py_trees_js repository

Repository Summary

Checkout URI https://github.com/splintered-reality/py_trees_js.git
VCS Type git
VCS Version release/0.5.x
Last Updated 2019-10-29
Dev Status DEVELOPED
Released RELEASED

Packages

Name Version
py_trees_js 0.5.1

README

PyTrees Js

[About] [Roadmap] [Usage] [Example Qt-Js Integration] [The JSON Specification]

About

Javascript libraries for visualisation and monitoring of behaviour trees at runtime or when replaying a log.

  • Visualise runtime snapshots of behaviour trees
  • Collapse subtrees
  • Zoom and scale contents to fit
  • Timeline rewind & resume

Despite primarily catering for use cases with py_trees, these libraries can be used for behaviour trees in general since it requires only properties common to most behaviour tree implementations and flexibly opts into implementation specific data via passing of a key-value dictionary that is appropriately formatted in an implementation-agnostic manner inside the web application.

This repository also includes a hybrid Qt-JS application used for development and demonstration purposes.

For a quick preview of it's capabilities:

git clone https://github.com/splintered-reality/py_trees_js
cd py_trees_js
. ./virtualenv.bash
# launch the demo viewer
py-trees-demo-viewer
# OR launch the demo viewer with a js console for debugging
./scripts/py-trees-devel-viewer

Roadmap

See the Projects page for progress and planned milestones.

Usage

Building a complete application that can render a behaviour tree stream is an effort that can be decomposed into two tasks:

  1. Creating the web app for rendering trees and visualising a timeline
  2. Wrapping the web app in a framework and connecting it to an external stream

The first stage is purely an exercise with html, css and javascript. The latter will depend on your use case - it could be a qt-js hybrid application (as exemplified here) for developers, an electron application for cross-platform and mobile deployment or a cloud based service.

This section will walk through how to build a web application with the provided js libraries. An example of wrapping the web app within a Qt-Js application will follow.

To get started, let's begin with a basic html page with two divs, one for the tree canvas and one for the timeline:

<html>
<head>
  <meta charset="utf-8">
  <title>PyTrees Viewer</title>
</head>
<style>
  body {
    margin: 0;
    overflow:hidden;  <!-- no scrollbars -->
  }
</style>
<body>
  <div id="window">
    <div id="canvas"></div>
    <div id="timeline"></div>
  </div>
</body>
</html>

Next, bring in the javascript libraries. For exemplar purposes, it is assumed here that the libraries have been made available alongside the html page - how is an integration detail depending on the mode of deployment (see next section for an example).

Note that the py_trees-<version>.js library has only one dependency, jointjs, but that in turn has a few dependencies of it's own. The bundled libraries in the js/jointjs folder of this repository correspond to the requirements for a specific version of jointjs and have been tested to work with the accompany py_trees-<version>.js library.

You can verify that the libraries have been properly imported by calling py_trees.hello() which will print version information of the loaded javascript libraries (if found) to the javascript console.

<html>
<head>
  <meta charset="utf-8">
  <title>PyTrees Viewer</title>
</head>
<script src="js/jointjs/dagre-0.8.4.min.js"></script>
<script src="js/jointjs/graphlib-2.1.7.min.js"></script>
<script src="js/jointjs/jquery-3.4.1.min.js"></script>
<script src="js/jointjs/lodash-4.17.11.min.js"></script>
<script src="js/jointjs/backbone-1.4.0.js"></script>
<script src="js/jointjs/joint-3.0.4.min.js"></script>
<script src="js/py_trees-0.5.1.js"></script>
<link rel="stylesheet" href="js/py_trees-0.5.1.css">
<link rel="stylesheet" type="text/css" href="js/jointjs/joint-3.0.4.min.css"/>
<style>
  body {
    margin: 0;
    overflow:hidden;  <!-- no scrollbars -->
  }
</style>
<body>
  <script type="text/javascript">
    py_trees.hello()
  </script>
  <div id="window">
    <div id="canvas"></div>
    <div id="timeline"></div>
  </div>
</body>
</html>

Output from py_trees.hello():

********************************************************************************
                               Py Trees JS

 A javascript library for visualisation of executing behaviour trees.

 Version & Dependency Info:
  - py_trees:  0.3.1
    - jointjs :  3.0.4
       - backbone:  1.4.0
       - dagre   :  0.8.4
       - jquery  :  3.4.1
       - lodash  :  4.17.11
********************************************************************************

In the next iteration, the canvas is initialised and a callback for accepting incoming trees from an external source is prepared. To test it, pass it the demo tree provided by the library.

<html>
<head>
  <meta charset="utf-8">
  <title>PyTrees Viewer</title>
</head>
<script src="js/jointjs/dagre-0.8.4.min.js"></script>
<script src="js/jointjs/graphlib-2.1.7.min.js"></script>
<script src="js/jointjs/jquery-3.4.1.min.js"></script>
<script src="js/jointjs/lodash-4.17.11.min.js"></script>
<script src="js/jointjs/backbone-1.4.0.js"></script>
<script src="js/jointjs/joint-3.0.4.min.js"></script>
<script src="js/py_trees-0.5.1.js"></script>
<link rel="stylesheet" href="js/py_trees-0.5.1.css">
<link rel="stylesheet" type="text/css" href="js/jointjs/joint-3.0.4.min.css"/>
<style>
  body {
    margin: 0;
    overflow:hidden;  <!-- no scrollbars -->
  }
</style>
<body>
  <script type="text/javascript">
    py_trees.hello()
  </script>
  <div id="window">
    <div id="canvas"></div>
    <div id="timeline"></div>
  </div>
  <script type="text/javascript">
    // rendering canvas
    canvas_graph = py_trees.canvas.create_graph()
    canvas_paper = py_trees.canvas.create_paper({graph: canvas_graph})

    render_tree = function({tree}) {
      py_trees.canvas.update_graph({graph: canvas_graph, tree: tree})
      py_trees.canvas.layout_graph({graph: canvas_graph})
      if ( canvas_graph.get('scale_content_to_fit') ) {
        py_trees.canvas.scale_content_to_fit(canvas_paper)
      }
      return "rendered"
    }
    render_tree({tree: py_trees.experimental.create_demo_tree_definition()})
  </script>
</body>
</html>

At this point, your web app should be visualising a single tree and zoom/collapse/scale to fit interactions functional. I'm happy, you should be too!

Adding a timeline to the application is optional, but the code does not change significantly and is a very useful feature to have. The built-in demo app's index.html does exactly this. The code is reproduced below for convenience.

<html>
<head>
  <meta charset="utf-8">
  <title>PyTrees Viewer</title>
</head>
<script src="js/jointjs/dagre-0.8.4.min.js"></script>
<script src="js/jointjs/graphlib-2.1.7.min.js"></script>
<script src="js/jointjs/jquery-3.4.1.min.js"></script>
<script src="js/jointjs/lodash-4.17.11.min.js"></script>
<script src="js/jointjs/backbone-1.4.0.js"></script>
<script src="js/jointjs/joint-3.0.4.min.js"></script>
<script src="js/py_trees-0.5.1.js"></script>
<link rel="stylesheet" href="js/py_trees-0.5.1.css">
<link rel="stylesheet" type="text/css" href="js/jointjs/joint-3.0.4.min.css"/>
<style>
  body {
    margin: 0;
    overflow:hidden;  <!-- no scrollbars -->
  }
</style>
<body>
  <script type="text/javascript">
    py_trees.hello()
  </script>
  <div id="window">
    <div id="canvas"></div>
    <div id="timeline"></div>
  </div>
  <script type="text/javascript">
    // rendering canvas
    canvas_graph = py_trees.canvas.create_graph()
    canvas_paper = py_trees.canvas.create_paper({graph: canvas_graph})

    // event timeline
    timeline_graph = py_trees.timeline.create_graph({event_cache_limit: 100});
    timeline_paper = py_trees.timeline.create_paper({
        timeline_graph: timeline_graph,
        canvas_graph: canvas_graph,
        canvas_paper: canvas_paper,
    })

    // react to window resizing events
    $(window).resize(function() {
      py_trees.canvas.on_window_resize(canvas_paper)
      py_trees.timeline.on_window_resize(timeline_paper)
    })

    render_tree = function({tree}) {
      py_trees.timeline.add_tree_to_cache({
          timeline_graph: timeline_graph,
          canvas_graph: canvas_graph,
          canvas_paper: canvas_paper,
          tree: tree
      })
      return "rendered"
    }
  </script>
</body>
</html>

Example Qt-Js Integration

The demonstration application py-trees-demo-viewer is a qt-js hybrid application and serves two purposes.

The first purpose is to demonstrate connecting an external source to the render_tree() method in the web application (as defined above). The demo application here cycles through serialised trees whenever a Qt button is pressed.

The second purpose is to demonstrate the ease and utility of building a hybrid Qt-JS application. This is especially useful for robotics teams that lack a dedicated web team to help build and serve web applications. The usual problem is that developers need visual and interactive applications in their typical development workflow, whilst the product needs similar applications that can run as cloud services or on handheld devices. All too often, robotics developers build Qt applications to meet their needs but not the products, or endeavour to meet the product needs with web applications only to make their own development environment overly complex and cumbersome (and all too often not having the skills themselves to create polished web applications).

A Qt-Js hybrid application can help meet both needs. Javscript libraries can be shared as libraries / modules (via the .qrc generation mechanisms), the application itself can drop neatly into the developer's environment and if the Qt side endeavours to merely wrap the bundled web application, then migrating to a cloud service / mobile devices merely requires rewriting the wrapper to fit the framework of choice.

Step-by-step, how does this work?

The JS Libraries

The JS libraries are handled separately from the application and even deployed separately, with the obvious advantage that multiple applications can then take advantage of them without vendoring them into each and every application that uses them. Typical steps involve:

  1. Bundle the javascript resources into a .qrc file
  2. Generate the resources as a c++ library / python module
  3. Deploy the c++ library/python module in your development environment

In this case, the py_trees and jointjs javascript libraries have been listed in py_trees_js/resources.qrc, generated using py_trees_js/gen.bash, resulting in the importable module py_trees_js/resources.py. From this point, any pythonic Qt application wishing to visualise behaviour trees need only import this module from the py_trees_js package.

The Web App

The web application itself is made available similarly via .qrc resources, though the need to distribute it as a shareable package is not necessary. Typical steps involve:

  1. Bundle the .html/.css pages into a .qrc file
  2. Import into directly into designer when building your Qt application

In this case, our web app (py_trees_js/viewer/html/index.html) has been rolled into py_trees_js/viewer/web_app.qrc which is directly loaded into py_trees_js/viewer/web_view.ui where the URL property of the QWebEngineView widget has been configured with the resources index.html.

You could alternatively, generate a module from the .qrc and import that into the relevant python code as was done for the javascript resources.

The Qt Application

The Qt application can be designed in whatever way you're most comfortable with - via Designer, pure C++ or python. In this case, Qt's Designer is used to produce the .ui files which are generated into python modules and finally customised and brought together as a PyQt5 application. Refer to py_trees_js/viewer for more details or as a reference example from which to start your own Qt-Js hybrid application.

Key elements:

  1. Build your Qt application around a QWebEngineView widget
  2. Link/import the javascript module in the web engine view class
  3. Load the html page into the QWebEngineView view

Do not use the QWebView widget - this is deprecating in favour of the QWebEngineView widget. The most notable difference is that QWebView uses Qt's old webkit, while QWebEngineView makes use of Chromium's webkit.

Note that the second step automagically makes available the javascript resources to the application when it's loaded. It's not terribly fussy about where it gets loaded, see py_trees_js/viewer/web_view.py for an example:

# This is the module generated by running pyrcc5 on the js libraries .qrc
# It could have been equivalently deployed in a completely different python package
import py_trees_js.resources

Loading the web page can be accomplished in designer. Simply point it at your qresource file and set the dynamic URL property on the QWebEngineView widget. Alternatively you can import the resource module and load it via QWebEngineView's load api.

Qt-Js Interactions

Qt and JS can interact directly over snippets of javascript code (via runJavaScript() or over QWebChannel (a mechanism similar to sigslots) where more complexity is needed. The example application here calls on the render_tree() method we created earlier in the web application to send trees to the app. Example code from py_trees_js/viewer/viewer.py which handles button clicks to cycle through a list of demonstration trees:

def send_tree_response(reply):
    console.logdebug("reply: '{}' [viewer]".format(reply))


@qt_core.pyqtSlot()
def send_tree(web_view_page, demo_trees, unused_checked):
    demo_trees[send_tree.index]['timestamp'] = time.time()
    console.logdebug("send: tree '{}' [{}][viewer]".format(
        send_tree.index, demo_trees[send_tree.index]['timestamp'])
    )
    javascript_command = "render_tree({{tree: {}}})".format(demo_trees[send_tree.index])
    web_view_page.runJavaScript(javascript_command, send_tree_response)
    send_tree.index = 0 if send_tree.index == 2 else send_tree.index + 1

send_tree.index = 0

The JSON Specification

TODO: A JSon schema

Roughly, the specification expects json objects of the form:

  • timestamp: int
  • behaviours: dict[str, dict]
  • (optional) visited_path: list[str]

where each behaviour in the dict has specification:

  • id: str
  • status: Union[INVALID,FAILURE, RUNNING, SUCCESS]
  • name: str
  • colour:
  • (optional) children: List[str]
  • (optional) data:

Identification strings (id's) must be unique and are used as both keys for the behaviours dictionary, children and visited_path variables.

An example (extracted from py_trees.experimental.create_demo_tree_definition()):

{
    timestamp: 1563938995,
    visited_path: ['1', '2', '3', '4', '5', '7', '8'],
    behaviours: {
        '1': {
            id: '1',
            status: 'RUNNING',
            name: 'Selector',
            colour: '#00FFFF',
            children: ['2', '3', '4', '6'],
            data: {
                Type: 'py_trees.composites.Selector',
                Feedback: "Decision maker",
            },
        },
        '2': {
            id: '2',
            status: 'FAILURE',
            name: 'Sequence',
            colour: '#FFA500',
            children: ['7', '8', '9'],
            data: {
                Type: 'py_trees.composites.Sequence',
                Feedback: "Worker"
            },
        },
    }
}