semvec/pypi-packages huggingface dataset:
Wack 0.2.0
A simple CLI and automation tool
Requires Python: >=3.6

# Wack

## Aim
Wack is a personal project to provide a simple [click](https://github.com/pallets/click) (maybe [typer](https://github.com/tiangolo/typer) / or custom in the future) cli tool.

Wack gives the user the ability to:
* Make simple files quickly (e.g. setup.py)
* Ability to write simple and quick cli commands, found when typing `$ wack` into the terminal
* To automate simple tasks (e.g. making `pip install` write to requirements.txt by default)

## Install:
```
pip install wack
```

Now `wack` will be added to your terminal commands. Typing `wack` will give you:
```
Usage: wack [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  init
  install
  make
```

## Setup wack:
```
wack init
```

This will make a wack.py file in the directory you're in. My recommendation is to do this in the root dir. It will look like the below:
```
from wack import command
from wack import echo


@command()
def hello_world():
    echo("hello world")
```

Now when typing `$ wack` you will get:
```
Usage: wack [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  hello_world
  init
  install
  make
```


Make features:
* .pre-commit-config.yaml
* .travis.yml (for pypi)
* setup.py
* wack.py

FYI, as of 0.1.4 (current version), click groups aren't supported, only commands, which need to be imported from `wack`. All other click features should work by default and can be imported from click or wack (as wack impliments `from click import *`).

## TODO
* have a `make` command for:
    * dotenv
    * pre-commit
* more tests
* cookie-cutter for simple flask app?
* sqlalchemy / alembic setup
pacsltk 0.2.0
Developed in PACS Lab to ease the process of deployment and testing of our benchmarking workload to AWS Lambda.

PACSLTK (PACS Lambda ToolKit)
=============================

Developed in PACS Lab to ease the process of deployment and testing of
our benchmarking workload to AWS Lambda. To see how you can use pacsltk,
check out the `github
repository <https://github.com/pacslab/serverless-performance-modeling>`__.

Installation
------------

.. code:: sh

   pip install pacsltk

Examples
--------

You can use the package as simple as the short code snippet below:

.. code:: py

   from pacsltk import perfmodel

   arrival_rate = 100
   warm_service_time = 2
   cold_service_time = 25
   idle_time_before_kill = 10*60

   print("arrival_rate:", arrival_rate)
   print("warm_service_time:", warm_service_time)
   print("cold_service_time:", cold_service_time)
   print("idle_time_before_kill:", idle_time_before_kill)

   props1, props2 = perfmodel.get_sls_warm_count_dist(arrival_rate, warm_service_time, cold_service_time, idle_time_before_kill)
   perfmodel.print_props(props1)

which produces an output similar to the following:

::

   arrival_rate: 100
   warm_service_time: 2
   cold_service_time: 25
   idle_time_before_kill: 600

   Properties:
   ------------------
   avg_server_count: 251.043927
   avg_running_count: 200.148828
   avg_running_warm_count: 199.987058
   avg_idle_count: 51.056869
   cold_prob: 0.000065
   avg_utilization: 0.796622
   avg_resp_time: 2.001488
   rejection_prob: 0.000000
   rejection_rate: 0.000000
   ------------------

Updating README in RST file
---------------------------

.. code:: sh

   pandoc -s README.md -o README.rst