def dofit(xs, slambdas, alpha, sinbeta, gamma, delta, band, y):
xs = np.array(xs)
ok = np.isfinite(slambdas)
lsf = Fit.do_fit_wavelengths(xs[ok], lines[ok], alpha, sinbeta,
gamma, delta, band, y, error=slambdas[ok])
(order, pixel_y, alpha, sinbeta, gamma, delta) = lsf.params
print "order alpha sinbeta gamma delta"
print "%1.0i %5.7f %3.5f %3.2e %5.2f" % (order, alpha, sinbeta,
gamma, delta)
ll = Fit.wavelength_model(lsf.params, pix)
if DRAW:
pl.figure(3)
pl.clf()
pl.plot(ll, spec)
pl.title("Pixel %4.4f" % pos)
for lam in lines:
pl.axvline(lam, color='r')
pl.draw()
return [np.abs((
Fit.wavelength_model(lsf.params, xs[ok]) - lines[ok]))*1e4,
lsf.params]
def fit_spec(data, pos, alpha, sinbeta, gamma, delta, band):
global DRAW
ar_h_lines = np.array([1.465435, 1.474317, 1.505062, 1.517684, 1.530607,
1.533334, 1.540685, 1.590403, 1.599386, 1.618444, 1.644107,
1.674465, 1.744967, 1.791961])
Ar_K_lines = np.array([1.982291, 1.997118, 2.032256, 2.057443,
2.062186, 2.099184, 2.133871, 2.15409, 2.204558, 2.208321,
2.313952, 2.385154])
lines = ar_h_lines
bmap = {"Y": 6, "J": 5, "H": 4, "K": 3}
order = bmap[band]
d = 1e3/110.5
pix = np.arange(2048)
ll = Fit.wavelength_model([order, alpha, sinbeta, gamma, delta], pix)
spec = np.median(data[pos-3:pos+3, :], axis=0)
[xs, sxs, sigmas] = find_peaks(ll, spec, lines)
slambdas = d/order * np.array(sxs)
[deltas, params] = dofit(xs, slambdas, lines, alpha, sinbeta, gamma,
delta, band)
return (params[1], params[2], params[3], params[4],
np.median(deltas), np.std(deltas), pixel, sigmas)
def dofit(xs, slambdas, lines, alpha, sinbeta, gamma, delta, band):
xs = np.array(xs)
ok = np.isfinite(slambdas)
lsf = Fit.do_fit_wavelengths(xs[ok], lines[ok], alpha, sinbeta,
gamma, delta, band, error=slambdas[ok])
(order, alpha, sinbeta, gamma, delta) = lsf.params
print "order alpha sinbeta gamma delta"
print "%1.0i %5.7f %3.5f %3.2e %5.2f" % (order, alpha, sinbeta,
gamma, delta)
pix = np.arange(2048.)
ll = Fit.wavelength_model(lsf.params, pix)
return [np.abs((
Fit.wavelength_model(lsf.params, xs[ok]) - lines[ok]))*1e4,
lsf.params]
def dofit(xs, sxs, alpha, beta, band):
xs = np.array(xs)
sxs = np.array(sxs)/1000.
lsf = Fit.do_fit_wavelengths(xs, lines, alpha, beta, band, error=sxs)
(order, alpha, beta, gamma, delta) = lsf.params
print "alpha beta gamma delta"
print "%5.7f %3.5f %3.2e %5.2f" % (alpha, beta, gamma, delta)
ll = Fit.wavelength_model(lsf.params, pix)
if False:
pl.figure(3)
pl.clf()
pl.plot(ll, spec)
pl.title("Pixel %4.4f" % pos)
for lam in lines:
pl.axvline(lam, color='r')
pl.draw()
return [np.abs((Fit.wavelength_model(lsf.params, xs) - lines))/lines,
lsf.params]
def do_fits(params, pos, dy):
(alpha, sinbeta, gamma, delta) = params[1:]
thispos = pos - dy
yposs.append(thispos)
spec = np.median(data[thispos-3:thispos+3, :], axis=0)
ll = Fit.wavelength_model(params, pix)
[xs, sxs, sigmas] = find_peaks(ll, spec, lines)
slambdas = d/order * np.array(sxs)
[deltas, params] = dofit(xs, slambdas, lines, alpha, sinbeta,
gamma, delta, band)
results.append(np.array(params))
resdelt.append(deltas)
def fit_spec(data, pos, alpha, sinbeta, gamma, delta, band):
global DRAW
ar_h_lines = np.array([1.465435, 1.474317, 1.505062, 1.517684, 1.530607,
1.533334, 1.540685, 1.590403, 1.599386, 1.618444, 1.644107,
1.674465, 1.744967, 1.791961])
Ar_K_lines = np.array([1.982291, 1.997118, 2.032256, 2.057443,
2.062186, 2.099184, 2.133871, 2.15409, 2.204558, 2.208321,
2.313952, 2.385154])
Ne_J_lines = np.array([1.149125, 1.16719, 1.17227, 1.194655, 1.202994,
1.211564, 1.214306, 1.234677, 1.240622, 1.244273, 1.249108,
1.27369, 1.280624, 1.296021, 1.301182, 1.321761, 1.327627,
1.331685, 1.337077, 1.341026, 1.350788, 1.362638])
Ne_J_lines = np.array([1.149125, 1.16719, 1.117227])
AR_Y_lines = np.array([0.966043, 0.978718, 1.005481, 1.04809,
1.067649, 1.07039, 1.088394, 1.110819])
# NeAr H (1961, 1949)
lines = np.array([1.465435, 1.474317, 1.505062, 1.517684, 1.530607,
1.533334, 1.540685, 1.590403, 1.599386, 1.618444, 1.644107,
1.674465, 1.744967, 1.791961, \
1.493386, 1.499041, 1.523487, 1.5352384, 1.5411803, 1.5608478,
1.6027147, 1.6272797, 1.6409737, 1.6479254, 1.6793378,
1.7166622])
lines = np.array([ 1.540685, 1.5411803])
lines.sort()
bmap = {"Y": 6, "J": 5, "H": 4, "K": 3}
order = bmap[band]
d = 1e3/110.5
pix = np.arange(2048.)
ll = Fit.wavelength_model([order, pos, alpha, sinbeta, gamma, delta], pix)
spec = np.median(data[pos-1:pos+1, :], axis=0)
if DRAW:
pl.figure(2)
pl.clf()
pl.figure(3)
pl.clf()
pl.figure(1)
pl.clf()
pl.plot(ll, spec, '-+')
for lam in lines:
pl.axvline(lam, color='r')
pl.title("Pixel pos: %i"% pos)
pl.figure(2)
pl.clf()
xs = []
sxs = []
pks = []
rats = []
for lam in lines:
f = 0.9985
roi = (f*lam < ll) & (ll < lam/f)
if not roi.any():
xs.append(0.0)
sxs.append(np.inf)
continue
#lsf = Fit.mpfitpeak(pix[roi], spec[roi],
# error=np.sqrt(np.abs(spec[roi])))
lsf = Fit.mpfitpeak(pix[roi], spec[roi],
error=np.sqrt(np.abs(spec[roi])))
print lsf.params[2], lsf.params[1]
print "LSF"
pl.figure(6)
pl.clf()
pl.scatter(pix[roi], spec[roi])
pl.plot(pix[roi], Fit.gaussian(lsf.params, pix[roi]))
lsf = Fit.mpfitpeaks(pix[roi], spec[roi], 2,
error=np.sqrt(np.abs(spec[roi])))
print lsf.params[2], lsf.params[6], lsf.params[8]
print "LSF"
pl.figure(5)
pl.clf()
pl.scatter(pix[roi], spec[roi])
pl.plot(pix[roi], Fit.multi_gaussian(lsf.params, pix[roi]))
if lsf.perror is None:
xs.append(0.0)
sxs.append(np.inf)
continue
if lsf.status < 0:
xs.append(0.0)
sxs.append(np.inf)
continue
mnpix = np.min(pix[roi])
mxpix = np.max(pix[roi])
if (mnpix + 4) > lsf.params[1] < (mxpix-4):
xs.append(0.0)
sxs.append(np.inf)
continue
if mnpix < 7:
xs.append(0.0)
sxs.append(np.inf)
continue
if mxpix > 2040:
xs.append(0.)
sxs.append(np.inf)
continue
#if DRAW:
if DRAW:
pl.plot(pix[roi], spec[roi])
pl.plot(pix[roi], spec[roi], 'o')
pl.plot(pix[roi], Fit.gaussian(lsf.params, pix[roi]), 'r--')
xval = lsf.params[1]
xvals = lsf.perror[1]
xs.append(xval)
sxs.append(xvals)
if DRAW:
if np.isfinite(xvals):
pl.axvline(xval)
if DRAW:
pl.draw()
def dofit(xs, slambdas, alpha, sinbeta, gamma, delta, band, y):
xs = np.array(xs)
ok = np.isfinite(slambdas)
lsf = Fit.do_fit_wavelengths(xs[ok], lines[ok], alpha, sinbeta,
gamma, delta, band, y, error=slambdas[ok])
(order, pixel_y, alpha, sinbeta, gamma, delta) = lsf.params
print "order alpha sinbeta gamma delta"
print "%1.0i %5.7f %3.5f %3.2e %5.2f" % (order, alpha, sinbeta,
gamma, delta)
ll = Fit.wavelength_model(lsf.params, pix)
if DRAW:
pl.figure(3)
pl.clf()
pl.plot(ll, spec)
pl.title("Pixel %4.4f" % pos)
for lam in lines:
pl.axvline(lam, color='r')
pl.draw()
return [np.abs((
Fit.wavelength_model(lsf.params, xs[ok]) - lines[ok]))*1e4,
lsf.params]
slambdas = d/order * np.array(sxs)
[deltas, params] = dofit(xs, slambdas, alpha, sinbeta, gamma, delta,
band)
if 0.4 < np.median(deltas) < 0.8:
prev = np.median(deltas)
[deltas, params] = dofit(xs, slambdas, params[1], params[2],
params[3], params[4], band)
print "Previous MAD: %f is now %f" % (prev, np.median(deltas))
return (params[1], params[2], params[3], params[4],
np.median(deltas), np.std(deltas), pixel)
