PyPI官网下载|jupyterlab_thredds-0.2.0.tar.gz资源-CSDN文库

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《PyPI官网下载：jupyterlab_thredds-0.2.0.tar.gz——Python库的探索与应用》在Python编程世界中，PyPI（Python Package Index）是广大开发者获取和分享开源软件包的重要平台。本文将围绕“jupyterlab_thredds-0.2.0.tar.gz”这一资源展开，深入探讨它在Python库中的作用以及如何使用它来增强JupyterLab的科研数据访问能力。 “jupyterlab_thredds”是一个Python库，专为JupyterLab设计，目的是为了方便用户访问THREDDS（Thematic Real-time Environmental Distributed Data Services）服务器上的科学数据。THREDDS是一种数据服务框架，它使得科学家可以使用标准的网络协议（如HTTP、OPeNDAP）来访问和操作各种格式的科学数据，无论这些数据的原始格式如何。THREDDS提供了丰富的功能，包括数据发现、元数据检索、数据切片和转换等。 “jupyterlab_thredds-0.2.0.tar.gz”是该库的0.2.0版本，以tar.gz压缩格式提供，这是Python社区常用的源代码分发方式。这个压缩包包含了一系列的Python源代码文件、文档、测试用例等，用户可以解压后通过Python的安装工具（如pip）进行安装。解压后的文件名通常以库的名称加上版本号的形式出现，即“jupyterlab_thredds-0.2.0”。使用“jupyterlab_thredds”库，用户可以在JupyterLab环境中无缝集成THREDDS服务，这意味着可以直接在交互式的JupyterLab界面中浏览、探索和分析存储在THREDDS服务器上的大量科学数据。这个库提供了一个直观的界面，使得数据的访问和处理更加便捷，尤其适合于科研人员进行数据分析和可视化工作。具体使用步骤如下： 1. 确保已经安装了JupyterLab环境。如果没有，可以通过pip安装： ``` pip install jupyterlab ``` 2. 解压“jupyterlab_thredds-0.2.0.tar.gz”，然后在命令行中进入解压后的目录。 3. 使用pip安装库： ``` pip install . ``` 4. 在JupyterLab中，安装并加载THREDDS扩展： ``` jupyter labextension install @jupyterlab/thredds ``` 5. 现在，你可以启动JupyterLab，并通过其菜单或快捷方式访问THREDDS服务器，浏览并加载数据。通过“jupyterlab_thredds”，科研人员可以利用JupyterLab的强大功能，结合THREDDS的分布式数据服务，实现高效的数据探索和分析，进一步推动科研工作的进展。这不仅提升了数据处理的效率，也简化了对复杂科学数据的管理，使得更多的人能够参与到数据驱动的科学发现中来。

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jupyterlab_thredds-0.2.0.tar.gz （9个子文件）

jupyterlab_thredds-0.2.0

PKG-INFO 4KB

package.json 2KB

jupyterlab_thredds

__init__.py 2KB

version.py 22B

jupyter-config

jupyter_notebook_config.d

jupyterlab_thredds.json 95B

setup.cfg 160B

setup.py 2KB

setupbase.py 21KB

README.md 3KB

# jupyterlab_thredds [![Build Status](https://travis-ci.org/eWaterCycle/jupyterlab_thredds.svg?branch=master)](https://travis-ci.org/eWaterCycle/jupyterlab_thredds) [![SonarCloud Quality](https://sonarcloud.io/api/project_badges/measure?project=jupyterlab_thredds&metric=alert_status)](https://sonarcloud.io/dashboard?id=jupyterlab_thredds) [![SonarCloud Coverage](https://sonarcloud.io/api/project_badges/measure?project=jupyterlab_thredds&metric=coverage)](https://sonarcloud.io/component_measures?id=jupyterlab_thredds&metric=coverage) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1241007.svg)](https://doi.org/10.5281/zenodo.1241007) JupyterLab dataset browser for [THREDDS catalog](https://www.unidata.ucar.edu/software/thredds/v4.6/tds/catalog/index.html) Can inject [iris](http://scitools.org.uk/iris/docs/latest/index.html)/[xarray](https://xarray.pydata.org)/[leaflet](https://github.com/jupyter-widgets/ipyleaflet) code cells into a Python notebook of a selected dataset to further process/visualize the dataset. ![screenshot](https://github.com/eWaterCycle/jupyterlab_thredds/blob/master/jupyterlab_thredds.gif "Screenshot") ## Prerequisites * JupyterLab, `pip install jupyterlab` * ipywidgets, `jupyter labextension install @jupyter-widgets/jupyterlab-manager`, requirement for ipyleaflet * ipyleaflet, `jupyter labextension install jupyter-leaflet`, to load a WMS layer * [iris](http://scitools.org.uk/iris/docs/latest/index.html), `conda install -c conda-forge iris` ## Installation ```bash pip install jupyterlab_thredds jupyter labextension install @ewatercycle/jupyterlab_thredds ``` ## Usage 0. Start Jupyter lab with `jupyter lab` 1. In Jupyter lab open a notebook 2. Open the `THREDDS` tab on the left side. 3. Fill the catalog url 4. Press search button 5. Select how you would like to open the dataset, by default it uses [iris](http://scitools.org.uk/iris/docs/latest/index.html) Python package. 6. Press a dataset to insert code into a notebook ## Development For a development install (requires npm version 4 or later), do the following in the repository directory: ```bash npm install npm run build jupyter labextension link . python setup.py develop jupyter serverextension enable --sys-prefix jupyterlab_thredds ``` To rebuild the package and the JupyterLab app: ```bash npm run build jupyter lab build ``` Watch mode ```bash # shell 1 npm run watch # shell 2 jupyter lab --ip=0.0.0.0 --no-browser --watch ``` ## Release To make a new release perform the following steps: 1. Update version in `package.json` and `jupyterlab_thredds/version.py` 2. Make sure tests pass by running `npm test` and `pytest` 3. Publish lab extension to npmjs with `npm run build` and `npm publish --access=public` 4. Publish server extension to pypi with `python setup.py sdist bdist_wheel` and `twine upload dist/*`