fitbox=None, method='fast', guessmeth='max', searchsmooth=3,

refsign=1, guessFWHM=None, guessamp=None, guessbg=None,

fixFWHM=False, minamp=0.01, maxamp=None, maxFWHM=None,

minFWHM=None, verbose=False, plot=False, silent=False):

"""

detect and determine the shift or offset of one image either with respect

to a reference image or a reference position. In the first case, the

shift is determined with cross-correlation, while in the second a Gaussian

fit is performed

the convention of the shift is foundpos - refpos

OPTIONAL INPUT

- refpos: tuple, given (approx.) the reference position of a source that

can be used for fine centering before cropping

- method: string(max, mpfitgauss, fastgauss, skimage, ginsberg), giving the method that

should be used for the fine centering using the reference source.

'skimage' and 'ginsberg' do 2D cross-correlation

- refim: 2D array, giving a reference image if the method for the

centering is cross-correlation

- fitbox: scalar, giving the box length in x and y for the fitting of the

reference source

"""

s = np.array(np.shape(im))

if guesspos is None and refpos is not None:

guesspos = refpos

elif refpos is None and guesspos is not None:

refpos = guesspos

elif guesspos is None and refpos is None:

guesspos = 0.5*np.array(s)

refpos = guesspos

if verbose:

print("GET_SHIFT: guesspos: ", guesspos)

print("GET_SHIFT: refpos: ", refpos)

# --- if a reference image was provided then perform a cross-correlation

if method in ['cross', 'skimage', 'ginsberg']:

sr = np.array(np.shape(refim))

if verbose:

print("GET_SHIFT: input image dimension: ", s)

print("GET_SHIFT: reference image dimension: ", sr)

# --- for the cross-correlation, the image and reference must have the

# the same size

if (s[0] > sr[0]) | (s[1] > sr[1]):

cim = _crop_image(im, box=sr, cenpos=guesspos)

cenpos = guesspos

# # --- adjust the ref and guesspos

# refpos = refpos - guesspos + 0.5 * sr

# guesspos = 0.5 * sr

if verbose:

print("GET_SHIFT: refim smaller than im --> cut im")

print("GET_SHIFT: adjusted guesspos: ", guesspos)

print("GET_SHIFT: adjusted refpos: ", refpos)

elif (sr[0] > s[0]) | (sr[1] > s[1]):

cim = im

refim = _crop_image(refim, box=s)

cenpos = 0.5 * s

else:

cim = im

cenpos = 0.5 * s

# --- which cross-correlation algorithm should it be?

if (method == 'cross') | (method == 'skimage'):

from skimage.feature import register_translation

shift, error, diffphase = register_translation(cim, refim,

upsample_factor=100)

#print(shift,error)

error = [error, error] # apparently only on error value is returned?

elif method == 'ginsberg':

import warnings

with warnings.catch_warnings():

warnings.filterwarnings("ignore")

import image_registration

np.seterr(all='ignore') # silence warning messages about div-by-zero

dx, dy, ex, ey = image_registration.chi2_shift(refim, cim,

return_error=True,

zeromean=True,

upsample_factor='auto')

shift = [dy, dx]

error = [ey, ex]

else:

print("GET_SHIFT: ERROR: requested method not available: ", method)

sys.exit(0)

fitim = None

params = None

perrs = None

# --- correct shift for any difference between guesspos and refpos:

shift = [shift[0] + cenpos[0] - refpos[0],

shift[1] + cenpos[1] - refpos[1]]

# --- if not reference image is provided then perform a Gaussian fit

else:

# --- fit a Gaussian to find the center for the crop

params, perrs, fitim = _find_source(im, searchbox=searchbox,

fitbox=fitbox, method=method,

verbose=verbose,

guesspos=guesspos, sign=refsign,

plot=plot, guessFWHM=guessFWHM,

guessamp=guessamp,

guessbg=guessbg,

searchsmooth=searchsmooth,

fixFWHM=fixFWHM, minamp=minamp,

maxFWHM=maxFWHM,

minFWHM=minFWHM,

silent=silent)

# --- compute the shift from the fit results:

shift = np.array([params[2] - refpos[0], params[3] - refpos[1]])

# --- eror on the shift from the fit results:

error = np.array([perrs[2], perrs[3]])

if verbose:

print('GET_SHIFT: Found shift: ', shift)

print('GET_SHIFT: Uncertainty: ', error)

print('GET_SHIFT: Fit Params: ', params)