Spectroscopy tutorial

This tutorial provides information of how to install needed libraries for the package in https://github.com/JohanSchott/impurityModel and how to use the package on a laptop as well as on a computer cluster.

Install spectroscopy program

Choose machine: laptop or login to a computer cluster, e.g. Beskow, Tetralith, …

Download the spectroscopy program by typing:

git clone https://github.com/JohanSchott/impurityModel.git

For instructions of how to get started with the spectroscopy program, read the README file on the webpage: https://github.com/JohanSchott/impurityModel

Run simulations of NiO

Read and follow the README instructions in the link above.

Plot the generated spectra and the generated text output of NiO

Read and follow the README instructions in the link above.

Installation help of prerequisite libraries and compilers

The information below is probably of interest if you experience problems in executing the program package.

Practical help for usage on local computer

Installing missing Python 3.x

If Python 3.x is not installed it can be downloaded at e.g.: https://www.anaconda.com/download. This will also install many useful Python libraries. If only some Python libraries are missing, please look in the section below.

Installing missing Python libraries

If not all needed Python libraries are installed the program will stop and complain. We can try to install the missing libraries. For example, if the library numpy is missing, type:

pip install numpy

If Python is installed with Anaconda we can alternatively install the library with:

conda install numpy

Note, mpi4py might be a bit tricky to install, as it requires MPI compilers. Below follows instructions for how to install gfortran and open MPI compilers.

How to install gfortran compiler on mac

First check if gfortran already exists:

which gfortran

If not installed, we can install it using Homebrew.

Install Homebrew from the terminal (if not already installed):

ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Update Homebrew and make sure everything is ok.

brew update
brew doctor

Install gfortran from gcc, and swig:

brew install gcc
brew install swig

Alternative gfortran installation: Download .dmg file from: https://github.com/fxcoudert/gfortran-for-macOS/releases

How to install Open-MPI compiler

Download .tar.gz file from: https://www.open-mpi.org/software/ompi/v3.1/ Follow instructions in the INSTALL file:

cd openmpi-3.1.3/
mkdir /usr/local/openmpi
./configure --prefix=/usr/local/openmpi
make all
make install

If this is successful, we might still have to tell where the binaries are. This is done by typing:

export PATH=${PATH}:/usr/local/openmpi/bin

Finally, try to install mpi4py:

pip install mpi4py

Practical help for usage on computer clusters

On Beskow:

Load a module with all needed Python libraries installed, e.g.:

module load anaconda/py36/4.3
module load git/2.16.2

There might be a need to create a virtualenv. If needed, type e.g.:

conda create -n custom
source activate custom
conda install mpi4py

Start an interactive node (or create a job script):

interactive -t 00:30:00 -N 1 -A $ACCOUNT_NAME

Once in the interactive session, activate the virtual enviroment:

source activate custom
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib

On Beskow one should use aprun instead of mpirun. Therefore, in run_Ni_NiO_Xbath.sh, replace mpirun with aprun. Type e.g.:

path/to/folder/impurityModel/scripts/run_Ni_NiO_Xbath.sh 20 2 > output.txt

This will start a simulation with 2 MPI ranks. If RAM memory is an issue, one can use the MPI parameters aprun -n 8 -N 2 -d 1. Here 8 MPI ranks are used, 2 MPI ranks per node and no OpenMP threading.

For plotting, there might be some issues on Beskow. Therefor, it might be better to do plotting from Tegner instead. Login at Tegner and load the Python module:

module load anaconda/py36/5.0.1

Move to folder where the simulations are done and type:

python -m impurityModel.plotScripts.plotSpectra

On Tetralith:

Load modules, e.g.:

module load Python/3.6.7-env-nsc1-gcc-2018a-eb
module load buildenv-gcc/2018a-eb
module load git

Some Python libraries are not installed in the loaded Python module so we have to create a virtual environment:

virtualenv --system-site-packages impurityModelEnv

Now a folder called impurityModelEnv should exist. Activate the virtual environment (that we just created):

. impurityModelEnv/bin/activate

Install the following libraries (this is done only once):

pip install sympy
pip install PyQt5

Deactivate the virtual environment (that we created):

deactivate

Start an interactive node (or create a job script):

interactive -t 00:30:00 -N 1 -A $ACCOUNT_NAME

Once in the interactive session, activate the virtual enviroment:

. impurityModelEnv/bin/activate

On Tetralith one should use mpiexec instead of mpirun. Therefore, in run_Ni_NiO_Xbath.sh, replace mpirun with mpiexec. Type e.g.:

path/to/folder/impurityModel/scripts/run_Ni_NiO_Xbath.sh 20 2 > output.txt

to start a simulation with 2 MPI ranks. After simulations, deactivate the virtual environment:

deactivate

OpenMP

From experience it seems simulations run faster without OpenMP threading. This can typically be enforced by typing:

export OMP_NUM_THREADS=1

Practical help with matplotlib errors

In Python, when importing matplotlib.pylab, an error similar to:

ImportError: Matplotlib qt-based backends require an external PyQt4, PyQt5, PySide or PySide2 package to be installed, but it was not found.

might happen. In this case, a solution might be to change the backend used by matplotlib. This can be done by editing the matplotlibrc file (usually stored in ~/.config/matplotlib/matplotlibrc). Search for backend and type e.g.:

backend      : Qt5Agg

Another, more temporary, solution is to set the backend while inside the Python session:

import matplotlib
matplotlib.use('Qt5Agg')
import matplotlib.pylab as plt

JohanSchott/impurityModelTutorial