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Introduction to Matlab & Data Analysis

Tutorials 8 and 9: Cell Arrays

Advanced Text Processing And File Handling

Please change directory to directory E:\Matlab (cd E:\Matlab;)

From the course website

(http://www.carine.co.il/htmls/page_1176.aspx?c0=13889&bsp=14333&bssearch=4,0,5,3,41,0

)

Download:

t89.zip and unzip itWeizmann 2010 ©

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Outline

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Cell arrays: Creating and indexing Useful functions for strings lists

Structures Advanced string manipulation

Regular expressions File handling

Reading files Writing to files High-level file handling functions

Final example – P53

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Cell Arrays – Lecture Reminders Cell arrays –

Used for keeping different types of data in the same array

For example: A{1}= 2; A{2}= 4:2:44; A{3}= ‘hello’;

Extremely useful for handling lists of strings

Notice the curly brackets

2 4:2:44 hello

Cell Cell Cell Cell Array

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Creating Cell Arrays – Lecture Reminder

A(1) = {3}; A{2} = 3; A{3} = ‘radio blabla’; A{4} = 2:2:66;B(1:3) = {3, [1, 2], ’abc’};

C = {‘george clooney’ ; … ‘richard gere’ }; %Initializing an empty cell array:

D=cell(4,2);

>>A‘ans = [ 3][ 3]

' radio blabla'[ 1x33 double]

C = ' george clooney'

' richard gere'

D = ][ ][ ][ ][ ][ ][ ][ ][

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Indexing Cell Arrays Define a cell array:>> A(1) = {3};>> A{2} = 3;>> A{3} = ‘radio blabla’;

>> A{4} = 2:2:66; (or load A.mat;)

What is the difference?A(1)

A{1}

>>x=A(1) >>class(x)

>>x=A{1}>> class(x)

>>x=A(3)>> class(x)

>>x=A{3} >>class(x)

x = [3]cellx = 3doublex = 'radio blabla'cellx = radio blablachar

3 [1,2,7] ‘Str’

Cell Cell Cell Cell ArrayTry:

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Manipulating Cell arraysJust like numerical arrays…Examples:x([1,3,5]) = {'aaa','bbb','ccc'}x = repmat(x,2,3)x(:,4)x(1:2,3:5)

% Notice:% Using curly brackets returns couple of cells

[a, b]=x{1:2}

Numerical array default value is zero, in cell array it is []

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Cell Arrays Are Very Useful For Keeping Lists of Strings

Cell arrays of strings can be treated similarly to numerical arrays.

Many functions can work both numerical & cell arrays Many functions which work on strings can handle cell

arraysload fruit.mat;%fruit={‘mango’,’banana’,’melon’,’apple’,’kiwi’,’orange’};%fruit_prices=[30 15 10 5 35 8]; Find what is the price of melon?ind = find(strcmp(fruit,’melon’));fruit_prices(ind) Sort the fruits from cheapest to most expensive[sorted_p,y]=sort(fruit_prices);fruit(y)

ans = 10

{‘apple‘,’orange‘,’melon‘,’banana‘,’mango‘,’kiwi‘}

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Manipulating Cell Arrays That Hold Lists Of Strings

unique

intersect

setdiff

union

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Manipulating Cell Arrays That Hold Lists Of Strings - Example

%fruit={‘mango’,’banana’,’melon’,’apple’,’qiwi’,’orange’};

%fruit_sales={‘mango’,’banana’,’melon’,…

’mango’,’mango’,’qiwi’,’banana’,’mango’};

Which fruits were not sold today?setdiff(fruit,unique(fruit_sales))

{'apple‘,'orange‘}

For efficiency

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ismember Function Is Useful For Mapping One List To Another

Finds if an element exists in a list>> b = {‘z’,’y’,’x’,’w’};>> a = ismember(‘x’,b)a = 1

If it does – ismember can tell you where it is>>[a,map]= ismember(‘x’,b)a=1, map=3

ismember is good for mapping one list to another – when order is important! >>[a,map]= ismember({‘x’,’y’,‘c’},b);a=[1 1 0], map=[3 2 0]

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Comparing Two Lists of Strings:ismember, find and intersect

Which function to use? I want to find the order of

elements of one list in another list?

ismember I want to find which elements

of a list are also in another list?

intersect I want to find all the

occurrences of an element in a list?

find

When the element appears in the list more than once, ismember will return only the last position

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Using ismember - Example

>> a = ismember(‘banana’, fruit_sales)a=1>> a = ismember(‘orange’, fruit_sales)a=0>> a = ismember(fruit, fruit_sales);a = [1 1 1 0 1 0]% Reminder: fruit_prices = 30 15 10 5 35 8

Example: calculate the amount of money made by each fruit sale

>> [a,b]= ismember(fruit_sales, fruit);a = [1, 1, 1, 1, 1, 1, 1, 1]b = [1, 2, 3, 1, 1, 5, 2, 1]

>> sales_money = fruit_kilos .* fruit_prices(b)sales_money = [90, 30, 10, 60, 240, 17.5, 45, 150]

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Structures

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Lecture Reminder - Structures Creation

>> dogs.name = 'rufus';>> dogs.breed = 'Bulldog';>> dogs.age = 1.5; % in years>> dogs.special_food = 'none';>> dogsdogs =

name: 'rufus' breed: 'Bulldog ' age: 1.5000 special_food: 'none‘

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Lecture Reminder - Structures creation

Adding more dogs…>> dogs(2).name = 'king-kong';>> dogs(2).breed = ‘Chihuahua';>> dogs(2).age = 5; >> dogs(2).special_food = 'filet mignon';

>> dogs(3).name = 'wong';>> dogs(3).breed = 'pekingese';>> dogs(3).age = 20; >> dogs(3).special_food = 'sushi';

>> dogs =

1x3 struct array with fields: name breed age special_food

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Structures – Short Example

Define a “fruits” structure array that has the fields: name price color

and contains two fruits of your choice

Get: Cell array of the names Array of the prices The first fruit

>> fruits(1).name = 'Lemon';>> fruits(1).color = 'Yellow';>> fruits(1).price = 20; >> fruits(2).name = 'Apple';>> fruits(2).color = 'Green';>> fruits(2).price = 10;

>> {fruits.name}'Lemon' 'Apple'>> [fruits.price]20 10>> a = fruits(1)a = name: 'Lemon' color: 'Yellow' price: 20

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Structure Advertisement

Although this tutorial focuses on cells:

Using Structures to aggregate variables that belong to the same entity makes the program easier to design, more readable and easier to debug.

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Advanced Text processing (String Manipulation)

1. Review of useful functions:1. findstr, strfind, strtok, strtrim2. sprintf

2. Regular expressions

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Review of Useful Functions For String Manipulation

So far we learned simple string manipulations: str2num, num2str strcmp, strncmp, strcmpi, strncmpi

More advance string manipulation functions (used in text processing): findstr, strfind strtok strtrim sprintf (related functions: fprintf, sscanf)

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Finding One String Inside Another - findstr and strfind

findstr(str1,str2) – Searches the longer of the two input

strings for any occurrences of the shorter string (input order does not matter!):

>> k = findstr('beauty is in the eyes of the beholder','be')

k=[1, 30]

strfind(str1,str2) The order matters: finding str2 inside

str1 str1 can be a cell array of strings!!!

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Consider the line ‘this is an example’ How we write a program that breaks it to a

cell array of single words?rem=‘this is an example’;

words=cell(0);

while 1

[tok,rem] = strtok(rem);

if isempty(tok)

break;

end

words{end+1}=tok;

end

Example –Parsing a Line Using strtok

words'

ans =

'this' 'is' 'an' 'example'

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load fruit.mat;for i=1:length(fruit) s = sprintf('Fruit number %d: %s', i, fruit{i}); disp(s);end

sprintf – Write Formatted Data Into Strings

Fruit number 1: mangoFruit number 2: bananaFruit number 3: melonFruit number 4: appleFruit number 5: qiwiFruit number 6: orange

Number String

sprintf(format,…) – write formatted data into strings

Good for creating massages for disp Related functions: fprintf, sscanf

format special characters: %s – a string %d – an integer %f – a float (short double)

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sprintf - Example Consider the cell arraynames = {'Danny', 'Noa', 'Moti'}; Write a script that prints:Number:1, Name:Danny.Number:2, Name:Noa.Number:3, Name:Moti. Answer:for i=1:length(names) s = sprintf('Number:%d, Name:%s.',…

i, names{i}); disp(s);end

See also: sscanf & fprintf

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More Useful String Manipulation Functions

strtrim(str) – removes all leading and trailing white-space>> strtrim(' do not blink ')'do not blink‘

strtok(str,delim) - breaks a string into “tokens”>> [tok,rem]=strtok('this is an example', ' ')

tok =‘this’ rem = ‘ is an example’ strfind (str1,str2) - searches str2 in str1. str1 can be a cell array of strings! >> k = strfind('beauty is in the eyes of the

beholder','be') k=[1, 30] findstr(str1,str2) – Searches the longer of the two input

strings for any occurrences of the shorter string More useful functions at:

Help -> Matlab -> Functions by category -> Strings functions

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Regular expressions

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Regular Expression - Definition

Wikipedia – Regular expressions provide a concise and flexible means for identifying strings of text

of interest, such as particular characters, words, or patterns of characters.

ind = regexp(long_str,'\w+ain')

Regular expressions

We need to learn the regular expressions “language” syntax

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Regular Expressions Syntax

Defining a pattern: [] is like OR

Any character out of a,b,c or d: [abcd] Anything other than a,b,c or d : [^abcd]

Character range: (all characters a to z) [a-z] Special Charecters used in defining a pattern:

Any character: . Whitespace: \s Newline: \n Tab: \t Any alphanumeric character: \w [a-zA-Z_0-9] Any digit: \d [0-9]

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Pattern definition - Expression Quantifiers: One or more: exp+ (Example: ‘[\w]+’) Zero or more: exp* Between n and m times: exp{n,m}Examples

Read more about “regular expressions” in the MATLAB help!(search “regular expressions” )

Function: loc = regexp(str, pattern)

Regular Expressions Syntax

‘\w\s+\w’ – Two alphanumeric expressions with one or more spaces in the middle

‘[SRM]amy’ –

Ramy, Samy or Mamy

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Using Regular Expressions to Search For Pattern occurrences In a Long String

Example:

prof_higgins = 'The rain in Spain stays mainly in the plain.';

We would like to find all the words that rhyme with ‘ain’

1. Defining the pattern: new word (preceded with space) One or more alphanumeric characters ‘ain’ pattern= ‘\w+ain[\s\.]’ OR pattern= ‘[a-zA-Z]+ain [\s\.]’

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>> prof_higgins = … 'The rain in Spain falls mainly on the plain.';

Find occurrences indices: >> loc = regexp(prof_higgins,'\w+ain')loc = [5 13 25 39]

Get pattern occurrences:>> words = regexp(prof_higgins,'\w+ain','match')words = {'rain','Spain','main','plain'}

Using Regular Expressions to Search For Pattern occurrences In a Long String

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Replace all pattern occurrences:

>> eliza_doolittle=regexprep(prof_higgins,’ain’,’yne’)

elisa_doolittle = ‘The ryne in Spyne falls mynely on the plyne.’

Split a line to the words (Good for parsing lines of input file): >> words = regexp(prof_higgins, '\s', 'split');words ={'The‘, 'rain‘,'in‘,'Spain‘,'falls‘,'mainly‘,'on‘,'the‘,

'plain.‘}

Using Regular Expressions to Replace Pattern Occurrences In a Long String

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Using Regular Expression to Parse a line (see strtok for another option)

no_rhymes = regexp(prof_higgins, 'ain\w*\s', 'split')no_rhymes =

{'The r' 'in Sp' 'falls m' 'on the plain.‘}

Error: The last word does not have space after it

Fixing it:

no_rhymes = regexp(prof_higgins, '\w+ain[\s\.]', 'split')no_rhymes =

{'The ' 'in ' 'falls mainly on the ' '' }

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Running Example – Finding Bomb Threats

You are a CIA agent,who is in charge of identifying potential bombing threats of cities, by going over emails of terrorists .

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Using Regular Expression to Identify Significant Lines

Assume an email is stored as a cell array of strings (each line in a cell), called “email”

Using Regular expression: Identify lines that contain the expression “bomb” in it. When you find such a line, print: “Help!!!” load email.mat;for i=1:length(email)

line=email{i};if( )

disp(‘HELP!!!’);end

end

~isempty(regexp(line,’bomb’))

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Using Regular Expression to Identify Significant Lines

Notice there is a “bug” in the code: load email.mat;for i=1:length(email)

line=email{i};if(~isempty(regexp(line,’bomb’)) )

disp([‘HELP!!!:’ line]);end

end

HELP!!!:thinking of bombing rehovotHELP!!!:thinking of bombing sderotHELP!!!:thinking of going to the bombamella festival next week

How do we fix the bug?Hint | is or: ‘smil[e|ed|ing]’

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Using Regular Expression to Identify Significant Lines

Here is a fix for the bug:

load email.mat;for i=1:length(email)

line=email{i};if(~isempty(regexp(line,’[Bb]omb[ed|ing|s]*\s’)))

disp([‘HELP!!!:’ line]);end

end

HELP!!!:thinking of bombing rehovotHELP!!!:thinking of bombing sderot

| is or

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Regular Expression Tokens Are Used to Retrieve Specific Part of the Pattern Occurrences

tokens = regexp( …'bla bla ami@weizmann.ac.il bli bli tami@tau.ac.il ya', …

'(\w+)@(\w+)\.ac\.il', 'tokens')

Token 1 Token 2

tokens =

{ {‘ami’, ‘weizmann’} {‘tami’ ‘tau’} }

ocuurence1

tokens{1}{1} = ‘ami’

Token1

Token2

ocuurence2Token

1Token

2

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Using Tokens to Retrieve Specific Parts of the Pattern Occurrences

Now that you identified the suspicious email, take out the threatened city Hint: Use

regexp(line, <some expression>, ‘tokens’).

for i=1:length(email)line=email{i};if(~isempty(regexp(line,’[Bb]omb[ed|ing|s|\s]*\s’))) city = regexp(line,…

'[Bb]omb[ed|ing|s|\s]*\s(\w+)',…

'tokens');disp([‘HELP!!! Bomb threat on ‘ city{1}{1}]);

endend

HELP!!! Bomb threat on:rehovotHELP!!! Bomb threat on:sderot

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Using Tokens to Retrieve Specific Parts of the Pattern Occurrences

Here is a loop-less version: load email.mat;cities = regexp(email, '[Bb]omb[ed|ing|s]*\s(\w+).*', 'tokens')

is_threat = ~cellfun('isempty',cities);cities = cities(is_threat);cities = [cities{:}];cities = [cities{:}];warnings = strcat('HELP!!! Bomb threat on: ', cities)disp(strvcat(warnings))

HELP!!! Bomb threat on:rehovotHELP!!! Bomb threat on:sderot

regexp can handle cell array

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Handling Files

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Lecture Reminder –Opening and Closing Files

Opening a file for reading:fid=fopen(‘filename’,’r’); Opening a file for writing:fid=fopen(‘filename’,’w’); fid is a scalar MATLAB integer, called a

file identifier. You use the fid as the first argument to

other file input/output routines

Always close your file!!! fclose(fid);

Permissions: ‘a’ – append‘r+’- read and writeMore in the HELP…

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Lecture Reminder –Reading a File Line by Line

Reading line by line:line = fgetl(fid); How can we read the entire file?fid = fopen('names.txt');

while feof(fid)==0tline = fgetl(fid);

if ~ischar(tline) break; endtline = strtrim(tline);%<do whatever you want>

end

fclose(fid);

Open

Close

feof – did file reached the end

fgetl – file get linebreak if not char

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Lecture Reminder – Writing to a File

Open the file for writing permission Writing, line by line, using:

fprintf(fid,format,…); % similar to sprintf!!! Format – is a string with special characters:

%s – a string, %d – an integer, %f – a float (short double) Close the file Example:

fid = fopen(‘tmp.txt', 'w');for i=1:length(lines) fprintf(fid,’this is a line: %s\n’,lines{i});Endfclose(fid);

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fid = fopen('names.txt', 'r');l_cnt = 0;

while feof(fid)==0 line = fgetl(fid); if ~ischar(line) break; end l_cnt = l_cnt +1; disp(['Line number ' num2str(l_cnt) ':' line]); end

fclose(fid);

File handling - Example

Open the file names.txt for read

Display it with line numbers:Line number 1: <line1>Line number 2: <line2> …

Close the file

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File Handling - Example Congratulations!

You were just promoted to a senior spy. You have a directory full of emails text

files. Now you need to read all emails files,

identify the bomb threat, and write them into a summary threat_report.txt file.

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File Handling - ExampleSolution strategy:1. Open output the threats file 2. Go over all the emails in a given

directory:1. Open an input email file2. Read it, line by line 3. identify threats

When a threat is identified – Print the line

4. Close the input email file3. Close output threats file

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File Handling – Example:Programs Design

searchEmailsDirForThreats – Open report output file Open a directory and get all the files

names For each file run

searchEmailForThreats – Open email input file Search line by line for threat If threat is found –

Write the threat to the output file

1. Email file name2. Report output fid

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File Handling – Example:Main Function Design

function threats_found = searchEmailsDirForThreats(in_emails_dir, out_report_fname)

%<getting all files names> % <opening report output file>

% <going over the files>

% <closing report output file>

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File Handling – Example:Main Function Design

function threats_found = searchEmailsDirForThreats(in_emails_dir, out_report_fname

%<getting all files names>if (~isdir(in_emails_dir)) error([in_emails_dir ' is not a directory']);end % getting file namesfs = dir(in_emails_dir);file_names = {fs.name};

Directory management:

dir, pwd, cd, copyfile, delete, movefile, mkdir, rmdir, …

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File Handling – Example:Main Function Design

function threats_found = searchEmailsDirForThreats(in_emails_dir, out_report_fname

%<getting all files names> % <opening report output file>out_report_fid = fopen(out_report_fname, 'w');if out_report_fid < 0 error(['File ' ,out_report_fname ,' could not open']);end threats_found = 0;

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File Handling – Example:Main Function Design

function threats_found = searchEmailsDirForThreats(in_emails_dir, out_report_fname % <going over the files>for i=1:length(file_names) email_fname = file_names{i};

if (~isdir(email_fname)) threats_found = threats_found + ..

searchEmailForThreats(out_report_fid, … [in_emails_dir '/' email_fname]); end end% <closing report output file>fclose(out_report_fid);

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File Handling – Example:Looking for Threats In an Email

function threats_found = searchEmailForThreats(out_report_fid,email_fname)

% <opening email input file>%<going over the file line by line>while feof(in_fid) == 0 % <read line> if % <is found threat>

%<get the threatened city> % <adding to the report> endend%<closing input file>

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File Handling – Example:Opening File For Read

function threats_found = searchEmailForThreats(out_report_fid,email_fname)

% <opening email input file>in_fid = fopen(email_fname, 'rt');if in_fid < 0 error(['File ' , email_fname ,' was not found.']);end threats_found = 0; l_cnt = 0;

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File Handling – Example:Reading a File Line by Line

function threats_found = searchEmailForThreats(out_report_fid,email_fname)

% <opening email input file>%<going over the file line by line>while feof(in_fid) == 0 % <read line > line = fgetl(in_fid); if ~ischar(line) break; end l_cnt = l_cnt+1; line = strtrim(line); if % <is found threat>

… endend

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File Handling – Example:Using Regular expression to find and retrieve pattern occurences

while feof(in_fid) == 0 % <read line> % <is found threat> if (~isempty(regexp(line,'.*bomb.*'))) city = regexp(line, '.*bomb\w*\s([\w-]+).*', 'tokens'); % <adding to the report> fprintf(out_report_fid,'File: %s, Line number:%d, Threat on %s - %s\n', ... email_fname , l_cnt, city{1}{1},line); threats_found = threats_found + 1; end

end

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File Handling – Example:Looking For Threats in an Email

function threats_found = searchEmailForThreats(out_report_fid,email_fname)

% <opening email input file>%<going over the file line by line>while feof(in_fid) == 0 % <read line> if % <is found threat>

%<get the threatened city> % <adding to the report> endend%<closing input file>fclose(in_fid);

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High-Level File Handling Functions

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Matlab Has a Collection of High Level Write / Read Functions

Matlab has a collection of high level read and write functions

These functions can save the need to write read/ write the file line by line.

Examples: dlmread, dlmwrite textread, textscan xlsread importdata

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High-level File Reading Function Example- textread

Reading an entire text file in one line: lines=textread(filename,format,parameters) Example: When reading a file containing a single word in every

line: names=textread(‘names.txt’,’%s’);

If there are more words in a line – each word will be read separately

Example 1:

email=textread(‘email.txt’,’%s’); What happens?

email = {'thinking' 'of'' bombing' 'rehovot''thinking‘…}

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High-level File Reading Function Example- textread

Example 2: Reading a text file, line by line Try:

email = textread('email.txt', '%s', 'delimiter','\n‘);

What happens?

email = {'thinking of bombing rehovot''thinking of bombing sderot''thinking of going to the bombamella festival next week’}

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MATLAB functions for High-level file reading

Reading an entire Excel file in one line:

[nums,t]=xlsread(filename,options…) Will create a numerical array nums and a

cell array t. Try:

[n,t]=xlsread('rt_example3.xls') What happens?

Textual cells are set to NaNs in n Numerical cells are set to ‘’ (empty strings) in t

Note: can read each sheet (read the HELP)

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MATLAB functions for High-level file reading

Reading an entire Excel/tab delimited text file /other preformatted files:

A=importdata(filename,options…) Will create a structure A, which contains:

A.data - numerical array A.textdata - a cell array.

Try: A=importdata('rt_example3.xls') What happens?

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Summery – File Handeling

Matlab has diverse and powerful functions for text processing

Before you start coding using low levels I/O function – Check if one of the high level functions solves it.

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Final example:Looking for p53 TFBS

(Transcription Factor Binding Sites)in human promoters

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Looking for p53 TFBS in human promoters

A TF can recognize a variable site Some positions are fixed Some are optional, e.g. A/T are

acceptable, but not G/C. Consensus sequence: the pattern

representing all possible recognized sites.

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Looking for p53 TFBS in human promoters

Let’s define a consensus for p53 half-site:1. Pos #1: G/A/T2. Pos #2: G/A3. Pos #3: A/G/C4. Pos #4: C5. Pos #5: A/T6. Pos #6: A/T7. Pos #7: G8. Pos #8: N9. Pos #9: T/C/G10. Pos #10: T/C

Variable space0-13

Half-site Half-site

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Looking for p53 TFBS in human promoters

How do we even start???1. Read the promoter file into a cell array.2. Go through the promoters:

Look for the p53 consensus (need to define it – regular expression) When we find it store the data on the hit

3. Open a result file4. Go through all the hits you found

Print them into the results file

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Looking for p53 TFBS in human promoters

1. Reading the promoter file:

The file name: masked_promoters.some.txtThe file format: FASTA>gene1 header lineSequence…Sequence…> gene2 header lineSequence…Sequence…

>GENE=ENSG00000001036 Transcript=1 LLid=2519 orgDBsym=FUCA2 other details… CCATGTTCTAAACGACTTCATAGATTTATTTCTTTCAGTCAT…

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Looking for p53 TFBS in human promoters

1. Reading the promoter file:promoters={};ensID={};symb={};

fid=fopen('masked_promoters.all.txt');while feof(fid)==0 tline = fgetl(fid); >process the data> endfclose(fid);

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Looking for p53 TFBS in human promoters

1. Reading the promoter file:while 1 >from previous slide…> if(tline(1)=='>') %it is a header tmp=regexp(tline,…

'.*GENE=(\w+)\s.*orgDBsym=(\w+)',… 'tokens');

ensID{end+1}=tmp{1}{1}; symb{end+1}=tmp{1}{2}; else %it is a promoter promoters{end+1}=tline; endend

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Looking for p53 TFBS in human promoters

2. Go through the promoters:

hit_seq={};hit_gene=[];hit_pos=[];p53_consensus = ...'[GAT][GA][AGC]C[AT][AT]G.[TCG][TC].{0,13}[GAT][GA]

[AGC]C[AT][AT]G.[TCG][TC]';

for i=1:length(promoters) [m s e] = regexp(promoters{i}, p53_consensus, 'match', …

'start', 'end');%let’s ignore that DNA is double stranded…

if(~isempty(m)) hit_seq(end+1:end+length(m))=m; hit_gene(end+1:end+length(m))=repmat(i,1,length(m)); hit_pos(end+1:end+length(m))=s; endend

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Looking for p53 TFBS in human promoters

3&4. Open a result file, print all the hits

fid=fopen('p53_TFBS.txt','w');%printing a header linefprintf(fid,'gene ID\tgene name\tsite\tpos\n');for i=1:length(hit_gene) fprintf(fid,'%s\t%s\t%s\t%d\n',

ensID{hit_gene(i)},... symb{hit_gene(i)},...

hit_seq{i},... hit_pos(i));

endfclose(fid);