<commit_before># DIPPER
Dipper is a pure Python package to generate RDF triples from common scientific resources.
Dipper includes subpackages and modules to create graphical models of this data, including:

* Models for ternary associations and complex partonomies
* Graph utilities to create common relationships using a variety of ontologies
* Fetchers and Parsers that interface with remote databases and web services

## Beware: This code and resulting TTL is still in active development, and is subject to change.
The code in this repository is still under active development while we are refining the models for variant, 
genotype, and genotype-to-phenotype association.  This means that the graph patterns we are employing 
between the different entities is in flux.  **We will indicate here when the modeling has generally stabilized.**

* The dipper main wraps all of the source parsers, enabling users to specify one or more sources to process. 
The general strategy is that there is one class per data source.  We define the files to be fetched,
any file scrubbing, and then the parsing methods.  As the files are parsed, triples are loaded into an in-memory graph.  
This graph is then typically dumped into triples in turtle format.  For testing purposes,
 a subset of the graph is also dumped to *_test.ttl.
* Data generated from this pipeline can be used in a variety of ways downstream.  We recommend
loading the data into a graph database that is optimized for use with ontologies, such as <commit_msg>add travis icon to readme<commit_after>[![Build Status](https://travis-ci.org/monarch-initiative/dipper.svg?branch=master)](https://travis-ci.org/monarch-initiative/dipper)

# DIPPER
Dipper is a pure Python package to generate RDF triples from common scientific resources.
Dipper includes subpackages and modules to create graphical models of this data, including:

* Models for ternary associations and complex partonomies
* Graph utilities to create common relationships using a variety of ontologies
* Fetchers and Parsers that interface with remote databases and web services

## Beware: This code and resulting TTL is still in active development, and is subject to change.
The code in this repository is still under active development while we are refining the models for variant, 
genotype, and genotype-to-phenotype association.  This means that the graph patterns we are employing 
between the different entities is in flux.  **We will indicate here when the modeling has generally stabilized.**

* The dipper main wraps all of the source parsers, enabling users to specify one or more sources to process. 
The general strategy is that there is one class per data source.  We define the files to be fetched,
any file scrubbing, and then the parsing methods.  As the files are parsed, triples are loaded into an in-memory graph.  
This graph is then typically dumped into triples in turtle format.  For testing purposes,
 a subset of the graph is also dumped to *_test.ttl.
* Data generated from this pipeline can be used in a variety of ways downstream.  We recommend
loading the data into a graph database that is optimized for use with ontologies, such as 
{'fraction_non_alphanumeric': 0.03642072213500785, 'fraction_numerical': 0.0, 'mean_word_length': 4.5992970123022845, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6104172', 'n_tokens_mistral': 776, 'n_tokens_neox': 739, 'n_words': 477}