Spelling fixes in raster addons documentation (#664)

* Spelling fixes in r.accumulate
* Spelling fixes in r.flexure
* Spelling fixes in r.agent
* Fix r.connectivity page description truncated
* Spelling fixes r.area
* Spelling fixes in r.area.createweight. Also, some links had some spaces shown in the rendered page, since the line was wrapped in the middle of the URL. It is now fixed.
* Spelling fixes in r.connectivity.corridors
* Spelling fixes in r.basin
* Spelling fixes for r.bearing.distance
* Spelling fixes for r.bioclim
* Spelling fixes in r.bitpattern. And necessary indentation fixes to the touched header, to be consistent.
* Spelling fixes in r.catchment
* Spelling fixes in r.category.trim
* Spelling fixes for r.change.info
* Spelling fixes for r.clip
* Spelling fixes for r.colors.cubehelix
* Spelling fixes for r.colors.matplotlib
* Spelling fixes for r.colors.out_sld
* Spelling fixes in r.confusionmatrix
* Spelling fixes in r.cpt2grass
* Spelling fix in r.crater
* Spelling fixes in r.denoise
* r.diversity: HTML formating of parameter in manual
* r.droka: Spelling fixes
* r.droka: Spelling fixes and some english comments
* r.edm.eval: Spelling fixes
* r.edm.eval: HTML formating of parameters
* r.euro.ecosystem: Spelling fixes
* r.exdet: Spelling fixes
* r.fidimo: Spelling fixes
* r.fill.category: Spelling fixes
* r.findtheriver: Spelling fixes
* r.flexure: Spelling fixes
* r.flowfill: Spelling fixes
* r.forestfrag: Spelling fixes
* r.futures.demand: Spelling fixes
* r.futures.devpressure: Spelling fixes
* r.futures.parallelpga: Spelling fixes
* r.futures.pga: Spelling fixes
* r.futures.potential: Spelling fixes
* r.futures.potential: Spelling fixes
* r.futures.potsurface: Spelling fixes
* r.futures: Comment old SVN date
* r.futures.calib: Spelling fixes
* r.futures.demand: Additional spelling fixes
* r.droka: Apply translation suggestions

src/raster/r.accumulate/accumulate_iterative.c

@@ -86,7 +86,7 @@ static void trace_up(struct cell_map *dir_buf, struct raster_map *weight_buf,
         /* pop one upstream cell */
         struct neighbor *cur_up = get_up(&up_stack, 0);
 
-        /* only if the current cell is not process */
+        /* only if the current cell is not processed */
         if (!cur_up->done) {
             /* find its upstream cells */
             find_up(dir_buf, weight_buf, accum_buf, done, neg, cur_up->row,

src/raster/r.accumulate/delineate_streams.c

@@ -99,7 +99,7 @@ static void trace_down(struct cell_map *dir_buf, struct raster_map *accum_buf,
     int dir;
 
     /* if the current cell is outside the computational region or its
-     * acccumulation is less than the threshold, stop tracing */
+     * accumulation is less than the threshold, stop tracing */
     if (row < 0 || row >= nrows || col < 0 || col >= ncols ||
         get(accum_buf, row, col) < thresh)
         return;

src/raster/r.agent/libagent/ant.py

@@ -27,7 +27,7 @@ class Ant(agent.Agent):
 
     Ants are wandering around by chance until they find some goal cell,
     then they will mark their way back home with pheromone. Following ants
-    choose the marked cells on the playground more likely then unmarked
+    choose the marked cells on the playground more likely than unmarked
     spots.
 
     There are several optimizations / idealizations to choose from,
@@ -107,7 +107,7 @@ def costlymarkedposition(self, positions):
         """
         Avoiding high values on the costsurface, combined with the
         marked pheromone values on a certain layer combined with a random
-        value, pick a posiiton out of a list of positions.
+        value, pick a position out of a list of positions.
         @param positions list of possible positions
         @return position the decision for a position
         """
@@ -119,7 +119,7 @@ def costlymarkedposition(self, positions):
             if (penalty < self.world.minpenalty) or (penalty > self.world.maxpenalty):
                 positions.remove(p)
         if not positions:
-            # die as there is nowwhere to go to
+            # die as there is nowhere to go to
             self.snuffit()
             # make sure to not walk again..
             return [0, 0, 99, 99]
@@ -148,7 +148,7 @@ def costlymarkedposition(self, positions):
     def markedposition(self, positions):
         """
         Based on the value on a certain layer combined with a random
-        value, pick a posiiton out of a list of positions.
+        value, pick a position out of a list of positions.
         @param positions list of possible positions
         @return position the decision for a position
         """
@@ -184,7 +184,7 @@ def choose(self):
 
     def walkhome(self):
         """
-        Do all the things necessary for performing a regualar step when
+        Do all the things necessary for performing a regular step when
         walking back home.
         """
         self.position = self.nextstep
@@ -205,7 +205,7 @@ def walkhome(self):
 
     def walkaround(self):
         """
-        Do all the things necessary for performing a regualar step when
+        Do all the things necessary for performing a regular step when
         walking around.
         """
         self.laststeps.append(self.position)

src/raster/r.agent/libagent/anthill.py

@@ -24,7 +24,7 @@ class Anthill(world.World):
     The agents are implemented as ants, wandering around by chance
     if they find a goal cell they will mark their way back home
     with pheromone. The following ants then choose the marked cells
-    on the playground more likely then unmarked spots. The pheromone
+    on the playground more likely than unmarked spots. The pheromone
     evaporates over time.
     """
 

src/raster/r.agent/libagent/error.py

@@ -27,7 +27,7 @@ def __str__(self):
 
 
 class EnvError(Error):
-    """Exception raised for missing GRASS environement.
+    """Exception raised for missing GRASS environment.
 
     Attributes:
         expr -- Context expression in which the error occurred

src/raster/r.agent/libagent/playground.py

@@ -175,7 +175,7 @@ def getrandomposition(self):
 
     def isvalidposition(self, position):
         """
-        Test if a position realy is on the playground
+        Test if a position really is on the playground
         @return list position if on, boolean False if off the playground
         """
         if (
@@ -248,7 +248,7 @@ def getorderedneighbourpositions(self, position, freedom):
 
     def getneighbourpositions(self, position, freedom):
         """
-        Get all the positions reachable from a certain position and shuffel
+        Get all the positions reachable from a certain position and shuffle
         @param list coordinates of a certain cell
         @param int number of potentially reachable neighbours
         @return list of coordinates, or boolean False
@@ -278,7 +278,7 @@ def setcellvalue(self, layername, position, value):
     def decaycellvalues(self, layername, halflife, minimum=0):
         """
         Let the values in each cell decay, volatilize or evaporate over time.
-        This method is intendet for relativly small 2D python arrays, see
+        This method is intended for relatively small 2D python arrays, see
         grassland.Grassland.decaycellvalues for numpy arrays.
         @param string layername name of the layer to work on
         @param long halflife or number of years when to reach half of the value

src/raster/r.agent/libagent/world.py

@@ -16,7 +16,7 @@
 
 class World(object):
     """
-    Generic World class as basis for more complex worlds.
+    Generic World class as a basis for more complex worlds.
     A world is a place where things happen. A world normally
     consists of some playground, i.e. a setup of various
     layers. Furthermore it holds a list of agents that will
@@ -98,7 +98,7 @@ def bear(self, timetolive, position=None, agenttype=None):
         Set a new agent into the world
         @param int number of cycles the agent has to live
         @param list coordinates to put the agent on, none for a random position
-        @param agenttype the typ of agent to be spawned
+        @param agenttype the type of agent to be spawned
         @return agent the newly created agent
         """
         position = self.findposition(position)

src/raster/r.agent/r.agent.aco/r.agent.aco.html

@@ -6,9 +6,9 @@ <h2>DESCRIPTION</h2>
 <p>
 The basic concept of such an ACO world, is to take some cost surface
 and transform it to a penalty layer. Even if the algorithm comes from
-the realm of insects, it might be adopted to different animal kingdoms.
+the realm of insects, it might be adapted to different animal kingdoms.
 Depending on the type of agent this penalty layer must be reinterpreted:
-if for example we want to talk about human
+if for example, we want to talk about human
 agents
 the penalty layer may be expressed by the walking velocity, e.g. calculated
 with the algorithm proposed by
@@ -37,7 +37,7 @@ <h2>NOTES</h2>
 
 <h2>EXAMPLE</h2>
 
-A fictive usecase could look something like this
+A fictive use case could look something like this
 (note: at the moment the
 in- and output variables with <em>libold</em>, are still ascii-files):
 <p>

src/raster/r.agent/r.agent.aco/r.agent.aco.py

@@ -3,7 +3,7 @@
 MODULE:       r.agent.aco
 AUTHOR(S):    michael lustenberger inofix.ch
 PURPOSE:      r.agent.aco is used to organize ant-like agents in a raster
-              based playground. As decribed by the Ant Colony Optimization
+              based playground. As described by the Ant Colony Optimization
               algorithm, the ants wander around looking for attractors,
               marking their paths if they find any.
 COPYRIGHT:    (C) 2011 by Michael Lustenberger and the GRASS Development Team

src/raster/r.agent/r.agent.rand/r.agent.rand.html

@@ -7,6 +7,6 @@ <h2>DESCRIPTION</h2>
 As the name suggests, <em>r.agent.rand</em>
 lets agents wander around just based on chance.
 You only need to define a single input
-map, just some raster map, that will be completly ignored,
+map, just some raster map, that will be completely ignored,
 it is only used to set the frame for the agents to come.
 

src/raster/r.agent/tests/README

@@ -1,6 +1,6 @@
 README file for libagent's test suite
 
-This test cases are intended to contain all the tests for
+These test cases are intended to contain all the tests for
 libagent and to be usable with python 2.7 and higher, it
 should however run resp. via the separate unittest2 available
 as backport for python 2.3-2.6 too.
@@ -10,7 +10,7 @@ level issue
  user@host:~/path/r.agent.aco$ unit2 discover -v -p "test*.py"
 
 All tests except for tests.test_grassland should run without
-the GRASS environemnent. For tests.test_grassland you must start
+the GRASS environment. For tests.test_grassland you must start
 the tests inside the GRASS console respectively:
  GRASS 6.4.4 (foobar):~/path/r.agent.aco > unit2 discover -v -p "test*.py"
 

src/raster/r.area.createweight/r.area.createweight.html

@@ -42,7 +42,7 @@ <h2>DESCRIPTION</h2>
 grid. The spatial resolution of this output weighted grid should be
 specified via the <b>tile_size</b> parameter, and its name via the
 <b>output</b> parameter. The <b>tile_size</b> must be greater than
-the spatial resolution of <b>basemap_a</b>. It it considered good
+the spatial resolution of <b>basemap_a</b>. It is considered good
 practice that the <b>tile_size</b> also be greater than the spatial
 resolution of the other basemap and distance maps. The extent of the
 output weighted grid is created using the extent of the spatial units,
@@ -70,8 +70,8 @@ <h2>DESCRIPTION</h2>
 labels, using the flags <b>-a</b> and <b>-b</b>, for <b>basemap_a</b>
 and <b>basemap_b</b> respectively. If these flags are used, the raster
 should already contain the associated category labels. The module
-<a href="https://grass.osgeo.org/grass-stable/manuals/r.category.html">r.
-category</a> can be used to set category labels for a raster map. If
+<a href=https://grass.osgeo.org/grass-stable/manuals/r.category.html>r.category</a> 
+can be used to set category labels for a raster map. If
 the flag(s) <b>-a</b> and/or <b>-b</b> is(are) selected without existing
 categories for the corresponding raster map, the class value will be
 kept.
@@ -99,11 +99,9 @@ <h2>NOTES</h2>
 NULL values in the rasters). In the example below, numerous spatial
 units are not sufficiently covered, and should be removed prior to
 running the add-on. It is up to the users to choose an acceptable level of coverage
-
 for their analysis, and to ensure the quality of the input
-
 data, keeping in mind that the higher the coverage, the better the RF
-model. The RF model and its' prediction will only be as good as the
+model. The RF model and its prediction will only be as good as the
 input data it is given.
 
 <center>
@@ -120,18 +118,14 @@ <h2>NOTES</h2>
 rasters), the cell will be given a nodata value.
 
 <p>The module makes a temporary copy of the categorical input rasters
-
 (<b>basemap_a</b> and <b>basemap_b</b>) clipped to the
-
 area covered by the spatial units. This allows for the extraction of
 raster categories that exist only within the spatial units. If a user
 specifies a list of raster categories (<b>basemap_a_list</b>
-
 or <b>basemap_b_list</b>), these classes must be present in the area
 covered by the spatial units. The distance map (<b>distance_to</b>) is
 not changed, and it is kept in its original format.
 
-
 <p>The spatial units are rasterised to the extent, cell size and
 alignment of the output weighted grid, and then are re-vectorised. This
 results in spatial units whose boundaries will have a 'staircase'
@@ -147,27 +141,25 @@ <h2>NOTES</h2>
 units (see examples).
 
 <p>
-<table cellspacing="2" cellpadding="2" width="100%" align=center
-border="0">
+<table cellspacing="2" cellpadding="2" width="100%" align=center border="0">
 <tr>
-	<th><a href="r_area_createweight_input_spatial_units.png">
-    <img src="r_area_createweight_input_spatial_units.png"
-width=300></a></th>
-	<th><a href="r_area_createweight_gridded_spatial_units.png">
+  <th><a href="r_area_createweight_input_spatial_units.png">
+    <img src="r_area_createweight_input_spatial_units.png" width=300></a></th>
+  <th><a href="r_area_createweight_gridded_spatial_units.png">
     <img src="r_area_createweight_gridded_spatial_units.png" width=300></a>
 </th>
 </tr>
 <tr>
-	<th><i>Input spatial units</i></th>
-  	<th><i>'Gridded' spatial units</i></th>
+  <th><i>Input spatial units</i></th>
+    <th><i>'Gridded' spatial units</i></th>
 </tr>
 </table>
 
 <p>The response variable is log-transformed to avoid non-normal
 distribution and used to train the model. The prediction is then
 back-transformed and stored in the final weight raster. This approach
 is similar to the one proposed by [1]. Because of this log
-transformation (napierian logarithm), it is mandatory to not have zero
+transformation (Napierian logarithm), it is mandatory to not have zero
 or negative values in the column containing the response variable
 (<b>response_variable</b> parameter). It is also expected that the
 response variable column does not contain <em>NULL</em> values.
@@ -176,7 +168,7 @@ <h2>NOTES</h2>
 the weighting layer if the predicted weight is smaller than 0.0000000001
 obs./m².
 
-<p>The covariates whose feature importance is bellow 0.5% are, by
+<p>The covariates whose feature importance is below 0.5% are, by
 default, removed from the final model. The <b>-a</b> flag can be used to
 force keeping all the covariates in the final model.
 
@@ -229,7 +221,7 @@ <h4>Python libraries</h4>
 corresponding to each package and install them using the following
 command: <em>pip install packagename.whl</em>. Links for
 downloading wheels are provided below. The version installed should be
-compatible with user's Python version. If GRASS was not installed
+compatible with the user's Python version. If GRASS was not installed
 using the OSGeo4W method, the pip package manager can be installed by
 saving the "get-pip.py" python script provided
 <a href="https://bootstrap.pypa.io/get-pip.py">here</a> in the folder
@@ -239,11 +231,9 @@ <h4>Python libraries</h4>
 
 <ul>
 <li>Pandas Wheel (mainly relevant to Windows users):
-<a href="https://pypi.python.org/pypi/pandas">https://pypi.python.org/pypi/
-pandas</a></li>
+<a href="https://pypi.python.org/pypi/pandas">https://pypi.python.org/pypi/pandas</a></li>
 <li>Scikit-learn Wheel (mainly relevant to Windows users):
-<a href="https://pypi.python.org/pypi/scikit-learn/">https://pypi.python.
-org/pypi/scikit-learn/</a></li>
+<a href="https://pypi.python.org/pypi/scikit-learn/">https://pypi.python.org/pypi/scikit-learn/</a></li>
 <li>Unofficial Windows compiled binaries from <a href="https://www.lfd.uci.edu/~gohlke/pythonlibs/">
 Christoph Gohlke</a></li>
 </ul>
@@ -435,15 +425,12 @@ <h2>REFERENCES</h2>
 [1] Stevens, F.R., Gaughan, A.E., Linard, C. and Tatem, A.J., 2015.
 Disaggregating census data for population mapping using random forests
 with remotely-sensed and ancillary data. <i>PloS one, 10</i>(2),
-e0107042. <a
-href="https://doi.org/10.1371/journal.pone.0107042">https://doi.org/10.
-1371/journal.pone.0107042</a>
+e0107042. <a href="https://doi.org/10.1371/journal.pone.0107042">https://doi.org/10.1371/journal.pone.0107042</a>
 <p>[2] Grippa, T., Linard, C., Lennert, M., Georganos, S., Mboga, N.,
 Vanhuysse, S., Gadiaga, A., Wolff, E., 2019. Improving urban population
 distribution models with very-high resolution satellite information.
 <i>Data, 4</i>(1), 13. <a
-href="https://doi.org/10.3390/data4010013">https://doi.org/10.3390/
-data4010013</a>
+href="https://doi.org/10.3390/data4010013">https://doi.org/10.3390/data4010013</a>
 <p>[3] Flasse, C., T. Grippa, et S. Fennia. 2021. A TOOL FOR MACHINE
 LEARNING BASED DASYMETRIC MAPPING APPROACHES IN GRASS GIS. The
 International Archives of the Photogrammetry, Remote Sensing and
@@ -456,8 +443,8 @@ <h2>ACKNOWLEDGEMENT</h2>
 This work was funded by the Belgian Federal Science Policy Office
 (BELSPO) (Research Program for Earth Observation <a
 href="https://eo.belspo.be/About/Stereo3.aspx">STEREO III</a>),
-as part of the <a href="https://maupp.ulb.ac.be/">MAUPP project
-</a>(contract SR/00/304) and DASYWEIGHT project (contract SR/11/205).
+as part of the <a href="https://maupp.ulb.ac.be/">MAUPP project</a> 
+(contract SR/00/304) and DASYWEIGHT project (contract SR/11/205).
 
 <h2>SEE ALSO</h2>