Changes between Version 52 and Version 53 of GSoC/2014

Timestamp:

Feb 13, 2014, 8:48:15 AM (10 years ago)

Author:

wenzeslaus

Comment:

minor changes

GSoC/2014

@@ -131 +131 @@
  * It might also be advantageous to save the name of the color table after G7:r.colors somewhere in files for map, so that it can be reused/displayed later. (probably easiest to allow the colr/ files reading code to ignore #commented lines and put the name at the top of the colr/ file)
  * Language requirements: Python, wxPython
- * Co-mentor for non-wxPy bits: Hamish Bowman
+ * Co-mentor for non-wxPython bits: Hamish Bowman
 
 
@@ -166 +166 @@
  * Implement the raster attribute tables for 2D and for 3D rasters which would work similarly to attribute tables for vectors.
  * Attribute table can be filled with statistical data associated with one category, label (defined using G7:r.category), color (defined using G7:r.color) or generally it can contain any attributes as in case of vector attribute table.
- * See also G7:r.out.gdal `-t` flag (r58890) and G7:r.in.gdal (r54284) for reading a GDAL raster attribute table.
+ * See also G7:r.out.gdal `-t` flag (r58890) and G7:r.in.gdal (r54284) for reading and writing a GDAL raster attribute table (mainly related to colors).
 
 
@@ -180 +180 @@
  * All decorations should be supported by the export (save display to file) function because image outputs are the primary motivation of this project.
 
-=== Porting of g.infer to GRASS7 for rule-based analysis and workflow control ===
- * The add-on module [http://grass.osgeo.org/grass64/manuals/addons/g.infer.html g.infer] for GRASS6.4 provides a full rule-based inference engine for the use within GRASS.
- * Rule-based inference can be used both to analyse existing data layers (raster, raster3D, point vectors) but also to dynamically control GRASS-based workflows, including simulation models, agent-based simulation, and active retrieval of external input data.
- * g.infer is based on the [http://en.wikipedia.org/wiki/CLIPS CLIPS] expert system shell using the Python module [http://pyclips.sourceforge.net/web/ PyClips].
- * Porting the module to GRASS7 requires an object-oriented redesign of the g.infer architecture (currently: functional programming) 
- * A deep integration based on the G7 Python-API is desirable
+
+=== Porting of g.infer to GRASS GIS 7 for rule-based analysis and workflow control ===
+
+ * The add-on module [http://grass.osgeo.org/grass64/manuals/addons/g.infer.html g.infer] for GRASS 6.4 provides a full rule-based inference engine for the use within GRASS.
+ * Rule-based inference can be used both to analyse existing data layers (raster, 3D rasters, vectors points) but also to dynamically control GRASS-based workflows, including simulation models, agent-based simulation, and active retrieval of external input data.
+ * `g.infer` is based on the [http://en.wikipedia.org/wiki/CLIPS CLIPS] expert system shell using the Python module [http://pyclips.sourceforge.net/web/ PyClips].
+ * Porting the module to GRASS 7 (trunk) requires an object-oriented redesign of the `g.infer` architecture (currently, functional programming is used) 
+ * A deep integration based on the GRASS 7 Python API is desirable.
  * Language requirements: Python
  * Mentor: Peter Loewe
+
 
 == For students ==