Repository: https://bitbucket.org/vitotap/notebooks

Inside the notebook environment (notebooks.vgt.vito.be), these samples can be found under ~/Private/notebook-samples. You may want to run 'git pull' inside this directory to get the latest versions.

Quick start sample

histogramFinds PROBA-V products and computes a histogram using the processing cluster.

https://nbviewer.jupyter.org/urls/bitbucket.org/vitotap/notebooks/raw/master/QuickstartExample.ipynb

Keywords: catalog query, Spark, Python, histogram, matplotlib

 

R quick start

Users of the R programming language can use this sample to get started quickly with finding and reading PROBA-V data from inside their notebook or VM.

https://nbviewer.jupyter.org/urls/bitbucket.org/vitotap/notebooks/raw/ma...

Keywords: catalog query, raster, R

Time series analysis (cal/val)

timeseries  Uses MEP web services to retrieve time series from various products for a given point coordinate.

 https://nbviewer.jupyter.org/urls/bitbucket.org/vitotap/notebooks/raw/master/CalVal%20WCS%20Timeseries.ipynb

https://nbviewer.jupyter.org/urls/bitbucket.org/vitotap/notebooks/raw/master/AERONET%20CalVal%20TSViewer.ipynb

Keywords: Time series, REST, WCS, calibration, validation

Snow classification

Trains a machine learning classifier to classify snow pixels based on PROBA-V radiometry.

https://nbviewer.jupyter.org/urls/bitbucket.org/vitotap/notebooks/raw/master/SnowDetection.ipynb

Keywords: Spark, Machine learning, Python, Seaborn, graphs, plotting

 

 

 

Trend analysis

  Takes all timeseries from the time series viewer application, and runs a naïve trend detection algorithm on them using Spark. This simple approach   gives  some nice results.

 https://nbviewer.jupyter.org/urls/bitbucket.org/vitotap/notebooks/raw/master/Trend%20Analysis.ipynb

 Keywords: SparkSQL, time series, Python, plotting, maps, pandas