def fit_edge_poly(xposs, xposs_missing, yposs, order):
'''
fit_edge_poly fits a polynomial to the measured slit edges.
This polynomial is used to extract spectra.
fit_edge_poly computes a parabola, and fills in missing data with a
parabola
input-
xposs, yposs [N]: The x and y positions of the slit edge [pix]
order: the polynomial order
'''
# First fit low order polynomial to fill in missing data
fun = np.poly1d(Fit.polyfit_clip(xposs, yposs, 2))
xposs = np.append(xposs, xposs_missing)
yposs = np.append(yposs, fun(xposs_missing))
# Remove any fits that deviate wildly from the 2nd order polynomial
ok = np.abs(yposs - fun(xposs)) < 1
if not ok.any():
error("Flat is not well illuminated? Cannot find edges")
raise Exception("Flat is not well illuminated? Cannot find edges")
# Now refit to user requested order
fun = np.poly1d(Fit.polyfit_clip(xposs[ok], yposs[ok], order))
yposs_ok = yposs[ok]
res = fun(xposs[ok]) - yposs[ok]
sd = np.std(res)
ok = np.abs(res) < 2*sd
# Check to see if the slit edge funciton is sane,
# if it's not, then we fix it.
pix = np.arange(2048)
V = fun(pix)
if np.abs(V.max() - V.min()) > 10:
info ("Forcing a horizontal slit edge")
print("Forcing a horizontal slit edge")
tmp = yposs_ok[ok]
fun = np.poly1d(np.median(tmp))
#fun = np.poly1d(np.median(yposs[ok]))
return (fun, res, sd, ok)
def fit_edge_poly(xposs, xposs_missing, yposs, order):
'''
fit_edge_poly fits a polynomial to the measured slit edges.
This polynomial is used to extract spectra.
fit_edge_poly computes a parabola, and fills in missing data with a
parabola
input-
xposs, yposs [N]: The x and y positions of the slit edge [pix]
order: the polynomial order
'''
# First fit low order polynomial to fill in missing data
fun = np.poly1d(Fit.polyfit_clip(xposs, yposs, 2))
xposs = np.append(xposs, xposs_missing)
yposs = np.append(yposs, fun(xposs_missing))
# Remove any fits that deviate wildly from the 2nd order polynomial
ok = np.abs(yposs - fun(xposs)) < 1
if not ok.any():
error("Flat is not well illuminated? Cannot find edges")
raise Exception("Flat is not well illuminated? Cannot find edges")
# Now refit to user requested order
fun = np.poly1d(Fit.polyfit_clip(xposs[ok], yposs[ok], order))
yposs_ok = yposs[ok]
res = fun(xposs[ok]) - yposs[ok]
sd = np.std(res)
ok = np.abs(res) < 2 * sd
# Check to see if the slit edge funciton is sane,
# if it's not, then we fix it.
pix = np.arange(2048)
V = fun(pix)
if np.abs(V.max() - V.min()) > 10:
info("Forcing a horizontal slit edge")
print("Forcing a horizontal slit edge")
tmp = yposs_ok[ok]
fun = np.poly1d(np.median(tmp))
#fun = np.poly1d(np.median(yposs[ok]))
return (fun, res, sd, ok)
print "Skipping: %i" % (x+delt*i)
return map(np.array, (xposs, yposs, widths))
def fit_edge_poly(xposs, yposs, widths, order):
'''
fit_edge_poly fits a polynomial to the measured slit edges.
This polynomial is used to extract spectra.
input-
xposs, yposs [N]: The x and y positions of the slit edge [pix]
widths [N]: the offset from end of one slit to beginning of another [pix]
order: the polynomial order
'''
fun = np.poly1d(Fit.polyfit_clip(xposs, yposs, order))
wfun = np.poly1d(Fit.polyfit_clip(xposs, widths, order))
res = fun(xposs) - yposs
sd = np.std(res)
ok = np.abs(res) < 2*sd
return (fun, wfun, res, sd, ok)
def data_quality_plots(results):
y = 2028
toc = 0
slits = []
