oap 0.0.14

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____________________________________________________________________
                                                      Version 0.0.14
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
Last Update:    June 20, 2021

Introduction

The oap library is a transparent tool, written in Python and C, for working directly with image data from Optical Array Probes. It was initially developed for the preparation and classification of image data with neural networks.

This software is still in the alpha phase and will be further developed at irregular intervals! Comments on this project are always welcome! If you have suggestions for improvement, you can simply write me an email 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).

• DMT Monoscale Decompression coming next!

The complete documentation can be found at https://oap.readthedocs.io

Installation

The oap library is available on PyPi, so simply open a terminal window and type at the prompt:

pip install oap

I always recommend the use of virtual environments (e.g. venv & 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:

pip install oap --user

Usage

A short code snippet that decompresses an OAP imagefile and outputs the first 100 images to the console:

from oap import Imagefile

imagefile = Imagefile("Imagefile20200830120000")

# search for columns and rosettes
imagefile.classify()

# plot number of particles per flight second
imagefile.plot()

# plot number of rosettes per flight second
imagefile.plot(r=(0.5, 1))

# get all optical arrays containing particles of size
# 100 to 200 micrometers (area ratio) that were recorded
# between flight seconds 20000 and 22000.
array_list = imagefile.get_arrays(timespan=(20000, 22000),
                                  area_ratio=(100, 200))

# print particle images
for array in array_list:
    array.print()

Initialization & Compilation

Prerequisites

• Python >= 3.7
• Pipenv

pipenv install

python setup.py install