An Introduction to Programming with IDL. Interactive Data by Kenneth P. Bowman

By Kenneth P. Bowman

Content material:
Preface

, Pages xi-xii
Acknowledgments

, Page xiii
1 - Introduction

, Pages 3-7
2 - IDL Manuals and Books

, Pages 9-12
3 - Interactive IDL

, Pages 13-31,I
4 - IDL Scripts (Batch Jobs)

, Pages 33-38
5 - Integer Constants and Variables

, Pages 39-48
6 - Floating-Point Constants and Variables

, Pages 49-58
7 - utilizing Arrays

, Pages 59-76
8 - looking and Sorting

, Pages 77-82
9 - Structures

, Pages 83-90
10 - Printing Text

, Pages 93-100
11 - analyzing Text

, Pages 101-105
12 - Writing and studying Binary Files

, Pages 107-114
13 - interpreting NetCDF Files

, Pages 115-125
14 - Writing NetCDF Files

, Pages 127-133
15 - techniques and Functions

, Pages 137-151
16 - software Control

, Pages 153-157
17 - Line Graphs

, Pages 161-170
18 - Contour and floor Plots

, Pages 171-179
19 - Mapping

, Pages 181-191
20 - Printing Graphics

, Pages 193-200
21 - colour and photo Display

, Pages 201-218,II-V
22 - Animation

, Pages 219-224
23 - statistics and Pseudorandom Numbers

, Pages 227-235
24 - Interpolation

, Pages 237-246,VI-VII
25 - Fourier Analysis

, Pages 247-262,VIII
Appendix A - An IDL variety Guide

, Pages 263-270
Appendix B - instance tactics, capabilities, Scripts, and information Files

, Pages 271-276
Bibliography

, Page 277
Index

, Pages 279-286

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Extra resources for An Introduction to Programming with IDL. Interactive Data Language

Example text

Using double precision does not solve all floating-point problems; it just expands the range of problems that can be done with good precision. On some computers double-precision operations are slower than single-precision, so you probably do not want to automatically do everything in double precision. ) Also, double-precision numbers require twice as much computer memory as single-precision numbers. If you are using large arrays of numbers, the difference can be significant. Finally, if you write doubleprecision values to a file, the file will be twice as large as the equivalent file with single-precision values.

With current clock rates well above 1 GHz, most modern workstations and personal computers can perform more than 1 billion floating-point calculations per second (referred to as 1 gigaflop). This chapter covers the most important aspects of floating-point arithmetic for beginning programmers. 3 Limitations of Floating-Point Arithmetic To illustrate some of the problems that can occur when making calculations with floating-point numbers, we’ll start with some examples using decimal notation. As with integers, the fundamental problem with floating-point numbers on computers is that in practice we can use only a finite number of digits.

3 Commonly used integer types. Other integer types that are available in IDL are described in the IDL documentation. Type BYTE INT LONG LONG64 Bits Bytes 8 16 32 64 1 2 4 8 Range (powers of 2) Minimum Maximum 0 −215 −231 −263 28 − 1 215 − 1 231 − 1 263 − 1 Range (powers of 10) Minimum Maximum 0 −32,768 ∼ −2 · 109 ∼ −9 · 1018 255 32,767 ∼ 2 · 109 ∼ 9 · 1018 Chapter 5 48 Integer Constants and Variables 32/33 33/33 33/32 −33/32 3. Assume that you want to keep track of time using units of seconds. How many years can you count before a LONG variable is unable to represent the elapsed time?

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