Realizar o ping em uma url deteminada e anotar o IP, tempo médio e máximo.
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AZURE_STORAGE_CONNECTION_STRING=DefaultEndpointsProtocol=http;AccountName=devstoreaccount1;AccountKey=Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==;BlobEndpoint=http://host.docker.internal:10000/devstoreaccount1;QueueEndpoint=http://host.docker.internal:10001/devstoreaccount1; | |
AZURE_CONTAINER=ui-container | |
SFTP_HOST=host.docker.internal | |
SFTP_USER=foo | |
SFTP_PASS=pass | |
SFTP_FOLDER=/upload | |
POSTGRES_HOST=host.docker.internal | |
POSTGRES_PORT=5433 |
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@echo off | |
for /F "usebackq tokens=1,2 delims==" %%i in (`wmic os get LocalDateTime /VALUE 2^>NUL`) do if '.%%i.'=='.LocalDateTime.' set ldt=%%j | |
set actual_date=%ldt:~0,4%%ldt:~4,2%%ldt:~6,2% | |
echo Local date is %actual_date% | |
SET MY_PROGRAM=E:/TempoSilencio/silence_extractor.exe | |
SET MY_BASE_PATH=I:\SPEECHMINER\PROCESSADO | |
SET MY_COMMAND=%MY_PROGRAM% "%MY_BASE_PATH%" "%actual_date%" "%actual_date%" |
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library(foreach) | |
library(doParallel) | |
library(DBI) | |
#Creating the cluster | |
cl <- makeCluster(detectCores() -1) | |
# Defining packages and variables for cluster | |
clusterEvalQ(cl, { | |
library(odbc) |
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library(keras) | |
texts <- c("Today I got to the movie", | |
"Yesterday we got to the movie", | |
"Today was cold at the movie", | |
"Somethign happened yesterday") | |
tok <- keras::text_tokenizer(num_words = 5) | |
keras::fit_text_tokenizer(tok, texts) |
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FROM rocker/tidyverse | |
RUN echo "deb http://ftp.debian.org/debian sid main" | tee -a /etc/apt/sources.list.d/sid.list | |
RUN apt-get update -qq && apt-get -y --no-install-recommends install \ | |
apt-transport-https \ | |
libssl-dev \ | |
libsasl2-dev \ | |
openssl \ | |
curl \ |
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require(rvest) | |
#website | |
url <- ("http://www.anbima.com.br/est_termo/Curva_Zero.asp") | |
pgsession <- html_session(url) | |
pgform <-html_form(pgsession)[[1]] | |
fake_submit <- list("name" = NULL, | |
"type" = "submit", | |
"value" = "Submit", |
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library(tidyverse) | |
files <- list.files(path = "./data/", pattern = "*.csv") | |
df <- files %>% | |
map(function(x) { | |
read.csv(paste0("./data/", x)) | |
}) %>% | |
reduce(rbind) | |
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library(ggplot2) | |
library(tidyverse) | |
df <- mtcars %>% | |
gather(-mpg, key="var", value = "value") | |
ggplot(df, aes(x = value, y = mpg)) + | |
geom_point(alpha = 0.3) + | |
geom_smooth(method = "lm") + | |
facet_wrap(~var, scales = "free") + |
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df <- data.frame( | |
id = c(1,1,2,2,2), | |
columnFields = c("column 1", "column 2", "column 1", "column 2", "column 3"), | |
valueFields = c(1:5)) | |
tidyr::spread(df, columnFields, valueFields) |
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