Using the Outputs¶
This page gives details on how to open and use the pipeline output files.
It is recommended to use pandas or vaex to read the pipeline results from the parquet files. See the sections below for more information on using each library.
Note
It is also possible to use Dask to read the parquets in an out-of-core context but the general performance can sometimes be poor with many parquet files. vaex is the preferred out-of-core method.
Tip
Be sure to look at vast-tools, a ready-made library for exploring pipeline results!
Reading with pandas¶
Warning
pyarrow will be required to open parquets with pandas. We recommend using this instead of fastparquet.
To open a parquet using pandas use the read_parquet method:
import pandas as pd
sources = pd.read_parquet('pipeline-runs/new-test-data/sources.parquet')
sources.head()
n_meas_forced n_meas ... vs_abs_significant_max_int m_abs_significant_max_int
id ...
1 0 3 ... 55.050146 0.191083
2 1 3 ... 29.367098 0.525999
3 0 3 ... 4.388447 0.199877
4 1 3 ... 20.000058 1.047998
5 0 3 ... 0.000000 0.000000
[5 rows x 31 columns]
To read multiple parquets at once using pandas a loop must be used:
import glob
import pandas as pd
files = glob.glob("pipeline-runs/images/*/measurements.parquet")
data = [pd.read_parquet(f) for f in files]
measurements = pd.concat(data, ignore_index=True)
Tip
If you don't require all the columns you can specify which columes to read using the columns variable.
sources = pd.read_parquet('pipeline-runs/new-test-data/sources.parquet', columns=['id', 'n_meas'])
Reading with vaex¶
Warning
vaex is a young project so bugs may be expected along with frequent updates. It has currently been tested with version 3.0.0. Version 4.0.0 promises opening parquet files in an out-of-core context.
Warning
Some pipeline parquet format files do not open with vaex 3.0.0. arrow format files should open successfully.
A parquet, or arrow file, can be opened using the open() method:
import vaex
measurements = vaex.open('pipeline-runs/new-test-data/measurements.arrow')
measurements.head()
# source island_id component_id local_rms ra ra_err dec dec_err flux_peak flux_peak_err flux_int flux_int_err bmaj err_bmaj bmin err_bmin pa err_pa psf_bmaj psf_bmin psf_pa flag_c4 chi_squared_fit spectral_index spectral_index_from_TT has_siblings image_id time name snr compactness ew_sys_err ns_sys_err error_radius uncertainty_ew uncertainty_ns weight_ew weight_ns forced flux_int_isl_ratio flux_peak_isl_ratio id
0 730 SB00004_island_1 SB00004_component_1a 0.463596 321.902 4.42819e-06 -4.20097 2.2637e-06 307.991 0.4742 425.175 1.04728 20.95 1.08838e-05 12.28 4.32359e-06 108.19 0.00160065 15.58 0 -73.86 False 3516.59 -99 True True 2 2019-08-27 13:38:38.810000000 VAST_2118+00A_SB00004_component_1a 664.352 1.38048 0.000277778 0.000277778 5.05099e-06 0.000277824 0.000277824 1.29557e+07 1.29557e+07 False 0.651019 0.709182 204
1 730 SB00009_island_1 SB00009_component_1a 0.463422 321.902 3.52062e-06 -4.20103 2.45968e-06 318.544 0.472982 349.471 0.87264 21.4 8.50698e-06 12.78 5.46908e-06 107.02 0.0020224 5.72 0 46.03 True 15427.1 -99 True True 3 2019-08-27 18:52:00.556000000 VAST_2118-06A_SB00009_component_1a 687.374 1.09709 0.000277778 0.000277778 4.26887e-06 0.000277811 0.000277811 1.29569e+07 1.29569e+07 False 0.721399 0.7483 352
2 730 SB00006_island_1 SB00006_component_1a 0.627357 321.901 4.58076e-06 -4.20086 2.92861e-06 310.503 0.662503 421.137 1.4171 17.05 1.07873e-05 12.42 6.89559e-06 90.71 0.00438684 10.51 4.24 -82.52 False 4483.74 -99 True True 5 2019-10-29 10:28:07.911000000 VAST_2118+00A_SB00006_component_1a 494.938 1.35631 0.000277778 0.000277778 5.46682e-06 0.000277832 0.000277832 1.2955e+07 1.2955e+07 False 0.677562 0.721427 670
3 730 SB00011_island_1 SB00011_component_1a 0.627496 321.901 3.92144e-06 -4.20087 3.21715e-06 310.998 0.643049 350.901 1.21083 17.06 9.23494e-06 12.43 7.57501e-06 91.19 0.00481863 6.43 0 27.62 False 4405.81 -99 True True 4 2019-10-29 13:39:33.996000000 VAST_2118-06A_SB00011_component_1a 495.618 1.12831 0.000277778 0.000277778 5.05099e-06 0.000277824 0.000277824 1.29557e+07 1.29557e+07 False 0.677576 0.721816 511
4 730 SB00005_island_1 SB00005_component_1a 0.346147 321.901 1.83032e-06 -4.20051 1.71797e-06 299.072 0.342783 288.462 0.579893 14.13 4.37963e-06 12.14 3.97013e-06 65.14 0.00546325 2.86 0 6.42 False 2661.49 -99 True True 7 2019-10-30 09:10:04.340000000 VAST_2118+00A_SB00005_component_1a 864.004 0.964524 0.000277778 0.000277778 2.69987e-06 0.000277791 0.000277791 1.29588e+07 1.29588e+07 False 0.659887 0.719556 976
5 730 SB00010_island_1 SB00010_component_1a 0.347692 321.901 1.97328e-06 -4.20052 1.75466e-06 300.969 0.360198 353.643 0.695754 14.16 4.76862e-06 12.16 3.99059e-06 65.77 0.00546515 6.12 3.95 49.18 False 2412.18 -99 True True 6 2019-10-30 10:11:56.913000000 VAST_2118-06A_SB00010_component_1a 865.62 1.17502 0.000277778 0.000277778 2.56132e-06 0.00027779 0.00027779 1.29589e+07 1.29589e+07 False 0.664605 0.723765 816
6 730 SB00007_island_1 SB00007_component_1a 0.387605 321.901 2.03947e-06 -4.20032 1.77854e-06 317.014 0.392106 332.662 0.701451 14.56 4.96382e-06 11.54 3.99573e-06 64.78 0.00375775 4.81 0 24.08 False 1486.99 -99 True True 10 2020-01-11 05:27:24.605000000 VAST_2118+00A_SB00007_component_1a 817.88 1.04936 0.000277778 0.000277778 2.83165e-06 0.000277792 0.000277792 1.29587e+07 1.29587e+07 False 0.666924 0.716339 1493
7 730 SB00012_island_1 SB00012_component_1a 0.391978 321.901 2.12442e-06 -4.20032 1.81129e-06 318.042 0.404457 365.987 0.770202 14.57 5.18203e-06 11.53 4.0454e-06 65.33 0.00378202 5.75 3.63 46.53 False 1328.58 -99 True True 9 2020-01-11 05:40:11.007000000 VAST_2118-06A_SB00012_component_1a 811.376 1.15075 0.000277778 0.000277778 2.69987e-06 0.000277791 0.000277791 1.29588e+07 1.29588e+07 False 0.667317 0.717746 1330
8 730 SB00008_island_1 SB00008_component_1a 0.432726 321.901 2.98443e-06 -4.20052 2.32201e-06 293.737 0.436863 309.072 0.784657 18.35 7.14713e-06 12.12 5.31097e-06 105.78 0.00261377 4.82 0 18.62 False 2448.82 -99 True True 13 2020-01-12 05:23:07.478000000 VAST_2118+00A_SB00008_component_1a 678.807 1.05221 0.000277778 0.000277778 3.81819e-06 0.000277804 0.000277804 1.29576e+07 1.29576e+07 False 0.63994 0.725001 1889
9 730 SB00013_island_1 SB00013_component_1a 0.437279 321.901 3.14407e-06 -4.20052 2.36161e-06 294.141 0.451346 340.92 0.864347 18.38 7.55055e-06 12.12 5.36009e-06 106.18 0.00262701 6.01 4 51.55 False 2368.93 -99 True True 12 2020-01-12 05:36:03.834000000 VAST_2118-06A_SB00013_component_1a 672.663 1.15903 0.000277778 0.000277778 4.00455e-06 0.000277807 0.000277807 1.29573e+07 1.29573e+07 False 0.640807 0.72508 1740
Multiple parquet files can be opened at once using the open_many() method:
import glob
import vaex
files = glob.glob("pipeline-runs/images/*/measurements.parquet")
measurements = vaex.open_many(files)
Tip
You can convert a vaex dataframe to pandas by using the to_pandas_df() method:
import vaex
sources = vaex.open('pipeline-runs/new-test-data/sources.parquet')
sources = sources.to_pandas_df()
Linking the Results¶
The table below shows what parameters act as keys to link data from the different results tables.
Tip
If loading the measurements via the .arrow file, then the measurements already have the source column in-place.
Tip
The images.parquet file contains the column measurements_path which can be used to get the filepaths for all the selavy parquet files.
| Data | Column | Linked to | Column |
|---|---|---|---|
associations.parquet | meas_id | measurements.parquet, forced_measurements*.parquet | id |
associations.parquet | source_id | sources.parquet | id (index column) |
measurements.parquet, forced_measurements*.parquet | image_id | images.parquet | id |
images.parquet | band_id | bands.parquet | id |
images.parquet | skyreg_id | skyregions.parquet | id |
measurement_pairs.parquet | meas_id_a, meas_id_b | measurements.parquet, forced_measurements*.parquet | id |
measurement_pairs.parquet | source_id | sources.parquet | id (index column) |
relations.parquet | from_source_id, to_source_id | sources.parquet | id (index column) |
vast-tools¶
Link to the vast-tools documentation.
VAST has developed a python library called vast-tools that makes the exploration of results from the pipeline simple and efficient, in addition to being designed to be used in a Jupyter Notebook environment.
Full details can be found in the documentation linked above, which also includes example notebooks of how to interact with the data.