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.

Last update: Nov 04, 2022

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.

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

0 Jun 9, 2022

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

201 Nov 21, 2022

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.

Models Playground 🗂️ Upload a Preprocessed Dataset 🌠 Choose whether to perform Classification or Regression 🦹 Enter the Dependent Variable ?

19 Dec 10, 2022

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

8 Jul 9, 2021

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

276 Dec 20, 2022

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

36 Oct 4, 2022

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

144 Dec 30, 2022

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

1.3k Dec 29, 2022

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

1.1k Dec 26, 2022

Comments

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

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

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

Releases(v1.0.1)

v1.0.1(Dec 23, 2021)

QuakeLabeler-1.0.1.tar.gz
Source code(tar.gz)
Source code(zip)
v0.0.2(Jun 8, 2021)

Source code(tar.gz)
Source code(zip)
v0.0.1(Jun 8, 2021)

Source code(tar.gz)
Source code(zip)

Owner

Hao Mai

GitHub Repository

Kaggle-titanic - A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

Kaggle-titanic This is a tutorial in an IPython Notebook for the Kaggle competition, Titanic Machine Learning From Disaster. The goal of this reposito

800 Dec 15, 2022

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

Modeling High-Frequency Limit Order Book Dynamics Using Machine Learning Framework to capture the dynamics of high-frequency limit order books. Overvi

1.3k Jan 07, 2023

A novel framework to automatically learn high-quality scanning of non-planar, complex anisotropic appearance.

appearance-scanner About This repository is an implementation of the neural network proposed in Free-form Scanning of Non-planar Appearance with Neura

14 Oct 18, 2022

Implementation of momentum^2 teacher

Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning Requirements All experiments are done with python3.6, torch

121 Sep 26, 2022

Pytorch implementation of the paper DocEnTr: An End-to-End Document Image Enhancement Transformer.

DocEnTR Description Pytorch implementation of the paper DocEnTr: An End-to-End Document Image Enhancement Transformer. This model is implemented on to

74 Jan 07, 2023

Astrostatistics class for the MSc degree in Astrophysics at the University of Milan-Bicocca (Italy)

Astrostatistics Davide Gerosa - [email protected] University of Mil

25 Jan 02, 2023

Rethinking Nearest Neighbors for Visual Classification

Rethinking Nearest Neighbors for Visual Classification arXiv Environment settings Check out scripts/env_setup.sh Setup data Download the following fin

29 Oct 11, 2022

Official PyTorch Implementation of SSMix (Findings of ACL 2021)

SSMix: Saliency-based Span Mixup for Text Classification (Findings of ACL 2021) Official PyTorch Implementation of SSMix | Paper Abstract Data augment

52 Dec 27, 2022

A large-scale video dataset for the training and evaluation of 3D human pose estimation models

ASPset-510 ASPset-510 (Australian Sports Pose Dataset) is a large-scale video dataset for the training and evaluation of 3D human pose estimation mode

36 Oct 30, 2022

Use unsupervised and supervised learning to predict stocks

AIAlpha: Multilayer neural network architecture for stock return prediction This project is meant to be an advanced implementation of stacked neural n

1.5k Dec 26, 2022

TensorFlow CNN for fast style transfer

Fast Style Transfer in TensorFlow Add styles from famous paintings to any photo in a fraction of a second! It takes 100ms on a 2015 Titan X to style t

1 Dec 14, 2021

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

3k Dec 26, 2022

Gauge equivariant mesh cnn

Geometric Mesh CNN The code in this repository is an implementation of the Gauge Equivariant Mesh CNN introduced in the paper Gauge Equivariant Mesh C

50 Dec 18, 2022

Pytorch implementation of face attention network

Face Attention Network Pytorch implementation of face attention network as described in Face Attention Network: An Effective Face Detector for the Occ

312 Dec 09, 2022

NeRF visualization library under construction

NeRF visualization library using PlenOctrees, under construction pip install nerfvis Docs will be at: https://nerfvis.readthedocs.org import nerfvis s

196 Jan 04, 2023

[NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, Tengyu Ma This is the offi

528 Jan 01, 2023

OpenL3: Open-source deep audio and image embeddings

OpenL3 OpenL3 is an open-source Python library for computing deep audio and image embeddings. Please refer to the documentation for detailed instructi

326 Jan 02, 2023

Python scripts to detect faces in Python with the BlazeFace Tensorflow Lite models

Python scripts to detect faces using Python with the BlazeFace Tensorflow Lite models. Tested on Windows 10, Tensorflow 2.4.0 (Python 3.8).

46 Nov 17, 2022

Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR

Codebase for "INVASE: Instance-wise Variable Selection" Authors: Jinsung Yoon, James Jordon, Mihaela van der Schaar Paper: Jinsung Yoon, James Jordon,

50 Nov 11, 2022

Reproduction process of AlexNet

PaddlePaddle论文复现杂谈背景注：该repo基于PaddlePaddle，对AlexNet进行复现。时间仓促，难免有所疏漏，如果问题或者想法，欢迎随时提issue一块交流。飞桨论文复现赛地址：https://aistudio.baidu.com/aistudio/competitio

19 Nov 29, 2022