Skip to main content

automated tuning based on dependency graph

Project description

# AutoDepGraph [![Build Status](]( [![Codacy Badge](]( [![Coverage Status](](

AutoDepGraph is a QCoDeS based framework for using dependency graphs to calibrate a system. It is heavily inspired by ["Physical qubit calibration on a directed acyclic graph"](

## Overview
AutoDepGraph consists of two main classes, the CalibrationNode and the Graph.
Calibration is done by calling a node that one wants to execute, the node contains the logic required to satisfy the nodes it depends on (parents).

A CalibrationNode contains:

- parameters
- state
+ Good (green): check passes
+ needs calibration (yellow): calibration is not up to date anymore and needs to be updated
+ Bad (red): calibration or check has failed
+ unknown (grayed): checks of the node should be run
+ active (blue): calibration or check in progress
- parents: the nodes it depends on
- children: nodes that depend on this node
- check_function : name of function to be executed when check is called. This can be a method of another instrument.
- calibrate_function : name of function to be executed when calibrate is called. This can be a method of another instrument.
- calibration_timeout: time in (s) after which a calibration times out.

- function
- execute or call
+ Performs the logic of a node (check state, satisfy requirements) with the goal of moving to a "good" state
- check
+ Performs checks to determine and the state of a node
- calibrate
+ Executes the calibration routines of the node

A Graph is a container of nodes, it is used for:
- new graphs can be created by instantiating a graph and then using the add_node method to define new nodes.
- loading and saving the graph
- real-time visualization using pyqtgraph
- state of the node determines color of a node
- if a node has no calibrate function defined it is a manual node and has a hexagonal instead of a circle as symbol
- mouseover information lists more properties (planned)

![Example calibration graph](docs/example_graph.png)

## Examples
For an introductory example see the example notebook. If you want to see how to use a specific function, see the tests located in the autodepgraph/tests folder.

## Installation
- Clone the repository
- install the [requirements](requirements.txt)
- navigate to the repository and run `pip install -e .`
- verify success of installation by running `py.test`

#### N.B. windows can be "problematic"
Installation on windows is a bit more difficult, this relates mostly to the installation of pygraphviz. To install graphviz and pygraphviz on windows follow tthese steps:

- get the 64 bit version of ![graphviz for windows](, copy it to e.g., program files and add the bin folder to the system path.
- the 64 bit version lacks the libxml2.dll, you most likely have this from some other program. You can find this by searching for `libxml2.dll` in the program files folder. After that just copy paste it to the bin folder of graphviz.
- get pygraphviz by downloading the master from github.
- Now you will need to edit pygraphviz/graphviz.i and pygraphviz/graphviz_wrap.c according to the changes at A reference can be found in the _install folder
- Next install using
python install --include-path="C:\Program Files\graphviz-2.38_x64\include" --library-path="C:\Program Files\graphviz-2.38_x64\lib"

- then install autodepgraph and test the installation using `py.test`

## Acknowledgements
I would like to thank Julian Kelly for the idea of using a dependency graph for calibrations and for early discussions. I would like to thank Joe Weston for discussions and help in working out the initial design. I would like to acknowledge Livio Ciorciaro for disucssions and as a coauthor of this project.

Project details

Release history Release notifications

This version
History Node


History Node


History Node


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
autodepgraph-0.31.tar.gz (13.7 kB) Copy SHA256 hash SHA256 Source None May 18, 2018

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page