def test_catch_dup_name(self):
ns_builder1 = NWBNamespaceBuilder('Extension for us in my Lab',
"pynwb_test_extension1")
ext1 = NWBGroupSpec('A custom ElectricalSeries for my lab',
attributes=[
NWBAttributeSpec(name='trode_id',
doc='the tetrode id',
dtype='int')
],
neurodata_type_inc='ElectricalSeries',
neurodata_type_def='TetrodeSeries')
ns_builder1.add_spec(self.ext_source1, ext1)
ns_builder1.export(self.ns_path1, outdir=self.tempdir)
ns_builder2 = NWBNamespaceBuilder('Extension for us in my Lab',
"pynwb_test_extension1")
ext2 = NWBGroupSpec('A custom ElectricalSeries for my lab',
attributes=[
NWBAttributeSpec(name='trode_id',
doc='the tetrode id',
dtype='int')
],
neurodata_type_inc='ElectricalSeries',
neurodata_type_def='TetrodeSeries')
ns_builder2.add_spec(self.ext_source2, ext2)
ns_builder2.export(self.ns_path2, outdir=self.tempdir)
load_namespaces(os.path.join(self.tempdir, self.ns_path1))
with self.assertRaises(KeyError):
load_namespaces(os.path.join(self.tempdir, self.ns_path2))
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(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)
@register_class('MyTestMetaData', self.prefix)
class MyTestMetaData(LabMetaData):
__nwbfields__ = ('test_attr',)
@docval({'name': 'name', 'type': str, 'doc': 'name'},
{'name': 'test_attr', 'type': float, 'doc': 'test attribute'})
def __init__(self, **kwargs):
test_attr = popargs('test_attr', kwargs)
super(MyTestMetaData, self).__init__(**kwargs)
self.test_attr = test_attr
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 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_settings(dict):
""" WIP builds metadata based on passed in dict Does not support dict(s) deeper than 1 ATM"""
# Settings:
neurodata_type = 'MetaData'
prefix = 'NHP'
outdir = './'
extension_doc = 'lab metadata extension'
metadata_ext_group_spec = NWBGroupSpec(neurodata_type_def=neurodata_type,
neurodata_type_inc='LabMetaData',
doc=extension_doc,
attributes=[
NWBAttributeSpec(
name='experiment_id',
dtype='int',
doc='HW'),
NWBAttributeSpec(name='test',
dtype='text',
doc='HW')
])
#Export spec:
ext_source = '%s_extension.yaml' % prefix
ns_path = '%s_namespace.yaml' % prefix
ns_builder = NWBNamespaceBuilder(extension_doc, prefix, version=str(1))
ns_builder.add_spec(ext_source, metadata_ext_group_spec)
ns_builder.export(ns_path, outdir=outdir)
#Read spec and load namespace:
ns_abs_path = os.path.join(outdir, ns_path)
load_namespaces(ns_abs_path)
class MetaData(LabMetaData):
__nwbfields__ = ('experiment_id', 'test')
@docval({
'name': 'name',
'type': str,
'doc': 'name'
}, {
'name': 'experiment_id',
'type': int,
'doc': 'HW'
}, {
'name': 'test',
'type': str,
'doc': 'HW'
})
def __init__(self, **kwargs):
name, ophys_experiment_id, test = popargs('name', 'experiment_id',
'test', kwargs)
super(OphysBehaviorMetaData, self).__init__(name=name)
self.experiment_id = experiment_id
self.test = test
register_class('MetaData', prefix, MetaData)
return MetaData
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(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))
def main():
ep = """
use --nspath to validate against an extension. If --ns is not specified,
validate against all namespaces in namespace file.
"""
parser = ArgumentParser(description="Validate an NWB file", epilog=ep)
parser.add_argument("path", type=str, help="the path to the NWB file")
parser.add_argument('-p', '--nspath', type=str, help="the path to the namespace file")
parser.add_argument("-n", "--ns", type=str, help="the namespace to validate against")
args = parser.parse_args()
if not os.path.exists(args.path):
print('%s not found' % args.path, file=sys.stderr)
sys.exit(1)
io = HDF5IO(args.path, get_manager(), mode='r')
if args.nspath is not None:
namespaces = load_namespaces(args.nspath)
if args.ns is not None:
print('Validating against %s from %s.' % (args.ns, args.ns_path))
else:
print('Validating using namespaces in %s.' % args.nspath)
for ns in namespaces:
print('Validating against %s' % ns)
errors = validate(io, ns)
_print_errors(errors)
else:
errors = validate(io)
print('Validating against core namespace')
_print_errors(errors)
def init_extensions():
for namespace_file in glob.glob(EXTENSION_PATH + '/**/*.namespace.*ml'):
extension_path = os.path.dirname(namespace_file)
extension_name = os.path.basename(extension_path)
logging.info('Initializing extension ' + extension_name)
load_namespaces(namespace_file)
init_file = os.path.join(extension_path, 'init.py')
if os.path.exists(init_file):
try:
with open(init_file) as f:
for line in f:
eval(line)
except Exception as e:
logging.error("Error evaluating extension init file " +
init_file,
exc_info=True)
def load_icephys_meta_namespace():
"""
Internal helper function for loading the icephys_meta extension namespace for PyNWB
Uses the load_namespaces function from PyNWB and as such modifies the state of PyNWB
"""
# use function level imports here to avoid pulling these functions into the module namespace
import os
from pynwb import load_namespaces
# Set the path where the spec will be installed by default
ndx_icephys_meta_specpath = os.path.join(os.path.dirname(__file__),
'spec',
'ndx-icephys-meta.namespace.yaml')
# If the extensions has not been installed but we running directly from the git repo
if not os.path.exists(ndx_icephys_meta_specpath):
ndx_icephys_meta_specpath = os.path.abspath(os.path.join(os.path.dirname(__file__),
'../../../spec/',
'ndx-icephys-meta.namespace.yaml'))
# load namespace
load_namespaces(ndx_icephys_meta_specpath)
def load_LabMetaData_extension(schema, prefix):
docval_list, attributes, nwbfields_list = extract_from_schema(schema)
neurodata_type = schema.neurodata_type
outdir = os.path.abspath(os.path.dirname(__file__))
ns_path = '%s_namespace.yaml' % prefix
# Read spec and load namespace:
ns_abs_path = os.path.join(outdir, ns_path)
load_namespaces(ns_abs_path)
@register_class(neurodata_type, prefix)
class ExtensionClass(LabMetaData):
__nwbfields__ = tuple(nwbfields_list)
@docval(*docval_list)
def __init__(self, **kwargs):
name = kwargs.pop('name')
super(ExtensionClass, self).__init__(name=name)
for attr, val in kwargs.items():
setattr(self, attr, val)
return ExtensionClass
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)
def main():
ep = """
use --nspath to validate against an extension. If --ns is not specified,
validate against all namespaces in namespace file.
"""
parser = ArgumentParser(description="Validate an NWB file", epilog=ep)
parser.add_argument("paths", type=str, nargs='+', help="NWB file paths")
parser.add_argument('-p',
'--nspath',
type=str,
help="the path to the namespace file")
parser.add_argument("-n",
"--ns",
type=str,
help="the namespace to validate against")
args = parser.parse_args()
ret = 0
for path in args.paths:
if not os.path.exists(path):
print('%s not found' % path, file=sys.stderr)
ret = 1
continue
with NWBHDF5IO(path, get_manager(), mode='r') as io:
if args.nspath is not None:
namespaces = load_namespaces(args.nspath)
if args.ns is not None:
print('Validating %s against %s from %s.' %
(path, args.ns, args.ns_path))
ret = ret or _validate_helper(io=io, namespace=args.ns)
else:
print('Validating %s using namespaces in %s.' %
(path, args.nspath))
for ns in namespaces:
print('Validating against %s' % ns)
ret = ret or _validate_helper(io=io, namespace=ns)
else:
print('Validating %s against core namespace' % path)
ret = ret or _validate_helper(io=io)
sys.exit(ret)
import os
from pynwb import load_namespaces, get_class
# Set path of the namespace.yaml file to the expected install location
ndx_pose_specpath = os.path.join(os.path.dirname(__file__), 'spec',
'ndx-pose.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_pose_specpath):
ndx_pose_specpath = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', '..', 'spec',
'ndx-pose.namespace.yaml'))
# Load the namespace
load_namespaces(ndx_pose_specpath)
from . import io as __io
from .pose import PoseEstimationSeries, PoseEstimation, PoseGroupingSeries, AnimalIdentitySeries
import pandas as pd
import pynwb
import SimpleITK as sitk
import os
import collections
from allensdk.brain_observatory.running_speed import RunningSpeed
from allensdk.brain_observatory.behavior.image_api import ImageApi
from pynwb import load_namespaces
namespace_path = os.path.join(os.path.dirname(__file__),
'AIBS_ophys_behavior_namespace.yaml')
load_namespaces(namespace_path)
class NwbApi:
__slots__ = ('path', '_nwbfile')
@property
def nwbfile(self):
if hasattr(self, '_nwbfile'):
return self._nwbfile
io = pynwb.NWBHDF5IO(self.path, 'r')
return io.read()
def __init__(self, path, **kwargs):
''' Reads data for a single Brain Observatory session from an NWB 2.0 file
'''
# self.vertices = getargs('vertices', kwargs)
#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,
# .. _using_extension:
#
# Using extensions
# -----------------------------------------------------
#
# After an extension has been created, it can be used by downstream codes for reading and writing data.
# There are two main mechanisms for reading and writing extension data with PyNWB.
# The first involves defining new :py:class:`~pynwb.core.NWBContainer` classes that are then mapped
# to the neurodata types in the extension.
from pynwb import register_class, load_namespaces
from pynwb.ecephys import ElectricalSeries
from pynwb.form.utils import docval, call_docval_func, getargs, get_docval
ns_path = "mylab.namespace.yaml"
load_namespaces(ns_path)
@register_class('TetrodeSeries', 'mylab')
class TetrodeSeries(ElectricalSeries):
__nwbfields__ = ('trode_id', )
@docval(*get_docval(ElectricalSeries.__init__) + ({
'name': 'trode_id',
'type': int,
'doc': 'the tetrode id'
}, ))
def __init__(self, **kwargs):
call_docval_func(super(TetrodeSeries, self).__init__, kwargs)
self.trode_id = getargs('trode_id', kwargs)
# .. _using_extension:
#
# Using extensions
# -----------------------------------------------------
#
# After an extension has been created, it can be used by downstream codes for reading and writing data.
# There are two main mechanisms for reading and writing extension data with PyNWB.
# The first involves defining new :py:class:`~pynwb.core.NWBContainer` classes that are then mapped
# to the neurodata types in the extension.
from pynwb import register_class, load_namespaces
from pynwb.ecephys import ElectricalSeries
from hdmf.utils import docval, call_docval_func, getargs, get_docval
ns_path = "mylab.namespace.yaml"
load_namespaces(ns_path)
@register_class('TetrodeSeries', 'mylab')
class TetrodeSeries(ElectricalSeries):
__nwbfields__ = ('trode_id', )
@docval(*get_docval(ElectricalSeries.__init__) + ({
'name': 'trode_id',
'type': int,
'doc': 'the tetrode id'
}, ))
def __init__(self, **kwargs):
call_docval_func(super(TetrodeSeries, self).__init__, kwargs)
self.trode_id = getargs('trode_id', kwargs)
import os
from pynwb import load_namespaces
from ..auto_class import get_class, get_multi_container
import numpy as np
filepath = os.path.realpath(__file__)
basedir = os.path.split(filepath)[0]
name = 'ecog'
load_namespaces(os.path.join(basedir, name + '.namespace.yaml'))
def surface_init_add(faces, vertices, **kwargs):
if np.max(faces) >= len(vertices):
raise ValueError(
'index of faces exceeds number vertices for {}. '
'Faces should be 0-indexed, not 1-indexed'.format(name))
if np.min(faces < 0):
raise ValueError(
'faces hold indices of vertices and should be non-negative')
Surface = get_class(name, 'Surface', surface_init_add)
CorticalSurfaces = get_multi_container(name, 'CorticalSurfaces', Surface)
# 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 os
from pynwb import load_namespaces, get_class
# Set path of the namespace.yaml file to the expected install location
ndx_hierarchical_behavioral_data_specpath = os.path.join(
os.path.dirname(__file__),
'spec',
'ndx-hierarchical-behavioral-data.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_hierarchical_behavioral_data_specpath):
ndx_hierarchical_behavioral_data_specpath = os.path.abspath(os.path.join(
os.path.dirname(__file__),
'..', '..', '..',
'spec',
'ndx-hierarchical-behavioral-data.namespace.yaml'
))
# Load the namespace
load_namespaces(ndx_hierarchical_behavioral_data_specpath)
from .hierarchical_behavioral_data import *
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 os
from pynwb import load_namespaces
from ..auto_class import get_class
filepath = os.path.realpath(__file__)
basedir = os.path.split(filepath)[0]
load_namespaces(os.path.join(basedir, 'time_frequency.namespace.yaml'))
HilbertSeries = get_class('time_frequency', 'HilbertSeries')
def test_load_namespace(self):
self.test_export()
load_namespaces(os.path.join(self.tempdir, self.ns_path))
cat_cell_info = NWBGroupSpec(neurodata_type_def='CatCellInfo',
doc='Categorical Cell Info',
datasets=[gid_spec, cat_data_spec],
neurodata_type_inc='NWBDataInterface')
# export
ns_builder = NWBNamespaceBuilder(name + ' extensions', name)
for spec in [cat_cell_info]:
ns_builder.add_spec(ext_source, spec)
ns_builder.export(ns_path)
'''
EXAMPLE USAGE:
from pynwb import get_class, load_namespaces, NWBHDF5IO
ns_path = name + '.namespace.yaml'
load_namespaces(ns_path)
CatCellInfo = get_class('CatCellInfo', name)
import numpy as np
from pynwb import NWBFile
from datetime import datetime
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')
# -*- coding: utf-8 -*-
"""
Initialize the StimSeries and StimTable classes.
"""
# Standard libraries
import os
# Third party libraries
from pynwb import load_namespaces
# Set path of the namespace.yaml file to the expected install location
ndx_electrical_stim_specpath = os.path.join(
os.path.dirname(__file__), 'spec', 'ndx-electrical-stim.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_electrical_stim_specpath):
ndx_electrical_stim_specpath = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', '..', 'spec',
'ndx-electrical-stim.namespace.yaml'))
# Load the namespace
load_namespaces(ndx_electrical_stim_specpath)
from .ndx_electrical_stim import StimSeries, StimTable
import os
from pynwb import load_namespaces
# Set path of the namespace.yaml file to the expected install location
ndx_franklab_novela_specpath = os.path.join(
os.path.dirname(__file__), 'spec', 'ndx-franklab-novela.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_franklab_novela_specpath):
ndx_franklab_novela_specpath = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', '..', 'spec',
'ndx-franklab-novela.namespace.yaml'))
# Load the namespace
load_namespaces(ndx_franklab_novela_specpath)
