Metadata-Version: 2.1
Name: proMAD
Version: 0.1.0
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/v0.1.0.zip
Keywords: protein,microarrays,densitometric
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
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 :: 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

# proMAD
Semiquantitative densitometric measurement of protein microarrays


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## Setup
    pip install proMAD

## Usage
**ArrayAnalyse**
```python
from proMAD import ArrayAnalyse
aa = ArrayAnalyse('ARY022B')  # set array type
aa.load_collection('test_cases/exp_data')  # set input folder

aa.evaluate("A6")  # get result dictionary
aa.get_spot("A6")  # get raw data
aa.evaluate()  # get result dictionary for all spots
```
**Cutter**

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

c.load_collection('test_cases/raw_image_folder')  # 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 preview
c.save_images('test_cases/formated_image_folder')  # save to folder (will be created if it does not exist
```

* direct
```python
from proMAD import Cutter

c = Cutter()

c.load_collection('test_cases/raw_image_folder')  # 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/formated_image_folder')  # save to folder (will be created if it does not exist
```



