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Renames 'Flockers' to 'Flockers' for consistency.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,113 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from flockers.model import BoidModel\n", | ||
| "import numpy as np\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "%matplotlib inline" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def draw_boids(model):\n", | ||
| " x_vals = []\n", | ||
| " y_vals = []\n", | ||
| " for boid in model.schedule.agents:\n", | ||
| " x, y = boid.pos\n", | ||
| " x_vals.append(x)\n", | ||
| " y_vals.append(y)\n", | ||
| " fig = plt.figure(figsize=(10,10))\n", | ||
| " ax = fig.add_subplot(111)\n", | ||
| " ax.scatter(x_vals, y_vals)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "model = BoidModel(100, 100, 100, speed=5, vision=5, separation=1)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "for i in range(50):\n", | ||
| " model.step()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
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zzie5ZcbXAWC0Lp5M1o4k2TikeHJyf+8TavSMMSazBq4bktye5H9qrf1WVb03m3qyWmut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\ngQsAoDOBCwCgs/8fICoqGcqtXKgAAAAASUVORK5CYII=\n", | ||
| "text/plain": [ | ||
| "<matplotlib.figure.Figure at 0x108938a58>" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "draw_boids(model)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.4.2" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0 | ||
| } |
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| # Flockers | ||
|
|
||
| An implementation of Craig Reynolds's Boids flocker model. Agents (simulated birds) try to fly towards the average position of their neighbors and in the same direction as them, while maintaining a minimum distance. This produces flocking behavior. | ||
|
|
||
| This model tests Mesa's continuous space feature, and uses numpy arrays to represent vectors. It also demonstrates how to create custom visualization components. | ||
|
|
||
| ## How to Run | ||
|
|
||
| Launch the model: | ||
| ``` | ||
| $ python Flocker_Server.py | ||
| ``` | ||
|
|
||
| Then open your browser to [http://127.0.0.1:8521/](http://127.0.0.1:8521/) and press Reset, then Run. | ||
|
|
||
| ## Files | ||
|
|
||
| * [flockers/model.py](flockers/model.py): Core model file; contains the BoidModel class. | ||
| * [flockers/boid.py](flockers/boid.py): The Boid agent class. | ||
| * [flockers/SimpleContinuousModule.py](flockers/SimpleContinuousModule.py): Defines ``SimpleCanvas``, the Python side of a custom visualization module for drawing agents with continuous positions. | ||
| * [flockers/simple_continuous_canvas.js](flockers/simple_continuous_canvas.js): JavaScript side of the ``SimpleCanvas`` visualization module; takes the output genereated by the Python ``SimpleCanvas`` element and draws it in the browser window via HTML5 canvas. | ||
| * [flockers/server.py](flockers/server.py): Sets up the visualization; uses the SimpleCanvas element defined above | ||
| * [run.py](run.py) Launches the visualization. | ||
| * [Flocker Test.ipynb](Flocker Test.ipynb): Tests the model in a Jupyter notebook. | ||
|
|
||
| ## Further Reading | ||
|
|
||
| ======= | ||
| * Launch the visualization | ||
| ``` | ||
| $ python run.py | ||
| ``` | ||
| * Visit your browser: http://127.0.0.1:8521/ | ||
| * In your browser hit *run* |
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| from mesa.visualization.ModularVisualization import VisualizationElement | ||
|
|
||
|
|
||
| class SimpleCanvas(VisualizationElement): | ||
| local_includes = ["flockers/simple_continuous_canvas.js"] | ||
| portrayal_method = None | ||
| canvas_height = 500 | ||
| canvas_width = 500 | ||
|
|
||
| def __init__(self, portrayal_method, canvas_height=500, canvas_width=500): | ||
| ''' | ||
| Instantiate a new SimpleCanvas | ||
| ''' | ||
| self.portrayal_method = portrayal_method | ||
| self.canvas_height = canvas_height | ||
| self.canvas_width = canvas_width | ||
| new_element = ("new Simple_Continuous_Module({}, {})". | ||
| format(self.canvas_width, self.canvas_height)) | ||
| self.js_code = "elements.push(" + new_element + ");" | ||
|
|
||
| def render(self, model): | ||
| space_state = [] | ||
| for obj in model.schedule.agents: | ||
| portrayal = self.portrayal_method(obj) | ||
| x, y = obj.pos | ||
| x = ((x - model.space.x_min) / | ||
| (model.space.x_max - model.space.x_min)) | ||
| y = ((y - model.space.y_min) / | ||
| (model.space.y_max - model.space.y_min)) | ||
| portrayal["x"] = x | ||
| portrayal["y"] = y | ||
| space_state.append(portrayal) | ||
| return space_state |
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