snsim.post_sim_tools
====================

.. py:module:: snsim.post_sim_tools

.. autoapi-nested-parse::

   This module contains some tools for post-sim analysis.



Functions
---------

.. autoapisummary::

   snsim.post_sim_tools.SNR_pdet
   snsim.post_sim_tools.SNR_select


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

.. py:function:: SNR_pdet(SNR, SNR_mean, SNRp, p)

   Approximation of the SNR detection probability.

   :param SNR: The Signal to Noise Ratio.
   :type SNR: float or np.array(float)
   :param SNR_mean: The SNR for which p = 0.5.
   :type SNR_mean: float
   :param SNRp: The SNR for which we have a probability p of detection.
   :type SNRp: float
   :param p: The probability of detection at SNRp.
   :type p: float

   :returns: Probability of detection.
   :rtype: float or np.array(float)

   .. rubric:: Notes

   The detection probability function :

   .. math::
       P_{det}(SNR) = \frac{1}{1+\left(\frac{SNR_{mean}}{SNR}\right)^n}

   where :math:`n = \frac{\ln\left(\frac{1-p}{p}\right)}{\ln(SNR_{mean}) - \ln(SNR_p)}`


.. py:function:: SNR_select(selec_function, lcs, SNR_mean=5, SNR_limit=[15, 0.99], randseed=np.random.randint(1000, 100000))

   Run a SNR efficiency detection on all lcs.

   :param selec_function: Can be 'approx' function TODO : add interpolation for function from file.
   :type selec_function: str
   :param lcs: SN lcs data.
   :type lcs: pandas.DataFrame
   :param SNR_mean: The SNR for which the detection probability is 1/2 -> SNR_mean.
   :type SNR_mean: float or dic
   :param SNR_limit: A SNR and its probability of detection -> $SNR_p$ and p.
   :type SNR_limit: list of dic(list)
   :param randseed: Randseed for random detection.
   :type randseed: int

   :returns: The SNR selected epochs.
   :rtype: pandas.DataFrame

   .. rubric:: Notes

   The detection probability function :

   .. math::

       P_\text{det}(SNR) = \frac{1}{1+\left(\frac{SNR_\text{mean}}{SNR}\right)^n}

   where :math:`n = \frac{\ln\left(\frac{1-p}{p}\right)}{\ln(SNR_\text{mean}) - \ln(SNR_p)}`