def run(self, distribute: Optional[bool] = False, **kwargs):
config = general.load_config_file()
save_dir = self.step_local_staging_dir / "data"
save_dir.mkdir(parents=True, exist_ok=True)
space = shapespace.ShapeSpace(config)
space.load_shape_space_axes()
space.load_shapemode_manifest()
collector = ObjectCollector(config)
structure = "SLC25A17"
for shapemode in ['DNA_MEM_PC1']:
for b in [2, 8]:
prefix = f"{shapemode}_B{b}"
space.set_active_axis(shapemode, digitize=True)
space.set_active_structure(structure)
space.set_active_bin(b)
dna = space.get_dna_mesh_of_bin(b)
mem = space.get_mem_mesh_of_bin(b)
seg = cytoparam.voxelize_meshes([mem, dna])
save_as = save_dir / f"{prefix}.tif"
with writers.ome_tiff_writer.OmeTiffWriter(
save_as, overwrite_file=True) as writer:
writer.save(seg[0],
dimension_order='ZYX',
image_name=save_as.stem)
CellIds = space.get_active_cellids()
with concurrent.futures.ProcessPoolExecutor(
cluster.get_ncores()) as executor:
objs = list(
tqdm(executor.map(
collector.collect_segmented_objects,
[space.meta.loc[CellId] for CellId in CellIds]),
total=len(CellIds)))
objs = [o for obj in objs for o in obj]
img = collector.pack_objs(objs)
collector.save_img(
img, f"{structure}_{prefix}_ncells_{len(CellIds)}")
return None
self.domain = domain
self.origin = origin
self.coords_param = coords_param
return
def morph_on_shapemode_shape(self):
self.voxelize_and_parameterize_shapemode_shape()
self.morphed = cytoparam.morph_representation_on_shape(
img=self.domain,
param_img_coords=self.coords_param,
representation=self.aggregated_parameterized_intensity)
self.morphed = np.stack([self.domain, self.morphed])
return
if __name__ == "__main__":
config = general.load_config_file()
control = controller.Controller(config)
parser = argparse.ArgumentParser(description='Batch aggregation.')
parser.add_argument('--csv', help='Path to the dataframe.', required=True)
args = vars(parser.parse_args())
df = pd.read_csv(args['csv'], index_col=0)
aggregator = Aggregator(control)
for index, row in tqdm(df.iterrows(), total=len(df)):
'''Concurrent processes inside. Do not use concurrent here.'''
aggregator.execute(row)