pgmuvi 0.2.0rc2

A python package to interpret multiwavelength astronomical timeseries with GPs

pgmuvi

Python gaussian processes for multiwavelength variability inference

pgmuvi is based on GPyTorch and intended for us in infering the properties of astronomical sources with multiwavelength variability. It uses spectral-mixture kernels to learn an approximation of the PSD of the variability, which have been shown to be very effective for pattern discovery (see https://arxiv.org/pdf/1302.4245.pdf).

Installation and Quickstart

pgmuvi can be installed easily with pip::

$ pip install pgmuvi

You can also clone the latest version of pgmuvi from Github, for all the latest bugs but increased risk of features::

$ git clone git://github.com/ICSM/pgmuvi.git

and then you can install it::

$ cd pgmuvi
$ pip install .

If you want to contribute to pgmuvi and develop new features, you might want an editable install::

$ pip install -e .

this way you can test how things change as you go along.

Contributing

We very much welcome contributions to pgmuvi! Please take a look at our contributing guide for more information on how to contribute! But don't forget to read our code of conduct before you get started.

Citing pgmuvi

pgmuvi is currently under review in the Journal of Open Source Software. If you use pgmuvi in your research, please cite the paper (details will be given here when the paper is accepted!)

Using pgmuvi

You can find full documentation for pgmuvi at https://pgmuvi.readthedocs.io/. This includes a quickstart guide and a set of tutorials intended to get you up and running.

Sampling Quality Assessment

Before fitting a GP, you can check whether your lightcurve has adequate temporal sampling:

# Compute sampling metrics
metrics = lc.compute_sampling_metrics()
print(f"Nyquist period: {metrics['nyquist_period']:.2f} days")
print(f"Detectable range: {metrics['nyquist_period']:.1f}"
      f" - {metrics['longest_detectable_period']:.1f} days")

# Full quality assessment with detailed report
passes, diagnostics = lc.assess_sampling_quality(verbose=True)
if diagnostics['recommendation'] == 'PROCEED':
    lc.fit(...)

By default, fit() automatically checks sampling quality:

lc.fit(model='1D', ...)  # Raises ValueError if sampling is poor
lc.fit(model='1D', ..., check_sampling=False)  # Force fitting anyway


For multiband data:
# Check each wavelength band
results = lc2d.assess_sampling_quality_per_band()
print(f"{results['summary']['n_passing']}/{results['summary']['n_bands']} bands pass")

# Compute metrics per band
band_metrics = lc2d.compute_sampling_metrics_per_band()

# Filter to well-sampled bands only
lc_good = lc2d.filter_well_sampled_bands()
lc_good.fit(model='2D', ...)

Variability Detection

Before fitting a GP, you can check if your lightcurve shows significant variability using the built-in three-tier statistical testing framework (weighted chi-square, F_var excess variance, and Stetson K index):

from pgmuvi.lightcurve import Lightcurve

lc = Lightcurve(t, y, yerr)

# Check variability
diagnostics = lc.check_variability(verbose=True)

if diagnostics['decision'] == 'VARIABLE':
    # Proceed with fitting
    lc.fit(...)
else:
    print(f"Not variable: {diagnostics['decision']}")

You can also enable automatic variability checking inside fit():

# Raises ValueError if lightcurve is not variable
lc.fit(..., check_variability=True)

# To force fitting of a non-variable source:
lc.fit(..., check_variability=False)

For multiband data, each band can be checked independently:

lc2d = Lightcurve(xdata_2d, flux, error)


# Check each band
results = lc2d.check_variability_per_band(verbose=True)
print(f"{results['summary']['n_variable']} variable bands")

# Create a new Lightcurve with only variable bands retained
lc_var = lc2d.filter_variable_bands()
lc_var.fit(...)