#
# Build (or retrieve) the initial region
#
if opts.refine or opts.prerefine:
init_region, region_labels = amrlib.load_init_region(opts.refine or opts.prerefine, get_labels=True)
else:
####### BEGIN INITIAL GRID CODE #########
init_region, idx = determine_region(pt, pts, ovrlp, opts.overlap_threshold, expand_prms)
region_labels = intr_prms
# FIXME: To be reimplemented in a different way
#if opts.expand_param is not None:
#expand_param(init_region, opts.expand_param)
# TODO: Alternatively, check density of points in the region to determine
# the points to a side
grid, spacing = amrlib.create_regular_grid_from_cell(init_region, side_pts=5, return_cells=True)
# "Deactivate" cells not close to template points
# FIXME: This gets more and more dangerous in higher dimensions
# FIXME: Move to function
tree = BallTree(grid)
if opts.deactivate:
get_idx = set()
for pt in pts[idx:]:
get_idx.add(tree.query(pt, k=1, return_distance=False)[0][0])
selected = grid[numpy.array(list(get_idx))]
else:
selected = grid
# Make sure all our dimensions line up
# FIXME: We just need to be consistent from the beginning
opts.overlap_threshold,
expand_prms)
region_labels = intr_prms
# FIXME: To be reimplemented in a different way
#if opts.expand_param is not None:
#expand_param(init_region, opts.expand_param)
else:
# Override initial region -- use with care
_, init_region = common_cl.parse_param(opts.initial_region)
region_labels = init_region.keys()
init_region = amrlib.Cell(
numpy.vstack(init_region[k] for k in region_labels))
# TODO: Alternatively, check density of points in the region to determine
# the points to a side
grid, spacing = amrlib.create_regular_grid_from_cell(
init_region, side_pts=opts.points_per_side / 2, return_cells=True)
# "Deactivate" cells not close to template points
# FIXME: This gets more and more dangerous in higher dimensions
# FIXME: Move to function
tree = BallTree(grid)
if opts.deactivate:
get_idx = set()
for pt in pts[idx:]:
get_idx.add(
tree.query(numpy.atleast_2d(pt), k=1,
return_distance=False)[0][0])
selected = grid[numpy.array(list(get_idx))]
else:
selected = grid
init_region, region_labels = amrlib.load_init_region(opts.refine
or opts.prerefine,
get_labels=True)
else:
####### BEGIN INITIAL GRID CODE #########
init_region, idx = determine_region(pt, pts, ovrlp, opts.overlap_threshold,
expand_prms)
region_labels = intr_prms
# FIXME: To be reimplemented in a different way
#if opts.expand_param is not None:
#expand_param(init_region, opts.expand_param)
# TODO: Alternatively, check density of points in the region to determine
# the points to a side
grid, spacing = amrlib.create_regular_grid_from_cell(init_region,
side_pts=5,
return_cells=True)
# "Deactivate" cells not close to template points
# FIXME: This gets more and more dangerous in higher dimensions
# FIXME: Move to function
tree = BallTree(grid)
if opts.deactivate:
get_idx = set()
for pt in pts[idx:]:
get_idx.add(tree.query(pt, k=1, return_distance=False)[0][0])
selected = grid[numpy.array(list(get_idx))]
else:
selected = grid
# Make sure all our dimensions line up
####### BEGIN INITIAL GRID CODE #########
if opts.initial_region is None:
init_region, idx = determine_region(pt, pts, ovrlp, opts.overlap_threshold, expand_prms)
region_labels = intr_prms
# FIXME: To be reimplemented in a different way
#if opts.expand_param is not None:
#expand_param(init_region, opts.expand_param)
else:
# Override initial region -- use with care
_, init_region = common_cl.parse_param(opts.initial_region)
region_labels = init_region.keys()
init_region = amrlib.Cell(numpy.vstack(init_region[k] for k in region_labels))
# TODO: Alternatively, check density of points in the region to determine
# the points to a side
grid, spacing = amrlib.create_regular_grid_from_cell(init_region, side_pts=opts.points_per_side / 2, return_cells=True)
# "Deactivate" cells not close to template points
# FIXME: This gets more and more dangerous in higher dimensions
# FIXME: Move to function
tree = BallTree(grid)
if opts.deactivate:
get_idx = set()
for pt in pts[idx:]:
get_idx.add(tree.query(numpy.atleast_2d(pt), k=1, return_distance=False)[0][0])
selected = grid[numpy.array(list(get_idx))]
else:
selected = grid
# Make sure all our dimensions line up
# FIXME: We just need to be consistent from the beginning
