1. Technical computing with Matlab - University of Bath

Technical Computing with MATLAB

University Of Bath Seminar 19th November 2010 Adrienne James (Application Engineering)

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Agenda    

Introduction to MATLAB Importing, visualising and analysing data from multiple p sources Publishing results to reports Tools for efficient writing writing, maintaining and sharing of MATLAB routines

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Data Analysis Tasks Access

Explore & Discover

Share

Automate 3

Computational Finance Workflow Access

Research and Quantify

Files

Share Reporting

Data Analysis & Visualization

Databases

Datafeeds

Financial Modeling

Application Development

Applications

Production

Automate 4

Data Import

Datafeed toolbox

xml file

Web interface

Database toolbox

Proprietary file

Web interface

Text file

Text file

Spreadsheet Link EX

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Accessing Data from MATLAB Access 

Explore & Discover

Share

Files – Excel, text, or binary – Multimedia, Multimedia scientific – Web, XML



Applications and languages – C/C++, Java, FORTRAN – COM, .NET, shared libraries – Databases



Measurement hardware – Data acquisition q hardware for signals or images – Stand-alone instruments and devices

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Data Analysis and Visualization in MATLAB Access 

Explore & Discover

Share

Data Analysis – Manipulate, preprocess, and manage g data – Fast, accurate analysis with pre-built math, finance & engineering functions



Visualization – Built in graphics functions for engineering and science (2D, 3D, VolViz) – Interactive tools to annotate and customize graphics 7

Sharing Results from MATLAB Access 

Explore and Discover

Share

Automatically generate structured reports – Published MATLAB files – MATLAB® Report Generator



Feed your results into downstream design tools



Deploy applications to other environments

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Data Analysis with MATLAB Toolboxes

Adrienne James

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Agenda    

Signal processing (spectral analysis, filtering) Working with images, visualisation and analysis Accelerating data analysis with parallel computing Statistics and Optimisation

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Demo: Analysing live data    



Processing live data Analysing Spectra Developing Graphical User Interfaces (GUIs) Deploying standalone applications Find it on MATLAB Central! http://www.mathworks.in/matlabcentral/fileexchange/2904 12

B i i external Bringing t l Data D t into i t MATLAB MATLAB can read & write any file – High-level support for common formats – Functions to read custom formats

Some toolboxes provide additional accessibility – – – –

Data Acquisition Toolbox Image Acquisition Toolbox I t Instrument t Control C t l Toolbox T lb Database Toolbox

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Signal Processing with MATLAB Si Signal l Processing P i Toolbox: T lb – Filt Filter design, d i analysis l i and d implementation – Transforms – Statistical signal processing – – – – –

Spectral analysis Waveform generation Data windowing Linear prediction A d more… And 14

Introduction To MathWorks Deployment Products

MATLAB Compiler™

MATLAB Builder™ EX

.exe

.dll/.lib

Excel Add-in

MATLAB Builder™ JA

Java

MATLAB Builder™ NE

www

COM

.NET

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Introduction to MATLAB Compiler 



 

Automatically packages your MATLAB programs as standalone applications and software components S Supports t full f ll MATLAB language l and mostt toolboxes t lb Allows royalty-free deployment Provides shared infrastructure with MATLAB: – Speed of compiled application equivalent to speed in MATLAB

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Application Deployment Workflow End-User EndDesktop or Web Server

MATLAB Desktop

.EXE

.EXE

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High-Throughput Screening 





Typical goal – To observe the reaction of some biological entity of interest (e (e.g. g a protein protein, some cells, or an embryo) to exposure to multiple treatment conditions (e.g. times, drug compounds) Challenges g – Complex data to analyse (e.g. each measurement may require image processing, feature extraction, statistics) – Very large amounts of data (e.g. up to 100000 measurements, each a 100KB image file) – Very fast analysis required (e.g. up to 100000 measurements analysed per day) Solution – Implement and automate complex analyses using MATLAB and Toolboxes – Reduce time of analysis using the Parallel Computing Toolbox

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Data analysis (Image Processing Toolbox, Curve Fitting Toolbox, Parallel Computing Toolbox)

  

Enhancing, registering and segmenting images, and measuring image features Creating a script to batch process multiple images C Converting ti a b batch t h process tto run iin parallel ll l tto reduce computational time 19

What Can You Do with Image Processing Toolbox™? • Isolate regions of interest in an image to process. • Measure the properties of objects located in an image. • Preprocess p images g by y enhancing g image characteristics, and reducing the effects of noise and motion. • Design and implement 2-D 2 D spatial and frequency filters. • Extract image features using different methodologies. methodologies • Identify image objects by using image registration.

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Parallel Computing Toolbox p g Server MATLAB Distributed Computing 

Parallel Computing Toolbox: – Run up to 8 MATLAB processes in parallel on a single machine – Share data between processes – Run tasks in parallel across processes



MDCS – MATLAB engine on each node of a cluster – Ideal for large scale batch runs or time intensive calculations i.e. Monte Carlo simulations 21

Model-Ba ased Calibration Toolbox

Bioinform matics Toolbox

Statistics Optimiza ation

Commun nications Toolbox

Built in parallel functionality within specific toolboxes ( l requires (also i P Parallel ll l Computing Toolbox)

Global Optimization n Toolbox



Optimiza ation Toolbox

Parallel Computing with MATLAB

MATLAB and Parallel Computing Tools

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High level parallel functions



Low level parallel functions



Built on industry standard libraries

parfor

matlabpool

batch

jobs, tasks Industry Libraries Message Passing Interface (MPI) ScaLAPACK

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Parallel Computing on the Desktop Desktop Computer Parallel Computing Toolbox



Rapidly develop parallel applications on local computer

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Take full advantage of desktop power by using CPUs and GPUs

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Separate computer cluster not required 23

Scale Up to Clusters, Grids and Clouds Computer Cluster Desktop Computer

MATLAB Distributed Computing Server

Parallel Computing Toolbox

Scheduler

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Volume Correlation Analysis ((Statistics Toolbox)) 



We capture some observations on volumes of futures contracts traded in a certain period of time We are interested in trends in multivariate data x6E

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CL

0.11

0.00

0.10

0.51

0.07

0.34

0.03

0.05

0.03

0.04

0.07

0.03

0.07

ES

0.13

0.10

0.00

0.06

0.03

0.04

0.03

0.02

0.04

0.04

0.04

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0.03

HO

0.07

0.51

0.06

0.00

0.06

0.27

0.02

0.04

0.02

0.02

0.05

0.01

0.04

NG

0.05

0.07

0.03

0.06

0.00

0.03

0.01

0.10

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0.03

0.08

0.01

0.10

RB

0 04 0.04

0 34 0.34

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0.27 0 27 0.95

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0 02 0.02

ZB

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0.02 0.9

0.01

0.01

0.00

0.02

0.26

0.32

0.02

0.10

0.01

ZC

0.03

0.05

0.02

0.04

0.10

0.02

0.02

0.00

0.02

0.03

0.25

0.01

0.24

ZF

0.05

0.03

0.04

0.85 0.02

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0.26

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0.00

0.45

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0.15

0.03

ZN

0.06

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0.04

0.8 0.02

0.03

0.02

0.32

0.03

0.45

0.00

0.03

0.12

0.02

ZS

0.05

0.07

0.04

0.05 0.75

0.08

0.02

0.02

0.25

0.03

0.03

0.00

0.01

0.34

ZT

0 04 0.04

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0 02 0.02

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0 12 0.12

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0 00 0.00

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ZW

0.04

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0.04 0.65

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0.24

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0.00

x6E

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ES

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NG

RB

ZB

ZC

ZF

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ZS

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0.6

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0.5 CL

HO

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x6E

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ZT

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Electricity Forecasting 

We capture some observations on temperatures We are interested in forecasting electricity usage

Data & Model Prediction

100



Actual Model

80 60 40 20

0

-20 2004

2005

2006

2007

2008

2009

2010

2008

2009

2010

Residuals 40

20

0

-20

-40 2004

2005

2006

2007

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Global Optimization Toolbox

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