In [2]:
%matplotlib inline

from pangloss import BackgroundCatalog

B = BackgroundCatalog(N=100.0, domain=[1.43, 1.4, -1.43, -1.4], field=[0, 0, 0, 0])
B.plot()


---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-2874b641b70c> in <module>()
      4 
      5 B = BackgroundCatalog(N=100.0, domain=[1.43, 1.4, -1.43, -1.4], field=[0, 0, 0, 0])
----> 6 B.plot()

/Users/user/Code/Pangloss/pangloss/background.pyc in plot(self, subplot, mag_lim, mass_lim, z_lim, fig_size, graph, lensed)
   1013             # Adjust the subplot in wcs by half a pixel
   1014             #subplot = [subplot[0]-self.PIXSCALE[0]/2.0,subplot[1]-self.PIXSCALE[0]/2.0,subplot[2]-self.PIXSCALE[0]/2.0,subplot[3]-self.PIXSCALE[0]/2.0]
-> 1015 
   1016             # Create new imshow and world axes
   1017             imshow, world = pangloss.make_axes(fig,subplot)

TypeError: 'NoneType' object is not iterable

In [3]:
import pandas as pd

In [7]:
c = 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 [10]:
c[['pos_0[rad]','pos_1[rad]','Dc_los[Mpc/h]']].describe()


Out[10]:
pos_0[rad] pos_1[rad] Dc_los[Mpc/h]
count 287822.000000 287822.000000 287822.000000
mean -0.026113 -0.026166 2869.923881
std 0.005050 0.005034 1099.034985
min -0.034898 -0.034898 16.470900
25% -0.030429 -0.030497 1988.842500
50% -0.026116 -0.026196 2916.075000
75% -0.021708 -0.021799 3756.110000
max -0.017453 -0.017453 4820.910000

In [13]:
c.columns


Out[13]:
Index([u'GalID', u'HaloID', u'SubhaloID', u'Type', u'PlaneNumber', u'z_spec',
       u'pos_0[rad]', u'pos_1[rad]', u'Dc_los[Mpc/h]', u'M_Halo[M_sol/h]',
       u'M_Subhalo[M_sol/h]', u'M_Stellar[M_sol/h]', u'mag_SDSS_u',
       u'mag_SDSS_g', u'mag_SDSS_r', u'mag_SDSS_i', u'mag_SDSS_z', u'mag_J',
       u'mag_H', u'mag_K'],
      dtype='object')

In [14]:
c['z_spec']


Out[14]:
0         2.077090
1         2.004520
2         1.992640
3         1.995730
4         2.041820
5         2.124030
6         2.010650
7         1.994780
8         2.149090
9         1.991430
10        1.992220
11        2.042320
12        1.989740
13        2.120850
14        1.971940
15        1.994530
16        1.993080
17        2.032420
18        1.989440
19        1.973850
20        2.108280
21        2.085230
22        2.011700
23        2.026620
24        2.017230
25        2.149080
26        1.974910
27        1.987380
28        2.029520
29        2.091940
            ...   
287792    0.265379
287793    0.265488
287794    0.264504
287795    0.265127
287796    0.263735
287797    0.265274
287798    0.264283
287799    0.286548
287800    0.287163
287801    0.276481
287802    0.264856
287803    0.264549
287804    0.264315
287805    0.263421
287806    0.266278
287807    0.264629
287808    0.264358
287809    0.265545
287810    0.265313
287811    0.264455
287812    0.263553
287813    0.265252
287814    0.263759
287815    0.265385
287816    0.296568
287817    0.265297
287818    0.265640
287819    0.263647
287820    0.262917
287821    0.264561
Name: z_spec, dtype: float64

In [15]:
foo = pd.read_csv('~/Desktop/foo.csv')

In [17]:
foo[['RA', 'Dec']].describe()


Out[17]:
RA Dec
count 324.000000 324.000000
mean 0.024715 -0.024686
std 0.000154 0.000150
min 0.024439 -0.024957
25% 0.024577 -0.024822
50% 0.024724 -0.024665
75% 0.024860 -0.024563
max 0.024958 -0.024436

In [ ]: