Language notes
This page collects information on Visual Studio Code, C, C++, Fortran, Java, Perl, python and R.
Ada
From hello.adb,
with Ada.Text_IO;
procedure Hello is
begin
Ada.Text_IO.Put_Line ("Hello, World!");
end Hello;
We run
gnatmake hello
hello
BASIC
It is still possible to reflect the old language from here,
https://sourceforge.net/projects/devos-studio/.
C
There have been multiple instances for suggesting migration fromtempnam to mkstemp (/usr/include/stdlib.h under Bash but not Windows) and mktemp
-- the following code illustrates its use under both Bash and Windows, Nevertheless these are not standard routines, one still needs to add
char *mktemp(char *) for instance.
#include <stdio.h>
#include <fcntl.h> /* for open flags */
#include <limits.h> /* for PATH_MAX */
int main(void)
{
static char template[] = "/tmp/myfileXXXXXX";
char fname[PATH_MAX];
static char mesg[] =
"Here's lookin' at you, kid!\n"; /* beats "hello, world" */
int fd;
strcpy(fname, template);
mktemp(fname);
/* RACE CONDITION WINDOW OPENS */
printf("Filename is %s\n", fname);
/* RACE CONDITION WINDOW LASTS TO HERE */
fd = open(fname, O_CREAT|O_RDWR|O_TRUNC, 0600);
write(fd, mesg, strlen(mesg));
close(fd);
/* unlink(fname); */
return 0;
}
A script for testing UTF-8 support by PCRE,
#include <pcre.h>
#include <stdio.h>
#include <stdlib.h>
int main() {
int supports_utf8;
if (pcre_config (PCRE_CONFIG_UTF8, &supports_utf8)) {
fprintf(stderr, "pcre_config() failed\n");
exit(EXIT_FAILURE);
}
printf("UTF-8 is supported: %s\n", supports_utf8 ? "yes" : "no");
exit(EXIT_SUCCESS);
}
// gcc $(pkg-config --cflags --libs libpcre) pcreutf.c
// ./a.out
// pcretest -C
The following is Timsort implementation,
#include <stdio.h>
#define MIN_RUN 32
// 插入排序算法
void insertionSort(int arr[], int left, int right) {
for (int i = left + 1; i <= right; i++) {
int key = arr[i];
int j = i - 1;
while (j >= left && arr[j] > key) {
arr[j + 1] = arr[j];
j--;
}
arr[j + 1] = key;
}
}
// 归并函数
void merge(int arr[], int left, int mid, int right) {
int len1 = mid - left + 1;
int len2 = right - mid;
int L[len1], R[len2];
for (int i = 0; i < len1; i++)
L[i] = arr[left + i];
for (int j = 0; j < len2; j++)
R[j] = arr[mid + 1 + j];
int i = 0, j = 0, k = left;
while (i < len1 && j < len2) {
if (L[i] <= R[j])
arr[k++] = L[i++];
else
arr[k++] = R[j++];
}
while (i < len1)
arr[k++] = L[i++];
while (j < len2)
arr[k++] = R[j++];
}
// Timsort 算法
void timSort(int arr[], int n) {
for (int i = 0; i < n; i += MIN_RUN)
insertionSort(arr, i, (i + MIN_RUN - 1) < n ? (i + MIN_RUN - 1)
: (n - 1));
for (int size = MIN_RUN; size < n; size *= 2) {
for (int left = 0; left < n; left += 2 * size) {
int mid = left + size - 1;
int right = (left + 2 * size - 1) < (n - 1) ?
(left + 2 * size - 1) : (n - 1);
merge(arr, left, mid, right);
}
}
}
int main() {
int arr[] = {12, 11, 13, 5, 6, 7};
int n = sizeof(arr) / sizeof(arr[0]);
printf("Original array: ");
for (int i = 0; i < n; i++)
printf("%d ", arr[i]);
timSort(arr, n);
printf("\nSorted array: ");
for (int i = 0; i < n; i++)
printf("%d ", arr[i]);
return 0;
}
y gcc timsort.c -o timsort and timsort to get
Original array: 12 11 13 5 6 7
Sorted array: 5 6 7 11 12 13 14:40
C++
The use of Google Test is noted here,
Web: https://github.com/google/googletest.
wget -qO- https://github.com/google/googletest/archive/refs/tags/release-1.11.0.tar.gz | \
tar xvfz -
cd googletest-release-1.11.0
mkdir build && cd build
build ..
make
# amending set(CMAKE_INSTALL_PREFIX "/rds/user/jhz22/hpc-work") in `cmake_install.cmake`
make install
Now it is possible to compile R/glmnet 4.1-3, i.e., find_package(GTest 1.11 CONFIG REQUIRED) of src/glmnetpp/CMakeLists.txt.
Fortran
Information on modernising Fortran could be very useful in foreign language calls (e.g., R),
http://fortranwiki.org/fortran/show/Modernizing+Old+Fortran.
Debugging Fortran code
- gdb https://undo.io/resources/debugging-fortran-code-gdb/
- valgrind
program segfault1
implicit none
real, dimension(10) :: a
integer :: i
a = 0.
do i = 1, 12
a(i) = i
print*,a(i)
end do
end program segfault1
! gfortran -g -Wall -Wextra -Wimplicit-interface -fPIC -fmax-errors=1 -fcheck=all -fbacktrace segfault1.f90 -o segfault1
! valgrind --leak-check=full --dsymutil=yes --track-origins=yes ./segfault1
! MacOS --dsymutil=yes:
! valgrind --leak-check=full --dsymutil=yes --track-origins=yes ./segfault1
Java
The IDE of choice is NetBeans (e.g., DEPICT and JAM); however 8.1 from apt install under Ubuntu 18.04 crashes
so it is suggested to download directly from https://netbeans.org/downloads/. To enable JDK it is helpful to specify --javahome option.
sudo ./netbeans-8.2-linux.sh --javahome /usr/lib/jvm/java-8-oracale
or start with netbeans --javahome /usr/lib/jvm/java-8-oracle (more convenient to set alias netbeans='netbeans --javahome /usr/lib/jvm/java-8-oracle' at .bashrc).
NetBeans 9.0 is currently available from https://netbeans.apache.org/download/nb90/; the .zip file can be downloaded and unpacked for use.
For software such as cutadapt cython is required,
sudo apt install cython
JavaScript
A current JavaScript/TypeScript interpreter is deno, https://anaconda.org/conda-forge/deno/files.
wget https://anaconda.org/conda-forge/deno/1.40.2/download/linux-64/deno-1.40.2-hfc7925d_0.conda -O deno.conda
unzip deno.conda
tar --use-compress-program=unzstd -xvf pkg-deno-1.40.2-hfc7925d_0.tar.zst
deno --version
giving
deno 1.40.2 (release, x86_64-unknown-linux-gnu)
v8 12.1.285.6
typescript 5.3.3
The mermaid diagram is illustrated with mermaid.html using code available from here, https://cdnjs.cloudflare.com/ajax/libs/mermaid/8.0.0/mermaid.min.js.
The call can be embedded in markdown document,
<script type="module">
import mermaid from 'https://cdn.jsdelivr.net/npm/mermaid@10/dist/mermaid.esm.min.mjs';
mermaid.initialize({ startOnLoad: true });
</script>
The hello world example with plotly.js is https://plot.ly/javascript/getting-started/#hello-world-example and the 3D diagram is with 3d-scatter.html based on https://plot.ly/javascript/3d-scatter-plots/.
The base64 encode/decode is with https://www.base64encode.org/ & https://www.base64decode.org/.
Perl
sudo perl -MCPAN -e shell
install DBI
for instance, as used in VEP.
Another notable example is circos, http://circos.ca and its Google group,
wget -qO- http://www.circos.ca/distribution/circos-current.tgz | \
tar xvfz -
cd circos-0.69-9
bin/circos --modules
wget -qO- http://circos.ca/distribution/circos-tutorials-current.tgz | \
tar xvfz -
wget -qO- http://www.circos.ca/distribution/circos-tools-current.tgz | \
tar xvfz -
The following required modules can be installed
Config::General (v2.50 or later)
Font::TTF
GD
List::MoreUtils
Math::Bezier
Math::Round
Math::VecStat
Params::Validate
Readonly
Regexp::Common
Set::IntSpan (v1.16 or later)
Text::Format
and we can enter the example/ directory to run its script. The CircosAPI module requires namespace::autoclean, Moose, JSON::PP and String::Util.
Python
To disable upgrade of pip, add
[global]
disable-pip-version-check = True
option to $HOME/.config/pip/pip.conf
To install a particular version of package, e.g.,
sudo -H pip install pandas==0.20.1
which is required by DEPICT's munge_sumstats.py. Other pip options include uninstall.
The python programs in agotron_detector requires MySQL and can be installed as follows,
sudo apt-get install python-dev libmysqlclient-dev
sudo pip install MySQL-python
It is necessary to use --user option without super-user privilege.
PyStan is available with pip install pystan which uses matplotlib, https://github.com/matplotlib and Tkinter, established with sudo apt install python-tk or sudo apt install python3-tk.
import pystan
schools_code = """
data {
int<lower=0> J; // number of schools
real y[J]; // estimated treatment effects
real<lower=0> sigma[J]; // s.e. of effect estimates
}
parameters {
real mu;
real<lower=0> tau;
real eta[J];
}
transformed parameters {
real theta[J];
for (j in 1:J)
theta[j] = mu + tau * eta[j];
}
model {
eta ~ normal(0, 1);
y ~ normal(theta, sigma);
}
"""
schools_dat = {'J': 8,
'y': [28, 8, -3, 7, -1, 1, 18, 12],
'sigma': [15, 10, 16, 11, 9, 11, 10, 18]}
sm = pystan.StanModel(model_code=schools_code)
fit = sm.sampling(data=schools_dat, iter=1000, chains=4)
import matplotlib.pyplot as plt
def plotGraph():
fig = fit.plot()
# plt.show()
# use the save button or the following command,
# f.savefig("foo.pdf", bbox_inches='tight')
return fig
from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages('foo.pdf')
f = plotGraph()
pp.savefig(f)
pp.close()
To install jupyter-book,
module load python/2.7.10
python -m pip install jupyter-book --user
and we can check for $HOME/.local/lib/python2.7/site-packages and start from /home/jhz22/.local/bin. We can install notebook similarly.
python -m pip install notebook --user
Owing to recent changes, it is more appropriate to use python3
module load python/3.5
export PATH=$PATH:$HOME/.local/bin
export PYTHONPATH=/usr/local/Cluster-Apps/python/3.5.1/lib/python3.5/site-packages:$HOME/.local/lib/python3.5/site-packages
python3 -m pip install jupyter-book --user
To convert from parquet to csv is done as follows,
import pandas as pd
import pyspark
import pyarrow
import sys
import os
fn = sys.argv[1]
print(fn)
df = pd.read_parquet(fn)
outfn = "".join("GTEx_Analysis_v8_EUR_eQTL_all_associations_csv/" + os.path.splitext(os.path.basename(fn))[0] + ".csv")
print(outfn)
df.to_csv(outfn)
R
Information on R and RStudio can be seen from installation section of this, https://jinghuazhao.github.io/Computational-Statistics/INSTALL/.
The use of multi-byte string needs specific handling, e.g.,
# on Bash
iconv myfile -f UTF-8 -t ISO-8859-1 -c
and
# in R
Sys.setlocale("LC_ALL", "C")
See https://stackoverflow.com/questions/4993837/r-invalid-multibyte-string
plotly
It requires a number of software,
sudo dnf install udunits2-devel
sudo dnf install cairo-devel
sudo dnf install gdal gdal-devel
sudo dnf install proj-devel proj-static
sudo dnf install geos geos-devel
to be followed by
install.packages("plotly",depend=TRUE,repos="https://cran.r-project.org")
Calls from R
- Basic examples using OpenMP with R, for C, C++, F77, and Fortran 2003 using Romp, https://github.com/wrathematics/Romp
- RFI: R to Modern Fortran Interface, https://github.com/t-kalinowski/RFI
- Stanford Utility Tools for R packages using Fortran, https://bnaras.github.io/SUtools/articles/SUtools.html
Package examples for Fortran, * https://cran.r-project.org/web/packages/Delaporte/index.html. * https://cran.r-project.org/web/packages/spam/index.html * https://cran.r-project.org/web/packages/spam64/index.html
Documentation * https://www.avrahamadler.com/2018/12/09/the-need-for-speed-part-1-building-an-r-package-with-fortran/ * https://www.avrahamadler.com/2018/12/23/the-need-for-speed-part-2-c-vs-fortran-vs-c/ * https://www.sciencedirect.com/science/article/pii/S2352711018300785?via%3Dihub
R packages
See https://r-pkgs.org/index.html.
shinyapps
Web: https://www.shinyapps.io/, Shiny examples
The hello world version is as follows,
library(shiny)
ui <- fluidPage(
"Hello, world!"
)
server <- function(input, output, session) {
}
shinyApp(ui, server)
Suppose our a directory (called shinyapps here) contains files ui.R and server.R (or combined in app.R).
Go the web site, and register an account with email address. Login from https://www.shinyapps.io/admin/#/login and the following information is available:
Step 1 – Install rsconnect
The rsconnect package can be installed directly from CRAN. To make sure you have the latest version run following code in your R console:
install.packages('rsconnect')
Step 2 – Authorize Account
The rsconnect package must be authorized to your account using a token and secret. To do this, click the copy button below and we'll copy the whole command you need to your clipboard. Just paste it into your console to authorize your account. Once you've entered the command successfully in R, that computer is now authorized to deploy applications to your shinyapps.io account.
rsconnect::setAccountInfo(name='your-account', token='your token', secret='your secret')
In the future, you can manage your tokens from the Tokens page the settings menu.
Step 3 – Deploy
Once the rsconnect package has been configured, you're ready to deploy your first application. If you haven't written any applications yet, you can also checkout the Getting Started Guide for instructions on how to deploy our demo application. Run the following code in your R console.
library(rsconnect)
rsconnect::deployApp('path/to/your/app')
The shiny page is then up as https://your-account.shinyapps.io/shinyapps/.
A more sophisticated Dashboard using the Gapminer dataset is copied here from R-bloggers.
library(shiny)
library(dplyr)
library(purrr)
library(gapminder)
library(highcharter)
ui <- fluidPage(
tags$head(
tags$link(rel = "stylesheet", type = "text/css", href = "styles.css")
),
sidebarLayout(
sidebarPanel(
titlePanel("R Shiny Highcharts"),
selectInput(
inputId = "inContinent",
label = "Continent:",
choices = unique(gapminder$continent),
selected = "Europe"
),
selectInput(
inputId = "inYearMin",
label = "Start year:",
choices = unique(gapminder$year)[1:length(unique(gapminder$year)) - 1],
selected = min(gapminder$year)
),
selectInput(
inputId = "inYearMax",
label = "End year:",
choices = unique(gapminder$year)[2:length(unique(gapminder$year))],
selected = max(gapminder$year)
),
width = 3
),
mainPanel(
tags$h3("Latest stats:"),
tags$div(
tags$div(
tags$p("# Countries:"),
textOutput(outputId = "outNCountries")
) %>% tagAppendAttributes(class = "stat-card"),
tags$div(
tags$p("Median life exp:"),
textOutput(outputId = "outMedLifeExp")
) %>% tagAppendAttributes(class = "stat-card"),
tags$div(
tags$p("Median population:"),
textOutput(outputId = "outMedPop")
) %>% tagAppendAttributes(class = "stat-card"),
tags$div(
tags$p("Median GDP:"),
textOutput(outputId = "outMedGDP")
) %>% tagAppendAttributes(class = "stat-card")
) %>% tagAppendAttributes(class = "stat-card-container"),
tags$div(
tags$h3("Summary stats:"),
tags$div(
tags$div(
highchartOutput(outputId = "chartLifeExpByYear", height = 500)
) %>% tagAppendAttributes(class = "chart-card"),
tags$div(
highchartOutput(outputId = "chartGDPByYear", height = 500)
) %>% tagAppendAttributes(class = "chart-card"),
) %>% tagAppendAttributes(class = "base-charts-container")
) %>% tagAppendAttributes(class = "card-container"),
tags$div(
tags$h3("Drilldown:"),
tags$div(
highchartOutput(outputId = "chartDrilldown", height = 500)
) %>% tagAppendAttributes(class = "chart-card chart-card-full")
) %>% tagAppendAttributes(class = "card-container"),
width = 9
) %>% tagAppendAttributes(class = "main-container")
)
)
server <- function(input, output) {
data_cards <- reactive({
gapminder %>%
filter(
continent == input$inContinent,
year == max(year)
) %>%
summarise(
nCountries = n_distinct(country),
medianLifeExp = median(lifeExp),
medianPopM = median(pop / 1e6),
medianGDP = median(gdpPercap)
)
})
data_charts <- reactive({
gapminder %>%
filter(
continent == input$inContinent,
between(year, as.integer(input$inYearMin), as.integer(input$inYearMax))
) %>%
group_by(year) %>%
summarise(
medianLifeExp = round(median(lifeExp), 1),
medianGDP = round(median(gdpPercap), 2)
)
})
drilldown_chart_base_data <- reactive({
gapminder %>%
filter(
continent == input$inContinent,
year == max(year)
) %>%
group_by(country) %>%
summarise(
pop = round(pop, 1)
) %>%
arrange(desc(pop))
})
drilldown_chart_drilldown_data <- reactive({
gapminder %>%
filter(
continent == input$inContinent,
between(year, as.integer(input$inYearMin), as.integer(input$inYearMax))
) %>%
group_nest(country) %>%
mutate(
id = country,
type = "column",
data = map(data, mutate, name = year, y = pop),
data = map(data, list_parse)
)
})
output$outNCountries <- renderText({
data_cards()$nCountries
})
output$outMedLifeExp <- renderText({
paste(round(data_cards()$medianLifeExp, 1), "years")
})
output$outMedPop <- renderText({
paste0(round(data_cards()$medianPopM, 2), "M")
})
output$outMedGDP <- renderText({
paste0("$", round(data_cards()$medianGDP, 2))
})
output$chartLifeExpByYear <- renderHighchart({
hchart(data_charts(), "column", hcaes(x = year, y = medianLifeExp), color = "#0198f9", name = "Median life expectancy") |>
hc_title(text = "Median life expectancy by year", align = "left") |>
hc_xAxis(title = list(text = "Year")) |>
hc_yAxis(title = list(text = "Life expectancy"))
})
output$chartGDPByYear <- renderHighchart({
hchart(data_charts(), "line", hcaes(x = year, y = medianGDP), color = "#800000", name = "Median GDP") |>
hc_title(text = "Median GDP by year", align = "left") |>
hc_xAxis(title = list(text = "Year")) |>
hc_yAxis(title = list(text = "GDP"))
})
output$chartDrilldown <- renderHighchart({
hchart(
drilldown_chart_base_data(),
"column",
hcaes(x = country, y = pop, drilldown = country),
name = "Population"
) %>%
hc_drilldown(
allowPointDrilldown = TRUE,
series = list_parse(drilldown_chart_drilldown_data())
) |>
hc_colors(c("#004c5f")) |>
hc_title(text = "Population report", align = "left") |>
hc_xAxis(title = list(text = "")) |>
hc_yAxis(title = list(text = "Population"))
})
}
shinyApp(ui = ui, server = server)
and the www/styles.css is here,
www/styles.css
@import url('https:/s.googleapis.com/css2?family=Poppins:ital,wght@0,700;1,400&display=swap');
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
body {
-family: 'Poppins', sans-serif;
-weight: 400;
}
.main-container {
padding-top: 1rem;
}
.stat-card-container {
display: flex;
justify-content: space-between;
column-gap: 1rem;
}
.stat-card {
border: 2px solid #f2f2f2;
border-bottom: 2px solid #0198f9;
width: 100%;
padding: 0.5rem 0 0.5rem 1rem;
}
.stat-card > p {
text-transform: uppercase;
color: #808080;
}
.stat-card > div.shiny-text-output {
-size: 3rem;
-weight: 700;
}
.card-container {
padding-top: 2rem;
}
.base-charts-container {
display: flex;
justify-content: space-between;
column-gap: 1rem;
}
.chart-card {
border: 2px solid #f2f2f2;
width: 50%;
}
.chart-card-full {
width: 100%;
}
TeX/LaTeX
It is most convient to convert Tex/LaTex formulas into MicroSoft Word equtions via pandoc, i.e.,
pandoc README.md -o README.docx.
See https://pandoc.org/ and https://pandoc.org/try/.
For Chinese language support, try
sudo apt-get install texlive-latex-base
sudo apt-get install latex-cjk-all
sudo apt-get install texlive-latex-extra
sudo apt-get install texmaker
sudo apt-get install texlive-xetex
sudo apt-get install texlive-publishers
Now change latex to xelatex from Texmaker.
\documentclass{article}
\usepackage(xeCJK}
\begin{document}
How are you?你好吗？
\LaTeX
\end{document}
typescript
First, create hello.ts with two lines,
#!/usr/bin/env ts-node
console.log('Hello world!');
and set up the environment,
npm install -g npm
npm install typescript ts-node -g
chmod +x hello.ts
hello.ts
Visual Studio Code
It is available from https://code.visualstudio.com/download, so we could download a .tar.gz file and unpack.
There is a pointer from https://github.com/Microsoft/vscode to https://code.visualstudio.com/Download. Once downloaded, it can be installed with
sudo dpkg -i code_1.23.1-1525968403_amd64.deb
but it requires libgconf-2-4; when failed to install use sudo apt --fix-broken install.
See https://code.visualstudio.com/docs/python/python-tutorial for the hello world example.
ChatGPT
We have export OPENAI_API_KEY=$(grep sk ~/doc/OpenAI), and our first session:
import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
if openai.api_key is None:
raise ValueError("API key not found. Please set the OPENAI_API_KEY environment variable.")
question = input("What is your question? ")
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": question},
],
max_tokens=512,
n=1,
stop=None,
temperature=0.8,
)
print(response)
answer = response['choices'][0]['message']['content']
print("OpenAI: " + answer)
CodeGPT
We need sign up/in for an API key from https://platform.openai.com/overview, e.g., via your MicrsoSoft account.
From Visual Studio Code, add extension CodeGPT; Ctrl-Shit-p to saarch for CodeGPT: Set API Key and enter the key given above from https://platform.openai.com/account/api-keys.
Then we create a file, start with a comment, e.g., '# to draw a forest plot' and Ctrl-Shift-i to obtain the code.
Here is one of the results given,
# First, create a dataset with the relevant values for your forest plot
study_names <- c("Study A", "Study B", "Study C", "Study D")
odds_ratios <- c(1.2, 1.5, 0.8, 0.6)
lo_ci <- c(0.9, 1.0, 0.5, 0.3)
hi_ci <- c(1.5, 2.0, 1.2, 0.9)
# Combine data into a data frame
df <- data.frame(study_names, odds_ratios, lo_ci, hi_ci)
# Load the 'meta' package for the forest plot function
library(meta)
# Create the forest plot
forest(df$odds_ratios, ci.lb = df$lo_ci, ci.ub = df$hi_ci,
slab = df$study_names, xlab = "Odds Ratio",
main = "Forest Plot Example")