def test_qdcd_normalisation_profile(self):
"""
Test qdcd normalisation profile
"""
file_name = path.join(path.dirname(islatu.__file__),
"tests/test_files/qdcd_norm.dat")
normalisation_metadata, normalisation_data = io.i07_dat_parser(
file_name)
itp = splrep(normalisation_data["qdcd_"], normalisation_data["adc2"])
file_name1 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_a.dat")
file_name2 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_b.dat")
files = [file_name1, file_name2]
r = refl_data.Profile(files, io.i07_dat_parser)
r.crop(cropping.crop_around_peak_2d)
r.bkg_sub(background.fit_gaussian_2d)
r_store0 = r.scans[0].R
r_store1 = r.scans[1].R
r.qdcd_normalisation(itp)
np.any(
np.not_equal(unp.nominal_values(r.scans[0].R),
unp.nominal_values(r_store0)))
np.any(
np.not_equal(unp.nominal_values(r.scans[1].R),
unp.nominal_values(r_store1)))
def test_init_profile(self):
"""
Test init with correct file paths
"""
file_name1 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_a.dat")
file_name2 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_b.dat")
files = [file_name1, file_name2]
r = refl_data.Profile(files, io.i07_dat_parser)
assert_equal(len(r.scans), 2)
def test_rebin_profile(self):
"""
Test profile rebin
"""
file_name1 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_a.dat")
file_name2 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_b.dat")
files = [file_name1, file_name2]
r = refl_data.Profile(files, io.i07_dat_parser)
r.crop(cropping.crop_around_peak_2d)
r.bkg_sub(background.fit_gaussian_2d)
r.concatenate()
r.rebin(number_of_q_vectors=3)
assert_equal(r.q.size, 2)
assert_equal(r.R.size, 2)
def test_concatentate_profile(self):
"""
Test profile concatentation
"""
file_name1 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_a.dat")
file_name2 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_b.dat")
files = [file_name1, file_name2]
r = refl_data.Profile(files, io.i07_dat_parser)
r.crop(cropping.crop_around_peak_2d)
r.bkg_sub(background.fit_gaussian_2d)
r.concatenate()
assert_equal(r.q.size, 6)
assert_equal(r.R.size, 6)
assert_equal(r.dq.size, 6)
assert_equal(r.dR.size, 6)
def test_crop_bkg_profile(self):
"""
Test crop and bkg sub profile
"""
file_name1 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_a.dat")
file_name2 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_b.dat")
files = [file_name1, file_name2]
r = refl_data.Profile(files, io.i07_dat_parser)
r.crop(cropping.crop_around_peak_2d)
r.bkg_sub(background.fit_gaussian_2d)
a2 = np.zeros((3))
np.any(np.not_equal(unp.nominal_values(r.scans[0].R), a2))
np.any(np.not_equal(unp.nominal_values(r.scans[1].R), a2))
np.any(np.not_equal(unp.std_devs(r.scans[0].R), a2))
np.any(np.not_equal(unp.std_devs(r.scans[1].R), a2))
np.any(np.not_equal(r.scans[0].n_pixels, a2))
np.any(np.not_equal(r.scans[1].n_pixels, a2))
def test_resolution_function_profile(self):
"""
Test resolution_function from pixel profile
"""
file_name1 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_a.dat")
file_name2 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_b.dat")
files = [file_name1, file_name2]
r = refl_data.Profile(files, io.i07_dat_parser)
r.crop(cropping.crop_around_peak_2d)
r.bkg_sub(background.fit_gaussian_2d)
q_store0 = unp.nominal_values(r.scans[0].q)
q_store1 = unp.nominal_values(r.scans[1].q)
r.resolution_function(1)
assert_equal(unp.nominal_values(r.scans[0].q), q_store0)
np.any(np.not_equal(unp.std_devs(r.scans[0].q), np.zeros((3))))
assert_equal(unp.nominal_values(r.scans[1].q), q_store1)
np.any(np.not_equal(unp.std_devs(r.scans[1].q), np.zeros((3))))
def test_normalise_ter_profile(self):
"""
Test profile ter normalisation
"""
file_name1 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_a.dat")
file_name2 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_b.dat")
files = [file_name1, file_name2]
r = refl_data.Profile(files, io.i07_dat_parser)
r.crop(cropping.crop_around_peak_2d)
r.bkg_sub(background.fit_gaussian_2d)
r.concatenate()
store = r.R
dstore = r.dR
r.normalise_ter()
assert_equal(r.q.size, 6)
assert_equal(r.R.size, 6)
np.any(np.not_equal(r.R, store))
np.any(np.not_equal(r.dR, dstore))
def test_transmission_profile(self):
"""
Test transmission correction profile
"""
file_name1 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_a.dat")
file_name2 = path.join(path.dirname(islatu.__file__),
"tests/test_files/test_b.dat")
files = [file_name1, file_name2]
r = refl_data.Profile(files, io.i07_dat_parser)
r.crop(cropping.crop_around_peak_2d)
r.bkg_sub(background.fit_gaussian_2d)
r_store0 = r.scans[0].R
r_store1 = r.scans[1].R
r.transmission_normalisation()
np.any(
np.not_equal(unp.nominal_values(r.scans[0].R),
unp.nominal_values(r_store0)))
np.any(
np.not_equal(unp.nominal_values(r.scans[1].R),
unp.nominal_values(r_store1)))
def i07reduce(run_numbers, yaml_file, directory='/dls/{}/data/{}/{}/',
title='Unknown'):
"""
The runner that parses the yaml file and performs the data reduction.
run_numbers (:py:attr:`list` of :py:attr:`int`): Reflectometry scans that
make up the profile.
yaml_file (:py:attr:`str`): File path to instruction set.
directory (:py:attr:`str`): Outline for directory path.
title (:py:attr:`str`): A title for the experiment.
"""
the_boss = Foreperson(run_numbers, yaml_file, directory, title)
files_to_reduce = the_boss.reduction.input_files
print("-" * 10)
print('File Parsing')
print("-" * 10)
refl = refl_data.Profile(files_to_reduce, the_boss.reduction.parser,
the_boss.data_source.experiment.measurement.q_axis_name,
the_boss.data_source.experiment.measurement.theta_axis_name,
None, 0, the_boss.data_source.experiment.measurement.pixel_max,
the_boss.data_source.experiment.measurement.hot_pixel_max,
the_boss.data_source.experiment.measurement.transpose)
print("-" * 10)
print('Cropping')
print("-" * 10)
refl.crop(the_boss.reduction.crop_function, the_boss.reduction.crop_kwargs)
print("-" * 10)
print('Background Subtraction')
print("-" * 10)
refl.bkg_sub(the_boss.reduction.bkg_function,
the_boss.reduction.bkg_kwargs)
the_boss.reduction.data_state.background = 'corrected'
print("-" * 10)
print('Estimating Resolution Function')
print("-" * 10)
refl.resolution_function(
the_boss.data_source.experiment.measurement.qz_dimension,
progress=True)
the_boss.reduction.data_state.resolution = 'estimated'
print("-" * 10)
print('Performing Data Corrections')
print("-" * 10)
if the_boss.reduction.dcd_normalisation is not None:
itp = corrections.get_interpolator(
the_boss.reduction.dcd_normalisation, the_boss.reduction.parser)
refl.qdcd_normalisation(itp)
the_boss.reduction.data_state.dcd = 'normalised'
refl.footprint_correction(
the_boss.reduction.beam_width, the_boss.reduction.sample_size)
refl.transmission_normalisation()
the_boss.reduction.data_state.transmission = 'normalised'
refl.concatenate()
refl.normalise_ter()
the_boss.reduction.data_state.intensity = 'normalised'
if the_boss.data.rebin:
print("-" * 10)
print('Rebinning')
print("-" * 10)
if the_boss.data.q_min is None:
refl.rebin(number_of_q_vectors=the_boss.data.n_qvectors)
else:
if the_boss.data.q_space == 'linear':
spacing = np.linspace
elif the_boss.data.q_space == 'log':
spacing = np.logspace
refl.rebin(new_q=spacing(refl.q.min(), refl.q.max(),
the_boss.data.q_step))
the_boss.reduction.data_state.rebinned = the_boss.data.q_shape
the_boss.data_source.experiment.measurement.q_range = [
str(refl.q.min()), str(refl.q.max())]
the_boss.data.n_qvectors = str(len(refl.R))
try:
_ = the_boss.data.column_4
data = np.array([refl.q, refl.R, refl.dR, refl.dq]).T
np.savetxt(
(the_boss.directory_path + '/processing/XRR_{}.dat'.format(
run_numbers[0])), data,
header='{}\n 1 2 3 4'.format(dump(vars(the_boss))))
if the_boss.data.both:
data = np.array([refl.q, refl.R, refl.dR]).T
np.savetxt(
(the_boss.directory_path + '/processing/XRR_{}_3col.dat'.format(
run_numbers[0])), data,
header='{}\n 1 2 3'.format(dump(vars(the_boss))))
except:
data = np.array([refl.q, refl.R, refl.dR]).T
np.savetxt(
(the_boss.directory_path + '/processing/XRR_{}.dat'.format(
run_numbers[0])), data,
header='{}\n 1 2 3'.format(dump(vars(the_boss))))
print("-" * 10)
print('Reduced Data Stored in Processing Directory')
print("-" * 10)
