def test_matchdata():
ifile1 = 'data/newexp/segmented_image/T05.png'
ifile2 = 'data/newexp/segmented_image/T06.png'
lfile1 = 'data/newexp/l_numbers/T05.txt'
lfile2 = 'data/newexp/l_numbers/T06.txt'
celldata1 = celldata.CellData(ifile1, lfile1)
celldata2 = celldata.CellData(ifile2, lfile2)
del(celldata1[1])
del(celldata2[1])
md = matchdata.MatchData(celldata1, celldata2)
v = celldata.Coords2D((8, -36))
md.match_on_restricted_l(8, v)
print "On l, matched:", len(md.current_ml)
md.set_displacement(v)
#md.match_on_centroids_with_area(7)
md.match_with_displacement_field(7)
print "Initial centroid + area: matched:", len(md.current_ml)
#pprint.pprint(md.current_ml)
md.match_with_displacement_field(10)
print "Local displacement: matched:", len(md.current_ml), "of", len(md.cdfrom)
md.match_with_displacement_field(10)
print "Local displacement: matched:", len(md.current_ml), "of", len(md.cdfrom)
md.match_with_displacement_field(10)
print "Local displacement: matched:", len(md.current_ml), "of", len(md.cdfrom)
test_divide_tracker(md)
def matchdata_from_exp_and_tp(expname, tp, names):
expdata = load_data(expname, names)
ifile1, lfile1, pfile1, vfile1 = expdata[tp]
ifile2, lfile2, pfile2, vfile2 = expdata[tp + 1]
sx1, sy1, sz1 = get_voxel_spacing(vfile1)
sx2, sy2, sz2 = get_voxel_spacing(vfile2)
celldata1 = celldata.CellData(ifile1, lfile=lfile1)
celldata2 = celldata.CellData(ifile2,
lfile=lfile2,
scale=(sx2 / sy1, sy2 / sy1))
#celldata2 = celldata.CellData(ifile2)
#celldata1 = celldata.CellData(ifile1, lfile1)
#celldata2 = celldata.CellData(ifile2, lfile2)
# until we fix the l number generation
try:
del (celldata1[1])
del (celldata2[1])
except KeyError:
pass
mda = matchdata.MatchData(celldata1, celldata2)
return mda
def test_overall_centroid():
ifile = 'data/newexp/segmented_image/T00.png'
ifile2 = 'data/newexp/segmented_image/T01.png'
cd = celldata.CellData(ifile)
cd2 = celldata.CellData(ifile2)
print "Loaded celldata"
sys.stdout.flush()
#all = sum(cd.cells, celldata.Cell([]))
#allpl = [c.pl for c in cd.cells]
#myl = list(itertools.chain.from_iterable(allpl))
#xs, ys = zip(*myl)
#print sum(xs) / len(xs), sum(ys) / len(ys)
print cd.center
print cd2.center
def main():
#try:
# image_file1 = sys.argv[1]
# image_file2 = sys.argv[2]
#except:
# print "Usage %s image_file1 image_file2" % os.path.basename(sys.argv[0])
# sys.exit(0)
ifile1 = 'data/newexp/segmented_image/T05.png'
ifile2 = 'data/newexp/segmented_image/T06.png'
lfile1 = 'data/newexp/l_numbers/T05.txt'
lfile2 = 'data/newexp/l_numbers/T06.txt'
celldata1 = celldata.CellData(ifile1, lfile1)
celldata2 = celldata.CellData(ifile2, lfile2)
md = MatchData(celldata1, celldata2)
print md.best_matches_on_l(15, 1)
def matchdata_from_exp_and_tp(expname, tp, names):
#names = ['Segmented image', 'L numbers', 'Projection']
#names = ['New segmented image', 'L numbers', 'Gaussian projection']
expdata = load_data(expname, names)
ifile1, lfile1, pfile1 = expdata[tp]
ifile2, lfile2, pfile2 = expdata[tp + 1]
celldata1 = celldata.CellData(ifile1)
celldata2 = celldata.CellData(ifile2)
#celldata1 = celldata.CellData(ifile1, lfile1)
#celldata2 = celldata.CellData(ifile2, lfile2)
# until we fix the l number generation
try:
del (celldata1[1])
del (celldata2[1])
except KeyError:
pass
mda = matchdata.MatchData(celldata1, celldata2)
return mda
def test_for_ls(image_file, l_file):
ln = lnumbers.parse_l_file(l_file)
cd = celldata.CellData(image_file)
lkeys = set(ln.keys())
ckeys = set(cd.keys())
unique_to_l = list((lkeys | ckeys) - ckeys)
unique_to_c = list((lkeys | ckeys) - lkeys)
if len(unique_to_c) or len(unique_to_l):
print "For %s/%s" % (image_file, l_file)
if len(unique_to_c):
print " Unique to data from image file:", unique_to_c
if len(unique_to_l):
print " Unique to data from l file:", unique_to_l
def test_centroid_summer():
ifile = 'data/newexp/segmented_image/T05.png'
cd = celldata.CellData(ifile)
cells = [cd[150], cd[151], cd[152]]
for c in cells:
print c.centroid()
#print cells[0].pl + cells[1].pl
tpl = sum([c.pl for c in cells], [])
xs, ys = zip(*tpl)
x, y = sum(xs) / len(tpl), sum(ys) / len(tpl)
print Coords2D((x, y))
#newcell = cells[0] + cells[1] + cells[2]
newcell = sum(cells, celldata.Cell([]))
print newcell.centroid()
def test_l_numbers():
expname = 'newexp'
tp = 5
names = ['Segmented image', 'L numbers', 'Projection', 'Microscope metadata']
expdata = load_data(expname, names)
ifile1, lfile1, pfile1, vfile1 = expdata[tp]
ifile2, lfile2, pfile2, vfile2 = expdata[tp + 1]
celldata1 = celldata.CellData(ifile1, lfile=lfile1)
#celldata2 = celldata.CellData(ifile2, lfile=lfile2)
#md = matchdata.MatchData(celldata1, celldata2)
#print md.lmatrix.best_match(515)
lm = lnumbers.LMatrix.from_files(lfile1, lfile2)
#for id, vals in lm.ln1.iteritems():
# print id, lm.best_n_matches(id, 5), sum(vals[5:9])
lobiness = [(id, sum(vals[6:11])) for id, vals in lm.ln1.iteritems()]
slobi = sorted(lobiness, key=itemgetter(1))
blobi = slobi[-45:-5]
ml = read_ml('data/newexp/matches/T%02dT%02d.match' % (tp, tp + 1))
ts = 0
for id, score in blobi:
try:
real_m = ml[id]
except KeyError:
real_m = []
bm = lm.best_n_matches(id, 5)
success = False
for m in real_m:
if m in bm:
success = True
ts += 1
print id, score, bm, real_m, success
print ts
print zip(*blobi)[0]
def test_matchdata2():
expname = 'newexp'
tp = 5
names = ['Segmented image', 'L numbers', 'Projection', 'Microscope metadata']
expdata = load_data(expname, names)
ifile1, lfile1, pfile1, vfile1 = expdata[tp]
ifile2, lfile2, pfile2, vfile2 = expdata[tp + 1]
ml = read_ml('data/newexp/matches/T%02dT%02d.match' % (tp, tp + 1))
sx1, sy1, sz1 = get_voxel_spacing(vfile1)
sx2, sy2, sz2 = get_voxel_spacing(vfile2)
celldata1 = celldata.CellData(ifile1, lfile=lfile1)
celldata2 = celldata.CellData(ifile2, lfile=lfile2, scale=(sx2/sy1, sy2/sy1))
md = matchdata.MatchData(celldata1, celldata2)
allas = []
for cid in md.get_loby_candidates():
bm = md.lmatrix.best_n_matches(cid, 5)
areas = [float(md.cdto[tid].area)/md.cdfrom[cid].area for tid in bm]
try:
real_m = ml[cid]
except KeyError:
real_m = []
print cid
print bm
print real_m
print areas
for a in areas:
allas.append(a)
#print sorted(allas)[len(allas)/2], len(allas)
pml = {id : md.lmatrix.best_n_matches(id, 5)
for id in md.get_loby_candidates()}
pprint.pprint(pml)
for fid, tids in pml.iteritems():
for tid in tids:
delta_a = float(md.cdto[tid].area) / md.cdfrom[fid].area
if delta_a < 1.0 or delta_a > 1.7:
pml[fid].remove(tid)
pprint.pprint(pml)
ds = []
for fid, tids in pml.iteritems():
for tid in tids:
ds.append(abs(md.cdto[tid].centroid - md.cdfrom[fid].centroid))
med_d = sorted(ds)[len(ds)/3]
for fid, tids in pml.iteritems():
for tid in tids:
d = abs(md.cdto[tid].centroid - md.cdfrom[fid].centroid)
if d > med_d:
pml[fid].remove(tid)
pprint.pprint(pml)
failed = 0
good = 0
for fid in pml:
#print fid,
try:
real_m = ml[fid]
except KeyError:
real_m = []
if len(real_m) == 1 and len(pml[fid]):
if real_m[0] != pml[fid][0]:
failed +=1
print 'BAD'
else:
good += 1
print failed, good, float(good) / (failed + good)
#ml = {fid : pml[fid][:1] for fid in pml}
ml = md.get_possible_ml()
s = random.sample(ml, 5)
sd = {k : ml[k] for k in random.sample(ml, 5)}
cs = []
for fid, tids in sd.iteritems():
fc = md.cdfrom[fid].centroid
tc = md.cdto[tids[0]].centroid
cs.append((fc, tc))
#inp = [(f.centroid, msum([t for t in ts]).centroid )
#inp = [(f.centroid, msum([t.centroid for t in ts]) )
# for f, ts in self.itermatches()]
center, vd, s = md.calc_iso_params(cs)
print md.sampled_iso_params()
def test_smarter_centroid():
ifile = 'data/newexp/segmented_image/T05.png'
cd = celldata.CellData(ifile)
print cd[150].centroid
def run(self):
self.celld = celldata.CellData(self.ifile)
