|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "code", |
| 5 | + "execution_count": null, |
| 6 | + "metadata": {}, |
| 7 | + "outputs": [], |
| 8 | + "source": [ |
| 9 | + "from ipycanvas import Canvas, hold_canvas" |
| 10 | + ] |
| 11 | + }, |
| 12 | + { |
| 13 | + "cell_type": "code", |
| 14 | + "execution_count": null, |
| 15 | + "metadata": {}, |
| 16 | + "outputs": [], |
| 17 | + "source": [ |
| 18 | + "import numpy as np" |
| 19 | + ] |
| 20 | + }, |
| 21 | + { |
| 22 | + "cell_type": "code", |
| 23 | + "execution_count": null, |
| 24 | + "metadata": {}, |
| 25 | + "outputs": [], |
| 26 | + "source": [ |
| 27 | + "from math import pi, cos, sin" |
| 28 | + ] |
| 29 | + }, |
| 30 | + { |
| 31 | + "cell_type": "code", |
| 32 | + "execution_count": null, |
| 33 | + "metadata": {}, |
| 34 | + "outputs": [], |
| 35 | + "source": [ |
| 36 | + "def draw(canvas, t):\n", |
| 37 | + " size = 1000\n", |
| 38 | + " step = 20\n", |
| 39 | + " t1 = t / 1000.\n", |
| 40 | + "\n", |
| 41 | + " x = 0\n", |
| 42 | + " while x < size + step:\n", |
| 43 | + " y = 0\n", |
| 44 | + " while y < size + step:\n", |
| 45 | + " x_angle = y_angle = 2 * pi\n", |
| 46 | + "\n", |
| 47 | + " angle = x_angle * (x / size) + y_angle * (y / size)\n", |
| 48 | + "\n", |
| 49 | + " particle_x = x + 20 * cos(2 * pi * t1 + angle)\n", |
| 50 | + " particle_y = y + 20 * sin(2 * pi * t1 + angle)\n", |
| 51 | + "\n", |
| 52 | + " canvas.fill_circle(particle_x, particle_y, 6)\n", |
| 53 | + "\n", |
| 54 | + " y = y + step\n", |
| 55 | + "\n", |
| 56 | + " x = x + step" |
| 57 | + ] |
| 58 | + }, |
| 59 | + { |
| 60 | + "cell_type": "code", |
| 61 | + "execution_count": null, |
| 62 | + "metadata": {}, |
| 63 | + "outputs": [], |
| 64 | + "source": [ |
| 65 | + "def fast_draw(canvas, t):\n", |
| 66 | + " \"\"\"Same as draw, but using NumPy and the vectorized version of fill_circle: fill_circles\"\"\"\n", |
| 67 | + " size = 1000\n", |
| 68 | + " step = 20\n", |
| 69 | + " t1 = t / 1000.\n", |
| 70 | + " \n", |
| 71 | + " x = np.linspace(0, size, int(size / step))\n", |
| 72 | + " y = np.linspace(0, size, int(size / step))\n", |
| 73 | + " xv, yv = np.meshgrid(x, y)\n", |
| 74 | + " \n", |
| 75 | + " x_angle = y_angle = 2 * pi\n", |
| 76 | + "\n", |
| 77 | + " angle = x_angle * (xv / size) + y_angle * (yv / size)\n", |
| 78 | + "\n", |
| 79 | + " particle_x = xv + 20 * np.cos(2 * pi * t1 + angle)\n", |
| 80 | + " particle_y = yv + 20 * np.sin(2 * pi * t1 + angle)\n", |
| 81 | + "\n", |
| 82 | + " canvas.fill_circles(particle_x, particle_y, 6)" |
| 83 | + ] |
| 84 | + }, |
| 85 | + { |
| 86 | + "cell_type": "code", |
| 87 | + "execution_count": null, |
| 88 | + "metadata": {}, |
| 89 | + "outputs": [], |
| 90 | + "source": [ |
| 91 | + "size = 1000\n", |
| 92 | + "canvas = Canvas(width=size, height=size)\n", |
| 93 | + "canvas.fill_style = '#fcba03'\n", |
| 94 | + "canvas" |
| 95 | + ] |
| 96 | + }, |
| 97 | + { |
| 98 | + "cell_type": "markdown", |
| 99 | + "metadata": {}, |
| 100 | + "source": [ |
| 101 | + "### 1: Using `from time import sleep` and `fill_circle`\n", |
| 102 | + "\n", |
| 103 | + "**Worst approach: Slow locally, slow using a remote server (MyBinder)**" |
| 104 | + ] |
| 105 | + }, |
| 106 | + { |
| 107 | + "cell_type": "code", |
| 108 | + "execution_count": null, |
| 109 | + "metadata": {}, |
| 110 | + "outputs": [], |
| 111 | + "source": [ |
| 112 | + "from time import sleep\n", |
| 113 | + "\n", |
| 114 | + "for i in range(200):\n", |
| 115 | + " with hold_canvas(canvas):\n", |
| 116 | + " canvas.clear()\n", |
| 117 | + "\n", |
| 118 | + " draw(canvas, i * 20.)\n", |
| 119 | + "\n", |
| 120 | + " sleep(20 / 1000.)" |
| 121 | + ] |
| 122 | + }, |
| 123 | + { |
| 124 | + "cell_type": "markdown", |
| 125 | + "metadata": {}, |
| 126 | + "source": [ |
| 127 | + "### 2: Using `canvas.sleep` and `fill_circle`\n", |
| 128 | + "\n", |
| 129 | + "This caches the entire animation before sending it to the front-end. This results in a slow execution (caching), but it ensure a smooth animation on the front-end whichever the context (local or remote server).\n", |
| 130 | + "\n", |
| 131 | + "**Slow to execute, smooth animation**" |
| 132 | + ] |
| 133 | + }, |
| 134 | + { |
| 135 | + "cell_type": "code", |
| 136 | + "execution_count": null, |
| 137 | + "metadata": {}, |
| 138 | + "outputs": [], |
| 139 | + "source": [ |
| 140 | + "with hold_canvas(canvas):\n", |
| 141 | + " for i in range(200):\n", |
| 142 | + " canvas.clear()\n", |
| 143 | + "\n", |
| 144 | + " draw(canvas, i * 20.)\n", |
| 145 | + "\n", |
| 146 | + " canvas.sleep(20)" |
| 147 | + ] |
| 148 | + }, |
| 149 | + { |
| 150 | + "cell_type": "code", |
| 151 | + "execution_count": null, |
| 152 | + "metadata": {}, |
| 153 | + "outputs": [], |
| 154 | + "source": [ |
| 155 | + "canvas" |
| 156 | + ] |
| 157 | + }, |
| 158 | + { |
| 159 | + "cell_type": "markdown", |
| 160 | + "metadata": {}, |
| 161 | + "source": [ |
| 162 | + "### 3: Using `from time import sleep` and the vectorized `fill_circles`\n", |
| 163 | + "\n", |
| 164 | + "**Super fast locally, can be fast on a remote server if the latency is correct**" |
| 165 | + ] |
| 166 | + }, |
| 167 | + { |
| 168 | + "cell_type": "code", |
| 169 | + "execution_count": null, |
| 170 | + "metadata": {}, |
| 171 | + "outputs": [], |
| 172 | + "source": [ |
| 173 | + "from time import sleep\n", |
| 174 | + "\n", |
| 175 | + "for i in range(200):\n", |
| 176 | + " with hold_canvas(canvas):\n", |
| 177 | + " canvas.clear()\n", |
| 178 | + "\n", |
| 179 | + " fast_draw(canvas, i * 20.)\n", |
| 180 | + "\n", |
| 181 | + " sleep(20 / 1000.)" |
| 182 | + ] |
| 183 | + }, |
| 184 | + { |
| 185 | + "cell_type": "markdown", |
| 186 | + "metadata": {}, |
| 187 | + "source": [ |
| 188 | + "### 4: Using `canvas.sleep` and the vectorized `fill_circles`\n", |
| 189 | + "\n", |
| 190 | + "**Best approach: Super fast locally, super fast on a remote server**" |
| 191 | + ] |
| 192 | + }, |
| 193 | + { |
| 194 | + "cell_type": "code", |
| 195 | + "execution_count": null, |
| 196 | + "metadata": {}, |
| 197 | + "outputs": [], |
| 198 | + "source": [ |
| 199 | + "with hold_canvas(canvas):\n", |
| 200 | + " for i in range(200):\n", |
| 201 | + " canvas.clear()\n", |
| 202 | + "\n", |
| 203 | + " fast_draw(canvas, i * 20.)\n", |
| 204 | + "\n", |
| 205 | + " canvas.sleep(20)" |
| 206 | + ] |
| 207 | + }, |
| 208 | + { |
| 209 | + "cell_type": "markdown", |
| 210 | + "metadata": {}, |
| 211 | + "source": [ |
| 212 | + "### Conclusion\n", |
| 213 | + "\n", |
| 214 | + "Always use `hold_canvas`!\n", |
| 215 | + "\n", |
| 216 | + "As much as possible, try to use the vectorized version of the base methods if you want to exectute them multiple times (`fill_circles`, `fill_rects` etc).\n", |
| 217 | + "\n", |
| 218 | + "If you can, make use of `canvas.sleep` instead of `from time import sleep` so that the entire animation is sent at once to the front-end, making a smoother animation whatever the server latency." |
| 219 | + ] |
| 220 | + } |
| 221 | + ], |
| 222 | + "metadata": { |
| 223 | + "kernelspec": { |
| 224 | + "display_name": "Python 3", |
| 225 | + "language": "python", |
| 226 | + "name": "python3" |
| 227 | + }, |
| 228 | + "language_info": { |
| 229 | + "codemirror_mode": { |
| 230 | + "name": "ipython", |
| 231 | + "version": 3 |
| 232 | + }, |
| 233 | + "file_extension": ".py", |
| 234 | + "mimetype": "text/x-python", |
| 235 | + "name": "python", |
| 236 | + "nbconvert_exporter": "python", |
| 237 | + "pygments_lexer": "ipython3", |
| 238 | + "version": "3.9.0" |
| 239 | + } |
| 240 | + }, |
| 241 | + "nbformat": 4, |
| 242 | + "nbformat_minor": 4 |
| 243 | +} |
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