Installation¶
This tutorial will walk you through the process of installing pyvib. To follow, you really only need two basic things:
- A working Python3 installation. Python3.6 is required.
- The python packages numpy, scipy, matplotlib
Step 0: Install prerequisites¶
Recommended:
The necessary Python packages can be installed via the Anaconda Python distribution (https://www.anaconda.com/download/). Python 3 is needed. Anaconda is a collection of often used python packages, the python program and conda, a open source package management system. It runs on Windows, macOS and Linux. Especially on windows it makes the installation process simpler, as numpy and scipy depends on external libraries which can be difficult to install, but are automatically included when conda is used to install the python packages.
If you do not want to install all the packages included in Anaconda (some 3GB of space), miniconda can be installed instead which just includes conda and the python program. The needed packages NumPy, SciPy and matplotlib can be installed in miniconda via the command:
$ conda install numpy scipy matplotlib
Manual
If you do not want to use conda, the packages can be installed on a debian-like(linux) system:
$ sudo apt install python3 python3-pip
$ pip3 install numpy scipy matplotlib
On windows, check these wheels (binaries) for pip: numpy scipy
Step 1: Download and unpack pyvib¶
Download pyvib and unpack it:
$ unzip pyvib-master.zip
If you’re downloading from git, do:
$ git clone https://github.com/pawsen/pyvib.git
Step 2: Build pyvib¶
Just type:
$ cd pyvib-master # if you're not there already
$ python setup.py install
Advanced¶
Get better speed by linking numpy and scipy with optimised blas libraries. This is taken care of if you uses the anaconda distribution. Anaconda default ships with Intel Math Kernel Library (MKL) blas, which is probably the fastest blas implementation. It is Open License (not Open Source). If you wish an Open Source blas, numpy/scipy linked to OpenBlas can be installed with:
$ conda install -c conda-forge numpy scipy
-c specifies the channel to install from and conda-forge is a community driven package central. In the folder ~/miniconda3/conda-meta/ you can see which blas the current numpy is shipped with.
Another reason the prefer OpenBlas is space considerations. MKL takes around 800MB whereas OpenBlas is < 10MB.
If you install numpy/scipy from pip they are linked towards OpenBlas.