def build_toy_example():
load_namespaces(ns_path)
CompartmentSeries = get_class('CompartmentSeries', project_name)
membrane_potential = CompartmentSeries(source='source',
name='membrane_potential',
gid=[1, 2, 3],
index_pointer=[1, 2, 3],
cell_var='Membrane potential (mV)',
data=[[1., 2., 4.], [1., 2., 4.]],
timestamps=np.arange(2),
element_id=[0, 0, 0],
element_pos=[1., 1., 1.],
unit='µV')
nwbfile = NWBFile(source='source',
session_description='session_description',
identifier='identifier',
session_start_time=datetime.now(),
file_create_date=datetime.now(),
institution='institution',
lab='lab')
module = nwbfile.create_processing_module(name='membrane_potential',
source='source',
description='description')
module.add_container(membrane_potential)
with NWBHDF5IO('mem_potential_toy.nwb', mode='w') as io:
io.write(nwbfile)
def test_lab_meta_auto(self):
ns_builder = NWBNamespaceBuilder('Extension for use in my Lab',
self.prefix,
version='0.1.0')
test_meta_ext = NWBGroupSpec(neurodata_type_def='MyTestMetaData',
neurodata_type_inc='LabMetaData',
doc='my test meta data',
attributes=[
NWBAttributeSpec(name='test_attr',
dtype='float',
doc='test_dtype')
])
ns_builder.add_spec(self.ext_source, test_meta_ext)
ns_builder.export(self.ns_path, outdir=self.tempdir)
ns_abs_path = os.path.join(self.tempdir, self.ns_path)
load_namespaces(ns_abs_path)
MyTestMetaData = get_class('MyTestMetaData', self.prefix)
nwbfile = NWBFile("a file with header data", "NB123A",
datetime(2017, 5, 1, 12, 0, 0, tzinfo=tzlocal()))
nwbfile.add_lab_meta_data(
MyTestMetaData(name='test_name', test_attr=5.))
def build_real_example():
load_namespaces(ns_path)
VarTable = get_class('VarTable', project_name)
input_data = h5py.File('sim_data/cell_vars.h5', 'r')
vmtable = VarTable(source='source',
name='vm_table',
data=np.array(input_data['/v/data']),
gid=np.array(input_data['mapping/gids']),
index_pointer=np.array(
input_data['mapping/index_pointer']),
cell_var='Membrane potential (mV)',
element_id=np.array(input_data['mapping/element_id']),
element_pos=np.array(input_data['mapping/element_pos']))
nwbfile = NWBFile(source='source',
session_description='session_description',
identifier='identifier',
session_start_time=datetime.now(),
file_create_date=datetime.now(),
institution='institution',
lab='lab')
module = nwbfile.create_processing_module(name='name',
source='source',
description='description')
module.add_container(vmtable)
io = NWBHDF5IO('mem_potential_toy.nwb', mode='w')
io.write(nwbfile)
io.close()
def load_pynwb_extension(schema, prefix: str):
neurodata_type = schema.neurodata_type
outdir = os.path.abspath(os.path.dirname(__file__))
ns_path = f'{prefix}.namespace.yaml'
# Read spec and load namespace:
ns_abs_path = os.path.join(outdir, ns_path)
pynwb.load_namespaces(ns_abs_path)
return pynwb.get_class(neurodata_type, prefix)
def load_pynwb_extension(schema, prefix: str):
docval_list, attributes, nwbfields_list = extract_from_schema(schema)
neurodata_type = schema.neurodata_type
outdir = os.path.abspath(os.path.dirname(__file__))
ns_path = f'{prefix}.namespace.yaml'
# Read spec and load namespace:
ns_abs_path = os.path.join(outdir, ns_path)
pynwb.load_namespaces(ns_abs_path)
return pynwb.get_class(neurodata_type, prefix)
def test_load_namespace_with_reftype_attribute_check_autoclass_const(self):
ns_builder = NWBNamespaceBuilder('Extension for use in my Lab',
self.prefix)
test_ds_ext = NWBDatasetSpec(
doc='test dataset to add an attr',
name='test_data',
shape=(None, ),
attributes=[
NWBAttributeSpec(name='target_ds',
doc='the target the dataset applies to',
dtype=RefSpec('TimeSeries', 'object'))
],
neurodata_type_def='my_new_type')
ns_builder.add_spec(self.ext_source, test_ds_ext)
ns_builder.export(self.ns_path, outdir=self.tempdir)
load_namespaces(os.path.join(self.tempdir, self.ns_path))
my_new_type = get_class('my_new_type', self.prefix)
docval = None
for tmp in get_docval(my_new_type.__init__):
if tmp['name'] == 'target_ds':
docval = tmp
break
self.assertIsNotNone(docval)
self.assertEqual(docval['type'], TimeSeries)
import os
from pynwb import load_namespaces, get_class
import ndx_events
# Set path of the namespace.yaml file to the expected install location
ndx_ellipse_eye_tracking_specpath = os.path.join(
os.path.dirname(__file__),
'spec',
'ndx-ellipse-eye-tracking.namespace.yaml'
)
# If the extension has not been installed yet but we are running directly from
# the git repo
if not os.path.exists(ndx_ellipse_eye_tracking_specpath):
ndx_ellipse_eye_tracking_specpath = os.path.abspath(os.path.join(
os.path.dirname(__file__),
'..', '..', '..',
'spec',
'ndx-ellipse-eye-tracking.namespace.yaml'
))
# Load the namespace
load_namespaces(ndx_events.ndx_events_specpath)
load_namespaces(ndx_ellipse_eye_tracking_specpath)
# TODO: import your classes here or define your class using get_class to make
# them accessible at the package level
EllipseSeries = get_class('EllipseSeries', 'ndx-ellipse-eye-tracking')
EllipseEyeTracking = get_class('EllipseEyeTracking', 'ndx-ellipse-eye-tracking')
import numpy as np
from pynwb import get_class, load_namespaces
from datetime import datetime
from pynwb import NWBFile
from form.backends.hdf5 import HDF5IO
from pynwb import get_build_manager
ns_path = "alleninstitute.namespace.yaml"
load_namespaces(ns_path)
PopulationSpikeTrain = get_class('PopulationSpikeTrain', 'alleninstitute')
fs = PopulationSpikeTrain(data=(10*np.random.random(5)).astype(np.int),
name='my_population',
source='acquisition',
unit='second',
timestamps=np.random.rand(5))
f = NWBFile(source='noone',
session_description='my first synthetic recording',
file_name='tmp.nwb',
identifier='hi',
experimenter='Dr. Bilbo Baggins',
lab='Bag End Labatory',
institution='University of Middle Earth at the Shire',
experiment_description='empty',
session_id='LONELYMTN',
session_start_time=datetime.now())
f.add_raw_timeseries(fs)
def test_get_class(self):
self.test_load_namespace()
TetrodeSeries = get_class('TetrodeSeries',
'pynwb_test_extension') # noqa: F841
from pynwb import get_class
from .maze_extension import MazeExtension
LabMetaDataExtension = get_class('LabMetaDataExtension', 'ndx-tank-metadata')
RigExtension = get_class('RigExtension', 'ndx-tank-metadata')
import os
from pynwb import load_namespaces, get_class
# Set path of the namespace.yaml file to the expected install location
ndx_bipolar_scheme_specpath = os.path.join(
os.path.dirname(__file__), 'spec', 'ndx-bipolar-scheme.namespace.yaml')
# If the extension has not been installed yet but we are running directly from
# the git repo
if not os.path.exists(ndx_bipolar_scheme_specpath):
ndx_bipolar_scheme_specpath = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', '..', 'spec',
'ndx-bipolar-scheme.namespace.yaml'))
# Load the namespace
load_namespaces(ndx_bipolar_scheme_specpath)
EcephysExt = get_class('EcephysExt', 'ndx-bipolar-scheme')
# and :py:func:`~pynwb.form.utils.get_docval`
#
# When extending :py:class:`~pynwb.core.NWBContainer` or :py:class:`~pynwb.core.NWBContainer`
# subclasses, you should defining the class field ``__nwbfields__``. This will
# tell PyNWB the properties of the :py:class:`~pynwb.core.NWBContainer` extension.
#
# If you do not want to write additional code to read your extensions, PyNWB is able to dynamically
# create an :py:class:`~pynwb.core.NWBContainer` subclass for use within the PyNWB API.
# Dynamically created classes can be inspected using the built-in :py:func:`.help` or the :py:mod:`inspect` module.
from pynwb import get_class, load_namespaces
ns_path = "mylab.namespace.yaml"
load_namespaces(ns_path)
AutoTetrodeSeries = get_class('TetrodeSeries', 'mylab')
####################
# .. note::
#
# When defining your own :py:class:`~pynwb.core.NWBContainer`, the subclass name does not need to be the same
# as the extension type name. However, it is encouraged to keep class and extension names the same for the
# purposes of readibility.
####################
# .. _caching_extension:
#
# Caching extensions to file
# -----------------------------------------------------
#
# Extensions can be cached to file so that your NWB file will carry the extensions needed to read the file with it.
def test_get_class(self):
self.test_load_namespace()
TetrodeSeries = get_class('TetrodeSeries', self.prefix) # noqa: F841
where these are (this is) the compartment(s)
compartment_labels: str | Iterable(str) (optional)
or where these are (this is) the label(s)
Returns
-------
np.array(dtype=int)
"""
if compartment_numbers is not None and compartment_labels is not None:
raise ValueError('you cannot specify both compartments and compartment_labels')
if cell == 0:
start_ind = 0
else:
start_ind = self.compartments['number_index'].data[cell-1]
cell_compartments = self.compartments['number'][cell]
if compartment_numbers is not None:
return self._compartment_finder(cell_compartments, compartment_numbers, int, start_ind)
elif compartment_labels is not None:
return self._compartment_finder(cell_compartments, compartment_labels, str, start_ind)
else:
return np.arange(start_ind, start_ind + len(cell_compartments), dtype=int)
CompartmentSeries = get_class('CompartmentSeries', name)
CompartmentSeries._compartment_finder = _compartment_finder
CompartmentSeries.find_compartments = find_compartments
SimulationMetaData = get_class('SimulationMetaData', name)
#ns_builder.add_spec(ext_source, blender_surface)
blender_plane_segmentation = NWBGroupSpec(
'A plane to store data from blender',
neurodata_type_inc='PlaneSegmentation',
neurodata_type_def='BlenderPlaneSegmentation',
groups=[blender_surface])
ns_builder.add_spec(ext_source, blender_plane_segmentation)
#Writes YAML files
ns_builder.export(ns_path)
load_namespaces('blenderbits.namespace.yaml')
BlenderSurface = get_class('BlenderSurface', 'TanLab')
BlenderPlaneSegmentation = get_class('BlenderPlaneSegmentation', 'TanLab')
#Read in OBJ
os.chdir(
'C:/Users/Mrika/OneDrive/TanLab/NWBHackathonFiles/HackthonFiles/ObjectModels'
)
soma_triangles = o3d.io.read_triangle_mesh("soma.obj")
soma_triangles = np.asarray(soma_triangles.triangles)
#Testing stuff
soma_surface = BlenderSurface(vertices=[[0.0, 1.0, 1.0], [1.0, 1.0, 2.0],
[2.0, 2.0, 1.0], [2.0, 1.0, 1.0],
[1.0, 2.0, 1.0]],
faces=soma_triangles,
name='soma')
import os
from pynwb import load_namespaces, get_class
ibl_metadata_specpath = os.path.join(
os.path.dirname(__file__),
'spec',
'ndx-ibl-metadata.namespace.yaml'
)
if not os.path.exists(ibl_metadata_specpath):
ibl_metadata_specpath = os.path.abspath(os.path.join(
os.path.dirname(__file__),
'..', '..', '..',
'spec',
'ndx-ibl-metadata.namespace.yaml'
))
# Load the namespace
load_namespaces(ibl_metadata_specpath)
IblSessionData = get_class('IblSessionData', 'ndx-ibl-metadata')
IblSubject = get_class('IblSubject', 'ndx-ibl-metadata')
IblProbes = get_class('IblProbes', 'ndx-ibl-metadata')
import os
from pynwb import load_namespaces, get_class
from os import path
from pynwb.file import MultiContainerInterface
from pynwb import register_class
from pynwb.file import LabMetaData
name = 'ndx-task'
here = path.abspath(path.dirname(__file__))
ns_path = os.path.join(here, 'spec', name + '.namespace.yaml')
load_namespaces(ns_path)
Task = get_class('Task', name)
@register_class('Tasks', name)
class Tasks(MultiContainerInterface, LabMetaData):
"""
Purpose:
Topological graph representing connected components of a behavioral Environment.
Arguments:
name (str): name of this Environment
tasks (list): list of Environment objects
"""
__nwbfields__ = ('name', 'tasks')
from pynwb import load_namespaces, get_class
from os import path
from pynwb.file import MultiContainerInterface
from pynwb import register_class
from pynwb.file import LabMetaData
name = 'ndx-maze'
here = path.abspath(path.dirname(__file__))
ns_path = os.path.join(here, 'spec', name + '.namespace.yaml')
load_namespaces(ns_path)
Node = get_class('Node', name)
Edge = get_class('Edge', name)
PointNode = get_class('PointNode', name)
SegmentNode = get_class('SegmentNode', name)
PolygonNode = get_class('PolygonNode', name)
@register_class('Environment', name)
class Environment(MultiContainerInterface):
"""
Purpose:
Topological graph representing connected components of a beahvioral Environment.
Arguments:
name (str): name of this Environment
nodes (list): list of Node objects contained in this Environment
import os
from pynwb import load_namespaces, get_class
from os import path
name = 'ndx-nonrigid-motion-correction'
here = path.abspath(path.dirname(__file__))
ns_path = os.path.join(here, 'spec', name + '.namespace.yaml')
load_namespaces(ns_path)
NonrigidMotionCorrection = get_class('NonrigidMotionCorrection', name)
def local_interpolation(transform, image, npixels=1):
"""
Parameters
----------
transform
image
npixels: int
Returns
-------
"""
pass
def nonrigid_motion_correction_apply(self, ts):
import os
from pynwb import load_namespaces, get_class
from os import path
name = 'ndx-fret'
here = path.abspath(path.dirname(__file__))
ns_path = os.path.join(here, 'spec', name + '.namespace.yaml')
load_namespaces(ns_path)
FRETSeries = get_class('FRETSeries', name)
FRET = get_class('FRET', name)
# If the extension has not been installed yet but we are running directly from
# the git repo
if not os.path.exists(ndx_photometry_specpath):
ndx_photometry_specpath = os.path.abspath(
os.path.join(
os.path.dirname(__file__),
"..",
"..",
"..",
"spec",
"ndx-photometry.namespace.yaml",
))
# Load the namespace
load_namespaces(ndx_photometry_specpath)
# TODO: import your classes here or define your class using get_class to make
from .fibers_table import FibersTable
# them accessible at the package level
(CommandedVoltageSeries, ExcitationSourcesTable, PhotodetectorsTable,
DeconvolvedRoiResponseSeries, MultiCommandedVoltage, FiberPhotometry,
FluorophoresTable) = [
get_class(x, "ndx-photometry")
for x in ("CommandedVoltageSeries", "ExcitationSourcesTable",
"PhotodetectorsTable", "DeconvolvedRoiResponseSeries",
"MultiCommandedVoltage", 'FiberPhotometry', "FluorophoresTable")
]
import numpy as np
import pandas as pd
from bs4 import BeautifulSoup
from pynwb import load_namespaces, get_class
from pynwb.misc import UnitTimes
from general import gzip
import pdb
name = 'general'
ns_path = name + '.namespace.yaml'
load_namespaces(ns_path)
PopulationSpikeTimes = get_class('PopulationSpikeTimes', name)
def load_xml(filepath):
with open(filepath, 'r') as xml_file:
contents = xml_file.read()
soup = BeautifulSoup(contents, 'xml')
return soup
def get_channel_groups(xml_filepath):
"""Get the groups of channels that are recorded on each shank from the xml
file
Parameters
----------
# and :py:func:`~hdmf.utils.get_docval`
#
# When extending :py:class:`~pynwb.core.NWBContainer` or :py:class:`~pynwb.core.NWBContainer`
# subclasses, you should define the class field ``__nwbfields__``. This will
# tell PyNWB the properties of the :py:class:`~pynwb.core.NWBContainer` extension.
#
# If you do not want to write additional code to read your extensions, PyNWB is able to dynamically
# create an :py:class:`~pynwb.core.NWBContainer` subclass for use within the PyNWB API.
# Dynamically created classes can be inspected using the built-in :py:mod:`inspect` module.
from pynwb import get_class, load_namespaces
ns_path = "mylab.namespace.yaml"
load_namespaces(ns_path)
AutoTetrodeSeries = get_class('TetrodeSeries', 'mylab')
####################
# .. note::
#
# When defining your own :py:class:`~pynwb.core.NWBContainer`, the subclass name does not need to be the same
# as the extension type name. However, it is encouraged to keep class and extension names the same for the
# purposes of readability.
####################
# .. _caching_extension:
#
# Caching extensions to file
# -----------------------------------------------------
#
# By default, extensions are cached to file so that your NWB file will carry the extensions needed to read the file
import os
from pynwb import load_namespaces, get_class
from os import path
name = 'ndx-spectrum'
here = path.abspath(path.dirname(__file__))
ns_path = os.path.join(here, 'spec', name + '.namespace.yaml')
load_namespaces(ns_path)
Spectrum = get_class('Spectrum', name)
import os
import pandas as pd
from pynwb import load_namespaces, get_class
from pynwb.misc import AnnotationSeries
name = 'ndx-miniscope'
here = os.path.abspath(os.path.dirname(__file__))
ns_path = os.path.join(here, 'spec', name + '.namespace.yaml')
load_namespaces(ns_path)
Miniscope = get_class('Miniscope', name)
def load_miniscope_timestamps(fpath, cam_num=1):
"""Reads timestamp.dat and outputs a list of times in seconds
Parameters
----------
fpath: str
data directory or path to timestamp.dat
cam_num: int
number of feed
Returns
-------
numpy.ndarray list if times in seconds
"""
import os
from pynwb import load_namespaces, get_class
# Set path of the namespace.yaml file to the expected install location
ndx_stimulus_template_specpath = os.path.join(
os.path.dirname(__file__),
'ndx-aibs-stimulus-template.namespace.yaml'
)
# Load the namespace
load_namespaces(ndx_stimulus_template_specpath)
StimulusTemplateExtension = get_class('StimulusTemplate',
'ndx-aibs-stimulus-template')
import os
from pynwb import load_namespaces, get_class
from pynwb import register_class, docval
from hdmf.utils import call_docval_func
from pynwb.file import MultiContainerInterface
from hdmf.common.table import DynamicTable, ElementIdentifiers
name = 'ndx-tempo'
here = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', '..'))
ns_path = os.path.join(here, 'spec', name + '.namespace.yaml')
load_namespaces(ns_path)
Measurement = get_class('Measurement', name)
LaserLine = get_class('LaserLine', name)
PhotoDetector = get_class('PhotoDetector', name)
@register_class('LockInAmplifier', name)
class LockInAmplifier(DynamicTable):
__columns__ = (
{'name': 'channel_name',
'description': 'name of the channel of lock_in_amp'},
{'name': 'offset',
'description': 'offset for channel of lock_in_amp'},
{'name': 'gain',
'description': 'gain for channel of lock_in_amp'}
)
@docval({'name': 'name', 'type': str, 'doc': 'Name of this Compartments object',
'default': 'compartments'},
import os
from pynwb import load_namespaces, get_class
# Set path of the namespace.yaml file to the expected install location
ndx_ophys_events_specpath = os.path.join(
os.path.dirname(__file__),
'ndx-aibs-ophys-event-detection.namespace.yaml'
)
# Load the namespace
load_namespaces(ndx_ophys_events_specpath)
OphysEventDetection = get_class('OphysEventDetection',
'ndx-aibs-ophys-event-detection')
from pathlib import Path
import pynwb
file_dir = Path(__file__).parent
namespace_path = (file_dir / "ndx-aibs-ecephys.namespace.yaml").resolve()
pynwb.load_namespaces(str(namespace_path))
EcephysProbe = pynwb.get_class('EcephysProbe', 'ndx-aibs-ecephys')
EcephysElectrodeGroup = pynwb.get_class('EcephysElectrodeGroup',
'ndx-aibs-ecephys')
EcephysSpecimen = pynwb.get_class('EcephysSpecimen', 'ndx-aibs-ecephys')
EcephysEyeTrackingRigMetadata = pynwb.get_class(
'EcephysEyeTrackingRigMetadata', 'ndx-aibs-ecephys')
EcephysCSD = pynwb.get_class('EcephysCSD', 'ndx-aibs-ecephys')
import os
from pynwb import load_namespaces, get_class
name = 'ndx-bipolar-scheme'
# Set path of the namespace.yaml file to the expected install location
ndx_bipolar_scheme_specpath = os.path.join(os.path.dirname(__file__), 'spec',
name + '.namespace.yaml')
# If the extension has not been installed yet but we are running directly from
# the git repo
if not os.path.exists(ndx_bipolar_scheme_specpath):
ndx_bipolar_scheme_specpath = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', '..', 'spec',
name + '.namespace.yaml'))
# Load the namespace
load_namespaces(ndx_bipolar_scheme_specpath)
BipolarSchemeTable = get_class('BipolarSchemeTable', 'ndx-bipolar-scheme')
NdxBipolarScheme = get_class('NdxBipolarScheme', 'ndx-bipolar-scheme')