YouPlot : A command line tool for Unicode Plotting

Hello DEV.to
I recently created a command line tool called YouPlot. With this tool, you can plot unicode plots on the terminal. This post is the same as README. I wish you all the best.

https://github.com/kojix2/youplot

YouPlot is a command line tool for Unicode Plotting working with data from standard stream.

📊 Powered by UnicodePlot

Installation

gem install youplot

Quick Start

cat data.tsv | uplot <command> [options]

barplot

curl -sL https://git.io/ISLANDScsv \
| sort -nk2 -t, \
| tail -n15 \
| uplot bar -d, -t "Areas of the World's Major Landmasses"

histogram

echo -e "from numpy import random;" \
        "n = random.randn(10000);"  \
        "print('\\\n'.join(str(i) for i in n))" \
| python \
| uplot hist --nbins 20

lineplot

curl -sL https://git.io/AirPassengers \
| cut -f2,3 -d, \
| uplot line -d, -w 50 -h 15 -t AirPassengers --xlim 1950,1960 --ylim 0,600

scatter

curl -sL https://git.io/IRIStsv \
| cut -f1-4 \
| uplot scatter -H -t IRIS

density

curl -sL https://git.io/IRIStsv \
| cut -f1-4 \
| uplot density -H -t IRIS

boxplot

curl -sL https://git.io/IRIStsv \
| cut -f1-4 \
| uplot boxplot -H -t IRIS

count

In this example, YouPlot counts the number of chromosomes where the gene is located from the human gene annotation file and create a bar chart. The human gene annotation file can be downloaded from the following website.

• https://www.gencodegenes.org/human/

cat gencode.v35.annotation.gff3 \
| grep -v '#' | grep 'gene' | cut -f1 | \
 uplot count -t "The number of human gene annotations per chromosome"  -c blue

Note: count is not very fast because it runs in a Ruby script.
This is fine if the data is small, that is, in most cases. However, if you want to visualize huge data, it is faster to use a combination of common Unix commands as shown below.

cat gencode.v35.annotation.gff3 | grep -v '#' | grep 'gene' | cut -f1 \
|sort | uniq -c | sort -nrk2 | awk '{print $2,$1}' \
| uplot bar -d ' ' -t "The number of human gene annotations per chromosome"  -c blue

Usage

Why YouPlot?

Wouldn't it be a bit of pain to have to run R, Python, Julia, gnuplot or whatever REPL just to check your data?
YouPlot is a command line tool for this purpose. With YouPlot, you can continue working without leaving your terminal and shell.

how to use YouPlot?

uplot is the shortened form of youplot. You can use either.

• Reads data from standard input
  • cat data.tsv | uplot <command> [options]
• Reads data from files
  • uplot <command> [options] data.tsv ...
• Outputs data from stdin to stdout
  • pipeline1 | uplot <command> -O | pipeline2

Where to output the plot?

By default, the plot is output to standard error output.
The output file or stream for the plot can be specified with the -o option.

Where to output the input data?

By default, the input data is not output anywhere.
The -O option, with no arguments, outputs the input data directly to the standard output. This is useful when passing data to a subsequent pipeline.

What types of plots are available?

The following sub-commands are available

command	short	how it works
barplot	bar	draw a horizontal barplot
histogram	hist	draw a horizontal histogram
lineplot	line	draw a line chart
lineplots	lines	draw a line chart with multiple series
scatter	s	draw a scatter plot
density	d	draw a density plot
boxplot	box	draw a horizontal boxplot

See Quick Start for count.

command	short	how it works
count	c	draw a baplot based on the number of occurrences (slow)

How to view detailed command line options

Use --help to print command-specific options.

uplot hist --help

Usage: uplot histogram [options] <in.tsv>

Options for histogram:
        --symbol VAL         character to be used to plot the bars
        --closed VAL
    -n, --nbins VAL          approximate number of bins

Options:
...

How to view the list of available colors?

uplot colors

Contributing

• Report bugs
• Fix bugs and submit pull requests
• Write, clarify, or fix documentation
• Suggest or add new features

Development

git clone https://github.com/your_name/GR.rb # Clone the Git repo
cd GR.rb
bundle install             # Install the gem dependencies
bundle exec rake test      # Run the test
bundle exec rake install   # Installation from source code

License

MIT License.