Elias Soares eliassoares

## test_main.py
from main import calculate_simple_interest_investment, calculate_compound_interest_investment


def test_calculate_compound_interest_investment_when_principal_amount_is_zero():
    principal_amount = 0.0
    rate = 0.07
    time = 2
    expected_interest = 0

    interest = calculate_compound_interest_investment(

## main.py
def calculate_compound_interest_investment(
        principal_amount: float, rate: float, time: int
) -> float:
    """
    Returns the compound interest an accrued amount that includes
    principal plus interest.

    :param principal_amount: float
    :param rate: float
    :param time: int

## postgre_audit_trigger_aws_rds.sql
-- An audit history is important on most tables. Provide an audit trigger that logs to
-- a dedicated audit table for the major relations.
--
-- This file should be generic and not depend on application roles or structures,
-- as it's being listed here:
--
--    https://wiki.postgresql.org/wiki/Audit_trigger_91plus
--
-- This trigger was originally based on
--   http://wiki.postgresql.org/wiki/Audit_trigger

## parse_queries.py
from re import findall, M, I
import os.path


def parse_queries():
    """
        Irá parsear um arquivo .sql com as queries de SELECT necessárias
        e irá retornar um dicionário contendo o nome da query no arquivo, como chave
        e a query especificamente como valor.
    """

## json_to_map.go
package main

import (
	"encoding/json"
	"fmt"
)

func dumpMap(space string, m map[string]interface{}) {
	for k, v := range m {
		if mv, ok := v.(map[string]interface{}); ok {

## listAllDatabases.py
conn = psycopg2.connect("user='eliassoares' host='127.0.0.1' password='mysecretpassword'")

cur = conn.cursor()
cur.execute('SELECT datname FROM pg_database WHERE datistemplate = false;')
rows = cur.fetchall()
print(rows)

## neo4j_readme
1° Configura os nós:
	-Configurando o nó para busca:
		CREATE INDEX ON :Person(personID);
		CREATE INDEX ON :Person(name);
	-Configurando a chave:
		CREATE CONSTRAINT ON (p:Person) ASSERT p.personID IS UNIQUE;
	#Um INDEX não pode ser usado como chave, caso o tenha feito, usa-se:
			DROP INDEX ON :Person(name);
		para retirar o index.

## mysql-docker.sh
# Backup
docker exec CONTAINER /usr/bin/mysqldump -u root --password=root DATABASE > backup.sql

# Restore
cat backup.sql | docker exec -i CONTAINER /usr/bin/mysql -u root --password=root DATABASE


## gist:604fc73005584b6dd22b59091e03f33c
# When you're sure of the format, it's much quicker to explicitly convert your dates than use `parse_dates`
# Makes sense; was just surprised by the time difference.
import pandas as pd
from datetime import datetime
to_datetime = lambda d: datetime.strptime(d, '%m/%d/%Y %H:%M')

%time trips = pd.read_csv('data/divvy/Divvy_Trips_2013.csv', parse_dates=['starttime', 'stoptime'])
# CPU times: user 1min 29s, sys: 331 ms, total: 1min 29s
# Wall time: 1min 30s

## bitbot.py
#!/usr/bin/python
# -*- coding: utf-8 -*-

#Version: 2.0

import urllib
import json

#Coloque as informações sobre suas comprar aqui:
compras = [
	from main import calculate_simple_interest_investment, calculate_compound_interest_investment


	def test_calculate_compound_interest_investment_when_principal_amount_is_zero():
	principal_amount = 0.0
	rate = 0.07
	time = 2
	expected_interest = 0

	interest = calculate_compound_interest_investment(
	def calculate_compound_interest_investment(
	principal_amount: float, rate: float, time: int
	) -> float:
	"""
	Returns the compound interest an accrued amount that includes
	principal plus interest.

	:param principal_amount: float
	:param rate: float
	:param time: int
	-- An audit history is important on most tables. Provide an audit trigger that logs to
	-- a dedicated audit table for the major relations.
	--
	-- This file should be generic and not depend on application roles or structures,
	-- as it's being listed here:
	--
	-- https://wiki.postgresql.org/wiki/Audit_trigger_91plus
	--
	-- This trigger was originally based on
	-- http://wiki.postgresql.org/wiki/Audit_trigger
	from re import findall, M, I
	import os.path


	def parse_queries():
	"""
	Irá parsear um arquivo .sql com as queries de SELECT necessárias
	e irá retornar um dicionário contendo o nome da query no arquivo, como chave
	e a query especificamente como valor.
	"""
	package main

	import (
	"encoding/json"
	"fmt"
	)

	func dumpMap(space string, m map[string]interface{}) {
	for k, v := range m {
	if mv, ok := v.(map[string]interface{}); ok {
	conn = psycopg2.connect("user='eliassoares' host='127.0.0.1' password='mysecretpassword'")

	cur = conn.cursor()
	cur.execute('SELECT datname FROM pg_database WHERE datistemplate = false;')
	rows = cur.fetchall()
	print(rows)
	1° Configura os nós:
	-Configurando o nó para busca:
	CREATE INDEX ON :Person(personID);
	CREATE INDEX ON :Person(name);
	-Configurando a chave:
	CREATE CONSTRAINT ON (p:Person) ASSERT p.personID IS UNIQUE;
	#Um INDEX não pode ser usado como chave, caso o tenha feito, usa-se:
	DROP INDEX ON :Person(name);
	para retirar o index.
	# Backup
	docker exec CONTAINER /usr/bin/mysqldump -u root --password=root DATABASE > backup.sql

	# Restore
	cat backup.sql \| docker exec -i CONTAINER /usr/bin/mysql -u root --password=root DATABASE
	# When you're sure of the format, it's much quicker to explicitly convert your dates than use `parse_dates`
	# Makes sense; was just surprised by the time difference.
	import pandas as pd
	from datetime import datetime
	to_datetime = lambda d: datetime.strptime(d, '%m/%d/%Y %H:%M')

	%time trips = pd.read_csv('data/divvy/Divvy_Trips_2013.csv', parse_dates=['starttime', 'stoptime'])
	# CPU times: user 1min 29s, sys: 331 ms, total: 1min 29s
	# Wall time: 1min 30s
	#!/usr/bin/python
	# -- coding: utf-8 --

	#Version: 2.0

	import urllib
	import json

	#Coloque as informações sobre suas comprar aqui:
	compras = [