First steps in Yambopy: Difference between revisions

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* Create the yambo netCDF databases using the corresponding interface: (p2y for pw.x or a2y for abinit)
* Create the yambo netCDF databases using the corresponding interface: (p2y for pw.x or a2y for abinit)
* Run yambo once to complete the database
* Run yambo once to complete the database
* Run yambo specifying the run-levels
* Run yambo specifying the run-levels to generate the input file
* Edit the yambo input file
* Edit the yambo input file
* Run yambo
* Run various yambo simulations
* Plot the data results
* Plot the data results


Since many of the parameters of the calculation have to be converged the user might end up running the last three steps many times. This is rather time-consuming without an automatization script.
Since many of the parameters of the calculation have to be converged the user might end up running the last three steps many times. This is rather time-consuming without an automatization script.


The yambopy project aims to provide a simple set of python scripts to read and edit yambo input files. The primary objective is to make the convergence tests easier.
The yambopy project aims to provide a simple set of python scripts to (i) read and edit yambo and quantum espresso input files, and (ii) to easily perform pre- and post-processing of the simulation data for these two codes.  
Yambopy was born with the primary objective of making the convergence tests easier.
 
"""Installation instructions for general users"'"


A quick way to start using Yambopy is described here.
A quick way to start using Yambopy is described here.


<!--
* Enter in a node and create in the scratch a folder
* Enter in a node and create in the scratch a folder


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mkdir your_name; cd your_name
mkdir your_name; cd your_name
</source>
</source>
-->


* Clone yambopy from the git repository
* Make sure that you are using Python 3 and that you have the following python packages: <code>numpy</code>, <code>scipy</code>, <code>matplotlib</code>, <code>netCDF4</code>, <code>xml.etree</code>.
 
* Go to a directory of your choice and clone yambopy from the git repository


<source lang="python">
<source lang="python">
git clone https://github.com/henriquemiranda/yambopy.git
git clone https://github.com/yambo-code/yambopy.git
</source>
</source>


* Enter into the yambopy folder and install
* Enter into the yambopy folder and install
<source lang="python">
cd yambopy
sudo python setup.py install
</source>
If you don't have administrative privileges (for example on a computing cluster), type instead


<source lang="python">
<source lang="python">
cd yambopy
cd yambopy
python setup.py install --user
python setup.py install --user
</source>
* [OPTIONAL] Install abipy [WEBSITE] for band structure interpolations
<source lang="python">
pip install abipy
</source>
</source>


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<source lang="python">
<source lang="python">
cd tutorial/bn
cd tutorial/bn
</source>
And go to [[GW tutorial. Convergence and approximations (BN)]] or [[Bethe-Salpeter equation tutorial. Optical absorption (BN)]].
You can find all the documentation of yambopy here http://yambopy.readthedocs.io/en/latest/index.html
"""Installation instructions for the hands-on of the 2020 Yambo school at ICTP, Trieste"""
* Yambopy is already preinstalled in the Quantum Machine. Start the Quantum Machine and go to the tutorial folder
<source lang="python">
cd yambopy/tutorial/bn
</source>
</source>



Revision as of 13:37, 21 January 2020

A typical yambo calculation proceeds as follows:

  • Obtain the ground state properties from a DFT code (pw.x or abinit) First steps: a walk through from DFT to optical properties
  • Create the yambo netCDF databases using the corresponding interface: (p2y for pw.x or a2y for abinit)
  • Run yambo once to complete the database
  • Run yambo specifying the run-levels to generate the input file
  • Edit the yambo input file
  • Run various yambo simulations
  • Plot the data results

Since many of the parameters of the calculation have to be converged the user might end up running the last three steps many times. This is rather time-consuming without an automatization script.

The yambopy project aims to provide a simple set of python scripts to (i) read and edit yambo and quantum espresso input files, and (ii) to easily perform pre- and post-processing of the simulation data for these two codes. Yambopy was born with the primary objective of making the convergence tests easier.

"""Installation instructions for general users"'"

A quick way to start using Yambopy is described here.


  • Make sure that you are using Python 3 and that you have the following python packages: numpy, scipy, matplotlib, netCDF4, xml.etree.
  • Go to a directory of your choice and clone yambopy from the git repository

<source lang="python"> git clone https://github.com/yambo-code/yambopy.git </source>

  • Enter into the yambopy folder and install

<source lang="python"> cd yambopy sudo python setup.py install </source>

If you don't have administrative privileges (for example on a computing cluster), type instead

<source lang="python"> cd yambopy python setup.py install --user </source>

  • [OPTIONAL] Install abipy [WEBSITE] for band structure interpolations

<source lang="python"> pip install abipy </source>

  • Now yambopy is ready for use! Just go to tutorials folder and follow the docs!

<source lang="python"> cd tutorial/bn </source>

And go to GW tutorial. Convergence and approximations (BN) or Bethe-Salpeter equation tutorial. Optical absorption (BN).

You can find all the documentation of yambopy here http://yambopy.readthedocs.io/en/latest/index.html

"""Installation instructions for the hands-on of the 2020 Yambo school at ICTP, Trieste"""

  • Yambopy is already preinstalled in the Quantum Machine. Start the Quantum Machine and go to the tutorial folder

<source lang="python"> cd yambopy/tutorial/bn </source>

And go to GW tutorial. Convergence and approximations (BN) or Bethe-Salpeter equation tutorial. Optical absorption (BN).

You can find all the documentation of yambopy here http://yambopy.readthedocs.io/en/latest/index.html