In [8]:
%matplotlib inline
In [9]:
import pandas as pd
In [10]:
background = pd.read_csv('/Users/user/Desktop/background.csv')
halos = pd.read_csv('/Users/user/Desktop/halos.csv')
In [11]:
background
Out[11]:
Unnamed: 0
ID
RA
Dec
mag
Mstar_obs
z_obs
eMod_int
ePhi_int
e1_int
...
eMod
ePhi
kappa_halo
gamma1_halo
gamma2_halo
g_halo
e1_halo
e2_halo
eMod_halo
ePhi_halo
0
0
0.0
0.024513
-0.024514
13.497907
3.834421e+11
1.3857
0.298210
35.522894
0.096862
...
0.333865
38.316575
-0.000023
-0.067612
0.084970
(-0.0676106882294+0.0849684849947j)
0.019058
0.361238
0.361741
43.489989
1
1
1.0
0.024484
-0.024535
2.597448
7.917252e+11
1.3857
0.137932
-16.443335
0.115828
...
0.098430
-0.174268
-0.000032
-0.047347
0.027848
(-0.0473451876871+0.0278475422844j)
0.068990
-0.047428
0.083720
-17.253507
2
2
2.0
0.024504
-0.024496
0.872094
5.288954e+11
1.3857
0.065941
61.708902
-0.036317
...
0.104823
50.778468
-0.000024
-0.012544
0.106721
(-0.0125439131722+0.10671855771j)
-0.048044
0.160893
0.167913
53.313015
3 rows × 28 columns
In [12]:
background.columns
Out[12]:
Index([u'Unnamed: 0', u'ID', u'RA', u'Dec', u'mag', u'Mstar_obs', u'z_obs',
u'eMod_int', u'ePhi_int', u'e1_int', u'e2_int', u'strong_flag',
u'kappa', u'gamma1', u'gamma2', u'g', u'e1', u'e2', u'eMod', u'ePhi',
u'kappa_halo', u'gamma1_halo', u'gamma2_halo', u'g_halo', u'e1_halo',
u'e2_halo', u'eMod_halo', u'ePhi_halo'],
dtype='object')
In [13]:
halos
Out[13]:
Unnamed: 0
112009306000027
37000046000024
37014522009504
456000089002589
65000614000013
log-likelihood
0
0
1.368440e+12
1.876400e+13
1.782420e+12
5.606320e+12
8.288130e+11
2.340629
In [14]:
background['RA']
Out[14]:
0 0.024513
1 0.024484
2 0.024504
Name: RA, dtype: float64
In [15]:
background['e1_int']
Out[15]:
0 0.096862
1 0.115828
2 -0.036317
Name: e1_int, dtype: float64
In [16]:
background
Out[16]:
Unnamed: 0
ID
RA
Dec
mag
Mstar_obs
z_obs
eMod_int
ePhi_int
e1_int
...
eMod
ePhi
kappa_halo
gamma1_halo
gamma2_halo
g_halo
e1_halo
e2_halo
eMod_halo
ePhi_halo
0
0
0.0
0.024513
-0.024514
13.497907
3.834421e+11
1.3857
0.298210
35.522894
0.096862
...
0.333865
38.316575
-0.000023
-0.067612
0.084970
(-0.0676106882294+0.0849684849947j)
0.019058
0.361238
0.361741
43.489989
1
1
1.0
0.024484
-0.024535
2.597448
7.917252e+11
1.3857
0.137932
-16.443335
0.115828
...
0.098430
-0.174268
-0.000032
-0.047347
0.027848
(-0.0473451876871+0.0278475422844j)
0.068990
-0.047428
0.083720
-17.253507
2
2
2.0
0.024504
-0.024496
0.872094
5.288954e+11
1.3857
0.065941
61.708902
-0.036317
...
0.104823
50.778468
-0.000024
-0.012544
0.106721
(-0.0125439131722+0.10671855771j)
-0.048044
0.160893
0.167913
53.313015
3 rows × 28 columns
In [44]:
l0 = pd.read_csv('/Users/user/Desktop/lightcone_0.csv')
In [45]:
len(l0)
Out[45]:
472
In [40]:
l0
Out[40]:
Unnamed: 0
RA
Dec
z_obs
Mhalo_obs
Type
GalID
mag_SDSS_i
Mh
x
y
r
phi
z
Mh_obs
0
15442
0.024418
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2.096700
1.290990e+11
0
233002149000031
25.8436
1.290990e+11
0.327373
1.845184
1.874001
1.395203
2.096700
11.110923
1
15501
0.024783
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2.048570
7.048780e+11
0
22000121000031
25.8783
7.048780e+11
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1.002853
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2.048570
11.848114
2
15502
0.024523
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2.079910
5.267220e+11
0
233000373000031
24.0311
5.267220e+11
-0.032455
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0.725601
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2.079910
11.721581
3
15503
0.024137
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2.134700
2.082790e+11
0
279000084035274
25.1953
2.082790e+11
1.292045
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1.677378
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2.134700
11.318645
4
15504
0.025030
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2.029570
1.833200e+11
0
23003340009344
25.0323
1.833200e+11
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1.776110
3.126219
2.029570
11.263210
5
15505
0.025034
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2.032430
2.547540e+11
0
23003340005709
25.3176
2.547540e+11
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1.871895
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2.032430
11.406121
6
15507
0.024600
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2.103080
1.308200e+11
0
489021309000031
25.9926
1.308200e+11
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1.347647
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2.103080
11.116674
7
15508
0.024890
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2.101870
2.495910e+11
0
489000062000242
24.5315
2.495910e+11
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0.145562
1.302235
3.029580
2.101870
11.397229
8
15510
0.024678
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2.030510
1.222130e+11
0
23005429000031
25.6129
1.222130e+11
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0.934525
1.093369
2.116580
2.030510
11.087117
9
15511
0.024776
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2.095920
4.200010e+11
0
22004065000031
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4.200010e+11
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2.095920
11.623250
10
15512
0.024787
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2.102610
2.022540e+11
0
278017644000031
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2.022540e+11
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1.099517
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11.305897
11
15514
0.024287
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1.729920e+11
0
278017142000031
25.3238
1.729920e+11
0.778257
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11.238026
12
15516
0.025074
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3.743860e+11
0
22004094000031
25.7742
3.743860e+11
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1.994804
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13
15518
0.024848
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2.095910
1.919270e+11
0
22004065000093
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15519
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15845
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279000084041477
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...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
874
276123
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276124
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877
276126
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904 rows × 15 columns
In [26]:
l1 = pd.read_csv('/Users/user/Desktop/lightcone_1.csv')
In [27]:
l1
Out[27]:
Unnamed: 0
RA
Dec
z_obs
Mhalo_obs
Type
GalID
mag_SDSS_i
Mh
x
y
r
phi
z
Mh_obs
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15505
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...
...
...
...
...
...
...
...
...
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...
...
...
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910 rows × 15 columns
In [28]:
l2 = pd.read_csv('/Users/user/Desktop/lightcone_2.csv')
l2
Out[28]:
Unnamed: 0
RA
Dec
z_obs
Mhalo_obs
Type
GalID
mag_SDSS_i
Mh
x
y
r
phi
z
Mh_obs
0
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...
...
...
...
...
...
...
...
...
...
...
...
...
...
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...
865
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1.486480
-1.311886
0.160179
10.848644
895 rows × 15 columns
In [29]:
count = 287822
ra_max = -0.0174533
ra_min = -0.034898
dec_max = -0.0174533
dec_min = -0.0348981
xi = -0.0251909188704
xf = -0.0250163859452
yi = -0.0251909188704
yf = -0.0250163859452
In [30]:
area1 = (ra_max - ra_min)*(dec_max-dec_min)
area2 = (xf - xi)* (yf - yi)
In [31]:
area1
Out[31]:
0.00030431930255999995
In [32]:
area2
Out[32]:
3.0461741978869316e-08
In [33]:
frac = area2 / area1
ex_count = count * frac
In [34]:
ex_count
Out[34]:
28.81039561436791
In [37]:
out = pd.read_csv('/Users/user/Desktop/halos.csv')
In [38]:
out
Out[38]:
Unnamed: 0
111006726000565
111006726004073
111007167000028
111007406000027
111007703000027
111008078000027
112009253001465
112009253001583
112009306000027
...
97001163000025
97020576000025
98000008000404
98003134000025
98016480000024
98016480000159
98017091000024
98018070000024
98018361000024
log-likelihood
0
0
2.174880e+12
1.729920e+11
4.595910e+11
3.347960e+11
2.168860e+11
1.798770e+11
1.093030e+11
1.351230e+11
1.368440e+12
...
3.236070e+11
1.256560e+11
8.004110e+10
7.831980e+10
5.800830e+11
1.093030e+11
2.168860e+11
1.179100e+11
9.295100e+10
2.389705
1 rows × 546 columns
In [46]:
f = pd.read_csv('/Users/user/Desktop/foo.csv')
t = pd.read_csv('/Users/user/Desktop/foo2.csv')
In [48]:
t['r'].mean()
Out[48]:
1.3756341326660508
In [49]:
f['r'].mean()
Out[49]:
1.3756341326638073
In [50]:
print f['x'].mean()
print t['x'].mean()
0.0732333796174
0.0732333796496
In [51]:
print len(f)
print len(t)
472
472
In [54]:
diff = []
for i in f['x']:
if i not in t['x']:
diff.append(i)
In [55]:
diff2 = []
for i in t['x']:
if i not in f['x']:
diff2.append(i)
In [56]:
len(diff2)
Out[56]:
91
In [57]:
len(diff)
Out[57]:
91
In [74]:
diff[5]
Out[74]:
-1.2796314305700001
In [73]:
diff2[5]
Out[73]:
-1.2796314305399998
In [66]:
import numpy as np
In [67]:
np.cos(1000)
Out[67]:
0.56237907629070294
In [68]:
np.random.seed(111)
np.cos(1000)
Out[68]:
0.56237907629070294
In [85]:
np.cos(1002) * ((180.0 * 60.0) / np.pi)
Out[85]:
-3389.314804341921
In [86]:
np.cos(1002) * 180.0 * 60.0 / np.pi
Out[86]:
-3389.3148043419214
In [88]:
arcmin2rad = (1.0/60.0)*np.pi/180.0
arcmin2deg = (1.0/60.0)
rad2arcmin = 1.0/arcmin2rad
In [89]:
rad2arcmin
Out[89]:
3437.7467707849396
In [90]:
Angle.radian_to_arcmin(1)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-90-415a5b133522> in <module>()
----> 1 Angle.radian_to_arcmin(1)
NameError: name 'Angle' is not defined
In [91]:
diff[0:10]
Out[91]:
[-1.4813612131600002,
-1.1878732729600001,
-1.1483619360500001,
-1.1452715354799998,
-1.22773356119,
-1.2796314305700001,
-1.2469857679799998,
-1.2878785685900001,
-1.33324855801,
-1.5831085207100002]
In [93]:
np.pi
Out[93]:
3.141592653589793
In [97]:
np.pi*10**15 + 0.2
Out[97]:
3141592653589793.0
In [100]:
pred = pd.read_csv('/Users/user/Desktop/pred.csv')
In [104]:
pred.columns
Out[104]:
Index([u'Unnamed: 0', u'0.0', u'0.0.1', u'1.0', u'1.0.1', u'2.0', u'2.0.1'], dtype='object')
In [106]:
np.zeros((5,5))
Out[106]:
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.]])
In [109]:
d = pd.read_table('/Users/user/Code/Pangloss/data/GGL_los_8_0_0_0_0_N_4096_ang_4_Guo_galaxies_on_plane_27_to_63.images.txt')
In [110]:
d
Out[110]:
GalID
HaloID
SubhaloID
Type
PlaneNumber
z_spec
pos_0[rad]
pos_1[rad]
Dc_los[Mpc/h]
M_Halo[M_sol/h]
M_Subhalo[M_sol/h]
M_Stellar[M_sol/h]
mag_SDSS_u
mag_SDSS_g
mag_SDSS_r
mag_SDSS_i
mag_SDSS_z
mag_J
mag_H
mag_K
0
22005657000031
22005657000031
32002200007540
0
32
2.077090
-0.031597
-0.029582
3878.500
1.153280e+11
1.377050e+11
2.538560e+09
26.4515
25.9714
25.9779
25.9212
25.6947
24.8963
24.5919
24.3665
1
23003396000121
23003396000094
32002300002152
1
32
2.004520
-0.030872
-0.030504
3797.800
5.766400e+11
1.368440e+11
3.423400e+09
26.5567
26.0925
26.0241
25.9720
25.7234
24.7098
24.3246
24.0779
2
23003339006170
23003339002325
32002300002416
0
32
1.992640
-0.030766
-0.029958
3783.120
7.083200e+11
5.404930e+11
1.212330e+10
26.8170
25.8597
25.8092
25.5507
24.9120
23.9694
23.5292
23.0121
3
23003385000769
23003385000232
32002300005650
0
32
1.995730
-0.030627
-0.029594
3790.110
1.609430e+11
1.962300e+11
1.414890e+09
25.7759
25.6043
25.6101
25.6653
25.5959
24.7451
24.5413
24.4404
4
22000106000031
22000106000031
32002200006033
0
32
2.041820
-0.030769
-0.029931
3842.240
1.833200e+11
1.841810e+11
4.642430e+09
26.8309
25.9563
25.9070
25.6981
25.2301
24.3321
23.9834
23.6662
5
489020438001227
489020438001916
32049000000387
0
32
2.124030
-0.031282
-0.030422
3933.170
4.541690e+12
4.195700e+12
4.624150e+10
26.8694
26.1084
25.7770
25.5346
24.8123
23.2087
22.4581
22.0212
6
23005411000031
23005411000031
32002300010212
0
32
2.010650
-0.031274
-0.029770
3805.330
7.918040e+10
8.864770e+10
2.814590e+08
25.6097
25.5477
25.6489
25.7497
25.8087
25.3611
25.4079
25.4507
7
23003339000771
23003339001608
32002300000469
2
32
1.994780
-0.030579
-0.030002
3783.620
1.155350e+13
0.000000e+00
8.061260e+09
26.2996
25.5214
25.5081
25.3058
24.7935
23.8448
23.4391
23.0691
8
489020448004718
489020448001872
32049000005538
0
32
2.149090
-0.031530
-0.029740
3958.590
1.609430e+11
1.729920e+11
1.798840e+09
25.9459
25.5596
25.5285
25.5160
25.3935
24.6104
24.4181
24.2959
9
23003339001406
23003339001608
32002300000469
2
32
1.991430
-0.030633
-0.029939
3783.720
1.155350e+13
0.000000e+00
6.010610e+09
26.1708
25.5023
25.4905
25.3534
24.9863
24.0450
23.6678
23.3826
10
23003339001615
23003339001608
32002300000469
2
32
1.992220
-0.030625
-0.029962
3783.820
1.155350e+13
0.000000e+00
2.007910e+10
26.4384
25.4217
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...
...
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287822 rows × 20 columns
In [111]:
d = pd.read_csv('/Users/user/Desktop/output.csv')
In [112]:
d
Out[112]:
Unnamed: 0
37000046000024
37014522009504
456000089002589
65000614000013
82020319006828
log-likelihood
0
0
1.734220e+12
1.662620e+13
5.545210e+12
1.214470e+13
3.414230e+12
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1
1
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-0.322402
2
2
2.366810e+12
2.495040e+13
3.309230e+12
4.417750e+12
8.873370e+11
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3
3
1.562090e+12
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1.000080e+12
9.647970e+12
1.151820e+13
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4
4
4.222380e+12
1.774670e+12
8.224440e+12
2.154220e+12
1.779580e+13
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5
5
2.863410e+12
8.348370e+12
1.746270e+12
2.657710e+12
2.889230e+12
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6
6
2.143900e+12
6.251810e+12
9.777060e+11
1.187710e+12
1.183490e+13
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7
7
7.900830e+12
7.739890e+12
1.855580e+12
3.579470e+12
5.261200e+12
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8
8
1.230740e+12
8.480050e+12
5.666570e+12
2.214300e+13
2.009630e+12
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9
9
7.891360e+12
1.000080e+12
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1.090450e+12
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10
10
1.775110e+13
2.026850e+12
9.678090e+12
1.215160e+13
4.502100e+12
-0.338870
11
11
3.309230e+12
4.260250e+12
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1.302170e+12
1.047590e+13
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12
12
8.985260e+11
8.107390e+12
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2.071600e+12
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13
13
6.523780e+11
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14
14
4.920380e+12
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15
15
5.690660e+12
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16
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17
1.117650e+13
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18
6.713120e+10
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4.755990e+12
2.925370e+12
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19
19
6.337880e+12
2.235130e+12
1.274630e+12
1.517340e+12
1.445470e+13
-0.327558
In [ ]:
Content source: davidthomas5412/PanglossNotebooks
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