Other articles

1. 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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2. 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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3. 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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4. Time series analysis with pandas

   Task:

   analysis of several time series data (AO, NAO)
   
   

   Modules:

   pandas
   
   

   Notebook file

   Here I am going to show just some basic pandas stuff for time series analysis, as I think for the Earth Scientists it's the most interesting topic. If you find this small tutorial useful, I encourage you to watch this video, where Wes McKinney give extensive introduction to the time series data analysis with pandas.

   On the official website you can find explanation of what problems pandas solve in general, but I can tell you what problem pandas solve for me. It makes analysis and visualisation of 1D data, especially time series, MUCH faster. Before pandas working with time series in python was a pain for me, now it's fun. Ease of use stimulate in-depth exploration of the data: why wouldn't you make some additional analysis if it's just one line of code? Hope you will also find this great tool helpful and useful. So, let's begin.

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