Low-level Solver Interface

This is the low-level interface to the C++ implementation of the celerite algorithm. These methods do most of the heavy lifting but most users shouldn’t need to call these directly. This interface was built using pybind11.

celerite.solver.get_kernel_value(arg0: numpy.ndarray[float64[m, 1]], arg1: numpy.ndarray[float64[m, 1]], arg2: numpy.ndarray[float64[m, 1]], arg3: numpy.ndarray[float64[m, 1]], arg4: numpy.ndarray[float64[m, 1]], arg5: numpy.ndarray[float64[m, 1]], arg6: numpy.ndarray[float64]) → object

Get the value of the kernel for given parameters and lags

Parameters:
  • a_real (array[j_real]) – The coefficients of the real terms.
  • c_real (array[j_real]) – The exponents of the real terms.
  • a_comp (array[j_complex]) – The real part of the coefficients of the complex terms.
  • b_comp (array[j_complex]) – The imaginary part of the coefficients of the complex terms.
  • c_comp (array[j_complex]) – The real part of the exponents of the complex terms.
  • d_comp (array[j_complex]) – The imaginary part of the exponents of the complex terms.
  • tau (array[n]) – The time lags where the kernel should be evaluated.
Returns:

The kernel evaluated at tau.
Return type:

array[n]
celerite.solver.get_psd_value(arg0: numpy.ndarray[float64[m, 1]], arg1: numpy.ndarray[float64[m, 1]], arg2: numpy.ndarray[float64[m, 1]], arg3: numpy.ndarray[float64[m, 1]], arg4: numpy.ndarray[float64[m, 1]], arg5: numpy.ndarray[float64[m, 1]], arg6: numpy.ndarray[float64]) → object

Get the PSD of the kernel for given parameters and angular frequencies

Parameters:
  • a_real (array[j_real]) – The coefficients of the real terms.
  • c_real (array[j_real]) – The exponents of the real terms.
  • a_comp (array[j_complex]) – The real part of the coefficients of the complex terms.
  • b_comp (array[j_complex]) – The imaginary part of the coefficients of the complex terms.
  • c_comp (array[j_complex]) – The real part of the exponents of the complex terms.
  • d_comp (array[j_complex]) – The imaginary part of the exponents of the complex terms.
  • omega (array[n]) – The frequencies where the PSD should be evaluated.
Returns:

The PSD evaluated at omega.
Return type:

array[n]
celerite.solver.check_coefficients(arg0: numpy.ndarray[float64[m, 1]], arg1: numpy.ndarray[float64[m, 1]], arg2: numpy.ndarray[float64[m, 1]], arg3: numpy.ndarray[float64[m, 1]], arg4: numpy.ndarray[float64[m, 1]], arg5: numpy.ndarray[float64[m, 1]]) → bool

Apply Sturm’s theorem to check if parameters yield a positive PSD

Parameters:
  • a_real (array[j_real]) – The coefficients of the real terms.
  • c_real (array[j_real]) – The exponents of the real terms.
  • a_comp (array[j_complex]) – The real part of the coefficients of the complex terms.
  • b_comp (array[j_complex]) – The imaginary part of the coefficients of the complex terms.
  • c_comp (array[j_complex]) – The real part of the exponents of the complex terms.
  • d_comp (array[j_complex]) – The imaginary part of the exponents of the complex terms.
Returns:

True if the PSD is everywhere positive.
Return type:

bool
class celerite.solver.CholeskySolver

A thin wrapper around the C++ CholeskySolver class

compute(self: celerite.solver.CholeskySolver, arg0: float, arg1: numpy.ndarray[float64[m, 1]], arg2: numpy.ndarray[float64[m, 1]], arg3: numpy.ndarray[float64[m, 1]], arg4: numpy.ndarray[float64[m, 1]], arg5: numpy.ndarray[float64[m, 1]], arg6: numpy.ndarray[float64[m, 1]], arg7: numpy.ndarray[float64[m, 1]], arg8: numpy.ndarray[float64[m, n]], arg9: numpy.ndarray[float64[m, n]], arg10: numpy.ndarray[float64[m, 1]], arg11: numpy.ndarray[float64[m, 1]]) → None

Compute the Cholesky factorization of the matrix

Parameters:
  • jitter (float) – The jitter of the kernel.
  • a_real (array[j_real]) – The coefficients of the real terms.
  • c_real (array[j_real]) – The exponents of the real terms.
  • a_comp (array[j_complex]) – The real part of the coefficients of the complex terms.
  • b_comp (array[j_complex]) – The imaginary part of the coefficients of the complex terms.
  • c_comp (array[j_complex]) – The real part of the exponents of the complex terms.
  • d_comp (array[j_complex]) – The imaginary part of the exponents of the complex terms.
  • x (array[n]) – The _sorted_ array of input coordinates.
  • diag (array[n]) – An array that should be added to the diagonal of the matrix. This often corresponds to measurement uncertainties and in that case, diag should be the measurement _variance_ (i.e. sigma^2).
computed(self: celerite.solver.CholeskySolver) → bool

A flag that indicates if compute has been executed

Returns:True if solver.CholeskySolver.compute() was previously executed successfully.
Return type:bool
dot(self: celerite.solver.CholeskySolver, arg0: float, arg1: numpy.ndarray[float64[m, 1]], arg2: numpy.ndarray[float64[m, 1]], arg3: numpy.ndarray[float64[m, 1]], arg4: numpy.ndarray[float64[m, 1]], arg5: numpy.ndarray[float64[m, 1]], arg6: numpy.ndarray[float64[m, 1]], arg7: numpy.ndarray[float64[m, 1]], arg8: numpy.ndarray[float64[m, n]], arg9: numpy.ndarray[float64[m, n]], arg10: numpy.ndarray[float64[m, 1]], arg11: numpy.ndarray[float64[m, n]]) → numpy.ndarray[float64[m, n]]

Compute the dot product of a celerite matrix and another arbitrary matrix

This method computes A.b where A is defined by the parameters and b is an arbitrary matrix of the correct shape.

Parameters:
  • jitter (float) – The jitter of the kernel.
  • a_real (array[j_real]) – The coefficients of the real terms.
  • c_real (array[j_real]) – The exponents of the real terms.
  • a_comp (array[j_complex]) – The real part of the coefficients of the complex terms.
  • b_comp (array[j_complex]) – The imaginary part of the coefficients of the complex terms.
  • c_comp (array[j_complex]) – The real part of the exponents of the complex terms.
  • d_comp (array[j_complex]) – The imaginary part of the exponents of the complex terms.
  • x (array[n]) – The _sorted_ array of input coordinates.
  • b (array[n] or array[n, neq]) – The matrix b described above.
Returns:

The dot product A.b as described above.
Return type:

array[n] or array[n, neq]
Raises:

ValueError – For mismatched dimensions.
dot_L(self: celerite.solver.CholeskySolver, arg0: numpy.ndarray[float64[m, n]]) → numpy.ndarray[float64[m, n]]

Compute the dot product of the square root of a celerite matrix

This method computes L.z where A = L.L^T is the matrix defined in compute.

Parameters:z (array[n] or array[n, neq]) – The matrix z described above.
Returns:The dot product L.b as described above.
Return type:array[n] or array[n, neq]
Raises:ValueError – For mismatched dimensions.
dot_solve(self: celerite.solver.CholeskySolver, arg0: numpy.ndarray[float64[m, n]]) → float

Solve the system b^T . A^-1 . b

A previous call to solver.Solver.compute() defines a matrix A and this method solves b^T . A^-1 . b for a vector b.

Parameters:b (array[n]) – The right hand side of the linear system.
Returns:The solution of b^T . A^-1 . b.
Return type:float
Raises:ValueError – For mismatched dimensions.
grad_log_likelihood(self: celerite.solver.CholeskySolver, arg0: float, arg1: numpy.ndarray[float64[m, 1]], arg2: numpy.ndarray[float64[m, 1]], arg3: numpy.ndarray[float64[m, 1]], arg4: numpy.ndarray[float64[m, 1]], arg5: numpy.ndarray[float64[m, 1]], arg6: numpy.ndarray[float64[m, 1]], arg7: numpy.ndarray[float64[m, 1]], arg8: numpy.ndarray[float64[m, n]], arg9: numpy.ndarray[float64[m, n]], arg10: numpy.ndarray[float64[m, 1]], arg11: numpy.ndarray[float64[m, 1]], arg12: numpy.ndarray[float64[m, 1]]) → None

Compute the gradient of the log likelihood of the model using autodiff

The returned gradient is with respect to the jitter and the coefficients.

Parameters:
  • jitter (float) – The jitter of the kernel.
  • a_real (array[j_real]) – The coefficients of the real terms.
  • c_real (array[j_real]) – The exponents of the real terms.
  • a_comp (array[j_complex]) – The real part of the coefficients of the complex terms.
  • b_comp (array[j_complex]) – The imaginary part of the coefficients of the complex terms.
  • c_comp (array[j_complex]) – The real part of the exponents of the complex terms.
  • d_comp (array[j_complex]) – The imaginary part of the exponents of the complex terms.
  • x (array[n]) – The _sorted_ array of input coordinates.
  • y (array[n]) – The observations at x.
  • diag (array[n]) – An array that should be added to the diagonal of the matrix. This often corresponds to measurement uncertainties and in that case, diag should be the measurement _variance_ (i.e. sigma^2).
log_determinant(self: celerite.solver.CholeskySolver) → float

Get the log-determinant of the matrix defined by compute

Returns:The log-determinant of the matrix defined by solver.CholeskySolver.compute().
Return type:float
predict(self: celerite.solver.CholeskySolver, arg0: numpy.ndarray[float64[m, 1]], arg1: numpy.ndarray[float64[m, 1]]) → numpy.ndarray[float64[m, 1]]
solve(self: celerite.solver.CholeskySolver, arg0: numpy.ndarray[float64[m, n]]) → numpy.ndarray[float64[m, n]]

Solve a linear system for the matrix defined in compute

A previous call to solver.CholeskySolver.compute() defines a matrix A and this method solves for x in the matrix equation A.x = b.

Parameters:b (array[n] or array[n, nrhs]) – The right hand side of the linear system.
Returns:The solution of the linear system.
Return type:array[n] or array[n, nrhs]
Raises:ValueError – For mismatched dimensions.
class celerite.solver.CARMASolver

A thin wrapper around the C++ CARMASolver class

This solver is parameterized following carma_pack: https://github.com/brandonckelly/carma_pack

Parameters:
  • log_sigma (float) – The log of the variance of the process.
  • arparams (array[p]) – The parameters of the autoregressive component.
  • maparams (array[q]) – The parameters of the moving average component.
get_celerite_coeffs(self: celerite.solver.CARMASolver) → Tuple[numpy.ndarray[float64[m, 1]], numpy.ndarray[float64[m, 1]], numpy.ndarray[float64[m, 1]], numpy.ndarray[float64[m, 1]], numpy.ndarray[float64[m, 1]], numpy.ndarray[float64[m, 1]]]

Compute the coefficients of the celerite model for the given CARMA model

log_likelihood(self: celerite.solver.CARMASolver, arg0: numpy.ndarray[float64[m, 1]], arg1: numpy.ndarray[float64[m, 1]], arg2: numpy.ndarray[float64[m, 1]]) → float

Compute the log likelihood using a Kalman filter

Parameters:
  • t (array[n]) – The input coordinates of the observations.
  • y (array[n]) – The observations.
  • yerr (array[n]) – The measurement uncertainties of the observations.