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will no longer fail for insanely small i/sigma #46

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Jan 29, 2021
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will no longer fail for insanely small i/sigma #46

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67 changes: 3 additions & 64 deletions reciprocalspaceship/algorithms/scale_merged_intensities.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,8 +39,9 @@ def _french_wilson_posterior_quad(Iobs, SigIobs, Sigma, centric, npoints=200):
SigIobs = np.array(SigIobs, dtype=np.float64)
Sigma = np.array(Sigma, dtype=np.float64)

Jmin = np.maximum(Iobs - 20.*SigIobs, 0.)
Jmax = Iobs + 20.*SigIobs
half_window_size = 20.
Jmin = np.maximum(Iobs - half_window_size*SigIobs, 0.)
Jmax = Jmin + 2.*half_window_size*SigIobs
grid,weights = np.polynomial.legendre.leggauss(npoints)

J = (Jmax - Jmin)[:,None] * grid / 2. + (Jmax + Jmin)[:,None] / 2.
Expand All @@ -63,68 +64,6 @@ def _french_wilson_posterior_quad(Iobs, SigIobs, Sigma, centric, npoints=200):
variance_F = np.exp(log_prefactor + logsumexp(logweights+2.*logF+logP + logL - logZ[:,None], axis=1)) - mean_F**2
return mean, np.sqrt(variance), mean_F, np.sqrt(variance_F)

def _acentric_posterior(Iobs, SigIobs, Sigma, npoints=200):
"""
Compute the mean and std deviation of the acentric French-Wilson posterior.

Parameters
----------
Iobs : np.ndarray (float)
Observed merged refl intensities
SigIobs : np.ndarray (float)
Observed merged refl std deviation
Sigma : np.ndarray (float)
Average intensity in the resolution bin corresponding to Iobs, SigIobs
npoints : int
Number of grid points at which to evaluate the integrand. See the `deg`
parameter in the numpy
`documentation <https://numpy.org/doc/stable/reference/generated/numpy.polynomial.legendre.leggauss.html`_
for more details.

Returns
-------
mean_I : np.ndarray (float)
Mean posterior intensity
std_I : np.ndarray (float)
Standard deviation of posterior intensity
mean_F : np.ndarray (float)
Mean posterior structure factor amplitude
std_F :np.ndarray (float)
Standard deviation of posterior structure factor amplitude
"""
return _french_wilson_posterior_quad(Iobs, SigIobs, Sigma, False, npoints)

def _centric_posterior(Iobs, SigIobs, Sigma, npoints=200):
"""
Compute the mean and std deviation of the centric French-Wilson posterior.

Parameters
----------
Iobs : np.ndarray (float)
Observed merged refl intensities
SigIobs : np.ndarray (float)
Observed merged refl std deviation
Sigma : np.ndarray (float)
Average intensity in the resolution bin corresponding to Iobs, SigIobs
npoints : int
Number of grid points at which to evaluate the integrand. See the `deg`
parameter in the numpy
`documentation <https://numpy.org/doc/stable/reference/generated/numpy.polynomial.legendre.leggauss.html`_
for more details.

Returns
-------
mean_I : np.ndarray (float)
Mean posterior intensity
std_I : np.ndarray (float)
Standard deviation of posterior intensity
mean_F : np.ndarray (float)
Mean posterior structure factor amplitude
std_F :np.ndarray (float)
Standard deviation of posterior structure factor amplitude
"""
return _french_wilson_posterior_quad(Iobs, SigIobs, Sigma, True, npoints)

def mean_intensity_by_miller_index(I, H, bandwidth):
"""
Use a gaussian kernel smoother to compute mean intensities as a function of miller index.
Expand Down
34 changes: 24 additions & 10 deletions tests/algorithms/test_scale_merged_intensities.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,10 +5,13 @@

import reciprocalspaceship as rs
from reciprocalspaceship.algorithms import scale_merged_intensities
from reciprocalspaceship.algorithms.scale_merged_intensities import (
_acentric_posterior,
_centric_posterior
)
from reciprocalspaceship.algorithms.scale_merged_intensities import _french_wilson_posterior_quad

def _acentric_posterior(Iobs, SigIobs, Sigma, npoints=200):
return _french_wilson_posterior_quad(Iobs, SigIobs, Sigma, False, npoints)

def _centric_posterior(Iobs, SigIobs, Sigma, npoints=200):
return _french_wilson_posterior_quad(Iobs, SigIobs, Sigma, True, npoints)

def test_posteriors_fw1978(data_fw1978_input, data_fw1978_output):
"""
Expand All @@ -29,14 +32,14 @@ def test_posteriors_fw1978(data_fw1978_input, data_fw1978_output):
if "J" in data_fw1978_output.columns[0]:
refI = data_fw1978_output.iloc[:, 0].to_numpy()
refSigI = data_fw1978_output.iloc[:, 1].to_numpy()
assert np.allclose(mean, refI, rtol=0.03)
assert np.allclose(stddev, refSigI, rtol=0.03)
assert np.allclose(mean, refI, rtol=0.05)
assert np.allclose(stddev, refSigI, rtol=0.05)

# Compare structure factor amplitudes
else:
refF = data_fw1978_output.iloc[:, 0].to_numpy()
refSigF = data_fw1978_output.iloc[:, 1].to_numpy()
assert np.allclose(mean_f, refF, rtol=0.03)
assert np.allclose(mean_f, refF, rtol=0.05)
assert np.allclose(stddev_f, refSigF, rtol=0.05)

def test_centric_posterior(data_fw1978_input):
Expand Down Expand Up @@ -134,7 +137,7 @@ def test_scale_merged_intensities_validdata(hewl_merged, inplace, output_columns
def test_scale_merged_intensities_phenix(hewl_merged, ref_hewl, mean_intensity_method):
"""
Compare phenix.french_wilson to scale_merged_intensities(). Current
test criteria are that >95% of F and SigF are within 1%.
test criteria are that >95% of I, SigI, F, and SigF are within 2%.
"""
mtz = hewl_merged.dropna()
scaled = scale_merged_intensities(mtz, "IMEAN", "SIGIMEAN",
Expand All @@ -151,9 +154,20 @@ def test_scale_merged_intensities_phenix(hewl_merged, ref_hewl, mean_intensity_m
rsSigF = scaled["FW-SIGF"].to_numpy()
refF = ref_hewl["F"].to_numpy()
refSigF = ref_hewl["SIGF"].to_numpy()

rsI = scaled["FW-I"].to_numpy()
rsSigI = scaled["FW-SIGI"].to_numpy()
refI = ref_hewl["I"].to_numpy()
refSigI = ref_hewl["SIGI"].to_numpy()

assert (np.isclose(rsF, refF, rtol=0.01).sum()/len(scaled)) >= 0.95
assert (np.isclose(rsSigF, refSigF, rtol=0.01).sum()/len(scaled)) >= 0.95
assert (np.isclose(rsI, refI, rtol=0.02).sum()/len(scaled)) >= 0.95
assert (np.isclose(rsSigI, refSigI, rtol=0.02).sum()/len(scaled)) >= 0.95

assert (np.isclose(rsF, refF, rtol=0.02).sum()/len(scaled)) >= 0.95
assert (np.isclose(rsSigF, refSigF, rtol=0.02).sum()/len(scaled)) >= 0.95




@pytest.mark.parametrize("dropna", [True, False])
def test_scale_merged_intensities_dropna(data_hewl_all, dropna):
Expand Down