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
The official implementation of Theme Transformer

Theme Transformer This is the official implementation of Theme Transformer. Checkout our demo and paper : Demo | arXiv Environment: using python versi

Ian Shih 85 Dec 08, 2022
Manifold Alignment for Semantically Aligned Style Transfer

Manifold Alignment for Semantically Aligned Style Transfer [Paper] Getting Started MAST has been tested on CentOS 7.6 with python = 3.6. It supports

35 Nov 14, 2022
[ICCV 2021 Oral] Just Ask: Learning to Answer Questions from Millions of Narrated Videos

Just Ask: Learning to Answer Questions from Millions of Narrated Videos Webpage • Demo • Paper This repository provides the code for our paper, includ

Antoine Yang 87 Jan 05, 2023
DECA: Detailed Expression Capture and Animation (SIGGRAPH 2021)

DECA: Detailed Expression Capture and Animation (SIGGRAPH2021) input image, aligned reconstruction, animation with various poses & expressions This is

Yao Feng 1.5k Jan 02, 2023
tensorflow code for inverse face rendering

InverseFaceRender This is tensorflow code for our project: Learning Inverse Rendering of Faces from Real-world Videos. (https://arxiv.org/abs/2003.120

Yuda Qiu 18 Nov 16, 2022
Employs neural networks to classify images into four categories: ship, automobile, dog or frog

Neural Net Image Classifier Employs neural networks to classify images into four categories: ship, automobile, dog or frog Viterbi_1.py uses a classic

Riley Baker 1 Jan 18, 2022
The official PyTorch implementation for NCSNv2 (NeurIPS 2020)

Improved Techniques for Training Score-Based Generative Models This repo contains the official implementation for the paper Improved Techniques for Tr

174 Dec 26, 2022
Official PyTorch Implementation of GAN-Supervised Dense Visual Alignment

GAN-Supervised Dense Visual Alignment — Official PyTorch Implementation Paper | Project Page | Video This repo contains training, evaluation and visua

944 Jan 07, 2023
Data, notebooks, and articles associated with the RSNA AI Deep Learning Lab at RSNA 2021

RSNA AI Deep Learning Lab 2021 Intro Welcome Deep Learners! This document provides all the information you need to participate in the RSNA AI Deep Lea

RSNA 65 Dec 16, 2022
Bootstrapped Representation Learning on Graphs

Bootstrapped Representation Learning on Graphs This is the PyTorch implementation of BGRL Bootstrapped Representation Learning on Graphs The main scri

NerDS Lab :: Neural Data Science Lab 55 Jan 07, 2023
A Human-in-the-Loop workflow for creating HD images from text

A Human-in-the-Loop? workflow for creating HD images from text DALL·E Flow is an interactive workflow for generating high-definition images from text

Jina AI 2.5k Jan 02, 2023
EGNN - Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch

EGNN - Pytorch Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch. May be eventually used for Alphafold2 replication. This

Phil Wang 259 Jan 04, 2023
PyTorch implementation of SIFT descriptor

This is an differentiable pytorch implementation of SIFT patch descriptor. It is very slow for describing one patch, but quite fast for batch. It can

Dmytro Mishkin 150 Dec 24, 2022
Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces"

Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces" This repo contains the implementation of GEBO algorithm.

Jaeyeon Ahn 2 Mar 22, 2022
Testability-Aware Low Power Controller Design with Evolutionary Learning, ITC2021

Testability-Aware Low Power Controller Design with Evolutionary Learning This repo contains the source code of Testability-Aware Low Power Controller

Lee Man 1 Dec 26, 2021
A graphical Semi-automatic annotation tool based on labelImg and Yolov5

💕YOLOV5 semi-automatic annotation tool (Based on labelImg)

EricFang 247 Jan 05, 2023
Official code for On Path Integration of Grid Cells: Group Representation and Isotropic Scaling (NeurIPS 2021)

On Path Integration of Grid Cells: Group Representation and Isotropic Scaling This repo contains the official implementation for the paper On Path Int

Ruiqi Gao 39 Nov 10, 2022
Implementation of self-attention mechanisms for general purpose. Focused on computer vision modules. Ongoing repository.

Self-attention building blocks for computer vision applications in PyTorch Implementation of self attention mechanisms for computer vision in PyTorch

AI Summer 962 Dec 23, 2022
Pytorch implementation of Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization https://arxiv.org/abs/2008.11646

[TCSVT] Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization LPN [Paper] NEWs Prerequisites Python 3.6 GPU Memory = 8G Numpy 1.

46 Dec 14, 2022
Pytorch implementation of our paper under review — Lottery Jackpots Exist in Pre-trained Models

Lottery Jackpots Exist in Pre-trained Models (Paper Link) Requirements Python = 3.7.4 Pytorch = 1.6.1 Torchvision = 0.4.1 Reproduce the Experiment

Yuxin Zhang 27 Jun 28, 2022