semvec/pypi-packages huggingface dataset:
malix-rin 0.0.4
An api for generating disposable mails.
Requires Python: >=3.6
Keywords: temp mail,fake mail,disposable email,api.mail.tm

# MalixMail

API for generating disposable emails(technically it's an API wrapper of api.mail.tm).

## Why MalixMail?

Well don't bother to use this tool if you've got some premium mail services(testmail, tempmail.org) to do the work for you. it's for those who do not have access to those premium mail services and wondering how to create mass number of emails. goodluck!

You can find the documentation of this project - [here.](https://malixmail.readthedocs.io/en/latest/)

## From author

If you face any issue while using MailxMail lemme know by creating an issue.

## Installing

This library is available in PyPI, and you can get it by pip using the following command.

    py -m pip install -U malix-rin

Or you can clone the source code by git using the following commnad.

    git clone https://github.com/sunilswain/MalixMail

then go to same directory as MalixMail and run setup.py using this command.

    py setup.py install

## Contact me

**mail** - [sunilswain5555@gmail.com](mailto:sunilswain5555@protonmail.com)

**Join discord** - [here.](https://discord.gg/BsEsbUqn)
tsfresh-ppi 2021.8.29
Add peak-to-peak interval features to tsfresh

# tsfresh_ppi

This package provides some peak-to-peak interval (PPI) variability features to augment `tsfresh` [1].
Currently, `tsfresh` will find and count peaks using a couple of different methods.
However, it does not measure the variability in timing between those peaks.

## Installation

Option 1: Clone this repo.  Install prereqs from `requirements.txt`.  Then install the cloned directory, e.g. with `pip3 install -e [repo path]`.

Option 2: Install directly from PyPI (`tsfresh-ppi`).

## Usage

    from tsfresh import extract_features
    from tsfresh_ppi import get_fc_parameters

    my_signal = ...  # some pandas DataFrame

    # The default way to extract features with tsfresh looks something like this:
    features = extract_features(
        my_signal,
        ...
    )

    # This is how to get all of the default tsfresh features, plus default PPI features:
    fc_params_with_ppi = get_fc_parameters()
    features = extract_features(
        my_signal,
        default_fc_parameters = fc_params_with_ppi,
        ...
    )

## References
[1] https://github.com/blue-yonder/tsfresh