autofmu¶

Automatic FMU approximation tool.

Installation¶

Compilers¶

To correctly build an FMU this program needs to compile the generated C source into a shared library, therefore it requires the installation of C compilers.

If you are using the provided docker image to run the program then you are already able to cross compile the generated FMU to linux32, linux64, win32 and win64 platforms.

Otherwise if you are using a Linux distribution, you probably already have gcc installed, so you should be able to compile FMUs for your system. If you want to share the generated FMU it is advisable to also install a cross compiler to produce the binaries for Windows platforms (like MinGW). Below are the instructions to install with apt and dnf:

Debian/Ubuntu:

sudo apt install gcc-x86-64-linux-gnu gcc-i686-linux-gnu gcc-mingw-w64 gcc-mingw-w64-i686

Fedora:

sudo dnf install gcc-x86_64-linux-gnu mingw64-gcc mingw32-gcc

Usage¶

autofmu process a dataset

autofmu "dataset.csv" --inputs "x" "y" --outputs "z" -o "My Awesome Model.fmu"

This will read the dataset.csv file, select the x, y and z columns and find an approximation of the relation between the inputs and the outputs. Based on this relation, the sources files for the FMU will be generated and compiled, resulting in the My Awesome Model.fmu file ready to be used for simulations.

Contributing¶

License¶

This project is licensed under MIT license.

Table of contents¶

Installation¶

Compilers¶

To correctly build an FMU this program needs to compile the generated C source into a shared library, therefore it requires the installation of C compilers.

If you are using the provided docker image to run the program then you are already able to cross compile the generated FMU to linux32, linux64, win32 and win64 platforms.

Otherwise if you are using a Linux distribution, you probably already have gcc installed, so you should be able to compile FMUs for your system. If you want to share the generated FMU it is advisable to also install a cross compiler to produce the binaries for Windows platforms (like MinGW). Below are the instructions to install with apt and dnf:

Debian/Ubuntu:

sudo apt install gcc-x86-64-linux-gnu gcc-i686-linux-gnu gcc-mingw-w64 gcc-mingw-w64-i686

Fedora:

sudo dnf install gcc-x86_64-linux-gnu mingw64-gcc mingw32-gcc

Usage¶

autofmu process a dataset

autofmu "dataset.csv" --inputs "x" "y" --outputs "z" -o "My Awesome Model.fmu"

This will read the dataset.csv file, select the x, y and z columns and find an approximation of the relation between the inputs and the outputs. Based on this relation, the sources files for the FMU will be generated and compiled, resulting in the My Awesome Model.fmu file ready to be used for simulations.

License¶

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

API Reference¶

autofmu

autofmu¶

Automatic FMU approximation tool.

autofmu.main¶

Main entry point for running the program from the command line.

autofmu.main.main(args=None)[source]¶

Execute the program in a command line environment.

Parameters: args (Optional[Sequence[str]]) – sequence of command line arguments
Return type: None

autofmu.cli¶

Utilities for exposing a command line interface of the program.

autofmu.cli.create_argument_parser()[source]¶

Create an argument parser object to process command line arguments.

Return type: ArgumentParser
Returns: An argument parser object

autofmu.generator¶

Utilities for generating valid Functional Mockup Units.

autofmu.generator.generate_fmu(dataframe, model_name, inputs, outputs, outfile, strategy)[source]¶

Generate a valid FMU model.

Parameters

dataframe (DataFrame) – dataframe that contains the data used for the approximation
model_name (str) – name of the model as used in the modeling environment
inputs (Iterable[str]) – variable input names
outputs (Iterable[str]) – variable output names
outfile (Path) – path to the file to write the FMU
strategy (str) – strategy to use to find the approximation (e.g, “linear”)

Return type

None

autofmu.generator.generate_model_description(model_name, model_identifier, guid, inputs, outputs)[source]¶

Generate a valid FMI 2.0 model description XML document.

Parameters

model_name (str) – name of the model as used in the modeling environment
model_identifier (str) – short class name according to C syntax, for example, “A_B_C”
guid (str) – globaly unique identifier that identifies this model
inputs (Iterable[str]) – variable input names
outputs (Iterable[str]) – variable output names

Return type

ElementTree

Returns

Valid FMI 2.0 model description XML document

autofmu.generator.generate_model_source(guid, inputs, outputs, strategy, result)[source]¶

Generate a valid FMI 2.0 C source code implementation.

Parameters

guid (str) – globaly unique identifier that identifies this model
inputs (Iterable[str]) – variable input names
outputs (Iterable[str]) – variable output names
result (Union[LinearRegressionResult, LogisticRegressionResult]) – a result from an approximation calculation

Return type

str

Returns

Valid C source code that implements the FMI

autofmu.utils¶

General utilities.

autofmu.utils.compile_fmu(model_identifier, fmu_path)[source]¶

Compile the C sources files of an FMU.

Extracts the FMU into a temporary directory, calling cmake to build the FMU, copying the generated library back into the FMU file. If MinGW is installed, it also cross compiles the FMU for Linux and Windows.

Parameters

model_identifier (str) – short class name according to C syntax, for example, “A_B_C”
fmu_path (Path) – path to the FMU file

Return type

None

autofmu.utils.run_cmake(source_dir, build_dir, variables=None)[source]¶

Run cmake command and build the targets.

Roughly equivalent to running the following two commands:

cmake -S source_dir -B build_dir
cmake --build build_dir

Parameters

source_dir (Path) – path to source directory
build_dir (Path) – path to build directory
variables (Optional[Mapping[str, str]]) – a mapping between variable names and their values, e.g, {"CMAKE_PROJECT_NAME": "Unicorn"} would be passed as DCMAKE_PROJECT_NAME=Unicorn in the command line

Return type

None

autofmu.utils.slugify(value, allow_unicode=False)[source]¶

Convert a string to a URL slug.

Convert to ASCII if ‘allow_unicode’ is False. Convert spaces or repeated dashes to single dashes. Remove characters that aren’t alphanumerics, underscores, or hyphens. Convert to lowercase. Also strip leading and trailing whitespace, dashes, and underscores.

See:: https://docs.djangoproject.com/en/3.1/ref/utils/#django.utils.text.slugify

Return type: str

Command line reference¶

usage: autofmu [-h] [-o FILE] [-v] [-V] --inputs VARIABLE [VARIABLE ...]
               --outputs VARIABLE [VARIABLE ...] [-s {linear,logistic}]
               FILE

Positional Arguments¶

FILE: CSV files that contain the datasets for training the FMU model

Named Arguments¶

-o, --outfile

file to output the generated FMU model (default ‘model.fmu’)

Default: model.fmu

-v, --verbose

run the program in verbose mode

Default: False

-V, --version

show program’s version number and exit

--inputs

list of names of the model input variables

--outputs

list of names of the model output variables

-s, --strategy

Possible choices: linear, logistic

strategy to use to deduce the approximation

Default: “linear”

autofmu¶

Installation¶

Compilers¶

Usage¶

Contributing¶

License¶

Table of contents¶

Installation¶

Compilers¶

Usage¶

License¶

API Reference¶

autofmu¶

autofmu.main¶

autofmu.cli¶

autofmu.generator¶

autofmu.utils¶

Command line reference¶

Positional Arguments¶

Named Arguments¶

Indices and tables¶