def estimate_signal_to_noise(x, y):
raise
if 1:
x, y = interpolate(x, y)
#x, y_tr = fourier_filter(x, y)
x, y_tr = savitzky_golay_filter(x, y)
noise = y - y_tr
else:
from scitbx.math import chebyshev_polynome
from scitbx.math import chebyshev_lsq_fit
x_obs, y_obs = x, y
w_obs = flex.double(y_obs.size(), 1)
w_obs[0] = 1e16
w_obs[-1] = 1e16
## determining the number of terms takes much, much longer than the fit
n_terms = chebyshev_lsq_fit.cross_validate_to_determine_number_of_terms(
x_obs, y_obs, w_obs,
min_terms=2, max_terms=30,
n_goes=20, n_free=20)
#n_terms = 7
print "n_terms:", n_terms
fit = chebyshev_lsq_fit.chebyshev_lsq_fit(n_terms, x_obs, y_obs, w_obs)
fit_funct = chebyshev_polynome(
n_terms, fit.low_limit, fit.high_limit, fit.coefs)
y_fitted = fit_funct.f(x)
y_tr = y_fitted
n = y_tr.size()
noise = y - y_tr
noise_sq = flex.pow2(noise)
from xfel.command_line.view_pixel_histograms import sliding_average
#sigma_sq = sliding_average(noise_sq, n=31)
sigma_sq = sliding_average(noise_sq, n=15)
#sigma_sq = sliding_average(sigma_sq)
#signal_to_noise = y/flex.sqrt(sigma_sq)
import math
signal_to_noise = y/math.sqrt(flex.mean(noise_sq[50:200]))
#pyplot.plot(noise)
#pyplot.plot(x,y)
#pyplot.show()
offset = 0.2 * flex.max(y)
offset = 0
pyplot.plot(x, y, linewidth=2)
pyplot.plot(x, offset+y_tr, linewidth=2)
pyplot.show()
pyplot.plot(x, noise, linewidth=2)
#pyplot.plot(x, flex.sqrt(sigma_sq), linewidth=2)
#ax2 = pyplot.twinx()
#ax2.plot(x, y)
pyplot.show()
pyplot.plot(x[:375], signal_to_noise[:375])
#pyplot.xlim(
#ax2 = pyplot.twinx()
#ax2.plot(x, y)
pyplot.show()
def estimate_signal_to_noise(x, y_noisy, y_smoothed, plot=False):
"""Estimate noise in spectra by subtracting a smoothed spectrum from the
original noisy unsmoothed spectrum.
See:
The extraction of signal to noise values in x-ray absorption spectroscopy
A. J. Dent, P. C. Stephenson, and G. N. Greaves
Rev. Sci. Instrum. 63, 856 (1992); https://doi.org/10.1063/1.1142627
"""
noise = y_noisy - y_smoothed
noise_sq = flex.pow2(noise)
from xfel.command_line.view_pixel_histograms import sliding_average
sigma_sq = sliding_average(noise_sq, n=31)
sigma_sq = smoothing.savitzky_golay_filter(x.as_double(),
flex.pow2(noise),
half_window=20,
degree=1)[1]
sigma_sq.set_selected(sigma_sq <= 0, flex.mean(sigma_sq))
# or do this instead to use the background region as the source of noise:
#signal_to_noise = y_smoothed/math.sqrt(flex.mean(noise_sq[50:190]))
signal_to_noise = y_smoothed / flex.sqrt(sigma_sq)
#signal_to_noise.set_selected(x < 50, 0)
#signal_to_noise.set_selected(x > 375, 0)
if plot:
from matplotlib import pyplot
linewidth = 2
pyplot.plot(x, y_noisy, linewidth=linewidth)
pyplot.plot(x, y_smoothed, linewidth=linewidth)
pyplot_label_axes()
pyplot.show()
pyplot.plot(x, noise, linewidth=linewidth, label="noise")
pyplot.plot(x, flex.sqrt(sigma_sq), linewidth=linewidth, label="sigma")
pyplot_label_axes()
pyplot.legend(loc=2, prop={'size': 20})
pyplot.show()
pyplot.plot(x, signal_to_noise, linewidth=linewidth)
pyplot_label_axes()
pyplot.show()
return signal_to_noise
def estimate_signal_to_noise(x, y_noisy, y_smoothed, plot=False):
"""Estimate noise in spectra by subtracting a smoothed spectrum from the
original noisy unsmoothed spectrum.
See:
The extraction of signal to noise values in x-ray absorption spectroscopy
A. J. Dent, P. C. Stephenson, and G. N. Greaves
Rev. Sci. Instrum. 63, 856 (1992); http://dx.doi.org/10.1063/1.1142627
"""
noise = y_noisy - y_smoothed
noise_sq = flex.pow2(noise)
from xfel.command_line.view_pixel_histograms import sliding_average
sigma_sq = sliding_average(noise_sq, n=31)
sigma_sq = smoothing.savitzky_golay_filter(
x.as_double(), flex.pow2(noise), half_window=20, degree=1)[1]
sigma_sq.set_selected(sigma_sq <= 0, flex.mean(sigma_sq))
# or do this instead to use the background region as the source of noise:
#signal_to_noise = y_smoothed/math.sqrt(flex.mean(noise_sq[50:190]))
signal_to_noise = y_smoothed/flex.sqrt(sigma_sq)
#signal_to_noise.set_selected(x < 50, 0)
#signal_to_noise.set_selected(x > 375, 0)
if plot:
from matplotlib import pyplot
linewidth=2
pyplot.plot(x, y_noisy, linewidth=linewidth)
pyplot.plot(x, y_smoothed, linewidth=linewidth)
pyplot_label_axes()
pyplot.show()
pyplot.plot(x, noise, linewidth=linewidth, label="noise")
pyplot.plot(x, flex.sqrt(sigma_sq), linewidth=linewidth, label="sigma")
pyplot_label_axes()
pyplot.legend(loc=2, prop={'size':20})
pyplot.show()
pyplot.plot(x, signal_to_noise, linewidth=linewidth)
pyplot_label_axes()
pyplot.show()
return signal_to_noise
def estimate_signal_to_noise(x, y):
raise
if 1:
x, y = interpolate(x, y)
#x, y_tr = fourier_filter(x, y)
x, y_tr = savitzky_golay_filter(x, y)
noise = y - y_tr
else:
from scitbx.math import chebyshev_polynome
from scitbx.math import chebyshev_lsq_fit
x_obs, y_obs = x, y
w_obs = flex.double(y_obs.size(), 1)
w_obs[0] = 1e16
w_obs[-1] = 1e16
## determining the number of terms takes much, much longer than the fit
n_terms = chebyshev_lsq_fit.cross_validate_to_determine_number_of_terms(
x_obs,
y_obs,
w_obs,
min_terms=2,
max_terms=30,
n_goes=20,
n_free=20)
#n_terms = 7
print "n_terms:", n_terms
fit = chebyshev_lsq_fit.chebyshev_lsq_fit(n_terms, x_obs, y_obs, w_obs)
fit_funct = chebyshev_polynome(n_terms, fit.low_limit, fit.high_limit,
fit.coefs)
y_fitted = fit_funct.f(x)
y_tr = y_fitted
n = y_tr.size()
noise = y - y_tr
noise_sq = flex.pow2(noise)
from xfel.command_line.view_pixel_histograms import sliding_average
#sigma_sq = sliding_average(noise_sq, n=31)
sigma_sq = sliding_average(noise_sq, n=15)
#sigma_sq = sliding_average(sigma_sq)
#signal_to_noise = y/flex.sqrt(sigma_sq)
import math
signal_to_noise = y / math.sqrt(flex.mean(noise_sq[50:200]))
#pyplot.plot(noise)
#pyplot.plot(x,y)
#pyplot.show()
offset = 0.2 * flex.max(y)
offset = 0
pyplot.plot(x, y, linewidth=2)
pyplot.plot(x, offset + y_tr, linewidth=2)
pyplot.show()
pyplot.plot(x, noise, linewidth=2)
#pyplot.plot(x, flex.sqrt(sigma_sq), linewidth=2)
#ax2 = pyplot.twinx()
#ax2.plot(x, y)
pyplot.show()
pyplot.plot(x[:375], signal_to_noise[:375])
#pyplot.xlim(
#ax2 = pyplot.twinx()
#ax2.plot(x, y)
pyplot.show()
