{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e407ece",
   "metadata": {},
   "source": [
    "# Setting up the model and the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b256382f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlignmentParameters(min_score_v=0, min_score_j=0, max_error_d=100. left_v_cutoff=40)\n",
      "InferenceParameters(min_likelihood=0, min_ratio_likelihood=0, infer=false. store_best_event=true)\n"
     ]
    }
   ],
   "source": [
    "import righor\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# load the model\n",
    "model = righor.vdj.Model.load_model(\"../models/human_T_beta/model_params.txt\",\n",
    "\"../models/human_T_beta/model_marginals.txt\",\n",
    "\"../models/human_T_beta/V_gene_CDR3_anchors.csv\",\n",
    "\"../models/human_T_beta/J_gene_CDR3_anchors.csv\")\n",
    "\n",
    "\n",
    "# define parameters for the alignment and the inference\n",
    "align_params = righor.AlignmentParameters()\n",
    "infer_params = righor.InferenceParameters()\n",
    "\n",
    "# Default parameters favour accuracy rather than speed\n",
    "# generally no need to modify them\n",
    "print(align_params)\n",
    "print(infer_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0aec884a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "006d4327",
   "metadata": {},
   "source": [
    "## Evaluating a given sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4ddfef47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result:\n",
      "\tLikelihood: 3.24e-13, pgen: 0.00e0\n",
      "\tMost likely event:\n",
      "\t- CDR3 (nucleotides): TGCGCCAGCAGTGAGCGGACAGGGGGCCTAGCAATCAGCCCCAGCATTTT \n",
      "\t- CDR3 (amino acids):  \n",
      "\t- V name: TRBV10-1*02 \n",
      "\t- J name: TRBJ1-5*01 \n",
      "\t- likelihood ratio: 0.08366745966613477 \n",
      " \n"
     ]
    }
   ],
   "source": [
    "sequence = \"GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGGCGTGTCACCAGACTTGGAACCACAACAATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCCATTACTCATATGGTGTTCACGACACTAACAAAGGAGAAGTCTCAGATGGCTACAGTGTCTCTAGATCAAACACAGAGGACCTCCCCCTCACTCTGGAGTCTGCTGCCTCCTCCCAGACATCTGTATATTTCTGCGCCAGCAGTGAGCGGACAGGGGGCCTAGCAATCAGCCCCAGCATTTTGGTGATGGGACTCGACTCTCCATCCTAG\"\n",
    "\n",
    "# first align the sequence\n",
    "alignment = model.align_sequence(sequence, align_params)\n",
    "\n",
    "# then evaluate the alignment\n",
    "result = model.evaluate(alignment, infer_params)\n",
    "\n",
    "# Clean display of the result\n",
    "print(result.display(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea1b9367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([1.94021318e+00, 5.67251817e-02, 1.29175147e+00, 2.93673213e+00,\n",
       "       3.03304651e+00, 7.93925112e+00, 8.80454796e+00, 9.71969439e+00,\n",
       "       1.16137818e+01, 1.22176280e+01, 6.61989570e+00, 5.95615318e+00,\n",
       "       3.56701897e+00, 1.32475837e+00, 1.76946331e+00, 1.10985637e+00,\n",
       "       4.64892714e-01, 2.89894173e-01, 2.99503754e-01, 1.17364929e-02,\n",
       "       3.34552121e-02])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Representation of the probabilities of the events for that specific sequence\n",
    "fig, axes = plt.subplots(3, 3)\n",
    "\n",
    "for ax in axes.flatten():\n",
    "    ax.set_ylabel(\"Probabilities\")\n",
    "\n",
    "# Marginal probabilities of the V genes (for this specific sequence)\n",
    "pv = result.features.vdj.probas.sum(axis=(1,2))\n",
    "axes[0, 0].plot(pv, '.')\n",
    "axes[0, 0].set_xlabel(\"V gene\")\n",
    "\n",
    "# Marginal probabilities of the D genes\n",
    "pd = result.features.vdj.probas.sum(axis=(0,2))\n",
    "axes[0, 1].plot(pd, '.')\n",
    "axes[0, 1].set_xlabel(\"D gene\")\n",
    "\n",
    "# Marginal probabilities of the J genes\n",
    "pj = result.features.vdj.probas.sum(axis=(0,1))\n",
    "axes[0, 2].plot(pj, '.')\n",
    "axes[0, 2].set_xlabel(\"J gene\")\n",
    "\n",
    "# Marginal probabilities of the V deletion \n",
    "pdelv = result.features.delv.probas.sum(axis=(1))\n",
    "axes[1, 0].plot(pdelv, '.')\n",
    "axes[1, 0].set_xlabel(\"delV3'\")\n",
    "\n",
    "# Marginal probabilities of the D5 deletion \n",
    "pdeld5 = result.features.deld.probas.sum(axis=(1,2))\n",
    "axes[1, 1].plot(pdeld5, '.')\n",
    "axes[1, 1].set_xlabel(\"delD5'\")\n",
    "\n",
    "# Marginal probabilities of the D3 deletion \n",
    "pdeld3 = result.features.deld.probas.sum(axis=(0,2))\n",
    "axes[1, 2].plot(pdeld3, '.')\n",
    "axes[1, 2].set_xlabel(\"delD3'\")\n",
    "\n",
    "# Marginal probabilities of the J deletion \n",
    "pdelj = result.features.delj.probas.sum(axis=(1))\n",
    "axes[2, 0].plot(pdelj, '.')\n",
    "axes[2, 0].set_xlabel(\"delJ5'\")\n",
    "# Marginal probabilities of the insvd insertions\n",
    "pinsvd = result.features.insvd.length_distribution\n",
    "axes[2, 1].plot(pinsvd, '.')\n",
    "axes[2, 1].set_xlabel(\"insVD\")\n",
    "# Marginal probabilities of the insdj insertions\n",
    "pinsdj = result.features.insdj.length_distribution\n",
    "axes[2, 2].plot(pinsdj, '.')\n",
    "axes[2, 2].set_xlabel(\"insDJ\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Marginal probabilities of the deletion on V, 3'\n",
    "result.features.delv.probas.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d5eb874",
   "metadata": {},
   "source": [
    "## Generating sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "430b0b19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAAGCTGACATCTACCAGACCCCAAGATACCTTGCTATAGGGACAGGAAAGAAGATCACTCTGGAATGTTCTCTAACCATGGGCCATGAGAAAATGTACTGGTATCAACAAGATCCAGGAATGGAACTACACCTCATCCACTATTCCTATGGAGTTAATTCCACAGAGAAGGGAGATCTTTCCTCTGAGTCAACAGTCTCCAGAATAAGGACGGAGCATTTTCCCCTGACCCTGGAGTCTGCCAGGCCCTCACATACCTCTCAGTACCTCTGTGCCAGCAGTGAGGACAGGGACGTCACTGAAGCTTTCTTTGGACAAGGCACCAGACTCTCAGTTGTAG\n"
     ]
    }
   ],
   "source": [
    "seed = 42\n",
    "# or model.generator() if you don't want a seed\n",
    "generator = model.generator(seed) \n",
    "print(generator.generate(True).full_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc420ce9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAMDyCCwAAMDyCCwAAMDyCCwAAMDyCCwAAMDyCCwAAMDyCCwAAMDyCCwAAMDyCCwAAMDyCCwAAMDyuhVYCgsLlZiYKIfDIZfLpb17916yf2lpqVwulxwOh0aPHq3Nmzf79Vm3bp1uvPFGXXXVVRo5cqSysrLU2NjYnfIAAECICTqw7NixQ5mZmcrJyVFFRYXS0tI0Y8YMVVVVBex/4sQJzZw5U2lpaaqoqNDKlSu1aNEi7dy509vn+eef1/Lly/XII4+osrJSW7du1Y4dO7RixYruHxkAAAgZNsMwjGB2mDRpkiZOnKhNmzZ528aOHavZs2crPz/fr/+yZcu0e/duVVZWetsyMjL09ttvq6ysTJL04IMPqrKyUq+//rq3z5IlS/TWW29ddvSmg9vtVkxMjBoaGhQdHR3MIQEAAJNc6fk7qBGW5uZmlZeXKz093ac9PT1d+/fvD7hPWVmZX//p06fr4MGDamlpkSRNmTJF5eXleuuttyRJH3zwgYqKinTHHXd0WUtTU5PcbrfPBgAAQlNEMJ3r6+vl8XjkdDp92p1Op2prawPuU1tbG7B/a2ur6uvrdc011+iuu+7S2bNnNWXKFBmGodbWVv3bv/2bli9f3mUt+fn5+vnPfx5M+QAAoJ/q1qRbm83m89owDL+2y/Xv3P7GG28oLy9PhYWFOnTokHbt2qVXX31Vv/jFL7r8zBUrVqihocG7VVdXd+dQAABAPxDUCMuwYcMUHh7uN5pSV1fnN4rSIS4uLmD/iIgIDR06VJK0evVqzZ8/X/fdd58kafz48fr444/14x//WDk5OQoL889Vdrtddrs9mPIBAEA/FdQIS1RUlFwul4qLi33ai4uLlZqaGnCflJQUv/579uxRcnKyIiMjJUmffPKJXygJDw+XYRgKck4wAAAIQUFfEsrOztZvfvMbbdu2TZWVlcrKylJVVZUyMjIktV+qWbBggbd/RkaGTp06pezsbFVWVmrbtm3aunWrHn74YW+fWbNmadOmTdq+fbtOnDih4uJirV69Wt/61rcUHh7eA4cJAAD6s6AuCUnSvHnzdO7cOeXm5qqmpkZJSUkqKipSQkKCJKmmpsbnmSyJiYkqKipSVlaWNm7cqPj4eG3YsEFz5szx9lm1apVsNptWrVql06dPa/jw4Zo1a5by8vJ64BABAEB/F/RzWKyK57AAAND/9MpzWAAAAMxAYLmMOnejnig+pjo36xoBANpxbuh7BJZASvKl0gJJUt35Jq1//bjqzje1v1da0P4+AGDA8js3oNcRWAIJC5dK8ryhxau0oL09jDuXAADoS0HfJTQQ1E1YJMPdKGdJnoy/fyRpsow3HpeOb9QZ1xLZJixSrNlFAgD6VJ270TuicvR0g89PSYodYldstMOU2gYC7hIK4IniY1r/+nE9FL5LSyJfVJMRIbutVWta5upJz51afNsYZd1+Qw9VDgDoDzrODV3h3NA9V3r+JrAE0DlFj916ncLbWuQJi1Tlvf9XEikaAAaiz46wLN91RI/dOV5JI2IkcW7oris9f3NJKIDYaEf7P7rSAqmtpX2Epa1FSe8/LU1danZ5AAATeM8NnSSNiPEGFvQuJt125dMJtmdcS3Rj07M641oSeCIuAADodYywBNJxN9C0HNkmLNJiR5Vsk26Toh3t7RIjLQAwgMUOsWvxbWMUO8RudikDBoElkDaPNC1HmrpUsdLFSVQdIaXNY1ppAADzxUY7mGDbx5h0CwAATMNaQgAAIGQQWAAAgOURWAAAuBKd1pnzwzpzvY7AAgDAlWCdOVNxlxAAAFei407Rzo+36PQYDB530bsILAAAXKnOoeUvv5I8zYSVPsIlIQAAgjF1qRQe1R5WwqMIK32EwAIAQDBKCy6GFU8zS7b0EQILAABXqvOcldVn23+yzlyfYA4LAABXItAE20ATcdErCCwAAFyJTuvM+WCduT7BWkIAAMA0rCUEAABCBoEFAABYHoEFAABYHoEFAABYHoEFAABYHoEFAABYHoEFAABYHoEFAABYHoEFAABYHoEFAABYHoEFAABYHoEFAABYHoEFAABYXrcCS2FhoRITE+VwOORyubR3795L9i8tLZXL5ZLD4dDo0aO1efNmn/dvvfVW2Ww2v+2OO+7oTnkAACDEBB1YduzYoczMTOXk5KiiokJpaWmaMWOGqqqqAvY/ceKEZs6cqbS0NFVUVGjlypVatGiRdu7c6e2za9cu1dTUeLejR48qPDxc3/nOd7p/ZAAAIGTYDMMwgtlh0qRJmjhxojZt2uRtGzt2rGbPnq38/Hy//suWLdPu3btVWVnpbcvIyNDbb7+tsrKygL9j3bp1+tnPfqaamhoNGjToiupyu92KiYlRQ0ODoqOjgzkkAABgkis9fwc1wtLc3Kzy8nKlp6f7tKenp2v//v0B9ykrK/PrP336dB08eFAtLS0B99m6davuuuuuS4aVpqYmud1unw0AAISmoAJLfX29PB6PnE6nT7vT6VRtbW3AfWprawP2b21tVX19vV//t956S0ePHtV99913yVry8/MVExPj3UaOHBnMoQAAgH6kW5NubTabz2vDMPzaLtc/ULvUPrqSlJSkr33ta5esYcWKFWpoaPBu1dXVV1o+AADoZyKC6Txs2DCFh4f7jabU1dX5jaJ0iIuLC9g/IiJCQ4cO9Wn/5JNPtH37duXm5l62FrvdLrvdHkz5AACgnwpqhCUqKkoul0vFxcU+7cXFxUpNTQ24T0pKil//PXv2KDk5WZGRkT7tv//979XU1KR77rknmLIAAECIC/qSUHZ2tn7zm99o27ZtqqysVFZWlqqqqpSRkSGp/VLNggULvP0zMjJ06tQpZWdnq7KyUtu2bdPWrVv18MMP+3321q1bNXv2bL+RFwAAMLAFdUlIkubNm6dz584pNzdXNTU1SkpKUlFRkRISEiRJNTU1Ps9kSUxMVFFRkbKysrRx40bFx8drw4YNmjNnjs/nHjt2TG+++ab27NnzOQ8JAACEmqCfw2JVPIcFAID+p1eewwIAAGAGAgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8Agt81Lkb9UTxMdW5G80uBQAALwILfNSdb9L614+r7nyT2aUAAOBFYIFUki+VFgR+r7Sg/X0AAExEYIEutBhSSZ7OvJKro6cbJElHTzfozCu5Ukle+/sAAJgo6MUPEXqesc1VW8v/1ZLyNTrdckrSnTr98s/ljHxRa1rmKsw2V1lmFwkA6Fsl+VJYuDR1qf97pQVSm0eatqLPyiGwQN+bNEp149bqzKEELSlfowcjXpLd1qozriWaPnGxYofYzS4RANDXwsKlkjxJUt2ERXr+QJW+N2mUYis2tLdPy+nTcggsUGy0Q7HRDmnEz9RW8aTsbc1qC4uSc9bP5DS7OACAOTpGVkryZLgbtX6fS3c3bpfK17SHlUAjL72IwIKLSgsU1tasJiNC9rbm9iG/Pv4HCQCwkE/PAc6SPL1nj5C9vNWUsCIx6RYdSgvaJ9imLlPhlDJdSF3WPuTX1d1DAICQVudu1NHTDTp6/QPyhEXKbmuVJyxSR69/QEdPN/T587oYYYE3rGhajgZPXfrpBNuVkj3Ce/2SkRYAGFieP1Cl9a8f10Phu5QU2fLp6HuLXivM1pOeO7X4tjHKuv2GPquHwIJPZ3oHGOLreN3m6fuaAACm+t6kUbq7cbuc5S/qyJiFmnVksl4Zv09Ljm/UPV9PkG3SbX1aD4EFl74tjZEVABiQYis2eCfY2q5/QDrypmy3LpOuvVrOkjwp2tGn5wgCCwAA8Nd59P3Th4pKMm30ncACAAD8dRp9jx1i1+Lbxlx8LpcJo+8EFgAAcEmx0Y4+nWAbCLc1AwAAyyOwAAAAyyOwAAAAyyOwAAAAyyOwAAAAyyOwAAAAyyOwAAAAyyOwWElJfterI5cWtL8PAMAARGCxkrDw9tWRPxtaOlZTDgs3py4AAEzGk26tpONRxyV5F193hJVAqykDADBAEFisplNoaX2jQBFGC2EFADDgcUnIiqYuVVtYlCKMFrWFRRFWAAADHoHFikoLFNbWrCYjQmFtzV1PxAUAYIAgsFhInbtRZ17JlUrydGTMQt3Y9KyOjFkoleTpzCu5qnM3ml0iAACmILBYyPsv/kzO8jVa0zJXs45MliTNOjJZa1rmylm+Ru+/+DOTKwQAwBzdCiyFhYVKTEyUw+GQy+XS3r17L9m/tLRULpdLDodDo0eP1ubNm/36fPTRR1q4cKGuueYaORwOjR07VkVFRd0pr9+6JX6IzriWaPpP1uqxO8dLkh67c7ym/2StzriW6Jb4ISZXCACAOYK+S2jHjh3KzMxUYWGhJk+erKefflozZszQu+++q1GjRvn1P3HihGbOnKn7779fzz33nPbt26ef/OQnGj58uObMmSNJam5u1u23367Y2Fi9+OKLuvbaa1VdXa0hQwbWCXrQN1drkCRnp7akETFKGhEjjWB0BQAwcAUdWNauXat7771X9913nyRp3bp1eu2117Rp0ybl5/s/iXXz5s0aNWqU1q1bJ0kaO3asDh48qF//+tfewLJt2zb94x//0P79+xUZGSlJSkhI6O4xAQCAEBPUJaHm5maVl5crPT3dpz09PV379+8PuE9ZWZlf/+nTp+vgwYNqaWmRJO3evVspKSlauHChnE6nkpKS9Oijj8rj8XRZS1NTk9xut88WSmKH2LX4tjGKHWI3uxSgX6tzN+qJ4mNMWgf6uaACS319vTwej5xOp0+70+lUbW1twH1qa2sD9m9tbVV9fb0k6YMPPtCLL74oj8ejoqIirVq1SmvWrFFeXl6XteTn5ysmJsa7jRw5MphDsbzYaIeybr9BsdEOs0sB+p9O63LVnW/S+tePq+58U/t7rMsF9EvdmnRrs9l8XhuG4dd2uf6d29va2hQbG6stW7bI5XLprrvuUk5OjjZt2tTlZ65YsUINDQ3erbq6ujuHAiAUsS4XEHKCmsMybNgwhYeH+42m1NXV+Y2idIiLiwvYPyIiQkOHDpUkXXPNNYqMjFR4+MUvkbFjx6q2tlbNzc2Kiory+1y73S67ncslAPzVTVgkw90oZ0mejL9/JGmyjDcel45v1BnXEtkmLFKs2UWi+0ry20NnoKeAlxZIbR5p2oq+rwu9KqgRlqioKLlcLhUXF/u0FxcXKzU1NeA+KSkpfv337Nmj5ORk7wTbyZMn6/3331dbW5u3z7Fjx3TNNdcEDCsAcCnPH6jSpH0urWmZq/HHN+o9+wKNP75Ra1rmatI+l54/UGV2ifg8GEEbmIwgbd++3YiMjDS2bt1qvPvuu0ZmZqYxaNAg4+TJk4ZhGMby5cuN+fPne/t/8MEHxhe+8AUjKyvLePfdd42tW7cakZGRxosvvujtU1VVZQwePNh48MEHjffee8949dVXjdjYWOOXv/zlFdfV0NBgSDIaGhqCPSQAIeZMw/8zjvz9I+PI3z8yWn8+1DAeiTZafz7U23am4f+ZXSI+rzceN4xHott/BnqNfuNKz99B39Y8b948nTt3Trm5uaqpqVFSUpKKioq8tyHX1NSoqurif70kJiaqqKhIWVlZ2rhxo+Lj47VhwwbvLc2SNHLkSO3Zs0dZWVn68pe/rBEjRmjx4sVatmzZ5w5kAAae2GhH+4T10gKprUVNRoTsbS1Kev9pFhMNFaxsP+DYDOPTGbD9nNvtVkxMjBoaGhQdHW12OQDM9unlgTOuJZq0z6UDk8vlLF/DSS3EtOUOV1hbs9rCohT2s7Nml4NuuNLzd9AjLABgeR1zGablyDZhkRY7qmSbdJsU7WhvlwgtoaDTyvb2jpXt+XsNWQQWAKGnzeMdSYmVlHX7De3tHSeztq4fSgnrq3M3yigtkLN8jY6MWahZRybrlfH7NL4kT2fcjbJNXcozrEIQl4QAAP3K/m1LlVr1tNa0zNWTnju97Q+F79KSyBe1f9QDSv1RwSU+AVbCJSEAQEi6JX6IzgxfoukTF2vE6QYt33VEj905XkkjpujMoQTdEtn1g0zRfxFYAAD9CivbD0zdejQ/AABAXyKwAAD6LVa2Hzi4JAQA6Lc6VrZH6GOEBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQDQPSX5UmmBJKnO3agnio+pzt3Y/l5pQfv7QA8hsAAAuicsXCrJk0oLVHe+SetfP666802fhpW89vfx+XUKhn4GUDCMMLsAAEA/NXVp+8+SPA13N0pyafih9VL5GmlazsX38fl0BEPJ93/TjmA4LcecuvoYgQUA0C117kbVXf+Ahrsb5Sxfo/fsEbKXt+qMa4nOXv+AYt2Nio12mF1m/9cpGHpfdw4rAyQYElgAAN3y/IEqrX/9uCRXe1ixtarJiNCkfS5p35tafNsYZd1+g9llhobOoeUvv5I8zQMqrEgEFgBAN31v0ijdPs6p4YfWy17eHlbstlYdmFyusxMXK3aI3ewSQ8vUpRfDSnjUgAorUjcn3RYWFioxMVEOh0Mul0t79+69ZP/S0lK5XC45HA6NHj1amzdv9nn/t7/9rWw2m9/W2NjYnfIAAH0gNtqhpPeflrN8jc64lujGpmd1xrVEzvI1Snr/aS4H9bTSgothxdPc9UTcEBV0YNmxY4cyMzOVk5OjiooKpaWlacaMGaqqqgrY/8SJE5o5c6bS0tJUUVGhlStXatGiRdq5c6dPv+joaNXU1PhsDgf/2AHAsjrNozg7cbEktf+cluO9ewg9pPOcldVnB+T/xkFfElq7dq3uvfde3XfffZKkdevW6bXXXtOmTZuUn+9/a9XmzZs1atQorVu3TpI0duxYHTx4UL/+9a81Z84cbz+bzaa4uLhuHgYAoM+1ebzzKGLdjVp825j2y0AdlyraPObWFyoCTbANNBE3xAUVWJqbm1VeXq7ly5f7tKenp2v//v0B9ykrK1N6erpP2/Tp07V161a1tLQoMjJSknThwgUlJCTI4/HoK1/5in7xi19owoQJXdbS1NSkpqYm72u32x3MoQAAPq9pK7x/jI12+E6wHQAn0D7TKRj6GGDBMKhLQvX19fJ4PHI6nT7tTqdTtbW1Afepra0N2L+1tVX19fWSpJtuukm//e1vtXv3br3wwgtyOByaPHmyjh8/3mUt+fn5iomJ8W4jR44M5lAAAOgfpq3whhO/JwpPXeoTHENZtybd2mw2n9eGYfi1Xa5/5/avf/3ruueee3TLLbcoLS1Nv//973XDDTfoySef7PIzV6xYoYaGBu9WXV3dnUMBBhy/LzwA/YbPE4UHmKAuCQ0bNkzh4eF+oyl1dXV+oygd4uLiAvaPiIjQ0KFDA+4TFhamr371q5ccYbHb7bLbuWUOCFbHF97t45zcxQGg3whqhCUqKkoul0vFxcU+7cXFxUpNTQ24T0pKil//PXv2KDk52Tt/5bMMw9Dhw4d1zTXXBFMeAAAhp87dqKOnG7ybJJ/XA2W0NOi7hLKzszV//nwlJycrJSVFW7ZsUVVVlTIyMiS1X6o5ffq0nn32WUlSRkaGnnrqKWVnZ+v+++9XWVmZtm7dqhdeeMH7mT//+c/19a9/XWPGjJHb7daGDRt0+PBhbdy4sYcOExjY6tyN3iHkzl94HWKH2BltASzq4hOFL1q+64j3zwPlicJBB5Z58+bp3Llzys3NVU1NjZKSklRUVKSEhARJUk1Njc8zWRITE1VUVKSsrCxt3LhR8fHx2rBhg88tzR999JF+/OMfq7a2VjExMZowYYL+8pe/6Gtf+1oPHCIA077wSvLbF24LdMdIacGndz8MjAmDQHd1PFFYav8PjeW7juixO8craUSMJA2YJwrbjI4ZsP2c2+1WTEyMGhoaFB0dbXY5gKV8/Kdf6EKLobMTF/t94Q0/tF6DI20a9M3VPf+Lu1qgbQAu3Ab0hKOnG/R/nnxTrz40xRtY+rsrPX+zlhAwAAy6yq5B/50nZ7RDuv4BSVLSiBglvf+0VL6m95anZ5VZAD2EwAIMBJ2Cw3B3oySXhh9afzGs9GZwYJVZoMfEDrFffKLwAMMlIWAg+XR0o9UWqQijpU+Dg/GL4bJ5mmWER8m2+myf/M4+x5wdIGhXev7u1oPjAPRTU5dK4VHtYaUvl6cvLZDN06wmI0K2UF5lNiw88IJ0HZfBwsLNqQsIAQQWYCAxY3n6T0/WZ1xLdGPTszrjWhK6q8xOXeq/ii5zdoAewRwWYKD47Imz47XUayfSC3se1eD9j+uMa4lKnD+QdEQlzh9omktyluTpQlOrBqev7JXfbRrm7AC9gsACDAQmLU9/pPof2t8yV0/uc0lqf+5L+/NfXHoofK5Sq/+hlB7/rRYwdenFsNKXl96AEEZgAQYCk5anv+47v9SQ802arkAPvJoSunc6BLr0RmgBPhcCCzAQXOrOlF48kcZGO/we+Z80IiZkHngV0KejWRdSl+kZ21zdb7yowb186Q0YCJh0CwA9pdOlt5M3L9T614/r5M0L/SfiAggaIywA+sSAeOBV50tvnRaX7O1Lb8BAQGCBJdS5G/X8gSp9b9IoVg0OUbHRjpBfUbbOldW+KvbpBv9Vsa9/oH1VbBPrA/ozAgvM0+mpoHXnm7T+9eO6fZyzPbDwVFD0Q6atig0MAAQWmKfjqaCSd0E+Sb634AL9yPcmjdLt45ySAt0VpdC+HAb0MgILTFM3YZEMd6OcJXky/v6RpMky3nhcOr5RZ1xLZJuwiOFz9CsD8q4ooI9wlxBM8/yBKk3a59Kalrkaf3yj3rMv0PjjG7WmZa4m7XPp+QNVZpcIALAIRlhgmovD51Pk2fqy7G0t8oRFavpP1mq6GD5H/zYg7ooC+hCBBabxDp+XFkhtLWoyImRva1HS+0/zgC30ewPhriigL3FJCOYaSCv5AgC6jREWmKfT3UC2CYu02FEl26TbpGhHr68iDADoXwgsME+np4LGSheHz3kqKADgM2yGYRhmF9ET3G63YmJi1NDQoOjoaLPLAQAAV+BKz9/MYQEAAJZHYAEAAJZHYMGAV+du1BPFx1TnbjS7FABAFwgsGPA6Fl6sO99kdikAgC4QWAAAgOVxWzMGpI//9AtdaDF0duJiHT3dIEnen8MPrdfgSJsGfXO1mSUCADohsGBAevvD80qtelrP/fcpPem5U5K0fNcRPRS+S0siX9T+UQ8o1eQaAQAXcUkIA9L1c3N1xrVESyJf1Cvj90mSXhm/T0siX9QZ1xJdPzfX5AoBAJ0xwoIBKTbaIc36mRTt0PiSPL1nf1r2463StBw5WQ4AACyHERYMbFOXqi0sSnZbq9rColi7CAAsisCCga20QGFtzWq1RSqsrZlVogHAoggsGLg6rRYd8Uh9+0KMJXmEFgCwIOawYGDqFFa8l4E6fpbk+b4GAJiOwIKBqc3jG1Y6dLxu8/R9TQCALtkMwzDMLqInXOny1AAAwDqu9PzdrTkshYWFSkxMlMPhkMvl0t69ey/Zv7S0VC6XSw6HQ6NHj9bmzZu77Lt9+3bZbDbNnj27O6UBAIAQFHRg2bFjhzIzM5WTk6OKigqlpaVpxowZqqqqCtj/xIkTmjlzptLS0lRRUaGVK1dq0aJF2rlzp1/fU6dO6eGHH1ZaWlrwRwIAAEJW0JeEJk2apIkTJ2rTpk3etrFjx2r27NnKz8/3679s2TLt3r1blZWV3raMjAy9/fbbKisr87Z5PB5NnTpVP/zhD7V371599NFHeumll664Li4JAQDQ//TKJaHm5maVl5crPT3dpz09PV379+8PuE9ZWZlf/+nTp+vgwYNqaWnxtuXm5mr48OG69957r6iWpqYmud1unw0AAISmoAJLfX29PB6PnE6nT7vT6VRtbW3AfWprawP2b21tVX19vSRp37592rp1q5555pkrriU/P18xMTHebeTIkcEcCgAA6Ee6NenWZrP5vDYMw6/tcv072s+fP6977rlHzzzzjIYNG3bFNaxYsUINDQ3erbq6OogjAAAA/UlQz2EZNmyYwsPD/UZT6urq/EZROsTFxQXsHxERoaFDh+qdd97RyZMnNWvWLO/7bW1t7cVFROi9997Tdddd5/e5drtddrs9mPIBAEA/FdQIS1RUlFwul4qLi33ai4uLlZqaGnCflJQUv/579uxRcnKyIiMjddNNN+nIkSM6fPiwd/vWt76ladOm6fDhw1zqAQAAwT/pNjs7W/Pnz1dycrJSUlK0ZcsWVVVVKSMjQ1L7pZrTp0/r2WefldR+R9BTTz2l7Oxs3X///SorK9PWrVv1wgsvSJIcDoeSkpJ8fsfVV18tSX7tAABgYAo6sMybN0/nzp1Tbm6uampqlJSUpKKiIiUkJEiSampqfJ7JkpiYqKKiImVlZWnjxo2Kj4/Xhg0bNGfOnJ47CgAAENJC5tH8DQ0Nuvrqq1VdXc1zWAAA6CfcbrdGjhypjz76SDExMV32C5nFD8+fPy9JzHkBAKAfOn/+/CUDS8iMsLS1tenDDz/UkCFDLnmLdbA6kt9AGbkZaMcrDbxj5nhDG8cb2kLxeA3D0Pnz5xUfH6+wsK7vBQqZEZawsDBde+21vfb50dHRIfOP40oMtOOVBt4xc7yhjeMNbaF2vJcaWenQrQfHAQAA9CUCCwAAsDwCy2XY7XY98sgjA+apugPteKWBd8wcb2jjeEPbQDvezkJm0i0AAAhdjLAAAADLI7AAAADLI7AAAADLI7AAAADLI7BcRmFhoRITE+VwOORyubR3716zS+oV+fn5+upXv6ohQ4YoNjZWs2fP1nvvvWd2WX0mPz9fNptNmZmZZpfSa06fPq177rlHQ4cO1Re+8AV95StfUXl5udll9YrW1latWrVKiYmJuuqqqzR69Gjl5uaqra3N7NJ6zF/+8hfNmjVL8fHxstlseumll3zeNwxD//7v/674+HhdddVVuvXWW/XOO++YU2wPuNTxtrS0aNmyZRo/frwGDRqk+Ph4LViwQB9++KF5BX9Ol/v77eyBBx6QzWbTunXr+qw+MxBYLmHHjh3KzMxUTk6OKioqlJaWphkzZvisRh0qSktLtXDhQv33f/+3iouL1draqvT0dH388cdml9br/vrXv2rLli368pe/bHYpveZ///d/NXnyZEVGRuqPf/yj3n33Xa1Zs0ZXX3212aX1iscff1ybN2/WU089pcrKShUUFOhXv/qVnnzySbNL6zEff/yxbrnlFj311FMB3y8oKNDatWv11FNP6a9//avi4uJ0++23e9dd628udbyffPKJDh06pNWrV+vQoUPatWuXjh07pm9961smVNozLvf32+Gll17SgQMHFB8f30eVmchAl772ta8ZGRkZPm033XSTsXz5cpMq6jt1dXWGJKO0tNTsUnrV+fPnjTFjxhjFxcXG1KlTjcWLF5tdUq9YtmyZMWXKFLPL6DN33HGH8aMf/cin7c477zTuuecekyrqXZKMP/zhD97XbW1tRlxcnPHYY4952xobG42YmBhj8+bNJlTYsz57vIG89dZbhiTj1KlTfVNUL+rqeP/+978bI0aMMI4ePWokJCQYTzzxRJ/X1pcYYelCc3OzysvLlZ6e7tOenp6u/fv3m1RV32loaJAkffGLXzS5kt61cOFC3XHHHfrnf/5ns0vpVbt371ZycrK+853vKDY2VhMmTNAzzzxjdlm9ZsqUKXr99dd17NgxSdLbb7+tN998UzNnzjS5sr5x4sQJ1dbW+nx/2e12TZ06dUB8f0nt32E2my1kRxHb2to0f/58/fSnP9XNN99sdjl9ImQWP+xp9fX18ng8cjqdPu1Op1O1tbUmVdU3DMNQdna2pkyZoqSkJLPL6TXbt2/XoUOH9Ne//tXsUnrdBx98oE2bNik7O1srV67UW2+9pUWLFslut2vBggVml9fjli1bpoaGBt10000KDw+Xx+NRXl6e/vVf/9Xs0vpEx3dUoO+vU6dOmVFSn2psbNTy5ct19913h9QCgZ09/vjjioiI0KJFi8wupc8QWC7DZrP5vDYMw68t1Dz44IP629/+pjfffNPsUnpNdXW1Fi9erD179sjhcJhdTq9ra2tTcnKyHn30UUnShAkT9M4772jTpk0hGVh27Nih5557Tr/73e9088036/Dhw8rMzFR8fLy+//3vm11enxmI318tLS2666671NbWpsLCQrPL6RXl5eVav369Dh06FPJ/n51xSagLw4YNU3h4uN9oSl1dnd9/tYSShx56SLt371ZJSYmuvfZas8vpNeXl5aqrq5PL5VJERIQiIiJUWlqqDRs2KCIiQh6Px+wSe9Q111yjcePG+bSNHTs2JCeQS9JPf/pTLV++XHfddZfGjx+v+fPnKysrS/n5+WaX1ifi4uIkacB9f7W0tOi73/2uTpw4oeLi4pAdXdm7d6/q6uo0atQo7/fXqVOntGTJEn3pS18yu7xeQ2DpQlRUlFwul4qLi33ai4uLlZqaalJVvccwDD344IPatWuX/uu//kuJiYlml9SrbrvtNh05ckSHDx/2bsnJyfre976nw4cPKzw83OwSe9TkyZP9blM/duyYEhISTKqod33yyScKC/P9egsPDw+p25ovJTExUXFxcT7fX83NzSotLQ3J7y/pYlg5fvy4/vznP2vo0KFml9Rr5s+fr7/97W8+31/x8fH66U9/qtdee83s8noNl4QuITs7W/Pnz1dycrJSUlK0ZcsWVVVVKSMjw+zSetzChQv1u9/9Ti+//LKGDBni/S+zmJgYXXXVVSZX1/OGDBniNz9n0KBBGjp0aEjO28nKylJqaqoeffRRffe739Vbb72lLVu2aMuWLWaX1itmzZqlvLw8jRo1SjfffLMqKiq0du1a/ehHPzK7tB5z4cIFvf/++97XJ06c0OHDh/XFL35Ro0aNUmZmph599FGNGTNGY8aM0aOPPqovfOELuvvuu02suvsudbzx8fGaO3euDh06pFdffVUej8f7HfbFL35RUVFRZpXdbZf7+/1sIIuMjFRcXJxuvPHGvi6175h7k5L1bdy40UhISDCioqKMiRMnhuxtvpICbv/xH/9hdml9JpRvazYMw3jllVeMpKQkw263GzfddJOxZcsWs0vqNW6321i8eLExatQow+FwGKNHjzZycnKMpqYms0vrMSUlJQH/P/v973/fMIz2W5sfeeQRIy4uzrDb7cY3vvEN48iRI+YW/Tlc6nhPnDjR5XdYSUmJ2aV3y+X+fj9rINzWbDMMw+ijbAQAANAtzGEBAACWR2ABAACWR2ABAACWR2ABAACWR2ABAACWR2ABAACWR2ABAACWR2ABAACWR2ABAACWR2ABAACWR2ABAACWR2ABAACW9/8BW1cUfjd956IAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Sanity check, do the generated values follow the distribution of the models ?\n",
    "\n",
    "lst = []\n",
    "for _ in range(10000):\n",
    "    lst += [len(generator.generate(False).recombination_event.insdj.get_string())]\n",
    "dct = Counter(lst)\n",
    "result_gen = np.array([dct[ii] if ii in dct else 0 for ii in range(0, max(lst)+1)])\n",
    "result_gen = result_gen / result_gen.sum()\n",
    "plt.plot(model.p_ins_dj, '+')\n",
    "plt.plot(result_gen, 'x')\n",
    "plt.show()\n",
    "\n",
    "lst = []\n",
    "for _ in range(10000):\n",
    "    lst += [len(generator.generate(False).recombination_event.insvd.get_string())]\n",
    "dct = Counter(lst)\n",
    "result_gen = np.array([dct[ii] if ii in dct else 0 for ii in range(0, max(lst)+1) ])\n",
    "result_gen = result_gen / result_gen.sum()\n",
    "plt.plot(model.p_ins_vd, '+')\n",
    "plt.plot(result_gen, 'x')\n",
    "plt.show()\n",
    "\n",
    "lst = []\n",
    "for _ in range(10000):\n",
    "    lst += [generator.generate(False).recombination_event.delv]\n",
    "dct = Counter(lst)\n",
    "result_gen = np.array([dct[ii] if ii in dct else 0 for ii in range(0, max(lst)+1)])\n",
    "result_gen = result_gen / result_gen.sum()\n",
    "plt.plot((model.p_del_v_given_v @ model.p_v)/((model.p_del_v_given_v @ model.p_v)).sum(), '+')\n",
    "plt.plot(result_gen, 'x')\n",
    "plt.show()\n",
    "\n",
    "lst = []\n",
    "for _ in range(10000):\n",
    "    lst += [generator.generate(False).recombination_event.delj]\n",
    "dct = Counter(lst)\n",
    "result_gen = np.array([dct[ii] if ii in dct else 0 for ii in range(0, max(lst)+1)])\n",
    "result_gen = result_gen / result_gen.sum()\n",
    "plt.plot((model.p_del_j_given_j @ model.p_dj.sum(axis=0))/(model.p_del_j_given_j @ model.p_dj.sum(axis=0)).sum(), '+')\n",
    "plt.plot(result_gen, 'x')\n",
    "plt.show()\n",
    "\n",
    "lst = []\n",
    "for _ in range(100000):\n",
    "    lst += [generator.generate(False).recombination_event.deld5]\n",
    "dct = Counter(lst)\n",
    "result_gen = np.array([dct[ii] if ii in dct else 0 for ii in range(0, max(lst)+1)])\n",
    "result_gen = result_gen / result_gen.sum()\n",
    "plt.plot((model.p_del_d5_del_d3 @ model.p_dj.sum(axis=1)).sum(axis=(1))/(model.p_del_d5_del_d3 @ model.p_dj.sum(axis=1)).sum(), '+')\n",
    "plt.plot(result_gen, 'x')\n",
    "plt.show()\n",
    "\n",
    "lst = []\n",
    "for _ in range(10000):\n",
    "    lst += [generator.generate(False).recombination_event.v_index]\n",
    "dct = Counter(lst)\n",
    "result_gen = np.array([dct[ii] if ii in dct else 0 for ii in range(0, max(lst)+1)])\n",
    "result_gen = result_gen / result_gen.sum()\n",
    "plt.plot(model.p_v, '+')\n",
    "plt.plot(result_gen, 'x')\n",
    "plt.show()\n",
    "\n",
    "lst = []\n",
    "for _ in range(10000):\n",
    "    lst += [generator.generate(False).recombination_event.j_index]\n",
    "dct = Counter(lst)\n",
    "result_gen = np.array([dct[ii] if ii in dct else 0 for ii in range(0, max(lst)+1)])\n",
    "result_gen = result_gen / result_gen.sum()\n",
    "plt.plot(model.p_dj.sum(axis=0), '+')\n",
    "plt.plot(result_gen, 'x')\n",
    "plt.show()\n",
    "\n",
    "lst = []\n",
    "for _ in range(10000):\n",
    "    lst += [generator.generate(False).recombination_event.d_index]\n",
    "dct = Counter(lst)\n",
    "result_gen = np.array([dct[ii] if ii in dct else 0 for ii in range(0, max(lst)+1)])\n",
    "result_gen = result_gen / result_gen.sum()\n",
    "plt.plot(model.p_dj.sum(axis=1), '+')\n",
    "plt.plot(result_gen, 'x')\n",
    "plt.show()\n",
    "\n",
    "lst = []  ### deal with this bug\n",
    "lst2 = []\n",
    "for _ in range(10000):\n",
    "    s = generator.generate(False).recombination_event.insvd.get_string()\n",
    "    lst += [(a, b) for a, b in zip(s, s[1:])]\n",
    "    lst2 += [b for b in s[:-1]]\n",
    "dct = Counter(lst)\n",
    "dct2 = {a:Counter(lst2)[a]/sum(Counter(lst2).values()) for a in Counter(lst2)}\n",
    "dct = {a: dct[a]/dct2[a[0]] for a in dct}\n",
    "dct = {a: dct[a]/sum(dct.values()) for a in dct}\n",
    "\n",
    "result_gen = result_gen / result_gen.sum()\n",
    "plt.plot(model.markov_coefficients_vd.flatten()/model.markov_coefficients_vd.sum(), '+')\n",
    "plt.plot([dct[a] for a in [('A', 'A'),('A', 'C'),('A', 'G'),('A', 'T'),\n",
    "                           ('C', 'A'),('C', 'C'),('C', 'G'),('C', 'T'),\n",
    "                           ('G', 'A'),('G', 'C'),('G', 'G'),('G', 'T'),\n",
    "                           ('T', 'A'),('T', 'C'),('T', 'G'),('T', 'T')]], 'x')\n",
    "plt.show()\n",
    "\n",
    "lst = []  ### deal with this bug\n",
    "lst2 = []\n",
    "for _ in range(10000):\n",
    "    s = generator.generate(False).recombination_event.insdj.get_string()\n",
    "    # reversed direction\n",
    "    lst += [(b, a) for a, b in zip(s, s[1:])]\n",
    "    lst2 += [b for b in s[1:]]\n",
    "dct = Counter(lst)\n",
    "dct2 = {a:Counter(lst2)[a]/sum(Counter(lst2).values()) for a in Counter(lst2)}\n",
    "dct = {a: dct[a]/dct2[a[0]] for a in dct}\n",
    "dct = {a: dct[a]/sum(dct.values()) for a in dct}\n",
    "\n",
    "result_gen = result_gen / result_gen.sum()\n",
    "plt.plot(model.markov_coefficients_dj.flatten()/model.markov_coefficients_dj.sum(), '+')\n",
    "plt.plot([dct[a] for a in [('A', 'A'),('A', 'C'),('A', 'G'),('A', 'T'),\n",
    "                           ('C', 'A'),('C', 'C'),('C', 'G'),('C', 'T'),\n",
    "                           ('G', 'A'),('G', 'C'),('G', 'G'),('G', 'T'),\n",
    "                           ('T', 'A'),('T', 'C'),('T', 'G'),('T', 'T')]], 'x')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9ded95",
   "metadata": {},
   "source": [
    "## Inferring a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "52e20a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an uniform model with the same V/D/J genes & range for deletions\n",
    "uniform_model = model.uniform()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "59da6b55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generate...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [00:00<00:00, 121603.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Align...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [06:00<00:00, 27.77it/s]\n"
     ]
    }
   ],
   "source": [
    "# we're going to re-infer the model starting from the uniform_model\n",
    "\n",
    "seed = 42\n",
    "print(\"Generate...\")\n",
    "generator = model.generator(seed)\n",
    "sequences = [generator.generate(True).full_seq\n",
    "             for a in tqdm(range(10000))]\n",
    "\n",
    "print(\"Align...\")\n",
    "# first align the sequences\n",
    "alignments = [uniform_model.align_sequence(s, align_params) \n",
    "              for s in tqdm(sequences)]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f81d3ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Infer...\")\n",
    "models = {}\n",
    "for rd in range(5):\n",
    "    print(rd, end=\" \")\n",
    "    models[rd] = uniform_model.copy()\n",
    "    uniform_model.infer(alignments, infer_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab3e0173",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'models' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodels\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'models' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9173bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import numpy as np\n",
    "lst = []\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "pinsdj = model.p_ins_dj\n",
    "for ii in range(5):\n",
    "    pinsdj_inf = models[ii].p_ins_dj\n",
    "    ax.plot(pinsdj_inf, '.', label = f\"round {ii}\", alpha=0.2)\n",
    "ax.plot(pinsdj, '+', label=\"real\")\n",
    "\n",
    "ax.set_xlabel(\"insDJ\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "41e8ae5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "import numpy as np\n",
    "lst = []\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "pj = model.p_vdj.sum(axis=(0,1))\n",
    "for ii in range(5):\n",
    "    pj_inf = models[ii].p_vdj.sum(axis=(0,1))\n",
    "    ax.plot(pj_inf, '.', label = f\"round {ii}\", alpha=0.8)\n",
    "ax.plot(pj, '+', label=\"real\")\n",
    "\n",
    "ax.set_xlabel(\"J\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c51813e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models[1].p_v[32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89c24375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.71871807e-01, 2.25406620e-01, 7.08954802e-02, 2.19546126e-02,\n",
       "       6.81447692e-03, 2.10987010e-03, 6.53526204e-04, 2.02578706e-04,\n",
       "       6.28239414e-05, 1.94532147e-05, 6.03622810e-06, 1.87486937e-06,\n",
       "       5.79899843e-07, 1.79752881e-07, 5.55560326e-08, 1.71873986e-08,\n",
       "       5.31158250e-09, 1.64278396e-09, 5.08412434e-10, 1.57003166e-10,\n",
       "       4.84564612e-11])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models[1].p_del_v_given_v[:, 32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5afbbd32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8856"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pympler import asizeof\n",
    "asizeof.asizeof(alignments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "71d8dd7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pympler\n",
      "  Downloading Pympler-1.0.1-py3-none-any.whl (164 kB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m164.8/164.8 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: pympler\n",
      "Successfully installed pympler-1.0.1\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b4d2ef0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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