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pgmuvi

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Background
Key Concepts
Glossary

Preamble
The Lightcurve object
In this notebook, we will demonstrate some features available for Lightcurve objects to manipulate 1D and 2D light curve data.
Data structure and invariants
Missing and non-finite data
Generating synthetic data
Visualization using the .plot() method
Input/output to CSV
The band attribute; selecting, dropping, merging, and concatenating light curves.
Subsampling light curves
Statistical tests for variability
Preamble
Return modes of fit_LS()
Key points to keep in mind
Frequencies vs periods
Ordering of peaks
Meaning of the significance mask
Practical takeaway
Joint analysis across bands
Sensitivity to heterogeneous sampling
Interpretation of peaks
Significance in the multiband case
Practical takeaway
Why this matters
What use_best_band_init=True does
When to use it
Dominant component (~150 d)
Secondary component (~66 d)
Short-period region
Overall interpretation
Single-band case
Multiband case
Practical implication
Different roles of LS and GP models
Using LS to initialize GP models
Important caveat
Practical takeaway
What fit_LS() does well
Key points about interpretation
Multiband-specific considerations
Role of sampling
Relationship to GP modeling
Final takeaway
Gaussian-process fitting in pgmuvi
GP model families available in pgmuvi
A practical way to choose a GP model
Basic GP-fitting workflow in pgmuvi
Comparing GP packages on synthetic light curves
How to use pgmuvi - a brief introduction
Tutorial: Preprocessing and Data Quality Assessment
Tutorial: Generating Synthetic Light Curves
Tutorial: Model Selection

How-To Guides

Frequently Asked Questions

API reference