Transcript HTLTC-Lecture-4 - How to Learn to Code

File Input and Output
July 2nd, 2015
Inputs and Outputs
•
Inputs
•
Outputs
•
Keyboard
•
Graphs
•
Mouse
•
Images
•
storage(hard drive)
•
Videos(image stacks)
•
Networks
•
Text files
•
Statistical results
Keyboard Input
•
•
The simplest input is reading in lines from the
user
print (“Please enter a number: “)
x = scan()
•
Print (“Your number is “ + x)
Inputs and Outputs
•
Entering data on your own is time consuming
•
Large amounts of Data already exist in files
•
MATLAB, R, and Python all provide the ability
to read data from files (as well as write data to
files)
The basics of File Input and Output
File Input and Output are the bread and butter
of data manipulation and generation
File
Code
New
File
Part 1: File Types
Text Files
•
Images
•
•
Excel Files
•
FASTA files
•
Other
Text Files
•
•
A lot of data comes in the form of of
spreadsheets in text files (.txt or .csv extension)
Generally the easiest to manipulate
Image files
•
•
Images are stored as spreadsheets, where
each pixel is represented by an X and Y coordinate and a range of values for the
intensity of the pixel
These values depend on what type of file the
image is
Binary and Grayscale
•
Binary Image

•
Represented as either Boolean (TRUE, FALSE)
or numerical (0,1)
Grayscale Image

Represented with a range of numerical values
(usually between 0 and 255)
Binary and Grayscale
Grayscale
Binary
Color images
•
•
Color images can be thought of as a stack of 3
images (red, blue, green)
The intensity of each color is represented as a
number between 0-255
Color images
Common I/O themes
•
•
•
File type – The type of file the data will be saved
as.
Data type – The type (or mode) of the data
(integers, strings, characters)
Separator: What separates the data from each
other; in text files, this is often a ,
Reading a file
•
•
Most programming languages have functions
that “read” files.
You generally have to specify the type of file
being read, or the computer will not know how
to interpret what it's looking at
Reading a File
•
•
•
Reading data into a program can often times be
difficult and frustrating
Can sometimes take more time than the rest of
the coding!
It's important to understand the subtleties and
nuances that is specific to each language
“Data Preparation”
•
•
When working with any type of spread-sheet
like files (e.g., excel): easier to export the data as
a text file
Image files can be converted from one type to
another with relative ease. How you want to
manipulate an image will determine in what
format you want to save the image as.
Character Encoding
•
•
•
Used to represent all of the characters in a form
computers can understand it
There are different types of character coding,
the most common being Unicode
Modern operating systems are likely to use UTF8 or Unicode files
Character Encoding
•
•
If character encoding differs between your data
and what a computer uses to view the data,
your text files can look like gibberish.
Character Encoding can be changed using
programming language specific functions
Reading a File
•
R
•
Python
•
Matlab

read.table()

.read()

importdata()

read.csv

open()

fscanf()

scan()

readline()

fopen()
File Output
•
•
Generating a file is an easier process than
reading in a file
Stored data can be written to a file of your
choosing, whether it's an image, text file, etc.
•
write(x, file = “C:\Program Files\R\data.txt”)
•
write(x, file = “data.txt”, sep = “,”)
The Working Directory
•
•
The working directory can be thought of as the
area that the programming language currently
points to
When reading or writing files, if a full path name
is not given, the program will automatically look
in the working directory to read or write a file
Part 2: Pipelines
•
•
A way of structuring code that makes it simple
to work with
Simply, a pipeline is a chain of processes (such
as functions) arranged so that the output of
one process is the input to the next process
Part 2: Pipelines
Sequence
Data
Find Start
Codon
Convert to
Amino Acids
Save Amino
Acid File
Functions
findstart <- function(fullseq, codons = c("atg", "taa", "tag", "tga")){

startposition <- sapply(codons, function(x){start(matchPattern(x,
fullseq))})
return(substr(sequence, startposition, length(fullseq))
}

AminoSequence ← function(sequence){
amino ← *lots of code and a library of amino acid sequences*
return(amino)
}
Functions

simplesequence ← scan(“simplesequence.txt”)

sequenceTwo ← findstart(Thesequence)

AminoSeq ←AminoSequence(sequenceTwo)

write.file(AminoSeq, file = “Amino.txt”)
Pipelines


Structuring your code into functions can help
ease the readability of your code and allow
you to reuse functions for similar processes
Pipelines are excellent for automatic
processing of a large amount of data sets