Difference between revisions of "ESE315 Tutorial 2"
From atmoschem
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=Analyzing 2D and 3D atmospheric (geoscience) datasets with Python= | =Analyzing 2D and 3D atmospheric (geoscience) datasets with Python= | ||
| − | This tutorial shows you how to read 2D or 3D data in | + | This tutorial shows you how to read 2D or 3D data in NetCDF format into python and do a bunch of analyses. |
[https://www.unidata.ucar.edu/software/netcdf/ NetCDF] (network Common Data Form) is a set of interfaces for array-oriented data access and a freely distributed collection of data access libraries for C, Fortran, C++, Java, and other languages. The netCDF libraries support a machine-independent format for representing scientific data. Together, the interfaces, libraries, and format support the creation, access, and sharing of scientific data. | [https://www.unidata.ucar.edu/software/netcdf/ NetCDF] (network Common Data Form) is a set of interfaces for array-oriented data access and a freely distributed collection of data access libraries for C, Fortran, C++, Java, and other languages. The netCDF libraries support a machine-independent format for representing scientific data. Together, the interfaces, libraries, and format support the creation, access, and sharing of scientific data. | ||
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*Archivable. Access to all earlier forms of netCDF data will be supported by current and future versions of the software. | *Archivable. Access to all earlier forms of netCDF data will be supported by current and future versions of the software. | ||
| − | If you work in earth or environmental sciences, you will inevitably run into datasets in | + | If you work in earth or environmental sciences, you will inevitably run into datasets in NetCDF format. However, you do not need to know all the functionality of NetCDF in order to start working with it. As with anything else in research, start playing with the data and look up resources along the way. |
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=Reference= | =Reference= | ||
| + | *[https://www.unidata.ucar.edu/software/netcdf/ netcdf data format] | ||
*[http://xarray.pydata.org/en/stable/ Description of the xarray project, with links to tutorials] | *[http://xarray.pydata.org/en/stable/ Description of the xarray project, with links to tutorials] | ||
*[https://scitools.org.uk/cartopy/docs/latest/ Description of the cartopy project] | *[https://scitools.org.uk/cartopy/docs/latest/ Description of the cartopy project] | ||
* | * | ||
Revision as of 10:54, 21 October 2019
Contents
Analyzing 2D and 3D atmospheric (geoscience) datasets with Python
This tutorial shows you how to read 2D or 3D data in NetCDF format into python and do a bunch of analyses.
NetCDF (network Common Data Form) is a set of interfaces for array-oriented data access and a freely distributed collection of data access libraries for C, Fortran, C++, Java, and other languages. The netCDF libraries support a machine-independent format for representing scientific data. Together, the interfaces, libraries, and format support the creation, access, and sharing of scientific data.
NetCDF data is:
- Self-Describing. A netCDF file includes information about the data it contains.
- Portable. A netCDF file can be accessed by computers with different ways of storing integers, characters, and floating-point numbers.
- Scalable. A small subset of a large dataset may be accessed efficiently.
- Appendable. Data may be appended to a properly structured netCDF file without copying the dataset or redefining its structure.
- Sharable. One writer and multiple readers may simultaneously access the same netCDF file.
- Archivable. Access to all earlier forms of netCDF data will be supported by current and future versions of the software.
If you work in earth or environmental sciences, you will inevitably run into datasets in NetCDF format. However, you do not need to know all the functionality of NetCDF in order to start working with it. As with anything else in research, start playing with the data and look up resources along the way.
