Metadata-Version: 2.1
Name: oap
Version: 0.0.10
Summary: A transparent library for processing and analyzing individual images of Optical Array Probes (OAPs)
Home-page: https://github.com/lcsgrlch/oap
Author: Lucas Grulich (lucasgrulich@gmx.de)
Author-email: lucasgrulich@gmx.de
License: UNKNOWN
Description: [![license](https://img.shields.io/pypi/l/oap)](LICENSE)
        [![version](https://img.shields.io/pypi/pyversions/oap)](https://pypi.python.org/pypi/oap/)
        ```
                                                   1
                        111111                    1221  21
                      112222111       112211 2    11222331113
                     11211  1211    1122333222     12321  1211
                     1221   1211    1231  1221      2321   1221
                     1221   1221    1232  1221      2321   1121
                     1221  11221    1231   2321     13211  113
                     1112211211     1121  12321    1123332111
                       111111        111211 121    1123211
                                                    1221    1
                                                    121
                                                    111
                                                     1
        ____________________________________________________________________
                                                              Version 0.0.10
        Optical Array Processing (oap)
        Licensed under the MIT license (see `LICENSE` file)
        
        A transparent library for processing and analyzing individual
        images of Optical Array Probes (OAPs)
        
        Author:         Lucas Grulich (lucasgrulich@gmx.de)
        Last Update:    02. September 2020
        ```
        
        ### Introduction
        
        The [__oap__ library](https://pypi.python.org/pypi/oap/) is a transparent tool for working directly with image data from [Optical Array Probes](https://www.eol.ucar.edu/instruments/two-dimensional-optical-array-cloud-probe).
        It was initially developed for the preparation and classification of image data with neural networks.
        
        __This software will be continuously developed further!__ Comments on this project are welcome! If you have any suggestions for improvement, you can simply write me an [email](mailto:lucasgrulich@gmx.de) and I will try to implement them.
        
        At the current state, the decompression algorithm has only been implemented for grayscale probes by [Droplet Measurement Technologies (DMT)](https://www.dropletmeasurement.com/).
        
        * DMT Monoscale Decompression coming next!
        
        The complete __documentation__ can be found at https://oap.readthedocs.io
        
        ### Installation
        
        The __oap__ library is available on [PyPi](https://pypi.python.org/pypi/oap/), so simply open a terminal window and type at the prompt:
        ```bash
        pip install oap
        ```
        I always recommend the use of virtual environments (e.g. [venv](https://docs.python.org/3/library/venv.html) & [pipenv](https://github.com/pypa/pipenv)) to avoid destroying your Python installation.
        
        But if you don't want to work with virtual environments and still don't want to mess up your system directories, install the library as a user:
        ```bash
        pip install oap --user
        ```
        
        ### Usage
        
        A short code snippet that decompresses an OAP imagefile and outputs the first 100 images to the console:
        ```
        import oap
        
        images = []
        oap.imagefile("Imagefile20200830120000", images=images)
        
        for image in images[:100]:
            oap.print_array(image)
        ```
        
        ### Initialization & Compilation by yourself
        
        #### Prerequisites
        
        * Python >= 3.7
        * Pipenv
        
        ```bash
        pipenv install
        ```
        
        ```bash
        python setup.py install
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
