#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import math
import datetime
import pickle
## python2.6 tuft_nonlinearity.py
## Remove all interneurons in simset_odor
sys.path.extend(["..","../networks","../generators","../simulations"])
from moose_utils import * # imports moose
from data_utils import * # has mpi import and variables also
from OBNetwork import *
from sim_utils import *
from stimuliConstants import * # has SETTLETIME, inputList and pulseList, GLOMS_ODOR, GLOMS_NIL
from simset_odor import * # has REALRUNTIME, NUMBINS
from generate_constrate_files import * # has the firingrates list
RUNTIME = REALRUNTIME + SETTLETIME
from pylab import * # part of matplotlib that depends on numpy but not scipy
from plot_odor_morphs import *
#-----------------------------------------------------------
class constOdorResponse:
def __init__(self):
self.mpirank = mpirank
self.context = moose.PyMooseBase.getContext()
def setupStim(self, network, avgnum):
### connect the ORNs
for projname in network.projectionDict.keys():
#### Calling attach_spikes() for each projection,
#### would reconnect files to the same segment multiple times.
#### But attach_files_uniquely() checks whether timetable.tableSize is zero or not
#### i.e. files already attached or not.
### connect ORNs to mitrals
if 'ORN_mitral' in projname:
print "connecting ORN files to mitrals"
for i,proj in enumerate(network.projectionDict[projname][2]):
#get the glomnum from the post path proj[2]
mitname = string.split(proj[2],'/')[1] # name of the mitral cell from '/mitrals_2/...'
mitnum = int(string.split(mitname,'_')[1]) # mitral number from 'mitrals_2'
firingrate = firingrates[mitnum]
filebase = '../firefiles/firetimes_constrate_'+str(firingrate)+'.txt'
self.attach_files_uniquely(filebase,proj[0],proj[2],avgnum)
def attach_files_uniquely(self,filebase,synname,postsegpath,avgnum=None):
ttpath = postsegpath+'/'+synname+'_tt'
if self.context.exists(ttpath):
# timetable already created by networkML reader - just wrap it below.
tt = moose.TimeTable(ttpath) # post_segment_path+'/'+syn_name+'_tt'
else:
## if timetable was not already created by networkML reader,
## it means that the synaptic weights must be zero!
## (no extra inhibition - only main inhibition)
## hence do not attach spikefiles
return
if tt.tableSize != 0: return # if files are already attached, do nothing!
filebase += '_odor_'+str(odorA)+'_'+str(odorB)
if avgnum is not None: filebase += '_avgnum'+str(avgnum)
## attach_spikes() accesses filenumbers to this segment
## from 'fileNumbers' field (of the timetable object in MOOSE)
## which is created while reading in networkML.
attach_spikes(filebase, tt, self.mpirank)
def connect_granule_baselines_to_files(self, network, avgnum):
for projname in network.projectionDict.keys():
if 'granule_baseline' in projname:
for i,proj in enumerate(network.projectionDict[projname][2]):
## just wrap the existing timetable created when loading in neuroml
## it has a field fileNumbers that has which lines to take from the firingfile
tt = moose.TimeTable(proj[2]+'/'+proj[0]+'_tt') # post_segment_path+'/'+syn_name+'_tt'
if IN_VIVO:
if gran_base_resp_tuned:
filebase = '../firefiles/firetimes_gran_baseline_'+str(avgnum)
else:
filebase = '../firefiles/firetimes_gran_baseline_noresp_'+str(avgnum)
else:
filebase = '../firefiles/firetimes_gran_baseline_invitro_'+str(avgnum)
attach_spikes(filebase, tt, self.mpirank)
def run(self,network, binned):
print "Resetting MOOSE."
# from moose_utils.py sets clocks and resets
resetSim(network.context, SIMDT, PLOTDT)
print "Running at",self.mpirank
network.context.step(RUNTIME)
mitral_responses = []
mitral_responses_binned = []
if ONLY_TWO_MITS: num_mits = MIT_SISTERS
else: num_mits = NUM_GLOMS*MIT_SISTERS
for mitnum in range(num_mits):
mitral = network.mitralTable[mitnum]
## only the last respiration cycle is taken
if binned: mitral_responses_binned.append(
plotBins(mitral._vmTableSoma, NUMBINS, RUNTIME, (NUM_RESPS-1)*RESPIRATION+SETTLETIME) )
## need to convert to numpy's array(),
## else MOOSE table cannot be pickled for mpi4py send()
mitral_responses.append(array(mitral._vmTableSoma))
return (mitral_responses,mitral_responses_binned)
#----------------------------------------------------------------
if __name__ == "__main__":
sim = constOdorResponse()
includeProjections = []
tweaks = build_tweaks(CLUB_MITRALS, NO_SPINE_INH, NO_SINGLES,\
NO_JOINTS, NO_MULTIS, NO_PGS, ONLY_TWO_MITS, includeProjections)
BINNED = True
## if not BINNED, save the full mitral Vm-s
## and not just their spiketimes by setting spiketable = False below.
network = OBNetwork(OBNet_file, synchan_activation_correction, tweaks,\
mpirank=mpirank, invivo=IN_VIVO, spiketable=BINNED)
#printNetTree() # from moose_utils.py
## monitor those interneurons that are connected to mitral indices 0 and 1
## save only spiketimes by setting extras_spikes_only=True
sim.setupStim(network, avgnum=0)
mitral_responses,mitral_responses_binned = sim.run(network,BINNED)
figure()
title('Glomerulus 0')
if BINNED:
deltabin = RESPIRATION/NUMBINS
# Take only the last respiration cycle
timevec = arange(SETTLETIME+(NUM_RESPS-1)*RESPIRATION+deltabin/2,RUNTIME,deltabin)
mitral_responses = mitral_responses_binned
else:
timevec = arange(0.0,RUNTIME+1e-12,PLOTDT)
plot(timevec,mitral_responses[0],color=(0.0,1.0,0.0))
plot(timevec,mitral_responses[1],color=(0.0,1.0,0.5))
show()
