diff --git a/moose-examples/snippets/RandSpikeStats.py b/moose-examples/snippets/RandSpikeStats.py
index 725ae5d0ac9cc29af6fff70178ae2d747d7e4198..5bc14ae0517b873447cd93083510a7cf380e1b50 100644
--- a/moose-examples/snippets/RandSpikeStats.py
+++ b/moose-examples/snippets/RandSpikeStats.py
@@ -13,6 +13,7 @@ import moose
dt = 0.01
runtime = 100
+
def make_model():
sinePeriod = 50
maxFiringRate = 10
@@ -67,51 +68,14 @@ def make_model():
moose.connect( plotf, 'requestOut', fire, 'getVm' )
def main():
-<<<<<<< HEAD
- """
- 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.
- Each of these are recorded and plotted.
- The StimulusTable has a sine-wave waveform.
- """
- make_model()
-
- moose.reinit()
- moose.start( runtime )
- plots = moose.element( '/plots' )
- plot1 = moose.element( '/plot1' )
- plot2 = moose.element( '/plot2' )
- plotf = moose.element( '/plotf' )
- t = [i * dt for i in range( plot1.vector.size )]
- pylab.plot( t, plots.vector, label='stimulus' )
- pylab.plot( t, plot1.vector, label='spike rate mean' )
- pylab.plot( t, plot2.vector, label='Vm mean' )
- pylab.plot( t, plotf.vector, label='Vm' )
- 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' )
- '''
-=======
"""
- 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.
- Each of these are recorded and plotted.
- The StimulusTable has a sine-wave waveform.
+ 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.
+ Each of these are recorded and plotted.
+ The StimulusTable has a sine-wave waveform.
"""
make_model()
@@ -128,7 +92,7 @@ def main():
pylab.plot( t, plotf.vector, label='Vm' )
pylab.legend()
pylab.show()
->>>>>>> 0e491aa41584cf7a66c0e242374d8ee61660eb7b
+
'''
moose.useClock( 0, '/stim', 'process' )
diff --git a/moose-examples/snippets/analogStimTable.py b/moose-examples/snippets/analogStimTable.py
index 941de47ec9cf8bb309eee68a50e5cefa2d35dfc1..9b29b6f77ee92b59e66a1fabe9400c2c101d0a94 100644
--- a/moose-examples/snippets/analogStimTable.py
+++ b/moose-examples/snippets/analogStimTable.py
@@ -96,5 +96,6 @@ if __name__ == '__main__':
>>>>>>> 0e491aa41584cf7a66c0e242374d8ee61660eb7b
+
#
# stimtable.py ends here
diff --git a/moose-examples/snippets/funcInputToPools.py b/moose-examples/snippets/funcInputToPools.py
index 45f5c1da9d4f826fe92e3fc5207852e698fef3e7..6ab9b3e757fabfd345edd155cd68ef452a2750e8 100644
--- a/moose-examples/snippets/funcInputToPools.py
+++ b/moose-examples/snippets/funcInputToPools.py
@@ -12,95 +12,7 @@ import pylab
import numpy
import moose
import sys
-<<<<<<< HEAD
-def makeModel():
- if len( sys.argv ) == 1:
- useGsolve = True
- else:
- useGsolve = ( sys.argv[1] == 'True' )
- # create container for model
- model = moose.Neutral( 'model' )
- compartment = moose.CubeMesh( '/model/compartment' )
- compartment.volume = 1e-22
- # the mesh is created automatically by the compartment
- moose.le( '/model/compartment' )
- mesh = moose.element( '/model/compartment/mesh' )
-
- # create molecules and reactions
- a = moose.Pool( '/model/compartment/a' )
- b = moose.Pool( '/model/compartment/b' )
-
- # create functions of time
- f1 = moose.Function( '/model/compartment/f1' )
- f2 = moose.Function( '/model/compartment/f2' )
-
- # connect them up for reactions
- moose.connect( f1, 'valueOut', a, 'setConc' )
- moose.connect( f2, 'valueOut', b, 'increment' )
-
- # Assign parameters
- a.concInit = 0
- b.concInit = 1
- #f1.numVars = 1
- #f2.numVars = 1
- f1.expr = '1 + sin(t)'
- f2.expr = '10 * cos(t)'
-
- # Create the output tables
- graphs = moose.Neutral( '/model/graphs' )
- outputA = moose.Table2 ( '/model/graphs/nA' )
- outputB = moose.Table2 ( '/model/graphs/nB' )
-
- # connect up the tables
- moose.connect( outputA, 'requestOut', a, 'getN' );
- moose.connect( outputB, 'requestOut', b, 'getN' );
-
- # Set up the solvers
- if useGsolve:
- gsolve = moose.Gsolve( '/model/compartment/gsolve' )
- gsolve.useClockedUpdate = True
- else:
- gsolve = moose.Ksolve( '/model/compartment/gsolve' )
- stoich = moose.Stoich( '/model/compartment/stoich' )
- stoich.compartment = compartment
- stoich.ksolve = gsolve
- stoich.path = '/model/compartment/##'
- '''
- '''
-
- # We need a finer timestep than the default 0.1 seconds,
- # in order to get numerical accuracy.
- for i in range (10, 19 ):
- moose.setClock( i, 0.1 ) # for computational objects
-
-def main():
- """
-This example describes the special (and discouraged) use case where
-functions provide input to a reaction system. Here we have two functions of
-time which control the pool # and pool rate of change, respectively::
-
-number of molecules of a = 1 + sin(t)
-rate of change of number of molecules of b = 10 * cos(t)
-
-In the stochastic case one must set a special flag *useClockedUpdate*
-in order to achieve clock-triggered updates from the functions. This is
-needed because the functions do not have reaction events to trigger them,
-and even if there were reaction events they might not be frequent enough to
-track the periodic updates. The use of this flag slows down the calculations,
-so try to use a table to control a pool instead.
-
-To run in stochastic mode::
-
- ''python funcInputToPools''
-
-To run in deterministic mode::
-
- ''python funcInputToPools false''
-
- """
-
-=======
def makeModel():
if len( sys.argv ) == 1:
@@ -154,6 +66,9 @@ def makeModel():
stoich.compartment = compartment
stoich.ksolve = gsolve
stoich.path = '/model/compartment/##'
+ '''
+ '''
+
# We need a finer timestep than the default 0.1 seconds,
# in order to get numerical accuracy.
for i in range (10, 19 ):
@@ -177,46 +92,32 @@ so try to use a table to control a pool instead.
To run in stochastic mode::
- ''python funcInputToPools''
+ ''python funcInputToPools''
To run in deterministic mode::
- ''python funcInputToPools false''
+ ''python funcInputToPools false''
"""
->>>>>>> 0e491aa41584cf7a66c0e242374d8ee61660eb7b
makeModel()
moose.seed()
moose.reinit()
-<<<<<<< HEAD
moose.start( 50.0 ) # Run the model for 50 seconds.
-=======
- moose.start( 50.0 ) # Run the model for 100 seconds.
->>>>>>> 0e491aa41584cf7a66c0e242374d8ee61660eb7b
a = moose.element( '/model/compartment/a' )
b = moose.element( '/model/compartment/b' )
# Iterate through all plots, dump their contents to data.plot.
for x in moose.wildcardFind( '/model/graphs/n#' ):
-<<<<<<< HEAD
#x.xplot( 'scriptKineticModel.plot', x.name )
t = numpy.arange( 0, x.vector.size, 1 ) * x.dt # sec
pylab.plot( t, x.vector, label=x.name )
- pylab.legend()
- pylab.show()
-=======
- #x.xplot( 'scriptKineticModel.plot', x.name )
- t = numpy.arange( 0, x.vector.size, 1 ) * x.dt # sec
- pylab.plot( t, x.vector, label=x.name )
pylab.legend()
pylab.show()
->>>>>>> 0e491aa41584cf7a66c0e242374d8ee61660eb7b
-
quit()
# Run the 'main' if this script is executed standalone.
if __name__ == '__main__':
- main()
\ No newline at end of file
+ main()
diff --git a/moose-examples/snippets/intfire.py b/moose-examples/snippets/intfire.py
index 7cd93d3c35a92d3f28a4640cdcf94dc05367fe2e..8426052e245176f27458852cda4bc68bfcfa8e24 100644
--- a/moose-examples/snippets/intfire.py
+++ b/moose-examples/snippets/intfire.py
@@ -49,8 +49,8 @@ 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')
@@ -63,11 +63,13 @@ def connect_two_intfires():
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.
- """
+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
@@ -77,7 +79,8 @@ def connect_spikegen():
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' )
@@ -93,8 +96,9 @@ def setup_synapse():
def main():
"""
- Demonstrates connection between 2 IntFire neurons to observe
- spike generation.
+Demonstrates connection between 2 IntFire neurons to observe
+spike generation.
+
"""
connect_two_intfires()
connect_spikegen()
diff --git a/moose-examples/snippets/loadKineticModel.py b/moose-examples/snippets/loadKineticModel.py
index ad71c67a94451d6276dfbab3efd49bf947467e8b..25c3f69e5144bc7419ca827e16314e41d1b4c0cb 100644
--- a/moose-examples/snippets/loadKineticModel.py
+++ b/moose-examples/snippets/loadKineticModel.py
@@ -54,7 +54,13 @@ def main():
This example illustrates loading, running, and saving a kinetic
model defined in kkit format. It uses a default kkit model but
you can specify another using the command line
+<<<<<<< HEAD
+
``python filename runtime solver``.
+
+=======
+ ``python filename runtime solver``.
+>>>>>>> 0e491aa41584cf7a66c0e242374d8ee61660eb7b
We use the gsl solver here.
The model already defines a couple of plots and sets the runtime 20 secs.
diff --git a/moose-examples/snippets/loadSbmlmodel.py b/moose-examples/snippets/loadSbmlmodel.py
index 6288215d586b0767d17e8157d737dc31e569869e..744c723933686e32c5c095de1949cfacd10a3483 100644
--- a/moose-examples/snippets/loadSbmlmodel.py
+++ b/moose-examples/snippets/loadSbmlmodel.py
@@ -43,7 +43,7 @@ import pylab
import moose
from moose.SBML import *
-#from moose.chemUtil.add_Delete_ChemicalSolver import *
+from moose.chemUtil.add_Delete_ChemicalSolver import *
def main():
"""
diff --git a/moose-examples/snippets/multiComptSigNeur.py b/moose-examples/snippets/multiComptSigNeur.py
index bb964baf2e27fc021677298e6147ad1df665f66e..613249cbda288e14f3572b6a151f6b7c76e4af72 100644
--- a/moose-examples/snippets/multiComptSigNeur.py
+++ b/moose-examples/snippets/multiComptSigNeur.py
@@ -222,9 +222,9 @@ def dumpPlots( fname,runtime ):
for x in moose.wildcardFind( '/graphs/##[ISA=Table]' ):
x.xplot( fname, x.name )
t = numpy.linspace( 0, runtime, x.vector.size ) # sec
- plt.plot( t, x.vector, label=x.name )
- plt.legend()
- plt.show()
+ plt.plot( t, x.vector, label=x.name )
+ plt.legend()
+ plt.show()
quit()
def makeSpinyCompt():
comptLength = 30e-6
diff --git a/moose-examples/snippets/scaleVolumes.py b/moose-examples/snippets/scaleVolumes.py
index 965a8562f28d6b0c2b01825a2977df5064a854b5..02fec606d38dc27207785b1a75752740c30d244e 100644
--- a/moose-examples/snippets/scaleVolumes.py
+++ b/moose-examples/snippets/scaleVolumes.py
@@ -155,14 +155,9 @@ def main():
# Iterate through all plots, dump their contents to data.plot.
displayPlots()
pylab.show( block=False )
-<<<<<<< HEAD
- print(('vol = ', vol, 'hit 0 to go to next plot'))
- eval(str(input()))
-
-=======
print( 'vol = %f ' % vol )
response = input( "Press enter to go to next plot... " )
->>>>>>> 0e491aa41584cf7a66c0e242374d8ee61660eb7b
+
quit()
# Run the 'main' if this script is executed standalone.