Other articles

1. Seaborn library

   Task:

   Make matplotlib plots look nicer
   
   

   Solution:

   Seaborn

   Notebook file

   We all know and love matplotlib, but I guess most of you agree that the default output of matplotlib is ugly. One can spend hours tweaking the plots, and for sure you will get very nice result at the end - customization is one of the great powers of matplotlib after all. However there is another way - just rely on beautiful defaults created by someone else. Below I will show you couple of examples with Seaborn library, that is based on matplotlib, but make figures look much better. It also provide very simple way to draw statistical graphs, that I will also demonstrate.

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2. IPyhton interact and widgets (or how to write python version of ncview in about 3 lines of code)

   Task:

   Work with data interactively
   
   

   Solution:

   IPython widgets, interact

   Notebook file

   I always wanted to write a GUI to explore my data. This is probably one of the things that you can't get rid of as a former hardcore Windows user. You need all this buttons and sliders and check boxes, or at least you think you do. But every time I looked at GUI toolkits for python I was bored after 10 minutes of reading. I just want to show the plot and control couple of variables, and in order to do so I have to learn first how to place elements on the canvas, create handlers and so on and so forth. Boring and not worth it.

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3. Use of Basemap for Amazon river discharge visualization

   Task:

       Show how to work with river discharge data.
       Also show cople of ways to visualise this data with Basemap. 
   
   

   Solution

       Pandas, Basemap

   This notebook was originally created for Marinexplore Earth Data Challenge, in order to show how data for the submission was processed. I think it might be also interesting for those who begin to use python in geoscience, because it demonstrate couple of ways to handle csv and netCDF data and plotting capabilities of the Basemap module. There will be no extensive explanations though, moistly the code.

   I want to show a small example of the work flow that is more or less typical during research process. Often you see some interesting feature in your data and want to investigate it in more detail. If you not lucky enough to work with the model data, this would require dealing with multiple data sources, and possibly multiple file formats. Having all data sets in one place in consistent format becomes very handy for this type of applications.

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4. Plot grid and transect with PyNGL and komod

   Task:

   Draw a grid and a transect with PyNGL module
   
   

   Solution:

   komod module
   
   

   Notebook file

   This is a short follow up of the previous post about komod module, that is essentially a set of wrapper functions for PyNGL module. Here I am going to show how to plot a grid of your model and how to draw a transect. I am going to use the same data set af before: mean temperature from the World Ocean Atlas 2009 (5 deg. resolution).

   Import modules:

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5. Plot maps with PyNGL and komod

   Task:

   Quickly draw maps with PyNGL module
   
   

   Solution:

   komod module
   
   

   Notebook file

   The PyNGL module produce very nice looking maps, and it's capabilities in fine tuning the resulting image in many cases are much better compared to matplotlib Basemap module. However this flexibility come at a price: in order to draw a map of an acceptable appearance one has to write quite a long script, and specify many parameters. Of course once you find your "best ever" set of parameters, you basically copy/paste them from one script to another with only slight modifications. But at some point you get annoyed by this long sheets of code, that by the way do not look very nice in IPython notebooks, and you write a wrapper function.

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