Python库 | pycsep-0.1.0.dev1.tar.gz

# PyCSEP: Collaboratory for the Study of Earthquake Predictability    [](https://cseptesting.org) [](https://codecov.io/gh/SCECcode/csep2) The PyCSEP tools help 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 quantative 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. ## Installing PyCSEP PyCSEP can be installed using `pip` or built from source. We are working on a `conda-forge` recipe that will greatly simplify the installation process and remove the need to install system dependencies. If you plan on contributing to this package, visit the [contribution guidelines](https://github.com/SCECcode/pycsep/blob/master/CONTRIBUTING.md) for installation instructions. pip install pycsep Before this installation will work, you must **first** install the following system dependencies. The remaining dependencies should be installed by the installation script. To help manage dependency issues, we recommend using virtual environments like `virtualenv`. Python 3.7 or later (https://python.org) NumPy 1.10 or later (https://numpy.org)     Python package for scientific computing and numerical calculations. GEOS 3.3.3 or later (https://trac.osgeo.org/geos/)     C++ library for processing geometry. PROJ 4.9.0 or later (https://proj4.org/)     Library for cartographic projections. Example for Ubuntu: sudo apt-get install libproj-dev proj-data proj-bin sudo apt-get install libgeos-dev pip install --upgrade pip pip install numpy Example for MacOS: brew install proj geos pip install --upgrade pip pip install numpy ### From Source Use this approach if you want the most up-to-date code. This creates an editable installation that can be synced with the latest GitHub commit. We recommend using virtual environments when installing python packages from source to avoid any dependency conflicts. We prefer `conda` as the package manager over `pip`, because `conda` does a good job of handling binary distributions of packages across multiple platforms. Also, we recommend using the `miniconda` installer, because it is lightweight and only includes necessary pacakages like `pip` and `zlib`. #### Using Conda If you don't have `conda` on your machine, download and install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). git clone https://github.com/SCECcode/pycsep cd pycsep conda env create -f requirements.yml conda activate csep-dev # Installs in editor mode with all dependencies pip install -e . Note: If you want to go back to your default environment use the command `conda deactivate`. #### Using Pip / Virtualenv We highly recommend using Conda, because this tools helps to manage binary dependencies on Python packages. If you must use [Virtualenv](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) follow these instructions: git clone https://github.com/SCECcode/pycsep cd pycsep python -m virtualenv venv source venv/bin/activate # Installs in editor mode with all dependencies pip install -e .[all] Note: If you want to go back to your default environment use the command `deactivate`. ## Documentation and Changelog The documentation can be found at [here](https://cseptesting.org), and the changelog can be found [here](https://github.com/SCECcode/pycsep/blob/master/CHANGELOG.txt). ## Releases We follow [semver](https://semver.org/) for our versioning strategy.