– A tool to display and manipulate data.
– See ref. in your induction week notes.
– 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.
– Involves operations on 3 data types:-
– Vectors: 1, 2 or 3 dimensional arrays
– Histograms:
1 or 2 dimensional
– N-tuples: Tables of events
Postgraduate Computing Lectures PAW 1

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

– 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. a = b * c
~ ~ ~
– Select subrange e.g. vec(2:5,2)
– Draw (as histogram bins), Plot
(histogram points) and Fit to function
Postgraduate Computing Lectures PAW 3

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

– 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
Postgraduate Computing Lectures PAW 5

– Have numeric identifiers e.g. 123
– Record a set of n numbered (1..n) events each with m named attributes :-
********************************************************
* 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 *
********************************************************
– 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.
Postgraduate Computing Lectures PAW 6

– 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
Postgraduate Computing Lectures PAW 7

– 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)
– 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!
Postgraduate Computing Lectures PAW 8

– 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
Postgraduate Computing Lectures PAW 9

– 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)
– 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 .
Postgraduate Computing Lectures PAW 10