semvec/pypi-packages huggingface dataset:
hathitrust-api 0.1.1
Python wrappers for the HathiTrust APIs
License: MIT

hathitrust-api
=================

A simple interface for the HathiTrust APIs. The package contains basic classes and associated methods for querying the [Bibliographic API][bib api], [Data API][data api], and the [HTRC Solr Proxy][solr api].

The package is compatible with Python 2 and Python 3.

[bib api]: http://www.hathitrust.org/bib_api
[data api]: http://www.hathitrust.org/data_api
[solr api]: http://wiki.htrc.illinois.edu/display/COM/Solr+Proxy+API+User+Guide

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

Clone and install from this repository:
```
git clone https://github.com/rlmv/hathitrust-api.git
cd hathitrust-api
python setup.py install
```

Or install directly using `pip`:
```
pip install hathitrust-api
```

DataAPI
-------
The Data API retrieves non-google public domain works from the HathiTrust.

An OAuth [keyset][kgs] from HathiTrust is required to use the Data API.

Example usage:
```
>>> from hathitrust_api import DataAPI
>>> data_api = DataAPI(your_oauth_key, your_oauth_secret)
>>> ocrtext = data_api.getpageocr('nyp.33433082228226', 120)
```

[kgs]: http://babel.hathitrust.org/cgi/kgs/request

BibAPI
------
The bibliographic API delivers HathiTrust bibliographic data and MARC records in JSON format.

Example:
```
>>> from hathitrust_api import BibAPI
>>> bib_api = BibAPI()
>>> bib_info = bib_api.get_single_record_json('htid', 'dul1.ark:/13960/t00z82c1q')
>>> bib_info.keys()
[u'records', u'items']
>>> bib_info['records']['010944133']['publishDates']
[u'1670']
```

SolrAPI
-------
The HTRC Solr Proxy is a search index over the public domain collection.

```
>>> from hathitrust_api import SolrAPI
>>> solr = SolrAPI()
>>> results = solr.query("new zealand", fields=['title'])
>>> results
{u'responseHeader': {u'status': 0, u'QTime': 19}, u'response': {u'start': 0, u'numFound': 366613, u'docs': [{u'title': [u'The statues of New Zealand ...']}, {u'title': [u'New Zealand.']}, {u'title': [u"Wise's New Zealand index"]}, {u'title': [u'Palaeontological bulletin.']}, {u'title': [u'New Zealand,']}, {u'title': [u'The New Zealand official year-book.']}, {u'title': [u'The New Zealand official year-book.']}, {u'title': [u'The New Zealand official year-book.']}, {u'title': [u'The New Zealand official year-book.']}, {u'title': [u'The New Zealand official year-book.']}]}}
```

Needed:
------
* Write test cases.
q2 0.1.4
A reinforcement learning framework and command line tool
License: MIT
Keywords: deep reinforcement learning framework tool

# q2

A reinforcement learning framework and command line tool.


## Get it

```
pip install q2
q2 --help
```

## Use it

Make a folder, `cd` to it, then run

```
q2 init
```

to generate a new reinforcement learning project.

## Docs

Documentation is available at https://q2.readthedocs.io/.


## Develop


0. You'll need Python >= 3.5 (this is because q2 makes use of type annotations)
1. Clone this repository
2. I recommend setting up a virtual env in which to develop: `python -m venv env`
3. `pip install -r requirements.txt`

To test out the command line tools from your working copy of q2, run

```
pip install -e .
```

then run

```
q2 --help
```

to test that it worked.


## Contributing

If you want to submit a bug fix or minor change, feel free to make a pull request.

If you want to discuss bigger improvements, send an email to tdbalcorn at gmail dot com.