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
tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series classification, regression and forecasting.

Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai

timeseriesAI 2.8k Jan 08, 2023
An Implementation of Fully Convolutional Networks in Tensorflow.

Update An example on how to integrate this code into your own semantic segmentation pipeline can be found in my KittiSeg project repository. tensorflo

Marvin Teichmann 1.1k Dec 12, 2022
Diffgram - Supervised Learning Data Platform

Data Annotation, Data Labeling, Annotation Tooling, Training Data for Machine Learning

Diffgram 1.6k Jan 07, 2023
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
Perturb-and-max-product: Sampling and learning in discrete energy-based models

Perturb-and-max-product: Sampling and learning in discrete energy-based models This repo contains code for reproducing the results in the paper Pertur

Vicarious 2 Mar 14, 2022
The code for the CVPR 2021 paper Neural Deformation Graphs, a novel approach for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects.

Neural Deformation Graphs Project Page | Paper | Video Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction Aljaž Božič, Pablo P

Aljaz Bozic 134 Dec 16, 2022
Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces

This repository contains source code for the paper Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces a

9 Nov 21, 2022
NAS Benchmark in "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021

NAS-Bench-Macro This repository includes the benchmark and code for NAS-Bench-Macro in paper "Prioritized Architecture Sampling with Monto-Carlo Tree

35 Jan 03, 2023
i3DMM: Deep Implicit 3D Morphable Model of Human Heads

i3DMM: Deep Implicit 3D Morphable Model of Human Heads CVPR 2021 (Oral) Arxiv | Poject Page This project is the official implementation our work, i3DM

Tarun Yenamandra 60 Jan 03, 2023
Hierarchical Metadata-Aware Document Categorization under Weak Supervision (WSDM'21)

Hierarchical Metadata-Aware Document Categorization under Weak Supervision This project provides a weakly supervised framework for hierarchical metada

Yu Zhang 53 Sep 17, 2022
PyTorch trainer and model for Sequence Classification

PyTorch-trainer-and-model-for-Sequence-Classification After cloning the repository, modify your training data so that the training data is a .csv file

NhanTieu 2 Dec 09, 2022
NeuralDiff: Segmenting 3D objects that move in egocentric videos

NeuralDiff: Segmenting 3D objects that move in egocentric videos Project Page | Paper + Supplementary | Video About This repository contains the offic

Vadim Tschernezki 14 Dec 05, 2022
When in Doubt: Improving Classification Performance with Alternating Normalization

When in Doubt: Improving Classification Performance with Alternating Normalization Findings of EMNLP 2021 Menglin Jia, Austin Reiter, Ser-Nam Lim, Yoa

Menglin Jia 13 Nov 06, 2022
Alphabetical Letter Recognition

DecisionTrees-Image-Classification Alphabetical Letter Recognition In these demo we are using "Decision Trees" Our database is composed by Learning Im

Mohammed Firass 4 Nov 30, 2021
[ICCV 2021] Deep Hough Voting for Robust Global Registration

Deep Hough Voting for Robust Global Registration, ICCV, 2021 Project Page | Paper | Video Deep Hough Voting for Robust Global Registration Junha Lee1,

57 Nov 28, 2022
Fast and Easy Infinite Neural Networks in Python

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

Google 1.9k Jan 09, 2023
Canonical Appearance Transformations

CAT-Net: Learning Canonical Appearance Transformations Code to accompany our paper "How to Train a CAT: Learning Canonical Appearance Transformations

STARS Laboratory 54 Dec 24, 2022
Using fully convolutional networks for semantic segmentation with caffe for the cityscapes dataset

Using fully convolutional networks for semantic segmentation (Shelhamer et al.) with caffe for the cityscapes dataset How to get started Download the

Simon Guist 27 Jun 06, 2022
Establishing Strong Baselines for TripClick Health Retrieval; ECIR 2022

TripClick Baselines with Improved Training Data Welcome 🙌 to the hub-repo of our paper: Establishing Strong Baselines for TripClick Health Retrieval

Sebastian Hofstätter 3 Nov 03, 2022
A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.

Rockpool Rockpool is a Python package for developing signal processing applications with spiking neural networks. Rockpool allows you to build network

SynSense 21 Dec 14, 2022