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_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_peak_finder(self):
mean = 10.1234
sd = 5.55
x = np.linspace(-100, 100.5, 101)
y = peak_utils.gauss(x, 0, 10, mean, sd)
res = peak_utils.peak_finder(y, x=x)
assert_almost_equal(res[1][0], mean)
assert_almost_equal(res[1][1], sd)
def test_peak_finder(self):
mean = 10.1234
sd = 5.55
x = np.linspace(-100, 100.5, 101)
y = peak_utils.gauss(x, 0, 10, mean, sd)
res = peak_utils.peak_finder(y, x=x)
assert_almost_equal(res[1][0], mean)
assert_almost_equal(res[1][1], 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_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, bool)
mask[30:70] = True
mask[130:160] = True
profile, profile_sd = plp.background_subtract_line(
yvals, yvals_sd, mask)
verified_data = np.load(pjoin(self.pth, "background_subtract.npy"))
assert_almost_equal(verified_data, np.c_[profile, profile_sd])
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.pth, '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()
