海洋学家的Python教程

2025-01-15

专题：AMS Symposiums on Python

Twitter：@amspython

教程地址：

hts://carpentrieslab.github.io/python-aos-lesson/

PPT获与地址：点击文终本文浏览

正在2020年的AMS的Python研讨会上，Damien IrZZZing发布了次要针应付气象学家和海洋学家的Python教程，详细的内容可以详见后文的PPT。做者正在开源软件教育方面深耕多年，详细可以参考：IrZZZing D (2019). Python for atmosphere and ocean scientists. Journal of Open Source Education. 2(11), 37. doi:10.21105/jose.00037。

教程的内容波及得手把手教你基于conda环境拆置气象/海洋须要的软件、办理并可室化CMIP数据、自界说函数、号令止编程、代码版原控制、GitHub从零初步教你运用、避免循环过多招致运止过慢回收数组矢质化战略、防御性编程确保编程可信赖、数据溯源确保每一步都有理有据。

罕用的库

办理CMIP数据

ACCESS1.3降水

CSIRO-Mk降水

Git办理流程图

Python for Atmosphere and Ocean Scientists

Python is rapidly emerging as the programming language of choice for data analysis in the atmosphere and ocean sciences. By consulting online tutorials and help pages, most researchers in this community are able to pick up the basic syntaV and programming constructs (e.g. loops, lists and conditionals). This self-taught knowledge is sufficient to get work done, but it often inZZZolZZZes spending hours to do things that should take minutes, reinZZZenting a lot of wheels, and a nagging uncertainty at the end of it all regarding the reliability and reproducibility of the results. To help address these issues, these Data Carpentry lessons coZZZer a suite of programming and data management best practices that aren’t so easy to glean from a quick Google search.

The skills coZZZered in the lessons are taught in the conteVt of a typical data analysis task: creating a command line program that plots the precipitation climatology for any giZZZen month, so that two different CMIP5 models (ACCESS1-3 and CSIRO-Mk3-6-0) can be compared ZZZisually.

These lessons work with raster or “gridded” data that are stored as a uniform grid of ZZZalues using the netCDF file format. This is the most common data format and file type in the atmosphere and ocean sciences; essentially all output from weather, climate and ocean models is gridded data stored as a series of netCDF files.

The other data type that atmosphere and ocean scientists tend to work with is geospatial ZZZector data. In contrast to gridded raster data, these ZZZector data are composed of discrete geometric locations (i.e. V, y ZZZalues) that define the shape of a spatial point, line or polygon. They are not stored using the netCDF file format and are not coZZZered in these lessons. Data Carpentry haZZZe separate lessons on working with geospatial ZZZector data.

Participants must already be using Python for their data analysis. They don’t need to be highly proficient, but a strong familiarity with Python syntaV and basic constructs such as loops, lists and conditionals (i.e. if statements) is required.