semvec/pypi-packages huggingface dataset:
battle-cats-game-modder-cmd-line-version 1.0.8
A battle cats tool for modifying, encrypting, and decrypting game files
Requires Python: >=3.6

# read this please!
this is a port of the GUI [BCGM-python](https://github.com/fieryhenry/BCGM-Python) to a command line because i can't get wxpython to install.  
read [this](https://github.com/fieryhenry/BCGM-Python) for instructions on how to use
herhoopstats 0.0.1
Programmatic access to the Her Hoop Stats website
Requires Python: >=3
Keywords: sports,basketball,stats

# Her Hoop Stats

Provides access to the [Her Hoops Stats](https://herhoopstats.com) service for women's basketball (WNBA/NCAA) statistics. Subscription required.

## Installation

Python >= 3.5

`pip install herhoopstats`

## Usage

```python
from herhoopstats.api import HerHoopStats

api = HerHoopStats(email="<EMAIL>", password="<PASSWORD>")

teams = api.get_teams()
drexel = [t for t in teams if t.team == "Drexel"][0]
stats = api.get_team_statistics(drexel)
roster = api.get_roster_for_team(drexel)
```
snprocess 1.0.5
A SNP processing package
Requires Python: >=3.6

# SNProcess
![](img/logo.png)

  by Thomas Dokas
  
  <dokastho@umich.edu>

  SNProcess is a Single Nucleotide Polymorphism (SNP) Quality Control pipeline, written in python the procedure was developed by [Shing Wan Choi @ Mount Sinai, NYC](https://github.com/choishingwan/PRS-Tutorial) and for more info check out this [tutorial](https://github.com/MareesAT/GWA_tutorial).

## How to Install
  SNProcess is very easy for any user to install. It's listed on [pypi](https://pypi.org/project/snprocess), which means you can install it using pip:
  ```bash
  pip install snprocess
  ```
  and you can upgrade it by running:
  ```bash
  pip install snprocess --upgrade
  ```

## Results
  SNProcess will run a QC pipeline and produce the output files **qcplink.xyz**

## Evaluation
  SNProcess compiles relevant information with regard to the QC process into an HTML webpage. This can be found in your specified output folder, in the file **index.html**. SNProcess also provides a way of viewing this file easily, just run:
  ```bash
  snprocess -i
  ```
  and go to **localhost:8008**. This is a way of viewing this generated webpage using your own computer as a host. Pretty cool, right?

  ![](gif/tutorial.gif)

## FAQ
  * What should my input json look like? *Run snprocess -e for an example json printed to your console, or snprocess -g to generate your own to your output directory*
  * SNProcess won't run for me! What's wrong? *SNProcess is a somewhat unique program in that it uses [plink](https://www.cog-genomics.org/plink/1.9/) as a driver/helper. Make sure that's installed. If the issue persists, please file a bug report at the snprocess [repo](https://github.com/dokastho/snprocess)*

# More About SNProcess

## QC13. Remove individuals with outlying gender SNP's

Steps for QC:
1. Check missingness and generate plots
2. Remove individuals with high missingness
4. Select autosomal SNPs only and filter out SNPs with low minor allele frequency (MAF)
5. Delete SNPs not in the Hardy-WEinberg equilibrium (HWE)
6. Heterozygosity and LD Pruning

## QC2
This portion of the pipeline compares the user data with data in the 1,000 genome project and produces graphs that show the population stratification based on race & ethnicity

# TODO
* [ ] move final rscripts to python. shouldn't there be a python-like ggplot?
* [x] use tmp folder for files that arent used in index.html and qcplink.xyz
* [ ] 1kg check if exists, otherwise download. use settings input flag to do qc on 1kg or not
* [ ] remove snprocess.log
* [ ] try to redirect warnings to log
* [x] save the plink file to output dur BEFORE LD pruning (that step is just for qc2) but keep this in for qc2
* [x] also save MDS_merged.mds
TriangularMap 0.0.1
A Python library for working with triangular maps stored in linear arrays.
Requires Python: >=3.10

# TriangularMap

[![tests](https://github.com/robert-lieck/TriangularMap/actions/workflows/tests.yml/badge.svg)](https://github.com/robert-lieck/TriangularMap/actions/workflows/tests.yml)
[![codecov](https://codecov.io/gh/robert-lieck/TriangularMap/branch/main/graph/badge.svg?token=XAUCWNS7II)](https://codecov.io/gh/robert-lieck/TriangularMap)

![build](https://github.com/robert-lieck/TriangularMap/workflows/build/badge.svg)
[![PyPI version](https://badge.fury.io/py/TriangularMap.svg)](https://badge.fury.io/py/TriangularMap)

[![docs](https://github.com/robert-lieck/TriangularMap/actions/workflows/docs.yml/badge.svg)](https://robert-lieck.github.io/TriangularMap/)
[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
functional-functions 0.6.1
Commonly used functions by the Compass FBI Team
Requires Python: >=3.6

# Functional-Functions
Created by the FBI Compass team, this package contains our team's most commonly used and useful functions

# About
Contents include functions for connecting to snowflake, saving and loading pickles. For help with what functions are available, please run help()

# Installation
To install functional-functions, you can install it using pip:
````python
pip install functional_functions
````

# Additional Prequisites
To use the redshift query connector, you must additionally ensure you have downloaded/installed:
* Java (Optional)
    * You can usually install that from here: [JDK 16 Downloads](https://www.oracle.com/java/technologies/javase-jdk16-downloads.html)
* JDK8 (Optional)
    * For some reason, you may need this as well? This was troubleshooting I went through for setup
    ````python
    brew tap adoptopenjdk/openjdk
    brew install --cask adoptopenjdk8
    ````


# Usage
Currently all files are in the init.py. Import each function as needed. First time users are encouraged to import and run help() for more info.

````python
import functional_functions
````

````python
from functional_functions import help, query_snowflake
````

# Troubleshooting/Prequisite/Additional Notes
Some functions, especially the connections, are built off of a settings.py file which stores local creds. You can view a sample settings file called 'settings.py.sample' in the included files. Obviously feel free to use another method to store and provide creds if you want. We now have added the ability to directly reference environmental variables and/or a .env file. See creds.env.sample for example file.

However if you are using 'settings.py' as your creds file, you will need to place it in your overall site-packages folder. If you are using 'creds.env' you will need to place it in your root folder or have a direct reference to it when you are loading it in. I'd recommend using python package python-dotenv.

**NOTE**: Yes I know code shouldnt be stored in __init__.py, but I also am learning packages and such!.

# Help/Issues/Bugs
Please contact lawrence.chin@compass.com if there are any questions, bugs, or issues.
4to5 0.0.1
Replace the number 4 with the number 5.

4to5 - Replace the number 4 with the number 5.
==============================================

Unlike 2to3, this module finally does what it says! Replaces two numbers on your
interpreter. It's a true life-saver for both you and your colleagues.

Usage
======
.. code-block:: python

    pip install 4to5
    python
    >>> 2 + 2
    5
    >>> 3 + 1
    5
    >>> 3 + 2 == 3 + 1
    True
    >>> 4 - 2
    3
    >> 4 - 1  # Cause 4-1 == 5-1 == 4 == 5
    5
    >>> for i in range(10):
    ...   print(i)
    ...
    0
    1
    2
    3
    5
    5
    6
    7
    8
    9
    

Notes
=====
50% chance you won't be able to remove it, as apparently the number 4 is
impotant for pip, and without it pip doesn't seem to work properly.

To manually uninstall, delete ``sitecustomize.py`` from your ``site-packages`` directory.
Maybe I'll add a ``fix_my_system.py`` file in the future to remove it without using
the number 4.

Supports virtual environments.

Enjoy!
hotknots 2.4
A a tool to predict RNA secondary structure, including pseudoknots
Requires Python: >3.5.2

# HotKnots
Fast, and best, DNA/RNA folding algorithm


This was my attempt to get an RNA/DNA secondary structure program that included pseudoknot
structures working as a Python C Extenstion.  

I tried several different algoritms/packages, and Hotknots was not only the fastest, but
also the only that actually did pseudoknots.

I got it mostly working, but have not gottten around to making the parameter files as 
internal variables.  Having extenal data files with python PIP pacakges is a hassle
due to operating environments being virtual and not having actual file system paths.
Currently the params folder has to be in the same folder that you run HotKnots

To install:
```
git clone https://github.com/deprekate/HotKnots.git
pip install HotKnots/ --user
```



To use on the command line:
```
echo AACCCCUGCUGAAUAAAGCGGGGAAUAACUAUUCUAC | hotknots.py
```
and the output should be the sequence, followed by the structure and mfe of the best folding
```
AACCCCUGCUGAAUAAAGCGGGGAAUAACUAUUCUAC
...((((((([[[[[.)))))))......]]]]]... -9.883
```


To import and use in other python code, you need to import the package, and then find out where
it is installed, so it can find the various parameter files.  This is also when you can specify
which model and paramters to use:
```
import HotKnots as hk
# initialize everything first
params = os.path.dirname(hk.__file__)
hk.initialize( "DP", os.path.join(params,"parameters_DP09.txt") , os.path.join(params,"multirnafold.conf"), os.path.join(params,"pkenergy.conf") )

print(hk.fold("AACCCCUGCUGAAUAAAGCGGGGAAUAACUAUUCUAC", "DP"))
```
pydear 2.1.0
Dear imgui binding

# pydear 🦌

ImGui wrapper generated by clang.cindex. 

- [github repository](https://github.com/ousttrue/pydear)
- [pypi](https://pypi.org/project/pydear/)

Works in conjunction with [PyOpenGL](https://pypi.org/project/PyOpenGL/) and [glfw](https://pypi.org/project/glfw/).

[examples](./examples)

## PreBuild wheel

Only Python-3.10 (Windows 64bit) is provided.
It was developed on Windows and worked on Linux as well.

## Included Bindings

A luajit-like style using ctypes.
Pointer arguments are passed by an array of length 1.

```python
p_open = (ctypes.c_bool * 1)(True)
ImGui.Begin('name', p_open)
```

Also, all C structs are defined as `ctypes.Struct` generated using clang.cindex.

```python
class ImGuiIO(ctypes.Structure):
    _fields_=[
        ("ConfigFlags", ctypes.c_int32), # Int32Type: int,
        ("BackendFlags", ctypes.c_int32), # Int32Type: int,
```

The ImGUi backend is provided in a pure Python module `pydear.backends`.
Currently only glfw and OpenGL3 exist.

Most bindings are available, except for functions that have callback and va_list as arguments.

### ImGui-1.87 docking branch

<https://github.com/ocornut/imgui>

### ImNodes

<https://github.com/Nelarius/imnodes>

### NanoVG modified

<https://github.com/ousttrue/picovg/>

## Detail

<https://ousttrue.github.io/pydear/>

## Build

### Code generation

<https://github.com/ousttrue/rawtypes>

### Package check

```
py -m pip wheel .
twine check pydear-1.12.1-cp310-cp310-linux_x86_64.whl
```
fastapi-users-tortoise 0.2.0
FastAPI Users - Database adapter for Tortoise ORM
Requires Python: >=3.7

# FastAPI Users - Database adapter for Tortoise ORM

Tortoise orm database adapter for [fastapi-users v10](https://fastapi-users.github.io/fastapi-users/10.0/) and above.
If you want an example of a project using this or just bootstrap your project with fastapi-users and tortoise, you can check out the cookiecutter I'm working on [here](https://github.com/Tobi-De/cookiecutter-fastapi).

[![pypi](https://badge.fury.io/py/fastapi-users-tortoise.svg)](https://pypi.org/project/fastapi-users-tortoise/)
[![python](https://img.shields.io/pypi/pyversions/fastapi-users-tortoise)](https://github.com/Tobi-De/fastapi-users-tortoise)
[![MIT License](https://img.shields.io/apm/l/atomic-design-ui.svg?)](https://github.com/Tobi-De/fastapi-users-tortoise/blob/master/LICENSE)
[![black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

---

**Documentation**: <a href="https://tobi-de.github.io/fastapi-users-tortoise/" target="_blank">https://tobi-de.github.io/fastapi-users-tortoise</a>

**Source Code**: <a href="https://github.com/tobi-de/fastapi-users-tortoise" target="_blank">https://github.com/tobi-de/fastapi-users-tortoise</a>

---

Add quickly a registration and authentication system to your [FastAPI](https://fastapi.tiangolo.com/) project. **FastAPI Users** is designed to be as customizable and adaptable as possible.

**Sub-package for Tortoise ORM support in FastAPI Users.**

## Installation

```sh
pip install fastapi-users-tortoise
```

## Development

### Setup environment

You should create a virtual environment and activate it:

```bash
python -m venv venv/
```

```bash
source venv/bin/activate
```

And then install the development dependencies:

```bash
pip install -r requirements.dev.txt
```

### Run unit tests

You can run all the tests with:

```bash
make test
```

Alternatively, you can run `pytest` yourself:

```bash
pytest
```

There are quite a few unit tests, so you might run into ulimit issues where there are too many open file descriptors. You may be able to set a new, higher limit temporarily with:

```bash
ulimit -n 2048
```

### Format the code

Execute the following command to apply `isort` and `black` formatting:

```bash
make format
```

## License

This project is licensed under the terms of the MIT license.
jla-demultiplexer 0.0.8
Internal lab use tool for 3 end analysis
Requires Python: >=3.6
Keywords: Sequencing,ncRNAs,Bioinformatics

# jla-demultiplexer: Internal JLA lab tool for processing gene-specific sequencing experiments

## Installation

```bash
pip install jla-demultiplexer
```

## Usage

fastqbreakdown: Removes duplicate reads and trims barcodes and randommer addition
```bash
fastqbreakdown -r1 [read 1 location] -r2 [read 2 location] \
  -r [length of random-mer] -b [barcode]
```

## Notes

- Random-mer length supplied should be 10 or 11
- barcode supplied should be barcode column + gene specific column from manifest

## Usage

demultiplexer: Use JLA standard manifests to align and create tail files. Very specific to our gene-specific sequencing methodology
```bash
demultiplexer -r1 [read 1 location] -r2 [read 2 location] \
  -m [manifest location] -e [ensids] -f [reference fasta]
```
## Notes

- Must provide either -e (comma separated) or -f, but not both
- outputs to location of read 1
- tail file output is compatible with Tailer-Analysis which can be found at https://timnicholsonshaw.shinyapps.io/tailer-analysis/

## Usage

jla-trim: Removes duplicate reads and trims randommer addition only. FASTQ output
```bash
jla-trim -r1 [read 1 location] -r2 [read 2 location] \
  -r [length of random-mer]
```

## Notes

- Random-mer length supplied should be 10 or 11
mdtoolbelt 0.0.4
Tools por MD post-processing
Requires Python: >=3.6

# mdtoolbelt

Tools por MD post-processing

**Important**: PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA.


## Conversions

Convert structure and trajectory files from one format to another

### Python

```python
from mdtoolbelt.conversions import convert

convert(input_trajectory_filename='trajectory.xtc', output_trajectory_filename='trajectory.dcd')
```

### Bash

```bash
mdtb convert -it trajectory.xtc -ot trajectory.dcd
```
pysatModels 0.1.0
'Supports model analysis and model-data comparisons'
Requires Python: >=3.5

<div align="left">
        <img height="0" width="0px">
        <img width="20%" src="docs/images/pysatModels.png" alt="pysatModels" title="pysatModels"</img>
</div>

# pysatModels
[![Documentation Status](https://readthedocs.org/projects/pysatmodels/badge/?version=latest)](https://pysatmodels.readthedocs.io/en/latest/?badge=latest)
[![Build Status](https://github.com/pysat/pysatModels/actions/workflows/main.yml/badge.svg)](https://github.com/pysat/pysatModels/actions/workflows/main.yml/badge.svg)
[![Coverage Status](https://coveralls.io/repos/github/pysat/pysatModels/badge.svg?branch=main)](https://coveralls.io/github/pysat/pysatModels?branch=main)

pysatModels handles model-centric data loading through pysat and contains a
variety of tools to perform model-data analysis, including model validation.

Come join us on Slack! An invitation to the pysat workspace is available 
in the 'About' section of the
[pysat GitHub Repository.](https://github.com/pysat/pysat)

# Installation

The following instructions will allow you to install pysatModels and give some
examples on how to use the routines

## Prerequisites

pysatModels uses common Python modules, as well as modules developed by and for
the Space Physics community.  This module officially supports Python 3.6+.

| Common modules | Community modules |
| -------------- | ----------------- |
| numpy          | pysat             |
| pandas         | pyForecastTools   |
| requests       |                   |
| scipy          |                   |
| xarray         |                   |

## Installation through PyPi

```
pip install pysatmodels
```

## Installation from GitHub

1. Clone the git repository:
   `git clone https://github.com/pysat/pysatModels.git`

2. Install pysatModels:
   Change directories into the repository folder and run the setup.py file.
   There are a few ways you can do this:
   A. Install on the system (root privileges required):
      `sudo python3 setup.py install`
   B. Install at the user level:
      `python3 setup.py install --user`
   C. Install with the intent to develop locally:
      `python3 setup.py develop --user`

# Running the code

Examples for using the different pysatModels utilities are included in the
[documentation](https://pysatmodels.readthedocs.io/en/latest/).

If you're unfamiliar with pysat, tutorials and examples are available in its
[documentation](https://pysat.readthedocs.io/en/latest/).
cqh-util 0.1.26
cqh utils function
Requires Python: >=3.6

cqh_util
==============================================================


一些util
--------------------------------------------------------------



主要

* invoke_util

    invoke 脚本用到的一些东西

* mixin

    AsssertMixin 用于单元测试

* ansible_util

    一些ansible相关的东西


changes
---------------------------------------------------------

0.0.7
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

* 添加mod_util

0.0.8
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

* 添加 common_util
fastapi-firebase 0.3.1
FastAPI integration with firebase
Requires Python: >=3.8,<4.0

# FastAPI Firebase integration

This package contains some utilities to work with firebase in a FastAPI project.

## Example usage

For example, if you want to send the firebase app name:

```python
from fastapi import FastAPI, Depends
from fastapi_firebase import setup_firebase, firebase_app
from firebase_admin.app import App

app=FastAPI()

setup_firebase(app)

@app.get("/appname")
def get_appname(fb_app: App = Depends(firebase_app)):
    return fb_app.name

```

See the `setup_firebase` documentation for how to initialize.
lassa-picture 2.0.0
Printable picture for Python terminal.
Requires Python: >=3.6

# <p id="title">Lassa Picture</p>

Printable picture for Python terminal.

Python library by LassaInora.

--------
## Summary

- **[Links](#links)**
- **[Contacts](#contact)**
- **[Class Picture](#class_picture)**
  - ***[Picture initialization](#picture__init__)***
  - ***[Methods](#picture_methods)***
--------

## <p id="links">Links</p>

- [Personal GitHub](https://github.com/LassaInora)
- [GitHub project](https://github.com/LassaInora/Lassa_Picture)
- [Website project](https://lassainora.fr/projet/librairies/lassa_picture)

## <p id="contact">Contacts</p>

- [Personal email](mailto:axelleviandier@lassainora.fr)
- [Professional email](mailto:lassainora@lassainora.fr)
--------
<p id="class_picture">Class Picture:</p>
-------

### <p id="picture__init__">Picture initialization.</p>

- path: Picture file path.

### <p id="picture_methods">Methods:</p>

- get_data_file: 
  - Return file data in string.
- import_file:
  - Import the .lassa file
- export_file:
  - Export the .lassa file
- convert_grey:
  - Convert file to grayscale
- draw:
  - Draw the drawing