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refactored the Export file
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@AdityaPandeyCN
AdityaPandeyCN committed Aug 14, 2024
1 parent 9fa84cc commit 3a5a8cc
Showing 1 changed file with 67 additions and 72 deletions.
139 changes: 67 additions & 72 deletions pyneuroml/swc/ExportNML.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,6 @@
SegmentGroup,
)
from neuroml.nml.nml import Point3DWithDiam, SegmentParent
from neuroml.utils import component_factory

from .LoadSWC import SWCGraph, SWCNode

Expand Down Expand Up @@ -80,14 +79,9 @@ def create_cell(self, validate: bool = True) -> Cell:
"""
logger.info("Creating Cell object")
cell_name: str = self.get_cell_name()

notes: str = f"Neuronal morphology exported from Python Based Converter. Original file: {self.morphology_origin}"

self.cell = Cell(id=cell_name, notes=notes)

# Create an empty Morphology object
self.cell.morphology = Morphology(id=f"morphology_{cell_name}")

logger.debug(f"Created Cell object with name: {cell_name}")
return self.cell

Expand Down Expand Up @@ -204,20 +198,11 @@ def parse_tree(
cable_id = max(self.cable_ids_vs_indices.keys())
logger.debug(f"Using existing cable: Cable ID {cable_id}")

# Handle different cases for soma and dendrite connections
if this_point.type == SWCNode.SOMA:
# Case 1: Current point is part of the soma
# Handle soma point (may involve special soma representation)
self.handle_soma(this_point, parent_point, cable_id, new_cell)
elif this_point.type != SWCNode.SOMA and parent_point.type == SWCNode.SOMA:
# Case 2: Current point is the first point of a dendrite or axon, connected to the soma
# We don't create a 'real' segment here because:
# a) The soma representation might be simplified (e.g., sphere or cylinder)
# b) The connection to the soma will be handled differently (e.g., attached to soma surface)
logger.debug("Parent point is on soma! Not creating 'real' segment")
else:
# Case 3: Regular dendritic or axonal segment
# Create a normal segment
logger.debug(f"Creating segment for point {this_point.id}")
self.create_segment(this_point, parent_point, cable_id, new_cable)

Expand Down Expand Up @@ -251,8 +236,7 @@ def handle_soma(
new_cell: bool,
) -> None:
"""
Handle the creation of soma segments based on different soma representation cases.
Handle the creation of soma segments based on different soma representation cases.
This method implements the soma representation guidelines as described in
"Soma format representation in NeuroMorpho.Org as of version 5.3".
For full details, see: https://github.com/NeuroML/Cvapp-NeuroMorpho.org/blob/master/caseExamples/SomaFormat-NMOv5.3.pdf
Expand Down Expand Up @@ -287,7 +271,7 @@ def handle_soma(
logger.debug(f"Handling soma point: {this_point.id}")
soma_points: List[SWCNode] = [p for p in self.points if p.type == SWCNode.SOMA]

if len(soma_points) == 3:
if len(soma_points) <= 3:
if this_point.id == soma_points[0].id:
logger.debug("Processing first point of 3-point soma")
middle_point: SWCNode = soma_points[1]
Expand Down Expand Up @@ -336,57 +320,53 @@ def handle_soma(
this_point.id == soma_points[1].id or this_point.id == soma_points[2].id
):
pass # These points are already handled
elif len(soma_points) > 3:
if this_point.id == soma_points[0].id:
else:
# Sort soma points by x-coordinate, from most positive to most negative
sorted_soma_points = sorted(soma_points, key=lambda p: p.x)

if this_point == sorted_soma_points[0]:
logger.debug("Processing first point of multi-point soma")

# First segment
segment: Segment = Segment(
id=self.next_segment_id, name=f"Seg_{self.next_segment_id}"
)
segment.proximal = Point3DWithDiam(
x=this_point.x,
y=this_point.y,
z=this_point.z,
diameter=2 * this_point.radius,
)
next_point: SWCNode = soma_points[1]
segment.distal = Point3DWithDiam(
x=next_point.x,
y=next_point.y,
z=next_point.z,
diameter=2 * next_point.radius,
)
self.cell.morphology.segments.append(segment)
self.point_indices_vs_seg_ids[this_point.id] = self.next_segment_id
self.cable_ids_vs_indices[cable_id].append(self.next_segment_id)
self.segment_types[self.next_segment_id] = SWCNode.SOMA
self.add_segment_to_groups(self.next_segment_id, SWCNode.SOMA)
self.next_segment_id += 1
for i in range(len(sorted_soma_points) - 1):
current_point = sorted_soma_points[i]
next_point = sorted_soma_points[i + 1]

elif this_point.id != soma_points[-1].id:
# Middle segments
parent_seg_id: int = self.point_indices_vs_seg_ids[parent_point.id]
segment: Segment = Segment(
id=self.next_segment_id, name=f"Seg_{self.next_segment_id}"
)
segment.parent = SegmentParent(segments=parent_seg_id)
next_point: SWCNode = soma_points[soma_points.index(this_point) + 1]
segment.distal = Point3DWithDiam(
x=next_point.x,
y=next_point.y,
z=next_point.z,
diameter=2 * next_point.radius,
)
self.cell.morphology.segments.append(segment)
self.point_indices_vs_seg_ids[this_point.id] = self.next_segment_id
self.cable_ids_vs_indices[cable_id].append(self.next_segment_id)
self.segment_types[self.next_segment_id] = SWCNode.SOMA
self.add_segment_to_groups(self.next_segment_id, SWCNode.SOMA)
self.next_segment_id += 1
else:
logger.debug(f"Creating regular segment for soma point {this_point.id}")
self.create_segment(this_point, parent_point, cable_id, True)
segment = Segment(
id=self.next_segment_id, name=f"Seg_{self.next_segment_id}"
)

if i == 0:
segment.proximal = Point3DWithDiam(
x=current_point.x,
y=current_point.y,
z=current_point.z,
diameter=2 * current_point.radius,
)
else:
segment.parent = SegmentParent(
segments=self.next_segment_id - 1
)

segment.distal = Point3DWithDiam(
x=next_point.x,
y=next_point.y,
z=next_point.z,
diameter=2 * next_point.radius,
)

self.cell.morphology.segments.append(segment)
self.point_indices_vs_seg_ids[current_point.id] = (
self.next_segment_id
)
self.cable_ids_vs_indices[cable_id].append(self.next_segment_id)
self.segment_types[self.next_segment_id] = SWCNode.SOMA
self.add_segment_to_groups(self.next_segment_id, SWCNode.SOMA)

self.next_segment_id += 1

elif this_point != sorted_soma_points[0]:
# These points will be handled when processing the first point
pass

def create_segment(
self,
Expand Down Expand Up @@ -415,10 +395,28 @@ def create_segment(

segment: Segment = Segment(id=seg_id, name=f"Seg_{seg_id}")

# Always set a parent for the segment
if parent_point.id in self.point_indices_vs_seg_ids:
parent_seg_id: int = self.point_indices_vs_seg_ids[parent_point.id]
segment.parent = SegmentParent(segments=parent_seg_id)
else:
# If the parent point doesn't have a segment ID, it's likely the soma
# Find the last soma segment to use as the parent
soma_segments = [
seg
for seg in self.cell.morphology.segments
if self.segment_types.get(seg.id) == SWCNode.SOMA
]
if soma_segments:
last_soma_segment = soma_segments[-1]
segment.parent = SegmentParent(segments=last_soma_segment.id)
else:
logger.warning(
f"No parent segment found for segment {seg_id}. This should not happen."
)

# Always include proximal point for the first segment of a new cable
if new_cable:
segment.proximal = Point3DWithDiam(
x=parent_point.x,
y=parent_point.y,
Expand Down Expand Up @@ -484,24 +482,21 @@ def create_segment_groups(self) -> None:
"""
logger.info("Creating segment groups")

# Create SegmentGroup objects
for group_name, members in self.segment_groups.items():
if members:
group: SegmentGroup = SegmentGroup(id=group_name)
for member_id in sorted(members): # Sort to ensure consistent order
for member_id in sorted(members):
group.members.append(Member(segments=member_id))
self.cell.morphology.segment_groups.append(group)

# Add cable groups (assuming we're using NeuroML v2)
for cable_id, segments in self.cable_ids_vs_indices.items():
cable_group: SegmentGroup = SegmentGroup(
id=f"{self.cable_prefix_v2}{cable_id}"
)
for seg_id in sorted(set(segments)): # Remove duplicates and sort
for seg_id in sorted(set(segments)):
cable_group.members.append(Member(segments=seg_id))
self.cell.morphology.segment_groups.append(cable_group)

# Add the cell_type property
self.cell.properties.append(
Property(tag="cell_type", value="converted_from_swc")
)
Expand Down Expand Up @@ -536,7 +531,7 @@ def export_to_nml_file(self, filename: str) -> None:
:type filename: str
"""
if self.nml_doc is None:
self.nml_string() # This creates self.nml_doc if it doesn't exist
self.nml_string()

writers.NeuroMLWriter.write(self.nml_doc, filename)
logger.info(f"NeuroML file exported to: {filename}")

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