Package PyDSTool :: Package Toolbox :: Module neuralcomp
[hide private]
[frames] | no frames]

Module neuralcomp

source code


    An example set of basic compartmental model ModelSpec classes
    for use in computational neuroscience modeling.

    Basic classes provided:
    channel, compartment

    For multi-compartment models:
    soma, dendr_compartment, synapse, neuron, network, exc_synapse, inh_synapse

Rob Clewley, September 2005.

Classes [hide private]
  compatODEComponent
  compatODELeafComponent
  channel
  compartment
  channel_on
  channel_off
  soma
  dendr_compartment
  neurite_compartment
  neuron
  synapse
  exc_synapse
  inh_synapse
  pnnetwork
  network
Functions [hide private]
 
disconnectSynapse(syn, mspec)
Disconnect synapse object syn from ModelSpec object mspec in which it has been declared, and remove its target cell's synaptic channels.
source code
 
connectWithSynapse(synname, syntypestr, source_cell, dest_cell, dest_compartment_name='', threshfun=None, alpha=None, beta=None, threshfun_d=None, alpha_d=None, beta_d=None, adapt_typestr=None, vrev=None, g=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>)
Make a chemical or electrical synapse between two neurons.
source code
 
makeSynapseChannel(name, gatevarname=None, voltage='V', typestr=None, vrev=None, g=None, parlist=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None)
Make a chemical or electrical (gap junction) synapse channel in a soma.
source code
 
makeExtInputCurrentChannel(name, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None)
External input signal used directly as a current.
source code
 
makeExtInputConductanceChannel(name, voltage='V', g=None, vrev=None, parlist=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None)
External input signal used as a conductance.
source code
 
makeFunctionConductanceChannel(name, parameter_name, func_def_str, voltage='V', g=None, vrev=None, parlist=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None)
Explicit function waveform used as a conductance, e.g.
source code
 
makeBiasChannel(name, I=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None)
Constant bias / applied current "channel".
source code
 
makeChannel_halfact(name, voltage='V', s=None, isinstant=False, sinf=None, taus=None, spow=1, s2=None, isinstant2=False, sinf2=None, taus2=None, spow2=1, vrev=None, g=None, parlist=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None, nonlocal_variables=None)
Make an ionic membrane channel using the steady state and rate function formalism.
source code
 
makeChannel_rates(name, voltage='V', s=None, isinstant=False, arate=None, brate=None, spow=1, s2=None, isinstant2=False, arate2=None, brate2=None, spow2=1, vrev=None, g=None, parlist=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None, nonlocal_variables=None)
Make an ionic membrane channel using the forward and backward rate formalism.
source code
 
makeSoma(name, voltage='V', channelList=None, C=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.soma'>, channelclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>)
Build a soma type of "compartment" from a list of channels and a membrane capacitance C, using the local voltage name.
source code
 
makeDendrite(name, voltage='V', channelList=None, C=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.dendr_compartment'>, channelclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>)
Build a dendrite type of "compartment" from a list of channels and a membrane capacitance C, using the local voltage name.
source code
 
makeNeurite(name, voltage='V', channelList=None, C=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.dendr_compartment'>, channelclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>)
Build a neurite type of "compartment" from a list of channels and a membrane capacitance C, using the local voltage name.
source code
 
makePointNeuron(name, voltage='V', channelList=None, synapseList=None, C=None, noauxs=True)
Factory function for single compartment neurons ("point neurons").
source code
 
makePointNeuronNetwork(name, componentList)
Factory function returning a pnnetwork type object from a list of compatible components (somas and synapses).
source code
 
makeNeuronNetwork(name, neuronList)
Factory function returning a network type object from a list of compatible components (neurons).
source code
 
makeSynapse(name, gatevar, precompartment, typestr, threshfun=None, alpha=None, beta=None, targetchannel=None, evalopt=True, noauxs=True)
Make a chemical synapse channel object.
source code
 
makeAdaptingSynapse(name, gatevar, adaptvar, precompartment, typestr, adapt_typestr, threshfun=None, alpha=None, beta=None, threshfun_d=None, alpha_d=None, beta_d=None, targetchannel=None, evalopt=True, noauxs=True)
Make an adapting chemical synapse channel object.
source code
 
makePowerSpec(v, p) source code
 
makePar(parname, val=None) source code
 
makeFun(funname, sig, defn) source code
Variables [hide private]
  compatGens = ['Radau_ODEsystem', 'ADMC_ODEsystem', 'Vode_ODEsy...
  voltage = 'V'
  V = Var V (ExpFuncSpec)
  ALLOW_THREADS = 1
  Abs = Abs (ModelSpec wrapper)
  Acos = Acos (ModelSpec wrapper)
  Asin = Asin (ModelSpec wrapper)
  Atan = Atan (ModelSpec wrapper)
  Atan2 = Atan2 (ModelSpec wrapper)
  BUFSIZE = 10000
  Betavariate = Betavariate (ModelSpec wrapper)
  CLIP = 0
  Ceil = Ceil (ModelSpec wrapper)
  Choice = Choice (ModelSpec wrapper)
  Cos = Cos (ModelSpec wrapper)
  Cosh = Cosh (ModelSpec wrapper)
  Degrees = Degrees (ModelSpec wrapper)
  E = QuantSpec e (ExpFuncSpec)
  ERR_CALL = 3
  ERR_DEFAULT = 0
  ERR_DEFAULT2 = 2084
  ERR_IGNORE = 0
  ERR_LOG = 5
  ERR_PRINT = 4
  ERR_RAISE = 2
  ERR_WARN = 1
  Exp = Exp (ModelSpec wrapper)
  Expovariate = Expovariate (ModelSpec wrapper)
  FLOATING_POINT_SUPPORT = 1
  FPE_DIVIDEBYZERO = 1
  FPE_INVALID = 8
  FPE_OVERFLOW = 2
  FPE_UNDERFLOW = 4
  Fabs = Fabs (ModelSpec wrapper)
  False_ = False
  Floor = Floor (ModelSpec wrapper)
  Fmod = Fmod (ModelSpec wrapper)
  Frexp = Frexp (ModelSpec wrapper)
  Gammavariate = Gammavariate (ModelSpec wrapper)
  Gauss = Gauss (ModelSpec wrapper)
  Getrandbits = Getrandbits (ModelSpec wrapper)
  Getstate = Getstate (ModelSpec wrapper)
  Hypot = Hypot (ModelSpec wrapper)
  Infinity = inf
  Jumpahead = Jumpahead (ModelSpec wrapper)
  Ldexp = Ldexp (ModelSpec wrapper)
  Log = Log (ModelSpec wrapper)
  Log10 = Log10 (ModelSpec wrapper)
  Lognormvariate = Lognormvariate (ModelSpec wrapper)
  MAXDIMS = 32
  Max = Max (ModelSpec wrapper)
  Min = Min (ModelSpec wrapper)
  Modf = Modf (ModelSpec wrapper)
  NAN = nan
  NINF = -inf
  NZERO = -0.0
  Normalvariate = Normalvariate (ModelSpec wrapper)
  PINF = inf
  PZERO = 0.0
  Paretovariate = Paretovariate (ModelSpec wrapper)
  Pi = QuantSpec pi (ExpFuncSpec)
  Pow = Pow (ModelSpec wrapper)
  RAISE = 2
  Radians = Radians (ModelSpec wrapper)
  Randint = Randint (ModelSpec wrapper)
  Random = Random (ModelSpec wrapper)
  Randrange = Randrange (ModelSpec wrapper)
  SHIFT_DIVIDEBYZERO = 0
  SHIFT_INVALID = 9
  SHIFT_OVERFLOW = 3
  SHIFT_UNDERFLOW = 6
  Sample = Sample (ModelSpec wrapper)
  ScalarType = (<type 'int'>, <type 'float'>, <type 'complex'>, ...
  Seed = Seed (ModelSpec wrapper)
  Setstate = Setstate (ModelSpec wrapper)
  Shuffle = Shuffle (ModelSpec wrapper)
  Sin = Sin (ModelSpec wrapper)
  Sinh = Sinh (ModelSpec wrapper)
  Sqrt = Sqrt (ModelSpec wrapper)
  Sum = Sum (ModelSpec wrapper)
  Systemrandom = Systemrandom (ModelSpec wrapper)
  Tan = Tan (ModelSpec wrapper)
  Tanh = Tanh (ModelSpec wrapper)
  True_ = True
  UFUNC_BUFSIZE_DEFAULT = 10000
  UFUNC_PYVALS_NAME = 'UFUNC_PYVALS'
  Uniform = Uniform (ModelSpec wrapper)
  Vonmisesvariate = Vonmisesvariate (ModelSpec wrapper)
  WRAP = 1
  Weibullvariate = Weibullvariate (ModelSpec wrapper)
  Wichmannhill = Wichmannhill (ModelSpec wrapper)
  absolute = <ufunc 'absolute'>
  add = <ufunc 'add'>
  bitwise_and = <ufunc 'bitwise_and'>
  bitwise_not = <ufunc 'invert'>
  bitwise_or = <ufunc 'bitwise_or'>
  bitwise_xor = <ufunc 'bitwise_xor'>
  c_ = <numpy.lib.index_tricks.CClass object at 0x115c330>
  cast = {<type 'numpy.int64'>: <function <lambda> at 0x6d0c30>,...
  conj = <ufunc 'conjugate'>
  conjugate = <ufunc 'conjugate'>
  copysign = <ufunc 'copysign'>
  deg2rad = <ufunc 'deg2rad'>
  divide = <ufunc 'divide'>
  equal = <ufunc 'equal'>
  exp2 = <ufunc 'exp2'>
  expm1 = <ufunc 'expm1'>
  floor_divide = <ufunc 'floor_divide'>
  fmax = <ufunc 'fmax'>
  fmin = <ufunc 'fmin'>
  greater_equal = <ufunc 'greater_equal'>
  index_exp = <numpy.lib.index_tricks.IndexExpression object at ...
  inf = inf
  infty = inf
  invert = <ufunc 'invert'>
  isinf = <ufunc 'isinf'>
  left_shift = <ufunc 'left_shift'>
  little_endian = True
  log1p = <ufunc 'log1p'>
  logaddexp = <ufunc 'logaddexp'>
  logaddexp2 = <ufunc 'logaddexp2'>
  logical_and = <ufunc 'logical_and'>
  logical_not = <ufunc 'logical_not'>
  logical_xor = <ufunc 'logical_xor'>
  maximum = <ufunc 'maximum'>
  mgrid = <numpy.lib.index_tricks.nd_grid object at 0x1147490>
  minimum = <ufunc 'minimum'>
  multiply = <ufunc 'multiply'>
  n = 9
  nan = nan
  nbytes = {<type 'numpy.int64'>: 8, <type 'numpy.int16'>: 2, <t...
  negative = <ufunc 'negative'>
  newaxis = None
  nextafter = <ufunc 'nextafter'>
  not_equal = <ufunc 'not_equal'>
  ogrid = <numpy.lib.index_tricks.nd_grid object at 0x1147bb0>
  ones_like = <ufunc 'ones_like'>
  r_ = <numpy.lib.index_tricks.RClass object at 0x115c2f0>
  rad2deg = <ufunc 'rad2deg'>
  reciprocal = <ufunc 'reciprocal'>
  remainder = <ufunc 'remainder'>
  right_shift = <ufunc 'right_shift'>
  rint = <ufunc 'rint'>
  s_ = <numpy.lib.index_tricks.IndexExpression object at 0x115c3f0>
  sctypeDict = {0: <type 'numpy.bool_'>, 1: <type 'numpy.int8'>,...
  sctypeNA = {'?': 'Bool', 'B': 'UInt8', 'Bool': <type 'numpy.bo...
  sctypes = {'complex': [<type 'numpy.complex64'>, <type 'numpy....
  signbit = <ufunc 'signbit'>
  spacing = <ufunc 'spacing'>
  square = <ufunc 'square'>
  subtract = <ufunc 'subtract'>
  t = '0'
  true_divide = <ufunc 'true_divide'>
  trunc = <ufunc 'trunc'>
  typeDict = {0: <type 'numpy.bool_'>, 1: <type 'numpy.int8'>, 2...
  typeNA = {'?': 'Bool', 'B': 'UInt8', 'Bool': <type 'numpy.bool...
  typecodes = {'All': '?bhilqpBHILQPfdgFDGSUVOMm', 'AllFloat': '...
Function Details [hide private]

connectWithSynapse(synname, syntypestr, source_cell, dest_cell, dest_compartment_name='', threshfun=None, alpha=None, beta=None, threshfun_d=None, alpha_d=None, beta_d=None, adapt_typestr=None, vrev=None, g=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>)

source code 
Make a chemical or electrical synapse between two neurons. For
gap junctions this function returns None, otherwise will return the
synapse gating variable object.

Valid source_cell and dest_cell is a neuron or, for point neurons only,
  a soma is allowed.
The optional argument dest_compartment_name is a declared name in
dest_cell, or by default will be the soma.

The standard assumed rate equation for a chemical synapse conductance is

   s' = (1-alpha) * s - beta * s

Use the _d versions of threshfun, alpha, and beta, for an adapting chemical
synapse type's adapting variable.

makeSynapseChannel(name, gatevarname=None, voltage='V', typestr=None, vrev=None, g=None, parlist=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None)

source code 

Make a chemical or electrical (gap junction) synapse channel in a soma. To select these, the typestr argument must be one of 'exc', 'inh', or 'gap'. For a user-defined chemical synapse use a different string name for typestr. For gap junctions, use the pre-synaptic membrane potential's soma ModelSpec object or its full hierarchical name string for the argument vrev.

Returns a channel-type object by default, or some user-defined subclass if desired.

makeExtInputCurrentChannel(name, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None)

source code 

External input signal used directly as a current. Supply the Input having coordinate name 'ext_input'.

Returns a channel-type object by default, or some user-defined subclass if desired.

makeExtInputConductanceChannel(name, voltage='V', g=None, vrev=None, parlist=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None)

source code 

External input signal used as a conductance. Supply the Input having coordinate name 'ext_input'.

Returns a channel-type object by default, or some user-defined subclass if desired.

makeFunctionConductanceChannel(name, parameter_name, func_def_str, voltage='V', g=None, vrev=None, parlist=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None)

source code 

Explicit function waveform used as a conductance, e.g. for an alpha-function post-synaptic response. Creates a time-activated event to trigger the waveform based on a named parameter. The function will be a function of (local/relative) time t only.

Returns a channel-type object by default, or some user-defined subclass if desired.

makeBiasChannel(name, I=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None)

source code 

Constant bias / applied current "channel".

Returns a channel-type object by default, or some user-defined subclass if desired.

makeChannel_halfact(name, voltage='V', s=None, isinstant=False, sinf=None, taus=None, spow=1, s2=None, isinstant2=False, sinf2=None, taus2=None, spow2=1, vrev=None, g=None, parlist=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None, nonlocal_variables=None)

source code 
Make an ionic membrane channel using the steady state and rate function formalism.

i.e., that the gating variable s has a differential equation in the form:
   s' = (sinf(v) - v)/taus(v)
The channel may have up to two gating variables, each of which is given by an ODE.

If either gating variable has its corresponding 'isinstant' argument set to True, then
that variable is set to be instananeous (algebraic, not an ODE), i.e. s = sinf(v). The
taus function will then be ignored.

The resulting channel current will be of the form
    name.I = g * s^spow * s2^spow2 * (voltage - vrev)

Provide any additional Par or Fun objects necessary for the complete definition of the
channel kinetics in the optional parlist argument.

nonlocal_variables list (optional) provides string names of any dynamic variables
referenced in the declared specifications that are not local to this channel.

Returns a channel-type object by default, or some user-defined subclass if desired.

makeChannel_rates(name, voltage='V', s=None, isinstant=False, arate=None, brate=None, spow=1, s2=None, isinstant2=False, arate2=None, brate2=None, spow2=1, vrev=None, g=None, parlist=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>, gamma1=None, gamma2=None, nonlocal_variables=None)

source code 
Make an ionic membrane channel using the forward and backward rate formalism.

i.e., that the gating variable s has a differential equation in the form:
   s' = sa(v) * (1-s) - sb(v) * s
The channel may have up to two gating variables, each of which is given by an ODE.

If either gating variable has its corresponding 'isinstant' argument set to True, then
that variable is set to be instananeous (algebraic, not an ODE),
i.e. s = sinf(v) = a(v) / (a(v) + b(v))

The resulting channel current will be of the form
    name.I = g * s^spow * s2^spow2 * (voltage - vrev)

Provide any additional Par or Fun objects necessary for the complete definition of the
channel kinetics in the optional parlist argument.

nonlocal_variables list (optional) provides string names of any dynamic variables
referenced in the declared specifications that are not local to this channel.

Returns a channel-type object by default, or some user-defined subclass if desired.

makeSoma(name, voltage='V', channelList=None, C=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.soma'>, channelclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>)

source code 

Build a soma type of "compartment" from a list of channels and a membrane capacitance C, using the local voltage name.

Specify a sub-class for the channel to target specific types to summate, in case the user wants to have runtime control over which are "active" in the dynamics (mainly related to the DSSRT toolbox).

Returns a soma-type object by default, or some user-defined subclass if desired.

makeDendrite(name, voltage='V', channelList=None, C=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.dendr_compartment'>, channelclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>)

source code 

Build a dendrite type of "compartment" from a list of channels and a membrane capacitance C, using the local voltage name.

Specify a sub-class for the channel to target specific types to summate, in case the user wants to have runtime control over which are "active" in the dynamics (mainly related to the DSSRT toolbox).

Returns a dendrite-type object by default, or some user-defined subclass if desired.

makeNeurite(name, voltage='V', channelList=None, C=None, noauxs=True, subclass=<class 'PyDSTool.Toolbox.neuralcomp.dendr_compartment'>, channelclass=<class 'PyDSTool.Toolbox.neuralcomp.channel'>)

source code 

Build a neurite type of "compartment" from a list of channels and a membrane capacitance C, using the local voltage name.

Specify a sub-class for the channel to target specific types to summate, in case the user wants to have runtime control over which are "active" in the dynamics (mainly related to the DSSRT toolbox).

Returns a neurite-type object by default, or some user-defined subclass if desired.

makePointNeuron(name, voltage='V', channelList=None, synapseList=None, C=None, noauxs=True)

source code 

Factory function for single compartment neurons ("point neurons").

Returns a neuron type object from a list of ion channels and synapses (if any are defined). The ion channels will internally be built into a soma compartment type on the fly.

makeNeuronNetwork(name, neuronList)

source code 

Factory function returning a network type object from a list of compatible components (neurons). The neurons can be single or multi-compartmental.

Currently untested!


Variables Details [hide private]

compatGens

Value:
['Radau_ODEsystem',
 'ADMC_ODEsystem',
 'Vode_ODEsystem',
 'Euler_ODEsystem',
 'Dopri_ODEsystem']

ScalarType

Value:
(<type 'int'>,
 <type 'float'>,
 <type 'complex'>,
 <type 'long'>,
 <type 'bool'>,
 <type 'str'>,
 <type 'unicode'>,
 <type 'buffer'>,
...

cast

Value:
{<type 'numpy.int64'>: <function <lambda> at 0x6d0c30>, <type 'numpy.i\
nt16'>: <function <lambda> at 0x6d0c70>, <type 'numpy.complex128'>: <f\
unction <lambda> at 0x6d0cb0>, <type 'numpy.uint64'>: <function <lambd\
a> at 0x6d0cf0>, <type 'numpy.complex256'>: <function <lambda> at 0x6d\
0d70>, <type 'numpy.float32'>: <function <lambda> at 0x6d0db0>, <type \
'numpy.bool_'>: <function <lambda> at 0x6d0d30>, <type 'numpy.uint8'>:\
 <function <lambda> at 0x6d0e30>, <type 'numpy.int32'>: <function <lam\
bda> at 0x6d0f30>, <type 'numpy.int8'>: <function <lambda> at 0x6d0df0\
...

index_exp

Value:
<numpy.lib.index_tricks.IndexExpression object at 0x115c3b0>

nbytes

Value:
{<type 'numpy.int64'>: 8, <type 'numpy.int16'>: 2, <type 'numpy.comple\
x128'>: 16, <type 'numpy.uint64'>: 8, <type 'numpy.bool_'>: 1, <type '\
numpy.complex256'>: 32, <type 'numpy.float32'>: 4, <type 'numpy.int8'>\
: 1, <type 'numpy.uint8'>: 1, <type 'numpy.uint16'>: 2, <type 'numpy.o\
bject_'>: 4, <type 'numpy.float64'>: 8, <type 'numpy.int32'>: 4, <type\
 'numpy.string_'>: 0, <type 'numpy.void'>: 0, <type 'numpy.float128'>:\
 16, <type 'numpy.int32'>: 4, <type 'numpy.uint32'>: 4, <type 'numpy.u\
nicode_'>: 0, <type 'numpy.complex64'>: 8, <type 'numpy.uint32'>: 4}

sctypeDict

Value:
{0: <type 'numpy.bool_'>,
 1: <type 'numpy.int8'>,
 2: <type 'numpy.uint8'>,
 3: <type 'numpy.int16'>,
 4: <type 'numpy.uint16'>,
 5: <type 'numpy.int32'>,
 6: <type 'numpy.uint32'>,
 7: <type 'numpy.int32'>,
...

sctypeNA

Value:
{'?': 'Bool',
 'B': 'UInt8',
 'Bool': <type 'numpy.bool_'>,
 'Complex128': <type 'numpy.complex256'>,
 'Complex32': <type 'numpy.complex64'>,
 'Complex64': <type 'numpy.complex128'>,
 'D': 'Complex64',
 'F': 'Complex32',
...

sctypes

Value:
{'complex': [<type 'numpy.complex64'>,
             <type 'numpy.complex128'>,
             <type 'numpy.complex256'>],
 'float': [<type 'numpy.float32'>,
           <type 'numpy.float64'>,
           <type 'numpy.float128'>],
 'int': [<type 'numpy.int8'>,
         <type 'numpy.int16'>,
...

typeDict

Value:
{0: <type 'numpy.bool_'>,
 1: <type 'numpy.int8'>,
 2: <type 'numpy.uint8'>,
 3: <type 'numpy.int16'>,
 4: <type 'numpy.uint16'>,
 5: <type 'numpy.int32'>,
 6: <type 'numpy.uint32'>,
 7: <type 'numpy.int32'>,
...

typeNA

Value:
{'?': 'Bool',
 'B': 'UInt8',
 'Bool': <type 'numpy.bool_'>,
 'Complex128': <type 'numpy.complex256'>,
 'Complex32': <type 'numpy.complex64'>,
 'Complex64': <type 'numpy.complex128'>,
 'D': 'Complex64',
 'F': 'Complex32',
...

typecodes

Value:
{'All': '?bhilqpBHILQPfdgFDGSUVOMm',
 'AllFloat': 'fdgFDG',
 'AllInteger': 'bBhHiIlLqQpP',
 'Character': 'c',
 'Complex': 'FDG',
 'Datetime': 'Mm',
 'Float': 'fdg',
 'Integer': 'bhilqp',
...