sillasgonzaga

library(httr)
library(magrittr)
library(xml2)
library(rvest)

url_raw <- "http://dados.cvm.gov.br/dados/FI/DOC/INF_DIARIO/DADOS/"
output_file <- "fundos/api/informe_diario.csv"

csv_links <- httr::GET(url_raw) %>% 
  httr::content("text") %>% 

sillasgonzaga

import panel as pn  # GUI
pn.extension()

def get_completion(prompt, model="gpt-3.5-turbo"):
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0, # this is the degree of randomness of the model's output
    )

sillasgonzaga

# filtrar ano mais recente
df_idhm = df_idhm[df_idhm['ANO'] == np.max(df_idhm['ANO'])]
# selecionar colunas
df_idhm = df_idhm[['Codmun7', 'IDHM']]
# renomear colunas
df_idhm.columns = ['cod_municipio', 'idhm']
df_idhm.head()

sillasgonzaga

# baixar dados
library(tidyverse)
library(foreign)
library(readxl)
library(janitor)
library(fuzzyjoin)

# download.file("http://www.metro.sp.gov.br/pesquisa-od/arquivos/Pesquisa-Origem-Destino-2017-Banco-Dados.zip",
#               destfile = "od.zip")
# 

sillasgonzaga

cdb <- function(capital, data_aplicacao, data_resgate, di, pct_cdi){
  library(dplyr)
  library(bizdays)  
  
  dias_corridos <- as.numeric(difftime(data_resgate, data_aplicacao, units = "days"))
  
  # computar biz days entre datas
  bizdays::create.calendar("Brazil/ANBIMA",
                           bizdays::holidaysANBIMA,
                           weekdays=c("saturday", "sunday"))

sillasgonzaga

library(tabulizer)
library(tidyverse)

arquivo <- "C:/Users/sillas.gonzaga/Downloads/gsa.pdf"

tab <- tabulizer::extract_tables(arquivo, encoding = "UTF-8", method = "lattice")

map(tab,dim)

# extrair tabelas com 8 colunas

sillasgonzaga

plot_rf_tree <- function(final_model, tree_num, shorten_label = TRUE) {
  
  # source: https://shiring.github.io/machine_learning/2017/03/16/rf_plot_ggraph
  
  # get tree by index
  tree <- randomForest::getTree(final_model, 
                                k = tree_num, 
                                labelVar = TRUE) %>%
    tibble::rownames_to_column() %>%
    # make leaf split points to NA, so the 0s won't get plotted

sillasgonzaga

def correl_df(dframe):
    correl_matrix = dframe.corr()
    # Select upper triangle of correlation matrix
    upper = correl_matrix.where(np.triu(np.ones(correl_matrix.shape), k=1).astype(np.bool)).head()
    upper = upper.unstack().reset_index()
    # rename columns
    upper.columns = ['var1', 'var2', 'correl']
    # drop na
    upper = upper.dropna()
    upper['correl_abs'] = np.abs(upper['correl'])

sillasgonzaga

library(spotifyr)
library(tidyverse)


Sys.setenv(SPOTIFY_CLIENT_ID = '')
Sys.setenv(SPOTIFY_CLIENT_SECRET = '')

xml <- read_html("https://developer.spotify.com/documentation/general/guides/scopes/")
scopes <- xml %>% html_nodes("code") %>% html_text() %>% unique()
auth <- get_spotify_authorization_code(scope = scopes)

sillasgonzaga

import time
import sys
import logging
import pychromecast
from scapy.all import *

mac_address = '00:00:00:00:00:00'
def arp_display(pkt):
    if pkt[ARP].op == 1:
        if pkt[ARP].hwsrc == mac_address: