Giter VIP home page Giter VIP logo

pycsep's Introduction

pyCSEP: Collaboratory for the Study of Earthquake Predictability

DOI DOI badge

Description:

The pyCSEP Toolkit helps earthquake forecast model developers evaluate their forecasts with the goal of understanding earthquake predictability.

pyCSEP should:

  1. Help modelers become familiar with formats, procedures, and evaluations used in CSEP Testing Centers.
  2. Provide vetted software for model developers to use in their research.
  3. Provide quantitative and visual tools to assess earthquake forecast quality.
  4. Promote open-science ideas by ensuring transparency and availability of scientific code and results.
  5. Curate benchmark models and data sets for modelers to conduct retrospective experiments of their forecasts.

Table of Contents:

  1. Software Documentation
  2. Installation
  3. Usage
  4. Contributing
  5. Change Log
  6. Credits
  7. License

Installation:

pyCSEP can be installed in several ways. It can be installed using conda or pip package managers or from the source code found in the pyCSEP github repo. Researchers interested in contributing to pyCSEP development should install pyCSEP from source code. pyCSEP depends on the following software packages. These which may be installed automatically, or manually, depending on the installation method used.

Please see the requirements file for a complete list of requirements. These are installed automatically when using the conda distribution.

Detailed pyCSEP installation instructions can be found in the online pyCSEP documentation.

Usage:

Once installed, pyCSEP methods can be invoked from python code by importing package csep. pyCSEP provides objects and utilities related to several key concepts:

  • Earthquake Catalogs
  • Earthquake Forecasts
  • Earthquake Forecast Evaluations
  • Regions

An simple example to download and plot an earthquake catalog from the USGS ComCat:

import csep
from csep.core import regions
from csep.utils import time_utils
start_time = time_utils.strptime_to_utc_datetime('2019-01-01 00:00:00.0')
end_time = time_utils.utc_now_datetime()
catalog = csep.query_comcat(start_time, end_time)
catalog.plot(show=True)

Please see pyCSEP Getting Started documentation for more examples and tutorials.

Software Support:

Software support for pyCSEP is provided by that Southern California Earthquake Center (SCEC) Research Computing Group. This group supports several research software distributions including UCVM. Users can report issues and feature requests using the pyCSEP github-based issue tracking link below. Developers will also respond to emails sent to the SCEC software contact listed below.

  1. pyCSEP Issues
  2. Email Contact: software [at] scec [dot] org

Contributing:

We welcome contributions to the pyCSEP Toolkit. If you would like to contribute to this package, including software, tests, and documentation, please visit the contribution guidelines for guidelines on how to contribute to pyCSEP development. pyCSEP contributors agree to abide by the code of conduct found in our Code of Conduct guidelines.

Credits:

Development of pyCSEP is a group effort. A list of developers that have contributed to the PyCSEP Toolkit are listed in the credits file in this repository.

License:

The pyCSEP software is distributed under the BSD 3-Clause open-source license. Please see the license file for more information.

pycsep's People

Contributors

wsavran avatar pabloitu avatar khawajasim avatar gen2 avatar pjmaechling avatar bayonato89 avatar mherrmann3 avatar levin422 avatar fabiolsilva avatar hanbao-ucla avatar kennygraham1 avatar kirstybayliss avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.