Talking to Bruno about his project on stacking Swift observations and my project on Stripe82 SED we started to think about a collaboration to create a set of deep observations with Swift data. Deep Swift data being a great addition to the High-Energy end of Stripe82 data collection (see, for instance, LaMassa,2016).
Bruno then seached for all Swift-XRT observations inside the Stripe-82:
RA: 310 : 60
Dec: -1.25 : 1.25
Over the Stripe, Bruno has found ~3000 observations. See Figure 1 and tables Table 1 and Table 2.
Here, I'll do the filtering of the observations to keep only those useful for Paolo's stacking.
The selection looks for observations done within a time-range of a few days; for instance, 20 days is the window size I'll use here.
If all you want is to have a look at the final/filtered catalog, go straight to final section.
Otherwise, if the code used in this filtering does matter to you, you can show them out clicking the button below.
In [1]:
fromIPython.displayimportHTMLHTML('''<script>code_show=true; function code_toggle() { if (code_show){ $('div.input').hide(); } else { $('div.input').show(); } code_show = !code_show} $( document ).ready(code_toggle);</script><form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>''')
Out[1]:
In [2]:
fromIPython.displayimportHTMLHTML('''<figure> <img src="Stripe82_gal_projection.png" alt="Swift observations over Stripe82"> <figcaption>Figure 1: Swift observations over Stripe82</figcaption></figure>''')
Out[2]:
Figure 1: Swift observations over Stripe82
The base catalog
Right below, in Table 1, we can see a sample of the catalog (the first/last five lines).
In Table 2, we see a brief description of the catalog.
print"Table 1: Sample of the catalog"pandas.concat([cat.head(5),cat.tail(5)])
Table 1: Sample of the catalog
Out[4]:
Target_Name
obsid
RA_(J2000)
Dec_(J2000)
start_time
processing_date
xrt_exposure
uvot_exposure
bat_exposure
archive_date
Eclip_LONG
Eclip_LAT
GroupID
GroupSize
0
SAA-COLD-185-05
74146019
59.95865
0.01190
Jul 4, 2011 10:56:00
Jul 10, 2011
43.606
0.000
213.000
Jul 11, 2011
57.778385
-20.129974
1.0
28.0
1
SAA-COLD-78-07
74146024
59.97165
-0.00647
Mar 19, 2013 21:58:00
Mar 29, 2013
59.618
0.000
103.000
Mar 30, 2013
57.787768
-20.150683
1.0
28.0
2
SAA-COLD-184-06
74146006
59.97396
-0.01571
Jul 4, 2008 10:55:00
Oct 14, 2015
142.730
0.000
288.000
Jul 15, 2008
57.788085
-20.160203
1.0
28.0
3
SAA-COLD-184-06
74146028
59.97467
-0.00349
Jul 4, 2014 11:29:00
Jul 14, 2014
73.495
0.000
328.000
Jul 15, 2014
57.791585
-20.148411
1.0
28.0
4
SAA-COLD-77-07
74146026
59.97849
0.00060
Mar 18, 2014 23:21:00
Mar 28, 2014
72.428
0.000
160.000
Mar 29, 2014
57.796485
-20.145224
1.0
28.0
3030
SWIFT_GAL_SURVE
44610002
284.38862
1.09587
Mar 22, 2013 13:23:00
Apr 2, 2013
531.689
530.397
538.000
Apr 2, 2013
285.750199
23.751873
NaN
NaN
3031
3FGLJ1903.9+005
84846001
285.97019
0.93181
Nov 11, 2015 23:59:00
Nov 22, 2015
2817.892
2949.033
2999.000
Nov 22, 2015
287.444088
23.409176
NaN
NaN
3032
BURST (292.702,
680457000
292.70477
-0.66809
Mar 25, 2016 13:01:00
Apr 4, 2016
5.078
0.000
2017.941
Apr 5, 2016
294.396792
20.868780
NaN
NaN
3033
GRB 131127B
20329001
307.93677
0.98894
Nov 28, 2013 10:08:00
Dec 8, 2013
4976.248
4949.621
5005.000
Dec 9, 2013
310.621610
19.239110
NaN
NaN
3034
SWIFTJ2036.0-00
85659001
308.99192
-0.46204
Mar 16, 2016 07:25:00
Mar 26, 2016
839.939
814.009
847.000
Mar 27, 2016
311.296388
17.563196
NaN
NaN
In [5]:
print"Table 2: Summary of the catalog columns"cat.describe(include='all')
Table 2: Summary of the catalog columns
/home/chbrandt/.conda/envs/booq-dev/lib/python2.7/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile
RuntimeWarning)
Out[5]:
Target_Name
obsid
RA_(J2000)
Dec_(J2000)
start_time
processing_date
xrt_exposure
uvot_exposure
bat_exposure
archive_date
Eclip_LONG
Eclip_LAT
GroupID
GroupSize
count
3035
3.035000e+03
3035.000000
3035.000000
3035
3035
3035.000000
3035.000000
3035.000000
3035
3035.000000
3035.000000
2879.000000
2879.000000
unique
681
NaN
NaN
NaN
3035
1397
NaN
NaN
NaN
1889
NaN
NaN
NaN
NaN
top
AQLX-1
NaN
NaN
NaN
Apr 15, 2006 16:15:00
Oct 7, 2014
NaN
NaN
NaN
May 2, 2016
NaN
NaN
NaN
NaN
freq
278
NaN
NaN
NaN
1
45
NaN
NaN
NaN
36
NaN
NaN
NaN
NaN
mean
NaN
1.558216e+08
186.316193
0.075929
NaN
NaN
1517.361663
1422.427248
1667.715949
NaN
186.696292
0.341681
128.784300
54.054533
std
NaN
7.351472e+08
75.843331
0.555652
NaN
NaN
2853.121293
2728.280131
2970.263464
NaN
76.034196
18.148077
77.159774
79.198817
min
NaN
2.029400e+07
59.958650
-1.266100
NaN
NaN
0.000000
0.000000
0.000000
NaN
57.778385
-24.655739
1.000000
2.000000
25%
NaN
3.372302e+07
120.540790
-0.164905
NaN
NaN
128.620000
0.000000
317.500000
NaN
122.565842
-19.251349
NaN
NaN
50%
NaN
6.801500e+07
177.673620
0.007210
NaN
NaN
686.995000
618.296000
890.000000
NaN
177.675042
-1.338063
NaN
NaN
75%
NaN
7.504200e+07
265.013060
0.572640
NaN
NaN
1742.863500
1695.957500
1805.000000
NaN
264.565893
19.920627
NaN
NaN
max
NaN
7.002534e+09
310.920870
1.269710
NaN
NaN
47281.003000
44578.666000
49097.000000
NaN
313.379440
24.695579
244.000000
280.000000
Target_Name is the name of the (central) object at each observation, from that we see we have 681 unique sources out of the 3035 observations. GroupSize is the number of overlapping observations, the average number is ~54. Let's see how sparse are the observations in time and how do they distribute for each source.
To have a glue about the number of observations done over each object we can look the counts shown by Table 3 and the histogram below (Figure 2).
In [9]:
title="Figure 2: Number of sources(Y axis) observed number of times(X axis)"%matplotlibinlinefrommatplotlibimportpyplotaspltwidth=16height=4plt.figure(figsize=(width,height))yticks=[2,10,50,100,200,300]xticks=range(51)ax=cat_time[('start_time','count')].plot.hist(bins=xticks,xlim=(0,50),title=title,grid=True,xticks=xticks,yticks=yticks,align='left')ax.set_xlabel('Number of observations (per source)')
Out[9]:
<matplotlib.text.Text at 0x7fb9e9bd2bd0>
In [10]:
print"Table 3: Number counts and dates (first/last) of the observations (per object)"cat_time
Table 3: Number counts and dates (first/last) of the observations (per object)
Out[10]:
start_time
count
unique
top
freq
first
last
Target_Name
AQLX-1
278
278
2009-11-15 03:22:00
1
2006-03-07 00:16:00
2015-10-14 14:14:00
PSRJ1023+0038
123
123
2016-01-07 00:40:00
1
2013-06-10 14:00:00
2016-06-08 03:51:00
ARK120
87
87
2014-12-09 14:00:00
1
2014-03-22 10:23:00
2015-03-15 08:59:00
SWIFT_GAL_SURVE
77
77
2013-03-22 13:23:00
1
2011-02-25 11:01:00
2013-03-22 13:23:00
SN2007AF
55
55
2007-03-19 13:18:00
1
2007-03-02 19:14:00
2007-07-20 15:58:00
PMNJ0948+0022
43
43
2012-06-18 12:03:00
1
2008-12-05 02:21:00
2016-06-13 23:59:00
PS15AE
43
43
2015-02-19 06:57:00
1
2015-02-19 06:57:00
2016-03-10 08:40:00
1ES0414+009
40
40
2012-11-08 07:56:00
1
2006-10-21 00:30:00
2016-02-05 02:54:00
SA101
40
40
2009-06-30 18:48:00
1
2005-02-19 01:15:00
2015-11-17 00:20:00
AEAQR
39
39
2005-08-31 00:54:00
1
2005-08-30 18:56:00
2015-12-17 00:48:00
ASASSN-15HY
37
37
2015-05-02 18:52:00
1
2015-04-29 14:13:00
2015-06-23 11:17:00
ASASSN-14LP
27
27
2014-12-11 17:46:00
1
2014-12-10 09:35:00
2015-02-15 16:48:00
SWIFTJ185003.2-
25
25
2011-07-01 13:32:00
1
2011-06-24 23:50:00
2011-07-21 07:11:00
GRB141109A
24
24
2014-11-12 00:23:00
1
2014-11-09 05:34:00
2014-12-09 01:42:00
ASASSN-16AT
23
23
2016-02-06 23:59:00
1
2016-01-21 01:01:00
2016-03-05 07:56:00
2XMMJ185114.3-0
21
21
2012-06-19 17:17:00
1
2012-06-17 15:31:00
2012-08-31 14:59:00
SAA-COLD-309-06
21
21
2008-02-02 08:11:00
1
2008-02-02 08:11:00
2016-02-03 10:07:00
SAA-COLD-115-05
20
20
2016-05-01 20:45:00
1
2007-05-02 18:34:00
2016-05-01 20:45:00
SAA-COLD-100-07
19
19
2008-08-21 09:18:00
1
2006-08-03 08:22:00
2015-08-05 09:34:00
SA98OFFSET2
19
19
2014-05-15 07:07:00
1
2005-03-10 01:35:00
2016-04-23 21:08:00
SA104SW
18
18
2005-02-22 07:01:00
1
2005-02-22 07:01:00
2015-07-02 00:43:00
SAA-COLD-261-07
16
16
2006-12-28 14:44:00
1
2006-09-07 20:50:00
2015-12-30 12:25:00
SA104N
16
16
2005-03-21 08:34:00
1
2005-03-10 10:06:00
2013-12-04 23:59:00
3XMMJ185246.9+0
16
16
2013-08-29 00:57:00
1
2012-09-25 06:49:00
2013-10-30 09:13:00
SAA-COLD-254-05
16
16
2006-12-24 12:34:00
1
2006-08-21 20:50:00
2015-12-26 12:41:00
SAA-COLD-228-05
15
15
2013-08-27 09:42:00
1
2006-08-16 11:25:00
2015-09-28 06:24:00
UGC4179-SN06JD
15
15
2012-09-14 16:56:00
1
2008-12-09 01:08:00
2013-02-02 09:03:00
SAA-COLD-165-05
14
14
2011-06-11 17:28:00
1
2006-09-14 08:08:00
2015-09-22 06:52:00
V1647ORI
14
14
2010-03-05 06:36:00
1
2008-09-09 00:14:00
2015-01-09 12:52:00
SA101OFFSET1
13
13
2009-07-01 17:15:00
1
2005-03-10 13:12:00
2016-05-16 16:27:00
...
...
...
...
...
...
...
Q1016-006
1
1
2015-10-19 07:03:00
1
2015-10-19 07:03:00
2015-10-19 07:03:00
PKSB1130+008
1
1
2011-04-22 18:23:00
1
2011-04-22 18:23:00
2011-04-22 18:23:00
PKS0422+00
1
1
2010-10-18 19:20:00
1
2010-10-18 19:20:00
2010-10-18 19:20:00
OGP_3888
1
1
2016-05-21 00:04:00
1
2016-05-21 00:04:00
2016-05-21 00:04:00
SAA-COLD-113-06
1
1
2013-04-23 20:12:00
1
2013-04-23 20:12:00
2013-04-23 20:12:00
SAA-COLD-115-5
1
1
2010-04-25 17:40:00
1
2010-04-25 17:40:00
2010-04-25 17:40:00
SAA-COLD-118-05
1
1
2012-04-27 18:19:00
1
2012-04-27 18:19:00
2012-04-27 18:19:00
SAA-COLD-119-16
1
1
2009-04-29 22:25:00
1
2009-04-29 22:25:00
2009-04-29 22:25:00
SAA-COLD-185-05
1
1
2011-07-04 10:56:00
1
2011-07-04 10:56:00
2011-07-04 10:56:00
SAA-COLD-183-15
1
1
2006-07-02 11:19:00
1
2006-07-02 11:19:00
2006-07-02 11:19:00
SAA-COLD-169-07
1
1
2015-06-18 18:55:00
1
2015-06-18 18:55:00
2015-06-18 18:55:00
SAA-COLD-165-6
1
1
2006-06-14 19:34:00
1
2006-06-14 19:34:00
2006-06-14 19:34:00
SAA-COLD-164-6
1
1
2006-06-13 19:27:00
1
2006-06-13 19:27:00
2006-06-13 19:27:00
SAA-COLD-164-5
1
1
2010-06-13 17:29:00
1
2010-06-13 17:29:00
2010-06-13 17:29:00
SAA-COLD-164-13
1
1
2009-06-13 18:44:00
1
2009-06-13 18:44:00
2009-06-13 18:44:00
SAA-COLD-162-7
1
1
2005-06-11 20:23:00
1
2005-06-11 20:23:00
2005-06-11 20:23:00
1A 0535+262
1
1
2005-05-29 15:39:00
1
2005-05-29 15:39:00
2005-05-29 15:39:00
SAA-COLD-159-13
1
1
2009-06-08 16:33:00
1
2009-06-08 16:33:00
2009-06-08 16:33:00
SAA-COLD-158-6
1
1
2005-06-07 19:53:00
1
2005-06-07 19:53:00
2005-06-07 19:53:00
SAA-COLD-158-5
1
1
2009-06-07 18:08:00
1
2009-06-07 18:08:00
2009-06-07 18:08:00
SAA-COLD-156-14
1
1
2010-06-05 18:28:00
1
2010-06-05 18:28:00
2010-06-05 18:28:00
SAA-COLD-154-5
1
1
2005-06-03 18:00:00
1
2005-06-03 18:00:00
2005-06-03 18:00:00
SAA-COLD-146-5
1
1
2006-05-26 19:16:00
1
2006-05-26 19:16:00
2006-05-26 19:16:00
SAA-COLD-145-6
1
1
2005-05-25 20:05:00
1
2005-05-25 20:05:00
2005-05-25 20:05:00
SAA-COLD-144-5
1
1
2005-05-24 18:26:00
1
2005-05-24 18:26:00
2005-05-24 18:26:00
SAA-COLD-141-7
1
1
2005-05-21 21:27:00
1
2005-05-21 21:27:00
2005-05-21 21:27:00
SAA-COLD-122-5
1
1
2010-05-02 18:19:00
1
2010-05-02 18:19:00
2010-05-02 18:19:00
SAA-COLD-121-7
1
1
2005-05-01 18:48:00
1
2005-05-01 18:48:00
2005-05-01 18:48:00
SAA-COLD-121-24
1
1
2006-05-01 19:37:00
1
2006-05-01 19:37:00
2006-05-01 19:37:00
XTE J1752-223
1
1
2009-10-24 14:03:00
1
2009-10-24 14:03:00
2009-10-24 14:03:00
681 rows × 6 columns
Filtering the data
First, a closer look to an example
To have a better idea of what we should find regarding the observation time of these sources, I'll take a particular one -- V1647ORI -- and see what we have for this source.
In [11]:
print"Table 4: Observation carried out for source 'V1647ORI' sorted in time"g=cat_grouped_by_target.get_group('V1647ORI')g_sorted=g.sort_values('start_time')g_sorted
Table 4: Observation carried out for source 'V1647ORI' sorted in time
Out[11]:
start_time
345
2008-09-09 00:14:00
348
2008-09-10 01:55:00
337
2009-04-12 01:24:00
347
2009-09-11 09:14:00
344
2009-09-12 10:56:00
343
2009-09-13 03:00:00
346
2009-09-14 06:36:00
342
2010-02-24 05:33:00
340
2010-03-05 06:36:00
338
2015-01-05 05:00:00
339
2015-01-06 00:11:00
336
2015-01-07 14:31:00
335
2015-01-08 01:49:00
341
2015-01-09 12:52:00
If we consider each group of observations of our interest -- let me call them "chunk" -- observations that distance each other no more than "X" days (for example, X=20 days) we see from this example that it happens to exist more than one "chunk" of observations per object. Here, for instance, rows 347,344,343,346 and 338,339,336,335,341 form the cluster of observations of our interest, "chunk-1" and "chunk-2", respectively.
To select the candidates we need to run a window function over the 'start_time' sorted list, where the function has two elements (i.e, observations) to ask their distance in time. If the pair of observations is less than, say 20 days, they are selected for future processing.
Applying the filter to all objects
Now defining a 20 days window as the selection criterium to all objects in our catalog we end up with 2254 observations, done over 320 objects.
Table 5 add such information through column "obs_chunk", where "Not-Available" value means the observations that have not succeed in the filtering applied.
Note: obs_chunk values mean the groupings -- "chunks" -- formed within each object's set of observations. They are unique among each object's observations, but not accross the entire catalog.
In [12]:
deffind_clustered_observations(sorted_target_observations,time_range=10):# Let's select a 'time_range' days window to select valid observationswindow_size=time_rangeg_sorted=sorted_target_observations# an ordered dictionary works as a 'set' structurefromcollectionsimportOrderedDictselected_allObs=OrderedDict()# define en identificator for each cluster of observations, to ease future filteringgroup_obs=1_last_time=None_last_id=Nonefor_rowing_sorted.iterrows():ind,row=_rowif_last_timeisNone:_last_time=row.start_time_last_id=indcontinue_delta=row.start_time-_last_timeif_delta.days<=window_size:selected_allObs[_last_id]=group_obsselected_allObs[ind]=group_obselse:iflen(selected_allObs):group_obs=selected_allObs.values()[-1]+1_last_time=row.start_time_last_id=indreturnselected_allObs
print"Table 5: original catalog with column 'obs_chunk' to flag which rows succeed the filtering (non-NA values)."cat_with_obsChunksFlag=cat.join(obsChunks_forFilteringCat)cols=list(cat_with_obsChunksFlag.columns)cols.insert(2,cols.pop(-1))cat_with_obsChunksFlag=cat_with_obsChunksFlag.ix[:,cols]cat_with_obsChunksFlag
Table 5: original catalog with column 'obs_chunk' to flag which rows succeed the filtering (non-NA values).
Out[15]:
Target_Name
obsid
obs_chunk
RA_(J2000)
Dec_(J2000)
start_time
processing_date
xrt_exposure
uvot_exposure
bat_exposure
archive_date
Eclip_LONG
Eclip_LAT
GroupID
GroupSize
0
SAA-COLD-185-05
74146019
NaN
59.95865
0.01190
2011-07-04 10:56:00
Jul 10, 2011
43.606
0.000
213.000
Jul 11, 2011
57.778385
-20.129974
1.0
28.0
1
SAA-COLD-78-07
74146024
NaN
59.97165
-0.00647
2013-03-19 21:58:00
Mar 29, 2013
59.618
0.000
103.000
Mar 30, 2013
57.787768
-20.150683
1.0
28.0
2
SAA-COLD-184-06
74146006
1.0
59.97396
-0.01571
2008-07-04 10:55:00
Oct 14, 2015
142.730
0.000
288.000
Jul 15, 2008
57.788085
-20.160203
1.0
28.0
3
SAA-COLD-184-06
74146028
2.0
59.97467
-0.00349
2014-07-04 11:29:00
Jul 14, 2014
73.495
0.000
328.000
Jul 15, 2014
57.791585
-20.148411
1.0
28.0
4
SAA-COLD-77-07
74146026
3.0
59.97849
0.00060
2014-03-18 23:21:00
Mar 28, 2014
72.428
0.000
160.000
Mar 29, 2014
57.796485
-20.145224
1.0
28.0
5
SAA-COLD-184-5
74146018
1.0
59.97904
0.01818
2010-07-04 12:41:00
Jul 10, 2010
119.812
0.000
167.000
Jul 11, 2010
57.801026
-20.128160
1.0
28.0
6
SAA-COLD-185-17
74146010
1.0
59.98036
-0.04740
2009-07-04 10:37:00
Jul 10, 2009
158.473
0.000
250.000
Jul 11, 2009
57.787590
-20.192529
1.0
28.0
7
SAA-COLD-76-16
74146014
NaN
59.98080
-0.00729
2010-03-17 22:05:00
Mar 23, 2010
222.608
0.000
256.000
Mar 24, 2010
57.797108
-20.153424
1.0
28.0
8
SAA-COLD-184-5
74146017
1.0
59.98144
-0.00241
2010-07-04 10:58:00
Jul 10, 2010
126.487
0.000
170.000
Jul 11, 2010
57.798876
-20.148791
1.0
28.0
9
SAA-COLD-74-8
74146008
NaN
59.98251
0.00607
2009-03-15 22:51:00
Mar 21, 2009
166.006
0.000
225.000
Mar 22, 2009
57.801904
-20.140731
1.0
28.0
10
SAA-COLD-79-08
74146020
NaN
59.98477
0.00068
2012-03-19 22:21:00
Mar 25, 2012
82.033
0.000
225.000
Mar 26, 2012
57.803040
-20.146477
1.0
28.0
11
SAA-COLD-184-06
74146007
1.0
59.98622
-0.01296
2008-07-04 12:37:00
Oct 14, 2015
234.707
0.000
284.000
Jul 15, 2008
57.801470
-20.160114
1.0
28.0
12
SAA-COLD-77-07
74146025
3.0
59.98822
-0.00823
2014-03-18 21:38:00
Mar 28, 2014
31.865
0.000
272.000
Mar 29, 2014
57.804620
-20.155916
1.0
28.0
13
SAA-COLD-184-5
74146015
1.0
59.98907
-0.00997
2010-07-03 10:53:00
Jul 9, 2010
70.290
0.000
251.000
Jul 10, 2010
57.805112
-20.157796
1.0
28.0
14
SAA-COLD-185-17
74146009
1.0
59.98940
-0.03133
2009-07-03 12:11:00
Jul 9, 2009
387.867
0.000
532.000
Jul 10, 2009
57.800633
-20.178741
1.0
28.0
15
SAA-COLD-184-5
74146016
1.0
59.98946
-0.01475
2010-07-03 12:35:00
Jul 9, 2010
117.248
0.000
167.000
Jul 10, 2010
57.804439
-20.162551
1.0
28.0
16
SAA-COLD-79-08
74146022
1.0
59.98983
0.00349
2012-07-04 10:50:00
Jul 14, 2012
171.382
0.000
514.000
Jul 15, 2012
57.808942
-20.144803
1.0
28.0
17
SAA-COLD-184-06
74146031
3.0
60.00025
0.00071
2015-07-04 12:55:00
Jul 14, 2015
38.269
0.000
171.000
Jul 15, 2015
57.819161
-20.149728
1.0
28.0
18
SAA-COLD-185-17
74146012
1.0
60.00081
-0.00174
2009-07-04 14:04:00
Jul 10, 2009
151.024
0.000
170.000
Jul 11, 2009
57.819191
-20.152241
1.0
28.0
19
SAA-COLD-226-2
74146013
NaN
60.00347
0.00557
2009-08-14 06:27:00
Aug 20, 2009
124.227
0.000
414.000
Aug 21, 2009
57.823610
-20.145660
1.0
28.0
20
SAA-COLD-185-17
74146011
1.0
60.01651
0.00961
2009-07-04 12:20:00
Jul 10, 2009
70.611
0.000
167.000
Jul 11, 2009
57.838096
-20.144474
1.0
28.0
21
SAA-COLD-184-06
74146029
3.0
60.02220
-0.00581
2015-07-04 11:17:00
Jul 14, 2015
0.000
0.000
162.000
Jul 15, 2015
57.840542
-20.160749
1.0
28.0
22
SAA-COLD-79-08
74146023
1.0
60.02288
0.00310
2012-07-15 09:50:00
Jul 25, 2012
66.023
0.000
504.000
Jul 26, 2012
57.843259
-20.152185
1.0
28.0
23
SAA-COLD-184-06
74146001
NaN
60.02628
0.00252
2006-08-14 07:44:00
Apr 9, 2015
137.541
0.000
75.000
Aug 25, 2006
57.846668
-20.153471
1.0
28.0
24
SAA-COLD-184-06
74146004
1.0
60.02656
0.01513
2008-07-03 10:48:00
Oct 14, 2015
83.820
0.000
255.000
Jul 14, 2008
57.849803
-20.141206
1.0
28.0
25
SAA-COLD-184-06
74146027
2.0
60.03647
-0.00297
2014-07-03 11:30:00
Jul 13, 2014
35.066
0.000
346.000
Jul 14, 2014
57.856039
-20.160994
1.0
28.0
26
SAA-COLD-183-15
69966001
NaN
60.03900
-0.00459
2006-07-02 11:19:00
Apr 9, 2015
288.119
0.000
243.000
Jul 13, 2006
57.858308
-20.163113
1.0
28.0
27
SAA-COLD-184-06
74146003
NaN
60.05839
0.00134
2007-07-04 11:54:00
Jul 16, 2015
108.781
0.000
0.000
Jul 15, 2007
57.879833
-20.161419
1.0
28.0
28
1ES0414+009
30813032
10.0
64.15163
1.08873
2014-12-26 05:15:00
Jan 5, 2015
1029.447
0.000
1033.000
Jan 6, 2015
62.379521
-19.906271
2.0
42.0
29
1ES0414+009
30813018
5.0
64.16355
1.12553
2012-12-12 05:03:00
Dec 18, 2012
949.609
921.469
955.000
Jan 5, 2013
62.399193
-19.872299
2.0
42.0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
3005
SWIFT_GAL_SURVE
44585001
2.0
282.98759
0.93159
2011-11-24 21:48:00
Nov 30, 2011
496.440
493.999
501.000
Dec 1, 2011
284.209522
23.732510
NaN
NaN
3006
SWIFT_GAL_SURVE
44553001
4.0
283.07897
-0.69363
2012-11-22 14:22:00
Nov 28, 2012
486.409
486.132
494.000
Dec 16, 2012
284.136394
22.106117
NaN
NaN
3007
SWIFT_GAL_SURVE
44566001
4.0
283.08933
-0.10851
2012-11-22 16:18:00
Nov 28, 2012
511.483
509.015
517.000
Dec 16, 2012
284.209184
22.687446
NaN
NaN
3008
SWIFT_GAL_SURVE
44592001
4.0
283.11235
1.19251
2012-11-22 09:38:00
Nov 28, 2012
468.072
467.663
475.000
Dec 16, 2012
284.373273
23.979918
NaN
NaN
3009
SWIFT_GAL_SURVE
44547001
5.0
283.24691
-1.10987
2013-02-21 12:26:00
Mar 4, 2013
614.283
597.190
621.000
Mar 4, 2013
284.272706
21.675473
NaN
NaN
3010
SWIFT_GAL_SURVE
44560001
5.0
283.25028
-0.47929
2013-03-12 08:22:00
Mar 22, 2013
478.095
477.350
485.000
Mar 23, 2013
284.343181
22.302667
NaN
NaN
3011
SWIFT_GAL_SURVE
44573001
5.0
283.26883
0.13905
2013-03-11 22:34:00
Mar 21, 2013
476.379
473.908
482.000
Mar 22, 2013
284.429377
22.916146
NaN
NaN
3012
SWIFT_GAL_SURVE
44567001
5.0
283.35864
-0.23788
2013-03-02 17:29:00
Mar 12, 2013
534.048
533.078
541.000
Mar 13, 2013
284.485680
22.532160
NaN
NaN
3013
SWIFT_GAL_SURVE
44554001
5.0
283.35903
-0.87139
2013-03-02 18:44:00
Mar 12, 2013
586.702
584.634
592.000
Mar 13, 2013
284.418162
21.901741
NaN
NaN
3014
SWIFT_GAL_SURVE
44593001
5.0
283.39720
1.00263
2013-03-12 13:10:00
Mar 22, 2013
576.671
575.731
574.000
Mar 23, 2013
284.662450
23.762581
NaN
NaN
3015
SWIFT_GAL_SURVE
44574001
2.0
283.45141
0.04449
2011-11-23 11:57:00
Nov 29, 2011
883.320
883.395
891.000
Nov 30, 2011
284.616295
22.803845
NaN
NaN
3016
SWIFT_GAL_SURVE
44587001
3.0
283.48188
0.63861
2012-02-16 10:09:00
Feb 22, 2012
626.925
625.269
633.000
Feb 23, 2012
284.714299
23.391876
NaN
NaN
3017
SWIFT_GAL_SURVE
44561001
5.0
283.49456
-0.61071
2013-02-28 17:03:00
Mar 11, 2013
543.216
541.981
550.000
Mar 11, 2013
284.591639
22.147621
NaN
NaN
3018
SWIFT_GAL_SURVE
44600001
5.0
283.49601
1.24128
2013-02-16 20:04:00
Feb 27, 2013
518.206
518.106
525.000
Feb 27, 2013
284.796333
23.990013
NaN
NaN
3019
SWIFT_GAL_SURVE
44581001
5.0
283.63534
0.27559
2013-03-02 09:07:00
Mar 12, 2013
503.057
501.457
509.000
Mar 13, 2013
284.840323
23.015160
NaN
NaN
3020
SWIFT_GAL_SURVE
44594001
5.0
283.63718
0.86494
2013-03-04 17:13:00
Mar 14, 2013
528.232
524.957
534.000
Mar 15, 2013
284.907845
23.601242
NaN
NaN
3021
SWIFT_GAL_SURVE
44588001
4.0
283.72099
0.50529
2012-11-16 07:55:00
Nov 22, 2012
573.192
572.057
579.000
Dec 10, 2012
284.958512
23.234895
NaN
NaN
3022
SWIFT_GAL_SURVE
44575001
2.0
283.73082
-0.06610
2011-11-23 13:44:00
Nov 29, 2011
626.452
624.805
640.000
Nov 30, 2011
284.905530
22.665507
NaN
NaN
3023
SWIFT_GAL_SURVE
44601001
5.0
283.79479
1.12982
2013-03-04 14:01:00
Mar 14, 2013
536.555
533.210
542.000
Mar 15, 2013
285.108870
23.848477
NaN
NaN
3024
SWIFT_GAL_SURVE
44595001
4.0
283.85185
0.77289
2012-11-16 09:27:00
Nov 22, 2012
618.228
618.163
625.000
Dec 10, 2012
285.130386
23.487554
NaN
NaN
3025
SWIFT_GAL_SURVE
44589001
3.0
284.00077
0.37560
2012-03-01 02:36:00
Mar 7, 2012
481.395
480.064
487.000
Mar 8, 2012
285.246540
23.076907
NaN
NaN
3026
SWIFT_GAL_SURVE
44602001
5.0
284.00411
0.99232
2013-02-25 09:14:00
Mar 7, 2013
631.813
595.667
651.000
Mar 8, 2013
285.320615
23.689877
NaN
NaN
3027
SWIFT_GAL_SURVE
44609001
5.0
284.10874
1.23899
2013-03-01 23:31:00
Mar 11, 2013
481.394
480.594
488.000
Mar 12, 2013
285.462785
23.924122
NaN
NaN
3028
SWIFT_GAL_SURVE
44596001
1.0
284.12538
0.60116
2011-02-25 17:12:00
Mar 3, 2011
967.300
964.752
1000.000
Mar 4, 2011
285.407139
23.288115
NaN
NaN
3029
SWIFT_GAL_SURVE
44603001
1.0
284.24010
0.85737
2011-02-25 20:25:00
Mar 3, 2011
453.215
451.280
469.000
Mar 4, 2011
285.561083
23.530694
NaN
NaN
3030
SWIFT_GAL_SURVE
44610002
5.0
284.38862
1.09587
2013-03-22 13:23:00
Apr 2, 2013
531.689
530.397
538.000
Apr 2, 2013
285.750199
23.751873
NaN
NaN
3031
3FGLJ1903.9+005
84846001
NaN
285.97019
0.93181
2015-11-11 23:59:00
Nov 22, 2015
2817.892
2949.033
2999.000
Nov 22, 2015
287.444088
23.409176
NaN
NaN
3032
BURST (292.702,
680457000
NaN
292.70477
-0.66809
2016-03-25 13:01:00
Apr 4, 2016
5.078
0.000
2017.941
Apr 5, 2016
294.396792
20.868780
NaN
NaN
3033
GRB 131127B
20329001
NaN
307.93677
0.98894
2013-11-28 10:08:00
Dec 8, 2013
4976.248
4949.621
5005.000
Dec 9, 2013
310.621610
19.239110
NaN
NaN
3034
SWIFTJ2036.0-00
85659001
NaN
308.99192
-0.46204
2016-03-16 07:25:00
Mar 26, 2016
839.939
814.009
847.000
Mar 27, 2016
311.296388
17.563196
NaN
NaN
3035 rows × 15 columns
Filtered catalog
And here is the final catalog, where rows (i.e, observations) with out of our interest (i.e, "obs_chunk == NaN") were removed. This catalog is written to 'Swift_Master_Stripe82_groups_filtered.csv'.