Difference between revisions of "Python"

 
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[[Category: Python]][[Category:Training]]
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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.
  
=Learning Python=
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At NCI we maintain a [[Conda]] environment with a wide variety of libraries for climate and weather analysis.
* [http://learnpythonthehardway.org/book/ | Learn Python the Hard Way] - Python tutorial for beginners to programming
 
* [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]
 
  
=Resources for Climate Scientists=
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= Learning Python =
* [[Using Python with Climate Data]]
 
* [[Python Libraries on Raijin]]
 
* [http://christopherbull.com.au/blog/?page_id=180 | Chris Bull's getting started list]
 
* [http://oceanpython.org/ | OceanPython plotting examples]
 
* [[Running IPython Notebook from Raijin]]
 
* [[How to publish your Python code to PyPI | Publishing your code on PyPI]]
 
  
=Useful Libraries=
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*[http://learnpythonthehardway.org/book/ Learn Python the Hard Way] - Python tutorial for beginners to programming
* [http://docs.scipy.org/doc/numpy/ | NumPy] - Numerical Python Library
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*[http://swcarpentry.github.io/python-novice-inflammation/ Software Carpentry] - Programming for Scientists
* [http://docs.scipy.org/doc/scipy/reference/ | SciPy] - Scientific Python Tools
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*[http://www.johnny-lin.com/pyintro/ A Hands-On Introduction to Python in the Atmospheric and Oceanic Sciences]  
* [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)
 
  
* '''[https://accessdev.nci.org.au/trac/wiki/Raijin%20Apps#PythonLibraries | All libraries installed on Raijin]'''
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= Resources for Climate Scientists =
  
=<span style="font-size: 80%;">For using Python on your desktop computer, consider installing ''Enthought: Canopy''</span>=
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*[[Using_Python_with_Climate_Data|Using Python with Climate Data]]
* <span style="font-size: 14px; line-height: 15.6px;">The full version of the software is free for academic users</span>
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*[http://christopherbull.com.au/blog/?page_id=180 Chris Bull's getting started list]
* <span style="font-size: 14px; line-height: 15.6px;">and provides a large library of ready-made python packages</span>
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*[http://oceanpython.org/ OceanPython plotting examples]
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*[https://unidata.github.io/python-gallery/examples/index.html Unidata example python plot gallery]
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*[[Running IPython Notebook]]
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*[[How_to_publish_your_Python_code_to_PyPI|Publishing your code on PyPI]]
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= Useful Libraries =
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*[http://docs.scipy.org/doc/numpy/ NumPy] - Numerical Python Library
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*[http://docs.scipy.org/doc/scipy/reference/ SciPy] - Scientific Python Tools
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*[http://pandas.pydata.org Pandas] - Tabular data analysis
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*[http://xarray.pydata.org Xarray] - Gridded data & NetCDF analysis
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*[http://scitools.org.uk/iris/docs/latest/index.html Iris] - Read, Analyse and Plot Climate Datasets (NetCDF, GRIB, UM output)
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*[http://scitools.org.uk/cartopy/ Cartopy] - A library containing cartographic tools for python (alternative to basemap)
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= Creating a Local Environment =
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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
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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]]

Latest revision as of 22:34, 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.

Learning Python

Resources for Climate Scientists

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