semvec/pypi-packages huggingface dataset:
physquirrel 1.0.6
package for reconstructing semi-directed phylogenetic level-1 networks from four-leaved networks and sequence alignments
Requires Python: >=3.7

# `physquirrel`
`physquirrel` is a Python package for phylogenetic network analysis, with a focus on reconstructing semi-directed phylogenetic level-1 networks from quarnets and/or sequence alignments. The package provides tools to build and visualize phylogenetic networks, leveraging the **Squirrel** algorithm for efficient network reconstruction.


## List of important features
- **$\delta$-heuristic** to construct quarnets (4-leaf subnetworks) from a multiple sequence alignment (in `.fasta` or `.nexus` format)
- **Squirrel** algorithm to construct semi-directed phylogenetic level-1 networks from quarnets
- Basic visualization of networks
- Exporting phylogenetic trees and networks in `eNewick` format
- Methods to extract information from a network (e.g. its set of splits, its displayed quarnets)

## Installation
If you have an up-to-date version of [Python](https://www.python.org/downloads/) installed on your device, the standard package manager `pip` should come pre-installed. Then, you can install `physquirrel` from [PyPI](https://pypi.org/project/physquirrel/) by simply using the following command in a terminal:

`python -m pip install physquirrel`

  
## Example usage

### Importing the package
To get started with `physquirrel`, open a Python shell and import the package with:

```
import physquirrel as psq
```

### Creating a set of quarnets
Use the $\delta$-heuristic to create a dense set of tf-quarnets from a multiple sequence alignment as follows:
```
msa = psq.MSA('path/to/msa/file.fasta')
Q = msa.delta_heuristic()
```

Alternatively, the dense set of tf-quarnets can also be loaded directly from a `.txt` file as follows:
```
Q = psq.DenseQuarnetSet('path/to/quarnet/file.txt')
```
This method assumes that the `.txt` file contains one line per tf-quarnet. The quarnets need to be one of the following two types:
1. `SQ: a b c d` for a quarnet on leaves $\{a,b,c,d\}$ with a split $ab|cd$.
2. `4C: a b c d` for a quarnet on leaves $\{a,b,c,d\}$ with a four-cycle $a,b,c,d$ and the leaf $a$ below the reticulation.


### Reconstructing a network
To create a semi-directed network from the dense set of tf-quarnets, run the Squirrel algorithm:
```
N = Q.squirrel()
```
To view the semi-directed network and print its `eNewick` string (with an arbitrary rooting), run:
```
N.visualize()
eNewick = N.create_enewick()
print(eNewick)
```
        
For a complete overview of different methods and extra parameter options, please check the method descriptions in the [source code](https://github.com/nholtgrefe/squirrel/tree/main/physquirrel/src/physquirrel) of `physquirrel`.


## Citation
If you use `physquirrel`, please cite the corresponding paper:

*Squirrel: Reconstructing semi-directed phylogenetic level-1 networks from four-leaved networks and sequence alignments* by Niels Holtgrefe, Katharina T. Huber, Leo van Iersel, Mark Jones, Samuel Martin, and Vincent Moulton.
irnl-rdt-correction 1.1.2
Correction script to power the nonlinear correctors in the (HL-)LHC insertion regions based on RDTs.
Requires Python: >=3.9
Keywords: accelerator, correction, hl-lhc, lhc, nonlinear

# IRNL RDT Correction 

[![Cron Testing](https://github.com/pylhc/irnl_rdt_correction/workflows/Cron%20Testing/badge.svg)](https://github.com/pylhc/irnl_rdt_correction/actions?query=workflow%3A%22Cron+Testing%22)
[![Code Climate coverage](https://img.shields.io/codeclimate/coverage/pylhc/irnl_rdt_correction.svg?style=popout)](https://codeclimate.com/github/pylhc/irnl_rdt_correction)
[![Code Climate maintainability (percentage)](https://img.shields.io/codeclimate/maintainability-percentage/pylhc/irnl_rdt_correction.svg?style=popout)](https://codeclimate.com/github/pylhc/irnl_rdt_correction)
[![GitHub last commit](https://img.shields.io/github/last-commit/pylhc/irnl_rdt_correction.svg?style=popout)](https://github.com/pylhc/irnl_rdt_correction/)
[![GitHub release](https://img.shields.io/github/release/pylhc/irnl_rdt_correction.svg?style=popout)](https://github.com/pylhc/irnl_rdt_correction/)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6373374.svg)](https://doi.org/10.5281/zenodo.6373374)

Performs local correction of the Resonance Driving Terms (RDTs)
in the Insertion Regions (IRs) based on the principle described in
[[1]](#1) with the addition of correcting
feed-down and using feed-down to correct lower order RDTs.
Details can be found in [[2]](#2).


## Documentation

- The note containing background, derivation and implementation details can be found in the attached [note](latex/note.pdf).
- Autogenerated docs via `Sphinx` can be found at <https://pylhc.github.io/irnl_rdt_correction/>.

## Installing

This package is `Python 3.7+` compatible, and can be installed through `pip`:
```bash
python -m pip install irnl-rdt-correction
```

After installing, scripts can be run with either `python -m irnl_rdt_correction --FLAG ARGUMENT` or by calling the Python files directly.

For development purposes, we recommend creating a new virtual environment and installing from VCS in editable mode with all extra dependencies:
```bash
git clone https://github.com/pylhc/irnl_rdt_correction
python -m pip install --editable "irnl_rdt_correction[all]"
```

## Main Script 

-  [**irnl_rdt_correction.py**](irnl_rdt_correction/main.py) - Script to compute RDT correction in the (HL)LHC IRs including feed-down effects.

## License

This project is licensed under the `MIT` License - see the [LICENSE](LICENSE) file for details.


## References

<a id="1">[1]</a> :
O. Bruening et al.,
Dynamic aperture studies for the LHC separation dipoles. (2004)
https://cds.cern.ch/record/742967

<a id="2">[2]</a> :
J. Dilly and R. Tomás,
A flexible nonlinear Resonance Driving Term based Correction Algorithm with feed-down. (2022)
[note.pdf](latex/note.pdf)
hyperbee 0.0.2.9
The official Python library for the hyperbee API
Requires Python: >=3.7.1

# HyperChat™ Python Client

The HyperChat™ Python client provides convenient access to HyperChat™ REST APIs from any Python 3.7+ application. The library includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by [httpx](https://github.com/encode/httpx) as well as programmatic Collection management functions for RAG applications.

Installation:
```sh
pip install hyperbee
```

See the documentation and examples at [docs.hyperbee.ai](https://docs.hyperbee.ai/api_and_libraries/python_client).
snap-combine 0.9.0
SuperNova Async Pipeline: combining
Requires Python: >=3.7

# snap-combine 
![Tests](https://github.com/Sheshuk/snap-combine/workflows/Python%20package/badge.svg) ![Deploy](https://github.com/Sheshuk/snap-combine/workflows/Upload%20Python%20Package/badge.svg) ![PyPI](https://img.shields.io/pypi/v/snap-combine) ![release](https://img.shields.io/github/v/release/Sheshuk/snap-combine?include_prereleases) 

Code for combining SN significance from several detectors/experiments.

This package contains plugins for [SNAP](https://github.com/Sheshuk/snap-base) framework.

[Documentation](https://snap-combine.readthedocs.io)

## Installation
```
python -m pip install snap-combine
```

## Example

Example configurations provided the `examples` dir use these steps to 
* [client_sender.yml](examples/client_sender.yml): generate client data, calculate significance and send it (via ipc zeromq socket) to combiner node
* [combine.yml](examples/combine.yml): receive client significance time series, combine them and apply thresholds.

On client side run 
```
DETECTOR="det_name_1" snap_run client_sender.yml
```

Optionally in another terminal session:
```
DETECTOR="det_name_2" snap_run client_sender.yml
```

In another session (combination side):
```
snap_run combine.yml
```

You should see the monitoring ticks for all nodes.
Also you should see the files with the data output: `data_received.dat`,`data_combined.dat` and `data_triggered.dat`.

After about 120s from the client start, a supernova signal will be emitted, so the significance becomes elevated, and the Trigger counter will advance.
firstpackage-upload 0.0.1
A simple package that I cloned from Digital Innovation One studying for data engineering with Python.
Requires Python: >=3.8
Keywords: image processing

# image_processing

Description,
The package image_processing is used to:
    iamge_processing is used to:
        Processing:
            - Histogram matching
            - Structural similarity
            - Resize image
        Utils:
            - Read image
            - Save image
            - Plot image
            - Plot result
            - Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install image_processing

''''bash
pip install image_processing
''''

## Usage 

''''python
from image_processing import file1_name
file1_name.my_function()
''''

## Author
Andrew Mendes


## License
[MIT] (https://choosealicense.com)
simplepva 0.1.0
Help your python code have parameters and arguments

# simplepva

## Examples of How To Use 

Create a parameter

```python
from simplepva import AGR

parser=AGR()
# create two parameters --say and --cry
parser.add_agr('--say')
parser.add_agr('--cry')
# read all parameters and arguments, this is a required function
parser.parse_agrs()
# return argument of --say and --cry
say_data=parser.get_value('--say')
cry_data=parser.get_value('--cry')
print(say_data)
print(cry_data)
# Run it on terminal like this
# python yourfile.py --say "hello this is my test" --cry huhu
# output: hello this is my test
#         huhu
```
If you want a parameter to have a default argument,this default value can be changed.
```python
from simplepva import AGR

parser=AGR()
parser.add_agr('--say',default='hello world')
parser.parse_agrs()
say_data=parser.get_value('--say')
print(say_data)
# this is default:
# output: hello world
# change value:
# python yourfile.py --say "hello this is my test"
# output: hello this is my test
```
If you just want to add parameters without arguments
```python
from simplepva import AGR
parser=AGR()
parser.add_agr('--help',action='store_true')
parser.parse_agrs()
help_data=parser.get_value('--help')
if help_data:
    print('User entered help')
# python yourfile.py --help
# output: User entered help
```
Thanks from Phan Huynh Thien Phu