diff --git a/3413-differentials.ipynb b/3413-differentials.ipynb
new file mode 100644
index 000000000..3e8a413a4
--- /dev/null
+++ b/3413-differentials.ipynb
@@ -0,0 +1,2615 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1dee6578-0f48-43c8-8f50-20c9d34ad588",
+   "metadata": {},
+   "source": [
+    "# Differentials on (3, 4, 13)\n",
+    "\n",
+    "All computations are done on an unfolding of the (3, 4, 13) triangle, after inserting 4 marked points in some strategic locations. The (Delaunay triangulated) surface looks like this; the big red dot is the 26π singularity, the smaller one the 6π one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4b8cd8aa-49f4-4678-8217-5abaa36c10de",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'S' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m G \u001b[38;5;241m=\u001b[39m \u001b[43mS\u001b[49m\u001b[38;5;241m.\u001b[39mgraphical_surface(edge_labels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, zero_flags\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'S' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "G = S.graphical_surface(edge_labels=False, zero_flags=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "635ee889-b8ea-4779-86e7-fe378f59f3a3",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 202 graphics primitives"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "G.plot() + sum([v.plot(color=\"red\", size=v.angle() * 3) for v in S.singularities()])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "126967fe-2442-4bec-bd91-71a5b86924ec",
+   "metadata": {},
+   "source": [
+    "We now compute the harmonic differential with a cohomology class (real part) that comes from the tangent space, namely we take this cohomology class (the keys are the homology class where `B[(label, edge)]` refers to the class of the edge of the triangle with ``label`` in the above picture and the ``edge`` in counterclockwise order starting from 0 at the green segment.):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "c18c6225-0ecb-4018-a0aa-8235900efe93",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{B[(12, 1)] - B[(24, 0)]: 0.330984859362568, B[(20, 2)] + B[(27, 1)]: 0.0298448861506505, B[(14, 1)] - B[(18, 2)]: 1.00000000000000, B[(17, 0)]: 0.330984859362568, B[(28, 0)]: -0.409119785695342, B[(5, 1)] - B[(18, 2)]: 0.374920589337810, B[(8, 0)] + B[(18, 2)]: -0.613679678543013, B[(9, 2)]: -0.271295087061267, B[(0, 2)] + B[(6, 0)]: -0.758549799089383, B[(10, 0)]: 0.468809557996643, B[(2, 2)]: 0.468809557996643, B[(1, 0)] + B[(18, 2)]: -0.0737806161258918, B[(13, 2)]: -0.487254712028116, B[(6, 2)]: 0.319585127243391, B[(21, 0)]: 0.126424966514897, -B[(11, 0)] + B[(36, 0)]: -0.535544752210239}"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "F"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c520ae42-2864-4faf-ad95-36f6e90c65c4",
+   "metadata": {},
+   "source": [
+    "We represent the differential with a power series at each of the vertices in the surface. To compute these power series we solve a linear system that is roughly 350×350 (actually probably about twice that size) with double precision entries. The condition number of the system is 2e46. (For reference, a random permutation of the matrix entries only has condition number 1e4 or so.)\n",
+    "\n",
+    "This produces 6 power series. For somewhat mysterious empirical reasons, we use different (and probably far-from-optimal) numbers of coefficients for the different power series.\n",
+    "\n",
+    "At the non-singular points we get these series:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b42e4550-15d5-411d-b57e-22018e352552",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Power series at Vertex 1 of polygon 11\n",
+      "Power series: -0.039626706943261425 - 0.013105322709162814*I + (0.0007003925064263763 + 3.217643472853054e-06*I)*z3 + (0.01301872259659336 - 0.004151124519188824*I)*z3^2 + (0.10543841984858954 - 0.07575909835239229*I)*z3^3 + (0.36154084293331 - 0.4921861685384022*I)*z3^4 + (0.3373537624945319 - 1.020099734527775*I)*z3^5 + (-0.004245992234578906 + 0.8111943089020764*I)*z3^6 + (1.3378159815536195 + 4.193315850457734*I)*z3^7 + (9.224531185187683 + 12.84051582872556*I)*z3^8 + (26.138758576946763 + 19.20569820886117*I)*z3^9 + (20.34276256156427 + 6.746743578426327*I)*z3^10 + (-9.640376939100669 - 0.1320064702655412*I)*z3^11 + (338.1831346287219 - 109.78981660542647*I)*z3^12 + (2395.6780416926013 - 1736.4358954002716*I)*z3^13 + (8874.183792410706 - 12158.152031347276*I)*z3^14 + (20418.939108641283 - 61121.36018462475*I)*z3^15 + (6907.396314003927 - 269731.54784567206*I)*z3^16 + (-323895.6182359394 - 1202543.1442429975*I)*z3^17 + (-3945905.117917603 - 6187091.408351345*I)*z3^18 + (-39174013.969873145 - 31312997.46204437*I)*z3^19 + (-297474676.62793833 - 103579251.88849449*I)*z3^20 + (-1589838898.6635988 + 28033156.64278588*I)*z3^21 + (-5192998807.173564 + 2359899148.885962*I)*z3^22 + (-1396637516.8906097 + 9087275735.049881*I)*z3^23 + (100344072449.5138 - 51649409920.27526*I)*z3^24 + (640297481499.8428 - 930566358392.8811*I)*z3^25 + (1567596479523.5974 - 6761136119952.72*I)*z3^26 + (-4571898917812.0625 - 28069830608970.766*I)*z3^27 + (-35130336688827.906 - 21368652461015.35*I)*z3^28 + (333812597908064.8 + 661239515116375.5*I)*z3^29 + (7073635923717941.0 + 5406107234109717.0*I)*z3^30 + (6.480755046048237e+16 + 1.8305159634036184e+16*I)*z3^31 + (3.9323904138425197e+17 - 3.787209761509431e+16*I)*z3^32 + (1.6597542924111163e+18 - 8.650264499308404e+17*I)*z3^33 + (4.3482725143533706e+18 - 5.555535038969701e+18*I)*z3^34 + (1.8745157958662671e+18 - 1.96570668467186e+19*I)*z3^35 + (-4.034713792875166e+19 - 1.998958028006262e+19*I)*z3^36 + (-1.735057248571166e+20 + 2.201861659135004e+20*I)*z3^37 + (-1.0552114099872419e+20 + 1.6366426138786584e+21*I)*z3^38 + (2.3137889868900097e+21 + 6.491787631631122e+21*I)*z3^39 + (1.395915264959897e+22 + 1.7363864563682841e+22*I)*z3^40 + (4.594742016629936e+22 + 3.2361516005640255e+22*I)*z3^41 + (9.87081724344114e+22 + 4.369206225504879e+22*I)*z3^42 + (1.3836129123357765e+23 + 6.293321151578179e+22*I)*z3^43 + (1.2253763637486365e+23 + 1.614031714147279e+23*I)*z3^44 + (9.828107032187166e+22 + 3.991150747391167e+23*I)*z3^45 + (1.706245727263905e+23 + 6.122754296564376e+23*I)*z3^46 + (2.493828801608536e+23 + 5.0256991708346816e+23*I)*z3^47 + (1.4220530270635552e+23 + 1.6698117393764213e+23*I)*z3^48 + O(z3^49)\n",
+      "Absolute values of the coefficients:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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7dfrpp6u0tNS4kACAITN5srRiBWUOOFWGPst106ZNam1t1VNPPaXnn3++2+etra0655xztG/fPo0cObJf2+RZrgAAINgYOkOXlZWlUaNG9fr5yy+/rCuuuKLfZQ4AACAYDbjQVVRUaO7cuYqNjZXFYunxsOiaNWuUlJSk8PBwpaWlqbKy8pS+47nnntPixYsHGhEAACAoDLjQtbW1KSUlRUVFRT1+XlJSory8PK1cuVI7d+7UtGnTZLPZVNfP69Dtdru2bt2qOXPmDDQiAABAUBjwVa42m002m63Xzx9++GEtXbpUy5YtkyStXr1aGzdu1Nq1a1VYWHjS7b/00kuaPXu2wsPD+1zP4XDI4XC4x3a7vZ+/AQAAQGDwyTl07e3t2r59u7Kzsz2WZ2dnq6qqql/b6O/h1sLCQlmtVvcrPj5+QJkBAL7xySfSp58anQIIbD4pdE1NTero6FBMTIzH8piYGB08eNA9nj17thYtWqSysjLFxcVp27ZtkqSWlhb99a9/1ezZs0/6XQUFBWppaXG/6k+81TgAwDC33y6dd540frx0551GpwECl09vLGyxWDzGTqfTY9nGjRt7/Dmr1apDhw716zvCwsIUFhY28JAAAJ+orZVWr+4aP/SQdNttUkKCYZGAgOWTGbro6GiFhIR4zMZJUmNjY7dZOwBAYDrhv+l7XQZg8HxS6EJDQ5WWlqby8nKP5eXl5crMzPTFVwIA/Mw550h33901XrlS4jRnwDcGfMi1tbVVe/bscY9ra2tVU1OjqKgoJSQkKD8/Xzk5OUpPT1dGRoaKi4tVV1en5cuXeyU4AMD//frX0q23umbmzj7b6DRA4BpwoauurlZWVpZ7nJ+fL0nKzc3VunXrtHjxYjU3N2vVqlVqaGhQcnKyysrKlJiYOPjUAADTiIszOgEQ+Ax9lqsv8CxXAAAQbAx9lisAAAAGj0IHAABgchQ6AAAAk6PQAQAAmByFDgAAwOQodAAAACZHoQMA9NvRo64bBWdmSj/7mdTZaXQiANIgbiwMAAg+P/uZ9Pjjrvdvvy2NHSutWGFsJgDM0AEATsHf/tb3GIAxKHQAgH676irP8ZVXGpMDgCcOuQIA+u3WW6UxY6QdO6SsLGnOHKMTAZB4lisAAIDpccgVAADA5Ch0AAAAJkehAwAAMDkKHQAAgMlR6AAAAEyOQgcAAGByFDoAAACTo9ABAACYHIUOAADA5Ch0AAAAJkehA4Ag53BIr74qVVQYnQTAQFHoACCItbdLs2ZJc+ZIl18urVhhdCIAA0GhA4AgtnWr9NZbXePHHpOOHDEuD4CBodABQBCLjPQcjxghhYYaEgXAIFDoACCIXXyx9ItfSMOHSxER0jPPuN4DMBeL0+l0Gh3Cm+x2u6xWq1paWhQREWF0HAAwhY4OadgwyWIxOgmAgeC/wwAACgkxOgGAweCQKwAAgMlR6AAAAEyOQgcAAGByhha6BQsWaPTo0Vq4cGG3z44cOaLExETdcccdBiQDAAAwD0ML3YoVK/T000/3+NkDDzygKVOmDHEiAAAA8zG00GVlZWnUqFHdlv/jH//QRx99pDlz5hiQCgAAwFwGXOgqKio0d+5cxcbGymKxqLS0tNs6a9asUVJSksLDw5WWlqbKysp+bfuOO+5QYWHhQKMBAAAElQEXura2NqWkpKioqKjHz0tKSpSXl6eVK1dq586dmjZtmmw2m+rq6vrc7ksvvaQLLrhAF1xwwUCjAQAABJUB31jYZrPJZrP1+vnDDz+spUuXatmyZZKk1atXa+PGjVq7dm2fs2/vvPOOnn32Wa1fv16tra06duyYIiIidO+99/a4vsPhkMPhcI/tdvsAfyMAAABz8sk5dO3t7dq+fbuys7M9lmdnZ6uqqqrPny0sLFR9fb327t2rhx56SDfffHOvZe7r9a1Wq/sVHx/vld8BAADALHxS6JqamtTR0aGYmBiP5TExMTp48KB7PHv2bC1atEhlZWWKi4vTtm3bTvm7CgoK1NLS4n7V19cPOj8AAICZ+PRZrpYTnvLsdDo9lm3cuLHPn7/xxhtP+h1hYWEKCwsbUD4AAIBA4JMZuujoaIWEhHjMxklSY2Njt1k7AID3ffml9OGHUnu70UkADAWfFLrQ0FClpaWpvLzcY3l5ebkyMzN98ZUAgP+1ebOUkCBNnChdeqmr3AEIbAM+5Nra2qo9e/a4x7W1taqpqVFUVJQSEhKUn5+vnJwcpaenKyMjQ8XFxaqrq9Py5cu9EhwA0LN77pEOH3a937VLeuIJ6e67jc0EwLcGXOiqq6uVlZXlHufn50uScnNztW7dOi1evFjNzc1atWqVGhoalJycrLKyMiUmJg4+NQAEmZdechWzmBjp3/9dGju293VPOH252xhA4LE4nU6n0SG8yW63y2q1qqWlRREREUbHAYBBq6mR0tOljg7XePp0acuW3tffskWaO9c1S5eS4joEGxk5BEEBGManV7kCAAZv166uMidJO3b0vf7ll0t1ddLBg9L48dJpp/k2HwDjUegAwM9lZkojRkhHjrjGV1xx8p+JjGRWDggmFDoA8HPnny9t2iT94Q+uc+juusvoRAD8DefQAQAAmJxP7kMHAACAoUOhAwAAMDkKHQAAgMlR6ADAIK+/Lq1bJx06ZHQSAGbHVa4AYID77pNWrXK9P/tsqbpaOussYzMBMC9m6ADAAL/9bdf7/fulV14xLgsA86PQAYABTpyNY3YOwGBQ6ADAAH/8o3TRRdLo0dKdd0pXXWV0IgBmxo2FAQAATI4ZOgAAAJOj0AEAAJgchQ4AAMDkKHQAAAAmR6EDAAAwOQodAACAyVHoAMBLGhqkP/1Jevtto5MACDY8yxUAvGDfPunSS6XGRtf48celH//Y2EwAggczdADgBSUlXWVOkh57zLgsAIIPhQ4AvODMMz3HY8cakwNAcKLQAYAXLFki3XSTdPrpUnKyVFxsdCIAwYRnuQIAAJgcM3QAAAAmR6EDAAAwOQodAACAyVHoAAAATI5CBwAAYHIUOgAAAJOj0AEAAJicoYVuwYIFGj16tBYuXNiv5QAAAOjO0EK3YsUKPf300/1eDgAAgO4MLXRZWVkaNWpUv5cDwFA7fFhqbDQ6BQD0bcCFrqKiQnPnzlVsbKwsFotKS0u7rbNmzRolJSUpPDxcaWlpqqysHExWABhSf/yjNGaMFBMjLV1qdBoA6N2AC11bW5tSUlJUVFTU4+clJSXKy8vTypUrtXPnTk2bNk02m011dXUDDgsAQ6WzU/rhD6Vjx1zj3/9e2rLF2EwA0JvhA/1Bm80mm83W6+cPP/ywli5dqmXLlkmSVq9erY0bN2rt2rUqLCwc6Nd243A45HA43GO73e61bQMIXp2d0vHjnsva243JAgAn45Nz6Nrb27V9+3ZlZ2d7LM/OzlZVVZVXv6uwsFBWq9X9io+P9+r2AQSn4cOlBx7oGn/ve9LMmcblAYC++KTQNTU1qaOjQzExMR7LY2JidPDgQfd49uzZWrRokcrKyhQXF6dt27b1ubwnBQUFamlpcb/q6+t98SsBCEJ33in9/e9SdbX0yitSSIjRiQCgZwM+5NofFovFY+x0Oj2Wbdy4scef6215T8LCwhQWFjawgABwEuefb3QCADg5n8zQRUdHKyQkxGM2TpIaGxu7zdoBAABgcHxS6EJDQ5WWlqby8nKP5eXl5crMzPTFVwIAAAStAR9ybW1t1Z49e9zj2tpa1dTUKCoqSgkJCcrPz1dOTo7S09OVkZGh4uJi1dXVafny5V4JDgAAAJcBF7rq6mplZWW5x/n5+ZKk3NxcrVu3TosXL1Zzc7NWrVqlhoYGJScnq6ysTImJiYNPDQAAADeL0+l0Gh3Cm+x2u6xWq1paWhQREWF0HAAAAJ8z9FmuAAAAGDwKHQAAgMlR6AAAAEyOQgcAAGByFDoAAACT8+mjvwDA37z1lvTGG1JqqjR/vtFpAMA7KHQAgsabb0rZ2VJHh2v82GPSrbcamwkAvIFDrgCCRmlpV5mTpBdfNCwKAHgVM3QATK2zU3rtNen4cclmk047rfd1L7jAc3z++b7NBgBDhUIHwNQWL5aef971ftYsV7kLCel53R//WNq3T9q4UUpJkf7jP4YuJwD4Eo/+AmBan30mxcd7Ltu1S5o0yZg8AGAUzqEDYFpnnCGFhnaNhw2TIiMNiwMAhqHQATCtyEhp3TrX/z3jDOnxx6WEBINDAYABOOQKAABgcszQAQAAmByFDgAAwOQodAAAACZHoQPgV1pbpeuukxITpe9/X/rqK6MTAYD/o9AB8Cu/+IVUUiLV1Ul/+pP0wANGJwIA/0ehA+BX9u71HO/bZ0gMADAVCh0Av/Kv/+q6QbDkeoTX9dcbmwcAzIBnuQLwKwsWSBUV0rvvSpmZ0mWXGZ0IAPwfNxYGAAAwOQ65AgAAmByFDgAAwOQodAAAACZHoQMAADA5Ch2AIVFbK+3fb3QKAAhMFDoAPvfDH0rnnivFxUmFhUanAYDAw21LAPjUrl1SamrX2GKRvvhCsloNiwQAAYcZOgA+1dN/MgbWf0YCgPH8stA98sgjuuiiizRx4kStWLFCATaJCASV1FRp2bKu8a9+JUVGGpUGAAKT3x1y/ec//6nLLrtM77//vk477TRNnz5dDz30kDIyMvr18xxyBfzTJ59IoaFSfLzRSQAg8Pjls1yPHz+uo0ePSpKOHTumsWPHGpwIwGCNH290AgAIXF4/5FpRUaG5c+cqNjZWFotFpaWl3dZZs2aNkpKSFB4errS0NFVWVro/O/PMM3XHHXcoISFBsbGxmjVrlsbzbwIAAIBeeb3QtbW1KSUlRUVFRT1+XlJSory8PK1cuVI7d+7UtGnTZLPZVFdXJ0n64osv9Morr2jv3r3av3+/qqqqVFFR4e2YAAAAAcPrhc5ms+n+++/XNddc0+PnDz/8sJYuXaply5ZpwoQJWr16teLj47V27VpJ0htvvKHzzjtPUVFROv3003XllVfqnXfe8XZMAACAgDGkV7m2t7dr+/btys7O9lienZ2tqqoqSVJ8fLyqqqp09OhRdXR0aPPmzbrwwgt73abD4ZDdbvd4AQAABJMhLXRNTU3q6OhQTEyMx/KYmBgdPHhQknTZZZdpzpw5uvjiizVp0iSNHz9e8+bN63WbhYWFslqt7lc8l9ABAIAgY8h96CwWi8fY6XR6LHvggQf04Ycf6v3339dvfvObbut/U0FBgVpaWtyv+vp6n+UG4Mm/bnoEAMFrSAtddHS0QkJC3LNxX2tsbOw2a9dfYWFhioiI8HgB8K3aWiklxXVfuauvlv73LkMAAIMMaaELDQ1VWlqaysvLPZaXl5crMzNzKKMAGITbb5fee086flx6+WXpsceMTgQAwc3rNxZubW3Vnj173OPa2lrV1NQoKipKCQkJys/PV05OjtLT05WRkaHi4mLV1dVp+fLl3o4CwEeamz3Hn39uTA4AgIvXC111dbWysrLc4/z8fElSbm6u1q1bp8WLF6u5uVmrVq1SQ0ODkpOTVVZWpsTERG9HAeAjK1ZIb78tdXRIo0dLublGJwKA4OZ3z3IdLJ7lCgyN3buljz6SMjOls882Og0ABDcKHQAAgMkZctsSAAAAeA+FDgAAwOQodAAAACZHoQMAADA5Ch0AAIDJUegAAABMjkIHQJLU3i5t2eJ6pBcAwFwodAB09KiUlSXNmCGlpEi//rXRiQAAp4JCB0Cvvy5VVXWNf/lLKbBuOQ4AgY1CB0AjR3qOR4yQLBZjsgAATh2FDoCuuEL6P//H9X7kSOn3vzc2DwDg1PAsVwBura1SWJh02mlGJwEAnIrhRgcA4D/OOMPoBACAgeCQKwAAgMlR6AAAAEyOQgcAAGByFDoAAACTo9ABAACYHFe5AgGqs9N1P7l9+6SFC12P9AIABCbuQwcEqLw86dFHXe9PP12qrpYmTjQ0EgDARzjkCgSoDRu63n/1let5rQCAwEShAwLUt77lOb7wQmNyAAB8j3PogAD11FPSj38s1dVJ3/++ZLMZnQgA4CucQwcAAGByHHIFAAAwOQodAACAyVHoAAAATI5CBwAAYHIUOsBEDhyQ/vAH6Y03jE4CAPAn3LYEMIn6eumSS6RDh1zj+++XVq40NhMAwD8wQweYxEsvdZU5SXriCeOyAAD8i18WuuHDhys1NVWpqalatmyZ0XEAv3DWWZ7jceOMyQEA8D9+ecg1MjJSNTU1RscA/MrChVJenvT001J8vOtcOgAAJD99UkR0dLSampoG9LM8KQIAAAQbrx9yraio0Ny5cxUbGyuLxaLS0tJu66xZs0ZJSUkKDw9XWlqaKisrPT632+1KS0vT1KlTtWXLFm9HBAAACCheL3RtbW1KSUlRUVFRj5+XlJQoLy9PK1eu1M6dOzVt2jTZbDbV1dW519m7d6+2b9+u3/72t1qyZInsdru3YwIAAAQMnx5ytVgs2rBhg+bPn+9eNmXKFE2ePFlr1651L5swYYLmz5+vwsLCbtuw2Wz61a9+pfT09B6/w+FwyOFwuMd2u13x8fEccgUAAEFjSK9ybW9v1/bt25Wdne2xPDs7W1VVVZKkL774wl3QPvvsM33wwQc699xze91mYWGhrFar+xUfH++7XwAAAMAPDWmha2pqUkdHh2JiYjyWx8TE6ODBg5KkDz/8UOnp6UpJSdFVV12lRx99VFFRUb1us6CgQC0tLe5XfX29T38HwJt27JBSUqS4OOmhh4xOAwAwK0NuW2KxWDzGTqfTvSwzM1O7d+/u97bCwsIUFhbm1XzAUFm4UKqtdb2/804pM9P1AgDgVAzpDF10dLRCQkLcs3Ffa2xs7DZrBwSD/fv7HgMA0B9DWuhCQ0OVlpam8vJyj+Xl5eXKZFoCQeimm7reJyZKM2calwUAYF5eP+Ta2tqqPXv2uMe1tbWqqalRVFSUEhISlJ+fr5ycHKWnpysjI0PFxcWqq6vT8uXLvR0F8Htr10qzZknNzdKCBdKYMUYnAgCYkddvW7J582ZlZWV1W56bm6t169ZJct1Y+MEHH1RDQ4OSk5P1yCOPaPr06V75fp4UAQAAgo1fPvprMCh0AAAg2AzpOXQAAADwPgodAACAyVHoAC/7/HPp9delTz81OgkAIFhQ6AAv2rdP+va3pdmzpQkTpFdeMToRACAYUOgAL3rySenAAdf79napsNDYPACA4EChA7zojDM8x6NGGZMDABBcKHSAF916qzRjhut9QoL08MOGxgEABAmvPykCCGYjR0qbNkltbdKIEZLFYnQiAEAwoNABPjBypNEJAADBhEOuAAAAJkehAwAAMDkKHQAAgMlR6AAAAEyOiyKAk2hrk554Qjp6VFq6VIqJMToRAACeKHTASdhsUmWl6/1//Ze0a1f3GwgDAGAkDrkCfWhq6ipzkvTpp1JNjWFxAADoEYUO6IPVKp15Ztc4LExKTDQuDwAAPaHQAX047TSprEyaOlVKT5eef16Kjzc6FQAAnixOp9NpdAhvstvtslqtamlpUUREhNFxAAAAfI4ZOgAAAJOj0AEAAJgchQ4AAMDkKHQAAAAmR6EDAAAwOQodAACAyVHoAAAATI5CBwAAYHLDjQ4AGKGyUtq6Vbr0UmnmTKPTAAAwOBQ6BJ1XXpGuvlrq7HSN//u/peuuMzYTAACDwSFXBJ3167vKnCQ995xxWQAA8AYKHYLOuef2PQYAwGw45Iqgc889Un29VFEhTZki/fKXRicCAGBwLE6n02l0iG+qr69XTk6OGhsbNXz4cP385z/XokWL+v3zdrtdVqtVLS0tioiI8GFSAAAA/+B3ha6hoUGHDh1SamqqGhsbNXnyZH388ccaOXJkv36eQgcAAIKN3x1yHTdunMaNGydJGjt2rKKiovT555/3u9ABAAAEG69fFFFRUaG5c+cqNjZWFotFpaWl3dZZs2aNkpKSFB4errS0NFVWVva4rerqanV2dio+Pt7bMQEAAAKG1wtdW1ubUlJSVFRU1OPnJSUlysvL08qVK7Vz505NmzZNNptNdXV1Hus1NzdryZIlKi4u9nZEAACAgOLTc+gsFos2bNig+fPnu5dNmTJFkydP1tq1a93LJkyYoPnz56uwsFCS5HA49N3vflc333yzcnJy+vwOh8Mhh8PhHtvtdsXHx3MOHQAACBpDeh+69vZ2bd++XdnZ2R7Ls7OzVVVVJUlyOp268cYbNXPmzJOWOUkqLCyU1Wp1vzg8CwAAgs2QFrqmpiZ1dHQoJibGY3lMTIwOHjwoSdq6datKSkpUWlqq1NRUpaamavfu3b1us6CgQC0tLe5XfX29T38HAAAAf2PIVa4Wi8Vj7HQ63cumTp2qzm8+l+kkwsLCFBYW5tV8AAAAZjKkM3TR0dEKCQlxz8Z9rbGxsdusHQAAAPpnSAtdaGio0tLSVF5e7rG8vLxcmZmZQxkFAAAgYHj9kGtra6v27NnjHtfW1qqmpkZRUVFKSEhQfn6+cnJylJ6eroyMDBUXF6uurk7Lly/3dhT4md27pccflyIiXM9TjYoyOhEAAIHB64WuurpaWVlZ7nF+fr4kKTc3V+vWrdPixYvV3NysVatWqaGhQcnJySorK1NiYqK3o8CPNDRI06dLX37pGldWSm+/bWgkAAACht89y3WweJarf3r1VWnOHM9lX30lhYcbkwcAgEAypOfQIXhNnCidfnrX+KKL+i5zX3whXXONlJQk/fCH0rFjvs8IAIBZUegwJBITXbN0V18t5eS43vflrrukDRukvXul4mLp0UeHJCYAAKZkyH3oEBgaGqSlS6U9e6Rrr5Xuv7/v9S+/3PXqj717+x4DAIAuzNBhwG6+2TXT9o9/SA88ID37rPe2fcMNXe9PO81VGAEAQM+YocOAffpp3+PBuOkmKT5e2rVLmjFDSkvz3rYBAAg0zNBhwK6/vuv9iBHSvHne3f6sWdL//b/9K3OffSbdead0993SoUPezQEAgL/jtiUYlBdekD75RLrySteVq0Y4csT13V+fZ/etb0nvvec6VAsAQDCg0MH0du2SUlM9l33yiXTuuYbEAQBgyHHIFaaXkCBFRnaNY2Kks84yLA4AAEOOQgfTGz1a2rjR9SSKuXOl1193ndMHAECw4JArPDQ2Sn/5izRunPS97xmdBgAA9Ae3LYFbY6PritLPPnONf/pT1/3lAACAf+OQK9z+8peuMidJTzxhXBYAANB/FDq4nXghARcWAABgDhQ6uNlsUkGBFBXluq/bn/5kdCIAANAfXBQBAABgcszQAQAAmByFDgAAwOQodAAAACZHoTOptjYpsM5+BAAAA0WhM5mjR11Xo55xhhQXJ+3caXQiAABgNAqdyfzud9Jrr7neHzggrVhx8p85cEDavVs6fty32QAAgDEodCbT2tr3+ETPPCMlJkqTJkmzZkkOh++yAQAAY1Do/ERlpVRWdvLCdeONroImScOHu5632pc77uiamduyRXrxxUFHBQAAfma40QEg3X67tHq1631mpvTmm1JYWM/rjhsn1dRI1dXSOedI553X97ZDQvoeAwAA8+NJEQY7elQaMcLzitU33pCuuMI723/xRemGG1wzfzab9PLLrpk9AAAQOPhXu8FOO00aOdLzXLjISO9t/5prpIMHpZYWKSFBsli8t20AAOAfOIfOYCEhrgsXrFbXzNm990ppad79jshI13l3lDkAAAITh1z9hNMpdXZyjhsAADh1zND5CYuFMgcAAAaGQgcAAGByFDoAAACT88tCt2DBAo0ePVoLFy40OgoAAIDf88tCt2LFCj399NNGxxg0u93oBAAAIBj4ZaHLysrSqFGjjI4xYF98IU2Z4roVyfnnS59+anQiAAAQyE650FVUVGju3LmKjY2VxWJRaWlpt3XWrFmjpKQkhYeHKy0tTZWVld7IahoPPij99a+u93v2SAUFxuYBAACB7ZQLXVtbm1JSUlRUVNTj5yUlJcrLy9PKlSu1c+dOTZs2TTabTXV1de510tLSlJyc3O114MCBgf8mfqStzXP8zadAAAAAeNugbixssVi0YcMGzZ8/371sypQpmjx5stauXeteNmHCBM2fP1+FhYX93vbmzZtVVFSk559/vs/1HA6HHA6He2y32xUfH2/ojYU//liaOlVqanI9p/Uvf5FmzDAkCgAACAJePYeuvb1d27dvV3Z2tsfy7OxsVVVVefOr3AoLC2W1Wt2v+Ph4n3zPqbjwQumDD6TycumjjyhzAADAt7xa6JqamtTR0aGYmBiP5TExMTp48GC/tzN79mwtWrRIZWVliouL07Zt23pdt6CgQC0tLe5XfX39gPN705lnSrNmSX7QLwEAQIAb7ouNWk54CrzT6ey2rC8bN27s97phYWEKCwvr9/oAAACBxquFLjo6WiEhId1m4xobG7vN2vlCfb30m9+43h8+LBl0Ch0AAMCQ8uoh19DQUKWlpam8vNxjeXl5uTIzM735Vd20tEjf+Y700EOu8YIFPv06AAAAv3HKM3Stra3as2ePe1xbW6uamhpFRUUpISFB+fn5ysnJUXp6ujIyMlRcXKy6ujotX77cq8FPVFPjmqH72rZtUnOzNGaMT78WAADAcKdc6Kqrq5WVleUe5+fnS5Jyc3O1bt06LV68WM3NzVq1apUaGhqUnJyssrIyJSYmei91D8aPl8LDpaNHXeOzzpIiI336lQAAAH5hUPeh8zcbN0q/+IVd77xjVVVVizIyOIkOAAAEvoAqdJLrxsJWq9XQGwsDAAAMJa9eFAEAAIChR6E7BQcOSJ99ZnQKAAAATxS6frrvPunss11Pfrj7bqPTAAAAdOEcun44cMBV5r7pk0+kc8/1yuYBAAAGhRm6fujs7N8yAAAAI1Do+iEuTrrrrq7xbbdJ551nXB4AAIBv4pDrKfj0U9fMHGUOAAD4k1N+UkQw45w5AADgjzjkCgAAYHIUOgAAAJOj0AEAAJgchQ4AAMDkKHQAAAAmR6EDAAAwOQodAACAyVHoAAAATI5CBwAAYHIUOgAAAJOj0AEAAJgchQ4AAMDkKHQAAAAmN9zoAEb6n/+Rtm2Tpk51vQAAAMwoaAvdn/8s/eu/ut4PGyb9v/8nzZljbCYAAICBCNpDriUlXe87O6X1643LAgAAMBhBO0P30ktGJwAAAPAOi9PpdBodwpucTqcOHz6sUaNGyWKxGB0HAADA5wKu0AEAAASboD2HDgAAIFBQ6AAAAEyOQgcAAGByFDoAAACTo9ABAACYHIUOAADA5Ch0AAAAJkehAwAAMDkKHQAAgMlR6AAAAExuuNEB+uPr57MCAAAEm/48n94Uhe7w4cOyWq1GxwAAABhyLS0tioiI6HMdi9PpdA5RngHzlxk6u92u+Ph41dfXn/T/sfB/7M/Awb4MLOzPwMG+9I6AmaGzWCx+9Q9CRESEX+XB4LA/Awf7MrCwPwMH+9L3uCgCAADA5Ch0AAAAJkehOwVhYWG67777FBYWZnQUeAH7M3CwLwML+zNwsC+HjikuigAAAEDvmKEDAAAwOQodAACAyVHoAAAATI5CBwAAYHIUun4oLCzUJZdcolGjRmns2LGaP3++Pv74Y6NjoR8qKio0d+5cxcbGymKxqLS01P3ZsWPHdPfdd+vb3/62Ro4cqdjYWC1ZskQHDhwwLjB6dbK/Q/anuaxdu1aTJk1y33A2IyNDr776qiT2pdkVFhbKYrEoLy9PEvtzqFDo+mHLli265ZZb9M4776i8vFzHjx9Xdna22trajI6Gk2hra1NKSoqKioq6fXbkyBHt2LFDP//5z7Vjxw69+OKL+vvf/6558+YZkBQnc7K/Q/anucTFxenXv/61qqurVV1drZkzZ+rqq6/W+++/z740sW3btqm4uFiTJk1yL2N/DhEnTlljY6NTknPLli1GR8EpkOTcsGFDn+v89a9/dUpy7tu3b2hCYcD683fI/jSX0aNHO5988skeP2Nf+r/Dhw87zz//fGd5ebnz8ssvd/7kJz/pdV32p/cxQzcALS0tkqSoqCiDk8DbWlpaZLFYFBkZaXQUnER//g7Zn+bQ0dGhZ599Vm1tbcrIyOhxHfal/7vlllt05ZVXatasWSddl/3pfcONDmA2TqdT+fn5mjp1qpKTk42OAy86evSo7rnnHt1www08RNrP9efvkP3p/3bv3q2MjAwdPXpUZ5xxhjZs2KCJEyd2W4996f+effZZ7dixQ9u2bTvpuuxP36DQnaJbb71V7733nt566y2jo8CLjh07puuuu06dnZ1as2aN0XFwEif7O2R/msOFF16ompoaffnll3rhhReUm5urLVu2eJQ69qX/q6+v109+8hO9/vrrCg8P73Nd9qfv8OivU3DbbbeptLRUFRUVSkpKMjoOTpHFYtGGDRs0f/58j+XHjh3Ttddeq08//VRvvvmmxowZY0xA9MvJ/g7Zn+Y1a9YsjR8/Xk888YQk9qVZlJaWasGCBQoJCXEv6+jokMVi0bBhw+RwOBQSEsL+9DFm6PrB6XTqtttu04YNG7R582bKXAD5+n9g/vGPf2jTpk38D4wf68/fIfvT3JxOpxwOhyT2pZlcccUV2r17t8eym266Sd/61rd09913e5Q59qfvUOj64ZZbbtGf//xnvfTSSxo1apQOHjwoSbJarTr99NMNToe+tLa2as+ePe5xbW2tampqFBUVpdjYWC1cuFA7duzQK6+8oo6ODve+jYqKUmhoqFGx0YOT/R0eP36c/WkiP/3pT2Wz2RQfH6/Dhw/r2Wef1ebNm/Xaa6+xL01m1KhR3c5lHTlypMaMGaPk5GT251Ax8Apb05DU4+sPf/iD0dFwEps2bepx3+Xm5jpra2t73bebNm0yOjpOcLK/Q/anufzgBz9wJiYmOkNDQ51nnnmm84orrnC+/vrrTqeTfRkIvnnbEvbn0OAcOgAAAJPjPnQAAAAmR6EDAAAwOQodAACAyVHoAAAATI5CBwAAYHIUOgAAAJOj0AEAAJgchQ4AAMDkKHQAAAAmR6EDAAAwOQodAACAyVHoAAAATO7/A2HxaeS2TebhAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "Graphics object consisting of 1 graphics primitive"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "********************************************************************************\n",
+      "Power series at Vertex 0 of polygon 25\n",
+      "Power series: -0.039626826152550976 - 0.013104935278971774*I + (-0.0006996066004350295 - 3.943072963480528e-06*I)*z4 + (0.01301173003142985 - 0.004155187746843682*I)*z4^2 + (-0.10540893340092455 + 0.07575028371696343*I)*z4^3 + (0.36133180470501225 - 0.491915507165318*I)*z4^4 + (-0.33659659167511513 + 1.0180098074630088*I)*z4^5 + (-0.00447328804356661 + 0.8212904980398796*I)*z4^6 + (-1.349263615810134 - 4.2242108934029945*I)*z4^7 + (9.330753781503443 + 12.970762077205622*I)*z4^8 + (-26.876357846105805 - 19.688163579629183*I)*z4^9 + (24.25907545535865 + 7.876646371802032*I)*z4^10 + (-10.802502057101968 - 0.4833130727438283*I)*z4^11 + (455.8644849231911 - 138.15440067113116*I)*z4^12 + (-2969.880734581512 + 2088.8520627791663*I)*z4^13 + (10731.850907166629 - 14495.431915168903*I)*z4^14 + (-22706.623602914464 + 71236.71472273284*I)*z4^15 + (-8304.307126096342 - 297195.73780114256*I)*z4^16 + (455754.38946388743 + 1243336.936311184*I)*z4^17 + (-4803488.3327580355 - 6454779.807507232*I)*z4^18 + (47135544.27776013 + 35734776.45123968*I)*z4^19 + (-378298241.94548464 - 139886375.55011156*I)*z4^20 + (2263466417.162193 + 121540935.3503423*I)*z4^21 + (-9626276365.162226 + 2390547433.3684726*I)*z4^22 + (25037706213.72194 - 14127471415.77548*I)*z4^23 + (-3198414594.535721 - 13185747164.389809*I)*z4^24 + (-286043018931.2923 + 807036112719.1088*I)*z4^25 + (793569680697.582 - 7557591592644.157*I)*z4^26 + (5811581633267.72 + 45619822075088.99*I)*z4^27 + (-64792317416172.86 - 196355154838313.38*I)*z4^28 + (174147060509497.84 + 534363264995007.8*I)*z4^29 + (2293844845214284.0 - 313781601389311.1*I)*z4^30 + (-3.6373995733184456e+16 - 1609156237011220.5*I)*z4^31 + (2.9749499662982336e+17 - 4.683120720387627e+16*I)*z4^32 + (-1.6980415755371323e+18 + 7.631506354347342e+17*I)*z4^33 + (7.023674154614127e+18 - 6.218454574477141e+18*I)*z4^34 + (-1.921729467266083e+19 + 3.4777016745895956e+19*I)*z4^35 + (1.475846283507123e+19 - 1.433817984358538e+20*I)*z4^36 + (1.8241945144939728e+20 + 4.29011164677779e+20*I)*z4^37 + (-1.2831682679976868e+21 - 7.771499222967436e+20*I)*z4^38 + (5.343770431948028e+21 - 4.0586712716832604e+20*I)*z4^39 + (-1.6860640694734548e+22 + 1.0285463745338597e+22*I)*z4^40 + (4.255757498788444e+22 - 5.162838075933217e+22*I)*z4^41 + (-8.331802317892866e+22 + 1.80071889398865e+23*I)*z4^42 + (1.0352242896853371e+23 - 4.902829426132753e+23*I)*z4^43 + (1.5724233616103402e+22 + 1.0393327631207798e+24*I)*z4^44 + (-4.1640450514836607e+23 - 1.6285207564581654e+24*I)*z4^45 + (9.455696051518186e+23 + 1.7241346799925958e+24*I)*z4^46 + (-1.0215669559044802e+24 - 1.0575041470399436e+24*I)*z4^47 + (4.5595062975531706e+23 + 2.6333028209015294e+23*I)*z4^48 + O(z4^49)\n",
+      "Absolute values of the coefficients:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 1 graphics primitive"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "********************************************************************************\n",
+      "Power series at Vertex 0 of polygon 39\n",
+      "Power series: 0.03962660898233672 + 0.01310507477275552*I + (0.000412471116506685 - 0.0005648182652385445*I)*z0 + (0.007973924523693663 + 0.011089976185682564*I)*z0^2 + (-0.12367004838996652 + 0.039478000696758546*I)*z0^3 + (0.00305158095072742 - 0.6105270665124775*I)*z0^4 + (1.0192463446093674 + 0.33557515392858134*I)*z0^5 + (0.4887519793469451 - 0.6552557308282259*I)*z0^6 + (2.5674041552813973 + 3.595085999071444*I)*z0^7 + (-15.169476429216354 + 4.756073912896754*I)*z0^8 + (0.6938111262189829 - 32.92521819142214*I)*z0^9 + (21.69237585542908 + 9.750936538311704*I)*z0^10 + (-8.966428229542165 - 5.5511963802875295*I)*z0^11 + (-197.130902129379 - 377.7540982295914*I)*z0^12 + (3324.8576705467135 - 681.6211729728736*I)*z0^13 + (-1758.91307621844 + 17080.724876847526*I)*z0^14 + (-66429.1776899448 - 28316.43476854908*I)*z0^15 + (186326.5977687239 - 220083.6317194285*I)*z0^16 + (657419.7790154129 + 1060631.7263945022*I)*z0^17 + (-7006386.599811987 + 1615407.7822236808*I)*z0^18 + (5383104.828793738 - 50895606.87312515*I)*z0^19 + (309705701.8983907 + 145519331.14178714*I)*z0^20 + (-1313179727.582057 + 1327717579.6962543*I)*z0^21 + (-3235771981.4067464 - 6989195194.81525*I)*z0^22 + (19104989198.359978 - 244108002.20174426*I)*z0^23 + (-6149929550.861181 - 32917053750.637466*I)*z0^24 + (645373106779.422 + 359174676184.5778*I)*z0^25 + (-4504989364726.761 + 3283221492632.7056*I)*z0^26 + (-5666920015176.531 - 29095942878063.742*I)*z0^27 + (105046746128916.25 + 34664696977493.508*I)*z0^28 + (-213906848313265.94 - 37775993310276.59*I)*z0^29 + (3624365945580488.0 + 1297716808066956.0*I)*z0^30 + (-3.063231187252973e+16 + 2.8626891404530724e+16*I)*z0^31 + (-1.222939993113258e+17 - 2.8906461966058554e+17*I)*z0^32 + (1.8509750583555062e+18 - 1.5668625788677152e+17*I)*z0^33 + (-1.9033474457771945e+18 + 8.785583921459899e+18*I)*z0^34 + (-3.114045101639151e+19 - 1.676327611710632e+19*I)*z0^35 + (7.262699045433312e+19 - 7.961928848117904e+19*I)*z0^36 + (1.4280940161248667e+20 + 1.6041886824560224e+20*I)*z0^37 + (1.4142415182053915e+20 + 2.97832948917723e+20*I)*z0^38 + (-1.8261815429041218e+21 + 2.700778510421294e+21*I)*z0^39 + (-1.1493833394487768e+22 - 1.1345466990514745e+22*I)*z0^40 + (4.786041923321699e+22 - 2.7587498252863036e+22*I)*z0^41 + (3.255360652552698e+22 + 1.4008483527924302e+23*I)*z0^42 + (-2.8923973315285458e+23 - 2.1775412991000607e+22*I)*z0^43 + (1.6394482729679184e+23 - 4.162957971826421e+23*I)*z0^44 + (4.06158707036527e+23 + 3.104626565983201e+23*I)*z0^45 + (-3.157647922085262e+23 + 2.6546938561066224e+23*I)*z0^46 + (-1.2376971873644272e+23 - 1.8370745143889886e+23*I)*z0^47 + (5.633796713864565e+22 - 3.863221304858268e+22*I)*z0^48 + O(z0^49)\n",
+      "Absolute values of the coefficients:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 1 graphics primitive"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "********************************************************************************\n",
+      "Power series at Vertex 1 of polygon 2\n",
+      "Power series: 0.03962696661020537 + 0.013105193982045071*I + (0.0004047525736737061 + 0.0005687454665849342*I)*z1 + (7.927021554439065e-05 - 0.01365983485648687*I)*z1^2 + (-0.07686570995728084 + 0.10462018730320215*I)*z1^3 + (0.5816919482019224 - 0.18549241778181425*I)*z1^4 + (-1.0183854919658548 - 0.3383356796511043*I)*z1^5 + (-0.48220315218162246 - 0.6602236987178735*I)*z1^6 + (-0.008698731911758193 + 4.418953241991311*I)*z1^7 + (9.351219585906803 - 12.86542360819231*I)*z1^8 + (-31.240753622001968 + 10.4754527785256*I)*z1^9 + (23.165922904623812 + 5.395724018187037*I)*z1^10 + (-7.813242824170581 + 5.104634282019587*I)*z1^11 + (58.023211888956986 + 417.1635491683678*I)*z1^12 + (1653.328452300989 - 2923.036226863338*I)*z1^13 + (-15418.50782251449 + 6995.142640160614*I)*z1^14 + (69230.22550871974 + 15006.46921630137*I)*z1^15 + (-182914.56459924497 - 216892.8035738013*I)*z1^16 + (42772.175650655176 + 1260204.0868065374*I)*z1^17 + (4353149.034845048 - 6199552.110563924*I)*z1^18 + (-52002725.98090961 + 18780201.08690236*I)*z1^19 + (364431705.71484905 + 94549917.37910567*I)*z1^20 + (-1396237621.5900888 - 1591756897.9830675*I)*z1^21 + (1242141415.3857143 + 9182225813.496126*I)*z1^22 + (10116718021.899105 - 24655966352.414276*I)*z1^23 + (24907068215.604767 + 18455341804.204887*I)*z1^24 + (-834782805327.9172 - 339609292722.4559*I)*z1^25 + (4703726041850.886 + 6288159962627.907*I)*z1^26 + (-3192030593769.3384 - 46932536656199.18*I)*z1^27 + (-94599915491740.36 + 179869932058281.6*I)*z1^28 + (323481376139234.2 - 303553076721753.4*I)*z1^29 + (2541070565346227.5 + 1767717428857827.2*I)*z1^30 + (-2.384666906227862e+16 - 3.7065463725709656e+16*I)*z1^31 + (4851783042316304.0 + 3.445687089580052e+17*I)*z1^32 + (1.0959464833954391e+18 - 1.715874358372165e+18*I)*z1^33 + (-8.909411499934279e+18 + 3.928811298259652e+18*I)*z1^34 + (3.76500199811273e+19 + 6.341592448740794e+18*I)*z1^35 + (-8.957608928697673e+19 - 8.042500044881913e+19*I)*z1^36 + (9.157138258582733e+19 + 2.6991206843750266e+20*I)*z1^37 + (-2.962055778021626e+19 - 4.091391229708399e+20*I)*z1^38 + (8.22492906667656e+20 + 3.2321382714193235e+20*I)*z1^39 + (-4.561271708138317e+21 - 2.1301018063739072e+21*I)*z1^40 + (9.296142022741377e+21 + 1.08599475395796e+22*I)*z1^41 + (-8.179571330533686e+21 - 1.846117959981401e+22*I)*z1^42 + (3.388830678302479e+22 - 6.063871682774122e+21*I)*z1^43 + (-1.922490627257996e+23 + 1.6599311522556275e+22*I)*z1^44 + (4.4177446036804785e+23 + 1.9498717366525785e+23*I)*z1^45 + (-3.992951194622708e+23 - 5.9052038324165746e+23*I)*z1^46 + (1.0241536704894535e+22 + 6.088760116332556e+23*I)*z1^47 + (1.2615195948062235e+23 - 1.986988396786127e+23*I)*z1^48 + O(z1^49)\n",
+      "Absolute values of the coefficients:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 1 graphics primitive"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "********************************************************************************\n"
+     ]
+    }
+   ],
+   "source": [
+    "for v in S.vertices():\n",
+    "    if v.angle() != 1: continue\n",
+    "    print(f\"Power series at {v}\")\n",
+    "    series = f.series(v)\n",
+    "    print(f\"Power series: {series}\")\n",
+    "    print(\"Absolute values of the coefficients:\")\n",
+    "    list_plot([abs(c) for c in series.coefficients()], ticks=[[2 + n * 10 for n in range(20)], None], scale=\"semilogy\").show()\n",
+    "    print(\"*\" * 80)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9206fb1-e7bb-4681-8ed5-b38b9ec128eb",
+   "metadata": {},
+   "source": [
+    "At the singular points, the series use coordinates on a different chart, namely after taking a 13th and 3rd root respectively:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "3514063a-7fa2-4302-a320-3fd2b71d2cf9",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Power series at Vertex 0 of polygon 1; total angle 6π\n",
+      "Power series: 1.8491554953925515e-06 - 3.3605155635996198e-06*I + (6.426783547151665e-06 - 6.680071813536945e-06*I)*z2 + (2.999999199656937 - 2.1946874717571063e-06*I)*z2^2 + (7.770158528471763e-06 - 1.5758784540233184e-05*I)*z2^3 + (2.9183213096152637e-06 - 7.583734136989515e-06*I)*z2^4 + (2.153941442538074e-05 + 1.1024989568181827e-05*I)*z2^5 + (3.1213560959158908e-06 - 3.892477506333557e-06*I)*z2^6 + (1.3143696971461284e-05 + 1.5201995677887523e-06*I)*z2^7 + (-1.0376599249947807e-05 + 4.302613310577749e-06*I)*z2^8 + (-1.826940069136171e-05 + 8.488659667755522e-06*I)*z2^9 + (5.543610793635255e-07 + 1.610287123118194e-05*I)*z2^10 + (-2.82056287753828e-05 + 3.494972507731497e-05*I)*z2^11 + (-5.292681225152956 + 4.715637595640402*I)*z2^12 + (-1.8799100944140587e-06 + 1.6802346159854598e-05*I)*z2^13 + (-0.0006311116152307311 + 0.0008502559674807076*I)*z2^14 + (0.00028747248650970263 - 0.0005341816947149016*I)*z2^15 + (-0.0002269750319192932 + 0.0004406090847157835*I)*z2^16 + (7.304391926093376e-05 - 0.00036209193855853936*I)*z2^17 + (9.290609564540068e-05 - 0.0005103634015193376*I)*z2^18 + (0.0004870726043585265 - 0.004471168600968023*I)*z2^19 + (-2.1469606374987277e-06 - 0.002629108865323693*I)*z2^20 + (-5.1679818313125375e-05 - 0.002911525336053957*I)*z2^21 + (0.0002580007711471712 + 8.370047053410892e-05*I)*z2^22 + (0.0010404136026167412 + 0.0016196454523703498*I)*z2^23 + (0.00012915562769729035 - 0.0012108351456777927*I)*z2^24 + (0.015570948264655902 + 0.026996247058935375*I)*z2^25 + (-0.00017191709674912342 - 0.001393648354279001*I)*z2^26 + (8.429000996777656e-05 - 0.0007525536136629564*I)*z2^27 + (0.004723417607867483 + 0.0045814444394619*I)*z2^28 + (0.013882611545453977 + 0.010765496387116082*I)*z2^29 + (-0.20287764399357242 - 0.11678535938089117*I)*z2^30 + (0.025575265472688267 + 0.013900396908994063*I)*z2^31 + (-79.02220920955847 - 26.13434442072004*I)*z2^32 + (-0.06928572242125874 - 0.012126261329329969*I)*z2^33 + (1.0589904118462992 + 0.1163376008924791*I)*z2^34 + (-0.9339315367667916 - 0.011620023831913979*I)*z2^35 + (0.43450485513595766 - 0.0497211989648072*I)*z2^36 + (-0.48366702704530523 + 0.08605012646285151*I)*z2^37 + O(z2^38)\n",
+      "Absolute values of the coefficients:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 1 graphics primitive"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 1 graphics primitive"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "********************************************************************************\n",
+      "Power series at Vertex 0 of polygon 0; total angle 26π\n",
+      "Power series: 1.5617486326391372e-06 + 1.3112678232118924e-06*I + (-1.8118778461561955e-06 + 9.770517458834638e-06*I)*z5 + (1.1179991091956185 + 0.36973853955408453*I)*z5^2 + (1.3496612061170355e-05 - 1.0994238097867916e-06*I)*z5^3 + (1.6235814637010928e-06 - 1.313523166631233e-06*I)*z5^4 + (1.1609244803682127e-05 + 1.1290580750515483e-05*I)*z5^5 + (5.690662981447659e-06 + 5.127537815538312e-06*I)*z5^6 + (8.060939947373239e-06 - 5.0354806635273536e-06*I)*z5^7 + (-4.828588472932882e-06 - 1.5198561077011427e-06*I)*z5^8 + (6.696342646907342e-08 + 1.2805311630024201e-06*I)*z5^9 + (2.0896156890008822e-05 + 1.8821814209409613e-05*I)*z5^10 + (-1.4452351124366788e-05 + 6.194717472138143e-06*I)*z5^11 + (13.0 - 8.881784197001252e-15*I)*z5^12 + (-7.895640453061547e-06 - 1.8813636821989426e-05*I)*z5^13 + (-9.067876439190408e-06 - 1.24329270274052e-05*I)*z5^14 + (-0.0001089701836671363 - 4.607693418051475e-06*I)*z5^15 + (-2.2466123390434675e-05 - 2.5960455541104193e-05*I)*z5^16 + (-1.9419692688471545e-05 + 8.03252930213544e-06*I)*z5^17 + (-1.654342741884257e-05 + 5.669488837360489e-06*I)*z5^18 + (-9.119472337130918e-07 - 7.606381154869552e-06*I)*z5^19 + (4.057721297211845e-06 - 0.0004033458200529712*I)*z5^20 + (-7.785799227794449e-05 - 6.190312502424416e-06*I)*z5^21 + (5.7567027978970025 + 1.9038237362589072*I)*z5^22 + (-5.640608540066924e-05 + 1.771556211351444e-05*I)*z5^23 + (2.0087296536268948e-05 - 2.2558548278470994e-05*I)*z5^24 + (-0.0003360307334270723 - 2.980132144338405e-06*I)*z5^25 + (0.00016202792682176078 + 0.00022460939168871772*I)*z5^26 + (2.7846834521984357e-05 - 1.3774828257612954e-05*I)*z5^27 + (-2.4799819288917597e-05 + 2.1036417521839998e-05*I)*z5^28 + (-5.756821009745719e-05 - 3.4778368622055526e-05*I)*z5^29 + (-6.859368697198232e-05 + 0.0001910306050821468*I)*z5^30 + (-0.00033166070221157416 + 0.00022228409294728876*I)*z5^31 + (1.5158380033280811e-05 + 2.5828510289267544e-05*I)*z5^32 + (5.758395224497921e-06 + 3.47978835853344e-05*I)*z5^33 + (4.9817114274975625e-05 - 7.63338394965692e-05*I)*z5^34 + (-0.0009983739400116517 - 3.63280832048404e-06*I)*z5^35 + (-0.00032000115415226554 - 0.0004411670939992847*I)*z5^36 + (-3.496866131798745e-05 + 0.00016951148451276488*I)*z5^37 + (-0.0002838231573973152 + 9.909666680873155e-05*I)*z5^38 + (0.0003244490512694622 + 0.00023250784488076137*I)*z5^39 + (-5.719893226048699e-06 + 0.0009344615260302918*I)*z5^40 + (-0.0005722500663857267 + 0.0005441869420465689*I)*z5^41 + (4.763045480481761 + 1.575204928923894*I)*z5^42 + (9.093190130603865e-05 + 0.00039690969153135516*I)*z5^43 + (-0.0012456422496554792 + 0.001707240097203298*I)*z5^44 + (0.0005827058013515564 - 6.636765100418758e-05*I)*z5^45 + (0.0014608723770035662 + 0.001992131238792905*I)*z5^46 + (-4.0312627215403085e-05 + 0.00013958751284502777*I)*z5^47 + (0.0006083517314749731 - 0.00018719037671740887*I)*z5^48 + (-0.002632313438212433 - 0.001951257873985809*I)*z5^49 + (-2.6170091243465855e-05 - 0.0006346027142082997*I)*z5^50 + (-0.0030594187752498543 + 0.0021424285389495114*I)*z5^51 + (4.2645677423725644e-05 - 1.6672226058762013e-05*I)*z5^52 + (0.0002688366478833537 + 0.0009583055309093752*I)*z5^53 + (0.0014190856703851927 - 0.0019282260206248236*I)*z5^54 + (0.0018134803652109757 - 1.3237184339105518e-05*I)*z5^55 + (-0.002812632496221976 - 0.003931729266206724*I)*z5^56 + (-0.00028729265458646237 + 0.0011378247276093928*I)*z5^57 + (0.0005503457182352491 - 0.00017991379728711854*I)*z5^58 + (-0.016078702256616284 - 0.011769054588132613*I)*z5^59 + (-2.387201083224366e-05 - 0.0012042958671075507*I)*z5^60 + (-0.008741445117979982 + 0.0063127810367418855*I)*z5^61 + (-18.45208461837513 - 6.102391252421816*I)*z5^62 + (-0.0014536753157728696 - 0.004989236906314342*I)*z5^63 + (-0.08080493346979688 + 0.10997748411212677*I)*z5^64 + (-0.006234571311437778 + 3.689408803886758e-05*I)*z5^65 + (0.018372853687646653 + 0.02553588788714077*I)*z5^66 + (-0.0006229763748589145 + 0.0020906021717286625*I)*z5^67 + (-0.00663339295008935 + 0.0020063518096357243*I)*z5^68 + (-0.06713979326343311 - 0.049419084389026494*I)*z5^69 + (-0.0001832828579940715 + 0.007997924775058876*I)*z5^70 + (-0.03529826986163827 + 0.025384269388810346*I)*z5^71 + (-0.0023776015693054257 - 0.0006755943404913873*I)*z5^72 + (-0.0053667758967876735 - 0.01670824070400052*I)*z5^73 + (0.026547854730681546 - 0.03573423337750509*I)*z5^74 + (-0.05920196188419478 - 0.000318302205025543*I)*z5^75 + (-0.0313398573919947 - 0.04402838465719097*I)*z5^76 + (-0.01562198419436249 + 0.0474314616416832*I)*z5^77 + (0.026146172454298153 - 0.008467290912273905*I)*z5^78 + (-0.17579183076850735 - 0.12902569317046908*I)*z5^79 + (0.0015434110666354877 - 0.2965080177845695*I)*z5^80 + (-0.06266371785323782 + 0.04478838891726244*I)*z5^81 + (-19.23199190854966 - 6.360293068190409*I)*z5^82 + (-0.02511785951337825 - 0.0788741152970174*I)*z5^83 + (-0.7541813183970225 + 1.0266113097383005*I)*z5^84 + (-0.20771136952413397 - 0.0006804290059463905*I)*z5^85 + (0.12682768228565905 + 0.1761352866106644*I)*z5^86 + (-0.002630151824326384 + 0.008508960455557038*I)*z5^87 + (-0.10201999869336002 + 0.031909217349741704*I)*z5^88 + (-0.5645078231086699 - 0.41476446553662705*I)*z5^89 + (-0.0004703289705223807 + 0.14344282137022765*I)*z5^90 + (-0.25080975553066276 + 0.17965952489928239*I)*z5^91 + (-0.02300296441015208 - 0.007172353741761304*I)*z5^92 + (-0.0659358444876765 - 0.20522606845133076*I)*z5^93 + (0.20028241978062433 - 0.2731362965356272*I)*z5^94 + (-0.7972777271112339 - 0.004945369801545684*I)*z5^95 + (-0.22473199947999223 - 0.3127495548291743*I)*z5^96 + (-0.14079988504599633 + 0.41770754791774933*I)*z5^97 + (0.10706165272261822 - 0.034804182256949937*I)*z5^98 + (-0.9544463762151889 - 0.7014820627518382*I)*z5^99 + (0.015244552105782759 - 3.0876971639881146*I)*z5^100 + (-0.31819627960116503 + 0.22714201300559098*I)*z5^101 + (74.90704628829825 + 24.772874863000286*I)*z5^102 + (-0.1158222899522518 - 0.35563797001007613*I)*z5^103 + (-3.7016929391261675 + 5.038849481686784*I)*z5^104 + (-1.7184184350857012 - 0.007176559536418193*I)*z5^105 + (0.6668018437123011 + 0.9268252065969045*I)*z5^106 + (0.004912989898812908 - 0.00980712821727657*I)*z5^107 + (-0.5111565154153541 + 0.16384030616002754*I)*z5^108 + (-2.420474104129661 - 1.7757209235286855*I)*z5^109 + (-0.006977959033911963 + 1.0346424272638997*I)*z5^110 + (-1.270228979191212 + 0.9108491370119265*I)*z5^111 + (-0.09456168722551854 - 0.02758421770797267*I)*z5^112 + (-0.26747294345973543 - 0.8321585314579373*I)*z5^113 + (0.7527198367983092 - 1.0213774590228404*I)*z5^114 + (-5.432827716910198 - 0.030910786867869584*I)*z5^115 + (-0.9836025568213252 - 1.3674063514924657*I)*z5^116 + (-0.6486526115728204 + 1.9573754786621864*I)*z5^117 + O(z5^118)\n",
+      "Absolute values of the coefficients:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 1 graphics primitive"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 1 graphics primitive"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "********************************************************************************\n"
+     ]
+    }
+   ],
+   "source": [
+    "for v in S.vertices():\n",
+    "    if v.angle() == 1: continue\n",
+    "    print(f\"Power series at {v}; total angle {2*v.angle()}π\")\n",
+    "    series = f.series(v)\n",
+    "    print(f\"Power series: {series}\")\n",
+    "    print(\"Absolute values of the coefficients:\")\n",
+    "    list_plot([abs(c) for c in series.coefficients()], ticks=[[2 + n * 10 for n in range(20)], None], scale=\"semilogy\").show()\n",
+    "    list_plot([abs(c) for c in series.coefficients()], ticks=[[2 + n * 10 for n in range(20)], None]).show()\n",
+    "    print(\"*\" * 80)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e1247c5-7d14-44ef-ab31-29449407bcd9",
+   "metadata": {},
+   "source": [
+    "## Measuring the quality of these differentials\n",
+    "\n",
+    "We compute how closely these differentials track the prescribed cohomology that we were trying to solve for (very closely):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "56a82af4-c9bc-48c8-ab0e-209ff7f53b0d",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# This does not work in the setup of this notebook. The correct is shown instead.\n",
+    "# _ = f.error(kind=\"cohomology\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3206107d-a73f-47e2-8327-8109ac3ec65f",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "Integrating along cycle gives 0.33098486319992904 whereas the cocycle gave 0.330984859362568, i.e., an absolute error of 3.837360762481978e-09 and a relative error of 1.1593765255221076e-08.\n",
+    "Integrating along cycle gives 0.02984488314591953 whereas the cocycle gave 0.0298448861506505, i.e., an absolute error of 3.0047309293457225e-09 and a relative error of 1.0067825067847463e-07.\n",
+    "Integrating along cycle gives 0.999999986832941 whereas the cocycle gave 1.00000000000000, i.e., an absolute error of 1.316705899867543e-08 and a relative error of 1.316705899867543e-08.\n",
+    "Integrating along cycle gives 0.33098485971714564 whereas the cocycle gave 0.330984859362568, i.e., an absolute error of 3.5457742297850814e-10 and a relative error of 1.071279887730744e-09.\n",
+    "Integrating along cycle gives -0.4091197854215009 whereas the cocycle gave -0.409119785695342, i.e., an absolute error of 2.7384111644934706e-10 and a relative error of 6.693421487399474e-10.\n",
+    "Integrating along cycle gives 0.37492059415730333 whereas the cocycle gave 0.374920589337810, i.e., an absolute error of 4.8194937485313005e-09 and a relative error of 1.285470546454534e-08.\n",
+    "Integrating along cycle gives -0.6136796590818123 whereas the cocycle gave -0.613679678543013, i.e., an absolute error of 1.9461200650994215e-08 and a relative error of 3.1712310724055e-08.\n",
+    "Integrating along cycle gives -0.2712950885725277 whereas the cocycle gave -0.271295087061267, i.e., an absolute error of 1.5112603390932122e-09 and a relative error of 5.570540754952631e-09.\n",
+    "Integrating along cycle gives -0.7585497953711476 whereas the cocycle gave -0.758549799089383, i.e., an absolute error of 3.718235497274236e-09 and a relative error of 4.901768482092895e-09.\n",
+    "Integrating along cycle gives 0.46880955732332147 whereas the cocycle gave 0.468809557996643, i.e., an absolute error of 6.73321343125366e-10 and a relative error of 1.4362363813627445e-09.\n",
+    "Integrating along cycle gives 0.46880955732330515 whereas the cocycle gave 0.468809557996643, i.e., an absolute error of 6.733376634038279e-10 and a relative error of 1.4362711935336647e-09.\n",
+    "Integrating along cycle gives -0.07378060862277225 whereas the cocycle gave -0.0737806161258918, i.e., an absolute error of 7.50311958397365e-09 and a relative error of 1.0169499765590301e-07.\n",
+    "Integrating along cycle gives -0.487254709427468 whereas the cocycle gave -0.487254712028116, i.e., an absolute error of 2.6006478082152285e-09 and a relative error of 5.3373476828791845e-09.\n",
+    "Integrating along cycle gives 0.3195851237086348 whereas the cocycle gave 0.319585127243391, i.e., an absolute error of 3.5347559323994915e-09 and a relative error of 1.1060451914295373e-08.\n",
+    "Integrating along cycle gives 0.12642496736241948 whereas the cocycle gave 0.126424966514897, i.e., an absolute error of 8.475223023385325e-10 and a relative error of 6.70375738037997e-09.\n",
+    "Integrating along cycle gives -0.5355447481272093 whereas the cocycle gave -0.535544752210239, i.e., an absolute error of 4.083029914170311e-09 and a relative error of 7.624068571896737e-09.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02c67973-cfd5-43c7-b29a-39718c326749",
+   "metadata": {},
+   "source": [
+    "The above power series describe the differential close to the vertices of the surface. We use a cell decomposition to cut our surface into pieces such that each power series is used for computations in the cell that contains it. The cell decomposition used looks like this (the green segments are the cell boundaries, the arrow heads have no meaning here.):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "d687368f-7d19-4867-aefa-7906935dde6f",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 360 graphics primitives"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Ω._cells.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a16c1fb-b357-4984-8b64-d67213c0634d",
+   "metadata": {},
+   "source": [
+    "Two different power series \"meet\" at each of the green segment. We can compute their difference in L2 norm by integrating along the green segments. If we do this along all green segments we get a measure for how well the power series fit together, or rather a measure for the quality of the differential computed. In this example, we get for the *square* of the L2 norm:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "51c94926-59b5-4e43-92e1-1054456f3819",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "L2 norm of differential is 1.267333372785876e-07.\n"
+     ]
+    }
+   ],
+   "source": [
+    "_ = f.error(kind=\"L2\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38965d56-56ed-46f5-802b-608fd767a817",
+   "metadata": {},
+   "source": [
+    "## Roots of the differentials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50884b97-7a48-4fbb-b3b1-92b90d3258a1",
+   "metadata": {},
+   "source": [
+    "We can also compute where the roots of the differential are. They are all very close to the singularities so plotting them is not helpful.\n",
+    "\n",
+    "Since these roots are only approximations, we don't expect to see multiple roots. However, to detect their multiplicity, we can try to see how these roots cluster if we assume a certain numerical error in their positions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e068d7e8-2199-416a-b90d-ebc7bb0c51fc",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Distance of roots to vertices:\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Distance of roots to vertices:\")\n",
+    "roots = f.roots()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a10f5b17-edb2-4481-bff9-09dfae653b06",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clustering of roots:\n",
+      "Identifying roots contained in a 0.0 ball, there are 13 roots of orders (2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 1.38e-10 ball, there are 12 roots of orders (2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.00315 ball, there are 11 roots of orders (3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 10 roots of orders (4, 2, 1, 1, 1, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 9 roots of orders (5, 2, 1, 1, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 8 roots of orders (6, 2, 1, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 7 roots of orders (7, 2, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 6 roots of orders (8, 2, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 5 roots of orders (9, 2, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 4 roots of orders (10, 2, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 3 roots of orders (11, 2, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 2 roots of orders (12, 2)\n",
+      "Identifying roots contained in a 0.66 ball, there are 1 roots of orders (14,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Clustering of roots:\")\n",
+    "S.cluster_points(tuple(roots))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5ca7f71d-1b15-40a6-9fdc-d7553e947b51",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clustering of roots:\n",
+      "Identifying roots contained in a 0.0 ball, there are 13 roots of orders (2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 1.38e-10 ball, there are 12 roots of orders (2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.00315 ball, there are 11 roots of orders (3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 10 roots of orders (4, 2, 1, 1, 1, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 9 roots of orders (5, 2, 1, 1, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 8 roots of orders (6, 2, 1, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 7 roots of orders (7, 2, 1, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 6 roots of orders (8, 2, 1, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 5 roots of orders (9, 2, 1, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 4 roots of orders (10, 2, 1, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 3 roots of orders (11, 2, 1)\n",
+      "Identifying roots contained in a 0.0063 ball, there are 2 roots of orders (12, 2)\n",
+      "Identifying roots contained in a 0.66 ball, there are 1 roots of orders (14,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Clustering of roots:\")\n",
+    "S.cluster_points(tuple(roots))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a34f4e45-863a-41df-b161-5002cf29a46e",
+   "metadata": {},
+   "source": [
+    "## Implementation Details\n",
+    "\n",
+    "This is probably only of interest to Julian and Vincent.\n",
+    "\n",
+    "The differential was produced with 527d7e08d482f5b162af09cc161f2aa505a4d3b0 using this code snippet:\n",
+    "\n",
+    "```python\n",
+    "from flatsurf import similarity_surfaces, Polygon\n",
+    "S = similarity_surfaces.billiard(Polygon(angles=[3, 4, 13])).minimal_cover(\"translation\")\n",
+    "S = S.erase_marked_points().codomain().delaunay_triangulation()\n",
+    "S = S.relabel().codomain()\n",
+    "\n",
+    "from flatsurf import HarmonicDifferentials, ApproximateWeightedVoronoiCellDecomposition\n",
+    "S = S.insert_marked_points(*[S(label, S.polygon(label).centroid()) for label in (2, 18, 26, 31)]).codomain()\n",
+    "S = S.delaunay_triangulation()\n",
+    "S = S.relabel().codomain()\n",
+    "V = ApproximateWeightedVoronoiCellDecomposition(S)\n",
+    "\n",
+    "Omega = HarmonicDifferentials(S, error=1e-6, cell_decomposition=V)\n",
+    "from flatsurf import GL2ROrbitClosure\n",
+    "O = GL2ROrbitClosure(S)\n",
+    "for d in O.decompositions(4, 20):\n",
+    "    O.update_tangent_space_from_flow_decomposition(d)\n",
+    "    if O.dimension() == 7: break\n",
+    "\n",
+    "F = O._lift_to_simplicial_cohomology(O.lift(O.tangent_space_basis()[-1]))\n",
+    "F = F.parent()({k: v / max(F._values.values()) for (k, v) in F._values.items()})\n",
+    "f = Omega(F, check=False)\n",
+    "```\n",
+    "\n",
+    "The following is a binary representation of the differential (on the above commit it can be loaded with `loads`.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e3ab785f-4477-4c87-a8a3-dc7e23391c66",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import flatsurf\n",
+    "\n",
+    "pickle = 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+    "\n",
+    "f, F = loads(pickle)\n",
+    "Ω = f.parent()\n",
+    "S = Ω.surface()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b5cc350-4956-40a9-aa80-dffd50886384",
+   "metadata": {},
+   "source": [
+    "# Another Model of 3,4,13"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bcf924d-9332-4d10-ba76-d0fef84ac142",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from flatsurf import polygons, similarity_surfaces\n",
+    "\n",
+    "t = polygons.triangle(3, 4, 13)\n",
+    "B = similarity_surfaces.billiard(t)\n",
+    "S = B.minimal_cover('translation')\n",
+    "S = S.erase_marked_points().codomain()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "id": "8031a836-ee0d-4748-8d81-f3f4488dfafd",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attempt 0 with (1, 5/3) and 4: 3.68931570580045"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 97 graphics primitives"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/24 7.0 2/24 11. 3/24 11. 4/24 36. 5/24 21. 6/24 21. 7/24 7.1 8/24 16. 9/24 4.5 10/24 13. 11/24 10. 12/24 14. 13/24 6.6 14/24 5.2 15/24 3.8 16/28 5.6 17/28 4.3 18/28 21. 19/28 6.1 20/28 6.5 21/28 7.3 22/28 10. 23/28 11. 24/28 11. 25/28 29. 26/28 21. 27/28 27. 28/28 \n",
+      "Attempt 1 with (8, 1) and 3: 35. 1/8 16. 2/8 15. 3/8 4.1 4/8 3.0723753203620014"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 97 graphics primitives"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5/24 3.7 6/28 3.9 7/32 5.7 8/32 6.6 9/32 12. 10/32 21. 11/32 76. 12/32 800. 13/32 87. 14/32 23. 15/32 14. 16/32 8.1 17/32 5.5 18/32 4.3 19/32 3.3 20/36 8.3 21/36 10. 22/36 12. 23/36 4.7 24/36 3.5 25/40 4.7 26/40 6.2 27/40 8.1 28/40 6.3 29/40 3.9 30/40 6.3 31/40 8.3 32/40 7.6 33/40 3.5 34/40 6.9 35/40 3.8 36/40 3.2 37/40 5.9 38/40 12. 39/40 20. 40/40 \n",
+      "Attempt 2 with (23/2, 1/2) and 3: 9.2 1/8 15. 2/8 12. 3/8 60. 4/8 190. 5/8 21. 6/8 12. 7/8 13. 8/8 \n",
+      "Attempt 3 with (2/11, 8) and 3: 14. 1/8 3.6 2/12 5.8 3/12 17. 4/12 7.8 5/12 4.2 6/12 4.2 7/12 9.7 8/12 12. 9/12 6.3 10/12 5.2 11/12 10. 12/12 \n",
+      "Attempt 4 with (2, 0) and 5: 5.4 1/8 7.5 2/8 7.0 3/8 3.8 4/12 17. 5/12 3.9 6/16 7.1 7/16 6.3 8/16 7.1 9/16 4.1 10/16 4.4 11/16 6.9 12/16 9.1 13/16 7.5 14/16 5.0 15/16 16. 16/16 \n",
+      "Attempt 5 with (1, 2) and 3: 3.1 1/12 3.9 2/16 5.6 3/16 5.3 4/16 3.9 5/20 4.7 6/20 8.7 7/20 30. 8/20 27. 9/20 7.3 10/20 9.1 11/20 38. 12/20 12. 13/20 7.7 14/20 23. 15/20 9.0 16/20 5.4 17/20 8.4 18/20 31. 19/20 14. 20/20 \n",
+      "Attempt 6 with (1, 2) and 5: 7.8 1/8 5.0 2/8 3.2 3/12 8.8 4/12 9.3 5/12 9.1 6/12 3.4 7/16 4.9 8/16 18. 9/16 12. 10/16 10. 11/16 8.6 12/16 12. 13/16 4.0 14/20 7.0 15/20 17. 16/20 5.1 17/20 4.3 18/20 3.9 19/24 7.9 20/24 13. 21/24 11. 22/24 5.8 23/24 30. 24/24 \n",
+      "Attempt 7 with (1/5, 1/2) and 4: 4.5 1/8 23. 2/8 9.9 3/8 6.6 4/8 6.1 5/8 16. 6/8 8.2 7/8 8.3 8/8 \n",
+      "Attempt 8 with (3/2, 0) and 4: 4.5 1/8 7.4 2/8 6.3 3/8 7.5 4/8 8.1 5/8 4.0 6/8 5.2 7/8 7.0 8/8 \n",
+      "Attempt 9 with (1, 1/5) and 4: 3.7 1/12 6.7 2/12 8.4 3/12 9.3 4/12 6.2 5/12 4.5 6/12 3.7 7/16 10. 8/16 14. 9/16 4.5 10/16 6.9 11/16 4.7 12/16 3.3 13/20 19. 14/20 5.1 15/20 17. 16/20 5.2 17/20 13. 18/20 5.8 19/20 6.5 20/20 \n",
+      "Attempt 10 with (0, 2/3) and 4: 7.6 1/8 6.2 2/8 6.6 3/8 38. 4/8 12. 5/8 13. 6/8 26. 7/8 5.6 8/8 \n",
+      "Attempt 11 with (1/2, 9) and 4: 6.2 1/8 5.3 2/8 7.5 3/8 6.4 4/8 12. 5/8 7.4 6/8 4.2 7/8 5.1 8/8 \n",
+      "Attempt 12 with (1/2, 1) and 4: 17. 1/8 21. 2/8 47. 3/8 190. 4/8 40. 5/8 23. 6/8 14. 7/8 10. 8/8 \n",
+      "Attempt 13 with (1/4, 1) and 4: 8.3 1/8 9.8 2/8 4.9 3/8 4.3 4/8 13. 5/8 3.4 6/12 17. 7/12 16. 8/12 5.0 9/12 3.3 10/16 2.3704593437197317"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 97 graphics primitives"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11/32 11. 12/32 5.0 13/32 5.0 14/32 4.3 15/32 7.3 16/32 5.8 17/32 4.0 18/32 5.5 19/32 7.5 20/32 8.2 21/32 10. 22/32 5.3 23/32 12. 24/32 3.9 25/32 14. 26/32 7.6 27/32 10. 28/32 4.4 29/32 4.5 30/32 7.9 31/32 5.9 32/32 \n",
+      "Attempt 14 with (0, 1) and 4: 40. 1/8 3.9 2/12 6.2 3/12 6.0 4/12 4.8 5/12 8.0 6/12 14. 7/12 12. 8/12 4.3 9/12 5.6 10/12 21. 11/12 14. 12/12 \n",
+      "Attempt 15 with (0, 1) and 6: 5.4 1/8 68. 2/8 6.7 3/8 10. 4/8 6.9 5/8 6.0 6/8 7.7 7/8 5.4 8/8 \n",
+      "Attempt 16 with (1, 8) and 4: 5.9 1/8 4.3 2/8 11. 3/8 6.2 4/8 5.5 5/8 6.4 6/8 4.2 7/8 4.8 8/8 \n",
+      "Attempt 17 with (3/2, 0) and 3: 5.0 1/8 6.7 2/8 21. 3/8 14. 4/8 6.9 5/8 20. 6/8 19. 7/8 34. 8/8 \n",
+      "Attempt 18 with (2, 1) and 4: 12. 1/8 6.7 2/8 5.0 3/8 5.8 4/8 6.5 5/8 12. 6/8 7.5 7/8 3.3 8/12 7.8 9/12 5.3 10/12 4.7 11/12 3.3 12/16 15. 13/16 4.7 14/16 7.1 15/16 4.5 16/16 \n",
+      "Attempt 19 with (1/57, 1/2) and 4: 8.3 1/8 7.7 2/8 8.9 3/8 8.1 4/8 9.1 5/8 6.6 6/8 12. 7/8 6.6 8/8 \n",
+      "Attempt 20 with (3/2, 12) and 4: 17. 1/8 16. 2/8 150. 3/8 70. 4/8 12. 5/8 6.2 6/8 6.4 7/8 8.5 8/8 \n",
+      "Attempt 21 with (1, 1/12) and 4: 5.7 1/8 4.7 2/8 5.6 3/8 7.0 4/8 4.2 5/8 11. 6/8 19. 7/8 5.4 8/8 \n",
+      "Attempt 22 with (1, 0) and 5: 5.0 1/8 7.1 2/8 67. 3/8 27. 4/8 130. 5/8 79. 6/8 18. 7/8 10. 8/8 \n",
+      "Attempt 23 with (6, 1/16) and 4: 11. 1/8 16. 2/8 22. 3/8 6.7 4/8 7.0 5/8 7.0 6/8 10. 7/8 64. 8/8 \n",
+      "Attempt 24 with (4, 39/2) and 4: 5.0 1/8 4.3 2/8 13. 3/8 4.0 4/8 6.8 5/8 7.4 6/8 5.6 7/8 4.5 8/8 \n",
+      "Attempt 25 with (1/2, 0) and 4: 5.6 1/8 4.8 2/8 3.1 3/12 5.0 4/12 3.5 5/16 8.5 6/16 7.6 7/16 7.6 8/16 13. 9/16 16. 10/16 8.8 11/16 14. 12/16 5.4 13/16 11. 14/16 16. 15/16 3.3 16/20 3.9 17/24 6.6 18/24 5.1 19/24 5.3 20/24 11. 21/24 22. 22/24 8.9 23/24 3.5 24/28 8.8 25/28 16. 26/28 20. 27/28 15. 28/28 \n",
+      "Attempt 26 with (4, 1) and 4: 32. 1/8 38. 2/8 14. 3/8 7.3 4/8 9.4 5/8 17. 6/8 12. 7/8 4.5 8/8 \n",
+      "Attempt 27 with (14/3, 1/3) and 3: 10. 1/8 13. 2/8 26. 3/8 9.1 4/8 20. 5/8 22. 6/8 8.3 7/8 11. 8/8 \n",
+      "Attempt 28 with (1/6, 1/2) and 4: 12. 1/8 7.4 2/8 5.8 3/8 6.6 4/8 16. 5/8 5.2 6/8 4.4 7/8 15. 8/8 \n",
+      "Attempt 29 with (3, 0) and 5: 24. 1/8 25. 2/8 16. 3/8 13. 4/8 10. 5/8 9.2 6/8 6.9 7/8 8.1 8/8 \n",
+      "Attempt 30 with (1, 0) and 4: 5.2 1/8 4.5 2/8 4.8 3/8 3.5 4/12 3.9 5/16 6.8 6/16 4.3 7/16 4.9 8/16 10. 9/16 8.5 10/16 2.9 11/32 3.1 12/32 8.6 13/32 3.3 14/32 6.9 15/32 4.9 16/32 4.6 17/32 9.2 18/32 67. 19/32 31. 20/32 62. 21/32 100. 22/32 100. 23/32 18. 24/32 8.0 25/32 11. 26/32 8.7 27/32 6.5 28/32 2.8 29/32 4.6 30/32 18. 31/32 12. 32/32 \n",
+      "Attempt 31 with (6/7, 1) and 15: 11. 1/8 5.7 2/8 4.4 3/8 2.7 4/16 4.9 5/16 7.4 6/16 7.6 7/16 3.0 8/16 4.9 9/16 9.0 10/16 8.2 11/16 4.0 12/16 7.1 13/16 4.1 14/16 8.1 15/16 5.1 16/16 \n",
+      "Attempt 32 with (1, 1) and 17: 41. 1/8 3.6 2/12 4.6 3/12 7.7 4/12 3.8 5/16 5.4 6/16 7.9 7/16 6.2 8/16 5.6 9/16 5.4 10/16 8.2 11/16 9.6 12/16 4.6 13/16 8.8 14/16 22. 15/16 130. 16/16 \n",
+      "Attempt 33 with (3, 0) and 6: 20. 1/8 7.1 2/8 87. 3/8 13. 4/8 6.2 5/8 3.2 6/12 6.5 7/12 13. 8/12 27. 9/12 7.8 10/12 11. 11/12 6.5 12/12 \n",
+      "Attempt 34 with (1, 1) and 5: 6.6 1/8 9.6 2/8 18. 3/8 21. 4/8 11. 5/8 35. 6/8 19. 7/8 21. 8/8 \n",
+      "Attempt 35 with (0, 1/7) and 4: 34. 1/8 16. 2/8 11. 3/8 7.1 4/8 7.8 5/8 30. 6/8 27. 7/8 15. 8/8 \n",
+      "Attempt 36 with (1, 0) and 3: 8.8 1/8 7.7 2/8 7.3 3/8 14. 4/8 44. 5/8 32. 6/8 8.5 7/8 9.5 8/8 \n",
+      "Attempt 37 with (0, 1/114) and 3: 14. 1/8 12. 2/8 10. 3/8 13. 4/8 8.9 5/8 14. 6/8 9.4 7/8 7.9 8/8 \n",
+      "Attempt 38 with (0, 1/10) and 4: 8.3 1/8 32. 2/8 6.7 3/8 5.7 4/8 25. 5/8 7.0 6/8 9.3 7/8 31. 8/8 \n",
+      "Attempt 39 with (3/2, 1) and 3: 6.8 1/8 19. 2/8 7.7 3/8 8.8 4/8 13. 5/8 23. 6/8 79. 7/8 13. 8/8 \n",
+      "Attempt 40 with (0, 1/2) and 8: 12. 1/8 13. 2/8 27. 3/8 45. 4/8 7.5 5/8 7.3 6/8 4.2 7/8 7.0 8/8 \n",
+      "Attempt 41 with (0, 1/3) and 6: 14. 1/8 15. 2/8 6.1 3/8 14. 4/8 5.1 5/8 16. 6/8 27. 7/8 3.5 8/12 4.6 9/12 9.2 10/12 2.5 11/25 2.4 12/25 4.9 13/25 6.3 14/25 7.7 15/25 9.8 16/25 3.6 17/25 30. 18/25 15. 19/25 5.0 20/25 10. 21/25 13. 22/25 79. 23/25 13. 24/25 6.9 25/25 \n",
+      "Attempt 42 with (1, 0) and 42: 7.1 1/8 22. 2/8 5.9 3/8 3.3 4/12 6.8 5/12 10. 6/12 9.0 7/12 9.2 8/12 4.9 9/12 4.0 10/12 11. 11/12 320. 12/12 \n",
+      "Attempt 43 with (2, 0) and 4: 5.3 1/8 7.6 2/8 21. 3/8 4.5 4/8 8.0 5/8 4.8 6/8 8.5 7/8 25. 8/8 \n",
+      "Attempt 44 with (1/4, 0) and 17: 2.8 1/16 5.2 2/16 7.1 3/16 15. 4/16 5.2 5/16 5.9 6/16 20. 7/16 4.7 8/16 4.1 9/16 3.5 10/16 8.8 11/16 19. 12/16 5.6 13/16 4.5 14/16 4.2 15/16 49. 16/16 \n",
+      "Attempt 45 with (1/6, 0) and 5: 4.1 1/8 22. 2/8 4.4 3/8 13. 4/8 8.8 5/8 3.9 6/12 4.1 7/12 5.9 8/12 9.4 9/12 4.6 10/12 3.2 11/16 55. 12/16 2.9 13/28 4.2 14/28 16. 15/28 7.2 16/28 3.0 17/28 5.5 18/28 5.4 19/28 7.7 20/28 5.8 21/28 18. 22/28 6.3 23/28 31. 24/28 5.7 25/28 4.5 26/28 9.2 27/28 8.2 28/28 \n",
+      "Attempt 46 with (1/8, 2) and 13: 16. 1/8 4.6 2/8 5.4 3/8 8.4 4/8 5.0 5/8 7.3 6/8 4.5 7/8 27. 8/8 \n",
+      "Attempt 47 with (15, 1) and 6: 2.7 1/25 7.7 2/25 9.0 3/25 5.7 4/25 7.2 5/25 54. 6/25 16. 7/25 14. 8/25 10. 9/25 3.4 10/25 8.6 11/25 18. 12/25 4.1 13/25 8.5 14/25 7.1 15/25 4.9 16/25 5.7 17/25 6.9 18/25 4.7 19/25 7.5 20/25 35. 21/25 9.9 22/25 11. 23/25 2.5 24/25 8.9 25/25 \n",
+      "Attempt 48 with (0, 5/12) and 4: 30. 1/8 7.0 2/8 18. 3/8 11. 4/8 10. 5/8 14. 6/8 8.4 7/8 4.5 8/8 \n",
+      "Attempt 49 with (0, 1/42) and 3: 8.1 1/8 7.3 2/8 8.5 3/8 13. 4/8 5.5 5/8 7.0 6/8 7.6 7/8 4.0 8/12 3.7 9/16 8.1 10/16 9.4 11/16 5.7 12/16 5.0 13/16 3.0 14/20 4.1 15/20 8.9 16/20 7.4 17/20 29. 18/20 11. 19/20 15. 20/20 \n",
+      "Attempt 50 with (1, 1) and 4: 7.1 1/8 4.0 2/8 3.8 3/12 3.8 4/16 3.4 5/20 11. 6/20 4.2 7/20 4.2 8/20 5.0 9/20 5.0 10/20 5.7 11/20 4.8 12/20 4.9 13/20 2.9 14/32 3.4 15/32 4.8 16/32 7.3 17/32 10. 18/32 18. 19/32 30. 20/32 13. 21/32 26. 22/32 23. 23/32 19. 24/32 12. 25/32 48. 26/32 16. 27/32 7.4 28/32 3.3 29/32 3.9 30/32 4.2 31/32 3.5 32/32 \n",
+      "Attempt 51 with (1, 2) and 12: 21. 1/8 17. 2/8 4.9 3/8 4.7 4/8 7.6 5/8 8.4 6/8 4.1 7/8 18. 8/8 \n",
+      "Attempt 52 with (1, 1) and 153: 5.6 1/8 3.8 2/9 47. 3/9 7.7 4/9 16. 5/9 3.2 6/9 5.6 7/9 7.2 8/9 20. 9/9 \n",
+      "Attempt 53 with (1/38, 1) and 4: 17. 1/8 20. 2/8 40. 3/8 520. 4/8 110. 5/8 35. 6/8 21. 7/8 7.9 8/8 \n",
+      "Attempt 54 with (17, 2/3) and 36: 17. 1/8 29. 2/8 3.9 3/12 4.7 4/12 16. 5/12 16. 6/12 5.6 7/12 6.7 8/12 2.6 9/12 5.4 10/12 3.3 11/12 5.0 12/12 \n",
+      "Attempt 55 with (2/3, 0) and 5: 16. 1/8 8.7 2/8 3.2 3/12 3.1 4/16 5.4 5/16 27. 6/16 12. 7/16 4.6 8/16 6.8 9/16 6.7 10/16 8.8 11/16 12. 12/16 7.0 13/16 5.6 14/16 5.2 15/16 10. 16/16 \n",
+      "Attempt 56 with (3, 4/9) and 5: 12. 1/8 7.5 2/8 24. 3/8 5.5 4/8 5.5 5/8 5.4 6/8 3.0 7/12 6.6 8/12 5.0 9/12 33. 10/12 12. 11/12 3.0 12/28 6.2 13/28 7.9 14/28 7.8 15/28 2.8 16/28 10. 17/28 15. 18/28 9.0 19/28 6.8 20/28 9.6 21/28 4.0 22/28 3.4 23/28 5.8 24/28 4.6 25/28 2.6 26/28 7.1 27/28 4.2 28/28 \n",
+      "Attempt 57 with (4/33, 1) and 3: 14. 1/8 6.3 2/8 6.2 3/8 5.6 4/8 13. 5/8 15. 6/8 4.6 7/8 6.6 8/8 \n",
+      "Attempt 58 with (0, 1) and 3: 8.0 1/8 11. 2/8 25. 3/8 6.2 4/8 6.5 5/8 7.9 6/8 4.1 7/8 9.9 8/8 \n",
+      "Attempt 59 with (1/5, 7) and 5: 12. 1/8 20. 2/8 4.5 3/8 4.9 4/8 5.5 5/8 6.7 6/8 14. 7/8 19. 8/8 \n",
+      "Attempt 60 with (2, 11/26) and 4: 9.7 1/8 4.8 2/8 18. 3/8 3.5 4/12 4.6 5/12 4.6 6/12 6.4 7/12 12. 8/12 7.8 9/12 8.1 10/12 69. 11/12 8.6 12/12 \n",
+      "Attempt 61 with (1/4, 3) and 55: 6.0 1/8 10. 2/8 7.2 3/8 19. 4/8 6.6 5/8 3.1 6/11 4.6 7/11 4.2 8/11 13. 9/11 9.6 10/11 6.4 11/11 \n",
+      "Attempt 62 with (11, 1/4) and 4: 3.4 1/12 5.9 2/12 5.4 3/12 7.4 4/12 8.1 5/12 17. 6/12 53. 7/12 13. 8/12 11. 9/12 4.0 10/16 4.3 11/16 14. 12/16 11. 13/16 4.2 14/16 6.4 15/16 11. 16/16 \n",
+      "Attempt 63 with (1, 1/2) and 4: 8.9 1/8 7.9 2/8 5.2 3/8 9.1 4/8 9.9 5/8 9.9 6/8 6.9 7/8 9.5 8/8 \n",
+      "Attempt 64 with (0, 1/4) and 4: 8.8 1/8 5.4 2/8 7.7 3/8 8.7 4/8 17. 5/8 89. 6/8 40. 7/8 44. 8/8 \n",
+      "Attempt 65 with (3, 1) and 4: 50. 1/8 13. 2/8 4.4 3/8 6.2 4/8 7.1 5/8 4.2 6/8 43. 7/8 11. 8/8 \n",
+      "Attempt 66 with (1/22, 0) and 4: 4.0 1/8 5.4 2/8 7.1 3/8 30. 4/8 6.7 5/8 9.9 6/8 3.3 7/12 4.6 8/12 14. 9/12 4.7 10/12 3.0 11/16 2.5 12/32 3.9 13/32 3.5 14/32 13. 15/32 13. 16/32 36. 17/32 19. 18/32 21. 19/32 7.1 20/32 9.6 21/32 5.8 22/32 4.8 23/32 6.6 24/32 11. 25/32 16. 26/32 52. 27/32 36. 28/32 17. 29/32 11. 30/32 6.0 31/32 4.9 32/32 \n",
+      "Attempt 67 with (7, 2/3) and 4: 6.6 1/8 20. 2/8 17. 3/8 72. 4/8 130. 5/8 35. 6/8 13. 7/8 7.4 8/8 \n",
+      "Attempt 68 with (4, 1/2) and 3: 3.4 1/12 5.3 2/12 11. 3/12 13. 4/12 4.5 5/12 5.0 6/12 11. 7/12 8.5 8/12 7.2 9/12 5.6 10/12 47. 11/12 14. 12/12 \n",
+      "Attempt 69 with (1/9, 0) and 6: 7.1 1/8 12. 2/8 4.4 3/8 4.1 4/8 3.7 5/12 2.9 6/25 7.9 7/25 14. 8/25 4.5 9/25 7.4 10/25 27. 11/25 8.1 12/25 18. 13/25 4.9 14/25 20. 15/25 10. 16/25 4.5 17/25 6.0 18/25 2.7 19/25 4.6 20/25 6.3 21/25 3.7 22/25 5.2 23/25 12. 24/25 13. 25/25 \n",
+      "Attempt 70 with (1/3, 2) and 4: 7.1 1/8 4.8 2/8 15. 3/8 9.8 4/8 4.5 5/8 3.4 6/12 5.4 7/12 13. 8/12 3.9 9/16 3.7 10/20 8.1 11/20 7.6 12/20 3.9 13/24 9.4 14/24 4.6 15/24 3.9 16/28 4.2 17/28 22. 18/28 29. 19/28 8.2 20/28 5.5 21/28 9.3 22/28 6.5 23/28 7.9 24/28 15. 25/28 50. 26/28 30. 27/28 14. 28/28 \n",
+      "Attempt 71 with (0, 1/2) and 12: 5.4 1/8 7.2 2/8 14. 3/8 4.6 4/8 3.9 5/12 18. 6/12 4.8 7/12 12. 8/12 13. 9/12 6.1 10/12 11. 11/12 4.6 12/12 \n",
+      "Attempt 72 with (12, 3/2) and 14: 4.0 1/12 16. 2/12 2.8 3/17 12. 4/17 31. 5/17 4.9 6/17 3.7 7/17 3.7 8/17 3.6 9/17 13. 10/17 4.1 11/17 3.0 12/17 4.1 13/17 5.3 14/17 7.0 15/17 7.4 16/17 6.8 17/17 \n",
+      "Attempt 73 with (1/6, 13) and 5: 20. 1/8 13. 2/8 6.4 3/8 11. 4/8 5.4 5/8 8.7 6/8 9.1 7/8 35. 8/8 \n",
+      "Attempt 74 with (1/2, 1/6) and 10: 11. 1/8 160. 2/8 16. 3/8 7.0 4/8 4.3 5/8 5.9 6/8 6.6 7/8 5.9 8/8 \n",
+      "Attempt 75 with (1/386, 0) and 6: 4.9 1/8 5.4 2/8 3.5 3/12 9.5 4/12 6.1 5/12 7.6 6/12 7.3 7/12 9.3 8/12 27. 9/12 11. 10/12 26. 11/12 12. 12/12 \n",
+      "Attempt 76 with (1/4, 2) and 3: 30. 1/8 11. 2/8 4.5 3/8 3.0 4/40 5.6 5/40 4.7 6/40 3.8 7/40 6.0 8/40 3.1 9/40 19. 10/40 7.1 11/40 6.8 12/40 11. 13/40 10. 14/40 4.4 15/40 6.3 16/40 6.1 17/40 13. 18/40 7.9 19/40 4.2 20/40 6.6 21/40 6.2 22/40 5.5 23/40 4.5 24/40 17. 25/40 6.3 26/40 5.9 27/40 6.1 28/40 13. 29/40 9.6 30/40 5.3 31/40 5.9 32/40 4.9 33/40 10. 34/40 40. 35/40 8.3 36/40 4.9 37/40 7.1 38/40 6.1 39/40 9.5 40/40 \n",
+      "Attempt 77 with (1/18, 1/5) and 4: 13. 1/8 12. 2/8 9.3 3/8 11. 4/8 15. 5/8 8.3 6/8 8.5 7/8 7.9 8/8 \n",
+      "Attempt 78 with (1, 13) and 4: 14. 1/8 4.0 2/8 4.5 3/8 16. 4/8 7.4 5/8 7.7 6/8 13. 7/8 3.6 8/12 6.8 9/12 18. 10/12 4.0 11/16 4.5 12/16 3.1 13/20 7.3 14/20 6.0 15/20 4.6 16/20 3.5 17/24 7.2 18/24 4.4 19/24 8.6 20/24 33. 21/24 19. 22/24 7.3 23/24 3.9 24/28 13. 25/28 7.3 26/28 6.3 27/28 47. 28/28 \n",
+      "Attempt 79 with (1/23, 3) and 3: 13. 1/8 13. 2/8 6.6 3/8 7.0 4/8 8.8 5/8 16. 6/8 5.8 7/8 7.2 8/8 \n",
+      "Attempt 80 with (1/2, 1) and 3: 3.7 1/12 8.3 2/12 7.7 3/12 15. 4/12 13. 5/12 11. 6/12 10. 7/12 47. 8/12 140. 9/12 30. 10/12 18. 11/12 23. 12/12 \n",
+      "Attempt 81 with (1, 4) and 18: 3.3 1/12 19. 2/12 5.6 3/12 5.9 4/12 4.7 5/12 4.9 6/12 4.4 7/12 5.2 8/12 43. 9/12 21. 10/12 6.0 11/12 14. 12/12 \n",
+      "Attempt 82 with (95/12, 0) and 5: 6.3 1/8 4.0 2/12 3.0 3/16 4.4 4/16 10. 5/16 2.9 6/28 9.0 7/28 18. 8/28 6.9 9/28 4.2 10/28 6.8 11/28 11. 12/28 5.3 13/28 5.0 14/28 19. 15/28 15. 16/28 48. 17/28 12. 18/28 16. 19/28 20. 20/28 11. 21/28 5.9 22/28 6.9 23/28 6.2 24/28 10. 25/28 6.8 26/28 6.0 27/28 11. 28/28 \n",
+      "Attempt 83 with (1, 8/3) and 5: 3.3 1/12 7.1 2/12 8.1 3/12 14. 4/12 33. 5/12 4.4 6/12 5.4 7/12 8.9 8/12 6.2 9/12 6.1 10/12 12. 11/12 4.1 12/12 \n",
+      "Attempt 84 with (10, 1/3) and 3: 4.0 1/12 3.7 2/16 6.3 3/16 2.9 4/40 7.2 5/40 7.9 6/40 5.9 7/40 4.2 8/40 12. 9/40 6.8 10/40 7.2 11/40 17. 12/40 8.4 13/40 19. 14/40 3.6 15/40 12. 16/40 7.5 17/40 5.3 18/40 10. 19/40 3.7 20/40 3.3 21/40 5.9 22/40 4.1 23/40 12. 24/40 5.1 25/40 7.6 26/40 10. 27/40 3.6 28/40 8.9 29/40 5.7 30/40 6.4 31/40 6.2 32/40 9.6 33/40 6.1 34/40 6.2 35/40 6.3 36/40 5.1 37/40 7.3 38/40 4.6 39/40 5.1 40/40 \n",
+      "Attempt 85 with (3, 1) and 3: 26. 1/8 5.4 2/8 5.8 3/8 3.6 4/12 3.9 5/16 7.5 6/16 5.1 7/16 3.7 8/20 6.3 9/20 5.7 10/20 6.8 11/20 5.7 12/20 11. 13/20 110. 14/20 13. 15/20 4.0 16/24 2.7 17/40 11. 18/40 7.1 19/40 2.6 20/40 8.3 21/40 77. 22/40 11. 23/40 3.1 24/40 4.2 25/40 3.8 26/40 3.9 27/40 12. 28/40 10. 29/40 23. 30/40 17. 31/40 7.3 32/40 5.0 33/40 4.0 34/40 4.2 35/40 5.8 36/40 9.0 37/40 4.7 38/40 6.3 39/40 46. 40/40 \n",
+      "Attempt 86 with (0, 3/7) and 7: 8.3 1/8 10. 2/8 15. 3/8 6.6 4/8 14. 5/8 3.7 6/12 3.3 7/16 8.2 8/16 60. 9/16 9.1 10/16 49. 11/16 7.2 12/16 3.1 13/20 6.9 14/20 5.4 15/20 3.5 16/23 18. 17/23 22. 18/23 3.9 19/23 10. 20/23 4.2 21/23 9.4 22/23 4.3 23/23 \n",
+      "Attempt 87 with (38/11, 0) and 4: 5.6 1/8 3.9 2/12 17. 3/12 6.5 4/12 3.4 5/16 6.0 6/16 5.1 7/16 3.5 8/20 7.3 9/20 5.2 10/20 13. 11/20 17. 12/20 6.7 13/20 5.8 14/20 5.9 15/20 5.0 16/20 3.6 17/24 7.6 18/24 3.7 19/28 7.5 20/28 12. 21/28 5.1 22/28 56. 23/28 4.2 24/28 14. 25/28 2.9 26/32 3.6 27/32 10. 28/32 20. 29/32 16. 30/32 14. 31/32 9.3 32/32 \n",
+      "Attempt 88 with (0, 1/2) and 3: 5.2 1/8 27. 2/8 22. 3/8 7.4 4/8 7.7 5/8 5.9 6/8 6.5 7/8 15. 8/8 \n",
+      "Attempt 89 with (2, 1/2) and 4: 5.5 1/8 3.4 2/12 6.8 3/12 17. 4/12 16. 5/12 4.4 6/12 6.2 7/12 11. 8/12 4.8 9/12 21. 10/12 5.1 11/12 9.0 12/12 \n",
+      "Attempt 90 with (1, 4/3) and 6: 7.9 1/8 6.4 2/8 8.6 3/8 12. 4/8 3.8 5/12 23. 6/12 28. 7/12 6.7 8/12 7.7 9/12 3.8 10/16 26. 11/16 4.2 12/16 8.8 13/16 24. 14/16 4.2 15/16 6.0 16/16 \n",
+      "Attempt 91 with (0, 1) and 5: 4.4 1/8 5.9 2/8 8.0 3/8 9.8 4/8 9.4 5/8 7.6 6/8 6.2 7/8 3.4 8/12 3.0 9/28 5.5 10/28 5.9 11/28 4.7 12/28 3.3 13/28 3.8 14/28 8.6 15/28 7.3 16/28 24. 17/28 12. 18/28 4.7 19/28 11. 20/28 23. 21/28 5.2 22/28 10. 23/28 12. 24/28 3.7 25/28 3.6 26/28 6.7 27/28 3.8 28/28 \n",
+      "Attempt 92 with (5/2, 1/9) and 3: 9.4 1/8 12. 2/8 11. 3/8 11. 4/8 5.6 5/8 5.3 6/8 7.2 7/8 7.0 8/8 \n",
+      "Attempt 93 with (0, 78) and 4: 27. 1/8 14. 2/8 13. 3/8 39. 4/8 12. 5/8 6.6 6/8 5.9 7/8 12. 8/8 \n",
+      "Attempt 94 with (10, 0) and 4: 4.6 1/8 5.1 2/8 4.8 3/8 6.7 4/8 9.1 5/8 17. 6/8 3.3 7/12 11. 8/12 12. 9/12 7.3 10/12 3.7 11/16 5.8 12/16 11. 13/16 32. 14/16 6.9 15/16 6.5 16/16 \n",
+      "Attempt 95 with (1, 27/2) and 3: 8.1 1/8 6.0 2/8 3.6 3/12 13. 4/12 12. 5/12 16. 6/12 6.0 7/12 5.6 8/12 17. 9/12 16. 10/12 7.7 11/12 21. 12/12 \n",
+      "Attempt 96 with (1/3, 1) and 3: 15. 1/8 26. 2/8 270. 3/8 28. 4/8 23. 5/8 13. 6/8 12. 7/8 20. 8/8 \n",
+      "Attempt 97 with (0, 18/5) and 3: 8.8 1/8 15. 2/8 8.8 3/8 16. 4/8 5.1 5/8 5.8 6/8 7.8 7/8 10. 8/8 \n",
+      "Attempt 98 with (3/4, 1) and 4: 7.2 1/8 19. 2/8 24. 3/8 5.7 4/8 25. 5/8 12. 6/8 8.6 7/8 31. 8/8 \n",
+      "Attempt 99 with (3/2, 4) and 4: 4.3 1/8 6.3 2/8 10. 3/8 11. 4/8 5.6 5/8 14. 6/8 18. 7/8 10. 8/8 \n",
+      "Attempt 100 with (1/4, 1/2) and 4: 6.1 1/8 3.5 2/12 14. 3/12 12. 4/12 4.0 5/12 5.2 6/12 6.6 7/12 5.2 8/12 17. 9/12 4.8 10/12 5.2 11/12 17. 12/12 \n",
+      "Attempt 101 with (1/4, 0) and 3: 5.0 1/8 5.1 2/8 20. 3/8 6.9 4/8 5.3 5/8 28. 6/8 16. 7/8 3.4 8/12 5.6 9/12 17. 10/12 4.3 11/12 3.6 12/16 5.1 13/16 4.6 14/16 17. 15/16 13. 16/16 \n",
+      "Attempt 102 with (2, 1) and 8: 3.5 1/12 52. 2/12 9.5 3/12 4.8 4/12 8.9 5/12 13. 6/12 13. 7/12 6.3 8/12 6.3 9/12 6.3 10/12 4.1 11/12 23. 12/12 \n",
+      "Attempt 103 with (0, 1/3) and 3: 6.1 1/8 16. 2/8 20. 3/8 7.1 4/8 4.4 5/8 6.1 6/8 4.0 7/12 9.0 8/12 3.3 9/16 6.0 10/16 26. 11/16 5.5 12/16 4.8 13/16 7.7 14/16 14. 15/16 9.2 16/16 \n",
+      "Attempt 104 with (1, 16/17) and 6: 9.4 1/8 10. 2/8 7.5 3/8 34. 4/8 7.4 5/8 8.7 6/8 6.5 7/8 5.6 8/8 \n",
+      "Attempt 105 with (3, 4) and 6: 16. 1/8 52. 2/8 13. 3/8 4.6 4/8 4.3 5/8 11. 6/8 10. 7/8 16. 8/8 \n",
+      "Attempt 106 with (18, 1/11) and 6: 10. 1/8 4.0 2/8 20. 3/8 8.7 4/8 5.8 5/8 8.1 6/8 18. 7/8 7.4 8/8 \n",
+      "Attempt 107 with (0, 2) and 3: 13. 1/8 29. 2/8 9.7 3/8 7.9 4/8 3.9 5/12 6.9 6/12 7.6 7/12 6.2 8/12 16. 9/12 6.9 10/12 5.3 11/12 14. 12/12 \n",
+      "Attempt 108 with (1/4, 1/19) and 5: 28. 1/8 18. 2/8 8.2 3/8 3.8 4/12 4.3 5/12 6.8 6/12 6.2 7/12 5.5 8/12 6.0 9/12 4.9 10/12 3.8 11/16 4.3 12/16 8.3 13/16 6.3 14/16 6.5 15/16 6.6 16/16 \n",
+      "Attempt 109 with (1, 1/7) and 7: 3.8 1/12 23. 2/12 14. 3/12 4.3 4/12 5.7 5/12 13. 6/12 5.0 7/12 29. 8/12 4.9 9/12 13. 10/12 4.6 11/12 12. 12/12 \n",
+      "Attempt 110 with (3, 3/2) and 3: 14. 1/8 6.4 2/8 4.3 3/8 12. 4/8 15. 5/8 5.9 6/8 5.8 7/8 8.1 8/8 \n",
+      "Attempt 111 with (1/12, 1/2) and 5: 4.4 1/8 4.6 2/8 5.8 3/8 3.8 4/12 9.3 5/12 19. 6/12 8.2 7/12 140. 8/12 7.0 9/12 5.6 10/12 4.0 11/12 3.6 12/16 4.6 13/16 7.7 14/16 5.2 15/16 17. 16/16 \n",
+      "Attempt 112 with (7/2, 1/6) and 3: 13. 1/8 14. 2/8 13. 3/8 11. 4/8 4.6 5/8 4.9 6/8 6.5 7/8 9.6 8/8 \n",
+      "Attempt 113 with (6, 1) and 5: 23. 1/8 4.5 2/8 8.0 3/8 7.6 4/8 6.3 5/8 13. 6/8 5.9 7/8 5.0 8/8 \n",
+      "Attempt 114 with (1/9, 1) and 11: 6.6 1/8 13. 2/8 3.6 3/12 14. 4/12 3.1 5/16 9.1 6/16 56. 7/16 5.8 8/16 6.9 9/16 8.1 10/16 4.4 11/16 20. 12/16 5.5 13/16 11. 14/16 5.9 15/16 18. 16/16 \n",
+      "Attempt 115 with (0, 2) and 5: 15. 1/8 15. 2/8 21. 3/8 10. 4/8 8.0 5/8 38. 6/8 17. 7/8 9.6 8/8 \n",
+      "Attempt 116 with (1/5, 0) and 4: 6.0 1/8 5.6 2/8 26. 3/8 6.5 4/8 4.0 5/8 12. 6/8 4.4 7/8 6.2 8/8 \n",
+      "Attempt 117 with (1, 1) and 8: 7.6 1/8 46. 2/8 35. 3/8 14. 4/8 14. 5/8 5.0 6/8 5.3 7/8 6.5 8/8 \n",
+      "Attempt 118 with (0, 5/7) and 5: 3.3 1/12 4.0 2/12 6.4 3/12 19. 4/12 3.0 5/28 7.8 6/28 5.2 7/28 24. 8/28 7.1 9/28 9.6 10/28 7.1 11/28 35. 12/28 16. 13/28 30. 14/28 91. 15/28 14. 16/28 12. 17/28 7.9 18/28 19. 19/28 6.7 20/28 6.6 21/28 3.0 22/28 14. 23/28 19. 24/28 11. 25/28 5.4 26/28 2.5 27/28 4.0 28/28 \n",
+      "Attempt 119 with (2, 2) and 4: 13. 1/8 9.3 2/8 14. 3/8 6.0 4/8 6.8 5/8 21. 6/8 6.6 7/8 17. 8/8 \n",
+      "Attempt 120 with (1/2, 1/21) and 4: 7.1 1/8 7.8 2/8 9.8 3/8 18. 4/8 6.8 5/8 11. 6/8 5.9 7/8 13. 8/8 \n",
+      "Attempt 121 with (0, 1/2) and 9: 8.2 1/8 12. 2/8 2.7 3/20 10. 4/20 35. 5/20 11. 6/20 11. 7/20 3.6 8/20 10. 9/20 8.7 10/20 5.8 11/20 22. 12/20 11. 13/20 14. 14/20 51. 15/20 7.1 16/20 23. 17/20 5.6 18/20 4.3 19/20 14. 20/20 \n",
+      "Attempt 122 with (2, 1/4) and 4: 4.3 1/8 7.5 2/8 22. 3/8 12. 4/8 18. 5/8 38. 6/8 46. 7/8 22. 8/8 \n",
+      "Attempt 123 with (2, 1/2) and 19: 3.8 1/12 25. 2/12 3.9 3/15 11. 4/15 8.5 5/15 310. 6/15 4.9 7/15 9.9 8/15 7.9 9/15 16. 10/15 5.1 11/15 2.9 12/15 27. 13/15 3.5 14/15 9.4 15/15 \n",
+      "Attempt 124 with (1, 2/3) and 3: 4.8 1/8 11. 2/8 13. 3/8 14. 4/8 45. 5/8 19. 6/8 28. 7/8 85. 8/8 \n",
+      "Attempt 125 with (0, 4) and 4: 4.6 1/8 7.1 2/8 5.0 3/8 4.2 4/8 12. 5/8 7.8 6/8 8.3 7/8 15. 8/8 \n",
+      "Attempt 126 with (1/2, 1) and 5: 6.9 1/8 12. 2/8 5.0 3/8 2.8 4/28 11. 5/28 4.3 6/28 5.3 7/28 8.4 8/28 5.9 9/28 10. 10/28 8.2 11/28 5.5 12/28 6.3 13/28 8.8 14/28 16. 15/28 9.1 16/28 3.8 17/28 19. 18/28 6.6 19/28 12. 20/28 18. 21/28 160. 22/28 37. 23/28 7.5 24/28 12. 25/28 5.0 26/28 7.9 27/28 5.7 28/28 \n",
+      "Attempt 127 with (2/17, 1) and 4: 4.6 1/8 8.4 2/8 5.0 3/8 3.3 4/12 4.0 5/12 4.2 6/12 3.1 7/16 7.1 8/16 5.6 9/16 6.9 10/16 7.6 11/16 4.8 12/16 6.4 13/16 5.9 14/16 8.3 15/16 5.3 16/16 \n",
+      "Attempt 128 with (0, 2/5) and 9: 8.8 1/8 7.6 2/8 5.0 3/8 8.6 4/8 5.0 5/8 22. 6/8 6.5 7/8 8.8 8/8 \n",
+      "Attempt 129 with (2, 19) and 4: 14. 1/8 6.7 2/8 14. 3/8 8.2 4/8 4.3 5/8 4.7 6/8 20. 7/8 4.6 8/8 \n",
+      "Attempt 130 with (1, 1) and 6: 15. 1/8 13. 2/8 4.2 3/8 5.0 4/8 26. 5/8 22. 6/8 5.3 7/8 8.2 8/8 \n",
+      "Attempt 131 with (3, 0) and 4: 4.8 1/8 6.4 2/8 5.3 3/8 5.9 4/8 20. 5/8 27. 6/8 10. 7/8 12. 8/8 \n",
+      "Attempt 132 with (1/8, 2/3) and 12: 5.2 1/8 3.7 2/12 5.2 3/12 15. 4/12 7.4 5/12 5.8 6/12 4.7 7/12 13. 8/12 6.0 9/12 25. 10/12 28. 11/12 5.6 12/12 \n",
+      "Attempt 133 with (35, 2/3) and 9: 7.9 1/8 18. 2/8 7.5 3/8 3.8 4/12 25. 5/12 10. 6/12 7.7 7/12 4.4 8/12 16. 9/12 8.8 10/12 9.7 11/12 7.1 12/12 \n",
+      "Attempt 134 with (0, 7) and 7: 5.1 1/8 5.5 2/8 6.6 3/8 4.2 4/8 26. 5/8 8.1 6/8 8.7 7/8 10. 8/8 \n",
+      "Attempt 135 with (8/5, 1/2) and 5: 5.1 1/8 6.7 2/8 22. 3/8 14. 4/8 3.4 5/12 4.2 6/12 4.1 7/12 8.7 8/12 3.1 9/16 3.3 10/20 15. 11/20 8.4 12/20 4.6 13/20 6.9 14/20 9.0 15/20 8.0 16/20 22. 17/20 4.6 18/20 9.0 19/20 7.9 20/20 \n",
+      "Attempt 136 with (9/11, 20) and 4: 6.3 1/8 7.4 2/8 3.4 3/12 8.5 4/12 8.3 5/12 4.7 6/12 5.2 7/12 3.0 8/16 11. 9/16 5.0 10/16 2.9 11/32 7.1 12/32 5.3 13/32 3.6 14/32 7.8 15/32 3.6 16/32 6.1 17/32 7.8 18/32 6.7 19/32 3.8 20/32 9.5 21/32 7.0 22/32 6.9 23/32 8.5 24/32 6.0 25/32 10. 26/32 4.7 27/32 4.2 28/32 4.3 29/32 14. 30/32 7.5 31/32 4.9 32/32 \n",
+      "Attempt 137 with (1, 1/67) and 10: 2.9 1/19 11. 2/19 11. 3/19 13. 4/19 5.1 5/19 4.3 6/19 9.4 7/19 44. 8/19 4.2 9/19 6.4 10/19 5.6 11/19 5.9 12/19 8.6 13/19 26. 14/19 210. 15/19 16. 16/19 5.1 17/19 6.4 18/19 7.6 19/19 \n",
+      "Attempt 138 with (2/3, 1/6) and 7: 6.0 1/8 14. 2/8 7.5 3/8 9.3 4/8 55. 5/8 18. 6/8 8.5 7/8 9.6 8/8 \n",
+      "Attempt 139 with (0, 1/6) and 5: 8.8 1/8 11. 2/8 6.0 3/8 9.8 4/8 6.3 5/8 20. 6/8 51. 7/8 25. 8/8 \n",
+      "Attempt 140 with (2/17, 2) and 4: 6.1 1/8 10. 2/8 10. 3/8 7.0 4/8 14. 5/8 6.6 6/8 5.2 7/8 3.5 8/12 5.8 9/12 18. 10/12 4.5 11/12 4.4 12/12 \n",
+      "Attempt 141 with (0, 2) and 7: 13. 1/8 15. 2/8 7.1 3/8 3.8 4/12 23. 5/12 11. 6/12 29. 7/12 6.0 8/12 16. 9/12 2.9 10/23 3.4 11/23 8.1 12/23 10. 13/23 37. 14/23 17. 15/23 3.6 16/23 4.7 17/23 6.9 18/23 8.7 19/23 7.4 20/23 7.2 21/23 4.0 22/23 7.9 23/23 \n",
+      "Attempt 142 with (0, 3/2) and 4: 18. 1/8 5.9 2/8 4.7 3/8 11. 4/8 9.4 5/8 2.9 6/32 4.8 7/32 10. 8/32 25. 9/32 5.8 10/32 3.7 11/32 7.3 12/32 8.6 13/32 15. 14/32 4.0 15/32 3.0 16/32 4.5 17/32 15. 18/32 16. 19/32 7.9 20/32 16. 21/32 8.1 22/32 24. 23/32 18. 24/32 19. 25/32 4.9 26/32 8.9 27/32 6.9 28/32 8.9 29/32 19. 30/32 27. 31/32 240. 32/32 \n",
+      "Attempt 143 with (3, 1/2) and 3: 15. 1/8 15. 2/8 24. 3/8 11. 4/8 44. 5/8 6.6 6/8 11. 7/8 36. 8/8 \n",
+      "Attempt 144 with (3, 1) and 5: 6.4 1/8 5.8 2/8 6.0 3/8 13. 4/8 8.1 5/8 6.6 6/8 6.5 7/8 6.2 8/8 \n",
+      "Attempt 145 with (1, 0) and 13: 9.5 1/8 5.4 2/8 10. 3/8 12. 4/8 42. 5/8 11. 6/8 21. 7/8 2.9 8/17 9.9 9/17 3.3 10/17 4.7 11/17 5.2 12/17 2.9 13/17 7.4 14/17 17. 15/17 10. 16/17 5.5 17/17 \n",
+      "Attempt 146 with (0, 1/3) and 4: 7.7 1/8 14. 2/8 16. 3/8 6.4 4/8 5.1 5/8 13. 6/8 15. 7/8 11. 8/8 \n",
+      "Attempt 147 with (0, 3/4) and 4: 8.6 1/8 14. 2/8 5.2 3/8 6.2 4/8 7.7 5/8 31. 6/8 4.4 7/8 6.3 8/8 \n",
+      "Attempt 148 with (1/6, 0) and 14: 8.8 1/8 7.4 2/8 3.9 3/12 9.7 4/12 6.6 5/12 8.8 6/12 7.5 7/12 15. 8/12 4.1 9/12 6.6 10/12 15. 11/12 4.0 12/16 3.5 13/17 8.8 14/17 5.7 15/17 8.9 16/17 9.4 17/17 \n",
+      "Attempt 149 with (1/4, 1) and 5: 9.0 1/8 7.8 2/8 5.5 3/8 5.6 4/8 6.6 5/8 7.2 6/8 12. 7/8 4.3 8/8 \n",
+      "Attempt 150 with (1/5, 1) and 5: 5.6 1/8 4.7 2/8 5.8 3/8 28. 4/8 6.8 5/8 7.7 6/8 10. 7/8 8.5 8/8 \n",
+      "Attempt 151 with (5, 1) and 4: 6.4 1/8 5.0 2/8 7.8 3/8 15. 4/8 27. 5/8 12. 6/8 41. 7/8 5.6 8/8 \n",
+      "Attempt 152 with (5, 1/7) and 4: 13. 1/8 13. 2/8 8.6 3/8 10. 4/8 6.4 5/8 4.5 6/8 4.2 7/8 7.1 8/8 \n",
+      "Attempt 153 with (1, 4/3) and 4: 30. 1/8 8.8 2/8 17. 3/8 19. 4/8 19. 5/8 10. 6/8 8.5 7/8 9.4 8/8 \n",
+      "Attempt 154 with (1, 1/3) and 3: 7.3 1/8 5.0 2/8 5.5 3/8 11. 4/8 9.1 5/8 13. 6/8 4.9 7/8 4.4 8/8 \n",
+      "Attempt 155 with (1/29, 2) and 3: 16. 1/8 8.8 2/8 7.0 3/8 6.6 4/8 12. 5/8 12. 6/8 5.2 7/8 4.9 8/8 \n",
+      "Attempt 156 with (0, 3/2) and 3: 25. 1/8 47. 2/8 16. 3/8 25. 4/8 17. 5/8 42. 6/8 21. 7/8 7.3 8/8 \n",
+      "Attempt 157 with (0, 1/2) and 60: 4.1 1/8 10. 2/8 11. 3/8 12. 4/8 5.3 5/8 19. 6/8 13. 7/8 4.9 8/8 \n",
+      "Attempt 158 with (1/3, 1/6) and 3: 8.4 1/8 11. 2/8 63. 3/8 17. 4/8 24. 5/8 7.8 6/8 5.7 7/8 5.5 8/8 \n",
+      "Attempt 159 with (1/3, 1) and 4: 34. 1/8 14. 2/8 14. 3/8 5.4 4/8 8.7 5/8 6.8 6/8 6.8 7/8 4.3 8/8 \n",
+      "Attempt 160 with (1/3, 1/2) and 7: 4.6 1/8 16. 2/8 4.0 3/8 12. 4/8 3.6 5/12 7.1 6/12 7.9 7/12 9.1 8/12 6.0 9/12 12. 10/12 12. 11/12 6.6 12/12 \n",
+      "Attempt 161 with (1, 2/7) and 4: 5.8 1/8 7.8 2/8 7.3 3/8 23. 4/8 12. 5/8 12. 6/8 9.9 7/8 8.1 8/8 \n",
+      "Attempt 162 with (1/13, 0) and 4: 46. 1/8 4.5 2/8 5.1 3/8 5.9 4/8 8.1 5/8 4.1 6/8 7.0 7/8 5.8 8/8 \n",
+      "Attempt 163 with (2/13, 16) and 4: 15. 1/8 15. 2/8 15. 3/8 16. 4/8 18. 5/8 20. 6/8 22. 7/8 24. 8/8 \n",
+      "Attempt 164 with (0, 21) and 4: 4.3 1/8 4.5 2/8 5.2 3/8 3.6 4/12 5.2 5/12 4.5 6/12 3.6 7/16 6.2 8/16 14. 9/16 19. 10/16 8.0 11/16 6.5 12/16 7.6 13/16 14. 14/16 24. 15/16 92. 16/16 \n",
+      "Attempt 165 with (1, 1) and 7: 330. 1/8 130. 2/8 150. 3/8 410. 4/8 210. 5/8 74. 6/8 45. 7/8 28. 8/8 \n",
+      "Attempt 166 with (0, 3) and 47: 4.4 1/8 9.7 2/8 5.5 3/8 7.0 4/8 5.0 5/8 7.2 6/8 3.5 7/12 18. 8/12 75. 9/12 10. 10/12 8.8 11/12 9.3 12/12 \n",
+      "Attempt 167 with (1/2, 1/3) and 5: 8.3 1/8 40. 2/8 8.7 3/8 19. 4/8 5.1 5/8 3.3 6/12 4.3 7/12 9.4 8/12 2.8 9/28 8.8 10/28 12. 11/28 6.5 12/28 4.0 13/28 14. 14/28 8.3 15/28 22. 16/28 4.5 17/28 5.1 18/28 8.1 19/28 17. 20/28 4.1 21/28 6.1 22/28 7.5 23/28 4.9 24/28 16. 25/28 3.4 26/28 6.6 27/28 10. 28/28 \n",
+      "Attempt 168 with (0, 1) and 8: 12. 1/8 7.7 2/8 6.2 3/8 6.5 4/8 4.5 5/8 62. 6/8 7.3 7/8 3.4 8/12 6.9 9/12 5.8 10/12 5.4 11/12 5.3 12/12 \n",
+      "Attempt 169 with (0, 2/7) and 4: 3.8 1/12 8.6 2/12 9.0 3/12 5.6 4/12 6.6 5/12 5.4 6/12 7.5 7/12 26. 8/12 4.2 9/12 8.4 10/12 7.5 11/12 7.5 12/12 \n",
+      "Attempt 170 with (5/3, 2) and 7: 7.6 1/8 9.2 2/8 9.9 3/8 14. 4/8 4.4 5/8 7.2 6/8 49. 7/8 7.0 8/8 \n",
+      "Attempt 171 with (1/6, 1) and 4: 44. 1/8 60. 2/8 22. 3/8 12. 4/8 11. 5/8 6.2 6/8 5.5 7/8 4.7 8/8 \n",
+      "Attempt 172 with (0, 1/2) and 6: 15. 1/8 7.1 2/8 4.4 3/8 5.4 4/8 23. 5/8 30. 6/8 20. 7/8 27. 8/8 \n",
+      "Attempt 173 with (0, 1/6) and 3: 14. 1/8 16. 2/8 9.6 3/8 6.3 4/8 3.3 5/12 3.1 6/16 5.7 7/16 8.0 8/16 7.4 9/16 9.0 10/16 11. 11/16 4.7 12/16 2.8 13/40 3.0 14/40 11. 15/40 5.9 16/40 6.9 17/40 4.4 18/40 3.8 19/40 9.0 20/40 5.6 21/40 18. 22/40 8.4 23/40 4.1 24/40 4.1 25/40 10. 26/40 5.7 27/40 9.0 28/40 7.6 29/40 57. 30/40 21. 31/40 6.1 32/40 6.1 33/40 6.1 34/40 4.1 35/40 11. 36/40 6.0 37/40 5.6 38/40 22. 39/40 6.9 40/40 \n",
+      "Attempt 174 with (1/4, 2/3) and 3: 6.5 1/8 4.0 2/12 4.0 3/12 6.4 4/12 35. 5/12 9.1 6/12 52. 7/12 5.1 8/12 5.0 9/12 3.5 10/16 6.9 11/16 7.2 12/16 9.1 13/16 7.3 14/16 5.3 15/16 9.2 16/16 \n",
+      "Attempt 175 with (1, 1/7) and 6: 11. 1/8 13. 2/8 7.4 3/8 12. 4/8 13. 5/8 3.1 6/12 4.2 7/12 16. 8/12 7.3 9/12 6.5 10/12 9.8 11/12 5.4 12/12 \n",
+      "Attempt 176 with (4/55, 3/5) and 7: 4.5 1/8 22. 2/8 7.3 3/8 16. 4/8 7.3 5/8 4.8 6/8 10. 7/8 8.2 8/8 \n",
+      "Attempt 177 with (0, 3/2) and 5: 5.7 1/8 10. 2/8 16. 3/8 12. 4/8 20. 5/8 28. 6/8 18. 7/8 11. 8/8 \n",
+      "Attempt 178 with (1, 1/11) and 4: 4.6 1/8 5.1 2/8 7.2 3/8 6.5 4/8 11. 5/8 5.7 6/8 11. 7/8 5.7 8/8 \n",
+      "Attempt 179 with (2, 1) and 5: 4.4 1/8 8.2 2/8 5.3 3/8 45. 4/8 4.7 5/8 3.7 6/12 5.4 7/12 8.1 8/12 18. 9/12 16. 10/12 7.7 11/12 5.9 12/12 \n",
+      "Attempt 180 with (1/13, 1) and 4: 6.5 1/8 8.0 2/8 3.9 3/12 5.4 4/12 3.8 5/16 2.7 6/32 4.2 7/32 6.1 8/32 15. 9/32 9.2 10/32 5.1 11/32 3.6 12/32 6.1 13/32 17. 14/32 4.6 15/32 2.3703480660778453"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 97 graphics primitives"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16/32 5.7 17/32 27. 18/32 6.5 19/32 11. 20/32 5.8 21/32 4.6 22/32 5.8 23/32 3.6 24/32 7.2 25/32 5.8 26/32 9.8 27/32 8.1 28/32 7.5 29/32 17. 30/32 49. 31/32 12. 32/32 \n",
+      "Attempt 181 with (3/8, 0) and 5: 12. 1/8 7.6 2/8 8.6 3/8 22. 4/8 10. 5/8 34. 6/8 98. 7/8 54. 8/8 \n",
+      "Attempt 182 with (1/3, 1/4) and 12: 7.5 1/8 23. 2/8 22. 3/8 9.0 4/8 9.1 5/8 6.7 6/8 8.0 7/8 6.8 8/8 \n",
+      "Attempt 183 with (2, 4) and 3: 3.3 1/12 14. 2/12 4.7 3/12 4.9 4/12 9.8 5/12 12. 6/12 6.9 7/12 4.2 8/12 6.7 9/12 5.7 10/12 6.2 11/12 15. 12/12 \n",
+      "Attempt 184 with (5/19, 5) and 4: 4.8 1/8 24. 2/8 4.3 3/8 6.6 4/8 4.0 5/12 12. 6/12 9.7 7/12 5.9 8/12 7.7 9/12 4.7 10/12 14. 11/12 56. 12/12 \n",
+      "Attempt 185 with (52, 4) and 4: 17. 1/8 13. 2/8 22. 3/8 32. 4/8 9.2 5/8 3.2 6/12 3.4 7/16 4.0 8/20 5.1 9/20 4.6 10/20 5.4 11/20 7.3 12/20 6.1 13/20 12. 14/20 7.6 15/20 8.5 16/20 3.3 17/24 22. 18/24 3.6 19/28 2.7 20/32 4.4 21/32 11. 22/32 25. 23/32 33. 24/32 7.9 25/32 3.9 26/32 4.3 27/32 12. 28/32 10. 29/32 22. 30/32 7.0 31/32 8.0 32/32 \n",
+      "Attempt 186 with (1, 1) and 10: 5.0 1/8 14. 2/8 6.1 3/8 11. 4/8 2.8 5/19 3.9 6/19 11. 7/19 15. 8/19 11. 9/19 15. 10/19 7.9 11/19 6.8 12/19 2.8 13/19 5.6 14/19 9.9 15/19 6.4 16/19 8.2 17/19 13. 18/19 5.3 19/19 \n",
+      "Attempt 187 with (1/2, 0) and 7: 13. 1/8 8.6 2/8 5.1 3/8 3.6 4/12 5.3 5/12 70. 6/12 8.9 7/12 34. 8/12 9.0 9/12 12. 10/12 6.4 11/12 5.5 12/12 \n",
+      "Attempt 188 with (2, 1/2) and 5: 8.2 1/8 12. 2/8 11. 3/8 24. 4/8 7.8 5/8 4.7 6/8 7.4 7/8 4.7 8/8 \n",
+      "Attempt 189 with (2, 2/3) and 3: 4.4 1/8 3.6 2/12 6.2 3/12 6.1 4/12 4.1 5/12 4.6 6/12 12. 7/12 3.0 8/16 4.4 9/16 3.7 10/20 3.6 11/24 5.1 12/24 14. 13/24 21. 14/24 11. 15/24 18. 16/24 5.3 17/24 8.9 18/24 4.3 19/24 4.4 20/24 5.2 21/24 3.3 22/28 8.9 23/28 7.0 24/28 3.3 25/32 9.4 26/32 3.8 27/36 4.0 28/40 7.5 29/40 7.1 30/40 4.1 31/40 3.9 32/40 9.2 33/40 9.8 34/40 6.4 35/40 13. 36/40 32. 37/40 13. 38/40 4.9 39/40 4.4 40/40 \n",
+      "Attempt 190 with (3, 1/2) and 4: 4.9 1/8 3.8 2/12 3.9 3/16 3.4 4/20 4.5 5/20 17. 6/20 7.9 7/20 5.8 8/20 15. 9/20 4.1 10/20 14. 11/20 4.6 12/20 6.4 13/20 5.8 14/20 8.7 15/20 18. 16/20 6.8 17/20 4.8 18/20 6.9 19/20 11. 20/20 \n",
+      "Attempt 191 with (1, 3) and 6: 50. 1/8 16. 2/8 16. 3/8 9.7 4/8 7.2 5/8 20. 6/8 21. 7/8 45. 8/8 \n",
+      "Attempt 192 with (0, 1/7) and 3: 4.4 1/8 6.0 2/8 3.6 3/12 10. 4/12 5.4 5/12 7.0 6/12 5.0 7/12 4.6 8/12 3.9 9/16 3.2 10/20 5.3 11/20 7.4 12/20 15. 13/20 37. 14/20 16. 15/20 19. 16/20 7.2 17/20 7.4 18/20 7.8 19/20 34. 20/20 \n",
+      "Attempt 193 with (1, 1) and 22: 5.7 1/8 23. 2/8 14. 3/8 7.2 4/8 3.5 5/12 5.2 6/12 6.2 7/12 4.4 8/12 5.8 9/12 3.5 10/14 9.1 11/14 4.6 12/14 7.1 13/14 3.6 14/14 \n",
+      "Attempt 194 with (1, 4/3) and 3: 6.6 1/8 26. 2/8 8.3 3/8 5.7 4/8 5.8 5/8 8.5 6/8 7.6 7/8 21. 8/8 \n",
+      "Attempt 195 with (0, 2) and 26: 7.0 1/8 5.7 2/8 7.8 3/8 7.2 4/8 3.7 5/12 3.2 6/14 57. 7/14 5.6 8/14 6.7 9/14 7.9 10/14 12. 11/14 3.3 12/14 3.5 13/14 4.6 14/14 \n",
+      "Attempt 196 with (1/4, 0) and 11: 3.6 1/12 3.4 2/16 5.7 3/16 4.7 4/16 24. 5/16 5.1 6/16 6.4 7/16 39. 8/16 13. 9/16 3.9 10/19 8.7 11/19 28. 12/19 3.8 13/19 5.0 14/19 4.3 15/19 12. 16/19 7.5 17/19 22. 18/19 4.4 19/19 \n",
+      "Attempt 197 with (8, 1) and 4: 180. 1/8 23. 2/8 18. 3/8 6.6 4/8 4.3 5/8 3.4 6/12 25. 7/12 12. 8/12 4.9 9/12 6.5 10/12 6.2 11/12 4.2 12/12 \n",
+      "Attempt 198 with (0, 1) and 7: 4.6 1/8 3.8 2/12 6.9 3/12 7.0 4/12 4.2 5/12 12. 6/12 4.4 7/12 7.6 8/12 9.1 9/12 11. 10/12 4.2 11/12 6.7 12/12 \n",
+      "Attempt 199 with (3/16, 0) and 3: 3.9 1/12 3.3 2/16 4.8 3/16 9.6 4/16 21. 5/16 33. 6/16 43. 7/16 26. 8/16 5.9 9/16 3.4 10/20 5.0 11/20 5.2 12/20 8.3 13/20 6.5 14/20 7.7 15/20 3.0 16/40 8.3 17/40 4.1 18/40 18. 19/40 6.8 20/40 6.3 21/40 15. 22/40 8.6 23/40 12. 24/40 3.2 25/40 4.9 26/40 6.0 27/40 7.0 28/40 3.9 29/40 5.2 30/40 20. 31/40 9.9 32/40 10. 33/40 6.2 34/40 7.4 35/40 30. 36/40 9.1 37/40 4.5 38/40 10. 39/40 8.2 40/40 \n",
+      "Attempt 200 with (12, 0) and 3: 3.3 1/12 5.0 2/12 9.5 3/12 6.6 4/12 13. 5/12 9.9 6/12 10. 7/12 8.6 8/12 4.4 9/12 5.0 10/12 6.6 11/12 15. 12/12 \n",
+      "Attempt 201 with (1, 3) and 4: 8.1 1/8 29. 2/8 8.3 3/8 5.1 4/8 12. 5/8 6.6 6/8 7.7 7/8 3.7 8/12 3.2 9/16 7.3 10/16 4.9 11/16 4.6 12/16 6.8 13/16 25. 14/16 5.4 15/16 4.7 16/16 \n",
+      "Attempt 202 with (3, 50) and 7: 5.4 1/8 8.0 2/8 11. 3/8 11. 4/8 14. 5/8 24. 6/8 49. 7/8 290. 8/8 \n",
+      "Attempt 203 with (1/6, 1) and 5: 23. 1/8 11. 2/8 8.3 3/8 6.0 4/8 5.2 5/8 3.7 6/12 6.6 7/12 3.5 8/16 5.1 9/16 5.8 10/16 4.6 11/16 4.4 12/16 5.7 13/16 6.5 14/16 7.0 15/16 7.3 16/16 \n",
+      "Attempt 204 with (1, 0) and 56: 60. 1/8 12. 2/8 8.8 3/8 15. 4/8 5.1 5/8 4.3 6/8 5.0 7/8 76. 8/8 \n",
+      "Attempt 205 with (2, 1/3) and 4: 3.4 1/12 6.2 2/12 4.0 3/12 6.0 4/12 4.4 5/12 35. 6/12 8.1 7/12 7.3 8/12 16. 9/12 7.4 10/12 8.4 11/12 4.9 12/12 \n",
+      "Attempt 206 with (1, 2) and 4: 6.2 1/8 23. 2/8 6.7 3/8 8.0 4/8 31. 5/8 9.5 6/8 12. 7/8 3.6 8/12 6.0 9/12 5.9 10/12 3.0 11/16 8.5 12/16 5.2 13/16 16. 14/16 6.8 15/16 6.4 16/16 \n",
+      "Attempt 207 with (4, 1) and 6: 4.9 1/8 4.7 2/8 14. 3/8 7.5 4/8 7.9 5/8 11. 6/8 4.8 7/8 4.8 8/8 \n",
+      "Attempt 208 with (3, 1) and 6: 4.1 1/8 3.6 2/12 6.1 3/12 10. 4/12 5.7 5/12 7.4 6/12 18. 7/12 12. 8/12 12. 9/12 6.3 10/12 26. 11/12 6.1 12/12 \n",
+      "Attempt 209 with (1, 1/2) and 3: 15. 1/8 5.6 2/8 7.7 3/8 15. 4/8 58. 5/8 19. 6/8 10. 7/8 17. 8/8 \n",
+      "Attempt 210 with (1/2, 5) and 4: 5.8 1/8 14. 2/8 25. 3/8 8.5 4/8 3.8 5/12 9.0 6/12 4.3 7/12 6.0 8/12 4.9 9/12 10. 10/12 3.5 11/16 6.9 12/16 7.4 13/16 3.5 14/20 4.0 15/20 4.4 16/20 16. 17/20 130. 18/20 30. 19/20 6.0 20/20 \n",
+      "Attempt 211 with (0, 1/4) and 17: 12. 1/8 57. 2/8 3.7 3/12 3.7 4/16 4.8 5/16 3.2 6/16 7.8 7/16 3.3 8/16 5.5 9/16 28. 10/16 12. 11/16 8.6 12/16 13. 13/16 5.6 14/16 7.2 15/16 3.4 16/16 \n",
+      "Attempt 212 with (7, 1) and 8: 5.9 1/8 22. 2/8 16. 3/8 17. 4/8 23. 5/8 8.5 6/8 45. 7/8 35. 8/8 \n",
+      "Attempt 213 with (0, 4/65) and 4: 4.6 1/8 15. 2/8 4.6 3/8 7.5 4/8 8.6 5/8 17. 6/8 8.6 7/8 21. 8/8 \n",
+      "Attempt 214 with (0, 1) and 24: 73. 1/8 86. 2/8 20. 3/8 32. 4/8 10. 5/8 12. 6/8 23. 7/8 4.8 8/8 \n",
+      "Attempt 215 with (0, 7/2) and 61: 5.9 1/8 12. 2/8 12. 3/8 3.0 4/11 3.6 5/11 4.2 6/11 13. 7/11 6.0 8/11 5.0 9/11 5.9 10/11 5.8 11/11 \n",
+      "Attempt 216 with (28, 1/2) and 4: 7.9 1/8 5.5 2/8 3.0 3/12 6.8 4/12 18. 5/12 7.1 6/12 9.3 7/12 24. 8/12 190. 9/12 19. 10/12 11. 11/12 8.4 12/12 \n",
+      "Attempt 217 with (0, 1/11) and 4: 4.6 1/8 8.3 2/8 5.4 3/8 5.0 4/8 6.3 5/8 13. 6/8 4.9 7/8 6.2 8/8 \n",
+      "Attempt 218 with (1/4, 0) and 6: 4.7 1/8 5.5 2/8 5.3 3/8 3.8 4/12 17. 5/12 9.7 6/12 10. 7/12 8.7 8/12 5.6 9/12 6.7 10/12 4.3 11/12 29. 12/12 \n",
+      "Attempt 219 with (6, 1) and 6: 6.6 1/8 11. 2/8 74. 3/8 12. 4/8 22. 5/8 5.3 6/8 19. 7/8 6.1 8/8 \n",
+      "Attempt 220 with (1/3, 0) and 3: 4.9 1/8 7.4 2/8 5.7 3/8 17. 4/8 15. 5/8 4.9 6/8 17. 7/8 4.1 8/8 \n",
+      "Attempt 221 with (3/4, 2/3) and 3: 5.2 1/8 4.0 2/8 3.7 3/12 3.5 4/16 7.5 5/16 6.8 6/16 7.4 7/16 5.4 8/16 9.3 9/16 12. 10/16 9.1 11/16 8.8 12/16 9.5 13/16 3.6 14/20 23. 15/20 6.1 16/20 4.2 17/20 6.9 18/20 3.8 19/24 3.8 20/28 4.2 21/28 7.1 22/28 8.0 23/28 9.6 24/28 12. 25/28 4.6 26/28 5.0 27/28 9.6 28/28 \n",
+      "Attempt 222 with (1, 3/2) and 4: 7.6 1/8 13. 2/8 5.7 3/8 11. 4/8 3.2 5/12 8.0 6/12 6.3 7/12 25. 8/12 11. 9/12 14. 10/12 3.8 11/16 5.2 12/16 5.1 13/16 15. 14/16 3.7 15/20 6.1 16/20 2.5 17/32 10. 18/32 6.4 19/32 5.1 20/32 6.7 21/32 2.7 22/32 4.5 23/32 33. 24/32 48. 25/32 24. 26/32 8.3 27/32 4.0 28/32 7.1 29/32 5.8 30/32 4.9 31/32 5.1 32/32 \n",
+      "Attempt 223 with (2/3, 1/6) and 3: 19. 1/8 15. 2/8 16. 3/8 23. 4/8 19. 5/8 6.9 6/8 5.9 7/8 5.2 8/8 \n",
+      "Attempt 224 with (4/3, 3) and 6: 3.9 1/12 5.6 2/12 23. 3/12 7.1 4/12 8.8 5/12 4.5 6/12 14. 7/12 22. 8/12 5.2 9/12 4.3 10/12 12. 11/12 5.7 12/12 \n",
+      "Attempt 225 with (1, 1/6) and 4: 120. 1/8 12. 2/8 8.6 3/8 7.4 4/8 16. 5/8 4.2 6/8 1.8620434514711002"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 96 graphics primitives"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7/24 3.8 8/28 8.3 9/28 8.0 10/28 4.3 11/28 4.9 12/28 6.6 13/28 12. 14/28 6.4 15/28 5.8 16/28 3.7 17/32 3.5 18/32 16. 19/32 11. 20/32 12. 21/32 14. 22/32 3.8 23/32 3.0 24/32 7.5 25/32 5.4 26/32 6.3 27/32 4.8 28/32 6.9 29/32 5.7 30/32 15. 31/32 10. 32/32 \n",
+      "Attempt 226 with (6, 1/3) and 7: 12. 1/8 6.1 2/8 24. 3/8 7.0 4/8 4.5 5/8 5.6 6/8 17. 7/8 3.5 8/12 3.8 9/16 6.5 10/16 5.4 11/16 4.1 12/16 4.7 13/16 97. 14/16 29. 15/16 4.7 16/16 \n",
+      "Attempt 227 with (1/31, 1/11) and 3: 43. 1/8 31. 2/8 24. 3/8 22. 4/8 27. 5/8 25. 6/8 26. 7/8 22. 8/8 \n",
+      "Attempt 228 with (5/3, 9) and 7: 7.0 1/8 6.9 2/8 20. 3/8 10. 4/8 14. 5/8 4.3 6/8 15. 7/8 25. 8/8 \n",
+      "Attempt 229 with (2, 13/2) and 5: 9.6 1/8 4.1 2/8 14. 3/8 4.6 4/8 4.7 5/8 12. 6/8 6.2 7/8 13. 8/8 \n",
+      "Attempt 230 with (8, 1/15) and 5: 17. 1/8 31. 2/8 5.9 3/8 17. 4/8 15. 5/8 5.5 6/8 5.3 7/8 5.1 8/8 \n",
+      "Attempt 231 with (1/9, 3) and 4: 9.4 1/8 8.2 2/8 6.9 3/8 3.8 4/12 6.1 5/12 7.0 6/12 5.4 7/12 5.8 8/12 4.5 9/12 8.0 10/12 7.0 11/12 6.1 12/12 \n",
+      "Attempt 232 with (1/2, 2) and 3: 12. 1/8 4.3 2/8 2.3 3/40 4.1 4/40 3.9 5/40 6.0 6/40 6.1 7/40 4.9 8/40 7.3 9/40 3.9 10/40 4.5 11/40 5.3 12/40 11. 13/40 6.3 14/40 10. 15/40 8.6 16/40 31. 17/40 11. 18/40 18. 19/40 6.2 20/40 8.2 21/40 9.4 22/40 15. 23/40 27. 24/40 13. 25/40 11. 26/40 3.8 27/40 7.4 28/40 24. 29/40 11. 30/40 3.4 31/40 3.9 32/40 6.1 33/40 6.3 34/40 3.6 35/40 5.3 36/40 6.9 37/40 6.1 38/40 5.1 39/40 7.1 40/40 \n",
+      "Attempt 233 with (22, 19/10) and 8: 7.0 1/8 2.8 2/21 4.7 3/21 22. 4/21 7.1 5/21 3.8 6/21 4.3 7/21 4.6 8/21 7.7 9/21 11. 10/21 5.4 11/21 15. 12/21 23. 13/21 7.4 14/21 3.6 15/21 4.2 16/21 15. 17/21 7.7 18/21 6.7 19/21 26. 20/21 13. 21/21 \n",
+      "Attempt 234 with (3/2, 4/19) and 5: 3.9 1/12 6.2 2/12 9.7 3/12 12. 4/12 36. 5/12 50. 6/12 24. 7/12 39. 8/12 7.3 9/12 6.6 10/12 4.5 11/12 11. 12/12 \n",
+      "Attempt 235 with (1/4, 1/37) and 5: 9.1 1/8 7.4 2/8 7.4 3/8 9.8 4/8 18. 5/8 160. 6/8 9.3 7/8 5.7 8/8 \n",
+      "Attempt 236 with (0, 1/2) and 4: 9.3 1/8 9.9 2/8 4.0 3/8 6.0 4/8 7.7 5/8 15. 6/8 4.0 7/12 8.4 8/12 5.3 9/12 9.7 10/12 8.5 11/12 6.9 12/12 \n",
+      "Attempt 237 with (1, 1/4) and 5: 7.2 1/8 9.1 2/8 5.6 3/8 12. 4/8 5.0 5/8 4.9 6/8 5.8 7/8 9.0 8/8 \n",
+      "Attempt 238 with (0, 2) and 6: 9.7 1/8 6.8 2/8 3.3 3/12 4.8 4/12 21. 5/12 16. 6/12 8.5 7/12 30. 8/12 5.9 9/12 6.0 10/12 4.5 11/12 11. 12/12 \n",
+      "Attempt 239 with (2, 1/10) and 4: 11. 1/8 23. 2/8 9.0 3/8 9.7 4/8 4.1 5/8 6.6 6/8 3.9 7/12 4.3 8/12 3.0 9/32 4.0 10/32 37. 11/32 16. 12/32 11. 13/32 5.0 14/32 5.9 15/32 6.2 16/32 4.8 17/32 6.3 18/32 21. 19/32 4.6 20/32 11. 21/32 14. 22/32 14. 23/32 64. 24/32 8.2 25/32 15. 26/32 3.4 27/32 6.4 28/32 4.8 29/32 6.1 30/32 3.4 31/32 14. 32/32 \n",
+      "Attempt 240 with (13/6, 3/2) and 4: 3.3 1/12 5.3 2/12 9.0 3/12 6.5 4/12 11. 5/12 13. 6/12 5.4 7/12 2.3 8/32 9.1 9/32 6.4 10/32 2.9 11/32 8.4 12/32 8.5 13/32 19. 14/32 4.4 15/32 4.8 16/32 6.9 17/32 55. 18/32 13. 19/32 4.5 20/32 11. 21/32 13. 22/32 16. 23/32 7.0 24/32 4.4 25/32 7.7 26/32 7.1 27/32 10. 28/32 5.4 29/32 8.9 30/32 12. 31/32 16. 32/32 \n",
+      "Attempt 241 with (0, 2) and 4: 5.0 1/8 14. 2/8 3.9 3/12 6.5 4/12 4.3 5/12 5.5 6/12 11. 7/12 6.1 8/12 4.9 9/12 4.0 10/12 17. 11/12 4.2 12/12 \n",
+      "Attempt 242 with (5, 4) and 4: 5.1 1/8 6.3 2/8 26. 3/8 15. 4/8 20. 5/8 5.4 6/8 7.5 7/8 7.5 8/8 \n",
+      "Attempt 243 with (1/3, 0) and 4: 3.9 1/12 3.0 2/32 17. 3/32 17. 4/32 6.4 5/32 30. 6/32 6.0 7/32 4.3 8/32 17. 9/32 21. 10/32 17. 11/32 3.3 12/32 3.2 13/32 8.2 14/32 6.6 15/32 6.0 16/32 16. 17/32 5.1 18/32 9.8 19/32 3.5 20/32 7.1 21/32 6.7 22/32 4.3 23/32 6.0 24/32 3.3 25/32 8.3 26/32 4.2 27/32 7.8 28/32 3.9 29/32 5.3 30/32 19. 31/32 22. 32/32 \n",
+      "Attempt 244 with (1, 6) and 7: 8.5 1/8 3.3 2/12 4.2 3/12 5.7 4/12 12. 5/12 5.3 6/12 14. 7/12 30. 8/12 7.1 9/12 7.7 10/12 9.5 11/12 76. 12/12 \n",
+      "Attempt 245 with (1/3, 1) and 7: 21. 1/8 2.6 2/23 8.1 3/23 6.5 4/23 13. 5/23 94. 6/23 11. 7/23 5.5 8/23 4.0 9/23 7.3 10/23 4.1 11/23 7.4 12/23 130. 13/23 7.3 14/23 23. 15/23 5.6 16/23 3.7 17/23 5.6 18/23 8.3 19/23 7.1 20/23 4.6 21/23 8.9 22/23 14. 23/23 \n",
+      "Attempt 246 with (0, 3) and 10: 6.8 1/8 69. 2/8 7.8 3/8 8.3 4/8 8.3 5/8 9.5 6/8 8.5 7/8 9.3 8/8 \n",
+      "Attempt 247 with (1/11, 10/13) and 75: 4.3 1/8 5.7 2/8 21. 3/8 5.9 4/8 3.6 5/10 4.3 6/10 24. 7/10 4.6 8/10 17. 9/10 72. 10/10 \n",
+      "Attempt 248 with (4, 0) and 25: 51. 1/8 5.9 2/8 8.1 3/8 9.0 4/8 6.6 5/8 10. 6/8 12. 7/8 17. 8/8 \n",
+      "Attempt 249 with (16/11, 2) and 4: 120. 1/8 41. 2/8 33. 3/8 5.9 4/8 8.1 5/8 7.7 6/8 4.7 7/8 18. 8/8 \n",
+      "Attempt 250 with (1/40, 7) and 3: 17. 1/8 8.9 2/8 9.0 3/8 7.7 4/8 6.9 5/8 7.7 6/8 10. 7/8 11. 8/8 \n",
+      "Attempt 251 with (1, 1/2) and 10: 5.5 1/8 5.5 2/8 24. 3/8 7.1 4/8 14. 5/8 2.3 6/19 7.9 7/19 4.5 8/19 5.9 9/19 2.5 10/19 16. 11/19 4.9 12/19 3.4 13/19 11. 14/19 5.5 15/19 6.1 16/19 7.8 17/19 5.8 18/19 21. 19/19 \n",
+      "Attempt 252 with (1/47, 2) and 4: 15. 1/8 14. 2/8 6.6 3/8 6.1 4/8 8.3 5/8 4.1 6/8 5.3 7/8 13. 8/8 \n",
+      "Attempt 253 with (2, 1/8) and 4: 3.1 1/12 5.3 2/12 5.4 3/12 4.2 4/12 7.3 5/12 30. 6/12 7.1 7/12 7.4 8/12 7.4 9/12 2.9 10/32 3.3 11/32 9.0 12/32 8.9 13/32 14. 14/32 3.0 15/32 6.3 16/32 20. 17/32 13. 18/32 20. 19/32 22. 20/32 14. 21/32 6.1 22/32 11. 23/32 6.8 24/32 13. 25/32 4.6 26/32 5.4 27/32 4.0 28/32 18. 29/32 4.5 30/32 7.3 31/32 5.9 32/32 \n",
+      "Attempt 254 with (10/3, 1) and 16: 8.1 1/8 11. 2/8 3.3 3/12 8.9 4/12 35. 5/12 6.8 6/12 5.5 7/12 4.6 8/12 6.6 9/12 3.2 10/16 4.3 11/16 5.6 12/16 8.7 13/16 3.3 14/16 95. 15/16 28. 16/16 \n",
+      "Attempt 255 with (1/3, 5/4) and 4: 7.6 1/8 3.2 2/12 15. 3/12 12. 4/12 18. 5/12 6.6 6/12 8.8 7/12 3.1 8/16 7.7 9/16 12. 10/16 6.9 11/16 9.2 12/16 4.5 13/16 9.0 14/16 17. 15/16 15. 16/16 \n",
+      "Attempt 256 with (1, 7/2) and 9: 3.1 1/12 4.6 2/12 3.8 3/16 6.2 4/16 15. 5/16 8.7 6/16 5.0 7/16 7.9 8/16 8.2 9/16 5.3 10/16 15. 11/16 44. 12/16 7.2 13/16 4.8 14/16 11. 15/16 13. 16/16 \n",
+      "Attempt 257 with (1/9, 1/3) and 4: 3.7 1/12 5.8 2/12 4.7 3/12 14. 4/12 6.2 5/12 3.5 6/16 12. 7/16 8.2 8/16 19. 9/16 6.1 10/16 4.5 11/16 6.3 12/16 12. 13/16 4.6 14/16 3.1 15/20 6.1 16/20 7.3 17/20 11. 18/20 6.7 19/20 9.0 20/20 \n",
+      "Attempt 258 with (7, 1) and 6: 8.9 1/8 9.1 2/8 8.5 3/8 120. 4/8 16. 5/8 5.1 6/8 5.9 7/8 7.5 8/8 \n",
+      "Attempt 259 with (1/69, 1) and 4: 9.0 1/8 13. 2/8 6.6 3/8 9.9 4/8 11. 5/8 6.8 6/8 5.7 7/8 5.3 8/8 \n",
+      "Attempt 260 with (2, 4) and 6: 6.9 1/8 4.3 2/8 13. 3/8 21. 4/8 6.0 5/8 13. 6/8 4.2 7/8 12. 8/8 \n",
+      "Attempt 261 with (3, 1) and 18: 7.7 1/8 12. 2/8 4.6 3/8 3.8 4/12 4.4 5/12 45. 6/12 4.7 7/12 11. 8/12 4.4 9/12 6.6 10/12 11. 11/12 12. 12/12 \n",
+      "Attempt 262 with (1/2, 1/10) and 5: 4.5 1/8 4.2 2/8 5.8 3/8 6.3 4/8 5.8 5/8 12. 6/8 11. 7/8 5.2 8/8 \n",
+      "Attempt 263 with (1/84, 2/5) and 5: 9.1 1/8 7.6 2/8 9.2 3/8 7.2 4/8 16. 5/8 34. 6/8 9.1 7/8 6.4 8/8 \n",
+      "Attempt 264 with (3/2, 2/43) and 4: 26. 1/8 7.0 2/8 7.9 3/8 19. 4/8 23. 5/8 13. 6/8 4.7 7/8 5.0 8/8 \n",
+      "Attempt 265 with (0, 7/11) and 3: 3.2 1/12 3.3 2/16 5.0 3/16 6.9 4/16 6.6 5/16 9.7 6/16 24. 7/16 58. 8/16 17. 9/16 19. 10/16 18. 11/16 38. 12/16 17. 13/16 7.9 14/16 4.5 15/16 5.2 16/16 \n",
+      "Attempt 266 with (7, 4) and 4: 26. 1/8 12. 2/8 21. 3/8 21. 4/8 5.5 5/8 4.9 6/8 5.1 7/8 8.0 8/8 \n",
+      "Attempt 267 with (1/11, 1) and 8: 45. 1/8 51. 2/8 27. 3/8 17. 4/8 21. 5/8 35. 6/8 94. 7/8 42. 8/8 \n",
+      "Attempt 268 with (1, 5/2) and 336: 17. 1/8 6.0 2/8 17. 3/8 5.6 4/8 46. 5/8 4.2 6/8 9.9 7/8 4.8 8/8 \n",
+      "Attempt 269 with (2/7, 0) and 17: 8.3 1/8 7.6 2/8 13. 3/8 16. 4/8 6.1 5/8 6.3 6/8 14. 7/8 41. 8/8 \n",
+      "Attempt 270 with (26, 1/2) and 13: 11. 1/8 5.3 2/8 4.8 3/8 5.6 4/8 5.8 5/8 5.7 6/8 58. 7/8 6.6 8/8 \n",
+      "Attempt 271 with (11, 0) and 4: 11. 1/8 12. 2/8 14. 3/8 15. 4/8 12. 5/8 11. 6/8 13. 7/8 7.1 8/8 \n",
+      "Attempt 272 with (1, 0) and 7: 72. 1/8 420. 2/8 120. 3/8 68. 4/8 150. 5/8 28. 6/8 26. 7/8 18. 8/8 \n",
+      "Attempt 273 with (5, 2/7) and 4: 3.7 1/12 15. 2/12 7.3 3/12 23. 4/12 6.0 5/12 7.2 6/12 6.8 7/12 3.1 8/16 10. 9/16 4.3 10/16 6.0 11/16 4.2 12/16 10. 13/16 6.7 14/16 9.8 15/16 6.5 16/16 \n",
+      "Attempt 274 with (49, 0) and 10: 17. 1/8 4.3 2/8 3.6 3/12 9.6 4/12 5.5 5/12 6.6 6/12 5.9 7/12 10. 8/12 84. 9/12 23. 10/12 33. 11/12 59. 12/12 \n",
+      "Attempt 275 with (1, 1) and 3: 5.9 1/8 12. 2/8 4.6 3/8 3.8 4/12 5.2 5/12 3.4 6/16 5.2 7/16 7.2 8/16 5.4 9/16 5.3 10/16 2.6 11/40 6.5 12/40 11. 13/40 5.4 14/40 4.0 15/40 7.4 16/40 3.8 17/40 3.9 18/40 3.4 19/40 8.5 20/40 4.3 21/40 4.6 22/40 4.2 23/40 5.9 24/40 5.7 25/40 7.6 26/40 5.7 27/40 3.9 28/40 4.7 29/40 3.9 30/40 2.9 31/40 3.6 32/40 9.6 33/40 6.3 34/40 7.3 35/40 9.6 36/40 11. 37/40 29. 38/40 30. 39/40 17. 40/40 \n",
+      "Attempt 276 with (1/2, 1) and 72: 5.9 1/8 43. 2/8 14. 3/8 74. 4/8 7.6 5/8 47. 6/8 6.3 7/8 6.2 8/8 \n",
+      "Attempt 277 with (1, 0) and 11: 11. 1/8 8.8 2/8 22. 3/8 7.3 4/8 6.1 5/8 3.9 6/12 100. 7/12 5.7 8/12 4.0 9/16 22. 10/16 5.4 11/16 5.6 12/16 3.8 13/19 4.7 14/19 150. 15/19 9.5 16/19 6.0 17/19 3.1 18/19 5.9 19/19 \n",
+      "Attempt 278 with (1/3, 1/14) and 4: 11. 1/8 8.2 2/8 16. 3/8 9.6 4/8 6.6 5/8 5.2 6/8 5.2 7/8 11. 8/8 \n",
+      "Attempt 279 with (1, 5/2) and 4: 20. 1/8 8.9 2/8 19. 3/8 5.3 4/8 23. 5/8 18. 6/8 14. 7/8 8.2 8/8 \n",
+      "Attempt 280 with (7, 22/17) and 20: 3.6 1/12 17. 2/12 3.4 3/15 4.7 4/15 3.5 5/15 49. 6/15 33. 7/15 16. 8/15 5.9 9/15 6.3 10/15 23. 11/15 5.4 12/15 9.9 13/15 12. 14/15 4.1 15/15 \n",
+      "Attempt 281 with (2/7, 3/2) and 3: 9.0 1/8 2.9 2/40 2.7 3/40 11. 4/40 5.9 5/40 2.8 6/40 5.8 7/40 3.6 8/40 5.2 9/40 4.2 10/40 10. 11/40 14. 12/40 6.2 13/40 6.5 14/40 12. 15/40 2.9 16/40 5.9 17/40 5.1 18/40 8.1 19/40 13. 20/40 4.4 21/40 5.0 22/40 3.1 23/40 12. 24/40 5.9 25/40 16. 26/40 14. 27/40 14. 28/40 7.9 29/40 53. 30/40 9.0 31/40 10. 32/40 19. 33/40 5.4 34/40 4.0 35/40 7.5 36/40 4.3 37/40 18. 38/40 8.0 39/40 6.1 40/40 \n",
+      "Attempt 282 with (4/3, 1/24) and 4: 10. 1/8 7.2 2/8 11. 3/8 6.2 4/8 16. 5/8 5.8 6/8 4.7 7/8 13. 8/8 \n",
+      "Attempt 283 with (1/2, 0) and 5: 2.5 1/28 5.9 2/28 5.1 3/28 7.7 4/28 24. 5/28 8.3 6/28 3.5 7/28 6.8 8/28 20. 9/28 11. 10/28 5.6 11/28 7.3 12/28 8.2 13/28 3.3 14/28 4.2 15/28 62. 16/28 4.2 17/28 6.5 18/28 3.1 19/28 26. 20/28 3.9 21/28 4.0 22/28 4.5 23/28 6.4 24/28 7.1 25/28 6.7 26/28 4.2 27/28 17. 28/28 \n",
+      "Attempt 284 with (3/11, 3) and 4: 4.0 1/8 17. 2/8 4.2 3/8 15. 4/8 15. 5/8 7.2 6/8 7.3 7/8 11. 8/8 \n",
+      "Attempt 285 with (1/2, 1/3) and 3: 19. 1/8 7.6 2/8 5.8 3/8 8.0 4/8 4.2 5/8 5.1 6/8 6.3 7/8 8.3 8/8 \n",
+      "Attempt 286 with (3/2, 1/3) and 4: 12. 1/8 27. 2/8 14. 3/8 6.3 4/8 3.5 5/12 6.7 6/12 7.5 7/12 14. 8/12 7.8 9/12 4.4 10/12 14. 11/12 4.2 12/12 \n",
+      "Attempt 287 with (1, 1/29) and 6: 3.8 1/12 5.1 2/12 4.1 3/12 6.8 4/12 6.3 5/12 3.8 6/16 9.5 7/16 4.8 8/16 4.9 9/16 10. 10/16 13. 11/16 6.0 12/16 6.6 13/16 4.1 14/16 4.5 15/16 4.4 16/16 \n",
+      "Attempt 288 with (6, 0) and 4: 4.8 1/8 8.5 2/8 6.2 3/8 10. 4/8 62. 5/8 6.2 6/8 8.4 7/8 6.4 8/8 \n",
+      "Attempt 289 with (1/45, 1) and 4: 5.6 1/8 7.6 2/8 9.4 3/8 24. 4/8 110. 5/8 8.4 6/8 13. 7/8 5.9 8/8 \n",
+      "Attempt 290 with (6, 1/2) and 4: 15. 1/8 3.0 2/12 15. 3/12 4.3 4/12 5.2 5/12 4.5 6/12 4.0 7/12 5.8 8/12 3.7 9/16 5.6 10/16 4.6 11/16 3.5 12/20 5.4 13/20 28. 14/20 9.4 15/20 24. 16/20 7.0 17/20 4.3 18/20 7.0 19/20 5.7 20/20 \n",
+      "Attempt 291 with (0, 1) and 12: 6.4 1/8 9.7 2/8 6.5 3/8 14. 4/8 33. 5/8 4.3 6/8 6.7 7/8 6.1 8/8 \n",
+      "Attempt 292 with (3/4, 0) and 3: 3.6 1/12 6.5 2/12 6.3 3/12 6.0 4/12 8.6 5/12 16. 6/12 59. 7/12 20. 8/12 13. 9/12 4.8 10/12 3.4 11/16 5.7 12/16 7.8 13/16 4.8 14/16 7.7 15/16 4.4 16/16 \n",
+      "Attempt 293 with (0, 5/16) and 13: 37. 1/8 2.7 2/17 7.0 3/17 17. 4/17 4.1 5/17 4.3 6/17 8.9 7/17 14. 8/17 6.2 9/17 6.5 10/17 4.8 11/17 20. 12/17 6.3 13/17 8.6 14/17 6.6 15/17 37. 16/17 4.6 17/17 \n",
+      "Attempt 294 with (0, 7/3) and 45: 13. 1/8 10. 2/8 4.1 3/8 16. 4/8 4.7 5/8 6.0 6/8 12. 7/8 6.2 8/8 \n",
+      "Attempt 295 with (1, 12) and 3: 4.1 1/8 9.1 2/8 25. 3/8 9.5 4/8 7.0 5/8 14. 6/8 11. 7/8 12. 8/8 \n",
+      "Attempt 296 with (1, 2/5) and 14: 3.9 1/12 4.1 2/12 11. 3/12 11. 4/12 35. 5/12 24. 6/12 88. 7/12 27. 8/12 5.2 9/12 5.6 10/12 3.9 11/16 7.7 12/16 12. 13/16 4.6 14/16 14. 15/16 9.9 16/16 \n",
+      "Attempt 297 with (1/2, 1/2) and 3: 7.4 1/8 14. 2/8 6.5 3/8 7.0 4/8 20. 5/8 14. 6/8 29. 7/8 9.2 8/8 \n",
+      "Attempt 298 with (8, 3/10) and 4: 23. 1/8 5.4 2/8 5.1 3/8 6.1 4/8 31. 5/8 6.7 6/8 7.1 7/8 2.3 8/32 3.0 9/32 4.2 10/32 4.1 11/32 6.5 12/32 29. 13/32 4.8 14/32 6.6 15/32 2.7 16/32 3.3 17/32 5.8 18/32 3.8 19/32 6.1 20/32 19. 21/32 11. 22/32 5.6 23/32 6.1 24/32 8.2 25/32 16. 26/32 85. 27/32 23. 28/32 29. 29/32 8.0 30/32 4.5 31/32 3.3 32/32 \n",
+      "Attempt 299 with (45/2, 4) and 4: 4.1 1/8 5.7 2/8 3.0 3/12 5.1 4/12 25. 5/12 5.4 6/12 9.1 7/12 5.7 8/12 52. 9/12 5.8 10/12 7.9 11/12 6.6 12/12 \n",
+      "Attempt 300 with (1, 4) and 4: 8.8 1/8 18. 2/8 3.6 3/12 4.0 4/16 8.8 5/16 5.2 6/16 3.3 7/20 6.8 8/20 6.9 9/20 8.4 10/20 5.6 11/20 5.8 12/20 87. 13/20 9.6 14/20 18. 15/20 5.2 16/20 21. 17/20 5.2 18/20 6.1 19/20 4.6 20/20 \n",
+      "Attempt 301 with (3, 1/4) and 3: 6.6 1/8 12. 2/8 14. 3/8 5.5 4/8 8.2 5/8 12. 6/8 36. 7/8 11. 8/8 \n",
+      "Attempt 302 with (23/2, 2) and 4: 13. 1/8 15. 2/8 23. 3/8 5.6 4/8 4.4 5/8 3.6 6/12 11. 7/12 16. 8/12 6.5 9/12 5.7 10/12 8.8 11/12 7.6 12/12 \n",
+      "Attempt 303 with (1/7, 1) and 4: 3.6 1/12 10. 2/12 23. 3/12 5.5 4/12 8.0 5/12 5.3 6/12 6.2 7/12 3.6 8/16 9.5 9/16 4.5 10/16 6.1 11/16 20. 12/16 4.3 13/16 3.6 14/20 5.6 15/20 22. 16/20 30. 17/20 19. 18/20 9.0 19/20 8.2 20/20 \n",
+      "Attempt 304 with (0, 1/2) and 7: 20. 1/8 5.4 2/8 4.2 3/8 56. 4/8 4.6 5/8 16. 6/8 6.5 7/8 4.9 8/8 \n",
+      "Attempt 305 with (1/2, 4) and 21: 4.9 1/8 5.2 2/8 7.8 3/8 6.9 4/8 4.7 5/8 6.9 6/8 16. 7/8 15. 8/8 \n",
+      "Attempt 306 with (0, 3) and 5: 5.3 1/8 6.3 2/8 6.5 3/8 8.6 4/8 3.1 5/12 7.6 6/12 6.0 7/12 9.1 8/12 4.2 9/12 5.8 10/12 5.0 11/12 6.8 12/12 \n",
+      "Attempt 307 with (5, 5) and 11: 140. 1/8 19. 2/8 19. 3/8 6.4 4/8 6.4 5/8 4.7 6/8 7.2 7/8 34. 8/8 \n",
+      "Attempt 308 with (1, 26) and 4: 7.5 1/8 4.2 2/8 5.4 3/8 4.9 4/8 4.7 5/8 6.7 6/8 7.6 7/8 17. 8/8 \n",
+      "Attempt 309 with (1/5, 2) and 21: 31. 1/8 3.1 2/12 5.3 3/12 5.3 4/12 4.8 5/12 7.0 6/12 4.3 7/12 13. 8/12 19. 9/12 14. 10/12 5.4 11/12 38. 12/12 \n",
+      "Attempt 310 with (1/47, 6) and 6: 5.9 1/8 4.0 2/8 5.8 3/8 7.0 4/8 33. 5/8 11. 6/8 25. 7/8 8.6 8/8 \n",
+      "Attempt 311 with (1/78, 2/13) and 4: 25. 1/8 25. 2/8 26. 3/8 23. 4/8 33. 5/8 130. 6/8 32. 7/8 20. 8/8 \n",
+      "Attempt 312 with (290/3, 3) and 5: 11. 1/8 6.1 2/8 4.6 3/8 3.6 4/12 4.5 5/12 4.6 6/12 5.1 7/12 3.5 8/16 15. 9/16 5.8 10/16 8.4 11/16 16. 12/16 33. 13/16 11. 14/16 12. 15/16 5.6 16/16 \n",
+      "Attempt 313 with (1/3, 2) and 8: 10. 1/8 15. 2/8 5.7 3/8 37. 4/8 34. 5/8 8.7 6/8 6.5 7/8 4.6 8/8 \n",
+      "Attempt 314 with (16, 24) and 8: 10. 1/8 9.5 2/8 5.7 3/8 27. 4/8 6.2 5/8 8.0 6/8 8.7 7/8 8.7 8/8 \n",
+      "Attempt 315 with (5/6, 1/5) and 3: 6.3 1/8 7.2 2/8 19. 3/8 7.7 4/8 5.0 5/8 4.9 6/8 5.0 7/8 5.3 8/8 \n",
+      "Attempt 316 with (1/3, 1) and 6: 4.8 1/8 4.0 2/8 4.5 3/8 16. 4/8 7.7 5/8 9.7 6/8 3.6 7/12 11. 8/12 7.9 9/12 7.4 10/12 6.1 11/12 15. 12/12 \n",
+      "Attempt 317 with (3/5, 3/4) and 8: 130. 1/8 10. 2/8 13. 3/8 5.1 4/8 3.7 5/12 5.1 6/12 5.9 7/12 5.3 8/12 11. 9/12 4.7 10/12 4.2 11/12 5.2 12/12 \n",
+      "Attempt 318 with (2/3, 1/14) and 4: 10. 1/8 6.2 2/8 5.4 3/8 5.7 4/8 6.2 5/8 5.2 6/8 4.7 7/8 9.2 8/8 \n",
+      "Attempt 319 with (1, 0) and 12: 6.0 1/8 4.8 2/8 8.5 3/8 9.3 4/8 5.1 5/8 10. 6/8 7.0 7/8 15. 8/8 \n",
+      "Attempt 320 with (5, 2/5) and 4: 3.7 1/12 8.1 2/12 5.1 3/12 4.2 4/12 6.9 5/12 7.1 6/12 5.9 7/12 7.4 8/12 6.6 9/12 9.5 10/12 4.8 11/12 7.7 12/12 \n",
+      "Attempt 321 with (0, 3/5) and 4: 6.7 1/8 11. 2/8 14. 3/8 20. 4/8 5.3 5/8 2.8 6/32 5.8 7/32 3.4 8/32 3.8 9/32 15. 10/32 2.4 11/32 4.0 12/32 5.8 13/32 6.2 14/32 7.2 15/32 29. 16/32 11. 17/32 12. 18/32 8.4 19/32 8.8 20/32 7.0 21/32 4.5 22/32 8.5 23/32 5.2 24/32 6.5 25/32 8.0 26/32 7.5 27/32 12. 28/32 2.9 29/32 7.9 30/32 6.4 31/32 4.6 32/32 \n",
+      "Attempt 322 with (42, 1) and 4: 5.9 1/8 5.0 2/8 7.1 3/8 5.3 4/8 7.8 5/8 7.7 6/8 23. 7/8 17. 8/8 \n",
+      "Attempt 323 with (7, 3) and 5: 120. 1/8 16. 2/8 8.0 3/8 5.9 4/8 7.0 5/8 12. 6/8 10. 7/8 13. 8/8 \n",
+      "Attempt 324 with (1/6, 3) and 8: 9.5 1/8 4.8 2/8 8.4 3/8 9.5 4/8 5.2 5/8 11. 6/8 3.5 7/12 12. 8/12 4.7 9/12 5.9 10/12 4.2 11/12 3.9 12/16 5.5 13/16 14. 14/16 4.7 15/16 8.2 16/16 \n",
+      "Attempt 325 with (2, 15) and 3: 5.2 1/8 5.5 2/8 3.5 3/12 6.1 4/12 11. 5/12 4.3 6/12 4.0 7/16 5.1 8/16 5.0 9/16 6.3 10/16 4.2 11/16 5.6 12/16 15. 13/16 8.1 14/16 27. 15/16 35. 16/16 \n",
+      "Attempt 326 with (1/11, 1) and 9: 22. 1/8 7.8 2/8 5.9 3/8 13. 4/8 54. 5/8 18. 6/8 5.4 7/8 7.2 8/8 \n",
+      "Attempt 327 with (10/3, 2/9) and 4: 7.8 1/8 6.9 2/8 4.5 3/8 4.2 4/8 6.1 5/8 11. 6/8 8.2 7/8 13. 8/8 \n",
+      "Attempt 328 with (9, 0) and 4: 6.3 1/8 19. 2/8 15. 3/8 6.0 4/8 5.0 5/8 4.2 6/8 8.7 7/8 8.8 8/8 \n",
+      "Attempt 329 with (1/49, 2) and 5: 5.0 1/8 4.9 2/8 4.3 3/8 3.7 4/12 8.3 5/12 4.6 6/12 9.0 7/12 5.4 8/12 9.6 9/12 47. 10/12 12. 11/12 3.9 12/16 6.8 13/16 30. 14/16 9.7 15/16 16. 16/16 \n",
+      "Attempt 330 with (5/2, 1) and 4: 6.4 1/8 4.6 2/8 26. 3/8 3.7 4/12 3.0 5/16 6.0 6/16 14. 7/16 4.2 8/16 4.7 9/16 7.5 10/16 16. 11/16 6.7 12/16 30. 13/16 8.5 14/16 4.1 15/16 4.4 16/16 \n",
+      "Attempt 331 with (2, 3/4) and 3: 4.1 1/8 7.4 2/8 11. 3/8 28. 4/8 46. 5/8 15. 6/8 9.3 7/8 7.8 8/8 \n",
+      "Attempt 332 with (41/2, 1/14) and 3: 5.8 1/8 8.1 2/8 8.4 3/8 11. 4/8 7.4 5/8 13. 6/8 13. 7/8 7.3 8/8 \n",
+      "Attempt 333 with (1/2, 2) and 4: 9.3 1/8 10. 2/8 11. 3/8 13. 4/8 18. 5/8 10. 6/8 6.4 7/8 15. 8/8 \n",
+      "Attempt 334 with (0, 4) and 3: 16. 1/8 6.3 2/8 8.6 3/8 15. 4/8 4.9 5/8 4.6 6/8 3.5 7/12 11. 8/12 5.2 9/12 2.4 10/40 5.7 11/40 4.5 12/40 6.7 13/40 7.1 14/40 7.1 15/40 3.6 16/40 3.4 17/40 5.2 18/40 12. 19/40 8.7 20/40 18. 21/40 11. 22/40 15. 23/40 14. 24/40 11. 25/40 9.2 26/40 8.0 27/40 6.5 28/40 4.3 29/40 3.9 30/40 5.2 31/40 12. 32/40 5.8 33/40 5.6 34/40 5.7 35/40 6.3 36/40 5.9 37/40 7.5 38/40 16. 39/40 82. 40/40 \n",
+      "Attempt 335 with (17/23, 1/3) and 4: 7.3 1/8 10. 2/8 30. 3/8 34. 4/8 13. 5/8 12. 6/8 16. 7/8 29. 8/8 \n",
+      "Attempt 336 with (178, 3) and 4: 6.4 1/8 6.1 2/8 11. 3/8 4.1 4/8 4.2 5/8 4.8 6/8 5.6 7/8 8.5 8/8 \n",
+      "Attempt 337 with (1/4, 3/2) and 223: 16. 1/8 40. 2/8 21. 3/8 44. 4/8 14. 5/8 17. 6/8 8.8 7/8 5.9 8/8 \n",
+      "Attempt 338 with (1, 0) and 6: 5.3 1/8 5.9 2/8 5.5 3/8 11. 4/8 9.0 5/8 6.5 6/8 5.9 7/8 7.0 8/8 \n",
+      "Attempt 339 with (1/4, 1/9) and 12: 5.0 1/8 6.4 2/8 4.6 3/8 3.4 4/12 6.2 5/12 7.2 6/12 5.2 7/12 17. 8/12 18. 9/12 15. 10/12 92. 11/12 18. 12/12 \n",
+      "Attempt 340 with (4, 0) and 5: 5.5 1/8 3.2 2/12 3.8 3/16 4.0 4/20 9.5 5/20 4.2 6/20 4.7 7/20 6.5 8/20 29. 9/20 4.7 10/20 10. 11/20 3.7 12/24 11. 13/24 6.9 14/24 5.0 15/24 4.3 16/24 10. 17/24 6.9 18/24 9.9 19/24 8.5 20/24 6.3 21/24 6.3 22/24 5.4 23/24 3.7 24/28 5.9 25/28 9.4 26/28 24. 27/28 620. 28/28 \n",
+      "Attempt 341 with (5, 11/7) and 4: 4.6 1/8 4.9 2/8 9.9 3/8 8.3 4/8 29. 5/8 14. 6/8 4.1 7/8 3.6 8/12 5.4 9/12 5.3 10/12 3.8 11/16 5.8 12/16 8.0 13/16 7.9 14/16 24. 15/16 18. 16/16 \n",
+      "Attempt 342 with (8, 0) and 5: 6.9 1/8 8.0 2/8 24. 3/8 4.5 4/8 7.6 5/8 4.5 6/8 12. 7/8 7.1 8/8 \n",
+      "Attempt 343 with (1/2, 2) and 6: 34. 1/8 10. 2/8 12. 3/8 10. 4/8 6.3 5/8 4.7 6/8 3.1 7/12 4.9 8/12 34. 9/12 5.0 10/12 7.0 11/12 14. 12/12 \n",
+      "Attempt 344 with (0, 1/17) and 9: 8.2 1/8 7.2 2/8 6.7 3/8 4.3 4/8 15. 5/8 37. 6/8 7.8 7/8 37. 8/8 \n",
+      "Attempt 345 with (0, 1/4) and 7: 2.6 1/23 9.6 2/23 4.7 3/23 14. 4/23 13. 5/23 4.0 6/23 5.5 7/23 3.2 8/23 9.7 9/23 9.6 10/23 14. 11/23 13. 12/23 14. 13/23 30. 14/23 13. 15/23 11. 16/23 5.7 17/23 4.8 18/23 8.8 19/23 20. 20/23 12. 21/23 41. 22/23 130. 23/23 \n",
+      "Attempt 346 with (0, 4) and 8: 5.6 1/8 8.4 2/8 3.9 3/12 32. 4/12 9.7 5/12 4.5 6/12 36. 7/12 4.0 8/16 9.4 9/16 3.5 10/20 9.0 11/20 9.2 12/20 3.6 13/21 11. 14/21 7.5 15/21 5.0 16/21 16. 17/21 79. 18/21 17. 19/21 4.9 20/21 4.7 21/21 \n",
+      "Attempt 347 with (1/229, 1/2) and 4: 17. 1/8 17. 2/8 20. 3/8 19. 4/8 17. 5/8 14. 6/8 13. 7/8 13. 8/8 \n",
+      "Attempt 348 with (9, 45) and 5: 9.1 1/8 9.5 2/8 15. 3/8 13. 4/8 8.8 5/8 5.1 6/8 2.4 7/28 8.8 8/28 11. 9/28 6.8 10/28 3.8 11/28 4.7 12/28 21. 13/28 18. 14/28 31. 15/28 11. 16/28 4.2 17/28 9.7 18/28 3.4 19/28 6.4 20/28 22. 21/28 5.4 22/28 25. 23/28 4.4 24/28 3.5 25/28 5.4 26/28 6.4 27/28 41. 28/28 \n",
+      "Attempt 349 with (5/23, 910) and 5: 10. 1/8 6.7 2/8 6.2 3/8 12. 4/8 9.7 5/8 6.7 6/8 7.8 7/8 24. 8/8 \n",
+      "Attempt 350 with (4/3, 0) and 4: 25. 1/8 7.1 2/8 11. 3/8 8.4 4/8 9.6 5/8 7.0 6/8 6.4 7/8 6.1 8/8 \n",
+      "Attempt 351 with (0, 29) and 4: 4.3 1/8 7.3 2/8 8.5 3/8 18. 4/8 4.4 5/8 5.8 6/8 16. 7/8 14. 8/8 \n",
+      "Attempt 352 with (0, 1) and 9: 8.2 1/8 7.2 2/8 4.6 3/8 26. 4/8 4.5 5/8 13. 6/8 21. 7/8 3.5 8/12 8.3 9/12 6.1 10/12 7.4 11/12 3.2 12/16 8.7 13/16 24. 14/16 2.7 15/20 5.4 16/20 4.0 17/20 7.2 18/20 3.1 19/20 2.9 20/20 \n",
+      "Attempt 353 with (4, 17/9) and 17: 3.1 1/12 14. 2/12 3.3 3/16 2.9 4/16 4.4 5/16 5.5 6/16 13. 7/16 24. 8/16 8.6 9/16 4.3 10/16 11. 11/16 30. 12/16 3.5 13/16 4.3 14/16 39. 15/16 8.5 16/16 \n",
+      "Attempt 354 with (2, 11) and 4: 8.8 1/8 5.6 2/8 12. 3/8 4.4 4/8 8.3 5/8 17. 6/8 7.1 7/8 4.2 8/8 \n",
+      "Attempt 355 with (2/9, 1/3) and 7: 9.1 1/8 8.5 2/8 6.5 3/8 10. 4/8 6.0 5/8 31. 6/8 30. 7/8 16. 8/8 \n",
+      "Attempt 356 with (9/643, 2) and 13: 13. 1/8 13. 2/8 2.5 3/17 13. 4/17 13. 5/17 16. 6/17 4.1 7/17 4.9 8/17 3.5 9/17 12. 10/17 6.1 11/17 5.1 12/17 5.1 13/17 11. 14/17 6.3 15/17 3.9 16/17 11. 17/17 \n",
+      "Attempt 357 with (6, 0) and 5: 12. 1/8 17. 2/8 18. 3/8 5.6 4/8 3.4 5/12 7.3 6/12 13. 7/12 7.9 8/12 2.9 9/28 4.0 10/28 12. 11/28 3.9 12/28 12. 13/28 5.9 14/28 5.4 15/28 8.8 16/28 7.2 17/28 7.1 18/28 4.7 19/28 5.4 20/28 8.1 21/28 4.5 22/28 6.7 23/28 6.5 24/28 4.6 25/28 7.9 26/28 5.3 27/28 7.5 28/28 \n",
+      "Attempt 358 with (1, 2) and 49: 5.5 1/8 11. 2/8 13. 3/8 3.4 4/11 7.3 5/11 33. 6/11 1300. 7/11 19. 8/11 5.5 9/11 3.1 10/11 8.2 11/11 \n",
+      "Attempt 359 with (3, 1/8) and 4: 18. 1/8 12. 2/8 38. 3/8 5.6 4/8 3.7 5/12 10. 6/12 6.8 7/12 5.2 8/12 7.9 9/12 20. 10/12 5.0 11/12 6.3 12/12 \n",
+      "Attempt 360 with (1, 1/2) and 6: 4.0 1/12 8.3 2/12 140. 3/12 11. 4/12 8.9 5/12 5.5 6/12 8.7 7/12 18. 8/12 8.5 9/12 11. 10/12 8.3 11/12 5.4 12/12 \n",
+      "Attempt 361 with (1, 0) and 8: 5.9 1/8 6.7 2/8 14. 3/8 5.5 4/8 9.2 5/8 39. 6/8 3.6 7/12 8.5 8/12 4.7 9/12 13. 10/12 6.2 11/12 13. 12/12 \n",
+      "Attempt 362 with (3, 2) and 4: 12. 1/8 4.2 2/8 6.5 3/8 3.9 4/12 14. 5/12 10. 6/12 22. 7/12 26. 8/12 7.3 9/12 4.7 10/12 24. 11/12 7.6 12/12 \n",
+      "Attempt 363 with (2, 2) and 5: 430. 1/8 19. 2/8 12. 3/8 5.4 4/8 10. 5/8 6.1 6/8 6.1 7/8 3.7 8/12 18. 9/12 6.0 10/12 5.5 11/12 7.0 12/12 \n",
+      "Attempt 364 with (4, 1/19) and 6: 22. 1/8 5.3 2/8 3.8 3/12 7.1 4/12 4.1 5/12 4.9 6/12 3.1 7/16 8.7 8/16 9.0 9/16 3.7 10/20 7.8 11/20 11. 12/20 3.3 13/24 4.0 14/25 7.0 15/25 5.9 16/25 14. 17/25 5.2 18/25 8.6 19/25 24. 20/25 6.0 21/25 11. 22/25 5.6 23/25 25. 24/25 7.7 25/25 \n",
+      "Attempt 365 with (2/3, 3) and 3: 8.6 1/8 8.7 2/8 25. 3/8 16. 4/8 7.5 5/8 6.7 6/8 22. 7/8 9.5 8/8 \n",
+      "Attempt 366 with (1/5, 3) and 4: 2.7 1/32 9.5 2/32 4.1 3/32 7.2 4/32 3.8 5/32 6.4 6/32 30. 7/32 6.0 8/32 6.3 9/32 8.2 10/32 4.5 11/32 4.7 12/32 3.8 13/32 8.0 14/32 28. 15/32 12. 16/32 9.6 17/32 7.7 18/32 4.5 19/32 6.6 20/32 5.8 21/32 14. 22/32 4.7 23/32 6.2 24/32 4.6 25/32 6.7 26/32 15. 27/32 4.4 28/32 7.6 29/32 50. 30/32 7.2 31/32 5.8 32/32 \n",
+      "Attempt 367 with (1, 1) and 11: 11. 1/8 9.5 2/8 5.5 3/8 9.2 4/8 5.9 5/8 4.2 6/8 11. 7/8 6.9 8/8 \n",
+      "Attempt 368 with (0, 3) and 8: 13. 1/8 19. 2/8 6.0 3/8 7.3 4/8 7.2 5/8 4.7 6/8 5.7 7/8 4.2 8/8 \n",
+      "Attempt 369 with (1/2, 2/11) and 4: 28. 1/8 62. 2/8 24. 3/8 9.6 4/8 7.8 5/8 6.7 6/8 27. 7/8 61. 8/8 \n",
+      "Attempt 370 with (13/3, 2) and 4: 13. 1/8 30. 2/8 180. 3/8 25. 4/8 9.1 5/8 7.9 6/8 14. 7/8 19. 8/8 \n",
+      "Attempt 371 with (3, 7/3) and 5: 14. 1/8 3.9 2/12 5.2 3/12 7.3 4/12 3.7 5/16 4.8 6/16 9.9 7/16 16. 8/16 7.8 9/16 15. 10/16 14. 11/16 19. 12/16 5.8 13/16 9.2 14/16 12. 15/16 5.5 16/16 \n",
+      "Attempt 372 with (3, 4) and 4: 5.9 1/8 14. 2/8 5.8 3/8 4.2 4/8 6.1 5/8 5.2 6/8 12. 7/8 5.3 8/8 \n",
+      "Attempt 373 with (2/7, 1) and 8: 9.8 1/8 11. 2/8 8.8 3/8 4.0 4/8 9.3 5/8 11. 6/8 20. 7/8 50. 8/8 \n",
+      "Attempt 374 with (1/78, 1/40) and 4: 62. 1/8 51. 2/8 47. 3/8 52. 4/8 48. 5/8 47. 6/8 70. 7/8 77. 8/8 \n",
+      "Attempt 375 with (1/2, 3/4) and 3: 7.1 1/8 4.5 2/8 4.5 3/8 5.4 4/8 8.5 5/8 17. 6/8 16. 7/8 19. 8/8 \n",
+      "Attempt 376 with (1/72, 2/17) and 3: 53. 1/8 42. 2/8 40. 3/8 39. 4/8 36. 5/8 43. 6/8 62. 7/8 160. 8/8 \n",
+      "Attempt 377 with (1/4, 1/3) and 4: 6.4 1/8 11. 2/8 24. 3/8 18. 4/8 22. 5/8 13. 6/8 28. 7/8 8.0 8/8 \n",
+      "Attempt 378 with (0, 3) and 4: 6.7 1/8 8.4 2/8 11. 3/8 190. 4/8 13. 5/8 26. 6/8 17. 7/8 7.8 8/8 \n",
+      "Attempt 379 with (1/2, 0) and 30: 7.8 1/8 12. 2/8 16. 3/8 8.3 4/8 23. 5/8 4.9 6/8 12. 7/8 7.3 8/8 \n",
+      "Attempt 380 with (11/2, 5) and 3: 7.4 1/8 47. 2/8 25. 3/8 11. 4/8 6.6 5/8 10. 6/8 11. 7/8 11. 8/8 \n",
+      "Attempt 381 with (2, 1) and 11: 84. 1/8 6.5 2/8 5.5 3/8 6.3 4/8 16. 5/8 11. 6/8 3.0 7/19 12. 8/19 43. 9/19 6.2 10/19 12. 11/19 10. 12/19 6.0 13/19 5.1 14/19 22. 15/19 5.0 16/19 6.7 17/19 13. 18/19 17. 19/19 \n",
+      "Attempt 382 with (0, 1/3) and 7: 20. 1/8 28. 2/8 8.5 3/8 4.5 4/8 9.1 5/8 9.0 6/8 75. 7/8 6.6 8/8 \n",
+      "Attempt 383 with (5, 0) and 5: 10. 1/8 8.8 2/8 41. 3/8 6.9 4/8 7.0 5/8 4.6 6/8 3.9 7/12 4.4 8/12 11. 9/12 5.4 10/12 4.6 11/12 46. 12/12 \n",
+      "Attempt 384 with (0, 1/6) and 4: 3.0 1/32 11. 2/32 3.9 3/32 2.9 4/32 3.8 5/32 6.6 6/32 11. 7/32 8.4 8/32 3.3 9/32 12. 10/32 5.7 11/32 9.9 12/32 24. 13/32 21. 14/32 7.5 15/32 3.8 16/32 4.1 17/32 17. 18/32 6.0 19/32 22. 20/32 6.9 21/32 6.6 22/32 16. 23/32 51. 24/32 25. 25/32 23. 26/32 5.6 27/32 6.4 28/32 4.7 29/32 11. 30/32 6.4 31/32 3.0 32/32 \n",
+      "Attempt 385 with (7/4, 0) and 9: 3.1 1/12 5.0 2/12 4.7 3/12 13. 4/12 14. 5/12 11. 6/12 3.0 7/20 6.8 8/20 8.7 9/20 12. 10/20 13. 11/20 13. 12/20 6.9 13/20 4.6 14/20 9.9 15/20 7.5 16/20 4.8 17/20 8.9 18/20 5.6 19/20 8.3 20/20 \n",
+      "Attempt 386 with (4/3, 0) and 8: 11. 1/8 5.8 2/8 6.1 3/8 8.0 4/8 8.9 5/8 8.9 6/8 15. 7/8 8.6 8/8 \n",
+      "Attempt 387 with (1, 7) and 3: 33. 1/8 38. 2/8 10. 3/8 6.6 4/8 7.8 5/8 7.3 6/8 7.6 7/8 23. 8/8 \n",
+      "Attempt 388 with (1/5, 9) and 4: 8.8 1/8 6.0 2/8 4.6 3/8 11. 4/8 5.8 5/8 11. 6/8 90. 7/8 34. 8/8 \n",
+      "Attempt 389 with (1/5, 1/22) and 17: 12. 1/8 5.3 2/8 28. 3/8 5.5 4/8 6.1 5/8 6.5 6/8 41. 7/8 7.0 8/8 \n",
+      "Attempt 390 with (1/15, 0) and 3: 41. 1/8 6.8 2/8 8.2 3/8 6.5 4/8 4.3 5/8 5.3 6/8 6.0 7/8 8.0 8/8 \n",
+      "Attempt 391 with (1/2, 1) and 10: 4.5 1/8 9.7 2/8 3.9 3/12 3.8 4/16 16. 5/16 52. 6/16 4.2 7/16 6.0 8/16 4.2 9/16 3.5 10/19 14. 11/19 3.5 12/19 3.7 13/19 7.9 14/19 9.3 15/19 9.0 16/19 4.9 17/19 16. 18/19 15. 19/19 \n",
+      "Attempt 392 with (1/2, 1) and 8: 10. 1/8 6.5 2/8 6.8 3/8 5.7 4/8 9.6 5/8 2.9 6/21 4.1 7/21 4.7 8/21 19. 9/21 6.4 10/21 34. 11/21 19. 12/21 13. 13/21 6.5 14/21 4.5 15/21 9.4 16/21 7.7 17/21 8.7 18/21 9.5 19/21 14. 20/21 17. 21/21 \n",
+      "Attempt 393 with (1/2, 1/20) and 6: 34. 1/8 67. 2/8 42. 3/8 6.7 4/8 8.8 5/8 17. 6/8 84. 7/8 44. 8/8 \n",
+      "Attempt 394 with (5, 2) and 4: 6.1 1/8 7.5 2/8 11. 3/8 17. 4/8 12. 5/8 25. 6/8 28. 7/8 32. 8/8 \n",
+      "Attempt 395 with (7/26, 0) and 3: 12. 1/8 28. 2/8 3.7 3/12 2.8 4/40 6.8 5/40 8.6 6/40 3.4 7/40 4.3 8/40 5.1 9/40 9.8 10/40 7.3 11/40 26. 12/40 9.3 13/40 10. 14/40 8.5 15/40 8.3 16/40 33. 17/40 31. 18/40 15. 19/40 15. 20/40 14. 21/40 14. 22/40 8.0 23/40 8.5 24/40 13. 25/40 56. 26/40 13. 27/40 7.6 28/40 5.0 29/40 7.6 30/40 17. 31/40 11. 32/40 21. 33/40 13. 34/40 8.9 35/40 4.1 36/40 4.1 37/40 12. 38/40 6.1 39/40 6.1 40/40 \n",
+      "Attempt 396 with (1, 11/2) and 5: 7.3 1/8 5.2 2/8 12. 3/8 5.4 4/8 4.4 5/8 5.0 6/8 11. 7/8 5.5 8/8 \n",
+      "Attempt 397 with (1/25, 4) and 13: 21. 1/8 3.2 2/12 10. 3/12 10. 4/12 4.6 5/12 4.3 6/12 24. 7/12 7.2 8/12 5.5 9/12 23. 10/12 11. 11/12 8.0 12/12 \n",
+      "Attempt 398 with (1, 12) and 4: 5.6 1/8 15. 2/8 9.6 3/8 3.4 4/12 6.2 5/12 4.4 6/12 4.9 7/12 9.6 8/12 5.6 9/12 5.6 10/12 5.4 11/12 7.5 12/12 \n",
+      "Attempt 399 with (1/3, 0) and 12: 7.0 1/8 8.3 2/8 9.4 3/8 680. 4/8 26. 5/8 16. 6/8 7.6 7/8 9.7 8/8 \n",
+      "Attempt 400 with (7/2, 1/3) and 13: 2.6 1/17 7.1 2/17 9.7 3/17 7.2 4/17 7.8 5/17 8.2 6/17 5.8 7/17 8.0 8/17 6.7 9/17 2.8 10/17 3.6 11/17 180. 12/17 25. 13/17 4.1 14/17 7.7 15/17 6.2 16/17 4.0 17/17 \n",
+      "Attempt 401 with (0, 4/13) and 4: 10. 1/8 46. 2/8 8.3 3/8 6.9 4/8 12. 5/8 13. 6/8 25. 7/8 12. 8/8 \n",
+      "Attempt 402 with (16, 4) and 4: 9.3 1/8 5.5 2/8 5.8 3/8 7.5 4/8 3.3 5/12 3.4 6/16 5.9 7/16 10. 8/16 11. 9/16 21. 10/16 8.7 11/16 46. 12/16 18. 13/16 7.6 14/16 3.4 15/20 8.4 16/20 8.3 17/20 2.9 18/32 2.5 19/32 13. 20/32 22. 21/32 8.2 22/32 6.0 23/32 3.7 24/32 8.4 25/32 8.3 26/32 14. 27/32 3.4 28/32 3.1 29/32 17. 30/32 11. 31/32 4.1 32/32 \n",
+      "Attempt 403 with (10, 20/3) and 4: 8.5 1/8 3.4 2/12 5.9 3/12 6.8 4/12 14. 5/12 3.8 6/16 5.2 7/16 11. 8/16 13. 9/16 14. 10/16 8.4 11/16 7.1 12/16 35. 13/16 9.2 14/16 5.5 15/16 6.7 16/16 \n",
+      "Attempt 404 with (12, 2) and 4: 28. 1/8 3.9 2/12 2.7 3/32 7.0 4/32 13. 5/32 6.7 6/32 26. 7/32 27. 8/32 6.6 9/32 7.9 10/32 7.5 11/32 4.9 12/32 3.8 13/32 8.8 14/32 12. 15/32 9.6 16/32 10. 17/32 11. 18/32 11. 19/32 2.6 20/32 5.0 21/32 4.1 22/32 9.1 23/32 5.3 24/32 8.5 25/32 7.3 26/32 21. 27/32 30. 28/32 7.9 29/32 9.6 30/32 4.3 31/32 4.2 32/32 \n",
+      "Attempt 405 with (4, 20) and 5: 3.9 1/12 13. 2/12 3.3 3/16 4.6 4/16 4.5 5/16 14. 6/16 9.4 7/16 5.3 8/16 4.9 9/16 14. 10/16 4.2 11/16 6.6 12/16 4.3 13/16 3.9 14/20 5.6 15/20 29. 16/20 7.9 17/20 5.1 18/20 9.0 19/20 3.5 20/24 5.2 21/24 2.8 22/28 12. 23/28 7.3 24/28 15. 25/28 6.8 26/28 3.2 27/28 3.6 28/28 \n",
+      "Attempt 406 with (1, 1/2) and 8: 7.4 1/8 5.2 2/8 6.4 3/8 3.4 4/12 5.5 5/12 4.1 6/12 21. 7/12 7.8 8/12 14. 9/12 14. 10/12 6.1 11/12 3.3 12/16 5.2 13/16 35. 14/16 3.5 15/20 15. 16/20 6.2 17/20 13. 18/20 53. 19/20 8.2 20/20 \n",
+      "Attempt 407 with (1/2, 2/7) and 6: 10. 1/8 6.6 2/8 5.1 3/8 6.1 4/8 3.8 5/12 6.1 6/12 5.4 7/12 3.7 8/16 5.9 9/16 5.9 10/16 3.6 11/20 5.6 12/20 26. 13/20 36. 14/20 6.6 15/20 6.0 16/20 34. 17/20 17. 18/20 3.5 19/24 7.3 20/24 32. 21/24 33. 22/24 9.4 23/24 20. 24/24 \n",
+      "Attempt 408 with (1/2, 2/5) and 4: 57. 1/8 27. 2/8 14. 3/8 15. 4/8 7.0 5/8 9.3 6/8 15. 7/8 43. 8/8 \n",
+      "Attempt 409 with (1/2, 1/2) and 7: 5.8 1/8 3.6 2/12 13. 3/12 17. 4/12 2.9 5/23 6.1 6/23 7.6 7/23 4.7 8/23 3.9 9/23 6.5 10/23 15. 11/23 7.1 12/23 8.1 13/23 16. 14/23 9.4 15/23 4.8 16/23 24. 17/23 11. 18/23 12. 19/23 5.3 20/23 5.7 21/23 6.6 22/23 5.0 23/23 \n",
+      "Attempt 410 with (18, 8) and 3: 7.5 1/8 4.7 2/8 18. 3/8 14. 4/8 4.3 5/8 4.9 6/8 3.3 7/12 3.1 8/16 6.3 9/16 17. 10/16 6.6 11/16 3.9 12/20 8.0 13/20 5.0 14/20 6.2 15/20 4.2 16/20 11. 17/20 9.1 18/20 6.1 19/20 8.9 20/20 \n",
+      "Attempt 411 with (0, 1/14) and 11: 3.2 1/12 6.2 2/12 6.1 3/12 3.5 4/16 5.4 5/16 4.6 6/16 7.6 7/16 2.7 8/19 27. 9/19 37. 10/19 12. 11/19 4.6 12/19 6.2 13/19 9.3 14/19 25. 15/19 86. 16/19 190. 17/19 23. 18/19 21. 19/19 \n",
+      "Attempt 412 with (1, 0) and 15: 16. 1/8 5.0 2/8 3.8 3/12 6.0 4/12 21. 5/12 7.6 6/12 19. 7/12 4.1 8/12 11. 9/12 4.8 10/12 3.9 11/16 7.7 12/16 6.7 13/16 7.6 14/16 12. 15/16 5.9 16/16 \n",
+      "Attempt 413 with (2, 1/6) and 8: 42. 1/8 8.8 2/8 7.1 3/8 18. 4/8 3.7 5/12 4.5 6/12 4.0 7/16 10. 8/16 6.0 9/16 11. 10/16 2.9 11/21 3.8 12/21 2.2 13/21 10. 14/21 5.7 15/21 42. 16/21 8.4 17/21 4.9 18/21 4.5 19/21 16. 20/21 6.6 21/21 \n",
+      "Attempt 414 with (1/3, 1/6) and 4: 10. 1/8 9.0 2/8 4.0 3/8 6.6 4/8 5.2 5/8 7.7 6/8 8.9 7/8 4.9 8/8 \n",
+      "Attempt 415 with (84, 1) and 6: 5.7 1/8 10. 2/8 7.0 3/8 7.0 4/8 3.6 5/12 4.7 6/12 7.6 7/12 14. 8/12 58. 9/12 74. 10/12 15. 11/12 19. 12/12 \n",
+      "Attempt 416 with (1/3, 1/3) and 4: 26. 1/8 6.5 2/8 3.2 3/12 2.0 4/32 3.9 5/32 3.8 6/32 5.0 7/32 3.5 8/32 4.3 9/32 3.1 10/32 4.2 11/32 12. 12/32 4.4 13/32 18. 14/32 3.9 15/32 7.1 16/32 5.1 17/32 3.8 18/32 9.0 19/32 10. 20/32 6.1 21/32 3.8 22/32 4.3 23/32 22. 24/32 4.7 25/32 12. 26/32 4.1 27/32 13. 28/32 6.6 29/32 4.6 30/32 3.7 31/32 21. 32/32 \n",
+      "Attempt 417 with (2/3, 2) and 4: 3.5 1/12 10. 2/12 7.3 3/12 3.3 4/16 5.4 5/16 12. 6/16 12. 7/16 4.2 8/16 6.9 9/16 6.4 10/16 6.0 11/16 17. 12/16 3.1 13/20 9.3 14/20 4.4 15/20 13. 16/20 17. 17/20 4.0 18/20 4.4 19/20 8.6 20/20 \n",
+      "Attempt 418 with (0, 63/2) and 4: 4.9 1/8 4.8 2/8 13. 3/8 11. 4/8 4.4 5/8 8.9 6/8 7.6 7/8 5.8 8/8 \n",
+      "Attempt 419 with (5, 5/6) and 4: 7.7 1/8 18. 2/8 13. 3/8 9.8 4/8 5.7 5/8 4.9 6/8 5.7 7/8 7.8 8/8 \n",
+      "Attempt 420 with (0, 13/3) and 5: 13. 1/8 2.6 2/28 6.5 3/28 8.8 4/28 13. 5/28 8.3 6/28 3.9 7/28 7.3 8/28 4.3 9/28 5.1 10/28 5.3 11/28 3.9 12/28 47. 13/28 84. 14/28 28. 15/28 11. 16/28 3.1 17/28 8.2 18/28 9.7 19/28 4.1 20/28 5.2 21/28 6.0 22/28 15. 23/28 3.5 24/28 6.6 25/28 2.5 26/28 6.9 27/28 3.7 28/28 \n",
+      "Attempt 421 with (1, 9/2) and 4: 19. 1/8 43. 2/8 8.9 3/8 7.0 4/8 3.4 5/12 6.7 6/12 2.7 7/32 7.1 8/32 6.6 9/32 7.3 10/32 6.1 11/32 3.0 12/32 6.6 13/32 31. 14/32 11. 15/32 5.6 16/32 7.9 17/32 10. 18/32 8.5 19/32 17. 20/32 14. 21/32 72. 22/32 150. 23/32 16. 24/32 16. 25/32 3.8 26/32 6.6 27/32 5.0 28/32 5.7 29/32 14. 30/32 8.3 31/32 18. 32/32 \n",
+      "Attempt 422 with (1/4, 1) and 3: 4.5 1/8 8.4 2/8 8.1 3/8 8.4 4/8 4.0 5/12 3.9 6/16 5.8 7/16 9.6 8/16 10. 9/16 5.2 10/16 5.0 11/16 6.8 12/16 32. 13/16 5.3 14/16 9.8 15/16 11. 16/16 \n",
+      "Attempt 423 with (1/2, 9/2) and 4: 8.2 1/8 9.1 2/8 14. 3/8 49. 4/8 39. 5/8 150. 6/8 51. 7/8 27. 8/8 \n",
+      "Attempt 424 with (1/5, 1) and 4: 21. 1/8 4.5 2/8 31. 3/8 4.4 4/8 5.1 5/8 8.0 6/8 3.0 7/32 3.1 8/32 5.1 9/32 18. 10/32 7.1 11/32 5.1 12/32 19. 13/32 15. 14/32 39. 15/32 14. 16/32 8.2 17/32 3.4 18/32 5.8 19/32 30. 20/32 11. 21/32 3.9 22/32 5.6 23/32 10. 24/32 4.6 25/32 4.8 26/32 4.7 27/32 5.5 28/32 4.1 29/32 21. 30/32 8.4 31/32 2.4 32/32 \n",
+      "Attempt 425 with (1/5, 2) and 8: 19. 1/8 5.1 2/8 6.8 3/8 3.2 4/12 6.8 5/12 4.2 6/12 5.6 7/12 4.7 8/12 4.0 9/16 34. 10/16 28. 11/16 6.0 12/16 6.5 13/16 7.9 14/16 9.6 15/16 7.6 16/16 \n",
+      "Attempt 426 with (5/56, 2/5) and 4: 4.4 1/8 4.9 2/8 4.7 3/8 12. 4/8 19. 5/8 33. 6/8 58. 7/8 160. 8/8 \n",
+      "Attempt 427 with (14/3, 1) and 44: 5.7 1/8 7.3 2/8 14. 3/8 3.3 4/12 5.5 5/12 9.2 6/12 9.8 7/12 3.9 8/12 6.0 9/12 3.7 10/12 12. 11/12 4.1 12/12 \n",
+      "Attempt 428 with (8, 16/43) and 4: 30. 1/8 19. 2/8 6.1 3/8 3.7 4/12 4.4 5/12 4.6 6/12 11. 7/12 170. 8/12 12. 9/12 5.1 10/12 18. 11/12 9.0 12/12 \n",
+      "Attempt 429 with (1/2, 1) and 7: 4.9 1/8 5.2 2/8 5.7 3/8 3.1 4/12 27. 5/12 8.3 6/12 9.0 7/12 2.9 8/23 16. 9/23 3.7 10/23 3.8 11/23 16. 12/23 29. 13/23 5.0 14/23 9.2 15/23 5.1 16/23 5.0 17/23 8.6 18/23 26. 19/23 11. 20/23 34. 21/23 14. 22/23 7.0 23/23 \n",
+      "Attempt 430 with (1/10, 1) and 5: 7.3 1/8 4.8 2/8 4.2 3/8 8.3 4/8 13. 5/8 7.1 6/8 41. 7/8 8.8 8/8 \n",
+      "Attempt 431 with (1/3, 0) and 6: 6.9 1/8 5.0 2/8 4.3 3/8 5.5 4/8 6.9 5/8 22. 6/8 2.8 7/25 8.9 8/25 3.6 9/25 12. 10/25 13. 11/25 19. 12/25 7.6 13/25 4.0 14/25 19. 15/25 47. 16/25 13. 17/25 21. 18/25 9.7 19/25 6.9 20/25 18. 21/25 6.8 22/25 3.2 23/25 9.8 24/25 7.0 25/25 \n",
+      "Attempt 432 with (1, 3/17) and 7: 3.2 1/12 8.2 2/12 11. 3/12 8.4 4/12 3.6 5/16 5.4 6/16 11. 7/16 3.2 8/20 7.4 9/20 17. 10/20 3.3 11/23 6.1 12/23 12. 13/23 4.8 14/23 10. 15/23 17. 16/23 100. 17/23 8.6 18/23 17. 19/23 6.4 20/23 5.9 21/23 31. 22/23 16. 23/23 \n",
+      "Attempt 433 with (1/3, 2) and 3: 5.0 1/8 5.8 2/8 8.3 3/8 5.6 4/8 8.0 5/8 3.2 6/12 4.6 7/12 6.6 8/12 6.4 9/12 28. 10/12 4.0 11/12 8.3 12/12 \n",
+      "Attempt 434 with (7/3, 3) and 6: 6.7 1/8 11. 2/8 14. 3/8 16. 4/8 30. 5/8 8.2 6/8 4.7 7/8 5.6 8/8 \n",
+      "Attempt 435 with (1/5, 0) and 7: 14. 1/8 7.2 2/8 21. 3/8 8.2 4/8 2.9 5/23 5.4 6/23 21. 7/23 9.9 8/23 14. 9/23 6.3 10/23 7.6 11/23 9.1 12/23 24. 13/23 61. 14/23 13. 15/23 6.5 16/23 10. 17/23 6.1 18/23 7.2 19/23 4.2 20/23 8.0 21/23 7.5 22/23 8.1 23/23 \n",
+      "Attempt 436 with (0, 1/16) and 7: 21. 1/8 2.5 2/23 5.2 3/23 10. 4/23 7.6 5/23 6.4 6/23 5.2 7/23 13. 8/23 11. 9/23 70. 10/23 7.0 11/23 9.8 12/23 3.6 13/23 3.5 14/23 5.4 15/23 5.5 16/23 5.5 17/23 7.7 18/23 8.5 19/23 8.6 20/23 6.1 21/23 17. 22/23 180. 23/23 \n",
+      "Attempt 437 with (1/3, 1) and 12: 9.3 1/8 17. 2/8 15. 3/8 10. 4/8 6.9 5/8 32. 6/8 5.5 7/8 83. 8/8 \n",
+      "Attempt 438 with (0, 1/3) and 9: 6.4 1/8 4.3 2/8 2.6 3/20 11. 4/20 68. 5/20 10. 6/20 54. 7/20 7.4 8/20 5.6 9/20 3.7 10/20 5.6 11/20 3.5 12/20 3.2 13/20 9.2 14/20 3.6 15/20 5.6 16/20 3.5 17/20 8.7 18/20 23. 19/20 8.7 20/20 \n",
+      "Attempt 439 with (1, 7/4) and 6: 11. 1/8 66. 2/8 12. 3/8 5.4 4/8 7.7 5/8 3.3 6/12 4.8 7/12 6.2 8/12 69. 9/12 8.1 10/12 3.1 11/16 9.8 12/16 7.2 13/16 5.3 14/16 4.8 15/16 6.7 16/16 \n",
+      "Attempt 440 with (2/7, 1/6) and 4: 6.9 1/8 8.1 2/8 13. 3/8 5.2 4/8 8.8 5/8 8.7 6/8 3.2 7/12 7.5 8/12 3.4 9/16 5.8 10/16 4.5 11/16 4.2 12/16 8.1 13/16 24. 14/16 13. 15/16 36. 16/16 \n",
+      "Attempt 441 with (7, 0) and 4: 3.6 1/12 6.7 2/12 4.0 3/12 15. 4/12 4.3 5/12 5.6 6/12 3.1 7/16 2.8 8/32 16. 9/32 24. 10/32 14. 11/32 10. 12/32 19. 13/32 5.3 14/32 4.6 15/32 4.5 16/32 6.0 17/32 3.5 18/32 3.7 19/32 5.9 20/32 12. 21/32 4.9 22/32 14. 23/32 4.3 24/32 13. 25/32 22. 26/32 6.4 27/32 16. 28/32 88. 29/32 14. 30/32 8.5 31/32 5.9 32/32 \n",
+      "Attempt 442 with (5/4, 0) and 5: 24. 1/8 5.9 2/8 7.0 3/8 11. 4/8 11. 5/8 5.6 6/8 6.7 7/8 8.3 8/8 \n",
+      "Attempt 443 with (1/3, 2/3) and 24: 4.2 1/8 5.7 2/8 2.8 3/14 5.7 4/14 4.0 5/14 10. 6/14 17. 7/14 4.9 8/14 7.8 9/14 35. 10/14 5.6 11/14 5.9 12/14 7.0 13/14 8.0 14/14 \n",
+      "Attempt 444 with (1/5, 0) and 6: 8.3 1/8 18. 2/8 28. 3/8 160. 4/8 32. 5/8 14. 6/8 9.7 7/8 8.2 8/8 \n",
+      "Attempt 445 with (1/3, 1) and 5: 7.8 1/8 4.1 2/8 3.7 3/12 6.2 4/12 23. 5/12 4.2 6/12 5.8 7/12 6.3 8/12 6.3 9/12 6.1 10/12 12. 11/12 8.0 12/12 \n",
+      "Attempt 446 with (9/16, 0) and 5: 4.8 1/8 4.2 2/8 4.6 3/8 15. 4/8 4.3 5/8 5.5 6/8 12. 7/8 8.0 8/8 \n",
+      "Attempt 447 with (1/12, 0) and 7: 19. 1/8 10. 2/8 13. 3/8 5.4 4/8 8.0 5/8 5.2 6/8 4.9 7/8 11. 8/8 \n",
+      "Attempt 448 with (1/2, 11) and 3: 24. 1/8 6.7 2/8 26. 3/8 8.0 4/8 9.1 5/8 11. 6/8 3.8 7/12 2.9 8/40 14. 9/40 4.4 10/40 3.7 11/40 4.3 12/40 19. 13/40 19. 14/40 34. 15/40 11. 16/40 6.8 17/40 6.6 18/40 6.0 19/40 12. 20/40 4.2 21/40 14. 22/40 12. 23/40 3.9 24/40 4.4 25/40 6.0 26/40 9.6 27/40 6.1 28/40 4.6 29/40 5.3 30/40 4.0 31/40 2.8 32/40 6.3 33/40 26. 34/40 12. 35/40 28. 36/40 12. 37/40 6.4 38/40 4.5 39/40 6.7 40/40 \n",
+      "Attempt 449 with (19/7, 1/12) and 4: 6.2 1/8 25. 2/8 12. 3/8 6.9 4/8 3.2 5/12 7.7 6/12 6.6 7/12 3.5 8/16 2.3 9/32 14. 10/32 7.4 11/32 12. 12/32 4.4 13/32 4.4 14/32 3.3 15/32 3.5 16/32 8.4 17/32 22. 18/32 6.4 19/32 5.5 20/32 4.5 21/32 3.7 22/32 10. 23/32 4.9 24/32 8.7 25/32 6.7 26/32 4.3 27/32 4.7 28/32 9.3 29/32 15. 30/32 5.7 31/32 9.7 32/32 \n",
+      "Attempt 450 with (1/4, 1) and 7: 14. 1/8 18. 2/8 84. 3/8 16. 4/8 6.7 5/8 13. 6/8 4.8 7/8 3.2 8/12 9.5 9/12 5.5 10/12 6.9 11/12 9.4 12/12 \n",
+      "Attempt 451 with (4, 57/2) and 6: 5.6 1/8 26. 2/8 4.4 3/8 6.3 4/8 4.7 5/8 13. 6/8 17. 7/8 18. 8/8 \n",
+      "Attempt 452 with (1/16, 1) and 5: 7.0 1/8 7.2 2/8 7.0 3/8 11. 4/8 9.9 5/8 7.0 6/8 36. 7/8 8.9 8/8 \n",
+      "Attempt 453 with (0, 6) and 23: 27. 1/8 5.7 2/8 6.0 3/8 28. 4/8 8.5 5/8 7.0 6/8 6.3 7/8 5.6 8/8 \n",
+      "Attempt 454 with (2/3, 0) and 6: 10. 1/8 13. 2/8 6.1 3/8 8.8 4/8 5.6 5/8 3.1 6/12 91. 7/12 4.0 8/16 7.8 9/16 7.0 10/16 7.5 11/16 5.0 12/16 7.0 13/16 9.4 14/16 27. 15/16 5.7 16/16 \n",
+      "Attempt 455 with (1/3, 1/7) and 27: 5.5 1/8 4.3 2/8 4.1 3/8 8.5 4/8 4.7 5/8 36. 6/8 5.2 7/8 9.2 8/8 \n",
+      "Attempt 456 with (12, 5/7) and 4: 5.5 1/8 4.6 2/8 8.4 3/8 12. 4/8 5.1 5/8 8.2 6/8 11. 7/8 3.5 8/12 3.0 9/16 3.0 10/32 12. 11/32 5.6 12/32 7.5 13/32 4.6 14/32 6.8 15/32 24. 16/32 110. 17/32 19. 18/32 5.0 19/32 6.6 20/32 19. 21/32 11. 22/32 13. 23/32 3.0 24/32 3.8 25/32 11. 26/32 7.8 27/32 3.1 28/32 6.5 29/32 6.3 30/32 3.7 31/32 6.9 32/32 \n",
+      "Attempt 457 with (1/2, 1/2) and 4: 9.9 1/8 5.6 2/8 10. 3/8 11. 4/8 9.6 5/8 18. 6/8 7.5 7/8 18. 8/8 \n",
+      "Attempt 458 with (3, 4) and 3: 6.1 1/8 3.9 2/12 4.1 3/12 8.2 4/12 11. 5/12 31. 6/12 21. 7/12 11. 8/12 5.6 9/12 3.7 10/16 5.0 11/16 7.6 12/16 8.8 13/16 6.5 14/16 14. 15/16 7.6 16/16 \n",
+      "Attempt 459 with (1/2, 3) and 4: 22. 1/8 13. 2/8 5.4 3/8 11. 4/8 39. 5/8 690. 6/8 47. 7/8 12. 8/8 \n",
+      "Attempt 460 with (1, 0) and 25: 3.6 1/12 7.1 2/12 17. 3/12 5.5 4/12 8.5 5/12 29. 6/12 4.4 7/12 57. 8/12 5.0 9/12 3.4 10/14 50. 11/14 5.1 12/14 4.7 13/14 7.8 14/14 \n",
+      "Attempt 461 with (4, 4) and 4: 27. 1/8 17. 2/8 5.3 3/8 4.9 4/8 6.8 5/8 5.1 6/8 3.2 7/12 11. 8/12 8.2 9/12 12. 10/12 7.2 11/12 6.2 12/12 \n",
+      "Attempt 462 with (1/5, 0) and 3: 4.5 1/8 3.9 2/12 7.2 3/12 5.4 4/12 12. 5/12 32. 6/12 8.1 7/12 7.0 8/12 5.4 9/12 5.6 10/12 2.4 11/40 6.2 12/40 4.2 13/40 4.8 14/40 5.6 15/40 23. 16/40 11. 17/40 4.8 18/40 4.0 19/40 11. 20/40 3.1 21/40 8.1 22/40 6.2 23/40 3.0 24/40 9.6 25/40 4.1 26/40 3.1 27/40 4.8 28/40 4.0 29/40 4.1 30/40 9.8 31/40 11. 32/40 4.1 33/40 3.3 34/40 5.0 35/40 7.6 36/40 3.2 37/40 7.0 38/40 5.1 39/40 3.1 40/40 \n",
+      "Attempt 463 with (1, 1/3) and 8: 56. 1/8 7.7 2/8 12. 3/8 20. 4/8 6.1 5/8 3.3 6/12 15. 7/12 4.6 8/12 8.0 9/12 4.3 10/12 5.1 11/12 41. 12/12 \n",
+      "Attempt 464 with (0, 10) and 4: 9.8 1/8 31. 2/8 9.9 3/8 52. 4/8 76. 5/8 9.3 6/8 7.4 7/8 4.0 8/12 5.4 9/12 9.9 10/12 7.5 11/12 8.3 12/12 \n",
+      "Attempt 465 with (1/2, 1/28) and 3: 24. 1/8 27. 2/8 22. 3/8 22. 4/8 18. 5/8 20. 6/8 42. 7/8 32. 8/8 \n",
+      "Attempt 466 with (1, 29/2) and 3: 11. 1/8 7.0 2/8 29. 3/8 8.2 4/8 18. 5/8 8.5 6/8 13. 7/8 12. 8/8 \n",
+      "Attempt 467 with (1/6, 10) and 6: 48. 1/8 11. 2/8 48. 3/8 3.6 4/12 5.7 5/12 7.8 6/12 40. 7/12 8.3 8/12 11. 9/12 9.2 10/12 12. 11/12 6.4 12/12 \n",
+      "Attempt 468 with (1/4, 1/2) and 6: 5.6 1/8 13. 2/8 12. 3/8 11. 4/8 11. 5/8 9.7 6/8 10. 7/8 3.9 8/12 13. 9/12 4.7 10/12 7.8 11/12 3.1 12/16 2.4 13/25 11. 14/25 4.5 15/25 14. 16/25 4.2 17/25 4.2 18/25 5.3 19/25 13. 20/25 5.6 21/25 7.7 22/25 4.6 23/25 5.7 24/25 13. 25/25 \n",
+      "Attempt 469 with (1/5, 2) and 12: 3.8 1/12 3.7 2/16 5.6 3/16 11. 4/16 5.2 5/16 10. 6/16 13. 7/16 37. 8/16 3.2 9/18 6.2 10/18 8.8 11/18 7.9 12/18 18. 13/18 3.2 14/18 4.9 15/18 8.7 16/18 8.8 17/18 24. 18/18 \n",
+      "Attempt 470 with (2, 7/15) and 8: 6.1 1/8 5.9 2/8 8.2 3/8 8.8 4/8 9.7 5/8 15. 6/8 2.6 7/21 7.0 8/21 4.7 9/21 2.7 10/21 5.3 11/21 7.5 12/21 10. 13/21 33. 14/21 7.1 15/21 4.3 16/21 11. 17/21 13. 18/21 16. 19/21 6.5 20/21 3.7 21/21 \n",
+      "Attempt 471 with (1, 3) and 12: 4.4 1/8 9.2 2/8 4.4 3/8 3.8 4/12 35. 5/12 8.1 6/12 4.7 7/12 13. 8/12 2.7 9/18 8.7 10/18 5.5 11/18 7.7 12/18 3.2 13/18 7.2 14/18 19. 15/18 28. 16/18 4.4 17/18 15. 18/18 \n",
+      "Attempt 472 with (1, 3/2) and 5: 5.9 1/8 9.8 2/8 6.4 3/8 4.4 4/8 3.4 5/12 3.8 6/16 33. 7/16 45. 8/16 9.6 9/16 4.5 10/16 7.1 11/16 5.4 12/16 4.3 13/16 7.2 14/16 4.9 15/16 3.5 16/20 8.5 17/20 7.9 18/20 5.1 19/20 12. 20/20 \n",
+      "Attempt 473 with (0, 16) and 4: 16. 1/8 21. 2/8 30. 3/8 76. 4/8 140. 5/8 120. 6/8 42. 7/8 24. 8/8 \n",
+      "Attempt 474 with (2/23, 1/3) and 4: 52. 1/8 4.2 2/8 3.6 3/12 6.8 4/12 7.0 5/12 3.7 6/16 4.1 7/16 15. 8/16 13. 9/16 11. 10/16 3.2 11/20 6.4 12/20 8.0 13/20 7.4 14/20 4.0 15/24 3.0 16/32 7.9 17/32 15. 18/32 30. 19/32 26. 20/32 5.8 21/32 6.2 22/32 3.4 23/32 7.5 24/32 2.7 25/32 4.4 26/32 7.6 27/32 6.6 28/32 4.5 29/32 6.5 30/32 6.9 31/32 13. 32/32 \n",
+      "Attempt 475 with (1/3, 38) and 5: 5.7 1/8 11. 2/8 5.1 3/8 21. 4/8 5.2 5/8 3.6 6/12 6.1 7/12 3.2 8/16 12. 9/16 8.4 10/16 41. 11/16 6.5 12/16 5.0 13/16 12. 14/16 8.5 15/16 6.7 16/16 \n",
+      "Attempt 476 with (1, 2) and 36: 3.8 1/12 2.4 2/12 5.8 3/12 6.7 4/12 18. 5/12 58. 6/12 6.3 7/12 8.1 8/12 8.2 9/12 8.2 10/12 6.9 11/12 7.8 12/12 \n",
+      "Attempt 477 with (1/7, 31) and 3: 28. 1/8 23. 2/8 20. 3/8 18. 4/8 20. 5/8 24. 6/8 37. 7/8 60. 8/8 \n",
+      "Attempt 478 with (1/11, 4) and 6: 6.4 1/8 12. 2/8 5.9 3/8 2.9 4/25 4.8 5/25 11. 6/25 20. 7/25 22. 8/25 4.0 9/25 4.5 10/25 5.3 11/25 7.8 12/25 4.9 13/25 13. 14/25 4.1 15/25 3.8 16/25 19. 17/25 4.1 18/25 5.7 19/25 7.3 20/25 8.9 21/25 7.4 22/25 4.4 23/25 6.7 24/25 10. 25/25 \n",
+      "Attempt 479 with (0, 1/4) and 12: 14. 1/8 22. 2/8 20. 3/8 4.8 4/8 5.0 5/8 11. 6/8 5.3 7/8 17. 8/8 \n",
+      "Attempt 480 with (1/20, 2) and 3: 25. 1/8 24. 2/8 24. 3/8 26. 4/8 8.6 5/8 4.6 6/8 5.5 7/8 6.8 8/8 \n",
+      "Attempt 481 with (1/3, 0) and 7: 13. 1/8 3.3 2/12 5.0 3/12 6.9 4/12 6.4 5/12 37. 6/12 6.4 7/12 3.3 8/16 3.8 9/20 8.0 10/20 5.2 11/20 13. 12/20 6.2 13/20 37. 14/20 120. 15/20 22. 16/20 6.0 17/20 5.2 18/20 5.5 19/20 25. 20/20 \n",
+      "Attempt 482 with (2/5, 3) and 12: 150. 1/8 33. 2/8 25. 3/8 33. 4/8 67. 5/8 660. 6/8 71. 7/8 38. 8/8 \n",
+      "Attempt 483 with (0, 1/3) and 10: 6.3 1/8 19. 2/8 310. 3/8 18. 4/8 8.4 5/8 7.5 6/8 12. 7/8 4.2 8/8 \n",
+      "Attempt 484 with (8, 2) and 8: 24. 1/8 15. 2/8 4.5 3/8 2.8 4/21 18. 5/21 15. 6/21 4.0 7/21 4.1 8/21 6.4 9/21 9.1 10/21 5.7 11/21 52. 12/21 21. 13/21 5.4 14/21 7.1 15/21 5.8 16/21 5.4 17/21 54. 18/21 18. 19/21 6.3 20/21 3.4 21/21 \n",
+      "Attempt 485 with (11, 1) and 7: 8.2 1/8 4.4 2/8 5.5 3/8 11. 4/8 5.5 5/8 55. 6/8 12. 7/8 4.5 8/8 \n",
+      "Attempt 486 with (0, 17) and 4: 6.2 1/8 4.0 2/12 10. 3/12 6.4 4/12 8.7 5/12 28. 6/12 6.1 7/12 31. 8/12 9.8 9/12 19. 10/12 8.8 11/12 3.6 12/16 4.6 13/16 3.8 14/20 16. 15/20 7.5 16/20 5.6 17/20 11. 18/20 3.9 19/24 13. 20/24 4.7 21/24 7.6 22/24 140. 23/24 30. 24/24 \n",
+      "Attempt 487 with (1/43, 2/3) and 5: 11. 1/8 12. 2/8 12. 3/8 14. 4/8 5.6 5/8 13. 6/8 12. 7/8 3.8 8/12 9.1 9/12 4.8 10/12 7.8 11/12 3.4 12/16 6.3 13/16 4.2 14/16 24. 15/16 5.0 16/16 \n",
+      "Attempt 488 with (1, 1/4) and 4: 6.3 1/8 6.9 2/8 22. 3/8 15. 4/8 15. 5/8 89. 6/8 12. 7/8 10. 8/8 \n",
+      "Attempt 489 with (2, 1/76) and 3: 32. 1/8 24. 2/8 23. 3/8 24. 4/8 21. 5/8 18. 6/8 24. 7/8 17. 8/8 \n",
+      "Attempt 490 with (3, 0) and 3: 36. 1/8 22. 2/8 16. 3/8 17. 4/8 5.5 5/8 6.4 6/8 8.4 7/8 7.9 8/8 \n",
+      "Attempt 491 with (2, 1/2) and 3: 10. 1/8 15. 2/8 5.3 3/8 2.9 4/40 3.4 5/40 13. 6/40 17. 7/40 7.1 8/40 10. 9/40 3.0 10/40 3.4 11/40 4.6 12/40 6.6 13/40 3.8 14/40 3.4 15/40 3.7 16/40 8.3 17/40 21. 18/40 16. 19/40 5.5 20/40 3.9 21/40 10. 22/40 11. 23/40 4.5 24/40 8.8 25/40 16. 26/40 5.1 27/40 12. 28/40 8.2 29/40 30. 30/40 140. 31/40 61. 32/40 27. 33/40 8.8 34/40 5.6 35/40 12. 36/40 8.2 37/40 6.7 38/40 14. 39/40 24. 40/40 \n",
+      "Attempt 492 with (0, 1/2) and 10: 18. 1/8 6.2 2/8 18. 3/8 13. 4/8 10. 5/8 4.2 6/8 19. 7/8 4.3 8/8 \n",
+      "Attempt 493 with (2/7, 2/3) and 3: 30. 1/8 25. 2/8 64. 3/8 38. 4/8 37. 5/8 11. 6/8 14. 7/8 28. 8/8 \n",
+      "Attempt 494 with (1, 1/4) and 3: 10. 1/8 6.3 2/8 6.3 3/8 6.3 4/8 5.6 5/8 4.9 6/8 23. 7/8 14. 8/8 \n",
+      "Attempt 495 with (2/3, 2) and 6: 210. 1/8 220. 2/8 47. 3/8 12. 4/8 13. 5/8 8.7 6/8 9.2 7/8 5.1 8/8 \n",
+      "Attempt 496 with (1, 19/2) and 3: 3.8 1/12 12. 2/12 4.1 3/12 5.1 4/12 7.3 5/12 7.8 6/12 4.2 7/12 25. 8/12 7.2 9/12 8.6 10/12 3.3 11/16 7.8 12/16 9.5 13/16 4.7 14/16 7.1 15/16 22. 16/16 \n",
+      "Attempt 497 with (5/2, 1) and 3: 17. 1/8 9.4 2/8 11. 3/8 7.0 4/8 5.2 5/8 8.1 6/8 7.6 7/8 7.6 8/8 \n",
+      "Attempt 498 with (5, 0) and 4: 11. 1/8 8.6 2/8 3.8 3/12 9.8 4/12 7.5 5/12 4.2 6/12 38. 7/12 6.9 8/12 6.0 9/12 6.2 10/12 8.8 11/12 3.1 12/16 3.7 13/20 4.1 14/20 9.3 15/20 15. 16/20 12. 17/20 10. 18/20 41. 19/20 7.1 20/20 \n",
+      "Attempt 499 with (8/5, 0) and 3: 7.1 1/8 7.5 2/8 28. 3/8 8.4 4/8 11. 5/8 7.0 6/8 8.2 7/8 22. 8/8 \n",
+      "Attempt 500 with (3, 2) and 6: 32. 1/8 11. 2/8 7.3 3/8 9.8 4/8 5.9 5/8 16. 6/8 5.0 7/8 8.3 8/8 \n",
+      "Attempt 501 with (19, 1/15) and 7: 5.9 1/8 5.4 2/8 5.4 3/8 21. 4/8 7.1 5/8 6.2 6/8 20. 7/8 20. 8/8 \n",
+      "Attempt 502 with (1, 4) and 24: 16. 1/8 6.4 2/8 4.9 3/8 3.4 4/12 6.6 5/12 11. 6/12 5.1 7/12 11. 8/12 11. 9/12 22. 10/12 4.7 11/12 19. 12/12 \n",
+      "Attempt 503 with (116, 1/3) and 4: 8.9 1/8 5.6 2/8 10. 3/8 4.2 4/8 10. 5/8 3.8 6/12 9.0 7/12 5.7 8/12 5.2 9/12 6.9 10/12 9.8 11/12 9.7 12/12 \n",
+      "Attempt 504 with (0, 8) and 5: 16. 1/8 4.5 2/8 15. 3/8 10. 4/8 10. 5/8 13. 6/8 39. 7/8 4.2 8/8 \n",
+      "Attempt 505 with (3/2, 8) and 5: 74. 1/8 120. 2/8 26. 3/8 13. 4/8 9.7 5/8 32. 6/8 6.1 7/8 4.5 8/8 \n",
+      "Attempt 506 with (5/2, 17) and 5: 11. 1/8 7.4 2/8 4.5 3/8 3.6 4/12 14. 5/12 6.0 6/12 15. 7/12 130. 8/12 9.7 9/12 6.9 10/12 11. 11/12 8.6 12/12 \n",
+      "Attempt 507 with (1, 0) and 17: 23. 1/8 7.1 2/8 11. 3/8 2.8 4/16 13. 5/16 5.1 6/16 10. 7/16 2.8 8/16 9.5 9/16 7.8 10/16 9.7 11/16 8.0 12/16 25. 13/16 5.0 14/16 3.8 15/16 11. 16/16 \n",
+      "Attempt 508 with (1/3, 0) and 5: 11. 1/8 5.9 2/8 9.8 3/8 3.8 4/12 10. 5/12 9.0 6/12 6.0 7/12 4.6 8/12 9.5 9/12 6.1 10/12 3.9 11/16 8.6 12/16 9.8 13/16 13. 14/16 83. 15/16 16. 16/16 \n",
+      "Attempt 509 with (1/3, 1) and 10: 9.7 1/8 12. 2/8 11. 3/8 5.4 4/8 28. 5/8 10. 6/8 18. 7/8 2.7 8/19 4.3 9/19 12. 10/19 15. 11/19 6.7 12/19 6.6 13/19 22. 14/19 22. 15/19 10. 16/19 28. 17/19 14. 18/19 5.1 19/19 \n",
+      "Attempt 510 with (10/61, 1) and 3: 8.8 1/8 13. 2/8 28. 3/8 63. 4/8 20. 5/8 11. 6/8 30. 7/8 45. 8/8 \n",
+      "Attempt 511 with (2/5, 1/15) and 3: 19. 1/8 17. 2/8 17. 3/8 12. 4/8 11. 5/8 12. 6/8 13. 7/8 9.9 8/8 \n",
+      "Attempt 512 with (4, 0) and 4: 15. 1/8 13. 2/8 3.2 3/12 7.3 4/12 6.8 5/12 16. 6/12 9.5 7/12 6.2 8/12 5.0 9/12 6.9 10/12 4.5 11/12 15. 12/12 \n",
+      "Attempt 513 with (1/8, 1/2) and 6: 6.4 1/8 6.1 2/8 4.7 3/8 4.1 4/8 7.3 5/8 19. 6/8 14. 7/8 70. 8/8 \n",
+      "Attempt 514 with (2, 7) and 3: 14. 1/8 12. 2/8 6.0 3/8 27. 4/8 5.4 5/8 12. 6/8 2.8 7/40 5.1 8/40 17. 9/40 7.1 10/40 5.5 11/40 4.3 12/40 6.6 13/40 11. 14/40 6.0 15/40 5.0 16/40 14. 17/40 5.0 18/40 4.9 19/40 4.9 20/40 11. 21/40 11. 22/40 4.3 23/40 6.0 24/40 8.4 25/40 3.7 26/40 6.5 27/40 6.9 28/40 8.1 29/40 6.1 30/40 4.1 31/40 3.3 32/40 3.9 33/40 3.5 34/40 3.5 35/40 4.3 36/40 5.9 37/40 16. 38/40 62. 39/40 66. 40/40 \n",
+      "Attempt 515 with (3, 3) and 5: 16. 1/8 4.8 2/8 11. 3/8 6.6 4/8 4.6 5/8 12. 6/8 7.3 7/8 5.6 8/8 \n",
+      "Attempt 516 with (19, 1) and 4: 9.3 1/8 5.2 2/8 4.2 3/8 5.2 4/8 8.6 5/8 28. 6/8 19. 7/8 3.4 8/12 5.4 9/12 2.7 10/32 12. 11/32 3.9 12/32 8.4 13/32 6.2 14/32 22. 15/32 8.8 16/32 41. 17/32 7.6 18/32 12. 19/32 7.3 20/32 3.7 21/32 3.9 22/32 6.8 23/32 13. 24/32 5.1 25/32 7.6 26/32 21. 27/32 6.3 28/32 6.3 29/32 12. 30/32 2.9 31/32 5.8 32/32 \n",
+      "Attempt 517 with (1/2, 0) and 3: 6.9 1/8 6.1 2/8 9.6 3/8 21. 4/8 57. 5/8 15. 6/8 9.9 7/8 24. 8/8 \n",
+      "Attempt 518 with (1/3, 7) and 3: 4.5 1/8 9.6 2/8 3.8 3/12 11. 4/12 5.6 5/12 5.8 6/12 3.2 7/16 6.5 8/16 19. 9/16 16. 10/16 54. 11/16 13. 12/16 25. 13/16 24. 14/16 7.6 15/16 17. 16/16 \n",
+      "Attempt 519 with (1, 1/101) and 120: 8.4 1/8 3.9 2/9 17. 3/9 3.6 4/9 6.7 5/9 4.4 6/9 6.7 7/9 11. 8/9 48. 9/9 \n",
+      "Attempt 520 with (1, 1) and 13: 7.8 1/8 30. 2/8 4.0 3/8 6.3 4/8 19. 5/8 8.3 6/8 4.5 7/8 4.9 8/8 \n",
+      "Attempt 521 with (0, 1/2) and 5: 6.3 1/8 4.9 2/8 13. 3/8 6.6 4/8 3.7 5/12 19. 6/12 4.6 7/12 13. 8/12 13. 9/12 4.6 10/12 9.3 11/12 9.2 12/12 \n",
+      "Attempt 522 with (2, 0) and 3: 14. 1/8 7.6 2/8 5.0 3/8 14. 4/8 6.8 5/8 5.7 6/8 4.0 7/12 12. 8/12 11. 9/12 7.5 10/12 9.1 11/12 2.8 12/40 2.9 13/40 8.8 14/40 7.6 15/40 19. 16/40 11. 17/40 4.4 18/40 8.0 19/40 8.2 20/40 11. 21/40 18. 22/40 25. 23/40 4.8 24/40 6.1 25/40 7.5 26/40 12. 27/40 5.1 28/40 8.0 29/40 17. 30/40 80. 31/40 19. 32/40 9.6 33/40 5.1 34/40 5.6 35/40 7.5 36/40 18. 37/40 4.6 38/40 3.9 39/40 5.3 40/40 \n",
+      "Attempt 523 with (9, 1/4) and 3: 12. 1/8 15. 2/8 12. 3/8 16. 4/8 4.4 5/8 2.8 6/40 4.5 7/40 5.7 8/40 3.7 9/40 6.7 10/40 9.5 11/40 16. 12/40 8.2 13/40 8.1 14/40 4.9 15/40 3.8 16/40 4.1 17/40 13. 18/40 6.6 19/40 5.1 20/40 8.7 21/40 4.5 22/40 4.5 23/40 4.9 24/40 7.0 25/40 14. 26/40 19. 27/40 7.3 28/40 5.5 29/40 3.3 30/40 9.8 31/40 3.8 32/40 5.0 33/40 12. 34/40 12. 35/40 6.1 36/40 9.5 37/40 4.4 38/40 8.8 39/40 6.6 40/40 \n",
+      "Attempt 524 with (0, 1/13) and 5: 4.3 1/8 17. 2/8 46. 3/8 28. 4/8 10. 5/8 12. 6/8 11. 7/8 10. 8/8 \n",
+      "Attempt 525 with (5, 1/44) and 3: 16. 1/8 14. 2/8 14. 3/8 13. 4/8 9.9 5/8 10. 6/8 9.5 7/8 9.7 8/8 \n",
+      "Attempt 526 with (1/2, 2) and 10: 17. 1/8 4.5 2/8 4.5 3/8 15. 4/8 9.5 5/8 19. 6/8 6.6 7/8 5.9 8/8 \n",
+      "Attempt 527 with (2, 1) and 3: 25. 1/8 17. 2/8 51. 3/8 26. 4/8 9.7 5/8 10. 6/8 13. 7/8 5.0 8/8 \n",
+      "Attempt 528 with (1, 3) and 3: 20. 1/8 9.7 2/8 13. 3/8 24. 4/8 9.0 5/8 5.7 6/8 3.5 7/12 5.5 8/12 13. 9/12 47. 10/12 13. 11/12 6.8 12/12 \n",
+      "Attempt 529 with (1/3, 1/7) and 8: 120. 1/8 7.7 2/8 6.1 3/8 2.9 4/21 9.9 5/21 6.4 6/21 3.9 7/21 12. 8/21 5.4 9/21 2.5 10/21 4.9 11/21 3.3 12/21 44. 13/21 12. 14/21 4.1 15/21 6.5 16/21 29. 17/21 7.3 18/21 8.6 19/21 2.8 20/21 6.8 21/21 \n",
+      "Attempt 530 with (2/3, 3/2) and 5: 11. 1/8 4.2 2/8 35. 3/8 7.1 4/8 4.1 5/8 12. 6/8 4.3 7/8 28. 8/8 \n",
+      "Attempt 531 with (8/3, 1) and 4: 4.8 1/8 11. 2/8 4.0 3/8 4.3 4/8 5.4 5/8 15. 6/8 15. 7/8 4.6 8/8 \n",
+      "Attempt 532 with (33, 4) and 4: 16. 1/8 9.0 2/8 4.2 3/8 7.4 4/8 21. 5/8 4.5 6/8 8.7 7/8 6.6 8/8 \n",
+      "Attempt 533 with (1/3, 8) and 15: 5.0 1/8 2.9 2/16 14. 3/16 6.5 4/16 5.6 5/16 4.7 6/16 3.1 7/16 16. 8/16 7.2 9/16 13. 10/16 6.0 11/16 4.0 12/16 8.3 13/16 3.9 14/16 4.3 15/16 5.2 16/16 \n",
+      "Attempt 534 with (13/2, 1/3) and 5: 10. 1/8 3.3 2/12 6.0 3/12 15. 4/12 11. 5/12 11. 6/12 13. 7/12 7.9 8/12 5.9 9/12 22. 10/12 12. 11/12 8.2 12/12 \n",
+      "Attempt 535 with (1, 1/5) and 19: 5.6 1/8 3.7 2/12 4.6 3/12 7.1 4/12 7.8 5/12 8.1 6/12 11. 7/12 13. 8/12 9.6 9/12 40. 10/12 2.7 11/15 11. 12/15 24. 13/15 6.5 14/15 7.3 15/15 \n",
+      "Attempt 536 with (4, 2) and 7: 9.7 1/8 4.3 2/8 6.3 3/8 4.2 4/8 7.5 5/8 23. 6/8 10. 7/8 3.3 8/12 4.2 9/12 6.1 10/12 10. 11/12 4.3 12/12 \n",
+      "Attempt 537 with (1/2, 2/3) and 4: 18. 1/8 5.6 2/8 5.8 3/8 8.9 4/8 13. 5/8 160. 6/8 24. 7/8 12. 8/8 \n",
+      "Attempt 538 with (1/2, 1) and 6: 7.9 1/8 22. 2/8 65. 3/8 37. 4/8 8.6 5/8 5.4 6/8 29. 7/8 3.2 8/12 6.2 9/12 19. 10/12 10. 11/12 28. 12/12 \n",
+      "Attempt 539 with (1/15, 5) and 5: 8.6 1/8 2.8 2/28 4.6 3/28 12. 4/28 8.9 5/28 7.7 6/28 15. 7/28 4.4 8/28 4.5 9/28 8.9 10/28 12. 11/28 3.6 12/28 4.5 13/28 4.4 14/28 14. 15/28 4.0 16/28 8.5 17/28 20. 18/28 6.6 19/28 3.3 20/28 7.6 21/28 7.6 22/28 6.0 23/28 7.2 24/28 10. 25/28 36. 26/28 45. 27/28 6.6 28/28 \n",
+      "Attempt 540 with (2/3, 1/3) and 4: 14. 1/8 12. 2/8 15. 3/8 20. 4/8 9.7 5/8 8.1 6/8 12. 7/8 5.5 8/8 \n",
+      "Attempt 541 with (1/23, 1) and 5: 6.8 1/8 6.3 2/8 5.3 3/8 5.5 4/8 4.9 5/8 8.3 6/8 10. 7/8 3.2 8/12 6.4 9/12 6.1 10/12 9.2 11/12 8.2 12/12 \n",
+      "Attempt 542 with (4/105, 1/2) and 4: 6.3 1/8 8.9 2/8 25. 3/8 6.3 4/8 4.9 5/8 6.9 6/8 5.1 7/8 6.9 8/8 \n",
+      "Attempt 543 with (1/2, 3) and 5: 6.5 1/8 4.5 2/8 3.3 3/12 3.7 4/16 13. 5/16 5.4 6/16 5.1 7/16 13. 8/16 3.4 9/20 8.5 10/20 5.0 11/20 3.5 12/24 5.6 13/24 6.0 14/24 18. 15/24 32. 16/24 8.2 17/24 12. 18/24 11. 19/24 3.2 20/28 5.9 21/28 5.2 22/28 5.5 23/28 9.6 24/28 4.3 25/28 20. 26/28 24. 27/28 9.0 28/28 \n",
+      "Attempt 544 with (0, 1) and 15: 12. 1/8 10. 2/8 21. 3/8 7.1 4/8 7.3 5/8 5.0 6/8 5.7 7/8 3.7 8/12 14. 9/12 5.2 10/12 47. 11/12 7.6 12/12 \n",
+      "Attempt 545 with (1/4, 2) and 5: 19. 1/8 8.3 2/8 7.0 3/8 5.4 4/8 17. 5/8 14. 6/8 6.7 7/8 7.1 8/8 \n",
+      "Attempt 546 with (1/9, 2) and 4: 4.9 1/8 7.2 2/8 22. 3/8 21. 4/8 15. 5/8 93. 6/8 19. 7/8 24. 8/8 \n",
+      "Attempt 547 with (2, 2/9) and 4: 4.0 1/8 3.5 2/12 4.8 3/12 7.0 4/12 15. 5/12 13. 6/12 13. 7/12 13. 8/12 59. 9/12 6.9 10/12 10. 11/12 3.4 12/16 4.9 13/16 9.9 14/16 4.7 15/16 3.4 16/20 14. 17/20 4.5 18/20 4.6 19/20 11. 20/20 \n",
+      "Attempt 548 with (47, 1/2) and 4: 8.0 1/8 6.8 2/8 8.5 3/8 58. 4/8 29. 5/8 6.7 6/8 7.9 7/8 7.0 8/8 \n",
+      "Attempt 549 with (1/5, 1) and 12: 16. 1/8 6.5 2/8 4.9 3/8 4.4 4/8 7.4 5/8 36. 6/8 7.3 7/8 14. 8/8 \n",
+      "Attempt 550 with (0, 14) and 7: 6.9 1/8 4.9 2/8 8.9 3/8 7.3 4/8 11. 5/8 35. 6/8 41. 7/8 7.8 8/8 \n",
+      "Attempt 551 with (1/3, 1/2) and 4: 3.2 1/12 7.3 2/12 4.4 3/12 8.3 4/12 4.4 5/12 6.1 6/12 6.3 7/12 24. 8/12 12. 9/12 21. 10/12 4.8 11/12 6.1 12/12 \n",
+      "Attempt 552 with (2, 1) and 7: 4.1 1/8 12. 2/8 22. 3/8 7.7 4/8 13. 5/8 28. 6/8 33. 7/8 11. 8/8 \n",
+      "Attempt 553 with (1/15, 1/17) and 6: 5.8 1/8 5.2 2/8 3.8 3/12 5.9 4/12 4.5 5/12 4.0 6/12 5.3 7/12 8.3 8/12 6.4 9/12 4.8 10/12 4.8 11/12 3.8 12/16 5.2 13/16 3.9 14/20 4.7 15/20 3.6 16/24 3.5 17/25 3.9 18/25 3.8 19/25 5.3 20/25 8.7 21/25 26. 22/25 22. 23/25 16. 24/25 18. 25/25 \n",
+      "Attempt 554 with (1, 1/9) and 5: 12. 1/8 6.1 2/8 17. 3/8 18. 4/8 16. 5/8 11. 6/8 8.6 7/8 4.2 8/8 \n",
+      "Attempt 555 with (1/15, 5) and 4: 29. 1/8 11. 2/8 16. 3/8 22. 4/8 19. 5/8 8.7 6/8 10. 7/8 5.7 8/8 \n",
+      "Attempt 556 with (5, 1/3) and 8: 20. 1/8 5.9 2/8 7.7 3/8 6.8 4/8 3.5 5/12 10. 6/12 12. 7/12 10. 8/12 6.7 9/12 7.2 10/12 6.7 11/12 8.4 12/12 \n",
+      "Attempt 557 with (4, 2) and 6: 8.7 1/8 6.2 2/8 4.8 3/8 3.7 4/12 3.2 5/16 10. 6/16 6.3 7/16 9.2 8/16 7.6 9/16 15. 10/16 9.9 11/16 6.5 12/16 14. 13/16 11. 14/16 3.3 15/20 3.8 16/24 15. 17/24 6.3 18/24 5.8 19/24 9.0 20/24 5.0 21/24 15. 22/24 3.3 23/25 38. 24/25 4.1 25/25 \n",
+      "Attempt 558 with (0, 9) and 10: 5.2 1/8 57. 2/8 3.5 3/12 5.2 4/12 32. 5/12 4.6 6/12 6.0 7/12 7.1 8/12 3.4 9/16 4.1 10/16 20. 11/16 3.6 12/19 12. 13/19 15. 14/19 5.1 15/19 4.6 16/19 4.3 17/19 2.6 18/19 6.2 19/19 \n",
+      "Attempt 559 with (1, 1/6) and 5: 6.2 1/8 3.1 2/12 16. 3/12 13. 4/12 7.9 5/12 3.9 6/16 3.0 7/28 9.5 8/28 4.3 9/28 3.3 10/28 6.9 11/28 8.0 12/28 5.4 13/28 10. 14/28 5.1 15/28 5.1 16/28 29. 17/28 5.8 18/28 7.5 19/28 7.6 20/28 6.2 21/28 6.3 22/28 8.2 23/28 5.7 24/28 20. 25/28 20. 26/28 15. 27/28 4.7 28/28 \n",
+      "Attempt 560 with (4/5, 1/4) and 7: 9.8 1/8 5.1 2/8 3.9 3/12 6.5 4/12 18. 5/12 31. 6/12 8.2 7/12 12. 8/12 9.1 9/12 17. 10/12 4.5 11/12 4.1 12/12 \n",
+      "Attempt 561 with (1/2, 1/4) and 3: 9.2 1/8 13. 2/8 53. 3/8 51. 4/8 270. 5/8 420. 6/8 71. 7/8 33. 8/8 \n",
+      "Attempt 562 with (0, 6) and 6: 5.0 1/8 9.1 2/8 18. 3/8 12. 4/8 16. 5/8 16. 6/8 6.6 7/8 6.8 8/8 \n",
+      "Attempt 563 with (18, 1) and 4: 26. 1/8 6.6 2/8 2.7 3/32 8.3 4/32 6.4 5/32 14. 6/32 22. 7/32 7.4 8/32 4.9 9/32 9.4 10/32 7.5 11/32 6.1 12/32 3.0 13/32 6.9 14/32 13. 15/32 7.2 16/32 8.9 17/32 5.5 18/32 7.6 19/32 5.6 20/32 13. 21/32 7.4 22/32 5.9 23/32 26. 24/32 15. 25/32 3.2 26/32 13. 27/32 3.5 28/32 5.6 29/32 24. 30/32 7.6 31/32 10. 32/32 \n",
+      "Attempt 564 with (1/2, 4) and 6: 24. 1/8 6.3 2/8 9.7 3/8 18. 4/8 4.0 5/8 4.3 6/8 5.7 7/8 7.0 8/8 \n",
+      "Attempt 565 with (12/5, 1) and 3: 5.3 1/8 7.2 2/8 5.6 3/8 11. 4/8 3.6 5/12 8.2 6/12 3.7 7/16 8.7 8/16 6.1 9/16 11. 10/16 5.1 11/16 5.7 12/16 8.3 13/16 48. 14/16 27. 15/16 7.3 16/16 \n",
+      "Attempt 566 with (3, 0) and 301: 4.5 1/8 14. 2/8 4.6 3/8 6.8 4/8 130. 5/8 11. 6/8 55. 7/8 7.2 8/8 \n",
+      "Attempt 567 with (1, 11/4) and 6: 6.1 1/8 4.7 2/8 9.9 3/8 3.9 4/12 6.8 5/12 33. 6/12 16. 7/12 5.7 8/12 7.9 9/12 11. 10/12 7.7 11/12 19. 12/12 \n",
+      "Attempt 568 with (15, 1) and 4: 3.4 1/12 11. 2/12 3.9 3/16 5.6 4/16 8.1 5/16 8.1 6/16 6.6 7/16 3.8 8/20 25. 9/20 16. 10/20 43. 11/20 28. 12/20 11. 13/20 5.0 14/20 6.2 15/20 2.9 16/32 4.7 17/32 13. 18/32 3.3 19/32 3.3 20/32 3.3 21/32 12. 22/32 6.4 23/32 12. 24/32 8.6 25/32 5.5 26/32 19. 27/32 26. 28/32 5.9 29/32 7.3 30/32 7.6 31/32 18. 32/32 \n",
+      "Attempt 569 with (30, 1/3) and 5: 10. 1/8 2.9 2/28 9.2 3/28 15. 4/28 3.8 5/28 5.6 6/28 6.9 7/28 8.6 8/28 4.1 9/28 5.3 10/28 48. 11/28 8.7 12/28 5.4 13/28 3.0 14/28 9.2 15/28 7.8 16/28 10. 17/28 3.4 18/28 6.3 19/28 30. 20/28 10. 21/28 26. 22/28 4.7 23/28 8.3 24/28 7.5 25/28 7.5 26/28 6.0 27/28 4.3 28/28 \n",
+      "Attempt 570 with (1, 2/17) and 4: 33. 1/8 4.7 2/8 4.0 3/12 3.2 4/16 4.8 5/16 12. 6/16 12. 7/16 11. 8/16 5.7 9/16 6.3 10/16 4.3 11/16 11. 12/16 2.7 13/32 5.0 14/32 6.1 15/32 8.8 16/32 7.8 17/32 4.6 18/32 15. 19/32 15. 20/32 6.9 21/32 16. 22/32 13. 23/32 8.2 24/32 34. 25/32 110. 26/32 13. 27/32 11. 28/32 14. 29/32 4.2 30/32 5.1 31/32 5.8 32/32 \n",
+      "Attempt 571 with (2/3, 2/7) and 4: 9.6 1/8 11. 2/8 13. 3/8 5.5 4/8 6.5 5/8 4.9 6/8 6.9 7/8 6.6 8/8 \n",
+      "Attempt 572 with (1/2, 1) and 42: 19. 1/8 2.9 2/12 3.3 3/12 5.8 4/12 4.5 5/12 2.8 6/12 6.8 7/12 13. 8/12 7.6 9/12 21. 10/12 4.6 11/12 4.1 12/12 \n",
+      "Attempt 573 with (569/3, 5) and 7: 58. 1/8 4.4 2/8 3.0 3/23 7.9 4/23 8.9 5/23 13. 6/23 8.6 7/23 5.1 8/23 5.1 9/23 6.5 10/23 5.2 11/23 5.9 12/23 2.9 13/23 8.9 14/23 6.5 15/23 7.6 16/23 10. 17/23 8.8 18/23 9.8 19/23 5.3 20/23 9.1 21/23 5.0 22/23 3.9 23/23 \n",
+      "Attempt 574 with (1, 1/3) and 4: 4.5 1/8 8.3 2/8 5.4 3/8 4.7 4/8 13. 5/8 18. 6/8 5.4 7/8 13. 8/8 \n",
+      "Attempt 575 with (0, 31) and 4: 12. 1/8 21. 2/8 5.9 3/8 5.5 4/8 12. 5/8 12. 6/8 10. 7/8 7.7 8/8 \n",
+      "Attempt 576 with (14, 1) and 4: 14. 1/8 36. 2/8 9.0 3/8 4.1 4/8 3.6 5/12 5.7 6/12 9.3 7/12 5.0 8/12 4.0 9/12 12. 10/12 3.8 11/16 22. 12/16 7.9 13/16 4.3 14/16 3.2 15/20 7.9 16/20 7.6 17/20 5.6 18/20 6.8 19/20 6.7 20/20 \n",
+      "Attempt 577 with (1/3, 1/6) and 17: 6.6 1/8 4.5 2/8 9.9 3/8 6.0 4/8 3.2 5/12 5.8 6/12 29. 7/12 5.4 8/12 7.1 9/12 7.0 10/12 8.8 11/12 8.4 12/12 \n",
+      "Attempt 578 with (1/9, 5) and 4: 6.6 1/8 12. 2/8 2.9 3/32 4.2 4/32 9.6 5/32 9.3 6/32 2.7 7/32 14. 8/32 7.8 9/32 4.7 10/32 4.8 11/32 4.8 12/32 6.6 13/32 13. 14/32 5.2 15/32 3.4 16/32 8.0 17/32 12. 18/32 6.4 19/32 3.9 20/32 5.5 21/32 5.8 22/32 4.8 23/32 7.8 24/32 4.8 25/32 3.6 26/32 5.2 27/32 14. 28/32 7.7 29/32 8.8 30/32 18. 31/32 28. 32/32 \n",
+      "Attempt 579 with (64/3, 1) and 4: 5.4 1/8 19. 2/8 6.7 3/8 8.3 4/8 24. 5/8 7.4 6/8 3.8 7/12 6.6 8/12 3.5 9/16 8.1 10/16 5.4 11/16 27. 12/16 14. 13/16 10. 14/16 12. 15/16 7.5 16/16 \n",
+      "Attempt 580 with (0, 1/17) and 4: 12. 1/8 6.1 2/8 8.6 3/8 5.3 4/8 4.3 5/8 2.8 6/32 3.9 7/32 7.5 8/32 26. 9/32 15. 10/32 10. 11/32 6.6 12/32 26. 13/32 29. 14/32 22. 15/32 8.1 16/32 4.4 17/32 9.8 18/32 17. 19/32 6.4 20/32 5.9 21/32 8.9 22/32 4.1 23/32 4.4 24/32 4.0 25/32 2.9 26/32 25. 27/32 4.8 28/32 2.3 29/32 8.7 30/32 4.4 31/32 9.2 32/32 \n",
+      "Attempt 581 with (1/2, 1/5) and 18: 6.2 1/8 7.8 2/8 6.2 3/8 38. 4/8 27. 5/8 21. 6/8 21. 7/8 6.7 8/8 \n",
+      "Attempt 582 with (16, 2) and 8: 12. 1/8 5.5 2/8 6.1 3/8 7.9 4/8 4.3 5/8 7.9 6/8 4.4 7/8 16. 8/8 \n",
+      "Attempt 583 with (25, 3) and 4: 7.6 1/8 9.5 2/8 15. 3/8 3.6 4/12 4.7 5/12 6.2 6/12 20. 7/12 3.4 8/16 4.5 9/16 4.3 10/16 18. 11/16 29. 12/16 9.6 13/16 4.8 14/16 4.8 15/16 3.1 16/20 12. 17/20 12. 18/20 13. 19/20 13. 20/20 \n",
+      "Attempt 584 with (1/6, 1/7) and 5: 9.2 1/8 2.9 2/28 9.6 3/28 7.4 4/28 5.4 5/28 8.7 6/28 8.8 7/28 16. 8/28 120. 9/28 9.1 10/28 7.3 11/28 4.6 12/28 4.8 13/28 2.8 14/28 7.2 15/28 6.3 16/28 15. 17/28 21. 18/28 5.3 19/28 6.0 20/28 8.9 21/28 14. 22/28 15. 23/28 8.5 24/28 7.9 25/28 4.8 26/28 15. 27/28 40. 28/28 \n",
+      "Attempt 585 with (2, 5/11) and 3: 6.0 1/8 4.9 2/8 5.8 3/8 5.5 4/8 7.0 5/8 11. 6/8 44. 7/8 33. 8/8 \n",
+      "Attempt 586 with (10, 1/5) and 5: 8.9 1/8 6.7 2/8 19. 3/8 38. 4/8 9.7 5/8 7.1 6/8 5.9 7/8 6.9 8/8 \n",
+      "Attempt 587 with (1/2, 10) and 8: 9.8 1/8 4.7 2/8 15. 3/8 7.0 4/8 5.7 5/8 17. 6/8 6.9 7/8 3.9 8/12 6.3 9/12 11. 10/12 8.6 11/12 5.1 12/12 \n",
+      "Attempt 588 with (1, 10) and 3: 9.4 1/8 26. 2/8 8.0 3/8 5.1 4/8 3.7 5/12 6.2 6/12 3.1 7/16 4.4 8/16 9.5 9/16 21. 10/16 4.5 11/16 6.0 12/16 4.3 13/16 36. 14/16 8.7 15/16 6.6 16/16 \n",
+      "Attempt 589 with (4, 3) and 4: 21. 1/8 54. 2/8 53. 3/8 23. 4/8 9.4 5/8 9.0 6/8 7.3 7/8 4.6 8/8 \n",
+      "Attempt 590 with (1, 2/3) and 40: 23. 1/8 7.4 2/8 4.5 3/8 20. 4/8 5.9 5/8 10. 6/8 3.2 7/12 4.7 8/12 12. 9/12 13. 10/12 4.3 11/12 6.3 12/12 \n",
+      "Attempt 591 with (12, 1/2) and 4: 4.8 1/8 3.2 2/12 4.2 3/12 3.7 4/16 4.8 5/16 5.1 6/16 3.8 7/20 7.1 8/20 5.9 9/20 8.1 10/20 14. 11/20 40. 12/20 360. 13/20 190. 14/20 30. 15/20 12. 16/20 7.1 17/20 7.0 18/20 14. 19/20 4.1 20/20 \n",
+      "Attempt 592 with (3/7, 1/10) and 4: 12. 1/8 5.3 2/8 22. 3/8 6.4 4/8 4.7 5/8 4.3 6/8 6.6 7/8 4.0 8/12 4.9 9/12 4.8 10/12 5.1 11/12 8.4 12/12 \n",
+      "Attempt 593 with (1/322, 1/2) and 3: 57. 1/8 52. 2/8 44. 3/8 53. 4/8 82. 5/8 230. 6/8 110. 7/8 50. 8/8 \n",
+      "Attempt 594 with (1/3, 1/9) and 115: 4.6 1/8 7.4 2/8 16. 3/8 2.9 4/9 7.0 5/9 36. 6/9 20. 7/9 100. 8/9 5.1 9/9 \n",
+      "Attempt 595 with (1, 1) and 38: 10. 1/8 24. 2/8 5.4 3/8 3.4 4/12 9.6 5/12 10. 6/12 9.8 7/12 18. 8/12 6.7 9/12 5.9 10/12 12. 11/12 5.3 12/12 \n",
+      "Attempt 596 with (3, 2/3) and 4: 7.5 1/8 4.4 2/8 7.6 3/8 4.9 4/8 32. 5/8 36. 6/8 23. 7/8 8.6 8/8 \n",
+      "Attempt 597 with (1/15, 1) and 5: 7.4 1/8 5.9 2/8 12. 3/8 7.3 4/8 3.9 5/12 3.9 6/16 11. 7/16 4.9 8/16 5.2 9/16 6.2 10/16 8.0 11/16 10. 12/16 15. 13/16 28. 14/16 140. 15/16 26. 16/16 \n",
+      "Attempt 598 with (1/6, 34) and 24: 4.2 1/8 11. 2/8 7.5 3/8 7.0 4/8 15. 5/8 3.6 6/12 5.5 7/12 3.0 8/14 3.0 9/14 6.9 10/14 6.6 11/14 7.6 12/14 4.1 13/14 2.7 14/14 \n",
+      "Attempt 599 with (92, 1/160) and 7: 17. 1/8 9.8 2/8 18. 3/8 17. 4/8 6.5 5/8 17. 6/8 4.6 7/8 4.0 8/8 \n",
+      "Attempt 600 with (2, 1/6) and 3: 6.7 1/8 5.9 2/8 9.7 3/8 5.6 4/8 8.8 5/8 12. 6/8 6.5 7/8 17. 8/8 \n",
+      "Attempt 601 with (1, 2/5) and 4: 6.6 1/8 26. 2/8 8.3 3/8 4.0 4/8 4.6 5/8 6.6 6/8 4.3 7/8 4.9 8/8 \n",
+      "Attempt 602 with (1, 27) and 10: 13. 1/8 9.1 2/8 10. 3/8 7.9 4/8 8.4 5/8 8.2 6/8 6.1 7/8 56. 8/8 \n",
+      "Attempt 603 with (9, 0) and 3: 28. 1/8 300. 2/8 46. 3/8 11. 4/8 5.5 5/8 5.9 6/8 2.9 7/40 4.5 8/40 3.4 9/40 5.6 10/40 4.8 11/40 4.5 12/40 3.8 13/40 7.3 14/40 19. 15/40 27. 16/40 7.3 17/40 6.5 18/40 5.0 19/40 2.8 20/40 3.1 21/40 5.7 22/40 8.8 23/40 26. 24/40 13. 25/40 6.3 26/40 7.5 27/40 4.9 28/40 5.6 29/40 11. 30/40 4.4 31/40 15. 32/40 3.7 33/40 11. 34/40 3.0 35/40 6.4 36/40 5.9 37/40 11. 38/40 3.3 39/40 5.7 40/40 \n",
+      "Attempt 604 with (1, 1/6) and 18: 7.0 1/8 15. 2/8 12. 3/8 3.7 4/12 15. 5/12 17. 6/12 30. 7/12 6.2 8/12 5.8 9/12 6.5 10/12 5.0 11/12 4.1 12/12 \n",
+      "Attempt 605 with (1/2, 3/7) and 4: 4.9 1/8 5.8 2/8 8.2 3/8 5.0 4/8 9.7 5/8 8.2 6/8 5.0 7/8 3.5 8/12 4.2 9/12 5.4 10/12 40. 11/12 5.9 12/12 \n",
+      "Attempt 606 with (5, 1/13) and 11: 11. 1/8 8.0 2/8 5.0 3/8 5.5 4/8 6.8 5/8 6.9 6/8 19. 7/8 7.7 8/8 \n",
+      "Attempt 607 with (1, 2) and 65: 3.2 1/11 6.7 2/11 5.5 3/11 16. 4/11 8.7 5/11 44. 6/11 6.9 7/11 5.9 8/11 15. 9/11 7.7 10/11 9.4 11/11 \n",
+      "Attempt 608 with (3, 2) and 5: 7.8 1/8 16. 2/8 6.0 3/8 7.8 4/8 4.3 5/8 14. 6/8 4.1 7/8 3.6 8/12 7.0 9/12 16. 10/12 8.6 11/12 68. 12/12 \n",
+      "Attempt 609 with (0, 1/5) and 6: 8.6 1/8 4.7 2/8 12. 3/8 4.0 4/8 45. 5/8 4.7 6/8 57. 7/8 7.5 8/8 \n",
+      "Attempt 610 with (1/10, 0) and 5: 7.1 1/8 11. 2/8 9.6 3/8 6.9 4/8 5.6 5/8 6.5 6/8 19. 7/8 8.0 8/8 \n",
+      "Attempt 611 with (1/6, 1) and 6: 25. 1/8 50. 2/8 260. 3/8 41. 4/8 17. 5/8 14. 6/8 9.7 7/8 7.9 8/8 \n",
+      "Attempt 612 with (1/187, 1/2) and 12: 90. 1/8 7.0 2/8 10. 3/8 5.9 4/8 15. 5/8 54. 6/8 7.3 7/8 6.1 8/8 \n",
+      "Attempt 613 with (1/2, 1/4) and 5: 7.3 1/8 6.1 2/8 6.1 3/8 8.5 4/8 3.5 5/12 5.9 6/12 12. 7/12 3.8 8/16 3.0 9/28 13. 10/28 21. 11/28 11. 12/28 3.9 13/28 5.9 14/28 5.9 15/28 4.3 16/28 9.2 17/28 9.5 18/28 3.7 19/28 3.9 20/28 3.8 21/28 16. 22/28 4.2 23/28 2.9 24/28 4.2 25/28 6.4 26/28 23. 27/28 5.6 28/28 \n",
+      "Attempt 614 with (7, 1) and 48: 5.2 1/8 3.9 2/11 110. 3/11 8.3 4/11 5.7 5/11 5.8 6/11 8.8 7/11 5.7 8/11 110. 9/11 4.7 10/11 10. 11/11 \n",
+      "Attempt 615 with (0, 1/168) and 5: 5.2 1/8 15. 2/8 11. 3/8 4.8 4/8 3.8 5/12 4.8 6/12 3.2 7/16 21. 8/16 14. 9/16 4.7 10/16 3.6 11/20 8.6 12/20 2.8 13/28 5.8 14/28 13. 15/28 5.0 16/28 8.6 17/28 7.1 18/28 9.0 19/28 5.4 20/28 7.0 21/28 3.8 22/28 3.9 23/28 4.7 24/28 11. 25/28 8.0 26/28 5.2 27/28 44. 28/28 \n",
+      "Attempt 616 with (0, 63) and 5: 8.9 1/8 6.1 2/8 5.6 3/8 14. 4/8 4.1 5/8 3.1 6/12 5.1 7/12 9.2 8/12 4.1 9/12 14. 10/12 8.2 11/12 16. 12/12 \n",
+      "Attempt 617 with (3, 1/16) and 3: 11. 1/8 10. 2/8 8.9 3/8 9.7 4/8 45. 5/8 11. 6/8 7.1 7/8 7.3 8/8 \n",
+      "Attempt 618 with (0, 2/3) and 3: 4.6 1/8 5.6 2/8 11. 3/8 6.9 4/8 9.1 5/8 7.7 6/8 4.4 7/8 3.1 8/12 13. 9/12 9.5 10/12 8.7 11/12 40. 12/12 \n",
+      "Attempt 619 with (2, 81) and 11: 14. 1/8 10. 2/8 6.5 3/8 6.4 4/8 16. 5/8 19. 6/8 6.7 7/8 5.0 8/8 \n",
+      "Attempt 620 with (1/6, 1) and 12: 5.4 1/8 13. 2/8 5.0 3/8 13. 4/8 7.8 5/8 3.9 6/12 4.7 7/12 6.6 8/12 5.6 9/12 5.1 10/12 3.1 11/16 3.9 12/18 4.9 13/18 3.4 14/18 3.3 15/18 8.9 16/18 8.3 17/18 4.5 18/18 \n",
+      "Attempt 621 with (2/3, 2) and 5: 24. 1/8 5.2 2/8 4.4 3/8 13. 4/8 4.8 5/8 7.4 6/8 5.6 7/8 8.7 8/8 \n",
+      "Attempt 622 with (2/13, 2/21) and 4: 9.6 1/8 10. 2/8 8.6 3/8 6.7 4/8 10. 5/8 6.9 6/8 6.1 7/8 5.7 8/8 \n",
+      "Attempt 623 with (1/4, 0) and 399: 5.9 1/7 3.8 2/7 24. 3/7 6.4 4/7 13. 5/7 3.0 6/7 100. 7/7 \n",
+      "Attempt 624 with (4, 5) and 7: 5.9 1/8 5.2 2/8 3.7 3/12 6.2 4/12 6.9 5/12 35. 6/12 4.6 7/12 16. 8/12 150. 9/12 12. 10/12 11. 11/12 6.4 12/12 \n",
+      "Attempt 625 with (3/4, 1/397) and 6: 12. 1/8 11. 2/8 93. 3/8 12. 4/8 10. 5/8 12. 6/8 9.2 7/8 12. 8/8 \n",
+      "Attempt 626 with (1/2, 1/5) and 5: 5.0 1/8 4.2 2/8 5.2 3/8 4.6 4/8 5.3 5/8 4.1 6/8 17. 7/8 3.7 8/12 9.1 9/12 5.1 10/12 8.0 11/12 15. 12/12 \n",
+      "Attempt 627 with (1, 1/162) and 4: 20. 1/8 21. 2/8 19. 3/8 40. 4/8 17. 5/8 20. 6/8 20. 7/8 16. 8/8 \n",
+      "Attempt 628 with (13/2, 1/2) and 4: 6.0 1/8 8.9 2/8 45. 3/8 16. 4/8 5.3 5/8 4.3 6/8 9.0 7/8 7.1 8/8 \n",
+      "Attempt 629 with (1/3, 1/5) and 3: 13. 1/8 9.1 2/8 7.5 3/8 11. 4/8 6.1 5/8 6.7 6/8 7.6 7/8 12. 8/8 \n",
+      "Attempt 630 with (2, 2) and 6: 11. 1/8 7.1 2/8 5.6 3/8 15. 4/8 7.3 5/8 12. 6/8 9.3 7/8 4.5 8/8 \n",
+      "Attempt 631 with (22, 0) and 12: 3.6 1/12 200. 2/12 26. 3/12 13. 4/12 4.8 5/12 4.1 6/12 13. 7/12 4.1 8/12 7.7 9/12 5.5 10/12 15. 11/12 11. 12/12 \n",
+      "Attempt 632 with (3/10, 1) and 4: 7.0 1/8 14. 2/8 12. 3/8 5.5 4/8 3.4 5/12 6.7 6/12 31. 7/12 19. 8/12 4.9 9/12 7.5 10/12 4.3 11/12 3.7 12/16 5.0 13/16 19. 14/16 5.0 15/16 3.2 16/20 9.0 17/20 5.1 18/20 8.8 19/20 7.0 20/20 \n",
+      "Attempt 633 with (0, 40) and 4: 4.3 1/8 7.5 2/8 5.1 3/8 6.1 4/8 4.5 5/8 15. 6/8 7.8 7/8 6.5 8/8 \n",
+      "Attempt 634 with (1/6, 0) and 6: 3.3 1/12 3.5 2/16 29. 3/16 6.3 4/16 26. 5/16 11. 6/16 4.8 7/16 10. 8/16 7.7 9/16 8.3 10/16 6.1 11/16 5.4 12/16 8.7 13/16 7.2 14/16 27. 15/16 6.5 16/16 \n",
+      "Attempt 635 with (2/9, 45) and 24: 5.5 1/8 7.9 2/8 7.7 3/8 7.5 4/8 6.2 5/8 5.6 6/8 12. 7/8 8.3 8/8 \n",
+      "Attempt 636 with (5, 1/13) and 6: 7.7 1/8 6.3 2/8 190. 3/8 12. 4/8 8.8 5/8 12. 6/8 8.5 7/8 5.9 8/8 \n",
+      "Attempt 637 with (0, 1/7) and 6: 7.4 1/8 3.7 2/12 7.2 3/12 7.9 4/12 12. 5/12 4.0 6/12 3.2 7/16 5.5 8/16 5.7 9/16 54. 10/16 5.0 11/16 3.9 12/20 13. 13/20 9.9 14/20 7.1 15/20 3.1 16/24 11. 17/24 12. 18/24 6.6 19/24 5.7 20/24 6.1 21/24 93. 22/24 8.7 23/24 3.3 24/25 5.1 25/25 \n",
+      "Attempt 638 with (1, 2/3) and 6: 10. 1/8 7.3 2/8 7.5 3/8 5.1 4/8 33. 5/8 6.5 6/8 2.9 7/25 14. 8/25 5.3 9/25 4.0 10/25 3.7 11/25 4.9 12/25 13. 13/25 27. 14/25 23. 15/25 5.0 16/25 5.5 17/25 9.7 18/25 6.0 19/25 7.8 20/25 7.0 21/25 13. 22/25 5.5 23/25 4.8 24/25 6.8 25/25 \n",
+      "Attempt 639 with (2, 6) and 3: 5.1 1/8 6.0 2/8 4.2 3/8 3.1 4/12 13. 5/12 5.8 6/12 6.4 7/12 10. 8/12 8.2 9/12 12. 10/12 8.1 11/12 5.7 12/12 \n",
+      "Attempt 640 with (3/10, 1/8) and 4: 5.4 1/8 4.6 2/8 5.0 3/8 6.7 4/8 16. 5/8 110. 6/8 17. 7/8 23. 8/8 \n",
+      "Attempt 641 with (8, 1/2) and 11: 9.5 1/8 8.9 2/8 3.5 3/12 16. 4/12 49. 5/12 5.4 6/12 5.1 7/12 3.9 8/16 7.1 9/16 4.8 10/16 7.4 11/16 12. 12/16 9.0 13/16 8.3 14/16 3.2 15/19 12. 16/19 26. 17/19 11. 18/19 2.5 19/19 \n",
+      "Attempt 642 with (1/10, 0) and 4: 7.2 1/8 4.4 2/8 8.2 3/8 15. 4/8 20. 5/8 7.6 6/8 38. 7/8 53. 8/8 \n",
+      "Attempt 643 with (1/137, 1) and 4: 37. 1/8 14. 2/8 9.9 3/8 10. 4/8 9.7 5/8 9.2 6/8 10. 7/8 15. 8/8 \n",
+      "Attempt 644 with (10, 1/10) and 3: 5.9 1/8 4.6 2/8 8.8 3/8 12. 4/8 9.7 5/8 11. 6/8 20. 7/8 5.0 8/8 \n",
+      "Attempt 645 with (1, 0) and 18: 21. 1/8 6.5 2/8 7.1 3/8 8.6 4/8 8.5 5/8 7.6 6/8 4.9 7/8 6.0 8/8 \n",
+      "Attempt 646 with (0, 3) and 7: 8.6 1/8 24. 2/8 120. 3/8 17. 4/8 7.5 5/8 6.4 6/8 13. 7/8 6.9 8/8 \n",
+      "Attempt 647 with (0, 1) and 17: 12. 1/8 6.1 2/8 12. 3/8 23. 4/8 32. 5/8 11. 6/8 9.6 7/8 6.0 8/8 \n",
+      "Attempt 648 with (0, 1) and 11: 31. 1/8 8.1 2/8 5.9 3/8 6.4 4/8 6.3 5/8 4.5 6/8 4.3 7/8 5.9 8/8 \n",
+      "Attempt 649 with (10, 1/2) and 7: 8.3 1/8 9.7 2/8 4.2 3/8 15. 4/8 7.7 5/8 7.0 6/8 6.2 7/8 4.9 8/8 \n",
+      "Attempt 650 with (1/2, 0) and 8: 7.4 1/8 11. 2/8 3.6 3/12 45. 4/12 6.1 5/12 6.5 6/12 4.2 7/12 3.6 8/16 10. 9/16 4.0 10/20 12. 11/20 6.7 12/20 5.0 13/20 8.4 14/20 20. 15/20 11. 16/20 11. 17/20 4.7 18/20 6.3 19/20 7.1 20/20 \n",
+      "Attempt 651 with (1, 1/3) and 6: 6.5 1/8 9.7 2/8 10. 3/8 3.3 4/12 13. 5/12 5.6 6/12 14. 7/12 6.6 8/12 17. 9/12 10. 10/12 4.7 11/12 6.5 12/12 \n",
+      "Attempt 652 with (1/3, 2/3) and 6: 5.6 1/8 4.9 2/8 10. 3/8 7.5 4/8 14. 5/8 7.3 6/8 14. 7/8 5.7 8/8 \n",
+      "Attempt 653 with (3/2, 0) and 5: 17. 1/8 7.8 2/8 11. 3/8 7.3 4/8 11. 5/8 3.2 6/12 14. 7/12 5.3 8/12 11. 9/12 4.6 10/12 12. 11/12 4.6 12/12 \n",
+      "Attempt 654 with (3, 1) and 9: 4.3 1/8 6.2 2/8 20. 3/8 4.0 4/8 4.4 5/8 9.5 6/8 5.4 7/8 16. 8/8 \n",
+      "Attempt 655 with (2, 3) and 4: 9.2 1/8 19. 2/8 3.5 3/12 9.8 4/12 7.2 5/12 2.9 6/32 10. 7/32 2.9 8/32 9.2 9/32 7.0 10/32 8.7 11/32 4.9 12/32 8.0 13/32 4.2 14/32 6.5 15/32 8.9 16/32 41. 17/32 7.4 18/32 11. 19/32 26. 20/32 16. 21/32 18. 22/32 13. 23/32 17. 24/32 3.6 25/32 4.1 26/32 10. 27/32 7.0 28/32 12. 29/32 28. 30/32 51. 31/32 12. 32/32 \n",
+      "Attempt 656 with (1, 3) and 21: 25. 1/8 7.3 2/8 5.8 3/8 3.9 4/12 7.5 5/12 3.9 6/15 5.8 7/15 5.5 8/15 13. 9/15 4.8 10/15 18. 11/15 5.2 12/15 5.8 13/15 4.3 14/15 9.7 15/15 \n",
+      "Attempt 657 with (2, 8) and 4: 4.6 1/8 6.1 2/8 15. 3/8 180. 4/8 12. 5/8 8.3 6/8 4.2 7/8 8.2 8/8 \n",
+      "Attempt 658 with (1/6, 1) and 3: 5.5 1/8 5.0 2/8 4.7 3/8 4.4 4/8 3.5 5/12 4.4 6/12 5.9 7/12 6.0 8/12 6.7 9/12 10. 10/12 10. 11/12 15. 12/12 \n",
+      "Attempt 659 with (13, 7/4) and 4: 6.6 1/8 5.9 2/8 8.3 3/8 9.4 4/8 7.3 5/8 13. 6/8 11. 7/8 5.4 8/8 \n",
+      "Attempt 660 with (1/2, 80/7) and 4: 13. 1/8 3.7 2/12 6.4 3/12 3.7 4/16 5.6 5/16 7.8 6/16 12. 7/16 9.4 8/16 11. 9/16 11. 10/16 4.7 11/16 7.0 12/16 5.6 13/16 11. 14/16 9.3 15/16 18. 16/16 \n",
+      "Attempt 661 with (13/4, 1/2) and 4: 4.0 1/8 8.3 2/8 40. 3/8 16. 4/8 36. 5/8 13. 6/8 6.9 7/8 29. 8/8 \n",
+      "Attempt 662 with (2, 5) and 6: 15. 1/8 6.1 2/8 80. 3/8 7.2 4/8 7.3 5/8 3.8 6/12 5.0 7/12 5.1 8/12 4.4 9/12 5.9 10/12 11. 11/12 8.2 12/12 \n",
+      "Attempt 663 with (0, 5) and 3: 13. 1/8 7.0 2/8 13. 3/8 5.8 4/8 4.7 5/8 10. 6/8 6.2 7/8 5.3 8/8 \n",
+      "Attempt 664 with (0, 11/9) and 15: 9.4 1/8 8.8 2/8 6.8 3/8 10. 4/8 3.6 5/12 23. 6/12 8.0 7/12 17. 8/12 11. 9/12 8.1 10/12 7.1 11/12 78. 12/12 \n",
+      "Attempt 665 with (2/31, 2) and 4: 6.0 1/8 6.7 2/8 15. 3/8 10. 4/8 31. 5/8 9.1 6/8 27. 7/8 9.2 8/8 \n",
+      "Attempt 666 with (2, 1/29) and 4: 7.0 1/8 9.9 2/8 6.8 3/8 6.7 4/8 8.2 5/8 5.2 6/8 5.5 7/8 5.7 8/8 \n",
+      "Attempt 667 with (5, 46) and 3: 13. 1/8 7.2 2/8 7.0 3/8 3.2 4/12 2.7 5/40 6.1 6/40 2.8 7/40 6.9 8/40 5.6 9/40 4.9 10/40 4.7 11/40 5.9 12/40 7.2 13/40 17. 14/40 76. 15/40 9.7 16/40 5.8 17/40 3.6 18/40 5.8 19/40 5.2 20/40 20. 21/40 5.4 22/40 3.1 23/40 3.1 24/40 4.5 25/40 4.2 26/40 4.1 27/40 5.4 28/40 15. 29/40 14. 30/40 25. 31/40 42. 32/40 15. 33/40 30. 34/40 30. 35/40 11. 36/40 6.6 37/40 5.7 38/40 5.7 39/40 7.2 40/40 \n",
+      "Attempt 668 with (4, 0) and 3: 3.4 1/12 6.3 2/12 4.3 3/12 6.3 4/12 7.8 5/12 9.0 6/12 20. 7/12 19. 8/12 4.4 9/12 4.5 10/12 5.2 11/12 3.1 12/16 5.4 13/16 18. 14/16 13. 15/16 5.0 16/16 \n",
+      "Attempt 669 with (1/94, 2/3) and 4: 12. 1/8 13. 2/8 23. 3/8 36. 4/8 22. 5/8 22. 6/8 8.9 7/8 7.8 8/8 \n",
+      "Attempt 670 with (0, 1/9) and 4: 9.0 1/8 5.5 2/8 6.0 3/8 10. 4/8 11. 5/8 12. 6/8 8.4 7/8 12. 8/8 \n",
+      "Attempt 671 with (1, 16) and 6: 6.2 1/8 6.2 2/8 19. 3/8 3.7 4/12 14. 5/12 3.5 6/16 6.4 7/16 8.0 8/16 8.2 9/16 11. 10/16 7.5 11/16 5.0 12/16 50. 13/16 15. 14/16 2.7 15/25 4.8 16/25 9.5 17/25 6.3 18/25 3.6 19/25 5.0 20/25 2.9 21/25 22. 22/25 5.9 23/25 26. 24/25 12. 25/25 \n",
+      "Attempt 672 with (2/5, 1) and 4: 5.1 1/8 16. 2/8 6.2 3/8 12. 4/8 2.9 5/32 3.7 6/32 4.8 7/32 6.7 8/32 4.8 9/32 6.6 10/32 4.4 11/32 7.8 12/32 10. 13/32 27. 14/32 46. 15/32 13. 16/32 7.5 17/32 4.2 18/32 24. 19/32 11. 20/32 17. 21/32 15. 22/32 9.6 23/32 28. 24/32 12. 25/32 6.6 26/32 12. 27/32 5.0 28/32 12. 29/32 8.6 30/32 31. 31/32 59. 32/32 \n",
+      "Attempt 673 with (1/10, 3) and 5: 23. 1/8 5.8 2/8 19. 3/8 6.4 4/8 5.2 5/8 16. 6/8 6.6 7/8 6.4 8/8 \n",
+      "Attempt 674 with (14, 2/23) and 50: 11. 1/8 4.0 2/8 10. 3/8 53. 4/8 10. 5/8 5.1 6/8 6.3 7/8 7.5 8/8 \n",
+      "Attempt 675 with (1, 5) and 3: 4.7 1/8 4.2 2/8 10. 3/8 14. 4/8 4.1 5/8 8.8 6/8 5.9 7/8 21. 8/8 \n",
+      "Attempt 676 with (11/2, 1/2) and 9: 8.7 1/8 26. 2/8 3.5 3/12 6.8 4/12 8.5 5/12 5.4 6/12 14. 7/12 3.9 8/16 9.9 9/16 6.8 10/16 6.7 11/16 9.1 12/16 7.9 13/16 11. 14/16 9.3 15/16 9.1 16/16 \n",
+      "Attempt 677 with (15, 15/2) and 5: 5.9 1/8 8.4 2/8 3.4 3/12 20. 4/12 14. 5/12 6.5 6/12 16. 7/12 8.8 8/12 3.1 9/16 5.7 10/16 4.7 11/16 6.2 12/16 6.9 13/16 7.9 14/16 6.0 15/16 3.1 16/20 6.1 17/20 13. 18/20 7.9 19/20 27. 20/20 \n",
+      "Attempt 678 with (0, 11/2) and 4: 12. 1/8 4.3 2/8 8.0 3/8 17. 4/8 20. 5/8 110. 6/8 27. 7/8 7.0 8/8 \n",
+      "Attempt 679 with (1/165, 3/13) and 4: 23. 1/8 22. 2/8 21. 3/8 27. 4/8 22. 5/8 19. 6/8 19. 7/8 18. 8/8 \n",
+      "Attempt 680 with (6, 2) and 6: 6.5 1/8 6.3 2/8 12. 3/8 27. 4/8 320. 5/8 33. 6/8 10. 7/8 7.6 8/8 \n",
+      "Attempt 681 with (18, 2) and 5: 12. 1/8 7.7 2/8 3.6 3/12 9.5 4/12 5.7 5/12 5.7 6/12 5.4 7/12 2.8 8/28 14. 9/28 14. 10/28 44. 11/28 8.5 12/28 6.2 13/28 5.0 14/28 7.1 15/28 5.4 16/28 15. 17/28 7.2 18/28 8.2 19/28 4.0 20/28 12. 21/28 5.3 22/28 6.6 23/28 13. 24/28 6.8 25/28 8.5 26/28 27. 27/28 44. 28/28 \n",
+      "Attempt 682 with (1/2, 1) and 29: 11. 1/8 6.5 2/8 11. 3/8 8.8 4/8 5.4 5/8 22. 6/8 11. 7/8 20. 8/8 \n",
+      "Attempt 683 with (1, 1/9) and 3: 11. 1/8 15. 2/8 9.5 3/8 9.7 4/8 20. 5/8 7.7 6/8 6.6 7/8 26. 8/8 \n",
+      "Attempt 684 with (1, 1/7) and 4: 5.4 1/8 12. 2/8 3.7 3/12 7.4 4/12 3.2 5/16 2.6 6/32 4.7 7/32 7.7 8/32 20. 9/32 10. 10/32 18. 11/32 33. 12/32 15. 13/32 57. 14/32 81. 15/32 21. 16/32 14. 17/32 12. 18/32 7.3 19/32 13. 20/32 56. 21/32 15. 22/32 8.1 23/32 8.6 24/32 6.4 25/32 4.3 26/32 5.2 27/32 4.2 28/32 12. 29/32 13. 30/32 12. 31/32 5.7 32/32 \n",
+      "Attempt 685 with (1, 1/6) and 12: 5.0 1/8 19. 2/8 5.8 3/8 6.0 4/8 7.7 5/8 3.9 6/12 3.4 7/16 5.2 8/16 3.8 9/18 20. 10/18 9.5 11/18 7.3 12/18 4.1 13/18 6.6 14/18 3.4 15/18 6.7 16/18 7.6 17/18 9.0 18/18 \n",
+      "Attempt 686 with (1/16, 0) and 22: 10. 1/8 2.8 2/14 7.5 3/14 17. 4/14 7.7 5/14 4.2 6/14 5.8 7/14 8.8 8/14 7.5 9/14 5.1 10/14 9.5 11/14 6.8 12/14 3.7 13/14 13. 14/14 \n",
+      "Attempt 687 with (0, 2/5) and 4: 3.1 1/12 7.7 2/12 5.0 3/12 8.3 4/12 13. 5/12 10. 6/12 5.9 7/12 4.9 8/12 2.9 9/32 9.8 10/32 26. 11/32 14. 12/32 15. 13/32 4.5 14/32 7.8 15/32 22. 16/32 10. 17/32 14. 18/32 26. 19/32 100. 20/32 38. 21/32 25. 22/32 12. 23/32 7.6 24/32 5.6 25/32 9.4 26/32 17. 27/32 3.8 28/32 6.2 29/32 12. 30/32 19. 31/32 54. 32/32 \n",
+      "Attempt 688 with (1/24, 1/14) and 4: 16. 1/8 16. 2/8 13. 3/8 13. 4/8 21. 5/8 62. 6/8 12. 7/8 11. 8/8 \n",
+      "Attempt 689 with (2/3, 0) and 3: 11. 1/8 5.1 2/8 6.8 3/8 16. 4/8 18. 5/8 28. 6/8 8.7 7/8 27. 8/8 \n",
+      "Attempt 690 with (1/2, 3) and 12: 3.8 1/12 2.7 2/18 3.8 3/18 9.4 4/18 5.3 5/18 7.7 6/18 29. 7/18 20. 8/18 5.0 9/18 9.1 10/18 4.9 11/18 3.6 12/18 3.9 13/18 63. 14/18 13. 15/18 3.5 16/18 3.3 17/18 3.2 18/18 \n",
+      "Attempt 691 with (6, 4/161) and 4: 5.7 1/8 5.4 2/8 11. 3/8 5.3 4/8 6.7 5/8 4.3 6/8 4.5 7/8 4.7 8/8 \n",
+      "Attempt 692 with (1, 1) and 31: 3.7 1/12 12. 2/12 370. 3/12 9.7 4/12 3.6 5/13 6.0 6/13 6.0 7/13 11. 8/13 9.3 9/13 7.9 10/13 80. 11/13 19. 12/13 4.4 13/13 \n",
+      "Attempt 693 with (2125/2, 2) and 6: 22. 1/8 68. 2/8 7.1 3/8 9.3 4/8 5.3 5/8 3.9 6/12 11. 7/12 17. 8/12 9.4 9/12 5.0 10/12 15. 11/12 13. 12/12 \n",
+      "Attempt 694 with (2/3, 0) and 4: 4.2 1/8 7.6 2/8 5.6 3/8 9.4 4/8 25. 5/8 33. 6/8 11. 7/8 7.9 8/8 \n",
+      "Attempt 695 with (13, 1/2) and 3: 6.8 1/8 7.9 2/8 6.7 3/8 3.0 4/40 9.9 5/40 4.5 6/40 5.8 7/40 20. 8/40 17. 9/40 12. 10/40 7.4 11/40 11. 12/40 9.5 13/40 18. 14/40 16. 15/40 17. 16/40 34. 17/40 110. 18/40 1200. 19/40 190. 20/40 46. 21/40 20. 22/40 15. 23/40 13. 24/40 15. 25/40 7.2 26/40 9.7 27/40 12. 28/40 10. 29/40 26. 30/40 16. 31/40 8.8 32/40 5.4 33/40 5.2 34/40 8.9 35/40 26. 36/40 27. 37/40 12. 38/40 5.4 39/40 3.7 40/40 \n",
+      "Attempt 696 with (22, 1) and 3: 13. 1/8 12. 2/8 7.0 3/8 11. 4/8 28. 5/8 39. 6/8 9.8 7/8 6.6 8/8 \n",
+      "Attempt 697 with (3/2, 3) and 4: 3.9 1/12 3.0 2/16 4.9 3/16 6.3 4/16 4.0 5/20 15. 6/20 36. 7/20 6.2 8/20 9.1 9/20 13. 10/20 22. 11/20 110. 12/20 68. 13/20 16. 14/20 9.5 15/20 6.2 16/20 6.6 17/20 4.5 18/20 3.9 19/24 6.3 20/24 10. 21/24 28. 22/24 8.1 23/24 9.7 24/24 \n",
+      "Attempt 698 with (1, 1055/3) and 4: 5.4 1/8 5.8 2/8 3.6 3/12 8.2 4/12 9.0 5/12 11. 6/12 7.4 7/12 26. 8/12 31. 9/12 5.9 10/12 4.2 11/12 4.2 12/12 \n",
+      "Attempt 699 with (2, 23/13) and 5: 33. 1/8 11. 2/8 3.4 3/12 5.3 4/12 6.4 5/12 5.0 6/12 3.1 7/16 5.6 8/16 7.2 9/16 12. 10/16 13. 11/16 3.9 12/20 15. 13/20 8.4 14/20 48. 15/20 6.9 16/20 5.1 17/20 3.9 18/24 3.7 19/28 4.3 20/28 5.1 21/28 4.8 22/28 7.5 23/28 32. 24/28 9.1 25/28 5.1 26/28 4.0 27/28 3.3 28/28 \n",
+      "Attempt 700 with (2, 4/11) and 5: 7.8 1/8 5.3 2/8 20. 3/8 15. 4/8 5.4 5/8 4.4 6/8 23. 7/8 4.8 8/8 \n",
+      "Attempt 701 with (73, 1/2) and 4: 7.6 1/8 8.5 2/8 4.0 3/12 5.5 4/12 5.1 5/12 9.9 6/12 31. 7/12 6.6 8/12 7.4 9/12 4.5 10/12 4.9 11/12 6.3 12/12 \n",
+      "Attempt 702 with (1/8, 0) and 3: 9.8 1/8 14. 2/8 18. 3/8 26. 4/8 11. 5/8 11. 6/8 41. 7/8 71. 8/8 \n",
+      "Attempt 703 with (11, 1) and 5: 11. 1/8 7.4 2/8 7.1 3/8 17. 4/8 8.2 5/8 17. 6/8 27. 7/8 6.4 8/8 \n",
+      "Attempt 704 with (5, 1) and 3: 3.8 1/12 6.2 2/12 17. 3/12 5.4 4/12 9.3 5/12 3.6 6/16 9.5 7/16 4.0 8/20 8.7 9/20 4.8 10/20 8.9 11/20 3.3 12/24 3.0 13/40 9.5 14/40 5.0 15/40 4.7 16/40 13. 17/40 9.6 18/40 27. 19/40 16. 20/40 12. 21/40 30. 22/40 5.6 23/40 2.9 24/40 6.4 25/40 4.7 26/40 9.5 27/40 3.0 28/40 4.4 29/40 4.3 30/40 6.6 31/40 21. 32/40 16. 33/40 4.8 34/40 9.5 35/40 8.8 36/40 10. 37/40 45. 38/40 11. 39/40 10. 40/40 \n",
+      "Attempt 705 with (1/2, 1/4) and 8: 8.4 1/8 4.4 2/8 12. 3/8 3.7 4/12 4.9 5/12 22. 6/12 4.9 7/12 11. 8/12 19. 9/12 3.3 10/16 5.4 11/16 4.5 12/16 25. 13/16 4.7 14/16 21. 15/16 4.3 16/16 \n",
+      "Attempt 706 with (2, 1) and 12: 12. 1/8 5.2 2/8 19. 3/8 45. 4/8 3.4 5/12 7.5 6/12 35. 7/12 17. 8/12 4.5 9/12 5.5 10/12 5.1 11/12 26. 12/12 \n",
+      "Attempt 707 with (1/2, 1/3) and 4: 9.9 1/8 2.4 2/32 7.1 3/32 6.1 4/32 5.1 5/32 6.9 6/32 18. 7/32 5.1 8/32 8.1 9/32 5.1 10/32 6.3 11/32 8.9 12/32 10. 13/32 9.5 14/32 6.1 15/32 7.6 16/32 15. 17/32 28. 18/32 8.7 19/32 9.2 20/32 5.3 21/32 6.8 22/32 4.7 23/32 4.3 24/32 6.0 25/32 3.7 26/32 4.2 27/32 12. 28/32 12. 29/32 7.8 30/32 5.7 31/32 8.0 32/32 \n",
+      "Attempt 708 with (1, 1/40) and 4: 10. 1/8 9.7 2/8 8.7 3/8 7.8 4/8 14. 5/8 23. 6/8 8.0 7/8 8.1 8/8 \n",
+      "Attempt 709 with (0, 5/2) and 6: 5.8 1/8 4.5 2/8 17. 3/8 7.9 4/8 8.4 5/8 4.0 6/8 3.4 7/12 15. 8/12 4.8 9/12 4.7 10/12 6.2 11/12 13. 12/12 \n",
+      "Attempt 710 with (1/2, 1/6) and 5: 13. 1/8 13. 2/8 8.6 3/8 18. 4/8 11. 5/8 26. 6/8 10. 7/8 5.6 8/8 \n",
+      "Attempt 711 with (1, 5/2) and 11: 24. 1/8 14. 2/8 7.4 3/8 43. 4/8 4.1 5/8 6.2 6/8 7.2 7/8 13. 8/8 \n",
+      "Attempt 712 with (11, 2/3) and 5: 16. 1/8 22. 2/8 8.6 3/8 7.7 4/8 2.7 5/28 4.4 6/28 11. 7/28 14. 8/28 4.8 9/28 6.4 10/28 7.3 11/28 4.4 12/28 12. 13/28 5.7 14/28 14. 15/28 4.0 16/28 3.7 17/28 11. 18/28 6.1 19/28 3.4 20/28 5.1 21/28 13. 22/28 7.2 23/28 8.3 24/28 3.5 25/28 3.5 26/28 7.0 27/28 7.8 28/28 \n",
+      "Attempt 713 with (1/9, 1/3) and 7: 3.1 1/12 30. 2/12 3.2 3/16 3.6 4/20 7.9 5/20 3.4 6/23 21. 7/23 4.0 8/23 3.4 9/23 13. 10/23 9.7 11/23 6.6 12/23 5.0 13/23 4.7 14/23 4.6 15/23 16. 16/23 4.0 17/23 4.6 18/23 4.0 19/23 6.5 20/23 13. 21/23 4.5 22/23 39. 23/23 \n",
+      "Attempt 714 with (2, 1/11) and 11: 7.6 1/8 5.1 2/8 8.5 3/8 7.9 4/8 5.4 5/8 8.2 6/8 8.5 7/8 13. 8/8 \n",
+      "Attempt 715 with (31/3, 0) and 5: 4.8 1/8 21. 2/8 6.9 3/8 10. 4/8 4.5 5/8 21. 6/8 3.2 7/12 9.7 8/12 7.8 9/12 8.5 10/12 73. 11/12 7.7 12/12 \n",
+      "Attempt 716 with (6/7, 1/40) and 7: 4.2 1/8 4.8 2/8 5.1 3/8 4.4 4/8 6.5 5/8 8.1 6/8 34. 7/8 22. 8/8 \n",
+      "Attempt 717 with (1, 4) and 3: 13. 1/8 22. 2/8 19. 3/8 7.4 4/8 4.7 5/8 3.1 6/12 4.4 7/12 4.1 8/12 4.9 9/12 7.1 10/12 11. 11/12 2.7 12/40 2.5 13/40 9.3 14/40 18. 15/40 4.7 16/40 5.7 17/40 5.0 18/40 8.8 19/40 7.9 20/40 4.4 21/40 3.9 22/40 6.8 23/40 11. 24/40 3.9 25/40 8.9 26/40 5.6 27/40 4.1 28/40 15. 29/40 100. 30/40 9.6 31/40 15. 32/40 8.9 33/40 6.8 34/40 21. 35/40 7.4 36/40 7.9 37/40 7.8 38/40 4.6 39/40 12. 40/40 \n",
+      "Attempt 718 with (3/4, 1/6) and 5: 2.9 1/28 6.1 2/28 3.8 3/28 4.6 4/28 21. 5/28 17. 6/28 18. 7/28 6.4 8/28 10. 9/28 10. 10/28 4.2 11/28 3.3 12/28 4.2 13/28 8.4 14/28 5.9 15/28 11. 16/28 7.8 17/28 11. 18/28 3.5 19/28 2.9 20/28 3.1 21/28 16. 22/28 7.0 23/28 37. 24/28 15. 25/28 9.4 26/28 9.9 27/28 28. 28/28 \n",
+      "Attempt 719 with (7/37, 1/2) and 7: 6.6 1/8 6.2 2/8 5.0 3/8 4.9 4/8 6.4 5/8 5.1 6/8 9.4 7/8 19. 8/8 \n",
+      "Attempt 720 with (2/3, 1/5) and 4: 8.5 1/8 11. 2/8 2.6 3/32 5.2 4/32 7.6 5/32 9.4 6/32 14. 7/32 5.9 8/32 5.9 9/32 4.3 10/32 4.2 11/32 6.3 12/32 7.0 13/32 6.7 14/32 5.5 15/32 3.9 16/32 5.0 17/32 5.1 18/32 4.5 19/32 7.4 20/32 4.1 21/32 8.0 22/32 3.5 23/32 3.7 24/32 6.1 25/32 4.3 26/32 9.4 27/32 26. 28/32 7.1 29/32 18. 30/32 13. 31/32 6.7 32/32 \n",
+      "Attempt 721 with (0, 4/3) and 4: 12. 1/8 7.7 2/8 6.0 3/8 20. 4/8 7.3 5/8 5.6 6/8 12. 7/8 14. 8/8 \n",
+      "Attempt 722 with (43, 36) and 6: 4.8 1/8 14. 2/8 8.1 3/8 2.8 4/25 4.9 5/25 5.3 6/25 5.4 7/25 7.0 8/25 4.7 9/25 12. 10/25 10. 11/25 6.6 12/25 17. 13/25 110. 14/25 15. 15/25 10. 16/25 4.2 17/25 8.4 18/25 6.0 19/25 4.9 20/25 4.4 21/25 5.3 22/25 4.0 23/25 12. 24/25 8.0 25/25 \n",
+      "Attempt 723 with (0, 140) and 17: 7.1 1/8 9.0 2/8 10. 3/8 3.6 4/12 6.1 5/12 3.4 6/16 16. 7/16 120. 8/16 6.8 9/16 3.9 10/16 25. 11/16 4.1 12/16 5.4 13/16 7.9 14/16 9.0 15/16 3.2 16/16 \n",
+      "Attempt 724 with (0, 5/4) and 10: 10. 1/8 66. 2/8 28. 3/8 4.1 4/8 3.7 5/12 11. 6/12 7.1 7/12 9.5 8/12 5.1 9/12 21. 10/12 5.3 11/12 4.0 12/16 9.9 13/16 7.4 14/16 6.5 15/16 8.8 16/16 \n",
+      "Attempt 725 with (1/2, 31) and 4: 64. 1/8 100. 2/8 290. 3/8 300. 4/8 270. 5/8 61. 6/8 43. 7/8 38. 8/8 \n",
+      "Attempt 726 with (10, 2/5) and 6: 15. 1/8 15. 2/8 14. 3/8 4.0 4/8 7.7 5/8 5.1 6/8 5.3 7/8 6.7 8/8 \n",
+      "Attempt 727 with (1/173, 1) and 3: 31. 1/8 36. 2/8 29. 3/8 25. 4/8 24. 5/8 24. 6/8 20. 7/8 17. 8/8 \n",
+      "Attempt 728 with (1/2, 1/3) and 7: 4.4 1/8 25. 2/8 5.9 3/8 5.8 4/8 5.1 5/8 9.3 6/8 5.1 7/8 5.9 8/8 \n",
+      "Attempt 729 with (1/4, 3) and 6: 6.9 1/8 7.8 2/8 5.4 3/8 3.1 4/12 16. 5/12 3.2 6/16 3.1 7/20 5.9 8/20 3.0 9/24 8.6 10/24 9.2 11/24 7.1 12/24 32. 13/24 5.9 14/24 21. 15/24 4.8 16/24 4.6 17/24 8.1 18/24 3.1 19/25 5.1 20/25 8.9 21/25 8.0 22/25 21. 23/25 100. 24/25 36. 25/25 \n",
+      "Attempt 730 with (0, 3) and 27: 6.1 1/8 42. 2/8 10. 3/8 10. 4/8 4.7 5/8 8.1 6/8 8.3 7/8 4.0 8/12 12. 9/12 4.4 10/12 4.8 11/12 3.6 12/13 9.9 13/13 \n",
+      "Attempt 731 with (1/6, 0) and 16: 3.1 1/12 6.4 2/12 7.2 3/12 4.4 4/12 4.0 5/16 32. 6/16 4.1 7/16 4.8 8/16 8.4 9/16 6.5 10/16 11. 11/16 6.2 12/16 16. 13/16 4.9 14/16 28. 15/16 15. 16/16 \n",
+      "Attempt 732 with (0, 3/8) and 4: 5.1 1/8 25. 2/8 4.7 3/8 11. 4/8 39. 5/8 9.7 6/8 8.9 7/8 14. 8/8 \n",
+      "Attempt 733 with (1, 1) and 54: 7.8 1/8 3.8 2/11 6.8 3/11 3.6 4/11 7.1 5/11 5.1 6/11 6.3 7/11 6.4 8/11 4.7 9/11 5.4 10/11 6.5 11/11 \n",
+      "Attempt 734 with (5/2, 1/4) and 4: 5.7 1/8 6.1 2/8 5.1 3/8 4.6 4/8 21. 5/8 7.7 6/8 5.0 7/8 18. 8/8 \n",
+      "Attempt 735 with (0, 1) and 103: 5.5 1/8 27. 2/8 58. 3/8 6.7 4/8 14. 5/8 4.6 6/8 31. 7/8 6.9 8/8 \n",
+      "Attempt 736 with (1, 1) and 36: 6.2 1/8 21. 2/8 4.7 3/8 68. 4/8 6.5 5/8 6.3 6/8 8.5 7/8 28. 8/8 \n",
+      "Attempt 737 with (4, 5) and 12: 44. 1/8 5.8 2/8 18. 3/8 8.0 4/8 5.4 5/8 6.7 6/8 11. 7/8 8.6 8/8 \n",
+      "Attempt 738 with (1, 18) and 4: 4.3 1/8 4.8 2/8 6.3 3/8 7.6 4/8 6.6 5/8 8.3 6/8 9.9 7/8 6.9 8/8 \n",
+      "Attempt 739 with (1/19, 85/37) and 11: 10. 1/8 5.8 2/8 3.1 3/12 13. 4/12 6.3 5/12 6.2 6/12 6.2 7/12 34. 8/12 15. 9/12 43. 10/12 83. 11/12 16. 12/12 \n",
+      "Attempt 740 with (1, 3) and 5: 3.4 1/12 3.8 2/16 6.0 3/16 5.3 4/16 12. 5/16 9.2 6/16 8.5 7/16 5.0 8/16 11. 9/16 4.7 10/16 11. 11/16 15. 12/16 8.6 13/16 10. 14/16 24. 15/16 29. 16/16 \n",
+      "Attempt 741 with (1/2, 0) and 6: 3.8 1/12 12. 2/12 5.4 3/12 4.5 4/12 3.8 5/16 9.0 6/16 6.9 7/16 9.3 8/16 28. 9/16 5.6 10/16 3.5 11/20 19. 12/20 39. 13/20 22. 14/20 10. 15/20 8.5 16/20 18. 17/20 5.6 18/20 10. 19/20 40. 20/20 \n",
+      "Attempt 742 with (11/19, 1/2) and 5: 32. 1/8 5.8 2/8 2.9 3/28 6.7 4/28 3.9 5/28 3.8 6/28 6.5 7/28 7.8 8/28 9.7 9/28 3.5 10/28 14. 11/28 14. 12/28 7.5 13/28 7.9 14/28 7.6 15/28 6.1 16/28 11. 17/28 4.1 18/28 6.9 19/28 4.4 20/28 5.5 21/28 19. 22/28 4.5 23/28 6.9 24/28 3.9 25/28 4.2 26/28 8.2 27/28 11. 28/28 \n",
+      "Attempt 743 with (1/19, 2) and 3: 12. 1/8 38. 2/8 5.6 3/8 5.3 4/8 4.3 5/8 5.6 6/8 6.4 7/8 5.1 8/8 \n",
+      "Attempt 744 with (12, 14) and 4: 8.8 1/8 7.2 2/8 4.1 3/8 8.3 4/8 6.5 5/8 3.6 6/12 5.9 7/12 6.5 8/12 12. 9/12 3.6 10/16 10. 11/16 11. 12/16 7.5 13/16 18. 14/16 6.3 15/16 7.3 16/16 \n",
+      "Attempt 745 with (2, 4) and 82: 4.7 1/8 12. 2/8 6.1 3/8 16. 4/8 2.2 5/10 4.8 6/10 3.6 7/10 4.6 8/10 13. 9/10 6.1 10/10 \n",
+      "Attempt 746 with (0, 3) and 20: 16. 1/8 15. 2/8 5.6 3/8 7.0 4/8 6.8 5/8 13. 6/8 7.7 7/8 5.3 8/8 \n",
+      "Attempt 747 with (1/3, 4) and 4: 8.9 1/8 23. 2/8 11. 3/8 3.2 4/12 8.1 5/12 5.7 6/12 11. 7/12 3.7 8/16 4.6 9/16 35. 10/16 20. 11/16 8.6 12/16 8.6 13/16 3.7 14/20 4.1 15/20 2.8 16/32 10. 17/32 20. 18/32 5.0 19/32 11. 20/32 12. 21/32 3.4 22/32 7.8 23/32 6.4 24/32 13. 25/32 17. 26/32 4.5 27/32 2.6 28/32 7.0 29/32 23. 30/32 6.7 31/32 5.3 32/32 \n",
+      "Attempt 748 with (1/2, 0) and 23: 4.7 1/8 5.7 2/8 41. 3/8 10. 4/8 21. 5/8 8.2 6/8 8.4 7/8 13. 8/8 \n",
+      "Attempt 749 with (0, 15) and 6: 39. 1/8 7.8 2/8 4.1 3/8 4.6 4/8 12. 5/8 4.8 6/8 3.7 7/12 5.2 8/12 7.8 9/12 11. 10/12 33. 11/12 37. 12/12 \n",
+      "Attempt 750 with (1/3, 5/19) and 3: 11. 1/8 15. 2/8 6.6 3/8 8.2 4/8 5.6 5/8 5.1 6/8 4.2 7/8 4.3 8/8 \n",
+      "Attempt 751 with (28, 0) and 4: 6.3 1/8 3.8 2/12 8.0 3/12 14. 4/12 33. 5/12 22. 6/12 5.2 7/12 5.3 8/12 16. 9/12 16. 10/12 4.4 11/12 5.5 12/12 \n",
+      "Attempt 752 with (2, 1/4) and 3: 7.3 1/8 7.4 2/8 20. 3/8 25. 4/8 7.4 5/8 6.7 6/8 8.1 7/8 7.2 8/8 \n",
+      "Attempt 753 with (49, 1/3) and 10: 4.3 1/8 8.2 2/8 5.2 3/8 8.4 4/8 3.0 5/19 7.4 6/19 6.1 7/19 5.3 8/19 8.0 9/19 11. 10/19 6.1 11/19 8.7 12/19 19. 13/19 26. 14/19 6.1 15/19 5.9 16/19 6.9 17/19 5.3 18/19 7.7 19/19 \n",
+      "Attempt 754 with (1/32, 1/2) and 5: 7.9 1/8 8.1 2/8 4.0 3/8 6.4 4/8 9.4 5/8 6.2 6/8 5.1 7/8 15. 8/8 \n",
+      "Attempt 755 with (1/2, 3/38) and 4: 7.5 1/8 6.4 2/8 5.0 3/8 13. 4/8 7.9 5/8 7.6 6/8 8.8 7/8 7.5 8/8 \n",
+      "Attempt 756 with (1, 13/2) and 7: 12. 1/8 6.1 2/8 3.5 3/12 7.1 4/12 22. 5/12 9.6 6/12 4.4 7/12 7.3 8/12 7.4 9/12 31. 10/12 8.2 11/12 4.5 12/12 \n",
+      "Attempt 757 with (1, 1/3) and 5: 9.3 1/8 13. 2/8 13. 3/8 15. 4/8 4.1 5/8 4.4 6/8 3.8 7/12 3.8 8/16 4.5 9/16 4.6 10/16 7.3 11/16 8.0 12/16 5.6 13/16 5.1 14/16 8.9 15/16 4.9 16/16 \n",
+      "Attempt 758 with (1, 1/9) and 4: 5.7 1/8 4.2 2/8 4.1 3/8 4.0 4/12 12. 5/12 12. 6/12 6.7 7/12 4.0 8/16 5.2 9/16 10. 10/16 36. 11/16 14. 12/16 3.3 13/20 4.7 14/20 6.1 15/20 5.3 16/20 5.6 17/20 7.8 18/20 17. 19/20 12. 20/20 \n",
+      "Attempt 759 with (2/5, 2) and 4: 7.4 1/8 6.5 2/8 3.4 3/12 9.6 4/12 22. 5/12 9.8 6/12 11. 7/12 6.8 8/12 6.4 9/12 20. 10/12 12. 11/12 5.6 12/12 \n",
+      "Attempt 760 with (3, 0) and 9: 3.4 1/12 11. 2/12 8.0 3/12 9.2 4/12 2.9 5/20 13. 6/20 3.6 7/20 9.7 8/20 780. 9/20 9.5 10/20 4.0 11/20 20. 12/20 5.0 13/20 4.4 14/20 6.1 15/20 8.5 16/20 13. 17/20 2.8 18/20 24. 19/20 4.6 20/20 \n",
+      "Attempt 761 with (8/3, 1/2) and 4: 8.6 1/8 37. 2/8 5.5 3/8 13. 4/8 4.3 5/8 6.1 6/8 9.2 7/8 13. 8/8 \n",
+      "Attempt 762 with (6, 0) and 3: 2.8 1/40 5.3 2/40 7.4 3/40 5.0 4/40 7.5 5/40 40. 6/40 11. 7/40 8.0 8/40 9.1 9/40 6.1 10/40 6.4 11/40 3.6 12/40 12. 13/40 7.5 14/40 8.5 15/40 4.4 16/40 4.4 17/40 20. 18/40 62. 19/40 7.0 20/40 8.4 21/40 5.2 22/40 6.4 23/40 23. 24/40 13. 25/40 3.7 26/40 6.6 27/40 7.0 28/40 7.7 29/40 6.4 30/40 3.8 31/40 7.3 32/40 24. 33/40 20. 34/40 5.6 35/40 17. 36/40 12. 37/40 10. 38/40 8.0 39/40 11. 40/40 \n",
+      "Attempt 763 with (22, 6) and 3: 4.2 1/8 16. 2/8 48. 3/8 20. 4/8 5.4 5/8 7.5 6/8 3.8 7/12 7.5 8/12 22. 9/12 4.0 10/16 3.8 11/20 5.6 12/20 15. 13/20 11. 14/20 5.5 15/20 14. 16/20 39. 17/20 10. 18/20 15. 19/20 11. 20/20 \n",
+      "Attempt 764 with (1/2, 1/23) and 4: 9.7 1/8 14. 2/8 10. 3/8 8.0 4/8 10. 5/8 8.5 6/8 7.5 7/8 8.4 8/8 \n",
+      "Attempt 765 with (1, 2) and 8: 7.2 1/8 4.6 2/8 5.0 3/8 2.5 4/21 5.8 5/21 4.6 6/21 8.3 7/21 4.5 8/21 3.0 9/21 16. 10/21 5.7 11/21 8.0 12/21 7.3 13/21 5.5 14/21 11. 15/21 12. 16/21 16. 17/21 9.3 18/21 19. 19/21 6.7 20/21 3.8 21/21 \n",
+      "Attempt 766 with (1, 1/6) and 38: 26. 1/8 5.3 2/8 3.5 3/12 14. 4/12 13. 5/12 8.3 6/12 12. 7/12 3.9 8/12 14. 9/12 3.6 10/12 2.8 11/12 4.7 12/12 \n",
+      "Attempt 767 with (0, 1) and 10: 7.1 1/8 9.3 2/8 4.0 3/8 2.9 4/19 5.3 5/19 5.2 6/19 6.5 7/19 4.2 8/19 5.1 9/19 4.9 10/19 11. 11/19 5.6 12/19 11. 13/19 5.3 14/19 3.5 15/19 6.3 16/19 6.0 17/19 9.1 18/19 3.9 19/19 \n",
+      "Attempt 768 with (1/12, 1/6) and 9: 14. 1/8 5.2 2/8 5.0 3/8 8.7 4/8 4.8 5/8 5.5 6/8 13. 7/8 11. 8/8 \n",
+      "Attempt 769 with (2/7, 1) and 3: 4.7 1/8 3.9 2/12 4.2 3/12 3.1 4/16 3.5 5/20 4.4 6/20 4.7 7/20 14. 8/20 24. 9/20 16. 10/20 13. 11/20 19. 12/20 5.8 13/20 4.8 14/20 6.1 15/20 16. 16/20 13. 17/20 17. 18/20 30. 19/20 69. 20/20 \n",
+      "Attempt 770 with (1/9, 6/5) and 4: 3.4 1/12 10. 2/12 2.4 3/32 5.9 4/32 5.8 5/32 4.8 6/32 7.5 7/32 3.8 8/32 4.3 9/32 5.5 10/32 18. 11/32 4.3 12/32 6.9 13/32 8.3 14/32 13. 15/32 5.6 16/32 14. 17/32 5.6 18/32 12. 19/32 13. 20/32 21. 21/32 99. 22/32 35. 23/32 17. 24/32 12. 25/32 6.1 26/32 7.6 27/32 5.7 28/32 6.6 29/32 8.2 30/32 14. 31/32 13. 32/32 \n",
+      "Attempt 771 with (30, 1) and 4: 6.2 1/8 7.1 2/8 10. 3/8 3.1 4/12 2.8 5/32 7.5 6/32 3.2 7/32 9.1 8/32 13. 9/32 43. 10/32 19. 11/32 7.0 12/32 11. 13/32 21. 14/32 18. 15/32 39. 16/32 160. 17/32 45. 18/32 18. 19/32 16. 20/32 57. 21/32 11. 22/32 6.3 23/32 5.4 24/32 10. 25/32 6.6 26/32 8.1 27/32 5.4 28/32 4.5 29/32 7.7 30/32 4.4 31/32 6.4 32/32 \n",
+      "Attempt 772 with (0, 4/3) and 5: 12. 1/8 8.1 2/8 12. 3/8 36. 4/8 4.2 5/8 3.9 6/12 5.4 7/12 12. 8/12 11. 9/12 14. 10/12 6.8 11/12 3.5 12/16 2.9 13/28 6.1 14/28 4.6 15/28 12. 16/28 9.9 17/28 12. 18/28 22. 19/28 17. 20/28 90. 21/28 6.0 22/28 7.3 23/28 9.5 24/28 14. 25/28 33. 26/28 31. 27/28 100. 28/28 \n",
+      "Attempt 773 with (1/33, 0) and 4: 12. 1/8 3.7 2/12 5.8 3/12 6.9 4/12 7.4 5/12 14. 6/12 7.2 7/12 12. 8/12 4.4 9/12 11. 10/12 6.3 11/12 8.2 12/12 \n",
+      "Attempt 774 with (0, 2) and 9: 8.9 1/8 11. 2/8 11. 3/8 4.9 4/8 22. 5/8 13. 6/8 9.3 7/8 3.9 8/12 5.5 9/12 6.9 10/12 3.3 11/16 37. 12/16 2.6 13/20 2.9 14/20 9.9 15/20 14. 16/20 21. 17/20 14. 18/20 2.7 19/20 6.0 20/20 \n",
+      "Attempt 775 with (0, 10) and 3: 9.4 1/8 3.6 2/12 6.2 3/12 11. 4/12 5.4 5/12 2.6 6/40 8.9 7/40 7.4 8/40 6.3 9/40 7.7 10/40 19. 11/40 11. 12/40 4.6 13/40 19. 14/40 31. 15/40 11. 16/40 16. 17/40 120. 18/40 76. 19/40 13. 20/40 10. 21/40 6.8 22/40 4.0 23/40 3.6 24/40 5.6 25/40 6.2 26/40 7.5 27/40 5.7 28/40 10. 29/40 5.0 30/40 11. 31/40 5.2 32/40 9.5 33/40 9.2 34/40 4.4 35/40 12. 36/40 5.7 37/40 3.3 38/40 15. 39/40 7.3 40/40 \n",
+      "Attempt 776 with (30, 0) and 4: 7.0 1/8 13. 2/8 15. 3/8 3.1 4/12 11. 5/12 3.6 6/16 8.0 7/16 4.0 8/16 18. 9/16 3.7 10/20 5.4 11/20 35. 12/20 3.6 13/24 5.1 14/24 4.2 15/24 4.1 16/24 5.2 17/24 31. 18/24 60. 19/24 5.7 20/24 3.3 21/28 4.2 22/28 4.9 23/28 5.0 24/28 8.0 25/28 4.6 26/28 7.5 27/28 39. 28/28 \n",
+      "Attempt 777 with (4/3, 2) and 4: 4.6 1/8 5.6 2/8 7.2 3/8 11. 4/8 48. 5/8 64. 6/8 84. 7/8 26. 8/8 \n",
+      "Attempt 778 with (9, 9) and 7: 4.5 1/8 3.0 2/12 5.8 3/12 9.3 4/12 26. 5/12 20. 6/12 5.6 7/12 6.1 8/12 6.1 9/12 4.7 10/12 5.5 11/12 7.1 12/12 \n",
+      "Attempt 779 with (0, 1/3) and 5: 5.2 1/8 8.8 2/8 20. 3/8 8.5 4/8 8.6 5/8 7.1 6/8 11. 7/8 7.8 8/8 \n",
+      "Attempt 780 with (4, 1/2) and 4: 14. 1/8 3.2 2/12 3.4 3/16 6.3 4/16 6.6 5/16 3.3 6/20 12. 7/20 5.9 8/20 10. 9/20 5.3 10/20 3.8 11/24 11. 12/24 5.9 13/24 3.3 14/28 7.2 15/28 8.5 16/28 7.3 17/28 23. 18/28 6.6 19/28 27. 20/28 28. 21/28 5.2 22/28 5.3 23/28 8.1 24/28 9.8 25/28 5.5 26/28 2.9 27/32 5.9 28/32 10. 29/32 30. 30/32 4.3 31/32 6.4 32/32 \n",
+      "Attempt 781 with (5/3, 1) and 3: 3.8 1/12 4.1 2/12 3.4 3/16 11. 4/16 45. 5/16 17. 6/16 26. 7/16 9.9 8/16 30. 9/16 12. 10/16 5.1 11/16 4.6 12/16 10. 13/16 6.7 14/16 4.9 15/16 7.8 16/16 \n",
+      "Attempt 782 with (3/4, 0) and 4: 7.5 1/8 7.7 2/8 5.6 3/8 20. 4/8 5.1 5/8 6.7 6/8 8.5 7/8 3.2 8/12 13. 9/12 6.3 10/12 11. 11/12 12. 12/12 \n",
+      "Attempt 783 with (3, 11) and 10: 13. 1/8 6.5 2/8 9.1 3/8 5.4 4/8 13. 5/8 6.9 6/8 10. 7/8 27. 8/8 \n",
+      "Attempt 784 with (1, 1) and 21: 6.9 1/8 4.1 2/8 29. 3/8 8.9 4/8 9.0 5/8 16. 6/8 2.6 7/15 9.8 8/15 6.5 9/15 17. 10/15 6.2 11/15 22. 12/15 7.4 13/15 9.3 14/15 3.2 15/15 \n",
+      "Attempt 785 with (1/2, 15) and 5: 6.2 1/8 6.6 2/8 3.3 3/12 10. 4/12 5.3 5/12 3.5 6/16 3.9 7/20 7.2 8/20 23. 9/20 15. 10/20 7.8 11/20 24. 12/20 12. 13/20 15. 14/20 86. 15/20 290. 16/20 19. 17/20 14. 18/20 36. 19/20 8.6 20/20 \n",
+      "Attempt 786 with (0, 4/31) and 5: 3.8 1/12 14. 2/12 8.7 3/12 16. 4/12 6.6 5/12 4.2 6/12 5.4 7/12 4.4 8/12 18. 9/12 5.1 10/12 4.7 11/12 9.9 12/12 \n",
+      "Attempt 787 with (2/5, 1/2) and 4: 7.0 1/8 12. 2/8 7.2 3/8 11. 4/8 4.9 5/8 4.7 6/8 2.4 7/32 6.8 8/32 16. 9/32 6.5 10/32 4.9 11/32 13. 12/32 4.3 13/32 9.0 14/32 18. 15/32 4.2 16/32 16. 17/32 5.9 18/32 11. 19/32 4.1 20/32 13. 21/32 4.0 22/32 4.0 23/32 5.5 24/32 4.5 25/32 16. 26/32 11. 27/32 24. 28/32 16. 29/32 4.7 30/32 5.8 31/32 2.5 32/32 \n",
+      "Attempt 788 with (0, 2) and 10: 15. 1/8 26. 2/8 9.5 3/8 11. 4/8 6.3 5/8 8.3 6/8 19. 7/8 74. 8/8 \n",
+      "Attempt 789 with (1/7, 1/2) and 4: 7.1 1/8 9.3 2/8 9.2 3/8 19. 4/8 14. 5/8 11. 6/8 4.3 7/8 4.9 8/8 \n",
+      "Attempt 790 with (6/31, 14/3) and 4: 20. 1/8 23. 2/8 5.1 3/8 6.3 4/8 7.2 5/8 4.9 6/8 22. 7/8 5.1 8/8 \n",
+      "Attempt 791 with (4, 2/5) and 5: 14. 1/8 4.1 2/8 4.4 3/8 14. 4/8 5.5 5/8 7.0 6/8 20. 7/8 7.0 8/8 \n",
+      "Attempt 792 with (1/919, 1) and 4: 46. 1/8 46. 2/8 43. 3/8 40. 4/8 39. 5/8 34. 6/8 35. 7/8 46. 8/8 \n",
+      "Attempt 793 with (1/5, 1/4) and 7: 5.2 1/8 7.1 2/8 18. 3/8 4.1 4/8 8.9 5/8 7.3 6/8 21. 7/8 15. 8/8 \n",
+      "Attempt 794 with (6, 1/7) and 4: 4.3 1/8 5.4 2/8 6.3 3/8 5.8 4/8 8.5 5/8 4.4 6/8 10. 7/8 10. 8/8 \n",
+      "Attempt 795 with (7/12, 1) and 4: 38. 1/8 13. 2/8 8.8 3/8 3.5 4/12 4.4 5/12 5.5 6/12 6.2 7/12 4.6 8/12 8.5 9/12 6.7 10/12 11. 11/12 5.1 12/12 \n",
+      "Attempt 796 with (1/464, 13) and 4: 26. 1/8 24. 2/8 22. 3/8 22. 4/8 20. 5/8 20. 6/8 19. 7/8 18. 8/8 \n",
+      "Attempt 797 with (1/2, 6) and 4: 10. 1/8 15. 2/8 6.5 3/8 27. 4/8 18. 5/8 6.1 6/8 4.9 7/8 3.9 8/12 6.2 9/12 21. 10/12 6.4 11/12 3.5 12/16 4.7 13/16 6.7 14/16 26. 15/16 4.9 16/16 \n",
+      "Attempt 798 with (0, 5) and 6: 5.4 1/8 11. 2/8 4.3 3/8 11. 4/8 3.9 5/12 10. 6/12 13. 7/12 37. 8/12 18. 9/12 16. 10/12 9.0 11/12 4.7 12/12 \n",
+      "Attempt 799 with (2, 1/2) and 345: 27. 1/8 4.1 2/8 8.3 3/8 10. 4/8 5.4 5/8 5.5 6/8 9.6 7/8 5.9 8/8 \n",
+      "Attempt 800 with (1, 7/2) and 3: 4.9 1/8 4.2 2/8 9.1 3/8 5.5 4/8 2.8 5/40 7.2 6/40 8.0 7/40 39. 8/40 6.2 9/40 6.7 10/40 14. 11/40 33. 12/40 20. 13/40 6.1 14/40 6.4 15/40 4.1 16/40 7.3 17/40 13. 18/40 38. 19/40 11. 20/40 12. 21/40 7.7 22/40 7.6 23/40 2.8 24/40 6.3 25/40 4.2 26/40 7.5 27/40 9.8 28/40 26. 29/40 6.1 30/40 4.7 31/40 3.2 32/40 7.2 33/40 6.9 34/40 5.5 35/40 5.0 36/40 5.1 37/40 12. 38/40 5.8 39/40 7.1 40/40 \n",
+      "Attempt 801 with (0, 1/37) and 5: 19. 1/8 13. 2/8 18. 3/8 5.5 4/8 13. 5/8 5.4 6/8 3.9 7/12 20. 8/12 4.1 9/12 4.9 10/12 4.6 11/12 4.6 12/12 \n",
+      "Attempt 802 with (0, 21) and 7: 9.9 1/8 90. 2/8 8.1 3/8 3.6 4/12 14. 5/12 32. 6/12 24. 7/12 7.6 8/12 9.4 9/12 17. 10/12 160. 11/12 15. 12/12 \n",
+      "Attempt 803 with (7/4, 0) and 3: 4.9 1/8 7.2 2/8 14. 3/8 140. 4/8 17. 5/8 6.5 6/8 5.5 7/8 4.8 8/8 \n",
+      "Attempt 804 with (1/11, 1) and 3: 7.4 1/8 6.1 2/8 6.6 3/8 6.0 4/8 6.0 5/8 8.5 6/8 7.9 7/8 4.5 8/8 \n",
+      "Attempt 805 with (1/70, 1/3) and 4: 14. 1/8 13. 2/8 11. 3/8 13. 4/8 12. 5/8 34. 6/8 34. 7/8 12. 8/8 \n",
+      "Attempt 806 with (10/3, 0) and 4: 4.2 1/8 5.0 2/8 3.9 3/12 6.2 4/12 12. 5/12 4.3 6/12 5.5 7/12 4.6 8/12 2.6 9/32 4.1 10/32 9.6 11/32 11. 12/32 3.4 13/32 3.2 14/32 4.5 15/32 9.5 16/32 4.3 17/32 4.8 18/32 16. 19/32 6.3 20/32 11. 21/32 10. 22/32 15. 23/32 25. 24/32 48. 25/32 130. 26/32 860. 27/32 1200. 28/32 150. 29/32 52. 30/32 27. 31/32 16. 32/32 \n",
+      "Attempt 807 with (0, 6) and 12: 14. 1/8 6.5 2/8 6.3 3/8 7.1 4/8 6.1 5/8 18. 6/8 9.6 7/8 20. 8/8 \n",
+      "Attempt 808 with (1/3, 17) and 4: 6.1 1/8 15. 2/8 5.9 3/8 4.4 4/8 10. 5/8 11. 6/8 7.5 7/8 12. 8/8 \n",
+      "Attempt 809 with (0, 1/30) and 4: 14. 1/8 16. 2/8 5.4 3/8 4.3 4/8 6.1 5/8 9.7 6/8 4.1 7/8 6.5 8/8 \n",
+      "Attempt 810 with (2, 1/17) and 4: 6.8 1/8 4.2 2/8 5.6 3/8 4.6 4/8 19. 5/8 5.7 6/8 4.0 7/12 8.0 8/12 18. 9/12 10. 10/12 4.5 11/12 9.3 12/12 \n",
+      "Attempt 811 with (8/35, 1) and 3: 7.1 1/8 25. 2/8 40. 3/8 35. 4/8 15. 5/8 8.8 6/8 9.0 7/8 12. 8/8 \n",
+      "Attempt 812 with (4, 2) and 5: 24. 1/8 19. 2/8 23. 3/8 22. 4/8 27. 5/8 7.7 6/8 7.4 7/8 49. 8/8 \n",
+      "Attempt 813 with (0, 3) and 3: 14. 1/8 9.4 2/8 3.0 3/12 3.4 4/16 3.3 5/20 8.3 6/20 4.7 7/20 9.2 8/20 5.8 9/20 7.0 10/20 4.5 11/20 8.9 12/20 5.4 13/20 5.3 14/20 8.4 15/20 7.4 16/20 35. 17/20 100. 18/20 13. 19/20 13. 20/20 \n",
+      "Attempt 814 with (1/2, 1/8) and 5: 5.2 1/8 6.6 2/8 5.6 3/8 12. 4/8 20. 5/8 29. 6/8 21. 7/8 6.1 8/8 \n",
+      "Attempt 815 with (1/4, 0) and 4: 4.3 1/8 17. 2/8 14. 3/8 8.1 4/8 5.1 5/8 5.7 6/8 3.4 7/12 3.3 8/16 27. 9/16 3.0 10/32 4.1 11/32 3.9 12/32 11. 13/32 7.6 14/32 7.6 15/32 11. 16/32 3.0 17/32 6.9 18/32 4.0 19/32 5.8 20/32 4.9 21/32 7.2 22/32 20. 23/32 4.5 24/32 2.4 25/32 3.4 26/32 3.7 27/32 4.8 28/32 21. 29/32 52. 30/32 390. 31/32 26. 32/32 \n",
+      "Attempt 816 with (32, 1) and 5: 12. 1/8 26. 2/8 9.3 3/8 22. 4/8 27. 5/8 16. 6/8 4.9 7/8 6.3 8/8 \n",
+      "Attempt 817 with (2/49, 1) and 4: 20. 1/8 43. 2/8 12. 3/8 4.6 4/8 4.1 5/8 5.9 6/8 5.4 7/8 4.9 8/8 \n",
+      "Attempt 818 with (1/4, 2) and 4: 7.5 1/8 4.4 2/8 9.0 3/8 8.0 4/8 7.9 5/8 4.3 6/8 3.7 7/12 5.5 8/12 7.4 9/12 10. 10/12 5.9 11/12 5.1 12/12 \n",
+      "Attempt 819 with (5, 1/2) and 3: 14. 1/8 8.3 2/8 3.4 3/12 3.8 4/16 5.4 5/16 6.0 6/16 16. 7/16 3.9 8/20 5.7 9/20 4.9 10/20 7.0 11/20 6.5 12/20 13. 13/20 5.9 14/20 4.0 15/24 11. 16/24 15. 17/24 6.7 18/24 4.5 19/24 17. 20/24 5.5 21/24 10. 22/24 14. 23/24 22. 24/24 \n",
+      "Attempt 820 with (0, 1/24) and 4: 11. 1/8 3.3 2/12 7.4 3/12 5.8 4/12 7.2 5/12 5.7 6/12 3.0 7/32 5.9 8/32 5.4 9/32 3.8 10/32 14. 11/32 6.9 12/32 4.6 13/32 7.8 14/32 8.3 15/32 6.0 16/32 11. 17/32 69. 18/32 20. 19/32 6.9 20/32 4.4 21/32 3.7 22/32 8.3 23/32 31. 24/32 21. 25/32 14. 26/32 6.4 27/32 33. 28/32 13. 29/32 4.2 30/32 3.2 31/32 6.3 32/32 \n",
+      "Attempt 821 with (3, 2/5) and 8: 10. 1/8 14. 2/8 13. 3/8 5.0 4/8 2.9 5/21 6.0 6/21 6.0 7/21 9.1 8/21 3.2 9/21 5.3 10/21 6.2 11/21 4.6 12/21 7.3 13/21 3.5 14/21 45. 15/21 11. 16/21 11. 17/21 5.7 18/21 8.4 19/21 12. 20/21 7.8 21/21 \n",
+      "Attempt 822 with (31, 1) and 35: 14. 1/8 13. 2/8 5.8 3/8 4.9 4/8 18. 5/8 4.9 6/8 14. 7/8 16. 8/8 \n",
+      "Attempt 823 with (23, 1) and 9: 6.7 1/8 7.5 2/8 7.8 3/8 4.9 4/8 9.1 5/8 5.7 6/8 11. 7/8 3.5 8/12 10. 9/12 6.3 10/12 5.6 11/12 8.5 12/12 \n",
+      "Attempt 824 with (5/3, 0) and 4: 5.9 1/8 11. 2/8 31. 3/8 10. 4/8 3.6 5/12 12. 6/12 5.4 7/12 9.7 8/12 5.7 9/12 4.5 10/12 29. 11/12 5.7 12/12 \n",
+      "Attempt 825 with (2/3, 2) and 3: 11. 1/8 9.5 2/8 33. 3/8 23. 4/8 5.4 5/8 5.9 6/8 5.3 7/8 3.8 8/12 4.1 9/12 7.9 10/12 4.5 11/12 5.2 12/12 \n",
+      "Attempt 826 with (2, 1/5) and 6: 2.7 1/25 6.5 2/25 11. 3/25 9.0 4/25 7.7 5/25 5.6 6/25 3.2 7/25 6.9 8/25 6.2 9/25 14. 10/25 3.8 11/25 6.4 12/25 11. 13/25 4.7 14/25 3.5 15/25 3.7 16/25 7.5 17/25 3.5 18/25 7.3 19/25 6.1 20/25 4.4 21/25 4.1 22/25 12. 23/25 41. 24/25 15. 25/25 \n",
+      "Attempt 827 with (5/3, 0) and 25: 5.4 1/8 8.4 2/8 32. 3/8 6.6 4/8 12. 5/8 31. 6/8 3.4 7/12 6.8 8/12 5.2 9/12 3.7 10/14 4.3 11/14 26. 12/14 13. 13/14 14. 14/14 \n",
+      "Attempt 828 with (18, 0) and 4: 9.1 1/8 5.1 2/8 5.2 3/8 20. 4/8 5.2 5/8 6.2 6/8 4.8 7/8 12. 8/8 \n",
+      "Attempt 829 with (2, 1/2) and 6: 7.5 1/8 9.4 2/8 7.2 3/8 7.1 4/8 7.0 5/8 12. 6/8 5.7 7/8 8.5 8/8 \n",
+      "Attempt 830 with (9/49, 1) and 4: 9.0 1/8 7.3 2/8 6.7 3/8 25. 4/8 10. 5/8 10. 6/8 17. 7/8 4.3 8/8 \n",
+      "Attempt 831 with (0, 2/3) and 6: 6.8 1/8 13. 2/8 6.9 3/8 4.6 4/8 11. 5/8 4.1 6/8 7.6 7/8 4.3 8/8 \n",
+      "Attempt 832 with (1/2, 1/6) and 4: 5.9 1/8 4.8 2/8 4.4 3/8 5.7 4/8 5.8 5/8 4.9 6/8 8.6 7/8 7.9 8/8 \n",
+      "Attempt 833 with (1/56, 1/2) and 3: 18. 1/8 16. 2/8 16. 3/8 16. 4/8 14. 5/8 13. 6/8 12. 7/8 10. 8/8 \n",
+      "Attempt 834 with (0, 2/17) and 4: 11. 1/8 5.3 2/8 6.5 3/8 28. 4/8 12. 5/8 7.6 6/8 45. 7/8 7.3 8/8 \n",
+      "Attempt 835 with (125/2, 2) and 4: 4.9 1/8 11. 2/8 4.4 3/8 15. 4/8 7.2 5/8 11. 6/8 100. 7/8 15. 8/8 \n",
+      "Attempt 836 with (1/2, 1/5) and 11: 5.9 1/8 2.6 2/19 7.3 3/19 6.7 4/19 6.5 5/19 11. 6/19 27. 7/19 44. 8/19 13. 9/19 6.3 10/19 11. 11/19 9.8 12/19 4.7 13/19 8.8 14/19 29. 15/19 9.1 16/19 6.1 17/19 3.6 18/19 9.1 19/19 \n",
+      "Attempt 837 with (3, 11/6) and 4: 17. 1/8 23. 2/8 13. 3/8 15. 4/8 4.0 5/12 4.1 6/12 3.7 7/16 3.6 8/20 3.2 9/24 4.5 10/24 4.5 11/24 4.6 12/24 4.7 13/24 6.3 14/24 21. 15/24 5.9 16/24 4.4 17/24 11. 18/24 16. 19/24 4.9 20/24 4.7 21/24 6.2 22/24 7.5 23/24 3.8 24/28 13. 25/28 7.9 26/28 4.9 27/28 15. 28/28 \n",
+      "Attempt 838 with (1, 5) and 8: 18. 1/8 10. 2/8 4.1 3/8 8.2 4/8 32. 5/8 5.6 6/8 4.7 7/8 5.0 8/8 \n",
+      "Attempt 839 with (1/12, 1/2) and 4: 10. 1/8 15. 2/8 13. 3/8 9.2 4/8 3.3 5/12 4.8 6/12 6.9 7/12 8.7 8/12 21. 9/12 9.0 10/12 15. 11/12 4.1 12/12 \n",
+      "Attempt 840 with (14/3, 5/3) and 10: 16. 1/8 8.5 2/8 5.3 3/8 8.4 4/8 6.4 5/8 7.1 6/8 4.5 7/8 10. 8/8 \n",
+      "Attempt 841 with (1, 81) and 4: 5.2 1/8 4.4 2/8 5.8 3/8 18. 4/8 8.2 5/8 7.8 6/8 21. 7/8 3.8 8/12 11. 9/12 3.0 10/32 3.7 11/32 7.7 12/32 18. 13/32 8.5 14/32 8.1 15/32 13. 16/32 8.4 17/32 8.4 18/32 9.1 19/32 4.0 20/32 7.8 21/32 44. 22/32 8.3 23/32 7.9 24/32 7.1 25/32 7.1 26/32 10. 27/32 6.8 28/32 6.0 29/32 4.0 30/32 8.9 31/32 5.3 32/32 \n",
+      "Attempt 842 with (1, 1/11) and 6: 14. 1/8 7.5 2/8 7.2 3/8 5.3 4/8 2.9 5/25 4.3 6/25 4.5 7/25 18. 8/25 5.6 9/25 7.1 10/25 11. 11/25 13. 12/25 3.2 13/25 11. 14/25 8.2 15/25 18. 16/25 130. 17/25 9.7 18/25 36. 19/25 8.3 20/25 4.5 21/25 8.1 22/25 3.7 23/25 5.4 24/25 6.1 25/25 \n",
+      "Attempt 843 with (1/8, 4) and 4: 5.6 1/8 7.3 2/8 3.9 3/12 11. 4/12 4.0 5/16 8.9 6/16 12. 7/16 4.7 8/16 16. 9/16 8.0 10/16 8.4 11/16 12. 12/16 6.4 13/16 9.8 14/16 3.2 15/20 7.5 16/20 2.9 17/32 5.5 18/32 2.8 19/32 9.0 20/32 17. 21/32 15. 22/32 49. 23/32 12. 24/32 7.7 25/32 4.9 26/32 5.9 27/32 8.6 28/32 4.5 29/32 2.8 30/32 6.5 31/32 7.2 32/32 \n",
+      "Attempt 844 with (4, 1/9) and 5: 4.2 1/8 20. 2/8 5.2 3/8 4.0 4/12 9.0 5/12 7.1 6/12 4.2 7/12 41. 8/12 5.6 9/12 4.9 10/12 6.8 11/12 30. 12/12 \n",
+      "Attempt 845 with (1/46, 1/4) and 8: 14. 1/8 3.9 2/12 4.3 3/12 14. 4/12 6.4 5/12 13. 6/12 16. 7/12 25. 8/12 9.6 9/12 22. 10/12 24. 11/12 4.0 12/16 33. 13/16 9.4 14/16 9.6 15/16 14. 16/16 \n",
+      "Attempt 846 with (1/20, 0) and 10: 3.2 1/12 5.2 2/12 28. 3/12 5.0 4/12 7.9 5/12 17. 6/12 4.3 7/12 4.7 8/12 3.4 9/16 3.9 10/19 8.7 11/19 4.7 12/19 19. 13/19 55. 14/19 9.8 15/19 5.9 16/19 240. 17/19 4.9 18/19 7.0 19/19 \n",
+      "Attempt 847 with (0, 1/12) and 4: 8.2 1/8 9.3 2/8 19. 3/8 6.3 4/8 4.9 5/8 4.7 6/8 5.6 7/8 49. 8/8 \n",
+      "Attempt 848 with (1/11, 0) and 3: 25. 1/8 9.5 2/8 18. 3/8 14. 4/8 6.1 5/8 2.9 6/40 9.2 7/40 5.7 8/40 7.9 9/40 12. 10/40 3.8 11/40 4.5 12/40 7.0 13/40 12. 14/40 53. 15/40 9.9 16/40 6.5 17/40 3.8 18/40 7.6 19/40 11. 20/40 6.6 21/40 9.9 22/40 5.2 23/40 19. 24/40 6.6 25/40 3.2 26/40 9.7 27/40 5.0 28/40 14. 29/40 12. 30/40 12. 31/40 4.0 32/40 5.6 33/40 5.5 34/40 16. 35/40 5.6 36/40 3.2 37/40 6.2 38/40 4.4 39/40 6.6 40/40 \n",
+      "Attempt 849 with (89, 1) and 5: 4.2 1/8 4.8 2/8 2.4 3/28 12. 4/28 5.7 5/28 15. 6/28 8.9 7/28 12. 8/28 4.8 9/28 7.3 10/28 3.4 11/28 5.2 12/28 8.8 13/28 4.6 14/28 6.4 15/28 11. 16/28 4.1 17/28 9.5 18/28 6.5 19/28 6.3 20/28 11. 21/28 3.2 22/28 11. 23/28 3.0 24/28 4.6 25/28 5.2 26/28 11. 27/28 5.4 28/28 \n",
+      "Attempt 850 with (2/9, 2) and 4: 6.6 1/8 6.2 2/8 5.9 3/8 7.1 4/8 8.5 5/8 14. 6/8 37. 7/8 120. 8/8 \n",
+      "Attempt 851 with (8/3, 54) and 6: 11. 1/8 43. 2/8 4.6 3/8 3.7 4/12 4.1 5/12 12. 6/12 15. 7/12 11. 8/12 16. 9/12 7.2 10/12 4.7 11/12 6.3 12/12 \n",
+      "Attempt 852 with (34/3, 1) and 4: 5.6 1/8 3.5 2/12 5.3 3/12 24. 4/12 7.8 5/12 8.2 6/12 3.6 7/16 3.1 8/20 3.5 9/24 4.5 10/24 8.9 11/24 4.0 12/24 6.8 13/24 5.4 14/24 4.5 15/24 10. 16/24 6.0 17/24 4.1 18/24 5.5 19/24 11. 20/24 65. 21/24 18. 22/24 7.4 23/24 18. 24/24 \n",
+      "Attempt 853 with (1/2, 17/5) and 4: 24. 1/8 5.5 2/8 3.8 3/12 4.0 4/16 12. 5/16 6.7 6/16 11. 7/16 4.2 8/16 6.6 9/16 38. 10/16 7.6 11/16 4.6 12/16 5.4 13/16 6.3 14/16 6.4 15/16 3.8 16/20 4.6 17/20 8.8 18/20 3.3 19/24 8.7 20/24 5.6 21/24 17. 22/24 35. 23/24 7.2 24/24 \n",
+      "Attempt 854 with (3, 1/10) and 4: 15. 1/8 19. 2/8 5.4 3/8 3.1 4/12 17. 5/12 6.6 6/12 3.8 7/16 2.7 8/32 17. 9/32 8.8 10/32 11. 11/32 3.9 12/32 7.0 13/32 9.2 14/32 7.0 15/32 6.5 16/32 4.7 17/32 22. 18/32 7.3 19/32 7.8 20/32 5.0 21/32 9.4 22/32 4.5 23/32 9.3 24/32 4.8 25/32 18. 26/32 4.1 27/32 5.7 28/32 8.3 29/32 5.7 30/32 11. 31/32 15. 32/32 \n",
+      "Attempt 855 with (1/2, 1/9) and 4: 6.1 1/8 5.0 2/8 6.4 3/8 9.4 4/8 10. 5/8 7.2 6/8 16. 7/8 22. 8/8 \n",
+      "Attempt 856 with (5, 1/3) and 4: 15. 1/8 28. 2/8 8.8 3/8 6.1 4/8 4.2 5/8 15. 6/8 54. 7/8 11. 8/8 \n",
+      "Attempt 857 with (1/16, 1/8) and 4: 23. 1/8 29. 2/8 16. 3/8 11. 4/8 11. 5/8 9.1 6/8 8.1 7/8 8.1 8/8 \n",
+      "Attempt 858 with (57/11, 1) and 4: 4.2 1/8 13. 2/8 8.0 3/8 6.4 4/8 8.2 5/8 3.6 6/12 3.3 7/16 4.2 8/16 4.0 9/20 14. 10/20 6.3 11/20 8.5 12/20 6.0 13/20 4.8 14/20 24. 15/20 17. 16/20 7.9 17/20 8.1 18/20 23. 19/20 33. 20/20 \n",
+      "Attempt 859 with (27/2, 1) and 4: 5.0 1/8 6.8 2/8 7.3 3/8 5.9 4/8 5.2 5/8 8.2 6/8 5.0 7/8 5.0 8/8 \n",
+      "Attempt 860 with (4, 1/29) and 3: 19. 1/8 14. 2/8 10. 3/8 10. 4/8 15. 5/8 12. 6/8 8.3 7/8 8.4 8/8 \n",
+      "Attempt 861 with (4/3, 63) and 3: 8.4 1/8 4.6 2/8 3.4 3/12 6.6 4/12 12. 5/12 9.3 6/12 7.8 7/12 3.7 8/16 12. 9/16 5.8 10/16 6.9 11/16 3.5 12/20 5.8 13/20 4.7 14/20 11. 15/20 17. 16/20 3.1 17/24 8.2 18/24 4.6 19/24 3.8 20/28 4.7 21/28 4.6 22/28 3.7 23/32 7.5 24/32 5.3 25/32 5.5 26/32 5.0 27/32 3.2 28/36 18. 29/36 5.1 30/36 5.5 31/36 6.0 32/36 5.8 33/36 8.2 34/36 21. 35/36 30. 36/36 \n",
+      "Attempt 862 with (2/7, 1) and 25: 63. 1/8 11. 2/8 5.4 3/8 2.7 4/14 42. 5/14 5.0 6/14 6.6 7/14 8.5 8/14 34. 9/14 74. 10/14 12. 11/14 8.7 12/14 140. 13/14 18. 14/14 \n",
+      "Attempt 863 with (1/4, 2) and 7: 6.5 1/8 7.4 2/8 11. 3/8 12. 4/8 11. 5/8 2.7 6/23 2.6 7/23 24. 8/23 13. 9/23 9.1 10/23 3.1 11/23 22. 12/23 4.3 13/23 8.0 14/23 2.8 15/23 10. 16/23 4.6 17/23 14. 18/23 8.2 19/23 5.8 20/23 4.4 21/23 6.3 22/23 24. 23/23 \n",
+      "Attempt 864 with (1, 1/354) and 16: 5.9 1/8 27. 2/8 5.9 3/8 11. 4/8 6.1 5/8 6.3 6/8 32. 7/8 14. 8/8 \n",
+      "Attempt 865 with (12, 1/3) and 4: 9.0 1/8 3.0 2/32 18. 3/32 4.8 4/32 4.8 5/32 5.5 6/32 8.5 7/32 5.7 8/32 24. 9/32 9.1 10/32 8.2 11/32 6.4 12/32 4.2 13/32 16. 14/32 8.1 15/32 5.2 16/32 4.4 17/32 9.1 18/32 2.8 19/32 10. 20/32 7.2 21/32 4.1 22/32 13. 23/32 5.7 24/32 6.9 25/32 47. 26/32 5.0 27/32 5.4 28/32 11. 29/32 9.4 30/32 4.3 31/32 11. 32/32 \n",
+      "Attempt 866 with (1, 4) and 8: 28. 1/8 5.3 2/8 8.6 3/8 37. 4/8 5.1 5/8 11. 6/8 16. 7/8 4.3 8/8 \n",
+      "Attempt 867 with (6, 0) and 9: 7.3 1/8 8.8 2/8 6.9 3/8 21. 4/8 4.0 5/12 7.2 6/12 11. 7/12 12. 8/12 4.6 9/12 5.0 10/12 15. 11/12 3.7 12/16 6.6 13/16 7.3 14/16 8.0 15/16 15. 16/16 \n",
+      "Attempt 868 with (9/5, 1) and 4: 18. 1/8 4.4 2/8 3.9 3/12 7.7 4/12 4.7 5/12 8.7 6/12 3.8 7/16 16. 8/16 6.5 9/16 3.7 10/20 8.5 11/20 7.5 12/20 6.8 13/20 11. 14/20 3.9 15/24 2.4 16/32 9.7 17/32 3.7 18/32 5.0 19/32 3.7 20/32 5.5 21/32 5.5 22/32 8.0 23/32 7.5 24/32 9.4 25/32 18. 26/32 8.3 27/32 7.2 28/32 11. 29/32 4.8 30/32 4.8 31/32 5.3 32/32 \n",
+      "Attempt 869 with (5, 1/4) and 4: 12. 1/8 11. 2/8 15. 3/8 18. 4/8 3.3 5/12 5.7 6/12 25. 7/12 11. 8/12 11. 9/12 4.4 10/12 3.8 11/16 3.2 12/20 4.8 13/20 9.5 14/20 21. 15/20 6.7 16/20 4.4 17/20 6.1 18/20 5.9 19/20 5.6 20/20 \n",
+      "Attempt 870 with (7/27, 0) and 3: 4.6 1/8 4.0 2/8 10. 3/8 7.4 4/8 6.5 5/8 21. 6/8 6.1 7/8 4.1 8/8 \n",
+      "Attempt 871 with (6, 1/92) and 6: 9.4 1/8 2.9 2/25 6.4 3/25 9.7 4/25 9.5 5/25 5.8 6/25 4.7 7/25 4.5 8/25 6.5 9/25 8.7 10/25 13. 11/25 6.5 12/25 15. 13/25 5.2 14/25 3.4 15/25 3.5 16/25 8.4 17/25 14. 18/25 6.7 19/25 5.3 20/25 6.4 21/25 8.1 22/25 3.7 23/25 6.5 24/25 6.0 25/25 \n",
+      "Attempt 872 with (0, 12) and 4: 6.2 1/8 5.9 2/8 19. 3/8 8.1 4/8 11. 5/8 6.6 6/8 10. 7/8 4.3 8/8 \n",
+      "Attempt 873 with (6, 1) and 4: 4.7 1/8 9.2 2/8 7.0 3/8 15. 4/8 11. 5/8 8.4 6/8 8.5 7/8 9.0 8/8 \n",
+      "Attempt 874 with (8/3, 4/5) and 5: 27. 1/8 4.6 2/8 3.4 3/12 3.3 4/16 6.0 5/16 35. 6/16 16. 7/16 3.4 8/20 5.8 9/20 18. 10/20 36. 11/20 36. 12/20 7.4 13/20 4.2 14/20 11. 15/20 9.9 16/20 4.6 17/20 4.8 18/20 8.0 19/20 14. 20/20 \n",
+      "Attempt 875 with (17/7, 1/12) and 19: 14. 1/8 7.1 2/8 48. 3/8 16. 4/8 6.1 5/8 9.6 6/8 5.9 7/8 6.0 8/8 \n",
+      "Attempt 876 with (0, 3) and 6: 6.4 1/8 15. 2/8 15. 3/8 16. 4/8 11. 5/8 9.6 6/8 14. 7/8 100. 8/8 \n",
+      "Attempt 877 with (17, 2) and 149: 3.6 1/9 7.0 2/9 8.9 3/9 33. 4/9 5.0 5/9 13. 6/9 8.4 7/9 13. 8/9 6.1 9/9 \n",
+      "Attempt 878 with (3/5, 0) and 3: 13. 1/8 16. 2/8 9.7 3/8 6.4 4/8 5.7 5/8 5.5 6/8 4.7 7/8 9.1 8/8 \n",
+      "Attempt 879 with (1/5, 12) and 4: 8.8 1/8 4.3 2/8 9.3 3/8 5.4 4/8 5.3 5/8 8.3 6/8 12. 7/8 6.7 8/8 \n",
+      "Attempt 880 with (1/3, 2) and 12: 4.3 1/8 6.6 2/8 22. 3/8 7.0 4/8 4.5 5/8 3.9 6/12 9.8 7/12 9.6 8/12 8.8 9/12 4.4 10/12 11. 11/12 4.1 12/12 \n",
+      "Attempt 881 with (1/7, 3/2) and 11: 3.4 1/12 3.2 2/16 15. 3/16 17. 4/16 5.3 5/16 7.3 6/16 5.1 7/16 11. 8/16 8.6 9/16 2.4 10/19 4.3 11/19 5.0 12/19 7.6 13/19 11. 14/19 7.9 15/19 14. 16/19 25. 17/19 18. 18/19 3.9 19/19 \n",
+      "Attempt 882 with (1/4, 9) and 6: 6.0 1/8 5.3 2/8 4.9 3/8 7.5 4/8 14. 5/8 3.4 6/12 3.2 7/16 8.6 8/16 9.6 9/16 4.8 10/16 11. 11/16 6.6 12/16 12. 13/16 15. 14/16 9.7 15/16 3.5 16/20 5.8 17/20 10. 18/20 71. 19/20 11. 20/20 \n",
+      "Attempt 883 with (1/2, 1) and 12: 5.9 1/8 5.7 2/8 14. 3/8 10. 4/8 25. 5/8 7.8 6/8 6.6 7/8 8.9 8/8 \n",
+      "Attempt 884 with (1/3, 1/2) and 3: 4.7 1/8 8.0 2/8 4.8 3/8 6.0 4/8 9.6 5/8 5.3 6/8 12. 7/8 6.4 8/8 \n",
+      "Attempt 885 with (12, 1/72) and 26: 18. 1/8 36. 2/8 8.5 3/8 9.0 4/8 9.5 5/8 15. 6/8 8.9 7/8 19. 8/8 \n",
+      "Attempt 886 with (6, 13) and 5: 5.5 1/8 7.3 2/8 8.9 3/8 5.8 4/8 3.0 5/28 7.2 6/28 5.7 7/28 3.6 8/28 43. 9/28 5.6 10/28 14. 11/28 10. 12/28 5.2 13/28 6.0 14/28 5.0 15/28 5.6 16/28 5.1 17/28 10. 18/28 4.4 19/28 2.7 20/28 6.1 21/28 2.6 22/28 2.9 23/28 9.2 24/28 6.5 25/28 13. 26/28 25. 27/28 16. 28/28 \n",
+      "Attempt 887 with (3, 1/3) and 4: 5.1 1/8 3.5 2/12 4.4 3/12 5.5 4/12 6.9 5/12 32. 6/12 9.8 7/12 4.2 8/12 7.5 9/12 6.4 10/12 21. 11/12 5.4 12/12 \n",
+      "Attempt 888 with (6/35, 0) and 4: 36. 1/8 11. 2/8 5.0 3/8 6.4 4/8 6.3 5/8 7.1 6/8 11. 7/8 3.0 8/12 6.5 9/12 10. 10/12 3.0 11/32 6.8 12/32 12. 13/32 5.8 14/32 10. 15/32 7.6 16/32 7.2 17/32 10. 18/32 9.0 19/32 6.8 20/32 4.4 21/32 3.7 22/32 5.3 23/32 8.6 24/32 120. 25/32 54. 26/32 10. 27/32 7.2 28/32 5.1 29/32 2.6 30/32 7.5 31/32 5.7 32/32 \n",
+      "Attempt 889 with (1, 7) and 4: 14. 1/8 6.1 2/8 4.1 3/8 11. 4/8 4.8 5/8 4.3 6/8 5.0 7/8 5.3 8/8 \n",
+      "Attempt 890 with (0, 330) and 5: 5.4 1/8 2.6 2/28 4.2 3/28 5.3 4/28 3.1 5/28 3.1 6/28 8.5 7/28 17. 8/28 13. 9/28 48. 10/28 16. 11/28 14. 12/28 76. 13/28 11. 14/28 8.2 15/28 5.1 16/28 7.4 17/28 8.4 18/28 7.1 19/28 4.9 20/28 6.6 21/28 6.3 22/28 3.7 23/28 38. 24/28 4.3 25/28 8.6 26/28 4.4 27/28 9.3 28/28 \n",
+      "Attempt 891 with (1, 3/13) and 3: 6.3 1/8 19. 2/8 11. 3/8 10. 4/8 4.3 5/8 7.1 6/8 4.2 7/8 3.7 8/12 7.5 9/12 8.1 10/12 21. 11/12 12. 12/12 \n",
+      "Attempt 892 with (1/2, 1/7) and 4: 5.0 1/8 4.2 2/8 5.1 3/8 11. 4/8 7.7 5/8 8.2 6/8 12. 7/8 6.8 8/8 \n",
+      "Attempt 893 with (1/4, 1/5) and 4: 4.4 1/8 3.6 2/12 4.8 3/12 14. 4/12 11. 5/12 16. 6/12 3.9 7/16 4.2 8/16 5.9 9/16 9.4 10/16 8.4 11/16 6.5 12/16 15. 13/16 27. 14/16 16. 15/16 240. 16/16 \n",
+      "Attempt 894 with (2, 2) and 7: 6.3 1/8 3.9 2/12 5.5 3/12 5.4 4/12 4.8 5/12 5.0 6/12 9.9 7/12 60. 8/12 10. 9/12 6.9 10/12 14. 11/12 35. 12/12 \n",
+      "Attempt 895 with (1/2, 5) and 5: 11. 1/8 51. 2/8 30. 3/8 6.0 4/8 7.3 5/8 4.3 6/8 5.9 7/8 14. 8/8 \n",
+      "Attempt 896 with (5/7, 2) and 4: 7.1 1/8 3.8 2/12 4.7 3/12 6.6 4/12 5.9 5/12 12. 6/12 44. 7/12 12. 8/12 5.4 9/12 3.4 10/16 4.6 11/16 9.8 12/16 35. 13/16 11. 14/16 5.1 15/16 5.3 16/16 \n",
+      "Attempt 897 with (0, 37/6) and 4: 24. 1/8 8.7 2/8 5.0 3/8 7.1 4/8 42. 5/8 6.2 6/8 13. 7/8 14. 8/8 \n",
+      "Attempt 898 with (1/7, 1/21) and 5: 12. 1/8 12. 2/8 6.3 3/8 23. 4/8 5.6 5/8 5.1 6/8 17. 7/8 14. 8/8 \n",
+      "Attempt 899 with (0, 14) and 4: 3.1 1/12 15. 2/12 4.4 3/12 13. 4/12 5.1 5/12 4.3 6/12 6.8 7/12 4.8 8/12 10. 9/12 14. 10/12 10. 11/12 13. 12/12 \n",
+      "Attempt 900 with (0, 4) and 12: 21. 1/8 8.2 2/8 3.8 3/12 4.8 4/12 16. 5/12 4.0 6/12 3.0 7/18 5.7 8/18 5.3 9/18 27. 10/18 6.4 11/18 14. 12/18 9.6 13/18 3.7 14/18 4.5 15/18 8.3 16/18 7.1 17/18 9.0 18/18 \n",
+      "Attempt 901 with (1/3, 1/68) and 12: 4.3 1/8 11. 2/8 12. 3/8 6.6 4/8 14. 5/8 15. 6/8 4.9 7/8 15. 8/8 \n",
+      "Attempt 902 with (1/3, 1) and 8: 18. 1/8 4.6 2/8 4.6 3/8 11. 4/8 23. 5/8 11. 6/8 9.1 7/8 5.1 8/8 \n",
+      "Attempt 903 with (5/136, 1) and 8: 8.6 1/8 3.9 2/12 4.6 3/12 8.6 4/12 5.3 5/12 8.3 6/12 8.5 7/12 21. 8/12 5.7 9/12 11. 10/12 3.3 11/16 9.5 12/16 7.2 13/16 7.1 14/16 6.3 15/16 3.1 16/20 11. 17/20 7.3 18/20 4.3 19/20 5.4 20/20 \n",
+      "Attempt 904 with (1/2, 1/15) and 11: 4.0 1/12 26. 2/12 11. 3/12 4.3 4/12 2.8 5/19 15. 6/19 4.5 7/19 27. 8/19 7.7 9/19 6.6 10/19 20. 11/19 3.9 12/19 3.4 13/19 4.0 14/19 13. 15/19 4.5 16/19 5.8 17/19 5.5 18/19 14. 19/19 \n",
+      "Attempt 905 with (1/6, 4/21) and 9: 5.5 1/8 10. 2/8 3.8 3/12 6.6 4/12 7.2 5/12 7.3 6/12 11. 7/12 14. 8/12 52. 9/12 230. 10/12 20. 11/12 17. 12/12 \n",
+      "Attempt 906 with (0, 1/4) and 3: 5.6 1/8 2.9 2/40 4.7 3/40 5.6 4/40 4.1 5/40 8.4 6/40 21. 7/40 6.3 8/40 7.6 9/40 5.8 10/40 12. 11/40 4.0 12/40 6.9 13/40 4.8 14/40 5.4 15/40 4.6 16/40 7.2 17/40 8.6 18/40 17. 19/40 110. 20/40 43. 21/40 48. 22/40 44. 23/40 16. 24/40 8.3 25/40 7.1 26/40 4.6 27/40 8.7 28/40 9.6 29/40 6.9 30/40 9.4 31/40 7.9 32/40 4.1 33/40 7.1 34/40 4.4 35/40 3.7 36/40 4.2 37/40 7.5 38/40 2.9 39/40 4.7 40/40 \n",
+      "Attempt 907 with (0, 30) and 11: 4.3 1/8 8.5 2/8 9.5 3/8 47. 4/8 7.9 5/8 24. 6/8 6.5 7/8 12. 8/8 \n",
+      "Attempt 908 with (2, 1) and 6: 8.1 1/8 3.1 2/12 8.6 3/12 6.4 4/12 3.4 5/16 4.7 6/16 5.1 7/16 7.3 8/16 52. 9/16 6.6 10/16 4.9 11/16 69. 12/16 4.6 13/16 5.4 14/16 6.7 15/16 3.3 16/20 3.4 17/24 10. 18/24 3.2 19/25 8.0 20/25 3.9 21/25 2.9 22/25 14. 23/25 17. 24/25 33. 25/25 \n",
+      "Attempt 909 with (1/9, 1/2) and 9: 4.5 1/8 4.3 2/8 6.8 3/8 7.3 4/8 4.1 5/8 12. 6/8 6.3 7/8 7.4 8/8 \n",
+      "Attempt 910 with (1/8, 3/4) and 5: 2.7 1/28 3.9 2/28 5.5 3/28 8.0 4/28 18. 5/28 7.2 6/28 16. 7/28 16. 8/28 6.8 9/28 4.7 10/28 6.4 11/28 3.7 12/28 19. 13/28 4.3 14/28 2.7 15/28 3.1 16/28 7.9 17/28 4.4 18/28 5.9 19/28 14. 20/28 9.8 21/28 7.2 22/28 53. 23/28 9.1 24/28 5.5 25/28 45. 26/28 8.8 27/28 2.8 28/28 \n",
+      "Attempt 911 with (2/3, 1) and 4: 5.1 1/8 11. 2/8 6.2 3/8 6.2 4/8 6.8 5/8 8.1 6/8 9.1 7/8 12. 8/8 \n",
+      "Attempt 912 with (1, 1/2) and 86: 10. 1/8 2.9 2/10 12. 3/10 9.5 4/10 22. 5/10 7.2 6/10 5.1 7/10 7.3 8/10 5.2 9/10 8.3 10/10 \n",
+      "Attempt 913 with (6, 3/2) and 5: 3.1 1/12 5.4 2/12 5.8 3/12 27. 4/12 8.2 5/12 9.1 6/12 9.4 7/12 11. 8/12 6.0 9/12 12. 10/12 3.8 11/16 3.5 12/20 6.0 13/20 7.4 14/20 28. 15/20 11. 16/20 7.9 17/20 3.1 18/24 3.4 19/28 6.8 20/28 4.5 21/28 14. 22/28 3.6 23/28 5.3 24/28 3.3 25/28 5.7 26/28 28. 27/28 16. 28/28 \n",
+      "Attempt 914 with (8/3, 3/2) and 3: 2.9 1/40 5.4 2/40 25. 3/40 23. 4/40 18. 5/40 7.2 6/40 4.8 7/40 7.9 8/40 12. 9/40 6.0 10/40 15. 11/40 24. 12/40 5.4 13/40 3.3 14/40 7.1 15/40 7.7 16/40 14. 17/40 4.6 18/40 4.6 19/40 6.5 20/40 9.0 21/40 6.7 22/40 5.9 23/40 4.8 24/40 4.6 25/40 6.3 26/40 7.0 27/40 6.2 28/40 4.8 29/40 3.8 30/40 5.7 31/40 23. 32/40 15. 33/40 5.6 34/40 4.7 35/40 4.7 36/40 15. 37/40 7.0 38/40 5.5 39/40 6.5 40/40 \n",
+      "Attempt 915 with (1, 4) and 6: 7.0 1/8 16. 2/8 4.2 3/8 6.1 4/8 26. 5/8 5.6 6/8 11. 7/8 31. 8/8 \n",
+      "Attempt 916 with (5/9, 31/3) and 14: 5.3 1/8 7.8 2/8 26. 3/8 14. 4/8 24. 5/8 3.3 6/12 9.5 7/12 8.2 8/12 5.5 9/12 4.9 10/12 11. 11/12 9.4 12/12 \n",
+      "Attempt 917 with (9/2, 1/7) and 6: 6.6 1/8 18. 2/8 7.9 3/8 6.5 4/8 11. 5/8 17. 6/8 3.2 7/12 4.7 8/12 5.4 9/12 4.6 10/12 4.4 11/12 9.1 12/12 \n",
+      "Attempt 918 with (0, 6) and 4: 9.2 1/8 3.7 2/12 6.3 3/12 12. 4/12 6.1 5/12 3.5 6/16 2.8 7/32 11. 8/32 6.8 9/32 5.1 10/32 7.5 11/32 12. 12/32 9.5 13/32 5.2 14/32 5.3 15/32 6.1 16/32 8.5 17/32 4.9 18/32 8.6 19/32 6.1 20/32 4.4 21/32 6.9 22/32 21. 23/32 19. 24/32 120. 25/32 31. 26/32 12. 27/32 11. 28/32 24. 29/32 100. 30/32 1300. 31/32 74. 32/32 \n",
+      "Attempt 919 with (1/6, 9) and 3: 17. 1/8 4.7 2/8 8.4 3/8 79. 4/8 19. 5/8 14. 6/8 6.7 7/8 18. 8/8 \n",
+      "Attempt 920 with (1/2, 23) and 3: 95. 1/8 76. 2/8 250. 3/8 150. 4/8 290. 5/8 1500. 6/8 170. 7/8 71. 8/8 \n",
+      "Attempt 921 with (4, 1/2) and 5: 13. 1/8 14. 2/8 6.6 3/8 31. 4/8 10. 5/8 5.9 6/8 8.1 7/8 5.3 8/8 \n",
+      "Attempt 922 with (12, 1/3) and 3: 17. 1/8 84. 2/8 26. 3/8 7.0 4/8 12. 5/8 120. 6/8 13. 7/8 5.1 8/8 \n",
+      "Attempt 923 with (2, 1/6) and 31: 4.1 1/8 7.7 2/8 5.1 3/8 3.4 4/12 10. 5/12 15. 6/12 8.1 7/12 4.2 8/12 15. 9/12 6.9 10/12 3.4 11/13 11. 12/13 750. 13/13 \n",
+      "Attempt 924 with (1, 6) and 3: 11. 1/8 7.7 2/8 7.0 3/8 5.8 4/8 3.7 5/12 3.8 6/16 9.0 7/16 28. 8/16 11. 9/16 4.0 10/20 7.5 11/20 3.5 12/24 5.9 13/24 26. 14/24 6.2 15/24 5.4 16/24 6.9 17/24 18. 18/24 17. 19/24 29. 20/24 12. 21/24 9.4 22/24 5.6 23/24 8.0 24/24 \n",
+      "Attempt 925 with (7/9, 1/7) and 4: 3.8 1/12 4.2 2/12 8.0 3/12 4.7 4/12 9.8 5/12 20. 6/12 89. 7/12 7.9 8/12 5.4 9/12 3.2 10/16 3.9 11/20 6.7 12/20 8.4 13/20 4.8 14/20 7.2 15/20 4.2 16/20 11. 17/20 31. 18/20 8.7 19/20 7.9 20/20 \n",
+      "Attempt 926 with (1/5, 2) and 4: 4.9 1/8 14. 2/8 8.7 3/8 9.7 4/8 8.4 5/8 17. 6/8 120. 7/8 100. 8/8 \n",
+      "Attempt 927 with (3, 1/2) and 5: 10. 1/8 3.0 2/28 6.9 3/28 7.8 4/28 4.6 5/28 3.2 6/28 17. 7/28 6.0 8/28 8.0 9/28 7.0 10/28 9.8 11/28 38. 12/28 11. 13/28 7.1 14/28 9.9 15/28 3.9 16/28 5.7 17/28 3.9 18/28 14. 19/28 5.8 20/28 4.8 21/28 7.5 22/28 5.1 23/28 17. 24/28 24. 25/28 25. 26/28 6.8 27/28 9.0 28/28 \n",
+      "Attempt 928 with (1/5, 3) and 5: 5.9 1/8 13. 2/8 4.5 3/8 6.9 4/8 4.7 5/8 8.3 6/8 6.2 7/8 8.2 8/8 \n",
+      "Attempt 929 with (20, 0) and 6: 3.9 1/12 5.3 2/12 5.2 3/12 4.3 4/12 8.6 5/12 7.5 6/12 4.9 7/12 31. 8/12 7.1 9/12 8.3 10/12 18. 11/12 7.3 12/12 \n",
+      "Attempt 930 with (3/10, 3) and 5: 10. 1/8 13. 2/8 9.8 3/8 11. 4/8 78. 5/8 110. 6/8 18. 7/8 9.9 8/8 \n",
+      "Attempt 931 with (1/11, 8) and 6: 22. 1/8 8.3 2/8 5.7 3/8 13. 4/8 20. 5/8 2.5 6/25 7.6 7/25 6.5 8/25 20. 9/25 7.9 10/25 4.5 11/25 11. 12/25 12. 13/25 16. 14/25 3.9 15/25 6.3 16/25 7.1 17/25 4.9 18/25 30. 19/25 7.6 20/25 18. 21/25 54. 22/25 16. 23/25 6.1 24/25 17. 25/25 \n",
+      "Attempt 932 with (1/7, 1) and 13: 4.5 1/8 23. 2/8 3.8 3/12 5.0 4/12 4.4 5/12 11. 6/12 5.9 7/12 4.7 8/12 21. 9/12 190. 10/12 9.6 11/12 71. 12/12 \n",
+      "Attempt 933 with (18, 1/11) and 8: 7.0 1/8 14. 2/8 6.7 3/8 13. 4/8 7.1 5/8 4.5 6/8 5.7 7/8 17. 8/8 \n",
+      "Attempt 934 with (1/2, 1/4) and 4: 25. 1/8 9.5 2/8 6.8 3/8 4.7 4/8 3.6 5/12 4.3 6/12 4.2 7/12 4.4 8/12 4.2 9/12 5.0 10/12 6.3 11/12 5.9 12/12 \n",
+      "Attempt 935 with (1/39, 1) and 5: 3.8 1/12 4.7 2/12 5.7 3/12 7.5 4/12 8.2 5/12 4.6 6/12 18. 7/12 3.5 8/16 10. 9/16 13. 10/16 9.8 11/16 6.0 12/16 6.1 13/16 5.3 14/16 8.2 15/16 5.6 16/16 \n",
+      "Attempt 936 with (0, 9/2) and 3: 6.5 1/8 3.5 2/12 2.7 3/40 6.1 4/40 6.3 5/40 14. 6/40 5.8 7/40 5.9 8/40 3.0 9/40 2.5 10/40 4.4 11/40 17. 12/40 6.3 13/40 4.6 14/40 3.9 15/40 6.0 16/40 4.2 17/40 5.6 18/40 5.3 19/40 17. 20/40 28. 21/40 8.9 22/40 6.0 23/40 7.0 24/40 10. 25/40 6.4 26/40 13. 27/40 28. 28/40 21. 29/40 12. 30/40 5.4 31/40 4.6 32/40 3.1 33/40 4.0 34/40 5.4 35/40 17. 36/40 7.9 37/40 2.5 38/40 4.5 39/40 12. 40/40 \n",
+      "Attempt 937 with (1/3, 5) and 4: 57. 1/8 10. 2/8 7.3 3/8 6.9 4/8 39. 5/8 11. 6/8 84. 7/8 10. 8/8 \n",
+      "Attempt 938 with (0, 5/573) and 3: 15. 1/8 13. 2/8 11. 3/8 15. 4/8 8.9 5/8 8.9 6/8 8.2 7/8 8.7 8/8 \n",
+      "Attempt 939 with (1, 1/3) and 7: 5.3 1/8 7.2 2/8 12. 3/8 28. 4/8 8.5 5/8 6.1 6/8 4.3 7/8 15. 8/8 \n",
+      "Attempt 940 with (10, 0) and 6: 35. 1/8 4.1 2/8 7.1 3/8 16. 4/8 95. 5/8 19. 6/8 9.5 7/8 5.4 8/8 \n",
+      "Attempt 941 with (21/26, 1/2) and 4: 3.9 1/12 5.6 2/12 4.8 3/12 6.6 4/12 13. 5/12 7.4 6/12 6.6 7/12 8.3 8/12 3.8 9/16 9.3 10/16 6.7 11/16 18. 12/16 7.1 13/16 3.8 14/20 7.5 15/20 5.4 16/20 2.4 17/32 3.7 18/32 8.6 19/32 90. 20/32 14. 21/32 5.6 22/32 3.3 23/32 5.4 24/32 13. 25/32 5.6 26/32 9.0 27/32 20. 28/32 55. 29/32 29. 30/32 11. 31/32 15. 32/32 \n",
+      "Attempt 942 with (3/5, 2) and 14: 18. 1/8 4.4 2/8 18. 3/8 7.5 4/8 4.4 5/8 9.7 6/8 20. 7/8 7.8 8/8 \n",
+      "Attempt 943 with (1/7, 13/85) and 4: 9.7 1/8 10. 2/8 8.5 3/8 6.1 4/8 13. 5/8 14. 6/8 5.6 7/8 11. 8/8 \n",
+      "Attempt 944 with (5/2, 1) and 18: 3.6 1/12 6.0 2/12 6.6 3/12 3.4 4/15 8.5 5/15 33. 6/15 32. 7/15 8.3 8/15 6.1 9/15 3.6 10/15 32. 11/15 5.0 12/15 8.2 13/15 14. 14/15 4.5 15/15 \n",
+      "Attempt 945 with (10, 1) and 5: 16. 1/8 13. 2/8 15. 3/8 16. 4/8 25. 5/8 47. 6/8 160. 7/8 700. 8/8 \n",
+      "Attempt 946 with (1/4, 1/3) and 3: 8.9 1/8 7.0 2/8 7.0 3/8 31. 4/8 15. 5/8 6.1 6/8 6.1 7/8 7.6 8/8 \n",
+      "Attempt 947 with (4, 5/7) and 5: 8.4 1/8 16. 2/8 5.4 3/8 3.7 4/12 2.6 5/28 4.5 6/28 7.6 7/28 6.1 8/28 9.1 9/28 28. 10/28 35. 11/28 26. 12/28 7.2 13/28 13. 14/28 17. 15/28 29. 16/28 87. 17/28 250. 18/28 28. 19/28 18. 20/28 10. 21/28 7.9 22/28 6.9 23/28 5.4 24/28 13. 25/28 4.0 26/28 6.7 27/28 5.5 28/28 \n",
+      "Attempt 948 with (0, 17/11) and 3: 27. 1/8 31. 2/8 32. 3/8 9.2 4/8 14. 5/8 31. 6/8 12. 7/8 10. 8/8 \n",
+      "Attempt 949 with (2, 2) and 3: 14. 1/8 13. 2/8 7.5 3/8 26. 4/8 12. 5/8 17. 6/8 16. 7/8 20. 8/8 \n",
+      "Attempt 950 with (2/11, 0) and 64: 6.8 1/8 9.5 2/8 7.8 3/8 12. 4/8 7.9 5/8 7.0 6/8 36. 7/8 4.3 8/8 \n",
+      "Attempt 951 with (1, 1/18) and 10: 14. 1/8 8.7 2/8 3.0 3/19 4.2 4/19 9.8 5/19 4.3 6/19 9.2 7/19 3.1 8/19 4.3 9/19 7.0 10/19 2.5 11/19 4.9 12/19 48. 13/19 35. 14/19 15. 15/19 7.1 16/19 4.2 17/19 6.2 18/19 5.6 19/19 \n",
+      "Attempt 952 with (16, 0) and 5: 5.2 1/8 7.7 2/8 7.3 3/8 6.7 4/8 11. 5/8 5.3 6/8 8.4 7/8 4.2 8/8 \n",
+      "Attempt 953 with (1/160, 22/9) and 4: 11. 1/8 11. 2/8 10. 3/8 12. 4/8 35. 5/8 9.3 6/8 7.7 7/8 8.5 8/8 \n",
+      "Attempt 954 with (9, 1) and 6: 4.4 1/8 11. 2/8 3.7 3/12 7.4 4/12 5.4 5/12 11. 6/12 16. 7/12 6.2 8/12 6.0 9/12 11. 10/12 26. 11/12 4.4 12/12 \n",
+      "Attempt 955 with (0, 1/5) and 5: 8.1 1/8 6.9 2/8 8.5 3/8 14. 4/8 7.8 5/8 7.2 6/8 5.9 7/8 4.9 8/8 \n",
+      "Attempt 956 with (0, 1/10) and 3: 12. 1/8 13. 2/8 12. 3/8 6.2 4/8 5.5 5/8 5.5 6/8 7.9 7/8 5.0 8/8 \n",
+      "Attempt 957 with (3/2, 2/3) and 95: 9.8 1/8 5.5 2/8 41. 3/8 4.7 4/8 25. 5/8 16. 6/8 12. 7/8 5.3 8/8 \n",
+      "Attempt 958 with (2, 1/5) and 3: 39. 1/8 6.0 2/8 5.3 3/8 4.7 4/8 5.2 5/8 5.4 6/8 12. 7/8 6.8 8/8 \n",
+      "Attempt 959 with (5/26, 1) and 3: 5.1 1/8 4.9 2/8 11. 3/8 23. 4/8 23. 5/8 11. 6/8 16. 7/8 5.1 8/8 \n",
+      "Attempt 960 with (25/4, 0) and 5: 13. 1/8 9.5 2/8 8.3 3/8 15. 4/8 15. 5/8 5.9 6/8 14. 7/8 16. 8/8 \n",
+      "Attempt 961 with (1/4, 0) and 5: 4.4 1/8 8.2 2/8 4.0 3/8 3.1 4/12 6.2 5/12 8.0 6/12 4.5 7/12 3.7 8/16 3.9 9/20 4.3 10/20 21. 11/20 99. 12/20 71. 13/20 9.9 14/20 6.9 15/20 3.9 16/24 11. 17/24 4.1 18/24 5.3 19/24 5.7 20/24 9.6 21/24 6.9 22/24 3.8 23/28 11. 24/28 15. 25/28 4.7 26/28 6.0 27/28 11. 28/28 \n",
+      "Attempt 962 with (1/9, 27) and 4: 88. 1/8 180. 2/8 120. 3/8 48. 4/8 64. 5/8 37. 6/8 41. 7/8 22. 8/8 \n",
+      "Attempt 963 with (3, 16) and 3: 7.2 1/8 7.5 2/8 12. 3/8 8.3 4/8 10. 5/8 18. 6/8 13. 7/8 16. 8/8 \n",
+      "Attempt 964 with (1/3, 34) and 4: 11. 1/8 9.5 2/8 10. 3/8 8.3 4/8 11. 5/8 7.5 6/8 5.4 7/8 7.2 8/8 \n",
+      "Attempt 965 with (1, 1) and 24: 6.6 1/8 8.5 2/8 16. 3/8 14. 4/8 21. 5/8 130. 6/8 13. 7/8 5.0 8/8 \n",
+      "Attempt 966 with (5, 48) and 19: 31. 1/8 3.3 2/12 8.5 3/12 2.8 4/15 5.9 5/15 27. 6/15 8.7 7/15 16. 8/15 19. 9/15 14. 10/15 16. 11/15 19. 12/15 9.7 13/15 4.4 14/15 30. 15/15 \n",
+      "Attempt 967 with (8, 7) and 3: 7.6 1/8 15. 2/8 32. 3/8 6.4 4/8 5.3 5/8 4.5 6/8 2.8 7/40 8.8 8/40 8.8 9/40 4.4 10/40 7.8 11/40 8.6 12/40 7.5 13/40 7.2 14/40 22. 15/40 6.5 16/40 8.7 17/40 10. 18/40 14. 19/40 34. 20/40 22. 21/40 12. 22/40 5.6 23/40 17. 24/40 19. 25/40 13. 26/40 9.3 27/40 31. 28/40 34. 29/40 9.2 30/40 8.3 31/40 8.4 32/40 2.7 33/40 4.5 34/40 3.5 35/40 5.6 36/40 6.0 37/40 17. 38/40 4.4 39/40 3.7 40/40 \n",
+      "Attempt 968 with (4/3, 2/3) and 3: 23. 1/8 10. 2/8 5.6 3/8 7.4 4/8 5.7 5/8 4.2 6/8 3.7 7/12 5.4 8/12 11. 9/12 5.6 10/12 3.1 11/16 7.6 12/16 6.0 13/16 6.4 14/16 7.7 15/16 6.1 16/16 \n",
+      "Attempt 969 with (8/5, 0) and 4: 21. 1/8 15. 2/8 10. 3/8 3.9 4/12 5.4 5/12 9.0 6/12 14. 7/12 110. 8/12 18. 9/12 7.6 10/12 9.4 11/12 4.7 12/12 \n",
+      "Attempt 970 with (2, 0) and 7: 3.7 1/12 5.5 2/12 5.0 3/12 6.1 4/12 8.1 5/12 5.8 6/12 4.5 7/12 8.7 8/12 11. 9/12 7.0 10/12 54. 11/12 5.5 12/12 \n",
+      "Attempt 971 with (1/16, 2/11) and 15: 2.6 1/16 4.1 2/16 4.0 3/16 4.8 4/16 14. 5/16 7.6 6/16 6.9 7/16 19. 8/16 13. 9/16 5.3 10/16 4.2 11/16 7.8 12/16 5.2 13/16 39. 14/16 240. 15/16 22. 16/16 \n",
+      "Attempt 972 with (8, 1/4) and 8: 5.5 1/8 5.5 2/8 7.4 3/8 5.0 4/8 4.8 5/8 30. 6/8 4.2 7/8 35. 8/8 \n",
+      "Attempt 973 with (10, 0) and 5: 6.7 1/8 4.6 2/8 7.3 3/8 6.3 4/8 5.4 5/8 5.4 6/8 4.3 7/8 20. 8/8 \n",
+      "Attempt 974 with (9/26, 0) and 4: 2.4 1/32 9.1 2/32 7.2 3/32 4.6 4/32 13. 5/32 7.7 6/32 9.2 7/32 13. 8/32 37. 9/32 61. 10/32 18. 11/32 10. 12/32 39. 13/32 7.9 14/32 3.6 15/32 3.6 16/32 6.7 17/32 4.5 18/32 5.0 19/32 3.3 20/32 9.3 21/32 12. 22/32 12. 23/32 23. 24/32 5.7 25/32 6.9 26/32 4.2 27/32 10. 28/32 6.2 29/32 9.8 30/32 3.1 31/32 9.8 32/32 \n",
+      "Attempt 975 with (3, 1/9) and 9: 7.3 1/8 3.0 2/20 13. 3/20 5.6 4/20 7.5 5/20 23. 6/20 8.6 7/20 5.7 8/20 4.0 9/20 6.8 10/20 13. 11/20 27. 12/20 15. 13/20 3.8 14/20 4.9 15/20 14. 16/20 2.9 17/20 5.7 18/20 8.7 19/20 5.3 20/20 \n",
+      "Attempt 976 with (1/8, 5) and 6: 13. 1/8 5.3 2/8 6.7 3/8 2.0 4/25 3.7 5/25 22. 6/25 5.9 7/25 7.8 8/25 10. 9/25 11. 10/25 5.8 11/25 4.3 12/25 11. 13/25 63. 14/25 28. 15/25 73. 16/25 11. 17/25 7.1 18/25 11. 19/25 9.5 20/25 6.5 21/25 30. 22/25 18. 23/25 9.1 24/25 24. 25/25 \n",
+      "Attempt 977 with (1, 4/11) and 5: 11. 1/8 7.9 2/8 8.1 3/8 4.9 4/8 11. 5/8 4.7 6/8 5.8 7/8 8.6 8/8 \n",
+      "Attempt 978 with (1/6, 1/2) and 6: 5.7 1/8 16. 2/8 3.4 3/12 13. 4/12 5.1 5/12 3.9 6/16 25. 7/16 10. 8/16 3.3 9/20 7.6 10/20 2.3 11/25 11. 12/25 5.9 13/25 5.3 14/25 4.0 15/25 8.6 16/25 7.9 17/25 10. 18/25 11. 19/25 7.1 20/25 27. 21/25 6.5 22/25 12. 23/25 6.2 24/25 5.8 25/25 \n",
+      "Attempt 979 with (1, 12/97) and 4: 6.2 1/8 4.9 2/8 4.2 3/8 7.9 4/8 11. 5/8 16. 6/8 11. 7/8 4.5 8/8 \n",
+      "Attempt 980 with (1, 6) and 123: 13. 1/8 22. 2/8 9.8 3/8 10. 4/8 5.4 5/8 6.9 6/8 8.7 7/8 6.2 8/8 \n",
+      "Attempt 981 with (5, 5) and 4: 3.8 1/12 23. 2/12 9.8 3/12 2.7 4/32 4.8 5/32 3.5 6/32 5.9 7/32 4.5 8/32 19. 9/32 9.3 10/32 16. 11/32 6.0 12/32 16. 13/32 6.6 14/32 5.0 15/32 3.9 16/32 22. 17/32 3.4 18/32 3.3 19/32 3.5 20/32 36. 21/32 16. 22/32 2.2 23/32 3.0 24/32 5.5 25/32 14. 26/32 3.1 27/32 11. 28/32 3.8 29/32 4.8 30/32 23. 31/32 11. 32/32 \n",
+      "Attempt 982 with (8, 1/8) and 5: 17. 1/8 25. 2/8 8.0 3/8 8.0 4/8 9.7 5/8 9.7 6/8 13. 7/8 58. 8/8 \n",
+      "Attempt 983 with (1, 1/6) and 3: 33. 1/8 10. 2/8 7.7 3/8 6.9 4/8 5.2 5/8 9.4 6/8 49. 7/8 25. 8/8 \n",
+      "Attempt 984 with (1/7, 1) and 9: 68. 1/8 11. 2/8 6.1 3/8 4.1 4/8 6.8 5/8 12. 6/8 4.4 7/8 6.4 8/8 \n",
+      "Attempt 985 with (15/7, 1) and 4: 4.0 1/8 6.5 2/8 8.5 3/8 31. 4/8 9.2 5/8 5.1 6/8 17. 7/8 6.6 8/8 \n",
+      "Attempt 986 with (10, 1) and 6: 21. 1/8 4.0 2/8 10. 3/8 12. 4/8 8.4 5/8 22. 6/8 6.4 7/8 8.7 8/8 \n",
+      "Attempt 987 with (1/13, 2) and 4: 4.3 1/8 5.1 2/8 23. 3/8 6.7 4/8 7.0 5/8 5.9 6/8 30. 7/8 10. 8/8 \n",
+      "Attempt 988 with (1/4, 10) and 4: 6.9 1/8 5.4 2/8 19. 3/8 4.9 4/8 4.9 5/8 5.7 6/8 3.7 7/12 4.2 8/12 19. 9/12 10. 10/12 43. 11/12 14. 12/12 \n",
+      "Attempt 989 with (1/3, 18) and 4: 7.6 1/8 7.6 2/8 5.2 3/8 13. 4/8 5.5 5/8 5.2 6/8 5.5 7/8 4.7 8/8 \n",
+      "Attempt 990 with (2/287, 1/5) and 5: 310. 1/8 64. 2/8 29. 3/8 21. 4/8 17. 5/8 15. 6/8 13. 7/8 11. 8/8 \n",
+      "Attempt 991 with (5, 1/175) and 4: 13. 1/8 18. 2/8 12. 3/8 14. 4/8 11. 5/8 11. 6/8 10. 7/8 9.2 8/8 \n",
+      "Attempt 992 with (6/5, 137/2) and 3: 7.7 1/8 6.8 2/8 3.5 3/12 5.7 4/12 12. 5/12 4.4 6/12 8.6 7/12 5.7 8/12 3.7 9/16 5.1 10/16 3.2 11/20 8.0 12/20 7.0 13/20 45. 14/20 15. 15/20 6.3 16/20 4.1 17/20 4.6 18/20 3.7 19/24 18. 20/24 7.9 21/24 5.2 22/24 3.4 23/28 5.2 24/28 11. 25/28 4.4 26/28 4.8 27/28 5.9 28/28 \n",
+      "Attempt 993 with (1/3, 1/7) and 32: 6.8 1/8 5.5 2/8 6.5 3/8 12. 4/8 7.5 5/8 18. 6/8 5.9 7/8 7.3 8/8 \n",
+      "Attempt 994 with (2, 1/9) and 4: 32. 1/8 9.3 2/8 3.6 3/12 6.6 4/12 13. 5/12 9.1 6/12 4.3 7/12 6.2 8/12 6.1 9/12 5.3 10/12 3.8 11/16 5.7 12/16 15. 13/16 13. 14/16 5.1 15/16 16. 16/16 \n",
+      "Attempt 995 with (1, 13/11) and 3: 3.5 1/12 5.2 2/12 5.2 3/12 4.0 4/12 4.7 5/12 4.3 6/12 14. 7/12 30. 8/12 12. 9/12 10. 10/12 9.9 11/12 7.8 12/12 \n",
+      "Attempt 996 with (1/34, 1) and 9: 7.5 1/8 3.5 2/12 10. 3/12 9.7 4/12 11. 5/12 5.3 6/12 8.3 7/12 4.4 8/12 11. 9/12 13. 10/12 5.2 11/12 4.3 12/12 \n",
+      "Attempt 997 with (2/11, 1/5) and 4: 8.5 1/8 5.6 2/8 5.5 3/8 11. 4/8 12. 5/8 6.9 6/8 4.3 7/8 8.8 8/8 \n",
+      "Attempt 998 with (1/34, 0) and 9: 5.2 1/8 31. 2/8 3.6 3/12 2.9 4/20 4.5 5/20 13. 6/20 45. 7/20 5.7 8/20 17. 9/20 4.1 10/20 6.7 11/20 4.7 12/20 3.3 13/20 5.6 14/20 14. 15/20 18. 16/20 6.8 17/20 5.9 18/20 11. 19/20 7.4 20/20 \n",
+      "Attempt 999 with (1, 6) and 4: 26. 1/8 6.2 2/8 7.0 3/8 12. 4/8 17. 5/8 13. 6/8 11. 7/8 5.6 8/8 \n",
+      "Attempt 1000 with (2, 3/4) and 4: 6.9 1/8 9.4 2/8 25. 3/8 13. 4/8 3.1 5/12 10. 6/12 11. 7/12 5.7 8/12 9.5 9/12 3.0 10/16 8.8 11/16 9.0 12/16 11. 13/16 27. 14/16 8.0 15/16 3.2 16/20 6.3 17/20 21. 18/20 6.1 19/20 8.5 20/20 \n",
+      "Attempt 1001 with (4, 1) and 13: 4.7 1/8 4.9 2/8 15. 3/8 4.1 4/8 10. 5/8 9.5 6/8 47. 7/8 15. 8/8 \n",
+      "Attempt 1002 with (0, 1/7) and 7: 58. 1/8 4.2 2/8 5.1 3/8 6.0 4/8 3.5 5/12 5.4 6/12 4.3 7/12 24. 8/12 10. 9/12 6.7 10/12 6.3 11/12 9.3 12/12 \n",
+      "Attempt 1003 with (1/3, 134) and 5: 2.5 1/28 5.6 2/28 6.0 3/28 48. 4/28 35. 5/28 7.1 6/28 3.0 7/28 5.1 8/28 6.7 9/28 9.9 10/28 5.3 11/28 15. 12/28 6.2 13/28 4.0 14/28 8.1 15/28 9.9 16/28 9.6 17/28 4.3 18/28 3.5 19/28 3.6 20/28 11. 21/28 6.4 22/28 24. 23/28 4.7 24/28 5.1 25/28 9.0 26/28 7.3 27/28 4.8 28/28 \n",
+      "Attempt 1004 with (2, 3) and 7: 11. 1/8 49. 2/8 12. 3/8 4.2 4/8 3.4 5/12 5.5 6/12 2.6 7/23 9.5 8/23 4.0 9/23 35. 10/23 7.8 11/23 17. 12/23 13. 13/23 6.0 14/23 10. 15/23 9.2 16/23 5.6 17/23 4.3 18/23 8.8 19/23 4.3 20/23 18. 21/23 8.4 22/23 6.7 23/23 \n",
+      "Attempt 1005 with (22, 1/4) and 4: 4.5 1/8 11. 2/8 24. 3/8 26. 4/8 8.8 5/8 6.2 6/8 26. 7/8 5.0 8/8 \n",
+      "Attempt 1006 with (0, 7/2) and 3: 6.4 1/8 21. 2/8 15. 3/8 4.3 4/8 5.2 5/8 4.1 6/8 7.4 7/8 8.4 8/8 \n",
+      "Attempt 1007 with (3/11, 1) and 3: 7.9 1/8 7.1 2/8 2.5 3/40 3.6 4/40 5.9 5/40 4.7 6/40 5.7 7/40 4.0 8/40 15. 9/40 7.0 10/40 5.8 11/40 4.7 12/40 11. 13/40 8.1 14/40 8.8 15/40 5.2 16/40 7.4 17/40 8.4 18/40 3.0 19/40 8.7 20/40 8.1 21/40 4.0 22/40 11. 23/40 7.8 24/40 4.2 25/40 3.7 26/40 7.6 27/40 6.2 28/40 17. 29/40 14. 30/40 37. 31/40 14. 32/40 6.5 33/40 6.9 34/40 3.6 35/40 5.8 36/40 4.0 37/40 5.2 38/40 4.7 39/40 6.1 40/40 \n",
+      "Attempt 1008 with (1/2, 2) and 5: 9.7 1/8 5.3 2/8 6.7 3/8 6.6 4/8 18. 5/8 52. 6/8 77. 7/8 10. 8/8 \n",
+      "Attempt 1009 with (1/7, 0) and 11: 2.9 1/19 9.5 2/19 8.6 3/19 14. 4/19 6.8 5/19 5.3 6/19 11. 7/19 10. 8/19 12. 9/19 530. 10/19 10. 11/19 13. 12/19 7.3 13/19 5.4 14/19 10. 15/19 4.5 16/19 4.0 17/19 13. 18/19 11. 19/19 \n",
+      "Attempt 1010 with (6, 1) and 3: 6.4 1/8 3.3 2/12 11. 3/12 8.0 4/12 3.5 5/16 3.3 6/20 5.3 7/20 16. 8/20 14. 9/20 5.3 10/20 4.2 11/20 13. 12/20 9.8 13/20 9.5 14/20 9.2 15/20 6.8 16/20 2.9 17/40 11. 18/40 11. 19/40 18. 20/40 5.8 21/40 6.4 22/40 9.0 23/40 6.8 24/40 5.7 25/40 15. 26/40 5.6 27/40 3.6 28/40 10. 29/40 7.6 30/40 5.2 31/40 2.4 32/40 4.3 33/40 11. 34/40 24. 35/40 4.5 36/40 4.0 37/40 5.0 38/40 5.9 39/40 4.1 40/40 \n",
+      "Attempt 1011 with (3/16, 9/4) and 16: 5.8 1/8 5.2 2/8 4.5 3/8 5.2 4/8 2.5 5/16 3.6 6/16 15. 7/16 9.1 8/16 6.2 9/16 11. 10/16 16. 11/16 6.8 12/16 18. 13/16 6.4 14/16 3.8 15/16 9.4 16/16 \n",
+      "Attempt 1012 with (335, 5) and 10: 14. 1/8 4.6 2/8 25. 3/8 8.3 4/8 3.4 5/12 15. 6/12 8.2 7/12 4.0 8/16 6.8 9/16 13. 10/16 6.1 11/16 8.2 12/16 6.1 13/16 4.5 14/16 5.2 15/16 45. 16/16 \n",
+      "Attempt 1013 with (1/236, 1/3) and 6: 9.2 1/8 9.3 2/8 26. 3/8 9.7 4/8 7.7 5/8 8.3 6/8 20. 7/8 8.8 8/8 \n",
+      "Attempt 1014 with (5/23, 0) and 3: 5.1 1/8 5.1 2/8 3.9 3/12 6.9 4/12 11. 5/12 6.4 6/12 5.7 7/12 8.3 8/12 6.8 9/12 4.8 10/12 14. 11/12 7.2 12/12 \n",
+      "Attempt 1015 with (1/2, 4) and 3: 5.0 1/8 7.8 2/8 3.6 3/12 4.3 4/12 6.0 5/12 3.6 6/16 5.7 7/16 3.0 8/20 4.1 9/20 8.7 10/20 5.8 11/20 9.5 12/20 16. 13/20 46. 14/20 12. 15/20 6.8 16/20 4.5 17/20 7.1 18/20 16. 19/20 8.2 20/20 \n",
+      "Attempt 1016 with (1/19, 6) and 6: 9.7 1/8 2.9 2/25 15. 3/25 31. 4/25 10. 5/25 12. 6/25 11. 7/25 13. 8/25 8.4 9/25 11. 10/25 28. 11/25 17. 12/25 18. 13/25 28. 14/25 110. 15/25 3800. 16/25 100. 17/25 26. 18/25 26. 19/25 8.7 20/25 31. 21/25 12. 22/25 25. 23/25 9.2 24/25 7.5 25/25 \n",
+      "Attempt 1017 with (5/6, 6) and 3: 4.1 1/8 3.5 2/12 7.6 3/12 2.8 4/40 7.7 5/40 5.0 6/40 18. 7/40 4.0 8/40 3.8 9/40 8.0 10/40 9.2 11/40 7.1 12/40 12. 13/40 20. 14/40 47. 15/40 13. 16/40 12. 17/40 9.9 18/40 5.0 19/40 10. 20/40 16. 21/40 36. 22/40 14. 23/40 11. 24/40 5.7 25/40 3.2 26/40 5.3 27/40 8.1 28/40 4.9 29/40 5.7 30/40 15. 31/40 15. 32/40 5.3 33/40 6.3 34/40 6.0 35/40 4.8 36/40 4.6 37/40 6.2 38/40 14. 39/40 21. 40/40 \n",
+      "Attempt 1018 with (1, 9) and 4: 5.6 1/8 3.3 2/12 10. 3/12 5.0 4/12 9.7 5/12 6.5 6/12 11. 7/12 4.6 8/12 3.9 9/16 4.6 10/16 9.1 11/16 31. 12/16 8.6 13/16 27. 14/16 34. 15/16 24. 16/16 \n",
+      "Attempt 1019 with (3/4, 1) and 18: 8.2 1/8 19. 2/8 16. 3/8 5000. 4/8 22. 5/8 18. 6/8 19. 7/8 18. 8/8 \n",
+      "Attempt 1020 with (5/6, 0) and 3: 4.8 1/8 7.2 2/8 25. 3/8 7.8 4/8 12. 5/8 18. 6/8 25. 7/8 14. 8/8 \n",
+      "Attempt 1021 with (1/3, 1/7) and 7: 14. 1/8 5.8 2/8 4.8 3/8 7.5 4/8 6.8 5/8 9.3 6/8 18. 7/8 4.7 8/8 \n",
+      "Attempt 1022 with (1/4, 2) and 6: 7.8 1/8 5.8 2/8 5.1 3/8 11. 4/8 30. 5/8 56. 6/8 12. 7/8 15. 8/8 \n",
+      "Attempt 1023 with (1, 13) and 9: 6.4 1/8 4.6 2/8 55. 3/8 6.0 4/8 4.4 5/8 6.2 6/8 8.0 7/8 5.6 8/8 \n",
+      "Attempt 1024 with (2/23, 1) and 24: 11. 1/8 13. 2/8 12. 3/8 7.0 4/8 6.5 5/8 3.9 6/12 4.1 7/12 7.8 8/12 40. 9/12 7.3 10/12 5.1 11/12 3.5 12/14 33. 13/14 4.8 14/14 \n",
+      "Attempt 1025 with (85/6, 10/7) and 6: 5.5 1/8 14. 2/8 37. 3/8 18. 4/8 6.8 5/8 30. 6/8 8.9 7/8 40. 8/8 \n",
+      "Attempt 1026 with (2, 23/4) and 7: 5.3 1/8 3.1 2/12 4.6 3/12 7.2 4/12 13. 5/12 10. 6/12 7.8 7/12 7.4 8/12 3.9 9/16 9.4 10/16 58. 11/16 11. 12/16 6.4 13/16 5.5 14/16 4.0 15/20 6.6 16/20 3.3 17/23 6.0 18/23 37. 19/23 15. 20/23 5.4 21/23 11. 22/23 11. 23/23 \n",
+      "Attempt 1027 with (1/155, 1/3) and 14: 4.7 1/8 9.7 2/8 22. 3/8 15. 4/8 12. 5/8 4.7 6/8 24. 7/8 9.2 8/8 \n",
+      "Attempt 1028 with (1/2, 4) and 5: 9.4 1/8 6.9 2/8 11. 3/8 7.0 4/8 110. 5/8 6.2 6/8 5.3 7/8 8.8 8/8 \n",
+      "Attempt 1029 with (0, 1/8) and 4: 8.8 1/8 4.4 2/8 11. 3/8 3.4 4/12 2.8 5/32 4.2 6/32 4.6 7/32 6.1 8/32 9.6 9/32 25. 10/32 9.4 11/32 8.3 12/32 12. 13/32 10. 14/32 5.7 15/32 5.0 16/32 8.3 17/32 32. 18/32 6.3 19/32 5.4 20/32 21. 21/32 26. 22/32 4.2 23/32 7.7 24/32 4.3 25/32 7.9 26/32 19. 27/32 9.4 28/32 19. 29/32 5.0 30/32 5.0 31/32 10. 32/32 \n",
+      "Attempt 1030 with (0, 97/2) and 4: 28. 1/8 10. 2/8 7.5 3/8 4.7 4/8 6.1 5/8 3.1 6/12 7.6 7/12 7.2 8/12 7.9 9/12 28. 10/12 38. 11/12 18. 12/12 \n",
+      "Attempt 1031 with (13, 1) and 4: 6.6 1/8 5.2 2/8 3.7 3/12 3.4 4/16 8.5 5/16 11. 6/16 10. 7/16 96. 8/16 13. 9/16 8.3 10/16 4.6 11/16 10. 12/16 21. 13/16 5.6 14/16 18. 15/16 5.6 16/16 \n",
+      "Attempt 1032 with (3, 0) and 7: 3.5 1/12 16. 2/12 3.9 3/16 3.6 4/20 11. 5/20 7.2 6/20 7.6 7/20 4.7 8/20 8.9 9/20 10. 10/20 34. 11/20 7.4 12/20 5.7 13/20 4.4 14/20 5.3 15/20 12. 16/20 5.5 17/20 26. 18/20 6.2 19/20 4.6 20/20 \n",
+      "Attempt 1033 with (3/2, 1/2) and 3: 5.8 1/8 5.0 2/8 5.8 3/8 4.4 4/8 9.1 5/8 8.5 6/8 20. 7/8 16. 8/8 \n",
+      "Attempt 1034 with (1/31, 13/19) and 6: 4.3 1/8 3.9 2/12 4.0 3/12 51. 4/12 5.9 5/12 40. 6/12 4.5 7/12 26. 8/12 10. 9/12 6.7 10/12 6.5 11/12 5.1 12/12 \n",
+      "Attempt 1035 with (5/3, 1) and 4: 13. 1/8 4.9 2/8 5.3 3/8 6.9 4/8 3.2 5/12 17. 6/12 5.9 7/12 5.7 8/12 4.7 9/12 8.6 10/12 5.9 11/12 3.5 12/16 5.4 13/16 8.5 14/16 25. 15/16 6.5 16/16 \n",
+      "Attempt 1036 with (75, 1) and 5: 4.3 1/8 6.9 2/8 2.9 3/28 3.8 4/28 4.2 5/28 4.8 6/28 3.7 7/28 8.6 8/28 32. 9/28 19. 10/28 60. 11/28 38. 12/28 9.6 13/28 10. 14/28 14. 15/28 5.3 16/28 4.4 17/28 6.5 18/28 15. 19/28 4.0 20/28 8.3 21/28 5.0 22/28 3.4 23/28 2.7 24/28 5.5 25/28 25. 26/28 3.1 27/28 3.3 28/28 \n",
+      "Attempt 1037 with (1/2, 1) and 19: 5.2 1/8 7.7 2/8 71. 3/8 3.9 4/12 17. 5/12 3.8 6/15 5.2 7/15 8.2 8/15 37. 9/15 16. 10/15 17. 11/15 6.2 12/15 4.5 13/15 7.3 14/15 4.5 15/15 \n",
+      "Attempt 1038 with (3, 2/3) and 5: 11. 1/8 29. 2/8 22. 3/8 4.0 4/8 3.3 5/12 4.5 6/12 21. 7/12 6.8 8/12 17. 9/12 5.5 10/12 6.3 11/12 3.9 12/16 14. 13/16 9.1 14/16 9.3 15/16 5.3 16/16 \n",
+      "Attempt 1039 with (9/11, 1/5) and 4: 4.3 1/8 10. 2/8 6.8 3/8 27. 4/8 8.3 5/8 18. 6/8 10. 7/8 13. 8/8 \n",
+      "Attempt 1040 with (1/108, 0) and 4: 4.6 1/8 5.1 2/8 3.2 3/12 5.5 4/12 4.9 5/12 4.0 6/12 3.2 7/16 9.5 8/16 17. 9/16 5.2 10/16 3.5 11/20 3.7 12/24 18. 13/24 7.6 14/24 10. 15/24 7.9 16/24 7.0 17/24 26. 18/24 20. 19/24 130. 20/24 35. 21/24 7.0 22/24 5.9 23/24 9.8 24/24 \n",
+      "Attempt 1041 with (1/2, 15) and 4: 5.3 1/8 4.7 2/8 5.8 3/8 5.8 4/8 5.3 5/8 9.7 6/8 3.6 7/12 5.9 8/12 5.4 9/12 5.1 10/12 6.6 11/12 13. 12/12 \n",
+      "Attempt 1042 with (22, 2) and 10: 7.1 1/8 18. 2/8 5.0 3/8 8.8 4/8 4.2 5/8 9.7 6/8 59. 7/8 34. 8/8 \n",
+      "Attempt 1043 with (4, 1/3) and 4: 3.0 1/32 4.3 2/32 16. 3/32 21. 4/32 53. 5/32 19. 6/32 11. 7/32 9.3 8/32 26. 9/32 41. 10/32 12. 11/32 6.8 12/32 4.8 13/32 9.0 14/32 6.0 15/32 11. 16/32 5.2 17/32 3.7 18/32 7.4 19/32 7.9 20/32 5.9 21/32 5.9 22/32 13. 23/32 13. 24/32 3.7 25/32 10. 26/32 5.0 27/32 6.8 28/32 13. 29/32 8.4 30/32 5.4 31/32 10. 32/32 \n",
+      "Attempt 1044 with (2, 31/2) and 4: 5.6 1/8 6.0 2/8 6.5 3/8 9.3 4/8 9.6 5/8 9.4 6/8 8.4 7/8 13. 8/8 \n",
+      "Attempt 1045 with (1, 5/34) and 5: 7.9 1/8 6.8 2/8 32. 3/8 8.2 4/8 17. 5/8 7.5 6/8 12. 7/8 14. 8/8 \n",
+      "Attempt 1046 with (1/4, 1/10) and 10: 5.0 1/8 7.8 2/8 10. 3/8 5.5 4/8 3.7 5/12 3.6 6/16 18. 7/16 4.9 8/16 4.3 9/16 8.3 10/16 12. 11/16 16. 12/16 4.6 13/16 11. 14/16 6.8 15/16 14. 16/16 \n",
+      "Attempt 1047 with (10, 30) and 4: 30. 1/8 31. 2/8 60. 3/8 140. 4/8 410. 5/8 110. 6/8 42. 7/8 31. 8/8 \n",
+      "Attempt 1048 with (1/148, 0) and 5: 4.6 1/8 3.4 2/12 19. 3/12 4.2 4/12 3.5 5/16 6.4 6/16 17. 7/16 29. 8/16 11. 9/16 7.9 10/16 10. 11/16 3.6 12/20 13. 13/20 5.0 14/20 16. 15/20 8.7 16/20 9.5 17/20 3.6 18/24 2.5 19/28 8.5 20/28 6.5 21/28 29. 22/28 9.9 23/28 3.3 24/28 35. 25/28 5.1 26/28 9.6 27/28 2.7 28/28 \n",
+      "Attempt 1049 with (1, 2) and 17: 20. 1/8 7.2 2/8 8.8 3/8 7.8 4/8 7.4 5/8 3.5 6/12 7.7 7/12 6.2 8/12 17. 9/12 6.4 10/12 10. 11/12 21. 12/12 \n",
+      "Attempt 1050 with (9, 13) and 3: 12. 1/8 22. 2/8 6.2 3/8 7.1 4/8 19. 5/8 78. 6/8 11. 7/8 6.0 8/8 \n",
+      "Attempt 1051 with (1, 1/113) and 4: 17. 1/8 37. 2/8 17. 3/8 16. 4/8 15. 5/8 18. 6/8 12. 7/8 11. 8/8 \n",
+      "Attempt 1052 with (0, 1/20) and 4: 5.7 1/8 10. 2/8 4.0 3/12 6.4 4/12 5.0 5/12 7.0 6/12 7.9 7/12 34. 8/12 8.2 9/12 6.4 10/12 19. 11/12 6.8 12/12 \n",
+      "Attempt 1053 with (1/11, 0) and 4: 8.2 1/8 53. 2/8 6.8 3/8 3.8 4/12 7.6 5/12 6.8 6/12 22. 7/12 5.2 8/12 20. 9/12 4.6 10/12 9.7 11/12 6.7 12/12 \n",
+      "Attempt 1054 with (1, 3/4) and 4: 3.9 1/12 4.6 2/12 15. 3/12 4.6 4/12 6.2 5/12 4.2 6/12 3.8 7/16 5.3 8/16 9.2 9/16 11. 10/16 17. 11/16 5.6 12/16 4.1 13/16 6.4 14/16 9.9 15/16 3.4 16/20 10. 17/20 6.9 18/20 24. 19/20 18. 20/20 \n",
+      "Attempt 1055 with (1/5, 0) and 5: 5.0 1/8 7.6 2/8 7.5 3/8 5.0 4/8 4.1 5/8 10. 6/8 3.9 7/12 21. 8/12 15. 9/12 80. 10/12 7.5 11/12 6.2 12/12 \n",
+      "Attempt 1056 with (16/3, 1) and 4: 6.0 1/8 3.3 2/12 9.2 3/12 6.8 4/12 17. 5/12 2.9 6/32 4.8 7/32 6.8 8/32 18. 9/32 5.3 10/32 12. 11/32 9.3 12/32 5.8 13/32 11. 14/32 8.3 15/32 36. 16/32 4.1 17/32 4.0 18/32 11. 19/32 2.9 20/32 7.7 21/32 8.6 22/32 3.5 23/32 5.1 24/32 6.6 25/32 4.8 26/32 8.4 27/32 21. 28/32 6.5 29/32 9.2 30/32 6.6 31/32 3.0 32/32 \n",
+      "Attempt 1057 with (22, 0) and 5: 4.6 1/8 6.1 2/8 16. 3/8 28. 4/8 6.9 5/8 3.9 6/12 7.1 7/12 12. 8/12 5.1 9/12 7.9 10/12 2.7 11/28 5.5 12/28 3.9 13/28 7.0 14/28 11. 15/28 5.2 16/28 7.5 17/28 4.9 18/28 6.4 19/28 6.2 20/28 7.0 21/28 3.2 22/28 11. 23/28 8.3 24/28 3.9 25/28 4.6 26/28 11. 27/28 13. 28/28 \n",
+      "Attempt 1058 with (0, 4) and 13: 6.2 1/8 5.2 2/8 4.2 3/8 5.6 4/8 4.2 5/8 24. 6/8 7.7 7/8 8.5 8/8 \n",
+      "Attempt 1059 with (0, 7) and 3: 11. 1/8 4.2 2/8 4.8 3/8 4.4 4/8 14. 5/8 13. 6/8 7.9 7/8 4.5 8/8 \n",
+      "Attempt 1060 with (10/3, 0) and 3: 11. 1/8 13. 2/8 39. 3/8 15. 4/8 6.0 5/8 3.4 6/12 4.5 7/12 3.1 8/16 4.0 9/16 6.9 10/16 5.1 11/16 5.8 12/16 5.1 13/16 9.6 14/16 5.0 15/16 5.7 16/16 \n",
+      "Attempt 1061 with (1/2, 58/3) and 6: 22. 1/8 6.8 2/8 3.9 3/12 4.2 4/12 9.2 5/12 13. 6/12 4.9 7/12 4.8 8/12 6.0 9/12 5.2 10/12 22. 11/12 10. 12/12 \n",
+      "Attempt 1062 with (1/6, 2/3) and 4: 4.9 1/8 3.7 2/12 4.0 3/12 6.2 4/12 7.8 5/12 5.3 6/12 4.1 7/12 3.4 8/16 10. 9/16 7.3 10/16 11. 11/16 30. 12/16 6.7 13/16 24. 14/16 7.7 15/16 5.1 16/16 \n",
+      "Attempt 1063 with (1, 1/7) and 5: 16. 1/8 20. 2/8 7.3 3/8 19. 4/8 28. 5/8 7.3 6/8 8.6 7/8 8.2 8/8 \n",
+      "Attempt 1064 with (0, 13) and 4: 9.3 1/8 28. 2/8 16. 3/8 28. 4/8 30. 5/8 5.8 6/8 5.3 7/8 3.9 8/12 7.1 9/12 9.3 10/12 3.8 11/16 6.9 12/16 6.3 13/16 14. 14/16 15. 15/16 16. 16/16 \n",
+      "Attempt 1065 with (1/28, 1/2) and 4: 18. 1/8 17. 2/8 5.3 3/8 5.1 4/8 4.2 5/8 6.1 6/8 18. 7/8 3.8 8/12 3.6 9/16 7.6 10/16 3.7 11/20 7.6 12/20 15. 13/20 11. 14/20 5.1 15/20 40. 16/20 35. 17/20 8.5 18/20 14. 19/20 3.7 20/24 4.9 21/24 7.3 22/24 17. 23/24 20. 24/24 \n",
+      "Attempt 1066 with (3/2, 2) and 7: 12. 1/8 10. 2/8 36. 3/8 12. 4/8 4.3 5/8 3.7 6/12 9.8 7/12 410. 8/12 8.4 9/12 4.1 10/12 4.0 11/12 5.4 12/12 \n",
+      "Attempt 1067 with (3/8, 1) and 9: 13. 1/8 18. 2/8 33. 3/8 4.3 4/8 5.4 5/8 4.1 6/8 8.4 7/8 36. 8/8 \n",
+      "Attempt 1068 with (1/2, 0) and 10: 5.2 1/8 85. 2/8 12. 3/8 4.5 4/8 12. 5/8 23. 6/8 14. 7/8 31. 8/8 \n",
+      "Attempt 1069 with (2/27, 1/3) and 101: 9.9 1/8 2.8 2/10 6.1 3/10 5.1 4/10 7.9 5/10 3.3 6/10 5.8 7/10 3.5 8/10 6.2 9/10 6.3 10/10 \n",
+      "Attempt 1070 with (1, 2/3) and 4: 7.9 1/8 6.1 2/8 7.7 3/8 14. 4/8 40. 5/8 33. 6/8 11. 7/8 7.3 8/8 \n",
+      "Attempt 1071 with (1/8, 1/2) and 3: 9.2 1/8 6.1 2/8 5.7 3/8 5.5 4/8 10. 5/8 4.9 6/8 4.3 7/8 6.9 8/8 \n",
+      "Attempt 1072 with (1, 1/2) and 5: 5.4 1/8 8.4 2/8 16. 3/8 5.2 4/8 5.2 5/8 3.6 6/12 23. 7/12 5.2 8/12 3.8 9/16 6.4 10/16 8.5 11/16 6.2 12/16 5.7 13/16 9.2 14/16 4.3 15/16 15. 16/16 \n",
+      "Attempt 1073 with (5, 2) and 6: 6.4 1/8 10. 2/8 6.1 3/8 13. 4/8 15. 5/8 15. 6/8 7.7 7/8 14. 8/8 \n",
+      "Attempt 1074 with (1, 1/77) and 17: 7.9 1/8 110. 2/8 16. 3/8 3.2 4/12 4.8 5/12 13. 6/12 21. 7/12 7.6 8/12 5.1 9/12 6.0 10/12 4.5 11/12 26. 12/12 \n",
+      "Attempt 1075 with (1, 1/2) and 9: 14. 1/8 8.2 2/8 11. 3/8 2.5 4/20 11. 5/20 12. 6/20 19. 7/20 20. 8/20 11. 9/20 3.9 10/20 18. 11/20 12. 12/20 69. 13/20 27. 14/20 8.2 15/20 6.1 16/20 15. 17/20 180. 18/20 9.8 19/20 95. 20/20 \n",
+      "Attempt 1076 with (4/3, 1/2) and 11: 12. 1/8 12. 2/8 19. 3/8 8.5 4/8 3.9 5/12 8.2 6/12 3.9 7/16 65. 8/16 9.5 9/16 16. 10/16 20. 11/16 4.9 12/16 6.5 13/16 7.6 14/16 3.3 15/19 3.0 16/19 5.0 17/19 4.5 18/19 17. 19/19 \n",
+      "Attempt 1077 with (1/9, 0) and 4: 4.0 1/12 8.3 2/12 7.1 3/12 13. 4/12 52. 5/12 25. 6/12 11. 7/12 6.8 8/12 9.4 9/12 40. 10/12 13. 11/12 9.2 12/12 \n",
+      "Attempt 1078 with (1/8, 52) and 4: 8.8 1/8 6.8 2/8 9.6 3/8 6.2 4/8 7.7 5/8 3.0 6/12 6.6 7/12 4.5 8/12 5.1 9/12 19. 10/12 3.3 11/16 3.0 12/20 8.7 13/20 11. 14/20 9.8 15/20 7.8 16/20 3.6 17/24 5.4 18/24 40. 19/24 6.8 20/24 4.2 21/24 14. 22/24 4.9 23/24 8.7 24/24 \n",
+      "Attempt 1079 with (9/2, 6) and 3: 4.1 1/8 4.0 2/8 5.2 3/8 11. 4/8 3.1 5/12 3.6 6/16 11. 7/16 4.6 8/16 5.4 9/16 16. 10/16 13. 11/16 4.8 12/16 6.7 13/16 4.2 14/16 3.3 15/20 9.8 16/20 5.7 17/20 4.1 18/20 4.1 19/20 4.8 20/20 \n",
+      "Attempt 1080 with (1/4, 1/4) and 3: 50. 1/8 63. 2/8 9.2 3/8 7.1 4/8 7.5 5/8 6.1 6/8 6.9 7/8 8.3 8/8 \n",
+      "Attempt 1081 with (2/3, 1) and 9: 85. 1/8 7.7 2/8 15. 3/8 6.4 4/8 52. 5/8 13. 6/8 9.4 7/8 10. 8/8 \n",
+      "Attempt 1082 with (4, 3/106) and 3: 15. 1/8 13. 2/8 23. 3/8 12. 4/8 12. 5/8 9.0 6/8 9.2 7/8 7.8 8/8 \n",
+      "Attempt 1083 with (9, 4) and 3: 16. 1/8 64. 2/8 38. 3/8 110. 4/8 30. 5/8 38. 6/8 8.8 7/8 8.3 8/8 \n",
+      "Attempt 1084 with (2, 1/3) and 5: 6.5 1/8 11. 2/8 6.0 3/8 11. 4/8 4.6 5/8 5.2 6/8 3.9 7/12 7.5 8/12 24. 9/12 11. 10/12 5.2 11/12 5.7 12/12 \n",
+      "Attempt 1085 with (66, 1) and 4: 13. 1/8 8.2 2/8 3.4 3/12 3.9 4/16 5.7 5/16 4.7 6/16 3.5 7/20 4.5 8/20 2.3 9/32 3.6 10/32 4.4 11/32 6.4 12/32 33. 13/32 38. 14/32 16. 15/32 16. 16/32 5.9 17/32 4.3 18/32 4.4 19/32 4.3 20/32 6.0 21/32 5.0 22/32 8.7 23/32 9.7 24/32 8.0 25/32 11. 26/32 18. 27/32 19. 28/32 26. 29/32 36. 30/32 110. 31/32 100. 32/32 \n",
+      "Attempt 1086 with (1, 1/4) and 7: 9.0 1/8 12. 2/8 5.5 3/8 9.4 4/8 5.1 5/8 8.5 6/8 7.4 7/8 30. 8/8 \n",
+      "Attempt 1087 with (1/4, 1/2) and 5: 13. 1/8 11. 2/8 6.4 3/8 4.5 4/8 7.1 5/8 4.7 6/8 5.0 7/8 4.5 8/8 \n",
+      "Attempt 1088 with (13/4, 1/9) and 7: 20. 1/8 12. 2/8 4.4 3/8 6.3 4/8 5.9 5/8 6.6 6/8 7.5 7/8 9.8 8/8 \n",
+      "Attempt 1089 with (1/3, 13/37) and 5: 11. 1/8 9.1 2/8 5.6 3/8 3.9 4/12 9.4 5/12 9.0 6/12 4.5 7/12 3.8 8/16 3.8 9/20 13. 10/20 4.7 11/20 6.8 12/20 5.2 13/20 3.6 14/24 20. 15/24 3.4 16/28 7.7 17/28 7.8 18/28 4.4 19/28 13. 20/28 12. 21/28 7.7 22/28 13. 23/28 5.1 24/28 5.2 25/28 14. 26/28 6.0 27/28 5.6 28/28 \n",
+      "Attempt 1090 with (1/3, 1/6) and 5: 5.0 1/8 12. 2/8 18. 3/8 5.9 4/8 2.6 5/28 4.2 6/28 7.7 7/28 11. 8/28 3.3 9/28 2.3 10/28 12. 11/28 16. 12/28 15. 13/28 11. 14/28 5.5 15/28 15. 16/28 3.6 17/28 3.2 18/28 5.0 19/28 5.8 20/28 3.2 21/28 7.6 22/28 3.8 23/28 5.6 24/28 7.9 25/28 4.0 26/28 4.9 27/28 7.2 28/28 \n",
+      "Attempt 1091 with (9/8, 1/37) and 3: 21. 1/8 37. 2/8 22. 3/8 19. 4/8 17. 5/8 15. 6/8 17. 7/8 12. 8/8 \n",
+      "Attempt 1092 with (0, 6) and 3: 12. 1/8 37. 2/8 44. 3/8 76. 4/8 13. 5/8 5.5 6/8 7.7 7/8 3.7 8/12 3.4 9/16 6.0 10/16 3.6 11/20 5.6 12/20 23. 13/20 6.0 14/20 3.7 15/24 5.2 16/24 10. 17/24 17. 18/24 6.1 19/24 3.3 20/28 4.1 21/28 2.8 22/40 11. 23/40 11. 24/40 3.8 25/40 7.8 26/40 7.5 27/40 29. 28/40 4.5 29/40 8.7 30/40 5.2 31/40 6.7 32/40 3.0 33/40 11. 34/40 8.5 35/40 4.3 36/40 5.8 37/40 4.9 38/40 6.1 39/40 4.3 40/40 \n",
+      "Attempt 1093 with (1/3, 5) and 8: 9.5 1/8 13. 2/8 6.6 3/8 13. 4/8 6.5 5/8 13. 6/8 13. 7/8 8.5 8/8 \n",
+      "Attempt 1094 with (1/27, 1) and 532: 6.7 1/7 55. 2/7 6.4 3/7 4.6 4/7 6.4 5/7 5.0 6/7 48. 7/7 \n",
+      "Attempt 1095 with (147, 1) and 7: 7.1 1/8 6.5 2/8 7.2 3/8 6.0 4/8 16. 5/8 9.3 6/8 11. 7/8 13. 8/8 \n",
+      "Attempt 1096 with (2, 17/2) and 4: 7.1 1/8 12. 2/8 21. 3/8 35. 4/8 10. 5/8 12. 6/8 20. 7/8 9.3 8/8 \n",
+      "Attempt 1097 with (30, 2/3) and 5: 16. 1/8 12. 2/8 4.1 3/8 4.2 4/8 6.2 5/8 25. 6/8 8.5 7/8 26. 8/8 \n",
+      "Attempt 1098 with (1/8, 1) and 88: 160. 1/8 44. 2/8 7.9 3/8 7.1 4/8 4.0 5/8 46. 6/8 5.9 7/8 44. 8/8 \n",
+      "Attempt 1099 with (4/15, 1) and 4: 11. 1/8 4.0 2/8 3.6 3/12 5.4 4/12 9.6 5/12 5.5 6/12 5.7 7/12 5.0 8/12 6.3 9/12 7.5 10/12 29. 11/12 8.1 12/12 \n",
+      "Attempt 1100 with (1/60, 1) and 4: 7.6 1/8 7.0 2/8 9.8 3/8 6.6 4/8 11. 5/8 5.7 6/8 5.8 7/8 6.3 8/8 \n",
+      "Attempt 1101 with (51, 5) and 4: 11. 1/8 4.1 2/8 2.7 3/32 5.3 4/32 5.7 5/32 11. 6/32 7.2 7/32 3.2 8/32 4.4 9/32 5.0 10/32 7.0 11/32 7.4 12/32 10. 13/32 24. 14/32 5.8 15/32 12. 16/32 3.3 17/32 9.4 18/32 11. 19/32 3.4 20/32 2.6 21/32 12. 22/32 24. 23/32 9.4 24/32 37. 25/32 18. 26/32 47. 27/32 12. 28/32 4.7 29/32 19. 30/32 11. 31/32 3.5 32/32 \n",
+      "Attempt 1102 with (5/41, 4) and 5: 9.3 1/8 37. 2/8 11. 3/8 5.2 4/8 13. 5/8 8.0 6/8 4.6 7/8 6.3 8/8 \n",
+      "Attempt 1103 with (22, 0) and 7: 46. 1/8 8.4 2/8 6.9 3/8 5.8 4/8 7.0 5/8 5.3 6/8 6.2 7/8 5.9 8/8 \n",
+      "Attempt 1104 with (1, 5) and 4: 14. 1/8 4.6 2/8 4.2 3/8 3.6 4/12 5.7 5/12 6.8 6/12 12. 7/12 37. 8/12 67. 9/12 16. 10/12 7.8 11/12 7.4 12/12 \n",
+      "Attempt 1105 with (1/13, 26) and 3: 70. 1/8 48. 2/8 120. 3/8 170. 4/8 65. 5/8 41. 6/8 87. 7/8 190. 8/8 \n",
+      "Attempt 1106 with (5/18, 0) and 6: 3.4 1/12 7.4 2/12 4.8 3/12 8.4 4/12 9.6 5/12 4.1 6/12 12. 7/12 6.0 8/12 19. 9/12 3.3 10/16 15. 11/16 4.8 12/16 5.7 13/16 3.9 14/20 13. 15/20 8.9 16/20 8.1 17/20 9.6 18/20 12. 19/20 5.5 20/20 \n",
+      "Attempt 1107 with (0, 4/5) and 4: 11. 1/8 24. 2/8 41. 3/8 27. 4/8 12. 5/8 12. 6/8 15. 7/8 5.1 8/8 \n",
+      "Attempt 1108 with (16, 1/5) and 5: 4.2 1/8 5.0 2/8 4.9 3/8 7.7 4/8 16. 5/8 120. 6/8 130. 7/8 17. 8/8 \n",
+      "Attempt 1109 with (3, 1) and 8: 8.8 1/8 5.2 2/8 25. 3/8 42. 4/8 14. 5/8 3.5 6/12 7.6 7/12 7.8 8/12 6.9 9/12 20. 10/12 4.3 11/12 5.4 12/12 \n",
+      "Attempt 1110 with (0, 5/2) and 4: 5.6 1/8 7.1 2/8 7.4 3/8 7.1 4/8 5.0 5/8 5.7 6/8 11. 7/8 16. 8/8 \n",
+      "Attempt 1111 with (1/3, 3/4) and 3: 38. 1/8 26. 2/8 25. 3/8 55. 4/8 71. 5/8 19. 6/8 29. 7/8 10. 8/8 \n",
+      "Attempt 1112 with (16, 24) and 3: 23. 1/8 7.5 2/8 5.1 3/8 11. 4/8 6.9 5/8 8.2 6/8 4.3 7/8 6.1 8/8 \n",
+      "Attempt 1113 with (1/2, 17/121) and 4: 3.3 1/12 5.2 2/12 8.9 3/12 23. 4/12 20. 5/12 32. 6/12 34. 7/12 44. 8/12 100. 9/12 140. 10/12 46. 11/12 15. 12/12 \n",
+      "Attempt 1114 with (722/13, 1/8) and 4: 7.8 1/8 5.4 2/8 3.8 3/12 5.9 4/12 8.9 5/12 16. 6/12 6.1 7/12 9.8 8/12 9.4 9/12 6.7 10/12 11. 11/12 7.2 12/12 \n",
+      "Attempt 1115 with (1/9, 3/7) and 10: 43. 1/8 18. 2/8 24. 3/8 19. 4/8 9.5 5/8 5.1 6/8 4.2 7/8 3.9 8/12 3.5 9/16 4.9 10/16 5.5 11/16 4.9 12/16 24. 13/16 16. 14/16 31. 15/16 96. 16/16 \n",
+      "Attempt 1116 with (1/14, 1) and 4: 18. 1/8 20. 2/8 7.0 3/8 3.7 4/12 16. 5/12 7.0 6/12 4.9 7/12 7.3 8/12 16. 9/12 9.0 10/12 7.8 11/12 9.3 12/12 \n",
+      "Attempt 1117 with (3/31, 1/3) and 4: 7.2 1/8 6.1 2/8 9.4 3/8 4.0 4/12 5.6 5/12 7.0 6/12 12. 7/12 21. 8/12 4.3 9/12 3.4 10/16 5.5 11/16 5.4 12/16 18. 13/16 18. 14/16 15. 15/16 20. 16/16 \n",
+      "Attempt 1118 with (1/7, 4) and 5: 2.9 1/28 17. 2/28 22. 3/28 6.8 4/28 15. 5/28 3.9 6/28 5.0 7/28 3.6 8/28 4.6 9/28 13. 10/28 5.0 11/28 9.8 12/28 180. 13/28 14. 14/28 14. 15/28 5.3 16/28 7.0 17/28 3.7 18/28 8.5 19/28 9.4 20/28 5.4 21/28 6.5 22/28 6.3 23/28 22. 24/28 9.3 25/28 6.5 26/28 10. 27/28 8.5 28/28 \n",
+      "Attempt 1119 with (0, 9/2) and 8: 19. 1/8 12. 2/8 6.8 3/8 4.0 4/8 9.7 5/8 8.8 6/8 18. 7/8 8.8 8/8 \n",
+      "Attempt 1120 with (151/2, 1/5) and 14: 7.1 1/8 19. 2/8 3.7 3/12 5.1 4/12 9.6 5/12 6.2 6/12 7.0 7/12 3.4 8/16 6.4 9/16 3.8 10/17 5.8 11/17 5.1 12/17 23. 13/17 6.9 14/17 17. 15/17 6.7 16/17 18. 17/17 \n",
+      "Attempt 1121 with (0, 1/21) and 6: 7.3 1/8 4.1 2/8 4.7 3/8 31. 4/8 6.0 5/8 4.6 6/8 4.9 7/8 8.0 8/8 \n",
+      "Attempt 1122 with (1/8, 0) and 61: 8.7 1/8 6.3 2/8 7.7 3/8 5.7 4/8 12. 5/8 4.0 6/11 83. 7/11 11. 8/11 16. 9/11 5.9 10/11 5.5 11/11 \n",
+      "Attempt 1123 with (12, 5/2) and 3: 5.0 1/8 7.4 2/8 18. 3/8 110. 4/8 33. 5/8 8.6 6/8 6.3 7/8 3.3 8/12 4.2 9/12 7.0 10/12 45. 11/12 15. 12/12 \n",
+      "Attempt 1124 with (1/2, 1/2) and 5: 56. 1/8 10. 2/8 3.4 3/12 5.1 4/12 12. 5/12 2.3 6/28 4.4 7/28 5.8 8/28 5.5 9/28 8.9 10/28 5.1 11/28 30. 12/28 5.5 13/28 3.4 14/28 5.9 15/28 11. 16/28 5.6 17/28 4.4 18/28 4.3 19/28 12. 20/28 17. 21/28 4.9 22/28 13. 23/28 17. 24/28 11. 25/28 4.4 26/28 6.8 27/28 9.8 28/28 \n",
+      "Attempt 1125 with (14/3, 14) and 3: 6.7 1/8 8.7 2/8 27. 3/8 15. 4/8 5.6 5/8 9.3 6/8 6.4 7/8 5.7 8/8 \n",
+      "Attempt 1126 with (0, 2) and 8: 4.0 1/12 7.8 2/12 8.0 3/12 60. 4/12 7.8 5/12 3.9 6/16 11. 7/16 6.1 8/16 3.9 9/20 4.6 10/20 11. 11/20 6.6 12/20 5.3 13/20 9.8 14/20 7.5 15/20 5.0 16/20 5.6 17/20 10. 18/20 7.2 19/20 9.2 20/20 \n",
+      "Attempt 1127 with (1/30, 0) and 5: 4.9 1/8 6.4 2/8 14. 3/8 6.3 4/8 12. 5/8 4.7 6/8 4.4 7/8 19. 8/8 \n",
+      "Attempt 1128 with (2, 2/7) and 4: 26. 1/8 6.5 2/8 4.4 3/8 7.8 4/8 4.1 5/8 4.7 6/8 4.3 7/8 6.9 8/8 \n",
+      "Attempt 1129 with (724, 0) and 5: 3.5 1/12 4.7 2/12 6.7 3/12 3.9 4/16 3.3 5/20 8.5 6/20 13. 7/20 3.8 8/24 8.0 9/24 5.6 10/24 6.3 11/24 7.1 12/24 5.2 13/24 14. 14/24 13. 15/24 15. 16/24 18. 17/24 10. 18/24 7.9 19/24 3.4 20/28 23. 21/28 3.8 22/28 8.5 23/28 7.9 24/28 82. 25/28 15. 26/28 7.5 27/28 3.9 28/28 \n",
+      "Attempt 1130 with (0, 1/14) and 5: 19. 1/8 7.1 2/8 8.8 3/8 4.1 4/8 3.7 5/12 5.8 6/12 16. 7/12 17. 8/12 26. 9/12 8.0 10/12 8.5 11/12 4.8 12/12 \n",
+      "Attempt 1131 with (0, 6) and 18: 14. 1/8 5.7 2/8 8.0 3/8 17. 4/8 16. 5/8 7.6 6/8 7.3 7/8 14. 8/8 \n",
+      "Attempt 1132 with (7, 23/8) and 4: 15. 1/8 14. 2/8 6.7 3/8 4.9 4/8 11. 5/8 4.8 6/8 4.8 7/8 6.7 8/8 \n",
+      "Attempt 1133 with (1/8, 3/2) and 3: 5.1 1/8 9.9 2/8 13. 3/8 6.7 4/8 8.2 5/8 24. 6/8 16. 7/8 8.3 8/8 \n",
+      "Attempt 1134 with (1/3, 1/5) and 4: 7.7 1/8 6.0 2/8 5.7 3/8 4.6 4/8 3.5 5/12 6.4 6/12 5.9 7/12 9.8 8/12 8.9 9/12 21. 10/12 26. 11/12 12. 12/12 \n",
+      "Attempt 1135 with (5/3, 1) and 8: 6.0 1/8 13. 2/8 50. 3/8 19. 4/8 10. 5/8 19. 6/8 7.2 7/8 7.1 8/8 \n",
+      "Attempt 1136 with (1/49, 3) and 17: 7.4 1/8 5.3 2/8 5.6 3/8 6.3 4/8 8.0 5/8 15. 6/8 4.0 7/12 180. 8/12 3.8 9/16 29. 10/16 3.1 11/16 10. 12/16 8.2 13/16 8.8 14/16 3.4 15/16 4.1 16/16 \n",
+      "Attempt 1137 with (2/7, 0) and 4: 3.0 1/32 11. 2/32 14. 3/32 3.9 4/32 14. 5/32 3.8 6/32 7.4 7/32 16. 8/32 16. 9/32 4.7 10/32 6.4 11/32 13. 12/32 18. 13/32 5.4 14/32 5.3 15/32 20. 16/32 5.1 17/32 3.1 18/32 7.1 19/32 4.4 20/32 6.4 21/32 23. 22/32 7.5 23/32 17. 24/32 12. 25/32 21. 26/32 53. 27/32 5.7 28/32 14. 29/32 4.3 30/32 11. 31/32 9.4 32/32 \n",
+      "Attempt 1138 with (169, 1) and 4: 8.4 1/8 6.6 2/8 4.5 3/8 6.2 4/8 5.4 5/8 5.1 6/8 8.4 7/8 30. 8/8 \n",
+      "Attempt 1139 with (1/2, 4) and 4: 41. 1/8 12. 2/8 5.8 3/8 4.3 4/8 16. 5/8 7.3 6/8 23. 7/8 5.0 8/8 \n",
+      "Attempt 1140 with (17/2, 0) and 16: 6.9 1/8 12. 2/8 7.2 3/8 8.5 4/8 2.8 5/16 3.1 6/16 6.2 7/16 6.8 8/16 5.2 9/16 7.1 10/16 24. 11/16 3.6 12/16 3.5 13/16 9.0 14/16 17. 15/16 11. 16/16 \n",
+      "Attempt 1141 with (10/3, 1/2) and 5: 17. 1/8 6.6 2/8 18. 3/8 55. 4/8 25. 5/8 10. 6/8 6.5 7/8 11. 8/8 \n",
+      "Attempt 1142 with (1/6, 1/2) and 3: 5.7 1/8 5.5 2/8 6.2 3/8 9.5 4/8 11. 5/8 17. 6/8 7.8 7/8 9.4 8/8 \n",
+      "Attempt 1143 with (0, 3/4) and 137: 23. 1/8 9.0 2/8 23. 3/8 6.6 4/8 6.5 5/8 93. 6/8 24. 7/8 5.0 8/8 \n",
+      "Attempt 1144 with (3503, 5) and 64: 5.6 1/8 6.4 2/8 14. 3/8 7.7 4/8 5.1 5/8 11. 6/8 4.2 7/8 15. 8/8 \n",
+      "Attempt 1145 with (29, 0) and 4: 7.2 1/8 2.9 2/32 8.3 3/32 7.2 4/32 18. 5/32 9.2 6/32 3.3 7/32 3.7 8/32 12. 9/32 2.6 10/32 7.9 11/32 3.8 12/32 2.9 13/32 9.2 14/32 9.7 15/32 28. 16/32 310. 17/32 18. 18/32 6.8 19/32 4.6 20/32 7.1 21/32 5.5 22/32 6.3 23/32 4.5 24/32 9.6 25/32 65. 26/32 6.7 27/32 2.8 28/32 6.1 29/32 3.2 30/32 3.3 31/32 3.9 32/32 \n",
+      "Attempt 1146 with (2, 5) and 3: 6.0 1/8 18. 2/8 16. 3/8 57. 4/8 14. 5/8 6.3 6/8 5.8 7/8 4.0 8/12 5.9 9/12 7.9 10/12 11. 11/12 4.1 12/12 \n",
+      "Attempt 1147 with (1, 3/16) and 4: 7.9 1/8 6.9 2/8 3.9 3/12 8.7 4/12 5.6 5/12 4.3 6/12 4.3 7/12 8.8 8/12 14. 9/12 23. 10/12 20. 11/12 5.5 12/12 \n",
+      "Attempt 1148 with (25, 3/2) and 4: 2.4 1/32 19. 2/32 140. 3/32 7.9 4/32 4.2 5/32 5.4 6/32 5.5 7/32 2.7 8/32 5.9 9/32 8.5 10/32 12. 11/32 11. 12/32 10. 13/32 5.5 14/32 8.1 15/32 9.4 16/32 4.3 17/32 4.2 18/32 16. 19/32 2.4 20/32 3.7 21/32 5.9 22/32 5.1 23/32 9.8 24/32 96. 25/32 40. 26/32 7.0 27/32 4.6 28/32 4.9 29/32 6.4 30/32 34. 31/32 7.9 32/32 \n",
+      "Attempt 1149 with (1/7, 0) and 5: 5.5 1/8 21. 2/8 3.7 3/12 7.1 4/12 39. 5/12 11. 6/12 4.4 7/12 2.8 8/28 5.5 9/28 4.3 10/28 19. 11/28 23. 12/28 11. 13/28 8.5 14/28 38. 15/28 5.4 16/28 12. 17/28 14. 18/28 3.3 19/28 10. 20/28 9.2 21/28 39. 22/28 5.9 23/28 13. 24/28 3.5 25/28 8.0 26/28 3.4 27/28 6.4 28/28 \n",
+      "Attempt 1150 with (1/3, 1/2) and 5: 24. 1/8 5.0 2/8 4.6 3/8 14. 4/8 3.7 5/12 5.1 6/12 84. 7/12 5.4 8/12 5.4 9/12 4.4 10/12 13. 11/12 7.1 12/12 \n",
+      "Attempt 1151 with (0, 1/43) and 5: 5.4 1/8 6.6 2/8 8.8 3/8 13. 4/8 46. 5/8 18. 6/8 13. 7/8 8.9 8/8 \n",
+      "Attempt 1152 with (2, 9) and 3: 4.5 1/8 5.0 2/8 6.7 3/8 6.0 4/8 8.7 5/8 3.3 6/12 4.9 7/12 4.2 8/12 6.9 9/12 9.5 10/12 17. 11/12 29. 12/12 \n",
+      "Attempt 1153 with (3/8, 1/5) and 4: 4.4 1/8 3.4 2/12 12. 3/12 3.2 4/16 9.6 5/16 3.8 6/20 3.6 7/24 8.1 8/24 27. 9/24 4.3 10/24 4.6 11/24 8.7 12/24 6.2 13/24 5.3 14/24 4.4 15/24 11. 16/24 9.3 17/24 5.2 18/24 5.2 19/24 6.8 20/24 4.4 21/24 2.6 22/32 8.1 23/32 12. 24/32 7.1 25/32 22. 26/32 13. 27/32 6.4 28/32 10. 29/32 6.8 30/32 3.6 31/32 4.0 32/32 \n",
+      "Attempt 1154 with (5, 2) and 14: 54. 1/8 6.0 2/8 23. 3/8 58. 4/8 15. 5/8 13. 6/8 29. 7/8 9.5 8/8 \n",
+      "Attempt 1155 with (5/2, 1/2) and 16: 10. 1/8 2.8 2/16 6.5 3/16 4.3 4/16 11. 5/16 4.2 6/16 5.3 7/16 5.3 8/16 15. 9/16 11. 10/16 5.7 11/16 12. 12/16 8.4 13/16 5.7 14/16 25. 15/16 8.6 16/16 \n",
+      "Attempt 1156 with (0, 5) and 4: 14. 1/8 9.2 2/8 4.6 3/8 7.4 4/8 6.3 5/8 5.3 6/8 6.9 7/8 34. 8/8 \n",
+      "Attempt 1157 with (1, 6/7) and 9: 9.5 1/8 21. 2/8 8.8 3/8 13. 4/8 4.5 5/8 45. 6/8 6.1 7/8 29. 8/8 \n",
+      "Attempt 1158 with (0, 6) and 10: 3.0 1/12 9.8 2/12 11. 3/12 5.8 4/12 9.4 5/12 9.3 6/12 25. 7/12 31. 8/12 4.7 9/12 5.9 10/12 11. 11/12 25. 12/12 \n",
+      "Attempt 1159 with (1, 4) and 13: 3.6 1/12 5.4 2/12 14. 3/12 16. 4/12 8.3 5/12 5.4 6/12 5.4 7/12 4.4 8/12 12. 9/12 6.6 10/12 2.8 11/17 5.4 12/17 4.9 13/17 4.8 14/17 3.9 15/17 13. 16/17 8.3 17/17 \n",
+      "Attempt 1160 with (1/2, 1/62) and 5: 7.9 1/8 6.7 2/8 9.9 3/8 47. 4/8 10. 5/8 7.5 6/8 7.4 7/8 8.3 8/8 \n",
+      "Attempt 1161 with (1, 2) and 38: 5.4 1/8 9.5 2/8 4.4 3/8 4.7 4/8 12. 5/8 2.4 6/12 2.5 7/12 7.0 8/12 12. 9/12 6.2 10/12 280. 11/12 19. 12/12 \n",
+      "Attempt 1162 with (5/2, 1) and 12: 7.1 1/8 6.1 2/8 8.1 3/8 6.1 4/8 6.5 5/8 6.9 6/8 14. 7/8 3.9 8/12 5.3 9/12 5.3 10/12 7.5 11/12 19. 12/12 \n",
+      "Attempt 1163 with (7/2, 44) and 4: 8.9 1/8 9.5 2/8 15. 3/8 37. 4/8 84. 5/8 10. 6/8 5.1 7/8 24. 8/8 \n",
+      "Attempt 1164 with (23, 14) and 55: 4.0 1/8 7.7 2/8 3.6 3/11 18. 4/11 5.2 5/11 13. 6/11 4.3 7/11 3.8 8/11 17. 9/11 160. 10/11 3.8 11/11 \n",
+      "Attempt 1165 with (3, 1/3) and 85: 12. 1/8 7.8 2/8 9.0 3/8 3.8 4/10 6.6 5/10 15. 6/10 42. 7/10 140. 8/10 8.9 9/10 6.3 10/10 \n",
+      "Attempt 1166 with (10, 1) and 4: 17. 1/8 3.4 2/12 9.7 3/12 8.1 4/12 6.6 5/12 15. 6/12 9.7 7/12 12. 8/12 7.8 9/12 18. 10/12 4.5 11/12 7.9 12/12 \n",
+      "Attempt 1167 with (2, 2/11) and 4: 8.9 1/8 8.5 2/8 8.2 3/8 6.2 4/8 7.7 5/8 6.9 6/8 5.7 7/8 7.5 8/8 \n",
+      "Attempt 1168 with (1, 2/5) and 15: 8.9 1/8 5.1 2/8 5.9 3/8 9.8 4/8 3.9 5/12 3.8 6/16 3.3 7/16 13. 8/16 7.9 9/16 44. 10/16 3.2 11/16 11. 12/16 4.3 13/16 4.4 14/16 3.1 15/16 22. 16/16 \n",
+      "Attempt 1169 with (1/2, 2) and 9: 35. 1/8 2.5 2/20 9.2 3/20 5.8 4/20 9.8 5/20 3.5 6/20 3.5 7/20 9.2 8/20 8.8 9/20 11. 10/20 43. 11/20 39. 12/20 8.6 13/20 4.2 14/20 9.4 15/20 16. 16/20 8.6 17/20 6.2 18/20 6.5 19/20 8.0 20/20 \n",
+      "Attempt 1170 with (0, 17/2) and 7: 4.3 1/8 7.6 2/8 13. 3/8 4.5 4/8 9.4 5/8 4.2 6/8 5.5 7/8 15. 8/8 \n",
+      "Attempt 1171 with (47/2, 1) and 3: 8.2 1/8 10. 2/8 19. 3/8 7.7 4/8 6.1 5/8 9.0 6/8 16. 7/8 53. 8/8 \n",
+      "Attempt 1172 with (0, 1/12) and 3: 3.7 1/12 8.9 2/12 15. 3/12 7.4 4/12 13. 5/12 18. 6/12 7.7 7/12 5.9 8/12 8.8 9/12 12. 10/12 48. 11/12 12. 12/12 \n",
+      "Attempt 1173 with (2, 1/8) and 3: 7.8 1/8 7.4 2/8 8.2 3/8 12. 4/8 5.8 5/8 4.9 6/8 11. 7/8 4.8 8/8 \n",
+      "Attempt 1174 with (1/2, 1) and 24: 6.4 1/8 15. 2/8 5.8 3/8 15. 4/8 21. 5/8 5.1 6/8 2.8 7/14 4.2 8/14 4.2 9/14 16. 10/14 4.6 11/14 6.2 12/14 5.9 13/14 5.7 14/14 \n",
+      "Attempt 1175 with (0, 1/2) and 1336: 6.9 1/6 11. 2/6 6.2 3/6 4.8 4/6 4.2 5/6 15. 6/6 \n",
+      "Attempt 1176 with (14, 1/2) and 4: 3.4 1/12 6.6 2/12 13. 3/12 3.1 4/16 2.4 5/32 4.0 6/32 5.2 7/32 11. 8/32 50. 9/32 16. 10/32 7.9 11/32 14. 12/32 9.1 13/32 12. 14/32 3.9 15/32 6.3 16/32 5.5 17/32 4.6 18/32 12. 19/32 8.8 20/32 7.7 21/32 18. 22/32 4.6 23/32 5.2 24/32 5.9 25/32 26. 26/32 12. 27/32 4.4 28/32 11. 29/32 18. 30/32 4.7 31/32 3.3 32/32 \n",
+      "Attempt 1177 with (4, 3/2) and 3: 4.4 1/8 3.7 2/12 7.4 3/12 19. 4/12 6.9 5/12 5.9 6/12 13. 7/12 6.2 8/12 4.5 9/12 8.5 10/12 14. 11/12 4.3 12/12 \n",
+      "Attempt 1178 with (1, 8/3) and 3: 5.6 1/8 40. 2/8 6.8 3/8 7.5 4/8 11. 5/8 3.2 6/12 4.5 7/12 13. 8/12 13. 9/12 7.4 10/12 4.4 11/12 4.9 12/12 \n",
+      "Attempt 1179 with (0, 1/4) and 8: 22. 1/8 9.5 2/8 8.1 3/8 12. 4/8 4.0 5/12 3.6 6/16 50. 7/16 23. 8/16 590. 9/16 10. 10/16 15. 11/16 7.2 12/16 13. 13/16 5.5 14/16 6.0 15/16 17. 16/16 \n",
+      "Attempt 1180 with (1/2, 1/73) and 5: 9.7 1/8 9.1 2/8 8.5 3/8 27. 4/8 11. 5/8 7.4 6/8 9.4 7/8 8.5 8/8 \n",
+      "Attempt 1181 with (1/6, 5/12) and 3: 26. 1/8 70. 2/8 110. 3/8 190. 4/8 31. 5/8 20. 6/8 13. 7/8 8.6 8/8 \n",
+      "Attempt 1182 with (0, 9/7) and 3: 6.5 1/8 7.1 2/8 7.2 3/8 7.7 4/8 2.7 5/40 4.9 6/40 7.0 7/40 12. 8/40 32. 9/40 4.2 10/40 3.8 11/40 5.8 12/40 12. 13/40 5.5 14/40 7.0 15/40 7.4 16/40 5.0 17/40 11. 18/40 10. 19/40 5.7 20/40 11. 21/40 33. 22/40 15. 23/40 37. 24/40 14. 25/40 29. 26/40 10. 27/40 7.4 28/40 8.2 29/40 6.5 30/40 11. 31/40 8.1 32/40 5.8 33/40 15. 34/40 5.9 35/40 7.2 36/40 12. 37/40 6.1 38/40 18. 39/40 5.6 40/40 \n",
+      "Attempt 1183 with (0, 1/21) and 3: 6.2 1/8 5.5 2/8 4.1 3/8 7.4 4/8 22. 5/8 7.9 6/8 18. 7/8 5.1 8/8 \n",
+      "Attempt 1184 with (1/3, 13/4) and 4: 5.3 1/8 11. 2/8 9.3 3/8 8.0 4/8 7.1 5/8 7.2 6/8 7.9 7/8 14. 8/8 \n",
+      "Attempt 1185 with (1/3, 0) and 8: 8.1 1/8 6.1 2/8 4.6 3/8 8.9 4/8 45. 5/8 5.5 6/8 11. 7/8 3.1 8/12 15. 9/12 5.1 10/12 10. 11/12 6.6 12/12 \n",
+      "Attempt 1186 with (1/4, 2/5) and 3: 7.2 1/8 6.6 2/8 5.9 3/8 5.0 4/8 4.8 5/8 12. 6/8 28. 7/8 25. 8/8 \n",
+      "Attempt 1187 with (1/2, 3) and 16: 5.6 1/8 8.5 2/8 5.2 3/8 14. 4/8 18. 5/8 6.0 6/8 6.8 7/8 5.4 8/8 \n",
+      "Attempt 1188 with (17, 1) and 4: 11. 1/8 4.3 2/8 6.5 3/8 5.9 4/8 6.2 5/8 5.4 6/8 5.4 7/8 9.4 8/8 \n",
+      "Attempt 1189 with (3/2, 1/6) and 7: 12. 1/8 6.2 2/8 7.1 3/8 11. 4/8 28. 5/8 3.5 6/12 30. 7/12 9.0 8/12 12. 9/12 5.7 10/12 11. 11/12 4.7 12/12 \n",
+      "Attempt 1190 with (1/7, 1) and 5: 29. 1/8 15. 2/8 9.0 3/8 7.6 4/8 6.6 5/8 7.3 6/8 17. 7/8 62. 8/8 \n",
+      "Attempt 1191 with (1/3, 7/24) and 3: 33. 1/8 44. 2/8 13. 3/8 6.1 4/8 5.6 5/8 4.6 6/8 4.6 7/8 4.8 8/8 \n",
+      "Attempt 1192 with (3/2, 1/4) and 4: 17. 1/8 71. 2/8 780. 3/8 45. 4/8 29. 5/8 23. 6/8 5.2 7/8 4.5 8/8 \n",
+      "Attempt 1193 with (184, 7/80) and 66: 4.1 1/8 8.0 2/8 6.1 3/8 5.4 4/8 13. 5/8 16. 6/8 8.5 7/8 3.3 8/11 20. 9/11 7.5 10/11 3.7 11/11 \n",
+      "Attempt 1194 with (10/3, 1/5) and 3: 5.5 1/8 5.1 2/8 9.5 3/8 11. 4/8 10. 5/8 4.2 6/8 3.6 7/12 7.4 8/12 12. 9/12 5.5 10/12 4.8 11/12 8.6 12/12 \n",
+      "Attempt 1195 with (1/5, 2) and 3: 4.9 1/8 5.5 2/8 5.1 3/8 4.5 4/8 5.0 5/8 24. 6/8 5.1 7/8 4.2 8/8 \n",
+      "Attempt 1196 with (3, 1) and 50: 5.7 1/8 7.3 2/8 18. 3/8 5.9 4/8 6.4 5/8 5.1 6/8 3.6 7/11 3.3 8/11 42. 9/11 4.9 10/11 4.0 11/11 \n",
+      "Attempt 1197 with (1/2, 100) and 12: 8.5 1/8 3.5 2/12 20. 3/12 4.3 4/12 4.2 5/12 5.0 6/12 9.0 7/12 8.3 8/12 3.4 9/16 8.1 10/16 14. 11/16 4.5 12/16 7.4 13/16 3.8 14/18 12. 15/18 39. 16/18 12. 17/18 4.5 18/18 \n",
+      "Attempt 1198 with (0, 14) and 3: 7.1 1/8 4.9 2/8 8.8 3/8 60. 4/8 13. 5/8 6.9 6/8 33. 7/8 78. 8/8 \n",
+      "Attempt 1199 with (1/10, 1/2) and 6: 3.2 1/12 17. 2/12 3.3 3/16 6.1 4/16 16. 5/16 7.8 6/16 5.2 7/16 5.7 8/16 3.9 9/20 32. 10/20 4.0 11/20 9.1 12/20 13. 13/20 20. 14/20 6.2 15/20 110. 16/20 9.6 17/20 8.4 18/20 3.7 19/24 16. 20/24 9.8 21/24 16. 22/24 9.9 23/24 23. 24/24 \n",
+      "Attempt 1200 with (1, 1/23) and 4: 7.1 1/8 7.0 2/8 6.0 3/8 7.1 4/8 8.6 5/8 6.2 6/8 5.1 7/8 4.7 8/8 \n",
+      "Attempt 1201 with (3/20, 2/273) and 6: 14. 1/8 14. 2/8 11. 3/8 13. 4/8 9.5 5/8 23. 6/8 17. 7/8 12. 8/8 \n",
+      "Attempt 1202 with (1, 8/3) and 36: 12. 1/8 15. 2/8 12. 3/8 3.5 4/12 1.9 5/12 9.6 6/12 13. 7/12 18. 8/12 3.5 9/12 10. 10/12 21. 11/12 2.6 12/12 \n",
+      "Attempt 1203 with (0, 4) and 5: 5.2 1/8 6.3 2/8 7.7 3/8 29. 4/8 78. 5/8 26. 6/8 44. 7/8 33. 8/8 \n",
+      "Attempt 1204 with (9, 1/2) and 4: 6.3 1/8 11. 2/8 17. 3/8 5.0 4/8 6.3 5/8 3.3 6/12 7.1 7/12 12. 8/12 6.7 9/12 11. 10/12 6.2 11/12 9.6 12/12 \n",
+      "Attempt 1205 with (2, 19/3) and 4: 6.4 1/8 5.6 2/8 4.6 3/8 5.0 4/8 40. 5/8 15. 6/8 7.6 7/8 11. 8/8 \n",
+      "Attempt 1206 with (0, 3/29) and 56: 44. 1/8 5.9 2/8 4.9 3/8 5.2 4/8 9.7 5/8 5.9 6/8 5.2 7/8 9.6 8/8 \n",
+      "Attempt 1207 with (2, 7) and 5: 3.3 1/12 4.3 2/12 11. 3/12 49. 4/12 43. 5/12 440. 6/12 17. 7/12 5.1 8/12 4.8 9/12 4.8 10/12 4.7 11/12 6.2 12/12 \n",
+      "Attempt 1208 with (1/10, 0) and 15: 6.4 1/8 5.6 2/8 3.5 3/12 20. 4/12 14. 5/12 5.4 6/12 6.2 7/12 6.1 8/12 4.0 9/16 14. 10/16 7.2 11/16 5.3 12/16 7.8 13/16 13. 14/16 6.3 15/16 4.9 16/16 \n",
+      "Attempt 1209 with (1/8, 1/4) and 4: 90. 1/8 52. 2/8 28. 3/8 9.9 4/8 6.1 5/8 5.4 6/8 6.0 7/8 6.7 8/8 \n",
+      "Attempt 1210 with (1, 4) and 5: 11. 1/8 7.4 2/8 6.1 3/8 7.5 4/8 15. 5/8 5.9 6/8 5.3 7/8 4.0 8/8 \n",
+      "Attempt 1211 with (1/8, 1/4) and 3: 18. 1/8 17. 2/8 18. 3/8 19. 4/8 21. 5/8 24. 6/8 30. 7/8 56. 8/8 \n",
+      "Attempt 1212 with (1/2, 55) and 3: 7.7 1/8 7.5 2/8 6.5 3/8 12. 4/8 4.9 5/8 4.7 6/8 6.6 7/8 5.7 8/8 \n",
+      "Attempt 1213 with (1/22, 1/2) and 5: 5.1 1/8 5.5 2/8 6.5 3/8 4.7 4/8 9.5 5/8 4.8 6/8 16. 7/8 4.5 8/8 \n",
+      "Attempt 1214 with (1/20, 9) and 3: 5.1 1/8 5.1 2/8 7.0 3/8 10. 4/8 5.7 5/8 4.1 6/8 7.4 7/8 4.3 8/8 \n",
+      "Attempt 1215 with (21, 0) and 10: 14. 1/8 18. 2/8 5.3 3/8 5.2 4/8 25. 5/8 4.8 6/8 5.0 7/8 7.1 8/8 \n",
+      "Attempt 1216 with (2, 1/99) and 3: 32. 1/8 32. 2/8 23. 3/8 20. 4/8 29. 5/8 20. 6/8 24. 7/8 18. 8/8 \n",
+      "Attempt 1217 with (5, 1/2) and 7: 7.5 1/8 4.6 2/8 19. 3/8 18. 4/8 16. 5/8 19. 6/8 9.3 7/8 15. 8/8 \n",
+      "Attempt 1218 with (2, 0) and 6: 9.4 1/8 4.5 2/8 11. 3/8 6.6 4/8 7.0 5/8 11. 6/8 7.7 7/8 29. 8/8 \n",
+      "Attempt 1219 with (2, 4/5) and 4: 50. 1/8 10. 2/8 36. 3/8 16. 4/8 3.4 5/12 5.4 6/12 8.9 7/12 10. 8/12 33. 9/12 57. 10/12 15. 11/12 8.9 12/12 \n",
+      "Attempt 1220 with (1/3, 1/14) and 10: 18. 1/8 4.2 2/8 4.3 3/8 6.9 4/8 9.1 5/8 5.0 6/8 6.2 7/8 5.3 8/8 \n",
+      "Attempt 1221 with (9, 5) and 3: 5.6 1/8 16. 2/8 140. 3/8 56. 4/8 24. 5/8 11. 6/8 18. 7/8 49. 8/8 \n",
+      "Attempt 1222 with (1/8, 1/2) and 5: 6.3 1/8 10. 2/8 9.6 3/8 4.4 4/8 6.4 5/8 14. 6/8 10. 7/8 7.3 8/8 \n",
+      "Attempt 1223 with (1/51, 1) and 9: 6.5 1/8 21. 2/8 4.6 3/8 2.6 4/20 4.5 5/20 2.9 6/20 5.5 7/20 14. 8/20 4.3 9/20 2.8 10/20 21. 11/20 5.1 12/20 8.4 13/20 12. 14/20 13. 15/20 5.4 16/20 4.6 17/20 4.7 18/20 2.5 19/20 5.3 20/20 \n",
+      "Attempt 1224 with (2, 4) and 4: 4.1 1/8 2.8 2/32 6.1 3/32 8.1 4/32 15. 5/32 6.7 6/32 4.5 7/32 4.5 8/32 7.0 9/32 5.0 10/32 31. 11/32 14. 12/32 19. 13/32 59. 14/32 37. 15/32 21. 16/32 10. 17/32 8.5 18/32 4.9 19/32 3.4 20/32 13. 21/32 21. 22/32 7.4 23/32 5.5 24/32 8.3 25/32 9.4 26/32 4.4 27/32 6.1 28/32 8.0 29/32 3.5 30/32 13. 31/32 5.7 32/32 \n",
+      "Attempt 1225 with (7, 3/4) and 4: 20. 1/8 5.1 2/8 9.7 3/8 5.1 4/8 10. 5/8 2.8 6/32 7.1 7/32 18. 8/32 7.1 9/32 27. 10/32 6.7 11/32 15. 12/32 15. 13/32 19. 14/32 10. 15/32 37. 16/32 8.3 17/32 7.5 18/32 3.7 19/32 38. 20/32 9.7 21/32 14. 22/32 7.0 23/32 8.3 24/32 2.9 25/32 5.6 26/32 15. 27/32 6.2 28/32 5.0 29/32 8.1 30/32 6.0 31/32 4.8 32/32 \n",
+      "Attempt 1226 with (1/2, 21) and 4: 19. 1/8 10. 2/8 6.5 3/8 5.0 4/8 4.9 5/8 5.3 6/8 5.7 7/8 7.2 8/8 \n",
+      "Attempt 1227 with (3, 3/7) and 4: 43. 1/8 18. 2/8 9.0 3/8 4.1 4/8 7.8 5/8 39. 6/8 13. 7/8 5.0 8/8 \n",
+      "Attempt 1228 with (16/3, 1) and 3: 7.3 1/8 5.2 2/8 9.4 3/8 5.3 4/8 5.6 5/8 5.1 6/8 12. 7/8 25. 8/8 \n",
+      "Attempt 1229 with (13/32, 0) and 3: 13. 1/8 19. 2/8 5.3 3/8 4.6 4/8 23. 5/8 22. 6/8 5.4 7/8 4.3 8/8 \n",
+      "Attempt 1230 with (0, 2) and 19: 17. 1/8 53. 2/8 11. 3/8 18. 4/8 12. 5/8 22. 6/8 7.2 7/8 10. 8/8 \n",
+      "Attempt 1231 with (21/2, 1) and 4: 4.8 1/8 12. 2/8 13. 3/8 30. 4/8 24. 5/8 8.8 6/8 12. 7/8 17. 8/8 \n",
+      "Attempt 1232 with (1/834, 4) and 7: 7.0 1/8 8.5 2/8 8.4 3/8 5.6 4/8 18. 5/8 6.9 6/8 13. 7/8 5.3 8/8 \n",
+      "Attempt 1233 with (3, 1/2) and 6: 21. 1/8 3.2 2/12 4.0 3/12 5.1 4/12 12. 5/12 62. 6/12 18. 7/12 8.1 8/12 19. 9/12 250. 10/12 18. 11/12 12. 12/12 \n",
+      "Attempt 1234 with (1, 17/3) and 4: 4.4 1/8 3.0 2/32 9.4 3/32 5.6 4/32 6.6 5/32 5.3 6/32 3.9 7/32 7.2 8/32 12. 9/32 11. 10/32 5.0 11/32 4.7 12/32 3.9 13/32 6.7 14/32 4.2 15/32 6.5 16/32 25. 17/32 7.5 18/32 32. 19/32 18. 20/32 6.2 21/32 24. 22/32 12. 23/32 6.0 24/32 3.0 25/32 4.0 26/32 3.1 27/32 7.3 28/32 13. 29/32 8.8 30/32 3.3 31/32 5.2 32/32 \n",
+      "Attempt 1235 with (2/19, 1/2) and 7: 18. 1/8 8.4 2/8 12. 3/8 4.3 4/8 22. 5/8 7.3 6/8 11. 7/8 32. 8/8 \n",
+      "Attempt 1236 with (1/7, 2) and 4: 130. 1/8 880. 2/8 61. 3/8 32. 4/8 33. 5/8 10. 6/8 8.1 7/8 11. 8/8 \n",
+      "Attempt 1237 with (1/3, 0) and 102: 530. 1/8 14. 2/8 67. 3/8 11. 4/8 4.8 5/8 4.3 6/8 5.3 7/8 10. 8/8 \n",
+      "Attempt 1238 with (1/2, 1/3) and 30: 3.4 1/12 20. 2/12 7.0 3/12 9.5 4/12 4.9 5/12 6.0 6/12 6.1 7/12 12. 8/12 7.0 9/12 6.1 10/12 6.3 11/12 4.9 12/12 \n",
+      "Attempt 1239 with (8, 0) and 7: 9.4 1/8 4.5 2/8 7.2 3/8 6.3 4/8 8.8 5/8 7.3 6/8 5.3 7/8 17. 8/8 \n",
+      "Attempt 1240 with (2, 135/23) and 4: 5.8 1/8 4.9 2/8 3.9 3/12 4.2 4/12 3.4 5/16 23. 6/16 24. 7/16 8.8 8/16 4.7 9/16 8.3 10/16 15. 11/16 5.0 12/16 6.5 13/16 5.4 14/16 11. 15/16 4.4 16/16 \n",
+      "Attempt 1241 with (3/4, 4/49) and 3: 15. 1/8 12. 2/8 9.6 3/8 13. 4/8 16. 5/8 21. 6/8 9.5 7/8 8.7 8/8 \n",
+      "Attempt 1242 with (1/4, 1) and 12: 5.1 1/8 4.9 2/8 5.4 3/8 2.9 4/18 25. 5/18 32. 6/18 10. 7/18 4.1 8/18 17. 9/18 4.0 10/18 6.4 11/18 3.1 12/18 9.1 13/18 11. 14/18 11. 15/18 3.3 16/18 5.9 17/18 7.5 18/18 \n",
+      "Attempt 1243 with (29, 1) and 5: 6.6 1/8 5.8 2/8 3.6 3/12 3.6 4/16 5.1 5/16 4.8 6/16 7.1 7/16 18. 8/16 9.4 9/16 35. 10/16 8.2 11/16 5.1 12/16 27. 13/16 9.4 14/16 7.3 15/16 4.5 16/16 \n",
+      "Attempt 1244 with (1, 0) and 10: 3.9 1/12 4.5 2/12 4.7 3/12 87. 4/12 7.2 5/12 6.0 6/12 10. 7/12 4.8 8/12 6.4 9/12 7.3 10/12 8.3 11/12 9.0 12/12 \n",
+      "Attempt 1245 with (4, 7/67) and 3: 12. 1/8 8.0 2/8 17. 3/8 11. 4/8 9.1 5/8 23. 6/8 6.0 7/8 8.4 8/8 \n",
+      "Attempt 1246 with (0, 1/5) and 4: 6.9 1/8 16. 2/8 17. 3/8 3.8 4/12 6.5 5/12 10. 6/12 7.6 7/12 16. 8/12 3.2 9/16 6.2 10/16 8.3 11/16 57. 12/16 33. 13/16 7.3 14/16 4.2 15/16 6.8 16/16 \n",
+      "Attempt 1247 with (1/43, 0) and 3: 4.8 1/8 12. 2/8 4.1 3/8 5.8 4/8 2.7 5/40 3.5 6/40 6.2 7/40 5.6 8/40 4.5 9/40 6.5 10/40 13. 11/40 16. 12/40 5.6 13/40 6.7 14/40 3.0 15/40 3.6 16/40 13. 17/40 9.2 18/40 8.2 19/40 11. 20/40 5.6 21/40 7.8 22/40 6.2 23/40 5.1 24/40 10. 25/40 28. 26/40 25. 27/40 22. 28/40 24. 29/40 140. 30/40 160. 31/40 24. 32/40 9.1 33/40 7.7 34/40 11. 35/40 4.8 36/40 6.7 37/40 6.0 38/40 5.5 39/40 13. 40/40 \n",
+      "Attempt 1248 with (2/3, 31/2) and 7: 18. 1/8 6.3 2/8 5.9 3/8 12. 4/8 6.0 5/8 5.6 6/8 6.1 7/8 6.5 8/8 \n",
+      "Attempt 1249 with (1/5, 1/8) and 9: 4.2 1/8 7.1 2/8 9.9 3/8 5.3 4/8 5.9 5/8 7.1 6/8 5.1 7/8 6.0 8/8 \n",
+      "Attempt 1250 with (7/3, 2/3) and 4: 29. 1/8 16. 2/8 15. 3/8 4.5 4/8 4.9 5/8 3.2 6/12 3.6 7/16 4.5 8/16 3.1 9/20 4.4 10/20 4.6 11/20 5.2 12/20 4.1 13/20 4.1 14/20 4.4 15/20 7.8 16/20 5.6 17/20 6.9 18/20 7.9 19/20 10. 20/20 \n",
+      "Attempt 1251 with (1/2, 1/15) and 8: 4.0 1/8 6.4 2/8 12. 3/8 15. 4/8 8.0 5/8 5.6 6/8 5.6 7/8 15. 8/8 \n",
+      "Attempt 1252 with (1/14, 53) and 3: 7.7 1/8 5.5 2/8 6.2 3/8 5.3 4/8 4.6 5/8 4.9 6/8 5.1 7/8 4.8 8/8 \n",
+      "Attempt 1253 with (2/15, 0) and 5: 7.9 1/8 5.8 2/8 5.9 3/8 5.2 4/8 9.5 5/8 32. 6/8 8.1 7/8 4.9 8/8 \n",
+      "Attempt 1254 with (0, 1/7) and 13: 33. 1/8 38. 2/8 8.3 3/8 6.8 4/8 14. 5/8 100. 6/8 90. 7/8 8.9 8/8 \n",
+      "Attempt 1255 with (2/13, 1) and 4: 14. 1/8 17. 2/8 61. 3/8 5.3 4/8 4.7 5/8 7.3 6/8 4.9 7/8 5.2 8/8 \n",
+      "Attempt 1256 with (4, 1) and 3: 13. 1/8 17. 2/8 11. 3/8 5.9 4/8 3.2 5/12 4.8 6/12 5.0 7/12 11. 8/12 7.2 9/12 6.7 10/12 7.1 11/12 19. 12/12 \n",
+      "Attempt 1257 with (0, 1/109) and 4: 5.7 1/8 6.2 2/8 5.3 3/8 4.3 4/8 17. 5/8 11. 6/8 7.3 7/8 13. 8/8 \n",
+      "Attempt 1258 with (0, 1/68) and 8: 11. 1/8 9.6 2/8 5.1 3/8 9.6 4/8 17. 5/8 36. 6/8 4.8 7/8 5.3 8/8 \n",
+      "Attempt 1259 with (1, 1) and 15: 8.1 1/8 8.3 2/8 54. 3/8 5.5 4/8 6.7 5/8 5.1 6/8 2.6 7/16 5.3 8/16 4.1 9/16 4.2 10/16 8.6 11/16 5.3 12/16 7.2 13/16 10. 14/16 7.2 15/16 5.4 16/16 \n",
+      "Attempt 1260 with (3/2, 1/5) and 3: 7.1 1/8 6.6 2/8 6.5 3/8 6.4 4/8 18. 5/8 5.5 6/8 3.7 7/12 5.5 8/12 11. 9/12 7.5 10/12 13. 11/12 29. 12/12 \n",
+      "Attempt 1261 with (1/2, 3) and 7: 5.4 1/8 19. 2/8 6.5 3/8 17. 4/8 2.6 5/23 9.9 6/23 4.8 7/23 11. 8/23 7.6 9/23 10. 10/23 9.4 11/23 23. 12/23 12. 13/23 5.1 14/23 6.4 15/23 19. 16/23 4.9 17/23 11. 18/23 4.8 19/23 8.7 20/23 5.6 21/23 36. 22/23 49. 23/23 \n",
+      "Attempt 1262 with (2, 0) and 63: 12. 1/8 30. 2/8 7.6 3/8 18. 4/8 3.7 5/11 4.7 6/11 5.0 7/11 5.5 8/11 4.8 9/11 6.2 10/11 14. 11/11 \n",
+      "Attempt 1263 with (1/2, 4) and 14: 52. 1/8 4.9 2/8 6.4 3/8 7.4 4/8 4.8 5/8 26. 6/8 15. 7/8 100. 8/8 \n",
+      "Attempt 1264 with (7/59, 7) and 4: 10. 1/8 63. 2/8 18. 3/8 6.8 4/8 11. 5/8 13. 6/8 5.9 7/8 12. 8/8 \n",
+      "Attempt 1265 with (1, 17/4) and 4: 5.4 1/8 13. 2/8 12. 3/8 3.6 4/12 7.1 5/12 11. 6/12 4.9 7/12 7.8 8/12 10. 9/12 4.8 10/12 3.3 11/16 11. 12/16 14. 13/16 7.4 14/16 29. 15/16 8.9 16/16 \n",
+      "Attempt 1266 with (1/4, 1/2) and 7: 11. 1/8 4.8 2/8 5.0 3/8 5.8 4/8 7.3 5/8 11. 6/8 6.0 7/8 22. 8/8 \n",
+      "Attempt 1267 with (22, 2/17) and 4: 9.8 1/8 8.9 2/8 3.3 3/12 5.1 4/12 4.0 5/12 5.6 6/12 13. 7/12 3.6 8/16 4.9 9/16 20. 10/16 11. 11/16 6.4 12/16 6.0 13/16 23. 14/16 11. 15/16 5.4 16/16 \n",
+      "Attempt 1268 with (3, 0) and 96: 24. 1/8 11. 2/8 14. 3/8 5.0 4/8 5.5 5/8 7.7 6/8 41. 7/8 13. 8/8 \n",
+      "Attempt 1269 with (0, 1) and 16: 200. 1/8 43. 2/8 12. 3/8 6.1 4/8 9.0 5/8 14. 6/8 10. 7/8 9.9 8/8 \n",
+      "Attempt 1270 with (0, 12) and 9: 12. 1/8 8.5 2/8 11. 3/8 12. 4/8 11. 5/8 45. 6/8 6.1 7/8 5.0 8/8 \n",
+      "Attempt 1271 with (1, 1) and 25: 3.4 1/12 18. 2/12 6.6 3/12 8.7 4/12 2.6 5/14 4.6 6/14 16. 7/14 10. 8/14 4.8 9/14 5.5 10/14 32. 11/14 38. 12/14 6.8 13/14 13. 14/14 \n",
+      "Attempt 1272 with (6, 2) and 3: 7.5 1/8 4.7 2/8 5.8 3/8 2.6 4/40 4.0 5/40 4.6 6/40 11. 7/40 25. 8/40 6.9 9/40 8.7 10/40 4.6 11/40 4.8 12/40 3.6 13/40 12. 14/40 20. 15/40 12. 16/40 4.7 17/40 3.8 18/40 5.1 19/40 6.2 20/40 3.6 21/40 6.8 22/40 12. 23/40 9.2 24/40 2.3 25/40 4.0 26/40 5.0 27/40 8.8 28/40 13. 29/40 4.2 30/40 4.1 31/40 5.4 32/40 5.2 33/40 3.6 34/40 4.4 35/40 4.7 36/40 6.9 37/40 13. 38/40 6.9 39/40 13. 40/40 \n",
+      "Attempt 1273 with (0, 10) and 12: 7.8 1/8 25. 2/8 4.9 3/8 6.7 4/8 2.8 5/18 3.3 6/18 6.6 7/18 10. 8/18 42. 9/18 11. 10/18 4.4 11/18 15. 12/18 12. 13/18 9.4 14/18 4.0 15/18 5.0 16/18 7.4 17/18 5.4 18/18 \n",
+      "Attempt 1274 with (2, 1/6) and 9: 5.1 1/8 4.6 2/8 5.6 3/8 10. 4/8 6.9 5/8 28. 6/8 20. 7/8 33. 8/8 \n",
+      "Attempt 1275 with (3, 11/3) and 3: 9.6 1/8 5.7 2/8 13. 3/8 5.3 4/8 3.7 5/12 4.7 6/12 3.2 7/16 4.1 8/16 6.2 9/16 11. 10/16 11. 11/16 54. 12/16 90. 13/16 12. 14/16 8.2 15/16 5.3 16/16 \n",
+      "Attempt 1276 with (0, 1/3) and 15: 12. 1/8 24. 2/8 42. 3/8 6.2 4/8 5.7 5/8 5.1 6/8 12. 7/8 8.1 8/8 \n",
+      "Attempt 1277 with (7/3, 0) and 3: 4.2 1/8 8.6 2/8 4.3 3/8 10. 4/8 9.5 5/8 15. 6/8 4.8 7/8 3.9 8/12 14. 9/12 9.2 10/12 4.5 11/12 4.6 12/12 \n",
+      "Attempt 1278 with (1/11, 1/3) and 4: 5.4 1/8 7.2 2/8 4.4 3/8 3.8 4/12 5.7 5/12 6.6 6/12 18. 7/12 11. 8/12 11. 9/12 22. 10/12 21. 11/12 6.6 12/12 \n",
+      "Attempt 1279 with (759, 7) and 4: 13. 1/8 11. 2/8 17. 3/8 4.3 4/8 4.1 5/8 9.7 6/8 5.0 7/8 9.6 8/8 \n",
+      "Attempt 1280 with (1/13, 1/2) and 3: 10. 1/8 10. 2/8 15. 3/8 28. 4/8 83. 5/8 33. 6/8 28. 7/8 73. 8/8 \n",
+      "Attempt 1281 with (7, 2) and 7: 16. 1/8 4.2 2/8 5.9 3/8 4.8 4/8 5.9 5/8 4.9 6/8 21. 7/8 7.6 8/8 \n",
+      "Attempt 1282 with (4, 3) and 15: 11. 1/8 16. 2/8 7.8 3/8 5.1 4/8 15. 5/8 6.0 6/8 8.7 7/8 5.9 8/8 \n",
+      "Attempt 1283 with (13/6, 0) and 8: 26. 1/8 26. 2/8 7.4 3/8 10. 4/8 5.4 5/8 12. 6/8 9.0 7/8 4.5 8/8 \n",
+      "Attempt 1284 with (2, 1) and 19: 11. 1/8 6.5 2/8 17. 3/8 11. 4/8 10. 5/8 5.9 6/8 14. 7/8 3.9 8/12 6.0 9/12 18. 10/12 4.6 11/12 2.9 12/15 18. 13/15 11. 14/15 6.7 15/15 \n",
+      "Attempt 1285 with (1/78, 3/25) and 4: 20. 1/8 20. 2/8 20. 3/8 19. 4/8 18. 5/8 16. 6/8 17. 7/8 14. 8/8 \n",
+      "Attempt 1286 with (1/2, 1/3) and 20: 2.6 1/15 6.5 2/15 7.6 3/15 8.7 4/15 4.5 5/15 8.0 6/15 4.3 7/15 13. 8/15 7.8 9/15 6.1 10/15 26. 11/15 5.6 12/15 32. 13/15 7.2 14/15 4.9 15/15 \n",
+      "Attempt 1287 with (3, 1/10) and 12: 6.3 1/8 7.6 2/8 14. 3/8 6.6 4/8 49. 5/8 7.1 6/8 5.5 7/8 17. 8/8 \n",
+      "Attempt 1288 with (1/4, 1) and 6: 8.8 1/8 7.5 2/8 6.0 3/8 15. 4/8 4.0 5/12 8.1 6/12 8.4 7/12 22. 8/12 5.3 9/12 6.6 10/12 13. 11/12 8.4 12/12 \n",
+      "Attempt 1289 with (3/11, 2) and 3: 9.1 1/8 4.8 2/8 12. 3/8 5.3 4/8 7.6 5/8 4.2 6/8 3.3 7/12 6.5 8/12 7.9 9/12 21. 10/12 16. 11/12 9.7 12/12 \n",
+      "Attempt 1290 with (1, 12/5) and 4: 6.2 1/8 12. 2/8 4.6 3/8 7.1 4/8 4.8 5/8 12. 6/8 4.0 7/12 6.3 8/12 24. 9/12 5.5 10/12 15. 11/12 6.7 12/12 \n",
+      "Attempt 1291 with (1/2, 1/6) and 16: 3.8 1/12 22. 2/12 11. 3/12 4.5 4/12 9.6 5/12 11. 6/12 30. 7/12 33. 8/12 8.3 9/12 31. 10/12 4.2 11/12 7.6 12/12 \n",
+      "Attempt 1292 with (0, 29) and 6: 10. 1/8 29. 2/8 25. 3/8 9.6 4/8 5.6 5/8 11. 6/8 57. 7/8 25. 8/8 \n",
+      "Attempt 1293 with (1, 62/5) and 3: 7.6 1/8 3.9 2/12 7.4 3/12 18. 4/12 10. 5/12 3.8 6/16 13. 7/16 6.5 8/16 4.8 9/16 14. 10/16 4.7 11/16 4.8 12/16 6.8 13/16 3.4 14/20 4.0 15/20 6.1 16/20 23. 17/20 68. 18/20 7.0 19/20 3.8 20/24 5.4 21/24 7.1 22/24 6.6 23/24 5.8 24/24 \n",
+      "Attempt 1294 with (1/3, 3/55) and 14: 3.6 1/12 7.6 2/12 7.5 3/12 17. 4/12 4.6 5/12 5.7 6/12 30. 7/12 10. 8/12 9.4 9/12 6.5 10/12 40. 11/12 7.7 12/12 \n",
+      "Attempt 1295 with (1/4, 7) and 16: 3.5 1/12 13. 2/12 13. 3/12 9.7 4/12 18. 5/12 160. 6/12 14. 7/12 37. 8/12 5.7 9/12 7.0 10/12 8.4 11/12 4.2 12/12 \n",
+      "Attempt 1296 with (4, 3) and 14: 9.7 1/8 20. 2/8 5.9 3/8 7.2 4/8 5.2 5/8 5.3 6/8 5.7 7/8 4.5 8/8 \n",
+      "Attempt 1297 with (2, 1/48) and 8: 5.0 1/8 5.4 2/8 20. 3/8 12. 4/8 84. 5/8 18. 6/8 12. 7/8 16. 8/8 \n",
+      "Attempt 1298 with (3/4, 1/8) and 5: 9.5 1/8 15. 2/8 71. 3/8 10. 4/8 8.6 5/8 12. 6/8 5.7 7/8 4.3 8/8 \n",
+      "Attempt 1299 with (1/14, 1) and 6: 4.4 1/8 8.7 2/8 3.5 3/12 5.9 4/12 4.5 5/12 14. 6/12 6.9 7/12 21. 8/12 6.1 9/12 11. 10/12 3.5 11/16 7.6 12/16 2.7 13/25 14. 14/25 3.3 15/25 15. 16/25 6.2 17/25 9.9 18/25 5.0 19/25 4.9 20/25 11. 21/25 3.8 22/25 5.2 23/25 4.1 24/25 3.5 25/25 \n",
+      "Attempt 1300 with (7, 1) and 4: 7.2 1/8 14. 2/8 6.6 3/8 6.9 4/8 22. 5/8 18. 6/8 5.5 7/8 7.3 8/8 \n",
+      "Attempt 1301 with (2/7, 0) and 3: 5.4 1/8 5.0 2/8 8.1 3/8 6.5 4/8 7.0 5/8 8.8 6/8 8.3 7/8 3.3 8/12 3.8 9/16 11. 10/16 3.9 11/20 7.9 12/20 3.9 13/24 5.8 14/24 11. 15/24 20. 16/24 5.7 17/24 4.2 18/24 14. 19/24 6.6 20/24 8.4 21/24 6.1 22/24 16. 23/24 18. 24/24 \n",
+      "Attempt 1302 with (2, 3) and 44: 24. 1/8 4.3 2/8 4.6 3/8 2.2 4/12 5.2 5/12 8.0 6/12 18. 7/12 3.7 8/12 6.5 9/12 6.4 10/12 6.1 11/12 11. 12/12 \n",
+      "Attempt 1303 with (2, 42) and 17: 6.6 1/8 23. 2/8 14. 3/8 9.5 4/8 140. 5/8 11. 6/8 22. 7/8 18. 8/8 \n",
+      "Attempt 1304 with (2, 2/7) and 3: 27. 1/8 7.0 2/8 5.7 3/8 6.2 4/8 5.9 5/8 4.0 6/12 5.1 7/12 4.1 8/12 6.8 9/12 13. 10/12 18. 11/12 6.2 12/12 \n",
+      "Attempt 1305 with (1, 3/5) and 7: 14. 1/8 4.6 2/8 4.8 3/8 5.9 4/8 17. 5/8 17. 6/8 3.6 7/12 7.8 8/12 38. 9/12 11. 10/12 4.5 11/12 9.7 12/12 \n",
+      "Attempt 1306 with (0, 28/3) and 4: 11. 1/8 5.0 2/8 14. 3/8 7.7 4/8 6.9 5/8 3.3 6/12 4.9 7/12 4.4 8/12 7.0 9/12 11. 10/12 4.4 11/12 9.2 12/12 \n",
+      "Attempt 1307 with (6, 1/28) and 4: 6.1 1/8 18. 2/8 10. 3/8 7.7 4/8 6.8 5/8 13. 6/8 9.1 7/8 11. 8/8 \n",
+      "Attempt 1308 with (1/21, 3) and 4: 4.1 1/8 4.2 2/8 4.2 3/8 8.1 4/8 5.9 5/8 8.8 6/8 5.2 7/8 7.5 8/8 \n",
+      "Attempt 1309 with (1/29, 0) and 3: 3.7 1/12 4.4 2/12 17. 3/12 6.5 4/12 3.5 5/16 13. 6/16 6.2 7/16 7.3 8/16 8.0 9/16 29. 10/16 32. 11/16 210. 12/16 22. 13/16 7.1 14/16 7.6 15/16 7.8 16/16 \n",
+      "Attempt 1310 with (2, 1/5) and 4: 6.2 1/8 6.3 2/8 17. 3/8 7.7 4/8 16. 5/8 37. 6/8 14. 7/8 6.4 8/8 \n",
+      "Attempt 1311 with (1/4, 1/7) and 5: 3.3 1/12 15. 2/12 12. 3/12 35. 4/12 13. 5/12 33. 6/12 14. 7/12 5.9 8/12 8.8 9/12 7.9 10/12 9.4 11/12 3.6 12/16 3.7 13/20 6.0 14/20 9.5 15/20 9.3 16/20 6.3 17/20 9.4 18/20 12. 19/20 7.6 20/20 \n",
+      "Attempt 1312 with (1/4, 27/8) and 7: 7.4 1/8 4.5 2/8 2.4 3/23 3.7 4/23 4.4 5/23 8.5 6/23 38. 7/23 11. 8/23 29. 9/23 66. 10/23 50. 11/23 15. 12/23 9.1 13/23 6.2 14/23 5.2 15/23 3.8 16/23 11. 17/23 5.0 18/23 10. 19/23 14. 20/23 6.7 21/23 3.3 22/23 3.4 23/23 \n",
+      "Attempt 1313 with (7/5, 0) and 3: 37. 1/8 29. 2/8 140. 3/8 14. 4/8 5.6 5/8 4.8 6/8 4.0 7/12 3.9 8/16 5.7 9/16 7.0 10/16 8.5 11/16 4.3 12/16 21. 13/16 5.1 14/16 8.3 15/16 5.5 16/16 \n",
+      "Attempt 1314 with (2/29, 1) and 4: 5.2 1/8 11. 2/8 13. 3/8 7.6 4/8 16. 5/8 3.8 6/12 8.9 7/12 5.1 8/12 4.9 9/12 6.1 10/12 8.2 11/12 5.4 12/12 \n",
+      "Attempt 1315 with (1, 1/3) and 9: 15. 1/8 4.4 2/8 6.3 3/8 7.1 4/8 3.9 5/12 7.4 6/12 24. 7/12 3.8 8/16 7.2 9/16 9.0 10/16 10. 11/16 17. 12/16 15. 13/16 4.8 14/16 6.0 15/16 4.3 16/16 \n",
+      "Attempt 1316 with (1/3, 1/3) and 3: 5.7 1/8 5.3 2/8 8.3 3/8 22. 4/8 24. 5/8 21. 6/8 13. 7/8 28. 8/8 \n",
+      "Attempt 1317 with (2, 9) and 18: 6.2 1/8 8.1 2/8 16. 3/8 49. 4/8 10. 5/8 10. 6/8 7.6 7/8 50. 8/8 \n",
+      "Attempt 1318 with (2, 1/8) and 6: 18. 1/8 11. 2/8 6.0 3/8 12. 4/8 22. 5/8 45. 6/8 7.7 7/8 7.1 8/8 \n",
+      "Attempt 1319 with (3/2, 2) and 4: 6.3 1/8 3.9 2/12 11. 3/12 23. 4/12 4.4 5/12 5.2 6/12 6.0 7/12 7.2 8/12 8.1 9/12 7.8 10/12 8.4 11/12 13. 12/12 \n",
+      "Attempt 1320 with (5, 7) and 5: 2.8 1/28 5.9 2/28 5.3 3/28 5.9 4/28 4.0 5/28 6.8 6/28 4.2 7/28 11. 8/28 4.6 9/28 9.5 10/28 5.9 11/28 5.2 12/28 12. 13/28 14. 14/28 7.1 15/28 4.7 16/28 5.6 17/28 12. 18/28 18. 19/28 85. 20/28 29. 21/28 6.7 22/28 4.9 23/28 5.3 24/28 4.9 25/28 5.7 26/28 24. 27/28 14. 28/28 \n",
+      "Attempt 1321 with (1/4, 3/22) and 4: 10. 1/8 5.5 2/8 9.0 3/8 6.3 4/8 7.5 5/8 8.6 6/8 5.8 7/8 11. 8/8 \n",
+      "Attempt 1322 with (2, 5) and 4: 2.9 1/32 4.3 2/32 9.7 3/32 5.7 4/32 6.3 5/32 4.9 6/32 3.9 7/32 9.5 8/32 8.4 9/32 5.7 10/32 2.1 11/32 5.6 12/32 7.9 13/32 28. 14/32 18. 15/32 22. 16/32 8.8 17/32 9.6 18/32 15. 19/32 36. 20/32 80. 21/32 23. 22/32 14. 23/32 40. 24/32 11. 25/32 12. 26/32 15. 27/32 5.3 28/32 5.4 29/32 5.8 30/32 3.2 31/32 3.5 32/32 \n",
+      "Attempt 1323 with (1, 8) and 5: 17. 1/8 4.2 2/8 7.5 3/8 5.3 4/8 38. 5/8 5.5 6/8 6.4 7/8 13. 8/8 \n",
+      "Attempt 1324 with (0, 5) and 10: 21. 1/8 9.0 2/8 5.4 3/8 6.8 4/8 7.2 5/8 15. 6/8 4.2 7/8 4.2 8/8 \n",
+      "Attempt 1325 with (1/14, 1) and 10: 12. 1/8 17. 2/8 190. 3/8 38. 4/8 560. 5/8 92. 6/8 37. 7/8 36. 8/8 \n",
+      "Attempt 1326 with (3, 1/11) and 11: 10. 1/8 5.7 2/8 7.4 3/8 5.9 4/8 4.6 5/8 3.3 6/12 96. 7/12 2.7 8/19 16. 9/19 4.9 10/19 3.6 11/19 5.9 12/19 9.4 13/19 7.4 14/19 3.8 15/19 3.0 16/19 4.4 17/19 6.7 18/19 4.4 19/19 \n",
+      "Attempt 1327 with (1/2, 1/16) and 15: 5.5 1/8 4.8 2/8 13. 3/8 18. 4/8 14. 5/8 6.3 6/8 5.8 7/8 8.5 8/8 \n",
+      "Attempt 1328 with (2, 3/2) and 35: 9.4 1/8 8.5 2/8 3.3 3/12 32. 4/12 17. 5/12 11. 6/12 9.0 7/12 4.6 8/12 16. 9/12 55. 10/12 4.2 11/12 6.9 12/12 \n",
+      "Attempt 1329 with (1/3, 4) and 7: 12. 1/8 30. 2/8 15. 3/8 8.8 4/8 4.8 5/8 8.6 6/8 27. 7/8 5.5 8/8 \n",
+      "Attempt 1330 with (1, 1/8) and 4: 5.0 1/8 10. 2/8 16. 3/8 18. 4/8 13. 5/8 4.7 6/8 3.0 7/12 5.1 8/12 7.3 9/12 11. 10/12 4.9 11/12 24. 12/12 \n",
+      "Attempt 1331 with (3/2, 1/30) and 7: 4.0 1/8 6.2 2/8 6.3 3/8 4.5 4/8 76. 5/8 9.8 6/8 4.7 7/8 16. 8/8 \n",
+      "Attempt 1332 with (1, 3) and 9: 6.0 1/8 7.2 2/8 3.2 3/12 3.9 4/16 3.4 5/20 15. 6/20 3.7 7/20 5.4 8/20 17. 9/20 17. 10/20 7.1 11/20 11. 12/20 6.7 13/20 24. 14/20 4.4 15/20 4.6 16/20 7.7 17/20 11. 18/20 18. 19/20 5.7 20/20 \n",
+      "Attempt 1333 with (4, 1/8) and 3: 7.1 1/8 6.9 2/8 5.2 3/8 5.5 4/8 12. 5/8 15. 6/8 19. 7/8 6.5 8/8 \n",
+      "Attempt 1334 with (3/2, 1) and 4: 4.6 1/8 4.1 2/8 9.8 3/8 3.4 4/12 6.4 5/12 5.4 6/12 4.8 7/12 11. 8/12 13. 9/12 7.0 10/12 3.6 11/16 7.0 12/16 3.5 13/20 9.2 14/20 5.3 15/20 8.7 16/20 8.7 17/20 70. 18/20 8.8 19/20 5.0 20/20 \n",
+      "Attempt 1335 with (14, 0) and 5: 8.3 1/8 16. 2/8 9.4 3/8 3.6 4/12 6.7 5/12 5.8 6/12 8.5 7/12 6.0 8/12 15. 9/12 10. 10/12 12. 11/12 4.3 12/12 \n",
+      "Attempt 1336 with (2/5, 3/4) and 6: 6.9 1/8 6.4 2/8 6.0 3/8 13. 4/8 72. 5/8 13. 6/8 6.6 7/8 37. 8/8 \n",
+      "Attempt 1337 with (1/2, 1/7) and 14: 6.3 1/8 7.1 2/8 2.8 3/17 10. 4/17 7.5 5/17 5.2 6/17 16. 7/17 5.9 8/17 4.8 9/17 9.7 10/17 11. 11/17 6.8 12/17 3.3 13/17 13. 14/17 5.7 15/17 4.7 16/17 4.6 17/17 \n",
+      "Attempt 1338 with (7/17, 2) and 4: 2.6 1/32 14. 2/32 7.4 3/32 3.4 4/32 14. 5/32 9.8 6/32 4.2 7/32 5.5 8/32 32. 9/32 42. 10/32 22. 11/32 5.5 12/32 5.7 13/32 9.3 14/32 3.8 15/32 6.5 16/32 4.7 17/32 24. 18/32 7.5 19/32 6.4 20/32 4.3 21/32 4.9 22/32 7.0 23/32 8.4 24/32 55. 25/32 5.9 26/32 15. 27/32 2.4 28/32 6.0 29/32 11. 30/32 3.4 31/32 8.5 32/32 \n",
+      "Attempt 1339 with (1/48, 1/3) and 37: 5.2 1/8 4.1 2/8 7.6 3/8 6.8 4/8 3.6 5/12 6.0 6/12 41. 7/12 3.4 8/12 11. 9/12 13. 10/12 6.6 11/12 50. 12/12 \n",
+      "Attempt 1340 with (1/3, 49) and 9: 22. 1/8 9.1 2/8 5.0 3/8 27. 4/8 15. 5/8 31. 6/8 5.5 7/8 4.6 8/8 \n",
+      "Attempt 1341 with (65, 4) and 4: 24. 1/8 11. 2/8 5.8 3/8 4.4 4/8 7.7 5/8 23. 6/8 19. 7/8 14. 8/8 \n",
+      "Attempt 1342 with (1, 2) and 6: 7.2 1/8 7.7 2/8 16. 3/8 5.8 4/8 40. 5/8 17. 6/8 4.1 7/8 6.7 8/8 \n",
+      "Attempt 1343 with (1/2, 1/12) and 30: 17. 1/8 28. 2/8 20. 3/8 7.3 4/8 6.6 5/8 25. 6/8 8.4 7/8 14. 8/8 \n",
+      "Attempt 1344 with (3, 1/4) and 4: 3.4 1/12 3.4 2/16 4.8 3/16 31. 4/16 6.6 5/16 5.3 6/16 5.4 7/16 6.4 8/16 4.7 9/16 8.4 10/16 5.1 11/16 3.9 12/20 3.5 13/24 4.5 14/24 18. 15/24 8.8 16/24 17. 17/24 50. 18/24 53. 19/24 17. 20/24 4.5 21/24 5.5 22/24 8.5 23/24 2.8 24/32 2.9 25/32 8.5 26/32 15. 27/32 18. 28/32 4.8 29/32 15. 30/32 4.9 31/32 3.9 32/32 \n",
+      "Attempt 1345 with (3/2, 1) and 6: 4.8 1/8 7.4 2/8 15. 3/8 47. 4/8 3.5 5/12 6.7 6/12 7.4 7/12 3.2 8/16 4.6 9/16 14. 10/16 29. 11/16 6.4 12/16 6.8 13/16 10. 14/16 7.4 15/16 6.2 16/16 \n",
+      "Attempt 1346 with (9, 83) and 4: 21. 1/8 10. 2/8 5.0 3/8 7.2 4/8 3.7 5/12 8.1 6/12 13. 7/12 3.3 8/16 5.5 9/16 91. 10/16 8.2 11/16 3.4 12/20 5.6 13/20 15. 14/20 66. 15/20 13. 16/20 10. 17/20 6.5 18/20 5.3 19/20 7.5 20/20 \n",
+      "Attempt 1347 with (1, 4/25) and 3: 12. 1/8 16. 2/8 7.3 3/8 15. 4/8 20. 5/8 32. 6/8 34. 7/8 15. 8/8 \n",
+      "Attempt 1348 with (1/15, 1/2) and 14: 10. 1/8 11. 2/8 7.3 3/8 5.5 4/8 5.9 5/8 8.4 6/8 11. 7/8 4.4 8/8 \n",
+      "Attempt 1349 with (25, 21/2) and 4: 5.5 1/8 7.4 2/8 6.7 3/8 22. 4/8 6.4 5/8 7.7 6/8 2.6 7/32 4.3 8/32 9.5 9/32 14. 10/32 5.1 11/32 4.4 12/32 4.3 13/32 6.2 14/32 10. 15/32 49. 16/32 28. 17/32 15. 18/32 10. 19/32 32. 20/32 7.6 21/32 12. 22/32 6.3 23/32 4.3 24/32 9.2 25/32 5.0 26/32 2.5 27/32 12. 28/32 4.3 29/32 4.7 30/32 5.5 31/32 5.5 32/32 \n",
+      "Attempt 1350 with (0, 58) and 3: 79. 1/8 30. 2/8 19. 3/8 13. 4/8 37. 5/8 12. 6/8 5.9 7/8 5.3 8/8 \n",
+      "Attempt 1351 with (1, 4/3) and 33: 17. 1/8 5.0 2/8 12. 3/8 3.7 4/12 3.5 5/13 170. 6/13 20. 7/13 6.1 8/13 6.0 9/13 18. 10/13 14. 11/13 22. 12/13 7.2 13/13 \n",
+      "Attempt 1352 with (3/5, 0) and 9: 18. 1/8 12. 2/8 8.3 3/8 8.7 4/8 6.0 5/8 4.4 6/8 3.4 7/12 11. 8/12 5.1 9/12 5.3 10/12 2.8 11/20 64. 12/20 14. 13/20 8.1 14/20 3.1 15/20 4.1 16/20 5.7 17/20 5.2 18/20 2.9 19/20 3.6 20/20 \n",
+      "Attempt 1353 with (1/6, 0) and 3: 3.3 1/12 9.3 2/12 8.1 3/12 12. 4/12 26. 5/12 28. 6/12 12. 7/12 11. 8/12 4.0 9/16 5.3 10/16 6.5 11/16 11. 12/16 8.3 13/16 7.5 14/16 8.6 15/16 6.1 16/16 \n",
+      "Attempt 1354 with (1, 2) and 10: 47. 1/8 5.5 2/8 4.6 3/8 6.5 4/8 8.0 5/8 7.6 6/8 4.3 7/8 140. 8/8 \n",
+      "Attempt 1355 with (4/3, 1) and 3: 3.2 1/12 6.5 2/12 16. 3/12 8.2 4/12 9.6 5/12 9.6 6/12 8.3 7/12 10. 8/12 9.6 9/12 8.1 10/12 23. 11/12 8.0 12/12 \n",
+      "Attempt 1356 with (1, 5) and 5: 32. 1/8 110. 2/8 20. 3/8 10. 4/8 8.7 5/8 6.6 6/8 6.1 7/8 4.1 8/8 \n",
+      "Attempt 1357 with (6/13, 1) and 4: 29. 1/8 11. 2/8 9.1 3/8 7.4 4/8 4.9 5/8 5.3 6/8 6.3 7/8 4.6 8/8 \n",
+      "Attempt 1358 with (23, 1/3) and 3: 11. 1/8 4.0 2/12 5.1 3/12 5.2 4/12 4.1 5/12 5.7 6/12 4.1 7/12 11. 8/12 10. 9/12 4.1 10/12 9.1 11/12 10. 12/12 \n",
+      "Attempt 1359 with (1, 3/2) and 20: 3.7 1/12 7.0 2/12 8.8 3/12 8.8 4/12 5.0 5/12 14. 6/12 4.4 7/12 17. 8/12 5.6 9/12 5.7 10/12 3.1 11/15 8.3 12/15 7.7 13/15 27. 14/15 8.7 15/15 \n",
+      "Attempt 1360 with (57/2, 1) and 4: 2.8 1/32 3.8 2/32 4.0 3/32 15. 4/32 37. 5/32 12. 6/32 15. 7/32 23. 8/32 91. 9/32 43. 10/32 21. 11/32 28. 12/32 8.5 13/32 12. 14/32 5.3 15/32 5.5 16/32 4.5 17/32 5.1 18/32 7.1 19/32 6.8 20/32 8.6 21/32 9.8 22/32 19. 23/32 7.5 24/32 15. 25/32 8.4 26/32 5.7 27/32 5.8 28/32 8.5 29/32 7.5 30/32 15. 31/32 6.6 32/32 \n",
+      "Attempt 1361 with (1, 26) and 5: 4.3 1/8 23. 2/8 6.7 3/8 8.8 4/8 5.8 5/8 23. 6/8 8.1 7/8 5.7 8/8 \n",
+      "Attempt 1362 with (1, 1/19) and 3: 17. 1/8 36. 2/8 15. 3/8 22. 4/8 10. 5/8 20. 6/8 11. 7/8 10. 8/8 \n",
+      "Attempt 1363 with (0, 20) and 4: 8.9 1/8 6.5 2/8 13. 3/8 68. 4/8 11. 5/8 6.9 6/8 4.7 7/8 11. 8/8 \n",
+      "Attempt 1364 with (1/37, 1) and 3: 19. 1/8 12. 2/8 14. 3/8 12. 4/8 9.2 5/8 8.4 6/8 10. 7/8 7.8 8/8 \n",
+      "Attempt 1365 with (1/5, 6) and 3: 6.6 1/8 4.3 2/8 6.4 3/8 12. 4/8 10. 5/8 5.1 6/8 12. 7/8 5.5 8/8 \n",
+      "Attempt 1366 with (1/109, 6) and 4: 9.1 1/8 11. 2/8 10. 3/8 6.8 4/8 9.7 5/8 6.6 6/8 30. 7/8 6.9 8/8 \n",
+      "Attempt 1367 with (2, 2/3) and 9: 9.4 1/8 12. 2/8 4.9 3/8 6.4 4/8 24. 5/8 21. 6/8 4.3 7/8 6.0 8/8 \n",
+      "Attempt 1368 with (1/9, 0) and 5: 110. 1/8 2900. 2/8 110. 3/8 29. 4/8 13. 5/8 8.4 6/8 7.9 7/8 5.9 8/8 \n",
+      "Attempt 1369 with (2, 2) and 17: 13. 1/8 4.9 2/8 9.3 3/8 6.0 4/8 7.5 5/8 12. 6/8 51. 7/8 4.9 8/8 \n",
+      "Attempt 1370 with (31, 1/4) and 4: 4.9 1/8 8.0 2/8 6.7 3/8 4.9 4/8 8.9 5/8 4.5 6/8 7.6 7/8 12. 8/8 \n",
+      "Attempt 1371 with (2/3, 1/8) and 3: 12. 1/8 15. 2/8 34. 3/8 25. 4/8 11. 5/8 9.6 6/8 7.9 7/8 7.6 8/8 \n",
+      "Attempt 1372 with (1/2, 5/17) and 4: 50. 1/8 51. 2/8 15. 3/8 10. 4/8 8.3 5/8 6.4 6/8 5.7 7/8 11. 8/8 \n",
+      "Attempt 1373 with (28, 0) and 3: 4.8 1/8 6.8 2/8 8.0 3/8 8.1 4/8 6.4 5/8 19. 6/8 48. 7/8 16. 8/8 \n",
+      "Attempt 1374 with (1/8, 13/12) and 3: 6.0 1/8 8.4 2/8 23. 3/8 20. 4/8 45. 5/8 160. 6/8 51. 7/8 53. 8/8 \n",
+      "Attempt 1375 with (3, 9) and 6: 6.6 1/8 12. 2/8 11. 3/8 4.1 4/8 10. 5/8 10. 6/8 11. 7/8 5.8 8/8 \n",
+      "Attempt 1376 with (1/3, 1/17) and 10: 7.4 1/8 5.6 2/8 3.7 3/12 25. 4/12 13. 5/12 9.8 6/12 13. 7/12 5.8 8/12 18. 9/12 7.9 10/12 14. 11/12 7.0 12/12 \n",
+      "Attempt 1377 with (16/9, 1/8) and 4: 4.9 1/8 5.2 2/8 4.5 3/8 6.0 4/8 13. 5/8 18. 6/8 5.4 7/8 9.8 8/8 \n",
+      "Attempt 1378 with (1, 61) and 3: 5.2 1/8 19. 2/8 4.5 3/8 6.1 4/8 6.2 5/8 5.1 6/8 3.8 7/12 9.6 8/12 20. 9/12 25. 10/12 32. 11/12 6.9 12/12 \n",
+      "Attempt 1379 with (1/10, 0) and 10: 4.5 1/8 4.2 2/8 4.1 3/8 5.6 4/8 22. 5/8 9.2 6/8 8.6 7/8 7.0 8/8 \n",
+      "Attempt 1380 with (5, 1/2) and 4: 7.2 1/8 4.0 2/12 20. 3/12 11. 4/12 4.5 5/12 14. 6/12 11. 7/12 14. 8/12 44. 9/12 21. 10/12 9.7 11/12 6.4 12/12 \n",
+      "Attempt 1381 with (1/2, 0) and 45: 5.9 1/8 5.9 2/8 11. 3/8 13. 4/8 6.0 5/8 7.0 6/8 22. 7/8 16. 8/8 \n",
+      "Attempt 1382 with (1/15, 0) and 4: 8.5 1/8 4.7 2/8 16. 3/8 16. 4/8 9.4 5/8 9.5 6/8 6.2 7/8 8.6 8/8 \n",
+      "Attempt 1383 with (8, 1/2) and 179: 6.1 1/8 47. 2/8 8.6 3/8 13. 4/8 12. 5/8 6.1 6/8 16. 7/8 6.7 8/8 \n",
+      "Attempt 1384 with (2, 4) and 5: 10. 1/8 6.8 2/8 4.9 3/8 5.4 4/8 14. 5/8 9.4 6/8 5.5 7/8 7.1 8/8 \n",
+      "Attempt 1385 with (54, 1) and 3: 4.1 1/8 5.7 2/8 4.3 3/8 5.3 4/8 4.4 5/8 17. 6/8 8.1 7/8 17. 8/8 \n",
+      "Attempt 1386 with (5, 33/2) and 5: 6.5 1/8 6.9 2/8 8.4 3/8 9.6 4/8 19. 5/8 7.0 6/8 3.7 7/12 4.3 8/12 9.9 9/12 8.2 10/12 16. 11/12 6.1 12/12 \n",
+      "Attempt 1387 with (1/9, 1) and 4: 14. 1/8 6.8 2/8 8.6 3/8 6.2 4/8 2.8 5/32 6.2 6/32 6.2 7/32 6.7 8/32 4.8 9/32 8.7 10/32 43. 11/32 16. 12/32 24. 13/32 7.6 14/32 5.3 15/32 8.6 16/32 9.2 17/32 4.0 18/32 4.9 19/32 2.5 20/32 4.0 21/32 5.4 22/32 6.3 23/32 4.2 24/32 16. 25/32 5.6 26/32 20. 27/32 15. 28/32 3.1 29/32 5.8 30/32 5.5 31/32 8.5 32/32 \n",
+      "Attempt 1388 with (1/8, 3) and 3: 22. 1/8 11. 2/8 40. 3/8 36. 4/8 130. 5/8 1100. 6/8 100. 7/8 30. 8/8 \n",
+      "Attempt 1389 with (7/6, 2) and 4: 9.4 1/8 4.3 2/8 3.8 3/12 6.4 4/12 5.1 5/12 9.9 6/12 5.4 7/12 7.8 8/12 5.1 9/12 3.4 10/16 6.8 11/16 8.6 12/16 8.2 13/16 15. 14/16 43. 15/16 23. 16/16 \n",
+      "Attempt 1390 with (1/4, 3) and 4: 6.0 1/8 10. 2/8 3.1 3/12 7.8 4/12 6.0 5/12 6.2 6/12 6.5 7/12 35. 8/12 5.3 9/12 4.1 10/12 2.4 11/32 5.1 12/32 6.1 13/32 6.7 14/32 4.2 15/32 3.5 16/32 11. 17/32 2.9 18/32 5.9 19/32 4.9 20/32 37. 21/32 11. 22/32 13. 23/32 5.9 24/32 7.1 25/32 2.8 26/32 5.7 27/32 4.9 28/32 4.6 29/32 7.0 30/32 3.5 31/32 6.0 32/32 \n",
+      "Attempt 1391 with (1/17, 5) and 4: 12. 1/8 29. 2/8 9.2 3/8 4.1 4/8 10. 5/8 4.2 6/8 3.6 7/12 11. 8/12 4.0 9/16 6.2 10/16 7.5 11/16 18. 12/16 7.7 13/16 6.8 14/16 7.7 15/16 6.2 16/16 \n",
+      "Attempt 1392 with (3, 3) and 26: 3.2 1/12 9.8 2/12 8.5 3/12 4.4 4/12 17. 5/12 6.0 6/12 7.5 7/12 4.3 8/12 4.3 9/12 18. 10/12 3.4 11/14 7.2 12/14 3.1 13/14 17. 14/14 \n",
+      "Attempt 1393 with (2/9, 0) and 4: 11. 1/8 22. 2/8 13. 3/8 11. 4/8 5.9 5/8 33. 6/8 9.4 7/8 16. 8/8 \n",
+      "Attempt 1394 with (3, 3) and 4: 22. 1/8 18. 2/8 50. 3/8 98. 4/8 84. 5/8 37. 6/8 19. 7/8 26. 8/8 \n",
+      "Attempt 1395 with (0, 8) and 4: 12. 1/8 5.5 2/8 8.9 3/8 6.0 4/8 6.2 5/8 19. 6/8 15. 7/8 19. 8/8 \n",
+      "Attempt 1396 with (4, 4) and 3: 3.9 1/12 11. 2/12 16. 3/12 8.2 4/12 2.6 5/40 4.5 6/40 6.7 7/40 5.1 8/40 6.0 9/40 5.1 10/40 9.9 11/40 40. 12/40 160. 13/40 15. 14/40 17. 15/40 8.1 16/40 3.7 17/40 4.3 18/40 6.8 19/40 8.1 20/40 6.3 21/40 4.2 22/40 11. 23/40 5.3 24/40 18. 25/40 17. 26/40 7.2 27/40 4.1 28/40 4.1 29/40 4.4 30/40 5.7 31/40 4.6 32/40 12. 33/40 20. 34/40 19. 35/40 9.0 36/40 13. 37/40 8.9 38/40 13. 39/40 14. 40/40 \n",
+      "Attempt 1397 with (1/131, 57) and 4: 7.6 1/8 8.0 2/8 7.0 3/8 6.7 4/8 5.0 5/8 5.7 6/8 5.2 7/8 4.6 8/8 \n",
+      "Attempt 1398 with (2/5, 3/73) and 5: 7.2 1/8 6.7 2/8 4.8 3/8 4.7 4/8 9.3 5/8 85. 6/8 12. 7/8 5.7 8/8 \n",
+      "Attempt 1399 with (1/5, 3) and 6: 4.9 1/8 38. 2/8 6.9 3/8 2.5 4/25 5.5 5/25 15. 6/25 8.8 7/25 12. 8/25 10. 9/25 20. 10/25 2.7 11/25 3.4 12/25 6.3 13/25 7.5 14/25 9.6 15/25 3.7 16/25 7.5 17/25 4.5 18/25 7.1 19/25 12. 20/25 14. 21/25 92. 22/25 23. 23/25 8.0 24/25 11. 25/25 \n",
+      "Attempt 1400 with (1/10, 1) and 4: 3.8 1/12 3.4 2/16 9.1 3/16 15. 4/16 11. 5/16 9.4 6/16 8.1 7/16 3.6 8/20 2.6 9/32 5.7 10/32 4.7 11/32 9.4 12/32 6.8 13/32 5.3 14/32 4.4 15/32 6.5 16/32 7.7 17/32 4.9 18/32 4.2 19/32 12. 20/32 6.2 21/32 10. 22/32 9.3 23/32 41. 24/32 10. 25/32 10. 26/32 7.1 27/32 7.1 28/32 4.2 29/32 2.7 30/32 7.3 31/32 3.9 32/32 \n",
+      "Attempt 1401 with (1/15, 0) and 11: 4.8 1/8 14. 2/8 7.6 3/8 3.4 4/12 6.9 5/12 3.9 6/16 8.8 7/16 23. 8/16 6.4 9/16 5.3 10/16 3.5 11/19 4.3 12/19 7.1 13/19 11. 14/19 2.5 15/19 11. 16/19 23. 17/19 5.1 18/19 4.5 19/19 \n",
+      "Attempt 1402 with (29, 0) and 3: 12. 1/8 7.8 2/8 7.4 3/8 22. 4/8 10. 5/8 4.0 6/12 8.0 7/12 6.1 8/12 7.3 9/12 15. 10/12 12. 11/12 2.7 12/40 2.9 13/40 17. 14/40 2.9 15/40 3.7 16/40 4.2 17/40 13. 18/40 18. 19/40 19. 20/40 4.1 21/40 4.0 22/40 3.7 23/40 6.8 24/40 7.0 25/40 4.0 26/40 7.9 27/40 5.5 28/40 5.6 29/40 3.0 30/40 9.2 31/40 13. 32/40 14. 33/40 51. 34/40 310. 35/40 29. 36/40 8.8 37/40 4.6 38/40 4.6 39/40 5.2 40/40 \n",
+      "Attempt 1403 with (1/33, 1/7) and 3: 32. 1/8 30. 2/8 25. 3/8 26. 4/8 31. 5/8 73. 6/8 120. 7/8 32. 8/8 \n",
+      "Attempt 1404 with (5, 1) and 5: 6.0 1/8 8.8 2/8 30. 3/8 7.7 4/8 7.1 5/8 6.7 6/8 11. 7/8 20. 8/8 \n",
+      "Attempt 1405 with (1, 3/4) and 3: 24. 1/8 25. 2/8 7.2 3/8 5.0 4/8 5.3 5/8 5.8 6/8 6.2 7/8 10. 8/8 \n",
+      "Attempt 1406 with (6, 0) and 10: 4.2 1/8 3.2 2/12 4.1 3/12 3.3 4/16 2.8 5/19 4.9 6/19 6.0 7/19 5.1 8/19 5.0 9/19 5.4 10/19 5.7 11/19 4.5 12/19 8.0 13/19 34. 14/19 58. 15/19 140. 16/19 9.4 17/19 4.3 18/19 4.6 19/19 \n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/misc/cachefunc.pyx:1962\u001b[0m, in \u001b[0;36msage.misc.cachefunc.CachedMethodCaller.__call__ (build/cythonized/sage/misc/cachefunc.c:13400)\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1961\u001b[0m try:\n\u001b[0;32m-> 1962\u001b[0m     return cache[k]\n\u001b[1;32m   1963\u001b[0m except TypeError:  # k is not hashable\n",
+      "\u001b[0;31mKeyError\u001b[0m: (((-2, 6, 7),), ())",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/misc/cachefunc.pyx:1962\u001b[0m, in \u001b[0;36msage.misc.cachefunc.CachedMethodCaller.__call__ (build/cythonized/sage/misc/cachefunc.c:13400)\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1961\u001b[0m try:\n\u001b[0;32m-> 1962\u001b[0m     return cache[k]\n\u001b[1;32m   1963\u001b[0m except TypeError:  # k is not hashable\n",
+      "\u001b[0;31mKeyError\u001b[0m: (((-1, 4, 5), 1), ())",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[222], line 42\u001b[0m\n\u001b[1;32m     39\u001b[0m T \u001b[38;5;241m=\u001b[39m deformed\u001b[38;5;241m.\u001b[39mdelaunay_triangulation()\u001b[38;5;241m.\u001b[39mcodomain()\n\u001b[1;32m     41\u001b[0m \u001b[38;5;66;03m# Make sure the surface is not too terribly squeezed initially.\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mmax\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mT\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpolygon\u001b[49m\u001b[43m(\u001b[49m\u001b[43medge\u001b[49m\u001b[43m[\u001b[49m\u001b[43mInteger\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge\u001b[49m\u001b[43m(\u001b[49m\u001b[43medge\u001b[49m\u001b[43m[\u001b[49m\u001b[43mInteger\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mInteger\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mabs\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43medge\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mT\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medges\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     43\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(T\u001b[38;5;241m.\u001b[39mpolygon(edge[Integer(\u001b[38;5;241m0\u001b[39m)])\u001b[38;5;241m.\u001b[39medge(edge[Integer(\u001b[38;5;241m1\u001b[39m)])[Integer(\u001b[38;5;241m1\u001b[39m)]\u001b[38;5;241m.\u001b[39mabs() \u001b[38;5;28;01mfor\u001b[39;00m edge \u001b[38;5;129;01min\u001b[39;00m T\u001b[38;5;241m.\u001b[39medges())\n\u001b[1;32m     45\u001b[0m T \u001b[38;5;241m=\u001b[39m MutableOrientedSimilaritySurface\u001b[38;5;241m.\u001b[39mfrom_surface(T)\n",
+      "Cell \u001b[0;32mIn[222], line 42\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     39\u001b[0m T \u001b[38;5;241m=\u001b[39m deformed\u001b[38;5;241m.\u001b[39mdelaunay_triangulation()\u001b[38;5;241m.\u001b[39mcodomain()\n\u001b[1;32m     41\u001b[0m \u001b[38;5;66;03m# Make sure the surface is not too terribly squeezed initially.\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(T\u001b[38;5;241m.\u001b[39mpolygon(edge[Integer(\u001b[38;5;241m0\u001b[39m)])\u001b[38;5;241m.\u001b[39medge(edge[Integer(\u001b[38;5;241m1\u001b[39m)])[Integer(\u001b[38;5;241m0\u001b[39m)]\u001b[38;5;241m.\u001b[39mabs() \u001b[38;5;28;01mfor\u001b[39;00m edge \u001b[38;5;129;01min\u001b[39;00m T\u001b[38;5;241m.\u001b[39medges())\n\u001b[1;32m     43\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(T\u001b[38;5;241m.\u001b[39mpolygon(edge[Integer(\u001b[38;5;241m0\u001b[39m)])\u001b[38;5;241m.\u001b[39medge(edge[Integer(\u001b[38;5;241m1\u001b[39m)])[Integer(\u001b[38;5;241m1\u001b[39m)]\u001b[38;5;241m.\u001b[39mabs() \u001b[38;5;28;01mfor\u001b[39;00m edge \u001b[38;5;129;01min\u001b[39;00m T\u001b[38;5;241m.\u001b[39medges())\n\u001b[1;32m     45\u001b[0m T \u001b[38;5;241m=\u001b[39m MutableOrientedSimilaritySurface\u001b[38;5;241m.\u001b[39mfrom_surface(T)\n",
+      "File \u001b[0;32m~/proj/eskin/sage-flatsurf/flatsurf/geometry/surface.py:3171\u001b[0m, in \u001b[0;36mEdges.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3170\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__iter__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m-> 3171\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m label, polygon \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_surface\u001b[38;5;241m.\u001b[39mlabels(), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_surface\u001b[38;5;241m.\u001b[39mpolygons()):\n\u001b[1;32m   3172\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m edge \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(polygon\u001b[38;5;241m.\u001b[39mvertices())):\n\u001b[1;32m   3173\u001b[0m             \u001b[38;5;28;01myield\u001b[39;00m (label, edge)\n",
+      "File \u001b[0;32m~/proj/eskin/sage-flatsurf/flatsurf/geometry/surface.py:3102\u001b[0m, in \u001b[0;36mPolygons.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3083\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   3084\u001b[0m \u001b[38;5;124;03mIterate over the polygons in the same order as ``labels()`` does.\u001b[39;00m\n\u001b[1;32m   3085\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3099\u001b[0m \n\u001b[1;32m   3100\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   3101\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m label \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_surface\u001b[38;5;241m.\u001b[39mlabels():\n\u001b[0;32m-> 3102\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_surface\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpolygon\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/misc/cachefunc.pyx:1967\u001b[0m, in \u001b[0;36msage.misc.cachefunc.CachedMethodCaller.__call__ (build/cythonized/sage/misc/cachefunc.c:13536)\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1965\u001b[0m         return cache[k]\n\u001b[1;32m   1966\u001b[0m except KeyError:\n\u001b[0;32m-> 1967\u001b[0m     w = self._instance_call(*args, **kwds)\n\u001b[1;32m   1968\u001b[0m     cache[k] = w\n\u001b[1;32m   1969\u001b[0m     return w\n",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/misc/cachefunc.pyx:1842\u001b[0m, in \u001b[0;36msage.misc.cachefunc.CachedMethodCaller._instance_call (build/cythonized/sage/misc/cachefunc.c:12985)\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1840\u001b[0m         True\n\u001b[1;32m   1841\u001b[0m     \"\"\"\n\u001b[0;32m-> 1842\u001b[0m     return self.f(self._instance, *args, **kwds)\n\u001b[1;32m   1843\u001b[0m \n\u001b[1;32m   1844\u001b[0m cdef fix_args_kwds(self, tuple args, dict kwds) noexcept:\n",
+      "File \u001b[0;32m~/proj/eskin/sage-flatsurf/flatsurf/geometry/lazy.py:1105\u001b[0m, in \u001b[0;36mLazyDelaunayTriangulatedSurface.polygon\u001b[0;34m(self, label)\u001b[0m\n\u001b[1;32m   1102\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno polygon with this label\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1104\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m label \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_certified_labels:\n\u001b[0;32m-> 1105\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m certified_label \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_walk():\n\u001b[1;32m   1106\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m label \u001b[38;5;241m==\u001b[39m certified_label:\n\u001b[1;32m   1107\u001b[0m             \u001b[38;5;28;01massert\u001b[39;00m label \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_certified_labels\n",
+      "File \u001b[0;32m~/proj/eskin/sage-flatsurf/flatsurf/geometry/lazy.py:1129\u001b[0m, in \u001b[0;36mLazyDelaunayTriangulatedSurface._walk\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1126\u001b[0m visited\u001b[38;5;241m.\u001b[39madd(label)\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m edge \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m3\u001b[39m):\n\u001b[0;32m-> 1129\u001b[0m     \u001b[38;5;28mnext\u001b[39m\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopposite_edge\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/misc/cachefunc.pyx:1967\u001b[0m, in \u001b[0;36msage.misc.cachefunc.CachedMethodCaller.__call__ (build/cythonized/sage/misc/cachefunc.c:13536)\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1965\u001b[0m         return cache[k]\n\u001b[1;32m   1966\u001b[0m except KeyError:\n\u001b[0;32m-> 1967\u001b[0m     w = self._instance_call(*args, **kwds)\n\u001b[1;32m   1968\u001b[0m     cache[k] = w\n\u001b[1;32m   1969\u001b[0m     return w\n",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/misc/cachefunc.pyx:1842\u001b[0m, in \u001b[0;36msage.misc.cachefunc.CachedMethodCaller._instance_call (build/cythonized/sage/misc/cachefunc.c:12985)\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1840\u001b[0m         True\n\u001b[1;32m   1841\u001b[0m     \"\"\"\n\u001b[0;32m-> 1842\u001b[0m     return self.f(self._instance, *args, **kwds)\n\u001b[1;32m   1843\u001b[0m \n\u001b[1;32m   1844\u001b[0m cdef fix_args_kwds(self, tuple args, dict kwds) noexcept:\n",
+      "File \u001b[0;32m~/proj/eskin/sage-flatsurf/flatsurf/geometry/lazy.py:1151\u001b[0m, in \u001b[0;36mLazyDelaunayTriangulatedSurface.opposite_edge\u001b[0;34m(self, label, edge)\u001b[0m\n\u001b[1;32m   1149\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m   1150\u001b[0m     cross_label, cross_edge \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_surface\u001b[38;5;241m.\u001b[39mopposite_edge(label, edge)\n\u001b[0;32m-> 1151\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_certify_or_improve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcross_label\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m   1152\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m   1154\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_surface\u001b[38;5;241m.\u001b[39mopposite_edge(label, edge)\n",
+      "File \u001b[0;32m~/proj/eskin/sage-flatsurf/flatsurf/geometry/lazy.py:1224\u001b[0m, in \u001b[0;36mLazyDelaunayTriangulatedSurface._certify_or_improve\u001b[0;34m(self, label)\u001b[0m\n\u001b[1;32m   1221\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m   1223\u001b[0m \u001b[38;5;66;03m# If we reach here then we know that no flip was needed.\u001b[39;00m\n\u001b[0;32m-> 1224\u001b[0m ccc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_surface\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge_transformation\u001b[49m\u001b[43m(\u001b[49m\u001b[43mll\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mee\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcc\u001b[49m\n\u001b[1;32m   1226\u001b[0m \u001b[38;5;66;03m# Check if the disk passes through the next edge in the chain.\u001b[39;00m\n\u001b[1;32m   1227\u001b[0m lp \u001b[38;5;241m=\u001b[39m ccc\u001b[38;5;241m.\u001b[39mline_segment_position(\n\u001b[1;32m   1228\u001b[0m     ppp\u001b[38;5;241m.\u001b[39mvertex((eee \u001b[38;5;241m+\u001b[39m step) \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m3\u001b[39m), ppp\u001b[38;5;241m.\u001b[39mvertex((eee \u001b[38;5;241m+\u001b[39m step \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m3\u001b[39m)\n\u001b[1;32m   1229\u001b[0m )\n",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/structure/element.pyx:1527\u001b[0m, in \u001b[0;36msage.structure.element.Element.__mul__ (build/cythonized/sage/structure/element.c:20273)\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1525\u001b[0m     if not err:\n\u001b[1;32m   1526\u001b[0m         return (<Element>right)._mul_long(value)\n\u001b[0;32m-> 1527\u001b[0m     return coercion_model.bin_op(left, right, mul)\n\u001b[1;32m   1528\u001b[0m except TypeError:\n\u001b[1;32m   1529\u001b[0m     return NotImplemented\n",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/structure/coerce.pyx:1228\u001b[0m, in \u001b[0;36msage.structure.coerce.CoercionModel.bin_op (build/cythonized/sage/structure/coerce.c:15900)\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1226\u001b[0m # Now coerce to a common parent and do the operation there\n\u001b[1;32m   1227\u001b[0m try:\n\u001b[0;32m-> 1228\u001b[0m     xy = self.canonical_coercion(x, y)\n\u001b[1;32m   1229\u001b[0m except TypeError:\n\u001b[1;32m   1230\u001b[0m     self._record_exception()\n",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/structure/coerce.pyx:1422\u001b[0m, in \u001b[0;36msage.structure.coerce.CoercionModel.canonical_coercion (build/cythonized/sage/structure/coerce.c:18984)\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1420\u001b[0m             self._record_exception()\n\u001b[1;32m   1421\u001b[0m \n\u001b[0;32m-> 1422\u001b[0m     raise TypeError(\"no common canonical parent for objects with parents: '%s' and '%s'\"%(xp, yp))\n\u001b[1;32m   1423\u001b[0m \n\u001b[1;32m   1424\u001b[0m cpdef coercion_maps(self, R, S) noexcept:\n",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/structure/sage_object.pyx:221\u001b[0m, in \u001b[0;36msage.structure.sage_object.SageObject.__repr__ (build/cythonized/sage/structure/sage_object.c:4326)\u001b[0;34m()\u001b[0m\n\u001b[1;32m    219\u001b[0m     except AttributeError:\n\u001b[1;32m    220\u001b[0m         return super().__repr__()\n\u001b[0;32m--> 221\u001b[0m     return reprfunc()\n\u001b[1;32m    222\u001b[0m \n\u001b[1;32m    223\u001b[0m def _ascii_art_(self):\n",
+      "File \u001b[0;32m~/proj/eskin/sage-flatsurf/flatsurf/geometry/similarity.py:553\u001b[0m, in \u001b[0;36mSimilarityGroup._repr_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    545\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_repr_\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    546\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    547\u001b[0m \u001b[38;5;124;03m    TESTS::\u001b[39;00m\n\u001b[1;32m    548\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    551\u001b[0m \u001b[38;5;124;03m        Similarity group over Rational Field\u001b[39;00m\n\u001b[1;32m    552\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSimilarity group over \u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ring\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/structure/sage_object.pyx:221\u001b[0m, in \u001b[0;36msage.structure.sage_object.SageObject.__repr__ (build/cythonized/sage/structure/sage_object.c:4326)\u001b[0;34m()\u001b[0m\n\u001b[1;32m    219\u001b[0m     except AttributeError:\n\u001b[1;32m    220\u001b[0m         return super().__repr__()\n\u001b[0;32m--> 221\u001b[0m     return reprfunc()\n\u001b[1;32m    222\u001b[0m \n\u001b[1;32m    223\u001b[0m def _ascii_art_(self):\n",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/rings/number_field/number_field.py:3471\u001b[0m, in \u001b[0;36mNumberField_generic._repr_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3456\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_repr_\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   3457\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   3458\u001b[0m \u001b[38;5;124;03m    Return string representation of this number field.\u001b[39;00m\n\u001b[1;32m   3459\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3469\u001b[0m \n\u001b[1;32m   3470\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3471\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mNumber Field in \u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m with defining polynomial \u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvariable_name\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpolynomial\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3472\u001b[0m     gen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgen_embedding()\n\u001b[1;32m   3473\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m gen \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/structure/sage_object.pyx:221\u001b[0m, in \u001b[0;36msage.structure.sage_object.SageObject.__repr__ (build/cythonized/sage/structure/sage_object.c:4326)\u001b[0;34m()\u001b[0m\n\u001b[1;32m    219\u001b[0m     except AttributeError:\n\u001b[1;32m    220\u001b[0m         return super().__repr__()\n\u001b[0;32m--> 221\u001b[0m     return reprfunc()\n\u001b[1;32m    222\u001b[0m \n\u001b[1;32m    223\u001b[0m def _ascii_art_(self):\n",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/rings/polynomial/polynomial_element.pyx:2763\u001b[0m, in \u001b[0;36msage.rings.polynomial.polynomial_element.Polynomial._repr_ (build/cythonized/sage/rings/polynomial/polynomial_element.c:39548)\u001b[0;34m()\u001b[0m\n\u001b[1;32m   2761\u001b[0m         NotImplementedError: object does not support renaming: x^3 + 2/3*x^2 - 5/3\n\u001b[1;32m   2762\u001b[0m     \"\"\"\n\u001b[0;32m-> 2763\u001b[0m     return self._repr()\n\u001b[1;32m   2764\u001b[0m \n\u001b[1;32m   2765\u001b[0m def _latex_(self, name=None):\n",
+      "File \u001b[0;32m~/proj/umamba/envs/sage-flatsurf-build/lib/python3.10/site-packages/sage/rings/polynomial/polynomial_element.pyx:2727\u001b[0m, in \u001b[0;36msage.rings.polynomial.polynomial_element.Polynomial._repr (build/cythonized/sage/rings/polynomial/polynomial_element.c:38899)\u001b[0;34m()\u001b[0m\n\u001b[1;32m   2725\u001b[0m if n != m-1:\n\u001b[1;32m   2726\u001b[0m     sbuf.write(\" + \")\n\u001b[0;32m-> 2727\u001b[0m x = y = repr(x)\n\u001b[1;32m   2728\u001b[0m if y.find(\"-\") == 0:\n\u001b[1;32m   2729\u001b[0m     y = y[1:]\n",
+      "File \u001b[0;32msrc/cysignals/signals.pyx:310\u001b[0m, in \u001b[0;36mcysignals.signals.python_check_interrupt\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "best = (S, oo)\n",
+    "bounds = []\n",
+    "\n",
+    "while True:\n",
+    "    T = S\n",
+    "    \n",
+    "    from flatsurf import GL2ROrbitClosure\n",
+    "    from flatsurf.geometry.pyflatsurf.surface import Surface_pyflatsurf\n",
+    "    \n",
+    "    O = GL2ROrbitClosure(T)\n",
+    "    \n",
+    "    for decomposition in O.decompositions(64):\n",
+    "        O.update_tangent_space_from_flow_decomposition(decomposition)\n",
+    "        if O.dimension() > 3: break\n",
+    "    \n",
+    "    tangent = (\n",
+    "        O.lift(O.tangent_space_basis()[2]),\n",
+    "        O.lift(O.tangent_space_basis()[3]),\n",
+    "    )\n",
+    "    while True:\n",
+    "        upper_bound = (QQ.random_element().abs(), QQ.random_element().abs())\n",
+    "        if upper_bound == (0, 0):\n",
+    "            continue\n",
+    "        twist = ZZ.random_element().abs() + 3\n",
+    "        if twist == 0:\n",
+    "            continue\n",
+    "        bound = (upper_bound[0], upper_bound[1], twist)\n",
+    "        if bound not in bounds:\n",
+    "            bounds.append(bound)\n",
+    "            break\n",
+    "\n",
+    "    deformation = [O.V2(x / upper_bound[0] + y / upper_bound[1], x / (2*upper_bound[0]) + y / (3*upper_bound[1])).vector for (x, y) in zip(*tangent)]\n",
+    "    deformed = Surface_pyflatsurf((T.pyflatsurf()._flat_triangulation + deformation).codomain())\n",
+    "    \n",
+    "    from flatsurf import MutableOrientedSimilaritySurface\n",
+    "    deformed = MutableOrientedSimilaritySurface.from_surface(deformed)\n",
+    "    deformed.set_immutable()\n",
+    "    \n",
+    "    T = deformed.delaunay_triangulation().codomain()\n",
+    "\n",
+    "    # Make sure the surface is not too terribly squeezed initially.\n",
+    "    x = max(T.polygon(edge[0]).edge(edge[1])[0].abs() for edge in T.edges())\n",
+    "    y = max(T.polygon(edge[0]).edge(edge[1])[1].abs() for edge in T.edges())\n",
+    "\n",
+    "    T = MutableOrientedSimilaritySurface.from_surface(T)\n",
+    "    T.set_immutable()\n",
+    "    T = T.apply_matrix(matrix([[1, twist**3], [0, 1]]) * matrix([[1/x, 0], [0, 1/y]]), in_place=False).codomain().delaunay_triangulation().codomain()\n",
+    "    \n",
+    "    print(f\"Attempt {len(bounds) - 1} with {upper_bound} and {twist}: \", end=\"\")\n",
+    "\n",
+    "    iterations = 8\n",
+    "    iteration = 0\n",
+    "\n",
+    "    while iteration < iterations:\n",
+    "        iteration += 1\n",
+    "        T = MutableOrientedSimilaritySurface.from_surface(T)\n",
+    "        T.set_immutable()\n",
+    "        # T = T.apply_matrix(matrix([[3, 7], [2, 5]])).codomain().delaunay_triangulation().codomain()\n",
+    "        T = T.apply_matrix(matrix([[1, twist], [0, 1]])).codomain().delaunay_triangulation().codomain()\n",
+    "        edges = [e.change_ring(RDF).norm() for P in T.polygons() for e in P.edges()]\n",
+    "        rel = max(edges) / min(edges)\n",
+    "        if rel < best[1]:\n",
+    "            best = (T, rel)\n",
+    "            print(rel, end=\"\")\n",
+    "            T.plot(polygon_labels=False, edge_labels=False).show()\n",
+    "            iterations += 16\n",
+    "        else:\n",
+    "            print(f\"{rel.n(digits=2)} \", end=\"\")\n",
+    "\n",
+    "            if rel < 4:\n",
+    "                iterations += 4\n",
+    "            if rel < 3:\n",
+    "                iterations += 32\n",
+    "            if rel < 2:\n",
+    "                iterations += 128\n",
+    "\n",
+    "        from sage.all import log\n",
+    "        iterations = min(iterations, round(log(2**64, base=twist)))\n",
+    "        print(f\"{iteration}/{iterations} \", end=\"\")\n",
+    "    print(\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "id": "b734c554-f889-4ebe-aa48-d8acf61d1fc1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best[0].save(\"3413-186-model\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9ba34b6f-5107-4353-b095-4af36ba6fd83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import jurigged; _ = jurigged.watch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5a2abfc4-83d6-4e50-9758-37639b05502b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/__init__.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/version.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/__init__.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/homology.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/cohomology.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/harmonic_differentials.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/polygon.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/subfield.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/__init__.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/topological_surfaces.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/surface_category.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/polygonal_surfaces.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/euclidean_polygonal_surfaces.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/similarity_surfaces.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/cone_surfaces.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/dilation_surfaces.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/half_translation_surfaces.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/translation_surfaces.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/polygons.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/euclidean_polygons.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/euclidean.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/geometry.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/hyperbolic_polygons.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/categories/euclidean_polygons_with_angles.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/similarity_surface_generators.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/surface.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/surface_objects.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/similarity.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/surface_legacy.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/origami.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/saddle_connection.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/gl2r_orbit_closure.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/hyperbolic.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/voronoi.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/lazy.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/morphism.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/graphical/__init__.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/graphical/surface.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/graphical/polygon.py\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "Graphics object consisting of 97 graphics primitives"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "T = load(\"3413-186-model.sobj\")\n",
+    "T = T.relabel().codomain()\n",
+    "T.plot(polygon_labels=False, edge_labels=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c93629c5-8758-43f6-bfdb-018f1cdb7b41",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/circle.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "from flatsurf import HarmonicDifferentials, ApproximateWeightedVoronoiCellDecomposition\n",
+    "\n",
+    "S = T\n",
+    "S = S.insert_marked_points(*[S(label, S.polygon(label).centroid()) for label in (1, 21, 30)]).codomain().relabel().codomain().delaunay_triangulation().codomain()\n",
+    "S = S.insert_marked_points(*[S(label, S.polygon(label).centroid()) for label in (23,)]).codomain().relabel().codomain().delaunay_triangulation().codomain()\n",
+    "# S = S.insert_marked_points(*[S(label, S.polygon(label).centroid()) for label in (8,)]).codomain().relabel().codomain().delaunay_triangulation().codomain()\n",
+    "V = ApproximateWeightedVoronoiCellDecomposition(S)\n",
+    "Omega = HarmonicDifferentials(S, error=1e-6, cell_decomposition=V, check=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e1775eb9-5f3c-440a-baf8-0c7249814a04",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/pyflatsurf/surface.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/pyflatsurf_conversion.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/features.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/pyflatsurf/morphism.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/pyflatsurf/surface_point.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/cone.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/ray.py\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[0.7209091695651454,\n",
+       " 0.7519465252471788,\n",
+       " 0.8274565828494759,\n",
+       " 0.7519465252471788,\n",
+       " 0.8841048354386081,\n",
+       " 0.8274565828494759]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[Omega._relative_radius_of_convergence(v) for v in V.cells()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8144d67d-d6c6-4169-83f3-155b3651691a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "Graphics object consisting of 691 graphics primitives"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "GS = S.graphical_surface(edge_labels=False)\n",
+    "Omega.error_plot(GS, plot_points=10, cutoff=.7).show(dpi=1024)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "84ed5c3c-197c-431b-a993-6b713aef4aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/gl2r_orbit_closure.py:846: UserWarning: orbit_closure.decompositions() has been deprecated and will be removed in a future version of sage-flatsurf; use surface.flow_decompositions() instead.\n",
+      "  warnings.warn(\"orbit_closure.decompositions() has been deprecated and will be removed in a future version of sage-flatsurf; use surface.flow_decompositions() instead.\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/pyflatsurf/flow_decomposition.py\n",
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/flow_decomposition.py\n",
+      "2 2 2 4 4 4 4 4 4 4 4 5 6 "
+     ]
+    }
+   ],
+   "source": [
+    "from flatsurf import GL2ROrbitClosure\n",
+    "O = GL2ROrbitClosure(S)\n",
+    "for d in O.decompositions(6, 16):\n",
+    "    O.update_tangent_space_from_flow_decomposition(d)\n",
+    "    print(O.dimension(), end=\" \")\n",
+    "    if O.dimension() == 6: break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "38fbfcbc-2b13-404b-ad5b-92adab208df1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{B[(4, 0)] - B[(6, 0)]: 0.854101966249685, -B[(6, 0)] + B[(12, 1)]: 0.854101966249685, B[(23, 1)]: 0.381966011250105, -B[(19, 1)] + B[(34, 1)]: -1.00000000000000, B[(28, 0)]: -0.763932022500210, B[(5, 1)] + B[(6, 0)]: -0.381966011250105, B[(8, 0)]: 1.00000000000000, B[(19, 0)] + B[(19, 1)]: -0.236067977499790, B[(10, 0)]: 0.236067977499790, B[(6, 0)] + B[(11, 2)]: -1.23606797749979, B[(13, 2)]: 0.763932022500210, -B[(6, 0)] + B[(21, 0)]: 0.381966011250105, -B[(0, 0)] + B[(3, 0)]: -0.618033988749895}\n"
+     ]
+    }
+   ],
+   "source": [
+    "F = O._lift_to_simplicial_cohomology(O.lift(O.tangent_space_basis()[5]))\n",
+    "F = F.parent()({k: v / max(F._values.values()) for (k, v) in F._values.items()})\n",
+    "print(F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "273b4b69-ad91-426d-a615-a099a623d5bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"SAGE_NUM_THREADS\"] = '4'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f244f0e0-b41e-4036-8a0f-6d75b38df6e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "63 at Vertex 0 of polygon 14 with angle 3\n",
+      "53 at Vertex 0 of polygon 33 with angle 1\n",
+      "82 at Vertex 0 of polygon 16 with angle 1\n",
+      "53 at Vertex 0 of polygon 1 with angle 1\n",
+      "82 at Vertex 1 of polygon 19 with angle 1\n",
+      "256 at Vertex 0 of polygon 0 with angle 13\n"
+     ]
+    }
+   ],
+   "source": [
+    "Omega = HarmonicDifferentials(S, error=1e-6, cell_decomposition=V)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ca35979e-1c28-4a3b-97a5-bbd082945222",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Watch /home/jule/proj/eskin/sage-flatsurf/flatsurf/geometry/power_series.py\n",
+      "Adding L2 conditions\n",
+      "Ignoring L2 condition on edge of polygon\n",
+      "Ignoring L2 condition on edge of polygon\n",
+      "Ignoring L2 condition on edge of polygon\n",
+      "Ignoring L2 condition on edge of polygon\n",
+      "Ignoring L2 condition on edge of polygon\n",
+      "Ignoring L2 condition on edge of polygon\n",
+      "Ignoring L2 condition on edge of polygon\n",
+      "Ignoring L2 condition on edge of polygon\n",
+      "Ignoring L2 condition on edge of polygon\n",
+      "Delete flatsurf.geometry.harmonic_differentials.RationalMap.monodromy @L1139\n",
+      "Ignoring L2 condition on edge of polygon\n",
+      "Ignoring L2 condition on edge of polygon\n",
+      "Ignoring L2 condition on edge of polygon\n",
+      "Adding cohomology constraints\n",
+      "Creating Lagrange symbols\n",
+      "Denormalizing system\n",
+      "Constructing linear system\n",
+      "Solving 1194×1194 system\n",
+      "Computing condition\n",
+      "condition=4.625023334888032e+88\n",
+      "Solving system\n",
+      "Interpreting solution\n",
+      "Building series\n"
+     ]
+    }
+   ],
+   "source": [
+    "f = Omega(F, check=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a77c8cce-8841-4037-b0d5-547391fa44c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dumps(f)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "SageMath 10.2",
+   "language": "sage",
+   "name": "sagemath"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/benchmark/geometry/harmonic_differentials.py b/benchmark/geometry/harmonic_differentials.py
new file mode 100644
index 000000000..0f09270f9
--- /dev/null
+++ b/benchmark/geometry/harmonic_differentials.py
@@ -0,0 +1,44 @@
+# ********************************************************************
+#  This file is part of sage-flatsurf.
+#
+#        Copyright (C) 2022 Julian Rüth
+#
+#  sage-flatsurf is free software: you can redistribute it and/or modify
+#  it under the terms of the GNU General Public License as published by
+#  the Free Software Foundation, either version 2 of the License, or
+#  (at your option) any later version.
+#
+#  sage-flatsurf is distributed in the hope that it will be useful,
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#  GNU General Public License for more details.
+#
+#  You should have received a copy of the GNU General Public License
+#  along with sage-flatsurf. If not, see <https://www.gnu.org/licenses/>.
+# ********************************************************************
+import flatsurf
+
+
+def time_harmonic_differential(surface):
+    if surface == "TORUS":
+        surface = flatsurf.translation_surfaces.torus((1, 0), (0, 1))
+    elif surface == "3413":
+        E = flatsurf.EquiangularPolygons(3, 4, 13)
+        P = E.an_element()
+        surface = flatsurf.similarity_surfaces.billiard(P).minimal_cover(cover_type="translation")
+    else:
+        raise NotImplementedError
+
+    surface = surface.delaunay_triangulation()
+    surface.set_immutable()
+    V = flatsurf.ApproximateWeightedVoronoiCellDecomposition(surface)
+    Ω = flatsurf.HarmonicDifferentials(surface, cell_decomposition=V, error=1e-1)
+    a = flatsurf.SimplicialHomology(surface).gens()[0]
+    H = flatsurf.SimplicialCohomology(surface)
+    Ω(H({a: 1}), check=False)
+
+
+time_harmonic_differential.params = ([
+    "TORUS",
+    "3413",
+])
diff --git a/doc/examples/harmonic.md b/doc/examples/harmonic.md
new file mode 100644
index 000000000..3257c078b
--- /dev/null
+++ b/doc/examples/harmonic.md
@@ -0,0 +1,213 @@
+---
+jupyter:
+  jupytext:
+    formats: ipynb,md
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.3'
+      jupytext_version: 1.15.2
+  kernelspec:
+    display_name: SageMath 10.0
+    language: sage
+    name: sagemath
+---
+
+```sage
+import jurigged
+watcher = jurigged.watch("/")
+```
+
+# Harmonic Differentials
+
+
+First, some differentials on a square torus.
+
+```sage
+from flatsurf import translation_surfaces, HarmonicDifferentials, SimplicialHomology, SimplicialCohomology
+T = translation_surfaces.torus((1, 0), (0, 1))
+T.set_immutable()
+T.plot()
+```
+
+We create differentials with prescribed values on generators of homology. The differential is, modulo some numerical noise, a constant:
+
+```sage
+H = SimplicialHomology(T, generators="voronoi")
+a, b = H.gens()
+```
+
+```sage
+H = SimplicialCohomology(T, homology=H)
+f = H({a: 1})
+```
+
+```sage
+Omega = HarmonicDifferentials(T)
+omega = Omega(f, check=False)
+omega
+```
+
+The power series is developed around the centers of the circumcircle of the triangulation. In this example the centers for the triangles are the same, so the coefficients are (essentially) forced to be identical.
+
+We can recover the series for each triangle of the triangulation:
+
+```sage
+omega.series((0, 0, 0))
+```
+
+We can ask the differential how well it solves the constraints that were used to created it:
+
+```sage
+omega.error(verbose=True)
+```
+
+If we create the differential with much more precision, we see some numerical noise here:
+
+
+Omega(f, prec=40).error(verbose=True)
+
+
+We can also use other strategies to determine the differential. The supported strategies are `L2` (the default), `midpoint_derivatives` (forcing derivatives up to some point to match at the midpoints of the triangulation edges,) `area_upper_bound` (minimize an approximation of the area,) `area` (minimize the area).
+
+
+Omega(f, prec=2, algorithm=["midpoint_derivatives"])
+
+
+These strategies can also be mixed and weighted differently (in the case of `midpoint_derivatives`, this controls up to which derivative we force derivatives to match).
+
+There are checks for obvious errors in the computation, e.g., when the error in the L2 norm gets too big:
+
+
+Omega(f, prec=10, algorithm={"midpoint_derivatives": 1, "area_upper_bound": 0, "L2": 0})
+
+
+These checks can be disabled though:
+
+
+Omega(f, prec=10, algorithm={"midpoint_derivatives": 1, "area_upper_bound": 10, "L2": 0}, check=False)
+
+
+There are some other basic operations supported. We can, e.g., ask for the roots of a differential (TODO: This does not include roots at the vertices of the triangulation yet):
+
+
+omega.roots()
+
+
+At the vertices we can ask for the coefficients of the power series developed around that vertex:
+
+
+vertex = T.angles(return_adjacent_edges=True)[0][1]
+
+
+omega.cauchy_residue(vertex, 0)
+
+
+omega.cauchy_residue(vertex, -1)
+
+
+## A Less Trivial Example, the Regular Octagon
+
+```sage
+from flatsurf import translation_surfaces, HarmonicDifferentials, SimplicialHomology, SimplicialCohomology, TranslationSurface
+S = translation_surfaces.regular_octagon()
+
+scale = QQ(1.163592571218269375302518142809178538757590879116270587397 / ((1 + N(sqrt(2)))/2))
+S = S.apply_matrix(diagonal_matrix([scale, scale]), in_place=False)
+S.set_immutable()
+
+H = SimplicialHomology(S)
+HS = SimplicialCohomology(S, homology=H)
+a, b, c, d = HS.homology().gens()
+
+f = {
+    d: 0,
+    a: -0.681616747143081,
+    b: 0.963951648150378,
+    c: -0.681616747143081,
+    # d: 0,
+    # a: 0,
+    # b: 0,
+    # c: 1,
+}
+print(f)
+f = HS(f)
+f._values = {key: RealField(54)(value) for (key, value) in f._values.items()}
+
+Omega = HarmonicDifferentials(S, safety=0, singularities=True)
+```
+
+```sage
+omega = Omega(HS(f), prec=1, check=False)
+```
+
+```sage
+omega.error(verbose=True)
+```
+
+### An Explicit Series for the Octagon
+We can provide the series for the Voronoi cells explicitly if we don't want to solve for a cohomology class.
+
+```sage
+R.<z> = CC[[]]
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129456497689850077612339740942939156553059830972524183665072876965273817023921761892810147640056319233611703583418589154429913801911541671662946103353080378177574681769760298477719997882191855501255695580690828569986292166746254003766445910031217442744611928301515138488038005094432610315778976497019801406152624243184306356198755965911447519634068462166310014141391932/264033471183117610617483634005651878303489584617293169596541428412087253301006809518277111352506841760331072879270965157473887553225131821038588434054363367365464508195134938851926333258424733405731880909736874892158336241887935383572749118747828546816809859766112492603734005210687705524372620168629105603183269863073043942698310940871158090277230605143439811804867986965737806858712359646315582493231133983206019646279592253267765045166015625*z^746 + 155200961690731900153214055713130828453526630880670039350052523558144405415098628322245586952472440210904895628345152309403301551834600573156535825387813596743795665795436537393619532201063369775604173940322380488504216670763530610517848738364398736130445069648170609182033426001796323759331917884762685527826396397255138037558300977266082657143400797494207878292967827856/2011211660347701596925713402620366620907957308512008913915825781437184251167559537014701066804223740328143578702704979569245487352492798241796619989256200886068682160581904412433649157995014979629976345615293440541382524932815064737392195618318381514935595071060995536717330335430655349467827379569871267205102505211520958648302355018103946235836298392492781000022652760827721555588642339804405615103552165867425718866818700243718922138214111328125*z^754 - 680857066317421985016326359570906763067539323840670261832268133402291440997383900702068971797447124468001372193375548597140322729674041031297516552445983199189008379782718932851174327770988935937386826186808422782943100311132628748448732763892438542668332671679137792614366368746130550751618507370507765422074655526308986233120271246939468493203909258262612961119167189523984336/56057183629743158144257630224532479034901898829559458653400278131638082899618382263280988105968569970555367471591117887506373831597573420144077714449721177178571721896155890177066074104442305444820074226847931484723822489235810722829195714306285988375795117884054053329508568730040105280105753754375358640177541541910080621510821958603730794251163953067008805949045682249868517670628645799058338510630718696258946049892681006912827572785317897796630859375*z^762 + 7750058381013557722248770183009401926443887083742596092223058181846957838703144241498088283425767172647263825735980888076165949551791256760608557886338277525040272221521727426610711198539548617093038846798326890026921969833828298327442930654430169535922644402183187131151801193493883433872694706094156614992285441888597389206256197420958099336018186973319263476650888602778187102676868/4053930615846763871908499336428949116304108345886005870883271709176134423814109018742948638737780339249000835467474392467841609062391210625306974017949034010078735620485442727099595000693940605295650586093937478281573906472845315788325803848074541804741824541884493272140180493437701137654246360090889543130901282404559306165393609275294574780077091663387493066350780958786383398797990713465798876101400547088394024513448047964650024034608197398483753204345703125*z^770 - 174724720842993182580456361952574488844254609266145923882067687365633731298708563119363239907930205391985187107256598863329348062459111694386077493656676761084352934516917736472946966608837201435608057857961865315137564104729780639758140758262305274406307556904250422277795220014659975227349358370095617027511739573084462426241564060671550431487993651037662533810423249473655096293927372244524/580638849950743311804047395386986440139776702342726848028762235922963931202600334245091955342153490063656914571384097382784156613136964186504632990955629512224865300457203685834698519196677148799475091542755015583131734969772957191188946965748693540054745725896188973227638965628936397152352538975548139912174491662800297980097891796963740323033895456458334290238632251999673952604408079850454380270657349021372140478409107188038800669855079108873042277991771697998046875*z^778 + 9017968844029096356493162691498460296202723419273865136521657682453085168401884521032846935520636089758086381346815442424465524523128662029021925533844828540294451679041015413294487464151656629425264131583142828181551848182453186199505459407197374673557047108854266901115604674133846360241624292390951186986163955199855403681669117897155614683447995085519468535923033024315749138341913021508938701104/190381861777576997779073730510383059065424745576659230883847236770226280989740140929629550698635797303617177938480799697009973899461627169217048081498121718966922351575945615920549164333016982527732316810928146958385792485662248313500898047540402916172847196767902345743379247445540532552710363114979183073360650470126806746338654358842577341491843909762242494378398292964703218459308422571937428214848121846436083876107602356915567252989985630124190298754251562058925628662109375*z^786 + O(z^794)
+```
+
+```sage
+omega_exact = Omega({
+    S(0, S.polygon(0).circumscribing_circle().center()): g.change_ring(RR).add_bigoh(3),
+    next(iter(S.vertices())): g.change_ring(RR).add_bigoh(9) * 1000 + z**3})
+```
+
+```sage
+omega_exact
+```
+
+```sage
+omega_exact.error(verbose=True)
+```
+
+```sage
+omega.plot(versus=omega_exact)
+```
+
+We integrate to find the cohomology class this corresponds to.
+
+```sage
+{gen: omega_exact.integrate(gen).real() for gen in HS.homology().gens()}
+```
+
+```sage
+Δ = vector(list(Δ))
+```
+
+```sage
+Δ = RealField(54)(Δ[0]) + I*RealField(54)(Δ[1])
+```
+
+```sage
+Δ
+```
+
+```sage
+exact_sample = omega_exact.evaluate(0, edge=None, pos=None, Δ=Δ)
+exact_sample
+```
+
+Measuring the quality of the computed ω.
+
+```sage
+point = S.point(0, vector(Δ) + S.polygon(0).circumscribing_circle().center() )
+```
+
+```sage
+from flatsurf.geometry.cone_surface import ConeSurface
+singularities = [edges[0] for (angle, edges) in ConeSurface(S).angles(return_adjacent_edges=True) if angle > 1]
+S.plot() + point.plot(color="black", size=50) + sum([S.point(label, S.polygon(label).vertex(edge)).plot(color="red", size=25) for (label, edge) in singularities])
+```
+
+```sage
+approximate_sample = omega_exact.evaluate(0, edge=None, pos=None, Δ=Δ)
+
+print("|", exact_sample,"-",approximate_sample,"| = ", (exact_sample-approximate_sample).abs())
+```
diff --git a/doc/geometry.rst b/doc/geometry.rst
index aac9d393f..051ad3909 100644
--- a/doc/geometry.rst
+++ b/doc/geometry.rst
@@ -19,6 +19,7 @@ The flatsurf.geometry Package
    geometry/interval_exchange_transformation
    geometry/l_infinity_delaunay_cells
    geometry/lazy
+   geometry/lazy
    geometry/mappings
    geometry/mega_wollmilchsau
    geometry/minimal_cover
diff --git a/doc/news/deformation.rst b/doc/news/deformation.rst
new file mode 100644
index 000000000..570a9e7b5
--- /dev/null
+++ b/doc/news/deformation.rst
@@ -0,0 +1,59 @@
+**Added:**
+
+* Added ``bidict`` (providing dict encoding a bijection) as a dependency.
+
+* Added ``apply_matrix`` to all oriented similarity surfaces. (We apply the matrix to all polygons and keep the gluings intact. Probably not the most meaningful operation for non-dilation surfaces but it can be useful while building surfaces.)
+
+* Added ``homology`` and ``cohomology`` methods to surfaces.
+
+**Changed:**
+
+* Changed return type of ``flow_to_exit()``. It now returns just the point where the flow exits a polygon and not a description of the point anymore. **This is a breaking change.**
+
+* Changed ``billiard()`` to not triangulate non-convex polygons before creating the billiard. To restore the old behavior call ``triangulate()`` explicitly on the returned surface. Since surfaces built from non-convex polygons are quite limited, this might be a breaking change for some.
+
+* Changed ``standardize_polygons(in_place=False)`` to return (a morphism to) an immutable surface. Before, no morphism was returned and the surface was mutable.
+
+* Changed ``relabel()`` to accept a dict or a callable as the parameter ``relabeling`` (before this parameter had to be a dict and was called ``relabeling_map``.) Also, this method now returns a morphism and not a tuple containing a success flag.
+
+* Changed ``squared_length_bound`` parameter of ``saddle_connections``. The parameter is now optional. If no parameter is given, all saddle connections are enumerated (by length.)
+
+* Renamed module ``flatsurf.geometry.delaunay`` to ``flatsurf.geometry.lazy``.
+
+* Changed ``triangulate()`` to return a morphism to a triangulated surface (instead of just the triangulated surface.)
+
+**Deprecated:**
+
+* Deprecated ``polygon_double()`` since it is now identical with ``billiard()``.
+
+* Deprecated ``triangulation_mapping()`` since ``triangulate()`` now returns a morphism.
+
+* Deprecated ``canonicalize_mapping()`` (and moved it to the correct category) since ``canonicalize()`` now returns a morphism.
+
+**Removed:**
+
+* Removed ``flatsurf.geometry.mapping`` module. It has been replaced by ``flatsurf.geometry.morphism`` that can handle more general situations.
+
+* Removed the ``mapping`` keyword from ``apply_matrix()`` of dilation surfaces. The method now always returns a morphism.
+
+* Removed the previously deprecated ``rational`` keyword from ``billiard()``.
+
+* Removed ability to ``apply_matrix(in_place=True)`` with matrix with negative determinant. If you rely on this feature for some reason, you can use ``MutableOrientedSimilaritySurface.from_surface(apply_matrix(in_place=False))`` instead.
+
+* Removed the ``sc_list`` and ``check`` parameters of ``saddle_connections()``.
+
+**Fixed:**
+
+* Fixed ``flow_to_exit()`` to also work for polygons that are not strictly convex. (#258)
+
+* Fixed ``saddle_connections()`` to not throw a name error anymore in some edge cases. (#255)
+
+* Fixed ``saddle_connections()`` for surfaces built from polygons that are not strictly convex, for surfaces that contain self-glued edges, and for surfaces that contain unglued edges.
+
+* Fixed creating surface points at vertices of disconnected surfaces.
+
+* Fixed absolute position of polygon after ``subdivide_edges()``.
+
+**Performance:**
+
+* <news item>
diff --git a/environment.yml b/environment.yml
index 0e4edbfff..858b393c8 100644
--- a/environment.yml
+++ b/environment.yml
@@ -23,6 +23,8 @@ dependencies:
   - pytest-repeat
   - sagelib>=8.8
   # sagelib<9.2 does not explicitly install libiconv which is needed in lots of places.
+  # doctests call functionality in SageMath that relies on sympy being present
+  - sympy
   - libiconv
   - ruff=0.0.292
   - scipy
diff --git a/flatsurf.yml b/flatsurf.yml
index 18e32f7be..0f71c319e 100644
--- a/flatsurf.yml
+++ b/flatsurf.yml
@@ -15,8 +15,8 @@ dependencies:
   - maxima
   - gap-defaults
   - ipywidgets
-  - notebook
   - matplotlib-base
+  - notebook>=7
   - more-itertools
   - pip
   - pyintervalxt>=3,<4
diff --git a/flatsurf/__init__.py b/flatsurf/__init__.py
index 887547ada..757c26ed3 100644
--- a/flatsurf/__init__.py
+++ b/flatsurf/__init__.py
@@ -3,6 +3,10 @@
 """
 from flatsurf.version import version as __version__
 
+from flatsurf.geometry.homology import SimplicialHomology
+from flatsurf.geometry.cohomology import SimplicialCohomology
+from flatsurf.geometry.harmonic_differentials import HarmonicDifferentials
+
 from flatsurf.geometry.polygon import (
     Polygon,
     polygons,
@@ -19,12 +23,16 @@
     translation_surfaces,
 )
 
+from flatsurf.geometry.saddle_connection import SaddleConnection
+
 from flatsurf.geometry.surface import MutableOrientedSimilaritySurface
 
 from flatsurf.geometry.gl2r_orbit_closure import GL2ROrbitClosure
 
 from flatsurf.geometry.hyperbolic import HyperbolicPlane
 
+from flatsurf.geometry.euclidean import EuclideanPlane
+
 from flatsurf.geometry.homology import SimplicialHomology
 from flatsurf.geometry.cohomology import SimplicialCohomology
 
@@ -41,3 +49,8 @@
     HalfTranslationSurface,
     TranslationSurface,
 )
+
+from flatsurf.geometry.voronoi import (
+    VoronoiCellDecomposition,
+    ApproximateWeightedVoronoiCellDecomposition,
+)
diff --git a/flatsurf/features.py b/flatsurf/features.py
index 93b295652..fed9496bf 100644
--- a/flatsurf/features.py
+++ b/flatsurf/features.py
@@ -77,9 +77,19 @@ def unwrap_intrusive_ptr(K):
     "pyexactreal", url="https://github.com/flatsurf/exact-real/#install-with-conda"
 )
 
-pyflatsurf_feature = PythonModule(
-    "pyflatsurf", url="https://github.com/flatsurf/flatsurf/#install-with-conda"
-)
+
+class PyflatsurfModule(PythonModule):
+    def __init__(self):
+        super().__init__(
+            "pyflatsurf", url="https://github.com/flatsurf/flatsurf/#install-with-conda"
+        )
+
+    def is_saddle_connection_enumeration_functional(self):
+        # TODO: Check whether version is >=3.14.1
+        return False
+
+
+pyflatsurf_feature = PyflatsurfModule()
 
 gmpxxyy_feature = PythonModule(
     "gmpxxyy", url="https://github.com/flatsurf/flatsurf/#install-with-conda"
diff --git a/flatsurf/geometry/categories/cone_surfaces.py b/flatsurf/geometry/categories/cone_surfaces.py
index 8084f7272..c9c138015 100644
--- a/flatsurf/geometry/categories/cone_surfaces.py
+++ b/flatsurf/geometry/categories/cone_surfaces.py
@@ -59,6 +59,8 @@
 #  along with sage-flatsurf. If not, see <https://www.gnu.org/licenses/>.
 # ####################################################################
 
+from sage.misc.cachefunc import cached_in_parent_method, cached_method
+
 from flatsurf.geometry.categories.surface_category import (
     SurfaceCategory,
     SurfaceCategoryWithAxiom,
@@ -319,6 +321,133 @@ class WithoutBoundary(SurfaceCategoryWithAxiom):
                     True
 
                 """
+                class ElementMethods:
+                    # TODO: Only cache for vertices in parent, everything else, cache only in the point.
+                    @cached_in_parent_method
+                    def radius_of_convergence(self):
+                        r"""
+                        Return the distance of this point to the closest
+                        (other) singularity.
+
+                        EXAMPLES::
+
+                            sage: from flatsurf import translation_surfaces
+                            sage: S = translation_surfaces.regular_octagon()
+                            sage: S(0, S.polygon(0).centroid()).radius_of_convergence()
+                            √1/2*a + 1
+                            sage: next(iter(S.vertices())).radius_of_convergence()
+                            1
+                            sage: S(0, (1/2, 1/2)).radius_of_convergence()
+                            √1/2
+                            sage: S(0, (1/2, 0)).radius_of_convergence()
+                            1/2
+                            sage: S(0, (1/4, 0)).radius_of_convergence()
+                            1/4
+
+                        """
+                        # TODO: Require immutable for caching.
+
+                        surface = self.parent()
+
+                        norm = surface.euclidean_plane().norm()
+
+                        if all(vertex.angle() == 1 for vertex in surface.vertices()):
+                            return norm.infinite()
+
+                        erase_marked_points = surface.erase_marked_points()
+                        center = erase_marked_points(self)
+
+                        if not center.is_vertex():
+                            insert_marked_points = (
+                                center.surface().insert_marked_points(center)
+                            )
+                            center = insert_marked_points(center)
+
+                        surface = center.parent()
+
+                        for connection in surface.saddle_connections(
+                            initial_vertex=center
+                        ):
+                            end = surface(*connection.end())
+                            if end.angle() != 1:
+                                return norm.from_vector(connection.holonomy())
+
+                        assert False
+
+                class ParentMethods:
+                    r"""
+                    Provides methods available to all oriented cone surfaces
+                    without boundary.
+
+                    If you want to add functionality for such surfaces you most
+                    likely want to put it here.
+                    """
+
+                    def angles(self, numerical=False, return_adjacent_edges=False):
+                        r"""
+                        Return the set of angles around the vertices of the surface.
+
+                        EXAMPLES::
+
+                            sage: from flatsurf import polygons, similarity_surfaces
+                            sage: T = polygons.triangle(3, 4, 5)
+                            sage: S = similarity_surfaces.billiard(T)
+                            sage: S.angles()
+                            [1/3, 1/4, 5/12]
+                            sage: S.angles(numerical=True)   # abs tol 1e-14
+                            [0.333333333333333, 0.250000000000000, 0.416666666666667]
+
+                            sage: S.angles(return_adjacent_edges=True)
+                            [(1/3, [(0, 1), (1, 2)]), (1/4, [(0, 0), (1, 0)]), (5/12, [(1, 1), (0, 2)])]
+
+                        """
+                        if not numerical and any(
+                            not p.is_rational() for p in self.polygons()
+                        ):
+                            raise NotImplementedError(
+                                "cannot compute exact angles in this surface built from non-rational polygons yet"
+                            )
+
+                        edges = list(self.edges())
+                        edges = set(edges)
+                        angles = []
+
+                        if return_adjacent_edges:
+                            while edges:
+                                p, e = edges.pop()
+                                adjacent_edges = [(p, e)]
+                                angle = self.polygon(p).angle(e, numerical=numerical)
+                                pp, ee = self.opposite_edge(
+                                    p, (e - 1) % len(self.polygon(p).vertices())
+                                )
+                                while pp != p or ee != e:
+                                    edges.remove((pp, ee))
+                                    adjacent_edges.append((pp, ee))
+                                    angle += self.polygon(pp).angle(
+                                        ee, numerical=numerical
+                                    )
+                                    pp, ee = self.opposite_edge(
+                                        pp, (ee - 1) % len(self.polygon(pp).vertices())
+                                    )
+                                angles.append((angle, adjacent_edges))
+                        else:
+                            while edges:
+                                p, e = edges.pop()
+                                angle = self.polygon(p).angle(e, numerical=numerical)
+                                pp, ee = self.opposite_edge(
+                                    p, (e - 1) % len(self.polygon(p).vertices())
+                                )
+                                while pp != p or ee != e:
+                                    edges.remove((pp, ee))
+                                    angle += self.polygon(pp).angle(
+                                        ee, numerical=numerical
+                                    )
+                                    pp, ee = self.opposite_edge(
+                                        pp, (ee - 1) % len(self.polygon(pp).vertices())
+                                    )
+                                angles.append(angle)
+
+                        return angles
 
                 class Connected(SurfaceCategoryWithAxiom):
                     r"""
@@ -345,6 +474,190 @@ class ParentMethods:
                         most likely want to put it here.
                         """
 
+                        @cached_method(key=lambda self, distances: None)
+                        def distance_matrix_vertices(self, distance=lambda v, w: None):
+                            vertices = list(self.vertices())
+
+                            A = [
+                                [
+                                    0.0 if n == m else (distance(v, w) or float("inf"))
+                                    for n, w in enumerate(vertices)
+                                ]
+                                for m, v in enumerate(vertices)
+                            ]
+
+                            done = [[a != float("inf") for a in row] for row in A]
+
+                            def floyd():
+                                for k in range(len(vertices)):
+                                    for i in range(len(vertices)):
+                                        for j in range(len(vertices)):
+                                            A[i][j] = min(A[i][j], A[i][k] + A[k][j])
+
+                            for label in self.labels():
+                                polygon = self.polygon(label)
+                                for e, edge in enumerate(polygon.edges()):
+                                    start = self(label, e)
+                                    start = vertices.index(start)
+
+                                    end = self(label, (e + 1) % len(polygon.vertices()))
+                                    end = vertices.index(end)
+
+                                    from sage.all import RDF
+
+                                    A[start][end] = A[end][start] = min(
+                                        A[start][end], edge.change_ring(RDF).norm()
+                                    )
+
+                            floyd()
+
+                            todo = list(range(len(vertices)))
+
+                            while todo:
+                                v = max(
+                                    todo,
+                                    key=lambda v: (
+                                        len([not d for d in done[v]]),
+                                        -max(A[v]),
+                                    ),
+                                )
+
+                                todo.remove(v)
+
+                                if all(
+                                    d or i not in todo for (i, d) in enumerate(done[v])
+                                ):
+                                    continue
+
+                                vertex = vertices[v]
+
+                                for connection in self.saddle_connections(
+                                    initial_vertex=vertex,
+                                    squared_length_bound=max(A[v]) ** 2,
+                                ):
+                                    # TODO: Strangely, all this trickery is needed here since otherwise the symbolic machinery is confused.
+                                    from sage.all import RDF
+
+                                    length = float(
+                                        abs(
+                                            connection.holonomy()
+                                            .change_ring(RDF)
+                                            .norm()
+                                        )
+                                    )
+
+                                    if length >= max(A[v]):
+                                        break
+
+                                    w = vertices.index(self(*connection.end()))
+
+                                    if length < A[v][w]:
+                                        assert length < A[w][v]
+                                        A[v][w] = A[w][v] = length
+
+                                        floyd()
+
+                            from sage.all import matrix
+
+                            return matrix(A)
+
+                        def distance_matrix_points(
+                            self, points, distance=lambda v, w: None
+                        ):
+                            insertion = self.insert_marked_points(
+                                *[p for p in points if not p.is_vertex()]
+                            )
+
+                            V = list(insertion.codomain().vertices())
+
+                            inserted_points = [insertion(p) for p in points]
+
+                            def inserted_distance(v, w):
+                                if v in inserted_points and w in inserted_points:
+                                    return distance(
+                                        points[inserted_points.index(v)],
+                                        points[inserted_points.index(w)],
+                                    )
+
+                                return None
+
+                            D = insertion.codomain().distance_matrix_vertices(
+                                distance=inserted_distance
+                            )
+
+                            index = {
+                                p: V.index(q) for p, q in zip(points, inserted_points)
+                            }
+
+                            from sage.all import matrix
+
+                            return matrix(
+                                [
+                                    [D[index[p]][index[q]] for q in points]
+                                    for p in points
+                                ]
+                            )
+
+                        def cluster_points(self, points):
+                            points = tuple(points)
+                            D = self.distance_matrix_points(points)
+
+                            nclusters = len(points)
+                            for radius in sorted(set(D.list())):
+                                from sage.all import matrix
+
+                                adjacency = matrix(
+                                    [
+                                        [
+                                            d <= radius and i != j
+                                            for j, d in enumerate(row)
+                                        ]
+                                        for i, row in enumerate(D.rows())
+                                    ]
+                                )
+                                # print(radius)
+                                # print(adjacency)
+
+                                clusters = []
+                                ids = list(range(len(points)))
+                                while adjacency:
+                                    from sage.all import Graph
+
+                                    G = Graph(adjacency)
+
+                                    import sage.graphs.cliquer
+
+                                    clique = sage.graphs.cliquer.max_clique(G)
+                                    adjacency = matrix(
+                                        [
+                                            [
+                                                a
+                                                for j, a in enumerate(row)
+                                                if j not in clique
+                                            ]
+                                            for i, row in enumerate(adjacency.rows())
+                                            if i not in clique
+                                        ]
+                                    )
+                                    # print(adjacency)
+
+                                    clique = [ids[c] for c in clique]
+                                    # print("clique", clique)
+                                    clusters.append(clique)
+
+                                    ids = [id for id in ids if id not in clique]
+
+                                for p in range(len(points)):
+                                    if not any(p in cluster for cluster in clusters):
+                                        clusters.append([p])
+                                # print(clusters)
+                                if len(clusters) < nclusters:
+                                    nclusters = len(clusters)
+                                    # TODO: Actually it's not a ball of that radius.
+                                    print(
+                                        f"Identifying roots contained in a {radius:.3} ball, there are {nclusters} roots of orders {tuple(len(cluster) for cluster in clusters)}"
+                                    )
+
                         def _test_genus(self, **options):
                             r"""
                             Verify that the genus is compatible with the angles of the
@@ -378,6 +691,27 @@ def _test_genus(self, **options):
 
                             tester.assertAlmostEqual(
                                 self.genus(),
-                                sum(a - 1 for a in self.angles(numerical=True)) / 2.0
-                                + 1,
+                                float(
+                                    sum(a - 1 for a in self.angles(numerical=True))
+                                    / 2.0
+                                    + 1
+                                ),
                             )
+
+                    class ElementMethods:
+                        def distance(self, other):
+                            raise NotImplementedError
+
+                        def closest(self, points):
+                            return next(iter(self.nclosest()))[1]
+
+                        def nclosest(self, points, distance=None):
+                            distances = self.parent().distance_matrix_points(
+                                [self] + list(points), distance=distance
+                            )[0][1:]
+                            distances = [
+                                (distance, i) for (i, distance) in enumerate(distances)
+                            ]
+
+                            for (distance, i) in sorted(distances):
+                                yield distance, points[i]
diff --git a/flatsurf/geometry/categories/euclidean_polygonal_surfaces.py b/flatsurf/geometry/categories/euclidean_polygonal_surfaces.py
index 78dfc2cf0..744314159 100644
--- a/flatsurf/geometry/categories/euclidean_polygonal_surfaces.py
+++ b/flatsurf/geometry/categories/euclidean_polygonal_surfaces.py
@@ -44,6 +44,7 @@
 # ####################################################################
 
 from flatsurf.geometry.categories.surface_category import SurfaceCategory
+from sage.misc.cachefunc import cached_method
 
 
 class EuclideanPolygonalSurfaces(SurfaceCategory):
@@ -85,6 +86,12 @@ class ParentMethods:
         want to put it here.
         """
 
+        @cached_method
+        def euclidean_plane(self):
+            from flatsurf.geometry.euclidean import EuclideanPlane
+
+            return EuclideanPlane(self.base_ring())
+
         def graphical_surface(self, *args, **kwargs):
             r"""
             Return a graphical representation of this surface.
@@ -143,6 +150,7 @@ def plot(self, **kwargs):
                     "adjacencies",
                     "polygon_labels",
                     "edge_labels",
+                    "zero_flags",
                     "default_position_function",
                 ]
                 if key in kwargs
diff --git a/flatsurf/geometry/categories/euclidean_polygons.py b/flatsurf/geometry/categories/euclidean_polygons.py
index 32da380ba..1d9c9282d 100644
--- a/flatsurf/geometry/categories/euclidean_polygons.py
+++ b/flatsurf/geometry/categories/euclidean_polygons.py
@@ -340,6 +340,7 @@ def edges(self):
             """
             return [self.edge(i) for i in range(len(self.vertices()))]
 
+        @cached_method
         def edge(self, i):
             r"""
             Return the vector going from vertex ``i`` to the following vertex
@@ -757,7 +758,8 @@ def centroid(self):
                             for i in range(nvertices)
                         ]
                     ),
-                )
+                ),
+                immutable=True,
             )
 
         def get_point_position(self, point, translation=None):
@@ -832,7 +834,7 @@ def get_point_position(self, point, translation=None):
 
             # Determine whether the point is on an edge of the polygon.
             for i, (v, e) in enumerate(zip(self.vertices(), self.edges())):
-                if ccw(e, point - v) == 0:
+                if not ccw(e, point - v):
                     # The point lies on the line through this edge.
                     if 0 < e.dot_product(point - v) < e.dot_product(e):
                         return PolygonPosition(PolygonPosition.EDGE_INTERIOR, edge=i)
@@ -1215,6 +1217,9 @@ def triangulation(self):
                     sage: P = Polygon(vertices=[(0,0), (1,0), (1,1), (0,1), (0,2), (-1,2), (-1,1), (-2,1),
                     ....:                    (-2,0), (-1,0), (-1,-1), (0,-1)])
                     sage: P.triangulation()
+                    doctest:warning
+                    ...
+                    UserWarning: triangulation() has been deprecated and will be removed in a future version of sage-flatsurf. Use triangulate() instead.
                     [(0, 2), (2, 8), (3, 5), (6, 8), (8, 3), (3, 6), (9, 11), (0, 9), (2, 9)]
 
                 TESTS::
@@ -1309,6 +1314,12 @@ def triangulation(self):
                     [(0, 4), (1, 3), (4, 1)]
 
                 """
+                import warnings
+
+                warnings.warn(
+                    "triangulation() has been deprecated and will be removed in a future version of sage-flatsurf. Use triangulate() instead."
+                )
+
                 vertices = self.vertices()
 
                 n = len(vertices)
@@ -1387,6 +1398,76 @@ def triangulation(self):
 
                 assert False
 
+            def flow_to_exit(self, point, direction):
+                r"""
+                Flow a point in the direction of holonomy until the point
+                hits the boundary of the polygon and return the point on
+                the boundary where the trajectory exits.
+
+                INPUT:
+
+                - ``point`` -- a point in the closure of the polygon (as a vector)
+
+                - ``holonomy`` -- direction of motion (a vector of non-zero length)
+
+                TESTS::
+
+                    sage: from flatsurf import Polygon
+                    sage: P = Polygon(vertices=[(1, 0), (1, -2), (3/2, -5/2), (2, -2), (2, 0), (2, 1), (2, 3), (3/2, 7/2), (1, 3), (1, 1)])
+                    sage: P.flow_to_exit(vector((2, 1)), vector((0, 1)))
+                    (2, 3)
+                    sage: P.flow_to_exit(vector((1, 3)), vector((0, -1)))
+                    (1, 1)
+
+                """
+                if not direction:
+                    raise ValueError("direction must be non-zero")
+
+                vertices = self.vertices()
+
+                first_intersection = None
+
+                for v in range(len(vertices)):
+                    segment = vertices[v], vertices[(v + 1) % len(vertices)]
+
+                    from flatsurf.geometry.euclidean import ray_segment_intersection
+
+                    intersection = ray_segment_intersection(point, direction, segment)
+
+                    if intersection is None:
+                        continue
+
+                    if isinstance(intersection, tuple):
+                        if intersection[0] != point:
+                            # The flow overlaps with this edge but it hits
+                            # a vertex before it gets here.
+                            continue
+
+                        intersection = intersection[1]
+                        assert intersection != point
+
+                    if intersection == point:
+                        continue
+
+                    from flatsurf.geometry.euclidean import time_on_ray
+
+                    if (
+                        first_intersection is None
+                        or time_on_ray(point, direction, first_intersection)[0]
+                        > time_on_ray(point, direction, intersection)[0]
+                    ):
+                        first_intersection = intersection
+
+                if first_intersection is not None:
+                    return first_intersection
+
+                if self.get_point_position(point).is_outside():
+                    raise ValueError("Cannot flow from point outside of polygon")
+
+                raise ValueError(
+                    "Cannot flow from point on boundary if direction points out of the polygon"
+                )
+
             def triangulate(self):
                 r"""
                 Return a triangulation of this polygon.
@@ -1749,97 +1830,29 @@ def contains_point(self, point, translation=None):
                     r"""
                     Return whether the point is within the polygon (after the polygon is possibly translated)
                     """
+                    # TODO: Deprecate translation.
                     return self.get_point_position(
                         point, translation=translation
                     ).is_inside()
 
-                def flow_to_exit(self, point, direction):
+                def distance(self, point):
                     r"""
-                    Flow a point in the direction of holonomy until the point leaves the
-                    polygon.  Note that ValueErrors may be thrown if the point is not in the
-                    polygon, or if it is on the boundary and the holonomy does not point
-                    into the polygon.
-
-                    INPUT:
-
-                    - ``point`` -- a point in the closure of the polygon (as a vector)
-
-                    - ``holonomy`` -- direction of motion (a vector of non-zero length)
-
-                    OUTPUT:
-
-                    - The point in the boundary of the polygon where the trajectory exits
-
-                    - a PolygonPosition object representing the combinatorial position of the stopping point
+                    Return the distance of the boundary of this polygon to
+                    ``point``.
                     """
-                    from flatsurf.geometry.polygon import PolygonPosition
-
-                    V = self.base_ring().fraction_field() ** 2
-                    if direction == V.zero():
-                        raise ValueError("Zero vector provided as direction.")
-                    v0 = self.vertex(0)
-                    for i in range(len(self.vertices())):
-                        e = self.edge(i)
-                        from sage.all import matrix
-
-                        m = matrix([[e[0], -direction[0]], [e[1], -direction[1]]])
-                        try:
-                            ret = m.inverse() * (point - v0)
-                            s = ret[0]
-                            t = ret[1]
-                            # What if the matrix is non-invertible?
+                    return min(segment.distance(point) for segment in self.segments())
 
-                            # Answer: You'll get a ZeroDivisionError which means that the edge is parallel
-                            # to the direction.
+                def segments(self):
+                    E = self.euclidean_plane()
+                    V = self.vertices()
+                    return [
+                        E(start).segment(end) for (start, end) in zip(V, V[1:] + V[:1])
+                    ]
 
-                            # s is location it intersects on edge, t is the portion of the direction to reach this intersection
-                            if t > 0 and 0 <= s and s <= 1:
-                                # The ray passes through edge i.
-                                if s == 1:
-                                    # exits through vertex i+1
-                                    v0 = v0 + e
-                                    return v0, PolygonPosition(
-                                        PolygonPosition.VERTEX,
-                                        vertex=(i + 1) % len(self.vertices()),
-                                    )
-                                if s == 0:
-                                    # exits through vertex i
-                                    return v0, PolygonPosition(
-                                        PolygonPosition.VERTEX, vertex=i
-                                    )
-                                    # exits through vertex i
-                                # exits through interior of edge i
-                                prod = t * direction
-                                return point + prod, PolygonPosition(
-                                    PolygonPosition.EDGE_INTERIOR, edge=i
-                                )
-                        except ZeroDivisionError:
-                            # Here we know the edge and the direction are parallel
-                            if ccw(e, point - v0) == 0:
-                                # In this case point lies on the edge.
-                                # We need to work out which direction to move in.
-                                from flatsurf.geometry.euclidean import is_parallel
+                def euclidean_plane(self):
+                    from flatsurf import EuclideanPlane
 
-                                if (point - v0).is_zero() or is_parallel(e, point - v0):
-                                    # exits through vertex i+1
-                                    return self.vertex(i + 1), PolygonPosition(
-                                        PolygonPosition.VERTEX,
-                                        vertex=(i + 1) % len(self.vertices()),
-                                    )
-                                else:
-                                    # exits through vertex i
-                                    return v0, PolygonPosition(
-                                        PolygonPosition.VERTEX, vertex=i
-                                    )
-                            pass
-                        v0 = v0 + e
-                    # Our loop has terminated. This can mean one of several errors...
-                    pos = self.get_point_position(point)
-                    if pos.is_outside():
-                        raise ValueError("Started with point outside polygon")
-                    raise ValueError(
-                        "Point on boundary of polygon and direction not pointed into the polygon."
-                    )
+                    return EuclideanPlane(self.base_ring())
 
                 def flow_map(self, direction):
                     r"""
@@ -1963,6 +1976,7 @@ def flow(self, point, holonomy, translation=None):
                         sage: s.flow(p, w)
                         ((1, 1/2), (3/2, 0), point positioned on interior of edge 1 of polygon)
                     """
+                    # TODO: Deprecate translation.
                     from flatsurf.geometry.polygon import PolygonPosition
 
                     V = self.base_ring().fraction_field() ** 2
@@ -2059,22 +2073,26 @@ def circumscribed_circle(self):
                         sage: from flatsurf import Polygon
                         sage: P = Polygon(vertices=[(0,0),(1,0),(2,1),(-1,1)])
                         sage: P.circumscribed_circle()
-                        Circle((1/2, 3/2), 5/2)
+                        { (x - 1/2)² + (y - 3/2)² = 5/2 }
+
                     """
                     from flatsurf.geometry.circle import circle_from_three_points
 
                     circle = circle_from_three_points(
                         self.vertex(0), self.vertex(1), self.vertex(2), self.base_ring()
                     )
-                    for i in range(3, len(self.vertices())):
-                        if not circle.point_position(self.vertex(i)) == 0:
-                            raise ValueError(
-                                "Vertex " + str(i) + " is not on the circle."
-                            )
+                    # TODO: Temporarily disabled because it fails over inexact rings.
+                    # for i in range(3, len(self.vertices())):
+                    #     if not circle.point_position(self.vertex(i)) == 0:
+                    #         raise ValueError(
+                    #             "Vertex " + str(i) + " is not on the circle."
+                    #         )
                     return circle
 
-                def subdivide(self):
+                def subdivide(self, center=None):
                     r"""
+                    # TODO: If no point given, takes the centroid.
+
                     Return a list of triangles that partition this polygon.
 
                     For each edge of the polygon one triangle is created that joins this
@@ -2117,7 +2135,7 @@ def subdivide(self):
 
                     """
                     vertices = self.vertices()
-                    center = self.centroid()
+                    center = center or self.centroid()
                     from flatsurf import Polygon
 
                     return [
@@ -2153,13 +2171,22 @@ def subdivide_edges(self, parts=2):
                         Polygon(vertices=[(0, 0), (1/3, 0), (2/3, 0), (1, 0), (5/6, 1/6*a), (2/3, 1/3*a), (1/2, 1/2*a), (1/3, 1/3*a), (1/6, 1/6*a)])
 
                     """
-                    if parts < 1:
-                        raise ValueError("parts must be a positive integer")
+                    from collections.abc import Iterable
+
+                    if not isinstance(parts, Iterable):
+                        parts = [
+                            [1 / parts for k in range(parts)] for e in self.edges()
+                        ]
 
-                    steps = [e / parts for e in self.edges()]
                     from flatsurf import Polygon
 
-                    return Polygon(edges=[e for e in steps for p in range(parts)])
+                    return Polygon(
+                        edges=[
+                            e * part
+                            for (e, parts) in zip(self.edges(), parts)
+                            for part in parts
+                        ]
+                    ).translate(self.vertex(0))
 
                 def j_invariant(self):
                     r"""
diff --git a/flatsurf/geometry/categories/euclidean_polygons_with_angles.py b/flatsurf/geometry/categories/euclidean_polygons_with_angles.py
index ec8890779..050bc876b 100644
--- a/flatsurf/geometry/categories/euclidean_polygons_with_angles.py
+++ b/flatsurf/geometry/categories/euclidean_polygons_with_angles.py
@@ -481,12 +481,6 @@ def __slopes(self):
                 False
             ), "EuclideanPolygonsWithAngles should be a supercategory of this category"
 
-        # TODO: rather than lengths, it would be more convenient to have access
-        # to the tangent space (that is the space of possible holonomies). However,
-        # since it is not defined over the real numbers, there are several possible ways
-        # to handle the data.
-        # TODO: here we ignored the direction SO(2) which provides additional symmetry
-        # in the tangent space
         @cached_method
         def lengths_polytope(self):
             r"""
@@ -496,6 +490,18 @@ def lengths_polytope(self):
             equiangular polygons. Be careful that even though the lengths are
             admissible, they may not define a polygon without intersection.
 
+            .. TODO::
+
+                Rather than lengths, it would be more convenient to have access
+                to the tangent space (that is the space of possible
+                holonomies). However, since it is not defined over the real
+                numbers, there are several possible ways to handle the data.
+
+            .. TODO::
+
+                Here we ignored the direction SO(2) which provides additional
+                symmetry in the tangent space
+
             EXAMPLES::
 
                 sage: from flatsurf import EuclideanPolygonsWithAngles
diff --git a/flatsurf/geometry/categories/half_translation_surfaces.py b/flatsurf/geometry/categories/half_translation_surfaces.py
index 13f814589..0aab89c36 100644
--- a/flatsurf/geometry/categories/half_translation_surfaces.py
+++ b/flatsurf/geometry/categories/half_translation_surfaces.py
@@ -55,6 +55,7 @@
 )
 from sage.misc.lazy_import import LazyImport
 from sage.all import QQ, AA
+from sage.misc.cachefunc import cached_method
 
 
 class HalfTranslationSurfaces(SurfaceCategory):
@@ -193,7 +194,7 @@ def stratum(self):
                         Q_0(0, -1^4)
 
                     """
-                    angles = self.angles()
+                    angles = list(self.angles())
 
                     for a, b in self.gluings():
                         if a == b:
@@ -456,4 +457,5 @@ def normalized_coordinates(self):
                         S.glue((relabelling[p1], e1), (relabelling[p2], e2))
 
                     S._refine_category_(self.category())
+                    S.set_immutable()
                     return S, M
diff --git a/flatsurf/geometry/categories/polygonal_surfaces.py b/flatsurf/geometry/categories/polygonal_surfaces.py
index d6534bb2f..1584b4ae0 100644
--- a/flatsurf/geometry/categories/polygonal_surfaces.py
+++ b/flatsurf/geometry/categories/polygonal_surfaces.py
@@ -1270,6 +1270,10 @@ def is_with_boundary(self):
                     False
 
                 """
+                if "WithoutBoundary" in self.category().axioms():
+                    return False
+                if "WithBoundary" in self.category().axioms():
+                    return True
                 for label in self.labels():
                     for edge in range(len(self.polygon(label).vertices())):
                         cross = self.opposite_edge(label, edge)
@@ -1320,6 +1324,45 @@ def _test_labels(self, **options):
 
                 tester.assertEqual(len(list(self.labels())), len(self.labels()))
 
+            def some_elements(self):
+                r"""
+                Return some points in this surface.
+
+                EXAMPLES::
+
+                    sage: from flatsurf import Polygon, similarity_surfaces
+                    sage: P = Polygon(vertices=[(0,0), (2,0), (1,4), (0,5)])
+                    sage: S = similarity_surfaces.self_glued_polygon(P)
+                    sage: list(S.some_elements())
+                    [Vertex 0 of polygon 0,
+                     Point (3/4, 9/4) of polygon 0,
+                     Point (2/3, 0) of polygon 0,
+                     Point (4/3, 8/3) of polygon 0,
+                     Point (1/3, 14/3) of polygon 0,
+                     Point (0, 10/3) of polygon 0]
+
+                """
+                for vertex in self.vertices():
+                    yield vertex
+
+                from sage.categories.all import Fields
+
+                if self.base_ring() in Fields():
+                    for label in self.labels():
+                        polygon = self.polygon(label)
+                        vertices = polygon.vertices()
+
+                        yield self(label, sum(vertices) / len(vertices))
+                        for vertex in range(len(vertices)):
+                            yield self(
+                                label,
+                                (
+                                    polygon.vertex(vertex)
+                                    + 2 * polygon.vertex(vertex + 1)
+                                )
+                                / 3,
+                            )
+
     class InfiniteType(SurfaceCategoryWithAxiom):
         r"""
         The axiom satisfied by surfaces built from infinitely many polygons.
diff --git a/flatsurf/geometry/categories/similarity_surfaces.py b/flatsurf/geometry/categories/similarity_surfaces.py
index 930c3aa08..852af8ede 100644
--- a/flatsurf/geometry/categories/similarity_surfaces.py
+++ b/flatsurf/geometry/categories/similarity_surfaces.py
@@ -120,7 +120,6 @@
     SurfaceCategoryWithAxiom,
 )
 from flatsurf.cache import cached_surface_method
-
 from sage.categories.category_with_axiom import all_axioms
 from sage.all import QQ, AA
 
@@ -394,14 +393,16 @@ def is_rational_surface(self):
 
         def _mul_(self, matrix, switch_sides=True):
             r"""
+            Apply the 2×2 ``matrix`` to the polygons of this surface.
+
             EXAMPLES::
 
                 sage: from flatsurf import translation_surfaces
                 sage: s = translation_surfaces.infinite_staircase()
                 sage: s
                 The infinite staircase
-                sage: m=Matrix([[1,2],[0,1]])
-                sage: s2=m*s
+                sage: m = matrix([[1,2],[0,1]])
+                sage: s2 = m * s
                 sage: TestSuite(s2).run()
                 sage: s2.polygon(0)
                 Polygon(vertices=[(0, 0), (1, 0), (3, 1), (2, 1)])
@@ -420,16 +421,42 @@ def _mul_(self, matrix, switch_sides=True):
             if not switch_sides:
                 raise NotImplementedError
 
-            from sage.structure.element import is_Matrix
+            return self.apply_matrix(matrix).codomain()
 
-            if not is_Matrix(matrix):
-                raise NotImplementedError("only implemented for matrices")
-            if not matrix.dimensions != (2, 2):
-                raise NotImplementedError("only implemented for 2x2 matrices")
+        def harmonic_differentials(
+            self, error, cell_decomposition, check=True, category=None
+        ):
+            if self.is_mutable():
+                raise ValueError(
+                    "surface must be immutable to compute harmonic differentials"
+                )
 
+            from sage.all import RR
             from flatsurf.geometry.lazy import GL2RImageSurface
 
-            return GL2RImageSurface(self, matrix)
+            coefficients = RR
+
+            if category is None:
+                from sage.categories.all import Modules
+
+                category = Modules(coefficients)
+
+            return self._harmonic_differentials(
+                error=error,
+                cell_decomposition=cell_decomposition,
+                check=check,
+                category=category,
+            )
+
+        @cached_surface_method
+        def _harmonic_differentials(self, error, cell_decomposition, check, category):
+            from flatsurf.geometry.harmonic_differentials import (
+                HarmonicDifferentialSpace,
+            )
+
+            return HarmonicDifferentialSpace(
+                self, error, cell_decomposition, check, category
+            )
 
         def apply_matrix(self, m, in_place=None):
             r"""
@@ -1388,6 +1415,7 @@ def underlying_surface(self):
 
                 return self
 
+            @cached_surface_method
             def edge_transformation(self, p, e):
                 r"""
                 Return the similarity bringing the provided edge to the opposite edge.
@@ -1488,8 +1516,8 @@ def set_vertex_zero(self, label, v, in_place=False):
 
             def relabel(self, relabeling=None, in_place=False):
                 r"""
-                Return a surface whose polygons have been relabeled according
-                to ``relabeling``.
+                Return a morphism to a surface whose polygons have been
+                relabeled according to ``relabeling``.
 
                 INPUT:
 
@@ -1499,16 +1527,16 @@ def relabel(self, relabeling=None, in_place=False):
                   non-negative integers.
 
                 - ``in_place`` -- a boolean (default: ``False``); whether to
-                  modify this surface or return a relabeled copy instead.
+                  mutate this surface or return a morphism to an independent copy.
 
                 EXAMPLES::
 
                     sage: from flatsurf import translation_surfaces
                     sage: S = translation_surfaces.veech_double_n_gon(5)
-                    sage: SS = S.relabel({0: 1, 1: 2})
+                    sage: relabeling = S.relabel({0: 1, 1: 2})
+                    sage: SS = relabeling.codomain()
                     sage: SS
                     Translation Surface in H_2(2) built from 2 regular pentagons
-
                     sage: SS.root()
                     1
 
@@ -1519,7 +1547,7 @@ def relabel(self, relabeling=None, in_place=False):
 
                 The relabeling can also be a callable::
 
-                    sage: SSS = SS.relabel(lambda label: label -1)
+                    sage: SSS = SS.relabel(lambda label: label -1).codomain()
                     sage: SSS == S
                     True
 
@@ -1544,7 +1572,7 @@ def relabel(self, relabeling=None, in_place=False):
                 For infinite surfaces, we only support relabeling to the
                 non-negative integers:
 
-                    sage: S.relabel().labels()
+                    sage: S.relabel().codomain().labels()
                     (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, …)
 
                 """
@@ -1576,11 +1604,11 @@ def relabel(self, relabeling=None, in_place=False):
                     MutableOrientedSimilaritySurface,
                 )
 
-                S = MutableOrientedSimilaritySurface.from_surface(self)
-                S = S.relabel(relabeling=relabeling, in_place=True)
-                S.set_immutable()
+                s = MutableOrientedSimilaritySurface.from_surface(self)
+                morphism = s.relabel(relabeling=relabeling, in_place=True)
+                s.set_immutable()
 
-                return S
+                return morphism.change(domain=self, codomain=s, check=False)
 
             def copy(
                 self,
@@ -1666,7 +1694,7 @@ def copy(
                     message += " Use relabel({old: new for (new, old) in enumerate(surface.labels())}) for integer labels."
 
                 if not self.is_finite_type():
-                    message += " However, there is no immediate replacement for lazy copying of infinite surfaces. Have a look at the implementation of flatsurf.geometry.delaunay.LazyMutableSurface and adapt it to your needs."
+                    message += " However, there is no immediate replacement for lazy copying of infinite surfaces. Have a look at the implementation of flatsurf.geometry.lazy.LazyMutableSurface and adapt it to your needs."
 
                 if new_field is not None:
                     message += " Use change_ring() to change the field over which the surface is defined."
@@ -2041,19 +2069,21 @@ def random_flip(self, repeat=1, in_place=False):
                 EXAMPLES::
 
                     sage: from flatsurf import translation_surfaces
-                    sage: ss = translation_surfaces.ward(3).triangulate()
-                    sage: ss.random_flip(15)  # random
-                    Translation Surface in H_1(0^3) built from 6 triangles
+                    sage: ss = translation_surfaces.ward(3).triangulate().codomain()
+                    sage: ss.random_flip(15)
+                    Translation Surface in H_1(0^3) built from ...
 
                 """
                 if not self.is_triangulated():
                     raise ValueError("random_flip only works for triangulated surfaces")
+
                 if not in_place:
                     from flatsurf.geometry.surface import (
                         MutableOrientedSimilaritySurface,
                     )
 
                     self = MutableOrientedSimilaritySurface.from_surface(self)
+
                 labels = list(self.labels())
                 i = 0
                 from sage.misc.prandom import choice
@@ -2434,17 +2464,34 @@ def tangent_vector(self, lab, p, v, ring=None):
 
             def triangulation_mapping(self):
                 r"""
-                Return a ``SurfaceMapping`` triangulating the surface
-                or ``None`` if the surface is already triangulated.
+                Return a morphism triangulating the surface or
+                ``None`` if the surface is already triangulated.
+
+                EXAMPLES::
+
+                    sage: from flatsurf import translation_surfaces
+                    sage: S = translation_surfaces.mcmullen_L(1, 1, 1, 1)
+                    sage: S.triangulation_mapping()
+                    doctest:warning
+                    ...
+                    UserWarning: triangulation_mapping() has been deprecated and will be removed in a future version of sage-flatsurf; use triangulate() instead
+                    Triangulation morphism:
+                      From: Translation Surface in H_2(2) built from 3 squares
+                      To:   Triangulation of Translation Surface in H_2(2) built from 3 squares
+
                 """
-                from flatsurf.geometry.mappings import triangulation_mapping
+                import warnings
 
-                return triangulation_mapping(self)
+                warnings.warn(
+                    "triangulation_mapping() has been deprecated and will be removed in a future version of sage-flatsurf; use triangulate() instead"
+                )
 
+                return self.triangulate()
+
+            # TODO: Rename to triangulation()
             def triangulate(self, in_place=False, label=None, relabel=None):
                 r"""
-                Return a triangulated version of this surface. (This may be mutable
-                or not depending on the input.)
+                Return a morphism to a triangulated version of this surface.
 
                 INPUT:
 
@@ -2620,6 +2667,7 @@ def is_delaunay_triangulated(self, limit=None):
 
                 return True
 
+            # TODO: Should we rename this to is_delaunay? And remove is_delaunay_triangulated?
             def is_delaunay_decomposed(self, limit=None):
                 r"""
                 Return if the decomposition of the surface into polygons is Delaunay.
@@ -2667,6 +2715,17 @@ def is_delaunay_decomposed(self, limit=None):
 
                 return True
 
+            # TODO: In the long run, what should the naming strategy be?
+            # Should it say delaunay_triangulation() or delaunay_triangulate()?
+            # The latter sounds like an in-place operation. So maybe
+            # delaunay_triangulation() is better.
+            # Should there be any in-place operations at all? For anything that
+            # is linear-time or slower, it makes not a lot of sense really. If
+            # we want speed, we should call out to libflatsurf.
+            # TODO: Maybe the codomain should be a
+            # MutableOrientedSimilaritySurface that is immutable when
+            # this is finite type. (Here and for all the other functions
+            # returning a lazy surface at the moment.)
             def delaunay_triangulation(
                 self,
                 triangulated=None,
@@ -2854,7 +2913,7 @@ def delaunay_decomposition(
 
                     sage: p = Polygon(edges=[(4,0),(-2,1),(-2,-1)])
                     sage: s0 = similarity_surfaces.self_glued_polygon(p)
-                    sage: s = s0.delaunay_decomposition()
+                    sage: s = s0.delaunay_decomposition().codomain()
                     sage: TestSuite(s).run()
 
                     sage: m = matrix([[2,1],[1,1]])
@@ -2918,6 +2977,302 @@ def delaunay_decomposition(
 
                 return self.delaunay_decompose().codomain()
 
+            def _saddle_connections_unbounded(
+                self, initial_label, initial_vertex, algorithm
+            ):
+                r"""
+                Enumerate all saddle connections in this surface ordered by length.
+
+                This is a helper method for :meth:`saddle_connections`.
+                """
+
+                def squared_length(v):
+                    return v[0] ** 2 + v[1] ** 2
+
+                # Enumerate all saddle connections by length
+                connections = set()
+                shortest_edge = min(
+                    squared_length(self.polygon(label).edge(edge))
+                    for (label, edge) in self.edges()
+                )
+
+                length_bound = shortest_edge
+                while True:
+                    more_connections = [
+                        connection
+                        for connection in self.saddle_connections(
+                            squared_length_bound=length_bound,
+                            initial_label=initial_label,
+                            initial_vertex=initial_vertex,
+                            algorithm=algorithm,
+                        )
+                        if connection not in connections
+                    ]
+                    for connection in sorted(
+                        more_connections,
+                        key=lambda connection: squared_length(connection.holonomy()),
+                    ):
+                        connections.add(connection)
+                        yield connection
+
+                    length_bound *= 2
+
+            def _saddle_connections_generic_cone_bounded(
+                self, squared_length_bound, source, incoming_edge, similarity, cone
+            ):
+                r"""
+                Enumerate the saddle connections of length at most square root
+                of ``squared_length_bound`` and which are strictly inside the
+                ``cone``.
+
+                This is a helper method for :meth:`saddle_connections`.
+
+                ALGORITHM:
+
+                We check for each vertex of the polygon if it is contained in
+                the cone. If it is, it leads to a saddle connection (unless
+                it's hidden or too far away.)
+
+                Then we recursively propagate the cone across each edge it hits
+                into the neighboring polygons.
+
+                INPUT:
+
+                - ``squared_length_bound`` -- a number, the square of the
+                  length up to which saddle connections should be considered;
+                  the length of saddle connections is determined using their
+                  holonomy vector written in their source polygon.
+
+                - ``source`` -- a pair of a polygon label and a vertex
+                  index; the vertex where all saddle connections enumerated by
+                  this method start.
+
+                - ``incoming_edge`` -- a pair of a polygon label and an edge
+                  index; the ``cone`` is crossing over this ``incoming_edge``
+                  (after transforming that polygon with the inverse of the
+                   ``similarity``.)
+
+                - ``similarity`` -- a similarity of the plane; describes a
+                  translation, rotation, and dilation of the ``incoming_edge``
+                  polygon to position it relative to the ``cone``
+
+                - ``cone`` -- an open cone in the plane which bounds the
+                  holonomy of the saddle connections.
+
+                """
+                assert not cone.is_empty()
+
+                label = incoming_edge[0]
+                polygon = similarity(self.polygon(label))
+
+                incoming_edge_segment = (
+                    polygon.vertex(incoming_edge[1]),
+                    polygon.vertex(incoming_edge[1] + 1),
+                )
+                origin = (polygon.base_ring() ** 2).zero()
+
+                from flatsurf.geometry.cone import Cones
+
+                if polygon.vertex(incoming_edge[1]):
+                    incoming_edge_cone = Cones(self.base_ring())(
+                        polygon.vertex(incoming_edge[1] + 1),
+                        polygon.vertex(incoming_edge[1]),
+                    )
+                else:
+                    incoming_edge_cone = Cones(self.base_ring())(
+                        polygon.vertex(incoming_edge[1] + 1),
+                        polygon.vertex(incoming_edge[1] - 1),
+                    )
+
+                if not cone.is_subset(incoming_edge_cone):
+                    raise ValueError(
+                        "cone must be contained in the cone formed by the incoming edge"
+                    )
+
+                from flatsurf import EuclideanPlane
+
+                bounding_circle = EuclideanPlane(self.base_ring()).circle(
+                    (0, 0),
+                    radius_squared=squared_length_bound,
+                )
+
+                # Each vertex that is contained in the cone's interior yields a
+                # saddle connection (if it is "behind" the incoming edge and
+                # not hidden by some other edge; these conditions are only
+                # possible for polygons that are not strictly convex.)
+                for v, vertex in enumerate(polygon.vertices()):
+                    if cone.contains_point(vertex):
+                        if v == incoming_edge[1]:
+                            continue
+
+                        from flatsurf.geometry.euclidean import (
+                            time_on_ray,
+                            ray_segment_intersection,
+                        )
+
+                        vertex_time_on_ray = time_on_ray(
+                            origin,
+                            vertex,
+                            ray_segment_intersection(
+                                origin, vertex, incoming_edge_segment
+                            ),
+                        )
+                        if vertex_time_on_ray[0] > vertex_time_on_ray[1]:
+                            assert not polygon.is_convex()
+                            # The cone hits the vertex before entering the polygon.
+                            continue
+
+                        exit = polygon.flow_to_exit(vertex, -vertex)
+
+                        # TODO: This is probably very inefficient. It would be enough to check if this point is on the incoming_edge.
+                        exit = polygon.get_point_position(exit)
+
+                        if exit.is_vertex() and exit.get_vertex() != incoming_edge[1]:
+                            # Another vertex hides this vertex.
+                            continue
+
+                        if (
+                            exit.is_in_edge_interior()
+                            and exit.get_edge() != incoming_edge[1]
+                        ):
+                            # Another edge hides this vertex.
+                            continue
+
+                        # The vertex is not hidden by some other vertex or
+                        # edge. This is a saddle connection.
+                        holonomy = vertex
+                        if bounding_circle.point_position(holonomy) >= 0:
+                            # The saddle connection is within the squared_length_bound.
+                            from flatsurf.geometry.saddle_connection import (
+                                SaddleConnection,
+                            )
+
+                            yield SaddleConnection(
+                                surface=self,
+                                start=source,
+                                end=(label, v),
+                                holonomy=vertex,
+                                end_holonomy=~similarity.derivative() * vertex,
+                            )
+
+                # We need to propagate the cone across edges to neighboring
+                # polygons. For this, we split the cone into smaller subcones,
+                # such that each subcone contains no vertex in its interior.
+                cone_space = cone.parent()
+                ray_space = cone_space.rays()
+                vertex_directions = (
+                    [cone.start()]
+                    + cone.sorted_rays(
+                        [
+                            ray_space(vertex)
+                            for v, vertex in enumerate(polygon.vertices())
+                            if v != incoming_edge[1]
+                            and cone.contains_point(vertex)
+                            and ray_space(vertex) not in [cone.start(), cone.end()]
+                        ]
+                    )
+                    + [cone.end()]
+                )
+
+                subcones = [
+                    cone_space(v, w)
+                    for (v, w) in zip(vertex_directions, vertex_directions[1:])
+                ]
+                # Now we propagate each subcone across the first edge it hits
+                # after crossing over the incoming edge.
+                for subcone in subcones:
+                    ray = subcone.a_ray()
+                    from flatsurf.geometry.euclidean import ray_segment_intersection
+
+                    start = ray_segment_intersection(
+                        origin, ray.vector(), incoming_edge_segment
+                    )
+                    exit = polygon.flow_to_exit(start, ray.vector())
+                    exit = polygon.get_point_position(exit)
+                    assert exit.is_in_edge_interior()
+                    outgoing_edge = exit.get_edge()
+                    if (
+                        bounding_circle.line_segment_position(
+                            polygon.vertex(outgoing_edge),
+                            polygon.vertex(outgoing_edge + 1),
+                        )
+                        != 1
+                    ):
+                        # No part of the edge is inside the
+                        # squared_length_bound, search ends here.
+                        continue
+
+                    opposite_edge = self.opposite_edge(label, outgoing_edge)
+                    if opposite_edge is None:
+                        # Unglued edge. Search ends here.
+                        continue
+
+                    # Recurse
+                    yield from self._saddle_connections_generic_cone_bounded(
+                        squared_length_bound,
+                        source,
+                        opposite_edge,
+                        similarity * self.edge_transformation(*opposite_edge),
+                        subcone,
+                    )
+
+            def _saddle_connections_generic_from_vertex_bounded(
+                self, squared_length_bound, source
+            ):
+                r"""
+                Enumerate all the saddle connections up to length
+                ``squared_length_bound`` which start at ``source``.
+
+                This is a helper method for :meth:`saddle_connections`.
+
+                ALGORITHM:
+
+                We consider saddle connections that come from the edges
+                adjacent to the vertex of ``source`` and then use
+                :meth:`_saddle_connections_generic_cone_bounded` to enumerate the
+                saddle connections in the open cone formed by these edges.
+
+                INPUT:
+
+                - ``squared_length_bound`` -- a number, the square of the
+                  length up to which saddle connections should be considered;
+                  the length of saddle connections is determined using their
+                  holonomy vector written in their source polygon.
+
+                - ``source`` -- a pair consisting of a polygon label and a vertex
+                  index. The saddle connection starts at that vertex and is
+                  contained in the closed cone that is formed by the two edges
+                  adjacent to the vertex.
+
+                """
+                polygon = self.polygon(source[0])
+
+                from flatsurf.geometry.saddle_connection import SaddleConnection
+
+                for connection in [
+                    SaddleConnection.from_half_edge(self, source[0], source[1]),
+                    -SaddleConnection.from_half_edge(
+                        self, source[0], (source[1] - 1) % len(polygon.edges())
+                    ),
+                ]:
+                    if connection.length_squared() <= squared_length_bound:
+                        yield connection
+
+                from flatsurf.geometry.similarity import SimilarityGroup
+
+                G = SimilarityGroup(self.base_ring())
+                similarity = G.translation(*-polygon.vertex(source[1]))
+
+                from flatsurf.geometry.cone import Cones
+
+                cone = Cones(polygon.base_ring())(
+                    polygon.edge(source[1]), -polygon.edge(source[1] - 1)
+                )
+
+                yield from self._saddle_connections_generic_cone_bounded(
+                    squared_length_bound, source, source, similarity, cone
+                )
+
             def delaunay_decompose(self, codomain=None):
                 r"""
                 Return a Delaunay decomposition of this surface, i.e., a
@@ -3040,172 +3395,234 @@ def delaunay_decompose(self, codomain=None):
 
             def saddle_connections(
                 self,
-                squared_length_bound,
+                squared_length_bound=None,
                 initial_label=None,
                 initial_vertex=None,
-                sc_list=None,
-                check=False,
+                algorithm=None,
             ):
                 r"""
-                Returns a list of saddle connections on the surface whose length squared is less than or equal to squared_length_bound.
-                The length of a saddle connection is measured using holonomy from polygon in which the trajectory starts.
+                Return the saddle connections on this surface whose length
+                squared is at most ``squared_length_bound`` (ordered by
+                length.)
 
-                If initial_label and initial_vertex are not provided, we return all saddle connections satisfying the bound condition.
+                The length of a saddle connection is measured using holonomy
+                from the polygon in which the trajectory starts.
 
-                If initial_label and initial_vertex are provided, it only provides saddle connections emanating from the corresponding
-                vertex of a polygon. If only initial_label is provided, the added saddle connections will only emanate from the
-                corresponding polygon.
+                If no ``squared_length_bound`` is given, all saddle connections
+                are enumerated (ordered by length.)
 
-                If sc_list is provided the found saddle connections are appended to this list and the resulting list is returned.
+                If ``initial_label`` and ``initial_vertex`` are provided, only
+                saddle connections are returned which emanate from the
+                corresponding vertex of a polygon (and only pointing into the
+                polygon or along the edges adjacent to that vertex.)
 
-                If check==True it uses the checks in the SaddleConnection class to sanity check our results.
+                If only ``initial_label`` is provided, the saddle connections
+                will only emanate from vertices of the corresponding polygon.
+
+                If only ``initial_vertex`` is provided, the saddle connections
+                will only emanate from that vertex.
+
+                EXAMPLES:
+
+                Return the connections of length up to square root of 5::
 
-                EXAMPLES::
                     sage: from flatsurf import translation_surfaces
-                    sage: s = translation_surfaces.square_torus()
-                    sage: sc_list = s.saddle_connections(13, check=True)
-                    sage: len(sc_list)
-                    32
+                    sage: S = translation_surfaces.square_torus()
+                    sage: connections = S.saddle_connections(5)
+                    sage: list(connections)
+                    [Saddle connection (0, -1) from vertex 3 of polygon 0 to vertex 1 of polygon 0,
+                     Saddle connection (1, 0) from vertex 0 of polygon 0 to vertex 2 of polygon 0,
+                     Saddle connection (0, 1) from vertex 1 of polygon 0 to vertex 3 of polygon 0,
+                     Saddle connection (-1, 0) from vertex 2 of polygon 0 to vertex 0 of polygon 0,
+                     Saddle connection (1, 1) from vertex 0 of polygon 0 to vertex 2 of polygon 0,
+                     Saddle connection (1, -1) from vertex 3 of polygon 0 to vertex 1 of polygon 0,
+                     Saddle connection (-1, 1) from vertex 1 of polygon 0 to vertex 3 of polygon 0,
+                     Saddle connection (-1, -1) from vertex 2 of polygon 0 to vertex 0 of polygon 0,
+                     Saddle connection (-1, 2) from vertex 1 of polygon 0 to vertex 3 of polygon 0,
+                     Saddle connection (2, 1) from vertex 0 of polygon 0 to vertex 2 of polygon 0,
+                     Saddle connection (1, -2) from vertex 3 of polygon 0 to vertex 1 of polygon 0,
+                     Saddle connection (-2, 1) from vertex 1 of polygon 0 to vertex 3 of polygon 0,
+                     Saddle connection (-2, -1) from vertex 2 of polygon 0 to vertex 0 of polygon 0,
+                     Saddle connection (-1, -2) from vertex 2 of polygon 0 to vertex 0 of polygon 0,
+                     Saddle connection (2, -1) from vertex 3 of polygon 0 to vertex 1 of polygon 0,
+                     Saddle connection (1, 2) from vertex 0 of polygon 0 to vertex 2 of polygon 0]
+
+                We get the same result if we take the first 16 saddle
+                connections without a length bound::
+
+                    sage: from itertools import islice
+                    sage: set(connections) == set(islice(S.saddle_connections(), 16))
+                    True
+
+                While enumerating saddle connections without a bound is not
+                asymptotically slower than enumerating with a bound, in the
+                current implementation it is quite a bit slower in practice in
+                particular if the bound is small.
+
+                TESTS:
+
+                Verify that saddle connections are enumerated correctly when
+                there are unglued edges::
+
+                    sage: from flatsurf import Polygon, MutableOrientedSimilaritySurface
+                    sage: S = MutableOrientedSimilaritySurface(QQ)
+                    sage: S.add_polygon(Polygon(vertices=[(0, 0), (1, 0), (0, 1)]))
+                    0
+                    sage: S.set_immutable()
+                    sage: len(list(S.saddle_connections(10)))
+                    6
+
+                Verify that saddle connections are enumerated correctly when
+                there are self-glued edges::
+
+                    sage: from flatsurf import Polygon, MutableOrientedSimilaritySurface
+                    sage: S = MutableOrientedSimilaritySurface(QQ)
+                    sage: S.add_polygon(Polygon(vertices=[(0, 0), (1, 0), (0, 1)]))
+                    0
+                    sage: S.glue((0, 0), (0, 0))
+                    sage: S.glue((0, 1), (0, 1))
+                    sage: S.glue((0, 2), (0, 2))
+                    sage: S.set_immutable()
+
+                    sage: from itertools import islice
+                    sage: list(islice(S.saddle_connections(), 8))
+                    [Saddle connection (0, -1) from vertex 2 of polygon 0 to vertex 2 of polygon 0,
+                     Saddle connection (1, 0) from vertex 0 of polygon 0 to vertex 0 of polygon 0,
+                     Saddle connection (1, 1) from vertex 0 of polygon 0 to vertex 0 of polygon 0,
+                     Saddle connection (-1, 1) from vertex 1 of polygon 0 to vertex 1 of polygon 0,
+                     Saddle connection (2, 1) from vertex 0 of polygon 0 to vertex 0 of polygon 0,
+                     Saddle connection (1, -2) from vertex 2 of polygon 0 to vertex 2 of polygon 0,
+                     Saddle connection (-2, 1) from vertex 1 of polygon 0 to vertex 1 of polygon 0,
+                     Saddle connection (1, 2) from vertex 0 of polygon 0 to vertex 0 of polygon 0]
+
+                    sage: len(list(S.saddle_connections(1)))
+                    2
+
+                    sage: len(list(S.saddle_connections(1, initial_label=0, initial_vertex=1)))
+                    1
+
+                We can also enumerate saddle connections on surfaces that are
+                built from non-convex polygons such as this L shaped polygon::
+
+                    sage: from flatsurf import MutableOrientedSimilaritySurface, Polygon
+                    sage: L = MutableOrientedSimilaritySurface(QQ)
+                    sage: L.add_polygon(Polygon(vertices=[(0, 0), (3, 0), (7, 0), (7, 2), (3, 2), (3, 3), (0, 3), (0, 2)]))
+                    0
+                    sage: L.glue((0, 0), (0, 5))
+                    sage: L.glue((0, 1), (0, 3))
+                    sage: L.glue((0, 2), (0, 7))
+                    sage: L.glue((0, 4), (0, 6))
+                    sage: L.set_immutable()
+
+                    sage: connections = L.saddle_connections(128)
+                    sage: len(connections)
+                    164
+
+                Note that on translation surfaces, enumerating saddle
+                connections with the (default) ``"pyflatsurf"`` algorithm is
+                usually much faster than the ``"generic"`` algorithm::
+
+                    sage: connections = L.saddle_connections(128)
+                    sage: len(connections)
+                    164
+
+                    sage: connections = L.saddle_connections(128, algorithm="generic")
+                    sage: len(connections)
+                    164
+
                 """
-                if squared_length_bound <= 0:
-                    raise ValueError
+                # TODO: Add benchmarks of the generic "cone" algorithm against
+                # the pyflatsurf algorithm. Also benchmark how much slower this
+                # is now since we are supporting much more complicated
+                # geometries.
 
-                if sc_list is None:
-                    sc_list = []
-                if initial_label is None:
-                    if not self.is_finite_type():
-                        raise NotImplementedError
-                    if initial_vertex is not None:
-                        raise ValueError(
-                            "when initial_label is not provided, then initial_vertex must not be provided either"
-                        )
-                    for label in self.labels():
-                        self.saddle_connections(
-                            squared_length_bound, initial_label=label, sc_list=sc_list
-                        )
-                    return sc_list
-                if initial_vertex is None:
-                    for vertex in range(len(self.polygon(initial_label).vertices())):
-                        self.saddle_connections(
-                            squared_length_bound,
-                            initial_label=initial_label,
-                            initial_vertex=vertex,
-                            sc_list=sc_list,
-                        )
-                    return sc_list
+                if squared_length_bound is not None and squared_length_bound < 0:
+                    raise ValueError("length bound must be non-negative")
 
-                # Now we have a specified initial_label and initial_vertex
-                from flatsurf.geometry.similarity import SimilarityGroup
+                if algorithm is None:
+                    algorithm = "generic"
 
-                SG = SimilarityGroup(self.base_ring())
-                start_data = (initial_label, initial_vertex)
-                from flatsurf.geometry.circle import Circle
+                if algorithm == "generic":
+                    return self._saddle_connections_generic(
+                        squared_length_bound, initial_label, initial_vertex
+                    )
 
-                circle = Circle(
-                    (0, 0),
-                    squared_length_bound,
-                    base_ring=self.base_ring(),
-                )
-                p = self.polygon(initial_label)
-                v = p.vertex(initial_vertex)
-                last_sim = SG(-v[0], -v[1])
-
-                # First check the edge eminating rightward from the start_vertex.
-                e = p.edge(initial_vertex)
-                if e[0] ** 2 + e[1] ** 2 <= squared_length_bound:
-                    from flatsurf.geometry.surface_objects import SaddleConnection
-
-                    sc_list.append(SaddleConnection(self, start_data, e))
-
-                # Represents the bounds of the beam of trajectories we are sending out.
-                wedge = (
-                    last_sim(p.vertex((initial_vertex + 1) % len(p.vertices()))),
-                    last_sim(
-                        p.vertex(
-                            (initial_vertex + len(p.vertices()) - 1) % len(p.vertices())
-                        )
-                    ),
+                raise NotImplementedError(
+                    "cannot enumerate saddle connections with this algorithm yet"
                 )
 
-                # This will collect the data we need for a depth first search.
-                chain = [
-                    (
-                        last_sim,
-                        initial_label,
-                        wedge,
-                        [
-                            (initial_vertex + len(p.vertices()) - i) % len(p.vertices())
-                            for i in range(2, len(p.vertices()))
-                        ],
+            def _saddle_connections_generic(
+                self, squared_length_bound, initial_label, initial_vertex
+            ):
+                if squared_length_bound is None:
+                    # Enumerate all (usually infinitely many) saddle connections.
+                    return self._saddle_connections_unbounded(
+                        initial_label=initial_label,
+                        initial_vertex=initial_vertex,
+                        algorithm="generic",
                     )
-                ]
 
-                while len(chain) > 0:
-                    # Should verts really be edges?
-                    sim, label, wedge, verts = chain[-1]
-                    if len(verts) == 0:
-                        chain.pop()
-                        continue
-                    vert = verts.pop()
-                    p = self.polygon(label)
-                    # First check the vertex
-                    vert_position = sim(p.vertex(vert))
-                    from flatsurf.geometry.euclidean import ccw
+                connections = []
 
-                    if (
-                        ccw(wedge[0], vert_position) > 0
-                        and ccw(vert_position, wedge[1]) > 0
-                        and vert_position[0] ** 2 + vert_position[1] ** 2
-                        <= squared_length_bound
-                    ):
-                        sc_list.append(
-                            SaddleConnection(
-                                self,
-                                start_data,
-                                vert_position,
-                                end_data=(label, vert),
-                                end_direction=~sim.derivative() * -vert_position,
-                                holonomy=vert_position,
-                                end_holonomy=~sim.derivative() * -vert_position,
-                                check=check,
-                            )
+                if initial_label is None and initial_vertex is None:
+                    if not self.is_finite_type():
+                        raise NotImplementedError(
+                            "cannot enumerate saddle connections on surfaces that are built from inifinitely many polygons yet"
                         )
-                    # Now check if we should develop across the edge
-                    vert_position2 = sim(p.vertex((vert + 1) % len(p.vertices())))
-                    if (
-                        ccw(vert_position, vert_position2) > 0
-                        and ccw(wedge[0], vert_position2) > 0
-                        and ccw(vert_position, wedge[1]) > 0
-                        and circle.line_segment_position(vert_position, vert_position2)
-                        == 1
-                    ):
-                        if ccw(wedge[0], vert_position) > 0:
-                            # First in new_wedge should be vert_position
-                            if ccw(vert_position2, wedge[1]) > 0:
-                                new_wedge = (vert_position, vert_position2)
-                            else:
-                                new_wedge = (vert_position, wedge[1])
-                        else:
-                            if ccw(vert_position2, wedge[1]) > 0:
-                                new_wedge = (wedge[0], vert_position2)
-                            else:
-                                new_wedge = wedge
-                        new_label, new_edge = self.opposite_edge(label, vert)
-                        new_sim = sim * ~self.edge_transformation(label, vert)
-                        p = self.polygon(new_label)
-                        chain.append(
-                            (
-                                new_sim,
-                                new_label,
-                                new_wedge,
-                                [
-                                    (new_edge + len(p.vertices()) - i)
-                                    % len(p.vertices())
-                                    for i in range(1, len(p.vertices()))
-                                ],
+
+                    initial = [
+                        (label, range(len(self.polygon(label).vertices())))
+                        for label in self.labels()
+                    ]
+                elif initial_label is None:
+                    representatives = [
+                        (
+                            label,
+                            self.polygon(label)
+                            .get_point_position(coordinates)
+                            .get_vertex(),
+                        )
+                        for (label, coordinates) in initial_vertex.representatives()
+                    ]
+                    labels = {label for (label, _) in representatives}
+                    initial = [
+                        (
+                            label,
+                            [
+                                vertex
+                                for (lbl, vertex) in representatives
+                                if lbl == label
+                            ],
+                        )
+                        for label in labels
+                    ]
+                elif initial_vertex is None:
+                    initial = [
+                        (
+                            initial_label,
+                            range(len(self.polygon(initial_label).vertices())),
+                        )
+                    ]
+                for label, vertices in initial:
+                    for vertex in vertices:
+                        connections.extend(
+                            self._saddle_connections_generic_from_vertex_bounded(
+                                squared_length_bound=squared_length_bound,
+                                source=(label, vertex),
                             )
                         )
-                return sc_list
+
+                # The connections might contain duplicates because each glued
+                # edge can show up in two different polygons. Note that we
+                # cannot just change _saddle_connections_from_vertex_bounded()
+                # to only consider the edge clockwise from the vertex since we
+                # would then miss saddle connections in surfaces with
+                # self-glued and unglued edges.
+                connections = set(connections)
+
+                return sorted(
+                    connections, key=lambda connection: connection.length_squared()
+                )
 
             def ramified_cover(self, d, data):
                 r"""
@@ -3276,6 +3693,7 @@ def ramified_cover(self, d, data):
 
             def subdivide(self):
                 r"""
+                # TODO: Returns a morphism actually.
                 Return a copy of this surface whose polygons have been partitioned into
                 smaller triangles with
                 :meth:`~.euclidean_polygons.EuclideanPolygons.Simple.Convex.ParentMethods.subdivide`.
@@ -3292,7 +3710,7 @@ def subdivide(self):
 
                 Subdivision of this surface yields a surface with three triangles::
 
-                    sage: T = S.subdivide()
+                    sage: T = S.subdivide().codomain()
                     sage: T.labels()
                     (('Δ', 0), ('Δ', 1), ('Δ', 2))
 
@@ -3316,7 +3734,7 @@ def subdivide(self):
                     sage: S.glue(("Δ", 0), ("□", 2))
                     sage: S.glue(("□", 1), ("□", 3))
 
-                    sage: T = S.subdivide()
+                    sage: T = S.subdivide().codomain()
 
                     sage: T.labels()
                     (('Δ', 0), ('□', 2), ('Δ', 1), ('Δ', 2), ('□', 3), ('□', 1), ('□', 0))
@@ -3341,35 +3759,237 @@ def subdivide(self):
                      ((('□', 3), 2), (('□', 2), 1))]
 
                 """
-                labels = list(self.labels())
-                polygons = [self.polygon(label) for label in labels]
+                return self.insert_marked_points(
+                    *[
+                        self(label, self.polygon(label).centroid())
+                        for label in self.labels()
+                    ]
+                )
+
+            def insert_marked_points(self, *points):
+                if self.is_mutable():
+                    from flatsurf import MutableOrientedSimilaritySurface
+
+                    copy = MutableOrientedSimilaritySurface.from_surface(self)
+                    copy.set_immutable()
+
+                    codomain = copy.insert_marked_points(*points).codomain()
+
+                    # Since the domain is mutable, the morphism is not
+                    # functional but only has a .codomain().
+                    from flatsurf.geometry.morphism import SurfaceMorphism
+
+                    return SurfaceMorphism._create_morphism(None, codomain)
+
+                from flatsurf.geometry.morphism import IdentityMorphism
+
+                morphism = IdentityMorphism._create_morphism(self)
+
+                for p in points:
+                    if p.is_vertex():
+                        raise ValueError("cannot insert marked points at vertices")
+
+                edge_points = [p for p in points if p.is_in_edge_interior()]
+                face_points = [p for p in points if p not in edge_points]
+
+                assert len(edge_points) + len(face_points) == len(points)
 
-                subdivisions = [p.subdivide() for p in polygons]
+                if edge_points:
+                    insert_morphism = morphism.codomain()._insert_marked_points_edges(
+                        *edge_points
+                    )
+                    morphism = insert_morphism * morphism
+                    face_points = [insert_morphism(point) for point in face_points]
+                    self = insert_morphism.codomain()
+                if face_points:
+                    insert_morphism = morphism.codomain()._insert_marked_points_faces(
+                        *face_points
+                    )
+                    morphism = insert_morphism * morphism
+
+                return morphism
+
+            def _insert_marked_points_edges(self, *points):
+                assert (
+                    points
+                ), "_insert_marked_points_edges must be called with some points to insert"
+
+                from flatsurf.geometry.euclidean import time_on_ray
+
+                points = {
+                    label: {
+                        # TODO: Sort vertices along edge
+                        edge: sorted(
+                            [
+                                coordinates
+                                for point in points
+                                for (lbl, coordinates) in point.representatives()
+                                if lbl == label
+                                and self.polygon(label)
+                                .get_point_position(coordinates)
+                                .get_edge()
+                                == edge
+                            ],
+                            key=lambda coordinates: time_on_ray(
+                                self.polygon(label).vertex(edge),
+                                self.polygon(label).edge(edge),
+                                coordinates,
+                            ),
+                        )
+                        for edge in range(len(self.polygon(label).edges()))
+                    }
+                    for label in self.labels()
+                }
+
+                from flatsurf import MutableOrientedSimilaritySurface
+
+                surface = MutableOrientedSimilaritySurface(self.base_ring())
+
+                # Add polygons to surface with marked point
+                for label in self.labels():
+                    vertices = []
+                    for v, vertex in enumerate(self.polygon(label).vertices()):
+                        vertices.append(vertex)
+                        vertices.extend(points[label][v])
+
+                    from flatsurf import Polygon
+
+                    surface.add_polygon(Polygon(vertices=vertices), label=label)
+
+                # TODO: Make static on InsertMarkedPointsOnEdgeMorphism
+                def edgenum(label, edge, section):
+                    edgenum = 0
+                    for e in range(edge):
+                        edgenum += len(points[label][e]) + 1
+
+                    edgenum += section
+
+                    return edgenum
+
+                # Glue polygons in surface.
+                for label in self.labels():
+                    for edge in range(len(self.polygon(label).edges())):
+                        opposite = self.opposite_edge(label, edge)
+                        if opposite is None:
+                            continue
+
+                        opposite_label, opposite_edge = opposite
+                        for e in range(len(points[label][edge]) + 1):
+                            surface.glue(
+                                (label, edgenum(label, edge, e)),
+                                (
+                                    opposite_label,
+                                    edgenum(opposite_label, opposite_edge + 1, -1 - e),
+                                ),
+                            )
+
+                surface.set_immutable()
+
+                from flatsurf.geometry.morphism import InsertMarkedPointsOnEdgeMorphism
+
+                return InsertMarkedPointsOnEdgeMorphism._create_morphism(
+                    self, surface, points
+                )
+
+            def _insert_marked_points_faces(self, *points):
+                # Recursively insert points by only inserting at most one point
+                # in each face at a time.
+                first_point = {}
+                more_points = {}
+
+                for point in points:
+                    label, coordinates = point.representative()
+                    if label not in first_point:
+                        first_point[label] = point.coordinates(label)[0]
+                    else:
+                        more_points[label] = more_points.get(label, []) + [point]
+
+                assert (
+                    first_point
+                ), "_insert_marked_points_faces must be called with some points to insert"
+
+                def is_subdivided(label):
+                    return label in first_point
+
+                subdivisions = {
+                    label: self.polygon(label).subdivide(first_point[label])
+                    if is_subdivided(label)
+                    else [self.polygon(label)]
+                    for label in self.labels()
+                }
 
                 from flatsurf.geometry.surface import MutableOrientedSimilaritySurface
 
                 surface = MutableOrientedSimilaritySurface(self.base())
 
                 # Add subdivided polygons
-                for s, subdivision in enumerate(subdivisions):
-                    label = labels[s]
-                    for p, polygon in enumerate(subdivision):
-                        surface.add_polygon(polygon, label=(label, p))
-
-                surface.set_roots((label, 0) for label in self.roots())
-
-                # Add gluings between subdivided polygons
-                for s, subdivision in enumerate(subdivisions):
-                    label = labels[s]
-                    for p in range(len(subdivision)):
-                        surface.glue(
-                            ((label, p), 1), ((label, (p + 1) % len(subdivision)), 2)
-                        )
+                for label in self.labels():
+                    if is_subdivided(label):
+                        for p, polygon in enumerate(subdivisions[label]):
+                            surface.add_polygon(polygon, label=(label, p))
+                    else:
+                        surface.add_polygon(self.polygon(label), label=label)
+
+                surface.set_roots(
+                    (label, 0) if is_subdivided(label) else label
+                    for label in self.roots()
+                )
+
+                # Establish gluings
+                for label in self.labels():
+                    for e in range(len(self.polygon(label).vertices())):
+                        # Reestablish the original gluings
+                        opposite = self.opposite_edge(label, e)
 
-                        # Add gluing from original surface
-                        opposite = self.opposite_edge(label, p)
                         if opposite is not None:
-                            surface.glue(((label, p), 0), (opposite, 0))
+                            opposite_label, opposite_edge = opposite
+                            surface.glue(
+                                (
+                                    (label, e) if is_subdivided(label) else label,
+                                    0 if is_subdivided(label) else e,
+                                ),
+                                (
+                                    (opposite_label, opposite_edge)
+                                    if is_subdivided(opposite_label)
+                                    else opposite_label,
+                                    0
+                                    if is_subdivided(opposite_label)
+                                    else opposite_edge,
+                                ),
+                            )
+
+                        # Glue subdivided polygons internally
+                        if is_subdivided(label):
+                            surface.glue(
+                                ((label, e), 1),
+                                (
+                                    (
+                                        label,
+                                        (e + 1) % len(self.polygon(label).vertices()),
+                                    ),
+                                    2,
+                                ),
+                            )
+
+                surface.set_immutable()
+
+                from flatsurf.geometry.morphism import InsertMarkedPointsInFaceMorphism
+
+                insert_first_point = InsertMarkedPointsInFaceMorphism._create_morphism(
+                    self, surface, subdivisions
+                )
+
+                if more_points:
+                    from itertools import chain
+
+                    more_points = list(chain.from_iterable(more_points.values()))
+                    more_points = [insert_first_point(p) for p in more_points]
+                    return (
+                        insert_first_point.codomain().insert_marked_points(more_points)
+                        * insert_first_point
+                    )
+
+                return insert_first_point
 
                 surface.set_immutable()
 
@@ -3377,6 +3997,7 @@ def subdivide(self):
 
             def subdivide_edges(self, parts=2):
                 r"""
+                # TODO: Returns a morphism actually.
                 Return a copy of this surface whose edges have been split into
                 ``parts`` equal pieces each.
 
@@ -3398,7 +4019,7 @@ def subdivide_edges(self, parts=2):
                 Subdividing this triangle yields a triangle with marked points along
                 the edges::
 
-                    sage: T = S.subdivide_edges()
+                    sage: T = S.subdivide_edges().codomain()
 
                 If we add another polygon to the original surface and glue them, we
                 can see how existing gluings are preserved when subdividing::
@@ -3424,7 +4045,17 @@ def subdivide_edges(self, parts=2):
                 labels = list(self.labels())
                 polygons = [self.polygon(label) for label in labels]
 
-                subdivideds = [p.subdivide_edges(parts=parts) for p in polygons]
+                from collections.abc import Iterable
+
+                if not isinstance(parts, Iterable):
+                    parts = tuple(1 / parts for k in range(parts))
+                if sum(parts) != 1:
+                    raise ValueError
+
+                subdivideds = [
+                    p.subdivide_edges(parts=[parts for _ in p.edges()])
+                    for p in polygons
+                ]
 
                 from flatsurf.geometry.surface import MutableOrientedSimilaritySurface
 
@@ -3441,12 +4072,12 @@ def subdivide_edges(self, parts=2):
                     for e in range(len(polygon.vertices())):
                         opposite = self.opposite_edge(label, e)
                         if opposite is not None:
-                            for p in range(parts):
+                            for p in range(len(parts)):
                                 surface.glue(
-                                    (label, e * parts + p),
+                                    (label, e * len(parts) + p),
                                     (
                                         opposite[0],
-                                        opposite[1] * parts + (parts - p - 1),
+                                        opposite[1] * len(parts) + (len(parts) - p - 1),
                                     ),
                                 )
 
@@ -3787,11 +4418,12 @@ def reposition_polygons(self, in_place=False, relabel=None):
 
                 def standardize_polygons(self, in_place=False):
                     r"""
-                    Return a surface with each polygon replaced with a new
-                    polygon which differs by translation and reindexing. The
-                    new polygon will have the property that vertex zero is the
-                    origin, and all vertices lie either in the upper half
-                    plane, or on the x-axis with non-negative x-coordinate.
+                    Return a morphism to a surface with each polygon replaced
+                    with a new polygon which differs by translation and
+                    reindexing. The new polygon will have the property that
+                    vertex zero is the origin, and each vertex lies in the
+                    upper half plane or on the x-axis with non-negative
+                    x-coordinate.
 
                     EXAMPLES::
 
@@ -3801,7 +4433,7 @@ def standardize_polygons(self, in_place=False):
                         Polygon(vertices=[(0, 0), (-1, 0), (-1, -1), (0, -1)])
                         sage: [s.opposite_edge(0,i) for i in range(4)]
                         [(1, 0), (1, 1), (1, 2), (1, 3)]
-                        sage: ss=s.standardize_polygons()
+                        sage: ss = s.standardize_polygons().codomain()
                         sage: ss.polygon(1)
                         Polygon(vertices=[(0, 0), (1, 0), (1, 1), (0, 1)])
                         sage: [ss.opposite_edge(0,i) for i in range(4)]
@@ -3821,9 +4453,9 @@ def standardize_polygons(self, in_place=False):
                     S = MutableOrientedSimilaritySurface.from_surface(
                         self, category=self.category()
                     )
-                    S.standardize_polygons(in_place=True)
+                    morphism = S.standardize_polygons(in_place=True)
                     S.set_immutable()
-                    return S
+                    return morphism.change(domain=self, codomain=S)
 
                 def fundamental_group(self, base_label=None):
                     r"""
diff --git a/flatsurf/geometry/categories/translation_surfaces.py b/flatsurf/geometry/categories/translation_surfaces.py
index a9c7a0967..e4fc69a26 100644
--- a/flatsurf/geometry/categories/translation_surfaces.py
+++ b/flatsurf/geometry/categories/translation_surfaces.py
@@ -263,16 +263,6 @@ def edge_matrix(self, p, e=None):
 
             return identity_matrix(self.base_ring(), 2)
 
-        def canonicalize_mapping(self):
-            r"""
-            Return a SurfaceMapping canonicalizing this translation surface.
-            """
-            from flatsurf.geometry.mappings import (
-                canonicalize_translation_surface_mapping,
-            )
-
-            return canonicalize_translation_surface_mapping(self)
-
         def _test_translation_surface(self, **options):
             r"""
             Verify that this is a translation surface.
@@ -323,6 +313,110 @@ class ParentMethods:
             want to put it here.
             """
 
+            # TODO: Make sure this is cached for immutable surfaces.
+            def pyflatsurf(self):
+                r"""
+                Return an isomorphism to a surface backed by libflatsurf.
+
+                EXAMPLES::
+
+                    sage: from flatsurf import Polygon, MutableOrientedSimilaritySurface
+
+                    sage: S = MutableOrientedSimilaritySurface(QQ)
+                    sage: S.add_polygon(Polygon(vertices=[(0, 0), (1, 0), (1, 1)]), label=0)
+                    0
+                    sage: S.add_polygon(Polygon(vertices=[(0, 0), (1, 1), (0, 1)]), label=1)
+                    1
+
+                    sage: S.glue((0, 0), (1, 1))
+                    sage: S.glue((0, 1), (1, 2))
+                    sage: S.glue((0, 2), (1, 0))
+
+                    sage: S.set_immutable()
+
+                    sage: S.pyflatsurf().codomain()  # optional: pyflatsurf
+                    FlatTriangulationCombinatorial(vertices = (1, -3, 2, -1, 3, -2), faces = (1, 2, 3)(-1, -2, -3)) with vectors {1: (1, 0), 2: (0, 1), 3: (-1, -1)}
+
+                """
+                from flatsurf.geometry.pyflatsurf.surface import Surface_pyflatsurf
+
+                return Surface_pyflatsurf._from_flatsurf(self)
+
+            def saddle_connections(
+                self,
+                squared_length_bound=None,
+                initial_label=None,
+                initial_vertex=None,
+                algorithm=None,
+            ):
+                if squared_length_bound is not None and squared_length_bound < 0:
+                    raise ValueError("length bound must be non-negative")
+
+                if algorithm is None:
+                    from flatsurf.features import pyflatsurf_feature
+
+                    if (
+                        pyflatsurf_feature.is_present()
+                        and pyflatsurf_feature.is_saddle_connection_enumeration_functional()
+                    ):
+                        algorithm = "pyflatsurf"
+                    else:
+                        algorithm = "generic"
+
+                if algorithm == "pyflatsurf":
+                    from flatsurf.features import pyflatsurf_feature
+
+                    if (
+                        not flatsurf_feature.is_saddle_connection_enumeration_functional()
+                    ):
+                        import warnings
+
+                        warnings.warn(
+                            "enumerating saddle connections is broken in your version of pyflatsurf, namely saddle connections are enumerated in the wrong order and consequently some might be missing when enumerating with a length bound; upgrade to pyflatsurf >=3.14.1 to resolve this warning."
+                        )
+                    return self._saddle_connections_pyflatsurf(
+                        squared_length_bound, initial_label, initial_vertex
+                    )
+
+                from flatsurf.geometry.categories.similarity_surfaces import (
+                    SimilaritySurfaces,
+                )
+
+                return SimilaritySurfaces.Oriented.ParentMethods.saddle_connections(
+                    self, squared_length_bound, initial_label, initial_vertex
+                )
+
+            def _saddle_connections_pyflatsurf(
+                self, squared_length_bound, initial_label, initial_vertex
+            ):
+                pyflatsurf_conversion = self.pyflatsurf()
+
+                connections = (
+                    pyflatsurf_conversion.codomain()._flat_triangulation.connections()
+                )
+                connections = connections.byLength()
+
+                if initial_label is not None:
+                    raise NotImplementedError
+                    if initial_vertex is not None:
+                        raise NotImplementedError
+
+                for connection in connections:
+                    from flatsurf.geometry.pyflatsurf.saddle_connection import (
+                        SaddleConnection_pyflatsurf,
+                    )
+
+                    connection = SaddleConnection_pyflatsurf(
+                        connection, pyflatsurf_conversion.codomain()
+                    )
+                    connection = pyflatsurf_conversion.section()(connection)
+                    if squared_length_bound is not None:
+                        holonomy = connection.holonomy()
+                        # TODO: Use dot_product everywhere.
+                        if holonomy.dot_product(holonomy) > squared_length_bound:
+                            break
+                    yield connection
+
             @cached_surface_method
             def pyflatsurf(self):
                 r"""
@@ -395,6 +489,42 @@ def stratum(self):
 
                     return Stratum([ZZ(a - 1) for a in self.angles()], 1)
 
+                def canonicalize_mapping(self):
+                    r"""
+                    Return a SurfaceMapping canonicalizing this translation surface.
+
+                    EXAMPLES::
+
+                        sage: from flatsurf import translation_surfaces
+                        sage: s = translation_surfaces.octagon_and_squares()
+                        sage: s.canonicalize_mapping()
+                        doctest:warning
+                        ...
+                        UserWarning: canonicalize_mapping() has been deprecated and will be removed in a future version of sage-flatsurf; use canonicalize() instead
+                        Composite morphism:
+                          From: Translation Surface in H_3(4) built from 2 squares and a regular octagon
+                          To:   Translation Surface in H_3(4) built from 2 squares and a regular octagon
+                          Defn:   Delaunay Decomposition morphism:
+                                  From: Translation Surface in H_3(4) built from 2 squares and a regular octagon
+                                  To:   Translation Surface in H_3(4) built from 2 squares and a regular octagon
+                                then
+                                  Polygon Standardization morphism:
+                                  From: Translation Surface in H_3(4) built from 2 squares and a regular octagon
+                                  To:   Translation Surface in H_3(4) built from 2 squares and a regular octagon
+                                then
+                                  Relabeling morphism:
+                                  From: Translation Surface in H_3(4) built from 2 squares and a regular octagon
+                                  To:   Translation Surface in H_3(4) built from 2 squares and a regular octagon
+
+                    """
+                    import warnings
+
+                    warnings.warn(
+                        "canonicalize_mapping() has been deprecated and will be removed in a future version of sage-flatsurf; use canonicalize() instead"
+                    )
+
+                    return self.canonicalize()
+
                 def canonicalize(self, in_place=None):
                     r"""
                     Return a canonical version of this translation surface.
@@ -411,10 +541,10 @@ def canonicalize(self, in_place=None):
                         sage: s in TranslationSurfaces()
                         True
                         sage: a = s.base_ring().gen()
-                        sage: mat = Matrix([[1,2+a],[0,1]])
-                        sage: s1 = s.canonicalize()
+                        sage: mat = matrix([[1, 2 + a], [0, 1]])
+                        sage: s1 = s.canonicalize().codomain()
                         sage: s1.set_immutable()
-                        sage: s2 = (mat*s).canonicalize()
+                        sage: s2 = (mat * s).canonicalize().codomain()
                         sage: s2.set_immutable()
                         sage: s1.cmp(s2) == 0
                         True
@@ -434,31 +564,71 @@ def canonicalize(self, in_place=None):
                             "the in_place keyword of canonicalize() has been deprecated and will be removed in a future version of sage-flatsurf"
                         )
 
-                    s = self.delaunay_decompose().codomain().standardize_polygons()
+                    delaunay_decomposition = self.delaunay_decompose()
 
-                    from flatsurf.geometry.surface import (
-                        MutableOrientedSimilaritySurface,
+                    standardization = (
+                        delaunay_decomposition.codomain().standardize_polygons()
                     )
 
-                    s = MutableOrientedSimilaritySurface.from_surface(s)
-
                     from flatsurf.geometry.surface import (
                         MutableOrientedSimilaritySurface,
                     )
 
-                    ss = MutableOrientedSimilaritySurface.from_surface(s)
+                    s = MutableOrientedSimilaritySurface.from_surface(
+                        standardization.codomain()
+                    )
+
+                    ss = MutableOrientedSimilaritySurface.from_surface(
+                        standardization.codomain()
+                    )
 
                     for label in ss.labels():
                         ss.set_roots([label])
                         if ss.cmp(s) > 0:
                             s.set_roots([label])
 
-                    # We have chosen the root label such that this surface is minimal.
-                    # Now we relabel all the polygons so that they are natural
-                    # numbers in the order of the walk on the surface.
-                    labels = {label: i for (i, label) in enumerate(s.labels())}
-                    s.relabel(labels, in_place=True)
+                    # We have determined the root label such that this surface
+                    # is minimal. Now we relabel all the polygons so that they
+                    # are natural numbers in the order of the walk on the
+                    # surface.
+                    relabeling = s.relabel(
+                        {label: i for (i, label) in enumerate(s.labels())},
+                        in_place=True,
+                    )
                     s.set_immutable()
+
+                    return (
+                        relabeling.change(domain=standardization.codomain(), codomain=s)
+                        * standardization
+                        * delaunay_decomposition
+                    )
+
+                def flow_decomposition(self, direction):
+                    raise NotImplementedError  # TODO: Essentially invoke on pyflatsurf().
+
+                def flow_decompositions(self, algorithm="bfs", **kwargs):
+                    for slope in self._flow_decompositions_slopes(
+                        algorithm=algorithm, **kwargs
+                    ):
+                        yield self.flow_decomposition(slope)
+
+                def _flow_decompositions_slopes(self, algorithm, **kwargs):
+                    if algorithm == "bfs":
+                        return (
+                            self.pyflatsurf()
+                            .codomain()
+                            ._flow_decompositions_slopes_bfs(**kwargs)
+                        )
+                    if algorithm == "dfs":
+                        return (
+                            self.pyflatsurf()
+                            .codomain()
+                            ._flow_decompositions_slopes_dfs(**kwargs)
+                        )
+
+                    raise NotImplementedError(
+                        "unsupported algorithm to produce slopes for flow decompositions"
+                    )
                     return s
 
                 def j_invariant(self):
@@ -488,10 +658,17 @@ def j_invariant(self):
                         Jxy += xy
                     return (Jxx, Jyy, Jxy)
 
+                @cached_surface_method
                 def erase_marked_points(self):
                     r"""
-                    Return an isometric or similar surface with a minimal number of regular
-                    vertices of angle 2π.
+                    Return an isomorphism to a surface with a minimal regular vertices
+                    of angle 2π.
+
+                    ALGORITHM:
+
+                    We use the erasure of marked points implemented in pyflatsurf. For
+                    this we triangulate, then we Delaunay triangulate, then erase
+                    marked points with pyflatsurf, and then Delaunay triangulate again.
 
                     EXAMPLES::
 
@@ -501,14 +678,14 @@ def erase_marked_points(self):
                         sage: S = flatsurf.translation_surfaces.origami(G('(1,2,3,4)'), G('(1,4,2,3)'))
                         sage: S.stratum()
                         H_2(2, 0)
-                        sage: S.erase_marked_points().stratum() # optional: pyflatsurf  # long time (1s)  # random output due to matplotlib warnings with some combinations of setuptools and matplotlib
+                        sage: S.erase_marked_points().codomain().stratum() # optional: pyflatsurf  # long time (1s)
                         H_2(2)
 
                         sage: for (a,b,c) in [(1,4,11), (1,4,15), (3,4,13)]: # long time (10s), optional: pyflatsurf
                         ....:     T = flatsurf.polygons.triangle(a,b,c)
                         ....:     S = flatsurf.similarity_surfaces.billiard(T)
                         ....:     S = S.minimal_cover("translation")
-                        ....:     print(S.erase_marked_points().stratum())
+                        ....:     print(S.erase_marked_points().codomain().stratum())
                         H_6(10)
                         H_6(2^5)
                         H_8(12, 2)
@@ -517,9 +694,24 @@ def erase_marked_points(self):
                     function::
 
                         sage: O = flatsurf.translation_surfaces.regular_octagon()
-                        sage: O.erase_marked_points() is O
+                        sage: O.erase_marked_points().codomain() is O
                         True
 
+                    This method produces a morphism from the surface with marked points
+                    to the surface without marked points::
+
+                        sage: G = SymmetricGroup(4)
+                        sage: S = flatsurf.translation_surfaces.origami(G('(1,2,3,4)'), G('(1,4,2,3)'))
+                        sage: erasure = S.erase_marked_points()  # optional: pyflatsurf  # long time (1s)
+                        sage: marked_point = S(1, 1); marked_point  # optional: pyflatsurf  # long time (from above)
+                        Vertex 0 of polygon 2
+                        sage: erasure(marked_point)  # optional: pyflatsurf  # long time (from above)
+                        Point (-1, 0) of polygon (-3, -7, -2)
+                        sage: unmarked_point = S(1, 0); unmarked_point  # optional: pyflatsurf  # long time (from above)
+                        Vertex 0 of polygon 1
+                        sage: erasure(unmarked_point)  # optional: pyflatsurf  # long time (from above)
+                        Vertex 0 of polygon (-3, -7, -2)
+
                     TESTS:
 
                     Verify that https://github.com/flatsurf/flatsurf/issues/263 has been resolved::
@@ -528,7 +720,7 @@ def erase_marked_points(self):
                         sage: P = Polygon(angles=(10, 8, 3, 1, 1, 1), lengths=(1, 1, 2, 4))
                         sage: B = similarity_surfaces.billiard(P)
                         sage: S = B.minimal_cover(cover_type="translation")
-                        sage: S = S.erase_marked_points() # long time (3s), optional: pyflatsurf
+                        sage: S = S.erase_marked_points().codomain() # long time (3s), optional: pyflatsurf
 
                     ::
 
@@ -536,103 +728,319 @@ def erase_marked_points(self):
                         sage: P = Polygon(angles=(10, 7, 2, 2, 2, 1), lengths=(1, 1, 2, 3))
                         sage: B = similarity_surfaces.billiard(P)
                         sage: S_mp = B.minimal_cover(cover_type="translation")
-                        sage: S = S_mp.erase_marked_points() # long time (3s), optional: pyflatsurf
+                        sage: S = S_mp.erase_marked_points().codomain() # long time (3s), optional: pyflatsurf
 
                     """
                     if all(a != 1 for a in self.angles()):
                         # no 2π angle
-                        return self
-                    from flatsurf.geometry.pyflatsurf.conversion import (
-                        from_pyflatsurf,
-                        to_pyflatsurf,
-                    )
+                        from flatsurf.geometry.morphism import IdentityMorphism
 
-                    S = to_pyflatsurf(self)
-                    S.delaunay()
-                    S = S.eliminateMarkedPoints().surface()
-                    S.delaunay()
-                    return from_pyflatsurf(S)
+                        return IdentityMorphism._create_morphism(self)
 
-                def rel_deformation(self, deformation, local=None, limit=None):
-                    r"""
-                    Return a deformed surface obtained by shifting the vertices
-                    by ``deformation``.
+                    # Triangulate the surface: to_pyflatsurf maps self to a
+                    # triangulated libflatsurf surface (later called delaunay0_domain.)
+                    to_pyflatsurf = self.pyflatsurf()
 
-                    INPUT:
+                    # Delaunay triangulate: delaunay0 maps delaunay0_domain to delaunay0_codomain.
+                    # Since the flips of delaunay() are performed in-place, we create
+                    # the mapping using the Tracked[Deformation] feature of pyflatsurf.
+                    delaunay0_codomain = (
+                        to_pyflatsurf.codomain().flat_triangulation().clone()
+                    )
 
-                    - ``deformation`` -- a dict which maps the vertices of this
-                      surfaces to vectors. The rel deformation will move each
-                      vertex by that amount (relative to the others); any
-                      vertex not present in the dict will be treated as a
-                      deformation by the zero vector.
+                    from pyflatsurf import flatsurf
 
+                    delaunay0 = flatsurf.Tracked(
+                        delaunay0_codomain.combinatorial(),
+                        flatsurf.Deformation[type(delaunay0_codomain)](
+                            delaunay0_codomain.clone()
+                        ),
+                    )
 
-                    EXAMPLES::
+                    delaunay0_codomain.delaunay()
 
-                        sage: from flatsurf import translation_surfaces
-                        sage: S = translation_surfaces.arnoux_yoccoz(4)
-                        sage: S1 = S.rel_deformation({S(0, 0): (1, 0)}).canonicalize()  # optional: pyflatsurf
+                    # Erase marked points: elimination maps delaunay0_codomain to a
+                    # surface without marked points, later called delaunay1_domain.
+                    elimination = delaunay0_codomain.eliminateMarkedPoints()
 
-                        sage: a = S.base_ring().gen()
-                        sage: S2 = S.rel_deformation({S(0, 0): (a, 0)}).canonicalize()  # optional: pyflatsurf
+                    # Delaunay triangulate again: delaunay1 maps delaunay1_domain to delaunay1_codomain.
+                    # Again, we use a Tracked[Deformation] to create this mapping.
+                    delaunay1_codomain = elimination.codomain().clone()
 
-                        sage: M = matrix([[a, 0], [0, ~a]])
-                        sage: S2.cmp((M*S1).canonicalize())  # optional: pyflatsurf
-                        0
+                    delaunay1 = flatsurf.Tracked(
+                        delaunay1_codomain.combinatorial(),
+                        flatsurf.Deformation[type(delaunay1_codomain)](
+                            delaunay1_codomain.clone()
+                        ),
+                    )
 
-                    """
-                    if local is not None:
-                        import warnings
+                    delaunay1_codomain.delaunay()
 
-                        warnings.warn(
-                            "the local keyword has been removed from rel_deformation() without a replacement; do not use it anymore"
-                        )
+                    # Bring the surface back into sage-flatsurf: from_pyflatsurf maps a
+                    # sage-flatsurf surface to delaunay1_codomain.
+                    from flatsurf.geometry.pyflatsurf.surface import Surface_pyflatsurf
 
-                    if limit is not None:
-                        import warnings
+                    codomain_pyflatsurf = Surface_pyflatsurf(delaunay1_codomain)
 
-                        warnings.warn(
-                            "the limit keyword has been removed from rel_deformation() without a replacement; do not use it anymore"
-                        )
+                    from flatsurf.geometry.pyflatsurf.morphism import (
+                        Morphism_from_Deformation,
+                    )
 
-                    if not self.is_triangulated():
-                        raise NotImplementedError(
-                            "only triangulated surfaces can be rel-deformed"
-                        )
+                    pyflatsurf_morphism = Morphism_from_Deformation._create_morphism(
+                        to_pyflatsurf.codomain(),
+                        codomain_pyflatsurf,
+                        delaunay1.value() * elimination * delaunay0.value(),
+                    )
 
                     from flatsurf.geometry.pyflatsurf.conversion import (
                         FlatTriangulationConversion,
                     )
 
-                    conversion = FlatTriangulationConversion.to_pyflatsurf(self)
+                    from_pyflatsurf = FlatTriangulationConversion.from_pyflatsurf(
+                        delaunay1_codomain
+                    )
 
-                    from sage.all import vector
+                    from flatsurf.geometry.pyflatsurf.morphism import (
+                        Morphism_from_pyflatsurf,
+                    )
 
-                    deformation = {
-                        vertex: vector(
-                            self.base_ring(), deformation.get(vertex, (0, 0))
-                        )
-                        for vertex in self.vertices()
-                    }
-
-                    # pylint: disable=not-callable
-                    deformation = {
-                        edge: deformation[self(*self.opposite_edge(*edge))]
-                        - deformation[self(*edge)]
-                        for edge in self.edges()
-                    }
-                    # pylint: enable=not-callable
-
-                    vector_space_conversion = conversion.vector_space_conversion()
-                    deformation = [
-                        vector_space_conversion(
-                            deformation[conversion.section(edge.positive())]
-                        )
-                        for edge in conversion.codomain().edges()
-                    ]
+                    from_pyflatsurf = Morphism_from_pyflatsurf._create_morphism(
+                        codomain_pyflatsurf, from_pyflatsurf.domain(), from_pyflatsurf
+                    )
+
+                    return from_pyflatsurf * pyflatsurf_morphism * to_pyflatsurf
+
+                def rel_deformation(self, deformation, local=False, limit=100):
+                    r"""
+                    Perform a rel deformation of the surface and return the result.
+
+                    This algorithm currently assumes that all polygons affected by this deformation are
+                    triangles. That should be fixable in the future.
+
+                    INPUT:
+
+                    - ``deformation`` (dictionary) - A dictionary mapping singularities of
+                      the surface to deformation vectors (in some 2-dimensional vector
+                      space). The rel deformation being done will move the singularities
+                      (relative to each other) linearly to the provided vector for each
+                      vertex. If a singularity is not included in the dictionary then the
+                      vector will be treated as zero.
+
+                    - ``local`` - (boolean) - If true, the algorithm attempts to deform all
+                      the triangles making up the surface without destroying any of them.
+                      So, the area of the triangle must be positive along the full interval
+                      of time of the deformation.  If false, then the deformation must have
+                      a particular form: all vectors for the deformation must be parallel.
+                      In this case we achieve the deformation with the help of the SL(2,R)
+                      action and Delaunay triangulations.
 
-                    deformed = (conversion.codomain() + deformation).codomain()
+                    - ``limit`` (integer) - Restricts the length of the size of SL(2,R)
+                      deformations considered. The algorithm should be roughly worst time
+                      linear in limit.
 
-                    return FlatTriangulationConversion.from_pyflatsurf(
-                        deformed
-                    ).domain()
+                    .. TODO::
+
+                        - Support arbitrary rel deformations.
+                        - Remove the requirement that triangles be used.
+
+                    EXAMPLES::
+
+                        sage: from flatsurf import translation_surfaces
+                        sage: s = translation_surfaces.arnoux_yoccoz(4)
+                        sage: field = s.base_ring()
+                        sage: a = field.gen()
+                        sage: V = VectorSpace(field,2)
+                        sage: deformation1 = {s.singularity(0,0):V((1,0))}
+                        doctest:warning
+                        ...
+                        UserWarning: Singularity() is deprecated and will be removed in a future version of sage-flatsurf. Use surface.point() instead.
+                        sage: s1 = s.rel_deformation(deformation1).canonicalize().codomain()  # long time (.8s)
+                        sage: deformation2 = {s.singularity(0,0):V((a,0))}  # long time (see above)
+                        sage: s2 = s.rel_deformation(deformation2).canonicalize().codomain()  # long time (.6s)
+                        sage: m = Matrix([[a,0],[0,~a]])
+                        sage: s2.cmp((m*s1).canonicalize().codomain())  # long time (see above)
+                        0
+
+                    """
+                    s = self
+                    # Find a common field
+                    field = s.base_ring()
+                    for singularity, v in deformation.items():
+                        if v.parent().base_field() != field:
+                            from sage.structure.element import get_coercion_model
+
+                            cm = get_coercion_model()
+                            field = cm.common_parent(field, v.parent().base_field())
+                    from sage.modules.free_module import VectorSpace
+
+                    vector_space = VectorSpace(field, 2)
+
+                    from collections import defaultdict
+
+                    vertex_deformation = defaultdict(
+                        vector_space.zero
+                    )  # dictionary associating the vertices.
+                    deformed_labels = set()  # list of polygon labels being deformed.
+
+                    for singularity, vect in deformation.items():
+                        for label, coordinates in singularity.representatives():
+                            v = (
+                                self.polygon(label)
+                                .get_point_position(coordinates)
+                                .get_vertex()
+                            )
+                            vertex_deformation[(label, v)] = vect
+                            deformed_labels.add(label)
+                            assert len(s.polygon(label).vertices()) == 3
+
+                    from flatsurf.geometry.euclidean import ccw
+
+                    if local:
+                        from flatsurf.geometry.surface import (
+                            MutableOrientedSimilaritySurface,
+                        )
+
+                        ss = MutableOrientedSimilaritySurface.from_surface(s)
+                        ss.set_immutable()
+                        ss = MutableOrientedSimilaritySurface.from_surface(
+                            ss.change_ring(field)
+                        )
+                        us = ss
+
+                        for label in deformed_labels:
+                            polygon = s.polygon(label)
+                            a0 = vector_space(polygon.vertex(1))
+                            b0 = vector_space(polygon.vertex(2))
+                            v0 = vector_space(vertex_deformation[(label, 0)])
+                            v1 = vector_space(vertex_deformation[(label, 1)])
+                            v2 = vector_space(vertex_deformation[(label, 2)])
+                            a1 = v1 - v0
+                            b1 = v2 - v0
+                            # We deform by changing the triangle so that its vertices 1 and 2 have the form
+                            # a0+t*a1 and b0+t*b1
+                            # respectively. We are deforming from t=0 to t=1.
+                            # We worry that the triangle degenerates along the way.
+                            # The area of the deforming triangle has the form
+                            # A0 + A1*t + A2*t^2.
+                            A0 = ccw(a0, b0)
+                            A1 = ccw(a0, b1) + ccw(a1, b0)
+                            A2 = ccw(a1, b1)
+                            if A2:
+                                # Critical point of area function
+                                c = A1 / (-2 * A2)
+                                if field.zero() < c and c < 1:
+                                    if A0 + A1 * c + A2 * c**2 <= 0:
+                                        raise ValueError(
+                                            "Triangle with label %r degenerates at critical point before endpoint"
+                                            % label
+                                        )
+                            if A0 + A1 + A2 <= field.zero():
+                                raise ValueError(
+                                    "Triangle with label %r degenerates at or before endpoint"
+                                    % label
+                                )
+                            # Triangle does not degenerate.
+                            from flatsurf import Polygon
+
+                            us.replace_polygon(
+                                label,
+                                Polygon(
+                                    vertices=[vector_space.zero(), a0 + a1, b0 + b1],
+                                    base_ring=field,
+                                ),
+                            )
+                        ss.set_immutable()
+                        return ss
+
+                    else:  # Non local deformation
+                        # We can only do this deformation if all the rel vector are parallel.
+                        # Check for this.
+                        nonzero = None
+                        for singularity, vect in deformation.items():
+                            vvect = vector_space(vect)
+                            if vvect != vector_space.zero():
+                                if nonzero is None:
+                                    nonzero = vvect
+                                else:
+                                    assert (
+                                        ccw(nonzero, vvect) == 0
+                                    ), "In non-local deformation all deformation vectos must be parallel"
+                        assert nonzero is not None, "Deformation appears to be trivial."
+                        from sage.matrix.constructor import Matrix
+
+                        m = Matrix(
+                            [[nonzero[0], -nonzero[1]], [nonzero[1], nonzero[0]]]
+                        )
+                        mi = ~m
+                        g = Matrix([[1, 0], [0, 2]], ring=field)
+                        prod = m * g * mi
+                        ss = None
+                        k = 0
+                        while True:
+                            if ss is None:
+                                from flatsurf.geometry.surface import (
+                                    MutableOrientedSimilaritySurface,
+                                )
+
+                                ss = MutableOrientedSimilaritySurface.from_surface(
+                                    s.change_ring(field),
+                                    category=TranslationSurfaces(),
+                                )
+                            else:
+                                # In place matrix deformation
+                                ss.apply_matrix(prod, in_place=True)
+                            ss.delaunay_triangulation(direction=nonzero, in_place=True)
+                            deformation2 = {}
+                            for singularity, vect in deformation.items():
+                                found_start = None
+                                for label, coordinates in singularity.representatives():
+                                    v = (
+                                        s.polygon(label)
+                                        .get_point_position(coordinates)
+                                        .get_vertex()
+                                    )
+                                    if (
+                                        ccw(s.polygon(label).edge(v), nonzero) >= 0
+                                        and ccw(
+                                            nonzero, -s.polygon(label).edge((v + 2) % 3)
+                                        )
+                                        > 0
+                                    ):
+                                        found_start = (label, v)
+                                        found = None
+                                        for vv in range(3):
+                                            if (
+                                                ccw(ss.polygon(label).edge(vv), nonzero)
+                                                >= 0
+                                                and ccw(
+                                                    nonzero,
+                                                    -ss.polygon(label).edge(
+                                                        (vv + 2) % 3
+                                                    ),
+                                                )
+                                                > 0
+                                            ):
+                                                found = vv
+                                                deformation2[
+                                                    ss.point(
+                                                        label,
+                                                        ss.polygon(label).vertex(vv),
+                                                    )
+                                                ] = vect
+                                                break
+                                        assert found is not None
+                                        break
+                                assert found_start is not None
+
+                            try:
+                                sss = ss.rel_deformation(deformation2, local=True)
+                            except ValueError:
+                                k += 1
+                                if limit is not None and k >= limit:
+                                    raise Exception("exceeded limit iterations")
+                                continue
+
+                            sss = sss.apply_matrix(
+                                mi * g ** (-k) * m, in_place=False
+                            ).codomain()
+                            return sss.delaunay_triangulation(direction=nonzero)
diff --git a/flatsurf/geometry/chamanara.py b/flatsurf/geometry/chamanara.py
index 99cd0773b..dcc904f77 100644
--- a/flatsurf/geometry/chamanara.py
+++ b/flatsurf/geometry/chamanara.py
@@ -43,10 +43,11 @@
 #  along with sage-flatsurf. If not, see <https://www.gnu.org/licenses/>.
 # ********************************************************************
 
-from flatsurf.geometry.surface import OrientedSimilaritySurface
+from flatsurf.geometry.surface import OrientedSimilaritySurface, Labels
 from flatsurf.geometry.minimal_cover import MinimalTranslationCover
 from flatsurf.geometry.lazy import LazyRelabeledSurface
 from sage.rings.integer_ring import ZZ
+from flatsurf.geometry.surface import Labels
 
 
 def ChamanaraPolygon(alpha):
@@ -316,6 +317,41 @@ def graphical_surface(self, **kwds):
             label = self.opposite_edge(label, 3)[0]
         return super().graphical_surface(adjacencies=adjacencies, **kwds)
 
+    def labels(self):
+        r"""
+        Return the polygon labels of this surface.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.chamanara import chamanara_surface
+            sage: S = chamanara_surface(1/2)
+            sage: S.labels()
+            ((0, 1, 0), (1, -1, 0), (-1, 1/2, 0), (2, -1/2, 0), (-2, 1/4, 0), (3, -1/4, 0), (-3, 1/8, 0), (4, -1/8, 0), (-4, 1/16, 0), (5, -1/16, 0), (-5, 1/32, 0), (6, -1/32, 0), (-6, 1/64, 0), (7, -1/64, 0), (-7, 1/128, 0), (8, -1/128, 0), …)
+
+        """
+        return LazyLabels(self, finite=False)
+
+
+class LazyLabels(Labels):
+    def __contains__(self, label):
+        if not isinstance(label, tuple):
+            return False
+        if len(label) != 3:
+            return False
+
+        from sage.all import ZZ
+
+        if label[0] not in ZZ:
+            return False
+
+        if label[2] != 0:
+            return False
+
+        if label[0] >= 1:
+            return label[1] == -self._surface._alpha ** (label[0] - 1)
+
+        return label[1] == self._surface._alpha ** (-label[0])
+
 
 def chamanara_surface(alpha, n=None):
     r"""
diff --git a/flatsurf/geometry/circle.py b/flatsurf/geometry/circle.py
index e2db00f39..2984ef6f7 100644
--- a/flatsurf/geometry/circle.py
+++ b/flatsurf/geometry/circle.py
@@ -1,3 +1,4 @@
+# TODO: Delete this file.
 r"""
 This class contains methods useful for working with circles.
 
@@ -25,9 +26,9 @@
 # ****************************************************************************
 
 from sage.modules.free_module import VectorSpace
-from sage.modules.free_module_element import vector
 
 
+# TODO: This shoul be a method of EuclideanPlane.
 def circle_from_three_points(p, q, r, base_ring=None):
     r"""
     Construct a circle from three points on the circle.
@@ -47,233 +48,9 @@ def circle_from_three_points(p, q, r, base_ring=None):
     if center_3[2].is_zero():
         raise ValueError("The three points lie on a line.")
     center = V2((center_3[0] / center_3[2], center_3[1] / center_3[2]))
-    return Circle(center, (p[0] - center[0]) ** 2 + (p[1] - center[1]) ** 2)
 
+    from flatsurf import EuclideanPlane
 
-class Circle:
-    def __init__(self, center, radius_squared, base_ring=None):
-        r"""
-        Construct a circle from a Vector representing the center, and the
-        radius squared.
-        """
-        if base_ring is None:
-            self._base_ring = radius_squared.parent()
-        else:
-            self._base_ring = base_ring
-
-        # for calculations:
-        self._V2 = VectorSpace(self._base_ring, 2)
-        self._V3 = VectorSpace(self._base_ring, 3)
-
-        self._center = self._V2(center)
-        self._center.set_immutable()
-        self._radius_squared = self._base_ring(radius_squared)
-
-    def center(self):
-        r"""
-        Return the center of the circle as a vector.
-        """
-        return self._center
-
-    def radius_squared(self):
-        r"""
-        Return the square of the radius of the circle.
-        """
-        return self._radius_squared
-
-    def point_position(self, point):
-        r"""
-        Return 1 if point lies in the circle, 0 if the point lies on the circle,
-        and -1 if the point lies outide the circle.
-        """
-        value = (
-            (point[0] - self._center[0]) ** 2
-            + (point[1] - self._center[1]) ** 2
-            - self._radius_squared
-        )
-        if value > self._base_ring.zero():
-            return -1
-        if value < self._base_ring.zero():
-            return 1
-        return 0
-
-    def closest_point_on_line(self, point, direction_vector):
-        r"""
-        Consider the line through the provided point in the given direction.
-        Return the closest point on this line to the center of the circle.
-        """
-        cc = self._V3((self._center[0], self._center[1], self._base_ring.one()))
-        # point at infinite orthogonal to direction_vector:
-        dd = self._V3(
-            (direction_vector[1], -direction_vector[0], self._base_ring.zero())
-        )
-        l1 = cc.cross_product(dd)
-
-        pp = self._V3((point[0], point[1], self._base_ring.one()))
-        # direction_vector pushed to infinity
-        ee = self._V3(
-            (direction_vector[0], direction_vector[1], self._base_ring.zero())
-        )
-        l2 = pp.cross_product(ee)
-
-        # This is the point we want to return
-        rr = l1.cross_product(l2)
-        try:
-            return self._V2((rr[0] / rr[2], rr[1] / rr[2]))
-        except ZeroDivisionError:
-            raise ValueError(
-                "Division by zero error. Perhaps direction is zero. "
-                + "point="
-                + str(point)
-                + " direction="
-                + str(direction_vector)
-                + " circle="
-                + str(self)
-            )
-
-    def line_position(self, point, direction_vector):
-        r"""
-        Consider the line through the provided point in the given direction.
-        We return 1 if the line passes through the circle, 0 if it is tangent
-        to the circle and -1 if the line does not intersect the circle.
-        """
-        return self.point_position(self.closest_point_on_line(point, direction_vector))
-
-    def line_segment_position(self, p, q):
-        r"""
-        Consider the open line segment pq.We return 1 if the line segment
-        enters the interior of the circle, zero if it touches the circle
-        tangentially (at a point in the interior of the segment) and
-        and -1 if it does not touch the circle or its interior.
-        """
-        if self.point_position(p) == 1:
-            return 1
-        if self.point_position(q) == 1:
-            return 1
-        r = self.closest_point_on_line(p, q - p)
-        pos = self.point_position(r)
-        if pos == -1:
-            return -1
-        # This checks if r lies in the interior of pq
-        if p[0] == q[0]:
-            if (p[1] < r[1] and r[1] < q[1]) or (p[1] > r[1] and r[1] > q[1]):
-                return pos
-        elif (p[0] < r[0] and r[0] < q[0]) or (p[0] > r[0] and r[0] > q[0]):
-            return pos
-        # It does not lie in the interior.
-        return -1
-
-    def tangent_vector(self, point):
-        r"""
-        Return a vector based at the provided point (which must lie on the circle)
-        which is tangent to the circle and points in the counter-clockwise
-        direction.
-
-        EXAMPLES::
-
-            sage: from flatsurf.geometry.circle import Circle
-            sage: c=Circle(vector((0,0)), 2, base_ring=QQ)
-            sage: c.tangent_vector(vector((1,1)))
-            (-1, 1)
-        """
-        if not self.point_position(point) == 0:
-            raise ValueError("point not on circle.")
-        return vector((self._center[1] - point[1], point[0] - self._center[0]))
-
-    def other_intersection(self, p, v):
-        r"""
-        Consider a point p on the circle and a vector v. Let L be the line
-        through p in direction v. Then L intersects the circle at another
-        point q. This method returns q.
-
-        Note that if p and v are both in the field of the circle,
-        then so is q.
-
-        EXAMPLES::
-
-            sage: from flatsurf.geometry.circle import Circle
-            sage: c=Circle(vector((0,0)), 25, base_ring=QQ)
-            sage: c.other_intersection(vector((3,4)),vector((1,2)))
-            (-7/5, -24/5)
-        """
-        pp = self._V3((p[0], p[1], self._base_ring.one()))
-        vv = self._V3((v[0], v[1], self._base_ring.zero()))
-        L = pp.cross_product(vv)
-        cc = self._V3((self._center[0], self._center[1], self._base_ring.one()))
-        vvperp = self._V3((-v[1], v[0], self._base_ring.zero()))
-        # line perpendicular to L through center:
-        Lperp = cc.cross_product(vvperp)
-        # intersection of L and Lperp:
-        rr = L.cross_product(Lperp)
-        r = self._V2((rr[0] / rr[2], rr[1] / rr[2]))
-        return self._V2((2 * r[0] - p[0], 2 * r[1] - p[1]))
-
-    def __rmul__(self, similarity):
-        r"""
-        Apply a similarity to the circle.
-
-        EXAMPLES::
-
-            sage: from flatsurf import translation_surfaces
-            sage: s = translation_surfaces.square_torus()
-            sage: c = s.polygon(0).circumscribed_circle()
-            sage: c
-            Circle((1/2, 1/2), 1/2)
-            sage: s.edge_transformation(0,2)
-            (x, y) |-> (x, y - 1)
-            sage: s.edge_transformation(0,2) * c
-            Circle((1/2, -1/2), 1/2)
-        """
-        from .similarity import SimilarityGroup
-
-        SG = SimilarityGroup(self._base_ring)
-        s = SG(similarity)
-        return Circle(
-            s(self._center), s.det() * self._radius_squared, base_ring=self._base_ring
-        )
-
-    def __str__(self):
-        return (
-            "circle with center "
-            + str(self._center)
-            + " and radius squared "
-            + str(self._radius_squared)
-        )
-
-    def __repr__(self):
-        return "Circle(" + repr(self._center) + ", " + repr(self._radius_squared) + ")"
-
-    def __hash__(self):
-        r"""
-        Return a hash value for this circle that is compatible with
-        :meth:`__eq__`.
-
-        EXAMPLES::
-
-            sage: from flatsurf import translation_surfaces
-            sage: S = translation_surfaces.square_torus().triangulate().codomain().relabel()
-            sage: hash(S.polygon(0).circumscribed_circle()) == hash(S.polygon(1).circumscribed_circle())
-            True
-
-        """
-        return hash((self._center, self._radius_squared))
-
-    def __eq__(self, other):
-        r"""
-        Return whether this circle is indistinguishable from ``other``.
-
-        EXAMPLES::
-
-            sage: from flatsurf import translation_surfaces
-            sage: S = translation_surfaces.square_torus().triangulate().codomain().relabel()
-            sage: S.polygon(0).circumscribed_circle() == S.polygon(1).circumscribed_circle()
-            True
-
-        """
-        if not isinstance(other, Circle):
-            return False
-
-        return (
-            self._center == other._center
-            and self._radius_squared == other._radius_squared
-        )
+    return EuclideanPlane(base_ring).circle(
+        center, radius_squared=(p[0] - center[0]) ** 2 + (p[1] - center[1]) ** 2
+    )
diff --git a/flatsurf/geometry/cone.py b/flatsurf/geometry/cone.py
new file mode 100644
index 000000000..5464ee7e7
--- /dev/null
+++ b/flatsurf/geometry/cone.py
@@ -0,0 +1,177 @@
+from sage.all import UniqueRepresentation, Parent
+from sage.structure.element import Element
+from sage.misc.cachefunc import cached_method
+
+
+class Cone(Element):
+    # This is an open cone.
+    def __init__(self, parent, start, end):
+        super().__init__(parent)
+        self._start = parent.rays()(start)
+        self._end = parent.rays()(end)
+
+    @cached_method
+    def is_empty(self):
+        return self._start == self._end
+
+    def is_convex(self):
+        from flatsurf.geometry.euclidean import ccw
+
+        return ccw(self._start.vector(), self._end.vector()) >= 0
+
+    # TODO: Rename to contains_cone() [arguments are reversed!]
+    def is_subset(self, other):
+        r"""
+        Return whether this cone is contained in ``other``.
+        """
+        if not isinstance(other, Cone):
+            raise NotImplementedError
+
+        if other.parent() is not self.parent():
+            raise NotImplementedError
+
+        if self.is_empty():
+            return True
+
+        if other.is_empty():
+            return False
+
+        from flatsurf.geometry.euclidean import ccw
+
+        start_ccw = ccw(self._start.vector(), other._start.vector())
+        end_ccw = ccw(self._end.vector(), other._end.vector())
+
+        # Check whether the interior of this cone is contained in other.
+        if not self.is_convex():
+            if other.is_convex():
+                return False
+
+            # This non-convex cone C is contained in the other non-convex cone D,
+            # iff for the complements we have D^c ⊆ C^c.
+            return other.complement().is_subset(self.complement())
+
+        if other.is_convex():
+            if start_ccw > 0:
+                return False
+            if end_ccw < 0:
+                return False
+
+            return True
+
+        raise NotImplementedError
+
+    def complement(self):
+        r"""
+        Return the maximal cone contained in the complement of this cone, i.e.,
+        the complement of this cone with the boundaries (except for the origin)
+        missing.
+
+        """
+        if self.is_empty():
+            raise NotImplementedError
+
+        return self.parent()(self._end, self._start)
+
+    def contains_ray(self, ray):
+        if ray.parent() is not self.parent().rays():
+            raise NotImplementedError
+
+        if self.is_empty():
+            return False
+
+        from flatsurf.geometry.euclidean import ccw
+
+        ccw_from_start = ccw(self._start.vector(), ray.vector())
+        ccw_to_end = ccw(ray.vector(), self._end.vector())
+
+        if self.is_convex():
+            if ccw_from_start <= 0:
+                return False
+
+            if ccw_to_end <= 0:
+                return False
+
+            return True
+
+        return (
+            not self.complement().contains_ray(ray)
+            and ray != self._start
+            and ray != self._end
+        )
+
+    def sorted_rays(self, rays):
+        class Key:
+            def __init__(self, cone, ray):
+                self._cone = cone
+                self._ray = ray
+
+            def __lt__(self, rhs):
+                # TODO: Make sure all code paths are tested.
+                from flatsurf.geometry.euclidean import ccw
+
+                if not self._cone.is_convex():
+                    start_to_self = ccw(self._cone._start.vector(), self._ray.vector())
+                    start_to_rhs = ccw(self._cone._start.vector(), rhs._ray.vector())
+                    if start_to_self > 0 and start_to_rhs > 0:
+                        return ccw(self._ray.vector(), rhs._ray.vector()) > 0
+
+                    end_to_self = ccw(self._cone._end.vector(), self._ray.vector())
+                    end_to_rhs = ccw(self._cone._end.vector(), rhs._ray.vector())
+                    if end_to_self < 0 and end_to_rhs < 0:
+                        return ccw(self._ray.vector(), rhs._ray.vector()) > 0
+
+                    if start_to_self > 0 and end_to_rhs < 0:
+                        return True
+                    if end_to_self < 0 and start_to_rhs > 0:
+                        return False
+
+                    raise NotImplementedError
+                return ccw(self._ray.vector(), rhs._ray.vector()) > 0
+
+        rays = sorted(rays, key=lambda ray: Key(self, ray))
+
+        from itertools import groupby
+
+        return [ray for ray, _ in groupby(rays)]
+
+    def a_ray(self):
+        if self.is_empty():
+            raise TypeError
+
+        if self.is_convex():
+            return self.parent().rays()((self._start.vector() + self._end.vector()) / 2)
+
+        raise NotImplementedError
+
+    def start(self):
+        return self._start
+
+    def end(self):
+        return self._end
+
+    def contains_point(self, p):
+        if self.is_empty():
+            return False
+        if p.is_zero():
+            return True
+        return self.contains_ray(self.parent().rays()(p))
+
+    def _repr_(self):
+        if self.is_empty():
+            return "Empty cone"
+        return f"Open cone between {self._start} and {self._end}"
+
+
+class Cones(UniqueRepresentation, Parent):
+    Element = Cone
+
+    def __init__(self, base_ring, category=None):
+        from sage.categories.all import Sets
+
+        super().__init__(base_ring, category=category or Sets())
+
+    @cached_method
+    def rays(self):
+        from flatsurf.geometry.ray import Rays
+
+        return Rays(self.base_ring())
diff --git a/flatsurf/geometry/euclidean.py b/flatsurf/geometry/euclidean.py
index 8de66d96b..2f6ed0c8b 100644
--- a/flatsurf/geometry/euclidean.py
+++ b/flatsurf/geometry/euclidean.py
@@ -1,16 +1,44 @@
+# TODO: Benchmark how constructions here compare to constructions before we introduced the EuclideanPlane.
+
+
 r"""
-A loose collection of tools for Euclidean geometry in the plane.
+Two dimensional Euclidean geometry.
+
+EXAMPLES::
+
+    sage: from flatsurf import EuclideanPlane
+    sage: E = EuclideanPlane(QQ)
+    sage: E.circle((0, 0), radius=1)
+    { x² + y² = 1 }
+
+.. NOTE::
+
+    Most functionality in this module is also implemented in
+    SageMath in the context of linear programming/polyhedra/convex
+    geometry. However, that implementation is much more general
+    (higher dimensions) and therefore quite inefficient in two
+    dimensions.
+
+.. NOTE::
+
+    Explicitly creating Python objects for everything in the Euclidean plane
+    comes with a certain overhead. In practice, this overhead is negligible in
+    comparison to the cost of performing computations with these objects.
+    However, most classes here expose their core algorithms as static methods
+    so they can be called with the bare coordinates instead of on explicit
+    objects to gain a tiny bit of a speedup where this makes a difference.
 
-.. SEEALSO::
+.. SEEALSO
 
-    :mod:`flatsurf.geometry.circle` for everything specific to circles in the plane
+    :mod:`flatsurf.geometry.hyperbolic` for the geometry in the hyperbolic plane.
 
 """
 ######################################################################
 #  This file is part of sage-flatsurf.
 #
-#        Copyright (C) 2016-2020 Vincent Delecroix
-#                      2020-2023 Julian Rüth
+#        Copyright (C) 2013-2020 Vincent Delecroix
+#                      2013-2019 W. Patrick Hooper
+#                      2020-2024 Julian Rüth
 #
 #  sage-flatsurf is free software: you can redistribute it and/or modify
 #  it under the terms of the GNU General Public License as published by
@@ -25,702 +53,4261 @@
 #  You should have received a copy of the GNU General Public License
 #  along with sage-flatsurf. If not, see <https://www.gnu.org/licenses/>.
 ######################################################################
+from sage.structure.sage_object import SageObject
+from sage.structure.parent import Parent
+from sage.structure.element import Element
+from sage.structure.unique_representation import UniqueRepresentation
+from sage.misc.cachefunc import cached_method
 
+from flatsurf.geometry.geometry import Geometry, ExactGeometry, EpsilonGeometry
 
-def is_cosine_sine_of_rational(cos, sin, scaled=False):
-    r"""
-    Check whether the given pair is a cosine and sine of a same rational angle.
-
-    INPUT:
 
-    - ``cos`` -- a number
+class EuclideanPlane(Parent, UniqueRepresentation):
+    r"""
+    The Euclidean plane.
 
-    - ``sin`` -- a number
+    All objects in the plane must be specified over the given base ring.
 
-    - ``scaled`` -- a boolean (default: ``False``); whether to allow ``cos``
-      and ``sin`` to be scaled by the same positive algebraic number
+    The implemented objects of the plane are mostly convex (points, circles,
+    segments, rays, convex polygons.) But some are also non-convex such as
+    non-convex polygons.
 
     EXAMPLES::
 
-        sage: from flatsurf.geometry.euclidean import is_cosine_sine_of_rational
+        sage: from flatsurf import EuclideanPlane
+        sage: E = EuclideanPlane(QQ)
 
-        sage: c = s = AA(sqrt(2))/2
-        sage: is_cosine_sine_of_rational(c, s)
-        True
+    TESTS::
 
-        sage: c = AA(sqrt(3))/2
-        sage: s = AA(1/2)
-        sage: is_cosine_sine_of_rational(c, s)
+        sage: isinstance(E, EuclideanPlane)
         True
 
-        sage: c = AA(sqrt(5)/2)
-        sage: s = (1 - c**2).sqrt()
-        sage: c**2 + s**2
-        1.000000000000000?
-        sage: is_cosine_sine_of_rational(c, s)
-        False
+        sage: TestSuite(E).run()
 
-        sage: c = (AA(sqrt(5)) + 1)/4
-        sage: s = (1 - c**2).sqrt()
-        sage: is_cosine_sine_of_rational(c, s)
-        True
+    """
 
-        sage: K.<sqrt2> = NumberField(x**2 - 2, embedding=1.414)
-        sage: is_cosine_sine_of_rational(K.zero(), -K.one())
-        True
+    @staticmethod
+    def __classcall__(cls, base_ring=None, geometry=None, category=None):
+        r"""
+        Create the Euclidean plane with normalized arguments to make it a
+        unique SageMath parent.
 
-    TESTS::
+        TESTS::
 
-        sage: from pyexactreal import ExactReals # optional: pyexactreal  # random output due to matplotlib warnings with some combinations of setuptools and matplotlib
-        sage: R = ExactReals() # optional: pyexactreal
-        sage: is_cosine_sine_of_rational(R.one(), R.zero()) # optional: pyexactreal
-        True
+            sage: from flatsurf import EuclideanPlane
+            sage: from flatsurf.geometry.euclidean import EuclideanExactGeometry
 
-    """
-    from sage.all import AA
+            sage: EuclideanPlane() is EuclideanPlane(QQ)
+            True
 
-    # We cannot check in AA due to https://github.com/flatsurf/exact-real/issues/172
-    # We just trust that non-algebraic elements won't allow conversion to AA.
-    # if cos not in AA:
-    #     return False
-    # if sin not in AA:
-    #     return False
+            sage: EuclideanPlane() is EuclideanPlane(QQ, EuclideanExactGeometry(QQ))
+            True
 
-    if not scaled:
-        if cos**2 + sin**2 != 1:
-            return False
+        """
+        from sage.all import QQ
 
-    try:
-        cos = AA(cos)
-    except ValueError:
-        # This is a replacement for the "in AA" checked disabled above.
-        return False
+        base_ring = base_ring or QQ
 
-    cos = cos.as_number_field_element(embedded=True)
-    # We need an explicit conversion to the number field due to https://github.com/sagemath/sage/issues/35613
-    cos = cos[0](cos[1])
+        if geometry is None:
+            if not base_ring.is_exact():
+                raise ValueError("geometry must be specified over inexact rings")
 
-    try:
-        sin = AA(sin)
-    except ValueError:
-        # This is a replacement for the "in AA" checked disabled above.
-        return False
-    sin = sin.as_number_field_element(embedded=True)
-    # We need an explicit conversion to the number field due to https://github.com/sagemath/sage/issues/35613
-    sin = sin[0](sin[1])
+            geometry = EuclideanExactGeometry(base_ring)
 
-    from sage.all import ComplexBallField
+        from sage.categories.all import Sets
 
-    CBF = ComplexBallField(53)
+        category = category or Sets()
 
-    x = CBF(cos) + CBF.gen(0) * CBF(sin)
-    xN = x
+        return super().__classcall__(
+            cls, base_ring=base_ring, geometry=geometry, category=category
+        )
 
-    # Suppose that (cos, sin) are indeed sine and cosine of a rational angle.
-    # Then x = cos + I*sin generates a cyclotomic field C and for some N we
-    # have x^N = ±1. Since C is contained in the compositum of K=Q(cos) and
-    # L=Q(i*sin) and Q(cos) and Q(sin) are both contained in C, the degree of C
-    # is bounded from above by twice (accounting for the imaginary unit) the
-    # degrees of K and L. The degree of C is the totient of N which is bounded
-    # from below by n / (e^γ loglog n + 3 / loglog n) [cf. wikipedia].
-    degree_bound = 2 * cos.minpoly().degree() * sin.minpoly().degree()
+    def __init__(self, base_ring, geometry, category):
+        r"""
+        Create the Euclidean plane over ``base_ring``.
 
-    from itertools import count
+        TESTS::
 
-    for n in count(2):
-        xN *= x
+            sage: from flatsurf import EuclideanPlane
 
-        c = xN.real()
-        s = xN.imag()
+            sage: TestSuite(EuclideanPlane(QQ)).run()
+            sage: TestSuite(EuclideanPlane(AA)).run()
 
-        if xN.real().contains_zero() or xN.imag().contains_zero():
-            c, s = cos, sin
-            for i in range(n - 1):
-                c, s = c * cos - s * sin, s * cos + c * sin
+        """
+        from sage.all import RR
 
-            if c == 0 or s == 0:
-                return True
+        if geometry.base_ring() is not base_ring:
+            raise ValueError(
+                f"geometry base ring must be base ring of Euclidean plane but {geometry.base_ring()} is not {base_ring}"
+            )
 
-            CBF = ComplexBallField(CBF.precision() * 2)
-            x = CBF(cos) + CBF.gen(0) * CBF(sin)
-            xN = x**n
+        if not RR.has_coerce_map_from(geometry.base_ring()):
+            # We should check that the coercion is an embedding but this is not possible currently.
+            raise ValueError("base ring must embed into the reals")
 
-        from math import log
+        super().__init__(category=category)
+        self._base_ring = geometry.base_ring()
+        self.geometry = geometry
 
-        if n / (2.0 * log(log(n)) + 3 / log(log(n))) > 2 * degree_bound:
-            return False
+    def change_ring(self, ring, geometry=None):
+        r"""
+        Return the Euclidean plane over a different base ``ring``.
 
+        INPUT:
 
-def acos(cos_angle, numerical=False):
-    r"""
-    Return the arccosine of ``cos_angle`` as a multiple of 2π, i.e., as a value
-    between 0 and 1/2.
+        - ``ring`` -- a ring or ``None``; if ``None``, uses the current
+          :meth:`~EuclideanPlane.base_ring`.
 
-    INPUT:
+        - ``geometry`` -- a geometry or ``None``; if ``None``; trues to convert
+          the existing geometry to ``ring``.
 
-    - ``cos_angle`` -- a floating point number, the cosine of an angle
+        EXAMPLES::
 
-    - ``numerical`` -- a boolean (default: ``False``); whether to return a
-      numerical approximation of the arccosine or try to reconstruct an exact
-      rational value for the arccosine (in radians.)
+            sage: from flatsurf import EuclideanPlane
 
-    EXAMPLES::
+            sage: EuclideanPlane(QQ).change_ring(AA) is EuclideanPlane(AA)
+            True
 
-        sage: from flatsurf.geometry.euclidean import acos
+        """
+        if ring is None and geometry is None:
+            return self
 
-        sage: acos(1)
-        0
-        sage: acos(.5)
-        1/6
-        sage: acos(0)
-        1/4
-        sage: acos(-.5)
-        1/3
-        sage: acos(-1)
-        1/2
+        if ring is None:
+            ring = self.base_ring()
 
-        sage: acos(.25)
-        Traceback (most recent call last):
-        ...
-        NotImplementedError: cannot recover a rational angle from these numerical results
-        sage: acos(.25, numerical=True)
-        0.2097846883724169
+        if geometry is None:
+            geometry = self.geometry.change_ring(ring)
 
-    """
-    import math
+        return EuclideanPlane(ring, geometry)
 
-    angle = math.acos(cos_angle) / (2 * math.pi)
+    def _an_element_(self):
+        r"""
+        Return a typical point of the Euclidean plane.
 
-    assert 0 <= angle <= 0.5
+        EXAMPLES::
 
-    if numerical:
-        return angle
+            sage: from flatsurf import EuclideanPlane
 
-    # fast and dirty way using floating point approximation
-    from sage.all import RR
+            sage: E = EuclideanPlane()
+            sage: E.an_element()
+            (0, 0)
 
-    angle_rat = RR(angle).nearby_rational(0.00000001)
-    if angle_rat.denominator() > 256:
-        raise NotImplementedError(
-            "cannot recover a rational angle from these numerical results"
-        )
-    return angle_rat
+        """
+        return self.point(0, 0)
 
+    def some_subsets(self):
+        # TODO
+        raise NotImplementedError
 
-def angle(u, v, numerical=False):
-    r"""
-    Return the angle between the vectors ``u`` and ``v`` divided by `2 \pi`.
+    def some_elements(self):
+        r"""
+        Return some representative elements, i.e., points in the plane for
+        testing.
 
-    INPUT:
+        EXAMPLES::
 
-    - ``u``, ``v`` - vectors
+            sage: from flatsurf import EuclideanPlane
 
-    - ``numerical`` - boolean (default: ``False``), whether to return floating
-      point numbers
+            sage: EuclideanPlane().some_elements()
+            [(0, 0), (1, 0), (0, 1), ...]
 
-    EXAMPLES::
+        """
+        from sage.all import QQ
 
-        sage: from flatsurf.geometry.euclidean import angle
+        return [
+            self((0, 0)),
+            self((1, 0)),
+            self((0, 1)),
+            self((-QQ(1) / 2, QQ(1) / 2)),
+        ]
 
-    As the implementation is dirty, we at least check that it works for all
-    denominator up to 20::
+    def _test_some_subsets(self, tester=None, **options):
+        r"""
+        Run test suite on some representative subsets of the Euclidean plane.
 
-        sage: u = vector((AA(1),AA(0)))
-        sage: for n in xsrange(1,20):       # long time  (1.5s)
-        ....:     for k in xsrange(1,n):
-        ....:         v = vector((AA(cos(2*k*pi/n)), AA(sin(2*k*pi/n))))
-        ....:         assert angle(u,v) == k/n
+        EXAMPLES::
 
-    The numerical version (working over floating point numbers)::
+            sage: from flatsurf import EuclideanPlane
+            sage: EuclideanPlane()._test_some_subsets()
 
-        sage: import math
-        sage: u = (1, 0)
-        sage: for n in xsrange(1,20):
-        ....:     for k in xsrange(1,n):
-        ....:         a = 2 * k * math.pi / n
-        ....:         v = (math.cos(a), math.sin(a))
-        ....:         assert abs(angle(u,v,numerical=True) * 2 * math.pi - a) < 1.e-10
+        """
+        is_sub_testsuite = tester is not None
+        tester = self._tester(tester=tester, **options)
 
-    If the angle is not rational, then the method returns an element in the real
-    lazy field::
+        for x in self.some_elements():
+            tester.info(f"\n  Running the test suite of {x}")
 
-        sage: v = vector((AA(sqrt(2)), AA(sqrt(3))))
-        sage: a = angle(u, v)
-        Traceback (most recent call last):
-        ...
-        NotImplementedError: cannot recover a rational angle from these numerical results
-        sage: a = angle(u, v, numerical=True)
-        sage: a    # abs tol 1e-14
-        0.14102355421224375
-        sage: exp(2*pi.n()*CC(0,1)*a)
-        0.632455532033676 + 0.774596669241483*I
-        sage: v / v.norm()
-        (0.6324555320336758?, 0.774596669241484?)
+            from sage.all import TestSuite
 
-    """
-    import math
+            TestSuite(x).run(
+                verbose=tester._verbose,
+                prefix=tester._prefix + "  ",
+                raise_on_failure=is_sub_testsuite,
+            )
+            tester.info(tester._prefix + " ", newline=False)
 
-    u0 = float(u[0])
-    u1 = float(u[1])
-    v0 = float(v[0])
-    v1 = float(v[1])
+    def random_element(self, kind=None):
+        # TODO
+        raise NotImplementedError
 
-    cos_uv = (u0 * v0 + u1 * v1) / math.sqrt((u0 * u0 + u1 * u1) * (v0 * v0 + v1 * v1))
-    if cos_uv < -1.0:
-        assert cos_uv > -1.0000001
-        cos_uv = -1.0
-    elif cos_uv > 1.0:
-        assert cos_uv < 1.0000001
-        cos_uv = 1.0
+    def __call__(self, x):
+        r"""
+        Return ``x`` as an element of the Euclidean plane.
 
-    angle = acos(cos_uv, numerical=numerical)
-    return 1 - angle if u0 * v1 - u1 * v0 < 0 else angle
+        EXAMPLES::
 
-    # a neater way is provided below by working only with number fields
-    # but this method is slower...
-    # sqnorm_u = u[0]*u[0] + u[1]*u[1]
-    # sqnorm_v = v[0]*v[0] + v[1]*v[1]
-    #
-    # if sqnorm_u != sqnorm_v:
-    #    # we need to take a square root in order that u and v have the
-    #    # same norm
-    #    u = (1 / AA(sqnorm_u)).sqrt() * u.change_ring(AA)
-    #    v = (1 / AA(sqnorm_v)).sqrt() * v.change_ring(AA)
-    #    sqnorm_u = AA.one()
-    #    sqnorm_v = AA.one()
-    #
-    # cos_uv = (u[0]*v[0] + u[1]*v[1]) / sqnorm_u
-    # sin_uv = (u[0]*v[1] - u[1]*v[0]) / sqnorm_u
+            sage: from flatsurf import EuclideanPlane
 
+            sage: E = EuclideanPlane()
 
-def ccw(v, w):
-    r"""
-    Return a positive number if the turn from ``v`` to ``w`` is
-    counterclockwise, a negative number if it is clockwise, and zero if the two
-    vectors are collinear.
+            sage: E((1, 0))
+            (1, 0)
 
-    .. NOTE::
+        We need to override this method. The normal code path in SageMath
+        requires the argument to be an Element but facade sets are not
+        elements::
 
-        This function is sometimes also referred to as the wedge product or
-        simply the determinant. We chose the more customary name ``ccw`` from
-        computational geometry here.
+            sage: c = E.circle((0, 0), radius=1)
+            sage: Parent.__call__(E, c)
+            Traceback (most recent call last):
+            ...
+            TypeError: Cannot convert EuclideanCircle_with_category_with_category to sage.structure.element.Element
 
-    EXAMPLES::
+            sage: E(c)
+            { x² + y² = 1 }
 
-        sage: from flatsurf.geometry.euclidean import ccw
-        sage: ccw((1, 0), (0, 1))
-        1
-        sage: ccw((1, 0), (-1, 0))
-        0
-        sage: ccw((1, 0), (0, -1))
-        -1
-        sage: ccw((1, 0), (1, 0))
-        0
+        """
+        if isinstance(x, EuclideanFacade):
+            return self._element_constructor_(x)
 
-    """
-    return v[0] * w[1] - v[1] * w[0]
+        return super().__call__(x)
 
+    def _element_constructor_(self, x):
+        r"""
+        Return ``x`` as an element of the plane.
 
-def is_parallel(v, w):
-    r"""
-    Return whether the vectors ``v`` and ``w`` are parallel (but not
-    anti-parallel.)
+        EXAMPLES::
 
-    EXAMPLES::
+            sage: from flatsurf import EuclideanPlane
 
-        sage: from flatsurf.geometry.euclidean import is_parallel
-        sage: is_parallel((0, 1), (0, 1))
-        True
-        sage: is_parallel((0, 1), (0, 2))
-        True
-        sage: is_parallel((0, 1), (0, -2))
-        False
-        sage: is_parallel((0, 1), (0, 0))
-        False
-        sage: is_parallel((0, 1), (1, 0))
-        False
+            sage: E = EuclideanPlane()
 
-    TESTS::
+            sage: E(E.an_element()) in E
+            True
 
-        sage: V = QQ**2
+        Coordinates can be converted to points::
 
-        sage: is_parallel(V((0,1)), V((0,2)))
-        True
-        sage: is_parallel(V((1,-1)), V((2,-2)))
-        True
-        sage: is_parallel(V((4,-2)), V((2,-1)))
-        True
-        sage: is_parallel(V((1,2)), V((2,4)))
-        True
-        sage: is_parallel(V((0,2)), V((0,1)))
-        True
+            sage: E((1, 2))
+            (1, 2)
 
-        sage: is_parallel(V((1,1)), V((1,2)))
-        False
-        sage: is_parallel(V((1,2)), V((2,1)))
-        False
-        sage: is_parallel(V((1,2)), V((1,-2)))
-        False
-        sage: is_parallel(V((1,2)), V((-1,-2)))
-        False
-        sage: is_parallel(V((2,-1)), V((-2,1)))
-        False
+        Elements can be converted between planes with compatible base rings::
 
-    """
-    if ccw(v, w) != 0:
+            sage: EuclideanPlane(AA)(E((0, 0)))
+            (0, 0)
+
+        TESTS::
+
+            sage: E(0)
+            Traceback (most recent call last):
+            ...
+            NotImplementedError: cannot convert this element in Integer Ring to Euclidean Plane over Rational Field
+
+        """
+        from sage.all import parent
+
+        parent = parent(x)
+
+        if parent is self:
+            return x
+
+        if parent is self.vector_space():
+            x = tuple(x)
+
+        if isinstance(x, EuclideanSet):
+            return x.change(ring=self.base_ring(), geometry=self.geometry)
+
+        if isinstance(x, tuple):
+            if len(x) == 2:
+                return self.point(*x)
+
+            raise ValueError("coordinate tuple must have length 2")
+
+        raise NotImplementedError(f"cannot convert this element in {parent} to {self}")
+
+    def base_ring(self):
+        r"""
+        Return the base ring over which objects in the plane are defined.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+
+            sage: EuclideanPlane().base_ring()
+            Rational Field
+
+        """
+        return self._base_ring
+
+    def is_exact(self):
+        r"""
+        Return whether subsets have exact coordinates.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: E.is_exact()
+            True
+
+            sage: from flatsurf.geometry.euclidean import EuclideanEpsilonGeometry
+            sage: E = EuclideanPlane(RR, geometry=EuclideanEpsilonGeometry(RR, 1e-6))
+            sage: E.is_exact()
+            False
+
+        """
+        return self.base_ring().is_exact()
+
+    @cached_method
+    def vector_space(self):
+        r"""
+        Return the two dimensional standard vector space describing vectors in
+        this Euclidean plane.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: E.vector_space()
+            Vector space of dimension 2 over Rational Field
+
+        """
+        return self.base_ring() ** 2
+
+    def point(self, x, y):
+        r"""
+        Return the point in the Euclidean plane with coordinates ``x`` and
+        ``y``.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: E.point(1, 2)
+            (1, 2)
+
+        ::
+
+            sage: E.point(sqrt(2), sqrt(3))
+            Traceback (most recent call last):
+            ...
+            TypeError: unable to convert sqrt(2) to a rational
+
+        """
+        x = self._base_ring(x)
+        y = self._base_ring(y)
+
+        point = self.__make_element_class__(EuclideanPoint)(self, x, y)
+
+        return point
+
+    def circle(self, center, *, radius=None, radius_squared=None, check=True):
+        r"""
+        Return the circle around ``center`` with ``radius`` or
+        ``radius_squared``.
+
+        INPUT:
+
+        - ``center`` -- a point in the Euclidean plane
+
+        - ``radius`` or ``radius_squared`` -- exactly one of the parameters
+          must be specified.
+
+        - ``check`` -- whether to verify that the arguments define a circrle in the Euclidean plane (default: ``True``)
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: E.circle((0, 0), radius=2)
+            { x² + y² = 4 }
+            sage: E.circle((0, 0), radius_squared=4)
+            { x² + y² = 4 }
+
+        A circle with radius zero is a point::
+
+            sage: E.circle((0, 0), radius=0)
+            (0, 0)
+
+        We can explicity create a circle with radius zero by setting ``check``
+        to ``False``::
+
+            sage: E.circle((0, 0), radius=0, check=False)
+            { x² + y² = 0 }
+
+        """
+        # TODO: Allow radius to be an EuclideanDistance (and convert to one.)
+        if (radius is None) == (radius_squared is None):
+            raise ValueError(
+                "exactly one of radius or radius_squared must be specified"
+            )
+
+        if radius is not None:
+            if radius < 0:
+                raise ValueError("radius must not be negative")
+            radius_squared = radius**2
+
+        center = self(center)
+        radius_squared = self.base_ring()(radius_squared)
+
+        circle = self.__make_element_class__(EuclideanCircle)(
+            self, center, radius_squared
+        )
+        if check:
+            circle = circle._normalize()
+            circle._check()
+
+        return circle
+
+    def segment(
+        self,
+        line,
+        start=None,
+        end=None,
+        oriented=None,
+        check=True,
+        assume_normalized=False,
+    ):
+        r"""
+        Return the segment on the `line`` bounded by ``start`` and ``end``.
+
+        INPUT:
+
+        - ``line`` -- a line in the plane
+
+        - ``start`` -- ``None`` or a :meth:`point` on the line, e.g., obtained
+          as the :meth:`EuclideanLine.intersection` of ``line`` with another
+          line. If ``None``, a ray is returned, unbounded on one side.
+
+        - ``end`` -- ``None`` or a :meth:`point` on the line, e.g., obtained
+          as the :meth:`EuclideanLine.intersection` of ``line`` with another
+          line. If ``None``, a ray is returned, unbounded on one side. If
+          ``start`` is also ``None``, the ``line`` is returned.
+
+        - ``oriented`` -- whether to produce an oriented segment or an
+          unoriented segment. The default (``None``) is to produce an oriented
+          segment iff ``line`` is oriented or both ``start`` and ``end``
+          are provided so the orientation can be deduced from their order.
+
+        - ``check`` -- boolean (default: ``True``), whether validation is
+          performed on the arguments.
+
+        - ``assume_normalized`` -- boolean (default: ``False``), if not set,
+          the returned segment is normalized, i.e., if it is actually a point,
+          a :class:`EuclideanPoint` is returned.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: line = E.line((0, 0), (1, 1))
+            sage: E.segment(line, (0, 0), (1, 1))
+            (0, 0) → (1, 1)
+
+            sage: E.segment(line, (0, 0), (1, 1), oriented=False)
+            (0, 0) — (1, 1)
+
+        A segment that consists only of a single point gets returned as a
+        point::
+
+            sage: E.segment(line, (0, 0), (0, 0))
+            (0, 0)
+
+        To explicitly obtain a non-normalized segment in such cases, we can set
+        ``assume_normalized=True``::
+
+            sage: E.segment(line, (0, 0), (0, 0), assume_normalized=True)
+            (0, 0) → (0, 0)
+
+        A segment with a single endpoint is a ray::
+
+            sage: E.segment(line, start=(0, 0))
+            Ray from (0, 0) in direction (1, 1)
+            sage: E.segment(line, end=(0, 0))
+            Ray from (0, 0) in direction (-1, -1)
+
+        A segment without endpoints is a line::
+
+            sage: E.segment(line)
+            {-x + y = 0}
+
+        .. SEEALSO::
+
+            :meth:`EuclideanPoint.segment` to create a segment from its two
+            endpoints (without specifying a line.)
+
+        """
+        line = self(line)
+
+        if not isinstance(line, EuclideanLine):
+            raise TypeError("line must be a line")
+
+        if start is not None:
+            start = self(start)
+            if not isinstance(start, EuclideanPoint):
+                raise TypeError("start must be a point")
+
+        if end is not None:
+            end = self(end)
+            if not isinstance(end, EuclideanPoint):
+                raise TypeError("end must be a point")
+
+        if oriented is None:
+            oriented = line.is_oriented() or (start is not None and end is not None)
+
+        if not line.is_oriented():
+            line = line.change(oriented=True)
+
+            if start is None and end is None:
+                # any orientation of the line will do
+                pass
+            elif start is None or end is None or start == end:
+                raise ValueError(
+                    "cannot deduce segment from single endpoint on an unoriented line"
+                )
+            elif line.parametrize(start, check=False) > line.parametrize(
+                end, check=False
+            ):
+                line = -line
+
+        segment = self.__make_element_class__(
+            EuclideanOrientedSegment if oriented else EuclideanUnorientedSegment
+        )(self, line, start, end)
+
+        if check:
+            segment._check(require_normalized=False)
+
+        if not assume_normalized:
+            segment = segment._normalize()
+
+        if check:
+            segment._check(require_normalized=True)
+
+        return segment
+
+    def line(self, a, b, c=None, oriented=True, check=True):
+        r"""
+        Return a line in the Euclidean plane.
+
+        If only ``a`` and ``b`` are given, return the line going through the
+        points ``a`` and then ``b``.
+
+        If ``c`` is specified, return the line given by the equation
+
+        .. MATH::
+
+            a + bx + cy = 0
+
+        oriented such that the half plane
+
+        .. MATH::
+
+            a + bx + cy \ge 0
+
+        is to its left.
+
+        INPUT:
+
+        - ``a`` -- a point or an element of the :meth:`base_ring`
+
+        - ``b`` -- a point or an element of the :meth:`base_ring`
+
+        - ``c`` -- ``None`` or an element of the :meth:`base_ring` (default: ``None``)
+
+        - ``oriented`` -- whether the returned line is oriented (default: ``True``)
+
+        - ``check`` -- whether to verify that the arguments actually define a
+          line (default: ``True``)
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+
+            sage: E.line((0, 0), (1, 1))
+            {-x + y = 0}
+
+            sage: E.line(0, -1, 1)
+            {-x + y = 0}
+
+        """
+        if c is None:
+            a = self(a)
+            b = self(b)
+
+            if a == b:
+                raise ValueError("points specifying a line must be distinct")
+
+            ax, ay = a
+            bx, by = b
+
+            C = bx - ax
+            B = ay - by
+            A = -(B * ax + C * ay)
+
+            return self.line(A, B, C, oriented=oriented, check=check)
+
+        a = self.base_ring()(a)
+        b = self.base_ring()(b)
+        c = self.base_ring()(c)
+
+        line = self.__make_element_class__(
+            EuclideanOrientedLine if oriented else EuclideanUnorientedLine
+        )(self, a, b, c)
+
+        if check:
+            line = line._normalize()
+            line._check()
+
+        return line
+
+    geodesic = line
+
+    def ray(self, base, direction, check=True):
+        r"""
+        Return a ray from ``base`` in ``direction``.
+
+        INPUT:
+
+        - ``base`` -- a point in the Euclidean plane
+
+        - ``direction`` -- a vector in the two dimensional space over the
+          :meth:`base_ring`, see :meth:`vector_space`
+
+        - ``check`` -- a boolean (default: ``True``); whether to validate the
+          parameters
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+
+            sage: ray = E.ray((0, 0), (1, 1))
+
+        The base point is contained in the ray::
+
+            sage: E.point(0, 0) in ray
+            True
+
+        The direction must be non-zero::
+
+            sage: E.ray((0, 0), (0, 0))
+
+        """
+        base = self(base)
+        direction = self.vector_space()(direction)
+
+        if not isinstance(base, EuclideanPoint):
+            raise TypeError("base must be a point")
+
+        ray = self.__make_element_class__(EuclideanRay)(self, base, direction)
+
+        if check:
+            ray = ray._normalize()
+            ray._check()
+
+        return ray
+
+    @cached_method
+    def norm(self):
+        r"""
+        Return the Euclidean norm on this plane.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+
+            sage: norm = E.norm()
+            sage: norm(2)
+            2
+            sage: norm.from_square(2)
+            sqrt(2)
+            sage: norm.from_vector((1, 1))
+            sqrt(2)
+
+        """
+        return EuclideanDistances(self)
+
+    def polygon(self):
+        # TODO
+        raise NotImplementedError
+
+    def empty_set(self):
+        # TODO
+        raise NotImplementedError
+
+    def _repr_(self):
+        r"""
+        Return a printable representation of this Euclidean plane.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: EuclideanPlane(AA)
+            Euclidean Plane over Algebraic Real Field
+
+        """
+        return f"Euclidean Plane over {repr(self.base_ring())}"
+
+
+class EuclideanGeometry(Geometry):
+    r"""
+    Predicates and primitive geometric constructions over a base ``ring``.
+
+    This class and its subclasses implement the core underlying Euclidean
+    geometry that depends on the base ring. For example, when deciding whether
+    two points in the plane are equal, we cannot just compare their coordinates
+    if the base ring is inexact. Therefore, that predicate is implemented in
+    this "geometry" class and is implemented differently by
+    :class:`EuclideanExactGeometry` for exact and
+    :class:`EuclideanEpsilonGeometry` for inexact rings.
+
+    INPUT:
+
+    - ``ring`` -- a ring, the ring in which coordinates in the Euclidean plane
+      will be represented
+
+    .. NOTE::
+
+        Abstract methods are not marked with `@abstractmethod` since we cannot
+        use the ABCMeta metaclass to enforce their implementation; otherwise,
+        our subclasses could not use the unique representation metaclasses.
+
+    EXAMPLES:
+
+    The specific Euclidean geometry implementation is picked automatically,
+    depending on whether the base ring is exact or not::
+
+        sage: from flatsurf import EuclideanPlane
+        sage: E = EuclideanPlane()
+        sage: E.geometry
+        Exact geometry over Rational Field
+        sage: E((0, 0)) == E((1/1024, 0))
+        False
+
+    However, we can explicitly use a different or custom geometry::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanEpsilonGeometry
+        sage: E = EuclideanPlane(QQ, EuclideanEpsilonGeometry(QQ, 1/1024))
+        sage: E.geometry
+        Epsilon geometry with ϵ=1/1024 over Rational Field
+        sage: E((0, 0)) == E((1/2048, 0))
+        True
+
+    .. SEEALSO::
+
+        :class:`EuclideanExactGeometry`, :class:`EuclideanEpsilonGeometry`
+    """
+
+    def _equal_vector(self, v, w):
+        r"""
+        Return whether the vectors ``v`` and ``w`` should be considered equal.
+
+        .. NOTE::
+
+            This predicate should not be used directly in geometric
+            constructions since it does not specify the context in which this
+            question is asked. This makes it very difficult to override a
+            specific aspect in a custom geometry.
+
+        INPUT:
+
+        - ``v`` -- a vector over the :meth:`base_ring`
+
+        - ``w`` -- a vector over the :meth:`base_ring`
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import EuclideanExactGeometry
+            sage: G = EuclideanExactGeometry(QQ)
+            sage: G._equal_vector((0, 0), (0, 0))
+            True
+            sage: G._equal_vector((0, 0), (0, 1/1024))
+            False
+
+        """
+        if len(v) != len(w):
+            raise TypeError("v and w must be vectors in the same vector space")
+
+        return all(self._equal(vv, ww) for (vv, ww) in zip(v, w))
+
+    def _equal_point(self, p, q):
+        r"""
+        Return whether the points ``p`` and ``q`` should be considered equal.
+
+        .. NOTE::
+
+            This predicate should not be used directly in geometric
+            constructions since it does not specify the context in which this
+            question is asked. This makes it very difficult to override a
+            specific aspect in a custom geometry.
+
+        INPUT:
+
+        - ``p`` -- a point in the Euclidean plane
+
+        - ``q`` -- a point in the Euclidean plane
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import EuclideanExactGeometry
+            sage: G = EuclideanExactGeometry(QQ)
+            sage: G._equal_point((0, 0), (0, 0))
+            True
+            sage: G._equal_point((0, 0), (0, 1/1024))
+            False
+
+        """
+        return self._equal_vector(tuple(p), tuple(q))
+
+
+class EuclideanExactGeometry(UniqueRepresentation, EuclideanGeometry, ExactGeometry):
+    r"""
+    Predicates and primitive geometric constructions over an exact base ring.
+
+    EXAMPLES::
+
+        sage: from flatsurf import EuclideanPlane
+        sage: E = EuclideanPlane()
+        sage: E.geometry
+        Exact geometry over Rational Field
+
+    TESTS::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanExactGeometry
+        sage: isinstance(E.geometry, EuclideanExactGeometry)
+        True
+
+    .. SEEALSO::
+
+        :class:`EuclideanEpsilonGeometry` for an implementation over inexact rings
+
+    """
+
+    def change_ring(self, ring):
+        r"""
+        Return this geometry with the :meth:`~EuclideanGeometry.base_ring`
+        changed to ``ring``.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: E.geometry.change_ring(QQ) == E.geometry
+            True
+            sage: E.geometry.change_ring(AA)
+            Exact geometry over Algebraic Real Field
+
+        """
+        if not ring.is_exact():
+            raise ValueError("cannot change_ring() to an inexact ring")
+
+        return EuclideanExactGeometry(ring)
+
+
+class EuclideanEpsilonGeometry(
+    UniqueRepresentation, EuclideanGeometry, EpsilonGeometry
+):
+    r"""
+    Predicates and primitive geometric constructions over an inexact base ring.
+
+    EXAMPLES::
+
+        sage: from flatsurf import EuclideanPlane
+        sage: from flatsurf.geometry.euclidean import EuclideanEpsilonGeometry
+        sage: E = EuclideanPlane(RR, geometry=EuclideanEpsilonGeometry(RR, 1e-6))
+        sage: E.geometry
+        Epsilon geometry with ϵ=1.00000000000000e-6 over Real Field with 53 bits of precision
+
+    TESTS::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanEpsilonGeometry
+        sage: isinstance(E.geometry, EuclideanEpsilonGeometry)
+        True
+
+    .. SEEALSO::
+
+        :class:`EuclideanExactGeometry` for an implementation over exact rings
+
+    """
+
+    def _equal_vector(self, v, w):
+        r"""
+        Return whether the vectors ``v`` and ``w`` should be considered equal.
+
+        Implements :meth:`EuclideanGeometry._equal_vector` by comparing the
+        Euclidean distance of the points to this geometry's epsilon.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import EuclideanEpsilonGeometry
+            sage: G = EuclideanEpsilonGeometry(RR, 1e-3)
+
+            sage: G._equal_point((0, 0), (0, 0))
+            True
+            sage: G._equal_point((0, 0), (0, 1/1024))
+            True
+
+            sage: G._equal_vector((0, 0), (0, 0))
+            True
+            sage: G._equal_vector((0, 0), (0, 1/1024))
+            True
+
+        """
+        if len(v) != len(w):
+            raise TypeError("vectors must have same length")
+
+        return sum((vv - ww) ** 2 for (vv, ww) in zip(v, w)) < self._epsilon**2
+
+    def change_ring(self, ring):
+        r"""
+        Return this geometry with the :meth:`~EuclideanGeometry.base_ring`
+        changed to ``ring``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import EuclideanEpsilonGeometry
+            sage: G = EuclideanEpsilonGeometry(RR, 1e-3)
+            sage: G.change_ring(QQ)
+            Traceback (most recent call last):
+            ...
+            ValueError: cannot change_ring() to an exact ring
+            sage: G.change_ring(RDF)
+            Epsilon geometry with ϵ=0.001 over Real Double Field
+
+        """
+        if ring.is_exact():
+            raise ValueError("cannot change_ring() to an exact ring")
+
+        return EuclideanEpsilonGeometry(ring, self._epsilon)
+
+
+class EuclideanSet(SageObject):
+    r"""
+    Base class for subsets of :class:`EuclideanPlane`.
+
+    .. NOTE::
+
+        Concrete subclasses should apply the following rules.
+
+        There should only be a single type to describe a certain subset:
+        normally, a certain subset, say a point, should only be described by a
+        single class, namely :class:`Point`. Of course, one could
+        describe a point as a polygon delimited by some edges that all
+        intersect in that single point, such objects should be avoided. Namely,
+        the methods that create a subset, say :meth:`EuclideanPlane.polygon`
+        take care of this by calling a sets
+        :meth:`EuclideanSet._normalize` to rewrite a set in its most natural
+        representation. To get the denormalized representation, we can always
+        set `check=False` when creating the object. For this to work, the
+        `__init__` should not take care of any such normalization and accept
+        any input that can possibly be made sense of.
+
+        Comparison with ``==`` should mean "is essentially indistinguishable
+        from": Implementing == to mean anything else would get us into trouble
+        in the long run. In particular we cannot implement <= to mean "is
+        subset of" since then an oriented and an unoriented geodesic would be
+        `==`. So, objects of a different type should almost never be equal. A
+        notable exception are objects that are indistinguishable to the end
+        user but use different implementations.
+
+    TESTS::
+
+        sage: from flatsurf import EuclideanPlane
+        sage: from flatsurf.geometry.euclidean import EuclideanSet
+        sage: E = EuclideanPlane()
+
+        sage: isinstance(E((0, 0)), EuclideanSet)
+        True
+
+    """
+
+    def _check(self, require_normalized=True):
+        r"""
+        Validate this convex set.
+
+        Subclasses run specific checks here that can be disabled when creating
+        objects with ``check=False``.
+
+        INPUT:
+
+        - ``require_normalized`` -- a boolean (default: ``True``); whether to
+          include checks that assume that normalization has already happened
+
+        EXAMPLES:
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: P = E.point(0, 0)
+            sage: P._check()
+
+        """
+        pass
+
+    def _normalize(self):
+        r"""
+        Return this set possibly rewritten in a simpler form.
+
+        This method is only relevant for sets created with ``check=False``.
+        Such sets might have been created in a non-canonical way, e.g., when
+        creating a :class:`OrientedSegment` whose start and end point is
+        identical.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: segment = E.segment((0, 0), (0, 0), check=False, assume_normalized=True)
+            sage: segment
+            {-x - 1 = 0} ∩ {x - 1 ≥ 0} ∩ {x - 1 ≤ 0}
+            sage: segment._normalize()
+            (0, 0)
+
+        """
+        return self
+
+    def _test_normalize(self, **options):
+        r"""
+        Verify that normalization is idempotent.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: segment = E.segment((0, 0), (1, 0))
+            sage: segment._test_normalize()
+
+        """
+        tester = self._tester(**options)
+
+        normalization = self._normalize()
+
+        tester.assertEqual(normalization, normalization._normalize())
+
+    def __contains__(self, point):
+        r"""
+        Return whether this set contains the point ``point``.
+
+        INPUT:
+
+        - ``point`` -- a point in the Euclidean plane
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: c = E.circle((0, 0), radius=1)
+            sage: E((0, 0)) in c
+            True
+
+        """
+        raise NotImplementedError(
+            "this subset of the Euclidean plane cannot decide whether it contains a given point yet"
+        )
+
+    # TODO: Add a _test_contains test.
+
+    # TODO: Add is_bounded() and a test method.
+
+    def change(self, *, ring=None, geometry=None, oriented=None):
+        r"""
+        Return a modified copy of this set.
+
+        INPUT:
+
+        - ``ring`` -- a ring (default: ``None`` to keep the current
+          :meth:`~EuclideanPlane.base_ring`); the ring over which the new set
+          will be defined.
+
+        - ``geometry`` -- a :class:`EuclideanGeometry` (default: ``None`` to
+          keep the current geometry); the geometry that will be used for the
+          new set.
+
+        - ``oriented`` -- a boolean (default: ``None`` to keep the current
+          orientedness) whether the new set will be explicitly oriented.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+
+            sage: segment = E.segment((0, 0), (1, 1))
+
+        We can change the base ring over which this set is defined::
+
+            sage: segment.change(ring=AA)
+            {(x^2 + y^2) - x = 0}
+
+        We can drop the explicit orientation of a set::
+
+            sage: unoriented = segment.change(oriented=False)
+            sage: unoriented.is_oriented()
+            False
+
+        We can also take an unoriented set and pick an orientation::
+
+            sage: oriented = unoriented.change(oriented=True)
+            sage: oriented.is_oriented()
+            True
+
+        .. SEEALSO::
+
+            :meth:`is_oriented` to determine whether a set is oriented.
+
+        """
+        raise NotImplementedError(f"this {type(self)} does not implement change()")
+
+    def change_ring(self, ring):
+        r"""
+        Return this set as an element of the Euclidean plane over ``ring``.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+
+            sage: p = E((0, 0))
+            sage: p.change_ring(AA)
+
+        """
+        return self.change(ring=ring)
+
+    # TODO: Add change_ring, change and test methods.
+
+    # TODO: Add plot and test_plot()
+
+    # TODO: Add apply_similarity() and a test method.
+
+    # TODO: Add _acted_upon and a test method
+
+    # TODO: Add is_subset() and a test method.
+
+    # TODO: Add _an_element_ and some_elements()
+
+    # TODO: Add is_empty() and __bool__
+
+    # TODO: Add is_point()
+
+    def is_oriented(self):
+        r"""
+        Return whether this is a set with an explicit orientation.
+
+        Some sets come in two flavors. There are oriented segments and
+        unoriented segments.
+
+        This method answers whether a set is in the oriented kind if there is a
+        choice.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+
+        Segments are normally oriented::
+
+            sage: s = E.segment((0, 0), (1, 0))
+            sage: s.is_oriented()
+
+        We can explicitly ask for an unoriented segment::
+
+            sage: u = s.unoriented()
+            sage: u.is_oriented()
+            False
+
+        Points are not oriented, there is no choice of orientation::
+
+            sage: p = E((0, 0))
+            sage: p.is_oriented()
+            False
+
+        """
+        return isinstance(self, EuclideanOrientedSet)
+
+    # TODO: Add __hash__ and test method
+
+    # TODO: Add random_set for testing.
+
+
+class EuclideanOrientedSet(EuclideanSet):
+    r"""
+    Base class for sets that have an explicit orientation.
+
+    .. SEEALSO::
+
+        :meth:`EuclideanSet.is_oriented`
+
+    """
+
+
+class EuclideanFacade(EuclideanSet, Parent):
+    r"""
+    A subset of the Euclidean plane that is itself a parent.
+
+    This is the base class for all Euclidean sets that are not points.
+    This class solves the problem that we want sets to be "elements" of the
+    Euclidean plane but at the same time, we want these sets to live as parents
+    in the category framework of SageMath; so they have a Parent with Euclidean
+    points as their Element class.
+
+    SageMath provides the (not very frequently used and somewhat flaky) facade
+    mechanism for such parents. Such sets being a facade, their points can be
+    both their elements and the elements of the Euclidean plane.
+
+    EXAMPLES::
+
+        sage: from flatsurf import EuclideanPlane
+        sage: E = EuclideanPlane()
+        sage: c = E.circle((0, 0), radius=1)
+        sage: p = C.center()
+        sage: p in c
+        True
+        sage: p.parent() is E
+        True
+        sage: q = c.an_element()
+        sage: q
+        I
+        sage: q.parent() is E
+        True
+
+    TESTS::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanFacade
+        sage: isinstance(v, EuclideanFacade)
+        True
+
+    """
+
+    def __init__(self, parent, category=None):
+        Parent.__init__(self, facade=parent, category=category)
+
+    def parent(self):
+        r"""
+        Return the Euclidean plane this is a subset of.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: c = E.circle((0, 0), radius=1)
+            sage: c.parent()
+            Euclidean Plane over Rational Field
+
+        """
+        return self.facade_for()[0]
+
+    def _element_constructor_(self, x):
+        r"""
+        Return ``x`` as a point of this set.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: c = E.circle((0, 0), radius=1)
+            sage: c((1, 0))
+            (1, 0)
+            sage: v((0, 0))
+            Traceback (most recent call last):
+            ...
+            ValueError: point not contained in this set
+
+        """
+        x = self.parent()(x)
+
+        if isinstance(x, EuclideanPoint):
+            if not self.__contains__(x):
+                raise ValueError("point not contained in this set")
+
+        return x
+
+    def base_ring(self):
+        r"""
+        Return the ring over which points of this set are defined.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: c = E.circle((0, 0), radius=1)
+            sage: c.base_ring()
+            Rational Field
+
+        """
+        return self.parent().base_ring()
+
+
+class EuclideanCircle(EuclideanFacade):
+    r"""
+    A circle in the Euclidean plane.
+
+    INPUT:
+
+    - ``parent`` -- the :class:`EuclideanPlane` containing this circle
+
+    - ``center`` -- the :class:`EuclideanPoint`` at the center of this circle
+
+    - ``radius_squared`` -- the square of the radius of this circle
+
+    EXAMPLES::
+
+        sage: from flatsurf import EuclideanPlane
+        sage: c = EuclideanPlane().circle((0, 0), radius=1)
+
+    TESTS::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanCircle
+        sage: isinstance(c, EuclideanCircle)
+        True
+        sage: TestSuite(c).run()
+
+    .. SEEALSO::
+
+        :meth:`EuclideanPlane.circle` for a method to create circles
+
+    """
+
+    def __init__(self, parent, center, radius_squared):
+        super().__init__(parent)
+
+        self._center = center
+        self._radius_squared = radius_squared
+
+    def __eq__(self, other):
+        if not isinstance(other, EuclideanCircle):
+            return False
+
+        if self.parent() is not other.parent():
+            return False
+
+        return self._center == other._center and self._radius_squared == other._radius_squared
+
+    def __hash__(self):
+        return hash((self._center, self._radius_squared))
+
+    def _repr_(self):
+        r"""
+        Return a printable representation of this circle.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: c = EuclideanPlane().circle((0, 0), radius=1)
+            sage: c
+
+        """
+        x, y = self._center
+
+        x = f"(x - {x})" if x else "x"
+        y = f"(y - {y})" if y else "y"
+
+        return f"{{ {x}² + {y}² = {self._radius_squared} }}"
+
+    def change(self, *, ring=None, geometry=None, oriented=None):
+        r"""
+        Return a modified copy of this circle.
+
+        INPUT:
+
+        - ``ring`` -- a ring (default: ``None`` to keep the current
+          :meth:`~EuclideanPlane.base_ring`); the ring over which the new
+          circle will be defined.
+
+        - ``geometry`` -- a :class:`EuclideanGeometry` (default: ``None`` to
+          keep the current geometry); the geometry that will be used for the
+          new circle.
+
+        - ``oriented`` -- a boolean (default: ``None`` to keep the current
+          orientedness); must be ``None`` or ``False`` since circles cannot
+          have an explicit orientation. See :meth:`~EuclideanSet.is_oriented`.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+
+            sage: c = E.circle((0, 0), radius=1)
+
+        We change the base ring over which this circle is defined::
+
+            sage: c.change(ring=AA)
+
+        We cannot change the orientation of a circle::
+
+            sage: c.change(oriented=True)
+
+            sage: c.change(oriented=False)
+
+        """
+        if ring is not None or geometry is not None:
+            self = (
+                self.parent()
+                .change_ring(ring, geometry=geometry)
+                .circle(self._center, radius_squared=self._radius_squared, check=False)
+            )
+
+        if oriented is None:
+            oriented = self.is_oriented()
+
+        if oriented != self.is_oriented():
+            raise NotImplementedError("circles cannot have an explicit orientation")
+
+        return self
+
+    def _normalize(self):
+        r"""
+        Return this set possibly rewritten in a simpler form.
+
+        This implements :meth:`EuclideanSet._normalize`.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: circle = E.circle((0, 0), radius=0, check=False)
+            sage: circle
+            sage: circle._normalize()
+            (0, 0)
+
+        """
+        if self.parent().geometry._zero(self._radius_squared):
+            return self._center
+
+        return self
+
+    def center(self):
+        r"""
+        Return the point at the center of the circle.
+        """
+        return self._center
+
+    def radius_squared(self):
+        r"""
+        Return the square of the radius of the circle.
+        """
+        return self._radius_squared
+
+    def point_position(self, point):
+        r"""
+        Return 1 if point lies in the circle, 0 if the point lies on the circle,
+        and -1 if the point lies outide the circle.
+        """
+        # TODO: Deprecate?
+        value = (
+            (point[0] - self._center[0]) ** 2
+            + (point[1] - self._center[1]) ** 2
+            - self._radius_squared
+        )
+
+        if value > 0:
+            return -1
+        if value < 0:
+            return 1
+        return 0
+
+    def closest_point_on_line(self, point, direction_vector):
+        r"""
+        Consider the line through the provided point in the given direction.
+        Return the closest point on this line to the center of the circle.
+        """
+        # TODO: Rewrite or deprecate and generalize
+        V3 = self.parent().base_ring() ** 3
+        V2 = self.parent().base_ring() ** 2
+
+        cc = V3((self._center[0], self._center[1], 1))
+        # point at infinite orthogonal to direction_vector:
+        dd = V3((direction_vector[1], -direction_vector[0], 0))
+        l1 = cc.cross_product(dd)
+
+        pp = V3((point[0], point[1], 1))
+        # direction_vector pushed to infinity
+        ee = V3((direction_vector[0], direction_vector[1], 0))
+        l2 = pp.cross_product(ee)
+
+        # This is the point we want to return
+        rr = l1.cross_product(l2)
+        try:
+            return V2((rr[0] / rr[2], rr[1] / rr[2]))
+        except ZeroDivisionError:
+            raise ValueError(
+                "Division by zero error. Perhaps direction is zero. "
+                + "point="
+                + str(point)
+                + " direction="
+                + str(direction_vector)
+                + " circle="
+                + str(self)
+            )
+
+    # TODO: Not used anywhere.
+    ## def line_position(self, point, direction_vector):
+    ##     r"""
+    ##     Consider the line through the provided point in the given direction.
+    ##     We return 1 if the line passes through the circle, 0 if it is tangent
+    ##     to the circle and -1 if the line does not intersect the circle.
+    ##     """
+    ##     return self.point_position(self.closest_point_on_line(point, direction_vector))
+
+    # TODO: Create Segment class.
+    def line_segment_position(self, p, q):
+        r"""
+        Consider the open line segment pq.We return 1 if the line segment
+        enters the interior of the circle, zero if it touches the circle
+        tangentially (at a point in the interior of the segment) and
+        and -1 if it does not touch the circle or its interior.
+        """
+        if self.point_position(p) == 1:
+            return 1
+        if self.point_position(q) == 1:
+            return 1
+        r = self.closest_point_on_line(p, q - p)
+        pos = self.point_position(r)
+        if pos == -1:
+            return -1
+        # This checks if r lies in the interior of pq
+        if p[0] == q[0]:
+            if (p[1] < r[1] and r[1] < q[1]) or (p[1] > r[1] and r[1] > q[1]):
+                return pos
+        elif (p[0] < r[0] and r[0] < q[0]) or (p[0] > r[0] and r[0] > q[0]):
+            return pos
+        # It does not lie in the interior.
+        return -1
+
+    # TODO: Not used anywhere.
+    ## def tangent_vector(self, point):
+    ##     r"""
+    ##     Return a vector based at the provided point (which must lie on the circle)
+    ##     which is tangent to the circle and points in the counter-clockwise
+    ##     direction.
+
+    ##     EXAMPLES::
+
+    ##         sage: from flatsurf.geometry.circle import Circle
+    ##         sage: c=Circle(vector((0,0)), 2, base_ring=QQ)
+    ##         sage: c.tangent_vector(vector((1,1)))
+    ##         (-1, 1)
+    ##     """
+    ##     if not self.point_position(point) == 0:
+    ##         raise ValueError("point not on circle.")
+    ##     return vector((self._center[1] - point[1], point[0] - self._center[0]))
+
+    # TODO: Not used anywhere.
+    ## def other_intersection(self, p, v):
+    ##     r"""
+    ##     Consider a point p on the circle and a vector v. Let L be the line
+    ##     through p in direction v. Then L intersects the circle at another
+    ##     point q. This method returns q.
+
+    ##     Note that if p and v are both in the field of the circle,
+    ##     then so is q.
+
+    ##     EXAMPLES::
+
+    ##         sage: from flatsurf.geometry.circle import Circle
+    ##         sage: c=Circle(vector((0,0)), 25, base_ring=QQ)
+    ##         sage: c.other_intersection(vector((3,4)),vector((1,2)))
+    ##         (-7/5, -24/5)
+    ##     """
+    ##     pp = self._V3((p[0], p[1], self._base_ring.one()))
+    ##     vv = self._V3((v[0], v[1], self._base_ring.zero()))
+    ##     L = pp.cross_product(vv)
+    ##     cc = self._V3((self._center[0], self._center[1], self._base_ring.one()))
+    ##     vvperp = self._V3((-v[1], v[0], self._base_ring.zero()))
+    ##     # line perpendicular to L through center:
+    ##     Lperp = cc.cross_product(vvperp)
+    ##     # intersection of L and Lperp:
+    ##     rr = L.cross_product(Lperp)
+    ##     r = self._V2((rr[0] / rr[2], rr[1] / rr[2]))
+    ##     return self._V2((2 * r[0] - p[0], 2 * r[1] - p[1]))
+
+    def __rmul__(self, similarity):
+        r"""
+        Apply a similarity to the circle.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: s = translation_surfaces.square_torus()
+            sage: c = s.polygon(0).circumscribing_circle()
+            sage: c
+            Circle((1/2, 1/2), 1/2)
+            sage: s.edge_transformation(0,2)
+            (x, y) |-> (x, y - 1)
+            sage: s.edge_transformation(0,2) * c
+            Circle((1/2, -1/2), 1/2)
+        """
+        # TODO: Implement this properly for this parent
+        from .similarity import SimilarityGroup
+
+        SG = SimilarityGroup(self.parent().base_ring())
+        s = SG(similarity)
+        return self.parent().circle(
+            s(self._center), radius_squared=s.det() * self._radius_squared
+        )
+
+    ## def __str__(self):
+    ##     return (
+    ##         "circle with center "
+    ##         + str(self._center)
+    ##         + " and radius squared "
+    ##         + str(self._radius_squared)
+    ##     )
+
+
+class EuclideanPoint(EuclideanSet, Element):
+    r"""
+    A point in the :class:`EuclideanPlane`.
+
+    EXAMPLES::
+
+        sage: from flatsurf import EuclideanPlane
+        sage: E = EuclideanPlane()
+
+        sage: p = E.point(0, 0)
+
+    TESTS::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanPoint
+        sage: isinstance(p, EuclideanPoint)
+        True
+
+        sage: TestSuite(p).run()
+
+    .. SEEALSO::
+
+        :meth:`EuclideanPlane.point` for ways to create points
+
+    """
+
+    def __init__(self, parent, x, y):
+        super().__init__(parent)
+
+        self._x = x
+        self._y = y
+
+    def __iter__(self):
+        r"""
+        Return an iterator over the coordinates of this point.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+
+            sage: p = E.point(1, 2)
+            sage: list(p)
+            [1, 2]
+
+        """
+        yield self._x
+        yield self._y
+
+    def vector(self):
+        return self.parent().vector_space()((self._x, self._y))
+
+    def translate(self, v):
+        return self.parent().point(*(self.vector() + v))
+
+    def _richcmp_(self, other, op):
+        r"""
+        Return how this point compares to ``other`` with respect to the ``op``
+        operator.
+
+        This is only implemented for the operators ``==`` and ``!=``. It
+        returns whether two points are the same.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+
+            sage: E((0, 0)) = E((0, 0))
+            True
+
+        .. SEEALSO::
+
+            :meth:`EuclideanSet.__contains__` to check for containment of a
+            point in a set
+
+        """
+        from sage.structure.richcmp import op_EQ, op_NE
+
+        if op == op_NE:
+            return not self._richcmp_(other, op_EQ)
+
+        if op == op_EQ:
+            if not isinstance(other, EuclideanPoint):
+                return False
+
+            return self.parent().geometry._equal_point(self, other)
+
+        return super()._richcmp_(other, op)
+
+    def _repr_(self):
+        r"""
+        Return a printable representation of this point.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+
+            sage: p = E.point(0, 0)
+            sage: p
+            (0, 0)
+
+        """
+        return repr(tuple(self))
+
+    def __getitem__(self, i):
+        r"""
+        Return the ``i``-th coordinate of this point.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+
+            sage: p = E.point(1, 2)
+            sage: p[0]
+            1
+            sage: p[1]
+            2
+            sage: p[2]
+
+        """
+        if i == 0:
+            return self._x
+        if i == 1:
+            return self._y
+
+        raise NotImplementedError
+
+    def change(self, *, ring=None, geometry=None, oriented=None):
+        r"""
+        Return a modified copy of this point.
+
+        INPUT:
+
+        - ``ring`` -- a ring (default: ``None`` to keep the current
+          :meth:`~EuclideanPlane.base_ring`); the ring over which the new
+          point will be defined.
+
+        - ``geometry`` -- a :class:`EuclideanGeometry` (default: ``None`` to
+          keep the current geometry); the geometry that will be used for the
+          new point.
+
+        - ``oriented`` -- a boolean (default: ``None`` to keep the current
+          orientedness); must be ``None`` or ``False`` since points cannot
+          have an explicit orientation. See :meth:`~EuclideanSet.is_oriented`.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+
+            sage: p = E((0, 0))
+
+        We change the base ring over which this point is defined::
+
+            sage: p.change_ring(ring=AA)
+
+        We cannot change the orientation of a point:
+
+            sage: p.change(oriented=True)
+
+            sage: p.change(oriented=False)
+
+        """
+        if ring is not None or geometry is not None:
+            self = (
+                self.parent()
+                .change_ring(ring, geometry=geometry)
+                .point(self._x, self._y)
+            )
+
+        if oriented is None:
+            oriented = self.is_oriented()
+
+        if oriented != self.is_oriented():
+            raise NotImplementedError("points cannot have an explicit orientation")
+
+        return self
+
+    def segment(self, end):
+        end = self.parent()(end)
+
+        line = self.parent().line(self, end)
+
+        return self.parent().segment(line, start=self, end=end)
+
+    def __hash__(self):
+        return hash((self._x, self._y))
+
+
+class EuclideanLine(EuclideanFacade):
+    r"""
+    A line in the Euclidean plane.
+
+    This is a common base class for oriented and unoriented lines, see
+    :class:`EuclideanOrientedLine` and :class:`EuclideanUnorientedLine`.
+
+    Internally, we represent a line by its equation, i.e., the ``a``,
+    ``b``, ``c`` such that points on the line satisfy
+
+    .. MATH::
+
+        a + bx + cy = 0
+
+    EXAMPLES::
+
+        sage: from flatsurf import EuclideanPlane
+        sage: E = EuclideanPlane()
+        sage: line = E.line((0, 0), (1, 1))
+
+    TESTS::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanLine
+        sage: isinstance(line, EuclideanLine)
+        True
+        sage: TestSuite(line).run()
+
+    .. SEEALSO::
+
+        :meth:`EuclideanPlane.line`
+
+    """
+
+    def __init__(self, parent, a, b, c):
+        super().__init__(parent)
+
+        if not isinstance(a, Element) or a.parent() is not parent.base_ring():
+            raise TypeError("a must be an element of the base ring")
+        if not isinstance(b, Element) or b.parent() is not parent.base_ring():
+            raise TypeError("b must be an element of the base ring")
+        if not isinstance(c, Element) or c.parent() is not parent.base_ring():
+            raise TypeError("c must be an element of the base ring")
+
+        self._a = a
+        self._b = b
+        self._c = c
+
+    def change(self, *, ring=None, geometry=None, oriented=None):
+        r"""
+        Return a modified copy of this line.
+
+        INPUT:
+
+        - ``ring`` -- a ring (default: ``None`` to keep the current
+          :meth:`~EuclideanPlane.base_ring`); the ring over which the new line
+          will be defined.
+
+        - ``geometry`` -- a :class:`EuclideanGeometry` (default: ``None`` to
+          keep the current geometry); the geometry that will be used for the
+          new line.
+
+        - ``oriented`` -- a boolean (default: ``None`` to keep the current
+          orientedness); whether the new line should be oriented.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane(AA)
+
+        The base ring over which this line is defined can be changed::
+
+            sage: E.line((0, 0), (1, 1)).change_ring(QQ)
+
+        But we cannot change the base ring if the line's equation cannot be
+        expressed in the smaller ring::
+
+            sage: E.line((0, 0), (1, AA(2).sqrt())).change_ring(QQ)
+            Traceback (most recent call last):
+            ...
+            ValueError: Cannot coerce irrational Algebraic Real ... to Rational
+
+        We can forget the orientation of a line::
+
+            sage: line = E.line((0, 0), (1, 1))
+            sage: line.is_oriented()
+            True
+            sage: line = line.change(oriented=False)
+            sage: line.is_oriented()
+            False
+
+        We can (somewhat randomly) pick the orientation of a line::
+
+            sage: line = line.change(oriented=True)
+            sage: line.is_oriented()
+            True
+
+        """
+        if ring is not None or geometry is not None:
+            self = (
+                self.parent()
+                .change_ring(ring, geometry=geometry)
+                .line(
+                    self._a,
+                    self._b,
+                    self._c,
+                    check=False,
+                    oriented=self.is_oriented(),
+                )
+            )
+
+        if oriented is None:
+            oriented = self.is_oriented()
+
+        if oriented != self.is_oriented():
+            self = self.parent().line(
+                self._a, self._b, self._c, check=False, oriented=oriented
+            )
+
+        return self
+
+    def _repr_(self):
+        r"""
+        Return a printable representation of this line.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane(AA)
+            sage: E.line((0, 0), (1, 1))
+            sage: E.line((0, 1), (1, 1))
+            sage: E.line((1, 0), (1, 1))
+
+        """
+        a, b, c = self.equation(normalization=["gcd", None])
+
+        from sage.all import PolynomialRing
+
+        R = PolynomialRing(self.parent().base_ring(), names=["x", "y"])
+        polynomial_part = R({(1, 0): b, (0, 1): c})
+        if self.parent().geometry._sgn(a) != 0:
+            return f"{{{repr(a)} + {repr(polynomial_part)} = 0}}"
+        else:
+            return f"{{{repr(polynomial_part)} = 0}}"
+
+    def equation(self, normalization=None):
+        r"""
+        Return an equation for this line as a triple ``a``, ``b``, ``c`` such
+        that the line is given by the points satisfying
+
+        .. MATH::
+
+            a + bx + cy = 0
+
+        INPUT:
+
+        - ``normalization`` -- how to normalize the coefficients; the default
+          ``None`` is not to normalize at all. Other options are ``gcd``, to
+          divide the coefficients by their greatest common divisor, ``one``, to
+          normalize the first non-zero coefficient to ±1. This can also be a
+          list of such values which are then tried in order and exceptions are
+          silently ignored unless they happen at the last option.
+
+        If this line :meth;`is_oriented`, then the sign of the coefficients
+        is chosen to encode the orientation of this line. The sign is such
+        that the half plane obtained by replacing ``=`` with ``≥`` in the
+        equation is on the left of the line.
+
+        Note that the output might not uniquely describe the line. The
+        coefficients are only unique up to scaling.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane(AA)
+            sage: E.line((0, 0), (1, 1)).equation()
+            (0, -1, 1)
+            sage: E.line((0, 1), (1, 1)).equation()
+            (-1, 0, 1)
+            sage: E.line((1, 0), (1, 1)).equation()
+            (1, -1, 0)
+
+        Some normalizations might not be possible over some base rings::
+
+            sage; E = EuclideanPlane(ZZ)
+            sage: line = E.line((1, 3), (6, 8))
+            sage: line
+            {-2 + -x + y = 0}
+            sage: line.equation()
+            (-10, -5, 5)
+            sage: line.equation(normalization="one")
+            Traceback (most recent call last):
+            ...
+            TypeError: no conversion of this rational to integer
+            sage: line.equation(normalization="gcd")
+            (-2, -1, 1)
+
+        In such cases, we can also use a list of normalizations to select the
+        best one possible::
+
+            sage: line.equation(normalization=["one", "gcd", None])
+            (-2, -1, 1)
+
+        .. SEEALSO::
+
+            :meth:`HyperbolicGeodesic.equation` which does essentially the same
+            for geodesics in the hyperbolic plane
+
+        """
+        normalization = normalization or [None]
+
+        if isinstance(normalization, str):
+            normalization = [normalization]
+
+        from collections.abc import Sequence
+
+        if not isinstance(normalization, Sequence):
+            normalization = [normalization]
+
+        normalization = list(normalization)
+        normalization.reverse()
+
+        a, b, c = self._a, self._b, self._c
+
+        sgn = self.parent().geometry._sgn
+        sgn = (
+            -1
+            if (
+                sgn(a) < 0
+                or (sgn(a) == 0 and b < 0)
+                or (sgn(a) == 0 and sgn(b) == 0 and sgn(c) < 0)
+            )
+            else 1
+        )
+
+        while normalization:
+            strategy = normalization.pop()
+
+            from flatsurf.geometry.hyperbolic import HyperbolicGeodesic
+
+            try:
+                a, b, c = HyperbolicGeodesic._normalize_coefficients(
+                    a, b, c, strategy=strategy
+                )
+                break
+            except Exception:
+                if not normalization:
+                    raise
+
+        if not self.is_oriented():
+            a *= sgn
+            b *= sgn
+            c *= sgn
+
+        return a, b, c
+
+    def _an_element_(self):
+        if self._b:
+            return self.parent().point(-self._a / self._b, 0)
+
+        assert self._c
+        return self.parent().point(-self._a / self._c, 0)
+
+    def projection(self, point):
+        # Move the line to the origin, i.e., instead of a + bx + cy = 0,
+        # consider bx + cy = 0.
+        # Let v be a vector parallel to this line.
+        shift = self.an_element()
+
+        point = point.translate(-shift.vector())
+
+        (x, y) = point
+        v = (self._c, -self._b)
+        vv = ~(v[0] ** 2 + v[1] ** 2)
+
+        p = (v[0] ** 2 * x + v[0] * v[1] * y, v[0] * v[1] * x + v[1] * v[1] * y)
+        p = (p[0] * vv, p[1] * vv)
+
+        assert p[0] * self._b + p[1] * self._c == 0
+
+        return self.parent().point(*p).translate(shift.vector())
+
+    def contains_point(self, point):
+        x, y = point.vector()
+        return self._a + self._b * x + self._c * y == 0
+
+
+class EuclideanOrientedLine(EuclideanLine, EuclideanOrientedSet):
+    r"""
+    A line in the Euclidean plane with an explicit orientation.
+
+    Internally, we represent a line by its equation, i.e., the ``a``,
+    ``b``, ``c`` such that points on the line satisfy
+
+    .. MATH::
+
+        a + bx + cy = 0
+
+    The orientation of that line is such that
+
+    .. MATH::
+
+        a + bx + cy \ge 0
+
+    is to its left.
+
+    EXAMPLES::
+
+        sage: from flatsurf import EuclideanPlane
+        sage: E = EuclideanPlane()
+        sage: line = E.line((0, 0), (1, 1))
+
+    TESTS::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanOrientedLine
+        sage: isinstance(line, EuclideanOrientedLine)
+        True
+        sage: TestSuite(line).run()
+
+    """
+
+    def direction(self):
+        r"""
+        Return a vector pointing in the direction of this oriented line.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: line = E.line((0, 0), (1, 1))
+            sage: line.direction()
+            (1, 1)
+
+        """
+        return self.parent().vector_space()((self._c, -self._b))
+
+    def __neg__(self):
+        r"""
+        Return this line with reversed orientation.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: line = E.line((0, 0), (1, 1))
+            sage: -line
+
+        """
+        return self.parent().line(-self._a, -self._b, -self._c, check=False)
+
+
+class EuclideanUnorientedLine(EuclideanLine):
+    pass
+
+
+class EuclideanSegment(EuclideanFacade):
+    r"""
+    A line segment in the Euclidean plane.
+
+    This is a common base class for oriented and unoriented segments, see
+    :class:`EuclideanOrientedSegment` and :class:`EuclideanUnorientedSegment`.
+
+    EXAMPLES::
+
+        sage: from flatsurf import EuclideanPlane
+        sage: E = EuclideanPlane()
+        sage: start = E((0, 0))
+        sage: end = E((1, 1))
+        sage: segment = start.segment(end)
+
+    TESTS::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanSegment
+        sage: isinstance(segment, EuclideanSegment)
+        True
+        sage: TestSuite(segment).run()
+
+    .. SEEALSO::
+
+        :meth:`EuclideanPlane.segment`
+        :meth:`EuclideanPoint.segment`
+
+    """
+
+    def __init__(self, parent, line, start, end):
+        super().__init__(parent)
+
+        if not isinstance(line, EuclideanLine):
+            raise TypeError("line must be a Euclidean line")
+        if start is not None and not isinstance(start, EuclideanPoint):
+            raise TypeError("start must be a Euclidean point")
+        if end is not None and not isinstance(end, EuclideanPoint):
+            raise TypeError("end must be a Euclidean point")
+
+        self._line = line
+        self._start = start
+        self._end = end
+
+    def _normalize(self):
+        r"""
+        Return a normalized version of this segment.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: line = E.line((0, 0), (1, 1))
+
+        A segment with two identical endpoints, is a point::
+
+            sage: segment = E.segment(line, start=(0, 0), end=(0, 0), assume_normalized=True, check=False)
+            sage: segment
+            sage: segment._normalize()
+
+        A segment with a single endpoint is a ray::
+
+            sage: segment = E.segment(line, start=(0, 0), end=None, assume_normalized=True, check=False)
+            sage: segment
+            sage: segment._normalize()
+
+        A segment without endpoints is a line::
+
+            sage: segment = E.segment(line, start=None, end=None, assume_normalized=True, check=False)
+            sage: segment
+            sage: segment._normalize()
+
+        """
+        line = self._line
+        start = self._start
+        end = self._end
+
+        if start is None and end is None:
+            return self._line.change(oriented=self.is_oriented())
+
+        if start == end:
+            return start
+
+        if start is None:
+            start, end = end, start
+            line = -line
+
+        if end is None:
+            return self.parent().ray(start, line.direction())
+
+        return self
+
+    def distance(self, point):
+        point = self.parent()(point)
+
+        # To compute the distance from the point to the segment, we compute the
+        # distance from the point to the line containing the segment.
+        # If the closest point on the line is on the segment, that's the
+        # distance to the segment. If not, the minimum distance is at one of
+        # the endpoints of the segment.
+        norm = self.parent().norm()
+        p = self._line.projection(point)
+        if self.contains_point(p):
+            return norm.from_vector(point.vector() - p.vector())
+        return min(
+            norm.from_vector(point.vector() - self._start.vector()),
+            norm.from_vector(point.vector() - self._end.vector()),
+        )
+
+    def contains_point(self, point):
+        if not self._line.contains_point(point):
+            return False
+
+        return bool(
+            time_on_segment((self._start.vector(), self._end.vector()), point.vector())
+        )
+
+
+class EuclideanOrientedSegment(EuclideanSegment, EuclideanOrientedSet):
+    r"""
+    An oriented line segment in the Euclidean plane going from a ``start``
+    point to an ``end`` point.
+
+    EXAMPLES::
+
+        sage: from flatsurf import EuclideanPlane
+        sage: E = EuclideanPlane()
+        sage: start = E((0, 0))
+        sage: end = E((1, 0))
+        sage: segment = start.segment(end)
+
+    TESTS::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanOrientedSegment
+        sage: isinstance(segment, EuclideanOrientedSegment)
+        True
+        sage: TestSuite(segment).run()
+
+    """
+
+    def _repr_(self):
+        r"""
+        Return a printable representation of this segment.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: start = E((0, 0))
+            sage: end = E((1, 0))
+            sage: segment = start.segment(end)
+            sage: segment
+            (0, 0) → (1, 0)
+
+        """
+        return f"{self._start!r} → {self._end!r}"
+
+
+class EuclideanUnorientedSegment(EuclideanSegment):
+    r"""
+    An unoriented line segment in the Euclidean plane connecting ``start`` and
+    ``end``.
+
+    EXAMPLES::
+
+        sage: from flatsurf import EuclideanPlane
+        sage: E = EuclideanPlane()
+        sage: start = E((0, 0))
+        sage: end = E((1, 0))
+        sage: segment = start.segment(end)
+        sage: segment = segment.unoriented()
+
+    TESTS::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanUnorientedSegment
+        sage: isinstance(segment, EuclideanUnorientedSegment)
+        True
+        sage: TestSuite(segment).run()
+
+    """
+
+    def _repr_(self):
+        r"""
+        Return a printable representation of this segment.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: start = E((0, 0))
+            sage: end = E((1, 0))
+            sage: segment = start.segment(end).unoriented()
+            sage: segment
+            (0, 0) — (1, 0)
+
+        """
+        return f"{self._start!r} — {self._end!r}"
+
+
+class EuclideanRay(EuclideanFacade):
+    r"""
+    A ray emanating from a base point in the Euclidean plane.
+
+    EXAMPLES::
+
+        sage: from flatsurf import EuclideanPlane
+        sage: E = EuclideanPlane()
+        sage: ray = E.ray((0, 0), (1, 1))
+
+    TESTS::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanRay
+        sage: isinstance(ray, EuclideanRay)
+        True
+        sage: TestSuite(ray).run()
+
+    """
+
+    def __init__(self, parent, base, direction):
+        super().__init__(parent)
+
+        self._base = base
+        self._direction = direction
+
+    def _repr_(self):
+        r"""
+        Return a printable representation of this ray.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: E.ray((0, 0), (1, 1))
+
+        """
+        return f"Ray from {self._base!r} in direction {self._direction!r}"
+
+
+### TODO: PRE-EUCLIDEAN-PLANE CODE HERE
+
+
+def is_cosine_sine_of_rational(cos, sin, scaled=False):
+    r"""
+    Check whether the given pair is a cosine and sine of a same rational angle.
+
+    INPUT:
+
+    - ``cos`` -- a number
+
+    - ``sin`` -- a number
+
+    - ``scaled`` -- a boolean (default: ``False``); whether to allow ``cos``
+      and ``sin`` to be scaled by the same positive algebraic number
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import is_cosine_sine_of_rational
+
+        sage: c = s = AA(sqrt(2))/2
+        sage: is_cosine_sine_of_rational(c, s)
+        True
+
+        sage: c = AA(sqrt(3))/2
+        sage: s = AA(1/2)
+        sage: is_cosine_sine_of_rational(c, s)
+        True
+
+        sage: c = AA(sqrt(5)/2)
+        sage: s = (1 - c**2).sqrt()
+        sage: c**2 + s**2
+        1.000000000000000?
+        sage: is_cosine_sine_of_rational(c, s)
+        False
+
+        sage: c = (AA(sqrt(5)) + 1)/4
+        sage: s = (1 - c**2).sqrt()
+        sage: is_cosine_sine_of_rational(c, s)
+        True
+
+        sage: K.<sqrt2> = NumberField(x**2 - 2, embedding=1.414)
+        sage: is_cosine_sine_of_rational(K.zero(), -K.one())
+        True
+
+    TESTS::
+
+        sage: from pyexactreal import ExactReals # optional: pyexactreal  # random output due to matplotlib warnings with some combinations of setuptools and matplotlib
+        sage: R = ExactReals() # optional: pyexactreal
+        sage: is_cosine_sine_of_rational(R.one(), R.zero()) # optional: pyexactreal
+        True
+
+    """
+    from sage.all import AA
+
+    # We cannot check in AA due to https://github.com/flatsurf/exact-real/issues/172
+    # We just trust that non-algebraic elements won't allow conversion to AA.
+    # if cos not in AA:
+    #     return False
+    # if sin not in AA:
+    #     return False
+
+    if not scaled:
+        if cos**2 + sin**2 != 1:
+            return False
+
+    try:
+        cos = AA(cos)
+    except ValueError:
+        # This is a replacement for the "in AA" checked disabled above.
+        return False
+
+    cos = cos.as_number_field_element(embedded=True)
+    # We need an explicit conversion to the number field due to https://github.com/sagemath/sage/issues/35613
+    cos = cos[0](cos[1])
+
+    try:
+        sin = AA(sin)
+    except ValueError:
+        # This is a replacement for the "in AA" checked disabled above.
+        return False
+    sin = sin.as_number_field_element(embedded=True)
+    # We need an explicit conversion to the number field due to https://github.com/sagemath/sage/issues/35613
+    sin = sin[0](sin[1])
+
+    from sage.all import ComplexBallField
+
+    CBF = ComplexBallField(53)
+
+    x = CBF(cos) + CBF.gen(0) * CBF(sin)
+    xN = x
+
+    # Suppose that (cos, sin) are indeed sine and cosine of a rational angle.
+    # Then x = cos + I*sin generates a cyclotomic field C and for some N we
+    # have x^N = ±1. Since C is contained in the compositum of K=Q(cos) and
+    # L=Q(i*sin) and Q(cos) and Q(sin) are both contained in C, the degree of C
+    # is bounded from above by twice (accounting for the imaginary unit) the
+    # degrees of K and L. The degree of C is the totient of N which is bounded
+    # from below by n / (e^γ loglog n + 3 / loglog n) [cf. wikipedia].
+    degree_bound = 2 * cos.minpoly().degree() * sin.minpoly().degree()
+
+    from itertools import count
+
+    for n in count(2):
+        xN *= x
+
+        c = xN.real()
+        s = xN.imag()
+
+        if xN.real().contains_zero() or xN.imag().contains_zero():
+            c, s = cos, sin
+            for i in range(n - 1):
+                c, s = c * cos - s * sin, s * cos + c * sin
+
+            if c == 0 or s == 0:
+                return True
+
+            CBF = ComplexBallField(CBF.precision() * 2)
+            x = CBF(cos) + CBF.gen(0) * CBF(sin)
+            xN = x**n
+
+        from math import log
+
+        if n / (2.0 * log(log(n)) + 3 / log(log(n))) > 2 * degree_bound:
+            return False
+
+
+def acos(cos_angle, numerical=False):
+    r"""
+    Return the arccosine of ``cos_angle`` as a multiple of 2π, i.e., as a value
+    between 0 and 1/2.
+
+    INPUT:
+
+    - ``cos_angle`` -- a floating point number, the cosine of an angle
+
+    - ``numerical`` -- a boolean (default: ``False``); whether to return a
+      numerical approximation of the arccosine or try to reconstruct an exact
+      rational value for the arccosine (in radians.)
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import acos
+
+        sage: acos(1)
+        0
+        sage: acos(.5)
+        1/6
+        sage: acos(0)
+        1/4
+        sage: acos(-.5)
+        1/3
+        sage: acos(-1)
+        1/2
+
+        sage: acos(.25)
+        Traceback (most recent call last):
+        ...
+        NotImplementedError: cannot recover a rational angle from these numerical results
+        sage: acos(.25, numerical=True)
+        0.2097846883724169
+
+    """
+    import math
+
+    angle = math.acos(cos_angle) / (2 * math.pi)
+
+    assert 0 <= angle <= 0.5
+
+    if numerical:
+        return angle
+
+    # fast and dirty way using floating point approximation
+    from sage.all import RR
+
+    angle_rat = RR(angle).nearby_rational(0.00000001)
+    if angle_rat.denominator() > 256:
+        raise NotImplementedError(
+            "cannot recover a rational angle from these numerical results"
+        )
+    return angle_rat
+
+
+def angle(u, v, numerical=False):
+    r"""
+    Return the angle between the vectors ``u`` and ``v`` divided by `2 \pi`.
+
+    INPUT:
+
+    - ``u``, ``v`` - vectors in the plane
+
+    - ``numerical`` - boolean (default: ``False``), whether to return floating
+      point numbers
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import angle
+
+    As the implementation is dirty, we at least check that it works for all
+    denominator up to 20::
+
+        sage: u = vector((AA(1),AA(0)))
+        sage: for n in xsrange(1,20):       # long time  (1.5s)
+        ....:     for k in xsrange(1,n):
+        ....:         v = vector((AA(cos(2*k*pi/n)), AA(sin(2*k*pi/n))))
+        ....:         assert angle(u,v) == k/n
+
+    The numerical version (working over floating point numbers)::
+
+        sage: import math
+        sage: u = (1, 0)
+        sage: for n in xsrange(1,20):
+        ....:     for k in xsrange(1,n):
+        ....:         a = 2 * k * math.pi / n
+        ....:         v = (math.cos(a), math.sin(a))
+        ....:         assert abs(angle(u,v,numerical=True) * 2 * math.pi - a) < 1.e-10
+
+    If the angle is not rational, then the method returns an element in the real
+    lazy field::
+
+        sage: v = vector((AA(sqrt(2)), AA(sqrt(3))))
+        sage: a = angle(u, v)
+        Traceback (most recent call last):
+        ...
+        NotImplementedError: cannot recover a rational angle from these numerical results
+        sage: a = angle(u, v, numerical=True)
+        sage: a    # abs tol 1e-14
+        0.14102355421224375
+        sage: exp(2*pi.n()*CC(0,1)*a)
+        0.632455532033676 + 0.774596669241483*I
+        sage: v / v.norm()
+        (0.6324555320336758?, 0.774596669241484?)
+
+    """
+    import math
+
+    u0 = float(u[0])
+    u1 = float(u[1])
+    v0 = float(v[0])
+    v1 = float(v[1])
+
+    cos_uv = (u0 * v0 + u1 * v1) / math.sqrt((u0 * u0 + u1 * u1) * (v0 * v0 + v1 * v1))
+    if cos_uv < -1.0:
+        assert cos_uv > -1.0000001
+        cos_uv = -1.0
+    elif cos_uv > 1.0:
+        assert cos_uv < 1.0000001
+        cos_uv = 1.0
+
+    angle = acos(cos_uv, numerical=numerical)
+    return 1 - angle if u0 * v1 - u1 * v0 < 0 else angle
+
+
+def ccw(v, w):
+    r"""
+    Return a positive number if the turn from ``v`` to ``w`` is
+    counterclockwise, a negative number if it is clockwise, and zero if the two
+    vectors are collinear.
+
+    .. NOTE::
+
+        This function is sometimes also referred to as the wedge product or
+        simply the determinant. We chose the more customary name ``ccw`` from
+        computational geometry here.
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import ccw
+        sage: ccw((1, 0), (0, 1))
+        1
+        sage: ccw((1, 0), (-1, 0))
+        0
+        sage: ccw((1, 0), (0, -1))
+        -1
+        sage: ccw((1, 0), (1, 0))
+        0
+
+    """
+    return v[0] * w[1] - v[1] * w[0]
+
+
+def is_parallel(v, w):
+    r"""
+    Return whether the vectors ``v`` and ``w`` are parallel (but not
+    anti-parallel.)
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import is_parallel
+        sage: is_parallel((0, 1), (0, 1))
+        True
+        sage: is_parallel((0, 1), (0, 2))
+        True
+        sage: is_parallel((0, 1), (0, -2))
+        False
+        sage: is_parallel((0, 1), (0, 0))
+        False
+        sage: is_parallel((0, 1), (1, 0))
+        False
+
+    TESTS::
+
+        sage: V = QQ**2
+
+        sage: is_parallel(V((0,1)), V((0,2)))
+        True
+        sage: is_parallel(V((1,-1)), V((2,-2)))
+        True
+        sage: is_parallel(V((4,-2)), V((2,-1)))
+        True
+        sage: is_parallel(V((1,2)), V((2,4)))
+        True
+        sage: is_parallel(V((0,2)), V((0,1)))
+        True
+
+        sage: is_parallel(V((1,1)), V((1,2)))
+        False
+        sage: is_parallel(V((1,2)), V((2,1)))
+        False
+        sage: is_parallel(V((1,2)), V((1,-2)))
+        False
+        sage: is_parallel(V((1,2)), V((-1,-2)))
+        False
+        sage: is_parallel(V((2,-1)), V((-2,1)))
+        False
+
+    """
+    if ccw(v, w) != 0:
         # vectors are not collinear
         return False
 
-    return v[0] * w[0] + v[1] * w[1] > 0
+    return v[0] * w[0] + v[1] * w[1] > 0
+
+
+def is_anti_parallel(v, w):
+    r"""
+    Return whether the vectors ``v`` and ``w`` are anti-parallel, i.e., whether
+    ``v`` and ``-w`` are parallel.
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import is_anti_parallel
+        sage: V = QQ**2
+
+        sage: is_anti_parallel(V((0,1)), V((0,-2)))
+        True
+        sage: is_anti_parallel(V((1,-1)), V((-2,2)))
+        True
+        sage: is_anti_parallel(V((4,-2)), V((-2,1)))
+        True
+        sage: is_anti_parallel(V((-1,-2)), V((2,4)))
+        True
+
+        sage: is_anti_parallel(V((1,1)), V((1,2)))
+        False
+        sage: is_anti_parallel(V((1,2)), V((2,1)))
+        False
+        sage: is_anti_parallel(V((0,2)), V((0,1)))
+        False
+        sage: is_anti_parallel(V((1,2)), V((1,-2)))
+        False
+        sage: is_anti_parallel(V((1,2)), V((-1,2)))
+        False
+        sage: is_anti_parallel(V((2,-1)), V((-2,-1)))
+        False
+
+    """
+    return is_parallel(v, -w)
+
+
+def line_intersection(l, m):
+    r"""
+    Return the point of intersection between the lines ``l`` and ``m``. If the
+    lines do not have a single point of intersection, returns None.
+
+    INPUT:
+
+    - ``l`` -- a line in the plane given by two points (as vectors) in the plane
+
+    - ``m`` -- a line in the plane given by two points (as vectors) in the plane
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import line_intersection
+        sage: line_intersection((vector((-1, 0)), vector((1, 0))), (vector((0, -1)), vector((0, 1))))
+        (0, 0)
+
+    Parallel lines have no single point of intersection::
+
+        sage: line_intersection((vector((-1, 0)), vector((1, 0))), (vector((-1, 1)), vector((1, 1))))
+
+    Identical lines have no single point of intersection::
+
+        sage: line_intersection((vector((-1, 0)), vector((1, 0))), (vector((-2, 0)), vector((2, 0))))
+
+    """
+    Δl = l[1] - l[0]
+    Δm = m[1] - m[0]
+
+    # We solve the linear system determining the time when l[0] + t * Δl hits
+    # m, i.e., l[0] + t Δl == m[0] + s Δm.
+    a, b, c, d = Δl[0], -Δm[0], Δl[1], -Δm[1]
+    rhs = m[0] - l[0]
+
+    det = a * d - b * c
+    if det == 0:
+        # The lines are parallel
+        return None
+
+    # Solve the linear system. We only need t (and not s.)
+    t = (d * rhs[0] - b * rhs[1]) / det
+
+    return l[0] + t * Δl
+
+
+def time_on_ray(p, direction, q):
+    if direction[0]:
+        dim = 0
+    else:
+        dim = 1
+
+    delta = q[dim] - p[dim]
+    length = direction[dim]
+    if length < 0:
+        delta *= -1
+        length *= -1
+
+    return delta, length
+
+
+def time_on_segment(segment, p):
+    if p == segment[0]:
+        return 0
+    if not is_parallel(p - segment[0], segment[1] - segment[0]):
+        return None
+
+    delta, length = time_on_ray(segment[0], segment[1] - segment[0], p)
+    if delta > length:
+        return None
+    if delta < 0:
+        return None
+
+    return delta / length
+
+
+def ray_segment_intersection(p, direction, segment):
+    r"""
+    Return the intersection of the ray from ``p`` in ``direction`` with
+    ``segment``.
+
+    If the segment and the ray intersect in a point, return that point as a
+    vector.
+
+    If the segment and the ray overlap in a segment, return the end points of
+    that segment (in order.)
+
+    If the segment and the ray do not intersect, return ``None``.
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import ray_segment_intersection
+        sage: V = QQ**2
+
+        sage: ray_segment_intersection(V((0, 0)), V((1, 0)), (V((1, -1)), V((1, 1))))
+        (1, 0)
+        sage: ray_segment_intersection(V((0, 0)), V((1, 0)), (V((0, 0)), V((1, 0))))
+        ((0, 0), (1, 0))
+        sage: ray_segment_intersection(V((0, 0)), V((1, 0)), (V((1, 0)), V((2, 0))))
+        ((1, 0), (2, 0))
+        sage: ray_segment_intersection(V((0, 0)), V((1, 0)), (V((-1, 0)), V((1, 0))))
+        ((0, 0), (1, 0))
+        sage: ray_segment_intersection(V((0, 0)), V((1, 0)), (V((-1, -1)), V((-1, 1))))
+
+    TESTS::
+
+        sage: ray_segment_intersection(V((0, 0)), V((5, 1)), (V((3, 2)), V((3, 3))))
+
+    """
+    intersection = line_intersection((p, p + direction), segment)
+
+    if intersection is None:
+        # ray and segment are parallel.
+        if ccw(direction, segment[0] - p) != 0:
+            # ray and segment are not on the same line
+            return None
+
+        t0, length = time_on_ray(p, direction, segment[0])
+        t1, _ = time_on_ray(p, direction, segment[1])
+
+        if t1 < t0:
+            t0, t1 = t1, t0
+
+        if t1 < 0:
+            return None
+
+        if t1 == 0:
+            return p
+
+        t0 /= length
+        t1 /= length
+
+        if t0 < 0:
+            return (p, p + t1 * direction)
+
+        return (p + t0 * direction, p + t1 * direction)
+
+    if time_on_ray(p, direction, intersection)[0] < 0:
+        return None
+
+    if ccw(segment[0] - p, direction) * ccw(segment[1] - p, direction) > 0:
+        return None
+
+    return intersection
+
+
+def is_box_intersecting(b, c):
+    r"""
+    Return whether the (bounding) boxes ``b`` and ``c`` intersect.
+
+    INPUT:
+
+    - ``b`` -- a pair of corners
+    - ``c`` -- a pair of corners
+
+    OUTPUT:
+
+    - ``0`` -- do not intersect
+    - ``1`` -- intersect in a point
+    - ``2`` -- intersect in a segment
+    - ``3`` -- intersection has interior points
+
+    """
+
+    def normalize(b):
+        x_inverted = b[0][0] > b[1][0]
+        y_inverted = b[0][1] > b[1][1]
+
+        return (
+            (b[1][0] if x_inverted else b[0][0], b[1][1] if y_inverted else b[0][1]),
+            (b[0][0] if x_inverted else b[1][0], b[0][1] if y_inverted else b[1][1]),
+        )
+
+    b = normalize(b)
+    c = normalize(c)
+
+    if b[0][0] > c[1][0]:
+        return 0
+    if c[0][0] > b[1][0]:
+        return 0
+    if b[0][1] > c[1][1]:
+        return 0
+    if c[0][1] > b[1][1]:
+        return 0
+
+    # TODO: This is not the full algorithm yet.
+    return 3
+
+
+def is_segment_intersecting(s, t):
+    r"""
+    Return whether the segments ``s`` and ``t`` intersect.
+
+    INPUT:
+
+    - ``s`` -- a segment given as a pair of endpoints (given as vectors in the plane.)
+
+    - ``t`` -- a segment given as a pair of endpoints (given as vectors in the plane.)
+
+    OUTPUT:
+
+    - ``0`` - do not intersect
+    - ``1`` - exactly one endpoint in common
+    - ``2`` - non-trivial intersection
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import is_segment_intersecting
+        sage: is_segment_intersecting((vector((0, 0)), vector((1, 0))), (vector((0, 1)), vector((0, 3))))
+        0
+        sage: is_segment_intersecting((vector((0, 0)), vector((1, 0))), (vector((0, 0)), vector((0, 3))))
+        1
+        sage: is_segment_intersecting((vector((0, 0)), vector((1, 0))), (vector((0, -1)), vector((0, 3))))
+        2
+        sage: is_segment_intersecting((vector((-1, -1)), vector((1, 1))), (vector((0, 0)), vector((2, 2))))
+        2
+        sage: is_segment_intersecting((vector((-1, -1)), vector((1, 1))), (vector((1, 1)), vector((2, 2))))
+        1
+
+    """
+    if not is_box_intersecting(s, t):
+        return 0
+
+    Δs = s[1] - s[0]
+    turn_from_s = ccw(Δs, t[0] - s[0]) * ccw(Δs, t[1] - s[0])
+    if turn_from_s > 0:
+        # Both endpoints of t are on the same side of s
+        return 0
+
+    Δt = t[1] - t[0]
+    turn_from_t = ccw(Δt, s[0] - t[0]) * ccw(Δt, s[1] - t[0])
+    if turn_from_t > 0:
+        # Both endpoints of s are on the same side of t
+        return 0
+
+    if ccw(Δs, Δt) == 0:
+        # Segments are parallel
+        if is_anti_parallel(Δs, Δt):
+            # Ensure segments are oriented the same way.
+            t = (t[1], t[0])
+            Δt = -Δt
+
+        time_to_t0, length = time_on_ray(s[0], Δs, t[0])
+        time_to_t1, _ = time_on_ray(s[0], Δs, t[1])
+
+        if time_to_t0 < 0 and time_to_t1 < 0:
+            # Both endpoints of t are earlier than s
+            return 0
+
+        if time_to_t0 > length and time_to_t1 > length:
+            # Both endpoints of t are later than s
+            return 0
+
+        if time_to_t0 == length:
+            return 1
+
+        if time_to_t1 == 0:
+            return 1
+
+        return 2
+
+    if turn_from_t == 0 and turn_from_s == 0:
+        return 1
+
+    return 2
+
+
+def is_between(begin, end, v):
+    r"""
+    Check whether the vector ``v`` is strictly in the sector formed by the vectors
+    ``begin`` and ``end`` (in counter-clockwise order).
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import is_between
+        sage: is_between((1, 0), (1, 1), (2, 1))
+        True
+
+        sage: from itertools import product
+        sage: vecs = [(1, 0), (1, 1), (0, 1), (-1, 1), (-1, 0), (-1, -1), (0, -1), (1, -1)]
+        sage: for (i, vi), (j, vj), (k, vk) in product(enumerate(vecs), repeat=3):
+        ....:     assert is_between(vi, vj, vk) == ((i == j and i != k) or i < k < j or k < j < i or j < i < k), ((i, vi), (j, vj), (k, vk))
+    """
+    if begin[0] * end[1] > end[0] * begin[1]:
+        # positive determinant
+        # [ begin[0] end[0] ]^-1 = [ end[1] -end[0] ]
+        # [ begin[1] end[1] ]      [-begin[1]  begin[0] ]
+        # v[0] * end[1] - end[0] * v[1] > 0
+        # - v[0] * begin[1] + begin[0] * v[1] > 0
+        return end[1] * v[0] > end[0] * v[1] and begin[0] * v[1] > begin[1] * v[0]
+    elif begin[0] * end[1] == end[0] * begin[1]:
+        # aligned vector
+        if begin[0] * end[0] >= 0 and begin[1] * end[1] >= 0:
+            return (
+                v[0] * begin[1] != v[1] * begin[0]
+                or v[0] * begin[0] < 0
+                or v[1] * begin[1] < 0
+            )
+        else:
+            return begin[0] * v[1] > begin[1] * v[0]
+    else:
+        # negative determinant
+        # [ end[0] begin[0] ]^-1 = [ begin[1] -begin[0] ]
+        # [ end[1] begin[1] ]      [-end[1]  end[0] ]
+        # v[0] * begin[1] - begin[0] * v[1] > 0
+        # - v[0] * end[1] + end[0] * v[1] > 0
+        return begin[1] * v[0] < begin[0] * v[1] or end[0] * v[1] < end[1] * v[0]
+
+
+def solve(x, u, y, v):
+    r"""
+    Return (a,b) so that: x + au = y + bv
+
+    INPUT:
+
+    - ``x``, ``u``, ``y``, ``v`` -- two dimensional vectors
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import solve
+        sage: K.<sqrt2> = NumberField(x^2 - 2, embedding=AA(2).sqrt())
+        sage: V = VectorSpace(K,2)
+        sage: x = V((1,-sqrt2))
+        sage: y = V((1,1))
+        sage: a = V((0,1))
+        sage: b = V((-sqrt2, sqrt2+1))
+        sage: u = V((0,1))
+        sage: v = V((-sqrt2, sqrt2+1))
+        sage: a, b = solve(x,u,y,v)
+        sage: x + a*u == y + b*v
+        True
+
+        sage: u = V((1,1))
+        sage: v = V((1,sqrt2))
+        sage: a, b = solve(x,u,y,v)
+        sage: x + a*u == y + b*v
+        True
+
+    """
+    d = -u[0] * v[1] + u[1] * v[0]
+    if d.is_zero():
+        raise ValueError("parallel vectors")
+    a = v[1] * (x[0] - y[0]) + v[0] * (y[1] - x[1])
+    b = u[1] * (x[0] - y[0]) + u[0] * (y[1] - x[1])
+    return (a / d, b / d)
+
+
+def projectivization(x, y, signed=True, denominator=None):
+    r"""
+    Return a simplified version of the projective coordinate [x: y].
+
+    If ``signed`` (the default), the second coordinate is made non-negative;
+    otherwise the coordinates keep their signs.
+
+    If ``denominator`` is ``False``, returns [x/y: 1] up to sign. Otherwise,
+    the returned coordinates have no denominator and no non-unit gcd.
+
+    TESTS::
+
+        sage: from flatsurf.geometry.euclidean import projectivization
+
+        sage: projectivization(2/3, -3/5, signed=True, denominator=True)
+        (10, -9)
+        sage: projectivization(2/3, -3/5, signed=False, denominator=True)
+        (-10, 9)
+        sage: projectivization(2/3, -3/5, signed=True, denominator=False)
+        (10/9, -1)
+        sage: projectivization(2/3, -3/5, signed=False, denominator=False)
+        (-10/9, 1)
+
+        sage: projectivization(-1/2, 0, signed=True, denominator=True)
+        (-1, 0)
+        sage: projectivization(-1/2, 0, signed=False, denominator=True)
+        (1, 0)
+        sage: projectivization(-1/2, 0, signed=True, denominator=False)
+        (-1, 0)
+        sage: projectivization(-1/2, 0, signed=False, denominator=False)
+        (1, 0)
+
+    """
+    from sage.all import Sequence
+
+    parent = Sequence([x, y]).universe()
+    if y:
+        z = x / y
+        if denominator is True or (denominator is None and hasattr(z, "denominator")):
+            d = parent(z.denominator())
+        else:
+            d = parent(1)
+        if signed and y < 0:
+            d *= -1
+        return (z * d, d)
+    elif signed and x < 0:
+        return (parent(-1), parent(0))
+    else:
+        return (parent(1), parent(0))
+
+
+def slope(a, rotate=1):
+    r"""
+    Return either ``1`` (positive slope) or ``-1`` (negative slope).
+
+    If ``rotate`` is set to 1 then consider the edge as if it was rotated counterclockwise
+    infinitesimally.
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import slope
+        sage: slope((1, 1))
+        1
+        sage: slope((-1, 1))
+        -1
+        sage: slope((-1, -1))
+        1
+        sage: slope((1, -1))
+        -1
+
+        sage: slope((1, 0))
+        1
+        sage: slope((0, 1))
+        -1
+        sage: slope((-1, 0))
+        1
+        sage: slope((0, -1))
+        -1
+
+        sage: slope((1, 0), rotate=-1)
+        -1
+        sage: slope((0, 1), rotate=-1)
+        1
+        sage: slope((-1, 0), rotate=-1)
+        -1
+        sage: slope((0, -1), rotate=-1)
+        1
+
+        sage: slope((1, 0), rotate=0)
+        0
+        sage: slope((0, 1), rotate=0)
+        0
+        sage: slope((-1, 0), rotate=0)
+        0
+        sage: slope((0, -1), rotate=0)
+        0
+
+        sage: slope((0, 0))
+        Traceback (most recent call last):
+        ...
+        ValueError: zero vector
+    """
+    x, y = a
+    if not x and not y:
+        raise ValueError("zero vector")
+    if (x > 0 and y > 0) or (x < 0 and y < 0):
+        return 1
+    elif (x > 0 and y < 0) or (x < 0 and y > 0):
+        return -1
+    if rotate == 0:
+        return 0
+    if rotate == 1:
+        return 1 if x else -1
+    if rotate == -1:
+        return 1 if y else -1
+    raise ValueError("invalid argument rotate={}".format(rotate))
+
+
+class OrientedSegment:
+    r"""
+    A segment in the Euclidean plane with an explicit orientation.
+
+    .. NOTE::
+
+        SageMath provides polyhedra that implement more functionality than this
+        object. However, these polyhedra are notoriously slow for computations
+        in the Euclidean plane since they were (apparently) never optimized for
+        computations in low dimensions.
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import OrientedSegment
+        sage: S = OrientedSegment((0, 0), (1, 1))
+        sage: S
+        OrientedSegment((0, 0), (1, 1))
+
+    """
+
+    def __init__(self, a, b):
+        # TODO: Force a common base ring.
+        from sage.all import vector
+
+        self._a = vector(a, immutable=True)
+        self._b = vector(b, immutable=True)
+
+    def __hash__(self):
+        r"""
+        Return a hash value for this segment that is compatible with equality.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: hash(S) == hash(S)
+            True
+
+        """
+        return hash((self._a, self._b))
+
+    def __eq__(self, other):
+        r"""
+        Return whether this segment is indistinguishable from ``other``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: S == S
+            True
+
+        """
+        if not isinstance(other, OrientedSegment):
+            return False
+        return self._a == other._a and self._b == other._b
+
+    def __ne__(self, other):
+        return not (self == other)
+
+    def __repr__(self):
+        return f"OrientedSegment({self._a}, {self._b})"
+
+    def as_polyhedron(self):
+        r"""
+        Return this segment as a SageMath polyhedron.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: S.as_polyhedron()
+            A 1-dimensional polyhedron in ZZ^2 defined as the convex hull of 2 vertices
+
+        """
+        from sage.all import Polyhedron
+
+        return Polyhedron(vertices=[self._a, self._b])
+
+    def _parametrize(self, w):
+        r"""
+        Return a t such that `w = a + t (b-a)`.
+
+        Return ``None`` if no such ``t`` exists.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: S._parametrize((0, 0))
+            0
+            sage: S._parametrize((1, 1))
+            1
+            sage: S._parametrize((0, 1))
+
+        """
+        from sage.all import vector
+
+        w = vector(w)
+
+        v = self._b - self._a
+        wa = w - self._a
+        if v[0]:
+            t = wa[0] / v[0]
+        else:
+            t = wa[1] / v[1]
+
+        if self._a + t * v != w:
+            return None
+
+        return t
+
+    def is_subset(self, other):
+        r"""
+        Return whether this segment is a subset of ``other``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: S.is_subset(S)
+            True
+
+        """
+        if isinstance(other, OrientedSegment):
+            s = other._parametrize(self._a)
+            if s is None:
+                return False
+
+            t = other._parametrize(self._b)
+            if t is None:
+                return False
+
+            return 0 <= s <= 1 and 0 <= t <= 1
+        else:
+            raise NotImplementedError
+
+    def contains_point(self, point):
+        r"""
+        Return whether this segment contains ``point``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: S.contains_point((0, 0))
+            True
+            sage: S.contains_point((-1, -1))
+            False
+            sage: S.contains_point((-1, 0))
+            False
+
+        """
+        t = self._parametrize(point)
+        if t is None:
+            return False
+        return 0 <= t <= 1
+
+    def left_half_space(self):
+        r"""
+        Return the half space to the left of the line extending this segment.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: S.left_half_space()
+            {-x + y ≥ 0}
+
+        """
+        v = self._b - self._a
+
+        return HalfSpace(v[1] * self._a[0] - v[0] * self._a[1], -v[1], v[0])
+
+    def translate(self, delta):
+        r"""
+        Return this segment translated by the vector ``delta``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: S.translate((1, 2))
+            OrientedSegment((1, 2), (2, 3))
+
+        """
+        from sage.all import vector
+
+        delta = vector(delta)
+
+        return OrientedSegment(self._a + delta, self._b + delta)
+
+    def plot(self, **kwargs):
+        r"""
+        Return a graphical representation of this segment.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: S.plot()
+            Graphics object consisting of 2 graphics primitives
+
+        """
+        return self.as_polyhedron().plot(**kwargs)
+
+    def __neg__(self):
+        r"""
+        Return this segment with the endpoints swapped.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: -S
+            OrientedSegment((1, 1), (0, 0))
+
+        """
+        return OrientedSegment(self._b, self._a)
+
+    def intersection(self, other):
+        r"""
+        Return the intersection of this segment and ``other``.
+
+        Return ``None`` if the objects do not intersect.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: S.intersection(S) == S
+            True
+
+            sage: T = OrientedSegment((0, 0), (2, 1))
+            sage: S.intersection(T)
+            (0, 0)
+
+        """
+        # TODO: Test that everything can intersect with everything else and that the orientation of self is preserved in the intersection.
+        # TODO: Deprecate intersection functions (and everything else) defined at the top of this file.
+        if isinstance(other, OrientedSegment):
+            # TODO: Rewrite using line intersection.
+            intersecting = is_segment_intersecting(
+                (self._a, self._b), (other._a, other._b)
+            )
+            if not intersecting:
+                return None
+
+            intersection = line_intersection((self._a, self._b), (other._a, other._b))
+            if intersection is not None:
+                return intersection
+
+            s = self._parametrize(other._a)
+            t = self._parametrize(other._b)
+
+            if t < s:
+                s, t = t, s
+
+            if s < 0:
+                s = 0
+
+            if t > 1:
+                t = 1
+
+            assert 0 <= s < t <= 1
+
+            v = self._b - self._a
+            return OrientedSegment(self._a + s * v, self._a + t * v)
+        else:
+            raise NotImplementedError("cannot intersect a segment with this object yet")
+
+    def start(self):
+        r"""
+        Return the starting endpoint of this segment.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: S.start()
+            (0, 0)
+
+        """
+        return self._a
+
+    def end(self):
+        r"""
+        Return the final endpoint of this segment.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: S.end()
+            (1, 1)
+
+        """
+        return self._b
+
+    def line(self):
+        r"""
+        Return a line containing this segment.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: S.line()
+            {-x + y = 0}
+
+        """
+        return Ray(self.start(), self.end() - self.start()).line()
+
+    def midpoint(self):
+        r"""
+        Return the barycenter of this segment.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: S = OrientedSegment((0, 0), (1, 1))
+            sage: S.midpoint()
+            (1/2, 1/2)
+
+        """
+        from sage.all import vector
+
+        return vector((self.start() + self.end()) / 2, immutable=True)
+
+
+class HalfSpace:
+    r"""
+    The half plane in the Euclidean plane given by `bx + cy + a ≥ 0`.
+
+    .. NOTE::
+
+        SageMath provides polyhedra that implement more functionality than this
+        object. However, these polyhedra are notoriously slow for computations
+        in the Euclidean plane since they were (apparently) never optimized for
+        computations in low dimensions.
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import HalfSpace
+        sage: H = HalfSpace(1, 2, 3)
+        sage: H
+        {2 * x + 3 * y ≥ -1}
+
+    """
+
+    def __init__(self, a, b, c):
+        # TODO: Force a common base ring.
+        if not b and not c:
+            raise ValueError("b and c must not both be zero")
+
+        self._a = a
+        self._b = b
+        self._c = c
+
+    def __hash__(self):
+        r"""
+        Return a hash value for this half space that is compatible with equality.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import HalfSpace
+            sage: H = HalfSpace(0, 1, 2)
+            sage: hash(H) == hash(H)
+            True
+            sage: H = HalfSpace(1, 0, 2)
+            sage: hash(H) == hash(H)
+            True
+            sage: H = HalfSpace(1, 2, 0)
+            sage: hash(H) == hash(H)
+            True
+
+        """
+        if self._a:
+            return hash((self._b / self._a, self._c / self._a))
+        return hash((self._a / self._b, self._c / self._b))
+
+    def __eq__(self, other):
+        r"""
+        Return whether this half space is indistinguishable from ``other``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import HalfSpace
+            sage: H = HalfSpace(0, 1, 2)
+            sage: H == H
+            True
+
+            sage: G = HalfSpace(0, 2, 3)
+            sage: H == G
+            False
+
+            sage: G = HalfSpace(0, 2, 4)
+            sage: H == G
+            True
+
+        """
+        if not isinstance(other, HalfSpace):
+            return False
+
+        return (
+            self._a * other._c == other._a * self._c
+            and self._b * other._c == other._b * self._c
+        )
+
+    def __ne__(self, other):
+        return not (self == other)
+
+    def __repr__(self):
+        def render_term(coefficient, variable):
+            if not coefficient:
+                return ""
+            if coefficient == 1:
+                return variable
+            if coefficient == -1:
+                return f"-{variable}"
+            coefficient = repr(coefficient)
+            if "+" in coefficient or "-" in coefficient:
+                coefficient = f"({coefficient})"
+            return f"{coefficient} * {variable}"
+
+        lhs = [render_term(self._b, "x"), render_term(self._c, "y")]
+        lhs = [term for term in lhs if term]
+
+        lhs = (" + " if self._c >= 0 else " - ").join(lhs)
+
+        return f"{{{lhs} ≥ {-self._a}}}"
+
+    def as_polyhedron(self):
+        r"""
+        Return this half space as a SageMath polyhedron.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import HalfSpace
+            sage: H = HalfSpace(0, 1, 2)
+            sage: H.as_polyhedron()
+            A 2-dimensional polyhedron in QQ^2 defined as the convex hull of 1 vertex, 1 ray, 1 line
+
+        """
+        from sage.all import Polyhedron
+
+        return Polyhedron(ieqs=[(self._a, self._b, self._c)])
+
+    @staticmethod
+    def compact_intersection(*half_spaces):
+        r"""
+        Return the intersection of the ``half_spaces`` as the set of segments
+        on the boundary of the intersection (in no particular order but
+        oriented such that the intersection is on the left of each segment.)
+
+        Return ``None`` if the intersection is empty.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import HalfSpace
+            sage: HalfSpace.compact_intersection(
+            ....:     HalfSpace(1, 1, 0),
+            ....:     HalfSpace(1, -1, 0),
+            ....:     HalfSpace(1, 0, 1),
+            ....:     HalfSpace(1, 0, -1))
+            [OrientedSegment((-1, -1), (1, -1)),
+             OrientedSegment((-1, 1), (-1, -1)),
+             OrientedSegment((1, 1), (-1, 1)),
+             OrientedSegment((1, -1), (1, 1))]
+
+        """
+        from sage.all import Polyhedron
+
+        intersection = Polyhedron(ieqs=[(h._a, h._b, h._c) for h in half_spaces])
+
+        if intersection.is_empty():
+            return None
+
+        if not intersection.is_compact():
+            raise ValueError("half_spaces must have a bounded intersection")
+
+        interior_point = intersection.interior().an_element()
+
+        segments = [segment.as_polyhedron() for segment in intersection.faces(1)]
+
+        segments = [
+            OrientedSegment(*[vertex.vector() for vertex in segment.vertices()])
+            for segment in segments
+        ]
+
+        # Orient segments such that the interior is on the left hand side.
+        segments = [
+            segment
+            if segment.left_half_space().contains_point(interior_point)
+            else -segment
+            for segment in segments
+        ]
+
+        return segments
+
+    def contains_point(self, point):
+        r"""
+        Return whether the ``point`` is in this set.
 
+        EXAMPLES::
 
-def is_anti_parallel(v, w):
+            sage: from flatsurf.geometry.euclidean import HalfSpace
+            sage: H = HalfSpace(0, 1, 2)
+            sage: H.contains_point((0, 0))
+            True
+
+        """
+        return point[0] * self._b + point[1] * self._c + self._a >= 0
+
+
+class OrientedLine:
     r"""
-    Return whether the vectors ``v`` and ``w`` are anti-parallel, i.e., whether
-    ``v`` and ``-w`` are parallel.
+    The line in the Euclidean plane satisfying `bx + cy + a = 0` oriented such
+    that `bx + cy + a ≥ 0` is to the left of the line.
 
-    EXAMPLES::
+    .. NOTE::
 
-        sage: from flatsurf.geometry.euclidean import is_anti_parallel
-        sage: V = QQ**2
+        SageMath provides polyhedra that implement more functionality than this
+        object. However, these polyhedra are notoriously slow for computations
+        in the Euclidean plane since they were (apparently) never optimized for
+        computations in low dimensions.
 
-        sage: is_anti_parallel(V((0,1)), V((0,-2)))
-        True
-        sage: is_anti_parallel(V((1,-1)), V((-2,2)))
-        True
-        sage: is_anti_parallel(V((4,-2)), V((-2,1)))
-        True
-        sage: is_anti_parallel(V((-1,-2)), V((2,4)))
-        True
+    EXAMPLES::
 
-        sage: is_anti_parallel(V((1,1)), V((1,2)))
-        False
-        sage: is_anti_parallel(V((1,2)), V((2,1)))
-        False
-        sage: is_anti_parallel(V((0,2)), V((0,1)))
-        False
-        sage: is_anti_parallel(V((1,2)), V((1,-2)))
-        False
-        sage: is_anti_parallel(V((1,2)), V((-1,2)))
-        False
-        sage: is_anti_parallel(V((2,-1)), V((-2,-1)))
-        False
+        sage: from flatsurf.geometry.euclidean import OrientedLine
+        sage: L = OrientedLine(1, 2, 3)
+        sage: L
+        {2 * x + 3 * y = -1}
 
     """
-    return is_parallel(v, -w)
 
+    def __init__(self, a, b, c):
+        self._half_space = HalfSpace(a, b, c)
 
-def line_intersection(p1, p2, q1, q2):
-    r"""
-    Return the point of intersection between the line joining p1 to p2
-    and the line joining q1 to q2. If the lines do not have a single point of
-    intersection, we return None. Here p1, p2, q1 and q2 should be vectors in
-    the plane.
-    """
-    if ccw(p2 - p1, q2 - q1) == 0:
-        return None
+    def __repr__(self):
+        return repr(self._half_space).replace("≥", "=")
+
+    def __hash__(self):
+        return hash(self._half_space)
+
+    def __eq__(self, other):
+        if not isinstance(other, OrientedLine):
+            return False
+        return self._half_space == other._half_space
+
+    def __ne__(self, other):
+        return not (self == other)
+
+    def as_polyhedron(self):
+        r"""
+        Return this line as a SageMath polyhedron.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedLine
+            sage: L = OrientedLine(1, 2, 3)
+            sage: L.as_polyhedron()
+            A 1-dimensional polyhedron in QQ^2 defined as the convex hull of 1 vertex and 1 line
+
+        """
+        return self._half_space.as_polyhedron().faces(1)[0].as_polyhedron()
+
+    def __neg__(self):
+        return OrientedLine(
+            -self._half_space._a, -self._half_space._b, -self._half_space._c
+        )
+
+    def intersection(self, other):
+        r"""
+        Return the intersection of this line with the object ``other``.
+
+        Return ``None`` if they do not intersect.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedLine
+            sage: L = OrientedLine(1, 2, 3)
+            sage: M = OrientedLine(1, 2, 4)
+            sage: L.intersection(M)
+            (-1/2, 0)
+
+        """
+        if isinstance(other, OrientedLine):
+            if self == other:
+                return self
+            if self == -other:
+                return self
+            return line_intersection(self._points(), other._points())
+        if isinstance(other, OrientedSegment):
+            intersection = self.intersection(other.line())
+            if intersection is None:
+                return None
+            if intersection == self:
+                return other
+            if other.contains_point(intersection):
+                return intersection
+            return None
+        raise NotImplementedError("cannot intersect a line with this object yet")
+
+    def contains_point(self, point):
+        r"""
+        Return whether ``point`` is on this line.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import OrientedLine
+            sage: L = OrientedLine(1, 2, 3)
+            sage: L.contains_point((-1/2, 0))
+            True
+
+        """
+        return (
+            self._half_space._a
+            + point[0] * self._half_space._b
+            + point[1] * self._half_space._c
+            == 0
+        )
 
-    # Since the wedge product is non-zero, the following is invertible:
-    from sage.all import matrix
+    def _points(self):
+        r"""
+        Return a pair of points on this line.
 
-    m = matrix([[p2[0] - p1[0], q1[0] - q2[0]], [p2[1] - p1[1], q1[1] - q2[1]]])
-    return p1 + (m.inverse() * (q1 - p1))[0] * (p2 - p1)
+        EXAMPLES::
 
+            sage: from flatsurf.geometry.euclidean import OrientedLine
+            sage: L = OrientedLine(1, 2, 3)
+            sage: L._points()
+            ((0, -1/3), (1, -1))
 
-def is_segment_intersecting(e1, e2):
+        """
+        a, b, c = self._half_space._a, self._half_space._b, self._half_space._c
+
+        from sage.all import vector
+
+        if not c:
+            assert b
+            return vector((-a / b, 0)), vector((-a / b, 1))
+
+        return vector((0, -a / c)), vector((1, (-a - b) / c))
+
+
+# TODO: There is also another Ray from the origin in ray.py
+class Ray:
     r"""
-    Return whether the segments ``e1`` and ``e2`` intersect.
+    The infinite ray in the Euclidean plane from a finite point towards a direction.
 
-    OUTPUT:
+    .. NOTE::
 
-    - ``0`` - do not intersect
-    - ``1`` - one endpoint in common
-    - ``2`` - non-trivial intersection
+        SageMath provides polyhedra that implement more functionality than this
+        object. However, these polyhedra are notoriously slow for computations
+        in the Euclidean plane since they were (apparently) never optimized for
+        computations in low dimensions.
 
     EXAMPLES::
 
-        sage: from flatsurf.geometry.euclidean import is_segment_intersecting
-        sage: is_segment_intersecting(((0,0),(1,0)),((0,1),(0,3)))
-        0
-        sage: is_segment_intersecting(((0,0),(1,0)),((0,0),(0,3)))
-        1
-        sage: is_segment_intersecting(((0,0),(1,0)),((0,-1),(0,3)))
-        2
-        sage: is_segment_intersecting(((-1,-1),(1,1)),((0,0),(2,2)))
-        2
-        sage: is_segment_intersecting(((-1,-1),(1,1)),((1,1),(2,2)))
-        1
+        sage: from flatsurf.geometry.euclidean import Ray
+        sage: R = Ray((0, 0), (-1, 0))
+        sage: R
+        (0, 0) + λ (-1, 0)
 
     """
-    if e1[0] == e1[1] or e2[0] == e2[1]:
-        raise ValueError("degenerate segments")
 
-    elts = [e[i][j] for e in (e1, e2) for i in (0, 1) for j in (0, 1)]
+    def __init__(self, point, direction):
+        from sage.all import vector
+
+        self._point = vector(point, immutable=True)
+        self._direction = vector(direction, immutable=True)
+
+    def _normalized_direction(self):
+        r"""
+        Return the direction of this ray, normalized up to scaling.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import Ray
+            sage: R = Ray((0, 0), (-2, 0))
+            sage: R._normalized_direction()
+            (-1, 0)
+            sage: R = Ray((0, 0), (2, 0))
+            sage: R._normalized_direction()
+            (1, 0)
+            sage: R = Ray((0, 0), (-2, 3))
+            sage: R._normalized_direction()
+            (-2/3, 1)
+            sage: R = Ray((0, 0), (2, -3))
+            sage: R._normalized_direction()
+            (2/3, -1)
+
+        """
+        from sage.all import vector, sgn
+
+        if not self._direction[1]:
+            return vector((sgn(self._direction[0]), 0), immutable=True)
+
+        return vector(
+            (
+                sgn(self._direction[0]) * abs(self._direction[0] / self._direction[1]),
+                sgn(self._direction[1]),
+            ),
+            immutable=True,
+        )
 
-    from sage.structure.element import get_coercion_model
+    def __repr__(self):
+        return f"{self._point} + λ {self._normalized_direction()}"
 
-    cm = get_coercion_model()
+    def __hash__(self):
+        return hash((self._point, self._normalized_direction()))
 
-    base_ring = cm.common_parent(*elts)
-    if isinstance(base_ring, type):
-        from sage.structure.coerce import py_scalar_parent
+    def __eq__(self, other):
+        r"""
+        Return whether this ray is indistinguishable from ``other``.
+        """
+        if not isinstance(other, Ray):
+            return False
+        return (
+            self._point == other._point
+            and self._normalized_direction() == other._normalized_direction()
+        )
 
-        base_ring = py_scalar_parent(base_ring)
+    def __ne__(self, other):
+        return not (self == other)
 
-    from sage.all import matrix
+    def as_polyhedron(self):
+        r"""
+        Return a representation of this ray as a SageMath polyhedron.
 
-    m = matrix(base_ring, 3)
-    xs1, ys1 = map(base_ring, e1[0])
-    xt1, yt1 = map(base_ring, e1[1])
-    xs2, ys2 = map(base_ring, e2[0])
-    xt2, yt2 = map(base_ring, e2[1])
+        EXAMPLES::
 
-    m[0] = [xs1, ys1, 1]
-    m[1] = [xt1, yt1, 1]
-    m[2] = [xs2, ys2, 1]
-    s0 = m.det()
-    m[2] = [xt2, yt2, 1]
-    s1 = m.det()
-    if (s0 > 0 and s1 > 0) or (s0 < 0 and s1 < 0):
-        # e2 stands on one side of the line generated by e1
-        return 0
+            sage: from flatsurf.geometry.euclidean import Ray
+            sage: R = Ray((0, 0), (-2, 0))
+            sage: R.as_polyhedron()
+            A 1-dimensional polyhedron in ZZ^2 defined as the convex hull of 1 vertex and 1 ray
 
-    m[0] = [xs2, ys2, 1]
-    m[1] = [xt2, yt2, 1]
-    m[2] = [xs1, ys1, 1]
-    s2 = m.det()
-    m[2] = [xt1, yt1, 1]
-    s3 = m.det()
-    if (s2 > 0 and s3 > 0) or (s2 < 0 and s3 < 0):
-        # e1 stands on one side of the line generated by e2
-        return 0
+        """
+        from sage.all import Polyhedron
 
-    if s0 == 0 and s1 == 0:
-        assert s2 == 0 and s3 == 0
-        if xt1 < xs1 or (xt1 == xs1 and yt1 < ys1):
-            xs1, xt1 = xt1, xs1
-            ys1, yt1 = yt1, ys1
-        if xt2 < xs2 or (xt2 == xs2 and yt2 < ys2):
-            xs2, xt2 = xt2, xs2
-            ys2, yt2 = yt2, ys2
+        return Polyhedron(vertices=[self._point], rays=[self._direction])
 
-        if xs1 == xt1 == xs2 == xt2:
-            xs1, xt1, xs2, xt2 = ys1, yt1, ys2, yt2
+    def contains_point(self, point):
+        r"""
+        Return whether the Euclidean ``point`` is contained in this ray.
 
-        assert xs1 < xt1 and xs2 < xt2, (xs1, xt1, xs2, xt2)
+        EXAMPLES::
 
-        if (xs2 > xt1) or (xt2 < xs1):
-            return 0  # no intersection
-        elif (xs2 == xt1) or (xt2 == xs1):
-            return 1  # one endpoint in common
-        else:
-            assert (
-                xs1 <= xs2 < xt1
-                or xs1 < xt2 <= xt1
-                or (xs2 < xs1 and xt2 > xt1)
-                or (xs2 > xs1 and xt2 < xt1)
-            ), (xs1, xt1, xs2, xt2)
-            return 2  # one dimensional
-
-    elif s0 == 0 or s1 == 0:
-        # treat alignment here
-        if s2 == 0 or s3 == 0:
-            return 1  # one endpoint in common
-        else:
-            return 2  # intersection in the middle
+            sage: from flatsurf.geometry.euclidean import Ray
+            sage: R = Ray((0, 0), (-2, 0))
+            sage: R.contains_point((0, 0))
+            True
+            sage: R.contains_point((-2, 0))
+            True
+            sage: R.contains_point((2, 0))
+            False
 
-    return 2  # middle intersection
+        """
+        t = OrientedSegment(self._point, self._point + self._direction)._parametrize(
+            point
+        )
+        if t is None:
+            return False
+        return t >= 0
+
+    def intersection(self, other):
+        r"""
+        Return the intersection of this ray with the object ``other``.
+
+        Return ``None`` if the objects do not intersect.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import Ray, OrientedSegment
+            sage: R = Ray((0, 0), (-2, 0))
+            sage: R.intersection(OrientedSegment((1, 0), (-2, 0)))
+            OrientedSegment((0, 0), (-2, 0))
+            sage: R.intersection(OrientedSegment((-1, 0), (-2, 1)))
+            (-1, 0)
+            sage: R.intersection(OrientedSegment((-1, 1), (-2, -1)))
+            (-3/2, 0)
+
+        """
+        if isinstance(other, OrientedSegment):
+            line_intersection = self.line().intersection(other)
+            if line_intersection is None:
+                return None
+            if isinstance(line_intersection, OrientedSegment):
+                s = self._parametrize(line_intersection.start())
+                t = self._parametrize(line_intersection.end())
+                if s > t:
+                    s, t = t, s
+                if t < 0:
+                    return None
+                s = max(s, 0)
+                if s == t:
+                    from sage.all import vector
+
+                    return vector(self._point + s * self._direction)
+                return OrientedSegment(
+                    self._point + s * self._direction, self._point + t * self._direction
+                )
+            if self.contains_point(line_intersection):
+                return line_intersection
+            return None
+
+        raise NotImplementedError("cannot intersect a ray with this object yet")
+
+    def _parametrize(self, point):
+        return OrientedSegment(self._point, self._point + self._direction)._parametrize(
+            point
+        )
 
+    def line(self):
+        r"""
+        Return the oriented line containing this ray.
 
-def is_between(e0, e1, f):
-    r"""
-    Check whether the vector ``f`` is strictly in the sector formed by the vectors
-    ``e0`` and ``e1`` (in counter-clockwise order).
+        EXAMPLES::
 
-    EXAMPLES::
+            sage: from flatsurf.geometry.euclidean import Ray, OrientedSegment
+            sage: R = Ray((0, 0), (-2, 0))
+            sage: R.line()
+            {(-2) * y = 0}
 
-        sage: from flatsurf.geometry.euclidean import is_between
-        sage: is_between((1, 0), (1, 1), (2, 1))
-        True
+        """
+        b = -self._direction[1]
+        c = self._direction[0]
+        a = -(b * self._point[0] + c * self._point[1])
+        return OrientedLine(a, b, c)
 
-        sage: from itertools import product
-        sage: vecs = [(1, 0), (1, 1), (0, 1), (-1, 1), (-1, 0), (-1, -1), (0, -1), (1, -1)]
-        sage: for (i, vi), (j, vj), (k, vk) in product(enumerate(vecs), repeat=3):
-        ....:     assert is_between(vi, vj, vk) == ((i == j and i != k) or i < k < j or k < j < i or j < i < k), ((i, vi), (j, vj), (k, vk))
-    """
-    if e0[0] * e1[1] > e1[0] * e0[1]:
-        # positive determinant
-        # [ e0[0] e1[0] ]^-1 = [ e1[1] -e1[0] ]
-        # [ e0[1] e1[1] ]      [-e0[1]  e0[0] ]
-        # f[0] * e1[1] - e1[0] * f[1] > 0
-        # - f[0] * e0[1] + e0[0] * f[1] > 0
-        return e1[1] * f[0] > e1[0] * f[1] and e0[0] * f[1] > e0[1] * f[0]
-    elif e0[0] * e1[1] == e1[0] * e0[1]:
-        # aligned vector
-        if e0[0] * e1[0] >= 0 and e0[1] * e1[1] >= 0:
-            return f[0] * e0[1] != f[1] * e0[0] or f[0] * e0[0] < 0 or f[1] * e0[1] < 0
-        else:
-            return e0[0] * f[1] > e0[1] * f[0]
-    else:
-        # negative determinant
-        # [ e1[0] e0[0] ]^-1 = [ e0[1] -e0[0] ]
-        # [ e1[1] e0[1] ]      [-e1[1]  e1[0] ]
-        # f[0] * e0[1] - e0[0] * f[1] > 0
-        # - f[0] * e1[1] + e1[0] * f[1] > 0
-        return e0[1] * f[0] < e0[0] * f[1] or e1[0] * f[1] < e1[1] * f[0]
 
+class EuclideanDistance_base(Element):
+    def _acted_upon_(self, other, self_on_left):
+        return self.scale(other)
 
-def solve(x, u, y, v):
-    r"""
-    Return (a,b) so that: x + au = y + bv
+    def scale(self, scalar):
+        scalar = self.parent().base_ring()(scalar)
 
-    INPUT:
+        if scalar < 0:
+            raise ValueError("cannot scale a distance by a negative scalar")
 
-    - ``x``, ``u``, ``y``, ``v`` -- two dimensional vectors
+        return self._scale(scalar)
 
-    EXAMPLES::
+    def _richcmp_(self, other, op):
+        from sage.structure.richcmp import op_EQ, op_NE, op_LE, op_LT, op_GE, op_GT
 
-        sage: from flatsurf.geometry.euclidean import solve
-        sage: K.<sqrt2> = NumberField(x^2 - 2, embedding=AA(2).sqrt())
-        sage: V = VectorSpace(K,2)
-        sage: x = V((1,-sqrt2))
-        sage: y = V((1,1))
-        sage: a = V((0,1))
-        sage: b = V((-sqrt2, sqrt2+1))
-        sage: u = V((0,1))
-        sage: v = V((-sqrt2, sqrt2+1))
-        sage: a, b = solve(x,u,y,v)
-        sage: x + a*u == y + b*v
-        True
+        if op == op_LT:
+            return self <= other and not (self >= other)
+        if op == op_LE:
+            return self._le_(other)
+        if op == op_EQ:
+            return self._eq_(other)
+        if op == op_NE:
+            return not self == other
+        if op == op_GT:
+            return self >= other and not (self <= other)
+        if op == op_GE:
+            return self._ge_(other)
 
-        sage: u = V((1,1))
-        sage: v = V((1,sqrt2))
-        sage: a, b = solve(x,u,y,v)
-        sage: x + a*u == y + b*v
-        True
+        raise NotImplementedError("Operator not implemented for this distance")
 
-    """
-    d = -u[0] * v[1] + u[1] * v[0]
-    if d.is_zero():
-        raise ValueError("parallel vectors")
-    a = v[1] * (x[0] - y[0]) + v[0] * (y[1] - x[1])
-    b = u[1] * (x[0] - y[0]) + u[0] * (y[1] - x[1])
-    return (a / d, b / d)
+    def _le_(self, other):
+        if self.is_finite() and not other.is_finite():
+            return True
 
+        return self.norm_squared() <= other.norm_squared()
 
-def projectivization(x, y, signed=True, denominator=None):
-    r"""
-    Return a simplified version of the projective coordinate [x: y].
+    def _ge_(self, other):
+        if self.is_finite() and not other.is_finite():
+            return False
 
-    If ``signed`` (the default), the second coordinate is made non-negative;
-    otherwise the coordinates keep their signs.
+        return self.norm_squared() >= other.norm_squared()
 
-    If ``denominator`` is ``False``, returns [x/y: 1] up to sign. Otherwise,
-    the returned coordinates have no denominator and no non-unit gcd.
+    def _eq_(self, other):
+        return self.norm_squared() == other.norm_squared()
 
-    TESTS::
+    def _div_(self, other):
+        return self.parent().from_quotient(self, other)
 
-        sage: from flatsurf.geometry.euclidean import projectivization
+    def __float__(self):
+        from math import sqrt
 
-        sage: projectivization(2/3, -3/5, signed=True, denominator=True)
-        (10, -9)
-        sage: projectivization(2/3, -3/5, signed=False, denominator=True)
-        (-10, 9)
-        sage: projectivization(2/3, -3/5, signed=True, denominator=False)
-        (10/9, -1)
-        sage: projectivization(2/3, -3/5, signed=False, denominator=False)
-        (-10/9, 1)
+        f = float(self.norm_squared())
+        if f < 0:
+            print("Bug https://github.com/sagemath/sage/issues/37983.")
+            return float(0)
+        return sqrt(f)
 
-        sage: projectivization(-1/2, 0, signed=True, denominator=True)
-        (-1, 0)
-        sage: projectivization(-1/2, 0, signed=False, denominator=True)
-        (1, 0)
-        sage: projectivization(-1/2, 0, signed=True, denominator=False)
-        (-1, 0)
-        sage: projectivization(-1/2, 0, signed=False, denominator=False)
-        (1, 0)
 
-    """
-    from sage.all import Sequence
+class EuclideanDistance_squared(EuclideanDistance_base):
+    def __init__(self, parent, norm_squared):
+        super().__init__(parent)
 
-    parent = Sequence([x, y]).universe()
-    if y:
-        z = x / y
-        if denominator is True or (denominator is None and hasattr(z, "denominator")):
-            d = parent(z.denominator())
+        self._norm_squared = parent.base_ring()(norm_squared)
+
+    def norm_squared(self):
+        return self._norm_squared
+
+    def norm(self):
+        from sage.all import AA
+
+        return AA(self._norm_squared).sqrt()
+
+    def _scale(self, scalar):
+        return self.parent().from_norm_squared(scalar**2 * self._norm_squared)
+
+    def is_finite(self):
+        return True
+
+    def _add_(self, other):
+        return self.parent().from_sum(self, other)
+
+    def _repr_(self):
+        return f"√{self.norm_squared()}"
+
+
+# TODO: This is probably nonsense.
+class EuclideanDistance_sum(EuclideanDistance_base):
+    def __init__(self, parent, distances):
+        super().__init__(parent)
+
+        self._distances = distances
+
+    def _add_(self, other):
+        distances = list(self._distances)
+
+        if isinstance(other, EuclideanDistance_sum):
+            distances.extend(other._distances)
         else:
-            d = parent(1)
-        if signed and y < 0:
-            d *= -1
-        return (z * d, d)
-    elif signed and x < 0:
-        return (parent(-1), parent(0))
-    else:
-        return (parent(1), parent(0))
+            distances.append(other)
 
+        if any(not d.is_finite() for d in distances):
+            return self.parent().infinite()
 
-def slope(a, rotate=1):
-    r"""
-    Return either ``1`` (positive slope) or ``-1`` (negative slope).
+        return self.parent().from_sum(*distances)
 
-    If ``rotate`` is set to 1 then consider the edge as if it was rotated counterclockwise
-    infinitesimally.
+    def is_finite(self):
+        return True
 
-    EXAMPLES::
+    def norm_squared(self):
+        return sum(d.norm() for d in self._distances) ** 2
 
-        sage: from flatsurf.geometry.euclidean import slope
-        sage: slope((1, 1))
-        1
-        sage: slope((-1, 1))
-        -1
-        sage: slope((-1, -1))
-        1
-        sage: slope((1, -1))
-        -1
 
-        sage: slope((1, 0))
-        1
-        sage: slope((0, 1))
-        -1
-        sage: slope((-1, 0))
-        1
-        sage: slope((0, -1))
-        -1
+# TODO: This is probably nonsense.
+class EuclideanDistance_quotient(EuclideanDistance_base):
+    def __init__(self, parent, dividend, divisor):
+        super().__init__(parent)
 
-        sage: slope((1, 0), rotate=-1)
-        -1
-        sage: slope((0, 1), rotate=-1)
-        1
-        sage: slope((-1, 0), rotate=-1)
-        -1
-        sage: slope((0, -1), rotate=-1)
-        1
+        if not divisor.is_finite():
+            raise TypeError
 
-        sage: slope((1, 0), rotate=0)
-        0
-        sage: slope((0, 1), rotate=0)
-        0
-        sage: slope((-1, 0), rotate=0)
-        0
-        sage: slope((0, -1), rotate=0)
-        0
+        self._dividend = dividend
+        self._divisor = divisor
 
-        sage: slope((0, 0))
-        Traceback (most recent call last):
-        ...
-        ValueError: zero vector
-    """
-    x, y = a
-    if not x and not y:
-        raise ValueError("zero vector")
-    if (x > 0 and y > 0) or (x < 0 and y < 0):
-        return 1
-    elif (x > 0 and y < 0) or (x < 0 and y > 0):
-        return -1
-    if rotate == 0:
-        return 0
-    if rotate == 1:
-        return 1 if x else -1
-    if rotate == -1:
-        return 1 if y else -1
-    raise ValueError("invalid argument rotate={}".format(rotate))
+    def is_finite(self):
+        return self._dividend.is_finite()
+
+    def norm_squared(self):
+        return self._dividend.norm_squared() / self._divisor.norm_squared()
+
+
+class EuclideanDistance_infinite(EuclideanDistance_base):
+    def _scale(self, scalar):
+        if scalar == 0:
+            raise NotImplementedError
+
+        return self
+
+    def norm_squared(self):
+        from sage.all import oo
+
+        return oo
+
+    def is_finite(self):
+        return False
+
+    def _repr_(self):
+        return "∞"
+
+
+class EuclideanDistances(Parent):
+    def __init__(self, euclidean_plane, category=None):
+        # TODO: Pick a better category.
+        from sage.categories.all import Sets
+
+        super().__init__(euclidean_plane.base_ring(), category=category or Sets())
+        self._euclidean_plane = euclidean_plane
+
+    def _repr_(self):
+        return f"Euclidean Norm on {self._euclidean_plane}"
+
+    # TODO: We should probably also abstract away the concept of angles.
+
+    @cached_method
+    def infinite(self):
+        r"""
+        Return an infinite distance.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: E.norm().infinite()
+            oo
+
+        """
+        return self.__make_element_class__(EuclideanDistance_infinite)(self)
+
+    def zero(self):
+        return self.from_norm_squared(0)
+
+    def from_norm_squared(self, x):
+        return self.__make_element_class__(EuclideanDistance_squared)(self, x)
+
+    def from_vector(self, v):
+        return self.from_norm_squared(v[0] ** 2 + v[1] ** 2)
+
+    def from_sum(self, *distances):
+        return self.__make_element_class__(EuclideanDistance_sum)(self, distances)
+
+    def from_quotient(self, dividend, divisor):
+        return self.__make_element_class__(EuclideanDistance_quotient)(
+            self, dividend, divisor
+        )
+
+    def _element_constructor_(self, x):
+        from sage.all import vector
+
+        x = vector(x)
+
+        return self.from_norm_squared(x.dot_product(x))
diff --git a/flatsurf/geometry/flow_decomposition.py b/flatsurf/geometry/flow_decomposition.py
new file mode 100644
index 000000000..83ec865cc
--- /dev/null
+++ b/flatsurf/geometry/flow_decomposition.py
@@ -0,0 +1,17 @@
+from sage.all import SageObject
+
+
+class FlowDecomposition_base(SageObject):
+    def __init__(self, surface):
+        self._surface = surface
+
+    def surface(self):
+        return self._surface
+
+
+class FlowComponent_base(SageObject):
+    def __init__(self, flow_decomposition):
+        self._flow_decomposition = flow_decomposition
+
+    def flow_decomposition(self):
+        return self._flow_decomposition
diff --git a/flatsurf/geometry/geometry.py b/flatsurf/geometry/geometry.py
new file mode 100644
index 000000000..40ccada31
--- /dev/null
+++ b/flatsurf/geometry/geometry.py
@@ -0,0 +1,438 @@
+# TODO: Document module.
+
+
+class Geometry:
+    r"""
+    Predicates and primitive geometric constructions over a base ``ring``.
+
+    This is an abstract base class to collect shared functionality for concrete
+    geometries such as the :class:`EuclideanGeometry` and the
+    :class:`HyperbolicGeometry`.
+
+    INPUT:
+
+    - ``ring`` -- a ring, the ring in which object in this geometry will be
+      represented
+
+    TESTS::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanExactGeometry, Geometry
+        sage: geometry = EuclideanExactGeometry(QQ)
+        sage: isinstance(geometry, Geometry)
+        True
+
+    """
+
+    def __init__(self, ring):
+        r"""
+        TESTS::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: from flatsurf.geometry.euclidean import EuclideanGeometry
+            sage: E = EuclideanPlane()
+            sage: isinstance(E.geometry, EuclideanGeometry)
+            True
+
+        """
+        self._ring = ring
+
+    def base_ring(self):
+        r"""
+        Return the ring over which this geometry is implemented.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: E.geometry.base_ring()
+            Rational Field
+
+        """
+        return self._ring
+
+    def change_ring(self, ring):
+        r"""
+        Return this geometry with the :meth:`base_ring` changed to ``ring``.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: E.geometry
+            Exact geometry over Rational Field
+            sage: E.geometry.change_ring(AA)
+            Exact geometry over Algebraic Real Field
+
+        ::
+
+            sage: from flatsurf import HyperbolicPlane
+            sage: H = HyperbolicPlane()
+            sage: H.geometry
+            Exact geometry over Rational Field
+            sage: H.geometry.change_ring(AA)
+            Exact geometry over Algebraic Real Field
+
+        """
+        raise NotImplementedError("this geometry does not implement change_ring()")
+
+    def _zero(self, x):
+        r"""
+        Return whether ``x`` should be considered zero in the
+        :meth:`base_ring`.
+
+        .. NOTE::
+
+            This predicate should not be used directly in geometric
+            constructions since it does not specify the context in which this
+            question is asked. This makes it very difficult to override a
+            specific aspect in a custom geometry. Also, this predicate lacks
+            the context of other elements; a proper predicate should also take
+            other elements into account to decide this question relative to the
+            other values.
+
+        INPUT:
+
+        - ``x`` -- an element of the :meth:`base_ring`
+
+        EXAMPLES::
+
+            sage: from flatsurf import HyperbolicPlane
+            sage: H = HyperbolicPlane(RR)
+            sage: H.geometry._zero(1)
+            False
+            sage: H.geometry._zero(1e-9)
+            True
+
+        """
+        return self._cmp(x, 0) == 0
+
+    def _cmp(self, x, y):
+        r"""
+        Return how ``x`` compares to ``y``.
+
+        .. NOTE::
+
+            This predicate should not be used directly in geometric
+            constructions since it does not specify the context in which this
+            question is asked. This makes it very difficult to override a
+            specific aspect in a custom geometry.
+
+        INPUT:
+
+        - ``x`` -- an element of the :meth:`base_ring`
+
+        - ``y`` -- an element of the :meth:`base_ring`
+
+        EXAMPLES::
+
+            sage: from flatsurf import HyperbolicPlane
+            sage: H = HyperbolicPlane()
+            sage: H.geometry._cmp(0, 0)
+            0
+            sage: H.geometry._cmp(0, 1)
+            -1
+            sage: H.geometry._cmp(1, 0)
+            1
+
+        ::
+
+            sage: H = HyperbolicPlane(RR)
+            sage: H.geometry._cmp(0, 0)
+            0
+            sage: H.geometry._cmp(0, 1)
+            -1
+            sage: H.geometry._cmp(1, 0)
+            1
+            sage: H.geometry._cmp(1e-10, 0)
+            0
+
+        """
+        if self._equal(x, y):
+            return 0
+        if x < y:
+            return -1
+
+        assert (
+            x > y
+        ), "Geometry over this ring must override _cmp since not (x == y) and not (x < y) does not imply x > y"
+        return 1
+
+    def _sgn(self, x):
+        r"""
+        Return the sign of ``x``.
+
+        .. NOTE::
+
+            This predicate should not be used directly in geometric
+            constructions since it does not specify the context in which this
+            question is asked. This makes it very difficult to override a
+            specific aspect in a custom geometry. Also, this predicate lacks
+            the context of other elements; a proper predicate should also take
+            other elements into account to decide this question relative to the
+            other values.
+
+        INPUT:
+
+        - ``x`` -- an element of the :meth:`base_ring`.
+
+        EXAMPLES::
+
+            sage: from flatsurf import HyperbolicPlane
+            sage: H = HyperbolicPlane(RR)
+            sage: H.geometry._sgn(1)
+            1
+            sage: H.geometry._sgn(-1)
+            -1
+            sage: H.geometry._sgn(1e-10)
+            0
+
+        """
+        return self._cmp(x, 0)
+
+    def _equal(self, x, y):
+        r"""
+        Return whether ``x`` and ``y`` should be considered equal in the :meth:`base_ring`.
+
+        .. NOTE::
+
+            This predicate should not be used directly in geometric
+            constructions since it does not specify the context in which this
+            question is asked. This makes it very difficult to override a
+            specific aspect in a custom geometry.
+
+        INPUT:
+
+        - ``x`` -- an element of the :meth:`base_ring`
+
+        - ``y`` -- an element of the :meth:`base_ring`
+
+        EXAMPLES::
+
+            sage: from flatsurf import HyperbolicPlane
+            sage: H = HyperbolicPlane(RR)
+            sage: H.geometry._equal(0, 1)
+            False
+            sage: H.geometry._equal(0, 1e-10)
+            True
+
+        """
+        raise NotImplementedError("this geometry does not implement _equal()")
+
+    def _determinant(self, a, b, c, d):
+        r"""
+        Return the determinant of the 2×2 matrix ``[[a, b], [c, d]]`` or
+        ``None`` if the matrix is singular.
+
+        .. NOTE::
+
+            This predicate should not be used directly in geometric
+            constructions since it does not specify the context in which this
+            question is asked. This makes it very difficult to override a
+            specific aspect in a custom geometry.
+
+        INPUT:
+
+        - ``a`` -- an element of the :meth:`base_ring`
+
+        - ``b`` -- an element of the :meth:`base_ring`
+
+        - ``c`` -- an element of the :meth:`base_ring`
+
+        - ``d`` -- an element of the :meth:`base_ring`
+
+        EXAMPLES:
+
+            sage: from flatsurf import HyperbolicPlane
+            sage: H = HyperbolicPlane()
+
+            sage: H.geometry._determinant(1, 2, 3, 4)
+            -2
+            sage: H.geometry._determinant(0, 10^-10, 1, 1)
+            -1/10000000000
+
+        """
+        det = a * d - b * c
+        if self._zero(det):
+            return None
+        return det
+
+
+class ExactGeometry(Geometry):
+    r"""
+    Shared base class for predicates and geometric constructions over exact rings.
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanExactGeometry
+        sage: geometry = EuclideanExactGeometry(QQ)
+
+    TESTS::
+
+        sage: from flatsurf.geometry.geometry import ExactGeometry
+        sage: isinstance(geometry, ExactGeometry)
+        True
+
+    """
+
+    def _equal(self, x, y):
+        r"""
+        Return whether the numbers ``x`` and ``y`` should be considered equal
+        in exact geometry.
+
+        .. NOTE::
+
+            This predicate should not be used directly in geometric
+            constructions since it does not specify the context in which this
+            question is asked. This makes it very difficult to override a
+            specific aspect in a custom geometry.
+
+        EXAMPLES::
+
+            sage: from flatsurf import HyperbolicPlane
+            sage: H = HyperbolicPlane()
+            sage: H.geometry._equal(0, 1)
+            False
+            sage: H.geometry._equal(0, 1/2**64)
+            False
+            sage: H.geometry._equal(0, 0)
+            True
+
+        """
+        return x == y
+
+    def __repr__(self):
+        r"""
+        Return a printable representation of this geometry.
+
+        EXAMPLES::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: E = EuclideanPlane()
+            sage: E.geometry
+            Exact geometry over Rational Field
+
+        ::
+
+            sage: from flatsurf import HyperbolicPlane
+            sage: H = HyperbolicPlane()
+            sage: H.geometry
+            Exact geometry over Rational Field
+
+        """
+        return f"Exact geometry over {self._ring}"
+
+
+class EpsilonGeometry(Geometry):
+    r"""
+    Shared base class for predicates and primitive geometric constructions over
+    an inexact base ring.
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.euclidean import EuclideanEpsilonGeometry
+        sage: geometry = EuclideanEpsilonGeometry(RR, 1e-6)
+
+    TESTS::
+
+        sage: from flatsurf.geometry.geometry import EpsilonGeometry
+        sage: isinstance(geometry, EpsilonGeometry)
+        True
+
+    """
+
+    def __init__(self, ring, epsilon):
+        r"""
+        TESTS::
+
+            sage: from flatsurf import EuclideanPlane
+            sage: from flatsurf.geometry.euclidean import EuclideanEpsilonGeometry
+            sage: E = EuclideanPlane(RR, EuclideanEpsilonGeometry(RR, 1e-6))
+            sage: isinstance(E.geometry, EuclideanEpsilonGeometry)
+            True
+
+        """
+        super().__init__(ring)
+        self._epsilon = ring(epsilon)
+
+    def _equal(self, x, y):
+        r"""
+        Return whether ``x`` and ``y`` should be considered equal numbers with
+        respect to an ε error.
+
+        .. NOTE::
+
+            This method has not been tested much. Since this underlies much of
+            the inexact geometry, we should probably do something better here,
+            see e.g., https://floating-point-gui.de/errors/comparison/
+
+        EXAMPLES::
+
+            sage: from flatsurf import HyperbolicPlane
+            sage: H = HyperbolicPlane(RR)
+
+            sage: H.geometry._equal(1, 2)
+            False
+            sage: H.geometry._equal(1, 1 + 1e-32)
+            True
+            sage: H.geometry._equal(1e-32, 1e-32 + 1e-33)
+            False
+            sage: H.geometry._equal(1e-32, 1e-32 + 1e-64)
+            True
+
+        """
+        if x == 0 or y == 0:
+            return abs(x - y) < self._epsilon
+
+        return abs(x - y) <= (abs(x) + abs(y)) * self._epsilon
+
+    def _determinant(self, a, b, c, d):
+        r"""
+        Return the determinant of the 2×2 matrix ``[[a, b], [c, d]]`` or
+        ``None`` if the matrix is singular.
+
+        INPUT:
+
+        - ``a`` -- an element of the :meth:`~HyperbolicGeometry.base_ring`
+
+        - ``b`` -- an element of the :meth:`~HyperbolicGeometry.base_ring`
+
+        - ``c`` -- an element of the :meth:`~HyperbolicGeometry.base_ring`
+
+        - ``d`` -- an element of the :meth:`~HyperbolicGeometry.base_ring`
+
+        EXAMPLES:
+
+            sage: from flatsurf import HyperbolicPlane
+            sage: H = HyperbolicPlane(RR)
+
+            sage: H.geometry._determinant(1, 2, 3, 4)
+            -2
+            sage: H.geometry._determinant(1e-10, 0, 0, 1e-10)
+            1.00000000000000e-20
+
+        Unfortunately, we are not implementing any actual rank detecting
+        algorithm (QR decomposition or such) here. So, we do not detect that
+        this matrik is singular::
+
+            sage: H.geometry._determinant(1e-127, 1e-128, 1, 1)
+            9.00000000000000e-128
+
+        """
+        det = a * d - b * c
+        if det == 0:
+            # Note that we should instead numerically detect the rank here.
+            return None
+        return det
+
+    def __repr__(self):
+        r"""
+        Return a printable representation of this geometry.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.euclidean import EuclideanEpsilonGeometry
+            sage: EuclideanEpsilonGeometry(RR, 1e-6)
+            Epsilon geometry with ϵ=1.00000000000000e-6 over Real Field with 53 bits of precision
+
+        """
+        return f"Epsilon geometry with ϵ={self._epsilon} over {self._ring}"
diff --git a/flatsurf/geometry/gl2r_orbit_closure.py b/flatsurf/geometry/gl2r_orbit_closure.py
index 5665359fe..9a7bf7de2 100644
--- a/flatsurf/geometry/gl2r_orbit_closure.py
+++ b/flatsurf/geometry/gl2r_orbit_closure.py
@@ -13,21 +13,23 @@
 
 Let us first construct a Veech surface in the stratum H(2)::
 
-    sage: from flatsurf import translation_surfaces
-    sage: from flatsurf import GL2ROrbitClosure
+    sage: from flatsurf import translation_surfaces, GL2ROrbitClosure
 
     sage: x = polygen(QQ)
     sage: K.<a> = NumberField(x^3 - 2, embedding=AA(2)**(1/3))
     sage: S = translation_surfaces.mcmullen_L(1,1,1,a)
     sage: O = GL2ROrbitClosure(S) # optional: pyflatsurf  # random output due to matplotlib warnings with some combinations of setuptools and matplotlib
-    sage: O.decomposition((1,2)).cylinders() # optional: pyflatsurf
+    sage: O.decomposition((1,2)).cylinders() # optional: pyflatsurf  # TODO: Make this code not produce a warning.
+    doctest:warning
+    ...
+    UserWarning: orbit_closure.decomposition() has been deprecated and will be removed in a future version of sage-flatsurf; use surface.flow_decomposition(direction).decompose(limit) instead.
     [Cylinder with perimeter [...]]
 
 The following is also a Veech surface. However the flow decomposition
 in directions with long cylinders might not discover them if a limit
 is set::
 
-    sage: S = translation_surfaces.mcmullen_genus2_prototype(4,2,1,1,1/4)
+    sage: S = translation_surfaces.mcmullen_genus2_prototype(4, 2, 1, 1, 1/4)
     sage: l = S.base_ring().gen()
     sage: O = GL2ROrbitClosure(S) # optional: pyflatsurf
     sage: dec = O.decomposition((8*l - 25, 16), 9) # optional: pyflatsurf
@@ -47,7 +49,10 @@
 
     sage: S = translation_surfaces.veech_double_n_gon(5)
     sage: O = GL2ROrbitClosure(S)  # optional: pyflatsurf
-    sage: all(d.parabolic() for d in O.decompositions_depth_first(3))  # optional: pyflatsurf
+    sage: all(d.is_parabolic() for d in O.decompositions_depth_first(3))  # optional: pyflatsurf
+    doctest:warning
+    ...
+    UserWarning: orbit_closure.decompositions() has been deprecated and will be removed in a future version of sage-flatsurf; use surface.flow_decompositions() instead.
     True
 
 For surfaces in rank one loci, even though they are completely periodic,
@@ -55,15 +60,15 @@
 
     sage: S = translation_surfaces.mcmullen_genus2_prototype(4,2,1,1,1/4)
     sage: O = GL2ROrbitClosure(S)  # optional: pyflatsurf
-    sage: all((d.hasCylinder() == False) or d.parabolic() for d in O.decompositions(6))  # optional: pyflatsurf
+    sage: all((d.has_cylinder() is False) or (d.is_parabolic() is True) for d in O.decompositions(6))  # optional: pyflatsurf
     False
-    sage: all((d.completelyPeriodic() == True) or (d.hasCylinder() == False) for d in O.decompositions(6))  # optional: pyflatsurf
+    sage: all((d.is_completely_periodic() is True) or (d.has_cylinder() is False) for d in O.decompositions(6))  # optional: pyflatsurf
     True
 """
 # ****************************************************************************
 #  This file is part of sage-flatsurf.
 #
-#        Copyright (C) 2019-2022 Julian Rüth
+#        Copyright (C) 2019-2024 Julian Rüth
 #                      2020      Vincent Delecroix
 #
 #  sage-flatsurf is free software: you can redistribute it and/or modify
@@ -82,6 +87,8 @@
 
 from sage.all import FreeModule, matrix, identity_matrix, ZZ, QQ, Unknown, vector, prod
 
+from sage.misc.cachefunc import cached_method
+
 
 class GL2ROrbitClosure:
     r"""
@@ -116,6 +123,9 @@ class GL2ROrbitClosure:
         sage: for decomposition in O.decompositions(1):  # long time, optional: pyflatsurf, optional: pyexactreal
         ....:     O.update_tangent_space_from_flow_decomposition(decomposition)
         ....:     if O.dimension() == bound: break
+        doctest:warning
+        ...
+        UserWarning: orbit_closure.decompositions() has been deprecated and will be removed in a future version of sage-flatsurf; use surface.flow_decompositions() instead.
         sage: O  # long time, optional: pyflatsurf, optional: pyexactreal
         GL(2,R)-orbit closure of dimension at least 8 in H_7(4^3, 0) (ambient dimension 17)
 
@@ -150,22 +160,28 @@ class GL2ROrbitClosure:
     """
 
     def __init__(self, surface):
+        if surface.__class__.__name__.startswith("FlatTriangulation<"):
+            import warnings
+
+            warnings.warn(
+                "Creating a GL2ROrbitClosure from a FlatTriangulation has been deprecated and will be removed from a future version of sage-flatsurf; create GL2ROrbitClosure from a sage-flatsurf surface directly instead"
+            )
+
+            from flatsurf.geometry.pyflatsurf.surface import Surface_pyflatsurf
+
+            surface = Surface_pyflatsurf(surface)
+
         from flatsurf.geometry.categories import TranslationSurfaces
-        from flatsurf.geometry.surface import Surface_base
 
-        if isinstance(surface, Surface_base):
-            if surface not in TranslationSurfaces():
-                raise NotImplementedError(
-                    "cannot compute orbit closure of a non-translation surface"
-                )
+        if surface not in TranslationSurfaces():
+            raise NotImplementedError("surface must be a translation surface")
 
             base_ring = surface.base_ring()
             self._surface = surface.pyflatsurf().codomain().flat_triangulation()
         else:
             from flatsurf.geometry.pyflatsurf.conversion import sage_ring
 
-            base_ring = sage_ring(surface)
-            self._surface = surface
+        self._surface = surface
 
         # A model of the vector space R² in libflatsurf, e.g., to represent the
         # vector associated to a saddle connection.
@@ -180,14 +196,14 @@ def __init__(self, surface):
         # edges that form a basis of H_1(S, Sigma; Z)
         # It comes together with a projection matrix
         t, m = self._spanning_tree()
-        assert set(t.keys()) == {f[2] for f in self._surface.faces()}
+        assert set(t.keys()) == {f[2] for f in self._flat_triangulation().faces()}
         self.spanning_set = []
         v = set(t.values())
-        for e in self._surface.edges():
+        for e in self._flat_triangulation().edges():
             if e.positive() not in v and e.negative() not in v:
                 self.spanning_set.append(e)
         self.d = len(self.spanning_set)
-        assert 3 * self.d - 3 == self._surface.size()
+        assert 3 * self.d - 3 == self._flat_triangulation().size()
         assert m.rank() == self.d
         m = m.transpose()
         # projection matrix from Z^E to H_1(S, Sigma; Z) in the basis
@@ -199,7 +215,7 @@ def __init__(self, surface):
         self.V = FreeModule(self.V2.base_ring(), self.d)
         self.H = matrix(self.V2.base_ring(), self.d, 2)
         for i in range(self.d):
-            s = self._surface.fromHalfEdge(self.spanning_set[i].positive())
+            s = self._flat_triangulation().fromHalfEdge(self.spanning_set[i].positive())
             self.H[i] = self.V2._isomorphic_vector_space(self.V2(s))
         self.Hdual = self.Omega * self.H
 
@@ -219,6 +235,119 @@ def __init__(self, surface):
         self.update_tangent_space_from_vector(self.H.transpose()[0])
         self.update_tangent_space_from_vector(self.H.transpose()[1])
 
+    def _lift_to_simplicial_cohomology(self, v):
+        r"""
+        Convert an element from cohomology given by its values on all edges,
+        e.g., the output of :meth:`lift`, to the corresponding simplicial
+        cohomology class.
+
+        EXAMPLES::
+
+            sage: from flatsurf import polygons, translation_surfaces, similarity_surfaces
+            sage: from flatsurf import GL2ROrbitClosure  # optional: pyflatsurf
+
+            sage: T = polygons.triangle(3,4,13)
+            sage: S = similarity_surfaces.billiard(T)
+            sage: S = S.minimal_cover("translation").erase_marked_points().codomain() # long time (3s, #122), optional: pyflatsurf
+            sage: O = GL2ROrbitClosure(S)  # long time (above), optional: pyflatsurf
+            sage: for d in O.decompositions(4, 20):  # long time (2s, #124), optional: pyflatsurf
+            ....:     O.update_tangent_space_from_flow_decomposition(d)
+            ....:     if O.dimension() == 4:
+            ....:         break
+
+            sage: d1, d2, d3, d4 = [O.lift(b) for b in O.tangent_space_basis()]  # long time (above), optional: pyflatsurf
+            sage: O._lift_to_simplicial_cohomology(d3)
+
+        """
+        H = self._surface.cohomology()
+
+        values = {}
+
+        # TODO: Implement this in a more natural way without reaching
+        # into the internals of homology.
+        for homology_gen in H.homology().gens():
+            chain = homology_gen._chain
+            value = H._coefficients.zero()
+            for ((label, edge), coefficient) in chain.monomial_coefficients().items():
+                to_pyflatsurf = self._surface.pyflatsurf()
+                half_edge = to_pyflatsurf._pyflatsurf_conversion((label, edge))
+                if half_edge.id() < 0:
+                    value -= coefficient * v[-(half_edge.id() - 1)]
+                else:
+                    value += coefficient * v[half_edge.id() - 1]
+
+            values[homology_gen] = value
+
+        return H(values)
+
+    def deform(self):
+        # TODO: Move this to deformation branch.
+        tangents = self.tangent_space_basis()
+
+        if len(tangents) > 2:
+            # Ignore trivial deformations if we already discovered something in
+            # the tangent space.
+            tangents = tangents[2:]
+
+        # TODO: Currently, there's only a single tangent used here.
+        tangents = [sum(self.lift(v) for v in tangents)]
+
+        def upper_bound(v):
+            try:
+                length = sum(abs(x.parent().number_field(x)) for x in v) / len(v)
+            except TypeError:
+                length = sum(abs(x.parent().number_field()(x)) for x in v) / len(v)
+
+            n = 1
+            while n < length:
+                n *= 2
+            return n
+
+        tangents.sort(key=upper_bound)
+
+        scale = 2
+        while True:
+            eligibles = False
+
+            for tangent in tangents:
+                import cppyy
+
+                # What is a good vector to use to deform? See flatsurvey #3.
+                n = upper_bound(tangent) * scale
+                # n = upper_bound(tangent) // 4
+
+                # What is a good bound here? See flatsurvey #3.
+                # if n > 1e20:
+                #     print("Cannot deform. Deformation would lead to too much coefficient blowup.")
+                #     continue
+
+                eligibles = True
+
+                deformation = [self.V2(x / n, x / (2 * n)).vector for x in tangent]
+                try:
+                    # Valid deformations that require lots of flips take forever. It's crucial to pick n such that no/very few flips are sufficient. See #3.
+                    deformed = self._flat_triangulation() + deformation
+
+                    surface = deformed.surface()
+                    from flatsurf.geometry.pyflatsurf_conversion import (
+                        from_pyflatsurf,
+                    )
+
+                    return from_pyflatsurf(surface)
+                except cppyy.gbl.std.invalid_argument:
+                    continue
+
+            scale *= 2
+
+            if not eligibles:
+                raise Exception(
+                    "Cannot deform. No tangent vector can be used to deform."
+                )
+
+    @cached_method
+    def _flat_triangulation(self):
+        return self._surface.pyflatsurf().codomain().flat_triangulation()
+
     def dimension(self):
         r"""
         Return the current complex dimension of the GL(2,R)-orbit closure.
@@ -269,7 +398,7 @@ def ambient_stratum(self):
         """
         from surface_dynamics import Stratum
 
-        surface = self._surface
+        surface = self._flat_triangulation()
         angles = [surface.angle(v) for v in surface.vertices()]
         return Stratum([a - 1 for a in angles], 1)
 
@@ -336,10 +465,9 @@ def _half_edge_to_face(self, h):
         r"""
         Return a canonical half-edge encoding the face bounded by ``h``.
         """
-        surface = self._surface
         h1 = h
-        h2 = surface.nextInFace(h1)
-        h3 = surface.nextInFace(h2)
+        h2 = self._flat_triangulation().nextInFace(h1)
+        h3 = self._flat_triangulation().nextInFace(h2)
         return min([h1, h2, h3], key=lambda x: x.index())
 
     def __repr__(self):
@@ -360,9 +488,9 @@ def holonomy(self, v):
             sage: K.<a> = NumberField(x^3 - 2, embedding=AA(2)**(1/3))
             sage: S = translation_surfaces.mcmullen_L(1,1,1,a)
             sage: O = GL2ROrbitClosure(S) # optional: pyflatsurf
-            sage: edges = O._surface.edges() # optional: pyflatsurf
+            sage: edges = O._flat_triangulation().edges() # optional: pyflatsurf
             sage: F = FreeModule(ZZ, len(edges)) # optional: pyflatsurf
-            sage: all(O.V2(O.holonomy(O.proj * F.gen(i))).vector == O.V2(O._surface.fromHalfEdge(e.positive())).vector for i, e in enumerate(edges)) # optional: pyflatsurf
+            sage: all(O.V2(O.holonomy(O.proj * F.gen(i))).vector == O.V2(O._flat_triangulation().fromHalfEdge(e.positive())).vector for i, e in enumerate(edges)) # optional: pyflatsurf
             True
         """
         return self.V(v) * self.H
@@ -399,9 +527,12 @@ def lift(self, v):
 
         This can be used to deform the surface::
 
+            sage: from flatsurf import polygons, translation_surfaces, similarity_surfaces
+            sage: from flatsurf import GL2ROrbitClosure  # optional: pyflatsurf
+
             sage: T = polygons.triangle(3,4,13)
             sage: S = similarity_surfaces.billiard(T)
-            sage: S = S.minimal_cover("translation").erase_marked_points() # long time (3s, #122), optional: pyflatsurf
+            sage: S = S.minimal_cover("translation").erase_marked_points().codomain() # long time (3s, #122), optional: pyflatsurf
             sage: O = GL2ROrbitClosure(S)  # long time (above), optional: pyflatsurf
             sage: for d in O.decompositions(4, 20):  # long time (2s, #124), optional: pyflatsurf
             ....:     O.update_tangent_space_from_flow_decomposition(d)
@@ -411,9 +542,12 @@ def lift(self, v):
             sage: dreal = d1/132 + d2/227 + d3/1280 - d4/13201  # long time (above), optional: pyflatsurf
             sage: dimag = d1/141 - d2/233 + d4/1230 + d4/14250  # long time (above), optional: pyflatsurf
             sage: d = [O.V2((x,y)).vector for x,y in zip(dreal,dimag)]  # long time (above), optional: pyflatsurf
-            sage: S2 = O._surface + d  # long time (6s), optional: pyflatsurf
+            sage: S2 = O._flat_triangulation() + d  # long time (6s), optional: pyflatsurf  # TODO: Support this directly on a surface, i.e., fix the deprecation warning.
 
             sage: O2 = GL2ROrbitClosure(S2.surface())  # long time (above), optional: pyflatsurf
+            doctest:warning
+            ...
+            UserWarning: Creating a GL2ROrbitClosure from a FlatTriangulation has been deprecated and will be removed from a future version of sage-flatsurf; create GL2ROrbitClosure from a sage-flatsurf surface directly instead
             sage: for d in O2.decompositions(1, 20):  # long time (25s, #124), optional: pyflatsurf
             ....:     O2.update_tangent_space_from_flow_decomposition(d)
 
@@ -421,7 +555,7 @@ def lift(self, v):
         # given the values on the spanning edges we reconstruct the unique vector that
         # vanishes on the boundary
         bdry = self.boundaries()
-        n = self._surface.edges().size()
+        n = self._flat_triangulation().edges().size()
         k = len(self.spanning_set)
         assert k + len(bdry) == n + 1
         A = matrix(QQ, n + 1, n)
@@ -441,7 +575,7 @@ def lift(self, v):
         return A.solve_right(u)
 
     def absolute_homology(self):
-        vert_index = {v: i for i, v in enumerate(self._surface.vertices())}
+        vert_index = {v: i for i, v in enumerate(self._flat_triangulation().vertices())}
         m = len(vert_index)
         if m == 1:
             return self.V
@@ -456,12 +590,12 @@ def absolute_homology(self):
             r = [0] * m
             i = vert_index[
                 pyflatsurf.flatsurf.Vertex.target(
-                    e.positive(), self._surface.combinatorial()
+                    e.positive(), self._flat_triangulation().combinatorial()
                 )
             ]
             j = vert_index[
                 pyflatsurf.flatsurf.Vertex.source(
-                    e.positive(), self._surface.combinatorial()
+                    e.positive(), self._flat_triangulation().combinatorial()
                 )
             ]
             if i != j:
@@ -506,13 +640,14 @@ def _spanning_tree(self, root=None):
         r"""
         Return a pair ``(tree, proj)`` where
 
-        - ``tree`` is a tree encoded in a dictionary. Its keys are the faces
+        - ``tree`` is a spanning tree of the dual graph of the triangulation
+          encoded as a dictionary. Its keys are faces of the triangulation
           (coded by their minimal adjacent half-edge) and the corresponding
           value is the half-edge to cross to go toward the root face.
 
-        - ``proj`` a projection matrix : for a vector ``v``, the vector
-          ``v * proj`` is cohomologous to ``v`` and only takes values on the
-          spanning set.
+        - ``proj`` a projection matrix : for a vector ``v``, the vector ``v *
+          proj`` is cohomologous to ``v`` and only takes values on the spanning
+          set, i.e., on the triangulation edges not crossed by the ``tree``.
 
         EXAMPLES:
 
@@ -524,7 +659,7 @@ def _spanning_tree(self, root=None):
             sage: S = similarity_surfaces.billiard(T)
             sage: S = S.minimal_cover("translation")
             sage: O = GL2ROrbitClosure(S)  # optional: pyflatsurf
-            sage: num_edges = O._surface.edges().size()  # optional: pyflatsurf
+            sage: num_edges = O._flat_triangulation().edges().size()  # optional: pyflatsurf
             sage: V = VectorSpace(QQ, num_edges)  # optional: pyflatsurf
             sage: tree, proj = O._spanning_tree()  # optional: pyflatsurf
 
@@ -537,7 +672,7 @@ def _spanning_tree(self, root=None):
         takes values only on the chosen spanning set of edges::
 
             sage: values = tree.values()  # optional: pyflatsurf
-            sage: indices = set(e.index() for e in O._surface.edges() if e.positive() not in values and e.negative() not in values)  # optional: pyflatsurf
+            sage: indices = set(e.index() for e in O._flat_triangulation().edges() if e.positive() not in values and e.negative() not in values)  # optional: pyflatsurf
             sage: B = V.subspace(O.boundaries())  # optional: pyflatsurf
             sage: for e in range(num_edges):  # optional: pyflatsurf
             ....:     v = V.gen(e)
@@ -548,7 +683,7 @@ def _spanning_tree(self, root=None):
             ....:         assert (proj * v).is_zero()
         """
         if root is None:
-            root = next(iter(self._surface.edges())).positive()
+            root = next(iter(self._flat_triangulation().edges())).positive()
 
         root = self._half_edge_to_face(root)
         t = {root: None}  # face -> half edge to take to go to the root
@@ -564,16 +699,16 @@ def _spanning_tree(self, root=None):
                     todo.append(g)
                     edges.append(f1)
 
-                f = self._surface.nextInFace(f)
+                f = self._flat_triangulation().nextInFace(f)
 
         # gauss reduction
-        n = self._surface.size()
+        n = self._flat_triangulation().size()
         proj = identity_matrix(ZZ, n)
         edges.reverse()
         for f1 in edges:
-            f2 = self._surface.nextInFace(f1)
-            f3 = self._surface.nextInFace(f2)
-            assert self._surface.nextInFace(f3) == f1
+            f2 = self._flat_triangulation().nextInFace(f1)
+            f3 = self._flat_triangulation().nextInFace(f2)
+            assert self._flat_triangulation().nextInFace(f3) == f1
 
             i1 = f1.index()
             s1 = -1 if i1 % 2 else 1
@@ -605,9 +740,9 @@ def _intersection_matrix(self, t, spanning_set):
         while h not in contour_inv:
             contour_inv[h] = len(contour)
             contour.append(h)
-            h = self._surface.nextAtVertex(-h)
+            h = self._flat_triangulation().nextAtVertex(-h)
             while h not in all_edges:
-                h = self._surface.nextAtVertex(h)
+                h = self._flat_triangulation().nextAtVertex(h)
 
         assert len(contour) == len(all_edges)
 
@@ -680,10 +815,10 @@ def boundaries(self):
             ....:             for b in O.boundaries():
             ....:                 assert (O.proj * b).is_zero()
         """
-        n = self._surface.size()
+        n = self._flat_triangulation().size()
         V = FreeModule(ZZ, n)
         B = []
-        for f1, f2, f3 in self._surface.faces():
+        for f1, f2, f3 in self._flat_triangulation().faces():
             i1 = f1.index()
             s1 = -1 if i1 % 2 else 1
             i2 = f2.index()
@@ -703,45 +838,43 @@ def boundaries(self):
         return B
 
     def decomposition(self, v, limit=-1):
-        v = self.V2(v)
-
-        from flatsurf.features import pyflatsurf_feature
+        import warnings
 
-        pyflatsurf_feature.require()
-        import pyflatsurf
+        warnings.warn(
+            "orbit_closure.decomposition() has been deprecated and will be removed in a future version of sage-flatsurf; use surface.flow_decomposition(direction).decompose(limit) instead."
+        )
 
-        decomposition = pyflatsurf.flatsurf.makeFlowDecomposition(
-            self._surface, v.vector
+        decomposition = (
+            self._surface.pyflatsurf().codomain().flow_decomposition(direction=v)
         )
+        decomposition.decompose(limit=limit)
 
-        if limit != 0:
-            decomposition.decompose(int(limit))
-        return decomposition
+        return decomposition._flow_decomposition
 
     def decompositions(self, bound, limit=-1, bfs=False):
-        limit = int(limit)
+        import warnings
 
-        connections = self._surface.connections().bound(int(bound))
-        if bfs:
-            connections = connections.byLength()
-
-        slopes = None
-
-        from flatsurf.features import cppyy_feature
+        warnings.warn(
+            "orbit_closure.decompositions() has been deprecated and will be removed in a future version of sage-flatsurf; use surface.flow_decompositions() instead."
+        )
 
-        cppyy_feature.require()
-        import cppyy
+        if bfs:
+            algorithm = "bfs"
+        else:
+            algorithm = "dfs"
+
+        decompositions = (
+            self._surface.pyflatsurf()
+            .codomain()
+            .flow_decompositions(
+                algorithm=algorithm,
+                bound=bound,
+            )
+        )
 
-        for connection in connections:
-            direction = connection.vector()
-            if slopes is None:
-                slopes = cppyy.gbl.std.set[
-                    type(direction), type(direction).CompareSlope
-                ]()
-            if slopes.find(direction) != slopes.end():
-                continue
-            slopes.insert(direction)
-            yield self.decomposition(direction, limit)
+        for decomposition in decompositions:
+            decomposition.decompose(limit=limit)
+            yield decomposition
 
     def decompositions_depth_first(self, bound, limit=-1):
         return self.decompositions(bound, bfs=False, limit=limit)
@@ -790,17 +923,15 @@ def is_teichmueller_curve(self, bound, limit=-1):
             # square tiled
             return True
 
-        nv = len(self._surface.vertices())
-        ne = len(self._surface.edges())
-        nf = len(self._surface.faces())
+        nv = len(self._flat_triangulation().vertices())
+        ne = len(self._flat_triangulation().edges())
+        nf = len(self._flat_triangulation().faces())
         genus = (ne - nv - nf) // 2 + 1
         if k.degree() > genus or not k.is_totally_real():
             return False
 
         for decomposition in self.decompositions_depth_first(bound, limit):
-            if (
-                decomposition.parabolic() == False
-            ):  # noqa, we are comparing to a boost tribool so this cannot be replaced by "is False"
+            if decomposition.is_parabolic() is False:
                 return False
 
         return Unknown
@@ -834,12 +965,10 @@ def cylinder_circumference(self, component, A, sc_index, proj):
             (0, 0, 1, 0)
 
         """
-        if (
-            component.cylinder() != True
-        ):  # noqa, we are comparing to a boost tribool so this cannot be replaced by "is not True"
+        if component.is_cylinder() is not True:
             raise ValueError
 
-        perimeters = list(component.perimeter())
+        perimeters = list(component._flow_component().perimeter())
         per = perimeters[0]
         assert not per.vertical()
         sc = per.saddleConnection()
@@ -854,7 +983,7 @@ def cylinder_circumference(self, component, A, sc_index, proj):
 
         # check
         hol = self.holonomy_dual(circumference)
-        holbis = component.circumferenceHolonomy()
+        holbis = component._flow_component().circumferenceHolonomy()
         holbis = self.V2._isomorphic_vector_space(self.V2(holbis))
         assert hol == holbis, (hol, holbis)
 
@@ -889,21 +1018,19 @@ def eliminate_denominators(fractions):
         vcyls = []
         kz = self.flow_decomposition_kontsevich_zorich_cocycle(decomposition)
         for component in decomposition.components():
-            if (
-                component.cylinder() == False
-            ):  # noqa, we are comparing to a boost tribool so this cannot be replaced by "is False"
+            if component.is_cylinder() is False:
                 continue
-            elif (
-                component.cylinder() == True
-            ):  # noqa, we are comparing to a boost tribool so this cannot be replaced with "is True"
+            elif component.is_cylinder() is True:
                 vcyls.append(self.cylinder_circumference(component, *kz))
 
                 width = self.V2._isomorphic_vector_space.base_ring()(
-                    self.V2.base_ring()(component.width())
+                    self.V2.base_ring()(component._flow_component().width())
                 )
                 height = self.V2._isomorphic_vector_space.base_ring()(
                     self.V2.base_ring()(
-                        component.vertical().project(component.circumferenceHolonomy())
+                        component._flow_component()
+                        .vertical()
+                        .project(component._flow_component().circumferenceHolonomy())
                     )
                 )
                 module_fractions.append((width, height))
@@ -1013,7 +1140,7 @@ def _flow_decomposition_spanning_tree(self, decomposition, sc_index, sc_comp):
         assert n % 2 == 0
         n //= 2
 
-        for p in components[0].perimeter():
+        for p in components[0]._flow_component().perimeter():
             break
         t = {0: None}  # face -> half edge to take to go to the root
         todo = [0]
@@ -1021,7 +1148,7 @@ def _flow_decomposition_spanning_tree(self, decomposition, sc_index, sc_comp):
         while todo:
             i = todo.pop()
             c = components[i]
-            for sc in c.perimeter():
+            for sc in c._flow_component().perimeter():
                 sc1 = -sc.saddleConnection()
                 j = sc_comp[sc1]
                 if j not in t:
@@ -1042,7 +1169,7 @@ def _flow_decomposition_spanning_tree(self, decomposition, sc_index, sc_comp):
                 s1 = 1
             comp = components[sc_comp[sc1]]
             proj[i1] = 0
-            for p in comp.perimeter():
+            for p in comp._flow_component().perimeter():
                 sc = p.saddleConnection()
                 if sc == sc1:
                     continue
@@ -1116,7 +1243,7 @@ def flow_decomposition_kontsevich_zorich_cocycle(self, decomposition):
         components = list(decomposition.components())
         n_components = len(components)
         for i, comp in enumerate(components):
-            for p in comp.perimeter():
+            for p in comp._flow_component().perimeter():
                 sc = p.saddleConnection()
                 sc_comp[sc] = i
                 if sc not in sc_index:
@@ -1138,7 +1265,7 @@ def flow_decomposition_kontsevich_zorich_cocycle(self, decomposition):
         for i, sc in enumerate(spanning_set):
             sc = sc_pos[sc]
             c = sc.chain()
-            for edge in self._surface.edges():
+            for edge in self._flat_triangulation().edges():
                 A[i] += ZZ(str(c[edge])) * self.proj.column(edge.index())
         assert A.det().is_unit()
         return A, sc_index, proj
diff --git a/flatsurf/geometry/harmonic_differentials.py b/flatsurf/geometry/harmonic_differentials.py
new file mode 100644
index 000000000..68125eea9
--- /dev/null
+++ b/flatsurf/geometry/harmonic_differentials.py
@@ -0,0 +1,3660 @@
+r"""
+TODO: Document this module.
+TODO: Rename this module?
+TODO: Consider using a different basis than 1,z,z^2,..., see https://sagemath.zulipchat.com/#narrow/stream/271193-flatsurf/topic/numerical.20stability
+
+EXAMPLES:
+
+We compute harmonic differentials on the square torus::
+
+    sage: from flatsurf import translation_surfaces, HarmonicDifferentials, SimplicialCohomology
+    sage: T = translation_surfaces.square_torus()
+    sage: T.set_immutable()
+
+    sage: H = SimplicialCohomology(T)
+    sage: a, b = H.homology().gens()
+
+First, the harmonic differentials that sends the horizontal `b` to 1 and the
+vertical to zero (note that `a` is a diagonal)::
+
+    sage: f = H({b: 1, a: -1})
+    sage: Ω = HarmonicDifferentials(T)
+    sage: ω = Ω(f)
+    sage: ω
+    (1.0 + O(z0),)
+
+The harmonic differential that integrates as 1 on the vertical and 0 on the horizontal::
+
+    sage: g = H({a: -1, b: 0})
+    sage: Ω(g)
+    (-1.0*I + O(z0),)
+
+A less trivial example, the regular octagon::
+
+    sage: from flatsurf import translation_surfaces, HarmonicDifferentials, SimplicialCohomology
+    sage: S = translation_surfaces.regular_octagon().subdivide().codomain().delaunay_triangulate().codomain()
+
+    sage: H = SimplicialCohomology(S)
+    sage: a, b, c, d = H.homology().gens()
+
+    sage: # on the untriangulated surface f = H({ a: sqrt(2) + 1, b: 0, c: -sqrt(2) - 1, d: -sqrt(2) - 2})
+    sage: f = H({a: -sqrt(2), b: 0, c: -sqrt(2) - 1, d: sqrt(2) + 1})
+
+    sage: Omega = HarmonicDifferentials(S, error=1e-1)
+    sage: omega = Omega(f, check=False)
+    sage: omega.simplify(zero_threshold=1e-1)  # abs-tol 1e-4  # TODO: Why so much tolerance?
+    (1.3138012886047363*z0^2 + O(z0^5), -2.090515375137329 + (-3.2217148449767996)*z1^8 + (-2.515996975688303)*z1^16 + (-1.5095460414886475)*z1^24 + O(z1^25))
+
+The same computation, but we use a cell decomposition that is better adapted to
+computing harmonic differentials::
+
+    sage: from flatsurf.geometry.voronoi import ApproximateWeightedVoronoiCellDecomposition
+    sage: Omega = HarmonicDifferentials(S, error=1e-1, cell_decomposition=ApproximateWeightedVoronoiCellDecomposition(S))
+    sage: omega = Omega(f, check=False)
+    sage: omega.simplify(zero_threshold=1e-1)  # abs-tol 1e-1  # TODO: Why so much tolerance?
+    (1.333134175150692*z0^2 + O(z0^7), -2.090317964553833 + (-3.174059553834174)*z1^8 + O(z1^16))
+
+The same computation on a triangulation of the octagon::
+
+    sage: from flatsurf import HarmonicDifferentials, SimplicialCohomology, Polygon, translation_surfaces
+    sage: S = translation_surfaces.regular_octagon()
+    sage: S = S.subdivide().codomain()
+
+    sage: H = SimplicialCohomology(S)
+    sage: a, b, c, d = H.homology().gens()
+
+    sage: f = H({a: -sqrt(2), b: 0, c: -sqrt(2) - 1, d: sqrt(2) + 1})
+
+    sage: from flatsurf.geometry.voronoi import ApproximateWeightedVoronoiCellDecomposition
+    sage: Omega = HarmonicDifferentials(S, error=1e-1, cell_decomposition=ApproximateWeightedVoronoiCellDecomposition(S))
+    sage: omega = Omega(f, check=False)
+    sage: omega.simplify(zero_threshold=1e-1)  # abs-tol 1e-4  # TODO: Why so much tolerance?
+    (1.333134175150692*z0^2 + O(z0^7), -2.090317964553833 + (-3.174059553834174)*z1^8 + O(z1^16))
+
+The same surface but built as the unfolding of a right triangle. ``z2`` is the
+variable at the center of the octagon and ``z3`` is at the singularity. Note
+that another variable was chosen for ``z3`` than before, i.e., the output at
+``z3`` is rotated::
+
+    sage: from flatsurf import similarity_surfaces, HarmonicDifferentials, SimplicialCohomology, Polygon
+    sage: S = similarity_surfaces.billiard(Polygon(angles=[3/8, 1/2, 1/8], lengths=[1/2])).minimal_cover('translation')
+
+    sage: H = SimplicialCohomology(S)
+    sage: a, b, c, d = H.homology().gens()
+
+    sage: f = H({a: 0, b: sqrt(2) + 2, c: -1, d: -sqrt(2) - 1})
+
+    sage: from flatsurf.geometry.voronoi import ApproximateWeightedVoronoiCellDecomposition
+    sage: Omega = HarmonicDifferentials(S, error=1e-1, cell_decomposition=ApproximateWeightedVoronoiCellDecomposition(S))
+    sage: omega = Omega(f, check=False)  # random output due to L2 warnings
+    sage: omega.simplify(zero_threshold=1e-1)  # abs-tol 1e-4
+    (-1.4140331745147705 + (-5.2445968837090415)*z0^2 + (-14.135336687223573)*z0^4 + O(z0^5), 1.4160147905349731 + (-5.2437846751533215)*z1^2 + 14.13073027542154*z1^4 + O(z1^5), 1.3111448734204234*z2^2 + O(z2^7), 1.0453932285308838 + 1.8112497329711914*I + (-3.169093677628632)*z3^8 + (1.1057225941067088 - 1.9148742552824405*I)*z3^16 + O(z3^17), -1.4146366119384766*I + (-5.244161580049933)*z4^2 + 14.133169154040427*I*z4^4 + O(z4^5), 1.4153552055358887*I + (-5.244222128460129)*z5^2 + (-14.13234219652197*I)*z5^4 + O(z5^5))
+
+A deformed L::
+
+    sage: ### Does not work yet.
+    sage: ### from flatsurf import translation_surfaces, HarmonicDifferentials, ApproximateWeightedVoronoiCellDecomposition, GL2ROrbitClosure
+    sage: ### L = translation_surfaces.mcmullen_genus2_prototype(1, 1, 0, -1)
+    sage: ### L = GL2ROrbitClosure(L).deform()
+    sage: ### L = L.delaunay_triangulate().codomain()
+    sage: ### L = L.subdivide_edges(4).codomain()
+    sage: ### L = L.subdivide().codomain()
+    sage: ### # L = L.relabel({label: l for (l, label) in enumerate(L.labels())}).codomain()
+    sage: ### # L = L.insert_marked_points(*[L(label, L.polygon(label).centroid()) for label in [3]]).codomain()
+    sage: ### L = L.delaunay_triangulate().codomain()
+    sage: ### L = L.relabel({label: l for (l, label) in enumerate(L.labels())}).codomain()
+    sage: ### L.plot(edge_labels=False)
+    sage: ### V = ApproximateWeightedVoronoiCellDecomposition(L)
+    sage: ### Omega = HarmonicDifferentials(L, error=1e-3, cell_decomposition=V, check=False)
+    sage: ### Omega.error_plot(cutoff=.5)
+
+    sage: ### L = L.delaunay_triangulate().codomain()
+    sage: ### L = L.relabel({label: l for (l, label) in enumerate(L.labels())}).codomain()
+    sage: ### L = L.insert_marked_points(*[L(label, L.polygon(label).centroid()) for label in [8, 12]]).codomain()
+    sage: ### L = L.delaunay_triangulate().codomain()
+    sage: ### L = L.relabel({label: l for (l, label) in enumerate(L.labels())}).codomain()
+    sage: ### L = L.insert_marked_points(*[L(label, L.polygon(label).centroid()) for label in [1, 16]]).codomain()
+    sage: ### L = L.delaunay_triangulate().codomain()
+    sage: ### L = L.relabel({label: l for (l, label) in enumerate(L.labels())}).codomain()
+    sage: ### V = ApproximateWeightedVoronoiCellDecomposition(L)
+    sage: ### V.plot()
+
+    sage: ### Omega = HarmonicDifferentials(L, error=1e-3, cell_decomposition=V)
+
+Much more complicated, the unfolding of the (3, 4, 13) triangle::
+
+    sage: from flatsurf import similarity_surfaces, Polygon
+
+    sage: S = similarity_surfaces.billiard(Polygon(angles=[3, 4, 13])).minimal_cover("translation")
+    sage: S = S.erase_marked_points().codomain().delaunay_triangulate().codomain()
+    sage: S = S.relabel().codomain()
+
+If we develop power series at the vertices, not all of the surface is covered
+by their disks of convergence::
+
+    sage: from flatsurf import HarmonicDifferentials, ApproximateWeightedVoronoiCellDecomposition
+    sage: V = ApproximateWeightedVoronoiCellDecomposition(S)
+    sage: Omega = HarmonicDifferentials(S, error=1e-1, cell_decomposition=V)
+    Traceback (most recent call last):
+    ...
+    ValueError: cell decomposition is such that cells contain points outside of their center's radius of convergence
+
+We add marked points in the centers of some polygons::
+
+    sage: Omega = HarmonicDifferentials(S, error=1e-1, cell_decomposition=V, check=False)
+    sage: # Omega.error_plot()
+    sage: S = S.insert_marked_points(*[S(label, S.polygon(label).centroid()) for label in (2, 18, 26, 31)]).codomain()
+    sage: S = S.delaunay_triangulate().codomain()
+    sage: S = S.relabel().codomain()
+
+    sage: V = ApproximateWeightedVoronoiCellDecomposition(S)
+    sage: Omega = HarmonicDifferentials(S, error=1e-1, cell_decomposition=V)
+
+Given a vector from the tangent space, we can determine the corresponding differential::
+
+    sage: from flatsurf import GL2ROrbitClosure
+    sage: O = GL2ROrbitClosure(S)
+    sage: for d in O.decompositions(4, 20):  # random output due to deprecation warnings
+    ....:     O.update_tangent_space_from_flow_decomposition(d)
+    ....:     if O.dimension() == 7: break
+
+    sage: f = O._lift_to_simplicial_cohomology(O.lift(O.tangent_space_basis()[-1]))
+    sage: f = f.parent()({k: v / max(f._values.values()) for (k, v) in f._values.items()})
+    sage: f = Omega(f, check=False)  # long time
+
+We can determine the roots of this differential::
+
+    sage: # TODO
+
+There are precision problems in the above, so we add more centers at which we
+develop power series::
+
+    sage: # TODO
+
+"""
+######################################################################
+#  This file is part of sage-flatsurf.
+#
+#        Copyright (C) 2022-2024 Julian Rüth
+#
+#  sage-flatsurf is free software: you can redistribute it and/or modify
+#  it under the terms of the GNU General Public License as published by
+#  the Free Software Foundation, either version 2 of the License, or
+#  (at your option) any later version.
+#
+#  sage-flatsurf is distributed in the hope that it will be useful,
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#  GNU General Public License for more details.
+#
+#  You should have received a copy of the GNU General Public License
+#  along with sage-flatsurf. If not, see <https://www.gnu.org/licenses/>.
+######################################################################
+from sage.structure.parent import Parent
+from sage.structure.element import Element
+from sage.misc.cachefunc import cached_method, cached_function
+from sage.categories.all import SetsWithPartialMaps
+from scipy.integrate import quad
+
+import warnings
+
+with warnings.catch_warnings():
+    warnings.filterwarnings("ignore", category=DeprecationWarning)
+    import cppyy
+
+complex = None
+
+DEFAULT_BOUNDARY_ERROR = 1e-2
+
+
+def _cppyy():
+    global complex
+    if complex is None:
+        cppyy.include("complex")
+        complex = cppyy.gbl.std.complex["double"]
+    return cppyy
+
+
+def Ccpp(x):
+    _cppyy()
+    return complex(float(x.real()), float(x.imag()))
+
+
+@cached_function
+def zeta(d, n):
+    from sage.all import CDF
+
+    zeta = CDF.zeta(d)
+    return zeta**n / d
+
+
+def integral2arb(part, α, κ, d, ζd, n, β, λ, dd, ζdd, m, a, b, C, R):
+    r"""
+    Return the real/imaginary part of
+
+    \int_γ ζ_{d+1}^{κ (n+1)}/(d+1) (z-α)^\frac{n-d}{d+1} \overline{ζ_{dd+1}^{λ (m+1)}/(dd+1) (z-β)^\frac{m-dd}{dd+1}} dz
+
+    where γ(t) = (1-t)a + tb.
+    """
+    if not hasattr(_cppyy().gbl, "integral2arb"):
+        _cppyy().cppdef(
+            r"""
+        #include <acb_calc.h>
+        #include <string>
+
+        const int ARB_PREC=32;
+
+        struct Arb {
+            Arb() {
+                arb_init(value);
+            }
+
+            Arb(double d) {
+                arb_init(value);
+                arb_set_d(value, d);
+            }
+
+            ~Arb() {
+                arb_clear(value);
+            }
+
+            operator const arb_t&() const {
+                return value;
+            }
+
+            operator arb_t&() {
+                return value;
+            }
+
+            operator double() {
+                return arf_get_d(arb_midref(value), ARF_RND_NEAR);
+            }
+
+            arb_t value;
+        };
+
+        struct Mag {
+            Mag(double d) {
+                mag_init(value);
+                mag_set_d(value, d);
+            }
+
+            ~Mag() {
+                mag_clear(value);
+            }
+
+            operator const mag_t&() const {
+                return value;
+            }
+
+            mag_t value;
+        };
+
+        struct Acb {
+            Acb() {
+                acb_init(value);
+            }
+
+            Acb(const acb_t v) {
+                acb_init(value);
+                acb_set(value, v);
+            }
+
+            Acb(double re, double im) {
+                acb_init(value);
+                acb_set_d_d(value, re, im);
+            }
+
+            Arb real() {
+                Arb real;
+                acb_get_real(real, value);
+                return real;
+            }
+
+            Arb imag() {
+                Arb imag;
+                acb_get_imag(imag, value);
+                return imag;
+            }
+
+            ~Acb() {
+                acb_clear(value);
+            }
+
+            operator const acb_t&() const {
+                return value;
+            }
+
+            operator acb_t&() {
+                return value;
+            }
+
+            acb_t value;
+        };
+
+        struct Args {
+            Args(double Re_alpha, double Im_alpha, int kappa, int d, double Re_zeta_d, double Im_zeta_d, int n, double Re_beta, double Im_beta, int lambda, int dd, double Re_zeta_dd, double Im_zeta_dd, int m, double Re_a, double Im_a, double Re_b, double Im_b) :
+                alpha(Re_alpha, Im_alpha),
+                beta(Re_beta, Im_beta),
+                d(d),
+                dd(dd),
+                n(n),
+                m(m) {
+
+                Acb zeta_d_power(Re_zeta_d, Im_zeta_d);
+                acb_pow_si(zeta_d_power, zeta_d_power, kappa * (n + 1), ARB_PREC);
+                acb_div_si(zeta_d_power, zeta_d_power, d + 1, ARB_PREC);
+
+                Acb zeta_dd_power(Re_zeta_dd, Im_zeta_dd);
+                acb_pow_si(zeta_dd_power, zeta_dd_power, lambda * (m + 1), ARB_PREC);
+                acb_div_si(zeta_dd_power, zeta_dd_power, dd + 1, ARB_PREC);
+                acb_conj(zeta_dd_power, zeta_dd_power);
+
+                acb_sub(ba, Acb(Re_b, Im_b), Acb(Re_a, Im_a), ARB_PREC);
+
+                acb_abs(ba_, ba, ARB_PREC);
+
+                Acb ba__;
+                acb_set_round_arb(ba__, ba_, ARB_PREC);
+
+                acb_mul(constant, ba__, zeta_d_power, ARB_PREC);
+                acb_mul(constant, constant, zeta_dd_power, ARB_PREC);
+            }
+
+            Acb alpha, beta;
+            Acb ba;
+            Arb ba_;
+            int d, dd;
+            int n, m;
+            Acb constant;
+        };
+
+        double integral2arb(std::string part, double Re_alpha, double Im_alpha, int kappa, int d, double Re_zeta_d, double Im_zeta_d, int n, double Re_beta, double Im_beta, int lambda, int dd, double Re_zeta_dd, double Im_zeta_dd, int m, double Re_a, double Im_a, double Re_b, double Im_b) {
+            double res_d;
+
+            Acb res;
+
+            Args args(Re_alpha, Im_alpha, kappa, d, Re_zeta_d, Im_zeta_d, n, Re_beta, Im_beta, lambda, dd, Re_zeta_dd, Im_zeta_dd, m, Re_a, Im_a, Re_b, Im_b);
+
+            const auto func = [](acb_ptr out, const acb_t z, void * param, slong order, slong prec) -> int {
+                const Args* args = (const Args*)param;
+
+                if (order > 1) {
+                    // TODO: When order == 1 we need to verify that we are holomorphic somewhere. But where exactly?
+                    throw std::logic_error("derivatives of function not implemented/function not holomorphic");
+                }
+
+                Acb za(z);
+                acb_sub(za, za, args->alpha, ARB_PREC);
+                acb_pow_arb(za, za, Arb(1 / (double)(args->d + 1)), ARB_PREC);
+                acb_pow_si(za, za, args->n - args->d, ARB_PREC);
+
+                Acb zb(z);
+                acb_sub(zb, zb, args->beta, ARB_PREC);
+                acb_pow_arb(zb, zb, Arb(1 / (double)(args->dd + 1)), ARB_PREC);
+                acb_pow_si(zb, zb, args->m - args->dd, ARB_PREC);
+                acb_conj(zb, zb);
+
+                acb_mul(out, za, zb, ARB_PREC);
+
+                return 0;
+            };
+
+            if (acb_calc_integrate(res, func, &args, Acb(Re_a, Im_a), Acb(Re_b, Im_b), ARB_PREC /* rel_goal */, Mag(1e-64) /* abs_tol */, nullptr /* options */, ARB_PREC) != ARB_CALC_SUCCESS) {
+                throw std::logic_error("acb_calc_integrate() did not converge");
+            }
+
+            acb_mul(res, res, args.constant, ARB_PREC);
+            acb_div(res, res, args.ba, ARB_PREC);
+
+            if (part == "Re") {
+                return res.real();
+            } else if (part == "Im") {
+                return res.imag();
+            } else {
+                throw std::logic_error("unknown part");
+            }
+        }
+        """
+        )
+
+        _cppyy().load_library("arb")
+
+    return R(
+        _cppyy().gbl.integral2arb(
+            part,
+            float(α.real()),
+            float(α.imag()),
+            int(κ),
+            int(d),
+            float(ζd.real()),
+            float(ζd.imag()),
+            int(n),
+            float(β.real()),
+            float(β.imag()),
+            int(λ),
+            int(dd),
+            float(ζdd.real()),
+            float(ζdd.imag()),
+            m,
+            float(a.real()),
+            float(a.imag()),
+            float(b.real()),
+            float(b.imag()),
+        )
+    )
+
+
+def integral2cpp(part, α, κ, d, n, β, λ, dd, m, a, b, C, R):
+    r"""
+    Return the real/imaginary part of
+
+    \int_γ ζ_{d+1}^{κ (n+1)}/(d+1) (z-α)^\frac{n-d}{d+1} \overline{ζ_{dd+1}^{λ (m+1)}/(dd+1) (z-β)^\frac{m-dd}{dd+1}} dz
+
+    where γ(t) = (1-t)a + tb.
+    """
+    # Since γ(t) = (1 - t)a + tb, we have |·γ(t)| = |b - a|
+    constant = (
+        zeta(d + 1, κ * (n + 1)) * zeta(dd + 1, λ * (m + 1)).conjugate() * abs(b - a)
+    )
+
+    constant = Ccpp(constant)
+
+    α = Ccpp(α)
+    β = Ccpp(β)
+    a = Ccpp(a)
+    b = Ccpp(b)
+
+    n = int(n)
+    m = int(m)
+    d = int(d)
+    dd = int(dd)
+
+    if not hasattr(_cppyy().gbl, "value"):
+        _cppyy().cppdef(
+            r"""
+        #include <complex>
+
+        using complex = std::complex<double>;
+
+        complex pow(complex z, double e) {
+            if (e == 0) return 1;
+            if (e == 1) return z;
+            return std::pow(z, e);
+        }
+
+        complex value(double t, complex constant, complex a, complex alpha, int d, int n, complex b, complex beta, int dd, int m) {
+            complex z = (1 - t) * a + t * b;
+
+            complex za = pow(pow(z - alpha, 1 / (double)(d + 1)), n - d);
+            complex zb = std::conj(pow(pow(z - beta, 1 / (double)(dd + 1)), m - dd));
+
+            return constant * za * zb;
+        }
+        """
+        )
+
+    def pow(z, e):
+        z = complex(z)
+        e = complex(e)
+
+        if e == 0:
+            return complex(1)
+
+        if e == 1:
+            return z
+
+        return _cppyy().gbl.std.pow(z, e)
+
+    def value(t):
+        value = _cppyy().gbl.value(t, constant, a, α, d, n, b, β, dd, m)
+
+        if part == "Re":
+            return value.real
+        if part == "Im":
+            return value.imag
+
+        raise NotImplementedError
+
+    integral, error = quad(value, 0, 1)
+
+    return R(integral)
+
+
+def integral2(part, α, κ, d, ζd, n, β, λ, dd, ζdd, m, a, b, C, R):
+    r"""
+    Return the real/imaginary part of
+
+    \int_γ ζ_{d+1}^{κ (n+1)}/(d+1) (z-α)^\frac{n-d}{d+1} \overline{ζ_{dd+1}^{λ (m+1)}/(dd+1) (z-β)^\frac{m-dd}{dd+1}} dz
+
+    where γ(t) = (1-t)a + tb.
+    """
+    # Since γ(t) = (1 - t)a + tb, we have |·γ(t)| = |b - a|
+    constant = (
+        ζd ** (κ * (n + 1))
+        / (d + 1)
+        * (ζdd ** (λ * (m + 1)) / (dd + 1)).conjugate()
+        * abs(b - a)
+    )
+
+    def value(t):
+        z = (1 - t) * a + t * b
+
+        if d == 0:
+            za = (z - α) ** n
+        else:
+            za = (z - α).nth_root(d + 1) ** (n - d)
+
+        if dd == 0:
+            zb = ((z - β) ** m).conjugate()
+        else:
+            zb = ((z - β).nth_root(dd + 1) ** (m - dd)).conjugate()
+
+        value = constant * za * zb
+
+        if part == "Re":
+            return float(value.real())
+        if part == "Im":
+            return float(value.imag())
+
+        raise NotImplementedError
+
+    integral, error = quad(value, 0, 1)
+
+    return R(integral)
+
+
+@cached_function
+def Cab(C, a):
+    return C(*a)
+
+
+def define_solve():
+    if not hasattr(_cppyy().gbl, "solve"):
+        import os.path
+
+        _cppyy().include(
+            os.path.join(os.path.dirname(__file__), "..", "..", "mpreal-support.h")
+        )
+        _cppyy().cppdef(
+            r"""
+        #include <cassert>
+        #include <vector>
+        #include <iostream>
+        #include <eigen3/Eigen/LU>
+        #include <eigen3/Eigen/Sparse>
+        #include <eigen3/Eigen/OrderingMethods>
+
+        using namespace mpfr;
+        using namespace Eigen;
+        using std::vector;
+        typedef SparseMatrix<mpreal> MatrixXmp;
+        typedef Matrix<mpreal,Dynamic,1> VectorXmp;
+
+        VectorXmp solve(vector<vector<double>> _A, vector<double> _b)
+        {
+          // set precision to ? bits (double has only 53 bits)
+          mpreal::set_default_prec(256);
+          // Declare matrix and vector types with multi-precision scalar type
+
+          const int ROWS = _A.size();
+          const int COLS = _A[0].size();
+          MatrixXmp A = MatrixXmp(ROWS, COLS);
+          VectorXmp b = VectorXmp(ROWS);
+
+          for (int y = 0; y < ROWS; y++) {
+            for (int x = 0; x < COLS; x++) {
+              if (_A[y][x] != 0) {
+                A.insert(y, x) = _A[y][x];
+              }
+              b[y] = _b[y];
+            }
+          }
+
+          A.makeCompressed();
+
+          SparseQR<MatrixXmp, NaturalOrdering<int>> QRd;
+          QRd.compute(A);
+
+          assert(QRd.info() == Success);
+
+          VectorXmp x = QRd.solve(b);
+
+          assert(QRd.info() == Success);
+
+          return x;
+        }
+        """
+        )
+
+    return _cppyy().gbl.solve
+
+
+# TODO: This works around a problem when pyflatsurf is loaded. If pyflatsurf is loaded first, there are C++ errors.
+define_solve()
+
+
+class HarmonicDifferential(Element):
+    def __init__(self, parent, series, residue=None, cocycle=None):
+        super().__init__(parent)
+        if series is None:
+            raise ValueError  # why would we want series to be None again?
+        self._series = series
+        self._residue = residue
+        self._cocycle = cocycle
+
+    def _add_(self, other):
+        r"""
+        Return the sum of this harmonic differential and ``other`` by summing
+        their underlying power series.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, HarmonicDifferentials, SimplicialHomology, SimplicialCohomology
+            sage: T = translation_surfaces.square_torus().delaunay_triangulate().codomain()
+            sage: T.set_immutable()
+
+            sage: H = SimplicialHomology(T)
+            sage: a, b = H.gens()
+            sage: H = SimplicialCohomology(T)
+            sage: f = H({a: 1})
+
+            sage: Ω = HarmonicDifferentials(T, error=1e-3)
+
+            sage: ω = Ω(f) + Ω(f) + Ω(H({a: -2}))
+            sage: ω  # random output due to numerical noise
+            ((-2.00000000007046e-82*I) + (-9.99999999996877e-83*I)*z0 + (2.00000000003978e-81 + 3.99999999998751e-82*I)*z0^2 + (3.99999999995683e-81)*z0^3 + (-1.00000000001253e-79*I)*z0^4 + O(z0^5), (3.99999999996833e-83*I) + (-4.00000000006421e-83)*z1 + 0.000000000000000*z1^2 + (-1.99999999997841e-81*I)*z1^3 + (9.99999999983070e-81*I)*z1^4 + O(z1^5))
+            sage: ω.simplify()
+            (O(z0^5), O(z1^5))
+
+        """
+        return self.parent()(
+            {
+                triangle: self._series[triangle] + other._series[triangle]
+                for triangle in self._series
+            }
+        )
+
+    def _sub_(self, other):
+        return self.parent()(
+            {
+                triangle: self._series[triangle] - other._series[triangle]
+                for triangle in self._series
+            }
+        )
+
+    # TODO: Can we increase the default precision? Somehow, we do not get much more precision easily in the octagon. Why?
+    def error(self, kind=None, verbose=False, abs_tol=1e-4, rel_tol=1e-4):
+        r"""
+        Return whether this differential is likely inaccurate.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, HarmonicDifferentials, SimplicialHomology, SimplicialCohomology
+            sage: T = translation_surfaces.square_torus()
+            sage: T.set_immutable()
+
+            sage: H = SimplicialHomology(T)
+            sage: a, b = H.gens()
+            sage: H = SimplicialCohomology(T)
+            sage: f = H({a: 1})
+
+            sage: Ω = HarmonicDifferentials(T, error=1e-3)
+            sage: η = Ω(f)
+            sage: η.error()
+            False
+
+        """
+        error = False
+
+        def errors(expected, actual):
+            abs_error = float(abs(expected - actual))
+            rel_error = 0
+            if abs(expected) > 1e-12:
+                rel_error = abs_error / float(abs(expected))
+
+            return abs_error, rel_error
+
+        if kind is None or "residue" in kind:
+            if self._residue is not None:
+                report = f"Harmonic differential created by solving Ax=b with |Ax-b| = {self._residue}."
+                if verbose:
+                    print(report)
+                if self._residue > abs_tol:
+                    error = report
+                    if not verbose:
+                        return error
+
+        if kind is None or "cohomology" in kind:
+            if self._cocycle is not None:
+                for gen in self._cocycle.parent().homology().gens():
+                    expected = self._cocycle(gen)
+                    actual = self.integrate(gen).real
+                    if callable(actual):
+                        actual = actual()
+
+                    abs_error, rel_error = errors(expected, actual)
+
+                    report = f"Integrating along cycle gives {actual} whereas the cocycle gave {expected}, i.e., an absolute error of {abs_error} and a relative error of {rel_error}."
+                    if verbose:
+                        print(report)
+
+                    if abs_error > abs_tol or rel_error > rel_tol:
+                        error = report
+                        if not verbose:
+                            return error
+
+        if kind is None or "L2" in kind:
+            C = self.parent()._constraints()
+            consistency = self.parent()._L2_consistency_constraints()
+
+            abs_error = self._evaluate(consistency)
+
+            report = f"L2 norm of differential is {abs_error}."
+            if verbose:
+                print(report)
+
+            if abs_error > abs_tol:
+                error = report
+                if not verbose:
+                    return error
+
+            ## expected_cost = abs(abs_error / len(consistencies))
+
+            ## g = self.parent().surface().graphical_surface(polygon_labels=False, edge_labels=False)
+            ## plot = g.plot()
+            ## for (polygon_cell, segment, opposite_polygon_cell), cost in consistencies.items():
+            ##     cost =  abs(self._evaluate(cost))
+            ##     relative_cost = cost / expected_cost
+            ##     if relative_cost < 1:
+            ##         continue
+
+            ##     from sage.all import line2d
+            ##     polygon = g.graphical_polygon(polygon_cell.label())
+            ##     plot += line2d([polygon.transform(segment[0]), polygon.transform(segment[1])], color="red", thickness=int(relative_cost))
+            ## plot.show()
+
+        return error
+
+    def _error_l2(self):
+        C = self.parent()._constraints()
+        consistency = self.parent()._L2_consistency_constraints()
+
+        abs_error = self._evaluate(consistency)
+        return abs_error
+
+    def cohomology(self):
+        H = self.parent().cohomology()
+        return H({gen: self.integrate(gen).real() for gen in H.homology().gens()})
+
+    def _evaluate(self, expression):
+        r"""
+        Evaluate an expression by plugging in the coefficients of the power
+        series defining this differential.
+
+        This might not correspond to evaluating the actual power series somewhere.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, HarmonicDifferentials, SimplicialHomology, SimplicialCohomology
+            sage: T = translation_surfaces.square_torus()
+            sage: T.set_immutable()
+
+            sage: H = SimplicialHomology(T)
+            sage: a, b = H.gens()
+            sage: H = SimplicialCohomology(T)
+            sage: f = H({b: 1})
+
+            sage: Ω = HarmonicDifferentials(T)
+            sage: η = Ω(f)
+
+        Compute the constant coefficients::
+
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints
+            sage: C = PowerSeriesConstraints(Ω)
+            sage: R = C.symbolic_ring()
+            sage: gen = C._gen("Re", T(0, (1/2, 1/2)), 0)
+            sage: η._evaluate(gen)
+            1.00000000000000
+
+        """
+        coefficients = {}
+
+        for variable in expression.variables():
+            center = self.parent()._gen_center(variable)
+            degree = self.parent()._gen_degree(variable)
+
+            try:
+                coefficient = self._series[center][degree]
+            except (IndexError, KeyError):
+                import warnings
+
+                warnings.warn(
+                    f"expected a {degree}th coefficient of the power series around {center} but none found"
+                )
+                coefficients[variable] = 0
+                continue
+
+            if self.parent()._gen_is_real(variable):
+                coefficients[variable] = coefficient.real()
+            elif self.parent()._gen_is_imag(variable):
+                coefficients[variable] = coefficient.imag()
+            else:
+                raise NotImplementedError
+
+        value = expression(coefficients)
+
+        from flatsurf.geometry.power_series import PowerSeriesCoefficientExpression
+
+        if isinstance(value, PowerSeriesCoefficientExpression):
+            assert value.total_degree() <= 0
+            value = value.constant_coefficient()
+
+        return value
+
+    @cached_method
+    def precision(self):
+        # TODO: This is the number of coefficients of the power series but we use it as bit precision?
+        # TODO: There should not be a single global precision. Instead each
+        # cell has its own precision. Also these precisions are not comparable,
+        # since depending on the degree of the root, we need to scale things.
+        precisions = set(
+            series.precision_absolute() for series in self._series.values()
+        )
+        # assert len(precisions) == 1
+        return min(precisions)
+
+    def integrate(self, cycle, numerical=False):
+        # TODO: Generalize to more than just cycles.
+        r"""
+        Return the integral of this differential along the homology class
+        ``cycle``.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, HarmonicDifferentials, SimplicialHomology, SimplicialCohomology
+            sage: T = translation_surfaces.square_torus()
+            sage: T.set_immutable()
+
+            sage: H = SimplicialHomology(T)
+            sage: a, b = H.gens()
+            sage: H = SimplicialCohomology(T)
+
+        Construct a differential form such that integrating it along `a` yields real part
+        1, and along `b` real part 0::
+
+            sage: f = H({a: 1})
+
+            sage: Ω = HarmonicDifferentials(T)
+            sage: η = Ω(f)
+
+            sage: η.integrate(a).real()  # tol 1e-6
+            1
+
+            sage: η.integrate(b).real()  # tol 1e-6
+            0
+
+        """
+        if numerical:
+            raise NotImplementedError
+
+        C = self.parent()._constraints()
+        return self._evaluate(C.integrate(cycle))
+
+    def _repr_(self):
+        def sparse_parent(series):
+            return type(series.parent())(
+                series.parent().base_ring(),
+                series.parent().variable_name(),
+                sparse=True,
+            )
+
+        return repr(tuple(sparse_parent(s)(s) for s in self._series.values()))
+
+    def _series(self, label, coordinates):
+        r"""
+        Return a series `f(z) = Σ_{n ≥ 0} a_n (z-α)^\frac{n-d}{d+1}` such that
+        `f(z)dz` describes the differential near ``coordinates`` with a flat
+        coordinate ``z``.
+
+        The series is returned as a triple ``(d, α, a_n)`` where ``a_n`` is the
+        sequence of coefficients.
+        """
+        raise NotImplementedError
+
+    def rational_map(self, other=None):
+        r"""
+        Return the map to `\mathbb{P}^1` given by the quotient of this
+        differential and ``other``.
+
+        INPUT:
+
+        - ``other`` -- another differential of ``None`` (default: ``None``); if
+          ``None``, the quotient with `dz` is returned.
+        """
+        if other is None:
+            other = self.parent().dz()
+
+        return RationalMap(self, other)
+
+    def roots(self):
+        return self.rational_map().roots()
+
+    def simplify(self, zero_threshold=1e-6):
+        def simplify(series):
+            def simplify(coefficient):
+                if coefficient.real().abs() < zero_threshold:
+                    coefficient = coefficient.parent()(
+                        coefficient.imag() * coefficient.parent()("I")
+                    )
+                if coefficient.imag().abs() < zero_threshold:
+                    coefficient = coefficient.parent()(coefficient.real())
+
+                return coefficient
+
+            coefficients = {
+                exponent: simplify(coefficient)
+                for (exponent, coefficient) in zip(
+                    series.exponents(), series.coefficients()
+                )
+            }
+            coefficients = {
+                exponent: coefficient
+                for (exponent, coefficient) in coefficients.items()
+                if coefficient
+            }
+
+            return series.parent()(coefficients, prec=series.prec())
+
+        return type(self)(
+            self.parent(),
+            {center: simplify(series) for (center, series) in self._series.items()},
+            residue=self._residue,
+            cocycle=self._cocycle,
+        )
+
+
+class RationalMap:
+    r"""
+    A map from a translation surface to `\mathbb{P}^1` given by the quotient of
+    two differentials.
+    """
+
+    def __init__(self, numerator, denominator):
+        if numerator.parent() != denominator.parent():
+            raise ValueError(
+                "numerator and denominator must be from the same space of differentials"
+            )
+
+        self._numerator = numerator
+        self._denominator = denominator
+
+    def __repr__(self):
+        return f"({self._numerator})/({self._denominator})"
+
+    def __invert__(self):
+        return RationalMap(self._denominator, self._numerator)
+
+    def __call__(self, point):
+        # TODO: This should return a projective value.
+        differentials = self._numerator.parent()
+        cell = differentials._cells.cell(point)
+
+        complex = cell.complex_from_point(point)
+
+        center = cell.center()
+        numerator = self._numerator._series[center].polynomial()
+        denominator = self._denominator._series[center].polynomial()
+
+        while True:
+            n = numerator(complex)
+            d = denominator(complex)
+
+            if d == 0 and n == 0:
+                numerator = (numerator(numerator.parent().gen() + complex) >> 1)(
+                    numerator.parent().gen() - complex
+                )
+                denominator = (denominator(denominator.parent().gen() + complex) >> 1)(
+                    denominator.parent().gen() - complex
+                )
+                continue
+
+            return (n, d)
+
+    def derivative(self):
+        Omega = self._numerator.parent()
+
+        n = self._numerator._series
+        d = self._denominator._series
+
+        keys = n.keys()
+
+        return RationalMap(
+            Omega(
+                {
+                    key: d[key] * n[key].derivative() - n[key] * d[key].derivative()
+                    for key in keys
+                }
+            ),
+            Omega({key: d[key] * d[key] for key in keys}),
+        )
+
+    # TODO: Passing the boundary_error into every method is silly. There should
+    # be a global geometry that takes care of that.
+    def roots(self, boundary_error=DEFAULT_BOUNDARY_ERROR):
+        # TODO: Add optional parameter to control grouping of roots.
+        return self.preimages(0, boundary_error=boundary_error)
+
+    def ramification_points(self, boundary_error=DEFAULT_BOUNDARY_ERROR):
+        # Return triples (ramification point, ramification index, branch point)
+
+        # TODO: Add optional parameter to control grouping of points.
+        # TODO: This is missing points over infinity.
+        return self.derivative().roots(boundary_error=boundary_error)
+
+    def branch_points(self, boundary_error=DEFAULT_BOUNDARY_ERROR):
+        # TODO: Use the precision of the differential to determine which branch
+        # points should be identified.
+        raise NotImplementedError
+
+    def degree(self, boundary_error=DEFAULT_BOUNDARY_ERROR):
+        degrees = {}
+        for (p, e, q) in self.ramification_points(boundary_error=boundary_error):
+            if q not in degrees:
+                degrees[q] = 0
+            degrees[q] += e
+
+        assert len(set(degrees.values())) == 1
+
+        return next(iter(degrees.values()))
+
+    def _preimages_rational_roots(self, n, d, p, q):
+        r"""
+        Return the solutions of `n/d = p/q` where `n/d` is a meromorphic
+        function and `p/q` is a point on the projective line.
+        """
+        # TODO: Use the precision of the differential to determine which roots
+        # should be identified.
+
+        if q == 0:
+            return self._preimages_rational_roots(d, n, q, p)
+
+        n = n.polynomial()
+        d = d.polynomial()
+
+        roots = []
+
+        f = n * q - d * p
+
+        def is_zero(x):
+            return abs(x) < 1e-8
+
+        for (root, multiplicity) in f.roots():
+            if is_zero(d(root)):
+                if not is_zero(n(root)):
+                    # print(f"{n(root)} is not zero")
+                    pass
+
+                def mult(f, x):
+                    mult = 0
+                    while is_zero(f(x)):
+                        assert f != 0
+                        f //= f.parent().gen() - x
+                        mult += 1
+                    return mult
+
+                root_multiplicity = min(mult(n, root), mult(d, root))
+
+                if is_zero((f // (f.parent().gen() - root) ** root_multiplicity)(root)):
+                    multiplicity = 1
+                else:
+                    # print("no solution here")
+                    # print((f // (f.parent().gen() - root)**root_multiplicity)(root))
+                    multiplicity = 0
+
+            # if multiplicity != 1:
+            #     print(f"{multiplicity=}")
+
+            roots.extend([root] * multiplicity)
+
+        return roots
+
+    def preimages(self, p, q=1, boundary_error=DEFAULT_BOUNDARY_ERROR):
+        # For each cell contains a list of points that satisfy that
+        # numerator/denominator = p/q. Some points may be repeated if they are
+        # close to the boundary of a cell.
+        preimages = {}
+
+        differentials = self._numerator.parent()
+
+        surface = differentials.surface()
+        cells = differentials._cells
+
+        for center in surface.vertices():
+            cell = cells.cell_at_center(center)
+
+            numerator = self._numerator._series[center]
+            denominator = self._denominator._series[center]
+
+            complex_solutions = [
+                complex_solution
+                for complex_solution in self._preimages_rational_roots(
+                    numerator, denominator, p, q
+                )
+                if cell.point_from_complex(
+                    complex_solution, boundary_error=boundary_error
+                )
+                is not None
+            ]
+
+            preimages[center] = complex_solutions
+
+        return self._preimages_merge_repetitions(
+            preimages, boundary_error=boundary_error
+        )
+
+    def _preimages_normalize_opposite(self, complex, candidates, boundary_error):
+        candidate = min(candidates, key=lambda c: abs(c - complex))
+
+        # TODO: Compare to boundary_error to check plausibility
+
+        return candidate
+
+    def _preimages_merge_repetitions(self, preimages, boundary_error):
+        differentials = self._numerator.parent()
+
+        surface = differentials.surface()
+        cells = differentials._cells
+
+        identified_points = []
+
+        for center, complexes in preimages.items():
+            for complex in complexes:
+                cell = cells.cell_at_center(center)
+                # For each preimage we determine which other centers should see that
+                # preimage.
+                # (Any preimage that is close to a boundary, should be seen by the
+                # center on the other side of the boundary.)
+                for (
+                    opposite_center,
+                    opposite_complex,
+                ) in cell.opposite_representations(complex, boundary_error):
+                    # Find the representation of that point as seen from the
+                    # other side and group this pair of points.
+                    if not preimages[opposite_center]:
+                        # print("no opposite for this point")
+                        assert (
+                            cell.point_from_complex(
+                                complex, boundary_error=boundary_error / 2
+                            )
+                            is None
+                        )
+                        continue
+
+                    opposite_complex = self._preimages_normalize_opposite(
+                        opposite_complex,
+                        preimages[opposite_center],
+                        boundary_error=boundary_error,
+                    )
+                    # print(f"Identifying {(center, complex)} and {(opposite_center, opposite_complex)}")
+                    identified_points.append(
+                        ((center, complex), (opposite_center, opposite_complex))
+                    )
+
+        # Throw away any points that are too far across the boundary.
+        points_without_noise = []
+
+        for center, complexes in preimages.items():
+            for complex in complexes:
+                cell = cells.cell_at_center(center)
+
+                if (
+                    cell.point_from_complex(complex, boundary_error=boundary_error / 2)
+                    is not None
+                ):
+                    points_without_noise.append((center, complex))
+
+        points_without_noise.sort(key=lambda p: abs(p[1]))
+
+        # Pick one representative of each group of points.
+        points_without_repetitions = []
+
+        for p in points_without_noise:
+            # This could easily be done sub-cubic but it's likely not a bottleneck ever.
+            # TODO: Instead of picking any representative we should probably
+            # pick the one that has likely the best precision (or average all
+            # the representatives.)
+            for x, y in identified_points:
+                if x == p and y in points_without_repetitions:
+                    # print("!", p)
+                    break
+                if y == p and x in points_without_repetitions:
+                    # print("!", p)
+                    break
+            else:
+                # print(p)
+                points_without_repetitions.append(p)
+
+        # Turn the complex solutions into actual points of the surface.
+        points = []
+
+        for (center, complex) in points_without_repetitions:
+            cell = cells.cell_at_center(center)
+            point = cell.point_from_complex(complex, boundary_error=boundary_error)
+            assert point is not None
+            points.append(point)
+
+        return points
+
+    def monodromy(self, base_point=None, steps=None, branch_points=None):
+        r"""
+        Return the monodromy group of this rational map.
+
+        INPUT:
+
+        - ``base_point`` -- a point on the projective line
+
+        - ``steps`` -- an integer (default: ``None``); the number of steps to
+          use initially when walking from the ``base_point`` to the branch
+          point and around the branch point. If ``None``, a default is chosen
+          automatically.
+
+        - ``branch_points`` -- all branch points of this map as points on the
+          projective line
+
+        TODO: Add a check that verifies that Riemann Hurwitz and prod() == one
+        checks out. Show these things in the examples.
+
+        """
+        if branch_points is None:
+            branch_points = self.branch_points()
+
+        if base_point is None:
+            raise NotImplementedError(
+                "cannot select a monodromy base_point automatically yet"
+            )
+
+        finite_branch_points = [p for p in branch_points if p[1] != 0]
+        infinite_branch_points = [p for p in branch_points if p[1] == 0]
+
+        # Sort branch points counterclockwise around the base point
+        from sage.all import atan2
+
+        branch_points = (
+            sorted(
+                finite_branch_points,
+                key=lambda p: atan2(
+                    *list((p[0] / p[1]) - (base_point[0] / base_point[1]))[::-1]
+                ),
+            )
+            + infinite_branch_points
+        )
+
+        from sage.all import parallel
+
+        @parallel
+        def monodromy_generator(branch_point):
+            return self.monodromy_generator(
+                base_point=base_point,
+                branch_point=branch_point,
+                steps=steps,
+                branch_points=branch_points,
+            )
+
+        permutations = {}
+        for ((branch_point,), kwargs), generator in monodromy_generator(
+            [(branch_point,) for branch_point in branch_points]
+        ):
+            permutations[branch_point] = generator
+
+        generators = [permutations[branch_point] for branch_point in branch_points]
+
+        domain = list(generators[0].keys())
+
+        from sage.all import Permutation, PermutationGroup
+
+        return PermutationGroup(
+            [
+                Permutation([domain.index(gen[x]) + 1 for x in domain]).cycle_string()
+                for gen in generators
+            ],
+            canonicalize=False,
+        )
+
+    def monodromy_generator(
+        self, base_point, branch_point, steps=None, branch_points=None
+    ):
+        r"""
+        Return the permutation of preimages of ``base_point`` corresponding to
+        a loop around ``branch_point``.
+
+        INPUT:
+
+        - ``base_point`` -- a point on the projective line
+
+        - ``branch_point`` -- a point on the projective line
+
+        - ``steps`` -- an integer (default: ``None``); the number of steps to
+          use initially when walking from the ``base_point`` to the
+          ``branch_point`` and around the ``branch_point``. If ``None``, a
+          default is chosen automatically.
+
+        OUTPUT: A bidict encoding a bijection on the preimages of ``base_point``.
+        """
+        if branch_points is None:
+            branch_points = self.branch_points()
+
+        loop, paths = self._monodromy_loop(
+            base_point=base_point,
+            branch_point=branch_point,
+            branch_points=branch_points,
+            steps=steps,
+        )
+
+        from bidict import bidict
+
+        return bidict({path[0]: path[-1] for path in paths})
+
+    def _monodromy_loop(self, base_point, branch_point, branch_points, steps=None):
+        r"""
+        Return a loop in `\mathbb{P}^1` and paths in the surface formed by the
+        preimages of that loop.
+
+        The loop starts and ends at ``base_point`` and walks around
+        ``branch_point`` (counterclockwise) without walking around any of the
+        other ``branch_points``.
+
+        INPUT:
+
+        - ``base_point`` -- a point on the projective line
+
+        - ``branch_point`` -- a point on the projective line
+
+        - ``branch_points`` -- points on the projective line; all the branch
+          point of this rational function
+
+        - ``steps`` -- an integer (default: ``None``); the number of steps to
+          use initially when walking from the ``base_point`` to the
+          ``branch_point`` and around the ``branch_point``. If ``None``, a
+          default is chosen automatically.
+
+        OUTPUT: A tuple (loop, paths) where loop is a sequence of points in the
+        projective line and paths is a list of paths formed by points in the
+        surface.
+        """
+        if steps is None:
+            steps = 8
+
+        finite_branch_points = [p[0] / p[1] for p in branch_points if p[1] != 0]
+
+        is_finite = branch_point[1] != 0
+
+        if is_finite:
+            center = branch_point[0] / branch_point[1]
+
+            if center not in finite_branch_points:
+                raise ValueError("branch_point must be one of branch_points")
+
+            radius = (
+                min(
+                    abs(center - other)
+                    for other in finite_branch_points
+                    if other != center
+                )
+                / 2
+            )
+        else:
+            center = sum(finite_branch_points) / len(finite_branch_points)
+            radius = max(abs(center - other) for other in finite_branch_points) * 3 / 2
+
+        base_point = base_point[0] / base_point[1]
+
+        # First, we move from the base point to the closest point on the circle
+        # with "radius" around the center.
+        center_to_base_point = base_point - center
+        center_to_base_point /= center_to_base_point.abs()
+        circle_base_point = center + radius * center_to_base_point
+
+        loop = [base_point]
+        paths = [[p] for p in self.preimages(base_point)]
+
+        degree = len(paths)
+
+        def align_preimages(preimages):
+            previous = [path[-1] for path in paths]
+
+            aligned_preimages = [None] * len(preimages)
+
+            erasure = self.surface().erase_marked_points()
+            previous = [erasure(p) for p in previous]
+
+            distances = erasure.codomain().distance_matrix_points(previous)
+
+            insertion = erasure.codomain().insert_marked_points(*previous)
+            previous = [insertion(p) for p in previous]
+
+            def distance(v, w):
+                if v in previous and w in previous:
+                    return distances[previous.index(v)][previous.index(w)]
+
+                return None
+
+            for preimage in preimages:
+                nclosest = iter(
+                    insertion(erasure(preimage)).nclosest(previous, distance=distance)
+                )
+                closest_distance, closest_point = next(nclosest)
+                second_closest_distance, second_closest_point = next(nclosest)
+
+                # TODO: Make 2 configurable.
+                if second_closest_distance < 2 * closest_distance:
+                    print(
+                        f"Not sure which path preimage continues. Best options were too close at {closest_distance} and {second_closest_distance}"
+                    )
+                    return None
+
+                aligned_preimages[previous.index(closest_point)] = preimage
+
+            if None in aligned_preimages:
+                print("Several points continue the same path. Need to refine.")
+                return None
+
+            return aligned_preimages
+
+        def walk(step):
+            while True:
+                refinements = []
+                while True:
+                    next = step(len(refinements))
+
+                    if next is None:
+                        return
+
+                    if next in refinements:
+                        refinements.append(None)
+                        continue
+
+                    refinements.append(next)
+
+                    print(f"Walking to {next}")
+
+                    preimages = self.preimages(next)
+                    preimages = align_preimages(preimages)
+
+                    if preimages is None:
+                        print("refining path")
+                        continue
+
+                    loop.append(next)
+                    for path, preimage in zip(paths, preimages):
+                        path.append(preimage)
+
+                    break
+
+        def segment(destination):
+            total = destination - loop[0]
+
+            def step(refinements):
+                if loop[-1] == destination:
+                    return None
+
+                remaining = (destination - loop[-1]).abs()
+                delta = total / (steps * (refinements + 1))
+
+                if delta.abs() >= remaining:
+                    return destination
+
+                return loop[-1] + delta
+
+            walk(step)
+
+        def circle(center, radius):
+            angle = 0
+            angle_delta = 0
+
+            start = loop[-1]
+
+            def step(refinements):
+                nonlocal angle_delta, angle
+                if refinements == 0:
+                    angle += angle_delta
+
+                if angle >= 1:
+                    return None
+
+                from sage.all import QQ
+
+                angle_delta = QQ((1, steps * (refinements + 1)))
+
+                from sage.all import CDF
+
+                rotation = CDF.zeta(steps * (refinements + 1))
+
+                if angle + angle_delta >= 1:
+                    return start
+
+                return (loop[-1] - center) * rotation + center
+
+            walk(step)
+
+        segment(circle_base_point)
+
+        initial_loop = loop[:]
+        initial_paths = [path[:] for path in paths]
+
+        circle(center, radius)
+
+        # No need to do the walk to the starting point again, it's just the
+        # reverse of the initial segment.
+        # segment(base_point)
+
+        loop.extend(initial_loop[::-1][1:])
+
+        for initial_path in initial_paths:
+            for path in paths:
+                if initial_path[-1] == path[-1]:
+                    path.extend(initial_path[::-1][1:])
+                    break
+            else:
+                assert (
+                    False
+                ), f"no path in {paths} can be continued with reversed {initial_path}"
+
+        return loop, paths
+
+    def surface(self):
+        return self._numerator.parent().surface()
+
+    def monodromy_plots(self, base_point, steps=10, branch_points=None):
+        if base_point[1] == 0:
+            raise NotImplementedError
+
+        base_point = base_point[0] / base_point[1]
+
+        if branch_points is None:
+            branch_points = self.branch_points()
+
+        finite_branch_points = [p[0] / p[1] for p in branch_points if p[1] != 0]
+
+        infinite_branch_point = any(p[1] == 0 for p in branch_points)
+
+        finite_radii = {
+            p: min(abs(p - other) for other in finite_branch_points if other != p) / 2
+            for p in finite_branch_points
+        }
+
+        if infinite_branch_point:
+            infinite_center = sum(finite_branch_points) / len(finite_branch_points)
+            infinite_radius = (
+                max(abs(infinite_center - other) for other in finite_branch_points)
+                * 3
+                / 2
+            )
+
+        S = self._numerator.parent().surface()
+        GS = S.graphical_surface(edge_labels=False, polygon_labels=False)
+
+        from sage.all import oo
+
+        from sage.all import point2d, Graphics
+
+        P1 = Graphics()
+        for branch_point in finite_branch_points + (
+            [oo] if infinite_branch_point else []
+        ):
+            if branch_point == oo:
+                radius = infinite_radius
+            else:
+                radius = finite_radii[branch_point]
+
+            center = branch_point
+            if branch_point == oo:
+                center = infinite_center
+
+            def plot(x, color="blue"):
+                return point2d([x])
+
+            def move(P, Q):
+                delta = (Q - P) / steps
+                return sum(plot(P + delta * step) for step in range(steps))
+
+            def cycle(center, start, ccw=True):
+                from sage.all import CDF
+
+                rotation = CDF.zeta(steps)
+                if not ccw:
+                    rotation = rotation.conjugate()
+
+                start = min(
+                    range(steps),
+                    key=lambda step: (
+                        start - (center + rotation**step * radius)
+                    ).abs(),
+                )
+
+                return sum(
+                    plot(center + rotation ** (start + step) * radius)
+                    for step in range(steps)
+                )
+
+            start = (
+                center - (center - base_point) / (center - base_point).abs() * radius
+            )
+
+            P1 += move(base_point, start)
+            P1 += cycle(center, start)
+            P1 += move(start, base_point)
+
+        P1 += point2d(finite_branch_points, color="red")
+
+        P1 += plot(base_point, color="green")
+
+        def monodromy_plot(branch_point, all_ramification_points=None):
+            print(f"Creating animation around {branch_point}")
+
+            if all_ramification_points is None:
+                if branch_point == oo:
+                    ramification_points = self.preimages(1, 0)
+                else:
+                    ramification_points = self.preimages(branch_point, 1)
+            else:
+                # TODO: Only consider the ramification points over this branch point.
+                ramification_points = all_ramification_points
+
+            ramification_points = sum(
+                p.plot(GS, color="red") for p in ramification_points
+            )
+
+            if branch_point == oo:
+                radius = infinite_radius
+            else:
+                radius = finite_radii[branch_point]
+
+            center = branch_point
+            if branch_point == oo:
+                center = infinite_center
+
+            def plot(x, color="orange"):
+                G = GS.plot()
+                try:
+                    G += ramification_points + sum(
+                        p.plot(GS, color=color) for p in self.preimages(x)
+                    )
+                except Exception:
+                    print("frame missed")
+
+                return G.inset(
+                    P1 + point2d([x], color=color), pos=(0.8, 0.8, 0.2, 0.2), fontsize=5
+                )
+
+            def move(P, Q):
+                delta = (Q - P) / steps
+                return [plot(P + delta * step) for step in range(steps)]
+
+            def cycle(center, start, ccw=True):
+                from sage.all import CDF
+
+                rotation = CDF.zeta(steps)
+                if not ccw:
+                    rotation = rotation.conjugate()
+
+                start = min(
+                    range(steps),
+                    key=lambda step: (
+                        start - (center + rotation**step * radius)
+                    ).abs(),
+                )
+
+                return [
+                    plot(center + rotation ** (start + step) * radius)
+                    for step in range(steps)
+                ]
+
+            start = (
+                center - (center - base_point) / (center - base_point).abs() * radius
+            )
+
+            frames = [plot(base_point, color="green")]
+            print(f"moving from {base_point} to {start}")
+            frames.extend(move(base_point, start))
+            print(f"walking around {center}")
+            frames.extend(cycle(center, start))
+            print(f"moving back to {base_point} from {start}")
+            frames.extend(move(start, base_point))
+
+            from sage.all import animate
+
+            return animate(frames, dpi=512)
+
+        for p in finite_branch_points:
+            yield monodromy_plot(p)
+        if infinite_branch_point:
+            yield monodromy_plot(oo)
+
+
+# TODO: Make these unique for each surface (without using UniqueRepresentation because equal surfaces can be distinct.)
+class HarmonicDifferentialSpace(Parent):
+    r"""
+    The space of harmonic differentials on this surface.
+
+    EXAMPLES::
+
+        sage: from flatsurf import translation_surfaces, HarmonicDifferentials, SimplicialCohomology
+        sage: T = translation_surfaces.square_torus()
+        sage: T.set_immutable()
+
+        sage: Ω = HarmonicDifferentials(T); Ω
+        Ω(Translation Surface in H_1(0) built from a square)
+
+    ::
+
+        sage: H = SimplicialCohomology(T)
+        sage: Ω(H())
+        (O(z0^5), O(z1^5))
+
+    ::
+
+        sage: a, b = H.homology().gens()
+        sage: f = H({b: 1})
+        sage: η = Ω(f)
+        sage: η.integrate(a).real()  # tol 1e-6
+        0
+        sage: η.integrate(b).real()  # tol 1e-6
+        1
+
+    """
+    Element = HarmonicDifferential
+
+    def __init__(
+        self, surface, error=None, cell_decomposition=None, check=True, category=None
+    ):
+        # TODO: Just order labels by their order in surface.labels() instead.
+        try:
+            sorted(surface.labels())
+        except Exception:
+            raise NotImplementedError(
+                "labels on the surface must be sortable so we use label order to make a choice of n-th roots"
+            )
+
+        if cell_decomposition is None:
+            if surface.genus() == 1:
+                from flatsurf.geometry.voronoi import VoronoiCellDecomposition
+
+                cell_decomposition = VoronoiCellDecomposition(surface)
+            else:
+                from flatsurf.geometry.voronoi import VoronoiCellDecomposition
+
+                cell_decomposition = VoronoiCellDecomposition(surface)
+
+        if error is None:
+            error = 1e-3
+
+        # TODO: Add defaults for error and cell_decomposition.
+
+        Parent.__init__(self, category=category or SetsWithPartialMaps())
+
+        self._surface = surface
+        self._error = error
+        self._cells = cell_decomposition
+        self._centers = list(self._cells.centers())
+
+        if check:
+            if any(
+                self._relative_radius_of_convergence(cell) >= 1 for cell in self._cells
+            ):
+                raise ValueError(
+                    "cell decomposition is such that cells contain points outside of their center's radius of convergence"
+                )
+
+            [self.ncoefficients(v) for v in surface.vertices()]
+
+    def change(self, surface=None):
+        if surface is not None:
+            self = HarmonicDifferentials(
+                surface=surface,
+                error=self._error,
+                cell_decomposition=self._cells.change(surface=surface),
+            )
+
+        return self
+
+    # TODO: This does not cache :(
+    @cached_method
+    def ncoefficients(self, center):
+        r"""
+        Return the number of coefficients we need to use for the power series
+        at ``center`` to obtain the prescribed error in the differentials.
+
+        .. NOTE::
+
+            We need to clarify the notion of "error" here.
+
+            Let η be the differential we are trying to compute and let η' be an
+            approximation to that differential (given by a truncated power
+            series at each vertex of the surface.)
+
+            Away from its poles on the flat z-chart, we can write `η = f(z)dz`
+            and `η' = f'(z)dz`.. We want to bound the absolute error of `f'(z)`
+            at the place where it's approximating `f(z)` worst, namely far away
+            from the centers of the cells.
+
+            We are determining the number of power series coefficients needed
+            to bound this error for each approximation of the differential at
+            each center of a cell.
+
+            Note that this is a strange notion. Absolute error is not a
+            terribly meaningful notion in the first place but relative errors
+            are very hard to argue with when there are zeros. A good notion of
+            error would actually have been the error in the rational function
+            induced by η (as a distance on the unit sphere representing the
+            projective line.) But again, it is very hard to control the
+            estimates there.
+
+        ALGORITHM:
+
+        The tool we are using here is a standard approximation for the error
+        term when approximating an analytic function with a polynomial, see
+        https://en.wikipedia.org/wiki/Taylor's_theorem#Taylor's_theorem_in_complex_analysis
+
+        Namely, let `f(y) = \sum a_n y^n` be an analytic function on an open
+        disk of radius `R`. Let `P_k(y)=a_0 + \cdots + a_k y^k` be the
+        truncation of this power series and `R_k(y) = f(y) - P_k(y)`. We want
+        to estimate `|R_k(y)|` for all `|y|<r_\mathrm{cell}<R`.
+
+        Pick `r` with `r_\mathrm{cell} < r < R` and let `\beta =
+        r_\mathrm{cell} / r`. Then we have the estimate
+
+        .. MATH::
+
+            |R_k(y)| \le \frac{M_r\beta^{k+1}}{1-\beta}`
+
+        where `M_r` is the maximum of `f` on the circle of radius `r`.
+
+        To estimate `M_r` we can use any finite approximation of `f(y) \simeq
+        \sum b_n y^n` and get that approximately `M_r \le \sum |b_n| r^n`.
+
+        Since we can pick `r` arbitrarily with `r_\mathrm{cell} < r < R`, we
+        are going to pick it so it minimizes
+
+        .. MATH::
+
+            \epsilon_y := \sum |b_n| r^n\frac{r_\mathrm{cell}}{r - r_\mathrm{cell}}\left(\frac{r_\mathrm{cell}}{r}\right)^k
+
+        which is just the formula from above with `M_r` and `\beta` plugged in.
+
+        Algorithmically, if we want this error term to go below a certain
+        `\epsilon`, we iterate over all the integers `k` and numerically
+        optimize `r` until `\epsilon \le \epsilon_y`.
+
+        So far this discussion only directly applies to a differential that is
+        given by `f(z)dz` with an analytical `f(z)`. However, at singularities
+        the differential is of the form `g(y)dy` with a power series `g(y)=\sum
+        a_ny^n` centered at the singularity `y=0`. Let `d` be the degree of the
+        root at `y=0`, so for a regular point it would be `d=1`.
+
+        Since we have `y^d = z - z_0` for some base point `z_0`, we get
+
+        .. MATH::
+
+            dy = \frac{\zeta}{d} \left(z-z_0\right)^\frac{1-d}{d}dz
+
+        where `\zeta` is a certain root of unity.
+
+        If we plug this into `\eta = g(y)dy = f(z)dz`, we get
+
+        .. MATH::
+
+            f(z) = \frac{\zeta}{d} \left(z-z_0\right)^\frac{1-d}{d} g(y).
+
+        Let `\epsilon_z(z) = |f(z) - f'(z)|` be the absolute error in an
+        approximation to `f(z)`. From the above we get that
+
+        .. MATH::
+
+            \epsilon_z(z) = \frac{1}{d} |z|^\frac{1-d}{d} \epsilon_y(y) \le \frac{1}{d} r_{\mathrm{cell},z}^\frac{1-d}{d} \epsilon_y(y)
+
+        where `|z|` is the distance from `y=0` to `z` on the flat `z`-chart.
+
+        We plug in the description of `\epsilon_y(y)` we had determined at the
+        start and since `|z| \le r_{\mathrm{cell},z} =
+        r_{\mathrm{cell},y}^{1/d}`, we can simplify to
+
+        .. MATH::
+
+            \epsilon_z(z) \le \frac{1}{d} \frac{r_{\mathrm{cell},y}^{2-d}}{r_y - r_{\mathrm{cell},y}}\left(\frac{r_{\mathrm{cell},y}}{r_y}\right)^k \sum |b_n| r_y^n.
+
+        """
+        k = 1
+        while self._ncoefficients_epsilon_z(center, k) > self._error:
+            k += 1
+
+        # print(
+        #     f"{k} coefficients at {center} with degree {center.angle()} for an error of {self._ncoefficients_epsilon_z(center, k)}"
+        # )
+        return k
+
+    def _ncoefficients_epsilon_z(self, center, k):
+        d = center.angle()
+        cell = self._cells.cell_at_center(center)
+        rcelly = float(cell.radius()) ** (1 / d)
+
+        # TODO: Numerically optimize this pair of values.
+        ry = float(center.radius_of_convergence()) ** (1 / d)
+        mr = 1 / d
+
+        return rcelly ** (2 - d) / (ry - rcelly) / d * (rcelly / ry) ** k * mr
+
+    def dz(self):
+        return self(
+            {
+                v: v.angle()
+                * self._constraints().power_series_ring(v).gen() ** (v.angle() - 1)
+                for v in self.surface().vertices()
+            }
+        )
+
+        # TODO: We should maybe test against thi path:
+        # fdz = HH({gamma: RDF(sum(c * S.polygon(label).edge(edge)[0] for (label, edge), c in gamma._chain.monomial_coefficients().items())) for gamma in H.gens()})
+        # return self(self.fdz(), check=False)
+
+    def surface(self):
+        return self._surface
+
+    @cached_method
+    def _relative_radius_of_convergence(self, cell):
+        r"""
+        Return the :meth:`Cell.radius` of the ``cell`` divided by the radius of
+        convergence at the center point of that cell as a floating point number.
+        """
+        R = float(cell.center().radius_of_convergence())
+        r = float(cell.radius())
+
+        return r / R
+
+    @cached_method
+    def _relative_inradius_of_convergence(self, cell):
+        R = float(cell.center().radius_of_convergence())
+        r = float(cell.inradius())
+
+        return r / R
+
+    def error_plot(
+        self, graphical_surface=None, cutoff=1.0, plot_points=20, regions=True
+    ):
+        if graphical_surface is None:
+            graphical_surface = self.surface().graphical_surface(
+                polygon_labels=False, edge_labels=False
+            )
+
+        plot = graphical_surface.plot(fill=False)
+
+        from sage.all import var
+
+        x, y = var("x,y")
+
+        # TODO: Would be better to use a global region plot over the entire surface.
+        for label in self.surface().labels():
+            from sage.all import RDF
+
+            polygon = self.surface().polygon(label).change_ring(RDF)
+
+            graphical_polygon = graphical_surface.graphical_polygon(label)
+
+            for polygon_cell in self._cells.polygon_cells(label):
+                radius_of_convergence = float(
+                    polygon_cell.cell().center().radius_of_convergence()
+                )
+
+                def is_visible(x, y):
+                    from sage.all import vector
+
+                    xy = vector((x, y))
+
+                    xy_polygon = graphical_polygon.transform_back(xy)
+                    if polygon.get_point_position(xy_polygon).is_outside():
+                        return False
+
+                    error = (
+                        xy_polygon - polygon_cell.center()
+                    ).norm() / radius_of_convergence
+                    if error < cutoff:
+                        return False
+
+                    return polygon_cell.contains_point(xy_polygon)
+
+                from sage.all import region_plot
+
+                if regions:
+                    plot += region_plot(
+                        is_visible,
+                        (x, graphical_polygon.xmin(), graphical_polygon.xmax()),
+                        (y, graphical_polygon.ymin(), graphical_polygon.ymax()),
+                        plot_points=plot_points,
+                        incol="orange",
+                        outcol=None,
+                        bordercol="lightgrey",
+                        alpha=0.2,
+                    )
+
+                for corner in polygon_cell.corners():
+                    xy = graphical_polygon.transform(corner)
+                    if is_visible(*xy):
+                        from sage.all import point2d
+
+                        plot += point2d([xy], color="red")
+
+        return plot + self._cells.plot(graphical_surface)
+
+    # def error_location(self, cell=None):
+    #     if cell is None:
+    #         cell = self.error_cell()
+
+    #     return cell.furthest_point()
+
+    # def error_cell(self):
+    #     return max(self._voronoi_diagram().cells(), key=lambda cell: self.error(cell=cell))
+
+    def _repr_(self):
+        return f"Ω({self._surface})"
+
+    # TODO: Move to some class that is shared between harmonic differentials
+    # and constraints that abstracts away details of the symbolic ring.
+    def _gen_center(self, gen):
+        assert self._gen_is_real(gen) or self._gen_is_imag(gen)
+
+        gen, degree = gen.describe()
+
+        return self._centers[int(gen.split(",")[0][4:])]
+
+    # TODO: Move to some class that is shared between harmonic differentials
+    # and constraints that abstracts away details of the symbolic ring.
+    def _gen_is_lagrange(self, gen):
+        gen, degree = gen.describe()
+
+        return gen == "λ?"
+
+    # TODO: Move to some class that is shared between harmonic differentials
+    # and constraints that abstracts away details of the symbolic ring.
+    def _gen_is_real(self, gen):
+        gen, degree = gen.describe()
+
+        return gen.startswith("Re(a")
+
+    # TODO: Move to some class that is shared between harmonic differentials
+    # and constraints that abstracts away details of the symbolic ring.
+    def _gen_is_imag(self, gen):
+        gen, degree = gen.describe()
+
+        return gen.startswith("Im(a")
+
+    # TODO: Move to some class that is shared between harmonic differentials
+    # and constraints that abstracts away details of the symbolic ring.
+    def _gen_degree(self, gen):
+        gen, degree = gen.describe()
+        return degree
+
+    @cached_method(key=lambda self, check: None, do_pickle=True)
+    def basis(self, check=True):
+        return [self(gen, check=check) for gen in self.cohomology().gens()]
+
+    def cohomology(self):
+        from flatsurf.geometry.cohomology import SimplicialCohomology
+
+        return SimplicialCohomology(self._surface)
+
+    @cached_method(key=lambda self, check: None, do_pickle=True)
+    def period_matrix(self, check=True):
+        from flatsurf.geometry.homology import SimplicialHomology
+
+        symplectic_basis = SimplicialHomology(self._surface).symplectic_basis()
+        symplectic_basis = symplectic_basis[: len(symplectic_basis) // 2]
+
+        from sage.all import matrix
+
+        return matrix(
+            [
+                [
+                    differential.integrate(path)
+                    for differential in self.basis(check=check)
+                ]
+                for path in symplectic_basis
+            ]
+        )
+
+    def _element_constructor_(self, x, *args, **kwargs):
+        if not x:
+            return self.element_class(self, None, *args, **kwargs)
+
+        if isinstance(x, dict):
+            return self.element_class(self, x, *args, **kwargs)
+
+        return self._element_from_cohomology(x, *args, **kwargs)
+
+    def _constraints(self):
+        return PowerSeriesConstraints(self)
+
+    # TODO: The caching is a huge spaghetti mess.
+    @cached_method
+    def _L2_consistency_constraints(self):
+        return self._constraints()._L2_consistencies()
+
+    def _element_from_cohomology(self, cocycle, /, algorithm=["L2"], check=True):
+        # TODO: In practice we could speed things up a lot with some smarter
+        # caching. A lot of the quantities used in the computations only depend
+        # on the surface & precision. When computing things for many cocycles
+        # we do not need to recompute them. (But currently, we probably do
+        # because they live in the constraints instance.)
+
+        # We develop a consistent system of power series at each vertex of the Voronoi diagram
+        # to describe a differential.
+
+        # Let η be the differential we are looking for. To describe η we will use ω, the differential
+        # corresponding to the flat structure given by our triangulation on this Riemann surface.
+        # Then f:=η/ω is a meromorphic function which we can develop locally into a Laurent series.
+        # Away from the vertices of the triangulation, ω has no zeros, so f has no poles there and is
+        # thus given by a power series.
+
+        # At each vertex of the Voronoi diagram, write f=Σ a_k z^k + O(z^prec). Our task is now to determine
+        # the a_k.
+
+        constraints = self._constraints()
+
+        # We use a variety of constraints. Which ones to use exactly is
+        # determined by the "algorithm" parameter. If algorithm is a dict, it
+        # can be used to configure aspects of the constraints.
+        def get_parameter(alg, default):
+            nonlocal algorithm
+            assert alg in algorithm
+            if isinstance(algorithm, dict):
+                return algorithm.pop(alg)
+            algorithm = [a for a in algorithm if a != alg]
+            return default
+
+        # (1) The radius of convergence of the power series is the distance from the vertex of the Voronoi
+        # cell to the closest vertex of the triangulation (since we use a Delaunay triangulation, all vertices
+        # are at the same distance in fact.) So the radii of convergence of two neigbhouring cells overlap
+        # and the power series must coincide there. Note that this constraint is unrelated to the cohomology
+        # class Φ.
+        if "midpoint_derivatives" in algorithm:
+            derivatives = get_parameter("midpoint_derivatives", self.prec // 3)
+            constraints.require_midpoint_derivatives(derivatives)
+
+        # (1') TODO: Describe L2 optimization.
+        if "L2" in algorithm:
+            print("Adding L2 conditions")
+            weight = get_parameter("L2", 1)
+            constraints.optimize(weight * self._L2_consistency_constraints())
+
+        if "squares" in algorithm:
+            weight = get_parameter("squares", 1)
+            constraints.optimize(weight * constraints._squares())
+
+        # (2) We have that for any cycle γ, Re(∫fω) = Re(∫η) = Φ(γ). We can turn this into constraints
+        # on the coefficients as we integrate numerically following the path γ as it intersects the radii of
+        # convergence.
+        print("Adding cohomology constraints")
+        constraints.require_cohomology(cocycle)
+
+        if "force_singularities" in algorithm:
+            singularities = get_parameter(
+                "force_singularities",
+                {
+                    vertex: vertex.angle() - 1
+                    for vertex in self.surface().singularities()
+                },
+            )
+            for vertex in self.surface().singularities():
+                for degree in range(singularities.get(vertex, 0)):
+                    constraints.add_constraint(constraints._gen("Re", vertex, degree))
+                    constraints.add_constraint(constraints._gen("Im", vertex, degree))
+
+        # (3) Since the area ∫ η \wedge \overline{η} must be finite [TODO:
+        # REFERENCE?] we optimize for a proxy of this quantity to be minimal.
+        if "area_upper_bound" in algorithm:
+            weight = get_parameter("area_upper_bound", 1)
+            constraints.optimize(weight * constraints._area_upper_bound())
+
+        # (3') We can also optimize for the exact quantity to be minimal but
+        # this is much slower.
+        if "area" in algorithm:
+            weight = get_parameter("area", 1)
+            constraints.optimize(weight * constraints._area())
+
+        if "tykhonov" in algorithm:
+            # TODO: Should we still try to do something like this? (Whatever
+            # the idea was here?)
+            pass
+
+        if algorithm:
+            raise ValueError(f"unsupported algorithm {algorithm}")
+
+        solution, residue = constraints.solve()
+        η = self.element_class(self, solution, residue=residue, cocycle=cocycle)
+
+        if check:
+            if report := η.error():
+                raise ValueError(report)
+
+        return η
+
+
+class PowerSeriesConstraints:
+    r"""
+    A collection of (linear) constraints on the coefficients of power series
+    developed at the vertices of the Voronoi cells of a Delaunay triangulation.
+
+    This is used to create harmonic differentials from cohomology classes.
+    """
+
+    def __init__(self, differentials):
+        self._differentials = differentials
+        self._constraints = []
+        self._cost = self.symbolic_ring().zero()
+
+    def __repr__(self):
+        return repr(self._constraints)
+
+    @cached_method
+    def symbolic_ring(self, base_ring=None):
+        r"""
+        Return the polynomial ring in the coefficients of the power series of
+        the triangles.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, HarmonicDifferentials
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints
+            sage: T = translation_surfaces.square_torus()
+            sage: T.set_immutable()
+
+            sage: Ω = HarmonicDifferentials(T)
+
+            sage: C = PowerSeriesConstraints(Ω)
+            sage: C.symbolic_ring()
+            Ring of Power Series Coefficients in Re(a0,0),…,Re(a1,0),…,Im(a0,0),…,Im(a1,0),…,λ0,… over Complex Field with 54 bits of precision
+
+        """
+        # TODO: What's the correct precision here?
+
+        gens = (
+            [f"Re(a{n},?)" for n in range(len(self._differentials._centers))]
+            + [f"Im(a{n},?)" for n in range(len(self._differentials._centers))]
+            + ["λ?"]
+        )
+
+        from sage.all import ComplexField
+        from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+
+        return PowerSeriesCoefficientExpressionRing(
+            base_ring or self.complex_field(), tuple(gens)
+        )
+
+    @cached_method
+    def complex_field(self):
+        # TODO: Make this configurable.
+        from sage.all import CDF
+
+        return CDF
+
+    @cached_method
+    def real_field(self):
+        # TODO: Make this configurable.
+        from sage.all import RDF
+
+        return RDF
+
+    # TODO: Move to Harmonic Differentials; or maybe some other shared class
+    # that abstracts away details of the symbolic ring.
+    @cached_method
+    def lagrange(self, k):
+        return self.symbolic_ring().gen(("λ?", k))
+
+    def add_constraint(self, expression, rank_check=True):
+        total_degree = expression.total_degree()
+
+        if total_degree == -1:
+            return
+
+        if total_degree == 0:
+            raise ValueError(f"cannot solve for constraint {expression} == 0")
+
+        if total_degree > 1:
+            raise NotImplementedError("can only encode linear constraints")
+
+        if expression.parent().base_ring() is self.real_field():
+            # TODO: Should we scale?
+            # self._constraints.append(expression / expression.norm(1))
+            # TODO: This is a very expensive hack to detect dependent conditions.
+            if rank_check:
+                rank = self.matrix(nowarn=True)[0].rank()
+            self._constraints.append(expression)
+            if rank_check:
+                if self.matrix(nowarn=True)[0].rank() == rank:
+                    self._constraints.pop()
+        elif expression.parent().base_ring() is self.complex_field():
+            self.add_constraint(expression.real(), rank_check=rank_check)
+            self.add_constraint(expression.imag(), rank_check=rank_check)
+        else:
+            raise NotImplementedError("cannot handle expressions over this base ring")
+
+    def integrate(self, cycle):
+        r"""
+        Return the linear combination of the power series coefficients that
+        describe the integral of a differential along the homology class
+        ``cycle``.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, SimplicialHomology
+            sage: T = translation_surfaces.square_torus()
+            sage: T.set_immutable()
+
+            sage: H = SimplicialHomology(T)
+
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints, HarmonicDifferentials
+            sage: Ω = HarmonicDifferentials(T, centers="vertices", ncoefficients=3)
+            sage: C = PowerSeriesConstraints(Ω)
+
+            sage: C.integrate(H())
+            0
+
+        Integrating the power series developed around the vertex along the path
+        that loops horizontally from the center of the square to itself, we get
+        `a_0 - i/2 a_1 - a_2/6`::  # TODO: This is not true anymore.
+
+            sage: a, b = H.gens()
+            sage: C.integrate(b)  # TODO: There are many correct answers here. Test for something meaningful.
+            Re(a0,0) ...
+
+        :: # TODO: Explain what's the expected output here
+
+            sage: C.integrate(-a)  # TODO: There are many correct answers here. Test for something meaningful.
+            (-1.00000000000000*I)*Re(a0,0) ...
+
+        The same integrals but developing the power series at the vertex and at the center of the square::
+
+            sage: Ω = HarmonicDifferentials(T)
+            sage: C = PowerSeriesConstraints(Ω)
+
+            sage: C.integrate(a)   # not tested # TODO: Check these values
+            0.828427124746190*Re(a0,0) + 0.171572875253810*Re(a1,0) + 0.828427124746190*I*Im(a0,0) + 0.171572875253810*I*Im(a1,0) + (-1.38777878078145e-17 - 8.54260702578763e-18*I)*Re(a0,1) + (-3.03576608295941e-18 + 0.0857864376269050*I)*Re(a1,1) + (8.54260702578763e-18 - 1.38777878078145e-17*I)*Im(a0,1) + (-0.0857864376269050 - 3.03576608295941e-18*I)*Im(a1,1) + (0.0473785412436502 + 4.71795158413990e-18*I)*Re(a0,2) + (-0.0424723326565069 - 3.03576608295941e-18*I)*Re(a1,2) + (-4.71795158413990e-18 + 0.0473785412436502*I)*Im(a0,2) + (3.03576608295941e-18 - 0.0424723326565069*I)*Im(a1,2) + (-1.73472347597681e-18 - 2.19851947436667e-18*I)*Re(a0,3) + (2.16840434497101e-18 - 0.0208152801713079*I)*Re(a1,3) + (2.19851947436667e-18 - 1.73472347597681e-18*I)*Im(a0,3) + (0.0208152801713079 + 2.16840434497101e-18*I)*Im(a1,3) + (0.00487732352790257 + 9.71367022318980e-19*I)*Re(a0,4) + (0.0100938339276945 + 1.51788304147971e-18*I)*Re(a1,4) + (-9.71367022318980e-19 + 0.00487732352790257*I)*Im(a0,4) + (-1.51788304147971e-18 + 0.0100938339276945*I)*Im(a1,4)
+            sage: C.integrate(b)  # not tested # TODO: Check these values
+            (-0.828427124746190*I)*Re(a0,0) + (-0.171572875253810*I)*Re(a1,0) + 0.828427124746190*Im(a0,0) + 0.171572875253810*Im(a1,0) + (-1.38777878078145e-17 + 8.54260702578763e-18*I)*Re(a0,1) + (-3.03576608295941e-18 - 0.0857864376269050*I)*Re(a1,1) + (-8.54260702578763e-18 - 1.38777878078145e-17*I)*Im(a0,1) + (0.0857864376269050 - 3.03576608295941e-18*I)*Im(a1,1) + (7.70371977754894e-34 + 0.0473785412436502*I)*Re(a0,2) + (-2.60208521396521e-18 - 0.0424723326565069*I)*Re(a1,2) + (-0.0473785412436502 + 7.70371977754894e-34*I)*Im(a0,2) + (0.0424723326565069 - 2.60208521396521e-18*I)*Im(a1,2) + (1.73472347597681e-18 - 2.19851947436667e-18*I)*Re(a0,3) + (-2.16840434497101e-18 - 0.0208152801713079*I)*Re(a1,3) + (2.19851947436667e-18 + 1.73472347597681e-18*I)*Im(a0,3) + (0.0208152801713079 - 2.16840434497101e-18*I)*Im(a1,3) + (-9.62964972193618e-35 - 0.00487732352790257*I)*Re(a0,4) + (-1.08420217248550e-18 - 0.0100938339276945*I)*Re(a1,4) + (0.00487732352790257 - 9.62964972193618e-35*I)*Im(a0,4) + (0.0100938339276945 - 1.08420217248550e-18*I)*Im(a1,4)
+
+        ::
+
+            sage: from flatsurf import translation_surfaces, HarmonicDifferentials, SimplicialHomology
+            sage: S = translation_surfaces.regular_octagon()
+
+            sage: H = SimplicialHomology(S)
+            sage: Ω = HarmonicDifferentials(S)
+
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints
+            sage: C = PowerSeriesConstraints(Ω)
+
+            sage: C.integrate(H())
+            0
+
+            sage: a, b, c, d = H.gens()
+
+            sage: C.integrate(a)  # not tested # TODO: Check these values
+            (1.13290899470104 - 1.13290899470104*I)*Re(a0,0) + (1.13290899470104 + 1.13290899470104*I)*Im(a0,0) + (0.275277280554704 + 0.275277280554704*I)*Re(a1,0) + (-0.275277280554704 + 0.275277280554704*I)*Im(a1,0) + (0.327551243899061 + 6.93889390390723e-18*I)*Re(a1,1) + (-6.93889390390723e-18 + 0.327551243899061*I)*Im(a1,1) + (0.191399262161835 - 0.191399262161835*I)*Re(a1,2) + (0.191399262161835 + 0.191399262161835*I)*Im(a1,2)
+
+            sage: C.integrate(b)  # not tested # TODO: Check these values
+            1.60217526524068*Re(a0,0) + 1.60217526524068*I*Im(a0,0) + (-0.389300863573646 + 1.38777878078145e-17*I)*Re(a1,0) + (-1.38777878078145e-17 - 0.389300863573646*I)*Im(a1,0) + (-1.04083408558608e-17 - 0.327551243899061*I)*Re(a1,1) + (0.327551243899061 - 1.04083408558608e-17*I)*Im(a1,1) + 0.270679432377470*Re(a1,2) + 0.270679432377470*I*Im(a1,2)
+
+            sage: C.integrate(c)  # not tested # TODO: Check these values
+            1.60217526524068*Re(a0,0) + 1.60217526524068*I*Im(a0,0) + (-0.389300863573646 + 1.38777878078145e-17*I)*Re(a1,0) + (-1.38777878078145e-17 - 0.389300863573646*I)*Im(a1,0) + (-1.04083408558608e-17 - 0.327551243899061*I)*Re(a1,1) + (0.327551243899061 - 1.04083408558608e-17*I)*Im(a1,1) + 0.270679432377470*Re(a1,2) + 0.270679432377470*I*Im(a1,2)
+
+            sage: C.integrate(d)  # not tested # TODO: Check these values
+            (-1.60217526524068*I)*Re(a0,0) + 1.60217526524068*Im(a0,0) + (5.55111512312578e-17 - 0.389300863573646*I)*Re(a1,0) + (0.389300863573646 + 5.55111512312578e-17*I)*Im(a1,0) + (-2.77555756156289e-17 + 0.327551243899061*I)*Re(a1,1) + (-0.327551243899061 - 2.77555756156289e-17*I)*Im(a1,1) + (-0.270679432377470*I)*Re(a1,2) + 0.270679432377470*Im(a1,2)
+
+        """
+        return sum(
+            (
+                multiplicity * sgn * self._integrate_path(path)
+                for (
+                    label,
+                    edge,
+                ), multiplicity in cycle._chain.monomial_coefficients().items()
+                for (sgn, path) in self._integrate_path_along_edge(label, edge)
+            ),
+            start=self.symbolic_ring().zero(),
+        )
+
+    def _integrate_path_along_edge(self, label, edge):
+        r"""
+        Return the path along the ``edge`` of the polygon with ``label`` as a
+        sequence of :class:`Path` that we can integrate along.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, SimplicialHomology
+            sage: T = translation_surfaces.square_torus()
+            sage: T.set_immutable()
+
+            sage: H = SimplicialHomology(T)
+
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints, HarmonicDifferentials
+            sage: Ω = HarmonicDifferentials(T)
+            sage: C = PowerSeriesConstraints(Ω)
+            sage: C._integrate_path_along_edge(0, 1)
+            [(1, Path (0, 1) from (1, 0) in polygon 0 to (1, 1) in polygon 0)]
+
+        ::
+
+            sage: from flatsurf import translation_surfaces, HarmonicDifferentials
+            sage: S = translation_surfaces.regular_octagon()
+            sage: Ω = HarmonicDifferentials(S)
+            sage: PowerSeriesConstraints(Ω)._integrate_path_along_edge(0, 0)
+            [(1, Path (1, 0) from (0, 0) in polygon 0 to (1, 0) in polygon 0)]
+
+        """
+        surface = self._differentials.surface()
+        polygon = surface.polygon(label)
+
+        from flatsurf.geometry.voronoi import SurfaceLineSegment
+
+        return [
+            (
+                1,
+                SurfaceLineSegment(
+                    self._differentials.surface(),
+                    label,
+                    polygon.vertex(edge),
+                    polygon.edge(edge),
+                ),
+            )
+        ]
+
+    def _integrate_path(self, segment):
+        r"""
+        Return the linear combination of the power series coefficients that
+        describe the integral along the ``segment``.
+
+        This is a helper method for :meth:`integrate`.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, HarmonicDifferentials, SimplicialHomology
+            sage: S = translation_surfaces.regular_octagon()
+
+            sage: Ω = HarmonicDifferentials(S)
+
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints
+            sage: C = PowerSeriesConstraints(Ω)
+
+            sage: from flatsurf.geometry.harmonic_differentials import GeodesicPath
+            sage: path = GeodesicPath.along_edge(S, 0, 0)
+
+            sage: C._integrate_path(path)  # not tested # TODO: Check this value
+            (-5.55111512312578e-17 - 0.389300863573646*I)*Re(a0,0) + (-1.60217526524068*I)*Re(a1,0) + (0.389300863573646 - 5.55111512312578e-17*I)*Im(a0,0) + 1.60217526524068*Im(a1,0) + (-2.08166817117217e-17 - 0.327551243899060*I)*Re(a0,1) + (1.66533453693773e-16 + 3.19522800018857e-17*I)*Re(a1,1) + (0.327551243899060 - 2.08166817117217e-17*I)*Im(a0,1) + (-3.19522800018857e-17 + 1.66533453693773e-16*I)*Im(a1,1) + (-0.270679432377470*I)*Re(a0,2) + (-1.23259516440783e-32 + 0.342727396656658*I)*Re(a1,2) + 0.270679432377470*Im(a0,2) + (-0.342727396656658 - 1.23259516440783e-32*I)*Im(a1,2) + (1.38777878078145e-17 - 0.219471472765136*I)*Re(a0,3) + (-1.24900090270330e-16 - 3.07576511193400e-17*I)*Re(a1,3) + (0.219471472765136 + 1.38777878078145e-17*I)*Im(a0,3) + (3.07576511193400e-17 - 1.24900090270330e-16*I)*Im(a1,3) + (2.42861286636753e-17 - 0.174329399573979*I)*Re(a0,4) + (1.69481835106077e-32 - 0.131965414609324*I)*Re(a1,4) + (0.174329399573979 + 2.42861286636753e-17*I)*Im(a0,4) + (0.131965414609324 + 1.69481835106077e-32*I)*Im(a1,4) + (3.12250225675825e-17 - 0.135339716188735*I)*Re(a0,5) + (0.135339716188735 + 3.12250225675825e-17*I)*Im(a0,5) + (2.77555756156289e-17 - 0.102340546397005*I)*Re(a0,6) + (0.102340546397005 + 2.77555756156289e-17*I)*Im(a0,6) + (3.46944695195361e-17 - 0.0749845895064374*I)*Re(a0,7) + (0.0749845895064374 + 3.46944695195361e-17*I)*Im(a0,7) + (3.12250225675825e-17 - 0.0527958835241931*I)*Re(a0,8) + (0.0527958835241931 + 3.12250225675825e-17*I)*Im(a0,8) + (2.77555756156289e-17 - 0.0352191503025193*I)*Re(a0,9) + (0.0352191503025193 + 2.77555756156289e-17*I)*Im(a0,9) + (2.08166817117217e-17 - 0.0216611462547145*I)*Re(a0,10) + (0.0216611462547145 + 2.08166817117217e-17*I)*Im(a0,10) + (2.08166817117217e-17 - 0.0115239671919220*I)*Re(a0,11) + (0.0115239671919220 + 2.08166817117217e-17*I)*Im(a0,11) + (1.73472347597681e-17 - 0.00423065874117904*I)*Re(a0,12) + (0.00423065874117904 + 1.73472347597681e-17*I)*Im(a0,12) + (1.04083408558608e-17 + 0.000756226861613830*I)*Re(a0,13) + (-0.000756226861613830 + 1.04083408558608e-17*I)*Im(a0,13) + (8.67361737988404e-18 + 0.00392229868304028*I)*Re(a0,14) + (-0.00392229868304028 + 8.67361737988404e-18*I)*Im(a0,14)
+
+        """
+        return sum(
+            self._integrate_path_polygon_cell(polygon_cell, segment)
+            for (
+                segment,
+                polygon_cell,
+            ) in self._differentials._cells.split_segment_at_polygon_cells(segment)
+        )
+
+    def _integrate_path_polygon_cell(self, polygon_cell, segment):
+        r"""
+        Return a symbolic expression describing the integral along the
+        ``segment`` in the Voronoi cell ``polygon_cell``.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, HarmonicDifferentials, SimplicialHomology
+            sage: S = translation_surfaces.regular_octagon()
+
+            sage: Ω = HarmonicDifferentials(S)
+
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints
+            sage: C = PowerSeriesConstraints(Ω)
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+
+            sage: V = Ω._voronoi_diagram()
+            sage: C._integrate_path_cell(V.polygon_cell(0, (0, 0)), OrientedSegment((0, 0), (1/2, 0)))  # TODO: Check this value
+            0.793700525984099*Re(a0,0) + 0.793700525984099*I*Im(a0,0) + 0.314980262473718*Re(a0,1) + 0.314980262473718*I*Im(a0,1) + 0.166666666666667*Re(a0,2) + 0.166666666666667*I*Im(a0,2) + 0.0992125657480124*Re(a0,3) + 0.0992125657480124*I*Im(a0,3) + 0.0629960524947437*Re(a0,4) + 0.0629960524947437*I*Im(a0,4) + 0.0416666666666667*Re(a0,5) + 0.0416666666666667*I*Im(a0,5) + 0.0283464473520960*Re(a0,6) + 0.0283464473520960*I*Im(a0,6) + 0.0196862663993065*Re(a0,7) + 0.0196862663993065*I*Im(a0,7) + 0.0138888888888889*Re(a0,8) + 0.0138888888888889*I*Im(a0,8) + 0.00992125657430033*Re(a0,9) + 0.00992125657430033*I*Im(a0,9) + 0.00715864232879659*Re(a0,10) + 0.00715864232879659*I*Im(a0,10) + 0.00520833333333333*Re(a0,11) + 0.00520833333333333*I*Im(a0,11) + 0.00381586791339311*Re(a0,12) + 0.00381586791339311*I*Im(a0,12) + 0.00281232377208854*Re(a0,13) + 0.00281232377208854*I*Im(a0,13) + 0.00208333333333333*Re(a0,14) + 0.00208333333333333*I*Im(a0,14)
+
+        """
+        integrator = self.CellIntegrator(self, polygon_cell)
+        ncoefficients = self._differentials.ncoefficients(polygon_cell.cell().center())
+        return sum(
+            integrator.a(n) * integrator.integral(n, segment)
+            for n in range(ncoefficients)
+        )
+
+    @cached_method
+    def _L2_consistencies(self):
+        cells = self._differentials._cells
+
+        from sage.all import parallel
+
+        @parallel
+        def L2_cost(polygon_cell, boundary_segment, opposite_polygon_cell):
+            from flatsurf.geometry.euclidean import OrientedSegment
+
+            boundary_segment = OrientedSegment(*boundary_segment)
+
+            boundary_segments = polygon_cell.split_segment_with_constant_root_branches(
+                boundary_segment
+            )
+            boundary_segments = sum(
+                (
+                    opposite_polygon_cell.split_segment_with_constant_root_branches(
+                        segment
+                    )
+                    for segment in boundary_segments
+                ),
+                start=[],
+            )
+
+            ret = sum(
+                self._L2_consistency_voronoi_boundary(
+                    polygon_cell, segment, opposite_polygon_cell
+                )
+                for segment in boundary_segments
+            )
+
+            if not ret:
+                return {}
+
+            return ret._coefficients
+
+        # Get one copy of each cell boundary (with a random orientation.)
+        cell_boundaries = {
+            frozenset([boundary, -boundary]): boundary
+            for cell in cells
+            for boundary in cell.boundary()
+        }.values()
+
+        polygon_cell_boundaries = sum(
+            (boundary.polygon_cell_boundaries() for boundary in cell_boundaries),
+            start=[],
+        )
+
+        return sum(
+            self.symbolic_ring()(cost)
+            for ((args, kwargs), cost) in L2_cost(polygon_cell_boundaries)
+        )
+
+    # TODO: Move to HarmonicDifferentials
+    def _gen(self, kind, center, n):
+        return self.symbolic_ring(self.real_field()).gen(
+            (f"{kind}(a{self._differentials._centers.index(center)},?)", n)
+        )
+
+    class CellIntegrator:
+        def __init__(self, constraints, polygon_cell):
+            self._constraints = constraints
+            self._polygon_cell = polygon_cell
+
+        @cached_method
+        def a(self, n):
+            return self.Re_a(
+                n
+            ) + self._constraints.complex_field().gen() * self._constraints.symbolic_ring(
+                self._constraints.complex_field()
+            )(
+                self.Im_a(n)
+            )
+
+        @cached_method
+        def Re_a(self, n):
+            return self._constraints._gen("Re", self._polygon_cell.cell().center(), n)
+
+        @cached_method
+        def Im_a(self, n):
+            return self._constraints._gen("Im", self._polygon_cell.cell().center(), n)
+
+        def integral(self, n, segment):
+            r"""
+            Return the sum of
+
+            \int_γ ζ_{d+1}^{κ (n+1)}/(d+1) (z-α)^\frac{n-d}{d+1}
+
+            where `d` is the order of the singularity at the center of the
+            Voronoi cell containing ``segment``, γ are segments that partition
+            ``segment`` such that the `d+1`st root of `z-α` can be taken
+            consistently on the smaller segment and it is precisely the main
+            branch of the root up to `ζ^κ` where `ζ` is a `d+1`st root of
+            unity, `α` is the center of the Voronoi cell containing the
+            segment,
+            """
+            sum = self._constraints.complex_field().zero()
+
+            C = self._constraints.complex_field()
+
+            d = self._polygon_cell.cell().center().angle() - 1
+            α = C(*self._polygon_cell.center())
+
+            for γ in self._polygon_cell.split_segment_with_constant_root_branches(
+                segment
+            ):
+                a = C(*γ.start())
+                b = C(*γ.end())
+
+                constant = zeta(
+                    d + 1, self._polygon_cell.root_branch_for_segment(γ) * (n + 1)
+                ) * (C(b) - C(a))
+
+                def value(part, t):
+                    z = self._constraints.complex_field()(*((1 - t) * a + t * b))
+
+                    value = constant * (z - α).nth_root(d + 1) ** (n - d)
+
+                    if part == "Re":
+                        return float(value.real())
+                    if part == "Im":
+                        return float(value.imag())
+
+                    raise NotImplementedError
+
+                real, error = quad(lambda t: value("Re", t), 0, 1)
+                imag, error = quad(lambda t: value("Im", t), 0, 1)
+
+                sum += C(real, imag)
+
+            return sum
+
+        def mixed_integral(self, part, α, κ, d, n, β, λ, dd, m, γ):
+            # TODO: In the actual computation we are throwing an absolute value
+            # in somewhere, i.e., we are not using γ.(t) but |γ.(t)|.
+            # That's fine but the documentation is wrong here and some other places.
+            # TODO: It's weird that this one does not split for inuform roots but integral() does.
+            r"""
+            Return the real/imaginary part of
+
+            \int_γ ζ_{d+1}^{κ (n+1)}/(d+1) (z-α)^\frac{n-d}{d+1} \overline{ζ_{dd+1}^{λ (m+1)}/(dd+1) (z-β)^\frac{m-dd}{dd+1}} dz
+            """
+            R = self._constraints.real_field()
+            C = self._constraints.complex_field()
+            a = Cab(C, γ.start())
+            b = Cab(C, γ.end())
+            α = Cab(C, α)
+            β = Cab(C, β)
+
+            return integral2cpp(part, α, κ, d, n, β, λ, dd, m, a=a, b=b, C=C, R=R)
+
+    def _L2_consistency_voronoi_boundary(
+        self, polygon_cell, boundary_segment, opposite_polygon_cell
+    ):
+        r"""
+                ALGORITHM:
+
+                Two cells meet at the ``boundary_segment``. The harmonic differential
+                can on these cells abstractly be described as a power series around
+                the center of the cell
+
+                g(y) = Σ_{n ≥ 0} a_n y^n
+
+                This is, however, not the representation on any of the charts in which
+                the translation surface is given.
+
+                To describe this power series on such a chart, let `z` denote the
+                variable on that chart, we are going to have to takes `d+1`-st roots
+                of `y`. Note that ``boundary_segment`` is assumed to be such that this
+                is consistently possible, namely ``boundary_segment`` does not cross
+                the horizontal line on which the singularity lives in the `z`-chart.
+
+                Therefore, we write with the center y=0 being z=α
+
+                y(z) = ζ_{d+1}^κ (z-α)^{1/(d+1)}
+
+                where the last part denotes the principal `d+1`-st root of `z`.
+
+                Hence, we can rewrite `g(y)dy` on the `z`-chart:
+
+                g(y)dy = g(y(z)) dy/dz dz
+                       = Σ_{n ≥ 0} a_n ζ_{d+1}^{κ n} (z-α)^{n/(d+1)} ζ_{d+1}^κ 1/(d+1) (z-α)^{-d/(d+1)}dz
+                       = Σ_{n ≥ 0} a_n ζ_{d+1}^{κ (n+1)}/(d+1) (z-α)^\frac{n-d}{d+1} dz
+                       =: Σ_{n ≥ 0} a_n f_n(z) dz
+                       =: f(z) dz
+
+                Note that the formulas above also hold when the center is not an actual
+                singularity, i.e., d = 0.
+
+                Now, we want to describe the error between two such series when
+                integrating along the ``boundary_segment``, namely, for two such
+                differentials `f(z)dz` and `g(z)dz` we compute the L2 norm of `f-g` or
+                rather the square thereof `\int_γ (f-g)\overline{(f-g)} dz` where γ is
+                the ``boundary_segment``.
+
+                TODO: This is not actually the integral but a norm, we get |·γ| in the
+                integration and not just the signed derivative.
+
+                As discussed above, we have
+
+                f = Σ_{n ≥ 0} a_n f_n(z),
+
+                g = Σ_{m ≥ 0} b_m g_m(z).
+
+                The `a_n` and `b_n` are symbolic variables since we are going to
+                optimize for these later. If we evaluate that L2 norm squared we get a
+                polynomial of homogeneous degree two in these variables.
+
+                Namely,
+
+                (f-g)\overline{(f-g)} = f\overline{f} - 2 \Re f\overline{g} + g\overline{g}.
+
+                Note that all terms are real.
+
+                The first term is going to yield polynomials in `a_n a_m`, the second
+                term mixed polynomials `a_n b_m`, and the last term polynomials in `b_n
+                b_m`.
+
+                In fact, our symbolic variables are going to be `\Re a_n`, `\Im a_n`,
+                `\Re b_m`, `\Im b_m`.
+
+                Working through the first of the three components of the L2 norm
+                expression, we have
+
+                \int_γ f\overline{f}
+                = Σ_{n,m ≥ 0} a_n \overline{a_m} \int_γ f_n(z)\overline{f_m(z)}
+                =: Σ_{n,m ≥ 0} a_n \overline{a_m} (f_{\Re, n, m} + i f_{\Im, n, m})
+
+                We can simplify things a bit since we know that the result is real;
+                inside the series we have therefore
+
+                \Re (a_n \overline{a_m} (f_{\Re, n, m} + i f_{\Im, n, m}))
+                = (\Re a_n \Re a_m + \Im a_n \Im a_m - i \Re a_n \Im a_m + i \Im a_n \Re a_m) (f_{\Re, n, m} + i f_{\Im, n, m})
+                = \Re a_n \Re a_m f_{\Re, n, m} + \Im a_n \Im a_m f_{\Re, n, m} + \Re a_n \Im a_m f_{\Im, n, m} - \Im a_n \Re a_m f_{\Im, n, m}.
+
+                We get essentially the same terms for `g\overline{g}`.
+
+                Finally, we need to compute the middle term:
+
+                - \int_γ 2 \Re f\overline{g}
+                = - 2 \Re \int_γ f\overline{g}
+                = - 2 \Re Σ_{n,m ≥ 0} a_n \overline{b_m} \int_γ f_n{z)\overline{g_m(z)}
+                =: - 2 \Re Σ_{n,m ≥ 0} a_n \overline{b_m} ((f,g)_{\Re, n, m} + i (f,g)_{\Im, n, m})
+
+                Again we can simplify and get inside the series
+
+                \Re (a_n \overline{b_m} ((f,g)_{\Re, n, m} + i (f,g)_{\Im, n, m}))
+                = \Re a_n \Re b_m (f,g)_{\Re, n, m} + \Im a_n \Im b_m (f,g)_{\Re, n, m} + \Re a_n \Im b_m (f,g)_{\Im, n, m} - \Im a_n \Re b_m (f,g)_{\Im, n, m}
+        q
+                The integrals `f_{\Re, n, m}`, `f_{\Im, n, m}`, `(f,g)_{\Re, n, m}`,
+                and `(f,g)_{\Im, n, m}` are currently all computed numerically. We know
+                of but have not implemented a better approach, yet.
+
+                TESTS::
+
+                    sage: from flatsurf import translation_surfaces, HarmonicDifferentials, SimplicialHomology
+                    sage: S = translation_surfaces.regular_octagon()
+                    sage: H = SimplicialHomology(S)
+                    sage: Ω = HarmonicDifferentials(S)
+                    sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints
+                    sage: C = PowerSeriesConstraints(Ω)
+                    sage: V = Ω._voronoi_diagram()
+
+                    sage: cells, segment = next(iter(V.boundaries().items()))
+                    sage: cell, opposite_cell = list(cells)
+
+                    sage: E = C._L2_consistency_voronoi_boundary(cell, segment, opposite_cell)
+                    sage: F = C._L2_consistency_voronoi_boundary(opposite_cell, -segment, cell)
+                    sage: (E - F).map_coefficients(lambda c: c if abs(c) > 1e-15 else 0)
+                    0
+
+        """
+        # TODO: Do not ignore boundaries that are aligned with edges!
+        if polygon_cell.label() != opposite_polygon_cell.label():
+            print("Ignoring L2 condition on edge of polygon")
+            return 0
+
+        assert polygon_cell.label() == opposite_polygon_cell.label()
+        assert polygon_cell.contains_segment(boundary_segment)
+        assert opposite_polygon_cell.contains_segment(boundary_segment)
+
+        return (
+            self._L2_consistency_voronoi_boundary_f_overline_f(
+                polygon_cell, boundary_segment
+            )
+            - 2
+            * self._L2_consistency_voronoi_boundary_Re_f_overline_g(
+                polygon_cell, boundary_segment, opposite_polygon_cell
+            )
+            + self._L2_consistency_voronoi_boundary_g_overline_g(
+                opposite_polygon_cell, boundary_segment
+            )
+        )
+
+    def _L2_consistency_voronoi_boundary_f_overline_f(self, cell, boundary_segment):
+        r"""
+        Return the value of `\int_γ f\overline{f}.
+        """
+        center = self._differentials.surface()(cell.label(), cell.center())
+
+        cell_integrator = self.CellIntegrator(self, cell)
+
+        κ = cell.root_branch_for_segment(boundary_segment)
+        α = cell.center()
+        d = center.angle() - 1
+
+        # Cast to a builtin int to speed up some operations later.
+        d = int(d)
+
+        def f_(part, n, m):
+            return cell_integrator.mixed_integral(
+                part, α, κ, d, n, α, κ, d, m, boundary_segment
+            )
+
+        return self.symbolic_ring().sum(
+            [
+                (
+                    cell_integrator.Re_a(n) * cell_integrator.Re_a(m)
+                    + cell_integrator.Im_a(n) * cell_integrator.Im_a(m)
+                )
+                * f_("Re", n, m)
+                + (
+                    cell_integrator.Re_a(n) * cell_integrator.Im_a(m)
+                    - cell_integrator.Im_a(n) * cell_integrator.Re_a(m)
+                )
+                * f_("Im", n, m)
+                for n in range(self._differentials.ncoefficients(center))
+                for m in range(self._differentials.ncoefficients(center))
+            ]
+        )
+
+    def _L2_consistency_voronoi_boundary_Re_f_overline_g(
+        self, cell, boundary_segment, opposite_cell
+    ):
+        r"""
+        Return the real part of `\int_γ f\overline{g}.
+        """
+        center = self._differentials.surface()(cell.label(), cell.center())
+        opposite_center = self._differentials.surface()(
+            opposite_cell.label(), opposite_cell.center()
+        )
+
+        cell_integrator = self.CellIntegrator(self, cell)
+        opposite_cell_integrator = self.CellIntegrator(self, opposite_cell)
+
+        κ = cell.root_branch_for_segment(boundary_segment)
+        λ = opposite_cell.root_branch_for_segment(boundary_segment)
+        α = cell.center()
+        β = opposite_cell.center()
+        d = center.angle() - 1
+        dd = opposite_center.angle() - 1
+
+        # Cast to a builtin int to speed up some operations later.
+        d = int(d)
+        dd = int(dd)
+
+        def fg_(part, n, m):
+            return cell_integrator.mixed_integral(
+                part, α, κ, d, n, β, λ, dd, m, boundary_segment
+            )
+
+        return self.symbolic_ring().sum(
+            [
+                (
+                    cell_integrator.Re_a(n) * opposite_cell_integrator.Re_a(m)
+                    + cell_integrator.Im_a(n) * opposite_cell_integrator.Im_a(m)
+                )
+                * fg_("Re", n, m)
+                + (
+                    cell_integrator.Re_a(n) * opposite_cell_integrator.Im_a(m)
+                    - cell_integrator.Im_a(n) * opposite_cell_integrator.Re_a(m)
+                )
+                * fg_("Im", n, m)
+                for n in range(self._differentials.ncoefficients(center))
+                for m in range(self._differentials.ncoefficients(opposite_center))
+            ]
+        )
+
+    def _L2_consistency_voronoi_boundary_g_overline_g(
+        self, opposite_cell, boundary_segment
+    ):
+        r"""
+        Return the value of `\int_γ g\overline{g}.
+        """
+        opposite_center = self._differentials.surface()(
+            opposite_cell.label(), opposite_cell.center()
+        )
+
+        opposite_cell_integrator = self.CellIntegrator(self, opposite_cell)
+
+        λ = opposite_cell.root_branch_for_segment(boundary_segment)
+        β = opposite_cell.center()
+        dd = opposite_center.angle() - 1
+
+        # Cast to a builtin int to speed up some operations later.
+        dd = int(dd)
+
+        def g_(part, n, m):
+            return opposite_cell_integrator.mixed_integral(
+                part, β, λ, dd, n, β, λ, dd, m, boundary_segment
+            )
+
+        return self.symbolic_ring().sum(
+            [
+                (
+                    opposite_cell_integrator.Re_a(n) * opposite_cell_integrator.Re_a(m)
+                    + opposite_cell_integrator.Im_a(n)
+                    * opposite_cell_integrator.Im_a(m)
+                )
+                * g_("Re", n, m)
+                + (
+                    opposite_cell_integrator.Re_a(n) * opposite_cell_integrator.Im_a(m)
+                    - opposite_cell_integrator.Im_a(n)
+                    * opposite_cell_integrator.Re_a(m)
+                )
+                * g_("Im", n, m)
+                for n in range(self._differentials.ncoefficients(opposite_center))
+                for m in range(self._differentials.ncoefficients(opposite_center))
+            ]
+        )
+
+    @cached_method
+    def _elementary_line_integrals(self, label, n, m):
+        r"""
+        Return the integrals f(z)dx and f(z)dy where f(z) = z^n\overline{z}^m
+        along the boundary of the polygon ``label``.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, SimplicialCohomology, HarmonicDifferentials
+            sage: T = translation_surfaces.square_torus()
+            sage: T.set_immutable()
+
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints, HarmonicDifferentials
+            sage: Ω = HarmonicDifferentials(T)
+            sage: C = PowerSeriesConstraints(Ω)
+
+            sage: C._elementary_line_integrals(0, 0, 0)  # tol 1e-9
+            (0.0000000000000000, 0.0000000000000000)
+            sage: C._elementary_line_integrals(0, 1, 0)  # tol 1e-9
+            (0 - 1.0*I, 1.0 + 0.0*I)
+            sage: C._elementary_line_integrals(0, 0, 1)  # tol 1e-9
+            (0.0 + 1.0*I, 1.0 - 0.0*I)
+            sage: C._elementary_line_integrals(0, 1, 1)  # tol 1e-9
+            (0, 0)
+
+        """
+        ix = self.complex_field().zero()
+        iy = self.complex_field().zero()
+
+        polygon = self._differentials.surface().polygon(label)
+        center = polygon.circumscribed_circle().center()
+
+        for v, e in zip(polygon.vertices(), polygon.edges()):
+            Δx, Δy = e
+            x0, y0 = -center + v
+
+            def f(x, y):
+                from sage.all import I
+
+                return self.complex_field()((x + I * y) ** n * (x - I * y) ** m)
+
+            def fx(t):
+                if abs(Δx) < 1e-6:
+                    return self.complex_field().zero()
+                return f(x0 + t, y0 + t * Δy / Δx)
+
+            def fy(t):
+                if abs(Δy) < 1e-6:
+                    return self.complex_field().zero()
+                return f(x0 + t * Δx / Δy, y0 + t)
+
+            def integrate(value, t0, t1):
+                from sage.all import numerical_integral
+
+                # TODO: Should we do something about the error that is stored in [1]?
+                return numerical_integral(value, t0, t1)[0]
+
+            C = self.complex_field()
+            ix += C(
+                integrate(lambda t: fx(t).real(), 0, Δx),
+                integrate(lambda t: fx(t).imag(), 0, Δx),
+            )
+            iy += C(
+                integrate(lambda t: fy(t).real(), 0, Δy),
+                integrate(lambda t: fy(t).imag(), 0, Δy),
+            )
+
+        return ix, iy
+
+    @cached_method
+    def _elementary_area_integral(self, label, n, m):
+        r"""
+        Return the integral of z^n\overline{z}^m on the polygon with ``label``.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, SimplicialCohomology, HarmonicDifferentials
+            sage: T = translation_surfaces.square_torus()
+            sage: T.set_immutable()
+
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints, HarmonicDifferentials
+            sage: Ω = HarmonicDifferentials(T)
+            sage: C = PowerSeriesConstraints(Ω)
+            sage: C._elementary_area_integral(0, 0, 0)  # tol 1e-9
+            1.0 + 0.0*I
+
+            sage: C._elementary_area_integral(0, 1, 0)  # tol 1e-6
+            0.0
+
+        """
+        C = self.complex_field()
+        # Write f(n, m) for z^n\overline{z}^m.
+        # Then 1/(2m + 1) [d/dx f(n, m+1) - d/dy -i f(n, m+1)] = f(n, m).
+
+        # So we can use Green's theorem to compute this integral by integrating
+        # on the boundary of the triangle:
+        # -i/(2m + 1) f(n, m + 1) dx + 1/(2m + 1) f(n, m + 1) dy
+
+        ix, iy = self._elementary_line_integrals(label, n, m + 1)
+
+        from sage.all import I
+
+        return -I / (C(2) * (m + 1)) * ix + C(1) / (2 * (m + 1)) * iy
+
+    def optimize(self, f):
+        r"""
+        Add constraints that optimize the symbolic expression ``f``.
+
+        EXAMPLES:
+
+        TODO: All these examples are a bit pointless::
+
+            sage: from flatsurf import translation_surfaces, SimplicialCohomology
+            sage: T = translation_surfaces.square_torus()
+            sage: T.set_immutable()
+
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints, HarmonicDifferentials
+            sage: Ω = HarmonicDifferentials(T)
+            sage: C = PowerSeriesConstraints(Ω)
+            sage: R = C.symbolic_ring()
+
+        We optimize a function in two variables. Since there are no
+        constraints, we do not get any Lagrange multipliers in this
+        optimization but just for roots of the derivative::
+
+            sage: f = 10*R.gen(0)^2 + 16*R.gen(2)^2
+            sage: C.optimize(f)
+            sage: C._optimize_cost()
+            sage: C
+            [20.0000000000000*Re(a0,0), 32.0000000000000*Im(a0,0)]
+
+        """
+        if f:
+            self._cost += f
+
+    def variables(self):
+        terms = list(self._constraints)
+        if self._cost is not None:
+            terms.append(self._cost)
+
+        return set(
+            variable for constraint in terms for variable in constraint.variables()
+        )
+
+    def _optimize_cost(self):
+        # We use Lagrange multipliers to rewrite this expression.
+        # If we let
+        #   L(Re(a), Im(a), λ) = f(Re(a), Im(a)) + Σ λ_i g_i(Re(a), Im(a))
+        # and denote by g_i=0 all the affine linear conditions collected so
+        # far, then we get two constraints for each a_k, one real, one
+        # imaginary, namely that the partial derivative wrt Re(a_k) and Im(a_k)
+        # vanishes. Note that we have one Lagrange multiplier for each affine
+        # linear constraint collected so far.
+
+        # TODO: We add lots of lagrange coefficients even if our rank is very
+        # small. We should probably determine the rank here and reduce the
+        # system first.
+        lagranges = len(self._constraints)
+
+        g = self._constraints
+
+        # We form the partial derivative with respect to the variables Re(a_k)
+        # and Im(a_k).
+        for variable in self.variables():
+            if variable.describe()[0] == "λ?":
+                continue
+
+            L = self._cost.derivative(variable)
+
+            for i in range(lagranges):
+                L += g[i][variable] * self.lagrange(i)
+
+            self.add_constraint(L, rank_check=False)
+
+        # We form the partial derivatives with respect to the λ_i. This yields
+        # the condition -g_i=0 which is already recorded in the linear system.
+
+        # Prevent us from calling this method again.
+        self._cost = None
+
+    def require_cohomology(self, cocycle):
+        r""" "
+        Create a constraint by integrating numerically following the paths that
+        form a basis of homology.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, SimplicialCohomology
+            sage: T = translation_surfaces.square_torus()
+            sage: T.set_immutable()
+
+            sage: H = SimplicialCohomology(T)
+            sage: a, b = H.homology().gens()
+
+        Integrating along the (negative horizontal) cycle `b`, produces
+        something with `-Re(a_0)` in the real part.
+        Integration along the diagonal `a`, produces essentially `Re(a_0) -
+        Im(a_0)`. Note that the two variables ``a0`` and ``a1`` are the same
+        because the centers of the Voronoi cells for the two triangles are
+        identical::
+
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints, HarmonicDifferentials
+            sage: Ω = HarmonicDifferentials(T)
+            sage: C = PowerSeriesConstraints(Ω)
+            sage: C.require_cohomology(H({b: 1}))
+            sage: C  # tol 1e-9  # not tested  # TODO: Check this value
+            [0.828427124746190*Re(a0,0) + 0.171572875253810*Re(a1,0) - 1.38777878078145e-17*Re(a0,1) - 3.03576608295941e-18*Re(a1,1) + 8.54260702578763e-18*Im(a0,1) + 0.0857864376269050*Im(a1,1) + 0.0473785412436502*Re(a0,2) - 0.0424723326565069*Re(a1,2) - 4.71795158413990e-18*Im(a0,2) - 3.03576608295941e-18*Im(a1,2) - 1.73472347597681e-18*Re(a0,3) + 2.16840434497101e-18*Re(a1,3) + 2.19851947436667e-18*Im(a0,3) - 0.0208152801713079*Im(a1,3) + 0.00487732352790257*Re(a0,4) + 0.0100938339276945*Re(a1,4) - 9.71367022318980e-19*Im(a0,4) + 1.51788304147971e-18*Im(a1,4), 0.828427124746190*Im(a0,0) + 0.171572875253810*Im(a1,0) - 1.38777878078145e-17*Re(a0,1) - 3.03576608295941e-18*Re(a1,1) - 8.54260702578763e-18*Im(a0,1) + 0.0857864376269050*Im(a1,1) + 7.70371977754894e-34*Re(a0,2) - 2.60208521396521e-18*Re(a1,2) - 0.0473785412436502*Im(a0,2) + 0.0424723326565069*Im(a1,2) + 1.73472347597681e-18*Re(a0,3) - 2.16840434497101e-18*Re(a1,3) + 2.19851947436667e-18*Im(a0,3) + 0.0208152801713079*Im(a1,3) - 9.62964972193618e-35*Re(a0,4) - 1.08420217248550e-18*Re(a1,4) + 0.00487732352790257*Im(a0,4) + 0.0100938339276945*Im(a1,4) - 1.00000000000000]
+
+
+        If we increase precision, we see additional higher imaginary parts.
+        These depend on the choice of base point of the integration and will be
+        found to be zero by other constraints, not true anymore TODO::
+
+            sage: C = PowerSeriesConstraints(Ω)
+            sage: C.require_cohomology(H({b: 1}))
+            sage: C  # not tested  # TODO: check this value
+            [0.828427124746190*Re(a0,0) + 0.171572875253810*Re(a1,0) - 1.38777878078145e-17*Re(a0,1) - 3.03576608295941e-18*Re(a1,1) + 8.54260702578763e-18*Im(a0,1) + 0.0857864376269050*Im(a1,1) + 0.0473785412436502*Re(a0,2) - 0.0424723326565069*Re(a1,2) - 4.71795158413990e-18*Im(a0,2) - 3.03576608295941e-18*Im(a1,2) - 1.73472347597681e-18*Re(a0,3) + 2.16840434497101e-18*Re(a1,3) + 2.19851947436667e-18*Im(a0,3) - 0.0208152801713079*Im(a1,3) + 0.00487732352790257*Re(a0,4) + 0.0100938339276945*Re(a1,4) - 9.71367022318980e-19*Im(a0,4) + 1.51788304147971e-18*Im(a1,4), 0.828427124746190*Im(a0,0) + 0.171572875253810*Im(a1,0) - 1.38777878078145e-17*Re(a0,1) - 3.03576608295941e-18*Re(a1,1) - 8.54260702578763e-18*Im(a0,1) + 0.0857864376269050*Im(a1,1) + 7.70371977754894e-34*Re(a0,2) - 2.60208521396521e-18*Re(a1,2) - 0.0473785412436502*Im(a0,2) + 0.0424723326565069*Im(a1,2) + 1.73472347597681e-18*Re(a0,3) - 2.16840434497101e-18*Re(a1,3) + 2.19851947436667e-18*Im(a0,3) + 0.0208152801713079*Im(a1,3) - 9.62964972193618e-35*Re(a0,4) - 1.08420217248550e-18*Re(a1,4) + 0.00487732352790257*Im(a0,4) + 0.0100938339276945*Im(a1,4) - 1.00000000000000]
+
+        """
+        from sage.all import parallel
+
+        @parallel
+        def create_constraint(cycle):
+            return self.integrate(cycle).real() - self.real_field()(
+                cocycle(cycle).real()
+            )
+
+        for (args, kwargs), constraint in create_constraint(
+            (cycle,) for cycle in cocycle.parent().homology().gens()
+        ):
+            self.add_constraint(constraint, rank_check=False)
+
+    def lagrange_variables(self):
+        return set(
+            variable for variable in self.variables() if variable.describe()[0] == "λ?"
+        )
+
+    def matrix(self, nowarn=False):
+        r"""
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, SimplicialCohomology
+            sage: T = translation_surfaces.square_torus()
+            sage: T.set_immutable()
+
+            sage: H = SimplicialCohomology(T)
+            sage: a, b = H.homology().gens()
+
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints, HarmonicDifferentials
+            sage: Ω = HarmonicDifferentials(T)
+            sage: C = PowerSeriesConstraints(Ω)
+            sage: C.require_cohomology(H({a: 1}))
+            sage: C.optimize(C._L2_consistency())
+            sage: C._optimize_cost()
+            sage: C.matrix()  # not tested # TODO: Check this matrix.
+            (22 x 22 dense matrix over Real Field with 54 bits of precision,
+             (1.00000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000),
+             {Im(a0,4): 0,
+              Re(a0,2): 1,
+              Im(a1,1): 2,
+              Re(a1,3): 3,
+              Re(a1,1): 4,
+              Im(a0,1): 5,
+              Im(a0,3): 6,
+              Im(a1,3): 7,
+              Re(a0,1): 8,
+              Im(a1,2): 9,
+              Im(a0,0): 10,
+              Re(a0,3): 11,
+              Im(a1,0): 12,
+              Im(a0,2): 13,
+              Im(a1,4): 14,
+              Re(a1,2): 15,
+              Re(a1,4): 16,
+              Re(a0,0): 17,
+              Re(a0,4): 18,
+              Re(a1,0): 19},
+            set())
+
+        """
+        lagranges = {}
+
+        non_lagranges = set()
+        for row, constraint in enumerate(self._constraints):
+            assert constraint.total_degree() <= 1
+            for variable in constraint.variables():
+                gen, degree = variable.describe()
+                if gen != "λ?":
+                    non_lagranges.add(variable)
+
+        non_lagranges = {variable: i for (i, variable) in enumerate(non_lagranges)}
+
+        # TODO: Can we make this warning work again?
+        # if len(set(self.symbolic_ring()._regular_gens(self._prec))) != len(non_lagranges):
+        #     if not nowarn:
+        #         from warnings import warn
+        #         warn(f"Some power series coefficients are not constrained for this harmonic differential. They will be chosen to be 0 by the solver.")
+
+        from sage.all import matrix, vector
+
+        A = matrix(
+            self.real_field(),
+            len(self._constraints),
+            len(non_lagranges) + len(self.lagrange_variables()),
+        )
+        b = vector(self.real_field(), len(self._constraints))
+
+        for row, constraint in enumerate(self._constraints):
+            b[row] -= constraint.constant_coefficient()
+
+            for variable in constraint.variables():
+                if variable.describe()[0] == "λ?":
+                    if variable not in lagranges:
+                        lagranges[variable] = len(non_lagranges) + len(lagranges)
+                    column = lagranges[variable]
+                else:
+                    column = non_lagranges[variable]
+
+                A[row, column] += constraint[variable]
+
+        return A, b, non_lagranges, set()
+
+    @cached_method
+    def power_series_ring(self, *args):
+        r"""
+        Return the power series ring to write down the series describing a
+        harmonic differential in a Voronoi cell.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: T = translation_surfaces.square_torus()
+            sage: T.set_immutable()
+
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints, HarmonicDifferentials
+            sage: Ω = HarmonicDifferentials(T)
+            sage: Ω = PowerSeriesConstraints(Ω)
+            sage: Ω.power_series_ring(T(0, (1/2, 1/2)))
+            Power Series Ring in z0 over Complex Field with 54 bits of precision
+            sage: Ω.power_series_ring(T(0, 0))
+            Power Series Ring in z1 over Complex Field with 54 bits of precision
+
+        """
+        from sage.all import PowerSeriesRing
+
+        if len(args) != 1:
+            raise NotImplementedError
+
+        point = args[0]
+        return PowerSeriesRing(
+            self.complex_field(), f"z{self._differentials._centers.index(point)}"
+        )
+
+    def solve(self, algorithm="eigen+mpfr"):
+        r"""
+        Return a solution for the system of constraints with minimal error.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: T = translation_surfaces.square_torus()
+            sage: T.set_immutable()
+
+            sage: from flatsurf.geometry.harmonic_differentials import PowerSeriesConstraints, HarmonicDifferentials
+            sage: Ω = HarmonicDifferentials(T)
+            sage: C = PowerSeriesConstraints(Ω)
+            sage: R = C.symbolic_ring()
+            sage: C.add_constraint(R.gen(0) - R.gen(5))
+            sage: C.add_constraint(R.gen(0) - 1)
+            sage: C.solve()
+            ({Point (1/2, 1/2) of polygon 0: 1.00000000000000 + 1.00000000000000*z0 + O(z0^2)},
+             0.000000000000000)
+
+        """
+        print("Creating Lagrange symbols")
+        self._optimize_cost()
+
+        print("Denormalizing system")
+        self._denormalize()
+
+        print("Constructing linear system")
+        A, b, decode, _ = self.matrix()
+
+        rows, columns = A.dimensions()
+        # TODO
+        print(f"Solving {rows}×{columns} system")
+        rank = A.rank()
+
+        print("Computing condition")
+        from sage.all import RDF, oo
+
+        condition = A.change_ring(RDF).condition()
+        print(f"{condition=}")
+
+        if rank < columns:
+            # TODO: Warn?
+            print(f"system underdetermined: {rows}×{columns} matrix of rank {rank}")
+
+        print("Solving system")
+        if algorithm == "arb":
+            from sage.all import ComplexBallField
+
+            C = ComplexBallField(self.complex_field().prec())
+            CA = A.change_ring(C)
+            Cb = b.change_ring(C)
+
+            solution = CA.solve_right(Cb)
+
+            solution = solution.change_ring(self.real_field())
+        elif algorithm == "scipy":
+            CA = A.change_ring(RDF)
+            Cb = b.change_ring(RDF)
+            solution = CA.solve_right(Cb)
+        elif algorithm == "eigen+mpfr":
+            _cppyy().load_library("mpfr")
+
+            solution = define_solve()(A, b)
+
+            from sage.all import vector
+
+            solution = vector([self.real_field()(entry) for entry in solution])
+        else:
+            raise NotImplementedError
+
+        print("Interpreting solution")
+        residue = (A * solution - b).norm()
+
+        lagranges = len(self.lagrange_variables())
+
+        if lagranges:
+            solution = solution[:-lagranges]
+
+        series = {point: {} for point in self._differentials._centers}
+
+        for variable, column in decode.items():
+            value = solution[column]
+
+            gen, degree = variable.describe()
+
+            # TODO: Use generic functionality to parse variable name.
+            if gen.startswith("Re(a"):
+                part = 0
+            elif gen.startswith("Im(a"):
+                part = 1
+            else:
+                assert False
+
+            center = self._differentials._centers[int(gen.split(",")[0][4:])]
+
+            if degree not in series[center]:
+                series[center][degree] = [None, None]
+
+            series[center][degree][part] = self._normalize(
+                value, center=center, degree=degree, part=part
+            )
+
+        print("Building series")
+        series = {
+            point: sum(
+                (
+                    self.complex_field()(*entry)
+                    * self.power_series_ring(point).gen() ** k
+                    for (k, entry) in series[point].items()
+                ),
+                start=self.power_series_ring(point).zero(),
+            ).add_bigoh(max(series[point]) + 1)
+            for point in series
+            if series[point]
+        }
+
+        return series, residue
+
+    def _denormalize(self):
+        # TODO: Maybe call this one balance and the other one unbalance.
+        # Rewrite a * x_n as (a * r^-n) * (r^n * x_n) where r is the relative
+        # radius of convergence at the center of x (0 < r < 1.)
+        # The solution of the optimization problem (r^n * x_n) is then a much
+        # smaller number for large n which should help keep the condition of
+        # the problem in check.
+        def mul(scalar, expression):
+            # TODO: Why is there no scalar multiplication on power series expressions?
+            return expression.map_coefficients(lambda c: scalar * c)
+
+        def center(v):
+            return Ω._gen_center(v)
+
+        Ω = self._differentials
+        self._constraints = [
+            sum(
+                mul(
+                    Ω._relative_inradius_of_convergence(
+                        Ω._cells.cell_at_center(center(v))
+                    )
+                    ** -(v.describe()[1] / center(v).angle())
+                    * constraint[v]
+                    if not Ω._gen_is_lagrange(v)
+                    else constraint[v],
+                    v,
+                )
+                for v in constraint.variables()
+            )
+            + constraint.constant_coefficient()
+            for constraint in self._constraints
+        ]
+
+    def _normalize(self, x, center, degree, part):
+        # Undo the effect of _denormalize on x, i.e., return r^n * x.
+        Ω = self._differentials
+        r = Ω._relative_inradius_of_convergence(Ω._cells.cell_at_center(center))
+        return x * r ** -(degree / center.angle())
+
+
+class Path:
+    pass
+
+
+class GeodesicPath(Path):
+    def __init__(self, surface, start, end, holonomy):
+        if holonomy.is_mutable():
+            raise TypeError("holonomy must be immutable")
+        if start[1].is_mutable():
+            raise TypeError("start point must be immutable")
+        if end[1].is_mutable():
+            raise TypeError("end point must be immutable")
+
+        self._surface = surface
+        self._start = start
+        self._end = end
+        self._holonomy = holonomy
+
+    @staticmethod
+    def across_edge(surface, label, edge):
+        r"""
+        Return the :class:`GeodesicPath` that crosses from the center of the
+        polygon ``label`` to the center of the polygon across the ``edge`` in
+        a straight line.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.regular_octagon()
+
+            sage: from flatsurf.geometry.harmonic_differentials import GeodesicPath
+            sage: GeodesicPath.across_edge(S, 0, 0)
+            Path (0, -a - 1) from (1/2, 1/2*a + 1/2) in polygon 0 to (1/2, 1/2*a + 1/2) in polygon 0
+
+        """
+        polygon = surface.polygon(label)
+
+        if not polygon.is_convex():
+            raise NotImplementedError
+
+        opposite_label, opposite_edge = surface.opposite_edge(label, edge)
+        opposite_polygon = surface.polygon(opposite_label)
+
+        holonomy = (
+            polygon.vertex(edge)
+            - polygon.centroid()
+            + opposite_polygon.centroid()
+            - opposite_polygon.vertex(opposite_edge + 1)
+        )
+        # TODO: edge() should be immutable.
+        holonomy.set_immutable()
+
+        return GeodesicPath(
+            surface,
+            (label, polygon.centroid()),
+            (opposite_label, opposite_polygon.centroid()),
+            holonomy,
+        )
+
+    @staticmethod
+    def along_edge(surface, label, edge):
+        r"""
+        Return the :class:`GeodesicPath` along the ``edge`` of the polygon with
+        ``label``.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.regular_octagon()
+
+            sage: from flatsurf.geometry.harmonic_differentials import GeodesicPath
+            sage: GeodesicPath.along_edge(S, 0, 0)
+            Path (1, 0) from (0, 0) in polygon 0 to (1, 0) in polygon 0
+
+        """
+        polygon = surface.polygon(label)
+        holonomy = polygon.edge(edge)
+        # TODO: edge() should be immutable.
+        holonomy.set_immutable()
+
+        return GeodesicPath(
+            surface,
+            (label, polygon.vertex(edge)),
+            (label, polygon.vertex(edge + 1)),
+            holonomy,
+        )
+
+    def split(self):
+        r"""
+        Return the path as a sequence of segments in polygons.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.regular_octagon()
+
+            sage: from flatsurf.geometry.harmonic_differentials import GeodesicPath
+            sage: path = GeodesicPath.across_edge(S, 0, 0)
+            sage: path.split()
+            [(0, OrientedSegment((1/2, 1/2*a + 1/2), (1/2, 0))),
+             (0, OrientedSegment((1/2, a + 1), (1/2, 1/2*a + 1/2)))]
+
+        """
+        polygon = self._surface.polygon(self._start[0])
+        if not polygon.is_convex():
+            raise NotImplementedError
+
+        from flatsurf.geometry.euclidean import OrientedSegment
+
+        # TODO: This only works for convex polygons.
+        if (
+            self._end[0] == self._start[0]
+            and polygon.get_point_position(self._start[1] + self._holonomy).is_inside()
+        ):
+            return [
+                (
+                    self._start[0],
+                    OrientedSegment(self._start[1], self._start[1] + self._holonomy),
+                )
+            ]
+
+        path = OrientedSegment(self._start[1], self._start[1] + self._holonomy)
+        for v in range(len(polygon.vertices())):
+            edge = OrientedSegment(polygon.vertex(v), polygon.vertex(v + 1))
+            intersection = edge.intersection(path)
+
+            if intersection is None:
+                continue
+
+            if intersection == self._start[1]:
+                continue
+
+            assert (
+                self._surface(self._start[0], intersection).angle() == 1
+            ), "path crosses over a singularity"
+
+            segment = OrientedSegment(self._start[1], intersection)
+
+            prefix = [(self._start[0], segment)]
+
+            holonomy = self._holonomy - (intersection - self._start[1])
+            holonomy.set_immutable()
+
+            if not holonomy:
+                return prefix
+
+            from flatsurf.geometry.euclidean import is_parallel
+
+            assert is_parallel(holonomy, self._holonomy)
+
+            opposite_label, opposite_edge = self._surface.opposite_edge(
+                self._start[0], v
+            )
+
+            new_start = self._surface.polygon(opposite_label).vertex(
+                opposite_edge + 1
+            ) + (intersection - polygon.vertex(v))
+            new_start.set_immutable()
+
+            return (
+                prefix
+                + GeodesicPath(
+                    self._surface, (opposite_label, new_start), self._end, holonomy
+                ).split()
+            )
+
+        assert False
+
+    def __neg__(self):
+        holonomy = -self._holonomy
+        holonomy.set_immutable()
+        return GeodesicPath(self._surface, self._end, self._start, holonomy)
+
+    def __repr__(self):
+        return f"Path {self._holonomy} from {self._start[1]} in polygon {self._start[0]} to {self._end[1]} in polygon {self._end[0]}"
+
+    def __eq__(self, other):
+        if not isinstance(other, GeodesicPath):
+            return False
+        if self._start == other._start and self._holonomy == other._holonomy:
+            assert self._end == other._end
+            return True
+
+        return False
+
+    def __ne__(self, other):
+        return not (self == other)
+
+    def __hash__(self):
+        return hash((self._start, self._holonomy))
+
+
+def HarmonicDifferentials(
+    surface, error=None, cell_decomposition=None, check=True, category=None
+):
+    return surface.harmonic_differentials(error, cell_decomposition, check, category)
diff --git a/flatsurf/geometry/homology.py b/flatsurf/geometry/homology.py
index 58d0fb0de..4ff771006 100644
--- a/flatsurf/geometry/homology.py
+++ b/flatsurf/geometry/homology.py
@@ -426,6 +426,39 @@ def surface(self):
         """
         return self.parent().surface()
 
+    def path(self):
+        edges = []
+
+        for (label, edge), multiplicity in dict(self._chain).items():
+            from sage.all import ZZ
+
+            if multiplicity not in ZZ:
+                raise NotImplementedError("cannot lift this homology class to a path")
+
+            if multiplicity < 0:
+                multiplicity *= -1
+                label, edge = self.surface().opposite_edge(label, edge)
+
+            edges.extend([(label, edge)] * multiplicity)
+
+        connected_edges = [edges.pop()]
+        while edges:
+            for edge in edges:
+                if self.surface()(
+                    connected_edges[-1][0], connected_edges[-1][1] + 1
+                ) == self.surface()(*edge):
+                    edges.remove(edge)
+                    connected_edges.append(edge)
+                    break
+            else:
+                assert False, "path not connected"
+
+        assert self.surface()(
+            connected_edges[-1][0], connected_edges[-1][1] + 1
+        ) == self.surface()(*connected_edges[0]), "path not looping"
+
+        return connected_edges
+
     def __bool__(self):
         r"""
         Return whether this class is non-trivial.
@@ -757,6 +790,61 @@ def to_C0(point):
 
         return self.change(k=self._k - 1).chain_module().zero()
 
+    def _boundary_segment(self, gen):
+        if self._generators == "edge":
+            C0 = self.chain_module(dimension=0)
+            label, edge = gen
+            opposite_label, opposite_edge = self._surface.opposite_edge(label, edge)
+            return C0(
+                self._surface.point(
+                    opposite_label,
+                    self._surface.polygon(opposite_label).vertex(opposite_edge),
+                )
+            ) - C0(
+                self._surface.point(label, self._surface.polygon(label).vertex(edge))
+            )
+
+        if self._generators == "voronoi":
+            C0 = self.chain_module(dimension=0)
+            label, edge = gen
+            opposite_label, opposite_edge = self._surface.opposite_edge(label, edge)
+
+            return C0(opposite_label) - C0(label)
+
+        raise NotImplementedError
+
+    def _boundary_polygon(self, gen):
+        if self._generators == "edge":
+            C1 = self.chain_module(dimension=1)
+            boundary = C1.zero()
+            face = gen
+            for edge in range(len(self._surface.polygon(face).vertices())):
+                if (face, edge) in C1.indices():
+                    boundary += C1((face, edge))
+                else:
+                    boundary -= C1(self._surface.opposite_edge(face, edge))
+            return boundary
+
+        if self._generators == "voronoi":
+            C1 = self.chain_module(dimension=1)
+            boundary = C1.zero()
+            label, vertex = gen
+            # The counterclockwise walk around "vertex" is a boundary.
+            while True:
+                edge = (vertex - 1) % len(self._surface.polygon(label).vertices())
+                opposite_label, opposite_edge = self._surface.opposite_edge(label, edge)
+                if (label, edge) in C1.indices():
+                    boundary += C1((label, edge))
+                else:
+                    boundary -= C1((opposite_label, opposite_edge))
+
+                if (opposite_label, opposite_edge) == gen:
+                    return boundary
+
+                label, vertex = opposite_label, opposite_edge
+
+        raise NotImplementedError
+
     @cached_method
     def _chain_complex(self):
         r"""
diff --git a/flatsurf/geometry/hyperbolic.py b/flatsurf/geometry/hyperbolic.py
index da8e2c0ab..d3d11450d 100644
--- a/flatsurf/geometry/hyperbolic.py
+++ b/flatsurf/geometry/hyperbolic.py
@@ -224,6 +224,9 @@
 from sage.misc.cachefunc import cached_method
 
 
+from flatsurf.geometry.geometry import Geometry, ExactGeometry, EpsilonGeometry
+
+
 class HyperbolicPlane(Parent, UniqueRepresentation):
     r"""
     The hyperbolic plane.
@@ -1667,7 +1670,8 @@ def segment(
 
         .. SEEALSO::
 
-            :meth:`HyperbolicPoint.segment`
+            :meth:`HyperbolicPoint.segment` to create a segment from its two
+            endpoints (without specifying a geodesic.)
 
         """
         geodesic = self(geodesic)
@@ -3432,7 +3436,7 @@ def _repr_(self):
         return f"Hyperbolic Plane over {repr(self.base_ring())}"
 
 
-class HyperbolicGeometry:
+class HyperbolicGeometry(Geometry):
     r"""
     Predicates and primitive geometric constructions over a base ``ring``.
 
@@ -3476,236 +3480,20 @@ class HyperbolicGeometry:
         sage: H(0) == H(1/2048)
         True
 
+    TESTS::
+
+        sage: from flatsurf import HyperbolicPlane
+        sage: from flatsurf.geometry.hyperbolic import HyperbolicGeometry
+        sage: H = HyperbolicPlane()
+        sage: isinstance(H.geometry, HyperbolicGeometry)
+        True
+
     .. SEEALSO::
 
         :class:`HyperbolicExactGeometry`, :class:`HyperbolicEpsilonGeometry`
 
     """
 
-    def __init__(self, ring):
-        r"""
-        TESTS::
-
-            sage: from flatsurf import HyperbolicPlane
-            sage: from flatsurf.geometry.hyperbolic import HyperbolicGeometry
-            sage: H = HyperbolicPlane()
-            sage: isinstance(H.geometry, HyperbolicGeometry)
-            True
-
-        """
-        self._ring = ring
-
-    def base_ring(self):
-        r"""
-        Return the ring over which this geometry is implemented.
-
-        EXAMPLES::
-
-            sage: from flatsurf import HyperbolicPlane
-            sage: H = HyperbolicPlane()
-            sage: H.geometry.base_ring()
-            Rational Field
-
-        """
-        return self._ring
-
-    def _zero(self, x):
-        r"""
-        Return whether ``x`` should be considered zero in the
-        :meth:`base_ring`.
-
-        .. NOTE::
-
-            This predicate should not be used directly in geometric
-            constructions since it does not specify the context in which this
-            question is asked. This makes it very difficult to override a
-            specific aspect in a custom geometry. Also, this predicate lacks
-            the context of other elements; a proper predicate should also take
-            other elements into account to decide this question relative to the
-            other values.
-
-        INPUT:
-
-        - ``x`` -- an element of the :meth:`base_ring`
-
-        EXAMPLES::
-
-            sage: from flatsurf import HyperbolicPlane
-            sage: H = HyperbolicPlane(RR)
-            sage: H.geometry._zero(1)
-            False
-            sage: H.geometry._zero(1e-9)
-            True
-
-        """
-        return self._cmp(x, 0) == 0
-
-    def _cmp(self, x, y):
-        r"""
-        Return how ``x`` compares to ``y``.
-
-        .. NOTE::
-
-            This predicate should not be used directly in geometric
-            constructions since it does not specify the context in which this
-            question is asked. This makes it very difficult to override a
-            specific aspect in a custom geometry.
-
-        INPUT:
-
-        - ``x`` -- an element of the :meth:`base_ring`
-
-        - ``y`` -- an element of the :meth:`base_ring`
-
-        EXAMPLES::
-
-            sage: from flatsurf import HyperbolicPlane
-            sage: H = HyperbolicPlane()
-            sage: H.geometry._cmp(0, 0)
-            0
-            sage: H.geometry._cmp(0, 1)
-            -1
-            sage: H.geometry._cmp(1, 0)
-            1
-
-        ::
-
-            sage: H = HyperbolicPlane(RR)
-            sage: H.geometry._cmp(0, 0)
-            0
-            sage: H.geometry._cmp(0, 1)
-            -1
-            sage: H.geometry._cmp(1, 0)
-            1
-            sage: H.geometry._cmp(1e-10, 0)
-            0
-
-        """
-        if self._equal(x, y):
-            return 0
-        if x < y:
-            return -1
-
-        assert (
-            x > y
-        ), "Geometry over this ring must override _cmp since not (x == y) and not (x < y) does not imply x > y"
-        return 1
-
-    def _sgn(self, x):
-        r"""
-        Return the sign of ``x``.
-
-        .. NOTE::
-
-            This predicate should not be used directly in geometric
-            constructions since it does not specify the context in which this
-            question is asked. This makes it very difficult to override a
-            specific aspect in a custom geometry. Also, this predicate lacks
-            the context of other elements; a proper predicate should also take
-            other elements into account to decide this question relative to the
-            other values.
-
-        INPUT:
-
-        - ``x`` -- an element of the :meth:`base_ring`.
-
-        EXAMPLES::
-
-            sage: from flatsurf import HyperbolicPlane
-            sage: H = HyperbolicPlane(RR)
-            sage: H.geometry._sgn(1)
-            1
-            sage: H.geometry._sgn(-1)
-            -1
-            sage: H.geometry._sgn(1e-10)
-            0
-
-        """
-        return self._cmp(x, 0)
-
-    def _equal(self, x, y):
-        r"""
-        Return whether ``x`` and ``y`` should be considered equal in the :meth:`base_ring`.
-
-        .. NOTE::
-
-            This predicate should not be used directly in geometric
-            constructions since it does not specify the context in which this
-            question is asked. This makes it very difficult to override a
-            specific aspect in a custom geometry.
-
-        INPUT:
-
-        - ``x`` -- an element of the :meth:`base_ring`
-
-        - ``y`` -- an element of the :meth:`base_ring`
-
-        EXAMPLES::
-
-            sage: from flatsurf import HyperbolicPlane
-            sage: H = HyperbolicPlane(RR)
-            sage: H.geometry._equal(0, 1)
-            False
-            sage: H.geometry._equal(0, 1e-10)
-            True
-
-        """
-        raise NotImplementedError("this geometry does not implement _equal()")
-
-    def _determinant(self, a, b, c, d):
-        r"""
-        Return the determinant of the 2×2 matrix ``[[a, b], [c, d]]`` or
-        ``None`` if the matrix is singular.
-
-        .. NOTE::
-
-            This predicate should not be used directly in geometric
-            constructions since it does not specify the context in which this
-            question is asked. This makes it very difficult to override a
-            specific aspect in a custom geometry.
-
-        INPUT:
-
-        - ``a`` -- an element of the :meth:`base_ring`
-
-        - ``b`` -- an element of the :meth:`base_ring`
-
-        - ``c`` -- an element of the :meth:`base_ring`
-
-        - ``d`` -- an element of the :meth:`base_ring`
-
-        EXAMPLES:
-
-            sage: from flatsurf import HyperbolicPlane
-            sage: H = HyperbolicPlane()
-
-            sage: H.geometry._determinant(1, 2, 3, 4)
-            -2
-            sage: H.geometry._determinant(0, 10^-10, 1, 1)
-            -1/10000000000
-
-        """
-        det = a * d - b * c
-        if self._zero(det):
-            return None
-        return det
-
-    def change_ring(self, ring):
-        r"""
-        Return this geometry with the :meth:`base_ring` changed to ``ring``.
-
-        EXAMPLES::
-
-            sage: from flatsurf import HyperbolicPlane
-            sage: H = HyperbolicPlane()
-            sage: H.geometry
-            Exact geometry over Rational Field
-            sage: H.geometry.change_ring(AA)
-            Exact geometry over Algebraic Real Field
-
-        """
-        raise NotImplementedError("this geometry does not implement change_ring()")
-
     def projective(self, p, q, point):
         r"""
         Return the ideal point with projective coordinates ``[p: q]`` in the
@@ -3880,6 +3668,7 @@ def intersection(self, f, g):
             (0, 0)
 
         """
+        # TODO: Use Euclidean geometry?
         (fa, fb, fc) = f
         (ga, gb, gc) = g
         det = self._determinant(fb, fc, gb, gc)
@@ -3893,7 +3682,7 @@ def intersection(self, f, g):
         return (x, y)
 
 
-class HyperbolicExactGeometry(UniqueRepresentation, HyperbolicGeometry):
+class HyperbolicExactGeometry(UniqueRepresentation, HyperbolicGeometry, ExactGeometry):
     r"""
     Predicates and primitive geometric constructions over an exact base ring.
 
@@ -3916,32 +3705,6 @@ class HyperbolicExactGeometry(UniqueRepresentation, HyperbolicGeometry):
 
     """
 
-    def _equal(self, x, y):
-        r"""
-        Return whether the numbers ``x`` and ``y`` should be considered equal
-        in exact geometry.
-
-        .. NOTE::
-
-            This predicate should not be used directly in geometric
-            constructions since it does not specify the context in which this
-            question is asked. This makes it very difficult to override a
-            specific aspect in a custom geometry.
-
-        EXAMPLES::
-
-            sage: from flatsurf import HyperbolicPlane
-            sage: H = HyperbolicPlane()
-            sage: H.geometry._equal(0, 1)
-            False
-            sage: H.geometry._equal(0, 1/2**64)
-            False
-            sage: H.geometry._equal(0, 0)
-            True
-
-        """
-        return x == y
-
     def change_ring(self, ring):
         r"""
         Return this geometry with the :meth:`~HyperbolicGeometry.base_ring`
@@ -3975,22 +3738,10 @@ def change_ring(self, ring):
 
         return HyperbolicExactGeometry(ring)
 
-    def __repr__(self):
-        r"""
-        Return a printable representation of this geometry.
-
-        EXAMPLES::
 
-            sage: from flatsurf import HyperbolicPlane
-            sage: H = HyperbolicPlane()
-            sage: H.geometry
-            Exact geometry over Rational Field
-
-        """
-        return f"Exact geometry over {self._ring}"
-
-
-class HyperbolicEpsilonGeometry(UniqueRepresentation, HyperbolicGeometry):
+class HyperbolicEpsilonGeometry(
+    UniqueRepresentation, HyperbolicGeometry, EpsilonGeometry
+):
     r"""
     Predicates and primitive geometric constructions over a base ``ring`` with
     "precision" ``epsilon``.
@@ -4033,90 +3784,6 @@ class HyperbolicEpsilonGeometry(UniqueRepresentation, HyperbolicGeometry):
 
     """
 
-    def __init__(self, ring, epsilon):
-        r"""
-        TESTS::
-
-            sage: from flatsurf import HyperbolicPlane
-            sage: from flatsurf.geometry.hyperbolic import HyperbolicEpsilonGeometry
-            sage: H = HyperbolicPlane(RR)
-            sage: isinstance(H.geometry, HyperbolicEpsilonGeometry)
-            True
-
-        """
-        super().__init__(ring)
-        self._epsilon = ring(epsilon)
-
-    def _equal(self, x, y):
-        r"""
-        Return whether ``x`` and ``y`` should be considered equal numbers with
-        respect to an ε error.
-
-        .. NOTE::
-
-            This method has not been tested much. Since this underlies much of
-            the inexact geometry, we should probably do something better here,
-            see e.g., https://floating-point-gui.de/errors/comparison/
-
-        EXAMPLES::
-
-            sage: from flatsurf import HyperbolicPlane
-            sage: H = HyperbolicPlane(RR)
-
-            sage: H.geometry._equal(1, 2)
-            False
-            sage: H.geometry._equal(1, 1 + 1e-32)
-            True
-            sage: H.geometry._equal(1e-32, 1e-32 + 1e-33)
-            False
-            sage: H.geometry._equal(1e-32, 1e-32 + 1e-64)
-            True
-
-        """
-        if x == 0 or y == 0:
-            return abs(x - y) < self._epsilon
-
-        return abs(x - y) <= (abs(x) + abs(y)) * self._epsilon
-
-    def _determinant(self, a, b, c, d):
-        r"""
-        Return the determinant of the 2×2 matrix ``[[a, b], [c, d]]`` or
-        ``None`` if the matrix is singular.
-
-        INPUT:
-
-        - ``a`` -- an element of the :meth:`~HyperbolicGeometry.base_ring`
-
-        - ``b`` -- an element of the :meth:`~HyperbolicGeometry.base_ring`
-
-        - ``c`` -- an element of the :meth:`~HyperbolicGeometry.base_ring`
-
-        - ``d`` -- an element of the :meth:`~HyperbolicGeometry.base_ring`
-
-        EXAMPLES:
-
-            sage: from flatsurf import HyperbolicPlane
-            sage: H = HyperbolicPlane(RR)
-
-            sage: H.geometry._determinant(1, 2, 3, 4)
-            -2
-            sage: H.geometry._determinant(1e-10, 0, 0, 1e-10)
-            1.00000000000000e-20
-
-        Unfortunately, we are not implementing any actual rank detecting
-        algorithm (QR decomposition or such) here. So, we do not detect that
-        this matrik is singular::
-
-            sage: H.geometry._determinant(1e-127, 1e-128, 1, 1)
-            9.00000000000000e-128
-
-        """
-        det = a * d - b * c
-        if det == 0:
-            # Note that we should instead numerically detect the rank here.
-            return None
-        return det
-
     def projective(self, p, q, point):
         r"""
         Return the ideal point with projective coordinates ``[p: q]`` in the
@@ -4196,20 +3863,6 @@ def change_ring(self, ring):
 
         return HyperbolicEpsilonGeometry(ring, self._epsilon)
 
-    def __repr__(self):
-        r"""
-        Return a printable representation of this geometry.
-
-        EXAMPLES::
-
-            sage: from flatsurf import HyperbolicPlane
-            sage: H = HyperbolicPlane(RR)
-            sage: H.geometry
-            Epsilon geometry with ϵ=1.00000000000000e-6 over Real Field with 53 bits of precision
-
-        """
-        return f"Epsilon geometry with ϵ={self._epsilon} over {self._ring}"
-
 
 class HyperbolicConvexSet(SageObject):
     r"""
@@ -4796,7 +4449,7 @@ def _test_change_ring(self, **options):
         tester = self._tester(**options)
         tester.assertEqual(self, self.change_ring(self.parent().base_ring()))
 
-    def change(self, ring=None, geometry=None, oriented=None):
+    def change(self, *, ring=None, geometry=None, oriented=None):
         r"""
         Return a modified copy of this set.
 
@@ -4833,13 +4486,13 @@ def change(self, ring=None, geometry=None, oriented=None):
 
         We can also take an unoriented set and pick an orientation::
 
-            sage: oriented = geodesic.change(oriented=True)
+            sage: oriented = unoriented.change(oriented=True)
             sage: oriented.is_oriented()
             True
 
         .. SEEALSO::
 
-            :meth:`is_oriented` for oriented an unoriented sets.
+            :meth:`is_oriented` to determine whether a set is oriented.
 
         """
         raise NotImplementedError(f"this {type(self)} does not implement change()")
@@ -5900,7 +5553,7 @@ class HyperbolicConvexFacade(HyperbolicConvexSet, Parent):
     This is the base class for all hyperbolic convex sets that are not points.
     This class solves the problem that we want convex sets to be "elements" of
     the hyperbolic plane but at the same time, we want these sets to live as
-    parents in the category framework of SageMath; so they have be a Parent
+    parents in the category framework of SageMath; so they have a Parent
     with hyperbolic points as their Element class.
 
     SageMath provides the (not very frequently used and somewhat flaky) facade
@@ -6371,7 +6024,7 @@ def plot(self, model="half_plane", **kwds):
             .plot(model=model, **kwds)
         )
 
-    def change(self, ring=None, geometry=None, oriented=None):
+    def change(self, *, ring=None, geometry=None, oriented=None):
         r"""
         Return a modified copy of this half space.
 
@@ -6910,7 +6563,7 @@ def equation(self, model, normalization=None):
         If this geodesic :meth;`is_oriented`, then the sign of the coefficients
         is chosen to encode the orientation of this geodesic. The sign is such
         that the half plane obtained by replacing ``=`` with ``≥`` in above
-        equationsis on the left of the geodesic.
+        equations is on the left of the geodesic.
 
         Note that the output might not uniquely describe the geodesic since the
         coefficients are only unique up to scaling.
@@ -9667,7 +9320,7 @@ def _repr_(self):
             PowerSeriesRing(self.parent().base_ring(), names="I")(list(coordinates))
         )
 
-    def change(self, ring=None, geometry=None, oriented=None):
+    def change(self, *, ring=None, geometry=None, oriented=None):
         r"""
         Return a modified copy of this point.
 
@@ -10123,7 +9776,7 @@ def _repr_(self):
 
         return repr(self.change_ring(RR))
 
-    def change(self, ring=None, geometry=None, oriented=None):
+    def change(self, *, ring=None, geometry=None, oriented=None):
         r"""
         Return a modified copy of this point.
 
@@ -11898,7 +11551,7 @@ def plot(self, model="half_plane", **kwds):
 
         return self._enhance_plot(plot, model=model)
 
-    def change(self, ring=None, geometry=None, oriented=None):
+    def change(self, *, ring=None, geometry=None, oriented=None):
         r"""
         Return a modified copy of this polygon.
 
@@ -12740,7 +12393,7 @@ def __eq__(self, other):
             self.geodesic() == other.geodesic() and self.vertices() == other.vertices()
         )
 
-    def change(self, ring=None, geometry=None, oriented=None):
+    def change(self, *, ring=None, geometry=None, oriented=None):
         r"""
         Return a modified copy of this segment.
 
@@ -13535,7 +13188,7 @@ def half_spaces(self):
             ]
         )
 
-    def change(self, ring=None, geometry=None, oriented=None):
+    def change(self, *, ring=None, geometry=None, oriented=None):
         r"""
         Return a copy of the empty set.
 
@@ -14490,6 +14143,7 @@ def convex_hull(vertices):
         vertices = [vertex.coordinates(model="klein") for vertex in vertices]
         reference = min(vertices)
 
+        # TODO: Move to euclidean.
         class Slope:
             def __init__(self, xy):
                 self.dx = xy[0] - reference[0]
diff --git a/flatsurf/geometry/lazy.py b/flatsurf/geometry/lazy.py
index 6bbe04c72..48ba81d42 100644
--- a/flatsurf/geometry/lazy.py
+++ b/flatsurf/geometry/lazy.py
@@ -1184,6 +1184,53 @@ def __init__(self, similarity_surface, direction=None, relabel=None, category=No
             category=category or self._surface.category(),
         )
 
+    def _image_edge(self, label, edge):
+        r"""
+        Return the saddle connection in this surface that is identical to the
+        one given by the oriented ``edge`` in the polygon with ``label`` in the
+        original reference surface.
+        """
+        # Ensure that polygon with label is final in self._surface.
+        self.polygon(label)
+
+        from flatsurf.geometry.saddle_connection import SaddleConnection
+
+        saddle_connection = SaddleConnection.from_half_edge(
+            self._reference, label, edge
+        )
+
+        for flip in self._flips:
+            saddle_connection = flip(saddle_connection)
+
+        return SaddleConnection(
+            self,
+            start=saddle_connection.start(),
+            end=saddle_connection.end(),
+            holonomy=saddle_connection.holonomy(),
+            end_holonomy=saddle_connection.end_holonomy(),
+            check=False,
+        )
+
+    def _preimage_edge(self, label, edge):
+        # Ensure that polygon with label is final in self._surface.
+        self.polygon(label)
+
+        from flatsurf.geometry.saddle_connection import SaddleConnection
+
+        saddle_connection = SaddleConnection.from_half_edge(self._surface, label, edge)
+
+        for flip in self._flips[::-1]:
+            saddle_connection = flip.section()(saddle_connection)
+
+        return SaddleConnection(
+            self._reference,
+            start=saddle_connection.start(),
+            end=saddle_connection.end(),
+            holonomy=saddle_connection.holonomy(),
+            end_holonomy=saddle_connection.end_holonomy(),
+            check=False,
+        )
+
     def is_mutable(self):
         r"""
         Return whether this surface is mutable, i.e., return ``False``.
diff --git a/flatsurf/geometry/mappings.py b/flatsurf/geometry/mappings.py
deleted file mode 100644
index 134dc6293..000000000
--- a/flatsurf/geometry/mappings.py
+++ /dev/null
@@ -1,944 +0,0 @@
-r"""Mappings between translation surfaces."""
-# *********************************************************************
-#  This file is part of sage-flatsurf.
-#
-#        Copyright (C) 2016-2022 W. Patrick Hooper
-#                      2016-2022 Vincent Delecroix
-#                           2023 Julian Rüth
-#
-#  sage-flatsurf is free software: you can redistribute it and/or modify
-#  it under the terms of the GNU General Public License as published by
-#  the Free Software Foundation, either version 2 of the License, or
-#  (at your option) any later version.
-#
-#  sage-flatsurf is distributed in the hope that it will be useful,
-#  but WITHOUT ANY WARRANTY; without even the implied warranty of
-#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-#  GNU General Public License for more details.
-#
-#  You should have received a copy of the GNU General Public License
-#  along with sage-flatsurf. If not, see <https://www.gnu.org/licenses/>.
-# *********************************************************************
-
-
-class SurfaceMapping:
-    r"""Abstract class for any mapping between surfaces."""
-
-    def __init__(self, domain, codomain):
-        self._domain = domain
-        self._codomain = codomain
-
-    def domain(self):
-        r"""
-        Return the domain of the mapping.
-        """
-        return self._domain
-
-    def codomain(self):
-        r"""
-        Return the range of the mapping.
-        """
-        return self._codomain
-
-    def push_vector_forward(self, tangent_vector):
-        r"""Applies the mapping to the provided vector."""
-        raise NotImplementedError
-
-    def pull_vector_back(self, tangent_vector):
-        r"""Applies the inverse of the mapping to the provided vector."""
-        raise NotImplementedError
-
-    def __mul__(self, other):
-        # Compose SurfaceMappings
-        return SurfaceMappingComposition(other, self)
-
-    def __rmul__(self, other):
-        return SurfaceMappingComposition(self, other)
-
-
-class SurfaceMappingComposition(SurfaceMapping):
-    r"""
-    Composition of two mappings between surfaces.
-    """
-
-    def __init__(self, mapping1, mapping2):
-        r"""
-        Represent the mapping of mapping1 followed by mapping2.
-        """
-        if mapping1.codomain() != mapping2.domain():
-            raise ValueError(
-                "Codomain of mapping1 must be equal to the domain of mapping2"
-            )
-        self._m1 = mapping1
-        self._m2 = mapping2
-        SurfaceMapping.__init__(self, self._m1.domain(), self._m2.codomain())
-
-    def push_vector_forward(self, tangent_vector):
-        r"""Applies the mapping to the provided vector."""
-        return self._m2.push_vector_forward(
-            self._m1.push_vector_forward(tangent_vector)
-        )
-
-    def pull_vector_back(self, tangent_vector):
-        r"""Applies the inverse of the mapping to the provided vector."""
-        return self._m1.pull_vector_back(self._m2.pull_vector_back(tangent_vector))
-
-    def factors(self):
-        r"""
-        Return the two factors of this surface mapping as a pair (f,g),
-        where the original map is f o g.
-        """
-        return self._m2, self._m1
-
-
-class IdentityMapping(SurfaceMapping):
-    r"""
-    Construct an identity map between two "equal" surfaces.
-    """
-
-    def __init__(self, domain, codomain):
-        SurfaceMapping.__init__(self, domain, codomain)
-
-    def push_vector_forward(self, tangent_vector):
-        r"""Applies the mapping to the provided vector."""
-        ring = tangent_vector.bundle().base_ring()
-        return self._codomain.tangent_vector(
-            tangent_vector.polygon_label(),
-            tangent_vector.point(),
-            tangent_vector.vector(),
-            ring=ring,
-        )
-
-    def pull_vector_back(self, tangent_vector):
-        r"""Applies the pullback mapping to the provided vector."""
-        ring = tangent_vector.bundle().base_ring()
-        return self._domain.tangent_vector(
-            tangent_vector.polygon_label(),
-            tangent_vector.point(),
-            tangent_vector.vector(),
-            ring=ring,
-        )
-
-
-class SimilarityJoinPolygonsMapping(SurfaceMapping):
-    r"""
-    Return a SurfaceMapping joining two polygons together along the edge provided to the constructor.
-
-    EXAMPLES::
-
-        sage: from flatsurf import MutableOrientedSimilaritySurface, Polygon
-        sage: s = MutableOrientedSimilaritySurface(QQ)
-        sage: s.add_polygon(Polygon(edges=[(1,0),(0,1),(-1,-1)]))
-        0
-        sage: s.add_polygon(Polygon(edges=[(-1,0),(0,-1),(1,1)]))
-        1
-        sage: s.glue((0, 0), (1, 0))
-        sage: s.glue((0, 1), (1, 1))
-        sage: s.glue((0, 2), (1, 2))
-        sage: s.set_immutable()
-
-        sage: from flatsurf.geometry.mappings import SimilarityJoinPolygonsMapping
-        sage: m=SimilarityJoinPolygonsMapping(s, 0, 2)
-        sage: s2=m.codomain()
-        sage: s2.labels()
-        (0,)
-        sage: s2.polygons()
-        (Polygon(vertices=[(0, 0), (1, 0), (1, 1), (0, 1)]),)
-        sage: s2.gluings()
-        (((0, 0), (0, 2)), ((0, 1), (0, 3)), ((0, 2), (0, 0)), ((0, 3), (0, 1)))
-
-    """
-
-    def __init__(self, s, p1, e1):
-        r"""
-        Join polygon with label p1 of s to polygon sharing edge e1.
-        """
-        if s.is_mutable():
-            raise ValueError(
-                "Can only construct SimilarityJoinPolygonsMapping for immutable surfaces."
-            )
-
-        from flatsurf.geometry.surface import MutableOrientedSimilaritySurface
-
-        ss2 = MutableOrientedSimilaritySurface.from_surface(s)
-        s2 = ss2
-
-        poly1 = s.polygon(p1)
-        p2, e2 = s.opposite_edge(p1, e1)
-        poly2 = s.polygon(p2)
-        t = s.edge_transformation(p2, e2)
-        dt = t.derivative()
-        vs = []  # actually stores new edges...
-        edge_map = {}  # Store the pairs for the old edges.
-        for i in range(e1):
-            edge_map[len(vs)] = (p1, i)
-            vs.append(poly1.edge(i))
-        ne = len(poly2.vertices())
-        for i in range(1, ne):
-            ee = (e2 + i) % ne
-            edge_map[len(vs)] = (p2, ee)
-            vs.append(dt * poly2.edge(ee))
-        for i in range(e1 + 1, len(poly1.vertices())):
-            edge_map[len(vs)] = (p1, i)
-            vs.append(poly1.edge(i))
-
-        inv_edge_map = {}
-        for key, value in edge_map.items():
-            inv_edge_map[value] = (p1, key)
-
-        if p2 in s.roots():
-            # The polygon with the base label is being removed.
-            s2.set_roots(tuple(p1 if label == p2 else label for label in s.roots()))
-
-        s2.remove_polygon(p1)
-        from flatsurf import Polygon
-
-        s2.add_polygon(Polygon(edges=vs, base_ring=s.base_ring()), label=p1)
-
-        for i in range(len(vs)):
-            p3, e3 = edge_map[i]
-            p4, e4 = s.opposite_edge(p3, e3)
-            if p4 == p1 or p4 == p2:
-                pp, ee = inv_edge_map[(p4, e4)]
-                s2.glue((p1, i), (pp, ee))
-            else:
-                s2.glue((p1, i), (p4, e4))
-
-        s2.remove_polygon(p2)
-        s2.set_immutable()
-
-        self._saved_label = p1
-        self._removed_label = p2
-        self._remove_map = t
-        self._remove_map_derivative = dt
-        self._glued_edge = e1
-        SurfaceMapping.__init__(self, s, ss2)
-
-    def removed_label(self):
-        r"""
-        Return the label that was removed in the joining process.
-        """
-        return self._removed_label
-
-    def glued_vertices(self):
-        r"""
-        Return the vertices of the newly glued polygon which bound the diagonal formed by the glue.
-        """
-        return (
-            self._glued_edge,
-            self._glued_edge
-            + len(self._domain.polygon(self._removed_label).vertices()),
-        )
-
-    def push_vector_forward(self, tangent_vector):
-        r"""Applies the mapping to the provided vector."""
-        ring = tangent_vector.bundle().base_ring()
-        if tangent_vector.polygon_label() == self._removed_label:
-            return self._codomain.tangent_vector(
-                self._saved_label,
-                self._remove_map(tangent_vector.point()),
-                self._remove_map_derivative * tangent_vector.vector(),
-                ring=ring,
-            )
-        else:
-            return self._codomain.tangent_vector(
-                tangent_vector.polygon_label(),
-                tangent_vector.point(),
-                tangent_vector.vector(),
-                ring=ring,
-            )
-
-    def pull_vector_back(self, tangent_vector):
-        r"""
-        Applies the inverse of the mapping to the provided vector.
-        """
-        ring = tangent_vector.bundle().base_ring()
-        if tangent_vector.polygon_label() == self._saved_label:
-            p = tangent_vector.point()
-            v = self._domain.polygon(self._saved_label).vertex(self._glued_edge)
-            e = self._domain.polygon(self._saved_label).edge(self._glued_edge)
-            from flatsurf.geometry.euclidean import ccw
-
-            wp = ccw(p - v, e)
-            if wp > 0:
-                # in polygon with the removed label
-                return self.domain().tangent_vector(
-                    self._removed_label,
-                    (~self._remove_map)(tangent_vector.point()),
-                    (~self._remove_map_derivative) * tangent_vector.vector(),
-                    ring=ring,
-                )
-            if wp < 0:
-                # in polygon with the removed label
-                return self.domain().tangent_vector(
-                    self._saved_label,
-                    tangent_vector.point(),
-                    tangent_vector.vector(),
-                    ring=ring,
-                )
-            # Otherwise wp==0
-            w = tangent_vector.vector()
-            wp = ccw(w, e)
-            if wp > 0:
-                # in polygon with the removed label
-                return self.domain().tangent_vector(
-                    self._removed_label,
-                    (~self._remove_map)(tangent_vector.point()),
-                    (~self._remove_map_derivative) * tangent_vector.vector(),
-                    ring=ring,
-                )
-            return self.domain().tangent_vector(
-                self._saved_label,
-                tangent_vector.point(),
-                tangent_vector.vector(),
-                ring=ring,
-            )
-        else:
-            return self._domain.tangent_vector(
-                tangent_vector.polygon_label(),
-                tangent_vector.point(),
-                tangent_vector.vector(),
-                ring=ring,
-            )
-
-
-class SplitPolygonsMapping(SurfaceMapping):
-    r"""
-    Class for cutting a polygon along a diagonal.
-
-    EXAMPLES::
-
-        sage: from flatsurf import translation_surfaces
-        sage: s=translation_surfaces.veech_2n_gon(4)
-        sage: from flatsurf.geometry.mappings import SplitPolygonsMapping
-        sage: m = SplitPolygonsMapping(s,0,0,2)
-        sage: s2=m.codomain()
-        sage: TestSuite(s2).run()
-        sage: s2.labels()
-        (0, 1)
-        sage: s2.polygons()
-        (Polygon(vertices=[(0, 0), (1/2*a + 1, 1/2*a), (1/2*a + 1, 1/2*a + 1), (1, a + 1), (0, a + 1), (-1/2*a, 1/2*a + 1), (-1/2*a, 1/2*a)]), Polygon(vertices=[(0, 0), (-1/2*a - 1, -1/2*a), (-1/2*a, -1/2*a)]))
-        sage: s2.gluings()
-        (((0, 0), (1, 0)), ((0, 1), (0, 5)), ((0, 2), (0, 6)), ((0, 3), (1, 1)), ((0, 4), (1, 2)), ((0, 5), (0, 1)), ((0, 6), (0, 2)), ((1, 0), (0, 0)), ((1, 1), (0, 3)), ((1, 2), (0, 4)))
-
-    """
-
-    def __init__(self, s, p, v1, v2, new_label=None):
-        r"""
-        Split the polygon with label p of surface s along the diagonal joining vertex v1 to vertex v2.
-
-        Warning: We do not ensure that new_label is not already in the list of labels unless it is None (as by default).
-        """
-        if s.is_mutable():
-            raise ValueError("The surface should be immutable.")
-
-        poly = s.polygon(p)
-        ne = len(poly.vertices())
-        if v1 < 0 or v2 < 0 or v1 >= ne or v2 >= ne:
-            raise ValueError("Provided vertices out of bounds.")
-        if abs(v1 - v2) <= 1 or abs(v1 - v2) >= ne - 1:
-            raise ValueError("Provided diagonal is not a diagonal.")
-        if v2 < v1:
-            temp = v1
-            v1 = v2
-            v2 = temp
-
-        newedges1 = [poly.vertex(v2) - poly.vertex(v1)]
-        for i in range(v2, v1 + ne):
-            newedges1.append(poly.edge(i))
-
-        from flatsurf import Polygon
-
-        newpoly1 = Polygon(edges=newedges1, base_ring=s.base_ring())
-
-        newedges2 = [poly.vertex(v1) - poly.vertex(v2)]
-        for i in range(v1, v2):
-            newedges2.append(poly.edge(i))
-        newpoly2 = Polygon(edges=newedges2, base_ring=s.base_ring())
-
-        from flatsurf.geometry.surface import MutableOrientedSimilaritySurface
-
-        ss2 = MutableOrientedSimilaritySurface.from_surface(s)
-        s2 = ss2
-        s2.remove_polygon(p)
-        s2.add_polygon(newpoly1, label=p)
-        new_label = s2.add_polygon(newpoly2, label=new_label)
-
-        old_to_new_labels = {}
-        for i in range(ne):
-            if i < v1:
-                old_to_new_labels[i] = (p, i + ne - v2 + 1)
-            elif i < v2:
-                old_to_new_labels[i] = (new_label, i - v1 + 1)
-            else:  # i>=v2
-                old_to_new_labels[i] = (p, i - v2 + 1)
-        new_to_old_labels = {}
-        for i, pair in old_to_new_labels.items():
-            new_to_old_labels[pair] = i
-
-        # This glues the split polygons together.
-        s2.glue((p, 0), (new_label, 0))
-        for e in range(ne):
-            ll, ee = old_to_new_labels[e]
-            lll, eee = s.opposite_edge(p, e)
-            if lll == p:
-                gl, ge = old_to_new_labels[eee]
-                s2.glue((ll, ee), (gl, ge))
-            else:
-                s2.glue((ll, ee), (lll, eee))
-
-        s2.set_immutable()
-
-        self._p = p
-        self._v1 = v1
-        self._v2 = v2
-        self._new_label = new_label
-        from flatsurf.geometry.similarity import SimilarityGroup
-
-        TG = SimilarityGroup(s.base_ring())
-        self._tp = TG(-s.polygon(p).vertex(v1))
-        self._tnew_label = TG(-s.polygon(p).vertex(v2))
-        SurfaceMapping.__init__(self, s, ss2)
-
-    def push_vector_forward(self, tangent_vector):
-        r"""Applies the mapping to the provided vector."""
-        ring = tangent_vector.bundle().base_ring()
-        if tangent_vector.polygon_label() == self._p:
-            point = tangent_vector.point()
-            vertex1 = self._domain.polygon(self._p).vertex(self._v1)
-            vertex2 = self._domain.polygon(self._p).vertex(self._v2)
-
-            from flatsurf.geometry.euclidean import ccw
-
-            wp = ccw(vertex2 - vertex1, point - vertex1)
-
-            if wp > 0:
-                # in new polygon 1
-                return self.codomain().tangent_vector(
-                    self._p,
-                    self._tp(tangent_vector.point()),
-                    tangent_vector.vector(),
-                    ring=ring,
-                )
-            if wp < 0:
-                # in new polygon 2
-                return self.codomain().tangent_vector(
-                    self._new_label,
-                    self._tnew_label(tangent_vector.point()),
-                    tangent_vector.vector(),
-                    ring=ring,
-                )
-
-            # Otherwise wp==0
-            w = tangent_vector.vector()
-            wp = ccw(vertex2 - vertex1, w)
-            if wp > 0:
-                # in new polygon 1
-                return self.codomain().tangent_vector(
-                    self._p,
-                    self._tp(tangent_vector.point()),
-                    tangent_vector.vector(),
-                    ring=ring,
-                )
-            # in new polygon 2
-            return self.codomain().tangent_vector(
-                self._new_label,
-                self._tnew_label(tangent_vector.point()),
-                tangent_vector.vector(),
-                ring=ring,
-            )
-        else:
-            # Not in a polygon that was changed. Just copy the data.
-            return self._codomain.tangent_vector(
-                tangent_vector.polygon_label(),
-                tangent_vector.point(),
-                tangent_vector.vector(),
-                ring=ring,
-            )
-
-    def pull_vector_back(self, tangent_vector):
-        r"""Applies the pullback mapping to the provided vector."""
-        ring = tangent_vector.bundle().base_ring()
-        if tangent_vector.polygon_label() == self._p:
-            return self._domain.tangent_vector(
-                self._p,
-                (~self._tp)(tangent_vector.point()),
-                tangent_vector.vector(),
-                ring=ring,
-            )
-        elif tangent_vector.polygon_label() == self._new_label:
-            return self._domain.tangent_vector(
-                self._p,
-                (~self._tnew_label)(tangent_vector.point()),
-                tangent_vector.vector(),
-                ring=ring,
-            )
-        else:
-            # Not in a polygon that was changed. Just copy the data.
-            return self._domain.tangent_vector(
-                tangent_vector.polygon_label(),
-                tangent_vector.point(),
-                tangent_vector.vector(),
-                ring=ring,
-            )
-
-
-def subdivide_a_polygon(s):
-    r"""
-    Return a SurfaceMapping which cuts one polygon along a diagonal or None if the surface is triangulated.
-    """
-    from flatsurf.geometry.euclidean import ccw
-
-    for label, poly in zip(s.labels(), s.polygons()):
-        n = len(poly.vertices())
-        if n > 3:
-            for i in range(n):
-                e1 = poly.edge(i)
-                e2 = poly.edge((i + 1) % n)
-                if ccw(e1, e2) != 0:
-                    return SplitPolygonsMapping(s, label, i, (i + 2) % n)
-            raise ValueError(
-                "Unable to triangulate polygon with label "
-                + str(label)
-                + ": "
-                + str(poly)
-            )
-    return None
-
-
-def triangulation_mapping(s):
-    r"""
-    Return a SurfaceMapping triangulating ``s``.
-
-    EXAMPLES::
-
-        sage: from flatsurf import translation_surfaces
-        sage: s=translation_surfaces.veech_2n_gon(4)
-        sage: from flatsurf.geometry.mappings import triangulation_mapping
-        sage: m=triangulation_mapping(s)
-        sage: s2=m.codomain()
-        sage: TestSuite(s2).run()
-        sage: s2.polygons()
-        (Polygon(vertices=[(0, 0), (-1/2*a, 1/2*a + 1), (-1/2*a, 1/2*a)]),
-         Polygon(vertices=[(0, 0), (1/2*a, -1/2*a - 1), (1/2*a, 1/2*a)]),
-         Polygon(vertices=[(0, 0), (-1/2*a - 1, -1/2*a - 1), (0, -1)]),
-         Polygon(vertices=[(0, 0), (-1, -a - 1), (1/2*a, -1/2*a)]),
-         Polygon(vertices=[(0, 0), (0, -a - 1), (1, 0)]),
-         Polygon(vertices=[(0, 0), (-1/2*a - 1, -1/2*a), (-1/2*a, -1/2*a)]))
-
-    """
-    if not s.is_finite_type():
-        raise NotImplementedError
-
-    m = subdivide_a_polygon(s)
-    if m is None:
-        return None
-    s1 = m.codomain()
-    while True:
-        m2 = subdivide_a_polygon(s1)
-        if m2 is None:
-            return m
-        s1 = m2.codomain()
-        m = SurfaceMappingComposition(m, m2)
-    return m
-
-
-def flip_edge_mapping(s, p1, e1):
-    r"""
-    Return a mapping whose domain is s which flips the provided edge.
-    """
-    m1 = SimilarityJoinPolygonsMapping(s, p1, e1)
-    v1, v2 = m1.glued_vertices()
-    removed_label = m1.removed_label()
-    m2 = SplitPolygonsMapping(
-        m1.codomain(), p1, (v1 + 1) % 4, (v1 + 3) % 4, new_label=removed_label
-    )
-    return SurfaceMappingComposition(m1, m2)
-
-
-def one_delaunay_flip_mapping(s):
-    r"""
-    Returns one delaunay flip, or none if no flips are needed.
-    """
-    for p, poly in zip(s.labels(), s.polygons()):
-        for e in range(len(poly.vertices())):
-            if s._delaunay_edge_needs_flip(p, e):
-                return flip_edge_mapping(s, p, e)
-    return None
-
-
-def delaunay_triangulation_mapping(s):
-    r"""
-    Returns a mapping to a Delaunay triangulation or None if the surface already is Delaunay triangulated.
-    """
-    if not s.is_finite_type():
-        raise NotImplementedError
-
-    m = triangulation_mapping(s)
-    if m is None:
-        s1 = s
-    else:
-        s1 = m.codomain()
-    m1 = one_delaunay_flip_mapping(s1)
-    if m1 is None:
-        return m
-    if m is None:
-        m = m1
-    else:
-        m = SurfaceMappingComposition(m, m1)
-    s1 = m1.codomain()
-    while True:
-        m1 = one_delaunay_flip_mapping(s1)
-        if m1 is None:
-            return m
-        s1 = m1.codomain()
-        m = SurfaceMappingComposition(m, m1)
-
-
-def delaunay_decomposition_mapping(s):
-    r"""
-    Returns a mapping to a Delaunay decomposition or possibly None if the surface already is Delaunay.
-    """
-    m = delaunay_triangulation_mapping(s)
-    if m is None:
-        s1 = s
-    else:
-        s1 = m.codomain()
-
-    joins = set()
-    edge_vectors = []
-
-    for p, poly in zip(s1.labels(), s1.polygons()):
-        for e in range(len(poly.vertices())):
-            pp, ee = s1.opposite_edge(p, e)
-            if (pp, ee) in joins:
-                continue
-            if s1._delaunay_edge_needs_join(p, e):
-                joins.add((p, e))
-                edge_vectors.append(s1.tangent_vector(p, poly.vertex(e), poly.edge(e)))
-
-    if len(edge_vectors) > 0:
-        ev = edge_vectors.pop()
-        p, e = ev.edge_pointing_along()
-        m1 = SimilarityJoinPolygonsMapping(s1, p, e)
-        s2 = m1.codomain()
-        while len(edge_vectors) > 0:
-            ev = edge_vectors.pop()
-            ev2 = m1.push_vector_forward(ev)
-            p, e = ev2.edge_pointing_along()
-            mtemp = SimilarityJoinPolygonsMapping(s2, p, e)
-            m1 = SurfaceMappingComposition(m1, mtemp)
-            s2 = m1.codomain()
-        if m is None:
-            return m1
-        else:
-            return SurfaceMappingComposition(m, m1)
-    return m
-
-
-def canonical_first_vertex(polygon):
-    r"""
-    Return the index of the vertex with smallest y-coordinate.
-    If two vertices have the same y-coordinate, then the one with least x-coordinate is returned.
-    """
-    best = 0
-    best_pt = polygon.vertex(best)
-    for v in range(1, len(polygon.vertices())):
-        pt = polygon.vertex(v)
-        if pt[1] < best_pt[1]:
-            best = v
-            best_pt = pt
-    if best == 0:
-        if pt[1] == best_pt[1]:
-            return v
-    return best
-
-
-class CanonicalizePolygonsMapping(SurfaceMapping):
-    r"""
-    This is a mapping to a surface with the polygon vertices canonically determined.
-    A canonical labeling is when the canonocal_first_vertex is the zero vertex.
-    """
-
-    def __init__(self, s):
-        r"""
-        Split the polygon with label p of surface s along the diagonal joining vertex v1 to vertex v2.
-        """
-        if not s.is_finite_type():
-            raise ValueError("Currently only works with finite surfaces.")
-        ring = s.base_ring()
-        from flatsurf.geometry.similarity import SimilarityGroup
-
-        T = SimilarityGroup(ring)
-        cv = {}  # dictionary for canonical vertices
-        translations = {}  # translations bringing the canonical vertex to the origin.
-        from flatsurf.geometry.surface import MutableOrientedSimilaritySurface
-
-        s2 = MutableOrientedSimilaritySurface(ring)
-        for label, polygon in zip(s.labels(), s.polygons()):
-            cv[label] = cvcur = canonical_first_vertex(polygon)
-            newedges = []
-            for i in range(len(polygon.vertices())):
-                newedges.append(polygon.edge((i + cvcur) % len(polygon.vertices())))
-
-            from flatsurf import Polygon
-
-            s2.add_polygon(Polygon(edges=newedges, base_ring=ring), label=label)
-            translations[label] = T(-polygon.vertex(cvcur))
-        for l1, polygon in zip(s.labels(), s.polygons()):
-            for e1 in range(len(polygon.vertices())):
-                l2, e2 = s.opposite_edge(l1, e1)
-                ee1 = (e1 - cv[l1] + len(polygon.vertices())) % len(polygon.vertices())
-                polygon2 = s.polygon(l2)
-                ee2 = (e2 - cv[l2] + len(polygon2.vertices())) % len(
-                    polygon2.vertices()
-                )
-                # newgluing.append( ( (l1,ee1),(l2,ee2) ) )
-                s2.glue((l1, ee1), (l2, ee2))
-        s2.set_roots(s.roots())
-        s2.set_immutable()
-        ss2 = s2
-
-        self._cv = cv
-        self._translations = translations
-
-        SurfaceMapping.__init__(self, s, ss2)
-
-    def push_vector_forward(self, tangent_vector):
-        r"""Applies the mapping to the provided vector."""
-        ring = tangent_vector.bundle().base_ring()
-        label = tangent_vector.polygon_label()
-        return self.codomain().tangent_vector(
-            label,
-            self._translations[label](tangent_vector.point()),
-            tangent_vector.vector(),
-            ring=ring,
-        )
-
-    def pull_vector_back(self, tangent_vector):
-        r"""Applies the pullback mapping to the provided vector."""
-        ring = tangent_vector.bundle().base_ring()
-        label = tangent_vector.polygon_label()
-        return self.domain().tangent_vector(
-            label,
-            (~self._translations[label])(tangent_vector.point()),
-            tangent_vector.vector(),
-            ring=ring,
-        )
-
-
-class ReindexMapping(SurfaceMapping):
-    r"""
-    Apply a dictionary to relabel the polygons.
-    """
-
-    def __init__(self, s, relabler, new_base_label=None):
-        r"""
-        The parameters should be a surface and a dictionary which takes as input a label and produces a new label.
-        """
-        if not s.is_finite_type():
-            raise ValueError("Currently only works with finite surfaces." "")
-        f = {}  # map for labels going forward.
-        b = {}  # map for labels going backward.
-        for label in s.labels():
-            if label in relabler:
-                l2 = relabler[label]
-                f[label] = l2
-                if l2 in b:
-                    raise ValueError(
-                        "Provided dictionary has two keys mapping to the same value. Or you are mapping to a label you didn't change."
-                    )
-                b[l2] = label
-            else:
-                # If no key then don't change the label
-                f[label] = label
-                if label in b:
-                    raise ValueError(
-                        "Provided dictionary has two keys mapping to the same value. Or you are mapping to a label you didn't change."
-                    )
-                b[label] = label
-
-        self._f = f
-        self._b = b
-
-        if new_base_label is None:
-            if s.root() in f:
-                new_base_label = f[s.root()]
-            else:
-                new_base_label = s.root()
-        from flatsurf.geometry.surface import MutableOrientedSimilaritySurface
-
-        s2 = MutableOrientedSimilaritySurface.from_surface(s)
-        s2.relabel(relabler, in_place=True)
-        s2.set_roots([new_base_label])
-        s2.set_immutable()
-
-        SurfaceMapping.__init__(self, s, s2)
-
-    def push_vector_forward(self, tangent_vector):
-        r"""Applies the mapping to the provided vector."""
-        # There is no change- we just move it to the new surface.
-        ring = tangent_vector.bundle().base_ring()
-        return self.codomain().tangent_vector(
-            self._f[tangent_vector.polygon_label()],
-            tangent_vector.point(),
-            tangent_vector.vector(),
-            ring=ring,
-        )
-
-    def pull_vector_back(self, tangent_vector):
-        r"""Applies the pullback mapping to the provided vector."""
-        ring = tangent_vector.bundle().base_ring()
-        return self.domain().tangent_vector(
-            self._b[tangent_vector.polygon_label()],
-            tangent_vector.point(),
-            tangent_vector.vector(),
-            ring=ring,
-        )
-
-
-def my_sgn(val):
-    if val > 0:
-        return 1
-    elif val < 0:
-        return -1
-    else:
-        return 0
-
-
-def polygon_compare(poly1, poly2):
-    r"""
-    Compare two polygons first by area, then by number of sides,
-    then by lexicographical ordering on edge vectors."""
-    # This should not be used is broken!!
-    # from sage.functions.generalized import sgn
-    res = my_sgn(-poly1.area() + poly2.area())
-    if res != 0:
-        return res
-    res = my_sgn(len(poly1.vertices()) - len(poly2.vertices()))
-    if res != 0:
-        return res
-    ne = len(poly1.vertices())
-    for i in range(0, ne - 1):
-        edge_diff = poly1.edge(i) - poly2.edge(i)
-        res = my_sgn(edge_diff[0])
-        if res != 0:
-            return res
-        res = my_sgn(edge_diff[1])
-        if res != 0:
-            return res
-    return 0
-
-
-def canonicalize_translation_surface_mapping(s):
-    r"""
-    Return the translation surface in a canonical form.
-
-    EXAMPLES::
-
-        sage: from flatsurf import translation_surfaces
-        sage: s = translation_surfaces.octagon_and_squares().canonicalize()
-
-        sage: TestSuite(s).run()
-
-        sage: a = s.base_ring().gen()  # a is the square root of 2.
-
-        sage: from flatsurf.geometry.mappings import GL2RMapping
-        sage: from flatsurf.geometry.mappings import canonicalize_translation_surface_mapping
-        sage: mat=Matrix([[1,2+a],[0,1]])
-        sage: from flatsurf.geometry.mappings import GL2RMapping
-        sage: m1=GL2RMapping(s, mat)
-        sage: m2=canonicalize_translation_surface_mapping(m1.codomain())
-        sage: m=m2*m1
-        sage: m.domain().cmp(m.codomain())
-        0
-        sage: TestSuite(m.codomain()).run()
-        sage: s=m.domain()
-        sage: v=s.tangent_vector(0,(0,0),(1,1))
-        sage: w=m.push_vector_forward(v)
-        sage: print(w)
-        SimilaritySurfaceTangentVector in polygon 0 based at (0, 0) with vector (a + 3, 1)
-    """
-    from flatsurf.geometry.categories import TranslationSurfaces
-
-    if not s.is_finite_type():
-        raise NotImplementedError
-    if s not in TranslationSurfaces():
-        raise ValueError("Only defined for TranslationSurfaces")
-    m1 = delaunay_decomposition_mapping(s)
-    if m1 is None:
-        s2 = s
-    else:
-        s2 = m1.codomain()
-    m2 = CanonicalizePolygonsMapping(s2)
-    if m1 is None:
-        m = m2
-    else:
-        m = SurfaceMappingComposition(m1, m2)
-    s2 = m.codomain()
-
-    # This is essentially copy & paste from canonicalize() from TranslationSurfaces()
-    from flatsurf.geometry.surface import MutableOrientedSimilaritySurface
-
-    s2copy = MutableOrientedSimilaritySurface.from_surface(s2)
-    ss = MutableOrientedSimilaritySurface.from_surface(s2)
-    labels = set(s2.labels())
-    for label in labels:
-        ss.set_roots([label])
-        if ss.cmp(s2copy) > 0:
-            s2copy.set_roots([label])
-
-    s2copy.set_immutable()
-
-    # We now have the base_label correct.
-    # We will use the label walk to generate the canonical labeling of polygons.
-    labels = {label: i for (i, label) in enumerate(s2copy.labels())}
-
-    m3 = ReindexMapping(s2, labels, 0)
-    return SurfaceMappingComposition(m, m3)
-
-
-class GL2RMapping(SurfaceMapping):
-    r"""
-    This class pushes a surface forward under a matrix.
-
-    Note that for matrices of negative determinant we need to relabel edges (because
-    edges must have a counterclockwise cyclic order). For each n-gon in the surface,
-    we relabel edges according to the involution `e \mapsto n-1-e`.
-
-    EXAMPLE::
-
-        sage: from flatsurf import translation_surfaces
-        sage: s=translation_surfaces.veech_2n_gon(4)
-        sage: from flatsurf.geometry.mappings import GL2RMapping
-        sage: mat=Matrix([[2,1],[1,1]])
-        sage: m=GL2RMapping(s,mat)
-        sage: TestSuite(m.codomain()).run()
-    """
-
-    def __init__(self, s, m, category=None):
-        r"""
-        Hit the surface s with the 2x2 matrix m which should have positive determinant.
-        """
-        from flatsurf.geometry.lazy import GL2RImageSurface
-
-        codomain = GL2RImageSurface(s, m, category=category or s.category())
-        self._m = m
-        self._im = ~m
-        SurfaceMapping.__init__(self, s, codomain)
-
-    def push_vector_forward(self, tangent_vector):
-        r"""Applies the mapping to the provided vector."""
-        return self.codomain().tangent_vector(
-            tangent_vector.polygon_label(),
-            self._m * tangent_vector.point(),
-            self._m * tangent_vector.vector(),
-        )
-
-    def pull_vector_back(self, tangent_vector):
-        r"""Applies the inverse of the mapping to the provided vector."""
-        return self.domain().tangent_vector(
-            tangent_vector.polygon_label(),
-            self._im * tangent_vector.point(),
-            self._im * tangent_vector.vector(),
-        )
diff --git a/flatsurf/geometry/morphism.py b/flatsurf/geometry/morphism.py
index 4317b9977..9e98bed58 100644
--- a/flatsurf/geometry/morphism.py
+++ b/flatsurf/geometry/morphism.py
@@ -36,8 +36,7 @@
     sage: morphism = S.subdivide_edges(3)
     sage: morphism = morphism.codomain().triangulate() * morphism
     sage: T = morphism.codomain()
-
-    sage: morphism
+    sage: morphism  # TODO: Get rid of the identity in the sequence below.
     Composite morphism:
       From: Translation Surface in H_2(2) built from a regular octagon
       To:   Triangulation of Translation Surface in H_2(2, 0^8) built from a regular octagon with 16 marked vertices
@@ -1517,6 +1516,15 @@ def _section_homology_edge(self, label, edge, codomain):
             "not a single edge maps to this edge, cannot implement preimage of this edge yet"
         )
 
+    def _image_line_segment(self, s):
+        raise NotImplementedError(f"a {type(self).__name__} cannot compute the image of a line segment yet")
+
+    def _section_line_segment(self, t):
+        raise NotImplementedError(f"a {type(self).__name__} cannot compute a preimage of a line segment yet")
+
+    def _test_section_line_segment(self, **options):
+        raise NotImplementedError
+
     def _composition(self, other):
         r"""
         Return the composition of this morphism and ``other``.
@@ -1557,6 +1565,252 @@ def _composition(self, other):
 
         return CompositionMorphism._create_morphism(self, other)
 
+    def push_vector_forward(self, tangent_vector):
+        import warnings
+
+        warnings.warn(
+            "push_vector_forward() has been deprecated and will be removed in a future version of sage-flatsurf; call the morphism with the tangent vector instead, i.e., instead of morphism.push_vector_forward(t) use morphism(t)"
+        )
+
+        return self(tangent_vector)
+
+    def pull_vector_back(self, tangent_vector):
+        import warnings
+
+        warnings.warn(
+            "pull_vector_back() has been deprecated and will be removed in a future version of sage-flatsurf; call a section of morphism with the tangent vector instead, i.e., instead of morphism.pull_vector_back(t) use morphism.section()(t)"
+        )
+
+        return self.section()(tangent_vector)
+
+    def _image_saddle_connection(self, c):
+        r"""
+        Return the image of saddle connection ``c`` under this morphism.
+
+        This is a helper method for :meth:`__call__`.
+
+        Subclasses should implement this method if the morphism is meaningful
+        on the level of saddle connections.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.square_torus()
+            sage: morphism = S.apply_matrix(matrix([[2, 0], [0, 1]]), in_place=False)
+
+        The image of a saddle connection::
+
+            sage: from flatsurf.geometry.saddle_connection import SaddleConnection
+            sage: c = SaddleConnection.from_vertex(S, 0, 0, (1, 1))
+            sage: c
+            Saddle connection (1, 1) from vertex 0 of polygon 0 to vertex 2 of polygon 0
+
+            sage: morphism(c)
+            Saddle connection (2, 1) from vertex 0 of polygon 0 to vertex 2 of polygon 0
+
+        Not all morphisms are meaningful on the level of saddle connections::
+
+            sage: morphism = S.subdivide()
+            sage: morphism(c)
+            Traceback (most recent call last):
+            ...
+            NotImplementedError: a InsertMarkedPointsInFaceMorphism_with_category cannot compute the image of a saddle connection yet
+
+        """
+        raise NotImplementedError(
+            f"a {type(self).__name__} cannot compute the image of a saddle connection yet"
+        )
+
+    def _section_saddle_connection(self, t):
+        r"""
+        Return a preimage of a saddle connection ``t`` under this morphism.
+
+        This is a helper method for :meth:`__call__` of the :meth:`section`.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.square_torus()
+            sage: morphism = S.apply_matrix(matrix([[2, 0], [0, 1]]), in_place=False)
+            sage: T = morphism.codomain()
+
+            sage: from flatsurf.geometry.saddle_connection import SaddleConnection
+            sage: t = SaddleConnection.from_vertex(T, 0, 0, (2, 1)); t
+            Saddle connection (2, 1) from vertex 0 of polygon 0 to vertex 2 of polygon 0
+            sage: (morphism * morphism.section())(t)
+            Saddle connection (2, 1) from vertex 0 of polygon 0 to vertex 2 of polygon 0
+
+        """
+        raise NotImplementedError(
+            f"a {type(self).__name__} cannot compute a preimage of a saddle connection yet"
+        )
+
+    def _test_section_saddle_connection(self, **options):
+        r"""
+        Verify that :meth:`_section_saddle_connection` actually produces a
+        section.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.square_torus()
+            sage: morphism = S.apply_matrix(matrix([[2, 0], [0, 1]]), in_place=False)
+            sage: morphism._test_section_saddle_connection()
+
+        """
+        tester = self._tester(**options)
+
+        section = self.section()
+        identity = self * section
+
+        from itertools import islice
+
+        for q in tester.some_elements(islice(self.codomain().saddle_connections(), 16)):
+            tester.assertEqual(identity(q), q)
+
+    def _image_tangent_vector(self, t):
+        r"""
+        Return the image of the tangent vector ``v`` under this morphism.
+
+        This is a helper method for :meth:`__call__`.
+
+        Subclasses should implement this method if the morphism is meaningful
+        on the level of tangent vectors.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.square_torus()
+            sage: morphism = S.apply_matrix(matrix([[2, 0], [0, 1]]), in_place=False)
+
+        The image of a tangent vector::
+
+            sage: t = S.tangent_vector(0, (0, 0), (1, 1))
+            sage: morphism(t)
+            SimilaritySurfaceTangentVector in polygon 0 based at (0, 0) with vector (2, 1)
+
+        Not all morphisms are meaningful on the level of tangent vectors::
+
+            # TODO: Add an example of such a morphism
+
+        """
+        raise NotImplementedError(
+            f"a {type(self).__name__} cannot compute the image of a tangent vector yet"
+        )
+
+    def _section_tangent_vector(self, q):
+        r"""
+        Return a preimage of the tangent vector ``q`` under this morphism.
+
+        This is a helper method for :meth:`__call__` of the :meth:`section`.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.square_torus()
+            sage: morphism = S.apply_matrix(matrix([[2, 0], [0, 1]]), in_place=False)
+            sage: T = morphism.codomain()
+
+            sage: q = T.tangent_vector(0, (0, 0), (2, 1)); q
+            SimilaritySurfaceTangentVector in polygon 0 based at (0, 0) with vector (2, 1)
+            sage: (morphism * morphism.section())(q)
+            SimilaritySurfaceTangentVector in polygon 0 based at (0, 0) with vector (2, 1)
+
+        """
+        raise NotImplementedError(
+            f"a {type(self).__name__} cannot compute a preimage of a tangent vector yet"
+        )
+
+    def _test_section_tangent_vector(self, **options):
+        r"""
+        Verify that :meth:`_section_tangent_vector` actually produces a
+        section.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.square_torus()
+            sage: morphism = S.apply_matrix(matrix([[2, 0], [0, 1]]), in_place=False)
+            sage: morphism._test_section_tangent_vector()
+
+        """
+        tester = self._tester(**options)
+
+        section = self.section()
+        identity = self * section
+
+        for q in tester.some_elements(self.codomain().tangent_bundle().some_elements()):
+            tester.assertEqual(identity(q), q)
+
+    def change(self, domain=None, codomain=None, check=True):
+        r"""
+        Return a copy of this morphism with the domain or codomain replaced
+        with ``domain`` and ``codomain``, respectively.
+
+        For this to work, the ``domain`` must be trivially a replacement for
+        the original domain and the ``codomain`` must be trivially a
+        replacement for the original codomain. This method is sometimes useful
+        to implement new morphisms. It should not be necessary to call this
+        method otherwise. This method is usually used when the domain or
+        codomain was originally mutable or to replace the domain or codomain
+        with another indistinguishable domain or codomain.
+
+        INPUT:
+
+        - ``domain`` -- a surface (default: ``None``); if set, the surfaces
+          replaces the domain of this morphism
+
+        - ``codomain`` -- a surface (default: ``None``); if set, the surfaces
+          replaces the codomain of this morphism
+
+        - ``check`` -- a boolean (default: ``True``); whether to check
+          compatibility of the ``domain`` and ``codomain`` with the data
+          defining the original morphism.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, MutableOrientedSimilaritySurface
+            sage: S = translation_surfaces.square_torus()
+            sage: S = MutableOrientedSimilaritySurface.from_surface(S)
+            sage: morphism = S.apply_matrix(matrix([[2, 0], [0, 1]]), in_place=False)
+            sage: morphism.domain()
+            Unknown Surface
+
+            sage: S.set_immutable()
+            sage: morphism = morphism.change(domain=S)
+            sage: morphism.domain()
+            Translation Surface in H_1(0) built from a square
+
+        ::
+
+            sage: from flatsurf import translation_surfaces, MutableOrientedSimilaritySurface
+            sage: S = translation_surfaces.square_torus()
+            sage: S = MutableOrientedSimilaritySurface.from_surface(S)
+            sage: morphism = S.apply_matrix(matrix([[2, 0], [0, 1]]), in_place=True)
+            sage: morphism.domain()
+            Unknown Surface
+            sage: morphism.codomain()
+            Unknown Surface
+
+            sage: S.set_immutable()
+            sage: morphism = morphism.change(codomain=S)
+            sage: morphism.domain()
+            Unknown Surface
+            sage: morphism.codomain()
+            Translation Surface in H_1(0) built from a rectangle
+
+        """
+        if domain is not None:
+            raise NotImplementedError(
+                f"a {type(self).__name__} cannot swap out its domain yet"
+            )
+        if codomain is not None:
+            raise NotImplementedError(
+                f"a {type(self).__name__} cannot swap out its codomain yet"
+            )
+
+        return self
+
     def __eq__(self, other):
         r"""
         Return whether this morphism is indistinguishable from ``other``.
@@ -1767,6 +2021,9 @@ def _section_homology_edge(self, label, edge, codomain):
         """
         return self._morphism._image_homology_edge(label, edge, codomain=codomain)
 
+    def _image_line_segment(self, x): return self._morphism._section_line_segment(x)
+    def _section_line_segment(self, y): return self._morphism._image_line_segment(y)
+
     def __eq__(self, other):
         r"""
         Return whether this section is indistinguishable from ``other``.
@@ -1837,6 +2094,9 @@ def _repr_defn(self):
         return f"Section of {self._morphism}"
 
 
+# TODO: The whole composite setup is not great yet. The special handling of
+# contravariant induced maps in call is a hack.
+# TODO: We should reverse the order of _morphisms.
 class CompositionMorphism(SurfaceMorphism):
     r"""
     The formal composition of two morphisms between surfaces.
@@ -2025,6 +2285,16 @@ def _image_homology_edge(self, label, edge, codomain):
             self.domain().homology()((label, edge)), codomain=codomain
         )
 
+    def _image_line_segment(self, x):
+        for morphism in self._morphisms:
+            x = morphism._image_line_segment(x)
+        return x
+
+    def _section_line_segment(self, y):
+        for morphism in self._morphisms[::-1]:
+            y = morphism._section_line_segment(y)
+        return y
+
     def _repr_type(self):
         r"""
         Helper method for :meth:`__repr__`.
@@ -2542,6 +2812,12 @@ def _section_point(self, q):
         """
         return self._factorization()._section_point(q)
 
+    def _image_line_segment(self, x):
+        return self._factorization()._image_line_segment(x)
+
+    def _section_line_segment(self, y):
+        return self._factorization()._section_line_segment(y)
+
     def _factorization(self):
         r"""
         Return the morphism underlying this morphism.
@@ -2870,7 +3146,27 @@ def __hash__(self):
         return hash((self.__factorization, self._name))
 
 
-class TriangulationMorphism_base(SurfaceMorphism):
+class RepolygonizationMorphism(SurfaceMorphism):
+    def _image_line_segment(self, s):
+        raise NotImplementedError
+
+    def _section_line_segment(self, t):
+        from flatsurf.geometry.voronoi import SurfaceLineSegment
+
+        if not self.domain().is_translation_surface():
+            raise NotImplementedError
+
+        if t.start().angle() != 1:
+            raise NotImplementedError
+
+        start = self.section()(t.start())
+
+        assert start.angle() == 1, "preimage of a regular point must be regular"
+
+        return SurfaceLineSegment(self.domain(), *start.representative(), t.holonomy())
+
+
+class TriangulationMorphism_base(RepolygonizationMorphism):
     r"""
     Abstract base class for morphisms from a surface to its triangulation.
 
@@ -2951,6 +3247,53 @@ def _section_edge(self, label, edge):
 
         assert False, "triangle must come from a polygon before triangulation"
 
+    def _image_saddle_connection(self, connection):
+        (label, edge) = connection.start()
+
+        label, edge = self._image_edge(label, edge)
+
+        from flatsurf.geometry.euclidean import ccw
+
+        while (
+            ccw(
+                connection.direction().vector(),
+                -self.codomain()
+                .polygon(label)
+                .edges()[(edge - 1) % len(self.codomain().polygon(label).edges())],
+            )
+            <= 0
+        ):
+            (label, edge) = self.codomain().opposite_edge(
+                label, (edge - 1) % len(self.codomain().polygon(label).edges())
+            )
+
+        # TODO: This is extremely slow.
+        from flatsurf.geometry.saddle_connection import SaddleConnection
+
+        return SaddleConnection.from_vertex(
+            self.codomain(), label, edge, connection.direction().vector()
+        )
+
+    def _section_saddle_connection(self, connection):
+        (label, edge) = connection.start()
+
+        domain_label = self.codomain()._reference_label(label)
+        triangulation = self.codomain()._triangulation(domain_label)[1].inverse
+
+        while (label, edge) not in triangulation:
+            label, edge = self.codomain().opposite_edge(label, edge)
+            edge = (edge + 1) % len(self.codomain().polygon(label).edges())
+
+        # TODO: This is extremely slow.
+        from flatsurf.geometry.saddle_connection import SaddleConnection
+
+        return SaddleConnection.from_vertex(
+            self.domain(),
+            domain_label,
+            triangulation[(label, edge)],
+            connection.direction(),
+        )
+
     def _image_homology_edge(self, label, edge, codomain):
         r"""
         Implements :class:`SurfaceMorphism._image_homology_edge`.
@@ -3224,7 +3567,7 @@ def __hash__(self):
         return hash(self.parent())
 
 
-class DelaunayTriangulationMorphism(SurfaceMorphism):
+class DelaunayTriangulationMorphism(RepolygonizationMorphism):
     r"""
     A morphism from a triangulated surface to its Delaunay triangulation.
 
@@ -3869,6 +4212,36 @@ def __init__(self, parent, m):
 
         self._matrix = matrix(self.domain().base_ring(), m, immutable=True)
 
+    def change(self, domain=None, codomain=None, check=True):
+        return type(self)._create_morphism(
+            domain=domain or self.domain(),
+            codomain=codomain or self.codomain(),
+            m=self._matrix,
+            category=self.category_for(),
+        )
+
+    def _image_tangent_vector(self, t):
+        return self.codomain().tangent_vector(
+            t.polygon_label(), self._matrix * t.point(), self._matrix * t.vector()
+        )
+
+    def _image_saddle_connection(self, connection):
+        if self._matrix.det() <= 0:
+            raise NotImplementedError(
+                "cannot compute the image of a saddle connection for this matrix yet"
+            )
+
+        from flatsurf.geometry.saddle_connection import SaddleConnection
+
+        return SaddleConnection(
+            surface=self.codomain(),
+            start=connection.start(),
+            end=connection.end(),
+            holonomy=self._matrix * connection.holonomy(),
+            end_holonomy=self._matrix * connection.end_holonomy(),
+            check=False,
+        )
+
     def section(self):
         r"""
         Return an inverse of this morphism.
@@ -4129,3 +4502,289 @@ def __hash__(self):
 
         """
         return hash(self._parts)
+
+    def _image_saddle_connection(self, c):
+        start = c.start()
+        end = c.end()
+
+        from flatsurf.geometry.saddle_connection import SaddleConnection
+
+        return SaddleConnection(
+            surface=self.codomain(),
+            start=(start[0], start[1] * self._parts),
+            end=(end[0], end[1] * self._parts),
+            holonomy=c.holonomy(),
+            end_holonomy=c.end_holonomy(),
+            check=False,
+        )
+
+    def _section_saddle_connection(self, c):
+        start = c.start()
+        if start[1] % self._parts != 0:
+            # TODO: This is not true. Straight line trajectories are built of such segments.
+            raise NotImplementedError(
+                "cannot represent segments not starting at a vertex yet"
+            )
+
+        end = c.end()
+        if end[1] % self._parts != 0:
+            # TODO: This is not true. Straight line trajectories are built of such segments.
+            raise NotImplementedError(
+                "cannot represent segments not terminating at a vertex yet"
+            )
+
+        from flatsurf.geometry.saddle_connection import SaddleConnection
+
+        return SaddleConnection(
+            surface=self.domain(),
+            start=(start[0], start[1] // self._parts),
+            end=(end[0], end[1] // self._parts),
+            holonomy=c.holonomy(),
+            end_holonomy=c.end_holonomy(),
+            check=False,
+        )
+
+    def _image_tangent_vector(self, t):
+        return self.codomain().tangent_vector(t.polygon_label(), t.point(), t.vector())
+
+    def _section_tangent_vector(self, t):
+        return self.domain().tangent_vector(t.polygon_label(), t.point(), t.vector())
+
+
+# TODO: Can we use some generic machinery from RepolygonizationMorphism here?
+class InsertMarkedPointsInFaceMorphism(RepolygonizationMorphism):
+    def __init__(self, parent, subdivisions, category=None):
+        self._subdivisions = subdivisions
+
+        super().__init__(parent, category=category)
+
+    # TODO: docstring
+    def _image_point(self, p):
+        # TODO: docstring
+        from flatsurf.geometry.pyflatsurf_conversion import FlatTriangulationConversion
+
+        to_pyflatsurf = FlatTriangulationConversion.to_pyflatsurf(
+            domain=self.codomain()
+        )
+
+        label = next(iter(p.labels()))
+        coordinates = next(iter(p.coordinates(label))) - self.domain().polygon(
+            label
+        ).vertex(0)
+
+        coordinates = next(iter(p.coordinates(label)))
+        coordinates -= self.domain().polygon(label).vertex(0)
+
+        if len(self._subdivisions[label]) == 1:
+            # No point was inserted into this polygon.
+            coordinates += self.codomain().polygon(label).vertex(0)
+            return self.codomain()(label, coordinates)
+
+        # We take the translation of the point from a nearby vertex and then
+        # translate from the image of that vertex by the same amount.
+        # Since that translation is currently only implemented in libflatsurf,
+        # we leave the heavy lifting to libflatsurf here.
+        from flatsurf.geometry.pyflatsurf_conversion import VectorSpaceConversion
+
+        to_pyflatsurf_vector = VectorSpaceConversion.to_pyflatsurf(coordinates.parent())
+
+        def ccw(v, w):
+            r"""
+            Return whether v->w describe a non-clockwise turn.
+            """
+            return v[0] * w[1] >= w[0] * v[1]
+
+        initial_edge = ((label, 0), 0)
+        mid_edge = (
+            (
+                label,
+                len(self.codomain().polygons()) // len(self.domain().polygons()) - 1,
+            ),
+            1,
+        )
+
+        face = (
+            mid_edge
+            if ccw(self.codomain().polygon(mid_edge[0]).edge(mid_edge[1]), coordinates)
+            else initial_edge
+        )
+        face = to_pyflatsurf(face)
+        coordinates = to_pyflatsurf_vector(coordinates)
+
+        import pyflatsurf
+
+        p = pyflatsurf.flatsurf.Point[type(to_pyflatsurf.codomain())](
+            to_pyflatsurf.codomain(), face, coordinates
+        )
+
+        return to_pyflatsurf.section(p)
+
+    def _image_homology_edge(self, label, edge):
+        return [(1, (label, edge), 0)]
+
+    def __eq__(self, other):
+        if not isinstance(other, InsertMarkedPointsInFaceMorphism):
+            return False
+
+        return (
+            self.domain() == other.domain()
+            and self.codomain() == other.codomain()
+            and self._subdivisions == other._subdivisions
+        )
+
+    def _repr_type(self):
+        return "Marked-Point-Insertion"
+
+
+# TODO: Can we use some generic machinery from RepolygonizationMorphism here?
+class InsertMarkedPointsOnEdgeMorphism(RepolygonizationMorphism):
+    def __init__(self, parent, points, category=None):
+        super().__init__(parent, category=category)
+        self._points = points
+
+    def _image_point(self, p):
+        return self.codomain()(*p.representative())
+
+    def _repr_type(self):
+        return "InsertMarkedPoint"
+
+class PolygonStandardizationMorphism(SurfaceMorphism):
+    def __init__(self, parent, vertex_zero, category=None):
+        super().__init__(parent, category=category)
+        self._vertex_zero = vertex_zero
+
+    def change(self, domain=None, codomain=None, check=True):
+        # TODO: Check compatibility
+        return type(self)._create_morphism(
+            domain=domain or self.domain(),
+            codomain=codomain or self.codomain(),
+            vertex_zero=self._vertex_zero,
+            category=self.category_for(),
+        )
+
+    def __eq__(self, other):
+        if not isinstance(other, PolygonStandardizationMorphism):
+            return False
+
+        return (
+            self.parent() == other.parent() and self._vertex_zero == other._vertex_zero
+        )
+
+    def _repr_type(self):
+        return "Polygon Standardization"
+
+
+# TODO: Can we use some generic machinery from RepolygonizationMorphism here?
+class RelabelingMorphism(RepolygonizationMorphism):
+    def __init__(self, parent, relabeling, category=None):
+        super().__init__(parent, category=category)
+        # TODO: Compactify relabeling and make it frozen and hashable.
+        self._relabeling = relabeling
+
+    def change(self, domain=None, codomain=None, check=True):
+        # TODO: Check compatibility
+        return type(self)._create_morphism(
+            domain=domain or self.domain(),
+            codomain=codomain or self.codomain(),
+            relabeling=self._relabeling,
+            category=self.category_for(),
+        )
+
+    def __eq__(self, other):
+        if not isinstance(other, RelabelingMorphism):
+            return False
+
+        return self.parent() == other.parent() and self._relabeling == other._relabeling
+
+    def _repr_type(self):
+        return "Relabeling"
+
+
+class PolygonIsometryMorphism(SurfaceMorphism):
+    r"""
+    A morphism that maps each polygon to an isometric polygon.
+
+    The morphism is encoded as the mapping on polygon labels and an isometry
+    for each polygon.
+
+    # TODO: Currently, there is no isometry, so we encode things very explicitly.
+
+    EXAMPLES:
+
+    A rotation of a regular octagon::
+
+        sage: from flatsurf import translation_surfaces
+        sage: S = translation_surfaces.regular_octagon()
+
+        sage: from flatsurf.geometry.morphism import PolygonIsometryMorphism
+        sage: f = PolygonIsometryMorphism._create_morphism(S, S, {0: (0, 1)})
+        sage: f
+        Polygon Isometry endomorphism of Translation Surface in H_2(2) built from a regular octagon
+
+    The morphism can be applied to homology classes::
+
+        sage: from flatsurf import SimplicialHomology
+        sage: H = SimplicialHomology(S)
+        sage: a, b, c, d = H.gens()
+        sage: a, b, c, d
+        (B[(0, 1)], B[(0, 2)], B[(0, 3)], B[(0, 0)])
+        sage: f(a), f(b), f(c), f(d)
+        (B[(0, 2)], B[(0, 3)], -B[(0, 0)], B[(0, 1)])
+
+    A rotation of a triangle unfolding::
+
+        sage: from flatsurf import similarity_surfaces, Polygon
+        sage: S = similarity_surfaces.billiard(Polygon(angles=[3, 4, 13])).minimal_cover("translation")
+        sage: # TODO: complete example
+
+    """
+
+    def __init__(self, parent, polygon_mapping, category=None):
+        super().__init__(parent, category=category)
+        self._polygon_mapping = polygon_mapping
+
+        for source_label in self.domain().labels():
+            target_label, shift = self._polygon_mapping[source_label]
+
+            source_polygon = self.domain().polygon(source_label)
+            target_polygon = self.codomain().polygon(target_label)
+
+            if len(source_polygon.vertices()) != len(target_polygon.vertices()):
+                raise ValueError("isomorphism must map n-gons to n-gons")
+
+            for source_edge in range(len(source_polygon.vertices())):
+                (
+                    source_opposite_label,
+                    source_opposite_edge,
+                ) = self.domain().opposite_edge(source_label, source_edge)
+
+                target_edge = (source_edge + shift) % len(target_polygon.vertices())
+
+                (
+                    target_opposite_label,
+                    target_opposite_edge,
+                ) = self.codomain().opposite_edge(target_label, target_edge)
+
+                source_opposite_label_image, other_shift = self._polygon_mapping[
+                    source_opposite_label
+                ]
+                if target_opposite_label != source_opposite_label_image:
+                    raise ValueError(
+                        "gluings are not compatible with the provided isometries"
+                    )
+
+                if target_opposite_edge != (source_opposite_edge + other_shift) % len(
+                    self.codomain().polygon(target_opposite_label).vertices()
+                ):
+                    raise ValueError(
+                        "gluings are not compatible with the provided isometries"
+                    )
+
+    def _repr_type(self):
+        return "Polygon Isometry"
+
+    def _image_homology_edge(self, label, edge):
+        label, shift = self._polygon_mapping[label]
+        return [
+            (1, label, (edge + shift) % len(self.codomain().polygon(label).vertices()))
+        ]
diff --git a/flatsurf/geometry/polygon.py b/flatsurf/geometry/polygon.py
index 410df56be..719bfe7b2 100644
--- a/flatsurf/geometry/polygon.py
+++ b/flatsurf/geometry/polygon.py
@@ -38,6 +38,8 @@
 #  along with sage-flatsurf. If not, see <https://www.gnu.org/licenses/>.
 # ****************************************************************************
 
+# TODO: Could it make sense to make all vertex and edge indexes live in Z/nZ so we do not need this % len(...) all the time?
+
 from sage.all import (
     cached_method,
     Parent,
@@ -88,6 +90,18 @@ def __init__(self, parent, xy):
 
         super().__init__(parent)
 
+    def coordinates(self, edge=None):
+        if edge is None:
+            return self._xy
+
+        polygon = self.parent()
+
+        from sage.all import matrix
+
+        return matrix([polygon.edge(edge), -polygon.edge(edge - 1)]).solve_left(
+            self._xy - polygon.vertex(edge)
+        )
+
     def position(self):
         r"""
         Describe the position of this point in the polygon.
diff --git a/flatsurf/geometry/power_series.py b/flatsurf/geometry/power_series.py
new file mode 100644
index 000000000..f3d628946
--- /dev/null
+++ b/flatsurf/geometry/power_series.py
@@ -0,0 +1,1046 @@
+r"""
+Utilities to deal with power series and Laurent series defined on surfaces.
+"""
+######################################################################
+#  This file is part of sage-flatsurf.
+#
+#        Copyright (C) 2022-2023 Julian Rüth
+#
+#  sage-flatsurf is free software: you can redistribute it and/or modify
+#  it under the terms of the GNU General Public License as published by
+#  the Free Software Foundation, either version 2 of the License, or
+#  (at your option) any later version.
+#
+#  sage-flatsurf is distributed in the hope that it will be useful,
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#  GNU General Public License for more details.
+#
+#  You should have received a copy of the GNU General Public License
+#  along with sage-flatsurf. If not, see <https://www.gnu.org/licenses/>.
+######################################################################
+from sage.rings.ring import CommutativeRing
+from sage.structure.element import CommutativeRingElement
+from sage.structure.unique_representation import UniqueRepresentation
+from sage.misc.cachefunc import cached_method
+
+
+class PowerSeriesCoefficientExpression(CommutativeRingElement):
+    r"""
+    An expression in the (symbolic) coefficients of a multivariate power
+    series.
+
+    Consider a multivariate power series, for example, in two variables
+
+    .. MATH::
+
+        \sum a_i b_j x^i y^j
+
+    This element is an expression in the coefficients of such a series, for
+    example
+
+    .. MATH::
+
+        a_0^2 + 2 a_1 b_1 + b_1^2
+
+    In principle, this is just a multivariate polynomial in all the `a_i` and
+    `b_j`. However, for our use case, see :module:`harmonic_differentials`,
+    these expressions are of low degree (at most 2) but with lots of variables.
+    Multivariate polynomial rings in SageMath are bad at handling such
+    extremely sparse scenarios since some operations are implemented linearly
+    in the number of generators of the ring.
+
+    Therefore, we roll our own "sparse multivariate polynomial ring" here that
+    is asymptotically fast for the operations we care about (and slow for other
+    operations such as multiplication of expressions.)
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing  # random output due to deprecation warnings from cppyy
+        sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("a_?", "b_?"))
+        sage: a0 = R.gen(("a_?", 0))
+        sage: b0 = R.gen(("b_?", 0))
+        sage: a1 = R.gen(("a_?", 1))
+        sage: b1 = R.gen(("b_?", 1))
+
+        sage: a0^2 + b0^2 + 2 * a1 * b1
+        a_0^2 + b_0^2 + 2*a_1*b_1
+
+    """
+
+    def __init__(self, parent, coefficients):
+        super().__init__(parent)
+
+        # Zero coefficients must be removed. Otherwise, degree computations break.
+        assert all(v for v in coefficients.values())
+
+        self._coefficients = coefficients
+
+    def _richcmp_(self, other, op):
+        r"""
+        Compare this expression to ``other``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("a_?", "b_?"))
+            sage: a0 = R.gen(("a_?", 0))
+            sage: b0 = R.gen(("b_?", 0))
+
+            sage: a0 == b0
+            False
+
+            sage: a0^2 == a0
+            False
+
+            sage: a0 == a0
+            True
+
+        """
+        from sage.structure.richcmp import op_EQ, op_NE
+
+        if op == op_NE:
+            return not (self == other)
+
+        if op == op_EQ:
+            return self._coefficients == other._coefficients
+
+        raise NotImplementedError
+
+    def __bool__(self):
+        # Implemented directly for performance.
+        return bool(self._coefficients)
+
+    def is_one(self):
+        for key, value in self._coefficients.items():
+            if key == () and value.is_one():
+                continue
+            return False
+
+        return True
+
+    def _repr_(self):
+        r"""
+        Return a printable representation of this expression.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: re = R.gen(("Re(a0,?)", 0))
+            sage: im = R.gen(("Im(a0,?)", 0))
+
+            sage: re
+            Re(a0,0)
+            sage: im
+            Im(a0,0)
+            sage: re + im
+            Re(a0,0) + Im(a0,0)
+            sage: re + im + 1
+            Re(a0,0) + Im(a0,0) + 1
+
+        """
+
+        def variable_name(variable):
+            gen, degree = variable.describe()
+
+            return gen.replace("?", str(degree))
+
+        variables = list(self.variables())
+
+        def key(variable):
+            return next(iter(next(iter(variable._coefficients.keys()))))
+
+        variables.sort(key=key)
+
+        variable_names = tuple(variable_name(variable) for variable in variables)
+
+        def encode_variable_name(variable):
+            return (
+                variable.replace("(", "__open__")
+                .replace(")", "__close__")
+                .replace(",", "__comma__")
+            )
+
+        variable_names = [encode_variable_name(name) for name in variable_names]
+
+        def decode_variable_name(variable):
+            return (
+                variable.replace("__comma__", ",")
+                .replace("__close__", ")")
+                .replace("__open__", "(")
+            )
+
+        from sage.all import PolynomialRing
+
+        R = PolynomialRing(self.base_ring(), variable_names)
+
+        def monomial(gens):
+            monomial = R.one()
+            for gen in gens:
+                gen = self.parent().gen(gen)
+                monomial *= R(variable_names[variables.index(gen)])
+            return monomial
+
+        f = sum(
+            coefficient * monomial(gens)
+            for (gens, coefficient) in self._coefficients.items()
+        )
+
+        return decode_variable_name(repr(f))
+
+    def degree(self, gen):
+        r"""
+        Return the total degree of this expression in the variable ``gen``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: a = R.gen(("Re(a0,?)", 0))
+            sage: b = R.gen(("Im(a0,?)", 0))
+
+            sage: a.degree(a)
+            1
+            sage: (a + b).degree(a)
+            1
+            sage: (a * b + a).degree(a)
+            1
+            sage: R.one().degree(a)
+            0
+            sage: R.zero().degree(a)
+            -1
+
+        """
+        if not gen.is_variable():
+            raise ValueError(f"gen must be a variable not {gen}")
+
+        variable = next(iter(next(iter(gen._coefficients))))
+
+        return max(
+            [monomial.count(variable) for monomial in self._coefficients], default=-1
+        )
+
+    def is_monomial(self):
+        r"""
+        Return whether this expression is a non-constant monomial without a
+        leading coefficient.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: a = R.gen(("Re(a0,?)", 0))
+            sage: b = R.gen(("Im(a0,?)", 0))
+
+            sage: a.is_monomial()
+            True
+            sage: (2*a).is_monomial()
+            False
+            sage: (a + b).is_monomial()
+            False
+            sage: R.one().is_monomial()
+            False
+            sage: R.zero().is_monomial()
+            False
+            sage: (a * a).is_monomial()
+            True
+
+        """
+        if len(self._coefficients) != 1:
+            return False
+
+        ((key, value),) = self._coefficients.items()
+
+        return bool(key) and value.is_one()
+
+    def is_constant(self):
+        r"""
+        Return whether this expression is a constant from the base ring.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: a = R.gen(("Re(a0,?)", 0))
+            sage: b = R.gen(("Im(a0,?)", 0))
+
+            sage: a.is_constant()
+            False
+            sage: (a + b).is_constant()
+            False
+            sage: R.one().is_constant()
+            True
+            sage: R.zero().is_constant()
+            True
+            sage: (a * a).is_constant()
+            False
+
+        """
+        coefficients = len(self._coefficients)
+
+        if coefficients == 0:
+            return True
+
+        if coefficients > 1:
+            return False
+
+        monomial = next(iter(self._coefficients.keys()))
+
+        return not monomial
+
+    def norm(self, p=2):
+        r"""
+        Return the p-norm of the coefficient vector.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: a = R.gen(("Re(a0,?)", 0))
+            sage: b = R.gen(("Im(a0,?)", 0))
+
+            sage: x = a + 1
+
+            sage: x.norm(1)
+            2
+
+            sage: x.norm(oo)
+            1
+
+        """
+        from sage.all import vector
+
+        return vector(self._coefficients.values()).norm(p)
+
+    def _neg_(self):
+        r"""
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: a = R.gen(("Re(a0,?)", 0))
+            sage: b = R.gen(("Im(a0,?)", 0))
+
+            sage: -a
+            -Re(a0,0)
+            sage: -(a + b)
+            -Re(a0,0) - Im(a0,0)
+            sage: -(a * a)
+            -Re(a0,0)^2
+            sage: -R.one()
+            -1
+            sage: -R.zero()
+            0
+
+        """
+        parent = self.parent()
+        return type(self)(
+            parent,
+            {key: -coefficient for (key, coefficient) in self._coefficients.items()},
+        )
+
+    def _add_(self, other):
+        r"""
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: a = R.gen(("Re(a0,?)", 0))
+            sage: b = R.gen(("Im(a0,?)", 0))
+
+            sage: a + 1
+            Re(a0,0) + 1
+            sage: a + (-a)
+            0
+            sage: a + b
+            Re(a0,0) + Im(a0,0)
+            sage: a * a + b * b
+            Re(a0,0)^2 + Im(a0,0)^2
+
+        """
+        return self.parent().sum([self, other])
+
+    def _sub_(self, other):
+        r"""
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: a = R.gen(("Re(a0,?)", 0))
+            sage: b = R.gen(("Im(a0,?)", 0))
+
+            sage: a - 1
+            Re(a0,0) - 1
+            sage: a - a
+            0
+            sage: a * a - b * b
+            Re(a0,0)^2 - Im(a0,0)^2
+
+        """
+        return self._add_(-other)
+
+    def _mul_(self, other):
+        r"""
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: a = R.gen(("Re(a0,?)", 0))
+            sage: b = R.gen(("Im(a0,?)", 0))
+
+            sage: a * a
+            Re(a0,0)^2
+            sage: a * b
+            Re(a0,0)*Im(a0,0)
+            sage: a * R.one()
+            Re(a0,0)
+            sage: a * R.zero()
+            0
+            sage: (a + b) * (a - b)
+            Re(a0,0)^2 - Im(a0,0)^2
+
+        """
+        parent = self.parent()
+
+        if other.is_zero() or self.is_zero():
+            return parent.zero()
+
+        if other.is_one():
+            return self
+
+        if self.is_one():
+            return other
+
+        coefficients = {}
+
+        for self_monomial, self_coefficient in self._coefficients.items():
+            assert self_coefficient
+            for other_monomial, other_coefficient in other._coefficients.items():
+                assert other_coefficient
+
+                monomial = tuple(sorted(self_monomial + other_monomial))
+                coefficient = self_coefficient * other_coefficient
+
+                if monomial not in coefficients:
+                    coefficients[monomial] = coefficient
+                else:
+                    coefficients[monomial] += coefficient
+                    if not coefficients[monomial]:
+                        del coefficients[monomial]
+
+        return type(self)(self.parent(), coefficients)
+
+    def _rmul_(self, right):
+        r"""
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: a = R.gen(("Re(a0,?)", 0))
+            sage: b = R.gen(("Im(a0,?)", 0))
+
+            sage: a * 0
+            0
+            sage: a * 1
+            Re(a0,0)
+            sage: a * 2
+            2*Re(a0,0)
+
+        """
+        return self._lmul_(right)
+
+    def _lmul_(self, left):
+        r"""
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: a = R.gen(("Re(a0,?)", 0))
+            sage: b = R.gen(("Im(a0,?)", 0))
+
+            sage: 0 * a
+            0
+            sage: 1 * a
+            Re(a0,0)
+            sage: 2 * a
+            2*Re(a0,0)
+
+        """
+        return type(self)(
+            self.parent(),
+            {
+                key: coefficient
+                for (key, value) in self._coefficients.items()
+                if (coefficient := left * value)
+            },
+        )
+
+    def constant_coefficient(self):
+        r"""
+        Return the constant coefficient of this expression.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: a = R.gen(("Re(a0,?)", 0))
+            sage: b = R.gen(("Im(a0,?)", 0))
+
+            sage: a.constant_coefficient()
+            0
+            sage: (a + b).constant_coefficient()
+            0
+            sage: R.one().constant_coefficient()
+            1
+            sage: R.zero().constant_coefficient()
+            0
+
+        """
+        return self._coefficients.get((), self.parent().base_ring().zero())
+
+    def variables(self):
+        r"""
+        Return the variables that appear in this expression.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: re = R.gen(("Re(a0,?)", 0))
+            sage: im = R.gen(("Im(a0,?)", 0))
+
+            sage: R.zero().variables()
+            set()
+            sage: R.one().variables()
+            set()
+            sage: (re^2 * im + 1).variables()
+            {Im(a0,0), Re(a0,0)}
+            sage: (re + 1).variables()
+            {Re(a0,0)}
+
+        """
+        return set(
+            self.parent().gen(gen)
+            for monomial in self._coefficients.keys()
+            for gen in monomial
+        )
+
+    def is_variable(self):
+        r"""
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: re = R.gen(("Re(a0,?)", 0))
+            sage: im = R.gen(("Im(a0,?)", 0))
+
+            sage: re.is_variable()
+            True
+            sage: R.zero().is_variable()
+            False
+            sage: R.one().is_variable()
+            False
+            sage: (re + 1).is_variable()
+            False
+            sage: (re * im).is_variable()
+            False
+
+        """
+        if not self.is_monomial():
+            return False
+
+        monomial = next(iter(self._coefficients.keys()))
+
+        if len(monomial) != 1:
+            return False
+
+        return True
+
+    def describe(self):
+        r"""
+        Return a tuple describing the nature of this variable.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: re = R.gen(("Re(a0,?)", 0))
+            sage: im = R.gen(("Im(a0,?)", 0))
+
+            sage: re.describe()
+            ('Re(a0,?)', 0)
+            sage: im.describe()
+            ('Im(a0,?)', 0)
+            sage: (re + im).describe()
+            Traceback (most recent call last):
+            ...
+            ValueError: element must be a variable
+
+        """
+        if not self.is_variable():
+            raise ValueError("element must be a variable")
+
+        variable = next(iter(next(iter(self._coefficients.keys()))))
+
+        gens = self.parent()._gens
+
+        gen = gens[variable % len(gens)]
+        degree = variable // len(gens)
+
+        return gen, degree
+
+    def real(self):
+        r"""
+        Return the real part of this expression.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(CC, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: re = R.gen(("Re(a0,?)", 0))
+            sage: im = R.gen(("Im(a0,?)", 0))
+
+            sage: x = (re + I*im)**2
+            sage: x
+            Re(a0,0)^2 + 2.00000000000000*I*Re(a0,0)*Im(a0,0) - Im(a0,0)^2
+            sage: x.real()
+            Re(a0,0)^2 - Im(a0,0)^2
+
+        """
+        return self.map_coefficients(
+            lambda c: c.real(), self.parent().change_ring(self.parent().real_field())
+        )
+
+    def imag(self):
+        r"""
+        Return the imaginary part of this expression.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(CC, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: re = R.gen(("Re(a0,?)", 0))
+            sage: im = R.gen(("Im(a0,?)", 0))
+
+            sage: x = (re + I*im)**2
+            sage: x
+            Re(a0,0)^2 + 2.00000000000000*I*Re(a0,0)*Im(a0,0) - Im(a0,0)^2
+            sage: x.imag()
+            2.00000000000000*Re(a0,0)*Im(a0,0)
+
+        """
+        return self.map_coefficients(
+            lambda c: c.imag(), self.parent().change_ring(self.parent().real_field())
+        )
+
+    def __getitem__(self, gen):
+        r"""
+        Return the coefficient of the monomial ``gen``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(CC, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: re = R.gen(("Re(a0,?)", 0))
+            sage: im = R.gen(("Im(a0,?)", 0))
+
+            sage: re[re]
+            1.00000000000000
+            sage: re[im]
+            0.000000000000000
+            sage: (re + im)[re]
+            1.00000000000000
+            sage: (re * im)[re]
+            0.000000000000000
+            sage: (re * im)[re * im]
+            1.00000000000000
+
+        """
+        if not gen.is_monomial():
+            raise ValueError("gen must be a monomial")
+
+        return self._coefficients.get(
+            next(iter(gen._coefficients.keys())), self.parent().base_ring().zero()
+        )
+
+    def __hash__(self):
+        return hash(tuple(sorted(self._coefficients.items())))
+
+    def total_degree(self):
+        r"""
+        Return the total degree of this expression in its symbolic variables.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(CC, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: re = R.gen(("Re(a0,?)", 0))
+            sage: im = R.gen(("Im(a0,?)", 0))
+
+            sage: R.zero().total_degree()
+            -1
+            sage: R.one().total_degree()
+            0
+            sage: re.total_degree()
+            1
+            sage: (re * re + im).total_degree()
+            2
+
+        """
+        degrees = [len(monomial) for monomial in self._coefficients]
+        return max(degrees, default=-1)
+
+    def derivative(self, gen):
+        r"""
+        Return the derivative of this expression with respect to the variable
+        ``gen``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(CC, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: re = R.gen(("Re(a0,?)", 0))
+            sage: im = R.gen(("Im(a0,?)", 0))
+
+            sage: R.zero().derivative(re)
+            0
+            sage: R.one().derivative(re)
+            0
+            sage: re.derivative(re)
+            1.00000000000000
+            sage: re.derivative(im)
+            0
+            sage: x = (re + im) * (re - im)
+            sage: x.derivative(re)
+            2.00000000000000*Re(a0,0)
+            sage: x.derivative(im)
+            -2.00000000000000*Im(a0,0)
+
+        """
+        if not gen.is_variable():
+            raise ValueError("gen must be a variable")
+
+        gen = next(iter(gen._coefficients.keys()))[0]
+
+        derivative = self.parent().zero()
+
+        for monomial, coefficient in self._coefficients.items():
+            assert coefficient
+
+            exponent = monomial.count(gen)
+
+            if not exponent:
+                continue
+
+            monomial = list(monomial)
+            monomial.remove(gen)
+            monomial = tuple(monomial)
+
+            derivative += self.parent()({monomial: exponent * coefficient})
+
+        return derivative
+
+    def map_coefficients(self, f, ring=None):
+        r"""
+        Return the image of this expression by applying ``f`` to each non-zero
+        coefficient of the expression.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(CC, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: re = R.gen(("Re(a0,?)", 0))
+            sage: im = R.gen(("Im(a0,?)", 0))
+
+            sage: re.map_coefficients(lambda c: 2*c)
+            2.00000000000000*Re(a0,0)
+
+        """
+        if ring is None:
+            ring = self.parent()
+
+        return ring(
+            {
+                key: image
+                for key, value in self._coefficients.items()
+                if (image := f(value))
+            }
+        )
+
+    def __call__(self, values):
+        r"""
+        Return the value of this symbolic expression at ``values``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(CC, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: re = R.gen(("Re(a0,?)", 0))
+            sage: im = R.gen(("Im(a0,?)", 0))
+
+            sage: re({re: 1})
+            1.00000000000000
+            sage: re({re: 2, im: 1})
+            2.00000000000000
+            sage: (2 * re * im)({re: 3, im: 5})
+            30.0000000000000
+
+        """
+
+        def evaluate(monomial):
+            product = self.parent().base_ring().one()
+
+            for variable in monomial:
+                product *= values[self.parent().gen(variable)]
+
+            return product
+
+        return sum(
+            [
+                coefficient * evaluate(monomial)
+                for (monomial, coefficient) in self._coefficients.items()
+            ]
+        )
+
+
+class PowerSeriesCoefficientExpressionRing(UniqueRepresentation, CommutativeRing):
+    r"""
+    The ring of expressions in the symbolic coefficients of a multivariate
+    power series.
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+        sage: R = PowerSeriesCoefficientExpressionRing(CC, gens=("Re(a0,?)", "Im(a0,?)"))
+        sage: R
+        Ring of Power Series Coefficients in Re(a0,0),…,Im(a0,0),… over Complex Field with 53 bits of precision
+
+    TESTS::
+
+        sage: R.has_coerce_map_from(CC)
+        True
+        sage: TestSuite(R).run()
+
+    """
+
+    def __init__(self, base_ring, gens, category=None):
+        self._gens = gens
+
+        from sage.categories.all import CommutativeRings
+
+        CommutativeRing.__init__(
+            self, base_ring, category=category or CommutativeRings(), normalize=False
+        )
+        self.register_coercion(base_ring)
+
+    Element = PowerSeriesCoefficientExpression
+
+    def _repr_(self):
+        return f"Ring of Power Series Coefficients in {','.join(gen.replace('?', '0') + ',…' for gen in self._gens)} over {self.base_ring()}"
+
+    def change_ring(self, ring):
+        r"""
+        Return this ring with the ring of constants replaced by ``ring``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(CC, gens=("Re(a0,?)", "Im(a0,?)"))
+            sage: R.change_ring(RR)
+            Ring of Power Series Coefficients in Re(a0,0),…,Im(a0,0),… over Real Field with 53 bits of precision
+
+        """
+        return PowerSeriesCoefficientExpressionRing(
+            ring, self._gens, category=self.category()
+        )
+
+    def sum(self, summands):
+        r"""
+        Return the sum of ``summands``.
+
+        This is an optimized version of the builtin `sum` that creates fewer
+        temporary objects.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(CC, gens=("Re(a0,?)", "Im(a0,?)"))
+
+            sage: R.sum([R.gen(0), R.gen(1)])
+            Re(a0,0) + Im(a0,0)
+
+        """
+        # TODO: Add a benchmark to show that this is actually way faster when
+        # there are lots of generators.
+
+        summands = list(summands)
+
+        if len(summands) == 0:
+            return self.zero()
+
+        if len(summands) == 1:
+            return summands.pop()
+
+        coefficients = dict(summands.pop()._coefficients)
+
+        while summands:
+            summand = summands.pop()
+            for monomial, coefficient in summand._coefficients.items():
+                assert coefficient
+                if monomial not in coefficients:
+                    coefficients[monomial] = coefficient
+                else:
+                    coefficients[monomial] += coefficient
+
+                if not coefficients[monomial]:
+                    del coefficients[monomial]
+
+        return self(coefficients)
+
+    def real_field(self):
+        r"""
+        Return a real base field corresponding to the complex base field of
+        this ring.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(CC, gens=("Re(a0,?)", "Im(a0,?)"))
+
+            sage: R.real_field()
+            Real Field with 53 bits of precision
+
+        When the base ring is not complex, this method is not functional::
+
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("Re(a0,?)", "Im(a0,?)"))
+
+            sage: R.real_field()
+            Traceback (most recent call last):
+            ...
+            AttributeError: 'RationalField_with_category' object has no attribute 'prec'
+
+        """
+        # TODO: This should depend on the base ring.
+        from sage.all import RDF
+
+        return RDF
+
+    def is_exact(self):
+        r"""
+        Return whether this ring is implementing exact arithmetic.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("a", "b"))
+            sage: R.is_exact()
+            True
+
+        """
+        return self.base_ring().is_exact()
+
+    def _coerce_map_from_(self, other):
+        if isinstance(other, PowerSeriesCoefficientExpressionRing):
+            return self.base_ring().has_coerce_map_from(other.base_ring())
+
+    def _element_constructor_(self, x):
+        r"""
+        Return an element of this ring built from ``x``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("a?", "b?"))
+            sage: R(1)
+            1
+            sage: R({(1,): 3})
+            3*b0
+
+        """
+        if isinstance(x, PowerSeriesCoefficientExpression):
+            return x.map_coefficients(self.base_ring(), ring=self)
+
+        if isinstance(x, dict):
+            return self.element_class(
+                self,
+                {
+                    tuple(sorted(monomial)): self.base_ring()(coefficient)
+                    for (monomial, coefficient) in x.items()
+                    if coefficient
+                },
+            )
+
+        from sage.all import parent
+
+        if parent(x) is self.base_ring():
+            if not x:
+                return self.element_class(self, {})
+            return self.element_class(self, {(): x})
+
+        raise TypeError(f"cannot create a symbolic expression from this {type(x)}")
+
+    @cached_method
+    def gen(self, gen):
+        r"""
+        Return the generator identified by ``gen``.
+
+        INPUT:
+
+        - ``gen`` -- a tuple (name, degree) or an integer.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("a?", "b?"))
+            sage: R.gen(("a?", 0))
+            a0
+
+        SageMath wants us to also order the generators and return them when
+        calling ``gen`` with an integer argument::
+
+            sage: R.gen(0)
+            a0
+            sage: R.gen(1)
+            b0
+            sage: R.gen(2)
+            a1
+            sage: R.gen(3)
+            b1
+
+        """
+        if isinstance(gen, tuple):
+            if len(gen) == 2:
+                name, degree = gen
+                if name not in self._gens:
+                    raise ValueError(f"name must be one of {self._gens} not {name}")
+                gen = degree * len(self._gens) + self._gens.index(name)
+
+        from sage.all import parent, ZZ
+
+        if parent(gen) == ZZ:
+            gen = int(gen)
+
+        if isinstance(gen, int):
+            return self.element_class(self, {(gen,): self.base().one()})
+
+        raise TypeError("gen must be an integer or a tuple (name, degree)")
+
+    def ngens(self):
+        r"""
+        Return the number of generators of this ring.
+
+        Since there are infinitely many generators, this method is not
+        implemented (SageMath does not accept us returning +infinity here.)
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.power_series import PowerSeriesCoefficientExpressionRing
+            sage: R = PowerSeriesCoefficientExpressionRing(QQ, gens=("a?", "b?"))
+            sage: R.ngens()
+            Traceback (most recent call last):
+            ...
+            NotImplementedError
+
+        """
+        raise NotImplementedError
diff --git a/flatsurf/geometry/pyflatsurf/conversion.py b/flatsurf/geometry/pyflatsurf/conversion.py
index 7a1fb54ca..a65d52214 100644
--- a/flatsurf/geometry/pyflatsurf/conversion.py
+++ b/flatsurf/geometry/pyflatsurf/conversion.py
@@ -2146,6 +2146,71 @@ def _preimage_half_edge(self, half_edge):
         """
         return self._half_edge_to_label[half_edge.id()]
 
+    def _image_saddle_connection(self, saddle_connection):
+        r"""
+        Return the image of the ``saddle_connection``.
+
+        This is a helper method for :meth:`__call__`.
+
+        INPUT:
+
+        - ``saddle_connection`` -- a saddle connection defined in the :meth:`domain`.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: from flatsurf.geometry.pyflatsurf_conversion import FlatTriangulationConversion
+            sage: from flatsurf.geometry.surface_objects import SurfacePoint
+            sage: S = translation_surfaces.veech_double_n_gon(5).triangulate().codomain()
+            sage: conversion = FlatTriangulationConversion.to_pyflatsurf(S)
+
+            sage: conversion._image_saddle_connection(next(iter(S.saddle_connections(1))))
+            5
+
+        """
+        import pyflatsurf
+
+        return pyflatsurf.flatsurf.SaddleConnection[type(self.codomain())].inSector(
+            self.codomain(),
+            self._image_half_edge(*saddle_connection.start()),
+            self.vector_space_conversion()(saddle_connection.holonomy()),
+        )
+
+    def _preimage_saddle_connection(self, saddle_connection):
+        r"""
+        Return the preimage of the ``saddle_connection`` in the domain of this
+        conversion.
+
+        This is a helper method for :meth:`section`.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: from flatsurf.geometry.pyflatsurf_conversion import FlatTriangulationConversion
+            sage: from flatsurf.geometry.surface_objects import SurfacePoint
+            sage: S = translation_surfaces.veech_double_n_gon(5).triangulate().codomain()
+            sage: conversion = FlatTriangulationConversion.to_pyflatsurf(S)
+
+            sage: connection = next(iter(conversion.codomain().connections()))
+            sage: connection
+            1
+
+            sage: preimage = conversion._preimage_saddle_connection(connection)
+            sage: preimage
+            Saddle connection (1, 0) from vertex 0 of polygon (0, 0) to vertex 0 of polygon (1, 0)
+
+            sage: conversion(preimage)
+            1
+
+        """
+        from flatsurf.geometry.saddle_connection import SaddleConnection
+
+        # TODO: Speed this up!
+        return SaddleConnection.from_vertex(
+            self.domain(),
+            *self._preimage_half_edge(saddle_connection.source()),
+            self.vector_space_conversion().section(saddle_connection.vector()),
+        )
     def __eq__(self, other):
         r"""
         Return whether this conversion is indistinguishable from ``other``.
diff --git a/flatsurf/geometry/pyflatsurf/flow_decomposition.py b/flatsurf/geometry/pyflatsurf/flow_decomposition.py
new file mode 100644
index 000000000..e01a9b235
--- /dev/null
+++ b/flatsurf/geometry/pyflatsurf/flow_decomposition.py
@@ -0,0 +1,73 @@
+from flatsurf.geometry.flow_decomposition import (
+    FlowDecomposition_base,
+    FlowComponent_base,
+)
+
+
+def tribool_to_bool_or_none(tribool):
+    if tribool:
+        return True
+
+    import cppyy
+
+    if cppyy.gbl.boost.logic.indeterminate(tribool):
+        return None
+
+    return False
+
+
+class FlowDecomposition_pyflatsurf(FlowDecomposition_base):
+    def __init__(self, flow_decomposition, surface):
+        super().__init__(surface)
+
+        self._flow_decomposition = flow_decomposition
+        self._components = None
+
+    def decompose(self, limit=-1):
+        if limit != 0:
+            self.invalidate_components()
+            self._flow_decomposition.decompose(int(limit))
+
+    def is_parabolic(self):
+        return tribool_to_bool_or_none(self._flow_decomposition.parabolic())
+
+    def invalidate_components(self):
+        for component in self._components or []:
+            component._invalidate()
+
+        self._components = None
+
+    def components(self):
+        if self._components is None:
+            self._components = [
+                FlowComponent_pyflatsurf(component, self)
+                for component in self._flow_decomposition.components()
+            ]
+
+        return self._components
+
+    def has_cylinder(self):
+        return tribool_to_bool_or_none(self._flow_decomposition.hasCylinder())
+
+    def is_completely_periodic(self):
+        return tribool_to_bool_or_none(self._flow_decomposition.completelyPeriodic())
+
+
+class FlowComponent_pyflatsurf(FlowComponent_base):
+    def __init__(self, flow_component, flow_decomposition):
+        super().__init__(flow_decomposition)
+
+        self.__flow_component = flow_component
+
+    def _invalidate(self):
+        self.__flow_component = None
+
+    def _flow_component(self):
+        if self.__flow_component is None:
+            raise NotImplementedError(
+                "component of flow decomposition has been modified externally since it was created"
+            )
+        return self.__flow_component
+
+    def is_cylinder(self):
+        return tribool_to_bool_or_none(self._flow_component().cylinder())
diff --git a/flatsurf/geometry/pyflatsurf/morphism.py b/flatsurf/geometry/pyflatsurf/morphism.py
index 694c9e7c5..18c4f8a59 100644
--- a/flatsurf/geometry/pyflatsurf/morphism.py
+++ b/flatsurf/geometry/pyflatsurf/morphism.py
@@ -146,6 +146,15 @@ def _repr_type(self):
         """
         return "pyflatsurf conversion"
 
+    def _image_saddle_connection(self, connection):
+        from flatsurf.geometry.pyflatsurf.saddle_connection import (
+            SaddleConnection_pyflatsurf,
+        )
+
+        return SaddleConnection_pyflatsurf(
+            self._pyflatsurf_conversion(connection), self.codomain()
+        )
+
     def _test_section_point(self, **options):
         r"""
         Do not verify that :meth:`_section_point` has been implemented
diff --git a/flatsurf/geometry/pyflatsurf/saddle_connection.py b/flatsurf/geometry/pyflatsurf/saddle_connection.py
new file mode 100644
index 000000000..87a9659f0
--- /dev/null
+++ b/flatsurf/geometry/pyflatsurf/saddle_connection.py
@@ -0,0 +1,12 @@
+from flatsurf.geometry.saddle_connection import SaddleConnection_base
+
+
+class SaddleConnection_pyflatsurf(SaddleConnection_base):
+    def __init__(self, connection, surface):
+        super().__init__(surface)
+
+        self._connection = connection
+        self._surface = surface
+
+    def surface(self):
+        return self._surface
diff --git a/flatsurf/geometry/pyflatsurf/surface.py b/flatsurf/geometry/pyflatsurf/surface.py
index cdb78ab29..bf1869706 100644
--- a/flatsurf/geometry/pyflatsurf/surface.py
+++ b/flatsurf/geometry/pyflatsurf/surface.py
@@ -403,6 +403,48 @@ def opposite_edge(self, label, edge):
 
         return opposite_label, opposite_label.index(opposite_half_edge.id())
 
+    def flow_decomposition(self, direction):
+        direction = self._vector_space_conversion.domain()(direction)
+        direction = self._vector_space_conversion(direction)
+
+        import pyflatsurf
+
+        decomposition = pyflatsurf.flatsurf.makeFlowDecomposition(
+            self._flat_triangulation,
+            direction,
+        )
+
+        from flatsurf.geometry.pyflatsurf.flow_decomposition import (
+            FlowDecomposition_pyflatsurf,
+        )
+
+        return FlowDecomposition_pyflatsurf(decomposition, surface=self)
+
+    def _flow_decompositions_slopes_bfs(self, bound):
+        return self._flow_decompositions_slopes_from_connections(
+            self._flat_triangulation.connections().bound(int(bound)).byLength()
+        )
+
+    def _flow_decompositions_slopes_dfs(self, bound):
+        return self._flow_decompositions_slopes_from_connections(
+            self._flat_triangulation.connections().bound(int(bound))
+        )
+
+    def _flow_decompositions_slopes_from_connections(self, connections):
+        import cppyy
+
+        slopes = cppyy.gbl.std.set[
+            self._vector_space_conversion.codomain(),
+            self._vector_space_conversion.codomain().CompareSlope,
+        ]()
+
+        for connection in connections:
+            slope = connection.vector()
+            if slopes.find(slope) != slopes.end():
+                continue
+            slopes.insert(slope)
+            yield self._vector_space_conversion.section(slope)
+
     def __eq__(self, other):
         r"""
         Return whether this surface is indistinguishable from ``other``.
diff --git a/flatsurf/geometry/pyflatsurf/surface_point.py b/flatsurf/geometry/pyflatsurf/surface_point.py
new file mode 100644
index 000000000..15cf3414f
--- /dev/null
+++ b/flatsurf/geometry/pyflatsurf/surface_point.py
@@ -0,0 +1,8 @@
+from flatsurf.geometry.surface_objects import SurfacePoint_base
+
+
+class SurfacePoint_pyflatsurf(SurfacePoint_base):
+    def __init__(self, point, surface):
+        self._point = point
+
+        super().__init__(surface)
diff --git a/flatsurf/geometry/ray.py b/flatsurf/geometry/ray.py
new file mode 100644
index 000000000..c75a2e72e
--- /dev/null
+++ b/flatsurf/geometry/ray.py
@@ -0,0 +1,188 @@
+r"""
+Geometry with rays in the Euclidean plane.
+
+EXAMPLES::
+
+    sage: from flatsurf.geometry.ray import Rays
+    sage: R = Rays(QQ)
+    sage: R((1, 0))
+    Ray towards (1, 0)
+
+    sage: R((1, 0)) == R((2, 0))
+    True
+
+    sage: R((1, 0)) == R((-2, 0))
+    False
+
+"""
+######################################################################
+#  This file is part of sage-flatsurf.
+#
+#        Copyright (C) 2024 Julian Rüth
+#
+#  sage-flatsurf is free software: you can redistribute it and/or modify
+#  it under the terms of the GNU General Public License as published by
+#  the Free Software Foundation, either version 2 of the License, or
+#  (at your option) any later version.
+#
+#  sage-flatsurf is distributed in the hope that it will be useful,
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#  GNU General Public License for more details.
+#
+#  You should have received a copy of the GNU General Public License
+#  along with sage-flatsurf. If not, see <https://www.gnu.org/licenses/>.
+######################################################################
+from sage.structure.parent import Parent
+from sage.structure.element import Element
+from sage.structure.unique_representation import UniqueRepresentation
+from sage.misc.cachefunc import cached_method
+
+
+class Ray(Element):
+    r"""
+    A ray in the Euclidean plane.
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.ray import Rays
+        sage: R = Rays(QQ)
+        sage: r = R((1, 0)); r
+        Ray towards (1, 0)
+
+        sage: R((0, 0))
+        Traceback (most recent call last):
+        ...
+        ValueError: direction must not be the zero vector
+
+    TESTS::
+
+        sage: from flatsurf.geometry.ray import Ray
+        sage: isinstance(r, Ray)
+        True
+        sage: TestSuite(r).run()
+
+    """
+
+    def __init__(self, parent, direction):
+        super().__init__(parent)
+
+        direction = parent.ambient_space()(direction)
+        direction.set_immutable()
+
+        if direction.is_zero():
+            raise ValueError("direction must not be the zero vector")
+
+        self._direction = direction
+
+    def vector(self):
+        r"""
+        Return a vector in this ray.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.ray import Rays
+            sage: R = Rays(QQ)
+            sage: r = R((1, 0)); r
+            Ray towards (1, 0)
+            sage: r.vector()
+            (1, 0)
+
+        """
+        return self._direction
+
+    def _neg_(self):
+        return self.parent()(-self._direction)
+
+    def _repr_(self):
+        return f"Ray towards {self._direction}"
+
+    def _richcmp_(self, other, op):
+        r"""
+        Return how this ray compares to ``other``.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.ray import Rays
+            sage: R = Rays(QQ)
+
+            sage: R((0, 1)) == R((0, 2))
+            True
+            sage: R((1, 0)) == R((2, 0))
+            True
+            sage: R((1, 1)) == R((2, 2))
+            True
+            sage: R((1, 1)) == R((2, 1))
+            False
+            sage: R((0, 1)) == R((0, -2))
+            False
+            sage: R((1, 1)) == R((-2, -2))
+            False
+
+        """
+        from sage.structure.richcmp import op_EQ, op_NE
+
+        if op == op_NE:
+            return not self._richcmp_(other, op_EQ)
+
+        if op == op_EQ:
+            from sage.all import sgn
+
+            return (
+                self._direction[0] * other._direction[1]
+                == other._direction[0] * self._direction[1]
+                and sgn(self._direction[0]) == sgn(other._direction[0])
+                and sgn(self._direction[1]) == sgn(other._direction[1])
+            )
+
+
+class Rays(UniqueRepresentation, Parent):
+    r"""
+    The space of rays from the origin in the Euclidean plane.
+
+    EXAMPLES::
+
+        sage: from flatsurf.geometry.ray import Rays
+        sage: R = Rays(QQ)
+        sage: R
+        Rays in Vector space of dimension 2 over Rational Field
+
+    TESTS::
+
+        sage: isinstance(R, Rays)
+        True
+        sage: TestSuite(R).run()
+
+    """
+    Element = Ray
+
+    def __init__(self, base_ring, category=None):
+        from sage.categories.all import Sets, Rings
+
+        if base_ring not in Rings():
+            raise TypeError("base ring must be a ring")
+
+        super().__init__(base=base_ring, category=category or Sets())
+
+    def _an_element_(self):
+        return self((1, 0))
+
+    def some_element(self):
+        return [self(v) for v in self.ambient_space().some_elements() if v]
+
+    @cached_method
+    def ambient_space(self):
+        r"""
+        Return the ambient Euclidean space containing these rays.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.ray import Rays
+            sage: Rays(QQ).ambient_space()
+            Vector space of dimension 2 over Rational Field
+
+        """
+        return self.base_ring() ** 2
+
+    def _repr_(self):
+        return f"Rays in {self.ambient_space()}"
diff --git a/flatsurf/geometry/saddle_connection.py b/flatsurf/geometry/saddle_connection.py
new file mode 100644
index 000000000..4ffd7e80d
--- /dev/null
+++ b/flatsurf/geometry/saddle_connection.py
@@ -0,0 +1,756 @@
+from sage.all import SageObject
+from sage.misc.cachefunc import cached_method
+
+
+# TODO: SaddleConnection should be an element in the space of SaddleConnections or the space of Paths rather?
+
+
+class SaddleConnection_base(SageObject):
+    def __init__(self, surface):
+        self._surface = surface
+
+
+class SaddleConnection(SaddleConnection_base):
+    r"""
+    Represents a saddle connection on a SimilaritySurface.
+
+    TESTS::
+
+        sage: from flatsurf.geometry.saddle_connection import SaddleConnection
+        sage: from flatsurf import translation_surfaces
+        sage: S = translation_surfaces.cathedral(1, 2)
+        sage: SaddleConnection.from_vertex(S, 1, 8, (0, -1))
+        Saddle connection (0, -2) from vertex 8 of polygon 1 to vertex 5 of polygon 1
+
+    """
+
+    # TODO: The constructor should not do so much work. Missing parameters
+    # should be filled in by calling a static factory method.
+    # TODO: direction and end_direction should not be part of the data. It's
+    # just the tangent vector given by the holonomy.
+    # TODO: start and end should probably be just a tangent vector? And the
+    # naming of start() and end() should also reflect that? Then start() and
+    # end() could actually just return the surface points instead of returning
+    # a tuple.
+    def __init__(
+        self,
+        surface,
+        start,
+        direction=None,
+        end=None,
+        end_direction=None,
+        holonomy=None,
+        end_holonomy=None,
+        check=True,
+        limit=None,
+        start_data=None,
+        end_data=None,
+    ):
+        r"""
+        TODO: Cleanup documentation.
+
+        Construct a saddle connection on a SimilaritySurface.
+
+        The only necessary parameters are the surface, start_data, and direction
+        (to start). If there is missing data that can not be inferred from the surface
+        type, then a straight-line trajectory will be computed to confirm that this is
+        indeed a saddle connection. The trajectory will pass through at most limit
+        polygons before we give up.
+
+        Details of the parameters are provided below.
+
+        Parameters
+        ----------
+        surface : a SimilaritySurface
+            which will contain the saddle connection being constructed.
+        start : a pair
+            consisting of the label of the polygon where the saddle connection starts
+            and the starting vertex.
+        direction : 2-dimensional vector with entries in the base_ring of the surface
+            representing the direction the saddle connection is moving in (in the
+            coordinates of the initial polygon).
+        end : a pair
+            consisting of the label of the polygon where the saddle connection terminates
+            and the terminating vertex.
+        end_direction : 2-dimensional vector with entries in the base_ring of the surface
+            representing the direction to move backward from the end point (in the
+            coordinates of the terminal polygon). If the surface is a DilationSurface
+            or better this will be the negation of the direction vector. If the surface
+            is a HalfDilation surface or better, then this will be either the direction
+            vector or its negation. In either case the value can be inferred from the
+            end.
+        holonomy : 2-dimensional vector with entries in the base_ring of the surface
+            the holonomy of the saddle connection measured from the start. To compute this
+            you develop the saddle connection into the plane starting from the starting
+            polygon.
+        end_holonomy : 2-dimensional vector with entries in the base_ring of the surface
+            the holonomy of the saddle connection measured from the end (with the opposite
+            orientation). To compute this you develop the saddle connection into the plane
+            starting from the terminating polygon. For a translation surface, this will be
+            the negation of holonomy, and for a HalfTranslation surface it will be either
+            equal to holonomy or equal to its negation. In both these cases the end_holonomy
+            can be inferred and does not need to be passed to the constructor.
+        check : boolean
+            If all data above is provided or can be inferred, then when check=False this
+            geometric data is not verified. With check=True the data is always verified
+            by straight-line flow. Erroroneous data will result in a ValueError being thrown.
+            Defaults to true.
+        limit :
+            The combinatorial limit (in terms of number of polygons crossed) to flow forward
+            to check the saddle connection geometry.
+
+        TESTS:
+
+        Arguments are validated. If the direction points out of the polygon, no saddle connection can be created::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.cathedral(1, 2)
+            sage: from flatsurf.geometry.saddle_connection import SaddleConnection
+            sage: SaddleConnection(S, (1, 5), (1, 1))
+            Traceback (most recent call last):
+            ...
+            ValueError: Singular point with vector pointing away from polygon
+
+        """
+        from flatsurf.geometry.categories import SimilaritySurfaces
+
+        if surface not in SimilaritySurfaces():
+            raise TypeError("surface must be a similarity surface")
+
+        if start_data is not None:
+            import warnings
+
+            warnings.warn(
+                "start_data has been deprecated as a keyword argument for SaddleConnection() and will be removed in a future version of sage-flatsurf; use start instead"
+            )
+            start = start_data
+            del start_data
+
+        if start is None:
+            raise ValueError("start must be specified to create a SaddleConnection")
+
+        if end_data is not None:
+            import warnings
+
+            warnings.warn(
+                "end_data has been deprecated as a keyword argument for SaddleConnection() and will be removed in a future version of sage-flatsurf; use end instead"
+            )
+            end = end_data
+            del end_data
+
+        if direction is not None:
+            import warnings
+
+            if holonomy is None:
+                warnings.warn(
+                    "direction has been deprecated as a keyword argument for SaddleConnection() and will be removed in a future of sage-flatsurf; if you want to create a SaddleConnection without specifying the holonomy, use SaddleConnection.from_vertex() instead."
+                )
+                c = SaddleConnection.from_vertex(
+                    surface, *start, direction, limit=limit
+                )
+                start = c._start
+                holonomy = c._holonomy
+                end = c._end
+                end_holonomy = c._end_holonomy
+            else:
+                warnings.warn(
+                    "direction has been deprecated as a keyword argument for SaddleConnection() and will be removed in a future of sage-flatsurf; there is no need to pass this argument anymore when the holonomy is specified."
+                )
+            del direction
+
+        if holonomy is None:
+            raise ValueError("holonomy must be specified to create a SaddleConnection")
+
+        if end is None or end_holonomy is None:
+            import warnings
+
+            warnings.warn(
+                "end and end_holonomy must be provided as a keyword argument for SaddleConnection() in future versions of sage-flatsurf; use SaddleConnection.from_vertex() instead to create a SaddleConnection without specifying these."
+            )
+            c = SaddleConnection.from_vertex(surface, *start, holonomy, limit=limit)
+            start = c._start
+            holonomy = c._holonomy
+            end = c._end
+            end_holonomy = c._end_holonomy
+
+        if end_direction is not None:
+            import warnings
+
+            warnings.warn(
+                "end_direction has been deprecated as a keyword argument for SaddleConnection() and will be removed in a future version of sage-flatsurf; the end direction is deduced from the end holonomy automatically"
+            )
+            del end_direction
+
+        if limit is not None:
+            import warnings
+
+            warnings.warn(
+                "limit has been deprecated as a keyword argument for SaddleConnection() and will be removed in a future version of sage-flatsurf; use SaddleConnection.from_vertex() to search with a limit instead"
+            )
+            del limit
+
+        super().__init__(surface)
+
+        V = self._surface.base_ring() ** 2
+
+        self._start = tuple(start)
+        self._holonomy = V(holonomy)
+        self._start, self._holonomy = self._normalize(self._start, self._holonomy)
+        self._holonomy.set_immutable()
+
+        self._end = tuple(end)
+        self._end_holonomy = V(end_holonomy)
+        self._end, self._end_holonomy = self._normalize(self._end, self._end_holonomy)
+        self._end_holonomy.set_immutable()
+
+    def surface(self):
+        return self._surface
+
+    def _normalize(self, start, holonomy):
+        r"""
+        Normalize the ``start`` and ``holonomy`` data describing this saddle
+        connection.
+
+        When the saddle connection is parallel to a polygon's edge, there can
+        be two different descriptions of the same saddle connection.
+
+        EXAMPLES::
+
+            sage: from flatsurf import MutableOrientedSimilaritySurface, Polygon
+            sage: S = MutableOrientedSimilaritySurface(QQ)
+            sage: S.add_polygon(Polygon(vertices=((0, 0), (1, 0), (1, 1))))
+            0
+
+            sage: S.glue((0, 0), (0, 0))
+            sage: S.glue((0, 1), (0, 1))
+            sage: S.glue((0, 2), (0, 2))
+
+            sage: S.set_immutable()
+
+            sage: from flatsurf import SaddleConnection
+            sage: SaddleConnection.from_vertex(surface=S, label=0, vertex=0, direction=(1, 0))
+            Saddle connection (1, 0) from vertex 0 of polygon 0 to vertex 0 of polygon 0
+            sage: SaddleConnection.from_vertex(surface=S, label=0, vertex=0, direction=(1, 1))
+            Saddle connection (-1, -1) from vertex 2 of polygon 0 to vertex 2 of polygon 0
+
+        ::
+
+            sage: S = MutableOrientedSimilaritySurface(QQ)
+            sage: S.add_polygon(Polygon(vertices=((0, 0), (1, 0), (1, 1))))
+            0
+
+            sage: S.set_immutable()
+
+            sage: from flatsurf import SaddleConnection
+            sage: SaddleConnection(surface=S, start=(0, 0), direction=(1, 0))
+            Saddle connection (1, 0) from vertex 0 of polygon 0 to vertex 1 of polygon 0
+            sage: SaddleConnection(surface=S, start=(0, 0), direction=(1, 1))
+            Saddle connection (1, 1) from vertex 0 of polygon 0 to vertex 2 of polygon 0
+
+        """
+        label = start[0]
+        polygon = self._surface.polygon(label)
+        previous_edge = (start[1] - 1) % len(polygon.vertices())
+        if holonomy == -polygon.edge(previous_edge):
+            opposite_edge = self._surface.opposite_edge(label, previous_edge)
+            if opposite_edge is not None:
+                return (
+                    opposite_edge,
+                    self._surface.edge_transformation(label, previous_edge).derivative()
+                    * holonomy,
+                )
+
+        return start, holonomy
+
+    def __neg__(self):
+        return SaddleConnection(
+            surface=self._surface,
+            start=self._end,
+            end=self._start,
+            holonomy=self._end_holonomy,
+            end_holonomy=self._holonomy,
+            check=False,
+        )
+
+    @classmethod
+    def from_half_edge(self, surface, label, edge):
+        r"""
+        Return a saddle connection along the ``edge`` in the polygon ``label``
+        of ``surface``.
+
+        INPUT:
+
+        - ``surface`` -- a similarity surface
+
+        - ``label`` -- a polygon label in ``surface``
+
+        - ``edge`` -- the index of an edge in the polygon with ``label``
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, SaddleConnection
+            sage: S = translation_surfaces.square_torus()
+
+            sage: SaddleConnection.from_half_edge(S, 0, 0)
+            Saddle connection (1, 0) from vertex 0 of polygon 0 to vertex 2 of polygon 0
+
+        Saddle connections in a surface with boundary::
+
+            sage: from flatsurf import MutableOrientedSimilaritySurface, Polygon
+            sage: S = MutableOrientedSimilaritySurface(QQ)
+            sage: S.add_polygon(Polygon(vertices=((0, 0), (1, 0), (1, 1))))
+            0
+            sage: S.set_immutable()
+
+            sage: c = SaddleConnection.from_half_edge(S, 0, 0); c
+            Saddle connection (1, 0) from vertex 0 of polygon 0 to vertex 1 of polygon 0
+            sage: -c
+            Saddle connection (-1, 0) from vertex 1 of polygon 0 to vertex 0 of polygon 0
+
+            sage: c == -c
+            False
+
+        Saddle connections in a surface with self-glued edges::
+
+            sage: from flatsurf import MutableOrientedSimilaritySurface, Polygon
+            sage: S = MutableOrientedSimilaritySurface(QQ)
+            sage: S.add_polygon(Polygon(vertices=((0, 0), (1, 0), (1, 1))))
+            0
+            sage: S.glue((0, 0), (0, 0))
+            sage: S.glue((0, 1), (0, 1))
+            sage: S.glue((0, 2), (0, 2))
+
+            sage: c = SaddleConnection.from_half_edge(S, 0, 0); c
+            Saddle connection (1, 0) from vertex 0 of polygon 0 to vertex 0 of polygon 0
+            sage: -c
+            Saddle connection (1, 0) from vertex 0 of polygon 0 to vertex 0 of polygon 0
+
+            sage: c == -c
+            True
+
+        """
+        polygon = surface.polygon(label)
+        holonomy = polygon.edge(edge)
+
+        return SaddleConnection(
+            surface=surface,
+            start=(label, edge),
+            end=(label, (edge + 1) % len(polygon.vertices())),
+            holonomy=holonomy,
+            end_holonomy=-holonomy,
+            check=False,
+        )
+
+    @classmethod
+    def from_vertex(cls, surface, label, vertex, direction, limit=None):
+        r"""
+        Return the saddle connection emanating from the ``vertex`` of the
+        polygon with ``label`` following the ray ``direction``.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces, SaddleConnection
+            sage: S = translation_surfaces.square_torus()
+
+            sage: SaddleConnection.from_vertex(S, 0, 0, (1, 0))
+            Saddle connection (1, 0) from vertex 0 of polygon 0 to vertex 2 of polygon 0
+            sage: SaddleConnection.from_vertex(S, 0, 0, (2, 1))
+            Saddle connection (2, 1) from vertex 0 of polygon 0 to vertex 2 of polygon 0
+            sage: SaddleConnection.from_vertex(S, 0, 0, (0, 1))
+            Saddle connection (0, 1) from vertex 1 of polygon 0 to vertex 3 of polygon 0
+
+        TESTS::
+
+            sage: from flatsurf.geometry.saddle_connection import SaddleConnection
+            sage: from flatsurf import translation_surfaces
+
+            sage: S = translation_surfaces.cathedral(1, 2)
+            sage: SaddleConnection.from_vertex(S, 1, 8, (0, -1))
+            Saddle connection (0, -2) from vertex 8 of polygon 1 to vertex 5 of polygon 1
+
+        """
+        from flatsurf.geometry.ray import Rays
+
+        R = Rays(surface.base_ring())
+        direction = R(direction)
+
+        tangent_vector = surface.tangent_vector(
+            label, surface.polygon(label).vertex(vertex), direction.vector()
+        )
+        trajectory = tangent_vector.straight_line_trajectory()
+
+        if limit is None:
+            from sage.all import infinity
+
+            limit = infinity
+
+        trajectory.flow(steps=limit)
+
+        if not trajectory.is_saddle_connection():
+            raise ValueError(
+                "no saddle connection in this direction within the specified limit"
+            )
+
+        end_tangent_vector = trajectory.terminal_tangent_vector()
+
+        assert (
+            not trajectory.segments()[0].is_edge() or len(trajectory.segments()) == 1
+        ), "when the saddle connection is an edge it must not consist of more than that edge"
+
+        if trajectory.segments()[0].is_edge():
+            # When the saddle connection is just an edge, the similarity
+            # computation below can be wrong when that edge is glued to the
+            # same polygon. Namely, the similarity is missing the final factor
+            # that comes from that gluing. E.g., in the Cathedral test case
+            # above, the vertical saddle connection connecting the vertices of
+            # polygon 1 at (1, 3) and (2, 1) by going in direction (0, -1) is
+            # misinterpreted as the connection going in direction (1, -2).
+            return SaddleConnection.from_half_edge(
+                surface, label, trajectory.segments()[0].edge()
+            )
+
+        from flatsurf.geometry.similarity import SimilarityGroup
+
+        one = SimilarityGroup(surface.base_ring()).one()
+        segments = list(trajectory.segments())[1:]
+
+        from sage.all import prod
+
+        similarity = prod(
+            [
+                surface.edge_transformation(
+                    segment.start().polygon_label(),
+                    segment.start().position().get_edge(),
+                )
+                for segment in segments
+            ],
+            one,
+        )
+
+        holonomy = (
+            similarity(trajectory.segments()[-1].end().point())
+            - trajectory.initial_tangent_vector().point()
+        )
+        end_holonomy = (~similarity.derivative()) * holonomy
+
+        return SaddleConnection(
+            surface=surface,
+            start=(label, vertex),
+            holonomy=holonomy,
+            end=(end_tangent_vector.polygon_label(), end_tangent_vector.vertex()),
+            end_holonomy=end_holonomy,
+        )
+
+    @cached_method
+    def direction(self):
+        r"""
+        Return a ray parallel to the :meth:`holonomy`.
+        """
+        from flatsurf.geometry.ray import Rays
+
+        return Rays(self._holonomy.base_ring())(self._holonomy)
+
+    @cached_method
+    def end_direction(self):
+        r"""
+        Return a ray parallel to the :meth:`end_holonomy`.
+        """
+        from flatsurf.geometry.ray import Rays
+
+        return Rays(self._holonomy.base_ring())(self._end_holonomy)
+
+    def start_data(self):
+        r"""
+        Return the pair (l, v) representing the label and vertex of the corresponding polygon
+        where the saddle connection originates.
+        """
+        import warnings
+
+        warnings.warn(
+            "start_data() has been deprecated and will be removed from a future version of sage-flatsurf; use start() instead."
+        )
+
+        return self.start()
+
+    def start(self):
+        # TODO: Document that surface()(*start()) produces the actual vertex.
+        return self._start
+
+    def end_data(self):
+        r"""
+        Return the pair (l, v) representing the label and vertex of the corresponding polygon
+        where the saddle connection terminates.
+        """
+        import warnings
+
+        warnings.warn(
+            "end_data() has been deprecated and will be removed from a future version of sage-flatsurf; use end() instead."
+        )
+
+        return self.end()
+
+    def end(self):
+        # TODO: Document that surface()(*end()) produces the actual vertex.
+        return self._end
+
+    def holonomy(self):
+        r"""
+        Return the holonomy vector of the saddle connection (measured from the start).
+
+        In a SimilaritySurface this notion corresponds to developing the saddle connection into the plane
+        using the initial chart coming from the initial polygon.
+        """
+        return self._holonomy
+
+    def length(self):
+        r"""
+        In a cone surface, return the length of this saddle connection. Since
+        this may not lie in the field of definition of the surface, it is
+        returned as an element of the Algebraic Real Field.
+        """
+        from flatsurf.geometry.categories import ConeSurfaces
+
+        if self._surface not in ConeSurfaces():
+            raise NotImplementedError(
+                "length of a saddle connection only makes sense for cone surfaces"
+            )
+
+        from sage.all import vector, AA
+
+        return vector(AA, self._holonomy).norm()
+
+    def length_squared(self):
+        holonomy = self.holonomy()
+        return holonomy[0] ** 2 + holonomy[1] ** 2
+
+    def end_holonomy(self):
+        r"""
+        Return the holonomy vector of the saddle connection (measured from the end).
+
+        In a SimilaritySurface this notion corresponds to developing the saddle connection into the plane
+        using the initial chart coming from the initial polygon.
+        """
+        return self._end_holonomy
+
+    def start_tangent_vector(self):
+        r"""
+        Return a tangent vector to the saddle connection based at its
+        :meth:`start`.
+        """
+        return self._surface.tangent_vector(
+            self._start[0],
+            self._surface.polygon(self._start[0]).vertex(self._start[1]),
+            self.direction().vector(),
+        )
+
+    @cached_method(key=lambda self, limit, cache: None)
+    def trajectory(self, limit=1000, cache=None):
+        r"""
+        Return a straight line trajectory representing this saddle connection.
+        Fails if the trajectory passes through more than limit polygons.
+        """
+        if cache is not None:
+            import warnings
+
+            warnings.warn(
+                "The cache keyword argument of trajectory() is ignored. Trajectories are always cached."
+            )
+
+        v = self.start_tangent_vector()
+        traj = v.straight_line_trajectory()
+        traj.flow(limit)
+        if not traj.is_saddle_connection():
+            raise ValueError(
+                "Did not obtain saddle connection by flowing forward. Limit="
+                + str(limit)
+            )
+
+        return traj
+
+    def plot(self, *args, **options):
+        r"""
+        Equivalent to ``.trajectory().plot(*args, **options)``
+        """
+        return self.trajectory().plot(*args, **options)
+
+    def end_tangent_vector(self):
+        r"""
+        Return a tangent vector to the saddle connection based at its
+        :meth:`end`.
+        """
+        return self._surface.tangent_vector(
+            self._end[0],
+            self._surface.polygon(self._end[0]).vertex(self._end[1]),
+            self._end_direction.vector(),
+        )
+
+    def invert(self):
+        r"""
+        Return this saddle connection but with opposite orientation.
+        """
+        return SaddleConnection(
+            self._surface,
+            self._end,
+            self._end_direction,
+            self._start,
+            self._direction,
+            self._end_holonomy,
+            self._holonomy,
+            check=False,
+        )
+
+    def intersections(self, traj, count_singularities=False, include_segments=False):
+        r"""
+        See documentation of :meth:`~.straight_line_trajectory.AbstractStraightLineTrajectory.intersections`
+        """
+        return self.trajectory().intersections(
+            traj, count_singularities, include_segments
+        )
+
+    def intersects(self, traj, count_singularities=False):
+        r"""
+        See documentation of :meth:`~.straight_line_trajectory.AbstractStraightLineTrajectory.intersects`
+        """
+        return self.trajectory().intersects(
+            traj, count_singularities=count_singularities
+        )
+
+    def __eq__(self, other):
+        r"""
+        Return whether this saddle connection is indistinguishable from
+        ``other``.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.square_torus()
+            sage: connections = S.saddle_connections(13)  # random output due to deprecation warning from cppyy
+
+            sage: connections[0] == connections[0]
+            True
+            sage: connections[0] == connections[1]
+            False
+
+
+        TESTS:
+
+        Verify that saddle connections can be compared to arbitrary objects (so
+        they can be put into dicts with other objects)::
+
+            sage: connections[0] == 42
+            False
+
+        ::
+
+            sage: len(connections)
+            32
+            sage: len(set(connections))
+            32
+
+        """
+        if self is other:
+            return True
+
+        if not isinstance(other, SaddleConnection):
+            return False
+
+        if self._surface != other._surface:
+            return False
+
+        if self._start != other._start:
+            return False
+
+        if self._holonomy != other._holonomy:
+            return False
+
+        return True
+
+    def __hash__(self):
+        return hash((self._start, self._holonomy))
+
+    def _test_geometry(self, **options):
+        # Test that this saddle connection actually exists on the surface.
+        SaddleConnection(
+            self._surface,
+            self._start,
+            self._direction,
+            self._end,
+            self._end_direction,
+            self._holonomy,
+            self._end_holonomy,
+            check=True,
+        )
+
+    def __repr__(self):
+        return f"Saddle connection {self.holonomy()} from vertex {self.start()[1]} of polygon {self.start()[0]} to vertex {self.end()[1]} of polygon {self.end()[0]}"
+
+    def _test_inverse(self, **options):
+        # Test that inverting works properly.
+        SaddleConnection(
+            self._surface,
+            self._end,
+            self._end_direction,
+            self._start,
+            self._direction,
+            self._end_holonomy,
+            self._holonomy,
+            check=True,
+        )
+
+    def is_closed(self):
+        return self.surface()(*self.start()) == self.surface()(*self.end())
+
+    def homology(self):
+        r"""
+        Return a homology class (generated by edges) that is homologous to this saddle connection.
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.square_torus()
+            sage: connections = list(S.saddle_connections(13))
+            sage: connections[-1].homology()
+            -2*B[(0, 0)] - 3*B[(0, 1)]
+
+        ::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.cathedral(1, 2)
+            sage: connections = [connection for connection in S.saddle_connections(13) if connection.is_closed()]
+            sage: connections[-1].homology()
+            -B[(1, 1)] - 2*B[(1, 2)] - B[(1, 6)] - B[(3, 1)] + B[(3, 7)]
+
+        """
+        to_pyflatsurf = self._surface.pyflatsurf()
+
+        connection = to_pyflatsurf(self)
+
+        # TODO: We should probably make pyflatsurf chains and saddle
+        # connections proper objects in sage-flatsurf so that they can be
+        # mapped through to_pyflatsurf.section()
+        chain = connection._connection.chain()
+
+        chain = {
+            e.positive().id(): chain[e]
+            for e in to_pyflatsurf.codomain()._flat_triangulation.edges()
+        }
+
+        from sage.all import ZZ
+
+        chain = {
+            (
+                [label for label in to_pyflatsurf.codomain().labels() if e in label][0],
+                [label for label in to_pyflatsurf.codomain().labels() if e in label][
+                    0
+                ].index(e),
+            ): ZZ(multiplicity)
+            for (e, multiplicity) in chain.items()
+        }
+
+        from flatsurf.geometry.homology import SimplicialHomology
+
+        homology = SimplicialHomology(to_pyflatsurf.codomain())
+
+        chain = sum(multiplicity * homology(e) for (e, multiplicity) in chain.items())
+
+        return to_pyflatsurf.section()(chain)
diff --git a/flatsurf/geometry/similarity.py b/flatsurf/geometry/similarity.py
index b4ff0987a..c1c07484d 100644
--- a/flatsurf/geometry/similarity.py
+++ b/flatsurf/geometry/similarity.py
@@ -250,7 +250,7 @@ def __hash__(self):
             + 67 * hash(self._sign)
         )
 
-    def __call__(self, w, ring=None):
+    def __call__(self, w, ring=None, V=None):
         r"""
         Return the image of ``w`` under the similarity. Here ``w`` may be a
         convex polygon or a vector (or something that can be indexed in the
@@ -280,58 +280,42 @@ def __call__(self, w, ring=None):
             Category of convex simple euclidean polygons over Algebraic Real Field
 
         """
-        if ring is not None and ring not in Rings():
-            raise TypeError("ring must be a ring")
+        if ring is not None:
+            if ring not in Rings():
+                raise TypeError("ring must be a ring")
+            if V is not None:
+                if V.base_ring() is not ring:
+                    raise ValueError("ring and base ring of V must be identical")
+            else:
+                V = ring**2
 
         from flatsurf.geometry.polygon import EuclideanPolygon
 
-        if isinstance(w, EuclideanPolygon) and w.is_convex():
-            if ring is None:
+        if isinstance(w, EuclideanPolygon):
+            if V is None:
                 ring = self.parent().base_ring()
+                V = ring**2
+
+            if not self._sign.is_one():
+                raise TypeError("similarity must be orientation preserving.")
 
             from flatsurf import Polygon
 
-            try:
-                return Polygon(vertices=[self(v) for v in w.vertices()], base_ring=ring)
-            except ValueError:
-                if not self._sign.is_one():
-                    raise ValueError("Similarity must be orientation preserving.")
-
-                # Not sure why this would happen:
-                raise
-
-        if ring is None:
-            if self._sign.is_one():
-                return vector(
-                    [
-                        self._a * w[0] - self._b * w[1] + self._s,
-                        self._b * w[0] + self._a * w[1] + self._t,
-                    ]
-                )
-            else:
-                return vector(
-                    [
-                        self._a * w[0] + self._b * w[1] + self._s,
-                        self._b * w[0] - self._a * w[1] + self._t,
-                    ]
-                )
-        else:
-            if self._sign.is_one():
-                return vector(
-                    ring,
-                    [
-                        self._a * w[0] - self._b * w[1] + self._s,
-                        self._b * w[0] + self._a * w[1] + self._t,
-                    ],
-                )
-            else:
-                return vector(
-                    ring,
-                    [
-                        self._a * w[0] + self._b * w[1] + self._s,
-                        self._b * w[0] - self._a * w[1] + self._t,
-                    ],
-                )
+            return Polygon(
+                vertices=[self(v, V=V) for v in w.vertices()],
+                base_ring=ring,
+                check=False,
+            )
+
+        v = (
+            self._a * w[0] - self._sign * self._b * w[1] + self._s,
+            self._b * w[0] + self._sign * self._a * w[1] + self._t,
+        )
+
+        if V is None:
+            return vector(v)
+
+        return V(v)
 
     def _repr_(self):
         r"""
@@ -372,7 +356,7 @@ def __eq__(self, other):
         """
         if other is None:
             return False
-        if type(other) == int:
+        if type(other) is int:
             return False
         if self.parent() != other.parent():
             return False
@@ -473,8 +457,15 @@ def _vector_space(self):
 
         return VectorSpace(self._ring, 2)
 
+    def translation(self, x, y):
+        return self(x, y)
+
     def _element_constructor_(self, *args, **kwds):
         r"""
+        .. TODO::
+
+            This should also support 2×2 and 3×3 matrix inputs.
+
         TESTS::
 
             sage: from flatsurf.geometry.similarity import SimilarityGroup
@@ -498,8 +489,6 @@ def _element_constructor_(self, *args, **kwds):
         b = s = t = self._ring.zero()
         sign = ZZ_1
 
-        # TODO: 2x2 and 3x3 matrix input
-
         if isinstance(x, (tuple, list)):
             if len(x) == 2:
                 s, t = map(self._ring, x)
@@ -617,6 +606,7 @@ def base_ring(self):
         return self._ring
 
 
+# TODO: Make this a static method of SimilarityGroup
 def similarity_from_vectors(u, v, matrix_space=None):
     r"""
     Return the unique similarity matrix that maps ``u`` to ``v``.
diff --git a/flatsurf/geometry/similarity_surface_generators.py b/flatsurf/geometry/similarity_surface_generators.py
index 45870919a..0c016797f 100644
--- a/flatsurf/geometry/similarity_surface_generators.py
+++ b/flatsurf/geometry/similarity_surface_generators.py
@@ -712,7 +712,7 @@ def self_glued_polygon(P):
         return s
 
     @staticmethod
-    def billiard(P, rational=None):
+    def billiard(P):
         r"""
         Return the ConeSurface associated to the billiard in the polygon ``P``.
 
@@ -720,18 +720,12 @@ def billiard(P, rational=None):
 
         - ``P`` -- a polygon
 
-        - ``rational`` -- a boolean or ``None`` (default: ``None``) -- whether
-          to assume that all the angles of ``P`` are a rational multiple of π.
-
         EXAMPLES::
 
             sage: from flatsurf import Polygon, similarity_surfaces
 
             sage: P = Polygon(vertices=[(0,0), (1,0), (0,1)])
-            sage: Q = similarity_surfaces.billiard(P, rational=True)
-            doctest:warning
-            ...
-            UserWarning: the rational keyword argument of billiard() has been deprecated and will be removed in a future version of sage-flatsurf; rationality checking is now faster so this is not needed anymore
+            sage: Q = similarity_surfaces.billiard(P)
             sage: Q
             Genus 0 Rational Cone Surface built from 2 isosceles triangles
             sage: from flatsurf.geometry.categories import ConeSurfaces
@@ -757,13 +751,12 @@ def billiard(P, rational=None):
 
         A quadrilateral from Eskin-McMullen-Mukamel-Wright::
 
-            sage: from flatsurf import Polygon
             sage: P = Polygon(angles=(1, 1, 1, 7))
             sage: S = similarity_surfaces.billiard(P)
             sage: TestSuite(S).run()
             sage: S = S.minimal_cover(cover_type="translation")
             sage: TestSuite(S).run()
-            sage: S = S.erase_marked_points() # optional: pyflatsurf
+            sage: S = S.erase_marked_points().codomain() # optional: pyflatsurf
             sage: TestSuite(S).run()
             sage: S, _ = S.normalized_coordinates()
             sage: TestSuite(S).run()
@@ -785,144 +778,7 @@ def billiard(P, rational=None):
             True
 
         """
-        if not isinstance(P, EuclideanPolygon):
-            raise TypeError("invalid input")
-
-        if rational is not None:
-            import warnings
-
-            warnings.warn(
-                "the rational keyword argument of billiard() has been deprecated and will be removed in a future version of sage-flatsurf; rationality checking is now faster so this is not needed anymore"
-            )
-
-        from flatsurf.geometry.categories import ConeSurfaces
-
-        category = ConeSurfaces()
-        if P.is_rational():
-            category = category.Rational()
-
-        V = P.base_ring() ** 2
-
-        if not P.is_convex():
-            # triangulate non-convex ones
-            base_ring = P.base_ring()
-            comb_edges = P.triangulation()
-            vertices = P.vertices()
-            comb_triangles = SimilaritySurfaceGenerators._billiard_build_faces(
-                len(vertices), comb_edges
-            )
-            triangles = []
-            internal_edges = []  # list (p1, e1, p2, e2)
-            external_edges = []  # list (p1, e1)
-            edge_to_lab = {}
-            for num, (i, j, k) in enumerate(comb_triangles):
-                triangles.append(
-                    Polygon(
-                        vertices=[vertices[i], vertices[j], vertices[k]],
-                        base_ring=base_ring,
-                    )
-                )
-                edge_to_lab[(i, j)] = (num, 0)
-                edge_to_lab[(j, k)] = (num, 1)
-                edge_to_lab[(k, i)] = (num, 2)
-            for num, (i, j, k) in enumerate(comb_triangles):
-                if (j, i) in edge_to_lab:
-                    num2, e2 = edge_to_lab[j, i]
-                    internal_edges.append((num, 0, num2, e2))
-                else:
-                    external_edges.append((num, 0))
-                if (k, j) in edge_to_lab:
-                    num2, e2 = edge_to_lab[k, j]
-                    internal_edges.append((num, 1, num2, e2))
-                else:
-                    external_edges.append((num, 1))
-                if (i, k) in edge_to_lab:
-                    num2, e2 = edge_to_lab[i, k]
-                    internal_edges.append((num, 2, num2, e2))
-                else:
-                    external_edges.append((num, 1))
-            P = triangles
-        else:
-            internal_edges = []
-            external_edges = [(0, i) for i in range(len(P.vertices()))]
-            base_ring = P.base_ring()
-            P = [P]
-
-        surface = MutableOrientedSimilaritySurface(base_ring, category=category)
-
-        m = len(P)
-
-        for p in P:
-            surface.add_polygon(p)
-        for p in P:
-            surface.add_polygon(
-                Polygon(edges=[V((-x, y)) for x, y in reversed(p.edges())])
-            )
-        for p1, e1, p2, e2 in internal_edges:
-            surface.glue((p1, e1), (p2, e2))
-            ne1 = len(surface.polygon(p1).vertices())
-            ne2 = len(surface.polygon(p2).vertices())
-            surface.glue((m + p1, ne1 - e1 - 1), (m + p2, ne2 - e2 - 1))
-        for p, e in external_edges:
-            ne = len(surface.polygon(p).vertices())
-            surface.glue((p, e), (m + p, ne - e - 1))
-
-        surface.set_immutable()
-
-        return surface
-
-    @staticmethod
-    def _billiard_build_faces(n, edges):
-        r"""
-        Given a combinatorial list of pairs ``edges`` forming a cell-decomposition
-        of a polygon (with vertices labeled from ``0`` to ``n-1``) return the list
-        of cells.
-
-        This is a helper method for :meth:`billiard`.
-
-        EXAMPLES::
-
-            sage: from flatsurf.geometry.similarity_surface_generators import SimilaritySurfaceGenerators
-            sage: SimilaritySurfaceGenerators._billiard_build_faces(4, [(0,2)])
-            [[0, 1, 2], [2, 3, 0]]
-            sage: SimilaritySurfaceGenerators._billiard_build_faces(4, [(1,3)])
-            [[1, 2, 3], [3, 0, 1]]
-            sage: SimilaritySurfaceGenerators._billiard_build_faces(5, [(0,2), (0,3)])
-            [[0, 1, 2], [3, 4, 0], [0, 2, 3]]
-            sage: SimilaritySurfaceGenerators._billiard_build_faces(5, [(0,2)])
-            [[0, 1, 2], [2, 3, 4, 0]]
-            sage: SimilaritySurfaceGenerators._billiard_build_faces(5, [(1,4)])
-            [[1, 2, 3, 4], [4, 0, 1]]
-            sage: SimilaritySurfaceGenerators._billiard_build_faces(5, [(1,3),(3,0)])
-            [[1, 2, 3], [3, 4, 0], [0, 1, 3]]
-        """
-        polygons = [list(range(n))]
-        for u, v in edges:
-            j = None
-            for i, p in enumerate(polygons):
-                if u in p and v in p:
-                    if j is not None:
-                        raise RuntimeError
-                    j = i
-            if j is None:
-                raise RuntimeError
-            p = polygons[j]
-            i0 = p.index(u)
-            i1 = p.index(v)
-            if i0 > i1:
-                i0, i1 = i1, i0
-            polygons[j] = p[i0 : i1 + 1]
-            polygons.append(p[i1:] + p[: i0 + 1])
-        return polygons
-
-    @staticmethod
-    def polygon_double(P):
-        r"""
-        Return the ConeSurface associated to the billiard in the polygon ``P``.
-        Differs from billiard(P) only in the graphical display. Here, we display
-        the polygons separately.
-        """
-        from sage.matrix.constructor import matrix
+        from sage.all import matrix
 
         n = len(P.vertices())
         r = matrix(2, [-1, 0, 0, 1])
@@ -936,6 +792,16 @@ def polygon_double(P):
         surface.set_immutable()
         return surface
 
+    @staticmethod
+    def polygon_double(P):
+        import warnings
+
+        warnings.warn(
+            "polygon_double() has been deprecated and will be removed from a future version of sage-flatsurf. Use billiard() instead."
+        )
+
+        return SimilaritySurfaceGenerators.billiard(P)
+
     @staticmethod
     def right_angle_triangle(w, h):
         r"""
@@ -1084,13 +950,16 @@ def genus_two_square(a, b, c, d):
 
 
 class HalfTranslationSurfaceGenerators:
-    # TODO: ideally, we should be able to construct a non-convex polygon and make the construction
-    # below as a special case of billiard unfolding.
     @staticmethod
     def step_billiard(w, h):
         r"""
         Return a (finite) step billiard associated to the given widths ``w`` and heights ``h``.
 
+        .. TODO::
+
+            Ideally, we should be able to construct a non-convex polygon and
+            make this construction a special case of billiard unfolding.
+
         EXAMPLES::
 
             sage: from flatsurf import half_translation_surfaces
@@ -1955,7 +1824,7 @@ def arnoux_yoccoz(genus):
             sage: TestSuite(s).run()
             sage: s.is_delaunay_decomposed()
             True
-            sage: s = s.canonicalize()
+            sage: s = s.canonicalize().codomain()
             sage: s
             Translation Surface in H_4(3^2) built from 16 triangles
             sage: field=s.base_ring()
@@ -1963,7 +1832,7 @@ def arnoux_yoccoz(genus):
             sage: from sage.matrix.constructor import Matrix
             sage: m = Matrix([[a,0],[0,~a]])
             sage: ss = m*s
-            sage: ss = ss.canonicalize()
+            sage: ss = ss.canonicalize().codomain()
             sage: s.cmp(ss) == 0
             True
 
diff --git a/flatsurf/geometry/straight_line_trajectory.py b/flatsurf/geometry/straight_line_trajectory.py
index bee1d4d55..23a9aaf90 100644
--- a/flatsurf/geometry/straight_line_trajectory.py
+++ b/flatsurf/geometry/straight_line_trajectory.py
@@ -25,21 +25,6 @@
 #  You should have received a copy of the GNU General Public License
 #  along with sage-flatsurf. If not, see <https://www.gnu.org/licenses/>.
 # *********************************************************************
-from collections import deque
-
-from flatsurf.geometry.euclidean import line_intersection
-from flatsurf.geometry.surface_objects import SaddleConnection
-
-# Vincent question:
-# using deque has the disadvantage of losing the initial points
-# ideally doig
-#  my_line[i]
-# we should always access to the same element
-
-# I wanted to be able to flow backward thus inserting at the beginning of a list.
-# Perhaps it would be better to model this on a deque-like class that is indexed by
-# all integers rather than just the non-negative ones? Do you know of such
-# a class? Alternately, we could store an offset.
 
 
 def get_linearity_coeff(u, v):
@@ -481,6 +466,8 @@ def intersections(self, traj, count_singularities=False, include_segments=False)
             Point (0, 1/2) of polygon 0
             2 2
         """
+        from flatsurf.geometry.saddle_connection import SaddleConnection
+
         # Partition the segments making up the trajectories by label.
         if isinstance(traj, SaddleConnection):
             traj = traj.trajectory()
@@ -506,11 +493,17 @@ def intersections(self, traj, count_singularities=False, include_segments=False)
                 seg_list_2 = lab_to_seg2[label]
                 for seg1 in seg_list_1:
                     for seg2 in seg_list_2:
+                        from flatsurf.geometry.euclidean import line_intersection
+
                         x = line_intersection(
-                            seg1.start().point(),
-                            seg1.start().point() + seg1.start().vector(),
-                            seg2.start().point(),
-                            seg2.start().point() + seg2.start().vector(),
+                            (
+                                seg1.start().point(),
+                                seg1.start().point() + seg1.start().vector(),
+                            ),
+                            (
+                                seg2.start().point(),
+                                seg2.start().point() + seg2.start().vector(),
+                            ),
                         )
                         if x is not None:
                             pos = (
@@ -561,6 +554,8 @@ class StraightLineTrajectory(AbstractStraightLineTrajectory):
     """
 
     def __init__(self, tangent_vector):
+        from collections import deque
+
         self._segments = deque()
         seg = SegmentInPolygon(tangent_vector)
         self._segments.append(seg)
@@ -756,6 +751,8 @@ def __init__(self, tangent_vector):
         )
         x *= T.length_bot(i)
 
+        from collections import deque
+
         self._points = deque()  # we store triples (lab, edge, rel_pos)
         self._points.append((p, i, x))
 
diff --git a/flatsurf/geometry/surface.py b/flatsurf/geometry/surface.py
index 0b65cf223..7e23d9f81 100644
--- a/flatsurf/geometry/surface.py
+++ b/flatsurf/geometry/surface.py
@@ -436,7 +436,12 @@ def set_immutable(self):
              'area',
              'canonicalize',
              'canonicalize_mapping',
+             'cluster_points',
+             'distance_matrix_points',
+             'distance_matrix_vertices',
              'erase_marked_points',
+             'flow_decomposition',
+             'flow_decompositions',
              'holonomy_field',
              'is_veering_triangulated',
              'j_invariant',
@@ -445,6 +450,7 @@ def set_immutable(self):
              'normalized_coordinates',
              'pyflatsurf',
              'rel_deformation',
+             'singularities',
              'stratum',
              'veech_group',
              'veering_triangulation'}
@@ -488,6 +494,9 @@ def is_mutable(self):
         """
         return self._mutable
 
+    def __hash__(self):
+        return super().__hash__()
+
     def __eq__(self, other):
         r"""
         Return whether this surface is indistinguishable from ``other``.
@@ -1103,13 +1112,14 @@ def to_new(lbl, edge):
 
     def standardize_polygons(self, in_place=False):
         r"""
-        Replace each polygon with a new polygon which differs by
-        translation and reindexing. The new polygon will have the property
-        that vertex zero is the origin, and all vertices lie either in the
-        upper half plane, or on the x-axis with non-negative x-coordinate.
+        Return a morphism to a surface with each polygon replaced with a new
+        polygon which differs by translation and reindexing. The new polygon
+        will have the property that vertex zero is the origin, and each vertex
+        lies in the upper half plane or on the x-axis with non-negative
+        x-coordinate.
 
-        This is done to the current surface if in_place=True. A mutable
-        copy is created and returned if in_place=False (as default).
+        This is done to the current surface if in_place=True, otherwise an
+        immutable copy is created and returned.
 
         This overrides
         :meth:`flatsurf.geometry.categories.similarity_surfaces.SimilaritySurfaces.FiniteType.Oriented.ParentMethods.standardize_polygons`
@@ -1127,32 +1137,27 @@ def standardize_polygons(self, in_place=False):
             sage: s.set_root(0)
             sage: s.set_immutable()
 
-            sage: s.standardize_polygons().polygon(0)
+            sage: s.standardize_polygons().codomain().polygon(0)
             Polygon(vertices=[(0, 0), (1, 0), (1, 1), (0, 1)])
 
         """
         if not in_place:
             S = MutableOrientedSimilaritySurface.from_surface(self)
-            S.standardize_polygons(in_place=True)
-            return S
-
-        cv = {}  # dictionary for non-zero canonical vertices
-        for label, polygon in zip(self.labels(), self.polygons()):
-            best = 0
-            best_pt = polygon.vertex(best)
-            for v in range(1, len(polygon.vertices())):
-                pt = polygon.vertex(v)
-                if (pt[1] < best_pt[1]) or (pt[1] == best_pt[1] and pt[0] < best_pt[0]):
-                    best = v
-                    best_pt = pt
-            # We replace the polygon if the best vertex is not the zero vertex, or
-            # if the coordinates of the best vertex differs from the origin.
-            if not (best == 0 and best_pt.is_zero()):
-                cv[label] = best
-        for label, v in cv.items():
-            self.set_vertex_zero(label, v, in_place=True)
+            morphism = S.standardize_polygons(in_place=True)
+            S.set_immutable()
+            return morphism.change(domain=self, codomain=S)
+
+        vertex_zero = {}
+        for label in self.labels():
+            vertices = self.polygon(label).vertices()
+            vertex_zero[label] = min(
+                range(len(vertices)), key=lambda v: (vertices[v][1], vertices[v][0])
+            )
+            self.set_vertex_zero(label, vertex_zero[label], in_place=True)
 
-        return self
+        from flatsurf.geometry.morphism import PolygonStandardizationMorphism
+
+        return PolygonStandardizationMorphism._create_morphism(None, self, vertex_zero)
 
 
 class MutableOrientedSimilaritySurface(
@@ -1625,6 +1630,67 @@ def replace_polygon(self, label, polygon):
 
         self._polygons[label] = polygon
 
+    def apply_matrix(self, m, in_place=None):
+        r"""
+        Apply the 2×2 matrix ``m`` to the polygons of this surface.
+
+        INPUT:
+
+        - ``m`` -- a 2×2 matrix
+
+        - ``in_place`` -- a boolean (default: ``True``); whether to modify
+          this surface itself or return a modified copy of this surface
+          instead.
+
+        EXAMPLES::
+
+            sage: from flatsurf import Polygon, MutableOrientedSimilaritySurface
+            sage: S = MutableOrientedSimilaritySurface(QQ)
+            sage: S.add_polygon(Polygon(vertices=[(0, 0), (1, 0), (1, 1), (0, 1)]))
+            0
+            sage: S.glue((0, 0), (0, 2))
+            sage: S.glue((0, 1), (0, 3))
+
+            sage: deformation = S.apply_matrix(matrix([[1, 2], [3, 4]]), in_place=True)
+            Traceback (most recent call last):
+            ...
+            NotImplementedError: apply_matrix(in_place=True) not supported with negative determinant yet
+
+            sage: deformation = S.apply_matrix(matrix([[1, 2], [3, 4]]), in_place=False)
+            sage: S.polygon(0)
+            Polygon(vertices=[(0, 0), (1, 0), (1, 1), (0, 1)])
+
+            sage: deformation.codomain().polygon(0)
+            Polygon(vertices=[(0, 0), (2, 4), (3, 7), (1, 3)])
+
+        """
+        if in_place is None:
+            import warnings
+
+            warnings.warn(
+                "The defaults for apply_matrix() are going to change in a future version of sage-flatsurf; previously, apply_matrix() was performed in_place=True. In a future version of sage-flatsurf the default is going to change to in_place=False. In the meantime, please pass in_place=True/False explicitly."
+            )
+
+            in_place = True
+
+        if not in_place:
+            return super().apply_matrix(m, in_place=in_place)
+
+        if not m.det():
+            raise ValueError("matrix must not be degenerate")
+
+        if m.det() < 0:
+            raise NotImplementedError(
+                "apply_matrix(in_place=True) not supported with negative determinant yet"
+            )
+
+        for label in self.labels():
+            self.replace_polygon(label, m * self.polygon(label))
+
+        from flatsurf.geometry.morphism import GL2RMorphism
+
+        return GL2RMorphism._create_morphism(None, self, m)
+
     def opposite_edge(self, label, edge=None):
         r"""
         Return the edge that ``edge`` of ``label`` is glued to or ``None`` if this edge is unglued.
@@ -1713,14 +1779,6 @@ def set_vertex_zero(self, label, v, in_place=False):
         return self
 
     def relabel(self, relabeling=None, in_place=False):
-        r"""
-        Overrides
-        :meth:`flatsurf.geometry.categories.similarity_surfaces.SimilaritySurfaces.Oriented.ParentMethods.relabel`
-        to allow relabeling in-place.
-        """
-        if not in_place:
-            return super().relabel(relabeling=relabeling, in_place=in_place)
-
         if relabeling is None:
             relabeling = {label: l for (l, label) in enumerate(self.labels())}
 
@@ -2059,6 +2117,7 @@ def _triangulate(surface, label):
 
         return triangulation, edge_to_edge
 
+    # TODO: Deprecate?
     def delaunay_single_flip(self):
         r"""
         Perform a single in place flip of a triangulated mutable surface
@@ -2167,6 +2226,9 @@ def cmp(self, s2, limit=None):
                     count += 1
                 return 0
 
+    def __hash__(self):
+        return super().__hash__()
+
     def __eq__(self, other):
         r"""
         Return whether this surface is indistinguishable from ``other``.
@@ -2437,6 +2499,10 @@ def __getitem__(self, root):
             (0, 1)
 
         """
+        # TODO: This is very inefficient (and wont work on infinite surfaces?) But otherwise, any label is in surface.roots().
+        if root not in list(self):
+            raise KeyError
+
         return self._surface.component(root)
 
     def __iter__(self):
@@ -2910,6 +2976,13 @@ class LabelsFromView(Labels, LabeledView):
 
     """
 
+    def __eq__(self, other):
+        if isinstance(other, LabelsFromView):
+            if self._view == other._view:
+                return True
+
+        return super().__eq__(other)
+
 
 class Polygons(LabeledCollection):
     r"""
diff --git a/flatsurf/geometry/surface_legacy.py b/flatsurf/geometry/surface_legacy.py
index 56ebd235f..c9611e418 100644
--- a/flatsurf/geometry/surface_legacy.py
+++ b/flatsurf/geometry/surface_legacy.py
@@ -939,7 +939,7 @@ class Surface_list(Surface):
         ...
         UserWarning: copy() has been deprecated and will be removed from a future version of sage-flatsurf; for surfaces of finite type use MutableOrientedSimilaritySurface.from_surface() instead.
         Use relabel({old: new for (new, old) in enumerate(surface.labels())}) for integer labels. However, there is no immediate replacement for lazy copying of infinite surfaces.
-        Have a look at the implementation of flatsurf.geometry.delaunay.LazyMutableSurface and adapt it to your needs.
+        Have a look at the implementation of flatsurf.geometry.lazy.LazyMutableSurface and adapt it to your needs.
         sage: # Explore the surface a bit
         sage: ts.polygon(0)
         Polygon(vertices=[(0, 0), (4, 0), (0, 3)])
@@ -1101,7 +1101,7 @@ def _validate_init_parameters(cls, base_ring, surface, copy, mutable, finite):
 
         if surface is None:
             if copy is not None:
-                raise ValueError("Cannot copy when surface was provided.")
+                raise ValueError("Cannot copy when no surface was provided.")
 
             if mutable is None:
                 mutable = True
diff --git a/flatsurf/geometry/surface_objects.py b/flatsurf/geometry/surface_objects.py
index 581e5d6b0..6d74d688d 100644
--- a/flatsurf/geometry/surface_objects.py
+++ b/flatsurf/geometry/surface_objects.py
@@ -74,7 +74,11 @@ def Singularity(similarity_surface, label, v, limit=None):
     )
 
 
-class SurfacePoint(Element):
+class SurfacePoint_base(Element):
+    pass
+
+
+class SurfacePoint(SurfacePoint_base):
     r"""
     A point on ``surface``.
 
@@ -126,6 +130,15 @@ class SurfacePoint(Element):
 
     TESTS:
 
+    Test that points can be created on disconnected surfaces::
+
+        sage: from flatsurf import MutableOrientedSimilaritySurface, polygons
+        sage: S = MutableOrientedSimilaritySurface(QQ)
+        sage: S.add_polygon(polygons.square())
+        0
+        sage: S(0, 0)
+        Vertex 0 of polygon 0
+
     Verify that #275 has been resolved, i.e., points on the boundary can be
     created::
 
@@ -294,6 +307,11 @@ def is_vertex(self):
         position = self.surface().polygon(label).get_point_position(coordinates)
         return position.is_vertex()
 
+    def is_in_edge_interior(self):
+        label, coordinates = self.representative()
+        position = self.surface().polygon(label).get_point_position(coordinates)
+        return position.is_in_edge_interior()
+
     def one_vertex(self):
         r"""
         Return a pair (l, v) from the equivalence class of this singularity.
@@ -565,6 +583,7 @@ def __repr__(self):
             Point (1/2, 1/2) of polygon 0
 
         """
+        # TODO: Why is this a dunder method?
 
         def render(label, coordinates):
             if self.is_vertex():
@@ -612,7 +631,7 @@ def __eq__(self, other):
             return True
         if not isinstance(other, SurfacePoint):
             return False
-        if not self._surface == other._surface:
+        if self._surface != other._surface:
             return False
         return self._representatives == other._representatives
 
@@ -674,476 +693,6 @@ def __ne__(self, other):
         return not (self == other)
 
 
-class SaddleConnection(SageObject):
-    r"""
-    Represents a saddle connection on a SimilaritySurface.
-    """
-
-    def __init__(
-        self,
-        surface,
-        start_data,
-        direction,
-        end_data=None,
-        end_direction=None,
-        holonomy=None,
-        end_holonomy=None,
-        check=True,
-        limit=1000,
-    ):
-        r"""
-        Construct a saddle connection on a SimilaritySurface.
-
-        The only necessary parameters are the surface, start_data, and direction
-        (to start). If there is missing data that can not be inferred from the surface
-        type, then a straight-line trajectory will be computed to confirm that this is
-        indeed a saddle connection. The trajectory will pass through at most limit
-        polygons before we give up.
-
-        Details of the parameters are provided below.
-
-        Parameters
-        ----------
-        surface : a SimilaritySurface
-            which will contain the saddle connection being constructed.
-        start_data : a pair
-            consisting of the label of the polygon where the saddle connection starts
-            and the starting vertex.
-        direction : 2-dimensional vector with entries in the base_ring of the surface
-            representing the direction the saddle connection is moving in (in the
-            coordinates of the initial polygon).
-        end_data : a pair
-            consisting of the label of the polygon where the saddle connection terminates
-            and the terminating vertex.
-        end_direction : 2-dimensional vector with entries in the base_ring of the surface
-            representing the direction to move backward from the end point (in the
-            coordinates of the terminal polygon). If the surface is a DilationSurface
-            or better this will be the negation of the direction vector. If the surface
-            is a HalfDilation surface or better, then this will be either the direction
-            vector or its negation. In either case the value can be inferred from the
-            end_data.
-        holonomy : 2-dimensional vector with entries in the base_ring of the surface
-            the holonomy of the saddle connection measured from the start. To compute this
-            you develop the saddle connection into the plane starting from the starting
-            polygon.
-        end_holonomy : 2-dimensional vector with entries in the base_ring of the surface
-            the holonomy of the saddle connection measured from the end (with the opposite
-            orientation). To compute this you develop the saddle connection into the plane
-            starting from the terminating polygon. For a translation surface, this will be
-            the negation of holonomy, and for a HalfTranslation surface it will be either
-            equal to holonomy or equal to its negation. In both these cases the end_holonomy
-            can be inferred and does not need to be passed to the constructor.
-        check : boolean
-            If all data above is provided or can be inferred, then when check=False this
-            geometric data is not verified. With check=True the data is always verified
-            by straight-line flow. Erroroneous data will result in a ValueError being thrown.
-            Defaults to true.
-        limit :
-            The combinatorial limit (in terms of number of polygons crossed) to flow forward
-            to check the saddle connection geometry.
-        """
-        from flatsurf.geometry.categories import SimilaritySurfaces
-
-        if surface not in SimilaritySurfaces():
-            raise TypeError
-
-        self._surface = surface
-
-        # Sanitize the direction vector:
-        V = self._surface.base_ring().fraction_field() ** 2
-        self._direction = V(direction)
-        if self._direction == V.zero():
-            raise ValueError("Direction must be nonzero.")
-        # To canonicalize the direction vector we ensure its endpoint lies in the boundary of the unit square.
-        xabs = self._direction[0].abs()
-        yabs = self._direction[1].abs()
-        if xabs > yabs:
-            self._direction = self._direction / xabs
-        else:
-            self._direction = self._direction / yabs
-
-        # Fix end_direction if not standard.
-        if end_direction is not None:
-            xabs = end_direction[0].abs()
-            yabs = end_direction[1].abs()
-            if xabs > yabs:
-                end_direction = end_direction / xabs
-            else:
-                end_direction = end_direction / yabs
-
-        self._surfacetart_data = tuple(start_data)
-
-        if end_direction is None:
-            from flatsurf.geometry.categories import DilationSurfaces
-
-            # Attempt to infer the end_direction.
-            if self._surface in DilationSurfaces().Positive():
-                end_direction = -self._direction
-            elif self._surface in DilationSurfaces() and end_data is not None:
-                p = self._surface.polygon(end_data[0])
-                from flatsurf.geometry.euclidean import ccw
-
-                if (
-                    ccw(p.edge(end_data[1]), self._direction) >= 0
-                    and ccw(
-                        p.edge(
-                            (len(p.vertices()) + end_data[1] - 1) % len(p.vertices())
-                        ),
-                        self._direction,
-                    )
-                    > 0
-                ):
-                    end_direction = self._direction
-                else:
-                    end_direction = -self._direction
-
-        if end_holonomy is None and holonomy is not None:
-            # Attempt to infer the end_holonomy:
-            from flatsurf.geometry.categories import (
-                HalfTranslationSurfaces,
-                TranslationSurfaces,
-            )
-
-            if self._surface in TranslationSurfaces():
-                end_holonomy = -holonomy
-            if self._surface in HalfTranslationSurfaces():
-                if direction == end_direction:
-                    end_holonomy = holonomy
-                else:
-                    end_holonomy = -holonomy
-
-        if (
-            end_data is None
-            or end_direction is None
-            or holonomy is None
-            or end_holonomy is None
-            or check
-        ):
-            v = self.start_tangent_vector()
-            traj = v.straight_line_trajectory()
-            traj.flow(limit)
-            if not traj.is_saddle_connection():
-                raise ValueError(
-                    "Did not obtain saddle connection by flowing forward. Limit="
-                    + str(limit)
-                )
-            tv = traj.terminal_tangent_vector()
-            self._end_data = (tv.polygon_label(), tv.vertex())
-            if end_data is not None:
-                if end_data != self._end_data:
-                    raise ValueError(
-                        "Provided or inferred end_data="
-                        + str(end_data)
-                        + " does not match actual end_data="
-                        + str(self._end_data)
-                    )
-            self._end_direction = tv.vector()
-            # Canonicalize again.
-            xabs = self._end_direction[0].abs()
-            yabs = self._end_direction[1].abs()
-            if xabs > yabs:
-                self._end_direction = self._end_direction / xabs
-            else:
-                self._end_direction = self._end_direction / yabs
-            if end_direction is not None:
-                if end_direction != self._end_direction:
-                    raise ValueError(
-                        "Provided or inferred end_direction="
-                        + str(end_direction)
-                        + " does not match actual end_direction="
-                        + str(self._end_direction)
-                    )
-
-            if traj.segments()[0].is_edge():
-                # Special case (The below method causes error if the trajectory is just an edge.)
-                self._holonomy = self._surface.polygon(start_data[0]).edge(
-                    start_data[1]
-                )
-                self._end_holonomy = self._surface.polygon(self._end_data[0]).edge(
-                    self._end_data[1]
-                )
-            else:
-                from .similarity import SimilarityGroup
-
-                sim = SimilarityGroup(self._surface.base_ring()).one()
-                itersegs = iter(traj.segments())
-                next(itersegs)
-                for seg in itersegs:
-                    sim = sim * self._surface.edge_transformation(
-                        seg.start().polygon_label(), seg.start().position().get_edge()
-                    )
-                self._holonomy = (
-                    sim(traj.segments()[-1].end().point())
-                    - traj.initial_tangent_vector().point()
-                )
-                self._end_holonomy = -((~sim.derivative()) * self._holonomy)
-
-            if holonomy is not None:
-                if holonomy != self._holonomy:
-                    print("Combinatorial length: " + str(traj.combinatorial_length()))
-                    print("Start: " + str(traj.initial_tangent_vector().point()))
-                    print("End: " + str(traj.terminal_tangent_vector().point()))
-                    print("Start data:" + str(start_data))
-                    print("End data:" + str(end_data))
-                    raise ValueError(
-                        "Provided holonomy "
-                        + str(holonomy)
-                        + " does not match computed holonomy of "
-                        + str(self._holonomy)
-                    )
-            if end_holonomy is not None:
-                if end_holonomy != self._end_holonomy:
-                    raise ValueError(
-                        "Provided or inferred end_holonomy "
-                        + str(end_holonomy)
-                        + " does not match computed end_holonomy of "
-                        + str(self._end_holonomy)
-                    )
-        else:
-            self._end_data = tuple(end_data)
-            self._end_direction = end_direction
-            self._holonomy = holonomy
-            self._end_holonomy = end_holonomy
-
-        # Make vectors immutable
-        self._direction.set_immutable()
-        self._end_direction.set_immutable()
-        self._holonomy.set_immutable()
-        self._end_holonomy.set_immutable()
-
-    def surface(self):
-        return self._surface
-
-    def direction(self):
-        r"""
-        Returns a vector parallel to the saddle connection pointing from the start point.
-
-        The will be normalized so that its $l_\infty$ norm is 1.
-        """
-        return self._direction
-
-    def end_direction(self):
-        r"""
-        Returns a vector parallel to the saddle connection pointing from the end point.
-
-        The will be normalized so that its `l_\infty` norm is 1.
-        """
-        return self._end_direction
-
-    def start_data(self):
-        r"""
-        Return the pair (l, v) representing the label and vertex of the corresponding polygon
-        where the saddle connection originates.
-        """
-        return self._surfacetart_data
-
-    def end_data(self):
-        r"""
-        Return the pair (l, v) representing the label and vertex of the corresponding polygon
-        where the saddle connection terminates.
-        """
-        return self._end_data
-
-    def holonomy(self):
-        r"""
-        Return the holonomy vector of the saddle connection (measured from the start).
-
-        In a SimilaritySurface this notion corresponds to developing the saddle connection into the plane
-        using the initial chart coming from the initial polygon.
-        """
-        return self._holonomy
-
-    def length(self):
-        r"""
-        In a cone surface, return the length of this saddle connection. Since
-        this may not lie in the field of definition of the surface, it is
-        returned as an element of the Algebraic Real Field.
-        """
-        from flatsurf.geometry.categories import ConeSurfaces
-
-        if self._surface not in ConeSurfaces():
-            raise NotImplementedError(
-                "length of a saddle connection only makes sense for cone surfaces"
-            )
-
-        return vector(AA, self._holonomy).norm()
-
-    def end_holonomy(self):
-        r"""
-        Return the holonomy vector of the saddle connection (measured from the end).
-
-        In a SimilaritySurface this notion corresponds to developing the saddle connection into the plane
-        using the initial chart coming from the initial polygon.
-        """
-        return self._end_holonomy
-
-    def start_tangent_vector(self):
-        r"""
-        Return a tangent vector to the saddle connection based at its start.
-        """
-        return self._surface.tangent_vector(
-            self._surfacetart_data[0],
-            self._surface.polygon(self._surfacetart_data[0]).vertex(
-                self._surfacetart_data[1]
-            ),
-            self._direction,
-        )
-
-    @cached_method(key=lambda self, limit, cache: None)
-    def trajectory(self, limit=1000, cache=None):
-        r"""
-        Return a straight line trajectory representing this saddle connection.
-        Fails if the trajectory passes through more than limit polygons.
-        """
-        if cache is not None:
-            import warnings
-
-            warnings.warn(
-                "The cache keyword argument of trajectory() is ignored. Trajectories are always cached."
-            )
-
-        v = self.start_tangent_vector()
-        traj = v.straight_line_trajectory()
-        traj.flow(limit)
-        if not traj.is_saddle_connection():
-            raise ValueError(
-                "Did not obtain saddle connection by flowing forward. Limit="
-                + str(limit)
-            )
-
-        return traj
-
-    def plot(self, *args, **options):
-        r"""
-        Equivalent to ``.trajectory().plot(*args, **options)``
-        """
-        return self.trajectory().plot(*args, **options)
-
-    def end_tangent_vector(self):
-        r"""
-        Return a tangent vector to the saddle connection based at its start.
-        """
-        return self._surface.tangent_vector(
-            self._end_data[0],
-            self._surface.polygon(self._end_data[0]).vertex(self._end_data[1]),
-            self._end_direction,
-        )
-
-    def invert(self):
-        r"""
-        Return this saddle connection but with opposite orientation.
-        """
-        return SaddleConnection(
-            self._surface,
-            self._end_data,
-            self._end_direction,
-            self._surfacetart_data,
-            self._direction,
-            self._end_holonomy,
-            self._holonomy,
-            check=False,
-        )
-
-    def intersections(self, traj, count_singularities=False, include_segments=False):
-        r"""
-        See documentation of :meth:`~.straight_line_trajectory.AbstractStraightLineTrajectory.intersections`
-        """
-        return self.trajectory().intersections(
-            traj, count_singularities, include_segments
-        )
-
-    def intersects(self, traj, count_singularities=False):
-        r"""
-        See documentation of :meth:`~.straight_line_trajectory.AbstractStraightLineTrajectory.intersects`
-        """
-        return self.trajectory().intersects(
-            traj, count_singularities=count_singularities
-        )
-
-    def __eq__(self, other):
-        r"""
-        Return whether this saddle connection is indistinguishable from
-        ``other``.
-
-        EXAMPLES::
-
-            sage: from flatsurf import translation_surfaces
-            sage: S = translation_surfaces.square_torus()
-            sage: connections = S.saddle_connections(13)
-
-            sage: connections[0] == connections[0]
-            True
-            sage: connections[0] == connections[1]
-            False
-
-
-        TESTS:
-
-        Verify that saddle connections can be compared to arbitrary objects (so
-        they can be put into dicts with other objects)::
-
-            sage: connections[0] == 42
-            False
-
-        ::
-
-            sage: len(connections)
-            32
-            sage: len(set(connections))
-            32
-
-        """
-        if self is other:
-            return True
-        if not isinstance(other, SaddleConnection):
-            return False
-        if not self._surface == other._surface:
-            return False
-        if not self._direction == other._direction:
-            return False
-        if not self._surfacetart_data == other._surfacetart_data:
-            return False
-        # Initial data should determine the saddle connection:
-        return True
-
-    def __ne__(self, other):
-        return not self == other
-
-    def __hash__(self):
-        return 41 * hash(self._direction) - 97 * hash(self._surfacetart_data)
-
-    def _test_geometry(self, **options):
-        # Test that this saddle connection actually exists on the surface.
-        SaddleConnection(
-            self._surface,
-            self._surfacetart_data,
-            self._direction,
-            self._end_data,
-            self._end_direction,
-            self._holonomy,
-            self._end_holonomy,
-            check=True,
-        )
-
-    def __repr__(self):
-        return "Saddle connection in direction {} with start data {} and end data {}".format(
-            self._direction, self._surfacetart_data, self._end_data
-        )
-
-    def _test_inverse(self, **options):
-        # Test that inverting works properly.
-        SaddleConnection(
-            self._surface,
-            self._end_data,
-            self._end_direction,
-            self._surfacetart_data,
-            self._direction,
-            self._end_holonomy,
-            self._holonomy,
-            check=True,
-        )
-
-
 class Cylinder(SageObject):
     r"""
     Represents a cylinder in a SimilaritySurface. A cylinder for these purposes is a
@@ -1238,7 +787,7 @@ def __init__(self, s, label0, edges):
                     raise ValueError("Combinatorial data does not represent a cylinder")
 
         # Extract the saddle connections on the right side:
-        from flatsurf.geometry.surface_objects import SaddleConnection
+        from flatsurf.geometry.saddle_connection import SaddleConnection
 
         sc_set_right = set()
         vertices = []
@@ -1254,9 +803,10 @@ def __init__(self, s, label0, edges):
             li = (li[0], SG(-v) * li[1])
             lio = ss.opposite_edge(li, edges[i])
             lj = labels[j]
-            sc = SaddleConnection(
+            sc = SaddleConnection.from_vertex(
                 s,
-                (lio[0][0], (lio[1] + 1) % len(ss.polygon(lio[0]).vertices())),
+                lio[0][0],
+                (lio[1] + 1) % len(ss.polygon(lio[0]).vertices()),
                 (~lio[0][1])(vert_j) - (~lio[0][1])(vert_i),
             )
             sc_set_right.add(sc)
@@ -1268,9 +818,10 @@ def __init__(self, s, label0, edges):
                 li = (li[0], SG(-v) * li[1])
                 lio = ss.opposite_edge(li, edges[i])
                 lj = labels[j]
-                sc = SaddleConnection(
+                sc = SaddleConnection.from_vertex(
                     s,
-                    (lio[0][0], (lio[1] + 1) % len(ss.polygon(lio[0]).vertices())),
+                    lio[0][0],
+                    (lio[1] + 1) % len(ss.polygon(lio[0]).vertices()),
                     (~lio[0][1])(vert_j) - (~lio[0][1])(vert_i),
                     limit=j - i,
                 )
@@ -1293,9 +844,10 @@ def __init__(self, s, label0, edges):
             li = (li[0], SG(-v) * li[1])
             lio = ss.opposite_edge(li, edges[i])
             lj = labels[j]
-            sc = SaddleConnection(
+            sc = SaddleConnection.from_vertex(
                 s,
-                (lj[0], (edges[j] + 1) % len(ss.polygon(lj).vertices())),
+                lj[0],
+                (edges[j] + 1) % len(ss.polygon(lj).vertices()),
                 (~lj[1])(vert_i) - (~lj[1])(vert_j),
             )
             sc_set_left.add(sc)
@@ -1306,9 +858,10 @@ def __init__(self, s, label0, edges):
                 li = labels[i]
                 lio = ss.opposite_edge(li, edges[i])
                 lj = labels[j]
-                sc = SaddleConnection(
+                sc = SaddleConnection.from_vertex(
                     s,
-                    (lj[0], (edges[j] + 1) % len(ss.polygon(lj).vertices())),
+                    lj[0],
+                    (edges[j] + 1) % len(ss.polygon(lj).vertices()),
                     (~lj[1])(vert_i) - (~lj[1])(vert_j),
                 )
                 sc_set_left.add(sc)
diff --git a/flatsurf/geometry/tangent_bundle.py b/flatsurf/geometry/tangent_bundle.py
index aed1f381d..3c59d740d 100644
--- a/flatsurf/geometry/tangent_bundle.py
+++ b/flatsurf/geometry/tangent_bundle.py
@@ -80,6 +80,17 @@ class SimilaritySurfaceTangentVector:
         sage: s.tangent_vector(0, (1, 1), (0, -1))
         SimilaritySurfaceTangentVector in polygon 0 based at (0, 1) with vector (0, -1)
 
+    TESTS:
+
+    We verify that the saddle connections in a cathedral can be computed. This
+    failed at some point::
+
+        sage: from flatsurf import translation_surfaces
+        sage: S = translation_surfaces.cathedral(1, 2)
+        sage: connections = S.saddle_connections(2)  # random output due to cppyy deprecation warnings
+        sage: len(connections)
+        40
+
     """
 
     def __init__(self, tangent_bundle, polygon_label, point, vector):
@@ -118,18 +129,24 @@ def __init__(self, tangent_bundle, polygon_label, point, vector):
                 self._position = pos
         elif pos.is_vertex():
             v = pos.get_vertex()
-            p = self.surface().polygon(polygon_label)
             # subsequent edge:
             edge1 = p.edge(v)
             # prior edge:
-            edge0 = p.edge((v - 1) % len(p.vertices()))
+            edge0 = p.edge(v - 1)
             wp1 = ccw(edge1, vector)
             wp0 = ccw(edge0, vector)
             if wp1 < 0 or wp0 < 0:
                 raise ValueError(
                     "Singular point with vector pointing away from polygon"
                 )
-            if wp0 == 0:
+
+            if (
+                is_anti_parallel(edge0, vector)
+                and self.surface().opposite_edge(
+                    polygon_label, (v - 1) % len(p.vertices())
+                )
+                is not None
+            ):
                 # vector points backward along edge 0
                 label2, e2 = self.surface().opposite_edge(
                     polygon_label, (v - 1) % len(p.vertices())
@@ -146,7 +163,7 @@ def __init__(self, tangent_bundle, polygon_label, point, vector):
                     self.surface().polygon(label2).get_point_position(point2)
                 )
             else:
-                # vector points along edge1 in that directior or points into polygons interior
+                # vector points along edge1 or points into polygons interior
                 self._polygon_label = polygon_label
                 self._point = point
                 self._vector = vector
@@ -337,12 +354,10 @@ def forward_to_polygon_boundary(self):
             SimilaritySurfaceTangentVector in polygon 1 based at (2/3, 2) with vector (4, -3)
         """
         p = self.polygon()
-        point2, pos2 = p.flow_to_exit(self.point(), self.vector())
-        # diff=point2-point
-        new_vector = SimilaritySurfaceTangentVector(
+        point2 = p.flow_to_exit(self.point(), self.vector())
+        return SimilaritySurfaceTangentVector(
             self.bundle(), self.polygon_label(), point2, -self.vector()
         )
-        return new_vector
 
     def straight_line_trajectory(self):
         r"""
@@ -581,6 +596,48 @@ def __init__(self, similarity_surface, ring=None):
 
         self._V = VectorSpace(self._base_ring, 2)
 
+    def some_elements(self):
+        r"""
+        Return some typical tangent vectors (for testing).
+
+        EXAMPLES::
+
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.square_torus()
+            sage: T = S.tangent_bundle()
+            sage: list(T.some_elements())
+            [SimilaritySurfaceTangentVector in polygon 0 based at (0, 1) with vector (1/2, -1/2),
+             SimilaritySurfaceTangentVector in polygon 0 based at (1, 1) with vector (-1/2, -1/2),
+             SimilaritySurfaceTangentVector in polygon 0 based at (1, 0) with vector (-1/2, 1/2),
+             SimilaritySurfaceTangentVector in polygon 0 based at (0, 0) with vector (1/2, 1/2),
+             SimilaritySurfaceTangentVector in polygon 0 based at (1/2, 1/2) with vector (1, 2),
+             SimilaritySurfaceTangentVector in polygon 0 based at (2/3, 0) with vector (1, 0),
+             SimilaritySurfaceTangentVector in polygon 0 based at (2/3, 1) with vector (-1, 0),
+             SimilaritySurfaceTangentVector in polygon 0 based at (1, 2/3) with vector (0, 1),
+             SimilaritySurfaceTangentVector in polygon 0 based at (0, 2/3) with vector (0, -1),
+             SimilaritySurfaceTangentVector in polygon 0 based at (1/3, 0) with vector (1, 0),
+             SimilaritySurfaceTangentVector in polygon 0 based at (1/3, 1) with vector (-1, 0),
+             SimilaritySurfaceTangentVector in polygon 0 based at (1, 1/3) with vector (0, 1),
+             SimilaritySurfaceTangentVector in polygon 0 based at (0, 1/3) with vector (0, -1)]
+
+        """
+        S = self.surface()
+        for point in S.some_elements():
+            for label, coordinates in point.representatives():
+                polygon = S.polygon(label)
+                polygon_point = polygon(coordinates)
+                position = polygon_point.position()
+                if position.is_in_interior():
+                    yield self(label, coordinates, (1, 2))
+                elif position.is_in_edge_interior():
+                    edge = polygon.edge(position.get_edge())
+                    yield self(label, coordinates, edge)
+                else:
+                    assert position.is_vertex()
+                    vertex = position.get_vertex()
+                    bisector = (polygon.edge(vertex) - polygon.edge(vertex - 1)) / 2
+                    yield self(label, coordinates, bisector)
+
     def __call__(self, polygon_label, point, vector):
         r"""
         Construct a tangent vector from a polygon label, a point in the polygon and a vector. The point and the vector should have coordinates
diff --git a/flatsurf/geometry/voronoi.py b/flatsurf/geometry/voronoi.py
new file mode 100644
index 000000000..7502ad567
--- /dev/null
+++ b/flatsurf/geometry/voronoi.py
@@ -0,0 +1,2121 @@
+# TODO: Change the inheritance structure so it's clear which implementations
+# use the fact that the cells are convex, which use the fact that the cells are
+# given additionally by line segments (so given by their corners.)
+
+
+from sage.misc.cachefunc import cached_method
+
+
+class CellDecomposition:
+    Cell = None
+
+    def __init__(self, surface):
+        self._surface = surface
+
+    def surface(self):
+        return self._surface
+
+    @cached_method
+    def centers(self):
+        r"""
+        Return the center points of this cell decomposition.
+        """
+        return frozenset(self._surface.vertices())
+
+    def cell(self, point):
+        r"""
+        Return a cell containing ``point``.
+        """
+        return next(iter(self.cells(point)))
+
+    @cached_method
+    def cell_at_center(self, center):
+        r"""
+        Return the cell centered at ``center``.
+        """
+        if self.Cell is None:
+            raise NotImplementedError("this decomposition does not implement cells yet")
+
+        return self.Cell(self, center)
+
+    def cells(self, point=None):
+        r"""
+        Return the cells containing ``point``.
+        """
+        if point is None:
+            yield from self
+            return
+
+        for cell in self:
+            if cell.contains_point(point):
+                yield cell
+
+    @cached_method
+    def polygon_cells(self, label):
+        return tuple(
+            polygon_cell
+            for cell in self
+            for polygon_cell in cell.polygon_cells()
+            if polygon_cell.label() == label
+        )
+
+    def split_segment_at_cells(self, segment):
+        r"""
+        Return the ``segment`` split into shorter segments that each lie in a
+        single cell.
+
+        Returns a sequence of pairs, consisting of subsegment and a cell that
+        contains the segment.
+        """
+        raise NotImplementedError(
+            "this decomposition does not implement splitting of segments yet"
+        )
+
+    def split_segment_at_polygon_cells(self, segment):
+        r"""
+        Return the ``segment`` split into shorter segments that each lie in a
+        single polygon cell.
+
+        Returns a sequence of pairs, consisting of subsegment and a cell that
+        contains the segment.
+        """
+        from flatsurf.geometry.euclidean import OrientedSegment
+
+        return [
+            (OrientedSegment(*s), polygon_cell)
+            for (label, subsegment, _) in segment.split()
+            for (s, polygon_cell) in self._split_segment_at_polygon_cells(
+                label, subsegment
+            )
+        ]
+
+    def _split_segment_at_polygon_cells(self, label, segment):
+        start, end = segment
+
+        if start == end:
+            return []
+
+        for start_cell in self.polygon_cells(label=label):
+            if not start_cell.contains_point(start):
+                continue
+
+            polygon = start_cell.polygon()
+
+            try:
+                exit = polygon.flow_to_exit(start, end - start)
+            except ValueError:
+                continue
+
+            return [((start, exit), start_cell)] + self._split_segment_at_polygon_cells(
+                label, (exit, end)
+            )
+
+        assert False
+
+    def __iter__(self):
+        r"""
+        Return an iterator over the cells of this decomposition.
+        """
+        for center in self.centers():
+            yield self.cell_at_center(center)
+
+    @cached_method
+    def _neg_boundary_segment(self, boundary_segment):
+        r"""
+        Return the boundary segment that has the same segment as
+        ``boundary_segment`` but with the opposite orientation.
+        """
+        search = -boundary_segment.segment()
+
+        for cell in self:
+            for boundary in cell.boundary():
+                if boundary.segment() == search:
+                    return boundary
+
+        assert False, "boundary segment has no negative in this decomposition"
+
+    def __repr__(self):
+        raise NotImplementedError("this cell decomposition cannot print itself yet")
+
+    def plot(self, graphical_surface=None):
+        r"""
+        Return a graphical representation of this cell decomposition.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.voronoi import VoronoiDiagram
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.regular_octagon()
+            sage: center = S(0, S.polygon(0).centroid())
+            sage: S = S.insert_marked_points(center).codomain()
+            sage: V = VoronoiDiagram(S, S.vertices())
+            sage: V.plot()
+            Graphics object consisting of 73 graphics primitives
+
+        The underlying surface is not plotted automatically when it is provided
+        as a keyword argument::
+
+            sage: V.plot(graphical_surface=S.graphical_surface())
+            Graphics object consisting of 48 graphics primitives
+
+        """
+        plot_surface = graphical_surface is None
+
+        if graphical_surface is None:
+            graphical_surface = self._surface.graphical_surface(
+                edge_labels=False, polygon_labels=False
+            )
+
+        plot = []
+        if plot_surface:
+            plot.append(graphical_surface.plot())
+
+        for cell in self:
+            plot.append(cell.plot(graphical_surface))
+
+        return sum(plot)
+
+    def _test_boundary_consistency(self):
+        # TODO: Use SageMath test framework.
+        segments = [boundary.segment() for cell in self for boundary in cell.boundary()]
+
+        for cell in self:
+            for boundary in cell.boundary():
+                assert (
+                    -boundary.segment() in segments
+                ), f"{boundary} has no negative {-boundary.segment()}"
+
+
+class Cell:
+    BoundarySegment = None
+    PolygonCell = None
+
+    def __init__(self, decomposition, center):
+        self._decomposition = decomposition
+        self._center = center
+
+    def surface(self):
+        return self._decomposition.surface()
+
+    def decomposition(self):
+        return self._decomposition
+
+    def center(self):
+        r"""
+        Return the point of the surface that should be considered as the center
+        of this cell.
+        """
+        return self._center
+
+    def polygon_cell(self, label, coordinates):
+        r"""
+        Return a restriction of this cell to the polygon with ``label`` that
+        contains ``coordinates``.
+        """
+        for polygon_cell in self.polygon_cells(label=label):
+            if polygon_cell.contains_point(coordinates):
+                return polygon_cell
+
+        assert not self.contains_point(self.surface()(label, coordinates))
+        return None
+
+    def polygon_cells(self, label=None, branch=None):
+        r"""
+        Return restrictions of this cell to all polygons of the surface.
+
+        If ``label`` is given, return only the restrictions to that polygon.
+        """
+        if label is None:
+            return sum(
+                [
+                    self.polygon_cells(label=label, branch=branch)
+                    for label in self.surface().labels()
+                ],
+                start=(),
+            )
+
+        return self._polygon_cells(label=label, branch=branch)
+
+    def _polygon_cells(self, label, branch):
+        r"""
+        Return the restrictions of this cell to its connected components in the
+        polygon with ``label``.
+
+        Note that a cell can be separated by a boundary from itself so a single
+        cell can induce several polygon cells in a polygon even if these
+        polygon cells touch at their boundaries.
+        """
+        raise NotImplementedError(
+            "this cell decomposition cannot restrict cells to polygons yet"
+        )
+
+    @cached_method
+    def boundary(self):
+        r"""
+        Return the boundary of this cell in a counterclockwise walk.
+
+        This method is only available if the boundary is connected, i.e. the
+        cell is simply connected.
+        """
+        raise NotImplementedError(
+            "this cell decomposition cannot compute boundaries of cells yet"
+        )
+
+    def radius(self):
+        r"""
+        Return the distance from the center to the furthest point in this cell.
+        """
+        return max(boundary.radius() for boundary in self.boundary())
+
+    def inradius(self):
+        r"""
+        Return the distance from the center to the closest boundary point of this cell.
+        """
+        return min(boundary.inradius() for boundary in self.boundary())
+
+    def complex_from_point(self, p):
+        r"""
+        .. NOTE::
+
+        See :meth:`point_from_complex` for a description of the coordinate
+        system change involved.
+        """
+        for label, coordinates in p.representatives():
+            polygon_cell = self.polygon_cell(label, coordinates)
+            if polygon_cell is not None:
+                break
+        else:
+            raise ValueError("point not in cell")
+
+        return polygon_cell.complex_from_point(coordinates)
+
+    def polygon_point_from_complex(self, y, boundary_error=0):
+        # TODO: This is very slow because we have to lift complex coordinates
+        # to the base ring (i.e., the rationals.) There's not much we can do
+        # about this without supporting floating point surfaces?
+
+        from math import pi
+
+        d = self._center.angle() - 1
+
+        arg = y.arg() * (d + 1)
+
+        if arg < 0:
+            arg += 2 * pi * (d + 1)
+
+        branch = 0
+        # TODO: We could use > pi or >= pi here. This has to be compatible with
+        # root_branch() and polygon_cells().
+        while arg >= pi:
+            arg -= 2 * pi
+            branch += 1
+
+        if branch > d:
+            branch -= d + 1
+
+        from sage.all import vector
+
+        xy = vector(list(y ** (d + 1)))
+
+        xy_lift = xy.change_ring(self.surface().base_ring())
+
+        # TODO: This is all not too robust since we are feeding
+        # floating point numbers into exact polygons here. As a
+        # result roots could show up twice or never.
+        for polygon_cell in self.polygon_cells(branch=branch):
+            polygon = polygon_cell._polygon()
+            polygon_complex = polygon_cell._polygon_complex()
+            center = polygon_cell.center()
+            vertex = polygon.get_point_position(center).get_vertex()
+
+            if abs(xy) < 1e-24:
+                # print("identifying root with vertex")
+                return polygon_cell.label(), center
+
+            from flatsurf.geometry.euclidean import ccw
+
+            if (
+                ccw(polygon_complex.edge(vertex), xy) >= 0
+                and ccw(-polygon_complex.edge(vertex - 1), xy) < 0
+            ):
+                p = center + xy_lift
+
+                from flatsurf.geometry.euclidean import OrientedSegment
+
+                if polygon_cell.root_branch(p) == branch:
+                    if polygon.get_point_position(p).is_outside():
+                        return None
+
+                    if not polygon_cell.contains_point(
+                        p, boundary_error=boundary_error
+                    ):
+                        return None
+
+                    # print(f"Point at distance {xy.norm():.5} of {self.center()} (angle {2*self.center().angle()}π)")
+
+                    return polygon_cell.label(), p
+
+        assert False
+
+    def point_from_complex(self, y, boundary_error=0):
+        r"""
+        Return the point of this cell which is given by the local coordinate y.
+
+        Return ``None`` if there is no such point, i.e., the coordinate
+        describes a point outside of the cell.
+
+        .. NOTE::
+
+        Points of the surface are usually given by their flat coordinate `z` in
+        one of the polygons that forms the surface. However, at a singularity
+        of degree d, we can perform a change of coordinates and write
+
+        y(z) = ζ_{d+1}^κ (z-α)^{1/(d+1)}
+
+        where z=α and y=0 are coordinates for the singularity and the last
+        exponent denotes the principal `d+1`-st root of `z`, see also
+        :meth:`root_branch` for the choice of root.
+
+        Here, we take a `y` coordinate and transform it back to the
+        corresponding `z` coordinate. We have
+
+        z(y) = y^{d+1} + α.
+
+        """
+        p = self.polygon_point_from_complex(y=y, boundary_error=boundary_error)
+
+        if p is None:
+            return None
+
+        return self.surface()(*p)
+
+    def __eq__(self, other):
+        if not isinstance(other, Cell):
+            return False
+        return (
+            self._decomposition == other._decomposition
+            and self._center == other._center
+        )
+
+    def __hash__(self):
+        return hash(self._center)
+
+    def __repr__(self):
+        return f"Cell at {self.center()}"
+
+    def plot(self, graphical_surface=None):
+        r"""
+        Return a graphical representation of this cell.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.voronoi import VoronoiDiagram
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.regular_octagon()
+            sage: V = VoronoiDiagram(S, S.vertices())
+            sage: cell = V.cell(S(0, 0))
+            sage: cell.plot()
+            Graphics object consisting of 34 graphics primitives
+
+        """
+        plot_surface = graphical_surface is None
+
+        if graphical_surface is None:
+            graphical_surface = self.surface().graphical_surface(
+                edge_labels=False, polygon_labels=False
+            )
+
+        plot = []
+        if plot_surface:
+            plot.append(graphical_surface.plot())
+
+        for boundary in self.boundary():
+            plot.append(boundary.plot(graphical_surface=graphical_surface))
+
+        return sum(plot)
+
+    def contains_point(self, point, boundary_error=0):
+        for label, coordinates in point.representatives():
+            for polygon_cell in self.polygon_cells(label=label):
+                if polygon_cell.contains_point(
+                    coordinates, boundary_error=boundary_error
+                ):
+                    return True
+
+        return False
+
+
+class LineSegmentCell(Cell):
+    r"""
+    A cell whose boundary is given by line segments.
+    """
+
+    @cached_method
+    def corners(self):
+        return [segment.segment().start() for segment in self.boundary()]
+
+    @cached_method
+    def _polygon_cells(self, label, branch):
+        surface = self.surface()
+        polygon = surface.polygon(label)
+
+        if branch is not None:
+            return tuple(
+                polygon_cell
+                for polygon_cell in self._polygon_cells(label=label, branch=None)
+                if branch in polygon_cell.root_branches()
+            )
+
+        cell_boundary = self.boundary()
+
+        # TODO: Very slow, so disabled.
+        # if any(
+        #     cell_boundary[b].segment().start() != cell_boundary[b - 1].segment().end()
+        #     for b in range(len(cell_boundary))
+        # ):
+        #     raise NotImplementedError("boundary of cell must be connected")
+
+        from collections import namedtuple
+
+        PolygonCellBoundary = namedtuple(
+            "PolygonCellBoundary", ("cell_boundary", "label", "segment", "center")
+        )
+
+        # Filter out the bits of the boundary that lie in the polygon with label.
+        polygon_cells_boundary = []
+
+        for boundary in cell_boundary:
+            for lbl, subsegment, start_segment in boundary.segment().split(label=label):
+                assert lbl == label
+                center = (
+                    subsegment[0] - start_segment - boundary._center_to_start.holonomy()
+                )
+                center.set_immutable()
+                polygon_cells_boundary.append(
+                    PolygonCellBoundary(boundary, label, subsegment, center)
+                )
+
+        polygon_cells = []
+
+        if not polygon_cells_boundary:
+            # TODO: Here we use the assumption that entire polygons cannot be contained in a cell.
+            return ()
+
+        unused_polygon_cells_boundaries = set(polygon_cells_boundary)
+
+        from collections import defaultdict
+
+        polygon_cells_boundary_from = defaultdict(lambda: [])
+        for boundary in polygon_cells_boundary:
+            start, end = boundary.segment
+            polygon_cells_boundary_from[start].append(boundary)
+
+        while unused_polygon_cells_boundaries:
+            # Build a new polygon cell starting from a random boundary segment.
+            polygon_cell = [unused_polygon_cells_boundaries.pop()]
+            center = polygon_cell[0].center
+
+            # Walk the boundary of the polygon cell until it closed up.
+            while True:
+                # Find the first segment at the end point of the previous
+                # segment that is at a clockwise turn but still within the polygon.
+                start, end = polygon_cell[-1].segment
+
+                strictly_clockwise_from = start - end
+
+                end_position = polygon.get_point_position(end)
+                if end_position.is_vertex():
+                    counterclockwise_from = polygon.edge(end_position.get_vertex())
+                elif end_position.is_in_edge_interior():
+                    counterclockwise_from = polygon.edge(end_position.get_edge())
+                else:
+                    counterclockwise_from = start - end
+
+                # Find candidate next boundaries that are starting at the end
+                # point of the previous boundary and in the correct sector.
+                from flatsurf.geometry.euclidean import is_between, is_parallel
+
+                polygon_cells_boundary_from_in_sector = [
+                    boundary
+                    for boundary in polygon_cells_boundary_from[end]
+                    if (
+                        not is_parallel(counterclockwise_from, strictly_clockwise_from)
+                        and is_parallel(
+                            boundary.segment[1] - boundary.segment[0],
+                            counterclockwise_from,
+                        )
+                    )
+                    or is_between(
+                        counterclockwise_from,
+                        strictly_clockwise_from,
+                        boundary.segment[1] - boundary.segment[0],
+                    )
+                ]
+
+                # Pick the first such boundary turning clockwise from the
+                # previous boundary.
+                def angle_key(boundary):
+                    class AngleKey:
+                        def __init__(self, vector, sector):
+                            self._vector = vector
+                            self._sector = sector
+                            assert is_parallel(
+                                self._sector[0], self._vector
+                            ) or is_between(*self._sector, self._vector)
+
+                        def __gt__(self, other):
+                            from flatsurf.geometry.euclidean import is_parallel
+
+                            assert not is_parallel(
+                                self._vector, other._vector
+                            ), "cell must not have equally oriented parallel boundaries"
+
+                            if is_parallel(self._sector[0], self._vector):
+                                return False
+                            if is_parallel(self._sector[0], other._vector):
+                                return True
+                            return is_between(
+                                self._sector[0], self._vector, other._vector
+                            )
+
+                    return AngleKey(
+                        boundary.segment[1] - boundary.segment[0],
+                        (counterclockwise_from, strictly_clockwise_from),
+                    )
+
+                next_polygon_cell_boundary = max(
+                    polygon_cells_boundary_from_in_sector, key=angle_key, default=None
+                )
+
+                if next_polygon_cell_boundary is None:
+                    assert len(polygon_cells_boundary_from_in_sector) == 0
+
+                    # This boundary touches the polygon edges but no explicit
+                    # boundary starts at the point where it touches.
+                    if end_position.is_vertex():
+                        edge = end_position.get_vertex()
+
+                    else:
+                        # Part of a polygon edge needs to be added to the boundary
+                        # of this cell.
+                        # Walk the polygon edges until we find a starting point of
+                        # a boundary segment (or hit a vertex.)
+                        assert (
+                            end_position.is_in_edge_interior()
+                        ), "boundary segments ends in interior of polygon but no other segment continues here"
+                        edge = end_position.get_edge()
+
+                    from flatsurf.geometry.euclidean import time_on_ray
+
+                    polygon_cells_boundary_on_edge = [
+                        boundary
+                        for boundary in polygon_cells_boundary
+                        if polygon.get_point_position(
+                            boundary.segment[0]
+                        ).is_in_edge_interior()
+                        and polygon.get_point_position(boundary.segment[0]).get_edge()
+                        == edge
+                        and time_on_ray(end, polygon.edge(edge), boundary.segment[0])[0]
+                        > 0
+                    ]
+
+                    next_end = min(
+                        (
+                            (
+                                time_on_ray(
+                                    end, polygon.edge(edge), boundary.segment[0]
+                                )[0],
+                                boundary.segment[0],
+                            )
+                            for boundary in polygon_cells_boundary_on_edge
+                        ),
+                        default=(None, None),
+                    )[1]
+                    if next_end is None:
+                        # No segment starts on this edge. Go to the vertex.
+                        next_end = polygon.vertex(edge + 1)
+
+                    next_polygon_cell_boundary = PolygonCellBoundary(
+                        None, label, (end, next_end), center
+                    )
+                else:
+                    if next_polygon_cell_boundary == polygon_cell[0]:
+                        break
+                    assert (
+                        next_polygon_cell_boundary in unused_polygon_cells_boundaries
+                    ), f"boundary segment present in multiple polygon cells"
+                    unused_polygon_cells_boundaries.remove(next_polygon_cell_boundary)
+
+                assert (
+                    next_polygon_cell_boundary not in polygon_cell
+                ), "boundary segment must not repeat in polygon cell boundary"
+
+                assert (
+                    next_polygon_cell_boundary.center == center
+                ), f"segments in cell boundary refer to inconsistent cell centers, ({next_polygon_cell_boundary.center} != {center})"
+
+                polygon_cell.append(next_polygon_cell_boundary)
+
+            # Build an actual polygon cell from the boundary segments
+            assert center is not None
+            polygon_cells.append(
+                self.PolygonCell(
+                    cell=self,
+                    label=label,
+                    center=center,
+                    boundary=tuple([boundary.segment for boundary in polygon_cell]),
+                )
+            )
+
+        return tuple(polygon_cells)
+
+    def opposite_representations(self, complex, boundary_error):
+        from flatsurf.geometry.euclidean import EuclideanPlane
+
+        E = EuclideanPlane(self.surface().base_ring())
+
+        point = self.point_from_complex(complex, boundary_error=boundary_error)
+        assert point is not None
+
+        representations = set()
+
+        for boundary in self.boundary():
+            for (
+                polygon_cell,
+                subsegment,
+                opposite_polygon_cell,
+            ) in boundary.polygon_cell_boundaries():
+                subsegment = E(subsegment[0]).segment(subsegment[1])
+                for (label, coordinates) in point.representatives():
+                    if label == polygon_cell.label():
+                        from math import sqrt
+
+                        close_to_boundary = float(subsegment.distance(coordinates)) < (
+                            boundary_error / 2
+                        ) * sqrt(
+                            float(self.surface().polygon(polygon_cell.label()).area())
+                        )
+                        if close_to_boundary:
+                            representations.add(
+                                (
+                                    opposite_polygon_cell.cell().center(),
+                                    opposite_polygon_cell.complex_from_point(
+                                        coordinates
+                                    ),
+                                )
+                            )
+                        break
+
+        return representations
+
+
+class BoundarySegment:
+    def __init__(self, cell, segment):
+        self._cell = cell
+        self._segment = segment
+
+    def surface(self):
+        return self._cell.surface()
+
+    def cell(self):
+        return self._cell
+
+    def __bool__(self):
+        return bool(self._holonomy())
+
+    def __neg__(self):
+        return self.cell().decomposition()._neg_boundary_segment(self)
+
+    def segment(self):
+        return self._segment
+
+    def polygon_cell_boundaries(self):
+        r"""
+        Return this segment split into subsegments that each live entirely
+        within a polygon.
+
+        Returns the subsegments as triples (polygon cell, subsegment in polygon
+        cell, opposite polygon cell).
+        """
+        raise NotImplementedError(
+            "this cell decomposition cannot restrict its boundaries to polygons yet"
+        )
+
+    def plot(self, graphical_surface=None):
+        return self.segment().plot(graphical_surface=graphical_surface)
+
+    def __eq__(self, other):
+        if not isinstance(other, BoundarySegment):
+            return False
+
+        return self.cell() == other.cell() and self.segment() == other.segment()
+
+    def __hash__(self):
+        return hash(self.segment())
+
+    def __repr__(self):
+        return f"Boundary at {self.segment()}"
+
+
+class LineBoundarySegment(BoundarySegment):
+    r"""
+    A segment on the boundary of a cell that is a line segment.
+
+    This segment and the segments connecting the end points of the segment to
+    the center of the cell form a triangle in the plane.
+
+    INPUT:
+
+    - ``center_to_start`` -- a segment from the center of the Voronoi cell to
+      the start of ``segment``
+
+    """
+
+    def __init__(self, cell, segment, center_to_start):
+        super().__init__(cell, segment)
+
+        self._center_to_start = center_to_start
+
+    @cached_method
+    def polygon_cell_boundaries(self):
+        boundaries = []
+        for (label, subsegment, start_holonomy) in self.segment().split():
+            polygon = self.surface().polygon(label)
+            for polygon_cell in self._cell.polygon_cells(label):
+                if subsegment in polygon_cell.boundary():
+                    midpoint = (subsegment[0] + subsegment[1]) / 2
+                    midpoint_position = polygon.get_point_position(midpoint)
+                    assert midpoint_position.is_inside()
+                    assert not midpoint_position.is_vertex()
+
+                    opposite_label = None
+                    if not midpoint_position.is_in_edge_interior():
+                        opposite_label = label
+                    else:
+                        edge = midpoint_position.get_edge()
+                        opposite_label, opposite_edge = self.surface().opposite_edge(
+                            label, edge
+                        )
+                        opposite_polygon = self.surface().polygon(opposite_label)
+                        midpoint += opposite_polygon.vertex(
+                            opposite_edge + 1
+                        ) - polygon.vertex(edge)
+
+                    opposite_polygon_cell = [
+                        opposite_polygon_cell
+                        for opposite_polygon_cell in (-self)._cell.polygon_cells(
+                            opposite_label
+                        )
+                        if opposite_polygon_cell.contains_point(midpoint)
+                        and opposite_polygon_cell != polygon_cell
+                    ]
+
+                    assert (
+                        opposite_polygon_cell
+                    ), "no polygon cell on the other side of this boundary"
+                    assert (
+                        len(opposite_polygon_cell) == 1
+                    ), "more than one polygon cell on the other side of this boundary"
+                    opposite_polygon_cell = opposite_polygon_cell[0]
+
+                    boundaries.append((polygon_cell, subsegment, opposite_polygon_cell))
+                    break
+            else:
+                assert (
+                    False
+                ), "subsegment of boundary must also be a boundary on the level of polygons"
+
+        return boundaries
+
+    def radius(self):
+        norm = self.surface().euclidean_plane().norm()
+        return max(
+            [
+                norm.from_vector(self._center_to_start.holonomy()),
+                norm.from_vector(
+                    self._center_to_start.holonomy() + self._segment.holonomy()
+                ),
+            ]
+        )
+
+    def inradius(self):
+        from flatsurf.geometry.euclidean import EuclideanPlane
+
+        E = EuclideanPlane(self.surface().base_ring())
+        center = E.point(0, 0)
+        P = self._center_to_start.holonomy()
+        Q = P + self._segment.holonomy()
+        line = E.line(P, Q)
+        segment = E.segment(line, start=P, end=Q)
+        return segment.distance(center)
+
+
+class PolygonCell:
+    r"""
+    A :class:`Cell` restricted to a polygon of the surface.
+
+    INPUT:
+
+    - ``cell`` -- the :class:`Cell` which this polygon cell is a restriction of
+
+    - ``label`` -- the polygon to which this was restricted
+
+    """
+
+    def __init__(self, cell, label):
+        self._cell = cell
+        self._label = label
+
+    def label(self):
+        return self._label
+
+    def _polygon(self):
+        return self.cell().surface().polygon(self._label)
+
+    @cached_method
+    def _polygon_complex(self):
+        from sage.all import CDF
+
+        return self._polygon().change_ring(CDF)
+
+    def surface(self):
+        return self.cell().surface()
+
+    def cell(self):
+        return self._cell
+
+    def contains_point(self, point, boundary_error=0):
+        raise NotImplementedError
+
+    def contains_segment(self, segment):
+        raise NotImplementedError
+
+    def boundary(self):
+        raise NotImplementedError(
+            "this cell decomposition does not know how to compute the boundary segments of a polygon cell yet"
+        )
+
+    def __eq__(self, other):
+        raise NotImplementedError
+
+    def __hash__(self):
+        raise NotImplementedError
+
+    def corners(self):
+        raise NotImplementedError
+
+    def complex_from_point(self, coordinates):
+        k = self.root_branch(coordinates)
+        d = self.cell()._center.angle() - 1
+
+        from sage.all import CDF
+
+        zeta = CDF.zeta(d + 1) ** k
+        z = CDF(*(coordinates - self.center()))
+
+        return zeta * z.nth_root(d + 1)
+
+
+class PolygonCellWithCenter(PolygonCell):
+    r"""
+    A :class:`Cell` restricted to a polygon of the surface.
+
+    INPUT:
+
+    - ``cell`` -- the :class:`Cell` which this polygon cell is a restriction of
+
+    - ``label`` -- the polygon to which this was restricted
+
+    - ``center`` -- the position of the center of this cell; if outside of the
+      polygon, then a segment from any interior point of the polygon to this point is
+      assumed to correspond to an actual line segment in the surface connecting
+      that point to the center.
+
+    """
+
+    def __init__(self, cell, label, center):
+        super().__init__(cell, label)
+
+        if center is None:
+            raise ValueError
+
+        self._center = center
+
+    def center(self):
+        r"""
+        Return the point (not necessarily of the polygon) that should be
+        considered the center of this cell.
+        """
+        return self._center
+
+    def split_segment_with_constant_root_branches(self, segment):
+        r"""
+        Return the ``segment`` split into smaller segments such that these
+        segments do not cross the horizontal line left of the center of the
+        cell (if that center is an actual singularity and not just a marked
+        point.)
+
+        On such a shorter segment, we can then develop an n-th root
+        consistently where n-1 is the order of the singularity.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.voronoi import VoronoiDiagram
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.regular_octagon()
+            sage: V = VoronoiDiagram(S, S.vertices())
+            sage: cell = V.polygon_cell(0, (1, 0))
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: cell.split_segment_uniform_root_branch(OrientedSegment((0, -1), (0, 1)))
+            [OrientedSegment((0, -1), (0, 0)), OrientedSegment((0, 0), (0, 1))]
+
+        """
+        from flatsurf.geometry.euclidean import OrientedSegment
+
+        d = self.cell().center().angle()
+
+        assert d >= 1
+        if d == 1:
+            return [segment]
+
+        if (
+            segment.contains_point(self.center())
+            and segment.start() != self.center()
+            and segment.end() != self.center()
+        ):
+            return [
+                OrientedSegment(segment.start(), self.center()),
+                OrientedSegment(self.center(), segment.end()),
+            ]
+
+        from flatsurf.geometry.euclidean import Ray
+
+        ray = Ray(self.center(), (-1, 0))
+
+        if ray.contains_point(segment.start()) or ray.contains_point(segment.end()):
+            return [segment]
+
+        intersection = ray.intersection(segment)
+
+        if intersection is None:
+            return [segment]
+
+        return [
+            OrientedSegment(segment.start(), intersection),
+            OrientedSegment(intersection, segment.end()),
+        ]
+
+    @cached_method
+    def _root_branch_primitive(self):
+        S = self.surface()
+
+        center = self.cell().center()
+
+        angle = center.angle()
+
+        assert angle > 1
+
+        # Choose a horizontal ray to the right, that defines where the
+        # principal root is being used. We use the "smallest" vertex in the
+        # "smallest" polygon containing such a ray.
+        from flatsurf.geometry.euclidean import ccw
+
+        primitive_label, primitive_vertex = min(
+            (label, vertex)
+            for (label, _) in center.representatives()
+            for vertex in range(len(S.polygon(label).vertices()))
+            if S(label, vertex) == center
+            and ccw((1, 0), S.polygon(label).edge(vertex)) <= 0
+            and ccw((1, 0), -S.polygon(label).edge(vertex - 1)) >= 0
+        )
+
+        return primitive_label, primitive_vertex
+
+    def root_branch(self, point):
+        angle = self.cell().center().angle()
+
+        assert angle >= 1
+        if angle == 1:
+            return 0
+
+        # Choose a horizontal ray to the right, that defines where the
+        # principal root is being used. We use the "smallest" vertex in the
+        # "smallest" polygon containing such a ray.
+        primitive_label, primitive_vertex = self._root_branch_primitive()
+
+        branch = 0
+        label = primitive_label
+        vertex = primitive_vertex
+
+        from flatsurf.geometry.euclidean import ccw
+
+        # Walk around the vertex to determine the branch of the root.
+        while True:
+            polygon = self.surface().polygon(label)
+            if label == self.label() and polygon.vertex(vertex) == self.center():
+                low = (
+                    ccw((-1, 0), polygon.edge(vertex)) <= 0
+                    and ccw((-1, 0), point - polygon.vertex(vertex)) > 0
+                )
+                if low:
+                    return (branch + 1) % angle
+                return branch
+
+            if (
+                ccw((-1, 0), polygon.edge(vertex)) <= 0
+                and ccw((-1, 0), -polygon.edge(vertex - 1)) > 0
+            ):
+                branch += 1
+                branch %= angle
+
+            label, vertex = self.surface().opposite_edge(
+                label, (vertex - 1) % len(polygon.vertices())
+            )
+
+    @cached_method
+    def root_branches(self):
+        root_branches = []
+
+        polygon = self.surface().polygon(self.label())
+        center = self.center()
+        vertex = polygon.get_point_position(center).get_vertex()
+
+        root_branches.append(self.root_branch(center + polygon.edge(vertex)))
+        root_branches.append(self.root_branch(center - polygon.edge(vertex - 1)))
+
+        return frozenset(root_branches)
+
+    def root_branch_for_segment(self, segment):
+        r"""
+        Return which branch can be taken consistently along the ``segment``
+        when developing an n-th root at the center of this Voronoi cell.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.voronoi import VoronoiDiagram
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.regular_octagon()
+            sage: V = VoronoiDiagram(S, S.vertices())
+            sage: cell = V.polygon_cell(0, (0, 0))
+
+            sage: from flatsurf.geometry.euclidean import OrientedSegment
+            sage: cell.root_branch_for_segment(OrientedSegment((0, -1), (0, 1)))
+            Traceback (most recent call last):
+            ...
+            ValueError: segment does not permit a consistent choice of root
+
+            sage: cell.root_branch_for_segment(OrientedSegment((0, 0), (0, 1/2)))
+            0
+
+            sage: cell = V.polygon_cell(0, (1, 0))
+            sage: cell.root_branch_for_segment(OrientedSegment((0, 0), (0, 1/2)))
+            1
+
+            sage: a = S.base_ring().gen()
+            sage: cell = V.polygon_cell(0, (1 + a/2, a/2))
+            sage: cell.root_branch_for_segment(OrientedSegment((1, 1/2 + a/2), (1 + a/2, 1/2 + a/2)))
+            2
+
+        """
+        if self.split_segment_with_constant_root_branches(segment) != [segment]:
+            raise ValueError("segment does not permit a consistent choice of root")
+
+        return self.root_branch(segment.midpoint())
+
+    def __repr__(self):
+        return f"{self.cell()} restricted to polygon {self.label()} at {self._center}"
+
+
+class LineSegmentPolygonCell(PolygonCellWithCenter):
+    r"""
+    A :class:`Cell` restricted to a polygon of the surface.
+
+    INPUT:
+
+    - ``cell`` -- the :class:`Cell` which this polygon cell is a restriction of
+
+    - ``label`` -- the polygon to which this was restricted
+
+    - ``center`` -- the position of the center of this cell; if outside of the
+      polygon, then a segment from any interior point of the polygon to this point is
+      assumed to correspond to an actual line segment in the surface connecting
+      that point to the center.
+
+    - ``boundary`` -- segments that are restrictions of the boundary of the
+      cell to this polygon, in counterclockwise order around the ``center``.
+    """
+
+    def __init__(self, cell, label, center, boundary):
+        super().__init__(cell, label, center)
+
+        self._boundary = boundary
+
+    def boundary(self):
+        return self._boundary
+
+    def contains_point(self, point, boundary_error=0):
+        if boundary_error < 0:
+            raise NotImplementedError
+
+        P = self.polygon()
+        if P.contains_point(point):
+            return True
+
+        if boundary_error != 0:
+            from math import sqrt
+
+            if float(P.distance(point)) <= boundary_error * sqrt(
+                float(self.surface().polygon(self.label()).area())
+            ):
+                # print(f"Point {point} is not in {P} but at distance {float(P.distance(point))}")
+                return True
+
+        return False
+
+    def polygon(self):
+        r"""
+        Return a polygon (subset of :meth:`polygon`) that describes this cell.
+        """
+        from flatsurf import Polygon
+
+        return Polygon(vertices=[segment[0] for segment in self._boundary])
+
+    def __eq__(self, other):
+        if not isinstance(other, PolygonCell):
+            return False
+
+        return (
+            self.cell() == other.cell()
+            and self.label() == other.label()
+            and self.center() == other.center()
+        )
+
+    def __hash__(self):
+        return hash((self.label(), self.center()))
+
+    def plot(self, graphical_polygon=None):
+        r"""
+        Return a graphical representation of this cell.
+
+        EXAMPLES::
+
+            sage: from flatsurf.geometry.voronoi import VoronoiDiagram
+            sage: from flatsurf import translation_surfaces
+            sage: S = translation_surfaces.regular_octagon()
+            sage: V = VoronoiDiagram(S, S.vertices())
+            sage: cell = V.polygon_cell(0, (0, 0))
+            sage: cell.plot()
+            Graphics object consisting of 3 graphics primitives
+
+        """
+        plot_polygon = graphical_polygon is None
+
+        if graphical_polygon is None:
+            graphical_polygon = (
+                self.surface().graphical_surface().graphical_polygon(self.label())
+            )
+
+        shift = graphical_polygon.transformed_vertex(0) - self.surface().polygon(
+            self.label()
+        ).vertex(0)
+
+        from flatsurf.geometry.euclidean import OrientedSegment
+
+        plot = sum(
+            OrientedSegment(*segment).translate(shift).plot(point=False)
+            for segment in self.boundary()
+        )
+
+        if plot_polygon:
+            pass  # plot = graphical_polygon.plot_polygon() + plot
+
+        return plot
+
+    def corners(self):
+        return tuple(segment[0] for segment in self._boundary)
+
+
+class ConvexLineSegmentPolygonCell(LineSegmentPolygonCell):
+    def contains_segment(self, segment):
+        return self.contains_point(segment.start()) and self.contains_point(
+            segment.end()
+        )
+
+
+class VoronoiCellBoundarySegment(LineBoundarySegment):
+    pass
+
+
+class VoronoiPolygonCell(ConvexLineSegmentPolygonCell):
+    pass
+
+
+class VoronoiCell(LineSegmentCell):
+    BoundarySegment = VoronoiCellBoundarySegment
+    PolygonCell = VoronoiPolygonCell
+
+    @cached_method
+    def boundary(self):
+        surface = self.surface()
+
+        (label, coordinates) = self.center().representative()
+        vertex = surface.polygon(label).get_point_position(coordinates).get_vertex()
+
+        boundary = []
+
+        initial_label, initial_vertex = label, vertex
+        while True:
+            polygon = surface.polygon(label)
+
+            center = polygon.circumscribing_circle().center().vector()
+
+            next_label, next_vertex = surface.opposite_edge(
+                label, (vertex - 1) % len(polygon.vertices())
+            )
+            next_polygon = surface.polygon(next_label)
+
+            next_center = next_polygon.circumscribing_circle().center().vector()
+
+            # Bring next_center into the coordinate system of polygon
+            next_center += polygon.vertex(vertex) - next_polygon.vertex(next_vertex)
+
+            holonomy = next_center - center
+
+            if not holonomy:
+                # Ambiguous Delaunay triangulation. The two circumscribing circles coincide.
+                pass
+            else:
+                # Construct the start point of the boundary segment and a segment from the center to that start point.
+                start_label, start_edge = (label, vertex)
+                start_holonomy = center - polygon.vertex(start_edge)
+
+                from flatsurf.geometry.euclidean import ccw
+
+                if ccw(-polygon.edge(start_edge - 1), start_holonomy) > 0:
+                    start_label, start_edge = surface.opposite_edge(
+                        start_label, (start_edge - 1) % len(polygon.vertices())
+                    )
+                    polygon = surface.polygon(start_label)
+                elif ccw(polygon.edge(start_edge), start_holonomy) < 0:
+                    start_label, start_edge = surface.opposite_edge(
+                        start_label, start_edge
+                    )
+                    polygon = surface.polygon(start_label)
+                    start_edge = (start_edge + 1) % len(polygon.vertices())
+
+                assert (
+                    ccw(-polygon.edge(start_edge - 1), start_holonomy) < 0
+                    and ccw(polygon.edge(start_edge), start_holonomy) >= 0
+                ), "center of Voronoi cell must be within a neighboring triangle"
+
+                start_segment = SurfaceLineSegment(
+                    surface, start_label, polygon.vertex(start_edge), start_holonomy
+                )
+
+                assert (
+                    not start_segment.end().is_vertex()
+                ), "boundary of a Voronoi cell cannot go through a vertex"
+
+                segment = SurfaceLineSegment(
+                    surface, *start_segment.end().representative(), holonomy
+                )
+
+                boundary.append(
+                    VoronoiCellBoundarySegment(self, segment, start_segment)
+                )
+
+            label, vertex = next_label, next_vertex
+
+            if (label, vertex) == (initial_label, initial_vertex):
+                break
+
+            label, vertex = next_label, next_vertex
+
+        return tuple(boundary)
+
+
+class VoronoiCellDecomposition_delaunay(CellDecomposition):
+    r"""
+    EXAMPLES::
+
+        sage: from flatsurf import translation_surfaces, VoronoiCellDecomposition
+
+        sage: S = translation_surfaces.regular_octagon().subdivide().codomain().delaunay_triangulate().codomain()
+        sage: V = VoronoiCellDecomposition(S)
+
+        sage: V.plot()
+
+    ::
+
+        sage: from flatsurf import Polygon, similarity_surfaces, VoronoiCellDecomposition
+
+        sage: S = similarity_surfaces.billiard(Polygon(angles=[3, 4, 13])).minimal_cover("translation").erase_marked_points().codomain().delaunay_triangulate().codomain()
+        sage: V = VoronoiCellDecomposition(S)
+
+        sage: V.plot()
+
+    """
+    Cell = VoronoiCell
+
+    def __init__(self, surface):
+        if not surface.is_delaunay_triangulated():
+            raise NotImplementedError("surface must be Delaunay triangulated")
+        if not surface.is_translation_surface():
+            raise NotImplementedError("surface must be a translation surface")
+
+        super().__init__(surface)
+
+    def __repr__(self):
+        return f"Voronoi cell decomposition of {self.surface()}"
+
+    def change(self, surface=None):
+        if surface is not None:
+            self = VoronoiCellDecomposition(surface)
+
+        return self
+
+
+class ApproximateWeightedCellBoundarySegment(LineBoundarySegment):
+    pass
+
+
+class ApproximateWeightedVoronoiPolygonCell(ConvexLineSegmentPolygonCell):
+    pass
+
+
+class ApproximateWeightedVoronoiCell(LineSegmentCell):
+    BoundarySegment = ApproximateWeightedCellBoundarySegment
+    PolygonCell = ApproximateWeightedVoronoiPolygonCell
+
+    @cached_method
+    def boundary(self):
+        surface = self.surface()
+
+        (label, coordinates) = self.center().representative()
+        vertex = surface.polygon(label).get_point_position(coordinates).get_vertex()
+
+        boundary = []
+
+        initial_label, initial_vertex = label, vertex
+
+        while True:
+            polygon = surface.polygon(label)
+
+            corners = self.decomposition()._split_polygon_at_vertex(label, vertex)
+
+            assert (
+                len(corners) > 1
+            ), "cannot create segments in this polygon from a single corner"
+
+            next_label, next_vertex = surface.opposite_edge(
+                label, (vertex - 1) % len(polygon.vertices())
+            )
+
+            for corner, next_corner in zip(corners, corners[1:]):
+                segment = SurfaceLineSegment(
+                    surface, label, corner, next_corner - corner
+                )
+
+                start_segment_holonomy = corner - polygon.vertex(vertex)
+                from flatsurf.geometry.euclidean import is_anti_parallel
+
+                if is_anti_parallel(polygon.edge(vertex - 1), start_segment_holonomy):
+                    start_segment = SurfaceLineSegment(
+                        surface,
+                        next_label,
+                        surface.polygon(next_label).vertex(next_vertex),
+                        start_segment_holonomy,
+                    )
+                else:
+                    start_segment = SurfaceLineSegment(
+                        surface, label, polygon.vertex(vertex), start_segment_holonomy
+                    )
+                boundary.append(
+                    ApproximateWeightedCellBoundarySegment(self, segment, start_segment)
+                )
+
+                # TODO: Why is this needed?
+                if len(boundary) > 1:
+                    if boundary[-1] == boundary[-2]:
+                        boundary.pop()
+
+            ## Now taken care of by split_polygon_at_vertex()
+            ## corners_next_polygon = self.decomposition()._split_polygon_at_vertex(next_label, next_vertex)
+            ## if surface(label, corners[-1]) != surface(next_label, corners_next_polygon[0]):
+            ##     # The split of the polygons along the edge is not compatible,
+            ##     # add a short segment along that edge to connect the last
+            ##     # corner of this polygon to the first corner of the next
+            ##     # polygon.
+            ##     next_polygon = surface.polygon(next_label)
+            ##     corner_next_polygon = corners_next_polygon[0] + (polygon.vertex(vertex) - next_polygon.vertex(next_vertex))
+
+            ##     segment = SurfaceLineSegment(surface, label, corners[-1], corner_next_polygon - corners[-1])
+            ##     start_segment = SurfaceLineSegment(surface, next_label, next_polygon.vertex(next_vertex), corners[-1] - polygon.vertex(vertex))
+
+            ##     boundary.append(ApproximateWeightedCellBoundarySegment(self, segment, start_segment))
+
+            label, vertex = next_label, next_vertex
+
+            if (label, vertex) == (initial_label, initial_vertex):
+                break
+
+        # TODO: Why is this needed?
+        if len(boundary) > 1:
+            if boundary[-1] == boundary[0]:
+                boundary.pop()
+
+        return tuple(boundary)
+
+
+class ApproximateWeightedVoronoiCellDecomposition_delaunay(CellDecomposition):
+    r"""
+    EXAMPLES::
+
+        sage: from flatsurf import translation_surfaces, ApproximateWeightedVoronoiCellDecomposition
+
+        sage: S = translation_surfaces.regular_octagon().subdivide().codomain().delaunay_triangulate().codomain()
+        sage: V = ApproximateWeightedVoronoiCellDecomposition(S)
+
+        sage: V.plot()
+
+    ::
+
+        sage: from flatsurf import Polygon, similarity_surfaces, ApproximateWeightedVoronoiCellDecomposition
+
+        sage: S = similarity_surfaces.billiard(Polygon(angles=[3, 4, 13])).minimal_cover("translation").erase_marked_points().codomain().delaunay_triangulate().codomain()
+        sage: V = ApproximateWeightedVoronoiCellDecomposition(S)
+
+        sage: V.plot()
+
+    """
+    Cell = ApproximateWeightedVoronoiCell
+
+    def __init__(self, surface):
+        if not surface.is_delaunay_triangulated():
+            raise NotImplementedError("surface must be triangulated")
+        if not surface.is_translation_surface():
+            raise NotImplementedError("surface must be a translation surface")
+
+        super().__init__(surface)
+
+    def __repr__(self):
+        return f"Approximate weighted Voronoi cell decomposition of {self.surface()}"
+
+    def change(self, surface=None):
+        if surface is not None:
+            self = ApproximateWeightedVoronoiCellDecomposition(surface)
+
+        return self
+
+    def _exactify(self, x):
+        R = self.surface().base_ring()
+        return R(round(float(x), 3))
+
+    @cached_method
+    def _split_polygon_at_vertex(self, label, vertex):
+        # Return a refined version of _split_polygon(label)[vertex] that adds points for the split polygon that happened across any edges (so each segment has a negative.)
+        surface = self.surface()
+        polygon = surface.polygon(label)
+
+        split = [polygon.vertex(vertex)]
+
+        split.extend(self._split_polygon(label)[vertex])
+
+        # previous_label, previous_vertex = surface.opposite_edge(label, vertex)
+        # previous_polygon = surface.polygon(previous_label)
+        # previous_vertex = (previous_vertex + 1) % len(previous_polygon.vertices())
+        # previous_corner = self._split_polygon(previous_label)[previous_vertex][-1]
+        # previous_corner += (polygon.vertex(vertex) - previous_polygon.vertex(previous_vertex))
+
+        # assert polygon.get_point_position(previous_corner).is_in_edge_interior() and polygon.get_point_position(previous_corner).get_edge() == vertex
+
+        # if split[1] != previous_corner:
+        #     split = split[:1] + [previous_corner] + split[1:]
+
+        next_label, next_vertex = surface.opposite_edge(
+            label, (vertex - 1) % len(polygon.vertices())
+        )
+        next_polygon = surface.polygon(next_label)
+        next_corner = self._split_polygon(next_label)[next_vertex][0]
+        next_corner += polygon.vertex(vertex) - next_polygon.vertex(next_vertex)
+
+        assert polygon.get_point_position(
+            next_corner
+        ).is_in_edge_interior() and polygon.get_point_position(
+            next_corner
+        ).get_edge() == (
+            vertex - 1
+        ) % len(
+            polygon.vertices()
+        )
+
+        if split[-1] != next_corner:
+            split.append(next_corner)
+
+        refined_split = []
+        for corner, next_corner in zip(split, split[1:] + split[:1]):
+            assert corner != next_corner
+            midpoint = (corner + next_corner) / 2
+            midpoint_position = polygon.get_point_position(midpoint)
+            if midpoint_position.is_in_edge_interior():
+                edge = midpoint_position.get_edge()
+
+                opposite_label, opposite_edge = surface.opposite_edge(label, edge)
+                opposite_polygon = surface.polygon(opposite_label)
+
+                edge_points = self._split_polygon_edge(opposite_label, opposite_edge)
+                edge_points = [
+                    p
+                    + (
+                        polygon.vertex(edge)
+                        - opposite_polygon.vertex(opposite_edge + 1)
+                    )
+                    for p in edge_points
+                ]
+
+                assert all(
+                    polygon.get_point_position(p).get_edge() == edge
+                    for p in edge_points
+                )
+
+                from flatsurf.geometry.euclidean import time_on_segment
+
+                edge_points = [
+                    p
+                    for p in edge_points
+                    if time_on_segment((corner, next_corner), p) not in [0, 1, None]
+                ]
+                for p in sorted(
+                    edge_points, key=lambda p: time_on_segment((corner, next_corner), p)
+                ):
+                    assert p not in refined_split
+                    assert p != next_corner
+                    refined_split.append(p)
+
+            assert next_corner not in refined_split
+            refined_split.append(next_corner)
+
+        return tuple(refined_split[:-1])
+
+    @cached_method
+    def _split_polygon_edge(self, label, edge):
+        polygon = self.surface().polygon(label)
+
+        points = []
+
+        for split in self._split_polygon(label):
+            for point in split:
+                position = polygon.get_point_position(point)
+                if not position.is_in_edge_interior():
+                    continue
+                if position.get_edge() != edge:
+                    continue
+                if point not in points:
+                    points.append(point)
+
+        return tuple(points)
+
+    @cached_method
+    def _split_polygon(self, label):
+        surface = self.surface()
+        polygon = surface.polygon(label)
+        nvertices = len(polygon.vertices())
+        assert nvertices == 3
+
+        vertices = [surface(label, v) for v in range(nvertices)]
+
+        # TODO: If I remember correctly, the algorithm is such that
+        # essentially, the points on the boundary have distances from their
+        # respective centers according to the weights, i.e., if two vertices
+        # v0, v1 have weights w0, w1, then the distances will be such that
+        # d0/w0 == d1/w1. (So, large weight implies large distance.)
+
+        weights = [float(v.radius_of_convergence()) for v in vertices]
+
+        splits = [
+            self._split_segment(
+                (polygon.vertex(v), polygon.vertex(v + 1)),
+                polygon.vertex(v + 2),
+                weights[v:] + weights[:v],
+            )
+            for v in range(nvertices)
+        ]
+
+        lens = [len(split) for split in splits]
+
+        if lens == [1, 1, 1]:
+            # The edges are far away from the opposite corners.
+            # A---+---C
+            # |  /|  /
+            # | / | /
+            # |/C |/
+            # +---+
+            # |  /
+            # | /
+            # |/
+            # B
+            # We give each small triangle in this picture to its vertex and
+            # split the central triangle C between the three vertices. (So each
+            # cell will be a quadrilateral.)
+            center = sum(
+                splits[v][0]
+                * self._exactify(
+                    (weights[v] + weights[(v + 1) % nvertices]) / (2 * sum(weights))
+                )
+                for v in range(nvertices)
+            )
+
+            return [
+                [splits[0][0], center, splits[2][0]],
+                [splits[1][0], center, splits[0][0]],
+                [splits[2][0], center, splits[1][0]],
+            ]
+        if lens == [2, 1, 1]:
+            return [
+                [splits[0][0], splits[2][0]],
+                [splits[1][0], splits[0][1]],
+                [splits[2][0], splits[0][0], splits[0][1], splits[1][0]],
+            ]
+        if lens == [1, 1, 2]:
+            return [
+                [splits[0][0], splits[2][1]],
+                [splits[1][0], splits[2][0], splits[2][1], splits[0][0]],
+                [splits[2][0], splits[1][0]],
+            ]
+        if lens == [1, 2, 1]:
+            return [
+                [splits[0][0], splits[1][0], splits[1][1], splits[2][0]],
+                [splits[1][0], splits[0][0]],
+                [splits[2][0], splits[1][1]],
+            ]
+
+        raise NotImplementedError(f"non-trivial split for {label} with {lens}")
+
+    def _split_segment(self, AB, C, weights):
+        A, B = AB
+        wA, wB, wC = weights
+
+        def circle_of_apollonius(P, Q, w, v):
+            if w == v:
+                raise NotImplementedError("circle of Apollonius is a line")
+
+            # The foci of the circle.
+            C = (w * Q + v * P) / (w + v)
+            D = (v * P - w * Q) / (v - w)
+
+            center = (C + D) / 2
+
+            radius = (D - C).norm() / 2
+
+            return center, radius
+
+        def circle_segment_intersection(center, radius, segment):
+            # Shift the center to the origin.
+            segment = segment[0] - center, segment[1] - center
+
+            # Let P(t) be the point on the segment at time t in [0, 1].
+            # We solve the quadratic equation for |P(t)| = radius^2.
+
+            a = (segment[1][0] - segment[0][0]) ** 2 + (
+                segment[1][1] - segment[0][1]
+            ) ** 2
+            b = 2 * (
+                segment[0][0] * segment[1][0]
+                + segment[0][1] * segment[1][1]
+                - segment[0][0] ** 2
+                - segment[0][1] ** 2
+            )
+            c = segment[0][0] ** 2 + segment[0][1] ** 2 - radius**2
+
+            from math import sqrt
+
+            for t in [
+                (-b + sqrt(float(b**2 - 4 * a * c))) / (2 * a),
+                (-b - sqrt(float(b**2 - 4 * a * c))) / (2 * a),
+            ]:
+                if 0 <= t <= 1:
+                    return self._exactify(t)
+
+            assert (
+                False
+            ), f"intersection point must be on segment but time {t} is not in the unit interval, i.e., segment {segment} does not intersect circle at origin with radius {radius}"
+
+        A_equal_B = A + (B - A) * self._exactify(wA / (wA + wB))
+        if ((A - A_equal_B) / wA).norm() > ((C - A_equal_B) / wC).norm():
+            # C is closer to A on the segment AB than B.
+            # Construct the circle of Apollonius with foci A and C.
+            if wA == wC:
+                # Circle of Apollonius is a line.
+                AC = C - A
+                midpoint = (C + A) / 2
+                from sage.all import vector
+
+                orthogonal_ray = (midpoint, vector((AC[1], -AC[0])))
+
+                from flatsurf.geometry.euclidean import ray_segment_intersection
+
+                A_equal_C = ray_segment_intersection(*orthogonal_ray, (A, B))
+                assert A_equal_C is not None
+            else:
+                center, radius = circle_of_apollonius(A, C, wA, wC)
+                t = circle_segment_intersection(center, radius, (A, B))
+                A_equal_C = (1 - t) * A + t * B
+            if wB == wC:
+                # Circle of Apollonius is a line.
+                BC = C - B
+                midpoint = (C + B) / 2
+                from sage.all import vector
+
+                orthogonal_ray = (midpoint, vector((-BC[1], BC[0])))
+
+                from flatsurf.geometry.euclidean import ray_segment_intersection
+
+                B_equal_C = ray_segment_intersection(*orthogonal_ray, (B, A))
+                assert B_equal_C is not None
+            else:
+                center, radius = circle_of_apollonius(B, C, wB, wC)
+                t = circle_segment_intersection(center, radius, (B, A))
+                B_equal_C = (1 - t) * B + t * A
+
+            return [A_equal_C, B_equal_C]
+
+        return [A_equal_B]
+
+
+class MappedBoundarySegment(BoundarySegment):
+    def __init__(self, cell, boundary):
+        super().__init__(
+            cell, cell._decomposition._isomorphism.section()._image_line_segment(boundary.segment())
+        )
+        self._codomain_boundary = boundary
+
+    def radius(self):
+        return self._codomain_boundary.radius()
+
+    @cached_method
+    def polygon_cell_boundaries(self):
+        polygon_cell_boundaries = []
+
+        for (
+            polygon_cell,
+            subsegment,
+            opposite_polygon_cell,
+        ) in self._codomain_boundary.polygon_cell_boundaries():
+            raise NotImplementedError  # TODO: This seems to be quite complicated to compute.
+
+        return tuple(polygon_cell_boundaries)
+
+
+class MappedPolygonCell(PolygonCell):
+    pass
+
+
+class MappedCell(Cell):
+    def __init__(self, decomposition, center):
+        super().__init__(decomposition, center)
+        self._codomain_cell = decomposition._codomain_decomposition.cell(
+            decomposition._isomorphism(center)
+        )
+
+    @cached_method
+    def boundary(self):
+        return tuple(
+            MappedBoundarySegment(self, boundary)
+            for boundary in self._codomain_cell.boundary()
+        )
+
+
+class MappedCellDecomposition(CellDecomposition):
+    Cell = MappedCell
+
+    def __init__(self, codomain_decomposition, isomorphism):
+        super().__init__(isomorphism.domain())
+
+        self._codomain_decomposition = codomain_decomposition
+        self._isomorphism = isomorphism
+
+    def __repr__(self):
+        return f"{self._codomain_decomposition} pulled back to {self.surface()}"
+
+
+# TODO: Move to surface objects.
+class SurfaceLineSegment:
+    # TODO: Use tangent vectors instead of start (and end.)
+    def __init__(self, surface, label, start, holonomy):
+        if not holonomy:
+            raise ValueError
+
+        polygon = surface.polygon(label)
+
+        position = polygon.get_point_position(start)
+        if position.is_outside():
+            raise ValueError(
+                f"start point of segment must be in the polygon but {start} is not in {polygon}"
+            )
+        if position.is_in_edge_interior():
+            edge = position.get_edge()
+            from flatsurf.geometry.euclidean import ccw, is_anti_parallel
+
+            if ccw(polygon.edge(edge), holonomy) < 0 or (
+                ccw(polygon.edge(edge), holonomy) == 0
+                and is_anti_parallel(polygon.edge(edge), holonomy)
+            ):
+                start -= polygon.vertex(edge)
+                label, edge = surface.opposite_edge(label, edge)
+                polygon = surface.polygon(label)
+                start += polygon.vertex(edge + 1)
+        if position.is_vertex():
+            vertex = position.get_vertex()
+            from flatsurf.geometry.euclidean import ccw
+
+            if (
+                ccw(polygon.edge(vertex), holonomy) >= 0
+                and ccw(-polygon.edge(vertex - 1), holonomy) < 0
+            ):
+                # holonomy points into the polygon
+                pass
+            else:
+                if surface(label, vertex).angle() != 1:
+                    raise ValueError(
+                        "holonomy must point into the polygon at a singular point"
+                    )
+                raise NotImplementedError(
+                    "cannot handle holonomy vector points out of the polygon at a marked point yet"
+                )
+
+        self._surface = surface
+        self._label = label
+        self._start = start
+        self._holonomy = holonomy
+
+        # TODO: Instead, copy if mutable.
+        self._start.set_immutable()
+        self._holonomy.set_immutable()
+
+    @staticmethod
+    def from_linear_combination(saddle_connections, coordinates):
+        if len(saddle_connections) != len(coordinates):
+            raise ValueError
+
+        if len(saddle_connections) != 2:
+            raise NotImplementedError
+
+        surface = saddle_connections[0].surface()
+
+        if not surface.is_translation_surface():
+            raise NotImplementedError
+
+        if surface(*saddle_connections[0].start()) != surface(
+            *saddle_connections[1].start()
+        ):
+            raise ValueError
+
+        holonomy = sum(
+            [
+                coefficient * connection.holonomy()
+                for (coefficient, connection) in zip(coordinates, saddle_connections)
+            ]
+        )
+
+        label, edge = saddle_connections[0].start()
+        polygon = surface.polygon(label)
+
+        # TODO: We must factor this rotation algorithm out somehow. It's used in so many places by now.
+        from flatsurf.geometry.euclidean import ccw
+
+        while ccw(holonomy, -polygon.edge(edge - 1)) <= 0:
+            label, edge = surface.opposite_edge(
+                label, (edge - 1) % len(polygon.edges())
+            )
+            polygon = surface.polygon(label)
+
+        return SurfaceLineSegment(surface, label, polygon.vertex(edge), holonomy)
+
+    def surface(self):
+        return self._surface
+
+    # TODO: Could speed this up further by doing this upon construction; the expensive get_point_position() is already done there.
+    @cached_method
+    def start(self):
+        return self._surface(*self.start_representative())
+
+    def start_representative(self):
+        return self._label, self._start
+
+    def start_tangent_vector(self):
+        return self._surface.tangent_vector(self._label, self._start, self._holonomy)
+
+    def end_tangent_vector(self):
+        raise NotImplementedError
+
+    def an_inner_point(self):
+        segment = self.split()[0]
+        return self.surface()(segment[0], (segment[1][0] + segment[1][1]) / 2)
+
+    def end(self):
+        return self._surface(*self.end_representative())
+
+    def end_representative(self):
+        end = self.split()[-1]
+        return end[0], end[1][1]
+
+    def _flow_to_exit(self):
+        r"""
+        Split this segment into a segment that lies entirely in its source
+        polygon, and a rest segment.
+
+        If this segment lies entirely in its source segment, returns ``None``
+        as the rest.
+
+        The initial part is returned as a segment label and a Euclidean segment
+        in the corresponding polygon.
+        """
+        # TDOO: This generic flowing foo should probably be implemented in a better place.
+        surface = self.surface()
+        polygon = surface.polygon(self._label)
+
+        end = self._start + self._holonomy
+        end.set_immutable()
+
+        if polygon.get_point_position(end).is_outside():
+            exit = polygon.flow_to_exit(self._start, self._holonomy)
+            exit.set_immutable()
+
+            Δ = exit - self._start
+
+            rest = SurfaceLineSegment(surface, self._label, exit, self._holonomy - Δ)
+
+            return (self._label, (self._start, exit)), rest
+
+        return (self._label, (self._start, end)), None
+
+    def __bool__(self):
+        return bool(self._holonomy)
+
+    def holonomy(self, start=None):
+        if start is None:
+            start = self._label, self._start
+
+        if start == (self._label, self._start):
+            return self._holonomy
+
+        # TODO: This is only correct for translation surfaces. (And even there
+        # it's a bit unclear what this means if start has nothing to do with
+        # the representative chosen in _start.)
+        return self._holonomy
+
+    def __repr__(self):
+        return f"{self.start()}→{self.end()}"
+
+    def __neg__(self):
+        return SurfaceLineSegment(
+            self._surface, *self.end_representative(), -self._holonomy
+        )
+
+    def split(self, label=None):
+        r"""
+        Return this segment split into subsegments that live entirely in
+        polygons of the surface.
+
+        The subsegments are returned as triples (polygon label, segment in the
+        euclidean plane, holonomy from the start point of the segment to the
+        start point of the subsegment).
+
+        If ``label`` is set, only return the subsegments that are in the
+        polygon with ``label``.
+        """
+        surface = self.surface()
+
+        segments = []
+
+        holonomy = self.surface().euclidean_plane().vector_space()((0, 0))
+
+        while True:
+            if self is None:
+                break
+
+            (lbl, segment), self = self._flow_to_exit()
+
+            if label is None or label == lbl:
+                holonomy.set_immutable()
+                segments.append((lbl, segment, holonomy))
+
+            # TODO: This assumes that this is a translation surface.
+            holonomy += segment[1] - segment[0]
+
+        return segments
+
+    def __eq__(self, other):
+        if self.start() != other.start():
+            return False
+
+        start = self.start_representative()
+
+        return self.holonomy(start) == other.holonomy(start)
+
+    def __hash__(self):
+        return hash((self.start(), self._holonomy))
+
+    def plot(self, graphical_surface=None):
+        plot_surface = graphical_surface is None
+
+        if graphical_surface is None:
+            graphical_surface = self.surface().graphical_surface(
+                edge_labels=False, polygon_labels=False
+            )
+
+        plot = []
+        if plot_surface:
+            plot.append(graphical_surface.plot())
+
+        subsegments = self.split()
+        for s, (label, subsegment, _) in enumerate(subsegments):
+            polygon = self.surface().polygon(label)
+
+            graphical_polygon = graphical_surface.graphical_polygon(label)
+
+            # TODO: This should be implemented in graphical polygon probably.
+            # TODO: This assumes that the surface is a translation surface.
+            vertex = graphical_polygon.transformed_vertex(0)
+            graphical_subsegment = [
+                vertex + (subsegment[0] - polygon.vertex(0)),
+                vertex + (subsegment[1] - polygon.vertex(0)),
+            ]
+
+            if s == len(subsegments) - 1:
+                from sage.all import arrow2d
+
+                plot.append(
+                    arrow2d(
+                        *graphical_subsegment, arrowsize=1.5, width=0.4, color="green"
+                    )
+                )
+            else:
+                from sage.all import line2d
+
+                plot.append(line2d(graphical_subsegment))
+
+        return sum(plot)
+
+
+def VoronoiCellDecomposition(surface):
+    if surface.is_delaunay_triangulated():
+        return VoronoiCellDecomposition_delaunay(surface)
+
+    delaunay_triangulation = surface.delaunay_triangulate()
+    return MappedCellDecomposition(
+        VoronoiCellDecomposition(delaunay_triangulation.codomain()),
+        delaunay_triangulation,
+    )
+
+
+def ApproximateWeightedVoronoiCellDecomposition(surface):
+    if surface.is_delaunay_triangulated():
+        return ApproximateWeightedVoronoiCellDecomposition_delaunay(surface)
+
+    delaunay_triangulation = surface.delaunay_triangulate()
+    return MappedCellDecomposition(
+        ApproximateWeightedVoronoiCellDecomposition(delaunay_triangulation.codomain()),
+        delaunay_triangulation,
+    )
diff --git a/flatsurf/graphical/polygon.py b/flatsurf/graphical/polygon.py
index a54af7e8e..4825ea617 100644
--- a/flatsurf/graphical/polygon.py
+++ b/flatsurf/graphical/polygon.py
@@ -24,6 +24,7 @@
 from sage.plot.text import text
 from sage.plot.line import line2d
 from sage.plot.point import point2d
+from sage.misc.cachefunc import cached_method
 
 from flatsurf.geometry.similarity import SimilarityGroup
 
@@ -77,7 +78,7 @@ def __repr__(self):
             sage: gs.graphical_polygon(0)
             GraphicalPolygon with vertices [(0.0, 0.0), (2.0, -2.0), (2.0, 0.0)]
         """
-        return "GraphicalPolygon with vertices {}".format(self._v)
+        return "GraphicalPolygon with vertices {}".format(list(self._v()))
 
     def base_polygon(self):
         r"""
@@ -89,8 +90,13 @@ def transformed_vertex(self, e):
         r"""
         Return the graphical coordinates of the vertex in double precision.
         """
+        # TODO: Where would the double precision come from that the docstring talks about?
         return self._transformation(self._p.vertex(e))
 
+    @cached_method
+    def _v(self):
+        return tuple(V(self._transformation(v)) for v in self._p.vertices())
+
     def xmin(self):
         r"""
         Return the minimal x-coordinate of a vertex.
@@ -99,7 +105,7 @@ def xmin(self):
 
             to fit with Sage conventions this should be xmin
         """
-        return min([v[0] for v in self._v])
+        return min([v[0] for v in self._v()])
 
     def ymin(self):
         r"""
@@ -109,7 +115,7 @@ def ymin(self):
 
             to fit with Sage conventions this should be ymin
         """
-        return min([v[1] for v in self._v])
+        return min([v[1] for v in self._v()])
 
     def xmax(self):
         r"""
@@ -119,7 +125,7 @@ def xmax(self):
 
             to fit with Sage conventions this should be xmax
         """
-        return max([v[0] for v in self._v])
+        return max([v[0] for v in self._v()])
 
     def ymax(self):
         r"""
@@ -129,7 +135,7 @@ def ymax(self):
 
             To fit with Sage conventions this should be ymax
         """
-        return max([v[1] for v in self._v])
+        return max([v[1] for v in self._v()])
 
     def bounding_box(self):
         r"""
@@ -186,8 +192,7 @@ def set_transformation(self, transformation=None):
             self._transformation = SimilarityGroup(self._p.base_ring()).one()
         else:
             self._transformation = transformation
-        # recompute the location of vertices:
-        self._v = [V(self._transformation(v)) for v in self._p.vertices()]
+        self._v.clear_cache()
 
     def plot_polygon(self, **options):
         r"""
@@ -198,7 +203,7 @@ def plot_polygon(self, **options):
         """
         if "axes" not in options:
             options["axes"] = False
-        return polygon2d(self._v, **options)
+        return polygon2d(self._v(), **options)
 
     def plot_label(self, label, **options):
         r"""
@@ -214,7 +219,7 @@ def plot_label(self, label, **options):
         if "position" in options:
             return text(str(label), options.pop("position"), **options)
         else:
-            return text(str(label), sum(self._v) / len(self._v), **options)
+            return text(str(label), sum(self._v()) / len(self._v()), **options)
 
     def plot_edge(self, e, **options):
         r"""
@@ -224,7 +229,7 @@ def plot_edge(self, e, **options):
         Options are processed as in sage.plot.line.line2d.
         """
         return line2d(
-            [self._v[e], self._v[(e + 1) % len(self.base_polygon().vertices())]],
+            [self._v()[e], self._v()[(e + 1) % len(self.base_polygon().vertices())]],
             **options
         )
 
@@ -245,7 +250,7 @@ def plot_edge_label(self, i, label, **options):
 
         Other options are processed as in sage.plot.text.text.
         """
-        e = self._v[(i + 1) % len(self.base_polygon().vertices())] - self._v[i]
+        e = self._v()[(i + 1) % len(self.base_polygon().vertices())] - self._v()[i]
 
         if "position" in options:
             if options["position"] not in ["inside", "outside", "edge"]:
@@ -321,7 +326,7 @@ def plot_edge_label(self, i, label, **options):
         # Now push_off stores the amount it should be pushed into the polygon
 
         no = V((-e[1], e[0]))
-        return text(label, self._v[i] + t * e + push_off * no, **options)
+        return text(label, self._v()[i] + t * e + push_off * no, **options)
 
     def plot_zero_flag(self, **options):
         r"""
@@ -339,7 +344,10 @@ def plot_zero_flag(self, **options):
             t = 0.5
 
         return line2d(
-            [self._v[0], self._v[0] + t * (sum(self._v) / len(self._v) - self._v[0])],
+            [
+                self._v()[0],
+                self._v()[0] + t * (sum(self._v()) / len(self._v()) - self._v()[0]),
+            ],
             **options
         )
 
diff --git a/flatsurf/graphical/surface.py b/flatsurf/graphical/surface.py
index e8a23fd07..ebca09f52 100644
--- a/flatsurf/graphical/surface.py
+++ b/flatsurf/graphical/surface.py
@@ -1,3 +1,8 @@
+# TODO: It is annoying that the zero flag is not normally shown when plotting.
+# Whenever polygon labels are shown, the default should be to show the zero
+# flag. Also, there should be an option to enable it with a zero_flags=True or
+# something like that. Finally, it would be nice to not show edge gluings if
+# there is only one possible translation gluing.
 r"""
 .. jupyter-execute::
     :hide-code:
@@ -35,6 +40,8 @@
 #  along with sage-flatsurf. If not, see <https://www.gnu.org/licenses/>.
 # ****************************************************************************
 
+from sage.misc.cachefunc import cached_function
+
 from sage.rings.integer_ring import ZZ
 from sage.rings.rational_field import QQ
 from sage.modules.free_module_element import vector
@@ -134,18 +141,10 @@ class works. (Apologies!)
 
         - ``'letter'`` -- add matching letters to glued edges in an arbitrary way
 
-    - ``adjacencies`` -- a list of pairs ``(p,e)`` to be used to set
-      adjacencies of polygons.
-
     - ``default_position_function`` -- a function mapping polygon labels to
       similarities describing the position of the corresponding polygon.
 
-    If adjacencies is not defined and the surface is finite, make_all_visible()
-    is called to make all polygons visible.
-
-    EXAMPLES:
-
-    .. jupyter-execute::
+    EXAMPLES::
 
         sage: from flatsurf import similarity_surfaces
         sage: from flatsurf.graphical.surface import GraphicalSurface
@@ -180,9 +179,11 @@ def __init__(
         self,
         surface,
         adjacencies=None,
+        polygon_transformations=None,
         polygon_labels=True,
         edge_labels="gluings",
         default_position_function=None,
+        zero_flags=None,
     ):
         self._ss = surface
         self._default_position_function = default_position_function
@@ -193,9 +194,14 @@ def __init__(
 
         self._visible = set(self._ss.roots())
 
-        if adjacencies is None:
-            if self._ss.is_finite_type():
-                self.make_all_visible()
+        if adjacencies is not None:
+            # TODO: This is not implemented anymore. We should just revamp this whole interface.
+            for root in surface.roots():
+                self.make_visible(root)
+        else:
+            if polygon_transformations is None:
+                if self._ss.is_finite_type():
+                    self.layout()
         self._edge_labels = None
 
         self.will_plot_polygons = True
@@ -275,17 +281,19 @@ def __init__(
         r"""Options passed to :meth:`.polygon.GraphicalPolygon.plot_zero_flag` when plotting a zero_flag."""
 
         self.process_options(
-            adjacencies=adjacencies,
+            polygon_transformations=polygon_transformations,
             polygon_labels=polygon_labels,
             edge_labels=edge_labels,
+            zero_flags=zero_flags,
         )
 
     def process_options(
         self,
-        adjacencies=None,
+        polygon_transformations=None,
         polygon_labels=None,
         edge_labels=None,
         default_position_function=None,
+        zero_flags=None,
     ):
         r"""
         Process the options listed as if the graphical_surface was first
@@ -306,9 +314,13 @@ def process_options(
             ...
             ValueError: invalid value for edge_labels (='hey')
         """
-        if adjacencies is not None:
-            for p, e in adjacencies:
-                self.make_adjacent(p, e)
+        for label, transformation in (polygon_transformations or {}).items():
+            from flatsurf.graphical.polygon import GraphicalPolygon
+
+            self._polygons[label] = GraphicalPolygon(
+                self._ss.polygon(label), transformation=transformation
+            )
+            self.make_visible(label)
 
         if polygon_labels is not None:
             if not isinstance(polygon_labels, bool):
@@ -330,6 +342,12 @@ def process_options(
                     "invalid value for edge_labels (={!r})".format(edge_labels)
                 )
 
+        if zero_flags is not None:
+            if not isinstance(zero_flags, bool):
+                raise ValueError("zero_flags must be True, False or None")
+
+            self.will_plot_zero_flags = zero_flags
+
         if default_position_function is not None:
             self._default_position_function = default_position_function
 
@@ -361,6 +379,10 @@ def copy(self):
         """
         gs = GraphicalSurface(
             self.get_surface(),
+            polygon_transformations={
+                label: polygon.transformation()
+                for (label, polygon) in self._polygons.items()
+            },
             default_position_function=self._default_position_function,
         )
 
@@ -518,6 +540,131 @@ def make_all_visible(self, adjacent=None, limit=None):
                         if i >= limit:
                             return
 
+    def layout(self):
+        surface = self._ss
+
+        self.make_all_visible()
+
+        for label in surface.labels():
+            if label not in surface.roots():
+                self.hide(label)
+
+        @cached_function
+        def polygon_after_glue(label, edge):
+            assert not self.is_visible(label)
+            opposite_label, opposite_edge = surface.opposite_edge(label, edge)
+            assert self.is_visible(opposite_label)
+            self.make_adjacent(opposite_label, opposite_edge, visible=False)
+            assert self.is_adjacent(label, edge)
+            return self.graphical_polygon(label).copy()
+
+        @cached_function
+        def transformed_vertex_after_glue(label, edge, vertex):
+            return polygon_after_glue(label, edge).transformed_vertex(vertex)
+
+        @cached_function
+        def transformed_vertex(label, vertex):
+            assert self.is_visible(label)
+            return self.graphical_polygon(label).transformed_vertex(vertex)
+
+        while True:
+            invisible_labels = [
+                label for label in surface.labels() if not self.is_visible(label)
+            ]
+            if not invisible_labels:
+                break
+
+            def edge_score(label, edge):
+                polygon = surface.polygon(label)
+
+                opposite_edge = surface.opposite_edge(label, edge)
+                if opposite_edge is None:
+                    return -1e9
+                opposite_label, opposite_edge = opposite_edge
+
+                if not self.is_visible(opposite_label):
+                    return -1e9
+
+                score = 0
+
+                adjacents = []
+
+                for e in range(len(polygon.vertices())):
+                    opposite_edge = surface.opposite_edge(label, e)
+                    if opposite_edge is None:
+                        continue
+                    opposite_label, opposite_edge = opposite_edge
+
+                    if not self.is_visible(opposite_label):
+                        continue
+
+                    if transformed_vertex_after_glue(
+                        label, edge, e
+                    ) == transformed_vertex(
+                        opposite_label, opposite_edge + 1
+                    ) and transformed_vertex_after_glue(
+                        label, edge, e + 1
+                    ) == transformed_vertex(
+                        opposite_label, opposite_edge
+                    ):
+                        adjacents.append(opposite_label)
+
+                score += len(adjacents)
+
+                assert score >= 1
+
+                for visible in self.visible():
+                    if visible in adjacents:
+                        continue
+
+                    visible_polygon = surface.polygon(visible)
+
+                    intersections = 0
+                    for e in range(len(polygon.vertices())):
+                        for f in range(len(visible_polygon.vertices())):
+                            from flatsurf.geometry.euclidean import (
+                                is_segment_intersecting,
+                            )
+
+                            intersection = is_segment_intersecting(
+                                (
+                                    transformed_vertex_after_glue(label, edge, e),
+                                    transformed_vertex_after_glue(label, edge, e + 1),
+                                ),
+                                (
+                                    transformed_vertex(visible, f),
+                                    transformed_vertex(visible, f + 1),
+                                ),
+                            )
+                            if intersection == 2:
+                                intersections += 1
+
+                    score -= intersections * 100
+
+                return score
+
+            def label_score(label):
+                assert not self.is_visible(label)
+
+                polygon = surface.polygon(label)
+
+                return max(
+                    edge_score(label, edge) for edge in range(len(polygon.vertices()))
+                )
+
+            label = max(invisible_labels, key=label_score)
+            polygon = surface.polygon(label)
+
+            edge = max(
+                range(len(polygon.vertices())), key=lambda edge: edge_score(label, edge)
+            )
+            self.make_adjacent(*surface.opposite_edge(label, edge))
+            assert self.is_adjacent(label, edge)
+            assert (
+                polygon_after_glue(label, edge).transformation()
+                == self.graphical_polygon(label).transformation()
+            )
+
     def get_surface(self):
         r"""
         Return the underlying similarity surface.
@@ -676,6 +823,7 @@ def is_adjacent(self, p, e):
             return False
         pp, ee = opposite_edge
         if not self.is_visible(pp):
+            # TODO: Why does this only check visibility on pp? (and not also on p.)
             return False
         g = self.graphical_polygon(p)
         gg = self.graphical_polygon(pp)
@@ -742,10 +890,10 @@ def to_surface(
             sage: from flatsurf import similarity_surfaces
             sage: s = similarity_surfaces.example()
             sage: gs = s.graphical_surface()
-            sage: gs.to_surface((1,-2))
+            sage: gs.to_surface((1,1/2))
             Point (1, 1/2) of polygon 1
-            sage: gs.to_surface((1,-2), v=(1,0))
-            SimilaritySurfaceTangentVector in polygon 1 based at (1, 1/2) with vector (1, -1/2)
+            sage: gs.to_surface((1,1/2), v=(1,0))
+            SimilaritySurfaceTangentVector in polygon 1 based at (1, 1/2) with vector (1, 0)
 
             sage: from flatsurf import translation_surfaces
             sage: s = translation_surfaces.infinite_staircase()
diff --git a/mpreal-support.h b/mpreal-support.h
new file mode 100644
index 000000000..b82e41128
--- /dev/null
+++ b/mpreal-support.h
@@ -0,0 +1,213 @@
+// This file is part of a joint effort between Eigen, a lightweight C++ template library
+// for linear algebra, and MPFR C++, a C++ interface to MPFR library (http://www.holoborodko.com/pavel/)
+//
+// Copyright (C) 2010-2012 Pavel Holoborodko <pavel@holoborodko.com>
+// Copyright (C) 2010 Konstantin Holoborodko <konstantin@holoborodko.com>
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MPREALSUPPORT_MODULE_H
+#define EIGEN_MPREALSUPPORT_MODULE_H
+
+#include <eigen3/Eigen/Core>
+#include "mpreal.h"
+
+namespace Eigen {
+  
+/**
+  * \defgroup MPRealSupport_Module MPFRC++ Support module
+  * \code
+  * #include <Eigen/MPRealSupport>
+  * \endcode
+  *
+  * This module provides support for multi precision floating point numbers
+  * via the <a href="http://www.holoborodko.com/pavel/mpfr">MPFR C++</a>
+  * library which itself is built upon <a href="http://www.mpfr.org/">MPFR</a>/<a href="http://gmplib.org/">GMP</a>.
+  *
+  * \warning MPFR C++ is licensed under the GPL.
+  *
+  * You can find a copy of MPFR C++ that is known to be compatible in the unsupported/test/mpreal folder.
+  *
+  * Here is an example:
+  *
+\code
+#include <iostream>
+#include <Eigen/MPRealSupport>
+#include <Eigen/LU>
+using namespace mpfr;
+using namespace Eigen;
+int main()
+{
+  // set precision to 256 bits (double has only 53 bits)
+  mpreal::set_default_prec(256);
+  // Declare matrix and vector types with multi-precision scalar type
+  typedef Matrix<mpreal,Dynamic,Dynamic>  MatrixXmp;
+  typedef Matrix<mpreal,Dynamic,1>        VectorXmp;
+
+  MatrixXmp A = MatrixXmp::Random(100,100);
+  VectorXmp b = VectorXmp::Random(100);
+
+  // Solve Ax=b using LU
+  VectorXmp x = A.lu().solve(b);
+  std::cout << "relative error: " << (A*x - b).norm() / b.norm() << std::endl;
+  return 0;
+}
+\endcode
+  *
+  */
+	
+  template<> struct NumTraits<mpfr::mpreal>
+    : GenericNumTraits<mpfr::mpreal>
+  {
+    enum {
+      IsInteger = 0,
+      IsSigned = 1,
+      IsComplex = 0,
+      RequireInitialization = 1,
+      ReadCost = HugeCost,
+      AddCost  = HugeCost,
+      MulCost  = HugeCost
+    };
+
+    typedef mpfr::mpreal Real;
+    typedef mpfr::mpreal NonInteger;
+    
+    static inline Real highest  (long Precision = mpfr::mpreal::get_default_prec()) { return  mpfr::maxval(Precision); }
+    static inline Real lowest   (long Precision = mpfr::mpreal::get_default_prec()) { return -mpfr::maxval(Precision); }
+
+    // Constants
+    static inline Real Pi      (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::const_pi(Precision);        }
+    static inline Real Euler   (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::const_euler(Precision);     }
+    static inline Real Log2    (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::const_log2(Precision);      }
+    static inline Real Catalan (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::const_catalan(Precision);   }
+
+    static inline Real epsilon (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::machine_epsilon(Precision); }
+    static inline Real epsilon (const Real& x)                                      { return mpfr::machine_epsilon(x); }
+
+#ifdef MPREAL_HAVE_DYNAMIC_STD_NUMERIC_LIMITS
+    static inline int digits10 (long Precision = mpfr::mpreal::get_default_prec())  { return std::numeric_limits<Real>::digits10(Precision); }
+    static inline int digits10 (const Real& x)                                      { return std::numeric_limits<Real>::digits10(x); }
+    
+    static inline int digits ()               { return std::numeric_limits<Real>::digits(); }
+    static inline int digits (const Real& x)  { return std::numeric_limits<Real>::digits(x); }
+#endif
+
+    static inline Real dummy_precision()
+    {
+      mpfr_prec_t weak_prec = ((mpfr::mpreal::get_default_prec()-1) * 90) / 100;
+      return mpfr::machine_epsilon(weak_prec);
+    }
+  };
+
+  namespace internal {
+
+  template<> inline mpfr::mpreal random<mpfr::mpreal>()
+  {
+    return mpfr::random();
+  }
+
+  template<> inline mpfr::mpreal random<mpfr::mpreal>(const mpfr::mpreal& a, const mpfr::mpreal& b)
+  {
+    return a + (b-a) * random<mpfr::mpreal>();
+  }
+
+  inline bool isMuchSmallerThan(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& eps)
+  {
+    return mpfr::abs(a) <= mpfr::abs(b) * eps;
+  }
+
+  inline bool isApprox(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& eps)
+  {
+    return mpfr::isEqualFuzzy(a,b,eps);
+  }
+
+  inline bool isApproxOrLessThan(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& eps)
+  {
+    return a <= b || mpfr::isEqualFuzzy(a,b,eps);
+  }
+
+  template<> inline long double cast<mpfr::mpreal,long double>(const mpfr::mpreal& x)
+  { return x.toLDouble(); }
+
+  template<> inline double cast<mpfr::mpreal,double>(const mpfr::mpreal& x)
+  { return x.toDouble(); }
+
+  template<> inline long cast<mpfr::mpreal,long>(const mpfr::mpreal& x)
+  { return x.toLong(); }
+
+  template<> inline int cast<mpfr::mpreal,int>(const mpfr::mpreal& x)
+  { return int(x.toLong()); }
+
+  // Specialize GEBP kernel and traits for mpreal (no need for peeling, nor complicated stuff)
+  // This also permits to directly call mpfr's routines and avoid many temporaries produced by mpreal
+    template<>
+    class gebp_traits<mpfr::mpreal, mpfr::mpreal, false, false>
+    {
+    public:
+      typedef mpfr::mpreal ResScalar;
+      enum {
+        Vectorizable = false,
+        LhsPacketSize = 1,
+        RhsPacketSize = 1,
+        ResPacketSize = 1,
+        NumberOfRegisters = 1,
+        nr = 1,
+        mr = 1,
+        LhsProgress = 1,
+        RhsProgress = 1
+      };
+      typedef ResScalar LhsPacket;
+      typedef ResScalar RhsPacket;
+      typedef ResScalar ResPacket;
+      typedef LhsPacket LhsPacket4Packing;
+      
+    };
+
+
+
+    template<typename Index, typename DataMapper, bool ConjugateLhs, bool ConjugateRhs>
+    struct gebp_kernel<mpfr::mpreal,mpfr::mpreal,Index,DataMapper,1,1,ConjugateLhs,ConjugateRhs>
+    {
+      typedef mpfr::mpreal mpreal;
+
+      EIGEN_DONT_INLINE
+      void operator()(const DataMapper& res, const mpreal* blockA, const mpreal* blockB, 
+                      Index rows, Index depth, Index cols, const mpreal& alpha,
+                      Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0)
+      {
+        if(rows==0 || cols==0 || depth==0)
+          return;
+
+        mpreal  acc1(0,mpfr_get_prec(blockA[0].mpfr_srcptr())),
+                tmp (0,mpfr_get_prec(blockA[0].mpfr_srcptr()));
+
+        if(strideA==-1) strideA = depth;
+        if(strideB==-1) strideB = depth;
+
+        for(Index i=0; i<rows; ++i)
+        {
+          for(Index j=0; j<cols; ++j)
+          {
+            const mpreal *A = blockA + i*strideA + offsetA;
+            const mpreal *B = blockB + j*strideB + offsetB;
+            
+            acc1 = 0;
+            for(Index k=0; k<depth; k++)
+            {
+              mpfr_mul(tmp.mpfr_ptr(), A[k].mpfr_srcptr(), B[k].mpfr_srcptr(), mpreal::get_default_rnd());
+              mpfr_add(acc1.mpfr_ptr(), acc1.mpfr_ptr(), tmp.mpfr_ptr(),  mpreal::get_default_rnd());
+            }
+            
+            mpfr_mul(acc1.mpfr_ptr(), acc1.mpfr_srcptr(), alpha.mpfr_srcptr(), mpreal::get_default_rnd());
+            mpfr_add(res(i,j).mpfr_ptr(), res(i,j).mpfr_srcptr(), acc1.mpfr_srcptr(),  mpreal::get_default_rnd());
+          }
+        }
+      }
+    };
+  } // end namespace internal
+}
+
+#endif // EIGEN_MPREALSUPPORT_MODULE_H
diff --git a/mpreal.h b/mpreal.h
new file mode 100644
index 000000000..8a078ea2c
--- /dev/null
+++ b/mpreal.h
@@ -0,0 +1,3338 @@
+/*
+    MPFR C++: Multi-precision floating point number class for C++.
+    Based on MPFR library:    http://mpfr.org
+
+    Project homepage:    http://www.holoborodko.com/pavel/mpfr
+    Contact e-mail:      pavel@holoborodko.com
+
+    Copyright (c) 2008-2022 Pavel Holoborodko
+                       2024 Julian Rüth
+
+    Contributors:
+    Dmitriy Gubanov, Konstantin Holoborodko, Brian Gladman,
+    Helmut Jarausch, Fokko Beekhof, Ulrich Mutze, Heinz van Saanen,
+    Pere Constans, Peter van Hoof, Gael Guennebaud, Tsai Chia Cheng,
+    Alexei Zubanov, Jauhien Piatlicki, Victor Berger, John Westwood,
+    Petr Aleksandrov, Orion Poplawski, Charles Karney, Arash Partow,
+    Rodney James, Jorge Leitao, Jerome Benoit, Michal Maly.
+
+    Licensing:
+    (A) MPFR C++ is under GNU General Public License ("GPL").
+
+    (B) Non-free licenses may also be purchased from the author, for users who
+        do not want their programs protected by the GPL.
+
+        The non-free licenses are for users that wish to use MPFR C++ in
+        their products but are unwilling to release their software
+        under the GPL (which would require them to release source code
+        and allow free redistribution).
+
+        Such users can purchase an unlimited-use license from the author.
+        Contact us for more details.
+
+    GNU General Public License ("GPL") copyright permissions statement:
+    **************************************************************************
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+*/
+
+#ifndef __MPREAL_H__
+#define __MPREAL_H__
+
+#include <string>
+#include <iostream>
+#include <sstream>
+#include <stdexcept>
+#include <cfloat>
+#include <cmath>
+#include <cstring>
+#include <limits>
+#include <complex>
+#include <algorithm>
+#include <stdint.h>
+
+// Options
+#define MPREAL_HAVE_MSVC_DEBUGVIEW              // Enable Debugger Visualizer for "Debug" builds in MSVC.
+#define MPREAL_HAVE_DYNAMIC_STD_NUMERIC_LIMITS  // Enable extended std::numeric_limits<mpfr::mpreal> specialization.
+                                                // Meaning that "digits", "round_style" and similar members are defined as functions, not constants.
+                                                // See std::numeric_limits<mpfr::mpreal> at the end of the file for more information.
+
+// Library version
+#define MPREAL_VERSION_MAJOR 3
+#define MPREAL_VERSION_MINOR 6
+#define MPREAL_VERSION_PATCHLEVEL 9
+#define MPREAL_VERSION_STRING "3.6.9"
+
+// Detect compiler using signatures from http://predef.sourceforge.net/
+#if defined(__GNUC__) && defined(__INTEL_COMPILER)
+    #define MPREAL_ISINF(x) isinf(x)                   // Intel ICC compiler on Linux
+
+#elif defined(_MSC_VER)                         // Microsoft Visual C++
+    #define MPREAL_ISINF(x) (!_finite(x))
+
+#else
+    #define MPREAL_ISINF(x) std::isinf(x)              // GNU C/C++ (and/or other compilers), just hope for C99 conformance
+#endif
+
+// A Clang feature extension to determine compiler features.
+#ifndef __has_feature
+    #define __has_feature(x) 0
+#endif
+
+// Detect support for r-value references (move semantic).
+// Move semantic should be enabled with great care in multi-threading environments,
+// especially if MPFR uses custom memory allocators.
+// Everything should be thread-safe and support passing ownership over thread boundary.
+#if (__has_feature(cxx_rvalue_references) || \
+       defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L || \
+      (defined(_MSC_VER) && _MSC_VER >= 1600) && !defined(MPREAL_DISABLE_MOVE_SEMANTIC))
+
+    #define MPREAL_HAVE_MOVE_SUPPORT
+
+    // Use fields in mpfr_t structure to check if it was initialized / set dummy initialization
+    #define mpfr_is_initialized(x)      (0 != (x)->_mpfr_d)
+    #define mpfr_set_uninitialized(x)   ((x)->_mpfr_d = 0 )
+#endif
+
+// Detect support for explicit converters.
+#if (__has_feature(cxx_explicit_conversions) || \
+       (defined(__GXX_EXPERIMENTAL_CXX0X__) && __GNUC_MINOR >= 5) || __cplusplus >= 201103L || \
+       (defined(_MSC_VER) && _MSC_VER >= 1800) || \
+       (defined(__INTEL_COMPILER) && __INTEL_COMPILER >= 1300))
+
+    #define MPREAL_HAVE_EXPLICIT_CONVERTERS
+#endif
+
+#define MPFR_USE_INTMAX_T   // Enable 64-bit integer types - should be defined before mpfr.h
+
+#if defined(MPREAL_HAVE_MSVC_DEBUGVIEW) && defined(_MSC_VER) && defined(_DEBUG)
+    #define MPREAL_MSVC_DEBUGVIEW_CODE     DebugView = toString();
+    #define MPREAL_MSVC_DEBUGVIEW_DATA     std::string DebugView;
+#else
+    #define MPREAL_MSVC_DEBUGVIEW_CODE
+    #define MPREAL_MSVC_DEBUGVIEW_DATA
+#endif
+
+#define MPFR_USE_NO_MACRO
+#include <mpfr.h>
+
+#if (MPFR_VERSION < MPFR_VERSION_NUM(3,0,0))
+    #include <cstdlib>                          // Needed for random()
+#endif
+
+// Less important options
+#define MPREAL_DOUBLE_BITS_OVERFLOW -1          // Triggers overflow exception during conversion to double if mpreal
+                                                // cannot fit in MPREAL_DOUBLE_BITS_OVERFLOW bits
+                                                // = -1 disables overflow checks (default)
+
+// Fast replacement for mpfr_set_zero(x, +1):
+// (a) uses low-level data members, might not be forward compatible
+// (b) sign is not set, add (x)->_mpfr_sign = 1;
+#define mpfr_set_zero_fast(x)  ((x)->_mpfr_exp = __MPFR_EXP_ZERO)
+
+#if defined(__GNUC__)
+  #define MPREAL_PERMISSIVE_EXPR __extension__
+#else
+  #define MPREAL_PERMISSIVE_EXPR
+#endif
+
+namespace mpfr {
+
+class mpreal {
+private:
+    mpfr_t mp;
+
+public:
+
+    // Get default rounding mode & precision
+    inline static mp_rnd_t   get_default_rnd()    {    return (mp_rnd_t)(mpfr_get_default_rounding_mode());       }
+    inline static mp_prec_t  get_default_prec()   {    return (mpfr_get_default_prec)();                          }
+
+    // Constructors && type conversions
+    mpreal();
+    mpreal(const mpreal& u);
+    mpreal(const mpf_t u);
+    mpreal(const mpz_t u,                  mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
+    mpreal(const mpq_t u,                  mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
+    mpreal(const double u,                 mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
+    mpreal(const long double u,            mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
+    mpreal(const unsigned long long int u, mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
+    mpreal(const long long int u,          mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
+    mpreal(const unsigned long int u,      mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
+    mpreal(const unsigned int u,           mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
+    mpreal(const long int u,               mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
+    mpreal(const int u,                    mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
+
+    // Construct mpreal from mpfr_t structure.
+    // shared = true allows to avoid deep copy, so that mpreal and 'u' share the same data & pointers.
+    mpreal(const mpfr_t  u, bool shared = false);
+
+    mpreal(const char* s,             mp_prec_t prec = mpreal::get_default_prec(), int base = 10, mp_rnd_t mode = mpreal::get_default_rnd());
+    mpreal(const std::string& s,      mp_prec_t prec = mpreal::get_default_prec(), int base = 10, mp_rnd_t mode = mpreal::get_default_rnd());
+
+    ~mpreal();
+
+#ifdef MPREAL_HAVE_MOVE_SUPPORT
+    mpreal& operator=(mpreal&& v);
+    mpreal(mpreal&& u);
+#endif
+
+    // Operations
+    // =
+    // +, -, *, /, ++, --, <<, >>
+    // *=, +=, -=, /=,
+    // <, >, ==, <=, >=
+
+    // =
+    mpreal& operator=(const mpreal& v);
+    mpreal& operator=(const mpf_t v);
+    mpreal& operator=(const mpz_t v);
+    mpreal& operator=(const mpq_t v);
+    mpreal& operator=(const long double v);
+    mpreal& operator=(const double v);
+    mpreal& operator=(const unsigned long int v);
+    mpreal& operator=(const unsigned long long int v);
+    mpreal& operator=(const long long int v);
+    mpreal& operator=(const unsigned int v);
+    mpreal& operator=(const long int v);
+    mpreal& operator=(const int v);
+    mpreal& operator=(const char* s);
+    mpreal& operator=(const std::string& s);
+    template <typename real_t> mpreal& operator= (const std::complex<real_t>& z);
+
+    // +
+    mpreal& operator+=(const mpreal& v);
+    mpreal& operator+=(const mpf_t v);
+    mpreal& operator+=(const mpz_t v);
+    mpreal& operator+=(const mpq_t v);
+    mpreal& operator+=(const long double u);
+    mpreal& operator+=(const double u);
+    mpreal& operator+=(const unsigned long int u);
+    mpreal& operator+=(const unsigned int u);
+    mpreal& operator+=(const long int u);
+    mpreal& operator+=(const int u);
+
+    mpreal& operator+=(const long long int  u);
+    mpreal& operator+=(const unsigned long long int u);
+    mpreal& operator-=(const long long int  u);
+    mpreal& operator-=(const unsigned long long int u);
+    mpreal& operator*=(const long long int  u);
+    mpreal& operator*=(const unsigned long long int u);
+    mpreal& operator/=(const long long int  u);
+    mpreal& operator/=(const unsigned long long int u);
+
+    const mpreal operator+() const;
+    mpreal& operator++ ();
+    const mpreal  operator++ (int);
+
+    // -
+    mpreal& operator-=(const mpreal& v);
+    mpreal& operator-=(const mpz_t v);
+    mpreal& operator-=(const mpq_t v);
+    mpreal& operator-=(const long double u);
+    mpreal& operator-=(const double u);
+    mpreal& operator-=(const unsigned long int u);
+    mpreal& operator-=(const unsigned int u);
+    mpreal& operator-=(const long int u);
+    mpreal& operator-=(const int u);
+    const mpreal operator-() const;
+    friend const mpreal operator-(const unsigned long int b, const mpreal& a);
+    friend const mpreal operator-(const unsigned int b,      const mpreal& a);
+    friend const mpreal operator-(const long int b,          const mpreal& a);
+    friend const mpreal operator-(const int b,               const mpreal& a);
+    friend const mpreal operator-(const double b,            const mpreal& a);
+    mpreal& operator-- ();
+    const mpreal  operator-- (int);
+
+    // *
+    mpreal& operator*=(const mpreal& v);
+    mpreal& operator*=(const mpz_t v);
+    mpreal& operator*=(const mpq_t v);
+    mpreal& operator*=(const long double v);
+    mpreal& operator*=(const double v);
+    mpreal& operator*=(const unsigned long int v);
+    mpreal& operator*=(const unsigned int v);
+    mpreal& operator*=(const long int v);
+    mpreal& operator*=(const int v);
+
+    // /
+    mpreal& operator/=(const mpreal& v);
+    mpreal& operator/=(const mpz_t v);
+    mpreal& operator/=(const mpq_t v);
+    mpreal& operator/=(const long double v);
+    mpreal& operator/=(const double v);
+    mpreal& operator/=(const unsigned long int v);
+    mpreal& operator/=(const unsigned int v);
+    mpreal& operator/=(const long int v);
+    mpreal& operator/=(const int v);
+    friend const mpreal operator/(const unsigned long int b, const mpreal& a);
+    friend const mpreal operator/(const unsigned int b,      const mpreal& a);
+    friend const mpreal operator/(const long int b,          const mpreal& a);
+    friend const mpreal operator/(const int b,               const mpreal& a);
+    friend const mpreal operator/(const double b,            const mpreal& a);
+
+    //<<= Fast Multiplication by 2^u
+    mpreal& operator<<=(const unsigned long int u);
+    mpreal& operator<<=(const unsigned int u);
+    mpreal& operator<<=(const long int u);
+    mpreal& operator<<=(const int u);
+
+    //>>= Fast Division by 2^u
+    mpreal& operator>>=(const unsigned long int u);
+    mpreal& operator>>=(const unsigned int u);
+    mpreal& operator>>=(const long int u);
+    mpreal& operator>>=(const int u);
+
+    // Type Conversion operators
+    bool               toBool      (                        )    const;
+    long               toLong      (mp_rnd_t mode = GMP_RNDZ)    const;
+    unsigned long      toULong     (mp_rnd_t mode = GMP_RNDZ)    const;
+    long long          toLLong     (mp_rnd_t mode = GMP_RNDZ)    const;
+    unsigned long long toULLong    (mp_rnd_t mode = GMP_RNDZ)    const;
+    float              toFloat     (mp_rnd_t mode = GMP_RNDN)    const;
+    double             toDouble    (mp_rnd_t mode = GMP_RNDN)    const;
+    long double        toLDouble   (mp_rnd_t mode = GMP_RNDN)    const;
+
+#if defined (MPREAL_HAVE_EXPLICIT_CONVERTERS)
+    explicit operator bool               () const { return toBool();                 }
+    explicit operator signed char        () const { return (signed char)toLong();    }
+    explicit operator unsigned char      () const { return (unsigned char)toULong(); }
+    explicit operator short              () const { return (short)toLong();          }
+    explicit operator unsigned short     () const { return (unsigned short)toULong();}
+    explicit operator int                () const { return (int)toLong();            }
+    explicit operator unsigned int       () const { return (unsigned int)toULong();  }
+    explicit operator long               () const { return toLong();                 }
+    explicit operator unsigned long      () const { return toULong();                }
+    explicit operator long long          () const { return toLLong();                }
+    explicit operator unsigned long long () const { return toULLong();               }
+    explicit operator float              () const { return toFloat();                }
+    explicit operator double             () const { return toDouble();               }
+    explicit operator long double        () const { return toLDouble();              }
+#endif
+
+    // Get raw pointers so that mpreal can be directly used in raw mpfr_* functions
+    ::mpfr_ptr    mpfr_ptr();
+    ::mpfr_srcptr mpfr_ptr()    const;
+    ::mpfr_srcptr mpfr_srcptr() const;
+
+    // Convert mpreal to string with n significant digits in base b
+    // n = -1 -> convert with the maximum available digits
+    std::string toString(int n = -1, int b = 10, mp_rnd_t mode = mpreal::get_default_rnd()) const;
+
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
+    std::string toString(const std::string& format) const;
+#endif
+
+    std::ostream& output(std::ostream& os) const;
+
+    // Math Functions
+    friend const mpreal sqr (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal sqrt(const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal sqrt(const unsigned long int v, mp_rnd_t rnd_mode);
+    friend const mpreal cbrt(const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal root(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode);
+    friend const mpreal pow (const mpreal& a, const mpreal& b, mp_rnd_t rnd_mode);
+    friend const mpreal pow (const mpreal& a, const mpz_t b, mp_rnd_t rnd_mode);
+    friend const mpreal pow (const mpreal& a, const unsigned long int b, mp_rnd_t rnd_mode);
+    friend const mpreal pow (const mpreal& a, const long int b, mp_rnd_t rnd_mode);
+    friend const mpreal pow (const unsigned long int a, const mpreal& b, mp_rnd_t rnd_mode);
+    friend const mpreal pow (const unsigned long int a, const unsigned long int b, mp_rnd_t rnd_mode);
+    friend const mpreal fabs(const mpreal& v, mp_rnd_t rnd_mode);
+
+    friend const mpreal abs(const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal dim(const mpreal& a, const mpreal& b, mp_rnd_t rnd_mode);
+    friend inline const mpreal mul_2ui(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode);
+    friend inline const mpreal mul_2si(const mpreal& v, long int k, mp_rnd_t rnd_mode);
+    friend inline const mpreal div_2ui(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode);
+    friend inline const mpreal div_2si(const mpreal& v, long int k, mp_rnd_t rnd_mode);
+    friend int cmpabs(const mpreal& a,const mpreal& b);
+
+    friend const mpreal log  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal log2 (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal logb (const mpreal& v, mp_rnd_t rnd_mode);
+    friend     mp_exp_t ilogb(const mpreal& v);
+    friend const mpreal log10(const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal exp  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal exp2 (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal exp10(const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal log1p(const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal expm1(const mpreal& v, mp_rnd_t rnd_mode);
+
+    friend const mpreal nextpow2(const mpreal& v, mp_rnd_t rnd_mode);
+
+    friend const mpreal cos(const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal sin(const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal tan(const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal sec(const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal csc(const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal cot(const mpreal& v, mp_rnd_t rnd_mode);
+    friend int sin_cos(mpreal& s, mpreal& c, const mpreal& v, mp_rnd_t rnd_mode);
+
+    friend const mpreal acos  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal asin  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal atan  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal atan2 (const mpreal& y, const mpreal& x, mp_rnd_t rnd_mode);
+    friend const mpreal acot  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal asec  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal acsc  (const mpreal& v, mp_rnd_t rnd_mode);
+
+    friend const mpreal cosh  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal sinh  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal tanh  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal sech  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal csch  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal coth  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal acosh (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal asinh (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal atanh (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal acoth (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal asech (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal acsch (const mpreal& v, mp_rnd_t rnd_mode);
+
+    friend const mpreal hypot (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode);
+
+    friend const mpreal fac_ui (unsigned long int v,  mp_prec_t prec, mp_rnd_t rnd_mode);
+    friend const mpreal eint   (const mpreal& v, mp_rnd_t rnd_mode);
+
+    friend const mpreal gamma    (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal tgamma   (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal lngamma  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal lgamma   (const mpreal& v, int *signp, mp_rnd_t rnd_mode);
+    friend const mpreal zeta     (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal erf      (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal erfc     (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal besselj0 (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal besselj1 (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal besseljn (long n, const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal bessely0 (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal bessely1 (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal besselyn (long n, const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal fma      (const mpreal& v1, const mpreal& v2, const mpreal& v3, mp_rnd_t rnd_mode);
+    friend const mpreal fms      (const mpreal& v1, const mpreal& v2, const mpreal& v3, mp_rnd_t rnd_mode);
+    friend const mpreal agm      (const mpreal& v1, const mpreal& v2, mp_rnd_t rnd_mode);
+    friend const mpreal sum      (const mpreal tab[], const unsigned long int n, int& status, mp_rnd_t rnd_mode);
+    friend int          sgn      (const mpreal& v);
+
+// MPFR 2.4.0 Specifics
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
+    friend int          sinh_cosh   (mpreal& s, mpreal& c, const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal li2         (const mpreal& v,                       mp_rnd_t rnd_mode);
+    friend const mpreal fmod        (const mpreal& x, const mpreal& y,      mp_rnd_t rnd_mode);
+    friend const mpreal rec_sqrt    (const mpreal& v,                       mp_rnd_t rnd_mode);
+
+    // MATLAB's semantic equivalents
+    friend const mpreal rem (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode); // Remainder after division
+    friend const mpreal mod (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode); // Modulus after division
+#endif
+
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
+    friend const mpreal digamma (const mpreal& v,        mp_rnd_t rnd_mode);
+    friend const mpreal ai      (const mpreal& v,        mp_rnd_t rnd_mode);
+    friend const mpreal urandom (gmp_randstate_t& state, mp_rnd_t rnd_mode);     // use gmp_randinit_default() to init state, gmp_randclear() to clear
+#endif
+
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,1,0))
+    friend const mpreal grandom (gmp_randstate_t& state, mp_rnd_t rnd_mode);     // use gmp_randinit_default() to init state, gmp_randclear() to clear
+    friend const mpreal grandom (unsigned int seed);
+#endif
+
+    // Uniformly distributed random number generation in [0,1] using
+    // Mersenne-Twister algorithm by default.
+    // Use parameter to setup seed, e.g.: random((unsigned)time(NULL))
+    // Check urandom() for more precise control.
+    friend const mpreal random(unsigned int seed);
+
+    // Splits mpreal value into fractional and integer parts.
+    // Returns fractional part and stores integer part in n.
+    friend const mpreal modf(const mpreal& v, mpreal& n);
+
+    // Constants
+    // don't forget to call mpfr_free_cache() for every thread where you are using const-functions
+    friend const mpreal const_log2      (mp_prec_t prec, mp_rnd_t rnd_mode);
+    friend const mpreal const_pi        (mp_prec_t prec, mp_rnd_t rnd_mode);
+    friend const mpreal const_euler     (mp_prec_t prec, mp_rnd_t rnd_mode);
+    friend const mpreal const_catalan   (mp_prec_t prec, mp_rnd_t rnd_mode);
+
+    // returns +inf iff sign>=0 otherwise -inf
+    friend const mpreal const_infinity(int sign, mp_prec_t prec);
+
+    // Output/ Input
+    friend std::ostream& operator<<(std::ostream& os, const mpreal& v);
+    friend std::istream& operator>>(std::istream& is, mpreal& v);
+
+    // Integer Related Functions
+    friend const mpreal rint (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal ceil (const mpreal& v);
+    friend const mpreal floor(const mpreal& v);
+    friend const mpreal round(const mpreal& v);
+    friend long lround(const mpreal& v);
+    friend long long llround(const mpreal& v);
+    friend const mpreal trunc(const mpreal& v);
+    friend const mpreal rint_ceil   (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal rint_floor  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal rint_round  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal rint_trunc  (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal frac        (const mpreal& v, mp_rnd_t rnd_mode);
+    friend const mpreal remainder   (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode);
+    friend const mpreal remquo      (const mpreal& x, const mpreal& y, int* q, mp_rnd_t rnd_mode);
+
+    // Miscellaneous Functions
+    friend const mpreal nexttoward (const mpreal& x, const mpreal& y);
+    friend const mpreal nextabove  (const mpreal& x);
+    friend const mpreal nextbelow  (const mpreal& x);
+
+    // use gmp_randinit_default() to init state, gmp_randclear() to clear
+    friend const mpreal urandomb (gmp_randstate_t& state);
+
+// MPFR < 2.4.2 Specifics
+#if (MPFR_VERSION <= MPFR_VERSION_NUM(2,4,2))
+    friend const mpreal random2 (mp_size_t size, mp_exp_t exp);
+#endif
+
+    // Instance Checkers
+    friend bool isnan    (const mpreal& v);
+    friend bool isinf    (const mpreal& v);
+    friend bool isfinite (const mpreal& v);
+
+    friend bool isnum    (const mpreal& v);
+    friend bool iszero   (const mpreal& v);
+    friend bool isint    (const mpreal& v);
+
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
+    friend bool isregular(const mpreal& v);
+#endif
+
+    // Set/Get instance properties
+    inline mp_prec_t    get_prec() const;
+    inline void         set_prec(mp_prec_t prec, mp_rnd_t rnd_mode = get_default_rnd());    // Change precision with rounding mode
+
+    // Aliases for get_prec(), set_prec() - needed for compatibility with std::complex<mpreal> interface
+    inline mpreal&      setPrecision(int Precision, mp_rnd_t RoundingMode = get_default_rnd());
+    inline int          getPrecision() const;
+
+    // Set mpreal to +/- inf, NaN, +/-0
+    mpreal&        setInf  (int Sign = +1);
+    mpreal&        setNan  ();
+    mpreal&        setZero (int Sign = +1);
+    mpreal&        setSign (int Sign, mp_rnd_t RoundingMode = get_default_rnd());
+
+    //Exponent
+    mp_exp_t get_exp() const;
+    int set_exp(mp_exp_t e);
+    int check_range  (int t, mp_rnd_t rnd_mode = get_default_rnd());
+    int subnormalize (int t, mp_rnd_t rnd_mode = get_default_rnd());
+
+    // Inexact conversion from float
+    inline bool fits_in_bits(double x, int n);
+
+    // Set/Get global properties
+    static void            set_default_prec(mp_prec_t prec);
+    static void            set_default_rnd(mp_rnd_t rnd_mode);
+
+    static mp_exp_t  get_emin (void);
+    static mp_exp_t  get_emax (void);
+    static mp_exp_t  get_emin_min (void);
+    static mp_exp_t  get_emin_max (void);
+    static mp_exp_t  get_emax_min (void);
+    static mp_exp_t  get_emax_max (void);
+    static int       set_emin (mp_exp_t exp);
+    static int       set_emax (mp_exp_t exp);
+
+    // Efficient swapping of two mpreal values - needed for std algorithms
+    friend void swap(mpreal& x, mpreal& y);
+
+    friend const mpreal fmax(const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode);
+    friend const mpreal fmin(const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode);
+
+private:
+    // Human friendly Debug Preview in Visual Studio.
+    // Put one of these lines:
+    //
+    // mpfr::mpreal=<DebugView>                              ; Show value only
+    // mpfr::mpreal=<DebugView>, <mp[0]._mpfr_prec,u>bits    ; Show value & precision
+    //
+    // at the beginning of
+    // [Visual Studio Installation Folder]\Common7\Packages\Debugger\autoexp.dat
+    MPREAL_MSVC_DEBUGVIEW_DATA
+
+    // "Smart" resources deallocation. Checks if instance initialized before deletion.
+    void clear(::mpfr_ptr);
+};
+
+//////////////////////////////////////////////////////////////////////////
+// Exceptions
+class conversion_overflow : public std::exception {
+public:
+    std::string why() { return "inexact conversion from floating point"; }
+};
+
+//////////////////////////////////////////////////////////////////////////
+// Constructors & converters
+// Default constructor: creates mp number and initializes it to 0.
+inline mpreal::mpreal()
+{
+    mpfr_init2(mpfr_ptr(), mpreal::get_default_prec());
+    mpfr_set_zero_fast(mpfr_ptr());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const mpreal& u)
+{
+    mpfr_init2(mpfr_ptr(),mpfr_get_prec(u.mpfr_srcptr()));
+    mpfr_set  (mpfr_ptr(),u.mpfr_srcptr(),mpreal::get_default_rnd());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+#ifdef MPREAL_HAVE_MOVE_SUPPORT
+inline mpreal::mpreal(mpreal&& other)
+{
+    mpfr_set_uninitialized(mpfr_ptr());      // make sure "other" holds null-pointer (in uninitialized state)
+    mpfr_swap(mpfr_ptr(), other.mpfr_ptr());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal& mpreal::operator=(mpreal&& other)
+{
+    if (this != &other)
+    {
+        mpfr_swap(mpfr_ptr(), other.mpfr_ptr()); // destructor for "other" will be called just afterwards
+        MPREAL_MSVC_DEBUGVIEW_CODE;
+    }
+    return *this;
+}
+#endif
+
+inline mpreal::mpreal(const mpfr_t  u, bool shared)
+{
+    if(shared)
+    {
+        std::memcpy(mpfr_ptr(), u, sizeof(mpfr_t));
+    }
+    else
+    {
+        mpfr_init2(mpfr_ptr(), mpfr_get_prec(u));
+        mpfr_set  (mpfr_ptr(), u, mpreal::get_default_rnd());
+    }
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const mpf_t u)
+{
+    mpfr_init2(mpfr_ptr(),(mp_prec_t) mpf_get_prec(u)); // (gmp: mp_bitcnt_t) unsigned long -> long (mpfr: mp_prec_t)
+    mpfr_set_f(mpfr_ptr(),u,mpreal::get_default_rnd());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const mpz_t u, mp_prec_t prec, mp_rnd_t mode)
+{
+    mpfr_init2(mpfr_ptr(), prec);
+    mpfr_set_z(mpfr_ptr(), u, mode);
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const mpq_t u, mp_prec_t prec, mp_rnd_t mode)
+{
+    mpfr_init2(mpfr_ptr(), prec);
+    mpfr_set_q(mpfr_ptr(), u, mode);
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const double u, mp_prec_t prec, mp_rnd_t mode)
+{
+     mpfr_init2(mpfr_ptr(), prec);
+
+#if (MPREAL_DOUBLE_BITS_OVERFLOW > -1)
+    if(fits_in_bits(u, MPREAL_DOUBLE_BITS_OVERFLOW))
+    {
+        mpfr_set_d(mpfr_ptr(), u, mode);
+    }else
+        throw conversion_overflow();
+#else
+    mpfr_set_d(mpfr_ptr(), u, mode);
+#endif
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const long double u, mp_prec_t prec, mp_rnd_t mode)
+{
+    mpfr_init2 (mpfr_ptr(), prec);
+    mpfr_set_ld(mpfr_ptr(), u, mode);
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const unsigned long long int u, mp_prec_t prec, mp_rnd_t mode)
+{
+    mpfr_init2 (mpfr_ptr(), prec);
+    mpfr_set_uj(mpfr_ptr(), u, mode);
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const long long int u, mp_prec_t prec, mp_rnd_t mode)
+{
+    mpfr_init2 (mpfr_ptr(), prec);
+    mpfr_set_sj(mpfr_ptr(), u, mode);
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const unsigned long int u, mp_prec_t prec, mp_rnd_t mode)
+{
+    mpfr_init2 (mpfr_ptr(), prec);
+    mpfr_set_ui(mpfr_ptr(), u, mode);
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const unsigned int u, mp_prec_t prec, mp_rnd_t mode)
+{
+    mpfr_init2 (mpfr_ptr(), prec);
+    mpfr_set_ui(mpfr_ptr(), u, mode);
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const long int u, mp_prec_t prec, mp_rnd_t mode)
+{
+    mpfr_init2 (mpfr_ptr(), prec);
+    mpfr_set_si(mpfr_ptr(), u, mode);
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const int u, mp_prec_t prec, mp_rnd_t mode)
+{
+    mpfr_init2 (mpfr_ptr(), prec);
+    mpfr_set_si(mpfr_ptr(), u, mode);
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const char* s, mp_prec_t prec, int base, mp_rnd_t mode)
+{
+    mpfr_init2  (mpfr_ptr(), prec);
+    mpfr_set_str(mpfr_ptr(), s, base, mode);
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mpreal::mpreal(const std::string& s, mp_prec_t prec, int base, mp_rnd_t mode)
+{
+    mpfr_init2  (mpfr_ptr(), prec);
+    mpfr_set_str(mpfr_ptr(), s.c_str(), base, mode);
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline void mpreal::clear(::mpfr_ptr x)
+{
+#ifdef MPREAL_HAVE_MOVE_SUPPORT
+    if(mpfr_is_initialized(x))
+#endif
+    mpfr_clear(x);
+}
+
+inline mpreal::~mpreal()
+{
+    clear(mpfr_ptr());
+}
+
+// internal namespace needed for template magic
+namespace internal{
+
+    // Use SFINAE to restrict arithmetic operations instantiation only for numeric types
+    // This is needed for smooth integration with libraries based on expression templates, like Eigen.
+    // TODO: Do the same for boolean operators.
+    template <typename ArgumentType> struct result_type {};
+
+    template <> struct result_type<mpreal>              {typedef mpreal type;};
+    template <> struct result_type<mpz_t>               {typedef mpreal type;};
+    template <> struct result_type<mpq_t>               {typedef mpreal type;};
+    template <> struct result_type<long double>         {typedef mpreal type;};
+    template <> struct result_type<double>              {typedef mpreal type;};
+    template <> struct result_type<unsigned long int>   {typedef mpreal type;};
+    template <> struct result_type<unsigned int>        {typedef mpreal type;};
+    template <> struct result_type<long int>            {typedef mpreal type;};
+    template <> struct result_type<int>                 {typedef mpreal type;};
+    template <> struct result_type<long long>           {typedef mpreal type;};
+    template <> struct result_type<unsigned long long>  {typedef mpreal type;};
+}
+
+// + Addition
+template <typename Rhs>
+inline const typename internal::result_type<Rhs>::type
+    operator+(const mpreal& lhs, const Rhs& rhs){ return mpreal(lhs) += rhs;    }
+
+template <typename Lhs>
+inline const typename internal::result_type<Lhs>::type
+    operator+(const Lhs& lhs, const mpreal& rhs){ return mpreal(rhs) += lhs;    }
+
+// - Subtraction
+template <typename Rhs>
+inline const typename internal::result_type<Rhs>::type
+    operator-(const mpreal& lhs, const Rhs& rhs){ return mpreal(lhs) -= rhs;    }
+
+template <typename Lhs>
+inline const typename internal::result_type<Lhs>::type
+    operator-(const Lhs& lhs, const mpreal& rhs){ return mpreal(lhs) -= rhs;    }
+
+// * Multiplication
+template <typename Rhs>
+inline const typename internal::result_type<Rhs>::type
+    operator*(const mpreal& lhs, const Rhs& rhs){ return mpreal(lhs) *= rhs;    }
+
+template <typename Lhs>
+inline const typename internal::result_type<Lhs>::type
+    operator*(const Lhs& lhs, const mpreal& rhs){ return mpreal(rhs) *= lhs;    }
+
+// / Division
+template <typename Rhs>
+inline const typename internal::result_type<Rhs>::type
+    operator/(const mpreal& lhs, const Rhs& rhs){ return mpreal(lhs) /= rhs;    }
+
+template <typename Lhs>
+inline const typename internal::result_type<Lhs>::type
+    operator/(const Lhs& lhs, const mpreal& rhs){ return mpreal(lhs) /= rhs;    }
+
+//////////////////////////////////////////////////////////////////////////
+// sqrt
+const mpreal sqrt(const unsigned int v, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal sqrt(const long int v, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal sqrt(const int v, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal sqrt(const long double v, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal sqrt(const double v, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+
+// abs
+inline const mpreal abs(const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd());
+
+//////////////////////////////////////////////////////////////////////////
+// pow
+const mpreal pow(const mpreal& a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const mpreal& a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const mpreal& a, const long double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const mpreal& a, const double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+
+const mpreal pow(const unsigned int a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const long int a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const int a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const long double a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const double a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+
+const mpreal pow(const unsigned long int a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const unsigned long int a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const unsigned long int a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const unsigned long int a, const long double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const unsigned long int a, const double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+
+const mpreal pow(const unsigned int a, const unsigned long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const unsigned int a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const unsigned int a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const unsigned int a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const unsigned int a, const long double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const unsigned int a, const double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+
+const mpreal pow(const long int a, const unsigned long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const long int a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const long int a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const long int a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const long int a, const long double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const long int a, const double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+
+const mpreal pow(const int a, const unsigned long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const int a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const int a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const int a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const int a, const long double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const int a, const double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+
+const mpreal pow(const long double a, const long double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const long double a, const unsigned long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const long double a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const long double a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const long double a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+
+const mpreal pow(const double a, const double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const double a, const unsigned long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const double a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const double a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+const mpreal pow(const double a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+
+inline const mpreal mul_2ui(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+inline const mpreal mul_2si(const mpreal& v, long int k, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+inline const mpreal div_2ui(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+inline const mpreal div_2si(const mpreal& v, long int k, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
+
+//////////////////////////////////////////////////////////////////////////
+// Estimate machine epsilon for the given precision
+// Returns smallest eps such that 1.0 + eps != 1.0
+inline mpreal machine_epsilon(mp_prec_t prec = mpreal::get_default_prec());
+
+// Returns smallest eps such that x + eps != x (relative machine epsilon)
+inline mpreal machine_epsilon(const mpreal& x);
+
+// Gives max & min values for the required precision,
+// minval is 'safe' meaning 1 / minval does not overflow
+// maxval is 'safe' meaning 1 / maxval does not underflow
+inline mpreal minval(mp_prec_t prec = mpreal::get_default_prec());
+inline mpreal maxval(mp_prec_t prec = mpreal::get_default_prec());
+
+// 'Dirty' equality check 1: |a-b| < min{|a|,|b|} * eps
+inline bool isEqualFuzzy(const mpreal& a, const mpreal& b, const mpreal& eps);
+
+// 'Dirty' equality check 2: |a-b| < min{|a|,|b|} * eps( min{|a|,|b|} )
+inline bool isEqualFuzzy(const mpreal& a, const mpreal& b);
+
+// 'Bitwise' equality check
+//  maxUlps - a and b can be apart by maxUlps binary numbers.
+inline bool isEqualUlps(const mpreal& a, const mpreal& b, int maxUlps);
+
+//////////////////////////////////////////////////////////////////////////
+// Convert precision in 'bits' to decimal digits and vice versa.
+//    bits   = ceil(digits*log[2](10))
+//    digits = floor(bits*log[10](2))
+
+inline mp_prec_t digits2bits(int d);
+inline int       bits2digits(mp_prec_t b);
+
+//////////////////////////////////////////////////////////////////////////
+// min, max
+const mpreal (max)(const mpreal& x, const mpreal& y);
+const mpreal (min)(const mpreal& x, const mpreal& y);
+
+//////////////////////////////////////////////////////////////////////////
+// Implementation
+//////////////////////////////////////////////////////////////////////////
+
+//////////////////////////////////////////////////////////////////////////
+// Operators - Assignment
+inline mpreal& mpreal::operator=(const mpreal& v)
+{
+    if (this != &v)
+    {
+        mp_prec_t tp = mpfr_get_prec(  mpfr_srcptr());
+        mp_prec_t vp = mpfr_get_prec(v.mpfr_srcptr());
+
+        if(tp != vp){
+            clear(mpfr_ptr());
+            mpfr_init2(mpfr_ptr(), vp);
+        }
+
+        mpfr_set(mpfr_ptr(), v.mpfr_srcptr(), mpreal::get_default_rnd());
+
+        MPREAL_MSVC_DEBUGVIEW_CODE;
+    }
+    return *this;
+}
+
+inline mpreal& mpreal::operator=(const mpf_t v)
+{
+    mpfr_set_f(mpfr_ptr(), v, mpreal::get_default_rnd());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator=(const mpz_t v)
+{
+    mpfr_set_z(mpfr_ptr(), v, mpreal::get_default_rnd());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator=(const mpq_t v)
+{
+    mpfr_set_q(mpfr_ptr(), v, mpreal::get_default_rnd());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator=(const long double v)
+{
+    mpfr_set_ld(mpfr_ptr(), v, mpreal::get_default_rnd());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator=(const double v)
+{
+#if (MPREAL_DOUBLE_BITS_OVERFLOW > -1)
+    if(fits_in_bits(v, MPREAL_DOUBLE_BITS_OVERFLOW))
+    {
+        mpfr_set_d(mpfr_ptr(),v,mpreal::get_default_rnd());
+    }else
+        throw conversion_overflow();
+#else
+    mpfr_set_d(mpfr_ptr(),v,mpreal::get_default_rnd());
+#endif
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator=(const unsigned long int v)
+{
+    mpfr_set_ui(mpfr_ptr(), v, mpreal::get_default_rnd());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator=(const unsigned int v)
+{
+    mpfr_set_ui(mpfr_ptr(), v, mpreal::get_default_rnd());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator=(const unsigned long long int v)
+{
+    mpfr_set_uj(mpfr_ptr(), v, mpreal::get_default_rnd());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator=(const long long int v)
+{
+    mpfr_set_sj(mpfr_ptr(), v, mpreal::get_default_rnd());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator=(const long int v)
+{
+    mpfr_set_si(mpfr_ptr(), v, mpreal::get_default_rnd());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator=(const int v)
+{
+    mpfr_set_si(mpfr_ptr(), v, mpreal::get_default_rnd());
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator=(const char* s)
+{
+    // Use other converters for more precise control on base & precision & rounding:
+    //
+    //        mpreal(const char* s,        mp_prec_t prec, int base, mp_rnd_t mode)
+    //        mpreal(const std::string& s,mp_prec_t prec, int base, mp_rnd_t mode)
+    //
+    // Here we assume base = 10 and we use precision of target variable.
+
+    mpfr_t t;
+
+    mpfr_init2(t, mpfr_get_prec(mpfr_srcptr()));
+
+    if(0 == mpfr_set_str(t, s, 10, mpreal::get_default_rnd()))
+    {
+        mpfr_set(mpfr_ptr(), t, mpreal::get_default_rnd());
+        MPREAL_MSVC_DEBUGVIEW_CODE;
+    }
+
+    clear(t);
+    return *this;
+}
+
+inline mpreal& mpreal::operator=(const std::string& s)
+{
+    // Use other converters for more precise control on base & precision & rounding:
+    //
+    //        mpreal(const char* s,        mp_prec_t prec, int base, mp_rnd_t mode)
+    //        mpreal(const std::string& s,mp_prec_t prec, int base, mp_rnd_t mode)
+    //
+    // Here we assume base = 10 and we use precision of target variable.
+
+    mpfr_t t;
+
+    mpfr_init2(t, mpfr_get_prec(mpfr_srcptr()));
+
+    if(0 == mpfr_set_str(t, s.c_str(), 10, mpreal::get_default_rnd()))
+    {
+        mpfr_set(mpfr_ptr(), t, mpreal::get_default_rnd());
+        MPREAL_MSVC_DEBUGVIEW_CODE;
+    }
+
+    clear(t);
+    return *this;
+}
+
+template <typename real_t>
+inline mpreal& mpreal::operator= (const std::complex<real_t>& z)
+{
+    return *this = z.real();
+}
+
+//////////////////////////////////////////////////////////////////////////
+// + Addition
+inline mpreal& mpreal::operator+=(const mpreal& v)
+{
+    mpfr_add(mpfr_ptr(), mpfr_srcptr(), v.mpfr_srcptr(), mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator+=(const mpf_t u)
+{
+    *this += mpreal(u);
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator+=(const mpz_t u)
+{
+    mpfr_add_z(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator+=(const mpq_t u)
+{
+    mpfr_add_q(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator+= (const long double u)
+{
+    *this += mpreal(u);
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator+= (const double u)
+{
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
+    mpfr_add_d(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
+#else
+    *this += mpreal(u);
+#endif
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator+=(const unsigned long int u)
+{
+    mpfr_add_ui(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator+=(const unsigned int u)
+{
+    mpfr_add_ui(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator+=(const long int u)
+{
+    mpfr_add_si(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator+=(const int u)
+{
+    mpfr_add_si(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator+=(const long long int u)         {    *this += mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this;    }
+inline mpreal& mpreal::operator+=(const unsigned long long int u){    *this += mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this;    }
+inline mpreal& mpreal::operator-=(const long long int  u)        {    *this -= mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this;    }
+inline mpreal& mpreal::operator-=(const unsigned long long int u){    *this -= mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this;    }
+inline mpreal& mpreal::operator*=(const long long int  u)        {    *this *= mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this;    }
+inline mpreal& mpreal::operator*=(const unsigned long long int u){    *this *= mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this;    }
+inline mpreal& mpreal::operator/=(const long long int  u)        {    *this /= mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this;    }
+inline mpreal& mpreal::operator/=(const unsigned long long int u){    *this /= mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this;    }
+
+inline const mpreal mpreal::operator+()const    {    return mpreal(*this); }
+
+inline const mpreal operator+(const mpreal& a, const mpreal& b)
+{
+    mpreal c(0, (std::max)(mpfr_get_prec(a.mpfr_ptr()), mpfr_get_prec(b.mpfr_ptr())));
+    mpfr_add(c.mpfr_ptr(), a.mpfr_srcptr(), b.mpfr_srcptr(), mpreal::get_default_rnd());
+    return c;
+}
+
+inline mpreal& mpreal::operator++()
+{
+    return *this += 1;
+}
+
+inline const mpreal mpreal::operator++ (int)
+{
+    mpreal x(*this);
+    *this += 1;
+    return x;
+}
+
+inline mpreal& mpreal::operator--()
+{
+    return *this -= 1;
+}
+
+inline const mpreal mpreal::operator-- (int)
+{
+    mpreal x(*this);
+    *this -= 1;
+    return x;
+}
+
+//////////////////////////////////////////////////////////////////////////
+// - Subtraction
+inline mpreal& mpreal::operator-=(const mpreal& v)
+{
+    mpfr_sub(mpfr_ptr(),mpfr_srcptr(),v.mpfr_srcptr(),mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator-=(const mpz_t v)
+{
+    mpfr_sub_z(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator-=(const mpq_t v)
+{
+    mpfr_sub_q(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator-=(const long double v)
+{
+    *this -= mpreal(v);
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator-=(const double v)
+{
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
+    mpfr_sub_d(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+#else
+    *this -= mpreal(v);
+#endif
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator-=(const unsigned long int v)
+{
+    mpfr_sub_ui(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator-=(const unsigned int v)
+{
+    mpfr_sub_ui(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator-=(const long int v)
+{
+    mpfr_sub_si(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator-=(const int v)
+{
+    mpfr_sub_si(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline const mpreal mpreal::operator-()const
+{
+    mpreal u(*this);
+    mpfr_neg(u.mpfr_ptr(),u.mpfr_srcptr(),mpreal::get_default_rnd());
+    return u;
+}
+
+inline const mpreal operator-(const mpreal& a, const mpreal& b)
+{
+    mpreal c(0, (std::max)(mpfr_get_prec(a.mpfr_ptr()), mpfr_get_prec(b.mpfr_ptr())));
+    mpfr_sub(c.mpfr_ptr(), a.mpfr_srcptr(), b.mpfr_srcptr(), mpreal::get_default_rnd());
+    return c;
+}
+
+inline const mpreal operator-(const double  b, const mpreal& a)
+{
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
+    mpreal x(0, mpfr_get_prec(a.mpfr_ptr()));
+    mpfr_d_sub(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
+    return x;
+#else
+    mpreal x(b, mpfr_get_prec(a.mpfr_ptr()));
+    x -= a;
+    return x;
+#endif
+}
+
+inline const mpreal operator-(const unsigned long int b, const mpreal& a)
+{
+    mpreal x(0, mpfr_get_prec(a.mpfr_ptr()));
+    mpfr_ui_sub(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
+    return x;
+}
+
+inline const mpreal operator-(const unsigned int b, const mpreal& a)
+{
+    mpreal x(0, mpfr_get_prec(a.mpfr_ptr()));
+    mpfr_ui_sub(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
+    return x;
+}
+
+inline const mpreal operator-(const long int b, const mpreal& a)
+{
+    mpreal x(0, mpfr_get_prec(a.mpfr_ptr()));
+    mpfr_si_sub(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
+    return x;
+}
+
+inline const mpreal operator-(const int b, const mpreal& a)
+{
+    mpreal x(0, mpfr_get_prec(a.mpfr_ptr()));
+    mpfr_si_sub(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
+    return x;
+}
+
+//////////////////////////////////////////////////////////////////////////
+// * Multiplication
+inline mpreal& mpreal::operator*= (const mpreal& v)
+{
+    mpfr_mul(mpfr_ptr(),mpfr_srcptr(),v.mpfr_srcptr(),mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator*=(const mpz_t v)
+{
+    mpfr_mul_z(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator*=(const mpq_t v)
+{
+    mpfr_mul_q(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator*=(const long double v)
+{
+    *this *= mpreal(v);
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator*=(const double v)
+{
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
+    mpfr_mul_d(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+#else
+    *this *= mpreal(v);
+#endif
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator*=(const unsigned long int v)
+{
+    mpfr_mul_ui(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator*=(const unsigned int v)
+{
+    mpfr_mul_ui(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator*=(const long int v)
+{
+    mpfr_mul_si(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator*=(const int v)
+{
+    mpfr_mul_si(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline const mpreal operator*(const mpreal& a, const mpreal& b)
+{
+    mpreal c(0, (std::max)(mpfr_get_prec(a.mpfr_ptr()), mpfr_get_prec(b.mpfr_ptr())));
+    mpfr_mul(c.mpfr_ptr(), a.mpfr_srcptr(), b.mpfr_srcptr(), mpreal::get_default_rnd());
+    return c;
+}
+
+//////////////////////////////////////////////////////////////////////////
+// / Division
+inline mpreal& mpreal::operator/=(const mpreal& v)
+{
+    mpfr_div(mpfr_ptr(),mpfr_srcptr(),v.mpfr_srcptr(),mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator/=(const mpz_t v)
+{
+    mpfr_div_z(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator/=(const mpq_t v)
+{
+    mpfr_div_q(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator/=(const long double v)
+{
+    *this /= mpreal(v);
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator/=(const double v)
+{
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
+    mpfr_div_d(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+#else
+    *this /= mpreal(v);
+#endif
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator/=(const unsigned long int v)
+{
+    mpfr_div_ui(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator/=(const unsigned int v)
+{
+    mpfr_div_ui(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator/=(const long int v)
+{
+    mpfr_div_si(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator/=(const int v)
+{
+    mpfr_div_si(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline const mpreal operator/(const mpreal& a, const mpreal& b)
+{
+    mpreal c(0, (std::max)(mpfr_get_prec(a.mpfr_srcptr()), mpfr_get_prec(b.mpfr_srcptr())));
+    mpfr_div(c.mpfr_ptr(), a.mpfr_srcptr(), b.mpfr_srcptr(), mpreal::get_default_rnd());
+    return c;
+}
+
+inline const mpreal operator/(const unsigned long int b, const mpreal& a)
+{
+    mpreal x(0, mpfr_get_prec(a.mpfr_srcptr()));
+    mpfr_ui_div(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
+    return x;
+}
+
+inline const mpreal operator/(const unsigned int b, const mpreal& a)
+{
+    mpreal x(0, mpfr_get_prec(a.mpfr_srcptr()));
+    mpfr_ui_div(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
+    return x;
+}
+
+inline const mpreal operator/(const long int b, const mpreal& a)
+{
+    mpreal x(0, mpfr_get_prec(a.mpfr_srcptr()));
+    mpfr_si_div(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
+    return x;
+}
+
+inline const mpreal operator/(const int b, const mpreal& a)
+{
+    mpreal x(0, mpfr_get_prec(a.mpfr_srcptr()));
+    mpfr_si_div(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
+    return x;
+}
+
+inline const mpreal operator/(const double  b, const mpreal& a)
+{
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
+    mpreal x(0, mpfr_get_prec(a.mpfr_srcptr()));
+    mpfr_d_div(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
+    return x;
+#else
+    mpreal x(b, mpfr_get_prec(a.mpfr_ptr()));
+    x /= a;
+    return x;
+#endif
+}
+
+//////////////////////////////////////////////////////////////////////////
+// Shifts operators - Multiplication/Division by power of 2
+inline mpreal& mpreal::operator<<=(const unsigned long int u)
+{
+    mpfr_mul_2ui(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator<<=(const unsigned int u)
+{
+    mpfr_mul_2ui(mpfr_ptr(),mpfr_srcptr(),static_cast<unsigned long int>(u),mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator<<=(const long int u)
+{
+    mpfr_mul_2si(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator<<=(const int u)
+{
+    mpfr_mul_2si(mpfr_ptr(),mpfr_srcptr(),static_cast<long int>(u),mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator>>=(const unsigned long int u)
+{
+    mpfr_div_2ui(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator>>=(const unsigned int u)
+{
+    mpfr_div_2ui(mpfr_ptr(),mpfr_srcptr(),static_cast<unsigned long int>(u),mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator>>=(const long int u)
+{
+    mpfr_div_2si(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::operator>>=(const int u)
+{
+    mpfr_div_2si(mpfr_ptr(),mpfr_srcptr(),static_cast<long int>(u),mpreal::get_default_rnd());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline const mpreal operator<<(const mpreal& v, const unsigned long int k)
+{
+    return mul_2ui(v,k);
+}
+
+inline const mpreal operator<<(const mpreal& v, const unsigned int k)
+{
+    return mul_2ui(v,static_cast<unsigned long int>(k));
+}
+
+inline const mpreal operator<<(const mpreal& v, const long int k)
+{
+    return mul_2si(v,k);
+}
+
+inline const mpreal operator<<(const mpreal& v, const int k)
+{
+    return mul_2si(v,static_cast<long int>(k));
+}
+
+inline const mpreal operator>>(const mpreal& v, const unsigned long int k)
+{
+    return div_2ui(v,k);
+}
+
+inline const mpreal operator>>(const mpreal& v, const long int k)
+{
+    return div_2si(v,k);
+}
+
+inline const mpreal operator>>(const mpreal& v, const unsigned int k)
+{
+    return div_2ui(v,static_cast<unsigned long int>(k));
+}
+
+inline const mpreal operator>>(const mpreal& v, const int k)
+{
+    return div_2si(v,static_cast<long int>(k));
+}
+
+// mul_2ui
+inline const mpreal mul_2ui(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode)
+{
+    mpreal x(v);
+    mpfr_mul_2ui(x.mpfr_ptr(),v.mpfr_srcptr(),k,rnd_mode);
+    return x;
+}
+
+// mul_2si
+inline const mpreal mul_2si(const mpreal& v, long int k, mp_rnd_t rnd_mode)
+{
+    mpreal x(v);
+    mpfr_mul_2si(x.mpfr_ptr(),v.mpfr_srcptr(),k,rnd_mode);
+    return x;
+}
+
+inline const mpreal div_2ui(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode)
+{
+    mpreal x(v);
+    mpfr_div_2ui(x.mpfr_ptr(),v.mpfr_srcptr(),k,rnd_mode);
+    return x;
+}
+
+inline const mpreal div_2si(const mpreal& v, long int k, mp_rnd_t rnd_mode)
+{
+    mpreal x(v);
+    mpfr_div_2si(x.mpfr_ptr(),v.mpfr_srcptr(),k,rnd_mode);
+    return x;
+}
+
+//////////////////////////////////////////////////////////////////////////
+//Relational operators
+
+// WARNING:
+//
+// Please note that following checks for double-NaN are guaranteed to work only in IEEE math mode:
+//
+// isnan(b) =  (b != b)
+// isnan(b) = !(b == b)  (we use in code below)
+//
+// Be cautions if you use compiler options which break strict IEEE compliance (e.g. -ffast-math in GCC).
+// Use std::isnan instead (C++11).
+
+inline bool operator >  (const mpreal& a, const mpreal& b           ){  return (mpfr_greater_p(a.mpfr_srcptr(),b.mpfr_srcptr()) != 0 );            }
+inline bool operator >  (const mpreal& a, const unsigned long int b ){  return !isnan(a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) > 0 );                 }
+inline bool operator >  (const mpreal& a, const unsigned int b      ){  return !isnan(a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) > 0 );                 }
+inline bool operator >  (const mpreal& a, const long int b          ){  return !isnan(a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) > 0 );                 }
+inline bool operator >  (const mpreal& a, const int b               ){  return !isnan(a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) > 0 );                 }
+inline bool operator >  (const mpreal& a, const long double b       ){  return !isnan(a) && (b == b) && (mpfr_cmp_ld(a.mpfr_srcptr(),b) > 0 );    }
+inline bool operator >  (const mpreal& a, const double b            ){  return !isnan(a) && (b == b) && (mpfr_cmp_d (a.mpfr_srcptr(),b) > 0 );    }
+
+inline bool operator >= (const mpreal& a, const mpreal& b           ){  return (mpfr_greaterequal_p(a.mpfr_srcptr(),b.mpfr_srcptr()) != 0 );       }
+inline bool operator >= (const mpreal& a, const unsigned long int b ){  return !isnan(a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) >= 0 );                }
+inline bool operator >= (const mpreal& a, const unsigned int b      ){  return !isnan(a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) >= 0 );                }
+inline bool operator >= (const mpreal& a, const long int b          ){  return !isnan(a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) >= 0 );                }
+inline bool operator >= (const mpreal& a, const int b               ){  return !isnan(a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) >= 0 );                }
+inline bool operator >= (const mpreal& a, const long double b       ){  return !isnan(a) && (b == b) && (mpfr_cmp_ld(a.mpfr_srcptr(),b) >= 0 );   }
+inline bool operator >= (const mpreal& a, const double b            ){  return !isnan(a) && (b == b) && (mpfr_cmp_d (a.mpfr_srcptr(),b) >= 0 );   }
+
+inline bool operator <  (const mpreal& a, const mpreal& b           ){  return (mpfr_less_p(a.mpfr_srcptr(),b.mpfr_srcptr()) != 0 );               }
+inline bool operator <  (const mpreal& a, const unsigned long int b ){  return !isnan(a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) < 0 );                 }
+inline bool operator <  (const mpreal& a, const unsigned int b      ){  return !isnan(a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) < 0 );                 }
+inline bool operator <  (const mpreal& a, const long int b          ){  return !isnan(a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) < 0 );                 }
+inline bool operator <  (const mpreal& a, const int b               ){  return !isnan(a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) < 0 );                 }
+inline bool operator <  (const mpreal& a, const long double b       ){  return !isnan(a) && (b == b) && (mpfr_cmp_ld(a.mpfr_srcptr(),b) < 0 );    }
+inline bool operator <  (const mpreal& a, const double b            ){  return !isnan(a) && (b == b) && (mpfr_cmp_d (a.mpfr_srcptr(),b) < 0 );    }
+
+inline bool operator <= (const mpreal& a, const mpreal& b           ){  return (mpfr_lessequal_p(a.mpfr_srcptr(),b.mpfr_srcptr()) != 0 );          }
+inline bool operator <= (const mpreal& a, const unsigned long int b ){  return !isnan(a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) <= 0 );                }
+inline bool operator <= (const mpreal& a, const unsigned int b      ){  return !isnan(a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) <= 0 );                }
+inline bool operator <= (const mpreal& a, const long int b          ){  return !isnan(a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) <= 0 );                }
+inline bool operator <= (const mpreal& a, const int b               ){  return !isnan(a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) <= 0 );                }
+inline bool operator <= (const mpreal& a, const long double b       ){  return !isnan(a) && (b == b) && (mpfr_cmp_ld(a.mpfr_srcptr(),b) <= 0 );   }
+inline bool operator <= (const mpreal& a, const double b            ){  return !isnan(a) && (b == b) && (mpfr_cmp_d (a.mpfr_srcptr(),b) <= 0 );   }
+
+inline bool operator == (const mpreal& a, const mpreal& b           ){  return (mpfr_equal_p(a.mpfr_srcptr(),b.mpfr_srcptr()) != 0 );              }
+inline bool operator == (const mpreal& a, const unsigned long int b ){  return !isnan(a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) == 0 );                }
+inline bool operator == (const mpreal& a, const unsigned int b      ){  return !isnan(a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) == 0 );                }
+inline bool operator == (const mpreal& a, const long int b          ){  return !isnan(a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) == 0 );                }
+inline bool operator == (const mpreal& a, const int b               ){  return !isnan(a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) == 0 );                }
+inline bool operator == (const mpreal& a, const long double b       ){  return !isnan(a) && (b == b) && (mpfr_cmp_ld(a.mpfr_srcptr(),b) == 0 );   }
+inline bool operator == (const mpreal& a, const double b            ){  return !isnan(a) && (b == b) && (mpfr_cmp_d (a.mpfr_srcptr(),b) == 0 );   }
+
+inline bool operator != (const mpreal& a, const mpreal& b           ){  return !(a == b);  }
+inline bool operator != (const mpreal& a, const unsigned long int b ){  return !(a == b);  }
+inline bool operator != (const mpreal& a, const unsigned int b      ){  return !(a == b);  }
+inline bool operator != (const mpreal& a, const long int b          ){  return !(a == b);  }
+inline bool operator != (const mpreal& a, const int b               ){  return !(a == b);  }
+inline bool operator != (const mpreal& a, const long double b       ){  return !(a == b);  }
+inline bool operator != (const mpreal& a, const double b            ){  return !(a == b);  }
+
+inline bool isnan    (const mpreal& op){    return (mpfr_nan_p    (op.mpfr_srcptr()) != 0 );    }
+inline bool isinf    (const mpreal& op){    return (mpfr_inf_p    (op.mpfr_srcptr()) != 0 );    }
+inline bool isfinite (const mpreal& op){    return (mpfr_number_p (op.mpfr_srcptr()) != 0 );    }
+inline bool iszero   (const mpreal& op){    return (mpfr_zero_p   (op.mpfr_srcptr()) != 0 );    }
+inline bool isint    (const mpreal& op){    return (mpfr_integer_p(op.mpfr_srcptr()) != 0 );    }
+
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
+inline bool isregular(const mpreal& op){    return (mpfr_regular_p(op.mpfr_srcptr()));}
+#endif
+
+//////////////////////////////////////////////////////////////////////////
+// Type Converters
+inline bool               mpreal::toBool   (             )  const    {    return  mpfr_zero_p (mpfr_srcptr()) == 0;     }
+inline long               mpreal::toLong   (mp_rnd_t mode)  const    {    return  mpfr_get_si (mpfr_srcptr(), mode);    }
+inline unsigned long      mpreal::toULong  (mp_rnd_t mode)  const    {    return  mpfr_get_ui (mpfr_srcptr(), mode);    }
+inline float              mpreal::toFloat  (mp_rnd_t mode)  const    {    return  mpfr_get_flt(mpfr_srcptr(), mode);    }
+inline double             mpreal::toDouble (mp_rnd_t mode)  const    {    return  mpfr_get_d  (mpfr_srcptr(), mode);    }
+inline long double        mpreal::toLDouble(mp_rnd_t mode)  const    {    return  mpfr_get_ld (mpfr_srcptr(), mode);    }
+inline long long          mpreal::toLLong  (mp_rnd_t mode)  const    {    return  mpfr_get_sj (mpfr_srcptr(), mode);    }
+inline unsigned long long mpreal::toULLong (mp_rnd_t mode)  const    {    return  mpfr_get_uj (mpfr_srcptr(), mode);    }
+
+inline ::mpfr_ptr     mpreal::mpfr_ptr()             { return mp; }
+inline ::mpfr_srcptr  mpreal::mpfr_ptr()    const    { return mp; }
+inline ::mpfr_srcptr  mpreal::mpfr_srcptr() const    { return mp; }
+
+template <class T>
+inline std::string toString(T t, std::ios_base & (*f)(std::ios_base&))
+{
+    std::ostringstream oss;
+    oss << f << t;
+    return oss.str();
+}
+
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
+
+inline std::string mpreal::toString(const std::string& format) const
+{
+    char *s = NULL;
+    std::string out;
+
+    if( !format.empty() )
+    {
+        if(!(mpfr_asprintf(&s, format.c_str(), mpfr_srcptr()) < 0))
+        {
+            out = std::string(s);
+
+            mpfr_free_str(s);
+        }
+    }
+
+    return out;
+}
+
+#endif
+
+inline std::string mpreal::toString(int n, int b, mp_rnd_t mode) const
+{
+    // TODO: Add extended format specification (f, e, rounding mode) as it done in output operator
+    (void)b;
+    (void)mode;
+
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
+
+    std::ostringstream format;
+
+    int digits = (n >= 0) ? n : 2 + bits2digits(mpfr_get_prec(mpfr_srcptr()));
+
+    format << "%." << digits << "RNg";
+
+    return toString(format.str());
+
+#else
+
+    char *s, *ns = NULL;
+    size_t slen, nslen;
+    mp_exp_t exp;
+    std::string out;
+
+    if(mpfr_inf_p(mp))
+    {
+        if(mpfr_sgn(mp)>0) return "+Inf";
+        else               return "-Inf";
+    }
+
+    if(mpfr_zero_p(mp)) return "0";
+    if(mpfr_nan_p(mp))  return "NaN";
+
+    s  = mpfr_get_str(NULL, &exp, b, 0, mp, mode);
+    ns = mpfr_get_str(NULL, &exp, b, (std::max)(0,n), mp, mode);
+
+    if(s!=NULL && ns!=NULL)
+    {
+        slen  = strlen(s);
+        nslen = strlen(ns);
+        if(nslen<=slen)
+        {
+            mpfr_free_str(s);
+            s = ns;
+            slen = nslen;
+        }
+        else {
+            mpfr_free_str(ns);
+        }
+
+        // Make human eye-friendly formatting if possible
+        if (exp>0 && static_cast<size_t>(exp)<slen)
+        {
+            if(s[0]=='-')
+            {
+                // Remove zeros starting from right end
+                char* ptr = s+slen-1;
+                while (*ptr=='0' && ptr>s+exp) ptr--;
+
+                if(ptr==s+exp) out = std::string(s,exp+1);
+                else           out = std::string(s,exp+1)+'.'+std::string(s+exp+1,ptr-(s+exp+1)+1);
+
+                //out = string(s,exp+1)+'.'+string(s+exp+1);
+            }
+            else
+            {
+                // Remove zeros starting from right end
+                char* ptr = s+slen-1;
+                while (*ptr=='0' && ptr>s+exp-1) ptr--;
+
+                if(ptr==s+exp-1) out = std::string(s,exp);
+                else             out = std::string(s,exp)+'.'+std::string(s+exp,ptr-(s+exp)+1);
+
+                //out = string(s,exp)+'.'+string(s+exp);
+            }
+
+        }else{ // exp<0 || exp>slen
+            if(s[0]=='-')
+            {
+                // Remove zeros starting from right end
+                char* ptr = s+slen-1;
+                while (*ptr=='0' && ptr>s+1) ptr--;
+
+                if(ptr==s+1) out = std::string(s,2);
+                else         out = std::string(s,2)+'.'+std::string(s+2,ptr-(s+2)+1);
+
+                //out = string(s,2)+'.'+string(s+2);
+            }
+            else
+            {
+                // Remove zeros starting from right end
+                char* ptr = s+slen-1;
+                while (*ptr=='0' && ptr>s) ptr--;
+
+                if(ptr==s) out = std::string(s,1);
+                else       out = std::string(s,1)+'.'+std::string(s+1,ptr-(s+1)+1);
+
+                //out = string(s,1)+'.'+string(s+1);
+            }
+
+            // Make final string
+            if(--exp)
+            {
+                if(exp>0) out += "e+"+mpfr::toString<mp_exp_t>(exp,std::dec);
+                else       out += "e"+mpfr::toString<mp_exp_t>(exp,std::dec);
+            }
+        }
+
+        mpfr_free_str(s);
+        return out;
+    }else{
+        return "conversion error!";
+    }
+#endif
+}
+
+
+//////////////////////////////////////////////////////////////////////////
+// I/O
+inline std::ostream& mpreal::output(std::ostream& os) const
+{
+    std::ostringstream format;
+    const std::ios::fmtflags flags = os.flags();
+
+    format << ((flags & std::ios::showpos) ? "%+" : "%");
+    if (os.precision() >= 0)
+        format << '.' << os.precision() << "R*"
+               << ((flags & std::ios::floatfield) == std::ios::fixed ? 'f' :
+                   (flags & std::ios::floatfield) == std::ios::scientific ? 'e' :
+                   'g');
+    else
+        format << "R*e";
+
+    char *s = NULL;
+    if(!(mpfr_asprintf(&s, format.str().c_str(),
+                        mpfr::mpreal::get_default_rnd(),
+                        mpfr_srcptr())
+        < 0))
+    {
+        os << std::string(s);
+        mpfr_free_str(s);
+    }
+    return os;
+}
+
+inline std::ostream& operator<<(std::ostream& os, const mpreal& v)
+{
+    return v.output(os);
+}
+
+inline std::istream& operator>>(std::istream &is, mpreal& v)
+{
+    // TODO: use cout::hexfloat and other flags to setup base
+    std::string tmp;
+    is >> tmp;
+    mpfr_set_str(v.mpfr_ptr(), tmp.c_str(), 10, mpreal::get_default_rnd());
+    return is;
+}
+
+//////////////////////////////////////////////////////////////////////////
+//     Bits - decimal digits relation
+//        bits   = ceil(digits*log[2](10))
+//        digits = floor(bits*log[10](2))
+
+inline mp_prec_t digits2bits(int d)
+{
+    const double LOG2_10 = 3.3219280948873624;
+
+    return mp_prec_t(std::ceil( d * LOG2_10 ));
+}
+
+inline int bits2digits(mp_prec_t b)
+{
+    const double LOG10_2 = 0.30102999566398119;
+
+    return int(std::floor( b * LOG10_2 ));
+}
+
+//////////////////////////////////////////////////////////////////////////
+// Set/Get number properties
+inline mpreal& mpreal::setSign(int sign, mp_rnd_t RoundingMode)
+{
+    mpfr_setsign(mpfr_ptr(), mpfr_srcptr(), sign < 0, RoundingMode);
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline int mpreal::getPrecision() const
+{
+    return int(mpfr_get_prec(mpfr_srcptr()));
+}
+
+inline mpreal& mpreal::setPrecision(int Precision, mp_rnd_t RoundingMode)
+{
+    mpfr_prec_round(mpfr_ptr(), Precision, RoundingMode);
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::setInf(int sign)
+{
+    mpfr_set_inf(mpfr_ptr(), sign);
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::setNan()
+{
+    mpfr_set_nan(mpfr_ptr());
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mpreal& mpreal::setZero(int sign)
+{
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
+    mpfr_set_zero(mpfr_ptr(), sign);
+#else
+    mpfr_set_si(mpfr_ptr(), 0, (mpfr_get_default_rounding_mode)());
+    setSign(sign);
+#endif
+
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return *this;
+}
+
+inline mp_prec_t mpreal::get_prec() const
+{
+    return mpfr_get_prec(mpfr_srcptr());
+}
+
+inline void mpreal::set_prec(mp_prec_t prec, mp_rnd_t rnd_mode)
+{
+    mpfr_prec_round(mpfr_ptr(),prec,rnd_mode);
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+}
+
+inline mp_exp_t mpreal::get_exp () const
+{
+    return mpfr_get_exp(mpfr_srcptr());
+}
+
+inline int mpreal::set_exp (mp_exp_t e)
+{
+    int x = mpfr_set_exp(mpfr_ptr(), e);
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return x;
+}
+
+inline mpreal& negate(mpreal& x) // -x in place
+{
+    mpfr_neg(x.mpfr_ptr(),x.mpfr_srcptr(),mpreal::get_default_rnd());
+    return x;
+}
+
+inline const mpreal frexp(const mpreal& x, mp_exp_t* exp, mp_rnd_t mode = mpreal::get_default_rnd())
+{
+    mpreal y(x);
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,1,0))
+    mpfr_frexp(exp,y.mpfr_ptr(),x.mpfr_srcptr(),mode);
+#else
+    *exp = mpfr_get_exp(y.mpfr_srcptr());
+    mpfr_set_exp(y.mpfr_ptr(),0);
+#endif
+    return y;
+}
+
+inline const mpreal frexp(const mpreal& x, int* exp, mp_rnd_t mode = mpreal::get_default_rnd())
+{
+    mp_exp_t expl;
+    mpreal y = frexp(x, &expl, mode);
+    *exp = int(expl);
+    return y;
+}
+
+inline const mpreal ldexp(const mpreal& v, mp_exp_t exp)
+{
+    mpreal x(v);
+
+    // rounding is not important since we are just increasing the exponent (= exact operation)
+    mpfr_mul_2si(x.mpfr_ptr(), x.mpfr_srcptr(), exp, mpreal::get_default_rnd());
+    return x;
+}
+
+inline const mpreal scalbn(const mpreal& v, mp_exp_t exp)
+{
+    return ldexp(v, exp);
+}
+
+inline mpreal machine_epsilon(mp_prec_t prec)
+{
+    /* the smallest eps such that 1 + eps != 1 */
+    return machine_epsilon(mpreal(1, prec));
+}
+
+inline mpreal machine_epsilon(const mpreal& x)
+{
+    /* the smallest eps such that x + eps != x */
+    if( x < 0)
+    {
+        return nextabove(-x) + x;
+    }else{
+        return nextabove( x) - x;
+    }
+}
+
+// minval is 'safe' meaning 1 / minval does not overflow
+inline mpreal minval(mp_prec_t prec)
+{
+    /* min = 1/2 * 2^emin = 2^(emin - 1) */
+    return mpreal(1, prec) << mpreal::get_emin()-1;
+}
+
+// maxval is 'safe' meaning 1 / maxval does not underflow
+inline mpreal maxval(mp_prec_t prec)
+{
+    /* max = (1 - eps) * 2^emax, eps is machine epsilon */
+    return (mpreal(1, prec) - machine_epsilon(prec)) << mpreal::get_emax();
+}
+
+inline bool isEqualUlps(const mpreal& a, const mpreal& b, int maxUlps)
+{
+    return abs(a - b) <= machine_epsilon((max)(abs(a), abs(b))) * maxUlps;
+}
+
+inline bool isEqualFuzzy(const mpreal& a, const mpreal& b, const mpreal& eps)
+{
+    return abs(a - b) <= eps;
+}
+
+inline bool isEqualFuzzy(const mpreal& a, const mpreal& b)
+{
+    return isEqualFuzzy(a, b, machine_epsilon((max)(1, (min)(abs(a), abs(b)))));
+}
+
+//////////////////////////////////////////////////////////////////////////
+// C++11 sign functions.
+inline mpreal copysign(const mpreal& x, const  mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal rop(0, mpfr_get_prec(x.mpfr_ptr()));
+    mpfr_setsign(rop.mpfr_ptr(), x.mpfr_srcptr(), mpfr_signbit(y.mpfr_srcptr()), rnd_mode);
+    return rop;
+}
+
+inline bool signbit(const mpreal& x)
+{
+    return mpfr_signbit(x.mpfr_srcptr());
+}
+
+inline mpreal& setsignbit(mpreal& x, bool minus, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpfr_setsign(x.mpfr_ptr(), x.mpfr_srcptr(), minus, rnd_mode);
+    return x;
+}
+
+inline const mpreal modf(const mpreal& v, mpreal& n)
+{
+    mpreal f(v);
+
+    // rounding is not important since we are using the same number
+    mpfr_frac (f.mpfr_ptr(),f.mpfr_srcptr(),mpreal::get_default_rnd());
+    mpfr_trunc(n.mpfr_ptr(),v.mpfr_srcptr());
+    return f;
+}
+
+inline int mpreal::check_range (int t, mp_rnd_t rnd_mode)
+{
+    return mpfr_check_range(mpfr_ptr(),t,rnd_mode);
+}
+
+inline int mpreal::subnormalize (int t,mp_rnd_t rnd_mode)
+{
+    int r = mpfr_subnormalize(mpfr_ptr(),t,rnd_mode);
+    MPREAL_MSVC_DEBUGVIEW_CODE;
+    return r;
+}
+
+inline mp_exp_t mpreal::get_emin (void)
+{
+    return mpfr_get_emin();
+}
+
+inline int mpreal::set_emin (mp_exp_t exp)
+{
+    return mpfr_set_emin(exp);
+}
+
+inline mp_exp_t mpreal::get_emax (void)
+{
+    return mpfr_get_emax();
+}
+
+inline int mpreal::set_emax (mp_exp_t exp)
+{
+    return mpfr_set_emax(exp);
+}
+
+inline mp_exp_t mpreal::get_emin_min (void)
+{
+    return mpfr_get_emin_min();
+}
+
+inline mp_exp_t mpreal::get_emin_max (void)
+{
+    return mpfr_get_emin_max();
+}
+
+inline mp_exp_t mpreal::get_emax_min (void)
+{
+    return mpfr_get_emax_min();
+}
+
+inline mp_exp_t mpreal::get_emax_max (void)
+{
+    return mpfr_get_emax_max();
+}
+
+//////////////////////////////////////////////////////////////////////////
+// Mathematical Functions
+//////////////////////////////////////////////////////////////////////////
+
+// Unary function template with single 'mpreal' argument
+#define MPREAL_UNARY_MATH_FUNCTION_BODY(f)                    \
+        mpreal y(0, mpfr_get_prec(x.mpfr_srcptr()));          \
+        mpfr_##f(y.mpfr_ptr(), x.mpfr_srcptr(), r);           \
+        return y;
+
+// Binary function template with 'mpreal' and 'unsigned long' arguments
+#define MPREAL_BINARY_MATH_FUNCTION_UI_BODY(f, u)             \
+        mpreal y(0, mpfr_get_prec(x.mpfr_srcptr()));          \
+        mpfr_##f(y.mpfr_ptr(), x.mpfr_srcptr(), u, r);        \
+        return y;
+
+inline const mpreal sqr  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd())
+{   MPREAL_UNARY_MATH_FUNCTION_BODY(sqr );    }
+
+inline const mpreal sqrt (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd())
+{   MPREAL_UNARY_MATH_FUNCTION_BODY(sqrt);    }
+
+inline const mpreal sqrt(const unsigned long int x, mp_rnd_t r)
+{
+    mpreal y;
+    mpfr_sqrt_ui(y.mpfr_ptr(), x, r);
+    return y;
+}
+
+inline const mpreal sqrt(const unsigned int v, mp_rnd_t rnd_mode)
+{
+    return sqrt(static_cast<unsigned long int>(v),rnd_mode);
+}
+
+inline const mpreal sqrt(const long int v, mp_rnd_t rnd_mode)
+{
+    if (v>=0)   return sqrt(static_cast<unsigned long int>(v),rnd_mode);
+    else        return mpreal().setNan(); // NaN
+}
+
+inline const mpreal sqrt(const int v, mp_rnd_t rnd_mode)
+{
+    if (v>=0)   return sqrt(static_cast<unsigned long int>(v),rnd_mode);
+    else        return mpreal().setNan(); // NaN
+}
+
+inline const mpreal root(const mpreal& x, unsigned long int k, mp_rnd_t r = mpreal::get_default_rnd())
+{
+    mpreal y(0, mpfr_get_prec(x.mpfr_srcptr()));
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(4,0,0))
+    mpfr_rootn_ui(y.mpfr_ptr(), x.mpfr_srcptr(), k, r);
+#else
+    mpfr_root(y.mpfr_ptr(), x.mpfr_srcptr(), k, r);
+#endif
+    return y;
+}
+
+inline const mpreal dim(const mpreal& a, const mpreal& b, mp_rnd_t r = mpreal::get_default_rnd())
+{
+    mpreal y(0, mpfr_get_prec(a.mpfr_srcptr()));
+    mpfr_dim(y.mpfr_ptr(), a.mpfr_srcptr(), b.mpfr_srcptr(), r);
+    return y;
+}
+
+inline int cmpabs(const mpreal& a,const mpreal& b)
+{
+    return mpfr_cmpabs(a.mpfr_ptr(), b.mpfr_srcptr());
+}
+
+inline int sin_cos(mpreal& s, mpreal& c, const mpreal& v, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    return mpfr_sin_cos(s.mpfr_ptr(), c.mpfr_ptr(), v.mpfr_srcptr(), rnd_mode);
+}
+
+inline const mpreal sqrt  (const long double v, mp_rnd_t rnd_mode)    {   return sqrt(mpreal(v),rnd_mode);    }
+inline const mpreal sqrt  (const double v, mp_rnd_t rnd_mode)         {   return sqrt(mpreal(v),rnd_mode);    }
+
+inline const mpreal cbrt  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(cbrt );    }
+inline const mpreal fabs  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(abs  );    }
+inline const mpreal abs   (const mpreal& x, mp_rnd_t r)                             {   MPREAL_UNARY_MATH_FUNCTION_BODY(abs  );    }
+inline const mpreal log   (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(log  );    }
+inline const mpreal log2  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(log2 );    }
+inline const mpreal log10 (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(log10);    }
+inline const mpreal exp   (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(exp  );    }
+inline const mpreal exp2  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(exp2 );    }
+inline const mpreal exp10 (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(exp10);    }
+inline const mpreal cos   (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(cos  );    }
+inline const mpreal sin   (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(sin  );    }
+inline const mpreal tan   (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(tan  );    }
+inline const mpreal sec   (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(sec  );    }
+inline const mpreal csc   (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(csc  );    }
+inline const mpreal cot   (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(cot  );    }
+inline const mpreal acos  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(acos );    }
+inline const mpreal asin  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(asin );    }
+inline const mpreal atan  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(atan );    }
+
+inline const mpreal logb  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   return log2 (abs(x),r);                    }
+inline     mp_exp_t ilogb (const mpreal& x) { return x.get_exp(); }
+
+inline const mpreal acot  (const mpreal& v, mp_rnd_t r = mpreal::get_default_rnd()) {   return atan (1/v, r);                      }
+inline const mpreal asec  (const mpreal& v, mp_rnd_t r = mpreal::get_default_rnd()) {   return acos (1/v, r);                      }
+inline const mpreal acsc  (const mpreal& v, mp_rnd_t r = mpreal::get_default_rnd()) {   return asin (1/v, r);                      }
+inline const mpreal acoth (const mpreal& v, mp_rnd_t r = mpreal::get_default_rnd()) {   return atanh(1/v, r);                      }
+inline const mpreal asech (const mpreal& v, mp_rnd_t r = mpreal::get_default_rnd()) {   return acosh(1/v, r);                      }
+inline const mpreal acsch (const mpreal& v, mp_rnd_t r = mpreal::get_default_rnd()) {   return asinh(1/v, r);                      }
+
+inline const mpreal cosh  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(cosh );    }
+inline const mpreal sinh  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(sinh );    }
+inline const mpreal tanh  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(tanh );    }
+inline const mpreal sech  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(sech );    }
+inline const mpreal csch  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(csch );    }
+inline const mpreal coth  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(coth );    }
+inline const mpreal acosh (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(acosh);    }
+inline const mpreal asinh (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(asinh);    }
+inline const mpreal atanh (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(atanh);    }
+
+inline const mpreal log1p   (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(log1p  );    }
+inline const mpreal expm1   (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(expm1  );    }
+inline const mpreal eint    (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(eint   );    }
+inline const mpreal gamma   (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(gamma  );    }
+inline const mpreal tgamma  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(gamma  );    }
+inline const mpreal lngamma (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(lngamma);    }
+inline const mpreal zeta    (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(zeta   );    }
+inline const mpreal erf     (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(erf    );    }
+inline const mpreal erfc    (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(erfc   );    }
+inline const mpreal besselj0(const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(j0     );    }
+inline const mpreal besselj1(const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(j1     );    }
+inline const mpreal bessely0(const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(y0     );    }
+inline const mpreal bessely1(const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(y1     );    }
+
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(4,0,0))
+inline const mpreal gammainc (const mpreal& a, const mpreal& x, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    /*
+       The non-normalized (upper) incomplete gamma function of a and x:
+       gammainc(a,x) := Gamma(a,x) = int(t^(a-1) * exp(-t), t=x..infinity)
+    */
+    mpreal y(0,(std::max)(a.getPrecision(), x.getPrecision()));
+    mpfr_gamma_inc(y.mpfr_ptr(), a.mpfr_srcptr(), x.mpfr_srcptr(), rnd_mode);
+    return y;
+}
+
+inline const mpreal beta (const mpreal& z, const mpreal& w, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    /*
+       Beta function, uses formula (6.2.2) from Abramowitz & Stegun:
+       beta(z,w) = gamma(z)*gamma(w)/gamma(z+w)
+    */
+    mpreal y(0,(std::max)(z.getPrecision(), w.getPrecision()));
+    mpfr_beta(y.mpfr_ptr(), z.mpfr_srcptr(), w.mpfr_srcptr(), rnd_mode);
+    return y;
+}
+
+inline const mpreal log_ui (unsigned long int n, mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    /* Computes natural logarithm of an unsigned long  */
+    mpreal y(0, prec);
+    mpfr_log_ui(y.mpfr_ptr(),n,rnd_mode);
+    return y;
+}
+#endif
+
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(4,2,0))
+
+/* f(x,u) = f(2*pi*x/u) */
+inline const mpreal cosu   (const mpreal& x, unsigned long u, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_BINARY_MATH_FUNCTION_UI_BODY(cosu,  u); }
+inline const mpreal sinu   (const mpreal& x, unsigned long u, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_BINARY_MATH_FUNCTION_UI_BODY(sinu,  u); }
+inline const mpreal tanu   (const mpreal& x, unsigned long u, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_BINARY_MATH_FUNCTION_UI_BODY(tanu,  u); }
+inline const mpreal acosu  (const mpreal& x, unsigned long u, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_BINARY_MATH_FUNCTION_UI_BODY(acosu, u); }
+inline const mpreal asinu  (const mpreal& x, unsigned long u, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_BINARY_MATH_FUNCTION_UI_BODY(asinu, u); }
+inline const mpreal atanu  (const mpreal& x, unsigned long u, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_BINARY_MATH_FUNCTION_UI_BODY(atanu, u); }
+
+/* f(x) = f(pi*x) */
+inline const mpreal cospi  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(cospi ); }
+inline const mpreal sinpi  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(sinpi ); }
+inline const mpreal tanpi  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(tanpi ); }
+inline const mpreal acospi (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(acospi); }
+inline const mpreal asinpi (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(asinpi); }
+inline const mpreal atanpi (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(atanpi); }
+
+inline const mpreal log2p1 (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(log2p1 ); }  /* log2 (1+x) */
+inline const mpreal log10p1(const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(log10p1); }  /* log10(1+x) */
+inline const mpreal exp2m1 (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(exp2m1 ); }  /* 2^x-1      */
+inline const mpreal exp10m1(const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(exp10m1); }  /* 10^x-1     */
+
+inline const mpreal atan2u(const mpreal& y, const mpreal& x, unsigned long u, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    /*
+        atan2u(y,x,u) = atan(|y/x|)*u/(2*pi)   for x > 0
+        atan2u(y,x,u) = 1-atan(|y/x|)*u/(2*pi) for x < 0
+    */
+    mpreal a(0, (std::max)(y.getPrecision(), x.getPrecision()));
+    mpfr_atan2u(a.mpfr_ptr(), y.mpfr_srcptr(), x.mpfr_srcptr(), u, rnd_mode);
+    return a;
+}
+
+inline const mpreal atan2pi(const mpreal& y, const mpreal& x, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    /* atan2pi(x) = atan2u(u=2) */
+    mpreal a(0, (std::max)(y.getPrecision(), x.getPrecision()));
+    mpfr_atan2pi(a.mpfr_ptr(), y.mpfr_srcptr(), x.mpfr_srcptr(), rnd_mode);
+    return a;
+}
+
+inline const mpreal powr(const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    /* powr(x,y) = exp(y*log(x)) */
+    mpreal a(0, (std::max)(x.getPrecision(), y.getPrecision()));
+    mpfr_powr(a.mpfr_ptr(), x.mpfr_srcptr(), y.mpfr_srcptr(), rnd_mode);
+    return a;
+}
+
+inline const mpreal compound(const mpreal& x, long n, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    /* compound(x,n) = (1+x)^n */
+    mpreal y(0, x.getPrecision());
+    mpfr_compound_si(y.mpfr_ptr(),x.mpfr_srcptr(),n,rnd_mode);
+    return y;
+}
+
+inline const mpreal fmod(const mpreal& x, unsigned long u, mp_rnd_t r = mpreal::get_default_rnd())
+{
+    /* x modulo a machine integer u */
+    MPREAL_BINARY_MATH_FUNCTION_UI_BODY(fmod_ui, u);
+}
+#endif
+
+inline const mpreal nextpow2(const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd())
+{
+    mpreal y(0, x.getPrecision());
+
+    if(!iszero(x))
+        y = ceil(log2(abs(x,r),r));
+
+    return y;
+}
+
+inline const mpreal atan2 (const mpreal& y, const mpreal& x, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal a(0,(std::max)(y.getPrecision(), x.getPrecision()));
+    mpfr_atan2(a.mpfr_ptr(), y.mpfr_srcptr(), x.mpfr_srcptr(), rnd_mode);
+    return a;
+}
+
+inline const mpreal hypot (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal a(0,(std::max)(y.getPrecision(), x.getPrecision()));
+    mpfr_hypot(a.mpfr_ptr(), x.mpfr_srcptr(), y.mpfr_srcptr(), rnd_mode);
+    return a;
+}
+
+inline const mpreal hypot(const mpreal& a, const mpreal& b, const mpreal& c)
+{
+    if(isnan(a) || isnan(b) || isnan(c)) return mpreal().setNan();
+    else
+    {
+        mpreal absa = abs(a), absb = abs(b), absc = abs(c);
+        mpreal w = (std::max)(absa, (std::max)(absb, absc));
+        mpreal r;
+
+        if (!iszero(w))
+        {
+            mpreal iw = 1/w;
+            r = w * sqrt(sqr(absa*iw) + sqr(absb*iw) + sqr(absc*iw));
+        }
+
+        return r;
+    }
+}
+
+inline const mpreal hypot(const mpreal& a, const mpreal& b, const mpreal& c, const mpreal& d)
+{
+    if(isnan(a) || isnan(b) || isnan(c) || isnan(d)) return mpreal().setNan();
+    else
+    {
+        mpreal absa = abs(a), absb = abs(b), absc = abs(c), absd = abs(d);
+        mpreal w = (std::max)(absa, (std::max)(absb, (std::max)(absc, absd)));
+        mpreal r;
+
+        if (!iszero(w))
+        {
+            mpreal iw = 1/w;
+            r = w * sqrt(sqr(absa*iw) + sqr(absb*iw) + sqr(absc*iw) + sqr(absd*iw));
+        }
+
+        return r;
+    }
+}
+
+inline const mpreal remainder (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal a(0,(std::max)(y.getPrecision(), x.getPrecision()));
+    mpfr_remainder(a.mpfr_ptr(), x.mpfr_srcptr(), y.mpfr_srcptr(), rnd_mode);
+    return a;
+}
+
+inline const mpreal remquo (const mpreal& x, const mpreal& y, int* q, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    long lq;
+    mpreal a(0,(std::max)(y.getPrecision(), x.getPrecision()));
+    mpfr_remquo(a.mpfr_ptr(), &lq, x.mpfr_srcptr(), y.mpfr_srcptr(), rnd_mode);
+    if (q) *q = int(lq);
+    return a;
+}
+
+inline const mpreal fac_ui (unsigned long int v, mp_prec_t prec     = mpreal::get_default_prec(),
+                                                 mp_rnd_t  rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal x(0, prec);
+    mpfr_fac_ui(x.mpfr_ptr(),v,rnd_mode);
+    return x;
+}
+
+
+inline const mpreal lgamma (const mpreal& v, int *signp = 0, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal x(v);
+    int tsignp;
+
+    if(signp)   mpfr_lgamma(x.mpfr_ptr(),  signp,v.mpfr_srcptr(),rnd_mode);
+    else        mpfr_lgamma(x.mpfr_ptr(),&tsignp,v.mpfr_srcptr(),rnd_mode);
+
+    return x;
+}
+
+
+inline const mpreal besseljn (long n, const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd())
+{
+    mpreal  y(0, x.getPrecision());
+    mpfr_jn(y.mpfr_ptr(), n, x.mpfr_srcptr(), r);
+    return y;
+}
+
+inline const mpreal besselyn (long n, const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd())
+{
+    mpreal  y(0, x.getPrecision());
+    mpfr_yn(y.mpfr_ptr(), n, x.mpfr_srcptr(), r);
+    return y;
+}
+
+inline const mpreal fma (const mpreal& v1, const mpreal& v2, const mpreal& v3, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal a;
+    mp_prec_t p1, p2, p3;
+
+    p1 = v1.get_prec();
+    p2 = v2.get_prec();
+    p3 = v3.get_prec();
+
+    a.set_prec(p3>p2?(p3>p1?p3:p1):(p2>p1?p2:p1));
+
+    mpfr_fma(a.mp,v1.mp,v2.mp,v3.mp,rnd_mode);
+    return a;
+}
+
+inline const mpreal fms (const mpreal& v1, const mpreal& v2, const mpreal& v3, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal a;
+    mp_prec_t p1, p2, p3;
+
+    p1 = v1.get_prec();
+    p2 = v2.get_prec();
+    p3 = v3.get_prec();
+
+    a.set_prec(p3>p2?(p3>p1?p3:p1):(p2>p1?p2:p1));
+
+    mpfr_fms(a.mp,v1.mp,v2.mp,v3.mp,rnd_mode);
+    return a;
+}
+
+inline const mpreal agm (const mpreal& v1, const mpreal& v2, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal a;
+    mp_prec_t p1, p2;
+
+    p1 = v1.get_prec();
+    p2 = v2.get_prec();
+
+    a.set_prec(p1>p2?p1:p2);
+
+    mpfr_agm(a.mp, v1.mp, v2.mp, rnd_mode);
+
+    return a;
+}
+
+inline const mpreal sum (const mpreal tab[], const unsigned long int n, int& status, mp_rnd_t mode = mpreal::get_default_rnd())
+{
+    mpfr_srcptr *p = new mpfr_srcptr[n];
+
+    for (unsigned long int  i = 0; i < n; i++)
+        p[i] = tab[i].mpfr_srcptr();
+
+    mpreal x;
+    status = mpfr_sum(x.mpfr_ptr(), (mpfr_ptr*)p, n, mode);
+
+    delete [] p;
+    return x;
+}
+
+//////////////////////////////////////////////////////////////////////////
+// MPFR 2.4.0 Specifics
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
+
+inline int sinh_cosh(mpreal& s, mpreal& c, const mpreal& v, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    return mpfr_sinh_cosh(s.mp,c.mp,v.mp,rnd_mode);
+}
+
+inline const mpreal li2 (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd())
+{
+    MPREAL_UNARY_MATH_FUNCTION_BODY(li2);
+}
+
+inline const mpreal rem (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    /*  R = rem(X,Y) if Y != 0, returns X - n * Y where n = trunc(X/Y). */
+    return fmod(x, y, rnd_mode);
+}
+
+inline const mpreal mod (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    (void)rnd_mode;
+
+    /*
+
+    m = mod(x,y) if y != 0, returns x - n*y where n = floor(x/y)
+
+    The following are true by convention:
+    - mod(x,0) is x
+    - mod(x,x) is 0
+    - mod(x,y) for x != y and y != 0 has the same sign as y.
+
+    */
+
+    if(iszero(y)) return x;
+    if(x == y) return 0;
+
+    mpreal m = x - floor(x / y) * y;
+
+    return copysign(abs(m),y); // make sure result has the same sign as Y
+}
+
+inline const mpreal fmod (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal a;
+    mp_prec_t yp, xp;
+
+    yp = y.get_prec();
+    xp = x.get_prec();
+
+    a.set_prec(yp>xp?yp:xp);
+
+    mpfr_fmod(a.mp, x.mp, y.mp, rnd_mode);
+
+    return a;
+}
+
+inline const mpreal rec_sqrt(const mpreal& v, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal x(v);
+    mpfr_rec_sqrt(x.mp,v.mp,rnd_mode);
+    return x;
+}
+#endif //  MPFR 2.4.0 Specifics
+
+//////////////////////////////////////////////////////////////////////////
+// MPFR 3.0.0 Specifics
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
+inline const mpreal digamma (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(digamma);     }
+inline const mpreal ai      (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(ai);          }
+#endif // MPFR 3.0.0 Specifics
+
+//////////////////////////////////////////////////////////////////////////
+// Constants
+inline const mpreal const_log2 (mp_prec_t p = mpreal::get_default_prec(), mp_rnd_t r = mpreal::get_default_rnd())
+{
+    mpreal x(0, p);
+    mpfr_const_log2(x.mpfr_ptr(), r);
+    return x;
+}
+
+inline const mpreal const_pi (mp_prec_t p = mpreal::get_default_prec(), mp_rnd_t r = mpreal::get_default_rnd())
+{
+    mpreal x(0, p);
+    mpfr_const_pi(x.mpfr_ptr(), r);
+    return x;
+}
+
+inline const mpreal const_euler (mp_prec_t p = mpreal::get_default_prec(), mp_rnd_t r = mpreal::get_default_rnd())
+{
+    mpreal x(0, p);
+    mpfr_const_euler(x.mpfr_ptr(), r);
+    return x;
+}
+
+inline const mpreal const_catalan (mp_prec_t p = mpreal::get_default_prec(), mp_rnd_t r = mpreal::get_default_rnd())
+{
+    mpreal x(0, p);
+    mpfr_const_catalan(x.mpfr_ptr(), r);
+    return x;
+}
+
+inline const mpreal const_infinity (int sign = 1, mp_prec_t p = mpreal::get_default_prec())
+{
+    mpreal x(0, p);
+    mpfr_set_inf(x.mpfr_ptr(), sign);
+    return x;
+}
+
+//////////////////////////////////////////////////////////////////////////
+// Integer Related Functions
+inline const mpreal ceil(const mpreal& v)
+{
+    mpreal x(v);
+    mpfr_ceil(x.mp,v.mp);
+    return x;
+}
+
+inline const mpreal floor(const mpreal& v)
+{
+    mpreal x(v);
+    mpfr_floor(x.mp,v.mp);
+    return x;
+}
+
+inline const mpreal round(const mpreal& v)
+{
+    mpreal x(v);
+    mpfr_round(x.mp,v.mp);
+    return x;
+}
+
+inline long lround(const mpreal& v)
+{
+    long r = std::numeric_limits<long>::min();
+    mpreal x = round(v);
+    if (abs(x) < -mpreal(r))    // Assume mpreal(LONG_MIN) is exact
+      r = x.toLong();
+    return r;
+}
+
+inline long long llround(const mpreal& v)
+{
+    long long r = std::numeric_limits<long long>::min();
+    mpreal x = round(v);
+    if (abs(x) < -mpreal(r))    // Assume mpreal(LLONG_MIN) is exact
+      r = x.toLLong();
+    return r;
+}
+
+inline const mpreal trunc(const mpreal& v)
+{
+    mpreal x(v);
+    mpfr_trunc(x.mp,v.mp);
+    return x;
+}
+
+inline const mpreal rint       (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(rint      );     }
+inline const mpreal rint_ceil  (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(rint_ceil );     }
+inline const mpreal rint_floor (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(rint_floor);     }
+inline const mpreal rint_round (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(rint_round);     }
+inline const mpreal rint_trunc (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(rint_trunc);     }
+inline const mpreal frac       (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) {   MPREAL_UNARY_MATH_FUNCTION_BODY(frac      );     }
+
+//////////////////////////////////////////////////////////////////////////
+// Miscellaneous Functions
+inline int sgn(const mpreal& op)
+{
+    // Please note, this is classic signum function which ignores sign of zero.
+    // Use signbit if you need sign of zero.
+    return mpfr_sgn(op.mpfr_srcptr());
+}
+
+//////////////////////////////////////////////////////////////////////////
+// Miscellaneous Functions
+inline void         swap (mpreal& a, mpreal& b)            {    mpfr_swap(a.mpfr_ptr(),b.mpfr_ptr());   }
+inline const mpreal (max)(const mpreal& x, const mpreal& y){    return (x<y?y:x);       }
+inline const mpreal (min)(const mpreal& x, const mpreal& y){    return (y<x?y:x);       }
+
+inline const mpreal fmax(const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal a;
+    mpfr_max(a.mp,x.mp,y.mp,rnd_mode);
+    return a;
+}
+
+inline const mpreal fmin(const mpreal& x, const mpreal& y,  mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal a;
+    mpfr_min(a.mp,x.mp,y.mp,rnd_mode);
+    return a;
+}
+
+inline const mpreal nexttoward (const mpreal& x, const mpreal& y)
+{
+    mpreal a(x);
+    mpfr_nexttoward(a.mp,y.mp);
+    return a;
+}
+
+inline const mpreal nextabove  (const mpreal& x)
+{
+    mpreal a(x);
+    mpfr_nextabove(a.mp);
+    return a;
+}
+
+inline const mpreal nextbelow  (const mpreal& x)
+{
+    mpreal a(x);
+    mpfr_nextbelow(a.mp);
+    return a;
+}
+
+inline const mpreal urandomb (gmp_randstate_t& state)
+{
+    mpreal x;
+    mpfr_urandomb(x.mpfr_ptr(),state);
+    return x;
+}
+
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
+inline const mpreal urandom (gmp_randstate_t& state, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal x;
+    mpfr_urandom(x.mpfr_ptr(), state, rnd_mode);
+    return x;
+}
+#endif
+
+#if (MPFR_VERSION <= MPFR_VERSION_NUM(2,4,2))
+inline const mpreal random2 (mp_size_t size, mp_exp_t exp)
+{
+    mpreal x;
+    mpfr_random2(x.mpfr_ptr(),size,exp);
+    return x;
+}
+#endif
+
+// Uniformly distributed random number generation
+// a = random(seed); <- initialization & first random number generation
+// a = random();     <- next random numbers generation
+// seed != 0
+inline const mpreal random(unsigned int seed = 0)
+{
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
+    static gmp_randstate_t state;
+    static bool initialize = true;
+
+    if(initialize)
+    {
+        gmp_randinit_default(state);
+        gmp_randseed_ui(state,0);
+        initialize = false;
+    }
+
+    if(seed != 0) gmp_randseed_ui(state,seed);
+
+    return mpfr::urandom(state);
+#else
+    if(seed != 0)    std::srand(seed);
+    return mpfr::mpreal(std::rand()/(double)RAND_MAX);
+#endif
+}
+
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,1,0))
+inline const mpreal grandom (gmp_randstate_t& state, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal x;
+#if (MPFR_VERSION >= MPFR_VERSION_NUM(4,0,0))
+    mpfr_nrandom(x.mpfr_ptr(), state, rnd_mode);
+#else
+    mpfr_grandom(x.mpfr_ptr(), NULL, state, rnd_mode);
+#endif
+    return x;
+}
+
+inline const mpreal grandom(unsigned int seed = 0)
+{
+    static gmp_randstate_t state;
+    static bool initialize = true;
+
+    if(initialize)
+    {
+        gmp_randinit_default(state);
+        gmp_randseed_ui(state,0);
+        initialize = false;
+    }
+
+    if(seed != 0) gmp_randseed_ui(state,seed);
+
+    return mpfr::grandom(state);
+}
+#endif
+
+//////////////////////////////////////////////////////////////////////////
+// Set/Get global properties
+inline void mpreal::set_default_prec(mp_prec_t prec)
+{
+    mpfr_set_default_prec(prec);
+}
+
+inline void mpreal::set_default_rnd(mp_rnd_t rnd_mode)
+{
+    mpfr_set_default_rounding_mode(rnd_mode);
+}
+
+inline bool mpreal::fits_in_bits(double x, int n)
+{
+    int i;
+    double t;
+    return MPREAL_ISINF(x) || (std::modf ( std::ldexp ( std::frexp ( x, &i ), n ), &t ) == 0.0);
+}
+
+inline const mpreal pow(const mpreal& a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal x(a);
+    mpfr_pow(x.mp,x.mp,b.mp,rnd_mode);
+    return x;
+}
+
+inline const mpreal pow(const mpreal& a, const mpz_t b, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal x(a);
+    mpfr_pow_z(x.mp,x.mp,b,rnd_mode);
+    return x;
+}
+
+inline const mpreal pow(const mpreal& a, const long long b, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    (void)rnd_mode;
+    return pow(a,mpreal(b));
+}
+
+inline const mpreal pow(const mpreal& a, const unsigned long long b, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    (void)rnd_mode;
+    return pow(a,mpreal(b));
+}
+
+inline const mpreal pow(const mpreal& a, const unsigned long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal x(a);
+    mpfr_pow_ui(x.mp,x.mp,b,rnd_mode);
+    return x;
+}
+
+inline const mpreal pow(const mpreal& a, const unsigned int b, mp_rnd_t rnd_mode)
+{
+    return pow(a,static_cast<unsigned long int>(b),rnd_mode);
+}
+
+inline const mpreal pow(const mpreal& a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal x(a);
+    mpfr_pow_si(x.mp,x.mp,b,rnd_mode);
+    return x;
+}
+
+inline const mpreal pow(const mpreal& a, const int b, mp_rnd_t rnd_mode)
+{
+    return pow(a,static_cast<long int>(b),rnd_mode);
+}
+
+inline const mpreal pow(const mpreal& a, const long double b, mp_rnd_t rnd_mode)
+{
+    return pow(a,mpreal(b),rnd_mode);
+}
+
+inline const mpreal pow(const mpreal& a, const double b, mp_rnd_t rnd_mode)
+{
+    return pow(a,mpreal(b),rnd_mode);
+}
+
+inline const mpreal pow(const unsigned long int a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
+{
+    mpreal x(a);
+    mpfr_ui_pow(x.mp,a,b.mp,rnd_mode);
+    return x;
+}
+
+inline const mpreal pow(const unsigned int a, const mpreal& b, mp_rnd_t rnd_mode)
+{
+    return pow(static_cast<unsigned long int>(a),b,rnd_mode);
+}
+
+inline const mpreal pow(const long int a, const mpreal& b, mp_rnd_t rnd_mode)
+{
+    if (a>=0)     return pow(static_cast<unsigned long int>(a),b,rnd_mode);
+    else          return pow(mpreal(a),b,rnd_mode);
+}
+
+inline const mpreal pow(const int a, const mpreal& b, mp_rnd_t rnd_mode)
+{
+    if (a>=0)     return pow(static_cast<unsigned long int>(a),b,rnd_mode);
+    else          return pow(mpreal(a),b,rnd_mode);
+}
+
+inline const mpreal pow(const long double a, const mpreal& b, mp_rnd_t rnd_mode)
+{
+    return pow(mpreal(a),b,rnd_mode);
+}
+
+inline const mpreal pow(const double a, const mpreal& b, mp_rnd_t rnd_mode)
+{
+    return pow(mpreal(a),b,rnd_mode);
+}
+
+// pow unsigned long int
+inline const mpreal pow(const unsigned long int a, const unsigned long int b, mp_rnd_t rnd_mode)
+{
+    mpreal x(a);
+    mpfr_ui_pow_ui(x.mp,a,b,rnd_mode);
+    return x;
+}
+
+inline const mpreal pow(const unsigned long int a, const unsigned int b, mp_rnd_t rnd_mode)
+{
+    return pow(a,static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
+}
+
+inline const mpreal pow(const unsigned long int a, const long int b, mp_rnd_t rnd_mode)
+{
+    if(b>0)    return pow(a,static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
+    else       return pow(a,mpreal(b),rnd_mode); //mpfr_ui_pow
+}
+
+inline const mpreal pow(const unsigned long int a, const int b, mp_rnd_t rnd_mode)
+{
+    if(b>0)    return pow(a,static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
+    else       return pow(a,mpreal(b),rnd_mode); //mpfr_ui_pow
+}
+
+inline const mpreal pow(const unsigned long int a, const long double b, mp_rnd_t rnd_mode)
+{
+    return pow(a,mpreal(b),rnd_mode); //mpfr_ui_pow
+}
+
+inline const mpreal pow(const unsigned long int a, const double b, mp_rnd_t rnd_mode)
+{
+    return pow(a,mpreal(b),rnd_mode); //mpfr_ui_pow
+}
+
+// pow unsigned int
+inline const mpreal pow(const unsigned int a, const unsigned long int b, mp_rnd_t rnd_mode)
+{
+    return pow(static_cast<unsigned long int>(a),b,rnd_mode); //mpfr_ui_pow_ui
+}
+
+inline const mpreal pow(const unsigned int a, const unsigned int b, mp_rnd_t rnd_mode)
+{
+    return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
+}
+
+inline const mpreal pow(const unsigned int a, const long int b, mp_rnd_t rnd_mode)
+{
+    if(b>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
+    else    return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
+}
+
+inline const mpreal pow(const unsigned int a, const int b, mp_rnd_t rnd_mode)
+{
+    if(b>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
+    else    return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
+}
+
+inline const mpreal pow(const unsigned int a, const long double b, mp_rnd_t rnd_mode)
+{
+    return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
+}
+
+inline const mpreal pow(const unsigned int a, const double b, mp_rnd_t rnd_mode)
+{
+    return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
+}
+
+// pow long int
+inline const mpreal pow(const long int a, const unsigned long int b, mp_rnd_t rnd_mode)
+{
+    if (a>0) return pow(static_cast<unsigned long int>(a),b,rnd_mode); //mpfr_ui_pow_ui
+    else     return pow(mpreal(a),b,rnd_mode); //mpfr_pow_ui
+}
+
+inline const mpreal pow(const long int a, const unsigned int b, mp_rnd_t rnd_mode)
+{
+    if (a>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode);  //mpfr_ui_pow_ui
+    else     return pow(mpreal(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_pow_ui
+}
+
+inline const mpreal pow(const long int a, const long int b, mp_rnd_t rnd_mode)
+{
+    if (a>0)
+    {
+        if(b>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
+        else    return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
+    }else{
+        return pow(mpreal(a),b,rnd_mode); // mpfr_pow_si
+    }
+}
+
+inline const mpreal pow(const long int a, const int b, mp_rnd_t rnd_mode)
+{
+    if (a>0)
+    {
+        if(b>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
+        else    return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
+    }else{
+        return pow(mpreal(a),static_cast<long int>(b),rnd_mode); // mpfr_pow_si
+    }
+}
+
+inline const mpreal pow(const long int a, const long double b, mp_rnd_t rnd_mode)
+{
+    if (a>=0)   return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
+    else        return pow(mpreal(a),mpreal(b),rnd_mode); //mpfr_pow
+}
+
+inline const mpreal pow(const long int a, const double b, mp_rnd_t rnd_mode)
+{
+    if (a>=0)   return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
+    else        return pow(mpreal(a),mpreal(b),rnd_mode); //mpfr_pow
+}
+
+// pow int
+inline const mpreal pow(const int a, const unsigned long int b, mp_rnd_t rnd_mode)
+{
+    if (a>0) return pow(static_cast<unsigned long int>(a),b,rnd_mode); //mpfr_ui_pow_ui
+    else     return pow(mpreal(a),b,rnd_mode); //mpfr_pow_ui
+}
+
+inline const mpreal pow(const int a, const unsigned int b, mp_rnd_t rnd_mode)
+{
+    if (a>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode);  //mpfr_ui_pow_ui
+    else     return pow(mpreal(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_pow_ui
+}
+
+inline const mpreal pow(const int a, const long int b, mp_rnd_t rnd_mode)
+{
+    if (a>0)
+    {
+        if(b>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
+        else    return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
+    }else{
+        return pow(mpreal(a),b,rnd_mode); // mpfr_pow_si
+    }
+}
+
+inline const mpreal pow(const int a, const int b, mp_rnd_t rnd_mode)
+{
+    if (a>0)
+    {
+        if(b>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
+        else    return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
+    }else{
+        return pow(mpreal(a),static_cast<long int>(b),rnd_mode); // mpfr_pow_si
+    }
+}
+
+inline const mpreal pow(const int a, const long double b, mp_rnd_t rnd_mode)
+{
+    if (a>=0)   return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
+    else        return pow(mpreal(a),mpreal(b),rnd_mode); //mpfr_pow
+}
+
+inline const mpreal pow(const int a, const double b, mp_rnd_t rnd_mode)
+{
+    if (a>=0)   return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
+    else        return pow(mpreal(a),mpreal(b),rnd_mode); //mpfr_pow
+}
+
+// pow long double
+inline const mpreal pow(const long double a, const long double b, mp_rnd_t rnd_mode)
+{
+    return pow(mpreal(a),mpreal(b),rnd_mode);
+}
+
+inline const mpreal pow(const long double a, const unsigned long int b, mp_rnd_t rnd_mode)
+{
+    return pow(mpreal(a),b,rnd_mode); //mpfr_pow_ui
+}
+
+inline const mpreal pow(const long double a, const unsigned int b, mp_rnd_t rnd_mode)
+{
+    return pow(mpreal(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_pow_ui
+}
+
+inline const mpreal pow(const long double a, const long int b, mp_rnd_t rnd_mode)
+{
+    return pow(mpreal(a),b,rnd_mode); // mpfr_pow_si
+}
+
+inline const mpreal pow(const long double a, const int b, mp_rnd_t rnd_mode)
+{
+    return pow(mpreal(a),static_cast<long int>(b),rnd_mode); // mpfr_pow_si
+}
+
+inline const mpreal pow(const double a, const double b, mp_rnd_t rnd_mode)
+{
+    return pow(mpreal(a),mpreal(b),rnd_mode);
+}
+
+inline const mpreal pow(const double a, const unsigned long int b, mp_rnd_t rnd_mode)
+{
+    return pow(mpreal(a),b,rnd_mode); // mpfr_pow_ui
+}
+
+inline const mpreal pow(const double a, const unsigned int b, mp_rnd_t rnd_mode)
+{
+    return pow(mpreal(a),static_cast<unsigned long int>(b),rnd_mode); // mpfr_pow_ui
+}
+
+inline const mpreal pow(const double a, const long int b, mp_rnd_t rnd_mode)
+{
+    return pow(mpreal(a),b,rnd_mode); // mpfr_pow_si
+}
+
+inline const mpreal pow(const double a, const int b, mp_rnd_t rnd_mode)
+{
+    return pow(mpreal(a),static_cast<long int>(b),rnd_mode); // mpfr_pow_si
+}
+} // End of mpfr namespace
+
+// Explicit specialization of std::swap for mpreal numbers
+// Thus standard algorithms will use efficient version of swap (due to Koenig lookup)
+// Non-throwing swap C++ idiom: http://en.wikibooks.org/wiki/More_C%2B%2B_Idioms/Non-throwing_swap
+namespace std
+{
+
+    template <>
+    inline void swap(mpfr::mpreal& x, mpfr::mpreal& y)
+    {
+        return mpfr::swap(x, y);
+    }
+
+    template<>
+    class numeric_limits<mpfr::mpreal>
+    {
+    public:
+        static const bool is_specialized    = true;
+        static const bool is_signed         = true;
+        static const bool is_integer        = false;
+        static const bool is_exact          = false;
+        static const int  radix             = 2;
+
+        static const bool has_infinity      = true;
+        static const bool has_quiet_NaN     = true;
+        static const bool has_signaling_NaN = true;
+
+        static const bool is_iec559         = true;        // = IEEE 754
+        static const bool is_bounded        = true;
+        static const bool is_modulo         = false;
+        static const bool traps             = true;
+        static const bool tinyness_before   = true;
+
+        static const float_denorm_style has_denorm  = denorm_absent;
+
+        inline static mpfr::mpreal (min)    (mp_prec_t precision = mpfr::mpreal::get_default_prec()) {  return  mpfr::minval(precision);  }
+        inline static mpfr::mpreal (max)    (mp_prec_t precision = mpfr::mpreal::get_default_prec()) {  return  mpfr::maxval(precision);  }
+        inline static mpfr::mpreal lowest   (mp_prec_t precision = mpfr::mpreal::get_default_prec()) {  return -mpfr::maxval(precision);  }
+
+        // Returns smallest eps such that 1 + eps != 1 (classic machine epsilon)
+        inline static mpfr::mpreal epsilon(mp_prec_t precision = mpfr::mpreal::get_default_prec()) {  return  mpfr::machine_epsilon(precision); }
+
+        // Returns smallest eps such that x + eps != x (relative machine epsilon)
+        inline static mpfr::mpreal epsilon(const mpfr::mpreal& x) {  return mpfr::machine_epsilon(x);  }
+
+        inline static mpfr::mpreal round_error(mp_prec_t precision = mpfr::mpreal::get_default_prec())
+        {
+            mp_rnd_t r = mpfr::mpreal::get_default_rnd();
+
+            if(r == GMP_RNDN)  return mpfr::mpreal(0.5, precision);
+            else               return mpfr::mpreal(1.0, precision);
+        }
+
+        inline static const mpfr::mpreal infinity()         { return mpfr::const_infinity();     }
+        inline static const mpfr::mpreal quiet_NaN()        { return mpfr::mpreal().setNan();    }
+        inline static const mpfr::mpreal signaling_NaN()    { return mpfr::mpreal().setNan();    }
+        inline static const mpfr::mpreal denorm_min()       { return (min)();                    }
+
+        // Please note, exponent range is not fixed in MPFR
+        static const int min_exponent = MPFR_EMIN_DEFAULT;
+        static const int max_exponent = MPFR_EMAX_DEFAULT;
+        MPREAL_PERMISSIVE_EXPR static const int min_exponent10 = (int) (MPFR_EMIN_DEFAULT * 0.3010299956639811);
+        MPREAL_PERMISSIVE_EXPR static const int max_exponent10 = (int) (MPFR_EMAX_DEFAULT * 0.3010299956639811);
+
+#ifdef MPREAL_HAVE_DYNAMIC_STD_NUMERIC_LIMITS
+
+        // Following members should be constant according to standard, but they can be variable in MPFR
+        // So we define them as functions here.
+        //
+        // This is preferable way for std::numeric_limits<mpfr::mpreal> specialization.
+        // But it is incompatible with standard std::numeric_limits and might not work with other libraries, e.g. boost.
+        // See below for compatible implementation.
+        inline static float_round_style round_style()
+        {
+            mp_rnd_t r = mpfr::mpreal::get_default_rnd();
+
+            switch (r)
+            {
+            case GMP_RNDN: return round_to_nearest;
+            case GMP_RNDZ: return round_toward_zero;
+            case GMP_RNDU: return round_toward_infinity;
+            case GMP_RNDD: return round_toward_neg_infinity;
+            default: return round_indeterminate;
+            }
+        }
+
+        inline static int digits()                        {    return int(mpfr::mpreal::get_default_prec());    }
+        inline static int digits(const mpfr::mpreal& x)   {    return x.getPrecision();                         }
+
+        inline static int digits10(mp_prec_t precision = mpfr::mpreal::get_default_prec())
+        {
+            return mpfr::bits2digits(precision);
+        }
+
+        inline static int digits10(const mpfr::mpreal& x)
+        {
+            return mpfr::bits2digits(x.getPrecision());
+        }
+
+        inline static int max_digits10(mp_prec_t precision = mpfr::mpreal::get_default_prec())
+        {
+            return digits10(precision);
+        }
+#else
+        // Digits and round_style are NOT constants when it comes to mpreal.
+        // If possible, please use functions digits() and round_style() defined above.
+        //
+        // These (default) values are preserved for compatibility with existing libraries, e.g. boost.
+        // Change them accordingly to your application.
+        //
+        // For example, if you use 256 bits of precision uniformly in your program, then:
+        // digits       = 256
+        // digits10     = 77
+        // max_digits10 = 78
+        //
+        // Approximate formula for decimal digits is: digits10 = floor(log10(2) * digits). See bits2digits() for more details.
+
+        static const std::float_round_style round_style = round_to_nearest;
+        static const int digits       = 53;
+        static const int digits10     = 15;
+        static const int max_digits10 = 16;
+#endif
+    };
+
+}
+
+#endif /* __MPREAL_H__ */
diff --git a/test/geometry/gl2r_orbit_closure/test_rank2_quadrilaterals.py b/test/geometry/gl2r_orbit_closure/test_rank2_quadrilaterals.py
index 57351fced..f996ef9b4 100644
--- a/test/geometry/gl2r_orbit_closure/test_rank2_quadrilaterals.py
+++ b/test/geometry/gl2r_orbit_closure/test_rank2_quadrilaterals.py
@@ -34,8 +34,6 @@
 from flatsurf import EuclideanPolygonsWithAngles, similarity_surfaces, GL2ROrbitClosure
 
 
-# TODO: the test for field of definition with is_isomorphic() does not check
-# for embeddings... though for quadratic fields it does not matter much.
 @pytest.mark.parametrize(
     "a,b,c,d,l1,l2,veech,discriminant",
     [
@@ -51,11 +49,18 @@
     ],
 )
 def test_rank2_quadrilateral(a, b, c, d, l1, l2, veech, discriminant):
+    """
+    .. TODO::
+
+        The test for field of definition with is_isomorphic() does not check
+        for embeddings. Though for quadratic fields it does not matter much.
+
+    """
     E = EuclideanPolygonsWithAngles(a, b, c, d)
     P = E([l1, l2], normalized=True)
-    B = similarity_surfaces.billiard(P, rational=True)
+    B = similarity_surfaces.billiard(P)
     S = B.minimal_cover(cover_type="translation")
-    S = S.erase_marked_points()
+    S = S.erase_marked_points().codomain()
     S, _ = S.normalized_coordinates()
     orbit_closure = GL2ROrbitClosure(S)
     assert orbit_closure.ambient_stratum() == E.billiard_unfolding_stratum(
diff --git a/test/geometry/test_euclidean.py b/test/geometry/test_euclidean.py
index f52505af4..9366a1be7 100644
--- a/test/geometry/test_euclidean.py
+++ b/test/geometry/test_euclidean.py
@@ -22,7 +22,7 @@
 
 import pytest
 
-from sage.all import QQ, randint
+from sage.all import QQ, randint, vector
 
 
 @pytest.mark.repeat(1024)
@@ -60,10 +60,10 @@ def test_segment_intersect():
     from flatsurf.geometry.euclidean import is_segment_intersecting
 
     while True:
-        us = (randint(-4, 4), randint(-4, 4))
-        ut = (randint(-4, 4), randint(-4, 4))
-        vs = (randint(-4, 4), randint(-4, 4))
-        vt = (randint(-4, 4), randint(-4, 4))
+        us = vector((randint(-4, 4), randint(-4, 4)))
+        ut = vector((randint(-4, 4), randint(-4, 4)))
+        vs = vector((randint(-4, 4), randint(-4, 4)))
+        vt = vector((randint(-4, 4), randint(-4, 4)))
         if us != ut and vs != vt:
             break
 
diff --git a/test/geometry/test_saddle_connection.py b/test/geometry/test_saddle_connection.py
new file mode 100644
index 000000000..fc82cc576
--- /dev/null
+++ b/test/geometry/test_saddle_connection.py
@@ -0,0 +1,53 @@
+r"""
+Tests that saddle connections are enumerated correctly.
+"""
+# ****************************************************************************
+#  This file is part of sage-flatsurf.
+#
+#        Copyright (C) 2024 Julian Rüth
+#
+#  sage-flatsurf is free software: you can redistribute it and/or modify
+#  it under the terms of the GNU General Public License as published by
+#  the Free Software Foundation, either version 2 of the License, or
+#  (at your option) any later version.
+#
+#  sage-flatsurf is distributed in the hope that it will be useful,
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#  GNU General Public License for more details.
+#
+#  You should have received a copy of the GNU General Public License
+#  along with sage-flatsurf. If not, see <https://www.gnu.org/licenses/>.
+# ****************************************************************************
+
+import pytest
+
+
+def test_L():
+    r"""
+    Test that saddle connections in an L are counted the same no matter how the
+    L is represented.
+    """
+    from flatsurf import translation_surfaces
+
+    L_from_rectangles = translation_surfaces.mcmullen_L(1, 2, 3, 4)
+    connections_from_rectangles = L_from_rectangles.saddle_connections(128)
+    assert len(connections_from_rectangles) == 164
+
+    L_from_triangles = L_from_rectangles.triangulate().codomain()
+    connections_from_triangles = L_from_triangles.saddle_connections(128)
+    assert len(connections_from_triangles) == 164
+
+    from sage.all import QQ
+    from flatsurf import MutableOrientedSimilaritySurface, Polygon
+    L = MutableOrientedSimilaritySurface(QQ)
+    L.add_polygon(Polygon(vertices=[(0, 0), (3, 0), (7, 0), (7, 2), (3, 2), (3, 3), (0, 3), (0, 2)]))
+    L.glue((0, 0), (0, 5))
+    L.glue((0, 1), (0, 3))
+    L.glue((0, 2), (0, 7))
+    L.glue((0, 4), (0, 6))
+    L.set_immutable()
+
+    connections = L.saddle_connections(128)
+
+    assert len(connections) == 164