PASCAL II - Data Structures
Philip Fees
CS341
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Introduction

What will be studied?
– Data Types
– Arrays
– Records
– Abstract Data
– Pointers
– Linked lists, Stacks, Queues

What is a data structure?
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Workshop 1



File I/O Concepts
Pascal File I/O
User Defined Types
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File I/O Concepts

Generally termed Device I/O
– Could be hard disk, CD-ROM, tape, etc.
– Could be terminal, socket (IPC/Internet), etc.






Delimited files (whitespace, eol, eof) vs. non
delimited
Encoded (ASCII, EBCDIC) vs. binary
Open and Close
Read or write via file/device handle (symbolic name)
Seek vs. non-seek devices
Sequential vs. Indexed
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Pascal File I/O - Handles

Identify handle (symbolic name)
Program myProgram (input, output, fileHandle);
…
VAR
fileHandle : text;

Associate file to handle
– “create procedure file prior to compiling and
running the program”
– TP: assign(fileHandle,’myTextFile.txt’);
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Pascal File I/O - Open & Seek

Open the file
reset(fileHandle);


rewind is part of “seeking”
delimiter tests
WHILE not eol DO
WHILE not eol(fileHandle) DO
WHILE not eof DO
WHILE not eof(fileHandle) DO
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Pascal File I/O - Read & Write

Read from file via handle
readln(fileHandle, variable1, variable2, …);

Write to file via handle
writeln(fileHandle, ‘this is a test’, variable1, …);

Ease old file contents
rewrite(fileHandle);

See example pg. 444
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Exercises

Pg. 448 25 - 30
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User Defined Types

Characteristic of a data type (int, char,
boolean)
– set of value
– fixed amount of memory


Ordinal data type have pred() and succ()
values
User defined a.k.a. enumerated data type
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Enumerated Data Type

Syntax
Type
Weekday = (Mon, Tues, Wed, Thur, Fri);

Usage
VAR
Day : Weekday;
…
Day := Mon;
if (Day = Mon) then
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Subranges


Define one type as subset of another type
Examples
TYPE
USYears = 1776..1998;
Days = (Sun, Mon, Tues, Wed, Thur, Fri, Sat);
Weekdays = Mon..Fri;

Operations: succ() and pred()
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Exercises



Pg. 454-455 # 2, 15
Pg. 461 #22-26
Pg. 465 #8, 20, 21, 22
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Workshop 2
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


Single Dimensional Arrays
Selection Sort
Arrays and Subroutines
Searching Arrays
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Arrays
Collection of data elements
 Data elements are the same type
 Contiguous in memory
 Access individuals by subscript
 Array size determined by:

element size * number of elements
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Pascal Arrays
name : ARRAY [ index type ] OF type;
 Example 1:

VAR
List : ARRAY [1..5] OF integer;

Example 2:
TYPE
Numbers = ARRAY [1..5] OF integer;
VAR
List : Numbers;
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Pascal Arrays (cont.)

index type alternatives
TYPE
DAYS = (SUN, MON, TUES, WED, THUR, FRI,
SAT);
VAR
StockPriceList : ARRAY [9..16] OF real;
NegativeList : ARRAY [-2..3] OF char;
HoursWorked : ARRAY [MON..FRI] of real;
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Use of Constants

Good Pratice
CONST
ClassSize = 35;
Type
TestScores = 0..100;
VAR
Score : ARRAY [1..ClassSize] of TestScores;
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Arrays and Loops
“Looping” over the array
 Example:

FOR J := 1 to ClassSize DO
BEGIN
write(‘next: ‘);
readln(Score[J]);
writeln(‘value = ‘, Score[J]);
END
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Review
Example 10.7 pg. 491
 Example 10.9 pg. 493
 Example 10.10 pg. 494

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Selection Sort
Sorting a list of values
 Algorithm

– start with first element [1] (current)
– find smallest element in array and
exchange with current
– current := next array element (current + 1)
– continue to end of array

What is the best and worst case?
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Arrays and Subroutines
See GetData example on pg. 508
 Call by value vs. call by reference

– Call by value: create a copy of the array
– Call by reference: refer to the passed array
– Performance implications
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Search Algorithms

Sequential Search (pg. 526)
– search entire array until value located or
“hit” the end of the array
– Average of N iterations of loop

Binary Search (pg. 528)
– Assumes sorted array
– start in middle; look in upper or lower half
– Average of log N iterations of loop
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Analysis
Overhead of inserting new value in
sorted array
 What should maximum size of the array
be?
 When should an array be used?

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Exercises
Arrays: pp. 497-500 # 1-5, 10, 20
 Sorts: pg. 507 # 2, 3, 6
 Subroutines: pg. 516 # 16-19
 Searching: pg. #533 5, 15

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Workshop 3

Multi-dimensional arrays
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Array in Memory
Array stored in contiguous memory
 Location is calculated:

– Starting address + (row index * # of
columns) + column index

Row Major vs. Column Major
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Pascal Syntax

Syntax
<name> : ARRAY [ <row index>, <column
index> ] OF <element type>
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Two Dimensional Example

Example 1:
VAR
Table : ARRAY [ 1..3, 1.. 4 ] OF integer;

Example 2:
TYPE
Maxtrix = [ 1.. 3, 1.. 4] OF integer;
VAR
Table : Matrix;
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Iteration

Example:
FOR row := 1 to 3 DO
FOR column := 1 to 4 DO
writeln(‘Table[‘,row,’, ‘, column,’] = ‘,
Table[row][column]);
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Higher Dimensional Arrays

Syntax
<identifier> : ARRAY [ A1 .. B1, A2 .. B2, …,
An .. Bn] OF <type>

Example1:
cube : ARRAY [ 1..3, 1..4, 1..5] OF integer;

Example 2:
cube : ARRAY [ 1..3 ] OF ARRAY [ 1..4, 1..5 ]
OF integer;
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Exercises

pg. 567 # 4, 9-12, 13-15
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Workshop 4



Records
Variants
Binary Files
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Workshop 5

Sets
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Defining and Declaring

Syntax
TYPE
<type name> = SET OF <base type> ;
VAR
<variable name> : <type name> ;

Example
TYPE
Digits = SET OF 0..9;
VAR
numbers : Digits;
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Different than Subtypes

Assignment
numbers := [ 0, 2, 4, 6, 8 ];





Set is undefined until assignment
Assigned values must be in base type elements of the set
Universal sets contain all values
Subsets - one set contains all members of
another set
See set definitions on top of page 735.
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Set Operations
Union: A + B
 Intersection: A * B
 Difference: A - B

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Relational Operators
equal: A = B sets A and B are identical
 not equal: A <> B set A and B are not
identical
 subset: A <= B A is a subset of B
 superset A >= B A is a superset of B
(A<=B)

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Membership

Syntax
<element> IN <set>

Example
2 IN numbers

Boolean value: is 2 in the numbers set?
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Membership Example
Useful for “edit checks”
 Example

IF response IN [‘Y’, ‘y’] THEN
(continue action here)
ELSE
(alternative action here)

Review example on page 744.
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Exercises
Page 739 # 9-15, 26-30
 Page 743 # 2, 7-20, 27
 Programming Problem Page 759 # 1, 3

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Workshop 6
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Model Builder
Abstract Data Type (ADT)
String ADT
Linked List ADT
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Model Builder
Model, design, or abstraction - analysis
without being concern for details
 Examples

– World Wide Web
– Windows
– Virus
– UNIX: parent process, child process, kill,
fork, etc.
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Abstract Data Type




collection of data (objects)
shared properties
shared operations
Examples: Array, Integers,
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ADT Example - String





definition - finite sequence of characters
terminated with null character
access individual elements (substring)
Operations: create, read, write, assign, length
reconsider operations if string is defined by
length not null terminator.
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ADT Example - Linked List





Definition - list of data items associated by a
link to one or more nodes.
Typically point to next node (single linked) or
previous node (double linked)
Head node is first node in list
To “walk” the list, must start at head and
proceed sequentially.
Task: Define operations, define interfaces,
implement
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Workshop 7




Linked Lists
Array Implementation of Linked Lists
Pointers
Pointer Implementation of Linked Lists
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Why Linked Lists

Arrays
– Size fixed at Compile Time, inefficient space
utilization
– Inserting, deleting, organizing are costly. Why?

Requirement
– Size determined at Run Time
– Minimal cost to insert, delete, and organize data

Examples
– Sorting, data with undetermined number of items
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Linked Lists
Diagram data structure (See page 872)
 Define operations (See page 881)
 Define implementation base data
structures (See page 873)
 Diagram operation’s effect on sample
data structure (See page 887)

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Introduction to Pointers
A pointer doesn’t contain the data, but
contains a way of locating the data.
 Example: Array subscript

pointer : integer
data : array [1..100] of char;
…
pointer := 5;
writeln(array[pointer]);
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Array Implementation of Linked
Lists

Issues
– Still have compile time limit on number of nodes,
wasted space
– Which nodes are “free”

Benefits
– Simple base data type
– Ok if max nodes know at compile time
– Still have benefits of low cost insert, update, and
organize
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Array Implementation (cont.)

Free nodes
– Mark all free node - sequential search
– Keep linked list of free nodes


Book has special procedures to handle free
nodes
Modify linked list procedures to handle free
linked list
– Move InitializeSpace logic into Create Procedure
– Pass InsertNode the data in place of the pointer P
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Pointers & Dynamic Memory

Declaring a pointer (see page 874)
Var
intPtr : ^integer;

Allocate memory
new(intPtr);

Access and deallocate memory
intPtr^ := 10;
writeln(intPtr^);
dispose(intPtr^);
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Pointers & Dynamic Memory

Declaring a pointer (see page 874)
Var
intPtr : ^integer;

Allocate memory
new(intPtr);

Access and deallocate memory
intPtr^ := 10;
writeln(intPtr^);
dispose(intPtr^);
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Pointers & Dynamic Memory

Declaring a pointer (see page 874)
Var
intPtr : ^integer;

Allocate memory
new(intPtr);

Access and deallocate memory
intPtr^ := 10;
writeln(intPtr^);
dispose(intPtr);
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Pointers & Dynamic Memory
(cont.)




NIL value - doesn’t point to anything
Memory allocated by new comes from heap
Must deallocate via dispose
Must keep track of all allocated memory memory leaks
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Pointer Implementation of Linked
Lists

See page 889
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Variations on Linked List
Dummy Header - avoid test for empty
list
 Circularly Linked - no NIL
 Doubly Linked List - don’t need previous
node

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Exercises


Pg 872 # 16
Pg 880 # 9-19
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