In [3]:

    

import numpy as np
from astropy.table import Table
from astropy.io.ascii import InconsistentTableError
from astropy.io.fits import getheader
import matplotlib.pyplot as plt
from scipy import stats
import os
from glob import glob
from scipy.optimize import least_squares
from tqdm import tqdm_notebook


    

In [4]:

    

def model2(theta, mag, y, zp=22.5, sigdet=5, k=1):
    a, b, maglim = theta 
    teff = np.exp(a + b * (maglim - 21.)) 

    # Compute flux/limit. 
    F = 10**(-0.4 * (mag - zp)) 
    Flim = 10**(-0.4 * (maglim - zp)) 

    # Compute noise. 
    Fnoise = (Flim / sigdet)**2 * k * teff - Flim
    magerr = 2.5 / np.log(10.) * np.sqrt((1. + Fnoise / F) / (F * k * teff))

    return np.sum(np.abs(y - magerr))
    #return magerr - y


    

In [5]:

    

datapath = f'{os.environ["HOME"]}/Projects/planckClusters/data/proc2'
files = glob(f'{datapath}/PSZ**/*K_cal.cat')


    

In [6]:

    

files


    

    
Out[6]:





['/home/boada/Projects/planckClusters/data/proc2/PSZ1_G148.20+23.49/PSZ1_G148.20+23.49K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G142.17+37.28/PSZ1_G142.17+37.28K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G137.58+53.88/PSZ2_G137.58+53.88K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G286.25+62.68/PSZ1_G286.25+62.68K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G031.91+67.94/PSZ1_G031.91+67.94K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G244.48+34.06/PSZ1_G244.48+34.06K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G096.44-10.40/PSZ1_G096.44-10.40K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G071.67-42.76/PSZ2_G071.67-42.76K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G105.91-38.39/PSZ1_G105.91-38.39K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G108.90-52.04/PSZ1_G108.90-52.04K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G138.60-10.85/PSZ1_G138.60-10.85K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G189.79-37.25/PSZ2_G189.79-37.25K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G055.83-41.64/PSZ1_G055.83-41.64K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G093.04-32.38/PSZ2_G093.04-32.38K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G102.86-31.07/PSZ1_G102.86-31.07K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G146.00-49.42/PSZ1_G146.00-49.42K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G185.45-32.01/PSZ2_G185.45-32.01K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G206.64-21.17/PSZ1_G206.64-21.17K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G092.46-35.22/PSZ2_G092.46-35.22K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G127.35-10.69/PSZ2_G127.35-10.69K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G162.30-26.92/PSZ1_G162.30-26.92K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G127.55+20.84/PSZ1_G127.55+20.84K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G100.45+16.79/PSZ2_G100.45+16.79K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G107.41-09.57/PSZ2_G107.41-09.57K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G166.27-24.71/PSZ2_G166.27-24.71K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G166.27-25.02/PSZ2_G166.27-25.02K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G153.56+36.82/PSZ2_G153.56+36.82K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G044.83+10.02/PSZ2_G044.83+10.02K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G137.24+53.93/PSZ2_G137.24+53.93K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G310.81+83.91/PSZ2_G310.81+83.91K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G173.76+22.92/PSZ2_G173.76+22.92K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G210.71+63.08/PSZ2_G210.71+63.08K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G136.31+54.67/PSZ2_G136.31+54.67K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G320.94+83.69/PSZ2_G320.94+83.69K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G048.47+34.86/PSZ2_G048.47+34.86K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G163.22-26.48/PSZ2_G163.22-26.48K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G145.25+50.84/PSZ2_G145.25+50.84K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G125.55+32.72/PSZ2_G125.55+32.72K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G328.96+71.97/PSZ2_G328.96+71.97K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G146.88+17.13/PSZ2_G146.88+17.13K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G120.76+44.14/PSZ2_G120.76+44.14K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G305.76+44.79/PSZ2_G305.76+44.79K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G022.03+17.75/PSZ2_G022.03+17.75K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G104.15-38.85/PSZ2_G104.15-38.85K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G096.43-20.89/PSZ2_G096.43-20.89K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G112.07-39.86/PSZ2_G112.07-39.86K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G192.40-67.89/PSZ2_G192.40-67.89K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G092.11-33.73/PSZ2_G092.11-33.73K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G165.39+09.22/PSZ2_G165.39+09.22K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G086.35-13.94/PSZ2_G086.35-13.94K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G131.27-25.82/PSZ2_G131.27-25.82K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G144.84-35.16/PSZ2_G144.84-35.16K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G166.56-17.69/PSZ2_G166.56-17.69K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G183.92+16.36/PSZ2_G183.92+16.36K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G093.41-16.26/PSZ2_G093.41-16.26K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G128.15-24.17/PSZ2_G128.15-24.17K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G139.72-17.13/PSZ2_G139.72-17.13K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G230.28-28.57/PSZ2_G230.28-28.57K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G216.25+10.10/PSZ2_G216.25+10.10K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G032.12-14.96/PSZ2_G032.12-14.96K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G228.35-66.31/PSZ2_G228.35-66.31K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G098.38+77.22/PSZ2_G098.38+77.22K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G237.68+57.83/PSZ2_G237.68+57.83K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G112.54+59.53/PSZ2_G112.54+59.53K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G270.88+37.23/PSZ2_G270.88+37.23K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G254.52+62.52/PSZ2_G254.52+62.52K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G179.33-22.22/PSZ2_G179.33-22.22K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G191.57+58.88/PSZ2_G191.57+58.88K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G153.56+36.23/PSZ1_G153.56+36.23K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G093.71-30.90/PSZ2_G093.71-30.90K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G153.41+36.58/PSZ1_G153.41+36.58K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G206.45+13.89/PSZ1_G206.45+13.89K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G249.01+73.75/PSZ1_G249.01+73.75K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G318.46+83.79/PSZ2_G318.46+83.79K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G118.06+31.10/PSZ1_G118.06+31.10K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G185.93-31.21/PSZ1_G185.93-31.21K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ1_G142.38+22.82/PSZ1_G142.38+22.82K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G135.94-68.22/PSZ2_G135.94-68.22K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G203.32+08.91/PSZ2_G203.32+08.91K_cal.cat',
 '/home/boada/Projects/planckClusters/data/proc2/PSZ2_G032.31+66.07/PSZ2_G032.31+66.07K_cal.cat']

In [7]:

    

filters = ['g', 'r', 'i', 'z', 'K']
limits = {}
for filt in tqdm_notebook(filters, desc='filters'):
    maglim = []
    for f in tqdm_notebook(files, desc='files', leave=False):
        new_f = f'{f[:-9]}{filt}_cal.cat'        
        if os.path.isfile(new_f):
            f = new_f
        else:
            maglim.append(-1)
            continue
        cat = Table.read(f, format='ascii')
        # filter out non-detections
        cat = cat[cat['MAG_AUTO'] < 30]
        # filter out the stars
        cat = cat[cat['CLASS_STAR']< 0.8]
        if len(cat) < 100:
            maglim.append(-1)
        else:
            fits_file = f'{f[:-8]}.fits'
            hdr = getheader(fits_file)
            zp = hdr['magzero']
            result = least_squares(model2, (4, 1, 20), loss='cauchy', f_scale=np.std(cat['MAGERR_AUTO']),
                           args=(cat['MAG_AUTO'], cat['MAGERR_AUTO']), kwargs={'zp':zp, 'sigdet':3}, 
                           bounds=([3,0.5, 15], [5,2, 30]))
            maglim.append(result['x'][2])
    limits[filt] = maglim


    

In [13]:

    

import pandas as pd


    

In [14]:

    

df = pd.DataFrame(limits)


    

In [15]:

    

df.describe()


    

    
Out[15]:







  

    

      

      
g
      
r
      
i
      
z
      
K
    
  
  

    

      
count
      
80.000000
      
80.000000
      
80.000000
      
80.000000
      
80.000000
    
    

      
mean
      
16.249540
      
16.059091
      
16.026271
      
15.470404
      
21.318638
    
    

      
std
      
11.380659
      
11.255119
      
11.226700
      
11.181487
      
0.681492
    
    

      
min
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
18.801006
    
    

      
25%
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.814546
    
    

      
50%
      
23.446769
      
23.187573
      
23.237844
      
22.704416
      
21.491795
    
    

      
75%
      
23.919518
      
23.709339
      
23.555344
      
23.126283
      
21.823434
    
    

      
max
      
24.669797
      
24.345082
      
24.175711
      
24.127846
      
22.167039
    
  

In [16]:

    

df.replace(-1, np.nan).describe()


    

    
Out[16]:







  

    

      

      
g
      
r
      
i
      
z
      
K
    
  
  

    

      
count
      
56.000000
      
56.000000
      
56.000000
      
55.000000
      
80.000000
    
    

      
mean
      
23.642200
      
23.370131
      
23.323245
      
22.956951
      
21.318638
    
    

      
std
      
0.743882
      
0.738106
      
0.569614
      
0.481309
      
0.681492
    
    

      
min
      
20.533843
      
20.048669
      
21.216852
      
21.450051
      
18.801006
    
    

      
25%
      
23.377925
      
23.114832
      
23.107482
      
22.668354
      
20.814546
    
    

      
50%
      
23.761087
      
23.465553
      
23.366265
      
22.992246
      
21.491795
    
    

      
75%
      
24.051688
      
23.797146
      
23.699036
      
23.216523
      
21.823434
    
    

      
max
      
24.669797
      
24.345082
      
24.175711
      
24.127846
      
22.167039
    
  

In [17]:

    

fields = [f'{f.rsplit("/")[-1][:-9]}' for f in files]


    

In [18]:

    

df['fields'] = fields


    

In [19]:

    

df


    

    
Out[19]:







  

    

      

      
g
      
r
      
i
      
z
      
K
      
fields
    
  
  

    

      
0
      
23.552037
      
23.238198
      
23.253262
      
23.064814
      
21.928263
      
PSZ1_G148.20+23.49
    
    

      
1
      
23.462874
      
23.121495
      
23.233203
      
23.215255
      
21.954919
      
PSZ1_G142.17+37.28
    
    

      
2
      
23.733361
      
23.391225
      
23.486331
      
22.975911
      
21.681539
      
PSZ2_G137.58+53.88
    
    

      
3
      
23.333869
      
23.336319
      
23.427244
      
22.946449
      
22.118406
      
PSZ1_G286.25+62.68
    
    

      
4
      
23.603999
      
23.494681
      
23.460437
      
22.948528
      
22.065140
      
PSZ1_G031.91+67.94
    
    

      
5
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
21.324506
      
PSZ1_G244.48+34.06
    
    

      
6
      
23.307893
      
23.014027
      
23.315319
      
23.391770
      
21.996544
      
PSZ1_G096.44-10.40
    
    

      
7
      
24.032375
      
23.783068
      
23.677466
      
23.034849
      
22.066333
      
PSZ2_G071.67-42.76
    
    

      
8
      
24.088340
      
23.667774
      
23.517554
      
22.971916
      
22.090588
      
PSZ1_G105.91-38.39
    
    

      
9
      
23.907309
      
23.857055
      
23.826679
      
-1.000000
      
21.482872
      
PSZ1_G108.90-52.04
    
    

      
10
      
24.206176
      
23.777907
      
23.764127
      
23.160876
      
22.155448
      
PSZ1_G138.60-10.85
    
    

      
11
      
23.327755
      
22.005806
      
21.216852
      
21.955558
      
21.952371
      
PSZ2_G189.79-37.25
    
    

      
12
      
23.815991
      
23.408164
      
23.359358
      
22.834768
      
21.637327
      
PSZ1_G055.83-41.64
    
    

      
13
      
23.683143
      
23.228242
      
22.806845
      
22.331975
      
22.008086
      
PSZ2_G093.04-32.38
    
    

      
14
      
24.136263
      
23.713542
      
23.679194
      
23.153346
      
22.101006
      
PSZ1_G102.86-31.07
    
    

      
15
      
23.901936
      
23.676050
      
23.545750
      
23.045908
      
22.069240
      
PSZ1_G146.00-49.42
    
    

      
16
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.706868
      
PSZ2_G185.45-32.01
    
    

      
17
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.592505
      
PSZ1_G206.64-21.17
    
    

      
18
      
22.401153
      
21.709094
      
22.501569
      
22.416918
      
22.026540
      
PSZ2_G092.46-35.22
    
    

      
19
      
23.824819
      
23.436425
      
23.327462
      
23.305568
      
22.161771
      
PSZ2_G127.35-10.69
    
    

      
20
      
23.495649
      
23.333892
      
23.260735
      
23.217791
      
22.167039
      
PSZ1_G162.30-26.92
    
    

      
21
      
23.285940
      
22.993146
      
22.891321
      
23.051818
      
19.423085
      
PSZ1_G127.55+20.84
    
    

      
22
      
23.498881
      
23.281216
      
23.296631
      
23.138347
      
21.392570
      
PSZ2_G100.45+16.79
    
    

      
23
      
24.508927
      
24.255537
      
23.284348
      
24.127846
      
20.554182
      
PSZ2_G107.41-09.57
    
    

      
24
      
23.997023
      
23.675831
      
23.841795
      
23.608635
      
21.441228
      
PSZ2_G166.27-24.71
    
    

      
25
      
23.056980
      
22.793571
      
23.283847
      
23.313126
      
20.302829
      
PSZ2_G166.27-25.02
    
    

      
26
      
24.669797
      
23.890716
      
23.516643
      
22.674915
      
21.887894
      
PSZ2_G153.56+36.82
    
    

      
27
      
23.303081
      
23.088222
      
22.928222
      
22.860862
      
18.801006
      
PSZ2_G044.83+10.02
    
    

      
28
      
24.496035
      
24.001895
      
23.967519
      
22.526523
      
21.695187
      
PSZ2_G137.24+53.93
    
    

      
29
      
21.486490
      
22.285564
      
21.658246
      
23.168663
      
21.701451
      
PSZ2_G310.81+83.91
    
    

      
...
      
...
      
...
      
...
      
...
      
...
      
...
    
    

      
50
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.805966
      
PSZ2_G131.27-25.82
    
    

      
51
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.728862
      
PSZ2_G144.84-35.16
    
    

      
52
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.788421
      
PSZ2_G166.56-17.69
    
    

      
53
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.814900
      
PSZ2_G183.92+16.36
    
    

      
54
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.602994
      
PSZ2_G093.41-16.26
    
    

      
55
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.813484
      
PSZ2_G128.15-24.17
    
    

      
56
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.897196
      
PSZ2_G139.72-17.13
    
    

      
57
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.518969
      
PSZ2_G230.28-28.57
    
    

      
58
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.622390
      
PSZ2_G216.25+10.10
    
    

      
59
      
23.577113
      
23.424609
      
23.721942
      
23.187969
      
21.130331
      
PSZ2_G032.12-14.96
    
    

      
60
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.362136
      
PSZ2_G228.35-66.31
    
    

      
61
      
24.403918
      
24.105055
      
24.030546
      
23.280531
      
21.328997
      
PSZ2_G098.38+77.22
    
    

      
62
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.956816
      
PSZ2_G237.68+57.83
    
    

      
63
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.540980
      
PSZ2_G112.54+59.53
    
    

      
64
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
19.531491
      
PSZ2_G270.88+37.23
    
    

      
65
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
19.974693
      
PSZ2_G254.52+62.52
    
    

      
66
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.997525
      
PSZ2_G179.33-22.22
    
    

      
67
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.806465
      
PSZ2_G191.57+58.88
    
    

      
68
      
23.392610
      
23.292547
      
23.242485
      
22.776855
      
21.648347
      
PSZ1_G153.56+36.23
    
    

      
69
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.780143
      
PSZ2_G093.71-30.90
    
    

      
70
      
23.859063
      
23.514430
      
23.373171
      
22.495223
      
21.814751
      
PSZ1_G153.41+36.58
    
    

      
71
      
23.305324
      
22.877618
      
22.303899
      
22.178162
      
21.533171
      
PSZ1_G206.45+13.89
    
    

      
72
      
22.920207
      
22.871203
      
22.985992
      
22.116866
      
21.598057
      
PSZ1_G249.01+73.75
    
    

      
73
      
20.533843
      
20.048669
      
22.450358
      
22.661200
      
21.521523
      
PSZ2_G318.46+83.79
    
    

      
74
      
22.250233
      
22.335039
      
22.930601
      
22.793787
      
21.572631
      
PSZ1_G118.06+31.10
    
    

      
75
      
23.430664
      
23.094840
      
23.092001
      
22.585222
      
21.605050
      
PSZ1_G185.93-31.21
    
    

      
76
      
23.108123
      
22.836460
      
22.902435
      
22.733917
      
21.849481
      
PSZ1_G142.38+22.82
    
    

      
77
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.914606
      
PSZ2_G135.94-68.22
    
    

      
78
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.962021
      
PSZ2_G203.32+08.91
    
    

      
79
      
-1.000000
      
-1.000000
      
-1.000000
      
-1.000000
      
20.586275
      
PSZ2_G032.31+66.07
    
  

80 rows × 6 columns

In [20]:

    

df = df.replace(-1, np.nan)


    

In [22]:

    

df.to_csv('./psz_depths.csv', index=False)


    

In [ ]: