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kazimuth/Bitmap.ipynb

Created November 28, 2017 04:10
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Bitmap.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Bitwise operations on Maps\n",
"Note: you don't need the bindings built to use this notebook."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"try:\n",
" import peachpy as p\n",
" import peachpy.x86_64 as x86\n",
"except:\n",
" print('run `pip install --upgrade git+https://github.com/Maratyszcza/PeachPy`')\n",
"try:\n",
" from ipywidgets import interact\n",
"except:\n",
" print('run `pip install ipywidgets && jupyter nbextension enable --py --sys-prefix widgetsnbextension`')\n",
" \n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAe4AAAH0CAYAAAD7Ws6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ffb5ad69f98>"
]
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
],
"source": [
"def empty():\n",
" return np.zeros((8,8), dtype=bool)\n",
"\n",
"def show(q):\n",
" plt.imshow(q, vmin=0, vmax=1)\n",
"\n",
"q = empty()\n",
"q[4,5] = 1\n",
"q[0,2] = 1\n",
"\n",
"show(q)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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8qvZJ8o4k/5TkF9ZGO0nGGD/Y7DgAwDQr7qcneUKS/5Hktqp6YZJjknw/ySfHGB+fYAwA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ffb580952e8>"
]
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
],
"source": [
"def enc(arr):\n",
" result = 0\n",
" arr = arr.reshape(64)\n",
" for i in range(64):\n",
" result |= arr[i] << i\n",
" return result\n",
"\n",
"def dec(q):\n",
" assert q < 1 << 64\n",
" result = np.zeros(64)\n",
" for i in range(64):\n",
" result[i] = (q >> i) & 1\n",
" return result.reshape(8, 8)\n",
"\n",
"show(dec(enc(q)))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2c22ff5296094db2bdb569a2b2f53263",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</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": [
"interactive(children=(IntSlider(value=0, description='z', max=63), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"@interact(z=(0,63))\n",
"def move(z=0):\n",
" return dec(1 << z)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def make_function_reg(name, op):\n",
" target = x86.uarch.default + x86.isa.lzcnt + x86.isa.avx2 + x86.isa.popcnt\n",
" q = p.Argument(p.const_uint64_t, name=\"q\")\n",
"\n",
" with x86.Function(name, (q, ), p.uint64_t, target=target) as asm_function:\n",
" reg_q = x86.GeneralPurposeRegister64()\n",
" reg_out = x86.GeneralPurposeRegister64()\n",
" x86.LOAD.ARGUMENT(reg_q, q)\n",
" op(reg_out, reg_q)\n",
" x86.RETURN(reg_out)\n",
" \n",
" return asm_function.finalize(x86.abi.detect()).encode().load()\n",
"\n",
"bsr = make_function_reg('bsr', x86.BSR)\n",
"bsf = make_function_reg('bsf', x86.BSF)\n",
"lzcnt = make_function_reg('lzcnt', x86.LZCNT)\n",
"popcnt = make_function_reg('popcnt', x86.POPCNT)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e7d2c2ced27e4c0f900b18c423ed19e2",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</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": [
"interactive(children=(IntSlider(value=0, description='s', max=63), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"@interact(s=(0,63))\n",
"def ops(s=0):\n",
" return bsr(1 << s), bsf(1 << s), lzcnt(1 << s), popcnt(1 << s)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def ones(q):\n",
" result = []\n",
" i = 0\n",
" while q != 0:\n",
" next = bsf(q)\n",
" result.append(next)\n",
" q ^= 1 << next\n",
" \n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7a0bbc207eca432586671f6420dd73ba",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</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": [
"interactive(children=(IntSlider(value=0, description='s', max=63), IntSlider(value=63, description='t', max=63), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"@interact(s=(0,63), t=(0,63))\n",
"def ops(s=0,t=63):\n",
" q = (1 << s) | (1 << t)\n",
" return bsr(q), bsf(q), lzcnt(q), popcnt(q), ones(q)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0.]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"col = 0\n",
"for i in range(8):\n",
" col |= 1 << i*8\n",
"\n",
"dec(col)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0., 0., 0., 0., 1., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 1., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 1., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 1., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 1., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 1., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 1., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 1., 0.]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dec(col << 6)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1., 1., 1., 1., 1., 1., 1., 1.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"row = 255\n",
"dec(row)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 1., 1., 1., 1., 1., 1., 1.],\n",
" [ 0., 0., 0., 0., 0., 0., 0., 0.]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dec(row << 8 * 6)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1., 1., 1., 1., 1., 1., 1., 1.],\n",
" [ 1., 1., 1., 1., 1., 1., 1., 1.],\n",
" [ 1., 1., 1., 1., 1., 1., 1., 1.],\n",
" [ 1., 1., 1., 1., 1., 1., 1., 1.],\n",
" [ 1., 1., 1., 1., 1., 1., 1., 1.],\n",
" [ 1., 1., 1., 1., 1., 1., 1., 1.],\n",
" [ 1., 1., 1., 1., 1., 1., 1., 1.],\n",
" [ 1., 1., 1., 1., 1., 1., 1., 1.]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all1 = 0xffffffffffffffff\n",
"dec(all1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "24319bf4dfc14f96b884efb430126885",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</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": [
"interactive(children=(IntSlider(value=0, description='r', max=7), IntSlider(value=0, description='c', max=7), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"@interact(r=(0,7), c=(0,7))\n",
"def scopei(r=0,c=0):\n",
" return dec((row << (r*8)) | (col << c))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "59d72619e99944f09a090362cb2e9600",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</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": [
"interactive(children=(IntSlider(value=0, description='r', max=7), IntSlider(value=0, description='c', max=7), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def rowof(sq):\n",
" return sq >> 3\n",
"def colof(sq):\n",
" return sq & 7\n",
"def sqof(r, c):\n",
" return (r << 3) | c\n",
"\n",
"# note: an unfortunate consequence of this row-major layout\n",
"# w/ 0 at LSB is that << shifts the whole matrix *right*, and\n",
"# >> shifts the whole matrix *left*\n",
"\n",
"def point(sq):\n",
" row = rowof(sq)\n",
" col = colof(sq)\n",
" plt.scatter(col, row)\n",
"\n",
"@interact(r=(0,7), c=(0,7))\n",
"def scopeq(r=0,c=0):\n",
" q = (row << (r*8)) | (col << c)\n",
" show(dec(q))\n",
" point(bsr(q))\n",
" point(bsf(q))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9ea37c160f8a4a18bb949ac08a4bb086",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</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": [
"interactive(children=(IntSlider(value=3, description='c', max=7), IntSlider(value=2, description='ri', max=8), IntSlider(value=5, description='rj', max=8), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def between_vertical(c, ri, rj):\n",
" return (col << c) & (all1 << (8 * ri)) & (all1 >> (8 * (7-rj)))\n",
"\n",
"@interact(c=(0,7),ri=(0,8),rj=(0,8))\n",
"def betweenvi(c=3,ri=2,rj=5):\n",
" if rj < ri:\n",
" ri, rj = rj, ri\n",
" #show(dec(between_vertical(col,ri,rj)))\n",
" show(dec(between_vertical(c,ri,rj)))\n",
" point(sqof(ri, c))\n",
" point(sqof(rj, c))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "047ff33b53e04edfa9fa3854e8822d5a",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</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": [
"interactive(children=(IntSlider(value=3, description='r', max=7), IntSlider(value=5, description='ci', max=7), IntSlider(value=6, description='cj', max=7), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"@interact(r=(0,7), ci=(0,7), cj=(0,7))\n",
"def betweenhi(r=3, ci=5, cj=6):\n",
" if cj < ci:\n",
" ci, cj = cj, ci\n",
" q = row << (8*r)\n",
" q = (q << ci) & (q >> (7^cj))\n",
" show(dec(q))\n",
" point(sqof(r,ci))\n",
" point(sqof(r,cj))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a0ae48bc71546589a41e198cebedbe1",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</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": [
"interactive(children=(IntSlider(value=3, description='r', max=7), IntSlider(value=4, description='c', max=7), Dropdown(description='d', options=('nn', 'ee', 'ss', 'ww'), value='nn'), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"NN = 0\n",
"EE = 1\n",
"SS = 2\n",
"WW = 3\n",
"\n",
"def project(r,c,d):\n",
" if d == NN:\n",
" r-=1\n",
" return (col << c) & (all1 >> (8 * (7^r)))\n",
" elif d == SS:\n",
" r+=1\n",
" return (col << c) & (all1 << (8 * r))\n",
" elif d == EE:\n",
" c+=1\n",
" row_ = (row << 8*r)\n",
" return row_ & (row_ << c)\n",
" elif d == WW:\n",
" c-=1\n",
" row_ = (row << 8*r)\n",
" return row_ & (row_ >> (7^c))\n",
"\n",
"@interact(r=(0,7),c=(0,7),d=['nn', 'ee', 'ss', 'ww'])\n",
"def projecti(r=3,c=4,d='up'):\n",
" d = {'nn':NN,'ee':EE,'ss':SS,'ww':WW}[d]\n",
" show(dec(project(r,c,d)))\n",
" point(sqof(r,c))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dcca96c60bd74d93ad8a9f389ba5ca6c",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</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": [
"interactive(children=(IntSlider(value=1, description='r', max=7), IntSlider(value=2, description='c', max=7), IntSlider(value=2, description='d', max=3), Dropdown(description='q', index=1, options=(8192, 288234843186200593), value=288234843186200593), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"@interact(r=(0,7), c=(0,7), d=(0,3), q=[0x00000002000, 0x400041010010011])\n",
"def lazertrace(r=1, c=2, d=2, q=0x400041010010011):\n",
" point(sqof(r,c))\n",
"\n",
" trace = np.zeros((8,8,3))\n",
" trace[:,:,0] = dec(q)\n",
" proj = project(r, c, d)\n",
"\n",
" isxt = proj & q\n",
" if isxt:\n",
" if d == NN or d == WW:\n",
" sq = bsr(isxt)\n",
" trunc = proj & (all1 << (sq))\n",
" \n",
" if d == SS or d == EE:\n",
" sq = bsf(isxt)\n",
" trunc = proj & (all1 >> (63-sq))\n",
"\n",
" trace[:,:,1] = dec(trunc)\n",
" trace[:,:,2] = dec(trunc)\n",
" \n",
" point(sq)\n",
" else:\n",
" trace[:,:,2] = dec(proj)\n",
"\n",
" \n",
" plt.imshow(trace,vmin=0,vmax=1)\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"try scrubbing r.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "44382b545b864820bd85277bcf3582c2",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</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": [
"interactive(children=(IntSlider(value=6, description='r', max=7), IntSlider(value=3, description='c', max=7), IntSlider(value=1, description='d', max=3), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"NW = 0\n",
"NE = 1\n",
"SE = 2\n",
"SW = 3\n",
"\n",
"reflect = [\n",
" # NW NE SE SW\n",
" [ -1, -1, EE, WW], # NN\n",
" [ NN, -1, -1, SS], # EE\n",
" [ WW, EE, -1, -1 ], # SS\n",
" [ -1, NN, SS, -1 ] # WW\n",
"];\n",
"dx = [-1, 1, 1, -1]\n",
"dy = [-1, -1, 1, 1]\n",
"\n",
"def make_laser_map(q, directions, r, c, d):\n",
" # q: long representing locations\n",
" # directions: np.array((8,8), dtype=np.uint8) holding orientations\n",
" # in actual code can be implemented with bit lookups\n",
" result = 0\n",
" while True:\n",
" proj = project(r, c, d)\n",
" # sometimes is an np.uint64, which causes problems; just cast it\n",
" # not needed in C\n",
" q = int(q)\n",
" isxt = proj & q\n",
" if isxt:\n",
" if d == NN or d == WW:\n",
" sq = bsr(isxt)\n",
" trunc = proj & (all1 << (sq))\n",
" \n",
" if d == SS or d == EE:\n",
" sq = bsf(isxt)\n",
" trunc = proj & (all1 >> (63-sq))\n",
" point(sq)\n",
" else:\n",
" result |= proj\n",
" break\n",
" \n",
" result |= trunc\n",
" r, c = rowof(sq), colof(sq)\n",
" d = reflect[d][directions[r,c]]\n",
" if d == -1:\n",
" break\n",
"\n",
" return result\n",
"\n",
"directions = np.array([\n",
" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0,SE, 0, 0, 0, 0,SW, 0],\n",
" [ 0, 0, 0,SE, 0,SW, 0, 0],\n",
" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0, 0, 0,NE, 0, 0,NW, 0],\n",
" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0,SW, 0, 0, 0,NW, 0, 0],\n",
" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
"])\n",
"present = enc(np.array([\n",
" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0, 1, 0, 0, 0, 0, 1, 0],\n",
" [ 0, 0, 0, 1, 0, 1, 0, 0],\n",
" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0, 0, 0, 1, 0, 0, 1, 0],\n",
" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0, 1, 0, 0, 0, 1, 0, 0],\n",
" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
"]))\n",
"pdec = dec(present)\n",
"\n",
"print('try scrubbing r.')\n",
"\n",
"@interact(r=(0,7), c=(0,7), d=(0,3))\n",
"def make_laser_map_i(r=6, c=3, d=1):\n",
" trace = np.zeros((8,8,3))\n",
" trace[:,:,0] = pdec\n",
" trace[:,:,1] = dec(make_laser_map(present, directions, r, c, d))\n",
" \n",
" point(sqof(r,c))\n",
" \n",
" plt.imshow(trace, vmin=0, vmax=1)\n",
" for i in range(8):\n",
" for j in range(8):\n",
" if pdec[i,j]:\n",
" plt.arrow(j, i, dx[directions[i,j]] / 2, dy[directions[i,j]] / 2, color='white')\n",
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"text/plain": "<matplotlib.figure.Figure at 0x7ffb4eba27b8>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"01dd38e0b3884b50869bb8f2bd5d1277": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "d",
"layout": "IPY_MODEL_79ec0297d9d04797801957125a78e8f5",
"max": 3,
"style": "IPY_MODEL_67867e99c25e4d3b8a0969506488c49e",
"value": 2
}
},
"01ef3caa155f442bb322f5a033f39b97": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"0207c715195b45cda9737823f1445af0": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_1b734a97c2bd4146bc774b3c6eeae2ee",
"outputs": [
{
"data": {
"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f4982533b38>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"020ccfa5cfa945d0ae5dc394ea06a942": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"02111999c9644b0c912bb050d80a23fb": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "d",
"layout": "IPY_MODEL_510e65d6133b485bab8ebfdf505c1a9d",
"max": 3,
"style": "IPY_MODEL_8dfb2c70c5d44532839856e3160fd57d",
"value": 2
}
},
"02ef33642d224c4bb5669b928bf3d043": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"0329b642b8fe4a4996ffd9625329cdba": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_01ef3caa155f442bb322f5a033f39b97",
"outputs": [
{
"ename": "TypeError",
"evalue": "ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/data/vision/torralba/scratch2/jhgilles/miniconda3/envs/flowstone/lib/python3.6/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-73-93374ba37cee>\u001b[0m in \u001b[0;36mlazertrace\u001b[0;34m(r, c, d)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpdec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmake_laser_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpresent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-73-93374ba37cee>\u001b[0m in \u001b[0;36mmake_laser_map\u001b[0;34m(q, directions, r, c, d)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mproj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0misxt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproj\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 24\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misxt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mNN\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWW\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
]
}
]
}
},
"03364b5b3e5a45fb86c2c147e6539efe": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "r",
"layout": "IPY_MODEL_1ebd7cbbda344d63b522facddc389e75",
"max": 7,
"style": "IPY_MODEL_c1a242c0473a4f13a35c79c6740685d3",
"value": 6
}
},
"0338e0be8a714068b5c6969e6ee8db1f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "VBoxModel",
"state": {
"_dom_classes": [
"widget-interact"
],
"children": [
"IPY_MODEL_4658c4d7f582457684bbdfb3b777e19f",
"IPY_MODEL_fe90009675d84f84a72ed7f9423e9e74",
"IPY_MODEL_d37a965d3c044adda6d13ca28ae067f6",
"IPY_MODEL_eaf3430f943d4f7d9a3ff502101aabef",
"IPY_MODEL_36836afede0147e2a8b1c6f5af2b2933"
],
"layout": "IPY_MODEL_de9c6b130776413eac24ebafe9bdc47e"
}
},
"034f91ab73fc4edd8871743c4f07ca72": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_4f88323b009a45029c79bd871db026ea",
"outputs": [
{
"data": {
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"text/plain": "<matplotlib.figure.Figure at 0x7f498c0283c8>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"038522dec42b4c558d30573c7dd4ab0c": {
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h4a6qZ1dVm388f1H7gakQbRZBvPuzkHBX1TFJ3pzkS4vYPkyNaLNI4t2XwcNdVZXkt5N8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\nItwA0BHhBoCO/H8QvXn3YeSX3gAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f498211f630>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"05b687f486764b029e0335898bc35a37": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "DescriptionStyleModel",
"state": {
"description_width": ""
}
},
"05bb5910f56a4b2d8d7fb4938720829b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"05d563bdb4464b649eb7cbe850f91933": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"05d99ccf14db42ae9503134a0a10bcbb": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_76707a12dea84de2b6e8c2edad09d2bb",
"outputs": [
{
"data": {
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"text/plain": "<matplotlib.figure.Figure at 0x7f4981ba1048>"
},
"metadata": {
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"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"05e3a2135d9e4c93a6fa8ee4af26a945": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"06053f0e526e44cb8f896c8c936ba1d4": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_f3121e91a26a4239832c1162c5a58499",
"outputs": [
{
"data": {
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"text/plain": "<matplotlib.figure.Figure at 0x7f49819ebeb8>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"0606060ab3ed4c40847b1c483300e392": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "VBoxModel",
"state": {
"_dom_classes": [
"widget-interact"
],
"children": [
"IPY_MODEL_141b9593bf61468b9fffb600e0961fa4",
"IPY_MODEL_11926094e68a46d3bad79ab11cc62dd2",
"IPY_MODEL_ac189e6f00864cebb4bd5b4e6661eb29",
"IPY_MODEL_d816c11e250347d4a1aaee329ea2d42c"
],
"layout": "IPY_MODEL_a2a9e28f2eab40e2a28b2e7710ae5921"
}
},
"06077c7d9db247eb950b6d24ddd4e0e4": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_3452603c2dd84d37b5a19e18a8f64784",
"outputs": [
{
"data": {
"text/plain": "array([[ 0., 0., 0., 0., 0., 0., 0., 0.],\n [ 0., 0., 0., 0., 0., 0., 0., 0.],\n [ 0., 0., 0., 0., 0., 0., 0., 0.],\n [ 0., 0., 0., 0., 0., 0., 0., 0.],\n [ 0., 0., 0., 0., 0., 0., 0., 0.],\n [ 0., 1., 0., 0., 0., 0., 0., 0.],\n [ 0., 0., 0., 0., 0., 0., 0., 0.],\n [ 0., 0., 0., 0., 0., 0., 0., 0.]])"
},
"metadata": {},
"output_type": "display_data"
}
]
}
},
"0612de51ea804d87ae8a5933cebd9b18": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"0669f079eac74ecbadfb86099b9e314d": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_2b9dad91cfd54777bd78d28984d790a2",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "3 0\n"
},
{
"data": {
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"text/plain": "<matplotlib.figure.Figure at 0x7f49823de978>"
},
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}
},
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}
]
}
},
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"model_module_version": "1.0.0",
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},
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"model_name": "IntSliderModel",
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"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_4bc93c48a3b04e9dac31facb1c42e278",
"outputs": [
{
"data": {
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"text/plain": "<matplotlib.figure.Figure at 0x7ffb4edf1ef0>"
},
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-109-6b1961bc89a3>\u001b[0m in \u001b[0;36mmake_laser_map_i\u001b[0;34m(r, c, d)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpdec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmake_laser_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpresent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-109-6b1961bc89a3>\u001b[0m in \u001b[0;36mmake_laser_map\u001b[0;34m(q, directions, r, c, d)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mproj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mproj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-87-acc3aa59f506>\u001b[0m in \u001b[0;36mproject\u001b[0;34m(r, c, d)\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mNN\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m-=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m \u001b[0;34m<<\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mall1\u001b[0m \u001b[0;34m>>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m^\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mSS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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{
"data": {
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"text/plain": "<matplotlib.figure.Figure at 0x7f4982086e80>"
},
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},
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}
},
"16f4c58d09c1499fad1016169bb6f186": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "d",
"layout": "IPY_MODEL_b9d58b8a790f40eeb2722530e533ed4f",
"max": 3,
"style": "IPY_MODEL_c2b9466b09a84d4797068ad9b084af85",
"value": 3
}
},
"1759d5b8422241b7b47633db6a844f52": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_4993dc87014042e7863748b6d032d211",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "1 0\n0 0\n"
},
{
"data": {
"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f4981eef6a0>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
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{
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"text": "53 (6, 5) 1 0 0\n21 (2, 5) 0 3 3\n19 (2, 3) 3 2 2\n35 (4, 3) 2 1 1\n38 (4, 6) 1 0 0\n14 (1, 6) 0 3 3\n9 (1, 1) 3 2 2\n49 (6, 1) 2 3 -1\n"
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"layout": "IPY_MODEL_18d75d3007eb4c96ba5cd5537974f6aa",
"max": 3,
"style": "IPY_MODEL_d0cd10c993c4467aab9c527320914973",
"value": 3
}
},
"1a71430dab8647ad8733db3fc74ec243": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"1a7272e02f3e45db889f44e551988745": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_badfae5beecc44c1b9a66d402d2992d7",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "53 (6, 5) 1 0 0\n21 (2, 5) 0 3 3\n19 (2, 3) 3 2 2\n35 (4, 3) 2 1 1\n38 (4, 6) 1 0 0\n14 (1, 6) 0 3 3\n9 (1, 1) 3 2 2\n49 (6, 1) 2 3 -1\n"
},
{
"data": {
"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f49823a7978>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"27863ad2901d4943908866cdbfd6fcc3": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"27b53a4027b743069b2af602052987e5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "DescriptionStyleModel",
"state": {
"description_width": ""
}
},
"27d4254c83604a8a89631e8d0b74a385": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_61fbb3ba93f844c58cc7caf9ce5b7447",
"outputs": [
{
"data": {
"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f498c416978>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"2834656ce9ef461db0f98f3e143b9df4": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"28a1c945e6a94dcbab264efe493432e5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "DescriptionStyleModel",
"state": {
"description_width": ""
}
},
"28e4d118d637454e9b048e9bb704c2dc": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"292a4c669f8a4935982186b3b3952346": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "DropdownModel",
"state": {
"_options_labels": [
"8192",
"288234843186200593"
],
"description": "q",
"index": 1,
"layout": "IPY_MODEL_d1ff96239e88420f89cb44369bf51629",
"style": "IPY_MODEL_3d9d443179724175acc5979e799a6564"
}
},
"294c87e7334e488e8714c39f6b000736": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"296fc47bad2548ea893089c6226e4e64": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"298780bcf40142b1a8c69f2cfb38e307": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"29f8a4e3565a40589e7f7717965eab9c": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_0b720bf1e4394a3982463b9d4c8a72ff",
"outputs": [
{
"data": {
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"text/plain": "<matplotlib.figure.Figure at 0x7f498c43ec50>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"36914e48198d4cd2b70af213f6addf0b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "DropdownModel",
"state": {
"_options_labels": [
"8192",
"288234843186200593"
],
"description": "q",
"index": 1,
"layout": "IPY_MODEL_69ff519e87934ca99ee8b5f91fede255",
"style": "IPY_MODEL_c67a11e5c75e4959b28d710febc5bd63"
}
},
"369738e80d924734be4ec53929131e9b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"36d16d8ffc1f458fbde28061a32db46b": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_ab21a66dfb524b67a0cdcad88d365ef6",
"outputs": [
{
"data": {
"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f498c028a58>"
},
"metadata": {
"image/png": {
"height": 250,
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}
},
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}
]
}
},
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"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "c",
"layout": "IPY_MODEL_18a6388930084a1f80c51c34e7e552f2",
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}
},
"37085e29592040e881d5aedfef3f34b6": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
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}
},
"37898c0d500c4f61873c432da1019947": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
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},
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"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_9a0ac1bf194f451d84e2223afb6cf322",
"outputs": [
{
"data": {
"text/plain": "(63, 0, 0, 2, [0, 63])"
},
"metadata": {},
"output_type": "display_data"
}
]
}
},
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"model_name": "IntSliderModel",
"state": {
"description": "z",
"layout": "IPY_MODEL_cdf0bbc6e9e7472a8e3ce9794e51b68a",
"max": 63,
"style": "IPY_MODEL_fc5b1be973fd478bb2e4c666f967b16e"
}
},
"383a1b9492af45eb995eec9491b4ce06": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "VBoxModel",
"state": {
"_dom_classes": [
"widget-interact"
],
"children": [
"IPY_MODEL_405e8425cf3d49a291dca118bd0178f0",
"IPY_MODEL_feef695d2b2f4222bc955cff37e424da",
"IPY_MODEL_bc526c072c6c4f69b49043d538372017",
"IPY_MODEL_05d99ccf14db42ae9503134a0a10bcbb"
],
"layout": "IPY_MODEL_9225367327fd464db45ffb13bdcbee64"
}
},
"38e0525c10d248e7b57b5256c2726c44": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "r",
"layout": "IPY_MODEL_d443222e49c24f3f95c24c7804e77a47",
"max": 7,
"style": "IPY_MODEL_f1f8e2d18eee4ebdbd14c4817cdea579",
"value": 6
}
},
"38e796369c024b8582833c6d0124c9e2": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "r",
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"text/plain": "<matplotlib.figure.Figure at 0x7ffb4eb41518>"
},
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}
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},
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}
},
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"layout": "IPY_MODEL_9b3350ab62d340e192d6c6d5a2c5bdfe",
"outputs": [
{
"data": {
"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fb4774fcf28>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"3dc300c60e114e1b9c2989173c02435a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"3dd1a670485c45b494e9139b90022d3e": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_0cb160eb49654211973c20e17bb61d1a",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "2 578721382704613376\n578721382704613376 34360262656\n"
},
{
"data": {
"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f498c3d4cf8>"
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{
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"text/plain": "<matplotlib.figure.Figure at 0x7f498c25d3c8>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"3f3c8767ae32415a8fcae6f5f8acebd7": {
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"state": {
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"layout": "IPY_MODEL_4f53a54693c548dda3bdfa5b1a3eb97c",
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},
"3f78210380a341919c4a8500cb833923": {
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"model_name": "VBoxModel",
"state": {
"_dom_classes": [
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],
"children": [
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"IPY_MODEL_7bdd7e791aef402cb3e96394e2141d40",
"IPY_MODEL_1c5327719c2c4de4b64403da5f0e1596",
"IPY_MODEL_cd026e9e5d194f2e89d2f613d6a4e2f2"
],
"layout": "IPY_MODEL_0fa0e4b55a0c47e7981b648bc72639ab"
}
},
"3f9281576fac49d790044705976c535f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "DropdownModel",
"state": {
"_options_labels": [
"nn",
"ee",
"ss",
"ww"
],
"description": "d",
"index": 3,
"layout": "IPY_MODEL_fb764088a1e44975a1c5c64c6ed05fab",
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}
},
"3f984132df11489f973541206cfad626": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"3fb84ca3f344484194690b20ad420233": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"3fb9e7c346f0405bbe3823ef68e4abca": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "d",
"layout": "IPY_MODEL_b08c50ec161d4700ac68ab45188f3137",
"max": 3,
"style": "IPY_MODEL_e02223d1d14b498d9db4177b2e1c4ffc",
"value": 2
}
},
"3fdcfc5789724f2096e68782950ec2ed": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "c",
"layout": "IPY_MODEL_fa54609d1f9a41fb972b6acb105e7eb1",
"max": 7,
"style": "IPY_MODEL_5d507f2da6b149da84d2a54f0904422e",
"value": 4
}
},
"401d0db14d8c4e6d8b6c1cede49c89be": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"4046c9572eb54a9a9fb75d1659db6d1c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"405e8425cf3d49a291dca118bd0178f0": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "r",
"layout": "IPY_MODEL_e5d331cb56744786b61f43212d463a2d",
"max": 7,
"style": "IPY_MODEL_c2ca02aee836479397a2fd8bdba13b7e",
"value": 5
}
},
"406bcf60d82a4d54b1fec5e504932cfd": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"406ee844272b43fb93c114aaabb651b4": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_ab42abd541c14d92aefa301c76d0e4ec",
"outputs": [
{
"ename": "TypeError",
"evalue": "ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/data/vision/torralba/scratch2/jhgilles/miniconda3/envs/flowstone/lib/python3.6/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-74-72e070270bd6>\u001b[0m in \u001b[0;36mmake_laser_map_i\u001b[0;34m(r, c, d)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpdec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmake_laser_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpresent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-74-72e070270bd6>\u001b[0m in \u001b[0;36mmake_laser_map\u001b[0;34m(q, directions, r, c, d)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mproj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0misxt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproj\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 24\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misxt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mNN\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWW\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
]
}
]
}
},
"41600a93adc645248b7445d40e8165a4": {
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"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "r",
"layout": "IPY_MODEL_c6081582308543c39797e04b091650cb",
"max": 7,
"style": "IPY_MODEL_2bacd89597bc41349451abe4e1a7c738",
"value": 1
}
},
"41685720baf34440b6cef9d94f030868": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"4168bfd6b045479abcc55698fb450735": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"41dfce1db9e7462a8b5f5f0e073f8328": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"41ed47058a274215bd435c134f75217c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"421812c96f0349b883c2474bb573d393": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"421c339796ef4122bdff141545998343": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"422b5f00d1f1434197e5352ac45fa401": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"4270cfbd8b014e118e823ddd7fbdc55e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "VBoxModel",
"state": {
"_dom_classes": [
"widget-interact"
],
"children": [
"IPY_MODEL_aa726a27c609472eb2e646b7bf2a28e0",
"IPY_MODEL_47b4014199f34c39bddeec780a4c7d87",
"IPY_MODEL_5afd016b020944eca9aa0d514471b75d",
"IPY_MODEL_c28b7d2ab4494f8888d57883920ad9a0",
"IPY_MODEL_27d4254c83604a8a89631e8d0b74a385"
],
"layout": "IPY_MODEL_0c02866f25564ac9b8ffc3316341dcb5"
}
},
"4278f5e791c140749467634ba477d4de": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_bf7a65ff3cc64224a7f0c022965a7f2c",
"outputs": [
{
"data": {
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"\u001b[0;32m<ipython-input-101-010b77e58ed5>\u001b[0m in \u001b[0;36mmake_laser_map_i\u001b[0;34m(r, c, d)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpdec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmake_laser_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpresent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-101-010b77e58ed5>\u001b[0m in \u001b[0;36mmake_laser_map\u001b[0;34m(q, directions, r, c, d)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mproj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0misxt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproj\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 24\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misxt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mNN\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWW\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
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"text/plain": "<matplotlib.figure.Figure at 0x7f498c4cf588>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"4a88ffa4784e4ca2a741d3b0e43e978d": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_2cf8fc3c3dee4f7a96928d0a48c661d0",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "<class 'int'> <class 'numpy.int64'> 0b1000000000000000000000000000000000000000000000000000000000000000 0b100010000000000100100000000000001010000100001000000000\n"
},
{
"ename": "TypeError",
"evalue": "ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/data/vision/torralba/scratch2/jhgilles/miniconda3/envs/flowstone/lib/python3.6/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-111-6b1961bc89a3>\u001b[0m in \u001b[0;36mmake_laser_map_i\u001b[0;34m(r, c, d)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpdec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmake_laser_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpresent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-111-6b1961bc89a3>\u001b[0m in \u001b[0;36mmake_laser_map\u001b[0;34m(q, directions, r, c, d)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mproj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0misxt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproj\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misxt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mNN\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWW\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
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DR7ssVX7MKvyfa48/MiKpDIuBl/eYfgLYr5yhVFp7RludzXBL2VqM8W4lo632ZLilrC3G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\nAElFTkSuQmCC\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f4981ce7c88>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"504ecbb6840f4892a7ccb13931476d8a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"50a37b8fbdd04bd49fb198f11acdcfc5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"50d020c840714597996547b1913c7eed": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "VBoxModel",
"state": {
"_dom_classes": [
"widget-interact"
],
"children": [
"IPY_MODEL_20de5213b4fe4f16ab121791886f82e8",
"IPY_MODEL_490b069eeefe4c8e98bac096d886e38b",
"IPY_MODEL_fb30d2adabc74ae7aa7b8513b55accc5",
"IPY_MODEL_0669f079eac74ecbadfb86099b9e314d"
],
"layout": "IPY_MODEL_6001462796dd42b79e94bd15a0054c5d"
}
},
"50f0a7073db54ee78a81781a1699e4a2": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_a11844dd37134c33807b6b1fe59b8842",
"outputs": [
{
"data": {
"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f4982526748>"
},
"metadata": {
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"height": 250,
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}
},
"output_type": "display_data"
}
]
}
},
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"model_module_version": "1.0.0",
"model_name": "VBoxModel",
"state": {
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],
"children": [
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"51c7b3ae4cab483aaf051d08f5f2fd3f": {
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"IPY_MODEL_29f8a4e3565a40589e7f7717965eab9c"
],
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}
},
"51edbfb857b2498e974c2de0a38d6cc8": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
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"layout": "IPY_MODEL_6c846fb0fdef4944a315fdcc589543ac",
"max": 63,
"style": "IPY_MODEL_e9233b3174d74274a70db43f14cda7d3"
}
},
"52028c3485884ad7b8cb2548f73946cb": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_74062b9f2159431aa03140d85ea10764",
"outputs": [
{
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-102-f75464180bfc>\u001b[0m in \u001b[0;36mmake_laser_map_i\u001b[0;34m(r, c, d)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpdec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmake_laser_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpresent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-102-f75464180bfc>\u001b[0m in \u001b[0;36mmake_laser_map\u001b[0;34m(q, directions, r, c, d)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mproj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0misxt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproj\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 25\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misxt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mNN\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWW\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"layout": "IPY_MODEL_c5d7077605404488ab03b3c145318ebb",
"outputs": [
{
"data": {
"image/png": 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+rQ7qd//vE/Izf/ij2TeOSDKS1IGjJqkcUfvyqqe8Nt/9iD+dfPyqO38OwDr2jDFO2+hG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\naES4AaAR4QaARv4/0ycAdeMm+YEAAAAASUVORK5CYII=\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f498228fd68>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"5afd016b020944eca9aa0d514471b75d": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "d",
"layout": "IPY_MODEL_1c42d0a20b524fddb6f86ad382941218",
"max": 3,
"style": "IPY_MODEL_9dd22cc0f6cf4f4ab65c12edf69c27bc",
"value": 2
}
},
"5b1027d83b6b4a97a0b5c7a58208c9f0": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "c",
"layout": "IPY_MODEL_9c5bec08a846414a9deb54443aab3ce1",
"max": 7,
"style": "IPY_MODEL_6ab97527ad9f43d7aa4d1504f8c8ae62",
"value": 3
}
},
"5b1943d0adb543228e7ed77dbf53b404": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"5b2bd2fe178f415aa6b944e9a6ade8e7": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"5b39108059b345cab113d1cc8dbc5996": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "c",
"layout": "IPY_MODEL_ad23a7c3539345bf9b71852ea0975183",
"max": 7,
"style": "IPY_MODEL_5280453c57144e22a22c30580c844943",
"value": 2
}
},
"5b737e8ed0f94bd1be187f846f319072": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_83f1a2a10011427e8748266ce25169e6",
"outputs": [
{
"data": {
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"text/plain": "<matplotlib.figure.Figure at 0x7f4982341668>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"5fe6790f78284c5cba71c51e0b03e040": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_f74b0db43d7a4d93aa7efa08baf48ca4",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "53 (6, 5) 1 0 0\n21 (2, 5) 0 3 3\n19 (2, 3) 3 2 2\n35 (4, 3) 2 1 1\n38 (4, 6) 1 0 0\n14 (1, 6) 0 3 3\n9 (1, 1) 3 2 2\n49 (6, 1) 2 3 -1\n"
},
{
"data": {
"image/png": 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AW1NKbxnlfBcD8woZZIcp6Eff0OOP788DD84EAkj1y91L3n37sXNXccghzzSa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\nJUnKyP8H7CYhMBJvOFYAAAAASUVORK5CYII=\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f498e8ee9b0>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"5ff431b560c84010bbace62d7e22ce54": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"6001462796dd42b79e94bd15a0054c5d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"602ecd0e95b641a898b1a64a91630013": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_94578e031aca4d7ca6a9741f79c42f02",
"outputs": [
{
"data": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "578721382704613376 34360262656\n"
},
{
"ename": "TypeError",
"evalue": "unsupported operand type(s) for &: 'NoneType' and 'int'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/data/vision/torralba/scratch2/jhgilles/miniconda3/envs/flowstone/lib/python3.6/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-67-9da0b3813db0>\u001b[0m in \u001b[0;36mlazertrace\u001b[0;34m(r, c, d)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpdec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m \u001b[0mtrace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmake_laser_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpresent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-67-9da0b3813db0>\u001b[0m in \u001b[0;36mmake_laser_map\u001b[0;34m(q, directions, r, c, d)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mproj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0misxt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproj\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 24\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0misxt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misxt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for &: 'NoneType' and 'int'"
]
}
]
}
},
"6581688428944e908816ed139c1ed5fc": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"65b757a24e614c02a99b001c9fa7b938": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_7fd30956e32c4840a353ecbd4cfb0b11",
"outputs": [
{
"data": {
"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fb477c55ba8>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"660c98ff2b46440faf9c2c6a0136a7dd": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"660e1289bff043a9a991c1f8dca3b22d": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_974be0ea5a644c89934b47c4993447e3",
"outputs": [
{
"ename": "NameError",
"evalue": "name 'ones' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/data/vision/torralba/scratch2/jhgilles/miniconda3/envs/flowstone/lib/python3.6/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-9-6926fe8dafd6>\u001b[0m in \u001b[0;36mops\u001b[0;34m(s, t)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m63\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m<<\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m<<\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mbsr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbsf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlzcnt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpopcnt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'ones' is not defined"
]
}
]
}
},
"666e838eb6ea405d9f1f376af43d5580": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"6681d838101c476c8a07ea897a74695b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"66a1a6be627141ee974ef8598b962b8f": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"66aa76879ee645a2a88f79075fe16e8d": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_d3605a09dac3431b959f7a92d9159221",
"outputs": [
{
"data": {
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"text/plain": "<matplotlib.figure.Figure at 0x7f49817d3a90>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"67271e0a760a45e488d5c525f59e231a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "r",
"layout": "IPY_MODEL_7711d221952b41e69ab381b0e73d1c8f",
"max": 7,
"style": "IPY_MODEL_a4f2cc78aa224c71ab1409be7be49e40",
"value": 6
}
},
"6736582ecdc0488cb11bae74dafaf064": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "c",
"layout": "IPY_MODEL_19e568f179b745c0aa90cde002d091ad",
"max": 7,
"style": "IPY_MODEL_70818f8fc8e347c6bbd36682e01c0f15",
"value": 2
}
},
"673f45366f984800ace7b46675979569": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"6748bd399dae40b49ab4c12bc8b59f05": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"67530d8f1110490b86d65dd8da951864": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"677345b58461465b85af1be81c5daa44": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "DescriptionStyleModel",
"state": {
"description_width": ""
}
},
"6773954e0e634dfca9279e18a63bc206": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_7e29caf21b9e4aac84898447663a5e81",
"outputs": [
{
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"70a68b0e3eb34b5eaa4cd62b70d789a1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"70d71a011c4948b6b23ff62b5c6678b7": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "r",
"layout": "IPY_MODEL_054a5a68d582420da4adff64ff3ebd69",
"max": 7,
"style": "IPY_MODEL_50a37b8fbdd04bd49fb198f11acdcfc5",
"value": 3
}
},
"70e4555dfef14f1c8847f5d1a5559521": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"71a91b57451043918d6404597af22906": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_ead10c0d017541a681cbaa19b9180aa5",
"outputs": [
{
"data": {
"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f49817b32b0>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
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}
},
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"model_name": "VBoxModel",
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],
"children": [
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"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_422b5f00d1f1434197e5352ac45fa401",
"outputs": [
{
"data": {
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"text/plain": "<matplotlib.figure.Figure at 0x7f4981acaa20>"
},
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"\u001b[0;32m<ipython-input-3-0170cf0d9b16>\u001b[0m in \u001b[0;36menc\u001b[0;34m(arr)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0menc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m|=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<<\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'reshape'"
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"text/plain": "<matplotlib.figure.Figure at 0x7f498c425a90>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"7c6937baecf0411f82272079229c4d05": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"7c6b79f1bad546bb86f7e81b2a855957": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"7c72d534224d4aaaae112e631065c1a9": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
"state": {
"description": "c",
"layout": "IPY_MODEL_fa1e1f050f974b19b95486d5bf1e0ac8",
"max": 7,
"style": "IPY_MODEL_84b970950e6d43ebba6d793157633c8f",
"value": 7
}
},
"7c7b48fac22f48e995e7743cf76806d0": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"7ccfd3aa19e84f7b9f2920e73dc9b12b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"7d0bc612ba6141719cf58680f7ff0458": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"7d24db5ea21144508c6a4d399ebefc38": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"7d7d774954e7461fa99b6c5e588fbd3e": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_5e24fda600fd4cadbf7807c5f5c6351b",
"outputs": [
{
"data": {
"image/png": 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],
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"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_ee4308da6a0b43b686a2b672d0c85c50",
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{
"data": {
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"text/plain": "<matplotlib.figure.Figure at 0x7f498c448400>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
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"model_module_version": "1.0.0",
"model_name": "IntSliderModel",
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"layout": "IPY_MODEL_23eda160167b448a9773f9af2097de3b",
"max": 7,
"style": "IPY_MODEL_1823a0beb59e4237b3be1fc85bba3111",
"value": 6
}
},
"80ea10b7f8604a10867df604906aff15": {
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}
},
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"model_name": "IntSliderModel",
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},
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"children": [
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"layout": "IPY_MODEL_6181abb2dc024adc900877d2a33286fe",
"max": 7,
"style": "IPY_MODEL_0c52aba3dac04b14a4949af91656bec3",
"value": 3
}
},
"974be0ea5a644c89934b47c4993447e3": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.0.0",
"model_name": "LayoutModel",
"state": {}
},
"97777be91276419c801567df7e2c73bb": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "VBoxModel",
"state": {
"_dom_classes": [
"widget-interact"
],
"children": [
"IPY_MODEL_ee60453494114407b734636d7f7083a0",
"IPY_MODEL_1fb2ceeaa53146528e3a1378f6866bec",
"IPY_MODEL_e002f4f7d07d4062bb57e13a1a6c8c9f",
"IPY_MODEL_b5b37e76cae04e8e871e3bb5ab47a62c"
],
"layout": "IPY_MODEL_5c9bfac3b4d2484cbcc35fcdbec3df28"
}
},
"979201da9f5342a284e45ee333af6eb3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.0.0",
"model_name": "DescriptionStyleModel",
"state": {
"description_width": ""
}
},
"97af6a17a1e14aa2ab5ec81b1290c715": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_e6a38f949a7a43bb86eb6813f9beadce",
"outputs": [
{
"data": {
"image/png": 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7kEWH+18m+UqSa8cYh+3jMbYlOWHSibFwmzRvwg2sxyThXvRd5VfNtwcteBwAWAmLDvcp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\nRoQbABoRbgBoRLgBoBHhBoBG/j96kaj32ujlGgAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f49824d3b00>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"97b21dd08ae94e168cf89861f61cbf98": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_2d58d9227f3c4d9e8c9de08e46d4bca5",
"outputs": [
{
"data": {
"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f498c45c860>"
},
"metadata": {
"image/png": {
"height": 250,
"width": 247
}
},
"output_type": "display_data"
}
]
}
},
"97b2ce7c5a824ff49ffa25f76ef127aa": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_2d3725de9b904e1db22c88b402ea27d8",
"outputs": [
{
"data": {
"image/png": 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{
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"text": "1 0\n0\n0 0\n-1\n"
},
{
"data": {
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