temporal manual: do not abbreviate parameters in man as it is confusing

git-svn-id: https://svn.osgeo.org/grass/grass/trunk@66274 15284696-431f-4ddb-bdfa-cd5b030d7da7

temporal/t.connect/t.connect.html

@@ -13,7 +13,7 @@ default values.
 
 <h2>NOTES</h2>
 
-Setting the connection with t.connect will not test the connection for validity.
+Setting the connection with <em>t.connect</em> will not test the connection for validity.
 Hence a database connection will not be established.
 <p>
 The connection values are stored in the mapset's <tt>VAR</tt> file.
@@ -37,6 +37,7 @@ PostgreSQL and SQLite databases can not be mixed in a location.
 <h3>SQLite</h3>
 The SQLite database is created automatically when used the first time.
 <div class="code"><pre>
+# use single quotes here
 t.connect driver=sqlite database='$GISDBASE/$LOCATION_NAME/PERMANENT/tgis/sqlite.db'
 t.connect -p
 t.info -s

temporal/t.info/t.info.html

@@ -44,7 +44,7 @@ t.info -s
 In order to obtain information about a space time dataset, run:
 
 <div class="code"><pre>
-t.info in=tempmean_monthly
+t.info input=tempmean_monthly
  +-------------------- Space Time Raster Dataset -----------------------------+
  |                                                                            |
  +-------------------- Basic information -------------------------------------+
@@ -91,7 +91,7 @@ t.info in=tempmean_monthly
  |     output="tempmean_monthly" title="Monthly precipitation"
  |     description="Dataset with monthly precipitation"
  | # 2014-11-27 09:44:32
- | t.register -i type="rast" inp="tempmean_monthly" maps="2009_01_tempmean,...,2012_12_tempmean" start="2009-01-01" increment="1 months"
+ | t.register -i type="rast" input="tempmean_monthly" maps="2009_01_tempmean,...,2012_12_tempmean" start="2009-01-01" increment="1 months"
  |
  +----------------------------------------------------------------------------+
 </pre></div>
@@ -104,7 +104,7 @@ it the greatest common divisor between all gaps in the time series.
 In order to obtain information about a map in a space time dataset, run:
 
 <div class="code"><pre>
-t.info in=2009_01_tempmean type=rast
+t.info input=2009_01_tempmean type=raster
  +-------------------- Raster Dataset ----------------------------------------+
  |                                                                            |
  +-------------------- Basic information -------------------------------------+

temporal/t.merge/t.merge.html

@@ -26,8 +26,8 @@ register two unique maps in each of it. Then we merge the two space
 time raster datasets together.
 
 <div class="code"><pre>
-r.mapcalc expr="map1 = rand(0, 10)"  -s
-r.mapcalc expr="map2 = rand(10, 20)" -s
+r.mapcalc expression="map1 = rand(0, 10)"  -s
+r.mapcalc expression="map2 = rand(10, 20)" -s
 
 t.create type=strds temporaltype=absolute \
          output=precipitation_daily_1 \
@@ -92,8 +92,8 @@ t.info precipitation_daily_1
  +----------------------------------------------------------------------------+
 
 
-r.mapcalc expr="map3 = rand(20, 30)" -s
-r.mapcalc expr="map4 = rand(30, 40)" -s
+r.mapcalc expression="map3 = rand(20, 30)" -s
+r.mapcalc expression="map4 = rand(30, 40)" -s
 
 t.create type=strds temporaltype=absolute \
          output=precipitation_daily_2 \

temporal/t.rast.accumulate/t.rast.accumulate.html

@@ -66,11 +66,11 @@ wget http://www-pool.math.tu-berlin.de/~soeren/grass/temperature_mean_1990_2000_
 mkdir -p /tmp/grassdata/LL
 
 # Start GRASS and create a new location with PERMANENT mapset
-grass71  -c EPSG:4326 /tmp/grassdata/LL/PERMANENT
+grass71 -c EPSG:4326 /tmp/grassdata/LL/PERMANENT
 
 # Import the temperature data
-t.rast.import in=temperature_mean_1990_2000_daily_celsius.tar.gz \
-      out=temperature_mean_1990_2000_daily_celsius extr=/tmp
+t.rast.import input=temperature_mean_1990_2000_daily_celsius.tar.gz \
+      output=temperature_mean_1990_2000_daily_celsius directory=/tmp
 
 # We need to set the region correctly
 g.region -p raster=`t.rast.list input=temperature_mean_1990_2000_daily_celsius column=name | tail -1`
@@ -95,7 +95,7 @@ g.region -p zoom=`t.rast.list input=temperature_mean_1990_2000_daily_celsius col
 t.rast.accumulate input="temperature_mean_1990_2000_daily_celsius" \
       output="temperature_mean_1990_2000_daily_celsius_accumulated_10_30" \
       limits="10,30" start="1990-01-01" stop="2000-01-01" cycle="12 months" \
-      base="temp_acc_daily_10_30" method="bedd"
+      basename="temp_acc_daily_10_30" method="bedd"
 
 #############################################################################
 #### ACCUMULATION PATTERN DETECTION #########################################
@@ -105,17 +105,17 @@ t.rast.accumulate input="temperature_mean_1990_2000_daily_celsius" \
 # First cycle at 325°C - 427°C GDD
 t.rast.accdetect input=temperature_mean_1990_2000_daily_celsius_accumulated_10_30@PERMANENT \
       occ=leafhopper_occurrence_c1_1990_2000 start="1990-01-01" stop="2000-01-01" \
-      cycle="12 months" range=325,427 base=lh_c1 indicator=leafhopper_indicator_c1_1990_2000
+      cycle="12 months" range=325,427 basename=lh_c1 indicator=leafhopper_indicator_c1_1990_2000
 
 # Second cycle at 685°C - 813°C GDD
 t.rast.accdetect input=temperature_mean_1990_2000_daily_celsius_accumulated_10_30@PERMANENT \
       occ=leafhopper_occurrence_c2_1990_2000 start="1990-01-01" stop="2000-01-01" \
-      cycle="12 months" range=685,813 base=lh_c2 indicator=leafhopper_indicator_c2_1990_2000
+      cycle="12 months" range=685,813 basename=lh_c2 indicator=leafhopper_indicator_c2_1990_2000
 
 # Third cycle at 1047°C - 1179°C GDD
 t.rast.accdetect input=temperature_mean_1990_2000_daily_celsius_accumulated_10_30@PERMANENT \
       occ=leafhopper_occurrence_c3_1990_2000 start="1990-01-01" stop="2000-01-01" \
-      cycle="12 months" range=1047,1179 base=lh_c3 indicator=leafhopper_indicator_c3_1990_2000
+      cycle="12 months" range=1047,1179 basename=lh_c3 indicator=leafhopper_indicator_c3_1990_2000
 
 
 #############################################################################
@@ -124,31 +124,31 @@ t.rast.accdetect input=temperature_mean_1990_2000_daily_celsius_accumulated_10_3
 
 # Extract the areas that have full cycles
 t.rast.aggregate input=leafhopper_indicator_c1_1990_2000 gran="1 year" \
-      output=leafhopper_cycle_1_1990_2000_yearly method=maximum base=li_c1
+      output=leafhopper_cycle_1_1990_2000_yearly method=maximum basename=li_c1
 
-t.rast.mapcalc input=leafhopper_cycle_1_1990_2000_yearly base=lh_clean_c1 \
+t.rast.mapcalc input=leafhopper_cycle_1_1990_2000_yearly basename=lh_clean_c1 \
                output=leafhopper_cycle_1_1990_2000_yearly_clean \
-               expr="if(leafhopper_cycle_1_1990_2000_yearly == 3, 1, null())"
+               expression="if(leafhopper_cycle_1_1990_2000_yearly == 3, 1, null())"
 
 t.rast.aggregate input=leafhopper_indicator_c2_1990_2000 gran="1 year" \
-      output=leafhopper_cycle_2_1990_2000_yearly method=maximum base=li_c2
+      output=leafhopper_cycle_2_1990_2000_yearly method=maximum basename=li_c2
 
-t.rast.mapcalc input=leafhopper_cycle_2_1990_2000_yearly base=lh_clean_c2 \
+t.rast.mapcalc input=leafhopper_cycle_2_1990_2000_yearly basename=lh_clean_c2 \
                output=leafhopper_cycle_2_1990_2000_yearly_clean \
-               expr="if(leafhopper_cycle_2_1990_2000_yearly == 3, 2, null())"
+               expression="if(leafhopper_cycle_2_1990_2000_yearly == 3, 2, null())"
 
 t.rast.aggregate input=leafhopper_indicator_c3_1990_2000 gran="1 year" \
-      output=leafhopper_cycle_3_1990_2000_yearly method=maximum base=li_c3
+      output=leafhopper_cycle_3_1990_2000_yearly method=maximum basename=li_c3
 
-t.rast.mapcalc input=leafhopper_cycle_3_1990_2000_yearly base=lh_clean_c3 \
+t.rast.mapcalc input=leafhopper_cycle_3_1990_2000_yearly basename=lh_clean_c3 \
                output=leafhopper_cycle_3_1990_2000_yearly_clean \
-               expr="if(leafhopper_cycle_3_1990_2000_yearly == 3, 3, null())"
+               expression="if(leafhopper_cycle_3_1990_2000_yearly == 3, 3, null())"
 
 
 t.rast.mapcalc input=leafhopper_cycle_1_1990_2000_yearly_clean,leafhopper_cycle_2_1990_2000_yearly_clean,leafhopper_cycle_3_1990_2000_yearly_clean \
-               base=lh_cleann_all_cycles \
+               basename=lh_cleann_all_cycles \
                output=leafhopper_all_cycles_1990_2000_yearly_clean \
-               expr="if(isnull(leafhopper_cycle_3_1990_2000_yearly_clean), \
+               expression="if(isnull(leafhopper_cycle_3_1990_2000_yearly_clean), \
 	         if(isnull(leafhopper_cycle_2_1990_2000_yearly_clean), \
 		 if(isnull(leafhopper_cycle_1_1990_2000_yearly_clean), \
 		 null() ,1),2),3)"
@@ -170,35 +170,35 @@ t.rast.colors input=leafhopper_all_cycles_1990_2000_yearly_clean rules=color.tab
 
 # Extract the duration in days of the first cycle
 t.rast.aggregate input=leafhopper_occurrence_c1_1990_2000 gran="1 year" \
-      output=leafhopper_min_day_c1_1990_2000 method=minimum base=occ_min_day_c1
+      output=leafhopper_min_day_c1_1990_2000 method=minimum basename=occ_min_day_c1
 t.rast.aggregate input=leafhopper_occurrence_c1_1990_2000 gran="1 year" \
-      output=leafhopper_max_day_c1_1990_2000 method=maximum base=occ_max_day_c1
+      output=leafhopper_max_day_c1_1990_2000 method=maximum basename=occ_max_day_c1
 t.rast.mapcalc input=leafhopper_min_day_c1_1990_2000,leafhopper_max_day_c1_1990_2000 \
-               base=occ_duration_c1 \
+               basename=occ_duration_c1 \
                output=leafhopper_duration_c1_1990_2000 \
-               expr="leafhopper_max_day_c1_1990_2000 - leafhopper_min_day_c1_1990_2000"
+               expression="leafhopper_max_day_c1_1990_2000 - leafhopper_min_day_c1_1990_2000"
 
 
 # Extract the duration in days of the second cycle
 t.rast.aggregate input=leafhopper_occurrence_c2_1990_2000 gran="1 year" \
-      output=leafhopper_min_day_c2_1990_2000 method=minimum base=occ_min_day_c2
+      output=leafhopper_min_day_c2_1990_2000 method=minimum basename=occ_min_day_c2
 t.rast.aggregate input=leafhopper_occurrence_c2_1990_2000 gran="1 year" \
-      output=leafhopper_max_day_c2_1990_2000 method=maximum base=occ_max_day_c2
+      output=leafhopper_max_day_c2_1990_2000 method=maximum basename=occ_max_day_c2
 t.rast.mapcalc input=leafhopper_min_day_c2_1990_2000,leafhopper_max_day_c2_1990_2000 \
-               base=occ_duration_c2 \
+               basename=occ_duration_c2 \
                output=leafhopper_duration_c2_1990_2000 \
-               expr="leafhopper_max_day_c2_1990_2000 - leafhopper_min_day_c2_1990_2000"
+               expression="leafhopper_max_day_c2_1990_2000 - leafhopper_min_day_c2_1990_2000"
 
 
 # Extract the duration in days of the third cycle
 t.rast.aggregate input=leafhopper_occurrence_c3_1990_2000 gran="1 year" \
-      output=leafhopper_min_day_c3_1990_2000 method=minimum base=occ_min_day_c3
+      output=leafhopper_min_day_c3_1990_2000 method=minimum basename=occ_min_day_c3
 t.rast.aggregate input=leafhopper_occurrence_c3_1990_2000 gran="1 year" \
-      output=leafhopper_max_day_c3_1990_2000 method=maximum base=occ_max_day_c3
+      output=leafhopper_max_day_c3_1990_2000 method=maximum basename=occ_max_day_c3
 t.rast.mapcalc input=leafhopper_min_day_c3_1990_2000,leafhopper_max_day_c3_1990_2000 \
-               base=occ_duration_c3 \
+               basename=occ_duration_c3 \
                output=leafhopper_duration_c3_1990_2000 \
-               expr="leafhopper_max_day_c3_1990_2000 - leafhopper_min_day_c3_1990_2000"
+               expression="leafhopper_max_day_c3_1990_2000 - leafhopper_min_day_c3_1990_2000"
 
 t.rast.colors input=leafhopper_duration_c1_1990_2000 color=rainbow
 t.rast.colors input=leafhopper_duration_c2_1990_2000 color=rainbow
@@ -212,33 +212,33 @@ t.rast.colors input=leafhopper_duration_c3_1990_2000 color=rainbow
 
 # First cycle
 t.rast.aggregate input=leafhopper_indicator_c1_1990_2000 gran="1 month" \
-      output=leafhopper_indi_min_month_c1_1990_2000 method=minimum base=occ_indi_min_month_c1
+      output=leafhopper_indi_min_month_c1_1990_2000 method=minimum basename=occ_indi_min_month_c1
 t.rast.aggregate input=leafhopper_indicator_c1_1990_2000 gran="1 month" \
-      output=leafhopper_indi_max_month_c1_1990_2000 method=maximum base=occ_indi_max_month_c1
+      output=leafhopper_indi_max_month_c1_1990_2000 method=maximum basename=occ_indi_max_month_c1
 t.rast.mapcalc input=leafhopper_indi_min_month_c1_1990_2000,leafhopper_indi_max_month_c1_1990_2000 \
-               base=indicator_monthly_c1 \
+               basename=indicator_monthly_c1 \
                output=leafhopper_monthly_indicator_c1_1990_2000 \
-               expr="if(leafhopper_indi_min_month_c1_1990_2000 == 1, 1, if(leafhopper_indi_max_month_c1_1990_2000 == 3, 3, 2))"
+               expression="if(leafhopper_indi_min_month_c1_1990_2000 == 1, 1, if(leafhopper_indi_max_month_c1_1990_2000 == 3, 3, 2))"
 
 # Second cycle
 t.rast.aggregate input=leafhopper_indicator_c2_1990_2000 gran="1 month" \
-      output=leafhopper_indi_min_month_c2_1990_2000 method=minimum base=occ_indi_min_month_c2
+      output=leafhopper_indi_min_month_c2_1990_2000 method=minimum basename=occ_indi_min_month_c2
 t.rast.aggregate input=leafhopper_indicator_c2_1990_2000 gran="1 month" \
-      output=leafhopper_indi_max_month_c2_1990_2000 method=maximum base=occ_indi_max_month_c2
+      output=leafhopper_indi_max_month_c2_1990_2000 method=maximum basename=occ_indi_max_month_c2
 t.rast.mapcalc input=leafhopper_indi_min_month_c2_1990_2000,leafhopper_indi_max_month_c2_1990_2000 \
-               base=indicator_monthly_c2 \
+               basename=indicator_monthly_c2 \
                output=leafhopper_monthly_indicator_c2_1990_2000 \
-               expr="if(leafhopper_indi_min_month_c2_1990_2000 == 1, 1, if(leafhopper_indi_max_month_c2_1990_2000 == 3, 3, 2))"
+               expression="if(leafhopper_indi_min_month_c2_1990_2000 == 1, 1, if(leafhopper_indi_max_month_c2_1990_2000 == 3, 3, 2))"
 
 # Third cycle
 t.rast.aggregate input=leafhopper_indicator_c3_1990_2000 gran="1 month" \
-      output=leafhopper_indi_min_month_c3_1990_2000 method=minimum base=occ_indi_min_month_c3
+      output=leafhopper_indi_min_month_c3_1990_2000 method=minimum basename=occ_indi_min_month_c3
 t.rast.aggregate input=leafhopper_indicator_c3_1990_2000 gran="1 month" \
-      output=leafhopper_indi_max_month_c3_1990_2000 method=maximum base=occ_indi_max_month_c3
+      output=leafhopper_indi_max_month_c3_1990_2000 method=maximum basename=occ_indi_max_month_c3
 t.rast.mapcalc input=leafhopper_indi_min_month_c3_1990_2000,leafhopper_indi_max_month_c3_1990_2000 \
-               base=indicator_monthly_c3 \
+               basename=indicator_monthly_c3 \
                output=leafhopper_monthly_indicator_c3_1990_2000 \
-               expr="if(leafhopper_indi_min_month_c3_1990_2000 == 1, 1, if(leafhopper_indi_max_month_c3_1990_2000 == 3, 3, 2))"
+               expression="if(leafhopper_indi_min_month_c3_1990_2000 == 1, 1, if(leafhopper_indi_max_month_c3_1990_2000 == 3, 3, 2))"
 
 cat > color.table << EOF
 3 red

temporal/t.rast.aggregate.ds/t.rast.aggregate.ds.html

@@ -38,7 +38,7 @@ the raster map layers):
 MAPS="map_1 map_2 map_3 map_4 map_5 map_6 map_7"
 
 for map in ${MAPS} ; do
-    r.mapcalc --o expr="${map} = 1" 
+    r.mapcalc --o expression="${map} = 1" 
     echo ${map} >> map_list.txt 
 done
 
@@ -178,7 +178,7 @@ t.info type=stvds input=points
 t.rast.aggregate.ds input=precipitation_daily \
                     output=precipitation_agg \
                     sample=points type=stvds \
-                    base=prec_agg \
+                    basename=prec_agg \
                     method=sum sampling=contains
 
 t.support input=precipitation_agg \
@@ -230,7 +230,7 @@ t.info type=strds input=precipitation_agg
  | Command history:
  | # 2014-11-23 16:53:23 
  | t.rast.aggregate.ds input="precipitation_daily"
- |     output="precipitation_agg" sample="points" type="stvds" base="prec_agg"
+ |     output="precipitation_agg" sample="points" type="stvds" basename="prec_agg"
  |     method="sum" sampling="contains"
  | # 2014-11-23 16:53:28 
  | t.support input="precipitation_agg"

temporal/t.rast.aggregate/t.rast.aggregate.html

@@ -59,7 +59,7 @@ In this example the user is going to aggregate monthly data into yearly
 data, running:
 
 <div class="code"><pre>
-t.rast.aggregate in=tempmean_monthly out=tempmean_yearly basename=tempmean_year \
+t.rast.aggregate input=tempmean_monthly output=tempmean_yearly basename=tempmean_year \
                  granularity="1 years" method=average
 
 t.support input=tempmean_yearly \
@@ -109,8 +109,8 @@ t.info tempmean_yearly
  | Dataset with monthly precipitation
  | Command history:
  | # 2014-11-27 10:25:21
- | t.rast.aggregate in="tempmean_monthly"
- |     out="tempmean_yearly" basename="tempmean_year" granularity="1 years"
+ | t.rast.aggregate input="tempmean_monthly"
+ |     output="tempmean_yearly" basename="tempmean_year" granularity="1 years"
  |     method="average"
  |
  | # 2014-11-27 10:26:21

temporal/t.rast.extract/t.rast.extract.html

@@ -28,7 +28,7 @@ side, eg.:
 
 <div class="code"><pre>
 t.rast.extract input=tempmean_monthly where="start_time > '2010-01-05'" \
-               output=selected_tempmean_monthly base=new_tmean_month \
+               output=selected_tempmean_monthly basename=new_tmean_month \
                expression="if(tempmean_monthly < 0, null(), tempmean_monthly)"
 </div>
 

temporal/t.rast.gapfill/t.rast.gapfill.html

@@ -25,9 +25,9 @@ We expect one map with a value of 2 for the first gap and
 two maps (values 3.666 and 4.333) for the second gap after interpolation.
 
 <div class="code"><pre>
-r.mapcalc expr="map1 = 1" 
-r.mapcalc expr="map2 = 3" 
-r.mapcalc expr="map3 = 5" 
+r.mapcalc expression="map1 = 1" 
+r.mapcalc expression="map2 = 3" 
+r.mapcalc expression="map3 = 5" 
 
 t.register type=raster maps=map1 start=2012-08-20 end=2012-08-21
 t.register type=raster maps=map2 start=2012-08-22 end=2012-08-23
@@ -57,7 +57,7 @@ map2@PERMANENT|map2|PERMANENT|2012-08-22 00:00:00|2012-08-23 00:00:00|1.0|2.0
 None|None|None|2012-08-23 00:00:00|2012-08-24 00:00:00|1.0|3.0
 map3@PERMANENT|map3|PERMANENT|2012-08-24 00:00:00|2012-08-25 00:00:00|1.0|4.0
 
-t.rast.gapfill input=precipitation_daily base=gap
+t.rast.gapfill input=precipitation_daily basename=gap
 
 t.rast.list input=precipitation_daily columns=name,start_time,min,max
 

temporal/t.rast.import/t.rast.import.html

@@ -17,7 +17,7 @@ to import it running:
 
 <div class="code"><pre>
 t.rast.import input=lst_daily.tar.bzip2 output=lst_daily \
-              base=lst directory=/tmp
+              basename=lst directory=/tmp
 
 </pre></div>
 

temporal/t.rast.mapcalc/t.rast.mapcalc.html

@@ -91,8 +91,8 @@ equal time stamps if the month of the start time is May or June,
 otherwise we multiply the maps. The command will look as follows:
 <p>
 <div class="code"><pre>
-t.rast.mapcalc input=A,B output=C base=c method=equal \
-    expr="if(start_month() == 5 || start_month() == 6, (A + B), (A * B))"
+t.rast.mapcalc input=A,B output=C basename=c method=equal \
+    expression="if(start_month() == 5 || start_month() == 6, (A + B), (A * B))"
 </pre></div>
 <p>
 The resulting raster maps in dataset C can be listed with <em>t.rast.list</em>:
@@ -114,12 +114,12 @@ datasets will be replaced by the corresponding raster maps. The final
 expression will be passed to <em>r.mapcalc</em>, resulting in 6 runs:
 <p>
 <div class="code"><pre>
-r.mapcalc expr="c_1 = if(3 == 5 || 3 == 6, (a3 + b3), (a3 * b3))"
-r.mapcalc expr="c_2 = if(4 == 5 || 4 == 6, (a4 + b4), (a4 * b4))"
-r.mapcalc expr="c_3 = if(5 == 5 || 5 == 6, (a5 + b5), (a5 * b5))"
-r.mapcalc expr="c_4 = if(6 == 5 || 6 == 6, (a6 + b6), (a6 * b6))"
-r.mapcalc expr="c_5 = if(7 == 5 || 7 == 6, (a7 + b7), (a7 * b7))"
-r.mapcalc expr="c_6 = if(8 == 5 || 8 == 6, (a8 + b8), (a8 * b8))"
+r.mapcalc expression="c_1 = if(3 == 5 || 3 == 6, (a3 + b3), (a3 * b3))"
+r.mapcalc expression="c_2 = if(4 == 5 || 4 == 6, (a4 + b4), (a4 * b4))"
+r.mapcalc expression="c_3 = if(5 == 5 || 5 == 6, (a5 + b5), (a5 * b5))"
+r.mapcalc expression="c_4 = if(6 == 5 || 6 == 6, (a6 + b6), (a6 * b6))"
+r.mapcalc expression="c_5 = if(7 == 5 || 7 == 6, (a7 + b7), (a7 * b7))"
+r.mapcalc expression="c_6 = if(8 == 5 || 8 == 6, (a8 + b8), (a8 * b8))"
 </pre></div>
 <p>
 
@@ -131,8 +131,8 @@ null otherwise it set the original value. The other months are copied as
 the original one.
 
 <div class="code"><pre>
-t.rast.mapcalc input=tempmean_monthly output=january_under_0 base=january_under_0 \
-    expr="if(start_month() == 1 && tempmean_monthly > 0, null(), tempmean_monthly)"
+t.rast.mapcalc input=tempmean_monthly output=january_under_0 basename=january_under_0 \
+    expression="if(start_month() == 1 && tempmean_monthly > 0, null(), tempmean_monthly)"
 
 # printing the minimum or maximum values only for January
 t.rast.list january_under_0 columns=name,start_time,min,max | grep 01-01

temporal/t.rast.neighbors/t.rast.neighbors.html

@@ -23,7 +23,7 @@ To smooth the maps contained into a space time dataset run:
 
 <div class="code"><pre>
 t.rast.neighbors input=tempmean_monthly output=smooth_tempmean_monthly \
-                 base=tmean_smooth size=5 method=average nprocs=4
+                 basename=tmean_smooth size=5 method=average nprocs=4
 
 # show some info about the new space time dataset
 t.info smooth_tempmean_monthly
@@ -70,7 +70,7 @@ t.info smooth_tempmean_monthly
  | Command history:
  | # 2014-11-27 11:41:36
  | t.rast.neighbors input="tempmean_monthly"
- |     output="smooth_tempmean_monthly" base="tmean_smooth" size="5"
+ |     output="smooth_tempmean_monthly" basename="tmean_smooth" size="5"
  |     method="average" nprocs="4"
  |
  +----------------------------------------------------------------------------+

temporal/t.rast.to.rast3/t.rast.to.rast3.html

@@ -40,7 +40,7 @@ t.rast.extract input=tempmean_monthly output=tempmean_monthly_later_2012 \
 g.region -p3 res3=500
 
 # convert to 3D raster map
-t.rast.to.rast3 in=tempmean_monthly_later_2012@climate_2009_2012 out=tempmean_monthly_2012
+t.rast.to.rast3 input=tempmean_monthly_later_2012@climate_2009_2012 output=tempmean_monthly_2012
 
 t.info type=raster_3d input=tempmean_monthly_2012
  +-------------------- 3D Raster Dataset -------------------------------------+

temporal/t.rast.what/t.rast.what.html

@@ -37,10 +37,10 @@ In the following examples we sample a space time raster dataset that contains
 g.region s=0 n=80 w=0 e=120 b=0 t=50 res=10
 
 # Generate data
-r.mapcalc expr="a_1 = 1" -s
-r.mapcalc expr="a_2 = 2" -s
-r.mapcalc expr="a_3 = 3" -s
-r.mapcalc expr="a_4 = 4" -s
+r.mapcalc expression="a_1 = 1" -s
+r.mapcalc expression="a_2 = 2" -s
+r.mapcalc expression="a_3 = 3" -s
+r.mapcalc expression="a_4 = 4" -s
 
 t.create type=strds output=A title="A test" descr="A test"
 

temporal/t.register/t.register.html

@@ -128,12 +128,12 @@ generated timestamps will be inspected using <em>r.timestamp</em> and
 that was set with <em>r.timestamp</em>.
 
 <div class="code"><pre>
-r.mapcalc expr="prec_1 = 100"
-r.mapcalc expr="prec_2 = 200"
-r.mapcalc expr="prec_3 = 300"
-r.mapcalc expr="prec_4 = 400"
-r.mapcalc expr="prec_5 = 500"
-r.mapcalc expr="prec_6 = 600"
+r.mapcalc expression="prec_1 = 100"
+r.mapcalc expression="prec_2 = 200"
+r.mapcalc expression="prec_3 = 300"
+r.mapcalc expression="prec_4 = 400"
+r.mapcalc expression="prec_5 = 500"
+r.mapcalc expression="prec_6 = 600"
 
 t.create type=strds temporaltype=absolute \
     output=precip_abs title="Example" \
@@ -159,7 +159,7 @@ prec_4|PERMANENT|2001-04-01 00:00:00|2001-05-01 00:00:00
 prec_5|PERMANENT|2001-05-01 00:00:00|2001-06-01 00:00:00
 prec_6|PERMANENT|2001-06-01 00:00:00|2001-07-01 00:00:00
 
-r.mapcalc expr="prec_7 = 700"
+r.mapcalc expression="prec_7 = 700"
 r.timestamp map=prec_7 date="1 jul 2001 / 1 aug 2001"
 
 t.register type=raster input=precip_abs maps=prec_7

temporal/t.sample/t.sample.html

@@ -43,16 +43,16 @@ time stamped map layers in <em>A</em> and <em>P</em>.
 g.region s=0 n=80 w=0 e=120 b=0 t=50 res=10 res3=10 -p3
 
 # Generate the raster map layer
-r.mapcalc expr="a1 = rand(0, 550)" -s
-r.mapcalc expr="a2 = rand(0, 450)" -s
-r.mapcalc expr="a3 = rand(0, 320)" -s
-r.mapcalc expr="a4 = rand(0, 510)" -s
-r.mapcalc expr="a5 = rand(0, 300)" -s
-r.mapcalc expr="a6 = rand(0, 650)" -s
+r.mapcalc expression="a1 = rand(0, 550)" -s
+r.mapcalc expression="a2 = rand(0, 450)" -s
+r.mapcalc expression="a3 = rand(0, 320)" -s
+r.mapcalc expression="a4 = rand(0, 510)" -s
+r.mapcalc expression="a5 = rand(0, 300)" -s
+r.mapcalc expression="a6 = rand(0, 650)" -s
 
 # Generate the vector map layer
-v.random -z output=pnts1 n=20 zmin=0 zmax=100 column=height
-v.random -z output=pnts2 n=20 zmin=0 zmax=100 column=height
+v.random -z output=pnts1 n=20 zminput=0 zmax=100 column=height
+v.random -z output=pnts2 n=20 zminput=0 zmax=100 column=height
 
 n1=`g.tempfile pid=1 -d` 
 n2=`g.tempfile pid=2 -d`

temporal/t.shift/t.shift.html

@@ -32,12 +32,12 @@ dataset using an increment of one day. Then we shift the time intervals
 with a granularity of 12 hours.
 
 <div class="code"><pre>
-r.mapcalc expr="prec_1 = rand(0, 550)" -s
-r.mapcalc expr="prec_2 = rand(0, 450)" -s
-r.mapcalc expr="prec_3 = rand(0, 320)" -s
-r.mapcalc expr="prec_4 = rand(0, 510)" -s
-r.mapcalc expr="prec_5 = rand(0, 300)" -s
-r.mapcalc expr="prec_6 = rand(0, 650)" -s
+r.mapcalc expression="prec_1 = rand(0, 550)" -s
+r.mapcalc expression="prec_2 = rand(0, 450)" -s
+r.mapcalc expression="prec_3 = rand(0, 320)" -s
+r.mapcalc expression="prec_4 = rand(0, 510)" -s
+r.mapcalc expression="prec_5 = rand(0, 300)" -s
+r.mapcalc expression="prec_6 = rand(0, 650)" -s
 
 t.create type=strds temporaltype=absolute \
          output=precipitation_daily \

temporal/t.vect.db.select/t.vect.db.select.html

@@ -9,7 +9,7 @@ registered in a space time vector dataset.
 The example is querying the <em>shoreline</em> space time dataset:
 
 <div class="code"><pre>
-t.vect.db.select in=shoreline
+t.vect.db.select input=shoreline
 start_time|end_time|cat|DESCR|SOURCE|DATE
 1849|1873|1|T-736|NOAA/USGS|01/01/1858
 1849|1873|2|T-657|NOAA/USGS|01/01/1857
@@ -27,7 +27,7 @@ In the following example it print only DATE and SOURCE columns for data
 before 1900-01-01.
 
 <div class="code"><pre>
-t.vect.db.select in=shoreline column=DATE,SOURCE t_where="start_time < 1900"
+t.vect.db.select input=shoreline column=DATE,SOURCE t_where="start_time < 1900"
 start_time|end_time|DATE|SOURCE
 1849|1873|01/01/1858|NOAA/USGS
 1849|1873|01/01/1857|NOAA/USGS

temporal/t.vect.export/t.vect.export.html

@@ -29,7 +29,7 @@ within the boundary of the state taken at 1 month intervals.
 
 <div class="code"><pre>
     
-t.vect.export in=shoreline out=shoreline_nc
+t.vect.export input=shoreline output=shoreline_nc
 
 tar xvfj shoreline_nc
 shoreline_1849_1873.xml

temporal/t.vect.extract/t.vect.extract.html

@@ -10,8 +10,8 @@ In the following example a new space time vector dataset will be create
 with all the data later than 2000:
 
 <div class="code"><pre>
-t.vect.extract inp=shoreline where="start_time > 2000" \
-               out=shoreline_later_2000 basename=new_shoreline
+t.vect.extract input=shoreline where="start_time > 2000" \
+               output=shoreline_later_2000 basename=new_shoreline
 
 t.info shoreline_later_2000@shoreline type=stvds
  +-------------------- Space Time Vector Dataset -----------------------------+
@@ -60,12 +60,12 @@ t.info shoreline_later_2000@shoreline type=stvds
  | North Caroline shoreline from 2000 to 2009
  | Command history:
  | # 2014-11-29 08:43:50 
- | t.vect.extract inp="shoreline"
- |     where="start_time > 2000" out="shoreline_later_2000"
+ | t.vect.extract input="shoreline"
+ |     where="start_time > 2000" output="shoreline_later_2000"
  |     basename="new_shoreline"
  | # 2014-11-29 08:44:14 
  | t.support type="stvds"
- |     in="shoreline_later_2000@shoreline"
+ |     input="shoreline_later_2000@shoreline"
  |     descr="North Caroline shoreline from 2000 to 2009"
  +----------------------------------------------------------------------------+
 

temporal/t.vect.import/t.vect.import.html

@@ -35,7 +35,7 @@ t.vect.export input=random_locations output=random_locations.tar.gz \
               compression=gzip
 
 t.vect.import input=random_locations.tar.gz output=new_random_locations \
-              base=new_map directory=/tmp
+              basename=new_map directory=/tmp
 
 t.vect.list new_random_locations
 id|name|layer|mapset|start_time|end_time

temporal/t.vect.list/t.vect.list.html

@@ -34,7 +34,7 @@ value is used, the value <em>deltagaps</em> will print the delta between
 maps and also the gaps if they exist (like in this example).
 
 <div class="code"><pre>
-t.vect.list method=deltagaps in=shoreline
+t.vect.list method=deltagaps input=shoreline
 id|name|layer|mapset|start_time|end_time|interval_length|distance_from_begin
 shoreline_1849_1873@shoreline|shoreline_1849_1873|None|shoreline|1849|1873|24|0
 None|None|None|None|1873|1925|52|24

temporal/t.vect.observe.strds/t.vect.observe.strds.html

@@ -34,8 +34,8 @@ The example show how to create a space time vector dataset and a vector
 layer starting from a point vector and a space time raster dataset:
 
 <div class="code"><pre>
-t.vect.observe.strds in=precip_30ynormals_3d strds=tempmean_monthly \
-                     out=precip_stations vect=precip_stations_monthly \
+t.vect.observe.strds input=precip_30ynormals_3d strds=tempmean_monthly \
+                     output=precip_stations vect=precip_stations_monthly \
                      columns=month
 
 t.info precip_stations type=stvds
@@ -84,8 +84,8 @@ t.info precip_stations type=stvds
  | Observation of space time raster dataset(s) tempmean_monthly with vector map precip_30ynormals_3d
  | Command history:
  | # 2014-12-02 00:42:39
- | t.vect.observe.strds in="precip_30ynormals_3d"
- |     strds="tempmean_monthly" out="precip_stations"
+ | t.vect.observe.strds input="precip_30ynormals_3d"
+ |     strds="tempmean_monthly" output="precip_stations"
  |     vect="precip_stations_monthly" columns="month"
  |
  +----------------------------------------------------------------------------+

temporal/t.vect.univar/t.vect.univar.html

@@ -11,7 +11,7 @@ example; so create the <b>precip_stations</b> space time vector dataset
 and after run the following command:
 
 <div class="code"><pre>
-t.vect.univar in=precip_stations col=month
+t.vect.univar input=precip_stations col=month
 id|start|end|n|nmissing|nnull|min|max|range|mean|mean_abs|population_stddev|population_variance|population_coeff_variation|sample_stddev|sample_variance|kurtosis|skewness
 precip_stations_monthly@climate_2009_2012|2009-01-01 00:00:00|2009-02-01 00:00:00|132|0|4|-2.31832|7.27494|9.59326|3.44624|3.5316|1.79322|3.21564|0.520341|1.80005|3.24019|0.484515|-0.338519
 precip_stations_monthly@climate_2009_2012|2009-02-01 00:00:00|2009-03-01 00:00:00|132|0|4|-0.654152|7.90613|8.56028|5.47853|5.48844|1.73697|3.01708|0.317051|1.74359|3.04011|0.875252|-1.0632

temporal/t.vect.what.strds/t.vect.what.strds.html

@@ -12,7 +12,7 @@ In below example the command fills the new column "new_temp" with values
 extracted from the "tempmean_monthly" space time raster dataset:
 
 <div class="code"><pre>
-t.vect.what.strds in=precip_stations@climate_2009_2012 \
+t.vect.what.strds input=precip_stations@climate_2009_2012 \
                   strds=tempmean_monthly@climate_2009_2012 \
                   column=new_temp method=average
 </pre></div>