treetime/gtr.py
def prob_t(self, seq_p, seq_ch, t, pattern_multiplicity = None,
               return_log=False, ignore_gaps=True):
        """
        Compute the probability to observe seq_ch (child sequence) after time t starting from seq_p
        (parent sequence).

        Parameters
        ----------

         seq_p : character array
            Parent sequence

         seq_c : character array
            Child sequence

         t : double
            Time (branch len) separating the profiles.

         pattern_multiplicity : numpy array
            If sequences are reduced by combining identical alignment patterns,
            these multplicities need to be accounted for when counting the number
            of mutations across a branch. If None, all pattern are assumed to
            occur exactly once.

         return_log : bool
            It True, return log-probability.

        Returns
        -------
         prob : np.array
            Resulting probability

        """
        seq_pair, multiplicity = self.compress_sequence_pair(seq_p, seq_ch,
                                        pattern_multiplicity=pattern_multiplicity, ignore_gaps=ignore_gaps)
        return self.prob_t_compressed(seq_pair, multiplicity, t, return_log=return_log)
def prob_t(self, seq_p, seq_ch, t, pattern_multiplicity = None,
               return_log=False, ignore_gaps=True):
        """
        Compute the probability to observe seq_ch (child sequence) after time t starting from seq_p
        (parent sequence).

        Parameters
        ----------

         seq_p : character array
            Parent sequence

         seq_c : character array
            Child sequence

         t : double
            Time (branch len) separating the profiles.

         pattern_multiplicity : numpy array
            If sequences are reduced by combining identical alignment patterns,
            these multplicities need to be accounted for when counting the number
            of mutations across a branch. If None, all pattern are assumed to
            occur exactly once.

         return_log : bool
            It True, return log-probability.

        Returns
        -------
         prob : np.array
            Resulting probability

        """
        seq_pair, multiplicity = self.compress_sequence_pair(seq_p, seq_ch,
                                        pattern_multiplicity=pattern_multiplicity, ignore_gaps=ignore_gaps)
        return self.prob_t_compressed(seq_pair, multiplicity, t, return_log=return_log)
def prob_t ( self , seq_p , seq_ch , t , pattern_multiplicity = None , return_log = False , ignore_gaps = True ) : seq_pair , multiplicity = self . compress_sequence_pair ( seq_p , seq_ch , pattern_multiplicity = pattern_multiplicity , ignore_gaps = ignore_gaps ) return self . prob_t_compressed ( seq_pair , multiplicity , t , return_log = return_log )
Compute the probability to observe seq_ch (child sequence) after time t starting from seq_p
        (parent sequence).

        Parameters
        ----------

         seq_p : character array
            Parent sequence

         seq_c : character array
            Child sequence

         t : double
            Time (branch len) separating the profiles.

         pattern_multiplicity : numpy array
            If sequences are reduced by combining identical alignment patterns,
            these multplicities need to be accounted for when counting the number
            of mutations across a branch. If None, all pattern are assumed to
            occur exactly once.

         return_log : bool
            It True, return log-probability.

        Returns
        -------
         prob : np.array
            Resulting probability
Compute the probability to observe seq_ch ( child sequence ) after time t starting from seq_p ( parent sequence ) .