def analysis_run(args, file_name="summary_cell_counts.csv"):
args = prep_atlas_conf(args)
# if args.structures_file_path is None:
# args.structures_file_path = get_structures_path()
atlas = brainio.load_any(args.paths.registered_atlas_path)
hemisphere = brainio.load_any(args.paths.hemispheres_atlas_path)
cells = get_cells_data(
args.paths.classification_out_file, cells_only=args.cells_only,
)
max_coords = get_max_coords(cells) # Useful for debugging dimensions
# structures_reference_df = load_structures_as_df(args.structures_file_path)
structures_reference_df = load_structures_as_df(get_structures_path())
atlas_pixel_sizes = get_atlas_pixel_sizes(args.atlas_config)
sample_pixel_sizes = args.x_pixel_um, args.y_pixel_um, args.z_pixel_um
scales = get_scales(
sample_pixel_sizes, atlas_pixel_sizes, args.scale_cell_coordinates
)
structures_with_cells = set()
for i, cell in enumerate(tqdm(cells)):
transform_cell_coords(atlas, cell, scales)
structure_id = get_structure_from_coordinates(
atlas,
cell,
max_coords,
order=args.coordinates_order,
structures_reference_df=structures_reference_df,
)
if structure_id is not None:
cell.structure_id = structure_id
structures_with_cells.add(structure_id)
else:
continue
cell.hemisphere = get_structure_from_coordinates(
hemisphere, cell, max_coords, order=args.coordinates_order
)
sorted_cell_numbers = get_cells_nbs_df(
cells, structures_reference_df, structures_with_cells
)
combined_hemispheres = combine_df_hemispheres(sorted_cell_numbers)
df = calculate_densities(combined_hemispheres, args.paths.volume_csv_path)
df = sanitise_df(df)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
output_file = os.path.join(args.output_dir, file_name)
df.to_csv(output_file, index=False)
def xml_crop(args, df_query="name"):
args = prep_atlas_conf(args)
if args.structures_file_path is None:
args.structures_file_path = get_structures_path()
reference_struct_df = pd.read_csv(get_structures_path())
curate_struct_df = pd.read_csv(args.structures_file_path)
curate_struct_df = reference_struct_df[
reference_struct_df[df_query].isin(curate_struct_df[df_query])
]
curated_ids = list(curate_struct_df["structure_id_path"])
atlas = brainio.load_any(args.registered_atlas_path)
hemisphere = brainio.load_any(args.hemispheres_atlas_path)
structures_reference_df = load_structures_as_df(get_structures_path())
atlas_pixel_sizes = get_atlas_pixel_sizes(args.atlas_config)
sample_pixel_sizes = args.x_pixel_um, args.y_pixel_um, args.z_pixel_um
scales = cells_regions.get_scales(sample_pixel_sizes, atlas_pixel_sizes)
destination_folder = os.path.join(args.xml_dir, "xml_crop")
if not os.path.exists(destination_folder):
os.makedirs(destination_folder)
xml_names = [f for f in os.listdir(args.xml_dir) if f.endswith(".xml")]
xml_paths = [os.path.join(args.xml_dir, f) for f in xml_names]
for idx, xml_path in enumerate(xml_paths):
print("Curating file: {}".format(xml_names[idx]))
cells = cells_regions.get_cells_data(
xml_path, cells_only=args.cells_only,
)
max_coords = cells_regions.get_max_coords(cells)
curated_cells = []
for i, cell in enumerate(cells):
cells_regions.transform_cell_coords(atlas, cell, scales)
structure_id = cells_regions.get_structure_from_coordinates(
atlas,
cell,
max_coords,
order=args.coordinates_order,
structures_reference_df=structures_reference_df,
)
if structure_id in curated_ids:
if args.hemisphere_query in [1, 2]:
hemisphere = cells_regions.get_structure_from_coordinates(
hemisphere,
cell,
max_coords,
order=args.coordinates_order,
)
if hemisphere is args.hemisphere_query:
curated_cells.append(cell)
else:
curated_cells.append(cell)
cells_to_xml(
curated_cells,
os.path.join(destination_folder, xml_names[idx]),
artifact_keep=True,
)
print("Done!")
def main():
suppress_tf_logging(tf_suppress_log_messages)
import amap.main as register
from cellfinder.tools import prep
start_time = datetime.now()
args, what_to_run = prep.prep_cellfinder_general()
if what_to_run.register:
# TODO: add register_part_brain option
logging.info("Registering to atlas")
args, additional_images_downsample = prep.prep_registration(args)
register.main(
args.registration_config,
args.target_brain_path,
args.paths.registration_output_folder,
x_pixel_um=args.x_pixel_um,
y_pixel_um=args.y_pixel_um,
z_pixel_um=args.z_pixel_um,
orientation=args.orientation,
flip_x=args.flip_x,
flip_y=args.flip_y,
flip_z=args.flip_z,
affine_n_steps=args.affine_n_steps,
affine_use_n_steps=args.affine_use_n_steps,
freeform_n_steps=args.freeform_n_steps,
freeform_use_n_steps=args.freeform_use_n_steps,
bending_energy_weight=args.bending_energy_weight,
grid_spacing_x=args.grid_spacing_x,
smoothing_sigma_reference=args.smoothing_sigma_reference,
smoothing_sigma_floating=args.smoothing_sigma_floating,
histogram_n_bins_floating=args.histogram_n_bins_floating,
histogram_n_bins_reference=args.histogram_n_bins_reference,
sort_input_file=args.sort_input_file,
n_free_cpus=args.n_free_cpus,
save_downsampled=not (args.no_save_downsampled),
additional_images_downsample=additional_images_downsample,
boundaries=not (args.no_boundaries),
debug=args.debug,
)
else:
logging.info("Skipping registration")
if what_to_run.summarise:
args = prep.prep_atlas_conf(args)
if len(args.signal_planes_paths) > 1:
base_directory = args.output_dir
for idx, signal_paths in enumerate(args.signal_planes_paths):
channel = args.signal_ch_ids[idx]
logging.info("Processing channel: " + str(channel))
channel_directory = os.path.join(
base_directory, "channel_" + str(channel)
)
if not os.path.exists(channel_directory):
os.makedirs(channel_directory)
# prep signal channel specific args
args.signal_planes_paths[0] = signal_paths
# TODO: don't overwrite args.output_dir - use Paths instead
args.output_dir = channel_directory
args.signal_channel = channel
# Run for each channel
run_all(args, what_to_run)
else:
args.signal_channel = args.signal_ch_ids[0]
run_all(args, what_to_run)
logging.info(
"Finished. Total time taken: {}".format(datetime.now() - start_time)
)
