Metadata-Version: 1.2
Name: deepblink
Version: 0.0.2
Summary: Threshold independent detection and localization of diffraction-limited spots.
Home-page: https://github.com/bbquercus/deepblink/
Author: Bastian Eichenberger
Author-email: bastian@eichenbergers.ch
License: MIT
Project-URL: Documentation, https://deepblink.readthedocs.io/
Project-URL: Changelog, https://deepblink.readthedocs.io/en/latest/changelog.html
Project-URL: Issue Tracker, https://github.com/bbquercus/deepblink/issues
Description: .. image:: https://badge.fury.io/py/deepblink.svg
            :target: https://badge.fury.io/py/deepblink
            :alt: Pypi package version number.
        .. image:: https://travis-ci.org/BBQuercus/deepBlink.svg?branch=master
            :target: https://travis-ci.org/BBQuercus/deepBlink
            :alt: Travis CI build status.
        .. image:: https://ci.appveyor.com/api/projects/status/86ylig998derkv0c/branch/master?svg=true
            :target: https://ci.appveyor.com/project/BBQuercus/deepblink/branch/master
            :alt: Appveyor build status.
        .. image:: https://codecov.io/gh/BBQuercus/deepBlink/branch/master/graph/badge.svg
            :target: https://codecov.io/gh/BBQuercus/deepBlink
            :alt: Codecov test coverage.
        .. image:: https://img.shields.io/github/license/bbquercus/deepblink
            :alt: GitHub code licence is MIT.
        
        .. image:: https://github.com/bbquercus/deepblink/raw/master/images/logo.jpg
            :width: 200px
            :align: right
            :alt: Logo of deepBlink.
        
        ============
        deepBlink
        ============
        
        Threshold independent detection and localization of diffraction-limited spots.
        
        
        Overview
        ============
        In biomedical microscopy data, a common task involves the detection of
        diffraction-limited spots that visualize single proteins, domains, mRNAs,
        and many more. These spots were traditionally detected with mathematical
        operators such as Laplacian of Gaussian. These operators, however, rely
        on human input ranging from image-intensity thresholds, approximative
        spot sizes, etc. This process is tedious and not always reliable. DeepBlink
        relies on neural networks to automatically find spots without the need for
        human intervention. DeepBlink is available as a ready-to-use command-line
        interface.
        
        Example images will follow shortly.
        
        Installation
        ============
        
        ::
        
            pip install deepblink
        
        You can also install the in-development version with::
        
            pip install git+ssh://git@github.com/bbquercus/deepblink/bbquercus/deepblink.git@master
        
        Documentation
        =============
        
        
        https://deepblink.readthedocs.io/
        
        
        Development
        ===========
        
        To run the all tests run::
        
            tox
        
        Note, to combine the coverage data from all the tox environments run:
        
        .. list-table::
            :widths: 10 90
            :stub-columns: 1
        
            - - Windows
              - ::
        
                    set PYTEST_ADDOPTS=--cov-append
                    tox
        
            - - Other
              - ::
        
                    PYTEST_ADDOPTS=--cov-append tox
        
        
        Changelog
        =========
        
        0.0.1 (2020-06-24)
        ------------------
        
        * First release on PyPI with cookiecutter HelloWorld template to set everything up.
        
        0.0.2 (2020-06-29)
        ------------------
        
        * Still cookiecutter code but addition of docstrings to test sphinx apidoc capabilities.
        
Keywords: deep-learning,biomedical,image analysis,spot detection
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Scientific/Engineering :: Artificial Life
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Utilities
Requires-Python: >=3.6
