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CO907 Resources

Individual project work

Each student shoud choose one time series from the following source of X-ray emissoin time series: RTXE ASM Lightcurves . Which series you work on is negotiable, however:

  • choose a series with plenty of data points displayed in red;
  • duplication of main series with other students is to be avoided; allocated so far: casa, lmcx3, velax1, herx1, serx1, x1735-444, exo2030+375, aqlx1, gx301-2,x1822-000, tychosnr, gx339-4, cenx3, cygx3, crab smxc1, cygx1, mkn421, scox1 (18 chosen Sat 30/10).
  • the particular series used for the examples sheet cannot be your main one, that is not x1705-440 or x0512-401 .

You will also need to look at the Readme on these RTXE Quicklook datafiles .

You are expected to apply a variety of methods discussed in Prof Chapman's lectures to analyse your series and characterise it as best you can. The Problems Sheet also gives you a 'warm-up' on some of the analyses you could use.

Your work will be assessed through the following presentation and your answers to follow-up questions.

Please plan for 15 minutes presentation. No student will be allowed to present beyond 20 minutes. Please bring one printed copy of your slides.

Your end-of-module Presentation slides, plots etc should address the following:
  1. Irregularities in the timebase and data gaps (ie intervals where there are no observations) what/where are they and how did you deal with them?
  2. On what timescales (if any) is the signal time stationary?
  3. Fourier methods: Can the signal be meaningfully decomposed in the frequency domain- is there different behaviour at low and high frequencies or not? Can the signal be meaningfully decomposed in the time domain- is there different behaviour at different times? Mention the resolution, accuracy, physical units of your spectra.
  4. Wavelet methods: can the signal be meaningfully decomposed as a sum of signals on different scales?
  5. Try to characterize the timeseries. Suggest a simple model (eg, an oscillation, a stochastic process) that most closely describes your signal.

Anything you can add in relation to more ‘advanced methods’ eg bispectra, multiscaling, complexity measures, is a bonus. You will not be criticised for omitting these.

The follow-up questions will generally relate to what you have done or tried to do. However if you have missed out something obvious (such as one of 1-5 above) we may ask about that.

Schedule October/November 2010

Note supporting computing classes by Q Caudron. These relate to several Complexity modules and the early ones really matter for CO907. I have included ALL these classes weeks 1-5 below.

Week 1, Oct 4-

  • Mon 2 Computing class
  • Tues 10 CO907 Group Project Briefing
  • Tues 3 Computing class
  • Thurs 3-5 RCB will join a separate coordination & progress meeting of each group for ca 30 mins. You arrange these!!
  • Fri 9.30 briefing/class/surgery

Week 2, Oct 11-

  • Mon 2 Computing class
  • Tues 10-12 RCB will join a separate progress meeting of each group for ca 30 mins. You arrange these!!
  • Tues 3 lecture (Chapman)
  • Thurs 10-12 briefing/class/surgery. Matters arising (inc from Chapman lecture Tues) and presentation issues.
  • Thurs 3 Lecture (Chapman)
  • Fri 1.30 Group Project presentations; allocation of individual projects

Week 3, Oct 18-

  • Tues 10 Computing class
  • Tues 3 Lecture (Chapman)
  • Thurs 10 class/data surgery with RCB
  • Thurs 3 Lecture (Chapman)

Week 4, Oct 25-

  • Tues 10 Computing class
  • Tues 3 Lecture (Chapman)
  • Thurs 10 class/data surgery with RCB
  • Thurs 3 Lecture (Chapman)

Week 5, Nov 1-

  • Thurs, Fri: individual project presentations at times to be arranged.



2010 Wavelets

Lecture slides Feb 25-6: (from Dudok de Wit): wavelets.pdf

A useful case study to read: "A Practical Guide to Wavelet Analysis" torrence_compo_guide_1998.pdf .

MatLab has an extensive built in Wavelet Toolbox: follow Help -> Function Browser from inside MatLab. As a product it is described in Mathworks (I believe this is included in UoW site licence already) and there is a link there to an online Webinar introduction.


To participate in this course for credit you will need to take part in the individual exercise in data analysis. These are described in detail in the lectures.

You will need to look at the Readme on RTXE Quicklook datafiles and the datasets are: RTXE ASM Lightcurves



Background notes and papers to accompany the course:

Lecture notes:

on scaling (Chapman): Chapman_notes

bibliography on wavelets (Poli):polibibliography.pdf and plots to accompany the lectures poliwavelets_19feb09_web.pdf

Neil Johnson notes:








past presentations:

on statistical complexity (Feldman):


Feldman's introductory talk at Warwick

on Wavelets and HOS (Dudok de Wit):




HOS: Dudok_de_Wit_review





Buckingham PI theorem:



Power laws:





Other help:


For MATLAB demos (inc FFT): see under HELP in the application.