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Implementation of an Elasto Plastic Model

Home Page: https://tdegeus.github.io/GooseEPM/

License: MIT License

CMake 4.72% C++ 64.98% Python 30.30%

gooseepm's Introduction

GooseEPM

CI Doxygen -> gh-pages Conda Version

Implementation of an Elasto Plastic Model, please consult the documentation:

https://tdegeus.github.io/GooseEPM

Install the Python module

conda-forge

conda install -c conda-forge gooseepm

From source

# Download GooseEPM
git checkout https://github.com/tdegeus/GooseEPM.git
cd GooseEPM

# Get prerequisites. An example is given using conda, but there are many other ways
conda activate myenv
conda env update --file environment.yaml
# (if you use hardware optimisation, below, you also want)
conda install -c conda-forge xsimd

# Compile and install the Python module
# (-v can be omitted as is controls just the verbosity)
python -m pip install . -v --no-build-isolation

# Or, compile with hardware optimisation (fastest), see scikit-build docs
SKBUILD_CONFIGURE_OPTIONS="-DUSE_SIMD=1" python -m pip install . -v --no-build-isolation

# Note that you can also compile with debug assertions (very slow)
SKBUILD_CONFIGURE_OPTIONS="-USE_DEBUG=1" python -m pip install . -v --no-build-isolation

# Or, without any assertions (slightly faster, but more dangerous)
SKBUILD_CONFIGURE_OPTIONS="-USE_ASSERT=0" python -m pip install . -v --no-build-isolation

gooseepm's People

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laurentbugnard avatar tdegeus avatar

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laurentbugnard

gooseepm's Issues

Avoid negative yield stress values

Maybe we should make sure that yield stress values are always positive, which might not be the case when sigmay_std is not negligible w.r.t. sigmay_mean.

for (size_t i = 0; i < m_sigy.size(); ++i) {
m_sigy.flat(i) = m_gen.normal(m_sigy_mu.flat(i), m_sigy_std.flat(i));
}

The change should then also be made when resampling.

m_sigy.flat(idx) = m_gen.normal(m_sigy_mu.flat(idx), m_sigy_std.flat(idx));

What do you think?

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