gwsnr.jax.jaxjit_interpolators

Module Contents

Functions

find_index_1d_jax(x_array, x_new)

Find the index for cubic spline interpolation in 1D.

spline_interp_4pts_jax(x_eval, x_pts, y_pts, condition_i)

Performs piecewise interpolation using 4 points with JAX compatibility.

spline_interp_4x4x4x4pts_jax(q_array, mtot_array, ...)

Function that performs the FULL 4D interpolation for a SINGLE point.

spline_interp_4x4x4x4pts_batched_jax(q_array, ...)

Perform batched 4D cubic spline interpolation using JAX vectorization.

get_interpolated_snr_aligned_spins_jax(mass_1, mass_2, ...)

Calculate interpolated signal-to-noise ratio (SNR) for aligned spin gravitational wave signals using JAX.

get_interpolated_snr_aligned_spins_helper(mass_1, ...)

Calculate interpolated signal-to-noise ratio (SNR) for aligned spin gravitational wave signals using JAX.

spline_interp_4x4pts_jax(q_array, mtot_array, ...)

Function that performs the FULL 2D interpolation for a SINGLE point.

spline_interp_4x4pts_batched_jax(q_array, mtot_array, ...)

Perform batched 2D cubic spline interpolation using JAX vectorization.

get_interpolated_snr_no_spins_jax(mass_1, mass_2, ...)

Calculate interpolated signal-to-noise ratio (SNR) for aligned spin gravitational wave signals using JAX.

get_interpolated_snr_no_spins_helper(mass_1, mass_2, ...)

Function to calculate the interpolated snr for a given set of parameters
gwsnr.jax.jaxjit_interpolators.find_index_1d_jax(x_array, x_new)[source]

Find the index for cubic spline interpolation in 1D. Returns the index and a condition for edge handling.

Parameters:
x_arrayjnp.ndarray

The array of x values for interpolation. Must be sorted in ascending order.

x_newfloat or jnp.ndarray

The new x value(s) to find the index for.

Returns:
ijnp.ndarray

The index in x_array where x_new would fit, clipped to range [1, N-3] where N is the length of x_array.

condition_ijnp.ndarray

A condition indicating which interpolation branch to use: - 1: Use linear interpolation at the left edge (x_new <= x_array[1]). - 2: Use cubic interpolation in the middle. - 3: Use linear interpolation at the right edge (x_new >= x_array[N-2]).

Notes

Uses binary search with clipped indices to ensure valid 4-point stencils. The condition parameter determines linear vs cubic interpolation at boundaries.

gwsnr.jax.jaxjit_interpolators.spline_interp_4pts_jax(x_eval, x_pts, y_pts, condition_i)[source]

Performs piecewise interpolation using 4 points with JAX compatibility. This function implements a piecewise interpolation scheme that uses: - Linear interpolation at the left boundary (condition_i=1) - Cubic interpolation in the middle region (condition_i=2) - Linear interpolation at the right boundary (condition_i=3) The cubic interpolation uses cubic Hermite spline coefficients for smooth interpolation between the middle two points, while the boundary regions use linear interpolation for stability. :param x_eval: The x-coordinate(s) where interpolation is to be evaluated. :type x_eval: array_like :param x_pts: Array of 4 x-coordinates of the interpolation points, ordered as

[x0, x1, x2, x3] where x1 and x2 are the main interpolation interval.

Parameters:

  • y_pts (array_like) -- Array of 4 y-coordinates corresponding to x_pts, ordered as [y0, y1, y2, y3].

  • condition_i (int) -- Interpolation mode selector: - 1: Linear interpolation using points (x0, y0) and (x1, y1) - 2: Cubic interpolation using all 4 points with x_eval in [x1, x2] - 3: Linear interpolation using points (x2, y2) and (x3, y3)

Returns:
array_like

Interpolated value(s) at x_eval using the specified interpolation method.

Notes

  • The function handles degenerate cases where denominators are zero by returning appropriate fallback values (y0, y1, or y2 respectively).

  • Uses JAX’s lax.switch for efficient conditional execution.

  • The cubic interpolation uses normalized parameter t = (x_eval - x1) / (x2 - x1).

  • Cubic coefficients follow the pattern: a*t³ + b*t² + c*t + d where:

gwsnr.jax.jaxjit_interpolators.spline_interp_4x4x4x4pts_jax(q_array, mtot_array, a1_array, a2_array, snrpartialscaled_array, q_new, mtot_new, a1_new, a2_new)[source]

Function that performs the FULL 4D interpolation for a SINGLE point. This function finds indices, slices data, and then uses vmap internally to perform interpolation efficiently without Python loops.

gwsnr.jax.jaxjit_interpolators.spline_interp_4x4x4x4pts_batched_jax(q_array, mtot_array, a1_array, a2_array, snrpartialscaled_array, q_new_batch, mtot_new_batch, a1_new_batch, a2_new_batch)[source]

Perform batched 4D cubic spline interpolation using JAX vectorization.

gwsnr.jax.jaxjit_interpolators.get_interpolated_snr_aligned_spins_jax(mass_1, mass_2, luminosity_distance, theta_jn, psi, geocent_time, ra, dec, a_1, a_2, detector_tensor, snr_partialscaled, ratio_arr, mtot_arr, a1_arr, a_2_arr, batch_size=100000)[source]

Calculate interpolated signal-to-noise ratio (SNR) for aligned spin gravitational wave signals using JAX. This function computes the SNR for gravitational wave signals with aligned spins across multiple detectors using 4D cubic spline interpolation. It calculates the effective distance, partial SNR, and combines results from multiple detectors to produce the effective SNR.

Parameters:
mass_1jax.numpy.ndarray

Primary mass of the binary system in solar masses.

mass_2jax.numpy.ndarray

Secondary mass of the binary system in solar masses.

luminosity_distancejax.numpy.ndarray

Luminosity distance to the source in Mpc.

theta_jnjax.numpy.ndarray

Inclination angle between the orbital angular momentum and line of sight in radians.

psijax.numpy.ndarray

Polarization angle in radians.

geocent_timejax.numpy.ndarray

GPS time of coalescence at the geocenter in seconds.

rajax.numpy.ndarray

Right ascension of the source in radians.

decjax.numpy.ndarray

Declination of the source in radians.

a_1jax.numpy.ndarray

Dimensionless spin magnitude of the primary black hole.

a_2jax.numpy.ndarray

Dimensionless spin magnitude of the secondary black hole.

detector_tensorjax.numpy.ndarray

Detector tensor array containing detector response information. Shape: (n_detectors, …)

snr_partialscaledjax.numpy.ndarray

Pre-computed scaled partial SNR values for interpolation. Shape: (n_detectors, …)

ratio_arrjax.numpy.ndarray

Mass ratio grid points for interpolation (q = m2/m1).

mtot_arrjax.numpy.ndarray

Total mass grid points for interpolation.

a1_arrjax.numpy.ndarray

Primary spin grid points for interpolation.

a_2_arrjax.numpy.ndarray

Secondary spin grid points for interpolation.

Returns:
snrjax.numpy.ndarray

SNR values for each detector. Shape: (n_detectors, n_samples)

snr_effectivejax.numpy.ndarray

Effective SNR combining all detectors. Shape: (n_samples,)

snr_partial_jax.numpy.ndarray

Interpolated partial SNR values for each detector. Shape: (n_detectors, n_samples)

d_effjax.numpy.ndarray

Effective distance for each detector accounting for antenna response. Shape: (n_detectors, n_samples)

Notes

  • Uses 4D cubic spline interpolation for efficient SNR calculation

  • Assumes aligned spins (no precession)

  • Effective SNR is calculated as sqrt(sum(SNR_i^2)) across detectors

  • Chirp mass and inclination-dependent factors are computed analytically

gwsnr.jax.jaxjit_interpolators.get_interpolated_snr_aligned_spins_helper(mass_1, mass_2, luminosity_distance, theta_jn, a_1, a_2, snr_partialscaled, ratio_arr, mtot_arr, a1_arr, a_2_arr, Fp, Fc, detector_tensor)[source]

Calculate interpolated signal-to-noise ratio (SNR) for aligned spin gravitational wave signals using JAX. This function computes the SNR for gravitational wave signals with aligned spins across multiple detectors using 4D cubic spline interpolation. It calculates the effective distance, partial SNR, and combines results from multiple detectors to produce the effective SNR.

Parameters:
mass_1jax.numpy.ndarray

Primary mass of the binary system in solar masses.

mass_2jax.numpy.ndarray

Secondary mass of the binary system in solar masses.

luminosity_distancejax.numpy.ndarray

Luminosity distance to the source in Mpc.

theta_jnjax.numpy.ndarray

Inclination angle between the orbital angular momentum and line of sight in radians.

psijax.numpy.ndarray

Polarization angle in radians.

geocent_timejax.numpy.ndarray

GPS time of coalescence at the geocenter in seconds.

rajax.numpy.ndarray

Right ascension of the source in radians.

decjax.numpy.ndarray

Declination of the source in radians.

a_1jax.numpy.ndarray

Dimensionless spin magnitude of the primary black hole.

a_2jax.numpy.ndarray

Dimensionless spin magnitude of the secondary black hole.

detector_tensorjax.numpy.ndarray

Detector tensor array containing detector response information. Shape: (n_detectors, …)

snr_partialscaledjax.numpy.ndarray

Pre-computed scaled partial SNR values for interpolation. Shape: (n_detectors, …)

ratio_arrjax.numpy.ndarray

Mass ratio grid points for interpolation (q = m2/m1).

mtot_arrjax.numpy.ndarray

Total mass grid points for interpolation.

a1_arrjax.numpy.ndarray

Primary spin grid points for interpolation.

a_2_arrjax.numpy.ndarray

Secondary spin grid points for interpolation.

Returns:
snrjax.numpy.ndarray

SNR values for each detector. Shape: (n_detectors, n_samples)

snr_effectivejax.numpy.ndarray

Effective SNR combining all detectors. Shape: (n_samples,)

snr_partial_jax.numpy.ndarray

Interpolated partial SNR values for each detector. Shape: (n_detectors, n_samples)

d_effjax.numpy.ndarray

Effective distance for each detector accounting for antenna response. Shape: (n_detectors, n_samples)

Notes

  • Uses 4D cubic spline interpolation for efficient SNR calculation

  • Assumes aligned spins (no precession)

  • Effective SNR is calculated as sqrt(sum(SNR_i^2)) across detectors

  • Chirp mass and inclination-dependent factors are computed analytically

gwsnr.jax.jaxjit_interpolators.spline_interp_4x4pts_jax(q_array, mtot_array, snrpartialscaled_array, q_new, mtot_new)[source]

Function that performs the FULL 2D interpolation for a SINGLE point. This function finds indices, slices data, and then uses vmap internally to perform interpolation efficiently without Python loops.

gwsnr.jax.jaxjit_interpolators.spline_interp_4x4pts_batched_jax(q_array, mtot_array, snrpartialscaled_array, q_new_batch, mtot_new_batch)[source]

Perform batched 2D cubic spline interpolation using JAX vectorization.

gwsnr.jax.jaxjit_interpolators.get_interpolated_snr_no_spins_jax(mass_1, mass_2, luminosity_distance, theta_jn, psi, geocent_time, ra, dec, a_1, a_2, detector_tensor, snr_partialscaled, ratio_arr, mtot_arr, a1_arr, a_2_arr, batch_size=100000)[source]

Calculate interpolated signal-to-noise ratio (SNR) for aligned spin gravitational wave signals using JAX. This function computes the SNR for gravitational wave signals with aligned spins across multiple detectors using 4D cubic spline interpolation. It calculates the effective distance, partial SNR, and combines results from multiple detectors to produce the effective SNR.

Parameters:
mass_1jax.numpy.ndarray

Primary mass of the binary system in solar masses.

mass_2jax.numpy.ndarray

Secondary mass of the binary system in solar masses.

luminosity_distancejax.numpy.ndarray

Luminosity distance to the source in Mpc.

theta_jnjax.numpy.ndarray

Inclination angle between the orbital angular momentum and line of sight in radians.

psijax.numpy.ndarray

Polarization angle in radians.

geocent_timejax.numpy.ndarray

GPS time of coalescence at the geocenter in seconds.

rajax.numpy.ndarray

Right ascension of the source in radians.

decjax.numpy.ndarray

Declination of the source in radians.

a_1jax.numpy.ndarray

Dimensionless spin magnitude of the primary black hole.

a_2jax.numpy.ndarray

Dimensionless spin magnitude of the secondary black hole.

detector_tensorjax.numpy.ndarray

Detector tensor array containing detector response information. Shape: (n_detectors, …)

snr_partialscaledjax.numpy.ndarray

Pre-computed scaled partial SNR values for interpolation. Shape: (n_detectors, …)

ratio_arrjax.numpy.ndarray

Mass ratio grid points for interpolation (q = m2/m1).

mtot_arrjax.numpy.ndarray

Total mass grid points for interpolation.

a1_arrjax.numpy.ndarray

Primary spin grid points for interpolation.

a_2_arrjax.numpy.ndarray

Secondary spin grid points for interpolation.

Returns:
snrjax.numpy.ndarray

SNR values for each detector. Shape: (n_detectors, n_samples)

snr_effectivejax.numpy.ndarray

Effective SNR combining all detectors. Shape: (n_samples,)

snr_partial_jax.numpy.ndarray

Interpolated partial SNR values for each detector. Shape: (n_detectors, n_samples)

d_effjax.numpy.ndarray

Effective distance for each detector accounting for antenna response. Shape: (n_detectors, n_samples)

Notes

  • Uses 4D cubic spline interpolation for efficient SNR calculation

  • Assumes aligned spins (no precession)

  • Effective SNR is calculated as sqrt(sum(SNR_i^2)) across detectors

  • Chirp mass and inclination-dependent factors are computed analytically

gwsnr.jax.jaxjit_interpolators.get_interpolated_snr_no_spins_helper(mass_1, mass_2, luminosity_distance, theta_jn, snr_partialscaled, ratio_arr, mtot_arr, Fp, Fc, detector_tensor)[source]

Function to calculate the interpolated snr for a given set of parameters