def run_all(args, what_to_run, atlas):
from cellfinder.detect import detect
from cellfinder.classify import classify
from cellfinder.analyse import analyse
from cellfinder.figures import figures
from cellfinder.tools import prep
args, what_to_run = prep.prep_channel_specific_general(args, what_to_run)
if what_to_run.detect:
logging.info("Detecting cell candidates")
args = prep.prep_candidate_detection(args)
detect.main(args)
else:
logging.info("Skipping cell detection")
if what_to_run.classify:
args = prep.prep_classification(args, what_to_run)
if what_to_run.classify:
logging.info("Running cell classification")
what_to_run.cells_exist = classify.main(args)
else:
logging.info("No cells were detected, skipping classification.")
else:
logging.info("Skipping cell classification")
what_to_run.update_if_cells_required()
if what_to_run.analyse or what_to_run.figures:
downsampled_space = get_downsampled_space(
atlas, args.brainreg_paths.boundaries_file_path)
if what_to_run.analyse:
logging.info("Analysing cell positions")
analyse.run(args, atlas, downsampled_space)
else:
logging.info("Skipping cell position analysis")
if what_to_run.figures:
logging.info("Generating figures")
figures.run(args, atlas, downsampled_space.shape)
else:
logging.info("Skipping figure generation")
def run_all(args, what_to_run):
from cellfinder.detect import detect
from cellfinder.classify import classify
import cellfinder.summarise.count_summary as cell_count_summary
from cellfinder.figures import figures
from cellfinder.tools import prep
from cellfinder.standard_space.cells_to_standard_space import (
transform_cells_to_standard_space,
)
args, what_to_run = prep.prep_channel_specific_general(args, what_to_run)
if what_to_run.detect:
logging.info("Detecting cell candidates")
args = prep.prep_candidate_detection(args)
detect.main(args)
else:
logging.info("Skipping cell detection")
if what_to_run.classify:
logging.info("Running cell classification")
args = prep.prep_classification(args)
classify.main(args)
else:
logging.info("Skipping cell classification")
if what_to_run.summarise:
logging.info("Summarising cell counts")
cell_count_summary.analysis_run(args)
else:
logging.info("Skipping cell count summary")
if what_to_run.standard_space:
logging.info("Converting cells to standard space")
args = prep.standard_space_prep(args)
transform_cells_to_standard_space(args)
else:
logging.info("Skipping converting cells to standard space")
if what_to_run.figures:
logging.info("Generating figures")
args = prep.figures_prep(args)
figures.figures(args)
else:
logging.info("Skipping figure generation")
def run_all(args, what_to_run, atlas):
from cellfinder_core.detect import detect
from cellfinder_core.classify import classify
from cellfinder_core.tools import prep
from cellfinder_core.tools.IO import read_with_dask
from cellfinder.analyse import analyse
from cellfinder.figures import figures
from cellfinder.tools.prep import (
prep_candidate_detection,
prep_channel_specific_general,
)
points = None
signal_array = None
args, what_to_run = prep_channel_specific_general(args, what_to_run)
if what_to_run.detect:
logging.info("Detecting cell candidates")
args = prep_candidate_detection(args)
signal_array = read_with_dask(
args.signal_planes_paths[args.signal_channel]
)
points = detect.main(
signal_array,
args.start_plane,
args.end_plane,
args.voxel_sizes,
args.soma_diameter,
args.max_cluster_size,
args.ball_xy_size,
args.ball_z_size,
args.ball_overlap_fraction,
args.soma_spread_factor,
args.n_free_cpus,
args.log_sigma_size,
args.n_sds_above_mean_thresh,
)
ensure_directory_exists(args.paths.points_directory)
save_cells(
points,
args.paths.detected_points,
save_csv=args.save_csv,
artifact_keep=args.artifact_keep,
)
else:
logging.info("Skipping cell detection")
points = get_cells(args.paths.detected_points)
if what_to_run.classify:
model_weights = prep.prep_classification(
args.trained_model,
args.model_weights,
args.install_path,
args.model,
args.n_free_cpus,
)
if what_to_run.classify:
if points is None:
points = get_cells(args.paths.detected_points)
if signal_array is None:
signal_array = read_with_dask(
args.signal_planes_paths[args.signal_channel]
)
logging.info("Running cell classification")
background_array = read_with_dask(args.background_planes_path[0])
points = classify.main(
points,
signal_array,
background_array,
args.n_free_cpus,
args.voxel_sizes,
args.network_voxel_sizes,
args.batch_size,
args.cube_height,
args.cube_width,
args.cube_depth,
args.trained_model,
model_weights,
args.network_depth,
)
save_cells(
points,
args.paths.classified_points,
save_csv=args.save_csv,
)
what_to_run.cells_exist = cells_exist(args.paths.classified_points)
else:
logging.info("No cells were detected, skipping classification.")
else:
logging.info("Skipping cell classification")
what_to_run.update_if_cells_required()
if what_to_run.analyse or what_to_run.figures:
downsampled_space = get_downsampled_space(
atlas, args.brainreg_paths.boundaries_file_path
)
if what_to_run.analyse:
points = get_cells(args.paths.classified_points, cells_only=True)
if len(points) == 0:
logging.info("No cells detected, skipping cell position analysis")
else:
logging.info("Analysing cell positions")
analyse.run(args, points, atlas, downsampled_space)
else:
logging.info("Skipping cell position analysis")
if what_to_run.figures:
points = get_cells(args.paths.detected_points, cells_only=True)
if len(points) == 0:
logging.info("No cells detected, skipping")
else:
logging.info("Generating figures")
figures.run(args, atlas, downsampled_space.shape)
else:
logging.info("Skipping figure generation")