def __init__(self, OBfile, synchan_activation_correction,
tweaks, mpirank, uniquestr, granfilebase, resptuned=False, spiketable=False):
self.mpirank = mpirank
self.uniquestr = uniquestr
self.context = moose.PyMooseBase.getContext()
self.granfilebase = granfilebase
self.spiketable = spiketable
## netseed is between 'seed' and '_' / '.xml' in OBfile
## ensure there is an _ before .xml, to consider both endings.
OBfile_underscored = OBfile.replace('.xml','_.xml')
self.netseed = float((OBfile_underscored.split('seed')[1]).split('_')[0])
load_channels()
## synchan_activation_correction depends on SIMDT,
## hence passed all the way down to
## granule_mitral_GABA synapse from the top level
load_synapses(synchan_activation_correction)
self.cellSegmentDict = load_cells()
filename = OBfile
NML = NetworkML({'temperature':CELSIUS})
## Below returns populationDict = { 'populationname1':(cellname,{instanceid1:moosecell, ... }) , ... }
## and projectionDict = { 'projectionname1':(source,target,[(syn_name1,pre_seg_path,post_seg_path),...]) , ... }
(self.populationDict,self.projectionDict) = \
NML.readNetworkMLFromFile(filename,self.cellSegmentDict,params=tweaks)
if VARY_GRANS_RMP:
self.set_granules_rmp(gran_RMP_SD)
if VARY_PGS_RMP:
self.set_pgs_rmp(PG_RMP_SD) # let PGs vary also
self.set_different_pgs() # set cells as PG_LTS or PG
## Vary synapse/channel Gbar-s only after setting them as PG_LTS or PG above
self.set_pgs_input_conductances() # set input conductance based on PG type
#self.set_pgs_tca_var() # changes TCa for all PGs
## for PG_LTS, varying ELeak or TCa doesn't change RMP, varying KCa does
self.set_pgs_kca_var() # changes KCa for PG_LTSs
#### Granule baseline synapses and connected timetables are created
#### by the networkml reader from the network xml file.
#### But the firefiles need to be loaded into timetables.
self.connect_granule_baselines_to_files('granule_baseline')
#### mitral baseline synapses and connected timetables are created
#### by the networkml reader from the network xml file.
#### But the firefiles need to be loaded into timetables.
#print "Connecting 10000 files per mit"
## connect baseline inhibition to mitrals at lateral dendrites
#self.connect_granule_baselines_to_files('mitral_baseline')
## connect baseline inhibition to mitrals at tuft dendrites
#self.connect_granule_baselines_to_files('tuft_mitral')
#printCellTree(moose.Cell('granules_singles_0'))
# unordered dictionary self.populationDict['mitrals'][1] is
# renamed to a more friendly self.mitralTable
self.mitralTable = self.populationDict['mitrals'][1]
self.setupMitralRecordingTables()
#printNetTree()
print "OB Network from",OBfile,"loaded!"
def __init__(self,
OBfile,
synchan_activation_correction,
tweaks,
mpirank,
uniquestr,
granfilebase,
resptuned=False,
spiketable=False):
self.mpirank = mpirank
self.uniquestr = uniquestr
self.context = moose.PyMooseBase.getContext()
self.granfilebase = granfilebase
self.spiketable = spiketable
## netseed is between 'seed' and '_' / '.xml' in OBfile
## ensure there is an _ before .xml, to consider both endings.
OBfile_underscored = OBfile.replace('.xml', '_.xml')
self.netseed = float(
(OBfile_underscored.split('seed')[1]).split('_')[0])
load_channels()
## synchan_activation_correction depends on SIMDT,
## hence passed all the way down to
## granule_mitral_GABA synapse from the top level
load_synapses(synchan_activation_correction)
self.cellSegmentDict = load_cells()
filename = OBfile
NML = NetworkML({'temperature': CELSIUS})
## Below returns populationDict = { 'populationname1':(cellname,{instanceid1:moosecell, ... }) , ... }
## and projectionDict = { 'projectionname1':(source,target,[(syn_name1,pre_seg_path,post_seg_path),...]) , ... }
(self.populationDict,self.projectionDict) = \
NML.readNetworkMLFromFile(filename,self.cellSegmentDict,params=tweaks)
if VARY_GRANS_RMP:
self.set_granules_rmp(gran_RMP_SD)
if VARY_PGS_RMP:
self.set_pgs_rmp(PG_RMP_SD) # let PGs vary also
self.set_different_pgs() # set cells as PG_LTS or PG
## Vary synapse/channel Gbar-s only after setting them as PG_LTS or PG above
self.set_pgs_input_conductances(
) # set input conductance based on PG type
#self.set_pgs_tca_var() # changes TCa for all PGs
## for PG_LTS, varying ELeak or TCa doesn't change RMP, varying KCa does
self.set_pgs_kca_var() # changes KCa for PG_LTSs
#### Granule baseline synapses and connected timetables are created
#### by the networkml reader from the network xml file.
#### But the firefiles need to be loaded into timetables.
self.connect_granule_baselines_to_files('granule_baseline')
#### mitral baseline synapses and connected timetables are created
#### by the networkml reader from the network xml file.
#### But the firefiles need to be loaded into timetables.
#print "Connecting 10000 files per mit"
## connect baseline inhibition to mitrals at lateral dendrites
#self.connect_granule_baselines_to_files('mitral_baseline')
## connect baseline inhibition to mitrals at tuft dendrites
#self.connect_granule_baselines_to_files('tuft_mitral')
#printCellTree(moose.Cell('granules_singles_0'))
# unordered dictionary self.populationDict['mitrals'][1] is
# renamed to a more friendly self.mitralTable
self.mitralTable = self.populationDict['mitrals'][1]
self.setupMitralRecordingTables()
#printNetTree()
print "OB Network from", OBfile, "loaded!"
from neuroml_utils import *
from sim_utils import * # has build_tweaks()
from networkConstants import *
## these are needed to load mitral cell and get segids and synapse locations
from load_channels import *
from load_synapses import *
from load_cells import *
from simset_odor import * # has synchan_activation_correction
load_channels()
## synchan_activation_correction depends on SIMDT,
## hence passed all the way down to
## granule_mitral_GABA synapse from the top level
load_synapses(synchan_activation_correction)
## cellSegmentDict[<cellname>] = { segid1 : [ segname,(proximalx,proximaly,proximalz),
## (distalx,distaly,distalz),diameter,length,[potential_syn1, ... ] ] , ... }
cellSegmentDict = load_cells()
from pylab import *
## USAGE:
## python2.6 plot_mitral_connectivity_NetworkML.py <networkmlfile.xml>
## OR
## python2.6 plot_mitral_connectivity_NetworkML.py SYN_DECAY
## The plots are for weights and numbers of all exc/inh synapses on the central/lateral mitrals.
## The printing on the terminal gives weights located proximally vs distally from central mits.
TWOGLOMS = False#True # whether to check for mits 0,2 (True) or 0,1 (False)
INCLUDEMULTIS = True # whether to include multi-s in joint connectivity
class NetworkML():