class TestReduce(unittest.TestCase):
def setUp(self):
path = os.path.dirname(__file__)
self.path = path
self.cwd = os.getcwd()
self.tmpdir = TemporaryDirectory()
os.chdir(self.tmpdir.name)
return 0
def tearDown(self):
os.chdir(self.cwd)
self.tmpdir.cleanup()
def test_batch_reduce(self):
filename = os.path.join(self.path, "test_batch_reduction.xls")
b = BatchReducer(filename, data_folder=self.path, verbose=False)
b.reduce(show=False)
def test_batch_reduce_ipython(self):
filename = os.path.join(self.path, "test_batch_reduction.xls")
refnx.reduce.batchreduction._have_ipython = False
b = BatchReducer(filename, data_folder=self.path, verbose=False)
b.reduce(show=False)
refnx.reduce.batchreduction._have_ipython = True
b = BatchReducer(filename, data_folder=self.path, verbose=False)
b.reduce(show=False)
def setUp(self):
path = os.path.dirname(__file__)
self.path = path
self.cwd = os.getcwd()
self.tmpdir = TemporaryDirectory()
os.chdir(self.tmpdir.name)
return 0
def setUp(self):
path = os.path.dirname(__file__)
self.path = path
self.f113 = PlatypusNexus(os.path.join(self.path, "PLP0011613.nx.hdf"))
self.f641 = PlatypusNexus(os.path.join(self.path, "PLP0011641.nx.hdf"))
self.cwd = os.getcwd()
self.tmpdir = TemporaryDirectory()
os.chdir(self.tmpdir.name)
return 0
class TestPlatypusNexus(unittest.TestCase):
def setUp(self):
path = os.path.dirname(__file__)
self.path = path
self.f113 = PlatypusNexus(os.path.join(self.path, "PLP0011613.nx.hdf"))
self.f641 = PlatypusNexus(os.path.join(self.path, "PLP0011641.nx.hdf"))
self.cwd = os.getcwd()
self.tmpdir = TemporaryDirectory()
os.chdir(self.tmpdir.name)
return 0
def tearDown(self):
os.chdir(self.cwd)
self.tmpdir.cleanup()
def test_chod(self):
flight_length = self.f113.chod()
assert_almost_equal(flight_length[0], 7141.413818359375)
assert_almost_equal(flight_length[1], 808)
flight_length = self.f641.chod(omega=1.8, twotheta=3.6)
assert_almost_equal(flight_length[0], 7146.3567785516016)
assert_almost_equal(flight_length[1], 808)
def test_phase_angle(self):
# TODO. Add more tests where the phase_angle isn't zero.
phase_angle, master_opening = self.f641.phase_angle()
assert_almost_equal(phase_angle, 0)
assert_almost_equal(master_opening, 1.04719755)
def test_background_subtract_line(self):
# checked each step of the background subtraction with IGOR
# so this test background correction should be correct.
# create some test data
xvals = np.linspace(-10, 10, 201)
yvals = np.ceil(gauss(xvals, 0, 100, 0, 1) + 2 * xvals + 30)
# add some reproducible random noise
np.random.seed(1)
yvals += np.sqrt(yvals) * np.random.randn(yvals.size)
yvals_sd = np.sqrt(yvals)
mask = np.zeros(201, np.bool)
mask[30:70] = True
mask[130:160] = True
profile, profile_sd = plp.background_subtract_line(yvals, yvals_sd, mask)
verified_data = np.load(os.path.join(self.path, "background_subtract.npy"))
assert_almost_equal(verified_data, np.c_[profile, profile_sd])
def test_find_specular_ridge(self):
xvals = np.linspace(-10, 10, 201)
yvals = np.ceil(gauss(xvals, 0, 1000, 0, 1))
detector = np.repeat(yvals[:, np.newaxis], 1000, axis=1).T
detector_sd = np.sqrt(detector)
output = plp.find_specular_ridge(detector[np.newaxis, :], detector_sd[np.newaxis, :])
assert_(len(output) == 2)
assert_almost_equal(output[0][0], 100)
def test_background_subtract(self):
# create some test data
xvals = np.linspace(-10, 10, 201)
yvals = np.ceil(gauss(xvals, 0, 100, 0, 1) + 2 * xvals + 30)
# add some reproducible random noise
np.random.seed(1)
yvals += np.sqrt(yvals) * np.random.randn(yvals.size)
yvals_sd = np.sqrt(yvals)
# now make an (N, T, Y) detector image
n_tbins = 10
detector = np.repeat(yvals, n_tbins).reshape(xvals.size, n_tbins).T
detector_sd = np.repeat(yvals_sd, n_tbins).reshape(xvals.size, n_tbins).T
detector = detector.reshape(1, n_tbins, xvals.size)
detector_sd = detector_sd.reshape(1, n_tbins, xvals.size)
mask = np.zeros((1, n_tbins, 201), np.bool)
mask[:, :, 30:70] = True
mask[:, :, 130:160] = True
det_bkg, detSD_bkg = plp.background_subtract(detector, detector_sd, mask)
# each of the (N, T) entries should have the same background subtracted
# entries
verified_data = np.load(os.path.join(self.path, "background_subtract.npy"))
it = np.nditer(detector, flags=["multi_index"])
it.remove_axis(2)
while not it.finished:
profile = det_bkg[it.multi_index]
profile_sd = detSD_bkg[it.multi_index]
assert_almost_equal(verified_data, np.c_[profile, profile_sd])
it.iternext()
def test_calculate_bins(self):
bins = plp.calculate_wavelength_bins(2.0, 18, 2.0)
assert_almost_equal(bins[0], 1.98)
assert_almost_equal(bins[-1], 18.18)
def test_event(self):
# When you use event mode processing, make sure the right amount of
# spectra are created
out = self.f113.process(eventmode=[0, 900, 1800], integrate=0)
assert_(np.size(out[1], axis=0) == 2)
def test_event_folder(self):
# When you use event mode processing, make sure the right amount of
# spectra are created
out = self.f113.process(eventmode=[0, 900, 1800], integrate=0, event_folder=self.path)
def test_multiple_acquisitions(self):
"""
TODO: add a dataset which has multiple spectra in it, and make sure it
processes.
"""
pass
def test_reduction_runs(self):
# just check it runs
self.f113.process()
# check that event mode reduction gives the same output as non-event
# mode reduction.
spectrum0 = self.f113.process(direct=True)
spectrum1 = self.f113.process(direct=True, eventmode=[], integrate=0)
assert_allclose(spectrum0[1][0], spectrum1[1][0], rtol=0.001)
# check that the wavelength resolution is roughly right, between 7 and
# 8%.
res = self.f113.processed_spectrum["m_lambda_fwhm"][0] / self.f113.processed_spectrum["m_lambda"][0]
assert_array_less(res, np.ones_like(res) * 0.08)
assert_array_less(np.ones_like(res) * 0.07, res)
def test_save_spectrum(self):
# test saving spectrum
self.f113.process()
# can save the spectra by supplying a filename
self.f113.write_spectrum_xml("test.xml")
self.f113.write_spectrum_dat("test.dat")
# can save by supplying file handle:
with open("test.xml", "wb") as f:
self.f113.write_spectrum_xml(f)
# can save by supplying file handle:
with open("test.dat", "wb") as f:
self.f113.write_spectrum_xml(f)
def test_accumulate_files(self):
fnames = ["PLP0000708.nx.hdf", "PLP0000709.nx.hdf"]
pths = [os.path.join(self.path, fname) for fname in fnames]
plp.accumulate_HDF_files(pths)
f8, f9, fadd = None, None, None
try:
f8 = h5py.File(os.path.join(self.path, "PLP0000708.nx.hdf"), "r")
f9 = h5py.File(os.path.join(self.path, "PLP0000709.nx.hdf"), "r")
fadd = h5py.File(os.path.join(os.getcwd(), "ADD_PLP0000708.nx.hdf"), "r")
f8d = f8["entry1/data/hmm"][0]
f9d = f9["entry1/data/hmm"][0]
faddd = fadd["entry1/data/hmm"][0]
assert_equal(faddd, f8d + f9d)
finally:
if f8 is not None:
f8.close()
if f9 is not None:
f9.close()
if fadd is not None:
fadd.close()
def test_accumulate_files_reduce(self):
# test by adding a file to itself. Should have smaller stats
fnames = ["PLP0000708.nx.hdf", "PLP0000708.nx.hdf"]
pths = [os.path.join(self.path, fname) for fname in fnames]
plp.accumulate_HDF_files(pths)
# it should be processable
fadd = PlatypusNexus(os.path.join(os.getcwd(), "ADD_PLP0000708.nx.hdf"))
fadd.process()
# it should also be reduceable
reducer = ReducePlatypus(os.path.join(self.path, "PLP0000711.nx.hdf"))
reduced = reducer.reduce(os.path.join(os.getcwd(), "ADD_PLP0000708.nx.hdf"))
assert_("ydata" in reduced)
# the error bars should be smaller
reduced2 = reducer.reduce(os.path.join(self.path, "PLP0000708.nx.hdf"))
assert_(np.all(reduced["ydata_sd"] < reduced2["ydata_sd"]))
class TestReduce(unittest.TestCase):
def setUp(self):
path = os.path.dirname(__file__)
self.path = path
self.cwd = os.getcwd()
self.tmpdir = TemporaryDirectory()
os.chdir(self.tmpdir.name)
return 0
def tearDown(self):
os.chdir(self.cwd)
self.tmpdir.cleanup()
def test_smoke(self):
# a quick smoke test to check that the reduction can occur
a, fname = reduce_stitch([708, 709, 710], [711, 711, 711],
data_folder=self.path, rebin_percent=2)
a.save('test1.dat')
def test_reduction_method(self):
# a quick smoke test to check that the reduction can occur
a = PlatypusReduce('PLP0000711.nx.hdf', data_folder=self.path,
rebin_percent=4)
# try reduction with the reduce method
a.reduce('PLP0000708.nx.hdf', data_folder=self.path, rebin_percent=4)
# try reduction with the __call__ method
a('PLP0000708.nx.hdf', data_folder=self.path, rebin_percent=4)
# this should also have saved a couple of files in the current
# directory
assert_(os.path.isfile('./PLP0000708_0.dat'))
assert_(os.path.isfile('./PLP0000708_0.xml'))
# try writing offspecular data
a.write_offspecular('offspec.xml', 0)
def test_event_reduction(self):
# check that eventmode reduction can occur, and that there are the
# correct number of datasets produced.
a = PlatypusReduce(
os.path.join(self.path, 'PLP0011613.nx.hdf'),
reflect=os.path.join(self.path, 'PLP0011641.nx.hdf'),
integrate=0, rebin_percent=2,
eventmode=[0, 900, 1800])
assert_equal(a.ydata.shape[0], 2)
# check that the resolutions are pretty much the same
assert_allclose(a.xdata_sd[0] / a.xdata[0],
a.xdata_sd[1] / a.xdata[1],
atol=0.001)
# check that the (right?) timestamps are written into the datafile
tree = ET.parse(os.path.join(os.getcwd(), 'PLP0011641_1.xml'))
tree.find('.//REFentry').attrib['time']
# TODO, timestamp is created in the local time stamp of the testing
# machine. The following works if reduced with a computer in Australian
# EST.
# assert_(t == '2012-01-20T22:05:32')
# what happens if you have too many frame bins
a = PlatypusReduce(
os.path.join(self.path, 'PLP0011613.nx.hdf'),
reflect=os.path.join(self.path, 'PLP0011641.nx.hdf'),
integrate=0, rebin_percent=2,
eventmode=[0, 25200, 27000, 30000])
assert_equal(a.ydata.shape[0], 1)
class TestPlatypusNexus(unittest.TestCase):
def setUp(self):
path = os.path.dirname(__file__)
self.path = path
self.f113 = PlatypusNexus(os.path.join(self.path,
'PLP0011613.nx.hdf'))
self.f641 = PlatypusNexus(os.path.join(self.path,
'PLP0011641.nx.hdf'))
self.cwd = os.getcwd()
self.tmpdir = TemporaryDirectory()
os.chdir(self.tmpdir.name)
return 0
def tearDown(self):
os.chdir(self.cwd)
self.tmpdir.cleanup()
def test_chod(self):
flight_length = self.f113.chod()
assert_almost_equal(flight_length[0], 7141.413818359375)
assert_almost_equal(flight_length[1], 808)
flight_length = self.f641.chod(omega=1.8, twotheta=3.6)
assert_almost_equal(flight_length[0], 7146.3567785516016)
assert_almost_equal(flight_length[1], 808)
def test_phase_angle(self):
# TODO. Add more tests where the phase_angle isn't zero.
phase_angle, master_opening = self.f641.phase_angle()
assert_almost_equal(phase_angle, 0)
assert_almost_equal(master_opening, 1.04719755)
def test_background_subtract_line(self):
# checked each step of the background subtraction with IGOR
# so this test background correction should be correct.
# create some test data
xvals = np.linspace(-10, 10, 201)
yvals = np.ceil(gauss(xvals, 0, 100, 0, 1) + 2 * xvals + 30)
# add some reproducible random noise
np.random.seed(1)
yvals += np.sqrt(yvals) * np.random.randn(yvals.size)
yvals_sd = np.sqrt(yvals)
mask = np.zeros(201, np.bool)
mask[30:70] = True
mask[130:160] = True
profile, profile_sd = plp.background_subtract_line(yvals,
yvals_sd,
mask)
verified_data = np.load(os.path.join(self.path,
'background_subtract.npy'))
assert_almost_equal(verified_data, np.c_[profile, profile_sd])
def test_find_specular_ridge(self):
xvals = np.linspace(-10, 10, 201)
yvals = np.ceil(gauss(xvals, 0, 1000, 0, 1))
detector = np.repeat(yvals[:, np.newaxis], 1000, axis=1).T
detector_sd = np.sqrt(detector)
output = plp.find_specular_ridge(detector[np.newaxis, :],
detector_sd[np.newaxis, :])
assert_(len(output) == 5)
assert_almost_equal(output[0][0], 100)
def test_background_subtract(self):
# create some test data
xvals = np.linspace(-10, 10, 201)
yvals = np.ceil(gauss(xvals, 0, 100, 0, 1) + 2 * xvals + 30)
# add some reproducible random noise
np.random.seed(1)
yvals += np.sqrt(yvals) * np.random.randn(yvals.size)
yvals_sd = np.sqrt(yvals)
# now make an (N, T, Y) detector image
n_tbins = 10
detector = np.repeat(yvals,
n_tbins).reshape(xvals.size, n_tbins).T
detector_sd = np.repeat(yvals_sd,
n_tbins).reshape(xvals.size, n_tbins).T
detector = detector.reshape(1, n_tbins, xvals.size)
detector_sd = detector_sd.reshape(1, n_tbins, xvals.size)
mask = np.zeros((1, n_tbins, 201), np.bool)
mask[:, :, 30:70] = True
mask[:, :, 130:160] = True
det_bkg, detSD_bkg = plp.background_subtract(detector,
detector_sd,
mask)
# each of the (N, T) entries should have the same background subtracted
# entries
verified_data = np.load(os.path.join(self.path,
'background_subtract.npy'))
it = np.nditer(detector, flags=['multi_index'])
it.remove_axis(2)
while not it.finished:
profile = det_bkg[it.multi_index]
profile_sd = detSD_bkg[it.multi_index]
assert_almost_equal(verified_data, np.c_[profile, profile_sd])
it.iternext()
def test_calculate_bins(self):
bins = plp.calculate_wavelength_bins(2., 18, 2.)
assert_almost_equal(bins[0], 1.98)
assert_almost_equal(bins[-1], 18.18)
def test_event(self):
# When you use event mode processing, make sure the right amount of
# spectra are created
out = self.f113.process(eventmode=[0, 900, 1800], integrate=0)
assert_(np.size(out[1], axis=0) == 2)
def test_event_folder(self):
# When you use event mode processing, make sure the right amount of
# spectra are created
self.f113.process(eventmode=[0, 900, 1800], integrate=0,
event_folder=self.path)
def test_multiple_acquisitions(self):
"""
TODO: add a dataset which has multiple spectra in it, and make sure it
processes.
"""
pass
def test_reduction_runs(self):
# just check it runs
self.f113.process()
# check that event mode reduction gives the same output as non-event
# mode reduction.
spectrum0 = self.f113.process(direct=True)
spectrum1 = self.f113.process(direct=True, eventmode=[], integrate=0)
assert_allclose(spectrum0[1][0], spectrum1[1][0], rtol=0.001)
# check that the wavelength resolution is roughly right, between 7 and
# 8%.
res = (self.f113.processed_spectrum['m_lambda_fwhm'][0] /
self.f113.processed_spectrum['m_lambda'][0])
assert_array_less(res, np.ones_like(res) * 0.08)
assert_array_less(np.ones_like(res) * 0.07, res)
def test_save_spectrum(self):
# test saving spectrum
self.f113.process()
# can save the spectra by supplying a filename
self.f113.write_spectrum_xml('test.xml')
self.f113.write_spectrum_dat('test.dat')
# can save by supplying file handle:
with open('test.xml', 'wb') as f:
self.f113.write_spectrum_xml(f)
# can save by supplying file handle:
with open('test.dat', 'wb') as f:
self.f113.write_spectrum_xml(f)
def test_accumulate_files(self):
fnames = ['PLP0000708.nx.hdf', 'PLP0000709.nx.hdf']
pths = [os.path.join(self.path, fname) for fname in fnames]
plp.accumulate_HDF_files(pths)
f8, f9, fadd = None, None, None
try:
f8 = h5py.File(os.path.join(self.path,
'PLP0000708.nx.hdf'), 'r')
f9 = h5py.File(os.path.join(self.path,
'PLP0000709.nx.hdf'), 'r')
fadd = h5py.File(os.path.join(os.getcwd(),
'ADD_PLP0000708.nx.hdf'), 'r')
f8d = f8['entry1/data/hmm'][0]
f9d = f9['entry1/data/hmm'][0]
faddd = fadd['entry1/data/hmm'][0]
assert_equal(faddd, f8d + f9d)
finally:
if f8 is not None:
f8.close()
if f9 is not None:
f9.close()
if fadd is not None:
fadd.close()
def test_accumulate_files_reduce(self):
# test by adding a file to itself. Should have smaller stats
fnames = ['PLP0000708.nx.hdf', 'PLP0000708.nx.hdf']
pths = [os.path.join(self.path, fname) for fname in fnames]
plp.accumulate_HDF_files(pths)
# it should be processable
fadd = PlatypusNexus(os.path.join(os.getcwd(),
'ADD_PLP0000708.nx.hdf'))
fadd.process()
# it should also be reduceable
reducer = PlatypusReduce(os.path.join(self.path,
'PLP0000711.nx.hdf'))
reduced = reducer.reduce(os.path.join(os.getcwd(),
'ADD_PLP0000708.nx.hdf'))
assert_('ydata' in reduced)
# the error bars should be smaller
reduced2 = reducer.reduce(os.path.join(self.path, 'PLP0000708.nx.hdf'))
assert_(np.all(reduced['ydata_sd'] < reduced2['ydata_sd']))
def test_manual_beam_find(self):
# you can specify a function that finds where the specular ridge is.
def manual_beam_find(detector, detector_sd):
beam_centre = np.zeros(len(detector))
beam_sd = np.zeros(len(detector))
beam_centre += 50
beam_sd += 5
return beam_centre, beam_sd, np.array([40]), np.array([60]), [[]]
# the manual beam find is only mandatory when peak_pos == -1.
self.f113.process(manual_beam_find=manual_beam_find,
peak_pos=-1)
assert_equal(self.f113.processed_spectrum['m_beampos'][0], 50)
# manual beam finding also specifies the lower and upper pixel of the
# foreground
assert_equal(self.f113.processed_spectrum['lopx'][0], 40)
assert_equal(self.f113.processed_spectrum['hipx'][0], 60)
def test_fore_back_region(self):
# calculation of foreground and background regions is done correctly
centres = np.array([100., 90.])
sd = np.array([5., 11.5])
lopx, hipx, background_pixels = fore_back_region(centres, sd)
assert_(len(lopx) == 2)
assert_(len(hipx) == 2)
assert_(len(background_pixels) == 2)
assert_(isinstance(lopx[0], numbers.Integral))
assert_(isinstance(hipx[0], numbers.Integral))
calc_lower = np.floor(centres - sd * EXTENT_MULT)
assert_equal(lopx, calc_lower)
calc_higher = np.ceil(centres + sd * EXTENT_MULT)
assert_equal(hipx, calc_higher)
y1 = np.atleast_1d(
np.round(lopx - PIXEL_OFFSET).astype('int'))
y0 = np.atleast_1d(
np.round(lopx - PIXEL_OFFSET - EXTENT_MULT * sd).astype('int'))
y2 = np.atleast_1d(
np.round(hipx + PIXEL_OFFSET).astype('int'))
y3 = np.atleast_1d(
np.round(hipx + PIXEL_OFFSET + EXTENT_MULT * sd).astype('int'))
bp = np.r_[np.arange(y0[0], y1[0] + 1),
np.arange(y2[0], y3[0] + 1)]
assert_equal(bp, background_pixels[0])
def test_basename_datafile(self):
# check that the right basename is returned
pth = 'a/b/c.nx.hdf'
assert_(basename_datafile(pth) == 'c')
pth = 'c.nx.hdf'
assert_(basename_datafile(pth) == 'c')
