from __future__ import absolute_import, division, print_function
from os.path import join as pjoin

# Format expected by setup.py and doc/source/conf.py: string of form "X.Y.Z"
_version_major = 0
_version_minor = 1
_version_micro = 1  # use '' for first of series, number for 1 and above
_version_extra = 'dev'
# _version_extra = ''  # Uncomment this for full releases

# Construct full version string from these.
_ver = [_version_major, _version_minor]
if _version_micro:
    _ver.append(_version_micro)
if _version_extra:
    _ver.append(_version_extra)

__version__ = '.'.join(map(str, _ver))

CLASSIFIERS = ["Development Status :: 3 - Alpha",
               "Environment :: Console",
               "Intended Audience :: Science/Research",
               "License :: OSI Approved :: MIT License",
               "Operating System :: OS Independent",
               "Programming Language :: Python",
               "Topic :: Scientific/Engineering"]

# Description should be a one-liner:
description = "defectfinder: automated localization, classification and visualization of the defects in 2D materials from dynamic STEM data"
# Long description will go up on the pypi page
long_description = """
Understanding elementary mechanisms behind the solid-state phase
 transformations and reactions is key to the tuning of the functional
 properties of many technologically relevant materials. Recent advances
 in scanning transmission electron microscopy (STEM) have allowed
 unprecedented insight into these processes via observations of atomic
 motion induced by thermal stimuli or electron beam. At the same time,
 the ability to quickly acquire large, high-resolution datasets has created a
 challenge for rapid physics-based analysis of STEM images and movies
 that typically contain several hundreds to several thousand atomic units
 (per image/frame).

 Defectfinder enables the user to develop a convolutional-neural-network
 based framework for automated localization, classification and visualization
 of the defects using physics based models in 2D materials from dynamic
 STEM data. It also enables the user to gain insight into the defect coordinates
 with the help of class activation maps. Furthermore, it allows the user to
 visualize and understand the transformations and reaction pathways
 between defect types in these 2D materials.

=======
defectfinder  is licensed under the terms of the MIT license. See the file
"LICENSE" for information on the history of this software, terms & conditions
for usage, and a DISCLAIMER OF ALL WARRANTIES.
"""

NAME = "defectfinder"
MAINTAINER = "Sarthak, Jimin, Yiwen, Maxim"
MAINTAINER_EMAIL = "jariwala@uw.edu"
DESCRIPTION = description
LONG_DESCRIPTION = long_description
URL = "https://github.com/yiwen26/DLSSTRP"
DOWNLOAD_URL = ""
LICENSE = "MIT"
AUTHOR = "SARTHAK"
AUTHOR_EMAIL = "jariwala@uw.edu"
PLATFORMS = "OS Independent"
MAJOR = _version_major
MINOR = _version_minor
MICRO = _version_micro
VERSION = __version__
# PACKAGE_DATA = {'defectfinder': [pjoin('data', '*')]}
REQUIRES = [
    "numpy",
    "scipy",
    "scikit-image",
    "matplotlib",
    "keras",
    "scikit-learn",
    "opencv-python"]
