semvec/pypi-packages huggingface dataset:
ciborg 0.1
Generate CI configuration for various services
License: MIT
Requires Python: >=3.5

ciborg
======

|PyPI| |Pythons| |Azure| |codecov| |GitHub|

Assimilate your CI services

First targeted service:
- `Azure Pipelines`_

.. _Azure Pipelines: https://azure.microsoft.com/en-us/services/devops/pipelines/

.. |PyPI| image:: https://img.shields.io/pypi/v/ciborg.svg
   :alt: PyPI version
   :target: https://pypi.org/project/ciborg/

.. |Pythons| image:: https://img.shields.io/pypi/pyversions/ciborg.svg
   :alt: supported Python versions
   :target: https://pypi.org/project/ciborg/

.. |Azure| image:: https://dev.azure.com/altendky/ciborg/_apis/build/status/altendky.ciborg?branchName=develop
   :alt: Azure build status
   :target: https://dev.azure.com/altendky/ciborg/_build

.. |codecov| image:: https://codecov.io/gh/altendky/ciborg/branch/develop/graph/badge.svg
   :alt: codecov coverage status
   :target: https://codecov.io/gh/altendky/ciborg

.. |GitHub| image:: https://img.shields.io/github/last-commit/altendky/ciborg/develop.svg
   :alt: source on GitHub
   :target: https://github.com/altendky/ciborg
wff 0.0.0.1
Python library for building and working with propositional formulas.
License: MIT

===
wff
===

Python library for building and working with propositional formulas.

.. image:: https://badge.fury.io/py/wff.svg
   :target: https://badge.fury.io/py/wff
   :alt: PyPI version and link.

Package Installation and Usage
------------------------------
The package is available on PyPI::

    python -m pip install wff

The library can be imported in the usual way::

    import wff
    from wff import *

Unit tests can be executed using `doctest <https://docs.python.org/3/library/doctest.html>`_::

    python wff/wff.py -v
nr.dotconfig 0.0.6
A single configuration file for .bash_profile and .gitconfig.
License: MIT

# dotconfig

A single configuration file for `.bash_profile` and `.gitconfig`.

    $ pipx install nr.dotconfig
    $ dotconfig dotconfig.yaml -u
    Writing /home/me/.bash_profile
    Writing /home/me/.gitconfig

## Example

My own dotconfig can be found [here](https://git.niklasrosenstein.com/NiklasRosenstein/dotconfig-files/raw/branch/master/dotconfig.yaml).
An further example below shows how another config file can be taken as a basis for another (eg. to add work related settings on top
of a personal configuration).

```yml
base: https://git.niklasrosenstein.com/NiklasRosenstein/dotconfig-files/raw/branch/master/dotconfig.yaml
profile:
  blocks:
    work-functions: |
      function do-all-my-work() {
        # it's a secret
      }
gitconfig:
  user:
    email: my-work-mail@work.com
    signingkey: WORKSIGNINKEYID
  Profile.user: '{{base.gitconfig.user}}'
```

---

<p align="center">Copyright 2020 &copy; Niklas Rosenstein</p>
lamzip 0.4.1
A simple packaging utility that creates an AWS Lambda zip for distribution
License: MIT
Keywords: aws,lambda,package,function,zip


Install Notes
-------------
To install using pip:

```shell script
pip install lamzip
```

To update using pip:
```shell script
pip install lamzip --upgrade
```

Using LamZip
------------
#### Initial Project Setup
Create a defaults file in your project directory to simplify packaging:
```
lamzip make-config [--config-name .altname]
```
Just using `lamzip make-config` without any arguments will create the default
config file `.lamzip.yaml`.  Contained in the default config is an example
setup that looks like the following:
```yaml
# Lambda-Zip (lamzip)
#
# Defaults for packaging your AWS Lambda function
#

source-directory: ./appsrc
destination-directory: ./dist
package-prefix: foo
package-version: 0.0.1
```

#### Configure LamZip
Your project structure should look something like the following:
```
Main-Project-Folder\
   dist\
   appsrc\
      lib1\
      lib2\
      app.py
   .bumpversion
   .lamzip.yaml
   .gitignore
   LICENSE
   README
   requirements.txt
   setup.py
```

In this example you would use the following settings:<br>
`source-directory` to `./appsrc`<br>
`destination-directory` to `./dist` # the default is typically the case here<br>
`package-prefix` to `app` # zip file package name pre-pended to the version<br>
`package-version` to `0.0.1` # current app version

#### Run LamZip (Package your function)
Once configured, you can just run LamZip in the following way:
```
# lamzip use-config

LamZip using existing config file: .lamzip.yaml
  Source Dir: ./appsrc
  Destination Dir: ./dist
  Package Prefix: app
  Package Version: 0.0.1

Creating Archive: ./dist\app-0.0.1.zip
  adding: ./appsrc\app.py

``` 

#### Pass all parameters using CLI
It is not required to use the LamZip config file.  All arguments can be passed
using the CLI in the following way:
```shell script
lamzip specify --src-dir ./appsrc --dest-dir ./dist --zip-prefix app --zip-version 0.0.1
```

Integration
-----------
Now that you have the LamZip config deployed to your project folder it is
recommended to use a versioning utility like "Bumpversion" to easily maintain the version
configuration within the LamZip config file (.lamzip.yaml).<br>
https://github.com/peritus/bumpversion

Important
---------
Ensure the python "Scripts" directory is located in your system path, as this library
installs LamZip to that location for global use.

**Verify the path:**<br>
Use the version response to verify the interpreter location on your system.
```shell script
# pip3 -V

pip 20.0.2 from c:\program files\python37\lib\site-packages\pip (python 3.7)
```

```
Using BASH: "echo $PATH" to verify "<path-to-python-base>/scripts" exists
Using PowerShell: "$ENV:$PATH" to verify "<path-to-python-base>\Scripts" exists
```
shrynk 0.2.25
Using Machine Learning to learn how to Compress
License: MIT

<p align="center">
  <img src="./Shrynk.png" width="400rem"/>
</p>

[![Build Status](https://travis-ci.org/kootenpv/shrynk.svg?branch=master)](https://travis-ci.org/kootenpv/shrynk)
[![PyPI](https://img.shields.io/pypi/pyversions/shrynk.svg?style=flat-square&logo=python)](https://pypi.python.org/pypi/shrynk/)
[![PyPI](https://img.shields.io/pypi/v/shrynk.svg?style=flat-square&logo=pypi)](https://pypi.python.org/pypi/shrynk/)
[![HitCount](http://hits.dwyl.io/kootenpv/shrynk.svg)](http://hits.dwyl.io/kootenpv/shrynk)

You can read the [introductory blog post](https://vks.ai/2019-12-05-shrynk-using-machine-learning-to-learn-how-to-compress) or try it live at https://shrynk.ai

### Features

- ✓ Compress your data smartly based on **Machine Learning**
- ✓ Takes **User Requirements** in the form of weights for `size`, `write_time` and `read_time`
- ✓ Trains & caches a model based on **compression methods available** in the system, using packaged data
- ✓ **CLI** for compressing and decompressing
- ✓ Works with `CSV`, `JSON` and `Bytes` in general

### CLI

    shrynk compress myfile.json       # will yield e.g. myfile.json.gz or myfile.json.bz2
    shrynk decompress myfile.json.gz  # will yield myfile.json

    shrynk compress myfile.csv --size 0 --write 1 --read 0

    shrynk benchmark myfile.csv                  # shows benchmark results
    shrynk benchmark --predict myfile.csv        # will also show the current prediction
    shrynk benchmark --save --predict myfile.csv # will add the result to the training data too

### Usage

Installation:

    pip install shrynk

Then in Python:

```python
import pandas as pd
from shrynk import save, load

# save dataframe compressed
my_df = pd.DataFrame({"a": [1]})
file_path = save(my_df, "mypath.csv")
# e.g. mypath.csv.bz2

# load compressed file
loaded_df = load(file_path)
```

If you just want the prediction, you can also:

```python
import pandas as pd
from shrynk import infer

infer(pd.DataFrame({"a": [1]}))
# {"engine": "csv", "compression": "bz2"}
```

### Add your own data

If you want more control you can do the following:

```python
import pandas as pd
from shrynk import PandasCompressor

df = pd.DataFrame({"a": [1, 2, 3]})

pdc = PandasCompressor("default")
pdc.run_benchmarks(df) # adds data to the default

pdc.train_model(size=3, write=1, read=1)

pdc.predict(df)
```