3.8 KiB
YouPlot - Create ASCII charts on the terminal with data from standard streams in the pipeline.
📊 Powered by UnicodePlot
Installation
gem install youplot
Quick Start
barplot
curl -sL https://git.io/ISLANDScsv \
| sort -nk2 -t, \
| tail \
| 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.
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
file
stream
help
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:
...
colors
uplot colors
Development
Let's keep it simple.
Contributing
Bug reports and pull requests are welcome on GitHub at https://github.com/kojix2/youplot.