QuakeLabeler is a Python package to create and manage your seismic training data, processes, and visualization in a single place — so you can focus on building the next big thing.

Overview

QuakeLabeler

Quake Labeler was born from the need for seismologists and developers who are not AI specialists to easily, quickly, and independently build and visualize their training data set.

Introduction

QuakeLabeler is a Python package to customize, build and manage your seismic training data, processes, and visualization in a single place — so you can focus on building the next big thing. Current functionalities include retrieving waveforms from data centers, customizing seismic samples, auto-building datasets, preprocessing and augmenting for labels, and visualizing data distribution. The code helps all levels of AI developers and seismology researchers for querying and building their own earthquake datasets and can be used through an interactive command-line interface with little knowledge of Python.

Installation, Usage, documentation and scripts are described at https://maihao14.github.io/QuakeLabeler/

Author: Hao Mai(Developer and Maintainer) & Pascal Audet (Developer and Maintainer)

Installation

Conda environment

We recommend creating a custom conda environment where QuakeLabeler can be installed along with its dependencies.

  • Create a environment called ql and install pygmt:
conda create -n ql python=3.8 pygmt -c conda-forge
  • Activate the newly created environment:
conda activate ql

Installing from source

Download or clone the repository:

git clone https://github.com/maihao14/QuakeLabeler.git
cd QuakeLabeler
pip install .

If you work in development mode, use the -e argument as pip install -e .

Running the scripts

Create a work folder where you will run the scripts that accompany QuakeLabeler. For example:

mkdir ~/WorkFolder
cd WorkFolder

Run QuakeLabeler. Input QuakeLabeler to macOS terminal or Windows consoles:

QuakeLabeler

Or input quakelabeler also works:

quakelabeler

A QuakeLabeler welcome interface will be loading:

(ql) [email protected] QuakeLabeler % QuakeLabeler
Welcome to QuakeLabeler----Fast AI Earthquake Dataset Deployment Tool!
QuakeLabeler provides multiple modes for different levels of Seismic AI researchers

[Beginner] mode -- well prepared case studies;
[Advanced] mode -- produce earthquake samples based on Customized parameters.

Contributing

All constructive contributions are welcome, e.g. bug reports, discussions or suggestions for new features. You can either open an issue on GitHub or make a pull request with your proposed changes. Before making a pull request, check if there is a corresponding issue opened and reference it in the pull request. If there isn't one, it is recommended to open one with your rationale for the change. New functionality or significant changes to the code that alter its behavior should come with corresponding tests and documentation. If you are new to contributing, you can open a work-in-progress pull request and have it iteratively reviewed. Suggestions for improvements (speed, accuracy, etc.) are also welcome.

You might also like...
Spam your friends and famly and when you do your famly will disown you and you will have no friends.

SpamBot9000 Spam your friends and family and when you do your family will disown you and you will have no friends. Terms of Use Disclaimer: Please onl

The code for our paper
The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction"

The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction"

Ever felt tired after preprocessing the dataset, and not wanting to write any code further to train your model? Ever encountered a situation where you wanted to record the hyperparameters of the trained model and able to retrieve it afterward? Models Playground is here to help you do that. Models playground allows you to train your models right from the browser.
7th place solution of Human Protein Atlas - Single Cell Classification on Kaggle

kaggle-hpa-2021-7th-place-solution Code for 7th place solution of Human Protein Atlas - Single Cell Classification on Kaggle. A description of the met

Kaggle | 9th place single model solution for TGS Salt Identification Challenge

UNet for segmenting salt deposits from seismic images with PyTorch. General We, tugstugi and xuyuan, have participated in the Kaggle competition TGS S

Source codes of CenterTrack++ in 2021 ICME Workshop on Big Surveillance Data Processing and Analysis
Source codes of CenterTrack++ in 2021 ICME Workshop on Big Surveillance Data Processing and Analysis

MOT Tracked object bounding box association (CenterTrack++) New association method based on CenterTrack. Two new branches (Tracked Size and IOU) are a

AI Flow is an open source framework that bridges big data and artificial intelligence.
AI Flow is an open source framework that bridges big data and artificial intelligence.

Flink AI Flow Introduction Flink AI Flow is an open source framework that bridges big data and artificial intelligence. It manages the entire machine

In-Place Activated BatchNorm for Memory-Optimized Training of DNNs
In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

In-Place Activated BatchNorm In-Place Activated BatchNorm for Memory-Optimized Training of DNNs In-Place Activated BatchNorm (InPlace-ABN) is a novel

PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).

PyGAD: Genetic Algorithm in Python PyGAD is an open-source easy-to-use Python 3 library for building the genetic algorithm and optimizing machine lear

Comments
  • QuakeLabeler ModuleNotFoundError

    QuakeLabeler ModuleNotFoundError

    I followed the installation instructions to install the fascinating QuakeLabeler package But I encountered an error as follows Traceback (most recent call last): File "/home/panxiong/anaconda3/envs/ql/bin/QuakeLabeler", line 5, in <module> from quakelabeler.scripts.QuakeLabeler import main ModuleNotFoundError: No module named 'quakelabeler.scripts' Please give me a solution, thanks.

    opened by PANXIONG-CN 2
  • Error loading GMT shared library

    Error loading GMT shared library

    Hello,

    I was trying to use the QuakeLabeler package on some data and when I tried to run it I got the following error:

    pygmt.exceptions.GMTCLibNotFoundError: Error loading GMT shared library at 'libgmt.so'. libgmt.so: cannot open shared object file: No such file or directory

    I saw that there were some responses to a similar question in the past, but they all involved using conda, which I don't use at it interferes with other libraries I use.

    So far I tried using:

    pip install pygmt

    as well as GMT:

    sudo apt-get install gmt gmt-dcw gmt-gshhg sudo apt-get install ghostscript Unfortunately, it did not work.

    Any suggestions would be appreciated

    opened by sbrent88 1
  • the problem of QuakeLabeler used in the Ubuntu

    the problem of QuakeLabeler used in the Ubuntu

    After I create the python environment needed by QuakeLabeler and install it in my Ubuntu computer, there was the problem, "AttributeError: 'numpy.int64' object has no attribute 'split'" when I execute QuakeLabeler (quakelabeler) in the terminal.

    “”“ Traceback (most recent call last): File "/home/xxx/anaconda3/envs/slc/bin/QuakeLabeler", line 33, in sys.exit(load_entry_point('QuakeLabeler', 'console_scripts', 'QuakeLabeler')()) File "/home/xxx/anaconda3/envs/slc/bin/QuakeLabeler", line 25, in importlib_load_entry_point return next(matches).load() File "/home/xxx/anaconda3/envs/slc/lib/python3.8/importlib/metadata.py", line 77, in load module = import_module(match.group('module')) File "/home/xxx/anaconda3/envs/slc/lib/python3.8/importlib/init.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "", line 1014, in _gcd_import File "", line 991, in _find_and_load File "", line 961, in _find_and_load_unlocked File "", line 219, in _call_with_frames_removed File "", line 1014, in _gcd_import File "", line 991, in _find_and_load File "", line 961, in _find_and_load_unlocked File "", line 219, in _call_with_frames_removed File "", line 1014, in _gcd_import File "", line 991, in _find_and_load File "", line 975, in _find_and_load_unlocked File "", line 671, in _load_unlocked File "", line 843, in exec_module File "", line 219, in _call_with_frames_removed File "/home/xxx/EQ_Detection/QuakeLabeler/quakelabeler/init.py", line 5, in from .classes import QuakeLabeler, Interactive, CustomSamples, QueryArrival, BuiltInCatalog, MergeMetadata, GlobalMaps File "/home/xxx/EQ_Detection/QuakeLabeler/quakelabeler/classes.py", line 35, in from obspy.core.utcdatetime import UTCDateTime File "/home/xxx/.local/lib/python3.8/site-packages/obspy/init.py", line 39, in from obspy.core.utcdatetime import UTCDateTime # NOQA File "/home/xxx/.local/lib/python3.8/site-packages/obspy/core/init.py", line 124, in from obspy.core.utcdatetime import UTCDateTime # NOQA File "/home/xxx/.local/lib/python3.8/site-packages/obspy/core/utcdatetime.py", line 27, in from obspy.core.util.deprecation_helpers import ObsPyDeprecationWarning File "/home/xxx/.local/lib/python3.8/site-packages/obspy/core/util/init.py", line 27, in from obspy.core.util.base import (ALL_MODULES, DEFAULT_MODULES, File "/home/xxx/.local/lib/python3.8/site-packages/obspy/core/util/base.py", line 36, in from obspy.core.util.misc import to_int_or_zero, buffered_load_entry_point File "/home/xxx/.local/lib/python3.8/site-packages/obspy/core/util/misc.py", line 214, in loadtxt(np.array([0]), ndmin=1) File "/home/xxx/anaconda3/envs/slc/lib/python3.8/site-packages/numpy/lib/npyio.py", line 1086, in loadtxt ncols = len(usecols or split_line(first_line)) File "/home/xxx/anaconda3/envs/slc/lib/python3.8/site-packages/numpy/lib/npyio.py", line 977, in split_line line = line.split(comment, 1)[0] AttributeError: 'numpy.int64' object has no attribute 'split' "”"

    opened by Damin1909 3
Owner
Hao Mai
Hao Mai
A simple Python library for stochastic graphical ecological models

What is Viridicle? Viridicle is a library for simulating stochastic graphical ecological models. It implements the continuous time models described in

Theorem Engine 0 Dec 04, 2021
CondNet: Conditional Classifier for Scene Segmentation

CondNet: Conditional Classifier for Scene Segmentation Introduction The fully convolutional network (FCN) has achieved tremendous success in dense vis

ycszen 31 Jul 22, 2022
Blender add-on: Add to Cameras menu: View → Camera, View → Add Camera, Camera → View, Previous Camera, Next Camera

Blender add-on: Camera additions In 3D view, it adds these actions to the View|Cameras menu: View → Camera : set the current camera to the 3D view Vie

German Bauer 11 Feb 08, 2022
This repository is the official implementation of Open Rule Induction. This paper has been accepted to NeurIPS 2021.

Open Rule Induction This repository is the official implementation of Open Rule Induction. This paper has been accepted to NeurIPS 2021. Abstract Rule

Xingran Chen 16 Nov 14, 2022
Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction

Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction. arxiv This repository contains python scripts for tr

12 Dec 12, 2022
Torch-mutable-modules - Use in-place and assignment operations on PyTorch module parameters with support for autograd

Torch Mutable Modules Use in-place and assignment operations on PyTorch module p

Kento Nishi 7 Jun 06, 2022
Pytorch reimplementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)

Vision Transformer Pytorch reimplementation of Google's repository for the ViT model that was released with the paper An Image is Worth 16x16 Words: T

Eunkwang Jeon 1.4k Dec 28, 2022
🔥 TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement"

🆕 Are you looking for a new YOLOv3 implemented by TF2.0 ? If you hate the fucking tensorflow1.x very much, no worries! I have implemented a new YOLOv

3.6k Dec 26, 2022
Object Detection Projekt in GKI WS2021/22

tfObjectDetection Object Detection Projekt with tensorflow in GKI WS2021/22 Docker Container: docker run -it --name --gpus all -v path/to/project:p

Tim Eggers 1 Jul 18, 2022
This repository contains source code for the Situated Interactive Language Grounding (SILG) benchmark

SILG This repository contains source code for the Situated Interactive Language Grounding (SILG) benchmark. If you find this work helpful, please cons

Victor Zhong 17 Nov 27, 2022
Source code for our Paper "Learning in High-Dimensional Feature Spaces Using ANOVA-Based Matrix-Vector Multiplication"

NFFT4ANOVA Source code for our Paper "Learning in High-Dimensional Feature Spaces Using ANOVA-Based Matrix-Vector Multiplication" This package uses th

Theresa Wagner 1 Aug 10, 2022
Think Big, Teach Small: Do Language Models Distil Occam’s Razor?

Think Big, Teach Small: Do Language Models Distil Occam’s Razor? Software related to the paper "Think Big, Teach Small: Do Language Models Distil Occa

0 Dec 07, 2021
The code for Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation

BiMix The code for Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation arxiv Framework: visualization results: Requiremen

stanley 18 Sep 18, 2022
Code repository for the work "Multi-Domain Incremental Learning for Semantic Segmentation", accepted at WACV 2022

Multi-Domain Incremental Learning for Semantic Segmentation This is the Pytorch implementation of our work "Multi-Domain Incremental Learning for Sema

Pgxo20 24 Jan 02, 2023
Code for ICCV2021 paper SPEC: Seeing People in the Wild with an Estimated Camera

SPEC: Seeing People in the Wild with an Estimated Camera [ICCV 2021] SPEC: Seeing People in the Wild with an Estimated Camera, Muhammed Kocabas, Chun-

Muhammed Kocabas 187 Dec 26, 2022
Implementation of our paper 'RESA: Recurrent Feature-Shift Aggregator for Lane Detection' in AAAI2021.

RESA PyTorch implementation of the paper "RESA: Recurrent Feature-Shift Aggregator for Lane Detection". Our paper has been accepted by AAAI2021. Intro

137 Jan 02, 2023
YOLO-v5 기반 단안 카메라의 영상을 활용해 차간 거리를 일정하게 유지하며 주행하는 Adaptive Cruise Control 기능 구현

자율 주행차의 영상 기반 차간거리 유지 개발 Table of Contents 프로젝트 소개 주요 기능 시스템 구조 디렉토리 구조 결과 실행 방법 참조 팀원 프로젝트 소개 YOLO-v5 기반으로 단안 카메라의 영상을 활용해 차간 거리를 일정하게 유지하며 주행하는 Adap

14 Jun 29, 2022
Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".

Detecting Twenty-thousand Classes using Image-level Supervision Detic: A Detector with image classes that can use image-level labels to easily train d

Meta Research 1.3k Jan 04, 2023
Implementation of the federated dual coordinate descent (FedDCD) method.

FedDCD.jl Implementation of the federated dual coordinate descent (FedDCD) method. Installation To install, just call Pkg.add("https://github.com/Zhen

Zhenan Fan 6 Sep 21, 2022
JFB: Jacobian-Free Backpropagation for Implicit Models

JFB: Jacobian-Free Backpropagation for Implicit Models

Typal Research 28 Dec 11, 2022