def test_calc_ref_basic(self):
layer1 = dataformat.SLDPro(1., 0., 0.)
layer2 = dataformat.SLDPro(1., 5., 0.)
sld = [[layer1, layer2]]
data1 = dataformat.QData(0.05, 0., 0., 0.05 * 0.05)
data2 = dataformat.QData(0.25, 0., 0., 0.05 * 0.25)
data3 = dataformat.QData(0.50, 0., 0., 0.05 * 0.50)
data = [data1, data2, data3]
a = reflect.Reflect(sld, data)
a.calc_ref()
assert_equal(len(a.reflect), 1)
assert_equal(len(a.reflect[0]), 3)
def calc_ref(self):
"""Calculate reflectometry.
The calculation of the reflectometry profiles based on the sld profiles calculated from each of the timesteps
under study.
"""
if len(self.exp_data) > 0:
self.reflect = []
prog = 0
k = 0
print("Calculating reflectometry\n[ 0 % ]")
for i in range(0, len(self.sld_profile)):
k += 1
prog_new = np.floor(k / (len(self.sld_profile)) * 100)
if prog_new > prog + 9:
prog = prog_new
print("[{} {} % ]".format('#' * int(prog / 10),
int(prog / 10) * 10))
refl = convolution(self.exp_data, self.sld_profile[i])
a = []
for j in range(0, len(self.exp_data)):
a.append(
dataformat.QData(self.exp_data[j].q, refl[j], 0,
self.exp_data[j].dq))
self.reflect.append(a)
else:
raise ValueError(
'No q vectors have been defined -- either read a .dat file or get q vectors.'
)
def read_dat(self):
"""Parses .dat.
Parses the .dat file, supporting 2, 3, and 4 column files consisting of q, i, di, and dq with comments in lines
where the first character is a '#'. If there is no .dat file the get_qs() function should be used to generate
q vectors to allow for the calculation of the reflectometry profile.
"""
self.expdata = []
if self.datfile:
lines = line_count(self.datfile)
print("Reading DAT file")
percentage = 0
print_update(percentage)
file = open(self.datfile, 'r')
for i, line in enumerate(file):
percentage_new = np.floor(i / lines * 100)
percentage = check_update(percentage, percentage_new)
if line[0] != '#':
line_list = line.split()
if len(line_list) == 2:
self.expdata.append(
dataformat.QData(
float(line_list[0]), float(line_list[1]),
float(line_list[1]) * (self.ierror / 100),
float(line_list[0]) * (self.resolution / 100)))
if len(line_list) == 3:
self.expdata.append(
dataformat.QData(
float(line_list[0]), float(line_list[1]),
float(line_list[2]),
float(line_list[0]) * (self.resolution / 100)))
if len(line_list) == 4:
self.expdata.append(
dataformat.QData(float(line_list[0]),
float(line_list[1]),
float(line_list[2]),
float(line_list[3])))
print_update(100)
file.close()
else:
print(
'No DAT file has been given, therefore no comparison will be conducted, please use the get_qs '
'function. Alternatively the DAT file can be added using the setFile function.'
)
return
def return_fitted(self):
"""Return fitted.
Return the fitted calculated reflectometry data for use.
"""
self.sim_data_fitted = []
for i in range(0, len(self.sim_data)):
a = self.sim_data[i].i * self.scale + self.background
b = self.sim_data[i].di * self.scale
self.sim_data_fitted.append(
dataformat.QData(self.sim_data[i].q, a, b,
self.sim_data[i].dq))
def test_reflect(self):
layer1 = dataformat.SLDPro(1., 0., 0.)
layer2 = dataformat.SLDPro(1., 5., 0.)
sld = [[layer1, layer2]]
data1 = dataformat.QData(0.05, 0., 0., 0.05 * 0.05)
data2 = dataformat.QData(0.25, 0., 0., 0.05 * 0.25)
data3 = dataformat.QData(0.50, 0., 0., 0.05 * 0.50)
data = [data1, data2, data3]
a = reflect.Reflect(sld, data)
assert_almost_equal(a.sld_profile[0][0].thick, 1.)
assert_almost_equal(a.sld_profile[0][0].real, 0.)
assert_almost_equal(a.sld_profile[0][0].imag, 0.)
assert_almost_equal(a.sld_profile[0][1].thick, 1.)
assert_almost_equal(a.sld_profile[0][1].real, 5.)
assert_almost_equal(a.sld_profile[0][1].imag, 0.)
assert_almost_equal(a.exp_data[0].q, 0.05)
assert_almost_equal(a.exp_data[0].dq, 0.05 * 0.05)
assert_almost_equal(a.exp_data[1].q, 0.25)
assert_almost_equal(a.exp_data[1].dq, 0.25 * 0.05)
assert_almost_equal(a.exp_data[2].q, 0.50)
assert_almost_equal(a.exp_data[2].dq, 0.50 * 0.05)
def get_qs(self, start=0.005, end=0.5, number=50):
"""Make custom q-vectors.
If no datfile exists this function should be used to generate a linear-spaced range of q-vector for the
calculation of the reflectometry over.
Parameters
----------
start: float, optional
The first q-vector for which the reflectometry should be calculated.
end: float, optional
The last q-vector for which the reflectometry should be calculated.
number: int, optional
The number of q-vectors.
"""
q_values = np.linspace(start, end, number)
for i in range(0, len(q_values)):
self.expdata.append(
dataformat.QData(q_values[i], None, None,
q_values[i] * (self.resolution / 100)))
return
def test_return_fitted(self):
data1 = dataformat.QData(0.05, 3., 0.3, 0.05 * 0.05)
data2 = dataformat.QData(0.25, 2., 0.2, 0.05 * 0.25)
data3 = dataformat.QData(0.50, 1., 0.1, 0.05 * 0.50)
data = [data1, data2, data3]
sdata1 = dataformat.QData(0.05, 1., 0.1, 0.05 * 0.05)
sdata2 = dataformat.QData(0.25, 2., 0.2, 0.05 * 0.25)
sdata3 = dataformat.QData(0.50, 3., 0.3, 0.05 * 0.50)
sdata = [sdata1, sdata2, sdata3]
a = compare.Compare(data, sdata, 2., 0.)
a.return_fitted()
assert_almost_equal(a.sim_data_fitted[0].i, 2.)
assert_almost_equal(a.sim_data_fitted[1].i, 4.)
assert_almost_equal(a.sim_data_fitted[2].i, 6.)
assert_almost_equal(a.sim_data_fitted[0].di, 0.2)
assert_almost_equal(a.sim_data_fitted[1].di, 0.4)
assert_almost_equal(a.sim_data_fitted[2].di, 0.6)
def average_ref(self):
"""Average reflectometry profiles.
The averaging of the reflectometry profiles as calculated by the calc_ref() function.
"""
if len(self.exp_data) > 0:
self.averagereflect = []
for i in range(0, len(self.reflect[0])):
self.averagereflect.append(
dataformat.QData(self.exp_data[i].q, 0, 0,
self.exp_data[i].dq))
for i in range(0, len(self.reflect[0])):
for j in range(0, len(self.reflect)):
self.averagereflect[i].i += self.reflect[j][i].i
self.averagereflect[i].i /= len(self.reflect)
for j in range(0, len(self.reflect)):
self.averagereflect[i].di += np.square(
self.reflect[j][i].i - self.averagereflect[i].i)
self.averagereflect[i].di = np.sqrt(
1. / (len(self.reflect) - 1) * self.averagereflect[i].di)
else:
raise ValueError(
'No q vectors have been defined -- either read a .dat file or get q vectors.'
)
def test_compare(self):
data1 = dataformat.QData(0.05, 3., 0.3, 0.05 * 0.05)
data2 = dataformat.QData(0.25, 2., 0.2, 0.05 * 0.25)
data3 = dataformat.QData(0.50, 1., 0.1, 0.05 * 0.50)
data = [data1, data2, data3]
sdata1 = dataformat.QData(0.05, 1., 0.1, 0.05 * 0.05)
sdata2 = dataformat.QData(0.25, 2., 0.2, 0.05 * 0.25)
sdata3 = dataformat.QData(0.50, 3., 0.3, 0.05 * 0.50)
sdata = [sdata1, sdata2, sdata3]
a = compare.Compare(data, sdata, 1., 0.)
assert_almost_equal(a.exp_data[0].q, 0.05)
assert_almost_equal(a.exp_data[0].i, 3.)
assert_almost_equal(a.exp_data[0].di, 0.3)
assert_almost_equal(a.exp_data[0].dq, 0.05 * 0.05)
assert_almost_equal(a.exp_data[1].q, 0.25)
assert_almost_equal(a.exp_data[1].i, 2.)
assert_almost_equal(a.exp_data[1].di, 0.2)
assert_almost_equal(a.exp_data[1].dq, 0.05 * 0.25)
assert_almost_equal(a.exp_data[2].q, 0.50)
assert_almost_equal(a.exp_data[2].i, 1.)
assert_almost_equal(a.exp_data[2].di, 0.1)
assert_almost_equal(a.exp_data[2].dq, 0.05 * 0.50)
assert_almost_equal(a.sim_data[0].q, 0.05)
assert_almost_equal(a.sim_data[0].i, 1.)
assert_almost_equal(a.sim_data[0].di, 0.1)
assert_almost_equal(a.sim_data[0].dq, 0.05 * 0.05)
assert_almost_equal(a.sim_data[1].q, 0.25)
assert_almost_equal(a.sim_data[1].i, 2.)
assert_almost_equal(a.sim_data[1].di, 0.2)
assert_almost_equal(a.sim_data[1].dq, 0.05 * 0.25)
assert_almost_equal(a.sim_data[2].q, 0.50)
assert_almost_equal(a.sim_data[2].i, 3.)
assert_almost_equal(a.sim_data[2].di, 0.3)
assert_almost_equal(a.sim_data[2].dq, 0.05 * 0.50)
assert_almost_equal(a.scale, 1.)
assert_almost_equal(a.background, 0.)
def test_average_reflect(self):
data11 = dataformat.QData(0.05, 0.1, 0., 0.05 * 0.05)
data12 = dataformat.QData(0.25, 0.05, 0., 0.05 * 0.25)
data13 = dataformat.QData(0.50, 0.01, 0., 0.05 * 0.50)
data1 = [data11, data12, data13]
data21 = dataformat.QData(0.05, 0.12, 0., 0.05 * 0.05)
data22 = dataformat.QData(0.25, 0.03, 0., 0.05 * 0.25)
data23 = dataformat.QData(0.50, 0.015, 0., 0.05 * 0.50)
data2 = [data21, data22, data23]
data31 = dataformat.QData(0.05, 0.14, 0., 0.05 * 0.05)
data32 = dataformat.QData(0.25, 0.07, 0., 0.05 * 0.25)
data33 = dataformat.QData(0.50, 0.005, 0., 0.05 * 0.50)
data3 = [data31, data32, data33]
layer1 = dataformat.SLDPro(1., 0., 0.)
layer2 = dataformat.SLDPro(1., 5., 0.)
sld = [[layer1, layer2]]
ddata1 = dataformat.QData(0.05, 0., 0., 0.05 * 0.05)
ddata2 = dataformat.QData(0.25, 0., 0., 0.05 * 0.25)
ddata3 = dataformat.QData(0.50, 0., 0., 0.05 * 0.50)
ddata = [ddata1, ddata2, ddata3]
a = reflect.Reflect(sld, ddata)
a.reflect = [data1, data2, data3]
a.average_ref()
assert_almost_equal(a.averagereflect[0].i, 0.12)
assert_almost_equal(a.averagereflect[1].i, 0.05)
assert_almost_equal(a.averagereflect[2].i, 0.01)
def test_qdata(self):
a = dataformat.QData(1., 2., 3., 4.)
assert_equal(a.q, 1.)
assert_equal(a.i, 2.)
assert_equal(a.di, 3.)
assert_equal(a.dq, 4.)
def test_fit_noi(self):
data11 = dataformat.QData(0.05, 0.1, 0., 0.05 * 0.05)
data12 = dataformat.QData(0.25, 0.05, 0., 0.05 * 0.25)
data13 = dataformat.QData(0.50, 0.01, 0., 0.05 * 0.50)
data1 = [data11, data12, data13]
data21 = dataformat.QData(0.05, 0.12, 0., 0.05 * 0.05)
data22 = dataformat.QData(0.25, 0.03, 0., 0.05 * 0.25)
data23 = dataformat.QData(0.50, 0.015, 0., 0.05 * 0.50)
data2 = [data21, data22, data23]
data31 = dataformat.QData(0.05, 0.14, 0., 0.05 * 0.05)
data32 = dataformat.QData(0.25, 0.07, 0., 0.05 * 0.25)
data33 = dataformat.QData(0.50, 0.005, 0., 0.05 * 0.50)
data3 = [data31, data32, data33]
layer1 = dataformat.SLDPro(1., 0., 0.)
layer2 = dataformat.SLDPro(1., 5., 0.)
sld = [[layer1, layer2]]
ddata1 = dataformat.QData(0.05, 0., 0., 0.05 * 0.05)
ddata2 = dataformat.QData(0.25, 0., 0., 0.05 * 0.25)
ddata3 = dataformat.QData(0.50, 0., 0., 0.05 * 0.50)
ddata = [ddata1, ddata2, ddata3]
a = reflect.Reflect(sld, ddata)
a.reflect = [data1, data2, data3]
a.average_ref()
ddata1 = dataformat.QData(0.05, None, 0., 0.05 * 0.05)
ddata2 = dataformat.QData(0.25, 0., 0., 0.05 * 0.25)
ddata3 = dataformat.QData(0.50, 0., 0., 0.05 * 0.50)
ddata = [ddata1, ddata2, ddata3]
k = compare.Compare(ddata, a.averagereflect, 1, 0)
with self.assertRaises(ValueError) as context:
k.fit()
self.assertTrue(
'No experimental data has been set for comparison, please read in a a .dat file.'
in str(context.exception))
def test_fit_noq(self):
data11 = dataformat.QData(0.05, 0.1, 0., 0.05 * 0.05)
data12 = dataformat.QData(0.25, 0.05, 0., 0.05 * 0.25)
data13 = dataformat.QData(0.50, 0.01, 0., 0.05 * 0.50)
data1 = [data11, data12, data13]
data21 = dataformat.QData(0.05, 0.12, 0., 0.05 * 0.05)
data22 = dataformat.QData(0.25, 0.03, 0., 0.05 * 0.25)
data23 = dataformat.QData(0.50, 0.015, 0., 0.05 * 0.50)
data2 = [data21, data22, data23]
data31 = dataformat.QData(0.05, 0.14, 0., 0.05 * 0.05)
data32 = dataformat.QData(0.25, 0.07, 0., 0.05 * 0.25)
data33 = dataformat.QData(0.50, 0.005, 0., 0.05 * 0.50)
data3 = [data31, data32, data33]
layer1 = dataformat.SLDPro(1., 0., 0.)
layer2 = dataformat.SLDPro(1., 5., 0.)
sld = [[layer1, layer2]]
ddata1 = dataformat.QData(0.05, 0., 0., 0.05 * 0.05)
ddata2 = dataformat.QData(0.25, 0., 0., 0.05 * 0.25)
ddata3 = dataformat.QData(0.50, 0., 0., 0.05 * 0.50)
ddata = [ddata1, ddata2, ddata3]
a = reflect.Reflect(sld, ddata)
a.reflect = [data1, data2, data3]
a.average_ref()
ddata = []
k = compare.Compare(ddata, a.averagereflect, 1, 0)
with self.assertRaises(ValueError) as context:
k.fit()
self.assertTrue(
'No q vectors have been defined -- either read a .dat file or get q vectors.'
in str(context.exception))