style: Fix trailing whitespaces and end of file (#1130)

* style: Fix trailing whitespace

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src/imagery/i.spec.sam/i.spec.sam.html

@@ -16,7 +16,7 @@ <h2>DESCRIPTION</h2>
 row2: 28.26  34.82  38.27  40.1 38.27 23.7
 row3: 10.54  16.35  23.7   38.98 40.1 38.98
 
-  
+
 <h2>EXAMPLES</h2>
 
 <div class="code"><pre>

src/raster/r.boxplot/r.boxplot.html

@@ -11,10 +11,10 @@ <h2>DESCRIPTION</h2>
 represent the median and interquartile range (IQR) of the input layer. Note
 that all values of the input raster (within the region's extent)
 are used to compute the median and IQR. If the zones of the zonal map
-cover only part of the region, the user can mask out the non-covered 
+cover only part of the region, the user can mask out the non-covered
 parts of the input map first by means of <em>r.mask</em>. That will result in
 an IQR and median representing the values that fall within the zones of
-the zonal map only. Otherwise, the computational region can be changed to 
+the zonal map only. Otherwise, the computational region can be changed to
 fit the extent of the zonal map with <em>g.region</em>
 
 <p>
@@ -25,7 +25,7 @@ <h2>DESCRIPTION</h2>
 
 <p>
 The whiskers extend to the most extreme data point, which is no
-more than <b>range</b> &#10005; the IQR from the box. 
+more than <b>range</b> &#10005; the IQR from the box.
 By default, a <b>range</b> of 1.5 is used, but the user can change
 this. Note that range values need to be larger than 0.
 
@@ -64,11 +64,11 @@ <h2>NOTE</h2>
 with the error message, 'The zonal raster must be of type CELL (integer)'.
 
 <p>
-If the <b>c</b> flag is used, the <b>bxp_color</b> and <b>median_color</b> 
+If the <b>c</b> flag is used, the <b>bxp_color</b> and <b>median_color</b>
 are ignored, even if set by the user. The option to color boxploxs using the colors
 of the zonal raster categories (<b>c</b> flag) only works if the zonal
 map contains a color table. If it does not, the function exits with the
-error message that 'The zonal map does not have a color table'. If the user 
+error message that 'The zonal map does not have a color table'. If the user
 thinks there is a color table, run <em>r.colors.out</em> and check if the
 categories are integers. If not, that is the problem. If they are all
 integers, you probably have caught a bug.
@@ -83,7 +83,7 @@ <h2>EXAMPLE</h2>
 <h3>Example 1</h3>
 Draw a boxplot of the values of the <i>elevation</i> layer from the
 <a href="https://grass.osgeo.org/download/data/">NC sample
-dataset</a>. Set the <b>h</b> flag to print the boxplot horizontally. 
+dataset</a>. Set the <b>h</b> flag to print the boxplot horizontally.
 Set the plot dimensions to 7 inch wide, 1 inch high.
 
 <div class="code"><pre>

src/raster/r.catchment/r.catchment.html

@@ -46,7 +46,7 @@ <h3>Options and flags:</h3>
 the catchment configuration. The optional value <b>name_column</b>
 is to be used in conjunction with the <b>-i</b> flag (see below).
 There are three native flags for <em>r.catchment</em>. <b>-c</b>
-allows you to keep the interim cost surface maps made. <b>-l</b> allows 
+allows you to keep the interim cost surface maps made. <b>-l</b> allows
 you to show a list of the costv alues in that cost map, along with
 the size of the catchments they delineate. <b>-i</b> enable "iterative"
 mode. Here, the module will loop through all the points in the input
@@ -72,7 +72,7 @@ <h2>NOTES</h2>
 determine the catchment.
 
 The input <b>start_points</b> map should be a vector points map.
-If the file contains other types of features (areas, lines, centroids), 
+If the file contains other types of features (areas, lines, centroids),
 these will be ignored. If you desire, a start points map could be
 manually digitized (with <em>v.digit</em>) over topographic or
 cultural features, or could be created as a series of random points

src/raster/r.edm.eval/r.edm.eval.html

@@ -1,29 +1,29 @@
 <h2>DESCRIPTION</h2>
 
-The <em>r.edm.eval</em> addon returns a few evaluation statistics, 
-including the area under the curve (AUC), the maximum true skill 
-statistic (TSS), and the maximum kappa. For all three statistics, the 
-corresponding probability threshold values are also given. Optionally, 
+The <em>r.edm.eval</em> addon returns a few evaluation statistics,
+including the area under the curve (AUC), the maximum true skill
+statistic (TSS), and the maximum kappa. For all three statistics, the
+corresponding probability threshold values are also given. Optionally,
 it draws the receiver operating characteristic (ROC) curve.
 
 <p>
-The addon takes as input a raster layer with 
-observations(<em>observations</em>) and one or more 
-<em>predictions</em> layers. The <em>observations</em> layer 
-should be encoded with 1 (presence/cases) and 0 (absences/controls). 
-The (<em>predictions</em> layer gives the predicted values (e.g., the 
-outcome of a model). This typically are probability or suitability 
-scores between 0 and 1, but it will work on other scales too. With two 
-or more <em>predictions</em> layers, statistics are calculated for each 
-prediction layer separately. This allows one to compare predictions of 
+The addon takes as input a raster layer with
+observations(<em>observations</em>) and one or more
+<em>predictions</em> layers. The <em>observations</em> layer
+should be encoded with 1 (presence/cases) and 0 (absences/controls).
+The (<em>predictions</em> layer gives the predicted values (e.g., the
+outcome of a model). This typically are probability or suitability
+scores between 0 and 1, but it will work on other scales too. With two
+or more <em>predictions</em> layers, statistics are calculated for each
+prediction layer separately. This allows one to compare predictions of
 different models.
 
 <p>
-By default, the prediction layer is divided in 200 bins (the user can 
-change this number). For each bin, the module computes the cumulative 
-true and false positives (TP, FP), true and false negatives (TN, FN), the 
-true and false positive rate (TPR, FPR), the true negative rate (TNR), 
-Cohen's kappa and the true skill statistic. 
+By default, the prediction layer is divided in 200 bins (the user can
+change this number). For each bin, the module computes the cumulative
+true and false positives (TP, FP), true and false negatives (TN, FN), the
+true and false positive rate (TPR, FPR), the true negative rate (TNR),
+Cohen's kappa and the true skill statistic.
 
 <div class="code"><pre>
 TPR = TP / (TP + FN)
@@ -42,49 +42,49 @@ <h2>DESCRIPTION</h2>
 </pre></div>
 
 <p>
-These values can be saved to a CSV file, together with the 
-corresponding upper and lower boundaries of the bins values. These can 
+These values can be saved to a CSV file, together with the
+corresponding upper and lower boundaries of the bins values. These can
 be used to calculate additional statistics.
 
 <p>
-With the <em>-b</em> flag, the module will use the fraction of 
-points that is predicted to be presence/true instead of the false 
-positive rate to create the ROC curve and calculate the AUC. Use this 
-when the predictions are created using a model that works with 
+With the <em>-b</em> flag, the module will use the fraction of
+points that is predicted to be presence/true instead of the false
+positive rate to create the ROC curve and calculate the AUC. Use this
+when the predictions are created using a model that works with
 background points instead of (pseudo-)absence points (like Maxent).
 
 <p>
-A problem with the AUC is that it varies with the spatial extent used 
-to select background points (Lobo et al. 2008, Jiménez-Valverde 2012). 
-Generally, the larger that extent, the higher the AUC value. Therefore, 
-AUC values are generally biased and cannot be directly compared. An 
-admittedly simple way to evaluate this issue of changing AUC values 
-with changing extents is to only use (pseudo-)absence or background 
-points within certain distances of the presence points (raster cells). 
-To do so, the user can use the <em>buffer</em> parameter to limit the 
-absence/background points that are used to calculate the various 
-statistics to those within a certain distance from the presence 
-locations/areas. 
+A problem with the AUC is that it varies with the spatial extent used
+to select background points (Lobo et al. 2008, Jiménez-Valverde 2012).
+Generally, the larger that extent, the higher the AUC value. Therefore,
+AUC values are generally biased and cannot be directly compared. An
+admittedly simple way to evaluate this issue of changing AUC values
+with changing extents is to only use (pseudo-)absence or background
+points within certain distances of the presence points (raster cells).
+To do so, the user can use the <em>buffer</em> parameter to limit the
+absence/background points that are used to calculate the various
+statistics to those within a certain distance from the presence
+locations/areas.
 
 <p>
-The user can set the required ratio of presence and total points to be 
-used in the evaluation (<em>preval</em>; value between 0 an 1). This 
-allows the user to test how sensitive the evaluation statistics are for 
-the prevalence of presence points. 
+The user can set the required ratio of presence and total points to be
+used in the evaluation (<em>preval</em>; value between 0 an 1). This
+allows the user to test how sensitive the evaluation statistics are for
+the prevalence of presence points.
 
 <h2>NOTES</h2>
 
-The module expects an <em>reference</em> raster layer, encoded with 1 
-(presence/cases) and 0 (absences/controls). Optionally, the user can 
-define other values using the <em>absence</em> and <em>presence</em> 
-parameters. If the layer contains more than 2 unique values, or if the 
-layer is not of the type CELL (integer), the module will terminate with 
+The module expects an <em>reference</em> raster layer, encoded with 1
+(presence/cases) and 0 (absences/controls). Optionally, the user can
+define other values using the <em>absence</em> and <em>presence</em>
+parameters. If the layer contains more than 2 unique values, or if the
+layer is not of the type CELL (integer), the module will terminate with
 a warning.
 
 <p>
-The selection of the best evaluation statistic depends on your data and 
-what you want to test. This module provides a few only, but the user can 
-calculate his/her own using the table in the CSV file. See the 
+The selection of the best evaluation statistic depends on your data and
+what you want to test. This module provides a few only, but the user can
+calculate his/her own using the table in the CSV file. See the
 References list for a few useful references in that respect.
 
 <p>
@@ -93,9 +93,9 @@ <h2>NOTES</h2>
 
 <h2>EXAMPLES</h2>
 
-Run this example in the "landsat" mapset of the North Carolina sample 
-data set location. First, create a binary map with herbaceous land cover 
-(1) and other (0). Next, create a raster layer with training pixels 
+Run this example in the "landsat" mapset of the North Carolina sample
+data set location. First, create a binary map with herbaceous land cover
+(1) and other (0). Next, create a raster layer with training pixels
 using <em>r.learn.train</em> and <em>r.learn.predict</em> from the
 <a href="r.learn.ml2.html">r.learn.ml2</a> addon.
 Note, <em>r.learn.train</em> can compute its own accuracy
@@ -108,22 +108,22 @@ <h2>EXAMPLES</h2>
 </pre></div>
 
 <p>
-Then we can use these training pixels to perform a classification on 
+Then we can use these training pixels to perform a classification on
 the more recently obtained landsat 7 image using <em>r.learn.train</em>.
 
 <div class="code"><pre>
 # train a random forest regression model using r.learn.train
 r.learn.train group=lsat7_2000 training_map=training_pixels \
 	model_name=RandomForestRegressor n_estimators=500 \
 	save_model=rf_model.gz cv=2
-	
+
 # perform prediction using r.learn.predict
 r.learn.predict group=lsat7_2000 load_model=rf_model.gz \
    output=rf_regressor
 </pre></div>
 
 <p>
-Now we can see how well the model performed using the 
+Now we can see how well the model performed using the
 <em>r.edm.eval</em>.
 
 <div class="code"><pre>
@@ -154,14 +154,14 @@ <h2>EXAMPLES</h2>
 </div>
 
 <p>
-Let's try how well a logistic regression performs, and compare this 
+Let's try how well a logistic regression performs, and compare this
 with the previous results.
 
 <div class="code"><pre>
 # train a logistic regression model using r.learn.train
 r.learn.train group=lsat7_2000 training_map=training_pixels \
 	model_name=LogisticRegression save_model=lr_model.gz cv=2
-	
+
 # perform prediction using r.learn.predict -p
 r.learn.predict group=lsat7_2000 load_model=lr_model.gz \
    output=lr_regressor
@@ -191,7 +191,7 @@ <h2>EXAMPLES</h2>
 treshold minimum distance to (0,1) = 0.0848
 </pre></div>
 
-And the accompanying ROC curve is shown below (the figure can optionally be saved to file). 
+And the accompanying ROC curve is shown below (the figure can optionally be saved to file).
 
 <p>
 <div align="center" style="margin: 10px">
@@ -204,29 +204,29 @@ <h2>EXAMPLES</h2>
 <h2>REFERENCES</h2>
 
 Jiménez-Valverde, A. 2012. Insights into the area under the receiver
-operating characteristic curve (AUC) as a discrimination measure in 
-species distribution modelling. Global Ecology and Biogeography 21: 
+operating characteristic curve (AUC) as a discrimination measure in
+species distribution modelling. Global Ecology and Biogeography 21:
 498-507
 
 <p>
 Lobo, J. M., Jim&eacute;nez-Valverde, A., &amp; Real, R. 2008. AUC: a
-misleading measure of the performance of predictive distribution 
+misleading measure of the performance of predictive distribution
 models. Global Ecology and Biogeography 17: 145-151.
 
 <p>
 Hijmans, R. J. 2012. Cross-validation of species distribution models:
-removing spatial sorting bias and calibration with a null model. 
+removing spatial sorting bias and calibration with a null model.
 Ecology 93: 679-688.
 
 <p>
 Allouche, O., Tsoar, A., &amp; Kadmon, R. 2006. Assessing the accuracy of
-species distribution models: prevalence, kappa and the true skill 
+species distribution models: prevalence, kappa and the true skill
 statistic (TSS). Journal of Applied Ecology 43: 1223-1232.
 
 <p>
 McPherson, J. M., Jetz, W., &amp; Rogers, D. J. 2004. The effects of
-species' range sizes on the accuracy of distribution models: ecological 
-phenomenon or statistical artefact? Journal of Applied Ecology 41: 
+species' range sizes on the accuracy of distribution models: ecological
+phenomenon or statistical artefact? Journal of Applied Ecology 41:
 811-823.
 
 <h2>SEE ALSO</h2>

src/raster/r.fusion/r.fusion.html

@@ -1,15 +1,15 @@
 <h2>DESCRIPTION</h2>
 
-<em>r.fusion</em> enhances the resolution of a raster map by using spatial 
-detail of a high-resolution map. The actual values in the resultant output map 
-correspond to the input map while the spatial detail corresponds to the 
-high-resolution map. The effect is similar to pan-sharpening, but the method 
-can be applied more generally, not only to imagery but also to climatological 
+<em>r.fusion</em> enhances the resolution of a raster map by using spatial
+detail of a high-resolution map. The actual values in the resultant output map
+correspond to the input map while the spatial detail corresponds to the
+high-resolution map. The effect is similar to pan-sharpening, but the method
+can be applied more generally, not only to imagery but also to climatological
 data such as temperature or precipitation.
 
 <h2>NOTES</h2>
 
-Two different methods are available with the <b>method</b> option: 
+Two different methods are available with the <b>method</b> option:
 <em>difference</em> and <em>proportion</em>.
 
 <p>
@@ -21,11 +21,11 @@ <h2>NOTES</h2>
 <p>
 highres(A<sub>lowres</sub> - B<sub>lowres</sub>) + B<sub>highres</sub> = A<sub>highres</sub>
 <p>
-where <em>highres()</em> is a function to interpolate the differences. Here, 
+where <em>highres()</em> is a function to interpolate the differences. Here,
 <em>r.resamp.filter</em> is used for interpolation.
 
 <p>
-The <em>proportion</em> method is suitable for e.g. precipitation where zero 
+The <em>proportion</em> method is suitable for e.g. precipitation where zero
 precipition must stay zero precipition, and uses the formula
 <p>
 A / B * B = A
@@ -35,8 +35,8 @@ <h2>NOTES</h2>
 highres(A<sub>lowres</sub> / B<sub>lowres</sub>) * B<sub>highres</sub> = A<sub>highres</sub>
 <p>
 
-Again, <em>highres()</em> is a function to interpolate the proportions, and 
-<em>r.resamp.filter</em> is used for interpolation. For the <em>proportion</em> 
+Again, <em>highres()</em> is a function to interpolate the proportions, and
+<em>r.resamp.filter</em> is used for interpolation. For the <em>proportion</em>
 method, all values in the high-resolution B map must be &gt; 0.
 
 

src/raster/r.fuzzy.set/r.fuzzy.set.html

@@ -93,7 +93,7 @@ <h4>Calculation of boundary shape</h4>
 1-cos(x * Pi/2)^m (for negative shape parameter)
 
 where x: membership, and
-m = 2^exp(2 * |shape|) 
+m = 2^exp(2 * |shape|)
 For default shape=0, m = 2 (most common parameter for that equation).
 </code></pre>