diff --git a/snippets/RandSpikeStats.py b/snippets/RandSpikeStats.py
index dfe288f91441a02da4d5e62ad4df04c82b3fe6fc..1b494da48e7b17d51b17cfb7ead49d11078ca680 100644
--- a/snippets/RandSpikeStats.py
+++ b/snippets/RandSpikeStats.py
@@ -67,13 +67,14 @@ def make_model():
moose.connect( plotf, 'requestOut', fire, 'getVm' )
def main():
+
"""
- 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()
@@ -91,6 +92,7 @@ def main():
pylab.legend()
pylab.show()
+
'''
moose.useClock( 0, '/stim', 'process' )
moose.useClock( 1, '/spike', 'process' )
diff --git a/snippets/analogStimTable.py b/snippets/analogStimTable.py
index 006f47f8f4a9bd34ded56d9f8cbecaf58a0202b2..e6b7602cd5107f40735055142162e1281045f01c 100644
--- a/snippets/analogStimTable.py
+++ b/snippets/analogStimTable.py
@@ -92,5 +92,6 @@ if __name__ == '__main__':
main()
+
#
# stimtable.py ends here
diff --git a/snippets/cspaceSteadyState.py b/snippets/cspaceSteadyState.py
index 713e157bc3ea258b0682d65a544c05f592b9b695..7cd2f012fa2b0b359d8f6aa3e515c9fc0976d0d1 100644
--- a/snippets/cspaceSteadyState.py
+++ b/snippets/cspaceSteadyState.py
@@ -40,6 +40,15 @@ def main():
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
+<<<<<<< HEAD
+ 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.
+=======
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
@@ -47,6 +56,7 @@ def main():
- 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.
+>>>>>>> 0e491aa41584cf7a66c0e242374d8ee61660eb7b
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
@@ -55,6 +65,15 @@ def main():
seconds.
Looking at the output you will see many features of note:
- the first attractor (stable point) and the saddle point
+<<<<<<< HEAD
+ (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.
+=======
(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
@@ -62,6 +81,7 @@ def main():
- 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.
+>>>>>>> 0e491aa41584cf7a66c0e242374d8ee61660eb7b
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
diff --git a/snippets/funcInputToPools.py b/snippets/funcInputToPools.py
index b6f8f0b31d4531d50c04421b14fb8d877ddf8769..52978b4cad06fecb1a11e121db057eeacb2dede8 100644
--- a/snippets/funcInputToPools.py
+++ b/snippets/funcInputToPools.py
@@ -13,6 +13,7 @@ import numpy
import moose
import sys
+
def makeModel():
if len( sys.argv ) == 1:
useGsolve = True
@@ -65,6 +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 ):
@@ -88,11 +92,11 @@ 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''
"""
@@ -100,6 +104,7 @@ To run in deterministic mode::
moose.seed()
moose.reinit()
+
moose.start( 50.0 ) # Run the model for 100 seconds.
a = moose.element( '/model/compartment/a' )
@@ -107,12 +112,11 @@ To run in deterministic mode::
# Iterate through all plots, dump their contents to data.plot.
for x in moose.wildcardFind( '/model/graphs/n#' ):
- #x.xplot( 'scriptKineticModel.plot', x.name )
+ #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()
-
quit()
# Run the 'main' if this script is executed standalone.
diff --git a/snippets/intfire.py b/snippets/intfire.py
index 7cd93d3c35a92d3f28a4640cdcf94dc05367fe2e..8426052e245176f27458852cda4bc68bfcfa8e24 100644
--- a/snippets/intfire.py
+++ b/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/snippets/loadKineticModel.py b/snippets/loadKineticModel.py
index ad71c67a94451d6276dfbab3efd49bf947467e8b..25c3f69e5144bc7419ca827e16314e41d1b4c0cb 100644
--- a/snippets/loadKineticModel.py
+++ b/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/snippets/loadSbmlmodel.py b/snippets/loadSbmlmodel.py
index 6288215d586b0767d17e8157d737dc31e569869e..744c723933686e32c5c095de1949cfacd10a3483 100644
--- a/snippets/loadSbmlmodel.py
+++ b/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/snippets/multiComptSigNeur.py b/snippets/multiComptSigNeur.py
index bb964baf2e27fc021677298e6147ad1df665f66e..613249cbda288e14f3572b6a151f6b7c76e4af72 100644
--- a/snippets/multiComptSigNeur.py
+++ b/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/snippets/scaleVolumes.py b/snippets/scaleVolumes.py
index 965a8562f28d6b0c2b01825a2977df5064a854b5..02fec606d38dc27207785b1a75752740c30d244e 100644
--- a/snippets/scaleVolumes.py
+++ b/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.