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smartsplit.py
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smartsplit.py
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from enlib import config
parser = config.ArgumentParser()
parser.add_argument("sel")
parser.add_argument("odir")
parser.add_argument("-n", "--nsplit",type=int, default=4)
parser.add_argument("-R", "--rad", type=float, default=0.7)
parser.add_argument("-r", "--res", type=float, default=0.5)
parser.add_argument("-b", "--block", type=str, default="day")
parser.add_argument("-O", "--nopt", type=int, default=2000)
parser.add_argument("-m", "--mode", type=str, default="crosslink", help="plain, crosslink or scanpat")
parser.add_argument("-w", "--weight",type=str, default="plain")
parser.add_argument("--opt-mode", type=str, default="linear")
parser.add_argument("--constraint", type=str, default=None)
parser.add_argument( "--scanpat-tol", type=float, default=1)
args = parser.parse_args()
import numpy as np, sys, glob, re, os
from enlib import utils
with utils.nowarn():
from enlib import fastweight, enmap
from enact import filedb, actdata
filedb.init()
ids = filedb.scans[args.sel]
db = filedb.scans.select(ids)
ntod = len(db)
nsplit = args.nsplit
if len(ids) < nsplit:
print("%d tods is too few for %d splits" % (ntod, nsplit))
sys.exit(1)
nopt = args.nopt if nsplit > 1 else 0
optimize_subsets = (args.mode == "crosslink" or args.mode=="scanpat")
detdir = args.odir + "/details"
utils.mkdir(args.odir)
utils.mkdir(detdir)
# Determine which arrays we have. We can't process arrays independently,
# as they in principle have correlated noise. But we also want to distinguish
# between them
pre, _, anames = np.char.rpartition(ids,".").T
if args.mode == "crosslink":
# Treat rising and setting as separate arrays"
rise = utils.rewind(db.data["baz"],0,360) > 0
anames[rise] = np.char.add(anames[rise], "r")
anames[~rise] = np.char.add(anames[~rise],"s")
elif args.mode == "scanpat":
# Treat each scanning pattern as a different array
patterns = np.array([db.data["baz"],db.data["bel"],db.data["waz"]]).T
pids = utils.label_unique(patterns, axes=(1,), atol=args.scanpat_tol)
npat = np.max(pids)+1
for pid in range(npat):
anames[pids==pid] = np.char.add(anames[pids==pid], "p%d" % pid)
else:
# In this case we don't split arrays
pass
def ids2ctimes(ids): return np.char.partition(ids,".").T[0].astype(int)
def fix_aname(aname): return aname.replace("ar","pa").replace(":","_")
anames = np.array([fix_aname(aname) for aname in anames])
arrays, ais, nper = np.unique(anames, return_counts=True, return_inverse=True)
narray = len(arrays)
ctime = ids2ctimes(pre)
sys.stderr.write("found arrays " + " ".join(arrays) + "\n")
# Get our block splitting parameters
toks = args.block.split(":")
block_mode = toks[0]
block_size = float(toks[1]) if len(toks) > 1 else 1
def calc_ndig(a): return int(np.log10(a))+1 if a > 0 else 1
def atolist(a): return ",".join(["%d" % v for v in a])
def calc_overlap_matrix(split_ids):
flat = [[id.split(".")[0] for id in ids] for split in split_ids for ids in split]
return [[len(set(fa)&set(fb)) for fb in flat] for fa in flat]
def format_overlap_matrix(mat):
res = ""
for row in mat:
for col in row:
res += " %4d" % col
res += "\n"
return res
def read_existing(dirname):
# Read an existing split set, returning [(array,splits),(array,splits),...],
# where splits = [ids1, ids2, ...].
work = {}
for fname in glob.glob(dirname + "/ids*.txt"):
m = re.match(r"ids_([^_]+_f[^_]+[rs])_set(\d+)\.txt", os.path.basename(fname))
if not m: continue
array = m.group(1)
split = int(m.group(2))
ids = np.loadtxt(fname, usecols=(0,), dtype="U")
if array not in work: work[array] = {}
work[array][split] = ids
# Check that we have a consistent number of splits
shit = np.bincount([split for key in work for split in work[key]])
if np.any(shit != shit[0]): raise ValueError("Inconsistent number of splits in input directory")
nsplit = len(shit)
# Reformat to lists insted of dicts for the splits
aset = {array:[work[array][split] for split in range(nsplit)] for array in work}
return aset
def match_existing(aset, ctimes):
# Given an aset as returned by read_existing, convert TOD ids to ctimes and match them against
# our existing ctime list. Returns an [nctime] array containing the id of the input split
# it belongs to. If any id is present in aset but not in ctimes, an IndexError is raised.
# Unrestricted ctimes will be set to -1.
ind_ownership = np.full(len(ctimes),-1,int)
for array in aset:
for split, ids in enumerate(aset[array]):
my_ctimes = ids2ctimes(ids)
my_inds = utils.find(ctimes, my_ctimes)
ind_ownership[my_inds] = split
return ind_ownership
def get_block_ownership(ind_ownership, block_inds):
# Given information of which split already owns which ctime index,
# return the corresponding information at the block level. Returns
# ValueError if a block has conflicting ownership
block_ownership = np.full(len(block_inds),-1,int)
for bi, ablock in enumerate(block_inds):
bown = []
for inds in ablock:
bown.append(ind_ownership[inds])
bown = np.concatenate(bown)
# Filter out unowned markers
bown = bown[bown>=0]
if len(bown) == 0:
block_ownership[bi] = -1
else:
if np.any(bown != bown[0]): raise ValueError("Inconsistent ownership for block %d: %s" % (bi, str(np.unique(bown))))
block_ownership[bi] = bown[0]
return block_ownership
# Peform the actual splitting. The goal is to get a
# [nblock,narray][{tod_index}] list defining the blocks
# (with tod_index being an index into the original db)
if block_mode == "tod":
# TOD as the building block
_, bid_raw = np.unique(ctime, return_inverse=True)
elif block_mode == "day":
bid_raw = db.data["jon"]
else: raise ValueError("Unrecognized block mode '%s'" % block_mode)
bid_raw = (bid_raw//block_size).astype(int)
# We know know which block each tod belongs to. But some of these will
# be empty, so we want to prune those, This results in the proper block id
u, bid, ucounts = np.unique(bid_raw, return_counts=True, return_inverse=True)
nblock = len(u)
block_inds = [[[] for ai in range(narray)] for bi in range(nblock)]
block_size = np.zeros(nblock,int)
for i, bi in enumerate(bid):
block_inds[bi][ais[i]].append(i)
block_size[bi] += 1
block_inds = np.array(block_inds, object)
# Sort from biggest to smallest, to aid greedy algorithm
block_order = np.argsort(block_size)[::-1] # [nblock]
block_inds = block_inds[block_order] # [nblock,narr][{tod_indices}]
block_size = block_size[block_order] # [nblock]
# Apply any external constraints
if args.constraint:
aset = read_existing(args.constraint)
ind_ownership = match_existing(aset, ctime)
block_ownership = get_block_ownership(ind_ownership, block_inds)
else:
block_ownership = np.full(len(block_inds),-1,int)
fixed_blocks = np.where(block_ownership>=0)[0]
free_blocks = np.where(block_ownership<0)[0]
nfixed = len(fixed_blocks)
nfree = len(free_blocks)
sys.stderr.write("splitting %d:[%s] tods into %d splits via %d blocks%s" % (
ntod, atolist(nper), nsplit, nblock, (" with %d:%d free:fixed" % (nfree,nfixed)) if nfixed > 0 else "") + "\n")
# We assume that site and pointing offsets are the same for all tods,
# so get them based on the first one
entry = filedb.data[ids[0]]
site = actdata.read(entry, ["site"]).site
# Determine the bounding box of our selected data
bounds = db.data["bounds"].reshape(2,-1).copy()
bounds[0] = utils.rewind(bounds[0], bounds[0,0], 360)
box = utils.widen_box(utils.bounding_box(bounds.T), 4*args.rad, relative=False)
waz, wel = box[1]-box[0]
# Use fullsky horizontally if we wrap too far
if waz <= 180:
shape, wcs = enmap.geometry(pos=box[:,::-1]*utils.degree, res=args.res*utils.degree, proj="car", ref=(0,0))
else:
shape, wcs = enmap.fullsky_geometry(res=args.res*utils.degree)
y1, y2 = np.sort(enmap.sky2pix(shape, wcs, [box[:,1]*utils.degree,[0,0]])[0].astype(int))
shape, wcs = enmap.slice_geometry(shape, wcs, (slice(y1,y2),slice(None)))
sys.stderr.write("using %s workspace with resolution %.2f deg" % (str(shape), args.res) + "\n")
# Get the hitmap for each block
hits = enmap.zeros((nblock,narray)+shape, wcs)
ndig = calc_ndig(nblock)
sys.stderr.write("estimating hitmap for block %*d/%d" % (ndig,0,nblock))
for bi in range(nblock):
for ai in range(narray):
block_db = db.select(block_inds[bi,ai])
hits[bi,ai] = fastweight.fastweight(shape, wcs, block_db, array_rad=args.rad*utils.degree, site=site, weight=args.weight)
sys.stderr.write("%s%*d/%d" % ("\b"*(1+2*ndig),ndig,bi+1,nblock))
sys.stderr.write("\n")
# Build a mask for the region of interest per array
mask = enmap.zeros((narray,)+shape, wcs, bool)
nblock_per_pix = enmap.zeros((narray,)+shape, wcs, int)
nblock_lim = np.zeros(narray)
for ai in range(narray):
ahits = hits[:,ai]
ref = np.median(ahits[ahits>0])
nblock_per_pix[ai] = np.sum(ahits>ref*0.2,0)
nblock_ref = np.median(nblock_per_pix[ai][nblock_per_pix[ai]>0])
nblock_lim[ai] = min(2*nsplit, nblock_ref*0.2)
mask[ai] = nblock_per_pix[ai] > nblock_lim[ai]
sys.stderr.write("[%s] pixels hit by at least [%s] blocks\n" % (
atolist(np.sum(mask,(-2,-1))), atolist(nblock_lim)))
def calc_delta_score(split_hits, bhits, mask):
# fractional improvement is (split_hits + bhits)/split_hits -1 = bhits/split_hits
# This will often lead to division by zero. That is not catastrophic, but loses
# the ability to distinguish between multiple cases that would all fill in empty pixels.
# So we cap the ratio to a large number.
with utils.nowarn():
ratio = bhits/split_hits
ratio[np.isnan(ratio)] = 0
ratio = np.minimum(ratio, 1000)
return np.sum(ratio[:,mask],-1)
# Perform the split. Can't use the traditional greedy
# bucket algorithm where one always allocates to the
# emptiest one, because there are now npix ways to be
# empty. could allocate to the one that makes the biggest
# relative increase. That way filling in holes will always
# be prioritized, while increasing already high areas counts less.
#target = np.sum(hits,0)/nsplit
split_hits = enmap.zeros((nsplit,narray)+shape, wcs)
split_blocks = [[] for i in range(nsplit)]
split_fixed = [np.where(block_ownership==i)[0] for i in range(nsplit)]
ndig_free = calc_ndig(nfree)
ndig_fixed = calc_ndig(nfixed)
if nfixed > 0:
sys.stderr.write("allocating fixed block %*d/%d" % (ndig_fixed,0,nfixed))
for i, bi in enumerate(fixed_blocks):
split_hits[block_ownership[bi]] += hits[bi]
sys.stderr.write("%s%*d/%d" % ("\b"*(1+2*ndig_fixed),ndig_fixed,i+1,nfixed))
sys.stderr.write("\n")
sys.stderr.write("allocating free block %*d/%d" % (ndig_free,0,nfree))
else:
sys.stderr.write("allocating block %*d/%d" % (ndig_free,0,nfree))
for i, bi in enumerate(free_blocks):
bhits = hits[bi]
score = calc_delta_score(split_hits, bhits, mask)
best = np.argmax(score)
split_hits[best] += bhits
split_blocks[best].append(bi)
sys.stderr.write("%s%*d/%d" % ("\b"*(1+2*ndig_free),ndig_free,i+1,nfree))
sys.stderr.write("\n")
nswap = 0
odig = calc_ndig(nopt)
if args.opt_mode == "linear":
opt_order = np.arange(nopt)%max(1,nfree)
elif args.opt_mode == "random":
opt_order = np.random.randint(0, nfree, nopt)
sys.stderr.write("optimizing %*d/%d [%*d]" % (odig, 0, nopt, odig, 0))
for oi, i in enumerate(opt_order):
bi = free_blocks[i]
bhits = hits[bi]
# Which split is this block currently in? This could be sped up with a set, but
# will probably be fast enough anyway
for scur in range(nsplit):
if bi in split_blocks[scur]:
break
else: raise AssertionError("block not in any splits!")
# Simulate removing and readding
# Find the score decrease from removing it from this split
split_hits[scur] = np.maximum(0, split_hits[scur] - bhits)
split_blocks[scur].remove(bi)
score = calc_delta_score(split_hits, bhits, mask)
best = np.argmax(score)
split_hits[best] += bhits
split_blocks[best].append(bi)
if scur != best: nswap += 1
sys.stderr.write("%s%*d/%d [%*d]" % ("\b"*(3*odig+4),odig,oi+1,nopt,odig,nswap))
sys.stderr.write("\n")
# Merge the fixed and free groups
split_blocks = [np.concatenate([
np.array(split_blocks[i],int),
np.array(split_fixed[i], int),
]) for i in range(nsplit)]
# We now know which split each block belongs to. Use this and the block
# definitions to extract the ids that go into each split.
split_ids = [[[] for ai in range(narray)] for bi in range(nsplit)]
for si in range(nsplit):
for ai in range(narray):
for bi in split_blocks[si]:
split_ids[si][ai] += list(ids[block_inds[bi,ai]])
if optimize_subsets:
# If we are optimizing for crosslinking support we want to output information
# both about individual scanning directions and the overall ones. We do that
# by first handling the individual ones, and then combining them
for i in range(nsplit):
for ai, aname in enumerate(arrays):
enmap.write_map(detdir + "/hits_%s_set%d.fits" % (aname, i), split_hits[i,ai])
enmap.write_map(detdir + "/hits_masked_%s_set%d.fits" % (aname,i), split_hits[i,ai]*mask[ai])
with open(detdir + "/ids_%s_set%d.txt" % (aname,i), "w") as f:
for id in sorted(split_ids[i][ai]):
f.write(id + "\n")
sys.stderr.write("split stats by rise vs. set\n")
stats = ""
for ai, aname in enumerate(arrays):
stats += (" %-5s %s" % ("ntod",aname) + " ".join(["%7d" % len(split_ids[i][ai]) for i in range(nsplit)]) + "\n")
for name, op in [("min",np.min),("max",np.max),("mean",np.mean)]:
for ai, aname in enumerate(arrays):
stats += (" %-5s %s" % (name,aname) + " ".join(["%7.1f" % op(split_hits[i,ai][mask[ai]]) for i in range(nsplit)]) + "\n")
sys.stderr.write(stats)
with open(detdir + "/stats_rs.txt", "w") as f:
f.write(stats)
# Combine rising and setting versions of each array
if args.mode == "crosslink": patdig = 1
elif args.mode == "scanpat": patdig = calc_ndig(npat)+1
arrays_comb = sorted(list(set([a[:-patdig] for a in arrays])))
split_hits_comb = enmap.zeros((nsplit,len(arrays_comb))+shape, wcs)
split_ids_comb = [[[] for a in arrays_comb] for bi in range(nsplit)]
mask_comb = enmap.zeros((len(arrays_comb),)+shape, wcs, bool)
for an, anew in enumerate(arrays_comb):
for ao, aold in enumerate(arrays):
if not aold[:-patdig] == anew: continue
for i in range(nsplit):
split_hits_comb[i,an] += split_hits[i,ao]
split_ids_comb[i][an] += split_ids[i][ao]
mask_comb[an] |= mask[ao]
arrays, split_hits, split_ids, mask = arrays_comb, split_hits_comb, split_ids_comb, mask_comb
sys.stderr.write("split stats\n")
stats = ""
for ai, aname in enumerate(arrays):
stats += (" %-5s %s" % ("ntod",aname) + " ".join(["%7d" % len(split_ids[i][ai]) for i in range(nsplit)]) + "\n")
for name, op in [("min",np.min),("max",np.max),("mean",np.mean)]:
for ai, aname in enumerate(arrays):
stats += (" %-5s %s" % (name,aname) + " ".join(["%7.1f" % op(split_hits[i,ai][mask[ai]]) for i in range(nsplit)]) + "\n")
sys.stderr.write(stats)
with open(detdir + "/stats.txt", "w") as f:
f.write(stats)
# Print overlap matrix
sys.stderr.write("overlap matrix\n")
omat = calc_overlap_matrix(split_ids)
overlap = format_overlap_matrix(omat)
sys.stderr.write(overlap)
with open(detdir + "/overlap.txt", "w") as f:
f.write(overlap)
# Get the ids that go into each split
for i in range(nsplit):
for ai, aname in enumerate(arrays):
with open(args.odir + "/ids_%s_set%d.txt" % (aname,i), "w") as f:
for id in sorted(split_ids[i][ai]):
f.write(id + "\n")
enmap.write_map(detdir + "/hits_%s_set%d.fits" % (aname, i), split_hits[i,ai])
enmap.write_map(detdir + "/hits_masked_%s_set%d.fits" % (aname,i), split_hits[i,ai]*mask[ai])
enmap.write_map(detdir + "/nblock.fits", nblock_per_pix)
enmap.write_map(detdir + "/mask.fits", mask.astype(np.int16))