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module SModel.ReversibleMarkov (module SModel.ReversibleMarkov,
module SModel.Markov,
MkReversible(..),
reversible,
nonreversible)
where
import Bio.Alphabet
import Markov (MkReversible(..), reversible, nonreversible, CTMC(..))
import qualified Markov
import SModel.Markov
import SModel.Simple
import SModel.Rate
import Tree (HasBranchLengths(..))
equ a = Markov.equ (alphabetSize a) 1.0
gtr_sym exchange a = Markov.gtr_sym (alphabetSize a) exchange
gtr a s pi = reversible $ markov a (simple_smap a) (s %*% plus_f_matrix pi') pi' where pi' = list_to_vector pi
f81 pi a = gtr a (equ a) pi
jukes_cantor a = gtr a (equ a) (uniform_frequencies a)
gtr' s' pi a = gtr a (gtr_sym' s' a) (frequencies_from_dict a pi)
letter_pair_names a = pairNames $ Markov.all_pairs (getLetters a)
-- factor out code to get gtr exch list
-- maybe put ReversibleFrequency into this file.
-- clean up f1x4 and f3x4?
gtr_sym' es' a = gtr_sym es a where lpairs = Markov.all_pairs (getLetters a)
es :: [Double]
es = if length lpairs == length es' then
[Markov.get_element_exchange es' (l1++l2) (l2++l1)| (l1,l2) <- lpairs]
else
error $ "Expected "++show (length lpairs)++" exchangeabilities but got "++ show (length es')++"!"
plus_f a pi s = gtr a s pi
plus_fe a s = plus_f a (uniform_frequencies a) s
plus_gwf a pi f s = reversible $ markov a (simple_smap a) (s %*% plus_gwf_matrix pi' f) pi' where pi' = list_to_vector pi
plus_f' a pi s = plus_f a (frequencies_from_dict a pi) s
plus_gwf' a pi f s = plus_gwf a (frequencies_from_dict a pi) f s
type ReversibleMarkov = MkReversible Markov
-- This is used both for observations, and also to determine which states are the same for computing rates.
instance HasSMap m => HasSMap (MkReversible m) where
getSMap (Reversible m) = getSMap m
instance HasAlphabet m => HasAlphabet (MkReversible m) where
getAlphabet (Reversible m) = getAlphabet m
instance RateModel ReversibleMarkov where
rate (Reversible m) = rate m
instance HasBranchLengths t => SimpleSModel t (MkReversible Markov) where
type instance IsReversible (MkReversible Markov) = EquilibriumReversible
branch_transition_p (SModelOnTree tree smodel factor) b = [qExp $ scale (factor * branchLength tree b / r) smodel]
where r = rate smodel
distribution _ = [1.0]
nBaseModels _ = 1
stateLetters (SModelOnTree _ rm _) = getSMap rm
componentFrequencies (SModelOnTree tree smodel factor) i = [getStartFreqs smodel]!!i
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