Experiments with SDSS Data

In [1]:

    

Pkg.status("Celeste")


    

    




 - Celeste                       0.4.0+             reproducibility (dirty)

In [1]:

    

import Celeste: AccuracyBenchmark, SDSSIO, Synthetic, ParallelRun
using DataFrames, CSV
import PyPlot


    

    




INFO: Recompiling stale cache file /Users/acm/.julia/lib/v0.6/Celeste.ji for module Celeste.
ERROR: LoadError: LoadError: ArgumentError: Module NearestNeighbors not found in current path.
Run `Pkg.add("NearestNeighbors")` to install the NearestNeighbors package.
Stacktrace:
 [1] _require(::Symbol) at ./loading.jl:428
 [2] require(::Symbol) at ./loading.jl:398
 [3] include_from_node1(::String) at ./loading.jl:569
 [4] include(::String) at ./sysimg.jl:14
 [5] include_from_node1(::String) at ./loading.jl:569
 [6] include(::String) at ./sysimg.jl:14
 [7] anonymous at ./<missing>:2
while loading /Users/acm/.julia/v0.6/Celeste/src/Coordinates.jl, in expression starting on line 7
while loading /Users/acm/.julia/v0.6/Celeste/src/Celeste.jl, in expression starting on line 18






    




Failed to precompile Celeste to /Users/acm/.julia/lib/v0.6/Celeste.ji.

Stacktrace:
 [1] compilecache(::String) at ./loading.jl:703
 [2] _require(::Symbol) at ./loading.jl:456
 [3] require(::Symbol) at ./loading.jl:398

In [3]:

    

primary_catalog = AccuracyBenchmark.load_primary(AccuracyBenchmark.STRIPE82_RCF, AccuracyBenchmark.SDSS_DATA_DIR)
head(primary_catalog)


    

    
Out[3]:




objid
ra
dec
is_star
flux_r_nmgy
color_ug
color_gr
color_ri
color_iz
gal_frac_dev
gal_axis_ratio
gal_radius_px
gal_angle_deg
1
1237663784734490644
0.455258
0.611827
true
12.0878
3.11219
1.25308
0.833756
0.449864
0.0
0.999936
0.0245935
72.3498
2
1237663784734490645
0.457945
0.60102
false
42.4246
1.32123
0.702266
0.46665
0.0713212
0.0
0.600007
5.02739
54.279
3
1237663784734490646
0.452085
0.597542
false
10.7722
1.3876
1.62647
0.68046
0.512688
0.655229
0.644871
1.36462
134.109
4
1237663784734490668
0.468856
0.533093
true
379.548
1.08377
0.379158
0.118438
0.0499962
0.0
0.69987
0.0141502
105.359
5
1237663784734490670
0.471615
0.517553
true
90.514
1.20337
0.469821
0.167938
0.097679
0.9853
0.771631
0.0488277
105.466
6
1237663784734490672
0.481218
0.517017
true
227.672
1.23845
0.477644
0.162442
0.068806
0.503044
0.75223
0.0179534
105.48

In [4]:

    

coadd_catalog = AccuracyBenchmark.load_coadd_catalog(AccuracyBenchmark.COADD_CATALOG_FITS)

# filter the coadd catalog -- these columns can't be missing
no_na_cols = :flux_r_nmgy, :color_ug, :color_gr, :color_ri, :color_iz
for col in no_na_cols
    coadd_catalog = coadd_catalog[.!ismissing.(coadd_catalog[col]), :]
end

head(coadd_catalog)


    

    
Out[4]:




objid
ra
dec
is_star
flux_r_nmgy
color_ug
color_gr
color_ri
color_iz
gal_frac_dev
gal_axis_ratio
gal_radius_px
gal_angle_deg
1
8647474692482203784
0.536969
0.572232
false
4.20126
1.18763
0.664524
0.941524
-0.0294029
0.658111
0.204593
2.10751
5.16219
2
8647474692482203814
0.543336
0.460541
true
19.3121
2.29263
1.25516
0.825686
0.475343
missing
missing
missing
missing
3
8647474692482138371
0.452067
0.597554
false
13.5317
2.40159
1.45971
0.433763
0.481071
0.983442
0.622878
2.21404
133.298
4
8647474692482138403
0.461833
0.563745
false
1.89322
0.159879
1.63116
0.732745
0.575775
0.906633
0.0653559
0.205927
62.7757
5
8647474692482204160
0.515754
0.517092
true
1.71441
0.944377
0.34063
0.147833
0.0732357
missing
missing
missing
missing
6
8647474692482204190
0.520442
0.598824
true
4.15835
1.00537
0.292471
0.100794
0.0374307
missing
missing
missing
missing

In [5]:

    

# load SDSS images
dataset = SDSSIO.SDSSDataSet(AccuracyBenchmark.SDSS_DATA_DIR)
sdss_images = SDSSIO.load_field_images(dataset, AccuracyBenchmark.STRIPE82_RCF);
plot_data = AccuracyBenchmark.plot_image(sdss_images[3]);
PyPlot.imshow(plot_data)


    

    













    
Out[5]:





PyObject <matplotlib.image.AxesImage object at 0x7fb5b11977b8>

In [6]:

    

dummy_box = ParallelRun.BoundingBox(-1000.0, 1000.0, -1000.0, 1000.0)
sdss_results = ParallelRun.infer_box(sdss_images, dummy_box; method = :single_vi);


    

    




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[1]<4> INFO: #751 at (0.44876418624150277, 0.4811384945348197): 6.172206975 secs
[1]<4> INFO: #753 at (0.4528433945053536, 0.5018339804100775): 0.855251225 secs
[1]<4> INFO: #754 at (0.4532673687390285, 0.6170216801786995): 0.625938022 secs
[1]<3> INFO: #750 at (0.44849377012239866, 0.4795217665578252): 8.944321223 secs
[1]<4> INFO: #755 at (0.4546010231604616, 0.5940813085337987): 0.64927043 secs
[1]<4> INFO: #757 at (0.4547306181243341, 0.5780094448051661): 0.688244207 secs
[1]<3> INFO: #756 at (0.4547224761819082, 0.5763183948415322): 0.828284122 secs
[1]<3> INFO: #759 at (0.45583942415121226, 0.4796797065642941): 0.642175503 secs
[1]<4> INFO: #758 at (0.45495787451340064, 0.5953690578999821): 0.730297408 secs
[1]<3> INFO: #760 at (0.4568188181030662, 0.5888495922760255): 0.537966498 secs
[1]<4> INFO: #761 at (0.45714121664747026, 0.466359327540039): 2.879124554 secs
[1]<1> INFO: #558 at (0.5466245035009789, 0.4719827676305011): 118.040375902 secs
[1]<1> INFO: #764 at (0.45755596182043146, 0.603467959765893): 0.802817965 secs
[1]<2> INFO: #752 at (0.4517088432035764, 0.47811432390842606): 16.898931042 secs
[1]<1> INFO: #765 at (0.4580409769324549, 0.5193876689091195): 0.800063078 secs
[1]<2> INFO: #766 at (0.45878346305886997, 0.5423409169203393): 0.550408072 secs
[1]<3> INFO: #762 at (0.4531287982187669, 0.6038860257407163): 7.934698531 secs
[1]<1> INFO: #767 at (0.4591348424046805, 0.5495015353691642): 0.977401706 secs
[1]<1> INFO: #770 at (0.46004917615760804, 0.6307247854467449): 0.688868487 secs
[1]<1> INFO: #771 at (0.4603691542451165, 0.5944895319818109): 2.092409215 secs
[1]<2> INFO: #768 at (0.45661525086670374, 0.5073826756021669): 4.229388574 secs
[1]<1> INFO: #772 at (0.4604550738139219, 0.45681440394372885): 0.68201437 secs
[1]<3> INFO: #769 at (0.45718184443075083, 0.5068509908303509): 3.753896488 secs
[1]<2> INFO: #773 at (0.46101918627831656, 0.4679802470609131): 0.486312365 secs
[1]<4> INFO: #763 at (0.45330963820423076, 0.6058070144674119): 9.704304931 secs
[1]<1> INFO: #774 at (0.4611661379934198, 0.6293221111995193): 0.691805079 secs
[1]<3> INFO: #775 at (0.4614849722558901, 0.42799830527571386): 0.608284333 secs
[1]<1> INFO: #778 at (0.4632693730287665, 0.5910254286698714): 0.464011946 secs
[1]<2> INFO: #776 at (0.46231599576842586, 0.4112458191820528): 0.799869691 secs
[1]<3> INFO: #779 at (0.46336600558296787, 0.5838711619329731): 0.768260194 secs
[1]<4> INFO: #777 at (0.46299463587257156, 0.475725953903474): 1.032808863 secs
[1]<1> INFO: #780 at (0.46342513456602275, 0.5859431153370998): 0.798089446 secs
[1]<2> INFO: #781 at (0.46390125352269124, 0.5338933936982078): 0.767935288 secs
[1]<3> INFO: #782 at (0.46472962439706494, 0.5407637829808667): 0.475025551 secs
[1]<4> INFO: #783 at (0.4646516093229803, 0.5801279935262642): 0.567602876 secs
[1]<1> INFO: #784 at (0.4655483614733178, 0.5549005163326867): 0.560468054 secs
[1]<2> INFO: #785 at (0.46598539572818704, 0.6126109995010549): 0.609298658 secs
[1]<4> INFO: #787 at (0.46702738678748346, 0.4234926768510416): 0.663755925 secs
[1]<3> INFO: #786 at (0.4663440175239628, 0.5131080260269105): 0.984219662 secs
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[1]<2> INFO: #810 at (0.4764311105311999, 0.5193347821893634): 1.914419377 secs
[1]<4> INFO: #811 at (0.4765340991984349, 0.497444442255846): 1.840963313 secs
[1]<3> INFO: #809 at (0.4763811996466413, 0.4733625855705286): 2.094915149 secs
[1]<1> INFO: #812 at (0.47664837346264444, 0.6099299940448553): 1.905327755 secs

In [7]:

    

celeste_catalog = AccuracyBenchmark.celeste_to_df(sdss_results)
prediction_dfs = [primary_catalog, celeste_catalog,]
scores = AccuracyBenchmark.score_predictions(coadd_catalog, prediction_dfs)


    

    
Out[7]:




N
first
second
diff
diff_sd
field
1
59
0.0169492
0.0338983
-0.0169492
0.0169492
missed_stars
2
247
0.0283401
0.0242915
0.00404858
0.0119466
missed_galaxies
3
306
0.266245
0.274899
-0.00865445
0.00826177
position
4
306
0.181913
0.129396
0.0525175
0.013055
flux_r_mag
5
306
0.768396
0.645925
0.122471
0.0482431
flux_r_nmgy
6
75
16.1186
11.2796
4.83897
1.64226
gal_angle_deg
7
152
0.231962
0.139562
0.0924005
0.0260341
gal_frac_dev
8
152
0.192954
0.128236
0.0647183
0.0108201
gal_axis_ratio
9
152
1.31824
0.52948
0.788759
0.427942
gal_radius_px
10
293
1.01626
0.588286
0.427979
0.0568488
color_ug
11
305
0.277312
0.125628
0.151684
0.0197521
color_gr
12
306
0.1823
0.100734
0.0815658
0.0110732
color_ri
13
304
0.369671
0.178523
0.191148
0.0299815
color_iz

In [8]:

    

uncertainty_df = AccuracyBenchmark.get_uncertainty_df(coadd_catalog, celeste_catalog)
scores = AccuracyBenchmark.score_uncertainty(uncertainty_df)


    

    
Out[8]:




field
within_half_sd
within_1_sd
within_2_sd
within_3_sd
1
log_flux_r_nmgy
0.0921409
0.200542
0.395664
0.588076
2
color_ug
0.227642
0.425474
0.715447
0.872629
3
color_gr
0.235772
0.490515
0.758808
0.899729
4
color_ri
0.227642
0.409214
0.753388
0.867209
5
color_iz
0.284553
0.525745
0.794038
0.924119

In [2]:

    

scores_mc = CSV.read("mcmc_scripts/s82-results/uscore_mc.csv")


    

    




ArgumentError: Module CSV not found in current path.
Run `Pkg.add("CSV")` to install the CSV package.

Stacktrace:
 [1] _require(::Symbol) at ./loading.jl:428
 [2] require(::Symbol) at ./loading.jl:398