps of
-categorized cells and then filling the resulting data gaps without
-"inventing" any new values. In such cases, the "mode" or "median"
-interpolators should be used.
-
-
-<h3>Usage</h3>
-
-The most critical user-provided settings are the interpolation/gap
-filling method (<b>mode</b>) and the maximum distance, up to which
-<em>r.fill.gaps</em> will scan for cells with values (<b>distance</b>).
-The distance can be expressed as a number of cells (default) or in the
-current GRASS location's units (if the <b>-m</b> flag is given). The latter
-are typically meters, but can be any other units of a <em>planar</em>
-coordinate system.
-
-<p>
-
-Note that proper handling of geodetic coordinates (lat/lon) and
-distances is currently not implemented. For lat/lon data, the distance
-should therefore be specified in cells and usage of
-<em>r.fill.gaps</em> should be restricted to small areas to avoid large
-inaccuracies that may arise from the different extents of cells along
-the latitudinal and longitudinal axes.
-
-<p>
-
-Distances specified in map units (<b>-m</b> flag) will be approximated
-as accurately as the current region's cell resolution settings allow.
-The program will warn if the distance cannot be expressed as whole
-cells at the current region's resolution. In such case, the number of
-cells in the search window will be rounded up. Due to the rounding
-effect introduced by using cells as spatial units, the actual maximum
-distance considered by the interpolation may be up to half a cell
-diagonal larger than the one specified by the user.
-
-<p>
-
-The interpolator type "wmean" uses a precomputed matrix of spatial
-weights to speed up computation. This matrix can be examined (printed
-to the screen) before running the interpolation, by setting the
-<b>-w</b> flag. In mode "wmean", the <b>power</b> option has the usual
-meaning in IDW: higher values mean that cell values in the neighborhood
-lose their influence on the cell to be interpolated more rapidly with
-increasing distance from the latter. Another way of explaining this
-effect is to state that larger "power" settings result in more
-localized interpolation, smaller ones in more globalized interpolation.
-The default setting is <tt>power=2.0</tt>.
-
-<p>
-
-The interpolators "mean", "median" and "mode" are calculated from all
-cell values within the search radius. No spatial weighting is applied
-for these methods. The "mode" of the input data may not always be
-unique. In such case, the mode will be the smallest value with the
-highest frequency.
-
-<p>
-
-Often, input data will contain spurious extreme measurements (spikes,
-outliers, noise) caused by the limits of device sensitivity, hardware
-defects, environmental influences, etc. If the normal, valid range of
-input data is known beforehand, then the <b>minimum</b> and
-<b>maximum</b> options can be used to exclude those input cells that
-have values below or above that range, respectively. This will prevent
-the influence of spikes and outliers from spreading through the
-interpolation.
-
-<p>
-
-Unless the <b>-p</b> (preserve) flag is given, data cells of the input
-map will be replaced with interpolated values instead of preserving
-them in the output. In modes "wmean" and "mean", this results in a
-smoothing effect that includes the original data (see below)!
-
-<p>
-
-Besides the result of the interpolation/gap filling, a second output
-map can be specified via the <b>uncertainty</b> option. The cell values
-in this map represent a simple measure of how much uncertainty was
-involved in interpolating each cell value of the primary output map,
-with "0" being the lowest and "1" being the theoretic highest
-uncertainty. Uncertainty is measured by summing up all cells in the
-neighborhood (defined by the search radius <b>distance</b>) that
-contain a value in the <b>input</b> map, multiplied by their weights,
-and dividing the result by the sum of all weights in the neighborhood.
-For <tt>mode=wmean</tt>, this means that interpolated output cells that
-were computed from many nearby input cells have very low uncertainty
-and vice versa. For all other modes, all weights in the neighborhood
-are constant "1" and the uncertainty measure is a simple measure of how
-many input data cells were present in the search window.
-
-<h3>Smoothing</h3>
-
-The <em>r.fill.gaps</em> module uses the interpolated values to adjust
-the original values and create a smooth surface, which is akin to running
-a low-pass filter on the data. Since most high-resolution sensor data
-is noisy, this is normally a desired effect and results in output that
-is more suitable for deriving secondary products, such as slope, aspect
-and curvature maps. Larger settings for the search radius (<b>distance</b>)
-will result in a stronger smoothing. In practice, some experimentation
-with different settings for <b>distance</b> and <b>power</b> might be required
-to achieve good results. In some cases (e.g. when dealing with low-noise or
-classified data), it might be desirable to turn off data smoothing by
-setting the <b>-p</b> (preserve) flag. This will ensure that the original
-cell data is copied through to the result map without alteration.
-
-<center>
-<a href="r_fill_gaps_smoothing.png">
- <img src="r_fill_gaps_smoothing.png" alt="Smooth versus preserve" width="600" height="300">
-</a>
-<p><em>
-Effect of smoothing the original data: The top row shows a gap-filled surface computed from a rasterized Lidar point
-cloud (using <tt>mode=wmean</tt> and <tt>power=2</tt>), and the derived slope, aspect,
-and profile curvature maps. The smoothing effect is clearly visible.
-The bottom row shows the effect of setting the <b>-p</b> flag: Preserving the original
-cell values in the interpolated output produces and unsmoothed, noisy surface, and likewise
-noisy derivative maps.
-</em></p>
-</center>
-
-The effect can be seen in the illustration above:
-Slope, aspect, and profile curvature are computed using the
-<em><a href="r.slope.aspect.html">r.slope.aspect</a></em> module, which
-uses a window (kernel) for computations that considers only the
-immediate neighborhood of each cell. When performed on noisy data,
-such local operations result in equally noisy derivatives if the
-original data is preserved (by setting the <b>-p</b> flag) and no smoothing
-is performed.
-
-<p>
-
-(Note that the effects of noisy data can also be avoided by using modules
-that are not restricted to minimal kernel sizes. For example, aspect and other morphometric parameters can be computed
-using the <em><a href="r.param.scale.html">r.param.scale</a></em> module
-which operates with variable-size cell neighborhoods.)
-
-<!--
-wget http://fatra.cnr.ncsu.edu/uav-lidar-analytics-course/midpines_2015_spm.las -O points.las
-
-g.region n=220220 s=218690 e=637800 w=636270 res=1
-
-r.in.lidar input=points.las output=ground method=mean class_filter=2
-
-r.fill.gaps input=ground output=ground_filled mode=wmean power=2.0 cells=8
-r.fill.gaps -p input=ground output=ground_filled2 mode=wmean power=2.0 cells=8
-
-r.slope.aspect elevation=ground_filled slope=slope aspect=aspect pcurvature=pcur tcurvature=tcur
-r.slope.aspect elevation=ground_filled2 slope=slope2 aspect=aspect2 pcurvature=pcur2 tcurvature=tcur2
-
-g.region n=219537 s=219172 w=636852 e=637218 -p
-
-d.mon cairo output=r_fill_gaps_smoothing.png width=1464 height=730
-export GRASS_FONT=LiberationSans-Regular
-d.frame frame=u1 at=50,100,0,25 -c
-d.rast ground_filled
-d.text text="surf. (smoothed)" at=5,5 size=10 bgcolor=white color=black
-d.frame frame=u2 at=50,100,25,50 -c
-d.rast slope
-d.text text="slope" at=5,5 size=10 bgcolor=white color=black
-d.frame frame=u3 at=50,100,50,75 -c
-d.rast aspect
-d.text text="aspect" at=5,5 size=10 bgcolor=white color=black
-d.frame frame=u4 at=50,100,75,100 -c
-d.rast pcur
-d.text text="prof. curvature" at=5,5 size=10 bgcolor=white color=black
-d.frame frame=l1 at=0,50,0,25 -c
-d.rast ground_filled2
-d.text text="surf. (preserved)" at=5,5 size=10 bgcolor=white color=black
-d.frame frame=l2 at=0,50,25,50 -c
-d.rast slope2
-d.text text="slope" at=5,5 size=10 bgcolor=white color=black
-d.frame frame=l3 at=0,50,50,75 -c
-d.rast aspect2
-d.text text="aspect" at=5,5 size=10 bgcolor=white color=black
-d.frame frame=l4 at=0,50,75,100 -c
-d.rast pcur2
-d.text text="prof. curvature" at=5,5 size=10 bgcolor=white color=black
-d.mon stop=cairo
--->
-
-<h3>Spatial weighting scheme</h3>
-
-The key to getting good gap filling results is to understand the
-spatial weighting scheme used in mode "wmean". The weights are
-precomputed and assigned per cell within the search window centered on
-the location at which to interpolate/gap fill all cells within the
-user-specified distance.
-
-<p>
-
-The illustration below shows the precomputed weights matrix for a
-search distance of four cells from the center cell:
-
-<pre>
-000.00 000.01 000.04 000.07 000.09 000.07 000.04 000.01 000.00
-000.01 000.06 000.13 000.19 000.22 000.19 000.13 000.06 000.01
-000.04 000.13 000.25 000.37 000.42 000.37 000.25 000.13 000.04
-000.07 000.19 000.37 000.56 000.68 000.56 000.37 000.19 000.07
-000.09 000.22 000.42 000.68 001.00 000.68 000.42 000.22 000.09
-000.07 000.19 000.37 000.56 000.68 000.56 000.37 000.19 000.07
-000.04 000.13 000.25 000.37 000.42 000.37 000.25 000.13 000.04
-000.01 000.06 000.13 000.19 000.22 000.19 000.13 000.06 000.01
-000.00 000.01 000.04 000.07 000.09 000.07 000.04 000.01 000.00
-</pre>
-
-Note that the weights in such a small window drop rapidly for the
-default setting of <tt>power=2</tt>.
-
-<p>
-
-If the distance is given in map units (flag <tt>-m</tt>), then the
-search window can be modeled more accurately as a circle. The
-illustration below shows the precomputed weights for a distance in map
-units that is approximately equivalent to four cells from the center
-cell:
-
-<pre>
-...... ...... ...... 000.00 000.00 000.00 ...... ...... ......
-...... 000.00 000.02 000.06 000.09 000.06 000.02 000.00 ......
-...... 000.02 000.11 000.22 000.28 000.22 000.11 000.02 ......
-000.00 000.07 000.22 000.44 000.58 000.44 000.22 000.07 000.00
-000.00 000.09 000.28 000.58 001.00 000.58 000.28 000.09 000.00
-000.00 000.07 000.22 000.44 000.58 000.44 000.22 000.07 000.00
-...... 000.02 000.11 000.22 000.28 000.22 000.11 000.02 ......
-...... 000.00 000.02 000.06 000.09 000.06 000.02 000.00 ......
-...... ...... ...... 000.00 000.00 000.00 ...... ...... ......
-</pre>
-
-<p>
-
-When using a small search radius, <b>cells</b> must also be set to a
-small value. Otherwise, there may not be enough cells with data within
-the search radius to support interpolation.
-
-
-<h2>NOTES</h2>
-
-The straight-line metric used for converting distances in map units to
-cell numbers is only adequate for planar coordinate systems. Using this
-module with lat/lon input data will likely give inaccurate results,
-especially when interpolating over large geographical areas.
-
-<p>
-
-If the distance is set to a relatively large value, processing time
-will quickly approach and eventually exceed that of point-based
-interpolation modules such as
-<em><a href="v.surf.rst.html">v.surf.rst</a></em>.
-
-<p>
-
-This module can handle cells with different X and Y resolutions.
-However, note that the weight matrix will be skewed in such cases, with
-higher weights occurring close to the center and along the axis with
-the higher resolution. This is because weights on the lower resolution
-axis are less accurately calculated. The skewing effect will be
-stronger if the difference between the X and Y axis resolution is
-greater and a larger "power" setting is chosen. This property of the
-weights matrix directly reflects the higher information density along
-the higher resolution axis.
-
-<p>
-
-Note on printing the weights matrix (using the <b>-w</b> flag): the
-matrix cannot be printed if it is too large.
-
-<p>
-
-The memory estimate provided by the <b>-u</b> flag is a lower limit on
-the amount of RAM that will be needed.
-
-<p>
-
-If the <b>-s</b> flag is set, floating point type output will be
-saved as a "single precision" raster map, saving ~50% disk space
-compared to the default "double precision" output.
-
-
-<h2>EXAMPLES</h2>
-
-Gap-fill a dataset using spatially weighted mean (IDW) and a maximum
-search radius of 3.0 map units; also produce uncertainty estimation
-map:
-
-<div class="code"><pre>
-r.fill.gaps input=measurements output=result dist=3.0 -m mode=wmean uncertainty=uncert_map
-</pre></div>
-
-Run a fast low-pass filter (replacement all cells with mean value of
-neighboring cells) on the input map:
-
-<div class="code"><pre>
-r.fill.gaps input=measurements output=result dist=10 mode=mean
-</pre></div>
-
-Fill data gaps in a categorized raster map; preserve existing data:
-
-<div class="code"><pre>
-r.fill.gaps input=categories output=result dist=100 -m mode=mode -p
-</pre></div>
-
-<h3>Lidar point cloud example</h3>
-
-<!--
-Using:
-http://fatra.cnr.ncsu.edu/uav-lidar-analytics-course/midpines_2015_spm.las
--->
-
-Inspect the point density and determine the extent of the point cloud
-using the <em><a href="r.in.lidar.html">r.in.lidar</a></em> module:
-
-<div class="code"><pre>
-r.in.lidar -e input=points.las output=density method=n resolution=5 class_filter=2
-</pre></div>
-
-Based on the result, set computational region extent and desired
-resolution:
-
-<div class="code"><pre>
-g.region -pa raster=density res=1
-</pre></div>
-
-Import the point cloud as raster using binning:
-
-<div class="code"><pre>
-r.in.lidar input=points.las output=ground_raw method=mean class_filter=2
-</pre></div>
-
-Check that there are more non-NULL cells than NULL ("no data") cells:
-
-<div class="code"><pre>
-r.univar map=ground_raw
-</pre></div>
-
-<pre>
-total null and non-null cells: 2340900
-total null cells: 639184
-...
-</pre>
-
-Fill in the NULL cells using the default 3-cell search radius:
-
-<div class="code"><pre>
-r.fill.gaps input=ground output=ground_filled uncertainty=uncertainty distance=3 mode=wmean power=2.0 cells=8
-</pre></div>
-
-<center>
-<a href="r_fill_gaps_lidar.png"><img src="r_fill_gaps_lidar.png" alt="Point density and ground surface" width=600></a>
-<p><em>
-Binning of Lidar and resulting ground surface with filled gaps.
-Note the remaining NULL cells (white) in the resulting ground surface.
-These are areas with a lack of cells with values in close proximity.
-</em></p>
-</center>
-
-<!--
-d.mon cairo output=r_fill_gaps_lidar.png width=1530 height=1530
-export GRASS_FONT=LiberationSans-Regular
-d.frame frame=ul at=50,100,0,50 -c
-d.rast density3
-d.legend raster=density3 title="Density" at=50,95,2,10 -bsf
-d.text text="density" at=5,5 size=10 bgcolor=white color=black
-d.frame frame=ur at=50,100,50,100 -c
-d.rast ground
-d.text text="ground_raw" at=5,5 size=10 bgcolor=white color=black
-d.legend raster=ground title="Ground (raw)" at=50,95,2,10 -bsf
-d.frame frame=ll at=0,50,0,50 -c
-d.rast uncertainty
-d.text text="uncertainty" at=5,5 size=10 bgcolor=white color=black
-d.legend raster=uncertainty title="Uncertainty" at=50,95,2,10 -bsf
-d.frame frame=lr at=0,50,50,100 -c
-d.rast ground_filled
-d.text text="ground_filled" at=5,5 size=10 bgcolor=white color=black
-d.legend raster=ground title="Ground (filled)" at=50,95,2,10 -bsf
-d.mon stop=cairo
--->
-
-<h2>SEE ALSO</h2>
-
-<em>
-<a href="r.fillnulls.html">r.fillnulls</a>,
-<a href="r.neighbors.html">r.neighbors</a>,
-<a href="r.surf.idw2.html">r.surf.idw</a>,
-<a href="v.surf.idw.html">v.surf.idw</a>,
-<a href="v.surf.rst.html">v.surf.rst</a>
-</em>
-
-<p>
-<a href="http://en.wikipedia.org/wiki/Inverse_distance_weighting">Inverse Distance Weighting in Wikipedia</a>
-
-<h2>AUTHOR</h2>
-
-Benjamin Ducke
-
-<p><i>Last changed: $Date$</i>
-
Copied: grass/trunk/raster/r.fill.stats/r.fill.stats.html (from rev 71849, grass-addons/grass7/raster/r.fill.gaps/r.fill.gaps.html)
===================================================================
--- grass/trunk/raster/r.fill.stats/r.fill.stats.html (rev 0)
+++ grass/trunk/raster/r.fill.stats/r.fill.stats.html 2017-11-26 22:30:03 UTC (rev 71850)
@@ -0,0 +1,485 @@
+<h2>DESCRIPTION</h2>
+
+
+<em><b>r.fill.stats</b></em> is a module for fast gap filling and
+interpolation (with smoothing) of dense raster data.
+
+<p>
+
+The <em>r.fill.stats</em> module is capable of quickly filling small
+data gaps in large and high-resolution raster maps. It's primary purpose
+is to improve high-resolution, rasterized sensor data (such as Lidar
+data). As a rule of thumb, there
+should be at least as many data cells as there are "no data" (NULL) cells in
+the input raster map. Several interpolation modes are available. By
+default, all values of the input raster map will be replaced with
+locally interpolated values in the output raster map. This is the
+equivalent of running a low-pass smoothing filter on the interpolated
+data and is often desirable, owing to noisy nature of high-resolution
+sensor data. With dense data and small gaps, this should result in only slight
+deviations from the original data and produce smooth output. Alternatively,
+setting the <b>-p</b> flag will disable the low-pass filter and preserve
+the original cell values.
+
+<p>
+
+Available gap filling modes:
+<ul>
+<li>spatially weighted mean (default)</li>
+<li>simple mean</li>
+<li>simple median</li>
+<li>simple mode</li>
+</ul>
+
+<p>
+
+The spatially weighted mean is equivalent to an Inverse Distance
+Weighting (IDW;
+see also <em><a href="r.surf.idw.html">r.surf.idw</a></em>)
+interpolation. The simple mean is equivalent to a simple low-pass filter.
+Median and mode replacements can also be achieved using
+<em><a href="r.neighbors.html">r.neighbors</a></em>.
+
+<p>
+
+Note that <em>r.fill.stats</em> is highly optimized for fast processing
+of large raster datasets with a small interpolation distance threshold
+(i.e. closing small gaps). As a trade-off for speed and a small memory
+foot print, some spatial accuracy is lost due to the rounding of the
+distance threshold to the closest approximation in input raster cells
+and the use of a matrix of precomputed weights at cell resolution (see
+further down for details). Note also that processing time will increase
+exponentially with higher distance settings. Cells outside the distance
+threshold will not be interpolated, preserving the edges of the original data
+extent.
+
+<p>
+
+This module is not well suited for interpolating sparse input data and
+closing large gaps. For such purposes more appropriate
+methods are available, such as
+<em><a href="v.surf.idw.html">v.surf.idw</a></em> or
+<em><a href="v.surf.rst.html">v.surf.rst</a></em>.
+
+<p>
+
+Applications where the properties of <em>r.fill.stats</em> are
+advantageous include the processing of high resolution, close range
+scanning and remote sensing datasets. Such datasets commonly feature
+densely spaced measurements that have some gaps after rasterization,
+due to blind spots, instrument failures, and misalignments between the
+GIS' raster cell grids and the original measurement locations. In these
+cases, <em>r.fill.stats</em> should typically be run using the "weighted
+mean" (default) method and with a small distance setting (the default
+is to use a search radius of just three cells).
+
+<p>
+
+The images below show a gradiometer dataset with gaps and its
+interpolated equivalent, produced using the spatially weighted mean
+operator (<tt>mode="wmean"</tt>).
+
+<p>
+
+<img src="r_fill_stats_01.png" alt="Raw data"> <img src="r_fill_stats_02.png" alt="Gaps filled">
+
+<p>
+
+In addition, <em>r.fill.stats</em> can be useful for raster map
+generalization. Often, this involves removing small clumps of
+categorized cells and then filling the resulting data gaps without
+"inventing" any new values. In such cases, the "mode" or "median"
+interpolators should be used.
+
+
+<h3>Usage</h3>
+
+The most critical user-provided settings are the interpolation/gap
+filling method (<b>mode</b>) and the maximum distance, up to which
+<em>r.fill.stats</em> will scan for cells with values (<b>distance</b>).
+The distance can be expressed as a number of cells (default) or in the
+current GRASS location's units (if the <b>-m</b> flag is given). The latter
+are typically meters, but can be any other units of a <em>planar</em>
+coordinate system.
+
+<p>
+
+Note that proper handling of geodetic coordinates (lat/lon) and
+distances is currently not implemented. For lat/lon data, the distance
+should therefore be specified in cells and usage of
+<em>r.fill.stats</em> should be restricted to small areas to avoid large
+inaccuracies that may arise from the different extents of cells along
+the latitudinal and longitudinal axes.
+
+<p>
+
+Distances specified in map units (<b>-m</b> flag) will be approximated
+as accurately as the current region's cell resolution settings allow.
+The program will warn if the distance cannot be expressed as whole
+cells at the current region's resolution. In such case, the number of
+cells in the search window will be rounded up. Due to the rounding
+effect introduced by using cells as spatial units, the actual maximum
+distance considered by the interpolation may be up to half a cell
+diagonal larger than the one specified by the user.
+
+<p>
+
+The interpolator type "wmean" uses a precomputed matrix of spatial
+weights to speed up computation. This matrix can be examined (printed
+to the screen) before running the interpolation, by setting the
+<b>-w</b> flag. In mode "wmean", the <b>power</b> option has the usual
+meaning in IDW: higher values mean that cell values in the neighborhood
+lose their influence on the cell to be interpolated more rapidly with
+increasing distance from the latter. Another way of explaining this
+effect is to state that larger "power" settings result in more
+localized interpolation, smaller ones in more globalized interpolation.
+The default setting is <tt>power=2.0</tt>.
+
+<p>
+
+The interpolators "mean", "median" and "mode" are calculated from all
+cell values within the search radius. No spatial weighting is applied
+for these methods. The "mode" of the input data may not always be
+unique. In such case, the mode will be the smallest value with the
+highest frequency.
+
+<p>
+
+Often, input data will contain spurious extreme measurements (spikes,
+outliers, noise) caused by the limits of device sensitivity, hardware
+defects, environmental influences, etc. If the normal, valid range of
+input data is known beforehand, then the <b>minimum</b> and
+<b>maximum</b> options can be used to exclude those input cells that
+have values below or above that range, respectively. This will prevent
+the influence of spikes and outliers from spreading through the
+interpolation.
+
+<p>
+
+Unless the <b>-p</b> (preserve) flag is given, data cells of the input
+map will be replaced with interpolated values instead of preserving
+them in the output. In modes "wmean" and "mean", this results in a
+smoothing effect that includes the original data (see below)!
+
+<p>
+
+Besides the result of the interpolation/gap filling, a second output
+map can be specified via the <b>uncertainty</b> option. The cell values
+in this map represent a simple measure of how much uncertainty was
+involved in interpolating each cell value of the primary output map,
+with "0" being the lowest and "1" being the theoretic highest
+uncertainty. Uncertainty is measured by summing up all cells in the
+neighborhood (defined by the search radius <b>distance</b>) that
+contain a value in the <b>input</b> map, multiplied by their weights,
+and dividing the result by the sum of all weights in the neighborhood.
+For <tt>mode=wmean</tt>, this means that interpolated output cells that
+were computed from many nearby input cells have very low uncertainty
+and vice versa. For all other modes, all weights in the neighborhood
+are constant "1" and the uncertainty measure is a simple measure of how
+many input data cells were present in the search window.
+
+<h3>Smoothing</h3>
+
+The <em>r.fill.stats</em> module uses the interpolated values to adjust
+the original values and create a smooth surface, which is akin to running
+a low-pass filter on the data. Since most high-resolution sensor data
+is noisy, this is normally a desired effect and results in output that
+is more suitable for deriving secondary products, such as slope, aspect
+and curvature maps. Larger settings for the search radius (<b>distance</b>)
+will result in a stronger smoothing. In practice, some experimentation
+with different settings for <b>distance</b> and <b>power</b> might be required
+to achieve good results. In some cases (e.g. when dealing with low-noise or
+classified data), it might be desirable to turn off data smoothing by
+setting the <b>-p</b> (preserve) flag. This will ensure that the original
+cell data is copied through to the result map without alteration.
+
+<center>
+<a href="r_fill_stats_smoothing.png">
+ <img src="r_fill_stats_smoothing.png" alt="Smooth versus preserve" width="600" height="300">
+</a>
+<p><em>
+Effect of smoothing the original data: The top row shows a gap-filled surface computed from a rasterized Lidar point
+cloud (using <tt>mode=wmean</tt> and <tt>power=2</tt>), and the derived slope, aspect,
+and profile curvature maps. The smoothing effect is clearly visible.
+The bottom row shows the effect of setting the <b>-p</b> flag: Preserving the original
+cell values in the interpolated output produces and unsmoothed, noisy surface, and likewise
+noisy derivative maps.
+</em></p>
+</center>
+
+The effect can be seen in the illustration above:
+Slope, aspect, and profile curvature are computed using the
+<em><a href="r.slope.aspect.html">r.slope.aspect</a></em> module, which
+uses a window (kernel) for computations that considers only the
+immediate neighborhood of each cell. When performed on noisy data,
+such local operations result in equally noisy derivatives if the
+original data is preserved (by setting the <b>-p</b> flag) and no smoothing
+is performed.
+
+<p>
+
+(Note that the effects of noisy data can also be avoided by using modules
+that are not restricted to minimal kernel sizes. For example, aspect and other morphometric parameters can be computed
+using the <em><a href="r.param.scale.html">r.param.scale</a></em> module
+which operates with variable-size cell neighborhoods.)
+
+<!--
+wget http://fatra.cnr.ncsu.edu/uav-lidar-analytics-course/midpines_2015_spm.las -O points.las
+
+g.region n=220220 s=218690 e=637800 w=636270 res=1
+
+r.in.lidar input=points.las output=ground method=mean class_filter=2
+
+r.fill.stats input=ground output=ground_filled mode=wmean power=2.0 cells=8
+r.fill.stats -p input=ground output=ground_filled2 mode=wmean power=2.0 cells=8
+
+r.slope.aspect elevation=ground_filled slope=slope aspect=aspect pcurvature=pcur tcurvature=tcur
+r.slope.aspect elevation=ground_filled2 slope=slope2 aspect=aspect2 pcurvature=pcur2 tcurvature=tcur2
+
+g.region n=219537 s=219172 w=636852 e=637218 -p
+
+d.mon cairo output=r_fill_gaps_smoothing.png width=1464 height=730
+export GRASS_FONT=LiberationSans-Regular
+d.frame frame=u1 at=50,100,0,25 -c
+d.rast ground_filled
+d.text text="surf. (smoothed)" at=5,5 size=10 bgcolor=white color=black
+d.frame frame=u2 at=50,100,25,50 -c
+d.rast slope
+d.text text="slope" at=5,5 size=10 bgcolor=white color=black
+d.frame frame=u3 at=50,100,50,75 -c
+d.rast aspect
+d.text text="aspect" at=5,5 size=10 bgcolor=white color=black
+d.frame frame=u4 at=50,100,75,100 -c
+d.rast pcur
+d.text text="prof. curvature" at=5,5 size=10 bgcolor=white color=black
+d.frame frame=l1 at=0,50,0,25 -c
+d.rast ground_filled2
+d.text text="surf. (preserved)" at=5,5 size=10 bgcolor=white color=black
+d.frame frame=l2 at=0,50,25,50 -c
+d.rast slope2
+d.text text="slope" at=5,5 size=10 bgcolor=white color=black
+d.frame frame=l3 at=0,50,50,75 -c
+d.rast aspect2
+d.text text="aspect" at=5,5 size=10 bgcolor=white color=black
+d.frame frame=l4 at=0,50,75,100 -c
+d.rast pcur2
+d.text text="prof. curvature" at=5,5 size=10 bgcolor=white color=black
+d.mon stop=cairo
+-->
+
+<h3>Spatial weighting scheme</h3>
+
+The key to getting good gap filling results is to understand the
+spatial weighting scheme used in mode "wmean". The weights are
+precomputed and assigned per cell within the search window centered on
+the location at which to interpolate/gap fill all cells within the
+user-specified distance.
+
+<p>
+
+The illustration below shows the precomputed weights matrix for a
+search distance of four cells from the center cell:
+
+<pre>
+000.00 000.01 000.04 000.07 000.09 000.07 000.04 000.01 000.00
+000.01 000.06 000.13 000.19 000.22 000.19 000.13 000.06 000.01
+000.04 000.13 000.25 000.37 000.42 000.37 000.25 000.13 000.04
+000.07 000.19 000.37 000.56 000.68 000.56 000.37 000.19 000.07
+000.09 000.22 000.42 000.68 001.00 000.68 000.42 000.22 000.09
+000.07 000.19 000.37 000.56 000.68 000.56 000.37 000.19 000.07
+000.04 000.13 000.25 000.37 000.42 000.37 000.25 000.13 000.04
+000.01 000.06 000.13 000.19 000.22 000.19 000.13 000.06 000.01
+000.00 000.01 000.04 000.07 000.09 000.07 000.04 000.01 000.00
+</pre>
+
+Note that the weights in such a small window drop rapidly for the
+default setting of <tt>power=2</tt>.
+
+<p>
+
+If the distance is given in map units (flag <tt>-m</tt>), then the
+search window can be modeled more accurately as a circle. The
+illustration below shows the precomputed weights for a distance in map
+units that is approximately equivalent to four cells from the center
+cell:
+
+<pre>
+...... ...... ...... 000.00 000.00 000.00 ...... ...... ......
+...... 000.00 000.02 000.06 000.09 000.06 000.02 000.00 ......
+...... 000.02 000.11 000.22 000.28 000.22 000.11 000.02 ......
+000.00 000.07 000.22 000.44 000.58 000.44 000.22 000.07 000.00
+000.00 000.09 000.28 000.58 001.00 000.58 000.28 000.09 000.00
+000.00 000.07 000.22 000.44 000.58 000.44 000.22 000.07 000.00
+...... 000.02 000.11 000.22 000.28 000.22 000.11 000.02 ......
+...... 000.00 000.02 000.06 000.09 000.06 000.02 000.00 ......
+...... ...... ...... 000.00 000.00 000.00 ...... ...... ......
+</pre>
+
+<p>
+
+When using a small search radius, <b>cells</b> must also be set to a
+small value. Otherwise, there may not be enough cells with data within
+the search radius to support interpolation.
+
+
+<h2>NOTES</h2>
+
+The straight-line metric used for converting distances in map units to
+cell numbers is only adequate for planar coordinate systems. Using this
+module with lat/lon input data will likely give inaccurate results,
+especially when interpolating over large geographical areas.
+
+<p>
+
+If the distance is set to a relatively large value, processing time
+will quickly approach and eventually exceed that of point-based
+interpolation modules such as
+<em><a href="v.surf.rst.html">v.surf.rst</a></em>.
+
+<p>
+
+This module can handle cells with different X and Y resolutions.
+However, note that the weight matrix will be skewed in such cases, with
+higher weights occurring close to the center and along the axis with
+the higher resolution. This is because weights on the lower resolution
+axis are less accurately calculated. The skewing effect will be
+stronger if the difference between the X and Y axis resolution is
+greater and a larger "power" setting is chosen. This property of the
+weights matrix directly reflects the higher information density along
+the higher resolution axis.
+
+<p>
+
+Note on printing the weights matrix (using the <b>-w</b> flag): the
+matrix cannot be printed if it is too large.
+
+<p>
+
+The memory estimate provided by the <b>-u</b> flag is a lower limit on
+the amount of RAM that will be needed.
+
+<p>
+
+If the <b>-s</b> flag is set, floating point type output will be
+saved as a "single precision" raster map, saving ~50% disk space
+compared to the default "double precision" output.
+
+
+<h2>EXAMPLES</h2>
+
+Gap-fill a dataset using spatially weighted mean (IDW) and a maximum
+search radius of 3.0 map units; also produce uncertainty estimation
+map:
+
+<div class="code"><pre>
+r.fill.stats input=measurements output=result dist=3.0 -m mode=wmean uncertainty=uncert_map
+</pre></div>
+
+Run a fast low-pass filter (replacement all cells with mean value of
+neighboring cells) on the input map:
+
+<div class="code"><pre>
+r.fill.stats input=measurements output=result dist=10 mode=mean
+</pre></div>
+
+Fill data gaps in a categorized raster map; preserve existing data:
+
+<div class="code"><pre>
+r.fill.stats input=categories output=result dist=100 -m mode=mode -p
+</pre></div>
+
+<h3>Lidar point cloud example</h3>
+
+<!--
+Using:
+http://fatra.cnr.ncsu.edu/uav-lidar-analytics-course/midpines_2015_spm.las
+-->
+
+Inspect the point density and determine the extent of the point cloud
+using the <em><a href="r.in.lidar.html">r.in.lidar</a></em> module:
+
+<div class="code"><pre>
+r.in.lidar -e input=points.las output=density method=n resolution=5 class_filter=2
+</pre></div>
+
+Based on the result, set computational region extent and desired
+resolution:
+
+<div class="code"><pre>
+g.region -pa raster=density res=1
+</pre></div>
+
+Import the point cloud as raster using binning:
+
+<div class="code"><pre>
+r.in.lidar input=points.las output=ground_raw method=mean class_filter=2
+</pre></div>
+
+Check that there are more non-NULL cells than NULL ("no data") cells:
+
+<div class="code"><pre>
+r.univar map=ground_raw
+</pre></div>
+
+<pre>
+total null and non-null cells: 2340900
+total null cells: 639184
+...
+</pre>
+
+Fill in the NULL cells using the default 3-cell search radius:
+
+<div class="code"><pre>
+r.fill.stats input=ground output=ground_filled uncertainty=uncertainty distance=3 mode=wmean power=2.0 cells=8
+</pre></div>
+
+<center>
+<a href="r_fill_stats_lidar.png"><img src="r_fill_stats_lidar.png" alt="Point density and ground surface" width=600 height=600></a>
+<p><em>
+Binning of Lidar and resulting ground surface with filled gaps.
+Note the remaining NULL cells (white) in the resulting ground surface.
+These are areas with a lack of cells with values in close proximity.
+</em></p>
+</center>
+
+<!--
+d.mon cairo output=r_fill_gaps_lidar.png width=1530 height=1530
+export GRASS_FONT=LiberationSans-Regular
+d.frame frame=ul at=50,100,0,50 -c
+d.rast density3
+d.legend raster=density3 title="Density" at=50,95,2,10 -bsf
+d.text text="density" at=5,5 size=10 bgcolor=white color=black
+d.frame frame=ur at=50,100,50,100 -c
+d.rast ground
+d.text text="ground_raw" at=5,5 size=10 bgcolor=white color=black
+d.legend raster=ground title="Ground (raw)" at=50,95,2,10 -bsf
+d.frame frame=ll at=0,50,0,50 -c
+d.rast uncertainty
+d.text text="uncertainty" at=5,5 size=10 bgcolor=white color=black
+d.legend raster=uncertainty title="Uncertainty" at=50,95,2,10 -bsf
+d.frame frame=lr at=0,50,50,100 -c
+d.rast ground_filled
+d.text text="ground_filled" at=5,5 size=10 bgcolor=white color=black
+d.legend raster=ground title="Ground (filled)" at=50,95,2,10 -bsf
+d.mon stop=cairo
+-->
+
+<h2>SEE ALSO</h2>
+
+<em>
+<a href="r.fillnulls.html">r.fillnulls</a>,
+<a href="r.neighbors.html">r.neighbors</a>,
+<a href="r.surf.idw.html">r.surf.idw</a>,
+<a href="v.surf.idw.html">v.surf.idw</a>,
+<a href="v.surf.rst.html">v.surf.rst</a>
+</em>
+
+<p>
+<a href="http://en.wikipedia.org/wiki/Inverse_distance_weighting">Inverse Distance Weighting in Wikipedia</a>
+
+<h2>AUTHOR</h2>
+
+Benjamin Ducke
+
+<p><i>Last changed: $Date$</i>
+
Deleted: grass/trunk/raster/r.fill.stats/r_fill_gaps_01.png
===================================================================
(Binary files differ)
Deleted: grass/trunk/raster/r.fill.stats/r_fill_gaps_02.png
===================================================================
(Binary files differ)
Deleted: grass/trunk/raster/r.fill.stats/r_fill_gaps_lidar.png
===================================================================
(Binary files differ)
Deleted: grass/trunk/raster/r.fill.stats/r_fill_gaps_smoothing.png
===================================================================
(Binary files differ)
Copied: grass/trunk/raster/r.fill.stats/r_fill_stats_01.png (from rev 71849, grass-addons/grass7/raster/r.fill.gaps/r_fill_gaps_01.png)
===================================================================
(Binary files differ)
Copied: grass/trunk/raster/r.fill.stats/r_fill_stats_02.png (from rev 71849, grass-addons/grass7/raster/r.fill.gaps/r_fill_gaps_02.png)
===================================================================
(Binary files differ)
Copied: grass/trunk/raster/r.fill.stats/r_fill_stats_lidar.png (from rev 71849, grass-addons/grass7/raster/r.fill.gaps/r_fill_gaps_lidar.png)
===================================================================
(Binary files differ)
Copied: grass/trunk/raster/r.fill.stats/r_fill_stats_smoothing.png (from rev 71849, grass-addons/grass7/raster/r.fill.gaps/r_fill_gaps_smoothing.png)
===================================================================
(Binary files differ)
Added: grass/trunk/raster/r.fill.stats/testsuite/data/input_ascii.txt
===================================================================
--- grass/trunk/raster/r.fill.stats/testsuite/data/input_ascii.txt (rev 0)
+++ grass/trunk/raster/r.fill.stats/testsuite/data/input_ascii.txt 2017-11-26 22:30:03 UTC (rev 71850)
@@ -0,0 +1,13 @@
+north: 220542
+south: 220528
+east: 638492
+west: 638478
+rows: 7
+cols: 7
+3 2 5 3 5 3 4
+1 4 5 5 5 * 4
+2 1 * 3 5 5 2
+4 2 4 4 4 5 4
+4 4 2 * 5 2 4
+1 2 1 1 2 2 2
+5 4 1 2 3 4 2
Property changes on: grass/trunk/raster/r.fill.stats/testsuite/data/input_ascii.txt
___________________________________________________________________
Added: svn:mime-type
+ text/plain
Added: svn:keywords
+ Author Date Id
Added: svn:eol-style
+ native
Added: grass/trunk/raster/r.fill.stats/testsuite/data/output_mean.txt
===================================================================
--- grass/trunk/raster/r.fill.stats/testsuite/data/output_mean.txt (rev 0)
+++ grass/trunk/raster/r.fill.stats/testsuite/data/output_mean.txt 2017-11-26 22:30:03 UTC (rev 71850)
@@ -0,0 +1,13 @@
+north: 220542
+south: 220528
+east: 638492
+west: 638478
+rows: 7
+cols: 7
+2.5 3.3333333333333335 4 4.666666666666667 4.2000000000000002 4.2000000000000002 *
+2.1666666666666665 2.875 3.5 4.5 4.25 4.125 3.6000000000000001
+2.3333333333333335 2.875 3.5 4.375 4.5 4.25 4
+2.8333333333333335 2.875 2.8571428571428572 3.8571428571428572 4.125 4 3.6666666666666665
+2.8333333333333335 2.6666666666666665 2.5 2.875 3.125 3.3333333333333335 3.1666666666666665
+3.3333333333333335 2.6666666666666665 2.125 2.125 2.625 2.8888888888888888 2.6666666666666665
+3 2.3333333333333335 1.8333333333333333 1.6666666666666667 2.3333333333333335 2.5 2.5
Property changes on: grass/trunk/raster/r.fill.stats/testsuite/data/output_mean.txt
___________________________________________________________________
Added: svn:mime-type
+ text/plain
Added: svn:keywords
+ Author Date Id
Added: svn:eol-style
+ native
Added: grass/trunk/raster/r.fill.stats/testsuite/data/output_median.txt
===================================================================
--- grass/trunk/raster/r.fill.stats/testsuite/data/output_median.txt (rev 0)
+++ grass/trunk/raster/r.fill.stats/testsuite/data/output_median.txt 2017-11-26 22:30:03 UTC (rev 71850)
@@ -0,0 +1,13 @@
+north: 220542
+south: 220528
+east: 638492
+west: 638478
+rows: 7
+cols: 7
+2.5 3.5 4.5 5 5 4 *
+2 2.5 3.5 5 5 4.5 4
+2 3 4 4.5 5 4.5 4
+3 3 3 4 4.5 4 4
+3 2 2 3 3 4 3
+4 2 2 2 2 2 2
+3 1.5 1.5 1.5 2 2 2
Property changes on: grass/trunk/raster/r.fill.stats/testsuite/data/output_median.txt
___________________________________________________________________
Added: svn:mime-type
+ text/plain
Added: svn:keywords
+ Author Date Id
Added: svn:eol-style
+ native
Added: grass/trunk/raster/r.fill.stats/testsuite/data/output_mode.txt
===================================================================
--- grass/trunk/raster/r.fill.stats/testsuite/data/output_mode.txt (rev 0)
+++ grass/trunk/raster/r.fill.stats/testsuite/data/output_mode.txt 2017-11-26 22:30:03 UTC (rev 71850)
@@ -0,0 +1,13 @@
+north: 220542
+south: 220528
+east: 638492
+west: 638478
+rows: 7
+cols: 7
+3 5 5 5 5 5 *
+2 2 5 5 5 5 4
+1 4 4 5 5 5 4
+4 4 4 4 5 5 5
+4 4 2 4 2 2 2
+4 4 2 2 2 2 2
+1 1 1 1 2 2 2
Property changes on: grass/trunk/raster/r.fill.stats/testsuite/data/output_mode.txt
___________________________________________________________________
Added: svn:mime-type
+ text/plain
Added: svn:keywords
+ Author Date Id
Added: svn:eol-style
+ native
Added: grass/trunk/raster/r.fill.stats/testsuite/data/output_wmean.txt
===================================================================
--- grass/trunk/raster/r.fill.stats/testsuite/data/output_wmean.txt (rev 0)
+++ grass/trunk/raster/r.fill.stats/testsuite/data/output_wmean.txt 2017-11-26 22:30:03 UTC (rev 71850)
@@ -0,0 +1,13 @@
+north: 220542
+south: 220528
+east: 638492
+west: 638478
+rows: 7
+cols: 7
+2.7803300858899105 2.4093647857764431 4.6588626785196299 3.4093647857764431 4.7270901428157037 3.2196699141100891 *
+1.4093647857764431 3.5529114668595096 4.9317725357039253 4.7445208382054336 5 4.25 3.8535533905932735
+1.9999999999999996 1.3411373214803695 3.25 3.3411373214803692 4.8083906286540756 4.7953176071117767 2.4775922500725169
+3.7270901428157042 2.3193489522432071 3.7270901428157042 3.9317725357039262 4.1916093713459235 4.6806510477567924 3.9317725357039262
+3.795317607111778 3.6167812573081513 2.2046823928882215 3.0000000000000004 4.5224077499274822 2.5109583235891315 3.7270901428157051
+1.5458197143685908 2.1277395808972828 1.127739580897283 1.1364549285921475 2.1916093713459244 2.1277395808972828 2.1364549285921481
+4.6338834764831835 3.7270901428157042 1.2729098571842956 1.9317725357039264 2.9317725357039257 3.6588626785196308 2.1464466094067265
Property changes on: grass/trunk/raster/r.fill.stats/testsuite/data/output_wmean.txt
___________________________________________________________________
Added: svn:mime-type
+ text/plain
Added: svn:keywords
+ Author Date Id
Added: svn:eol-style
+ native
Added: grass/trunk/raster/r.fill.stats/testsuite/test_r_fill_stats.py
===================================================================
--- grass/trunk/raster/r.fill.stats/testsuite/test_r_fill_stats.py (rev 0)
+++ grass/trunk/raster/r.fill.stats/testsuite/test_r_fill_stats.py 2017-11-26 22:30:03 UTC (rev 71850)
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+from grass.gunittest.case import TestCase
+from grass.gunittest.main import test
+
+
+class TestRFillStats(TestCase):
+
+ ascii = 'ascii'
+ stats = 'stats'
+ wmean = 'wmean'
+ mean = 'mean'
+ median = 'median'
+ mode = 'mode'
+
+ @classmethod
+ def setUpClass(cls):
+ cls.use_temp_region()
+ cls.runModule('r.in.ascii', input='data/input_ascii.txt', output=cls.ascii, type='CELL')
+ cls.runModule('r.in.ascii', input='data/output_wmean.txt', output=cls.wmean, type='DCELL')
+ cls.runModule('r.in.ascii', input='data/output_mean.txt', output=cls.mean, type='DCELL')
+ cls.runModule('r.in.ascii', input='data/output_median.txt', output=cls.median, type='DCELL')
+ cls.runModule('r.in.ascii', input='data/output_mode.txt', output=cls.mode, type='CELL')
+ cls.runModule('g.region', raster=cls.ascii)
+
+ @classmethod
+ def tearDownClass(cls):
+ cls.del_temp_region()
+ cls.runModule('g.remove', flags='f', type='raster',
+ name=[cls.ascii, cls.wmean, cls.mean, cls.median, cls.mode])
+
+ def test_stats(self):
+ """Test if results match verified rasters"""
+ self.assertModule('r.fill.gaps', input=self.ascii, output=self.stats, distance=1, mode='wmean', power=2, cells=2, overwrite=True)
+ self.assertRastersNoDifference(self.stats, self.wmean, precision=1e-6)
+ self.assertModule('r.fill.gaps', input=self.ascii, output=self.stats, distance=1, mode='mean', power=2, cells=2, overwrite=True)
+ self.assertRastersNoDifference(self.stats, self.mean, precision=1e-6)
+ self.assertModule('r.fill.gaps', input=self.ascii, output=self.stats, distance=1, mode='median', power=2, cells=2, overwrite=True)
+ self.assertRastersNoDifference(self.stats, self.median, precision=1e-6)
+ self.assertModule('r.fill.gaps', input=self.ascii, output=self.stats, distance=1, mode='mode', power=2, cells=2, overwrite=True)
+ self.assertRastersNoDifference(self.stats, self.mode, precision=1e-6)
+
+if __name__ == '__main__':
+ test()
Property changes on: grass/trunk/raster/r.fill.stats/testsuite/test_r_fill_stats.py
___________________________________________________________________
Added: svn:mime-type
+ text/x-python
Added: svn:eol-style
+ native
More information about the grass-commit
mailing list