def test_tzero_limits(self):
""" Test that the output of ``exponential`` has the correct time-zero """
t = np.arange(-10, 50, step=0.3)
I = exponential(t, tzero=self.tzero, amp=self.amp, tconst=self.tconst)
# Check that all values before time-zero are the amplitude
self.assertTrue(np.all(np.equal(I[t < self.tzero], self.amp)))
self.assertTrue(np.all(np.less(I[t > self.tzero], self.amp)))
def test_exponential_amplitude():
""" Test that the output of ``exponential`` is at most ``amp``. """
tzero = 10 * (random() - 0.5) # between -5 and 5
amp = 5 * random() + 5 # between 5 and 10
tconst = random() + 0.3 # between 0.3 and 1.3
t = np.arange(-10, 50, step=0.3)
I = exponential(t, tzero=tzero, amp=amp, tconst=tconst)
assert np.all(np.less_equal(I, amp))
def test_exponential_positivity():
""" Test that the output of ``exponential`` is always positive. """
tzero = 10 * (random() - 0.5) # between -5 and 5
amp = 5 * random() + 5 # between 5 and 10
tconst = random() + 0.3 # between 0.3 and 1.3
t = np.arange(-10, 50, step=0.3)
I = exponential(t, tzero=tzero, amp=amp, tconst=tconst)
assert np.all(I > 0)
def test_offset(self):
""" Test that the output of ``exponential`` is at lest ``offset``. """
offset = 15
t = np.arange(-10, 50, step=0.3)
I = exponential(t,
tzero=self.tzero,
amp=self.amp,
tconst=self.tconst,
offset=offset)
self.assertTrue(np.all(np.greater_equal(I, offset)))
def test_exponential_offset():
""" Test that the output of ``exponential`` is at lest ``offset``. """
tzero = 10 * (random() - 0.5) # between -5 and 5
amp = 5 * random() + 5 # between 5 and 10
tconst = random() + 0.3 # between 0.3 and 1.3
offset = 15
t = np.arange(-10, 50, step=0.3)
I = exponential(t, tzero=tzero, amp=amp, tconst=tconst, offset=offset)
assert np.all(np.greater_equal(I, offset))
def test_exponential_tzero_limits():
""" Test that the output of ``exponential`` has the correct time-zero """
tzero = 10 * (random() - 0.5) # between -5 and 5
amp = 5 * random() + 5 # between 5 and 10
tconst = random() + 0.3 # between 0.3 and 1.3
t = np.arange(-10, 50, step=0.3)
I = exponential(t, tzero=tzero, amp=amp, tconst=tconst)
# Check that all values before time-zero are the amplitude
assert np.all(np.equal(I[t < tzero], amp))
assert np.all(np.less(I[t > tzero], amp))
def test_trivial_constant_spacing(self):
""" Test with_irf with a trivial IRF, with constant spacing """
params = (0, 1, 3)
times = np.linspace(-5, 15, 256)
data = exponential(times, *params)
@with_irf(0.00001) # vanishingly small irf
def exponential_with_irf(time, *args, **kwargs):
return exponential(time, *args, **kwargs)
conv = exponential_with_irf(times, *params)
self.assertTrue(np.allclose(data, conv))
def test_biexponential_against_exponential():
""" Test that ``biexponential`` reduces to ``exponential`` for appropriate parameters """
tzero = 10 * (random() - 0.5) # between -5 and 5
amp1 = 5 * random() + 5 # between 5 and 10
tconst1 = random() + 0.3 # between 0.3 and 1.3
amp2 = 5 * random() + 5 # between 5 and 10
tconst2 = random() + 0.3 # between 0.3 and 1.3
t = np.arange(-10, 50, step=0.3)
offset = 2
exp = exponential(t, tzero, amp1, tconst1, offset=offset)
biexp = biexponential(t, tzero, amp1, 0, tconst1, 1, offset=offset)
assert np.allclose(exp, biexp)
def test_against_exponential(self):
""" Test that ``biexponential`` reduces to ``exponential`` for appropriate parameters """
t = np.arange(-10, 50, step=0.3)
offset = 2
exp = exponential(t,
self.tzero,
self.amp1,
self.tconst1,
offset=offset)
biexp = biexponential(t,
self.tzero,
self.amp1,
0,
self.tconst1,
1,
offset=offset)
self.assertTrue(np.allclose(exp, biexp))
def test_trivial_nonconstant_spacing(self):
""" Test with_irf with a trivial IRF, with non-constant spacing """
# Note that the spacing of the steps is important for this test
# If the array `times` is of even length, then the convolution will result
# in one time-step shift
params = (0, 1, 3)
times = np.concatenate((
np.arange(-10, -2, step=1),
np.arange(-2, 2, step=0.04),
np.arange(2, 10, step=1),
))
data = exponential(times, *params)
@with_irf(0.00001) # vanishingly small irf
def exponential_with_irf(time, *args, **kwargs):
return exponential(time, *args, **kwargs)
conv = exponential_with_irf(times, *params)
self.assertTrue(np.allclose(data, conv))
def test_amplitude(self):
""" Test that the output of ``exponential`` is at most ``amp``. """
t = np.arange(-10, 50, step=0.3)
I = exponential(t, tzero=self.tzero, amp=self.amp, tconst=self.tconst)
self.assertTrue(np.all(np.less_equal(I, self.amp)))
def test_positivity(self):
""" Test that the output of ``exponential`` is always positive. """
t = np.arange(-10, 50, step=0.3)
I = exponential(t, tzero=self.tzero, amp=self.amp, tconst=self.tconst)
self.assertTrue(np.all(I > 0))
def exponential_with_irf(time, *args, **kwargs):
return exponential(time, *args, **kwargs)