Difference between revisions of "CodeBreak 1/9/2021"

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Unrelated to the original topics, but some of the attendees didn't know it was possible to <span style="background-color:#ffff00">connect a jupyter notebook directly to gadi</span> compute nodes, which is useful for anyone who must access data on /scratch, or have workloads that are currently too onerous for VDI or [https://ood.nci.org.au ood.nci.org.au] (though ood is designed to cater for larger jobs than VDI).&nbsp; This is all covered on our wiki &nbsp; [http://climate-cms.wikis.unsw.edu.au/Running_Jupyter_Notebook#On_Gadi http://climate-cms.wikis.unsw.edu.au/Running_Jupyter_Notebook#On_Gadi] &nbsp; which also covers creating symbolic links to access files in other locations on the file system, e.g. /g/data &nbsp;
 
Unrelated to the original topics, but some of the attendees didn't know it was possible to <span style="background-color:#ffff00">connect a jupyter notebook directly to gadi</span> compute nodes, which is useful for anyone who must access data on /scratch, or have workloads that are currently too onerous for VDI or [https://ood.nci.org.au ood.nci.org.au] (though ood is designed to cater for larger jobs than VDI).&nbsp; This is all covered on our wiki &nbsp; [http://climate-cms.wikis.unsw.edu.au/Running_Jupyter_Notebook#On_Gadi http://climate-cms.wikis.unsw.edu.au/Running_Jupyter_Notebook#On_Gadi] &nbsp; which also covers creating symbolic links to access files in other locations on the file system, e.g. /g/data &nbsp;
 +
  
 
= Fast percentile calculation in python =
 
= Fast percentile calculation in python =
  
&nbsp; The question was on how to calculate a percentile climatology where we calculate the 90th percentile for each day of the year considering the values for the 31 days surrounding each date.
+
&nbsp; The question was on <span style="background-color:#ffff00">how to calculate a percentile climatology</span> where we calculate the 90th percentile for each day of the year considering the values for the 31 days surrounding each date.
  
 
The notebook illustrates:
 
The notebook illustrates:
  
*the use of rolling() and construct() to build a DataArray with the 31 days windows for each day of the timeseries.  
+
*the use of rolling() and construct() to build a DataArray with the 31-day windows for each day of the timeseries.  
 
*the use of groupby() to do calculations for each day of the year.  
 
*the use of groupby() to do calculations for each day of the year.  
 
*the use of quantile() to calculate percentiles on DataArrays.  
 
*the use of quantile() to calculate percentiles on DataArrays.  
 
*the use of load() if a function complains about chunking.  
 
*the use of load() if a function complains about chunking.  
 
*the use of a list and xarray.concat() to create a DataArray of results.
 
*the use of a list and xarray.concat() to create a DataArray of results.

Revision as of 02:40, 6 September 2021

Summary of topics:

  • How to organise plotting large numbers of plots from heterogenous models in python
  • Layout of subplots with matplotlib in python
  • Fast percentile calculation in python

Organising workflows in python

cf-xarray is a useful library for writing general code for a heterogenous set of models that might not share the same names for things like dimensions (e.g. lat/latitude/LAT/) and variables. cf-xarray has code for inferring this information and allows you to refer to them in a general way. It is available in the conda environments and the documentation is here:

https://cf-xarray.readthedocs.io/en/latest/

The python3 pathlib library is an object oriented path manipulation library that makes working with paths a lot simpler and cleaner, e.g. when opening data files, and saving processed output or plots to disk:

https://docs.python.org/3/library/pathlib.html

As far as how to organise plotting of a large number of different plots from a range of models, there are a range of data structures that might suit this purpose, and it comes down to the specifics of what needs to be done and personal preference, but some options are:  

For more information, this is a pretty comprehensive write up of some of the commonly used data structures in python   https://realpython.com/python-data-structures/  

Subplots in matplotlib

  Scott wrote a blog showing a sophisticated use of subplot, but also has some tips for organising the plots by saving references to each in a dictionary named for the plot type:   https://climate-cms.org/2018/04/27/subplots.html

Jupyter Notebook

Unrelated to the original topics, but some of the attendees didn't know it was possible to connect a jupyter notebook directly to gadi compute nodes, which is useful for anyone who must access data on /scratch, or have workloads that are currently too onerous for VDI or ood.nci.org.au (though ood is designed to cater for larger jobs than VDI).  This is all covered on our wiki   http://climate-cms.wikis.unsw.edu.au/Running_Jupyter_Notebook#On_Gadi   which also covers creating symbolic links to access files in other locations on the file system, e.g. /g/data  


Fast percentile calculation in python

  The question was on how to calculate a percentile climatology where we calculate the 90th percentile for each day of the year considering the values for the 31 days surrounding each date.

The notebook illustrates:

  • the use of rolling() and construct() to build a DataArray with the 31-day windows for each day of the timeseries.
  • the use of groupby() to do calculations for each day of the year.
  • the use of quantile() to calculate percentiles on DataArrays.
  • the use of load() if a function complains about chunking.
  • the use of a list and xarray.concat() to create a DataArray of results.