def burst(self, wavelength0, wavelength):
"""
Calculates pdf(wavelength) for burst time component
Parameters
----------
wavelength0: float
Nominal wavelength, Angstrom
wavelength: float
Wavelength, Angstrom
Returns
-------
pdf: float
Probability density function at `wavelength`
"""
# burst time
tc = general.tauC(wavelength0,
xsi=self.xsi,
z0=self.z0 / 1000,
freq=self.freq)
# time of flight
TOF = self.L / 1000.0 / general.wavelength_velocity(wavelength0)
width = tc / TOF * wavelength0
return stats.uniform.pdf(wavelength - wavelength0, -width / 2, width)
def width(self, wavelength0):
# burst width
tc = general.tauC(wavelength0, xsi=self.xsi, z0=self.z0 / 1000,
freq=self.freq)
# time of flight
TOF = self.L / 1000. / general.wavelength_velocity(wavelength0)
width = tc / TOF * wavelength0
# da width
width += self._da * wavelength0
# crossing width
tau_h = self.H / self.R / (2 * np.pi * self.freq)
width += tau_h / TOF * wavelength0
return width
def crossing(self, wavelength0, wavelength):
"""
Calculates pdf(wavelength) due to crossing time
Parameters
----------
wavelength0: float
nominal wavelength, Angstrom
wavelength: float
Wavelength, Angstrom
Returns
-------
pdf: float
Probability density function at `wavelength`
"""
tau_h = self.H / self.R / (2 * np.pi * self.freq)
TOF = self.L / 1000.0 / general.wavelength_velocity(wavelength0)
width = tau_h / TOF * wavelength0
return stats.uniform.pdf(wavelength - wavelength0, -width / 2, width)
def _reduce_single_angle(self, scale=1):
"""
Reduce a single angle.
"""
n_spectra = self.reflected_beam.n_spectra
n_tpixels = np.size(self.reflected_beam.m_topandtail, 1)
n_ypixels = np.size(self.reflected_beam.m_topandtail, 2)
# calculate omega and two_theta depending on the mode.
mode = self.reflected_beam.mode
# we'll need the wavelengths to calculate Q.
wavelengths = self.reflected_beam.m_lambda
m_twotheta = np.zeros((n_spectra, n_tpixels, n_ypixels))
detector_z_difference = (self.reflected_beam.detector_z -
self.direct_beam.detector_z)
beampos_z_difference = (self.reflected_beam.m_beampos -
self.direct_beam.m_beampos)
Y_PIXEL_SPACING = self.reflected_beam.cat.y_pixels_per_mm[0]
total_z_deflection = (detector_z_difference +
beampos_z_difference * Y_PIXEL_SPACING)
if mode in ['FOC', 'POL', 'POLANAL', 'MT']:
# omega_nom.shape = (N, )
omega_nom = np.degrees(
np.arctan(total_z_deflection / self.reflected_beam.detector_y)
/ 2.)
'''
Wavelength specific angle of incidence correction
This involves:
1) working out the trajectory of the neutrons through the
collimation system.
2) where those neutrons intersect the sample.
3) working out the elevation of the neutrons when they hit the
sample.
4) correcting the angle of incidence.
'''
speeds = general.wavelength_velocity(wavelengths)
collimation_distance = self.reflected_beam.cat.collimation_distance
s2_sample_distance = (self.reflected_beam.cat.sample_distance -
self.reflected_beam.cat.slit2_distance)
# work out the trajectories of the neutrons for them to pass
# through the collimation system.
trajectories = find_trajectory(collimation_distance / 1000., 0,
speeds)
# work out where the beam hits the sample
res = parabola_line_intersection_point(s2_sample_distance / 1000,
0, trajectories, speeds,
omega_nom[:, np.newaxis])
intersect_x, intersect_y, x_prime, elevation = res
# correct the angle of incidence with a wavelength dependent
# elevation.
omega_corrected = omega_nom[:, np.newaxis] - elevation
m_twotheta += np.arange(n_ypixels * 1.)[np.newaxis, np.newaxis, :]
m_twotheta -= self.direct_beam.m_beampos[:, np.newaxis, np.newaxis]
m_twotheta *= Y_PIXEL_SPACING
m_twotheta += detector_z_difference
m_twotheta /= (self.reflected_beam.detector_y[:, np.newaxis,
np.newaxis])
m_twotheta = np.arctan(m_twotheta)
m_twotheta = np.degrees(m_twotheta)
# you may be reflecting upside down, reverse the sign.
upside_down = np.sign(omega_corrected[:, 0])
m_twotheta *= upside_down[:, np.newaxis, np.newaxis]
omega_corrected *= upside_down[:, np.newaxis]
elif mode in ['SB', 'DB']:
# the angle of incidence is half the two theta of the reflected
# beam
omega = np.arctan(
total_z_deflection / self.reflected_beam.detector_y) / 2.
# work out two theta for each of the detector pixels
m_twotheta += np.arange(n_ypixels * 1.)[np.newaxis, np.newaxis, :]
m_twotheta -= self.direct_beam.m_beampos[:, np.newaxis, np.newaxis]
m_twotheta += detector_z_difference
m_twotheta -= (
self.reflected_beam.detector_y[:, np.newaxis, np.newaxis] *
np.tan(omega[:, np.newaxis, np.newaxis]))
m_twotheta /= (self.reflected_beam.detector_y[:, np.newaxis,
np.newaxis])
m_twotheta = np.arctan(m_twotheta)
m_twotheta += omega[:, np.newaxis, np.newaxis]
# still in radians at this point
# add an extra dimension, because omega_corrected needs to be the
# angle of incidence for each wavelength. I.e. should be
# broadcastable to (N, T)
omega_corrected = np.degrees(omega)[:, np.newaxis]
m_twotheta = np.degrees(m_twotheta)
'''
--Specular Reflectivity--
Use the (constant wavelength) spectra that have already been integrated
over 2theta (in processnexus) to calculate the specular reflectivity.
Beware: this is because m_topandtail has already been divided through
by monitor counts and error propagated (at the end of processnexus).
Thus, the 2theta pixels are correlated to some degree. If we use the 2D
plot to calculate reflectivity
(sum {Iref_{2theta, lambda}}/I_direct_{lambda}) then the error bars in
the reflectivity turn out much larger than they should be.
'''
ydata, ydata_sd = EP.EPdiv(self.reflected_beam.m_spec,
self.reflected_beam.m_spec_sd,
self.direct_beam.m_spec,
self.direct_beam.m_spec_sd)
# calculate the 1D Qz values.
xdata = general.q(omega_corrected, wavelengths)
xdata_sd = (self.reflected_beam.m_lambda_fwhm /
self.reflected_beam.m_lambda)**2
xdata_sd += (self.reflected_beam.domega[:, np.newaxis] /
omega_corrected)**2
xdata_sd = np.sqrt(xdata_sd) * xdata
'''
---Offspecular reflectivity---
normalise the counts in the reflected beam by the direct beam
spectrum this gives a reflectivity. Also propagate the errors,
leaving the fractional variance (dr/r)^2.
--Note-- that adjacent y-pixels (same wavelength) are correlated in
this treatment, so you can't just sum over them.
i.e. (c_0 / d) + ... + c_n / d) != (c_0 + ... + c_n) / d
'''
m_ref, m_ref_sd = EP.EPdiv(
self.reflected_beam.m_topandtail,
self.reflected_beam.m_topandtail_sd,
self.direct_beam.m_spec[:, :, np.newaxis],
self.direct_beam.m_spec_sd[:, :, np.newaxis])
# you may have had divide by zero's.
m_ref = np.where(np.isinf(m_ref), 0, m_ref)
m_ref_sd = np.where(np.isinf(m_ref_sd), 0, m_ref_sd)
# calculate the Q values for the detector pixels. Each pixel has
# different 2theta and different wavelength, ASSUME that they have the
# same angle of incidence
qx, qy, qz = general.q2(omega_corrected[:, :, np.newaxis], m_twotheta,
0, wavelengths[:, :, np.newaxis])
reduction = {}
reduction['x'] = self.x = xdata
reduction['x_err'] = self.x_err = xdata_sd
reduction['y'] = self.y = ydata / scale
reduction['y_err'] = self.y_err = ydata_sd / scale
reduction['omega'] = omega_corrected
reduction['m_twotheta'] = m_twotheta
reduction['m_ref'] = self.m_ref = m_ref
reduction['m_ref_err'] = self.m_ref_err = m_ref_sd
reduction['qz'] = self.m_qz = qz
reduction['qx'] = self.m_qx = qx
reduction['nspectra'] = self.n_spectra = n_spectra
reduction['start_time'] = self.reflected_beam.start_time
reduction['datafile_number'] = self.datafile_number = (
self.reflected_beam.datafile_number)
fnames = []
datasets = []
datafilename = self.reflected_beam.datafilename
datafilename = os.path.basename(datafilename.split('.nx.hdf')[0])
for i in range(n_spectra):
data_tup = self.data(scanpoint=i)
datasets.append(ReflectDataset(data_tup))
if self.save:
for i, dataset in enumerate(datasets):
fname = '{0}_{1}.dat'.format(datafilename, i)
fnames.append(fname)
with open(fname, 'wb') as f:
dataset.save(f)
fname = '{0}_{1}.xml'.format(datafilename, i)
with open(fname, 'wb') as f:
dataset.save_xml(f, start_time=reduction['start_time'][i])
reduction['fname'] = fnames
return datasets, deepcopy(reduction)
def _reduce_single_angle(self, scale=1):
"""
Reduce a single angle.
"""
n_spectra = self.reflected_beam.n_spectra
n_tpixels = np.size(self.reflected_beam.m_topandtail, 1)
n_ypixels = np.size(self.reflected_beam.m_topandtail, 2)
# calculate omega and two_theta depending on the mode.
mode = self.reflected_beam.mode
# we'll need the wavelengths to calculate Q.
wavelengths = self.reflected_beam.m_lambda
m_twotheta = np.zeros((n_spectra, n_tpixels, n_ypixels))
if mode in ['FOC', 'POL', 'POLANAL', 'MT']:
detector_z_difference = (self.reflected_beam.detector_z -
self.direct_beam.detector_z)
beampos_z_difference = (self.reflected_beam.m_beampos
- self.direct_beam.m_beampos)
total_z_deflection = (detector_z_difference
+ beampos_z_difference * Y_PIXEL_SPACING)
# omega_nom.shape = (N, )
omega_nom = np.degrees(np.arctan(total_z_deflection
/ self.reflected_beam.detector_y) / 2.)
'''
Wavelength specific angle of incidence correction
This involves:
1) working out the trajectory of the neutrons through the
collimation system.
2) where those neutrons intersect the sample.
3) working out the elevation of the neutrons when they hit the
sample.
4) correcting the angle of incidence.
'''
speeds = general.wavelength_velocity(wavelengths)
collimation_distance = self.reflected_beam.cat.collimation_distance
s2_sample_distance = (self.reflected_beam.cat.sample_distance
- self.reflected_beam.cat.slit2_distance)
# work out the trajectories of the neutrons for them to pass
# through the collimation system.
trajectories = pm.find_trajectory(collimation_distance / 1000.,
0, speeds)
# work out where the beam hits the sample
res = pm.parabola_line_intersection_point(s2_sample_distance / 1000,
0,
trajectories,
speeds,
omega_nom[:, np.newaxis])
intersect_x, intersect_y, x_prime, elevation = res
# correct the angle of incidence with a wavelength dependent
# elevation.
omega_corrected = omega_nom[:, np.newaxis] - elevation
elif mode == 'SB' or mode == 'DB':
omega = self.reflected_beam.M_beampos + self.reflected_beam.detectorZ[:, np.newaxis]
omega -= self.direct_beam.M_beampos + self.direct_beam.detectorZ
omega /= 2 * self.reflected_beam.detectorY[:, np.newaxis, np.newaxis]
omega = np.arctan(omega)
m_twotheta += np.arange(n_ypixels * 1.)[np.newaxis, np.newaxis, :] * Y_PIXEL_SPACING
m_twotheta += self.reflected_beam.detectorZ[:, np.newaxis, np.newaxis]
m_twotheta -= self.direct_beam.M_beampos[:, :, np.newaxis] + self.direct_beam.detectorZ
m_twotheta -= self.reflected_beam.detectorY[:, np.newaxis, np.newaxis] * np.tan(omega[:, :, np.newaxis])
m_twotheta /= self.reflected_beam.detectorY[:, np.newaxis, np.newaxis]
m_twotheta = np.arctan(m_twotheta)
m_twotheta += omega[:, :, np.newaxis]
'''
--Specular Reflectivity--
Use the (constant wavelength) spectra that have already been integrated
over 2theta (in processnexus) to calculate the specular reflectivity.
Beware: this is because m_topandtail has already been divided through
by monitor counts and error propagated (at the end of processnexus).
Thus, the 2theta pixels are correlated to some degree. If we use the 2D
plot to calculate reflectivity
(sum {Iref_{2theta, lambda}}/I_direct_{lambda}) then the error bars in
the reflectivity turn out much larger than they should be.
'''
ydata, ydata_sd = EP.EPdiv(self.reflected_beam.m_spec,
self.reflected_beam.m_spec_sd,
self.direct_beam.m_spec,
self.direct_beam.m_spec_sd)
# calculate the 1D Qz values.
xdata = general.q(omega_corrected, wavelengths)
xdata_sd = (self.reflected_beam.m_lambda_fwhm
/ self.reflected_beam.m_lambda) ** 2
xdata_sd += (self.reflected_beam.domega[:, np.newaxis]
/ omega_corrected) ** 2
xdata_sd = np.sqrt(xdata_sd) * xdata
'''
---Offspecular reflectivity---
normalise the counts in the reflected beam by the direct beam
spectrum this gives a reflectivity. Also propagate the errors,
leaving the fractional variance (dr/r)^2.
--Note-- that adjacent y-pixels (same wavelength) are correlated in this
treatment, so you can't just sum over them.
i.e. (c_0 / d) + ... + c_n / d) != (c_0 + ... + c_n) / d
'''
m_ref, m_ref_sd = EP.EPdiv(self.reflected_beam.m_topandtail,
self.reflected_beam.m_topandtail_sd,
self.direct_beam.m_spec[:, :, np.newaxis],
self.direct_beam.m_spec_sd[:, :, np.newaxis])
# you may have had divide by zero's.
m_ref = np.where(np.isinf(m_ref), 0, m_ref)
m_ref_sd = np.where(np.isinf(m_ref_sd), 0, m_ref_sd)
# calculate the Q values for the detector pixels. Each pixel has
# different 2theta and different wavelength, ASSUME that they have the
# same angle of incidence
qx, qy, qz = general.q2(omega_corrected[:, :, np.newaxis],
m_twotheta,
0,
wavelengths[:, :, np.newaxis])
reduction = {}
reduction['xdata'] = self.xdata = xdata
reduction['xdata_sd'] = self.xdata_sd = xdata_sd
reduction['ydata'] = self.ydata = ydata
reduction['ydata_sd'] = self.ydata_sd = ydata_sd
reduction['m_ref'] = self.m_ref = m_ref
reduction['m_ref_sd'] = self.m_ref_sd = m_ref_sd
reduction['qz'] = self.m_qz = qz
reduction['qy'] = self.m_qy = qy
reduction['nspectra'] = self.n_spectra = n_spectra
reduction['datafile_number'] = self.datafile_number = (
self.reflected_beam.datafile_number)
fnames = []
if self.save:
for i in range(n_spectra):
data_tup = self.data(scanpoint=i)
dataset = ReflectDataset(data_tup)
fname = 'PLP{0:07d}_{1}.dat'.format(self.datafile_number, i)
fnames.append(fname)
with open(fname, 'wb') as f:
dataset.save(f)
fname = 'PLP{0:07d}_{1}.xml'.format(self.datafile_number, i)
with open(fname, 'wb') as f:
dataset.save_xml(f)
reduction['fname'] = fnames
return deepcopy(reduction)
def sample(self, samples, random_state=None):
"""
Sample the beam for reflected signal.
2400000 samples roughly corresponds to 1200 sec of *PLATYPUS* using
dlambda=3.3 and dtheta=3.3 at angle=0.65.
150000000 samples roughly corresponds to 3600 sec of *PLATYPUS* using
dlambda=3.3 and dtheta=3.3 at angle=3.0.
(The sample number <--> actual acquisition time correspondence has
not been checked fully)
Parameters
----------
samples: int
How many samples to run.
random_state: {int, `~np.random.RandomState`, `~np.random.Generator`}, optional
If `random_state` is not specified the
`~np.random.RandomState` singleton is used.
If `random_state` is an int, a new ``RandomState`` instance is used,
seeded with seed.
If `random_state` is already a ``RandomState`` or a ``Generator``
instance, then that object is used.
Specify `random_state` for repeatable minimizations.
"""
# grab a random number generator
rng = check_random_state(random_state)
# generate neutrons of various wavelengths
wavelengths = self.spectrum_dist.rvs(size=samples, random_state=rng)
# generate neutrons of different angular divergence
angles = self.angular_dist.rvs(samples, random_state=rng) + self.angle
# angular deviation due to gravity
# --> no correction for gravity affecting width of angular resolution
if self.gravity:
speeds = general.wavelength_velocity(wavelengths)
# trajectories through slits for different wavelengths
trajectories = pm.find_trajectory(self.L12 / 1000.0, 0, speeds)
# elevation at sample
elevations = pm.elevation(
trajectories, speeds, (self.L12 + self.L2S) / 1000.0
)
angles -= elevations
# calculate Q
q = general.q(angles, wavelengths)
# calculate reflectivities for a neutron of a given Q.
# the angular resolution smearing has already been done. The wavelength
# resolution smearing follows.
r = self.model(q, x_err=0.0)
# accept or reject neutrons based on the reflectivity of
# sample at a given Q.
criterion = rng.uniform(size=samples)
accepted = criterion < r
# implement wavelength smearing from choppers. Jitter the wavelengths
# by a uniform distribution whose full width is dlambda / 0.68.
if self.force_gaussian:
noise = rng.standard_normal(size=samples)
jittered_wavelengths = wavelengths * (
1 + self.dlambda / 2.3548 * noise
)
else:
noise = rng.uniform(-0.5, 0.5, size=samples)
jittered_wavelengths = wavelengths * (
1 + self.dlambda / 0.68 * noise
)
# update reflected beam counts. Rebin smearing
# is taken into account due to the finite size of the wavelength
# bins.
hist = np.histogram(
jittered_wavelengths[accepted], self.wavelength_bins
)
self.reflected_beam += hist[0]
self.bmon_reflect += float(samples)
# update resolution kernel. If we have more than 100000 in all
# bins skip
if (
len(self._res_kernel)
and np.min([len(v) for v in self._res_kernel.values()]) > 500000
):
return
bin_loc = np.digitize(jittered_wavelengths, self.wavelength_bins)
for i in range(1, len(self.wavelength_bins)):
# extract q values that fall in each wavelength bin
q_for_bin = np.copy(q[bin_loc == i])
q_samples_so_far = self._res_kernel.get(i - 1, np.array([]))
updated_samples = np.concatenate((q_samples_so_far, q_for_bin))
# no need to keep double precision for these sample arrays
self._res_kernel[i - 1] = updated_samples.astype(np.float32)
def __init__(
self,
model,
angle,
L12=2859,
footprint=60,
L2S=120,
dtheta=3.3,
lo_wavelength=2.8,
hi_wavelength=18,
dlambda=3.3,
rebin=2,
gravity=False,
force_gaussian=False,
force_uniform_wavelength=False,
):
self.model = model
self.bkg = model.bkg.value
self.angle = angle
# dlambda refers to the FWHM of the gaussian approximation to a uniform
# distribution. The full width of the uniform distribution is
# dlambda/0.68.
self.dlambda = dlambda / 100.0
# the rebin percentage refers to the full width of the bins. You have to
# multiply this value by 0.68 to get the equivalent contribution to the
# resolution function.
self.rebin = rebin / 100.0
self.wavelength_bins = calculate_wavelength_bins(
lo_wavelength, hi_wavelength, rebin
)
bin_centre = 0.5 * (
self.wavelength_bins[1:] + self.wavelength_bins[:-1]
)
# angular deviation due to gravity
# --> no correction for gravity affecting width of angular resolution
elevations = 0
if gravity:
speeds = general.wavelength_velocity(bin_centre)
# trajectories through slits for different wavelengths
trajectories = pm.find_trajectory(L12 / 1000.0, 0, speeds)
# elevation at sample
elevations = pm.elevation(
trajectories, speeds, (L12 + L2S) / 1000.0
)
# nominal Q values
self.q = general.q(angle - elevations, bin_centre)
# keep a tally of the direct and reflected beam
self.direct_beam = np.zeros((self.wavelength_bins.size - 1))
self.reflected_beam = np.zeros((self.wavelength_bins.size - 1))
# beam monitor counts for normalisation
self.bmon_direct = 0
self.bmon_reflect = 0
self.gravity = gravity
# wavelength generator
self.force_uniform_wavelength = force_uniform_wavelength
if force_uniform_wavelength:
self.spectrum_dist = uniform(
loc=lo_wavelength - 1, scale=hi_wavelength - lo_wavelength + 1
)
else:
a = PN("PLP0000711.nx.hdf")
q, i, di = a.process(
normalise=False,
normalise_bins=False,
rebin_percent=0.5,
lo_wavelength=max(0, lo_wavelength - 1),
hi_wavelength=hi_wavelength + 1,
)
q = q.squeeze()
i = i.squeeze()
self.spectrum_dist = SpectrumDist(q, i)
self.force_gaussian = force_gaussian
# angular resolution generator, based on a trapezoidal distribution
# The slit settings are the optimised set typically used in an
# experiment. dtheta/theta refers to the FWHM of a Gaussian
# approximation to a trapezoid.
# stores the q vectors contributing towards each datapoint
self._res_kernel = {}
self._min_samples = 0
self.dtheta = dtheta / 100.0
self.footprint = footprint
self.angle = angle
self.L2S = L2S
self.L12 = L12
s1, s2 = general.slit_optimiser(
footprint,
self.dtheta,
angle=angle,
L2S=L2S,
L12=L12,
verbose=False,
)
div, alpha, beta = general.div(s1, s2, L12=L12)
self.div, self.s1, self.s2 = s1, s2, div
if force_gaussian:
self.angular_dist = norm(scale=div / 2.3548)
else:
self.angular_dist = trapz(
c=(alpha - beta) / 2.0 / alpha,
d=(alpha + beta) / 2.0 / alpha,
loc=-alpha,
scale=2 * alpha,
)
def test_wavelength_velocity(self):
speed = general.wavelength_velocity(20.)
assert_almost_equal(speed, 197.8017006541796, 5)
def test_wavelength_velocity(self):
speed = general.wavelength_velocity(20.)
assert_almost_equal(speed, 197.8017006541796, 5)
def process(
self,
h5norm=None,
lo_wavelength=2.5,
hi_wavelength=19.0,
background=True,
direct=False,
omega=None,
twotheta=None,
rebin_percent=1.0,
wavelength_bins=None,
normalise=True,
integrate=-1,
eventmode=None,
peak_pos=None,
background_mask=None,
normalise_bins=True,
**kwds
):
"""
Processes the ProcessNexus object to produce a time of flight spectrum.
The processed spectrum is stored in the `processed_spectrum` attribute.
The specular spectrum is also returned from this function.
Parameters
----------
h5norm : HDF5 NeXus file
The hdf5 file containing the floodfield data.
lo_wavelength : float
The low wavelength cutoff for the rebinned data (A).
hi_wavelength : float
The high wavelength cutoff for the rebinned data (A).
background : bool
Should a background subtraction be carried out?
direct : bool
Is it a direct beam you measured? This is so a gravity correction
can be applied.
omega : float
Expected angle of incidence of beam. If this is None, then the
rough angle of incidence is obtained from the NeXus file.
twotheta : float
Expected two theta value of specular beam. If this is None then
the rough angle of incidence is obtained from the NeXus file.
rebin_percent : float
Specifies the rebinning percentage for the spectrum. If
`rebin_percent is None`, then no rebinning is done.
wavelength_bins : array_like
The wavelength bins for rebinning. If `wavelength_bins is not
None` then the `rebin_percent` parameter is ignored.
normalise : bool
Normalise by the monitor counts.
integrate : int
- integrate == -1
the spectrum is integrated over all the scanpoints.
- integrate >= 0
the individual spectra are calculated individually.
If `eventmode is not None` then integrate specifies which
scanpoint to examine.
eventmode : None or array_like
If eventmode is `None` then the integrated detector image is used.
If eventmode is an array then the array specifies the integration
times (in seconds) for the detector image, e.g. [0, 20, 30] would
result in two spectra. The first would contain data for 0 s to 20s,
the second would contain data for 20 s to 30 s. This option can
only be used when `integrate >= -1`.
If eventmode has zero length (e.g. []), then a single time interval
for the entire acquisition is used, [0, acquisition_time]. This
would source the image from the eventmode file, rather than the
NeXUS file.
peak_pos : None or (float, float)
Specifies the peak position and peak standard deviation to use.
background_mask : array_like
An array of bool that specifies which y-pixels to use for
background subtraction. Should be the same length as the number of
y pixels in the detector image. Otherwise an automatic mask is
applied (if background is True).
normalise_bins : bool
Divides the intensity in each wavelength bin by the width of the
bin. This allows one to compare spectra even if they were processed
with different rebin percentages.
Notes
-----
After processing this object contains the following the following
attributes:
- path - path to the data file
- datafilename - name of the datafile
- datafile_number - datafile number.
- m_topandtail - the corrected 2D detector image, (n_spectra, TOF, Y)
- m_topandtail_sd - corresponding standard deviations
- n_spectra - number of spectra in processed data
- bm1_counts - beam montor counts, (n_spectra,)
- m_spec - specular intensity, (n_spectra, TOF)
- m_spec_sd - corresponding standard deviations
- m_beampos - beam_centre for each spectrum, (n_spectra, )
- m_lambda - wavelengths for each spectrum, (n_spectra, TOF)
- m_lambda_fwhm - corresponding FWHM of wavelength distribution
- m_lambda_hist - wavelength bins for each spectrum, (n_spectra, TOF)
- m_spec_tof - TOF for each wavelength bin, (n_spectra, TOF)
- mode - the Platypus mode, e.g. FOC/MT/POL/POLANAL/SB/DB
- detector_z - detector height, (n_spectra, )
- detector_y - sample-detector distance, (n_spectra, )
- domega - collimation uncertainty
- lopx - lowest extent of specular beam (in y pixels), (n_spectra, )
- hipx - highest extent of specular beam (in y pixels), (n_spectra, )
Returns
-------
m_lambda, m_spec, m_spec_sd: np.ndarray
Arrays containing the wavelength, specular intensity as a function
of wavelength, standard deviation of specular intensity
"""
cat = self.cat
scanpoint = 0
# beam monitor counts for normalising data
bm1_counts = cat.bm1_counts.astype("float64")
# TOF bins
TOF = cat.t_bins.astype("float64")
# This section controls how multiple detector images are handled.
# We want event streaming.
if eventmode is not None:
scanpoint = integrate
if integrate == -1:
scanpoint = 0
output = self.process_event_stream(scanpoint=scanpoint, frame_bins=eventmode)
frame_bins, detector, bm1_counts = output
else:
# we don't want detector streaming
detector = cat.detector
scanpoint = 0
# integrate over all spectra
if integrate == -1:
detector = np.sum(detector, 0)[np.newaxis,]
bm1_counts[:] = np.sum(bm1_counts)
n_spectra = np.size(detector, 0)
# Up until this point detector.shape=(N, T, Y,
# pre-average over x, leaving (n, t, y) also convert to dp
detector = np.sum(detector, axis=3, dtype="float64")
# detector shape should now be (n, t, y)
# calculate the counting uncertainties
detector_sd = np.sqrt(detector)
bm1_counts_sd = np.sqrt(bm1_counts)
# detector normalisation with a water file
if h5norm:
x_bins = cat.x_bins[scanpoint]
# shape (y,)
detector_norm, detector_norm_sd = create_detector_norm(h5norm, x_bins[0], x_bins[1])
# detector has shape (N, T, Y), shape of detector_norm should
# broadcast to (1, 1, y)
# TODO: Correlated Uncertainties?
detector, detector_sd = EP.EPdiv(detector, detector_sd, detector_norm, detector_norm_sd)
# shape of these is (n_spectra, TOFbins)
m_spec_tof_hist = np.zeros((n_spectra, np.size(TOF, 0)), dtype="float64")
m_lambda_hist = np.zeros((n_spectra, np.size(TOF, 0)), dtype="float64")
m_spec_tof_hist[:] = TOF
"""
chopper to detector distances
note that if eventmode is specified the n_spectra is NOT
equal to the number of entries in e.g. /longitudinal_translation
this means you have to copy values in from the correct scanpoint
"""
flight_distance = np.zeros(n_spectra, dtype="float64")
d_cx = np.zeros(n_spectra, dtype="float64")
detpositions = np.zeros(n_spectra, dtype="float64")
# The angular divergence of the instrument
domega = np.zeros(n_spectra, dtype="float64")
phase_angle = np.zeros(n_spectra, dtype="float64")
# process each of the spectra taken in the detector image
originalscanpoint = scanpoint
for idx in range(n_spectra):
freq = cat.frequency[scanpoint]
# calculate the angular divergence
domega[idx] = general.div(
cat.ss2vg[scanpoint], cat.ss3vg[scanpoint], (cat.slit3_distance[0] - cat.slit2_distance[0])
)[0]
"""
work out the total flight length
IMPORTANT: this varies as a function of twotheta. This is
because the Platypus detector does not move on an arc.
At high angles chod can be ~ 0.75% different. This is will
visibly shift fringes.
"""
if omega is None:
omega = cat.omega[scanpoint]
if twotheta is None:
twotheta = cat.twotheta[scanpoint]
output = self.chod(omega, twotheta, scanpoint=scanpoint)
flight_distance[idx], d_cx[idx] = output
# calculate phase openings
output = self.phase_angle(scanpoint)
phase_angle[scanpoint], master_opening = output
"""
toffset - the time difference between the magnet pickup on the
choppers (TTL pulse), which is situated in the middle of the
chopper window, and the trailing edge of chopper 1, which is
supposed to be time0. However, if there is a phase opening this
time offset has to be relocated slightly, as time0 is not at the
trailing edge.
"""
poff = cat.chopper1_phase_offset[0]
poffset = 1.0e6 * poff / (2.0 * 360.0 * freq)
toffset = (
poffset
+ 1.0e6 * master_opening / 2 / (2 * np.pi) / freq
- 1.0e6 * phase_angle[scanpoint] / (360 * 2 * freq)
)
m_spec_tof_hist[idx] -= toffset
detpositions[idx] = cat.dy[scanpoint]
if eventmode is not None:
m_spec_tof_hist[:] = TOF - toffset
flight_distance[:] = flight_distance[0]
detpositions[:] = detpositions[0]
break
else:
scanpoint += 1
scanpoint = originalscanpoint
# convert TOF to lambda
# m_spec_tof_hist (n, t) and chod is (n,)
m_lambda_hist = general.velocity_wavelength(1.0e3 * flight_distance[:, np.newaxis] / m_spec_tof_hist)
m_lambda = 0.5 * (m_lambda_hist[:, 1:] + m_lambda_hist[:, :-1])
TOF -= toffset
# gravity correction if direct beam
if direct:
# TODO: Correlated Uncertainties?
output = correct_for_gravity(
detector,
detector_sd,
m_lambda,
self.cat.collimation_distance,
self.cat.dy,
lo_wavelength,
hi_wavelength,
)
detector, detector_sd, m_gravcorrcoefs = output
beam_centre, beam_sd = find_specular_ridge(detector, detector_sd)
# beam_centre = m_gravcorrcoefs
else:
beam_centre, beam_sd = find_specular_ridge(detector, detector_sd)
# you want to specify the specular ridge on the averaged detector image
if peak_pos is not None:
beam_centre = np.ones(n_spectra) * peak_pos[0]
beam_sd = np.ones(n_spectra) * peak_pos[1]
"""
Rebinning in lambda for all detector
Rebinning is the default option, but sometimes you don't want to.
detector shape input is (n, t, y)
"""
if wavelength_bins is not None:
rebinning = wavelength_bins
elif 0.0 < rebin_percent < 15.0:
rebinning = calculate_wavelength_bins(lo_wavelength, hi_wavelength, rebin_percent)
# rebin_percent percentage is zero. No rebinning, just cutoff
# wavelength
else:
rebinning = m_lambda_hist[0, :]
rebinning = rebinning[np.searchsorted(rebinning, lo_wavelength) : np.searchsorted(rebinning, hi_wavelength)]
"""
now do the rebinning for all the N detector images
rebin.rebinND could do all of these at once. However, m_lambda_hist
could vary across the range of spectra. If it was the same I could
eliminate the loop.
"""
output = []
output_sd = []
for idx in range(n_spectra):
# TODO: Correlated Uncertainties?
plane, plane_sd = rebin.rebin_along_axis(
detector[idx], m_lambda_hist[idx], rebinning, y1_sd=detector_sd[idx]
)
output.append(plane)
output_sd.append(plane_sd)
detector = np.array(output)
detector_sd = np.array(output_sd)
if len(detector.shape) == 2:
detector = detector[np.newaxis,]
detector_sd = detector_sd[np.newaxis,]
# (1, T)
m_lambda_hist = np.atleast_2d(rebinning)
"""
Divide the detector intensities by the width of the wavelength bin.
This is so the intensities between different rebinning strategies can
be compared.
"""
if normalise_bins:
div = 1 / np.ediff1d(m_lambda_hist[0])[:, np.newaxis]
detector, detector_sd = EP.EPmulk(detector, detector_sd, div)
# convert the wavelength base to a timebase
m_spec_tof_hist = 0.001 * flight_distance[:, np.newaxis] / general.wavelength_velocity(m_lambda_hist)
m_lambda = 0.5 * (m_lambda_hist[:, 1:] + m_lambda_hist[:, :-1])
m_spec_tof = 0.001 * flight_distance[:, np.newaxis] / general.wavelength_velocity(m_lambda)
# we want to integrate over the following pixel region
lopx = np.floor(beam_centre - beam_sd * EXTENT_MULT).astype("int")
hipx = np.ceil(beam_centre + beam_sd * EXTENT_MULT).astype("int")
m_spec = np.zeros((n_spectra, np.size(detector, 1)))
m_spec_sd = np.zeros_like(m_spec)
# background subtraction
if background:
if background_mask is not None:
# background_mask is (Y), need to make 3 dimensional (N, T, Y)
# first make into (T, Y)
backgnd_mask = np.repeat(background_mask[np.newaxis, :], detector.shape[1], axis=0)
# make into (N, T, Y)
full_backgnd_mask = np.repeat(backgnd_mask[np.newaxis, :], n_spectra, axis=0)
else:
# there may be different background regions for each spectrum
# in the file
y1 = np.round(lopx - PIXEL_OFFSET).astype("int")
y0 = np.round(y1 - (EXTENT_MULT * beam_sd)).astype("int")
y2 = np.round(hipx + PIXEL_OFFSET).astype("int")
y3 = np.round(y2 + (EXTENT_MULT * beam_sd)).astype("int")
full_backgnd_mask = np.zeros_like(detector, dtype="bool")
for i in range(n_spectra):
full_backgnd_mask[i, :, y0[i] : y1[i]] = True
full_backgnd_mask[i, :, y2[i] + 1 : y3[i] + 1] = True
# TODO: Correlated Uncertainties?
detector, detector_sd = background_subtract(detector, detector_sd, full_backgnd_mask)
"""
top and tail the specular beam with the known beam centres.
All this does is produce a specular intensity with shape (N, T),
i.e. integrate over specular beam
"""
for i in range(n_spectra):
m_spec[i] = np.sum(detector[i, :, lopx[i] : hipx[i] + 1], axis=1)
sd = np.sum(detector_sd[i, :, lopx[i] : hipx[i] + 1] ** 2, axis=1)
m_spec_sd[i] = np.sqrt(sd)
# assert np.isfinite(m_spec).all()
# assert np.isfinite(m_specSD).all()
# assert np.isfinite(detector).all()
# assert np.isfinite(detectorSD).all()
# normalise by beam monitor 1.
if normalise:
m_spec, m_spec_sd = EP.EPdiv(m_spec, m_spec_sd, bm1_counts[:, np.newaxis], bm1_counts_sd[:, np.newaxis])
output = EP.EPdiv(
detector, detector_sd, bm1_counts[:, np.newaxis, np.newaxis], bm1_counts_sd[:, np.newaxis, np.newaxis]
)
detector, detector_sd = output
"""
now work out dlambda/lambda, the resolution contribution from
wavelength.
van Well, Physica B, 357(2005) pp204-207), eqn 4.
this is only an approximation for our instrument, as the 2nd and 3rd
discs have smaller openings compared to the master chopper.
Therefore the burst time needs to be looked at.
"""
tau_da = m_spec_tof_hist[:, 1:] - m_spec_tof_hist[:, :-1]
m_lambda_fwhm = general.resolution_double_chopper(
m_lambda,
z0=d_cx[:, np.newaxis] / 1000.0,
freq=cat.frequency[:, np.newaxis],
L=flight_distance[:, np.newaxis] / 1000.0,
H=cat.ss2vg[originalscanpoint] / 1000.0,
xsi=phase_angle[:, np.newaxis],
tau_da=tau_da,
)
m_lambda_fwhm *= m_lambda
# put the detector positions and mode into the dictionary as well.
detector_z = cat.dz
detector_y = cat.dy
mode = cat.mode
d = dict()
d["path"] = cat.path
d["datafilename"] = cat.filename
d["datafile_number"] = cat.datafile_number
if h5norm is not None:
d["normfilename"] = h5norm.filename
d["m_topandtail"] = detector
d["m_topandtail_sd"] = detector_sd
d["n_spectra"] = n_spectra
d["bm1_counts"] = bm1_counts
d["m_spec"] = m_spec
d["m_spec_sd"] = m_spec_sd
d["m_beampos"] = beam_centre
d["m_lambda"] = m_lambda
d["m_lambda_fwhm"] = m_lambda_fwhm
d["m_lambda_hist"] = m_lambda_hist
d["m_spec_tof"] = m_spec_tof
d["mode"] = mode
d["detector_z"] = detector_z
d["detector_y"] = detector_y
d["domega"] = domega
d["lopx"] = lopx
d["hipx"] = hipx
self.processed_spectrum = d
return m_lambda, m_spec, m_spec_sd
def correct_for_gravity(detector, detector_sd, lamda, coll_distance, sample_det, lo_wavelength, hi_wavelength, theta=0):
"""
Returns a gravity corrected yt plot, given the data, its associated errors,
the wavelength corresponding to each of the time bins, and the trajectory
of the neutrons. Low lambda and high Lambda are wavelength cutoffs to
ignore.
Parameters
----------
detector : np.ndarray
Detector image. Has shape (N, T, Y)
detector_sd : np.ndarray
Standard deviations of detector image
lamda : np.ndarray
Wavelengths corresponding to the detector image, has shape (N, T)
coll_distance : float
Collimation distance between slits, mm
sample_det : float
Sample - detector distance, mm
lo_wavelength : float
Low wavelength cut off, Angstrom
hi_wavelength : float
High wavelength cutoff, Angstrom
theta : float
Angle between second collimation slit, first collimation slit, and
horizontal
Returns
-------
corrected_data, corrected_data_sd, m_gravcorrcoefs :
np.ndarray, np.ndarray, np.ndarray
Corrected image. This is a theoretical prediction where the spectral
ridge is for each wavelength. This will be used to calculate the
actual angle of incidence in the reduction process.
"""
num_lambda = np.size(lamda, axis=1)
x_init = np.arange((np.size(detector, axis=2) + 1) * 1.0) - 0.5
m_gravcorrcoefs = np.zeros((np.size(detector, 0)), dtype="float64")
corrected_data = np.zeros_like(detector)
corrected_data_sd = np.zeros_like(detector)
for spec in range(np.size(detector, 0)):
neutron_speeds = general.wavelength_velocity(lamda[spec])
trajectories = pm.find_trajectory(coll_distance / 1000.0, theta, neutron_speeds)
travel_distance = (coll_distance + sample_det[spec]) / 1000.0
# centres(t,)
# TODO, don't use centroids, use Gaussian peak
centroids = np.apply_along_axis(ut.centroid, 1, detector[spec])
lopx = np.searchsorted(lamda[spec], lo_wavelength)
hipx = np.searchsorted(lamda[spec], hi_wavelength)
def f(tru_centre):
deflections = pm.y_deflection(trajectories[lopx:hipx], neutron_speeds[lopx:hipx], travel_distance)
model = 1000.0 * deflections / Y_PIXEL_SPACING + tru_centre
diff = model - centroids[lopx:hipx, 0]
diff = diff[~np.isnan(diff)]
return diff
# find the beam centre for an infinitely fast neutron
x0 = np.array([np.nanmean(centroids[lopx:hipx, 0])])
res = leastsq(f, x0)
m_gravcorrcoefs[spec] = res[0][0]
total_deflection = 1000.0 * pm.y_deflection(trajectories, neutron_speeds, travel_distance)
total_deflection /= Y_PIXEL_SPACING
x_rebin = x_init.T + total_deflection[:, np.newaxis]
for wavelength in range(np.size(detector, axis=1)):
output = rebin.rebin(
x_init, detector[spec, wavelength], x_rebin[wavelength], y1_sd=detector_sd[spec, wavelength]
)
corrected_data[spec, wavelength] = output[0]
corrected_data_sd[spec, wavelength] = output[1]
return corrected_data, corrected_data_sd, m_gravcorrcoefs