diff --git a/ipynb/Advent 2017.ipynb b/ipynb/Advent 2017.ipynb
index 0267aa5..0f8750a 100644
--- a/ipynb/Advent 2017.ipynb	
+++ b/ipynb/Advent 2017.ipynb	
@@ -29,7 +29,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -290,9 +290,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -377,22 +375,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2014"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "digits = mapt(int, '3294199471327195994824832197564859876682638188889768298894243832665654681412886862234525991553276578641265589959178414218389329361496673991614673626344552179413995562266818138372393213966143124914469397692587251112663217862879233226763533911128893354536353213847122251463857894159819828724827969576432191847787772732881266875469721189331882228146576832921314638221317393256471998598117289632684663355273845983933845721713497811766995367795857965222183668765517454263354111134841334631345111596131682726196574763165187889337599583345634413436165539744188866156771585647718555182529936669683581662398618765391487164715724849894563314426959348119286955144439452731762666568741612153254469131724137699832984728937865956711925592628456617133695259554548719328229938621332325125972547181236812263887375866231118312954369432937359357266467383318326239572877314765121844831126178173988799765218913178825966268816476559792947359956859989228917136267178571776316345292573489873792149646548747995389669692188457724414468727192819919448275922166321158141365237545222633688372891451842434458527698774342111482498999383831492577615154591278719656798277377363284379468757998373193231795767644654155432692988651312845433511879457921638934877557575241394363721667237778962455961493559848522582413748218971212486373232795878362964873855994697149692824917183375545192119453587398199912564474614219929345185468661129966379693813498542474732198176496694746111576925715493967296487258237854152382365579876894391815759815373319159213475555251488754279888245492373595471189191353244684697662848376529881512529221627313527441221459672786923145165989611223372241149929436247374818467481641931872972582295425936998535194423916544367799522276914445231582272368388831834437562752119325286474352863554693373718848649568451797751926315617575295381964426843625282819524747119726872193569785611959896776143539915299968276374712996485367853494734376257511273443736433464496287219615697341973131715166768916149828396454638596713572963686159214116763')\n",
-    "N = len(digits)"
+    "N = len(digits)\n",
+    "N"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -421,9 +427,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -478,9 +482,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -507,9 +509,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -575,9 +575,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -624,9 +622,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -646,9 +642,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -691,9 +685,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -720,9 +712,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -751,9 +741,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -784,9 +772,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -824,9 +810,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -857,9 +841,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -898,9 +880,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -954,9 +934,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1027,9 +1005,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1049,9 +1025,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1078,9 +1052,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1107,9 +1079,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1137,10 +1107,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1148,7 +1116,7 @@
        "8038"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1180,7 +1148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {
     "collapsed": true
    },
@@ -1210,10 +1178,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 31,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1221,7 +1187,7 @@
        "{'wiapj'}"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1241,9 +1207,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1259,9 +1225,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1281,10 +1247,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1292,7 +1256,7 @@
        "{'eionkb', 'lsire', 'wiapj', 'ycpcv'}"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1310,10 +1274,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 35,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1321,7 +1283,7 @@
        "'eionkb'"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1344,10 +1306,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 38,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1355,7 +1315,7 @@
        "1072"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1380,10 +1340,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 39,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1391,7 +1349,7 @@
        "6828"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1421,10 +1379,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 40,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1432,7 +1388,7 @@
        "7234"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1461,10 +1417,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 41,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1472,7 +1426,7 @@
        "'{{{{{{{},},{{},}},{{{{}},{{{{{}}},{}},},{{{{},{,{{{}}}}},},{{{}},{{}}}'"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1493,10 +1447,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 42,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1504,7 +1456,7 @@
        "9662"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1536,10 +1488,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 43,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1547,7 +1497,7 @@
        "5989"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1565,10 +1515,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 44,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1576,7 +1524,7 @@
        "4903"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1603,9 +1551,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 45,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1633,9 +1581,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 46,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1645,9 +1593,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 47,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1657,10 +1605,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 48,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1687,10 +1633,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 49,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1698,7 +1642,7 @@
        "4480"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1718,10 +1662,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 50,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1729,7 +1671,7 @@
        "'c500ffe015c83b60fad2e4b7d59dabc4'"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1792,7 +1734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 51,
    "metadata": {
     "collapsed": true
    },
@@ -1810,10 +1752,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 52,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1821,7 +1761,7 @@
        "705"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1852,10 +1792,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 53,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1863,7 +1801,7 @@
        "1469"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1893,9 +1831,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 54,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1916,9 +1854,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 55,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1936,10 +1874,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 56,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1947,7 +1883,7 @@
        "115"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1969,10 +1905,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 57,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1980,7 +1914,7 @@
        "221"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2004,9 +1938,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 58,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -2025,10 +1959,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((0, 3), (1, 2), (2, 4), (4, 6), (6, 4))"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scanners = mapt(integers, Input(13))\n",
+    "scanners[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2036,13 +1989,12 @@
        "1504"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "scanners = mapt(integers, Input(13))\n",
     "trip_severity(scanners)"
    ]
   },
@@ -2057,10 +2009,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 60,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2068,7 +2018,7 @@
        "10"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2088,10 +2038,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 61,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2099,7 +2047,7 @@
        "3823370"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2119,9 +2067,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 62,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -2130,10 +2078,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 63,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2141,7 +2087,7 @@
        "8316"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2166,9 +2112,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 64,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -2188,9 +2134,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 65,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -2204,9 +2150,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 66,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -2223,10 +2169,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 67,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2234,7 +2178,7 @@
        "1074"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2254,10 +2198,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 68,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2265,7 +2207,7 @@
        "597"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2300,10 +2242,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 69,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2311,7 +2251,7 @@
        "303"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2324,6 +2264,200 @@
     "judge(criteria(4, A()), criteria(8, B()), 5*10**6)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# [Day 16](https://adventofcode.com/2017/day/16): Permutation Promenade\n",
+    "\n",
+    "Let's read the input and check that it looks reasonable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('x5/15',\n",
+       " 's15',\n",
+       " 'x1/3',\n",
+       " 'pn/f',\n",
+       " 'x11/2',\n",
+       " 's13',\n",
+       " 'x6/3',\n",
+       " 'pe/a',\n",
+       " 'x14/12',\n",
+       " 's15')"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dance = vector(Input(16).read())\n",
+    "dance[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10000"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(dance)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'll define `perform` to perform the dance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'lbdiomkhgcjanefp'"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dancers = 'abcdefghijklmnop'\n",
+    "\n",
+    "def perform(dance, dancers=dancers):\n",
+    "    D = deque(dancers)\n",
+    "    def swap(i, j): D[i], D[j] = D[j], D[i]\n",
+    "    for move in dance:\n",
+    "        op, arg = move[0], move[1:]\n",
+    "        if   op == 's': D.rotate(int(arg))\n",
+    "        elif op == 'x': swap(*integers(arg))\n",
+    "        elif op == 'p': swap(D.index(arg[0]), D.index(arg[2]))\n",
+    "    return cat(D)\n",
+    "            \n",
+    "perform(dance)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's the right answer.\n",
+    "\n",
+    "**Part Two**\n",
+    "\n",
+    "My first thought was to define a dance as a permutation: a list of numbers `[11, 1, 9, ...]` which says that the net effect of the dance is that the first dancer (`a`) ends up in position, the second (`b`) stays in position 1, and so on. Applying that permutation once is a lot faster than interpreting all 10,000 moves of the dance, and it is feasible to apply the permutation a billion times. I tried that (code not shown here), but that was a mistake: it took 15 minutes to run, and it got the wrong answer. The problem is that a dance is *not* just a permutation, because a dance can reference dancer *names*, not just positions.\n",
+    "\n",
+    "It would take about 10,000 times 20 minutes to perform a billion repetitions of the dance, so that's out. But even though the dance is not a permutation, it might repeat after a short period. Let's check:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "abcdefghijklmnop is seen in iterations (0, 56)\n"
+     ]
+    }
+   ],
+   "source": [
+    "seen = {dancers: 0}\n",
+    "d = dancers\n",
+    "for i in range(1, 1000):\n",
+    "    d = perform(dance, d)\n",
+    "    if d in seen:\n",
+    "        print(d, 'is seen in iterations', (seen[d], i))\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we get back to the start position after 56 repetitions of the dance. What happens after a billion repetitions?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "48"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1000000000 % 56"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The end position after a billion repetitions is the same as after 48:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'ejkflpgnamhdcboi'"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def whole(N, dance, dancers=dancers):\n",
+    "    \"Repeat `perform(dance)` N times.\"\n",
+    "    for i in range(N):\n",
+    "        dancers = perform(dance, dancers)\n",
+    "    return dancers\n",
+    "    \n",
+    "whole(48, dance)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2335,12 +2469,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 8s, sys: 171 ms, total: 1min 8s\n",
+      "Wall time: 1min 8s\n"
+     ]
+    }
+   ],
    "source": [
+    "%%time\n",
     "def day(n, compute1, answer1, compute2, answer2):\n",
     "    \"Assert that we get the right answers for this day.\"\n",
     "    assert compute1 == answer1\n",
@@ -2367,7 +2509,9 @@
     "day(14, sum(bits(key, i).count('1') for i in range(128)), 8316, \n",
     "        flood_all(Grid(key)), 1074)\n",
     "day(15, judge(A(), B()), 597, \n",
-    "        judge(criteria(4, A()), criteria(8, B()), 5*10**6), 303)"
+    "        judge(criteria(4, A()), criteria(8, B()), 5*10**6), 303)\n",
+    "day(16, perform(dance), 'lbdiomkhgcjanefp', \n",
+    "        whole(48, dance), 'ejkflpgnamhdcboi')"
    ]
   },
   {
@@ -2379,16 +2523,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 186,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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0R6BhN5b8dYGNJ+yrdNkp0p8LmQXkFzl2AVV+UQmPLjvIgQtZNhlvcs9mDHPg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S26pUKlxcXAzrCgYHB3Pp0iUiIyMNt77vdfXqVXbt2oW/vz8A7dq1Y/DgwezYsUMs6SQI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8tQ+uQ4hC4TuPZFt39E6ZPwBh+3qdAzd0HxP79wATz6rbvCwPpNSHLiuDZNdV9X7Tf65FkM7MpT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\nKjsKhUKhtHj+Hwxq4YL9S3IjAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1177ff518>"
+       "<matplotlib.figure.Figure at 0x112458588>"
       ]
      },
      "metadata": {},
@@ -2397,17 +2539,18 @@
    ],
    "source": [
     "def plot_times(times):\n",
-    "    X = ints(1, len(times[0][2:]))\n",
+    "    plt.style.use('seaborn-whitegrid')\n",
+    "    X = ints(1, len(times[0]) - 2)\n",
     "    for (label, mark, *Y) in times:\n",
     "        plt.plot(X, Y, mark, label=label)\n",
-    "    plt.xlabel('Day Number'); plt.ylabel('Minutes')\n",
-    "    plt.legend()\n",
+    "    plt.xlabel('Day Number'); plt.ylabel('Minutes to Solve Both')\n",
+    "    plt.legend(loc='upper left')\n",
     "\n",
     "x = None\n",
     "plot_times([\n",
-    "    ('Me',    'd:', (4), 6,(20), 5, 12, 30, 33,(10), 21, 40, 13, 12,(30),(40), 13),\n",
-    "    ('100th', 'v:',  6,  6, 23,  4,  5,  9, 25,  8,  12, 25, 12,  9, 22,  25,  10),\n",
-    "    ('1st',   '^:',  1,  1,  4,  1,  2,  3, 10,  3,   4,  6,  3,  2,  6,   5,   2)])"
+    "    ('Me',    'd:', (4), 6,(20), 5, 12, 30, 33,(10), 21, 40, 13, 12,(30),(41), 13, 64),\n",
+    "    ('100th', 'v:',  6,  6, 23,  4,  5,  9, 25,  8,  12, 25, 12,  9, 22,  25,  10, 27),\n",
+    "    ('1st',   '^:',  1,  1,  4,  1,  2,  3, 10,  3,   4,  6,  3,  2,  6,   5,   2,  5)])"
    ]
   }
  ],
@@ -2427,7 +2570,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.5.3"
   }
  },
  "nbformat": 4,