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diff --git a/docs/source/user/py/tutorials/ChemicalBistables.rst b/docs/source/user/py/tutorials/ChemicalBistables.rst
index 7a520c4ab6561e33c28f548fdd3faad344652210..c9e926b2c5ebb3e2679eade5dce5a84d5c718651 100644
--- a/docs/source/user/py/tutorials/ChemicalBistables.rst
+++ b/docs/source/user/py/tutorials/ChemicalBistables.rst
@@ -2,8 +2,6 @@
Chemical Bistables
******************
-
-
A `bistable system <https://en.wikipedia.org/wiki/Bistability>`_ is a dynamic system that has two stable equilibrium states. The following examples can be used to teach and demonstrate different aspects of bistable systems or to learn how to model them using moose. Each example contains a short description, the model's code, and the output with default settings.
Each example can be found as a python file within the main moose folder under
@@ -16,14 +14,16 @@ In order to run the example, run the script
python filename.py
-in your command line, where ``filename.py`` is the name of the python file you would like to run. The filenames are written in **bold** at the beginning of each section, and the files themselves can be found in the aformentioned directory.
+in command line, where ``filename.py`` is the name of the python file you would like to run. The filenames of each example are written in **bold** at the beginning of their respective sections, and the files themselves can be found in the aformentioned directory.
In chemical bistable models that use solvers, there are optional arguments that allow you to specify which solver you would like to use.
::
python filename.py [gsl | gssa | ee]
-All the following examples can be run with either of the three solvers, which in some cases produces a different outcome. However, simply running the file without the optional arguement will by default use the ``gsl`` solver, which will produce the outputs shown below.
+Where gsl is a deterministic solver, gssa stands for Gillespie stochastic simulation algorithm, and ee stands for exponential euler.
+
+All the following examples can be run with either of the three solvers, which in some cases produces a different outcome. However, simply running the file without the optional argument will by default use the ``gsl`` solver. These ``gsl`` outputs are the ones shown below.
Simple Bistables
================
@@ -59,6 +59,8 @@ thresholds different for the different states?
2. Try different volumes in line 31, and rerun using the gssa. Will you
see more or less noise if you increase the volume to 1e-20 m^3?
+**Code:**
+
.. hidden-code-block:: python
:linenos:
:label: Show/Hide code
@@ -223,6 +225,8 @@ automatically scales the volumes from 1e-19 down to smaller values.
Note how the simulation successively becomes noisier, until at very
small volumes there are spontaneous state transitions.
+**Code:**
+
.. hidden-code-block:: python
:linenos:
:label: Show/Hide code
@@ -389,7 +393,6 @@ small volumes there are spontaneous state transitions.
.. parsed-literal::
vol = 1e-19, a.concInit = 1.0, a.nInit = 60221.415
- Close graph to go to next plot
@@ -400,7 +403,6 @@ small volumes there are spontaneous state transitions.
.. parsed-literal::
vol = 1e-20, a.concInit = 1.0, a.nInit = 6022.1415
- Close graph to go to next plot
@@ -411,7 +413,6 @@ small volumes there are spontaneous state transitions.
.. parsed-literal::
vol = 1e-21, a.concInit = 1.0, a.nInit = 602.21415
- Close graph to go to next plot
@@ -422,7 +423,6 @@ small volumes there are spontaneous state transitions.
.. parsed-literal::
vol = 3e-22, a.concInit = 1.0, a.nInit = 180.664245
- Close graph to go to next plot
@@ -433,7 +433,6 @@ small volumes there are spontaneous state transitions.
.. parsed-literal::
vol = 1e-22, a.concInit = 1.0, a.nInit = 60.221415
- Close graph to go to next plot
@@ -444,7 +443,6 @@ small volumes there are spontaneous state transitions.
.. parsed-literal::
vol = 3e-23, a.concInit = 1.0, a.nInit = 18.0664245
- Close graph to go to next plot
@@ -455,7 +453,6 @@ small volumes there are spontaneous state transitions.
.. parsed-literal::
vol = 1e-23, a.concInit = 1.0, a.nInit = 6.0221415
- Close graph to go to next plot
@@ -486,6 +483,8 @@ original levels. At t = 320 we apply a strong push to take it over to a
state where **b** is larger. At t = 430 we give it a strong push to take
it back to the **c** dominant state.
+**Code:**
+
.. hidden-code-block:: python
:linenos:
:label: Show/Hide code
@@ -599,6 +598,8 @@ Note that this is a somewhat unphysiological manipulation! Following
this the model settles back to the same 'off' state it was in
originally.
+**Code:**
+
.. hidden-code-block:: python
:linenos:
:label: Show/Hide code
@@ -724,6 +725,8 @@ the cylinder. As there is a slightly larger volume on the left, the
transition point gradually advances to the right, as molecule **b**
yields to the slightly larger amounts of molecule **c**.
+**Code:**
+
.. hidden-code-block:: python
:linenos:
:label: Show/Hide code
@@ -935,6 +938,8 @@ steady states are.
For more information on the algorithm used, look in the comments within
the main method of the code below.
+**Code:**
+
.. hidden-code-block:: python
:linenos:
:label: Show/Hide code
@@ -1192,8 +1197,8 @@ the main method of the code below.
.. image:: ../../../images/findS.png
-Dose Response
-=============
+Dose Response (Under construction)
+==================================
File name: **doseResponse.py**
@@ -1204,6 +1209,8 @@ steady-state solver to find the stable points for each value of the
control parameter. Unfortunately it doesn't work right now. Seems like
the kcat scaling isn't being registered.
+**Code:**
+
.. hidden-code-block:: python
:linenos:
:label: Show/Hide code
diff --git a/docs/source/user/py/tutorials/ChemicalOscillators.rst b/docs/source/user/py/tutorials/ChemicalOscillators.rst
new file mode 100644
index 0000000000000000000000000000000000000000..564df326bc9d105bb7233c0ba554f42aeeb30b3f
--- /dev/null
+++ b/docs/source/user/py/tutorials/ChemicalOscillators.rst
@@ -0,0 +1,558 @@
+********************
+Chemical Oscillators
+********************
+
+`Chemical Oscillators <https://en.wikipedia.org/wiki/Chemical_clock>`_, also known as chemical clocks, are chemical systems in which the concentrations of one or more reactants undergoes periodic changes.
+
+These Oscillatory reactions can be modelled using moose. The examples below demonstrate different types of chemical oscillators, as well as how they can be simulated using moose. Each example has a short description, the code used in the simulation, and the default (gsl solver) output of the code.
+
+Each example can be found as a python file within the main moose folder under
+::
+
+ (...)/moose/moose-examples/tutorials/ChemicalOscillators
+
+In order to run the example, run the script
+::
+
+ python filename.py
+
+in command line, where ``filename.py`` is the name of the python file you would like to run. The filenames of each example are written in **bold** at the beginning of their respective sections, and the files themselves can be found in the aformentioned directory.
+
+In chemical models that use solvers, there are optional arguments that allow you to specify which solver you would like to use.
+::
+
+ python filename.py [gsl | gssa | ee]
+
+Where gsl is a deterministic solver, gssa stands for Gillespie stochastic simulation algorithm, and ee stands for exponential euler.
+
+All the following examples can be run with either of the three solvers, which in some cases produces a different outcome. However, simply running the file without the optional argument will by default use the ``gsl`` solver. These ``gsl`` outputs are the ones shown below.
+
+|
+|
+
+Slow Feedback Oscillator
+========================
+
+File name: **slowFbOsc.py**
+
+This example illustrates loading, and running a kinetic model for a
+delayed -ve feedback oscillator, defined in kkit format. The model is
+one by Boris N. Kholodenko from Eur J Biochem. (2000) 267(6):1583-8
+
+.. image:: ../../../images/Kholodenko_tut.png
+
+This model has a high-gain MAPK stage, whose effects are visible whem
+one looks at the traces from successive stages in the plots. The
+upstream pools have small early peaks, and the downstream pools have
+large delayed ones. The negative feedback step is mediated by a simple
+binding reaction of the end-product of oscillation with an upstream
+activator.
+
+We use the gsl solver here. The model already defines some plots and
+sets the runtime to 4000 seconds. The model does not really play nicely
+with the GSSA solver, since it involves some really tiny amounts of the
+MAPKKK.
+
+Things to do with the model:
+
+::
+
+ - Look at model once it is loaded in::
+
+ moose.le( '/model' )
+ moose.showfields( '/model/kinetics/MAPK/MAPK' )
+
+ - Behold the amplification properties of the cascade. Could do this by blocking the feedback step and giving a small pulse input.
+ - Suggest which parameters you would alter to change the period of the oscillator:
+ - Concs of various molecules, for example::
+
+ ras_MAPKKKK = moose.element( '/model/kinetics/MAPK/Ras_dash_MKKKK' )
+ moose.showfields( ras_MAPKKKK )
+ ras_MAPKKKK.concInit = 1e-5
+ - Feedback reaction rates
+ - Rates of all the enzymes::
+
+ for i in moose.wildcardFind( '/##[ISA=EnzBase]'):
+ i.kcat *= 10.0
+
+**Code:**
+
+.. hidden-code-block:: python
+ :linenos:
+ :label: Show/Hide code
+
+ #########################################################################
+ ## This program is part of 'MOOSE', the
+ ## Messaging Object Oriented Simulation Environment.
+ ## Copyright (C) 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.
+ #########################################################################
+
+ import moose
+ import matplotlib.pyplot as plt
+ import matplotlib.image as mpimg
+ import pylab
+ import numpy
+ import sys
+
+ def main():
+
+ solver = "gsl"
+ mfile = '../../genesis/Kholodenko.g'
+ runtime = 5000.0
+ if ( len( sys.argv ) >= 2 ):
+ solver = sys.argv[1]
+ modelId = moose.loadModel( mfile, 'model', solver )
+ dt = moose.element( '/clock' ).tickDt[18]
+ moose.reinit()
+ moose.start( runtime )
+
+ # Display all plots.
+ img = mpimg.imread( 'Kholodenko_tut.png' )
+ fig = plt.figure( figsize=( 12, 10 ) )
+ png = fig.add_subplot( 211 )
+ imgplot = plt.imshow( img )
+ ax = fig.add_subplot( 212 )
+ x = moose.wildcardFind( '/model/#graphs/conc#/#' )
+ t = numpy.arange( 0, x[0].vector.size, 1 ) * dt
+ ax.plot( t, x[0].vector * 100, 'b-', label='Ras-MKKK * 100' )
+ ax.plot( t, x[1].vector, 'y-', label='MKKK-P' )
+ ax.plot( t, x[2].vector, 'm-', label='MKK-PP' )
+ ax.plot( t, x[3].vector, 'r-', label='MAPK-PP' )
+ plt.ylabel( 'Conc (mM)' )
+ plt.xlabel( 'Time (seconds)' )
+ pylab.legend()
+ pylab.show()
+
+ # Run the 'main' if this script is executed standalone.
+ if __name__ == '__main__':
+ main()
+
+|
+**Output:**
+
+
+.. image:: ../../../images/FB.png
+
+|
+|
+
+
+Turing Pattern Oscillator in One Dimension
+==========================================
+
+File name: **TuringOneDim.py**
+
+This example illustrates how to set up a oscillatory Turing pattern in
+1-D using reaction diffusion calculations. 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.
+
+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**.
+
+.. image:: ../../../images/turingPatternTut.png
+
+This chemical system is present in a 1-dimensional (cylindrical)
+compartment. The entire reaction-diffusion system is set up within the
+script.
+
+**Code:**
+
+.. hidden-code-block:: python
+ :linenos:
+ :label: Show/Hide code
+
+ #########################################################################
+ ## This program is part of 'MOOSE', the
+ ## Messaging Object Oriented Simulation Environment.
+ ## Copyright (C) 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.
+ #########################################################################
+
+ import math
+ import numpy
+ import matplotlib.pyplot as plt
+ import matplotlib.image as mpimg
+ import moose
+
+ def makeModel():
+
+ # create container for model
+ r0 = 1e-6 # m
+ r1 = 1e-6 # m
+ num = 100
+ diffLength = 1e-6 # m
+ len = num * diffLength # m
+ diffConst = 5e-12 # m^2/sec
+ motorRate = 1e-6 # m/sec
+ concA = 1 # millimolar
+ dt4 = 0.02 # for the diffusion
+ dt5 = 0.2 # for the reaction
+
+ model = moose.Neutral( 'model' )
+ compartment = moose.CylMesh( '/model/compartment' )
+ compartment.r0 = r0
+ compartment.r1 = r1
+ compartment.x0 = 0
+ compartment.x1 = len
+ compartment.diffLength = diffLength
+
+ assert( compartment.numDiffCompts == num )
+
+ # create molecules and reactions
+ a = moose.Pool( '/model/compartment/a' )
+ b = moose.Pool( '/model/compartment/b' )
+ s = moose.Pool( '/model/compartment/s' )
+ e1 = moose.MMenz( '/model/compartment/e1' )
+ e2 = moose.MMenz( '/model/compartment/e2' )
+ e3 = moose.MMenz( '/model/compartment/e3' )
+ r1 = moose.Reac( '/model/compartment/r1' )
+ moose.connect( e1, 'sub', s, 'reac' )
+ moose.connect( e1, 'prd', a, 'reac' )
+ moose.connect( a, 'nOut', e1, 'enzDest' )
+ e1.Km = 1
+ e1.kcat = 1
+
+ moose.connect( e2, 'sub', s, 'reac' )
+ moose.connect( e2, 'prd', b, 'reac' )
+ moose.connect( a, 'nOut', e2, 'enzDest' )
+ e2.Km = 1
+ e2.kcat = 0.5
+
+ moose.connect( e3, 'sub', a, 'reac' )
+ moose.connect( e3, 'prd', s, 'reac' )
+ moose.connect( b, 'nOut', e3, 'enzDest' )
+ e3.Km = 0.1
+ e3.kcat = 1
+
+ moose.connect( r1, 'sub', b, 'reac' )
+ moose.connect( r1, 'prd', s, 'reac' )
+ r1.Kf = 0.3 # 1/sec
+ r1.Kb = 0 # 1/sec
+
+ # Assign parameters
+ a.diffConst = diffConst/10
+ b.diffConst = diffConst
+ s.diffConst = 0
+
+ # Make solvers
+ ksolve = moose.Ksolve( '/model/compartment/ksolve' )
+ dsolve = moose.Dsolve( '/model/dsolve' )
+ # Set up clocks. The dsolver to know before assigning stoich
+ moose.setClock( 4, dt4 )
+ moose.setClock( 5, dt5 )
+ moose.useClock( 4, '/model/dsolve', 'process' )
+ # Ksolve must be scheduled after dsolve.
+ moose.useClock( 5, '/model/compartment/ksolve', 'process' )
+
+ stoich = moose.Stoich( '/model/compartment/stoich' )
+ stoich.compartment = compartment
+ stoich.ksolve = ksolve
+ stoich.dsolve = dsolve
+ stoich.path = "/model/compartment/##"
+ assert( dsolve.numPools == 3 )
+ a.vec.concInit = [0.1]*num
+ a.vec[0].concInit *= 1.2 # slight perturbation at one end.
+ b.vec.concInit = [0.1]*num
+ s.vec.concInit = [1]*num
+
+ def displayPlots():
+ a = moose.element( '/model/compartment/a' )
+ b = moose.element( '/model/compartment/b' )
+ pos = numpy.arange( 0, a.vec.conc.size, 1 )
+ pylab.plot( pos, a.vec.conc, label='a' )
+ pylab.plot( pos, b.vec.conc, label='b' )
+ pylab.legend()
+ pylab.show()
+
+ def main():
+ runtime = 400
+ displayInterval = 2
+ makeModel()
+ dsolve = moose.element( '/model/dsolve' )
+ moose.reinit()
+ #moose.start( runtime ) # Run the model for 10 seconds.
+
+ a = moose.element( '/model/compartment/a' )
+ b = moose.element( '/model/compartment/b' )
+ s = moose.element( '/model/compartment/s' )
+
+ img = mpimg.imread( 'turingPatternTut.png' )
+ #imgplot = plt.imshow( img )
+ #plt.show()
+
+ plt.ion()
+ fig = plt.figure( figsize=(12,10) )
+ png = fig.add_subplot(211)
+ imgplot = plt.imshow( img )
+ ax = fig.add_subplot(212)
+ ax.set_ylim( 0, 0.5 )
+ plt.ylabel( 'Conc (mM)' )
+ plt.xlabel( 'Position along cylinder (microns)' )
+ pos = numpy.arange( 0, a.vec.conc.size, 1 )
+ line1, = ax.plot( pos, a.vec.conc, label='a' )
+ line2, = ax.plot( pos, b.vec.conc, label='b' )
+ timeLabel = plt.text(60, 0.4, 'time = 0')
+ plt.legend()
+ fig.canvas.draw()
+
+ for t in range( displayInterval, runtime, displayInterval ):
+ moose.start( displayInterval )
+ line1.set_ydata( a.vec.conc )
+ line2.set_ydata( b.vec.conc )
+ timeLabel.set_text( "time = %d" % t )
+ fig.canvas.draw()
+
+ print( "Hit 'enter' to exit" )
+ raw_input( )
+
+
+
+ # Run the 'main' if this script is executed standalone.
+ if __name__ == '__main__':
+ main()
+
+|
+
+**Output:**
+
+.. image:: ../../../images/turing.png
+
+|
+|
+
+
+Relaxation Oscillator
+=====================
+
+File name: **relaxationOsc.py**
+
+This example illustrates a **Relaxation Oscillator**. This is an
+oscillator built around a switching reaction, which tends to flip into
+one or other state and stay there. The relaxation bit comes in because
+once it is in state 1, a slow (relaxation) process begins which
+eventually flips it into state 2, and vice versa.
+
+.. image:: ../../../images/relaxOsc_tut.png
+
+The model is based on Bhalla, Biophys J. 2011. It is defined in kkit
+format. It uses the deterministic gsl solver by default. You can specify
+the stochastic Gillespie solver on the command line
+
+::
+
+ ``python relaxationOsc.py gssa``
+
+Things to do with the model:
+
+::
+
+ * Figure out what determines its frequency. You could change
+ the initial concentrations of various model entities::
+
+ ma = moose.element( '/model/kinetics/A/M' )
+ ma.concInit *= 1.5
+
+ Alternatively, you could scale the rates of molecular traffic
+ between the compartments::
+
+ exo = moose.element( '/model/kinetics/exo' )
+ endo = moose.element( '/model/kinetics/endo' )
+ exo.Kf *= 1.0
+ endo.Kf *= 1.0
+
+ * Play with stochasticity. The standard thing here is to scale the
+ volume up and down::
+
+ compt.volume = 1e-18
+ compt.volume = 1e-20
+ compt.volume = 1e-21
+
+**Code:**
+
+.. hidden-code-block:: python
+ :linenos:
+ :label: Show/Hide code
+
+ #########################################################################
+ ## This program is part of 'MOOSE', the
+ ## Messaging Object Oriented Simulation Environment.
+ ## Copyright (C) 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.
+ #########################################################################
+
+ import moose
+ import matplotlib.pyplot as plt
+ import matplotlib.image as mpimg
+ import pylab
+ import numpy
+ import sys
+
+ def main():
+
+ solver = "gsl" # Pick any of gsl, gssa, ee..
+ #solver = "gssa" # Pick any of gsl, gssa, ee..
+ mfile = '../../genesis/OSC_Cspace.g'
+ runtime = 4000.0
+ if ( len( sys.argv ) >= 2 ):
+ solver = sys.argv[1]
+ modelId = moose.loadModel( mfile, 'model', solver )
+ # Increase volume so that the stochastic solver gssa
+ # gives an interesting output
+ compt = moose.element( '/model/kinetics' )
+ compt.volume = 1e-19
+ dt = moose.element( '/clock' ).tickDt[18] # 18 is the plot clock.
+
+ moose.reinit()
+ moose.start( runtime )
+
+ # Display all plots.
+ img = mpimg.imread( 'relaxOsc_tut.png' )
+ fig = plt.figure( figsize=(12, 10 ) )
+ png = fig.add_subplot( 211 )
+ imgplot = plt.imshow( img )
+ ax = fig.add_subplot( 212 )
+ x = moose.wildcardFind( '/model/#graphs/conc#/#' )
+ t = numpy.arange( 0, x[0].vector.size, 1 ) * dt
+ ax.plot( t, x[0].vector, 'b-', label=x[0].name )
+ ax.plot( t, x[1].vector, 'c-', label=x[1].name )
+ ax.plot( t, x[2].vector, 'r-', label=x[2].name )
+ ax.plot( t, x[3].vector, 'm-', label=x[3].name )
+ plt.ylabel( 'Conc (mM)' )
+ plt.xlabel( 'Time (seconds)' )
+ pylab.legend()
+ pylab.show()
+
+ # Run the 'main' if this script is executed standalone.
+ if __name__ == '__main__':
+ main()
+
+|
+
+**Output:**
+
+.. image:: ../../../images/relax.png
+
+
+|
+|
+
+Repressilator
+=============
+
+File name: **repressilator.py**
+
+This example illustrates the classic **Repressilator** model, based on
+Elowitz and Liebler, Nature 2000. The model has the basic architecture
+
+.. image:: ../../../images/repressillatorOsc.png
+
+where **TetR**, **Lac**, and **Lcl** are genes whose products repress
+eachother. The circle symbol indicates inhibition. The model uses the
+Gillespie (stochastic) method by default but you can run it using a
+deterministic method by saying ``python repressillator.py gsl``
+
+Good things to do with this model include:
+
+::
+
+ * Ask what it would take to change period of repressillator:
+
+ * Change inhibitor rates::
+
+ inhib = moose.element( '/model/kinetics/TetR_gene/inhib_reac' )
+ moose.showfields( inhib )
+ inhib.Kf *= 0.1
+
+ * Change degradation rates::
+
+ degrade = moose.element( '/model/kinetics/TetR_gene/TetR_degradation' )
+ degrade.Kf *= 10.0
+ * Run in stochastic mode:
+
+ * Change volumes, figure out how many molecules are present::
+
+ lac = moose.element( '/model/kinetics/lac_gene/lac' )
+ print lac.n``
+
+ * Find when it becomes hopelessly unreliable with small volumes.
+
+**Code:**
+
+.. hidden-code-block:: python
+ :linenos:
+ :label: Show/Hide code
+
+ #########################################################################
+ ## This program is part of 'MOOSE', the
+ ## Messaging Object Oriented Simulation Environment.
+ ## Copyright (C) 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.
+ #########################################################################
+
+ import moose
+ import matplotlib.pyplot as plt
+ import matplotlib.image as mpimg
+ import pylab
+ import numpy
+ import sys
+
+ def main():
+
+ #solver = "gsl" # Pick any of gsl, gssa, ee..
+ solver = "gssa" # Pick any of gsl, gssa, ee..
+ mfile = '../../genesis/Repressillator.g'
+ runtime = 6000.0
+ if ( len( sys.argv ) >= 2 ):
+ solver = sys.argv[1]
+ modelId = moose.loadModel( mfile, 'model', solver )
+ # Increase volume so that the stochastic solver gssa
+ # gives an interesting output
+ compt = moose.element( '/model/kinetics' )
+ compt.volume = 1e-19
+ dt = moose.element( '/clock' ).tickDt[18]
+
+ moose.reinit()
+ moose.start( runtime )
+
+ # Display all plots.
+ img = mpimg.imread( 'repressillatorOsc.png' )
+ fig = plt.figure( figsize=(12, 10 ) )
+ png = fig.add_subplot( 211 )
+ imgplot = plt.imshow( img )
+ ax = fig.add_subplot( 212 )
+ x = moose.wildcardFind( '/model/#graphs/conc#/#' )
+ plt.ylabel( 'Conc (mM)' )
+ plt.xlabel( 'Time (seconds)' )
+ for x in moose.wildcardFind( '/model/#graphs/conc#/#' ):
+ t = numpy.arange( 0, x.vector.size, 1 ) * dt
+ pylab.plot( t, x.vector, label=x.name )
+ pylab.legend()
+ pylab.show()
+
+ # Run the 'main' if this script is executed standalone.
+ if __name__ == '__main__':
+ main()
+
+|
+
+**Output:**
+
+.. image:: ../../../images/repris.png
+
+
diff --git a/docs/source/user/py/tutorials/index_tut.rst b/docs/source/user/py/tutorials/index_tut.rst
index afeee8e51cb1b7f9e155fd4773d6914321923733..79fd95714440639df91137148697ac30309b9e7b 100644
--- a/docs/source/user/py/tutorials/index_tut.rst
+++ b/docs/source/user/py/tutorials/index_tut.rst
@@ -8,8 +8,7 @@ Moose tutorials demonstrate how to do a variety of things with moose. It is reco
:maxdepth: 2
ChemicalBistables
+ ChemicalOscillators
ExcInhNet
ExcInhNetPlast
- ChemicalOscillators
- Rdesigneur