[GRASS-SVN] r38829 - grass-addons/vector/v.krige

svn_grass at osgeo.org svn_grass at osgeo.org
Fri Aug 21 16:45:12 EDT 2009 

Author: martinl
Date: 2009-08-21 16:45:11 -0400 (Fri, 21 Aug 2009)
New Revision: 38829

Modified:
   grass-addons/vector/v.krige/description.html
   grass-addons/vector/v.krige/v.krige.py
Log:
modules manual page - indentation, some redundant tags removed
more keywords


Modified: grass-addons/vector/v.krige/description.html
===================================================================
--- grass-addons/vector/v.krige/description.html	2009-08-21 19:31:57 UTC (rev 38828)
+++ grass-addons/vector/v.krige/description.html	2009-08-21 20:45:11 UTC (rev 38829)
@@ -1,12 +1,21 @@
 <h2>DESCRIPTION</h2>
 
-<i>v.krige</i> allows to perform kriging operations in GRASS environment, using R software functions in background.
+<em>v.krige</em> allows to perform kriging operations in GRASS
+environment, using R software functions in background.
 
 <h2>NOTES</h2>
 
-<i>v.krige</i> is just a front-end to R. The options and parameters are the same offered by packages <i>automap</i> and <i>gstat</i>.<br>
-Kriging, like other interpolation methods, is fully dependent on input data features. Exploratory analysis of data is encouraged to find out outliers, trends, anisotropies, uneven distributions and consequently choose the kriging algorithm that will give the most acceptable result. Good knowledge of the dataset is more valuable than hundreds of parameters or powerful hardware. See Isaaks and Srivastava's book, exhaustive and clear even if a bit outdated.
+<em>v.krige</em> is just a front-end to R. The options and parameters are the same offered by packages <i>automap</i> and <i>gstat</i>.
 
+<p>
+Kriging, like other interpolation methods, is fully dependent on input
+data features. Exploratory analysis of data is encouraged to find out
+outliers, trends, anisotropies, uneven distributions and consequently
+choose the kriging algorithm that will give the most acceptable
+result. Good knowledge of the dataset is more valuable than hundreds
+of parameters or powerful hardware. See Isaaks and Srivastava's book,
+exhaustive and clear even if a bit outdated.
+
 <h3>Dependencies</h3>
 
 <dl>
@@ -29,105 +38,137 @@
 
 Simply run:
 
-<br>
 <div class="code"><pre>
   g.extension v.krige
 <pre></div>
-<p>
 
 <h4>Less easy way: manual install</h4>
 
-It is sufficient to copy it into the (GRASS binaries) path somewhere. Alternatively, install addons into a separate GRASS addons binaries/scripts directory which is easier to maintain. It avoids getting clobbered every time you reinstall GRASS. To use these separately stored scripts, set and export the GRASS_ADDON_PATH environment variable before starting GRASS and it will automatically be added to the module search path. To simplify this, do for example:
+It is sufficient to copy it into the (GRASS binaries) path
+somewhere. Alternatively, install addons into a separate GRASS addons
+binaries/scripts directory which is easier to maintain. It avoids
+getting clobbered every time you reinstall GRASS. To use these
+separately stored scripts, set and export the GRASS_ADDON_PATH
+environment variable before starting GRASS and it will automatically
+be added to the module search path. To simplify this, do for example:
 
-<br>
 <div class="code"><pre>
   # add in $HOME/.bashrc:
   GRASS_ADDON_PATH=/usr/local/grass/addons/
   export GRASS_ADDON_PATH
 <pre></div>
-<p>
 
-Make sure that the script is executable, then just call it in GRASS typing the filename. Python scripts need to be called writing the extension as well, like:
+Make sure that the script is executable, then just call it in GRASS
+typing the filename. Python scripts need to be called writing the
+extension as well, like:
 
-<br>
 <div class="code"><pre>
   GRASS 6.5.svn (spearfish60):~ &gt; v.krige.py
 <pre></div>
+
 <h4>Notes for Debian GNU/Linux</h4>
-<p>
-Install the dependiencies. <b>Attention! python-rpy IS NOT SUITABLE.</b>: 
-<br>
+
+
+Install the dependiencies. <b>Attention! python-rpy IS NOT
+SUITABLE.</b>:
+
 <div class="code"><pre>
   aptitude install R python-rpy2
 <pre></div>
-<p>
 
 To install R packages, use either R's function (as root):
-<br>
+
 <div class="code"><pre>
   install.packages("gstat", dep=T)
   install.packages("spgrass6", dep=T)
 <pre></div>
-<p>
-either the brand new Debian packages [5], add to repositories' list for 32bit or 64bit (pick up the suitable line):
-<br>
+
+either the brand new Debian packages [5], add to repositories' list
+for 32bit or 64bit (pick up the suitable line):
+
 <div class="code"><pre>
   deb <a href="http://debian.cran.r-project.org/cran2deb/debian-i386">http://debian.cran.r-project.org/cran2deb/debian-i386</a> testing/
   deb <a href="http://debian.cran.r-project.org/cran2deb/debian-amd64">http://debian.cran.r-project.org/cran2deb/debian-amd64</a> testing/
 <pre></div>
 
-<p>
 and get the packages via aptitude:
-<br>
+
 <div class="code"><pre>
   aptitude install r-cran-gstat r-cran-spgrass6
 <pre></div>
 
 <h4>Notes for Windows</h4>
+
+At this very moment, v.krige is developed against GRASS 6.5
+(6_develbranch) and no backward compatibility with OSGeo4W's packaged
+GRASS 6.4svn2 is provided, nor with WinGRASS.
+
 <p>
-At this very moment, v.krige is developed against GRASS 6.5 (6_develbranch) and no backward compatibility with OSGeo4W's packaged GRASS 6.4svn2 is provided, nor with WinGRASS. 
-</p>
-<p>
-If you really need to run v.krige right now on Windows, I suggest to compile GRASS following this guide: <a href>http://trac.osgeo.org/grass/wiki/CompileOnWindows</a>. 
-You could also use Linux in a virtual machine. Or install Linux in a separate partition of the HD. This is not as painful as it appears, there are lots of guides over the Internet to help you.
-</p>
+If you really need to run v.krige right now on Windows, I suggest to
+compile GRASS following this
+<a href="http://trac.osgeo.org/grass/wiki/CompileOnWindows">guide</a>.
+You could also use Linux in a virtual machine. Or install Linux in a
+separate partition of the HD. This is not as painful as it appears,
+there are lots of guides over the Internet to help you.
 
+
 <h3>Computation time issues</h3>
 
-Please note that kriging calculation is slown down both by high number of input data points and/or high region resolution, even if they both contribute to a better output. 
+Please note that kriging calculation is slown down both by high number
+of input data points and/or high region resolution, even if they both
+contribute to a better output.
 
 <h2>EXAMPLES</h2>
 
-Kriging example based on elevation map (Spearfish data set).<br><br>
+Kriging example based on elevation map (Spearfish data set).
 
-<b>Part 1: random sampling</b> of 2000 vector points from known elevation map. Each point will receive the elevation value from the elevation raster, as if it came from a point survey.
-<br>
+<p>
+<b>Part 1: random sampling</b> of 2000 vector points from known
+elevation map. Each point will receive the elevation value from the
+elevation raster, as if it came from a point survey.
+
 <div class="code"><pre>
  g.region rast=elevation.10m -p
  v.random output=rand2k_elev n=2000
  v.db.addtable map=rand2k_elev column="elevation double precision"
  v.what.rast vect=rand2k_elev rast=elevation.10m column=elevation
 <pre></div>
+
 <p>
+<b>Part 2: remove points lacking elevation attributes</b>. Points
+sampled at the border of the elevation map didn't receive any
+value. v.krige has no preferred action to cope with no data values, so
+the user must check for them and decide what to do (remove points,
+fill with the value of the nearest point, fill with the global/local
+mean...). In the following line of code, points with no data are
+removed from the map.
 
-<b>Part 2: remove points lacking elevation attributes</b>. Points sampled at the border of the elevation map didn't receive any value. v.krige has no preferred action to cope with no data values, so the user must check for them and decide what to do (remove points, fill with the value of the nearest point, fill with the global/local mean...). In the following line of code, points with no data are removed from the map.
-
-<br>
 <div class="code"><pre>
  v.extract rand2k_elev output=rand2k_elev_filt where="elevation not NULL"
 <pre></div>
-<p>
 
-Check the result of previous line ("number of NULL attributes" must be 0):
+Check the result of previous line ("number of NULL attributes" must be
+0):
 
-<br>
 <div class="code"><pre>
  v.univar rand2k_elev_filt type=point column=elevation
 <pre></div>
+
 <p>
+<b>Part 3: reconstruct DEM through kriging</b>. Using automatic
+variogram fit is the simplest way to run v.krige from CLI (note:
+requires R's automap package). Output map name is optional, the
+modules creates it automatically appending "_kriging" the the input
+map name and also checks for overwrite. If output_var is specified,
+the variance map is also created. Automatic variogram fit is provided
+by R package automap, the variogram models tested by the fitting
+functions are: exponential, spherical, Gaussian, Matern, M.Stein's
+parametrisation. A wider range of models is available from gstat
+package and can be tested on the GUI via the variogram plotting. If
+model is specified in the CLI, also sill, nugget and range values are
+to be provided, otherwise an error is raised (see second example of
+v.krige command).
 
-<b>Part 3: reconstruct DEM through kriging</b>. Using automatic variogram fit is the simplest way to run v.krige from CLI (note: requires R's automap package). Output map name is optional, the modules creates it automatically appending "_kriging" the the input map name and also checks for overwrite. If output_var is specified, the variance map is also created. Automatic variogram fit is provided by R package automap, the variogram models tested by the fitting functions are: exponential, spherical, Gaussian, Matern, M.Stein's parametrisation. A wider range of models is available from gstat package and can be tested on the GUI via the variogram plotting. If model is specified in the CLI, also sill, nugget and range values are to be provided, otherwise an error is raised (see second example of v.krige command).
-<br>
 <div class="code"><pre>
  v.krige.py input=rand2k_elev_filt column=elevation output=rand2k_elev_kriging \
                output_var=rand2k_elev_kriging_var
@@ -135,39 +176,48 @@
                output_var=rand2k_elev_kriging_var model=Lin sill=2500 nugget=0 range=1000 \
                --overwrite
 <pre></div>
-<p>
+
 Or run wxGUI, to interactively fit the variogram and explore options:
-<br>
+
 <div class="code"><pre>
   v.krige.py
 </pre></div>
-<p>
 
 <b>Calculate prediction error</b>:
-<br>
+
 <div class="code"><pre>
  r.mapcalc "rand2k_elev_kriging_pe = sqrt(rand2k_elev_kriging_var)"
  r.univar elevation.10m
  r.univar rand2k_elev_kriging
  r.univar rand2k_elev_kriging_pe
 <pre></div>
-<p>
 
-The results show high errors, as the kriging techniques (ordinary and block kriging) are unable to handle a dataset with a trend, like the one used in this example: elevation is higher in the southwest corner and lower on northeast corner. Universal kriging can give far better results in these cases as it can handle the trend. It is available in R package gstat and will be part of a future v.krige release.
+The results show high errors, as the kriging techniques (ordinary and
+block kriging) are unable to handle a dataset with a trend, like the
+one used in this example: elevation is higher in the southwest corner
+and lower on northeast corner. Universal kriging can give far better
+results in these cases as it can handle the trend. It is available in
+R package gstat and will be part of a future v.krige release.
 
 <h2>SEE ALSO</h2>
 
-R package <A hREF="http://cran.r-project.org/web/packages/gstat/index.html">gstat</A>, mantained by Edzer J. Pebesma and others <br>
-R package <A hREF="http://cran.r-project.org/web/packages/spgrass6/index.html">spgrass6</A>, mantained by Roger Bivand
 
+R package <a href="http://cran.r-project.org/web/packages/gstat/index.html">gstat</a>,
+mantained by Edzer J. Pebesma and others
+<br>
+R
+package <a href="http://cran.r-project.org/web/packages/spgrass6/index.html">spgrass6</a>,
+mantained by Roger Bivand
 
-<h2>AUTHOR</h2>
 
-Anne Ghisla, Google Summer of Code 2009
-
 <h2>REFERENCES</h2>
 
 Isaaks and Srivastava, 1989: "An Introduction to Applied Geostatistics" (ISBN 0-19-505013-4) 
 
-<p><i>Last changed: $Date$</i>
+<h2>AUTHOR</h2>
 
+Anne Ghisla, Google Summer of Code 2009
+
+<p>
+<i>Last changed: $Date$</i>
+

Modified: grass-addons/vector/v.krige/v.krige.py
===================================================================
--- grass-addons/vector/v.krige/v.krige.py	2009-08-21 19:31:57 UTC (rev 38828)
+++ grass-addons/vector/v.krige/v.krige.py	2009-08-21 20:45:11 UTC (rev 38829)
@@ -19,7 +19,7 @@
 
 #%module
 #% description: Performs ordinary or block kriging.
-#% keywords: kriging
+#% keywords: vector, kriging
 #%end
 
 #%option

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