eztaox.models ============= .. py:module:: eztaox.models .. autoapi-nested-parse:: Light curve models. A module of light curve models. which are the interface for modeling uni/multi-band light curves using Gaussian Processes (GPs). Classes ------- .. autoapisummary:: eztaox.models.MultiVarModel eztaox.models.UniVarModel Module Contents --------------- .. py:class:: MultiVarModel(X: tuple[tinygp.helpers.JAXArray | numpy.typing.NDArray, tinygp.helpers.JAXArray | numpy.typing.NDArray], y: tinygp.helpers.JAXArray | numpy.typing.NDArray, yerr: tinygp.helpers.JAXArray | numpy.typing.NDArray, base_kernel: tinygp.kernels.Kernel | eztaox.kernels.quasisep.Quasisep, n_band: int, *, multiband_kernel: tinygp.kernels.Kernel | tinygp.kernels.quasisep.Wrapper | None = None, mean_func: collections.abc.Callable | None = None, amp_scale_func: collections.abc.Callable | None = None, lag_func: collections.abc.Callable | None = None, **kwargs) Bases: :py:obj:`equinox.Module` An interface for modeling multivariate/multi-band time series using GPs. This interface only takes GP kernels that can be evaluated using the scalable method of `DFM+17 `. This interface allows fitting for a parameterized mean function of the time series, additional variance to the measurement uncertainty, and time delays between each uni-variate/single-band time series. :param X: Input data containing time and band indices as a tuple. :type X: JAXArray|NDArray :param y: Observed data values. :type y: JAXArray|NDArray :param yerr: Observational uncertainties. :type yerr: JAXArray|NDArray :param base_kernel: A GP kernel from the kernels.quasisep module. :type base_kernel: Quasisep :param n_band: An integer number of bands in the input light curve. :type n_band: int :param multiband_kernel: A multiband kernel specifying the cross-band covariance, defaults to kernels.quasisep.MultibandLowRank. :type multiband_kernel: Quasisep, optional :param mean_func: A callable mean function for the GP, defaults to None. :type mean_func: Callable, optional :param amp_scale_func: A callable amplitude scaling function, defaults to None. :type amp_scale_func: Callable, optional :param lag_func: A callable function for time delays between bands, defaults to None. :type lag_func: Callable, optional :param \*\*kwargs: Additional keyword arguments. - `zero_mean` (bool): If True, assumes zero-mean GP. Defaults to True. - `has_jitter` (bool): If True, assumes the input observational erros are underestimated. Defaults to False. - `has_lag` (bool): If True, assumes time delays between time series in each band. Defaults to False. .. py:method:: get_mean(zero_mean: bool, params: dict[str, tinygp.helpers.JAXArray], X: tinygp.helpers.JAXArray) -> tinygp.helpers.JAXArray Return the mean of the GP. .. py:method:: get_amp_scale(params: dict[str, tinygp.helpers.JAXArray]) -> tinygp.helpers.JAXArray Return the ampltiude of GP in each individaul band. .. py:method:: lag_transform(has_lag: bool, params: dict[str, tinygp.helpers.JAXArray], X: tinygp.helpers.JAXArray) -> tuple[tuple[tinygp.helpers.JAXArray, tinygp.helpers.JAXArray], tinygp.helpers.JAXArray] Shift the time axis by the lag in each band. :param has_lag: should we introduce lag? :type has_lag: bool :param params: argument to pass to the lag function (callable for time delays between bands). :type params: dict :param X: times and bands :type X: JAXArray :returns: modified times and bands and indexes of the new times. :rtype: tuple(tuple(JAXArray, JAXArray), JAXArray) .. py:method:: log_prior(params: dict[str, tinygp.helpers.JAXArray]) -> tinygp.helpers.JAXArray Calculate the log prior of the input parameters. :param params: Model parameters. :type params: dict[str, JAXArray] :returns: Log prior of the input parameters. :rtype: JAXArray .. py:method:: log_prob(params: dict[str, tinygp.helpers.JAXArray]) -> tinygp.helpers.JAXArray Calculate the log probability of the input parameters. :param params: Model parameters. :type params: dict[str, JAXArray] :returns: Log probability of the input parameters. :rtype: JAXArray .. py:method:: aic(params: dict[str, tinygp.helpers.JAXArray]) -> tinygp.helpers.JAXArray Calculate the Akaike Information Criterion (AIC) for the model. :param params: Model parameters. :type params: dict[str, JAXArray] :returns: AIC value. :rtype: JAXArray .. py:method:: bic(params: dict[str, tinygp.helpers.JAXArray]) -> tinygp.helpers.JAXArray Calculate the Bayesian Information Criterion (BIC) for the model. :param params: Model parameters. :type params: dict[str, JAXArray] :returns: BIC value. :rtype: JAXArray .. py:method:: sample(params: dict[str, tinygp.helpers.JAXArray]) -> None Integrate with numpyro for MCMC sampling. :param params: Model parameters. :type params: dict[str, JAXArray] .. py:method:: pred(params: dict[str, tinygp.helpers.JAXArray], X: tinygp.helpers.JAXArray) -> tuple[tinygp.helpers.JAXArray, tinygp.helpers.JAXArray] Make conditional GP prediction. :param params: A dictionary containing model parameters. :type params: dict[str, JAXArray] :param X: The time and band information for creating the conditional GP prediction. :type X: JAXArray :returns: A tuple of the mean GP prediction and its uncertainty (square root of the predicted variance). :rtype: tuple[JAXArray, JAXArray] .. py:class:: UniVarModel(t: tinygp.helpers.JAXArray | numpy.typing.NDArray, y: tinygp.helpers.JAXArray | numpy.typing.NDArray, yerr: tinygp.helpers.JAXArray | numpy.typing.NDArray, kernel: tinygp.kernels.Kernel | eztaox.kernels.quasisep.Quasisep, *, mean_func: collections.abc.Callable | None = None, amp_scale_func: collections.abc.Callable | None = None, **kwargs) Bases: :py:obj:`MultiVarModel` Subclass MultiVarModel for modeling univariate/single-band time series data. :param t: Time stamps of the input light curve. :type t: JAXArray|NDArray :param y: Observed data values at the corresponding time stamps. :type y: JAXArray|NDArray :param yerr: Observational uncertainties. :type yerr: JAXArray|NDArray :param kernel: A GP kernel from the eztaox.kernels.quasisep module. :type kernel: Quasisep :param mean_func: A callable mean function for the GP, defaults to None. :type mean_func: Callable, optional :param amp_scale_func: A callable amplitude scaling function, defaults to None. :type amp_scale_func: Callable, optional :param \*\*kwargs: Additional keyword arguments. - `zero_mean` (bool): If True, assumes zero-mean GP. Defaults to True. - `has_jitter` (bool): If True, assumes the input observational erros are underestimated. Defaults to False. .. py:method:: pred(params, t) -> tuple[tinygp.helpers.JAXArray, tinygp.helpers.JAXArray] Make conditional GP prediction. :param params: A dictionary containing model parameters. :type params: dict[str, JAXArray] :param t: The time information for creating the conditional GP prediction. :type t: JAXArray :returns: A tuple of the mean GP prediction and its uncertainty (square root of the predicted variance). :rtype: tuple[JAXArray, JAXArray]