A Python library for electronic structure pre/post-processing

PyProcar PyProcar is a robust, open-source Python library used for pre- and post-processing of the electronic structure data coming from DFT calculations. PyProcar provides a set of functions that manage data obtained from the PROCAR format. Basically, the PROCAR format is a projection of the Kohn-Sham states over atomic orbitals. That projection is performed to every k-point in the considered mesh, every energy band and every atom. PyProcar is capable of performing a multitude of tasks including plotting plain and […]

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Histogramming for analysis powered by boost-histogram

Hist Hist is a analyst friendly front-end for boost-histogram, designed for Python 3.7+ (3.6 users get version 2.3). Installation You can install this library from PyPI with pip: python3 -m pip install “hist[plot]” If you do not need the plotting features, you can skip the [plot] extra. Features Hist currently provides everything boost-histogram provides, and the following enhancements: Hist augments axes with names: name= is a unique label describing each axis label= is an optional string that is used in […]

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Self-Supervised Learning for General-Purpose Audio Representation

BYOL for Audio This is a demo implementation of BYOL for Audio (BYOL-A), a self-supervised learning method for general-purpose audio representation, includes: Training code that can train models with arbitrary audio files. Evaluation code that can evaluate trained models with downstream tasks. Pretrained weights. If you find BYOL-A useful in your research, please use the following BibTeX entry for citation. @misc{niizumi2021byol-a, title={BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation}, author={Daisuke Niizumi and Daiki Takeuchi and Yasunori Ohishi and Noboru […]

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TensorFlow implementation of EigenGAN: Layer-Wise Eigen-Learning for GANs

EigenGAN TensorFlow implementation of EigenGAN: Layer-Wise Eigen-Learning for GANs EigenGAN Usage Environment Python 3.6 TensorFlow 1.15 OpenCV, scikit-image, tqdm, oyaml we recommend Anaconda or Miniconda, then you can create the environment with commands below conda create -n EigenGAN python=3.6 source activate EigenGAN conda install opencv scikit-image tqdm tensorflow-gpu=1.15 conda install -c conda-forge oyaml NOTICE: if you create a new conda environment, remember to activate it before any other command source activate EigenGAN Data Preparation CelebA-unaligned (10.2GB, higher quality than the […]

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Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video

TCMR_RELEASE This repository is the official Pytorch implementation of Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video. The base codes are largely borrowed from VIBE. Qualtitative result Paper teaser video Installation TCMR is tested on Ubuntu 16.04 with Pytorch 1.4 and Python 3.7.10. You may need sudo privilege for the installation. source scripts/install_pip.sh Quick demo Download the pre-trained demo TCMR and required data by below command and download SMPL layers from here (male&female) and […]

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A lightweight engine for slicing a PyTorch tensor into parallel shards

TorchShard TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards. It can reduce GPU memory and scale up the training when the model has massive linear layers (e.g., ViT, BERT and GPT) or huge classes (millions). It has the same API design as PyTorch. Installation pip install torchshard More options in INSTALL.md. Usage import torchshard as ts ts.init_process_group(group_size=2) # init parallel groups m = torch.nn.Sequential( torch.nn.Linear(20, 30, bias=True), ts.nn.ParallelLinear(30, 30, bias=True, dim=None), # equal to nn.Linear() […]

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Monocular 360˚ Layout Estimation via Differentiable Depth Rendering

LED2-Net This is PyTorch implementation of our CVPR 2021 Oral paper “LED2-Net: Monocular 360˚ Layout Estimation via Differentiable Depth Rendering”. You can visit our project website and upload your own panorama to see the 3D results! Prerequisite This repo is primarily based on PyTorch. You can use the follwoing command to intall the dependencies. pip install -r requirements.txt Preparing Training Data Under LED2Net/Dataset, we provide the dataloader of Matterport3D and Realtor360. The annotation formats of the two datasets follows PanoAnnotator. […]

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WhatsApp Group Chat Analysis using Python

Introduction Today one of the trendy social media platforms is…. guess what? One and only Whatsapp😅. It is one of the favorite social media platforms among all of us because of its attractive features. It has more than 2B users worldwide and “According to one survey an average user spends more than 195 minutes per week on WhatsApp”. How terrible the above statement is. Leave all these things and let’s understand what actually WhatsApp analyzer means? WhatsApp Analyzer means we […]

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A Python package for Galactic and gravitational dynamics

Gala Gala is a Python package for Galactic and gravitational dynamics. Galactic Dynamics is the study of the formation, history, and evolution of galaxies using the orbits of objects — numerically-integrated trajectories of stars, dark matter particles, star clusters, or galaxies themselves. Installation and Dependencies The easiest way to get Gala is to install with conda or pip. If you are on Linux or Mac, you can install with conda using the conda-forge channel: conda install gala –channel conda-forge If […]

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An implementation of chunked, compressed, N-dimensional arrays for Python

Zarr Zarr is a Python package providing an implementation of compressed, chunked, N-dimensional arrays, designed for use in parallel computing. Main Features Create N-dimensional arrays with any NumPy dtype. Chunk arrays along any dimension. Compress and/or filter chunks using any NumCodecs codec. Store arrays in memory, on disk, inside a zip file, on S3, etc… Read an array concurrently from multiple threads or processes. Write to an array concurrently from multiple threads or processes. Organize arrays into hierarchies via groups. […]

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