robustnet.sampling ================== .. py:module:: robustnet.sampling .. autoapi-nested-parse:: Define class for sampling model parameters. Attributes ---------- .. autoapisummary:: robustnet.sampling.logger Classes ------- .. autoapisummary:: robustnet.sampling.Sampler robustnet.sampling.SamplingCallback Module Contents --------------- .. py:data:: logger .. py:class:: Sampler(model, exclude_metabs=None) Bases: :py:obj:`robustnet.kinetics.Fitter` .. py:method:: _make_value_array(data, varnames, argname, method='geomean') Make an array with elements ordered according to ``varnames``. If ``data`` is a dict or ``pandas.Series``, missing values (either absent or ``NaN``) are imputed by ``method``. .. py:method:: _prepare_prior(mu, sigma, init, name, label) Prepare prior ``mu``, ``sigma`` and ``initvalues`` for variables used in sampling. .. py:method:: log_transform(log_mu, log_sigma) :staticmethod: Assume ``X ~ LogNormal(log_mu, log_sigma)``. Compute the ``mu`` and ``sigma`` of the corresponding normal distribution: ``log(X) ~ Normal(mu, sigma)``. .. py:method:: _to_ser(data, name) Convert a ``numpy.ndarray`` to a ``pandas.Series`` .. py:method:: sample_with_omics(ref_v_prior_mu, kparam_prior_mu, x_prior_mu, e_prior_mu, ref_v_prior_sigma=0.01, kparam_prior_sigma=0.1, x_prior_sigma=0.1, e_prior_sigma=0.001, ref_v_initvalues=None, kparam_initvalues=None, x_initvalues=None, e_initvalues=None, alpha=None, n_tunes=10000, n_samples=10000, n_chains=10, n_jobs=1) Sample model parameters with fluxomics, metabolomics, proteomics data and enzyme kinetic parameters. :param ref_v_prior_mu: Reference-state flux distribution in units of mmol/L/s (cell-based). If ``None``, reference fluxes loaded by ``load_priors`` are used. :type ref_v_prior_mu: dict or pandas.Series :param kparam_prior_mu: Mean values of the prior distributions for kinetic parameters. Catalytic constants have units of 1/s, Michaelis, activation, and inhibition constants have units of mM, and equilibrium constants are dimensionless. Missing kinetic parameters are allowed. If ``None``, kinetic parameters loaded by ``load_priors`` are used. :type kparam_prior_mu: dict or pandas.Series :param x_prior_mu: Mean values of prior metabolite concentrations in mM (cell-based). Missing metabolites are allowed. If ``None``, metabolomics data loaded by ``load_priors`` are used. :type x_prior_mu: dict, pandas.Series or None :param e_prior_mu: Mean values of prior enzyme concentrations in mM (cell-based). Missing enzymes are allowed. If ``None``, proteomics data loaded by ``load_priors`` are used. :type e_prior_mu: dict, pandas.Series or None :param ref_v_prior_sigma: Standard deviations of reference-state fluxes. If a scalar is provided, the same value is used for all fluxes. Missing flux values are allowed when using a dict or ``pandas.Series``. If ``None``, standard deviations from reference fluxes loaded by ``load_priors`` are used. Defaults to ``0.01``. :type ref_v_prior_sigma: scalar, dict, pandas.Series, optional :param kparam_prior_sigma: Standard deviations of kinetic parameters. If a scalar is provided, the same value is used for all parameters. Missing parameter values are allowed. If ``None``, standard deviations from kinetic parameters loaded by ``load_priors`` are used. Default to ``0.1``. :type kparam_prior_sigma: scalar, dict or pandas.Series, optional :param x_prior_sigma: Standard deviations of metabolite concentrations. If a scalar is provided, the same value is used for all metabolites. Missing metabolite values are allowed. If ``None``, standard deviations from metabolomics data loaded by ``load_priors`` are used. Default to ``0.1``. :type x_prior_sigma: scalar, dict or pandas.Series or None, optional :param e_prior_sigma: Standard deviations of enzyme concentrations. If a scalar is provided, the same value is used for all enzymes. Missing enzyme values are allowed. If ``None``, standard deviations from proteomics data loaded by ``load_priors`` are used. Default to ``0.001``. :type e_prior_sigma: scalar, dict or pandas.Series or None, optional :param ref_v_initvalues: Initial values for reference flux sampling. Missing fluxes are allowed. If ``None``, ``ref_v_prior_mu`` is used. :type ref_v_initvalues: dict, pandas.Series or None, optional :param kparam_initvalues: Initial values for kinetic parameter sampling. Missing parameter values are allowed. If ``None``, ``kparam_prior_mu`` is used. :type kparam_initvalues: dict, pandas.Series or None, optional :param x_initvalues: Initial values for metabolite concentration sampling. Missing metabolite values are allowed. If ``None``, ``x_prior_mu`` is used. :type x_initvalues: dict, pandas.Series or None, optional :param e_initvalues: Initial values for enzyme concentration sampling. Missing enzyme values are allowed. If ``None``, ``e_prior_mu`` is used. :type e_initvalues: dict, pandas.Series or None, optional :param alpha: Gaussian penalty strength used in parameter balancing. Larger values impose stronger penalties in log-posterior space. A reasonable choice is often on the same order of magnitude as ``1 / ref_flux_sigma**2``. If ``None``, ``geomean(1 / ref_flux_sigma**2)`` is used. :type alpha: float or None, optional :param n_tunes: Number of tuning iterations performed before sampling in each chain. :type n_tunes: int, optional :param n_samples: Number of samples drawn in each chain. :type n_samples: int, optional :param n_chains: Number of sampling chains. :type n_chains: int, optional :param n_jobs: Number of parallel jobs to run in parallel. :type n_jobs: int, optional .. py:class:: SamplingCallback(pbar) .. py:attribute:: pbar .. py:attribute:: traces .. py:method:: __call__(trace, draw)