Changes between Version 3 and Version 4 of GSoC/2014/ImplementationOf3DRasterFlowLine

Timestamp:

Jun 6, 2014, 2:24:01 PM (10 years ago)

Author:

annakrat

Comment:

--

GSoC/2014/ImplementationOf3DRasterFlowLine

@@ -54 +54 @@
 || August 22 || Students can begin submitting required code samples to Google
 
-'''Week 1'''
+== Reports ==
+
+=== Week 1 ===
 
 I discussed the implementation with mentors. Based on that I will use similar approach as implemented in VTK, which computes the flowlines by integrating the vector field. To have a first prototype quickly, I will implement it in Python using PyGRASS to write vector lines and Python scripting library (array.py). I will write the Python code in a way which would make it easy to port to C.
@@ -68 +70 @@
 I will probably keep the code in sandbox and once it is usable, I will put it in addons
 
-'''Week 2'''
+=== Week 2 ===
 
 I started to implement the prototype in Python:
@@ -78 +80 @@
  * Soeren suggests using interpolation approach by [http://www.isws.illinois.edu/pubdoc/b/iswsb-65.pdf Prickett, 1981] instead of trilinear interpolation from voxel centers.
  * r3.flow should be able to compute the gradient so that the input can be also just one 3D raster map instead of 3 maps representing the vector field. For now I could probably use !NumPy's method `gradient` based on central differences. When porting to C, there is [http://grass.osgeo.org/programming7/gpdelib.html GPDE] library in GRASS.
+
+=== Week 3 ===
+
+I have a running prototype producing flowlines, I tested it on artificial data created from the first example in !r3.gwflow. Here are some results:
+
+[[Image(regular_flowlines.png, 300px)]] [[Image(random_flowlines.png, 300px)]]
+
+The flowlines can be generated with forward or backward integration (upstream or downstream). 
+The integration step can be defined:
+ * as a time step - it is difficult to guess the right value
+ * as a lenght step in map units or cells (voxels) - that's now the default. The drawback is that it doesn't behave so well around singular points (where velocity is close to zero) as the time step does.
+
+Another parameter is the maximum number of steps which prevents infinite loops I got around the singular point. 
+
+I added another input - besides the vector field components, the input can be only one 3D raster (scalar field) and I compute gradient using NumPy's method `gradient`.
+
+For the next week, I would like to test it on more complicated data (space-time cube data of terrain evolution) and implement a flow accumulation analogue in 3D. I am still not sure how to proceed, I have to either ensure that the step is so small that I will have at least one point of the flowline in the intersected cell or I have to check between each two steps that I didn't skip any cells on the way.
+
+Also I will look at implementation of Runge Kutta method with adaptive step size  which might help with the flowlines near a singular point.