Difference between revisions of "Python"

m (Removed redundant | characters in link text)
Line 1: Line 1:
 +
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.
  
{{Needs Update}}
+
At NCI we maintain a [[Conda]] environment with a wide variety of libraries for climate and weather analysis.
  
 
= Learning Python =
 
= Learning Python =
Line 7: Line 8:
 
*[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]  
 
 
 
  
 
= Resources for Climate Scientists =
 
= Resources for Climate Scientists =
Line 23: Line 22:
  
 
*[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)  
  
*'''[https://accessdev.nci.org.au/trac/wiki/Raijin%20Apps#PythonLibraries All libraries installed on Raijin]'''
+
= Creating a Local Environment =
  
= <span style="font-size: 80%;">For using Python on your desktop computer, consider installing ''Enthought: Canopy''</span> =
+
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
  
*<span style="font-size: 14px; line-height: 15.6px;">The full version of the software is free for academic users</span>
+
Or try [https://www.anaconda.com/distribution/ Anaconda], which we use for the [[Conda|Conda environment]] at NCI
*<span style="font-size: 14px; line-height: 15.6px;">and provides a large library of ready-made python packages</span>
 
  
 
[[Category:Python]] [[Category:Training]]
 
[[Category:Python]] [[Category:Training]]

Revision as of 22:32, 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.

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