{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "116bcc5c",
   "metadata": {},
   "source": [
    "# Example\n",
    "\n",
    "In this section, we show a specific application of `GaugeFixer` to study the local sequence requirements around different peaks in the Shine-Dalgarno sequence landscape as described in [Martí-Gómez et al. (2026)](https://www.biorxiv.org/content/10.64898/2025.12.08.693054v2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "23db982a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import logomaker\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from itertools import combinations, product\n",
    "from plot import heatmap_pairwise\n",
    "from scipy.stats import pearsonr\n",
    "from gaugefixer import AllOrderModel\n",
    "from gaugefixer.utils import get_subsets_of_set, get_orbits_features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab3054d0",
   "metadata": {},
   "source": [
    "### Defining an all-order model for the Shine-Dalgarno sequence\n",
    "\n",
    "In this case, we use a previously inferred complete combinatorial landscape by [Martí-Gómez et al. (2026)](https://academic.oup.com/mbe/article/43/2/msag023/8456298) using high-throughput experimental data collected by [Kuo et al. (2020)](https://genome.cshlp.org/content/30/5/711) on nearly every possible 9-nucleotide sequence in the 5'UTR of the dmsC gene in *E. coli*. \n",
    "\n",
    "We start by loading the inferred landscape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ef8bc62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "f",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "7d72ac85-df93-44e9-bb26-9ab116789062",
       "rows": [
        [
         "AAAAAAAAA",
         "0.5638568624854088"
        ],
        [
         "AAAAAAAAC",
         "0.7015107274055481"
        ],
        [
         "AAAAAAAAG",
         "0.6191882789134979"
        ],
        [
         "AAAAAAAAU",
         "0.6287718787789345"
        ],
        [
         "AAAAAAACA",
         "0.594683438539505"
        ],
        [
         "AAAAAAACC",
         "0.5868444815278053"
        ],
        [
         "AAAAAAACG",
         "0.6727708727121353"
        ],
        [
         "AAAAAAACU",
         "0.6478897631168365"
        ],
        [
         "AAAAAAAGA",
         "0.6396320313215256"
        ],
        [
         "AAAAAAAGC",
         "0.5802233070135117"
        ],
        [
         "AAAAAAAGG",
         "0.6302464604377747"
        ],
        [
         "AAAAAAAGU",
         "0.6269779577851295"
        ],
        [
         "AAAAAAAUA",
         "0.5150142461061478"
        ],
        [
         "AAAAAAAUC",
         "0.6858302354812622"
        ],
        [
         "AAAAAAAUG",
         "0.72210294008255"
        ],
        [
         "AAAAAAAUU",
         "0.6273685991764069"
        ],
        [
         "AAAAAACAA",
         "0.6003869958221912"
        ],
        [
         "AAAAAACAC",
         "0.6278582885861397"
        ],
        [
         "AAAAAACAG",
         "0.4741915259510278"
        ],
        [
         "AAAAAACAU",
         "0.5944587886333466"
        ],
        [
         "AAAAAACCA",
         "0.5504545196890831"
        ],
        [
         "AAAAAACCC",
         "0.5203661322593689"
        ],
        [
         "AAAAAACCG",
         "0.5586934387683868"
        ],
        [
         "AAAAAACCU",
         "0.5673647373914719"
        ],
        [
         "AAAAAACGA",
         "0.6045751720666885"
        ],
        [
         "AAAAAACGC",
         "0.6105850348249078"
        ],
        [
         "AAAAAACGG",
         "0.4990684352815151"
        ],
        [
         "AAAAAACGU",
         "0.6573664750903845"
        ],
        [
         "AAAAAACUA",
         "0.5626135319471359"
        ],
        [
         "AAAAAACUC",
         "0.5640361309051514"
        ],
        [
         "AAAAAACUG",
         "0.6872833054512739"
        ],
        [
         "AAAAAACUU",
         "0.6237415429204702"
        ],
        [
         "AAAAAAGAA",
         "0.6685855761170387"
        ],
        [
         "AAAAAAGAC",
         "0.6549443751573563"
        ],
        [
         "AAAAAAGAG",
         "0.6668623015284538"
        ],
        [
         "AAAAAAGAU",
         "0.5943528115749359"
        ],
        [
         "AAAAAAGCA",
         "0.5456972662359476"
        ],
        [
         "AAAAAAGCC",
         "0.4914389997720718"
        ],
        [
         "AAAAAAGCG",
         "0.6260806508362293"
        ],
        [
         "AAAAAAGCU",
         "0.5608344525098801"
        ],
        [
         "AAAAAAGGA",
         "0.6515529751777649"
        ],
        [
         "AAAAAAGGC",
         "0.7578512728214264"
        ],
        [
         "AAAAAAGGG",
         "0.4236851334571838"
        ],
        [
         "AAAAAAGGU",
         "0.7181397974491119"
        ],
        [
         "AAAAAAGUA",
         "0.5852416604757309"
        ],
        [
         "AAAAAAGUC",
         "0.716905027627945"
        ],
        [
         "AAAAAAGUG",
         "0.6548958718776703"
        ],
        [
         "AAAAAAGUU",
         "0.6019890904426575"
        ],
        [
         "AAAAAAUAA",
         "0.5940462350845337"
        ],
        [
         "AAAAAAUAC",
         "0.7444006204605103"
        ]
       ],
       "shape": {
        "columns": 1,
        "rows": 262144
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAAAAAAAA</th>\n",
       "      <td>0.563857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAAAAAAC</th>\n",
       "      <td>0.701511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAAAAAAG</th>\n",
       "      <td>0.619188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAAAAAAU</th>\n",
       "      <td>0.628772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAAAAACA</th>\n",
       "      <td>0.594683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UUUUUUUGU</th>\n",
       "      <td>0.615910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UUUUUUUUA</th>\n",
       "      <td>0.539058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UUUUUUUUC</th>\n",
       "      <td>0.548782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UUUUUUUUG</th>\n",
       "      <td>0.536610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UUUUUUUUU</th>\n",
       "      <td>0.564960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>262144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  f\n",
       "AAAAAAAAA  0.563857\n",
       "AAAAAAAAC  0.701511\n",
       "AAAAAAAAG  0.619188\n",
       "AAAAAAAAU  0.628772\n",
       "AAAAAAACA  0.594683\n",
       "...             ...\n",
       "UUUUUUUGU  0.615910\n",
       "UUUUUUUUA  0.539058\n",
       "UUUUUUUUC  0.548782\n",
       "UUUUUUUUG  0.536610\n",
       "UUUUUUUUU  0.564960\n",
       "\n",
       "[262144 rows x 1 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function = pd.read_csv(\"shine_dalgarno.csv\", index_col=0)\n",
    "function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97207fcd",
   "metadata": {},
   "source": [
    "and define an all-order interaction model specifying this particular sequence-function relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3f9be424",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AllOrderModel(L=9, alphabet_name=\"rna\")\n",
    "model.set_landscape(function[\"f\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90bd0c8b",
   "metadata": {},
   "source": [
    "### Define gauges of interest\n",
    "\n",
    "We have previously shown that this landscape consists of a series of fitness peaks that correspond to sequences that have the core AGGAG motif at different distances from the start codon. In order to study the local structure of these different peaks, we define a series of probability distributions with the core motif at different positions and uniform allele probabilities at the remaining sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac064604",
   "metadata": {},
   "outputs": [],
   "source": [
    "pi_uniform = [0.25 * np.ones(4)]\n",
    "pi_motif = [\n",
    "    np.array([1, 0, 0, 0]),\n",
    "    np.array([0, 0, 1, 0]),\n",
    "    np.array([0, 0, 1, 0]),\n",
    "    np.array([1, 0, 0, 0]),\n",
    "    np.array([0, 0, 1, 0]),\n",
    "]\n",
    "\n",
    "positions = [-13, -12, -11, -10, -9]\n",
    "alphabet = list(\"ACGU\")\n",
    "gauges = []\n",
    "for p, position in enumerate(positions):\n",
    "    pi_lc = pi_uniform * p + pi_motif + pi_uniform * (4 - p)\n",
    "    gauges.append(pi_lc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c05f84b4",
   "metadata": {},
   "source": [
    "We can visualize these probability distributions easily using site-frequency logos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52d7e00a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot probability distributions\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 275x600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, subplots = plt.subplots(\n",
    "    5,\n",
    "    1,\n",
    "    figsize=(2.75, 6),\n",
    ")\n",
    "\n",
    "print(\"Plot probability distributions\")\n",
    "for axes, (p, position), pi_lc in zip(subplots, enumerate(positions), gauges):\n",
    "    pi_lc = pd.DataFrame(pi_lc, columns=alphabet)\n",
    "    logo = logomaker.Logo(pi_lc, ax=axes, show_spines=False, baseline_width=0)\n",
    "    \n",
    "    # Add bracket and \"Core\" label above the AGGAG motif\n",
    "    bracket_y = 1.15\n",
    "    text_y = 1.15\n",
    "    x_left = p - 0.5\n",
    "    x_right = p + 4 + 0.5\n",
    "\n",
    "    # Draw bracket: horizontal line with small vertical ends\n",
    "    axes.plot([x_left, x_left, x_right, x_right], \n",
    "                [bracket_y - 0.08, bracket_y, bracket_y, bracket_y - 0.08], \n",
    "                color='black', linewidth=0.5, clip_on=False)\n",
    "\n",
    "    # Add \"Core\" label\n",
    "    axes.text((x_left + x_right) / 2, text_y, 'core', \n",
    "                ha='center', va='bottom', fontsize=7, clip_on=False)\n",
    "\n",
    "    axes.set(\n",
    "        ylim=(0, 1),\n",
    "        yticks=[], #(0, 1),\n",
    "        yticklabels=[], #[\"0\", \"1\"],\n",
    "        ylabel=f\"register {position}\",\n",
    "        xticks=np.arange(9),\n",
    "        xticklabels=np.arange(-13, -4),\n",
    "    )\n",
    "    axes.tick_params(axis='x', rotation=90, length=0, pad=1.5)  # xtick marks of length 0\n",
    "    axes.tick_params(axis='y', length=2)\n",
    "    if position == -9:  # Use 'position' instead of 'p'\n",
    "        axes.set_xlabel(\"position relative to AUG\", labelpad=1)\n",
    "    # Hide the x-axis line but keep the xtick labels\n",
    "    axes.spines['bottom'].set_visible(False)\n",
    "\n",
    "    for p in list(range(p)) + list(range(p + 5, 9)):\n",
    "        for c in alphabet:\n",
    "            logo.style_single_glyph(p, c, alpha=0.3)\n",
    "\n",
    "fig.tight_layout(h_pad=1, w_pad=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "470134e0",
   "metadata": {},
   "source": [
    "### Computing gauge-fixed parameters\n",
    "\n",
    "Once we have defined the probability distributions capturing the sequences of interest at the different fitness peaks, we compute the gauge-fixed parameters under the hierarchical gauges defined by these probability distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd1f1d64",
   "metadata": {},
   "outputs": [
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       "\n",
       "                                               -10        -9  \\\n",
       "((), )                                    1.906598  1.101074   \n",
       "((0,), A)                                 0.180145  0.095675   \n",
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       "((0,), U)                                 0.114324  0.020898   \n",
       "...                                            ...       ...   \n",
       "((0, 1, 2, 3, 4, 5, 6, 7, 8), UUUUUUUGU)  0.000000  0.019661   \n",
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       "\n",
       "                                                                orbit  \\\n",
       "((), )                                                             ()   \n",
       "((0,), A)                                                        (0,)   \n",
       "((0,), C)                                                        (0,)   \n",
       "((0,), G)                                                        (0,)   \n",
       "((0,), U)                                                        (0,)   \n",
       "...                                                               ...   \n",
       "((0, 1, 2, 3, 4, 5, 6, 7, 8), UUUUUUUGU)  (0, 1, 2, 3, 4, 5, 6, 7, 8)   \n",
       "((0, 1, 2, 3, 4, 5, 6, 7, 8), UUUUUUUUA)  (0, 1, 2, 3, 4, 5, 6, 7, 8)   \n",
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       "((0, 1, 2, 3, 4, 5, 6, 7, 8), UUUUUUUUG)  (0, 1, 2, 3, 4, 5, 6, 7, 8)   \n",
       "((0, 1, 2, 3, 4, 5, 6, 7, 8), UUUUUUUUU)  (0, 1, 2, 3, 4, 5, 6, 7, 8)   \n",
       "\n",
       "                                             subseq  \n",
       "((), )                                               \n",
       "((0,), A)                                         A  \n",
       "((0,), C)                                         C  \n",
       "((0,), G)                                         G  \n",
       "((0,), U)                                         U  \n",
       "...                                             ...  \n",
       "((0, 1, 2, 3, 4, 5, 6, 7, 8), UUUUUUUGU)  UUUUUUUGU  \n",
       "((0, 1, 2, 3, 4, 5, 6, 7, 8), UUUUUUUUA)  UUUUUUUUA  \n",
       "((0, 1, 2, 3, 4, 5, 6, 7, 8), UUUUUUUUC)  UUUUUUUUC  \n",
       "((0, 1, 2, 3, 4, 5, 6, 7, 8), UUUUUUUUG)  UUUUUUUUG  \n",
       "((0, 1, 2, 3, 4, 5, 6, 7, 8), UUUUUUUUU)  UUUUUUUUU  \n",
       "\n",
       "[1953125 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thetas = {}\n",
    "for position, pi_lc in zip(positions, gauges):\n",
    "    theta_fixed = model.get_fixed_params(gauge=\"hierarchical\", pi_lc=pi_lc)\n",
    "    thetas[position] = theta_fixed\n",
    "thetas = pd.DataFrame(thetas)\n",
    "thetas[\"orbit\"] = [x[0] for x in thetas.index]\n",
    "thetas[\"subseq\"] = [x[1] for x in thetas.index]\n",
    "thetas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9dccfe",
   "metadata": {},
   "source": [
    "As we want to compare the local sequence requirements in the core motif at each register, we need to align the gauge-fixed parameters of the core positions across the different positions, which we do as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7029f475",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>((0, 1, 2, 3, 4), UUUGU)</th>\n",
       "      <td>-0.036878</td>\n",
       "      <td>-0.095886</td>\n",
       "      <td>0.384084</td>\n",
       "      <td>0.153701</td>\n",
       "      <td>-0.012828</td>\n",
       "      <td>(0, 1, 2, 3, 4)</td>\n",
       "      <td>UUUGU</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>((0, 1, 2, 3, 4), UUUUA)</th>\n",
       "      <td>0.222282</td>\n",
       "      <td>-0.106347</td>\n",
       "      <td>0.074687</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>-0.060240</td>\n",
       "      <td>(0, 1, 2, 3, 4)</td>\n",
       "      <td>UUUUA</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>((0, 1, 2, 3, 4), UUUUC)</th>\n",
       "      <td>0.040322</td>\n",
       "      <td>-0.345761</td>\n",
       "      <td>-0.079473</td>\n",
       "      <td>-0.121644</td>\n",
       "      <td>-0.042035</td>\n",
       "      <td>(0, 1, 2, 3, 4)</td>\n",
       "      <td>UUUUC</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>((0, 1, 2, 3, 4), UUUUG)</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(0, 1, 2, 3, 4)</td>\n",
       "      <td>UUUUG</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>((0, 1, 2, 3, 4), UUUUU)</th>\n",
       "      <td>-0.133842</td>\n",
       "      <td>-0.119921</td>\n",
       "      <td>0.181592</td>\n",
       "      <td>0.015053</td>\n",
       "      <td>0.109169</td>\n",
       "      <td>(0, 1, 2, 3, 4)</td>\n",
       "      <td>UUUUU</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3125 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               -13       -12       -11       -10        -9  \\\n",
       "((), )                    1.861897  2.186185  2.211720  1.906598  1.101074   \n",
       "((0,), A)                 0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "((0,), C)                -0.646397 -0.966215 -0.780846 -0.678776 -0.501391   \n",
       "((0,), G)                 0.149598 -0.152077 -0.269654 -0.416939 -0.221410   \n",
       "((0,), U)                 0.000753 -0.699675 -0.522361 -0.472846 -0.368357   \n",
       "...                            ...       ...       ...       ...       ...   \n",
       "((0, 1, 2, 3, 4), UUUGU) -0.036878 -0.095886  0.384084  0.153701 -0.012828   \n",
       "((0, 1, 2, 3, 4), UUUUA)  0.222282 -0.106347  0.074687  0.016267 -0.060240   \n",
       "((0, 1, 2, 3, 4), UUUUC)  0.040322 -0.345761 -0.079473 -0.121644 -0.042035   \n",
       "((0, 1, 2, 3, 4), UUUUG)  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "((0, 1, 2, 3, 4), UUUUU) -0.133842 -0.119921  0.181592  0.015053  0.109169   \n",
       "\n",
       "                                    orbit subseq  k  \n",
       "((), )                                 ()         0  \n",
       "((0,), A)                            (0,)      A  1  \n",
       "((0,), C)                            (0,)      C  1  \n",
       "((0,), G)                            (0,)      G  1  \n",
       "((0,), U)                            (0,)      U  1  \n",
       "...                                   ...    ... ..  \n",
       "((0, 1, 2, 3, 4), UUUGU)  (0, 1, 2, 3, 4)  UUUGU  5  \n",
       "((0, 1, 2, 3, 4), UUUUA)  (0, 1, 2, 3, 4)  UUUUA  5  \n",
       "((0, 1, 2, 3, 4), UUUUC)  (0, 1, 2, 3, 4)  UUUUC  5  \n",
       "((0, 1, 2, 3, 4), UUUUG)  (0, 1, 2, 3, 4)  UUUUG  5  \n",
       "((0, 1, 2, 3, 4), UUUUU)  (0, 1, 2, 3, 4)  UUUUU  5  \n",
       "\n",
       "[3125 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_aligned = {}\n",
    "orbits = get_subsets_of_set((0, 1, 2, 3, 4))\n",
    "features = get_orbits_features(orbits, model.alphabet_list)\n",
    "for p, position in enumerate(positions):\n",
    "    features_p = [\n",
    "        (tuple(x + p for x in orbit), subseq) for orbit, subseq in features\n",
    "    ]\n",
    "    theta_p = thetas.loc[features_p, position]\n",
    "    theta_p.index = features\n",
    "    theta_aligned[position] = theta_p\n",
    "theta_aligned = pd.DataFrame(theta_aligned)\n",
    "theta_aligned[\"orbit\"] = [x[0] for x in theta_aligned.index]\n",
    "theta_aligned[\"subseq\"] = [x[1] for x in theta_aligned.index]\n",
    "theta_aligned[\"k\"] = [len(x) for x in theta_aligned[\"subseq\"]]\n",
    "theta_aligned\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d97ed9a",
   "metadata": {},
   "source": [
    "### Visualizing gauge-fixed parameters across binding positions\n",
    "\n",
    "Next, we summarize the local sequence requirements by visualizing the constant, additive and pairwise coefficients in the core positions across the different gauges.\n",
    "\n",
    "We start by simply visualizing the average fitness of sequences having the core AGGAG motif at different positions by plotting the constant term under each gauge and comparing it with the average fitness in the whole landscape and the fitness of the wild-type sequence AAGGAGGUG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7b8a10b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "-13",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "-12",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "-11",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "-10",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "-9",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "orbit",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "subseq",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "k",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "cd2d4e9c-b3f1-4c2f-8a66-8cad6c119faa",
       "rows": [
        [
         "((), '')",
         "1.8618965897809971",
         "2.1861845029616234",
         "2.2117199462280723",
         "1.9065979073527615",
         "1.1010743734042236",
         "()",
         "",
         "0"
        ]
       ],
       "shape": {
        "columns": 8,
        "rows": 1
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>-13</th>\n",
       "      <th>-12</th>\n",
       "      <th>-11</th>\n",
       "      <th>-10</th>\n",
       "      <th>-9</th>\n",
       "      <th>orbit</th>\n",
       "      <th>subseq</th>\n",
       "      <th>k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>((), )</th>\n",
       "      <td>1.861897</td>\n",
       "      <td>2.186185</td>\n",
       "      <td>2.21172</td>\n",
       "      <td>1.906598</td>\n",
       "      <td>1.101074</td>\n",
       "      <td>()</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             -13       -12      -11       -10        -9 orbit subseq  k\n",
       "((), )  1.861897  2.186185  2.21172  1.906598  1.101074    ()         0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta0 = theta_aligned.loc[theta_aligned[\"k\"] == 0, :].copy()\n",
    "theta0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "14ae52c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 300x175 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_f = function.values.mean()\n",
    "wt_f = function.loc[\"AAGGAGGUG\"].values[0]\n",
    "\n",
    "mean_color = \"C3\"\n",
    "cons_color = \"C2\"\n",
    "fig, ax = plt.subplots(1, 1, figsize=(3, 1.75))\n",
    "means = theta0[positions].values.flatten()\n",
    "ax.scatter(positions, means, c=\"black\", s=7)\n",
    "ax.plot(positions, means, c=\"black\", lw=0.9)\n",
    "ax.axhline(mean_f, c=mean_color, linestyle=\"--\", lw=0.5)\n",
    "ax.axhline(wt_f, c=cons_color, linestyle=\"--\", lw=0.5)\n",
    "ax.set(\n",
    "    xticks=positions,  # Set ticks BEFORE ticklabels\n",
    "    xticklabels=positions,\n",
    "    ylim=(0.4, 2.75),\n",
    ")\n",
    "ax.text(x=-10, y=wt_f, s=\"wild type\", fontsize=9, ha=\"left\", va=\"bottom\", color=cons_color)\n",
    "ax.text(x=-13, y=mean_f, s=\"average\", fontsize=9, ha=\"left\", va=\"bottom\", color=mean_color)\n",
    "ax.tick_params(axis='both', which='major', length=2)\n",
    "ax.set_xlabel(\"register\", labelpad=1)\n",
    "ax.set_ylabel(\"parameter\", labelpad=1)\n",
    "fig.tight_layout(pad=0.2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33064008",
   "metadata": {},
   "source": [
    "This shows that the optimal position of the AGGAG motif relative to the start codon in the dmsC gene is at registers -12 and -11, and fitness drops dramatically when the motif is located at position -9. \n",
    "\n",
    "Next, we visualize the additive coefficients around each fitness peak at the core positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "12da8843",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_add = theta_aligned.loc[theta_aligned[\"k\"] == 1, :].copy()\n",
    "theta_add[\"position\"] = [x[0]+1 for x in theta_add[\"orbit\"]]\n",
    "theta_lc_positions = {\n",
    "    position: pd.pivot(\n",
    "        theta_add, index=\"subseq\", columns=\"position\", values=position\n",
    "    )\n",
    "    for position in positions\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "99e9c0e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(\n",
    "    5,\n",
    "    1,\n",
    "    figsize=(2, 6),\n",
    ")\n",
    "\n",
    "for ax, (position, theta_lc) in zip(axes, theta_lc_positions.items()):\n",
    "    # Invert the y-axis order so lower y values are at the top\n",
    "    sns.heatmap(\n",
    "        theta_lc.iloc[::-1, :],  # reverse rows for y-axis inversion\n",
    "        ax=ax,\n",
    "        cmap=\"coolwarm\",\n",
    "        center=0,\n",
    "        cbar=False,\n",
    "        vmin=-1.5,\n",
    "        vmax=1.5,\n",
    "    )\n",
    "    xs = np.arange(5)\n",
    "    ys = np.array([0, 2, 2, 0, 2])\n",
    "    # Adjust scatter y positions to match inverted y-axis\n",
    "    ax.scatter(xs + 0.5, (3 - ys) + 0.5, c=\"black\", s=2)\n",
    "    ax.set(\n",
    "        ylabel=f\"register {position}\",\n",
    "        xlabel=\"\",\n",
    "        aspect=.8,\n",
    "        ylim=(0, 4),   # 4 rows (ACGU): still needed for axis scaling, y inverted by data order\n",
    "        xlim=(0, 5),   # 5 columns (positions 1-5)\n",
    "    )\n",
    "    # Keep yticklabels in same logical order (e.g., ACGU mapped to 0,1,2,3 originally)\n",
    "    # But now since the DataFrame is inverted, flip the order of ticklabels\n",
    "    ax.set_yticks(np.arange(4)+0.5, labels=list('ACGU')[::-1], rotation=0, fontsize=8)\n",
    "    ax.set_xticks(np.arange(5)+0.5, labels=np.arange(1, 6), rotation=90, fontsize=8)\n",
    "    ax.tick_params(axis='x', length=0)\n",
    "    ax.tick_params(axis='y', length=0)\n",
    "    sns.despine(ax=ax, top=False, right=False)\n",
    "ax.set(xlabel=\"position in core\")\n",
    "fig.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cf2d94f",
   "metadata": {},
   "source": [
    "These heatmaps show that mutations away from the canonical AGGAG motif are deleterious across all registers, but also that their quantitative effects are very similar to each other.\n",
    "\n",
    "Next, we investigate whether the same applies to the pairwise coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "347c3b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_alleles = [f\"{p}{a}\" for p, a in product(range(5), alphabet)]\n",
    "theta_pw = theta_aligned.loc[theta_aligned[\"k\"] == 2, :].copy()\n",
    "theta_pw[\"pos_allele_1\"] = [\n",
    "    f\"{orbit[0]}{subseq[0]}\"\n",
    "    for orbit, subseq in theta_pw[[\"orbit\", \"subseq\"]].values\n",
    "]\n",
    "theta_pw[\"pos_allele_2\"] = [\n",
    "    f\"{orbit[1]}{subseq[1]}\"\n",
    "    for orbit, subseq in theta_pw[[\"orbit\", \"subseq\"]].values\n",
    "]\n",
    "\n",
    "theta_lclc_positions = {\n",
    "    position: pd.pivot(\n",
    "        theta_pw,\n",
    "        index=\"pos_allele_1\",\n",
    "        columns=\"pos_allele_2\",\n",
    "        values=position,\n",
    "    )\n",
    "    .reindex(pos_alleles)\n",
    "    .T.reindex(pos_alleles)\n",
    "    .T\n",
    "    for position in positions\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "acdce34b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x800 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(\n",
    "    5,\n",
    "    1,\n",
    "    figsize=(3, 8),\n",
    ")\n",
    "\n",
    "for ax, (position, theta_lclc) in zip(\n",
    "    axes, theta_lclc_positions.items()\n",
    "):\n",
    "    theta_lclc = theta_lclc.to_numpy().reshape(5, 4, 5, 4)\n",
    "    ax, cbar = heatmap_pairwise(\n",
    "        theta_lclc,\n",
    "        alphabet=alphabet,\n",
    "        seq=\"AGGAG\",\n",
    "        ax=ax,\n",
    "        clim=(-1.5, 1.5),\n",
    "        ccenter=0,\n",
    "        sepline_kwargs={\"lw\": 0.5, \"c\": \"black\"},\n",
    "        show_alphabet=True,\n",
    "        show_position=False,\n",
    "        alphabet_size=4,\n",
    "        cmap_size=\"4%\",\n",
    "    )\n",
    "    cbar.set_ticks([-1.0, 0.0, 1.0], \n",
    "                    labels=[\"-1\", \"0\", \"1\"], \n",
    "                    size=0,  # Turn off major tick length\n",
    "                    minor=False, \n",
    "                    fontsize=6)\n",
    "    # Set cbar tick_params to half the default length (default is 4.0, so use 2.0)\n",
    "    cbar.ax.tick_params(length=2)  # halve the tick length\n",
    "    cbar.set_label(r\"parameter\")\n",
    "    ax.set_xticklabels(np.arange(1, 6))\n",
    "    ax.tick_params(axis='x', length=0, rotation=90, pad=0.5)\n",
    "    ax.set(ylabel=f\"register {position}\")\n",
    "ax.set(xlabel=\"position in core\")\n",
    "\n",
    "fig.tight_layout(h_pad=0.5, w_pad=1.0, pad=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca1b0d57",
   "metadata": {},
   "source": [
    "These heatmaps show that the pairwise coefficients describing the interactions between pairs of core positions are also very similar across the different registers. Overall, these results support the hypothesis that the different peaks correspond to binding of the same element at different distances from the start codon.\n",
    "\n",
    "Finally, we quantitatively compare the values of these parameters across the different registers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7135492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting comparison of coefficients\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6b789b1660>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x325 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(\n",
    "    2,\n",
    "    5,\n",
    "    figsize=(7, 3.25),\n",
    "    sharey=True,\n",
    "    sharex=True\n",
    ")\n",
    "\n",
    "print(\"Plotting comparison of coefficients\")\n",
    "pos_pairs = list(combinations(positions, 2))\n",
    "theta_up_to_pw = theta_aligned.loc[theta_aligned[\"k\"] <= 2, :]\n",
    "idx = ~np.all(theta_up_to_pw[positions].values == 0.0, axis=1)\n",
    "theta_up_to_pw = theta_up_to_pw.loc[idx, :]\n",
    "\n",
    "lims = (-2.5, 2.5)\n",
    "sizes = [6, 4, 2]\n",
    "lables = [\"constant\", \"additive\", \"pairwise\"]\n",
    "palette = {0: \"C6\", 1: \"C8\", 2: \"C9\"}\n",
    "\n",
    "for (p, q), ax in zip(pos_pairs, axes.flatten()):           \n",
    "    for k, theta_k in theta_up_to_pw.groupby(\"k\"):\n",
    "        ax.scatter(\n",
    "            theta_k[p],\n",
    "            theta_k[q],\n",
    "            c=palette[k],\n",
    "            s=sizes[k],\n",
    "            lw=0,\n",
    "            label=lables[k],\n",
    "        )\n",
    "    ax.axline((0, 0), slope=1, lw=0.5, linestyle=\"--\", c=\"grey\", zorder=-100)\n",
    "    ax.set(aspect=\"equal\", xlim=lims, ylim=lims)\n",
    "    ax.set_xticks([-2,0,2])\n",
    "    ax.set_yticks([-2,0,2])\n",
    "    ax.tick_params(axis='both', which='major', length=2)\n",
    "    ax.tick_params(axis='both', which='minor', length=2)\n",
    "\n",
    "    ax.text(\n",
    "        0.05,\n",
    "        0.95,\n",
    "        f\"{p} vs {q}\",\n",
    "        fontsize=8,\n",
    "        transform=ax.transAxes,\n",
    "        ha=\"left\",\n",
    "        va=\"top\",\n",
    "    )\n",
    "    r = pearsonr(theta_up_to_pw[p], theta_up_to_pw[q])[0]\n",
    "    ax.text(\n",
    "        0.95,\n",
    "        0.05,\n",
    "        \"r={:.2f}\".format(r),\n",
    "        fontsize=8,\n",
    "        transform=ax.transAxes,\n",
    "        ha=\"right\",\n",
    "        va=\"bottom\",\n",
    "    )\n",
    "    \n",
    "fig.supylabel('2nd register', fontsize=10)\n",
    "fig.supxlabel(\"1st register\", fontsize=10)\n",
    "fig.tight_layout(h_pad=0.5, w_pad=0.5, pad=0.5)\n",
    "fig.subplots_adjust(top=0.95)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(\n",
    "    handles,\n",
    "    labels,\n",
    "    loc=\"upper center\",\n",
    "    ncol=3,\n",
    "    fontsize=8,\n",
    "    frameon=True,\n",
    "    handletextpad=0.2,           # reduce space between symbol and text\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gauge",
   "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": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}