P

preprocessing

Displam is a legacy Fortran tool for the calculation of the phase velocity of Lamb waves in plate composite structures.

The Siril image processing software for amateur astronomy

Runtime environment for software projects developed by the Department of Structural Mechanics at the Institute of Lightweight Structures of the German Aerospace Center. It uses a customized Jupyterlab instance as its graphical user interface.

PyCODAC is a software project for researchers, students, mechanical and computiational engineers written primarily in Python. It provides a modern pre- and postprocessing unit for computation jobs using MCODAC. Full utilization of this project requires additional proprietary computation software.

An ABAQUS plugin to create statistical representative unit volumes with varying fiber volume fractions.

Non destructive damage segmentation and mapping tool for composites.

These Siril preprocessing scripts extract the SII, Ha, and OIII data from Seestar S50 telescope captures, and either Drizzle 1.5x or not. Note that these scripts do not align the SII, Ha, and OIII channels, for that you will either need PixInsight, or a Python pipeline to do so. Setting up this pipeline is not difficult. If need be, AI can walk you though the process, just upload the Read Me documents and ask it to help you create the Python Venv. Consult the project Read Me documents for more information. Clear skies!

SiriLic (Siril's Interactive Companion) is a software that prepares acquisition files (RAW, Offset, Flat and Dark) for processing with the SiriL software.

pySiriL ( python SiriL) is a python library to interface python to a SiriL script.

pySiriL ( python SiriL) est une librairie python pour interfacer python au langage de script de SiriL.

U-Net Adaptive Generalized Image Binarization for Documents

Mirrored from: https://github.com/venkatakolagotla/robin Originally forked from: https://github.com/masyagin1998/robin

Code Repetition Preprocessor

Exploratory Data Analysis (EDA) on Haberman Dataset. Feature Importance.