Description

Technical info

Repository Instructions (see README.md)

# frustration cooling paper
Here we only detail how to generate the plots and the data used in the published manuscript. Other code that was used in testing can also be found in the /code folder. 

Explicit dependencies on the Python packages within conda environment is given in environment.yml

The parallel implementation of the cooling is published on GitHub separately and a link is:https://github.com/HybridScale/Entanglement-Cooling-Algorithm

The plots have went thought three iteration during the review process. Therefore in this reposity there are folders : plots/, /plots2, /plots3
Folder /plot3 is the published article plot generation. 

The codes in this repository are published under GNU General Public License v3.0 (GNU GPLv3).

## plots3/Fig1 
- main plot data generated with /code/Fig1/main.py
- inset plot data generated with /code/Fig1/inset.py
- plot generated with /plots/Fig1/Fig1.py
- N.B. the numerical algorithm for the Exact Diagonalization suffers from a numerical instability and few points have to accounted for

## plots3/Fig2
- data generated with Gianpaolo code in /code/Code_Fig_11.ipynb within Jupyter notebook environemnt
- data stored in /data/data_for_referee/plot2/
- plot generate with /plots3/Fig2.py

## plot3/Fig3
- we use Tikz to generate the sketch. It can be found in overleaf/Entanglement Cooling Circuit Diagram (https://www.overleaf.com/project/630f411a7baa816d3670a620)
- from the output folder we just use the one automatically generate by executing the code on overleaf
- Tikz generation also given in /plots3/Tikz.zip

## plots3/Fig4
- data generate using parallized version of the /code/coolingalgorithm.py made and used on GPU cluster by Nenad Mijić and Davor Davidović (GitHub reference will be given above)
- to generate the plot first apply /data/collect.py to extract the averaged MC trajectories and than just use this data from the folders /data/reduced and /data/unreduced which contain average_MCtrajectory.data
- plotting is done with /plots3/Fig4/Fig4.py
- extra code in /plots/Fig3 was used for testing

## plots3/Fig5
- code in /plots3/ folder used to collect the date from the output of parallel codes
- N.B. all the datafolders used in generating the Fig5 are provided in the folder as well. In particular /plots3/Fig5/Fig5_process.py and Fig5_process_plato.py Fig5starting.py
- the relevant data is save in /plots3/Fig5
- final plot generated with /plots3/Fig5.py

## plots3/Fig6
- all calculations for this plots are done with the same plotting script and that is /plots3/Fig6

## plots3/Fig7
- data generate using parallized version of the /code/coolingalgorithm.py made and used on GPU cluster by Nenad Mijić and Davor Davidović (GitHub reference will be given above)
- plot generated with /plots3/Fig7.py

## plots3/Fig8
- data generate using parallized version of the /code/coolingalgorithm.py made and used on GPU cluster by Nenad Mijić and Davor Davidović (GitHub reference will be given above)
- plot generated with /plots3/Fig.py

## plots3/Fig9
 - data generate /code/coolingalgorithm.py 
 - first processing of the data is done with /plots3/prepare.py
 - final plot is done with /plots3/Fig9.py
 
## plots3/Fig10
- data generate using parallized version of the /code/coolingalgorithm.py made and used on GPU cluster by Nenad Mijić and Davor Davidović (GitHub reference will be given above)
- first processing of the date done with /plots3/data_preparation.py
- final plot is done with /plots3/Fig10.py

## plots3/Fig11
- data generated with Gianpaolo code in /code/Code_Fig_11.ipynb within Jupyter notebook environemnt
- data stored in /data/data_for_referee/plot1/
- plot generate with /plots3/Fig11.py
 

## data/
- contains all the data used in this manuscript
- the relevant datasets are only the ones used to generate the figures
- The following codes are used to verify the output of the parallel algorithm implementations
- /data/checkED.py
- /data/checkED_int.py
- /data/checkED_int_initialstatistics.py
- /data/checkED_int_loop.py
- /data/collect.py
- /data/test.py

## dependencies/
- this folder contains all the necessary functions written by Jovan and Gianapaolo that are used in this manuscript