semvec/pypi-packages huggingface dataset:
tweetypy 1.0.0rc6
Bird song analysis tools
License: MIT

TweetyPy
========

TweetyPy is a direct descendant of the FEX frequency excursion calculator.
Frequency excursion is the first (and only in v1) analysis that TweetyPy is
capable of, but there are plans for adding several additional calculations
starting with trill rate and bandwidth followed by multi-song comparisons for
similarity and eventually clustering and categorizations.

The current code is still in an alpha state.  This means it should only be used
for testing and cannot be considered reliable for analyses yet.  Please report
any bugs, but do not be surprised if this_ happens while using the alpha/beta
releases.

.. _this: https://www.youtube.com/watch?v=iWtohHwNXoU
PlagiarismDetector 0.2.2
Calculates percentage of N-Tuples from file1 found in another file2
License: MIT

===================
Plagiarism Detector
===================

Calculates percentage of N-Tuples from file1 found in another file2::

    from plagiarismdetector.detector import Detector

    print Detector.detect(synonyms_file_path,
                           eval_file_path,
                           source_file_path,
                           n_tuples_value=3)



Running
=======
``python plagiarismdetector/main.py synonyms_file_path eval_file_path source_file_path 3``


Assumptions & Overview
======================
* Dependent on Python2.7
* Tokenizer is only adapted to English language text using Penn TreeBank tokenizer, reason being it divides strings based off structures in english language that might fail in other languages e.g. in Hindi since sentence separators and punctuations are entirely different.
* The module is optimized to be as fast as possible, some of the optimizations are:
    * Only n-grams for file 2 are generated and stored, file 1 n-tuples are generated but not stored.
    * Not holding generated n-grams in memory, a generator is used
    * Dictionary of n-grams is created from file2 n-grams for constant time lookup of file1 tuples
    * Keys in file2 n-gram dictionary contains hashes for tuples instead of actual tuples to reduce space complexity.
    * Since we are only concerned with percentage of file1 n-tuples found in file2, we do not need to store any tuples. Therefore, we first generate n-grams for file2 and then calculate count for file1 on the fly instead of generating all n-tuples of file1 and cross referencing it with those of file2.


Testing
=======
``python -m unittest discover tests``

Help
====
``python plagiarismdetector/main.py -h``

positional arguments
--------------------
  ========================  ==============================================
  ``synonym_file_path``     Path to file to be used for synonyms
  ``evaluation_file_path``  Path to file to be evaluated
  ``source_file_path``      Path to file to be used as source for matching
  ``n-tuples``              Number of N-tuples, Optional and Defaults to 3
  ========================  ==============================================

optional arguments
------------------
  -h, --help            show this help message and exit

Example
=======
  ================   ===============
  Returns            100.0
  ================   ===============
  Evaluation File    go for a run
  Source File        go for a jog
  N-tuples           3
  ================   ===============
spitzer 1.1.0
Django-based multi-target migration tool
License: MIT
Keywords: migration,database,devops

# Spitzer

Django-based multi-target migration tool

# Install requires
- Python 3.4+
- Django
- PyYAML
- terminaltables
- cymysql
- django-cymysq

```PHP
pip install rinzler
```

# Config

Place a copy of the sample [spitzer.yaml](https://github.com/feliphebueno/Spitzer/wiki/spitzer.yaml) at 
your app's root directory.

# Usage
```PHP
//Configure e install Spitzer
$ python -m spitzer install

//Create and register a new blank migration file
$ python -m spitzer create

//Register your self-created migration file
$ python -m spitzer make_migrations

//Execute migrations on the configured target
$ python -m spitzer migrate

//List your migrations
$ python -m spitzer show_migrations

//You will see something like this:
```
<img width="847" alt="captura de tela 2017-09-06 as 10 19 50" src="https://user-images.githubusercontent.com/6662338/30114124-41cbd75c-92ed-11e7-82ef-c05632c8e25a.png">