exoplasim 3.4.2

Exoplanet GCM

Created by Adiv Paradise

Copyright 2020, Distributed under the General Public License

This API was written with Python 3 in mind, but should work with Python 2 and outdated versions of NumPy.

Read the full documentation at http://exoplasim.readthedocs.io.

Requirements

• numpy

• scipy

• matplotlib (only needed for additional utilities)

• GNU C (gcc/g++) and Fortran (gfortran) compilers (for Python utilities)

• (optionally) Other compilers whose use you prefer for the model itself

• (optionally) MPI libraries for those compilers

• netCDF4 (optional)

• h5py (optional)

Compatibility

• Linux (tested on Ubuntu 18.04, CentOS 6.10): Yes

• Google Colaboratory: Yes (note that OpenMPI support on Colaboratory is limited due to automatic root privileges; look up how to run OpenMPI executables with root permissions and note that this is not recommended)

• Windows 10: Yes, via Windows Subsystem for Linux

• Mac OS X: Yes, requires Xcode and developer tools, and OpenMPI support requires that Fortran-compatible libraries be built. Tested on Mac OS X Catalina and Big Sur (with MacPorts, GCC10, OpenMPI, and Anaconda3), Apple M1 compatibility has not been tested.

New in 3.0:

• ExoPlaSim no longer depends on X11 libraries for installation and compilation!

• Revamped postprocessor no longer depends on NetCDF-C libraries, and supports additional output formats (including netCDF, HDF5, NumPy archives, and archives of CSV files).

• GCC and gfortran support through GCC 10.

• Improved cross-platform compatibility

• Numerous bugfixes

Installation

pip install exoplasim

OR:

python setup.py install

The first time you import the module and try to create a model after either installing or updating, ExoPlaSim will run a configuration script.

Multiple output formats are supported by the built-in pyburn postprocessor. If you wish to use HDF5 or NetCDF output formats, you will need the netCDF4-python and h5py libraries, respectively. You can ensure these are included at install-time by specifying them:

::

pip install exoplasim[netCDF4]

OR::

pip install exoplasim[HDF5]

OR::

pip install exoplasim[netCDF4,HDF5]

You may also configure and compile the model manually if you wish to not use the Python API, by entering the exoplasim/ directory and running first configure.sh, then compile.sh (compilation flags are shown by running ./compile.sh -h).

PlaSim Documentation

Original PlaSim documentation is available in the exoplasim/docs/ folder.

Usage

To use the ExoPlaSim Python API, you must import the module, create a Model or one of its subclasses, call its configure method and/or modify method, and then run it.

An IPython notebook is included with ExoPlaSim; which demonstrates basic usage. It can be found in the ExoPlaSim installation directory, or downloaded directly here.

Basic example::

import exoplasim as exo
mymodel = exo.Model(workdir="mymodel_testrun",modelname="mymodel",resolution="T21",layers=10,ncpus=8)
mymodel.configure()
mymodel.exportcfg()
mymodel.run(years=100,crashifbroken=True)
mymodel.finalize("mymodel_output")

In this example, we initialize a model that will run in the directory “mymodel_testrun”, and has the name “mymodel”, which will be used to label output and error logs. The model has T21 resolution, or 32x64, 10 layers, and will run on 8 CPUs. By default, the compiler will use 8-byte precision. 4-byte may run slightly faster, but possibly at the cost of reduced stability. If there are machine-specific optimization flags you would like to use when compiling, you may specify them as a string to the optimization argument, e.g. optimization='mavx'. ExoPlaSim will check to see if an appropriate executable has already been created, and if not (or if flags indicating special compiler behavior such as debug=True or an optimization flag are set) it will compile one. We then configure the model with all the default parameter choices, which means we will get a model of Earth. We then export the model configurations to a .cfg file (named automatically after the model), which will allow the model configuration to be recreated exactly by other users. We run the model for 100 years, with error-handling enabled. Finally, we tell the model to clean up after itself. It will take the most recent output files and rename them after the model name we chose, and delete all the intermediate output and configuration files.