Getting started with python scripting for MOOSE¶
To see an interactive version of this page, click the following link
This document describes how to use the
moose module in Python
scripts or in an interactive Python shell. It aims to give you enough
overview to help you start scripting using MOOSE and extract farther
information that may be required for advanced work. Knowledge of
Python or programming in general will be helpful. If you just want to
simulate existing models in one of the supported formats, you can fire
the MOOSE GUI and locate the model file using the
File menu and
load it. The GUI is described in separate document. If you
are looking for recipes for specific tasks, take a look at
cookbook. The example code in the boxes can be entered in
a Python shell.
MOOSE is object-oriented. Biological concepts are mapped into classes, and a model is built by creating instances of these classes and connecting them by messages. MOOSE also has numerical classes whose job is to take over difficult computations in a certain domain, and do them fast. There are such solver classes for stochastic and deterministic chemistry, for diffusion, and for multicompartment neuronal models.
MOOSE is a simulation environment, not just a numerical engine: It provides data representations and solvers (of course!), but also a scripting interface with Python, graphical displays with Matplotlib, PyQt, and OpenGL, and support for many model formats. These include SBML, NeuroML, GENESIS kkit and cell.p formats, HDF5 and NSDF for data writing.
This page acts as the first stepping stone for learning how moose works. The tutorials here are intended to be interactive, and are presented as python commands. Python commands are identifiable by the
>>> before the command as opposed to
$ which identifies a command-line command.
You are encouraged to run a python shell while reading through this documentation and trying out each command for yourself. Python shells are environments within your terminal wherein everything you type is interpreted as a python command. They can be accessed by typing
in your command-line terminal.
While individually typing lines of code in a python terminal is useful for practicing using moose and coding in general, note that once you close the python environment all the code you typed is gone and the moose models created are also lost. In order to 'save' models that you create, you would have to type your code in a text file with a
.py extension. The easiest way to do this is to create a text file in command line, open it with a text editor (for example, gedit), and simply type your code in (make sure you indent correctly).
$ touch code.py $ gedit code.py
Once you have written your code in the file, you can run it through your python environment.
$ python code.py
Note that apart from this section of the quickstart, most of the moose documentation is in the form of
snippets. These are basically
.py files with code that demonstrates a certain functionality in moose. If you see a dialogue box like this one:
Demonstrate the use of Func class
You can view the code by clicking the green source button on the left side of the box. Alternatively, the source code for all of the examples in the documentation can be found in
moose/moose-examples/snippets. Once you run each file in python, it is encouraged that you look through the code to understand how it works.
In the quickstart, most of the snippets demonstrate the functionality of specific classes. However, snippets in later sections such as the cookbook show how to do specific things in moose such as creating networks, chemical models, and synaptic channels.
In a python script you import modules to access the functionalities they provide. In order to use moose, you need to import it within a python environment or at the beginning of your python script.
>>> import moose
This make the
moose module available for use in Python. You can use Python's built-in
help function to read the top-level documentation for the moose module.
This will give you an overview of the module. Press
q to exit the pager and get back to the interpreter. You can also access the documentation for individual classes and functions this way.
To list the available functions and classes you can use
MOOSE has built-in documentation in the C++-source-code independent of
moose module has a separate
doc function to extract
The class level documentation will show whatever the author/maintainer of the class wrote for documentation followed by a list of various kinds of fields and their data types. This can be very useful in an interactive session.
Each field can have its own detailed documentation, too.
Note that you need to put the class-name followed by dot followed by
field-name within quotes. Otherwise,
moose.doc will receive the
field value as parameter and get confused.
Alternatively, if you want to see a full list of classes, functions and their fields, you can browse through the following pages. This is especially helpful when going through snippets.
Creating objects and traversing the object hierarchy¶
Different types of biological entities like neurons, enzymes, etc are
represented by classes and individual instances of those types are
objects of those classes. Objects are the building-blocks of models in
MOOSE. We call MOOSE objects
element and use object and element
interchangeably in the context of MOOSE. Elements are conceptually laid
out in a tree-like hierarchical structure. If you are familiar with file
system hierarchies in common operating systems, this should be simple.
At the top of the object hierarchy sits the
Shell, equivalent to the
root directory in UNIX-based systems and represented by the path
You can list the existing objects under
/ using the
>>> moose.le() Elements under / /Msgs /clock /classes /postmaster
classes are predefined objects in MOOSE. And
each object can contain other objects inside them. You can see them by
passing the path of the parent object to
>>> moose.le('/Msgs') Elements under /Msgs /Msgs/singleMsg /Msgs/oneToOneMsg /Msgs/oneToAllMsg /Msgs/diagonalMsg /Msgs/sparseMsg
Now let us create some objects of our own. This can be done by invoking MOOSE class constructors (just like regular Python classes).
>>> model = moose.Neutral('/model')
The above creates a
Neutral object named
the most basic class in MOOSE. A
Neutral element can act as a
container for other elements. We can create something under
>>> soma = moose.Compartment('/model/soma')
Every element has a unique path. This is a concatenation of the names of all the objects one has to traverse starting with the root to reach that element.
>>> print soma.path /model/soma
The name of the element can be printed, too.
>>> print soma.name soma
Compartment elements model small sections of a neuron. Some
basic experiments can be carried out using a single compartment. Let us
create another object to act on the
soma. This will be a step
current generator to inject a current pulse into the soma.
>>> pulse = moose.PulseGen('/model/pulse')
You can use
le at any point to see what is there
>>> moose.le('/model') Elements under /model /model/soma /model/pulse
And finally, we can create a
Table to record the time series of the
soma's membrane potential. It is good practice to organize the data
separately from the model. So we do it as below
>>> data = moose.Neutral('/data') >>> vmtab = moose.Table('/data/soma_Vm')
Now that we have the essential elements for a small model, we can go on to set the properties of this model and the experimental protocol.
Elements have several kinds of fields. The simplest ones are the
value fields. These can be accessed like ordinary Python members.
You can list the available value fields using
valueFinfo is the type name for value fields.
short form of field information. For each type of field there is a
name ending with
-Finfo. The above will display the following
('this', 'name', 'me', 'parent', 'children', 'path', 'class', 'linearSize', 'objectDimensions', 'lastDimension', 'localNumField', 'pathIndices', 'msgOut', 'msgIn', 'Vm', 'Cm', 'Em', 'Im', 'inject', 'initVm', 'Rm', 'Ra', 'diameter', 'length', 'x0', 'y0', 'z0', 'x', 'y', 'z')
Some of these fields are for internal or advanced use, some give access
to the physical properties of the biological entity we are trying to
model. Now we are interested in
In the most basic form, a neuronal compartment acts like a parallel
RC circuit with a battery attached. Here
resistor and capacitor connected in parallel, and the battery with
Em is in series with the resistor, as shown below:
The fields are populated with some defaults.
>>> print soma.Cm, soma.Rm, soma.Vm, soma.Em, soma.initVm 1.0 1.0 -0.06 -0.06 -0.06
You can set the
Rm fields to something realistic using
simple assignment (we follow SI unit) .
>>> soma.Cm = 1e-9 >>> soma.Rm = 1e7 >>> soma.initVm = -0.07
Instead of writing print statements for each field, you could use the utility function showfield to see that the changes took effect
>>> moose.showfield(soma) [ /soma ] diameter = 0.0 Ra = 1.0 y0 = 0.0 Rm = 10000000.0 numData = 1 inject = 0.0 initVm = -0.07 Em = -0.06 y = 0.0 numField = 1 path = /soma dt = 5e-05 tick = 4 z0 = 0.0 name = soma Cm = 1e-09 x0 = 0.0 Vm = -0.06 className = Compartment length = 0.0 Im = 0.0 x = 0.0 z = 0.0
Now we can setup the current pulse to be delivered to the soma
>>> pulse.delay = 50e-3 >>> pulse.width = 100e-3 >>> pulse.level = 1e-9 >>> pulse.delay = 1e9
This tells the pulse generator to create a 100 ms long pulse 50 ms after
the start of the simulation. The amplitude of the pulse is set to 1 nA.
We set the delay for the next pulse to a very large value (larger than
the total simulation time) so that the stimulation stops after the first
pulse. Had we set
pulse.delay = 0 , it would have generated a pulse
train at 50 ms intervals.
In order for the elements to interact during simulation, we need to
connect them via messages. Elements are connected to each other using
special source and destination fields. These types are named
destFinfo. You can query the available source and
destination fields on an element using
getFieldNames as before. This
time, let us do it another way: by the class name
>>> moose.getFieldNames('PulseGen', 'srcFinfo') ('childMsg', 'output')
This form has the advantage that you can get information about a class without creating elements of that class.
childMsg is a source field that is used by the MOOSE internals
to connect child elements to parent elements. The second one is of our
interest. Check out the built-in documentation here
>>> moose.doc('PulseGen.output') PulseGen.output: double - source field Current output level.
so this is the output of the pulse generator and this must be injected
soma to stimulate it. But where in the
soma can we send
it? Again, MOOSE has some introspection built in.
>>> soma.getFieldNames('destFinfo') ('parentMsg', 'setThis', 'getThis', ... 'setZ', 'getZ', 'injectMsg', 'randInject', 'cable', 'process', 'reinit', 'initProc', 'initReinit', 'handleChannel', 'handleRaxial', 'handleAxial')
Now that is a long list. But much of it are fields for internal or
special use. Anything that starts with
set are internal
destFinfo used for accessing value fields (we shall use one of those
when setting up data recording). Among the rest
injectMsg seems to
be the most likely candidate. Use the
connect function to connect
the pulse generator output to the soma input
>>> m = moose.connect(pulse, 'output', soma, 'injectMsg')
connect(source, source_field, dest, dest_field) creates a
source_field field to
dest_field field and returns that message. Messages are
also elements. You can print them to see their identity
>>> print m <moose.SingleMsg: id=5, dataId=733, path=/Msgs/singleMsg>
You can print any element as above and the string representation will
show you the class, two numbers(
identifying it among all elements, and its path. You can get some more
information about a message
>>> print m.e1.path, m.e2.path, m.srcFieldsOnE1, m.destFieldsOnE2 /model/pulse /model/soma ('output',) ('injectMsg',)
will confirm what you already know.
A message element has fields
e2 referring to the elements
it connects. For single one-directional messages these are source and
destination elements, which are
soma respectively. The
next two items are lists of the field names which are connected by this
You could also check which elements are connected to a particular field
>>> print soma.neighbors['injectMsg'] [<moose.vec: class=PulseGen, id=729,path=/model/pulse>]
Notice that the list contains something called vec. We discuss this
neighbors is a new kind of
lookupFinfo which behaves like a dictionary. Next we connect
the table to the soma to retrieve its membrane potential
Vm. This is
where all those
destFinfo starting with
set come in
use. For each value field
X, there is a
retrieve the value at simulation time. This is used by the table to
record the values
>>> moose.connect(vmtab, 'requestOut', soma, 'getVm') <moose.SingleMsg: id=5, dataIndex=0, path=/Msgs/singleMsg>
This finishes our model and recording setup. You might be wondering
about the source-destination relationship above. It is natural to think
soma is the source of
Vm values which should be sent to
vmtab. But here
requestOut is a
srcFinfo acting like a
reply card. This mode of obtaining data is called pull mode. 
You can skip the next section on fine control of the timing of updates and read Running the simulation.
With the model all set up, we have to schedule the simulation. Different components in a model may have different rates of update. For example, the dynamics of electrical components require the update intervals to be of the order 0.01 ms whereas chemical components can be as slow as 1 s. Also, the results may depend on the sequence of the updates of different components. These issues are addressed in MOOSE using a clock-based update scheme. Each model component is scheduled on a clock tick (think of multiple hands of a clock ticking at different intervals and the object being updated at each tick of the corresponding hand). The scheduling also guarantees the correct sequencing of operations. For example, your Table objects should always be scheduled after the computations that they are recording, otherwise they will miss the outcome of the latest calculation.
MOOSE has a central clock element (
/clock) to manage
time. Clock has a set of
Tick elements under it that take care of
advancing the state of each element with time as the simulation
progresses. Every element to be included in a simulation must be
assigned a tick. Each tick can have a different ticking interval
dt) that allows different elements to be updated at different
By default, every object is assigned a clock tick with reasonable default timesteps as soon it is created:
Class type tick dt Electrical computations: 0-7 50 microseconds electrical compartments, V and ligand-gated ion channels, Calcium conc and Nernst, stimulus generators and tables, HSolve. Table (to plot elec. signals) 8 100 microseconds Diffusion solver 10 0.01 seconds Chemical computations: 11-17 0.1 seconds Pool, Reac, Enz, MMEnz, Func, Function, Gsolve, Ksolve, Stats (to do stats on outputs) Table2 (to plot chem. signals) 18 1 second HDF5DataWriter 30 1 second Postmaster (for parallel 31 0.01 seconds computations)
There are 32 available clock ticks. Numbers 20 to 29 are unassigned so you can use them for whatever purpose you like.
If you want fine control over the scheduling, there are three things you can do.
- Alter the 'tick' field on the object
- Alter the dt associated with a given tick, using the moose.setClock( tick, newdt) command
- Go through a wildcard path of objects reassigning there clock ticks, using moose.useClock( path, newtick, function).
Here we discuss these in more detail.
Altering the 'tick' field
Every object knows which tick and dt it uses:
>>> a = moose.Pool( '/a' ) >>> print a.tick, a.dt 13 0.1
tick field on every object can be changed, and the object will
adopt whatever clock dt is used for that tick. The
dt field is
readonly, because changing it would have side-effects on every object
associated with the current tick.
Ticks -1 and -2 are special: They both tell the object that it is disabled (not scheduled for any operations). An object with a tick of -1 will be left alone entirely. A tick of -2 is used in solvers to indicate that should the solver be removed, the object will revert to its default tick.
Altering the dt associated with a given tick
We initialize the ticks and set their
dt values using the
>>> moose.setClock(0, 0.025e-3) >>> moose.setClock(1, 0.025e-3) >>> moose.setClock(2, 0.25e-3)
This will initialize tick #0 and tick #1 with
dt = 25 Î¼s and tick #2
dt = 250 Î¼s. Thus all the elements scheduled on ticks #0 and 1
will be updated every 25 Î¼s and those on tick #2 every 250 Î¼s. We use
the faster clocks for the model components where finer timescale is
required for numerical accuracy and the slower clock to sample the
Note that if you alter the dt associated with a given tick, this will affect the update time for all the objects using that clock tick. If you're unsure that you want to do this, use one of the vacant ticks.
Assigning clock ticks to all objects in a wildcard path
To assign tick #2 to the table for recording
Vm, we pass its
whole path to the
>>> moose.useClock(2, '/data/soma_Vm', 'process')
Read this as "use tick # 2 on the element at path
process method at every step". Every class that is supposed
to update its state or take some action during simulation implements a
process method. And in most cases that is the method we want the
ticks to call at every time step. A less common method is
which is implemented in some classes to interleave actions or updates
that must be executed in a specific order . The
class is one such case where a neuronal compartment has to know the
Vm of its neighboring compartments before it can calculate its
Vm for the next step. This is done with:
>>> moose.useClock(0, soma.path, 'init')
Here we used the
path field instead of writing the path explicitly.
Next we assign tick #1 to process method of everything under
>>> moose.useClock(1, '/model/##', 'process')
Here the second argument is an example of wild-card path. The
matches everything under the path preceding it at any depth. Thus if we
had some other objects under
process method of
those would also have been scheduled on tick #1. This is very useful for
complex models where it is tedious to scheduled each element
individually. In this case we could have used
/model/# as well for
the path. This is a single level wild-card which matches only the
/model but does not go farther down in the hierarchy.
Once the model is all set up, we can put the model to its initial state using
You may remember that we had changed initVm from
The reinit call we initialize
Vm to that value. You can verify that
>>> print soma.Vm -0.07
Finally, we run the simulation for 300 ms
The data will be recorded by the
soma_vm table, which is referenced
by the variable
Table class provides a numpy array
interface to its content. The field is
vector. So you can easily plot
the membrane potential using the matplotlib
>>> import pylab >>> t = pylab.linspace(0, 300e-3, len(vmtab.vector)) >>> pylab.plot(t, vmtab.vector) >>> pylab.show()
The first line imports the pylab submodule from matplotlib. This useful
for interactive plotting. The second line creates the time points to
match our simulation time and length of the recorded data. The third
line plots the
Vm and the fourth line makes it visible. Does the
plot match your expectation?
MOOSE elements are instances of the class
PulseGen and other MOOSE classes are derived classes of
melement instances are contained in array-like
vec object has a numerical
id_ field uniquely identifying it. An
vec can have one or
more elements. You can create an array of elements
>>> comp_array = moose.vec('/model/comp', n=3, dtype='Compartment')
This tells MOOSE to create an
vec of 3
vec objects with multiple
elements, the index in the
vec is part of the element path.
>>> print comp_array.path, type(comp_array)
comp_array is an instance of
vec class. You can
loop through the elements in an
vec like a Python list
>>> for comp in comp_array: ... print comp.path, type(comp) ...
/model/comp <type 'moose.Compartment'> /model/comp <type 'moose.Compartment'> /model/comp <type 'moose.Compartment'>
Thus elements are instances of class
melement. All elements in an
vec share the
id_ of the
vec which can retrieved by
A frequent use case is that after loading a model from a file one knows
the paths of various model components but does not know the appropriate
class name for them. For this scenario there is a function called
element which converts ("casts" in programming jargon) a path or any
moose object to its proper MOOSE class. You can create additional
soma in the example this way
x = moose.element('/model/soma')
Any MOOSE class can be extended in Python. But any additional attributes
added in Python are invisible to MOOSE. So those can be used for
functionalities at the Python level only. You can see
moose-examples/squid/squid.py for an example.
The following kinds of
Finfo are accessible in Python
- ``valueFinfo`` : simple values. For each readable
XYZthere is a
getXYZthat can be used for reading the value at run time. If
XYZis writable then there will also be
destFinfoto set it:
- ``lookupFinfo`` : lookup tables. These fields act like Python
dictionaries but iteration is not supported. Example:
- ``srcFinfo`` : source of a message. Example:
- ``destFinfo`` : destination of a message. Example:
Compartment.injectMsg. Apart from being used in setting up messages, these are accessible as functions from Python.
HHGate.setupAlphais an example.
- ``sharedFinfo`` : a composition of source and destination fields.
Now you know the basics of pymoose and how to access the help
system. You can figure out how to do specific things by looking at the
'cookbook`. In addition, the
in your MOOSE installation has small executable python scripts that
show usage of specific classes or functionalities. Beyond that you can
browse the code in the
moose-examples directory to see some more complex
MOOSE is backward compatible with GENESIS and most GENESIS classes have been reimplemented in MOOSE. There is slight change in naming (MOOSE uses CamelCase), and setting up messages are different. But GENESIS documentation is still a good source for documentation on classes that have been ported from GENESIS.
If the built-in MOOSE classes do not satisfy your needs entirely, you are welcome to add new classes to MOOSE. The API documentation will help you get started.
|||To list the classes only, use |
|||MOOSE is unit agnostic and things should work fine as long as you use values all converted to a consistent unit system.|
|||This apparently convoluted implementation is for performance reason. Can you figure out why? Hint: the table is driven by a slower clock than the compartment.|
|||In principle any function available in a MOOSE class can be executed periodically this way as long as that class exposes the function for scheduling following the MOOSE API. So you have to consult the class' documentation for any nonstandard methods that can be scheduled this way.|