Docker-in-Docker (DinD) capabilities of public runners deactivated. More info

Commit 30a50eba authored by Philippe Veber's avatar Philippe Veber
Browse files

récupération des noeuds de chaque modèle pour bppseqgen

parent 25fa66f4
......@@ -4,19 +4,21 @@ open Core
open File_formats
let env = docker_image ~account:"carinerey" ~name:"bppsuite:06182018" ()
let env = docker_image ~account:"carinerey" ~name:"bppsuite" ~tag:"06182018" ()
let assign k v =
seq ~sep:"=" [ string k ; v ]
let conf_file_bppseqgen ~tree ~nb_sites ~config =
seq ~sep:"\n" [
assign "input.tree.file" (dep tree) ;
assign "output.sequence.file" dest ;
assign "number_of_sites" (int nb_sites) ;
string config ;
]
seq ~sep:"\n" (
[
assign "input.tree.file" (dep tree) ;
assign "output.sequence.file" dest ;
assign "number_of_sites" (int nb_sites) ;
]
@ config
)
let bppseqgen ~nb_sites ~tree ~config : nucleotide_fasta workflow =
workflow ~descr:"bppsuite.bppseqgen" [
......
......@@ -5,7 +5,7 @@ open File_formats
val bppseqgen :
nb_sites:int ->
tree:nhx workflow ->
config: string ->
config:Bistro.Template.t list ->
nucleotide_fasta workflow
val fna2faa :
......
......@@ -10,30 +10,31 @@ let string_of_model m = match m with
| H0 -> "H0"
| Ha -> "Ha"
let bpp_config = function
| H0 -> {|\
let bpp_config_base = {|
alphabet=Codon(letter=DNA)
genetic_code = Standard
input.tree.format=Nhx
output.internal.sequences=no
nonhomogeneous = general
nonhomogeneous.number_of_models = 1
model1=Codon_AAFit(model=K80, fitness=FromModel(model=LGL08_CAT_C1(nbCat=10)))
model1.nodes_id=0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17
nonhomogeneous.root_freq=Fixed()
rate_distribution=Constant()
|};
| Ha -> {|\
alphabet=Codon(letter=DNA)
genetic_code = Standard
input.tree.format=Nhx
output.internal.sequences=no
nonhomogeneous = general
nonhomogeneous.number_of_models = 2
|}
let bpp_config_H0 = {|nonhomogeneous.number_of_models = 1
model1=Codon_AAFit(model=K80, fitness=FromModel(model=LGL08_CAT_C1(nbCat=10)))
|}
let bpp_config_Ha = {|nonhomogeneous.number_of_models = 2
model1=Codon_AAFit(model=K80, fitness=FromModel(model=LGL08_CAT_C1(nbCat=10)))
model1.nodes_id=15,1,13,14,4
model2=Codon_AAFit(model=K80, fitness=FromModel(model=LGL08_CAT_C7(nbCat=10)))
model2.nodes_id=0,2,3,5,6,7,8,9,10,11,12,16,17
nonhomogeneous.root_freq=Fixed()
rate_distribution=Constant()
|};
|}
let bpp_config nodes hyp = [
string bpp_config_base ;
insert nodes ;
string (
match hyp with
| H0 -> bpp_config_H0
| Ha -> bpp_config_Ha
) ;
]
......@@ -33,7 +33,8 @@ let repo_of_raw_dataset (raw_dataset:raw_dataset) =
let derive_from_model ~model ~tree ~tree_dataset ~preview =
let nb_sites = if preview then 10 else 100 in
let config = Convergence_hypothesis.bpp_config model in
let nodes = Tree_dataset.nodes tree_dataset model in
let config = Convergence_hypothesis.bpp_config nodes model in
let tree = Tree_dataset.tree tree_dataset `Simulation in
let fna = Bppsuite.bppseqgen ~nb_sites ~tree ~config in
let faa = Bppsuite.fna2faa ~fna in
......
......@@ -140,7 +140,7 @@ t.write(format=1, features=["ND"], outfile = "%s/tree.only_node_ids.nhx" %(OutDi
#### -> 1 line: all nodes ids
all_node_ids = range(nodeId-1)
with open("%s/tree.H0.node_ids" %(OutDirName), "w") as output_H0_node_ids:
output_H0_node_ids.write(",".join(map(str, all_node_ids)))
output_H0_node_ids.write("model1.nodes_id="+",".join(map(str, all_node_ids)))
### tree.Ha.node_ids: alternative hypothesis
#### -> 2 lines: 1) node ids under the ancestral model
......@@ -149,9 +149,9 @@ with open("%s/tree.H0.node_ids" %(OutDirName), "w") as output_H0_node_ids:
with open("%s/tree.Ha.node_ids" %(OutDirName), "w") as output_Ha_node_ids:
conv_node_ids = [n.ND for n in t.search_nodes(Condition = "1")]
not_conv_node_ids = [i for i in all_node_ids if i not in conv_node_ids]
output_Ha_node_ids.write(",".join(map(str, conv_node_ids)))
output_Ha_node_ids.write("model2.nodes_id="+",".join(map(str, conv_node_ids)))
output_Ha_node_ids.write("\n")
output_Ha_node_ids.write(",".join(map(str, not_conv_node_ids)))
output_Ha_node_ids.write("model1.nodes_id="+",".join(map(str, not_conv_node_ids)))
######### output trees for detection
......
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