Vini2

-- Projection example
SELECT DISTINCT member_id FROM borrow;
SELECT DISTINCT member_id,book_id FROM borrow;

-- Selection example
SELECT * FROM member WHERE date_of_birth='1997-10-21';

-- Rename example
WITH library_member AS (SELECT * FROM member) SELECT * FROM library_member;

Vini2

import tweepy as tw
import pandas as pd
import json

# App Auth
consumer_key = 'XXX'
consumer_secret = 'XXX'
access_key = 'XXX'
access_secret = 'XXX'

Vini2

class Individual {
    int fitness = 0;
    int geneLength = 5;
    int[] genes = new int[5];

    public Individual() {
        Random rn = new Random();
        //Set genes randomly for each individual
        for (int i = 0; i < geneLength; i++) {
            genes[i] = rn.nextInt() % 2;

Vini2

# Read the file and get the DNA string
file = open('sample_dna.txt', 'r')
dna = file.read()

# Print the original DNA string
print "DNA String: ", dna

# Print the count of each letter
print "Count of A: ", dna.count("A")
print "Count of C: ", dna.count("C")

Vini2

# Read the file and get the DNA string
file = open("sample_dna.txt", "r")
dna = file.read()

# Print the original DNA string
print "DNA String: ", dna

# Create dictionary of complementing nucleobase pairs
comp_pairs = {"A" : "T", "T" : "A", "G" : "C", "C" : "G"}

Vini2

# Read the file and get the DNA string
file = open('sample_dna.txt', 'r')
dna = file.read()
print "DNA: ", dna

rna = ""

# Generate the RNA string
for i in dna:
    # Replace all occurrences of T with U

Vini2

# Read the file and get the RNA string
file = open('sample_rna.txt', 'r')
rna = file.read()

print "RNA String: ", rna

# RNA codon table
rna_codon = {"UUU" : "F", "CUU" : "L", "AUU" : "I", "GUU" : "V",
           "UUC" : "F", "CUC" : "L", "AUC" : "I", "GUC" : "V",
           "UUA" : "L", "CUA" : "L", "AUA" : "I", "GUA" : "V",

Vini2

import argparse
import scipy.special
import csv

parser = argparse.ArgumentParser(description="""Evaluate clustering results. This scripts will return the 
                                                precision, recall, F1-score and ARI of the provided clustering result""")

parser.add_argument("--clustered", 
                    required=True, 
                    type=str,

Vini2

Vini2

# Read the file and get the DNA string
file = open('sample_dna.txt', 'r')
dna = file.read()

print "DNA Sequence: ", dna

# DNA codon table
protein = {"TTT" : "F", "CTT" : "L", "ATT" : "I", "GTT" : "V",
           "TTC" : "F", "CTC" : "L", "ATC" : "I", "GTC" : "V",
           "TTA" : "L", "CTA" : "L", "ATA" : "I", "GTA" : "V",