semvec/pypi-packages huggingface dataset:
setFTs 0.0.1.1
A collection of Discrete Set Fourier Transformation Algorithms

setFTs
======
This library provides functionalities for calculating the Fourier transform on set functions, 
based on the novel mathematical foundation of discrete signal processing on set functions [1].
We provide functionalities for:
- Initializing a set function object from a full set of function evaluations.
- Inititalizing a set function object from a queryable python function.
- Applying the fast Fourier transform algorithm [1]
- Applying sparse Fourier transform algorithm [2]
- Set function minimization algorithms [3]
- Shapley Values Calculation
## 1 Documentation
Full documentation of the setfunctions and plotting modules can be found at: https://ebners.github.io/setFTs_docs/
or in the Documentation_setFTs.pdf provided in the repositors

## 2 Requirements
setFTs uses the python library pySCIPOpt for the implementation of the MIP-based minimization algorithm. 
pySCIPOpt requires a working installation of the SCIP Optimization Suite. The creators of pySCIPOpt recommend using conda as it installs SCIP automatically. And allows the installation of pySCIPOpt in one command:
```
conda install --channel conda-forge pyscipopt
```
More information about installing pySCIPOpt can be found at: https://github.com/scipopt/PySCIPOpt/blob/master/INSTALL.md

## 3 Installation
The installation of our package works over pypi and therefore a working installation of pip is needed. The pip command to install setFTs is the following:
```
pip install setFTs
```
## References
[1]
```
@article{Discrete_Signal_Proc, 
     title={Discrete signal processing with set functions},
     volume={69},
     DOI={10.1109/tsp.2020.3046972},
     journal={IEEE Transactions on Signal Processing},
     author={Puschel, Markus and Wendler, Chris},
     year={2021}, 
     pages={1039–1053}
 } 
```
 [2]
```
 @article{Sparse,
    author    = {Chris Wendler and
               Andisheh Amrollahi and
               Bastian Seifert and
               Andreas Krause and
               Markus P{\"{u}}schel},
    title     = {Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases},
    journal   = {CoRR},
    volume    = {abs/2010.00439},
    year      = {2020},
    url       = {https://arxiv.org/abs/2010.00439},
}
```
[3]
```
@article{MIPS,
    author={Weissteiner,Jakob and Wendler, Chris and Seuken, Sven and Lubin,Ben and Püschel, Markus},
    title={Fourier analysis-based iterative combinatorial auctions},
    DOI={10.24963/ijcai.2022/78},
    journal={Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence},
    year={2022}
    } 
```
mlmprovisions 0.1.8
Reusable Django app to track provisions for affiliate partners. Includes tools for multi-level marketing provisions.
Requires Python: >=3.4

# mlmprovisions

Reusable Django app, that allows you to track provisions
for your affiliate partners. It has a flexible way of configuring how to 
split the provision and is capable of running 
**multi-level marketing**

## Screenshots
![ScreenShot](docs/screenshot1.png "Marketer node detail")
![ScreenShot](docs/screenshot2.png "Discount code detail")
![ScreenShot](docs/screenshot3.png "Admin config for all")
![ScreenShot](docs/screenshot4.png "Admin config of marketer node")

## Instalation
```bash
pip install mlmprovisions
```
Then add required apps **settings.py**
```python
INSTALLED_APPS = [
    ...
    'treenode',
    'mlmtools',
    'floppyforms',
    ...
]
```

Add to the **urls.py**
```python
    path('mlmtools/', include('mlmtools.urls'))
```
And sync the db
```bash
python manage.py makemigrations
python manage.py migrate
```


## Configuration and usage
Using the admin interface add marketer nodes and tracking codes for them.
We suggest adding 1 central node and several subnodes

Fields:
* **Owner** - the user this note is associated to
* **Split type** - how to split the provision
  * **fixed** - this node takes a fixed percentage*
  * **tree split** - MultiLevel split. This split is how much is sent up the tree
* **Split cut** - the slider how much we keep and how much we pass to the child nodes
* **Can have children** - whether the subnodes can create subnodes (MLM needs this)  
* **track code usage** - how many times can the code be used (limitless/only once for all/only once per user)

To add attribution you have these methods
```python
from mlmtools.models import TrackCode, CodeUse
str_code = "CFHUMJ"

#tells if this code can be used
CodeUse.test_use(str_code, client_user) #tells if this code can be used

#lets have something like an order (the track code uses generic relation)
dt = DummyTarget(name="my order target 2", amount=1000.0)
dt.save() 

#and attach the code
CodeUse.use(str_code, client_user, target=dt, amount=dt.amount)
```

And finaly you can check how it looks by visiting
```
http://<yoursite>/mlmtools/list_codes/
```

ENJOY!
streamlit-draggable-list 0.0.1
Streamlit component of draggable list
Requires Python: >=3.6

# streamlit-draggable-list
A Streamlit wrapper component on react-smooth-dnd

### Exmple
```python
import streamlit as st
from st_draggable_list import DraggableList

st.title("DraggableList")

data = [
    {"id": "oct", "order": 10, "name": "Oct"},
    {"id": "nov", "order": 11, "name": "Nov"},
    {"id": "dec", "order": 12, "name": "Dec"},
    {"id": "jan", "order": 1, "name": "Jan"},
    {"id": "feb", "order": 2, "name": "Feb"},
    {"id": "mar", "order": 3, "name": "Mar"},
    {"id": "apr", "order": 4, "name": "Apr"},
    {"id": "may", "order": 5, "name": "May"},
    {"id": "jun", "order": 6, "name": "Jun"},
    {"id": "jul", "order": 7, "name": "Jul"},
    {"id": "aug", "order": 8, "name": "Aug"},
    {"id": "Sep", "order": 9, "name": "Sep"},
]

slist = DraggableList(data, key="foo")
st.write(slist)
```
![draggable_list](./draggable_list.png)

### Component value
![component_value](./component_value.png)

### Install
```
pip install streamlit-draggable-list
```
SwifTCR 0.4.12
Cluster and construct netwotk graph of highly similar sequences
Keywords: cluster,TCR,edit,distance

SwifTCR is a Python package provides a relatively quick way to find all sequences with shared single-only-replacement, only-deletion or both, creating clusters of CDR3s highly similar and construct a network graph. The tool, designed to enable researchers to quickly identify clusters in a relatively small data-set, is also available for large-scale data collection thanks its implementation of Spark, making it available for big-data immunological analysis.
dlns3-wrapper 0.0.1
DevsLane S3 Wrapper in Python
Requires Python: >=3.8

# DevsLane S3 Wrapper in Python

## Install

```bash
pip3 install dlns3
```

## Available Methods

- delete
- getSignedUrl
- upload
- changeObjectACL
- getBaseURL
- getRelativePath
- getFullURL
- copy
- download

## Code Examples

```python
from dlns3.constant import s3Credentials, s3UploadConfig, constants
from dlns3 import DLNS3
from settings import settings

credentials = s3Credentials.S3Credentials(
    bucket=settings['AWS_BUCKET'], secret=settings['AWS_SECRET_KEY'], key=settings['AWS_ACCESS_KEY'], region=settings['AWS_REGION'])

s3 = DLNS3(credentials)

config = s3UploadConfig.S3UploadConfig(
    acl=constants.S3ACL.PUBLIC_READ, contentType="image/jpeg")

print(s3.upload('./temp/abhi-hum-zinda-hai-new.jpeg',
      'abhi-hum-zinda-hai-new.jpeg', config))
```
lnschema_drylab 0.1.0
Schema module drylab (`xqfj`).

[![pypi](https://img.shields.io/pypi/v/lnschema_drylab?color=%2334D058&label=pypi%20package)](https://pypi.org/project/lnschema_drylab)

# lnschema-drylab: Drylab (`xqfj`)

Read the [docs](https://lamin.ai/docs/lnschema-drylab).