Packaging setup and registration to PyPi

.gitignore

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 # Distribution / packaging
 .Python
 env/
-build/
 develop-eggs/
-dist/
 downloads/
 eggs/
 .eggs/
-lib/
 lib64/
 parts/
 sdist/
 var/
-*.egg-info/
 .installed.cfg
 *.egg
 

MANIFEST

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+# file GENERATED by distutils, do NOT edit
+setup.py
+neuropredict/__init__.py
+neuropredict/freesurfer.py
+neuropredict/neuropredict.py
+neuropredict/posthoc.py
+neuropredict/rhst.py
+neuropredict/test_rhst.py

README.md

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-# psy
+# neuropredict
 
 Automatic estimation of predictive power of commonly used structural imaging features as well as user-defined features.
 

build/lib/neuropredict/__init__.py

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+
+all = ['neuropredict']

neuropredict.egg-info/PKG-INFO

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+Metadata-Version: 1.0
+Name: neuropredict
+Version: 0.1
+Summary: Neuroimaging Predictive Analysis
+Home-page: https://github.com/raamana/neuropredict
+Author: Pradeep Reddy Raamana
+Author-email: [email protected]
+License: UNKNOWN
+Description: # neuropredict
+
+        Automatic estimation of predictive power of commonly used structural imaging features as well as user-defined features.
+
+        The aim of this python module would be to automatically assess the predictive power of commonly used neuroimaging features (such as resting-state connectibity, fractional anisotropy, subcortical volumes and cortical thickness features) automatically read from the processing of popular tools such as FSL, DTIstudio, AFNI and Freesurfer, and present a comprehensive report on a given dataset. It is mainly aimed (to lower or remove the barriers) at clinical users who would like to understand what features and brain regions are discriminative in their shiny new dataset before diving into the deep grey sea of feature extraction and optimization.
+
+        PS: It sounds similar (on the surface) to other software available, however it is aimed to lower the barriers even further, or remove them altogether! All the user would need to provide are commonly used features (such as a Freesurfer output directory) and obtain an easy to read report, along with well-packaged export of performance metrics (for sharing and posthoc comparison) on the predictive power of the features they are interested in.
+
+        ## usage:
+
+        ```
+        usage: neuropredict [-h] -m METADATAFILE [-f FSDIR] [-u USERDIR] [-o OUTDIR]
+
+        optional arguments:
+          -h, --help            show this help message and exit
+          -m METADATAFILE, --metadatafile METADATAFILE
+                                Abs path to file containing metadata for subjects to
+                                be included for analysis. At the minimum, each subject
+                                should have an id per row followed by the class it
+                                belongs to. E.g. sub001,control sub002,control
+                                sub003,disease sub004,disease
+          -f FSDIR, --fsdir FSDIR
+                                Abs. path of SUBJECTS_DIR containing the finished runs
+                                of Freesurfer parcellation
+          -u USERDIR, --userdir USERDIR
+                                Abs. path to an user's own features.This contains a
+                                separate folder for each subject (named after its ID
+                                in the metadata file) containing a file called
+                                features.txt with one number per line. All the
+                                subjects must have the number of features (#lines in
+                                file)
+          -o OUTDIR, --outdir OUTDIR
+                                Output folder to store features and results.
+        ```
+
+
+
+
+
+Platform: UNKNOWN

neuropredict.egg-info/SOURCES.txt

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+setup.py
+neuropredict/__init__.py
+neuropredict/freesurfer.py
+neuropredict/neuropredict.py
+neuropredict/posthoc.py
+neuropredict/rhst.py
+neuropredict/test_rhst.py
+neuropredict.egg-info/PKG-INFO
+neuropredict.egg-info/SOURCES.txt
+neuropredict.egg-info/dependency_links.txt
+neuropredict.egg-info/requires.txt
+neuropredict.egg-info/top_level.txt

neuropredict.egg-info/dependency_links.txt

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+

neuropredict.egg-info/requires.txt

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+numpy
+sklearn

neuropredict.egg-info/top_level.txt

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+neuropredict

neuropredict/__init__.py

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+
+all = ['neuropredict']

requirements.txt

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+numpy
+sklearn
+nibabel
+pyradigm

setup.py

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+#!/usr/bin/env python
+
+from setuptools import setup
+
+setup(name='neuropredict',
+      version='0.1',
+      description='Neuroimaging Predictive Analysis',
+      long_description=open('README.md').read(),
+      author='Pradeep Reddy Raamana',
+      author_email='[email protected]',
+      url='https://github.com/raamana/neuropredict',
+      packages=['neuropredict'],
+      install_requires=['numpy', 'sklearn'],
+     )