/
calc_lick.py
executable file
·274 lines (258 loc) · 12 KB
/
calc_lick.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
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 8 10:01:07 2013
@author: cbarbosa
Study velocity dispersion correction of Lick indices in the Hydra I sample
"""
import os
import pickle
import numpy as np
import pyfits as pf
from scipy.interpolate import NearestNDInterpolator as interpolator
from scipy.interpolate import interp1d
from config import *
import lector as lector
from run_ppxf import pPXF
def correct_indices(indices, inderr, indtempl, indtempl_b, types):
""" Make corrections for the broadening in the spectra."""
corrected = np.zeros_like(indices)
errors = np.zeros_like(indices)
for i,t in enumerate(types):
if t == 0:
C = indtempl[i] / indtempl_b[i]
if C >= 1:
corrected[i] = indices[i] * C
errors[i] = C * inderr[i]
else:
corrected[i] = indices[i]
errors[i] = inderr[i]
# print "Warning: Meaningless correction for {0}.".format(
# lick_indices[i])
elif t in [1, 2]:
C = indtempl[i] - indtempl_b[i]
corrected[i] = indices[i] + C
errors[i] = inderr[i]
# print lick_indices[i], t, indices[i], C, corrected[i]
return corrected, errors
def correct_indices2(indices, inderr, indtempl, indtempl_b, types):
""" Make corrections for the broadening in the spectra."""
corrected = np.zeros_like(indices)
errors = np.zeros_like(indices)
for i,t in enumerate(types):
C = indtempl[i] - indtempl_b[i]
corrected[i] = indices[i] + C
errors[i] = inderr[i]
return corrected, errors
def save(results, output):
""" Save results to a output file. """
with open(output, "w") as f:
f.write("\n".join(results))
def wavelength_array(spec):
""" Produces array for wavelenght of a given array. """
w0 = pf.getval(spec, "CRVAL1")
deltaw = pf.getval(spec, "CD1_1")
pix0 = pf.getval(spec, "CRPIX1")
npix = pf.getval(spec, "NAXIS1")
return w0 + deltaw * (np.arange(npix) + 1 - pix0)
def check_intervals(setupfile, bands, vel):
""" Check which indices are defined in the spectrum. """
c = 299792.458 # speed of light in km/s
with open(setupfile) as f:
lines = [x for x in f.readlines()]
lines = [x for x in lines if x.strip()]
intervals = np.array(lines[5:]).astype(float)
intervals = intervals.reshape((len(intervals)/2, 2))
bands = np.loadtxt(bands, usecols=(2,7))
bands *= np.sqrt((1 + vel/c)/(1 - vel/c))
goodbands = np.zeros(len(bands))
for i, (b1, b2) in enumerate(bands):
for (i1, i2) in intervals:
if i1 < b1 and b2 < i2:
goodbands[i] = 1
return np.where(goodbands == 1, 1, np.nan)
class BroadCorr:
""" Wrapper for the interpolated model."""
def __init__(self, table):
self.interpolate(table)
def interpolate(self, table):
sigmas = np.loadtxt(table, dtype=np.double, usecols=(0,))
inds = np.loadtxt(table, dtype=np.double, usecols=np.arange(1,51, 2)).T
corrs = np.loadtxt(table, dtype=np.double,usecols=np.arange(2,51, 2)).T
self.fs = []
self.indlims = []
for i, (idx, corr) in enumerate(zip(inds, corrs)):
f = interpolator(np.column_stack((sigmas, idx)), corr)
self.fs.append(f)
self.indlims.append([idx.min(), idx.max()])
self.siglims = [sigmas.min(), sigmas.max()]
return
def __call__(self, sigma, lick):
b = np.zeros(len(lick))
for i,l in enumerate(lick):
if np.isnan(l):
b[i] = 0.
else:
b[i] = self.fs[i](sigma, l)
return b
class Vdisp_corr_k04():
""" Correction for LOSVD only for multiplicative indices from
Kuntschner 2004."""
def __init__(self):
""" Load tables. """
table = os.path.join(tables_dir, "kuntschner2004.tab")
bands = os.path.join(tables_dir, "BANDS")
self.indices_k04 = np.loadtxt(table, usecols=(1,), dtype=str).tolist()
self.type_k04 = np.loadtxt(table, usecols=(2,),
dtype=str).tolist()
self.coeff_k04 = np.loadtxt(table, usecols=np.arange(3,10))
self.lick_indices = np.loadtxt(bands, usecols=(0,), dtype=str)
self.lick_types = np.loadtxt(bands, usecols=(8,))
def __call__(self, lick, sigma, h3=0., h4=0.):
newlick = np.zeros(25)
for i,index in enumerate(lick_indices):
if index in self.indices_k04:
idx = self.indices_k04.index(index)
a1, a2, a3, b1, b2, c1, c2 = self.coeff_k04[idx]
if self.type_k04[idx] == "m":
C_k04 = 1. + a1 * sigma + a2 * sigma**2 + \
a3 * sigma**3 + b1 * sigma * h3 + \
b2 * sigma**2 * h3 + \
c1 * sigma * h4 + c2 * sigma**2 * h4
newlick[i] = C_k04 * lick[i]
else:
C_k04 = a1 * sigma + a2 * sigma**2 + \
a3 * sigma**3 + b1 * sigma * h3 + \
b2 * sigma**2 * h3 + \
c1 * sigma * h4 + c2 * sigma**2 * h4
newlick[i] = lick[i] + C_k04
else:
newlick[i] = lick[i]
return newlick
if __name__ == "__main__":
workdir = os.path.join(home, "single2")
os.chdir(workdir)
if not os.path.exists(os.path.join(workdir, "logs")):
os.mkdir(os.path.join(workdir, "logs"))
kinfile = "ppxf_results.dat"
specs = np.genfromtxt(kinfile, usecols = (0,), dtype=None).tolist()
K04 = Vdisp_corr_k04()
bands = os.path.join(tables_dir, "BANDS")
lick_types = np.loadtxt(bands, usecols=(8,))
lick_indices = np.genfromtxt(bands, usecols=(0,), dtype=None).tolist()
header = "# Spectra\t" + "\t".join(lick_indices)
# Initiating outputss
results, results_err = [header], [header]
results5, results5_err = [header], [header]
bcorr = BroadCorr(os.path.join(tables_dir, "lickcorr_m.txt"))
offset = np.loadtxt(os.path.join(tables_dir,"LICK_OFFSETS.dat"),
usecols=(1,)).T
broad2lick = False
for i, spec in enumerate(specs):
setupfile = os.path.join(home, "single1/{0}.setup".format(spec))
if not os.path.exists(setupfile):
print "Setup file not found: ", setupfile
continue
# Read the spectrum file and pPXF results
pp = pPXF(spec, velscale)
pp.calc_arrays_emission()
pp.sky_sub()
if pp.ncomp > 1:
v, s, h3, h4 = pp.sol[0]
else:
v, s, h3, h4 = pp.sol
print spec, v, s
goodindices = check_intervals(setupfile, bands, v)
######################################################################
# Check problem with broadening
bf = interp1d(pp.w_log, pp.bestfit, bounds_error=False,
fill_value="extrapolate")
bestfit = bf(pp.w)
bfu = interp1d(pp.w_log, pp.bestfit_unbroad, bounds_error=False,
fill_value="extrapolate")
bestfitunb = bfu(pp.w)
flux = pp.flux
######################################################################
# Broadening of the spectra to the Lick resolution
if broad2lick:
pp.flux = lector.broad2lick(pp.w, pp.flux, 2.1, vel=v)
pp.bestfit = lector.broad2lick(pp.w_log, pp.bestfit, 2.54, vel=v)
pp.bestfit_unbroad = lector.broad2lick(pp.w_log, pp.bestfit_unbroad,
2.54, vel=v)
flux = lector.broad2lick2(pp.w, pp.flux, 2.1, vel=v)
bestfit = lector.broad2lick2(pp.w, bestfit, 2.54, vel=v)
bestfitunb = lector.broad2lick2(pp.w, bestfitunb, 2.54, vel=v)
noise = pp.flux / pp.noise[0]
#####################################################################
# Make Lick indices measurements
#####################################################################
lick, lickerrs = lector.lector(pp.w, pp.flux-pp.em_linear, noise, bands,
vel = v, cols=(0,8,2,3,4,5,6,7),
keeplog=0, output="logs/lick_{0}".format(
spec.replace(".fits", ".pdf")), title=spec)
# Measuring Lick indices in the templates
noise2 = pp.bestfit / pp.noise[0]
lick_bf, tmp = lector.lector(pp.w_log, pp.bestfit - pp.em, noise2, bands,
vel = v, cols=(0,8,2,3,4,5,6,7),
keeplog=0, output="a1.pdf", title=spec)
noise3 = pp.bestfit_unbroad / pp.noise[0]
lick_bf_unb, tmp = lector.lector(pp.w_log, pp.bestfit_unbroad - pp.em,
noise3,
bands, vel = v, cols=(0,8,2,3,4,5,6,7),
keeplog=0, title=spec)
####################################################################
# Measure in new specs
noise2 = pp.bestfit / pp.noise[0]
lnew, tmp = lector.lector(pp.w, flux-pp.em_linear, noise, bands,
vel = v, cols=(0,8,2,3,4,5,6,7),
keeplog=0, output="logs/lick_{0}".format(
spec.replace(".fits", ".pdf")), title=spec)
lbf, tmp = lector.lector(pp.w, bestfit-pp.em_linear, noise, bands,
vel = v, cols=(0,8,2,3,4,5,6,7),
keeplog=0, output="a1.pdf", title=spec)
noise3 = pp.bestfit_unbroad / pp.noise[0]
lbfu, tmp = lector.lector(pp.w, bestfitunb-pp.em_linear, noise,
bands, vel = v, cols=(0,8,2,3,4,5,6,7),
keeplog=0, title=spec)
####################################################################
igood = np.array([12,13,16,17,18,19,20])
# Removing bad indices
lick *= goodindices
lickerrs *= goodindices
lick_bf *= goodindices
lick_bf_unb *= goodindices
####################################################################
# LOSVD correction using averages over templates
lick2 = lick * bcorr(s, lick)
lickerrs2 = lickerrs * bcorr(s, lick)
####################################################################
# LOSVD correction using best fit templates
####################################################################
lick3, lickerrs3 = correct_indices(lick, lickerrs, lick_bf_unb, lick_bf,
lick_types)
######################################################################
# LOSVD correction for some indices using Kuntschner 2004.
######################################################################
lick4 = K04(lick, s, h3, h4)
######################################################################
# New correction
lick5, lickerrs5 = correct_indices(lnew, lickerrs, lbfu, lbf,
lick_types)
######################################################################
# Offset correction
lick += offset
lick2 += offset
lick3 += offset
lick4 += offset
lick5 += offset
######################################################################
# Convert to string
######################################################################
lick = "".join(["{0:14}".format("{0:.5f}".format(x)) for x in lick])
lick2 = "".join(["{0:14}".format("{0:.5f}".format(x)) for x in lick2])
lick5 = "".join(["{0:14}".format("{0:.5f}".format(x)) for x in lick5])
# Append to output
results.append("{0:28s}".format(spec) + lick)
results5.append("{0:28s}".format(spec) + lick5)
res = "lickres" if broad2lick else "instres"
save(results5, "lick_vdcorr_{0}.tsv".format(res))
save(results, "lick_novdcorr_{0}.tsv".format(res))