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kilianvolmer merged 16 commits into from
May 11, 2026
Merged

1462 Add ScalarType to Code examples in the documentation #1546

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421d728
switch to ScalarType in example docs
charlie0614 Apr 20, 2026
c90a58d
update code examples in docu for lct and ide models
annawendler Apr 22, 2026
86c54fb
Merge branch 'main' into 2026_04_doc
kilianvolmer Apr 23, 2026
5785fdf
review suggestions for ide and lct
annawendler May 7, 2026
5152663
update docu for installing python packages
annawendler May 7, 2026
3b9e3d8
review suggestions
annawendler May 11, 2026
48536b3
Merge branch 'main' into 2026_04_doc
annawendler May 11, 2026
38b194c
CHG: Change double to Scalartype
kilianvolmer May 11, 2026
de8d896
Update docs/source/python/python_packages.rst
annawendler May 11, 2026
4928657
CHG: Use FP in backend
kilianvolmer May 11, 2026
bb5380f
CHG: Use ScalarType instead of double in feedback examples
kilianvolmer May 11, 2026
42dac68
CHG: ScalarType to double
kilianvolmer May 11, 2026
5098e3b
Replace double by ScalarType
jubicker May 11, 2026
0e5e36f
Merge branch '2026_04_doc' of github.com:SciCompMod/memilio into 2026…
kilianvolmer May 11, 2026
a422e5c
Merge branch 'main' into 2026_04_doc
annawendler May 11, 2026
30f044e
update docu regarding memilio-simulation wheels
annawendler May 11, 2026
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13 changes: 7 additions & 6 deletions cpp/examples/d_abm.cpp
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Expand Up @@ -21,6 +21,7 @@
#include "d_abm/quad_well.h"
#include "d_abm/simulation.h"
#include "d_abm/parameters.h"
#include "memilio/config.h"
#include "memilio/utils/random_number_generator.h"
#include "memilio/data/analyze_result.h"
#include "memilio/epidemiology/adoption_rate.h"
Expand All @@ -44,10 +45,10 @@ int main()
using Model = mio::dabm::Model<QuadWell<InfectionState>>;
std::vector<Model::Agent> agents(1000);
//Random variables for initialization of agents' position and infection state
auto& pos_rng = mio::UniformDistribution<double>::get_instance();
auto& pos_rng = mio::UniformDistribution<ScalarType>::get_instance();
auto& sta_rng = mio::DiscreteDistribution<size_t>::get_instance();
//Infection state distribution
std::vector<double> pop_dist{0.98, 0.01, 0.005, 0.005, 0., 0.};
std::vector<ScalarType> pop_dist{0.98, 0.01, 0.005, 0.005, 0., 0.};
for (auto& a : agents) {
//Agents are equally distributed in [-2,2]x[-2,2] at the beginning
a.position =
Expand All @@ -71,11 +72,11 @@ int main()
}

//Set interaction radius and noise term of the diffusion process
double interaction_radius = 0.5;
double noise = 0.4;
ScalarType interaction_radius = 0.5;
ScalarType noise = 0.4;

double dt = 0.1;
double tmax = 30.;
ScalarType dt = 0.1;
ScalarType tmax = 30.;

Model model(agents, adoption_rates, interaction_radius, noise, {InfectionState::D});
auto sim = mio::dabm::Simulation(model, 0.0, dt);
Expand Down
65 changes: 34 additions & 31 deletions cpp/examples/ode_secir_feedback.cpp
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Expand Up @@ -21,32 +21,33 @@
#include "memilio/compartments/feedback_simulation.h"
#include "memilio/utils/logging.h"

void initialize_model(mio::osecir::Model<double>& model, int total_population, double cont_freq)
void initialize_model(mio::osecir::Model<ScalarType>& model, int total_population, ScalarType cont_freq)
{
model.parameters.set<mio::osecir::StartDay<double>>(60);
model.parameters.set<mio::osecir::Seasonality<double>>(0.2);
model.parameters.set<mio::osecir::StartDay<ScalarType>>(60);
model.parameters.set<mio::osecir::Seasonality<ScalarType>>(0.2);

// time-related parameters
model.parameters.get<mio::osecir::TimeExposed<double>>() = 3.2;
model.parameters.get<mio::osecir::TimeInfectedNoSymptoms<double>>() = 2.0;
model.parameters.get<mio::osecir::TimeInfectedSymptoms<double>>() = 5.8;
model.parameters.get<mio::osecir::TimeInfectedSevere<double>>() = 9.5;
model.parameters.get<mio::osecir::TimeInfectedCritical<double>>() = 7.1;
model.parameters.get<mio::osecir::TimeExposed<ScalarType>>() = 3.2;
model.parameters.get<mio::osecir::TimeInfectedNoSymptoms<ScalarType>>() = 2.0;
model.parameters.get<mio::osecir::TimeInfectedSymptoms<ScalarType>>() = 5.8;
model.parameters.get<mio::osecir::TimeInfectedSevere<ScalarType>>() = 9.5;
model.parameters.get<mio::osecir::TimeInfectedCritical<ScalarType>>() = 7.1;

// Set transmission and isolation parameters
model.parameters.get<mio::osecir::TransmissionProbabilityOnContact<double>>() = 0.05;
model.parameters.get<mio::osecir::RelativeTransmissionNoSymptoms<double>>() = 0.7;
model.parameters.get<mio::osecir::RecoveredPerInfectedNoSymptoms<double>>() = 0.09;
model.parameters.get<mio::osecir::RiskOfInfectionFromSymptomatic<double>>() = 0.25;
model.parameters.get<mio::osecir::MaxRiskOfInfectionFromSymptomatic<double>>() = 0.45;
model.parameters.get<mio::osecir::TestAndTraceCapacity<double>>() = 35;
model.parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>() = 0.2;
model.parameters.get<mio::osecir::CriticalPerSevere<double>>() = 0.25;
model.parameters.get<mio::osecir::DeathsPerCritical<double>>() = 0.3;
model.parameters.get<mio::osecir::TransmissionProbabilityOnContact<ScalarType>>() = 0.05;
model.parameters.get<mio::osecir::RelativeTransmissionNoSymptoms<ScalarType>>() = 0.7;
model.parameters.get<mio::osecir::RecoveredPerInfectedNoSymptoms<ScalarType>>() = 0.09;
model.parameters.get<mio::osecir::RiskOfInfectionFromSymptomatic<ScalarType>>() = 0.25;
model.parameters.get<mio::osecir::MaxRiskOfInfectionFromSymptomatic<ScalarType>>() = 0.45;
model.parameters.get<mio::osecir::TestAndTraceCapacity<ScalarType>>() = 35;
model.parameters.get<mio::osecir::SeverePerInfectedSymptoms<ScalarType>>() = 0.2;
model.parameters.get<mio::osecir::CriticalPerSevere<ScalarType>>() = 0.25;
model.parameters.get<mio::osecir::DeathsPerCritical<ScalarType>>() = 0.3;

// contact matrix
mio::ContactMatrixGroup<double>& contact_matrix = model.parameters.get<mio::osecir::ContactPatterns<double>>();
contact_matrix[0] = mio::ContactMatrix<double>(Eigen::MatrixXd::Constant(1, 1, cont_freq));
mio::ContactMatrixGroup<ScalarType>& contact_matrix =
model.parameters.get<mio::osecir::ContactPatterns<ScalarType>>();
contact_matrix[0] = mio::ContactMatrix<ScalarType>(Eigen::MatrixXd::Constant(1, 1, cont_freq));

// initial population
model.populations[{mio::AgeGroup(0), mio::osecir::InfectionState::Exposed}] = 40;
Expand All @@ -65,23 +66,24 @@ void initialize_model(mio::osecir::Model<double>& model, int total_population, d
model.apply_constraints();
}

void initialize_feedback(mio::FeedbackSimulation<double, mio::Simulation<double, mio::osecir::Model<double>>,
mio::osecir::ContactPatterns<double>>& feedback_simulation)
void initialize_feedback(
mio::FeedbackSimulation<ScalarType, mio::Simulation<ScalarType, mio::osecir::Model<ScalarType>>,
mio::osecir::ContactPatterns<ScalarType>>& feedback_simulation)
{
// nominal ICU capacity
feedback_simulation.get_parameters().template get<mio::NominalICUCapacity<double>>() = 10;
feedback_simulation.get_parameters().template get<mio::NominalICUCapacity<ScalarType>>() = 10;

// ICU occupancy in the past for memory kernel
auto& icu_occupancy = feedback_simulation.get_parameters().template get<mio::ICUOccupancyHistory<double>>();
auto& icu_occupancy = feedback_simulation.get_parameters().template get<mio::ICUOccupancyHistory<ScalarType>>();
Eigen::VectorXd icu_day = Eigen::VectorXd::Constant(1, 1);
const auto cutoff = static_cast<int>(feedback_simulation.get_parameters().template get<mio::GammaCutOff>());
for (int t = -cutoff; t <= 0; ++t) {
icu_occupancy.add_time_point(t, icu_day);
}

// bounds for contact reduction measures
feedback_simulation.get_parameters().template get<mio::ContactReductionMin<double>>() = {0.1};
feedback_simulation.get_parameters().template get<mio::ContactReductionMax<double>>() = {0.8};
feedback_simulation.get_parameters().template get<mio::ContactReductionMin<ScalarType>>() = {0.1};
feedback_simulation.get_parameters().template get<mio::ContactReductionMax<ScalarType>>() = {0.8};
}

int main()
Expand All @@ -92,23 +94,24 @@ int main()
// risk which is calculated from the ICU occupancy using a memory kernel.
mio::set_log_level(mio::LogLevel::warn);

const double tmax = 35;
const ScalarType tmax = 35;
const int total_population = 1000;
const double cont_freq = 10;
const ScalarType cont_freq = 10;

// create and initialize ODE model for a single age group
mio::osecir::Model<double> model(1);
mio::osecir::Model<ScalarType> model(1);
initialize_model(model, total_population, cont_freq);

// determine the index for the ICU state (InfectedCritical) for feedback mechanism
auto icu_index = std::vector<size_t>{
model.populations.get_flat_index({mio::AgeGroup(0), mio::osecir::InfectionState::InfectedCritical})};

// create simulation objects: first a secir simulation, then a feedback simulation
auto simulation = mio::osecir::Simulation<double, mio::Simulation<double, mio::osecir::Model<double>>>(model);
auto simulation =
mio::osecir::Simulation<ScalarType, mio::Simulation<ScalarType, mio::osecir::Model<ScalarType>>>(model);
auto feedback_simulation =
mio::FeedbackSimulation<double, mio::Simulation<double, mio::osecir::Model<double>>,
mio::osecir::ContactPatterns<double>>(std::move(simulation), icu_index);
mio::FeedbackSimulation<ScalarType, mio::Simulation<ScalarType, mio::osecir::Model<ScalarType>>,
mio::osecir::ContactPatterns<ScalarType>>(std::move(simulation), icu_index);

// set up the parameters for the feedback simulation
initialize_feedback(feedback_simulation);
Expand Down
69 changes: 35 additions & 34 deletions cpp/examples/ode_secir_feedback_graph.cpp
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Expand Up @@ -26,71 +26,72 @@
#include <iostream>

// alias for the type of the simulation with feedback
using FeedbackSim = mio::FeedbackSimulation<double, mio::Simulation<double, mio::osecir::Model<double>>,
mio::osecir::ContactPatterns<double>>;
using FeedbackSim = mio::FeedbackSimulation<ScalarType, mio::Simulation<ScalarType, mio::osecir::Model<ScalarType>>,
mio::osecir::ContactPatterns<ScalarType>>;

// helper function to initialize the model with population and parameters
void initialize_model(mio::osecir::Model<double>& model, double cont_freq)
void initialize_model(mio::osecir::Model<ScalarType>& model, ScalarType cont_freq)
{
model.parameters.set<mio::osecir::StartDay<double>>(60);
model.parameters.set<mio::osecir::Seasonality<double>>(0.2);
model.parameters.set<mio::osecir::StartDay<ScalarType>>(60);
model.parameters.set<mio::osecir::Seasonality<ScalarType>>(0.2);

// Mean stay times per compartment
model.parameters.get<mio::osecir::TimeExposed<double>>() = 3.2;
model.parameters.get<mio::osecir::TimeInfectedNoSymptoms<double>>() = 2.0;
model.parameters.get<mio::osecir::TimeInfectedSymptoms<double>>() = 5.8;
model.parameters.get<mio::osecir::TimeInfectedSevere<double>>() = 9.5;
model.parameters.get<mio::osecir::TimeInfectedCritical<double>>() = 7.1;
model.parameters.get<mio::osecir::TimeExposed<ScalarType>>() = 3.2;
model.parameters.get<mio::osecir::TimeInfectedNoSymptoms<ScalarType>>() = 2.0;
model.parameters.get<mio::osecir::TimeInfectedSymptoms<ScalarType>>() = 5.8;
model.parameters.get<mio::osecir::TimeInfectedSevere<ScalarType>>() = 9.5;
model.parameters.get<mio::osecir::TimeInfectedCritical<ScalarType>>() = 7.1;

// Set transmission and isolation parameters
model.parameters.get<mio::osecir::TransmissionProbabilityOnContact<double>>() = 0.05;
model.parameters.get<mio::osecir::RelativeTransmissionNoSymptoms<double>>() = 0.7;
model.parameters.get<mio::osecir::RecoveredPerInfectedNoSymptoms<double>>() = 0.09;
model.parameters.get<mio::osecir::RiskOfInfectionFromSymptomatic<double>>() = 0.25;
model.parameters.get<mio::osecir::MaxRiskOfInfectionFromSymptomatic<double>>() = 0.45;
model.parameters.get<mio::osecir::TestAndTraceCapacity<double>>() = 35;
model.parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>() = 0.2;
model.parameters.get<mio::osecir::CriticalPerSevere<double>>() = 0.25;
model.parameters.get<mio::osecir::DeathsPerCritical<double>>() = 0.3;
model.parameters.get<mio::osecir::TransmissionProbabilityOnContact<ScalarType>>() = 0.05;
model.parameters.get<mio::osecir::RelativeTransmissionNoSymptoms<ScalarType>>() = 0.7;
model.parameters.get<mio::osecir::RecoveredPerInfectedNoSymptoms<ScalarType>>() = 0.09;
model.parameters.get<mio::osecir::RiskOfInfectionFromSymptomatic<ScalarType>>() = 0.25;
model.parameters.get<mio::osecir::MaxRiskOfInfectionFromSymptomatic<ScalarType>>() = 0.45;
model.parameters.get<mio::osecir::TestAndTraceCapacity<ScalarType>>() = 35;
model.parameters.get<mio::osecir::SeverePerInfectedSymptoms<ScalarType>>() = 0.2;
model.parameters.get<mio::osecir::CriticalPerSevere<ScalarType>>() = 0.25;
model.parameters.get<mio::osecir::DeathsPerCritical<ScalarType>>() = 0.3;

// contact matrix
mio::ContactMatrixGroup<double>& contact_matrix = model.parameters.get<mio::osecir::ContactPatterns<double>>();
contact_matrix[0] = mio::ContactMatrix<double>(Eigen::MatrixXd::Constant(1, 1, cont_freq));
mio::ContactMatrixGroup<ScalarType>& contact_matrix =
model.parameters.get<mio::osecir::ContactPatterns<ScalarType>>();
contact_matrix[0] = mio::ContactMatrix<ScalarType>(Eigen::MatrixXd::Constant(1, 1, cont_freq));
}

// helper function to initialize the feedback mechanism parameters for a simulation
void initialize_feedback(FeedbackSim& feedback_simulation)
{
// nominal ICU capacity
feedback_simulation.get_parameters().template get<mio::NominalICUCapacity<double>>() = 10;
feedback_simulation.get_parameters().template get<mio::NominalICUCapacity<ScalarType>>() = 10;

// ICU occupancy in the past for memory kernel
auto& icu_occupancy = feedback_simulation.get_parameters().template get<mio::ICUOccupancyHistory<double>>();
auto& icu_occupancy = feedback_simulation.get_parameters().template get<mio::ICUOccupancyHistory<ScalarType>>();
Eigen::VectorXd icu_day = Eigen::VectorXd::Constant(1, 1);
const auto cutoff = static_cast<int>(feedback_simulation.get_parameters().template get<mio::GammaCutOff>());
for (int t = -cutoff; t <= 0; ++t) {
icu_occupancy.add_time_point(t, icu_day);
}

// bounds for contact reduction measures
feedback_simulation.get_parameters().template get<mio::ContactReductionMin<double>>() = {0.1};
feedback_simulation.get_parameters().template get<mio::ContactReductionMax<double>>() = {0.8};
feedback_simulation.get_parameters().template get<mio::ContactReductionMin<ScalarType>>() = {0.1};
feedback_simulation.get_parameters().template get<mio::ContactReductionMax<ScalarType>>() = {0.8};

// Set blending factors. The global blending factor is implicitly defined as 1 - local - regional.
feedback_simulation.get_parameters().template get<mio::BlendingFactorLocal<double>>() = 0.5;
feedback_simulation.get_parameters().template get<mio::BlendingFactorRegional<double>>() = 0.3;
feedback_simulation.get_parameters().template get<mio::BlendingFactorLocal<ScalarType>>() = 0.5;
feedback_simulation.get_parameters().template get<mio::BlendingFactorRegional<ScalarType>>() = 0.3;
}

// helper function to create the graph with nodes and edges
mio::Graph<mio::SimulationNode<double, FeedbackSim>, mio::MobilityEdge<double>>
create_graph(int num_nodes, int total_population, double cont_freq)
mio::Graph<mio::SimulationNode<ScalarType, FeedbackSim>, mio::MobilityEdge<ScalarType>>
create_graph(int num_nodes, int total_population, ScalarType cont_freq)
{
// Create a graph for the metapopulation simulation
mio::Graph<mio::SimulationNode<double, FeedbackSim>, mio::MobilityEdge<double>> g;
mio::Graph<mio::SimulationNode<ScalarType, FeedbackSim>, mio::MobilityEdge<ScalarType>> g;

// Create models and add nodes to the graph
for (int i = 0; i < num_nodes; ++i) {
mio::osecir::Model<double> model(1);
mio::osecir::Model<ScalarType> model(1);
initialize_model(model, cont_freq);

// Set initial populations (infection starts in the first node)
Expand Down Expand Up @@ -120,7 +121,7 @@ create_graph(int num_nodes, int total_population, double cont_freq)
model.populations.get_flat_index({mio::AgeGroup(0), mio::osecir::InfectionState::InfectedCritical})};

// Create feedback simulation
auto feedback_sim = FeedbackSim(mio::Simulation<double, mio::osecir::Model<double>>(model), icu_index);
auto feedback_sim = FeedbackSim(mio::Simulation<ScalarType, mio::osecir::Model<ScalarType>>(model), icu_index);
initialize_feedback(feedback_sim);

// Node-ID-Logic: 1001-1005, 2001-2005, ...
Expand Down Expand Up @@ -153,15 +154,15 @@ int main()
const auto tmax = 10.;
const auto dt = 0.5;
const int total_population = 1000;
const double cont_freq = 2.7;
const ScalarType cont_freq = 2.7;
const int num_nodes = 10;

// Create the graph
auto g = create_graph(num_nodes, total_population, cont_freq);

// Create and run the simulation
using Graph = decltype(g);
auto sim = mio::FeedbackGraphSimulation<double, Graph>(std::move(g), t0, dt);
auto sim = mio::FeedbackGraphSimulation<ScalarType, Graph>(std::move(g), t0, dt);
sim.advance(tmax);

// The output shows the compartments sizes for a node without any initial infections.
Expand Down
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