Following a question from my dear colleague Devy, here is how to plot a set of events, occurring at random moments in time. The idea is to plot the waveform of each event with the beginning at the top and the end at the bottom (along the “y” axis) and centred on the origin time…
This morning, North Korea tested some nuclear “bomb” somewhere in the middle of the country (confirmed by Pyongyang officials and CTBTO), and many seismic sensors worldwide recorded the triggered waveforms. The location of the test is the same as the 2009 one, confirmed by the location provided by global monitoring networks (USGS, GEOFON). To pythonise…
In the coming months, I’ll prepare some tutorials over an excellent data analysis package called pandas ! To show you the power of pandas, just take a look at this old tutorial, where I exploited the power of itertools to group sparse data into 5 seconds bins. The magic of pandas is that, when you…
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…
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…
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 =…
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…
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,…
Following a very old post (link), and questions from Matthias and Kevin, I’ve finally managed to test the R2-related scripts I wrote long-long time ago… I’m really sorry, but don’t quite have the time now to really document all functions/actions, but it should be quite straightforward. This script assumes you start from a ABEM S4K…
Everyone used to search for code snippets on the web know the importance of a “Gallery” linking to all examples and tutorials… So, here is my really ugly Gallery :
In the previous tutorial, I defined a “shoot” method to compute the landing point of a shoot from one point, to a given azimuth and distance. Using this logic, it’s possible to find the points situated at a given distance from a “centre” point, a circle. The goal: Drawing circles of a given radius around…
Following a question from Ricardo Gama (see his comment), I’ve prepared this new tutorial. He wondered if Basemap has a function similar to the track1 function in matlab (you know, that crappy costly thing…)… Here is what I obtained : The goal: Plotting great circles with Basemap, but knowing only the longitude, latitude, the azimuth…
Dear visitors, I’m always searching new ideas of preparing new tutorials for things doable with Basemap, but I’d like these examples to be as useful as possible, which means : If you have something you would like to appear here, please, use the Comment box below to tell me ! I’ll do my best to…
New version here Following a question in the matplotlib mailing list, I dug inside the code of readshapefile, in order to gain power : The goal: The data: http://www.gadm.org/ saved inside a new “borders/” folder ! The idea: Opening a GADM shapefile, get region names, and plot filled regions with random color ! The process:…
Here is the trick (well documented on the matplotlib webpage) to define the font family and size of what appears on your matplotlib plot:
Here is a new tutorial that will include “a bit of all” tutorials previously published on this blog and some new cool stuff to play with ! Idea: Find some resources on the Internet and plot them on the map of Europe ! In brief: do that: Step 0: Input Preparation I found some statistics…
Hi folkes, Following a question by Gui on this website, I checked the “how to” draw pie charts on a basemap plot ! Here below I generalise the example given by Manuel Metz on the matplotlib website. The idea is to draw a pie from scratch, in a method that allows a custom number of…
# remember to define the ax instance : ax = plt.subplot(111) zone = “Roetgen – Monschau” x,y = m(6.15,50.41) x2,y2 = m(6.59,50.67) x3,y3 = m(6.34,50.855) x4,y4 = m(5.83,50.65) data = np.array(([x,x2,x3,x4], [y,y2,y3,y4])) p = Polygon(data.T,edgecolor=’red’,linewidth=1.5,facecolor=’none’) ax.add_artist(p) Note that we have an array containing the points already projected using m(x,y) : basemapped_points x,y = m(data[:,0],data[:,1]) data[:,0]=x…
Here, we will focus on adding a “zoom box” on the top left corner of the plot. But before that, we will mask a part of the earthquakes, in order to have a “cleaner” map ! This is achieved by using the numpy.ma module : import numpy.ma as ma Mlon = ma.masked_outside(lon, 5.6, 7.5) #…
Now, let’s imagine we have a dataset containing latitude/longitudes of earthquakes, plus their depth and magnitude. Of course, you don’t always have this dataset available, so let’s build a fake one : import numpy as np lon = np.random.random_integers(11,79,1000)/10. lat = np.random.random_integers(491,519,1000)/10. depth = np.random.random_integers(0,300,1000)/10. magnitude = np.random.random_integers(0,100,1000)/10. We create random integer values, and we…
# # BaseMap example by geophysique.be # tutorial 01 from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np fig = plt.figure(figsize=(11.7,8.3)) #Custom adjust of the subplots plt.subplots_adjust(left=0.05,right=0.95,top=0.90,bottom=0.05,wspace=0.15,hspace=0.05) ax = plt.subplot(111) #Let’s create a basemap around Belgium m = Basemap(resolution=’i’,projection=’merc’, llcrnrlat=49.0,urcrnrlat=52.0,llcrnrlon=1.,urcrnrlon=8.0,lat_ts=51.0) m.drawcountries(linewidth=0.5) m.drawcoastlines(linewidth=0.5) m.drawparallels(np.arange(49.,53.,1.),labels=[1,0,0,0],color=’black’,dashes=[1,0],labelstyle=’+/-‘,linewidth=0.2) # draw parallels m.drawmeridians(np.arange(1.,9.,1.),labels=[0,0,0,1],color=’black’,dashes=[1,0],labelstyle=’+/-‘,linewidth=0.2) # draw meridians plt.show()
I just discovered that I was using from griddata import griddata in quite a lot of python scripts I wrote for scientific data plotting. While struggling to recompile it under my new win7 x64 box, I googled a little and found out that it’s now included in matplotlib (which was already installed…). So, now, I’ll…