semvec/pypi-packages huggingface dataset:
pypi-api-installer 1.4
A python library with a set of tools for installing packages from pypi dynamically

# pypi_api_installer 🪄
A python library with a set of tools for installing python packages from [PYPI](https://pypi.org/) dynamicly and without using `pip` or any other shell tools.

# Important Note ⚠️
You can install libraries from PyPI and use them immediately. However, libraries that utilize C extensions or require compilation may not function correctly.


## installation ⬇️
Use this `pip` command to install the package:
```zsh
pip install pypi_api_installer
```

## usage 🙌

The easiest way to use this library to Install packages with its dependencies automatically is by using `MagicLibraryInstaller` class. An example of utilizing it to install the `requests` library:

```python
from pypi_api_installer import MagicLibraryInstaller

MagicLibraryInstaller("requests", lib_folder_path="custom_lib")
```

This script will install the latest version of the `requests` library, and then install all of its dependencies on the folder `custom_lib`.
rs-docs-loader 0.1.1
RS Docs Loader will help you to integrate different data sources

# rs-docs-loaders
Python project designed to provide different document loaders from external sources like notion, Google Drive, etc.

## Setup environment
For a proper use of python, we should create a virtual environment (venv) to isolate the project packages from the global python configuration, to create this venv run:
* `python -m venv path/to/venv`

Then you need to activate this new venv, so for that run:
* `source path/to/venv/bin/activate`

Please take a look to this little guide about how to create and activate a [venv](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/#create-and-use-virtual-environments)

## Install dependencies
To install the project dependencies run `pip install -r requirements.txt`. This will install all the requirements for the project and all loaders requirements.

If you are a `dev`, you may want to install some dev dependencies to be able to use for example `black`, `isort`, `flake8`, etc. To install the dev dependencies run `pip install -r dev_requirements.txt`. This will install the same things as `requirements.txt`(step before) plus dev tools.

### Requirements structure
```
rs-docs-loaders/
|-- common/
|-- loaders/
|   |-- first_loader/
|   |   |-- tests/
|   |   |-- other_folder/
|   |   |-- first_loader.py
|   |   |-- requirements.txt
|   |   |-- README.md
|   |-- second_loader/
|   |   |-- tests/
|   |   |-- other_folder/
|   |   |-- second_loader.py
|   |   |-- requirements.txt
|   |   |-- README.md
|   |-- loaders_requirements.txt
|-- base_requirements.txt
|-- dev_requirements.txt
|-- requirements.txt
```

`requirements.txt` --> invoke `base_requirements.txt` and `loaders/loders_requirements.txt`

`base_requirements.txt` --> has all the dependencies for the base project.

`loaders/loaders_requirements.txt` --> has all the dependencies for the loaders.

`loaders/[loader-name]/requirements.txt` --> has all the dependencies for an specific loader.

`dev_requirements.txt` --> invoke `requirements.txt` + devs tools

### Install specific loader dependencies
To install a specific loader dependencies, you will need to install the base requirements for the proyect and then the loader specific requirements, so you can do something like: `pip install -r base_requirements.txt` and then `pip install -r loaders/[loader-name]/requirements.txt`

### Add a new loader
To add a new loader, you just need to create a new folder inside `loaders/` and must create a `requirements.txt` for this loader. Then you will need to update the `loaders/loaders_requirements.txt` to invoke the requirements for the new loader.

So in the `loaders/loaders_requirements.txt` you will need to add a new line: `-r [loader-name]/requirements.txt`. This will make that the new loaders requirements were installed when you install the `requirements.txt` placed in the root folder, making it dependencies available for CI, and basic project configuration.
rhe 1.0.0
Gene Environment Interaction Estimator
Requires Python: >=3.8

# RHE

Randomized Haseman–Elston regression for Multi-variance Components: Python wrappers for **G**ene-**EN**viroment **I**nteraction **E**stimator


## Installation

```
pip install rhe
```

This package provides basic Python wrappers for the GENIE executables.
Full documentation for the GENIE project, as well as sample data necessary to run the toy examples below, is available here:
https://github.com/sriramlab/GENIE


## Interface

This package provides the following functions:

- `rhe.run_genie`
- `rhe.run_genie_mem`
- `rhe.run_genie_multi_pheno`


### Run GENIE with a list of command line arguments

```py
from rhe import run_genie

gen = 'path/to/test'
phen = 'path/to/test.pheno'
covar = 'path/to/test.cov'
annot = 'path/to/single.annot'
env = 'path/to/test.env'

run_genie(
    ['-g', gen, '-p', phen, '-c', covar, '-e', env, '-m', 'G+GxE+NxE', '-k', '10', '-jn', '10', '-o',
     'path/to/analysis.out', '-annot', annot, '-t', '6']
)
```


### Run GENIE with a single string of command line arguments

```py
from rhe import run_genie

gen = 'path/to/test'
phen = 'path/to/test.pheno'
covar = 'path/to/test.cov'
annot = 'path/to/single.annot'
env = 'path/to/test.env'

run_genie(
    f'-g {gen} -p {phen} -c {covar} -e {env} -m G+GxE+NxE -k 10 -jn 10 -o path/to/analysis.out -annot {annot} -t 6'
)
```


### Run GENIE with using a config file

```py
from rhe import run_genie_mem

with open('config.txt', 'w') as f:
    f.write('genotype=path/to/test\n')
    f.write('phenotype=path/to/test.pheno\n')
    f.write('covariate=path/to/test.cov\n')
    f.write('environment=path/to/test.env\n')
    f.write('annotation=path/to/single.annot\n')
    f.write('output=path/to/test.py.list.3.out\n')
    f.write('nthreads=6\n')
    f.write('num_vec=20\n')
    f.write('num_jack=10\n')
    f.write('trace=1\n')
    f.write('model=G\n')
    f.write('verbose=1\n')

run_genie_mem(
    ['--config', 'config.list.txt']
)
```


## Citation
```
1. Ali Pazokitoroudi, Andrew Dahl, Noah Zaitlen, Saharon Rosset, Sriram Sankararaman.
bioRxiv 2023.12.12.571316; doi: https://doi.org/10.1101/2023.12.12.571316
2. Ali Pazokitoroudi, Yue Wu, Kathryn S. Burch, Kangcheng Hou, Aaron Zhou, 
Bogdan Pasaniuc, Sriram Sankararaman. Nature Communications (2020); doi: https://doi.org/10.1101/522003
```


## Version
```
v1.0.0
```
madml 2.6.7
Application domain of machine learning in materials science.
Requires Python: >=3.6

# Materials Application Domain Machine Learning (MADML)

Research with respect to application domain with a materials science emphasis is contained within. The GitHub repo can be found in [here](https://github.com/leschultz/application_domain.git).

## Examples

* Tutorial 1: Assess and fit a single model from all data: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leschultz/materials_application_domain_machine_learning/blob/main/examples/jupyter/tutorial_1.ipynb)
* Tutorial 2: Use model hosted on Docker Hub: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leschultz/materials_application_domain_machine_learning/blob/main/examples/jupyter/tutorial_2.ipynb)

## Structure
The structure of the code packages is as follows:

```
materials_application_domain_machine_learning/
├── examples
│   ├── jupyter
│   └── single_runs
├── src
│   └── madml
└── tests
```

## Coding Style

Python scripts follow PEP 8 guidelines. A usefull tool to use to check a coding style is pycodestyle.

```
pycodestyle <script>
```

## Authors

### Graduate Students
* **Lane Schultz** - *Main Contributer* - [leschultz](https://github.com/leschultz)

## Acknowledgments

* The [Computational Materials Group (CMG)](https://matmodel.engr.wisc.edu/) at the University of Wisconsin - Madison
* Professor Dane Morgan [ddmorgan](https://github.com/ddmorgan) and Dr. Ryan Jacobs [rjacobs914](https://github.com/rjacobs914) for computational material science guidence