def write_segmentation(
segext_obj: SegmentationExtractor,
save_path: PathType = None,
plane_num=0,
metadata: dict = None,
overwrite: bool = True,
buffer_size: int = 10,
nwbfile=None,
):
assert (
save_path is None or nwbfile is None
), "Either pass a save_path location, or nwbfile object, but not both!"
# parse metadata correctly:
if isinstance(segext_obj, MultiSegmentationExtractor):
segext_objs = segext_obj.segmentations
if metadata is not None:
assert isinstance(metadata, list), (
"For MultiSegmentationExtractor enter 'metadata' as a list of "
"SegmentationExtractor metadata")
assert len(metadata) == len(segext_objs), (
"The 'metadata' argument should be a list with the same "
"number of elements as the segmentations in the "
"MultiSegmentationExtractor")
else:
segext_objs = [segext_obj]
if metadata is not None and not isinstance(metadata, list):
metadata = [metadata]
metadata_base_list = [
NwbSegmentationExtractor.get_nwb_metadata(sgobj)
for sgobj in segext_objs
]
print(f"writing nwb for {segext_obj.extractor_name}\n")
# updating base metadata with new:
for num, data in enumerate(metadata_base_list):
metadata_input = metadata[num] if metadata else {}
metadata_base_list[num] = dict_recursive_update(
metadata_base_list[num], metadata_input)
metadata_base_common = metadata_base_list[0]
# build/retrieve nwbfile:
if nwbfile is not None:
assert isinstance(
nwbfile, NWBFile), "'nwbfile' should be of type pynwb.NWBFile"
write = False
else:
write = True
save_path = Path(save_path)
assert save_path.suffix == ".nwb"
if save_path.is_file() and not overwrite:
nwbfile_exist = True
file_mode = "r+"
else:
if save_path.is_file():
os.remove(save_path)
if not save_path.parent.is_dir():
save_path.parent.mkdir(parents=True)
nwbfile_exist = False
file_mode = "w"
io = NWBHDF5IO(str(save_path), file_mode)
if nwbfile_exist:
nwbfile = io.read()
else:
nwbfile = NWBFile(**metadata_base_common["NWBFile"])
# Subject:
if metadata_base_common.get("Subject") and nwbfile.subject is None:
nwbfile.subject = Subject(**metadata_base_common["Subject"])
# Processing Module:
if "ophys" not in nwbfile.processing:
ophys = nwbfile.create_processing_module(
"ophys", "contains optical physiology processed data")
else:
ophys = nwbfile.get_processing_module("ophys")
for plane_no_loop, (segext_obj, metadata) in enumerate(
zip(segext_objs, metadata_base_list)):
# Device:
if metadata["Ophys"]["Device"][0]["name"] not in nwbfile.devices:
nwbfile.create_device(**metadata["Ophys"]["Device"][0])
# ImageSegmentation:
image_segmentation_name = (
"ImageSegmentation" if plane_no_loop == 0 else
f"ImageSegmentation_Plane{plane_no_loop}")
if image_segmentation_name not in ophys.data_interfaces:
image_segmentation = ImageSegmentation(
name=image_segmentation_name)
ophys.add(image_segmentation)
else:
image_segmentation = ophys.data_interfaces.get(
image_segmentation_name)
# OpticalChannel:
optical_channels = [
OpticalChannel(**i) for i in metadata["Ophys"]["ImagingPlane"]
[0]["optical_channel"]
]
# ImagingPlane:
image_plane_name = ("ImagingPlane" if plane_no_loop == 0 else
f"ImagePlane_{plane_no_loop}")
if image_plane_name not in nwbfile.imaging_planes.keys():
input_kwargs = dict(
name=image_plane_name,
device=nwbfile.get_device(
metadata_base_common["Ophys"]["Device"][0]["name"]),
)
metadata["Ophys"]["ImagingPlane"][0][
"optical_channel"] = optical_channels
input_kwargs.update(**metadata["Ophys"]["ImagingPlane"][0])
if "imaging_rate" in input_kwargs:
input_kwargs["imaging_rate"] = float(
input_kwargs["imaging_rate"])
imaging_plane = nwbfile.create_imaging_plane(**input_kwargs)
else:
imaging_plane = nwbfile.imaging_planes[image_plane_name]
# PlaneSegmentation:
input_kwargs = dict(
description="output from segmenting imaging plane",
imaging_plane=imaging_plane,
)
ps_metadata = metadata["Ophys"]["ImageSegmentation"][
"plane_segmentations"][0]
if ps_metadata[
"name"] not in image_segmentation.plane_segmentations:
ps_exist = False
else:
ps = image_segmentation.get_plane_segmentation(
ps_metadata["name"])
ps_exist = True
roi_ids = segext_obj.get_roi_ids()
accepted_list = segext_obj.get_accepted_list()
accepted_list = [] if accepted_list is None else accepted_list
rejected_list = segext_obj.get_rejected_list()
rejected_list = [] if rejected_list is None else rejected_list
accepted_ids = [1 if k in accepted_list else 0 for k in roi_ids]
rejected_ids = [1 if k in rejected_list else 0 for k in roi_ids]
roi_locations = np.array(segext_obj.get_roi_locations()).T
def image_mask_iterator():
for id in segext_obj.get_roi_ids():
img_msks = segext_obj.get_roi_image_masks(
roi_ids=[id]).T.squeeze()
yield img_msks
if not ps_exist:
input_kwargs.update(
**ps_metadata,
columns=[
VectorData(
data=H5DataIO(
DataChunkIterator(image_mask_iterator(),
buffer_size=buffer_size),
compression=True,
compression_opts=9,
),
name="image_mask",
description="image masks",
),
VectorData(
data=roi_locations,
name="RoiCentroid",
description=
"x,y location of centroid of the roi in image_mask",
),
VectorData(
data=accepted_ids,
name="Accepted",
description=
"1 if ROi was accepted or 0 if rejected as a cell during segmentation operation",
),
VectorData(
data=rejected_ids,
name="Rejected",
description=
"1 if ROi was rejected or 0 if accepted as a cell during segmentation operation",
),
],
id=roi_ids,
)
ps = image_segmentation.create_plane_segmentation(
**input_kwargs)
# Fluorescence Traces:
if "Flourescence" not in ophys.data_interfaces:
fluorescence = Fluorescence()
ophys.add(fluorescence)
else:
fluorescence = ophys.data_interfaces["Fluorescence"]
roi_response_dict = segext_obj.get_traces_dict()
roi_table_region = ps.create_roi_table_region(
description=f"region for Imaging plane{plane_no_loop}",
region=list(range(segext_obj.get_num_rois())),
)
rate = (np.float("NaN")
if segext_obj.get_sampling_frequency() is None else
segext_obj.get_sampling_frequency())
for i, j in roi_response_dict.items():
data = getattr(segext_obj, f"_roi_response_{i}")
if data is not None:
data = np.asarray(data)
trace_name = "RoiResponseSeries" if i == "raw" else i.capitalize(
)
trace_name = (trace_name if plane_no_loop == 0 else
trace_name + f"_Plane{plane_no_loop}")
input_kwargs = dict(
name=trace_name,
data=data.T,
rois=roi_table_region,
rate=rate,
unit="n.a.",
)
if trace_name not in fluorescence.roi_response_series:
fluorescence.create_roi_response_series(**input_kwargs)
# create Two Photon Series:
if "TwoPhotonSeries" not in nwbfile.acquisition:
warn(
"could not find TwoPhotonSeries, using ImagingExtractor to create an nwbfile"
)
# adding images:
images_dict = segext_obj.get_images_dict()
if any([image is not None for image in images_dict.values()]):
images_name = ("SegmentationImages" if plane_no_loop == 0 else
f"SegmentationImages_Plane{plane_no_loop}")
if images_name not in ophys.data_interfaces:
images = Images(images_name)
for img_name, img_no in images_dict.items():
if img_no is not None:
images.add_image(
GrayscaleImage(name=img_name, data=img_no.T))
ophys.add(images)
# saving NWB file:
if write:
io.write(nwbfile)
io.close()
# test read
with NWBHDF5IO(str(save_path), "r") as io:
io.read()
def write_segmentation(segext_obj,
save_path,
plane_num=0,
metadata=None,
overwrite=True):
save_path = Path(save_path)
assert save_path.suffix == '.nwb'
if save_path.is_file() and not overwrite:
nwbfile_exist = True
file_mode = 'r+'
else:
if save_path.is_file():
os.remove(save_path)
if not save_path.parent.is_dir():
save_path.parent.mkdir(parents=True)
nwbfile_exist = False
file_mode = 'w'
# parse metadata correctly:
if isinstance(segext_obj, MultiSegmentationExtractor):
segext_objs = segext_obj.segmentations
if metadata is not None:
assert isinstance(metadata, list), "For MultiSegmentationExtractor enter 'metadata' as a list of " \
"SegmentationExtractor metadata"
assert len(metadata) == len(segext_objs), "The 'metadata' argument should be a list with the same " \
"number of elements as the segmentations in the " \
"MultiSegmentationExtractor"
else:
segext_objs = [segext_obj]
if metadata is not None and not isinstance(metadata, list):
metadata = [metadata]
metadata_base_list = [
NwbSegmentationExtractor.get_nwb_metadata(sgobj)
for sgobj in segext_objs
]
print(f'writing nwb for {segext_obj.extractor_name}\n')
# updating base metadata with new:
for num, data in enumerate(metadata_base_list):
metadata_input = metadata[num] if metadata else {}
metadata_base_list[num] = dict_recursive_update(
metadata_base_list[num], metadata_input)
# loop for every plane:
with NWBHDF5IO(str(save_path), file_mode) as io:
metadata_base_common = metadata_base_list[0]
if nwbfile_exist:
nwbfile = io.read()
else:
nwbfile = NWBFile(**metadata_base_common['NWBFile'])
# Subject:
if metadata_base_common.get('Subject'):
nwbfile.subject = Subject(
**metadata_base_common['Subject'])
# Processing Module:
if 'ophys' not in nwbfile.processing:
ophys = nwbfile.create_processing_module(
'ophys', 'contains optical physiology processed data')
else:
ophys = nwbfile.get_processing_module('ophys')
for plane_no_loop, (segext_obj, metadata) in enumerate(
zip(segext_objs, metadata_base_list)):
# Device:
if metadata['Ophys']['Device'][0][
'name'] not in nwbfile.devices:
nwbfile.create_device(**metadata['Ophys']['Device'][0])
# ImageSegmentation:
image_segmentation_name = 'ImageSegmentation' if plane_no_loop == 0 else f'ImageSegmentation_Plane{plane_no_loop}'
if image_segmentation_name not in ophys.data_interfaces:
image_segmentation = ImageSegmentation(
name=image_segmentation_name)
ophys.add_data_interface(image_segmentation)
else:
image_segmentation = ophys.data_interfaces.get(
image_segmentation_name)
# OpticalChannel:
optical_channels = [
OpticalChannel(**i) for i in metadata['Ophys']
['ImagingPlane'][0]['optical_channel']
]
# ImagingPlane:
image_plane_name = 'ImagingPlane' if plane_no_loop == 0 else f'ImagePlane_{plane_no_loop}'
if image_plane_name not in nwbfile.imaging_planes.keys():
input_kwargs = dict(
name=image_plane_name,
device=nwbfile.get_device(metadata_base_common['Ophys']
['Device'][0]['name']),
)
metadata['Ophys']['ImagingPlane'][0][
'optical_channel'] = optical_channels
input_kwargs.update(**metadata['Ophys']['ImagingPlane'][0])
if 'imaging_rate' in input_kwargs:
input_kwargs['imaging_rate'] = float(
input_kwargs['imaging_rate'])
imaging_plane = nwbfile.create_imaging_plane(
**input_kwargs)
else:
imaging_plane = nwbfile.imaging_planes[image_plane_name]
# PlaneSegmentation:
input_kwargs = dict(
description='output from segmenting imaging plane',
imaging_plane=imaging_plane)
ps_metadata = metadata['Ophys']['ImageSegmentation'][
'plane_segmentations'][0]
if ps_metadata[
'name'] not in image_segmentation.plane_segmentations:
input_kwargs.update(**ps_metadata)
ps = image_segmentation.create_plane_segmentation(
**input_kwargs)
ps_exist = False
else:
ps = image_segmentation.get_plane_segmentation(
ps_metadata['name'])
ps_exist = True
# ROI add:
image_masks = segext_obj.get_roi_image_masks()
roi_ids = segext_obj.get_roi_ids()
accepted_list = segext_obj.get_accepted_list()
accepted_list = [] if accepted_list is None else accepted_list
rejected_list = segext_obj.get_rejected_list()
rejected_list = [] if rejected_list is None else rejected_list
accepted_ids = [
1 if k in accepted_list else 0 for k in roi_ids
]
rejected_ids = [
1 if k in rejected_list else 0 for k in roi_ids
]
roi_locations = np.array(segext_obj.get_roi_locations()).T
if not ps_exist:
ps.add_column(
name='RoiCentroid',
description=
'x,y location of centroid of the roi in image_mask')
ps.add_column(
name='Accepted',
description=
'1 if ROi was accepted or 0 if rejected as a cell during segmentation operation'
)
ps.add_column(
name='Rejected',
description=
'1 if ROi was rejected or 0 if accepted as a cell during segmentation operation'
)
for num, row in enumerate(roi_ids):
ps.add_roi(id=row,
image_mask=image_masks[:, :, num],
RoiCentroid=roi_locations[num, :],
Accepted=accepted_ids[num],
Rejected=rejected_ids[num])
# Fluorescence Traces:
if 'Flourescence' not in ophys.data_interfaces:
fluorescence = Fluorescence()
ophys.add_data_interface(fluorescence)
else:
fluorescence = ophys.data_interfaces['Fluorescence']
roi_response_dict = segext_obj.get_traces_dict()
roi_table_region = ps.create_roi_table_region(
description=f'region for Imaging plane{plane_no_loop}',
region=list(range(segext_obj.get_num_rois())))
rate = np.float('NaN') if segext_obj.get_sampling_frequency(
) is None else segext_obj.get_sampling_frequency()
for i, j in roi_response_dict.items():
data = getattr(segext_obj, f'_roi_response_{i}')
if data is not None:
data = np.asarray(data)
trace_name = 'RoiResponseSeries' if i == 'raw' else i.capitalize(
)
trace_name = trace_name if plane_no_loop == 0 else trace_name + f'_Plane{plane_no_loop}'
input_kwargs = dict(name=trace_name,
data=data.T,
rois=roi_table_region,
rate=rate,
unit='n.a.')
if trace_name not in fluorescence.roi_response_series:
fluorescence.create_roi_response_series(
**input_kwargs)
# create Two Photon Series:
if 'TwoPhotonSeries' not in nwbfile.acquisition:
warn(
'could not find TwoPhotonSeries, using ImagingExtractor to create an nwbfile'
)
# adding images:
images_dict = segext_obj.get_images_dict()
if any([image is not None for image in images_dict.values()]):
images_name = 'SegmentationImages' if plane_no_loop == 0 else f'SegmentationImages_Plane{plane_no_loop}'
if images_name not in ophys.data_interfaces:
images = Images(images_name)
for img_name, img_no in images_dict.items():
if img_no is not None:
images.add_image(
GrayscaleImage(name=img_name, data=img_no))
ophys.add(images)
# saving NWB file:
io.write(nwbfile)
# test read
with NWBHDF5IO(str(save_path), 'r') as io:
io.read()
