semvec/pypi-packages huggingface dataset:
vasp-phonopy-sscha 1.0
An interface between VASP, PHONOPY and SSCHA

##########################################################################
############################ vaspsscha 1.0 ##############################
#########################################################################

VASP = Vienna Ab Initio Simulation Package
SSCHA = Stochastic Self Consistent Harmonic Approximation

This is the first version of the python package for the treatment of VASP and SSCHA input/output.


### 1# HOW TO INSTALL ###

Two ways:

1) pip install vaspsscha

This will safely store your package in the python environment in which you are running the command. In general, i really advice adopting the conda environment manager. If this is the case, it would be really useful to set an alias for the main script  "vaspsscha/vaspsscha/interface.py", creating a line in ~/.bashrc, ~/.myaliases (or any sourced file):

alias vaspsscha="python /YOUR-ANACONDA-INSTALLATION-PATH/anaconda3/envs/YOUR-ENVIRONMENT/lib/python3.10/site-packages/vaspsscha/interface.py"

2) python setup.py install

This will store the package folder as a subdirectory of your python in use. If you use the built-in python (highgly not recommended), it will end up in /usr/local/lib/python2.X.

FINAL REMARK: Wherever the package is installed, assign an alias to it in order to be able to call all the vaspsscha modules without referring to the installation path.


### 2# FOLDER STRUCTURE ###
                                              pop_n
                                                |
    dyn                                        data                                                    vasp
                                 -where the SSCHA ensemble are stored                            - VASP execution directory 
                                                                                                    containing the ensembles POSCARs
### 3# HOW TO USE ###

vaspsscha intervenes in different points in the VASP+SSCHA workflow. All the scripts 
reside in the same folder vaspsscha/vaspsscha. 

FIRST THINGS FIRST: you need to have executed a phonopy harmonic calculation (even of very low precision), wheter using DFPT in the unit cell or FINITE DIFFERENCES in the supercell. Furthermore, you should have extracted the force constants through PHONOPY. 

THE MAIN SCRIPT

interface.py

1) 
    i) Creation of the Gamma point dynamical matrix (dynq1) from the just calculated PHONOPY FC (trial harmonic guess for the SSCHA cycle)
    ii) Interpolation on any q-grid of choice. These matrixes are grouped by reciprocal space symmetry in as many "dynq" files as the number of irreducible q* points.
    iii) generation of the first population from the harmonic guess. In case the dynamical matrix is not positive definite (aka unstable/imaginary phonons), it can be positivized in order to be able to generate the ensembles.
vasp-sscha 0.1
An interface between VASP and SSCHA

##########################################################################
############################ vasp_sscha 1.0 ##############################
#########################################################################

VASP = Vienna Ab Initio Simulation Package
SSCHA = Stochastic Self Consistent Harmonic Approximation

This is the first version of the python package for the treatment of VASP and SSCHA input/output.


### HOW TO INSTALL ###

### HOW TO USE ###
pycollage 0.0.0
Recreate an image with many others
Requires Python: >=3.8

Create collages of images.

![](tomato.png)

## Installation

Install the package via either of these options:
1. ```console
   $ git clone https://github.com/kklein/pycollage.git
   $ conda env create -f environment.yml
   $ conda activate pycollage
   $ pip install --no-build-isolation -e .
   ```
2. `$ pip install pycollage`
3. `$ conda install pycollage -c conda-forge`

## Usage

In order to use `pycollage`, you should have a collection of images
which makes up for the individual components of the collage as well
as a target image which the collage seeks to imitate.

In the following we will assume that you saved the image collection under
`/users/Anne/image_collection` and the target image under
`/users/Anne/target_image.png`. Best use absolute paths.

### Processing the collection

The first step consists of processing the image collection. In order to do so,
run

```
$ pycollage process-collection /users/Anne/image_collection
```

By default, this will resize the images to each be 20x20 pixels. If you'd like
a different component size, e.g. 30x30 pixels, run

```
$ pycollage process-collection /users/Anne/image_collection --component_size 30
```

instead.

You will notice that executing this will tell you where an index directory has been
created. Given the paths from above, this should be `/uers/Anne/index`. Moreover
another new directory has been created: `/users/Anne/image_collection_resized`.

### Building a collage

Now that the collection has been duely processed, we can embark onto actually building a collage.

For that purpose, simply run

```
$ pycollage build /users/Anne/index /users/Anne/target_image
```

where the first path corresponds to the path that was mentioned to you
when executing `process_collection`.

At the end of the process both the original target image as well as the collage
imitating the target image will be opened. You can then save the collage to wherever
you like.


### Runtime
Note that this process will take longer
* the larger the `component_size` you provide
* the larger your collection of images
* the larger the resolution of the target image

Since the collage will scale up the original target image by `component_size` per
dimensions - i.e., 20 by default - you might want to scale down your original
target image to below 1000x1000 pixels.