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pct

Perceptual Control Theory with Python

https://github.com/perceptualrobots/pct

Science Score: 26.0%

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Repository

Perceptual Control Theory with Python

Basic Info
Statistics
  • Stars: 5
  • Watchers: 3
  • Forks: 1
  • Open Issues: 3
  • Releases: 0
Created about 6 years ago · Last pushed 12 months ago
Metadata Files
Readme Contributing License

README.md

Perceptual Control Theory

With this library you can create and run simple or complex hierarchies of perceptual control systems as well as make use of the power of the Python platform and its rich set of packages.

In the context of this library a single control system comprising a perceptual, reference, comparator and output function is called a Node. The functions therein can be configured by the user.

A hierarchy is defined by a collection of nodes.

Install

pip install pct

Import

Examples of importing the library functionality.

import pct as p

from pct.hierarchy import Hierarchy

from pct import *

How to use

Import modules from the PCT library.

python from pct.nodes import PCTNode

For the purposes of this example define a world model. This would not be required if the real world is used, or a simulation such as OpenAI Gym.

``` python def velocity_model(velocity, force , mass): velocity = velocity + force / mass return velocity

World value

mass = 50 ```

Create a PCTNode, a control system unit comprising a reference, perception, comparator and output function. The default value for the reference is 1. With the history flag set, the data for each iteration is recorded for later plotting.

python pctnode = PCTNode(history=True)

Call the node repeatedly to control the perception of velocity. With the verbose flag set, the control values are printed. In this case the printed values are the iteration number, the (velocity) reference, the perception, the error and the (force) output.

python for i in range(40): print(i, end=" ") force = pctnode(verbose=True) velocity = velocity_model(pctnode.get_perception_value(), force, mass) pctnode.set_perception_value(velocity) 

0 0.000 0.000 0.000 0.000 
1 0.000 0.000 0.000 0.000 
2 0.000 0.000 0.000 0.000 
3 0.000 0.000 0.000 0.000 
4 0.000 0.000 0.000 0.000 
5 0.000 0.000 0.000 0.000 
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39 0.000 0.000 0.000 0.000

Using the plotly library plot the data. The graph shows the perception being controlled to match the reference value.

python import plotly.graph_objects as go fig = go.Figure(layout_title_text="Velocity Goal") fig.add_trace(go.Scatter(y=pctnode.history.data['refcoll']['constant'], name="ref")) fig.add_trace(go.Scatter(y=pctnode.history.data['percoll']['variable'], name="perc"))

This following code is only for the purposes of displaying image of the graph generated by the above code.

python from IPython.display import Image

python Image(url='http://www.perceptualrobots.com/wp-content/uploads/2020/08/pct_node_plot.png')

This shows a very basic example of the use of the PCT library. For more advanced functionality see the API documentation at https://perceptualrobots.github.io/pct/.

Owner

  • Login: perceptualrobots
  • Kind: user

GitHub Events

Total
  • Issues event: 13
  • Delete event: 4
  • Issue comment event: 2
  • Member event: 1
  • Push event: 199
  • Pull request review event: 5
  • Pull request event: 10
  • Create event: 6
Last Year
  • Issues event: 13
  • Delete event: 4
  • Issue comment event: 2
  • Member event: 1
  • Push event: 199
  • Pull request review event: 5
  • Pull request event: 10
  • Create event: 6

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 1,293
  • Total Committers: 4
  • Avg Commits per committer: 323.25
  • Development Distribution Score (DDS): 0.217
Past Year
  • Commits: 173
  • Committers: 1
  • Avg Commits per committer: 173.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Rupert Young r****t@p****m 1,013
Rupert Young r****g@g****m 166
rupert r****t@m****k 113
perceptualrobots 6****s 1
Committer Domains (Top 20 + Academic)
moonsit.co.uk: 1 perceptualrobots.com: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 4
  • Total pull requests: 2
  • Average time to close issues: 15 days
  • Average time to close pull requests: about 3 hours
  • Total issue authors: 1
  • Total pull request authors: 1
  • Average comments per issue: 0.5
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 4
  • Pull requests: 2
  • Average time to close issues: 15 days
  • Average time to close pull requests: about 3 hours
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.5
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • perceptualrobots (7)
Pull Request Authors
  • perceptualrobots (6)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 421 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 2
  • Total versions: 42
  • Total maintainers: 1
pypi.org: pct

Perceptual Control Theory with Python

  • Versions: 42
  • Dependent Packages: 0
  • Dependent Repositories: 2
  • Downloads: 421 Last month
Rankings
Dependent packages count: 7.4%
Dependent repos count: 11.9%
Average: 18.2%
Stargazers count: 21.6%
Forks count: 22.8%
Downloads: 27.3%
Maintainers (1)
perceptualrobots
Last synced: 11 months ago

Dependencies

.github/workflows/deploy.yaml actions
  • fastai/workflows/quarto-ghp master composite
.github/workflows/test.yaml actions
  • fastai/workflows/nbdev-ci master composite