def test_initial_data_processing(self):
"""
Test whether the background and scale are handled properly
when creating an InvariantCalculator object
"""
length = len(self.data.x)
self.assertEqual(length, len(self.data.y))
inv = invariant.InvariantCalculator(self.data)
self.assertEqual(length, len(inv._data.x))
self.assertEqual(inv._data.x[0], self.data.x[0])
# Now the same thing with a background value
bck = 0.1
inv = invariant.InvariantCalculator(self.data, background=bck)
self.assertEqual(inv._background, bck)
self.assertEqual(length, len(inv._data.x))
self.assertEqual(inv._data.y[0]+bck, self.data.y[0])
# Now the same thing with a scale value
scale = 0.1
inv = invariant.InvariantCalculator(self.data, scale=scale)
self.assertEqual(inv._scale, scale)
self.assertEqual(length, len(inv._data.x))
self.assertAlmostEqual(inv._data.y[0]/scale, self.data.y[0],7)
def test_qstar_low_q_guinier(self):
"""
Test low-q extrapolation with a Guinier
"""
inv = invariant.InvariantCalculator(self.data)
# Basic sanity check
_qstar = inv.get_qstar()
qstar, dqstar = inv.get_qstar_with_error()
self.assertEqual(qstar, _qstar)
# Low-Q Extrapolation
# Check that the returned invariant is what we expect given
# the result we got without extrapolation
inv.set_extrapolation('low', npts=10, function='guinier')
qs_extr, dqs_extr = inv.get_qstar_with_error('low')
delta_qs_extr, delta_dqs_extr = inv.get_qstar_low()
self.assertEqual(qs_extr, _qstar+delta_qs_extr)
self.assertEqual(dqs_extr, math.sqrt(dqstar*dqstar + delta_dqs_extr*delta_dqs_extr))
# We don't expect the extrapolated invariant to be very far from the
# result without extrapolation. Let's test for a result within 10%.
self.assertTrue(math.fabs(qs_extr-qstar)/qstar<0.1)
# Check that the two results are consistent within errors
# Note that the error on the extrapolated value takes into account
# a systematic error for the fact that we may not know the shape of I(q) at low Q.
self.assertTrue(math.fabs(qs_extr-qstar)<dqs_extr)
def test_qstar_high_q(self):
"""
Test high-q extrapolation
"""
inv = invariant.InvariantCalculator(self.data)
# Basic sanity check
_qstar = inv.get_qstar()
qstar, dqstar = inv.get_qstar_with_error()
self.assertEqual(qstar, _qstar)
# High-Q Extrapolation
# Check that the returned invariant is what we expect given
# the result we got without extrapolation
inv.set_extrapolation('high', npts=20, function='power_law')
qs_extr, dqs_extr = inv.get_qstar_with_error('high')
delta_qs_extr, delta_dqs_extr = inv.get_qstar_high()
# From previous analysis using SasView, we expect an exponent of about 3
self.assertTrue(math.fabs(inv._high_extrapolation_function.power-3)<0.1)
self.assertEqual(qs_extr, _qstar+delta_qs_extr)
self.assertAlmostEqual(dqs_extr, math.sqrt(dqstar*dqstar + delta_dqs_extr*delta_dqs_extr), 10)
# We don't expect the extrapolated invariant to be very far from the
# result without extrapolation. Let's test for a result within 10%.
#TODO: verify whether this test really makes sense
#self.assertTrue(math.fabs(qs_extr-qstar)/qstar<0.1)
# Check that the two results are consistent within errors
self.assertTrue(math.fabs(qs_extr-qstar)<dqs_extr)
def test_use_case_6(self):
"""
Invariant with high-Q extrapolation
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data)
# Set the extrapolation parameters for the high-Q range
inv.set_extrapolation(range='low',
npts=10,
function='power_law',
power=4)
# The version of the call without error
# The function parameter defaults to None, then is picked to be 'power_law' for extrapolation='high'
qstar = inv.get_qstar(extrapolation='low')
# The version of the call with error
qstar, qstar_err = inv.get_qstar_with_error(extrapolation='low')
# Get the volume fraction and surface
v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
# Test results
self.assertAlmostEquals(qstar, 7.49e-5, 2)
self.assertAlmostEquals(v, 0.005952674, 3)
self.assertAlmostEquals(s, 941.7452, 3)
def test_qstar_high_q(self):
"""
Test high-q extrapolation
"""
inv = invariant.InvariantCalculator(self.data)
# Basic sanity check
_qstar = inv.get_qstar()
qstar, dqstar = inv.get_qstar_with_error()
self.assertEqual(qstar, _qstar)
# High-Q Extrapolation
# Check that the returned invariant is what we expect given
# the result we got without extrapolation
inv.set_extrapolation('high', npts=75, function='power_law')
qs_extr, dqs_extr = inv.get_qstar_with_error('high')
delta_qs_extr, delta_dqs_extr = inv.get_qstar_high()
# In principle the slope should be -4. However due to the oscillations
# and finite number of points we need quite a lot of points to ensure
# that the fit averages close to 4. SasView estimates 3.92 at the
# moment
self.assertTrue(math.fabs(inv._high_extrapolation_function.power-4)<0.1)
self.assertEqual(qs_extr, _qstar+delta_qs_extr)
self.assertAlmostEqual(dqs_extr, math.sqrt(dqstar*dqstar \
+ delta_dqs_extr*delta_dqs_extr), 10)
def test_use_case_2(self):
"""
Test the Invariant with the low-Q Guinier extrapolation. This
should now capture 98.5% of the invariant so in principle the
calculation should give a value that is 1.5% below the theoretical
value. This should be within tolerance so leave the invariant
test value at the true theoretical value here.
"""
# Create an invariant object with the background set to zero
inv = invariant.InvariantCalculator(data=self.data_q_smear,
background=0)
# Set the extrapolation parameters for the low-Q range
inv.set_extrapolation(range='low', npts=10, function='guinier')
# again using calls to both for reasons described in previous test
# classes above.
qstar1 = inv.get_qstar(extrapolation='low')
qstar, qstar_err = inv.get_qstar_with_error(extrapolation='low')
# Get the volume fraction and surface
v, dv = inv.get_volume_fraction_with_error(contrast=2.2e-6)
s, ds = inv.get_surface_with_error(contrast=2.2e-6,
porod_const=1.825e-8)
# Test results
self.assertEqual(qstar1, qstar)
self.assertAlmostEqual(qstar, 9.458239e-5, delta=1.5e-6)
self.assertAlmostEqual(v, 0.01, delta=15e-4)
self.assertAlmostEqual(s, 6.000e-6, 7)
def test_use_case_4(self):
"""
Test the Invariant with both the low Q Guinier and the high-Q -4
power law extrapolations. This should now capture all the invariant
so we now again give the true theoretical invariant as the test
value to compare agains.
"""
# Create an invariant object with the background set to zero
inv = invariant.InvariantCalculator(data=self.data_q_smear,
background=0)
# Set the extrapolation parameters for the low- and high-Q ranges
inv.set_extrapolation(range='low', npts=10, function='guinier')
inv.set_extrapolation(range='high',
npts=10,
function='power_law',
power=4)
# again using calls to both for reasons described in previous test
# classes above.
qstar1 = inv.get_qstar(extrapolation='low')
qstar, qstar_err = inv.get_qstar_with_error(extrapolation='low')
# Get the volume fraction and surface
v, dv = inv.get_volume_fraction_with_error(contrast=2.2e-6)
s, ds = inv.get_surface_with_error(contrast=2.2e-6,
porod_const=1.825e-8)
# Test results
self.assertEqual(qstar1, qstar)
self.assertAlmostEqual(qstar, 9.458239e-5, delta=1.5e-6)
self.assertAlmostEqual(v, 0.01, delta=15e-4)
self.assertAlmostEqual(s, 6.000e-6, 7)
def test_use_case_1(self):
"""
Test the Invariant without extrapolation. Because the object is
so large and in this case I is only multiplied by q rather than q^2,
a significant part of the invariant (~6.5%)is not captured with
most of that (~5%) being in the low Q region. Thus the valuefor the
invariant here is being adjusted down by 6.5% As seen when both
extrapolations are included the integration seems to be computing
a percent or two high.
"""
# Create an invariant object with background of zero as that is how the
# data was created. A different background could cause negative
# intensities. Leave scale as defaults.
inv = invariant.InvariantCalculator(data=self.data_q_smear,
background=0)
# again using calls to both for reasons described in previous test
# classes above.
qstar1 = inv.get_qstar()
qstar, qstar_err = inv.get_qstar_with_error()
# Get the volume fraction and surface
v, dv = inv.get_volume_fraction_with_error(contrast=2.2e-6)
s, ds = inv.get_surface_with_error(contrast=2.2e-6,
porod_const=1.825e-8)
# Test results
self.assertEqual(qstar1, qstar)
self.assertAlmostEqual(qstar, 8.8434e-5, delta=1.5e-6)
self.assertAlmostEqual(v, 0.00935, delta=1.5e-4)
self.assertAlmostEqual(s, 6.000e-6, 7)
def test_low_q(self):
"""
Invariant with low-Q extrapolation
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data)
# Set the extrapolation parameters for the low-Q range
inv.set_extrapolation(range='low', npts=20, function='guinier')
self.assertEqual(inv._low_extrapolation_npts, 20)
self.assertEqual(inv._low_extrapolation_function.__class__,
invariant.Guinier)
# Data boundaries for fiiting
qmin = inv._data.x[0]
qmax = inv._data.x[inv._low_extrapolation_npts - 1]
# Extrapolate the low-Q data
inv._fit(model=inv._low_extrapolation_function,
qmin=qmin,
qmax=qmax,
power=inv._low_extrapolation_power)
self.assertAlmostEqual(self.scale,
inv._low_extrapolation_function.scale, 6)
self.assertAlmostEqual(self.rg,
inv._low_extrapolation_function.radius, 6)
def test_low_data(self):
"""
Invariant with low-Q extrapolation with slit smear
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data)
# Set the extrapolation parameters for the low-Q range
inv.set_extrapolation(range='low', npts=self.npts, function='guinier')
self.assertEqual(inv._low_extrapolation_npts, self.npts)
self.assertEqual(inv._low_extrapolation_function.__class__, invariant.Guinier)
# Data boundaries for fiiting
qmin = inv._data.x[0]
qmax = inv._data.x[inv._low_extrapolation_npts - 1]
# Extrapolate the low-Q data
inv._fit(model=inv._low_extrapolation_function,
qmin=qmin,
qmax=qmax,
power=inv._low_extrapolation_power)
qstar = inv.get_qstar(extrapolation='low')
#Compution the y 's coming out of the invariant when computing extrapolated
#low data . expect the fit engine to have been already called and the guinier
# to have the radius and the scale fitted
data_in_range = inv.get_extra_data_low(q_start=self.data.x[0],
npts = inv._low_extrapolation_npts)
test_y = data_in_range.y
self.assertTrue(len(test_y) == len(self.data.y[:inv._low_extrapolation_npts]))
for i in range(inv._low_extrapolation_npts):
value = math.fabs(test_y[i]-self.data.y[i])/self.data.y[i]
self.assertTrue(value < 0.001)
def test_use_case_1(self):
"""
Invariant without extrapolation
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data)
# We have to be able to tell the InvariantCalculator whether we want the
# extrapolation or not. By default, when the user doesn't specify, we
# should compute Q* without extrapolation. That's what should be done
# in __init__.
# We call get_qstar() with no argument, which signifies that we do NOT
# want extrapolation.
qstar = inv.get_qstar()
# The volume fraction and surface use Q*. That means that the following
# methods should check that Q* has been computed. If not, it should
# compute it by calling get_qstare(), leaving the parameters as default.
v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
# Test results
self.assertAlmostEquals(qstar, 7.48959e-5, 2)
self.assertAlmostEquals(v, 0.005644689, 4)
self.assertAlmostEquals(s, 941.7452, 3)
def test_low_q(self):
"""
Invariant with low-Q extrapolation
NOTE: as noted in the class docs, this should probalby be removed.
But until we remove the option for low Q power law extrapolation
will leave this.
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data)
# Set the extrapolation parameters for the low-Q range
inv.set_extrapolation(range='low', npts=20, function='power_law')
self.assertEqual(inv._low_extrapolation_npts, 20)
self.assertEqual(inv._low_extrapolation_function.__class__,
invariant.PowerLaw)
# Data boundaries for fitting
qmin = inv._data.x[0]
qmax = inv._data.x[inv._low_extrapolation_npts - 1]
# Extrapolate the low-Q data
inv._fit(model=inv._low_extrapolation_function,
qmin=qmin,
qmax=qmax,
power=inv._low_extrapolation_power)
self.assertAlmostEqual(self.scale,
inv._low_extrapolation_function.scale, 6)
self.assertAlmostEqual(self.m, inv._low_extrapolation_function.power, 6)
def test_low_q(self):
"""
Invariant with low-Q extrapolation with no slit smear
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data)
# Set the extrapolation parameters for the low-Q range
inv.set_extrapolation(range='low', npts=10, function='guinier')
self.assertEqual(inv._low_extrapolation_npts, 10)
self.assertEqual(inv._low_extrapolation_function.__class__,
invariant.Guinier)
# Data boundaries for fiiting
qmin = inv._data.x[0]
qmax = inv._data.x[inv._low_extrapolation_npts - 1]
# Extrapolate the low-Q data
inv._fit(model=inv._low_extrapolation_function,
qmin=qmin,
qmax=qmax,
power=inv._low_extrapolation_power)
self.assertAlmostEqual(self.scale,
inv._low_extrapolation_function.scale, 6)
self.assertAlmostEqual(self.rg,
inv._low_extrapolation_function.radius, 6)
qstar = inv.get_qstar(extrapolation='low')
test_y = inv._low_extrapolation_function.evaluate_model(x=self.data.x)
for i in range(len(self.data.x)):
value = math.fabs(test_y[i]-self.data.y[i])/self.data.y[i]
self.assertTrue(value < 0.001)
def test_use_case_3(self):
"""
Invariant with low-Q extrapolation
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data)
# Set the extrapolation parameters for the low-Q range
# The npts parameter should have a good default.
# The range parameter should be 'high' or 'low'
# The function parameter should default to None. If it is None,
# the method should pick a good default (Guinier at low-Q and 1/q^4 at high-Q).
# The method should also check for consistency of the extrapolation and function
# parameters. For instance, you might not want to allow 'high' and 'guinier'.
# The power parameter (not shown below) should default to 4.
inv.set_extrapolation(range='low', npts=10, function='guinier')
# The version of the call without error
# At this point, we could still compute Q* without extrapolation by calling
# get_qstar with arguments, or with extrapolation=None.
qstar = inv.get_qstar(extrapolation='low')
# The version of the call with error
qstar, qstar_err = inv.get_qstar_with_error(extrapolation='low')
# Get the volume fraction and surface
v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
# Test results
self.assertAlmostEquals(qstar, 7.49e-5, 1)
self.assertAlmostEquals(v, 0.005648401, 4)
self.assertAlmostEquals(s , 941.7452, 3)
def test_high_data(self):
"""
Invariant with low-Q extrapolation with slit smear
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data)
# Set the extrapolation parameters for the low-Q range
inv.set_extrapolation(range='high', npts=self.npts, function='power_law')
self.assertEqual(inv._high_extrapolation_npts, self.npts)
self.assertEqual(inv._high_extrapolation_function.__class__, invariant.PowerLaw)
# Data boundaries for fiiting
xlen = len(self.data.x)
start = xlen - inv._high_extrapolation_npts
qmin = inv._data.x[start]
qmax = inv._data.x[xlen-1]
# Extrapolate the high-Q data
inv._fit(model=inv._high_extrapolation_function,
qmin=qmin,
qmax=qmax,
power=inv._high_extrapolation_power)
qstar = inv.get_qstar(extrapolation='high')
data_in_range= inv.get_extra_data_high(q_end = max(self.data.x),
npts = inv._high_extrapolation_npts)
test_y = data_in_range.y
self.assertTrue(len(test_y) == len(self.data.y[start:]))
temp = self.data.y[start:]
for i in range(len(self.data.x[start:])):
value = math.fabs(test_y[i]- temp[i])/temp[i]
self.assertTrue(value < 0.001)
def test_use_case_3(self):
"""
Test the Invariant with the high-Q -4 power law extrapolation.
This should still only capture 95% of the true invariant so i
principle the calculation should give a value that is 5% lower than
the true theoretical value. Thus here again we adjust the test
value for the invariant down, this time by 5%.
"""
# Create an invariant object with the background set to zero
inv = invariant.InvariantCalculator(data=self.data_q_smear,
background=0)
# Set the extrapolation parameters for the high-Q range
inv.set_extrapolation(range='high',
npts=10,
function='power_law',
power=4)
# again using calls to both for reasons described in previous test
# classes above.
qstar1 = inv.get_qstar(extrapolation='high')
qstar, qstar_err = inv.get_qstar_with_error(extrapolation='high')
# Get the volume fraction and surface
v, dv = inv.get_volume_fraction_with_error(contrast=2.2e-6)
s, ds = inv.get_surface_with_error(contrast=2.2e-6,
porod_const=1.825e-8)
# Test results
self.assertEqual(qstar1, qstar)
self.assertAlmostEqual(qstar, 8.9853e-5, delta=1.5e-6)
self.assertAlmostEqual(v, 0.0095, delta=1.5e-4)
self.assertAlmostEqual(s, 6.000e-6, 7)
def test_bad_parameter_name(self):
"""
The set_extrapolation method checks that the name of the extrapolation
function and the name of the q-range to extrapolate (high/low) is
recognized.
"""
inv = invariant.InvariantCalculator(self.data)
self.assertRaises(ValueError, inv.set_extrapolation, 'low', npts=4, function='not_a_name')
self.assertRaises(ValueError, inv.set_extrapolation, 'not_a_range', npts=4, function='guinier')
self.assertRaises(ValueError, inv.set_extrapolation, 'high', npts=4, function='guinier')
def test_use_case_1(self):
"""
Invariant without extrapolation
"""
inv = invariant.InvariantCalculator(data=self.data_q_smear)
qstar = inv.get_qstar()
v = inv.get_volume_fraction(contrast=2.6e-6)
s = inv.get_surface(contrast=2.6e-6, porod_const=2)
# Test results
self.assertAlmostEquals(qstar, 1.361677e-3, 4)
self.assertAlmostEquals(v, 0.115352622, 2)
self.assertAlmostEquals(s, 941.7452, 3)
def test_qstar_low_q_power_law(self):
"""
Test low-q extrapolation with a power law
"""
inv = invariant.InvariantCalculator(self.data)
# Basic sanity check
_qstar = inv.get_qstar()
qstar, dqstar = inv.get_qstar_with_error()
self.assertEqual(qstar, _qstar)
# Low-Q Extrapolation
# Check that the returned invariant is what we expect given
inv.set_extrapolation('low', npts=10, function='power_law')
qs_extr, dqs_extr = inv.get_qstar_with_error('low')
delta_qs_extr, delta_dqs_extr = inv.get_qstar_low()
# A fit using SasView gives 0.0655 for the value of the exponent
self.assertAlmostEqual(inv._low_extrapolation_function.power, 0.0655,
3)
if False:
npts = len(inv._data.x) - 1
import matplotlib.pyplot as plt
plt.loglog(inv._data.x[:npts],
inv._data.y[:npts],
'o',
label='Original data',
markersize=10)
plt.loglog(inv._data.x[:npts],
inv._low_extrapolation_function.evaluate_model(
inv._data.x[:npts]),
'r',
label='Fitted line')
plt.legend()
plt.show()
self.assertEqual(qs_extr, _qstar + delta_qs_extr)
self.assertAlmostEqual(
dqs_extr,
math.sqrt(dqstar * dqstar + delta_dqs_extr * delta_dqs_extr), 15)
# We don't expect the extrapolated invariant to be very far from the
# result without extrapolation. Let's test for a result within 10%.
self.assertTrue(math.fabs(qs_extr - qstar) / qstar < 0.1)
# Check that the two results are consistent within errors
# Note that the error on the extrapolated value takes into account
# a systematic error for the fact that we may not know the shape of I(q) at low Q.
self.assertTrue(math.fabs(qs_extr - qstar) < dqs_extr)
def test_use_case_4(self):
"""
Invariant with high-Q extrapolation
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data_q_smear)
# Set the extrapolation parameters for the high-Q range
inv.set_extrapolation(range='high', npts=10, function='power_law', power=4)
# The version of the call without error
qstar = inv.get_qstar(extrapolation='high')
# The version of the call with error
qstar, qstar_err = inv.get_qstar_with_error(extrapolation='high')
# Test results
self.assertAlmostEqual(qstar, 0.0045773,2)
def test_error_treatment(self):
x = np.asarray(np.asarray([0,1,2,3]))
y = np.asarray(np.asarray([1,1,1,1]))
# These are all the values of the dy array that would cause
# us to set all dy values to 1.0 at __init__ time.
dy_list = [ [], None, [0,0,0,0] ]
for dy in dy_list:
data = Data1D(x=x, y=y, dy=dy)
inv = invariant.InvariantCalculator(data)
self.assertEqual(len(inv._data.x), len(inv._data.dy))
self.assertEqual(len(inv._data.dy), 4)
for i in range(4):
self.assertEqual(inv._data.dy[i],1)
def test_use_case_2(self):
"""
Tes the Invariant with the low-Q Guinier extrapolation
"""
# Create an invariant object with background of zero as that is how the
# data was created. A different background could cause negative
# intensities. Leave scale as defaults.
inv = invariant.InvariantCalculator(data=self.data, background=0)
# Now we do want to use the extrapoloation so first we need to set
# the extrapolation parameters, in thsi case for the low-Q range
# The npts parameter should have a good default.
# The range parameter should be 'high' or 'low'
# The function parameter should default to None. If it is None,
# the method should pick a good default
# (Guinier at low-Q and 1/q^4 at high-Q).
# The method should also check for consistency of the extrapolation
# and function parameters. For instance, you might not want to allow
# 'high' and 'guinier'.
# The power parameter (not shown below) should default to 4.
inv.set_extrapolation(range='low', npts=10, function='guinier')
# The version of the call without error again checks that the function
# returns the same value as it calculates and passes to the verion of
# the call with uncertainties.
#
# Note that at this point, we could still compute Q* without
# extrapolation by calling get_qstar with no arguments, or with
# extrapolation=None.
# But of course we want to test the low Q Guinier extrapolation so...
qstar1 = inv.get_qstar(extrapolation='low')
# And again, using the version of the call which also retruns
# the uncertainties.
qstar, qstar_err = inv.get_qstar_with_error(extrapolation='low')
# Get the volume fraction and surface
v, dv = inv.get_volume_fraction_with_error(contrast=2.2e-6)
s, ds = inv.get_surface_with_error(contrast=2.2e-6,
porod_const=1.825e-7)
# Test results
self.assertEqual(qstar1, qstar)
self.assertAlmostEqual(qstar, 1.088e-4, delta=1e-6)
self.assertAlmostEqual(v, 0.01150, delta=1e-4)
self.assertAlmostEqual(s, 6.000e-5, 7)
def test_use_case_2(self):
"""
Invariant without extrapolation. Invariant, volume fraction and surface
are given with errors.
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data_q_smear)
# Get the invariant with errors
qstar, qstar_err = inv.get_qstar_with_error()
# Get the volume fraction and surface
v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
# Test results
self.assertAlmostEquals(qstar, 1.361677e-3, 4)
self.assertAlmostEquals(v, 0.115352622, 2)
self.assertAlmostEquals(s, 941.7452, 3)
def test_use_case_5(self):
"""
Invariant with both high- and low-Q extrapolation
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data_q_smear)
# Set the extrapolation parameters for the low- and high-Q ranges
inv.set_extrapolation(range='low', npts=10, function='guinier')
inv.set_extrapolation(range='high', npts=10, function='power_law',
power=4)
# The version of the call without error
# The function parameter defaults to None, then is picked to be
# 'power_law' for extrapolation='high'
qstar = inv.get_qstar(extrapolation='both')
# The version of the call with error
qstar, qstar_err = inv.get_qstar_with_error(extrapolation='both')
# Test results
self.assertAlmostEqual(qstar, 0.00460319,3)
def test_use_case_2(self):
"""
Invariant without extrapolation. Invariant, volume fraction and surface
are given with errors.
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data)
# Get the invariant with errors
qstar, qstar_err = inv.get_qstar_with_error()
# The volume fraction and surface use Q*. That means that the following
# methods should check that Q* has been computed. If not, it should
# compute it by calling get_qstare(), leaving the parameters as default.
v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
# Test results
self.assertAlmostEquals(qstar, 7.48959e-5, 2)
self.assertAlmostEquals(v, 0.005644689, 1)
self.assertAlmostEquals(s, 941.7452, 3)
def test_use_case_3(self):
"""
Invariant with low-Q extrapolation
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data_q_smear)
# Set the extrapolation parameters for the low-Q range
inv.set_extrapolation(range='low', npts=20, function='guinier')
# The version of the call without error
qstar = inv.get_qstar(extrapolation='low')
# The version of the call with error
qstar, qstar_err = inv.get_qstar_with_error(extrapolation='low')
# Get the volume fraction and surface
v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
# Test results
self.assertAlmostEquals(qstar, 0.00138756, 2)
self.assertAlmostEquals(v, 0.117226896, 2)
self.assertAlmostEquals(s, 941.7452, 3)
def test_qstar_low_q_guinier(self):
"""
Test low-q extrapolation with a Guinier
"""
inv = invariant.InvariantCalculator(self.data)
# Basic sanity check
_qstar = inv.get_qstar()
qstar, dqstar = inv.get_qstar_with_error()
self.assertEqual(qstar, _qstar)
# Low-Q Extrapolation
# Check that the returned invariant is what we expect given
# the result we got without extrapolation
inv.set_extrapolation('low', npts=10, function='guinier')
qs_extr, dqs_extr = inv.get_qstar_with_error('low')
delta_qs_extr, delta_dqs_extr = inv.get_qstar_low()
self.assertEqual(qs_extr, _qstar+delta_qs_extr)
self.assertEqual(dqs_extr, math.sqrt(dqstar*dqstar +
delta_dqs_extr*delta_dqs_extr))
def test_high_q(self):
"""
Invariant with high-Q extrapolation with slit smear
TODO:: As of 3/23/2020 by PDB - this data is NOT smeared as far as
I can see. On the other hand it is the only high q
extrapolation test? Need to double check then rewrite doc
strings accordingly.
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data)
# Set the extrapolation parameters for the low-Q range
inv.set_extrapolation(range='high', npts=self.npts,
function='power_law')
self.assertEqual(inv._high_extrapolation_npts, self.npts)
self.assertEqual(inv._high_extrapolation_function.__class__,
invariant.PowerLaw)
# Data boundaries for fiiting
xlen = len(self.data.x)
start = xlen - inv._high_extrapolation_npts
qmin = inv._data.x[start]
qmax = inv._data.x[xlen-1]
# Extrapolate the high-Q data
inv._fit(model=inv._high_extrapolation_function,
qmin=qmin,
qmax=qmax,
power=inv._high_extrapolation_power)
qstar = inv.get_qstar(extrapolation='high')
test_y = inv._high_extrapolation_function.evaluate_model(x=self.data.x[start: ])
self.assertTrue(len(test_y) == len(self.data.y[start:]))
for i in range(len(self.data.x[start:])):
value = math.fabs(test_y[i]-self.data.y[start+i])/self.data.y[start+i]
self.assertTrue(value < 0.001)
def test_low_data(self):
"""
Invariant with low-Q extrapolation with slit smear
TODO:: on 3/23/2020 PDB says: a) there is no slit smear data here
and b) this seems to be the exactly the same tests as
test_low_q and of the whole class TestDataExtraLow which
iteself is a superset of the class TestGunierExtrapolation?
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data)
# Set the extrapolation parameters for the low-Q range
inv.set_extrapolation(range='low', npts=self.npts, function='guinier')
self.assertEqual(inv._low_extrapolation_npts, self.npts)
self.assertEqual(inv._low_extrapolation_function.__class__,
invariant.Guinier)
# Data boundaries for fiiting
qmin = inv._data.x[0]
qmax = inv._data.x[inv._low_extrapolation_npts - 1]
# Extrapolate the low-Q data
inv._fit(model=inv._low_extrapolation_function,
qmin=qmin,
qmax=qmax,
power=inv._low_extrapolation_power)
qstar = inv.get_qstar(extrapolation='low')
#Computing the ys coming out of the invariant when computing
# extrapolated low data . expect the fit engine to have been already
# called and the guinier to have the radius and the scale fitted
data_in_range = inv.get_extra_data_low(q_start=self.data.x[0],
npts = inv._low_extrapolation_npts)
test_y = data_in_range.y
self.assertTrue(len(test_y) == len(self.data.y[:inv._low_extrapolation_npts]))
for i in range(inv._low_extrapolation_npts):
value = math.fabs(test_y[i]-self.data.y[i])/self.data.y[i]
self.assertTrue(value < 0.001)
def test_use_case_5(self):
"""
Invariant with both high- and low-Q extrapolation
"""
# Create invariant object. Background and scale left as defaults.
inv = invariant.InvariantCalculator(data=self.data_q_smear)
# Set the extrapolation parameters for the low- and high-Q ranges
inv.set_extrapolation(range='low', npts=10, function='guinier')
inv.set_extrapolation(range='high', npts=10, function='power_law', power=4)
# The version of the call without error
# The function parameter defaults to None, then is picked to be 'power_law' for extrapolation='high'
qstar = inv.get_qstar(extrapolation='both')
# The version of the call with error
qstar, qstar_err = inv.get_qstar_with_error(extrapolation='both')
# Get the volume fraction and surface
# WHY SHOULD THIS FAIL?
#self.assertRaises(RuntimeError, inv.get_volume_fraction_with_error, 2.6e-6)
#self.assertRaises(RuntimeError, inv.get_surface_with_error, 2.6e-6, 2)
# Test results
self.assertAlmostEquals(qstar, 0.00460319,3)
