def test_linear(self):
'''Test linear fitting.'''
order = 1
results0 = numpy.array([456.0, 0.3, -0.9])
z = numpy.sum(cf * v for cf, v in zip(results0.flat, self.grid0))
results, = imsurfit(z, order=order)
for i0, i in zip(results0.flat, results.flat):
self.assertAlmostEqual(i0, i)
z = numpy.sum(cf * v for cf, v in zip(results0.flat, self.grid1))
results, = imsurfit(z, order=order)
for i0, i in zip(results0.flat, results.flat):
self.assertAlmostEqual(i0, i)
def test_cubic(self):
'''Test cubic fitting.'''
order = 3
results0 = numpy.array([456.0, 0.3, -0.9, 0.03, -0.01, 0.07,
0.0, -10, 0.0, 0.04])
z = numpy.sum(cf * v for cf, v in zip(results0.flat, self.grid0))
results, = imsurfit(z, order=order)
for i0, i in zip(results0.flat, results.flat):
self.assertAlmostEqual(i0, i)
z = numpy.sum(cf * v for cf, v in zip(results0.flat, self.grid1))
results, = imsurfit(z, order=order)
for i0, i in zip(results0.flat, results.flat):
self.assertAlmostEqual(i0, i)
def test_cuadratic(self):
'''Test cuadratic fitting.'''
order = 2
results0 = numpy.array([1.0, 0.1, 12, 3.0, -11.8, 9.2])
z = numpy.sum(cf * v for cf, v in zip(results0.flat, self.grid0))
results, = imsurfit(z, order=order)
for i0, i in zip(results0.flat, results.flat):
self.assertAlmostEqual(i0, i)
z = numpy.sum(cf * v for cf, v in zip(results0.flat, self.grid1))
results, = imsurfit(z, order=order)
for i0, i in zip(results0.flat, results.flat):
self.assertAlmostEqual(i0, i)
def test_cuartic(self):
'''Test cuartic fitting.'''
order = 4
results0 = numpy.array([-11.0, -1.5, -0.1, 0.14, -15.03, 0.07,
0.448, -0.28, 1.4, 1.24,
-3.2, -1.2, 2.24, -8.1, -0.03])
z = numpy.sum(cf * v for cf, v in zip(results0.flat, self.grid0))
results, = imsurfit(z, order=order)
for i0, i in zip(results0.flat, results.flat):
self.assertAlmostEqual(i0, i)
z = numpy.sum(cf * v for cf, v in zip(results0.flat, self.grid1))
results, = imsurfit(z, order=order)
for i0, i in zip(results0.flat, results.flat):
self.assertAlmostEqual(i0, i)
def offset_from_crosscor(arr0,
arr1,
region,
refine=True,
refine_box=3,
order='ij'):
# import astropy.io.fits as fits
# allowed values for order
if order not in ['xy', 'ij']:
raise ValueError("'order' must be either 'ij' or 'xy'")
ref_array = arr0
shape = ref_array.shape
data1 = filter_region(ref_array[region])
d1 = standarize(data1)
# fits.writeto('cutout_%d.fits' % 0, d1, overwrite=True)
dcenter = numpy.asarray(d1.shape) // 2
# Correlate
data2 = filter_region(arr1[region])
d2 = standarize(data2)
# fits.writeto('cutout_%d.fits' % idx, d2, overwrite=True)
#corr = scipy.signal.correlate2d(d1, d2, mode='same', boundary='fill', fillvalue=fillvalue)
# correlation is equivalent to convolution with inverted image
corr = scipy.signal.fftconvolve(d1, d2[::-1, ::-1], mode='same')
# normalize
corr /= corr.max()
# fits.writeto('corr_%d.fits' % idx, corr, overwrite=True)
# fits.writeto('corr2_%d.fits' % idx, corr2 / corr2.max(), overwrite=True)
# Find peak in cross-cor
maxindex = numpy.unravel_index(corr.argmax(), corr.shape)
# Check the peak is above n times the background
median_corr = numpy.median(corr)
std_corr = numpy.std(corr)
peakvalue = corr[maxindex]
threshold = median_corr + 5 * std_corr
_logger.debug('The peak value is %f, threshold %f', peakvalue, threshold)
if peakvalue < threshold:
raise ValueError('Peak below threshold')
#baseoff = dcenter - numpy.asarray(maxindex)
# Pixel (0,0) in reference corresponds to baseoff in image
# print("Pixel (0,0) in reference corresponds to %s in image" % baseoff)
if refine:
# Refine to subpixel
# Fit a 2D surface to the peak of the crosscorr
region_ref = utils.image_box(maxindex,
shape,
box=(refine_box, refine_box))
coeffs, = imsurfit.imsurfit(corr[region_ref], order=2)
# coefss are a + b *x + c * y + d*x**2 + e * x* y + f * y**2
try:
# get peak from coeffs
xm, ym = vertex_of_quadratic(coeffs)
# xm ym are offsets
if abs(xm) > 1 or abs(ym) > 1:
# probably bad fit
# dont apply
final = maxindex
else:
final = maxindex + numpy.asarray([ym, xm])
except ValueError as error:
_logger.debug('Error fitting peak, %s', error)
final = maxindex
else:
final = maxindex
refoff = dcenter - final
# Pixel (0,0) in reference corresponds to baseoff in image
# print("Pixel (0,0) in reference corresponds to %s in image" % refoff)
# result xy
if order == 'xy':
return refoff[::-1]
else:
return refoff
def offset_from_crosscor(arr0, arr1, region, refine=True, refine_box=3, order='ij'):
# import astropy.io.fits as fits
# allowed values for order
if order not in ['xy', 'ij']:
raise ValueError("'order' must be either 'ij' or 'xy'")
ref_array = arr0
shape = ref_array.shape
data1 = filter_region(ref_array[region])
d1 = standarize(data1)
# fits.writeto('cutout_%d.fits' % 0, d1, overwrite=True)
dcenter = numpy.asarray(d1.shape) // 2
# Correlate
data2 = filter_region(arr1[region])
d2 = standarize(data2)
# fits.writeto('cutout_%d.fits' % idx, d2, overwrite=True)
#corr = scipy.signal.correlate2d(d1, d2, mode='same', boundary='fill', fillvalue=fillvalue)
# correlation is equivalent to convolution with inverted image
corr = scipy.signal.fftconvolve(d1, d2[::-1, ::-1], mode='same')
# normalize
corr /= corr.max()
# fits.writeto('corr_%d.fits' % idx, corr, overwrite=True)
# fits.writeto('corr2_%d.fits' % idx, corr2 / corr2.max(), overwrite=True)
# Find peak in cross-cor
maxindex = numpy.unravel_index(corr.argmax(), corr.shape)
# Check the peak is above n times the background
median_corr = numpy.median(corr)
std_corr = numpy.std(corr)
peakvalue = corr[maxindex]
threshold = median_corr + 5 * std_corr
_logger.debug('The peak value is %f, threshold %f', peakvalue, threshold)
if peakvalue < threshold:
raise ValueError('Peak below threshold')
#baseoff = dcenter - numpy.asarray(maxindex)
# Pixel (0,0) in reference corresponds to baseoff in image
# print("Pixel (0,0) in reference corresponds to %s in image" % baseoff)
if refine:
# Refine to subpixel
# Fit a 2D surface to the peak of the crosscorr
region_ref = utils.image_box(maxindex, shape, box=(refine_box, refine_box))
coeffs, = imsurfit.imsurfit(corr[region_ref], order=2)
# coefss are a + b *x + c * y + d*x**2 + e * x* y + f * y**2
try:
# get peak from coeffs
xm, ym = vertex_of_quadratic(coeffs)
# xm ym are offsets
if abs(xm) > 1 or abs(ym) > 1:
# probably bad fit
# dont apply
final = maxindex
else:
final = maxindex + numpy.asarray([ym, xm])
except ValueError as error:
_logger.debug('Error fitting peak, %s', error)
final = maxindex
else:
final = maxindex
refoff = dcenter - final
# Pixel (0,0) in reference corresponds to baseoff in image
# print("Pixel (0,0) in reference corresponds to %s in image" % refoff)
# result xy
if order == 'xy':
return refoff[::-1]
else:
return refoff
