def iterateFit(fit_func, fit_data):
if (type(fit_data.peaks) == type(numpy.array([]))):
# Calculate "regularized" image.
regularized_image = fit_data.regularizer.regularizeImage(fit_data.image)
#regularized_image = fit_data.regularizer.normalizeImage(fit_data.image)
#regularized_image = fit_data.image
# Fit to update peak locations.
result = fit_func(regularized_image,
fit_data.peaks,
scmos_cal = fit_data.lg_scmos_cal)
fit_peaks = multi_c.getGoodPeaks(result[0],
0.9*fit_data.threshold,
0.5*fit_data.sigma)
# Remove peaks that are too close to each other & refit.
fit_peaks = util_c.removeClosePeaks(fit_peaks, fit_data.sigma, fit_data.neighborhood)
result = fit_func(regularized_image,
fit_peaks,
scmos_cal = fit_data.lg_scmos_cal)
fit_peaks = multi_c.getGoodPeaks(result[0],
0.9*fit_data.threshold,
0.5*fit_data.sigma)
fit_data.peaks = fit_peaks
# FIXME:
# For multi-peak finding to work correctly we should subtract the
# peaks out of the actual image, not the regularized image, which
# is what this is.
fit_data.residual = result[1]
def iterateFit(fit_func, fit_data):
if (type(fit_data.peaks) == type(numpy.array([]))):
# Fit to update peak locations.
result = fit_func(fit_data.image, fit_data.peaks)
fit_peaks = multi_c.getGoodPeaks(result[0],
0.9*fit_data.threshold,
0.5*fit_data.sigma)
# Remove peaks that are too close to each other & refit.
fit_peaks = util_c.removeClosePeaks(fit_peaks, fit_data.sigma, fit_data.neighborhood)
result = fit_func(fit_data.image, fit_peaks)
fit_peaks = multi_c.getGoodPeaks(result[0],
0.9*fit_data.threshold,
0.5*fit_data.sigma)
fit_data.peaks = fit_peaks
fit_data.residual = result[1]
