.. _vect:
Vector
------
.. class:: Vector
This class was implemented by
.. code-block::
none
-----------------------------
Zach Mainen and Michael Hines
-----------------------------
Syntax:
``obj = h.Vector()``
``obj = h.Vector(size)``
``obj = h.Vector(size, init)``
``obj = h.Vector(python_iterable)``
Description:
NEURON's Vector class provides functionality that is similar (and partly interchangeable) with a numpy
one-dimensional array of doubles.
The reason for the continued use of Vector is both due to back-compatibility and due to the many faster C-level
extensions that have been written as NMOD programs that make use of this class.
A Vector is itself an iterable and can be used in any context that takes an iterable, e.g.,
.. code-block::
python
for x in vec: print(x)
[x for x in vec]
numpy.array(vec)
A Vector object created with this class can be thought of as
containing a one dimensional x array with elements of type float.
The :samp:`{objref}[{index}]` notation can be used to read and set Vector elements
(setting requires NEURON 7.7+). An older syntax :samp:`{objref}.x[{index}]` works on
all Python-supporting versions of NEURON.
Vector slices are not directly supported but are replicated with the functionality
of Vector.c() (see below).
A vector can be created with length *size* and with each element set to the value of *init* or can be created using
a Python iterable.
Vector methods that modify the elements are generally of the form
.. code-block::
none
obj = vsrcdest.method(...)
in which the values of vsrcdest on entry to the
method are used as source values by the method to compute values which replace
the old values in vsrcdest. The return value is simply an additional reference to the same Vector.
Beginning with NEURON 7.7, Vectors support arithmetic operations; e.g. one can write
``v1 = v2*s2 + v3*s3 + v4*s4``.
.. note::
In older code, you may see the use of the arithmetic functions
add, mul, etc. Those functions changed the vectors they operated on, so to avoid this,
the .c() method was used to create a new copy of a vector. The expression that can
now be written ``v1 = v2*s2 + v3*s3 + v4*s4`` using the older form would be written as
.. code-block::
none
v1 = v2.c().mul(s2).add(v3.c().mul(s3)).add(v4.c().mul(s4))
Examples:
.. code-block::
none
vec = h.Vector(20,5)
will create a vector with 20 indices, each having the value of 5.
.. code-block::
python
vec1 = h.Vector()
will create a vector with 0 size. It is seldom
necessary to specify a size for a Vector since most operations, if necessary,
increase or decrease the number of elements as needed.
.. code-block::
python
v = h.Vector([1, 2, 3])
will create a vector of length 3 whose entries are: 1, 2, and 3. The
constructor takes any Python iterable. In particular, it also works
with numpy arrays:
.. code-block::
python
import numpy
x = numpy.linspace(0, 2 * numpy.pi, 50)
y = h.Vector(numpy.sin(x))
produces a vector ``y`` of length 50 corresponding to the sine of evenly
spaced points between 0 and 2 pi, inclusive.
.. seealso::
:data:`Vector.x`, :meth:`Vector.resize`, :meth:`Vector.apply`
----
.. data:: Vector.x
Syntax:
``vec.x[index]``
Description:
Elements of a vector can be accessed with ``vec.x[index]`` notation for either access or assignment.
Vector indices range from 0 to len(Vector)-1
Vector contents can also be accessed with ``vec.get(index)`` or set with ``vec.set(index, value)``
**This is not recommended for new code; use vec[index] instead.**
Example:
``print(vec.x[0], vec[0])`` prints the value of the 0th (first) element twice.
``vec.x[i] = 3`` sets the i'th element to 3. Beginning with NEURON 7.7, it suffices
to write ``vec[i] = 3`` instead.
.. code-block::
python
h.xpanel("show a field editor")
h.xpvalue("last element", vec._ref_x[len(vec)-1])
h.xpanel()
Note, however, that there is a potential difficulty with the :func:`xpvalue` field
editor since, if vec is resized to be larger than vec.buffer_size() a reallocation of the
memory will cause the pointer to be invalid. In this case, the field editor will display the string, "Free'd".
.. warning::
``vec.x[-1]`` or ``vec[-1]`` return or set the value of the last element of the vector but ``vec._ref_x`` cannot be accessed in
this way.
----
.. method:: Vector.size
Syntax:
``size = vec.size()``
Description:
Deprecated in favor of len(vec); note that ``len(vec) == vec.size()``
Return the number of elements in the vector. The last element has the index:
``vec.size() - 1`` which can be abbreviated using -1 as above.
.. code-block::
python
for i in range(vec.size()):
print(vec[i])
.. note::
``for`` loops can also use Vector as an iterable
.. code-block::
python
for item in vec: print(item)
.. note::
There is a distinction between the size of a vector and the
amount of memory allocated to hold the vector. Generally, memory is only
freed and reallocated if the size needed is greater than the memory storage
previously allocated to the vector. Thus the memory used by vectors
tends to grow but not shrink. To reduce the memory used by a vector, one
can explicitly call :func:`buffer_size` .
.. seealso::
:meth:`Vector.buffer_size`
----
.. method:: Vector.resize
Syntax:
``obj = vsrcdest.resize(new_size)``
Description:
Resize the vector. If the vector is made smaller, then trailing elements
will be zeroed. If it is expanded, the new elements will be initialized to 0.0;
original elements will remain unchanged.
Warning: Any function that
resizes the vector to a larger size than its available space will reallocate and thereby
make existing pointers to the elements invalid
(see note in :meth:`Vector.size`).
For example, resizing vectors that have been plotted will remove that vector
from the plot list. Other functions may not be so forgiving and result in
a memory error (segmentation violation or unhandled exception).
Example:
.. code-block::
python
vec = h.Vector(20,5)
vec.resize(30) # Appends 10 elements, each having a value of 0
vec.printf()
vec.resize(10) # removes the last 20 elements; values of the first 10 elements are unchanged
.. seealso::
:meth:`Vector.buffer_size`
----
.. method:: Vector.buffer_size
Syntax:
``space = vsrc.buffer_size()``
``space = vsrc.buffer_size(request)``
Description:
Returns the length of the double precision array memory allocated to hold the
vector. This is NOT the size of the vector. The vector size can efficiently
grow up to this value without reallocating memory.
With an argument, frees the old memory space and allocates new
memory space for the vector, copying old element values to the new elements.
If the request is less than the size, the size is truncated to the request.
For vectors that grow continuously, it may be more efficient to
allocate enough space at the outset, or else occasionally change the
buffer_size by larger chunks. It is not necessary to worry about the
efficiency of growth during a Vector.record since the space available
automatically increases by doubling.
Example:
.. code-block::
python
y = h.Vector(10)
print(len(y))
print(y.buffer_size())
y.resize(5)
print(len(y))
print(y.buffer_size())
print(y.buffer_size(100))
print(len(y))
----
.. method:: Vector.get
Syntax:
``x = vec.get(index)``
Description:
Return the value of a vector element index.
----
.. method:: Vector.set
Syntax:
``obj = vsrcdest.set(index,value)``
Description:
Set vector element index to value. Equivalent to ``vec[i] = expr`` notation.
----
.. method:: Vector.fill
Syntax:
``obj = vsrcdest.fill(value)``
``obj = vsrcdest.fill(value, start, end)``
Description:
The first form assigns *value* to every element in vsrcdest.
If *start* and *end* arguments are present, they specify the index range for the assignment.
Example:
.. code-block::
python
vec = h.Vector(20,5)
vec.fill(9,2,7)
assigns 9 to vec[2] through vec[7]
(a total of 6 elements)
.. seealso::
:meth:`Vector.indgen`, :meth:`Vector.append`
----
.. method:: Vector.label
Syntax:
``s = vec.label()``
``s = vec.label(str_type)``
Description:
Label the vector with a string.
The return value is the label, which is an empty string if no label has been set.
Labels are printed on a Graph when the :meth:`Graph.plot` method is called.
Example:
.. code-block::
python
from neuron import h
vec = h.Vector()
print(vec.label())
vec.label("hello")
print(vec.label())
.. seealso::
:meth:`Graph.family`, :meth:`Graph.beginline`
----
.. method:: Vector.record
Syntax:
``vdest = vdest.record(var_reference)``
``vdest = vdest.record(var_reference, Dt)``
``vdest = vdest.record(var_reference, tvec)``
``vdest = vdest.record(point_process_object, var_reference, ...)``
Description:
Save the stream of values of "*var*" during a simulation into the vdest vector.
Previous record and play specifications of this Vector (if any) are destroyed.
Details:
NEURON pointers in python are handled using the _ref_ syntax. e.g., soma(0.5)._ref_v
To save a scalar from NEURON that scalar must exist in NEURON's scope.
Transfers take place on exit from ``finitialize()`` and on exit from ``fadvance()``.
At the end of ``finitialize()``, ``v[0] = var``. At the end of ``fadvance``,
*var* will be saved if ``t`` (after being incremented by ``fadvance``)
is equal or greater than the associated time of the
next index. The system maintains a set of record vectors and the vector will
be removed from the list if the vector or var is destroyed.
The vector is automatically increased in size by 100 elements at a time
if more space is required, so efficiency will be slightly improved if one
creates vectors with sufficient size to hold the entire stream, and plots will
be more persistent (recall that resizing may cause reallocation of memory
to hold elements and this will make pointers invalid).
The record semantics can be thought of as:
``var(t) -> v[index]``
The default relationship between ``index`` and
``t`` is ``t = index*dt``.
In the second form, ``t = index*Dt``.
In the third form, ``t = tvec[index]``.
For the local variable timestep method, :meth:`CVode.use_local_dt` and/or multiple
threads, :meth:`ParallelContext.nthread` , it is
often helpful to provide specific information about which cell the
*var* pointer is associated with by inserting as the first arg some POINT_PROCESS
object which is located on the cell. This is necessary if the pointer is not
a RANGE variable and is much more efficient if it is. The fixed step and global
variable time step method do not need or use this information for the
local step method but will use it for multiple threads. It is therefore
a good idea to supply it if possible.
Prior to version 7.7, the record methode returned 1.0 .
.. warning::
record/play behavior is reasonable but surprising if :data:`dt` is greater than
``Dt``. Things work best if ``Dt`` happens to be a multiple of :data:`dt`. All combinations
of record ; play ; ``Dt =>< dt`` ; and tvec sequences
have not been tested.
Example:
If NEURON has loaded its standard run library, the time course of membrane potential in the
middle of a section called "terminal" can be captured to a vector called dv by
.. code-block::
python
dv = h.Vector().record(terminal(0.5)._ref_v)
h.run()
Note that the next "run" will overwrite the previous time course stored
in the vector as it automatically performs an "init" before running a simulation.
Thus dv should be copied to another vector ( see :func:`copy` ).
To remove
dv from the list of record vectors, the easiest method is to destroy the instance
with
``dv = h.Vector()``
Any of the following makes NEURON load its standard run library:
- starting NEURON by executing `nrngui -python`
- executing any of the following statements:
- from neuron import gui # also brings up the NEURON Main Menu
- h.load_file("noload.hoc") # does not bring up the NEURON Main Menu
- h.load_file("stdrun.hoc") # does not bring up the NEURON Main Menu
.. seealso::
:func:`finitialize`, :func:`fadvance`, :func:`play`, :data:`t`, :func:`play_remove`
----
.. method:: Vector.play
Syntax:
``vdest = vsrc.play(var_reference, Dt)``
``vdest = vsrc.play(var_reference, tvec)``
``vdest = vsrc.play(index)``
``vdest = vsrc.play(var_reference or stmt, tvec, continuous)``
``vdest = vsrc.play(var_reference or stmt, tvec, indices_of_discontinuities_vector)``
``vdest = vsrc.play(point_process_object, var_reference, ...)``
Description:
The ``vsrc`` vector values are assigned to the "*var*" variable during a simulation.
The same vector can be played into different variables.
The index form immediately sets the var (or executes the stmt) with the
value of vsrc[index]
The play semantics can be thought of as
``v[index] -> var(t)`` where t(index) is Dt*index or tvec[index]
The discrete event delivery system is used to determine the precise
time at which values are copied from vsrc to var. Note that for variable
step methods, unless continuity is specifically requested, the function
is a step function. Also, for the local variable dt method, var MUST be
associated with the cell that contains the section accessed via sec=sec in the arg list
(but see the paragraph below about the use of a point_process_object
inserted as the first arg).
For the fixed step method,
transfers take place on entry to :func:`finitialize` and on entry to :func:`fadvance`.
At the beginning of :func:`finitialize`, ``var = v[0]``. On :func:`fadvance` a transfer will
take place if t will be equal
or greater than the associated time of the next index after the ``fadvance`` increment.
For the variable step methods, transfers take place exactly at the times specified by the Dt
or tvec arguments.
The system maintains a set of play vectors and the vector will be removed
from the list if the vector or var is destroyed.
If the end of the vector is reached, no further transfers are made (``var`` becomes
constant)
Note well: for the fixed step method,
if ``fadvance`` exits with time equal to ``t`` (ie enters at time t-dt),
then on entry to ``fadvance``, *var* is set equal to the value of
the vector at the index
appropriate to time t. Execute tests/nrniv/vrecord.py to see what this implies
during a simulation. ie the value of var from ``t-dt`` to t played into by
a vector is equal to the value of the vector at ``index(t)``. If the vector
was meant to serve as a continuous stimulus function, this results in
a first order correct simulation with respect to dt. If a second order correct
simulation is desired, it is necessary (though perhaps not sufficient since
all other equations in the system must also be solved using methods at least
second order correct) to fill the vector with function values at f((i-.5)*dt).
When continuous is 1 then linear interpolation is used to define the values
between time points. However, events at each Dt or tvec are still used
and that has beneficial performance implications for variable step methods
since vsrc is equivalent to a piecewise linear function and variable step
methods can excessively reduce dt as one approaches a discontinuity in
the first derivative. Note that if there are discontinuities in the
function itself, then tvec should have adjacent elements with the same
time value. When a value is greater than the range of
the t vector, linear extrapolation of the last two points is used
instead of a constant last value. If a constant outside the range
is desired, make sure the last two points have the same y value and
have different t values (if the last two values are at the same time,
the constant average will be returned).
The indices_of_discontinuities_vector argument is used to
specifying the indices in tvec of the times at which discrete events should
be used to notify that a discontinuity in the function, or any derivative
of the function, occurs. Presently, linear interpolation is used to
determine var(t) in the interval between these discontinuities (instead of
cubic spline) so the length of steps used by variable step methods near
the breakpoints depends on the details of how the parameter being played
into affects the states.
For the local variable timestep method, :meth:`CVode.use_local_dt` and/or multiple
threads, :meth:`ParallelContext.nthread` , it is
often helpful to provide specific information about which cell the
*var* pointer is associated with by inserting as the first arg some POINT_PROCESS
object which is located on the cell. This is necessary if the pointer is not
a RANGE variable and is much more efficient if it is. The fixed step and global
variable time step method do not need or use this information for the
local step method but will use it for multiple threads. It is therefore
a good idea to supply it if possible.
Prior to version 7.7, the play method returned 1.0 .
.. seealso::
:meth:`Vector.record`, :meth:`Vector.play_remove`
Example of playing into a segment's ina:
.. code-block::
python
from neuron import h, gui
import numpy
# create a geometry
soma = h.Section(name='soma')
# insert variables for sodium ions
soma.insert('na_ion')
# driving stimulus
t = h.Vector(numpy.linspace(0, 2 * numpy.pi, 50))
y = h.Vector(numpy.sin(t))
# play the stimulus into soma(0.5)'s ina
# the last True means to interpolate; it's not the default, but unless
# you know what you're doing, you probably want to pass True there
y.play(soma(0.5)._ref_ina, t, True)
# setup a graph
g = h.Graph()
g.addvar("ina", soma(0.5)._ref_ina)
g.size(0, 6.28, -1, 1)
h.graphList[0].append(g)
# run the simulation
h.finitialize(-65)
h.continuerun(6.28)
----
.. method:: Vector.play_remove
Syntax:
``v.play_remove()``
Description:
Removes the vector from BOTH record and play lists.
Note that the vector is automatically removed if
the variable which is recorded or played is destroyed
or if the vector is destroyed.
This function is used in those
cases where one wishes to keep the vector data even under subsequent runs.
.. seealso::
:meth:`Vector.record`, :meth:`Vector.play`
----
.. method:: Vector.indgen
Syntax:
``obj = vsrcdest.indgen()``
``obj = vsrcdest.indgen(stepsize)``
``obj = vsrcdest.indgen(start,stepsize)``
``obj = vsrcdest.indgen(start,stop,stepsize)``
Description:
Fill the elements of a vector with a sequence of values. With no
arguments, the sequence is integers from 0 to (size-1).
With only *stepsize* passed, the sequence goes from 0 to
*stepsize**(size-1)
in steps of *stepsize*. *Stepsize* does not have to be an integer.
With *start*, *stop* and *stepsize*,
the vector is resized to be 1 + (*stop* - $varstart)/*stepsize* long and the sequence goes from
*start* up to and including *stop* in increments of *stepsize*.
Example:
.. code-block::
python
vec = h.Vector(100)
vec.indgen(5)
creates a vector with 100 elements going from 0 to 495 in increments of 5.
.. code-block::
python
vec.indgen(50, 100, 10)
reduces the vector to 6 elements going from 50 to 100 in increments of 10.
.. code-block::
python
vec.indgen(90, 1000, 30)
expands the vector to 31 elements going from 90 to 990 in increments of 30.
.. seealso::
:meth:`Vector.fill`, :meth:`Vector.append`
----
.. method:: Vector.append
Syntax:
``obj = vsrcdest.append(vec1, vec2, ...)``
Description:
Concatenate values onto the end of a vector.
The arguments may be either scalars or vectors.
The values are appended to the end of the ``vsrcdest`` vector.
Example:
.. code-block::
python
vec = h.Vector(10,4)
vec1 = h.Vector(10,5)
vec2 = h.Vector(10,6)
vec.append(vec1, vec2, 7, 8, 9)
vec.append(h.Vector([4,1,2,7]))
turns ``vec`` into a 37 element vector, whose first ten elements = 4, whose
second ten elements = 5, whose third ten elements = 6, and whose 31st, 32nd,
and 33rd elements = 7, 8, and 9, and 34-37 are 4,1,2,7. Note that the Vector created to pass the Python list
into append is immediately discarded. Remember, index 32 refers to the 33rd element.
----
.. method:: Vector.insrt
Syntax:
``obj = vsrcdest.insrt(index, vec1, vec2, ...)``
Description:
Inserts values before the index element.
The arguments may be either scalars or vectors.
``obj.insrt(obj.size, ...)`` is equivalent to ``obj.append(...)``
----
.. method:: Vector.remove
Syntax:
``obj = vsrcdest.remove(index)``
``obj = vsrcdest.remove(start, end)``
Description:
Remove the indexed element (or inclusive range) from the vector.
The vector is resized.
----
.. method:: Vector.contains
Syntax:
``numerical_truth_value = vsrc.contains(value)``
Description:
Return whether or not
the vector contains *value* as at least one
of its elements (to within :data:`float_epsilon`). It returns True if the value is found; otherwise
it returns False. (In NEURON 7.5 and before, this method returned 1 or 0 instead of True or False, respectively.)
Example:
.. code-block::
python
vec = h.Vector(10)
vec.indgen(5)
vec.contains(30)
returns True, meaning the vector does contain an element whose value is 30.
.. code-block::
python
vec.contains(50)
returns False. The vector does not contain an element whose value is 50.
.. note::
An h.Vector is a Python iterable, so you can also use Python's ``in``
keyword: ``5 in h.Vector([1, 5])`` returns True.
----
.. method:: Vector.copy
Syntax:
``obj = vdest.copy(vsrc)``
``obj = vdest.copy(vsrc, dest_start)``
``obj = vdest.copy(vsrc, src_start, src_end)``
``obj = vdest.copy(vsrc, dest_start, src_start, src_end)``
``obj = vdest.copy(vsrc, dest_start, src_start, src_end, dest_inc, src_inc)``
``obj = vdest.copy(vsrc, vsrcdestindex)``
``obj = vdest.copy(vsrc, vsrcindex, vdestindex)``
Description:
Copies some or all of *vsrc* into *vdest*.
If the dest_start argument is present (an integer index),
source elements (beginning at *src*``[0]``)
are copied to *vdest* beginning at *dest*``[dest_start]``,
*Src_start* and *src_end* here refer to indices of *vsrcx*,
not *vdest*. If *vdest* is too small for the size required by *vsrc* and the
arguments, then it is resized to hold the data.
If the *dest* is larger than required AND there is more than one
argument the *dest* is NOT resized.
One may use -1 for the
src_end argument to specify the entire size (instead of the tedious ``len(src)-1``)
If the second (and third) argument is a vector,
the elements of that vector are the
indices of the vsrc to be copied to the same indices of the vdest.
In this case the vdest is not resized and any indices that are out of
range of either vsrc or vdest are ignored. This function allows mapping
of a subset of a source vector into the subset of a destination vector.
This function can be slightly more efficient than :func:`c` since
if vdest contains enough space, memory will not have to
be allocated for it. Also it is convenient for those cases
in which vdest is being plotted and therefore reallocation
of memory (with consequent removal of vdest from the Graph)
is to be explicitly avoided.
Example:
To copy the odd elements use:
.. code-block::
python
v1 = h.Vector(30)
v1.indgen()
v1.printf()
v2 = h.Vector()
v2.copy(v1, 0, 1, -1, 1, 2)
v2.printf()
To merge or shuffle two vectors into a third, use:
.. code-block::
python
v1 = h.Vector(15)
v1.indgen()
v1.printf()
v2 = h.Vector(15)
v2.indgen(10)
v2.printf()
v3 = h.Vector()
v3.copy(v1, 0, 0, -1, 2, 1)
v3.copy(v2, 1, 0, -1, 2, 1)
v3.printf()
Example:
.. code-block::
python
vec = h.Vector(100,10)
vec1 = h.Vector()
vec1.indgen(5,105,10)
vec.copy(vec1, 50, 3, 6)
turns ``vec`` from a 100 element into a 54 element vector.
The first 50 elements will each have the value 10 and the last four will
have the values 35, 45, 55, and 65 respectively.
.. warning::
Vectors copied to themselves are not usually what is expected. eg.
.. code-block::
python
vec = h.Vector(20)
vec.indgen()
vec.copy(vec, 10)
produces a 30 element vector cycling three times from 0 to 9. However
the self copy may work if the src index is always greater than or equal
to the destination index.
----
.. method:: Vector.c
Syntax:
``newvec = vsrc.c()``
``newvec = vsrc.c(srcstart)``
``newvec = vsrc.c(srcstart, srcend)``
Description:
Return a h.Vector which is a copy of the vsrc Vector, but does not copy
the label. For a complete copy including the label use :meth:`Vector.cl`.
(Identical to the :meth:`Vector.at` function but has a short name that suggests
copy or clone). Useful in the construction of filter chains.
In versions of NEURON before 7.7, this was often used in building Vectors
from other Vectors, e.g. ``vec2 = vec1.c().add(1)``; in new code, it is
recommended to use the shorter equivalent ``vec2 = vec1 + 1``.
----
.. method:: Vector.cl
Syntax:
``newvec = vsrc.cl()``
``newvec = vsrc.cl(srcstart)``
``newvec = vsrc.cl(srcstart, srcend)``
Description:
Return a h.Vector which is a copy, including the label, of the vsrc vector.
(Similar to the :meth:`Vector.c` function which does not copy the label)
Useful in the construction of filter chains.
Note that with no arguments, it is not necessary to type the
parentheses.
----
.. method:: Vector.at
Syntax:
``newvec = vsrc.at()``
``newvec = vsrc.at(start)``
``newvec = vsrc.at(start,end)``
Description:
Return a h.Vector consisting of all or part of another.
This function predates the introduction of the vsrc.c, "clone", function
which is synonymous but is retained for backward compatibility.
It merely avoids the necessity of a ``vdest = h.Vector()`` command and
is equivalent to
.. code-block::
python
vdest = h.Vector()
vdest.copy(vsrc, start, end)
Example:
.. code-block::
python
vec = h.Vector()
vec.indgen(10,50,2)
vec1 = vec.at(2, 10)
creates ``vec1`` with 9 elements which correspond to the values at indices
2 - 10 in ``vec``. The contents of ``vec1`` would then be, in order: 14, 16, 18,
20, 22, 24, 26, 28, 30.
----
.. method:: Vector.from_double
Syntax:
``obj = vdest.from_double(n, pointer)``
Description:
Resizes the vector to size n and copies the values from the double array
to the vector.
Examples:
Interacting with a HOC array:
.. code-block::
python
from neuron import h
# create and populate a HOC array
h('double px[5]')
h.px[0] = 5
h.px[3] = 2
# transfer the data
v.from_double(5, h._ref_px[0])
# print out the vector
v.printf()
Copying from a numpy array into an existing vector:
.. code-block::
python
from neuron import h
import neuron
import numpy
a = numpy.array([5, 1, 6], 'd')
v = h.Vector()
v.from_double(3, neuron.numpy_element_ref(a, 0))
v.printf()
.. note::
To create
a new vector from a numpy array just use
``v = h.Vector(python_iterable)``.
----
.. method:: Vector.where
Syntax:
``obj = vdest.where(vsource, opstring, value1)``
``obj = vdest.where(vsource, op2string, value1, value2)``
``obj = vsrcdest.where(opstring, value1)``
``obj = vsrcdest.where(op2string, value1, value2)``
Description:
``vdest`` is vector consisting of those elements of the given vector, ``vsource``
that match the condition opstring.
Opstring is a string matching one of these (all comparisons
are with respect to :data:`float_epsilon` ): ``"=="``, ``"!="``, ``">"``, ``"<"``, ``">="``, ``"<="``
Op2string requires two numbers defining open/closed ranges and matches one
of these: ``"[]"``, ``"[)"``, ``"(]"``, ``"()"``
Example:
.. code-block::
python
vec = h.Vector(25)
vec1 = h.Vector()
vec.indgen(10)
vec1.where(vec, ">=", 50)
creates ``vec1`` with 20 elements ranging in value from 50 to 240 in
increments of 10.
.. code-block::
python
r = h.Random()
vec = h.Vector(25)
vec1 = h.Vector()
r.uniform(10,20)
vec.fill(r)
vec1.where(vec, ">", 15)
creates ``vec1`` with random elements gotten from ``vec`` which have values
greater than 15. The h.elements in vec1 will be ordered
according to the order of their appearance in ``vec``.
.. seealso::
:meth:`Vector.indvwhere`, :meth:`Vector.indwhere`
----
.. method:: Vector.indwhere
.. seealso::
:meth:`Vector.indvwhere`
----
.. method:: Vector.indvwhere
Syntax:
``i = vsrc.indwhere(opstring, value)``
``i = vsrc.indwhere(op2string, low, high)``
``obj = vsrcdest.indvwhere(opstring,value)``
``obj = vsrcdest.indvwhere(opstring,value)``
``obj = vdest.indvwhere(vsource,op2string,low, high)``
``obj = vdest.indvwhere(vsource,op2string,low, high)``
Description:
The i = vsrc form returns the index of the first element of v matching
the criterion given by the opstring. If there is no match, the return value
is -1.
``vdest`` is a vector consisting of the indices of those elements of
the source vector that match the condition opstring.
Opstring is a string matching one of these: ``"=="``, ``"!="``, ``">"``, ``"<"``, ``">="``, ``"<="``
Op2string is a string matching one of these: ``"[]"``, ``"[)"``, ``"(]"``, ``"()"``
Comparisons are relative to the :data:`float_epsilon` global variable.
Example:
.. code-block::
python
vs = h.Vector()
vs.indgen(0, .9, .1)
vs.printf()
print(vs.indwhere(">", .3))
print("note roundoff error, vs[3] - 0.3 = %g" % (vs[3] - 0.3))
print(vs.indwhere("==", .5))
vd = vs.c().indvwhere(vs, "[)", .3, .7)
vd.printf()
.. seealso::
:meth:`Vector.where`
----
.. method:: Vector.fwrite
Syntax:
``n = vsrc.fwrite(fileobj)``
``n = vsrc.fwrite(fileobj, start, end)``
Description:
Write the vector ``vec`` to an open *fileobj* of type :class:`File` in
machine dependent binary format.
You must keep track of the vector's
size for later reading, so it is recommended that you store the size of the
vector as the first element of the file.
It is almost always better to use :func:`vwrite` since it stores the size
of the vector automatically and is more portable since the corresponding
vread will take care of machine dependent binary byte ordering differences.
Return value is the number of items. (0 if error)
:func:`fread` is used to read a file containing numbers stored by ``fwrite`` but
must have the same size.
----
.. method:: Vector.fread
Syntax:
``n = vdest.fread(fileobj)``
``n = vdest.fread(fileobj, n)``
``n = vdest.fread(fileobj, n, precision)``
Description:
Read the elements of a vector from the file in binary as written by ``fwrite.``
If *n* is present, the vector is resized before reading. Note that
files created with fwrite cannot be fread on a machine with different
byte ordering. E.g. spark and intel cpus have different byte ordering.
It is almost always better to use ``vwrite`` in combination with ``vread``.
See vwrite for the meaning of the *precision* argment.
Return value is 1 (no error checking).
----
.. method:: Vector.vwrite
Syntax:
``n = vec.vwrite(fileobj)``
``n = vec.vwrite(fileobj, precision)``
Description:
Write the vector in binary format
to an already opened for writing * fileobj* of type
:class:`File`.
:meth:`~Vector.vwrite` is easier to use than ``fwrite()``
since it stores the size of the vector and type information
for a more
automated read/write. The file data can also be vread on a machine with
different byte ordering. e.g. you can vwrite with an intel cpu and vread
on a sparc.
Precision formats 1 and 2 employ a simple automatic
compression which is uncompressed automatically by vread. Formats 3 and 4
remain uncompressed.
Default precision is 4 (double) because this is the usual type
used for numbers in oc and therefore requires no conversion or
compression
.. code-block::
python
* 1 : char shortest 8 bits
* 2 : short 16 bits
3 : float 32 bits
4 : double longest 64 bits
5 : int sizeof(int) bytes
.. warning::
These are useful primarily for storage of data: exact
values will not necessarily be maintained due to the conversion
process.
Return value is 1. Only if the type field is invalid will the return
value be 0.
----
.. method:: Vector.vread
Syntax:
``n = vec.vread(fileobj)``
Description:
Read vector from binary format file written with ``vwrite()``.
Size and data type have
been stored by ``vwrite()`` to allow correct retrieval syntax, byte ordering, and
decompression (where necessary). The vector is automatically resized.
Return value is 1. (No error checking.)
Example:
.. code-block::
python
v1 = h.Vector()
v1.indgen(20,30,2)
v1.printf()
f = h.File()
f.wopen("temp.tmp")
v1.vwrite(f)
v2 = h.Vector()
f.ropen("temp.tmp")
v2.vread(f)
v2.printf()
----
.. method:: Vector.printf
Syntax:
``n = vec.printf()``
``n = vec.printf(format_string)``
``n = vec.printf(format_string, start, end)``
``n = vec.printf(fileobj)``
``n = vec.printf(fileobj, format_string)``
``n = vec.printf(fileobj, format_string, start, end)``
Description:
Print the values of the vector in ascii either to the screen or a File instance
(if ``fileobj`` is present). *Start* and *end* enable you to specify
which particular set of indexed values to print.
Use ``format_string`` for formatting the output of each element.
This string must contain exactly one ``%f``, ``%g``, or ``%e``,
but can also contain additional formatting instructions.
Return value is number of items printed.
Example:
.. code-block::
python
vec = h.Vector()
vec.indgen(0, 1, 0.1)
vec.printf("%8.4f\n")
prints the numbers 0.0000 through 0.9000 in increments of 0.1. Each number will
take up a total of eight spaces, will have four decimal places
and will be printed on a h.line.
.. warning::
No error checking is done on the format string and invalid formats can cause
segmentation violations.
----
.. method:: Vector.scanf
Syntax:
``n = vec.scanf(fileobj)``
``n = vec.scanf(fileobj, n)``
``n = vec.scanf(fileobj, c, nc)``
``n = vec.scanf(fileobj, n, c, nc)``
Description:
Read ascii values from a :class:`File` instance (must already be opened for reading)
into vector. If present, scanning takes place til *n* items are
read or until EOF. Otherwise, ``vec.scanf`` reads until end of file.
If reading
til eof, a number followed
by a newline must be the last string in the file. (no trailing spaces
after the number and no extra newlines).
When reading til EOF, the vector grows approximately by doubling when
its currently allocated space is filled. To avoid the overhead of
memory reallocation when scanning very long vectors (e.g. > 50000 elements)
it is a good idea to presize the vector to a larger value than the
expected number of elements to be scanned.
Note that although the vector is resized to
the actual number of elements scanned, the space allocated to the
vector remains available for growth. See :meth:`Vector.buffer_size` .
Read from
column *c* of *nc* columns when data is in column format. It numbers
the columns beginning from 1.
The scan takes place at the current position of the file.
Return value is number of items read.
.. seealso::
:meth:`Vector.scantil`
----
.. method:: Vector.scantil
Syntax:
``n = vec.scantil(fileobj, sentinel)``
``n = vec.scantil(fileobj, sentinel, c, nc)``
Description:
Like :meth:`Vector.scanf` but scans til it reads a value equal to the
sentinel. e.g. -1e15 is a possible sentinel value in many situations.
The vector does not include the sentinel value. The file pointer is
left at the character following the sentinel.
Read from
column *c* of *nc* columns when data is in column format. It numbers
the columns beginning from 1. The scan stops when the sentinel is found in
any position prior to column c+1 but it is recommended that the sentinel
appear by itself on its own line. The file pointer is left at the
character following the sentinel.
The scan takes place at the current position of the file.
Return value is number of items read.
----
.. method:: Vector.plot
Syntax:
``obj = vec.plot(graphobj)``
``obj = vec.plot(graphobj, color, brush)``
``obj = vec.plot(graphobj, x_vec)``
``obj = vec.plot(graphobj, x_vec, color, brush)``
``obj = vec.plot(graphobj, x_increment)``
``obj = vec.plot(graphobj, x_increment, color, brush)``
Description:
Plot vector in a :class:`Graph` object. The default is to plot the elements of the
vector as y values with their indices as x values. An optional
argument can be used to
specify the x-axis. Such an argument can be either a
vector, *x_vec*, in which case its values are used for x values, or
a scalar, *x_increment*, in
which case x is incremented according to this number.
This function plots the
``vec`` values that exist in the vector at the time of graph flushing or window
resizing. The alternative is ``vec.line()`` which plots the vector values
that exist at the time of the call to ``plot``. It is therefore possible with
``vec.line()`` to produce multiple plots
on the same graph.
Once a vector is plotted, it is only necessary to call ``graphobj.flush()``
in order to display further changes to the vector. In this way it
is possible to produce rather rapid line animation.
If the vector :meth:`Graph.label` is not empty it will be used as the label for
the line on the Graph.
Resizing a vector that has been plotted will remove it from the Graph.
The number of points plotted is the minimum of vec.size and x_vec.size
at the time vec.plot is called. x_vec is assumed to be an unchanging
Vector.
Example:
.. code-block::
python
from neuron import h, gui
import time
g = h.Graph()
g.size(0,10,-1,1)
vec = h.Vector()
vec.indgen(0,10, .1)
vec.apply("sin")
vec.plot(g, .1)
def do_run():
for i in range(len(vec)):
vec.rotate(1)
g.flush()
h.doNotify()
time.sleep(0.01)
h.xpanel("")
h.xbutton("run", do_run)
h.xpanel()
.. image:: ../../images/vector-plot.png
:align: center
.. seealso::
:meth:`Graph.Vector`
----
.. method:: Vector.line
Syntax:
``obj = vec.line(graphobj)``
``obj = vec.line(graphobj, color, brush)``
``obj = vec.line(graphobj, x_vec)``
``obj = vec.line(graphobj, x_vec, color, brush)``
``obj = vec.line(graphobj, x_increment)``
``obj = vec.line(graphobj, x_increment, color, brush)``
Description:
Plot vector on a :class:`Graph`. Exactly like ``.plot()`` except the vector
is *not* plotted by reference so that the values may be changed
subsequently w/o disturbing the plot. It is therefore possible to produce
a number of plots of the same function on the same graph,
without erasing any previous plot.
The line on a graph is given the :meth:`Graph.label` if the label is not empty.
The number of point plotted is the minimum of vec.size and x_vec.size .
Example:
.. code-block::
python
from neuron import h, gui
g = h.Graph()
g.size(0,10,-1,1)
vec = h.Vector()
vec.indgen(0,10, .1)
vec.apply("sin")
for i in range(4):
vec.line(g, 0.1)
vec.rotate(10)
.. image:: ../../images/vector-line.png
:align: center
.. seealso::
:meth:`Graph.family`
----
.. method:: Vector.ploterr
Syntax:
``obj = vec.ploterr(graphobj, x_vec, err_vec)``
``obj = vec.ploterr(graphobj, x_vec, err_vec, size)``
``obj = vec.ploterr(graphobj, x_vec, err_vec, size, color, brush)``
Description:
Similar to ``vec.line()``, but plots error bars with size +/- the elements
of vector *err_vec*.
*size* sets the width of the seraphs on the error bars to a number
of printer dots.
*brush* sets the width of the plot line. 0=invisible,
1=minimum width, 2=1point, etc.
Example:
.. code-block::
python
g = h.Graph()
g.size(0,100, 0,250)
vec = h.Vector()
xvec = h.Vector()
errvec = h.Vector()
vec.indgen(0,200,20)
xvec.indgen(0,100,10)
errvec.copy(xvec)
errvec.apply("sqrt")
vec.ploterr(g, xvec, errvec, 10)
vec.mark(g, xvec, "O", 5)
.. image:: ../../images/vector-ploterr.png
:align: center
creates a graph which has x values of 0 through 100 in increments of 10 and
y values of 0 through 200 in increments of 20. At each point graphed, vertical
error bars are also drawn which are the +/- the length of the square root of the
values 0 through 100 in increments of 10. Each error bar has seraphs which are
ten printer points wide. The graph is also marked with filled circles 5 printers
points in diameter.
----
.. method:: Vector.mark
Syntax:
``obj = vec.mark(graphobj, x_vector)``
``obj = vec.mark(graphobj, x_vector, "style")``
``obj = vec.mark(graphobj, x_vector, "style", size)``
``obj = vec.mark(graphobj, x_vector, "style", size, color, brush)``
``obj = vec.mark(graphobj, x_increment)``
``obj = vec.mark(graphobj, x_increment, "style", size, color, brush)``
Description:
Similar to ``vec.line``, but instead of connecting by lines, it make marks,
centered at the indicated position, which do not change size when
window is zoomed or resized. The style is a single character
``|,-,+,o,O,t,T,s,S`` where ``o,t,s`` stand for circle, triangle, square
and capitalized means filled. Default size is 12 points.
----
.. method:: Vector.histogram
Syntax:
``newvect = vsrc.histogram(low, high, width)``
Description:
Create a histogram constructed by binning the values in ``vsrc``.
Bins run from *low* to *high* in divisions of *width*. Data outside
the range is not binned.
This function returns a vector that contains the counts in each bin, so while it is
to execute ``newvect = h.Vector()``.
The first element of ``newvect`` is 0 (``newvect[0] = 0``).
For ``ii > 0``, ``newvect[ii]`` equals the number of
items
in ``vsrc`` whose values lie in the half open interval
``[a,b)``
where ``b = low + ii*width`` and ``a = b - width``.
In other words, ``newvect[ii]`` is the number of items in
``vsrc``
that fall in the bin just below the boundary ``b``.
Example:
.. code-block::
python
rand = h.Random()
rand.negexp(1)
interval = h.Vector(100)
interval.setrand(rand) # random intervals
hist = interval.histogram(0, 10, .1)
# and for a manhattan style plot ...
g = h.Graph()
g.size(0,10,0,30)
# create an index vector with 0,0, 1,1, 2,2, 3,3, ...
v2 = h.Vector(2*len(hist))
v2.indgen(.5)
v2.apply("int")
#
v3 = h.Vector(1)
v3.index(hist, v2)
v3.rotate(-1) # so different y's within each pair
v3[0] = 0
v3.plot(g, v2)
.. image:: ../../images/vector-histogram.png
:align: center
creates a histogram of the occurrences of random numbers
ranging from 0 to 10 in divisions of 0.1.
----
.. method:: Vector.hist
Syntax:
``obj = vdest.hist(vsrc, low, size, width)``
Description:
Similar to :func:`histogram` (but notice the different argument meanings.
Put a histogram in *vdest* by binning
the data in *vsrc*.
Bins run from *low* to ``low + size * width``
in divisions of *width*.
Data outside
the range is not binned.
----
.. method:: Vector.sumgauss
Syntax:
``newvect = vsrc.sumgauss(low, high, width, var)``
``newvect = vsrc.sumgauss(low, high, width, var, weight_vec)``
Description:
Create a vector which is a curve calculated by summing gaussians of
area 1 centered on all the points in the vector. This has the
advantage over ``histogram`` of not imposing arbitrary bins. *low*
and *high* set the range of the curve.
*width* determines the granularity of the
curve. *var* sets the variance of the gaussians.
The optional argument ``weight_vec`` is a vector which should be the same
size as ``vec`` and is used to scale or weight the gaussians (default is
for them all to have areas of 1 unit).
This function returns a vector, so while it is
to declare *vectobj* as a ``h.Vector()``.
To plot, use ``v.indgen(low,high,width)`` for the x-vector argument.
Example:
.. code-block::
python
r = h.Random()
r.normal(1, 2)
data = h.Vector(100)
data.setrand(r)
hist = data.sumgauss(-4, 6, .5, 1)
x = h.Vector(len(hist))
x.indgen(-4, 6, .5)
g = h.Graph()
g.size(-4, 6, 0, 30)
hist.plot(g, x)
----
.. method:: Vector.smhist
Syntax:
``obj = vdest.smhist(vsrc, start, size, step, var)``
``obj = vdest.smhist(vsrc, start, size, step, var, weight_vec)``
Description:
Very similar to :func:`sumgauss` . Calculate a smooth histogram by convolving
the raw data set with a gaussian kernel. The histogram begins at
``varstart`` and has ``varsize`` values in increments of size ``varstep``.
``varvar`` sets the variance of the gaussians.
The optional argument ``weight_vec``
is a vector which should be the same size as ``vsrc`` and is used to scale or
weight the number of data points at a particular value.
----
.. method:: Vector.ind
Syntax:
``newvect = vsrc.ind(vindex)``
Description:
Return a h.Vector consisting of the elements of ``vsrc`` whose indices are given
by the elements of ``vindex``.
Example:
.. code-block::
python
vec = h.Vector(100)
vec2 = h.Vector()
vec.indgen(5)
vec2.indgen(49, 59, 1)
vec1 = vec.ind(vec2)
creates ``vec1`` to contain the fiftieth through the sixtieth elements of ``vec2``
which would have the values 245 through 295 in increments of 5.
----
.. method:: Vector.addrand
Syntax:
``obj = vsrcdest.addrand(randobj)``
``obj = vsrcdest.addrand(randobj, start, end)``
Description:
Adds random values to the elements of the vector by sampling from the
same distribution as last picked in the Random object *randobj*.
Example:
.. code-block::
python
from neuron import h, gui
vec = h.Vector(50)
g = h.Graph()
g.size(0,50,0,100)
r = h.Random()
r.poisson(.2)
vec.plot(g)
def race():
vec.fill(0)
for i in range(300):
vec.addrand(r)
g.flush()
h.doNotify()
race()
----
.. method:: Vector.setrand
Syntax:
``obj = vdest.setrand(randobj)``
``obj = vdest.setrand(randobj, start, end)``
Description:
Sets random values for the elements of the vector by sampling from the
same distribution as last picked in *randobj*.
----
.. method:: Vector.sin
Syntax:
``obj = vdest.sin(freq, phase)``
``obj = vdest.sin(freq, phase, dt)``
Description:
Generate a sin function in vector ``vec`` with frequency *freq* hz, phase
*phase* in radians. *dt* is assumed to be 1 msec unless specified.
----
.. method:: Vector.apply
Syntax:
``obj = vsrcdest.apply("func")``
``obj = vsrcdest.apply("func", start, end)``
Description:
Apply a hoc function to each of the elements in the vector.
The function can be any function that is accessible in oc. It
must take only one scalar argument and return a scalar.
Note that the function name must be in quotes and that the parentheses
are omitted.
Example:
.. code-block::
python
vec.apply("sin", 0, 9)
applies the sin function to the first ten elements of the vector ``vec``.
----
.. method:: Vector.reduce
Syntax:
``x = vsrc.reduce("func")``
``x = vsrc.reduce("func", base)``
``x = vsrc.reduce("func", base, start, end)``
Description:
Pass all elements of a vector through a HOC function and return the sum of
the results. Use *base* to initialize the value x.
Note that the function name must be in quotes and that the parentheses
are omitted.
Example:
.. code-block::
python
from neuron import h
vec = h.Vector()
vec.indgen(0, 10, 2)
h("func sq(){return $1*$1}")
print(vec.reduce("sq", 100))
displays the value 320.
100 + 0*0 + 2*2 + 4*4 + 6*6 + 8*8 + 10*10 = 320
Although reduce only works with HOC functions, it can be emulated in Python
using generators and the ``sum`` function. For example, the last
two lines of the above example are equivalent to:
.. code-block::
python
def sq(x):
return x * x
print(sum((sq(x) for x in vec), 100))
----
.. method:: Vector.floor
Syntax:
``vec.floor()``
Description:
Rounds toward negative infinity. Note that :data:`float_epsilon` is not
used in this calculation.
----
.. method:: Vector.to_python
Syntax:
``pythonlist = vec.to_python()``
``pythonlist = vec.to_python(pythonlist)``
``numpyarray = vec.to_python(numpyarray)``
Description:
Copy the vector elements from the hoc vector to a pythonlist or
1-d numpyarray. If the arg exists the pythonobject must have the same
size as the hoc vector.
----
.. method:: Vector.from_python
Syntax:
``vec = vec.from_python(pythonlist)``
``vec = vec.from_python(numpyarray)``
Description:
Copy the python list elements into the hoc vector. The elements must be
numbers that are convertable to doubles.
Copy the numpy 1-d array elements into the hoc vector.
The hoc vector is resized.
----
.. method:: Vector.as_numpy()
Syntax:
``numpyarray = vec.as_numpy()``
Description:
The numpyarray points into the data of the Hoc Vector, i.e. does not
copy the data. Do not
use the numpyarray if the Vector is destroyed.
Example:
.. code-block::
python
from neuron import h
v = h.Vector(5).indgen()
n = v.as_numpy()
print(n) #[0. 1. 2. 3. 4.]
v[1] += 10
n[2] += 20
print(n) #[ 0. 11. 22. 3. 4.]
v.printf() #0 11 22 3 4
----
.. method:: Vector.fit
Syntax:
``error = data_vec.fit(fit_vec,"fcn",indep_vec, pointer1, [pointer2], ... [pointerN])``
Description:
Use a simplex algorithm to find parameters *p1* through *pN* such to
minimize the mean squared error between the "data" contained in
``data_vec`` and the approximation generated by the user-supplied "*fcn*"
applied to the elements of ``indep_vec``.
*fcn* must take one argument which is the main independent variable
followed by one or more arguments which are tunable parameters which
will be optimized. Thus the arguments to .fit following "*fcn*" should
be completely analogous to the arguments to fcn itself. The
difference is that the args to fcn must all be scalars while the
corresponding args to .fit will be a vector object (for the
independent variable) and pointers to scalars (for the remaining
parameters).
The results of a call to .fit are three-fold. First, the parameters
of best fit are returned by setting the values of the variables *p1* to
*pN* (possible because they are passed as pointers). Second, the values
of the vector fit_vec are set to the fitted function. If ``fit_vec`` is
not passed with the same size as ``indep_vec`` and ``data_vec``, it is resized
accordingly. Third, the mean squared error between the fitted
function and the data is returned by ``.fit``. The ``.fit()`` call may be
reiterated several times until the error has reached an acceptable
level.
Care must be taken in selecting an initial set of parameter values.
Although you need not be too close, wild discrepancies will cause the
simplex algorithm to give up. Values of 0 are to be avoided. Trial
and error is sometimes necessary.
Because calls to hoc have a high overhead, this procedure can be
rather slow. Several commonly-used functions are provided directly
in c code and will work much faster. In each case, if the name below
is used, the builtin function will be used and the user is expected to
provide the correct number of arguments (here denoted ``a,b,c``...).
.. code-block::
python
"exp1": y = a * exp(-x/b)
"exp2": y = a * exp(-x/b) + c * exp (-x/d)
"charging": y = a * (1-exp(-x/b)) + c * (1-exp(-x/d))
"line": y = a * x + b
"quad": y = a * x^2 + b*x + c
.. warning::
This function is not very useful for fitting the results of simulation runs
due to its argument organization. For that purpose the :func:`fit_praxis` syntax
is more suitable. This function should become a top-level function which
merely takes a user error function name and a parameter list.
An alternative implementation of the simplex fitting algorithm is in
the scopmath library.
.. seealso::
:func:`fit_praxis`
Example:
The :menuselection:`NEURON Main Menu --> Miscellaneous --> Parameterized Function` widget uses this function
and is implemented in :file:`nrn/lib/hoc/funfit.hoc`
The following example demonstrates the strategy used by the simplex
fitting algorithm to search for a minimum. The location of the parameter
values is plotted on each call to the function.
The sample function has a minimum at the point (1, .5)
.. code-block::
python
from neuron import h, gui
g = h.Graph()
g.size(0, 3, 0, 3)
def fun(a, x, y):
if a == 0:
g.line(x, y)
g.flush()
print('{} {} {}'.format(a, x, y))
return (x - 1) ** 2 + (y - 0.5) ** 2
dvec = h.Vector(2)
fvec = h.Vector(2)
fvec.fill(1)
ivec = h.Vector(2)
ivec.indgen()
a = h.ref(2)
b = h.ref(1)
g.beginline()
error = dvec.fit(fvec, fun, ivec, a, b)
print('{} {} {}'.format(a[0], b[0], error))
.. warning::
Does not currently work with Python functions. It requires a string whose
value is the name of a HOC function instead.
----
.. _vect2:
.. method:: Vector.interpolate
Syntax:
``obj = ysrcdest.interpolate(xdest, xsrc)``
``obj = ydest.interpolate(xdest, xsrc, ysrc)``
Description:
Linearly interpolate points from (xsrc,ysrc) to (xdest,ydest)
In the second form, xsrc and ysrc remain unchanged.
Destination points outside the domain of xsrc are set to
``ysrc[0]`` or ``ysrc[ysrc.size-1]``
Example:
.. code-block::
python
g = h.Graph()
g.size(0,10,0,100)
#...
xs = h.Vector(10)
xs.indgen()
ys = xs * xs
ys.line(g, xs, 1, 0) # black reference line
xd = h.Vector()
xd.indgen(-.5, 10.5, .1)
yd = ys.c().interpolate(xd, xs)
yd.line(g, xd, 3, 0) # blue more points than reference
xd.indgen(-.5, 13, 3)
yd = ys.c().interpolate(xd, xs)
yd.line(g, xd, 2, 0) # red fewer points than reference
----
.. method:: Vector.deriv
Syntax:
``obj = vdest.deriv(vsrc)``
``obj = vdest.deriv(vsrc, dx)``
``obj = vdest.deriv(vsrc, dx, method)``
``obj = vsrcdest.deriv()``
``obj = vsrcdest.deriv(dx)``
``obj = vsrcdest.deriv(dx, method)``
Description:
The numerical Euler derivative or the central difference derivative of ``vec``
is placed in ``vdest``.
The variable *dx* gives the increment of the independent variable
between successive elements of ``vec``.
*method* = 1 = Euler derivative:
``vec1[i] = (vec[i+1] - vec[i])/dx``
Each time this method is used,
the first element
of ``vec`` is lost since *i* cannot equal -1. Therefore, since the
``integral`` function performs an Euler
integration, the integral of ``vec1`` will reproduce ``vec`` minus the first
element.
*method* = 2 = Central difference derivative:
``vec1[i] = ((vec[i+1]-vec[i-1])/2)/dx``
This method produces an Euler derivative for the first and last
elements of ``vec1``. The central difference method maintains the
same number of elements in ``vec1``
as were in ``vec`` and is a more accurate method than the Euler method.
A vector differentiated by this method cannot, however, be integrated
to reproduce the original ``vec``.
Example:
.. code-block::
python
from neuron import h
vec = h.Vector(range(6))
vec = vec * vec
vec1 = h.Vector()
vec1.deriv(vec, 0.1)
creates ``vec1`` with elements:
.. code-block::
python
10 20
40 60
80 90
Since *dx*\ =0.1, and there are eleven elements including 0,
the entire function exists between the values of 0 and 1, and the derivative
values are large compared to the function values. With *dx*\ =1,the vector
``vec1`` would consist of the following elements:
.. code-block::
python
1 2
4 6
8 9
The Euler method vs. the Central difference method:
Beginning with the vector ``vec``:
.. code-block::
python
0 1
4 9
16 25
``vec1.deriv(vec, 1, 1)`` (Euler) would go about
producing ``vec1`` by the following method:
.. code-block::
python
1-0 = 1 4-1 = 3
9-4 = 5 16-9 = 7
25-16 = 9
whereas ``vec1.deriv(vec, 1, 2)`` (Central difference) would go about
producing ``vec1`` as such:
.. code-block::
python
1-0 = 1 (4-0)/2 = 2
(9-1)/2 = 4 (16-4)/2 = 6
(25-9)/2 = 8 25-16 = 9
----
.. method:: Vector.integral
Syntax:
``obj = vdest.integral(vsrc)``
``obj = vdest.integral(vsrc, dx)``
``obj = vsrcdest.integral()``
``obj = vsrcdest.integral(dx)``
Description:
Places a numerical Euler integral of the vsrc elements in vdest.
*dx* sets the size of the discretization.
``vdest[i+1] = vdest[i] + vsrc[i+1]`` and the first element of ``vdest`` is always
equal to the first element of ``vsrc``.
Example:
.. code-block::
python
from neuron import h
vec = h.Vector([0, 1, 4, 9, 16, 25])
vec1 = h.Vector()
vec1.integral(vec, 1) # Euler integral of vec elements approximating
# an x-squared function, dx = 0.1
vec1.printf()
will print the following elements in ``vec1`` to the screen:
.. code-block::
python
0 1 5
14 30 55
In order to make the integral values more accurate, it is necessary to increase
the size of the vector and to decrease the size of *dx*.
.. code-block::
python
from neuron import h
import numpy
# set vec to the squares of 51 values from 0 to 5
vec = h.Vector(numpy.linspace(0, 5, 51))
vec.pow(2)
vec1 = h.Vector()
vec1.integral(vec, 0.1) # Euler integral of vec elements approximating
# an x-squared function, dx = 0.1
# print every 10th index
for i in range(0, len(vec1), 10):
print(vec1[i])
will print the following elements of
``vec1`` corresponding to the integers 0-5 to the screen:
.. code-block::
python
0
0.385
2.87
9.455
22.14
42.925
The integration naturally becomes more accurate as
*dx* is reduced and the size of the vector is increased. If the vector
is taken to 501 elements from 0-5 and *dx* is made to equal 0.01, the integrals
of the integers 0-5 yield the following (compared to their continuous values
on their right).
.. code-block::
python
0.00000 -- 0.00000 0.33835 -- 0.33333 2.6867 -- 2.6666
9.04505 -- 9.00000 21.4134 -- 21.3333 41.7917 -- 41.6666
----
.. method:: Vector.median
Syntax:
``median = vsrc.median()``
Description:
Find the median value of ``vec``.
----
.. method:: Vector.medfltr
Syntax:
``obj = vdest.medfltr(vsrc)``
``obj = vdest.medfltr(vsrc, points)``
``obj = vsrcdest.medfltr()``
``obj = vsrcdest.medfltr( points)``
Description:
Apply a median filter to vsrc, producing a smoothed version in vdest.
Each point is replaced with the median value of the *points* on
either side.
This is typically used for eliminating spikes from data.
----
.. method:: Vector.sort
Syntax:
``obj = vsrcdest.sort()``
Description:
Sort the elements of ``vec1`` in place, putting them in numerical order.
----
.. method:: Vector.sortindex
Syntax:
``vdest = vsrc.sortindex()``
``vdest = vsrc.sortindex(vdest)``
Description:
Return a h.Vector of indices which sort the vsrc elements in numerical
order. That is vsrc.index(vsrc.sortindex) is equivalent to vsrc.sort().
If vdest is present, use that as the destination vector for the indices.
This, if it is large enough, avoids the destruct/construct of vdest.
Example:
.. code-block::
python
from neuron import h
r = h.Random()
r.uniform(0, 100)
a = h.Vector(10)
a.setrand(r)
a.printf()
si = a.sortindex()
si.printf()
a.index(si).printf()
----
.. method:: Vector.reverse
Syntax:
``obj = vsrcdest.reverse()``
Description:
Reverses the elements of ``vec`` in place.
----
.. method:: Vector.rotate
Syntax:
``obj = vsrcdest.rotate(value)``
``obj = vsrcdest.rotate(value, 0)``
Description:
A negative *value* will move elements to the left. A positive argument
will move elements to the right. In both cases, the elements shifted off one
end of the vector will reappear at the other end.
If a 2nd arg is present, 0 values get shifted in and elements shifted off
one end are lost.
Example:
.. code-block::
python
vec.indgen(1, 10, 1)
vec.rotate(3)
orders the elements of ``vec`` as follows:
.. code-block::
python
8 9 10 1 2 3 4 5 6 7
whereas,
.. code-block::
python
vec.indgen(1, 10, 1)
vec.rotate(-3)
orders the elements of ``vec`` as follows:
.. code-block::
python
4 5 6 7 8 9 10 1 2 3
.. code-block::
python
vec = h.Vector()
vec.indgen(1,5,1)
vec.printf()
vec.c().rotate(2).printf()
vec.c().rotate(2, 0).printf()
vec.c().rotate(-2).printf()
vec.c().rotate(-2, 0).printf()
----
.. method:: Vector.rebin
Syntax:
``obj = vdest.rebin(vsrc,factor)``
``obj = vsrcdest.rebin(factor)``
Description:
Compresses length of vector ``vsrc`` by an integer *factor*. The sum of
elements is conserved, unless the *factor* produces a remainder,
in which case the remainder values are truncated from ``vdest``.
Example:
.. code-block::
python
vec.indgen(1, 10, 1)
vec1.rebin(vec, 2)
produces ``vec1``:
.. code-block::
python
3 7 11 15 19
where each pair of ``vec`` elements is added together into one element.
But,
.. code-block::
python
vec.indgen(1, 10, 1)
vec1.rebin(vec, 3)
adds trios ``vec`` elements and gets rid of the value 10, producing
``vec1``:
.. code-block::
python
6 15 24
----
.. method:: Vector.pow
Syntax:
``obj = vdest.pow(vsrc, power)``
``obj = vsrcdest.pow(power)``
Description:
Raise each element to some power. A power of -1, 0, .5, 1, or 2
are efficient.
----
.. method:: Vector.sqrt
Syntax:
``obj = vdest.sqrt(vsrc)``
``obj = vsrcdest.sqrt()``
Description:
Take the square root of each element. No domain checking.
----
.. method:: Vector.log
Syntax:
``obj = vdest.log(vsrc)``
``obj = vsrcdest.log()``
Description:
Take the natural log of each element. No domain checking.
----
.. method:: Vector.log10
Syntax:
``obj = vdest.log10(vsrc)``
``obj = vsrcdest.log10()``
Description:
Take the logarithm to the base 10 of each element. No domain checking.
----
.. method:: Vector.tanh
Syntax:
``obj = vdest.tanh(vsrc)``
``obj = vsrcdest.tanh()``
Description:
Take the hyperbolic tangent of each element.
----
.. method:: Vector.abs
Syntax:
``obj = vdest.abs(vsrc)``
``obj = vsrcdest.abs()``
Description:
Take the absolute value of each element.
Example:
.. code-block::
python
v1 = h.Vector()
v1.indgen(-.5, .5, .1)
v1.printf()
v1.abs().printf()
.. seealso::
:func:`abs`
----
.. method:: Vector.index
Syntax:
``obj = vdest.index(vsrc, indices)``
Description:
The values of the vector ``vsrc`` indexed by the vector *indices* are collected
into ``vdest``.
Example:
.. code-block::
python
from neuron import h
vec = h.Vector()
vec1 = h.Vector()
vec2 = h.Vector()
vec3 = h.Vector(6)
vec.indgen(0, 5.1, 0.1) # vec will have 51 values from 0 to 5, with increment=0.1
vec1.integral(vec, 0.1) # Euler integral of vec elements approximating
# an x-squared function, dx = 0.1
vec2.indgen(0, 50, 10)
vec3.index(vec1, vec2) # put the value of every 10th index in vec2
makes ``vec3`` with six elements corresponding to the integrated integers from
``vec``.
----
.. method:: Vector.min
Syntax:
``x = vec.min()``
``x = vec.min(start, end)``
Description:
Return the minimum value.
----
.. method:: Vector.min_ind
Syntax:
``i = vec.min_ind()``
``i = vec.min_ind(start, end)``
Description:
Return the index of the minimum value.
----
.. method:: Vector.max
Syntax:
``x = vec.max()``
``x = vec.max(start, end)``
Description:
Return the maximum value.
----
.. method:: Vector.max_ind
Syntax:
``i = vec.max_ind()``
``i = vec.max_ind(start, end)``
Description:
Return the index of the maximum value.
----
.. method:: Vector.sum
Syntax:
``x = vec.sum()``
``x = vec.sum(start, end)``
Description:
Return the sum of element values.
----
.. method:: Vector.sumsq
Syntax:
``x = vec.sumsq()``
``x = vec.sumsq(start, end)``
Description:
Return the sum of squared element values.
----
.. method:: Vector.mean
Syntax:
``x = vec.mean()``
``x = vec.mean(start, end)``
Description:
Return the mean of element values.
----
.. method:: Vector.var
Syntax:
``x = vec.var()``
``x = vec.var(start, end)``
Description:
Return the variance of element values.
----
.. method:: Vector.stdev
Syntax:
``vec.stdev()``
``vec.stdev(start,end)``
Description:
Return the standard deviation of the element values.
----
.. method:: Vector.stderr
Syntax:
``x = vec.stderr()``
``x = vec.stderr(start, end)``
Description:
Return the standard error of the mean (SEM) of the element values.
----
.. method:: Vector.dot
Syntax:
``x = vec.dot(vec1)``
Description:
Return the dot (inner) product of ``vec`` and *vec1*.
----
.. method:: Vector.mag
Syntax:
``x = vec.mag()``
Description:
Return the vector length or magnitude.
----
.. method:: Vector.add
Syntax:
``obj = vsrcdest.add(scalar)``
``obj = vsrcdest.add(vec1)``
Description:
Add either a scalar to each element of the vector or add the corresponding
elements of *vec1* to the elements of ``vsrcdest``.
``vsrcdest`` and *vec1* must have the same size.
----
.. method:: Vector.sub
Syntax:
``obj = vsrcdest.sub(scalar)``
``obj = vsrcdest.sub(vec1)``
Description:
Subtract either a scalar from each element of the vector or subtract the
corresponding elements of *vec1* from the elements of ``vsrcdest``.
``vsrcdest`` and *vec1* must have the same size.
----
.. method:: Vector.mul
Syntax:
``obj = vsrcdest.mul(scalar)``
``obj = vsrcdest.mul(vec1)``
Description:
Multiply each element of ``vsrcdest`` either by either a scalar or the
corresponding elements of *vec1*. ``vsrcdest``
and *vec1* must have the same size.
----
.. method:: Vector.div
Syntax:
``obj = vsrcdest.div(scalar)``
``obj = vsrcdest.div(vec1)``
Description:
Divide each element of ``vsrcdest`` either by a scalar or by the
corresponding elements of *vec1*. ``vsrcdest``
and *vec1* must have the same size.
----
.. method:: Vector.scale
Syntax:
``scale = vsrcdest.scale(low, high)``
Description:
Scale values of the elements of a vector to lie within the given range.
Return the scale factor used.
----
.. method:: Vector.eq
Syntax:
``numerical_truth_value = vec.eq(vec1)``
Description:
Test equality of vectors. Returns 1 if all elements of vec ==
corresponding elements of *vec1* (to within :data:`float_epsilon`).
Otherwise it returns 0. This can be made into a boolean truth value with Python function bool()
----
.. method:: Vector.meansqerr
Syntax:
``x = vec.meansqerr(vec1)``
``x = vec.meansqerr(vec1, weight_vec)``
Description:
Return the mean squared error between values of the elements of ``vec`` and
the corresponding elements of *vec1*. ``vec`` and *vec1* must have the
same size.
If the second vector arg is present, it also must have the same size and the
return value is sum of ``w[i]*(v1[i] - v2[i])^2 / size``
Fourier Analysis
~~~~~~~~~~~~~~~~
The following routines are based on the fast fourier transform (FFT)
and are implemented using code from Numerical Recipes in C (2nd ed.)
Refer to this source for further information.
.. method:: Vector.correl
Syntax:
``obj = vdest.correl(src)``
``obj = vdest.correl(src, vec2)``
Description:
Compute the cross-correlation function of *src* and *vec2* (or the
autocorrelation of *src* if *vec2* is not present).
----
.. method:: Vector.convlv
Syntax:
``obj = vdest.convlv(src,filter)``
``obj = vdest.convlv(src,filter, sign)``
Description:
Compute the convolution of *src* with *filter*. If =-1 then
compute the deconvolution.
Assumes filter is given in "wrap-around" order, with countup
``t=0..t=n/2`` followed by countdown ``t=n..t=n/2``. The size of *filter*
should be an odd <= the size of *v1*>.
Example:
.. code-block::
python
v1 = h.Vector(16)
v2 = h.Vector(16)
v3 = h.Vector()
v1[5] = v1[6] = 1
v2[3] = v2[4] = 3
v3.convlv(v1, v2)
v1.printf()
v2.printf()
v3.printf()
----
.. method:: Vector.spctrm
Syntax:
``obj = vdest.spctrm(vsrc)``
Description:
Return the power spectral density function of vsrc.
----
.. method:: Vector.filter
Syntax:
``obj = vdest.filter(src,filter)``
``obj = vsrcdest.filter(filter)``
Description:
Digital filter implemented by taking the inverse fft of
*filter* and convolving it with *vec1*. *vec* and *vec1*
are in the time
domain and *filter* is in the frequency domain.
----
.. method:: Vector.fft
Syntax:
``obj = vdest.fft(vsrc, sign)``
``obj = vsrcdest.fft(sign)``
Description:
Compute the fast fourier transform of the source data vector. If
*sign*\ =-1 then compute the inverse fft.
If vsrc.\ :meth:`~Vector.size` is not an integral power of 2, it is padded with 0's to
the next power of 2 size.
The complex frequency domain is represented in the vector as pairs of
numbers --- except for the first two numbers.
vec[0] is the amplitude of the 0 frequency cosine (constant)
and vec[1] is the amplitude of the highest (N/2) frequency cosine
(ie. alternating 1,-1's in the time domain)
vec[2, 3] is the amplitude of the cos(2*PI*i/n), sin(2*PI*i/n) components
(ie. one whole wave in the time domain)
vec[n-2, n-1] is the amplitude of the cos(PI*(n-1)*i/n), sin(PI*(n-1)*i/n)
components. The following example of a pure time domain sine wave
sampled at 16 points should be played with to see where
the specified frequency appears in the frequency domain vector (note that if the
frequency is greater than 8, aliasing will occur, ie sampling makes it appear
as a lower frequency)
Also note that the forward transform does not produce the amplitudes of
the frequency components that goes up to make the time domain function but
instead each element is the integral of the product of the time domain
function and a specific pure frequency. Thus the 0 and highest frequency
cosine are N times the amplitudes and all others are N/2 times the amplitudes.
.. code-block::
python
from neuron import h, gui
N = 16 # should be a power of 2
class MyGUI:
def __init__(self):
self.c = 1
self.f = 1 # waves per domain, max is N/2
self.box = h.VBox()
self.box.intercept(1)
h.xpanel('', 1)
h.xradiobutton('sin ', lambda: self.p(0))
h.xradiobutton('cos ', lambda: self.p(1), 1)
h.xvalue('freq (waves/domain)', (self, 'f'), 1, lambda: self.p(self.c))
h.xpanel()
self.g1 = h.Graph()
self.g2 = h.Graph()
self.g3 = h.Graph()
self.box.intercept(0)
self.box.map()
self.g1.size(0, N, -1, 1)
self.g2.size(0, N, -N, N)
self.g3.size(0, N, -N, N)
self.p(self.c)
def p(self, c):
self.v1 = h.Vector(N)
self.v1.sin(self.f, c * h.PI / 2, 1000. / N)
self.v1.plot(self.g1)
self.v2 = h.Vector()
self.v2.fft(self.v1, 1) # forward
self.v2.plot(self.g2)
self.v3 = h.Vector()
self.v3.fft(self.v2, -1) # inverse
self.v3.plot(self.g3) # amplitude N/2 times the original
gui = MyGUI()
.. image:: ../../images/fft1.png
:align: center
The inverse fft is mathematically almost identical
to the forward transform but often
has a different operational interpretation. In this
case the result is a time domain function which is merely the sum
of all the pure sinusoids weighted by the (complex) frequency function
(although, remember, points 0 and 1 in the frequency domain are special,
being the constant and the highest alternating cosine, respectively).
The example below shows the index of a particular frequency and phase
as well as the time domain pattern. Note that index 1 is for the higest
frequency cosine instead of the 0 frequency sin.
Because the frequency domain representation is something only a programmer
could love, and because one might wish to plot the real and imaginary
frequency spectra, one might wish to encapsulate the fft in a function
which uses a more convenient representation.
Below is an alternative FFT function where the frequency
values are spectrum amplitudes (no need to divide anything by N)
and the real and complex frequency components are
stored in separate vectors (of length N/2 + 1).
Consider the functions
.. code-block::
python
FFT(1, vt_src, vfr_dest, vfi_dest)
FFT(-1, vt_dest, vfr_src, vfi_src)
The forward transform (first arg = 1) requires
a time domain source vector with a length of N = 2^n where n is some positive
integer. The resultant real (cosine amplitudes) and imaginary (sine amplitudes)
frequency components are stored in the N/2 + 1
locations of the vfr_dest and vfi_dest vectors respectively (Note:
vfi_dest[0] and vfi_dest[N/2] are always set to 0. The index i in the
frequency domain is the number of full pure sinusoid waves in the time domain.
ie. if the time domain has length T then the frequency of the i'th component
is i/T.
The inverse transform (first arg = -1) requires two freqency domain
source vectors for the cosine and sine amplitudes. The size of these
vectors must be N/2+1 where N is a power of 2. The resultant time domain
vector will have a size of N.
If the source vectors are not a power of 2, then the vectors are padded
with 0's til vtsrc is 2^n or vfr_src is 2^n + 1. The destination vectors
are resized if necessary.
This function has the property that the sequence
.. code-block::
python
FFT(1, vt, vfr, vfi)
FFT(-1, vt, vfr, vfi)
leaves vt unchanged. Reversal of the order would leave vfr and vfi unchanged.
The implementation is:
.. code-block::
python
def FFT(direction, vt, vfr, vfi):
if direction == 1: # forward
vfr.fft(vt, 1)
n = len(vfr)
vfr.div(n/2)
vfr[0] /= 2 # makes the spectrum appear discontinuous
vfr[1] /= 2 # but the amplitudes are intuitive
vfi.copy(vfr, 0, 1, -1, 1, 2) # odd elements
vfr.copy(vfr, 0, 0, -1, 1, 2) # even elements
vfr.resize(n/2+1)
vfi.resize(n/2+1)
vfr[n/2] = vfi[0] #highest cos started in vfr[1]
vfi[0] = vfi[n/2] = 0 # weights for sin(0*i)and sin(PI*i)
else: # inverse
# shuffle vfr and vfi into vt
n = len(vfr)
vt.copy(vfr, 0, 0, n-2, 2, 1)
vt[1] = vfr[n-1]
vt.copy(vfi, 3, 1, n-2, 2, 1)
vt[0] *= 2
vt[1] *= 2
vt.fft(vt, -1)
If you load the previous example so that FFT is defined, the following
example shows the cosine and sine spectra of a pulse.
.. code-block::
python
from neuron import h, gui
N = 128
class MyGUI:
def __init__(self):
self.delay = 0
self.duration = N / 2
self.box = h.VBox()
self.box.intercept(1)
h.xpanel('')
h.xvalue('delay (points)', (self, 'delay'), 1, self.p)
h.xvalue('duration (points)', (self, 'duration'), 1, self.p)
h.xpanel()
self.g1 = h.Graph()
self.b1 = h.HBox()
self.b1.intercept(1)
self.g2 = h.Graph()
self.g3 = h.Graph()
self.b1.intercept(0)
self.b1.map()
self.g4 = h.Graph()
self.box.intercept(0)
self.box.map()
self.g1.size(0, N, -1, 1)
self.g2.size(0, N / 2, -1, 1)
self.g3.size(0, N / 2, -1, 1)
self.g4.size(0, N, -1, 1)
self.p()
def p(self):
self.v1 = h.Vector(N)
self.v1.fill(1, self.delay, self.delay + self.duration - 1)
self.v1.plot(self.g1)
self.v2 = h.Vector()
self.v3 = h.Vector()
FFT(1, self.v1, self.v2, self.v3)
self.v2.plot(self.g2)
self.v3.plot(self.g3)
self.v4 = h.Vector()
FFT(-1, self.v4, self.v2, self.v3)
self.v4.plot(self.g4)
mygui = MyGUI()
.. image:: ../../images/fft2.png
:align: center
.. seealso::
:func:`fft`, :func:`spctrm`
.. method:: Vector.trigavg
Syntax:
``v1.trigavg(data,trigger,pre,post)``
Description:
Perform an event-triggered average of <*data*> using times given by
<*trigger*>. The duration of the average is from -<*pre*> to <*post*>.
This is useful, for example, in calculating a spike triggered stimulus
average.
----
.. method:: Vector.spikebin
Syntax:
``v.spikebin(data,thresh)``
Description:
Used to make a binary version of a spike train. <*data*> is a vector
of membrane potential. <*thresh*> is the voltage threshold for spike
detection. <*v*> is set to all zeros except at the onset of spikes
(the first dt which the spike crosses threshold)
----
.. method:: Vector.psth
Syntax:
``vmeanfreq = vdest.psth(vsrchist,dt,trials,size)``
Description:
The name of this function is somewhat misleading, since its
input, vsrchist, is a finely-binned post-stimulus time histogram,
and its output, vdest, is an array whose elements are the mean
frequencies f_mean[i] that correspond to each bin of vsrchist.
For bin i, the corresponding mean frequency f_mean[i] is
determined by centering an adaptive square window on i and
widening the window until the number of spikes under the
window equals size. Then f_mean[i] is calculated as
``f_mean[i] = N[i] / (m dt trials)``
where
.. code-block::
python
f_mean[i] is in spikes per _second_ (Hz).
N[i] = total number of events in the window
centered on bin i
m = total number of bins in the window
centered on bin i
dt = binwidth of vsrchist in _milliseconds_
(so m dt is the width of the window in milliseconds)
trials = an integer scale factor
trials is used to adjust for the number of traces that were
superimposed to compute the elements of vsrchist. In other words,
suppose the elements of vsrchist were computed by adding up the
number of spikes in n traces
.. math::
v1[i] = \sum_{j=1}^n {\text{number of spikes in bin i of trace j}}
Then trials would be assigned the value n. Of course, if
the elements of vsrchist are divided by n before calling psth(),
then trials should be set to 1.
Acknowledgment:
The documentation and example for psth was prepared by Ted Carnevale.
.. warning::
The total number of spikes in vsrchist must be greater than size.
Example:
.. code-block::
python
from neuron import h, gui
b = h.VBox()
b.intercept(1)
g1 = h.Graph()
g1.size(0,200,0,10)
g2 = h.Graph()
g2.size(0,200,0,10)
b.intercept(0)
b.map("psth and mean freq")
VECSIZE = 200
MINSUM = 50
DT = 1000 # ms per bin of v1 (vsrchist)
TRIALS = 1
v1 = h.Vector(VECSIZE)
r = h.Random()
for ii in range(VECSIZE):
v1[ii] = int(r.uniform(0, 10))
v1.plot(g1)
v2 = h.Vector()
v2.psth(v1, DT, TRIALS, MINSUM)
v2.plot(g2)
.. image:: ../../images/vector-psth.png
:align: center
----
.. method:: Vector.inf
Syntax:
``v.inf(i,dt,gl,el,cm,th,res,[ref])``
Description:
Simulate a leaky integrate and fire neuron. <*i*> is a vector containing
the input. <*dt*> is the timestep. <*gl*> and <*el*> are the conductance
and reversal potential of the leak term <*cm*> is capacitance. <*th*>
is the threshold voltage and <*res*> is the reset voltage. <*ref*>, if
present sets the duration of ab absolute refractory period.
N.b. Currently working with forward Euler integration, which may give
spurious results.
----
.. method:: Vector.resample
Syntax:
``v1.resample(v2,rate)``
Description:
Resamples the vector at another rate -- integers work best.
.. seealso::
:func:`copy`