def summary_run(args):
# if args.structures_file_path is None:
# args.structures_file_path = get_structures_path()
# reference_structures_table = pd.read_csv(args.structures_file_path)
# else:
# raise NotImplementedError(
# "Only the Allen adult mouse atlas is " "currently supported."
# )
reference_structures_table = pd.read_csv(get_structures_path())
if not args.regions and not args.regions_list:
# regions = list(pd.read_csv(args.structures_file_path)["name"])
regions = list(pd.read_csv(get_structures_path())["name"])
else:
regions = []
if args.regions:
regions = regions + args.regions
if args.regions_list:
regions = regions + list(pd.read_csv(args.regions_list)["name"])
regions = remove_empty_string(regions)
regions = unique_elements_lists(regions)
csvs_folder = args.csv_dir
destination_folder = os.path.join(args.csv_dir, "summary")
csvs_names = [f for f in os.listdir(csvs_folder) if f.endswith(".csv")]
if len(csvs_names) == 0:
raise FileNotFoundError(
"No CSV files were found in the directory: "
"{}. Please check the arguments".format(csvs_folder))
csvs_paths = [os.path.join(csvs_folder, f) for f in csvs_names]
if not os.path.exists(destination_folder):
os.makedirs(destination_folder)
for csv_file_path in csvs_paths:
print("CSV file: {}".format(os.path.basename(csv_file_path)))
summary = make_summary_df_for_brain(
csv_file_path,
regions,
reference_structures_table,
sum_regions=args.sum_regions,
)
filename = os.path.basename(csv_file_path)
dest_path = os.path.join(destination_folder, filename)
summary.to_csv(dest_path, index=False)
print("Done!")
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 generate_region_volume(structure_names,
atlas_path,
output_path,
atlas_config,
glass=False):
structure_csv_file = get_structures_path()
reference_structures_table = pd.read_csv(structure_csv_file)
# ensure all names are valid
for indv_structure_name in structure_names:
try:
get_substructures(indv_structure_name, reference_structures_table)
except IndexError:
raise ValueError(
f"Brain region: '{indv_structure_name}' cannot be found "
f"in file: {structure_csv_file}. Please choose "
f"another structure.")
print(f"Loading atlas from: {atlas_path}")
atlas = brainio.load_nii(atlas_path, as_array=False)
atlas_scale = atlas.header.get_zooms()
atlas = atlas.get_data()
transformation_matrix = brain_tools.get_transformation_matrix(atlas_config)
if len(structure_names) > 1:
# Initialise an image to add each subimage to.
final_image = np.zeros_like(atlas)
for indv_structure_name in structure_names:
print(f"Analysing brain region: {indv_structure_name}")
substructures = get_substructures(indv_structure_name,
reference_structures_table)
print("This includes structures:")
indv_substructure_names = substructures["name"].values
for indv_substructure_name in indv_substructure_names:
print(indv_substructure_name)
list_vals = substructures["id"].values
print("Generating image with specified regions \n")
sub_image = np.isin(atlas, list_vals)
if glass:
print("Generating glass brain")
sub_image = sk_segmentation.find_boundaries(sub_image)
# If multiple structures, add them together
if len(structure_names) > 1:
final_image = np.logical_or(final_image, sub_image)
else:
final_image = sub_image
print("Converting image to 16 bit")
final_image = tools.scale_and_convert_to_16_bits(final_image)
print("Saving image")
brainio.to_nii(
final_image,
output_path,
scale=atlas_scale,
affine_transform=transformation_matrix,
)
print(f"Saved image at: {output_path}")
def xml_crop(args, df_query="name"):
args = prep_atlas_conf(args)
if args.reference_structures_file_path is None:
args.reference_structures_file_path = get_structures_path()
if args.structures_file_path is None:
args.structures_file_path = get_structures_path()
reference_struct_df = pd.read_csv(args.reference_structures_file_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(
args.reference_structures_file_path)
atlas_pixel_sizes = cells_regions.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!")