def findxcorfit(self):
"""Maximize the normalized correlation coefficient using the full wavelength solution.
"""
self.ws = st.fitxcor(
self.xarr,
self.farr,
self.swarr,
self.sfarr,
self.ws,
interptype='interp')
self.plotArt()
self.redraw_canvas()
def arcstraight(data, xarr, istart, ws=None, function='poly', order=3,
rstep=1, nrows=1, dcoef=None, y1=None, y2=None, log=None, verbose=True):
"""For a given image, assume that the line given by istart is the fiducial and then calculate
the transformation between each line and that line in order to straighten the arc
returns Wavlenght solution
"""
ImageSolution = {}
#set up the edges
if y1 is None: y1=0
if y2 is None: y2=data.shape[0]
# extract the central row
oxarr = xarr.copy()
ofarr = data[istart]
ws = WavelengthSolution.WavelengthSolution(xarr, xarr, function, order)
ws.fit()
ImageSolution[istart] = ws
if isinstance(dcoef, str):
if dcoef=='':
dcoef = None
else:
try:
dcoef = [float(w) for w in dcoef.replace('[','').replace(']','').split()]
except:
raise SaltError('dcoef is not the right format')
if dcoef is not None: ws.coef = dcoef
data = nd.gaussian_filter(data, 3)
# now step around the central row
for i in range(rstep, int(0.5 * len(data)), rstep):
for k in [istart - i, istart + i]:
if k < y1 or k > y2: continue
lws = getwsfromIS(k, ImageSolution)
xarr = np.arange(len(data[k]))
farr = apext.makeflat(data, k, k + nrows)
nws = st.fitxcor(
xarr,
farr,
oxarr,
ofarr,
lws,
interptype='interp')
ImageSolution[k] = nws
return ImageSolution
def arcstraight(data,
xarr,
istart,
ws=None,
function='poly',
order=3,
rstep=1,
nrows=1,
dcoef=None,
y1=None,
y2=None,
log=None,
verbose=True):
"""For a given image, assume that the line given by istart is the fiducial and then calculate
the transformation between each line and that line in order to straighten the arc
returns Wavlenght solution
"""
ImageSolution = {}
#set up the edges
if y1 is None: y1 = 0
if y2 is None: y2 = data.shape[0]
# extract the central row
oxarr = xarr.copy()
ofarr = data[istart]
ws = WavelengthSolution.WavelengthSolution(xarr, xarr, function, order)
ws.fit()
ImageSolution[istart] = ws
if isinstance(dcoef, str):
if dcoef == '':
dcoef = None
else:
try:
dcoef = [
float(w)
for w in dcoef.replace('[', '').replace(']', '').split()
]
except:
raise SaltError('dcoef is not the right format')
if dcoef is not None: ws.coef = dcoef
data = nd.gaussian_filter(data, 3)
# now step around the central row
for i in range(rstep, int(0.5 * len(data)), rstep):
for k in [istart - i, istart + i]:
if k < y1 or k > y2: continue
lws = getwsfromIS(k, ImageSolution)
xarr = np.arange(len(data[k]))
farr = apext.makeflat(data, k, k + nrows)
nws = st.fitxcor(xarr,
farr,
oxarr,
ofarr,
lws,
interptype='interp')
ImageSolution[k] = nws
return ImageSolution
