{
"cells": [
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from jp_doodle import doodle_files\n",
"vf_js = doodle_files.vendor_path(\"js/canvas_2d_vector_field.js\")\n",
"from jp_doodle import dual_canvas\n",
"import jp_proxy_widget"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "22ce57770b1e44a286e0141ec05cd91c",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n",
"<p>\n",
" If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another notebook frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"VBox(children=(JSProxyWidget(status=u'Not yet rendered'), Text(value=u'Not yet rendered', description=u'status:'), Text(value=u'No error', description=u'error'), Output(layout=Layout(border=u'1px solid black'))))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"w = jp_proxy_widget.JSProxyWidget()\n",
"w.load_js_files([vf_js])\n",
"dual_canvas.load_requirements(w)\n",
"w.js_init(\"\"\"\n",
"debugger;\n",
"element.canvas_2d_vector_field.example(element);\n",
"\"\"\")\n",
"\n",
"w.debugging_display()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"element"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w.element.reset_canvas()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.60555127546\n"
]
},
{
"data": {
"text/plain": [
"(array([[ 0.30242086, 0.57327401, 0.87441002],\n",
" [ 0.8699484 , 0.32549768, 0.29876122]]),\n",
" array([[ 0.27784884, 0.52669488, 0.80336326],\n",
" [ 0.89160212, 0.33359958, 0.30619763]]))"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"print np.linalg.norm((2,3))\n",
"x = np.random.random((2,3))\n",
"\n",
"def unitize(vectors):\n",
" (nr, nc) = vectors.shape\n",
" norms = np.linalg.norm(vectors, axis=1)\n",
" return vectors/norms.reshape((nr, 1))\n",
"\n",
"(x, unitize(x))\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# random motions from 1000 random positions\n",
"npoints = 100\n",
"duration = 1\n",
"shape = (npoints, 2)\n",
"points = np.random.random(shape) * 2.0 - 1.0\n",
"max_movement = 0.2\n",
"movement = max_movement * (np.random.random(shape) * 2.0 - 1.0)\n",
"offsets = np.random.random((npoints,)) * duration\n",
"motions = []\n",
"for i in range(npoints):\n",
" (x, y) = points[i]\n",
" (dx, dy) = movement[i]\n",
" motion = {\n",
" \"sx\": x-dx, \"sy\": y-dy, \"ex\": x+dx, \"ey\": y+dy, \"dt\": offsets[i]\n",
" }\n",
" motions.append(motion)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f5d113cb241b4215afa3110934a1a330",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Tab</code>.</p>\n",
"<p>\n",
" If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another notebook frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Tab(children=(VBox(children=(SnapshotCanvas(status=u'Not yet rendered'), JSProxyWidget(status=u'Not yet rendered'))), HTML(value=u'<img src=\"vector_field.png?id=snapshot_id_9_1539974981245\" id=\"snapshot_id_9_1539974981245\"/>\\n <div id=\"snapshot_id_10_1539974981246\">vector_field.png</div>')), _titles={u'1': 'Snapshot', u'0': 'Canvas'})"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def show_motions(motions, duration=1, radius=0.01): \n",
" #c2 = dual_canvas.DualCanvasWidget(width=800, height=800)\n",
" c2 = dual_canvas.SnapshotCanvas(\"vector_field.png\", width=800, height=800)\n",
" c2.js_init(\"\"\"\n",
" var frame0 = element.frame_region(50, 50, 300, 300, -1, -1, 1, 1);\n",
" element.frame0 = frame0;\n",
" frame0.frame_rect({x:-1, y:-1, w:2, h:2, color:\"#ddf\"});\n",
" frame0.lower_left_axes({min_x:-1, min_y:-1, max_x:1, max_y:1, max_tick_count:4});\n",
" var frame = element.frame_region(50, 50, 300, 300, -1, -1, 1, 1);\n",
" var f_options = {\n",
" frame: frame,\n",
" duration: duration, // seconds\n",
" initial_color: \"red\",\n",
" final_color: \"red\",\n",
" radius: radius,\n",
" motions: motions,\n",
" shape: \"line\",\n",
" };\n",
" element.fit(null, 50)\n",
" element.canvas_2d_vector_field(f_options);\n",
" \"\"\", motions=motions, duration=duration, radius=radius)\n",
" #return c2.debugging_display()\n",
" return c2\n",
"\n",
"c2 = show_motions(motions, duration)\n",
"c2.display_all()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"element"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c2.element.reset_canvas()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#points"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#motions"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# attraction towards the center\n",
"xc = 0.3\n",
"yc = 0.4\n",
"npoints = 100\n",
"duration = 1\n",
"shape = (npoints, 2)\n",
"points = np.random.random(shape) * 2.0 - 1.0\n",
"max_movement = 0.2\n",
"shift = np.array([[xc, yc]]) - points\n",
"movement = max_movement * shift\n",
"offsets = np.random.random((npoints,)) * duration\n",
"motions = []\n",
"for i in range(npoints):\n",
" (x, y) = points[i]\n",
" (dx, dy) = movement[i]\n",
" motion = {\n",
" \"sx\": x-dx, \"sy\": y-dy, \"ex\": x+dx, \"ey\": y+dy, \"dt\": offsets[i]\n",
" }\n",
" motions.append(motion)\n",
" \n",
"c3 = show_motions(motions, duration)\n",
"#c3"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# motion around the center point\n",
"xc = 0.3\n",
"yc = 0.4\n",
"npoints = 100\n",
"duration = 1\n",
"shape = (npoints, 2)\n",
"points = np.random.random(shape) * 2.0 - 1.0\n",
"max_movement = 0.2\n",
"shift = np.array([[xc, yc]]) - points\n",
"shift2 = np.zeros(shape)\n",
"shift2[:,0] = shift[:,1]\n",
"shift2[:,1] = -shift[:,0]\n",
"movement = max_movement * shift2\n",
"offsets = np.random.random((npoints,)) * duration\n",
"motions = []\n",
"for i in range(npoints):\n",
" (x, y) = points[i]\n",
" (dx, dy) = movement[i]\n",
" motion = {\n",
" \"sx\": x-dx, \"sy\": y-dy, \"ex\": x+dx, \"ey\": y+dy, \"dt\": offsets[i]\n",
" }\n",
" motions.append(motion)\n",
" \n",
"c4 = show_motions(motions, duration)\n",
"c4.js_init(\"\"\"\n",
"debugger;\n",
"element.frame0.frame_circle({x: xc, y:yc, r:max_movement, color:\"rgba(255,200,100,0.7)\"})\n",
"\"\"\", xc=xc, yc=yc, max_movement=max_movement)\n",
"#c4"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"element"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c4.element.reset_canvas()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4992e17da5dd456c9daa3070f0667fed",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>SnapshotCanvas</code>.</p>\n",
"<p>\n",
" If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another notebook frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"SnapshotCanvas(status=u'Not yet rendered')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def attraction(cx, cy, points, factor=1.0):\n",
" shift = factor * (np.array([[cx, cy]]) - points)\n",
" return unitize(shift)\n",
"\n",
"def clockwise(cx, cy, points, factor=1.0):\n",
" shift = attraction(cx, cy, points, factor=1.0)\n",
" shift2 = np.zeros(shift.shape)\n",
" shift2[:,0] = shift[:,1]\n",
" shift2[:,1] = -shift[:,0]\n",
" return shift2\n",
"\n",
"npoints = 1000\n",
"duration = 1\n",
"shape = (npoints, 2)\n",
"points = np.random.random(shape) * 2.0 - 1.0\n",
"shift = 0.25 * (\n",
" #+ attraction(0.5, 0.5, points)\n",
" + 2* attraction(-0.5, -0.5, points)\n",
" + clockwise(0.5, -0.5, points, -1)\n",
" + 3*clockwise(-0.5, 0.5, points, -1)\n",
")\n",
"#shift = unitize(shift)\n",
"max_movement = 0.2\n",
"movement = max_movement * shift\n",
"offsets = np.random.random((npoints,)) * duration\n",
"motions = []\n",
"for i in range(npoints):\n",
" (x, y) = points[i]\n",
" (dx, dy) = movement[i]\n",
" motion = {\n",
" \"sx\": x-dx, \"sy\": y-dy, \"ex\": x+dx, \"ey\": y+dy, \"dt\": offsets[i]\n",
" }\n",
" motions.append(motion)\n",
" \n",
"c4 = show_motions(motions, duration)\n",
"c4"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"element"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c4.element.reset_canvas()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# dump motions to file\n",
"filename = \"motions.json\"\n",
"f = open(filename, \"w\")\n",
"f.write(\"[\\n\")\n",
"for m in motions:\n",
" f.write(\"{\")\n",
" for k in m:\n",
" v = m[k]\n",
" f.write(\"%s:%2.2f,\" % (k, v))\n",
" f.write(\"},\\n\")\n",
"f.write(\"]\\n\")\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"print(open(filename).read(3200))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"motions[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}