In [1]:
import simlibs.simlib as sl
import os
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
print sl.__file__
/Users/rbiswas/.local/lib/python2.7/site-packages/OpSimSummary-0.0.1.dev0-py2.7.egg/simlibs/simlib.pyc
In [2]:
loc = '/Users/rbiswas/data/SNDATA/INTERNAL/LSST/simlibs/2_168'
In [38]:
import simlibs.summarize_opsim as so
In [39]:
opsimout = '/Users/rbiswas/data/LSST/OpSimData/opsim2_168_sqlite.db'
In [41]:
opsimSummary = os.SummaryOpsim.fromOpSimDB(opsimout,
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-41-155d3073250a> in <module>()
----> 1 opsimSummary = os.SummaryOpsim.fromOpSimDB(opSimDB=opsimout)
TypeError: fromOpSimDB() takes exactly 3 arguments (2 given)
In [9]:
basicSimlib = 'opsim2_168.SIMLIB'
In [10]:
oldsimlib = sl.Simlib.fromSimlibFile(basicSimlib)
In [12]:
len(oldsimlib.fieldIDs)
Out[12]:
3636
In [17]:
newSimlib = sl.Simlib.fromSimlibFile('opsim_2168.SIMLIB.DEEP.FULL')
In [19]:
newSimlib.fieldIDs
Out[19]:
[2082, 519, 2731, 2412, 2786, 526, 1427, 311, 2712, 764]
In [33]:
newvals = set(newSimlib.simlibData(2082).IDEXPT)
# print newvals.size
In [3]:
snanaDeepFileName = os.path.join(loc,'output_opsim2_168.SIMLIB.COADD.DEEP')
snanaDeep = sl.Simlaib.fromSimlibFile(snanaDeepFileName)
In [32]:
oldvals = set(oldsimlib.simlibData(2082).IDEXPT)
# print oldvals.size
In [35]:
newinnew = list(newvals - oldvals)
In [36]:
newinold = list(oldvals - newvals)
In [37]:
newinnew
Out[37]:
[39447760,
39187937,
41533445,
41541505,
38835547,
40631995,
39187938,
40894074,
39474527,
38759093,
39510913,
39116670,
38746031,
38835553,
40140335,
39474530,
39187939,
39474531,
38759097,
39474532,
41541507,
39856826,
39474533,
40970939,
39474534,
39856828,
39474535,
39187940,
39856829,
40512742,
39856830,
39474537,
41541508,
39856831,
39510915,
39474538,
39856832,
39474539,
39856833,
39474540,
41533463,
39856834,
39474541,
39856835,
39474542,
41541509,
39905988,
41325935,
39813277,
39813279,
39813280,
39813281,
39813282,
39813283,
39813284,
39813285,
39813286,
39813287,
39813288,
39813289,
39813290,
39813291,
39813292,
39813293,
39813294,
39813295,
39813296,
39813297,
39813298,
39813299,
39813300,
39813301,
39813302,
39813303,
39813304,
39813305,
39813306,
39813307,
39813308,
39856842,
39813310,
39813311,
39813312,
39813313,
39813314,
39813315,
39813316,
39813317,
39813318,
39813319,
39813320,
39813321,
39813322,
39813323,
39813324,
39813325,
39813326,
39813327,
39813328,
41325944,
39813331,
39813332,
39856846,
39813334,
39813335,
39813336,
39878873,
39878874,
39878875,
39878876,
39878877,
39878878,
39878879,
39878880,
39878881,
39878882,
39878883,
39878884,
39878885,
39878886,
39813351,
39878888,
39878889,
39813354,
39878891,
39878892,
39856850,
39813358,
39813359,
39813360,
39813361,
39813362,
39813363,
41517300,
41517301,
41517302,
41517303,
41517304,
41517305,
41517306,
41517307,
41517308,
41517309,
41517310,
41517311,
41517312,
41517313,
41517314,
41517315,
41517316,
41517317,
41517318,
41517319,
41517320,
41517321,
41517322,
41517323,
41517324,
41517325,
41517326,
41517327,
41517328,
41517329,
41517330,
41517331,
41517332,
41517333,
41517334,
41517335,
41517336,
41517337,
41517338,
41517339,
41517340,
41517341,
41517342,
41517343,
41517344,
41517345,
41517346,
41517347,
41517348,
41517349,
41517350,
41517351,
41517352,
40970972,
41517354,
41571719,
38764844,
41517357,
38764846,
38764847,
38764848,
38764849,
38764850,
38764851,
38764852,
38764853,
38764854,
38764855,
38764856,
38764857,
38764858,
41517371,
38764860,
38764861,
38764862,
38764863,
38764864,
41517377,
41517378,
39474571,
41517380,
41517381,
41517382,
41517383,
41517384,
41517385,
41517386,
41517387,
41517388,
41517389,
41517390,
41517391,
41517392,
41517393,
41517394,
41517395,
41539333,
39474574,
40890574,
40970980,
39474575,
39187948,
40970981,
39474576,
38742758,
39474577,
38742759,
39510923,
39474578,
38742760,
41539334,
39474579,
40924970,
38742761,
39474580,
40272250,
40272251,
40272252,
40272253,
40272254,
40272255,
40272256,
40272257,
40272258,
40272259,
40272260,
40272261,
40272262,
40272263,
40272264,
40272265,
40272266,
40272267,
40272268,
40272269,
40272270,
40272271,
40272272,
40272273,
40272274,
40272275,
40272276,
40272277,
40272278,
40272279,
40272280,
40272281,
40272282,
40272283,
40272284,
40272285,
40272286,
40599967,
40599968,
40599969,
40599970,
40599971,
40599972,
40599973,
40599974,
40599975,
40599976,
40272297,
40272298,
40599979,
40599980,
40599981,
40599982,
40599983,
40599984,
40599985,
40599986,
40599987,
40599988,
40599989,
40599990,
40599991,
40599992,
40599993,
40599994,
40599995,
40599996,
40927677,
40927678,
40927679,
40927680,
40927681,
40927682,
40272323,
40272324,
40600005,
40272326,
40272327,
40272328,
40272329,
40272330,
40272331,
40272332,
40272333,
40272334,
40272335,
40272336,
40272337,
40272338,
40272339,
40272340,
40272341,
40272342,
40272343,
40272344,
40272345,
40600026,
40600027,
40600028,
40600029,
40600030,
40600031,
40600032,
40600033,
40600034,
40600035,
40600036,
40600037,
40600038,
40600039,
40600040,
40600041,
40600042,
41571751,
39856893,
41571752,
39844667,
38742772,
41571753,
38742783,
39474602,
41541521,
38742784,
39510928,
39474603,
38742785,
39454482,
40971010,
41571757,
38742773,
39474606,
40971012,
39474607,
39856901,
39510929,
39474608,
40971014,
39474609,
40971015,
38794626,
39474610,
40971016,
39474611,
40971017,
39474612,
41541523,
40971018,
39510930,
39474613,
40971019,
39474614,
40971020,
40149183,
39474615,
40971021,
39474616,
40973045,
40971022,
39474617,
41558982,
40971023,
39510931,
39474618,
38742800,
41748839,
40971025,
39474620,
38742776,
38858292,
39474621,
41246106,
38742803,
40654270,
40971028,
39510932,
41571775,
40971029,
40654272,
40971030,
40654273,
38742777,
41735618,
40971032,
41735619,
40971033,
39510933,
41571780,
39450921,
40654277,
40654278,
40654279,
41571784,
40583283,
40943303,
40654281,
41768192,
40654282,
39861187,
41735627,
39213056,
40654284,
41571790,
40654285,
40654286,
41539346,
41190107,
41190108,
41190109,
41190110,
41190111,
41190112,
41190113,
41190114,
41190115,
41190116,
41190117,
41190118,
41190119,
41190120,
41190121,
41190122,
41190123,
41190124,
41190125,
41190126,
40654291,
39450924,
41539347,
40654292,
39447761,
40654293,
39187962,
38794614,
40654294,
39133567,
40654295,
41539348,
40964035,
38742782,
40583305,
39813337,
39184632,
39450927,
41539350,
39187942,
38732631,
38732632,
38732633,
38732634,
38732635,
38732636,
38732637,
38732638,
38732639,
38732640,
38732641,
38732642,
38732643,
38732644,
38732645,
38732646,
38732647,
38732648,
38732649,
38732650,
38732651,
40567660,
40567661,
40567662,
40567663,
40567664,
40567665,
40567666,
40567667,
40567668,
40567669,
40567670,
40567671,
40567672,
40567673,
40567674,
40567675,
40567676,
40567677,
40567678,
40567679,
40567680,
40567681,
40567682,
40567683,
40567684,
40567685,
40567686,
40567687,
40567688,
40567689,
40567690,
40567691,
40567692,
40567693,
40567694,
40567695,
40567696,
40567697,
41747346,
41747347,
41747348,
41747349,
41747350,
41747351,
41747352,
41747353,
41747354,
41747355,
41747356,
41747357,
41747358,
41747359,
41747360,
41747361,
41747362,
41747363,
41747364,
41747365,
41747366,
41747367,
41747368,
41747369,
41747370,
41747371,
41747372,
41747373,
41747374,
41747375,
41747376,
41747377,
41747378,
41747379,
41747380,
41747381,
41747382,
41747383,
41747384,
41747385,
41747386,
40567739,
40567740,
40567741,
40567742,
40567743,
41747392,
41747393,
40273643,
38742788,
40174553,
40174554,
40174555,
40174556,
40174557,
40174558,
40174559,
40174560,
40174561,
40174562,
40174563,
40174564,
40174565,
40174566,
40174567,
40174568,
40174569,
40174570,
40174571,
40174572,
39116671,
39551983,
39551985,
39551986,
39551987,
39551988,
39551989,
39551990,
39551991,
39551992,
39551993,
39551994,
39551995,
39551996,
39551997,
39551998,
39551999,
39552000,
39552001,
39552002,
39552003,
39552004,
39450933,
39552006,
39552007,
39552008,
39552009,
39552010,
39552011,
39552012,
39552013,
39552014,
39552015,
39898046,
39552017,
39552018,
39552019,
39552020,
39552021,
39552022,
39552023,
39552024,
39552025,
39552026,
39552027,
39552028,
39552029,
39552030,
39552031,
39552032,
39552033,
39552034,
39552035,
39552036,
39552037,
39552038,
39552039,
39552040,
39552041,
39552042,
39552043,
39552044,
39552045,
39552046,
39552047,
39552048,
39552049,
39552050,
39552051,
39552052,
39552053,
39552054,
39552055,
39552056,
39552057,
39552058,
39552059,
39552060,
39552061,
39552062,
40273647,
39552064,
39552065,
39552066,
39552067,
39552068,
39552069,
39552070,
39552071,
39552072,
39552073,
39552074,
40207435,
40502348,
40502349,
40502350,
40502351,
40502352,
40502353,
40502354,
40502355,
40502356,
40502357,
40502358,
40502359,
40502360,
40502361,
40502362,
40502363,
40502364,
40207453,
40207454,
40502367,
40502368,
40502369,
40502370,
40502371,
40502372,
40502373,
40502374,
40502375,
40502376,
40502377,
40502378,
40502379,
40502380,
40502381,
40502382,
40502383,
40502384,
40502385,
40502386,
40502387,
40502388,
40502389,
40502390,
40502391,
40502392,
40502393,
40502394,
40502395,
40502396,
40502397,
40502398,
40502399,
40502400,
40502401,
40502402,
40502403,
40502404,
40502405,
40502406,
40502407,
40502408,
40502409,
38742794,
40512749,
40502412,
40502413,
40502414,
40502415,
39856838,
40949272,
40502418,
40502419,
40502420,
40502421,
40502422,
40949273,
40502424,
40502425,
40502426,
40502427,
40502428,
40502429,
40502430,
40502431,
40502432,
40502433,
40502434,
40502435,
40502436,
40502437,
40502438,
40502439,
38742795,
40502441,
40502442,
40502443,
39856839,
40949277,
41186095,
40137403,
39450939,
41723626,
40234243,
40142018,
40142019,
40142020,
40142021,
40142022,
40142023,
40142024,
40142025,
40142026,
40142027,
40142028,
40142029,
40142030,
38798543,
40142032,
38798545,
38798546,
38798547,
38798548,
38798549,
38798550,
38798551,
38798552,
38798553,
38798554,
38798555,
38798556,
38798557,
38798558,
38798559,
38798560,
38798561,
38798562,
38798563,
38798564,
38798566,
38798567,
38798568,
38798569,
38798570,
38798571,
38798572,
38798573,
38798574,
38798575,
38864112,
38798577,
38798578,
38798579,
38798580,
38798581,
38798582,
38798583,
38798584,
38798585,
38798586,
38798587,
38798588,
38798589,
38798590,
38798591,
38798592,
38798593,
38798594,
38798595,
41223428,
41223429,
41223430,
41223431,
41223432,
41223433,
41223434,
41223435,
41223436,
41223437,
41223438,
41223439,
41223440,
38798609,
38798610,
38798611,
38798612,
38798613,
38798614,
38798615,
38798616,
38798617,
38798618,
38798619,
38798620,
38798621,
38798622,
38798623,
38798624,
38798625,
38798626,
38798627,
38798628,
38798629,
38798630,
38798631,
38798632,
38798633,
38798634,
38798635,
38798636,
38798637,
38798638,
39813341,
38798640,
38798641,
39813342,
41515527,
39813343,
39813344,
39856844,
41216961,
38831430,
39813345,
38831432,
38831433,
38831434,
38831435,
38831436,
38831437,
38831438,
38831439,
38831440,
38831441,
38831442,
38831443,
38831444,
38831445,
38831446,
38831447,
38831448,
38831449,
38831450,
38831451,
38831453,
38831454,
38831455,
38831456,
38831457,
38831458,
38831459,
38831460,
38831461,
38831462,
38831464,
38831465,
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38831468,
38831469,
38831470,
38831471,
38831472,
38831473,
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38831475,
38831476,
38831477,
38831478,
38831479,
38831480,
38831481,
38831482,
38831483,
38831484,
38831485,
38831486,
38831487,
38831488,
38831489,
38831490,
38831491,
38831492,
38831493,
38831494,
38831495,
38831496,
...]
In [4]:
snanaDeep.meta['COMMENTS']
Out[4]:
"COMMENT: LSST output_opsim2_168 year 01\nCOMMENT: deep fields\nCOMMENT: This SIMLIB created with command\nCOMMENT: ' simlib_coadd.exe output_opsim2_168.SIMLIB '\nCOMMENT: Process input SIMLIB file : output_opsim2_168.SIMLIB\nCOMMENT: Create ouptut SIMLIB file : output_opsim2_168.SIMLIB.COADD\nCOMMENT: Select MJD between 20000.00 and 80000.00\nCOMMENT: Select LIBID between 0 and 5000\nCOMMENT: Reject LIBID with < 3 exposures\nCOMMENT: Combine consecutive exposures within 0.400 days\nCOMMENT: Multiple exposures are 'SUMMED'"
In [5]:
snanaDeep.fieldIDs
Out[5]:
[2786, 519, 2731, 2412, 2082, 526, 1427, 311, 2712, 764]
In [6]:
snanaDeep.simlibDict[2786].meta['RA']
Out[6]:
150.361542
In [8]:
fieldIds = opsimSimlib.fieldIDs
In [14]:
lst = []
for fieldID in fieldIds:
# My Calculations from OpSim
opsimNobs = opsimSimlib.simlibData(fieldID).MJD.size
# From the SNANA simlibs
snanaData = snanaDeep.simlibData(fieldID)
print snanaData.MJD.min(), snanaData.MJD.max()
snanaData = snanaData[snanaData['MJD'] < 49561]
print snanaData.MJD.min(), snanaData.MJD.max()
snanaNobs = int(snanaData.NOISE.apply(lambda x: round((x / 0.25)**2.)).sum())
# print fieldID, opsimNobs, snanaNobs, float(opsimNobs) / float(snanaNobs)
lst.append([fieldID, opsimNobs, snanaNobs, float(opsimNobs) / float(snanaNobs)])
49400.27 52825.082
49400.27 49560.988
49354.066 52993.172
49354.066 49416.082
49498.406 52958.098
49498.406 49556.305
49530.441 52987.121
49530.441 49559.359
49365.324 52758.0
49365.324 49484.984
49471.418 52951.195
49471.418 49559.184
49354.035 52991.344
49354.035 49411.051
49501.43 52937.051
49501.43 49555.156
49530.43 52984.23
49530.43 49559.332
49495.402 52992.074
49495.402 49552.277
In [15]:
df = pd.DataFrame(np.array(lst), columns=['fieldID', 'OpSimVisits', 'SNANAVisits', 'frac'])
df
Out[15]:
fieldID
OpSimVisits
SNANAVisits
frac
0
2082
1995
2225
0.896629
1
519
2111
298
7.083893
2
2731
1095
357
3.067227
3
2412
1762
16
110.125000
4
2786
1709
1820
0.939011
5
526
1859
406
4.578818
6
1427
1762
33
53.393939
7
311
1781
591
3.013536
8
2712
2062
12
171.833333
9
764
2779
351
7.917379
In [16]:
opsimSimlib.simlibData(2082).MJD
Out[16]:
0 49400.276
1 49400.276
2 49400.277
3 49400.277
4 49400.278
5 49400.278
6 49400.278
7 49400.279
8 49400.279
9 49400.280
10 49403.244
11 49403.245
12 49403.245
13 49403.246
14 49403.246
15 49403.247
16 49403.247
17 49403.247
18 49403.248
19 49403.248
20 49406.235
21 49406.235
22 49406.235
23 49406.236
24 49406.236
25 49406.237
26 49406.237
27 49406.238
28 49406.238
29 49406.238
...
1965 49534.124
1966 49534.125
1967 49534.125
1968 49534.125
1969 49558.007
1970 49558.008
1971 49558.008
1972 49558.009
1973 49558.009
1974 49558.010
1975 49558.010
1976 49558.010
1977 49558.011
1978 49558.011
1979 49558.012
1980 49558.012
1981 49558.012
1982 49558.013
1983 49558.013
1984 49558.014
1985 49558.014
1986 49558.015
1987 49558.015
1988 49558.015
1989 49558.016
1990 49558.016
1991 49558.017
1992 49558.017
1993 49558.017
1994 49558.018
Name: MJD, dtype: float64
In [18]:
snanaDeep.simlibData(fieldID).MJD.max()
Out[18]:
52992.074000000001
In [19]:
snanaData.MJD.max()
Out[19]:
49552.277000000002
In [21]:
d = snanaDeep.simlibData(2082)
d['numCoadded'] = d['NOISE'].apply(lambda x: round((x / 0.25)**2.))
d = d[d['MJD'] < 49561]
d
Out[21]:
MJD
IDEXPT
FLT
GAIN
NOISE
SKYSIG
PSF1
PSF2
PSFRatio
ZPTAVG
ZPTERR
MAG
numCoadded
0
49400.281
3602082
g
1
0.90
71.72
1.72
0
0
34.50
0.005
-99
13
1
49400.270
3602082
r
1
1.15
123.29
1.68
0
0
35.04
0.005
-99
21
2
49400.293
3602082
i
1
1.27
197.76
1.69
0
0
35.09
0.005
-99
26
3
49400.297
3602082
z
1
1.27
199.13
1.68
0
0
34.54
0.005
-99
26
4
49400.309
3602082
Y
1
1.12
169.88
1.68
0
0
32.77
0.005
-99
20
5
49403.246
3602082
g
1
0.79
63.98
1.49
0
0
34.16
0.005
-99
10
6
49403.238
3602082
r
1
1.15
126.78
1.49
0
0
35.02
0.005
-99
21
7
49403.258
3602082
i
1
1.20
191.78
1.47
0
0
34.94
0.005
-99
23
8
49403.266
3602082
z
1
1.30
208.19
1.49
0
0
34.57
0.005
-99
27
9
49403.277
3602082
Y
1
1.15
178.04
1.49
0
0
32.83
0.005
-99
21
10
49406.238
3602082
g
1
0.79
174.40
2.00
0
0
34.12
0.005
-99
10
11
49406.230
3602082
r
1
1.12
224.81
2.00
0
0
34.94
0.005
-99
20
12
49406.246
3602082
i
1
1.17
243.83
2.04
0
0
34.88
0.005
-99
22
13
49406.258
3602082
z
1
1.35
249.12
2.05
0
0
34.64
0.005
-99
29
14
49406.266
3602082
Y
1
1.15
178.99
1.99
0
0
32.82
0.005
-99
21
15
49407.281
3612082
Y
1
0.35
54.50
1.90
0
0
30.27
0.005
-99
2
16
49416.406
3602082
g
1
0.25
107.20
2.00
0
0
31.69
0.005
-99
1
17
49416.402
3602082
r
1
1.12
515.13
2.00
0
0
34.98
0.005
-99
20
18
49419.387
3602082
g
1
0.83
76.29
2.21
0
0
34.33
0.005
-99
11
19
49419.379
3602082
r
1
1.15
121.98
2.20
0
0
35.06
0.005
-99
21
20
49419.395
3602082
i
1
1.15
177.60
2.20
0
0
34.86
0.005
-99
21
21
49419.406
3602082
z
1
0.94
467.19
2.20
0
0
33.85
0.005
-99
14
22
49421.375
3602082
u
1
1.20
24.16
1.68
0
0
33.28
0.005
-99
23
23
49432.242
3602082
g
1
0.90
70.30
1.85
0
0
34.55
0.005
-99
13
24
49432.230
3602082
r
1
1.15
118.57
1.85
0
0
35.07
0.005
-99
21
25
49432.258
3602082
i
1
1.27
192.34
1.83
0
0
35.11
0.005
-99
26
26
49432.258
3602082
z
1
1.27
193.30
1.85
0
0
34.55
0.005
-99
26
27
49432.266
3602082
Y
1
1.12
165.93
1.85
0
0
32.78
0.005
-99
20
28
49435.160
3602082
g
1
0.79
176.63
2.11
0
0
34.13
0.005
-99
10
29
49435.152
3602082
r
1
1.12
230.92
2.11
0
0
34.94
0.005
-99
20
...
...
...
...
...
...
...
...
...
...
...
...
...
...
89
49504.977
3602082
r
1
1.15
123.59
2.40
0
0
35.02
0.005
-99
21
90
49504.996
3602082
i
1
1.20
187.30
2.36
0
0
34.95
0.005
-99
23
91
49505.004
3602082
z
1
1.27
199.71
2.40
0
0
34.53
0.005
-99
26
92
49505.016
3602082
Y
1
1.12
170.21
2.40
0
0
32.77
0.005
-99
20
93
49507.988
3602082
g
1
0.90
71.41
2.06
0
0
34.49
0.005
-99
13
94
49507.977
3602082
r
1
1.15
122.71
2.05
0
0
35.04
0.005
-99
21
95
49507.996
3602082
i
1
1.20
185.93
2.04
0
0
34.96
0.005
-99
23
96
49508.004
3602082
z
1
1.27
198.40
2.05
0
0
34.54
0.005
-99
26
97
49508.016
3602082
Y
1
1.12
169.29
2.05
0
0
32.77
0.005
-99
20
98
49509.980
3602082
u
1
1.22
23.62
2.78
0
0
33.17
0.005
-99
24
99
49511.977
3602082
u
1
1.20
23.30
2.53
0
0
33.15
0.005
-99
23
100
49526.164
3612082
z
1
0.35
95.98
1.44
0
0
31.70
0.005
-99
2
101
49531.004
3602082
r
1
0.61
65.05
2.20
0
0
33.71
0.005
-99
6
102
49534.105
3602082
g
1
0.90
71.85
2.29
0
0
34.46
0.005
-99
13
103
49534.094
3602082
r
1
1.15
121.49
2.19
0
0
35.05
0.005
-99
21
104
49534.113
3602082
i
1
1.20
191.13
2.22
0
0
34.93
0.005
-99
23
105
49534.121
3602082
z
1
1.27
208.28
2.19
0
0
34.51
0.005
-99
26
106
49534.133
3602082
Y
1
1.12
182.16
2.19
0
0
32.73
0.005
-99
20
107
49540.980
3602082
u
1
1.20
24.21
1.73
0
0
33.35
0.005
-99
23
108
49542.980
3602082
u
1
1.20
24.57
1.42
0
0
33.39
0.005
-99
23
109
49547.031
3612082
z
1
0.56
95.25
1.57
0
0
32.75
0.005
-99
5
110
49551.090
3612082
Y
1
0.35
58.96
1.76
0
0
30.23
0.005
-99
2
111
49552.980
3612082
z
1
0.43
147.85
1.52
0
0
32.20
0.005
-99
3
112
49557.023
3612082
z
1
0.50
94.51
1.59
0
0
32.50
0.005
-99
4
113
49557.992
3602082
g
1
0.83
64.64
2.38
0
0
34.35
0.005
-99
11
114
49557.988
3602082
r
1
1.15
117.79
2.37
0
0
35.07
0.005
-99
21
115
49558.004
3602082
i
1
1.20
186.40
2.37
0
0
34.96
0.005
-99
23
116
49558.012
3602082
z
1
1.27
198.39
2.37
0
0
34.53
0.005
-99
26
117
49558.023
3602082
Y
1
1.12
172.64
2.37
0
0
32.76
0.005
-99
20
118
49560.988
3602082
r
1
0.56
58.00
1.82
0
0
33.51
0.005
-99
5
119 rows × 13 columns
In [22]:
0.25 * np.sqrt(13.)
Out[22]:
0.90138781886599728
In [23]:
d.numCoadded.sum()
Out[23]:
2225.0
In [26]:
d = snanaDeep.simlibData(519)
d['numCoadded'] = d['NOISE'].apply(lambda x: round((x / 0.25)**2.))
d = d[d['MJD'] < 49561]
d
Out[26]:
MJD
IDEXPT
FLT
GAIN
NOISE
SKYSIG
PSF1
PSF2
PSFRatio
ZPTAVG
ZPTERR
MAG
numCoadded
0
49354.070
3600519
g
1
0.83
65.00
1.79
0
0
34.35
0.005
-99
11
1
49354.066
3600519
r
1
1.15
119.05
1.78
0
0
35.07
0.005
-99
21
2
49354.098
3600519
i
1
1.35
211.92
1.82
0
0
35.21
0.005
-99
29
3
49354.090
3600519
z
1
1.27
195.51
1.78
0
0
34.55
0.005
-99
26
4
49354.102
3600519
Y
1
1.12
168.15
1.78
0
0
32.78
0.005
-99
20
5
49355.211
3610519
Y
1
0.79
125.56
1.63
0
0
32.00
0.005
-99
10
6
49356.199
3610519
Y
1
0.61
95.94
1.76
0
0
31.46
0.005
-99
6
7
49363.070
3600519
u
1
1.30
26.45
1.50
0
0
33.50
0.005
-99
27
8
49365.066
3600519
u
1
1.20
24.38
1.51
0
0
33.33
0.005
-99
23
9
49375.070
3600519
g
1
0.79
152.61
2.18
0
0
34.23
0.005
-99
10
10
49375.062
3600519
r
1
1.12
197.59
2.18
0
0
35.00
0.005
-99
20
11
49375.078
3600519
i
1
1.17
231.20
2.17
0
0
34.92
0.005
-99
22
12
49375.090
3600519
z
1
1.41
256.76
2.14
0
0
34.76
0.005
-99
32
13
49375.098
3600519
Y
1
1.15
174.93
2.17
0
0
32.83
0.005
-99
21
14
49377.188
3610519
z
1
0.43
89.46
2.51
0
0
32.15
0.005
-99
3
15
49382.055
3610519
z
1
0.43
137.86
2.14
0
0
32.19
0.005
-99
3
16
49411.117
3610519
Y
1
0.35
59.28
1.54
0
0
30.23
0.005
-99
2
17
49412.070
3610519
Y
1
0.79
126.16
2.09
0
0
32.00
0.005
-99
10
18
49416.082
3610519
r
1
0.35
38.93
2.03
0
0
32.45
0.005
-99
2
In [27]:
fig, ax = plt.subplots()
f1 = opsimSimlib.simlibData(519).hist(column='MJD', by='FLT', histtype='step', ax=ax, lw=2.0, sharex=True)
/usr/local/manual/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.py:3227: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared
"is being cleared", UserWarning)
In [28]:
d.hist(column='MJD', by='FLT', histtype='step', lw=2.0, ls='dashed', color='r')
Out[28]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x10af66250>,
<matplotlib.axes._subplots.AxesSubplot object at 0x109d0e110>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x109df5a90>,
<matplotlib.axes._subplots.AxesSubplot object at 0x109d52a90>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x109dce8d0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x10b23a710>]], dtype=object)
In [ ]:
In [ ]:
Content source: rbiswas4/simlib
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