def test_amplitude(self):
""" Test that the output of ``biexponential`` is at most ``amp1 + amp2``. """
t = np.arange(-10, 50, step=0.3)
I = biexponential(
t,
tzero=self.tzero,
amp1=self.amp1,
amp2=self.amp2,
tconst1=self.tconst1,
tconst2=self.tconst2,
)
self.assertTrue(np.all(np.less_equal(I, self.amp1 + self.amp2)))
def test_positivity(self):
""" Test that the output of ``biexponential`` is always positive. """
t = np.arange(-10, 50, step=0.3)
I = biexponential(
t,
tzero=self.tzero,
amp1=self.amp1,
amp2=self.amp2,
tconst1=self.tconst1,
tconst2=self.tconst2,
)
self.assertTrue(np.all(I > 0))
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_offset(self):
""" Test that the output of ``biexponential`` is at least ``offset``. """
offset = 15
t = np.arange(-10, 50, step=0.3)
I = biexponential(
t,
tzero=self.tzero,
amp1=self.amp1,
amp2=self.amp2,
tconst1=self.tconst1,
tconst2=self.tconst2,
offset=offset,
)
self.assertTrue(np.all(np.greater_equal(I, offset)))
def test_tzero_limits(self):
""" Test that the output of ``biexponential`` has the correct time-zero """
t = np.arange(-10, 50, step=0.3)
I = biexponential(
t,
tzero=self.tzero,
amp1=self.amp1,
amp2=self.amp2,
tconst1=self.tconst1,
tconst2=self.tconst2,
)
# Check that all values before time-zero are the amplitude
self.assertTrue(
np.all(np.equal(I[t < self.tzero], self.amp1 + self.amp2)))
self.assertTrue(
np.all(np.less(I[t > self.tzero], self.amp1 + self.amp2)))
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_biexponential_positivity():
""" Test that the output of ``biexponential`` is always positive. """
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)
I = biexponential(
t,
tzero=tzero,
amp1=amp1,
amp2=amp2,
tconst1=tconst1,
tconst2=tconst2,
)
assert np.all(I > 0)
def test_biexponential_amplitude():
""" Test that the output of ``biexponential`` is at most ``amp1 + amp2``. """
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)
I = biexponential(
t,
tzero=tzero,
amp1=amp1,
amp2=amp2,
tconst1=tconst1,
tconst2=tconst2,
)
assert np.all(np.less_equal(I, amp1 + amp2))
def test_biexponential_tzero_limits():
""" Test that the output of ``biexponential`` has the correct time-zero """
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)
I = biexponential(
t,
tzero=tzero,
amp1=amp1,
amp2=amp2,
tconst1=tconst1,
tconst2=tconst2,
)
# Check that all values before time-zero are the amplitude
assert np.all(np.equal(I[t < tzero], amp1 + amp2))
assert np.all(np.less(I[t > tzero], amp1 + amp2))
def test_biexponential_offset():
""" Test that the output of ``biexponential`` is at least ``offset``. """
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
offset = 15
t = np.arange(-10, 50, step=0.3)
I = biexponential(
t,
tzero=tzero,
amp1=amp1,
amp2=amp2,
tconst1=tconst1,
tconst2=tconst2,
offset=offset,
)
assert np.all(np.greater_equal(I, offset))