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In [19]:



    


import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits as pf
import collections



    











In [126]:



    


#open the All candidate data
file1 = 'HZLZ_Candidate_pixels.fits'

data = pf.open(file1)[1].data

RA1 = data.RA
DEC1 = data.DEC
ID1 = data.ID
print max(data.ID)

objects = collections.Counter(ID1)
unique = list(objects)
print unique[51]
print objects[2175.0]



    


















    






3255.0
2175.0
1

















In [127]:



    


numbers = [objects[i] for i in unique]

print np.shape(unique)
print np.shape(numbers)


print numbers[51],unique[51]



    


















    






(551,)
(551,)
1 2175.0

















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In [150]:



    


#open the Cut candidate data
file2 = 'HZLZ_Candidate_pixels_extcut.fits'

data2 = pf.open(file2)[1].data

RA2 = data2.RA
DEC2 = data2.DEC
ID2 = data2.ID

objects2 = collections.Counter(ID2)
unique2 = list(objects2)
print np.sort(unique2)[:10]
print np.sort(unique)[:10]



    


















    






[  0.   1.   8.  12.  14.  19.  25.  27.  30.  36.]
[  0.   1.   8.  12.  14.  19.  25.  27.  30.  33.]

















In [151]:



    


print objects2[33.0]
print objects[33.0]



    


















    






0
1

















In [68]:



    


ratios=[]
for i in unique:
    ratios.append(float(objects2[i])/objects[i])



    











In [69]:



    


print len(unique)
print len(ratios)



    


















    






551
551

















In [86]:



    


gdx = (np.asarray(ratios) == 0.0)
bdx = np.asarray(unique)[gdx]



    











In [115]:



    


for i in bdx:
    idx = np.where(ID1==i)[0][0]
    print data.ID[idx], data.RA[idx]



    


















    






33.0 4.048489
2097.0 15.525495
115.0 13.489963
126.0 15.819953
131.0 17.76704
136.0 19.758388
203.0 20.253153
2256.0 7.614062
2301.0 16.801855
278.0 14.760863
2358.0 17.084808
396.0 15.340738
371.0 17.491417
373.0 17.612263
2458.0 33.840324
2511.0 24.913902
2537.0 27.460066
2635.0 25.188074
2686.0 24.256901
2887.0 335.519684
2800.0 25.277014
2868.0 331.879961
2875.0 332.951447
2883.0 334.691681
2891.0 334.982941
848.0 35.492756
2902.0 336.403839
2905.0 336.396606
2908.0 337.492676
2926.0 334.726135
881.0 25.342705
2952.0 337.265442
2956.0 337.478394
2965.0 337.978149
924.0 25.829706
2992.0 341.463562
2994.0 341.021332
2996.0 342.223846
2998.0 341.926269
3004.0 342.89209
3014.0 342.894562
3017.0 343.512848
3024.0 343.873077
3036.0 345.724567
3042.0 346.777191
1025.0 23.709494
3161.0 338.203174
3172.0 340.957916
3201.0 345.418488
3208.0 346.802612
1204.0 331.488586
1206.0 331.508026
1208.0 331.830536
1232.0 334.216553
1245.0 336.701355
1256.0 337.447968
2942.0 337.195751
1306.0 337.833404
1309.0 338.851013
1310.0 339.548218
1313.0 339.652629
1326.0 340.782715
1333.0 341.952972
1340.0 342.386932
1345.0 342.796661
1348.0 342.657772
1356.0 344.147675
1359.0 344.271942
1365.0 344.597229
1380.0 346.840485
1449.0 355.020782
2981.0 339.674255
1507.0 340.246778
1550.0 344.463783
1643.0 3.46643
1721.0 11.789654
1741.0 16.370077
1746.0 14.265687
1748.0 16.967621
1767.0 18.63913
1784.0 21.091387
1799.0 20.866165
1829.0 15.525127
1898.0 14.58845
1973.0 18.507606
1979.0 18.058155
2005.0 20.259586

















In [123]:



    


cut = (bdx == 116)
print cut
print len(bdx[cut])



    


















    






[False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False]
0

















In [124]:



    


# Write the file that gives a zero weight to the IDs that dropped to zero after the cut

Total_Polygons = 3260
f = open('Weight_mask_cut_objects.txt','w')
f.write('#Weights\n')
for i in range(Total_Polygons):
    cut = (bdx == float(i))
    if len(bdx[cut]) >0:
        print i
        print 'Bad Polygon'
        f.write('0\n')
    else:
        f.write('1\n')

f.close()



    


















    






33
Bad Polygon
115
Bad Polygon
126
Bad Polygon
131
Bad Polygon
136
Bad Polygon
203
Bad Polygon
278
Bad Polygon
371
Bad Polygon
373
Bad Polygon
396
Bad Polygon
848
Bad Polygon
881
Bad Polygon
924
Bad Polygon
1025
Bad Polygon
1204
Bad Polygon
1206
Bad Polygon
1208
Bad Polygon
1232
Bad Polygon
1245
Bad Polygon
1256
Bad Polygon
1306
Bad Polygon
1309
Bad Polygon
1310
Bad Polygon
1313
Bad Polygon
1326
Bad Polygon
1333
Bad Polygon
1340
Bad Polygon
1345
Bad Polygon
1348
Bad Polygon
1356
Bad Polygon
1359
Bad Polygon
1365
Bad Polygon
1380
Bad Polygon
1449
Bad Polygon
1507
Bad Polygon
1550
Bad Polygon
1643
Bad Polygon
1721
Bad Polygon
1741
Bad Polygon
1746
Bad Polygon
1748
Bad Polygon
1767
Bad Polygon
1784
Bad Polygon
1799
Bad Polygon
1829
Bad Polygon
1898
Bad Polygon
1973
Bad Polygon
1979
Bad Polygon
2005
Bad Polygon
2097
Bad Polygon
2256
Bad Polygon
2301
Bad Polygon
2358
Bad Polygon
2458
Bad Polygon
2511
Bad Polygon
2537
Bad Polygon
2635
Bad Polygon
2686
Bad Polygon
2800
Bad Polygon
2868
Bad Polygon
2875
Bad Polygon
2883
Bad Polygon
2887
Bad Polygon
2891
Bad Polygon
2902
Bad Polygon
2905
Bad Polygon
2908
Bad Polygon
2926
Bad Polygon
2942
Bad Polygon
2952
Bad Polygon
2956
Bad Polygon
2965
Bad Polygon
2981
Bad Polygon
2992
Bad Polygon
2994
Bad Polygon
2996
Bad Polygon
2998
Bad Polygon
3004
Bad Polygon
3014
Bad Polygon
3017
Bad Polygon
3024
Bad Polygon
3036
Bad Polygon
3042
Bad Polygon
3161
Bad Polygon
3172
Bad Polygon
3201
Bad Polygon
3208
Bad Polygon

















In [ ]:

Content source: JDTimlin/QSO_Clustering

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