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        "author": "Anna Jaeschke; Hagen Eckert",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Image Recognition",
            "Topic :: Scientific/Engineering :: Information Analysis"
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        "description": "# proMAD <img src='https://github.com/theia-dev/proMAD/raw/master/proMAD/data/templates/logo.png' align=\"right\"/>\nSemiquantitative densitometric measurement of protein microarrays\n\n\n[![PyPi](https://img.shields.io/pypi/v/proMAD.svg?style=for-the-badge)](https://pypi.org/project/proMAD/)\n[![Status](https://img.shields.io/pypi/status/proMAD.svg?style=for-the-badge)](https://pypi.org/project/proMAD/)\n[![Github issues](https://img.shields.io/github/issues/theia-dev/proMAD.svg?style=for-the-badge)](https://github.com/theia-dev/proMAD/issues)\n[![License](https://img.shields.io/github/license/theia-dev/proMAD.svg?style=for-the-badge)](https://github.com/theia-dev/proMAD/blob/master/LICENSE.txt)\n\n[![Documentation](https://img.shields.io/readthedocs/promad.svg?style=for-the-badge)](https://promad.readthedocs.io)\n[![Coverage](https://img.shields.io/coveralls/github/theia-dev/proMAD?style=for-the-badge)](https://coveralls.io/github/theia-dev/proMAD)\n[![Build](https://img.shields.io/travis/theia-dev/proMAD.svg?style=for-the-badge)](https://travis-ci.org/theia-dev/proMAD)\n\n\n\n## Setup\n    pip install proMAD\n\nYou can also install the latest version directly from GitHub.\n\n    pip install -U git+https://github.com/theia-dev/proMAD.git#egg=proMAD\n\n\n## Usage\n**ArrayAnalyse**\n```python\nfrom proMAD import ArrayAnalyse\naa = ArrayAnalyse('ARY022B')  # set array type\naa.load_collection('tests/cases/prepared', rotation=90)  # set input folder\n\naa.evaluate(\"A6\")  # get result dictionary\naa.get_spot(\"A6\")  # get underlying image data\naa.evaluate()  # get result dictionary for all spots\n\naa.report('report.xlsx')  # export the results\n```\n**Cutter**\n\n* interactive\n```python\nfrom proMAD import Cutter\nc = Cutter()\n\nc.load_collection('tests/cases/raw')  # set input folder\nc.set_shape()  # ask for the shape\nc.guess_positions()  # use a simple guess as a starting point\nc.preview()  # display guess (uses the last loaded image as default)\n\nc.set_positions()  # ask for refined cut positions\nc.set_names()  # ask for names\nc.preview()  # check in the preview\nc.save_images('test/cases/formatted_image_folder')  # save to folder (will be created if it does not exist)\n```\n\n* direct\n```python\nfrom proMAD import Cutter\n\nc = Cutter()\n\nc.load_collection('tests/cases/raw')  # set input folder\nc.shape = (2, 3)\nc.cut_positions = [[20, 225, 445], [40, 130, 217, 315]]\nc.names = [['OL', 'ML', 'UL'], [None, 'MR', 'UR']]\nc.preview()\nc.save_images('test/cases/formatted_image_folder')  # save to folder (will be created if it does not exist)\n```\n\n***\n\nThe full source code can be accessed on [GitHub](https://github.com/theia-dev/proMAD) with the corresponding documentation hosted at [Read the docs](https://promad.readthedocs.io).\n\n\n",
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