This notebook shows how to reproduce the experiments in Approximate Inference for Constructing Astronomical Catalogs from Images using the Celeste.jl package and Julia 0.6.4. To install Celeste, run
Pkg.update()
Pkg.add("Celeste")
In [1]:
Pkg.status("Celeste")
- Celeste 0.4.0+ reproducibility (dirty)
In [2]:
import Celeste: AccuracyBenchmark, SDSSIO, Synthetic, ParallelRun
using DataFrames
import PyPlot
In [3]:
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
In [4]:
# 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[4]:
PyObject <matplotlib.image.AxesImage object at 0x7f20417187f0>
In [5]:
# generate images by conditioning on the coadd catalog
conditional_images = deepcopy(sdss_images)
catalog_entries = [
AccuracyBenchmark.make_catalog_entry(row)
for row in eachrow(coadd_catalog)]
Synthetic.gen_images!(conditional_images, catalog_entries)
plot_data = AccuracyBenchmark.plot_image(conditional_images[3]);
PyPlot.imshow(plot_data)
Out[5]:
PyObject <matplotlib.image.AxesImage object at 0x7f2048d8f5c0>
In [6]:
prior_catalog = AccuracyBenchmark.generate_catalog_from_celeste_prior(500, 12345)
head(prior_catalog)
Out[6]:
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 0.535903 0.627301 false 7.5945 -0.172541 1.09821 0.643075 -0.381725 0.272905 0.096291 1.84125 166.017 2 0.544344 0.550995 false 0.989773 -4.28371 1.37187 0.496161 1.30101 0.720652 0.655117 6.1787 50.211 3 0.455679 0.442466 true 8.91759 3.31271 1.22492 0.358178 0.269155 missing missing missing missing 4 0.469984 0.473291 false 4.05966 0.597365 -0.00497866 0.288615 0.598756 0.386987 0.463955 10.2972 87.5557 5 0.492634 0.452557 true 5.92552 0.72135 2.00172 1.53289 1.0992 missing missing missing missing 6 0.524173 0.563189 false 0.217006 -1.08506 1.64567 0.811115 -0.0872554 0.209362 0.503684 7.22285 97.8714
In [7]:
# generate images based on the prior catalog
prior_images = deepcopy(sdss_images)
catalog_entries = [
AccuracyBenchmark.make_catalog_entry(row)
for row in eachrow(prior_catalog)]
Synthetic.gen_images!(prior_images, catalog_entries)
plot_data = AccuracyBenchmark.plot_image(prior_images[3]);
PyPlot.imshow(plot_data)
Out[7]:
PyObject <matplotlib.image.AxesImage object at 0x7f2048bfb4e0>
The next block of code takes about 30 minutes single threaded. To enable more threads, before launching Jupyter, set two environment variables:
export OMP_NUM_THREADS=1
export JULIA_NUM_THREADS=4
In [8]:
box = ParallelRun.BoundingBox(-1000.0, 1000.0, -1000.0, 1000.0)
vi_results = ParallelRun.infer_box(prior_images, box; method = :single_vi);
celeste_catalog = AccuracyBenchmark.celeste_to_df(vi_results);
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[1]<1> INFO: infer_box for Celeste.BoundingBox(-1000.0, 1000.0, -1000.0, 1000.0) took 167.766488851 seconds
ti profile:
prep: 2.789e-06 (2789)
fork: 7.112e-06 (75.5701 - 56.6776)
user: 163.751 (164.517 - 164.067)
join: 5e-07 (0.660964 - 0.165241)
MCMC is parallelized differently (using pmap), which requires running an exterior script to schedule a set of jobs. For the MCMC SDSS Stripe2 experiment script to run, we need a few files to be saved (based on the synthetic catalog and the image pixels generated above). The following commands will reproduce our experiments
Create ground truth files (repeats code above)
$ cd Celeste.jl/benchmark/accuracy
$ julia write_ground_truth_catalog_csv.jl prior
$ julia generate_synthetic_field.jl prior_<hash>.csvwhich will create the following files in Celeste.jl/benchmark/accuracy/output:
prior_<hash>.csv : synthetic catalog drawn from the priorprior_<hash>_synthetic_<hash>.jld : synthetic image of sourcesRun the AIS-MCMC code on each source
$ cd Celeste.jl/experiments/mcmc_scripts
$ ./run_synthetic_shards.shwhich will schedule jobs for each set of sources and store samples in directory ais-output-synthetic.
Score results
julia score_mcmc_results.jl \
--ais-output ais-output-synthetic \
--output-dir synthetic-results \
--truth-csv ~/Proj/Celeste.jl/benchmark/accuracy/output/prior_<hash>.csv
--vb-csv ~/Proj/Celeste.jl/benchmark/accuracy/output/prior_<hash>_synthetic_<hash>_predictions_<hash>.csvwhich saves a bunch of csv files to experiments/mcmc_scripts/synthetic-results/ needed to reproduce plots (including the prediction score and uncertainty score dataframes that mirror the results below).
In [9]:
prediction_dfs = [celeste_catalog,]
scores = AccuracyBenchmark.score_predictions(prior_catalog, prediction_dfs)
Out[9]:
N first field 1 124 0.0483871 missed_stars 2 305 0.0262295 missed_galaxies 3 429 0.107366 position 4 429 0.124774 flux_r_mag 5 429 0.349698 flux_r_nmgy 6 44 5.25679 gal_angle_deg 7 80 0.250228 gal_frac_dev 8 80 0.0991387 gal_axis_ratio 9 80 0.551287 gal_radius_px 10 429 0.372569 color_ug 11 429 0.136951 color_gr 12 429 0.115546 color_ri 13 429 0.163566 color_iz
In [10]:
uncertainty_df = AccuracyBenchmark.get_uncertainty_df(prior_catalog, celeste_catalog)
scores = AccuracyBenchmark.score_uncertainty(uncertainty_df)
Out[10]:
field within_half_sd within_1_sd within_2_sd within_3_sd 1 log_flux_r_nmgy 0.15814 0.316279 0.497674 0.646512 2 color_ug 0.265116 0.502326 0.77907 0.9 3 color_gr 0.24186 0.455814 0.737209 0.853488 4 color_ri 0.22093 0.44186 0.690698 0.830233 5 color_iz 0.353488 0.574419 0.827907 0.927907
In [ ]:
scores_mc = CSV.read("mcmc_scripts/synthetic-results/uscore_mc.csv")
Content source: jeff-regier/Celeste.jl
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