Source code for pynwb

'''This package will contain functions, classes, and objects
for reading and writing data in NWB format
import os.path
from copy import deepcopy
from warnings import warn
import h5py

from hdmf.spec import NamespaceCatalog
from hdmf.utils import docval, getargs, popargs, call_docval_func, get_docval
from import HDMFIO
from hdmf.backends.hdf5 import HDF5IO as _HDF5IO
from hdmf.validate import ValidatorMap
from import BuildManager, TypeMap
import hdmf.common

__core_ns_file_name = 'nwb.namespace.yaml'

from .spec import NWBDatasetSpec, NWBGroupSpec, NWBNamespace  # noqa E402

def __get_resources():
    from pkg_resources import resource_filename
    ret = dict()
    ret['namespace_path'] = os.path.join(resource_filename(__name__, 'nwb-schema/core'), __core_ns_file_name)
    return ret

def _get_resources():
    # LEGACY: Needed to support legacy implementation.
    return __get_resources()

# a global namespace catalog
global __NS_CATALOG
global __TYPE_MAP

__NS_CATALOG = NamespaceCatalog(NWBGroupSpec, NWBDatasetSpec, NWBNamespace)

hdmf_typemap = hdmf.common.get_type_map()


[docs]@docval({'name': 'extensions', 'type': (str, TypeMap, list), 'doc': 'a path to a namespace, a TypeMap, or a list consisting of paths to namespaces and TypeMaps', 'default': None}, returns="TypeMap loaded for the given extension or NWB core namespace", rtype=tuple, is_method=False) def get_type_map(**kwargs): ''' Get the TypeMap for the given extensions. If no extensions are provided, return the TypeMap for the core namespace ''' extensions = getargs('extensions', kwargs) type_map = None if extensions is None: type_map = deepcopy(__TYPE_MAP) else: if isinstance(extensions, TypeMap): type_map = extensions else: type_map = deepcopy(__TYPE_MAP) if isinstance(extensions, list): for ext in extensions: if isinstance(ext, str): type_map.load_namespaces(ext) elif isinstance(ext, TypeMap): type_map.merge(ext) else: raise ValueError('extensions must be a list of paths to namespace specs or a TypeMaps') elif isinstance(extensions, str): type_map.load_namespaces(extensions) elif isinstance(extensions, TypeMap): type_map.merge(extensions) return type_map
[docs]@docval(*get_docval(get_type_map), returns="the namespaces loaded from the given file", rtype=tuple, is_method=False) def get_manager(**kwargs): ''' Get a BuildManager to use for I/O using the given extensions. If no extensions are provided, return a BuildManager that uses the core namespace ''' type_map = call_docval_func(get_type_map, kwargs) return BuildManager(type_map)
[docs]@docval({'name': 'namespace_path', 'type': str, 'doc': 'the path to the YAML with the namespace definition'}, returns="the namespaces loaded from the given file", rtype=tuple, is_method=False) def load_namespaces(**kwargs): ''' Load namespaces from file ''' namespace_path = getargs('namespace_path', kwargs) return __TYPE_MAP.load_namespaces(namespace_path)
# load the core namespace i.e. base NWB specification __resources = __get_resources() if os.path.exists(__resources['namespace_path']): load_namespaces(__resources['namespace_path'])
[docs]def available_namespaces(): """Returns all namespaces registered in the namespace catalog""" return __NS_CATALOG.namespaces
# a function to register a container classes with the global map
[docs]@docval({'name': 'neurodata_type', 'type': str, 'doc': 'the neurodata_type to get the spec for'}, {'name': 'namespace', 'type': str, 'doc': 'the name of the namespace'}, {"name": "container_cls", "type": type, "doc": "the class to map to the specified neurodata_type", 'default': None}, is_method=False) def register_class(**kwargs): """Register an NWBContainer class to use for reading and writing a neurodata_type from a specification If container_cls is not specified, returns a decorator for registering an NWBContainer subclass as the class for neurodata_type in namespace. """ neurodata_type, namespace, container_cls = getargs('neurodata_type', 'namespace', 'container_cls', kwargs) def _dec(cls): __TYPE_MAP.register_container_type(namespace, neurodata_type, cls) return cls if container_cls is None: return _dec else: _dec(container_cls)
# a function to register an object mapper for a container class
[docs]@docval({"name": "container_cls", "type": type, "doc": "the Container class for which the given ObjectMapper class gets used"}, {"name": "mapper_cls", "type": type, "doc": "the ObjectMapper class to use to map", 'default': None}, is_method=False) def register_map(**kwargs): """Register an ObjectMapper to use for a Container class type If mapper_cls is not specified, returns a decorator for registering an ObjectMapper class as the mapper for container_cls. If mapper_cls is specified, register the class as the mapper for container_cls """ container_cls, mapper_cls = getargs('container_cls', 'mapper_cls', kwargs) def _dec(cls): __TYPE_MAP.register_map(container_cls, cls) return cls if mapper_cls is None: return _dec else: _dec(mapper_cls)
[docs]@docval({'name': 'neurodata_type', 'type': str, 'doc': 'the neurodata_type to get the NWBContainer class for'}, {'name': 'namespace', 'type': str, 'doc': 'the namespace the neurodata_type is defined in'}, is_method=False) def get_class(**kwargs): """ Parse the YAML file for a given neurodata_type that is a subclass of NWBContainer and automatically generate its python API. This will work for most containers, but is known to not work for descendants of MultiContainerInterface and DynamicTable, so these must be defined manually (for now). `get_class` infers the API mapping directly from the specification. If you want to define a custom mapping, you should not use this function and you should define the class manually. Examples: Generating and registering an extension is as simple as:: MyClass = get_class('MyClass', 'ndx-my-extension') `get_class` defines only the `__init__` for the class. In cases where you want to provide additional methods for querying, plotting, etc. you can still use `get_class` and attach methods to the class after-the-fact, e.g.:: def get_sum(self, a, b): return self.feat1 + self.feat2 MyClass.get_sum = get_sum """ neurodata_type, namespace = getargs('neurodata_type', 'namespace', kwargs) return __TYPE_MAP.get_container_cls(namespace, neurodata_type)
[docs]@docval({'name': 'io', 'type': HDMFIO, 'doc': 'the HDMFIO object to read from'}, {'name': 'namespace', 'type': str, 'doc': 'the namespace to validate against', 'default': CORE_NAMESPACE}, returns="errors in the file", rtype=list, is_method=False) def validate(**kwargs): """Validate an NWB file against a namespace""" io, namespace = getargs('io', 'namespace', kwargs) builder = io.read_builder() validator = ValidatorMap(io.manager.namespace_catalog.get_namespace(name=namespace)) return validator.validate(builder)
[docs]class NWBHDF5IO(_HDF5IO): @docval({'name': 'path', 'type': str, 'doc': 'the path to the HDF5 file'}, {'name': 'mode', 'type': str, 'doc': 'the mode to open the HDF5 file with, one of ("w", "r", "r+", "a", "w-", "x")'}, {'name': 'load_namespaces', 'type': bool, 'doc': 'whether or not to load cached namespaces from given path - not applicable in write mode', 'default': False}, {'name': 'manager', 'type': BuildManager, 'doc': 'the BuildManager to use for I/O', 'default': None}, {'name': 'extensions', 'type': (str, TypeMap, list), 'doc': 'a path to a namespace, a TypeMap, or a list consisting paths to namespaces and TypeMaps', 'default': None}, {'name': 'file', 'type': h5py.File, 'doc': 'a pre-existing h5py.File object', 'default': None}, {'name': 'comm', 'type': "Intracomm", 'doc': 'the MPI communicator to use for parallel I/O', 'default': None}) def __init__(self, **kwargs): path, mode, manager, extensions, load_namespaces, file_obj, comm =\ popargs('path', 'mode', 'manager', 'extensions', 'load_namespaces', 'file', 'comm', kwargs) if load_namespaces: if manager is not None: warn("loading namespaces from file - ignoring 'manager'") if extensions is not None: warn("loading namespaces from file - ignoring 'extensions' argument") # namespaces are not loaded when creating an NWBHDF5IO object in write mode if 'w' in mode or mode == 'x': raise ValueError("cannot load namespaces from file when writing to it") tm = get_type_map() super(NWBHDF5IO, self).load_namespaces(tm, path, file=file_obj) manager = BuildManager(tm) # XXX: Leaving this here in case we want to revert to this strategy for # loading cached namespaces # ns_catalog = NamespaceCatalog(NWBGroupSpec, NWBDatasetSpec, NWBNamespace) # super(NWBHDF5IO, self).load_namespaces(ns_catalog, path) # tm = TypeMap(ns_catalog) # tm.copy_mappers(get_type_map()) else: if manager is not None and extensions is not None: raise ValueError("'manager' and 'extensions' cannot be specified together") elif extensions is not None: manager = get_manager(extensions=extensions) elif manager is None: manager = get_manager() super(NWBHDF5IO, self).__init__(path, manager=manager, mode=mode, file=file_obj, comm=comm)
[docs] @docval({'name': 'src_io', 'type': HDMFIO, 'doc': 'the HDMFIO object for reading the data to export'}, {'name': 'nwbfile', 'type': 'NWBFile', 'doc': 'the NWBFile object to export. If None, then the entire contents of src_io will be exported', 'default': None}, {'name': 'write_args', 'type': dict, 'doc': 'arguments to pass to :py:meth:`write_builder`', 'default': dict()}) def export(self, **kwargs): nwbfile = popargs('nwbfile', kwargs) kwargs['container'] = nwbfile call_docval_func(super().export, kwargs)
from . import io as __io # noqa: F401,E402 from .core import NWBContainer, NWBData # noqa: F401,E402 from .base import TimeSeries, ProcessingModule # noqa: F401,E402 from .file import NWBFile # noqa: F401,E402 from . import behavior # noqa: F401,E402 from . import device # noqa: F401,E402 from . import ecephys # noqa: F401,E402 from . import epoch # noqa: F401,E402 from . import icephys # noqa: F401,E402 from . import image # noqa: F401,E402 from . import misc # noqa: F401,E402 from . import ogen # noqa: F401,E402 from . import ophys # noqa: F401,E402 from . import retinotopy # noqa: F401,E402 from . import legacy # noqa: F401,E402 from ._version import get_versions # noqa: E402 __version__ = get_versions()['version'] del get_versions