Using Computer Code to Decipher Genetic Code - Part 2

Using Computer Code to Decipher Genetic Code - Part 2 (Bioinformatics 101)
This is a 2 Part series (Bioinformatics 101). I will provide a Non-Technical Introduction to the Exciting field of Bioinformatics so that you can get started in applying Data Science / Machine Learning to explore and model interesting data sets in biology, medicine and the life sciences. This is Part 2 and make sure to watch Part 1

#computer code #decipher genetic code

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Using Computer Code to Decipher Genetic Code - Part 2

Using Computer Code to Decipher Genetic Code - Part 2

Using Computer Code to Decipher Genetic Code - Part 2 (Bioinformatics 101)
This is a 2 Part series (Bioinformatics 101). I will provide a Non-Technical Introduction to the Exciting field of Bioinformatics so that you can get started in applying Data Science / Machine Learning to explore and model interesting data sets in biology, medicine and the life sciences. This is Part 2 and make sure to watch Part 1

#computer code #decipher genetic code

Using Computer Code to Decipher Genetic Code - Part 1

Using Computer Code to Decipher Genetic Code - Part 1 (Bioinformatics 101)
This is a 2 Part series (Bioinformatics 101). I will provide a Non-Technical Introduction to the Exciting field of Bioinformatics so that you can get started in applying Data Science / Machine Learning to explore and model interesting data sets in biology, medicine and the life sciences. This is Part 1 and please also stay tuned for Part 2 which is coming up really soon.

#computer code #decipher genetic code

Houston  Sipes

Houston Sipes

1604088000

How to Find the Stinky Parts of Your Code (Part II)

There are more code smells. Let’s keep changing the aromas. We see several symptoms and situations that make us doubt the quality of our development. Let’s look at some possible solutions.

Most of these smells are just hints of something that might be wrong. They are not rigid rules.

This is part II. Part I can be found here.

Code Smell 06 - Too Clever Programmer

The code is difficult to read, there are tricky with names without semantics. Sometimes using language’s accidental complexity.

_Image Source: NeONBRAND on _Unsplash

Problems

  • Readability
  • Maintainability
  • Code Quality
  • Premature Optimization

Solutions

  1. Refactor the code
  2. Use better names

Examples

  • Optimized loops

Exceptions

  • Optimized code for low-level operations.

Sample Code

Wrong

function primeFactors(n){
	  var f = [],  i = 0, d = 2;  

	  for (i = 0; n >= 2; ) {
	     if(n % d == 0){
	       f[i++]=(d); 
	       n /= d;
	    }
	    else{
	      d++;
	    }     
	  }
	  return f;
	}

Right

function primeFactors(numberToFactor){
	  var factors = [], 
	      divisor = 2,
	      remainder = numberToFactor;

	  while(remainder>=2){
	    if(remainder % divisor === 0){
	       factors.push(divisor); 
	       remainder = remainder/ divisor;
	    }
	    else{
	      divisor++;
	    }     
	  }
	  return factors;
	}

Detection

Automatic detection is possible in some languages. Watch some warnings related to complexity, bad names, post increment variables, etc.

#pixel-face #code-smells #clean-code #stinky-code-parts #refactor-legacy-code #refactoring #stinky-code #common-code-smells

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

Wiley  Mayer

Wiley Mayer

1603890000

How to Find the Stinky Parts of Your Code (Part I)

of something that might be wrong. They are not rigid rules.

Code Smell 01 — Anemic Models

Your objects are a bunch of public attributes without behavior.

Photo by Stacey Vandergriff on Unsplash

Protocol is empty (with setters/getters).

If we ask a domain expert to describe an entity he/she would hardly tell it is ‘a bunch of attributes’.

Problems

  • No Encapsulation.
  • No mapping to real world entities.
  • Duplicate Code
  • Coupling

Solutions

    1. Find Responsibilities.
    1. Protect your attributes.
    1. Hide implementations.
    1. Delegate

Examples

  • DTOs

Sample Code

	<?

	Class Window{
	  public height;
	  public width;

	  function getHeight(){
	    return $this->height;
	  }

	  function setHeight($height){
	    $this->height = $height;
	  }

	  function getWidth(){
	    return $this->width;
	  }

	  function setWidth($width){
	    $this->width = $width;
	  }

	}

Wrong

<?

	final Class Window{ 

	  function area(){
	    //...
	  }

	  function open(){
	    //..
	  }

	  function isOpen(){
	    //..
	  }

	}

Right

#code-smells #clean-code #refactoring #refactor-legacy-code #stinky-code #stinky-code-parts #pixel-face #hackernoon-top-story