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HBOOK
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Preliminary remarks
Contents
History
Preliminary remarks
List of Figures
List of Tables
Introduction
Data processing flow in particle experiments
HBOOK and its output options
The functionality of HBOOK
What you should know before you start
HBOOK parameter conventions
Histogram or Ntuple Identiers
Parameter types
Data packing
A basic example
HBOOK batch as the first step of the analysis
Adding some data to the RZ file
HPLOT interface for high quality graphics
One and two dimensional histograms -- Basics
Booking
One-dimensional case
Special values:
Two-dimensional case
Filling
Editing
Copy, rename, reset and delete
Ntuples
CWN and RWN -- Two kinds of Ntuples
Row-Wise-Ntuples (RWN)
Booking a RWN
Filling a RWN
Memory resident Ntuples
Circular buffer
Disk resident Ntuples
More general Ntuples: Column-Wise-Ntuples (CWN)
Data Compression
Storage Model
Performance
Booking a CWN
Describing the columns of a CWN
Alternative way of booking a CWN
Filling a CWN
Recovery procedure
Disk resident Ntuples
Making projections of a RWN
Get information about an Ntuple
Retrieve the contents of a RWN into an array
Retrieve the contents of a CWN into a common block
Generate a user function
Optimizing event loops
Ntuple operations
Duplicate Ntuples
Rename a column in an Ntuple
One-dimensional convolution of Ntuple
Discussion
Ntuple examples
A more complex example - CERN personnel statistics
Advanced features for booking and editing operations
Overview of booking options
Histograms with non-equidistant bins
Profile histograms
Rounding
Projections, Slices, Bands
Statistics
Function Representation
Reserve array in memory
Axis labels and histograms
Filling Operations
Fast Filling Entries
Global Filling
Filling histograms using character variables
Editing operations
One-dimensional histograms
Two-dimensional histograms
Index and General Title
What to Print (1-dimensional histogram)
Graphic Choices (1-dimensional histogram)
Scale Definition and Normalization
Page Control
Selective Editing
Printing after System Error Recovery
Changing Logical unit numbers for output and message files
Accessing Information
Testing if a histogram exists in memory
List of histograms
Number of entries
Histrogram attributes Contents
Contents
Errors
Associated function
Abscissa to channel number
Maximum and Minimum
Rebinning
Integrated contents
Histogram definition
Statistics
Operations on Histograms
Arithmetic Operations
Statistical differences between histograms
Weights and Saturation
Weighted 1-dimensional histograms
Saturated 1-dimensional histograms
2-dimensional histograms
Statistical Considerations
The Kolmogorov Test
The Power
The Confidence Level for 1-dimensional data
The Confidence Level for Two-dimensional Data
Bin by bin histogram comparisons
When to use #HDIFFB>HDIFFB instead of #HDIFF>HDIFF:
Choice of 590:
When to use the 594 option:
When to use the 598 option:
When to use the 600 option:
Comparison of Weighted versus Unweighted events:
Using Profile histograms:
Values of 617:
Other notes:
Statistical methods and numerical notes:
Error messages of #HDIFFB>HDIFFB:
Statistical comments:
Fitting, parameterization and smoothing
Fitting
One and two-dimensional distributions
Fitting one-dimensional histograms with special functions
Fitting one or multi-demensional arrays
Results of the fit
Naming the parameters of a fit
Retrieving the fitted parameters
The user parametric function
User specified derivatives
Basic concepts of MINUIT
Basic concepts - The transformation for parameters with limits.
How to get the right answer from MINUIT
Getting the right minimum with limits.
Getting the right parameter errors with limits.
Interpretation of Parameter Errors:
Statistical interpretation:
Reliability of MINUIT error estimates.
A non-physical region:
An underdetermined problem:
Numerical inaccuracies:
An ill-posed problem:
Excessive numerical roundoff:
Starting too far from the solution:
MINUIT interactive mode
Overview of available MINUIT commands
CLEar
CONtour par1 par2 devs ngrid
EXIT
FIX parno
HELP SET SHOw
HESse maxcalls
IMProve maxcalls
MIGrad maxcalls tolerance
MINImize maxcalls tolerance
MINOs maxcalls parno parno ...
RELease parno
REStore code
SCAn parno numpts from to
SEEk maxcalls devs
SET ERRordef up
SET LIMits parno lolim uplim
SET PARameter parno value
SET PRIntout level
SET STRategy level
SHOw XXXX
SHOw CORrelations
SHOw COVariance
SIMplex maxcalls tolerance
Deprecated fitting routines
Fitting histograms -- Long version
Fitting histograms -- Short version
Non-equidistant points in a multi-dimensional space -- Long version
Non-equidistant points in a multi-dimensional space -- Short version
Histogram Fitting with Special Functions
Parametrization
Histograms and plots
Distributions
Smoothing
Random Number Generation
Fitting with finite Monte Carlo statistics
The Problem.
Methodology.
The Solution.
Other points concerning the solution
Weighted Events
HBOOK routines
Example of fits
Memory Management and input/output Routines
Memory usage and ZEBRA
The use of ZEBRA
Memory size control
Space requirements
Directories
Input/Output Routines
Reading and writing histograms to a direct access file
Open an RZ direct access file or map a Global Section
Writing to a file
Reading from a direct-access file or global section
Merging HBOOK files into a new file
Scratching histogram in a file
Close a file
Exchange of histograms between different machines
Transfer between Unix machines with FTP
Transfer from CERNVM to a Unix workstation
Running FTP on a VAX/VMS systen
Proposed HBOOK file naming convention
RZ directories and HBOOK files
Using subdirectories
Global sections and shared memory
Sharing histograms in memory on remote machines
Memory communication
Mapping global sections on VMS
Using PAW as a presenter on VMS systems (global section)
Unix shared memory (Sun and DecStation only!)
Using PAW and Unix shared memory (Sun and DecStation only)
Access to remote files from a PAW session
Using PAW as a presenter on OS9 systems
HBOOK Tabular Overview
References
Index
List of Subroutines
Last update: Tue May 16 09:09:27 METDST 1995