Metadata-Version: 2.1
Name: proMAD
Version: 0.3.0
Summary: Semiquantitative densitometric measurement of protein microarrays
Home-page: https://proMAD.dev
Author: Anna Jaeschke; Hagen Eckert
Author-email: 
License: MIT
Download-URL: https://github.com/theia-dev/proMAD/archive/v0.3.0.zip
Keywords: protein,microarrays,densitometric
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: ~=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: scipy
Requires-Dist: scikit-image
Requires-Dist: imageio
Requires-Dist: openpyxl
Requires-Dist: requests
Requires-Dist: ipython
Requires-Dist: xmltodict
Requires-Dist: jinja2
Requires-Dist: pylatexenc

# proMAD <img src='https://github.com/theia-dev/proMAD/raw/master/proMAD/data/templates/logo.png' align="right"/>
Semiquantitative densitometric measurement of protein microarrays


[![PyPi](https://img.shields.io/pypi/v/proMAD.svg?style=for-the-badge)](https://pypi.org/project/proMAD/)
[![Status](https://img.shields.io/pypi/status/proMAD.svg?style=for-the-badge)](https://pypi.org/project/proMAD/)
[![Coverage](https://img.shields.io/coveralls/github/theia-dev/proMAD?style=for-the-badge)](https://coveralls.io/github/theia-dev/proMAD)
[![Build](https://img.shields.io/travis/theia-dev/proMAD.svg?style=for-the-badge)](https://travis-ci.org/theia-dev/proMAD)
[![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)


## Setup
    pip install proMAD

You can also install the latest version directly from GitHub.

    pip install -U git+https://github.com/theia-dev/proMAD.git#egg=proMAD


## Usage
**ArrayAnalyse**
```python
from proMAD import ArrayAnalyse
aa = ArrayAnalyse('ARY022B')  # set array type
aa.load_collection('tests/cases/prepared', rotation=90)  # set input folder

aa.evaluate("A6")  # get result dictionary
aa.get_spot("A6")  # get underlying image data
aa.evaluate()  # get result dictionary for all spots

aa.report('report.xlsx')  # export the results
```
**Cutter**

* interactive
```python
from proMAD import Cutter
c = Cutter()

c.load_collection('tests/cases/raw')  # set input folder
c.set_shape()  # ask for the shape
c.guess_positions()  # use a simple guess as a starting point
c.preview()  # display guess (uses the last loaded image as default)

c.set_positions()  # ask for refined cut positions
c.set_names()  # ask for names
c.preview()  # check in the preview
c.save_images('test/cases/formatted_image_folder')  # save to folder (will be created if it does not exist)
```

* direct
```python
from proMAD import Cutter

c = Cutter()

c.load_collection('tests/cases/raw')  # set input folder
c.shape = (2, 3)
c.cut_positions = [[20, 225, 445], [40, 130, 217, 315]]
c.names = [['OL', 'ML', 'UL'], [None, 'MR', 'UR']]
c.preview()
c.save_images('test/cases/formatted_image_folder')  # save to folder (will be created if it does not exist)
```



