![]() ![]() ![]() bootstrap.sh -with-libraries=serialization bootstrap.sh -with-libraries=python,serialization The boost site has detailed instructions for this, but here’s an overview: usr/local/src/boost_1_58_0)īuild the required boost libraries. There is a workaround for this below in the (see FAQ) section.ĭownload the boost source distribution from the boost web siteĮxtract the source somewhere on your machine (e.g. We’ve seen at least one example on a Fedora system where cmake compiled using a user-installed version of boost and then linked against the system version. If you do have a version of the boost libraries pre-installed and you want to use your own version, be careful when you build the code. This may require that you install a sqlite3-dev package. This probably means that you need to install the python-dev package (or whatever it’s called) for your linux distribution. The following are required if you are planning on using the Python wrappers if your linux distribution doesn’t have an appropriate package. Visual Studio 2015: it may be that older versions of the compiler also work, but we haven’t tested them.Ĭmake. It will automatically be disabled when this older compiler is used.Ĭlang v3.9: it may be that older versions of the compiler also work, but we haven’t tested them. G++ v4.8: though note that the SLN parser code cannot be built with v4.8. This means that the compilers used to build it cannot be completely ancient. Starting with the 2018_03 release, the RDKit core C++ code is written in modern C++ for this release that means C++11. Thanks to Gianluca Sforna’s work, binary RPMs for the RDKit are now part of the official Fedora repositories:Įddie Cao has produced a homebrew formula that can be used to easily build the RDKit Building from Source ¶ $ sudo apt-get install python-rdkit librdkit1 rdkit-data How to install RDKit with Conda ¶Ĭreating a new conda environment with the RDKit installed requires one single command similar to the following:: ![]() The conda source code repository is available on github and additional documentation is provided by the project website. A possible (but a bit more complex to use) alternative is provided with the smaller and more self-contained Miniconda. The easiest way to get Conda is having it installed as part of the Anaconda Python distribution. It has several analogies with pip and virtualenv, but it is designed to be more “python-agnostic” and more suitable for the distribution of binary packages and their dependencies. It supports the packaging and distribution of software components, and manages their installation inside isolated execution environments. Cross-platform using Conda ¶ Introduction to Conda ¶Ĭonda is an open-source, cross-platform, software package manager. A conda package for pyGPlates is being developed.Ī summary of all the commands are below.Below a number of installation recipes is presented, with varying degree of complexity. Now you should have a working pyGPlates installation with additional dependencies required to for scientific Python applications! Additional details on installing pyGPlates can be found here. Install any additional dependendies (that don’t have a conda forumla) using pip: pip install PlateTectonicTools.In my case conda develop /Users/ben/Downloads/pygplates_0.35.0_p圓9_Darwin-arm64 Link to your pygplates installation using the python path you wrote down at step 1.Then you should be able to install your Python packages: conda install numpy scipy netcdf4 geopandas pandas cartopy jupyter rasterio conda-build.Some packages require additional dependencies such as compilers and mapping libraries outside of conda (I’m looking at you GDAL!) To do this install homebrew, then run brew install qt5 gcc gdal cgal.Activate your new python environment with conda activate pygplates.Install conda (we recommend miniforge) and create a new environment e.g.Download and install the latest pygplates version corresponding to your system architecture (Mac M1 ARM) and take note of the installation directory and python path.We will use conda and brew to install packages: Instead, these instructions will get you a fully functioning scientific Python environment working natively on M1 architecture. Some instructions I have seen have recommended installing the x86 (Intel) packages, but there is a performance penalty when x86 programs are interpreted by Rosetta2 to work on the M1 Macs. are all ARM-based there are some differences to be aware of when installing your Python environment. The new Mac M1 processors are pretty cool, and are enough to convert this veteran Linux user to Mac. ![]()
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