ndio/ramon/__init__.py
def from_hdf5(hdf5, anno_id=None):
    """
    Converts an HDF5 file to a RAMON object. Returns an object that is a child-
    -class of RAMON (though it's determined at run-time what type is returned).

    Accessing multiple IDs from the same file is not supported, because it's
    not dramatically faster to access each item in the hdf5 file at the same
    time It's semantically and computationally easier to run this function
    several times on the same file.

    Arguments:
        hdf5 (h5py.File): A h5py File object that holds RAMON data
        anno_id (int): The ID of the RAMON obj to extract from the file. This
            defaults to the first one (sorted) if none is specified.

    Returns:
        ndio.RAMON object
    """
    if anno_id is None:
        # The user just wants the first item we find, so... Yeah.
        return from_hdf5(hdf5, list(hdf5.keys())[0])

    # First, get the actual object we're going to download.
    anno_id = str(anno_id)
    if anno_id not in list(hdf5.keys()):
        raise ValueError("ID {} is not in this file. Options are: {}".format(
            anno_id,
            ", ".join(list(hdf5.keys()))
        ))

    anno = hdf5[anno_id]
    # anno now holds just the RAMON of interest

    # This is the most complicated line in here: It creates an object whose
    # type is conditional on the ANNOTATION_TYPE of the hdf5 object.
    try:
        r = AnnotationType.get_class(anno['ANNOTATION_TYPE'][0])()
    except:
        raise InvalidRAMONError("This is not a valid RAMON type.")

    # All RAMON types definitely have these attributes:
    metadata = anno['METADATA']
    r.author = metadata['AUTHOR'][0]
    r.confidence = metadata['CONFIDENCE'][0]
    r.status = metadata['STATUS'][0]
    r.id = anno_id

    # These are a little tougher, some RAMON types have special attributes:

    if type(r) in [RAMONNeuron, RAMONSynapse]:
        r.segments = metadata['SEGMENTS'][()]

    if 'KVPAIRS' in metadata:
        kvs = metadata['KVPAIRS'][()][0].split()
        if len(kvs) != 0:

            for i in kvs:
                k, v = str(i).split(',')
                r.kvpairs[str(k)] = str(v)
        else:
            r.kvpairs = {}

    if issubclass(type(r), RAMONVolume):
        if 'CUTOUT' in anno:
            r.cutout = anno['CUTOUT'][()]
        if 'XYZOFFSET' in anno:
            r.cutout = anno['XYZOFFSET'][()]
        if 'RESOLUTION' in anno:
            r.cutout = anno['RESOLUTION'][()]

    if type(r) is RAMONSynapse:
        r.synapse_type = metadata['SYNAPSE_TYPE'][0]
        r.weight = metadata['WEIGHT'][0]

    if type(r) is RAMONSegment:
        if 'NEURON' in metadata:
            r.neuron = metadata['NEURON'][0]
        if 'PARENTSEED' in metadata:
            r.parent_seed = metadata['PARENTSEED'][0]
        if 'SEGMENTCLASS' in metadata:
            r.segmentclass = metadata['SEGMENTCLASS'][0]
        if 'SYNAPSES' in metadata:
            r.synapses = metadata['SYNAPSES'][()]
        if 'ORGANELLES' in metadata:
            r.organelles = metadata['ORGANELLES'][()]

    if type(r) is RAMONOrganelle:
        r.organelle_class = metadata['ORGANELLECLASS'][0]

    return r
def from_hdf5(hdf5, anno_id=None):
    """
    Converts an HDF5 file to a RAMON object. Returns an object that is a child-
    -class of RAMON (though it's determined at run-time what type is returned).

    Accessing multiple IDs from the same file is not supported, because it's
    not dramatically faster to access each item in the hdf5 file at the same
    time It's semantically and computationally easier to run this function
    several times on the same file.

    Arguments:
        hdf5 (h5py.File): A h5py File object that holds RAMON data
        anno_id (int): The ID of the RAMON obj to extract from the file. This
            defaults to the first one (sorted) if none is specified.

    Returns:
        ndio.RAMON object
    """
    if anno_id is None:
        # The user just wants the first item we find, so... Yeah.
        return from_hdf5(hdf5, list(hdf5.keys())[0])

    # First, get the actual object we're going to download.
    anno_id = str(anno_id)
    if anno_id not in list(hdf5.keys()):
        raise ValueError("ID {} is not in this file. Options are: {}".format(
            anno_id,
            ", ".join(list(hdf5.keys()))
        ))

    anno = hdf5[anno_id]
    # anno now holds just the RAMON of interest

    # This is the most complicated line in here: It creates an object whose
    # type is conditional on the ANNOTATION_TYPE of the hdf5 object.
    try:
        r = AnnotationType.get_class(anno['ANNOTATION_TYPE'][0])()
    except:
        raise InvalidRAMONError("This is not a valid RAMON type.")

    # All RAMON types definitely have these attributes:
    metadata = anno['METADATA']
    r.author = metadata['AUTHOR'][0]
    r.confidence = metadata['CONFIDENCE'][0]
    r.status = metadata['STATUS'][0]
    r.id = anno_id

    # These are a little tougher, some RAMON types have special attributes:

    if type(r) in [RAMONNeuron, RAMONSynapse]:
        r.segments = metadata['SEGMENTS'][()]

    if 'KVPAIRS' in metadata:
        kvs = metadata['KVPAIRS'][()][0].split()
        if len(kvs) != 0:

            for i in kvs:
                k, v = str(i).split(',')
                r.kvpairs[str(k)] = str(v)
        else:
            r.kvpairs = {}

    if issubclass(type(r), RAMONVolume):
        if 'CUTOUT' in anno:
            r.cutout = anno['CUTOUT'][()]
        if 'XYZOFFSET' in anno:
            r.cutout = anno['XYZOFFSET'][()]
        if 'RESOLUTION' in anno:
            r.cutout = anno['RESOLUTION'][()]

    if type(r) is RAMONSynapse:
        r.synapse_type = metadata['SYNAPSE_TYPE'][0]
        r.weight = metadata['WEIGHT'][0]

    if type(r) is RAMONSegment:
        if 'NEURON' in metadata:
            r.neuron = metadata['NEURON'][0]
        if 'PARENTSEED' in metadata:
            r.parent_seed = metadata['PARENTSEED'][0]
        if 'SEGMENTCLASS' in metadata:
            r.segmentclass = metadata['SEGMENTCLASS'][0]
        if 'SYNAPSES' in metadata:
            r.synapses = metadata['SYNAPSES'][()]
        if 'ORGANELLES' in metadata:
            r.organelles = metadata['ORGANELLES'][()]

    if type(r) is RAMONOrganelle:
        r.organelle_class = metadata['ORGANELLECLASS'][0]

    return r
def from_hdf5 ( hdf5 , anno_id = None ) : if anno_id is None : # The user just wants the first item we find, so... Yeah. return from_hdf5 ( hdf5 , list ( hdf5 . keys ( ) ) [ 0 ] ) # First, get the actual object we're going to download. anno_id = str ( anno_id ) if anno_id not in list ( hdf5 . keys ( ) ) : raise ValueError ( "ID {} is not in this file. Options are: {}" . format ( anno_id , ", " . join ( list ( hdf5 . keys ( ) ) ) ) ) anno = hdf5 [ anno_id ] # anno now holds just the RAMON of interest # This is the most complicated line in here: It creates an object whose # type is conditional on the ANNOTATION_TYPE of the hdf5 object. try : r = AnnotationType . get_class ( anno [ 'ANNOTATION_TYPE' ] [ 0 ] ) ( ) except : raise InvalidRAMONError ( "This is not a valid RAMON type." ) # All RAMON types definitely have these attributes: metadata = anno [ 'METADATA' ] r . author = metadata [ 'AUTHOR' ] [ 0 ] r . confidence = metadata [ 'CONFIDENCE' ] [ 0 ] r . status = metadata [ 'STATUS' ] [ 0 ] r . id = anno_id # These are a little tougher, some RAMON types have special attributes: if type ( r ) in [ RAMONNeuron , RAMONSynapse ] : r . segments = metadata [ 'SEGMENTS' ] [ ( ) ] if 'KVPAIRS' in metadata : kvs = metadata [ 'KVPAIRS' ] [ ( ) ] [ 0 ] . split ( ) if len ( kvs ) != 0 : for i in kvs : k , v = str ( i ) . split ( ',' ) r . kvpairs [ str ( k ) ] = str ( v ) else : r . kvpairs = { } if issubclass ( type ( r ) , RAMONVolume ) : if 'CUTOUT' in anno : r . cutout = anno [ 'CUTOUT' ] [ ( ) ] if 'XYZOFFSET' in anno : r . cutout = anno [ 'XYZOFFSET' ] [ ( ) ] if 'RESOLUTION' in anno : r . cutout = anno [ 'RESOLUTION' ] [ ( ) ] if type ( r ) is RAMONSynapse : r . synapse_type = metadata [ 'SYNAPSE_TYPE' ] [ 0 ] r . weight = metadata [ 'WEIGHT' ] [ 0 ] if type ( r ) is RAMONSegment : if 'NEURON' in metadata : r . neuron = metadata [ 'NEURON' ] [ 0 ] if 'PARENTSEED' in metadata : r . parent_seed = metadata [ 'PARENTSEED' ] [ 0 ] if 'SEGMENTCLASS' in metadata : r . segmentclass = metadata [ 'SEGMENTCLASS' ] [ 0 ] if 'SYNAPSES' in metadata : r . synapses = metadata [ 'SYNAPSES' ] [ ( ) ] if 'ORGANELLES' in metadata : r . organelles = metadata [ 'ORGANELLES' ] [ ( ) ] if type ( r ) is RAMONOrganelle : r . organelle_class = metadata [ 'ORGANELLECLASS' ] [ 0 ] return r
Converts an HDF5 file to a RAMON object. Returns an object that is a child-
    -class of RAMON (though it's determined at run-time what type is returned).

    Accessing multiple IDs from the same file is not supported, because it's
    not dramatically faster to access each item in the hdf5 file at the same
    time It's semantically and computationally easier to run this function
    several times on the same file.

    Arguments:
        hdf5 (h5py.File): A h5py File object that holds RAMON data
        anno_id (int): The ID of the RAMON obj to extract from the file. This
            defaults to the first one (sorted) if none is specified.

    Returns:
        ndio.RAMON object
Converts an HDF5 file to a RAMON object . Returns an object that is a child - - class of RAMON ( though it s determined at run - time what type is returned ) .