1. Postgraduate Computing Lectures PAW 1 PAW: Physicist Analysis Workstation What is PAW? –A tool to display and manipulate data. Learning PAW –See ref. in your induction week notes. Running PAW: 2 Versions:- –PAW: 2 windows: A terminal window for command input A graphics window for output –PAW++: GUI version A terminal window for command input A terminal window for log output Multiple graphics windows for in/output –Same functionality, but PAW++ allows interaction with data points. Using PAW –Involves operations on 3 data types:- –Vectors: 1, 2 or 3 dimensional arrays –Histograms: 1 or 2 dimensional –N-tuples: Tables of events

2. Postgraduate Computing Lectures PAW 2 PAW: Data Flows Vector N-tuple Histogram HBOOK File ASCII File Main internal data flows (but all are possible) External data flows but can also create internally Multiple open at once Organised into directories Each directory can have multiple versions of same object (n-tuple or histogram) Can duplicate and merge Multiple open at once Organised into directories Each directory can have multiple versions of same object (n-tuple or histogram) Can duplicate and merge PAW

3. Postgraduate Computing Lectures PAW 3 PAW: Vectors Vectors –Have character identifier e.g. vec –1, 2 or 3 dim. arrays e.g. vec(10,3) –Arbitrary size and number (almost!) –Create in memory, Read from disk (can filter) and Write to disk –Combine e.g. –Select subrange e.g. vec(2:5,2) –Draw (as histogram bins), Plot (histogram points) and Fit to function a = b * c ~ ~ ~

4. Postgraduate Computing Lectures PAW 4 PAW: Functions Functions –Plot user or built-in functions –1, 2 or 3dimensional –Wide range of representations (this is just a few of them!)

5. Postgraduate Computing Lectures PAW 5 PAW: Histograms Histograms –Have a numeric identifier e.g. 123 –1 or 2 dimensional –Can associate errors with bins –Read from / Write to disk / Create in memory. –Combine e.g. A = B * C –Select subrange e.g.123(1:20) –Wide range of plotting and fitting facilities

6. Postgraduate Computing Lectures PAW 6 N-tuples –Have numeric identifiers e.g. 123 –Record a set of n numbered (1..n) events each with m named attributes:- –Two types Row-Wise –Stored as a set of rows –Each attribute is a 4-byte floating point Column-Wise –Stored as a set of attributes –Attributes can be a integer, real or char –Fixed or variable length –Create in memory, I/O to disk. –Merge two or more. PAW: N-tuples ******************************************************** * NTUPLE ID= 10 ENTRIES= 3354 CERN Population * ******************************************************** * Var numb * Name * Lower * Upper * ******************************************************** * 1 * CATEGORY * 0.102000E+03 * 0.567000E+03 * * 2 * DIVISION * 0.100000E+01 * 0.130000E+02 * * 3 * FLAG * 0.000000E+00 * 0.310000E+02 * * 4 * AGE * 0.210000E+02 * 0.640000E+02 * * 5 * SERVICE * 0.000000E+00 * 0.350000E+02 * ********************************************************

7. Postgraduate Computing Lectures PAW 7 PAW: N-tuples (cont) –Can plot functions of attributes, e.g. if have attributes x,y plot:- sqrt(x**2+y**2) –Can apply cuts on points to plot e.g.:- sin(x)+log(y) z>1.0.and.z<10.0 This is very powerful! –Can also Print rows on basis of cuts –Masks:- Used to store commonly used cuts Saves time: only computed once Can have 32 masks per n-tuple –Can plot in 1, 2 or 3 dimensions, e.g. z%y%x to plot Z v. Y v. X –Plotting, PAW generates histograms:- Automatically choosing suitable scales For fine control can define histogram and the plot into that. –Scanning: Each entry in n-tuple is processed by user code –Can plot functions of attributes, e.g. if have attributes x,y plot:- sqrt(x**2+y**2) –Can apply cuts on points to plot e.g.:- sin(x)+log(y) z>1.0.and.z<10.0 This is very powerful! –Can also Print rows on basis of cuts –Masks:- Used to store commonly used cuts Saves time: only computed once Can have 32 masks per n-tuple –Can plot in 1, 2 or 3 dimensions, e.g. z%y%x to plot Z v. Y v. X –Plotting, PAW generates histograms:- Automatically choosing suitable scales For fine control can define histogram and the plot into that. –Scanning: Each entry in n-tuple is processed by user code

8. Postgraduate Computing Lectures PAW 8 PAW: Programming SIGMA –A system for vector operation e.g:- sigma x=array(200,0#2*pi) sigma s=sin(x) Create a 200 point vector x running 0.. 2  Create a 200 point vector s of sin(x) COMIS –A FORTRAN interpreter –Supports a subset of FORTRAN77 –Has access to PAW’s internal data structures –Can make calls to a wide range of CERNlib routines –Allows users to add their own code, without linking, for:- N-tuple plotting and cutting functions Fitting and plotting functions Non-standard operations on internal data structures. Another very powerful feature!

9. Postgraduate Computing Lectures PAW 9 PAW: Command Interface Commands –Have a tree structure e.g. vector/operations/vscale –Can abbreviate if unambiguous e.g. ve/op/vsc or even vsc –Case insensitive –Built-in help: summary (usage) or detailed (help) –Command line recall with arrow keys –Can be stored in files as macros. Files have the extension.kumac –Complete programming language with:- Local variables Global variables Flow control Argument passing - so that macros can be used like subroutines Embedded data files –Typically users develop kumac files as part of their analysis tool set

10. Postgraduate Computing Lectures PAW 10 PAW: Graphics Options to Control Appearance –Position on screen. Direct plots into tiled zones on the screen –Log and linear scales –Titles –Attributes of graphical elements:- Colour Thickness Character font Line type (solid or broken) HIGZ –Is the underlying plotting package –Has a wide range of primitives e.g. titles, axes, polylines etc. that can supplement raw plots –All graphical output can be stored in files and output as Postscript.