Introduction¶
The package extends the PythonFMU package. It includes the necessary modules to construct a component model according to the fmi, OSP and DNV-RP-0513 standards with focus on the following features:
seamless translation of a Python model to an FMU package with minimal overhead (definition of FMU interface)
support of vector variables (numpy)
support of variable units and display units
support of range checking of variables
Features which facilitate Assurance of Simulation Models, DNV-RP-0513 shall have a special focus in this package.
Installation¶
pip install component-model
Getting Started¶
A new model can consist of any python code. To turn the python code into an FMU the following is necessary
The model code is wrapped into a Python class which inherits from Model
The exposed interface variables (model parameters, input- and output connectors) are defined as Variable objects
The (model).do_step( time, dt) function of the model class is extended with model internal code, i.e. model evolves from time to time+dt.
Calling the method Model.build() will then compile the FMU and package it into a suitable FMU file.
See the files example_models/bouncing_ball.py and tests/test_make_bouncingBall.py supplied with this package as a simple example of this process. The first file defines the model class and the second file demonstrates the process of making the FMU and using it within fmpy and OSP.
Usage example¶
This is another BouncingBall example, using 3D vectors and units.
from math import sqrt
import numpy as np
from component_model.model import Model
from component_model.variable import Variable
class BouncingBall3D(Model):
"""Another Python-based BouncingBall model, using PythonFMU to construct a FMU.
Special features:
* The ball has a 3-D vector as position and speed
* As output variable the model estimates the next bouncing point
* As input variables, the restitution coefficient `e`, the gravitational acceleration `g`
and the initial speed can be changed.
* Internal units are SI (m,s,rad)
Args:
pos (np.array)=(0,0,1): The 3-D position in of the ball at time [m]
speed (np.array)=(1,0,0): The 3-D speed of the ball at time [m/s]
g (float)=9.81: The gravitational acceleration [m/s^2]
e (float)=0.9: The coefficient of restitution (dimensionless): |speed after| / |speed before| collision
min_speed_z (float)=1e-6: The minimum speed in z-direction when bouncing stops [m/s]
"""
def __init__(
self,
name: str = "BouncingBall3D",
description="Another Python-based BouncingBall model, using Model and Variable to construct a FMU",
pos: tuple = ("0 m", "0 m", "10 inch"),
speed: tuple = ("1 m/s", "0 m/s", "0 m/s"),
g: float = "9.81 m/s^2",
e: float = 0.9,
min_speed_z: float = 1e-6,
**kwargs,
):
super().__init__(name, description, author="DNV, SEACo project", **kwargs)
self._pos = self._interface("pos", pos)
self._speed = self._interface("speed", speed)
self._g = self._interface("g", g)
self.a = np.array((0, 0, -self.g), float)
self._e = self._interface("e", e)
self.min_speed_z = min_speed_z
self.stopped = False
self.time = 0.0
self._p_bounce = self._interface("p_bounce", ("0m", "0m", "0m")) # Note: 3D, but z always 0
self.t_bounce, self.p_bounce = (-1.0, self.pos) # provoke an update at simulation start
def do_step(self, _, dt):
"""Perform a simulation step from `self.time` to `self.time + dt`.
With respect to bouncing (self.t_bounce should be initialized to a negative value)
.t_bounce <= .time: update .t_bounce
.time < .t_bounce <= .time+dt: bouncing happens within time step
.t_bounce > .time+dt: no bouncing. Just advance .pos and .speed
"""
if not super().do_step(self.time, dt):
return False
if self.t_bounce < self.time: # calculate first bounce
self.t_bounce, self.p_bounce = self.next_bounce()
while self.t_bounce <= self.time + dt: # bounce happens within step or at border
dt1 = self.t_bounce - self.time
self.pos = self.p_bounce
self.speed += self.a * dt1 # speed before bouncing
self.speed[2] = -self.speed[2] # speed after bouncing if e==1.0
self.speed *= self.e # speed reduction due to coefficient of restitution
if self.speed[2] < self.min_speed_z:
self.stopped = True
self.a[2] = 0.0
self.speed[2] = 0.0
self.pos[2] = 0.0
self.time += dt1 # jump to the exact bounce time
dt -= dt1
self.t_bounce, self.p_bounce = self.next_bounce() # update to the next bounce
if dt > 0:
# print(f"pos={self.pos}, speed={self.speed}, a={self.a}, dt={dt}")
self.pos += self.speed * dt + 0.5 * self.a * dt**2
self.speed += self.a * dt
self.time += dt
if self.pos[2] < 0:
self.pos[2] = 0
return True
def next_bounce(self):
"""Calculate time of next bounce and position where the ground will be hit,
based on .time, .pos and .speed.
"""
if self.stopped: # stopped bouncing
return (1e300, np.array((1e300, 1e300, 0), float))
else:
dt_bounce = (self.speed[2] + sqrt(self.speed[2] ** 2 + 2 * self.g * self.pos[2])) / self.g
p_bounce = self.pos + self.speed * dt_bounce # linear. not correct for z-direction!
p_bounce[2] = 0
return (self.time + dt_bounce, p_bounce)
def setup_experiment(self, start: float):
"""Set initial (non-interface) variables."""
super().setup_experiment(start)
self.stopped = False
self.time = start
def exit_initialization_mode(self):
"""Initialize the model after initial variables are set."""
super().exit_initialization_mode()
self.a = np.array((0, 0, -self.g), float)
def _interface(self, name: str, start: float | tuple):
"""Define a FMU2 interface variable, using the variable interface.
Args:
name (str): base name of the variable
start (str|float|tuple): start value of the variable (optionally with units)
Returns:
the variable object. As a side effect the variable value is made available as self.<name>
"""
if name == "pos":
return Variable(
self,
name="pos",
description="The 3D position of the ball [m] (height in inch as displayUnit example.",
causality="output",
variability="continuous",
initial="exact",
start=start,
rng=((0, "100 m"), None, (0, "10 m")),
)
elif name == "speed":
return Variable(
self,
name="speed",
description="The 3D speed of the ball, i.e. d pos / dt [m/s]",
causality="output",
variability="continuous",
initial="exact",
start=start,
rng=((0, "1 m/s"), None, ("-100 m/s", "100 m/s")),
)
elif name == "g":
return Variable(
self,
name="g",
description="The gravitational acceleration (absolute value).",
causality="parameter",
variability="fixed",
start=start,
rng=(),
)
elif name == "e":
return Variable(
self,
name="e",
description="The coefficient of restitution, i.e. |speed after| / |speed before| bounce.",
causality="parameter",
variability="fixed",
start=start,
rng=(),
)
elif name == "p_bounce":
return Variable(
self,
name="p_bounce",
description="The expected position of the next bounce as 3D vector",
causality="output",
variability="continuous",
start=start,
rng=(),
)
The following might be noted:
The interface variables are defined in a separate local method
_interface_variables, keeping it separate from the model code.The
do_step()method contains the essential code, describing how the ball moves through the air. It calls thesuper().do_step()method, which is essential to link it toModel. The return True statement is also essential for the working of the emerging FMU.The
next_bounce()method is a helper method.In addition to the extension of
do_step(), here also thesetup_experiment()method is extended. Local (non-interface) variables can thus be initialized in a convenient way.
It should be self-evident that thorough testing of any model is necessary before translation to a FMU.
The simulation orchestration engine (e.g. OSP) used to run FMUs obfuscates error messages,
such that first stage assurance of a model should aways done using e.g. pytest.
The minimal code to make the FMU file package is
from component_model.model import Model
from fmpy.util import fmu_info
asBuilt = Model.build("../component_model/example_models/bouncing_ball.py")
info = fmu_info(asBuilt.name) # not necessary, but it lists essential properties of the FMU
The model can then be run using fmpy
from fmpy import plot_result, simulate_fmu
result = simulate_fmu(
"BouncingBall.fmu",
stop_time=3.0,
step_size=0.1,
validate=True,
solver="Euler",
debug_logging=True,
logger=print,
start_values={"pos[2]": 2}, # optional start value settings
)
plot_result(result)
Similarly, the model can be run using OSP (or rather libcosimpy - OSP wrapped into Python):
from libcosimpy.CosimEnums import CosimExecutionState
from libcosimpy.CosimExecution import CosimExecution
from libcosimpy.CosimSlave import CosimLocalSlave
sim = CosimExecution.from_step_size(step_size=1e7) # empty execution object with fixed time step in nanos
bb = CosimLocalSlave(fmu_path="./BouncingBall.fmu", instance_name="bb")
print("SLAVE", bb, sim.status())
ibb = sim.add_local_slave(bb)
assert ibb == 0, f"local slave number {ibb}"
reference_dict = {var_ref.name.decode(): var_ref.reference for var_ref in sim.slave_variables(ibb)}
sim.real_initial_value(ibb, reference_dict["pos[2]"], 2.0) # Set initial values
sim_status = sim.status()
assert sim_status.current_time == 0
assert CosimExecutionState(sim_status.state) == CosimExecutionState.STOPPED
infos = sim.slave_infos()
print("INFOS", infos)
sim.simulate_until(target_time=3e9) # Simulate for 1 second
This is admittedly more complex than the fmpy example,
but it should be emphasised that fmpy is made for single component model simulation (testing),
while OSP is made for multi-component systems.
Development Setup¶
1. Install uv¶
This project uses uv as package manager.
If you haven’t already, install uv, preferably using it’s “Standalone installer” method:
..on Windows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
..on MacOS and Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh
(see docs.astral.sh/uv for all / alternative installation methods.)
Once installed, you can update uv to its latest version, anytime, by running:
uv self update
2. Install Python¶
This project requires Python 3.10 or later.
If you don’t already have a compatible version installed on your machine, the probably most comfortable way to install Python is through uv:
uv python install
This will install the latest stable version of Python into the uv Python directory, i.e. as a uv-managed version of Python.
Alternatively, and if you want a standalone version of Python on your machine, you can install Python either via winget:
winget install --id Python.Python
or you can download and install Python from the python.org website.
3. Clone the repository¶
Clone the component-model repository into your local development directory:
git clone https://github.com/dnv-opensource/component-model path/to/your/dev/component-model
4. Install dependencies¶
Run uv sync to create a virtual environment and install all project dependencies into it:
uv sync
5. (Optional) Activate the virtual environment¶
When using uv, there is in almost all cases no longer a need to manually activate the virtual environment.
uv will find the .venv virtual environment in the working directory or any parent directory, and activate it on the fly whenever you run a command via uv inside your project folder structure:
uv run <command>
However, you still can manually activate the virtual environment if needed. When developing in an IDE, for instance, this can in some cases be necessary depending on your IDE settings. To manually activate the virtual environment, run one of the “known” legacy commands:
..on Windows:
.venv\Scripts\activate.bat
..on Linux:
source .venv/bin/activate
6. Install pre-commit hooks¶
The .pre-commit-config.yaml file in the project root directory contains a configuration for pre-commit hooks.
To install the pre-commit hooks defined therein in your local git repository, run:
uv run pre-commit install
All pre-commit hooks configured in .pre-commit-config.yam will now run each time you commit changes.
7. Test that the installation works¶
To test that the installation works, run pytest in the project root folder:
uv run pytest
Meta¶
Copyright (c) 2024 DNV AS. All rights reserved.
Siegfried Eisinger - siegfried.eisinger@dnv.com
Distributed under the MIT license. See LICENSE for more information.
Contribute¶
Anybody in the FMU and OSP community is welcome to contribute to this code, to make it better, and especially including other features from model assurance, as we firmly believe that trust in our models is needed if we want to base critical decisions on the support from these models.
To contribute, follow these steps:
Fork it https://github.com/dnv-opensource/component-model/fork/
Create an issue in your GitHub repo
Create your branch based on the issue number and type (
git checkout -b issue-name)Evaluate and stage the changes you want to commit (
git add -i)Commit your changes (
git commit -am 'place a descriptive commit message here')Push to the branch (
git push origin issue-name)Create a new Pull Request in GitHub
For your contribution, please make sure you follow the STYLEGUIDE before creating the Pull Request.