if opts.refine:
# FIXME: We use overlap threshold as a proxy for confidence level
selected = get_cr_from_grid(selected, results, cr_thr=opts.overlap_threshold)
print "Selected %d cells from %3.2f%% confidence region" % (len(selected), opts.overlap_threshold*100)
if opts.prerefine:
print "Performing refinement for points with overlap > %1.3f" % opts.overlap_threshold
pt_select = results > opts.overlap_threshold
selected = selected[pt_select]
results = results[pt_select]
grid, spacing = amrlib.refine_regular_grid(selected, spacing, return_cntr=True)
else:
grid, spacing = amrlib.refine_regular_grid(selected, spacing, return_cntr=opts.setup)
print "%d cells after refinement" % len(grid)
grid = amrlib.prune_duplicate_pts(grid, init_region._bounds, spacing)
#
# Clean up
#
grid = numpy.array(grid)
bounds_mask = amrlib.check_grid(grid, intr_prms, opts.distance_coordinates)
grid = grid[bounds_mask]
print "%d cells after bounds checking" % len(grid)
if len(grid) == 0:
exit("All cells would be removed by physical boundaries.")
# Convert back to physical mass
grid = amrlib.apply_inv_transform(grid, intr_prms, opts.distance_coordinates)
if opts.prerefine:
print "Performing refinement for points with overlap > %1.3f" % opts.overlap_threshold
pt_select = results > opts.overlap_threshold
selected = selected[pt_select]
results = results[pt_select]
grid, spacing = amrlib.refine_regular_grid(selected,
spacing,
return_cntr=True)
else:
grid, spacing = amrlib.refine_regular_grid(selected,
spacing,
return_cntr=opts.setup)
print "%d cells after refinement" % len(grid)
grid = amrlib.prune_duplicate_pts(grid, init_region._bounds, spacing)
#
# Clean up
#
grid = numpy.array(grid)
bounds_mask = amrlib.check_grid(grid, intr_prms, opts.distance_coordinates)
grid = grid[bounds_mask]
print "%d cells after bounds checking" % len(grid)
if len(grid) == 0:
exit("All cells would be removed by physical boundaries.")
# Convert back to physical mass
grid = amrlib.apply_inv_transform(grid, intr_prms, opts.distance_coordinates)