Update main BMC struct

Project.toml

@@ -1,38 +1,38 @@
 name = "BioMarkovChains"
 uuid = "f861b655-cb5f-42ce-b66a-341b542d4f2c"
-authors = ["Camilo García<[email protected]>"]
+authors = ["Camilo García-Botero<[email protected]>"]
 version = "0.6.0"
 
-[weakdeps]
-DiscreteMarkovChains = "8abcb7ef-b365-4f7b-ac38-56893fb62f9f"
-MarkovChainHammer = "38c40fd0-bccb-4723-b30d-b2caea0ad8d9"
-
-[extensions]
-DiscreteMarkovChainsExt = "DiscreteMarkovChains"
-
 [deps]
 BioSequences = "7e6ae17a-c86d-528c-b3b9-7f778a29fe59"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
-StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
 TestItems = "1c621080-faea-4a02-84b6-bbd5e436b8fe"
 VectorizedKmers = "2ef45bd6-4c2b-43d8-88b3-40597287359a"
+TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
+
+[weakdeps]
+DiscreteMarkovChains = "8abcb7ef-b365-4f7b-ac38-56893fb62f9f"
+MarkovChainHammer = "38c40fd0-bccb-4723-b30d-b2caea0ad8d9"
+
+[extensions]
+DiscreteMarkovChainsExt = "DiscreteMarkovChains"
+MarkovChainHammerExt = "MarkovChainHammer"
 
 [compat]
 BioSequences = "3"
 DiscreteMarkovChains = "0.2"
 MarkovChainHammer = "0.0.11"
 PrecompileTools = "1"
 StatsAPI = "1.6"
-StatsBase = "0.34.0"
 TestItemRunner = "0.2"
 TestItems = "0.1"
 VectorizedKmers = "0.7"
 julia = "1"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+TestItems = "1c621080-faea-4a02-84b6-bbd5e436b8fe"
 TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
 
 [targets]

ext/DiscreteMarkovChainsExt.jl

@@ -3,7 +3,9 @@ module DiscreteMarkovChainsExt
 using BioMarkovChains
 using DiscreteMarkovChains:
     DiscreteMarkovChains, DiscreteMarkovChain,
-    is_absorbing, is_ergodic, is_regular, is_reversible
+    is_absorbing, is_ergodic, is_regular, is_reversible,
+    stationary_distribution, first_passage_probabilities, exit_probabilities,
+    fundamental_matrix, mean_first_passage_time, mean_recurrence_time, mean_time_to_absorption
 
 ## Boolean functions ##
 
@@ -25,6 +27,40 @@ function DiscreteMarkovChains.is_reversible(bmc::BioMarkovChain)
     return is_reversible(DiscreteMarkovChain(bmc.tpm))
 end
 
-## 
+## Probability functions
+
+import DiscreteMarkovChains: stationary_distribution, first_passage_probabilities, exit_probabilities
+
+function DiscreteMarkovChains.exit_probabilities(bmc::BioMarkovChain)
+    return exit_probabilities(DiscreteMarkovChain(bmc.tpm))
+end
+
+function DiscreteMarkovChains.stationary_distribution(bmc::BioMarkovChain)
+    return stationary_distribution(DiscreteMarkovChain(bmc.tpm))
+end
+
+function DiscreteMarkovChains.first_passage_probabilities(bmc::BioMarkovChain, t) # it recieves other args...
+    return first_passage_probabilities(DiscreteMarkovChain(bmc.tpm), t)
+end
+
+## Mean time functions
+
+import DiscreteMarkovChains: fundamental_matrix, mean_first_passage_time, mean_recurrence_time, mean_time_to_absorption
+
+function DiscreteMarkovChains.fundamental_matrix(bmc::BioMarkovChain)
+    return fundamental_matrix(DiscreteMarkovChain(bmc.tpm))
+end
+
+function DiscreteMarkovChains.mean_first_passage_time(bmc::BioMarkovChain)
+    return mean_first_passage_time(DiscreteMarkovChain(bmc.tpm))
+end
+
+function DiscreteMarkovChains.mean_recurrence_time(bmc::BioMarkovChain)
+    return mean_recurrence_time(DiscreteMarkovChain(bmc.tpm))
+end
+
+function DiscreteMarkovChains.mean_time_to_absorption(bmc::BioMarkovChain)
+    return mean_time_to_absorption(DiscreteMarkovChain(bmc.tpm))
+end
 
 end

ext/MarkovChainHammerExt.jl

@@ -1,6 +1,12 @@
-# module MarkovChainHammerExt
+module MarkovChainHammerExt
 
-# using BioMarkovChains
-# using MarkovChainHammer.Trajectory: generate
+using BioMarkovChains
+using MarkovChainHammer: generate
 
-# end
+# function MarkovChainHammer.generate(pf::Matrix{Float64}, steps::Int64)
+#     return generate(pf, steps)
+# end
+
+export generate
+
+end

src/BioMarkovChains.jl

@@ -11,11 +11,9 @@ using BioSequences:
     NucleicAcidAlphabet,
     DNA,
     DNAAlphabet,
-    DNA_Gap, DNA_A, DNA_C, DNA_M, DNA_G, DNA_R, DNA_S, DNA_V, DNA_T, DNA_W, DNA_Y, DNA_H, DNA_K, DNA_D, DNA_B, DNA_N,
 
     #RNA
     RNA,
-    RNA_Gap, RNA_A, RNA_C, RNA_M, RNA_G, RNA_R, RNA_S, RNA_V, RNA_U, RNA_W, RNA_Y, RNA_H, RNA_K, RNA_D, RNA_B, RNA_N,
 
     #AminoAcids
     AminoAcid,
@@ -24,32 +22,27 @@ using BioSequences:
     AA_A, AA_R, AA_N, AA_D, AA_C, AA_Q, AA_E, AA_G, AA_H, AA_I, AA_L, AA_K, AA_M, AA_F, AA_P, AA_S, AA_T, AA_W, AA_Y, AA_V, AA_O, AA_U, AA_B, AA_J, AA_Z, AA_X, AA_Term, AA_Gap,
 
     # Other functions
-    SeqOrView,
-    alphabet,
-    @biore_str
-
+    SeqOrView, NucleicSeqOrView
+
 using PrecompileTools: @setup_workload, @compile_workload
 using TestItems: @testitem
 using StatsAPI: StatsAPI, fit, fit!
-using StatsBase: countmap
 using VectorizedKmers: count_kmers
 
 include("types.jl")
 export BioMarkovChain, BMC
 
 include("utils.jl")
-export transitions, hasprematurestop, randbmc
+export randbmc
 
 include("transitions.jl")
-export initials, transition_count_matrix, transition_probability_matrix, dnaseqprobability, logoddsratio, logoddsratioscore
+export initials, transition_count_matrix, transition_probability_matrix, logoddsratio, logoddsratioscore
 
 include("models.jl")
 export ECOLICDS, ECOLINOCDS
 
 include("perronfrobenius.jl")
-export perronfrobenius, generatedna
-
-include("constants.jl")
+export perronfrobenius # generatedna
 
 include("extended.jl")
 
@@ -61,12 +54,10 @@ include("extended.jl")
     @compile_workload begin
         # all calls in this block will be precompiled, regardless of whether
         # they belong to your package or not (on Julia 1.8 and higher)
-        transitions(seq)
         transition_count_matrix(seq)
         transition_probability_matrix(seq)
-        dnaseqprobability(seq, ECOLICDS)
-        BioMarkovChain(seq)
         perronfrobenius(seq)
+        BioMarkovChain(seq)
     end
 end
 

src/constants.jl

@@ -2,25 +2,5 @@
 
 # const LongAminoAcidOrView = Union{LongSequence{<:AminoAcidAlphabet},LongSubSeq{<:AminoAcidAlphabet}}
 
-const NUCLEICINDEXES = Dict(DNA_A => 1, DNA_C => 2, DNA_G => 3, DNA_T => 4)
 
-const DINUCLEICINDEXES = Dict(
-    LongDNA{4}("AA") => [1, 1],
-    LongDNA{4}("AC") => [1, 2],
-    LongDNA{4}("AG") => [1, 3],
-    LongDNA{4}("AT") => [1, 4],
-    LongDNA{4}("CA") => [2, 1],
-    LongDNA{4}("CC") => [2, 2],
-    LongDNA{4}("CG") => [2, 3],
-    LongDNA{4}("CT") => [2, 4],
-    LongDNA{4}("GA") => [3, 1],
-    LongDNA{4}("GC") => [3, 2],
-    LongDNA{4}("GG") => [3, 3],
-    LongDNA{4}("GT") => [3, 4],
-    LongDNA{4}("TA") => [4, 1],
-    LongDNA{4}("TC") => [4, 2],
-    LongDNA{4}("TG") => [4, 3],
-    LongDNA{4}("TT") => [4, 4],
-)
-
-const AA20 = (AA_A, AA_R, AA_N, AA_D, AA_C, AA_Q, AA_E, AA_G, AA_H, AA_I, AA_L, AA_K, AA_M, AA_F, AA_P, AA_S, AA_T, AA_W, AA_Y, AA_V)
+# const AA20 = (AA_A, AA_R, AA_N, AA_D, AA_C, AA_Q, AA_E, AA_G, AA_H, AA_I, AA_L, AA_K, AA_M, AA_F, AA_P, AA_S, AA_T, AA_W, AA_Y, AA_V)

src/extended.jl

@@ -6,7 +6,8 @@ function Base.show(io::IO, model::BioMarkovChain)
     # println(io, "BioMarkovChain:")
 
     # Determine the alphabet type
-    alphabet_type = length(model) == 20 ? "AminoAcids" : "DNA/RNA"
+    # alphabet_type = length(model) == 20 ? "AminoAcids" : "DNA/RNA"
+    alphabet_type = eltype(model)
 
     # Print the type name with inferred alphabet type
     println(io, "BioMarkovChain with $alphabet_type Alphabet:")
@@ -47,7 +48,7 @@ end
 Base.length(bmc::BioMarkovChain) = length(bmc.inits)
 
 function Base.eltype(bmc::BioMarkovChain)
-    return length(bmc) == 4 ? DNA : AminoAcid
+    return bmc.statespace
 end
 
 """

src/models.jl

@@ -8,7 +8,7 @@ const ECOLICDS = begin
 
     inits = [0.245, 0.243, 0.273, 0.239]
 
-    BMC(tpm, inits)
+    BMC(DNA, tpm, inits)
 end
 
 const ECOLINOCDS = begin
@@ -21,7 +21,7 @@ const ECOLINOCDS = begin
 
     inits = [0.262, 0.239, 0.240, 0.259]
 
-    BMC(tpm, inits)
+    BMC(DNA, tpm, inits)
 end
 
 
@@ -35,7 +35,7 @@ const CPGPOS = begin
 
     inits = [0.262, 0.239, 0.240, 0.259] # not stablished
 
-    BMC(tpm, inits)
+    BMC(DNA, tpm, inits)
 end
 
 
@@ -49,5 +49,5 @@ const CPGNEG = begin
 
     inits = [0.262, 0.239, 0.240, 0.259] # not stablished
 
-    BMC(tpm, inits)
+    BMC(DNA, tpm, inits)
 end

src/perronfrobenius.jl

@@ -1,14 +1,14 @@
 ### MarkdovChainHammer.jl ###
 
 """
-    perronfrobenius(sequence::LongNucOrView{4}, n::Int64=1)
+    perronfrobenius(sequence::NucleicSeqOrView{A}, n::Int64=1) where A
 
 Compute the Perron-Frobenius matrix, a column-wise version of the transition probability matrix (TPM), for a given nucleotide sequence.
 
 The Perron-Frobenius matrix captures the asymptotic probabilities of transitioning between nucleotides in the sequence over a specified number of steps `n`. It provides insight into the long-term behavior of a Markov chain or a dynamical system associated with the sequence.
 
 # Arguments
-- `sequence::LongNucOrView{4}`: A nucleotide sequence represented as a `LongNucOrView{4}` object.
+- `sequence::NucleicSeqOrView{A}`: A nucleotide sequence represented as a `NucleicSeqOrView{A}` object.
 - `n::Int64=1`: The number of steps to consider for the transition probability matrix. Default is 1.
 
 # Returns
@@ -20,7 +20,7 @@ sequence = LongSequence{DNAAlphabet{4}}("ACGTCGTCCACTACGACATCAGC")  # Replace wi
 n = 2
 pf = perronfrobenius(sequence, n)
 """
-function perronfrobenius(sequence::LongNucOrView{4}; n::Int64=1)
+function perronfrobenius(sequence::NucleicSeqOrView{A}; n::Int64=1) where A
     tpm = transition_probability_matrix(sequence, n)
     return copy(tpm')
 end
@@ -66,12 +66,12 @@ pf = perronfrobenius(orfdna)
 newdna = generatedna(pf, 100)
 ```
 """
-function generatedna(pf::Matrix{Float64}, steps::Int64; extended_alphabet::Bool = false)
-    newseq = LongDNA{4}()
-    # pf = transpose(tpm) # The Perron-Frobenius matrix
-    trajectory = MarkovChainHammer.generate(pf, steps)
-    for i in trajectory
-        newseq = append!(newseq, _int_to_dna(i; extended_alphabet))
-    end
-    return newseq
-end
+# function generatedna(pf::Matrix{Float64}, steps::Int64; extended_alphabet::Bool = false)
+#     newseq = LongDNA{4}()
+#     # pf = transpose(tpm) # The Perron-Frobenius matrix
+#     trajectory = generate(pf, steps)
+#     for i in trajectory
+#         newseq = append!(newseq, _int_to_dna(i; extended_alphabet))
+#     end
+#     return newseq
+# end