# 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)