{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4eb18968",
   "metadata": {},
   "source": [
    "# Fibonacci and Lucas-type sequences modulo prime powers\n",
    "\n",
    "**A computational replication and comparison based on Bragman–Rowland (2025).** This notebook counts exact residue sets over complete modular state orbits for Fibonacci, Lucas, and Pell sequences. The Fibonacci section reproduces published behavior; the Lucas and Pell sections are exploratory comparisons. No originality claim is made.\n",
    "\n",
    "Built and verified independently. I wrote and reran the code, checked the outputs against the tests and published benchmarks, and prepared this explanation myself."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8243b3fa",
   "metadata": {},
   "source": [
    "## tl;dr\n",
    "\n",
    "- For every sequence and prime tested, the observed-residue proportion is nonincreasing as $k$ grows. This is required by reduction modulo $p^k$, not a new discovery.\n",
    "- The Fibonacci data reproduce published finite counts and approach the Bragman–Rowland limiting densities. At $p=13$, the proportion is exactly $9/13$ at every tested level, matching their theorem and example.\n",
    "- Lucas numbers show the same monotonicity but often different levels: for example, the tested $p=13$ proportion is $12/13$ at every level, while $p=5$ decreases toward roughly one third over the tested range.\n",
    "- The Pell recurrence has exceptional lifts at $p=13$ and $p=31$: the first lift does not multiply the state period by $p$, the residue proportion drops sharply, and later tested lifts stabilize. The period exceptions are already documented; this experiment uses them to motivate a narrower residue-count question."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "461fad10",
   "metadata": {},
   "source": [
    "## Context & Methods\n",
    "\n",
    "For a recurrence\n",
    "\n",
    "\\[\n",
    "x_{n+2}=a x_{n+1}+b x_n \\pmod m,\n",
    "\\]\n",
    "\n",
    "the pair $s_n=(x_n,x_{n+1})$ determines all future terms. When $\\gcd(b,m)=1$, the map\n",
    "\n",
    "\\[\n",
    "(x,y)\\mapsto (y,ay+bx) \\pmod m\n",
    "\\]\n",
    "\n",
    "is invertible, so the starting pair lies on a pure cycle. The code records the first coordinate over that entire cycle and stops only when the **pair**, not merely one term, returns to the start.\n",
    "\n",
    "For each prime $p$ and exponent $k$, the measured quantity is\n",
    "\n",
    "\\[\n",
    "d_k(p)=\\frac{|\\{x_n\\bmod p^k:n\\ge 0\\}|}{p^k}.\n",
    "\\]\n",
    "\n",
    "We test $p\\in\\{2,3,5,7,11,13,19,31\\}$, with larger $k$ for smaller primes, for Fibonacci, standard Lucas, and Pell recurrences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "91326732",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-13T16:15:46.534295Z",
     "iopub.status.busy": "2026-08-13T16:15:46.534186Z",
     "iopub.status.idle": "2026-08-13T16:15:47.764485Z",
     "shell.execute_reply": "2026-08-13T16:15:47.764033Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exact experiment rows: 147\n",
      "Validation status: passed\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import json\n",
    "import sys\n",
    "\n",
    "import pandas as pd\n",
    "from IPython.display import Image, display\n",
    "\n",
    "ROOT = Path.cwd()\n",
    "if not (ROOT / \"src\").exists():\n",
    "    ROOT = ROOT.parent\n",
    "sys.path.insert(0, str(ROOT / \"src\"))\n",
    "sys.path.insert(0, str(ROOT))\n",
    "\n",
    "from modular_recurrences import EXPERIMENT_PLAN, SEQUENCES, run_experiments\n",
    "from run_study import PUBLISHED_FIBONACCI_LIMITS, build_validation, make_density_plot, make_growth_plot\n",
    "\n",
    "records, small_sets = run_experiments()\n",
    "df = pd.DataFrame(records)\n",
    "validation = build_validation(df)\n",
    "\n",
    "print(f\"Exact experiment rows: {len(df)}\")\n",
    "print(f\"Validation status: {validation['status']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dde175a",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "Each row below is one exact complete-orbit computation. `period` counts pair states; `residue_count` counts distinct first coordinates. The full machine-readable table is saved in `data/results.csv`; exact residue lists are saved for moduli at most 400."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86f7c076",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-13T16:15:47.765967Z",
     "iopub.status.busy": "2026-08-13T16:15:47.765853Z",
     "iopub.status.idle": "2026-08-13T16:15:47.791295Z",
     "shell.execute_reply": "2026-08-13T16:15:47.790949Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_edc1f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_edc1f_level0_col0\" class=\"col_heading level0 col0\" >sequence</th>\n",
       "      <th id=\"T_edc1f_level0_col1\" class=\"col_heading level0 col1\" >prime</th>\n",
       "      <th id=\"T_edc1f_level0_col2\" class=\"col_heading level0 col2\" >k</th>\n",
       "      <th id=\"T_edc1f_level0_col3\" class=\"col_heading level0 col3\" >modulus</th>\n",
       "      <th id=\"T_edc1f_level0_col4\" class=\"col_heading level0 col4\" >period</th>\n",
       "      <th id=\"T_edc1f_level0_col5\" class=\"col_heading level0 col5\" >residue_count</th>\n",
       "      <th id=\"T_edc1f_level0_col6\" class=\"col_heading level0 col6\" >residue_fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row0_col0\" class=\"data row0 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row0_col1\" class=\"data row0 col1\" >2</td>\n",
       "      <td id=\"T_edc1f_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "      <td id=\"T_edc1f_row0_col3\" class=\"data row0 col3\" >2</td>\n",
       "      <td id=\"T_edc1f_row0_col4\" class=\"data row0 col4\" >3</td>\n",
       "      <td id=\"T_edc1f_row0_col5\" class=\"data row0 col5\" >2</td>\n",
       "      <td id=\"T_edc1f_row0_col6\" class=\"data row0 col6\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row1_col0\" class=\"data row1 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row1_col1\" class=\"data row1 col1\" >2</td>\n",
       "      <td id=\"T_edc1f_row1_col2\" class=\"data row1 col2\" >2</td>\n",
       "      <td id=\"T_edc1f_row1_col3\" class=\"data row1 col3\" >4</td>\n",
       "      <td id=\"T_edc1f_row1_col4\" class=\"data row1 col4\" >6</td>\n",
       "      <td id=\"T_edc1f_row1_col5\" class=\"data row1 col5\" >4</td>\n",
       "      <td id=\"T_edc1f_row1_col6\" class=\"data row1 col6\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row2_col0\" class=\"data row2 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row2_col1\" class=\"data row2 col1\" >2</td>\n",
       "      <td id=\"T_edc1f_row2_col2\" class=\"data row2 col2\" >3</td>\n",
       "      <td id=\"T_edc1f_row2_col3\" class=\"data row2 col3\" >8</td>\n",
       "      <td id=\"T_edc1f_row2_col4\" class=\"data row2 col4\" >12</td>\n",
       "      <td id=\"T_edc1f_row2_col5\" class=\"data row2 col5\" >6</td>\n",
       "      <td id=\"T_edc1f_row2_col6\" class=\"data row2 col6\" >3/4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row3_col0\" class=\"data row3 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row3_col1\" class=\"data row3 col1\" >2</td>\n",
       "      <td id=\"T_edc1f_row3_col2\" class=\"data row3 col2\" >4</td>\n",
       "      <td id=\"T_edc1f_row3_col3\" class=\"data row3 col3\" >16</td>\n",
       "      <td id=\"T_edc1f_row3_col4\" class=\"data row3 col4\" >24</td>\n",
       "      <td id=\"T_edc1f_row3_col5\" class=\"data row3 col5\" >11</td>\n",
       "      <td id=\"T_edc1f_row3_col6\" class=\"data row3 col6\" >11/16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row4_col0\" class=\"data row4 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row4_col1\" class=\"data row4 col1\" >2</td>\n",
       "      <td id=\"T_edc1f_row4_col2\" class=\"data row4 col2\" >5</td>\n",
       "      <td id=\"T_edc1f_row4_col3\" class=\"data row4 col3\" >32</td>\n",
       "      <td id=\"T_edc1f_row4_col4\" class=\"data row4 col4\" >48</td>\n",
       "      <td id=\"T_edc1f_row4_col5\" class=\"data row4 col5\" >21</td>\n",
       "      <td id=\"T_edc1f_row4_col6\" class=\"data row4 col6\" >21/32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row5_col0\" class=\"data row5 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row5_col1\" class=\"data row5 col1\" >2</td>\n",
       "      <td id=\"T_edc1f_row5_col2\" class=\"data row5 col2\" >6</td>\n",
       "      <td id=\"T_edc1f_row5_col3\" class=\"data row5 col3\" >64</td>\n",
       "      <td id=\"T_edc1f_row5_col4\" class=\"data row5 col4\" >96</td>\n",
       "      <td id=\"T_edc1f_row5_col5\" class=\"data row5 col5\" >42</td>\n",
       "      <td id=\"T_edc1f_row5_col6\" class=\"data row5 col6\" >21/32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row6_col0\" class=\"data row6 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row6_col1\" class=\"data row6 col1\" >2</td>\n",
       "      <td id=\"T_edc1f_row6_col2\" class=\"data row6 col2\" >7</td>\n",
       "      <td id=\"T_edc1f_row6_col3\" class=\"data row6 col3\" >128</td>\n",
       "      <td id=\"T_edc1f_row6_col4\" class=\"data row6 col4\" >192</td>\n",
       "      <td id=\"T_edc1f_row6_col5\" class=\"data row6 col5\" >84</td>\n",
       "      <td id=\"T_edc1f_row6_col6\" class=\"data row6 col6\" >21/32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row7_col0\" class=\"data row7 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row7_col1\" class=\"data row7 col1\" >2</td>\n",
       "      <td id=\"T_edc1f_row7_col2\" class=\"data row7 col2\" >8</td>\n",
       "      <td id=\"T_edc1f_row7_col3\" class=\"data row7 col3\" >256</td>\n",
       "      <td id=\"T_edc1f_row7_col4\" class=\"data row7 col4\" >384</td>\n",
       "      <td id=\"T_edc1f_row7_col5\" class=\"data row7 col5\" >168</td>\n",
       "      <td id=\"T_edc1f_row7_col6\" class=\"data row7 col6\" >21/32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row8_col0\" class=\"data row8 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row8_col1\" class=\"data row8 col1\" >2</td>\n",
       "      <td id=\"T_edc1f_row8_col2\" class=\"data row8 col2\" >9</td>\n",
       "      <td id=\"T_edc1f_row8_col3\" class=\"data row8 col3\" >512</td>\n",
       "      <td id=\"T_edc1f_row8_col4\" class=\"data row8 col4\" >768</td>\n",
       "      <td id=\"T_edc1f_row8_col5\" class=\"data row8 col5\" >336</td>\n",
       "      <td id=\"T_edc1f_row8_col6\" class=\"data row8 col6\" >21/32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row9_col0\" class=\"data row9 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row9_col1\" class=\"data row9 col1\" >2</td>\n",
       "      <td id=\"T_edc1f_row9_col2\" class=\"data row9 col2\" >10</td>\n",
       "      <td id=\"T_edc1f_row9_col3\" class=\"data row9 col3\" >1024</td>\n",
       "      <td id=\"T_edc1f_row9_col4\" class=\"data row9 col4\" >1536</td>\n",
       "      <td id=\"T_edc1f_row9_col5\" class=\"data row9 col5\" >672</td>\n",
       "      <td id=\"T_edc1f_row9_col6\" class=\"data row9 col6\" >21/32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row10_col0\" class=\"data row10 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row10_col1\" class=\"data row10 col1\" >3</td>\n",
       "      <td id=\"T_edc1f_row10_col2\" class=\"data row10 col2\" >1</td>\n",
       "      <td id=\"T_edc1f_row10_col3\" class=\"data row10 col3\" >3</td>\n",
       "      <td id=\"T_edc1f_row10_col4\" class=\"data row10 col4\" >8</td>\n",
       "      <td id=\"T_edc1f_row10_col5\" class=\"data row10 col5\" >3</td>\n",
       "      <td id=\"T_edc1f_row10_col6\" class=\"data row10 col6\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row11_col0\" class=\"data row11 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row11_col1\" class=\"data row11 col1\" >3</td>\n",
       "      <td id=\"T_edc1f_row11_col2\" class=\"data row11 col2\" >2</td>\n",
       "      <td id=\"T_edc1f_row11_col3\" class=\"data row11 col3\" >9</td>\n",
       "      <td id=\"T_edc1f_row11_col4\" class=\"data row11 col4\" >24</td>\n",
       "      <td id=\"T_edc1f_row11_col5\" class=\"data row11 col5\" >9</td>\n",
       "      <td id=\"T_edc1f_row11_col6\" class=\"data row11 col6\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row12_col0\" class=\"data row12 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row12_col1\" class=\"data row12 col1\" >3</td>\n",
       "      <td id=\"T_edc1f_row12_col2\" class=\"data row12 col2\" >3</td>\n",
       "      <td id=\"T_edc1f_row12_col3\" class=\"data row12 col3\" >27</td>\n",
       "      <td id=\"T_edc1f_row12_col4\" class=\"data row12 col4\" >72</td>\n",
       "      <td id=\"T_edc1f_row12_col5\" class=\"data row12 col5\" >27</td>\n",
       "      <td id=\"T_edc1f_row12_col6\" class=\"data row12 col6\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row13_col0\" class=\"data row13 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row13_col1\" class=\"data row13 col1\" >3</td>\n",
       "      <td id=\"T_edc1f_row13_col2\" class=\"data row13 col2\" >4</td>\n",
       "      <td id=\"T_edc1f_row13_col3\" class=\"data row13 col3\" >81</td>\n",
       "      <td id=\"T_edc1f_row13_col4\" class=\"data row13 col4\" >216</td>\n",
       "      <td id=\"T_edc1f_row13_col5\" class=\"data row13 col5\" >81</td>\n",
       "      <td id=\"T_edc1f_row13_col6\" class=\"data row13 col6\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_edc1f_row14_col0\" class=\"data row14 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_edc1f_row14_col1\" class=\"data row14 col1\" >3</td>\n",
       "      <td id=\"T_edc1f_row14_col2\" class=\"data row14 col2\" >5</td>\n",
       "      <td id=\"T_edc1f_row14_col3\" class=\"data row14 col3\" >243</td>\n",
       "      <td id=\"T_edc1f_row14_col4\" class=\"data row14 col4\" >648</td>\n",
       "      <td id=\"T_edc1f_row14_col5\" class=\"data row14 col5\" >243</td>\n",
       "      <td id=\"T_edc1f_row14_col6\" class=\"data row14 col6\" >1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x112563440>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "    df[[\"sequence\", \"prime\", \"k\", \"modulus\", \"period\", \"residue_count\", \"residue_fraction\"]]\n",
    "    .head(15)\n",
    "    .style.hide(axis=\"index\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ab5e185",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-13T16:15:47.792410Z",
     "iopub.status.busy": "2026-08-13T16:15:47.792341Z",
     "iopub.status.idle": "2026-08-13T16:15:47.796859Z",
     "shell.execute_reply": "2026-08-13T16:15:47.796554Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_93c90\">\n",
       "  <caption>Exact observed-residue proportions modulo p</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >sequence</th>\n",
       "      <th id=\"T_93c90_level0_col0\" class=\"col_heading level0 col0\" >Fibonacci</th>\n",
       "      <th id=\"T_93c90_level0_col1\" class=\"col_heading level0 col1\" >Lucas</th>\n",
       "      <th id=\"T_93c90_level0_col2\" class=\"col_heading level0 col2\" >Pell</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >prime</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_93c90_level0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
       "      <td id=\"T_93c90_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_93c90_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "      <td id=\"T_93c90_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93c90_level0_row1\" class=\"row_heading level0 row1\" >3</th>\n",
       "      <td id=\"T_93c90_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "      <td id=\"T_93c90_row1_col1\" class=\"data row1 col1\" >1</td>\n",
       "      <td id=\"T_93c90_row1_col2\" class=\"data row1 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93c90_level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
       "      <td id=\"T_93c90_row2_col0\" class=\"data row2 col0\" >1</td>\n",
       "      <td id=\"T_93c90_row2_col1\" class=\"data row2 col1\" >4/5</td>\n",
       "      <td id=\"T_93c90_row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93c90_level0_row3\" class=\"row_heading level0 row3\" >7</th>\n",
       "      <td id=\"T_93c90_row3_col0\" class=\"data row3 col0\" >1</td>\n",
       "      <td id=\"T_93c90_row3_col1\" class=\"data row3 col1\" >1</td>\n",
       "      <td id=\"T_93c90_row3_col2\" class=\"data row3 col2\" >4/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93c90_level0_row4\" class=\"row_heading level0 row4\" >11</th>\n",
       "      <td id=\"T_93c90_row4_col0\" class=\"data row4 col0\" >7/11</td>\n",
       "      <td id=\"T_93c90_row4_col1\" class=\"data row4 col1\" >7/11</td>\n",
       "      <td id=\"T_93c90_row4_col2\" class=\"data row4 col2\" >9/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93c90_level0_row5\" class=\"row_heading level0 row5\" >13</th>\n",
       "      <td id=\"T_93c90_row5_col0\" class=\"data row5 col0\" >9/13</td>\n",
       "      <td id=\"T_93c90_row5_col1\" class=\"data row5 col1\" >12/13</td>\n",
       "      <td id=\"T_93c90_row5_col2\" class=\"data row5 col2\" >9/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93c90_level0_row6\" class=\"row_heading level0 row6\" >19</th>\n",
       "      <td id=\"T_93c90_row6_col0\" class=\"data row6 col0\" >12/19</td>\n",
       "      <td id=\"T_93c90_row6_col1\" class=\"data row6 col1\" >12/19</td>\n",
       "      <td id=\"T_93c90_row6_col2\" class=\"data row6 col2\" >15/19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93c90_level0_row7\" class=\"row_heading level0 row7\" >31</th>\n",
       "      <td id=\"T_93c90_row7_col0\" class=\"data row7 col0\" >19/31</td>\n",
       "      <td id=\"T_93c90_row7_col1\" class=\"data row7 col1\" >19/31</td>\n",
       "      <td id=\"T_93c90_row7_col2\" class=\"data row7 col2\" >19/31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x112894b30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_level = df[df[\"k\"] == 1].pivot(index=\"prime\", columns=\"sequence\", values=\"residue_fraction\")\n",
    "display(first_level.style.set_caption(\"Exact observed-residue proportions modulo p\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b576d176",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "The following plot is the main requested result. Lines may have different lengths because the experiment uses deeper exponents only where the modulus and period remain inexpensive enough for a laptop-scale exact enumeration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fb8017a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-13T16:15:47.797829Z",
     "iopub.status.busy": "2026-08-13T16:15:47.797768Z",
     "iopub.status.idle": "2026-08-13T16:15:48.056855Z",
     "shell.execute_reply": "2026-08-13T16:15:48.056461Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "density_path = ROOT / \"figures\" / \"density_by_k.png\"\n",
    "make_density_plot(df, density_path)\n",
    "display(Image(filename=str(density_path)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb6f92ad",
   "metadata": {},
   "source": [
    "### Fibonacci reproduces the published benchmarks\n",
    "\n",
    "Finite densities are compared with Bragman–Rowland's exact limiting values. Since (d_k(p)) decreases to the limit, a positive finite-minus-limit gap is expected when stabilization has not yet occurred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "de9d0628",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-13T16:15:48.058145Z",
     "iopub.status.busy": "2026-08-13T16:15:48.058054Z",
     "iopub.status.idle": "2026-08-13T16:15:48.061259Z",
     "shell.execute_reply": "2026-08-13T16:15:48.060936Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_462a6\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_462a6_level0_col0\" class=\"col_heading level0 col0\" >prime</th>\n",
       "      <th id=\"T_462a6_level0_col1\" class=\"col_heading level0 col1\" >max_k</th>\n",
       "      <th id=\"T_462a6_level0_col2\" class=\"col_heading level0 col2\" >observed_fraction</th>\n",
       "      <th id=\"T_462a6_level0_col3\" class=\"col_heading level0 col3\" >published_limit</th>\n",
       "      <th id=\"T_462a6_level0_col4\" class=\"col_heading level0 col4\" >observed_minus_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_462a6_row0_col0\" class=\"data row0 col0\" >2</td>\n",
       "      <td id=\"T_462a6_row0_col1\" class=\"data row0 col1\" >10</td>\n",
       "      <td id=\"T_462a6_row0_col2\" class=\"data row0 col2\" >21/32</td>\n",
       "      <td id=\"T_462a6_row0_col3\" class=\"data row0 col3\" >21/32</td>\n",
       "      <td id=\"T_462a6_row0_col4\" class=\"data row0 col4\" >0.00000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_462a6_row1_col0\" class=\"data row1 col0\" >3</td>\n",
       "      <td id=\"T_462a6_row1_col1\" class=\"data row1 col1\" >8</td>\n",
       "      <td id=\"T_462a6_row1_col2\" class=\"data row1 col2\" >1</td>\n",
       "      <td id=\"T_462a6_row1_col3\" class=\"data row1 col3\" >1</td>\n",
       "      <td id=\"T_462a6_row1_col4\" class=\"data row1 col4\" >0.00000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_462a6_row2_col0\" class=\"data row2 col0\" >5</td>\n",
       "      <td id=\"T_462a6_row2_col1\" class=\"data row2 col1\" >7</td>\n",
       "      <td id=\"T_462a6_row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "      <td id=\"T_462a6_row2_col3\" class=\"data row2 col3\" >1</td>\n",
       "      <td id=\"T_462a6_row2_col4\" class=\"data row2 col4\" >0.00000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_462a6_row3_col0\" class=\"data row3 col0\" >7</td>\n",
       "      <td id=\"T_462a6_row3_col1\" class=\"data row3 col1\" >6</td>\n",
       "      <td id=\"T_462a6_row3_col2\" class=\"data row3 col2\" >86137/117649</td>\n",
       "      <td id=\"T_462a6_row3_col3\" class=\"data row3 col3\" >41/56</td>\n",
       "      <td id=\"T_462a6_row3_col4\" class=\"data row3 col4\" >0.00000956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_462a6_row4_col0\" class=\"data row4 col0\" >11</td>\n",
       "      <td id=\"T_462a6_row4_col1\" class=\"data row4 col1\" >5</td>\n",
       "      <td id=\"T_462a6_row4_col2\" class=\"data row4 col2\" >88457/161051</td>\n",
       "      <td id=\"T_462a6_row4_col3\" class=\"data row4 col3\" >145/264</td>\n",
       "      <td id=\"T_462a6_row4_col4\" class=\"data row4 col4\" >0.00000595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_462a6_row5_col0\" class=\"data row5 col0\" >13</td>\n",
       "      <td id=\"T_462a6_row5_col1\" class=\"data row5 col1\" >5</td>\n",
       "      <td id=\"T_462a6_row5_col2\" class=\"data row5 col2\" >9/13</td>\n",
       "      <td id=\"T_462a6_row5_col3\" class=\"data row5 col3\" >9/13</td>\n",
       "      <td id=\"T_462a6_row5_col4\" class=\"data row5 col4\" >0.00000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_462a6_row6_col0\" class=\"data row6 col0\" >19</td>\n",
       "      <td id=\"T_462a6_row6_col1\" class=\"data row6 col1\" >4</td>\n",
       "      <td id=\"T_462a6_row6_col2\" class=\"data row6 col2\" >75621/130321</td>\n",
       "      <td id=\"T_462a6_row6_col3\" class=\"data row6 col3\" >441/760</td>\n",
       "      <td id=\"T_462a6_row6_col4\" class=\"data row6 col4\" >0.00000403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_462a6_row7_col0\" class=\"data row7 col0\" >31</td>\n",
       "      <td id=\"T_462a6_row7_col1\" class=\"data row7 col1\" >4</td>\n",
       "      <td id=\"T_462a6_row7_col2\" class=\"data row7 col2\" >19/31</td>\n",
       "      <td id=\"T_462a6_row7_col3\" class=\"data row7 col3\" >19/31</td>\n",
       "      <td id=\"T_462a6_row7_col4\" class=\"data row7 col4\" >0.00000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x112d29d60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "benchmark = pd.DataFrame(validation[\"published_fibonacci_limit_comparison\"])\n",
    "display(benchmark.style.hide(axis=\"index\").format({\"observed_minus_limit\": \"{:.8f}\"}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f67a536c",
   "metadata": {},
   "source": [
    "### Lucas and Pell resemble Fibonacci in monotonicity, not in exact density\n",
    "\n",
    "Reduction from modulo (p^{k+1}) to modulo (p^k) forces every density curve to be nonincreasing. Initial conditions and recurrence coefficients still matter: the Lucas and Pell curves can stabilize at very different proportions from Fibonacci for the same prime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b8e861c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-13T16:15:48.062318Z",
     "iopub.status.busy": "2026-08-13T16:15:48.062241Z",
     "iopub.status.idle": "2026-08-13T16:15:48.067093Z",
     "shell.execute_reply": "2026-08-13T16:15:48.066831Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_1071a\">\n",
       "  <caption>Deepest exact level computed for each sequence and prime</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_1071a_level0_col0\" class=\"col_heading level0 col0\" >sequence</th>\n",
       "      <th id=\"T_1071a_level0_col1\" class=\"col_heading level0 col1\" >prime</th>\n",
       "      <th id=\"T_1071a_level0_col2\" class=\"col_heading level0 col2\" >k</th>\n",
       "      <th id=\"T_1071a_level0_col3\" class=\"col_heading level0 col3\" >modulus</th>\n",
       "      <th id=\"T_1071a_level0_col4\" class=\"col_heading level0 col4\" >period</th>\n",
       "      <th id=\"T_1071a_level0_col5\" class=\"col_heading level0 col5\" >residue_count</th>\n",
       "      <th id=\"T_1071a_level0_col6\" class=\"col_heading level0 col6\" >residue_fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row0_col0\" class=\"data row0 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_1071a_row0_col1\" class=\"data row0 col1\" >2</td>\n",
       "      <td id=\"T_1071a_row0_col2\" class=\"data row0 col2\" >10</td>\n",
       "      <td id=\"T_1071a_row0_col3\" class=\"data row0 col3\" >1024</td>\n",
       "      <td id=\"T_1071a_row0_col4\" class=\"data row0 col4\" >1536</td>\n",
       "      <td id=\"T_1071a_row0_col5\" class=\"data row0 col5\" >672</td>\n",
       "      <td id=\"T_1071a_row0_col6\" class=\"data row0 col6\" >21/32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row1_col0\" class=\"data row1 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_1071a_row1_col1\" class=\"data row1 col1\" >3</td>\n",
       "      <td id=\"T_1071a_row1_col2\" class=\"data row1 col2\" >8</td>\n",
       "      <td id=\"T_1071a_row1_col3\" class=\"data row1 col3\" >6561</td>\n",
       "      <td id=\"T_1071a_row1_col4\" class=\"data row1 col4\" >17496</td>\n",
       "      <td id=\"T_1071a_row1_col5\" class=\"data row1 col5\" >6561</td>\n",
       "      <td id=\"T_1071a_row1_col6\" class=\"data row1 col6\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row2_col0\" class=\"data row2 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_1071a_row2_col1\" class=\"data row2 col1\" >5</td>\n",
       "      <td id=\"T_1071a_row2_col2\" class=\"data row2 col2\" >7</td>\n",
       "      <td id=\"T_1071a_row2_col3\" class=\"data row2 col3\" >78125</td>\n",
       "      <td id=\"T_1071a_row2_col4\" class=\"data row2 col4\" >312500</td>\n",
       "      <td id=\"T_1071a_row2_col5\" class=\"data row2 col5\" >78125</td>\n",
       "      <td id=\"T_1071a_row2_col6\" class=\"data row2 col6\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row3_col0\" class=\"data row3 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_1071a_row3_col1\" class=\"data row3 col1\" >7</td>\n",
       "      <td id=\"T_1071a_row3_col2\" class=\"data row3 col2\" >6</td>\n",
       "      <td id=\"T_1071a_row3_col3\" class=\"data row3 col3\" >117649</td>\n",
       "      <td id=\"T_1071a_row3_col4\" class=\"data row3 col4\" >268912</td>\n",
       "      <td id=\"T_1071a_row3_col5\" class=\"data row3 col5\" >86137</td>\n",
       "      <td id=\"T_1071a_row3_col6\" class=\"data row3 col6\" >86137/117649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row4_col0\" class=\"data row4 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_1071a_row4_col1\" class=\"data row4 col1\" >11</td>\n",
       "      <td id=\"T_1071a_row4_col2\" class=\"data row4 col2\" >5</td>\n",
       "      <td id=\"T_1071a_row4_col3\" class=\"data row4 col3\" >161051</td>\n",
       "      <td id=\"T_1071a_row4_col4\" class=\"data row4 col4\" >146410</td>\n",
       "      <td id=\"T_1071a_row4_col5\" class=\"data row4 col5\" >88457</td>\n",
       "      <td id=\"T_1071a_row4_col6\" class=\"data row4 col6\" >88457/161051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row5_col0\" class=\"data row5 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_1071a_row5_col1\" class=\"data row5 col1\" >13</td>\n",
       "      <td id=\"T_1071a_row5_col2\" class=\"data row5 col2\" >5</td>\n",
       "      <td id=\"T_1071a_row5_col3\" class=\"data row5 col3\" >371293</td>\n",
       "      <td id=\"T_1071a_row5_col4\" class=\"data row5 col4\" >799708</td>\n",
       "      <td id=\"T_1071a_row5_col5\" class=\"data row5 col5\" >257049</td>\n",
       "      <td id=\"T_1071a_row5_col6\" class=\"data row5 col6\" >9/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row6_col0\" class=\"data row6 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_1071a_row6_col1\" class=\"data row6 col1\" >19</td>\n",
       "      <td id=\"T_1071a_row6_col2\" class=\"data row6 col2\" >4</td>\n",
       "      <td id=\"T_1071a_row6_col3\" class=\"data row6 col3\" >130321</td>\n",
       "      <td id=\"T_1071a_row6_col4\" class=\"data row6 col4\" >123462</td>\n",
       "      <td id=\"T_1071a_row6_col5\" class=\"data row6 col5\" >75621</td>\n",
       "      <td id=\"T_1071a_row6_col6\" class=\"data row6 col6\" >75621/130321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row7_col0\" class=\"data row7 col0\" >Fibonacci</td>\n",
       "      <td id=\"T_1071a_row7_col1\" class=\"data row7 col1\" >31</td>\n",
       "      <td id=\"T_1071a_row7_col2\" class=\"data row7 col2\" >4</td>\n",
       "      <td id=\"T_1071a_row7_col3\" class=\"data row7 col3\" >923521</td>\n",
       "      <td id=\"T_1071a_row7_col4\" class=\"data row7 col4\" >893730</td>\n",
       "      <td id=\"T_1071a_row7_col5\" class=\"data row7 col5\" >566029</td>\n",
       "      <td id=\"T_1071a_row7_col6\" class=\"data row7 col6\" >19/31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row8_col0\" class=\"data row8 col0\" >Lucas</td>\n",
       "      <td id=\"T_1071a_row8_col1\" class=\"data row8 col1\" >2</td>\n",
       "      <td id=\"T_1071a_row8_col2\" class=\"data row8 col2\" >10</td>\n",
       "      <td id=\"T_1071a_row8_col3\" class=\"data row8 col3\" >1024</td>\n",
       "      <td id=\"T_1071a_row8_col4\" class=\"data row8 col4\" >1536</td>\n",
       "      <td id=\"T_1071a_row8_col5\" class=\"data row8 col5\" >652</td>\n",
       "      <td id=\"T_1071a_row8_col6\" class=\"data row8 col6\" >163/256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row9_col0\" class=\"data row9 col0\" >Lucas</td>\n",
       "      <td id=\"T_1071a_row9_col1\" class=\"data row9 col1\" >3</td>\n",
       "      <td id=\"T_1071a_row9_col2\" class=\"data row9 col2\" >8</td>\n",
       "      <td id=\"T_1071a_row9_col3\" class=\"data row9 col3\" >6561</td>\n",
       "      <td id=\"T_1071a_row9_col4\" class=\"data row9 col4\" >17496</td>\n",
       "      <td id=\"T_1071a_row9_col5\" class=\"data row9 col5\" >6561</td>\n",
       "      <td id=\"T_1071a_row9_col6\" class=\"data row9 col6\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row10_col0\" class=\"data row10 col0\" >Lucas</td>\n",
       "      <td id=\"T_1071a_row10_col1\" class=\"data row10 col1\" >5</td>\n",
       "      <td id=\"T_1071a_row10_col2\" class=\"data row10 col2\" >7</td>\n",
       "      <td id=\"T_1071a_row10_col3\" class=\"data row10 col3\" >78125</td>\n",
       "      <td id=\"T_1071a_row10_col4\" class=\"data row10 col4\" >62500</td>\n",
       "      <td id=\"T_1071a_row10_col5\" class=\"data row10 col5\" >26044</td>\n",
       "      <td id=\"T_1071a_row10_col6\" class=\"data row10 col6\" >26044/78125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row11_col0\" class=\"data row11 col0\" >Lucas</td>\n",
       "      <td id=\"T_1071a_row11_col1\" class=\"data row11 col1\" >7</td>\n",
       "      <td id=\"T_1071a_row11_col2\" class=\"data row11 col2\" >6</td>\n",
       "      <td id=\"T_1071a_row11_col3\" class=\"data row11 col3\" >117649</td>\n",
       "      <td id=\"T_1071a_row11_col4\" class=\"data row11 col4\" >268912</td>\n",
       "      <td id=\"T_1071a_row11_col5\" class=\"data row11 col5\" >86137</td>\n",
       "      <td id=\"T_1071a_row11_col6\" class=\"data row11 col6\" >86137/117649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row12_col0\" class=\"data row12 col0\" >Lucas</td>\n",
       "      <td id=\"T_1071a_row12_col1\" class=\"data row12 col1\" >11</td>\n",
       "      <td id=\"T_1071a_row12_col2\" class=\"data row12 col2\" >5</td>\n",
       "      <td id=\"T_1071a_row12_col3\" class=\"data row12 col3\" >161051</td>\n",
       "      <td id=\"T_1071a_row12_col4\" class=\"data row12 col4\" >146410</td>\n",
       "      <td id=\"T_1071a_row12_col5\" class=\"data row12 col5\" >88457</td>\n",
       "      <td id=\"T_1071a_row12_col6\" class=\"data row12 col6\" >88457/161051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row13_col0\" class=\"data row13 col0\" >Lucas</td>\n",
       "      <td id=\"T_1071a_row13_col1\" class=\"data row13 col1\" >13</td>\n",
       "      <td id=\"T_1071a_row13_col2\" class=\"data row13 col2\" >5</td>\n",
       "      <td id=\"T_1071a_row13_col3\" class=\"data row13 col3\" >371293</td>\n",
       "      <td id=\"T_1071a_row13_col4\" class=\"data row13 col4\" >799708</td>\n",
       "      <td id=\"T_1071a_row13_col5\" class=\"data row13 col5\" >342732</td>\n",
       "      <td id=\"T_1071a_row13_col6\" class=\"data row13 col6\" >12/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row14_col0\" class=\"data row14 col0\" >Lucas</td>\n",
       "      <td id=\"T_1071a_row14_col1\" class=\"data row14 col1\" >19</td>\n",
       "      <td id=\"T_1071a_row14_col2\" class=\"data row14 col2\" >4</td>\n",
       "      <td id=\"T_1071a_row14_col3\" class=\"data row14 col3\" >130321</td>\n",
       "      <td id=\"T_1071a_row14_col4\" class=\"data row14 col4\" >123462</td>\n",
       "      <td id=\"T_1071a_row14_col5\" class=\"data row14 col5\" >75621</td>\n",
       "      <td id=\"T_1071a_row14_col6\" class=\"data row14 col6\" >75621/130321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row15_col0\" class=\"data row15 col0\" >Lucas</td>\n",
       "      <td id=\"T_1071a_row15_col1\" class=\"data row15 col1\" >31</td>\n",
       "      <td id=\"T_1071a_row15_col2\" class=\"data row15 col2\" >4</td>\n",
       "      <td id=\"T_1071a_row15_col3\" class=\"data row15 col3\" >923521</td>\n",
       "      <td id=\"T_1071a_row15_col4\" class=\"data row15 col4\" >893730</td>\n",
       "      <td id=\"T_1071a_row15_col5\" class=\"data row15 col5\" >566029</td>\n",
       "      <td id=\"T_1071a_row15_col6\" class=\"data row15 col6\" >19/31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row16_col0\" class=\"data row16 col0\" >Pell</td>\n",
       "      <td id=\"T_1071a_row16_col1\" class=\"data row16 col1\" >2</td>\n",
       "      <td id=\"T_1071a_row16_col2\" class=\"data row16 col2\" >10</td>\n",
       "      <td id=\"T_1071a_row16_col3\" class=\"data row16 col3\" >1024</td>\n",
       "      <td id=\"T_1071a_row16_col4\" class=\"data row16 col4\" >1024</td>\n",
       "      <td id=\"T_1071a_row16_col5\" class=\"data row16 col5\" >768</td>\n",
       "      <td id=\"T_1071a_row16_col6\" class=\"data row16 col6\" >3/4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row17_col0\" class=\"data row17 col0\" >Pell</td>\n",
       "      <td id=\"T_1071a_row17_col1\" class=\"data row17 col1\" >3</td>\n",
       "      <td id=\"T_1071a_row17_col2\" class=\"data row17 col2\" >8</td>\n",
       "      <td id=\"T_1071a_row17_col3\" class=\"data row17 col3\" >6561</td>\n",
       "      <td id=\"T_1071a_row17_col4\" class=\"data row17 col4\" >17496</td>\n",
       "      <td id=\"T_1071a_row17_col5\" class=\"data row17 col5\" >6561</td>\n",
       "      <td id=\"T_1071a_row17_col6\" class=\"data row17 col6\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row18_col0\" class=\"data row18 col0\" >Pell</td>\n",
       "      <td id=\"T_1071a_row18_col1\" class=\"data row18 col1\" >5</td>\n",
       "      <td id=\"T_1071a_row18_col2\" class=\"data row18 col2\" >7</td>\n",
       "      <td id=\"T_1071a_row18_col3\" class=\"data row18 col3\" >78125</td>\n",
       "      <td id=\"T_1071a_row18_col4\" class=\"data row18 col4\" >187500</td>\n",
       "      <td id=\"T_1071a_row18_col5\" class=\"data row18 col5\" >78125</td>\n",
       "      <td id=\"T_1071a_row18_col6\" class=\"data row18 col6\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row19_col0\" class=\"data row19 col0\" >Pell</td>\n",
       "      <td id=\"T_1071a_row19_col1\" class=\"data row19 col1\" >7</td>\n",
       "      <td id=\"T_1071a_row19_col2\" class=\"data row19 col2\" >6</td>\n",
       "      <td id=\"T_1071a_row19_col3\" class=\"data row19 col3\" >117649</td>\n",
       "      <td id=\"T_1071a_row19_col4\" class=\"data row19 col4\" >100842</td>\n",
       "      <td id=\"T_1071a_row19_col5\" class=\"data row19 col5\" >67228</td>\n",
       "      <td id=\"T_1071a_row19_col6\" class=\"data row19 col6\" >4/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row20_col0\" class=\"data row20 col0\" >Pell</td>\n",
       "      <td id=\"T_1071a_row20_col1\" class=\"data row20 col1\" >11</td>\n",
       "      <td id=\"T_1071a_row20_col2\" class=\"data row20 col2\" >5</td>\n",
       "      <td id=\"T_1071a_row20_col3\" class=\"data row20 col3\" >161051</td>\n",
       "      <td id=\"T_1071a_row20_col4\" class=\"data row20 col4\" >351384</td>\n",
       "      <td id=\"T_1071a_row20_col5\" class=\"data row20 col5\" >131769</td>\n",
       "      <td id=\"T_1071a_row20_col6\" class=\"data row20 col6\" >9/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row21_col0\" class=\"data row21 col0\" >Pell</td>\n",
       "      <td id=\"T_1071a_row21_col1\" class=\"data row21 col1\" >13</td>\n",
       "      <td id=\"T_1071a_row21_col2\" class=\"data row21 col2\" >5</td>\n",
       "      <td id=\"T_1071a_row21_col3\" class=\"data row21 col3\" >371293</td>\n",
       "      <td id=\"T_1071a_row21_col4\" class=\"data row21 col4\" >61516</td>\n",
       "      <td id=\"T_1071a_row21_col5\" class=\"data row21 col5\" >28561</td>\n",
       "      <td id=\"T_1071a_row21_col6\" class=\"data row21 col6\" >1/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row22_col0\" class=\"data row22 col0\" >Pell</td>\n",
       "      <td id=\"T_1071a_row22_col1\" class=\"data row22 col1\" >19</td>\n",
       "      <td id=\"T_1071a_row22_col2\" class=\"data row22 col2\" >4</td>\n",
       "      <td id=\"T_1071a_row22_col3\" class=\"data row22 col3\" >130321</td>\n",
       "      <td id=\"T_1071a_row22_col4\" class=\"data row22 col4\" >274360</td>\n",
       "      <td id=\"T_1071a_row22_col5\" class=\"data row22 col5\" >102885</td>\n",
       "      <td id=\"T_1071a_row22_col6\" class=\"data row22 col6\" >15/19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1071a_row23_col0\" class=\"data row23 col0\" >Pell</td>\n",
       "      <td id=\"T_1071a_row23_col1\" class=\"data row23 col1\" >31</td>\n",
       "      <td id=\"T_1071a_row23_col2\" class=\"data row23 col2\" >4</td>\n",
       "      <td id=\"T_1071a_row23_col3\" class=\"data row23 col3\" >923521</td>\n",
       "      <td id=\"T_1071a_row23_col4\" class=\"data row23 col4\" >28830</td>\n",
       "      <td id=\"T_1071a_row23_col5\" class=\"data row23 col5\" >21142</td>\n",
       "      <td id=\"T_1071a_row23_col6\" class=\"data row23 col6\" >22/961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x112b68380>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_rows = (\n",
    "    df.sort_values(\"k\")\n",
    "      .groupby([\"sequence\", \"prime\"], as_index=False)\n",
    "      .tail(1)\n",
    "      [[\"sequence\", \"prime\", \"k\", \"modulus\", \"period\", \"residue_count\", \"residue_fraction\"]]\n",
    "      .sort_values([\"sequence\", \"prime\"])\n",
    ")\n",
    "display(final_rows.style.hide(axis=\"index\").set_caption(\"Deepest exact level computed for each sequence and prime\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f482677",
   "metadata": {},
   "source": [
    "### Two known exceptional Pell period lifts motivate a focused residue question\n",
    "\n",
    "Normally in these experiments, the state period and distinct-residue count both multiply by $p$ when lifting $p^k\\to p^{k+1}$, leaving the proportion unchanged. At $p=13$ and $p=31$, the first period lift is a known exception: $K(p^2)=K(p)$. Our computation adds the corresponding distinct-residue counts. For $p=13$, $P_7=169=13^2$, so the anomaly also has a direct arithmetic witness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd7d49df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-13T16:15:48.068253Z",
     "iopub.status.busy": "2026-08-13T16:15:48.068191Z",
     "iopub.status.idle": "2026-08-13T16:15:48.173650Z",
     "shell.execute_reply": "2026-08-13T16:15:48.173249Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_1ca2f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_1ca2f_level0_col0\" class=\"col_heading level0 col0\" >prime</th>\n",
       "      <th id=\"T_1ca2f_level0_col1\" class=\"col_heading level0 col1\" >k</th>\n",
       "      <th id=\"T_1ca2f_level0_col2\" class=\"col_heading level0 col2\" >period</th>\n",
       "      <th id=\"T_1ca2f_level0_col3\" class=\"col_heading level0 col3\" >period_growth</th>\n",
       "      <th id=\"T_1ca2f_level0_col4\" class=\"col_heading level0 col4\" >residue_count</th>\n",
       "      <th id=\"T_1ca2f_level0_col5\" class=\"col_heading level0 col5\" >residue_growth</th>\n",
       "      <th id=\"T_1ca2f_level0_col6\" class=\"col_heading level0 col6\" >residue_fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_1ca2f_row0_col0\" class=\"data row0 col0\" >13</td>\n",
       "      <td id=\"T_1ca2f_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "      <td id=\"T_1ca2f_row0_col2\" class=\"data row0 col2\" >28</td>\n",
       "      <td id=\"T_1ca2f_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_1ca2f_row0_col4\" class=\"data row0 col4\" >9</td>\n",
       "      <td id=\"T_1ca2f_row0_col5\" class=\"data row0 col5\" >nan</td>\n",
       "      <td id=\"T_1ca2f_row0_col6\" class=\"data row0 col6\" >9/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1ca2f_row1_col0\" class=\"data row1 col0\" >13</td>\n",
       "      <td id=\"T_1ca2f_row1_col1\" class=\"data row1 col1\" >2</td>\n",
       "      <td id=\"T_1ca2f_row1_col2\" class=\"data row1 col2\" >28</td>\n",
       "      <td id=\"T_1ca2f_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_1ca2f_row1_col4\" class=\"data row1 col4\" >13</td>\n",
       "      <td id=\"T_1ca2f_row1_col5\" class=\"data row1 col5\" >1.444444</td>\n",
       "      <td id=\"T_1ca2f_row1_col6\" class=\"data row1 col6\" >1/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1ca2f_row2_col0\" class=\"data row2 col0\" >13</td>\n",
       "      <td id=\"T_1ca2f_row2_col1\" class=\"data row2 col1\" >3</td>\n",
       "      <td id=\"T_1ca2f_row2_col2\" class=\"data row2 col2\" >364</td>\n",
       "      <td id=\"T_1ca2f_row2_col3\" class=\"data row2 col3\" >13.000000</td>\n",
       "      <td id=\"T_1ca2f_row2_col4\" class=\"data row2 col4\" >169</td>\n",
       "      <td id=\"T_1ca2f_row2_col5\" class=\"data row2 col5\" >13.000000</td>\n",
       "      <td id=\"T_1ca2f_row2_col6\" class=\"data row2 col6\" >1/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1ca2f_row3_col0\" class=\"data row3 col0\" >13</td>\n",
       "      <td id=\"T_1ca2f_row3_col1\" class=\"data row3 col1\" >4</td>\n",
       "      <td id=\"T_1ca2f_row3_col2\" class=\"data row3 col2\" >4732</td>\n",
       "      <td id=\"T_1ca2f_row3_col3\" class=\"data row3 col3\" >13.000000</td>\n",
       "      <td id=\"T_1ca2f_row3_col4\" class=\"data row3 col4\" >2197</td>\n",
       "      <td id=\"T_1ca2f_row3_col5\" class=\"data row3 col5\" >13.000000</td>\n",
       "      <td id=\"T_1ca2f_row3_col6\" class=\"data row3 col6\" >1/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1ca2f_row4_col0\" class=\"data row4 col0\" >13</td>\n",
       "      <td id=\"T_1ca2f_row4_col1\" class=\"data row4 col1\" >5</td>\n",
       "      <td id=\"T_1ca2f_row4_col2\" class=\"data row4 col2\" >61516</td>\n",
       "      <td id=\"T_1ca2f_row4_col3\" class=\"data row4 col3\" >13.000000</td>\n",
       "      <td id=\"T_1ca2f_row4_col4\" class=\"data row4 col4\" >28561</td>\n",
       "      <td id=\"T_1ca2f_row4_col5\" class=\"data row4 col5\" >13.000000</td>\n",
       "      <td id=\"T_1ca2f_row4_col6\" class=\"data row4 col6\" >1/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1ca2f_row5_col0\" class=\"data row5 col0\" >31</td>\n",
       "      <td id=\"T_1ca2f_row5_col1\" class=\"data row5 col1\" >1</td>\n",
       "      <td id=\"T_1ca2f_row5_col2\" class=\"data row5 col2\" >30</td>\n",
       "      <td id=\"T_1ca2f_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_1ca2f_row5_col4\" class=\"data row5 col4\" >19</td>\n",
       "      <td id=\"T_1ca2f_row5_col5\" class=\"data row5 col5\" >nan</td>\n",
       "      <td id=\"T_1ca2f_row5_col6\" class=\"data row5 col6\" >19/31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1ca2f_row6_col0\" class=\"data row6 col0\" >31</td>\n",
       "      <td id=\"T_1ca2f_row6_col1\" class=\"data row6 col1\" >2</td>\n",
       "      <td id=\"T_1ca2f_row6_col2\" class=\"data row6 col2\" >30</td>\n",
       "      <td id=\"T_1ca2f_row6_col3\" class=\"data row6 col3\" >1.000000</td>\n",
       "      <td id=\"T_1ca2f_row6_col4\" class=\"data row6 col4\" >22</td>\n",
       "      <td id=\"T_1ca2f_row6_col5\" class=\"data row6 col5\" >1.157895</td>\n",
       "      <td id=\"T_1ca2f_row6_col6\" class=\"data row6 col6\" >22/961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1ca2f_row7_col0\" class=\"data row7 col0\" >31</td>\n",
       "      <td id=\"T_1ca2f_row7_col1\" class=\"data row7 col1\" >3</td>\n",
       "      <td id=\"T_1ca2f_row7_col2\" class=\"data row7 col2\" >930</td>\n",
       "      <td id=\"T_1ca2f_row7_col3\" class=\"data row7 col3\" >31.000000</td>\n",
       "      <td id=\"T_1ca2f_row7_col4\" class=\"data row7 col4\" >682</td>\n",
       "      <td id=\"T_1ca2f_row7_col5\" class=\"data row7 col5\" >31.000000</td>\n",
       "      <td id=\"T_1ca2f_row7_col6\" class=\"data row7 col6\" >22/961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_1ca2f_row8_col0\" class=\"data row8 col0\" >31</td>\n",
       "      <td id=\"T_1ca2f_row8_col1\" class=\"data row8 col1\" >4</td>\n",
       "      <td id=\"T_1ca2f_row8_col2\" class=\"data row8 col2\" >28830</td>\n",
       "      <td id=\"T_1ca2f_row8_col3\" class=\"data row8 col3\" >31.000000</td>\n",
       "      <td id=\"T_1ca2f_row8_col4\" class=\"data row8 col4\" >21142</td>\n",
       "      <td id=\"T_1ca2f_row8_col5\" class=\"data row8 col5\" >31.000000</td>\n",
       "      <td id=\"T_1ca2f_row8_col6\" class=\"data row8 col6\" >22/961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x112cf7f20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "growth_path = ROOT / \"figures\" / \"pell_lift_growth.png\"\n",
    "make_growth_plot(df, growth_path)\n",
    "display(Image(filename=str(growth_path)))\n",
    "\n",
    "display(\n",
    "    df[(df[\"sequence\"] == \"Pell\") & (df[\"prime\"].isin([13, 31]))]\n",
    "    [[\"prime\", \"k\", \"period\", \"period_growth\", \"residue_count\", \"residue_growth\", \"residue_fraction\"]]\n",
    "    .style.hide(axis=\"index\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a678a03",
   "metadata": {},
   "source": [
    "## Validation and limitations\n",
    "\n",
    "The computations are exact for the tested finite moduli, but they do not prove limiting behavior for Lucas or Pell sequences. The validation checks pair-state return, zero preperiod for the invertible recurrences, monotonicity under lifting, known Pisano periods, independently checked residue counts, and comparison with published Fibonacci limits.\n",
    "\n",
    "Important limitations:\n",
    "\n",
    "- Only eight primes and bounded exponents are tested.\n",
    "- Numerical stabilization is evidence, not proof of a limiting formula.\n",
    "- This study counts which residues appear, not how frequently each residue appears within a period.\n",
    "- The literature search was targeted, not exhaustive; an observed Lucas/Pell pattern must not be described as new without a deeper review and proof."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "992f7f67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-13T16:15:48.174945Z",
     "iopub.status.busy": "2026-08-13T16:15:48.174862Z",
     "iopub.status.idle": "2026-08-13T16:15:48.177232Z",
     "shell.execute_reply": "2026-08-13T16:15:48.176903Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': 'passed',\n",
       " 'record_count': 147,\n",
       " 'all_invertible_orbits_returned_to_start': True,\n",
       " 'all_preperiods_zero': True,\n",
       " 'density_monotonicity_violations': [],\n",
       " 'independent_small_modulus_crosscheck': {'rows_checked': 78,\n",
       "  'maximum_modulus': 400,\n",
       "  'errors': []},\n",
       " 'published_fibonacci_limit_comparison': [{'prime': 2,\n",
       "   'max_k': 10,\n",
       "   'observed_fraction': '21/32',\n",
       "   'published_limit': '21/32',\n",
       "   'observed_minus_limit': 0.0},\n",
       "  {'prime': 3,\n",
       "   'max_k': 8,\n",
       "   'observed_fraction': '1',\n",
       "   'published_limit': '1',\n",
       "   'observed_minus_limit': 0.0},\n",
       "  {'prime': 5,\n",
       "   'max_k': 7,\n",
       "   'observed_fraction': '1',\n",
       "   'published_limit': '1',\n",
       "   'observed_minus_limit': 0.0},\n",
       "  {'prime': 7,\n",
       "   'max_k': 6,\n",
       "   'observed_fraction': '86137/117649',\n",
       "   'published_limit': '41/56',\n",
       "   'observed_minus_limit': 9.562342221353348e-06},\n",
       "  {'prime': 11,\n",
       "   'max_k': 5,\n",
       "   'observed_fraction': '88457/161051',\n",
       "   'published_limit': '145/264',\n",
       "   'observed_minus_limit': 5.950496012650237e-06},\n",
       "  {'prime': 13,\n",
       "   'max_k': 5,\n",
       "   'observed_fraction': '9/13',\n",
       "   'published_limit': '9/13',\n",
       "   'observed_minus_limit': 0.0},\n",
       "  {'prime': 19,\n",
       "   'max_k': 4,\n",
       "   'observed_fraction': '75621/130321',\n",
       "   'published_limit': '441/760',\n",
       "   'observed_minus_limit': 4.028514207226771e-06},\n",
       "  {'prime': 31,\n",
       "   'max_k': 4,\n",
       "   'observed_fraction': '19/31',\n",
       "   'published_limit': '19/31',\n",
       "   'observed_minus_limit': 0.0}],\n",
       " 'interpretation': 'Finite densities must be at or above their limit. Exact agreement at the tested maximum is expected for p=2,3,5,13,31; other tested primes converge from above.'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assert validation[\"status\"] == \"passed\"\n",
    "assert validation[\"all_invertible_orbits_returned_to_start\"]\n",
    "assert validation[\"all_preperiods_zero\"]\n",
    "assert validation[\"density_monotonicity_violations\"] == []\n",
    "validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e2e8e51",
   "metadata": {},
   "source": [
    "## Takeaways\n",
    "\n",
    "**Working conjecture to investigate:** For the Pell recurrence at an odd prime $p$, if at some lift $p^k\\to p^{k+1}$ both the pair-state period and the distinct-residue count multiply by $p$, then both continue to multiply by $p$ at every higher lift. Consequently, the residue proportion stabilizes from that level onward.\n",
    "\n",
    "This statement is supported by the tested data but is not proved and is not claimed to be original. A serious next step is to formulate it for general Lucas $U_n(P,Q)$ and $V_n(P,Q)$ sequences, then connect failures of period lifting to Wieferich-type divisibility conditions.\n",
    "\n",
    "### Primary references\n",
    "\n",
    "- N. Bragman and E. Rowland, [*Limiting density of the Fibonacci sequence modulo powers of a prime*](https://doi.org/10.1007/s40993-025-00667-1), *Research in Number Theory* 11 (2025), 88.\n",
    "- B. Avila and Y. Chen, [*On Moduli for Which the Lucas Numbers Contain a Complete Residue System*](https://www.fq.math.ca/Papers1/51-2/AvilaChen.pdf), *Fibonacci Quarterly* 51 (2013).\n",
    "- R. Bundschuh and P. Bundschuh, [*Distribution of Fibonacci and Lucas Numbers Modulo $3^k$*](https://www.fq.math.ca/Papers1/49-3/Bundschuh2.pdf), *Fibonacci Quarterly* 49 (2011).\n",
    "- J. Klaška, [*Donald Dines Wall's Conjecture*](https://www.fq.math.ca/Papers1/56-1/Klaska10917.pdf), *Fibonacci Quarterly* 56 (2018), which records $13$, $31$, and $1{,}546{,}463$ as the Pell period-lifting exceptions below $10^8$."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
