Simulation code and tutorial for BBHnet training data

Overview

Simulation Dataset for BBHnet

NOTE: OLD README, UPDATE IN PROGRESS

We generate simulation dataset to train BBHnet, our deep learning framework for detection of compact binary coalescene (CBC) gravitational-wave (GW) signals .

Example

To generate a noise dataset, simply run generateRealNoise.py:

python generateRealNoise.py -t0 1186729980 -t1 1186734086 -t0-psd 1186729980 -t1-psd 1186734086
    -fs 1024 -fmin 20 -o test_noise.h5

To also add CBC signals, enable the flag -S and add the prior distribution file in Bilby format with -p

python generateRealNoise.py -t0 1186729980 -t1 1186734086 -t0-psd 1186729980 -t1-psd 1186734086
    -fs 1024 -fmin 20 -S -p config/priors/nonspin_BBH.prior -o test_signal.h5

A full list of generateRealNoise.py arguments can be found below:

usage: generateRealNoise.py [-h] -t0 FRAME_START -t1 FRAME_STOP -t0-psd FRAME_START_PSD -t1-psd FRAME_STOP_PSD -o OUTFILE [-S]
                            [-fs SAMPLE_RATE] [-fmin HIGH_PASS] [-T SAMPLE_DURATION] [-dt TIME_STEP] [-p PRIOR_FILE]
                            [--correlation-shift CORRELATION_SHIFT] [--min-trigger MIN_TRIGGER] [--max-trigger MAX_TRIGGER]
                            [-s SEED]

optional arguments:
  -h, --help            show this help message and exit
  -t0 FRAME_START, --frame-start FRAME_START
                        starting GPS time of strain
  -t1 FRAME_STOP, --frame-stop FRAME_STOP
                        stopping GPS time of strain
  -t0-psd FRAME_START_PSD, --frame-start-psd FRAME_START_PSD
                        starting GPS time of strain for PSD estimation
  -t1-psd FRAME_STOP_PSD, --frame-stop-psd FRAME_STOP_PSD
                        stopping GPS time of strain for PSD estimation
  -o OUTFILE, --outfile OUTFILE
                        path to write output file in HDF5 format
  -S, --signal          Enable to add GW signal on top of background noise
  -fs SAMPLE_RATE, --sample-rate SAMPLE_RATE
                        sampling rate of strain
  -fmin HIGH_PASS, --high-pass HIGH_PASS
                        frequency of highpass filter
  -T SAMPLE_DURATION, --sample-duration SAMPLE_DURATION
                        duration in seconds of each sample
  -dt TIME_STEP, --time-step TIME_STEP
                        time step size in seconds between consecutive samples
  -p PRIOR_FILE, --prior-file PRIOR_FILE
                        path to prior config file. Required for signal simulation
  --correlation-shift CORRELATION_SHIFT
                        if given, also compute the correlation with given shift value
  --min-trigger MIN_TRIGGER
                        mininum trigger time w.r.t to sample. must be within [0, sample_duration]
  --max-trigger MAX_TRIGGER
                        maximum trigger time w.r.t to sample. must be within [0, sample_duration]
  -s SEED, --seed SEED  random seed for reproducibility

ROS-UGV-Control-Interface - Control interface which can be used in any UGV

ROS-UGV-Control-Interface Cam Closed: Cam Opened:

Ahmet Fatih Akcan 1 Nov 04, 2022
NBEATSx: Neural basis expansion analysis with exogenous variables

NBEATSx: Neural basis expansion analysis with exogenous variables We extend the NBEATS model to incorporate exogenous factors. The resulting method, c

Cristian Challu 100 Dec 31, 2022
Convolutional Neural Network to detect deforestation in the Amazon Rainforest

Convolutional Neural Network to detect deforestation in the Amazon Rainforest This project is part of my final work as an Aerospace Engineering studen

5 Feb 17, 2022
Code for Massive-scale Decoding for Text Generation using Lattices

Massive-scale Decoding for Text Generation using Lattices Jiacheng Xu, Greg Durrett TL;DR: a new search algorithm to construct lattices encoding many

Jiacheng Xu 37 Dec 18, 2022
Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)

Junction Tree Variational Autoencoder for Molecular Graph Generation Official implementation of our Junction Tree Variational Autoencoder https://arxi

Wengong Jin 418 Jan 07, 2023
Awesome Weak-Shot Learning

Awesome Weak-Shot Learning In weak-shot learning, all categories are split into non-overlapped base categories and novel categories, in which base cat

BCMI 162 Dec 30, 2022
Flask101 - FullStack Web Development with Python & JS - From TAQWA

Task: Create a CLI Calculator Step 0: Creating Virtual Environment $ python -m

Hossain Foysal 1 May 31, 2022
Official PyTorch implementation of "Evolving Search Space for Neural Architecture Search"

Evolving Search Space for Neural Architecture Search Usage Install all required dependencies in requirements.txt and replace all ..path/..to in the co

Yuanzheng Ci 10 Oct 24, 2022
Official PyTorch Implementation of paper "Deep 3D Mask Volume for View Synthesis of Dynamic Scenes", ICCV 2021.

Deep 3D Mask Volume for View Synthesis of Dynamic Scenes Official PyTorch Implementation of paper "Deep 3D Mask Volume for View Synthesis of Dynamic S

Ken Lin 17 Oct 12, 2022
[ICME 2021 Oral] CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning

CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning This repository is the official PyTorch implementation of CORE-Text, a

Jingyang Lin 18 Aug 11, 2022
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions

torch-imle Concise and self-contained PyTorch library implementing the I-MLE gradient estimator proposed in our NeurIPS 2021 paper Implicit MLE: Backp

UCL Natural Language Processing 249 Jan 03, 2023
Try out deep learning models online on Google Colab

Try out deep learning models online on Google Colab

Erdene-Ochir Tuguldur 1.5k Dec 27, 2022
Source Code for ICSE 2022 Paper - ``Can We Achieve Fairness Using Semi-Supervised Learning?''

Fair-SSL Source Code for ICSE 2022 Paper - Can We Achieve Fairness Using Semi-Supervised Learning? Ethical bias in machine learning models has become

1 Dec 18, 2021
Dynamic View Synthesis from Dynamic Monocular Video

Dynamic View Synthesis from Dynamic Monocular Video Project Website | Video | Paper Dynamic View Synthesis from Dynamic Monocular Video Chen Gao, Ayus

Chen Gao 139 Dec 28, 2022
Direct design of biquad filter cascades with deep learning by sampling random polynomials.

IIRNet Direct design of biquad filter cascades with deep learning by sampling random polynomials. Usage git clone https://github.com/csteinmetz1/IIRNe

Christian J. Steinmetz 55 Nov 02, 2022
Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms applied on Continuous Control Tasks

Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms applied on Continuous Control Tasks This is the master thesi

Giacomo Arcieri 1 Mar 21, 2022
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation [arxiv] This is the official repository for CDTrans: Cross-domain Transformer for

238 Dec 22, 2022
Unofficial PyTorch implementation of MobileViT based on paper "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer".

MobileViT RegNet Unofficial PyTorch implementation of MobileViT based on paper MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TR

Hong-Jia Chen 91 Dec 02, 2022
[ICCV 2021] Target Adaptive Context Aggregation for Video Scene Graph Generation

Target Adaptive Context Aggregation for Video Scene Graph Generation This is a PyTorch implementation for Target Adaptive Context Aggregation for Vide

Multimedia Computing Group, Nanjing University 44 Dec 14, 2022
Official PyTorch Implementation of "Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs". NeurIPS 2020.

Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs This repository is the implementation of SELAR. Dasol Hwang* , Jinyoung Pa

MLV Lab (Machine Learning and Vision Lab at Korea University) 48 Nov 09, 2022