def test_atlas_value_to_name():
structure_df = load_structures_as_df(structures_csv)
assert "Interpeduncular nucleus" == structures_tree.atlas_value_to_name(
100, structure_df
)
with pytest.raises(structures_tree.UnknownAtlasValue):
structures_tree.atlas_value_to_name(100000, structure_df)
def test_atlas_value_to_structure_id():
structure_df = load_structures_as_df(structures_csv)
assert structure_id_100 == structures_tree.atlas_value_to_structure_id(
100, structure_df
)
with pytest.raises(structures_tree.UnknownAtlasValue):
structures_tree.atlas_value_to_structure_id(100000, structure_df)
def main():
print("Starting amap viewer")
args = parser().parse_args()
structures_path = get_structures_path()
structures_df = load_structures_as_df(structures_path)
if not args.memory:
print("By default amap_vis does not load data into memory. "
"To speed up visualisation, use the '-m' flag. Be aware "
"this will make the viewer slower to open initially.")
paths = Paths(args.amap_directory)
with napari.gui_qt():
v = napari.Viewer(title="amap viewer")
if (Path(paths.registered_atlas_path).exists()
and Path(paths.boundaries_file_path).exists()):
if args.raw:
image_scales = display_raw(v, args)
else:
if Path(paths.downsampled_brain_path).exists():
image_scales = display_downsampled(v, args, paths)
else:
raise FileNotFoundError(
f"The downsampled image: "
f"{paths.downsampled_brain_path} could not be found. "
f"Please ensure this is the correct "
f"directory and that amap has completed. ")
labels = display_registration(
v,
paths.registered_atlas_path,
paths.boundaries_file_path,
image_scales,
memory=args.memory,
)
@labels.mouse_move_callbacks.append
def get_connected_component_shape(layer, event):
val = layer.get_value()
if val != 0 and val is not None:
try:
region = atlas_value_to_name(val, structures_df)
msg = f"{region}"
except UnknownAtlasValue:
msg = "Unknown region"
else:
msg = "No label here!"
layer.help = msg
else:
raise FileNotFoundError(
f"The directory: '{args.amap_directory}' does not "
f"appear to be complete. Please ensure this is the correct "
f"directory and that amap has completed.")
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 test_load_structures_df():
structures = load_structures_as_df(structures_csv)
assert len(structures) == 1299
assert (structures.keys() == Index(structure_headers)).all()
structures_test = list(structures.iloc[100].array)
assert structures_test[1] == structures_line_100[1]
assert structures_test[2] == structures_line_100[2]
assert structures_test[3] == structures_line_100[3]
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)
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(
# args.reference_structures_file_path
# )
structures_reference_df = load_structures_as_df(get_structures_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!")