Salem
Salem is a python library developed by Fabien Maussion, a researcher and lecturer at the Institute of Atmospheric and Cryospheric Sciences, at the University of Innsbruck.
It extends xarray and adds notably geolocalised subsetting (via shapefiles), masking, and geographical mapping operations to xarray’s DataArray DataSet datasetdata structures.
It is still ayoung project, but has already proven extremely useful to me in a number of occasions, and I think it's the perfect illustration of how one can build clever and useful extensions build on the very powerful xarray data structures. I hope to be able to contribute to this project, and this post is intended to be a shoutout and acknowledgment of Fabien Maussion's work.
If you work in climate / ocean science and are already using (as you should !) xarray, Salem is definitely worth a try
Wavelet analysis in Python
This notebook contains a brief overview of 3 convenient packages implementing wavelet analysis in Python:
- waipy
- kPywavelets
- wavelets
we will try and reproduce the examples found in:
A practical guide to wavelet analysis
from Christopher Torrence and Gil P. Compo
which use the NINO3.4 seasonal time series (The NINO3.4 index is calculated as the regional average of Sea - Surface - Temperature (SST) anomalies in the central eastern Pacific [5°N to 5°S, 170°W to 120°W.] and is one of the most used indices for tracking the the El Niño - Southern Oscillation phenomenon)
See also the Interactive Wavelet page
We will also see the example of rectification of the bias that exist in favor of large scales
troubleshooting
In this post I am giving some pointers to troubleshoot issues when installing packages via conda or pip, as well as some pointers to solve issues running the ipython (now jupyter) notebook on a machine behind a proxy.
building an ENSO tracker with Matplotlib
In this post, I will show how to play with matplotlib's patches to create a gauge or meter, the goal will be to get something like the Australian Bureau of Meteorology's ENSO tracker below
Miniconda installation
Below is a bash script to install Miniconda and set up a root environment containing all the libraries that I routinely use and that are necessary to
replicate the examples given in this blog.
EOF analysis with scikit-learn
In this notebook I give a very simple (and rather uncommented) example of how to use scikit-learn to perform an Empirical Orthogonal Function decomposition (EOF analysis, often referred to as well as Principal Component Analysis or PCA) of a climate field, in this case the monthly Sea Surface Temperature (SST) anomalies in the Pacific.
xray
xray has been originally developed by scientists and engineers working at the Climate Corporation
xray is a Python package that allows to define and manipulate N-Dimensional labelled arrays. In a nutschell, whenever you've got data that is defined over more than 2 dimensions, and to each point along those dimensions can be associated a label (e.g. a latitute, a longitude, a timestamp, a depth, etc) then you definitely need to have a look at xray.
If this data model reminds you of the data structures introduced by the widely used pandas library, this is not a coincidence, coming right from the xray documentation is this disclaimer: "xray is an open source project and Python package that aims to bring the labeled data power of pandas to the physical sciences, by providing N-dimensional variants of the core pandas data structures."
In this post I will give a few reasons why I think that xray is destined to become a core Python package for people working with multi-dimensional arrays, especially - but not only - in the geosciences, before illustrating the power of xray with a few examples of how it simplifies considerably common operations on climate datasets.
resources
A - totally incomplete - list of resources I have come across on Python and Python for data analysis and visualization, loosely organized by category:
About this blog
I am a climate scientist (to know more about me, head here) and have been using Python for data analysis and visualisation for about 10 years, slowly transitioning from Matlab and R.
In this blog, I will try and share what I have learned along the way, and give full examples of how I use Python in my research or operational workflows.
I will occasionally reflect on open science, and how open-source in general, and Python in particular can be used to help make climate science more open.