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moving stuff around and stuff

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commit cc23087ba974de2dc2b1cbb6dc8ca7996f35468b 1 parent 89721e9
Jen Fong-Adwent authored
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5 MANIFEST
@@ -0,0 +1,5 @@
+# file GENERATED by distutils, do NOT edit
+setup.py
+auto_tagify/__init__.py
+auto_tagify/auto_tagify.py
+auto_tagify/auto_tagifytests.py
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2  README → README.rst
@@ -1,3 +1,5 @@
+# Auto Tagify
+
Auto Tagify is a simple auto tagging module that uses NLTK to generate tags out of a selection of text. Any text that is less than 3 characters long or matches a particular POS (part-of-speech) will be ignored.
There are two operations Auto Tagify performs - one returns the selection of text with links embedded in the string and the other returns a list of all the taggable words as the stem word (using lemmatization).
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0  __init__.py → auto_tagify/__init__.py
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0  auto_tagify.py → auto_tagify/auto_tagify.py
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0  auto_tagifytests.py → auto_tagify/auto_tagifytests.py
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BIN  dist/auto_tagify-1.2.macosx-10.7-intel.tar.gz
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BIN  dist/auto_tagify-1.2.tar.gz
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23 setup.py 100644 → 100755
@@ -1,20 +1,23 @@
from distutils.core import setup
-VERSION = '1.2'
+
setup(name='auto_tagify',
- version=VERSION,
+ version='1.2',
author='Edna Piranha',
author_email='jen@ednapiranha.com',
url='https://github.com/ednapiranha/auto-tagify',
- download_url='http://bitworking.org/projects/httplib2/dist/httplib2-%s.tar.gz' % VERSION,
description='Auto-tags a selection of text and generates links to the tagified versions of the words',
- license='MIT',
+ license='BSD',
long_description="""
-Auto Tagify is a simple auto tagging module that uses NLTK to generate tags out of a selection of text. Any text that is less than 3 characters long or matches a particular POS (part-of-speech) will be ignored.
+Auto Tagify is a simple auto tagging module that uses NLTK to generate tags out of a selection of text. Any
+text that is less than 3 characters long or matches a particular POS (part-of-speech) will be ignored.
-There are two operations Auto Tagify performs - one returns the selection of text with links embedded in the string and the other returns a list of all the taggable words as the stem word (using lemmatization).
+There are two operations Auto Tagify performs - one returns the selection of text with links embedded in the
+string and the other returns a list of all the taggable words as the stem word (using lemmatization).
-For the first operation, everything is optional, but it is most effective to enter some text. Optional parameters you can set are the paths for tag links and the css classes for link. For instance, if you set your tag routing to a relative path such as /tags/<tagged_word> and want to use the css class named "tagged":
+For the first operation, everything is optional, but it is most effective to enter some text. Optional
+parameters you can set are the paths for tag links and the css classes for link. For instance, if you set
+your tag routing to a relative path such as /tags/<tagged_word> and want to use the css class named "tagged":
from auto_tagify import AutoTagify
@@ -40,7 +43,9 @@
The result will be a list: ['text', 'tag', 'kitten']
-By default, generate() and tag_list() will be in strict mode, which means all special characters will be stripped. If generate(False) or tag_list(False) is set, then special characters will be url encoded.
+By default, generate() and tag_list() will be in strict mode, which means all special characters will be stripped and
+lemmatization will be enforced. If generate(strict=False) or tag_list(strict=False) is set, then special characters will be url encoded
+and lemmatization will be ignored.
These two operations are sufficient for you to maintain tag counts and tag references to text in your application.
""",
@@ -51,7 +56,7 @@
'Environment :: Web Environment',
'Intended Audience :: Developers',
'Intended Audience :: Science/Research',
- 'License :: OSI Approved :: MIT License',
+ 'License :: OSI Approved :: BSD License',
'Natural Language :: English',
'Operating System :: OS Independent',
'Programming Language :: Python',
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