def process_data(num,
name=None,
microns=None,
outdir=None,
adir=None,
zdir=None,
gtype=None):
tic = time.time()
e = cranium.embryo(name, num, outdir)
e.add_channel(os.path.join(adir, 'AT_' + num + '_Probabilities.h5'), 'AT')
e.add_channel(os.path.join(zdir, gtype + '_' + num + '_Probabilities.h5'),
gtype)
e.process_channels(0.25, 0.5, 7, [1, 1, 1], microns, 2, 'AT', [0, 2, 1],
['x', 'z'])
e.save_psi()
toc = time.time()
print(num, toc - tic)
def process(num, expname=None, outdir=None, gtype=None, adir=None, zdir=None):
tic = time.time()
e = cranium.embryo(expname, num, outdir)
e.add_channel(os.path.join(adir, 'AT_' + num + '_Probabilities.h5'), 'AT')
e.add_channel(os.path.join(zdir, gtype + '_' + num + '_Probabilities.h5'),
gtype)
e.chnls['AT'].preprocess_data(genthresh, [1, 1, 1], microns)
e.chnls[gtype].preprocess_data(genthresh, [1, 1, 1], microns)
e.chnls['AT'].calculate_pca_median(e.chnls['AT'].raw_data, medthresh,
radius, microns)
pca = e.chnls['AT'].pcamed
e.chnls['AT'].pca_transform_3d(e.chnls['AT'].df_thresh,
pca,
comporder,
fitdim,
deg=2)
mm = e.chnls['AT'].mm
vertex = e.chnls['AT'].vertex
e.chnls[gtype].pca_transform_3d(e.chnls[gtype].df_thresh,
pca,
comporder,
fitdim,
deg=2,
mm=mm,
vertex=vertex)
e.chnls['AT'].transform_coordinates()
e.chnls[gtype].transform_coordinates()
e.save_psi()
toc = time.time()
print(num, toc - tic)
import cranium
expname = 'test'
samplenum = '01'
directory = 'data'
#Create an embryo object that facilitates data processing
e = cranium.embryo(expname, samplenum, directory)
#For each channel in your sample, add a channel with a unique name, e.g. 'c1' or 'c2'
e.add_channel('data/C1/AT_01_Probabilities.h5', 'c1')
# e.add_channel('data/C2/ZRF1_01_Probabilities.h5','c2')
genthresh = 0.5
scale = [1, 1, 1]
microns = [0.16, 0.16, 0.21]
#Threshold each channel and scale points according to voxel dimension in microns
e.chnls['c1'].preprocess_data(genthresh, scale, microns)
# e.chnls['c2'].preprocess_data(genthresh,scale,microns)
medthresh = 0.25
radius = 20
microns = [0.16, 0.16, 0.21]
#Run PCA on the structural channel, in this case, c1
e.chnls['c1'].calculate_pca_median(e.chnls['c1'].raw_data, medthresh, radius,
microns)
#Save the pca object that includes the transformation matrix
pca = e.chnls['c1'].pcamed
def process(num, P=None):
'''
Run through the processing steps for a single sample through saving psi files
:param int num: Index of the file that is currently being processed
:param :class:`paramClass` P: Object containing all variables from config file
'''
tic = time.time()
print(num, 'Starting sample')
#Extract sample number from c1 filename
snum = re.findall(r'\d+', P.c1_files[num].split('.')[0])[0]
e = cranium.embryo(P.expname, snum, P.outdir)
#Add channels and preprocess data
try:
e.add_channel(os.path.join(P.c1_dir, P.c1_files[num]), P.c1_key)
e.chnls[P.c1_key].preprocess_data(P.genthresh, P.scale, P.microns)
for i in range(len(P.Lcdir)):
e.add_channel(os.path.join(P.Lcdir[i], P.Lcfiles[i][num]),
P.Lckey[i])
e.chnls[P.Lckey[i]].preprocess_data(P.genthresh, P.scale,
P.microns)
except:
print(num, 'failed on preprocess_data', time.time() - tic)
#Calculate PCA transformation for structural channel, c1
try:
if P.twoD == True:
e.chnls[P.c1_key].calculate_pca_median_2d(
e.chnls[P.c1_key].raw_data, P.medthresh, P.radius, P.microns)
pca = e.chnls[P.c1_key].pcamed
e.chnls[P.c1_key].pca_transform_2d(e.chnls[P.c1_key].df_thresh,
pca,
P.comporder,
P.fitdim,
deg=P.deg)
#Transform additional channels
for i in range(len(P.Lcdir)):
e.chnls[P.Lckey[i]].pca_transform_2d(
e.chnls[P.Lckey[i]].df_thresh,
pca,
P.comporder,
P.fitdim,
deg=P.deg)
else:
e.chnls[P.c1_key].calculate_pca_median(e.chnls[P.c1_key].raw_data,
P.medthresh, P.radius,
P.microns)
pca = e.chnls[P.c1_key].pcamed
e.chnls[P.c1_key].pca_transform_3d(e.chnls[P.c1_key].df_thresh,
pca,
P.comporder,
P.fitdim,
deg=P.deg)
#Transform additional channels
for i in range(len(P.Lcdir)):
e.chnls[P.Lckey[i]].pca_transform_3d(
e.chnls[P.Lckey[i]].df_thresh,
pca,
P.comporder,
P.fitdim,
deg=P.deg)
except:
print(num, 'failed on pca', time.time() - tic)
print(num, 'Starting coordinate transformation')
try:
e.chnls[P.c1_key].transform_coordinates()
for i in range(len(P.Lcdir)):
e.chnls[P.Lckey[i]].transform_coordinates()
except:
print(num, 'failed on coordinate transform', time.time() - tic)
try:
e.save_psi()
except:
print(num, 'failed on save_psi', time.time() - tic)
toc = time.time()
print(num, 'Complete', toc - tic)
def first_alignment(self, p, Lc, D):
tic = time.time()
for i, c in enumerate(Lc):
print(i, c.name, c.dir)
pc = self.check_settings(p)
e = cranium.embryo(pc['expname'], self.key, pc['outdir'])
for i, c in enumerate(Lc):
print(i, c.dir, c.key)
if c.dir != None:
print(i, 'data processing')
e.add_channel(os.path.join(c.dir, self.fs[i]), c.key)
e.chnls[c.key].preprocess_data(pc['genthresh'], [1, 1, 1],
pc['microns'])
#Processing for the structural channel
if i == 0:
if D == 3:
print('comporder', pc['comporder'])
e.chnls[c.key].calculate_pca_median(
e.chnls[c.key].raw_data, pc['medthresh'],
pc['radius'], pc['microns'])
pca = e.chnls[c.key].pcamed
e.chnls[c.key].pca_transform_3d(
e.chnls[c.key].df_thresh,
pca,
pc['comporder'],
pc['fitdim'],
deg=pc['deg'])
elif D == 2:
e.chnls[c.key].calculate_pca_median_2d(
e.chnls[c.key].raw_data, pc['medthresh'],
pc['radius'], pc['microns'])
pca = e.chnls[c.key].pcamed
e.chnls[c.key].pca_transform_2d(
e.chnls[c.key].df_thresh,
pca,
pc['comporder'],
pc['fitdim'],
deg=pc['deg'])
mm = e.chnls[c.key].mm
vertex = e.chnls[c.key].vertex
#Secondary channel processing
if D == 3:
e.chnls[c.key].pca_transform_3d(e.chnls[c.key].df_thresh,
pca,
pc['comporder'],
pc['fitdim'],
deg=pc['deg'],
mm=mm,
vertex=vertex)
if D == 2:
e.chnls[c.key].pca_transform_2d(e.chnls[c.key].df_thresh,
pca,
pc['comporder'],
pc['fitdim'],
deg=pc['deg'],
mm=mm,
vertex=vertex)
print('sample alignment complete', time.time() - tic)
#Save data
for i, c in enumerate(Lc):
if c.dir != None:
print(i, 'data saving')
cname = c.name.get()
for p in ':,./\\[]{};() ':
cname = cname.replace(p, '')
fpath = os.path.join(
pc['outdir'],
'_'.join([cname, pc['expname'], self.key]) + '.psi')
cranium.write_data(fpath, e.chnls[c.key].df_align)
self.outpaths[i] = fpath
self.plot_projections(
[e.chnls[Lc[0].key].df_thresh, e.chnls[Lc[0].key].df_align], mm)