# FIXME: If we have more than 1 copies -- This is tricky because we need
# to pare down the duplicate sngl rows too
maxlnevid = numpy.max([s.snr for s in results])
total_evid = numpy.exp([s.snr - maxlnevid for s in results]).sum()
for res in results:
res.snr = numpy.exp(res.snr - maxlnevid) / total_evid
# FIXME: this needs to be done in a more consistent way
results = numpy.array([res.snr for res in results])
#
# 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)
# overflows later on
# FIXME: If we have more than 1 copies -- This is tricky because we need
# to pare down the duplicate sngl rows too
maxlnevid = numpy.max([s.snr for s in results])
total_evid = numpy.exp([s.snr - maxlnevid for s in results]).sum()
for res in results:
res.snr = numpy.exp(res.snr - maxlnevid)/total_evid
# FIXME: this needs to be done in a more consistent way
results = numpy.array([res.snr for res in results])
#
# 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
# overflows later on
# FIXME: If we have more than 1 copies -- This is tricky because we need
# to pare down the duplicate sngl rows too
maxlnevid = numpy.max([s.snr for s in results])
total_evid = numpy.exp([s.snr - maxlnevid for s in results]).sum()
for res in results:
res.snr = numpy.exp(res.snr - maxlnevid)/total_evid
# FIXME: this needs to be done in a more consistent way
results = numpy.array([res.snr for res in results])
#
# Build (or retrieve) the initial region
#
if opts.refine or opts.prerefine:
init_region = amrlib.load_init_region(opts.refine or opts.prerefine)
else:
####### BEGIN INITIAL GRID CODE #########
init_region, idx = determine_region(pt, pts, ovrlp, opts.overlap_threshold, expand_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)
