semvec/pypi-packages huggingface dataset:
flexfs 2.0.2
Python tools for querying Flexible File Structure
Requires Python: >=3.7, <4.0

# ffs

Python tools for querying a Flexible File Structure as specified in https://gitlab.com/cardonazlaticlabs/data-policy

The name of the PyPI package is `flexfs` to avoid naming collisions with the unrelated [`ffs`](https://pypi.org/project/ffs/) project.
The import and CLI name is `ffs`.

## Usage

The entry point is a command line tool called `ffs` with a number of subcommands:

```_main
Usage: ffs [OPTIONS] COMMAND [ARGS]...

  Command line tool for working with a Flexible File Structure.

Options:
  --version      Show the version and exit.
  -v, --verbose  Increase logging verbosity.  [x>=0]
  --help         Show this message and exit.

Commands:
  book      Export the FFS metadata into files for mdbook.
  export    Read the FFS and its metadata into JSON.
  problems  List problems with the structure of the FFS.
```

### Export

```_export
Usage: ffs export [OPTIONS] [ROOT]

  Read the FFS and its metadata into JSON.

Options:
  -s, --sort               Whether to sort keys in outupt.
  -s, --indent INTEGER     Indentation of output: none by default, 0 for
                           newlines, a positive number N for N spaces, a
                           negative number -N for N tabs.
  -l, --flatlines          Un-nest the entries and print one object per line.
                           The 'children' attribute is replaced by an array of
                           string names, and the 'name' attribute now includes
                           the entry's ancestors (/-separated). '--indent'
                           option is ignored.
  -r, --recursion INTEGER  Depth to recurse into entries; negative (default)
                           for infinite. Directories which are not valid
                           entries are not explored.
  --help                   Show this message and exit.
```

### Book

```_book
Usage: ffs book [OPTIONS] [ROOT] TARGET

  Export the FFS metadata into files for mdbook.

Options:
  -t, --title TEXT         Title for generated book, default
                           '{FQDN}:{ROOT_REAL_PATH}'.
  -r, --recursion INTEGER  Depth to recurse into entries; negative (default)
                           for infinite. Directories which are not valid
                           entries are not explored.
  --help                   Show this message and exit.
```

### Problems

```_problems
Usage: ffs problems [OPTIONS] [ROOT]

  List problems with the structure of the FFS.

Options:
  -c, --check          Exit with an error code at the first problem
  -s, --skip-problems  Do not attempt to traverse below problematic
                       directories
  --help               Show this message and exit.
```

## Development

A number of `make` recipes are included for convenience of regular development tasks.
In particular, see `make {install-dev,update-spec,fmt,lint,test,readme,book}`.
mizar-labs 0.2.3
Package for building financial machine learning models
Requires Python: >=3.8

![CI](https://github.com/MizarAI/mizar-labs/workflows/CI/badge.svg?branch=master)
[![codecov](https://codecov.io/gh/MizarAI/mizar-labs/branch/master/graph/badge.svg?token=W6VKLA1OF9)](https://codecov.io/gh/MizarAI/mizar-labs)
[![GitHub](https://img.shields.io/github/license/mizarai/mizar-labs)](https://img.shields.io/github/license/mizarai/mizar-labs)
[![PyPi](https://img.shields.io/pypi/v/mizar-labs)](https://img.shields.io/pypi/v/mizar-labs)

# Welcome to Mizar Labs

Mizar Labs is an open source repository containing state of the art machine learning modules specifically designed for working with financial time series. These modules aim to assist the user with building financial machine learning models by taking into account the caveats that arise when dealing with financial time series. A lot of the concepts are inspired by [Marcos Lopez de Prado](https://www.quantresearch.org/) and his book [Advances in Financial Machine Learning (2018)](https://www.amazon.com/Advances-Financial-Machine-Learning-Marcos/dp/1119482089).

Most of the modules follow the [scikit-learn](https://scikit-learn.org/stable/) API and work with methods like `fit` and `transform`.

Please read the [documentation](https://mizarlabs.readthedocs.io/) for further explanation of the modules.

## Installing

Before installing the package please install [TA-Lib dependencies](https://mrjbq7.github.io/ta-lib/install.html)

Install and update with [pip](https://pip.pypa.io/en/stable/quickstart/):

```bash
$ pip install mizar-labs
```

## Tutorials

In the future this section will have links to Jupyter Notebooks explaining how to use the modules.

## Mizar

Mizar is building a marketplace for algo-traders to deploy and share their trading strategies with investors. Please visit the website [mizar.ai](https://mizar.ai/) to learn more.

## Links

- Documentation: [https://mizarlabs.readthedocs.io/](https://mizarlabs.readthedocs.io)
- Website: [https://mizar.ai/](https://mizar.ai/)
- PyPi Releases: [https://pypi.org/project/mizar-labs/](https://pypi.org/project/mizar-labs/)
- Source Code: [https://github.com/MizarAI/mizar-labs](https://github.com/MizarAI/mizar-labs)