OpenSim is an open-source biomechanical package with a variety of applications. It is available for many users with bindings in MATLAB, Python, and Java via its application programming interfaces (APIs). Although the developers described well the OpenSim installation on different operating systems (Windows, Mac, and Linux), it is time-consuming and complex since each operating system requires a different configuration. This project aims to demystify the development of neuro-musculoskeletal modeling in OpenSim with zero configuration on any operating system for installation (thus cross-platform), easy to share models while accessing free graphical processing units (GPUs) on a web-based platform of Google Colab. To achieve this, OpenColab was developed where OpenSim source code was used to build a Conda package that can be installed on the Google Colab with only one block of code in less than 7 min. To use OpenColab, one requires a connection to the internet and a Gmail account. Moreover, OpenColab accesses vast libraries of machine learning methods available within free Google products, e.g. TensorFlow. Next, we performed an inverse problem in biomechanics and compared OpenColab results with OpenSim graphical user interface (GUI) for validation. The outcomes of OpenColab and GUI matched well (r≥0.82). OpenColab takes advantage of the zero-configuration of cloud-based platforms, accesses GPUs, and enables users to share and reproduce modeling approaches for further validation, innovative online training, and research applications. Step-by-step installation processes and examples are available at: https://simtk.org/projects/opencolab.

PROGRAM SUMMARY No. of lines in distributed program, including test data, etc.: 481 No. of bytes in distributed program, including test data, etc.: 14540.8 Distribution format: .py, .csv Programming language: Python Computer: Any workstation or laptop computer running TensorFlow, Google Colab, Anaconda, Jupyter, pandas, NumPy, Microsoft Azure and Alteryx. Operating system: Windows and Mac OS, Linux.

Nature of problem: Navier-Stokes equations are solved numerically in ANSYS Fluent using Reynolds stress model for turbulence. The simulated values of friction factor are validated with theoretical and experimental data obtained from literature. Artificial neural networks are then used for a prediction-based augmentation of friction factor. The capabilities of the neural networks is discussed, in regard to computational cost and domain limitations.

Solution method: The simulation data is obtained through Reynolds stress modelling of fluid flow through pipe. This data is augmented using the artificial neural network model that predicts within and without data domain.

Restrictions: The code used in this research is limited to smooth pipe bends, in which friction factor is analysed using a steady state incompressible fluid flow.

Runtime: The artificial neural network produces results within a span of 20 seconds for three-dimensional geometry, using the allocated free computational resources of Google Colaboratory cloud-based computing system.

Dataset Details

  Dataset Description

TP4 is a comprehensive dataset containing a curated collection of questions and answers from Stack Overflow. Focused on the realms of Python programming, NumPy, Pandas, TensorFlow, and PyTorch, TP4 includes essential attributes such as question ID, title, question body, answer body, associated tags, and score. This dataset is designed to facilitate research, analysis, and exploration of inquiries and solutions within the Python and… See the full description on the dataset page: https://huggingface.co/datasets/Syed-Hasan-8503/StackOverflow-TP4-1M.

This repository contains a Python script for classifying apple leaf diseases using a Vision Transformer (ViT) model. The dataset used is the Plant Village dataset, which contains images of apple leaves with four classes: Healthy, Apple Scab, Black Rot, and Cedar Apple Rust. The script includes data preprocessing, model training, and evaluation steps.

• Introduction
• Code Explanation
• Steps for Implementation
• Example Usage
• Conclusion

The goal of this project is to classify apple leaf diseases using a Vision Transformer (ViT) model. The dataset is divided into four classes: Healthy, Apple Scab, Black Rot, and Cedar Apple Rust. The script includes data preprocessing, model training, and evaluation steps.

• The script starts by importing necessary libraries such as matplotlib, seaborn, numpy, pandas, tensorflow, and sklearn. These libraries are used for data visualization, data manipulation, and building/training the deep learning model.

• The walk_through_dir function is used to explore the dataset directory structure and count the number of images in each class.
• The dataset is divided into Train, Val, and Test directories, each containing subdirectories for the four classes.

• The script uses ImageDataGenerator from Keras to apply data augmentation techniques such as rotation, horizontal flipping, and rescaling to the training data. This helps in improving the model's generalization ability.
• Separate generators are created for training, validation, and test datasets.

• The script defines a Patches layer that extracts patches from the images. This is a crucial step in Vision Transformers, where images are divided into smaller patches that are then processed by the transformer.
• The script visualizes these patches for different patch sizes (32x32, 16x16, 8x8) to understand how the image is divided.

• The script defines a Vision Transformer (ViT) model using TensorFlow and Keras. The model is compiled with the Adam optimizer and categorical cross-entropy loss.
• The model is trained for a specified number of epochs, and the training history is stored for later analysis.

• After training, the model is evaluated on the test dataset. The script generates a confusion matrix and a classification report to assess the model's performance.
• The confusion matrix is visualized using seaborn to provide a clear understanding of the model's predictions.

• The script includes functionality to visualize misclassified images, which helps in understanding where the model is making errors.

• The script demonstrates how to fine-tune the model by adjusting the learning rate and re-training the model.

1. Dataset Preparation

   • Ensure that the dataset is organized into Train, Val, and Test directories, with each directory containing subdirectories for each class (Healthy, Apple Scab, Black Rot, Cedar Apple Rust).

2. Install Required Libraries

   • Install the necessary Python libraries using pip:

     pip install tensorflow matplotlib seaborn numpy pandas scikit-learn

3. Run the Script

   • Execute the script in a Python environment. The script will automatically:
     • Load and preprocess the dataset.
     • Apply data augmentation.
     • Train the Vision Transformer model.
     • Evaluate the model and generate performance metrics.

4. Analyze Results

   • Review the confusion matrix and classification report to understand the model's performance.
   • Visualize misclassified images to identify potential areas for improvement.

5. Fine-Tuning

   • Experiment with different patch sizes, learning rates, and data augmentation techniques to improve the model's accuracy.

PyLibAPIs.7z : contains public API data (mongodb dump) for these frameworks:

TensorFlow

Keras

scikit-learn

Pandas

Flask

Django

Label.xlsx: cintains issues and their labels

PyLibAPIs.7z : contains public API data (MongoDB dump) for these frameworks:

• TensorFlow
• Keras
• Scikit-learn
• Pandas
• Flask
• Django

Label.xlsx: contains issues and their labels

Breaking Changes for All Frameworks.pdf: contains the breaking change distributions of all six frameworks

https://github.githubassets.com/images/modules/site/home/footer-illustration.svg" alt="GitHub">

Image credits: https://github.com

Introduction

This is a dataset that contains all commit messages and its related metadata from 34 popular GitHub repositories. These repositories are:

• tensorflow/tensorflow
• pytorch/pytorch
• torvalds/linux
• python/cpython
• rust-lang/rust
• microsoft/TypeScript
• microsoft/vscode
• golang/go
• numpy/numpy
• scikit-learn/scikit-learn
• openbsd/src
• freebsd/freebsd-src
• pandas-dev/pandas
• scipy/scipy
• tidyverse/ggplot2
• kubernetes/kubernetes
• postgres/postgres
• nodejs/node
• facebook/react
• angular/angular
• matplotlib/matplotlib
• apache/httpd
• nginx/nginx
• opencv/opencv
• ipython/ipython
• rstudio/rstudio
• jupyterlab/jupyterlab
• gcc-mirror/gcc
• apple/swift
• denoland/deno
• apache/spark
• llvm/llvm-project
• chromium/chromium
• v8/v8

Data as of Wed Apr 21 03:42:44 PM IST 2021

Credits

Image credits: Unsplash - plhnk

This dataset contains the codes to reproduce the results of "Time resolved micro-XRCT dataset of Enzymatically Induced Calcite Precipitation (EICP) in sintered glass bead columns", cf. https://doi.org/10.18419/darus-2227. The code takes "low-dose" images as an input where the images contain many artifacts and noise as a trade-off of a fast data acquisition (6 min / dataset while 3 hours / dataset ("high-dose") in normal configuration). These low quality images are able to be improved with the help of a pre-trained model. The pre-trained model provided in here is trained with pairs of "high-dose" and "low-dose" data of above mentioned EICP application. The examples of used training, input and output data can be also found in this dataset. Although we showed only limited examples in here, we would like to emphasize that the used workflow and codes can be further extended to general image enhancement applications. The code requires a Python version above 3.7.7 with packages such as tensorflow, kears, pandas, scipy, scikit, numpy and patchify libraries. For further details of operation, please refer to the readme.txt file.

This dataset contains the codes to reproduce the five different segmentation results of the paper Lee et al (2021). The original dataset before applying these segmentation codes could be found in Ruf & Steeb (2020). The adopted segmentation methods in order to identify the micro fractures within the original dataset are the Local threshold, Sato, Chan-Vese, Random forest and U-net model. The Local threshold, Sato and U-net models are written in Python. The codes require a version above Python 3.7.7 with tensorflow, keras, pandas, scipy, scikit and numpy libraries. The workflow of the Chan-Vese method is interpreted in Matlab2018b. The result of the Random forest method could be reproduced with the uploaded trained model in an open source program ImageJ and trainableWeka library. For further details of operation, please refer to the readme.txt file.