# Last Earthquakes tool – ETS powered

While in Indonesia last July, I created a small tool for the Kawah Ijen observers to allow them to search and plot teleseismic events and to calculate theoretical arrival times of the waves at the Ijen stations. It took roughly 2 hours to have a working version of the software, with: a GUI to plot…

# Matplotlib & Datetimes – Tutorial 04: Grouping & Analysing Sparse Data

To extend the previous tutorial (see here), we define a data array that has some information about the event that occurred for each datetime. The plot of data vs time now looks like: The data array is constructed with numpy.random: data = np.random.randint(10000,size=len(times)) Now, we will modify the example from tutorial 03: def group(di): return…

# Matplotlib & Datetimes – Tutorial 03: Grouping Sparse Data

New tutorial, more advanced this time ! Let’s say we have a number of observations, like occurrences of earthquakes, or visitors connecting to a webserver, etc. These observations don’t occur every second, they are sparse on the time axis. To prepare an example, I’ve created a set of random datetimes like this : N =…

# Matplotlib & Datetimes – Tutorial 02: Bar Plot

To add some interesting information to the previous tutorial, I’ve downloaded the number of licence plates given for new cars in Belgium for the same time span: 2005 587764 2006 633570 2007 644313 2008 652590 2009 571001 2010 642086 2011 679619 Load them in the same fashion: plates, number = np.loadtxt(‘newplates.txt’,skiprows=1,unpack=True) xdates2 = [datetime.datetime.strptime(str(int(date)),’%Y’) for…

# Matplotlib & Datetimes – Tutorial 01: Fuel Prices

Anyone who has played a little with dates know how painful it can be… Even more when you want to plot this data !! Matplotlib provides (link) a dates API, but to be honnest, even if the documentation is well maintained, I find it confusing. Maybe because they made the choice of a Gregorian-based calendar,…