Transcript Building SNOMAN - University of Oxford

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
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–
–
–
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
PAW: Data Flows
Vector
ASCII
File
PAW
Histogram
N-tuple
External data flows
but can also create
internally
HBOOK
File
•Multiple open at once
Main internal data flows •Organised into directories
•Each directory can have
(but all are possible)
multiple versions of same
object (n-tuple or histogram)
•Can duplicate and merge
Postgraduate Computing Lectures
PAW 2
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. 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
PAW: Functions
• Functions
– 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
PAW: Histograms
• Histograms
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–
–
–
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
PAW: N-tuples
• N-tuples
– 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
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
Postgraduate Computing Lectures
PAW 7
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!
Postgraduate Computing Lectures
PAW 8
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
Postgraduate Computing Lectures
PAW 9
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.
Postgraduate Computing Lectures
PAW 10