semvec/pypi-packages huggingface dataset:
lazyeda 0.1.4
Lazy EDA is a python library to help shorten the coding time for basic understanding  of data, EDA techniques and repetitive tasks with less lines of code
License: MIT

lazyeda
==============================

Lazy EDA is a python library to help shorten the coding time for basic understanding  of data, EDA techniques and repetitive tasks with less lines of code

Project Organization
------------

    ├── LICENSE
    ├── Makefile           <- Makefile with commands like `make data` or `make train`
    ├── README.md          <- The top-level README for developers using this project.
    ├── data
    │   ├── external       <- Data from third party sources.
    │   ├── interim        <- Intermediate data that has been transformed.
    │   ├── processed      <- The final, canonical data sets for modeling.
    │   └── raw            <- The original, immutable data dump.
    │
    ├── docs               <- A default Sphinx project; see sphinx-doc.org for details
    │
    ├── models             <- Trained and serialized models, model predictions, or model summaries
    │
    ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
    │                         the creator's initials, and a short `-` delimited description, e.g.
    │                         `1.0-jqp-initial-data-exploration`.
    │
    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
    │
    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
    │   └── figures        <- Generated graphics and figures to be used in reporting
    │
    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
    │                         generated with `pip freeze > requirements.txt`
    │
    ├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
    ├── src                <- Source code for use in this project.
    │   ├── __init__.py    <- Makes src a Python module
    │   │
    │   ├── data           <- Scripts to download or generate data
    │   │   └── make_dataset.py
    │   │
    │   ├── features       <- Scripts to turn raw data into features for modeling
    │   │   └── build_features.py
    │   │
    │   ├── models         <- Scripts to train models and then use trained models to make
    │   │   │                 predictions
    │   │   ├── predict_model.py
    │   │   └── train_model.py
    │   │
    │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
    │       └── visualize.py
    │
    └── tox.ini            <- tox file with settings for running tox; see tox.testrun.org


--------

<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
torch-stethoscope 0.0.1
Automatic deep learning research report generator
License: MIT

#  <img src="/icons/stethoscope.png" width="60" vertical-align="bottom">Stethoscope
> Medical imaging toolbox for Pytorch

## Contributing

#### How to contribute ?
- [X] Create a branch by feature and/or bug fix
- [X] Get the code
- [X] Commit and push
- [X] Create a pull request

#### Branch naming

##### Feature branch
> feature/ [Short feature description] [Issue number]

##### Bug branch
> fix/ [Short fix description] [Issue number]

#### Commits syntax:

##### Adding code:
> \+ Added [Short Description] [Issue Number]

##### Deleting code:
> \- Deleted [Short Description] [Issue Number]

##### Modifying code:
> \* Changed [Short Description] [Issue Number]

##### Merging code:
> Y Merged [Short Description] [Issue Number]


Icons made by <a href="http://www.flaticon.com/authors/freepik" title="Freepik">Freepik</a> from <a href="http://www.flaticon.com" title="Flaticon">www.flaticon.com</a> is licensed by <a href="http://creativecommons.org/licenses/by/3.0/" title="Creative Commons BY 3.0" target="_blank">CC 3.0 BY</a>
torch-radiate 0.0.1
Automatic deep learning research report generator
License: MIT

#  <img src="/icons/sunglasses.png" width="150" vertical-align="bottom">Radiate
> Automatic deep learning reasearch report generator

## Contributing

#### How to contribute ?
- [X] Create a branch by feature and/or bug fix
- [X] Get the code
- [X] Commit and push
- [X] Create a pull request

#### Branch naming

##### Feature branch
> feature/ [Short feature description] [Issue number]

##### Bug branch
> fix/ [Short fix description] [Issue number]

#### Commits syntax:

##### Adding code:
> \+ Added [Short Description] [Issue Number]

##### Deleting code:
> \- Deleted [Short Description] [Issue Number]

##### Modifying code:
> \* Changed [Short Description] [Issue Number]

##### Merging code:
> Y Merged [Short Description] [Issue Number]


Icons made by <a href="http://www.flaticon.com/authors/freepik" title="Freepik">Freepik</a> from <a href="http://www.flaticon.com" title="Flaticon">www.flaticon.com</a> is licensed by <a href="http://creativecommons.org/licenses/by/3.0/" title="Creative Commons BY 3.0" target="_blank">CC 3.0 BY</a>
SeismicReduction v0.3
Perform unsupervised machine learning on seismic data.
License: MIT
Keywords: seismicdata,unsupervisedlearning,machinelearning

# SEISMIC REDUCTION 
## Unsupervised Machine Learning: An Application to Seismic Amplitude vs Offset Interpretation ##

This project delivers a set of tools to run unsupervised machine learning on seismic data sets. The aim is to enable the  efficient experimentation of an array of models and the range of paramaters therein. Specifically the tools allow for efficient recognition of clustering of low fluid-factor anomalies derived from AVO analysis.

---

# Getting started:

### Installation:
- Install with the following in terminal: