def setUp(self):
os.makedirs(self.tmp_folder, exist_ok=True)
os.makedirs(self.config_folder, exist_ok=True)
global_config = NodeLabelWorkflow.get_config()['global']
global_config['shebang'] = self.shebang
global_config['block_shape'] = self.block_shape
with open(os.path.join(self.config_folder, 'global.config'), 'w') as f:
json.dump(global_config, f)
config = NodeLabelWorkflow.get_config()['merge_node_labels']
config.update({'threads_per_job': self.n_jobs})
with open(os.path.join(self.config_folder, 'merge_node_labels.config'),
'w') as f:
json.dump(config, f)
def setUp(self):
self._mkdir(self.tmp_folder)
self._mkdir(self.config_folder)
global_config = NodeLabelWorkflow.get_config()['global']
global_config['shebang'] = self.shebang
global_config['block_shape'] = [10, 256, 256]
with open(os.path.join(self.config_folder, 'global.config'), 'w') as f:
json.dump(global_config, f)
def get_config():
configs = super(UnmergeWorkflow, UnmergeWorkflow).get_config()
configs.update({
'fix_merges':
fix_tasks.FixMergesLocal.default_task_config(),
'find_merges':
find_tasks.FindMergesLocal.default_task_config(),
'write':
write_tasks.WriteLocal.default_task_config(),
**NodeLabelWorkflow.get_config()
})
return configs
def test_node_labels(self):
config = NodeLabelWorkflow.get_config()['merge_node_labels']
config.update({'threads_per_job': 8})
with open(os.path.join(self.config_folder,
'merge_node_labels.config'), 'w') as f:
json.dump(config, f)
task = NodeLabelWorkflow(tmp_folder=self.tmp_folder,
config_dir=self.config_folder,
target=self.target, max_jobs=8,
ws_path=self.path, ws_key=self.ws_key,
input_path=self.path, input_key=self.input_key,
output_path=self.output_path, output_key=self.output_key)
ret = luigi.build([task], local_scheduler=True)
self.assertTrue(ret)
self._check_result()
def test_cell_nucleus_mappings(self):
from mmpb.attributes.cell_nucleus_mapping import map_cells_to_nuclei
segmentation_folder = '../../data/0.1.1/segmentations'
seg_path = os.path.join(segmentation_folder,
'sbem-6dpf-1-whole-segmented-cells-labels.h5')
segmentation_folder = '../../data/0.0.0/segmentations'
nuc_path = os.path.join(
segmentation_folder,
'sbem-6dpf-1-whole-segmented-nuclei-labels.h5')
with h5py.File(seg_path, 'r') as f:
max_id = f['t00000/s00/0/cells'].attrs['maxId']
label_ids = np.arange(max_id + 1, dtype='uint64')
output_path = os.path.join(self.tmp_folder, 'table-test.csv')
config_folder = os.path.join(self.tmp_folder, 'configs')
os.makedirs(config_folder, exist_ok=True)
conf = NodeLabelWorkflow.get_config()['global']
shebang = '#! /g/kreshuk/pape/Work/software/conda/miniconda3/envs/cluster_env37/bin/python'
conf.update({'shebang': shebang})
with open(os.path.join(config_folder, 'global.config'), 'w') as f:
json.dump(conf, f)
target = 'local'
max_jobs = 60
map_cells_to_nuclei(label_ids,
seg_path,
nuc_path,
output_path,
tmp_folder=self.tmp_folder,
target=target,
max_jobs=max_jobs)
table = pandas.read_csv(output_path, sep='\t')
assert len(table) == max_id + 1
# make sure each nucleus is mapped only once
nucleus_ids = table['nucleus_id'].values
nucleus_ids, id_counts = np.unique(nucleus_ids, return_counts=True)
nucleus_ids, id_counts = nucleus_ids[1:], id_counts[1:]
self.assertEqual(id_counts.sum(), id_counts.size)
def test_regions(self):
from mmpb.attributes.region_attributes import region_attributes
image_folder = '../../data/0.0.0/images'
segmentation_folder = '../../data/0.0.0/segmentations'
seg_path = os.path.join(segmentation_folder,
'sbem-6dpf-1-whole-segmented-cells-labels.h5')
seg_key = 't00000/s00/0/cells'
with h5py.File(seg_path, 'r') as f:
max_id = f[seg_key].attrs['maxId']
output_path = os.path.join(self.tmp_folder, 'table-test.csv')
label_ids = np.arange(max_id + 1, dtype='uint64')
# write the global config
config_folder = os.path.join(self.tmp_folder, 'configs')
os.makedirs(config_folder, exist_ok=True)
conf = NodeLabelWorkflow.get_config()['global']
shebang = '#! /g/kreshuk/pape/Work/software/conda/miniconda3/envs/cluster_env37/bin/python'
conf.update({'shebang': shebang})
with open(os.path.join(config_folder, 'global.config'), 'w') as f:
json.dump(conf, f)
target = 'local'
max_jobs = 8
region_attributes(seg_path, output_path, image_folder,
segmentation_folder, label_ids, self.tmp_folder,
target, max_jobs)
table = pandas.read_csv(output_path, sep='\t')
assert len(table) == max_id + 1
base_path = '../../data/0.0.0/tables/sbem-6dpf-1-whole-segmented-cells-labels/default.csv'
base_table = pandas.read_csv(base_path, sep='\t')
seg_key = 't00000/s00/2/cells'
tissue_path = '../../data/rawdata/sbem-6dpf-1-whole-segmented-tissue-labels.h5'
tissue_key = 't00000/s00/0/cells'
self.check_result(table, base_table, seg_path, seg_key, tissue_path,
tissue_key)