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// Copyright (c) 2013-2020, SIB - Swiss Institute of Bioinformatics and
// Biozentrum - University of Basel
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <boost/python.hpp>
#include <boost/python/iterator.hpp>
#include <boost/python/suite/indexing/vector_indexing_suite.hpp>
#include <promod3/core/export_helper.hh>
#include <ost/export_helper/pair_to_tuple_conv.hh>
#include <promod3/modelling/loop_candidate.hh>
using namespace promod3;
using namespace boost::python;
using namespace promod3::modelling;
namespace {
loop::BackboneList& lc_getitem(LoopCandidatesPtr lcs, size_t i) {
return (*lcs)[i];
}
boost::python::list ApplyCCDWithAllSamplers(LoopCandidatesPtr p,
const ost::mol::ResidueHandle& n_stem,
const ost::mol::ResidueHandle& c_stem,
boost::python::list l,
int max_iterations, Real rmsd_cutoff,
bool keep_non_converged, int random_seed) {
// do work
loop::TorsionSamplerList v;
core::ConvertListToVector(l, v);
std::vector<uint> indices;
indices = p->ApplyCCD(n_stem, c_stem, v, max_iterations, rmsd_cutoff,
keep_non_converged, random_seed);
// convert and return
boost::python::list return_list;
core::AppendVectorToList(indices, return_list);
return return_list;
}
boost::python::list ApplyCCDWithSampler(LoopCandidatesPtr p,
const ost::mol::ResidueHandle& n_stem,
const ost::mol::ResidueHandle& c_stem,
loop::TorsionSamplerPtr torsion_sampler,
int max_iterations, Real rmsd_cutoff,
bool keep_non_converged, int random_seed) {
// do work
std::vector<uint> indices;
indices = p->ApplyCCD(n_stem, c_stem, torsion_sampler, max_iterations,
rmsd_cutoff, keep_non_converged, random_seed);
// convert and return
boost::python::list return_list;
core::AppendVectorToList(indices, return_list);
return return_list;
}
boost::python::list ApplyCCDWithoutSampler(LoopCandidatesPtr p,
const ost::mol::ResidueHandle& n_stem,
const ost::mol::ResidueHandle& c_stem,
int max_iterations,
Real rmsd_cutoff, bool keep_non_converged) {
// do work
std::vector<uint> indices;
indices = p->ApplyCCD(n_stem, c_stem, max_iterations, rmsd_cutoff,
keep_non_converged);
// convert and return
boost::python::list return_list;
core::AppendVectorToList(indices, return_list);
return return_list;
}
boost::python::list WrapApplyKIC(LoopCandidatesPtr p,
const ost::mol::ResidueHandle& n_stem,
const ost::mol::ResidueHandle& c_stem,
uint pivot_one, uint pivot_two,
uint pivot_three) {
// do work
std::vector<uint> indices;
indices = p->ApplyKIC(n_stem, c_stem, pivot_one, pivot_two, pivot_three);
// convert and return
boost::python::list return_list;
core::AppendVectorToList(indices, return_list);
return return_list;
}
LoopCandidatesPtr FillFromMC(const String& seq,
uint num_loops, uint steps,
MonteCarloSamplerPtr sampler,
MonteCarloCloserPtr closer,
MonteCarloScorerPtr scorer,
MonteCarloCoolerPtr cooler,
int random_seed) {
return LoopCandidates::FillFromMonteCarloSampler(seq,num_loops,steps,
sampler,closer,scorer,cooler,
random_seed);
}
LoopCandidatesPtr FillFromMCInit(const loop::BackboneList& initial_bb,
const String& seq,
uint num_loops, uint steps,
MonteCarloSamplerPtr sampler,
MonteCarloCloserPtr closer,
MonteCarloScorerPtr scorer,
MonteCarloCoolerPtr cooler,
int random_seed) {
return LoopCandidates::FillFromMonteCarloSampler(initial_bb,seq, num_loops,
steps, sampler, closer,
scorer, cooler,
random_seed);
}
boost::python::list WrapGetClusters(LoopCandidatesPtr p, Real max_dist,
bool superposed_rmsd) {
// get them
std::vector< std::vector<uint> > clusters;
p->GetClusters(max_dist, clusters, superposed_rmsd);
// convert and return
boost::python::list return_list;
for (uint i = 0; i < clusters.size(); ++i) {
boost::python::list lc_list;
core::AppendVectorToList(clusters[i], lc_list);
return_list.append(lc_list);
}
return return_list;
}
boost::python::list WrapGetClusteredCandidates(LoopCandidatesPtr p,
Real max_dist,
bool neglect_size_one,
bool superposed_rmsd) {
// get them
LoopCandidatesList clustered_lc
= p->GetClusteredCandidates(max_dist, neglect_size_one, superposed_rmsd);
// convert and return
boost::python::list return_list;
core::AppendVectorToList(clustered_lc, return_list);
return return_list;
}
void WrapCalculateBackboneScoresMH(LoopCandidatesPtr p,
ScoreContainer& score_container,
const ModellingHandle& mhandle,
uint start_resnum, uint chain_idx) {
p->CalculateBackboneScores(score_container, mhandle, start_resnum, chain_idx);
}
void WrapCalculateBackboneScoresMH_K(LoopCandidatesPtr p,
ScoreContainer& score_container,
const ModellingHandle& mhandle,
const boost::python::list& keys,
uint start_resnum, uint chain_idx) {
std::vector<String> v_keys;
core::ConvertListToVector(keys, v_keys);
p->CalculateBackboneScores(score_container, mhandle, v_keys, start_resnum,
chain_idx);
}
void WrapCalculateBackboneScores(LoopCandidatesPtr p,
ScoreContainer& score_container,
scoring::BackboneOverallScorerPtr scorer,
scoring::BackboneScoreEnvPtr scorer_env,
uint start_resnum, uint chain_idx) {
p->CalculateBackboneScores(score_container, scorer, scorer_env, start_resnum,
chain_idx);
}
void WrapCalculateBackboneScoresK(LoopCandidatesPtr p,
ScoreContainer& score_container,
scoring::BackboneOverallScorerPtr scorer,
scoring::BackboneScoreEnvPtr scorer_env,
const boost::python::list& keys,
uint start_resnum, uint chain_idx) {
std::vector<String> v_keys;
core::ConvertListToVector(keys, v_keys);
p->CalculateBackboneScores(score_container, scorer, scorer_env, v_keys,
start_resnum, chain_idx);
}
void WrapCalculateAllAtomScores(LoopCandidatesPtr p,
ScoreContainer& score_container,
const ModellingHandle& mhandle,
uint start_resnum, uint chain_idx) {
p->CalculateAllAtomScores(score_container, mhandle, start_resnum, chain_idx);
}
void WrapCalculateAllAtomScoresK(LoopCandidatesPtr p,
ScoreContainer& score_container,
const ModellingHandle& mhandle,
const boost::python::list& keys,
uint start_resnum, uint chain_idx) {
std::vector<String> v_keys;
core::ConvertListToVector(keys, v_keys);
p->CalculateAllAtomScores(score_container, mhandle, v_keys, start_resnum,
chain_idx);
}
boost::python::list WrapCalcSequenceProfScores(LoopCandidatesPtr p,
loop::StructureDBPtr structure_db,
const ost::seq::ProfileHandle& prof,
uint offset) {
// get scores
std::vector<Real> scores = p->CalculateSequenceProfileScores(structure_db,
prof, offset);
// convert and return
boost::python::list return_list;
core::AppendVectorToList(scores, return_list);
return return_list;
}
void WrapCalcSequenceProfScoresSC(LoopCandidatesPtr p,
ScoreContainer& score_container,
loop::StructureDBPtr structure_db,
const ost::seq::ProfileHandle& prof,
uint offset) {
p->CalculateSequenceProfileScores(score_container, structure_db, prof,
offset);
}
boost::python::list WrapCalcStructureProfScores(LoopCandidatesPtr p,
loop::StructureDBPtr structure_db,
const ost::seq::ProfileHandle& prof,
uint offset) {
// get scores
std::vector<Real> scores = p->CalculateStructureProfileScores(structure_db,
prof, offset);
// convert and return
boost::python::list return_list;
core::AppendVectorToList(scores, return_list);
return return_list;
}
void WrapCalcStructureProfScoresSC(LoopCandidatesPtr p,
ScoreContainer& score_container,
loop::StructureDBPtr structure_db,
const ost::seq::ProfileHandle& prof,
uint offset) {
p->CalculateStructureProfileScores(score_container, structure_db, prof,
offset);
}
boost::python::list WrapCalculateStemRMSDs(LoopCandidatesPtr p,
const ost::mol::ResidueHandle& n_stem,
const ost::mol::ResidueHandle& c_stem) {
// get scores
std::vector<Real> scores = p->CalculateStemRMSDs(n_stem, c_stem);
// convert and return
boost::python::list return_list;
core::AppendVectorToList(scores, return_list);
return return_list;
}
void WrapCalculateStemRMSDsSC(LoopCandidatesPtr p,
ScoreContainer& score_container,
const ost::mol::ResidueHandle& n_stem,
const ost::mol::ResidueHandle& c_stem) {
p->CalculateStemRMSDs(score_container, n_stem, c_stem);
}
LoopCandidatesPtr WrapExtractList(LoopCandidatesPtr p,
const boost::python::list& idx_list) {
std::vector<uint> indices;
core::ConvertListToVector(idx_list, indices);
return p->Extract(indices);
}
} //ns
void export_loop_candidate() {
class_<LoopCandidates, LoopCandidatesPtr>
("LoopCandidates", init<const String&>())
.def("FillFromDatabase", &LoopCandidates::FillFromDatabase,
(arg("n_stem"), arg("c_stem"), arg("seq"), arg("frag_db"),
arg("structure_db"), arg("extended_search")=false))
.staticmethod("FillFromDatabase")
.def("FillFromMonteCarloSampler", &FillFromMCInit,
(arg("initial_bb"), arg("seq"), arg("num_loops"), arg("steps"),
arg("sampler"), arg("closer"), arg("scorer"), arg("cooler"),
arg("random_seed")=0))
.def("FillFromMonteCarloSampler", &FillFromMC,
(arg("seq"), arg("num_loops"), arg("steps"), arg("sampler"),
arg("closer"), arg("scorer"), arg("cooler"), arg("random_seed")=0))
.staticmethod("FillFromMonteCarloSampler")
.def("GetSequence",&LoopCandidates::GetSequence,
return_value_policy<copy_const_reference>())
.def("ApplyCCD", &ApplyCCDWithSampler,
(arg("n_stem"), arg("c_stem"), arg("torsion_sampler"),
arg("max_iterations")=1000, arg("rmsd_cutoff")=0.1,
arg("keep_non_converged")=false, arg("random_seed")=0))
.def("ApplyCCD", &ApplyCCDWithAllSamplers,
(arg("n_stem"), arg("c_stem"), arg("torsion_samplers"),
arg("max_iterations")=1000, arg("rmsd_cutoff")=0.1,
arg("keep_non_converged")=false, arg("random_seed")=0))
.def("ApplyCCD", &ApplyCCDWithoutSampler,
(arg("n_stem"), arg("c_stem"), arg("max_iterations")=1000,
arg("rmsd_cutoff")=0.1, arg("keep_non_converged")=false))
.def("ApplyKIC", &WrapApplyKIC,
(arg("n_stem"), arg("c_stem"), arg("pivot_one"), arg("pivot_two"),
arg("pivot_three")))
.def("CalculateBackboneScores", &WrapCalculateBackboneScoresMH,
(arg("score_container"), arg("mhandle"),
arg("start_resnum"), arg("chain_idx")=0))
.def("CalculateBackboneScores", &WrapCalculateBackboneScoresMH_K,
(arg("score_container"), arg("mhandle"), arg("keys"),
arg("start_resnum"), arg("chain_idx")=0))
.def("CalculateBackboneScores", &WrapCalculateBackboneScores,
(arg("score_container"), arg("scorer"), arg("scorer_env"),
arg("start_resnum"), arg("chain_idx")=0))
.def("CalculateBackboneScores", &WrapCalculateBackboneScoresK,
(arg("score_container"), arg("scorer"), arg("scorer_env"),
arg("keys"), arg("start_resnum"), arg("chain_idx")=0))
.def("CalculateAllAtomScores", &WrapCalculateAllAtomScores,
(arg("score_container"), arg("mhandle"),
arg("start_resnum"), arg("chain_idx")=0))
.def("CalculateAllAtomScores", &WrapCalculateAllAtomScoresK,
(arg("score_container"), arg("mhandle"), arg("keys"),
arg("start_resnum"), arg("chain_idx")=0))
.def("CalculateSequenceProfileScores", &WrapCalcSequenceProfScores,
(arg("structure_db"), arg("prof"), arg("offset")=0))
.def("CalculateSequenceProfileScores", &WrapCalcSequenceProfScoresSC,
(arg("score_container"), arg("structure_db"), arg("prof"),
arg("offset")=0))
.def("CalculateStructureProfileScores", &WrapCalcStructureProfScores,
(arg("structure_db"), arg("prof"), arg("offset")=0))
.def("CalculateStructureProfileScores", &WrapCalcStructureProfScoresSC,
(arg("score_container"), arg("structure_db"), arg("prof"),
arg("offset")=0))
.def("CalculateStemRMSDs", &WrapCalculateStemRMSDs,
(arg("n_stem"), arg("c_stem")))
.def("CalculateStemRMSDs", &WrapCalculateStemRMSDsSC,
(arg("score_container"), arg("n_stem"), arg("c_stem")))
.def("__iter__", iterator<LoopCandidates>())
.def("__len__", &LoopCandidates::size)
.def("__getitem__", lc_getitem,
return_value_policy<reference_existing_object>())
.def("Add", &LoopCandidates::Add, (arg("bb_list")))
.def("AddFragmentInfo", &LoopCandidates::AddFragmentInfo, (arg("fragment")))
.def("Remove", &LoopCandidates::Remove, (arg("index")))
.def("HasFragmentInfos", &LoopCandidates::HasFragmentInfos)
.def("GetFragmentInfo", &LoopCandidates::GetFragmentInfo, (arg("index")),
return_value_policy<copy_const_reference>())
.def("Extract", &WrapExtractList, (arg("indices")))
.def("GetClusters", &WrapGetClusters, (arg("max_dist"),
arg("superposed_rmsd")=false))
.def("GetClusteredCandidates", &WrapGetClusteredCandidates,
(arg("max_dist"), arg("neglect_size_one")=true,
arg("superposed_rmsd")=false))
.def("GetLargestCluster", &LoopCandidates::GetLargestCluster,
(arg("max_dist"), arg("superposed_rmsd")=false))
;
}
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