diff --git a/docs/source/concepts/AbinsInterpolatedBroadening.rst b/docs/source/concepts/AbinsInterpolatedBroadening.rst
index f222922755c2b1ccbd3392d61eb6731bdd57a743..ec6e2da241a7b5ad67602b95f01772143f28ca33 100644
--- a/docs/source/concepts/AbinsInterpolatedBroadening.rst
+++ b/docs/source/concepts/AbinsInterpolatedBroadening.rst
@@ -315,35 +315,27 @@ We can build on this by performing convolution of the full spectrum with each of
ax1.plot(frequencies, intensities, 'k-', label='Unbroadened spectrum')
# Narrow limit
- freq_points, spectrum = Broadening.broaden_spectrum(frequencies=frequencies,
- bins=bins,
- s_dft=intensities,
- sigma=(peak1_sigma * np.ones_like(frequencies)),
- scheme='gaussian')
+ freq_points, spectrum = Broadening.broaden_spectrum(
+ frequencies, bins, intensities,
+ (peak1_sigma * np.ones_like(frequencies)),
+ scheme='gaussian')
ax2.plot(freq_points, spectrum, label='Convolve with min(sigma)')
# Broad limit
- freq_points, spectrum = Broadening.broaden_spectrum(frequencies=frequencies,
- bins=bins,
- s_dft=intensities,
- sigma=(peak2_sigma * np.ones_like(frequencies)),
- scheme='gaussian')
+ freq_points, spectrum = Broadening.broaden_spectrum(
+ frequencies, bins, intensities,
+ (peak2_sigma * np.ones_like(frequencies)),
+ scheme='gaussian')
ax2.plot(freq_points, spectrum, label='Convolve with max(sigma)')
# Reference method: sum individually
- freq_points, spectrum = Broadening.broaden_spectrum(frequencies=frequencies,
- bins=bins,
- s_dft=intensities,
- sigma=sigma,
- scheme='gaussian')
+ freq_points, spectrum = Broadening.broaden_spectrum(
+ frequencies, bins, intensities, sigma, scheme='gaussian')
ax3.plot(freq_points, spectrum, 'k-', label='Sum individual peaks')
# Interpolated
- freq_points, spectrum = Broadening.broaden_spectrum(frequencies=frequencies,
- bins=bins,
- s_dft=intensities,
- sigma=sigma,
- scheme='interpolate')
+ freq_points, spectrum = Broadening.broaden_spectrum(
+ frequencies, bins, intensities, sigma, scheme='interpolate')
ax2.plot(freq_points, spectrum, c='C2', linestyle='--', label='Interpolated', zorder=0.5)
ax3.plot(freq_points, spectrum, c='C2', linestyle='--', label='Interpolated', zorder=0.5)
diff --git a/scripts/AbinsModules/Instruments/Broadening.py b/scripts/AbinsModules/Instruments/Broadening.py
index 2e3f77f6c6270e60985569a8f3c978befb3ea80d..a8a20f01d2f45e6dd9d2352f339055659fa001f7 100644
--- a/scripts/AbinsModules/Instruments/Broadening.py
+++ b/scripts/AbinsModules/Instruments/Broadening.py
@@ -12,7 +12,7 @@ from scipy.signal import convolve
prebin_required_schemes = ['interpolate', 'interpolate_coarse']
-def broaden_spectrum(frequencies=None, bins=None, s_dft=None, sigma=None, scheme='gaussian_truncated'):
+def broaden_spectrum(frequencies, bins, s_dft, sigma, scheme='gaussian_truncated'):
"""Convert frequency/S data to broadened spectrum on a regular grid
Several algorithms are implemented, for purposes of
@@ -28,13 +28,13 @@ def broaden_spectrum(frequencies=None, bins=None, s_dft=None, sigma=None, scheme
:param s_dft: scattering values corresponding to *frequencies*
:type s_dft: 1D array-like
:param sigma:
- width of broadening function. This may be a scalar used over the whole spectrum, or a series of values
+ width of broadening function. This should be a scalar used over the whole spectrum, or a series of values
corresponding to *frequencies*.
:type sigma: float or 1D array-like
:param scheme:
Name of broadening method used. Options:
- - none: Return the input data
+ - none: Return the input data as a histogram, ignoring the value of sigma
- gaussian: Evaluate a Gaussian on the output grid for every input point and sum them. Simple but slow, and
recommended only for benchmarking and reference calculations.
- normal: Generate histograms with appropriately-located normal distributions for every input point. In
diff --git a/scripts/AbinsModules/Instruments/ToscaInstrument.py b/scripts/AbinsModules/Instruments/ToscaInstrument.py
index 582b97de9a3fec178e473054b92175d7b0b87e00..9bc3b9b42a0252012b55022359de94d7c8872a65 100644
--- a/scripts/AbinsModules/Instruments/ToscaInstrument.py
+++ b/scripts/AbinsModules/Instruments/ToscaInstrument.py
@@ -105,9 +105,6 @@ class ToscaInstrument(Instrument, FrequencyPowderGenerator):
sigma = self.get_sigma(frequencies)
- points_freq, broadened_spectrum = broaden_spectrum(frequencies=frequencies,
- s_dft=s_dft,
- bins=bins,
- sigma=sigma,
- scheme=selected_scheme)
+ points_freq, broadened_spectrum = broaden_spectrum(frequencies, bins, s_dft,
+ sigma, scheme=selected_scheme)
return points_freq, broadened_spectrum
diff --git a/scripts/test/Abins/AbinsBroadeningTest.py b/scripts/test/Abins/AbinsBroadeningTest.py
index 917aae8783b5fdcca52897e9d157387ba69af2b3..c92f9e73d3f1f9acd7a1845ebe5db91cff5cfa01 100644
--- a/scripts/test/Abins/AbinsBroadeningTest.py
+++ b/scripts/test/Abins/AbinsBroadeningTest.py
@@ -86,11 +86,8 @@ class AbinsBroadeningTest(unittest.TestCase):
results = {}
for scheme in schemes:
- _, results[scheme] = Broadening.broaden_spectrum(frequencies=freq_points,
- bins=bins,
- s_dft=s_dft,
- sigma=sigma,
- scheme=scheme)
+ _, results[scheme] = Broadening.broaden_spectrum(
+ freq_points, bins, s_dft, sigma, scheme)
for scheme in schemes:
# Interpolate scheme is approximate so just check a couple of sig.fig.
@@ -114,9 +111,7 @@ class AbinsBroadeningTest(unittest.TestCase):
'normal', 'normal_truncated']
for scheme in schemes:
- Broadening.broaden_spectrum(frequencies=frequencies,
- bins=bins, s_dft=s_dft, sigma=sigma,
- scheme=scheme)
+ Broadening.broaden_spectrum(frequencies, bins, s_dft, sigma, scheme=scheme)
def test_broadening_normalisation(self):
"""Check broadening implementations do not change overall intensity"""
@@ -137,20 +132,14 @@ class AbinsBroadeningTest(unittest.TestCase):
# Full Gaussian should reproduce null total
for scheme in ('none', 'gaussian'):
- freq_points, spectrum = Broadening.broaden_spectrum(frequencies=frequencies,
- bins=bins,
- s_dft=s_dft,
- sigma=sigma,
- scheme=scheme)
+ freq_points, spectrum = Broadening.broaden_spectrum(
+ frequencies, bins, s_dft, sigma, scheme=scheme)
self.assertAlmostEqual(sum(spectrum),
pre_broadening_total,)
# Normal scheme reproduces area as well as total;
- freq_points, full_spectrum = Broadening.broaden_spectrum(frequencies=frequencies,
- bins=bins,
- s_dft=s_dft,
- sigma=sigma,
- scheme='normal')
+ freq_points, full_spectrum = Broadening.broaden_spectrum(
+ frequencies, bins, s_dft, sigma, scheme='normal')
self.assertAlmostEqual(np.trapz(spectrum, x=freq_points),
pre_broadening_total * (bins[1] - bins[0]),)
self.assertAlmostEqual(sum(spectrum), pre_broadening_total)
@@ -158,22 +147,16 @@ class AbinsBroadeningTest(unittest.TestCase):
# truncated forms will be a little off but shouldn't be _too_ off
for scheme in ('gaussian_truncated', 'normal_truncated'):
- freq_points, trunc_spectrum = Broadening.broaden_spectrum(frequencies=frequencies,
- bins=bins,
- s_dft=s_dft,
- sigma=sigma,
- scheme=scheme)
+ freq_points, trunc_spectrum = Broadening.broaden_spectrum(
+ frequencies, bins, s_dft, sigma, scheme)
self.assertLess(abs(sum(full_spectrum) - sum(trunc_spectrum)) / sum(full_spectrum),
0.03)
# Interpolated methods need histogram input and smooth sigma
hist_spec, _ = np.histogram(frequencies, bins, weights=s_dft)
hist_sigma = sigma_func(freq_points)
- freq_points, interp_spectrum = Broadening.broaden_spectrum(frequencies=freq_points,
- bins=bins,
- s_dft=hist_spec,
- sigma=hist_sigma,
- scheme='interpolate')
+ freq_points, interp_spectrum = Broadening.broaden_spectrum(
+ freq_points, bins, hist_spec, hist_sigma, scheme='interpolate')
self.assertLess(abs(sum(interp_spectrum) - pre_broadening_total) / pre_broadening_total,
0.05)