def testInhAdaptingMarkov(self):
stg = stgen.StGen()
from numpy import array
t = array([0.0, 5000.0])
# using parameters from paper
a = array([23.18, 47.24])
bq = array([0.10912, 0.09794]) * 14.48
# expected mean firing rates for 2D case
alpha = array([7.60, 10.66])
tau = 110.0
t_stop = 10000.0
st = stg.inh_adaptingmarkov_generator(a, bq, tau, t, t_stop)
assert isinstance(st, signals.SpikeTrain)
st = stg.inh_adaptingmarkov_generator(a,
bq,
tau,
t,
t_stop,
array=True)
assert isinstance(st, numpy.ndarray)
assert st[-1] < t_stop
assert st[0] > t[0]
def testStatsInhGamma(self):
# this is a statistical test with non-zero chance of failure
from numpy import array
import neurotools.signals
stg = stgen.StGen()
# gamma step rate= 100Hz -> 200Hz, a = 3.0
st = stg.inh_gamma_generator(
array([3.0, 3.0]),
array([1.0 / 100.0 / 3.0, 1.0 / 200.0 / 3.0]),
array([500.0, 1500.0]),
2500.0,
array=True)
assert (type(st) == numpy.ndarray)
n1 = len(st[st < 1500.0])
n2 = len(st[st > 1500.0])
err = """
Number of spikes should be within 3 standard deviations of mean.
There is a non-zero chance for this to occur during normal operation.
Re-run the test to see if the error persists."""
if n2 > 200.0 + 3.0 * numpy.sqrt(
200.0) or n2 < 200.0 - 3.0 * numpy.sqrt(200.0):
raise StatisticalError(err)
if n1 > 100.0 + 3.0 * numpy.sqrt(
100.0) or n1 < 100.0 - 3.0 * numpy.sqrt(100.0):
raise StatisticalError(err)
def testStatsOUGen(self):
# this is a statistical test with non-zero chance of failure
from numpy import array
stg = stgen.StGen()
(ou, t) = stg.OU_generator(0.1,
10.0,
2.0,
10.0,
500.0,
1500.0,
array=True)
def testInh2DAdaptingMarkov(self):
stg = stgen.StGen()
from numpy import array
t = array([0.0, 10000.0])
# using parameters from paper
a = array([23.18, 47.24])
bq = array([0.10912, 0.09794]) * 14.48
# expected mean firing rates for 2D case
alpha = array([7.60, 10.66])
tau_s = 110.0
tau_r = 1.97
qrqs = 221.96
t_stop = 20000.0
st = stg.inh_2Dadaptingmarkov_generator(a, bq, tau_s, tau_r, qrqs, t,
t_stop)
assert isinstance(st, signals.SpikeTrain)
st = stg.inh_2Dadaptingmarkov_generator(a,
bq,
tau_s,
tau_r,
qrqs,
t,
t_stop,
array=True)
assert isinstance(st, numpy.ndarray)
assert st[-1] < t_stop
assert st[0] > t[0]
spikes1 = len(st[st < 10000])
spikes2 = len(st[st > 10000])
# should be approximately alpha[0]*10.0 spikes
assert numpy.clip(spikes1, 60, 100) == spikes1
# should be approximately alpha[1]*10.0 spikes
assert numpy.clip(spikes2, 80, 140) == spikes2
def test_poisson(rate, t_start, t_stop):
stg = stgen.StGen()
dt = t_stop - t_start
N = rate * dt / 1000.0
st = stg.poisson_generator(rate,
t_start=t_start,
t_stop=t_stop,
array=True)
if len(st) in (0, 1, 2, 3):
assert N < 15
return
assert st[-1] < t_stop
assert st[0] > t_start
# last spike should not be more than 4 ISI away from t_stop
err = """
Last spike should not be more than 4 ISI behind t_stop.
There is a non-zero chance for this to occur during normal operation.
Re-run the test to see if the error persists."""
if st[-1] < t_stop - 4.0 * 1.0 / rate * 1000.0:
raise StatisticalError(err)
# first spike should not be more than 4 ISI away from t_start
err = """
First spike should not be more than 4 ISI in front of t_start.
There is a non-zero chance for this to occur during normal operation.
Re-run the test to see if the error persists."""
if st[0] > t_start + 4.0 * 1.0 / rate * 1000.0:
raise StatisticalError(err)
err = """
Number of spikes should be within 3 standard deviations of mean.
There is a non-zero chance for this to occur during normal operation.
Re-run the test to see if the error persists."""
if len(st) > N + 3.0 * numpy.sqrt(N) or len(
st) < N - 3.0 * numpy.sqrt(N):
raise StatisticalError(err)
def testShotNoiseFromSpikes(self):
stg = stgen.StGen()
from numpy import array
st = stg.poisson_generator(10.0, 0.0, 1000.0)
ge = stgen.shotnoise_fromspikes(st, 2.0, 10.0, dt=0.1)
assert ge.t_start == 0.0
assert ge.t_stop == 1000.0
st = stg.poisson_generator(10.0, 0.0, 1000.0)
ge = stgen.shotnoise_fromspikes(st,
2.0,
10.0,
dt=0.1,
t_start=500.0,
t_stop=1500.0)
assert ge.t_start == 500.0
assert ge.t_stop == 1500.0
def testMethodsBasic(self):
stg = stgen.StGen()
rate = 100.0 #Hz
t_stop = 1000.0 # milliseconds
st = stg.poisson_generator(rate, 0.0, t_stop)
assert isinstance(st, signals.SpikeTrain)
st = stg.poisson_generator(rate, 0.0, t_stop, array=True)
assert isinstance(st, numpy.ndarray)
st = stg.poisson_generator(rate, 0.0, t_stop, array=True, debug=True)
assert isinstance(st[0], numpy.ndarray)
assert isinstance(st[1], list)
st = stg.poisson_generator(rate, 0.0, t_stop, debug=True)
assert isinstance(st[0], signals.SpikeTrain)
assert isinstance(st[1], list)
def testInhGammaBasic(self):
from numpy import array
import neurotools.signals
stg = stgen.StGen()
st = stg.inh_gamma_generator(
array([3.0, 3.0]),
array([1.0 / 100.0 / 3.0, 1.0 / 200.0 / 3.0]),
array([500.0, 1500.0]),
2500.0,
array=True)
assert (type(st) == numpy.ndarray)
st = stg.inh_gamma_generator(
array([3.0, 3.0]),
array([1.0 / 100.0 / 3.0, 1.0 / 200.0 / 3.0]),
array([500.0, 1500.0]),
2500.0,
array=False)
assert (type(st) == neurotools.signals.spikes.SpikeTrain)
def testCreateWithNoArgs(self):
"""
With no arguments for the constructor, we should get a Numpy RNG.
"""
stg = stgen.StGen()
assert isinstance(stg.rng, numpy.random.RandomState)
def testStatsInhPoisson(self):
# this is a statistical test with non-zero chance of failure
stg = stgen.StGen()
rate = 100.0 #Hz
t_start = 500.0
t_stop = 1500.0 # milliseconds
st = stg.inh_poisson_generator(numpy.array([rate]),
numpy.array([t_start]),
t_stop,
array=True)
assert st[-1] < t_stop
assert st[0] > t_start
# last spike should not be more than 4 ISI away from t_stop
err = """
Last spike should not be more than 4 ISI behind t_stop.
There is a non-zero chance for this to occur during normal operation.
Re-run the test to see if the error persists."""
if st[-1] < t_stop - 4.0 * 1.0 / rate * 1000.0:
raise StatisticalError(err)
# first spike should not be more than 4 ISI away from t_start
err = """
First spike should not be more than 4 ISI in front of t_start.
There is a non-zero chance for this to occur during normal operation.
Re-run the test to see if the error persists."""
if st[0] > t_start + 4.0 * 1.0 / rate * 1000.0:
raise StatisticalError(err)
err = """
Number of spikes should be within 3 standard deviations of mean.
There is a non-zero chance for this to occur during normal operation.
Re-run the test to see if the error persists."""
# time interval is one second
if len(st) > rate + 3.0 * numpy.sqrt(rate) or len(
st) < rate - 3.0 * numpy.sqrt(rate):
raise StatisticalError(err)
# step in the rate
st = stg.inh_poisson_generator(numpy.array([100.0, 200.0]),
numpy.array([500.0, 1500.0]),
2500.0,
array=True)
n1 = len(st[st < 1500.0])
n2 = len(st[st > 1500.0])
err = """
Number of spikes should be within 3 standard deviations of mean.
There is a non-zero chance for this to occur during normal operation.
Re-run the test to see if the error persists."""
if n2 > 200.0 + 3.0 * numpy.sqrt(
200.0) or n2 < 200.0 - 3.0 * numpy.sqrt(200.0):
raise StatisticalError(err)
if n1 > 100.0 + 3.0 * numpy.sqrt(
100.0) or n1 < 100.0 - 3.0 * numpy.sqrt(100.0):
raise StatisticalError(err)