def run_canonical_measurements(experiment_dir):
'''Run standard measurements on the specified experiment directory'''
experiment_dir = pathlib.Path(experiment_dir)
process_data.update_annotations(experiment_dir)
position_features = ['stage_x', 'stage_y', 'starting_stage_z', 'notes']
annotations = load_data.read_annotations(experiment_dir)
annotations = load_data.filter_annotations(annotations,
load_data.filter_excluded)
annotations = load_data.filter_annotations(annotations,
filter_worm_positions)
#print('warning: Im using a custom filter function')
#annotations = load_data.filter_annotations(annotations, lambda name, pa, ta: name < '25') # Remove me later
if any([
'lawn_area' in position_annotations
for (position_annotations,
timepoint_annotations) in annotations.items()
]):
position_features.append('lawn_area')
make_basic_measurements(experiment_dir, annotations)
process_data.collate_data(experiment_dir,
position_features=position_features)
return
make_pose_measurements(experiment_dir, annotations)
process_data.collate_data(experiment_dir,
position_features=position_features)
#make_mask_measurements(experiment_dir, annotations)
image_channels = elegant_hacks.get_image_channels(experiment_dir)
print(f'Image channels: {image_channels}')
if 'bf_1' in image_channels:
print('Found multipass movement channel bf_1; making measurements')
make_multipass_measurements(experiment_dir, annotations)
process_data.collate_data(
experiment_dir, position_features=position_features
) # For convenience since autofluorescence can take a little while....
if 'green_yellow_excitation_autofluorescence' in image_channels or 'autofluorescence' in image_channels:
fl_measurement_name = 'autofluorescence' if 'autofluorescence' in image_channels else 'green_yellow_excitation_autofluorescence'
print(
f'Found autofluorescence channel {fl_measurement_name}; making measurements'
)
make_af_measurements(experiment_dir,
annotations,
fl_measurement_name=fl_measurement_name)
process_data.collate_data(experiment_dir,
position_features=position_features)
def rw_load_position_images(rw, expt_dir, position, channels='bf'):
def position_filter(position_name, position_annotations,
timepoint_annotations):
return position_name == position
process_data.update_annotations(expt_dir)
position_images = load_data.scan_positions(expt_dir,
position_filter,
channels=channels)
image_files = position_images[position].values()
rw.add_image_files_to_flipbook(image_files)
def compile_annotations_from_tsv(experiment_root):
if type(experiment_root) is str:
experiment_root = pathlib.Path(experiment_root.replace('\\ ', ' '))
_check_metadata_for_timepoints(experiment_root)
process_data.update_annotations(experiment_root)
with (experiment_root /
'experiment_metadata.json').open('r') as mdata_file:
experiment_metadata = json.load(mdata_file)
annotation_data = {}
with list(experiment_root.glob('*.tsv'))[0].open('r') as annotation_file:
reader = csv.reader(annotation_file, delimiter='\t')
_ = reader.__next__() # Header
for line in reader:
position = line[0][1:] # Starts with '\'
notes = line[-1]
annotation_data[position] = {}
if 'DEAD' in notes:
for field, frame_num in zip(previous_annotations, line[1:]):
annotation_data[position][field] = experiment_metadata[
'timepoints'][int(frame_num)]
annotation_data[position]['notes'] = line[-1]
annotations = load_data.read_annotations(experiment_root)
# Note: process_data.propagate_worm_stages assumes that stages are monotonically increasing.
# To make use of propagate_worm_stages, prepopulate only the timepoints where there's a transition.
for position, (position_annotations,
timepoint_annotations) in annotations.items():
if 'DEAD' in annotation_data[position]['notes']:
first_timepoint = list(timepoint_annotations.keys())[0]
timepoint_annotations[first_timepoint]['stage'] = 'egg'
for field in previous_annotations:
transition_timepoint = annotation_data[position][field]
timepoint_annotations[transition_timepoint]['stage'] = field
position_annotations['exclude'] = False
else:
position_annotations['exclude'] = True
position_annotations['notes'] = annotation_data[position]['notes']
load_data.write_annotations(experiment_root, annotations)
process_data.annotate(
experiment_root,
position_annotators=[process_data.propagate_worm_stage])
def process_experiment_with_filter(experiment_root,
model,
image_filter,
mask_root=None,
overwrite_existing=False,
channels='bf',
make_masks=True,
do_annotations=True):
'''
image_filter - filter for scan_experiment_dir
'''
if mask_root is None:
mask_root = pathlib.Path(experiment_root) / 'derived_data' / 'mask'
# Temporary hacks until migration to new elegant complete (while zpl-9000 no longer updates annotations automatically)
process_data.update_annotations(experiment_root)
elegant_hacks.propagate_stages(experiment_root)
start_t = time.time()
positions = load_data.scan_experiment_dir(experiment_root,
timepoint_filter=image_filter,
channels=channels)
scan_t = time.time()
print(f'scanning done after {(scan_t-start_t)} s')
if make_masks:
process = segment_images.segment_positions(positions,
model,
mask_root,
use_gpu=True,
overwrite_existing=False)
if process.stderr:
print(f'Errors during segmentation: {process.stderr}'
) #raise Exception)
#raise Exception()
segment_t = time.time()
print(f'segmenting done after {(segment_t-scan_t)} s')
with (mask_root / 'notes.txt').open('a+') as notes_file:
notes_file.write(
f'{datetime.datetime.today().strftime("%Y-%m-%dt%H%M")} These masks were segmented with model {model}\n'
)
else:
print(f'No segmenting performed')
if do_annotations:
annotations = load_data.read_annotations(experiment_root)
metadata = load_data.read_metadata(experiment_root)
age_factor = metadata.get('age_factor', 1)
width_estimator = worm_widths.WidthEstimator.from_experiment_metadata(
metadata, age_factor)
segment_images.annotate_poses_from_masks(positions, mask_root,
annotations,
overwrite_existing,
width_estimator)
load_data.write_annotations(experiment_root, annotations)
annotation_t = time.time()
print(
f'annotation done after {(annotation_t - segment_t)} s') # ~3.5 hr
else:
print('No annotations done')
] #, elegant_filters.filter_live_animals] #, elegant_filters.filter_after_timepoint('2019-05-20t0000')] #, elegant_filters.select_worms(['15']), elegant_filters.filter_live_animals, elegant_filters.filter_by_stage('adult')] #, elegant_filters.filter_after_timepoint('2019-02-02t1200')]
channels = [
'bf'
] #, 'gfp'] #, 'autofluorescence'] #, 'green_yellow_excitation_autofluorescence'] # First one is the one used to load poses when specified.
# Only use one RisWidget for annotations
rw_defined = 'rw' in globals()
global rw
if not rw_defined: rw = ris_widget.RisWidget()
# Allows one to restart annotating in the same ris_widget window
if hasattr(rw, 'annotator'):
rw.annotator.close()
del (rw.annotator)
process_data.update_annotations(expt_dir)
experiment_annotations = load_data.read_annotations(
expt_dir, annotation_dir=annotation_dir)
if timepoint_filters:
experiment_annotations = load_data.filter_annotations(
experiment_annotations,
elegant_filters.compose_timepoint_filters(*timepoint_filters))
expt_pos = load_data.scan_experiment_dir(
expt_dir,
channels=channels,
timepoint_filter=lambda position_name, timepoint_name:
not experiment_annotations or
(position_name in experiment_annotations and timepoint_name in
experiment_annotations[position_name][1]))