def reduce_xrdml(f, bkg=None, scale=1, sample_length=None):
"""
Reduces a Panalytical XRDML file
Parameters
----------
f: file-like object or string
The specular reflectivity (XRDML) file of interest
bkg: list
A list of file-like objects or strings that contain background
measurements. The background is assumed to have the same number of
points as the specular reflectivity curve. The backgrounds are
averaged and subtracted from the specular reflectivity
scale: float
The direct beam intensity (cps)
sample_length: None or float
If None then no footprint correction is done. Otherwise the transverse
footprint of the sample (mm).
Returns
-------
specular_q, specular_r, specular_dr: np.ndarray
The specular reflectivity as a function of momentum transfer, Q.
"""
spec = parse_xrdml_file(f)
reflectivity = spec['intensities'] / spec['count_time']
reflectivity_s = np.sqrt(reflectivity) / spec['count_time']
# do the background subtraction
if bkg is not None:
bkgds = [parse_xrdml_file(fi) for fi in bkg]
bkgd_refs = np.r_[[bkgd['intensities'] for bkgd in bkgds]]
bkgd_refs_s = np.r_[[np.sqrt(bkgd['intensities']) / bkgd['count_time']
for bkgd in bkgds]]
bkgd_refs_var = bkgd_refs_s ** 2
weights = 1. / bkgd_refs_var
numerator = np.sum(bkgd_refs * weights, axis=0)
denominator = np.sum(weights, axis=0)
total_bkgd = numerator / denominator
total_bkgd_s = np.sqrt(1 / denominator)
reflectivity, reflectivity_s = EP.EPsub(reflectivity,
reflectivity_s,
total_bkgd,
total_bkgd_s)
# work out the Q values
qx, qy, qz = general.q2(spec['omega'],
spec['twotheta'],
np.zeros_like(spec['omega']),
spec['wavelength'])
# do a footprint correction
if sample_length is not None:
footprint_correction = general.beamfrac(np.array([XRR_BEAMWIDTH_SD]) *
2.35,
np.array([sample_length]),
spec['omega'])
reflectivity /= footprint_correction
reflectivity_s /= footprint_correction
# divide by the direct beam intensity
# assumes that the direct beam intensity is enormous, so the counting
# uncertainties in the scale factor are negligible.
reflectivity /= scale
reflectivity_s /= scale
return qz, reflectivity, reflectivity_s
def reduce_xrdml(f, bkg=None, scale=None, sample_length=None):
"""
Reduces a Panalytical XRDML file
Parameters
----------
f: file-like object or string
The specular reflectivity (XRDML) file of interest
bkg: list
A list of file-like objects or strings that contain background
measurements. The background is assumed to have the same number of
points as the specular reflectivity curve. The backgrounds are
averaged and subtracted from the specular reflectivity
scale: float, None
The direct beam intensity (cps). If `scale is None` then the dataset
is scaled by the point with maximum intensity below Q = 0.0318 (Q_crit
for Si at 8.048 keV).
sample_length: None or float
If None then no footprint correction is done. Otherwise the transverse
footprint of the sample (mm).
Returns
-------
dataset: refnx.dataset.ReflectDataset
The specular reflectivity as a function of momentum transfer, Q.
"""
spec = parse_xrdml_file(f)
reflectivity = spec["intensities"] / spec["count_time"]
reflectivity_s = np.sqrt(reflectivity) / spec["count_time"]
# do the background subtraction
if bkg is not None:
bkgds = [parse_xrdml_file(fi) for fi in bkg]
bkgd_refs = np.r_[[bkgd["intensities"] for bkgd in bkgds]]
bkgd_refs_s = np.r_[[
np.sqrt(bkgd["intensities"]) / bkgd["count_time"] for bkgd in bkgds
]]
bkgd_refs_var = bkgd_refs_s**2
weights = 1.0 / bkgd_refs_var
numerator = np.sum(bkgd_refs * weights, axis=0)
denominator = np.sum(weights, axis=0)
total_bkgd = numerator / denominator
total_bkgd_s = np.sqrt(1 / denominator)
reflectivity, reflectivity_s = EP.EPsub(reflectivity, reflectivity_s,
total_bkgd, total_bkgd_s)
# work out the Q values
qx, qy, qz = general.q2(
spec["omega"],
spec["twotheta"],
np.zeros_like(spec["omega"]),
spec["wavelength"],
)
# do a footprint correction
if sample_length is not None:
footprint_correction = general.beamfrac(
np.array([XRR_BEAMWIDTH_SD]) * 2.35,
np.array([sample_length]),
spec["omega"],
)
reflectivity /= footprint_correction
reflectivity_s /= footprint_correction
# divide by the direct beam intensity
# assumes that the direct beam intensity is enormous, so the counting
# uncertainties in the scale factor are negligible.
if scale is None:
# no scale factor was specifed, so normalise by highest intensity point
# below Qc for Silicon at 8.048 keV
below_qc = qz[qz < 0.0318]
if len(below_qc):
scale = np.max(reflectivity[qz < 0.0318])
reflectivity /= scale
reflectivity_s /= scale
d = ReflectDataset(data=(qz, reflectivity, reflectivity_s))
return d
def reduce_xrdml(f, bkg=None, scale=1, sample_length=None):
"""
Reduces a Panalytical XRDML file
Parameters
----------
f: file-like object or string
The specular reflectivity (XRDML) file of interest
bkg: list
A list of file-like objects or strings that contain background
measurements. The background is assumed to have the same number of
points as the specular reflectivity curve. The backgrounds are
averaged and subtracted from the specular reflectivity
scale: float
The direct beam intensity (cps)
sample_length: None or float
If None then no footprint correction is done. Otherwise the transverse
footprint of the sample (mm).
Returns
-------
specular_q, specular_r, specular_dr: np.ndarray
The specular reflectivity as a function of momentum transfer, Q.
"""
spec = parse_xrdml_file(f)
reflectivity = spec['intensities'] / spec['count_time']
reflectivity_s = np.sqrt(reflectivity) / spec['count_time']
# do the background subtraction
if bkg is not None:
bkgds = [parse_xrdml_file(fi) for fi in bkg]
bkgd_refs = np.r_[[bkgd['intensities'] for bkgd in bkgds]]
bkgd_refs_s = np.r_[[
np.sqrt(bkgd['intensities']) / bkgd['count_time'] for bkgd in bkgds
]]
bkgd_refs_var = bkgd_refs_s**2
weights = 1. / bkgd_refs_var
numerator = np.sum(bkgd_refs * weights, axis=0)
denominator = np.sum(weights, axis=0)
total_bkgd = numerator / denominator
total_bkgd_s = np.sqrt(1 / denominator)
reflectivity, reflectivity_s = EP.EPsub(reflectivity, reflectivity_s,
total_bkgd, total_bkgd_s)
# work out the Q values
qx, qy, qz = general.q2(spec['omega'], spec['twotheta'],
np.zeros_like(spec['omega']), spec['wavelength'])
# do a footprint correction
if sample_length is not None:
footprint_correction = general.beamfrac(
np.array([XRR_BEAMWIDTH_SD]) * 2.35, np.array([sample_length]),
spec['omega'])
reflectivity /= footprint_correction
reflectivity_s /= footprint_correction
# divide by the direct beam intensity
# assumes that the direct beam intensity is enormous, so the counting
# uncertainties in the scale factor are negligible.
reflectivity /= scale
reflectivity_s /= scale
return qz, reflectivity, reflectivity_s