Merge pull request #6 from ecodiv/workinprogress

Various small improvements / correction typo's en update email / url

grass7/display/d.vect.colbp/d.vect.colbp.html

@@ -23,7 +23,7 @@ <h2>DESCRIPTION</h2>
 <h2>NOTE</h2>
 <p>
 This script, based on d.vect.colhist, is a quick and dirty solution 
-using basic matplotlib, and will need some further fine-tuning and 
+sing basic matplotlib, and will need some further fine-tuning and 
 testing.
 
 <h2>EXAMPLE</h2>

grass7/raster/r.meb/r.meb.py

@@ -19,7 +19,7 @@
 #               and median of MES values in B (MESb), divided by the median of
 #               the absolute deviations of MESb from the median of MESb (MAD)
 #
-# COPYRIGHT: (C) 2014-2016 by Paulo van Breugel and the GRASS Development Team
+# COPYRIGHT: (C) 2014-2019 by Paulo van Breugel and the GRASS Development Team
 #
 #            This program is free software under the GNU General Public
 #            License (>=v2). Read the file COPYING that comes with GRASS
@@ -129,9 +129,9 @@
 100% 50:136:189
 """
 
-#----------------------------------------------------------------------------
+# ---------------------------------------------------------------------------
 # Functions
-#----------------------------------------------------------------------------
+# ---------------------------------------------------------------------------
 
 # create set to store names of temporary maps to be deleted upon exit
 CLEAN_RAST = []
@@ -258,9 +258,9 @@ def main(options, flags):
     text_file.write(hist)
     text_file.close()
 
-    #-------------------------------------------------------------------------
+    # ------------------------------------------------------------------------
     # Compute MES
-    #-------------------------------------------------------------------------
+    # ------------------------------------------------------------------------
 
     # Create temporary copy of ref layer
     tmpref0 = tmpname("reb1")
@@ -321,9 +321,9 @@ def main(options, flags):
         os.remove(tmprule)
         ipi.append(tmpf3)
 
-    #-----------------------------------------------------------------------
+    # ----------------------------------------------------------------------
     # Calculate EB statistics
-    #-----------------------------------------------------------------------
+    # ----------------------------------------------------------------------
 
     # EB MES
     if flag_m:
@@ -376,7 +376,7 @@ def main(options, flags):
         if not out:
             CLEAN_RAST.append(nmn)
         else:
-        # Write layer metadata
+            # Write layer metadata
             gs.run_command("r.support", map=nmn,
                            title="Multivariate environmental similarity (MES)",
                            units="0-100 (relative score",

grass7/raster/r.mess/r.mess.html

@@ -152,7 +152,7 @@ <h2>REFERENCES</h2>
 
 <h2>AUTHOR</h2>
 
-Paulo van Breugel, paulo at ecodiv.org
+Paulo van Breugel, paulo at ecodiv.earth
 
 
 <p><i>Last changed: $Date$</i>

grass7/raster/r.mess/r.mess.py

@@ -4,7 +4,7 @@
 ########################################################################
 #
 # MODULE:       r.mess
-# AUTHOR(S):    Paulo van Breugel <p.vanbreugel AT gmail.com>
+# AUTHOR(S):    Paulo van Breugel <paulo ecodiv earth>
 # PURPOSE:      Calculate the multivariate environmental similarity
 #               surface (MESS) as proposed by Elith et al., 2010,
 #               Methods in Ecology & Evolution, 1(330–342).
@@ -201,8 +201,8 @@ def main(options, flags):
            reftype['max'] != 1):
             gs.fatal(_("The ref_rast map must be a binary raster,"
                        " i.e. it should contain only values 0 and 1 or 1 only"
-                       " (now the minimum is %d and maximum is %d)")
-                     % (reftype['min'], reftype['max']))
+                       " (now the minimum is {} and maximum is {})".
+                       format(reftype['min'], reftype['max'])))
 
     # old environmental layers & variable names
     REF = options['env']
@@ -222,8 +222,8 @@ def main(options, flags):
         raster_exists(PROJ)
         if len(PROJ) != len(REF) and len(PROJ) != 0:
             gs.fatal(_("The number of reference and predictor variables"
-                       " should be the same. You provided %d reference and %d"
-                       " projection variables") % (len(REF), len(PROJ)))
+                       " should be the same. You provided {} reference and {}"
+                       " projection variables".format(len(REF), len(PROJ))))
 
     # output layers
     opl = options['output']
@@ -368,7 +368,7 @@ def main(options, flags):
             gs.run_command("r.mask", quiet=True, flags="r")
 
         # Upload raster values and get value in python as frequency table
-        sql1 = "SELECT cat FROM %s" % tmpf0
+        sql1 = "SELECT cat FROM {}".format(str(tmpf0))
         cn = len(np.hstack(db.db_select(sql=sql1)))
         for m in xrange(len(REF)):
 
@@ -382,12 +382,12 @@ def main(options, flags):
             mid = str(m)
             laytype = gs.raster_info(REF[m])['datatype']
             if laytype == 'CELL':
-                columns = "envvar_%s integer" % mid
+                columns = "envvar_{} integer".format(str(mid))
             else:
                 columns = "envvar_%s double precision" % mid
             gs.run_command("v.db.addcolumn", map=tmpf0, columns=columns,
                            quiet=True)
-            sql2 = "UPDATE %s SET envvar_%s = NULL" % (tmpf0, mid)
+            sql2 = "UPDATE {} SET envvar_{} = NULL".format(str(tmpf0), str(mid))
             gs.run_command("db.execute", sql=sql2, quiet=True)
             coln = "envvar_%s" % mid
             gs.run_command("v.what.rast", quiet=True, map=tmpf0,
@@ -402,9 +402,9 @@ def main(options, flags):
 
             # Check for point without values
             if b < cn:
-                gs.info(_("Please note that there were %d points without "
+                gs.info(_("Please note that there were {} points without "
                           "value. This is probably because they are outside "
-                          "the computational region or mask") % (cn - b))
+                          "the computational region or mask".format((cn - b))))
 
             # Set region to env_proj layers (if different from env) and remove
             # mask (if set above)

grass7/raster/r.mess/tests/r_mess_tests.txt

@@ -0,0 +1,115 @@
+#-----------------------------------------------------------------------
+# Test 1 - same area, without mask
+#-----------------------------------------------------------------------
+g.remove type=raster pattern=Ex_* -f
+g.region raster=bio1
+r.mess2 env=bio1,bio12,bio15 \
+        output=Ex_01
+r.mess2 -c -k -m -n env=bio1,bio12,bio15 \
+        ref_rast=ppa \
+        output=Ex_02
+r.mess2 -c -k -m -n \
+        env=bio1,bio12,bio15 \
+        env_proj=IPSL_bio1,IPSL_bio12,IPSL_bio15 \
+        output=Ex_03
+r.mess2 -c -k -m -n \
+        env=bio1,bio12,bio15 \
+        ref_vect=sample_locations@mess \
+        output=Ex_04
+r.mess2 -c -k -m -n env=bio1,bio12,bio15 \
+        env_proj=IPSL_bio1,IPSL_bio12,IPSL_bio15 \
+        ref_vect=sample_locations@mess \
+        output=Ex_05
+
+#-----------------------------------------------------------------------
+# Test 2 - same area, with mask
+#-----------------------------------------------------------------------
+g.remove type=raster pattern=Ex_* -f
+g.region raster=bio1
+r.mask raster=pnvmap
+r.mess2 env=bio1,bio12,bio15 \
+        output=Ex_06
+r.mask raster=pnvmap
+r.mess2 -c -k -m -n env=bio1,bio12,bio15 \
+        ref_rast=ppa \
+        output=Ex_07
+r.mask raster=pnvmap
+r.mess2 -c -k -m -n \
+        env=bio1,bio12,bio15 \
+        env_proj=IPSL_bio1,IPSL_bio12,IPSL_bio15 \
+        output=Ex_08
+r.mask raster=pnvmap
+r.mess2 -c -k -m -n \
+        env=bio1,bio12,bio15 \
+        ref_vect=sample_locations@mess \
+        output=Ex_09
+r.mask raster=pnvmap
+r.mess2 -c -k -m -n env=bio1,bio12,bio15 \
+        env_proj=IPSL_bio1,IPSL_bio12,IPSL_bio15 \
+        ref_vect=sample_locations@mess \
+        output=Ex_10
+
+#-----------------------------------------------------------------------
+# Test 3 - different reference and projection area, without mask
+#-----------------------------------------------------------------------
+g.remove type=raster pattern=Ex_* -f
+r.mask -r
+g.region raster=bio1
+r.mess2 -c -k -m -n \
+        env=bio1,bio12,bio15 \
+        env_proj=bio1_Eth,bio12_Eth,bio15_Eth \
+        output=Ex_11
+g.region raster=bio1
+r.mess2 -c -k -m -n \
+        env=bio1,bio12,bio15 \
+        env_proj=bio1_Eth,bio12_Eth,bio15_Eth \
+        ref_rast=ppa \
+        output=Ex_12
+g.region raster=bio1
+r.mess2 -c -k -m -n \
+        env=bio1,bio12,bio15 \
+        env_proj=bio1_Eth,bio12_Eth,bio15_Eth \
+        ref_vect=sample_locations@mess \
+        output=Ex_13
+
+# Test 3 - different reference and projection area, with mask
+#-----------------------------------------------------------------------
+g.remove type=raster pattern=Ex_* -f
+g.region raster=bio1
+r.mask raster=pnvmap
+r.mess2 -c -k -m -n \
+        env=bio1,bio12,bio15 \
+        env_proj=bio1_Eth,bio12_Eth,bio15_Eth \
+        output=Ex_14
+g.region raster=bio1
+r.mask raster=pnvmap
+r.mess2 -c -k -m -n \
+        env=bio1,bio12,bio15 \
+        env_proj=bio1_Eth,bio12_Eth,bio15_Eth \
+        ref_rast=ppa \
+        output=Ex_15
+g.region raster=bio1
+r.mask raster=pnvmap
+r.mess2 -c -k -m -n \
+        env=bio1,bio12,bio15 \
+        env_proj=bio1_Eth,bio12_Eth,bio15_Eth \
+        ref_vect=sample_locations@mess \
+        output=Ex_16
+
+# Test 4 - Compare tests that should give the same results
+#-----------------------------------------------------------------------
+g.region raster=bio1
+r.mask -r
+r.mess2 -c -k -m -n env=bio1,bio12,bio15 \
+        ref_rast=ppa \
+        output=Ex_02
+
+r.mask raster=ppa
+r.mess2 -c -k -m -n env=bio1,bio12,bio15 output=Ex_17
+
+
+
+
+
+
+

grass7/raster/r.niche.similarity/r.niche.similarity.py

@@ -4,15 +4,14 @@
 ########################################################################
 #
 # MODULE:       r.niche.similarity
-# AUTHOR(S):    Paulo van Breugel <p.vanbreugel AT gmail.com>
+# AUTHOR(S):    Paulo van Breugel <paulo ecodiv earth>
 # PURPOSE:      Compute  degree of niche overlap using the statistics D
 #               and I (as proposed by Warren et al., 2008) based on
 #               Schoeners D (Schoener, 1968) and Hellinger Distances
 #               (van der Vaart, 1998)
 #
-# COPYRIGHT: (C) 2015 Paulo van Breugel
-#            http://ecodiv.org
-#            http://pvanb.wordpress.com/
+# COPYRIGHT: (c) 2015-2019 Paulo van Breugel and GRASS Development Team
+#            http://ecodiv.earth
 #
 #            This program is free software under the GNU General Public
 #            License (>=v2). Read the file COPYING that comes with GRASS

grass7/raster/r.out.legend/r.out.legend.py

@@ -345,10 +345,10 @@ def main():
     d_legend = Module("d.legend", flags=flag, raster=inmap, font=font,
                       at=at, fontsize=fz, labelnum=labelnum, run_=False)
     if vr:
-        val_range = list(map(float, vr.split(',')))
+        val_range = map(float, vr.split(','))
         d_legend.inputs.range = val_range
     if labval:
-        label_values = list(map(float, labval.split(',')))
+        label_values = map(float, labval.split(','))
         d_legend.inputs.label_values = label_values
     if labstep:
         label_step = float(labstep)