-
Notifications
You must be signed in to change notification settings - Fork 1
/
findz_VIMOS.py
257 lines (225 loc) · 8.78 KB
/
findz_VIMOS.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
#!/usr/bin/env python
import pyfits
import numpy as np
import matplotlib.pyplot as pl
from astro.io import saveobj
from astro.utilities import between, adict
from astro.convolve import convolve_psf
from astro.spec import find_cont
import astro.sed as sed
from VIMOS_util import \
get_object_IDs, get_1st_order_region, get_1d_2d_spectra, \
WMIN, WMAX, MASKATMOS
from glob import glob
import sys, os
PLOT = 0
np.seterr(all='ignore')
# regions to mask when doing cross correlation; atmospheric lines and
# fringing.
LOG10_MASKATMOS = np.log10(MASKATMOS)
def prepare_templates(templates, convolve_pix=None, plot=0):
""" Add continua to templates and interpolate to log-linear wav
scale. Returns a new set of templates.
"""
tnew = []
for t in templates:
# fudge to remove bad template areas
print t.label
wgood = t.wa[t.fl > 1e-99]
w0,w1 = wgood[0], wgood[-1]
cond = between(t.wa, w0, w1)
wa0, fl0 = t.wa[cond], t.fl[cond]
if plot:
ax = pl.gca()
ax.cla()
pl.plot(wa0, fl0, 'b')
# rebin to a log-linear wavelength scale. To shift wavescale
# to redshift z, add log10(1+z) to wa or twa.
dw = (np.log10(wa0[-1]) - np.log10(wa0[0])) / (len(wa0) - 1)
logtwa = np.log10(wa0[0]) + dw * np.arange(len(wa0))
twa = 10**logtwa
tfl = np.interp(twa, wa0, fl0)
if t.label.startswith('sdss') and convolve_pix is not None:
# convolve to VIMOS resolution
tfl = convolve_psf(tfl, convolve_pix)
fwhm1 = len(wa0) // 7
fwhm2 = len(wa0) // 9
#print fwhm1, fwhm2
tco = find_cont(tfl, fwhm1=fwhm1, fwhm2=fwhm2)
if plot:
pl.plot(twa, tfl, 'r')
pl.plot(twa, tco, 'r--')
pl.show()
raw_input()
t1 = adict(logwa=logtwa, fl=tfl, co=tco, label=t.label)
tnew.append(t1)
return tnew
#@profile
def xcorr_template(wa, fl, er, co, logtwa, tfl, tco, redshifts, plot=False):
""" Cross-correlate a template with a spectrum for a series of
redshifts. Return the value of cross correlation at each
redshift. Peaks in the xcorr denote matches between the spectrum
and template.
"""
logwa = np.log10(wa)
wrange = WMAX - WMIN
if plot:
fig = pl.gcf()
pl.clf()
ax = fig.add_subplot(111)
artist_template, = ax.plot(wa, fl, 'b',alpha=0.5)
artist_spec, = ax.plot([], [], 'r', alpha=0.5)
artist_templateco, = ax.plot([], [], 'b--',alpha=0.5)
artist_specco, = ax.plot([], [], 'r--')
artist_line1, = ax.plot([], [], 'k:')
artist_line2, = ax.plot([], [], 'k:')
for w0,w1 in MASKATMOS:
ymin,ymax = ax.get_ylim()
ax.fill([w0,w0,w1,w1], [ymin,ymax,ymax,ymin], color='y', alpha=0.3)
wshifts = np.log10(np.asarray(redshifts) + 1)
xcorr = []
nchi2vals = []
masked = np.empty(len(logtwa), bool)
for wshift in wshifts:
#print 'z', 10**wshift -1
logtwa_z = logtwa + wshift
# In principle we could do this loop just once for a given set of
# templates and redshifts, instead of once for each spectrum.
masked.fill(0)
i0 = 0
for w0,w1 in LOG10_MASKATMOS:
i,j = i0 + logtwa_z[i0:].searchsorted([w0, w1])
masked[i:j] = True
i0 = j
if masked.all():
xcorr.append(0)
continue
notmasked = ~masked
fl0 = np.interp(logtwa_z, logwa, fl)
er0 = np.interp(logtwa_z, logwa, er)
co0 = np.interp(logtwa_z, logwa, co)
# find the common overlapping wavelength range
logtwa_z_notmasked = logtwa_z[notmasked]
logwmin = max(logtwa_z_notmasked[0], logwa[0])
logwmax = min(logtwa_z_notmasked[-1], logwa[-1])
good = notmasked & between(logtwa_z, logwmin, logwmax) & (er0 > 0)
if good.sum() == 0:
xcorr.append(0)
continue
# weight by (approx) good wavelength range over (approx) total
# available wavelength range
lw = logtwa_z[good]
weight = (10**lw[-1] - 10**lw[0]) / wrange
assert not np.isnan(weight)
assert 0 <= weight <= 1, '%.3f, %.1f - %.1f' % (weight,10**lw[0],10**lw[-1])
if weight < 0.1:
#print 'z=%.3f' % (10**wshift - 1), t.label, 'Less than 10% overlap'
xcorr.append(0)
continue
fl1 = fl0[good]
co1 = co0[good]
er1 = er0[good]
tfl1 = tfl[good]
tco1 = tco[good]
# scale template to match spectrum and then subtract continua
mult = np.median(fl1) / np.median(tfl1)
fl2 = fl1 - co1
tfl2 = (tfl1 - tco1)* mult
if plot:
w = 10**logtwa_z
artist_template.set_data(w, tfl*mult)
artist_spec.set_data(w, fl0)
artist_templateco.set_data(w, tco*mult)
artist_specco.set_data(w, co0)
artist_line1.set_data([10**logwmin]*2, [ymin, ymax])
artist_line2.set_data([10**logwmax]*2, [ymin, ymax])
val = (fl2 * tfl2).sum() / np.sqrt((tfl2**2).sum() * (fl2**2).sum())
assert not np.isnan(val)
resid = (fl1 - tfl1*mult)/er1
# chi2 per deg freedom
nchi2 = np.dot(resid, resid) / len(resid)
#print nchi2
if plot:
ax.set_title('%.4f %.3f' % (val,nchi2))
ax.set_xlim(5000, 9500)
ymax = np.percentile(fl1, 95)
#ax.plot(10**logtwa_z[good], -0.1*ymax + 0.05*ymax*resid, '.k',alpha=0.5)
ax.set_ylim(-0.2*ymax, 1.5*ymax)
pl.show()
raw_input('z=' + str(10**wshift - 1) + ', %i points' % good.sum())
xcorr.append(val * weight)
nchi2vals.append(nchi2)
return np.array(xcorr), np.array(nchi2vals)
if 1:
#####################################
# Prepare templates
#####################################
temp0 = sed.get_SEDs('sdss') #+ sed.get_SEDs('LBG')
# remove BAL and high_lum qsos
temp1 = [t for t in temp0 if
'qsoBAL' not in t.label and 'highlum' not in t.label]
# note sdss templates have resolution of 2000 or 150 km/s, VIMOS
# spectra have resolution of ~210 or 1428 km/s. So convolve sdss
# templates with Gaussian of width sqrt(1428^2 - 150^2) = 1420 km/s,
# or 20.6 pixels (sdss pixels are 69 km/s).
#
# (20.6 seems too big, do 10 instead)
#
templates = prepare_templates(temp1, convolve_pix=10, plot=PLOT)
saveobj('templates.sav', templates, overwrite=1)
#######################################
# Do xcorr
#######################################
IDs = get_object_IDs('object_sci_table.fits')
if not os.path.lexists('xcorr'):
os.mkdir('xcorr')
# median sky
msky = np.median(pyfits.getdata('mos_sci_sky_reduced.fits', 1), axis=0)
msky[msky == 0] = np.median(msky)
if PLOT:
pl.figure(figsize=(10, 6))
for ind,ID in enumerate(IDs[:5]):
print '%i of %i, cross-correlating %s with:' % (ind+1, len(IDs), ID)
wa, fl, er, sky, im, pos = get_1d_2d_spectra(ID)
# mask regions where the extracted sky differs strongly from
# the median extracted sky (this is usually a first-order
# line)
i0,i1 = get_1st_order_region(wa, sky, msky, wmin=WMIN)
if i0 is not None:
print ' Masking 1st order region'
er[i0:i1] = 0
# fwhm1 and fwhm2 need to be tweaked depending on resolution,
# size, etc
sp_co = find_cont(fl, fwhm1=200, fwhm2=150)
if PLOT:
pl.cla()
plot(wa, fl, wa, sp_co, wa, 50*er)
mult = np.median(fl) / np.median(msky)
plot(wa, msky *mult, wa, sky*mult)
pl.show()
raw_input('')
xcorrs = []
nchi2 = []
redshifts = []
for t in templates:
# find xcorr
tname = t.label.split('/')[-1][:-4]
if tname.startswith('star'):
z = [0]
elif tname.startswith('gal'):
z = np.arange(0, 1.5, 0.001)
elif tname.startswith('lbg'):
z = np.arange(1.5, 4.0, 0.001)
elif tname.startswith('qso'):
z = np.arange(0.5, 4.0, 0.001)
if not tname.startswith('star'):
print ' %s %.2f < z < %.2f' % (t.label, min(z), max(z))
xcorr,nchi = xcorr_template(wa, fl, er, sp_co,
t.logwa, t.fl, t.co, z, plot=PLOT)
nchi2.append(nchi)
xcorrs.append(xcorr)
redshifts.append(z)
name = ('xcorr/q%i_e%i_s%04i_o%i_xcorr.sav' % ID)
print 'Saving', name
saveobj(name, adict(z=redshifts, xcorr=xcorrs, nchi2=nchi2),
overwrite=1)