semvec/pypi-packages huggingface dataset:
qsft 0.0.2
Efficiently computing sparse Fourier transforms of functions
Requires Python: >=3.8

# Efficient Sparse q-ary Fourier Transforms

This repository contains code for the paper:

_"Efficiently Computing Sparse Fourier Transforms of_ $q$_-ary Functions" Yigit Erginbas*, Justin Kang*, Amirali Aghazadeh, Kannan Ramchandran_

*Equal contribution: These authors contributed equally.

This package may be useful to you if you deal with complicated functions of $q$-ary sequences, for example, 
functions of protiens, DNA or RNA. 
### Table of Contents
* [Abstract](#abstract)

### Abstract
<a id=abstract></a>
Fourier transformations of pseudo-Boolean functions are popular tools for analyzing functions of binary sequences. Real-world functions often have structures that manifest in a sparse Fourier transform, and previous works have shown that under the assumption of sparsity the transform can be computed efficiently. But what if we want to compute the Fourier transform of functions defined over a $q$-ary alphabet? These types of functions arise naturally in many areas including biology. A typical workaround is to encode the $q$-ary sequence in binary however, this approach is computationally inefficient and fundamentally incompatible with the existing sparse Fourier transform techniques. Herein, we develop a sparse Fourier transform algorithm specifically for $q$-ary functions of length $n$ sequences, dubbed $q$-SFT, which provably computes an $S$-sparse transform with vanishing error as $q^n$ goes to $\infty$ in $O(Sn)$ function evaluations and $O(S n^2 \log q)$ computations, where $S = q^{n\delta}$ for some $\delta < 1$. Under certain assumptions, we show that for fixed $q$, a robust version of $q$-SFT has a sample complexity of $O(Sn^2)$ and a computational complexity of $O(Sn^3)$ with the same asymptotic guarantees. We present numerical simulations on synthetic and real-world RNA data, demonstrating the scalability of $q$-SFT to massively high dimensional $q$-ary functions.
pyDSA-gui 1.5.4
GUI for pyDSA_core
Keywords: GUI DSA drop shape contact angle hysteresis

<div align="center">
  <img width=500px" src="https://framagit.org/gabylaunay/pyDSA_core/raw/master/branding/pyDSA_logo_text.svg"><br><br>
</div>


# Drop shape analysis software.


PyDSA is a software for drop shape analysis.
It allows to measure the properties of droplets in contact with solids (contact angle, droplet volume, evaporation rate, displacement velocity, ...) through an intuitive interface.
PyDSA is developed in Python and use Qt5 for it graphical interface.

## Installation<a name="installation"></a>

``pip install pyDSA_gui``

To update PyDSA, use:

``pip install -U pyDSA_gui``

## Usage

Just start PyDSA from the terminal (or anaconda console) with:

``pyDSA``

## Screenshots
<div>
Image scaling and pre-processing:<br>
<a href="doc/screenshot1.png">
<img src="doc/screenshot1.png" alt="Import" width="300"/>
</a>
</div>
<div>
<br>
Droplet edge detection:<br>
<a href="doc/screenshot2.png">
<img src="doc/screenshot2.png" alt="Import" width="300"/>
</a>
</div>
<div>
<br>
Droplet edge fitting:<br>
<a href="doc/screenshot3.png">
<img src="doc/screenshot3.png" alt="Import" width="300"/>
</a>
</div>
<div>
<br>
Droplets properties evolution after full analysis:<br>
<a href="doc/screenshot4.png">
<img src="doc/screenshot4.png" alt="Import" width="300"/>
</a>
</div>

## Documentation

PyDSA is designed to be simple and straightforward to use.
If you don't understand the effect of an option,
please refers to the inline documentation of [pyDSA_core](https://framagit.org/gabylaunay/pyDSA_core),
where all the features are documented in details.

## Citing this software

If PyDSA have been usefull for you, please consider citing it:
```
Launay G. PyDSA: Drop shape analysis in Python, 2018-, https://framagit.org/gabylaunay/pyDSA_core [Online; accessed <today>].
```

bibtex entry:
``` bibtex
@Misc{launay_pydsa_2018,
  author =    {Gaby Launay},
  title =     {{PyDSA}: Drop shape analysis in {Python}},
  year =      {2018--},
  url = "https://framagit.org/gabylaunay/pyDSA_core",
  note = {[Online; accessed <today>]}
}

```

## Issues and bugs

PyDSA is hosted on [Framagit](https://framagit.org/gabylaunay/pyDSA_gui).
If PyDSA crashes or behaves abnormally, just send me an email at [gaby.launay@protonmail.com](mailto:gaby.launay@protonmail.com).
Any of the followings will greatly help me fix the issue:

- A description of the problem
- The logs from the terminal
- A test case to reproduce the problem
pyDSA-core 1.4.1
Python Drop Shape Analyzer
Keywords: DSA drop shape contact angle hysteresis

<div align="center">
  <img width=500px" src="https://framagit.org/gabylaunay/pyDSA_core/raw/master/branding/pyDSA_logo_python_text.svg"><br><br>
</div>

# PyDSA_core: python3 package for drop shape analysis.

## Features

PyDSA_core allow to import videos and images of droplets and to get their properties, including

  - Drop edges positions
  - Contact angles
  - Contact angle hysteresis
  - Radius
  - Volume
  - Triple point positions (for SLIPS surfaces)
  - ...

A Graphical interface is also available [here](https://framagit.org/gabylaunay/pyDSA_gui).

A tutorial is available [here](https://gabylaunay.github.io/Python-cookbook/).

## Dependencies

pyDSA_core relies on matplotlib, numpy, scipy, IMTreatment and OpenCV.

## Installation

``pip install pydsa_core``

To update pyDSA_core, use:

``pip install -U pydsa_core``

## Citing this software

If PyDSA_core have been usefull for you, please consider citing it:
```
Launay G. PyDSA: Drop shape analysis in Python, 2018-, https://framagit.org/gabylaunay/pyDSA_core [Online; accessed <today>].
```

bibtex entry:
``` bibtex
@Misc{launay_pydsa_2018,
  author =    {Gaby Launay},
  title =     {{PyDSA}: Drop shape analysis in {Python}},
  year =      {2018--},
  url = "https://framagit.org/gabylaunay/pyDSA_core",
  note = {[Online; accessed <today>]}
}

```
prokerala-api 0.1.0
SDK for accessing Prokerala API
Requires Python: >=3.0

## Python client

This is a sample API client written in [Python](https://www.python.org/) for accessing Prokerala Astrology API.

## Installation

Install the package from Python Package Index

```
pip install prokerala-api
```

## Usage

You can find your `clientId` and `clientSecret` in your [dashboard](https://api.prokerala.com/account/client)


### [Kundli](https://api.prokerala.com/docs#operation/get-kundli)

```python
#!/usr/bin/env python

from prokerala_api import ApiClient
import json

def run():
    client = ApiClient('YOUR_CLIENT_ID', 'YOUR_CLIENT_SECRET')
    result = client.get('v2/astrology/kundli/advanced', {
        'ayanamsa': 1,
        'coordinates': '23.1765,75.7885',
        'datetime': '2020-10-19T12:31:14+00:00'
    })
    print json.dumps(result, indent=4)

if __name__ == '__main__':
    run()

```
personality_questionnaire 1.1.2
Personality and general experiment questionnaire processing methods.
Requires Python: >=3.11
Keywords: bfi-2, personality, questionnaire, vas-f

# personality_questionnaire
[![License](https://img.shields.io/badge/license-MIT-yellow.svg)](LICENSE)
[![python](https://img.shields.io/badge/Python-3.11-3776AB.svg?style=flat&logo=python&logoColor=white)](https://www.python.org)
[![Checked with mypy](https://www.mypy-lang.org/static/mypy_badge.svg)](https://www.mypy-lang.org/)

Personality and general questionnaire processing methods for various experiments.

# Supported Questionnaires
* BFI-2
* VAS-F

## Inputs
Questionnaire answers are...
* read via command line interface
* ints in a numpy ndarray (.npy)
* ints in a csv file (.csv)
* strings in a csv file (.csv)

## Outputs
For BFI-2, the OCEAN and FACET values are scaled to the range [0..1] per participant.

For VAS-F, the relative values are calculated.

# Setup
### Install package from PyPI
```
pip install personality_questionnaire
```

### Install package for development
```
git clone https://github.com/fodorad/personality_questionnaire
cd personality_questionnaire
pip install .
pip install -U -r requirements.txt
python -m unittest discover -s test
```

# Quick start
### Run interactive BFI-2 questionnaire
If you installed it via pip:
```
personality_questionnaire --participant_id test --questionnaire bfi2
```
alternative call:
```
python personality_questionnaire/api.py --participant_id test --questionnaire bfi2
```

### Run interactive VAS-F (pre- and post-)questionnaires
If you installed it via pip:
```
personality_questionnaire --participant_id test --questionnaire vasf --vasf_tag pre
personality_questionnaire --participant_id test --questionnaire vasf --vasf_tag post
```
alternative call:
```
python personality_questionnaire/api.py --participant_id test --questionnaire vasf --vasf_tag pre
python personality_questionnaire/api.py --participant_id test --questionnaire vasf --vasf_tag post
```

# Projects using exordium

### (2022) PersonalityLinMulT
LinMulT is trained for Big Five personality trait estimation using the First Impressions V2 dataset and sentiment estimation using the MOSI and MOSEI datasets.
* paper: Multimodal Sentiment and Personality Perception Under Speech: A Comparison of Transformer-based Architectures ([pdf](https://proceedings.mlr.press/v173/fodor22a/fodor22a.pdf), [website](https://proceedings.mlr.press/v173/fodor22a.html))
* code: https://github.com/fodorad/PersonalityLinMulT

# What's next
* Add support for the following questionnaires: HEXACO

# Updates
* 1.1.0: Add support for VAS-F and interactive CMD interface.
* 1.0.0: Add support for BFI-2 and PyPI publish.

# Contact
* Ádám Fodor (foauaai@inf.elte.hu)