AMR test case:

- Doxygen documentation

modified:   Tagging_nd.f90
new file:   analyse.py
modified:   error.cpp
modified:   vis_iter.py
modified:   visualize.py

ippl/test/AMR/Tagging_nd.f90

+!> @file Tagging_nd.f90
+!! @author Weiqun Zhang
+!! @date October 2016, LBNL
+!! @details This Fortran routine is used for tagging
+!!          the cells at a level for refinement.
+!!          In case of refinement a cell of the tagbox is
+!!          marked "set" otherwise "clear".
+!! @brief Fortran routine for tagging cells.
 
 ! ::: -----------------------------------------------------------
 ! ::: This routine will tag high error cells based on the state
@@ -17,6 +25,19 @@
 ! ::: level       => refinement level of this array
 ! ::: -----------------------------------------------------------
 
+!> Called by AmrOpal::ErrorEst.
+!! @param tag integer tag array
+!! @param tag_lo lower index extent of tag array
+!! @param tag_hi upper index extent of tag array
+!! @param state is the state array
+!! @param set is the integer value to tag cell fo refinement
+!! @param clear is the integer value to untag a cell
+!! @param lo is the lower left corner of the work region we are allowed to change
+!! @param hi is the upper right corner of the work region we are allowed to change
+!! @param dx is the cell size
+!! @param problo is the physical location of the lower left corner of the problem domain
+!! @param time is the problem evolution time
+!! @param level is the refinement level of this array
 subroutine state_error(tag,tag_lo,tag_hi, &
                        state,state_lo,state_hi, &
                        set,clear,&

ippl/test/AMR/analyse.py

+##
+# @file analyse.py
+# @author Matthias Frey
+# @date 26. Oct. 2016
+# @details The files generated by error.cpp can be visualized using this script.
+#          It plots the number of levels vs. the Euclidean error norm where the
+#          error is computed between the single- and multi-level solution.
+# @pre Environment variable OPAL_BUILD has to be set.
+# @brief Plot the Euclidean error norm vs. the level of refinement
+
+
+import numpy as np
+import os
+import matplotlib.pyplot as plt
+
+## Absolute path to OPAL build directory
+opal = os.environ['OPAL_BUILD']
+
+##
+# Create an error plot
+# @param filename is the absolute path
+# @param xlab is the label on the x-axis
+# @param ylab is the label on the y-axis
+def doPlot(filename, xlab, ylab):
+    # error with respect to single-level solve.
+    nLevels, l2err = np.loadtxt(filename, unpack=True)
+
+    plt.figure()
+    plt.plot(nLevels, l2err, "-o")
+    plt.xlabel(xlab)
+    plt.ylabel(ylab)
+    plt.show()
+
+
+doPlot(opal + "/ippl/test/AMR/l2_error_NOPARTICLES.dat",
+       '#levels', r'$L_{2}$-error')
+
+doPlot(opal + "/ippl/test/AMR/l2_error_UNIFORM.dat",
+       '#levels', r'$L_{2}$-error')
+
+doPlot(opal + "/ippl/test/AMR/l2_error_GAUSSIAN.dat",
+       '#levels', r'$L_{2}$-error')

ippl/test/AMR/error.cpp

@@ -84,7 +84,7 @@ int main(int argc, char* argv[]) {
     ParallelDescriptor::Barrier();
     
     if ( ParallelDescriptor::MyProc() == 0 && solved) {
-        std::ofstream out("l2_error.dat", std::ios::app);
+        std::ofstream out("l2_error_" + std::string(argv[6]) + ".dat", std::ios::app);
         out << nLevels << " " << l2error << std::endl;
         out.close();
     }

ippl/test/AMR/vis_iter.py

-# Author: Matthias Frey
-# Date: 19. Oct. 2016, LBNL
+##
+# @file vis_iter.py
+# @author Matthias Frey
+# @date 19. Oct. 2016, LBNL
 #
-# Script for visualizing the outpuf files
-# of iterative.cpp
-# It plots the potential (centered in longitudinal direction)
-# and the elecric field
-# components (centered)
+# @pre Environment variable OPAL_BUILD has to be set.
+# @details Script for visualizing the output files of iterative.cpp.
+#          It plots the potential (centered in longitudinal direction)
+#          and the elecric field components (centered)
+# @brief Plot potential and electric field of iterative.cpp
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -37,6 +39,9 @@ xi = np.extract(k == h, i)
 yi = np.extract(k == h, j)
 phi = np.extract(k == h, phi)
 
+print max(phi)
+print min(phi)
+
 
 
 doPlot(xi, yi, phi, 'grid in x', 'grid in y', r'$\phi$', 221, 'Density Plot',

ippl/test/AMR/visualize.py

-# Matthias Frey
-# 14. October 2016, LBNL
+##
+# @file visualize.py
+# @author Matthias Frey
+# @date 14. October 2016, LBNL
 # 
-# Plot the electric self-field, density and self-field
+# @pre Environment variable OPAL_BUILD has to be set.
+# @details Plot the electric self-field, density and self-field
 # potential using the yt framework.
 # 1. mpirun -np #cores testSolver
 # 2. python visualize.py (make sure you sourced the yt directory $YT_DIR/yt-x86_64/bin/activate)
-
+# @brief Slice plots of plotfiles generated by writePlotFile.H
 
 import os
 import yt