Software: Difference between revisions
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'''Data analysis''': |
'''Data analysis''': |
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*NumPy [https://numpy.org/] |
*NumPy [https://numpy.org/] |
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*xarray [https://docs.xarray.dev/en/stable/#] |
*xarray [https://docs.xarray.dev/en/stable/#], easy handling of large nc files. |
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*pandas [https://pandas.pydata.org/] |
*pandas [https://pandas.pydata.org/] |
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*SciPy [https://scipy.org/] |
*SciPy [https://scipy.org/] |
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* Paraview for 3D rendering [https://www.paraview.org/], note this one requires more computational power and should ideally be run through a supercomputer via VNC client. |
* Paraview for 3D rendering [https://www.paraview.org/], note this one requires more computational power and should ideally be run through a supercomputer via VNC client. |
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* Cartopy (python package) for maps [https://scitools.org.uk/cartopy/docs/latest/] |
* Cartopy (python package) for maps [https://scitools.org.uk/cartopy/docs/latest/] |
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* Matplotlib (standard python package) [https://matplotlib.org/] |
Revision as of 09:55, 26 May 2023
Essential Software
The following is a list of essential software packages and the links to their web pages:
Many of these can be installed using anaconda [1].
CDO
https://code.mpimet.mpg.de/projects/cdo/.
Note that CDO has a lot of built-in functions that are not well documented, but details about these can be usually found in their discussion forums, https://code.mpimet.mpg.de/projects/cdo/boards.
Python
Instructions on how to download and install Python for all OSs can be found at: https://www.python.org/.
It is usually recommended to use the Anaconda distribution to install Python. Details on how to do this are here: https://www.anaconda.com/
Python can be combined with a good Integrated Development Environment (IDE) of your choice. All existing IDEs have their pros and cons. Some of the most popular IDEs are the following:
Python boasts a large number of packages. Some of the most used packages for manipulating large files in NetCDF, HDF5 or CSV formats are the following:
Data analysis:
Parallel computing, Machine learning, Deep learning etc.:
Data visualization:
Most of these packages are distributed through either Conda [23] or Pip [24].
On top of these, various users also create packages tuned for specific purposes. They are usually made public through GitHub [25].
Various forums exist for the sole purpose of clearing specific questions about coding. One such forum is stackoverflow[26]. Medium [27] is also a good source for reading up about new ideas and tools in Python and other languages.
Visualisation
The visualisation of "nc" files can be made easier using: