def setUpContainer(self):
self.device = Device(name='dev1')
self.optical_channel = OpticalChannel('optchan1',
'a fake OpticalChannel', 500.)
return ImagingPlane('imgpln1', self.optical_channel,
'a fake ImagingPlane', self.device, 600., 300.,
'GFP', 'somewhere in the brain')
def buildPlaneSegmentationNoRois(self):
""" Return an PlaneSegmentation and set related objects """
ts = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
self.image_series = ImageSeries(name='test_iS',
dimension=[2],
external_file=['images.tiff'],
starting_frame=[1, 2, 3],
format='tiff',
timestamps=ts)
self.device = Device(name='dev1')
self.optical_channel = OpticalChannel(
name='test_optical_channel',
description='optical channel description',
emission_lambda=500.)
self.imaging_plane = ImagingPlane(
name='test_imaging_plane',
optical_channel=self.optical_channel,
description='imaging plane description',
device=self.device,
excitation_lambda=600.,
imaging_rate=300.,
indicator='GFP',
location='somewhere in the brain',
reference_frame='a frame to refer to')
return PlaneSegmentation(description='description',
imaging_plane=self.imaging_plane,
name='test_plane_seg_name',
reference_images=self.image_series)
def CreatePlaneSegmentation():
w, h = 5, 5
img_mask = [[[1.0 for x in range(w)] for y in range(h)],
[[2.0 for x in range(w)] for y in range(h)]]
pix_mask = [[1, 2, 1.0], [3, 4, 1.0], [5, 6, 1.0], [7, 8, 2.0],
[9, 10, 2.0]]
iSS = ImageSeries(name='test_iS',
data=np.ones((2, 2, 2)),
unit='unit',
external_file=['external_file'],
starting_frame=[1, 2, 3],
format='tiff',
timestamps=[1., 2.])
oc = OpticalChannel('test_optical_channel', 'description', 500.)
device = Device(name='device_name')
ip = ImagingPlane('test_imaging_plane', oc, 'description', device, 600.,
300., 'indicator', 'location', (1, 2, 1, 2, 3), 4.0,
'unit', 'reference_frame')
pS = PlaneSegmentation('description', ip, 'test_name', iSS)
pS.add_roi(pixel_mask=pix_mask[0:3], image_mask=img_mask[0])
pS.add_roi(pixel_mask=pix_mask[3:5], image_mask=img_mask[1])
return pS
def test_init(self):
w, h = 5, 5
img_mask = [[[1.0 for x in range(w)] for y in range(h)],
[[2.0 for x in range(w)] for y in range(h)]]
pix_mask = [[1, 2, 1.0], [3, 4, 1.0], [5, 6, 1.0], [7, 8, 2.0],
[9, 10, 2.0]]
iSS = ImageSeries(name='test_iS',
source='a hypothetical source',
data=list(),
unit='unit',
external_file=['external_file'],
starting_frame=[1, 2, 3],
format='tiff',
timestamps=list())
oc = OpticalChannel('test_optical_channel', 'test_source',
'description', 500.)
ip = ImagingPlane('test_imaging_plane', 'test_source', oc,
'description', 'device', 600., 'imaging_rate',
'indicator', 'location', (1, 2, 1, 2, 3), 4.0,
'unit', 'reference_frame')
pS = PlaneSegmentation('test source', 'description', ip, 'test_name',
iSS)
pS.add_roi("1234", pix_mask[0:3], img_mask[0])
pS.add_roi("5678", pix_mask[3:5], img_mask[1])
self.assertEqual(pS.description, 'description')
self.assertEqual(pS.source, 'test source')
self.assertEqual(pS.imaging_plane, ip)
self.assertEqual(pS.reference_images, iSS)
self.assertEqual(pS.pixel_masks.data, pix_mask)
self.assertEqual(pS.image_masks.data, img_mask)
def addContainer(self, file):
dev1 = file.create_device('dev1', 'dev1 description')
oc = OpticalChannel('optchan1', 'a fake OpticalChannel', 3.14)
ip = file.create_imaging_plane('imgpln1',
oc,
description='a fake ImagingPlane',
device=dev1,
excitation_lambda=6.28,
indicator='GFP',
location='somewhere in the brain',
imaging_rate=2.718)
data = np.ones((3, 3, 3))
timestamps = list(range(10))
fov = [2.0, 2.0, 5.0]
tps = TwoPhotonSeries('test_2ps',
ip,
data,
'image_unit',
'raw',
fov,
1.7,
3.4,
timestamps=timestamps,
dimension=[200, 200])
file.add_acquisition(tps)
return tps
def buildPlaneSegmentation(self):
w, h = 5, 5
img_mask = [[[1.0 for x in range(w)] for y in range(h)],
[[2.0 for x in range(w)] for y in range(h)]]
pix_mask = [(1, 2, 1.0), (3, 4, 1.0), (5, 6, 1.0), (7, 8, 2.0),
(9, 10, 2.)]
ts = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
self.image_series = ImageSeries(name='test_iS',
source='a hypothetical source',
dimension=[2],
external_file=['images.tiff'],
starting_frame=[1, 2, 3],
format='tiff',
timestamps=ts)
self.optical_channel = OpticalChannel('test_optical_channel',
'optical channel source',
'optical channel description',
500.)
self.imaging_plane = ImagingPlane(
'test_imaging_plane', 'ophys integration tests',
self.optical_channel, 'imaging plane description',
'imaging_device_1', 600., '2.718', 'GFP', 'somewhere in the brain',
(1, 2, 1, 2, 3), 4.0, 'manifold unit', 'A frame to refer to')
self.img_mask = deepcopy(img_mask)
self.pix_mask = deepcopy(pix_mask)
pS = PlaneSegmentation('integration test PlaneSegmentation',
'plane segmentation description',
self.imaging_plane, 'test_plane_seg_name',
self.image_series)
pS.add_roi('1234', pix_mask[0:3], img_mask[0])
pS.add_roi('5678', pix_mask[3:5], img_mask[1])
return pS
def __init__(self,
nwbfile,
emission_lambda=np.nan,
excitation_lambda=np.nan,
frame_rate=np.nan,
indicator='unknown',
location='unknown'):
super(OphysProcessing2NWB, self).__init__(nwbfile)
device = Device('microscope')
self.nwbfile.add_device(device)
optical_channel = OpticalChannel('OpticalChannel',
'description',
emission_lambda=emission_lambda)
imaging_plane = self.nwbfile.create_imaging_plane(
'ImagingPlane',
optical_channel,
description='description',
device=device,
excitation_lambda=excitation_lambda,
imaging_rate=frame_rate,
indicator=indicator,
location=location)
self.ophys_mod = self.nwbfile.create_processing_module(
'ophys', 'contains optical physiology processed data')
img_seg = ImageSegmentation()
self.ophys_mod.add_data_interface(img_seg)
self.ps = img_seg.create_plane_segmentation(
'output from segmenting my favorite imaging plane', imaging_plane,
'PlaneSegmentation')
def test_init(self):
oc = OpticalChannel('test_name', 'description', 500.)
self.assertEqual(oc.description, 'description')
self.assertEqual(oc.emission_lambda, 500.)
device = Device(name='device_name')
ip = ImagingPlane('test_imaging_plane', oc, 'description', device, 600.,
300., 'indicator', 'location', (50, 100, 3), 4.0, 'unit', 'reference_frame')
self.assertEqual(ip.optical_channel[0], oc)
self.assertEqual(ip.device, device)
self.assertEqual(ip.excitation_lambda, 600.)
self.assertEqual(ip.imaging_rate, 300.)
self.assertEqual(ip.indicator, 'indicator')
self.assertEqual(ip.location, 'location')
self.assertEqual(ip.manifold, (50, 100, 3))
self.assertEqual(ip.conversion, 4.0)
self.assertEqual(ip.unit, 'unit')
self.assertEqual(ip.reference_frame, 'reference_frame')
tPS = TwoPhotonSeries('test_tPS', unit='unit', field_of_view=[2., 3.],
imaging_plane=ip, pmt_gain=1.0, scan_line_rate=2.0, external_file=['external_file'],
starting_frame=[1, 2, 3], format='tiff', timestamps=list())
self.assertEqual(tPS.name, 'test_tPS')
self.assertEqual(tPS.unit, 'unit')
self.assertEqual(tPS.field_of_view, [2., 3.])
self.assertEqual(tPS.imaging_plane, ip)
self.assertEqual(tPS.pmt_gain, 1.0)
self.assertEqual(tPS.scan_line_rate, 2.0)
self.assertEqual(tPS.external_file, ['external_file'])
self.assertEqual(tPS.starting_frame, [1, 2, 3])
self.assertEqual(tPS.format, 'tiff')
self.assertIsNone(tPS.dimension)
def _get_imaging_plane(self):
"""Add new / return existing Imaging Plane"""
meta_imgplane = self.metadata['Ophys']['ImagingPlane'][0]
if meta_imgplane['name'] in self.nwbfile.imaging_planes:
imaging_plane = self.nwbfile.imaging_planes[meta_imgplane['name']]
else:
with h5py.File(self.source_paths['path_processed'], 'r') as f:
if 'depth' in f:
description = 'high zoom'
else:
description = 'low zoom'
animal_id = str(int(f['aid'][0]))
subject_info = subjects_info[animal_id]
indicator = subject_info['indicator']
imaging_rate = 1 / f['dto'][0]
# Create Imaging Plane
meta_optch = meta_imgplane['optical_channel'][0]
optical_channel = OpticalChannel(**meta_optch)
imaging_plane = self.nwbfile.create_imaging_plane(
name='imaging_plane',
optical_channel=optical_channel,
description=description,
device=self.nwbfile.devices[meta_imgplane['device']],
excitation_lambda=meta_imgplane['excitation_lambda'],
indicator=indicator,
location=meta_imgplane['location'],
imaging_rate=imaging_rate,
)
return imaging_plane
def CreatePlaneSegmentation():
w, h = 5, 5
img_mask = [[0 for x in range(w)] for y in range(h)]
w, h = 5, 2
pix_mask = [[0 for x in range(w)] for y in range(h)]
pix_mask_weight = [0 for x in range(w)]
iSS = ImageSeries(name='test_iS',
source='a hypothetical source',
data=list(),
unit='unit',
external_file=['external_file'],
starting_frame=[1, 2, 3],
format='tiff',
timestamps=list())
roi1 = ROI('roi1', 'test source', 'roi description1', pix_mask,
pix_mask_weight, img_mask, iSS)
roi2 = ROI('roi2', 'test source', 'roi description2', pix_mask,
pix_mask_weight, img_mask, iSS)
roi_list = (roi1, roi2)
oc = OpticalChannel('test_optical_channel', 'test_source', 'description',
'emission_lambda')
ip = ImagingPlane('test_imaging_plane', 'test_source', oc, 'description',
'device', 'excitation_lambda', 'imaging_rate',
'indicator', 'location', (1, 2, 1, 2, 3), 4.0, 'unit',
'reference_frame')
ps = PlaneSegmentation('name', 'test source', 'description', roi_list, ip,
iSS)
return ps
def test_init(self):
oc = OpticalChannel(name='test_optical_channel',
description='description',
emission_lambda=500.)
self.assertEqual(oc.name, 'test_optical_channel')
self.assertEqual(oc.description, 'description')
self.assertEqual(oc.emission_lambda, 500.)
def CreatePlaneSegmentation():
w, h = 5, 5
img_mask = [[[1.0 for x in range(w)] for y in range(h)],
[[2.0 for x in range(w)] for y in range(h)]]
pix_mask = [[1, 2, 1.0], [3, 4, 1.0], [5, 6, 1.0], [7, 8, 2.0],
[9, 10, 2.0]]
iSS = ImageSeries(name='test_iS',
source='a hypothetical source',
data=list(),
unit='unit',
external_file=['external_file'],
starting_frame=[1, 2, 3],
format='tiff',
timestamps=list())
oc = OpticalChannel('test_optical_channel', 'test_source', 'description',
'emission_lambda')
ip = ImagingPlane('test_imaging_plane', 'test_source', oc, 'description',
'device', 'excitation_lambda', 'imaging_rate',
'indicator', 'location', (1, 2, 1, 2, 3), 4.0, 'unit',
'reference_frame')
pS = PlaneSegmentation('test source', 'description', ip, 'test_name', iSS)
pS.add_roi(pix_mask[0:3], img_mask[0])
pS.add_roi(pix_mask[3:5], img_mask[1])
return pS
def addContainer(self, file):
dev1 = file.create_device('imaging_device_1', 'dev1 description')
oc = OpticalChannel('optchan1', 'a fake OpticalChannel', 3.14)
ip = file.create_imaging_plane('imgpln1', oc, 'a fake ImagingPlane',
dev1, 6.28, 2.718, 'GFP',
'somewhere in the brain')
return ip
def addContainer(self, file):
oc = OpticalChannel('optchan1', 'unit test TestImagingPlaneIO',
'a fake OpticalChannel', '3.14')
ip = ImagingPlane('imgpln1', 'unit test TestImagingPlaneIO', oc,
'a fake ImagingPlane', 'imaging_device_1', '6.28',
'2.718', 'GFP', 'somewhere in the brain')
file.set_imaging_plane(ip)
return ip
def make_imaging_plane(self):
self.device = Device(name='dev1')
self.optical_channel = OpticalChannel('optchan1',
'a fake OpticalChannel', 500.)
self.imaging_plane = ImagingPlane('imgpln1', self.optical_channel,
'a fake ImagingPlane', self.device,
600., 300., 'GFP',
'somewhere in the brain')
def make_imaging_plane(self, source):
self.optical_channel = OpticalChannel('optchan1', source,
'a fake OpticalChannel', 500.)
self.imaging_plane = ImagingPlane('imgpln1', source,
self.optical_channel,
'a fake ImagingPlane',
'imaging_device_1', 600., '2.718',
'GFP', 'somewhere in the brain')
def setUpContainer(self):
self.optical_channel = OpticalChannel('optchan1',
'unit test TestImagingPlaneIO',
'a fake OpticalChannel', 500.)
return ImagingPlane('imgpln1', 'unit test TestImagingPlaneIO',
self.optical_channel, 'a fake ImagingPlane',
'imaging_device_1', 600., '2.718', 'GFP',
'somewhere in the brain')
def test_add_fretseries(self):
# Create and add device
device = Device(name='Device')
self.nwbfile.add_device(device)
# Create optical channels
opt_ch_d = OpticalChannel(name='optical_channel',
description='optical_channel_description',
emission_lambda=529.)
opt_ch_a = OpticalChannel(name='optical_channel',
description='optical_channel_description',
emission_lambda=633.)
# Create FRET
fs_d = FRETSeries(name='donor',
fluorophore='mCitrine',
optical_channel=opt_ch_d,
device=device,
description='description of donor series',
data=np.random.randn(100, 10, 10),
rate=200.,
unit='no unit')
fs_a = FRETSeries(name='acceptor',
fluorophore='mKate2',
optical_channel=opt_ch_a,
device=device,
description='description of acceptor series',
data=np.random.randn(100, 10, 10),
rate=200.,
unit='no unit')
fret = FRET(name='FRET',
excitation_lambda=482.,
donor=fs_d,
acceptor=fs_a)
self.nwbfile.add_acquisition(fret)
filename = 'test_fret.nwb'
with NWBHDF5IO(filename, 'w') as io:
io.write(self.nwbfile)
with NWBHDF5IO(filename, mode='r', load_namespaces=True) as io:
io.read()
os.remove(filename)
def test_args(self):
oc = OpticalChannel('test_name', 'description', 500.)
device = Device(name='device_name')
ip = ImagingPlane('test_imaging_plane', oc, 'description', device, 600.,
300., 'indicator', 'location', (50, 100, 3), 4.0, 'unit', 'reference_frame')
with self.assertRaises(ValueError): # no data or external file
TwoPhotonSeries('test_tPS', unit='unit', field_of_view=[2., 3.],
imaging_plane=ip, pmt_gain=1.0, scan_line_rate=2.0,
starting_frame=[1, 2, 3], format='tiff', timestamps=[1., 2.])
def getBoilerPlateObjects(self):
iSS = ImageSeries(name='test_iS', data=np.ones((2, 2, 2)), unit='unit',
external_file=['external_file'], starting_frame=[1, 2, 3], format='tiff', timestamps=list())
device = Device(name='device_name')
oc = OpticalChannel('test_optical_channel', 'description', 500.)
ip = ImagingPlane('test_imaging_plane', oc, 'description', device, 600.,
300., 'indicator', 'location', (1, 2, 1, 2, 3), 4.0, 'unit', 'reference_frame')
return iSS, ip
def test_conversion_deprecated(self):
oc = OpticalChannel('test_name', 'description', 500.)
self.assertEqual(oc.description, 'description')
self.assertEqual(oc.emission_lambda, 500.)
device = Device(name='device_name')
msg = "The 'conversion' argument is deprecated in favor of 'origin_coords' and 'grid_spacing'."
with self.assertWarnsWith(DeprecationWarning, msg):
ImagingPlane('test_imaging_plane', oc, 'description', device, 600., 300., 'indicator', 'location',
None, 2.0)
def addContainer(self, file):
dev1 = file.create_device('imaging_device_1', 'dev1 description')
oc = OpticalChannel('optchan1', 'a fake OpticalChannel', 3.14)
ip = file.create_imaging_plane('imgpln1',
oc,
description='a fake ImagingPlane',
device=dev1,
excitation_lambda=6.28,
indicator='GFP',
location='somewhere in the brain',
imaging_rate=2.718)
return ip
def create_optical_channel(self, metadata=None):
input_kwargs = dict(name='OpticalChannel',
description='no description',
emission_lambda=np.nan)
if metadata is None and 'Ophys' in self.metadata and 'OpticalChannel' in self.metadata[
'Ophys']:
metadata = self.metadata['Ophys']['OpticalChannel']
input_kwargs.update(metadata)
return OpticalChannel(**input_kwargs)
def setUpContainer(self):
""" Return the test ImagingPlane to read/write """
self.device = Device(name='dev1')
self.optical_channel = OpticalChannel('optchan1',
'a fake OpticalChannel', 500.)
return ImagingPlane('imgpln1',
self.optical_channel,
'a fake ImagingPlane',
self.device,
600.,
300.,
'GFP',
'somewhere in the brain',
reference_frame='unknonwn')
def make_imaging_plane(self):
""" Make an ImagingPlane and related objects """
self.device = Device(name='dev1')
self.optical_channel = OpticalChannel('optchan1',
'a fake OpticalChannel', 500.)
self.imaging_plane = ImagingPlane('imgpln1',
self.optical_channel,
'a fake ImagingPlane',
self.device,
600.,
300.,
'GFP',
'somewhere in the brain',
reference_frame='unknown')
def make_imaging_plane(self):
""" Make an ImagingPlane and related objects """
self.device = Device(name='dev1')
self.optical_channel = OpticalChannel(
name='optchan1',
description='a fake OpticalChannel',
emission_lambda=500.)
self.imaging_plane = ImagingPlane(name='imgpln1',
optical_channel=self.optical_channel,
description='a fake ImagingPlane',
device=self.device,
excitation_lambda=600.,
imaging_rate=300.,
indicator='GFP',
location='somewhere in the brain',
reference_frame='unknown')
def setBoilerPlateObjects(self):
ts = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
self.image_series = ImageSeries(name='test_iS', dimension=[2],
external_file=['images.tiff'],
starting_frame=[1, 2, 3], format='tiff', timestamps=ts)
self.device = Device(name='dev1')
self.optical_channel = OpticalChannel('test_optical_channel',
'optical channel description', 500.)
self.imaging_plane = ImagingPlane('test_imaging_plane',
self.optical_channel,
'imaging plane description',
self.device,
600., 300., 'GFP', 'somewhere in the brain',
(1, 2, 1, 2, 3), 4.0, 'manifold unit', 'A frame to refer to')
return PlaneSegmentation('description', self.imaging_plane, 'test_plane_seg_name',
self.image_series)
def test_init(self):
oc = OpticalChannel('test_name', 'test_source', 'description',
'emission_lambda')
self.assertEqual(oc.description, 'description')
self.assertEqual(oc.emission_lambda, 'emission_lambda')
ip = ImagingPlane('test_imaging_plane', 'test source', oc,
'description', 'device', 'excitation_lambda',
'imaging_rate', 'indicator', 'location',
(1, 2, 1, 2, 3), 4.0, 'unit', 'reference_frame')
self.assertEqual(ip.optical_channel[0], oc)
self.assertEqual(ip.device, 'device')
self.assertEqual(ip.excitation_lambda, 'excitation_lambda')
self.assertEqual(ip.imaging_rate, 'imaging_rate')
self.assertEqual(ip.indicator, 'indicator')
self.assertEqual(ip.location, 'location')
self.assertEqual(ip.manifold, (1, 2, 1, 2, 3))
self.assertEqual(ip.conversion, 4.0)
self.assertEqual(ip.unit, 'unit')
self.assertEqual(ip.reference_frame, 'reference_frame')
tPS = TwoPhotonSeries('test_tPS',
'a hypothetical source',
data=list(),
unit='unit',
field_of_view=list(),
imaging_plane=ip,
pmt_gain=1.0,
scan_line_rate=2.0,
external_file=['external_file'],
starting_frame=[1, 2, 3],
format='tiff',
timestamps=list())
self.assertEqual(tPS.name, 'test_tPS')
self.assertEqual(tPS.source, 'a hypothetical source')
self.assertEqual(tPS.unit, 'unit')
self.assertEqual(tPS.field_of_view, list())
self.assertEqual(tPS.imaging_plane, ip)
self.assertEqual(tPS.pmt_gain, 1.0)
self.assertEqual(tPS.scan_line_rate, 2.0)
self.assertEqual(tPS.external_file, ['external_file'])
self.assertEqual(tPS.starting_frame, [1, 2, 3])
self.assertEqual(tPS.format, 'tiff')
self.assertEqual(tPS.dimension, [np.nan])
def add_two_photon_series(imaging, nwbfile, metadata, buffer_size=10):
"""
Auxiliary static method for nwbextractor.
Adds two photon series from imaging object as TwoPhotonSeries to nwbfile object.
"""
metadata = dict_recursive_update(get_default_nwb_metadata(), metadata)
metadata = update_dict(metadata,
NwbImagingExtractor.get_nwb_metadata(imaging))
# Tests if ElectricalSeries already exists in acquisition
nwb_es_names = [ac for ac in nwbfile.acquisition]
opts = metadata["Ophys"]["TwoPhotonSeries"][0]
if opts["name"] not in nwb_es_names:
# retrieve device
device = nwbfile.devices[list(nwbfile.devices.keys())[0]]
metadata["Ophys"]["ImagingPlane"][0]["optical_channel"] = [
OpticalChannel(**i) for i in metadata["Ophys"]["ImagingPlane"]
[0]["optical_channel"]
]
metadata["Ophys"]["ImagingPlane"][0] = update_dict(
metadata["Ophys"]["ImagingPlane"][0], {"device": device})
imaging_plane = nwbfile.create_imaging_plane(
**metadata["Ophys"]["ImagingPlane"][0])
def data_generator(imaging):
for i in range(imaging.get_num_frames()):
yield imaging.get_frames(frame_idxs=[i]).T
data = H5DataIO(
DataChunkIterator(data_generator(imaging),
buffer_size=buffer_size),
compression=True,
)
# using internal data. this data will be stored inside the NWB file
two_p_series_kwargs = update_dict(
metadata["Ophys"]["TwoPhotonSeries"][0],
dict(data=data, imaging_plane=imaging_plane),
)
ophys_ts = TwoPhotonSeries(**two_p_series_kwargs)
nwbfile.add_acquisition(ophys_ts)
return nwbfile
def cicada_create_optical_channel(self):
required_metadata = [
"optical_channel_name", "optical_channel_description",
"optical_channel_emission_lambda"
]
metadata_to_add = list(
set(required_metadata) -
set(list(self.data['ophys_metadata'].keys())))
for i in metadata_to_add:
self.data["ophys_metadata"] = self.add_required_metadata(
self.data["ophys_metadata"], i, "ophys")
self.optical_channel = OpticalChannel(
name=self.data['ophys_metadata'].get("optical_channel_name"),
description=self.data['ophys_metadata'].get(
"optical_channel_description"),
emission_lambda=self.data['ophys_metadata'].get(
"optical_channel_emission_lambda"))
