snsim.salt_utils
================

.. py:module:: snsim.salt_utils

.. autoapi-nested-parse::

   Contains function related to SALT model.



Functions
---------

.. autoapisummary::

   snsim.salt_utils.x0_to_mb
   snsim.salt_utils.n21_x1_model
   snsim.salt_utils.x1_mass_model
   snsim.salt_utils.n21_x1_mass_model
   snsim.salt_utils.cov_x0_to_mb
   snsim.salt_utils.compute_salt_fit_error
   snsim.salt_utils.salt_fitter


Module Contents
---------------

.. py:function:: x0_to_mb(x0, source_name, source_version)

.. py:function:: n21_x1_model(z, seed=None)

   X1 distribution redshift dependant model from  Nicolas et al. 2021.

   :param z: Redshift(s) of the SN.
   :type z: numpy.array(float)
   :param seed: Random seed.
   :type seed: int, opt

   :returns: **X1** -- Stretch parameters of supernovae.
   :rtype: numpy.array(float)


.. py:function:: x1_mass_model(host_mass, seed=None)

.. py:function:: n21_x1_mass_model(z, host_mass=None, seed=None)

.. py:function:: cov_x0_to_mb(x0, cov)

   Convert x0,x1,c covariance into mB,x1,c covariance.

   :param x0: SALT x0 parameter.
   :type x0: float
   :param cov: SALT x0, x1, c covariance matrix
   :type cov: numpy.array(float, size = (3,3))

   :returns: SALT mb, x1, c covariance matrix.
   :rtype: numpy.array(float, size = (3,3))


.. py:function:: compute_salt_fit_error(fit_model, cov, band, time_th, zp, magsys='ab')

   Compute fit error on flux from sncosmo fit covariance x0,x1,c.

   :param fit_model: The model used to fit the sn lightcurve.
   :type fit_model: sncosmo.Model
   :param cov: sncosmo x0,x1,c covariance matrix from SALT fit.
   :type cov: numpy.ndarray(float, size=(3,3))
   :param band: The band in which the error is computed.
   :type band: str
   :param time_th: Time for which compute the flux error.
   :type time_th: numpy.ndarray(float)
   :param zp: zeropoint to scale the error.
   :type zp: float
   :param magsys: Magnitude system to use.
   :type magsys: str

   :returns: Flux error for each input time.
   :rtype: numpy.ndarray(float)

   .. rubric:: Notes

   Compute theorical fluxerr from fit :math:`err = \sqrt{COV}`
   where :math:`COV = J^T  COV(x0,x1,c)  J` with :math:`J = (dF/dx0, dF/dx1, dF/dc)`
   the jacobian.

   .. math::

       F_{norm} = \frac{x_0}{1+z} \int_\lambda \left(M_0(\lambda_s, p) + x_1 M_1(\lambda_s, p)\right)\
       10^{-0.4cCL(\lambda_s)}T_b(\lambda) \frac{\lambda}{hc} d\lambda \times \text{NF}

   where the Norm Factor is :math:`\text{NF} = 10^{0.4(ZP_{norm} -ZP_{magsys})}`.

   We found :

   .. math::
       \frac{dF}{dx_0} = \frac{F}{x_0}

   .. math::
       \frac{dF}{dx_1} = \frac{x_0}{1+z} \int_\lambda M_1(\lambda_s, p) * 10^{-0.4cCL(\lambda_s)}\
                                T_b(\lambda)\frac{\lambda}{hc} d\lambda \times \text{NF}

   .. math::
       \frac{dF}{dc}  =  -\frac{\ln(10)}{2.5}\frac{x_0}{1+z} \int_\lambda \left(M_0(\lambda_s, p) + x_1 M_1(\lambda_s, p)\right)\
                       CL(\lambda_s)10^{-0.4 c CL(\lambda_s)}T_b(\lambda) \frac{\lambda}{hc} d\lambda \times \text{NF}


.. py:function:: salt_fitter(lc, fit_model, fit_par, **kwargs)

   Fit a given lightcurve with sncosmo salt model.

   :param lc: The SN lightcurve.
   :type lc: astropy.Table
   :param fit_model: Model used to fit the ligthcurve.
   :type fit_model: sncosmo.Model
   :param fit_par: The parameters to fit.
   :type fit_par: list(str)

   :returns: sncosmo dict of fit results.
   :rtype: sncosmo.utils.Result (numpy.nan if no result)