Quickstart

CLI

From version 1.0.10 there’s a suggested analysis pipeline in the form of a CLI tool to facilitate usage. To use it, create a script like the following, and run it:

from scludam import launch

if __name__ == "__main__":
    launch()

From there, you can download data from GAIA, load a file, run the analysis pipeline, plot and save results.

NOTES:

  • For plotting to display correctly, you need to have the corresponding library installed, such as PyQt6 in your environment.

  • The CLI tool is still rudimentary, it will exit on some incorrect inputs. Follow the directions of the prompts carefully.

Main functionality example

 1import matplotlib.pyplot as plt
 2
 3from scludam import DEP, CountPeakDetector, HopkinsTest, Query, RipleysKTest
 4
 5# search some data from GAIA
 6# with error and correlation
 7# columns for better KDE
 8df = (
 9    Query()
10    .select(
11        "ra",
12        "dec",
13        "ra_error",
14        "dec_error",
15        "ra_dec_corr",
16        "pmra",
17        "pmra_error",
18        "ra_pmra_corr",
19        "dec_pmra_corr",
20        "pmdec",
21        "pmdec_error",
22        "ra_pmdec_corr",
23        "dec_pmdec_corr",
24        "pmra_pmdec_corr",
25        "parallax",
26        "parallax_error",
27        "parallax_pmra_corr",
28        "parallax_pmdec_corr",
29        "ra_parallax_corr",
30        "dec_parallax_corr",
31        "phot_g_mean_mag",
32    )
33    # search for this identifier in simbad
34    # and bring data in a circle of radius
35    # 1/2 degree
36    .where_in_circle("ngc2168", 0.5)
37    .where(("parallax", ">", 0))
38    .where(("phot_g_mean_mag", "<", 18))
39    # include some common criteria
40    # for data precision
41    .where_arenou_criterion()
42    .where_aen_criterion()
43    .get()
44    .to_pandas()
45)
46
47# If data already has been downloaded, you can load it from a file:
48# > from astropy.table.table import Table
49# > df = Table.read("path_to_my_file/ngc2168_data.fits").to_pandas()
50
51
52# Build Detection-Estimation Pipeline
53dep = DEP(
54    # Detector configuration for the detection step
55    detector=CountPeakDetector(
56        bin_shape=[0.3, 0.3, 0.07],
57        min_score=3,
58        min_count=5,
59    ),
60    det_cols=["pmra", "pmdec", "parallax"],
61    sample_sigma_factor=2,
62    # Clusterability test configuration
63    tests=[
64        RipleysKTest(pvalue_threshold=0.05, max_samples=100),
65        HopkinsTest(),
66    ],
67    test_cols=[["ra", "dec"]] * 2,
68    # Membership columns to use
69    mem_cols=["pmra", "pmdec", "parallax", "ra", "dec"],
70).fit(df)
71
72# plot the results
73dep.scatterplot(["pmra", "pmdec"])
74# zoom on interesting area
75plt.axis([-1, 3, -5, 1])
76plt.show()
77
78# write results to file
79dep.write("ngc2168_result.fits")
_images/dep_pmra_pmdec.png

Documentation quick guide

  • Building queries for GAIA catalogues and retrieving data: Query

  • Detection and membership estimation pipeline: DEP

  • Detection method: CountPeakDetector

  • Clusterability tests: stat_tests

  • Clustering method: SHDBSCAN

  • Probability Estimation: DBME

  • Kernel Density Estimation with per-observation or per-dimension bandwidth, plugin or rule-of-thumb methods: HKDE

Documentation module guide

  • Query building, SIMBAD object searching and data related functionality: fetcher

  • Detection and membership estimation pipeline: pipeline

  • Detection: CountPeakDetector

  • Clusterability tests: stat_tests

  • Clustering: shdbscan

  • Probability estimation: membership

  • Kernel Density Estimation: hkde

  • Utils such as GAIA column names interpretation and one hot encoding: utils

  • Custom ploting functions: plots

  • Utils for R communication: rutils

  • Utils for masking data: masker

  • Useful distributions for data generation: synthetic