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What's new in this version

nothing changed, only fixed 'supported field'


Yet Another pButtons Extractor

NOTE: Development of yape stopped in February 2021. Replaced by yaspe
See: yaspe has a different internal structure and additional reporting and exporting options.

Use at your own risk and if you find any issues, please feel free to file it here.

There is a link to how-to videos on InterSystems Developer Community.

yape automatically creates charts that can be used for trouble-shooting performance issues or informing performance questions.

Below is the example chart of a weeks data.

For more fancy charts or to combine mgstat, vmstat an iostat use pretty_pButtons

Yape Installation: Docker Container (recommended)

Use image from docker hub

Of course, you need docker installed first :)


The same usage information applies, for example:

docker pull yape/yape
docker container run --rm -v "$(pwd)":/data yape/yape --help

For example to extract mgstat and vmstat quietly and also output csv files:

cd <directory with your pButtons file.html>
docker container run --rm -v "$(pwd)":/data yape/yape --mgstat --vmstat -qc /data/<name of your pButtons file.html>

Create image yourself

To avoid any fighting with python versions there is a Dockerfile for building a container

cd <some place you want the files>
git clone
cd yape
docker build -t yape2 .

You can run the container like this example to get parameters:

docker container run --rm -v "$(pwd)":/data yape2 --help

For example to extract mgstat and vmstat quietly and also output csv files:

cd <directory with your pButtons file.html>
docker container run --rm -v "$(pwd)":/data yape2 --mgstat --vmstat -qc /data/<name of your pButtons file.html>

Note: This installs the command line version only, for the interactive version you will need to install locally in your OS (see below).

If you want to remove the container version. Stop all containers and remove images.

Installation: Local

Requires git, pip and Python installed aready as per Python Version as below. Includes Alpha of the interactive version.

About Python Version

Python can be a beast about versions, for the simplest experience a container is a good way to go. You have been warned.


The following was used to test and build yape for the container version:

$ python --version
Python 3.7.4

To use pip to install packages to Anaconda Environment

$ sudo conda install pip
$ pip --version
pip 19.2.3 from /Users/mo/opt/anaconda3/lib/python3.7/site-packages/pip (python 3.7)

Install for running locally

cd <some place you want the files>
git clone
sudo pip install . --upgrade

yape --help

Other things you can try

Personal default options

You can specify a configuration file with the

yape --config config.example.yml ...

parameter. This allows to set things like the default output graph size. See config.example.yml for possible options and their default value. Yape will always check if there is a ~/.yape.yml file and load that.

Example: Dotgraphs plotting

To switch to dot graphs instead of line graphs set the style to "". For example, define the following in your config:

  dim: 16,6
  style: ""
  markersize: 2

dim is dimensions in inches.

Choices for style are: '-' solid line '--' dashed line '-.' dash-dotted line ':' dotted line

The defaults for configuration are:

  dim: 16,6
  style: "-"
  markersize: 1

Weekly overview graphs

To create a week overview graph you can currently parse a number of pbuttons into a file and then plot that:

yape --filedb data.db pbuttons1.html
yape --filedb data.db pbuttons2.html
yape --filedb data.db pbuttons3.html
yape --filedb data.db pbuttons4.html
yape --filedb data.db pbuttons5.html
yape --filedb data.db pbuttons6.html
yape --filedb data.db pbuttons7.html
yape --filedb data.db --timeframe "2018-06-11 00:00:00,2018-06-17 22:00:00" -a --skip-parse -o testdata/ 

Note: the last positional argument (pButtons file name) is not entered when --skip-parse and --filedb are used.

Experimental interactive version

Change to the base of your checked out yape directory and run:

bokeh serve --show yapesrv --args /path/to/your.html

This will give you (maybe) an interactive display of the pbuttons passed in. If you run into any errors, feel free to create an issue here:


To run the unittests, use

python nosetests

(you might need to use python3 as command if you have multiple versions of python installed)

It will run tests with all pbutton files found in the test-data directory.

Related Discussion

See the detailed description and discussion in this article. Have a look at InterSystems Developer Community to learn about InterSystems technology, sharing solutions and staying up-to-date on the latest developments.

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