ResultCode CreateExtSaveData(MediaType media_type, u32 high, u32 low, VAddr icon_buffer,
u32 icon_size, const FileSys::ArchiveFormatInfo& format_info) {
// Construct the binary path to the archive first
FileSys::Path path =
FileSys::ConstructExtDataBinaryPath(static_cast<u32>(media_type), high, low);
auto archive = id_code_map.find(media_type == MediaType::NAND ? ArchiveIdCode::SharedExtSaveData
: ArchiveIdCode::ExtSaveData);
if (archive == id_code_map.end()) {
return UnimplementedFunction(ErrorModule::FS); // TODO(Subv): Find the right error
}
auto ext_savedata = static_cast<FileSys::ArchiveFactory_ExtSaveData*>(archive->second.get());
ResultCode result = ext_savedata->Format(path, format_info);
if (result.IsError())
return result;
if (!Memory::IsValidVirtualAddress(icon_buffer))
return ResultCode(-1); // TODO(Subv): Find the right error code
std::vector<u8> smdh_icon(icon_size);
Memory::ReadBlock(icon_buffer, smdh_icon.data(), smdh_icon.size());
ext_savedata->WriteIcon(path, smdh_icon.data(), smdh_icon.size());
return RESULT_SUCCESS;
}
inline void Restaurant::FillInPredictivesNonGraphical(
const std::vector<double>& parent_predictives,
int type,
int depth,
const HPYParameter& hpy_parameter,
std::vector<double>& predictives) const {
for (size_t i = 0; i < parent_predictives.size(); ++i) {
predictives[i] = parent_predictives[i];
}
for (auto floor_it = floor2c_t_.begin(); floor_it != floor2c_t_.end(); ++floor_it) {
auto floor_id = (*floor_it).first;
auto& c_t = (*floor_it).second;
tmp_c_[floor_id] = c_t.first;
predictives[floor_id] *=
(hpy_parameter.concentration(depth, floor_id)
+ hpy_parameter.discount(depth, floor_id) * c_t.second)
/ (c_t.first + hpy_parameter.concentration(depth, floor_id));
}
auto type_it = type2internal_.find(type);
if (type_it == type2internal_.end()) return;
auto& sections = (*type_it).second.sections_;
auto section_it = sections.begin();
for (; section_it != sections.end(); ++section_it) {
auto floor_id = (*section_it).first;
auto& section = (*section_it).second;
auto cw = section.customers;
auto tw = section.tables;
predictives[floor_id] +=
(cw - hpy_parameter.discount(depth, floor_id) * tw)
/ (tmp_c_[floor_id] + hpy_parameter.concentration(depth, floor_id));
}
}
/*
* Retrieve a material by tag
*/
boost::optional<std::size_t> get_material_by_tag(const std::string &tag) noexcept {
boost::optional<std::size_t> result;
auto finder = material_defs_idx.find(tag);
if (finder != material_defs_idx.end()) {
result = finder->second;
}
return result;
}
ResultVal<FileSys::ArchiveFormatInfo> GetArchiveFormatInfo(ArchiveIdCode id_code,
FileSys::Path& archive_path) {
auto archive = id_code_map.find(id_code);
if (archive == id_code_map.end()) {
return UnimplementedFunction(ErrorModule::FS); // TODO(Subv): Find the right error
}
return archive->second->GetFormatInfo(archive_path);
}
ResultCode FormatArchive(ArchiveIdCode id_code, const FileSys::ArchiveFormatInfo& format_info,
const FileSys::Path& path) {
auto archive_itr = id_code_map.find(id_code);
if (archive_itr == id_code_map.end()) {
return UnimplementedFunction(ErrorModule::FS); // TODO(Subv): Find the right error
}
return archive_itr->second->Format(path, format_info);
}
ResultVal<std::unique_ptr<FileSys::FileSystemBackend>> OpenFileSystem(Type type,
FileSys::Path& path) {
LOG_TRACE(Service_FS, "Opening FileSystem with type=%d", type);
auto itr = filesystem_map.find(type);
if (itr == filesystem_map.end()) {
// TODO(bunnei): Find a better error code for this
return ResultCode(-1);
}
return itr->second->Open(path);
}
double Restaurant::predictive_probability(topic_t floor_id,
int type,
double parent_probability,
double discount,
double concentration) const {
int cw = 0;
int tw = 0;
int c = 0;
int t = 0;
auto section_it = type2internal_.find(type);
if (section_it != type2internal_.end()) {
cw = (*section_it).second.customers(floor_id);
tw = (*section_it).second.tables(floor_id);
}
auto floor_it = floor2c_t_.find(floor_id);
if (floor_it != floor2c_t_.end()) {
c = (*floor_it).second.first;
t = (*floor_it).second.second;
}
return ComputePYPPredictive(cw, tw, c, t, parent_probability, discount, concentration);
}
std::pair<double,double> FermiTable::calcAverageAtomicProperties
(const boost::container::flat_map<string,double>& tracers) const
{
double total = 0;
double aa = 0;
double zz = 0;
for(std::map<string,std::pair<double,double> >::const_iterator it=
atomic_properties_.begin();
it!=atomic_properties_.end();++it){
assert(tracers.count(it->first)==1);
const double mass_frac = tracers.find(it->first)->second;
const double A = it->second.first;
const double Z = it->second.second;
total += mass_frac;
aa += mass_frac/A;
zz += mass_frac*Z/A;
}
return std::pair<double,double>(total/aa,zz/aa);
}
ResultVal<ArchiveHandle> OpenArchive(ArchiveIdCode id_code, FileSys::Path& archive_path) {
LOG_TRACE(Service_FS, "Opening archive with id code 0x%08X", id_code);
auto itr = id_code_map.find(id_code);
if (itr == id_code_map.end()) {
// TODO: Verify error against hardware
return ResultCode(ErrorDescription::NotFound, ErrorModule::FS, ErrorSummary::NotFound,
ErrorLevel::Permanent);
}
CASCADE_RESULT(std::unique_ptr<ArchiveBackend> res, itr->second->Open(archive_path));
// This should never even happen in the first place with 64-bit handles,
while (handle_map.count(next_handle) != 0) {
++next_handle;
}
handle_map.emplace(next_handle, std::move(res));
return MakeResult<ArchiveHandle>(next_handle++);
}
bool is_better_armor(const std::string &item_tag, boost::container::flat_map<item_location_t, float> &ac_by_loc) {
auto finder = get_clothing_by_tag(item_tag);
if (!finder) return false;
const float item_ac = finder->armor_class;
item_location_t loc = INVENTORY;
if (finder->slot == "head") loc = HEAD;
if (finder->slot == "torso") loc = TORSO;
if (finder->slot == "legs") loc = LEGS;
if (finder->slot == "shoes") loc = FEET;
auto tester = ac_by_loc.find(loc);
if (tester == ac_by_loc.end()) {
return true;
} else {
if (item_ac > tester->second) return true;
}
return false;
}
void workflow_system::update(const double duration_ms) {
if (dirty) {
automatic_reactions.clear();
// Enumerate buildings and see which ones have reactions.
each<position_t, building_t>([this] (entity_t &e, position_t &pos, building_t &b) {
if (b.complete) {
auto finder = reaction_building_defs.find(b.tag);
if (finder != reaction_building_defs.end()) {
for (const std::string &reaction_name : finder->second) {
auto reactor = reaction_defs.find(reaction_name);
if (reactor != reaction_defs.end()) {
// Automatic reactions are added to the auto reactor list
if (reactor->second.automatic) {
auto automatic_finder = automatic_reactions.find(e.id);
if (automatic_finder == automatic_reactions.end()) {
automatic_reactions[e.id] = std::vector<std::string>{ reaction_name };
} else {
automatic_finder->second.push_back(reaction_name);
}
}
}
}
}
}
});
// Erase all completed jobs
designations->build_orders.erase(
std::remove_if(designations->build_orders.begin(),
designations->build_orders.end(),
[] (auto order_pair) { return order_pair.first == 0; }),
designations->build_orders.end());
// Not dirty anymore!
dirty = false;
}
}
/*
* Linter for materials, used in raw loader.
*/
void sanity_check_materials() noexcept
{
for (const auto &mat : material_defs) {
if (mat.tag.empty()) std::cout << "WARNING: Empty material tag\n";
if (mat.name.empty()) std::cout << "WARNING: Empty material name, tag: " << mat.tag << "\n";
/*if (!mat.mines_to_tag.empty()) {
auto finder = item_defs.find(mat.mines_to_tag);
if (finder == item_defs.end()) std::cout << "WARNING: Unknown mining result " << mat.mines_to_tag << ", tag: " << mat.tag << "\n";
}
if (!mat.mines_to_tag_second.empty()) {
auto finder = item_defs.find(mat.mines_to_tag_second);
if (finder == item_defs.end()) std::cout << "WARNING: Unknown mining result " << mat.mines_to_tag_second << ", tag: " << mat.tag << "\n";
}*/
if (!mat.ore_materials.empty()) {
for (const std::string &metal : mat.ore_materials) {
auto finder = material_defs_idx.find(metal);
if (finder == material_defs_idx.end()) {
std::cout << "WARNING: Substance " << mat.tag << " produces a non-existent ore: " << metal << "\n";
}
}
}
}
}
void Restaurant::FillInPredictives(const std::vector<double>& parent_predictives,
int type,
const LambdaManagerInterface& lmanager,
int depth,
const HPYParameter& hpy_parameter,
std::vector<double>& predictives) const {
auto floor_it = floor2c_t_.begin();
auto type_it = type2internal_.find(type);
predictives[0] = parent_predictives[0];
if (floor2c_t_.empty()) {
for (size_t i = 1; i < predictives.size(); ++i) {
double lambda = lmanager.lambda(i, depth);
predictives[i] = lambda * parent_predictives[i] + (1 - lambda) * predictives[0];
}
return;
}
if ((*floor_it).first == 0) {
auto& c_t = (*floor_it).second;
predictives[0] *= (hpy_parameter.concentration(depth, 0) + hpy_parameter.discount(depth, 0) * c_t.second) / (c_t.first + hpy_parameter.concentration(depth, 0));
if (type_it != type2internal_.end()) {
auto& sections = (*type_it).second.sections_;
if (!sections.empty()) {
auto section_begin = sections.begin();
if ((*section_begin).first == 0) {
int cw = (*section_begin).second.customers;
int tw = (*section_begin).second.tables;
predictives[0] += (cw - hpy_parameter.discount(depth, 0) * tw) / (c_t.first + hpy_parameter.concentration(depth, 0));
}
}
}
}
for (size_t i = 1; i < predictives.size(); ++i) {
double lambda = lmanager.lambda(i, depth);
predictives[i] = lambda * parent_predictives[i] + (1 - lambda) * predictives[0];
}
if ((*floor_it).first == 0) {
++floor_it;
}
for (; floor_it != floor2c_t_.end(); ++floor_it) {
auto floor_id = (*floor_it).first;
auto& c_t = (*floor_it).second;
tmp_c_[floor_id] = c_t.first;
predictives[floor_id] *=
(hpy_parameter.concentration(depth, floor_id)
+ hpy_parameter.discount(depth, floor_id) * c_t.second)
/ (c_t.first + hpy_parameter.concentration(depth, floor_id));
}
if (type_it == type2internal_.end()) return;
auto& sections = (*type_it).second.sections_;
auto section_it = sections.begin();
if (section_it != sections.end() && (*section_it).first == 0) {
++section_it;
}
for (; section_it != sections.end(); ++section_it) {
auto floor_id = (*section_it).first;
auto& section = (*section_it).second;
int cw = section.customers;
int tw = section.tables;
predictives[floor_id] +=
(cw - hpy_parameter.discount(depth, floor_id) * tw)
/ (tmp_c_[floor_id] + hpy_parameter.concentration(depth, floor_id));
}
}