Skip to content

Commit

Permalink
bugfixes
Browse files Browse the repository at this point in the history
  • Loading branch information
@schlegelp
schlegelp committed May 3, 2017
1 parent 59280d1 commit 81d8503
Show file tree
Hide file tree
Showing 2 changed files with 81 additions and 66 deletions.
145 changes: 80 additions & 65 deletions CATMAIDImport.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,10 @@

### CATMAID to Blender Import Script - Version History:

### V5.73 03/05/2017:
- bugfixes
- made plotting of dendrogram using pylab optional

### V5.72 19/04/2017:
- added import of tags

Expand Down Expand Up @@ -355,7 +359,7 @@
bl_info = {
"name": "CATMAIDImport",
"author": "Philipp Schlegel",
"version": (5, 7, 1),
"version": (5, 7, 3),
"for_catmaid_version": '2017.01.19-3-g1e99030',
"blender": (2, 7, 8),
"location": "Properties > Scene > CATMAID Import",
Expand Down Expand Up @@ -1767,7 +1771,7 @@ def execute(self,context):
for skid in skdata:
CATMAIDtoBlender.extract_nodes( skdata[skid], str(skid),
neuron_name = neuron_names[str(skid)],
resampling = self.resampling,
resampling = resampling_factors[skid],
import_synapses = self.import_synapses,
import_gap_junctions = self.import_gap_junctions,
import_abutting = self.import_abutting,
Expand Down Expand Up @@ -2460,7 +2464,7 @@ def get_connectors(self, active_skeleton, node_data, cndata, neuron_names ,mesh_
for connector in connector_data_post:
number_of_targets[connector[0]] = len(connector[1]['postsynaptic_to'])

print('Number of postsynapses/connector:', number_of_targets)
#print('Number of postsynapses/connector:', number_of_targets)

### Create a sphere for every connector - presynapses will be scaled based on number of postsynaptic targets
if self.color_prop == 'Black':
Expand Down Expand Up @@ -2526,8 +2530,10 @@ class ConnectorsToSVG(Operator, ExportHelper):
proximity_radius_for_density = FloatProperty(name="Proximity Threshold (Blender Units!)",
default = 0.25,
description = "Maximum allowed distance between Connector and a Node")
export_inputs = BoolProperty(name="Export Inputs", default = True)
export_outputs = BoolProperty(name="Export Outputs", default = True)
export_inputs = BoolProperty(name="Export Synaptic Inputs", default = True )
export_outputs = BoolProperty(name="Export Synaptic Outputs", default = True )
export_gaps = BoolProperty(name="Export Gap Junctions", default = True )
export_abutting = BoolProperty(name="Export Abutting Connectors", default = False )
scale_outputs = BoolProperty(name="Scale Presynapses", default = False,
description = "Size of Presynapses based on number of postsynaptically connected neurons")
basic_radius = FloatProperty(name="Base Radius", default = 0.5)
Expand Down Expand Up @@ -2629,7 +2635,13 @@ def execute(self, context):
self.mesh_color[skid] = neuron.active_material.diffuse_color

print("Retrieving connector data for %i neurons" % len(skids_to_export))
skdata,errors = retrieveSkeletonData(skids_to_export, time_out = context.user_preferences.addons['CATMAIDImport'].preferences.time_out, skip_existing = False)
skdata,errors = retrieveSkeletonData( skids_to_export,
time_out = context.user_preferences.addons['CATMAIDImport'].preferences.time_out,
skip_existing = False,
get_abutting = self.export_abutting
)

#Cndata is a dictionary containing details of all connectors
cndata = self.get_all_connectors( skdata )

if cndata is None:
Expand All @@ -2639,13 +2651,16 @@ def execute(self, context):
self.report({'ERROR'},errors)

for skid in skids_to_export:
connector_data[skid] = self.get_connectors(skid,skdata[skid],cndata)
connector_data[skid] = self.get_connectors(skid, skdata[skid], cndata)

if self.color_by_connections:
#If outputs are exported then count only upstream connections (upstream sources of these outputs)
#If inputs are exported then count only downstream connections (downstream targets of these inputs)
#-> just use them invertedly for use_inputs/outputs when calling get_connectivity
self.connections_for_color = self.get_connectivity(skids_to_export,self.export_outputs,self.export_inputs)
self.connections_for_color = self.get_connectivity( skids_to_export,
self.export_outputs,
self.export_inputs
)

if self.export_neuron is True:
neurons_svg_string = self.create_svg_for_neuron(neurons_to_export)
Expand All @@ -2667,6 +2682,8 @@ def execute(self, context):


def get_all_connectors(self, skdata):
""" Get details for connectors for all neuronss
"""
connector_id_list = []
connector_postdata = {}

Expand All @@ -2676,6 +2693,10 @@ def get_all_connectors(self, skdata):
connector_id_list.append(c[1])
if self.export_inputs is True and c[2] == 1:
connector_id_list.append(c[1])
if self.export_gaps is True and c[2] == 2:
connector_id_list.append(c[1])
if self.export_abutting is True and c[2] == 3:
connector_id_list.append(c[1])

if not connector_id_list:
print('ERROR: no connectors found!')
Expand All @@ -2684,34 +2705,33 @@ def get_all_connectors(self, skdata):

for i, c in enumerate( list( set( connector_id_list ) ) ):
connector_tag = 'connector_ids[%i]' % i
connector_postdata[connector_tag] = c
connector_postdata[ connector_tag ] = c

remote_connector_url = remote_instance.get_connector_details_url( project_id )

"""
Format of temp_data = [ [ cn_id, { 'connector_id' : int(),
'presynaptic_to': skid,
'postsynaptic_to' : [skid, skid, ...],
'presynaptic_to_node' : tn_id,
'postsynaptic_to_node': [tn_id, tn_id, ...] }
] ]
"""
temp_data = remote_instance.fetch( remote_connector_url , connector_postdata )

skids_to_check = []
cn_data = {}
print('TEMP DATA:',temp_data)
for c in temp_data:
cn_data[ c[0] ] = c[1]

#print('C:',c)

if c[1]['presynaptic_to'] != None:
skids_to_check.append(c[1]['presynaptic_to'])

for target_skid in c[1]['postsynaptic_to']:
if target_skid != None:
skids_to_check.append(target_skid)
cn_data = { c[0] : c[1] for c in temp_data }
skids_to_check = [ c[1]['presynaptic_to'] for c in temp_data if c[1]['presynaptic_to'] != None ] + [ s for c in temp_data for s in c['postsynaptic_to'] if s != None ]

self.check_ancestry ( list ( set( skids_to_check + list(skdata) ) ) )

#Format of cn_data = { connector_id : {} }
return cn_data


def get_connectors(self, active_skeleton, node_data, cndata):
connector_ids = []
def get_connectors(self, active_skeleton, node_data, cndata ):
""" Get a list of connectors for each neuron. Apply filters if necessary
"""

if self.filter_connectors:
filter_list = self.filter_connectors.split(',')
Expand All @@ -2723,59 +2743,54 @@ def get_connectors(self, active_skeleton, node_data, cndata):
filter_exclusion = True
else:
filter_inclusion = True

i = 0
connector_postdata_postsynapses = {}
connector_postdata_presynapses = {}

connector_post_coords = {}
connector_pre_coords = {}
nodes_list = {}
connector_pre_coords = {}

connector_data_post = []
connector_data_pre = []

print('Extracting coordinates..')
print('Extracting coordinates..')

### Convert coordinates to Blender
for node in node_data[0]:

X = float(node[3])/self.conversion_factor
Y = float(node[4])/-self.conversion_factor
Z = float(node[5])/self.conversion_factor

nodes_list[node[0]] = (X,Z,Y)
nodes_list = { n[0] : ( float(node[3])/self.conversion_factor,
float(node[4])/-self.conversion_factor,
float(node[5])/self.conversion_factor
) for n in node_data[0] }

for connection in node_data[1]:

connector_coords = { cn[1] : (cn[3]/self.conversion_factor,cn[5]/self.conversion_factor,cn[4]/-self.conversion_factor) for cn in node_data[1] }

for cn in node_data[1]:

if connection[2] == 1 and self.export_inputs is True:
if cn[2] == 1 and self.export_inputs is True:
### For Sources the Treenodes the Connector is connecting TO are listed
### Reason: One connector can connector to the same neuron (at different treenodes) multiple times!!!
### !!!Attention: Treenode can be connected to multiple connectors (up- and downstream)

if connection[0] not in connector_pre_coords:
connector_pre_coords[connection[0]] = {}
if cn[0] not in connector_pre_coords:
connector_pre_coords[cn[0]] = {}

#Format: connector_pre_coord[target_treenode_id][upstream_connector_id] = coords of target treenode
connector_pre_coords[connection[0]][connection[1]] = {}
connector_pre_coords[connection[0]][connection[1]]['coords'] = nodes_list[connection[0]] #these are treenode coords, NOT connector coords
connector_pre_coords[cn[0]][cn[1]] = {}
connector_pre_coords[cn[0]][cn[1]]['coords'] = nodes_list[cn[0]] #these are treenode coords, NOT connector coords

connector_data_pre.append( [ connection[1] , cndata[ connection[1] ] ] )
connector_data_pre.append( [ cn[1] , cndata[ cn[1] ] ] )

if connection[2] == 0 and self.export_outputs is True:
connector_post_coords[connection[1]] = {}
connector_post_coords[connection[1]]['id'] = connection[1]
connector_post_coords[connection[1]]['coords'] = (connection[3]/self.conversion_factor,connection[5]/self.conversion_factor,connection[4]/-self.conversion_factor)
if cn[2] == 0 and self.export_outputs is True:
connector_post_coords[cn[1]] = {}
connector_post_coords[cn[1]]['id'] = cn[1]
connector_post_coords[cn[1]]['coords'] = (cn[3]/self.conversion_factor,cn[5]/self.conversion_factor,cn[4]/-self.conversion_factor)

connector_data_post.append( [ connection[1] , cndata[ connection[1] ] ] )

i += 1
connector_data_post.append( [ cn[1] , cndata[ cn[1] ] ] )


print('%s Down- / %s Upstream connectors for skid %s found' % (len(connector_post_coords), len(connector_pre_coords), active_skeleton))
remote_connector_url = remote_instance.get_connector_details_url( project_id )

if connector_data_pre or connector_data_post:
print("Connectors successfully retrieved")
number_of_targets = {}
number_of_targets = { }
presynaptic_to = {}
postsynaptic_to = {}

Expand All @@ -2797,8 +2812,8 @@ def get_connectors(self, active_skeleton, node_data, cndata):


print('Retrieving Ancestry of all downstream neurons...')
self.check_ancestry(skids_to_check)
print('Done')
self.check_ancestry( skids_to_check )
print('Done')

neurons_included = []
entries_to_delete = {}
Expand All @@ -2815,7 +2830,7 @@ def get_connectors(self, active_skeleton, node_data, cndata):
if connector_id not in postsynaptic_to:
postsynaptic_to[connector_id] = []

entries_to_delete[connector_id] = True
entries_to_delete[ connector_id ] = True

if self.filter_connectors:
print('Filtering Connector %i (postsynaptic to: %s) for: < %s >' % (connector[0], str(connector[1]['postsynaptic_to']), self.filter_connectors))
Expand Down Expand Up @@ -2877,11 +2892,10 @@ def get_connectors(self, active_skeleton, node_data, cndata):

if self.filter_connectors:
print('Downstream Neurons remaining after filtering:')
print(set(neurons_included))

print(set(neurons_included))

### Only proceed if neuron actually has Inputs (e.g. sensory neurons)
if len(connector_pre_coords) > 0:
if len( connector_pre_coords ) > 0:
print('Total of %s connectors for %s inputs found: ' % (str(len(connector_data_pre)), str(len(connector_pre_coords))))

### Retrieve Ancestry(= name for all upstream neurons):
Expand Down Expand Up @@ -2915,7 +2929,6 @@ def get_connectors(self, active_skeleton, node_data, cndata):
if treenode not in presynaptic_to:
presynaptic_to[treenode] = []
entries_to_delete[treenode] = True


### Connector_data_XXX is a list NOT a dictionary, so we have to cycle through it
for connector in connector_data_pre:
Expand Down Expand Up @@ -2979,8 +2992,7 @@ def get_connectors(self, active_skeleton, node_data, cndata):
print('Upstream Neurons remaining after filtering:')
print(set(neurons_included))


return((number_of_targets, connector_pre_coords, connector_post_coords, presynaptic_to))
return( ( number_of_targets, connector_pre_coords, connector_post_coords, presynaptic_to ) )

else:
print('No data retrieved')
Expand Down Expand Up @@ -3028,7 +3040,7 @@ def export_to_svg(self, skids_to_export, connector_data, neurons_svg_string):

density_gradient = {'start_rgb': (0,255,0),
'end_rgb':(255,0,0)}
density_data = []
density_data = []

brain_shape_top_string = '<g id="brain shape top">\n <polyline points="28.3,-5.8 34.0,-7.1 38.0,-9.4 45.1,-15.5 50.8,-20.6 57.7,-25.4 59.6,-25.6 63.2,-22.8 67.7,-18.7 70.7,-17.2 74.6,-14.3 78.1,-12.8 84.3,-12.6 87.7,-15.5 91.8,-20.9 98.1,-32.4 99.9,-38.3 105.2,-48.9 106.1,-56.4 105.6,-70.1 103.2,-75.8 97.7,-82.0 92.5,-87.2 88.8,-89.1 82.6,-90.0 75.0,-89.9 67.4,-89.6 60.8,-85.6 55.3,-77.2 52.4,-70.2 51.9,-56.7 55.0,-47.0 55.9,-36.4 56.0,-32.1 54.3,-31.1 51.0,-33.4 50.7,-42.5 52.7,-48.6 49.9,-58.4 44.3,-70.8 37.4,-80.9 33.1,-84.0 24.7,-86.0 14.2,-83.9 8.3,-79.1 2.9,-68.3 1.3,-53.5 2.5,-46.9 3.0,-38.3 6.3,-28.2 10.9,-18.7 16.3,-9.7 22.2,-6.4 28.3,-5.8" \n style="fill:none;stroke:darkslategray;stroke-width:0.5;stroke-linecap:round;stroke-linejoin:round"/> \n <polyline points="88.8,-89.1 90.9,-97.7 92.9,-111.3 95.6,-125.6 96.7,-139.4 95.9,-152.0 92.8,-170.2 89.4,-191.0 87.2,-203.7 80.6,-216.6 73.4,-228.3 64.5,-239.9 56.4,-247.3 48.8,-246.9 39.0,-238.3 29.6,-226.9 24.7,-212.0 22.9,-201.2 23.1,-186.9 18.7,-168.3 14.1,-150.4 12.6,-138.0 13.7,-121.5 16.3,-105.1 18.3,-84.8 " \n style="fill:none;stroke:darkslategray;stroke-width:0.5;stroke-linecap:round;stroke-linejoin:round"/> \n </g>'
brain_shape_front_string = '<g id="brain shape front"> \n <polyline points="51.5,24.0 52.0,21.3 52.0,17.6 50.2,11.2 46.8,6.5 40.5,2.5 33.8,1.1 25.4,3.4 18.8,8.0 13.2,12.5 8.3,17.9 4.3,23.8 1.8,29.3 1.4,35.6 1.6,42.1 4.7,48.3 7.9,52.5 10.8,56.9 13.1,64.3 14.3,73.2 12.8,81.0 16.2,93.6 20.9,101.5 28.2,107.5 35.3,112.7 42.2,117.0 50.8,119.3 57.9,119.3 67.1,118.0 73.9,114.1 79.0,110.4 91.1,102.7 96.3,94.2 96.3,85.3 94.0,81.4 95.4,74.8 96.6,68.3 97.5,64.7 100.9,59.7 103.8,52.5 105.4,46.7 106.1,38.8 105.4,32.4 103.1,26.4 98.9,21.0 94.1,16.3 88.3,11.1 82.0,6.5 74.8,3.3 67.8,3.1 61.7,5.1 56.8,9.6 53.4,15.2 52.2,19.7 52.3,25.3 51.4,24.1 " \n style="fill:none;stroke:darkslategray;stroke-width:0.5;stroke-linecap:round;stroke-linejoin:round"/> \n <polyline points="46.6,34.0 45.5,36.1 43.2,38.6 41.1,43.3 39.7,48.7 39.7,51.0 42.6,55.2 51.4,59.5 54.9,60.9 60.8,60.8 62.9,58.2 62.9,52.6 60.3,47.6 57.7,43.9 56.1,40.2 55.1,35.9 55.1,34.4 51.8,33.6 49.1,33.5 46.6,34.0 " \n style="fill:none;stroke:darkslategray;stroke-width:0.5;stroke-linecap:round;stroke-linejoin:round"/> \n </g>'
Expand Down Expand Up @@ -4711,7 +4723,7 @@ def extract_partners (self, connectivity_data, get_inputs, get_outputs, make_cur
start = time.clock()

skdata, errors = retrieveSkeletonData( filtered_partners ,
time_out = context.user_preferences.addons['CATMAIDImport'].preferences.time_out,
time_out = bpy.context.user_preferences.addons['CATMAIDImport'].preferences.time_out,
get_abutting = self.import_abutting )

print("Creating meshes for %i neurons" % len(skdata))
Expand Down Expand Up @@ -9233,7 +9245,10 @@ def after_calc(self):
osd_timed.start()
ahd.clear()

self.matplot_dendrogram()
try:
self.matplot_dendrogram()
except:
pass

if self.save_dendrogram is True and os.path.exists ( os.path.normpath ( self.path_dendrogram ) ):
print('Creating Dendrogram.svg')
Expand Down
2 changes: 1 addition & 1 deletion update.txt
View file
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
#Provides current version, last stable version,
#recently added features and a message if need be

current_version = {5.72}
current_version = {5.73}
last_stable = {4.1}
new_features = {import of gap junction + abutting connectors; import of tags; improved volume import; code clean-up}
message = {}

0 comments on commit 81d8503

Please sign in to comment.