Skip to content
forked from atmtools/konrad

Implementation of a radiative-convective equilibrium model.

License

Notifications You must be signed in to change notification settings

pczarnecki/konrad

 
 

Repository files navigation

PyPI version Test Documentation Status DOI

Logo konrad

konrad is a one-dimensional radiative-convective equilibrium (RCE) model. It is build in an object oriented structure to allow simple modifications of the model setup.

You can find various tutorials that illustrate the usage of konrad in our "How to konrad" Jupyter book.

Requirements

konrad requires Python 3.6 or higher. The recommended way to get Python is through Anaconda. But of course, any other Python distribution is also working.

Install stable release

You can install the latest stable version of konrad using pip:

python -m pip install konrad

Konrad depends on the CliMT package. CliMT handles a variety of underlying FORTRAN code and provides precompiled binary wheels for some Python versions and operating systems.

However (for Python >3.7) the FORTRAN libraries need to be compiled locally. In this case, you need to specify a C compiler, a FORTRAN compiler, and the target architecture using the corresponding environment variables:

CC=gcc FC=gfortran TARGET=HASWELL python -m pip install konrad

macOS

On macOS, you may need to install the GCC compiler suite beforehand:

# Install GCC 11 and set it as C and Fortran compiler.
brew install gcc@12
CC=gcc-12 FC=gfortran-12

# Set the target architecture (different for Apple M1 [arm64]).
[[ $(uname -p) == arm64 ]] && TARGET=ARMV8 || TARGET=HASWELL

# Install a trimmed down version of CliMT that ships RRTMG only.
export CC FC TARGET
python -m pip install git+https://github.com/atmtools/climt@rrtmg-only

# Install konrad itself
python -m pip install konrad

About

Implementation of a radiative-convective equilibrium model.

Resources

License

Code of conduct

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 98.6%
  • TeX 1.4%