zeta1999 / pylightcurve-torch

An exoplanet transit modelling package for deep learning applications in Pytorch.

PyLightcurve-torch

An exoplanet transit modelling package for deep learning applications in Pytorch.

The code for orbit and flux drop computation is largely adapted from https://github.com/ucl-exoplanets/pylightcurve/ (under a MIT license).

The module pylightcurve_torch.functional.py contains the functions implemented in Pytorch and computing the orbital positions, transit durations and flux drops. (see PyLightcurve repository for more information about the numerical models used).

A TransitModule class is implemented in pylightcurve_torch.nn.py with the following features:

• Computes time series of planetary positions and primary/secondary flux drops
• it inherits torch.nn.Module class to benefit from its parameters optimisation and management capabilities and facilitated combination with neural networks
• native GPU compatibility

Installation

$ pip install pylightcurve-torch

Basic use

from pylightcurve_torch import TransitModule

tm = TransitModule(time, **transit_params)

flux_drop = tm()

If needs be, the returned torch.Tensor can be converted to a numpy.ndarrray using flux_drop.numpy() torch method or flux.cpu().numpy() if the computation took place on a gpu.

Transit parameters

Below is a summary table of the planetary orbital and transit parameters use in PyLightcurve-torch:

Name	Pylightcurve alias	Description	Python type	Unit	Transit type
a	sma_over_rs	ratio of semi-major axis by the stellar radius	float	unitless	primary/secondary
P	period	orbital period	float	days	primary/secondary
e	eccentricity	orbital eccentricity	float	unitless	primary/secondary
i	inclination	orbital inclination	float	degrees	primary/secondary
p	periastron	orbital argument of periastron	float	degrees	primary/secondary
t0	mid_time	transit mid-time epoch	float	days	primary/secondary
rp	rp_over_rs	ratio of planetary by stellar radii	float	unitless	primary/secondary
method	method	limb-darkening law	str		primary
ldc	limb_darkening_coefficients	limb-darkening coefficients	list	unitless	primary
fp	fp_over_fs	ratio of planetary by stellar fluxes	float	unitless	secondary

A short version of each parameter has been introduced, while maintaining a compatibility with origin PyLightcurve parameter names. All the parameters except method are converted to torch.Parameters when passed to a ``TransitModule```, with double dtype.

Differentiation

One of the main benefits of having a pytorch implementation for modelling transits is offered by its automatic differentiation feature with torch.autograd, stemming from autograd library.

Here is an example of basic usage:

...
tm.fit_param('rp')                  # activates the gradient computation for parameter 'rp'
err = loss(flux, **data)            # loss computation in pytorch 
err.backward()                      # gradients computation 
tm.rp.grad                          # access to computed gradient for parameter 'rp'

More Pytorch support

Several utility methods inherited from PyTorch modules are listed below, simplifying operations on all module's defined tensor parameters.

tm = TransitModule()

# Parameters access (iterators)
tm.parameters()
tm.named_parameters()

# dtype conversions
tm.float()
tm.double()

# Gradient local deactivation
with torch.no_grad():
    flux_no_grad = tm()

# device conversion
tm.cpu()
tm.cuda()

Running performance tests

In addition to traditional unit tests, computation performance tests can be executed this way:

 python tests/performance.py --plot
 

This will measure the computation time for computing forward transits as a function of transit duration, time vector length or batch size. If data have been savec previously, these will be plotted to with the name of the corresponding tag.