selected = get_evidence_grid(selected, res_pts, intr_prms)
if opts.verbose:
print "Loaded %d result points" % len(selected)
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 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)
#spacing /= 2
selected = amrlib.apply_transform(selected, intr_prms, opts.distance_coordinates)
grid_tree = BallTree(selected)
grid_idx = []
# Reorder the grid points to match their weight indices
for res in res_pts:
dist, idx = grid_tree.query(res, k=1)
# Stupid floating point inexactitude...
#assert all([numpy.isclose(a, b) for a, b in zip(res, selected[idx[0][0]])])
grid_idx.append(idx[0][0])
selected = get_cr_from_grid(selected[grid_idx], results, cr_thr=0.9)
print "Selected %d cells from %.2f%% confidence region" % (len(selected), 0.9)
####
grid, spacing = amrlib.refine_regular_grid(selected, spacing)
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.")
opts.distance_coordinates)
grid_tree = BallTree(selected)
grid_idx = []
# Reorder the grid points to match their weight indices
for res in res_pts:
dist, idx = grid_tree.query(res, k=1)
# Stupid floating point inexactitude...
#assert all([numpy.isclose(a, b) for a, b in zip(res, selected[idx[0][0]])])
grid_idx.append(idx[0][0])
selected = get_cr_from_grid(selected[grid_idx], results, cr_thr=0.9)
print "Selected %d cells from %.2f%% confidence region" % (len(selected),
0.9)
####
grid, spacing = amrlib.refine_regular_grid(selected, spacing)
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.")
