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neuron.h
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neuron.h.Section

neuron.hoc
  execute · hoc_ac

neuron.hoc.HocObject

PythonObject

# Python Language

This document describes installation and basic use of NEURON's Python interface. For information on the modules in the `neuron` namespace, see:

- Basic Reaction-Diffusion

## Installation

Syntax:

  `./configure --with-nrnpython ...`

  `make`

  `make install`

Description:

  Builds NEURON with Python embedded as an alternative interpreter to HOC. The python version used is that found from `which python`.

  NEURON can be used as an extension to Python if, after building as above, one goes to the src/nrnpython directory containing the Makefile and types something analogous to

      python setup.py install --home=$HOME

  Which on my machine installs in `/home/hines/lib64/python/neuron` and can be imported into NEURON with

      ipython
      import sys
      sys.path.append("/home/hines/lib64/python")
      import neuron

  It is probably better to avoid the incessant `import sys`... and instead add to your shell environment something analogous to

      export PYTHONPATH=$PYTHONPATH:/home/hines/lib64/python

  since when launching NEURON and embedding Python, the path is automatically defined so that `import neuron` does not require any prerequisites. If there is a `@<host-cpu@>/.libs/libnrnmech.so` file in your working directory, those nmodl mechanisms will be loaded as well. After this, you will probably want to:

      h = neuron.h # neuron imports hoc and does a  h = hoc.HocObject()

  In the past we also recommended an "import nrn" but this is no longer necessary as everything in that module is also directly available from the "h" object. You can use the hoc function `nrn_load_dll()` to load mechanism files as well, e.g. if neurondemo was used earlier so the shared object exists,

      h = hoc.HocObject()
      h('nrn_load_dll("$(NEURONHOME)/demo/release/x86_64/.libs/libnrnmech.so")')

## Python Accessing HOC

Syntax:

  `nrniv -python [file.hoc file.py -c "python_statement"]`

  `nrngui -python ...`

  `neurondemo -python ...`

Description:
  Launches NEURON with Python as the command line interpreter. File arguments with a .hoc suffix are interpreted using the Hoc interpreter. File arguments with the .py suffix are interpreted using the Python interpreter. The -c statement causes python to execute the statement. The import statements allow use of the following

`neuron.hoc.``execute`()
  Syntax:

    `import neuron`

    `neuron.hoc.execute('any hoc statement')`

  Description:

    Execute any statement or expression using the Hoc interpreter. This is obsolete since the same thing can be accomplished with HocObject with less typing. Note that triple quotes can be used for multiple line statements. A 'n' should be escaped as '\n'.

        hoc.execute('load_file("nrngui.hoc")')

  See also

  `nrnpython()`

class `neuron.hoc.``HocObject`
  Syntax:

    `import neuron`

    `h = neuron.hoc.HocObject()`

  Description:

    Allow access to anything in the Hoc interpreter. Note that `h = neuron.h` is the typical statement used since the neuron module creates an h field. When created via `hoc.HocObject()` its print string is "TopLevelHocInterpreter".

        h("any hoc statement")

    is the same as hoc.execute(...)

    Any hoc variable or string in the Hoc world can be accessed in the Python world:

        h('strdef s')
        h('{x = 3  s = "hello"}')
        print h.x          # prints 3.0
        print h.s          # prints hello

    And if it is assigned a value in the python world it will be that value in the Hoc world. (Note that any numeric python type becomes a double in Hoc.)

        h.x = 25
        h.s = 'goodbye'
        h('print x, s')    #prints 25 goodbye

    Any hoc object can be handled in Python.

        h('objref vec')
        h('vec = new Vector(5)')
        print h.vec        # prints Vector[0]
        print h.vec.size() # prints 5.0

    Note that any hoc object method or field may be called, or evaluated/assigned using the normal dot notation which is consistent between hoc and python. However, hoc object methods MUST have the parentheses or else the Python object is not the return value of the method but a method object. ie.

        x = h.vec.size     # not 5 but a python callable object
        print x            # prints: Vector[0].size()
        print x()          # prints 5.0

    This is also true for indices

        h.vec.indgen().add(10) # fills elements with 10, 11, ..., 14
        print h.vec.x[2]   # prints 12.0
        x = h.vec.x        # a python indexable object
        print x            # prints Vector[0].x[?]
        print x[2]         # prints 12.0

    The hoc object can be created directly in Python. E.g.

        v = h.Vector(10).indgen.add(10)

    Iteration over hoc Vector, List, and arrays is supported. e.g.

        v = h.Vector(4).indgen().add(10)
        for x in v :
          print x

        l = h.List() ; l.append(v); l.append(v); l.append(v)
        for x in l :
          print x

        h('objref o[2][3]')
        for x in h.o :
          for y in x :
            print x, y

    Any hoc Section can be handled in Python. E.g.

        h('create soma, axon')
        ax = h.axon

    makes ax a Python `Section` which references the hoc axon section. Many hoc functions require a currently accessed section and for these a typical idiom is

        ax.push() ; print secname() ; h.pop_section()

    More compact is to use the "sec" keyword parameter after the last positional parameter which makes the Section value the currently accessed section during the scope of the function call. e.g

        print secname(sec=ax)

    Point processes are handled by direct object creation as in

        stim = IClamp(1.0, sec = ax)
        // or
        stim = IClamp(ax(1.0))

    The latter is a somewhat simpler idiom that uses the Segment object which knows both the section and the location in the section and can also be used with the stim.loc function.

    Many hoc functions use call by reference and return information by changing the value of an argument. These are called from the python world by passing a HocObject.ref() object. Here is an example that changes a string.

        h('proc chgstr() { $s1 = "goodbye" }')
        s = h.ref('hello')
        print s[0]          # notice the index to dereference. prints hello
        h.chgstr(s)
        print s[0]          # prints goodbye
        h.sprint(s, 'value is %d', 2+2)
        print s[0]          # prints value is 4

    and here is an example that changes a pointer to a double

        h('proc chgval() { $&1 = $2 }')
        x = h.ref(5)
        print x[0]          # prints 5.0
        h.chgval(x, 1+1)
        print x[0]          # prints 2.0

    Finally, here is an example that changes a objref arg.

        h('proc chgobj() { $o1 = new List() }')
        v = h.ref([1,2,3])  # references a Python object
        print v[0]          # prints [1, 2, 3]
        h.chgobj(v)
        print v[0]          # prints List[0]

    Unfortunately, the HocObject.ref() is not often useful since it is not really a pointer to a variable. For example consider

        h('x = 1')
        y = h.ref(h.x)
        print y         # prints hoc ref value 1
        print h.x, y[0] # prints 1.0 1.0
        h.x = 2
        print h.x, y[0] # prints 2.0 1.0

    and thus in not what is needed in the most common case of a hoc function holding a pointer to a variable such as `Vector.record()` or `Vector.play()`. For this one needs the `_ref_varname` idiom which works for any hoc variable and acts exactly like a c pointer. eg:

        h('x = 1')
        y = h._ref_x
        print y          # prints pointer to hoc value 1
        print h.x, y[0]  # prints 1.0 1.0
        h.x = 2
        print h.x, y[0]  # prints 2.0 2.0
        y[0] = 3
        print h.x, y[0]  # prints 3.0 3.0

    Of course, this works only for hoc variables, not python variables. For arrays, use all the index arguments and prefix the name with _ref_. The pointer will be to the location indexed and one may access any element beyond the location by giving one more non-negative index. No checking is done with regard to array bounds errors. e.g

        v = h.Vector(4).indgen().add(10)
        y = v._ref_x[1]    # holds pointer to second element of v
        print v.x[2], y[1] # prints 12.0 12.0
        y[1] = 50
        v.printf()         # prints 10 11 50 13

    The idiom is used to record from (or play into) voltage and mechanism variables. eg

        v = h.Vector()
        v.record(h.soma(.5)._ref_v, sec = h.soma)
        pi = h.Vector()
        pi.record(h.soma(.5).pas._ref_i, sec = h.soma)
        ip = h.Vector()
        ip.record(h.soma(.5)._ref_i_pas, sec = h.soma)

    The factory idiom is one way to create Hoc objects and use them in Python.

        h('obfunc newvec() { return new Vector($1) }')
        v = h.newvec(10).indgen().add(10)
        v.printf()          # prints 10 11 ... 19 (not 10.0 ... since printf is a hoc function)

    but that idiom is more or less obsolete as the same thing can be accomplished directly as shown a few fragments back. Also consider the minimalist

        vt = h.Vector
        v = vt(4).indgen().add(10)

    Any Python object can be stored in a Hoc List. It is more efficient when navigating the List to use a python callable that avoids repeated lookup of a Hoc method symbol. Note that in the Hoc world a python object is of type PythonObject but python strings and scalars are translated back and forth as strdef and scalar doubles respectively.

        h('obfunc newlist() { return new List() }')
        list = h.newlist()
        apnd = list.append
        apnd([1,2,3])      # Python list in hoc List
        apnd(('a', 'b', 'c')) # Python tuple in hoc List
        apnd({'a':1, 'b':2, 'c':3}) # Python dictionary in hoc List
        item = list.object
        for i in range(0, int(list.count())) : # notice the irksome cast to int.
          print item(i)

        h('for i=0, List[0].count-1 print List[0].object(i)')

    To see all the methods available for a hoc object, use, for example,

        dir(h.Vector)

    h.anyclass can be subclassed with

        class MyVector(neuron.hclass(neuron.h.Vector)) :
          pass
        v = MyVector(10)
        v.zzz = 'hello' # a new attribute
        print v.size() # call any base method

    If you override a base method such as 'size' use

        v.baseattr('size')()

    to access the base method. Multiple inheritance involving hoc classes probably does not make sense. If you override the __init__ procedure when subclassing a Section, be sure to explicitly initialize the Section part of the instance with

        nrn.Section.__init__()

    Since nrn.Section is a standard Python class one can subclass it normally with

        class MySection(neuron.nrn.Section):
          pass

    The hoc setpointer statement is effected in Python as a function call with a syntax for POINT_PROCESS and SUFFIX (density)mechanisms respectively of

        h.setpointer(_ref_hocvar, 'POINTER_name', point_proces_object)
        h.setpointer(_ref_hocvar, 'POINTER_name', nrn.Mechanism_object)

    See `nrn/share/examples/nrniv/nmodl/`(`tstpnt1.py` and `tstpnt2.py`) for examples of usage. For a density mechanism, the 'POINTER_name' cannot have the SUFFIX appended. For example if a mechanism with suffix foo has a POINTER bar and you want it to point to t use

        h.setpointer(_ref_t, 'bar', sec(x).foo)

  See also

  `Vector.to_python()`, `Vector.from_python()`

`neuron.hoc.``hoc_ac`()
  Syntax:

    `import hoc`

    `double_value = hoc.hoc_ac()`

    `hoc.hoc_ac(double_value)`

  Description:

    Get and set the hoc global scalar, `hoc_ac_`-variables. This is obsolete since HocObject is far more general.

        import hoc
        hoc.hoc_ac(25)
        hoc.execute('print hoc_ac_') # prints 25
        hoc.execute('hoc_ac_ = 17')
        print hoc.hoc_ac()  # prints 17

`neuron.h.``cas`()
  Syntax:

    `sec = h.cas()`

    `or`

    `import nrn`

    `sec = nrn.cas()`

  Description:

    Returns the currently accessed section as a Python `Section` object.

        import neuron
        neuron.h('''
          create soma, dend[3], axon
          access dend[1]
        ''')

        sec = h.cas()
        print sec, sec.name()

class `neuron.h.``Section`
  Syntax:

    `sec = h.Section()`

    `sec = h.Section([name='string', [cell=self])`

    `or`

    `import nrn`

    `sec = nrn.Section()`

  Description:

    The Python Section object allows modification and evaluation of the information associated with a NEURON Conceptual Overview of Sections. The typical way to get a reference to a Section in Python is with `neuron.h.cas()` or by using the hoc section name as in `asec = h.dend[4]`. The `sec = Section()` will create an anonymous Section with a hoc name constructed from "Section" and the Python reference address. Access to Section variables is through standard dot notation. The "anonymous" python section can be given a name with the named parameter and/or associated with a cell object using the named cell parameter. Note that a cell association is required if one anticipates using the `gid2cell()` method of `ParallelContext`.

        import neuron
        h = neuron.h
        sec = h.Section()
        print sec        # prints <nrn.Section object at 0x2a96982108>
        print sec.name() # prints PySec_2a96982108
        sec.nseg = 3     # section has 3 segments (compartments)
        sec.insert("hh") # all compartments have the hh mechanism
        sec.L = 20       # Length of the entire section is 20 um.
        for seg in sec :   # iterates over the section compartments
          for mech in seg : # iterates over the segment mechanisms
            print sec.name(), seg.x, mech.name()

    A Python Section can be made the currently accessed section by using its push method. Be sure to use `pop_section()` when done with it to restore the previous currently accessed section. I.e, given the above fragment,

        from neuron import h
        h('''
        objref p
        p = new PythonObject()
        {p.sec.push() psection() pop_section()}
        ''')
        #or
        sec.push()
        h.secname()
        h.psection()
        h.pop_section()

    When calling a hoc function it is generally preferred to named sec arg style to automatically push and pop the section stack during the scope of the hoc function. ie

        h.psection(sec=sec)

    With a `SectionRef` one can, for example,

        h.dend[2].push() ; sr = h.SectionRef() ; h.pop_section()
        sr.root.push() ; print h.secname() ; h.pop_section()

    or, more compactly,

        sr = h.SectionRef(sec=h.dend[2])
        print sr.root.name(), h.secname(sec=sr.root)

    Iteration over sections is accomplished with

        for s in h.allsec() :
          print h.secname()

        sl = h.SectionList() ; sl.wholetree()
        for s in sl :
          print h.secname()

    Connecting a child section to a parent section uses the connect method using either

        childsec.connect(parentsec, parentx, childx)
        childsec.connect(parentsegment, childx)

    In the first form parentx and childx are optional with default values of 1 and 0 respectively. Parentx must be 0 or 1. In the second form, childx is optional and by default is 0. The parentsegment must be either parentsec(0) or parentsec(1).

    sec.cell() returns the cell object that 'owns' the section. The return value is None if no object owns the section (a top level section), the instance of the hoc template that created the section, or the python object specified by the named cell parameter when the python section was created.

### Segment

> Syntax:
>   `seg = section(x)`
> Description:
>   A Segment object is obtained from a Section with the function notation where the argument is 0 <= x <= 1 an the segment is the compartment that contains the location x. The x value of the segment is seg.x and the section is seg.sec . From a Segment one can obtain a Mechanism.

### Mechanism

> Syntax:
>   `mech = segment.mechname`
> Description:
>   A Mechanism object is obtained from a Segment. From a Mechanism one can obtain a range variable. The range variable can also be obtained from the segment by using the hoc range variable name that has the mechanism suffix.

## HOC accessing Python

> Syntax:
>   `nrniv [file.hoc...]`
> Description:
>   The absence of a -python argument causes NEURON to launch with Hoc as the command line interpreter. At present, no `file.py` arguments are allowed as all named files are treated as hoc files. Nevertheless, from the hoc world any python statement can be executed and anything in the python world can be assigned or evaluated.

`nrnpython`()
  Syntax:
    `nrnpython("any python statement")`
  Description:

    Executes any python statement.

        nrnpython("import sys")
        nrnpython("print sys.path")
        nrnpython("a = [1,2,3]")
        nrnpython("print a")
        nrnpython("import hoc")
        nrnpython("hoc.execute('print PI')")

class `PythonObject`
  Syntax:
    `p = new PythonObject()`
  Description:

    Accesses any python object. Almost equivalent to `HocObject` in the python world but because of some hoc syntax limitations, ie. hoc does not allow an object to be a callable function, and top level indices have different semantics, we sometimes need to use a special idiom, ie. the '_' method. Strings and double numbers move back and forth between Python and Hoc (but Python integers, etc. become double values in Hoc, and when they get back to the Python world, they are doubles).

        objref p
        p = new PythonObject()
        nrnpython("ev = lambda arg : eval(arg)") // interprets the string arg as an
                                  //expression and returns the value
        objref tup
        print p.ev("3 + 4")       // prints 7
        print p.ev("'hello' + 'world'") // prints helloworld
        tup = p.ev("('xyz',2,3)") // tup is a PythonObject wrapping a Python tuple
        print tup                 // prints PythonObject[1]
        print tup._[2]            // the 2th tuple element is 3
        print tup._[0]            // the 0th tuple element is xyz

        nrnpython("import hoc")   // back in the Python world
        nrnpython("h = hoc.HocObject()") // tup is a Python Tuple object
        nrnpython("print h.tup")   // prints ('xyz', 2, 3)

    Note that one needs the '_' method, equivalent to 'this', because trying to get at an element through the built-in python method name via

        tup.__getitem__(0)

    gives the error "TypeError: tuple indices must be integers" since the Hoc 0 argument is a double 0.0 when it gets into Python. It is difficult to pass an integer to a Python function from the hoc world. The only time Hoc doubles appear as integers in Python, is when they are the value of an index. If the index is not an integer, e.g. a string, use the __getitem__ idiom.

        objref p
        p = new PythonObject()
        nrnpython("ev = lambda arg : eval(arg)")
        objref d
        d = p.ev("{'one':1, 'two':2, 'three':3}")
        print d.__getitem__("two")        // prints 2

        objref dg
        dg = d.__getitem__
        print dg._("two")                // prints 2

    To assign a value to a python variable that exists in a module use

        nrnpython("a = 10")
        p = new PythonObject()
        p.a = 25
        p.a = "hello"
        p.a = new Vector(4)
        nrnpython("b = []")
        p.a = p.b

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