From 95862fa1a725aff67210f38023acb482088ce3a2 Mon Sep 17 00:00:00 2001 From: jackiekazil Date: Mon, 14 May 2018 13:19:04 -0400 Subject: [PATCH] Removing 'F'lockers. --- examples/Flockers/Flocker Test.ipynb | 113 ------------------ .../flockers/SimpleContinuousModule.py | 33 ----- examples/Flockers/flockers/boid.py | 92 -------------- examples/Flockers/flockers/model.py | 71 ----------- examples/Flockers/flockers/server.py | 21 ---- .../flockers/simple_continuous_canvas.js | 83 ------------- examples/Flockers/run.py | 3 - 7 files changed, 416 deletions(-) delete mode 100644 examples/Flockers/Flocker Test.ipynb delete mode 100644 examples/Flockers/flockers/SimpleContinuousModule.py delete mode 100644 examples/Flockers/flockers/boid.py delete mode 100644 examples/Flockers/flockers/model.py delete mode 100644 examples/Flockers/flockers/server.py delete mode 100644 examples/Flockers/flockers/simple_continuous_canvas.js delete mode 100644 examples/Flockers/run.py diff --git a/examples/Flockers/Flocker Test.ipynb b/examples/Flockers/Flocker Test.ipynb deleted file mode 100644 index b530d5258b1..00000000000 --- a/examples/Flockers/Flocker Test.ipynb +++ /dev/null @@ -1,113 +0,0 @@ -{ - "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": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAAJPCAYAAACpXgqFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngQsAoDOBCwCgs/8fICoqGcqtXKgAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "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 -} diff --git a/examples/Flockers/flockers/SimpleContinuousModule.py b/examples/Flockers/flockers/SimpleContinuousModule.py deleted file mode 100644 index 3067f66ac16..00000000000 --- a/examples/Flockers/flockers/SimpleContinuousModule.py +++ /dev/null @@ -1,33 +0,0 @@ -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 diff --git a/examples/Flockers/flockers/boid.py b/examples/Flockers/flockers/boid.py deleted file mode 100644 index 13aab718f4d..00000000000 --- a/examples/Flockers/flockers/boid.py +++ /dev/null @@ -1,92 +0,0 @@ -import numpy as np - -from mesa import Agent - - -class Boid(Agent): - ''' - A Boid-style flocker agent. - - The agent follows three behaviors to flock: - - Cohesion: steering towards neighboring agents. - - Separation: avoiding getting too close to any other agent. - - Alignment: try to fly in the same direction as the neighbors. - - Boids have a vision that defines the radius in which they look for their - neighbors to flock with. Their speed (a scalar) and velocity (a vector) - define their movement. Separation is their desired minimum distance from - any other Boid. - ''' - def __init__(self, unique_id, model, pos, speed, velocity, vision, - separation, cohere=0.025, separate=0.25, match=0.04): - ''' - Create a new Boid flocker agent. - - Args: - unique_id: Unique agent identifyer. - pos: Starting position - speed: Distance to move per step. - heading: numpy vector for the Boid's direction of movement. - vision: Radius to look around for nearby Boids. - separation: Minimum distance to maintain from other Boids. - cohere: the relative importance of matching neighbors' positions - separate: the relative importance of avoiding close neighbors - match: the relative importance of matching neighbors' headings - - ''' - super().__init__(unique_id, model) - self.pos = np.array(pos) - self.speed = speed - self.velocity = velocity - self.vision = vision - self.separation = separation - self.cohere_factor = cohere - self.separate_factor = separate - self.match_factor = match - - def cohere(self, neighbors): - ''' - Return the vector toward the center of mass of the local neighbors. - ''' - cohere = np.zeros(2) - if neighbors: - for neighbor in neighbors: - cohere += self.model.space.get_heading(self.pos, neighbor.pos) - cohere /= len(neighbors) - return cohere - - def separate(self, neighbors): - ''' - Return a vector away from any neighbors closer than separation dist. - ''' - me = self.pos - them = (n.pos for n in neighbors) - separation_vector = np.zeros(2) - for other in them: - if self.model.space.get_distance(me, other) < self.separation: - separation_vector -= self.model.space.get_heading(me, other) - return separation_vector - - def match_heading(self, neighbors): - ''' - Return a vector of the neighbors' average heading. - ''' - match_vector = np.zeros(2) - if neighbors: - for neighbor in neighbors: - match_vector += neighbor.velocity - match_vector /= len(neighbors) - return match_vector - - def step(self): - ''' - Get the Boid's neighbors, compute the new vector, and move accordingly. - ''' - - neighbors = self.model.space.get_neighbors(self.pos, self.vision, False) - self.velocity += (self.cohere(neighbors) * self.cohere_factor + - self.separate(neighbors) * self.separate_factor + - self.match_heading(neighbors) * self.match_factor) / 2 - self.velocity /= np.linalg.norm(self.velocity) - new_pos = self.pos + self.velocity * self.speed - self.model.space.move_agent(self, new_pos) diff --git a/examples/Flockers/flockers/model.py b/examples/Flockers/flockers/model.py deleted file mode 100644 index 1297d9efe83..00000000000 --- a/examples/Flockers/flockers/model.py +++ /dev/null @@ -1,71 +0,0 @@ -''' -Flockers -============================================================= -A Mesa implementation of Craig Reynolds's Boids flocker model. -Uses numpy arrays to represent vectors. -''' - - -import random -import numpy as np - -from mesa import Model -from mesa.space import ContinuousSpace -from mesa.time import RandomActivation - -from .boid import Boid - - -class BoidModel(Model): - ''' - Flocker model class. Handles agent creation, placement and scheduling. - ''' - - def __init__(self, - population=100, - width=100, - height=100, - speed=1, - vision=10, - separation=2, - cohere=0.025, - separate=0.25, - match=0.04): - ''' - Create a new Flockers model. - - Args: - population: Number of Boids - width, height: Size of the space. - speed: How fast should the Boids move. - vision: How far around should each Boid look for its neighbors - separation: What's the minimum distance each Boid will attempt to - keep from any other - cohere, separate, match: factors for the relative importance of - the three drives. ''' - self.population = population - self.vision = vision - self.speed = speed - self.separation = separation - self.schedule = RandomActivation(self) - self.space = ContinuousSpace(width, height, True) - self.factors = dict(cohere=cohere, separate=separate, match=match) - self.make_agents() - self.running = True - - def make_agents(self): - ''' - Create self.population agents, with random positions and starting headings. - ''' - for i in range(self.population): - x = random.random() * self.space.x_max - y = random.random() * self.space.y_max - pos = np.array((x, y)) - velocity = np.random.random(2) * 2 - 1 - boid = Boid(i, self, pos, self.speed, velocity, self.vision, - self.separation, **self.factors) - self.space.place_agent(boid, pos) - self.schedule.add(boid) - - def step(self): - self.schedule.step() diff --git a/examples/Flockers/flockers/server.py b/examples/Flockers/flockers/server.py deleted file mode 100644 index 6af4d29883c..00000000000 --- a/examples/Flockers/flockers/server.py +++ /dev/null @@ -1,21 +0,0 @@ -from mesa.visualization.ModularVisualization import ModularServer - -from .model import BoidModel -from .SimpleContinuousModule import SimpleCanvas - - -def boid_draw(agent): - return {"Shape": "circle", "r": 2, "Filled": "true", "Color": "Red"} - - -boid_canvas = SimpleCanvas(boid_draw, 500, 500) -model_params = { - "population": 100, - "width": 100, - "height": 100, - "speed": 5, - "vision": 10, - "separation": 2 -} - -server = ModularServer(BoidModel, [boid_canvas], "Boids", model_params) diff --git a/examples/Flockers/flockers/simple_continuous_canvas.js b/examples/Flockers/flockers/simple_continuous_canvas.js deleted file mode 100644 index 95e2d87ef9f..00000000000 --- a/examples/Flockers/flockers/simple_continuous_canvas.js +++ /dev/null @@ -1,83 +0,0 @@ -var ContinuousVisualization = function(height, width, context) { - var height = height; - var width = width; - var context = context; - - this.draw = function(objects) { - for (var i in objects) { - var p = objects[i]; - if (p.Shape == "rect") - this.drawRectange(p.x, p.y, p.w, p.h, p.Color, p.Filled); - if (p.Shape == "circle") - this.drawCircle(p.x, p.y, p.r, p.Color, p.Filled); - }; - - }; - - this.drawCircle = function(x, y, radius, color, fill) { - var cx = x * width; - var cy = y * height; - var r = radius; - - context.beginPath(); - context.arc(cx, cy, r, 0, Math.PI * 2, false); - context.closePath(); - - context.strokeStyle = color; - context.stroke(); - - if (fill) { - context.fillStyle = color; - context.fill(); - } - - }; - - this.drawRectange = function(x, y, w, h, color, fill) { - context.beginPath(); - var dx = w * width; - var dy = h * height; - - // Keep the drawing centered: - var x0 = (x*width) - 0.5*dx; - var y0 = (y*height) - 0.5*dy; - - context.strokeStyle = color; - context.fillStyle = color; - if (fill) - context.fillRect(x0, y0, dx, dy); - else - context.strokeRect(x0, y0, dx, dy); - }; - - this.resetCanvas = function() { - context.clearRect(0, 0, height, width); - context.beginPath(); - }; -}; - -var Simple_Continuous_Module = function(canvas_width, canvas_height) { - // Create the element - // ------------------ - - // Create the tag: - var canvas_tag = "