hfp.data_from_file(filepath=params['intermedfolder'] +
params['locmaxfile'],
skeys=('disttransf', 'disttransfm'),
tkeys=('disttransf', 'disttransfm'))
hfp.startlogger()
try:
hfp.logging('hfp datastructure:\n---\n{}---',
hfp.datastructure2string(maxdepth=2))
hfp.anytask(lib.getvaluesfromcoords,
reciprocal=True,
keys='disttransfm',
indict=hfp['false', '6155_9552'],
tkeys='result_false')
hfp.logging('hfp datastructure:\n---\n{}---',
hfp.datastructure2string(maxdepth=2))
# y = []
# maxlen = 0
# for d, k, v, kl in hfp['result_false'].data_iterator():
# y.append(v)
# x = range(0, len(v))
# plt.plot(x, v)
# if len(v) > maxlen:
# maxlen = len(v)
# # x = range(0, len(hfp['path_dt_6155_9552_0']))
# split_sample_a.write('/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.raw_neurons.crop.split_xyz.h5')
# Sample B
sample = IPL(
filepath=
'/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splB.raw_neurons.crop.h5'
)
sample.logging('Sample B datastructure\n---\n{}',
sample.datastructure2string())
reskeys = ('0', '1')
split_sample = IPL()
split_sample['z'] = sample.anytask(lib.split,
2,
axis=0,
result_keys=reskeys,
return_only=True)
split_sample['y'] = sample.anytask(lib.split,
2,
axis=1,
result_keys=reskeys,
return_only=True)
split_sample['x'] = sample.anytask(lib.split,
2,
axis=2,
result_keys=reskeys,
return_only=True)
split_sample = split_sample.switch_levels(1, 2)
sample.logging('Split sample B datastructure\n---\n{}',
# print k2
# hfp.anytask(lib.getvaluesfromcoords, v2,
# reciprocal=False,
# keys='disttransfm',
# tkeys='{}.{}.{}'.format('result_false', k, k2))
# Pop selected labels
a = hfp['true', 'border']
a.pop('63')
for k, v in a.iteritems():
print k
for k2, v2 in v.iteritems():
print k2
hfp.anytask(lib.getvaluesfromcoords, v2,
reciprocal=False,
keys=('disttransf', 'raw'),
tkeys='{}.{}.{}'.format('result_true', k, k2))
#'{}.{}.{}'.format('result_true', k, k2)
a = hfp['false', 'border']
# a.pop('63')
for k, v in a.iteritems():
print k
for k2, v2 in v.iteritems():
print k2
hfp.anytask(lib.getvaluesfromcoords, v2,
reciprocal=False,
keys=('disttransfm', 'raw'),
from hdf5_image_processing import Hdf5ImageProcessingLib as IPL
import processing_lib as lib
# Sample A probs
probs_a = IPL(
filepath=
'/mnt/localdata01/jhennies/neuraldata/cremi_2016/sample_A_train_betas/sample_A_train_mcseg_beta_0.5.h5'
)
probs_a.logging('Probs A datastructure\n---\n{}',
probs_a.datastructure2string())
probs_a.anytask(lib.swapaxes, 0, 2)
probs_a.write(
'/mnt/localdata01/jhennies/neuraldata/cremi_2016/sample_A_train_betas/cremi.splA.train.seg_beta_0.5.crop.h5'
)
reskeys = ('0', '1')
split_probs_a = IPL()
split_probs_a['z'] = probs_a.anytask(lib.split,
2,
axis=0,
result_keys=reskeys,
return_only=True,
rtrntype=IPL)
split_probs_a['y'] = probs_a.anytask(lib.split,
2,
axis=1,
result_keys=reskeys,
return_only=True,
from hdf5_image_processing import Hdf5ImageProcessingLib as IPL
import processing_lib as lib
# Sample A probs
probs_a = IPL(
filepath='/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.probs_cantorV1.h5'
)
probs_a.logging('Probs A datastructure\n---\n{}', probs_a.datastructure2string())
probs_a.anytask(lib.swapaxes, 0, 2)
probs_a.write('/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.train.probs.crop.h5')
reskeys = ('0', '1')
split_probs_a = IPL()
split_probs_a['z'] = probs_a.anytask(lib.split, 2, axis=0, result_keys=reskeys, return_only=True, rtrntype=IPL)
split_probs_a['y'] = probs_a.anytask(lib.split, 2, axis=1, result_keys=reskeys, return_only=True, rtrntype=IPL)
split_probs_a['x'] = probs_a.anytask(lib.split, 2, axis=2, result_keys=reskeys, return_only=True, rtrntype=IPL)
split_probs_a = split_probs_a.switch_levels(1, 2)
probs_a.logging('Split sample A datastructure\n---\n{}', split_probs_a.datastructure2string())
split_probs_a.write('/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.train.probs.crop.split_xyz.h5')
#
# split_sample_a = split_sample_a.switch_levels(1, 2)
# sample_a.logging('Split sample A datastructure\n---\n{}', split_sample_a.datastructure2string())
#
# split_sample_a.write('/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.raw_neurons.crop.split_xyz.h5')
# Sample B
sample = IPL(
filepath='/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splB.raw_neurons.crop.h5'
)
sample.logging('Sample B datastructure\n---\n{}', sample.datastructure2string())
reskeys = ('0', '1')
split_sample = IPL()
split_sample['z'] = sample.anytask(lib.split, 2, axis=0, result_keys=reskeys, return_only=True)
split_sample['y'] = sample.anytask(lib.split, 2, axis=1, result_keys=reskeys, return_only=True)
split_sample['x'] = sample.anytask(lib.split, 2, axis=2, result_keys=reskeys, return_only=True)
split_sample = split_sample.switch_levels(1, 2)
sample.logging('Split sample B datastructure\n---\n{}', split_sample.datastructure2string())
split_sample.write('/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splB.raw_neurons.crop.split_xyz.h5')
# Sample C
sample = IPL(
filepath='/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splC.raw_neurons.crop.h5'
)
sample.logging('Sample C datastructure\n---\n{}', sample.datastructure2string())
