Difference between revisions of "Python"
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| + | We encourage our researchers to use Python for analysing large climate datasets. The Xarray and Dask libraries in particular make it possible to analyse very large datasets with relative ease. | ||
| − | + | At NCI we maintain a [[Conda]] environment with a wide variety of libraries for climate and weather analysis. | |
= Learning Python = | = Learning Python = | ||
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*[http://swcarpentry.github.io/python-novice-inflammation/ Software Carpentry] - Programming for Scientists | *[http://swcarpentry.github.io/python-novice-inflammation/ Software Carpentry] - Programming for Scientists | ||
*[http://www.johnny-lin.com/pyintro/ A Hands-On Introduction to Python in the Atmospheric and Oceanic Sciences] | *[http://www.johnny-lin.com/pyintro/ A Hands-On Introduction to Python in the Atmospheric and Oceanic Sciences] | ||
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= Resources for Climate Scientists = | = Resources for Climate Scientists = | ||
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*[http://docs.scipy.org/doc/numpy/ NumPy] - Numerical Python Library | *[http://docs.scipy.org/doc/numpy/ NumPy] - Numerical Python Library | ||
| − | *[http://docs.scipy.org/doc/scipy/reference/ SciPy] - Scientific Python Tools | + | *[http://docs.scipy.org/doc/scipy/reference/ SciPy] - Scientific Python Tools |
| + | *[http://pandas.pydata.org Pandas] - Tabular data analysis | ||
| + | *[http://xarray.pydata.org Xarray] - Gridded data & NetCDF analysis | ||
*[http://scitools.org.uk/iris/docs/latest/index.html Iris] - Read, Analyse and Plot Climate Datasets (NetCDF, GRIB, UM output) | *[http://scitools.org.uk/iris/docs/latest/index.html Iris] - Read, Analyse and Plot Climate Datasets (NetCDF, GRIB, UM output) | ||
*[http://scitools.org.uk/cartopy/ Cartopy] - A library containing cartographic tools for python (alternative to basemap) | *[http://scitools.org.uk/cartopy/ Cartopy] - A library containing cartographic tools for python (alternative to basemap) | ||
| − | + | = Creating a Local Environment = | |
| − | + | For using Python on your desktop computer, consider installing [https://docs.enthought.com/canopy/2.0/index.html Enthought Canopy]. The full version of the software is free for academic users and provides a large library of ready-made python packages | |
| − | + | Or try [https://www.anaconda.com/distribution/ Anaconda], which we use for the [[Conda|Conda environment]] at NCI | |
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[[Category:Python]] [[Category:Training]] | [[Category:Python]] [[Category:Training]] | ||
Revision as of 23:32, 22 January 2020
We encourage our researchers to use Python for analysing large climate datasets. The Xarray and Dask libraries in particular make it possible to analyse very large datasets with relative ease.
At NCI we maintain a Conda environment with a wide variety of libraries for climate and weather analysis.
Contents
Learning Python
- Learn Python the Hard Way - Python tutorial for beginners to programming
- Software Carpentry - Programming for Scientists
- A Hands-On Introduction to Python in the Atmospheric and Oceanic Sciences
Resources for Climate Scientists
- Using Python with Climate Data
- Python Libraries on Raijin
- Chris Bull's getting started list
- OceanPython plotting examples
- Unidata example python plot gallery
- Running IPython Notebook from Raijin
- Publishing your code on PyPI
Useful Libraries
- NumPy - Numerical Python Library
- SciPy - Scientific Python Tools
- Pandas - Tabular data analysis
- Xarray - Gridded data & NetCDF analysis
- Iris - Read, Analyse and Plot Climate Datasets (NetCDF, GRIB, UM output)
- Cartopy - A library containing cartographic tools for python (alternative to basemap)
Creating a Local Environment
For using Python on your desktop computer, consider installing Enthought Canopy. The full version of the software is free for academic users and provides a large library of ready-made python packages
Or try Anaconda, which we use for the Conda environment at NCI
