diff --git a/.travis.yml b/.travis.yml
index 8534d5d56d248f845566a8d5a1979f549df71ed3..aa5980c723c16c68a29053c04d1f06595a57bafc 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -6,7 +6,7 @@ notifications:
recipients:
- dilawar.s.rajput@gmail.com
- upi.bhalla@gmail.com
- - malavs@ncbs.res.in
+ - ray.shubhasis@gmail.com
- hrani@ncbs.res.in
diff --git a/neuroml/GranuleCell/Granule98_hsolve.py b/neuroml/GranuleCell/Granule98_hsolve.py
index 6825a4a4fc4c3996d4034ce2b292ea721168fb7c..7f62f0eb6e4d70da672f9dc3ec37a450c1aaac8c 100644
--- a/neuroml/GranuleCell/Granule98_hsolve.py
+++ b/neuroml/GranuleCell/Granule98_hsolve.py
@@ -8,7 +8,10 @@ The soma name below is hard coded for gran98, else any other file can be used by
"""
import os
+os.environ['NUMPTHREADS'] = '1'
import sys
+sys.path.append('../../../python')
+
import moose
from moose.utils import *
from moose.neuroml.NeuroML import NeuroML
@@ -27,9 +30,9 @@ def loadGran98NeuroML_L123(filename):
soma_path = populationDict['Gran'][1][0].path+'/Soma_0'
somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/Gran_CaPool_98'),'Ca')
- somaIKCa = setupTable('somaIKCa',moose.element(soma_path+'/Gran_KCa_98'),'Gk')
+ somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/Gran_KCa_98'),'Gk')
## Am not able to plot KDr gating variable X when running under hsolve
- #KDrX = setupTable('ChanX',moose.element(soma_path+'/Gran_KDr_98'),'X')
+ #KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
print("Reinit MOOSE ... ")
resetSim(['/elec',cells_path], simdt, plotdt, simmethod='hsolve')
diff --git a/snippets/GraupnerBrunel2012_STDPfromCaPlasticity.py b/snippets/GraupnerBrunel2012_STDPfromCaPlasticity.py
index 556639dbfb2c0477f3bbe56316ad4d3fca1bce19..257add4833b5c355792e22dc261df84fde6f7819 100644
--- a/snippets/GraupnerBrunel2012_STDPfromCaPlasticity.py
+++ b/snippets/GraupnerBrunel2012_STDPfromCaPlasticity.py
@@ -1,3 +1,20 @@
+
+#/**********************************************************************
+#** This program is part of 'MOOSE', the
+#** Messaging Object Oriented Simulation Environment.
+#** Copyright (C) 2003-2014 Upinder S. Bhalla. and NCBS
+#** It is made available under the terms of the
+#** GNU Lesser General Public License version 2.1
+#** See the file COPYING.LIB for the full notice.
+#**********************************************************************/
+
+'''
+Simulate a pseudo-STDP protocol and plot the STDP kernel
+that emerges from Ca plasticity of Graupner and Brunel 2012.
+
+Author: Aditya Gilra, NCBS, Bangalore, October, 2014.
+'''
+
import moose
from pylab import *
@@ -54,9 +71,9 @@ network.vec.initVm = Vrest
#weight = 0.43 # initial synaptic weight
## gammaP/(gammaP+gammaD) = eq weight w/o noise
## see eqn (22), noiseSD also appears
- ## but doesn't work here,
+ ## but doesn't work here,
## weights away from 0.4 - 0.5 screw up the STDP rule!!
-
+
#bistable = True # if bistable is True, use bistable potential for weights
#noisy = False # use noisy weight updates given by noiseSD
#noiseSD = 3.3501 # if noisy, use noiseSD (3.3501 from Higgins et al 2014)
@@ -79,9 +96,9 @@ J = 5e-3 # V # delta function synapse, adds to Vm
weight = 0.5 # initial synaptic weight
# gammaP/(gammaP+gammaD) = eq weight w/o noise
# see eqn (22), noiseSD also appears
- # but doesn't work here,
+ # but doesn't work here,
# weights away from 0.4 - 0.5 screw up the STDP rule!!
-
+
bistable = True # if bistable is True, use bistable potential for weights
noisy = False # use noisy weight updates given by noiseSD
noiseSD = 2.8284 # if noisy, use noiseSD (3.3501 in Higgins et al 2014)
@@ -132,9 +149,9 @@ moose.connect( network, 'VmOut', Vms, 'input', 'OneToOne')
spikes = moose.Table( '/plotSpikes', 2 )
moose.connect( network, 'spikeOut', spikes, 'input', 'OneToOne')
CaTable = moose.Table( '/plotCa', 1 )
-moose.connect( CaTable, 'requestOut', syn, 'getCa')
+moose.connect( CaTable, 'requestOut', syn, 'getCa')
WtTable = moose.Table( '/plotWeight', 1 )
-moose.connect( WtTable, 'requestOut', syn.synapse[0], 'getWeight')
+moose.connect( WtTable, 'requestOut', syn.synapse[0], 'getWeight')
# ###########################################
# Simulate the STDP curve with spaced pre-post spike pairs
@@ -157,11 +174,9 @@ moose.reinit()
# function to make the aPlus and aMinus settle to equilibrium values
settletime = 100e-3 # s
-
def reset_settle():
- """
-Call this between every pre-post pair
-to reset the neurons and make them settle to rest.
+ """ Call this between every pre-post pair
+ to reset the neurons and make them settle to rest.
"""
syn.synapse[0].weight = weight
syn.Ca = 0.0
@@ -175,136 +190,125 @@ to reset the neurons and make them settle to rest.
# function to inject a sharp current pulse to make neuron spike
# immediately at a given time step
def make_neuron_spike(nrnidx,I=1e-7,duration=1e-3):
- """
-Inject a brief current pulse to
-make a neuron spike
+ """ Inject a brief current pulse to
+ make a neuron spike
"""
network.vec[nrnidx].inject = I
moose.start(duration)
network.vec[nrnidx].inject = 0.
-def main():
- """
-Simulate a pseudo-STDP protocol and plot the STDP kernel
-that emerges from Ca plasticity of Graupner and Brunel 2012.
-Author: Aditya Gilra, NCBS, Bangalore, October, 2014.
+dwlist_neg = []
+ddt = 2e-3 # s
+# since CaPlasticitySynHandler is event based
+# multiple pairs are needed for Ca to be registered above threshold
+# Values from Fig 2, last line of legend
+numpairs = 60 # number of spike parts per deltat
+t_between_pairs = 1.0 # time between each spike pair
+t_extent = 100e-3 # s # STDP kernel extent,
+ # t_extent > t_between_pairs/2 inverts pre-post pairing!
+# dt = tpost - tpre
+# negative dt corresponds to post before pre
+print('-----------------------------------------------')
+for deltat in arange(t_extent,0.0,-ddt):
+ reset_settle()
+ for i in range(numpairs):
+ # post neuron spike
+ make_neuron_spike(1)
+ moose.start(deltat)
+ # pre neuron spike after deltat
+ make_neuron_spike(0)
+ moose.start(t_between_pairs) # weight changes after pre-spike+delayD
+ # must run for at least delayD after pre-spike
+ dw = ( syn.synapse[0].weight - weight ) / weight
+ print(('post before pre, dt = %1.3f s, dw/w = %1.3f'%(-deltat,dw)))
+ dwlist_neg.append(dw)
+print('-----------------------------------------------')
+# positive dt corresponds to pre before post
+dwlist_pos = []
+for deltat in arange(ddt,t_extent+ddt,ddt):
+ reset_settle()
+ for i in range(numpairs):
+ # pre neuron spike
+ make_neuron_spike(0)
+ moose.start(deltat)
+ # post neuron spike after deltat
+ make_neuron_spike(1)
+ moose.start(t_between_pairs)
+ dw = ( syn.synapse[0].weight - weight ) / weight
+ print(('pre before post, dt = %1.3f s, dw/w = %1.3f'%(deltat,dw)))
+ dwlist_pos.append(dw)
+print('-----------------------------------------------')
+print(('Each of the above pre-post pairs was repeated',\
+ numpairs,'times, with',t_between_pairs,'s between pairs.'))
+print()
+print('Due to event based updates, Ca decays suddenly at events:')
+print('pre-spike, pre-spike + delayD, and post-spike;')
+print('apart from the usual CaPre and CaPost jumps at')
+print('pre-spike + delayD and post-spike respectively.')
+print('Because of the event based update, multiple pre-post pairs are used.')
+print()
+print('If you reduce the t_between_pairs,')
+print(' you\'ll see potentiation for the LTD part without using any triplet rule!')
+print()
+print("If you turn on noise, the weights fluctuate too much,")
+print(" not sure if there's a bug in my noise implementation.")
+print('-----------------------------------------------')
- """
- dwlist_neg = []
- ddt = 2e-3 # s
- # since CaPlasticitySynHandler is event based
- # multiple pairs are needed for Ca to be registered above threshold
- # Values from Fig 2, last line of legend
- numpairs = 60 # number of spike parts per deltat
- t_between_pairs = 1.0 # time between each spike pair
- t_extent = 100e-3 # s # STDP kernel extent,
- # t_extent > t_between_pairs/2 inverts pre-post pairing!
- # dt = tpost - tpre
- # negative dt corresponds to post before pre
- print('-----------------------------------------------')
- for deltat in arange(t_extent,0.0,-ddt):
- reset_settle()
- for i in range(numpairs):
- # post neuron spike
- make_neuron_spike(1)
- moose.start(deltat)
- # pre neuron spike after deltat
- make_neuron_spike(0)
- moose.start(t_between_pairs) # weight changes after pre-spike+delayD
- # must run for at least delayD after pre-spike
- dw = ( syn.synapse[0].weight - weight ) / weight
- print(('post before pre, dt = %1.3f s, dw/w = %1.3f'%(-deltat,dw)))
- dwlist_neg.append(dw)
- print('-----------------------------------------------')
- # positive dt corresponds to pre before post
- dwlist_pos = []
- for deltat in arange(ddt,t_extent+ddt,ddt):
- reset_settle()
- for i in range(numpairs):
- # pre neuron spike
- make_neuron_spike(0)
- moose.start(deltat)
- # post neuron spike after deltat
- make_neuron_spike(1)
- moose.start(t_between_pairs)
- dw = ( syn.synapse[0].weight - weight ) / weight
- print(('pre before post, dt = %1.3f s, dw/w = %1.3f'%(deltat,dw)))
- dwlist_pos.append(dw)
- print('-----------------------------------------------')
- print(('Each of the above pre-post pairs was repeated',\
- numpairs,'times, with',t_between_pairs,'s between pairs.'))
- print()
- print('Due to event based updates, Ca decays suddenly at events:')
- print('pre-spike, pre-spike + delayD, and post-spike;')
- print('apart from the usual CaPre and CaPost jumps at')
- print('pre-spike + delayD and post-spike respectively.')
- print('Because of the event based update, multiple pre-post pairs are used.')
- print()
- print('If you reduce the t_between_pairs,')
- print(' you\'ll see potentiation for the LTD part without using any triplet rule!')
- print()
- print("If you turn on noise, the weights fluctuate too much,")
- print(" not sure if there's a bug in my noise implementation.")
- print('-----------------------------------------------')
-
- # ###########################################
- # Plot the simulated Vm-s and STDP curve
- # ###########################################
-
- # insert spikes so that Vm reset doesn't look weird
- Vmseries0 = Vms.vec[0].vector
- numsteps = len(Vmseries0)
- for t in spikes.vec[0].vector:
- Vmseries0[int(t/dt)-1] = 30e-3 # V
- Vmseries1 = Vms.vec[1].vector
- for t in spikes.vec[1].vector:
- Vmseries1[int(t/dt)-1] = 30e-3 # V
-
- timeseries = linspace(0.,1000*numsteps*dt,numsteps)
- # Voltage plots
- figure(facecolor='w')
- plot(timeseries,Vmseries0,color='r') # pre neuron's vm
- plot(timeseries,Vmseries1,color='b') # post neuron's vm
- xlabel('time (ms)')
- ylabel('Vm (V)')
- title("pre (r) and post (b) neurons' Vm")
-
- # Ca plots for the synapse
- figure(facecolor='w')
- plot(timeseries,CaTable.vector[:len(timeseries)],color='r')
- plot((timeseries[0],timeseries[-1]),(thetaP,thetaP),color='k',\
- linestyle='dashed',label='pot thresh')
- plot((timeseries[0],timeseries[-1]),(thetaD,thetaD),color='b',\
- linestyle='dashed',label='dep thresh')
- legend()
- xlabel('time (ms)')
- ylabel('Ca (arb)')
- title("Ca conc in the synapse")
-
- # Weight plots for the synapse
- figure(facecolor='w')
- plot(timeseries,WtTable.vector[:len(timeseries)],color='r')
- xlabel('time (ms)')
- ylabel('Efficacy')
- title("Efficacy of the synapse")
-
- # STDP curve
- fig = figure(facecolor='w')
- ax = fig.add_subplot(111)
- ax.plot(arange(-t_extent,0,ddt)*1000,array(dwlist_neg),'.-r')
- ax.plot(arange(ddt,(t_extent+ddt),ddt)*1000,array(dwlist_pos),'.-b')
- xmin,xmax = ax.get_xlim()
- ymin,ymax = ax.get_ylim()
- ax.set_xticks([xmin,0,xmax])
- ax.set_yticks([ymin,0,ymax])
- ax.plot((0,0),(ymin,ymax),linestyle='dashed',color='k')
- ax.plot((xmin,xmax),(0,0),linestyle='dashed',color='k')
- ax.set_xlabel('$t_{post}-t_{pre}$ (ms)')
- ax.set_ylabel('$\Delta w / w$')
- fig.tight_layout()
- #fig.subplots_adjust(hspace=0.3,wspace=0.5) # use after tight_layout()
-
- show()
-
-if __name__ == '__main__':
- main()
+# ###########################################
+# Plot the simulated Vm-s and STDP curve
+# ###########################################
+
+# insert spikes so that Vm reset doesn't look weird
+Vmseries0 = Vms.vec[0].vector
+numsteps = len(Vmseries0)
+for t in spikes.vec[0].vector:
+ Vmseries0[int(t/dt)-1] = 30e-3 # V
+Vmseries1 = Vms.vec[1].vector
+for t in spikes.vec[1].vector:
+ Vmseries1[int(t/dt)-1] = 30e-3 # V
+
+timeseries = linspace(0.,1000*numsteps*dt,numsteps)
+# Voltage plots
+figure(facecolor='w')
+plot(timeseries,Vmseries0,color='r') # pre neuron's vm
+plot(timeseries,Vmseries1,color='b') # post neuron's vm
+xlabel('time (ms)')
+ylabel('Vm (V)')
+title("pre (r) and post (b) neurons' Vm")
+
+# Ca plots for the synapse
+figure(facecolor='w')
+plot(timeseries,CaTable.vector[:len(timeseries)],color='r')
+plot((timeseries[0],timeseries[-1]),(thetaP,thetaP),color='k',\
+ linestyle='dashed',label='pot thresh')
+plot((timeseries[0],timeseries[-1]),(thetaD,thetaD),color='b',\
+ linestyle='dashed',label='dep thresh')
+legend()
+xlabel('time (ms)')
+ylabel('Ca (arb)')
+title("Ca conc in the synapse")
+
+# Weight plots for the synapse
+figure(facecolor='w')
+plot(timeseries,WtTable.vector[:len(timeseries)],color='r')
+xlabel('time (ms)')
+ylabel('Efficacy')
+title("Efficacy of the synapse")
+
+# STDP curve
+fig = figure(facecolor='w')
+ax = fig.add_subplot(111)
+ax.plot(arange(-t_extent,0,ddt)*1000,array(dwlist_neg),'.-r')
+ax.plot(arange(ddt,(t_extent+ddt),ddt)*1000,array(dwlist_pos),'.-b')
+xmin,xmax = ax.get_xlim()
+ymin,ymax = ax.get_ylim()
+ax.set_xticks([xmin,0,xmax])
+ax.set_yticks([ymin,0,ymax])
+ax.plot((0,0),(ymin,ymax),linestyle='dashed',color='k')
+ax.plot((xmin,xmax),(0,0),linestyle='dashed',color='k')
+ax.set_xlabel('$t_{post}-t_{pre}$ (ms)')
+ax.set_ylabel('$\Delta w / w$')
+fig.tight_layout()
+#fig.subplots_adjust(hspace=0.3,wspace=0.5) # use after tight_layout()
+
+show()
diff --git a/snippets/HigginsGraupnerBrunel2014_LifetimeCaPlasticity.py b/snippets/HigginsGraupnerBrunel2014_LifetimeCaPlasticity.py
index 6acef833fc706bcb4e3a7c0ab0ac4e9a717738ed..1d3ddff2b9b8a5931eb66e5cab764c41461b02fb 100644
--- a/snippets/HigginsGraupnerBrunel2014_LifetimeCaPlasticity.py
+++ b/snippets/HigginsGraupnerBrunel2014_LifetimeCaPlasticity.py
@@ -1,3 +1,22 @@
+
+#/**********************************************************************
+#** This program is part of 'MOOSE', the
+#** Messaging Object Oriented Simulation Environment.
+#** Copyright (C) 2003-2014 Upinder S. Bhalla. and NCBS
+#** It is made available under the terms of the
+#** GNU Lesser General Public License version 2.1
+#** See the file COPYING.LIB for the full notice.
+#**********************************************************************/
+
+'''
+Simulate pre and post Poisson firing for a synapse with
+Ca plasticity of Graupner and Brunel 2012.
+See the trace over time (lifetime) for the synaptic efficacy,
+similar to figure 2A of Higgins, Graupner, Brunel, 2014.
+
+Author: Aditya Gilra, NCBS, Bangalore, October, 2014.
+'''
+
import moose
from pylab import *
@@ -34,131 +53,117 @@ J = 5e-3 # V # delta function synapse, adds to Vm
weight = 0.43 # initial synaptic weight
# gammaP/(gammaP+gammaD) = eq weight w/o noise
# see eqn (22), noiseSD also appears
- # but doesn't work here,
+ # but doesn't work here,
# weights away from 0.4 - 0.5 screw up the STDP rule!!
-
+
bistable = True # if bistable is True, use bistable potential for weights
noisy = True # use noisy weight updates given by noiseSD
noiseSD = 3.3501 # if noisy, use noiseSD (3.3501 from Higgins et al 2014)
##########################################
-def main():
- """
-Simulate pre and post Poisson firing for a synapse with
-Ca plasticity of Graupner and Brunel 2012.
-See the trace over time (lifetime) for the synaptic efficacy,
-similar to figure 2A of Higgins, Graupner, Brunel, 2014.
-
-Author: Aditya Gilra, NCBS, Bangalore, October, 2014.
-
- """
-
- prePoisson = moose.RandSpike('/pre')
- prePoisson.rate = frate
- postPoisson = moose.RandSpike('/post')
- postPoisson.rate = frate
-
- syn = moose.GraupnerBrunel2012CaPlasticitySynHandler( '/syn' )
- syn.numSynapses = 1 # 1 synapse
- # many pre-synaptic inputs can connect to a synapse
-
- # spikes from presynaptic Poisson generator
- moose.connect( prePoisson,'spikeOut', syn.synapse[0], 'addSpike')
-
- # post-synaptic spikes from postsynaptic Poisson generator
- moose.connect( postPoisson, 'spikeOut', syn, 'addPostSpike')
-
- syn.synapse[0].delay = 0.0
+prePoisson = moose.RandSpike('/pre')
+prePoisson.rate = frate
+postPoisson = moose.RandSpike('/post')
+postPoisson.rate = frate
+
+syn = moose.GraupnerBrunel2012CaPlasticitySynHandler( '/syn' )
+syn.numSynapses = 1 # 1 synapse
+ # many pre-synaptic inputs can connect to a synapse
+
+# spikes from presynaptic Poisson generator
+moose.connect( prePoisson,'spikeOut', syn.synapse[0], 'addSpike')
+
+# post-synaptic spikes from postsynaptic Poisson generator
+moose.connect( postPoisson, 'spikeOut', syn, 'addPostSpike')
+
+syn.synapse[0].delay = 0.0
+syn.synapse[0].weight = 1.0 # starting weight to decay from
+syn.CaInit = 0.0
+syn.tauCa = tauCa
+syn.tauSyn = tauSyn
+syn.CaPre = CaPre
+syn.CaPost = CaPost
+syn.delayD = delayD
+syn.thetaD = thetaD
+syn.thetaP = thetaP
+syn.gammaD = gammaD
+syn.gammaP = gammaP
+syn.weightMax = 1.0 # bounds on the weight
+syn.weightMin = 0.
+
+syn.noisy = noisy
+syn.noiseSD = noiseSD
+syn.bistable = bistable
+
+# ###########################################
+# Setting up tables
+# ###########################################
+
+CaTable = moose.Table( '/plotCa', 1 )
+moose.connect( CaTable, 'requestOut', syn, 'getCa')
+WtTable = moose.Table( '/plotWeight', 1 )
+moose.connect( WtTable, 'requestOut', syn.synapse[0], 'getWeight')
+
+# ###########################################
+# Simulate
+# ###########################################
+
+dt = 1e-3 # s
+# moose simulation
+#moose.useClock( 0, '/syn', 'process' )
+#moose.useClock( 1, '/pre', 'process' )
+#moose.useClock( 1, '/post', 'process' )
+# I think MOOSE uses a different clock than 3 for Tables
+# anyway, we use the default one and set all clocks to dt below
+#moose.useClock( 3, '/plotCa', 'process' )
+#moose.useClock( 3, '/plotWeight', 'process' )
+## use setClock to set the dt different from default dt
+for i in range(10):
+ moose.setClock( i, dt )
+
+moose.seed(100)
+moose.reinit()
+
+# function to make the aPlus and aMinus settle to equilibrium values
+print(("Rates of pre- and post-syanptic neurons =",frate))
+WtSeries = []
+numsteps = int(runtime/dt)
+for i in range(numrepeats):
syn.synapse[0].weight = 1.0 # starting weight to decay from
- syn.CaInit = 0.0
- syn.tauCa = tauCa
- syn.tauSyn = tauSyn
- syn.CaPre = CaPre
- syn.CaPost = CaPost
- syn.delayD = delayD
- syn.thetaD = thetaD
- syn.thetaP = thetaP
- syn.gammaD = gammaD
- syn.gammaP = gammaP
- syn.weightMax = 1.0 # bounds on the weight
- syn.weightMin = 0.
-
- syn.noisy = noisy
- syn.noiseSD = noiseSD
- syn.bistable = bistable
-
- # ###########################################
- # Setting up tables
- # ###########################################
-
- CaTable = moose.Table( '/plotCa', 1 )
- moose.connect( CaTable, 'requestOut', syn, 'getCa')
- WtTable = moose.Table( '/plotWeight', 1 )
- moose.connect( WtTable, 'requestOut', syn.synapse[0], 'getWeight')
-
- # ###########################################
- # Simulate
- # ###########################################
-
- dt = 1e-3 # s
- # moose simulation
- #moose.useClock( 0, '/syn', 'process' )
- #moose.useClock( 1, '/pre', 'process' )
- #moose.useClock( 1, '/post', 'process' )
- # I think MOOSE uses a different clock than 3 for Tables
- # anyway, we use the default one and set all clocks to dt below
- #moose.useClock( 3, '/plotCa', 'process' )
- #moose.useClock( 3, '/plotWeight', 'process' )
- ## use setClock to set the dt different from default dt
- for i in range(10):
- moose.setClock( i, dt )
-
- moose.seed(100)
- moose.reinit()
-
- # function to make the aPlus and aMinus settle to equilibrium values
- print(("Rates of pre- and post-syanptic neurons =",frate))
- WtSeries = []
- numsteps = int(runtime/dt)
- for i in range(numrepeats):
- syn.synapse[0].weight = 1.0 # starting weight to decay from
- syn.Ca = 0.0
- print(("Repeat number",i,"running..."))
- moose.start(runtime)
- WtSeries.append(WtTable.vector[i*numsteps:(i+1)*numsteps])
- WtSeries = array(WtSeries)
- WtMean = mean(WtSeries,axis=0)
- print("plotting...")
-
- # ###########################################
- # Plot the simulated weights and Ca vs time
- # ###########################################
-
- timeseries = linspace(0.0,runtime,numsteps)
- # Ca plots for the synapse
- # only the first repeat
- figure(facecolor='w')
- plot(timeseries,CaTable.vector[:numsteps],color='r')
- plot((timeseries[0],timeseries[-1]),(thetaP,thetaP),color='k',\
- linestyle='dashed',label='pot thresh')
- plot((timeseries[0],timeseries[-1]),(thetaD,thetaD),color='b',\
- linestyle='dashed',label='dep thresh')
- legend()
- xlabel('time (s)')
- ylabel('Ca (arb)')
- title("Ca conc in the synapse @ "+str(frate)+" Hz firing")
-
- # Weight plots for the synapse
- figure(facecolor='w')
- for i in range(numrepeats):
- plot(timeseries,WtSeries[i],alpha=0.5)
- plot(timeseries,WtMean,color='k',linewidth=2)
- xlabel('time (s)')
- ylabel('Efficacy')
- title("Efficacy of the synapse @ "+str(frate)+" Hz firing")
-
- show()
-
-if __name__ == '__main__':
- main()
+ syn.Ca = 0.0
+ print(("Repeat number",i,"running..."))
+ moose.start(runtime)
+ WtSeries.append(WtTable.vector[i*numsteps:(i+1)*numsteps])
+WtSeries = array(WtSeries)
+WtMean = mean(WtSeries,axis=0)
+print("plotting...")
+
+# ###########################################
+# Plot the simulated weights and Ca vs time
+# ###########################################
+
+timeseries = linspace(0.0,runtime,numsteps)
+# Ca plots for the synapse
+# only the first repeat
+figure(facecolor='w')
+plot(timeseries,CaTable.vector[:numsteps],color='r')
+plot((timeseries[0],timeseries[-1]),(thetaP,thetaP),color='k',\
+ linestyle='dashed',label='pot thresh')
+plot((timeseries[0],timeseries[-1]),(thetaD,thetaD),color='b',\
+ linestyle='dashed',label='dep thresh')
+legend()
+xlabel('time (s)')
+ylabel('Ca (arb)')
+title("Ca conc in the synapse @ "+str(frate)+" Hz firing")
+
+# Weight plots for the synapse
+figure(facecolor='w')
+for i in range(numrepeats):
+ plot(timeseries,WtSeries[i],alpha=0.5)
+plot(timeseries,WtMean,color='k',linewidth=2)
+xlabel('time (s)')
+ylabel('Efficacy')
+title("Efficacy of the synapse @ "+str(frate)+" Hz firing")
+
+show()
diff --git a/snippets/HsolveInstability.py b/snippets/HsolveInstability.py
index a0b54828fbd973b1c8ac7507789b51db35286228..0595c65f754dfefab8557f2b7e4e66f0f5bf31d1 100644
--- a/snippets/HsolveInstability.py
+++ b/snippets/HsolveInstability.py
@@ -1,22 +1,26 @@
-# HsolveInstability.py ---
+# HsolveInstability.py ---
-# Commentary:
-#
+# Commentary:
+#
+# A toy compartmental neuronal + chemical model that causes bad things
+# to happen to the hsolver, as of 28 May 2013. Hopefully this will
+# become irrelevant soon.
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
+#
# Code:
@@ -35,7 +39,7 @@ EREST_ACT = -70e-3
# Gate equations have the form:
#
# y(x) = (A + B * x) / (C + exp((x + D) / F))
-#
+#
# where x is membrane voltage and y is the rate constant for gate
# closing or opening
@@ -48,7 +52,7 @@ Na_m_params = [1e5 * (25e-3 + EREST_ACT), # 'A_A':
0.0, # 'B_B':
0.0, # 'B_C':
0.0 - EREST_ACT, # 'B_D':
- 18e-3 # 'B_F':
+ 18e-3 # 'B_F':
]
Na_h_params = [ 70.0, # 'A_A':
0.0, # 'A_B':
@@ -59,8 +63,8 @@ Na_h_params = [ 70.0, # 'A_A':
0.0, # 'B_B':
1.0, # 'B_C':
-30e-3 - EREST_ACT, # 'B_D':
- -0.01 # 'B_F':
- ]
+ -0.01 # 'B_F':
+ ]
K_n_params = [ 1e4 * (10e-3 + EREST_ACT), # 'A_A':
-1e4, # 'A_B':
-1.0, # 'A_C':
@@ -70,7 +74,7 @@ K_n_params = [ 1e4 * (10e-3 + EREST_ACT), # 'A_A':
0.0, # 'B_B':
0.0, # 'B_C':
0.0 - EREST_ACT, # 'B_D':
- 80e-3 # 'B_F':
+ 80e-3 # 'B_F':
]
VMIN = -30e-3 + EREST_ACT
VMAX = 120e-3 + EREST_ACT
@@ -80,12 +84,12 @@ def create_na_proto():
lib = moose.Neutral('/library')
na = moose.HHChannel('/library/na')
na.Xpower = 3
- xGate = moose.HHGate(na.path + '/gateX')
+ xGate = moose.HHGate(na.path + '/gateX')
xGate.setupAlpha(Na_m_params +
[VDIVS, VMIN, VMAX])
na.Ypower = 1
yGate = moose.HHGate(na.path + '/gateY')
- yGate.setupAlpha(Na_h_params +
+ yGate.setupAlpha(Na_h_params +
[VDIVS, VMIN, VMAX])
return na
@@ -93,7 +97,7 @@ def create_k_proto():
lib = moose.Neutral('/library')
k = moose.HHChannel('/library/k')
k.Xpower = 4.0
- xGate = moose.HHGate(k.path + '/gateX')
+ xGate = moose.HHGate(k.path + '/gateX')
xGate.setupAlpha(K_n_params +
[VDIVS, VMIN, VMAX])
return k
@@ -110,12 +114,12 @@ def create_squid():
compt.Ra = 7639.44e3
nachan = moose.HHChannel( '/n/compt/Na' )
nachan.Xpower = 3
- xGate = moose.HHGate(nachan.path + '/gateX')
+ xGate = moose.HHGate(nachan.path + '/gateX')
xGate.setupAlpha(Na_m_params +
[VDIVS, VMIN, VMAX])
nachan.Ypower = 1
yGate = moose.HHGate(nachan.path + '/gateY')
- yGate.setupAlpha(Na_h_params +
+ yGate.setupAlpha(Na_h_params +
[VDIVS, VMIN, VMAX])
nachan.Gbar = 0.942e-3
nachan.Ek = 115e-3+EREST_ACT
@@ -123,7 +127,7 @@ def create_squid():
kchan = moose.HHChannel( '/n/compt/K' )
kchan.Xpower = 4.0
- xGate = moose.HHGate(kchan.path + '/gateX')
+ xGate = moose.HHGate(kchan.path + '/gateX')
xGate.setupAlpha(K_n_params +
[VDIVS, VMIN, VMAX])
kchan.Gbar = 0.2836e-3
@@ -287,5 +291,5 @@ def main():
if __name__ == '__main__':
main()
-#
+#
# HsolveInstability.py ends here
diff --git a/snippets/IntegrateFireZoo.py b/snippets/IntegrateFireZoo.py
index 8707a5e8cda202975180eb566a1a6ac046350fa1..4618dfc35179bb1618716224f5861e8891a3615e 100644
--- a/snippets/IntegrateFireZoo.py
+++ b/snippets/IntegrateFireZoo.py
@@ -11,6 +11,60 @@
#### Author: Aditya Gilra, NCBS, Bangalore, October, 2014.
+'''
+Simulate current injection into various Integrate and Fire neurons.
+
+All integrate and fire (IF) neurons are subclasses of compartment,
+so they have all the fields of a passive compartment. Multicompartmental neurons can be created.
+Even ion channels and synaptic channels can be added to them, say for sub-threshold behaviour.
+
+The key addition is that they have a reset mechanism when the membrane potential Vm crosses a threshold.
+On each reset, a spikeOut message is generated, and the membrane potential is reset to Vreset.
+The threshold may be the spike generation threshold as for LIF and AdThreshIF,
+or it may be the peak of the spike as for QIF, ExIF, AdExIF, and IzhIF.
+The adaptive IFs have an extra adapting variable apart from membrane potential Vm.
+
+Details of the IFs are given below.
+The fields of the MOOSE objects are named exactly as the parameters in the equations below.
+
+ LIF: Leaky Integrate and Fire:
+ Rm*Cm * dVm/dt = -(Vm-Em) + Rm*I
+
+ QIF: Quadratic LIF:
+ Rm*Cm * dVm/dt = a0*(Vm-Em)*(Vm-vCritical) + Rm*I
+
+ ExIF: Exponential leaky integrate and fire:
+ Rm*Cm * dVm/dt = -(Vm-Em) + deltaThresh * exp((Vm-thresh)/deltaThresh) + Rm*I
+
+ AdExIF: Adaptive Exponential LIF:
+ Rm*Cm * dVm/dt = -(Vm-Em) + deltaThresh * exp((Vm-thresh)/deltaThresh) + Rm*I - w,
+
+ tau_w * dw/dt = a0*(Vm-Em) - w,
+
+ At each spike, w -> w + b0 "
+
+ AdThreshIF: Adaptive threshold LIF:
+ Rm*Cm * dVm/dt = -(Vm-Em) + Rm*I,
+
+ tauThresh * d threshAdaptive / dt = a0*(Vm-Em) - threshAdaptive,
+
+ At each spike, threshAdaptive is increased by threshJump
+ the spiking threshold adapts as thresh + threshAdaptive
+
+ IzhIF: Izhikevich:
+ d Vm /dt = a0 * Vm^2 + b0 * Vm + c0 - u + I/Cm,
+
+ d u / dt = a * ( b * Vm - u ),
+
+ At each spike, u -> u + d,
+
+ By default, a0 = 0.04e6/V/s, b0 = 5e3/s, c0 = 140 V/s are set to SI units,
+ so use SI consistently, or change a0, b0, c0 also if you wish to use other units.
+ Rm from Compartment is not used here, vReset is same as c in the usual formalism.
+ At rest, u0 = b V0, and V0 = ( -(-b0-b) +/- sqrt((b0-b)^2 - 4*a0*c0)) / (2*a0).
+
+'''
+
import moose
import matplotlib.pyplot as plt
import numpy as np
@@ -55,63 +109,11 @@ uRest = -16 # V/s # uRest = b*Em = -16 V/s
d = 10 # V/s # d is added to u at every spike
def main():
- """
-Simulate current injection into various Integrate and Fire neurons.
-
-All integrate and fire (IF) neurons are subclasses of compartment,
-so they have all the fields of a passive compartment. Multicompartmental neurons can be created.
-Even ion channels and synaptic channels can be added to them, say for sub-threshold behaviour.
-
-The key addition is that they have a reset mechanism when the membrane potential Vm crosses a threshold.
-On each reset, a spikeOut message is generated, and the membrane potential is reset to Vreset.
-The threshold may be the spike generation threshold as for LIF and AdThreshIF,
-or it may be the peak of the spike as for QIF, ExIF, AdExIF, and IzhIF.
-The adaptive IFs have an extra adapting variable apart from membrane potential Vm.
-
-Details of the IFs are given below.
-The fields of the MOOSE objects are named exactly as the parameters in the equations below.
-
- LIF: Leaky Integrate and Fire:
- Rm*Cm * dVm/dt = -(Vm-Em) + Rm*I
-
- QIF: Quadratic LIF:
- Rm*Cm * dVm/dt = a0*(Vm-Em)*(Vm-vCritical) + Rm*I
-
- ExIF: Exponential leaky integrate and fire:
- Rm*Cm * dVm/dt = -(Vm-Em) + deltaThresh * exp((Vm-thresh)/deltaThresh) + Rm*I
-
- AdExIF: Adaptive Exponential LIF:
- Rm*Cm * dVm/dt = -(Vm-Em) + deltaThresh * exp((Vm-thresh)/deltaThresh) + Rm*I - w,
-
- tau_w * dw/dt = a0*(Vm-Em) - w,
-
- At each spike, w -> w + b0 "
-
- AdThreshIF: Adaptive threshold LIF:
- Rm*Cm * dVm/dt = -(Vm-Em) + Rm*I,
-
- tauThresh * d threshAdaptive / dt = a0*(Vm-Em) - threshAdaptive,
-
- At each spike, threshAdaptive is increased by threshJump
- the spiking threshold adapts as thresh + threshAdaptive
-
- IzhIF: Izhikevich:
- d Vm /dt = a0 * Vm^2 + b0 * Vm + c0 - u + I/Cm,
-
- d u / dt = a * ( b * Vm - u ),
-
- At each spike, u -> u + d,
-
- By default, a0 = 0.04e6/V/s, b0 = 5e3/s, c0 = 140 V/s are set to SI units,
- so use SI consistently, or change a0, b0, c0 also if you wish to use other units.
- Rm from Compartment is not used here, vReset is same as c in the usual formalism.
- At rest, u0 = b V0, and V0 = ( -(-b0-b) +/- sqrt((b0-b)^2 - 4*a0*c0)) / (2*a0).
-
-On the command-line, in moose-examples/snippets directory, run ``python IntegrateFireZoo.py``.
-The script will ask you which neuron you want to simulate and you can choose and run what you want.
-Play with the parameters of the IF neurons in the source code.
-
- """
+ '''
+ On the command-line, in moose-examples/snippets directory, run ``python IntegrateFireZoo.py``.
+ The script will ask you which neuron you want to simulate and you can choose and run what you want.
+ Play with the parameters of the IF neurons in the source code.
+ '''
neuronChoices = {'LIF':moose.LIF, 'QIF':moose.QIF, 'ExIF':moose.ExIF, 'AdExIF':moose.AdExIF,
'AdThreshIF':moose.AdThreshIF, 'IzhIF':moose.IzhIF}
#### CHOOSE ONE OF THE NEURON KEYS AS choiceKey FROM BELOW DICTIONARY ####
@@ -221,5 +223,5 @@ Play with the parameters of the IF neurons in the source code.
# Run below if script is executed standalone.
if __name__ == '__main__':
-# The main function to simulate an integrate and fire neuron.
+ '''The main function to simulate an integrate and fire neuron.'''
main()
diff --git a/snippets/RandSpikeStats.py b/snippets/RandSpikeStats.py
index b7e5240c69d18f18289ec69176e14083b74a41e5..e92ab49acc217b6ec9cd5b422a136898b48d97e0 100644
--- a/snippets/RandSpikeStats.py
+++ b/snippets/RandSpikeStats.py
@@ -6,7 +6,13 @@
#** GNU Lesser General Public License version 2.1
#** See the file COPYING.LIB for the full notice.
#**********************************************************************/
-
+# This snippet shows the use of several objects.
+# This snippet sets up a StimulusTable to control a RandSpike which
+# sends its outputs to two places: to a SimpleSynHandler on an IntFire,
+# which is used to monitor spike arrival, and to various Stats objects.
+# I record and plot each of these.
+# The StimulusTable has a sine-wave waveform
+#
import numpy
import pylab
import moose
@@ -34,8 +40,8 @@ def make_model():
plotf = moose.Table( 'plotf' )
############### Set up parameters ###############
- stim.vector = [ maxFiringRate *
- numpy.sin(x * 2 * numpy.pi / sinePeriod)
+ stim.vector = [ maxFiringRate *
+ numpy.sin(x * 2 * numpy.pi / sinePeriod)
for x in range( sinePeriod )]
stim.startTime = 0
stim.stopTime = sinePeriod
@@ -77,6 +83,17 @@ def main():
"""
make_model()
+ '''
+ moose.useClock( 0, '/stim', 'process' )
+ moose.useClock( 1, '/spike', 'process' )
+ moose.useClock( 2, '/syn', 'process' )
+ moose.useClock( 3, '/fire', 'process' )
+ moose.useClock( 4, '/stats#', 'process' )
+ moose.useClock( 8, '/plot#', 'process' )
+ for i in range (10):
+ moose.setClock( i, dt )
+ moose.useClock( 8, '/plot#', 'process' )
+ '''
moose.reinit()
moose.start( runtime )
plots = moose.element( '/plots' )
@@ -91,19 +108,6 @@ def main():
pylab.legend()
pylab.show()
- '''
- moose.useClock( 0, '/stim', 'process' )
- moose.useClock( 1, '/spike', 'process' )
- moose.useClock( 2, '/syn', 'process' )
- moose.useClock( 3, '/fire', 'process' )
- moose.useClock( 4, '/stats#', 'process' )
- moose.useClock( 8, '/plot#', 'process' )
- for i in range (10):
- moose.setClock( i, dt )
- moose.useClock( 8, '/plot#', 'process' )
- '''
-
-
# Run the 'main' if this script is executed standalone.
if __name__ == '__main__':
main()
diff --git a/snippets/STDP.py b/snippets/STDP.py
index bee1f8b81b03e2301466fca8a397de9ea020eecc..31eb6d43a7548235123df1c1a47ad4ae9b64b374 100644
--- a/snippets/STDP.py
+++ b/snippets/STDP.py
@@ -15,9 +15,6 @@ import moose
import matplotlib.pyplot as plt
from numpy import arange, array
-##### Author: Aditya Gilra, NCBS, Bangalore, October, 2014.
-##### Fixed numpy imports and global variables: Subhasis Ray, Fri Jul 10 19:34:53 IST 2015
-
# ###########################################
# Neuron models
# ###########################################
@@ -37,11 +34,10 @@ spikes = None
dt = 1e-6
def setupModel():
- """
-Set up two LIF neurons and connect them by an STDPSynHandler.
-Set up some tables, and reinit MOOSE before simulation.
-
- """
+ '''
+ Set up two LIF neurons and connect them by an STDPSynHandler.
+ Set up some tables, and reinit MOOSE before simulation.
+ '''
global network, syn, Vms, weight, spikes, dt
# ###########################################
# Initialize neuron group
@@ -126,10 +122,8 @@ Set up some tables, and reinit MOOSE before simulation.
# function to make the aPlus and aMinus settle to equilibrium values
def reset_settle():
- """
-Call this between every pre-post pair
-to reset the neurons and make them settle to rest.
-
+ """ Call this between every pre-post pair
+ to reset the neurons and make them settle to rest.
"""
settletime = 100e-3 # s
syn.synapse[0].weight = weight # V
@@ -138,10 +132,8 @@ to reset the neurons and make them settle to rest.
# function to inject a sharp current pulse to make neuron spike
# immediately at a given time step
def make_neuron_spike(nrnidx,I=1e-7,duration=1e-3):
- """
-Inject a brief current pulse to
-make a neuron spike
-
+ """ Inject a brief current pulse to
+ make a neuron spike
"""
network.vec[nrnidx].inject = I
moose.start(duration)
diff --git a/snippets/analogStimTable.py b/snippets/analogStimTable.py
index 05889bc84736f45a6debafbe5f49d96d94b849df..13a3fc925ce94852617e446c2f80486dd157796e 100644
--- a/snippets/analogStimTable.py
+++ b/snippets/analogStimTable.py
@@ -1,24 +1,24 @@
-# analogStimTable.py ---
-#
+# analogStimTable.py ---
+#
# Filename: analogStimTable.py
-# Description:
+# Description:
# Author: Upi Bhalla
-#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
+#
import numpy as np
import pylab
@@ -87,9 +87,7 @@ def analogStimTable():
if __name__ == '__main__':
analogStimTable()
+
-if __name__ == '__main__':
- main
-
-#
+#
# stimtable.py ends here
diff --git a/snippets/analogStimTable.py~ b/snippets/analogStimTable.py~
deleted file mode 100644
index f1cf3c6c21846263b634a0317f373133f90ad808..0000000000000000000000000000000000000000
--- a/snippets/analogStimTable.py~
+++ /dev/null
@@ -1,95 +0,0 @@
-# analogStimTable.py ---
-#
-# Filename: analogStimTable.py
-# Description:
-# Author: Upi Bhalla
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; either version 3, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; see the file COPYING. If not, write to
-# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
-# Floor, Boston, MA 02110-1301, USA.
-#
-
-import numpy as np
-import pylab
-import moose
-from moose import utils
-
-def analogStimTable():
- simtime = 150
- simdt = 0.1
- model = moose.Neutral('/model')
- data = moose.Neutral('/data')
- # This is the stimulus generator
- stimtable = moose.StimulusTable('/model/stim')
- a = moose.BufPool( '/model/a' )
- b = moose.Pool( '/model/b' )
- reac = moose.Reac( '/model/reac' )
- reac.Kf = 0.1
- reac.Kb = 0.1
- moose.connect( stimtable, 'output', a, 'setConcInit' )
- moose.connect( reac, 'sub', a, 'reac' )
- moose.connect( reac, 'prd', b, 'reac' )
- aPlot = moose.Table('/data/aPlot')
- moose.connect(aPlot, 'requestOut', a, 'getConc')
- bPlot = moose.Table('/data/bPlot')
- moose.connect(bPlot, 'requestOut', b, 'getConc')
- moose.setClock( stimtable.tick, simdt )
- moose.setClock( a.tick, simdt )
- moose.setClock( aPlot.tick, simdt )
-
- ####################################################
- # Here we set up the stimulus table. It is half a sine-wave.
- stim = [ np.sin(0.01 * float(i) ) for i in range( 314 )]
- stimtable.vector = stim
- stimtable.stepSize = 0 # This forces use of current time as x value
-
- # The table will interpolate its contents over the time start to stop:
- # At values less than startTime, it emits the first value in table
- stimtable.startTime = 5
- # At values more than stopTime, it emits the last value in table
- stimtable.stopTime = 60
- stimtable.doLoop = 1 # Enable repeat playbacks.
- stimtable.loopTime = 10 + simtime / 2.0 # Repeat playback over this time
-
- moose.reinit()
- moose.start(simtime)
- t = [ x * simdt for x in range( len( aPlot.vector ) )]
- pylab.plot( t, aPlot.vector, label='Stimulus waveform' )
- pylab.plot( t, bPlot.vector, label='Reaction product b' )
- pylab.legend()
- pylab.show()
-
-def main():
- """
-Example of using a StimulusTable as an analog signal source in
-a reaction system. It could be used similarly to give other
-analog inputs to a model, such as a current or voltage clamp signal.
-
-This demo creates a StimulusTable and assigns it half a sine wave.
-Then we assign the start time and period over which to emit the wave.
-The output of the StimTable is sent to a pool **a**, which participates
-in a trivial reaction::
-
- table ----> a <===> b
-
-The output of **a** and **b** are recorded in a regular table
-for plotting.
- """
- analogStimTable()
-
-if __name__ == '__main__':
- main
-
-#
-# stimtable.py ends here
diff --git a/snippets/bidirectionalPlasticity.py b/snippets/bidirectionalPlasticity.py
index e60616d427c296d0464e9da7065c69a1494c6c71..c9e17143cc0deb2e55d7cf5488e2f09b4d2d11bf 100644
--- a/snippets/bidirectionalPlasticity.py
+++ b/snippets/bidirectionalPlasticity.py
@@ -32,9 +32,9 @@ def displayPlots():
def main():
"""
- This is a toy model of synaptic bidirectional plasticity. The model has
+ This is a toy model of synaptic bidirectional plasticity. The model has
a small a bistable chemical switch, and a small set of reactions that
- decode calcium input. One can turn the switch on with short high
+ decode calcium input. One can turn the switch on with short high
calcium pulses (over 2 uM for about 10 sec). One can turn it back off
again using a long, lower calcium pulse (0.2 uM, 2000 sec).
"""
diff --git a/snippets/compartment_net.py b/snippets/compartment_net.py
index be91b5477bceeb23fd8198aadca295efb59552a1..58b32e9fa14b3d2deb3ad4791101c5fe5ffd145a 100644
--- a/snippets/compartment_net.py
+++ b/snippets/compartment_net.py
@@ -1,44 +1,52 @@
-# compartment_net.py ---
-#
+# compartment_net.py ---
+#
# Filename: compartment_net.py
-# Description:
-# Author:Subhasis Ray
-# Maintainer:
+# Description:
+# Author:Subhasis Ray
+# Maintainer:
# Created: Sat Aug 11 14:30:21 2012 (+0530)
-# Version:
+# Version:
# Last-Updated: Sun Aug 12 15:45:38 2012 (+0530)
# By: subha
# Update #: 521
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+# A demo to create a network of single compartmental neurons connected
+# via alpha synapses.
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
-
+"""This script demonstrates the use of SynChan class to create a
+network of single-compartment neurons connected by synapse."""
import sys
sys.path.append('../../python')
import os
@@ -48,7 +56,7 @@ import matplotlib.pyplot as plt
import moose
from ionchannel import create_1comp_neuron
-
+
def create_population(container, size):
"""Create a population of `size` single compartmental neurons with Na+
and K+ channels. Also create SpikeGen objects and SynChan objects
@@ -87,13 +95,13 @@ def make_synapses(spikegen, synhandler, connprob=1.0, delay=5e-3):
synhandler : vec of SynHandler elements
Handles presynaptic spike event inputs to synchans.
-
+
connprob: float in range (0, 1]
connection probability between any two neurons
delay: float
mean delay of synaptic transmission. Individual delays are
- normally distributed with sd=0.1*mean.
+ normally distributed with sd=0.1*mean.
"""
for sh in synhandler:
scount = len(spikegen)
@@ -102,12 +110,10 @@ def make_synapses(spikegen, synhandler, connprob=1.0, delay=5e-3):
for ii, syn in enumerate(sh.synapse):
msg = moose.connect(spikegen[ii], 'spikeOut', syn, 'addSpike')
print(('Connected', spikegen[ii].path, 'to', syn.path, 'on', sh.path))
-
+
def create_network(size=2):
- """
- Create a network containing two neuronal populations, pop_A and
- pop_B and connect them up.
- """
+ """Create a network containing two neuronal populations, pop_A and
+ pop_B and connect them up"""
net = moose.Neutral('network')
pop_a = create_population(moose.Neutral('/network/pop_A'), size)
print(pop_a)
@@ -135,12 +141,7 @@ def create_network(size=2):
'Gsyn_B': gksyn_b
}
-def main():
- """
- This example illustrates how to create a network of single compartmental neurons
- connected via alpha synapses. It also shows the use of SynChan class to create a
- network of single-compartment neurons connected by synapse.
- """
+if __name__ == '__main__':
simtime = 0.1
simdt = 0.25e-5
plotdt = 0.25e-3
@@ -167,10 +168,7 @@ def main():
plt.plot(oid.vector, label=oid.path)
plt.legend()
plt.show()
+
-
-if __name__ == '__main__':
- main()
-
-#
+#
# compartment_net.py ends here
diff --git a/snippets/compartment_net_no_array.py b/snippets/compartment_net_no_array.py
index 84c9492f4f90ac95cf181007943bdf22e2b67bbf..47beb305eaeca27293e22322990153e3565645e2 100644
--- a/snippets/compartment_net_no_array.py
+++ b/snippets/compartment_net_no_array.py
@@ -1,39 +1,49 @@
-# compartment_net_no_array.py ---
-#
+# compartment_net_no_array.py ---
+#
# Filename: compartment_net_no_array.py
-# Description:
-# Author:Subhasis Ray
-# Maintainer:
+# Description:
+# Author:Subhasis Ray
+# Maintainer:
# Created: Sat Aug 11 14:30:21 2012 (+0530)
-# Version:
+# Version:
# Last-Updated: Tue May 7 18:26:26 2013 (+0530)
# By: subha
# Update #: 974
-# URL:
-# Keywords:
-# Compatibility:
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+# A demo to create a network of single compartmental neurons connected
+# via alpha synapses. This is same as compartment_net.py except that
+# we avoid ematrix and use single melements.
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
@@ -52,7 +62,7 @@ EREST_ACT = -70e-3
# Gate equations have the form:
#
# y(x) = (A + B * x) / (C + exp((x + D) / F))
-#
+#
# where x is membrane voltage and y is the rate constant for gate
# closing or opening
@@ -65,7 +75,7 @@ Na_m_params = [1e5 * (25e-3 + EREST_ACT), # 'A_A':
0.0, # 'B_B':
0.0, # 'B_C':
0.0 - EREST_ACT, # 'B_D':
- 18e-3 # 'B_F':
+ 18e-3 # 'B_F':
]
Na_h_params = [ 70.0, # 'A_A':
0.0, # 'A_B':
@@ -76,8 +86,8 @@ Na_h_params = [ 70.0, # 'A_A':
0.0, # 'B_B':
1.0, # 'B_C':
-30e-3 - EREST_ACT, # 'B_D':
- -0.01 # 'B_F':
- ]
+ -0.01 # 'B_F':
+ ]
K_n_params = [ 1e4 * (10e-3 + EREST_ACT), # 'A_A':
-1e4, # 'A_B':
-1.0, # 'A_C':
@@ -87,7 +97,7 @@ K_n_params = [ 1e4 * (10e-3 + EREST_ACT), # 'A_A':
0.0, # 'B_B':
0.0, # 'B_C':
0.0 - EREST_ACT, # 'B_D':
- 80e-3 # 'B_F':
+ 80e-3 # 'B_F':
]
VMIN = -40e-3 + EREST_ACT
VMAX = 120e-3 + EREST_ACT
@@ -98,12 +108,12 @@ soma_dia = 30e-6
def create_na_chan(path):
na = moose.HHChannel('%s/na' % (path))
na.Xpower = 3
- xGate = moose.HHGate(na.path + '/gateX')
+ xGate = moose.HHGate(na.path + '/gateX')
xGate.setupAlpha(Na_m_params +
[VDIVS, VMIN, VMAX])
na.Ypower = 1
yGate = moose.HHGate(na.path + '/gateY')
- yGate.setupAlpha(Na_h_params +
+ yGate.setupAlpha(Na_h_params +
[VDIVS, VMIN, VMAX])
na.Ek = 115e-3 + EREST_ACT
return na
@@ -111,7 +121,7 @@ def create_na_chan(path):
def create_k_chan(path):
k = moose.HHChannel('%s/k' % (path))
k.Xpower = 4.0
- xGate = moose.HHGate(k.path + '/gateX')
+ xGate = moose.HHGate(k.path + '/gateX')
xGate.setupAlpha(K_n_params +
[VDIVS, VMIN, VMAX])
k.Ek = -12e-3 + EREST_ACT
@@ -141,7 +151,7 @@ def create_compartment(path):
synchan = moose.SynChan(comp.path + '/synchan')
synchan.Gbar = 1e-8
synchan.tau1 = 2e-3
- synchan.tau2 = 2e-3
+ synchan.tau2 = 2e-3
synchan.Ek = 0.0
m = moose.connect(comp, 'channel', synchan, 'channel')
spikegen = moose.SpikeGen(comp.path + '/spikegen')
@@ -198,15 +208,15 @@ def test_compartment():
plt.legend()
plt.show()
plt.close()
-
+
def create_population(container, size):
"""Create a population of `size` single compartmental neurons with
Na and K channels. Also create SpikeGen objects and SynChan
objects connected to these which can act as plug points for
setting up synapses later."""
path = container.path
- # Contrast this with
- # comps = moose.vec(path+'/soma', size, 'Compartment')
+ # Contrast this with
+ # comps = moose.vec(path+'/soma', size, 'Compartment')
comps = [create_compartment(path+'/soma_%d' % (ii)) for ii in range(size)]
spikegens = []
synchans = []
@@ -222,7 +232,7 @@ def create_population(container, size):
synchans.append(synchan)
spikegen = moose.element(comp.path + '/spikegen')
spikegens.append(spikegen)
-
+
return {'compartment': comps,
'spikegen': spikegens,
'synchan': synchans}
@@ -242,20 +252,20 @@ def make_synapses(spikegen, synchan, delay=5e-3):
"""
scount = len(spikegen)
- for ii, sid in enumerate(synchan):
+ for ii, sid in enumerate(synchan):
s = moose.SynChan(sid)
sh = moose.SimpleSynHandler( sid.path + "/synh" )
moose.connect( sh, "activationOut", s, "activation" )
sh.synapse.num = scount
delay_list = np.random.normal(delay, delay*0.1, scount)
# print delay_list
- for jj in range(scount):
+ for jj in range(scount):
sh.synapse[jj].delay = delay_list[jj]
# Connect all spikegens to this synchan except that from
# same compartment - we assume if parents are same the two belong to the same compartment
if s.parent.path != spikegen[jj].parent.path:
m = moose.connect(spikegen[jj], 'spikeOut', moose.element(sh.path + '/synapse'), 'addSpike')
-
+
def two_populations(size=2):
"""An example with two population connected via synapses."""
net = moose.Neutral('network2')
@@ -320,7 +330,7 @@ def single_population(size=2):
inited = False
def assign_clocks(model_container_list, simdt, plotdt):
"""Assign clocks to elements under the listed paths.
-
+
This should be called only after all model components have been
created. Anything created after this will not be scheduled.
@@ -344,13 +354,7 @@ def assign_clocks(model_container_list, simdt, plotdt):
inited = True
moose.reinit()
-def main():
- """
- A demo to create a network of single compartmental neurons connected
- via alpha synapses. This is same as compartment_net.py except that
- we avoid ematrix and use single melements.
- """
-
+if __name__ == '__main__':
# test_compartment() # this calls assign_clocks - after which nothing else will be scheduled.
simtime = 0.1
simdt = 0.25e-5
@@ -369,7 +373,7 @@ def main():
plt.plot(t, vm.vector, label=vm.path)
plt.plot(np.linspace(0, simtime, len(data1['pulse'].vector)), data1['pulse'].vector * 1e6, label='Inject(uA)')
plt.legend()
- plt.subplot(212)
+ plt.subplot(212)
for gk in data1['gksyn']:
t = np.linspace(0, simtime, len(gk.vector))
plt.plot(t, gk.vector, label=gk.path)
@@ -398,8 +402,7 @@ def main():
plt.plot(t, gk.vector, label=gk.path)
plt.legend()
plt.show()
+
-if __name__ == '__main__':
- main()
-#
+#
# compartment_net_no_array.py ends here
diff --git a/snippets/compartmental_neuron.csv b/snippets/compartmental_neuron.csv
deleted file mode 100644
index f71941703822bee01fe6612f5a4e8ec406467bbb..0000000000000000000000000000000000000000
--- a/snippets/compartmental_neuron.csv
+++ /dev/null
@@ -1,2602 +0,0 @@
-# time,Vm1,Vm2
-0.000000000000000000e+00,-6.500000000000000222e-02,-6.500000000000000222e-02
-2.500000000000000052e-04,-6.500000000000000222e-02,-6.500000000000000222e-02
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-6.412499999999999867e-01,-6.499679093102707361e-02,-6.499679093102707361e-02
-6.414999999999999591e-01,-6.499680054217388259e-02,-6.499680054217388259e-02
-6.417500000000000426e-01,-6.499681012453534246e-02,-6.499681012453534246e-02
-6.420000000000000151e-01,-6.499681967819767592e-02,-6.499681967819767592e-02
-6.422499999999999876e-01,-6.499682920324677260e-02,-6.499682920324677260e-02
-6.424999999999999600e-01,-6.499683869976845274e-02,-6.499683869976845274e-02
-6.427500000000000435e-01,-6.499684816784812025e-02,-6.499684816784812025e-02
-6.430000000000000160e-01,-6.499685760757088759e-02,-6.499685760757088759e-02
-6.432499999999999885e-01,-6.499686701902175623e-02,-6.499686701902175623e-02
-6.434999999999999609e-01,-6.499687640228536678e-02,-6.499687640228536678e-02
-6.437500000000000444e-01,-6.499688575744613783e-02,-6.499688575744613783e-02
-6.440000000000000169e-01,-6.499689508458825205e-02,-6.499689508458825205e-02
-6.442499999999999893e-01,-6.499690438379560065e-02,-6.499690438379560065e-02
-6.444999999999999618e-01,-6.499691365515185282e-02,-6.499691365515185282e-02
-6.447500000000000453e-01,-6.499692289874048345e-02,-6.499692289874048345e-02
-6.450000000000000178e-01,-6.499693211464462050e-02,-6.499693211464462050e-02
-6.452499999999999902e-01,-6.499694130294711436e-02,-6.499694130294711436e-02
-6.454999999999999627e-01,-6.499695046373071827e-02,-6.499695046373071827e-02
-6.457500000000000462e-01,-6.499695959707782467e-02,-6.499695959707782467e-02
-6.460000000000000187e-01,-6.499696870307056229e-02,-6.499696870307056229e-02
-6.462499999999999911e-01,-6.499697778179094887e-02,-6.499697778179094887e-02
-6.464999999999999636e-01,-6.499698683332051641e-02,-6.499698683332051641e-02
-6.467500000000000471e-01,-6.499699585774089405e-02,-6.499699585774089405e-02
-6.470000000000000195e-01,-6.499700485513314197e-02,-6.499700485513314197e-02
-6.472499999999999920e-01,-6.499701382557825091e-02,-6.499701382557825091e-02
-6.474999999999999645e-01,-6.499702276915692023e-02,-6.499702276915692023e-02
-6.477500000000000480e-01,-6.499703168594955782e-02,-6.499703168594955782e-02
-6.480000000000000204e-01,-6.499704057603647445e-02,-6.499704057603647445e-02
-6.482499999999999929e-01,-6.499704943949760616e-02,-6.499704943949760616e-02
-6.484999999999999654e-01,-6.499705827641275024e-02,-6.499705827641275024e-02
-6.487500000000000488e-01,-6.499706708686135703e-02,-6.499706708686135703e-02
-6.490000000000000213e-01,-6.499707587092269645e-02,-6.499707587092269645e-02
-6.492499999999999938e-01,-6.499708462867583025e-02,-6.499708462867583025e-02
-6.494999999999999662e-01,-6.499709336019954264e-02,-6.499709336019954264e-02
-6.497500000000000497e-01,-6.499710206557239578e-02,-6.499710206557239578e-02
-6.500000000000000222e-01,-6.499711074487263263e-02,-6.499711074487263263e-02
diff --git a/snippets/compartmental_neuron.py b/snippets/compartmental_neuron.py
index 69b894bfb5125cf998886af2888329dcaf96dbc5..33a5db56bbbe9bec7f080bd545f13942f69b13db 100644
--- a/snippets/compartmental_neuron.py
+++ b/snippets/compartmental_neuron.py
@@ -1,51 +1,50 @@
-# compartmental_neuron.py ---
-#
+# compartmental_neuron.py ---
+#
# Filename: compartmental_neuron.py
-# Description:
-# Author:Subhasis Ray
-# Maintainer:
+# Description:
+# Author:Subhasis Ray
+# Maintainer:
# Created: Tue Aug 7 10:27:26 2012 (+0530)
-# Version:
+# Version:
# Last-Updated: Tue Dec 29 12:40:48 2015 (-0500)
# By: Subhasis Ray
# Update #: 86
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
+# Commentary:
+#
# Demostrates how to connect two compartments to form a simple neuron.
# All units are in SI
-#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
-"""
-Setting up a compartmental neuron from scratch using MOOSE classes.
+"""Setting up a compartmental neuron from scratch using MOOSE classes.
A simple electrophysiological simulation of a neuron composed of two
passive compartments. A current pulse is injected into the `soma`
@@ -92,8 +91,8 @@ moose.setClock(1, 0.025e-3)
moose.setClock(2, 0.25e-3)
# useClock: First argument is clock no.
# Second argument is a wildcard path matching all elements of type Compartment
-# Last argument is the processing function to be executed at each tick of clock 0
-moose.useClock(0, '/model/#[TYPE=Compartment]', 'init')
+# Last argument is the processing function to be executed at each tick of clock 0
+moose.useClock(0, '/model/#[TYPE=Compartment]', 'init')
moose.useClock(1, '/model/#[TYPE=Compartment]', 'process')
moose.useClock(2, axon_Vm.path, 'process')
moose.useClock(2, axon_Vm2.path, 'process')
@@ -125,5 +124,5 @@ data = np.vstack((time, axon_Vm.vector, axon_Vm2.vector))
np.savetxt('compartmental_neuron.csv', data.T, delimiter=',', header='time,Vm1,Vm2')
print('Saved data in compartmental_neuron.csv')
-#
+#
# compartmental_neuron.py ends here
diff --git a/snippets/crossComptNeuroMesh.py b/snippets/crossComptNeuroMesh.py
index 24e125b6b18fb00a868eee0f9cdc6be7edf61221..70f23542b837c4485992a91189b16bb08a3c350f 100644
--- a/snippets/crossComptNeuroMesh.py
+++ b/snippets/crossComptNeuroMesh.py
@@ -74,10 +74,10 @@ def makeNeuron( numSeg ):
shaftDia = 0.2e-6
headLength = 0.5e-6
headDia = 0.5e-6
-
+
cell = moose.Neutral( '/model/cell' )
model = moose.element( '/model' )
- prev = makeCompt( '/model/cell/soma',
+ prev = makeCompt( '/model/cell/soma',
model, 0.0, segmentLength, segmentDia )
dend = prev
for i in range( 0, numSeg ):
@@ -191,26 +191,23 @@ def makeModel():
def main():
"""
-This example illustrates how to define a kinetic model embedded in
-a NeuroMesh, and undergoing cross-compartment reactions. It is
-completely self-contained and does not use any external model definition
-files. Normally one uses standard model formats like
-SBML or kkit to concisely define kinetic and neuronal models.
-This example creates a simple reaction::
-
- a <==> b <==> c
-
-in which
-
-**a, b**, and **c** are in the dendrite, spine head, and PSD
-respectively.
-The model is set up to run using the Ksolve for integration. Although
-a diffusion solver is set up, the diff consts here are set to zero.
-The display has two parts:
-Above is a line plot of concentration against compartment#.
-Below is a time-series plot that appears after # the simulation has
-ended. The plot is for the last (rightmost) compartment.
-Concs of **a**, **b**, **c** are plotted for both graphs.
+ This example illustrates how to define a kinetic model embedded in
+ a NeuroMesh, and undergoing cross-compartment reactions. It is
+ completely self-contained and does not use any external model definition
+ files. Normally one uses standard model formats like
+ SBML or kkit to concisely define kinetic and neuronal models.
+ This example creates a simple reaction::
+ a <==> b <==> c
+ in which
+ **a, b**, and **c** are in the dendrite, spine head, and PSD
+ respectively.
+ The model is set up to run using the Ksolve for integration. Although
+ a diffusion solver is set up, the diff consts here are set to zero.
+ The display has two parts:
+ Above is a line plot of concentration against compartment#.
+ Below is a time-series plot that appears after # the simulation has
+ ended. The plot is for the last (rightmost) compartment.
+ Concs of **a**, **b**, **c** are plotted for both graphs.
"""
simdt = 0.01
plotdt = 0.01
@@ -260,7 +257,7 @@ def display():
line4, = timeseries.plot( t, x.vector, label=x.name )
plt.legend()
fig.canvas.draw()
- outfile = '%s.png' % sys.argv[0]
+ outfile = '%s.png' % sys.argv[0]
# print( "Hit 'enter' to exit" )
# raw_input()
plt.savefig( outfile )
diff --git a/snippets/crossComptSimpleReac.py b/snippets/crossComptSimpleReac.py
index 5763f502fb741d9d5ee81c4d38ca4caa61c32a84..808be6747a693b101ff1c8fc0a6f69dad07261fc 100644
--- a/snippets/crossComptSimpleReac.py
+++ b/snippets/crossComptSimpleReac.py
@@ -120,29 +120,29 @@ def main():
Here each molecule is in a different compartment.
The initial conditions are such that the end conc on all compartments
should be 2.0.
- The time course depends on which compartment the Reac object is
+ The time course depends on which compartment the Reac object is
embedded in.
- The cleanest thing numerically and also conceptually is to have both
+ The cleanest thing numerically and also conceptually is to have both
reactions in the same compartment, in this case the middle one
(**compt1**).
- The initial conditions have a lot of **B**. The equilibrium with
- **C** is fast and so **C** shoots up and passes **B**, peaking at
- about (2.5,9). This is also just
+ The initial conditions have a lot of **B**. The equilibrium with
+ **C** is fast and so **C** shoots up and passes **B**, peaking at
+ about (2.5,9). This is also just
about the crossover point.
**A** starts low and slowly climbs up to equilibrate.
- If we put **reac0** in **compt0** and **reac1** in **compt1**,
+ If we put **reac0** in **compt0** and **reac1** in **compt1**,
it behaves the same
qualitiatively but now the peak is at around (1, 5.2)
- This configuration of reactions makes sense from the viewpoint of
+ This configuration of reactions makes sense from the viewpoint of
having the
reactions always in the compartment with the smaller volume, which is
important if we need to have junctions where many small voxels talk to
one big voxel in another compartment.
Note that putting the reacs in other compartments doesn't work and in
- some combinations (e.g., **reac0** in **compt0** and **reac1** in
+ some combinations (e.g., **reac0** in **compt0** and **reac1** in
**compt2**) give
numerical instability.
"""
diff --git a/snippets/crossComptSimpleReacGSSA.py b/snippets/crossComptSimpleReacGSSA.py
index ff9edf52ffd0b92f5b518d99918ed11bd0f6fad3..bc6ce78b934591b9c19085baef7434625352dbd2 100644
--- a/snippets/crossComptSimpleReacGSSA.py
+++ b/snippets/crossComptSimpleReacGSSA.py
@@ -120,29 +120,29 @@ def main():
Here each molecule is in a different compartment.
The initial conditions are such that the end conc on all compartments
should be 2.0.
- The time course depends on which compartment the Reac object is
+ The time course depends on which compartment the Reac object is
embedded in.
- The cleanest thing numerically and also conceptually is to have both
+ The cleanest thing numerically and also conceptually is to have both
reactions in the same compartment, in this case the middle one
(**compt1**).
- The initial conditions have a lot of **B**. The equilibrium with
- **C** is fast and so **C** shoots up and passes **B**, peaking at
- about (2.5,9). This is also just
+ The initial conditions have a lot of **B**. The equilibrium with
+ **C** is fast and so **C** shoots up and passes **B**, peaking at
+ about (2.5,9). This is also just
about the crossover point.
**A** starts low and slowly climbs up to equilibrate.
- If we put **reac0** in **compt0** and **reac1** in **compt1**,
+ If we put **reac0** in **compt0** and **reac1** in **compt1**,
it behaves the same
qualitiatively but now the peak is at around (1, 5.2)
- This configuration of reactions makes sense from the viewpoint of
+ This configuration of reactions makes sense from the viewpoint of
having the
reactions always in the compartment with the smaller volume, which is
important if we need to have junctions where many small voxels talk to
one big voxel in another compartment.
Note that putting the reacs in other compartments doesn't work and in
- some combinations (e.g., **reac0** in **compt0** and **reac1** in
+ some combinations (e.g., **reac0** in **compt0** and **reac1** in
**compt2**) give
numerical instability.
"""
diff --git a/snippets/cspaceSteadyState.py b/snippets/cspaceSteadyState.py
index 2a5c7ad188b5397a28a1578f673999da651ccbae..dbe58af9c3f77fcba455ca5916154c44b30be7c9 100644
--- a/snippets/cspaceSteadyState.py
+++ b/snippets/cspaceSteadyState.py
@@ -29,6 +29,43 @@
## See the file COPYING.LIB for the full notice.
#########################################################################
+# This example sets up the kinetic solver and steady-state finder, on
+# a bistable model.
+# It looks for the fixed points 100 times, as follows:
+# - Set up the random initial condition that fits the conservation laws
+# - Run for 2 seconds. This should not be mathematically necessary, but
+# for obscure numerical reasons it makes it much more likely that the
+# steady state solver will succeed in finding a state.
+# - Find the fixed point
+# - Print out the fixed point vector and various diagnostics.
+# - Run for 10 seconds. This is completely unnecessary, and is done here
+# just so that the resultant graph will show what kind of state has been
+# found.
+# After it does all this, the program runs for 100 more seconds on the last
+# found fixed point (which turns out to be a saddle node), then
+# is hard-switched in the script to the first attractor basin from which
+# it runs for another 100 seconds till it settles there, and then
+# is hard-switched yet again to the second attractor and runs for 100
+# seconds.
+# Looking at the output you will see many features of note:
+# - the first attractor (stable point) and the saddle point
+# (unstable fixed point) are both found quite often. But the second
+# attractor is found just once. Has a very small basin of attraction.
+# - The values found for each of the fixed points match well with the
+# values found by running the system to steady-state at the end.
+# - There are a large number of failures to find a fixed point. These are
+# found and reported in the diagnostics. They show up on the plot
+# as cases where the 10-second runs are not flat.
+#
+# If you wanted to find fixed points in a production model, you would
+# not need to do the 10-second runs, and you would need to eliminate the
+# cases where the state-finder failed. Then you could identify the good
+# points and keep track of how many of each were found.
+# There is no way to guarantee that all fixed points have been found using
+# this algorithm!
+# You may wish to sample concentration space logarithmically rather than
+# linearly.
+
import math
import pylab
import numpy
diff --git a/snippets/cubeMeshSigNeur.py b/snippets/cubeMeshSigNeur.py
index 38671bcf01fdc345c7bed5da1e63d4177ac2d6ea..53589275a72237fd264f8530a7082f113c09c3ec 100644
--- a/snippets/cubeMeshSigNeur.py
+++ b/snippets/cubeMeshSigNeur.py
@@ -1,18 +1,19 @@
-# HsolveInstability.py ---
+# HsolveInstability.py ---
-# Commentary:
-#
-#
+# Commentary:
+#
+# A toy compartmental neuronal + chemical model in just a cubic volume
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
@@ -35,7 +36,7 @@ EREST_ACT = -70e-3
# Gate equations have the form:
#
# y(x) = (A + B * x) / (C + exp((x + D) / F))
-#
+#
# where x is membrane voltage and y is the rate constant for gate
# closing or opening
@@ -48,7 +49,7 @@ Na_m_params = [1e5 * (25e-3 + EREST_ACT), # 'A_A':
0.0, # 'B_B':
0.0, # 'B_C':
0.0 - EREST_ACT, # 'B_D':
- 18e-3 # 'B_F':
+ 18e-3 # 'B_F':
]
Na_h_params = [ 70.0, # 'A_A':
0.0, # 'A_B':
@@ -59,8 +60,8 @@ Na_h_params = [ 70.0, # 'A_A':
0.0, # 'B_B':
1.0, # 'B_C':
-30e-3 - EREST_ACT, # 'B_D':
- -0.01 # 'B_F':
- ]
+ -0.01 # 'B_F':
+ ]
K_n_params = [ 1e4 * (10e-3 + EREST_ACT), # 'A_A':
-1e4, # 'A_B':
-1.0, # 'A_C':
@@ -70,7 +71,7 @@ K_n_params = [ 1e4 * (10e-3 + EREST_ACT), # 'A_A':
0.0, # 'B_B':
0.0, # 'B_C':
0.0 - EREST_ACT, # 'B_D':
- 80e-3 # 'B_F':
+ 80e-3 # 'B_F':
]
VMIN = -30e-3 + EREST_ACT
VMAX = 120e-3 + EREST_ACT
@@ -91,7 +92,7 @@ def createSquid():
compt.diameter = 4e-6
nachan = moose.HHChannel( '/n/elec/compt/Na' )
nachan.Xpower = 3
- xGate = moose.HHGate(nachan.path + '/gateX')
+ xGate = moose.HHGate(nachan.path + '/gateX')
xGate.setupAlpha(Na_m_params + [VDIVS, VMIN, VMAX])
xGate.useInterpolation = 1
nachan.Ypower = 1
@@ -104,7 +105,7 @@ def createSquid():
kchan = moose.HHChannel( '/n/elec/compt/K' )
kchan.Xpower = 4.0
- xGate = moose.HHGate(kchan.path + '/gateX')
+ xGate = moose.HHGate(kchan.path + '/gateX')
xGate.setupAlpha(K_n_params + [VDIVS, VMIN, VMAX])
xGate.useInterpolation = 1
kchan.Gbar = 0.2836e-3
@@ -205,7 +206,7 @@ def makeModelInCubeMesh():
# Make adaptors
# Note that we can do this two ways: We can use an existing output
# msg from the object, which will come whenever the object processes,
- # or the adapator can request the object for the field, which happens
+ # or the adapator can request the object for the field, which happens
# whenever the adaptor processes. Here we illustrate both alternatives.
adaptK = moose.Adaptor( '/n/chem/neuroMesh/adaptK' )
chemK = moose.element( '/n/chem/neuroMesh/kChan' )
diff --git a/snippets/cylinderDiffusion.py b/snippets/cylinderDiffusion.py
index 799e0230cb79d19a711b3322d0c6e5ace4133671..624566c7418efb028e2bb6d2f71b387ff59410e2 100644
--- a/snippets/cylinderDiffusion.py
+++ b/snippets/cylinderDiffusion.py
@@ -17,7 +17,7 @@ import matplotlib.pyplot as plt
import moose
import os
-import signal
+import signal
PID = os.getpid()
def doNothing( *args ):
@@ -43,7 +43,7 @@ def makeModel():
compartment.x0 = 0
compartment.x1 = len
compartment.diffLength = diffLength
-
+
assert( compartment.numDiffCompts == num )
# create molecules and reactions
@@ -118,7 +118,7 @@ def updatePlots( plotlist, time ):
plotlist[5].set_ydata( c.conc )
plotlist[6].set_ydata( d.conc )
plotlist[0].canvas.draw()
-
+
def main():
"""
diff --git a/snippets/cylinderMotor.py b/snippets/cylinderMotor.py
index 1f0cf28179f96979f6f27fb9325d2766853eaed2..2aefc323c9fb7a7b969f2a65e6770b589997707c 100644
--- a/snippets/cylinderMotor.py
+++ b/snippets/cylinderMotor.py
@@ -28,13 +28,26 @@
## See the file COPYING.LIB for the full notice.
#########################################################################
+# This example illustrates how to set up a transport model with
+# four non-reacting molecules in a cylinder.
+# Molecule a and b have a positive motorConst so they are
+# are transported from soma (voxel 0) to the end of the cylinder.
+# Molecules c and d have a negative motorConst so they are transported
+# from the end of the cylinder to the soma.
+# Rate of all motors is 1e-6 microns/sec.
+# Pools a and c start out with all molecules at the soma, b and d
+# start with all molecules at the end of the cylinder.
+# Net effect is that only molecules a and d actually move. B and c
+# stay put as their motors are pushing further toward their respective
+# ends, and I assume all cells have sealed ends.
+
import math
import pylab
import numpy
import moose
import os
-import signal
+import signal
PID = os.getpid()
def doNothing( *args ):
diff --git a/snippets/diffEqSolution.py b/snippets/diffEqSolution.py
index 63edeec08a1fcde1dd66d6645b00d9664f90a450..7911480b8d7de397deb1f4b3e4c41063e0c0c22a 100644
--- a/snippets/diffEqSolution.py
+++ b/snippets/diffEqSolution.py
@@ -74,28 +74,28 @@ def makePlot( name, src, field, tick ):
def main():
"""
- This snippet illustrates the solution of an arbitrary set of
- differential equations using the **Func** class and the **Pool**
+ This snippet illustrates the solution of an arbitrary set of
+ differential equations using the **Func** class and the **Pool**
class. The equations solved here are::
tauI.m' = Ca - Ca_tgt
tauG.chan' = m - chan
These equations are taken from: O'Leary et al *Neuron* 2014.
-
+
**Func** evaluates an arbitrary function each timestep.
Here this function is the rate of change from the equations above.
- The rate of change is passed to the *increment* message of the
+ The rate of change is passed to the *increment* message of the
**Pool**. The numerical integration method is the Exponential
Euler method but this will work fine if the rates are slow compared to
the simulation timestep.
-
+
Conceptually, the idea is that if Ca is greater than the target level,
then more mRNA is made, which makes more channels.
- Although the equations have no upper or lower bounds on **m** or
+ Although the equations have no upper or lower bounds on **m** or
**chan**, MOOSE is sensible about preventing the molecular pools from
- having negative concentrations. This does mean that the solution method
- employed here won't work for the general solution of differential
+ having negative concentrations. This does mean that the solution method
+ employed here won't work for the general solution of differential
equations in non-chemical systems.
"""
elecdt = 25e-6
diff --git a/snippets/diffSpinyNeuron.py b/snippets/diffSpinyNeuron.py
index e4b61dac9011883f6993ac058b623ee527894902..241e64b1188adf68fcbd6e05d27e8fca9bc5b99f 100644
--- a/snippets/diffSpinyNeuron.py
+++ b/snippets/diffSpinyNeuron.py
@@ -100,7 +100,7 @@ def makeModel():
stoich0.filterXreacs()
stoich1.filterXreacs()
stoich2.filterXreacs()
-
+
Ca_input_dend = moose.vec( '/model/chem/compt0/Ca_input' )
print((len( Ca_input_dend )))
for i in range( 60 ):
@@ -110,7 +110,7 @@ def makeModel():
print((len( Ca_input_PSD )))
for i in range( 5 ):
Ca_input_PSD[ 2 + i * 2].conc = 1.0
-
+
# Create the output tables
num = compt0.numDiffCompts - 1
graphs = moose.Neutral( '/model/graphs' )
@@ -201,11 +201,11 @@ def finalizeDisplay( plotlist, cPlotDt ):
def makeChemModel( compt, doInput ):
"""
- This function setus up a simple chemical system in which Ca input
+ This function setus up a simple chemical system in which Ca input
comes to the dend and to selected PSDs. There is diffusion between
PSD and spine head, and between dend and spine head.
-
- :: Ca_input ------> Ca // in dend and spine head only.
+
+ Ca_input ------> Ca // in dend and spine head only.
"""
# create molecules and reactions
Ca = moose.Pool( compt.path + '/Ca' )
@@ -232,29 +232,29 @@ def makeChemModel( compt, doInput ):
def main():
"""
This example illustrates and tests diffusion embedded in
- the branching pseudo-1-dimensional geometry of a neuron.
- An input pattern of Ca stimulus is applied in a periodic manner both
+ the branching pseudo-1-dimensional geometry of a neuron.
+ An input pattern of Ca stimulus is applied in a periodic manner both
on the dendrite and on the PSDs of the 13 spines. The Ca levels in
each of the dend, the spine head, and the spine PSD are monitored.
Since the same molecule name is used for Ca in the three compartments,
these are automagially connected up for diffusion. The simulation
shows the outcome of this diffusion.
- This example uses an external electrical model file with basal
+ This example uses an external electrical model file with basal
dendrite and three branches on
the apical dendrite. One of those branches has the 13 spines.
The model is set up to run using the Ksolve for integration and the
Dsolve for handling diffusion.
- The timesteps here are not the defaults. It turns out that the
+ The timesteps here are not the defaults. It turns out that the
chem reactions and diffusion in this example are sufficiently fast
that the chemDt has to be smaller than default. Note that this example
uses rates quite close to those used in production models.
The display has four parts:
- a. animated line plot of concentration against main compartment#.
- b. animated line plot of concentration against spine compartment#.
- c. animated line plot of concentration against psd compartment#.
- d. time-series plot that appears after the simulation has
- ended.
+ a. animated line plot of concentration against main compartment#.
+ b. animated line plot of concentration against spine compartment#.
+ c. animated line plot of concentration against psd compartment#.
+ d. time-series plot that appears after the simulation has
+ ended.
"""
diff --git a/snippets/findChemSteadyState.py b/snippets/findChemSteadyState.py
index bbf19453e2cf509ab640fc8c40393d1e8c5caf6f..85783ced1b7ae91cfa1245f3df5421dd0204e514 100644
--- a/snippets/findChemSteadyState.py
+++ b/snippets/findChemSteadyState.py
@@ -19,12 +19,12 @@ def main():
This example sets up the kinetic solver and steady-state finder, on
a bistable model of a chemical system. The model is set up within the
script.
- The algorithm calls the steady-state finder 50 times with different
+ The algorithm calls the steady-state finder 50 times with different
(randomized) initial conditions, as follows:
* Set up the random initial condition that fits the conservation laws
- * Run for 2 seconds. This should not be mathematically necessary, but
- for obscure numerical reasons it makes it much more likely that the
+ * Run for 2 seconds. This should not be mathematically necessary, but
+ for obscure numerical reasons it makes it much more likely that the
steady state solver will succeed in finding a state.
* Find the fixed point
* Print out the fixed point vector and various diagnostics.
@@ -35,30 +35,30 @@ def main():
After it does all this, the program runs for 100 more seconds on the
last found fixed point (which turns out to be a saddle node), then
is hard-switched in the script to the first attractor basin from which
- it runs for another 100 seconds till it settles there, and then
- is hard-switched yet again to the second attractor and runs for 400
+ it runs for another 100 seconds till it settles there, and then
+ is hard-switched yet again to the second attractor and runs for 400
seconds.
Looking at the output you will see many features of note:
- * the first attractor (stable point) and the saddle point (unstable
+ * the first attractor (stable point) and the saddle point (unstable
fixed point) are both found quite often. But the second
- attractor is found just once.
+ attractor is found just once.
It has a very small basin of attraction.
* The values found for each of the fixed points match well with the
values found by running the system to steady-state at the end.
* There are a large number of failures to find a fixed point. These are
found and reported in the diagnostics. They show up on the plot
as cases where the 10-second runs are not flat.
-
+
If you wanted to find fixed points in a production model, you would
not need to do the 10-second runs, and you would need to eliminate the
cases where the state-finder failed. Then you could identify the good
points and keep track of how many of each were found.
- There is no way to guarantee that all fixed points have been found
- using this algorithm! If there are points in an obscure corner of state
- space (as for the singleton second attractor convergence in this
+ There is no way to guarantee that all fixed points have been found
+ using this algorithm! If there are points in an obscure corner of state
+ space (as for the singleton second attractor convergence in this
example) you may have to iterate very many times to find them.
You may wish to sample concentration space logarithmically rather than
@@ -111,7 +111,7 @@ def makeModel():
compartment = moose.CubeMesh( '/model/compartment' )
compartment.volume = 1e-15
# the mesh is created automatically by the compartment
- mesh = moose.element( '/model/compartment/mesh' )
+ mesh = moose.element( '/model/compartment/mesh' )
# create molecules and reactions
a = moose.Pool( '/model/compartment/a' )
@@ -173,7 +173,7 @@ def displayPlots():
pylab.show()
def getState( ksolve, state ):
- """ This function finds a steady state starting from a random
+ """ This function finds a steady state starting from a random
initial condition that is consistent with the stoichiometry rules
and the original model concentrations.
"""
diff --git a/snippets/func.py b/snippets/func.py
index 9c371cdc0c6247386630c7ef1497b2330d37bfbe..b521fe5948ad008afc34576eb09a24c529602587 100644
--- a/snippets/func.py
+++ b/snippets/func.py
@@ -1,51 +1,52 @@
-# func.py ---
-#
+# func.py ---
+#
# Filename: func.py
-# Description:
+# Description:
# Author: Subhasis Ray
# Maintainer: Subhasis Ray
# Created: Mon May 27 17:45:05 2013 (+0530)
-# Version:
+# Version:
# Last-Updated: Sat Jun 1 19:07:20 2013 (+0530)
# By: subha
# Update #: 253
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
-
-# Commentary:
-#
-#
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
+"""Demonstrate the use of Func class"""
import numpy as np
-import pylab
+import pylab
import moose
@@ -61,7 +62,7 @@ def test_func_nosim():
for ii in range(num-1):
expr += 'x_%d, ' % (ii)
expr += 'x_%d)' % (num-1)
- print(('Expression:', expr))
+ print(('Expression:', expr))
func_0.expr = expr
for ii in range(num):
var = 'x_%d' % (ii)
@@ -98,7 +99,7 @@ def test_func():
pylab.plot(xarr, values, 'g-', label='f(no-sim)')
pylab.plot(xarr, np.array(deriv)/1000, 'k-.', label="1e-3 * f'(no-sim)")
-
+
simdt = xarr[1] - xarr[0]
input = moose.StimulusTable('%s/xtab' % (model.path))
input.vector = xarr
@@ -106,7 +107,7 @@ def test_func():
input.stepPosition = xarr[0]
input.stopTime = xarr[-1] - xarr[0]
print((input.startTime, input.stopTime))
-
+
moose.connect(input, 'output', func_1, 'xIn')
x_tab = moose.Table('/data/xtab')
@@ -124,7 +125,7 @@ def test_func():
moose.setClock(3, simdt)
moose.useClock(0, '%s/##[TYPE=StimulusTable]' % (model.path), 'process')
moose.useClock(1, '%s/##[TYPE=Func]' % (model.path), 'process')
- moose.useClock(2, '%s/##[TYPE=DiffAmp]' % (model.path), 'process')
+ moose.useClock(2, '%s/##[TYPE=DiffAmp]' % (model.path), 'process')
moose.useClock(3, '%s/##' % (data.path), 'process')
moose.reinit()
t = xarr[-1] - xarr[0]
@@ -137,13 +138,10 @@ def test_func():
pylab.legend()
pylab.show()
-def main():
- """Demonstrate the use of Func class"""
+if __name__ == '__main__':
test_func_nosim()
test_func()
-if __name__ == '__main__':
- main()
-#
+#
# func.py ends here
diff --git a/snippets/funcRateHarmonicOsc.py b/snippets/funcRateHarmonicOsc.py
index 662078815b60dda9d4c4868cda7105d0e616826f..4198a86d009831d027ce9f9681439e6b707bbe95 100644
--- a/snippets/funcRateHarmonicOsc.py
+++ b/snippets/funcRateHarmonicOsc.py
@@ -1,3 +1,12 @@
+#########################################################################
+## This program is part of 'MOOSE', the
+## Messaging Object Oriented Simulation Environment.
+## Copyright (C) 2013 Upinder S. Bhalla. and NCBS
+## It is made available under the terms of the
+## GNU Lesser General Public License version 2.1
+## See the file COPYING.LIB for the full notice.
+#########################################################################
+
import math
import pylab
import numpy
@@ -104,4 +113,4 @@ def main():
# Run the 'main' if this script is executed standalone.
if __name__ == '__main__':
- main()
+ main()
diff --git a/snippets/funcReacLotkaVolterra.py b/snippets/funcReacLotkaVolterra.py
index 7c702b97666d2fbdb46a9f7669164d8634ae5ca0..ee76186a33b7f7b7cfa113b7a5f0311186d46a5b 100644
--- a/snippets/funcReacLotkaVolterra.py
+++ b/snippets/funcReacLotkaVolterra.py
@@ -1,3 +1,13 @@
+#########################################################################
+## This program is part of 'MOOSE', the
+## Messaging Object Oriented Simulation Environment.
+## Copyright (C) 2013 Upinder S. Bhalla. and NCBS
+## It is made available under the terms of the
+## GNU Lesser General Public License version 2.1
+## See the file COPYING.LIB for the full notice.
+#########################################################################
+
+
import math
import pylab
import numpy
@@ -60,34 +70,32 @@ def makeModel():
def main():
"""
-The funcReacLotkaVolterra example shows how to use function objects
-as part of differential equation systems in the framework of the MOOSE
-kinetic solvers. Here the system is set up explicitly using the
-scripting, in normal use one would expect to use SBML.
-
-In this example we set up a Lotka-Volterra system. The equations
-are readily expressed as a pair of reactions each of whose rate is
-governed by a function::
-
- x' = x( alpha - beta.y )
- y' = -y( gamma - delta.x )
-
-This translates into two reactions::
-
- x ---> z Kf = beta.y - alpha
- y ---> z Kf = gamma - delta.x
-
-Here z is a dummy molecule whose concentration is buffered to zero.
-
-The model first runs using default Exponential Euler integration.
-This is not particularly accurate even with a small timestep.
-The model is then converted to use the deterministic Kinetic solver
-Ksolve. This is accurate and faster.
-Note that we cannot use the stochastic GSSA solver for this system, it
-cannot handle a reaction term whose rate keeps changing.
-
+ The funcReacLotkaVolterra example shows how to use function objects
+ as part of differential equation systems in the framework of the MOOSE
+ kinetic solvers. Here the system is set up explicitly using the
+ scripting, in normal use one would expect to use SBML.
+
+ In this example we set up a Lotka-Volterra system. The equations
+ are readily expressed as a pair of reactions each of whose rate is
+ governed by a function::
+
+ x' = x( alpha - beta.y )
+ y' = -y( gamma - delta.x )
+
+ This translates into two reactions::
+
+ x ---> z Kf = beta.y - alpha
+ y ---> z Kf = gamma - delta.x
+
+ Here z is a dummy molecule whose concentration is buffered to zero.
+
+ The model first runs using default Exponential Euler integration.
+ This is not particularly accurate even with a small timestep.
+ The model is then converted to use the deterministic Kinetic solver
+ Ksolve. This is accurate and faster.
+ Note that we cannot use the stochastic GSSA solver for this system, it
+ cannot handle a reaction term whose rate keeps changing.
"""
-
makeModel()
for i in range( 11, 18 ):
diff --git a/snippets/function.py b/snippets/function.py
index 2bd87c5770d4d7cee9ac1259f0315f31927c1946..c0727f1dfb96e4b40d9f6b435f58c8d2aab9a8d2 100644
--- a/snippets/function.py
+++ b/snippets/function.py
@@ -1,47 +1,47 @@
-# function.py ---
-#
+# function.py ---
+#
# Filename: function.py
-# Description:
+# Description:
# Author: Subhasis Ray
-# Maintainer:
+# Maintainer:
# Created: Tue Sep 9 17:59:50 2014 (+0530)
-# Version:
+# Version:
# Last-Updated: Sun Dec 20 00:02:50 2015 (-0500)
# By: subha
# Update #: 4
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
-
-# Commentary:
-#
-#
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
@@ -54,6 +54,55 @@ import moose
simtime = 1.0
def example():
+ """Function objects can be used to evaluate expressions with arbitrary
+ number of variables and constants. We can assign expression of the
+ form::
+
+ f(c0, c1, ..., cM, x0, x1, ..., xN, y0,..., yP )
+
+ where `ci`'s are constants and `xi`'s and `yi`'s are variables.
+
+ The constants must be defined before setting the expression and
+ variables are connected via messages. The constants can have any
+ name, but the variable names must be of the form x{i} or y{i}
+ where i is increasing integer starting from 0.
+
+ The `xi`'s are field elements and you have to set their number
+ first (function.x.num = N). Then you can connect any source field
+ sending out double to the 'input' destination field of the
+ `x[i]`.
+
+ The `yi`'s are useful when the required variable is a value field
+ and is not available as a source field. In that case you connect
+ the `requestOut` source field of the function element to the
+ `get{Field}` destination field on the target element. The `yi`'s
+ are automatically added on connecting. Thus, if you call::
+
+ moose.connect(function, 'requestOut', a, 'getSomeField')
+ moose.connect(function, 'requestOut', b, 'getSomeField')
+
+ then ``a.someField`` will be assigned to ``y0`` and
+ ``b.someField`` will be assigned to ``y1``.
+
+ In this example we evaluate the expression: ``z = c0 * exp(c1 *
+ x0) * cos(y0)``
+
+ with x0 ranging from -1 to +1 and y0 ranging from -pi to
+ +pi. These values are stored in two stimulus tables called xtab
+ and ytab respectively, so that at each timestep the next values of
+ x0 and y0 are assigned to the function.
+
+ Along with the value of the expression itself we also compute its
+ derivative with respect to y0 and its derivative with respect to
+ time (rate). The former uses a five-point stencil for the
+ numerical differentiation and has a glitch at y=0. The latter uses
+ backward difference divided by dt.
+
+ Unlike Func class, the number of variables and constants are
+ unlimited in Function and you can set all the variables via
+ messages.
+
+ """
demo = moose.Neutral('/model')
function = moose.Function('/model/function')
function.c['c0'] = 1.0
@@ -78,7 +127,7 @@ def example():
input_x.stepPosition = xarr[0]
input_x.stopTime = simtime
moose.connect(input_x, 'output', function.x[0], 'input')
-
+
yarr = np.linspace(-np.pi, np.pi, nsteps)
input_y = moose.StimulusTable('/ytab')
input_y.vector = yarr
@@ -97,14 +146,14 @@ def example():
x_rec = moose.Table('/xrec')
moose.connect(x_rec, 'requestOut', input_x, 'getOutputValue')
y_rec = moose.Table('/yrec')
- moose.connect(y_rec, 'requestOut', input_y, 'getOutputValue')
+ moose.connect(y_rec, 'requestOut', input_y, 'getOutputValue')
- dt = simtime/nsteps
+ dt = simtime/nsteps
for ii in range(32):
moose.setClock(ii, dt)
moose.reinit()
moose.start(simtime)
-
+
# Uncomment the following lines and the import matplotlib.pyplot as plt on top
# of this file to display the plot.
plt.subplot(3,1,1)
@@ -136,65 +185,11 @@ def example():
plt.legend()
plt.tight_layout()
plt.show()
-
-def main():
- """
- Function objects can be used to evaluate expressions with arbitrary
- number of variables and constants. We can assign expression of the
- form::
-
- f(c0, c1, ..., cM, x0, x1, ..., xN, y0,..., yP )
-
- where `ci`'s are constants and `xi`'s and `yi`'s are variables.
-
- The constants must be defined before setting the expression and
- variables are connected via messages. The constants can have any
- name, but the variable names must be of the form x{i} or y{i}
- where i is increasing integer starting from 0.
-
- The `xi`'s are field elements and you have to set their number
- first (function.x.num = N). Then you can connect any source field
- sending out double to the 'input' destination field of the
- `x[i]`.
-
- The `yi`'s are useful when the required variable is a value field
- and is not available as a source field. In that case you connect
- the `requestOut` source field of the function element to the
- `get{Field}` destination field on the target element. The `yi`'s
- are automatically added on connecting. Thus, if you call::
-
- moose.connect(function, 'requestOut', a, 'getSomeField')
- moose.connect(function, 'requestOut', b, 'getSomeField')
-
- then ``a.someField`` will be assigned to ``y0`` and
- ``b.someField`` will be assigned to ``y1``.
-
- In this example we evaluate the expression: ``z = c0 * exp(c1 *
- x0) * cos(y0)``
-
- with x0 ranging from -1 to +1 and y0 ranging from -pi to
- +pi. These values are stored in two stimulus tables called xtab
- and ytab respectively, so that at each timestep the next values of
- x0 and y0 are assigned to the function.
-
- Along with the value of the expression itself we also compute its
- derivative with respect to y0 and its derivative with respect to
- time (rate). The former uses a five-point stencil for the
- numerical differentiation and has a glitch at y=0. The latter uses
- backward difference divided by dt.
-
- Unlike Func class, the number of variables and constants are
- unlimited in Function and you can set all the variables via
- messages.
-
- """
- example()
-
+
if __name__ == '__main__':
- main()
-
-
-
+ example()
+
+
-#
+#
# function.py ends here
diff --git a/snippets/function.py~ b/snippets/function.py~
deleted file mode 100644
index 8b574ea0e842549f2b96304a7795df254ba13e6d..0000000000000000000000000000000000000000
--- a/snippets/function.py~
+++ /dev/null
@@ -1,200 +0,0 @@
-# function.py ---
-#
-# Filename: function.py
-# Description:
-# Author: Subhasis Ray
-# Maintainer:
-# Created: Tue Sep 9 17:59:50 2014 (+0530)
-# Version:
-# Last-Updated: Sun Dec 20 00:02:50 2015 (-0500)
-# By: subha
-# Update #: 4
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
-
-# Commentary:
-#
-#
-#
-#
-
-# Change log:
-#
-#
-#
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; either version 3, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; see the file COPYING. If not, write to
-# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
-# Floor, Boston, MA 02110-1301, USA.
-#
-#
-
-# Code:
-
-import numpy as np
-import sys
-import matplotlib.pyplot as plt
-
-import moose
-
-simtime = 1.0
-
-def example():
- demo = moose.Neutral('/model')
- function = moose.Function('/model/function')
- function.c['c0'] = 1.0
- function.c['c1'] = 2.0
- function.x.num = 1
- function.expr = 'c0 * exp(c1*x0) * cos(y0) + sin(t)'
- # mode 0 - evaluate function value, derivative and rate
- # mode 1 - just evaluate function value,
- # mode 2 - evaluate derivative,
- # mode 3 - evaluate rate
- function.mode = 0
- function.independent = 'y0'
- nsteps = 1000
- xarr = np.linspace(0.0, 1.0, nsteps)
- # Stimulus tables allow you to store sequences of numbers which
- # are delivered via the 'output' message at each time step. This
- # is a placeholder and in real scenario you will be using any
- # sourceFinfo that sends out a double value.
- input_x = moose.StimulusTable('/xtab')
- input_x.vector = xarr
- input_x.startTime = 0.0
- input_x.stepPosition = xarr[0]
- input_x.stopTime = simtime
- moose.connect(input_x, 'output', function.x[0], 'input')
-
- yarr = np.linspace(-np.pi, np.pi, nsteps)
- input_y = moose.StimulusTable('/ytab')
- input_y.vector = yarr
- input_y.startTime = 0.0
- input_y.stepPosition = yarr[0]
- input_y.stopTime = simtime
- moose.connect(function, 'requestOut', input_y, 'getOutputValue')
-
- # data recording
- result = moose.Table('/ztab')
- moose.connect(result, 'requestOut', function, 'getValue')
- derivative = moose.Table('/zprime')
- moose.connect(derivative, 'requestOut', function, 'getDerivative')
- rate = moose.Table('/dz_by_dt')
- moose.connect(rate, 'requestOut', function, 'getRate')
- x_rec = moose.Table('/xrec')
- moose.connect(x_rec, 'requestOut', input_x, 'getOutputValue')
- y_rec = moose.Table('/yrec')
- moose.connect(y_rec, 'requestOut', input_y, 'getOutputValue')
-
- dt = simtime/nsteps
- for ii in range(32):
- moose.setClock(ii, dt)
- moose.reinit()
- moose.start(simtime)
-
- # Uncomment the following lines and the import matplotlib.pyplot as plt on top
- # of this file to display the plot.
- plt.subplot(3,1,1)
- plt.plot(x_rec.vector, result.vector, 'r-', label='z = {}'.format(function.expr))
- z = function.c['c0'] * np.exp(function.c['c1'] * xarr) * np.cos(yarr) + np.sin(np.arange(len(xarr)) * dt)
- plt.plot(xarr, z, 'b--', label='numpy computed')
- plt.xlabel('x')
- plt.ylabel('z')
- plt.legend()
-
- plt.subplot(3,1,2)
- plt.plot(y_rec.vector, derivative.vector, 'r-', label='dz/dy0')
- # derivatives computed by putting x values in the analytical formula
- dzdy = function.c['c0'] * np.exp(function.c['c1'] * xarr) * (- np.sin(yarr))
- plt.plot(yarr, dzdy, 'b--', label='numpy computed')
- plt.xlabel('y')
- plt.ylabel('dz/dy')
- plt.legend()
-
- plt.subplot(3,1,3)
- # *** BEWARE *** The first two entries are spurious. Entry 0 is
- # *** from reinit sending out the defaults. Entry 2 is because
- # *** there is no lastValue for computing real forward difference.
- plt.plot(np.arange(2, len(rate.vector), 1) * dt, rate.vector[2:], 'r-', label='dz/dt')
- dzdt = np.diff(z)/dt
- plt.plot(np.arange(0, len(dzdt), 1.0) * dt, dzdt, 'b--', label='numpy computed')
- plt.xlabel('t')
- plt.ylabel('dz/dt')
- plt.legend()
- plt.tight_layout()
- plt.show()
-
-def main():
- """
- Function objects can be used to evaluate expressions with arbitrary
- number of variables and constants. We can assign expression of the
- form::
-
- f(c_, c1, ..., cM, x0, x1, ..., xN, y0,..., yP )
-
- where `ci`'s are constants and `xi`'s and `yi`'s are variables.
-
- The constants must be defined before setting the expression and
- variables are connected via messages. The constants can have any
- name, but the variable names must be of the form x{i} or y{i}
- where i is increasing integer starting from 0.
-
- The `xi`'s are field elements and you have to set their number
- first (function.x.num = N). Then you can connect any source field
- sending out double to the 'input' destination field of the
- `x[i]`.
-
- The `yi`'s are useful when the required variable is a value field
- and is not available as a source field. In that case you connect
- the `requestOut` source field of the function element to the
- `get{Field}` destination field on the target element. The `yi`'s
- are automatically added on connecting. Thus, if you call::
-
- moose.connect(function, 'requestOut', a, 'getSomeField')
- moose.connect(function, 'requestOut', b, 'getSomeField')
-
- then ``a.someField`` will be assigned to ``y0`` and
- ``b.someField`` will be assigned to ``y1``.
-
- In this example we evaluate the expression: ``z = c0 * exp(c1 *
- x0) * cos(y0)``
-
- with x0 ranging from -1 to +1 and y0 ranging from -pi to
- +pi. These values are stored in two stimulus tables called xtab
- and ytab respectively, so that at each timestep the next values of
- x0 and y0 are assigned to the function.
-
- Along with the value of the expression itself we also compute its
- derivative with respect to y0 and its derivative with respect to
- time (rate). The former uses a five-point stencil for the
- numerical differentiation and has a glitch at y=0. The latter uses
- backward difference divided by dt.
-
- Unlike Func class, the number of variables and constants are
- unlimited in Function and you can set all the variables via
- messages.
-
- """
- example()
-
-if __name__ == '__main__':
- main()
-
-
-
-
-#
-# function.py ends here
diff --git a/snippets/gapjunction.py b/snippets/gapjunction.py
index 7afb339c82904f2ff726a62c7113cd4cb77babf1..0839e6fad3d217769368688566e2b3d70afd39ad 100644
--- a/snippets/gapjunction.py
+++ b/snippets/gapjunction.py
@@ -1,47 +1,47 @@
-# gapjunction.py ---
-#
+# gapjunction.py ---
+#
# Filename: gapjunction.py
-# Description:
-# Author:Subhasis Ray
-# Maintainer:
+# Description:
+# Author:Subhasis Ray
+# Maintainer:
# Created: Tue Jul 2 14:28:35 2013 (+0530)
-# Version:
+# Version:
# Last-Updated: Tue Jul 23 21:28:45 2013 (+0530)
# By: subha
# Update #: 57
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
-#
-#
-#
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
@@ -101,14 +101,12 @@ def gapjunction_demo():
pylab.plot(t, pulse_tab.vector*1e9, label='inject (nA)')
pylab.legend()
pylab.show()
+
+
+if __name__ == '__main__':
+ gapjunction_demo()
-def main():
- """
-This example is to demonstrate, how gap junction can be modeled using MOOSE.
- """
- gapjunction_demo()
-if __name__ == '__main__':
- main()
-#
+
+#
# gapjunction.py ends here
diff --git a/snippets/gssaCylinderDiffusion.py b/snippets/gssaCylinderDiffusion.py
index 7933230df23bfa72ecbb75305e4f904682ab8bf6..932c4aa23319d27fd6d73326df7482d0c4c23607 100644
--- a/snippets/gssaCylinderDiffusion.py
+++ b/snippets/gssaCylinderDiffusion.py
@@ -17,7 +17,7 @@ import moose
print(('[INFO] Using moose from %s' % moose.__file__ ))
import os
-import signal
+import signal
PID = os.getpid()
def doNothing( *args ):
@@ -27,6 +27,36 @@ signal.signal( signal.SIGUSR1, doNothing )
concA = 0.005 # millimolar
def makeModel():
+ """
+ This example illustrates how to set up a diffusion/transport model with
+ a simple reaction-diffusion system in a tapering cylinder:
+
+ | Molecule **a** diffuses with diffConst of 10e-12 m^2/s.
+ | Molecule **b** diffuses with diffConst of 5e-12 m^2/s.
+ | Molecule **b** also undergoes motor transport with a rate of 10e-6 m/s
+ | Thus it 'piles up' at the end of the cylinder.
+ | Molecule **c** does not move: diffConst = 0.0
+ | Molecule **d** does not move: diffConst = 10.0e-12 but it is buffered.
+ | Because it is buffered, it is treated as non-diffusing.
+
+ All molecules other than **d** start out only in the leftmost (first)
+ voxel, with a concentration of 1 mM. **d** is present throughout
+ at 0.2 mM, except in the last voxel, where it is at 1.0 mM.
+
+ The cylinder has a starting radius of 2 microns, and end radius of
+ 1 micron. So when the molecule undergoing motor transport gets to the
+ narrower end, its concentration goes up.
+
+ There is a little reaction in all compartments: ``b + d <===> c``
+
+ As there is a high concentration of **d** in the last compartment,
+ when the molecule **b** reaches the end of the cylinder, the reaction
+ produces lots of **c**.
+
+ Note that molecule **a** does not participate in this reaction.
+
+ The concentrations of all molecules are displayed in an animation.
+ """
# create container for model
r0 = 2e-6 # m
r1 = 1e-6 # m
@@ -45,7 +75,7 @@ def makeModel():
compartment.x0 = 0
compartment.x1 = len
compartment.diffLength = diffLength
-
+
assert( compartment.numDiffCompts == num )
# create molecules and reactions
@@ -123,40 +153,9 @@ def updatePlots( plotlist, time ):
plotlist[5].set_ydata( c.conc )
plotlist[6].set_ydata( d.conc )
plotlist[0].canvas.draw()
-
+
def main():
-
- """
- This example illustrates how to set up a diffusion/transport model with
- a simple reaction-diffusion system in a tapering cylinder:
-
- | Molecule **a** diffuses with diffConst of 10e-12 m^2/s.
- | Molecule **b** diffuses with diffConst of 5e-12 m^2/s.
- | Molecule **b** also undergoes motor transport with a rate of 10e-6 m/s
- | Thus it 'piles up' at the end of the cylinder.
- | Molecule **c** does not move: diffConst = 0.0
- | Molecule **d** does not move: diffConst = 10.0e-12 but it is buffered.
- | Because it is buffered, it is treated as non-diffusing.
-
- All molecules other than **d** start out only in the leftmost (first)
- voxel, with a concentration of 1 mM. **d** is present throughout
- at 0.2 mM, except in the last voxel, where it is at 1.0 mM.
-
- The cylinder has a starting radius of 2 microns, and end radius of
- 1 micron. So when the molecule undergoing motor transport gets to the
- narrower end, its concentration goes up.
-
- There is a little reaction in all compartments: ``b + d <===> c``
-
- As there is a high concentration of **d** in the last compartment,
- when the molecule **b** reaches the end of the cylinder, the reaction
- produces lots of **c**.
-
- Note that molecule **a** does not participate in this reaction.
-
- The concentrations of all molecules are displayed in an animation.
- """
runtime = 20.0
diffdt = 0.005
plotdt = 0.1
diff --git a/snippets/gssaRDspiny.py b/snippets/gssaRDspiny.py
index 6308e5bc6e4c3c0f2b6cdc5c2e89a09b26fcc00c..ad5262444f6211986825611d51cd7526361a50ab 100644
--- a/snippets/gssaRDspiny.py
+++ b/snippets/gssaRDspiny.py
@@ -86,7 +86,7 @@ def makeModel():
stoich0.filterXreacs()
stoich1.filterXreacs()
stoich2.filterXreacs()
-
+
moose.element( '/model/chem/compt2/a[0]' ).concInit *= 1.5
# Create the output tables
@@ -199,7 +199,7 @@ def makeChemModel( compt ):
"""
# create container for model
concA = 1 # millimolar
-
+
# create molecules and reactions
a = moose.Pool( compt.path + '/a' )
b = moose.Pool( compt.path + '/b' )
@@ -244,13 +244,13 @@ def makeChemModel( compt ):
def main():
"""
This example illustrates how to define a kinetic model embedded in
- the branching pseudo-1-dimensional geometry of a neuron. The model
+ the branching pseudo-1-dimensional geometry of a neuron. The model
oscillates in space and time due to a Turing-like reaction-diffusion
mechanism present in all compartments. For the sake of this demo,
the initial conditions are set up slightly different on the PSD
compartments, so as to break the symmetry and initiate oscillations
in the spines.
- This example uses an external electrical model file with basal
+ This example uses an external electrical model file with basal
dendrite and three branches on
the apical dendrite. One of those branches has a dozen or so spines.
In this example we build an identical model in each compartment, using
@@ -260,10 +260,10 @@ def main():
Dsolve for handling diffusion.
The display has four parts:
- a. animated line plot of concentration against main compartment#.
- b. animated line plot of concentration against spine compartment#.
- c. animated line plot of concentration against psd compartment#.
- d. time-series plot that appears after the simulation has
+ a. animated line plot of concentration against main compartment#.
+ b. animated line plot of concentration against spine compartment#.
+ c. animated line plot of concentration against psd compartment#.
+ d. time-series plot that appears after the simulation has
ended. The plot is for the last (rightmost) compartment.
"""
diff --git a/snippets/hdfdemo.py b/snippets/hdfdemo.py
index 9f55e8683c568cc8f5872ccf7a93ae663195cbc2..081b2c1feea0bf491127584e5c3004b308952299 100644
--- a/snippets/hdfdemo.py
+++ b/snippets/hdfdemo.py
@@ -13,35 +13,71 @@ import numpy
import moose
def example():
+ """In this example
+
+ 1. We create a passive neuronal compartment `comp`.
+
+ 2. We create an HDF5DataWriter object `hdfwriter` for writing to a
+ file ``output_hdfdemo.h5``. The `mode` of the HDF5DataWriter
+ is set to `2` which means if a file of the same name exists, it
+ will be overwritten.
+
+ 3. The membrane voltage `Vm` and membrane current `Im` of `comp`
+ are connected to `hdfwriter` for recording.
+
+ 4. We set some attributes of the datasets and the file.
+
+ 5. We run the simulation, `hdfwriter` records and stores the `Vm`
+ and `Im` values over time.
+
+ 6. We close the file by calling close() method of `hdfwriter`.
+
+ Running this snippet creates the file ``output_hdfdemo.h5`` which
+ reflects the structure of the model::
+
+ model
+ |
+ |
+ c[0]
+ |
+ |___im
+ |
+ |___vm
+
+
+ `im` and `vm` are datasets containing `Im` and `Vm` field values
+ recorded from `comp`.
+
+ """
model = moose.Neutral('/model')
comp = moose.Compartment('/model/c')
hdfwriter = moose.HDF5DataWriter('h')
hdfwriter.mode = 2 # Truncate existing file
moose.connect(hdfwriter, 'requestOut', comp, 'getVm')
moose.connect(hdfwriter, 'requestOut', comp, 'getIm')
-
+
hdfwriter.filename = 'output_hdfdemo.h5'
hdfwriter.compressor = 'zlib'
hdfwriter.compression = 7
-
+
# Flush data from memory to disk after accumulating every 1K entries.
hdfwriter.flushLimit = 1024
-
+
# We allow simple attributes of type string, double and long.
# This allows for file-level metadata/annotation.
hdfwriter.stringAttr['note'] = 'This is a test.'
-
+
# All paths are taken relative to the root. The last token is the name
# of the attribute.
hdfwriter.doubleAttr['{}/vm/a_double_attribute'.format(comp.path)] = 3.141592
hdfwriter.longAttr['an_int_attribute'] = 8640
-
+
# In addition, vectors of string, long and double can also be stored
# as attributes.
hdfwriter.stringVecAttr['stringvec'] = ['I wonder', 'why', 'I wonder']
hdfwriter.doubleVecAttr['{}/dvec'.format(comp.path)] = [3.141592, 2.71828]
hdfwriter.longVecAttr['{}/lvec'.format(comp.path)] = [3, 14, 1592, 271828]
-
+
vm_tab = moose.Table('Vm')
moose.connect(vm_tab, 'requestOut', comp, 'getVm')
moose.setClock(0, 1e-3)
@@ -50,7 +86,7 @@ def example():
moose.useClock(0, '/model/c', 'init')
moose.useClock(1, '/##[TYPE!=HDF5DataWriter]', 'process')
moose.useClock(2, '/##[TYPE=HDF5DataWriter]', 'process')
-
+
moose.reinit()
comp.inject = 0.1
moose.start(30.0)
@@ -58,45 +94,6 @@ def example():
vm_tab.plainPlot('hdfdemo_Vm.csv')
print(('Finished simulation. Data was saved in', hdfwriter.filename))
-def main():
- """
- In this example
-
- 1. We create a passive neuronal compartment `comp`.
-
- 2. We create an HDF5DataWriter object `hdfwriter` for writing to a
- file ``output_hdfdemo.h5``. The `mode` of the HDF5DataWriter
- is set to `2` which means if a file of the same name exists, it
- will be overwritten.
-
- 3. The membrane voltage `Vm` and membrane current `Im` of `comp`
- are connected to `hdfwriter` for recording.
-
- 4. We set some attributes of the datasets and the file.
-
- 5. We run the simulation, `hdfwriter` records and stores the `Vm`
- and `Im` values over time.
-
- 6. We close the file by calling close() method of `hdfwriter`.
-
- Running this snippet creates the file ``output_hdfdemo.h5`` which
- reflects the structure of the model::
-
- model
- |
- |
- c[0]
- |
- |___im
- |
- |___vm
-
-
- `im` and `vm` are datasets containing `Im` and `Vm` field values
- recorded from `comp`.
-
- """
- example()
-
+
if __name__ == '__main__':
- main()
+ example()
diff --git a/snippets/hdfdemo_Vm.csv b/snippets/hdfdemo_Vm.csv
deleted file mode 100644
index fa7948e9956a0d9f9b8f6e98e8efec8902395af5..0000000000000000000000000000000000000000
--- a/snippets/hdfdemo_Vm.csv
+++ /dev/null
@@ -1,30002 +0,0 @@
--5.999999999999999778e-02
--5.999999999999999778e-02
--5.990004998333750552e-02
--5.980019986673331539e-02
--5.970044955033731310e-02
--5.960079893439916565e-02
--5.950124791926825196e-02
--5.940179640539355183e-02
--5.930244429332354184e-02
--5.920319148370609824e-02
--5.910403787728840674e-02
--5.900498337491685147e-02
--5.890602787753691783e-02
--5.880717128619310924e-02
--5.870841350202881531e-02
--5.860975442628624937e-02
--5.851119396030633052e-02
--5.841273200552857953e-02
--5.831436846349103559e-02
--5.821610323583015223e-02
--5.811793622428068629e-02
--5.801986733067562158e-02
--5.792189645694605787e-02
--5.782402350512110684e-02
--5.772624837732781566e-02
--5.762857097579104221e-02
--5.753099120283338558e-02
--5.743350896087505431e-02
--5.733612415243380389e-02
--5.723883668012481885e-02
--5.714164644666061554e-02
--5.704455335485095896e-02
--5.694755730760273776e-02
--5.685065820791990882e-02
--5.675385595890335838e-02
--5.665715046375082581e-02
--5.656054162575681332e-02
--5.646402934831247500e-02
--5.636761353490551962e-02
--5.627129408912013436e-02
--5.617507091463685986e-02
--5.607894391523251393e-02
--5.598291299478008742e-02
--5.588697805724865408e-02
--5.579113900670327336e-02
--5.569539574730487941e-02
--5.559974818331020474e-02
--5.550419621907168310e-02
--5.540873975903733845e-02
--5.531337870775070165e-02
--5.521811296985072032e-02
--5.512294245007164084e-02
--5.502786705324293898e-02
--5.493288668428920885e-02
--5.483800124823007272e-02
--5.474321065018009080e-02
--5.464851479534865025e-02
--5.455391358903989574e-02
--5.445940693665261151e-02
--5.436499474368013118e-02
--5.427067691571026142e-02
--5.417645335842516396e-02
--5.408232397760127236e-02
--5.398828867910919482e-02
--5.389434736891363098e-02
--5.380049995307326083e-02
--5.370674633774066148e-02
--5.361308642916221001e-02
--5.351952013367798633e-02
--5.342604735772168989e-02
--5.333266800782053563e-02
--5.323938199059517068e-02
--5.314618921275956337e-02
--5.305308958112093382e-02
--5.296008300257963597e-02
--5.286716938412908823e-02
--5.277434863285566241e-02
--5.268162065593860049e-02
--5.258898536064991747e-02
--5.249644265435431112e-02
--5.240399244450907185e-02
--5.231163463866397162e-02
--5.221936914446120848e-02
--5.212719586963527468e-02
--5.203511472201288734e-02
--5.194312560951289126e-02
--5.185122844014616178e-02
--5.175942312201552847e-02
--5.166770956331566411e-02
--5.157608767233300140e-02
--5.148455735744564277e-02
--5.139311852712326323e-02
--5.130177108992702711e-02
--5.121051495450949093e-02
--5.111935002961451313e-02
--5.102827622407715702e-02
--5.093729344682360743e-02
--5.084640160687108751e-02
--5.075560061332774070e-02
--5.066489037539257528e-02
--5.057427080235533945e-02
--5.048374180359645191e-02
--5.039330328858690478e-02
--5.030295516688818025e-02
--5.021269734815214658e-02
--5.012252974212098172e-02
--5.003245225862706924e-02
--4.994246480759291507e-02
--4.985256729903106421e-02
--4.976275964304400362e-02
--4.967304174982406506e-02
--4.958341352965334875e-02
--4.949387489290362624e-02
--4.940442575003625714e-02
--4.931506601160208503e-02
--4.922579558824136808e-02
--4.913661439068367498e-02
--4.904752232974780163e-02
--4.895851931634168097e-02
--4.886960526146228584e-02
--4.878078007619555956e-02
--4.869204367171631187e-02
--4.860339595928812179e-02
--4.851483685026327514e-02
--4.842636625608265355e-02
--4.833798408827565812e-02
--4.824969025846011228e-02
--4.816148467834217850e-02
--4.807336725971627506e-02
--4.798533791446496499e-02
--4.789739655455890754e-02
--4.780954309205672631e-02
--4.772177743910495373e-02
--4.763409950793793396e-02
--4.754650921087772569e-02
--4.745900646033401893e-02
--4.737159116880406556e-02
--4.728426324887256144e-02
--4.719702261321157694e-02
--4.710986917458047374e-02
--4.702280284582580072e-02
--4.693582353988122458e-02
--4.684893116976743266e-02
--4.676212564859204973e-02
--4.667540688954954775e-02
--4.658877480592115566e-02
--4.650222931107479002e-02
--4.641577031846494394e-02
--4.632939774163261776e-02
--4.624311149420522182e-02
--4.615691148989650716e-02
--4.607079764250646137e-02
--4.598476986592123228e-02
--4.589882807411303778e-02
--4.581297218114007558e-02
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-3.999999999998787859e-02
-3.999999999998788552e-02
-3.999999999998789246e-02
-3.999999999998789940e-02
-3.999999999998790634e-02
-3.999999999998791328e-02
-3.999999999998792022e-02
-3.999999999998792716e-02
-3.999999999998793410e-02
-3.999999999998794104e-02
-3.999999999998794797e-02
-3.999999999998795491e-02
-3.999999999998796185e-02
-3.999999999998796879e-02
-3.999999999998797573e-02
-3.999999999998798267e-02
-3.999999999998798961e-02
-3.999999999998799655e-02
-3.999999999998800349e-02
-3.999999999998801042e-02
-3.999999999998801736e-02
-3.999999999998802430e-02
-3.999999999998803124e-02
-3.999999999998803818e-02
-3.999999999998804512e-02
-3.999999999998805206e-02
-3.999999999998805900e-02
-3.999999999998806594e-02
-3.999999999998807287e-02
-3.999999999998807981e-02
-3.999999999998808675e-02
-3.999999999998809369e-02
-3.999999999998810063e-02
-3.999999999998810757e-02
-3.999999999998811451e-02
-3.999999999998812145e-02
-3.999999999998812839e-02
-3.999999999998813532e-02
-3.999999999998814226e-02
-3.999999999998814920e-02
-3.999999999998815614e-02
-3.999999999998816308e-02
-3.999999999998817002e-02
-3.999999999998817696e-02
-3.999999999998818390e-02
-3.999999999998819084e-02
-3.999999999998819777e-02
-3.999999999998820471e-02
-3.999999999998821165e-02
-3.999999999998821859e-02
-
diff --git a/snippets/helloMoose.py b/snippets/helloMoose.py
index 0a617e7407fafb9f2acb4ed16d72195604f0e65d..c9fe293384d0e1a9cf1d5094edce92b841e71286 100644
--- a/snippets/helloMoose.py
+++ b/snippets/helloMoose.py
@@ -14,15 +14,15 @@ import numpy
def main():
"""
This is the Hello MOOSE program. It shows how to get MOOSE to do
- its most basic operations: to load, run, and graph a model defined
+ its most basic operations: to load, run, and graph a model defined
in an external model definition file.
-
+
The loadModel function is the core of this example. It can accept
a range of file and model types, including kkit, cspace and GENESIS .p
files. It autodetects the file type and loads in the simulation.
-
+
The wildcardFind function finds all objects matching the specified path,
- in this case Table objects hoding the simulation results. They were
+ in this case Table objects hoding the simulation results. They were
all defined in the model file.
"""
mfile = '../genesis/kkit_objects_example.g'
diff --git a/snippets/hhcomp.py b/snippets/hhcomp.py
index caa82ba15b0b3e13b1aa539b9155493210011af3..3a7e46fda4a78a60ef4b5da022641ffbabb703c3 100644
--- a/snippets/hhcomp.py
+++ b/snippets/hhcomp.py
@@ -1,45 +1,50 @@
-# hhcomp.py ---
-#
+# hhcomp.py ---
+#
# Filename: hhcomp.py
-# Description:
+# Description:
# Author: Subhasis Ray
-# Maintainer:
+# Maintainer:
# Created: Tue May 7 12:11:22 2013 (+0530)
-# Version:
+# Version:
# Last-Updated: Tue May 7 19:21:43 2013 (+0530)
# By: subha
# Update #: 309
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
-#
-#
-#
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
+#
+#
+
+# Code:
+"""A compartment with hodgkin-huxley ion channels"""
import sys
sys.path.append('../../python')
@@ -55,11 +60,10 @@ EREST_ACT = -70e-3
per_ms = 1e3
def create_na_chan(parent='/library', name='na', vmin=-110e-3, vmax=50e-3, vdivs=3000):
- """
- Create a Hodhkin-Huxley Na channel under `parent`.
-
+ """Create a Hodhkin-Huxley Na channel under `parent`.
+
vmin, vmax, vdivs: voltage range and number of divisions for gate tables
-
+
"""
na = moose.HHChannel('%s/%s' % (parent, name))
na.Xpower = 3
@@ -96,9 +100,9 @@ def create_na_chan(parent='/library', name='na', vmin=-110e-3, vmax=50e-3, vdivs
def create_k_chan(parent='/library', name='k', vmin=-120e-3, vmax=40e-3, vdivs=3000):
"""Create a Hodhkin-Huxley K channel under `parent`.
-
+
vmin, vmax, vdivs: voltage range and number of divisions for gate tables
-
+
"""
k = moose.HHChannel('%s/%s' % (parent, name))
k.Xpower = 4
@@ -158,11 +162,11 @@ def create_passive_comp(parent='/library', name='comp', diameter=30e-6, length=0
else:
sarea = np.pi * diameter * length
# specific conductance gm = 0.3 mS/cm^2
- comp.Rm = 1 / (0.3e-3 * sarea * 1e4)
+ comp.Rm = 1 / (0.3e-3 * sarea * 1e4)
# Specific capacitance cm = 1 uF/cm^2
comp.Cm = 1e-6 * sarea * 1e4
return comp, sarea
-
+
def create_hhcomp(parent='/library', name='hhcomp', diameter=-30e-6, length=0.0):
"""Create a compartment with Hodgkin-Huxley type ion channels (Na and
K).
@@ -177,7 +181,7 @@ def create_hhcomp(parent='/library', name='hhcomp', diameter=-30e-6, length=0.0)
create_na_chan(parent=comp.path)
na = moose.element('%s/na' % (comp.path))
# Na-conductance 120 mS/cm^2
- na.Gbar = 120e-3 * sarea * 1e4
+ na.Gbar = 120e-3 * sarea * 1e4
na.Ek = 115e-3 + EREST_ACT
moose.connect(comp, 'channel', na, 'channel')
if moose.exists('/library/k'):
@@ -186,7 +190,7 @@ def create_hhcomp(parent='/library', name='hhcomp', diameter=-30e-6, length=0.0)
create_k_chan(parent=comp.path)
k = moose.element('%s/k' % (comp.path))
# K-conductance 36 mS/cm^2
- k.Gbar = 36e-3 * sarea * 1e4
+ k.Gbar = 36e-3 * sarea * 1e4
k.Ek = -12e-3 + EREST_ACT
moose.connect(comp, 'channel', k, 'channel')
return comp, na, k
@@ -199,7 +203,7 @@ def test_hhcomp():
"""
model = moose.Neutral('/model')
- data = moose.Neutral('/data')
+ data = moose.Neutral('/data')
comp, na, k = create_hhcomp(parent=model.path)
print((comp.Rm, comp.Cm, na.Ek, na.Gbar, k.Ek, k.Gbar))
pg = moose.PulseGen('%s/pg' % (model.path))
@@ -245,14 +249,14 @@ def test_hhcomp():
# moose.showfield(comp)
# moose.showfield(na)
# moose.showfield(k)
+
+if __name__ == '__main__':
+ test_channel_gates()
+ test_hhcomp()
+
-def main():
- """A compartment with hodgkin-huxley ion channels"""
- test_channel_gates()
- test_hhcomp()
-if __name__ == '__main__':
- main()
-#
+
+#
# hhcomp.py ends here
diff --git a/snippets/insertSpinesWithoutRdesigneur.py b/snippets/insertSpinesWithoutRdesigneur.py
index 9975a96834af798ad673c11244e8400465dfe400..cf4fcc56f2e3919601c7ade3a4a071be15285369 100644
--- a/snippets/insertSpinesWithoutRdesigneur.py
+++ b/snippets/insertSpinesWithoutRdesigneur.py
@@ -82,11 +82,9 @@ def makeSpineProto():
def main():
"""
-This snippet illustrates how the Neuron class does the spine
-specification, without the rdesigneur intermediate.
-
+ This snippet illustrates how the Neuron class does the spine
+ specification, without the rdesigneur intermediate.
"""
-
app = QtGui.QApplication(sys.argv)
moose.Neutral( '/library' )
makeSpineProto()
diff --git a/snippets/interpol.py b/snippets/interpol.py
index 67a31b0c5c56cbca46d36423c675517b58da8852..a2f031f00acb8fae09d8fff257218650d9fab024 100644
--- a/snippets/interpol.py
+++ b/snippets/interpol.py
@@ -1,49 +1,52 @@
-# interpol.py ---
-#
+# interpol.py ---
+#
# Filename: interpol.py
-# Description:
+# Description:
# Author: Subhasis Ray
-# Maintainer:
+# Maintainer:
# Created: Wed Jun 25 16:13:26 2014 (+0530)
-# Version:
-# Last-Updated:
-# By:
+# Version:
+# Last-Updated:
+# By:
# Update #: 0
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
-#
-#
-#
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
+"""Example of Interpol object."""
+__author__ = 'Subhasis Ray'
+
import sys
import math
import numpy as np
@@ -52,43 +55,39 @@ from matplotlib import pyplot as plt
sys.path.append('../../python')
import moose
-def main():
- """Example of Interpol object."""
- simtime = 1.0
- simdt = 0.001
- model = moose.Neutral('/model')
- data = moose.Neutral('/data')
- interpol = moose.Interpol('/model/sin')
- vec = np.sin(np.linspace(-3.14, 3.14, 100))
- interpol.vector = vec
- interpol.xmax = 3.14
- interpol.xmin = -3.14
- recorded = moose.Table('/data/output')
- moose.connect(recorded, 'requestOut', interpol, 'getY')
+simtime = 1.0
+simdt = 0.001
+model = moose.Neutral('/model')
+data = moose.Neutral('/data')
+interpol = moose.Interpol('/model/sin')
+vec = np.sin(np.linspace(-3.14, 3.14, 100))
+interpol.vector = vec
+interpol.xmax = 3.14
+interpol.xmin = -3.14
+recorded = moose.Table('/data/output')
+moose.connect(recorded, 'requestOut', interpol, 'getY')
- stimtab = moose.StimulusTable('/model/x')
- stimtab.stepSize = 0.0
- # stimtab.startTime = 0.0
- # stimtab.stopTime = simtime
- stimtab.vector = np.linspace(-4, 4, 1000)
- print((stimtab.vector))
- print((interpol.vector))
- moose.connect(stimtab, 'output', interpol, 'input')
+stimtab = moose.StimulusTable('/model/x')
+stimtab.stepSize = 0.0
+# stimtab.startTime = 0.0
+# stimtab.stopTime = simtime
+stimtab.vector = np.linspace(-4, 4, 1000)
+print((stimtab.vector))
+print((interpol.vector))
+moose.connect(stimtab, 'output', interpol, 'input')
- moose.setClock(0, simdt)
- moose.useClock(0, '/data/##,/model/##', 'process')
- moose.reinit()
- moose.start(simtime)
- plt.plot(np.linspace(0, simtime, len(recorded.vector)), recorded.vector, 'b-+', label='interpolated')
- plt.plot(np.linspace(0, simtime, len(vec)), vec, 'r-+', label='original')
- plt.show()
+moose.setClock(0, simdt)
+moose.useClock(0, '/data/##,/model/##', 'process')
+moose.reinit()
+moose.start(simtime)
+plt.plot(np.linspace(0, simtime, len(recorded.vector)), recorded.vector, 'b-+', label='interpolated')
+plt.plot(np.linspace(0, simtime, len(vec)), vec, 'r-+', label='original')
+plt.show()
-if __name__ == '__main__':
- main()
-#
+#
# interpol.py ends here
diff --git a/snippets/interpol2d.py b/snippets/interpol2d.py
index b4642cae49a38e8e0e9c860b2953af3f3f63aae2..a6ccef2e09e4b0f807f7544b750beb66a3251e38 100644
--- a/snippets/interpol2d.py
+++ b/snippets/interpol2d.py
@@ -1,50 +1,50 @@
-# interpol2d.py ---
-#
+# interpol2d.py ---
+#
# Filename: interpol2d.py
-# Description:
-# Author:Subhasis Ray
-# Maintainer:
+# Description:
+# Author:Subhasis Ray
+# Maintainer:
# Created: Thu Jun 28 15:19:46 2012 (+0530)
-# Version:
+# Version:
# Last-Updated: Thu Jun 28 17:11:42 2012 (+0530)
# By: subha
# Update #: 49
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
-#
-#
-#
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
-"""Example of Interpol object in 2 dimensions."""
+
import numpy as np
import sys
@@ -65,16 +65,16 @@ def interpolation_demo():
print(matrix)
interpol.tableVector2D = matrix
# interpolating beyond top left corner.
- # value should be
+ # value should be
pos = (0.8, 0.3)
-
+
print(('Interpolated value at', pos))
print((interpol.z[pos[0], pos[1]]))
-
+
print(('Point going out of bound on both x and y', interpol.z[1.1, 1.1]))
print(('Point going out of bound on both x and y', interpol.z[0.5, 1.1]))
if __name__ == '__main__':
interpolation_demo()
-#
+#
# interpol2d.py ends here
diff --git a/snippets/intfire.py b/snippets/intfire.py
index bfd6c80885e667a0a9647722d8573f2786df9074..6b1e4c9794761dd5fb711c260a0aec7be0f1d048 100644
--- a/snippets/intfire.py
+++ b/snippets/intfire.py
@@ -1,75 +1,69 @@
-# intfire.py ---
-#
+# intfire.py ---
+#
# Filename: intfire.py
-# Description:
-# Author:Subhasis Ray
-# Maintainer:
+# Description:
+# Author:Subhasis Ray
+# Maintainer:
# Created: Thu Jun 21 16:40:25 2012 (+0530)
-# Version:
+# Version:
# Last-Updated: Sat Jun 23 13:44:10 2012 (+0530)
# By: subha
# Update #: 35
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
+# Commentary:
+#
# Code snippet to show some operations on IntFire.
-#
-#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
import moose
def connect_two_intfires():
- """
-Connect two IntFire neurons so that spike events in one gets
-transmitted to synapse of the other.
-
- """
+ """Connect two IntFire neurons so that spike events in one gets
+ transmitted to synapse of the other."""
if1 = moose.IntFire('if1')
if2 = moose.IntFire('if2')
sf1 = moose.SimpleSynHandler( 'if1/sh' )
moose.connect( sf1, 'activationOut', if1, 'activation' )
- sf1.synapse.num = 1
+ sf1.synapse.num = 1
syn1 = moose.element(sf1.synapse)
# Connect the spike message of if2 to the first synapse on if1
moose.connect(if2, 'spikeOut', syn1, 'addSpike')
def connect_spikegen():
- """
-Connect a SpikeGen object to an IntFire neuron such that spike
-events in spikegen get transmitted to the synapse of the IntFire
-neuron.
-
-if3 = moose.IntFire('if3')
- """
+ """Connect a SpikeGen object to an IntFire neuron such that spike
+ events in spikegen get transmitted to the synapse of the IntFire
+ neuron."""
+ if3 = moose.IntFire('if3')
sf3 = moose.SimpleSynHandler( 'if3/sh' )
moose.connect( sf3, 'activationOut', if3, 'activation' )
sf3.synapse.num = 1
@@ -78,10 +72,7 @@ if3 = moose.IntFire('if3')
moose.connect(sg, 'spikeOut', syn, 'addSpike')
def setup_synapse():
- """
-Create an intfire object and create two synapses on it.
-
- """
+ """Create an intfire object and create two synapses on it."""
if4 = moose.IntFire('if4')
sf4 = moose.SimpleSynHandler( 'if4/sh' )
sg1 = moose.SpikeGen('sg1')
@@ -94,17 +85,10 @@ Create an intfire object and create two synapses on it.
moose.connect(sg1, 'spikeOut', sf4.synapse[0], 'addSpike')
moose.connect(sg2, 'spikeOut', sf4.synapse[1], 'addSpike')
-def main():
- """
-Demonstrates connection between 2 IntFire neurons to observe
-spike generation.
-
- """
+if __name__ == '__main__':
connect_two_intfires()
connect_spikegen()
setup_synapse()
-
-if __name__ == '__main__':
- main()
-#
+
+#
# intfire.py ends here
diff --git a/snippets/ionchannel.py b/snippets/ionchannel.py
index fb3ce5a4261fa0ab5a642cf8da5c6a47f63ea151..4be6a84bc8375736739511b4728c5f151276538f 100644
--- a/snippets/ionchannel.py
+++ b/snippets/ionchannel.py
@@ -1,49 +1,81 @@
-# ionchannel.py ---
-#
+# ionchannel.py ---
+#
# Filename: ionchannel.py
-# Description:
+# Description:
# Author: Subhasis Ray
-# Maintainer:
+# Maintainer:
# Created: Wed Sep 17 10:33:20 2014 (+0530)
-# Version:
-# Last-Updated:
-# By:
+# Version:
+# Last-Updated:
+# By:
# Update #: 0
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
-
-# Commentary:
-#
-#
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
+"""This demo shows how to set the parameters for a Hodgkin-Huxley type ion channel.
+
+Hodgkin-Huxley type ion channels are composed of one or more gates
+that allow ions to cross the membrane. The gates transition between
+open and closed states and this, taken over a large population of
+ion channels over a patch of membrane has first order kinetics, where
+the rate of change of fraction of open gates (n) is given by::
+
+ dn/dt = alpha(Vm) * (1 - n) - beta(Vm) * n
+
+where alpha and beta are rate parameters for gate opening and
+closing respectively that depend on the membrane potential.
+The final channel conductance is computed as::
+
+ Gbar * m^x * h^y ...
+
+where m, n are the fraction of open gates of different types and x,
+y are the number of such gates in each channel. We can define the
+channel by specifying the alpha and beta parameters as functions of
+membrane potential and the exponents for each gate.
+The number gates is commonly one or two.
+
+Gate opening/closing rates have the form::
+
+ y(x) = (A + B * x) / (C + exp((x + D) / F))
+
+where x is membrane voltage and y is the rate parameter for gate
+closing or opening.
+
+
+"""
+
import numpy as np
import matplotlib.pyplot as plt
import moose
@@ -61,7 +93,7 @@ Na_m_params = [1e5 * (25e-3 + EREST_ACT), # 'A_A':
0.0, # 'B_B':
0.0, # 'B_C':
0.0 - EREST_ACT, # 'B_D':
- 18e-3 # 'B_F':
+ 18e-3 # 'B_F':
]
#: Parameters for defining h gate of Na+ channel
@@ -74,7 +106,7 @@ Na_h_params = [ 70.0, # 'A_A':
0.0, # 'B_B':
1.0, # 'B_C':
-30e-3 - EREST_ACT, # 'B_D':
- -0.01 # 'B_F':
+ -0.01 # 'B_F':
]
#: K+ channel in Hodgkin-Huxley model has only one gate, n and these
@@ -88,7 +120,7 @@ K_n_params = [ 1e4 * (10e-3 + EREST_ACT), # 'A_A':
0.0, # 'B_B':
0.0, # 'B_C':
0.0 - EREST_ACT, # 'B_D':
- 80e-3 # 'B_F':
+ 80e-3 # 'B_F':
]
#: We define the rate parameters, which are functions of Vm as
@@ -101,12 +133,11 @@ VMAX = 120e-3 + EREST_ACT
VDIVS = 3000
def create_na_proto():
- """
- Create and return a Na+ channel prototype '/library/na'
+ """Create and return a Na+ channel prototype '/library/na'
The Na+ channel conductance has the equation::
- g = Gbar * m^3 * h
+ g = Gbar * m^3 * h
For each gate, we use the HHChannel.setupAlpha function to set up
the interpolation table.
@@ -119,20 +150,19 @@ def create_na_proto():
na.Xpower = 3
#: channel/gateX is the m gate
#: setting Xpower to a positive number automatically creates this gate.
- xGate = moose.element(na.path + '/gateX')
+ xGate = moose.element(na.path + '/gateX')
xGate.setupAlpha(Na_m_params +
[VDIVS, VMIN, VMAX])
#: channel/gateY is the h gate
#: Exponent for h gate is 1
na.Ypower = 1
yGate = moose.element(na.path + '/gateY')
- yGate.setupAlpha(Na_h_params +
+ yGate.setupAlpha(Na_h_params +
[VDIVS, VMIN, VMAX])
return na
-
+
def create_k_proto():
- """
- Create and return a K+ channel prototype '/library/k'.
+ """Create and return a K+ channel prototype '/library/k'.
The K+ channel conductance has the equation::
@@ -143,7 +173,7 @@ def create_k_proto():
k = moose.HHChannel('/library/k')
k.tick = -1
k.Xpower = 4.0
- xGate = moose.HHGate(k.path + '/gateX')
+ xGate = moose.HHGate(k.path + '/gateX')
xGate.setupAlpha(K_n_params +
[VDIVS, VMIN, VMAX])
return k
@@ -156,7 +186,7 @@ def create_1comp_neuron(path, number=1):
path : str
path of the compartment to be created
- number : int
+ number : int
number of compartments to be created. If n is greater than 1,
we create a vec with that size, each having the same property.
@@ -193,8 +223,7 @@ def create_1comp_neuron(path, number=1):
return comps
def current_step_test(simtime, simdt, plotdt):
- """
- Create a single compartment and set it up for applying a step
+ """Create a single compartment and set it up for applying a step
current injection.
We use a PulseGen object to generate a 40 ms wide 1 nA current
@@ -221,39 +250,8 @@ def current_step_test(simtime, simdt, plotdt):
moose.start(simtime)
ts = np.linspace(0, simtime, len(vm_tab.vector))
return ts, current_tab.vector, vm_tab.vector,
-
-def main():
- """
-This demo shows how to set the parameters for a Hodgkin-Huxley type ion channel.
-
-Hodgkin-Huxley type ion channels are composed of one or more gates
-that allow ions to cross the membrane. The gates transition between
-open and closed states and this, taken over a large population of
-ion channels over a patch of membrane has first order kinetics, where
-the rate of change of fraction of open gates (n) is given by::
-
- dn/dt = alpha(Vm) * (1 - n) - beta(Vm) * n
-
-where alpha and beta are rate parameters for gate opening and
-closing respectively that depend on the membrane potential.
-The final channel conductance is computed as::
-
- Gbar * m^x * h^y ...
-
-where m, n are the fraction of open gates of different types and x,
-y are the number of such gates in each channel. We can define the
-channel by specifying the alpha and beta parameters as functions of
-membrane potential and the exponents for each gate.
-The number gates is commonly one or two.
-
-Gate opening/closing rates have the form::
-
- y(x) = (A + B * x) / (C + exp((x + D) / F))
-
-where x is membrane voltage and y is the rate parameter for gate
-closing or opening.
- """
-
+
+if __name__ == '__main__':
simtime = 0.1
simdt = 0.25e-5
plotdt = 0.25e-3
@@ -262,9 +260,7 @@ closing or opening.
plt.plot(ts, current * 1e9, label='current (nA)')
plt.legend()
plt.show()
+
-if __name__ == '__main__':
- main()
-
-#
+#
# ionchannel.py ends here
diff --git a/snippets/lif.py b/snippets/lif.py
index 0e091c2deacd4804dad9f910d00da59cdc48ea64..74fbbf895aabfe7f86579ad8912ca47f2350dd10 100644
--- a/snippets/lif.py
+++ b/snippets/lif.py
@@ -1,51 +1,51 @@
-# lif.py ---
-#
+# lif.py ---
+#
# Filename: lif.py
-# Description:
-# Author:Subhasis Ray
-# Maintainer:
+# Description:
+# Author:Subhasis Ray
+# Maintainer:
# Created: Sun Jul 8 14:00:31 2012 (+0530)
-# Version:
+# Version:
# Last-Updated: Tue Jul 17 16:45:44 2012 (+0530)
# By: subha
# Update #: 187
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
-#
+# Commentary:
+#
+# Demonstrates use of Leaky Integrate and Fire (LeakyIaf class) in
+# moose.
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
-"""
-Demonstrates use of Leaky Integrate and Fire (LeakyIaf class) in moose.
-"""
import sys
sys.path.append('../../python')
import os
@@ -86,7 +86,7 @@ def setupmodel(modelpath, iaf_Rm, iaf_Cm, pulse_interval):
pg.delay[0] = pulse_interval
pg.width[0] = 1e-3
pg.level[0] = 0.5
- moose.connect(pg, 'output', sg, 'Vm')
+ moose.connect(pg, 'output', sg, 'Vm')
return {
'model': model_container,
'iaf': iaf,
@@ -131,5 +131,5 @@ if __name__ == '__main__':
plt.legend()
plt.show()
-#
+#
# lif.py ends here
diff --git a/snippets/lifcomp.csv b/snippets/lifcomp.csv
deleted file mode 100644
index a1317b07069afb7f4200e8cbe969b1303dcaf6fd..0000000000000000000000000000000000000000
--- a/snippets/lifcomp.csv
+++ /dev/null
@@ -1,1002 +0,0 @@
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diff --git a/snippets/lifcomp.py b/snippets/lifcomp.py
index 2d534d6bccbe78c43ce2cb328b7b1eb812261e00..d1b114e0ec2daf12617b4fcc8274ef60e8aa3148 100644
--- a/snippets/lifcomp.py
+++ b/snippets/lifcomp.py
@@ -1,50 +1,55 @@
-# lifcomp.py ---
-#
+# lifcomp.py ---
+#
# Filename: lifcomp.py
# Description: Leaky Integrate and Fire using regular neuronal compartment
# Author: Subhasis Ray
-# Maintainer:
+# Maintainer:
# Created: Fri Feb 7 16:26:05 2014 (+0530)
-# Version:
-# Last-Updated:
-# By:
+# Version:
+# Last-Updated:
+# By:
# Update #: 0
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
-
-# Commentary:
-#
-#
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
+"""This is an example of how you can create a Leaky Integrate and Fire
+compartment using regular compartment and Func to check for thresold
+crossing and resetting the Vm."""
+
+
import moose
from moose import utils
from pylab import *
@@ -54,7 +59,7 @@ stepsize = 100e-3 # Time step for pauses between runs
simdt = 1e-4 # time step for numerical integration
plotdt = 0.25e-3 # time step for plotting
-delayMax = 5e-3 # Maximum synaptic delay
+delayMax = 5e-3 # Maximum synaptic delay
class LIFComp(moose.Compartment):
"""Leaky integrate and fire neuron using regular compartments,
@@ -70,7 +75,7 @@ class LIFComp(moose.Compartment):
self.Cm = 100e-9
self.Em = 0 #-65e-3
self.initVm = 0 #self.Em
-
+
# Note that the result is dependent on exact order of
# execution of SpikeGen and Func. If Func gets executed first
# SpikeGen will never cross threshold.
@@ -101,8 +106,7 @@ class LIFComp(moose.Compartment):
self.spikegen.threshold = value
def setup_two_cells():
- """
- Create two cells with leaky integrate and fire compartments. Each
+ """Create two cells with leaky integrate and fire compartments. Each
cell is a single compartment a1 and b2. a1 is stimulated by a step
current injection.
@@ -130,7 +134,7 @@ def setup_two_cells():
moose.connect(b2, 'channel', syn, 'channel')
## Single message works most of the time but occassionally gives a
## core dump
-
+
# m = moose.connect(a1.spikegen, 'spikeOut',
# syn.synapse.vec, 'addSpike')
@@ -145,7 +149,7 @@ def setup_two_cells():
stim.level[0] = 11e-9
moose.connect(stim, 'output', a1, 'injectMsg')
tables = []
- data = moose.Neutral('/data')
+ data = moose.Neutral('/data')
for c in moose.wildcardFind('/##[ISA=Compartment]'):
tab = moose.Table('%s/%s_Vm' % (data.path, c.name))
moose.connect(tab, 'requestOut', c, 'getVm')
@@ -157,14 +161,9 @@ def setup_two_cells():
syn.Gbar = 1e-6
return tables
-def main():
- """
- This is an example of how you can create a Leaky Integrate and Fire
- compartment using regular compartment and Func to check for thresold
- crossing and resetting the Vm.
- """
+if __name__ == '__main__':
tables = setup_two_cells()
-
+
utils.setDefaultDt(elecdt=simdt, plotdt2=plotdt)
utils.assignDefaultTicks(modelRoot='/model', dataRoot='/data', solver='ee')
moose.reinit()
@@ -179,14 +178,11 @@ def main():
np.savetxt('lifcomp.csv', data.transpose(), delimiter='\t', header=' '.join([tab.name for tab in tables]))
except TypeError as e: # old numpy may not have header.
np.savetxt('lifcomp.csv', data.transpose(), delimiter='\t' )
+
# subplot(len(tables), 1, ii+1)
# t = np.linspace(0, simtime, len(tab.vector))*1e3
# plot(t, tab.vector*1e3, label=tab.name)
# legend()
# show()
-
-if __name__ == '__main__':
- main()
-
-#
+#
# lifcomp.py ends here
diff --git a/snippets/loadCspaceModel.py b/snippets/loadCspaceModel.py
index 398a78059722634c256af6006e01762181f6c5ee..6a177622b76065e54248304d4850316e399a1c2a 100644
--- a/snippets/loadCspaceModel.py
+++ b/snippets/loadCspaceModel.py
@@ -1,47 +1,47 @@
-# loadCspaceModel.py ---
-#
+# loadCspaceModel.py ---
+#
# Filename: loadCspaceModel.py
-# Description:
-# Author: Upi Bhalla
-# Maintainer:
+# Description:
+# Author: Upi Bhalla
+# Maintainer:
# Created: Sat Oct 04 10:14:15 2014 (+0530)
-# Version:
-# Last-Updated:
-# By:
+# Version:
+# Last-Updated:
+# By:
# Update #: 0
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
-#
-#
-#
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
import math
diff --git a/snippets/loadKineticModel.py b/snippets/loadKineticModel.py
index 4c5473866bf0d2322622af33da7cb996b0de0f8c..3b931638ba068796b7abed02247c34e51f0f7e57 100644
--- a/snippets/loadKineticModel.py
+++ b/snippets/loadKineticModel.py
@@ -1,47 +1,47 @@
-# loadKineticModel.py ---
-#
+# loadKineticModel.py ---
+#
# Filename: loadKineticModel.py
-# Description:
-# Author: Upi Bhalla
-# Maintainer:
+# Description:
+# Author: Upi Bhalla
+# Maintainer:
# Created: Sat Oct 04 12:14:15 2014 (+0530)
# Version:
# Last-Updated: Tue Apr 18 17:40:00 2017(+0530)
# By:
# Update #: 0
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
-#
-#
-#
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
import moose
diff --git a/snippets/loadSbmlmodel.py b/snippets/loadSbmlmodel.py
index 6288215d586b0767d17e8157d737dc31e569869e..1a717245c46aa94048d709e5127f8fd344373574 100644
--- a/snippets/loadSbmlmodel.py
+++ b/snippets/loadSbmlmodel.py
@@ -1,40 +1,40 @@
# -*- coding: utf-8 -*-
# Filename: loadSbmlmodel.py
-# Description:
-# Author: Harsha rani
-# Maintainer:
+# Description:
+# Author: Harsha rani
+# Maintainer:
# Created: Fri Jan 29 11:43:15 2016 (+0530)
-# Version:
-# Last-Updated:
-# By:
+# Version:
+# Last-Updated:
+# By:
# Update #: 0
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
-
-# Commentary:
-#
-#
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
import sys
import os.path
@@ -43,16 +43,14 @@ import pylab
import moose
from moose.SBML import *
-#from moose.chemUtil.add_Delete_ChemicalSolver import *
+from moose.chemUtil.add_Delete_ChemicalSolver import *
def main():
- """
-This example illustrates loading, running of an SBML model defined in XML format.
-The model 00001-sbml-l3v1.xml is taken from l3v1 SBML testcase.
-Plots are setup.
-Model is run for 20sec.
-As a general rule we created model under '/path/model' and plots under '/path/graphs'.
-
+ """ This example illustrates loading, running of an SBML model defined in XML format.\n
+ The model 00001-sbml-l3v1.xml is taken from l3v1 SBML testcase.\n
+ Plots are setup.\n
+ Model is run for 20sec.\n
+ As a general rule we created model under '/path/model' and plots under '/path/graphs'.\n
"""
mfile = "../genesis/00001-sbml-l3v1.xml"
@@ -62,26 +60,26 @@ As a general rule we created model under '/path/model' and plots under '/path/gr
pass
else:
mfile = sys.argv[1]
-
+
try:
sys.argv[2]
except:
runtime = 20.0
else:
runtime = float(sys.argv[2])
-
+
# Loading the sbml file into MOOSE, models are loaded in path/model
sbmlId = moose.SBML.readSBML.mooseReadSBML(mfile,'sbml')
-
+
# Loading the sbml file into MOOSE, models are loaded in path/model
sbmlId = mooseReadSBML(mfile,'/sbml')
if isinstance(sbmlId, (list, tuple)):
print(sbmlId)
elif sbmlId.path != '/':
-
+
s1 = moose.element('/sbml/model/compartment/S1')
s2= moose.element('/sbml/model/compartment/S2')
-
+
# Creating MOOSE Table, Table2 is for the chemical model
graphs = moose.Neutral( '/sbml/graphs' )
outputs1 = moose.Table2 ( '/sbml/graphs/concS1')
@@ -93,7 +91,7 @@ As a general rule we created model under '/path/model' and plots under '/path/gr
# gsl solver is added, default is ee
mooseaddChemSolver(sbmlId.path,"ee")
-
+
# Reset and Run
moose.reinit()
moose.start(runtime)
@@ -105,13 +103,13 @@ def displayPlots():
for x in moose.wildcardFind( '/sbml/graphs/#[TYPE=Table2]' ):
t = np.arange( 0, x.vector.size, 1 ) #sec
pylab.plot( t, x.vector, label=x.name )
-
+
pylab.legend()
pylab.show()
quit()
-
if __name__=='__main__':
+
modelPath, modelpathexist = main()
if modelpathexist == True:
displayPlots()
diff --git a/snippets/mcs.plot b/snippets/mcs.plot
deleted file mode 100644
index 4868164922e2143ad51b2b0995056046c7ae1c39..0000000000000000000000000000000000000000
--- a/snippets/mcs.plot
+++ /dev/null
@@ -1,38076 +0,0 @@
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-/plotname psd0R
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-1.89596e-07
-1.89596e-07
-1.89596e-07
-1.896e-07
-1.896e-07
-1.896e-07
-1.896e-07
-1.89604e-07
-1.89604e-07
-1.89604e-07
-1.89604e-07
-1.89607e-07
-1.89607e-07
-1.89607e-07
-1.89607e-07
-1.89609e-07
-1.89609e-07
-1.89609e-07
-1.89609e-07
-1.89611e-07
-1.89611e-07
-1.89611e-07
-1.89611e-07
-1.89613e-07
-1.89613e-07
-1.89613e-07
-1.89613e-07
-1.89614e-07
-1.89614e-07
-1.89614e-07
-1.89614e-07
-1.89615e-07
-1.89615e-07
-1.89615e-07
-1.89615e-07
-1.89616e-07
-1.89616e-07
-1.89616e-07
-1.89616e-07
-1.89616e-07
-1.89616e-07
-1.89616e-07
-1.89616e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89617e-07
-1.89619e-07
-1.89619e-07
-1.89619e-07
-1.89619e-07
-1.89621e-07
-1.89621e-07
-1.89621e-07
-1.89621e-07
-1.89624e-07
-1.89624e-07
-1.89624e-07
-1.89624e-07
-1.89627e-07
-1.89627e-07
-1.89627e-07
-1.89627e-07
-1.89629e-07
-1.89629e-07
-1.89629e-07
-1.89629e-07
-1.89632e-07
-1.89632e-07
-1.89632e-07
-1.89632e-07
-1.89635e-07
-1.89635e-07
-1.89635e-07
-1.89635e-07
-1.89639e-07
-1.89639e-07
-1.89639e-07
-1.89639e-07
-1.89642e-07
-1.89642e-07
-1.89642e-07
-1.89642e-07
-1.89645e-07
-1.89645e-07
-1.89645e-07
-1.89645e-07
-1.89648e-07
-1.89648e-07
-1.89648e-07
-1.89648e-07
-1.8965e-07
-1.8965e-07
-1.8965e-07
-1.8965e-07
-1.89652e-07
-1.89652e-07
-1.89652e-07
-1.89652e-07
-1.89653e-07
-1.89653e-07
-1.89653e-07
-1.89653e-07
-1.89654e-07
-1.89654e-07
-1.89654e-07
-1.89654e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89656e-07
-1.89656e-07
-1.89656e-07
-1.89656e-07
-1.89656e-07
-1.89656e-07
-1.89656e-07
-1.89656e-07
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-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89655e-07
-1.89656e-07
-1.89656e-07
-1.89656e-07
-1.89656e-07
-1.89657e-07
-1.89657e-07
-1.89657e-07
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-1.8966e-07
-1.89663e-07
-
diff --git a/snippets/mgblock.py b/snippets/mgblock.py
index 370073280b8b24281cf3d2c6df141e06d830b5f8..0c66ec532393641cffb858b74cd668ff5ed2d224 100644
--- a/snippets/mgblock.py
+++ b/snippets/mgblock.py
@@ -1,47 +1,47 @@
-# mgblock.py ---
-#
+# mgblock.py ---
+#
# Filename: mgblock.py
-# Description:
-# Author:Upi Bhalla
-# Maintainer:
+# Description:
+# Author:Upi Bhalla
+# Maintainer:
# Created: Wed Jul 3 09:36:06 2013 (+0530)
-# Version:
+# Version:
# Last-Updated: Fri Sep 12 10:56:18 2014 (+0530)
# By: Upi
-# Update #:
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
-
-# Commentary:
-#
-#
-#
-#
+# Update #:
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
@@ -54,6 +54,20 @@ simdt = 1e-6
plotdt = 1e-4
def test_mgblock():
+ """
+ Demonstrates the use of MgBlock.
+ Creates an NMDA channel with MgBlock and another without.
+ Connects them up to the compartment on one hand, and to a
+ SynHandler on the other, so as to receive synaptic input.
+ Delivers two pulses to each receptor, with a small delay in between.
+
+ Plots out the conductance change at each receptor and the reslting
+ membrane potential rise at the compartment.
+
+ Note that these NMDA channels do NOT separate out the contributions
+ due to calcium and other ions. To do this correctly one should use
+ the GHK object.
+ """
model = moose.Neutral('/model')
data = moose.Neutral('/data')
soma = moose.Compartment('/model/soma')
@@ -77,13 +91,13 @@ def test_mgblock():
moose.connect( synHandler, 'activationOut', nmda, 'activation' )
-
+
# MgBlock sits between original channel nmda and the
# compartment. The origChannel receives the channel message from
# the nmda SynChan.
moose.connect(soma, 'VmOut', nmda, 'Vm' )
moose.connect(nmda, 'channelOut', mgblock, 'origChannel')
- moose.connect(mgblock, 'channel', soma, 'channel')
+ moose.connect(mgblock, 'channel', soma, 'channel')
# This is for comparing with MgBlock
nmda_noMg = moose.copy(nmda, soma, 'nmda_noMg')
moose.connect( nmda_noMg, 'channel', soma, 'channel')
@@ -130,25 +144,10 @@ def test_mgblock():
#data = pylab.vstack((t, Gnmda.vector, Gnmda_noMg.vector)).transpose()
#pylab.savetxt('mgblock.dat', data)
pylab.show()
+
-def main():
- """
- Demonstrates the use of MgBlock.
- Creates an NMDA channel with MgBlock and another without.
- Connects them up to the compartment on one hand, and to a
- SynHandler on the other, so as to receive synaptic input.
- Delivers two pulses to each receptor, with a small delay in between.
-
- Plots out the conductance change at each receptor and the reslting
- membrane potential rise at the compartment.
-
- Note that these NMDA channels do NOT separate out the contributions
- due to calcium and other ions. To do this correctly one should use
- the GHK object.
- """
+if __name__ == '__main__':
test_mgblock()
-if __name__ == '__main__':
- main()
-#
+#
# mgblock.py ends here
diff --git a/snippets/multiComptSigNeur.py b/snippets/multiComptSigNeur.py
index 15190c04c2906b11b40e59ddbca05d437313f6a2..f580751db9ae1d2fade7ac0870495aaa2327cc26 100644
--- a/snippets/multiComptSigNeur.py
+++ b/snippets/multiComptSigNeur.py
@@ -1,22 +1,36 @@
-# multiComptSigNeur.py ---
+# multiComptSigNeur.py ---
# Upi Bhalla NCBS Bangalore 2013.
-# Commentary:
-
+# Commentary:
+
+# A toy compartmental neuronal + chemical model. The neuronal model is in
+# a dendrite and five dendritic spines. The chemical model is in three
+# compartments: one for the dendrite,
+# one for the spine head, and one for the postsynaptic density. However,
+# the spatial geometry of the neuronal model is ignored and the chemical
+# model just has three cubic volumes for each compartment. So there
+# is a functional mapping but spatial considerations are lost.
+# The electrical model contributes the incoming calcium flux to the
+# chemical model. This comes from the synaptic channels.
+# The signalling here does two things to the electrical model. First, the
+# amount of receptor in the chemical model controls the amount of glutamate
+# receptor in the PSD. Second, there is a small kinase reaction that
+# phosphorylates and inactivates the dendritic potassium channel.
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
+#
# Code:
@@ -32,7 +46,7 @@ EREST_ACT = -70e-3
# Gate equations have the form:
#
# y(x) = (A + B * x) / (C + exp((x + D) / F))
-#
+#
# where x is membrane voltage and y is the rate constant for gate
# closing or opening
@@ -45,7 +59,7 @@ Na_m_params = [1e5 * (25e-3 + EREST_ACT), # 'A_A':
0.0, # 'B_B':
0.0, # 'B_C':
0.0 - EREST_ACT, # 'B_D':
- 18e-3 # 'B_F':
+ 18e-3 # 'B_F':
]
Na_h_params = [ 70.0, # 'A_A':
0.0, # 'A_B':
@@ -56,8 +70,8 @@ Na_h_params = [ 70.0, # 'A_A':
0.0, # 'B_B':
1.0, # 'B_C':
-30e-3 - EREST_ACT, # 'B_D':
- -0.01 # 'B_F':
- ]
+ -0.01 # 'B_F':
+ ]
K_n_params = [ 1e4 * (10e-3 + EREST_ACT), # 'A_A':
-1e4, # 'A_B':
-1.0, # 'A_C':
@@ -67,7 +81,7 @@ K_n_params = [ 1e4 * (10e-3 + EREST_ACT), # 'A_A':
0.0, # 'B_B':
0.0, # 'B_C':
0.0 - EREST_ACT, # 'B_D':
- 80e-3 # 'B_F':
+ 80e-3 # 'B_F':
]
VMIN = -30e-3 + EREST_ACT
VMAX = 120e-3 + EREST_ACT
@@ -85,7 +99,7 @@ def createSquid():
compt.Ra = 7639.44e3
nachan = moose.HHChannel( '/n/compt/Na' )
nachan.Xpower = 3
- xGate = moose.HHGate(nachan.path + '/gateX')
+ xGate = moose.HHGate(nachan.path + '/gateX')
xGate.setupAlpha(Na_m_params + [VDIVS, VMIN, VMAX])
xGate.useInterpolation = 1
nachan.Ypower = 1
@@ -98,7 +112,7 @@ def createSquid():
kchan = moose.HHChannel( '/n/compt/K' )
kchan.Xpower = 4.0
- xGate = moose.HHGate(kchan.path + '/gateX')
+ xGate = moose.HHGate(kchan.path + '/gateX')
xGate.setupAlpha(K_n_params + [VDIVS, VMIN, VMAX])
xGate.useInterpolation = 1
kchan.Gbar = 0.2836e-3
@@ -328,19 +342,19 @@ def createChemModel( neuroCompt, spineCompt, psdCompt ):
# Just for printf debugging
def printMolVecs( title ):
print(title)
- '''
+ """
nCa = moose.vec( '/model/chem/neuroMesh/Ca' )
sCa = moose.vec( '/model/chem/spineMesh/Ca' )
sR = moose.vec( '/model/chem/spineMesh/headGluR' )
pR = moose.vec( '/model/chem/psdMesh/psdGluR' )
- print 'sizes: nCa, sCa, sR, pR = ', len(nCa), len(sCa), len(sR), len(pR)
+ print 'sizes: nCa, sCa, sR, pR = ', len(nCa), len(sCa), len(sR), len(pR)
#print 'nCa=', nCa.conc, ', sCa=', sCa.conc, ', sR=', sR.n, ', pR=', pR.n
print 'nCaConcInit=', nCa.concInit, ', sCa=', sCa.concInit
#print 'sRnInit=', sR.nInit, ', pR=', pR.nInit
print 'sRconcInit=', sR.concInit, ', pR=', pR.concInit
#print 'nCaSize=', nCa.volume, ', sCa=', sCa.volume, ', sR=', sR.n, ', pR=', pR.n
- '''
+ """
def makeChemInCubeMesh():
dendSide = 10.8e-6
@@ -415,7 +429,7 @@ def makeCubeMultiscale():
diffReac = moose.Reac( '/model/chem/spineMesh/diff' )
moose.connect( diffReac, 'sub', headCa, 'reac' )
moose.connect( diffReac, 'prd', dendCa, 'reac' )
- diffReac.Kf = 1
+ diffReac.Kf = 1
diffReac.Kb = headCa.volume / dendCa.volume
# set up adaptors
@@ -501,25 +515,10 @@ def testCubeMultiscale( useSolver ):
dumpPlots( plotName )
def main():
- """
- A toy compartmental neuronal + chemical model. The neuronal model is in
- a dendrite and five dendritic spines. The chemical model is in three
- compartments: one for the dendrite,
- one for the spine head, and one for the postsynaptic density. However,
- the spatial geometry of the neuronal model is ignored and the chemical
- model just has three cubic volumes for each compartment. So there
- is a functional mapping but spatial considerations are lost.
- The electrical model contributes the incoming calcium flux to the
- chemical model. This comes from the synaptic channels.
- The signalling here does two things to the electrical model. First, the
- amount of receptor in the chemical model controls the amount of glutamate
- receptor in the PSD. Second, there is a small kinase reaction that
- phosphorylates and inactivates the dendritic potassium channel.
- """
- testCubeMultiscale( 1 )
+ testCubeMultiscale( 1 )
if __name__ == '__main__':
main()
-#
+#
# HsolveInstability.py ends here
diff --git a/snippets/multicomp_lif.py b/snippets/multicomp_lif.py
index 999c09f82d00e580af7a45befa0ac62fa6b80d44..8b29501f765c755882ead7748708d505abdcd961 100644
--- a/snippets/multicomp_lif.py
+++ b/snippets/multicomp_lif.py
@@ -1,50 +1,55 @@
-# multicomp_lif.py ---
-#
+# multicomp_lif.py ---
+#
# Filename: multicomp_lif.py
# Description: Leaky Integrate and Fire using regular neuronal compartment
# Author: Subhasis Ray
-# Maintainer:
+# Maintainer:
# Created: Fri Feb 7 16:26:05 2014 (+0530)
-# Version:
-# Last-Updated:
-# By:
+# Version:
+# Last-Updated:
+# By:
# Update #: 0
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
-
-# Commentary:
-#
-#
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
+"""This is an example of how you can create a Leaky Integrate and Fire
+compartment using regular compartment and Func to check for thresold
+crossing and resetting the Vm."""
+
+
import sys
sys.path.append('../../python')
@@ -57,14 +62,11 @@ stepsize = 100e-3 # Time step for pauses between runs
simdt = 1e-4 # time step for numerical integration
plotdt = 0.25e-3 # time step for plotting
-delayMax = 5e-3 # Maximum synaptic delay
+delayMax = 5e-3 # Maximum synaptic delay
class LIFComp(moose.Compartment):
- """
- Leaky integrate and fire neuron using regular compartments,
- spikegen and Func.
-
- """
+ """Leaky integrate and fire neuron using regular compartments,
+ spikegen and Func."""
def __init__(self, *args):
moose.Compartment.__init__(self, *args)
self.spikegen = moose.SpikeGen('%s/spike' % (self.path))
@@ -76,7 +78,7 @@ class LIFComp(moose.Compartment):
self.Cm = 100e-9
self.Em = 0 #-65e-3
self.initVm = 0 #self.Em
-
+
# Note that the result is dependent on exact order of
# execution of SpikeGen and Func. If Func gets executed first
# SpikeGen will never cross threshold.
@@ -107,12 +109,11 @@ class LIFComp(moose.Compartment):
self.spikegen.threshold = value
def setup_two_cells():
- """
- Create two cells with leaky integrate and fire compartments. The
+ """Create two cells with leaky integrate and fire compartments. The
first cell is composed of two compartments a1 and a2 and the
second cell is composed of compartments b1 and b2. Each pair is
connected via raxial message so that the voltage of one
- compartment influences the other through axial resistance Ra.
+ compartment influences the other through axial resistance Ra.
The compartment a1 of the first neuron is connected to the
compartment b2 of the second neuron through a synaptic channel.
@@ -145,7 +146,7 @@ def setup_two_cells():
moose.connect(b2, 'channel', syn, 'channel')
## Single message works most of the time but occassionally gives a
## core dump
-
+
# m = moose.connect(a1.spikegen, 'spikeOut',
# syn.synapse.vec, 'addSpike')
@@ -160,7 +161,7 @@ def setup_two_cells():
stim.level[0] = 11e-9
moose.connect(stim, 'output', a1, 'injectMsg')
tables = []
- data = moose.Neutral('/data')
+ data = moose.Neutral('/data')
for c in moose.wildcardFind('/##[ISA=Compartment]'):
tab = moose.Table('%s/%s' % (data.path, c.name))
moose.connect(tab, 'requestOut', c, 'getVm')
@@ -175,14 +176,9 @@ def setup_two_cells():
syn.Gbar = 1e-6
return tables
-def main():
- """
- This is an example of how you can create a Leaky Integrate and Fire
- compartment using regular compartment and Func to check for thresold
- crossing and resetting the Vm.
- """
+if __name__ == '__main__':
tables = setup_two_cells()
-
+
utils.setDefaultDt(elecdt=simdt, plotdt2=plotdt)
utils.assignDefaultTicks(modelRoot='/model', dataRoot='/data', solver='ee')
moose.reinit()
@@ -193,8 +189,5 @@ def main():
plot(t, tab.vector*1e3, label=tab.name)
legend()
show()
-
-if __name__ == '__main__':
- main()
-#
+#
# multicomp_lif.py ends here
diff --git a/snippets/neuronFromDotp.py b/snippets/neuronFromDotp.py
index 2e9ce7bb200d62fc413b4a6c71358725a9ca97a8..31c0ec384fca9d1f43a413695a4a8e3e07daf4b8 100644
--- a/snippets/neuronFromDotp.py
+++ b/snippets/neuronFromDotp.py
@@ -1,22 +1,26 @@
-# neuronFromDotp.py ---
+# neuronFromDotp.py ---
# Author: Upi Bhalla NCBS Bangalore 2014.
-# Commentary:
+# Commentary:
+# Demonstrates how to load a simple neuronal model in GENESIS dotp format.
+# The model has branches and a few spines.
+# It is adorned just with classic HH squid channels.
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
+#
# Code:
@@ -32,7 +36,7 @@ EREST_ACT = -70e-3
# Gate equations have the form:
#
# y(x) = (A + B * x) / (C + exp((x + D) / F))
-#
+#
# where x is membrane voltage and y is the rate constant for gate
# closing or opening
@@ -45,7 +49,7 @@ Na_m_params = [1e5 * (25e-3 + EREST_ACT), # 'A_A':
0.0, # 'B_B':
0.0, # 'B_C':
0.0 - EREST_ACT, # 'B_D':
- 18e-3 # 'B_F':
+ 18e-3 # 'B_F':
]
Na_h_params = [ 70.0, # 'A_A':
0.0, # 'A_B':
@@ -56,8 +60,8 @@ Na_h_params = [ 70.0, # 'A_A':
0.0, # 'B_B':
1.0, # 'B_C':
-30e-3 - EREST_ACT, # 'B_D':
- -0.01 # 'B_F':
- ]
+ -0.01 # 'B_F':
+ ]
K_n_params = [ 1e4 * (10e-3 + EREST_ACT), # 'A_A':
-1e4, # 'A_B':
-1.0, # 'A_C':
@@ -67,7 +71,7 @@ K_n_params = [ 1e4 * (10e-3 + EREST_ACT), # 'A_A':
0.0, # 'B_B':
0.0, # 'B_C':
0.0 - EREST_ACT, # 'B_D':
- 80e-3 # 'B_F':
+ 80e-3 # 'B_F':
]
VMIN = -30e-3 + EREST_ACT
VMAX = 120e-3 + EREST_ACT
@@ -154,5 +158,5 @@ def main():
if __name__ == '__main__':
main()
-#
+#
# neuronFromDotp.py ends here
diff --git a/snippets/nsdf.py b/snippets/nsdf.py
index 7c8765f2be0233a0834308c99a29b97ff23f05e5..fc6d20070ee061ebd17ff2db310ce63b432caaea 100644
--- a/snippets/nsdf.py
+++ b/snippets/nsdf.py
@@ -1,51 +1,51 @@
-# nsdf.py ---
-#
+# nsdf.py ---
+#
# Filename: nsdf.py
-# Description:
+# Description:
# Author: subha
-# Maintainer:
+# Maintainer:
# Created: Fri Jun 26 12:23:07 2015 (-0400)
-# Version:
+# Version:
# Last-Updated: Tue Dec 29 12:50:27 2015 (-0500)
# By: Subhasis Ray
# Update #: 6
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
-
-# Commentary:
-#
-#
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
-"""
-NSDF : Neuroscience Simulation Data Format
+"""NSDF : Neuroscience Simulation Data Format
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
NSDF is an HDF5 based format for storing data from neuroscience
simulation.
@@ -85,13 +85,15 @@ implemented in NSDFWriter. But units can be easily added as dataset
attribute if desired as shown in this example.
-References:
+References:
Ray, Chintaluri, Bhalla and Wojcik. NSDF: Neuroscience Simulation Data
Format, Neuroinformatics, 2015.
http://nsdf.readthedocs.org/en/latest/
+See also:
+nsdf_vec.py
"""
import numpy as np
@@ -101,8 +103,7 @@ import getpass
import moose
def setup_model():
- """
- Setup a dummy model with a PulseGen and a SpikeGen. The SpikeGen
+ """Setup a dummy model with a PulseGen and a SpikeGen. The SpikeGen
detects the leading edges of the pulses created by the PulseGen
and sends out the event times. We record the PulseGen outputValue
as Uniform data and leading edge time as Event data in the NSDF
@@ -150,7 +151,7 @@ def setup_model():
this simulation we generate square pules from a PulseGen object and
use a SpikeGen to detect the threshold crossing events of rising
edges. We store the pulsegen output as Uniform data and the threshold
-crossing times as Event data. '''
+crossing times as Event data. '''
nsdf.stringAttr['creator'] = getpass.getuser()
nsdf.stringVecAttr['software'] = ['python2.7', 'moose3' ]
nsdf.stringVecAttr['method'] = ['']
@@ -161,15 +162,14 @@ crossing times as Event data. '''
nsdf.stringAttr['/data/uniform/PulseGen/outputValue/tunit'] = 's'
nsdf.stringAttr['/data/uniform/PulseGen/outputValue/unit'] = 'A'
eventDataPath = '/data/event/SpikeGen/spikeOut/{}_{}_{}/unit'.format(t_lead.vec.value,
- t_lead.getDataIndex(),
+ t_lead.getDataIndex(),
t_lead.fieldIndex)
nsdf.stringAttr[eventDataPath] = 's'
-def main():
- setup_model()
if __name__ == '__main__':
- main()
+ setup_model()
+
-#
+#
# nsdf.py ends here
diff --git a/snippets/nsdf.txt b/snippets/nsdf.txt
deleted file mode 100644
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--- a/snippets/nsdf.txt
+++ /dev/null
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diff --git a/snippets/nsdf_demo.h5 b/snippets/nsdf_demo.h5
deleted file mode 100644
index 1e67dd0d1e6ab30bb8aa15494b479f51f9f84404..0000000000000000000000000000000000000000
Binary files a/snippets/nsdf_demo.h5 and /dev/null differ
diff --git a/snippets/nsdf_vec.py b/snippets/nsdf_vec.py
index ae25942576f9680e7b16eded8096beeb3f6a6fe0..7a35586db66bbe3996921a65447b7db64f08418c 100644
--- a/snippets/nsdf_vec.py
+++ b/snippets/nsdf_vec.py
@@ -1,49 +1,71 @@
-# nsdf_vec.py ---
-#
+# nsdf_vec.py ---
+#
# Filename: nsdf_vec.py
-# Description:
+# Description:
# Author: subha
-# Maintainer:
+# Maintainer:
# Created: Sat Dec 19 22:27:27 2015 (-0500)
-# Version:
+# Version:
# Last-Updated: Thu Aug 11 11:09:33 2016 (-0400)
# By: Subhasis Ray
# Update #: 135
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
-
-# Commentary:
-#
-#
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
+"""Example code to dump data from multiple elements in a vector.
+
+In this demo we create a PulseGen vector where each element has a
+different set of pulse parameters. After saving the output vector
+directly using MOOSE NSDFWriter we open the NSDF file using h5py and
+plot the saved data.
+
+You need h5py module installed to run this simulation.
+
+References:
+
+Ray, Chintaluri, Bhalla and Wojcik. NSDF: Neuroscience Simulation Data
+Format, Neuroinformatics, 2015.
+
+http://nsdf.readthedocs.org/en/latest/
+
+See also:
+
+nsdf.py
+
+"""
+
import numpy as np
from datetime import datetime
@@ -54,8 +76,7 @@ from matplotlib import pyplot as plt
import moose
def write_nsdf():
- """
- Setup a dummy model with a PulseGen vec and dump the outputValue in
+ """Setup a dummy model with a PulseGen vec and dump the outputValue in
NSDF file
"""
@@ -101,7 +122,7 @@ def write_nsdf():
this simulation we generate square pules from a PulseGen object and
use a SpikeGen to detect the threshold crossing events of rising
edges. We store the pulsegen output as Uniform data and the threshold
-crossing times as Event data. '''
+crossing times as Event data. '''
nsdf.stringAttr['creator'] = getpass.getuser()
nsdf.stringVecAttr['software'] = ['python2.7', 'moose3' ]
nsdf.stringVecAttr['method'] = ['']
@@ -109,7 +130,7 @@ crossing times as Event data. '''
nsdf.stringAttr['license'] = 'CC-BY-NC'
####################################################
## !! Work in progress: concurrent write via h5py does not work !!
- ####################################################
+ ####################################################
## Now write some custom stuff via h5py
print('Closing nsdf handle')
nsdf.close() #explicitly close the file so we do not interfere with h5py
@@ -127,7 +148,7 @@ crossing times as Event data. '''
pulse_info['level', ii] = pulsegen.vec[ii].level[0]
map_pulse[ii] = pulsegen.vec[ii].path
#TODO: connect this as a dimension scale on pulse_info
-
+
return nsdf.filename
def read_nsdf(fname):
@@ -146,36 +167,16 @@ def read_nsdf(fname):
source = pulse_src[ii]
data = pulse_data[ii, :]
dt = pulse_data.attrs['dt']
- plt.figure(source)
+ plt.figure(source)
ts = np.arange(len(data)) * dt
plt.plot(ts, data)
plt.suptitle(source)
plt.show()
+
-def main():
- """
- Example code to dump data from multiple elements in a vector.
-
- In this demo we create a PulseGen vector where each element has a
- different set of pulse parameters. After saving the output vector
- directly using MOOSE NSDFWriter we open the NSDF file using h5py and
- plot the saved data.
-
- You need h5py module installed to run this simulation.
-
- References:
-
- Ray, Chintaluri, Bhalla and Wojcik. NSDF: Neuroscience Simulation Data
- Format, Neuroinformatics, 2015.
-
- http://nsdf.readthedocs.org/en/latest/
-
- """
+if __name__ == '__main__':
fname = write_nsdf()
read_nsdf(fname)
-if __name__ == '__main__':
- main()
-
-#
+#
# nsdf_vec.py ends here
diff --git a/snippets/nsdf_vec_demo.h5 b/snippets/nsdf_vec_demo.h5
deleted file mode 100644
index c58441f1caffa9b7954c35b989672a7dd23f99b1..0000000000000000000000000000000000000000
Binary files a/snippets/nsdf_vec_demo.h5 and /dev/null differ
diff --git a/snippets/onetoonemsg.py b/snippets/onetoonemsg.py
index 6d8391397c1fd9708ccef3cbc751c81801285c1f..7f875f6f3abfebfae55d1f1d073e68f4887fc5cc 100644
--- a/snippets/onetoonemsg.py
+++ b/snippets/onetoonemsg.py
@@ -1,47 +1,47 @@
-# onetoonemsg.py ---
-#
+# onetoonemsg.py ---
+#
# Filename: onetoonemsg.py
-# Description:
-# Author:Subhasis Ray
-# Maintainer:
+# Description:
+# Author:Subhasis Ray
+# Maintainer:
# Created: Wed May 1 11:05:30 2013 (+0530)
-# Version:
+# Version:
# Last-Updated: Wed May 1 11:12:07 2013 (+0530)
# By: subha
# Update #: 24
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
-#
-#
-#
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
@@ -67,14 +67,8 @@ def test_one_to_one(size=2):
pylab.plot(t)
pylab.show()
-def main():
- """
-Demonstrates one-to-one connection between objects through ''moose.connect''.
- """
- test_one_to_one()
-
if __name__ == '__main__':
- main()
+ test_one_to_one()
-#
+#
# onetoonemsg.py ends here
diff --git a/snippets/output_hdfdemo.h5 b/snippets/output_hdfdemo.h5
deleted file mode 100644
index 392542373caba52878c01627ad94146bcf81babf..0000000000000000000000000000000000000000
Binary files a/snippets/output_hdfdemo.h5 and /dev/null differ
diff --git a/snippets/output_tabledemo.csv b/snippets/output_tabledemo.csv
deleted file mode 100644
index 3ab58b1c63aacae053c136670941b1626ce19a88..0000000000000000000000000000000000000000
--- a/snippets/output_tabledemo.csv
+++ /dev/null
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-
diff --git a/snippets/proto18.py b/snippets/proto18.py
index 55794bce45b037aa70e33a970953358923b2a75d..b2fecbb3aa0ee275de072b61752e9087f8c1f511 100644
--- a/snippets/proto18.py
+++ b/snippets/proto18.py
@@ -1,30 +1,29 @@
-#FILE INFORMATION
-"""
-Based mostly on the traub91proto.g by Dave Beeman
-Main difference is addition of Glu and NMDA channels
-The 1991 Traub set of voltage and concentration dependent channels
-Implemented as tabchannels by : Dave Beeman
- R.D.Traub, R. K. S. Wong, R. Miles, and H. Michelson
- Journal of Neurophysiology, Vol. 66, p. 635 (1991)
-
-This file depends on functions and constants defined in defaults.g
-As it is also intended as an example of the use of the tabchannel
-object to implement concentration dependent channels, it has extensive
-comments. Note that the original units used in the paper have been
-converted to SI (MKS) units. Also, we define the ionic equilibrium
-potentials relative to the resting potential, EREST_ACT. In the
-paper, this was defined to be zero. Here, we use -0.060 volts, the
-measured value relative to the outside of the cell.
-
-
-November 1999 update for GENESIS 2.2: Previous versions of this file used
-a combination of a table, tabgate, and vdep_channel to implement the
-Ca-dependent K Channel - K(C). This new version uses the new tabchannel
-"instant" field, introduced in GENESIS 2.2, to implement an
-"instantaneous" gate for the multiplicative Ca-dependent factor in the
-conductance. This allows these channels to be used with the fast
-hsolve chanmodes > 1.
-"""
+#/* FILE INFORMATION
+#** Based mostly on the traub91proto.g by Dave Beeman
+#** Main difference is addition of Glu and NMDA channels
+#** The 1991 Traub set of voltage and concentration dependent channels
+#** Implemented as tabchannels by : Dave Beeman
+#** R.D.Traub, R. K. S. Wong, R. Miles, and H. Michelson
+#** Journal of Neurophysiology, Vol. 66, p. 635 (1991)
+#**
+#** This file depends on functions and constants defined in defaults.g
+#** As it is also intended as an example of the use of the tabchannel
+#** object to implement concentration dependent channels, it has extensive
+#** comments. Note that the original units used in the paper have been
+#** converted to SI (MKS) units. Also, we define the ionic equilibrium
+#** potentials relative to the resting potential, EREST_ACT. In the
+#** paper, this was defined to be zero. Here, we use -0.060 volts, the
+#** measured value relative to the outside of the cell.
+#*/
+
+#/* November 1999 update for GENESIS 2.2: Previous versions of this file used
+# a combination of a table, tabgate, and vdep_channel to implement the
+# Ca-dependent K Channel - K(C). This new version uses the new tabchannel
+# "instant" field, introduced in GENESIS 2.2, to implement an
+# "instantaneous" gate for the multiplicative Ca-dependent factor in the
+# conductance. This allows these channels to be used with the fast
+# hsolve chanmodes > 1.
+#*/
# Apr 2012 update for pymoose. Converted to equivalent MOOSE funcs.
@@ -314,9 +313,9 @@ def make_Na():
xgate = moose.element( 'Na/gateX' )
xA = numpy.array( [ 320e3 * (0.0131 + EREST_ACT),
- -320e3, -1.0, -1.0 * (0.0131 + EREST_ACT), -0.004,
- -280e3 * (0.0401 + EREST_ACT), 280e3, -1.0,
- -1.0 * (0.0401 + EREST_ACT), 5.0e-3,
+ -320e3, -1.0, -1.0 * (0.0131 + EREST_ACT), -0.004,
+ -280e3 * (0.0401 + EREST_ACT), 280e3, -1.0,
+ -1.0 * (0.0401 + EREST_ACT), 5.0e-3,
3000, -0.1, 0.05 ] )
xgate.alphaParms = xA
@@ -326,7 +325,7 @@ def make_Na():
ygate = moose.element( 'Na/gateY' )
yA = numpy.array( [ 128.0, 0.0, 0.0, -1.0 * (0.017 + EREST_ACT), 0.018,
- 4.0e3, 0.0, 1.0, -1.0 * (0.040 + EREST_ACT), -5.0e-3,
+ 4.0e3, 0.0, 1.0, -1.0 * (0.040 + EREST_ACT), -5.0e-3,
3000, -0.1, 0.05 ] )
ygate.alphaParms = yA
@@ -348,7 +347,7 @@ def make_K_DR():
K_DR.Zpower = 0
xgate = moose.element( 'K_DR/gateX' )
- xA = numpy.array( [ 16e3 * (0.0351 + EREST_ACT),
+ xA = numpy.array( [ 16e3 * (0.0351 + EREST_ACT),
-16e3, -1.0, -1.0 * (0.0351 + EREST_ACT), -0.005,
250, 0.0, 0.0, -1.0 * (0.02 + EREST_ACT), 0.04,
3000, -0.1, 0.05 ] )
@@ -371,9 +370,9 @@ def make_K_A():
K_A.Zpower = 0
xgate = moose.element( 'K_A/gateX' )
- xA = numpy.array( [ 20e3 * (0.0131 + EREST_ACT),
+ xA = numpy.array( [ 20e3 * (0.0131 + EREST_ACT),
-20e3, -1.0, -1.0 * (0.0131 + EREST_ACT), -0.01,
- -17.5e3 * (0.0401 + EREST_ACT),
+ -17.5e3 * (0.0401 + EREST_ACT),
17.5e3, -1.0, -1.0 * (0.0401 + EREST_ACT), 0.01,
3000, -0.1, 0.05 ] )
xgate.alphaParms = xA
@@ -508,3 +507,4 @@ def make_axon():
axon = moose.SpikeGen( 'axon' )
axon.threshold = -40e-3 # V
axon.abs_refract = 10e-3 # sec
+
diff --git a/snippets/pulsegen.py b/snippets/pulsegen.py
index ffc67278c4bf1e4be26df990f87467ec8a34562f..2f6cfa36c27b127fffe439235b4938286a4817bc 100644
--- a/snippets/pulsegen.py
+++ b/snippets/pulsegen.py
@@ -1,48 +1,48 @@
-# pulsegen.py ---
-#
+# pulsegen.py ---
+#
# Filename: pulsegen.py
-# Description:
-# Author:Subhasis Ray
-# Maintainer:
+# Description:
+# Author:Subhasis Ray
+# Maintainer:
# Created: Sat Jun 23 10:26:25 2012 (+0530)
-# Version:
+# Version:
# Last-Updated: Sat Jun 23 13:51:28 2012 (+0530)
# By: subha
# Update #: 34
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
-#
-#
-#
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
@@ -53,6 +53,8 @@ from matplotlib import pyplot as plt
import numpy as np
def multilevel_pulsegen():
+ """Demonstrates a pulsegen with multiple levels, delays and
+ widths."""
pg = moose.PulseGen('pulsegen')
pg.count = 5
for ii in range(pg.count):
@@ -68,16 +70,9 @@ def multilevel_pulsegen():
plt.plot(tab.vector)
plt.show()
-def main():
- """
- Demonstrates a pulsegen with multiple levels, delays and
- widths.
- """
- multilevel_pulsegen()
-
if __name__ == '__main__':
- main()
+ multilevel_pulsegen()
-#
+#
# pulsegen.py ends here
diff --git a/snippets/pulsegen2.py b/snippets/pulsegen2.py
index dbe15cbc1472684950d7a50d7f30e3ea15dfba18..b5c9f0ca90de3064a5090a3e8ee1bd6a120257ca 100644
--- a/snippets/pulsegen2.py
+++ b/snippets/pulsegen2.py
@@ -1,52 +1,74 @@
-# pulsegen2.py ---
-#
+# pulsegen2.py ---
+#
# Filename: pulsegen2.py
-# Description:
+# Description:
# Author: Subhasis Ray
-# Maintainer:
+# Maintainer:
# Created: Wed Jul 8 17:06:23 2015 (+0530)
-# Version:
-# Last-Updated:
-# By:
+# Version:
+# Last-Updated:
+# By:
# Update #: 0
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
-
-# Commentary:
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
# Recovered old demo from moose branch (beta 1.4) and converted to
# current API.
-#
-#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
+"""Pulse generator example
+^^^^^^^^^^^^^^^^^^^^^^^
+
+This example shows the full range of operations of PulseGen objects
+with a reimplementation of corresponding GENESIS demo.
+
+A PulseGen object can be run in three modes: free running
+(trigMode=0), triggered (trigMode=1) and gated (trigMode=2).
+
+In the free running mode it keeps repeating the pulse series
+indefinitely.
+
+In triggered mode, it generates a pulse series on the leading edge of
+the trigger signal coming to its `input` field. The trigger can be the
+`output` of another PulseGen as in this example.
+
+In gated mode, the PulseGen acts as if it was free-running as long as
+the `input` remains high.
+
+"""
+
+
import moose
import pylab
@@ -54,121 +76,95 @@ import numpy
RUNTIME = 200.0
SIMDT = 1.0
-def main():
- """
- Pulse generator example
-
- This example shows the full range of operations of PulseGen objects
- with a reimplementation of corresponding GENESIS demo.
-
- A PulseGen object can be run in three modes: free running
- (trigMode=0), triggered (trigMode=1) and gated (trigMode=2).
-
- In the free running mode it keeps repeating the pulse series
- indefinitely.
-
- In triggered mode, it generates a pulse series on the leading edge of
- the trigger signal coming to its `input` field. The trigger can be the
- `output` of another PulseGen as in this example.
-
- In gated mode, the PulseGen acts as if it was free-running as long as
- the `input` remains high.
-
- """
-
-
- # Pulse generator with trigger mode = 0 This is free running - and
- # creates a series of pulses
- pulse0 = moose.PulseGen("/pulse0")
- pulse0.level[0] = 50.0
- pulse0.width[0] = 3.0
- pulse0.delay[0] = 5.0
- pulse0.level[1] = -20.0
- pulse0.width[1] = 5.0
- pulse0.delay[1] = 8.0
- pulse0.baseLevel = 10.0
- pulse0.trigMode = 0
-
- trig = moose.PulseGen("/trig")
- trig.level[0] = 20.0
- trig.width[0] = 1.0
- trig.delay[0] = 5.0
- trig.width[1] = 30.0
-
- # Pulse generator with trigger mode = 1
- pulse1 = moose.PulseGen("/pulse1")
- pulse1.level[0] = 50.0
- pulse1.width[0] = 3.0
- pulse1.delay[0] = 5.0
- pulse1.level[1] = -20.0
- pulse1.width[1] = 5.0
- pulse1.delay[1] = 8.0
- pulse1.baseLevel = 10.0
- pulse1.trigMode = 1
- trig.connect("output", pulse1, "input")
-
- # Gated pulse
- gate = moose.PulseGen("/gate")
- gate.level[0] = 20.0
- gate.width[0] = 30.0
- gate.delay[0] = 15.0
- gate.width[1] = 30.0
-
- # Pulse generator with trigger mode = 2
- pulse2 = moose.PulseGen("/pulse2")
- pulse2.level[0] = 50.0
- pulse2.width[0] = 3.0
- pulse2.delay[0] = 5.0
- pulse2.level[1] = -20.0
- pulse2.width[1] = 5.0
- pulse2.delay[1] = 8.0
- pulse2.baseLevel = 10.0
- pulse2.trigMode = 2
- gate.connect("output", pulse2, "input")
-
- plot0 = moose.Table("/plot0")
- plot0.connect("requestOut", pulse0, "getOutputValue")
-
- plot1 = moose.Table("/plot1")
- plot1.connect("requestOut", pulse1, "getOutputValue")
-
- plot2 = moose.Table("/plot2")
- plot2.connect("requestOut", pulse2, "getOutputValue")
-
- plotGate = moose.Table("/plotGate")
- plotGate.connect("requestOut", gate, "getOutputValue")
-
- plotTrig = moose.Table("/plotTrig")
- plotTrig.connect("requestOut", trig, "getOutputValue")
-
- moose.useClock(0, "/#[TYPE=PulseGen]", 'process')
- moose.useClock(1, "/#[TYPE=Table]", 'process')
- moose.setClock(0, SIMDT)
- moose.setClock(1, SIMDT)
- moose.reinit()
- moose.start(RUNTIME)
-
- pylab.subplot(511)
- pylab.plot(plot0.vector)
- pylab.title('Free Run')
- pylab.subplot(512)
- pylab.plot(plot1.vector)
- pylab.title('Triggered (below)')
- pylab.subplot(513)
- pylab.plot(plotTrig.vector)
- pylab.title('Free Running Trigger')
- pylab.subplot(514)
- pylab.plot(plot2.vector)
- pylab.title('Gated (below)')
- pylab.subplot(515)
- pylab.plot(plotGate.vector)
- pylab.title('Free Running Gate')
- pylab.tight_layout()
- pylab.show()
- print("pulsegen.py: finished simulation")
-
-if __name__ == '__main__':
- main()
-
-#
+# Pulse generator with trigger mode = 0 This is free running - and
+# creates a series of pulses
+pulse0 = moose.PulseGen("/pulse0")
+pulse0.level[0] = 50.0
+pulse0.width[0] = 3.0
+pulse0.delay[0] = 5.0
+pulse0.level[1] = -20.0
+pulse0.width[1] = 5.0
+pulse0.delay[1] = 8.0
+pulse0.baseLevel = 10.0
+pulse0.trigMode = 0
+
+trig = moose.PulseGen("/trig")
+trig.level[0] = 20.0
+trig.width[0] = 1.0
+trig.delay[0] = 5.0
+trig.width[1] = 30.0
+
+# Pulse generator with trigger mode = 1
+pulse1 = moose.PulseGen("/pulse1")
+pulse1.level[0] = 50.0
+pulse1.width[0] = 3.0
+pulse1.delay[0] = 5.0
+pulse1.level[1] = -20.0
+pulse1.width[1] = 5.0
+pulse1.delay[1] = 8.0
+pulse1.baseLevel = 10.0
+pulse1.trigMode = 1
+trig.connect("output", pulse1, "input")
+
+# Gated pulse
+gate = moose.PulseGen("/gate")
+gate.level[0] = 20.0
+gate.width[0] = 30.0
+gate.delay[0] = 15.0
+gate.width[1] = 30.0
+
+# Pulse generator with trigger mode = 2
+pulse2 = moose.PulseGen("/pulse2")
+pulse2.level[0] = 50.0
+pulse2.width[0] = 3.0
+pulse2.delay[0] = 5.0
+pulse2.level[1] = -20.0
+pulse2.width[1] = 5.0
+pulse2.delay[1] = 8.0
+pulse2.baseLevel = 10.0
+pulse2.trigMode = 2
+gate.connect("output", pulse2, "input")
+
+plot0 = moose.Table("/plot0")
+plot0.connect("requestOut", pulse0, "getOutputValue")
+
+plot1 = moose.Table("/plot1")
+plot1.connect("requestOut", pulse1, "getOutputValue")
+
+plot2 = moose.Table("/plot2")
+plot2.connect("requestOut", pulse2, "getOutputValue")
+
+plotGate = moose.Table("/plotGate")
+plotGate.connect("requestOut", gate, "getOutputValue")
+
+plotTrig = moose.Table("/plotTrig")
+plotTrig.connect("requestOut", trig, "getOutputValue")
+
+moose.useClock(0, "/#[TYPE=PulseGen]", 'process')
+moose.useClock(1, "/#[TYPE=Table]", 'process')
+moose.setClock(0, SIMDT)
+moose.setClock(1, SIMDT)
+moose.reinit()
+moose.start(RUNTIME)
+
+pylab.subplot(511)
+pylab.plot(plot0.vector)
+pylab.title('Free Run')
+pylab.subplot(512)
+pylab.plot(plot1.vector)
+pylab.title('Triggered (below)')
+pylab.subplot(513)
+pylab.plot(plotTrig.vector)
+pylab.title('Free Running Trigger')
+pylab.subplot(514)
+pylab.plot(plot2.vector)
+pylab.title('Gated (below)')
+pylab.subplot(515)
+pylab.plot(plotGate.vector)
+pylab.title('Free Running Gate')
+pylab.tight_layout()
+pylab.show()
+print("pulsegen.py: finished simulation")
+
+#
# pulsegen2.py ends here
diff --git a/snippets/pyrun.py b/snippets/pyrun.py
index 80bcaf38680756f0a862952c21acb1e87d5c09e0..6eac0f3f8c2186b992748059a802fa0c6afe3134 100644
--- a/snippets/pyrun.py
+++ b/snippets/pyrun.py
@@ -1,48 +1,59 @@
-# pyrun.py ---
-#
+# pyrun.py ---
+#
# Filename: pyrun.py
-# Description:
+# Description:
# Author: Subhasis Ray
-# Maintainer:
+# Maintainer:
# Created: Wed Oct 15 10:14:15 2014 (+0530)
-# Version:
-# Last-Updated:
-# By:
+# Version:
+# Last-Updated:
+# By:
# Update #: 0
-# URL:
-# Keywords:
-# Compatibility:
-#
-
-# Commentary:
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
+# Code:
+"""You can use the PyRun class to run Python statements from MOOSE at
+runtime. This opens up many possibilities of interleaving computing in
+Python and MOOSE. You can also use this for debugging simulations.
+"""
import numpy as np
from matplotlib import pyplot as plt
import moose
def run_sequence():
- """
- In this example we demonstrate the use of PyRun objects to execute
+ """In this example we demonstrate the use of PyRun objects to execute
Python statements from MOOSE. Here is a couple of fun things to
indicate the power of MOOSE-Python integration.
@@ -61,14 +72,14 @@ def run_sequence():
In the `runString` we put a couple of print statements to indicate
the name fof the object which is running and the current
count. Then we increase the count directly.
-
+
When we call ``moose.start()``, the `runString` gets executed at
each time step.
The other PyRun object we create, is `/World`. In its `initString`
apart from ordinary print statements and initialization, we define
a Python function called ``incr_count``. This silly little
- function just increments the global `world_count` by 1.
+ function just increments the global `world_count` by 1.
The `runString` for `World` simply calls this function to
increment the count and print it.
@@ -108,15 +119,14 @@ incr_count()
"""
world_runner.run('from datetime import datetime')
world_runner.run('print "World: current time:", datetime.now().isoformat()')
-
+
moose.useClock(0, world_runner.path, 'process')
moose.reinit()
moose.start(0.001)
-
+
def input_output():
- """
- The PyRun class can take a double input through `trigger`
+ """The PyRun class can take a double input through `trigger`
field. Whenever another object sends an input to this field, the
`runString` is executed.
@@ -148,15 +158,15 @@ def input_output():
one of the above. We set ``mode = 2`` to disable the `process`
method. Note that this could also have been done by setting its
``tick = -1``.
-
+
``mode = 1`` will disable `trigger` message and ``mode = 0``, the
default, enables both.
- """
+ """
model = moose.Neutral('/model')
input_pulse = moose.PulseGen('/model/pulse')
#: set the baseline output 0
input_pulse.baseLevel = 0.0
- #: We make it generate three pulses
+ #: We make it generate three pulses
input_pulse.count = 3
input_pulse.level[0] = 1.0
input_pulse.level[1] = 2.0
@@ -188,29 +198,22 @@ print 'output =', output
moose.useClock(0, input_pulse.path, 'process')
#: this is unnecessary because the mode=2 ensures that `process`
#: does nothing
- moose.useClock(1, pyrun.path, 'process')
+ moose.useClock(1, pyrun.path, 'process')
moose.useClock(2, '/model/#[ISA=Table]', 'process')
moose.reinit()
moose.start(10.0)
- #ts =
+ #ts =
plt.plot(input_table.vector, label='input')
plt.plot(output_table.vector, label='output')
plt.legend()
plt.show()
-def main():
- """
-You can use the PyRun class to run Python statements from MOOSE at
-runtime. This opens up many possibilities of interleaving computing in
-Python and MOOSE. You can also use this for debugging simulations.
- """
- run_sequence()
- moose.delete('/model')
- input_output()
-
if __name__ == '__main__':
- main()
+ run_sequence()
+ moose.delete('/model')
+ input_output()
+
-#
+#
# pyrun.py ends here
diff --git a/snippets/pyrun1.py b/snippets/pyrun1.py
index 312cdf5c46d43da3989833015503f598d1f49941..6eac0f3f8c2186b992748059a802fa0c6afe3134 100644
--- a/snippets/pyrun1.py
+++ b/snippets/pyrun1.py
@@ -1,50 +1,53 @@
-# pyrun.py ---
-#
+# pyrun.py ---
+#
# Filename: pyrun.py
-# Description:
+# Description:
# Author: Subhasis Ray
-# Maintainer:
+# Maintainer:
# Created: Wed Oct 15 10:14:15 2014 (+0530)
-# Version:
-# Last-Updated:
-# By:
+# Version:
+# Last-Updated:
+# By:
# Update #: 0
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
-
-# Commentary:
-#
-#
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
-
+"""You can use the PyRun class to run Python statements from MOOSE at
+runtime. This opens up many possibilities of interleaving computing in
+Python and MOOSE. You can also use this for debugging simulations.
+"""
import numpy as np
from matplotlib import pyplot as plt
import moose
@@ -69,14 +72,14 @@ def run_sequence():
In the `runString` we put a couple of print statements to indicate
the name fof the object which is running and the current
count. Then we increase the count directly.
-
+
When we call ``moose.start()``, the `runString` gets executed at
each time step.
The other PyRun object we create, is `/World`. In its `initString`
apart from ordinary print statements and initialization, we define
a Python function called ``incr_count``. This silly little
- function just increments the global `world_count` by 1.
+ function just increments the global `world_count` by 1.
The `runString` for `World` simply calls this function to
increment the count and print it.
@@ -116,18 +119,54 @@ incr_count()
"""
world_runner.run('from datetime import datetime')
world_runner.run('print "World: current time:", datetime.now().isoformat()')
-
+
moose.useClock(0, world_runner.path, 'process')
moose.reinit()
moose.start(0.001)
-
+
def input_output():
+ """The PyRun class can take a double input through `trigger`
+ field. Whenever another object sends an input to this field, the
+ `runString` is executed.
+
+ The fun part of this is that you can use the input value in your
+ python statements in `runString`. This is stored in a local
+ variable called `input_`. You can rename this by setting `inputVar`
+ field.
+
+ Things become even more interesting when you can send out a value
+ computed using Python. PyRun objects allow you to define a local
+ variable called `output` and whatever value you assign to this,
+ will be sent out through the source field `output` on successful
+ execution of the `runString`.
+
+ You can rename the output variable by setting `outputVar` field.
+
+ In this example, we send the output of a pulsegen object sending
+ out the values 1, 2, 3 during each pulse and compute the square of
+ these numbers in Python and set output to this square.
+
+ The calculated value is assigned to the `output` variable and in
+ turn sent out to a Table object's input and gets recorded.
+
+ By default PyRun executes the `runString` whenever a `trigger`
+ message is received and when its process method is called at each
+ timestep. In both cases it sends out the `output` value. Since
+ this may cause inaccuracies depending on what the Python
+ statements in `runString` do, a `mode` can be specified to disable
+ one of the above. We set ``mode = 2`` to disable the `process`
+ method. Note that this could also have been done by setting its
+ ``tick = -1``.
+
+ ``mode = 1`` will disable `trigger` message and ``mode = 0``, the
+ default, enables both.
+ """
model = moose.Neutral('/model')
input_pulse = moose.PulseGen('/model/pulse')
#: set the baseline output 0
input_pulse.baseLevel = 0.0
- #: We make it generate three pulses
+ #: We make it generate three pulses
input_pulse.count = 3
input_pulse.level[0] = 1.0
input_pulse.level[1] = 2.0
@@ -159,63 +198,22 @@ print 'output =', output
moose.useClock(0, input_pulse.path, 'process')
#: this is unnecessary because the mode=2 ensures that `process`
#: does nothing
- moose.useClock(1, pyrun.path, 'process')
+ moose.useClock(1, pyrun.path, 'process')
moose.useClock(2, '/model/#[ISA=Table]', 'process')
moose.reinit()
moose.start(10.0)
- #ts =
+ #ts =
plt.plot(input_table.vector, label='input')
plt.plot(output_table.vector, label='output')
plt.legend()
plt.show()
-def main():
- """You can use the PyRun class to run Python statements from MOOSE at
- runtime. This opens up many possibilities of interleaving computing in
- Python and MOOSE. You can also use this for debugging simulations.
-
- The PyRun class can take a double input through `trigger`
- field. Whenever another object sends an input to this field, the
- `runString` is executed.
-
- The fun part of this is that you can use the input value in your
- python statements in `runString`. This is stored in a local
- variable called `input_`. You can rename this by setting `inputVar`
- field.
-
- Things become even more interesting when you can send out a value
- computed using Python. PyRun objects allow you to define a local
- variable called `output` and whatever value you assign to this,
- will be sent out through the source field `output` on successful
- execution of the `runString`.
-
- You can rename the output variable by setting `outputVar` field.
-
- In this example, we send the output of a pulsegen object sending
- out the values 1, 2, 3 during each pulse and compute the square of
- these numbers in Python and set output to this square.
-
- The calculated value is assigned to the `output` variable and in
- turn sent out to a Table object's input and gets recorded.
-
- By default PyRun executes the `runString` whenever a `trigger`
- message is received and when its process method is called at each
- timestep. In both cases it sends out the `output` value. Since
- this may cause inaccuracies depending on what the Python
- statements in `runString` do, a `mode` can be specified to disable
- one of the above. We set ``mode = 2`` to disable the `process`
- method. Note that this could also have been done by setting its
- ``tick = -1``.
-
- ``mode = 1`` will disable `trigger` message and ``mode = 0``, the
- default, enables both.
- """
+if __name__ == '__main__':
run_sequence()
moose.delete('/model')
input_output()
+
-if __name__ == '__main__':
- main()
-#
+#
# pyrun.py ends here
diff --git a/snippets/randomspike.py b/snippets/randomspike.py
index 86a33f98ae22ba93d62fa3997d86be2ea19015ef..a89884020ebf0e6324bb27302ef7c05e73422ec3 100644
--- a/snippets/randomspike.py
+++ b/snippets/randomspike.py
@@ -1,48 +1,51 @@
# randomspike.py ---
-#
-# Filename: randomspike.py
+#
+# Filename: randomspike.py
# Author: Subhasis Ray
-# Maintainer:
+# Maintainer:
# Created: Tue Sep 30 10:58:09 2014 (+0530)
-# Version:
-# Last-Updated:
-# By:
+# Version:
+# Last-Updated:
+# By:
# Update #: 0
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
-
-# Commentary:
-#
-#
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
+
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
+"""This is an example of simulating random events from a Poisson
+process and applying the event as spike input to a
+single-compartmental Hodgekin-Huxley type neuron model."""
import sys
sys.path.append('../../python')
@@ -57,7 +60,7 @@ SIMTIME = 5.0
def create_cell():
"""Create a single-compartment Hodgking-Huxley neuron with a
- synaptic channel.
+ synaptic channel.
This uses the :func:`ionchannel.create_1comp_neuron` function for
model creation.
@@ -91,7 +94,7 @@ def example():
process, i.e., the probability of `k` spikes in any interval `t`
is given by the Poisson distribution:
- exp(-ut)(ut)^k/k!
+ exp(-ut)(ut)^k/k!
for k = 0, 1, 2, ... u is the rate of spiking (the mean of the
Poisson distribution). See `wikipedia
@@ -100,7 +103,7 @@ def example():
Many cortical neuron types spontaneously fire action
potentials. These are called ectopic spikes. In this example we
simulate this with a RandSpike object with rate 10 spikes/s and
- send this to a single compartmental neuron via a synapse.
+ send this to a single compartmental neuron via a synapse.
In this model the synaptic conductance is set so high that each
incoming spike evokes an action potential.
@@ -108,7 +111,7 @@ def example():
ectopic = moose.RandSpike('ectopic_input')
ectopic.rate = 10.0
cellmodel = create_cell()
- moose.connect(ectopic, 'spikeOut',
+ moose.connect(ectopic, 'spikeOut',
cellmodel['synhandler'].synapse[0], 'addSpike')
tab_vm = moose.Table('/Vm')
moose.connect(tab_vm, 'requestOut', cellmodel['neuron'], 'getVm')
@@ -116,21 +119,13 @@ def example():
moose.start(SIMTIME)
return tab_vm
-def main():
-
- """This is an example of simulating random events from a Poisson
- process and applying the event as spike input to a
- single-compartmental Hodgekin-Huxley type neuron model."""
-
- tab_vm = example()
- ts = np.linspace(0, SIMTIME, len(tab_vm.vector))
- plt.plot(ts, tab_vm.vector)
- plt.ylabel('Vm (Volt)')
- plt.xlabel('Time (s)')
- plt.show()
-
if __name__ == '__main__':
- main()
-
-#
+ tab_vm = example()
+ ts = np.linspace(0, SIMTIME, len(tab_vm.vector))
+ plt.plot(ts, tab_vm.vector)
+ plt.ylabel('Vm (Volt)')
+ plt.xlabel('Time (s)')
+ plt.show()
+
+#
# randomspike.py ends here
diff --git a/snippets/reacDiffBranchingNeuron.py b/snippets/reacDiffBranchingNeuron.py
index d51265e9933fd1a8ee36cc3a1d0e797b59b77070..3aea5ff7fc91dd9a9c04828ad81949ca26a1723a 100644
--- a/snippets/reacDiffBranchingNeuron.py
+++ b/snippets/reacDiffBranchingNeuron.py
@@ -52,7 +52,7 @@ def makeModel():
assert( stoich0.numVarPools == 3 )
assert( stoich0.numProxyPools == 0 )
assert( stoich0.numRates == 4 )
-
+
num = compt0.numDiffCompts - 1
moose.element( '/model/chem/compt0/a[' + str(num) + ']' ).concInit *= 1.5
@@ -117,24 +117,23 @@ def finalizeDisplay( plotlist, cPlotDt ):
def makeChemModel( compt ):
"""
-This function sets up a simple oscillatory chemical system within
-the script. The reaction system is::
-
- s ---a---> a // s goes to a, catalyzed by a.
- s ---a---> b // s goes to b, catalyzed by a.
- a ---b---> s // a goes to s, catalyzed by b.
- b -------> s // b is degraded irreversibly to s.
+ This function sets up a simple oscillatory chemical system within
+ the script. The reaction system is::
-in sum, **a** has a positive feedback onto itself and also forms **b**.
-**b** has a negative feedback onto **a**.
-Finally, the diffusion constant for **a** is 1/10 that of **b**.
+ s ---a---> a // s goes to a, catalyzed by a.
+ s ---a---> b // s goes to b, catalyzed by a.
+ a ---b---> s // a goes to s, catalyzed by b.
+ b -------> s // b is degraded irreversibly to s.
+ in sum, **a** has a positive feedback onto itself and also forms **b**.
+ **b** has a negative feedback onto **a**.
+ Finally, the diffusion constant for **a** is 1/10 that of **b**.
"""
# create container for model
diffConst = 10e-12 # m^2/sec
motorRate = 1e-6 # m/sec
concA = 1 # millimolar
-
+
# create molecules and reactions
a = moose.Pool( compt.path + '/a' )
b = moose.Pool( compt.path + '/b' )
@@ -178,39 +177,40 @@ Finally, the diffusion constant for **a** is 1/10 that of **b**.
def main():
"""
-This example illustrates how to define a kinetic model embedded in
-the branching pseudo 1-dimensional geometry of a neuron. This means
-that diffusion only happens along the axis of dendritic segments, not
-radially from inside to outside a dendrite, nor tangentially around
-the dendrite circumference. The model
-oscillates in space and time due to a Turing-like reaction-diffusion
-mechanism present in all compartments. For the sake of this demo,
-the initial conditions are set to be slightly different on one of the
-terminal dendrites, so as to break the symmetry and initiate
-oscillations.
-This example uses an external model file to specify a binary branching
-neuron. This model does not have any spines. The electrical model is
-used here purely for the geometry and is not part of the computations.
-In this example we build an identical chemical model throughout the
-neuronal geometry, using the makeChemModel function.
-The model is set up to run using the Ksolve for integration and the
-Dsolve for handling diffusion.
-
-The display has two parts:
-
- a. Animated pseudo-3D plot of neuronal geometry, where each point
- represents a diffusive voxel and moves in the y-axis to show
- changes in concentration.
- b. Time-series plot that appears after the simulation has
- ended. The plots are for the first and last diffusive voxel,
- that is, the soma and the tip of one of the apical dendrites.
+ reacDiffBranchingNeuron:
+ This example illustrates how to define a kinetic model embedded in
+ the branching pseudo 1-dimensional geometry of a neuron. This means
+ that diffusion only happens along the axis of dendritic segments, not
+ radially from inside to outside a dendrite, nor tangentially around
+ the dendrite circumference. The model
+ oscillates in space and time due to a Turing-like reaction-diffusion
+ mechanism present in all compartments. For the sake of this demo,
+ the initial conditions are set to be slightly different on one of the
+ terminal dendrites, so as to break the symmetry and initiate
+ oscillations.
+ This example uses an external model file to specify a binary branching
+ neuron. This model does not have any spines. The electrical model is
+ used here purely for the geometry and is not part of the computations.
+ In this example we build an identical chemical model throughout the
+ neuronal geometry, using the makeChemModel function.
+ The model is set up to run using the Ksolve for integration and the
+ Dsolve for handling diffusion.
+
+ The display has two parts:
+
+ a. Animated pseudo-3D plot of neuronal geometry, where each point
+ represents a diffusive voxel and moves in the y-axis to show
+ changes in concentration.
+ b. Time-series plot that appears after the simulation has
+ ended. The plots are for the first and last diffusive voxel,
+ that is, the soma and the tip of one of the apical dendrites.
"""
chemdt = 0.1 # Tested various dts, this is reasonable.
diffdt = 0.01
plotdt = 1
animationdt = 5
- runtime = 750
+ runtime = 750
makeModel()
plotlist = makeDisplay()
diff --git a/snippets/reacDiffConcGradient.py b/snippets/reacDiffConcGradient.py
index 1f28c488cf66e54272c3e1fbbebd4bb5df02f83e..7a6b5dd1f9a6c96fc98f1aea8b0f4d1abd62e84e 100644
--- a/snippets/reacDiffConcGradient.py
+++ b/snippets/reacDiffConcGradient.py
@@ -146,7 +146,7 @@ def main():
4x 2x 1x [Ratios of vols of compts]
If there is no diffusion then the ratio of concs should be 1:10:100
- If there is no x-compt reac, then clearly the concs should all be
+ If there is no x-compt reac, then clearly the concs should all be
the same, in this case they should be 2.0.
If both are happening then the final concs are 1.4, 2.5, 3.4.
"""
diff --git a/snippets/reacDiffSpinyNeuron.py b/snippets/reacDiffSpinyNeuron.py
index 17110f8a26506525750a5f4057ef8c4b4ccaf254..87f6145c29c82d8014f20c30c81af9fb6ed84456 100644
--- a/snippets/reacDiffSpinyNeuron.py
+++ b/snippets/reacDiffSpinyNeuron.py
@@ -85,7 +85,7 @@ def makeModel():
stoich0.filterXreacs()
stoich1.filterXreacs()
stoich2.filterXreacs()
-
+
moose.element( '/model/chem/compt2/a[0]' ).concInit *= 1.5
# Create the output tables
@@ -184,24 +184,23 @@ def finalizeDisplay( plotlist, cPlotDt ):
def makeChemModel( compt ):
"""
-This function setus up a simple oscillatory chemical system within
-the script. The reaction system is::
-
- s ---a---> a // s goes to a, catalyzed by a.
- s ---a---> b // s goes to b, catalyzed by a.
- a ---b---> s // a goes to s, catalyzed by b.
- b -------> s // b is degraded irreversibly to s.
+ This function setus up a simple oscillatory chemical system within
+ the script. The reaction system is::
-in sum, **a** has a positive feedback onto itself and also forms **b**.
-**b** has a negative feedback onto **a**.
-Finally, the diffusion constant for **a** is 1/10 that of **b**.
+ s ---a---> a // s goes to a, catalyzed by a.
+ s ---a---> b // s goes to b, catalyzed by a.
+ a ---b---> s // a goes to s, catalyzed by b.
+ b -------> s // b is degraded irreversibly to s.
+ in sum, **a** has a positive feedback onto itself and also forms **b**.
+ **b** has a negative feedback onto **a**.
+ Finally, the diffusion constant for **a** is 1/10 that of **b**.
"""
# create container for model
diffConst = 10e-12 # m^2/sec
motorRate = 1e-6 # m/sec
concA = 1 # millimolar
-
+
# create molecules and reactions
a = moose.Pool( compt.path + '/a' )
b = moose.Pool( compt.path + '/b' )
@@ -245,28 +244,28 @@ Finally, the diffusion constant for **a** is 1/10 that of **b**.
def main():
"""
-This example illustrates how to define a kinetic model embedded in
-the branching pseudo-1-dimensional geometry of a neuron. The model
-oscillates in space and time due to a Turing-like reaction-diffusion
-mechanism present in all compartments. For the sake of this demo,
-the initial conditions are set up slightly different on the PSD
-compartments, so as to break the symmetry and initiate oscillations
-in the spines.
-This example uses an external electrical model file with basal
-dendrite and three branches on
-the apical dendrite. One of those branches has a dozen or so spines.
-In this example we build an identical model in each compartment, using
-the makeChemModel function. One could readily define a system with
-distinct reactions in each compartment.
-The model is set up to run using the Ksolve for integration and the
-Dsolve for handling diffusion.
-The display has four parts:
-
- a. animated line plot of concentration against main compartment#.
- b. animated line plot of concentration against spine compartment#.
- c. animated line plot of concentration against psd compartment#.
- d. time-series plot that appears after the simulation has
- ended. The plot is for the last (rightmost) compartment.
+ This example illustrates how to define a kinetic model embedded in
+ the branching pseudo-1-dimensional geometry of a neuron. The model
+ oscillates in space and time due to a Turing-like reaction-diffusion
+ mechanism present in all compartments. For the sake of this demo,
+ the initial conditions are set up slightly different on the PSD
+ compartments, so as to break the symmetry and initiate oscillations
+ in the spines.
+ This example uses an external electrical model file with basal
+ dendrite and three branches on
+ the apical dendrite. One of those branches has a dozen or so spines.
+ In this example we build an identical model in each compartment, using
+ the makeChemModel function. One could readily define a system with
+ distinct reactions in each compartment.
+ The model is set up to run using the Ksolve for integration and the
+ Dsolve for handling diffusion.
+ The display has four parts:
+
+ a. animated line plot of concentration against main compartment#.
+ b. animated line plot of concentration against spine compartment#.
+ c. animated line plot of concentration against psd compartment#.
+ d. time-series plot that appears after the simulation has
+ ended. The plot is for the last (rightmost) compartment.
"""
chemdt = 0.1 # Tested various dts, this is reasonable.
diff --git a/snippets/recurrentIntFire.py b/snippets/recurrentIntFire.py
index 43af8c2b8d335a087b87b6df8182cd9c697d6304..2d6de1b3762b579930b83965cb0075809027cb0e 100644
--- a/snippets/recurrentIntFire.py
+++ b/snippets/recurrentIntFire.py
@@ -6,7 +6,12 @@
#** GNU Lesser General Public License version 2.1
#** See the file COPYING.LIB for the full notice.
#**********************************************************************/
-
+# This snippet sets up a recurrent network of IntFire objects, using
+# SimpleSynHandlers to deal with spiking events.
+# It isn't very satisfactory as activity runs down after a while.
+# It is a good example for using the IntFire, setting up random
+# connectivity, and using SynHandlers.
+#
import os
import random
import time
diff --git a/snippets/rxdFuncDiffusionStoch.py b/snippets/rxdFuncDiffusionStoch.py
index 0a3315ff79046bb0a00e259de7829f633ea1891a..e19c11707e00f42c13e5e8cee1f06b672e03db29 100644
--- a/snippets/rxdFuncDiffusionStoch.py
+++ b/snippets/rxdFuncDiffusionStoch.py
@@ -73,5 +73,3 @@ def main():
if __name__ == '__main__':
main()
-if __name__ == '__main__':
- main()
diff --git a/snippets/rxdSpineSize.py b/snippets/rxdSpineSize.py
index 741a3c5e5bd3180ba6373782ad27379b413adbd4..9d9a4e09936f755c1b8cf48a60aa434ad0c5a41e 100644
--- a/snippets/rxdSpineSize.py
+++ b/snippets/rxdSpineSize.py
@@ -1,3 +1,14 @@
+##################################################################
+## This program is part of 'MOOSE', the
+## Messaging Object Oriented Simulation Environment.
+## Copyright (C) 2015 Upinder S. Bhalla. and NCBS
+## It is made available under the terms of the
+## GNU Lesser General Public License version 2.1
+## See the file COPYING.LIB for the full notice.
+##
+## rxdSpineSize.py: Builds a cell with spines and a propagating reaction
+## wave. Products diffuse into the spine and cause it to get bigger.
+##################################################################
import math
import pylab
import numpy
@@ -117,14 +128,17 @@ def displayPlots():
def main():
"""
-This illustrates the use of rdesigneur to build a simple dendrite with
-spines, and then to resize them using spine fields. These are the
-fields that would be changed dynamically in a simulation with reactions
-that affect spine geometry.
-In this simulation there is a propagating reaction wave using a
-highly abstracted equation, whose product diffuses into the spines and
-makes them bigger.
+ This illustrates the use of rdesigneur to build a simple dendrite with
+ spines, and then to resize them using spine fields. These are the
+ fields that would be changed dynamically in a simulation with reactions
+ that affect spine geometry.
+ In this simulation there is a propagating reaction wave using a
+ highly abstracted equation, whose product diffuses into the spines and
+ makes them bigger.
"""
+ makeModel()
+ elec = moose.element( '/model/elec' )
+ elec.setSpineAndPsdMesh( moose.element('/model/chem/spine'), moose.element('/model/chem/psd') )
eHead = moose.wildcardFind( '/model/elec/#head#' )
oldDia = [ i.diameter for i in eHead ]
@@ -161,6 +175,7 @@ makes them bigger.
# Run the 'main' if this script is executed standalone.
+def showVisualization():
makeModel()
elec = moose.element( '/model/elec' )
elec.setSpineAndPsdMesh( moose.element('/model/chem/spine'), moose.element('/model/chem/psd') )
@@ -257,7 +272,7 @@ def create_viewer(path, moose_dendrite, dendZ, diaTab, psdZ):
if __name__ == '__main__':
- if doMoo
+ if doMoo:
showVisualization()
else:
main()
diff --git a/snippets/savemodel.py b/snippets/savemodel.py
index d913d9352e04e93ef6a7f549f6cd74570a801a62..4997743170642226b607676faff58824838e3262 100644
--- a/snippets/savemodel.py
+++ b/snippets/savemodel.py
@@ -1,49 +1,53 @@
-# savemodel.py ---
-#
+# savemodel.py ---
+#
# Filename: savemodel.py
-# Description:
+# Description:
# Author: Subhasis Ray
-# Maintainer:
+# Maintainer:
# Created: Wed Oct 29 00:08:50 2014 (+0530)
-# Version:
-# Last-Updated:Thr Dec 23 16:31:00 2015 (+0530)
-# By:
+# Version:
+# Last-Updated:Thr Dec 23 16:31:00 2015 (+0530)
+# By:
# Update #: 0
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
-#
-#
-#
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
+"""This example illustrates loading a kinetic model defined in Genesis format
+into Moose using "loadModel" function and using "saveModel" function one can
+save the model back to Genesis format
+"""
import os
import sys
diff --git a/snippets/scaleVolumes.py b/snippets/scaleVolumes.py
index 08ed0c025385fa30f3fc626126e0be237966f461..ad6c5c3e1c3c6a8d44e52ca3663c04b46bedd5d5 100644
--- a/snippets/scaleVolumes.py
+++ b/snippets/scaleVolumes.py
@@ -12,12 +12,6 @@ import pylab
import numpy
import moose
-# Ugly python hack to make input behave the same on python2 and python3.
-try:
- input = raw_input
-except NameError as e:
- pass
-
def makeModel():
# create container for model
model = moose.Neutral( 'model' )
@@ -100,14 +94,14 @@ def main():
else is set up correctly, then this change propagates through to all
reactions molecules.
- For a deterministic reaction one would not see any change in output
+ For a deterministic reaction one would not see any change in output
concentrations.
- For a stochastic reaction illustrated here, one sees the level of
- 'noise'
+ For a stochastic reaction illustrated here, one sees the level of
+ 'noise'
changing, even though the concentrations are similar up to a point.
This example creates a bistable model having two enzymes and a reaction.
One of the enzymes is autocatalytic.
- This model is set up within the script rather than using an external
+ This model is set up within the script rather than using an external
file.
The model is set up to run using the GSSA (Gillespie Stocahstic systems
algorithim) method in MOOSE.
@@ -160,4 +154,4 @@ def main():
eval(str(input()))
# Run the 'main' if this script is executed standalone.
if __name__ == '__main__':
- main( )
+ main()
diff --git a/snippets/scriptGssaSolver.py b/snippets/scriptGssaSolver.py
index 281adddd5d9e55fa56eb88f4cc165e059c8058ab..7dece423850732b4274b656cff089e4d8282ab0d 100644
--- a/snippets/scriptGssaSolver.py
+++ b/snippets/scriptGssaSolver.py
@@ -7,6 +7,14 @@
## See the file COPYING.LIB for the full notice.
#########################################################################
+# This example illustrates how to set up a kinetic solver and kinetic model
+# using the scripting interface. Normally this would be done using the
+# Shell::doLoadModel command, and normally would be coordinated by the
+# SimManager as the base of the entire model.
+# This example creates a bistable model having two enzymes and a reaction.
+# One of the enzymes is autocatalytic.
+# The model is set up to run using Exponential Euler integration.
+
import math
import pylab
import numpy
diff --git a/snippets/scriptKineticModel.py b/snippets/scriptKineticModel.py
index 2227c16baed21fcd1558452abf325e6b7f0228a1..a56db7977a14d2fac08c8511bee2b3fee60c21c2 100644
--- a/snippets/scriptKineticModel.py
+++ b/snippets/scriptKineticModel.py
@@ -18,7 +18,7 @@ def makeModel():
compartment.volume = 1e-15
# the mesh is created automatically by the compartment
moose.le( '/model/compartment' )
- mesh = moose.element( '/model/compartment/mesh' )
+ mesh = moose.element( '/model/compartment/mesh' )
# create molecules and reactions
a = moose.Pool( '/model/compartment/a' )
@@ -68,7 +68,7 @@ def makeModel():
moose.connect( outputA, 'requestOut', a, 'getConc' );
moose.connect( outputB, 'requestOut', b, 'getConc' );
- # We need a finer timestep than the default 0.1 seconds,
+ # We need a finer timestep than the default 0.1 seconds,
# in order to get numerical accuracy.
for i in range (11, 15 ):
moose.setClock( i, 0.001 ) # for computational objects
@@ -82,7 +82,7 @@ def main():
This example creates a bistable model having two enzymes and a reaction.
One of the enzymes is autocatalytic.
The model is set up to run using default Exponential Euler integration.
- The snippet scriptKineticSolver.py uses the much better GSL
+ The snippet scriptKineticSolver.py uses the much better GSL
Runge-Kutta-Fehlberg integration scheme on this same model.
"""
makeModel()
diff --git a/snippets/scriptKineticSolver.py b/snippets/scriptKineticSolver.py
index 77c585976b96fdcffd0c16a53c45ab28eda2b907..71cf795d343acc61d1ef48f18ccbfeb1669f273e 100644
--- a/snippets/scriptKineticSolver.py
+++ b/snippets/scriptKineticSolver.py
@@ -7,6 +7,14 @@
## See the file COPYING.LIB for the full notice.
#########################################################################
+# This example illustrates how to set up a kinetic solver and kinetic model
+# using the scripting interface. Normally this would be done using the
+# Shell::doLoadModel command, and normally would be coordinated by the
+# SimManager as the base of the entire model.
+# This example creates a bistable model having two enzymes and a reaction.
+# One of the enzymes is autocatalytic.
+# The model is set up to run using Exponential Euler integration.
+
import math
import pylab
import numpy
diff --git a/snippets/showclocks.py b/snippets/showclocks.py
index 4523630796af077a736d1a6afe2796936d1fc735..1bcd23ee77622767621f836ff562df790406d7e3 100644
--- a/snippets/showclocks.py
+++ b/snippets/showclocks.py
@@ -1,11 +1,8 @@
# -*- coding: utf-8 -*-
# Author: Subhasis Ray
# Date: Mon Jun 10 16:23:41 IST 2013
-import moose
-def main():
- """
-This snippet shows various ways of displaying scheduling
+"""This snippet shows various ways of displaying scheduling
information of moose model components.
The `/clock/tick` ematrix has 10 elements, any of which can be setup
@@ -24,51 +21,52 @@ which connect to all the targets of the corresponding `tickNo`. You
can display the neighbors of these finfos also to see what is
scheduled on each tick.
- """
+"""
+
+import moose
- comp = moose.Compartment('/comp')
+comp = moose.Compartment('/comp')
# Setup the ticks
- moose.setClock(0, 1e-6)
- moose.setClock(1, 1e-6)
+moose.setClock(0, 1e-6)
+moose.setClock(1, 1e-6)
# Schedule the element.
- moose.useClock(0, '/##[ISA=Compartment]', 'init')
- moose.useClock(1, '/##[ISA=Compartment]', 'process')
+moose.useClock(0, '/##[ISA=Compartment]', 'init')
+moose.useClock(1, '/##[ISA=Compartment]', 'process')
# List the ticks connected to an element.
- print(('Ticks connected to `process` method of', comp.path))
- for tick in comp.neighbors['process']:
- print((' ->',tick.path))
+print(('Ticks connected to `process` method of', comp.path))
+for tick in comp.neighbors['process']:
+ print((' ->',tick.path))
# Different ticks can be connected to different fields.
- print(('Ticks connected to `init` method of', comp.path))
- for tick in comp.neighbors['init']:
- print((' ->',tick.path))
+print(('Ticks connected to `init` method of', comp.path))
+for tick in comp.neighbors['init']:
+ print((' ->',tick.path))
# View the scheduled elements using the tick nos.
- t = moose.element('/clock')
- print('Elements on tick 0')
- for e in t.neighbors['proc0']:
- print((' ->', e.path))
- print('Elements on tick 1')
- for e in t.neighbors['proc1']:
- print((' ->', e.path))
-
- ch = moose.HHChannel('/comp/chan')
- moose.useClock(1, ch.path, 'process')
- print((ch.path, 'has been scheduled'))
- print('Elements on tick 1')
- for e in t.neighbors['proc1']:
- print((' ->', e.path))
+t = moose.element('/clock')
+print('Elements on tick 0')
+for e in t.neighbors['proc0']:
+ print((' ->', e.path))
+print('Elements on tick 1')
+for e in t.neighbors['proc1']:
+ print((' ->', e.path))
+
+ch = moose.HHChannel('/comp/chan')
+moose.useClock(1, ch.path, 'process')
+print((ch.path, 'has been scheduled'))
+print('Elements on tick 1')
+for e in t.neighbors['proc1']:
+ print((' ->', e.path))
# Go through elements by wildcard search and list the ticks connected.
# This can be slow when the model is too big.
- for el in moose.wildcardFind('/##[ISA=Compartment]'):
- print(('Ticks connected to `process` method of', el.path))
- for t in moose.element(el).neighbors['process']:
- print((' ->', t.path))
-
-if __name__ == '__main__':
- main()
+for el in moose.wildcardFind('/##[ISA=Compartment]'):
+ print(('Ticks connected to `process` method of', el.path))
+ for t in moose.element(el).neighbors['process']:
+ print((' ->', t.path))
# <codecell>
+
+
diff --git a/snippets/showmsg.py b/snippets/showmsg.py
index 757db1205d58659b78117ca4874770baf6ca84e6..f981956acf360eeca4f73fbdaeb45561f640323f 100644
--- a/snippets/showmsg.py
+++ b/snippets/showmsg.py
@@ -1,23 +1,22 @@
+
+"""This is to show a _raw_ way of traversing messages."""
import sys
sys.path.append('../../python')
import moose
-def main():
- """This is to show a _raw_ way of traversing messages."""
- connectionProbability = 0.5
- net = moose.IntFire('/net1', 10)
- syn = moose.SimpleSynHandler( '/net1/sh', 10 )
- moose.connect( syn, 'activationOut', net, 'activation', 'OneToOne' )
- synapse = syn.synapse.vec
- mid = moose.connect(net, 'spikeOut', synapse, 'addSpike', 'Sparse') # This creates a `Sparse` message from `spikeOut` source of net to `addSpike` destination on synapse.
- msg = moose.element(mid)
- msg.setRandomConnectivity(connectionProbability, 5)
- for n in net.vec:
- print(('Messages from %s.spikeOut' % (n.path)))
- node = moose.element(n)
- for dest, df in zip(node.msgDests['spikeOut'], node.msgDestFunctions['spikeOut']):
- print(('\t--> %s.%s' % (dest.path, df)))
+connectionProbability = 0.5
+net = moose.IntFire('/net1', 10)
+syn = moose.SimpleSynHandler( '/net1/sh', 10 )
+moose.connect( syn, 'activationOut', net, 'activation', 'OneToOne' )
+synapse = syn.synapse.vec
+mid = moose.connect(net, 'spikeOut', synapse, 'addSpike', 'Sparse') # This creates a `Sparse` message from `spikeOut` source of net to `addSpike` destination on synapse.
+msg = moose.element(mid)
+msg.setRandomConnectivity(connectionProbability, 5)
+for n in net.vec:
+ print(('Messages from %s.spikeOut' % (n.path)))
+ node = moose.element(n)
+ for dest, df in zip(node.msgDests['spikeOut'], node.msgDestFunctions['spikeOut']):
+ print(('\t--> %s.%s' % (dest.path, df)))
+
-if __name__ == '__main__':
- main()
diff --git a/snippets/singlemsgcross.py b/snippets/singlemsgcross.py
index 6ffb04fda6de11c65ab1507e8bbe682237e84a79..ee0a67a1795fdefe4b75fa7ab62b672b45168691 100644
--- a/snippets/singlemsgcross.py
+++ b/snippets/singlemsgcross.py
@@ -1,50 +1,53 @@
-# singlemsgcross.py ---
-#
+# singlemsgcross.py ---
+#
# Filename: singlemsgcross.py
-# Description:
-# Author:Subhasis Ray
-# Maintainer:
+# Description:
+# Author:Subhasis Ray
+# Maintainer:
# Created: Wed May 1 11:05:30 2013 (+0530)
-# Version:
+# Version:
# Last-Updated: Wed May 1 12:16:14 2013 (+0530)
# By: subha
# Update #: 43
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
-#
-#
-#
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
-#
+#
+#
# Code:
+"""This example shows that you can have two ematrix objects and
+connect individual elements using `Single` message"""
+
import pylab
import sys
sys.path.append('../../python')
@@ -85,15 +88,8 @@ def test_crossing_single():
pylab.plot(t)
pylab.show()
-def main():
- """
- This example shows that you can have two ematrix objects and
- connect individual elements using `Single` message
- """
- test_crossing_single()
-
if __name__ == '__main__':
- main()
+ test_crossing_single()
-#
+#
# singlemsgcross.py ends here
diff --git a/snippets/startstop.py b/snippets/startstop.py
index 8b1cfd447b1ed0a1b73fe67b167e981489474bdc..fd66b4f904fedbb07dd357b94a1c020bc8f1b76f 100644
--- a/snippets/startstop.py
+++ b/snippets/startstop.py
@@ -1,67 +1,67 @@
# startstop.py ---
-#
+#
# Filename: startstop.py
-# Description:
-# Author:Subhasis Ray
-# Maintainer:
+# Description:
+# Author:Subhasis Ray
+# Maintainer:
# Created: Sun Jul 1 13:42:28 2012 (+0530)
-# Version:
+# Version:
# Last-Updated: Sun Jul 1 14:41:34 2012 (+0530)
# By: subha
# Update #: 62
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
-#
-#
-#
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
-#
-#
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
-#
+#
import pylab
import moose
-
+
def main():
"""
- This demo shows how to start, stop, and continue a simulation.
+ This demo shows how to start, stop, and continue a simulation.
This is commonly done when we want to run a model till settling, then
change a parameter or deliver a stimulus, and then continue the
simulation.
- Here, the model is just the output of a PulseGen object which
+ Here, the model is just the output of a PulseGen object which
generates periodic pulses.
- The demo shows how to start the simulation. using the
+ The demo shows how to start the simulation. using the
*moose.reinit* command to reset the model to its initial state,
- and *moose.start* command to run the model for the specified duration.
+ and *moose.start* command to run the model for the specified duration.
We issue multiple *moose.start* commands and do different things to
the model between them. First, we change the delay of the pulseGen.
Then we show a number of ways to assign the timestep (dt) to the
table object in the simulation.
- Note that throughout this simulation the pulsegen is going at a
+ Note that throughout this simulation the pulsegen is going at a
uniform rate, it is just being sampled by the output table at
different intervals.
"""
@@ -70,7 +70,7 @@ def main():
steps = 100
simtime = dt * steps
# Pulsegen is on tick 0, we can pre-emptively set its dt.
- moose.setClock(0, dt)
+ moose.setClock(0, dt)
table = setup_model()
pulse = moose.element( '/model/pulse' )
# The 'tick' field is on every object, we can use this to set its dt.
@@ -100,11 +100,11 @@ def main():
# Finally, here we change the pulse delay to 1 second and run again.
print(('Simulator time at end of simulation', clock.currentTime))
- pylab.plot(pylab.linspace(0, clock.currentTime, len(table.vector)), table.vector)
+ pylab.plot(pylab.linspace(0, clock.currentTime, len(table.vector)), table.vector)
pylab.show()
def setup_model():
- model_container = moose.Neutral('/model')
+ model_container = moose.Neutral('/model')
pulse = moose.PulseGen('/model/pulse')
pulse.level[0] = 1.0
pulse.delay[0] = 0.5
diff --git a/snippets/stimtable.py b/snippets/stimtable.py
index a8381ff18bfd22f9b8726a05346da3a330781eb8..7234f5ea566bd17dda87416325321a7a24d9dcd8 100644
--- a/snippets/stimtable.py
+++ b/snippets/stimtable.py
@@ -1,42 +1,56 @@
-# stimtable.py ---
-#
+# stimtable.py ---
+#
# Filename: stimtable.py
-# Description:
+# Description:
# Author: Subhasis Ray
-# Maintainer:
+# Maintainer:
# Created: Wed May 8 18:51:07 2013 (+0530)
-# Version:
+# Version:
# Last-Updated: Mon May 27 21:15:36 2013 (+0530)
# By: subha
# Update #: 124
-# URL:
-# Keywords:
-# Compatibility:
-#
-#
+# URL:
+# Keywords:
+# Compatibility:
+#
+#
-# Commentary:
-#
-#
-#
-#
+# Commentary:
+#
+#
+#
+#
# Change log:
-#
+#
+#
+#
+#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
-#
+#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
+#
+#
+
+# Code:
+"""Example of StimulusTable using Poisson random numbers.
+
+Creates a StimulusTable and assigns it signal representing events in a
+Poisson process. The output of the StimTable is sent to a DiffAmp
+object for buffering and then recorded in a regular table.
+
+"""
import numpy as np
from matplotlib import pyplot as plt
@@ -78,7 +92,7 @@ def stimulus_table_demo():
# consecutive entries to have same value to get correct
# magnitude. And still we shall be off by at least one time step.
indices = np.concatenate((stimidx-1, stimidx, stimidx+1))
- stim[indices] = 1.0
+ stim[indices] = 1.0
stimtable.vector = stim
stimtable.stepSize = 0 # This forces use of current time as x value for interpolation
stimtable.stopTime = simtime
@@ -93,18 +107,9 @@ def stimulus_table_demo():
plt.title('Exmaple of StimulusTable')
plt.show()
-def main():
- """
-Example of StimulusTable using Poisson random numbers.
-
-Creates a StimulusTable and assigns it signal representing events in a
-Poisson process. The output of the StimTable is sent to a DiffAmp
-object for buffering and then recorded in a regular table.
-
- """
- stimulus_table_demo()
-
if __name__ == '__main__':
- main()
+ stimulus_table_demo()
+
+#
# stimtable.py ends here
diff --git a/snippets/stochasticLotkaVolterra.py b/snippets/stochasticLotkaVolterra.py
index 04921a1c9c5c1d1b4560b2bde33c57196d47ba42..049933bd63e87eda05228ab8a16c2b6cfb6c15d5 100644
--- a/snippets/stochasticLotkaVolterra.py
+++ b/snippets/stochasticLotkaVolterra.py
@@ -1,3 +1,13 @@
+#########################################################################
+## This program is part of 'MOOSE', the
+## Messaging Object Oriented Simulation Environment.
+## Copyright (C) 2013 Upinder S. Bhalla. and NCBS
+## It is made available under the terms of the
+## GNU Lesser General Public License version 2.1
+## See the file COPYING.LIB for the full notice.
+#########################################################################
+
+
import math
import pylab
import numpy
@@ -60,40 +70,38 @@ def makeModel():
def main():
"""
-The stochasticLotkaVolterra example is almost identical to the
-funcReacLotkaVolterra. It shows how to use function objects
-as part of differential equation systems in the framework of the MOOSE
-kinetic solvers. Here the difference is that we use a a stochastic
-solver. The system is interesting because it illustrates the
-instability of Lotka-Volterra systems in stochastic conditions. Here we
-see exctinction of one of the species and runaway buildup of the other.
-The simulation has to be halted at this point.
-
-Here the system is set up explicitly using the
-scripting, in normal use one would expect to use SBML.
-
-In this example we set up a Lotka-Volterra system. The equations
-are readily expressed as a pair of reactions each of whose rate is
-governed by a function::
-
- x' = x( alpha - beta.y )
- y' = -y( gamma - delta.x )
-
-This translates into two reactions::
-
- x ---> z Kf = beta.y - alpha
- y ---> z Kf = gamma - delta.x
-
-Here z is a dummy molecule whose concentration is buffered to zero.
-
-The model first runs using default Exponential Euler integration.
-This is not particularly accurate even with a small timestep.
-The model is then converted to use the deterministic Kinetic solver
-Ksolve. This is accurate and faster.
-
-Note that we cannot use the stochastic GSSA solver for this system, it
-cannot handle a reaction term whose rate keeps changing.
-
+ The stochasticLotkaVolterra example is almost identical to the
+ funcReacLotkaVolterra. It shows how to use function objects
+ as part of differential equation systems in the framework of the MOOSE
+ kinetic solvers. Here the difference is that we use a a stochastic
+ solver. The system is interesting because it illustrates the
+ instability of Lotka-Volterra systems in stochastic conditions. Here we
+ see exctinction of one of the species and runaway buildup of the other.
+ The simulation has to be halted at this point.
+
+ Here the system is set up explicitly using the
+ scripting, in normal use one would expect to use SBML.
+
+ In this example we set up a Lotka-Volterra system. The equations
+ are readily expressed as a pair of reactions each of whose rate is
+ governed by a function::
+
+ x' = x( alpha - beta.y )
+ y' = -y( gamma - delta.x )
+
+ This translates into two reactions::
+
+ x ---> z Kf = beta.y - alpha
+ y --->