semvec/pypi-packages huggingface dataset:
email-notify-magic 0.0.2
A Jupyter magic for sending e-mail after cell completion
License: MIT

# Email-Notify-Magic

Send an email after cell execution complete.

Obs: Currently only used with gmail.

## Install

```
pip install email-notify-magic
```

## Usage

- Load the magic extension:

```
%load_ext email_notify_magic
```

- Run the cell magic in jupyter notebook (see options bellow):

```
%%email email@gmail.com
```

- Insert password in the prompt and **done**

- Make sure your email account is enable for such usage (see [link](https://realpython.com/python-send-email/) and [less-secure-apps](https://myaccount.google.com/lesssecureapps)). It is recommend not to use personal accounts.

## Option:

- Sender and receiver same email address if `--to` is not supplied.

```
%%email email@gmail.com
```

- Set receiver with `--to` [`-t`]:

```
%%email email@gmail.com --to receiver@gmail.com
```

- Set e-mail subject `--subject` [`-s`]:

```
%%email email@gmail.com --s 'My subject'
```

- Set e-mail body `--body` [`-b`]:

```
%%email email@gmail.com --body 'From cell x'
```

Notice that the cell output is used if `--body` is not provided.

- Save password in jupyter session for latter usage `--keep-password` [`-k`]:

```
%%email email@gmail.com --body 'From cell x' -k
```

The next cell evoked with `%%email` will not require the password.


## Others

- Notification after cell execution inspired by [jupyter-notify](https://github.com/ShopRunner/jupyter-notify)
nosypy-gpu 0.0.2
NosyPy is a self supervised novelty detection package built for time lapse imagery using Python.
License: MIT

NosyPy is a self supervised novelty detection package built for time lapse imagery using Python.

- It is built ontop of Tensorflow 
- It uses a self supervised learning approach, removing the need for manual annotation
- It identifies areas in each image which likely contain an object, or objects, of interest
update-req-spec 1.0.2
Update Python requirements in a setup.py file to permit latest available versions
License: MIT

# update-req-spec

[![Build Status](https://travis-ci.org/RobbieClarken/update-req-spec.svg?branch=master)](https://travis-ci.org/RobbieClarken/update-req-spec)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/RobbieClarken/update-req-spec/blob/master/LICENSE)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)

Update Python requirements in a setup.py file to permit latest available versions.

## Motivation

You wish to cap the versions of the dependencies in your library to the major versions
that are currently released since you have tested your library against these versions and, given
everyone follows semantic versioning, minor updates should remain compatible. But keeping these
version ranges up to date with new major releases can be tedious.

`update-req-spec` makes this a little easier by updating the `install_requirements` in your
`setup.py` so that the version specifications allow the latest major releases while retaining the
minimum permitted versions. Then all you need to do is run your tests and publish a new release.

For example, given:

```python
from setuptools import setup

setup(
    # --- snip ---
    install_requires=[
        "requests",
        "Click>=6.1",
        "attrs~=18.1",
    ],
    # --- snip ---
)
```

will be transformed to:

```python
from setuptools import setup

setup(
    # --- snip ---
    install_requires=[
        "requests<3",
        "Click>=6.1,<8",
        "attrs>=18.1,<19",
    ],
    # --- snip ---
)
```

## Installation

Install with [pipsi](https://github.com/mitsuhiko/pipsi):

```bash
pipsi install update-req-sepc
```

or with pip:

```bash
python3 -m pip install update-req-spec
```

## Usage

```bash
update-req-spec setup.py
```

If you use a private packge repository:

```bash
update-req-spec --index-url http://private-repo.example/ setup.py

# or

export PIP_INDEX_URL=http://private-repo.example/
update-req-spec setup.py
```
nosypy 0.0.2
NosyPy is a self supervised novelty detection package built for time lapse imagery using Python.
License: MIT

NosyPy is a self supervised novelty detection package built for time lapse imagery using Python.

- It is built ontop of Tensorflow 
- It uses a self supervised learning approach, removing the need for manual annotation
- It identifies areas in each image which likely contain an object, or objects, of interest