libcosimpy 0.0.4

Python wrapper for the libcosim library

libcosimpy

Python wrapper for the libcosim library. The wrapper uses the libcosimc C wrapper and the ctypes library to make OSP accessible to Python developers.

Getting Started

libcosimpy is available from PyPI. Run the following command to install the package:

pip install libcosimpy

To install from the source, run the following command at the root directory of the repository:

pip install .

libcosimpy requires ctypes to call libcosimc functions. ctypes is included with Python and does not have to be installed.

Usage

Create execution

Import CosimExecution from libcosimpy

from libcosimpy.CosimExecution import CosimExecution

Empty execution object

execution = CosimExecution.from_step_size(step_size=1e3)

With a 0.01s fixed time step

From OSP config

execution = CosimExecution.from_osp_config_file(osp_path=f'[PATH_TO_OSP_DIRECTORY]')

From SSP config

execution = CosimExecution.from_ssp_file(ssp_path=f'[PATH_TO_SSP_DIRECTORY]')

Add slave

FMUs can be added manually to execution. OSP and SSP config executions will import all required slaves automatically and this step is not required

Import CosimLocalSlave from libcosimpy

from libcosimpy.CosimSlave import CosimLocalSlave

Add slave to existing execution

local_slave = CosimLocalSlave(fmu_path=f'[PATH_WITH_FILENAME_TO_FMU]', instance_name='[SOME_UNIQUE_NAME]')
slave_index = execution.add_local_slave(local_slave=local_slave)

Slave index is used for future referencing to the model

Run simulation

Simulations can either be run continiously for a duration

execution.simulate_until(target_time=10e9)

To simulate for 10s

Or stepped manually

execution.step()

With option for stepping multiple steps at once

execution.step(step_count=10)

Finding slave and variable indices

List of slave indices and corresponding indices can be fetched from execution

slave_infos = list(execution.slave_infos())

List of model variables and corresponding indices can be fetched

variables = execution.slave_variables(slave_index=slave_index)

The indices can also be found by unzipping the FMU-file and inspecting the modelDescription.xml file

Retrieving values from simulation

Import CosimObserver from libcosimpy

from libcosimpy.CosimObserver import CosimObserver

Observers can be used to retrieve values as Python list

observer = CosimObserver.create_last_value()
execution.add_observer(observer=observer)

# Run simulation
...
# Retrieve floating point values
values = observer.last_real_values(slave_index=[SLAVE_INDEX], # Model to monitor (integer)
                                   variable_references=[VALUE_REFERENCE(s)]) # List of indices to monitor (integer)

Time series and file export observers are also supported

Overriding values in simulation

Import CosimManipulator from libcosimpy

from libcosimpy.CosimManipulator import CosimManipulator

Manipulators are used to override values

manipulator = CosimManipulator.create_override()
execution.add_manipulator(manipulator=manipulator)

# Run simulation
...
# Override floating point values
manipulator.slave_real_values(slave_index=[SLAVE_INDEX], # Model to monitor (integer) 
                              variable_references=[VALUE_REFERENCE(s)], # Index or list of indices to manipulate (integer)
                              values=[SOME_OVERRIDE_VALUE(s)]) # Floating point values used for override. Equal length to variable references
execution.step()

Scenario manipulators are also supported

Using ECCO algorithm

Libcosimpy supports ECCO (Energy-Conservation-based Co-Simulation) algorithm based on the work in [1] for adaptively updating the step size of the simulation. The algorithm uses the law of conservation of energy between FMU models that represent power bonds from bond graph theory.

Creating ECCO algorithm manually

The parameters of the algorithm can be specified via the EccoParam class:

params = EccoParams(
    safety_factor=0.8,
    step_size=1e-4,
    min_step_size=1e-4,
    max_step_size=0.01,
    min_change_rate=0.2,
    max_change_rate=1.5,
    abs_tolerance=1e-4,
    rel_tolerance=1e-4,
    p_gain=0.2,
    i_gain=0.15,
)

The algorithm be created via create_ecco_algorithm, which can be used to create a new execution instance:

# Create an algorithm instance
ecco_algorithm = CosimAlgorithm.create_ecco_algorithm(params)

# Create execution
execution = CosimExecution.from_algorithm(ecco_algorithm)

The power bond between models is represented by input and output connection pair between two models:

# Indicating a power bond between models (indicated by index chassis_index and wheel_index)
ecco_algorithm.add_power_bond(
    chassis_index,
    chassis_v_out,
    chassis_f_in,
    wheel_index,
    wheel_f_out,
    wheel_v_in,
)

The simulation is started as usual via simulate_until function from CosimExecution:

execution.simulate_until(target_time=10e9)

See test_ecco_algorithm for detailed usage of ECCO algorithm.

Creating ECCO algorithm via system structure file

Alternatively, ECCO algorithm can also be created via system structure file:

<OspSystemStructure xmlns="http://opensimulationplatform.com/MSMI/OSPSystemStructure" version="0.1">
    ...
    <!-- Specify ecco algorithm -->
    <Algorithm>ecco</Algorithm>
    ...
    <Connections>
        <!-- Annotate variable connection as power bond via `powerBond` attribute. Specify
             causality of the variable (input or output) -->
        <VariableConnection powerBond="wheelchassis">
            <Variable simulator="chassis" name="velocity" causality="output"/>
            <Variable simulator="wheel" name="in_vel" causality="input"/>
        </VariableConnection>
        <VariableConnection powerBond="wheelchassis">
            <Variable simulator="wheel" name="out_spring_damper_f" causality="output"/>
            <Variable simulator="chassis" name="force" causality="input"/>
        </VariableConnection>
    </Connections>
    <!-- Specify ecco algorithm parameters -->
    <EccoConfiguration>
        <SafetyFactor>0.99</SafetyFactor>
        <StepSize>0.0001</StepSize>
        <MinimumStepSize>0.00001</MinimumStepSize>
        <MaximumStepSize>0.01</MaximumStepSize>
        <MinimumChangeRate>0.2</MinimumChangeRate>
        <MaximumChangeRate>1.5</MaximumChangeRate>
        <ProportionalGain>0.2</ProportionalGain>
        <IntegralGain>0.15</IntegralGain>
        <RelativeTolerance>1e-6</RelativeTolerance>
        <AbsoluteTolerance>1e-6</AbsoluteTolerance>
    </EccoConfiguration>
</OspSystemStructure>

Then this file can be loaded via a usual way via CosimExecution.from_osp_config_file:

execution = CosimExecution.from_osp_config_file(osp_path="tests/data/fmi2/quarter_truck")

See Quarter truck example for detailed usage of ECCO algorithm via system structure file.

Reference

[1] Sadjina, S. and Pedersen, E., 2020. Energy conservation and coupling error reduction in non-iterative co-simulations. Engineering with Computers, 36, pp.1579-1587.

Tests

Tests can be run using the pytest command in the terminal. libcosimc log level for all tests can be set in the ./tests/conftest.py file.