semvec/pypi-packages huggingface dataset:
ax3-email 2.0.0
AX3 Email is a Django application designed to send emails using Huey tasks.
Requires Python: >=3.10

# AX3 Email

AX3 Email is a Django application designed to send emails using Huey tasks.

## Installation

You can easily install AX3 Email from the PyPI package using the following command:

```bash
pip install ax3-email
```

After the package is installed, you need to configure your project settings. Specifically, you need to add `ax3_email` to your `INSTALLED_APPS`:

```python
INSTALLED_APPS += ['ax3_email']
```

## Configuration

Next, you need to add the email backend settings. Here's how you can do it:

```python
# app/settings.py
EMAIL_BACKEND = 'ax3_email.backends.AX3EmailBackend'
```

You can also specify additional settings for AX3 Email:

```python
# app/settings.py
AX3_EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend' # Optional - Default is smtp (django). This is the Django Email that actually sends the emails.
AX3_EMAIL_RETRIES  # Optional - Default is 3. This is the maximum number of times to retry sending an email.
AX3_EMAIL_DELAY # Optional - Default is 600. This is the time in seconds between attempts to send an email.
AX3_EMAIL_BACKUP_LIST # Optional - This is a list of emails to send a BCC backup copy of each email.
AX3_ONLY_BACKUP_LIST # Optional - If set to True, emails will only be sent to the backup list.
EMAIL_SUBJECT # Optional - This is a string format for all email subjects. For example, you can use '[ax3_prefix] {} ' as a prefix.
```

## Publish package

To build the package, you need to run the following command:

```bash
rye build && rye publish

```
pyMechkar 0.1.6
Useful Tools for Scientific Research
Requires Python: >=3.4

Utilities that help researchers in various phases of their research work. This package offer a function that automate the exploratory data analysis, a function for the generation of Table 1 (required on many epidemiological papers) which can be exported to excel. There is also a function for generation of a forestplot with a table and relative risks or odds ratios that can be used for publication. Additionally, there is a function that generates train/test random partitions used for model evaluation that checks for the balance of the partitions.
LOTUS-regression 0.8.3
Regression model developed for the LOTUS project
Requires Python: >=3.10

# LOTUS Regression
A multi-linear regression model specifically designed for use in calculation of trends for atmospheric constituents.
Developed as part of the LOTUS initiative (https://www.sparc-climate.org/activities/ozone-trends/, https://lotus.aeronomie.be)

## Installation Requirements
The code is tested on Python versions `3.8, 3.9, 3,10, 3.11` and should work on any of them,
however we recommend using the [Anaconda python distribution](https://www.continuum.io/downloads).


## Documentation
For more indepth documentation see http://arg.usask.ca/docs/LOTUS_regression/
fletmint 0.1.6.2
A sharp and modern components library for Flet
Requires Python: >=3.8

<h1 align="center">
  <img src="https://github.com/Bbalduzz/fletmint/assets/81587335/c9ee557f-4362-432e-9d02-47995bc63585" alt="fletmint logo" width="200"/>
  </br>
  Flet Mint
</h1>

<h4 align="center">A sharp and modern components library for Flet</h4>

<p align="center">
  <a href="https://github.com/Bbalduzz/fletmint?tab=readme-ov-file#documentation">Documentation</a> â€¢
  <a href="https://github.com/Bbalduzz/fletmint?tab=readme-ov-file#support">Support the dev</a>
</p>

<kbd>
<img src="https://github.com/Bbalduzz/fletmint/assets/81587335/16351382-8148-4421-b9a3-599003caeda9" alt="fletmint logo"/>
</kbd>