Dose_algo.Depsolver
Dependency solver. Implementation of the Edos algorithms
val load : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list -> Cudf.universe -> solver
initialize the solver.
val is_consistent : Cudf.universe -> Diagnostic.diagnosis
check if the universe universe is consistent (all installed packages are coinstallable) This function is a wrapper of Cudf_checker.is_consistent.
val edos_install : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list ->
Cudf.universe -> Cudf.package -> Diagnostic.diagnosis
check if the given package can be installed in the universe
Packages marked as `Keep_package must be always installed.
val edos_coinstall : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list ->
Cudf.universe -> Cudf.package list -> Diagnostic.diagnosis
check if the give package list can be installed in the universe
val edos_coinstall_prod : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list
-> Cudf.universe -> Cudf.package list list -> Diagnostic.diagnosis list
accept a list of list of packages and return the coinstallability test of the cartesian product.
val trim : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list ->
Cudf.universe -> Cudf.universe
remove uninstallable packages from the universe .
val trimlist : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list ->
Cudf.universe -> Cudf.package list -> Cudf.package list
remove uninstallable packages from the pkglist.
val find_broken : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list ->
Cudf.universe -> Cudf.package list
return the list of broken packages.
val find_installable : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list ->
Cudf.universe -> Cudf.package list
return the list of installable packages.
val find_listbroken : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list ->
Cudf.universe -> Cudf.package list -> Cudf.package list
return the list of broken packages.
val find_listinstallable : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list
-> Cudf.universe -> Cudf.package list -> Cudf.package list
return the list of installable packages.
val univcheck : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list ->
?callback:(Diagnostic.diagnosis -> unit) -> ?explain:bool -> Cudf.universe -> int
univcheck
check if all packages in the universe can be installed. Since not all packages are directly tested for installation, if a packages is installable, the installation might be empty. To obtain an installation set for each installable packages, the correct procedure is to iter on the list of packages.
val univcheck_lowmem : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list ->
?callback:(Diagnostic.diagnosis -> unit) -> ?explain:bool -> Cudf.universe -> int
val listcheck : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list ->
?callback:(Diagnostic.diagnosis -> unit) -> ?explain:bool -> Cudf.universe -> Cudf.package list -> int
listcheck ~callback:c subuniverse l
check if all packages in l
can be installed.
Invariant : l is a subset of universe can be installed in the solver universe.
It is responsability of the user to pass listcheck an appropriate subuniverse`
val dependency_closure : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list
-> ?maxdepth:int -> ?conjunctive:bool -> Cudf.universe -> Cudf.package list -> Cudf.package list
dependency_closure index l
return the union of the dependency closure of all packages in l
.
reverse_dependencies univ
compute the reverse dependency list of all packages in the universe univ
val reverse_dependency_closure : ?maxdepth:int ->
Cudf.universe -> Cudf.package list -> Cudf.package list
reverse_dependencies_closure univ
compute the reverse dependency list of all packages in l
in the universe univ
val output_clauses : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list ->
?enc:enc -> Cudf.universe -> string
output_clauses enc univ
return a string encoded accordingly to enc
(default cnf).
type depclean_result = Cudf.package * (Cudf_types.vpkglist * Cudf_types.vpkg * Cudf.package list) list * (Cudf_types.vpkg * Cudf.package list) list
The result of the depclean function is a tuple containing a package, a list of dependencies that are redundant and a list of conflicts that are redundant
val depclean : ?global_constraints:(Cudf_types.vpkglist * Cudf.package list) list ->
?callback:(depclean_result -> unit) -> Cudf.universe -> Cudf.package list -> depclean_result list
For each package p
in packagelist
, depclean univ packagelist
detect redundundant dependencies that refer to packages that are either missing or not co-installable together with the root package p
type solver_result =
| Sat of Cudf.preamble option * Cudf.universe |
| Unsat of Diagnostic.diagnosis option |
| Error of string |
val check_request : ?cmd:string -> ?criteria:string -> ?dummy:Cudf.package ->
?explain:bool -> Cudf.cudf -> solver_result
check_request
check if there exists a solution for the give cudf document if ?dummy is specified, adds this dummy package to the user request. This parameter is used to encode a list of 'essential' packages that must always be installed in the solution alongside with the user request. if ?cmd is specified, it will be used to call an external cudf solver to satisfy the request. if ?criteria is specified it will be used as optimization criteria. if ?explain is specified and there is no solution for the give request, the result will contain the failure reason.
val check_request_using : ?call_solver:(Cudf.cudf -> Cudf.preamble option * Cudf.universe) ->
?dummy:Cudf.package -> ?explain:bool -> Cudf.cudf -> solver_result
Same as check_request
, but allows to specify any function to call the external solver. It should raise Depsolver.Unsat
on failure.
val installation_graph : solution:Cudf.universe ->
(Dose_common.CudfAdd.Cudf_set.t * Dose_common.CudfAdd.Cudf_set.t) -> Defaultgraphs.ActionGraph.G.t
Build the installation graph from a cudf solution universe and sets of packages to be installed/removed (see CudfAdd.make_summary)