def eval_grid(t1, cells, intr_prms):
t1_norm = None
t1_copy = copy(t1)
overlaps = []
for cell in cells:
try:
prms = intr_prms
tmp = numpy.array(cell._center)[numpy.newaxis,:]
tmp = amrlib.apply_inv_transform(tmp, prms, opts.distance_coordinates).T
for p, pval in zip(prms, tmp):
#if p == "lam_tilde":
#t1_copy.psi0 = pval[0]
#t1_copy.psi3 = 0
#else:
setattr(t1_copy, p, pval[0])
except AssertionError:
rejected.append((cell, 0.0))
continue
# FIXME: Hardcoded waveforms
olap, t1_norm, _ = lalsimutils.overlap(t1, t1_copy, ovrlp_obj, 0.125, 40, "TaylorF2", "TaylorF2", t1_norm=t1_norm)
overlaps.append(olap)
return overlaps
def eval_grid(t1, cells, intr_prms):
t1_norm = None
t1_copy = copy(t1)
overlaps = []
for cell in cells:
try:
prms = intr_prms
tmp = numpy.array(cell._center)[numpy.newaxis, :]
tmp = amrlib.apply_inv_transform(tmp, prms,
opts.distance_coordinates).T
for p, pval in zip(prms, tmp):
#if p == "lam_tilde":
#t1_copy.psi0 = pval[0]
#t1_copy.psi3 = 0
#else:
setattr(t1_copy, p, pval[0])
except AssertionError:
rejected.append((cell, 0.0))
continue
# FIXME: Hardcoded waveforms
olap, t1_norm, _ = lalsimutils.overlap(t1,
t1_copy,
ovrlp_obj,
0.125,
40,
"TaylorF2",
"TaylorF2",
t1_norm=t1_norm)
overlaps.append(olap)
return overlaps
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)
cells = amrlib.grid_to_cells(grid, spacing)
if opts.setup:
grid_group = amrlib.init_grid_hdf(init_region, opts.setup + ".hdf", opts.overlap_threshold, opts.distance_coordinates, intr_prms=intr_prms)
level = amrlib.save_grid_cells_hdf(grid_group, cells, "mass1_mass2", intr_prms=intr_prms)
else:
grp = amrlib.load_grid_level(opts.refine, None)
level = amrlib.save_grid_cells_hdf(grp, cells, "mass1_mass2", intr_prms)
print "Selected %d cells for further analysis." % len(cells)
if opts.setup:
fname = "HL-MASS_POINTS_LEVEL_0-0-1.xml.gz"
write_to_xml(cells, intr_prms, pin_prms, None, fname, verbose=opts.verbose)
else:
#m = re.search("LEVEL_(\d+)", opts.result_file)
ovrlp = lalsimutils.Overlap(fLow=f_low, fMax=2000, deltaF=delta_f, psd=psd, analyticPSD_Q=False)
idx_range = range(args.tmplt_start_index or 0, args.tmplt_end_index or len(tmplt_bank))
# FIXME:
npts = len(tmplt_bank)
import h5py
h5file = h5py.File("test.hdf", "w")
olapdata = h5file.create_group(wtype)
for iprm in intr_prms:
dat = numpy.array([getattr(t, iprm) for t in tmplt_bank])
olapdata.create_dataset(iprm, maxshape=(npts,), data=dat)
olapmat = olapdata.create_dataset("overlaps", shape=(npts, npts))
for i1, pt in enumerate(pts):
opt = amrlib.apply_inv_transform(pts[i1,numpy.newaxis].copy(), intr_prms, "mchirp_eta")[0]
fname = "%s/%s_%d.json" % (bdir, wtype, i1)
# FIXME: This makes my eyes bleed...
toc["types"][wtype].append({})
toc["types"][wtype][-1]["mass1"] = opt[0]
toc["types"][wtype][-1]["mass2"] = opt[1]
toc["types"][wtype][-1]["filename"] = fname
if i1 not in idx_range:
continue
if os.path.exists(fname):
continue
dist, idx = tree.query(pt, k=npts, return_distance=True)
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)
cells = amrlib.grid_to_cells(grid, spacing)
if opts.setup:
grid_group = amrlib.init_grid_hdf(init_region,
opts.setup + ".hdf",
opts.overlap_threshold,
opts.distance_coordinates,
intr_prms=intr_prms)
level = amrlib.save_grid_cells_hdf(grid_group,
cells,
"mass1_mass2",
intr_prms=intr_prms)
else:
grp = amrlib.load_grid_level(opts.refine, None)
level = amrlib.save_grid_cells_hdf(grp, cells, "mass1_mass2", intr_prms)
idx_range = range(args.tmplt_start_index or 0, args.tmplt_end_index
or len(tmplt_bank))
# FIXME:
npts = len(tmplt_bank)
import h5py
h5file = h5py.File("test.hdf", "w")
olapdata = h5file.create_group(wtype)
for iprm in intr_prms:
dat = numpy.array([getattr(t, iprm) for t in tmplt_bank])
olapdata.create_dataset(iprm, maxshape=(npts, ), data=dat)
olapmat = olapdata.create_dataset("overlaps", shape=(npts, npts))
for i1, pt in enumerate(pts):
opt = amrlib.apply_inv_transform(pts[i1, numpy.newaxis].copy(), intr_prms,
"mchirp_eta")[0]
fname = "%s/%s_%d.json" % (bdir, wtype, i1)
# FIXME: This makes my eyes bleed...
toc["types"][wtype].append({})
toc["types"][wtype][-1]["mass1"] = opt[0]
toc["types"][wtype][-1]["mass2"] = opt[1]
toc["types"][wtype][-1]["filename"] = fname
if i1 not in idx_range:
continue
if os.path.exists(fname):
continue
dist, idx = tree.query(pt, k=npts, return_distance=True)
