semvec/pypi-packages huggingface dataset:
pidfile 0.1.1
A Pidfile Context Manager compatible with python-daemon's DaemonContext()
License: MIT

python-pidfile
==============

A Pidfile Context Manager compatible with python-daemon's DaemonContext()


Thanks
======

This is a direct copy (minus example usage in file) from an ActiveState recipe found here:
http://code.activestate.com/recipes/577911-context-manager-for-a-daemon-pid-file/

Example Usage
=============
```python
import daemon
context = daemon.DaemonContext()
context.pidfile = PidFile("/var/run/mydaemon")
```

or

```python
with daemon.DaemonContext(working_directory=".", pidfile=PidFile(pidpath)):
  #do your daemonstuff here
```
edi 0.6
A tool for quickly opening files deep in your git repo with a minimum of keystrokes.
License: MIT
Keywords: development environment tool git find files editor

Project Info
============

* Project Page: https://github.com/crdoconnor/edi
* Docs: https://edi.readthedocs.org/
* Bug Tracking: https://github.com/crdoconnor/edi/issues

Quickstart
==========
unique 0.1
Unique/Primary Key Index Store for normalize objects
License: MIT

README for Unique 0.1
=====================

Unique will be a system for uniquely storing ``normalize`` objects in
a unique B-Tree, with a backing store which records history and makes
it hard to tamper with: git.  It could be used as a basic key/value
store, given a suitably lax storage schema.  But its goal is to be a
useful first normal form (1NF) store.

This is currently in very early stages of development.  Please contact
the author or send pull requests if you want to get involved!  See
TODO.org for the roadmap.

Project License
---------------
This project is licensed under the MIT license.  Go wild!

Author
------
Sam Vilain <sam@vilain.net>
kHLL 0.0.4
Memory saving and fast k-deterministic k-means with HyperLogLog
License: MIT
Keywords: HyperLogLog,k-means,Machine Learning

# kHLL
HLL module for determining k-means seed

# Test

    $ python -m kHLL.test.test_image_hash

# Run example

Measure kmeans performance and accuracy.

    $ python -m kHLL.example.kmeans_example


# Run IPython notebook
Show IPython notebook whiich describes the performance results of HyperKEstimator

    $ ipython notebook --matplotlib inline