def circuit_example():
"""Example that shows how to plot a morphology with synapses from a Sonata circuit.
To make this example work, you would need a proper SONATA circuit.
"""
from bluepysnap import Circuit
from bluepysnap.bbp import Synapse
circuit = Circuit('/path/to/sonata_circuit_config.json')
edge_properties = [
'afferent_section_id',
'afferent_segment_id',
'afferent_segment_offset',
Synapse.U_SYN,
Synapse.D_SYN,
Synapse.F_SYN,
Synapse.G_SYNX,
]
morph_id = 110
synapses = circuit.edges['default'].afferent_edges(morph_id,
edge_properties)
morph_filepath = circuit.nodes['All'].morph.get_filepath(morph_id)
m = nm.load_morphology(morph_filepath)
fig = morphology.draw(m, synapses)
fig.show()
def NeuroM_section_to_NRN_compartment_paths(morph_path: Path):
"""Returns a dictionary NeuroM section id -> path of each compartment for the section.
Path are formed by following the section points until the pathlength of the compartment is
reached.
Args:
morph_path: the morphology path
1) Compute the cumulative pathlength along the section segments_directions
2) Get the compartment pathlengths (compartment are of equal pathlength in a given section)
3) Compartment by compartment, follow the points until the compartment pathlength is reached
Example for one section::
(1, 2) ------ (2, 2)
|
|
|
(0, 0) ------- (1, 0)
If n_compartments == 3, three paths are returned::
[array([[0. , 0. , 0. ],
[1. , 0. , 0. ],
[1. , 0.33333333, 0. ]]),
array([[1. , 0.33333333, 0. ],
[1. , 1.66666667, 0. ]]),
array([[1. , 1.66666667, 0. ],
[1. , 2. , 0. ],
[2. , 2. , 0. ]])]
"""
NeuroM_cell = load_morphology(morph_path)
NRN_neuron = get_NRN_cell(morph_path)
NRN_sections = list(NRN_neuron.icell.all)
mapping = NeuroM_section_to_NRN_section(morph_path)
NeuroM_to_compartment_position_mapping = {}
for section in NeuroM_cell.sections:
if section.type == NeuriteType.soma:
continue
NRN_section = NRN_sections[mapping[section.id]]
NeuroM_to_compartment_position_mapping[
section.id] = _compartment_paths(section.points[:, COLS.XYZ],
NRN_section.nseg)
return NeuroM_to_compartment_position_mapping
def NeuroM_section_to_NRN_section(filename: Path):
"""Returns a mapping from NeuroM section IDs to NRN ones."""
NeuroM_cell = load_morphology(filename)
NRN_cell = get_NRN_cell(filename)
mapping = {}
NRN_sections = list(NRN_cell.icell.all)
def is_soma(NRN_section):
"""Is the NRN section a soma section."""
return NRN_section.name().endswith('.soma[0]')
# Skip soma if exists
counter = 1 if is_soma(NRN_sections[0]) else 0
for NeuroM_section in iter_sections(NeuroM_cell,
neurite_order=NeuriteIter.NRN):
if _zero_length_section(NeuroM_section):
mapping[NeuroM_section.id] = None
if not NeuroM_section.children:
L.debug(
'Zero length section without children (NeuroM section id: %s)',
NeuroM_section.id)
continue
L.debug('Zero length section with children')
NRN_section = NRN_sections[counter]
counter -= 1
else:
mapping[NeuroM_section.id] = counter
NRN_section = NRN_sections[counter]
L.debug('NeuroM section (%s) has been mapped to NRN section (%s)',
NeuroM_section.id, mapping[NeuroM_section.id])
# Skip single child NeuroM_section because they have already been
# merged in the NeuroM morphology
while _has_single_child(NRN_section):
L.debug('Skipping single child')
counter += 1
NRN_section = NRN_section.children()[0]
counter += 1
_validate_section_mapping(NeuroM_cell, NRN_cell, mapping)
return mapping
def plain_example():
"""Example that shows how to plot a neuron dendrogram with a plain synapses dataframe."""
# Those properties are required in synapses dataframe for positioning
required_synapse_properties = [
consts.SOURCE_NODE_ID,
consts.TARGET_NODE_ID,
consts.POST_SECTION_ID,
consts.POST_SECTION_POS,
consts.PRE_SECTION_ID,
consts.PRE_SECTION_POS,
]
# or use plain strings
# required_columns = ['@source_node', '@target_node',
# 'afferent_section_id', 'afferent_section_pos',
# 'efferent_section_id', 'efferent_section_pos']
data = np.array([
[0, 116, 4, 0.81408846, 3, 0.7344886],
[0, 116, 5, 0.145983203, 4, 0.24454929],
[0, 116, 3, 0.968469656, 1, 0.4290702],
[116, 0, 2, 0.84480673, 1, 0.29180855],
[116, 0, 2, 0.5815143, 1, 0.68261607],
])
synapses = pd.DataFrame(columns=required_synapse_properties, data=data)
synapses = synapses.astype({
'@target_node': int,
'@source_node': int,
'afferent_section_id': int,
'efferent_section_id': int
})
m = nm.load_morphology('dendrogram_plain_example.swc')
fig = dendrogram.draw(m, synapses, 116)
fig.show()
# If you want to show additional data with synapses then just use additional columns in you
# dataframe. These data properties can have any names.
synapse_data_properties = [
'u_syn', 'depression_time', 'facilitation_time', 'conductance'
]
data = np.array([
[0.16718547, 153.8097, 8.452671, 1.9140357],
[0.16718547, 153.8097, 8.452671, 1.9140357],
[0.16718547, 153.8097, 8.452671, 1.9140357],
[0.29116565, 116.06434, 10.367496, 3.1585026],
[0.29116565, 116.06434, 10.367496, 3.1585026],
])
synapses_data = pd.DataFrame(columns=synapse_data_properties, data=data)
synapses = pd.concat([synapses, synapses_data], axis=1)
fig = dendrogram.draw(m, synapses, 116)
fig.show()
def example_afferent():
m = nm.load_morphology('dendrogram_plain_example.swc')
data = np.array([
[4, 0, 0.81408846, 'additional value'],
[6, 2, 0.545983203, 'additional value'],
[1, 0, 0.4290702, 'additional value'],
])
columns = [
consts.POST_SECTION_ID, consts.POST_SEGMENT_ID,
consts.POST_SEGMENT_OFFSET, 'additional_data'
]
synapses = pd.DataFrame(data, columns=columns)
fig = morphology.draw(m, synapses)
fig.show()
def example_afferent_efferent():
m = nm.load_morphology('dendrogram_plain_example.swc')
data = np.array([
[4, 0, 0.81408846, np.nan, np.nan, np.nan],
[6, 2, 0.145983203, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, 1, 0, 0.4290702],
[np.nan, np.nan, np.nan, 1, 0, 0.29180855],
[np.nan, np.nan, np.nan, 1, 0, 0.68261607],
])
columns = [
consts.POST_SECTION_ID, consts.POST_SEGMENT_ID,
consts.POST_SEGMENT_OFFSET, consts.PRE_SECTION_ID,
consts.PRE_SEGMENT_ID, consts.PRE_SEGMENT_OFFSET
]
synapses = pd.DataFrame(data, columns=columns)
fig = morphology.draw(m, synapses)
fig.show()
def circuit_example():
"""Example that shows how to plot a neuron dendrogram with synapses from a bluepysnap circuit.
To make this example work, you would need a proper SONATA circuit.
"""
from bluepysnap import Circuit
from bluepysnap.sonata_constants import Edge
from bluepysnap.bbp import Synapse
circuit = Circuit('/path/to/sonata_circuit_config.json')
# you can use `bluepysnap.sonata_constants.Edge` instead of `morph_tool.draw.consts`
edge_properties = [
Edge.SOURCE_NODE_ID,
Edge.TARGET_NODE_ID,
Edge.POST_SECTION_ID,
Edge.POST_SECTION_POS,
Edge.PRE_SECTION_ID,
Edge.PRE_SECTION_POS,
Synapse.U_SYN,
Synapse.D_SYN,
Synapse.F_SYN,
Synapse.G_SYNX,
]
source_node_id = 0
target_node_id = 116
synapses1 = circuit.edges['default'].pair_edges(source_node_id,
target_node_id,
edge_properties)
synapses2 = circuit.edges['default'].pair_edges(target_node_id,
source_node_id,
edge_properties)
synapses = pd.concat([synapses1, synapses2])
morph_filepath = circuit.nodes['default'].morph.get_filepath(
target_node_id)
m = nm.load_morphology(morph_filepath)
fig = dendrogram.draw(m, synapses, target_node_id)
fig.show()
def _create_test_neuron():
return nm.load_morphology(DATA / 'simple.swc')