r.resamp.bspline: cosmetics in module description

                  update manual to use standard formatting


git-svn-id: https://svn.osgeo.org/grass/grass/trunk@52197 15284696-431f-4ddb-bdfa-cd5b030d7da7

raster/r.resamp.bspline/main.c

@@ -7,12 +7,11 @@
  *
  * PURPOSE:      Spline Interpolation
  *
- * COPYRIGHT:    (C) 2010 GRASS development team
+ * COPYRIGHT:    (C) 2010, 2012 by GRASS development team
  *
- *               This program is free software under the
- *               GNU General Public License (>=v2).
- *               Read the file COPYING that comes with GRASS
- *               for details.
+ *               This program is free software under the GNU General
+ *               Public License (>=v2).  Read the file COPYING that
+ *               comes with GRASS for details.
  *
  **********************************************************************/
 
@@ -83,7 +82,7 @@ int main(int argc, char *argv[])
     G_add_keyword(_("resample"));
     G_add_keyword(_("interpolation"));
     module->description =
-	_("Bicubic or bilinear spline interpolation with Tykhonov regularization.");
+	_("Performs bicubic or bilinear spline interpolation with Tykhonov regularization.");
 
     in_opt = G_define_standard_option(G_OPT_R_INPUT);
 
@@ -91,12 +90,12 @@ int main(int argc, char *argv[])
 
     grid_opt = G_define_standard_option(G_OPT_V_OUTPUT);
     grid_opt->key = "grid";
-    grid_opt->description = _("Output vector with interpolation grid");
+    grid_opt->description = _("Name for output vector map with interpolation grid");
     grid_opt->required = NO;
     
     mask_opt = G_define_standard_option(G_OPT_R_INPUT);
     mask_opt->key = "mask";
-    mask_opt->label = _("Raster map to use for masking");
+    mask_opt->label = _("Name of raster map to use for masking");
     mask_opt->description = _("Only cells that are not NULL and not zero are interpolated");
     mask_opt->required = NO;
 

raster/r.resamp.bspline/r.resamp.bspline.html

@@ -3,21 +3,21 @@
 Tykhonov regularization. The input is a raster surface map, e.g. elevation,
 temperature, precipitation etc. Output is a raster map. Optionally, only
 input NULL cells are interpolated, useful to fill NULL cells, an alternative
-to <a href="r.fillnulls.html">r.fillnulls</a>. Using the -n flag to only
+to <em><a href="r.fillnulls.html">r.fillnulls</a></em>. Using the <b>-n</b> flag to only
 interpolate NULL cells will considerably speed up the module.
 <p>The input raster map is read at its native resolution, the output raster
 map will be produced for the current computational region set with
-<a href="g.region.html">g.region</a>. Any MASK will be respected, masked
+<em><a href="g.region.html">g.region</a></em>. Any MASK will be respected, masked
 values will be treated as NULL cells in both the input and the output map.
-<p>Spline step values <b><i>se</i></b> for the east-west direction and
-<b><i>sn</i></b> for the north-south direction should not be smaller than
+<p>Spline step values <b>se</b> for the east-west direction and
+<b>sn</b> for the north-south direction should not be smaller than
 the east-west and north-south resolutions of the input map. For a raster
 map without NULL cells, 1 * resolution can be used, but check for
 undershoots and overshoots. For very large areas with missing values
 (NULL cells), larger spline step values may be required, but most of the
 time the defaults (1.5 x resolution) should be fine.
-<p>The Tykhonov regularization parameter ("<b><i>lambda</i></b>") acts to
-smooth the interpolation. With a small <b><i>lambda</i></b>, the
+<p>The Tykhonov regularization parameter (<b>lambda</b>) acts to
+smooth the interpolation. With a small <b>lambda</b>, the
 interpolated surface closely follows observation points; a larger value
 will produce a smoother interpolation. Reasonable values are 0.0001,
 0.001, 0.005, 0.01, 0.02, 0.05, 0.1 (needs more testing). For seamless
@@ -28,8 +28,8 @@ these are derived from the observation points using a least squares regression;
 second is the computation of the interpolated surface (or interpolated vector
 points). As used here, the splines are 2D piece-wise non-zero polynomial
 functions calculated within a limited 2D area. The length of each spline step
-is defined by <b><i>se</i></b> for the east-west direction and
-<b><i>sn</i></b> for the north-south direction. For optimal performance, the
+is defined by <b>se</b> for the east-west direction and
+<b>sn</b> for the north-south direction. For optimal performance, the
 spline step values should be no less than the east-west and north-south
 resolutions of the input map. Each non-NULL cell observation is modeled as a
 linear function of the non-zero splines in the area around the observation.
@@ -38,14 +38,14 @@ Regularization avoids the need to have one one observation and one coefficient
 for each spline (in order to avoid instability). 
 
 <p>A cross validation "leave-one-out" analysis is available to help to determine
-the optimal <b><i>lambda</i></b> value that produces an interpolation that
+the optimal <b>lambda</b> value that produces an interpolation that
 best fits the original observation data. The more points used for
 cross-validation, the longer the time needed for computation. Empirical testing
 indicates a threshold of a maximum of 100 points is recommended. Note that cross
 validation can run very slowly if more than 100 observations are used. The
 cross-validation output reports <i>mean</i> and <i>rms</i> of the residuals from
 the true point value and the estimated from the interpolation for a fixed series
-of <b><i>lambda</i></b> values. No vector nor raster output will be created
+of <b>lambda</b> values. No vector nor raster output will be created
 when cross-validation is selected. 
 
 <h2>EXAMPLES</h2>
@@ -72,46 +72,48 @@ g.region rast=input -p
 r.patch input=input_raster,interpolated_nulls output=input_raster_gapfilled
 </pre></div>
 
-<h3>Estimation of <b><i>lambda</i></b> parameter with a cross validation proccess</h3>
+<h3>Estimation of <b>lambda</b> parameter with a cross validation proccess</h3>
 
 A random sample of points should be generated first with
-<a href="r.random.html">r.random</a>, and the current region should not
+<em><a href="r.random.html">r.random</a></em>, and the current region should not
 include more than 100 non-NULL random cells. 
 
 <div class="code"><pre>
 r.resamp.bspline -c input=input_raster 
 </pre></div>
 
+<h2>REFERENCES</h2>
+
+<ul>
+<li>Brovelli M. A., Cannata M., and Longoni U.M., 2004, LIDAR Data
+Filtering and DTM Interpolation Within GRASS, Transactions in GIS,
+April 2004, vol. 8, iss. 2, pp. 155-174(20), Blackwell Publishing Ltd</li>
+<li>Brovelli M. A. and Cannata M., 2004, Digital Terrain model
+reconstruction in urban areas from airborne laser scanning data: the
+method and an example for Pavia (Northern Italy). Computers and
+Geosciences 30, pp.325-331</li>
+<li>Brovelli M. A e Longoni U.M., 2003, Software per il filtraggio di
+dati LIDAR, Rivista dell'Agenzia del Territorio, n. 3-2003, pp. 11-22
+(ISSN 1593-2192)</li>
+<li>Antolin R. and Brovelli M.A., 2007, LiDAR data Filtering with GRASS GIS for the Determination of Digital Terrain Models. Proceedings of Jornadas de SIG Libre,
+Girona, Espa&ntilde;a. CD ISBN: 978-84-690-3886-9</li>
+</ul>
+
 <h2>SEE ALSO</h2>
 
 <em>
-<a href="r.fillnulls.html">r.fillnulls</a><br>
-<a href="r.resamp.rst.html">r.resamp.rst</a><br>
-<a href="r.resamp.interp.html">r.resamp.interp</a><br>
+<a href="r.fillnulls.html">r.fillnulls</a>,
+<a href="r.resamp.rst.html">r.resamp.rst</a>,
+<a href="r.resamp.interp.html">r.resamp.interp</a>,
 <a href="v.surf.bspline.html">v.surf.bspline</a>
 </em>
 
 <h2>AUTHORS</h2>
 Markus Metz<br>
 <br>
-based on <a href="v.surf.bspline.html">v.surf.bspline</a> by
+based on <em><a href="v.surf.bspline.html">v.surf.bspline</a></em> by
 <br>
 Maria Antonia Brovelli, Massimiliano Cannata, Ulisse Longoni, Mirko Reguzzoni, Roberto Antolin
 
-<h2>REFERENCES</h2>
-
-Brovelli M. A., Cannata M., and Longoni U.M., 2004, LIDAR Data
-Filtering and DTM Interpolation Within GRASS, Transactions in GIS,
-April 2004, vol. 8, iss. 2, pp. 155-174(20), Blackwell Publishing Ltd
-<p>Brovelli M. A. and Cannata M., 2004, Digital Terrain model
-reconstruction in urban areas from airborne laser scanning data: the
-method and an example for Pavia (Northern Italy). Computers and
-Geosciences 30, pp.325-331
-<p>Brovelli M. A e Longoni U.M., 2003, Software per il filtraggio di
-dati LIDAR, Rivista dell'Agenzia del Territorio, n. 3-2003, pp. 11-22
-(ISSN 1593-2192)
-<p>Antolin R. and Brovelli M.A., 2007, LiDAR data Filtering with GRASS GIS for the 
-Determination of Digital Terrain Models. Proceedings of Jornadas de SIG Libre,
-Girona, Espa&ntilde;a. CD ISBN: 978-84-690-3886-9 <br>
-
-<p><i>Last changed: $Date$</i>
+<p>
+<i>Last changed: $Date$</i>