{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ad1521ca",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Spike Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "472a19d2-7d4c-4557-a61c-dfb69e526667",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "This notebook contains the entire empirical analysis on the three spike homologs as seen in our manuscript _Jointly modeling deep mutational scans identifies shifted mutational effects among SARS-CoV-2 spike homologs_."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e30c6e6a-4f17-4394-a819-1313f178da4e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Computational platform environment\n",
    "\n",
    "This section shows the attributes of the machine which ran this notebook, as well as imports the necessary dependencies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68baf77-4b26-4103-832b-784ae38aa147",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Operating system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f4fa571-9ce4-4f77-8006-0f39d1e0bd22",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAME=\"Ubuntu\"\n",
      "VERSION=\"18.04.6 LTS (Bionic Beaver)\"\n"
     ]
    }
   ],
   "source": [
    "! grep -E '^(VERSION|NAME)=' /etc/os-release"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "496d5fc4-a7c8-449f-b76c-b6c8bd5df07a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Hardware (Processors and RAM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a88613cd-86ec-44ac-b22e-a6a9909b914b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: you should run this program as super-user.\n",
      "PCI (sysfs)  \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  *-memory                  \n",
      "       description: System memory\n",
      "       physical id: 0\n",
      "       size: 996GiB\n",
      "  *-cpu\n",
      "       product: AMD EPYC 75F3 32-Core Processor\n",
      "       vendor: Advanced Micro Devices [AMD]\n",
      "       physical id: 1\n",
      "       bus info: cpu@0\n",
      "       size: 3855MHz\n",
      "       width: 64 bits\n",
      "       capabilities: fpu fpu_exception wp vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp x86-64 constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca cpufreq\n",
      "WARNING: output may be incomplete or inaccurate, you should run this program as super-user.\n"
     ]
    }
   ],
   "source": [
    "! lshw -class memory -class processor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef7ba5a7-7605-4375-9af6-4907000cdb57",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "GPU's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a51e6af0-304a-4e2d-8914-54b9667f901d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_VISIBLE_DEVICES=0\n",
      "GPU 0: NVIDIA A100 80GB PCIe (UUID: GPU-414cb1bd-372a-4926-b140-b734687c927f)\n",
      "GPU 1: NVIDIA A100 80GB PCIe (UUID: GPU-e54c2054-5be3-ebd0-e22e-b98441ec664f)\n"
     ]
    }
   ],
   "source": [
    "%env CUDA_VISIBLE_DEVICES=0\n",
    "! nvidia-smi -L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "53fa951c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# built-in libraries\n",
    "import os\n",
    "import sys\n",
    "from itertools import combinations\n",
    "from collections import defaultdict\n",
    "import time\n",
    "import pprint\n",
    "import copy\n",
    "import pickle\n",
    "from functools import reduce\n",
    "\n",
    "# external dependencies\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy.stats import pearsonr\n",
    "from matplotlib.lines import Line2D\n",
    "import matplotlib.lines as mlines\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.transforms import (\n",
    "    Bbox, TransformedBbox, blended_transform_factory)\n",
    "from mpl_toolkits.axes_grid1.inset_locator import (\n",
    "    BboxPatch, BboxConnector, BboxConnectorPatch)\n",
    "import matplotlib.patches as patches\n",
    "import matplotlib.colors as colors\n",
    "import numpy as np\n",
    "import scipy\n",
    "from tqdm.notebook import tqdm\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import shutil\n",
    "from Bio.PDB.PDBParser import PDBParser\n",
    "from Bio.PDB.PDBList import PDBList\n",
    "from Bio.PDB.DSSP import DSSP\n",
    "from Bio import SeqIO\n",
    "import multidms\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b7f070-1910-4d3d-a37d-8cc202bc5ed3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "This was notebook was run with the following `multidms` version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e1626de0-261d-49be-be47-a920ad38a788",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.0'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multidms.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66cd6595",
   "metadata": {},
   "source": [
    "set papermill parameters which define notebook behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2938554",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_dir = 'results/spike_analysis'\n",
    "# define the fitting parameters\n",
    "scale_coeff_lasso_shift = [0.0, 5.00e-6, 1.00e-05, 2.00e-05, 4.00e-05, 8.00e-05, 1.60e-04, 3.20e-04, 6.40e-04] # the sweep of lasso coefficient params\n",
    "alpha_d=True\n",
    "scale_coeff_ridge_alpha_d=1e-3\n",
    "num_training_steps = 30\n",
    "iterations_per_step = 1000 # default 20000\n",
    "# init_beta_naught = 5.0 # We've found that we need to start with a higher beta_naught to get the model to converge correctly,\n",
    "scale_coeff_ridge_beta = 0.0 # the sweep of ridge coefficient params\n",
    "train_frac = 0.8 # fraction of data to use for cross validation training.\n",
    "lasso_choice = 4.00e-05 # the lasso coefficient to use for the final model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7febf8c-7159-4eba-8df1-ba7461b74e98",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Set some global configurations for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1aef9cfe-89e0-4560-9dab-a0cfa2ab5bd4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(output_dir): os.mkdir(output_dir)\n",
    "\n",
    "rc_kwargs = {\n",
    "    'legend.frameon': False,\n",
    "    \"font.size\" : 11,\n",
    "    \"font.weight\" : \"normal\"\n",
    "}\n",
    "\n",
    "plt.rcParams.update(**rc_kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f26463-32e6-4bb5-a04d-366df3157fc9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Input Data\n",
    "Load and organize the funcational score dms data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f8e5e1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "We begin with 16 individual sets of barcoded variants and their associated functional scores pre-computed. Each set derives from a single DMS experiment using one of Delta, Omicron BA.1, or Omicron BA.2 as the experimental wildtype. First, we parse the filenames to get experimental attributes tied in with the individual datasets as nested pd.DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "325abd2b-e238-472d-b49f-8a96ba064bd9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>library</th>\n",
       "      <th>replicate</th>\n",
       "      <th>filename</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lib-1</td>\n",
       "      <td>1</td>\n",
       "      <td>data/Delta/Lib-1_2021-10-28_thaw-1_VSVG_contro...</td>\n",
       "      <td>Delta-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lib-1</td>\n",
       "      <td>2</td>\n",
       "      <td>data/Delta/Lib-1_2021-10-28_thaw-1_VSVG_contro...</td>\n",
       "      <td>Delta-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lib-3</td>\n",
       "      <td>1</td>\n",
       "      <td>data/Delta/Lib-3_2021-10-28_thaw-1_VSVG_contro...</td>\n",
       "      <td>Delta-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lib-3</td>\n",
       "      <td>2</td>\n",
       "      <td>data/Delta/Lib-3_2021-10-28_thaw-1_VSVG_contro...</td>\n",
       "      <td>Delta-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lib-4</td>\n",
       "      <td>1</td>\n",
       "      <td>data/Delta/Lib-4_2021-10-28_thaw-1_VSVG_contro...</td>\n",
       "      <td>Delta-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lib-4</td>\n",
       "      <td>2</td>\n",
       "      <td>data/Delta/Lib-4_2021-10-28_thaw-1_VSVG_contro...</td>\n",
       "      <td>Delta-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lib-2</td>\n",
       "      <td>1</td>\n",
       "      <td>data/Delta/Lib-2_2021-10-28_thaw-1_VSVG_contro...</td>\n",
       "      <td>Delta-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lib-2</td>\n",
       "      <td>2</td>\n",
       "      <td>data/Delta/Lib-2_2021-10-28_thaw-1_VSVG_contro...</td>\n",
       "      <td>Delta-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lib-1</td>\n",
       "      <td>1</td>\n",
       "      <td>data/Omicron_BA1/Lib-1_2022-03-25_thaw-1_VSVG_...</td>\n",
       "      <td>Omicron_BA1-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lib-1</td>\n",
       "      <td>2</td>\n",
       "      <td>data/Omicron_BA1/Lib-1_2022-03-25_thaw-1_VSVG_...</td>\n",
       "      <td>Omicron_BA1-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lib-2</td>\n",
       "      <td>1</td>\n",
       "      <td>data/Omicron_BA1/Lib-2_2022-06-22_thaw-1_VSVG_...</td>\n",
       "      <td>Omicron_BA1-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lib-3</td>\n",
       "      <td>1</td>\n",
       "      <td>data/Omicron_BA1/Lib-3_2022-06-22_thaw-1_VSVG_...</td>\n",
       "      <td>Omicron_BA1-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lib-1</td>\n",
       "      <td>1</td>\n",
       "      <td>data/Omicron_BA2/Lib-1_2022-10-22_thaw-1_VSVG_...</td>\n",
       "      <td>Omicron_BA2-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Lib-2</td>\n",
       "      <td>1</td>\n",
       "      <td>data/Omicron_BA2/Lib-2_2022-10-22_thaw-1_VSVG_...</td>\n",
       "      <td>Omicron_BA2-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lib-1</td>\n",
       "      <td>2</td>\n",
       "      <td>data/Omicron_BA2/Lib-1_2022-10-22_thaw-2_VSVG_...</td>\n",
       "      <td>Omicron_BA2-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lib-2</td>\n",
       "      <td>2</td>\n",
       "      <td>data/Omicron_BA2/Lib-2_2022-10-22_thaw-2_VSVG_...</td>\n",
       "      <td>Omicron_BA2-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   library  replicate                                           filename  \\\n",
       "0    Lib-1          1  data/Delta/Lib-1_2021-10-28_thaw-1_VSVG_contro...   \n",
       "1    Lib-1          2  data/Delta/Lib-1_2021-10-28_thaw-1_VSVG_contro...   \n",
       "2    Lib-3          1  data/Delta/Lib-3_2021-10-28_thaw-1_VSVG_contro...   \n",
       "3    Lib-3          2  data/Delta/Lib-3_2021-10-28_thaw-1_VSVG_contro...   \n",
       "4    Lib-4          1  data/Delta/Lib-4_2021-10-28_thaw-1_VSVG_contro...   \n",
       "5    Lib-4          2  data/Delta/Lib-4_2021-10-28_thaw-1_VSVG_contro...   \n",
       "6    Lib-2          1  data/Delta/Lib-2_2021-10-28_thaw-1_VSVG_contro...   \n",
       "7    Lib-2          2  data/Delta/Lib-2_2021-10-28_thaw-1_VSVG_contro...   \n",
       "8    Lib-1          1  data/Omicron_BA1/Lib-1_2022-03-25_thaw-1_VSVG_...   \n",
       "9    Lib-1          2  data/Omicron_BA1/Lib-1_2022-03-25_thaw-1_VSVG_...   \n",
       "10   Lib-2          1  data/Omicron_BA1/Lib-2_2022-06-22_thaw-1_VSVG_...   \n",
       "11   Lib-3          1  data/Omicron_BA1/Lib-3_2022-06-22_thaw-1_VSVG_...   \n",
       "12   Lib-1          1  data/Omicron_BA2/Lib-1_2022-10-22_thaw-1_VSVG_...   \n",
       "13   Lib-2          1  data/Omicron_BA2/Lib-2_2022-10-22_thaw-1_VSVG_...   \n",
       "14   Lib-1          2  data/Omicron_BA2/Lib-1_2022-10-22_thaw-2_VSVG_...   \n",
       "15   Lib-2          2  data/Omicron_BA2/Lib-2_2022-10-22_thaw-2_VSVG_...   \n",
       "\n",
       "        condition  \n",
       "0         Delta-1  \n",
       "1         Delta-1  \n",
       "2         Delta-3  \n",
       "3         Delta-3  \n",
       "4         Delta-4  \n",
       "5         Delta-4  \n",
       "6         Delta-2  \n",
       "7         Delta-2  \n",
       "8   Omicron_BA1-1  \n",
       "9   Omicron_BA1-1  \n",
       "10  Omicron_BA1-2  \n",
       "11  Omicron_BA1-3  \n",
       "12  Omicron_BA2-1  \n",
       "13  Omicron_BA2-2  \n",
       "14  Omicron_BA2-1  \n",
       "15  Omicron_BA2-2  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func_score_data = pd.DataFrame()\n",
    "\n",
    "for homolog in [\"Delta\", \"Omicron_BA1\", \"Omicron_BA2\"]:\n",
    "    \n",
    "    # functional scores\n",
    "    func_sel = (\n",
    "        pd.read_csv(f\"data/{homolog}/functional_selections.csv\")\n",
    "        .assign(\n",
    "            filename = lambda x: f\"data/{homolog}/\" + \n",
    "            x.library + \"_\" + \n",
    "            x.preselection_sample + \n",
    "            \"_vs_\" + x.postselection_sample + \n",
    "            \"_func_scores.csv\"\n",
    "        )\n",
    "        .assign(\n",
    "            func_sel_scores_df = lambda x: x.filename.apply(\n",
    "                lambda f: pd.read_csv(f)\n",
    "            )   \n",
    "        )\n",
    "        .assign(\n",
    "            len_func_sel_scores_df = lambda x: x.func_sel_scores_df.apply(\n",
    "                lambda x: len(x)\n",
    "            )\n",
    "        )\n",
    "        .assign(homolog = homolog)\n",
    "    )\n",
    "    func_score_data = pd.concat([func_score_data, func_sel]).reset_index(drop=True)\n",
    "\n",
    "# Add a column that gives a unique ID to each homolog/DMS experiment\n",
    "func_score_data['condition'] = func_score_data.apply(\n",
    "    lambda row: f\"{row['homolog']}-{row['library']}\".replace('-Lib',''),\n",
    "    axis=1\n",
    ")\n",
    "func_score_data[['library', 'replicate', 'filename', 'condition']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f1dcb565-4aba-4ce4-a7a7-c406371fd89f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available conditions for fitting are:\n",
      "- Delta-1\n",
      "- Delta-3\n",
      "- Delta-4\n",
      "- Delta-2\n",
      "- Omicron_BA1-1\n",
      "- Omicron_BA1-2\n",
      "- Omicron_BA1-3\n",
      "- Omicron_BA2-1\n",
      "- Omicron_BA2-2\n"
     ]
    }
   ],
   "source": [
    "avail_cond_str = '\\n- '.join(list(func_score_data.condition.unique()))\n",
    "print(f\"Available conditions for fitting are:\\n- {avail_cond_str}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d4dbd8d-c028-41c2-a891-f001f6a9b237",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Concatentate each of the individual experiments, keeping track of the library and homolog of each. Output noteable features, for a random sample of 10 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "45085862-0225-428a-bdf2-6c806660338b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "69d566a3c3bc4f44932157fe2058c4a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>library</th>\n",
       "      <th>barcode</th>\n",
       "      <th>aa_substitutions</th>\n",
       "      <th>func_score</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>721925</th>\n",
       "      <td>Lib-3</td>\n",
       "      <td>CGTAAAGTTCCAACAA</td>\n",
       "      <td>G769R D950F R1107M N1192S</td>\n",
       "      <td>-2.1765</td>\n",
       "      <td>Omicron_BA1-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239549</th>\n",
       "      <td>Lib-4</td>\n",
       "      <td>AATAATTTTCCTACAC</td>\n",
       "      <td></td>\n",
       "      <td>-2.0202</td>\n",
       "      <td>Delta-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816259</th>\n",
       "      <td>Lib-1</td>\n",
       "      <td>GATGATACCAAACTAT</td>\n",
       "      <td>K814T L1024I E1207K</td>\n",
       "      <td>-2.1526</td>\n",
       "      <td>Omicron_BA2-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612980</th>\n",
       "      <td>Lib-2</td>\n",
       "      <td>AAATATCCTACAAGAA</td>\n",
       "      <td>C738Y A890T H1058Y</td>\n",
       "      <td>-9.0995</td>\n",
       "      <td>Omicron_BA1-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368871</th>\n",
       "      <td>Lib-1</td>\n",
       "      <td>TAATACCGAATCCCCC</td>\n",
       "      <td>A893V S939D A1078T</td>\n",
       "      <td>-4.1550</td>\n",
       "      <td>Omicron_BA1-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115330</th>\n",
       "      <td>Lib-2</td>\n",
       "      <td>GTATACATGTATGATG</td>\n",
       "      <td>S71L D1163E S1242N</td>\n",
       "      <td>0.2762</td>\n",
       "      <td>Omicron_BA2-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410949</th>\n",
       "      <td>Lib-1</td>\n",
       "      <td>GCATTACTACAAATAA</td>\n",
       "      <td>N960K</td>\n",
       "      <td>0.6777</td>\n",
       "      <td>Omicron_BA1-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971589</th>\n",
       "      <td>Lib-1</td>\n",
       "      <td>CAATATAGCATAGAGG</td>\n",
       "      <td>R78L</td>\n",
       "      <td>0.1378</td>\n",
       "      <td>Omicron_BA2-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592643</th>\n",
       "      <td>Lib-2</td>\n",
       "      <td>ACAAGCTTTGCAACAA</td>\n",
       "      <td>Y200H</td>\n",
       "      <td>1.3313</td>\n",
       "      <td>Omicron_BA1-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381265</th>\n",
       "      <td>Lib-1</td>\n",
       "      <td>CTAGTCTCCGACAAAA</td>\n",
       "      <td>F347S D627G I850L</td>\n",
       "      <td>-6.8325</td>\n",
       "      <td>Omicron_BA1-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        library           barcode           aa_substitutions  func_score  \\\n",
       "721925    Lib-3  CGTAAAGTTCCAACAA  G769R D950F R1107M N1192S     -2.1765   \n",
       "239549    Lib-4  AATAATTTTCCTACAC                                -2.0202   \n",
       "816259    Lib-1  GATGATACCAAACTAT        K814T L1024I E1207K     -2.1526   \n",
       "612980    Lib-2  AAATATCCTACAAGAA         C738Y A890T H1058Y     -9.0995   \n",
       "368871    Lib-1  TAATACCGAATCCCCC         A893V S939D A1078T     -4.1550   \n",
       "1115330   Lib-2  GTATACATGTATGATG         S71L D1163E S1242N      0.2762   \n",
       "410949    Lib-1  GCATTACTACAAATAA                      N960K      0.6777   \n",
       "971589    Lib-1  CAATATAGCATAGAGG                       R78L      0.1378   \n",
       "592643    Lib-2  ACAAGCTTTGCAACAA                      Y200H      1.3313   \n",
       "381265    Lib-1  CTAGTCTCCGACAAAA          F347S D627G I850L     -6.8325   \n",
       "\n",
       "             condition  \n",
       "721925   Omicron_BA1-3  \n",
       "239549         Delta-4  \n",
       "816259   Omicron_BA2-1  \n",
       "612980   Omicron_BA1-2  \n",
       "368871   Omicron_BA1-1  \n",
       "1115330  Omicron_BA2-2  \n",
       "410949   Omicron_BA1-1  \n",
       "971589   Omicron_BA2-1  \n",
       "592643   Omicron_BA1-2  \n",
       "381265   Omicron_BA1-1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func_score_df = pd.DataFrame()\n",
    "for idx, row in tqdm(func_score_data.iterrows(), total=len(func_score_data)):\n",
    "    mut_df_replicates = row.func_sel_scores_df.assign(\n",
    "        homolog=row.homolog,\n",
    "        library = row.library,\n",
    "        replicate = row.replicate,\n",
    "        condition=row.condition\n",
    "    )\n",
    "    func_score_df = pd.concat([func_score_df, mut_df_replicates])\n",
    "\n",
    "# rename, sort index, and fill na (wildtype values) with empty strings\n",
    "func_score_df = (func_score_df\n",
    "    .rename(\n",
    "        {\"aa_substitutions_reference\":\"aa_substitutions\"}, \n",
    "        axis=1\n",
    "    )\n",
    "    .reset_index(drop=True)\n",
    "    .fillna(\"\")\n",
    "    .sort_values(by=\"condition\")\n",
    ")\n",
    "func_score_df[[\"library\", \"barcode\", \"aa_substitutions\", \"func_score\", \"condition\"]].sample(10, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dabb345-ef8f-4a97-b8eb-cea29a4e4597",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Discard all variants with a pre-selection count of 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "35d1b1ae-881a-4031-81d4-89effe50b11c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Of 1135096 variants, 120164 had fewer than the threshold of counts before selection, and were filtered out\n"
     ]
    }
   ],
   "source": [
    "n_pre_threshold = len(func_score_df)\n",
    "func_score_df.query(\"pre_count >= 100\", inplace=True)\n",
    "print(f\"Of {n_pre_threshold} variants, {n_pre_threshold - len(func_score_df)} had fewer than the threshold of counts before selection, and were filtered out\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a0d44e1-7953-453a-abf1-d69c594d51f4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "We only require a functional score, aa substitutions, and condition column for instatiating the `multidms.Data` object. drop the rest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "88fb9dc0-0b5d-49e6-8e69-8b38665c3531",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "required_cols = ['func_score', 'aa_substitutions', 'condition']\n",
    "func_score_df.drop([c for c in func_score_df if c not in required_cols], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a6d285-4f71-4c1a-917b-cae96fe3ba0a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Remove all variants with string-suffixed sites (indels) and stop codon wildtypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b2640fd3-d17e-49cb-bea5-122ca5a6ba1d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bbebea446b3b44159a5ebc89d27d277c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1014932 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stop_wt_vars = []\n",
    "non_numeric_sites = []\n",
    "for idx, row in tqdm(func_score_df.iterrows(), total=len(func_score_df)):\n",
    "    for sub in row[\"aa_substitutions\"].split():\n",
    "        if sub[0] == \"*\":\n",
    "            stop_wt_vars.append(idx)\n",
    "        if not sub[-2].isnumeric():\n",
    "            non_numeric_sites.append(idx)\n",
    "\n",
    "to_drop = set.union(set(stop_wt_vars), set(non_numeric_sites))\n",
    "func_score_df.drop(to_drop, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b488806a-7942-45a5-8435-295dc487f83f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "We clip all functional scores at a lower bound of -3.5, and an upper bound of 2.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "56d9c8a5-1d05-448a-a2e8-1fb059e00087",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 143177 variants below the clip theshold, and 345 above.\n"
     ]
    }
   ],
   "source": [
    "n_below_clip = len(func_score_df.query(f\"func_score < -3.5\"))\n",
    "n_above_clip = len(func_score_df.query(f\"func_score > 2.5\"))\n",
    "print(f\"There are {n_below_clip} variants below the clip theshold, and {n_above_clip} above.\")\n",
    "func_score_df = func_score_df.assign(\n",
    "    func_score = func_score_df.func_score.clip(-3.5, 2.5)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "016a81aa",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Choose two representative biological replicates for each of the three homologs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "351cebdb-0bdc-49e2-b181-d9f72f5a7a07",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "experiment_conditions = [\"Delta\", \"Omicron_BA1\", \"Omicron_BA2\"]\n",
    "replicate_1_experiments = [\"Delta-2\", \"Omicron_BA1-2\", \"Omicron_BA2-1\"]\n",
    "replicate_2_experiments = [\"Delta-4\", \"Omicron_BA1-3\", \"Omicron_BA2-2\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38584590-dceb-4f8b-a8c2-d98eb94bd501",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Organize the two replicates and annotate replicates \"1\" and \"2\". These each represent a distinct training set such that we may train replicate models and compare their results. Output a random sample of 10 variants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "97cefc92-4ab6-4f50-b23c-4baf7ef017a1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>func_score</th>\n",
       "      <th>aa_substitutions</th>\n",
       "      <th>condition</th>\n",
       "      <th>replicate</th>\n",
       "      <th>n_subs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>623435</th>\n",
       "      <td>-2.5084</td>\n",
       "      <td>K764D</td>\n",
       "      <td>Omicron_BA1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064675</th>\n",
       "      <td>-3.5000</td>\n",
       "      <td>E324K F375R V1033A</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073643</th>\n",
       "      <td>-3.5000</td>\n",
       "      <td>I584P G891R V1068F</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211227</th>\n",
       "      <td>-1.1056</td>\n",
       "      <td>D936G</td>\n",
       "      <td>Delta</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808624</th>\n",
       "      <td>-0.0815</td>\n",
       "      <td></td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967641</th>\n",
       "      <td>-1.5393</td>\n",
       "      <td>T76K T284I V1176F</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675886</th>\n",
       "      <td>-1.1126</td>\n",
       "      <td>I105V W152C D178H K764V S803T</td>\n",
       "      <td>Omicron_BA1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853582</th>\n",
       "      <td>-3.5000</td>\n",
       "      <td>T76A R158S A771P</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662082</th>\n",
       "      <td>0.3180</td>\n",
       "      <td></td>\n",
       "      <td>Omicron_BA1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764857</th>\n",
       "      <td>-3.5000</td>\n",
       "      <td>Y265F T307N T678F K764N Y873S</td>\n",
       "      <td>Omicron_BA1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         func_score               aa_substitutions    condition  replicate  \\\n",
       "623435      -2.5084                          K764D  Omicron_BA1          1   \n",
       "1064675     -3.5000             E324K F375R V1033A  Omicron_BA2          2   \n",
       "1073643     -3.5000             I584P G891R V1068F  Omicron_BA2          2   \n",
       "211227      -1.1056                          D936G        Delta          2   \n",
       "808624      -0.0815                                 Omicron_BA2          1   \n",
       "967641      -1.5393              T76K T284I V1176F  Omicron_BA2          1   \n",
       "675886      -1.1126  I105V W152C D178H K764V S803T  Omicron_BA1          2   \n",
       "853582      -3.5000               T76A R158S A771P  Omicron_BA2          1   \n",
       "662082       0.3180                                 Omicron_BA1          2   \n",
       "764857      -3.5000  Y265F T307N T678F K764N Y873S  Omicron_BA1          2   \n",
       "\n",
       "         n_subs  \n",
       "623435        1  \n",
       "1064675       3  \n",
       "1073643       3  \n",
       "211227        1  \n",
       "808624        0  \n",
       "967641        3  \n",
       "675886        5  \n",
       "853582        3  \n",
       "662082        0  \n",
       "764857        5  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func_score_df = pd.concat(\n",
    "    [\n",
    "        (\n",
    "            func_score_df\n",
    "            .query(\"condition in @replicate_1_experiments\")\n",
    "            .replace(dict(zip(replicate_1_experiments, experiment_conditions)))\n",
    "            .assign(replicate=1)\n",
    "        ),\n",
    "        (\n",
    "            func_score_df\n",
    "            .query(\"condition in @replicate_2_experiments\")\n",
    "            .replace(dict(zip(replicate_2_experiments, experiment_conditions)))\n",
    "            .assign(replicate=2)\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "func_score_df = func_score_df.assign(\n",
    "    n_subs = [\n",
    "        len(aa_subs.split()) \n",
    "        for aa_subs in func_score_df.aa_substitutions\n",
    "    ]\n",
    ")\n",
    "func_score_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2a6cfa16-1e96-4acf-8877-9c795bb85233",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "func_score_df.to_csv(f\"{output_dir}/training_functional_scores.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e9db4d-6b54-4fff-ae53-663b64f7b03e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Variant barcode and mutation background stats\n",
    "\n",
    "In this section we briedly query and visualize charictaristics of the replicate training sets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "753635de-d647-41b8-8cdd-712da222fd32",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Get the expected number substitutions per variant for each condition replicate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "da58d067-e124-402b-9169-6746b5844ee9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delta - rep 1 has 2.18671 subs per variant, on average\n",
      "Delta - rep 2 has 2.29472 subs per variant, on average\n",
      "Omicron_BA1 - rep 1 has 1.802 subs per variant, on average\n",
      "Omicron_BA1 - rep 2 has 1.75802 subs per variant, on average\n",
      "Omicron_BA2 - rep 1 has 2.31117 subs per variant, on average\n",
      "Omicron_BA2 - rep 2 has 2.32827 subs per variant, on average\n"
     ]
    }
   ],
   "source": [
    "for group, group_df in func_score_df.groupby([\"condition\", \"replicate\"]):\n",
    "    print(f\"{group[0]} - rep {group[1]} has {round(group_df.n_subs.mean(), 5)} subs per variant, on average\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a80cb6-a561-440a-a71c-8060d572bc12",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Get the number of unique mutations seen in each condition replicate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "72f352bd-ce16-439c-8267-729de5f5b517",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delta - rep 1 has 28515\n",
      "Delta - rep 2 has 29158\n",
      "Omicron_BA1 - rep 1 has 70597\n",
      "Omicron_BA1 - rep 2 has 62129\n",
      "Omicron_BA2 - rep 1 has 60397\n",
      "Omicron_BA2 - rep 2 has 57719\n"
     ]
    }
   ],
   "source": [
    "for group, group_df in func_score_df.groupby([\"condition\", \"replicate\"]):\n",
    "    print(f\"{group[0]} - rep {group[1]} has {len(group_df.aa_substitutions.unique())}\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f199e90-91a2-49d0-b6e4-6f2e339340e6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Visualize the distribution of barcodes per variant, as well as the distribution of unique backgrounds per mutation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3a795651-b750-4613-b4b0-13577ba7a645",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "99d3b95a-a259-4815-9edf-52c5a7fdcb7c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x550 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saveas = f\"raw_data_summary_barcodes_backgrounds_hist\"\n",
    "logscale=False\n",
    "fig, ax = plt.subplots(2,3, sharex=\"row\", sharey=\"row\", figsize=[6.4, 5.5])\n",
    "\n",
    "condition_title = {\n",
    "    \"Delta\":\"Delta\",\n",
    "    \"Omicron_BA1\" : \"BA.1\",\n",
    "    \"Omicron_BA2\" : \"BA.2\"\n",
    "}\n",
    "\n",
    "row = 0\n",
    "for col, (condition, condition_df) in enumerate(func_score_df.groupby(\"condition\")):\n",
    "    iter_ax = ax[row, col]\n",
    "    \n",
    "    mut_df_replicates = condition_df.query(\"aa_substitutions != ''\")\n",
    "    mut_df_replicates = mut_df_replicates.assign(\n",
    "        num_muts = [\n",
    "            len(aa_subs.split())\n",
    "            for aa_subs in mut_df_replicates.aa_substitutions\n",
    "        ]\n",
    "    )\n",
    "    \n",
    "    sns.histplot(mut_df_replicates.query(\"num_muts <= 10\"), x=\"num_muts\", ax=iter_ax, hue=\"replicate\", discrete=True)\n",
    "    for rep, rep_df in mut_df_replicates.groupby(\"replicate\"):\n",
    "        mean = rep_df['num_muts'].mean()\n",
    "        iter_ax.axvline(mean, linestyle=(\"-\" if rep == 1 else \"--\"))\n",
    "    \n",
    "    if logscale: iter_ax.set_yscale('log')\n",
    "    if col != 2: \n",
    "        iter_ax.get_legend().remove()\n",
    "    n_rep1 = len(mut_df_replicates.query(\"replicate == 1\"))//1000\n",
    "    n_rep2 = len(mut_df_replicates.query(\"replicate == 2\"))//1000\n",
    "    iter_ax.text(\n",
    "        0.1, 1.1, \n",
    "        f\"$N={n_rep1}K, {n_rep2}K$\", \n",
    "        ha=\"left\", va=\"top\", \n",
    "        transform=iter_ax.transAxes\n",
    "    )\n",
    "    xscale = \"number of amino-acid substitutions per variant\" if col == 1 else \"\"\n",
    "    iter_ax.set_xlabel(xscale)\n",
    "    \n",
    "    ylabel = f\"variant counts\" if col == 0 else \"\"\n",
    "    iter_ax.set_ylabel(ylabel)\n",
    "    iter_ax.set_xticks(\n",
    "        [i+1 for i in range(10)],\n",
    "        labels=[i+1 for i in range(10)], \n",
    "        ha=\"center\",\n",
    "        size=7,\n",
    "        rotation=0\n",
    "    )\n",
    "    sns.despine(ax=iter_ax)\n",
    "    iter_ax.set_title(condition_title[condition], y=1.15)\n",
    "\n",
    "row = 1\n",
    "collapsed_bc_df = func_score_df.groupby(\n",
    "    [\"replicate\", \"condition\", \"aa_substitutions\"]\n",
    ").aggregate(\"mean\").reset_index()\n",
    "for col, (condition, condition_df) in enumerate(collapsed_bc_df.groupby(\"condition\")):\n",
    "    iter_ax = ax[row, col]\n",
    "    mut_df_replicates = pd.DataFrame()\n",
    "    for rep, rep_df in condition_df.groupby(\"replicate\"):\n",
    "        \n",
    "        times_seen = (\n",
    "            rep_df[\"aa_substitutions\"].str.split().explode().value_counts()\n",
    "        )\n",
    "        if (times_seen == times_seen.astype(int)).all():\n",
    "            times_seen = times_seen.astype(int)\n",
    "        times_seen = pd.DataFrame(times_seen)\n",
    "        times_seen.index.name = f\"mutation\"\n",
    "        mut_df_replicates = pd.concat([mut_df_replicates, times_seen.assign(replicate=rep).reset_index()])\n",
    "\n",
    "    sns.histplot(\n",
    "        mut_df_replicates.query(\"count <= 50\"), \n",
    "        x=\"count\", \n",
    "        ax=iter_ax, \n",
    "        element='step', \n",
    "        hue=\"replicate\", \n",
    "        discrete=True\n",
    "    )\n",
    "    \n",
    "    for rep, rep_df in mut_df_replicates.groupby(\"replicate\"):\n",
    "        mean = rep_df['count'].mean()\n",
    "        iter_ax.axvline(mean, linestyle=(\"-\" if rep == 1 else \"--\"))\n",
    "        \n",
    "    iter_ax.get_legend().remove()\n",
    "    n_rep1 = len(mut_df_replicates.query(\"replicate == 1\"))\n",
    "    n_rep2 = len(mut_df_replicates.query(\"replicate == 2\"))\n",
    "    iter_ax.text(\n",
    "        0.1, 1.1, \n",
    "        f\"$N={n_rep1}, {n_rep2}$\", \n",
    "        ha=\"left\", va=\"top\", \n",
    "        transform=iter_ax.transAxes\n",
    "    )\n",
    "    \n",
    "    xscale = \"number of variant backgrounds \\nfor a given amino-acid substitution\" if col == 1 else \"\"\n",
    "    iter_ax.set_xlabel(xscale)\n",
    "    \n",
    "    ylabel = f\"mutation counts\" if col == 0 else \"\"\n",
    "    iter_ax.set_ylabel(ylabel)\n",
    "    \n",
    "    xticks = [i for i in range(0, 51) if i % 5 == 0]\n",
    "    iter_ax.set_xticks(\n",
    "        xticks,\n",
    "        labels=xticks, \n",
    "        ha=\"center\",\n",
    "        size=7,\n",
    "        rotation=0\n",
    "    )\n",
    "    \n",
    "    sns.despine(ax=iter_ax)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "ax[0,0].text(\n",
    "    -0.1, 1.06, \n",
    "    f\"A\", \n",
    "    ha=\"right\", va=\"bottom\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=ax[0,0].transAxes\n",
    ")\n",
    "ax[1,0].text(\n",
    "    -0.1, 1.06, \n",
    "    f\"B\", \n",
    "    ha=\"right\", va=\"bottom\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=ax[1,0].transAxes\n",
    ")\n",
    "\n",
    "fig.subplots_adjust(hspace=.6)\n",
    "fig.savefig(f\"{output_dir}/{saveas}.pdf\")\n",
    "fig.savefig(f\"{output_dir}/{saveas}.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d53bf8b7-318a-43f2-8852-8c830dc8e538",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Plot the correlation of variant functional scores (averaged across barcodes) between replicates in each condition, as well as the full distribution of functional scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "97e5ea85-cccc-43ff-a7c5-f0ca3d60c1ae",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x530 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saveas = \"replicate_functional_score_correlation_scatter\"\n",
    "pal = sns.color_palette('tab20')\n",
    "\n",
    "fig, ax = plt.subplots(2,3, sharex=\"row\", sharey=\"row\", figsize=[6.4, 5.3])\n",
    "collapsed_bc_df = func_score_df.groupby(\n",
    "    [\"replicate\", \"condition\", \"aa_substitutions\"]\n",
    ").aggregate(\"mean\").reset_index()\n",
    "collapsed_bc_df = collapsed_bc_df.assign(\n",
    "    is_stop=[True if \"*\" in aasubs else False for aasubs in collapsed_bc_df.aa_substitutions]\n",
    ")\n",
    "\n",
    "is_stop_alpha_dict = {\n",
    "    True : 0.5,\n",
    "    False : 0.2\n",
    "}\n",
    "\n",
    "lim = [-3.8, 2.8]\n",
    "ticks = np.linspace(-3, 2, 6)\n",
    "for col, (condition, condition_df) in enumerate(collapsed_bc_df.groupby(\"condition\")):\n",
    "    \n",
    "    row = 0\n",
    "    iter_ax = ax[row, col]\n",
    "    \n",
    "    mut_df_replicates = reduce(\n",
    "        lambda left, right: pd.merge(\n",
    "            left, right, left_index=True, right_index=True, how=\"inner\"\n",
    "        ),\n",
    "        [\n",
    "            rep_df.rename({\"func_score\":f\"rep_{rep}_func_score\"}, axis=1).set_index(\"aa_substitutions\")\n",
    "            for rep, rep_df in condition_df.groupby(\"replicate\") \n",
    "        ],\n",
    "    )\n",
    "    \n",
    "    mut_df_replicates = mut_df_replicates.assign(\n",
    "        is_stop=[True if \"*\" in aasubs else False for aasubs in mut_df_replicates.index.values]\n",
    "    )\n",
    "    mut_df_replicates = mut_df_replicates.assign(\n",
    "        n_subs=[len(aasubs.split()) for aasubs in mut_df_replicates.index.values]\n",
    "    )\n",
    "    \n",
    "    # alpha = [is_stop_alpha_dict[istp] for istp in mut_df_replicates.is_stop]\n",
    "    for istp, color in zip([False, True], [\"darkgrey\", \"red\"]):\n",
    "        sns.scatterplot(\n",
    "            mut_df_replicates.query(\"is_stop == @istp\"), \n",
    "            x=\"rep_1_func_score\", \n",
    "            y=\"rep_2_func_score\", \n",
    "            ax =iter_ax,\n",
    "            c=color,\n",
    "            alpha=is_stop_alpha_dict[istp],\n",
    "            legend=False\n",
    "        )\n",
    "    \n",
    "    iter_ax.plot([-3.5, 2.5], [-3.5, 2.5], \"--\", lw=2, c=\"royalblue\")\n",
    "    \n",
    "    iter_ax.set_ylim(lim)\n",
    "    iter_ax.set_xlim(lim)\n",
    "    if col == 0:\n",
    "        iter_ax.set_yticks(ticks, labels=ticks)\n",
    "    iter_ax.set_xticks(ticks, labels=ticks, rotation=90)\n",
    "    \n",
    "    corr = pearsonr(mut_df_replicates[\"rep_1_func_score\"], mut_df_replicates[\"rep_2_func_score\"])[0]\n",
    "    iter_ax.annotate(\n",
    "        f\"$r = {corr:.2f}$\", \n",
    "        (0.1, 0.9), \n",
    "        xycoords=\"axes fraction\", \n",
    "        fontsize=12\n",
    "    )\n",
    "    iter_ax.set_title(condition)\n",
    "    # iter_ax.get_legend().remove()\n",
    "    sns.despine(ax=iter_ax)\n",
    "    \n",
    "    row = 1\n",
    "    iter_ax = ax[row, col]\n",
    "    sns.violinplot(\n",
    "        condition_df,\n",
    "        x=\"is_stop\",\n",
    "        y=\"func_score\",\n",
    "        hue=\"replicate\",\n",
    "        split=True,\n",
    "        gap=.1, inner=\"quart\",\n",
    "        palette=[\"0.5\", \"0.75\"],\n",
    "        ax=iter_ax\n",
    "    )\n",
    "    \n",
    "    sns.despine(ax=iter_ax)\n",
    "    if col != 2:\n",
    "        iter_ax.get_legend().remove()\n",
    "    else:\n",
    "        iter_ax.legend(bbox_to_anchor = (1.25, 1.05), title=\"replicate\")\n",
    "    if col == 0:\n",
    "        iter_ax.set_yticks(ticks, labels=ticks)\n",
    "\n",
    "ax[0,0].set_xlabel(\"\")\n",
    "ax[0,0].set_ylabel(\"replicate 2 \\n functional score\")\n",
    "\n",
    "ax[0,1].set_xlabel(\"replicate 1 functional score\")\n",
    "ax[0,1].set_title(\"BA.1\")\n",
    "ax[0,2].set_xlabel(\"\")\n",
    "ax[0,2].set_title(\"BA.2\")\n",
    "\n",
    "ax[1,0].set_xlabel(\"\")\n",
    "ax[1,0].set_ylabel(\"functional score\")\n",
    "\n",
    "ax[1,1].set_xlabel(\"variants contain stop codon mutations\")\n",
    "ax[1,2].set_xlabel(\"\")\n",
    "ax[1,2].set_ylabel(\"\")\n",
    "ax[1,1].set_ylabel(\"\")\n",
    "\n",
    "ax[0,0].text(\n",
    "    -0.1, 1.06, \n",
    "    f\"A\", \n",
    "    ha=\"right\", va=\"bottom\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=ax[0,0].transAxes\n",
    ")\n",
    "ax[1,0].text(\n",
    "    -0.1, 1.06, \n",
    "    f\"B\", \n",
    "    ha=\"right\", va=\"bottom\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=ax[1,0].transAxes\n",
    ")\n",
    "\n",
    "# fig.suptitle(\"Variant Functional Score \\nReplicate Correlation\")\n",
    "plt.tight_layout()\n",
    "fig.subplots_adjust(wspace=0.08, hspace = 0.5)\n",
    "fig.savefig(f\"{output_dir}/{saveas}.pdf\")\n",
    "fig.savefig(f\"{output_dir}/{saveas}.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10a940c0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Encode data for fitting\n",
    "\n",
    "Next, we use the `multidms.Data` class to prep our data for fitting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623bbb1a-2763-4ebe-863a-48564bbd0df1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Instantiate an object for each of our two replicate training sets, and append them to a list "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "71575337",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Delta\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6087de71ca7f426a8ca27732d3c944ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/28515 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3bf08fccb3f4484fb6281ef24e07c832",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/70597 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      },
      "text/plain": [
       "  0%|          | 0/60397 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 8 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=19939), Label(value='0 / 19939')))…"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [144, 143, 69, 145, 70, 211, 25, 26, 24, 157, 158, 898],\n",
      "dropping: 10983 variantswhich have mutations at those sites.\n"
     ]
    },
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=18566), Label(value='0 / 18566')))…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [371], dropping 2041 variants\n",
      "Converting mutations for Delta\n"
     ]
    },
    {
     "data": {
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=3325), Label(value='0 / 3325'))), …"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting mutations for Omicron_BA1\n",
      "is reference, skipping\n",
      "Converting mutations for Omicron_BA2\n"
     ]
    },
    {
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=6824), Label(value='0 / 6824'))), …"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished, time: 37\n",
      "inferring site map for Delta\n"
     ]
    },
    {
     "data": {
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       "model_id": "cbae9380194248d1bcbac07661e7295e",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA1\n"
     ]
    },
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA2\n"
     ]
    },
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       "model_id": "6bb467a0bda746878e2aaf210491624f",
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       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/57719 [00:00<?, ?it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 8 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=18626), Label(value='0 / 18626')))…"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [145, 70, 144, 143, 69, 211, 422, 26, 24, 25, 157, 158],\n",
      "dropping: 10129 variantswhich have mutations at those sites.\n"
     ]
    },
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=17360), Label(value='0 / 17360')))…"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [371], dropping 1873 variants\n",
      "Converting mutations for Delta\n"
     ]
    },
    {
     "data": {
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=3399), Label(value='0 / 3399'))), …"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting mutations for Omicron_BA1\n",
      "is reference, skipping\n",
      "Converting mutations for Omicron_BA2\n"
     ]
    },
    {
     "data": {
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=6536), Label(value='0 / 6536'))), …"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished, time: 35\n"
     ]
    }
   ],
   "source": [
    "datasets = []\n",
    "for res, fsdf in func_score_df.groupby(\"replicate\"):       \n",
    "\n",
    "    start = time.time()\n",
    "\n",
    "    # instantiate data object\n",
    "    data = multidms.Data(\n",
    "        fsdf,\n",
    "        collapse_identical_variants=\"mean\",       # take the average variant func score across barcode replicates\n",
    "        alphabet=multidms.AAS_WITHSTOP_WITHGAP,   # \n",
    "        reference=\"Omicron_BA1\",\n",
    "        assert_site_integrity=False,\n",
    "        verbose=True,\n",
    "        nb_workers=8,\n",
    "        name=f\"rep-{res}\" \n",
    "    )\n",
    "\n",
    "    end = time.time()\n",
    "    prep_time = round(end-start)\n",
    "    print(f\"Finished, time: {prep_time}\")\n",
    "\n",
    "    datasets.append(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c83765e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Change the datasets colors - the only mutible thing about `Data` Objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cc25fb48",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# condition colors must be hex-encoded,\n",
    "palette = [\n",
    "    '#F97306',\n",
    "    '#BFBFBF',\n",
    "    '#9400D3'\n",
    "]\n",
    "\n",
    "conditions = [\n",
    "    \"Delta\",\n",
    "    \"Omicron_BA1\",\n",
    "    \"Omicron_BA2\"\n",
    "]\n",
    "\n",
    "cc = {con:col for con, col in zip(conditions, palette)}\n",
    "for data in datasets:\n",
    "    data.condition_colors = cc"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ecbf7c95-7264-474b-9630-2e13725b304b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Fit Models (Shrinkage Analysis)\n",
    "\n",
    "For each replicate training set, initialize and fit a set models with variable lasso strength coefficients. Here, we use the `multidms.utils.fit_wrapper` function to handle the instatiation, and parameter fitting. Expand the cell output below to view the function docstring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "56cda88a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [],
   "source": [
    "fit_params = {\n",
    "    'scale_coeff_lasso_shift': scale_coeff_lasso_shift,\n",
    "    'alpha_d': [alpha_d],\n",
    "    'scale_coeff_ridge_alpha_d':[scale_coeff_ridge_alpha_d],\n",
    "    'scale_coeff_ridge_beta': [scale_coeff_ridge_beta],\n",
    "    'num_training_steps': [num_training_steps],\n",
    "    'iterations_per_step':[iterations_per_step],\n",
    "}\n",
    "\n",
    "fit_params[\"dataset\"] = datasets\n",
    "_, _, models = multidms.fit_models(fit_params, n_threads=-1)\n",
    "pickle.dump(models, open(f\"{output_dir}/full_models.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a5d5a99-b809-44a2-ae1f-ef5215f4bb92",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "The cell above saves the models and their relevant hyper-parameters in a `DataFrame` to a `pickle` binary file. Hense, If it's already been run, and you want to execute the code below _without_ re-fitting, the following cell will load that binary file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cb41b672",
   "metadata": {},
   "outputs": [],
   "source": [
    "models[\"replicate\"] = models.dataset_name.str.split(\"-\").str[-1].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "151ebbac-2b80-4219-bf08-a3e1d47b1f66",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# set some global variables\n",
    "# columns name\n",
    "model_name = \"model\"\n",
    "\n",
    "# the number of times a mutation must be seen in each condition to be included in downstream analysis\n",
    "times_seen_threshold = 1\n",
    "\n",
    "# The chosen lasso strength for our final spike model results\n",
    "chosen_lasso_strength = 4e-5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd8b5620-89bc-48ed-8227-d957189eafc2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Plot the model loss over training steps, as provided by `fit_wrapper`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1503f9d3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saveas=\"convergence_all_lasso_lines\"\n",
    "cmap=plt.get_cmap(\"tab20\")\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1,figsize=[6.4,4.5])\n",
    "color_idx = -1\n",
    "for i, (model, model_row) in enumerate(models.iterrows()):\n",
    "    if i%2 == 0: color_idx += 1\n",
    "\n",
    "    ax.plot(\n",
    "        [(1000 * s) for s in range(len(model_row.step_loss))],\n",
    "        model_row.step_loss,\n",
    "        c=cmap.colors[color_idx],\n",
    "        lw=3,\n",
    "        linestyle=\"-\" if model_row.replicate == 0 else \"--\",\n",
    "        label=f\"rep: {model_row.replicate} scale_coeff: {model_row.scale_coeff_lasso_shift}\"\n",
    "    )\n",
    "\n",
    "ticks = range(0, 30001, 5000)\n",
    "labels = [f\"{t//1000}K\" for t in ticks]\n",
    "ax.set_xticks(ticks, labels, rotation=0, ha='center')\n",
    "ax.set_ylabel(\"Model Loss (w/o L1 penalty)\")\n",
    "ax.set_xlabel(\"Optimization iterations\")\n",
    "\n",
    "black_line = mlines.Line2D([], [], color='black', linestyle='-',\n",
    "                          markersize=5, label='rep 1')\n",
    "black_dashed = mlines.Line2D([], [], color='black',linestyle='--',\n",
    "                          markersize=5, label='rep 2')\n",
    "lasso_color_handles = [\n",
    "    mlines.Line2D(\n",
    "        [], [], \n",
    "        color=color, \n",
    "        linestyle='-',\n",
    "        markersize=5,\n",
    "        linewidth=3,\n",
    "        label=\"$\\lambda$: \"+str(lasso)\n",
    "    )\n",
    "    for lasso, color in zip(models.scale_coeff_lasso_shift.unique(), cmap.colors)\n",
    "]\n",
    "\n",
    "elements = [black_line, black_dashed] + lasso_color_handles\n",
    "ax.legend(handles=elements, bbox_to_anchor = (1, 1), loc='upper left', frameon=False, fontsize=9)\n",
    "sns.despine(ax=ax)\n",
    "ax.set_ylim()\n",
    "plt.tight_layout()\n",
    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddfe76d8",
   "metadata": {},
   "source": [
    "## Model Evaluation and Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3dac6610",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_collection = multidms.ModelCollection(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3ec54100",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cache miss - this could take a moment\n",
      "  dataset_name  scale_coeff_lasso_shift       mut_type    mut_param  sparsity\n",
      "0        rep-1                 0.000000  nonsynonymous  shift_Delta  0.234082\n",
      "1        rep-1                 0.000000           stop  shift_Delta  0.247678\n",
      "2        rep-1                 0.000005  nonsynonymous  shift_Delta  0.312857\n",
      "3        rep-1                 0.000005           stop  shift_Delta  0.396285\n",
      "4        rep-1                 0.000010  nonsynonymous  shift_Delta  0.381122\n"
     ]
    },
    {
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0.826625386996904}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.6209183673469387}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.9752321981424149}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.8726530612244898}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.9921428571428571}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", 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\"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9998979591836735}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.FacetChart(...)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart, sparsity_df = model_collection.shift_sparsity(return_data=True, height_scalar=100) # TODO raise issue to fix height scalar\n",
    "print(sparsity_df.head())\n",
    "chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a1931818",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      datasets mut_param  correlation  scale_coeff_lasso_shift\n",
      "0  rep-1,rep-2      beta     0.837209                 0.000000\n",
      "0  rep-1,rep-2      beta     0.839758                 0.000005\n",
      "0  rep-1,rep-2      beta     0.838824                 0.000010\n",
      "0  rep-1,rep-2      beta     0.838579                 0.000020\n",
      "0  rep-1,rep-2      beta     0.837910                 0.000040\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-51662a7681b849f08f9bdc84fc3d6071.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-51662a7681b849f08f9bdc84fc3d6071.vega-embed details,\n",
       "  #altair-viz-51662a7681b849f08f9bdc84fc3d6071.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-51662a7681b849f08f9bdc84fc3d6071\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-51662a7681b849f08f9bdc84fc3d6071\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-51662a7681b849f08f9bdc84fc3d6071\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.15.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.15.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-528af9f3e7ff66e88d8f3546ebedc5ad\"}, \"facet\": {\"column\": {\"field\": \"datasets\", \"title\": \"Experiment comparison\", \"type\": \"nominal\"}}, \"spec\": {\"layer\": [{\"mark\": {\"type\": \"point\"}, \"encoding\": {\"color\": {\"field\": \"mut_param\", \"title\": \"Parameter\", \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"datasets\", \"type\": \"nominal\"}, {\"field\": \"correlation\", \"type\": \"quantitative\"}, {\"field\": \"mut_param\", \"type\": \"nominal\"}], \"x\": {\"axis\": {\"format\": \".1e\"}, \"field\": \"scale_coeff_lasso_shift\", \"title\": \"Lasso Penalty\", \"type\": \"nominal\"}, \"y\": {\"field\": \"correlation\", \"title\": \"Correlation\", \"type\": \"quantitative\"}}}, {\"mark\": {\"type\": \"line\"}, \"encoding\": {\"color\": {\"field\": \"mut_param\", \"title\": \"Parameter\", \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"datasets\", \"type\": \"nominal\"}, {\"field\": \"correlation\", \"type\": \"quantitative\"}, {\"field\": \"mut_param\", \"type\": \"nominal\"}], \"x\": {\"axis\": {\"format\": \".1e\"}, \"field\": \"scale_coeff_lasso_shift\", \"title\": \"Lasso Penalty\", \"type\": \"nominal\"}, \"y\": {\"field\": \"correlation\", \"title\": \"Correlation\", \"type\": \"quantitative\"}}}], \"height\": 200, \"width\": 200}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.15.1.json\", \"datasets\": {\"data-528af9f3e7ff66e88d8f3546ebedc5ad\": [{\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.8372091048402562, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.839758470866261, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.8388239747300699, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.8385792300278926, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.8379096375470336, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.8208707002034907, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.7974527452084917, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.7840767464276763, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"beta\", \"correlation\": 0.780621374390125, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7111845164132226, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7249261264558755, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7301481445204143, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7334960305283997, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7490186077918847, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.8136853699674704, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.8370136573262008, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.833384467543261, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.8320243726685691, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8401159054391213, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8378323734379057, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8277362205885377, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8100703654501927, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7956989226754073, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8259965308617166, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8370343748715462, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8326461559980456, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.8314172424574497, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.821537274254335, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.8199699883111343, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.8110598559779172, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7938391288532309, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7811071520399709, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.8200811231056834, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.8359421057151138, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.8321069045925462, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.8310287515366845, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.5148353166710564, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.5789024998355374, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.6212610866995948, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.6662643650074891, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.6844296489543019, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.7004066139413471, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.59136190596675, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.03520946742125117, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": null, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.5817616167305147, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.5599610107141831, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.568664841463541, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.6434680575655477, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.7210213514327054, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.748040207058886, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.8192028523176726, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.3443723725879912, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": null, \"scale_coeff_lasso_shift\": 0.00064}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.FacetChart(...)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart, corr_df = model_collection.mut_param_dataset_correlation(width_scalar=200, return_data=True)\n",
    "print(corr_df.head())\n",
    "chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c3825bc",
   "metadata": {},
   "source": [
    "## Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1099a2ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Delta\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f1408b8f3ddc4e2396bfd818fe5f6887",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/27342 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4952d428723b41f0bef3c157cad6aed5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/57528 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb61d051734d44969f428634275c4120",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/56907 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 4 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05708f93c8634954a0e633129a517f8d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=35445), Label(value='0 / 35445')))…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [144, 143, 69, 145, 70, 211, 25, 26, 24, 157, 158, 898],\n",
      "dropping: 9793 variantswhich have mutations at those sites.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49edfbc26b5e47b5b69d4328f25e3a1a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=32996), Label(value='0 / 32996')))…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [371], dropping 1732 variants\n",
      "Converting mutations for Delta\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c692bb55298f4414b0fe320c3b27a15f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=6380), Label(value='0 / 6380'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting mutations for Omicron_BA1\n",
      "is reference, skipping\n",
      "Converting mutations for Omicron_BA2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45e8cad6c06c4ca09063b89bb503382f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=12861), Label(value='0 / 12861')))…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Delta\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cec4015ae36e49c08108f9bf055b4df1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/27945 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94a41b71590441bb8681b91879639ca7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50638 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d831d5fa1df4b3abb2a2973a28f99ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/54489 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 4 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e57a33bc2a146178616b97256cea862",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=33268), Label(value='0 / 33268')))…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [145, 70, 144, 143, 69, 211, 422, 26, 24, 25, 157, 158],\n",
      "dropping: 9059 variantswhich have mutations at those sites.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ff84f7cc48e44dc8e800f11bf90eb2c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=31004), Label(value='0 / 31004')))…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [371], dropping 1643 variants\n",
      "Converting mutations for Delta\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "445ff54b452e4ba788c861c82b3a6ebf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=6516), Label(value='0 / 6516'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting mutations for Omicron_BA1\n",
      "is reference, skipping\n",
      "Converting mutations for Omicron_BA2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c21d9143301462d81198df7d1b2f70c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=12347), Label(value='0 / 12347')))…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_frac=0.8\n",
    "seed=4\n",
    "train, test = [], {}\n",
    "for replicate, fs_df in func_score_df.groupby(\"replicate\"):\n",
    "\n",
    "    dataset = fs_df.sample(frac=1, random_state=seed)\n",
    "    train_split, test_split = dataset.iloc[:int(len(dataset) * train_frac)], dataset.iloc[int(len(dataset) * train_frac):]\n",
    "    name = f\"rep-{replicate}\"\n",
    "    \n",
    "    train.append(\n",
    "        multidms.Data(\n",
    "            train_split,\n",
    "            collapse_identical_variants=\"mean\",\n",
    "            reference=\"Omicron_BA1\", \n",
    "            alphabet = multidms.AAS_WITHSTOP_WITHGAP, \n",
    "            verbose=True,\n",
    "            name = name\n",
    "        )\n",
    "    )\n",
    "\n",
    "    test[name] = test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0ce07b8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_params[\"dataset\"] = train \n",
    "_, _, models_cv = multidms.model_collection.fit_models(fit_params, n_threads = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e4b14aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "mc = multidms.model_collection.ModelCollection(models_cv)\n",
    "mc.add_validation_loss(test, overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "56c50e17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset_name</th>\n",
       "      <th>scale_coeff_lasso_shift</th>\n",
       "      <th>condition</th>\n",
       "      <th>loss</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.217511</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.222574</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.227321</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.235090</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>0.00004</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.249183</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dataset_name scale_coeff_lasso_shift condition      loss     split\n",
       "0        rep-1                     0.0     Delta  0.217511  training\n",
       "1        rep-1                0.000005     Delta  0.222574  training\n",
       "2        rep-1                 0.00001     Delta  0.227321  training\n",
       "3        rep-1                 0.00002     Delta  0.235090  training\n",
       "4        rep-1                 0.00004     Delta  0.249183  training"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validation_df = mc.get_conditional_loss_df()\n",
    "cross_validation_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "99afacd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x750 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saveas=\"shrinkage_analysis_trace_plots_beta\"\n",
    "\n",
    "fig, ax = plt.subplots(3, figsize=[4.5, 7.5], sharex=True)\n",
    "\n",
    "# replicate correlation\n",
    "iter_ax = ax[0]\n",
    "sns.lineplot(\n",
    "    data=(\n",
    "        corr_df\n",
    "        .query(\"mut_param.str.contains('shift')\")\n",
    "        .rename({\"mut_param\":\"shift params\"}, axis=1)\n",
    "        # .replace({\"Data-1\":\"rep-1\", \"Data-2\":\"rep-2\"})\n",
    "        .replace({\"shift_Delta\":\"Delta\", \"shift_Omicron_BA2\":\"BA.2\"})\n",
    "        .assign(\n",
    "            scale_coeff_lasso_shift = [\n",
    "                f\"{l:.1e}\" \n",
    "                for l in corr_df.query(\"mut_param.str.contains('shift')\").scale_coeff_lasso_shift\n",
    "            ],\n",
    "            correlation = lambda x: x.correlation**2\n",
    "        )\n",
    "        .reset_index(drop=True)\n",
    "    ),\n",
    "    x=\"scale_coeff_lasso_shift\",\n",
    "    y=\"correlation\",\n",
    "    style=\"shift params\",\n",
    "    markers=True,\n",
    "    ax=iter_ax,\n",
    "    linewidth=3,\n",
    "    color=\"black\"\n",
    ")\n",
    "iter_ax.set_ylabel(\"rep1 v. rep2\\nshift $(R^2)$\")\n",
    "# move legend outside of plot\n",
    "iter_ax.legend(\n",
    "    bbox_to_anchor = (1, 1), \n",
    "    loc='upper left', \n",
    "    frameon=False\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "# plot loss\n",
    "iter_ax = ax[1]\n",
    "sns.lineplot(\n",
    "    data = (\n",
    "        cross_validation_df.query(\"condition=='total'\")\n",
    "        .assign(\n",
    "            # lasso_strength = [f\"{l:.1e}\" for l in sparsity_df.scale_coeff_lasso_shift]\n",
    "            # lasso_strength = lambda x: f\"{x.scale_coeff_lasso_shift:.1e}\"\n",
    "            lasso_strength = lambda x: x['scale_coeff_lasso_shift'].apply(lambda y: f'{y:.1e}')\n",
    "        )\n",
    "    ),\n",
    "    x=\"lasso_strength\",\n",
    "    y=\"loss\",\n",
    "    ax=iter_ax,\n",
    "    hue=\"split\",\n",
    "    style=\"dataset_name\",\n",
    "    palette={\"training\":\"slategrey\", \"validation\":\"#2CA02C\"},\n",
    "    markers=True,\n",
    "    linewidth=3\n",
    ")\n",
    "# move legend outside of plot\n",
    "iter_ax.legend(\n",
    "    bbox_to_anchor = (1, 1), \n",
    "    loc='upper left', \n",
    "    frameon=False\n",
    ")\n",
    "\n",
    "\n",
    "# plot sparsity\n",
    "iter_ax = ax[2]\n",
    "sns.lineplot(\n",
    "    data=(\n",
    "        sparsity_df\n",
    "        .rename({\"dataset_name\":\"replicate\"}, axis=1)\n",
    "        .rename({\"mut_param\":\"shift params\", \"mut_type\":\"mutation type\"}, axis=1)\n",
    "        # .replace({\"Data-0\":\"rep-1\", \"Data-1\":\"rep-2\"})\n",
    "        .replace({\"nonsynonymous\":\"nonsynonymous\", \"stop\":\"stop\"})\n",
    "        .replace({\"shift_Delta\":\"Delta\", \"shift_Omicron_BA2\":\"BA.2\"})\n",
    "        .assign(\n",
    "            scale_coeff_lasso_shift = [f\"{l:.1e}\" for l in sparsity_df.scale_coeff_lasso_shift],\n",
    "            sparsity_percent = lambda x: x.sparsity * 100,\n",
    "        )\n",
    "    ),\n",
    "    x=\"scale_coeff_lasso_shift\",\n",
    "    y=\"sparsity_percent\",\n",
    "    hue=\"mutation type\",\n",
    "    style=\"replicate\",\n",
    "    palette={\"nonsynonymous\":\"grey\", \"stop\":\"#E377C2\"},\n",
    "    markers=True,\n",
    "    legend=True,\n",
    "    ax=iter_ax,\n",
    "    linewidth=3\n",
    ")\n",
    "# move legend outside of plot\n",
    "iter_ax.legend(\n",
    "    bbox_to_anchor = (1, 1), \n",
    "    loc='upper left', \n",
    "    frameon=False\n",
    ")\n",
    "# rotate x labels\n",
    "iter_ax.set_xticklabels(\n",
    "    iter_ax.get_xticklabels(), \n",
    "    rotation=90, \n",
    "    ha='center'\n",
    ")\n",
    "iter_ax.set_ylabel(\"sparsity\\n$(\\%\\Delta=0)$\")\n",
    "iter_ax.set_xlabel(f\"lasso regularization strength ($\\lambda$)\")\n",
    "\n",
    "for axes in ax:\n",
    "    axes.axvline(\n",
    "        f\"{chosen_lasso_strength:.1e}\", \n",
    "        color=\"grey\",\n",
    "        linewidth=10,\n",
    "        alpha=0.35\n",
    "    )\n",
    "\n",
    "sns.despine(fig)\n",
    "plt.tight_layout()\n",
    "# plt.tight_layout()\n",
    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
    "# plt.show()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298ecafb-3806-4cf9-a4d2-e390e8c5b2d4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Global epistasis fits\n",
    "\n",
    "Here, we take a look at the fit of the sigmoidal global epistasis function (at the chosen lasso coefficient of 5e-5) to the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7ef04f4-6905-4c6f-8150-8fa2149d3ec0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "For each replicate at the chosen lasso strength, we get the training data predictions using `model.get_variants_df`, and use `model.get_condition_params` paried with `model.model_components` for visualizing the global epistasis function with the current model parameters. See the function docs strings for the relevant details of each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "641e76a5-0c30-48c9-ab3a-e60fd6532ee0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "chosen_replicate_models = models.query(\"scale_coeff_lasso_shift == @chosen_lasso_strength\")\n",
    "replicate_data = {}\n",
    "for row_idx, replicate_row in chosen_replicate_models.iterrows():\n",
    "    model = replicate_row[model_name]\n",
    "\n",
    "    # get training data variants and their respective \n",
    "    mut_df_replicates = model.get_variants_df(phenotype_as_effect=False)\n",
    "\n",
    "    # find the low/high bound of the training data and use those to make\n",
    "    # global epistasis predictions across the range for plotting\n",
    "    xlb, xub = [-1, 1] + np.quantile(mut_df_replicates.predicted_latent, [0.05, 1.0])\n",
    "    additive_model_grid = np.linspace(xlb, xub, num=1000)\n",
    "\n",
    "    # make predictions on hypothetical data points between lower, and upper bound\n",
    "    current_params = model.get_condition_params(model.data.reference)\n",
    "    latent_preds = model.model_components[\"g\"](current_params[\"theta\"], additive_model_grid)\n",
    "    shape = (additive_model_grid, latent_preds)   \n",
    "\n",
    "    # save and organize the data for plotting\n",
    "    replicate_data[replicate_row.replicate] = {\n",
    "        \"variants_df\" : mut_df_replicates,\n",
    "        \"wildtype_df\" : model.wildtype_df,\n",
    "        \"epistasis_shape\" : shape,\n",
    "        \"condition_colors\" : model.data.condition_colors\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f09023eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([1, 2])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replicate_data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0348eef2-a50e-4b71-a277-848a0b480b4a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Plot the observed functional scores of a random sample of all variants (20%), as function of both latent phenotype prediction (top), and functional score phenotype prediction (bottom)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ba43db23-d144-445e-8962-fe9faa887440",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saveas=\"global_epistasis_and_prediction_correlations\"\n",
    "fig, ax = plt.subplots(2,2, figsize=[6.4,6], sharey='row')    \n",
    "\n",
    "row=0\n",
    "for replicate, data in replicate_data.items():\n",
    "\n",
    "    iter_ax = ax[row, replicate-1]\n",
    "    sns.scatterplot(\n",
    "        data=data[\"variants_df\"].sample(frac=0.2),\n",
    "        x=\"predicted_latent\",\n",
    "        y=f\"func_score\",\n",
    "        hue=\"condition\",\n",
    "        palette=model.data.condition_colors,\n",
    "        ax=iter_ax,\n",
    "        legend=False,\n",
    "        size=5,\n",
    "        alpha=0.2,\n",
    "        # lw=3\n",
    "    )\n",
    "    \n",
    "    for condition, values in data[\"wildtype_df\"].iterrows():\n",
    "        iter_ax.axvline(\n",
    "            values.predicted_latent,\n",
    "            label=condition,\n",
    "            c=model.data.condition_colors[condition],\n",
    "            lw=2,\n",
    "        )\n",
    "    \n",
    "    iter_ax.plot(*data[\"epistasis_shape\"], color=\"k\", lw=2)\n",
    "    \n",
    "    xlb, xub = [-1, 1] + np.quantile(data[\"variants_df\"].predicted_latent, [0.05, 1.0])\n",
    "    ylb, yub = [-1, 1] + np.quantile(data[\"variants_df\"].func_score, [0.05, 1.0])\n",
    "    iter_ax.set_xlim([xlb, xub])\n",
    "    iter_ax.set_ylim([ylb, yub])\n",
    "    iter_ax.set_title(f\"replicate {replicate}\")\n",
    "    iter_ax.set_ylabel(\"observed\\nfunctional score\")\n",
    "    iter_ax.set_xlabel(\"predicted latent phenotype ($\\phi$)\")\n",
    "\n",
    "row=1\n",
    "for replicate, data in replicate_data.items():\n",
    "\n",
    "    iter_ax = ax[row, replicate-1]\n",
    "    sns.scatterplot(\n",
    "        data=data[\"variants_df\"].sample(frac=0.1),\n",
    "        x=\"predicted_func_score\",\n",
    "        y=f\"func_score\",\n",
    "        hue=\"condition\",\n",
    "        palette=model.data.condition_colors,\n",
    "        ax=iter_ax,\n",
    "        legend=False,\n",
    "        size=5,\n",
    "        alpha=0.2\n",
    "    )\n",
    "    \n",
    "    for condition, values in data[\"wildtype_df\"].iterrows():\n",
    "        iter_ax.axvline(\n",
    "            values.predicted_latent,\n",
    "            label=condition,\n",
    "            c=model.data.condition_colors[condition],\n",
    "            lw=2,\n",
    "        )\n",
    "    \n",
    "    iter_ax.set_ylabel(\"observed\\nfunctional score\")\n",
    "    iter_ax.set_xlabel(\"predicted functional score\")\n",
    "\n",
    "    start_y = 0.9\n",
    "    for c, cdf in data[\"variants_df\"].groupby(\"condition\"):\n",
    "        r = pearsonr(\n",
    "            cdf[\"predicted_func_score\"],\n",
    "            cdf[\"func_score\"]\n",
    "        )[0]\n",
    "        iter_ax.annotate(\n",
    "            f\"$r = {r:.2f}$\",\n",
    "            (0.1, start_y),\n",
    "            xycoords=\"axes fraction\",\n",
    "            fontsize=12,\n",
    "            c=model.data.condition_colors[c],\n",
    "        )\n",
    "        start_y += -0.1\n",
    "\n",
    "\n",
    "elements = [\n",
    "    mlines.Line2D([], [], color=color, marker='o', linestyle='None',markersize=5, label=condition)\n",
    "    for condition, color in replicate_data[1][\"condition_colors\"].items()\n",
    "]\n",
    "\n",
    "\n",
    "ax[0, 0].legend(\n",
    "    handles=elements, \n",
    "    bbox_to_anchor = (0., .99), \n",
    "    loc='upper left', \n",
    "    frameon=False, \n",
    "    fontsize=9\n",
    ")\n",
    "    \n",
    "    \n",
    "plt.tight_layout()\n",
    "fig.subplots_adjust(wspace=0.05)\n",
    "\n",
    "ax[0,0].text(\n",
    "    -0.1, 1.00, \n",
    "    f\"A\", \n",
    "    ha=\"right\", va=\"center\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=ax[0,0].transAxes\n",
    ")\n",
    "ax[1,0].text(\n",
    "    -0.1, 1.00, \n",
    "    f\"B\", \n",
    "    ha=\"right\", va=\"center\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=ax[1,0].transAxes\n",
    ")\n",
    "\n",
    "\n",
    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5dc5e8b-f236-4b17-8104-eeaa4b53a63f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Shifted mutations (interactive altair chart)\n",
    "\n",
    "The easiest way to view shifted mutations is to create an interactive `altair` chart using `multidms.plot.mut_shift_plot`. This function can take a single model, or a collection of models in a dictionary if you want to visualize the aggregated (mean) results of shared mutations between models. Toggle the drop down for the cell below to see details on using this function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5c3ffdf7-acdc-4028-ae11-80628782da5b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function mut_param_heatmap in module multidms.model_collection:\n",
      "\n",
      "mut_param_heatmap(self, query=None, mut_param='shift', aggregate_func='mean', inner_merge_dataset_muts=True, times_seen_threshold=0, phenotype_as_effect=True, **kwargs)\n",
      "    Create lineplot and heatmap altair chart\n",
      "    across replicate datasets.\n",
      "    This function optionally applies a given `pandas.query`\n",
      "    on the fit_models dataframe that should result in a subset of\n",
      "    fit's which make sense to aggregate mutational data across, e.g.\n",
      "    replicate datasets.\n",
      "    It then computes the mean or median mutational parameter value\n",
      "    (\"beta\", \"shift\", or \"predicted_func_score\")\n",
      "    between the remaining fits. and creates an interactive altair chart.\n",
      "    \n",
      "    \n",
      "    Note that this will throw an error if the queried fits have more\n",
      "    than one unique hyper-parameter besides \"dataset_name\".\n",
      "    \n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    query : str\n",
      "        The query to apply to the fit_models dataframe. This should be\n",
      "        used to subset the fits to only those which make sense to aggregate\n",
      "        mutational data across, e.g. replicate datasets.\n",
      "        For example, if you have a collection of\n",
      "        fits with different epistatic models, you may want to query\n",
      "        for only those fits with the same epistatic model. e.g.\n",
      "        `query=\"epistatic_model == 'Sigmoid'\"`. For more on the query\n",
      "        syntax, see the\n",
      "        `pandas.query <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html>`_\n",
      "        documentation.\n",
      "    mut_param : str, optional\n",
      "        The mutational parameter to plot. The default is \"shift\".\n",
      "        Must be one of \"shift\", \"predicted_func_score\", or \"beta\".\n",
      "    aggregate_func : str, optional\n",
      "        The function to aggregate the mutational parameter values\n",
      "        between dataset fits. The default is \"mean\".\n",
      "    inner_merge_dataset_muts : bool, optional\n",
      "        Whether to toss mutations which are _not_ shared across all datasets\n",
      "        before aggregation of group mutation parameter values.\n",
      "        The default is True.\n",
      "    times_seen_threshold : int, optional\n",
      "        The minimum number of times a mutation must be seen across\n",
      "        all conditions within a single fit to be included in the\n",
      "        aggregation. The default is 0.\n",
      "    phenotype_as_effect : bool, optional\n",
      "        Passed to `Model.get_mutations_df()`,\n",
      "        Only applies if `mut_param=\"predicted_func_score\"`.\n",
      "    **kwargs : dict\n",
      "        Keyword arguments to pass to\n",
      "        :func:`multidms.plot._lineplot_and_heatmap`.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    altair.Chart\n",
      "        A chart object which can be displayed in a jupyter notebook\n",
      "        or saved to a file.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(multidms.ModelCollection.mut_param_heatmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54c5f17c-4e75-4a6c-8e11-703ddbdd9ffb",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Here, we create the interactive chart by feeding the function a dictionary containing the two replicate models, and specifying:\n",
    "\n",
    "1. times_seen_threshold = 1, meaning for a mutation to be included, it must be seen at least once in every condition\n",
    "2. inlcude_beta = False, we only wish to see the shifted parameters visualized, not the respective _effect_ (beta) parameters. (note that respective effect values will be added as a tooltip when hovering over any shift mutation).\n",
    "\n",
    "To view the chart, toggle the output of the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dd89416e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mc = multidms.ModelCollection(models.drop(columns=\"replicate\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6d450c2b-2f9e-4313-ad40-6c8221beb8f2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cache miss - this could take a moment\n"
     ]
    },
    {
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\"_stat_site_order\": 1196}, {\"site\": 1215, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 1202}, {\"site\": 160, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 149}, {\"site\": 170, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 159}, {\"site\": 200, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 189}, {\"site\": 204, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 193}, {\"site\": 248, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 236}, {\"site\": 265, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 253}, {\"site\": 266, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 254}, {\"site\": 269, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 257}, {\"site\": 279, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 267}, {\"site\": 28, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 24}, {\"site\": 313, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 301}, {\"site\": 351, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 339}, {\"site\": 365, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 353}, {\"site\": 369, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 357}, {\"site\": 37, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 33}, {\"site\": 38, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 34}, {\"site\": 380, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 367}, {\"site\": 396, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 383}, {\"site\": 421, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 408}, {\"site\": 423, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 410}, {\"site\": 449, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 436}, {\"site\": 451, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 438}, {\"site\": 453, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 440}, {\"site\": 473, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 460}, {\"site\": 489, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 476}, {\"site\": 495, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 482}, {\"site\": 501, \"wildtype\": \"Y\", \"wildtype_Delta\": \"N\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 488}, {\"site\": 508, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 495}, {\"site\": 612, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 599}, {\"site\": 636, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 623}, {\"site\": 655, \"wildtype\": \"Y\", \"wildtype_Delta\": \"H\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 642}, {\"site\": 660, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 647}, {\"site\": 674, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 661}, {\"site\": 695, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 682}, {\"site\": 707, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 694}, {\"site\": 741, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 728}, {\"site\": 756, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 743}, {\"site\": 789, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 776}, {\"site\": 796, \"wildtype\": \"Y\", \"wildtype_Delta\": \"D\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 783}, {\"site\": 837, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 824}, {\"site\": 873, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 860}, {\"site\": 904, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 891}, {\"site\": 91, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 85}, {\"site\": 917, \"wildtype\": \"Y\", \"wildtype_Delta\": \"Y\", \"wildtype_Omicron_BA2\": \"Y\", \"_stat_site_order\": 904}, {\"site\": 422, \"wildtype\": \"N\", \"wildtype_Delta\": \"N\", \"wildtype_Omicron_BA2\": \"N\", \"_stat_site_order\": 409}, {\"site\": 818, \"wildtype\": \"I\", \"wildtype_Delta\": \"I\", \"wildtype_Omicron_BA2\": \"I\", \"_stat_site_order\": 805}, {\"site\": 898, \"wildtype\": \"F\", \"wildtype_Delta\": \"F\", \"wildtype_Omicron_BA2\": \"F\", \"_stat_site_order\": 885}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart = mc.mut_param_heatmap(query=f\"scale_coeff_lasso_shift == {chosen_lasso_strength}\", times_seen_threshold=times_seen_threshold)\n",
    "chart.save(f\"{output_dir}/interactive_shift_chart.html\")\n",
    "chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49e626f6-9cbe-4d40-ad9c-d7e8d56538d2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Shifted mutations (manually queried)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "50d97f84",
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_replicate_muts(fit_dict, predicted_func_scores = False, how=\"inner\", **kwargs):\n",
    "    \"\"\"\n",
    "    Take a dictionary of fit objects, with key's as the prefix for individual\n",
    "    replicate values, and merge then such that all individual and average mutation\n",
    "    values are present in both.\n",
    "    \"\"\"\n",
    "    # obtain and curate each of the replicate mutational dataframes\n",
    "    mutations_dfs = []\n",
    "    for replicate, fit in fit_dict.items():\n",
    "        fit_mut_df = fit.get_mutations_df(**kwargs)\n",
    "        # drop all \"predicted_func_score\" and \"times seen\" columns\n",
    "        fit_mut_df = fit_mut_df.drop(\n",
    "            [c for c in fit_mut_df.columns if \"times_seen\" in c], \n",
    "            axis=1\n",
    "        )\n",
    "\n",
    "        new_column_name_map = {c: f\"{replicate}_{c}\" for c in fit_mut_df.columns}\n",
    "        fit_mut_df = fit_mut_df.rename(new_column_name_map, axis=1)\n",
    "\n",
    "        mutations_dfs.append(fit_mut_df)\n",
    "\n",
    "    # merge each of the replicate mutational dataframes\n",
    "    mut_df = reduce(\n",
    "        lambda left, right: pd.merge(\n",
    "            left, right, left_index=True, right_index=True, how=how\n",
    "        ),\n",
    "        mutations_dfs,\n",
    "    )\n",
    "\n",
    "    column_order = []\n",
    "    # now compute replicate averages\n",
    "    for c in fit.mutations_df.columns:\n",
    "\n",
    "        if not predicted_func_scores and \"predicted_func_score\" in c:\n",
    "            continue\n",
    "\n",
    "        if c == \"mutation\" or \"times_seen\" in c: # or \"predicted_func_score\" in c:\n",
    "            continue\n",
    "        \n",
    "        cols_to_combine = [f\"{replicate}_{c}\" for replicate in fit_dict.keys()]\n",
    "\n",
    "        # just keep one replicate wt, site, mut .. as they are shared.\n",
    "        if c in [\"wts\", \"sites\", \"muts\"]:\n",
    "            mut_df[c] = mut_df[cols_to_combine[0]]\n",
    "            mut_df.drop(cols_to_combine, axis=1, inplace=True)\n",
    "\n",
    "        # take the average.\n",
    "        else:\n",
    "            mut_df[f\"avg_{c}\"] = mut_df[cols_to_combine].mean(axis=1)\n",
    "            column_order += cols_to_combine + [f\"avg_{c}\"]\n",
    "\n",
    "    return mut_df.loc[:, [\"wts\", \"sites\", \"muts\"] + column_order]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "dfc964eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wts</th>\n",
       "      <th>sites</th>\n",
       "      <th>muts</th>\n",
       "      <th>1_beta</th>\n",
       "      <th>2_beta</th>\n",
       "      <th>avg_beta</th>\n",
       "      <th>1_shift_Delta</th>\n",
       "      <th>2_shift_Delta</th>\n",
       "      <th>avg_shift_Delta</th>\n",
       "      <th>1_shift_Omicron_BA2</th>\n",
       "      <th>...</th>\n",
       "      <th>avg_shift_Omicron_BA2</th>\n",
       "      <th>1_predicted_func_score_Delta</th>\n",
       "      <th>2_predicted_func_score_Delta</th>\n",
       "      <th>avg_predicted_func_score_Delta</th>\n",
       "      <th>1_predicted_func_score_Omicron_BA1</th>\n",
       "      <th>2_predicted_func_score_Omicron_BA1</th>\n",
       "      <th>avg_predicted_func_score_Omicron_BA1</th>\n",
       "      <th>1_predicted_func_score_Omicron_BA2</th>\n",
       "      <th>2_predicted_func_score_Omicron_BA2</th>\n",
       "      <th>avg_predicted_func_score_Omicron_BA2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mutation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M1I</th>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "      <td>-2.924932</td>\n",
       "      <td>-4.256726</td>\n",
       "      <td>-3.590829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.162696</td>\n",
       "      <td>-3.348626</td>\n",
       "      <td>-3.255661</td>\n",
       "      <td>-3.009065</td>\n",
       "      <td>-3.236155</td>\n",
       "      <td>-3.122610</td>\n",
       "      <td>-3.085516</td>\n",
       "      <td>-3.409368</td>\n",
       "      <td>-3.247442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2L</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>L</td>\n",
       "      <td>0.200928</td>\n",
       "      <td>0.207115</td>\n",
       "      <td>0.204021</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.204654</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.102327</td>\n",
       "      <td>0.287689</td>\n",
       "      <td>0.366644</td>\n",
       "      <td>0.327167</td>\n",
       "      <td>0.405955</td>\n",
       "      <td>0.487242</td>\n",
       "      <td>0.446598</td>\n",
       "      <td>-0.200109</td>\n",
       "      <td>0.107339</td>\n",
       "      <td>-0.046385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2S</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>0.194773</td>\n",
       "      <td>-0.074343</td>\n",
       "      <td>0.060215</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.275053</td>\n",
       "      <td>-0.286178</td>\n",
       "      <td>-0.005562</td>\n",
       "      <td>0.393345</td>\n",
       "      <td>-0.166355</td>\n",
       "      <td>0.113495</td>\n",
       "      <td>0.195843</td>\n",
       "      <td>-0.525584</td>\n",
       "      <td>-0.164871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2V</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>0.239144</td>\n",
       "      <td>-0.030672</td>\n",
       "      <td>0.104236</td>\n",
       "      <td>-0.086489</td>\n",
       "      <td>-0.153066</td>\n",
       "      <td>-0.119778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.188819</td>\n",
       "      <td>-0.521577</td>\n",
       "      <td>-0.166379</td>\n",
       "      <td>0.484417</td>\n",
       "      <td>-0.069216</td>\n",
       "      <td>0.207601</td>\n",
       "      <td>0.286191</td>\n",
       "      <td>-0.431950</td>\n",
       "      <td>-0.072880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3A</th>\n",
       "      <td>V</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>-0.007044</td>\n",
       "      <td>-0.047157</td>\n",
       "      <td>-0.027101</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001301</td>\n",
       "      <td>-0.133532</td>\n",
       "      <td>-0.225989</td>\n",
       "      <td>-0.179760</td>\n",
       "      <td>-0.013975</td>\n",
       "      <td>-0.106083</td>\n",
       "      <td>-0.060029</td>\n",
       "      <td>-0.206590</td>\n",
       "      <td>-0.473092</td>\n",
       "      <td>-0.339841</td>\n",
       "    </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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252T</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>T</td>\n",
       "      <td>-0.132241</td>\n",
       "      <td>-0.189524</td>\n",
       "      <td>-0.160882</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.074971</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.037485</td>\n",
       "      <td>-0.379343</td>\n",
       "      <td>-0.533716</td>\n",
       "      <td>-0.456529</td>\n",
       "      <td>-0.258642</td>\n",
       "      <td>-0.414259</td>\n",
       "      <td>-0.336450</td>\n",
       "      <td>-0.586871</td>\n",
       "      <td>-0.763869</td>\n",
       "      <td>-0.675370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252V</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>V</td>\n",
       "      <td>0.161672</td>\n",
       "      <td>0.177089</td>\n",
       "      <td>0.169381</td>\n",
       "      <td>0.262923</td>\n",
       "      <td>-0.185348</td>\n",
       "      <td>0.038788</td>\n",
       "      <td>-0.044192</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.080998</td>\n",
       "      <td>0.750125</td>\n",
       "      <td>-0.138738</td>\n",
       "      <td>0.305693</td>\n",
       "      <td>0.325693</td>\n",
       "      <td>0.414606</td>\n",
       "      <td>0.370149</td>\n",
       "      <td>0.039893</td>\n",
       "      <td>-0.233608</td>\n",
       "      <td>-0.096858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252W</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>W</td>\n",
       "      <td>0.046494</td>\n",
       "      <td>0.283281</td>\n",
       "      <td>0.164887</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015843</td>\n",
       "      <td>-0.026414</td>\n",
       "      <td>0.553441</td>\n",
       "      <td>0.263513</td>\n",
       "      <td>0.092733</td>\n",
       "      <td>0.674209</td>\n",
       "      <td>0.383471</td>\n",
       "      <td>-0.064218</td>\n",
       "      <td>0.168365</td>\n",
       "      <td>0.052073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252Y</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.349203</td>\n",
       "      <td>0.464681</td>\n",
       "      <td>0.406942</td>\n",
       "      <td>-0.103307</td>\n",
       "      <td>-0.228062</td>\n",
       "      <td>-0.165685</td>\n",
       "      <td>-0.029801</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.107036</td>\n",
       "      <td>0.380213</td>\n",
       "      <td>0.438552</td>\n",
       "      <td>0.409383</td>\n",
       "      <td>0.711687</td>\n",
       "      <td>1.132873</td>\n",
       "      <td>0.922280</td>\n",
       "      <td>0.450806</td>\n",
       "      <td>0.282706</td>\n",
       "      <td>0.366756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252*</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.069944</td>\n",
       "      <td>-0.002437</td>\n",
       "      <td>-0.036191</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071298</td>\n",
       "      <td>0.035649</td>\n",
       "      <td>0.113153</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020770</td>\n",
       "      <td>-0.257914</td>\n",
       "      <td>0.038034</td>\n",
       "      <td>-0.109940</td>\n",
       "      <td>-0.137814</td>\n",
       "      <td>-0.005529</td>\n",
       "      <td>-0.071672</td>\n",
       "      <td>-0.107910</td>\n",
       "      <td>-0.524964</td>\n",
       "      <td>-0.316437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5934 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         wts  sites muts    1_beta    2_beta  avg_beta  1_shift_Delta  \\\n",
       "mutation                                                                \n",
       "M1I        M      1    I -2.924932 -4.256726 -3.590829       0.000000   \n",
       "F2L        F      2    L  0.200928  0.207115  0.204021      -0.000000   \n",
       "F2S        F      2    S  0.194773 -0.074343  0.060215      -0.000000   \n",
       "F2V        F      2    V  0.239144 -0.030672  0.104236      -0.086489   \n",
       "V3A        V      3    A -0.007044 -0.047157 -0.027101      -0.000000   \n",
       "...       ..    ...  ...       ...       ...       ...            ...   \n",
       "S1252T     S   1252    T -0.132241 -0.189524 -0.160882      -0.000000   \n",
       "S1252V     S   1252    V  0.161672  0.177089  0.169381       0.262923   \n",
       "S1252W     S   1252    W  0.046494  0.283281  0.164887       0.000000   \n",
       "S1252Y     S   1252    Y  0.349203  0.464681  0.406942      -0.103307   \n",
       "S1252*     S   1252    * -0.069944 -0.002437 -0.036191       0.000000   \n",
       "\n",
       "          2_shift_Delta  avg_shift_Delta  1_shift_Omicron_BA2  ...  \\\n",
       "mutation                                                       ...   \n",
       "M1I            0.000000         0.000000            -0.000000  ...   \n",
       "F2L           -0.000000         0.000000            -0.204654  ...   \n",
       "F2S            0.000000         0.000000             0.000000  ...   \n",
       "F2V           -0.153066        -0.119778             0.000000  ...   \n",
       "V3A           -0.000000         0.000000            -0.000000  ...   \n",
       "...                 ...              ...                  ...  ...   \n",
       "S1252T         0.000000         0.000000            -0.074971  ...   \n",
       "S1252V        -0.185348         0.038788            -0.044192  ...   \n",
       "S1252W         0.000000         0.000000             0.018787  ...   \n",
       "S1252Y        -0.228062        -0.165685            -0.029801  ...   \n",
       "S1252*         0.071298         0.035649             0.113153  ...   \n",
       "\n",
       "          avg_shift_Omicron_BA2  1_predicted_func_score_Delta  \\\n",
       "mutation                                                        \n",
       "M1I                    0.000000                     -3.162696   \n",
       "F2L                   -0.102327                      0.287689   \n",
       "F2S                    0.000000                      0.275053   \n",
       "F2V                    0.000000                      0.188819   \n",
       "V3A                   -0.001301                     -0.133532   \n",
       "...                         ...                           ...   \n",
       "S1252T                -0.037485                     -0.379343   \n",
       "S1252V                -0.080998                      0.750125   \n",
       "S1252W                -0.015843                     -0.026414   \n",
       "S1252Y                -0.107036                      0.380213   \n",
       "S1252*                 0.020770                     -0.257914   \n",
       "\n",
       "          2_predicted_func_score_Delta  avg_predicted_func_score_Delta  \\\n",
       "mutation                                                                 \n",
       "M1I                          -3.348626                       -3.255661   \n",
       "F2L                           0.366644                        0.327167   \n",
       "F2S                          -0.286178                       -0.005562   \n",
       "F2V                          -0.521577                       -0.166379   \n",
       "V3A                          -0.225989                       -0.179760   \n",
       "...                                ...                             ...   \n",
       "S1252T                       -0.533716                       -0.456529   \n",
       "S1252V                       -0.138738                        0.305693   \n",
       "S1252W                        0.553441                        0.263513   \n",
       "S1252Y                        0.438552                        0.409383   \n",
       "S1252*                        0.038034                       -0.109940   \n",
       "\n",
       "          1_predicted_func_score_Omicron_BA1  \\\n",
       "mutation                                       \n",
       "M1I                                -3.009065   \n",
       "F2L                                 0.405955   \n",
       "F2S                                 0.393345   \n",
       "F2V                                 0.484417   \n",
       "V3A                                -0.013975   \n",
       "...                                      ...   \n",
       "S1252T                             -0.258642   \n",
       "S1252V                              0.325693   \n",
       "S1252W                              0.092733   \n",
       "S1252Y                              0.711687   \n",
       "S1252*                             -0.137814   \n",
       "\n",
       "          2_predicted_func_score_Omicron_BA1  \\\n",
       "mutation                                       \n",
       "M1I                                -3.236155   \n",
       "F2L                                 0.487242   \n",
       "F2S                                -0.166355   \n",
       "F2V                                -0.069216   \n",
       "V3A                                -0.106083   \n",
       "...                                      ...   \n",
       "S1252T                             -0.414259   \n",
       "S1252V                              0.414606   \n",
       "S1252W                              0.674209   \n",
       "S1252Y                              1.132873   \n",
       "S1252*                             -0.005529   \n",
       "\n",
       "          avg_predicted_func_score_Omicron_BA1  \\\n",
       "mutation                                         \n",
       "M1I                                  -3.122610   \n",
       "F2L                                   0.446598   \n",
       "F2S                                   0.113495   \n",
       "F2V                                   0.207601   \n",
       "V3A                                  -0.060029   \n",
       "...                                        ...   \n",
       "S1252T                               -0.336450   \n",
       "S1252V                                0.370149   \n",
       "S1252W                                0.383471   \n",
       "S1252Y                                0.922280   \n",
       "S1252*                               -0.071672   \n",
       "\n",
       "          1_predicted_func_score_Omicron_BA2  \\\n",
       "mutation                                       \n",
       "M1I                                -3.085516   \n",
       "F2L                                -0.200109   \n",
       "F2S                                 0.195843   \n",
       "F2V                                 0.286191   \n",
       "V3A                                -0.206590   \n",
       "...                                      ...   \n",
       "S1252T                             -0.586871   \n",
       "S1252V                              0.039893   \n",
       "S1252W                             -0.064218   \n",
       "S1252Y                              0.450806   \n",
       "S1252*                             -0.107910   \n",
       "\n",
       "          2_predicted_func_score_Omicron_BA2  \\\n",
       "mutation                                       \n",
       "M1I                                -3.409368   \n",
       "F2L                                 0.107339   \n",
       "F2S                                -0.525584   \n",
       "F2V                                -0.431950   \n",
       "V3A                                -0.473092   \n",
       "...                                      ...   \n",
       "S1252T                             -0.763869   \n",
       "S1252V                             -0.233608   \n",
       "S1252W                              0.168365   \n",
       "S1252Y                              0.282706   \n",
       "S1252*                             -0.524964   \n",
       "\n",
       "          avg_predicted_func_score_Omicron_BA2  \n",
       "mutation                                        \n",
       "M1I                                  -3.247442  \n",
       "F2L                                  -0.046385  \n",
       "F2S                                  -0.164871  \n",
       "F2V                                  -0.072880  \n",
       "V3A                                  -0.339841  \n",
       "...                                        ...  \n",
       "S1252T                               -0.675370  \n",
       "S1252V                               -0.096858  \n",
       "S1252W                                0.052073  \n",
       "S1252Y                                0.366756  \n",
       "S1252*                               -0.316437  \n",
       "\n",
       "[5934 rows x 21 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mut_df_replicates = combine_replicate_muts(\n",
    "    {\n",
    "        f\"{fit.dataset_name}\".split(\"-\")[-1]: fit.model\n",
    "        for fit in models.query(f\"scale_coeff_lasso_shift == {chosen_lasso_strength}\").itertuples()\n",
    "    },\n",
    "    predicted_func_scores=True,\n",
    "    how=\"inner\",\n",
    "    times_seen_threshold=times_seen_threshold\n",
    ")\n",
    "mut_df_replicates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bb4df58-4301-4356-874d-544b1f6d6837",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "We need the sitemap of inferred wildtype amino acids at each site, for each condition. This is easily accessible via the `Model.data` attribute "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "acd8e8e4-2270-4d4d-a9b6-49ace3ae67bc",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delta</th>\n",
       "      <th>Omicron_BA1</th>\n",
       "      <th>Omicron_BA2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>V</td>\n",
       "      <td>V</td>\n",
       "      <td>V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Q</td>\n",
       "      <td>Q</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>V</td>\n",
       "      <td>V</td>\n",
       "      <td>V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>R</td>\n",
       "      <td>T</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Delta Omicron_BA1 Omicron_BA2\n",
       "10     L           L           L\n",
       "11     V           V           V\n",
       "12     S           S           S\n",
       "13     S           S           S\n",
       "14     Q           Q           Q\n",
       "15     C           C           C\n",
       "16     V           V           V\n",
       "17     N           N           N\n",
       "18     L           L           L\n",
       "19     R           T           I\n",
       "20     T           T           T"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "site_map = model.data.site_map\n",
    "site_map.loc[10:20, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "253b1f06-f9a2-4d89-ae66-9df6fa3e8356",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x900 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# some renaming and wrangling\n",
    "mut_df_replicates[\"sense\"] = [\"stop\" if mut == \"*\" else \"nonsynonymous\" for mut in mut_df_replicates.muts]\n",
    "rename_omicron = {\n",
    "    c:\"_\".join(c.split(\"_\")[:2]+[c.split(\"_\")[3]])\n",
    "    for c in mut_df_replicates.columns if \"Omicron\" in c\n",
    "}\n",
    "mut_df_replicates.rename(rename_omicron, axis=1, inplace=True)\n",
    "site_map = site_map.reset_index().rename(\n",
    "    {\"index\":\"sites\", \"Omicron_BA2\": \"BA2\", \"Omicron_BA1\":\"BA1\"}, axis=1\n",
    ").set_index(\"sites\")\n",
    "\n",
    "saveas = \"shift_by_site_heatmap_zoom\"\n",
    "\n",
    "site_ranges = {\n",
    "    \"zoom1\" : [137, 142],\n",
    "    \"zoom2\" : [416, 421],\n",
    "    \"zoom3\" : [568, 573],\n",
    "    \"zoom4\" : [843, 856],\n",
    "    \"zoom5\" : [1019, 1029]\n",
    "}\n",
    "\n",
    "# heatmap ax width ratios\n",
    "width_ratios = [(end-start) for key, (start, end) in site_ranges.items()]\n",
    "\n",
    "# make the first one a little bigger for the color bar\n",
    "width_ratios[0] += width_ratios[0] * 0.5\n",
    "\n",
    "\n",
    "# Plot domain architecture in primary sequence\n",
    "# based on structure papers (Gobeil et al., 2022, Molecular Cell)\n",
    "# (Duan et al., 2020, Frotiers in Immunology)\n",
    "domain_dict = {\n",
    "    'NTD' : [13, 293],\n",
    "    'RBD' : [330, 528],\n",
    "    'SD1' : [528, 590],\n",
    "    'SD2' : [590, 685],\n",
    "    'FP' : [815, 834],\n",
    "    'HR1' : [907, 985],\n",
    "    'CH' : [985, 1035],\n",
    "    'CD' : [1075, 1162],\n",
    "    'HR2' : [1162, 1211],\n",
    "    'TM' : [1211, 1234],\n",
    "}\n",
    "\n",
    "sort_order = [\n",
    "    \"R\",\"K\",\"H\",\"D\",\"E\",\"Q\",\"N\",\"S\",\n",
    "    \"T\",\"Y\",\"W\",\"F\",\"A\",\"I\",\"L\",\"M\",\n",
    "    \"V\",\"G\",\"P\",\"C\",\"-\",\"*\",\n",
    "]\n",
    "\n",
    "fig = plt.figure( figsize=[6.4, 9])\n",
    "axs = fig.subplot_mosaic(\n",
    "    [\n",
    "        [\"Annotation\"]*5,\n",
    "        [\"Delta\"]*5,\n",
    "        [f\"{k}_Delta\" for k in list(site_ranges.keys())],\n",
    "        [f\"{k}_Delta\" for k in list(site_ranges.keys())],\n",
    "        [\".\"]*5,\n",
    "        [\"BA2\"]*5,\n",
    "        [f\"{k}_BA2\" for k in list(site_ranges.keys())],\n",
    "        [f\"{k}_BA2\" for k in list(site_ranges.keys())]\n",
    "    ],\n",
    "    \n",
    "    height_ratios=[\n",
    "        1.5, \n",
    "        2, \n",
    "        2, \n",
    "        2,\n",
    "        0.3,\n",
    "        2,\n",
    "        2, \n",
    "        2\n",
    "    ],\n",
    "    empty_sentinel=\".\",\n",
    "    # set the width ratios between the columns\n",
    "    width_ratios=width_ratios,\n",
    "    gridspec_kw={\n",
    "        \"wspace\": 0.20,\n",
    "        \"hspace\": 0.4,\n",
    "    }\n",
    ")\n",
    "\n",
    "# derived from\n",
    "# https://matplotlib.org/stable/gallery/subplots_axes_and_figures/axes_zoom_effect.html\n",
    "def connect_bbox(bbox1, bbox2,\n",
    "                 loc1a, loc2a, loc1b, loc2b,\n",
    "                 prop_lines, prop_patches=None):\n",
    "    if prop_patches is None:\n",
    "        prop_patches = {\n",
    "            **prop_lines,\n",
    "            \"alpha\": prop_lines.get(\"alpha\", 1) * 0.2,\n",
    "            \"clip_on\": False,\n",
    "        }\n",
    "\n",
    "    c1 = BboxConnector(\n",
    "        bbox1, bbox2, loc1=loc1a, loc2=loc2a, clip_on=False, **prop_lines)\n",
    "    c2 = BboxConnector(\n",
    "        bbox1, bbox2, loc1=loc1b, loc2=loc2b, clip_on=False, **prop_lines)\n",
    "\n",
    "    bbox_patch1 = BboxPatch(bbox1, **prop_patches)\n",
    "    bbox_patch2 = BboxPatch(bbox2, **prop_patches)\n",
    "\n",
    "    p = BboxConnectorPatch(bbox1, bbox2,\n",
    "                           loc1a=loc1a, loc2a=loc2a, loc1b=loc1b, loc2b=loc2b,\n",
    "                           clip_on=False,\n",
    "                           **prop_patches)\n",
    "\n",
    "    return c1, c2, bbox_patch1, bbox_patch2, p\n",
    "\n",
    "def zoom_effect03(ax1, ax2, xmin, xmax, **kwargs):\n",
    "\n",
    "    mybbox1 = ax1.bbox\n",
    "    \n",
    "    bbox = Bbox.from_extents(xmin, 0, xmax, 1)\n",
    "    mybbox2 = TransformedBbox(bbox, ax2.get_xaxis_transform())\n",
    "\n",
    "    prop_patches = {**kwargs, \"ec\": \"none\", \"alpha\": 0.2}\n",
    "\n",
    "    c1, c2, bbox_patch1, bbox_patch2, p = connect_bbox(\n",
    "        mybbox1, mybbox2,\n",
    "        loc1a=2, loc2a=3, loc1b=1, loc2b=4,\n",
    "        prop_lines=kwargs, prop_patches=prop_patches)\n",
    "\n",
    "    ax2.add_patch(c1)\n",
    "    ax2.add_patch(c2)\n",
    "    ax2.add_patch(p)\n",
    "\n",
    "    return c1, c2, bbox_patch1, bbox_patch2, p\n",
    "\n",
    "#############\n",
    "# sitewise\n",
    "#############\n",
    "\n",
    "\n",
    "pal = sns.color_palette('colorblind')\n",
    "cs = ['darkviolet', 'orange']\n",
    "cs = {\n",
    "    'BA2' : 'darkviolet', \n",
    "    'Delta' : 'orange'\n",
    "}\n",
    "\n",
    "# Plot per-site metric\n",
    "metric_prefix = 'max_abs_shift_'\n",
    "for (i, homolog) in enumerate(['BA2', 'Delta']):\n",
    "\n",
    "    sns.scatterplot(\n",
    "        x='sites', \n",
    "        y=f'avg_shift_{homolog}',\n",
    "        data=mut_df_replicates, \n",
    "        s=15,\n",
    "        alpha=0.7,\n",
    "        edgecolor=\"grey\",\n",
    "        linewidth=0.05,\n",
    "        ax=axs[homolog], \n",
    "        color=cs[homolog],\n",
    "        label=\"\"\n",
    "    )\n",
    "    \n",
    "    nis = site_map.query(f\"{homolog} != BA1\")\n",
    "    sns.scatterplot(\n",
    "        x='sites', \n",
    "        y=np.repeat(2.9, len(nis)),\n",
    "        data=nis, \n",
    "        s=30,\n",
    "        ax=axs[homolog],\n",
    "        marker='v',\n",
    "        facecolor=cs[homolog], \n",
    "        edgecolor=\"k\"\n",
    "    )\n",
    "\n",
    "    axs[homolog].grid()\n",
    "    axs[homolog].set(\n",
    "        xlim=[-10,1260],\n",
    "        ylim=[-2, 3],\n",
    "        yticks=[-2, -1, 0, 1, 2],\n",
    "    )\n",
    "    sns.despine(ax=axs[homolog])\n",
    "    \n",
    "    axs[homolog].tick_params(\n",
    "        axis='x', \n",
    "        bottom=False,\n",
    "        labelbottom=False,\n",
    "        labeltop=True if homolog == \"Delta\" else False,\n",
    "        top=True if homolog == \"Delta\" else False,\n",
    "        size=9\n",
    "    )\n",
    "    axs[homolog].set_xlabel(None)\n",
    "    axs[homolog].set_ylabel('shift ($\\Delta_{d,m}$)', size=10)\n",
    "\n",
    "\n",
    "axs[\"BA2\"]._shared_axes['x'].join(axs[\"BA2\"], axs[\"Delta\"])\n",
    "\n",
    "plot_rectangles = True\n",
    "for zoom, site_range in site_ranges.items():\n",
    "    if not plot_rectangles:\n",
    "        continue\n",
    "    (site_i, site_j) = site_range\n",
    "    for (i, homolog) in enumerate(['BA2', 'Delta']):\n",
    "        rect = patches.Rectangle(\n",
    "            (site_i-5, -2), site_j-site_i+11, 4,\n",
    "            edgecolor='none', facecolor='0.75', zorder=0\n",
    "        )\n",
    "        axs[homolog].add_patch(rect)\n",
    "        \n",
    "        \n",
    "#############\n",
    "# Annotation\n",
    "#############\n",
    "\n",
    "for (domain, (start, end)) in domain_dict.items():\n",
    "    rectangle = patches.Rectangle((start, 1), end-start, 2, edgecolor='0.25', facecolor='white')\n",
    "    axs[\"Annotation\"].add_patch(rectangle)\n",
    "    rx, ry = rectangle.get_xy()\n",
    "    cx = rx + rectangle.get_width()/2.0\n",
    "    cy = ry - 0.05 + rectangle.get_height()/2.0\n",
    "    if domain in ['FP', 'TM']:\n",
    "        cy += 2\n",
    "    axs[\"Annotation\"].annotate(\n",
    "        domain, (cx, cy), color='black', ha='center', va='center',\n",
    "        fontsize=7\n",
    "    )\n",
    "\n",
    "axs[\"Annotation\"].set(ylim=[-0.5,4], yticks=[])\n",
    "sns.despine(left=True, bottom=True, ax=axs[\"Annotation\"])\n",
    "\n",
    "axs[\"Annotation\"].sharex(axs[\"BA2\"])\n",
    "axs[\"Annotation\"].axhline(2, c='0.25', zorder=0)\n",
    "axs[\"Annotation\"].xaxis.set_tick_params(which='both', bottom=False, labelbottom=False, labeltop=False)\n",
    "\n",
    "#############\n",
    "# Heatmap\n",
    "#############\n",
    "\n",
    "for (i, homolog) in enumerate(['Delta', 'BA2']):\n",
    "    \n",
    "    df_shifts_wide = mut_df_replicates.pivot(\n",
    "        index='muts', \n",
    "        columns='sites', \n",
    "        values=f'avg_shift_{homolog}'\n",
    "    ).loc[sort_order, :]\n",
    "\n",
    "    for zoom, (start, end) in site_ranges.items():\n",
    "        \n",
    "        iter_ax = axs[f\"{zoom}_{homolog}\"]\n",
    "        iter_ax.set_facecolor(\"lightgrey\")\n",
    "        sites = [s for s in list(range(start, end+1)) if s in df_shifts_wide.columns]\n",
    "        \n",
    "        sns.heatmap(\n",
    "            df_shifts_wide.loc[:, sites], \n",
    "            cbar=True if zoom == \"zoom1\" else False,\n",
    "            cbar_kws={\n",
    "                \"shrink\": 0.5, \n",
    "                \"location\":'left',\n",
    "                'anchor': (-1.5, 0.5),\n",
    "                'label' : None\n",
    "            },\n",
    "            ax = iter_ax,\n",
    "            linewidth=.5, \n",
    "            linecolor=\"darkgrey\",\n",
    "            center=0,\n",
    "            cmap='RdBu',\n",
    "            vmin=-2.0,\n",
    "            vmax=2.0,\n",
    "            xticklabels=False,\n",
    "            yticklabels=False,\n",
    "        )\n",
    "\n",
    "        for i, site in enumerate(sites):\n",
    "            for j, mut in enumerate(sort_order):\n",
    "                \n",
    "                is_ref_wt = True if mut == site_map.loc[site,\"BA1\"] else False\n",
    "                if is_ref_wt:\n",
    "                    iter_ax.scatter(\n",
    "                        [i+0.5], \n",
    "                        [j+0.5], \n",
    "                        marker=\"x\", \n",
    "                        s=8, \n",
    "                        c=\"black\"\n",
    "                    )\n",
    "                is_nis = (\n",
    "                    True \n",
    "                    if mut ==  site_map.loc[site,homolog] and\n",
    "                    mut !=  site_map.loc[site,\"BA1\"]\n",
    "                    else False\n",
    "                )\n",
    "                \n",
    "                if is_nis:\n",
    "                    iter_ax.scatter(\n",
    "                        [i+0.5], \n",
    "                        [j+0.5], \n",
    "                        marker=\"o\", \n",
    "                        s=12, \n",
    "                        facecolors=cs[homolog],\n",
    "                        edgecolors=\"black\"\n",
    "                    )\n",
    "\n",
    "        if zoom != \"zoom1\":\n",
    "            axs[f\"{zoom}_{homolog}\"].tick_params(axis='y', left=False, labelleft=False)\n",
    "            sns.despine(left=True, bottom=True, ax=axs[f\"{zoom}_{homolog}\"])  \n",
    "        else:\n",
    "            axs[f\"{zoom}_{homolog}\"].set_yticks(\n",
    "                [s+0.5 for s in range(len(sort_order))],\n",
    "                labels=sort_order, \n",
    "                va=\"center\",\n",
    "                size=6\n",
    "            )\n",
    "            \n",
    "        axs[f\"{zoom}_{homolog}\"].set_ylabel(None)\n",
    "            \n",
    "        if homolog != \"Delta\":\n",
    "            axs[f\"{zoom}_{homolog}\"].sharex(axs[f\"{zoom}_Delta\"])\n",
    "        axs[f\"{zoom}_{homolog}\"].set_xticks(\n",
    "            [s+0.5 for s in range(len(sites))],\n",
    "             labels=sites, \n",
    "             ha=\"center\",\n",
    "            rotation=90,\n",
    "            size=7\n",
    "        )\n",
    "        axs[f\"{zoom}_{homolog}\"].set_xlabel(None)\n",
    "\n",
    "for zoom, (start, end) in site_ranges.items():\n",
    "    for homolog in ['Delta', 'BA2']:\n",
    "        zoom_effect03(axs[f\"{zoom}_{homolog}\"], axs[homolog], start, end, alpha= 0.2)\n",
    "\n",
    "fig.text(\n",
    "    0.5, 0.05, 'sites',\n",
    "    ha='center'\n",
    ")\n",
    "\n",
    "axs[\"Delta\"].text(\n",
    "    -0.1, 1.25, \n",
    "    f\"A\", \n",
    "    ha=\"right\", va=\"center\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=axs[\"Delta\"].transAxes\n",
    ")\n",
    "axs[\"Delta\"].text(\n",
    "    0.035, 1.15, \n",
    "    f\"Delta\", \n",
    "    ha=\"left\", va=\"center\", \n",
    "    size=12,\n",
    "    transform=axs[\"Delta\"].transAxes\n",
    ")\n",
    "\n",
    "axs[\"BA2\"].text(\n",
    "    -0.1, 1.25, \n",
    "    f\"B\", \n",
    "    ha=\"right\", va=\"center\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=axs[\"BA2\"].transAxes\n",
    ")\n",
    "axs[\"BA2\"].text(\n",
    "    0.035, 1.15, \n",
    "    f\"BA.2\", \n",
    "    ha=\"left\", va=\"center\", \n",
    "    size=12,\n",
    "    transform=axs[\"BA2\"].transAxes\n",
    ")\n",
    "\n",
    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9187c02e-51a5-4c66-9d62-5422319dc433",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Comparison of results to the naive approach (independent-condition fits)\n",
    "\n",
    "here we fit a model to each homolog individually, so that we may compare the results to our joint fitting process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "45cfc79e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>func_score</th>\n",
       "      <th>aa_substitutions</th>\n",
       "      <th>condition</th>\n",
       "      <th>replicate</th>\n",
       "      <th>n_subs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>272676</th>\n",
       "      <td>0.4616</td>\n",
       "      <td>N87T L517F T1006A</td>\n",
       "      <td>Delta</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272787</th>\n",
       "      <td>1.5096</td>\n",
       "      <td>D80L</td>\n",
       "      <td>Delta</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272779</th>\n",
       "      <td>-0.7202</td>\n",
       "      <td>A1026V D1168Y</td>\n",
       "      <td>Delta</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272786</th>\n",
       "      <td>1.1890</td>\n",
       "      <td>G75W K1154E</td>\n",
       "      <td>Delta</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272781</th>\n",
       "      <td>-1.2116</td>\n",
       "      <td>T307S S803L A893V</td>\n",
       "      <td>Delta</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930492</th>\n",
       "      <td>-3.5000</td>\n",
       "      <td>K182R N405L S408* T941S D1163Y</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930493</th>\n",
       "      <td>-0.7129</td>\n",
       "      <td>P82S S112T D138T K1038N</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930494</th>\n",
       "      <td>-0.8500</td>\n",
       "      <td>L179P A222T G261H N405D</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930495</th>\n",
       "      <td>-2.3933</td>\n",
       "      <td>S27F G413E</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930497</th>\n",
       "      <td>0.6685</td>\n",
       "      <td>A222V S1242I</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>689433 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        func_score                aa_substitutions    condition  replicate  \\\n",
       "272676      0.4616               N87T L517F T1006A        Delta          1   \n",
       "272787      1.5096                            D80L        Delta          1   \n",
       "272779     -0.7202                   A1026V D1168Y        Delta          1   \n",
       "272786      1.1890                     G75W K1154E        Delta          1   \n",
       "272781     -1.2116               T307S S803L A893V        Delta          1   \n",
       "...            ...                             ...          ...        ...   \n",
       "930492     -3.5000  K182R N405L S408* T941S D1163Y  Omicron_BA2          2   \n",
       "930493     -0.7129         P82S S112T D138T K1038N  Omicron_BA2          2   \n",
       "930494     -0.8500         L179P A222T G261H N405D  Omicron_BA2          2   \n",
       "930495     -2.3933                      S27F G413E  Omicron_BA2          2   \n",
       "930497      0.6685                    A222V S1242I  Omicron_BA2          2   \n",
       "\n",
       "        n_subs  \n",
       "272676       3  \n",
       "272787       1  \n",
       "272779       2  \n",
       "272786       2  \n",
       "272781       3  \n",
       "...        ...  \n",
       "930492       5  \n",
       "930493       4  \n",
       "930494       4  \n",
       "930495       2  \n",
       "930497       2  \n",
       "\n",
       "[689433 rows x 5 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func_score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dc7b6777-c3ca-4455-b9d9-a68c8cc3d7f1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Delta\n"
     ]
    },
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       "  0%|          | 0/28515 [00:00<?, ?it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 8 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=3565), Label(value='0 / 3565'))), …"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [],\n",
      "dropping: 0 variantswhich have mutations at those sites.\n"
     ]
    },
    {
     "data": {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [], dropping 0 variants\n",
      "Converting mutations for Delta\n",
      "is reference, skipping\n",
      "inferring site map for Omicron_BA1\n"
     ]
    },
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       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/70597 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 8 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=8825), Label(value='0 / 8825'))), …"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [],\n",
      "dropping: 0 variantswhich have mutations at those sites.\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [], dropping 0 variants\n",
      "Converting mutations for Omicron_BA1\n",
      "is reference, skipping\n",
      "inferring site map for Omicron_BA2\n"
     ]
    },
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       "  0%|          | 0/60397 [00:00<?, ?it/s]"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 8 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=7550), Label(value='0 / 7550'))), …"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [],\n",
      "dropping: 0 variantswhich have mutations at those sites.\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [], dropping 0 variants\n",
      "Converting mutations for Omicron_BA2\n",
      "is reference, skipping\n",
      "inferring site map for Delta\n"
     ]
    },
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       "version_minor": 0
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       "  0%|          | 0/29158 [00:00<?, ?it/s]"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 8 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=3645), Label(value='0 / 3645'))), …"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [],\n",
      "dropping: 0 variantswhich have mutations at those sites.\n"
     ]
    },
    {
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=3645), Label(value='0 / 3645'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [], dropping 0 variants\n",
      "Converting mutations for Delta\n",
      "is reference, skipping\n",
      "inferring site map for Omicron_BA1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "833419cd5e6a40558c9ef39691651c65",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/62129 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 8 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2650128e0f3343eaa563f0719eb69f9c",
       "version_major": 2,
       "version_minor": 0
      },
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=7767), Label(value='0 / 7767'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [],\n",
      "dropping: 0 variantswhich have mutations at those sites.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a4bc8cbcfb2479999bf082318670af9",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=7767), Label(value='0 / 7767'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [], dropping 0 variants\n",
      "Converting mutations for Omicron_BA1\n",
      "is reference, skipping\n",
      "inferring site map for Omicron_BA2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f15124742efa4c2f8b9147dcd26dc71d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/57719 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 8 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "320fadb7d3914d348ba5304607b3fd6b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=7215), Label(value='0 / 7215'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [],\n",
      "dropping: 0 variantswhich have mutations at those sites.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cfd47475cb3d4b05b9551a4bd0e57402",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=7215), Label(value='0 / 7215'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [], dropping 0 variants\n",
      "Converting mutations for Omicron_BA2\n",
      "is reference, skipping\n"
     ]
    }
   ],
   "source": [
    "single_condition_datasets = []\n",
    "for (replicate, condition), condition_fsdf in func_score_df.groupby([\"replicate\", \"condition\"]):\n",
    "    start = time.time()\n",
    "    data = multidms.Data(\n",
    "        condition_fsdf,\n",
    "        collapse_identical_variants=\"mean\",\n",
    "        alphabet=multidms.AAS_WITHSTOP_WITHGAP,\n",
    "        reference=condition,\n",
    "        assert_site_integrity=False,\n",
    "        verbose=True,\n",
    "        nb_workers=8,\n",
    "        name=f\"{replicate}-{condition}\"\n",
    "    )\n",
    "    data.condition_colors = cc\n",
    "    single_condition_datasets.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0aae707a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Data(1-Delta),\n",
       " Data(1-Omicron_BA1),\n",
       " Data(1-Omicron_BA2),\n",
       " Data(2-Delta),\n",
       " Data(2-Omicron_BA1),\n",
       " Data(2-Omicron_BA2)]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_condition_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ca644ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "single_condition_fit_params = {\n",
    "    'init_beta_naught' : [0.0],\n",
    "    'epistatic_model' : [\"Sigmoid\"],\n",
    "    'output_activation' : [\"Identity\"],\n",
    "    'tol':[1e-6],\n",
    "    'num_training_steps': [3],\n",
    "    'iterations_per_step':[1000]\n",
    "}\n",
    "single_condition_fit_params[\"dataset\"] = single_condition_datasets\n",
    "_, _, naive_models = multidms.fit_models(single_condition_fit_params, n_threads=-1)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3b691b07-81ce-4552-b2fc-0b9fb2572fb2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>dataset_name</th>\n",
       "      <th>step_loss</th>\n",
       "      <th>epistatic_model</th>\n",
       "      <th>output_activation</th>\n",
       "      <th>scale_coeff_lasso_shift</th>\n",
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       "      <th>scale_coeff_ridge_shift</th>\n",
       "      <th>scale_coeff_ridge_gamma</th>\n",
       "      <th>scale_coeff_ridge_alpha_d</th>\n",
       "      <th>...</th>\n",
       "      <th>gamma_corrected</th>\n",
       "      <th>alpha_d</th>\n",
       "      <th>init_beta_naught</th>\n",
       "      <th>lock_beta_naught_at</th>\n",
       "      <th>tol</th>\n",
       "      <th>num_training_steps</th>\n",
       "      <th>iterations_per_step</th>\n",
       "      <th>n_hidden_units</th>\n",
       "      <th>lower_bound</th>\n",
       "      <th>PRNGKey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Model(Model-0)</td>\n",
       "      <td>1-Delta</td>\n",
       "      <td>[1.6401410102844238, 0.3213399648666382, 0.238...</td>\n",
       "      <td>Sigmoid</td>\n",
       "      <td>Identity</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
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       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Model(Model-0)</td>\n",
       "      <td>1-Omicron_BA1</td>\n",
       "      <td>[1.498695969581604, 0.2580853998661041, 0.2273...</td>\n",
       "      <td>Sigmoid</td>\n",
       "      <td>Identity</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>None</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Model(Model-0)</td>\n",
       "      <td>1-Omicron_BA2</td>\n",
       "      <td>[1.3041640520095825, 0.21561458706855774, 0.19...</td>\n",
       "      <td>Sigmoid</td>\n",
       "      <td>Identity</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Model(Model-0)</td>\n",
       "      <td>2-Delta</td>\n",
       "      <td>[1.4836487770080566, 0.2918327748775482, 0.251...</td>\n",
       "      <td>Sigmoid</td>\n",
       "      <td>Identity</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.000001</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Model(Model-0)</td>\n",
       "      <td>2-Omicron_BA1</td>\n",
       "      <td>[1.414493441581726, 0.19964061677455902, 0.177...</td>\n",
       "      <td>Sigmoid</td>\n",
       "      <td>Identity</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Model(Model-0)</td>\n",
       "      <td>2-Omicron_BA2</td>\n",
       "      <td>[1.3287856578826904, 0.20366378128528595, 0.18...</td>\n",
       "      <td>Sigmoid</td>\n",
       "      <td>Identity</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>None</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
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       "      <td>None</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            model   dataset_name  \\\n",
       "0  Model(Model-0)        1-Delta   \n",
       "1  Model(Model-0)  1-Omicron_BA1   \n",
       "2  Model(Model-0)  1-Omicron_BA2   \n",
       "3  Model(Model-0)        2-Delta   \n",
       "4  Model(Model-0)  2-Omicron_BA1   \n",
       "5  Model(Model-0)  2-Omicron_BA2   \n",
       "\n",
       "                                           step_loss epistatic_model  \\\n",
       "0  [1.6401410102844238, 0.3213399648666382, 0.238...         Sigmoid   \n",
       "1  [1.498695969581604, 0.2580853998661041, 0.2273...         Sigmoid   \n",
       "2  [1.3041640520095825, 0.21561458706855774, 0.19...         Sigmoid   \n",
       "3  [1.4836487770080566, 0.2918327748775482, 0.251...         Sigmoid   \n",
       "4  [1.414493441581726, 0.19964061677455902, 0.177...         Sigmoid   \n",
       "5  [1.3287856578826904, 0.20366378128528595, 0.18...         Sigmoid   \n",
       "\n",
       "  output_activation scale_coeff_lasso_shift scale_coeff_ridge_beta  \\\n",
       "0          Identity                 0.00002                      0   \n",
       "1          Identity                 0.00002                      0   \n",
       "2          Identity                 0.00002                      0   \n",
       "3          Identity                 0.00002                      0   \n",
       "4          Identity                 0.00002                      0   \n",
       "5          Identity                 0.00002                      0   \n",
       "\n",
       "  scale_coeff_ridge_shift scale_coeff_ridge_gamma scale_coeff_ridge_alpha_d  \\\n",
       "0                       0                       0                         0   \n",
       "1                       0                       0                         0   \n",
       "2                       0                       0                         0   \n",
       "3                       0                       0                         0   \n",
       "4                       0                       0                         0   \n",
       "5                       0                       0                         0   \n",
       "\n",
       "   ... gamma_corrected alpha_d init_beta_naught lock_beta_naught_at       tol  \\\n",
       "0  ...           False   False              0.0                None  0.000001   \n",
       "1  ...           False   False              0.0                None  0.000001   \n",
       "2  ...           False   False              0.0                None  0.000001   \n",
       "3  ...           False   False              0.0                None  0.000001   \n",
       "4  ...           False   False              0.0                None  0.000001   \n",
       "5  ...           False   False              0.0                None  0.000001   \n",
       "\n",
       "  num_training_steps iterations_per_step n_hidden_units lower_bound PRNGKey  \n",
       "0                  3                1000              5        None       0  \n",
       "1                  3                1000              5        None       0  \n",
       "2                  3                1000              5        None       0  \n",
       "3                  3                1000              5        None       0  \n",
       "4                  3                1000              5        None       0  \n",
       "5                  3                1000              5        None       0  \n",
       "\n",
       "[6 rows x 21 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naive_models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe42406-43b4-413d-af53-fd33024c8049",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Combine the results of the individual models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e5284e45-f066-408b-8f59-5e9bc2065137",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>wts</th>\n",
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       "      <td>-0.162207</td>\n",
       "      <td>0.666150</td>\n",
       "      <td>0.560069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3A</th>\n",
       "      <td>V</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>0.152784</td>\n",
       "      <td>0.079664</td>\n",
       "      <td>-0.049184</td>\n",
       "      <td>-0.023562</td>\n",
       "      <td>0.038327</td>\n",
       "      <td>-0.134961</td>\n",
       "      <td>0.010511</td>\n",
       "    </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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252T</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>T</td>\n",
       "      <td>-0.340671</td>\n",
       "      <td>-0.268261</td>\n",
       "      <td>-0.571357</td>\n",
       "      <td>-0.691662</td>\n",
       "      <td>-0.331198</td>\n",
       "      <td>-0.534978</td>\n",
       "      <td>-0.456354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252V</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>V</td>\n",
       "      <td>0.542526</td>\n",
       "      <td>0.436843</td>\n",
       "      <td>0.308891</td>\n",
       "      <td>0.242965</td>\n",
       "      <td>0.544752</td>\n",
       "      <td>0.226868</td>\n",
       "      <td>0.383807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252W</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>W</td>\n",
       "      <td>1.142103</td>\n",
       "      <td>0.211932</td>\n",
       "      <td>0.312270</td>\n",
       "      <td>2.329360</td>\n",
       "      <td>0.828580</td>\n",
       "      <td>0.878538</td>\n",
       "      <td>0.950464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252Y</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.990768</td>\n",
       "      <td>0.969647</td>\n",
       "      <td>0.879357</td>\n",
       "      <td>1.086219</td>\n",
       "      <td>1.231161</td>\n",
       "      <td>0.910776</td>\n",
       "      <td>1.011322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252*</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.159378</td>\n",
       "      <td>-0.199520</td>\n",
       "      <td>0.032886</td>\n",
       "      <td>-0.186606</td>\n",
       "      <td>0.224432</td>\n",
       "      <td>-0.334765</td>\n",
       "      <td>-0.103825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5635 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         wts  sites muts  1-Delta_beta  1-Omicron_BA1_beta  \\\n",
       "mutation                                                     \n",
       "M1I        M      1    I     -1.870671           -4.543005   \n",
       "F2L        F      2    L      0.719424            0.616395   \n",
       "F2S        F      2    S      0.886682            0.227816   \n",
       "F2V        F      2    V      1.321443            1.497329   \n",
       "V3A        V      3    A      0.152784            0.079664   \n",
       "...       ..    ...  ...           ...                 ...   \n",
       "S1252T     S   1252    T     -0.340671           -0.268261   \n",
       "S1252V     S   1252    V      0.542526            0.436843   \n",
       "S1252W     S   1252    W      1.142103            0.211932   \n",
       "S1252Y     S   1252    Y      0.990768            0.969647   \n",
       "S1252*     S   1252    *     -0.159378           -0.199520   \n",
       "\n",
       "          1-Omicron_BA2_beta  2-Delta_beta  2-Omicron_BA1_beta  \\\n",
       "mutation                                                         \n",
       "M1I                -5.079311     -6.021095           -4.960052   \n",
       "F2L                -0.345233      1.548375            0.809705   \n",
       "F2S                 1.413247     -0.468354           -0.126621   \n",
       "F2V                 0.099748     -0.062049           -0.162207   \n",
       "V3A                -0.049184     -0.023562            0.038327   \n",
       "...                      ...           ...                 ...   \n",
       "S1252T             -0.571357     -0.691662           -0.331198   \n",
       "S1252V              0.308891      0.242965            0.544752   \n",
       "S1252W              0.312270      2.329360            0.828580   \n",
       "S1252Y              0.879357      1.086219            1.231161   \n",
       "S1252*              0.032886     -0.186606            0.224432   \n",
       "\n",
       "          2-Omicron_BA2_beta  avg_beta  \n",
       "mutation                                \n",
       "M1I                -6.756710 -4.871808  \n",
       "F2L                 0.415978  0.627441  \n",
       "F2S                -0.354802  0.262995  \n",
       "F2V                 0.666150  0.560069  \n",
       "V3A                -0.134961  0.010511  \n",
       "...                      ...       ...  \n",
       "S1252T             -0.534978 -0.456354  \n",
       "S1252V              0.226868  0.383807  \n",
       "S1252W              0.878538  0.950464  \n",
       "S1252Y              0.910776  1.011322  \n",
       "S1252*             -0.334765 -0.103825  \n",
       "\n",
       "[5635 rows x 10 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_dict = {row.dataset_name:row.model for _, row in naive_models.iterrows()}\n",
    "naive_mut_df = combine_replicate_muts(fit_dict,how=\"inner\",times_seen_threshold=times_seen_threshold)\n",
    "naive_mut_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74209b8d-e99d-4c11-a0b0-786e4c6657c0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Using BA.1 as a reference fit, compute the difference of betas. Which is the naive approach to computing \"shifts\" in mutation effect between experiments without using the multidms joint modeling infrastructure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e85041f5-30de-4722-aad3-61e2b6b63cec",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wts</th>\n",
       "      <th>sites</th>\n",
       "      <th>muts</th>\n",
       "      <th>1-Delta_beta</th>\n",
       "      <th>1-Omicron_BA1_beta</th>\n",
       "      <th>1-Omicron_BA2_beta</th>\n",
       "      <th>2-Delta_beta</th>\n",
       "      <th>2-Omicron_BA1_beta</th>\n",
       "      <th>2-Omicron_BA2_beta</th>\n",
       "      <th>avg_beta</th>\n",
       "      <th>1-Delta_S</th>\n",
       "      <th>2-Delta_S</th>\n",
       "      <th>1-Omicron_BA2_S</th>\n",
       "      <th>2-Omicron_BA2_S</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mutation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M1I</th>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "      <td>-1.870671</td>\n",
       "      <td>-4.543005</td>\n",
       "      <td>-5.079311</td>\n",
       "      <td>-6.021095</td>\n",
       "      <td>-4.960052</td>\n",
       "      <td>-6.756710</td>\n",
       "      <td>-4.871808</td>\n",
       "      <td>2.672334</td>\n",
       "      <td>-1.061042</td>\n",
       "      <td>-0.536307</td>\n",
       "      <td>-1.796658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2L</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>L</td>\n",
       "      <td>0.719424</td>\n",
       "      <td>0.616395</td>\n",
       "      <td>-0.345233</td>\n",
       "      <td>1.548375</td>\n",
       "      <td>0.809705</td>\n",
       "      <td>0.415978</td>\n",
       "      <td>0.627441</td>\n",
       "      <td>0.103029</td>\n",
       "      <td>0.738670</td>\n",
       "      <td>-0.961628</td>\n",
       "      <td>-0.393727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2S</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>0.886682</td>\n",
       "      <td>0.227816</td>\n",
       "      <td>1.413247</td>\n",
       "      <td>-0.468354</td>\n",
       "      <td>-0.126621</td>\n",
       "      <td>-0.354802</td>\n",
       "      <td>0.262995</td>\n",
       "      <td>0.658866</td>\n",
       "      <td>-0.341734</td>\n",
       "      <td>1.185431</td>\n",
       "      <td>-0.228181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2V</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>1.321443</td>\n",
       "      <td>1.497329</td>\n",
       "      <td>0.099748</td>\n",
       "      <td>-0.062049</td>\n",
       "      <td>-0.162207</td>\n",
       "      <td>0.666150</td>\n",
       "      <td>0.560069</td>\n",
       "      <td>-0.175887</td>\n",
       "      <td>0.100159</td>\n",
       "      <td>-1.397581</td>\n",
       "      <td>0.828358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3A</th>\n",
       "      <td>V</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>0.152784</td>\n",
       "      <td>0.079664</td>\n",
       "      <td>-0.049184</td>\n",
       "      <td>-0.023562</td>\n",
       "      <td>0.038327</td>\n",
       "      <td>-0.134961</td>\n",
       "      <td>0.010511</td>\n",
       "      <td>0.073120</td>\n",
       "      <td>-0.061889</td>\n",
       "      <td>-0.128848</td>\n",
       "      <td>-0.173288</td>\n",
       "    </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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252T</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>T</td>\n",
       "      <td>-0.340671</td>\n",
       "      <td>-0.268261</td>\n",
       "      <td>-0.571357</td>\n",
       "      <td>-0.691662</td>\n",
       "      <td>-0.331198</td>\n",
       "      <td>-0.534978</td>\n",
       "      <td>-0.456354</td>\n",
       "      <td>-0.072410</td>\n",
       "      <td>-0.360464</td>\n",
       "      <td>-0.303095</td>\n",
       "      <td>-0.203781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252V</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>V</td>\n",
       "      <td>0.542526</td>\n",
       "      <td>0.436843</td>\n",
       "      <td>0.308891</td>\n",
       "      <td>0.242965</td>\n",
       "      <td>0.544752</td>\n",
       "      <td>0.226868</td>\n",
       "      <td>0.383807</td>\n",
       "      <td>0.105683</td>\n",
       "      <td>-0.301787</td>\n",
       "      <td>-0.127952</td>\n",
       "      <td>-0.317884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252W</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>W</td>\n",
       "      <td>1.142103</td>\n",
       "      <td>0.211932</td>\n",
       "      <td>0.312270</td>\n",
       "      <td>2.329360</td>\n",
       "      <td>0.828580</td>\n",
       "      <td>0.878538</td>\n",
       "      <td>0.950464</td>\n",
       "      <td>0.930171</td>\n",
       "      <td>1.500780</td>\n",
       "      <td>0.100338</td>\n",
       "      <td>0.049959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252Y</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.990768</td>\n",
       "      <td>0.969647</td>\n",
       "      <td>0.879357</td>\n",
       "      <td>1.086219</td>\n",
       "      <td>1.231161</td>\n",
       "      <td>0.910776</td>\n",
       "      <td>1.011322</td>\n",
       "      <td>0.021121</td>\n",
       "      <td>-0.144942</td>\n",
       "      <td>-0.090290</td>\n",
       "      <td>-0.320385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252*</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.159378</td>\n",
       "      <td>-0.199520</td>\n",
       "      <td>0.032886</td>\n",
       "      <td>-0.186606</td>\n",
       "      <td>0.224432</td>\n",
       "      <td>-0.334765</td>\n",
       "      <td>-0.103825</td>\n",
       "      <td>0.040142</td>\n",
       "      <td>-0.411038</td>\n",
       "      <td>0.232406</td>\n",
       "      <td>-0.559197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5635 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         wts  sites muts  1-Delta_beta  1-Omicron_BA1_beta  \\\n",
       "mutation                                                     \n",
       "M1I        M      1    I     -1.870671           -4.543005   \n",
       "F2L        F      2    L      0.719424            0.616395   \n",
       "F2S        F      2    S      0.886682            0.227816   \n",
       "F2V        F      2    V      1.321443            1.497329   \n",
       "V3A        V      3    A      0.152784            0.079664   \n",
       "...       ..    ...  ...           ...                 ...   \n",
       "S1252T     S   1252    T     -0.340671           -0.268261   \n",
       "S1252V     S   1252    V      0.542526            0.436843   \n",
       "S1252W     S   1252    W      1.142103            0.211932   \n",
       "S1252Y     S   1252    Y      0.990768            0.969647   \n",
       "S1252*     S   1252    *     -0.159378           -0.199520   \n",
       "\n",
       "          1-Omicron_BA2_beta  2-Delta_beta  2-Omicron_BA1_beta  \\\n",
       "mutation                                                         \n",
       "M1I                -5.079311     -6.021095           -4.960052   \n",
       "F2L                -0.345233      1.548375            0.809705   \n",
       "F2S                 1.413247     -0.468354           -0.126621   \n",
       "F2V                 0.099748     -0.062049           -0.162207   \n",
       "V3A                -0.049184     -0.023562            0.038327   \n",
       "...                      ...           ...                 ...   \n",
       "S1252T             -0.571357     -0.691662           -0.331198   \n",
       "S1252V              0.308891      0.242965            0.544752   \n",
       "S1252W              0.312270      2.329360            0.828580   \n",
       "S1252Y              0.879357      1.086219            1.231161   \n",
       "S1252*              0.032886     -0.186606            0.224432   \n",
       "\n",
       "          2-Omicron_BA2_beta  avg_beta  1-Delta_S  2-Delta_S  1-Omicron_BA2_S  \\\n",
       "mutation                                                                        \n",
       "M1I                -6.756710 -4.871808   2.672334  -1.061042        -0.536307   \n",
       "F2L                 0.415978  0.627441   0.103029   0.738670        -0.961628   \n",
       "F2S                -0.354802  0.262995   0.658866  -0.341734         1.185431   \n",
       "F2V                 0.666150  0.560069  -0.175887   0.100159        -1.397581   \n",
       "V3A                -0.134961  0.010511   0.073120  -0.061889        -0.128848   \n",
       "...                      ...       ...        ...        ...              ...   \n",
       "S1252T             -0.534978 -0.456354  -0.072410  -0.360464        -0.303095   \n",
       "S1252V              0.226868  0.383807   0.105683  -0.301787        -0.127952   \n",
       "S1252W              0.878538  0.950464   0.930171   1.500780         0.100338   \n",
       "S1252Y              0.910776  1.011322   0.021121  -0.144942        -0.090290   \n",
       "S1252*             -0.334765 -0.103825   0.040142  -0.411038         0.232406   \n",
       "\n",
       "          2-Omicron_BA2_S  \n",
       "mutation                   \n",
       "M1I             -1.796658  \n",
       "F2L             -0.393727  \n",
       "F2S             -0.228181  \n",
       "F2V              0.828358  \n",
       "V3A             -0.173288  \n",
       "...                   ...  \n",
       "S1252T          -0.203781  \n",
       "S1252V          -0.317884  \n",
       "S1252W           0.049959  \n",
       "S1252Y          -0.320385  \n",
       "S1252*          -0.559197  \n",
       "\n",
       "[5635 rows x 14 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reference = \"Omicron_BA1\"\n",
    "for i, condition in enumerate([\"Delta\", \"Omicron_BA2\"]): \n",
    "    for replicate in [1, 2]:\n",
    "        reference_betas = naive_mut_df[f\"{replicate}-{reference}_beta\"]\n",
    "        condition_betas = naive_mut_df[f\"{replicate}-{condition}_beta\"]\n",
    "        naive_mut_df[f\"{replicate}-{condition}_S\"] = condition_betas - reference_betas\n",
    "naive_mut_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "753fe01e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mut_df_replicates = combine_replicate_muts(\n",
    "    {\n",
    "        f\"{fit.dataset_name}\".split(\"-\")[-1]: fit.model\n",
    "        for fit in models.query(f\"scale_coeff_lasso_shift == {chosen_lasso_strength}\").itertuples()\n",
    "    },\n",
    "    predicted_func_scores=False,\n",
    "    how=\"inner\",\n",
    "    times_seen_threshold=times_seen_threshold\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2b29adfb-24d3-4ddf-9459-cab064570d62",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x900 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saveas=\"shift_distribution_correlation_naive\"\n",
    "pal = sns.color_palette('colorblind')\n",
    "fig = plt.figure(figsize=[6.4, 9])\n",
    "\n",
    "# the ratio of plot to divider between \n",
    "dist_sf = 3\n",
    "\n",
    "# three rows of distributions\n",
    "dist_rows = [[] for _ in range(3)]\n",
    "for row, mut_type in enumerate([\"m\", \"i\", \"n\"]):\n",
    "    dist_rows[row].extend([f\"dist_{mut_type}_beta\"]*dist_sf)\n",
    "    dist_rows[row].append(\".\")\n",
    "    for param in [\"shift_Delta\", \"shift_Omicron_BA2\"]:\n",
    "        dist_rows[row].extend([f\"dist_{mut_type}_{param}\"]*dist_sf)\n",
    "\n",
    "# empty space row\n",
    "number_of_rows = len(dist_rows[0])\n",
    "empty_row = [\".\"] * len(dist_rows[0])\n",
    "\n",
    "# correlation plot row\n",
    "corr_row = [\"corr_beta\"]*dist_sf+ [\".\"]\n",
    "for param in [\"shift_Delta\", \"shift_Omicron_BA2\"]:\n",
    "    corr_row.extend([f\"corr_{param}\"]*dist_sf)\n",
    "    \n",
    "# niave correlation plot row\n",
    "naive_corr_row = [\"naive_corr_Omicron_BA1_beta\"]*dist_sf+ [\".\"]\n",
    "for param in [\"Delta_S\", \"Omicron_BA2_S\"]:\n",
    "    naive_corr_row.extend([f\"naive_corr_{param}\"]*dist_sf)\n",
    "\n",
    "axs = fig.subplot_mosaic(\n",
    "    dist_rows+[empty_row, corr_row, empty_row, naive_corr_row],\n",
    "    height_ratios=[0.3, 0.3, 0.3] + [0.38, 0.7, 0.55, 0.7],\n",
    "    empty_sentinel=\".\",\n",
    "    gridspec_kw={\n",
    "        \"wspace\": 0.05,\n",
    "        \"hspace\": 0.05,\n",
    "    }\n",
    ")\n",
    "\n",
    "query_dict = {\n",
    "    \"m\" : \"muts != '*' and muts != '-'\",\n",
    "    \"i\" : \"muts == '-'\",\n",
    "    \"n\" : \"muts == '*'\"\n",
    "}\n",
    "\n",
    "bins_dict = {\n",
    "    \"beta\" : np.arange(-5.25, 1.0, 0.5),\n",
    "    \"shift_Delta\" : np.arange(-3.25, 3.25, 0.5),\n",
    "    \"shift_Omicron_BA2\" : np.arange(-3.25, 3.25, 0.5)\n",
    "}\n",
    "\n",
    "#################\n",
    "# DISTRIBUTIONS\n",
    "#################\n",
    "mut_df_replicates[\"sense\"] = [\"stop\" if mut == \"*\" else \"nonsynonymous\" for mut in mut_df_replicates.muts]\n",
    "\n",
    "prefix = \"avg\"\n",
    "bins = np.arange(-5.25, 1.0, 0.5)\n",
    "# df = mut_df_replicates.copy()\n",
    "mut_df_replicates[\"avg_beta\"].clip(lower=-5, inplace=True)\n",
    "\n",
    "for col, param in enumerate([\"beta\", \"shift_Delta\", \"shift_Omicron_BA2\"]):\n",
    "    for row, mut_type in enumerate([\"m\", \"i\", \"n\"]):\n",
    "        \n",
    "        iter_ax = axs[f\"dist_{mut_type}_{param}\"]\n",
    "        sns.histplot(\n",
    "            mut_df_replicates.query(query_dict[mut_type]), \n",
    "            x=f\"{prefix}_{param}\", \n",
    "            ax=iter_ax,\n",
    "            stat='probability',\n",
    "            bins=bins_dict[param],\n",
    "            label=\"stop\",\n",
    "            color=\"red\" if mut_type == \"n\" else pal.as_hex()[row],\n",
    "            alpha=0.5\n",
    "        )\n",
    "        iter_ax.set_ylim(-0.05,1.05)\n",
    "        \n",
    "        # remove the ylabel from all but the first column\n",
    "        if col != 0: \n",
    "            iter_ax.tick_params(axis='y', labelleft=False)\n",
    "        iter_ax.set_yticks(\n",
    "            [0.0, 0.5], [0.0, 0.5], rotation=0, ha=\"right\",size=9\n",
    "        )\n",
    "            \n",
    "        if col != 0 or row != 1:\n",
    "            iter_ax.set_ylabel(None)\n",
    "        else:\n",
    "            iter_ax.set_ylabel(\"probability\")\n",
    "        \n",
    "        # remove the x labels from all but bottom\n",
    "        if row != 2:\n",
    "            iter_ax.tick_params(axis='x', labelbottom=False)\n",
    "            iter_ax.set_xlabel(None)\n",
    "\n",
    "        iter_ax.set_xlabel(None)\n",
    "        \n",
    "        sns.despine(ax=iter_ax)\n",
    "        \n",
    "axs[\"dist_n_beta\"].set_xlabel(r\"mut. effect $(\\beta_{m})$\")\n",
    "axs[\"dist_n_beta\"].xaxis.set_label_coords(0.5, -.5)\n",
    "\n",
    "axs[\"dist_m_beta\"].text(\n",
    "    0.1, 0.9, \n",
    "    f\"nonsynonymous\\nmuts\", \n",
    "    ha=\"left\", va=\"top\", \n",
    "    size=9,\n",
    "    transform=axs[\"dist_m_beta\"].transAxes\n",
    ")\n",
    "axs[\"dist_i_beta\"].text(\n",
    "    0.1, 0.9, \n",
    "    f\"in-frame codon\\ndeletion muts\", \n",
    "    ha=\"left\", va=\"top\", \n",
    "    size=9,\n",
    "    transform=axs[\"dist_i_beta\"].transAxes\n",
    ")\n",
    "axs[\"dist_n_beta\"].text(\n",
    "    0.1, 0.9, \n",
    "    f\"stop muts\", \n",
    "    ha=\"left\", va=\"top\", \n",
    "    size=9,\n",
    "    transform=axs[\"dist_n_beta\"].transAxes\n",
    ")\n",
    "\n",
    "axs[\"dist_n_shift_Delta\"].set_xlabel('shift ($\\Delta_{d,m}$)')\n",
    "axs[\"dist_n_shift_Delta\"].xaxis.set_label_coords(1.0, -.5)\n",
    "\n",
    "axs[\"dist_m_shift_Delta\"].set_title(\"Delta\")\n",
    "axs[\"dist_m_shift_Omicron_BA2\"].set_title(\"BA.2\")\n",
    "\n",
    "#################\n",
    "# CORRELATIONS\n",
    "#################\n",
    "\n",
    "data = mut_df_replicates.dropna().copy()\n",
    "data['1_beta'].clip(lower=-5, inplace=True)\n",
    "data['2_beta'].clip(lower=-5, inplace=True)\n",
    "# plot the correlations of parameters\n",
    "for col, param in enumerate([\"beta\", \"shift_Delta\", \"shift_Omicron_BA2\"]):\n",
    "    iter_ax = axs[f\"corr_{param}\"]\n",
    "    x, y = data[f\"1_{param}\"], data[f\"2_{param}\"]\n",
    "    sns.scatterplot(\n",
    "        data = data,\n",
    "        x = f\"1_{param}\",\n",
    "        y = f\"2_{param}\",\n",
    "        ax=iter_ax,\n",
    "        s=60, \n",
    "        alpha=0.3,     \n",
    "        c='0.25'\n",
    "    )\n",
    "    \n",
    "    # remove y labels from all but first column\n",
    "    if col != 0: \n",
    "        \n",
    "        if col == 2:\n",
    "            iter_ax.tick_params(axis='y', labelleft=False)\n",
    "        iter_ax.set_ylabel(None)\n",
    "        \n",
    "        lim = [-2.8, 2.8]\n",
    "        ticks = [-2, 0, 2]\n",
    "        \n",
    "        iter_ax.set_ylim(lim)\n",
    "        iter_ax.set_xlim(lim)\n",
    "        iter_ax.set_yticks(\n",
    "            ticks, ticks, size=12\n",
    "        )        \n",
    "        iter_ax.set_xticks(\n",
    "            ticks, ticks, rotation=0, size=12\n",
    "        )\n",
    "            # line of equivilence\n",
    "        iter_ax.plot(\n",
    "            lim, \n",
    "            lim,\n",
    "            linestyle=\"--\", \n",
    "            lw=2,\n",
    "            c='royalblue'\n",
    "        )\n",
    "        \n",
    "    else:\n",
    "        lim = [-6, 3]\n",
    "        ticks = [-4, -2, 0, 2]\n",
    "        \n",
    "        iter_ax.set_ylim(lim)\n",
    "        iter_ax.set_xlim(lim)\n",
    "        iter_ax.set_yticks(\n",
    "            ticks, ticks, size=12\n",
    "        )        \n",
    "        iter_ax.set_xticks(\n",
    "            ticks, ticks, rotation=0, size=12\n",
    "        )\n",
    "            # line of equivilence\n",
    "        iter_ax.plot(\n",
    "            lim, \n",
    "            lim,\n",
    "            linestyle=\"--\", \n",
    "            lw=2,\n",
    "            c='royalblue'\n",
    "        )\n",
    "        \n",
    "        iter_ax.set_ylabel(\"replicate 2\")\n",
    "        \n",
    "    iter_ax.set_xlabel(\"replicate 1\")\n",
    "    iter_ax.grid()\n",
    "\n",
    "    \n",
    "    corr = pearsonr(x, y)[0]**2\n",
    "    iter_ax.annotate(\n",
    "        f\"$R^2 = {corr:.2f}$\", \n",
    "        (0.07, 0.8), \n",
    "        xycoords=\"axes fraction\", \n",
    "        fontsize=11\n",
    "    )\n",
    "    sns.despine(ax=iter_ax)\n",
    "    \n",
    "#################\n",
    "# NAIVE CORRELATIONS\n",
    "#################\n",
    "\n",
    "data = naive_mut_df.dropna().copy()\n",
    "data[\"1-Omicron_BA1_beta\"].clip(lower=-10, inplace=True)\n",
    "data[\"2-Omicron_BA1_beta\"].clip(lower=-10, inplace=True)\n",
    "\n",
    "# plot the correlations of parameters\n",
    "for col, param in enumerate([\"Omicron_BA1_beta\", \"Delta_S\", \"Omicron_BA2_S\"]):\n",
    "    iter_ax = axs[f\"naive_corr_{param}\"]\n",
    "    x, y = data[f\"1-{param}\"], data[f\"2-{param}\"]\n",
    "    sns.scatterplot(\n",
    "        data = data,\n",
    "        x = f\"1-{param}\",\n",
    "        y = f\"2-{param}\",\n",
    "        ax=iter_ax,\n",
    "        s=60, \n",
    "        alpha=0.3,     \n",
    "        c='0.25'\n",
    "    )\n",
    "    \n",
    "    # remove y labels from all but first column\n",
    "    if col != 0: \n",
    "        \n",
    "        if col == 2:\n",
    "            iter_ax.tick_params(axis='y', labelleft=False)\n",
    "        iter_ax.set_ylabel(None)\n",
    "        \n",
    "        lim = [-8.2, 12.2]\n",
    "        ticks = range(-8, 12, 4)\n",
    "        \n",
    "        iter_ax.set_ylim(lim)\n",
    "        iter_ax.set_xlim(lim)\n",
    "        iter_ax.set_yticks(\n",
    "            ticks, ticks, size=12\n",
    "        )        \n",
    "        iter_ax.set_xticks(\n",
    "            ticks, ticks, rotation=0, size=12\n",
    "        )\n",
    "            # line of equivilence\n",
    "        iter_ax.plot(\n",
    "            lim, \n",
    "            lim,\n",
    "            linestyle=\"--\", \n",
    "            lw=2,\n",
    "            c='royalblue'\n",
    "        )\n",
    "        \n",
    "    else:\n",
    "        lim = [-11, 6]\n",
    "        ticks = range(-10, 6, 5)\n",
    "        \n",
    "        iter_ax.set_ylim(lim)\n",
    "        iter_ax.set_xlim(lim)\n",
    "        iter_ax.set_yticks(\n",
    "            ticks, ticks, size=12\n",
    "        )        \n",
    "        iter_ax.set_xticks(\n",
    "            ticks, ticks, rotation=0, size=12\n",
    "        )\n",
    "            # line of equivilence\n",
    "        iter_ax.plot(\n",
    "            lim, \n",
    "            lim,\n",
    "            linestyle=\"--\", \n",
    "            lw=2,\n",
    "            c='royalblue'\n",
    "        )\n",
    "        \n",
    "        iter_ax.set_ylabel(\"replicate 2\")\n",
    "        \n",
    "    iter_ax.set_xlabel(\"replicate 1\")\n",
    "    iter_ax.grid()\n",
    "\n",
    "    \n",
    "    corr = pearsonr(x, y)[0]**2\n",
    "    iter_ax.annotate(\n",
    "        f\"$R^{2} = {corr:.2f}$\", \n",
    "        (0.07, 0.8), \n",
    "        xycoords=\"axes fraction\", \n",
    "        fontsize=11\n",
    "    )\n",
    "    sns.despine(ax=iter_ax)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Add subpanel labels\n",
    "axs[\"dist_m_beta\"].text(\n",
    "    -0.2, 1.05, \n",
    "    f\"A\", \n",
    "    ha=\"right\", va=\"center\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=axs[\"dist_m_beta\"].transAxes\n",
    ")\n",
    "\n",
    "\n",
    "axs[\"dist_m_shift_Delta\"].text(\n",
    "    -0.15, 1.05, \n",
    "    f\"B\", \n",
    "    ha=\"right\", va=\"center\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=axs[\"dist_m_shift_Delta\"].transAxes\n",
    ")\n",
    "\n",
    "axs[\"corr_beta\"].text(\n",
    "    -0.2, 1.05, \n",
    "    f\"C\", \n",
    "    ha=\"right\", va=\"center\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=axs[\"corr_beta\"].transAxes\n",
    ")\n",
    "\n",
    "axs[\"corr_shift_Delta\"].text(\n",
    "    -0.15, 1.05, \n",
    "    f\"D\", \n",
    "    ha=\"right\", va=\"center\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=axs[\"corr_shift_Delta\"].transAxes\n",
    ")\n",
    "\n",
    "axs[\"naive_corr_Omicron_BA1_beta\"].text(\n",
    "    -0.2, 1.05, \n",
    "    f\"E\", \n",
    "    ha=\"right\", va=\"center\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=axs[\"naive_corr_Omicron_BA1_beta\"].transAxes\n",
    ")\n",
    "\n",
    "axs[\"naive_corr_Delta_S\"].text(\n",
    "    -0.15, 1.05, \n",
    "    f\"F\", \n",
    "    ha=\"right\", va=\"center\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=axs[\"naive_corr_Delta_S\"].transAxes\n",
    ")\n",
    "\n",
    "# titles\n",
    "axs[\"dist_m_beta\"].set_title(\"BA.1\")\n",
    "axs[\"corr_beta\"].set_title(r\"mut. effect $(\\beta_{m})$\", size=10)\n",
    "axs[\"corr_shift_Delta\"].set_title(\"shift ($\\Delta_{Delta, m}$)\", size=10)\n",
    "axs[\"corr_shift_Omicron_BA2\"].set_title(\"shift ($\\Delta_{BA.2, m}$)\", size=10)\n",
    "\n",
    "axs[\"naive_corr_Omicron_BA1_beta\"].set_title(\"mut. effect in BA.1\", size=10)\n",
    "axs[\"naive_corr_Delta_S\"].set_title(\"shift in mut. effect\\n(Delta - BA.1)\", size=10)\n",
    "axs[\"naive_corr_Omicron_BA2_S\"].set_title(\"shift in mut. effect\\n(BA.2 - BA.1)\", size=10)\n",
    "\n",
    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
    "\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00006ef8",
   "metadata": {},
   "source": [
    "## Comparison to linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d5861707",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_params_linear = fit_params.copy()\n",
    "fit_params_linear[\"dataset\"] = datasets\n",
    "fit_params_linear[\"epistatic_model\"] = [\"Identity\"]\n",
    "\n",
    "_, _, linear_models = multidms.fit_models(fit_params_linear, n_threads=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "093863d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_collection_linear = multidms.ModelCollection(linear_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1a4dd7f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cache miss - this could take a moment\n",
      "  dataset_name  scale_coeff_lasso_shift       mut_type    mut_param  sparsity\n",
      "0        rep-1                 0.000000  nonsynonymous  shift_Delta  0.234082\n",
      "1        rep-1                 0.000000           stop  shift_Delta  0.247678\n",
      "2        rep-1                 0.000005  nonsynonymous  shift_Delta  0.320816\n",
      "3        rep-1                 0.000005           stop  shift_Delta  0.318885\n",
      "4        rep-1                 0.000010  nonsynonymous  shift_Delta  0.413163\n"
     ]
    },
    {
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\"mut_param\": \"shift_Delta\", \"sparsity\": 0.9888775510204082}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Delta\", \"sparsity\": 0.9968367346938776}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"stop\", \"mut_param\": \"shift_Delta\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.1483673469387755}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.1826625386996904}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.2553061224489796}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.30340557275541796}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.3754081632653061}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.4674922600619195}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.5646938775510204}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.7585139318885449}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.7470408163265306}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.8978328173374613}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.8965306122448979}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9876160990712074}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9703061224489796}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9931632653061224}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9993877551020408}, {\"dataset_name\": \"rep-1\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.1526530612244898}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.0, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.17647058823529413}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.2530612244897959}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 5e-06, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.33746130030959753}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.3673469387755102}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 1e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.4613003095975232}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.5478571428571428}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 2e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.7554179566563467}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.7328571428571429}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 4e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.913312693498452}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.8863265306122449}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 8e-05, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9814241486068112}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9679591836734693}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00016, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9933673469387755}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00032, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"nonsynonymous\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 0.9990816326530613}, {\"dataset_name\": \"rep-2\", \"scale_coeff_lasso_shift\": 0.00064, \"mut_type\": \"stop\", \"mut_param\": \"shift_Omicron_BA2\", \"sparsity\": 1.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.FacetChart(...)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart, sparsity_df = model_collection_linear.shift_sparsity(return_data=True, height_scalar=100) # TODO raise issue to fix height scalar\n",
    "print(sparsity_df.head())\n",
    "chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2801f2bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      datasets mut_param  correlation  scale_coeff_lasso_shift\n",
      "0  rep-1,rep-2      beta     0.723252                 0.000000\n",
      "0  rep-1,rep-2      beta     0.742715                 0.000005\n",
      "0  rep-1,rep-2      beta     0.746827                 0.000010\n",
      "0  rep-1,rep-2      beta     0.737302                 0.000020\n",
      "0  rep-1,rep-2      beta     0.726406                 0.000040\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-7c5c1e1fbf75488ead4a03c6859c29cb.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-7c5c1e1fbf75488ead4a03c6859c29cb.vega-embed details,\n",
       "  #altair-viz-7c5c1e1fbf75488ead4a03c6859c29cb.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-7c5c1e1fbf75488ead4a03c6859c29cb\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-7c5c1e1fbf75488ead4a03c6859c29cb\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-7c5c1e1fbf75488ead4a03c6859c29cb\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.15.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
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       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
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       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.15.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
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\"mut_param\": \"predicted_func_score_Delta\", \"correlation\": 0.7052239734411834, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7232518073041926, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7427151621476121, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7468271946958948, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7373015947537663, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7264061844161868, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7166822403508754, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7109316799561909, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7079732878500581, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA1\", \"correlation\": 0.7052664174967669, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.6260249542137526, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.6625960786837645, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.6882333140191729, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7048258276418253, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7106087199686132, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.711837959569212, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7097347978403256, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7077474216005084, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"predicted_func_score_Omicron_BA2\", \"correlation\": 0.7052315149449219, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.3609446176893114, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.3838436429115677, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.4031193130568265, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.4407312910656222, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.5179240996765818, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.6015798222149723, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.6468273491478964, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.6694203079489227, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Delta\", \"correlation\": 0.8754983696991615, \"scale_coeff_lasso_shift\": 0.00064}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.2993643256933147, \"scale_coeff_lasso_shift\": 0.0}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.32937124289948083, \"scale_coeff_lasso_shift\": 5e-06}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.36227721605135715, \"scale_coeff_lasso_shift\": 1e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.4355317608040999, \"scale_coeff_lasso_shift\": 2e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.5224288314801461, \"scale_coeff_lasso_shift\": 4e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.6408989719229061, \"scale_coeff_lasso_shift\": 8e-05}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.7101979537174019, \"scale_coeff_lasso_shift\": 0.00016}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.7594567199257061, \"scale_coeff_lasso_shift\": 0.00032}, {\"datasets\": \"rep-1,rep-2\", \"mut_param\": \"shift_Omicron_BA2\", \"correlation\": 0.9835332722377108, \"scale_coeff_lasso_shift\": 0.00064}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.FacetChart(...)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart, corr_df = model_collection_linear.mut_param_dataset_correlation(width_scalar=200, return_data=True)\n",
    "print(corr_df.head())\n",
    "chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "41c62ccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_params_linear[\"dataset\"] = train \n",
    "_, _, linear_models_cv = multidms.model_collection.fit_models(fit_params_linear, n_threads = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3ab400e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_mc = multidms.model_collection.ModelCollection(linear_models_cv)\n",
    "linear_mc.add_validation_loss(test, overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3ae9ba45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset_name</th>\n",
       "      <th>scale_coeff_lasso_shift</th>\n",
       "      <th>condition</th>\n",
       "      <th>loss</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.209246</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.213277</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.217899</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.225076</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>0.00004</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.237947</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dataset_name scale_coeff_lasso_shift condition      loss     split\n",
       "0        rep-1                     0.0     Delta  0.209246  training\n",
       "1        rep-1                0.000005     Delta  0.213277  training\n",
       "2        rep-1                 0.00001     Delta  0.217899  training\n",
       "3        rep-1                 0.00002     Delta  0.225076  training\n",
       "4        rep-1                 0.00004     Delta  0.237947  training"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validation_df = mc.get_conditional_loss_df()\n",
    "cross_validation_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7cab36ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x750 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "saveas=\"shrinkage_analysis_linear_models\"\n",
    "\n",
    "fig, ax = plt.subplots(3, figsize=[4.5, 7.5], sharex=True)\n",
    "\n",
    "# replicate correlation\n",
    "iter_ax = ax[0]\n",
    "sns.lineplot(\n",
    "    data=(\n",
    "        corr_df\n",
    "        .query(\"mut_param.str.contains('shift')\")\n",
    "        .rename({\"mut_param\":\"shift params\"}, axis=1)\n",
    "        # .replace({\"Data-1\":\"rep-1\", \"Data-2\":\"rep-2\"})\n",
    "        .replace({\"shift_Delta\":\"Delta\", \"shift_Omicron_BA2\":\"BA.2\"})\n",
    "        .assign(\n",
    "            scale_coeff_lasso_shift = [\n",
    "                f\"{l:.1e}\" \n",
    "                for l in corr_df.query(\"mut_param.str.contains('shift')\").scale_coeff_lasso_shift\n",
    "            ],\n",
    "            correlation = lambda x: x.correlation**2\n",
    "        )\n",
    "        .reset_index(drop=True)\n",
    "    ),\n",
    "    x=\"scale_coeff_lasso_shift\",\n",
    "    y=\"correlation\",\n",
    "    style=\"shift params\",\n",
    "    markers=True,\n",
    "    ax=iter_ax,\n",
    "    linewidth=3,\n",
    "    color=\"black\"\n",
    ")\n",
    "iter_ax.set_ylabel(\"rep1 v. rep2\\nshift $(R^2)$\")\n",
    "# move legend outside of plot\n",
    "iter_ax.legend(\n",
    "    bbox_to_anchor = (1, 1), \n",
    "    loc='upper left', \n",
    "    frameon=False\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "# plot loss\n",
    "iter_ax = ax[1]\n",
    "sns.lineplot(\n",
    "    data = (\n",
    "        cross_validation_df.query(\"condition=='total'\")\n",
    "        .assign(\n",
    "            # lasso_strength = [f\"{l:.1e}\" for l in sparsity_df.scale_coeff_lasso_shift]\n",
    "            # lasso_strength = lambda x: f\"{x.scale_coeff_lasso_shift:.1e}\"\n",
    "            lasso_strength = lambda x: x['scale_coeff_lasso_shift'].apply(lambda y: f'{y:.1e}')\n",
    "        )\n",
    "    ),\n",
    "    x=\"lasso_strength\",\n",
    "    y=\"loss\",\n",
    "    ax=iter_ax,\n",
    "    hue=\"split\",\n",
    "    style=\"dataset_name\",\n",
    "    palette={\"training\":\"slategrey\", \"validation\":\"#2CA02C\"},\n",
    "    markers=True,\n",
    "    linewidth=3\n",
    ")\n",
    "# move legend outside of plot\n",
    "iter_ax.legend(\n",
    "    bbox_to_anchor = (1, 1), \n",
    "    loc='upper left', \n",
    "    frameon=False\n",
    ")\n",
    "\n",
    "\n",
    "# plot sparsity\n",
    "iter_ax = ax[2]\n",
    "sns.lineplot(\n",
    "    data=(\n",
    "        sparsity_df\n",
    "        .rename({\"dataset_name\":\"replicate\"}, axis=1)\n",
    "        .rename({\"mut_param\":\"shift params\", \"mut_type\":\"mutation type\"}, axis=1)\n",
    "        # .replace({\"Data-0\":\"rep-1\", \"Data-1\":\"rep-2\"})\n",
    "        .replace({\"nonsynonymous\":\"nonsynonymous\", \"stop\":\"stop\"})\n",
    "        .replace({\"shift_Delta\":\"Delta\", \"shift_Omicron_BA2\":\"BA.2\"})\n",
    "        .assign(\n",
    "            scale_coeff_lasso_shift = [f\"{l:.1e}\" for l in sparsity_df.scale_coeff_lasso_shift],\n",
    "            sparsity_percent = lambda x: x.sparsity * 100,\n",
    "        )\n",
    "    ),\n",
    "    x=\"scale_coeff_lasso_shift\",\n",
    "    y=\"sparsity_percent\",\n",
    "    hue=\"mutation type\",\n",
    "    style=\"replicate\",\n",
    "    palette={\"nonsynonymous\":\"grey\", \"stop\":\"#E377C2\"},\n",
    "    markers=True,\n",
    "    legend=True,\n",
    "    ax=iter_ax,\n",
    "    linewidth=3\n",
    ")\n",
    "# move legend outside of plot\n",
    "iter_ax.legend(\n",
    "    bbox_to_anchor = (1, 1), \n",
    "    loc='upper left', \n",
    "    frameon=False\n",
    ")\n",
    "# rotate x labels\n",
    "iter_ax.set_xticklabels(\n",
    "    iter_ax.get_xticklabels(), \n",
    "    rotation=90, \n",
    "    ha='center'\n",
    ")\n",
    "iter_ax.set_ylabel(\"sparsity\\n$(\\%\\Delta=0)$\")\n",
    "iter_ax.set_xlabel(f\"lasso regularization strength ($\\lambda$)\")\n",
    "\n",
    "for axes in ax:\n",
    "    axes.axvline(\n",
    "        f\"{chosen_lasso_strength:.1e}\", \n",
    "        color=\"grey\",\n",
    "        linewidth=10,\n",
    "        alpha=0.35\n",
    "    )\n",
    "\n",
    "sns.despine(fig)\n",
    "plt.tight_layout()\n",
    "# plt.tight_layout()\n",
    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
    "# plt.show()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e73c8774-b6b5-4bf1-98c0-e8f9633da993",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Validation mutations\n",
    "\n",
    "Next, we compare the results of the model to mutations tested individually in _in-vitro_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0ce65a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wts</th>\n",
       "      <th>sites</th>\n",
       "      <th>muts</th>\n",
       "      <th>1_beta</th>\n",
       "      <th>2_beta</th>\n",
       "      <th>avg_beta</th>\n",
       "      <th>1_shift_Delta</th>\n",
       "      <th>2_shift_Delta</th>\n",
       "      <th>avg_shift_Delta</th>\n",
       "      <th>1_shift_Omicron_BA2</th>\n",
       "      <th>...</th>\n",
       "      <th>avg_shift_Omicron_BA2</th>\n",
       "      <th>1_predicted_func_score_Delta</th>\n",
       "      <th>2_predicted_func_score_Delta</th>\n",
       "      <th>avg_predicted_func_score_Delta</th>\n",
       "      <th>1_predicted_func_score_Omicron_BA1</th>\n",
       "      <th>2_predicted_func_score_Omicron_BA1</th>\n",
       "      <th>avg_predicted_func_score_Omicron_BA1</th>\n",
       "      <th>1_predicted_func_score_Omicron_BA2</th>\n",
       "      <th>2_predicted_func_score_Omicron_BA2</th>\n",
       "      <th>avg_predicted_func_score_Omicron_BA2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mutation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M1I</th>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "      <td>-2.924932</td>\n",
       "      <td>-4.256726</td>\n",
       "      <td>-3.590829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.162696</td>\n",
       "      <td>-3.348626</td>\n",
       "      <td>-3.255661</td>\n",
       "      <td>-3.009065</td>\n",
       "      <td>-3.236155</td>\n",
       "      <td>-3.122610</td>\n",
       "      <td>-3.085516</td>\n",
       "      <td>-3.409368</td>\n",
       "      <td>-3.247442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2L</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>L</td>\n",
       "      <td>0.200928</td>\n",
       "      <td>0.207115</td>\n",
       "      <td>0.204021</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.204654</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.102327</td>\n",
       "      <td>0.287689</td>\n",
       "      <td>0.366644</td>\n",
       "      <td>0.327167</td>\n",
       "      <td>0.405955</td>\n",
       "      <td>0.487242</td>\n",
       "      <td>0.446598</td>\n",
       "      <td>-0.200109</td>\n",
       "      <td>0.107339</td>\n",
       "      <td>-0.046385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2S</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>0.194773</td>\n",
       "      <td>-0.074343</td>\n",
       "      <td>0.060215</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.275053</td>\n",
       "      <td>-0.286178</td>\n",
       "      <td>-0.005562</td>\n",
       "      <td>0.393345</td>\n",
       "      <td>-0.166355</td>\n",
       "      <td>0.113495</td>\n",
       "      <td>0.195843</td>\n",
       "      <td>-0.525584</td>\n",
       "      <td>-0.164871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2V</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>0.239144</td>\n",
       "      <td>-0.030672</td>\n",
       "      <td>0.104236</td>\n",
       "      <td>-0.086489</td>\n",
       "      <td>-0.153066</td>\n",
       "      <td>-0.119778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.188819</td>\n",
       "      <td>-0.521577</td>\n",
       "      <td>-0.166379</td>\n",
       "      <td>0.484417</td>\n",
       "      <td>-0.069216</td>\n",
       "      <td>0.207601</td>\n",
       "      <td>0.286191</td>\n",
       "      <td>-0.431950</td>\n",
       "      <td>-0.072880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3A</th>\n",
       "      <td>V</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>-0.007044</td>\n",
       "      <td>-0.047157</td>\n",
       "      <td>-0.027101</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001301</td>\n",
       "      <td>-0.133532</td>\n",
       "      <td>-0.225989</td>\n",
       "      <td>-0.179760</td>\n",
       "      <td>-0.013975</td>\n",
       "      <td>-0.106083</td>\n",
       "      <td>-0.060029</td>\n",
       "      <td>-0.206590</td>\n",
       "      <td>-0.473092</td>\n",
       "      <td>-0.339841</td>\n",
       "    </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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252T</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>T</td>\n",
       "      <td>-0.132241</td>\n",
       "      <td>-0.189524</td>\n",
       "      <td>-0.160882</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.074971</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.037485</td>\n",
       "      <td>-0.379343</td>\n",
       "      <td>-0.533716</td>\n",
       "      <td>-0.456529</td>\n",
       "      <td>-0.258642</td>\n",
       "      <td>-0.414259</td>\n",
       "      <td>-0.336450</td>\n",
       "      <td>-0.586871</td>\n",
       "      <td>-0.763869</td>\n",
       "      <td>-0.675370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252V</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>V</td>\n",
       "      <td>0.161672</td>\n",
       "      <td>0.177089</td>\n",
       "      <td>0.169381</td>\n",
       "      <td>0.262923</td>\n",
       "      <td>-0.185348</td>\n",
       "      <td>0.038788</td>\n",
       "      <td>-0.044192</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.080998</td>\n",
       "      <td>0.750125</td>\n",
       "      <td>-0.138738</td>\n",
       "      <td>0.305693</td>\n",
       "      <td>0.325693</td>\n",
       "      <td>0.414606</td>\n",
       "      <td>0.370149</td>\n",
       "      <td>0.039893</td>\n",
       "      <td>-0.233608</td>\n",
       "      <td>-0.096858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252W</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>W</td>\n",
       "      <td>0.046494</td>\n",
       "      <td>0.283281</td>\n",
       "      <td>0.164887</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015843</td>\n",
       "      <td>-0.026414</td>\n",
       "      <td>0.553441</td>\n",
       "      <td>0.263513</td>\n",
       "      <td>0.092733</td>\n",
       "      <td>0.674209</td>\n",
       "      <td>0.383471</td>\n",
       "      <td>-0.064218</td>\n",
       "      <td>0.168365</td>\n",
       "      <td>0.052073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252Y</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.349203</td>\n",
       "      <td>0.464681</td>\n",
       "      <td>0.406942</td>\n",
       "      <td>-0.103307</td>\n",
       "      <td>-0.228062</td>\n",
       "      <td>-0.165685</td>\n",
       "      <td>-0.029801</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.107036</td>\n",
       "      <td>0.380213</td>\n",
       "      <td>0.438552</td>\n",
       "      <td>0.409383</td>\n",
       "      <td>0.711687</td>\n",
       "      <td>1.132873</td>\n",
       "      <td>0.922280</td>\n",
       "      <td>0.450806</td>\n",
       "      <td>0.282706</td>\n",
       "      <td>0.366756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252*</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.069944</td>\n",
       "      <td>-0.002437</td>\n",
       "      <td>-0.036191</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071298</td>\n",
       "      <td>0.035649</td>\n",
       "      <td>0.113153</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020770</td>\n",
       "      <td>-0.257914</td>\n",
       "      <td>0.038034</td>\n",
       "      <td>-0.109940</td>\n",
       "      <td>-0.137814</td>\n",
       "      <td>-0.005529</td>\n",
       "      <td>-0.071672</td>\n",
       "      <td>-0.107910</td>\n",
       "      <td>-0.524964</td>\n",
       "      <td>-0.316437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5934 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         wts  sites muts    1_beta    2_beta  avg_beta  1_shift_Delta  \\\n",
       "mutation                                                                \n",
       "M1I        M      1    I -2.924932 -4.256726 -3.590829       0.000000   \n",
       "F2L        F      2    L  0.200928  0.207115  0.204021      -0.000000   \n",
       "F2S        F      2    S  0.194773 -0.074343  0.060215      -0.000000   \n",
       "F2V        F      2    V  0.239144 -0.030672  0.104236      -0.086489   \n",
       "V3A        V      3    A -0.007044 -0.047157 -0.027101      -0.000000   \n",
       "...       ..    ...  ...       ...       ...       ...            ...   \n",
       "S1252T     S   1252    T -0.132241 -0.189524 -0.160882      -0.000000   \n",
       "S1252V     S   1252    V  0.161672  0.177089  0.169381       0.262923   \n",
       "S1252W     S   1252    W  0.046494  0.283281  0.164887       0.000000   \n",
       "S1252Y     S   1252    Y  0.349203  0.464681  0.406942      -0.103307   \n",
       "S1252*     S   1252    * -0.069944 -0.002437 -0.036191       0.000000   \n",
       "\n",
       "          2_shift_Delta  avg_shift_Delta  1_shift_Omicron_BA2  ...  \\\n",
       "mutation                                                       ...   \n",
       "M1I            0.000000         0.000000            -0.000000  ...   \n",
       "F2L           -0.000000         0.000000            -0.204654  ...   \n",
       "F2S            0.000000         0.000000             0.000000  ...   \n",
       "F2V           -0.153066        -0.119778             0.000000  ...   \n",
       "V3A           -0.000000         0.000000            -0.000000  ...   \n",
       "...                 ...              ...                  ...  ...   \n",
       "S1252T         0.000000         0.000000            -0.074971  ...   \n",
       "S1252V        -0.185348         0.038788            -0.044192  ...   \n",
       "S1252W         0.000000         0.000000             0.018787  ...   \n",
       "S1252Y        -0.228062        -0.165685            -0.029801  ...   \n",
       "S1252*         0.071298         0.035649             0.113153  ...   \n",
       "\n",
       "          avg_shift_Omicron_BA2  1_predicted_func_score_Delta  \\\n",
       "mutation                                                        \n",
       "M1I                    0.000000                     -3.162696   \n",
       "F2L                   -0.102327                      0.287689   \n",
       "F2S                    0.000000                      0.275053   \n",
       "F2V                    0.000000                      0.188819   \n",
       "V3A                   -0.001301                     -0.133532   \n",
       "...                         ...                           ...   \n",
       "S1252T                -0.037485                     -0.379343   \n",
       "S1252V                -0.080998                      0.750125   \n",
       "S1252W                -0.015843                     -0.026414   \n",
       "S1252Y                -0.107036                      0.380213   \n",
       "S1252*                 0.020770                     -0.257914   \n",
       "\n",
       "          2_predicted_func_score_Delta  avg_predicted_func_score_Delta  \\\n",
       "mutation                                                                 \n",
       "M1I                          -3.348626                       -3.255661   \n",
       "F2L                           0.366644                        0.327167   \n",
       "F2S                          -0.286178                       -0.005562   \n",
       "F2V                          -0.521577                       -0.166379   \n",
       "V3A                          -0.225989                       -0.179760   \n",
       "...                                ...                             ...   \n",
       "S1252T                       -0.533716                       -0.456529   \n",
       "S1252V                       -0.138738                        0.305693   \n",
       "S1252W                        0.553441                        0.263513   \n",
       "S1252Y                        0.438552                        0.409383   \n",
       "S1252*                        0.038034                       -0.109940   \n",
       "\n",
       "          1_predicted_func_score_Omicron_BA1  \\\n",
       "mutation                                       \n",
       "M1I                                -3.009065   \n",
       "F2L                                 0.405955   \n",
       "F2S                                 0.393345   \n",
       "F2V                                 0.484417   \n",
       "V3A                                -0.013975   \n",
       "...                                      ...   \n",
       "S1252T                             -0.258642   \n",
       "S1252V                              0.325693   \n",
       "S1252W                              0.092733   \n",
       "S1252Y                              0.711687   \n",
       "S1252*                             -0.137814   \n",
       "\n",
       "          2_predicted_func_score_Omicron_BA1  \\\n",
       "mutation                                       \n",
       "M1I                                -3.236155   \n",
       "F2L                                 0.487242   \n",
       "F2S                                -0.166355   \n",
       "F2V                                -0.069216   \n",
       "V3A                                -0.106083   \n",
       "...                                      ...   \n",
       "S1252T                             -0.414259   \n",
       "S1252V                              0.414606   \n",
       "S1252W                              0.674209   \n",
       "S1252Y                              1.132873   \n",
       "S1252*                             -0.005529   \n",
       "\n",
       "          avg_predicted_func_score_Omicron_BA1  \\\n",
       "mutation                                         \n",
       "M1I                                  -3.122610   \n",
       "F2L                                   0.446598   \n",
       "F2S                                   0.113495   \n",
       "F2V                                   0.207601   \n",
       "V3A                                  -0.060029   \n",
       "...                                        ...   \n",
       "S1252T                               -0.336450   \n",
       "S1252V                                0.370149   \n",
       "S1252W                                0.383471   \n",
       "S1252Y                                0.922280   \n",
       "S1252*                               -0.071672   \n",
       "\n",
       "          1_predicted_func_score_Omicron_BA2  \\\n",
       "mutation                                       \n",
       "M1I                                -3.085516   \n",
       "F2L                                -0.200109   \n",
       "F2S                                 0.195843   \n",
       "F2V                                 0.286191   \n",
       "V3A                                -0.206590   \n",
       "...                                      ...   \n",
       "S1252T                             -0.586871   \n",
       "S1252V                              0.039893   \n",
       "S1252W                             -0.064218   \n",
       "S1252Y                              0.450806   \n",
       "S1252*                             -0.107910   \n",
       "\n",
       "          2_predicted_func_score_Omicron_BA2  \\\n",
       "mutation                                       \n",
       "M1I                                -3.409368   \n",
       "F2L                                 0.107339   \n",
       "F2S                                -0.525584   \n",
       "F2V                                -0.431950   \n",
       "V3A                                -0.473092   \n",
       "...                                      ...   \n",
       "S1252T                             -0.763869   \n",
       "S1252V                             -0.233608   \n",
       "S1252W                              0.168365   \n",
       "S1252Y                              0.282706   \n",
       "S1252*                             -0.524964   \n",
       "\n",
       "          avg_predicted_func_score_Omicron_BA2  \n",
       "mutation                                        \n",
       "M1I                                  -3.247442  \n",
       "F2L                                  -0.046385  \n",
       "F2S                                  -0.164871  \n",
       "F2V                                  -0.072880  \n",
       "V3A                                  -0.339841  \n",
       "...                                        ...  \n",
       "S1252T                               -0.675370  \n",
       "S1252V                               -0.096858  \n",
       "S1252W                                0.052073  \n",
       "S1252Y                                0.366756  \n",
       "S1252*                               -0.316437  \n",
       "\n",
       "[5934 rows x 21 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mut_df_replicates = combine_replicate_muts(\n",
    "    {\n",
    "        f\"{fit.dataset_name}\".split(\"-\")[-1]: fit.model\n",
    "        for fit in models.query(f\"scale_coeff_lasso_shift == {chosen_lasso_strength}\").itertuples()\n",
    "    },\n",
    "    predicted_func_scores=True,\n",
    "    phenotype_as_effect=True,\n",
    "    how=\"inner\",\n",
    "    times_seen_threshold=times_seen_threshold\n",
    ")\n",
    "mut_df_replicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "535e949f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phenotypic_effect_Delta</th>\n",
       "      <th>phenotypic_effect_Omicron_BA2</th>\n",
       "      <th>phenotypic_effect_Omicron_BA1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5934.000000</td>\n",
       "      <td>5934.000000</td>\n",
       "      <td>5934.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.685844</td>\n",
       "      <td>0.555077</td>\n",
       "      <td>0.691773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.333441</td>\n",
       "      <td>0.300639</td>\n",
       "      <td>0.398528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.092687</td>\n",
       "      <td>0.094148</td>\n",
       "      <td>0.101826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.468865</td>\n",
       "      <td>0.278595</td>\n",
       "      <td>0.306873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.742053</td>\n",
       "      <td>0.595659</td>\n",
       "      <td>0.748459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.909277</td>\n",
       "      <td>0.786314</td>\n",
       "      <td>0.983936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.126436</td>\n",
       "      <td>2.176377</td>\n",
       "      <td>2.776442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       phenotypic_effect_Delta  phenotypic_effect_Omicron_BA2  \\\n",
       "count              5934.000000                    5934.000000   \n",
       "mean                  0.685844                       0.555077   \n",
       "std                   0.333441                       0.300639   \n",
       "min                   0.092687                       0.094148   \n",
       "25%                   0.468865                       0.278595   \n",
       "50%                   0.742053                       0.595659   \n",
       "75%                   0.909277                       0.786314   \n",
       "max                   4.126436                       2.176377   \n",
       "\n",
       "       phenotypic_effect_Omicron_BA1  \n",
       "count                    5934.000000  \n",
       "mean                        0.691773  \n",
       "std                         0.398528  \n",
       "min                         0.101826  \n",
       "25%                         0.306873  \n",
       "50%                         0.748459  \n",
       "75%                         0.983936  \n",
       "max                         2.776442  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mut_df = (\n",
    "    mut_df_replicates\n",
    "    .assign(\n",
    "        phenotypic_effect_Delta = 2**mut_df_replicates[\"avg_predicted_func_score_Delta\"],\n",
    "        phenotypic_effect_Omicron_BA2 = 2**mut_df_replicates[\"avg_predicted_func_score_Omicron_BA2\"],\n",
    "        phenotypic_effect_Omicron_BA1 = 2**mut_df_replicates[\"avg_predicted_func_score_Omicron_BA1\"]\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "mut_df[[c for c in mut_df.columns if \"phenotypic_effect\" in c]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7d31b330-1b7c-47cf-abe6-385faa435cf6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x700 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saveas=\"validation_titer_fold_change\"\n",
    "\n",
    "row1 = ['titer', 'titer', '.']\n",
    "row2 = ['D142L', 'A419S', 'A570D'] \n",
    "row3 = ['K854N', 'T1027I', 'legend']\n",
    "empty_row = [\".\"] * 3\n",
    "\n",
    "fig = plt.figure( figsize=[6.4, 7])\n",
    "axs = fig.subplot_mosaic(\n",
    "    [row1, empty_row, row2, row3],\n",
    "    height_ratios=[\n",
    "        1, 0.39, 0.7, 0.7\n",
    "    ],\n",
    "    empty_sentinel=\".\",\n",
    "    gridspec_kw={\n",
    "        \"wspace\": 0.20,\n",
    "        \"hspace\": 0.4,\n",
    "    }\n",
    ")\n",
    "\n",
    "#############\n",
    "# TITERS\n",
    "#############\n",
    "\n",
    "# Read in data\n",
    "titers_df = pd.read_csv('data/viral_titers.csv')\n",
    "titers_df.rename(\n",
    "    columns={'RLUperuL':'titer', 'background':'homolog'},\n",
    "    inplace=True\n",
    ")\n",
    "\n",
    "# Add a column giving the replicate and mutation\n",
    "titers_df['replicate'] = titers_df['virus'].apply(lambda x: x[-1])\n",
    "titers_df['mutation'] = titers_df['virus'].str.extract(r'_(\\S+)_')\n",
    "titers_df['mutation'].fillna('unmutated', inplace=True)\n",
    "titers_df['mutation'].replace('142L', 'D142L', inplace=True)\n",
    "\n",
    "# Plot data for a given mutation\n",
    "validation_mutations = ['D142L', 'A419S', 'A570D', 'K854N', 'T1027I']\n",
    "homologs = ['Delta', 'BA.1', 'BA.2']\n",
    "replicates = ['1', '2', '3']\n",
    "xticklabels = ['unmutated'] + validation_mutations\n",
    "pal = sns.color_palette('colorblind')\n",
    "hex_codes = pal.as_hex()\n",
    "for (i, homolog) in enumerate(homologs):\n",
    "    \n",
    "    data = titers_df[(titers_df['homolog'] == homolog)]\n",
    "    \n",
    "    sns.stripplot(\n",
    "        x='mutation', y='titer', data=data, ax=axs['titer'],\n",
    "        order=xticklabels, s=10, alpha=0.5,\n",
    "        hue='homolog', hue_order=['Delta', 'BA.1', 'BA.2'],\n",
    "    )\n",
    "    sns.boxplot(\n",
    "        x='mutation', y='titer', data=data, ax=axs['titer'],\n",
    "        order=xticklabels,\n",
    "        showfliers=False, showbox=False, showcaps=False,\n",
    "        medianprops={'visible': False}, #dict(color=hex_codes[i]),\n",
    "        whiskerprops={'visible': False},\n",
    "    )\n",
    "\n",
    "handles, labels = axs['titer'].get_legend_handles_labels()\n",
    "by_label = dict(zip(labels, handles))\n",
    "axs['titer'].legend(by_label.values(), by_label.keys(), bbox_to_anchor=[1,0.5])\n",
    "\n",
    "axs['titer'].set_yscale('log')\n",
    "axs['titer'].set_yticks([1e2, 1e4, 1e6])\n",
    "axs['titer'].set_xticklabels(axs['titer'].get_xticklabels(), rotation = 90)\n",
    "axs['titer'].set_ylabel(r'viral titer (RLU/$\\mu$L)')\n",
    "axs['titer'].set_xlabel('')\n",
    "axs['titer'].grid()\n",
    "sns.despine(ax = axs['titer'])\n",
    "\n",
    "\n",
    "#############\n",
    "# FOLD CHANGE\n",
    "#############\n",
    "\n",
    "# Read in data\n",
    "val_df = pd.read_csv('data/spike_validation_data.csv')\n",
    "\n",
    "# Restructure the data\n",
    "val_dict = {\n",
    "    key : []\n",
    "    for key in [\n",
    "        'mutation', 'fold_change', 'homolog', 'replicate',\n",
    "        'predicted_beta'\n",
    "    ]\n",
    "}\n",
    "validation_mutations = ['D142L', 'A419S', 'A570D', 'K854N', 'T1027I']\n",
    "for i, row in val_df.iterrows():\n",
    "    for mutation in validation_mutations:\n",
    "        homolog = row['background'].replace('.', '')\n",
    "        homolog = \"Omicron_\" + homolog if \"BA\" in homolog else homolog\n",
    "\n",
    "        val_dict['mutation'].append(mutation)\n",
    "        val_dict['fold_change'].append(row[mutation])\n",
    "        val_dict['homolog'].append(homolog)\n",
    "        val_dict['replicate'].append(row['replicate'])\n",
    "\n",
    "        predicted_beta = float(mut_df[\n",
    "            mut_df['mutation'] == mutation\n",
    "        ][f'phenotypic_effect_{homolog}'].values[0])\n",
    "        val_dict['predicted_beta'].append(predicted_beta)\n",
    "\n",
    "val_df = pd.DataFrame(val_dict)\n",
    "val_df['site'] = val_df['mutation'].apply(lambda x: int(x[1:-1]))\n",
    "val_df['homolog'].replace('Omicron_BA1', 'BA.1', inplace=True)\n",
    "val_df['homolog'].replace('Omicron_BA2', 'BA.2', inplace=True)\n",
    "val_df.sort_values('site', inplace=True)\n",
    "\n",
    "for (i, mutation) in enumerate(validation_mutations):\n",
    "    data = val_df[val_df['mutation'] == mutation]\n",
    "    iter_ax = axs[mutation]\n",
    "    sns.scatterplot(\n",
    "        x='predicted_beta', y='fold_change', data=data,\n",
    "        hue='homolog', ax=iter_ax, s=100, alpha=0.7,\n",
    "        hue_order=['Delta', 'BA.1', 'BA.2']\n",
    "    )\n",
    "    iter_ax.set(\n",
    "        title=mutation, xlabel='', ylabel='',\n",
    "        yscale='log', ylim=[1e-5,2], yticks=[1, 1e-2, 1e-4],\n",
    "    )\n",
    "    iter_ax.set_xscale('log', base=2)\n",
    "    iter_ax.set_xlim([0.1, 1.3])\n",
    "    iter_ax.set_xticks([2**-3, 2**-2, 2**-1, 2**0])\n",
    "\n",
    "    iter_ax.grid()\n",
    "    iter_ax.get_legend().remove()\n",
    "    sns.despine(ax=iter_ax)\n",
    "    \n",
    "    if mutation in ['D142L', 'A419S']:\n",
    "        iter_ax.tick_params(axis=\"x\", labelbottom=False)\n",
    "    \n",
    "    if mutation not in ['D142L', 'K854N']:\n",
    "        iter_ax.tick_params(axis=\"y\", labelleft=False)\n",
    "\n",
    "fig.text(\n",
    "    0.5, 0.02, \"predicted enrichment ratio in DMS experiment \\n(mutant : unmutated)\",\n",
    "    ha='center'\n",
    ")\n",
    "fig.text(\n",
    "    0.000, 0.31, 'fold change in viral titer\\n (mutant : unmutated)',\n",
    "    va='center', rotation='vertical'\n",
    ")\n",
    "axs[\"legend\"].set_axis_off()\n",
    "\n",
    "axs[\"titer\"].text(\n",
    "    -0.05, 1.15, \n",
    "    f\"A\", \n",
    "    ha=\"right\", va=\"center\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=axs[\"titer\"].transAxes\n",
    ")\n",
    "axs[\"D142L\"].text(\n",
    "    -0.1, 1.25, \n",
    "    f\"B\", \n",
    "    ha=\"right\", va=\"center\", \n",
    "    size=15,\n",
    "    weight=\"bold\",\n",
    "    transform=axs[\"D142L\"].transAxes\n",
    ")\n",
    "\n",
    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d5b17fe-bfa8-4f4e-9b06-f2e704adc275",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Model-reference choice comparison\n",
    "\n",
    "Here, we fit each of the replicate dataset as before, but we also fit models where using Delta and BA.2 as a reference to show the model is robust to choice of reference "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "fac1a21d-bc2c-4f20-b768-2af1869af792",
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Delta\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc6c6f97c3c14a9ba1b8f063102568ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/28515 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a82127905444e05b7569311d8cfaeb5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/70597 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d100e5c3692f475d976a4e577538d2ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/60397 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 16 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c03d0ae119604c81995ae5ca455657e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=9970), Label(value='0 / 9970'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [144, 143, 69, 145, 70, 211, 25, 26, 24, 157, 158, 898],\n",
      "dropping: 10983 variantswhich have mutations at those sites.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00f9be87aba64fbdb7e6a8710e897dd5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=9283), Label(value='0 / 9283'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [371, 376], dropping 3499 variants\n",
      "Converting mutations for Delta\n",
      "is reference, skipping\n",
      "Converting mutations for Omicron_BA1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0a6e650fa9d4ceabfbd9dd9ff396b68",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=4049), Label(value='0 / 4049'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting mutations for Omicron_BA2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5bdee774e26d435dbba3345a1fcb2449",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=3357), Label(value='0 / 3357'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Delta\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "422768a36d184e12a7bd4c96b1dc3c69",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA1\n"
     ]
    },
    {
     "data": {
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       "model_id": "d0b838739e3740f7a7630bbdc4f44de7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/62129 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA2\n"
     ]
    },
    {
     "data": {
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       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 16 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7881e34cb5e54bf8a1d498ca5ed8d1e1",
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       "version_minor": 0
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      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=9313), Label(value='0 / 9313'))), …"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [145, 70, 144, 143, 69, 211, 422, 26, 24, 25, 157, 158],\n",
      "dropping: 10129 variantswhich have mutations at those sites.\n"
     ]
    },
    {
     "data": {
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       "model_id": "fe564c8f6e8a4091a4b3d16dab3a2163",
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      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=8680), Label(value='0 / 8680'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [371], dropping 1873 variants\n",
      "Converting mutations for Delta\n",
      "is reference, skipping\n",
      "Converting mutations for Omicron_BA1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ec81eb594624bc6b1d9a751e9ea0222",
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=3596), Label(value='0 / 3596'))), …"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting mutations for Omicron_BA2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ecfe8f4c5e584fd29a9ce69b945a8ea2",
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      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=3268), Label(value='0 / 3268'))), …"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Delta\n"
     ]
    },
    {
     "data": {
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       "model_id": "5775b19f2b46461ea151a00558565d27",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA1\n"
     ]
    },
    {
     "data": {
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA2\n"
     ]
    },
    {
     "data": {
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       "model_id": "58585ab630154c2b9de66f8983506907",
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       "version_minor": 0
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 16 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6c910b2deae4ce2939a14e5221fa6bd",
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      },
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=9970), Label(value='0 / 9970'))), …"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [144, 143, 69, 145, 70, 211, 25, 26, 24, 157, 158, 898],\n",
      "dropping: 10983 variantswhich have mutations at those sites.\n"
     ]
    },
    {
     "data": {
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       "model_id": "37df08caa86c4124b838863d664b6e2b",
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=9283), Label(value='0 / 9283'))), …"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [371], dropping 2041 variants\n",
      "Converting mutations for Delta\n"
     ]
    },
    {
     "data": {
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       "model_id": "85eb48fcbdc84c8f8d1771e412414e3e",
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=1663), Label(value='0 / 1663'))), …"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting mutations for Omicron_BA1\n",
      "is reference, skipping\n",
      "Converting mutations for Omicron_BA2\n"
     ]
    },
    {
     "data": {
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=3412), Label(value='0 / 3412'))), …"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Delta\n"
     ]
    },
    {
     "data": {
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       "model_id": "d827f138e1af4ec082d49efaadc0571d",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA1\n"
     ]
    },
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA2\n"
     ]
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    {
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       "model_id": "bd78c40e9b4144918ee5e5ee0a7fd69a",
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 16 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dfd78b4ca2ad4d1e91091982050fa1d6",
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=9313), Label(value='0 / 9313'))), …"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [145, 70, 144, 143, 69, 211, 422, 26, 24, 25, 157, 158],\n",
      "dropping: 10129 variantswhich have mutations at those sites.\n"
     ]
    },
    {
     "data": {
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       "model_id": "d28922cce2cc41f5b6c8e6e4c401434f",
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       "version_minor": 0
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=8680), Label(value='0 / 8680'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [371], dropping 1873 variants\n",
      "Converting mutations for Delta\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "222c58b26f234dbc8db382aab4b71d4c",
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       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=1700), Label(value='0 / 1700'))), …"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting mutations for Omicron_BA1\n",
      "is reference, skipping\n",
      "Converting mutations for Omicron_BA2\n"
     ]
    },
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     "name": "stdout",
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     "text": [
      "inferring site map for Delta\n"
     ]
    },
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     "text": [
      "inferring site map for Omicron_BA1\n"
     ]
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     "text": [
      "inferring site map for Omicron_BA2\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 16 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
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     "text": [
      "unknown cond wildtype at sites: [144, 143, 69, 145, 70, 211, 25, 26, 24, 157, 158, 898],\n",
      "dropping: 10983 variantswhich have mutations at those sites.\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [376], dropping 1486 variants\n",
      "Converting mutations for Delta\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting mutations for Omicron_BA1\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting mutations for Omicron_BA2\n",
      "is reference, skipping\n",
      "inferring site map for Delta\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inferring site map for Omicron_BA1\n"
     ]
    },
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     "text": [
      "inferring site map for Omicron_BA2\n"
     ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 16 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown cond wildtype at sites: [145, 70, 144, 143, 69, 211, 422, 26, 24, 25, 157, 158],\n",
      "dropping: 10129 variantswhich have mutations at those sites.\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid non-identical-sites: [], dropping 0 variants\n",
      "Converting mutations for Delta\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting mutations for Omicron_BA1\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting mutations for Omicron_BA2\n",
      "is reference, skipping\n"
     ]
    }
   ],
   "source": [
    "variable_reference_datasets = []\n",
    "\n",
    "for reference in [\"Delta\", \"Omicron_BA1\", \"Omicron_BA2\"]:\n",
    "    for replicate, rep_fsdf in func_score_df.groupby(\"replicate\"):\n",
    "        \n",
    "        start = time.time()\n",
    "\n",
    "        # initialize data object\n",
    "        data = multidms.Data(\n",
    "            rep_fsdf,\n",
    "            collapse_identical_variants=\"mean\",\n",
    "            alphabet=multidms.AAS_WITHSTOP_WITHGAP,\n",
    "            reference=reference,\n",
    "            assert_site_integrity=False,\n",
    "            verbose=True,\n",
    "            nb_workers=16,\n",
    "            name=f\"{replicate}-{reference}\"\n",
    "        )\n",
    "        data.condition_colors = cc\n",
    "        variable_reference_datasets.append(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2cccd793",
   "metadata": {},
   "outputs": [],
   "source": [
    "variable_reference_fit_params = fit_params.copy()\n",
    "variable_reference_fit_params[\"dataset\"] = variable_reference_datasets\n",
    "variable_reference_fit_params[\"scale_coeff_lasso_shift\"] = [chosen_lasso_strength]\n",
    "_, _, variable_reference_models = multidms.fit_models(variable_reference_fit_params, n_threads=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "19cfad8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "variable_reference_models = (\n",
    "    variable_reference_models\n",
    "    .assign(\n",
    "        reference=lambda x: x.dataset_name.str.split(\"-\").str[-1],\n",
    "        replicate=lambda x: x.dataset_name.str.split(\"-\").str[0]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad5bffb-b049-438c-bc0e-ffc77498d830",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "For each of the reference fits, compute parameters relative to a BA.1 reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cdd5d948-b2c8-4c23-aab5-192dc19233a8",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beta_Omicron_BA1</th>\n",
       "      <th>shift_Delta</th>\n",
       "      <th>shift_Omicron_BA2</th>\n",
       "      <th>reference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mutation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M1I</th>\n",
       "      <td>-3.362432</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2L</th>\n",
       "      <td>0.232434</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.066369</td>\n",
       "      <td>Delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2S</th>\n",
       "      <td>0.142001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2V</th>\n",
       "      <td>0.277621</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3A</th>\n",
       "      <td>-0.026383</td>\n",
       "      <td>-0.045533</td>\n",
       "      <td>-0.045533</td>\n",
       "      <td>Delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252T</th>\n",
       "      <td>-0.155242</td>\n",
       "      <td>-0.036178</td>\n",
       "      <td>-0.036178</td>\n",
       "      <td>BA2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252V</th>\n",
       "      <td>0.166300</td>\n",
       "      <td>0.030589</td>\n",
       "      <td>-0.078923</td>\n",
       "      <td>BA2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252W</th>\n",
       "      <td>0.165440</td>\n",
       "      <td>0.014676</td>\n",
       "      <td>0.014676</td>\n",
       "      <td>BA2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252Y</th>\n",
       "      <td>0.420877</td>\n",
       "      <td>-0.176302</td>\n",
       "      <td>-0.121661</td>\n",
       "      <td>BA2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252*</th>\n",
       "      <td>-0.061469</td>\n",
       "      <td>0.114527</td>\n",
       "      <td>0.062062</td>\n",
       "      <td>BA2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17804 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          beta_Omicron_BA1  shift_Delta  shift_Omicron_BA2 reference\n",
       "mutation                                                            \n",
       "M1I              -3.362432     0.000000           0.000000     Delta\n",
       "F2L               0.232434     0.000000          -0.066369     Delta\n",
       "F2S               0.142001     0.000000           0.000000     Delta\n",
       "F2V               0.277621     0.000000           0.000000     Delta\n",
       "V3A              -0.026383    -0.045533          -0.045533     Delta\n",
       "...                    ...          ...                ...       ...\n",
       "S1252T           -0.155242    -0.036178          -0.036178       BA2\n",
       "S1252V            0.166300     0.030589          -0.078923       BA2\n",
       "S1252W            0.165440     0.014676           0.014676       BA2\n",
       "S1252Y            0.420877    -0.176302          -0.121661       BA2\n",
       "S1252*           -0.061469     0.114527           0.062062       BA2\n",
       "\n",
       "[17804 rows x 4 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relative_params = pd.DataFrame()\n",
    "homologs = [\"Delta\", \"Omicron_BA1\", \"Omicron_BA2\"]\n",
    "\n",
    "# For each respective model fit \n",
    "for reference, replicate_models in variable_reference_models.groupby(\"reference\"):\n",
    "    \n",
    "    # combine the replicate mutational \n",
    "    mut_df = combine_replicate_muts(\n",
    "        {\n",
    "            f\"rep_{row.replicate}\":row[\"model\"] \n",
    "            for idx, row in replicate_models.iterrows()\n",
    "        },\n",
    "        times_seen_threshold = times_seen_threshold\n",
    "    )\n",
    "    \n",
    "    mut_df = mut_df.copy()[[c for c in mut_df.columns if \"avg\" in c]]\n",
    "    \n",
    "    # Compute mut effect (beta+shift) relative to each homolog\n",
    "    for homolog in homologs:\n",
    "        if homolog == reference:\n",
    "            mut_df[f\"beta_{homolog}\"] = mut_df[\"avg_beta\"]\n",
    "        else:\n",
    "            mut_df[f\"beta_{homolog}\"] = mut_df[\"avg_beta\"] + mut_df[f\"avg_shift_{homolog}\"]\n",
    "       \n",
    "    # Compute shifts relative to BA1 (betas_h - beta_BA1)\n",
    "    for homolog in homologs:\n",
    "        mut_df[f\"shift_{homolog}\"] = mut_df[f\"beta_{homolog}\"] - mut_df[f\"beta_Omicron_BA1\"]\n",
    "    \n",
    "    # drop un-neccessary columns\n",
    "    mut_df.drop([c for c in mut_df.columns if \"avg\" in c], axis=1, inplace=True)\n",
    "    \n",
    "    mut_df = mut_df.assign(reference = reference)\n",
    "    \n",
    "    relative_params = pd.concat([relative_params, mut_df])\n",
    "\n",
    "    \n",
    "relative_params.drop([\"beta_Delta\", \"beta_Omicron_BA2\", \"shift_Omicron_BA1\"], axis=1, inplace=True)\n",
    "relative_params.reference.replace({\"Omicron_BA2\":\"BA2\", \"Omicron_BA1\": \"BA1\"}, inplace=True)\n",
    "relative_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a6e371d1-09de-4225-b503-7117c3ab5894",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saveas = \"reference_model_comparison_params_scatter\"\n",
    "parameters = [\"beta_Omicron_BA1\", \"shift_Delta\", \"shift_Omicron_BA2\"]\n",
    "\n",
    "\n",
    "fig = plt.figure( figsize=[6.4, 8])\n",
    "axs = fig.subplot_mosaic(\n",
    "    [[f\"{param}_{col}\" for col in range(3)] for param in parameters],\n",
    "    gridspec_kw={\n",
    "        \"wspace\": 0.20,\n",
    "        \"hspace\": 1.0,\n",
    "    }\n",
    ")\n",
    "\n",
    "param_limits = {\n",
    "    \"beta_Omicron_BA1\" : range(-8, 2, 2),\n",
    "    \"shift_Delta\" : range(-2, 3),\n",
    "    \"shift_Omicron_BA2\" : range(-2, 3) \n",
    "}\n",
    "\n",
    "param_titles = {\n",
    "    \"beta_Omicron_BA1\" : r\"Mutation effect in BA.1\",\n",
    "    \"shift_Delta\" : \"shift in Delta, relative to BA.1\",\n",
    "    \"shift_Omicron_BA2\" : \"shift in BA.2, relative to BA.1\"\n",
    "}\n",
    "\n",
    "\n",
    "for row, param in enumerate(parameters):\n",
    "    \n",
    "    # pivot to each fit on columns, drop NIS.\n",
    "    data = relative_params.pivot(\n",
    "        columns = \"reference\",\n",
    "        values = param\n",
    "    ).dropna()\n",
    "\n",
    "    # plot each combination of fits\n",
    "    for col, (x, y) in enumerate(combinations(data.columns, 2)):\n",
    "        iter_ax = axs[f\"{param}_{col}\"]\n",
    "        \n",
    "        sns.scatterplot(\n",
    "            data = data,\n",
    "            x = x,\n",
    "            y = y,\n",
    "            ax = iter_ax,\n",
    "            alpha=0.3,\n",
    "            c='0.25'\n",
    "        )\n",
    "        \n",
    "        corr = pearsonr(data[x], data[y])[0]**2\n",
    "        iter_ax.annotate(\n",
    "            f\"$R^2 = {corr:.2f}$\", \n",
    "            (0.07, 0.8), \n",
    "            xycoords=\"axes fraction\", \n",
    "            fontsize=11\n",
    "        )\n",
    "        \n",
    "        limits = param_limits[param]\n",
    "        iter_ax.set_yticks(limits)\n",
    "        iter_ax.set_xticks(limits)\n",
    "        mmin, mmax = min(limits), max(limits)\n",
    "        iter_ax.plot([mmin, mmax], [mmin, mmax], \"--\", lw=2, c=\"royalblue\")\n",
    "        label_fn = lambda x: x if x == \"Delta\" else f\"{x[0]}{x[1]}.{x[2]}\"\n",
    "        \n",
    "        \n",
    "        xl = f\"{label_fn(x)}\"\n",
    "        yl = f\"{label_fn(y)}\"\n",
    "        if col == 1: xl += \"\\nmodel fit reference\"\n",
    "        if col == 0: yl = \"model fit reference\\n\" + yl\n",
    "        iter_ax.set_xlabel(xl)\n",
    "        iter_ax.set_ylabel(yl)            \n",
    "            \n",
    "        \n",
    "        \n",
    "        iter_ax.grid()\n",
    "        sns.despine(ax = iter_ax)\n",
    "        \n",
    "        if col != 0:\n",
    "            iter_ax.tick_params(\"y\", labelleft=False)\n",
    "            \n",
    "        if col == 1:\n",
    "            iter_ax.set_title(param_titles[param], size=13)\n",
    "            \n",
    "\n",
    "\n",
    "for param, sub_anno in zip(parameters, [\"A\", \"B\", \"C\"]):\n",
    "    iter_ax = axs[f\"{param}_0\"]    \n",
    "    axs[f\"{param}_0\"].text(\n",
    "        -0.2, 1.15, \n",
    "        sub_anno, \n",
    "        ha=\"right\", va=\"center\", \n",
    "        size=15,\n",
    "        weight=\"bold\",\n",
    "        transform=iter_ax.transAxes\n",
    "    )\n",
    "\n",
    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "746aac9d-d84d-40fc-bb66-f4f3fd181a2e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Cumulative distribution of model sparsity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3c9dc700",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cache miss - this could take a moment\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>replicate</th>\n",
       "      <th>mutation</th>\n",
       "      <th>sense</th>\n",
       "      <th>condition</th>\n",
       "      <th>shift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>A1015D</td>\n",
       "      <td>nonsynonymous</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.316587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>A1015S</td>\n",
       "      <td>nonsynonymous</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>A1015T</td>\n",
       "      <td>nonsynonymous</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.117340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>A1016D</td>\n",
       "      <td>nonsynonymous</td>\n",
       "      <td>Delta</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rep-1</td>\n",
       "      <td>A1016S</td>\n",
       "      <td>nonsynonymous</td>\n",
       "      <td>Delta</td>\n",
       "      <td>-0.081105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25609</th>\n",
       "      <td>rep-2</td>\n",
       "      <td>Y917C</td>\n",
       "      <td>nonsynonymous</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25610</th>\n",
       "      <td>rep-2</td>\n",
       "      <td>Y917F</td>\n",
       "      <td>nonsynonymous</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>-0.001024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25611</th>\n",
       "      <td>rep-2</td>\n",
       "      <td>Y917H</td>\n",
       "      <td>nonsynonymous</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25612</th>\n",
       "      <td>rep-2</td>\n",
       "      <td>Y91C</td>\n",
       "      <td>nonsynonymous</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25613</th>\n",
       "      <td>rep-2</td>\n",
       "      <td>Y91H</td>\n",
       "      <td>nonsynonymous</td>\n",
       "      <td>Omicron_BA2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25614 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      replicate mutation          sense    condition     shift\n",
       "0         rep-1   A1015D  nonsynonymous        Delta  0.316587\n",
       "1         rep-1   A1015S  nonsynonymous        Delta  0.000000\n",
       "2         rep-1   A1015T  nonsynonymous        Delta  0.117340\n",
       "3         rep-1   A1016D  nonsynonymous        Delta  0.000000\n",
       "4         rep-1   A1016S  nonsynonymous        Delta -0.081105\n",
       "...         ...      ...            ...          ...       ...\n",
       "25609     rep-2    Y917C  nonsynonymous  Omicron_BA2  0.000000\n",
       "25610     rep-2    Y917F  nonsynonymous  Omicron_BA2 -0.001024\n",
       "25611     rep-2    Y917H  nonsynonymous  Omicron_BA2  0.000000\n",
       "25612     rep-2     Y91C  nonsynonymous  Omicron_BA2  0.000000\n",
       "25613     rep-2     Y91H  nonsynonymous  Omicron_BA2  0.000000\n",
       "\n",
       "[25614 rows x 5 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tall_mut_df_chosen = (\n",
    "    mc.split_apply_combine_muts(query=f\"scale_coeff_lasso_shift == {chosen_lasso_strength}\", times_seen_threshold=times_seen_threshold)\n",
    "    .reset_index()\n",
    "    .rename(columns={\"dataset_name\":\"replicate\"})\n",
    "    .assign(sense=lambda x: [\"stop\" if \"*\" in mut else \"nonsynonymous\" for mut in x.mutation])\n",
    "    .melt(\n",
    "        id_vars=[\"replicate\", \"mutation\", \"sense\"],\n",
    "        value_vars=[\"shift_Delta\", \"shift_Omicron_BA2\"],\n",
    "        var_name=\"condition\",\n",
    "        value_name=\"shift\"\n",
    "    )\n",
    "    .replace({\"shift_Delta\":\"Delta\", \"shift_Omicron_BA2\":\"Omicron_BA2\"})\n",
    ")\n",
    "tall_mut_df_chosen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "47784572-2636-4707-81ea-873a633ad642",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saveas = \"percent_shifts_under_x_lineplot\"\n",
    "fig, ax = plt.subplots(1,2, figsize=[6.4,3.5], sharey='row')\n",
    "\n",
    "condition_col = {\n",
    "    \"Delta\" : 0,\n",
    "    \"Omicron_BA2\" : 1\n",
    "}\n",
    "\n",
    "replicate_line_style = {\n",
    "    \"rep-1\" : \"-\",\n",
    "    \"rep-2\" : \"--\"\n",
    "}\n",
    "\n",
    "sense_colors = {\n",
    "    \"nonsynonymous\" : \"darkgrey\",\n",
    "    \"stop\" : \"red\"\n",
    "}\n",
    "\n",
    "t_points = np.linspace(0, 2.5, 100)\n",
    "def perc_abs_lte(x, t):\n",
    "    abs_x = np.abs(x)\n",
    "    return len(abs_x[abs_x<=t]) / len(x) #)*100\n",
    "\n",
    "for (condition, replicate, sense), mut_df_replicates in tall_mut_df_chosen.groupby([\"condition\", \"replicate\", \"sense\"]):\n",
    "    iter_ax = ax[condition_col[condition]]\n",
    "    lt_percentages = [perc_abs_lte(mut_df_replicates[\"shift\"], t)*100 for t in t_points]\n",
    "    iter_ax.plot(\n",
    "        t_points, \n",
    "        lt_percentages,\n",
    "        linestyle = replicate_line_style[replicate],\n",
    "        color = sense_colors[sense],\n",
    "        linewidth = 3\n",
    "    )\n",
    "    iter_ax.set_xticks(np.linspace(0,2.5,6),np.linspace(0,2.5,6), rotation=0, ha=\"center\",size=10)\n",
    "    sns.despine(ax=iter_ax)\n",
    "    iter_ax.grid(visible=True)\n",
    "    \n",
    "ax[condition_col[\"Delta\"]].set_title(\"Delta\")\n",
    "ax[condition_col[\"Omicron_BA2\"]].set_title(\"BA.2\")\n",
    "\n",
    "ax[0].set_ylabel(\"percent \\n$|\\Delta_{d,m}| <= x$\")\n",
    "ax[0].set_xlabel(\"$x$\")\n",
    "ax[1].set_xlabel(\"$x$\")\n",
    "\n",
    "black_line = mlines.Line2D([], [], color='black', linestyle='-',\n",
    "                          markersize=5, label='rep 1')\n",
    "black_dashed = mlines.Line2D([], [], color='black',linestyle='--',\n",
    "                          markersize=5, label='rep 2')\n",
    "red_line = mlines.Line2D([], [], color='red', linewidth=2,linestyle='-',markersize=5, label='stop')\n",
    "grey_line = mlines.Line2D([], [], color='grey',linewidth=2, linestyle='-',markersize=5, label='nonsynonymous')\n",
    "ax[1].legend(\n",
    "    handles=[black_line, black_dashed, red_line, grey_line], \n",
    "    bbox_to_anchor = (1, 0), \n",
    "    loc='lower right', \n",
    "    frameon=True, \n",
    "    fontsize=9\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.subplots_adjust(wspace=0.05)\n",
    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3511314a-0ce8-478a-850e-e5f07447e7e4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Correlation of BA2 and Delta Shifts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4f644e9e-e4ff-4e30-a7c4-b5c3147fab9b",
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wts</th>\n",
       "      <th>sites</th>\n",
       "      <th>muts</th>\n",
       "      <th>1_beta</th>\n",
       "      <th>2_beta</th>\n",
       "      <th>avg_beta</th>\n",
       "      <th>1_shift_Delta</th>\n",
       "      <th>2_shift_Delta</th>\n",
       "      <th>avg_shift_Delta</th>\n",
       "      <th>1_shift_Omicron_BA2</th>\n",
       "      <th>2_shift_Omicron_BA2</th>\n",
       "      <th>avg_shift_Omicron_BA2</th>\n",
       "      <th>sense</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mutation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M1I</th>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "      <td>-2.924932</td>\n",
       "      <td>-4.256726</td>\n",
       "      <td>-3.590829</td>\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>0.000000</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2L</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>L</td>\n",
       "      <td>0.200928</td>\n",
       "      <td>0.207115</td>\n",
       "      <td>0.204021</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.204654</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.102327</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2S</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>0.194773</td>\n",
       "      <td>-0.074343</td>\n",
       "      <td>0.060215</td>\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>0.000000</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2V</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>0.239144</td>\n",
       "      <td>-0.030672</td>\n",
       "      <td>0.104236</td>\n",
       "      <td>-0.086489</td>\n",
       "      <td>-0.153066</td>\n",
       "      <td>-0.119778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3A</th>\n",
       "      <td>V</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>-0.007044</td>\n",
       "      <td>-0.047157</td>\n",
       "      <td>-0.027101</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.002601</td>\n",
       "      <td>-0.001301</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         wts  sites muts    1_beta    2_beta  avg_beta  1_shift_Delta  \\\n",
       "mutation                                                                \n",
       "M1I        M      1    I -2.924932 -4.256726 -3.590829       0.000000   \n",
       "F2L        F      2    L  0.200928  0.207115  0.204021      -0.000000   \n",
       "F2S        F      2    S  0.194773 -0.074343  0.060215      -0.000000   \n",
       "F2V        F      2    V  0.239144 -0.030672  0.104236      -0.086489   \n",
       "V3A        V      3    A -0.007044 -0.047157 -0.027101      -0.000000   \n",
       "\n",
       "          2_shift_Delta  avg_shift_Delta  1_shift_Omicron_BA2  \\\n",
       "mutation                                                        \n",
       "M1I            0.000000         0.000000            -0.000000   \n",
       "F2L           -0.000000         0.000000            -0.204654   \n",
       "F2S            0.000000         0.000000             0.000000   \n",
       "F2V           -0.153066        -0.119778             0.000000   \n",
       "V3A           -0.000000         0.000000            -0.000000   \n",
       "\n",
       "          2_shift_Omicron_BA2  avg_shift_Omicron_BA2          sense  \n",
       "mutation                                                             \n",
       "M1I                  0.000000               0.000000  nonsynonymous  \n",
       "F2L                  0.000000              -0.102327  nonsynonymous  \n",
       "F2S                 -0.000000               0.000000  nonsynonymous  \n",
       "F2V                  0.000000               0.000000  nonsynonymous  \n",
       "V3A                 -0.002601              -0.001301  nonsynonymous  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mut_df_replicates = (\n",
    "    combine_replicate_muts(\n",
    "        {\n",
    "            f\"{fit.dataset_name}\".split(\"-\")[-1]: fit.model\n",
    "            for fit in models.query(f\"scale_coeff_lasso_shift == {chosen_lasso_strength}\").itertuples()\n",
    "        },\n",
    "        times_seen_threshold=times_seen_threshold,\n",
    "        how=\"inner\"\n",
    "    )\n",
    "    .assign(\n",
    "        sense=lambda x: [\"stop\" if \"*\" in mut else \"nonsynonymous\" for mut in x.muts]\n",
    "    )\n",
    ")\n",
    "mut_df_replicates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c96eab17-2c6b-47aa-ad7a-00f35dfa89b4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data = mut_df_replicates.dropna()\n",
    "saveas=\"shift_corr_Delta_BA2\"\n",
    "fig, ax = plt.subplots(1,figsize=[4,4])\n",
    "lim = [-1.6, 2.6]\n",
    "ticks = range(-1, 2)\n",
    "sns.scatterplot(\n",
    "    data=mut_df_replicates,\n",
    "    x=\"avg_shift_Delta\",\n",
    "    y=\"avg_shift_Omicron_BA2\",\n",
    "#     hue = \"sense\",\n",
    "    alpha = 0.35,\n",
    "    ax=ax,\n",
    "    c='0.25'\n",
    ")\n",
    "ax.plot()\n",
    "ax.plot(\n",
    "    [-1.5, 2.5], \n",
    "    [-1.5, 2.5],\n",
    "    linestyle=\"--\", \n",
    "    lw=2,\n",
    "    c='royalblue'\n",
    ")\n",
    "corr = pearsonr(mut_df_replicates[\"avg_shift_Delta\"], mut_df_replicates[\"avg_shift_Omicron_BA2\"])[0]**2\n",
    "ax.annotate(\n",
    "    f\"$R^2 = {corr:.2f}$\", \n",
    "    (0.05, 0.8), \n",
    "    xycoords=\"axes fraction\", \n",
    "    fontsize=12\n",
    ")\n",
    "ax.set_ylim(lim)\n",
    "ax.set_xlim(lim)\n",
    "ax.set_yticks(ticks, labels=ticks)\n",
    "ax.set_xticks(ticks, labels=ticks)\n",
    "ax.set_ylabel(\"BA.2 shift ($\\Delta_{d,m}$)\")\n",
    "ax.set_xlabel(\"Delta shift ($\\Delta_{d,m}$)\")\n",
    "# ax.set(xticks=np.linspace(-1.5, 2.5, 5))\n",
    "ax.grid()\n",
    "sns.despine(ax=ax)\n",
    "\n",
    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31de53e4-f1d9-4e7f-9cf4-a12c6c420e66",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Structural Analysis\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "95ae0f57-512a-4478-8cc5-131f624db3b1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# mut_df_replicates = pd.read_csv(f\"{output_dir}/mutations_df.csv\")\n",
    "mut_df_replicates.rename(\n",
    "    columns={\n",
    "        col : col.replace('Omicron_BA2', 'BA2')\n",
    "        for col in mut_df_replicates\n",
    "    }, \n",
    "    inplace=True\n",
    ")\n",
    "\n",
    "# mut_df_replicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "e244ce31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wts</th>\n",
       "      <th>sites</th>\n",
       "      <th>muts</th>\n",
       "      <th>1_beta</th>\n",
       "      <th>2_beta</th>\n",
       "      <th>avg_beta</th>\n",
       "      <th>1_shift_Delta</th>\n",
       "      <th>2_shift_Delta</th>\n",
       "      <th>avg_shift_Delta</th>\n",
       "      <th>1_shift_BA2</th>\n",
       "      <th>2_shift_BA2</th>\n",
       "      <th>avg_shift_BA2</th>\n",
       "      <th>sense</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mutation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M1I</th>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "      <td>-2.924932</td>\n",
       "      <td>-4.256726</td>\n",
       "      <td>-3.590829</td>\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>0.000000</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2L</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>L</td>\n",
       "      <td>0.200928</td>\n",
       "      <td>0.207115</td>\n",
       "      <td>0.204021</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.204654</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.102327</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2S</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>0.194773</td>\n",
       "      <td>-0.074343</td>\n",
       "      <td>0.060215</td>\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>0.000000</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2V</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>0.239144</td>\n",
       "      <td>-0.030672</td>\n",
       "      <td>0.104236</td>\n",
       "      <td>-0.086489</td>\n",
       "      <td>-0.153066</td>\n",
       "      <td>-0.119778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3A</th>\n",
       "      <td>V</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>-0.007044</td>\n",
       "      <td>-0.047157</td>\n",
       "      <td>-0.027101</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.002601</td>\n",
       "      <td>-0.001301</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252T</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>T</td>\n",
       "      <td>-0.132241</td>\n",
       "      <td>-0.189524</td>\n",
       "      <td>-0.160882</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.074971</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.037485</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252V</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>V</td>\n",
       "      <td>0.161672</td>\n",
       "      <td>0.177089</td>\n",
       "      <td>0.169381</td>\n",
       "      <td>0.262923</td>\n",
       "      <td>-0.185348</td>\n",
       "      <td>0.038788</td>\n",
       "      <td>-0.044192</td>\n",
       "      <td>-0.117804</td>\n",
       "      <td>-0.080998</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252W</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>W</td>\n",
       "      <td>0.046494</td>\n",
       "      <td>0.283281</td>\n",
       "      <td>0.164887</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018787</td>\n",
       "      <td>-0.050473</td>\n",
       "      <td>-0.015843</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252Y</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.349203</td>\n",
       "      <td>0.464681</td>\n",
       "      <td>0.406942</td>\n",
       "      <td>-0.103307</td>\n",
       "      <td>-0.228062</td>\n",
       "      <td>-0.165685</td>\n",
       "      <td>-0.029801</td>\n",
       "      <td>-0.184271</td>\n",
       "      <td>-0.107036</td>\n",
       "      <td>nonsynonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1252*</th>\n",
       "      <td>S</td>\n",
       "      <td>1252</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.069944</td>\n",
       "      <td>-0.002437</td>\n",
       "      <td>-0.036191</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071298</td>\n",
       "      <td>0.035649</td>\n",
       "      <td>0.113153</td>\n",
       "      <td>-0.071614</td>\n",
       "      <td>0.020770</td>\n",
       "      <td>stop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5934 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         wts  sites muts    1_beta    2_beta  avg_beta  1_shift_Delta  \\\n",
       "mutation                                                                \n",
       "M1I        M      1    I -2.924932 -4.256726 -3.590829       0.000000   \n",
       "F2L        F      2    L  0.200928  0.207115  0.204021      -0.000000   \n",
       "F2S        F      2    S  0.194773 -0.074343  0.060215      -0.000000   \n",
       "F2V        F      2    V  0.239144 -0.030672  0.104236      -0.086489   \n",
       "V3A        V      3    A -0.007044 -0.047157 -0.027101      -0.000000   \n",
       "...       ..    ...  ...       ...       ...       ...            ...   \n",
       "S1252T     S   1252    T -0.132241 -0.189524 -0.160882      -0.000000   \n",
       "S1252V     S   1252    V  0.161672  0.177089  0.169381       0.262923   \n",
       "S1252W     S   1252    W  0.046494  0.283281  0.164887       0.000000   \n",
       "S1252Y     S   1252    Y  0.349203  0.464681  0.406942      -0.103307   \n",
       "S1252*     S   1252    * -0.069944 -0.002437 -0.036191       0.000000   \n",
       "\n",
       "          2_shift_Delta  avg_shift_Delta  1_shift_BA2  2_shift_BA2  \\\n",
       "mutation                                                             \n",
       "M1I            0.000000         0.000000    -0.000000     0.000000   \n",
       "F2L           -0.000000         0.000000    -0.204654     0.000000   \n",
       "F2S            0.000000         0.000000     0.000000    -0.000000   \n",
       "F2V           -0.153066        -0.119778     0.000000     0.000000   \n",
       "V3A           -0.000000         0.000000    -0.000000    -0.002601   \n",
       "...                 ...              ...          ...          ...   \n",
       "S1252T         0.000000         0.000000    -0.074971    -0.000000   \n",
       "S1252V        -0.185348         0.038788    -0.044192    -0.117804   \n",
       "S1252W         0.000000         0.000000     0.018787    -0.050473   \n",
       "S1252Y        -0.228062        -0.165685    -0.029801    -0.184271   \n",
       "S1252*         0.071298         0.035649     0.113153    -0.071614   \n",
       "\n",
       "          avg_shift_BA2          sense  \n",
       "mutation                                \n",
       "M1I            0.000000  nonsynonymous  \n",
       "F2L           -0.102327  nonsynonymous  \n",
       "F2S            0.000000  nonsynonymous  \n",
       "F2V            0.000000  nonsynonymous  \n",
       "V3A           -0.001301  nonsynonymous  \n",
       "...                 ...            ...  \n",
       "S1252T        -0.037485  nonsynonymous  \n",
       "S1252V        -0.080998  nonsynonymous  \n",
       "S1252W        -0.015843  nonsynonymous  \n",
       "S1252Y        -0.107036  nonsynonymous  \n",
       "S1252*         0.020770           stop  \n",
       "\n",
       "[5934 rows x 13 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mut_df_replicates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5467b38-01a6-400c-ab43-ba8c572284f0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Compute site-wise summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "cf871238-2934-4284-ae88-50f0f0eebc02",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sites</th>\n",
       "      <th>max_abs_S_BA2</th>\n",
       "      <th>max_abs_S_Delta</th>\n",
       "      <th>mean_abs_S_BA2</th>\n",
       "      <th>mean_abs_S_Delta</th>\n",
       "      <th>mean_S_BA2</th>\n",
       "      <th>mean_S_Delta</th>\n",
       "      <th>sum_S_BA2</th>\n",
       "      <th>sum_S_Delta</th>\n",
       "      <th>res_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.102327</td>\n",
       "      <td>0.119778</td>\n",
       "      <td>0.034109</td>\n",
       "      <td>0.039926</td>\n",
       "      <td>-0.034109</td>\n",
       "      <td>-0.039926</td>\n",
       "      <td>-0.102327</td>\n",
       "      <td>-0.119778</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.115220</td>\n",
       "      <td>0.104436</td>\n",
       "      <td>0.042435</td>\n",
       "      <td>0.017570</td>\n",
       "      <td>0.033281</td>\n",
       "      <td>0.017242</td>\n",
       "      <td>0.199683</td>\n",
       "      <td>0.103452</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.060115</td>\n",
       "      <td>0.107191</td>\n",
       "      <td>0.023524</td>\n",
       "      <td>0.052339</td>\n",
       "      <td>-0.023524</td>\n",
       "      <td>-0.019122</td>\n",
       "      <td>-0.070573</td>\n",
       "      <td>-0.057365</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.686307</td>\n",
       "      <td>0.291834</td>\n",
       "      <td>0.089656</td>\n",
       "      <td>0.090932</td>\n",
       "      <td>0.041643</td>\n",
       "      <td>-0.086132</td>\n",
       "      <td>0.707937</td>\n",
       "      <td>-1.464238</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sites  max_abs_S_BA2  max_abs_S_Delta  mean_abs_S_BA2  mean_abs_S_Delta  \\\n",
       "0      1       0.000000         0.000000        0.000000          0.000000   \n",
       "1      2       0.102327         0.119778        0.034109          0.039926   \n",
       "2      3       0.115220         0.104436        0.042435          0.017570   \n",
       "3      4       0.060115         0.107191        0.023524          0.052339   \n",
       "4      5       0.686307         0.291834        0.089656          0.090932   \n",
       "\n",
       "   mean_S_BA2  mean_S_Delta  sum_S_BA2  sum_S_Delta  res_n  \n",
       "0    0.000000      0.000000   0.000000     0.000000      1  \n",
       "1   -0.034109     -0.039926  -0.102327    -0.119778      2  \n",
       "2    0.033281      0.017242   0.199683     0.103452      3  \n",
       "3   -0.023524     -0.019122  -0.070573    -0.057365      4  \n",
       "4    0.041643     -0.086132   0.707937    -1.464238      5  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_collapse = [col for col in mut_df_replicates.columns if \"avg_\" in col]\n",
    "cols_to_collapse.extend(['sites']) #, 'BA1_wt', 'BA2_wt', 'Delta_wt', 'is_BA2_wt', 'is_Delta_wt'])\n",
    "shifts_by_site = mut_df_replicates[cols_to_collapse].groupby(\"sites\").agg(\n",
    "    max_abs_S_BA2 = ('avg_shift_BA2', lambda x: np.max(np.abs(x))),\n",
    "    max_abs_S_Delta = ('avg_shift_Delta', lambda x: np.max(np.abs(x))),\n",
    "    \n",
    "    mean_abs_S_BA2 = ('avg_shift_BA2', lambda x: np.mean(np.abs(x))),\n",
    "    mean_abs_S_Delta = ('avg_shift_Delta', lambda x: np.mean(np.abs(x))),\n",
    "    \n",
    "    mean_S_BA2 = ('avg_shift_BA2', np.mean),\n",
    "    mean_S_Delta = ('avg_shift_Delta', np.mean),\n",
    "    \n",
    "    sum_S_BA2 = ('avg_shift_BA2',  np.sum),\n",
    "    sum_S_Delta = ('avg_shift_Delta', np.sum),\n",
    ")\n",
    "\n",
    "shifts_by_site.reset_index(inplace=True)\n",
    "shifts_by_site[\"res_n\"] = shifts_by_site[\"sites\"].astype(int)\n",
    "shifts_by_site.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4e47b3-1bf4-4f64-846d-6fd4a36d7e9e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Read alignment of homologs, identify nonidentical sites in Delta and BA.2 relative to BA.1, using WH1 as the numbering reference, and add to data frame with site-level data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "d67ac38a-d4f8-4229-ad8d-40dafe70e156",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delta\n",
      "n_diffs 43 0.9657\n",
      "BA.2\n",
      "n_diffs 27 0.9785\n"
     ]
    }
   ],
   "source": [
    "# Read in alignment\n",
    "alignment_file = 'data/clustalo-I20230702-193723-0021-19090519-p1m.clustal_num'\n",
    "align_dict = {}\n",
    "with open(alignment_file) as handle:\n",
    "    for record in SeqIO.parse(handle, \"clustal\"):\n",
    "        align_dict[record.id] = record.seq\n",
    "        \n",
    "# Identify non-identical sites realtive to BA.1\n",
    "ref_seq = align_dict['WH1_QHD43416.1']\n",
    "homologs = ['Delta', 'BA.2']\n",
    "align_nis_dict = {\n",
    "    homolog : []\n",
    "    for homolog in homologs\n",
    "}\n",
    "align_gaps_dict = {\n",
    "    homolog : []\n",
    "    for homolog in homologs\n",
    "}\n",
    "for homolog in homologs:\n",
    "    print(homolog)\n",
    "    ref_n = 1\n",
    "    n_diffs = 0\n",
    "    for (n, (ref, i, j)) in enumerate(zip(ref_seq, align_dict['BA.1'], align_dict[homolog])):\n",
    "        if i != j:\n",
    "            \n",
    "            # If the mut is an indel, then record it in the gap dict\n",
    "            if (i == '-' or j == '-'):\n",
    "                if True: # ref_n not in align_gaps_dict[homolog]:\n",
    "                    align_gaps_dict[homolog].append(ref_n)\n",
    "                pass # continue\n",
    "            \n",
    "            # Record the mut in the nis dict\n",
    "            #print(ref_n, i, j)\n",
    "            align_nis_dict[homolog].append(ref_n)\n",
    "            n_diffs += 1\n",
    "        if ref != '-':\n",
    "            ref_n += 1\n",
    "    print('n_diffs', n_diffs, 1-round(n_diffs/1254, 4))\n",
    "    \n",
    "# Add columns to shifts_by_site that indicate if a site is nonidentical\n",
    "for homolog in ['Delta', 'BA.2']:\n",
    "    hn = homolog.replace('.', '')\n",
    "    shifts_by_site[f\"is_{hn}_nis\"] = shifts_by_site['res_n'].apply(lambda x: x in align_nis_dict[homolog])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "842938aa-3c91-4a23-b611-f0ce4578f213",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Define function for getting distance matrix from structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "28974a14-fead-4750-86b0-a85fdc649cef",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "def get_distance_matrix(structure, d_metric='interatomic'):\n",
    "\n",
    "    # Get a list of residues, ignorning heteroatoms\n",
    "    residues = [r for r in structure.get_residues() if r.get_id()[0] == \" \"]\n",
    "    res_ns = []\n",
    "    res_ids = []\n",
    "    for res in residues:\n",
    "        full_id = res.get_full_id()\n",
    "        chain = full_id[2]\n",
    "        res_n = ''.join(map(str, full_id[3][1:])).strip()\n",
    "        if (res_n not in res_ns) and chain == 'A':\n",
    "            res_ns.append(res_n)\n",
    "        res_ids.append(f'{chain}_{res_n}')\n",
    "        \n",
    "    # Compute a distance matrix between all pairs of C-alpha carbons\n",
    "    dist_dict = {\n",
    "        key : []\n",
    "        for key in ['res_n', 'res_id'] + res_ids\n",
    "    }\n",
    "    for res_i in residues:\n",
    "        full_id_i = res_i.get_full_id()\n",
    "        chain_i = full_id_i[2]\n",
    "        res_n_i = ''.join(map(str, full_id_i[3][1:])).strip()\n",
    "        dist_dict['res_n'].append(res_n_i)\n",
    "        dist_dict['res_id'].append(f'{chain_i}_{res_n_i}')\n",
    "        \n",
    "        #print(res_n_i)\n",
    "        \n",
    "        for res_j in residues:\n",
    "            full_id_j = res_j.get_full_id()\n",
    "            chain_j = full_id_j[2]\n",
    "            res_n_j = ''.join(map(str, full_id_j[3][1:])).strip()\n",
    "            \n",
    "            if d_metric == 'CA':\n",
    "                xyz_i = res_i[\"CA\"].get_coord()\n",
    "                xyz_j = res_j[\"CA\"].get_coord()\n",
    "                d = np.linalg.norm(xyz_j-xyz_i)\n",
    "\n",
    "            elif d_metric == 'interatomic':\n",
    "                interatomic_dists = []\n",
    "                for atom_i in res_i:\n",
    "                    for atom_j in res_j:\n",
    "                        interatomic_dists.append(atom_i-atom_j)\n",
    "                d = min(interatomic_dists)\n",
    "            \n",
    "            else:\n",
    "                raise ValueError(f'{d_metric} not recognized')\n",
    "            \n",
    "            dist_dict[f'{chain_j}_{res_n_j}'].append(d)\n",
    "    dist_df = pd.DataFrame(dist_dict)\n",
    "    \n",
    "    return dist_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62809f1c-a9fe-41ef-8345-32fb378e1647",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "For each input PDB, compute a distance matrix that quantifies the minimum inter-atomic distance between all atom pairs from a given pair of residues. Since this takes a long time, save the results to a file and only rerun if the file doesn't exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "0e4f24e2-f119-49b5-a7a8-1c8b03533197",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7tf8\n",
      "Downloading PDB structure '7tf8'...\n",
      "Writing distance matrix for 7tf8 to results/spike_analysis/7tf8_dist_matrix.csv\n",
      "7tl9\n",
      "Downloading PDB structure '7tl9'...\n",
      "Writing distance matrix for 7tl9 to results/spike_analysis/7tl9_dist_matrix.csv\n",
      "7tge\n",
      "Downloading PDB structure '7tge'...\n",
      "Writing distance matrix for 7tge to results/spike_analysis/7tge_dist_matrix.csv\n"
     ]
    }
   ],
   "source": [
    "# Make a list of PDBs to analyze, one per spike conformation\n",
    "pdbs = ['7tf8', '7tl9', '7tge']\n",
    "\n",
    "# Loop over PDBs and compute distance matrices for each\n",
    "dfs = []\n",
    "for pdb in pdbs:\n",
    "\n",
    "    # Download structure\n",
    "    print(pdb)\n",
    "    pdbl = PDBList()\n",
    "    pdbl.retrieve_pdb_file(pdb, pdir=f'{output_dir}/', file_format='pdb')\n",
    "    shutil.copy(f'{output_dir}/pdb{pdb}.ent', f'{output_dir}/pdb{pdb}.pdb')\n",
    "\n",
    "    # Read in structure\n",
    "    parser = PDBParser()\n",
    "    structure = parser.get_structure(pdb, f'results/pdb{pdb}.ent')\n",
    "    \n",
    "    # Compute distance matrix and write to output file\n",
    "    output_file = os.path.join(output_dir, f'{pdb}_dist_matrix.csv')\n",
    "    if not os.path.isfile(output_file):\n",
    "        mut_df_replicates = get_distance_matrix(structure, d_metric='interatomic')\n",
    "        print(f'Writing distance matrix for {pdb} to {output_file}')\n",
    "        mut_df_replicates.to_csv(output_file, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61b2fe9e-baca-41fe-b663-a78bd323e3c1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Read in the distance matrices generated above. Then make an NxN matrix with the minimum distance between each pair of residues, considering all instances of a residue in a given trimer, and across all conformations being analyzed. Finally, Compute summary statistics quantifying the level of shifts in a neighborhood around a residue and compute the nearest distance to a non-identical site."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "803f763f-bd3a-48d7-9150-7e6ffa4162e2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1050 1050\n",
      "1224 1050 1033\n",
      "Using a neighbor distance cutoff of 5 Angstroms\n"
     ]
    }
   ],
   "source": [
    "# Read in data\n",
    "dfs = []\n",
    "for pdb in pdbs:\n",
    "    output_file = os.path.join(output_dir, f'{pdb}_dist_matrix.csv')\n",
    "    mut_df_replicates = pd.read_csv(output_file)\n",
    "    dfs.append(mut_df_replicates)\n",
    "dist_df = pd.concat(dfs)\n",
    "\n",
    "# Ignore subset of sites\n",
    "dist_df = dist_df[\n",
    "    ~dist_df['res_n'].isin(['214A', '214B', '214C'])\n",
    "].copy()\n",
    "dist_df['res_n'] = dist_df['res_n'].astype(int)\n",
    "\n",
    "# First, collapse rows by site (ignoring chain),\n",
    "# taking the min value across common entries\n",
    "min_dist_df = dist_df.groupby('res_n').min()\n",
    "min_dist_df.index.name = None\n",
    "del min_dist_df['res_id']\n",
    "\n",
    "# Then, transpose the dataframe and do that same\n",
    "# as above, ignoring the same subset as above\n",
    "min_dist_df = min_dist_df.T\n",
    "min_dist_df['res_n'] = min_dist_df.apply(\n",
    "    lambda row: row.name[2:],\n",
    "    axis=1\n",
    ")\n",
    "min_dist_df = min_dist_df[\n",
    "    ~min_dist_df['res_n'].isin(['214A', '214B', '214C'])\n",
    "].copy()\n",
    "min_dist_df['res_n'] = min_dist_df['res_n'].astype(int)\n",
    "\n",
    "min_dist_df = min_dist_df.groupby('res_n').min()\n",
    "print(len(min_dist_df.columns.values), len(min_dist_df.index))\n",
    "res_ns = sorted(min_dist_df.index.unique())\n",
    "# Merge per-site data with distance matrix\n",
    "data = shifts_by_site.merge(\n",
    "    min_dist_df, on='res_n', how='inner'\n",
    ")\n",
    "print(len(shifts_by_site), len(min_dist_df), len(data))\n",
    "\n",
    "# For each site, compute the average metric among\n",
    "# all neighbors\n",
    "metric_prefix =\"max_abs_S\"\n",
    "nbr_dist_cutoff = 5\n",
    "print(f'Using a neighbor distance cutoff of {nbr_dist_cutoff} Angstroms')\n",
    "metrics = [f'{metric_prefix}_{h}' for h in ['Delta', 'BA2']]\n",
    "nbr_score_dict = {\n",
    "    key : []\n",
    "    for key in ['res_n', 'n_nbrs'] + [f'nbr_{metric}' for metric in metrics]\n",
    "}\n",
    "for res_n in list(set(res_ns)):\n",
    "    nbr_data = data[\n",
    "        (data[res_n] < nbr_dist_cutoff) &\n",
    "        (data['res_n'] != res_n)\n",
    "    ]\n",
    "    nbr_score_dict['res_n'].append(res_n)\n",
    "    nbr_score_dict['n_nbrs'].append(len(nbr_data))\n",
    "    for metric in metrics:\n",
    "        nbr_score_dict[f'nbr_{metric}'].append(nbr_data[metric].mean())\n",
    "\n",
    "nbr_score_df = pd.DataFrame(nbr_score_dict)\n",
    "nbr_score_df = nbr_score_df.merge(data, on='res_n')\n",
    "\n",
    "for homolog in ['BA2', 'Delta']:\n",
    "    \n",
    "    # Get a list of non-identical sites\n",
    "    nis = nbr_score_df.query(f\"is_{homolog}_nis == True\")[\"res_n\"]\n",
    "    \n",
    "    # Add a column that gives the minimum distance to non-identical\n",
    "    # sites for a given homolog, not counting the query site, which\n",
    "    # we achieve by ignoring entries of 0 in the distance matrix\n",
    "    nbr_score_df[f\"dist_nearest_{homolog}_nis\"] = nbr_score_df.replace(0, np.nan)[nis].min(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495843e2-8df7-42f4-af19-fc7e54c2e459",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Make plots analyzing the distribution shifts on the structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "4e2443a8-ebb0-49b5-b285-25d6d35405f8",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1rW99C9u2p/1qzCU44pAMrz6+gZzZC8DyLoeGjIFlCpYfcjgb1r8w6XzFQp6B/l5WrDgCy5z+mhs3bsTzPC699FKam5tpbm7msssuA+Ccc87h/PPPH9v3yCOPnJKTzefz9Pb2Tsnhxth3sKf33sSvTZs2TTl2vu+L4447Dtu2WbduHTC3e/SlRBzh7weoeZKhQkhDxpgmBQINGYOhQkjXNCmQnR8rZjzWdV1efPFFPvKRj+x0bLN9rD70kMVsXK8/YEnHoLXB5ORXXcB3//tLFPKjNDQ2AfDzn92OYRhcfNGFM6ZzTjjhBB544IFJrz355JP83d/9HTfddBOnnHLK2Ouve93ruOGGGxgdHR2LFn/4wx9iGAYXXnjhLscd46XHfN9706V05vu++MMf/oDv+6xYsQKY2z36UiIm/P0AQQhBqMimpidAxxJUanLaFMjuHvvss88SBAEnnHDCTsfW2to6JknbGXZUHCxos3jbNX/JHd+/ifddeyXv+8BH6O7p4Us3fJwrr76W449ZOrbvt7/9bd71rndx//33c9ZZZ9HU1MTZZ5897XVOOukkTjzxxLHf3/e+93HjjTdyxRVX8PGPf5zu7m4+/OEP8773vY+urq5djjvGS4+9fe/B7O+LHe89gDe+8Y2cfPLJHHfccaRSKVavXs0Xv/hFjjvuOK644gqAOd2jLyViwt8PYJlgmQIvUKRMMWW7F+hF2OlSILt77OrVq3Ech1e84hXz9TYmIZ00OOnYTm7733v4xMf/jr9+75vJZHJc/efv5Iv/esOkNQUpJWEY7pbWvbm5mfvvv5/rrruOK664glwux7XXXsvnPve5+Xw7MeYRe/veg9nfF9Pde6eeeio/+MEP+MIXvoCUkuXLl/Oe97yHD33oQziOs9fGPB8Q6gCoGCkUCjQ2NpLP53db/uT7PqtWreKSSy7Btu15HuGeY8M2j+7BgI5mEyEEYejz4lP3sfLY8xkqCBa1WaxYPP3NtuOxdSil6B8Jd3rsfGFnf9+5SEVfKtTHe/nll+90v/m49yZeb1+9/3bE/jTe/WmssHfHG0f4+wkWtFkUK5L+EZ2Prwfrg6MhjVmHBW0z/yt3PNaxdMRfKEsSNmQzxk4lkHsb+wrJx4hxoCMm/P0E6aTByqXOBB2+TrovbDVZ1DmzJHO6Yys1SSjB9SSup1iz0SWdNHapy48RI8b+jZjw9yOkkwYrFjt0eZKaa/DrTVrmaNu7JuiJx/YPB/zphRpDBUnSESRtRTkpyZfCneryY8SIsX8jJvz9EEnHwBS7R8hSwrPrXfqGQxa2Wdi2IAgUxbIinVQMjAbk0sa85PQn5uanWS+OESPGS4yY8A8ybO712D4csLDNJBl52JiOwLEVIwWJaSqGCiEtpQDLNHZrIXU6G4emzO55/8SIEWP+EBP+QYSaJxkYlSQcA8eaTOJCCDIpQaEkKZU9qq7EtsSsPHcmYiYbh94hveZQdSX7gVAiRowDEnPOC3z605+mp6dn2m29vb18+tOf3uNBxdg70MVVCscGP5yqxpVS0T0QMFKUpBIGTVkTxxZ0Dwas3eJRqY1H6TVPUqpKat7kyH0mG4e2Ji307xvaB7r+xIhxkGLOhP+pT32Kbdu2Tbutp6eHT33qU3s8qBh7B0GoydkxBeWqmlLINDgSUq6ELFto0Zg1p/XcqdQkG7Z5PL3O5el1NZ5e57Jhm54MdmUBATBcmDpJTMTu9hmd7XGz2W/dunV84AMf4Prrr8eyLI455phdXj/G/o997d5LpVLzfu/NOaWjlJryYa6jt7d3p+52MV4eTMypD42GDIwGWAZ4vklzg4FtCsq1kK39Pm1NBk05E9eXJCL1T91zp2fQZzAf4PpM67q5sN3aqY0DQCDVjC6Yu9tndLbHzXa/Z599lp///OcsWbKEXC6HlPH6w4GOffHey2az895JbVaE/73vfY/vfe97Y7///d///RRir9VqPPbYY7zmNa+Z1wHG2H3UPEmhLNnU442R9PKFNlLB9iEfPwwxTQUIRgoBpgGZlMm2fv1zY8akrUkv7jqWoH80IJcyWbrAHpv0601L+kdCnNFgpzYOAMXRIaoVRTY11db2pptuolAocOedd45Z1AZBwPvf/34+/vGPz+h9M9vjZrvfZZddxiWXXMKqVau48847eeyxx3b/nxBjn8HAwAC5XI5cLjdl2754791xxx088cQT8/o3mFVKx/M8isXiWBu3crk89nv9SwjBNddcw9e//vV5HWCMuWNi2uW3q8s8u8HF8yQCyKRNVi5xOHJZAsfWi7IdzSadLRbtLTa5jEkuZWBbgoHRkHNfcyif/eRH+fAH38s7rjiMN5zXxd/+1VvJ50fGrld/AihVdXRfKMtJkUkQBNx/78/4/Oc/z+svOILurRunHffu9hmd7XGz3c8w4hqEfQGLFy/mIx/5CNdeey0dHR00NDRw5ZVXMjIysuuDIwRBwN13383nP/95li9fzvr166fd72C592Z19nw+zw9+8AMeeOABli1bxje/+U0eeOCBSV+/+MUv+OpXvzpnB8If/vCHXH755SxevJhMJsMJJ5zArbfeGjeF3k3UVTLdkce9koKGrMlgXnegqrmSZMJgyQKHYw5NsKDNpr3JorPV5rDFNkEAwoCEYxC4wwwN9PCD277OcCHgbR+4mbe/5x/51X0/48v/8olJ13UsQRAq2ppMGtIG/SMhzz77HF/47Ec587QV/O1fvRXXdfnPm26Z0RRrd/uMzva4+ehjGuOlweDgIN3d3Xzta19DKcX3vvc9Pv3pT3P33XfzsY99bJfHr1mzhg9/+MMsXryYq666Ctd1+cY3vnHQ33uzSun83d/9HaeffjodHR1s3ryZarU6bwP4yle+wvLly/nyl79Me3s79957L+95z3vYunUr//zP/zxv1zlYMFElU3P1pJlJGRgCRgqSwdGQxZ16ns+mTKpuQL4c0pg1ERhUXZ+RgsS24A9/1I+Tr3z1W/jrj3wFGYLiXNa/+Cy/eeAXALi+JAzBDySWKVBhiYfu/T7/9V//zZN/epRlhxzB1W/7AG+5+mqGtz+3U0Oo3e0zOtvj4v62+w+efPJJAN7xjnfwta99DYDzzjuPp59+mp///OfTHlMoFPj+97/Prbfeyh/+8AeOPPJIrr/+eq6++mpWr14d33vMkvBbW1tZv379mGn/TIu2u4O7776btra2sd/PPfdchoaG+MpXvsL/+T//J368ngN2VMmEUhEEimpNkU0bZFKCfDmk3TdJ2HrBFSFQUuFYAtMULO206R4MeGZdladWr8a0HF7zuutJJQySjqBQlrQtOJSRkR+xrc8nX9bFVaWKZGDzr/nkR/6cRCLB1VdfzVf/3//lpJNfFVXahqxaNf99RWMcmHjyySdxHId//Md/nPT64Ycfzg9+8IMp+99zzz288Y1vHL/3vvpVXvWqVwHafbLeFP1gx6zY9NJLL+Waa65h+fLlAFxxxRWsWLFi2q9DDz10TgOYSPZ1vPKVr6RQKFAul+d0roMd9WYnUiq29fts3u4xWgpZ3+3SN+QjpSKUEIZabVUoS9obDdLJiPwj1FyJlILS0HMsP+xEDlm2mEpNUq3pqtmBgV5yjZ1sG/CRCoIAGjImhuGQTGZwXZd8Pk+tUiTlqFlX6u5un9HZHhf3t91/8OSTT3LqqadOSRH39vZOmzZOJBJkMuP3Xj6fJwxnkINNg4Pl3ptVhH/zzTfz2te+ljVr1vCVr3yFM888kwULFuy1Qf32t79l0aJF066mx5gZlglhqNjQHRCEkEkJFrTbyH6fbf0B6aSgvcnEDyTFCjSkDZYt1C6a3YMBSUcwmA8pVhS5jMH2Lc/QsehImnMm7c0mIwVJJqlY/cd7WHbk2VgGmAZ0tlq0NZocc+h5nHjKWtY8cQ8/u+vbXHrppXR2dvK2t72NP//zP9/l+He3z+hsj4v72+4/ePLJJzn66KMnvRaGIT/5yU+46KKLpux/zjnn0N3dzU9+8hNuvfXW+N6bAbMifNu2efe73w3AHXfcwUc/+lGOP/74vTKg3/72t3z/+9/ny1/+8oz7uK6L67pjvxcKBUA/uk3Xzmw2qB+3u8fvCVxPEkiwosXS2WC68ZpCqxKG8x5LOq2xdNjiDoNsMmRzr0c6YSFDkwXNBp2tAtsMaWtS5IuSrX0+Q6MBGUcwPFyit3sdppUgnQxBQToh+flP/oeh/m186J/fy6GLTAwDTEOhVICUguacxcmvvpS/eNsbGBrczre//W2+/e1v86//+q8cdthhrF+/nmuvvZZUKjXlvV944YX8y7/8CwMDA2P5zu9///sYhsE555wz4/9mtsfN5fwzXWtv3HsTr/dy3H+7g7053lqtxgsvvEAikZh0/m9/+9ts2bKFv/qrv5r2ukIILr/8ci6//HJ6enp2ee9NxL547+2N+o99quPVtm3bOO200zjqqKP45S9/OWP+/pOf/OS0Fb233XYb6XR6bw/zoMC6dev40Ic+RGtrK2effTYnnHACa9as4fbbb+etb30rb3jDG+Z0vmeffZb777+fhx9+mBtuuGGs2fNElEolrrvuOrq6urjyyisZHh7m1ltv5ayzzuIv//Ivx/Z74IEHuPHGG/n0pz/NMcccM+vjZruf67o8/vjjADz66KOsX7+er3zlK4BuVv2lL31pytjje2/+EN97+t5btWoV+Xx+7N4766yzaG9vn9N73xGzIvwf/ehHnHvuuTQ1NfGjH/1olyd94xvfOOeBjI6OcsYZZyCE4KGHHqKxcWphTh3TRVlLlixhcHBwj1oc3nvvvVxwwQUvSRu0qitZv9WnWJXk0lr37geKYkWSSxks6jRxbGPGqH+68ZZrkmc3uCQtwWhJUqiE1DyFAJqyJu1NJrVAcfSKBJnICG1ihA3wh2er9A2H3POTb/G9Wz7Ouz+2ip9998P0bn2WptYuLrz8r7ng0rdjCkHZlUgZ2SYoLedszpp0ddqceHhi0rjr4z399NMJlcX2YWfa9z7Q8yI3fvkjPPqHR8jlcrztbW/j05/+9KReod/+9re59tpruffee8caS69Zs4a/+7u/4/e///2Mx812v02bNnH44YdP+3/7xS9+MbYYCPNz7830/9yXsTfHe+utt/K3f/u3PPLII7zvfe/jySefZMmSJXz4wx/mne98526P9fTTT8dxHDKZzLT7zebeeDnvvQceeGDGxuizxawI3zAMHnnkEU499dRdqmZ0v9XZL5YAVKtVLrjgArZs2cLvf/97Fi1aNKfj98eetjP1ma3WQtZu9bFt6Gi2sExBNqXNx3IZY2wBdLrx1jzJ0+tcHFsggJ4hn9GCRAGphMCxBQ1pg5NfkUJKLeHsHQ7wfEXCFixosVjX7bJ2i8ft//0PrHv+Mf720/fh+QrPlwyNSlYstlm60OK5DT5NWYN0ShukhaGiUpWMliSvOjrJBa+avP4ycbxb+9TL3mN3V4h72u4ce3O8f/3Xf80jjzwybxXO8d92HLPK4W/cuJGFCxeO/TyfCIKAN7/5zaxZs4aHHnpozmS/P2Imk7GaK9naF1B1FVIKDAEDowFPrw9J2oKlC226Wi0WtFnY5tTzJh3dpnBDj7Y3rtQgmzawLIHvK7b1BSzpsChVJOu3eWztCwilgihC39zrU6iEeAGsX/s0Sw85lqacSRDqJ5KWRjAtQe9AQMIRIEApAUp/D6TCMGAgH87YI9f1JEMFOYPBmq7YHSqEdL2MPXZjvLx48sknOeGEE17uYRyQmBXhL1u2bNqf5wPvf//7+elPf8qXv/xlCoUCjzzyyNi2V77ylSQSiXm93r6AunxyR5OxwdGQck3R1mwwWpBs69dqm/Ymk2JFUiyFdCsoViSHdE1fC7GgzeLp9VW29gV0tVvYpoEfKCo1xcJWE9sWPPFClb6hENsSZNOReVpVsmXQY323B1LSt/U5Vh5/ORt7fBrSBh2tFi2NJgPDPj2jksMWOyilj8vXJLWanjgSlsHm7T7Pbahx5PLkFA/9QE7/3utwLEGlJmc0WItxYEMpxVNPPcXVV1/9cg/lgMRuN0Dp6elh27Zt1Gq1KdvOPPPMWZ+n7iPx93//91O2bdy4cUz7fyDBMpliMub6knw5JJMShCGUapKUNOho0WkPIaDmKxZlBfmSZMt2vYLveuMNRSo1yeZen+EojbO1LyTphDQ3WLQ3aSM015f8/pkquZRJZ6s+t+dJRoohfgA1D3xf8PF/X08YKi29jOSeYaBIWCagTdKaGyzKVUnPoI9K6MrdIAzJlwTbh0NMw5vSH9cyxt+7KfV5TVOnmwC8QGv9rWmeYGIc+BBCjCmfYsw/5kz4GzZs4O1vf/tYJL7jEsBcc/ibNm2a6xD2e9RTL3Xtu/6bQSghZcBQXoJSNOXG0x62Kah5umq2VA7Z0uuRBZ7d4NHeLGjIGnT3B/SPBiQcg84WC9fXC6G2yZjrZbkWUixLFrRYY+ceLUqqrk7HpGyo1bTRWlNOE7plCKquZPugYlGnRVPJpFyVNOUU5apeuG3OmQgBxbKgpUGwuMMiX9K2zBPz8QnHIJNQPLvJxTQESukJpSFj0tpgUKgoFrVZcTonRoy9gDkT/nve8x62bdvGrbfeyite8YopK9ExZocFbRbFiqR/ROfyQRGGioG8JO0YGIaJZY2nbfxQISX0DARUPTXWUKTeker5zSFOtPBaqkikgmzaJJMyJnno+KEmdjM6gR9IRooB5aqkb0QihZ54ugd8Fnfo8xerISnHIJHQKZejljlsHw7pHwoYLUkStjZOq9QUoOhssZFSkHTUWD6+7pZcdSXFqhzz+WnICoQSdA/4bOuHI5Y6LGiLO2/GiLE3MOdP1qOPPsq3vvWt3ZJexhhHOmmwcqkzqdl3KiEwDFi60NL5+0BhOgKlFOWqQiqFUoJc2iAMBbiQTAgsy+CZ9VWacxbNDSaNWZOBkRDHVmO9avPlkIYylCqK5pxBxZPklEG1phjMh8gQpJL4viSdEAQBbOwOaMoKEkmDtkYBQtCcMzlsicP6bR7rtvlUahKpBIYQSKnIJAU1V7FumwcoTCFYvtCmKavfd99QgBcIjjsswVBep7HC6Dgp9ZrCbHrnxngZUAF8wI6+Yux3mDPhL1q0CNOME6zzgXTSYMVihy5PL1J6vmTrdu12mXAE+aL2la/UwLa0Z00qCZWaoq3RpJSHfCnkhc0eG7sDNuLTPRDQ1miScGCkoO0V/EDqdM9wQDZt0JA26R8JQSlqnsL3JaYJhaLEl4KFrSYtOW1x7PvgKEgmdcrl8GUOLQ26oXlDxiQIFUrp8bmewrYE6ZReCC7VtExzU4/PEcs0iQ8XJA0Zm1TCYHHSpN03CUOd1pFSv7eZFD4xXiYUgY1AL1BCO3AtfFlHFGM3MWfC/9znPscXvvAFzjjjjNhwap4wRm4prbPfPhjQM6gYKYQMjup8e0PWYPP2gFJFkUwI7Og/9/hzVUbKJskEgH69dyggnRAsWyioVGFrf0DNlRxzWIKlnTa+r6h5Ln3DAX4IpinoHQpxfYltGSgFridoa9Jpp7ZGk6QDK5c4OLZBzZOkkwZHHZIAFNv6A8JQkS8rmhv0uoNSiiCApQssar4aa14eRM6cdSRsYyxaDEMVK3T2NRSBJ4BuoBb9XgOeAk4AtgNLXq7BxZgrZkX4r3/96yf9vm3bNpYvX84JJ5wwxeNZCMFdd901bwM82DAW9XdYHL4swdBoQKmqqLoS11eEYchISbCpx2dFBp7f4rN0gYFlGpRrWu7YmIXewZBKDZpzgoQtOHpFkhWLIolrCk46UvDMBo9t/R6OCYaA1kYT2zIIQ8VgIQClSKcMEo5gJC/pjXL2lilobTBZ0GaxbKFDsaIrfBuypnbP9CXlqiKTErQ3WShguKD9QSxj5haIsUJnH8RGNNkXo68GoAmoF7rfB1wJxD6H+wVmRfiFQmFSkcxhhx029nO97WGM+UXS0dF+R7M11pu2fyhgzWZJNmVgm3rR0zQV2wZCHAsasyaVmiKVFOQygi19PiNlg4VtFl1tk5OuqaTJ4Usd+ocDUkmDI9IWAsiXAwZHFBVXEYSSUAlKlZB0yiSdNGjMmJMal69c6rC8y2HbgA8SyhWdHmpvNvWTQUJPICWpx9vSYLB9RI6pk+qo2zXHCp19CBV0Gqce2Xeii/QA6rZBW4DngNNe8tHF2A3MivAffPDBvTyMGDtD0jHo6Q9wA724aVlCV7gCKMiXQ2wTcikDCdRqCql05e7CNosVi2ySiakkalsghCJhGzgW+IHCD3TZbToBQSgQSEpVg2xa6+dNU0xqXL59MKCrw2JJh1Zr1Rup1HX1EEXuhv69s9Wi4qoxdZJj6Yi/UJY0pI1YobM7mLiYujv+bTMd76Nz9vXIfrpavxTQE50j9o7b5zFvny7P82KJ5l5CzZP0Dutip+ULHaquom/YA6DqKhoi+wSpQIZgWpB0YEGrxZJOG8OYviq35ipyGZOmjEn3gMe6bR6VqsSyDbQzq6BaCbGtEDcwNSlndL5lkg1ChzVWV7CjZUI9cl/QbLAdSCUMVi41x9RJlZpOES1q05YRsUJnDpi4mBqgCXsBcAgzp1gmknu4i+NtNMnX0Gmc6ZAEZHTOGPs85vzp+s53vsONN9449vszzzzDypUrSafTnH322fT398/rAGMQKXi0dUEmpW0OWhv0XJ10DBoyBpYp9OcukPQO+jy/yceKcvMDI8GUAjmlFPlySC5t0NFq4jgGoyWFGy2YJhMChZaDDhcko4WA9ds8CqXxFdV64/JyVWpjN1tH/VVXEoZ63aF/JKQhbdDZOh5b1Ncpjj0swbGHJTn2sAQrFjsx2c8F9cXU9WjSbQMS0e9PRNt33P8p4CHgt8C9wA+BZ3ZyfBpYDJTRpD8R9dspx/jkEGOfx5w/YV/84hcnOWZed911OI7DV7/6VXp7e/n4xz8+rwOMoa0YHFublPmhwg8krq+tFTJJwUhR6oKmUsBQPqTqKjJJg2ULbfxAMTgasnabR74UEoaKkWLAU+tqjBYkSFiz2WNwJKA5Z7C43SKXNgkChe9DY1bLL2ueYnA0YHOvT83V1x7K+2zt93lqfY112zzcQLdXLJZDRkshnq+rZlcudUhNk1JKOgbZlBHn7HcHG4FhtEImi35Wz0a/D0fb65huchgCXoi2iZ0cfxSwDNiAfjoIgCpQj+sS6KeCOJ2zX2DOKZ1Nmzbxile8AoDBwUEeeughfvrTn3LxxRfT3t7Ohz70oXkf5IGGWqS7t0xmRXZJx2Bhi0XvQECpIkknBWEUYS1baLGhRzFc0GZojVnBglaTpgaLbNqkXJEMF0KGRgOGRkISCfB9RSphckiXhW0Jnlnv0jMcYAvIpizKNYkQggWtBo5lUKqGuL4ikRBUPUX3gI9SsHptTa8nSGhpgMaMgasEjg3LuxwaJtk5x1rLeUN9MbWFqXl1Eb2+HTgMTcQTJ4d6iqYCrAAK6Bz8yhmOzwGvi17fjM7Zp6J9ALrQKaAY+wXmTPiGYeB5On/8wAMPYNs255xzDgALFy5kaGhofkd4AKFSk5MqayfKG3eVzljQZrFk1OLFLR5DeUngK7Cg6kJbk01zzmRBm01zg4FpCCpVXfA0nA+pBQokZFEMDIUkHMFxhyVJJXU+flmXxbpuk+FiSN+Ij2UIWnImDVkDhaDqhRTKkLAECRte2ORRdiW2KTh8iY1pGgwXJK6nWNJpUagoSmVJR3O8ALtX4KMj7eQM25PoxVaf6SeHAJ2/z6Gf8YfQk0FymuNBk/pVaDVODzpnn0VPHMczdb1gTxeRY+w1zPkTefzxx/Mf//EfLF68mH/7t3/j3HPPHbMw3rJlCx0dHfM+yAMBlZpk7RaPQkV7wWdTxhR5485IP500OPawJNm0EeXS9evZtKCtyabqKpobTQwBIwVJzVMM50MsC7IpA9+XJBwDNwipeIrewYAVizXhtzTYLG53sE1fyzEDyGQMDEM3NpGhQVuToClnMjAaMlIOaMmZdDTbILQBWjatbRuG8pLWJjP2tN+bsNGf3BqaeHdEjXH7g+kmBwsw0Vp6h/FUzY7H+0A++jmHll7WyVwAD+xw/d1ZRI7xkmLOhH/DDTfwZ3/2Zxx33HHkcjnuu+++sW133nknp5566rwO8EDB9kFtmTCxy9OO8sZddXlKJw2OXpHk0MUO2wdNVv8RlnclaMzabNnuU65KggAcC4ZHdZ69bn+AJUg4gnTSoOpKtvT7LOq0tSTTFizutChUQkIVaqmkp/DQna4asoITDk+wuNNh9QsuacegUJKMlmogDRxHXyedEAwKaGk0CEIVV8zuLnYVIafR1gbrgQyT0zoKnb45NNqvwtTJoZ7H34aWW1qMM4FCF1o5wOPMTNw7qnLq6wTD6KeJZHTN9cAIcCIx6e8DmDPhv+Y1r2HLli28+OKLHHrooZMqbd/97ndPKsqKoVHzdJWqbYEfgDNB0bA7XZ6SjsGidofVQEeTSammEIYiX5Qs7bRxbMHGXp9cykAIqNYUzQ0mCUene1KOQamiVTQJW19vUbtNvhjSM6StForlkFTSoNm06Gq3OaTLYaQQ8OI2Fym1TbMSkEtDqARKhbieoFhRdLYYpJNmXDG7O3gW6GPXEfIhaCLdymSCrRNuPa8+0+TQhY7e1wNHoJmghCb7fqAjOudMxF2NzlONxrnjOgHoCSYTjXEjcNzu/UlizB92K8may+U46aSTprx+ySWX7PGADjRUapIN3bqTVNIxcOyAhsx4FSpoeeNoMSRfkpDd9ULucCFgW6+ubS9WJWCwtMOm2qQAgVISqRReKHFLglRS0JQ1xjpcbR/2x2V1ERKOoK3ZYmG7TSgVm7f7BKFiUbvNwlaLqid57HmXmqtobTRQKsTzdXEXtp5YbFMQSknPQMhpRztxOmcuiFJ0bGR2EXIuem0jeoG1hCbeQ5k6QUw3Oahon8Oj1waj4x002R/O9MT9TPRzT7Tt90AzeqKY7SJyjJcN8araXkQ9bz9UCEjYgoQDpiEYGNEFR0s7dai/bcBnJC8RQhcmzbSQW6lJNvZ4PLPOpVT1aAPKVUljDqqenjiyaYPRovasKZQUSzpNslEFbqUmSTjgeZBw9GcxDNVYpWt7k5ZQGgYce6j20C9V9XpAz6APQrFyiU33QIhlGFTCEK+mKCLJJKHqWrqLlgFNDZPH7npTO3TFmIDN0fdF6Pw67DpCzkWvHcbOU0AzTQ7HoCcDMzreR6dxkkxP3Engd9EYF6LXABLAi+j00EzZ3B0XgWO8bIgJfy+inrdf1G6DEgyMhjQ3aNuBkYKke0BH0b2DIUs6LNqbrBkXcuuTx9qtHl4AC9ss/EG9QOsF+vhCRZFyDA49yiFhwxPP18iXQgZGQ6o13dVKKYVlwZJOB9eHIAynrXRNOgbtkY9PuSqp1CQLWwGlWLfNJwgkSdvANCTVGgznJblsyCtbEyxoMXHs8XFvHwwYGNHKrnqHrriqdgIqaCKG3YuQZxM1z2ZyyLNz9c8Q+knhZPRElI++L0eT/ia0amdHTFxEjvGyIib8vYSaJxkqhGNWA21NJpWaZKQgyaQEKUewsUeHPAtaTFoaTd3iMGFM8akJQti6XT8pGIa2ILZM/bltyukK2aGCpLVRq2Namgw6WiySCcFAv6RSCwklmAZIqWhJ2zQ3WCQswfIuW1fJOtr2uFSVk+oDko5BEIJtaX39cEFqa2YlqHgSFYJhStJpQWuDQSZpkEnp/P1EZVI2qZms3qFrNsqkgwZ1Jc1MmM8IeWeTw87UPzW0+qaZqcSdQpP+ZnQqKDVh246LyDFeVsSEv5cQhBCE2q4YIJkwWLrAZnA0JF/WKZ2h0ZCFrSZBCNv6A0xzPL/v2PD0hhq9wxZBoNjWF5BO6b62meR4GFjvaFUoh2SSgu5Bn6qrGMz71HxoTAtAy0BtS9CQNkBo8h/I+5gmLO+y6ekfrw9QChoyBl3tFi0NFpapJyLHNBgYdlFKp4EaMwaGoSWfZVf73G8fCVm20I4mKX9MmSQjt8xkQpBOmbNWJh0UqBPtTKqmlypC3pn6xwdG0ZW3df+ciViOXuzdxLimf7pF5BgvK2LC30uwTO0uOdH7PZkwWNxp0O6bdPf7bOn1SSQM0iltfhYEioGRkNFiiJTaEiGVENgmhEpSqsBoSZF0JNkJUZRlCYplyfpuH9eTdDYrlBQ0Zgy2lvRTRlebTSqpq2K39fts2OZhW4LHVY2mlElLs8nSDouaD8OFkGJFkk0bHLciwfJFDumkYLgQ0D8SUnEVpgGFiiSdUNQ8aExrBVLPoM/WfpOB0YC+YUlzg0Fjxhhr2AK7p0w6oJFGq3E2M2Ux/SWPkGdS/2xHG6i1znCciZ4MFqBTPTtbRI7xsmFeCf+73/0uUkquueaa+TztPo26TUIQSizTGEuHJB1jzEFyR+93xxKMFEOchKApZ5CICM90BI6teH5jjaF8gGEIihUdSlVcyYIWExXCtoGA5QvGxxAEimI1JAh0E/BsygTCMd19EELNUyQdwYYej54B3aFq8QKLwVFJ70hAqRYyNBrS3GCNkfRAPuTZTS7FqsT1JF6oe+Q2piFUimJZUa0qmnO6j+5wMcQtS/qGQ1KOYKQQEgTaeXNx2+TktGOJuLvVRCxDE35d7fJyRcgzLfC+IhrjdnY+KR1HXGm7D2NeCf+d73znQUP49cXInsGAgZGAiqtIJaGjSTcbWRAtghYrcor3+2A+xEBwWJdDpQYJR41NCJWqJsz+4ZCF7TaNOQOhBGVX8uIWT1fNhlAshxzTAtv6XAKpr7N8oUN7s4Vh6GboSunUjTBgtBDSPxywtc/D9RWOZdDTH1KqSJobDfpGQ9RwyAlpQcLRt0VzTnvkb+jxsU3BsYcmGM6HFCuKhK23B0rR2WhRrIa4ni66amvUna+qgyGGITGLegIAXRNgOxIpibtbTUQ9Z34IOjUy2wh5OnLdU8KdaYG3CHjo6L852reMfiLYUfsfY5/EvBL+/fffP8WG90BEfTFyYDQgX9KtB5MJgesq+oZDap4aW5RcudSZ4v3e2WRiGtCYNugeCMYWcm1TsH1YO17aNixstUg6mhHbGkwGh0NGitpzPpnQf+en1ntkU4pDF9ms6BpvdNKYNRkYCckkDfpHdGvCmqdQgCEEri8plHURmG3oVI8XKDb3+TQ3WGTSJpYlKFUkfqAwhF5wXdJh0zcSEIbQkNUL0tsHfEZLkpYGQWuDjW0LbMtgYavUaxNGyMYeSbuADT0ehhEipeCoQ2Kt/hQcjV743BVhT2djUJ8UCuy6cGs2k8KOr0+M/nuj11zitM1+hHkl/DPPPHM+T7fPoi63FOjF2bYmbZeg0oqRgtbTFypybFFyxWKHrgkOmQDVdS6GKSYt5BYrkmJZaquCtEUiYRAEkmJF6+BdL8R1JcMSFjRrojxqmU2pZlCsKPKVEMsWJGwdZVdqktEAyjVJoaS7YhUjAldSkExok7Wt/boQK5SSgWFFz4DPymXaIlmI8T602/p98mVJuSZ1sVgh1FW5VZ3vz6ZMmnO6Ly5Ac06neTb0BjSkQtpbdXFWoarloaWKlnvubaWO7/vY+5P4f1cR8nQ2BsPA/dH2k9DWCdMVbu2p3009+l+O9tI5HW3PEGO/wLwQ/qZNm1i3bh0nnngiLS0tuz5gP0Zdbpl0YHBUR+b1dExdMVOsSBa1TzYQ2zGSref3O5pNFnfatPsmpYrE80NGSiZJx0BJSd9IqNMoZYkXghsIMlaIaeqZY1G7Q/+I4oVtPtWnFSuX2jRlLdqaTJZ22pimYsM2aMgKBvMS39dPGem0wPeh6iuKVYlh6hRQ1Q15eqOLRJFJWnS1W/T0+2zu80k5BpYFodQFX6YQGKYi6UDS0e+9KTf+Ph1HyzSFgmSky1coFrU7tDYYFCpqryp1wjAkn8/jeR4LFizY9QH7C6azMciji6DqP3cwtXDrEKZOFHl0Y5Re4NXMPkpPTfge5+z3G8yZ8P/+7/+eMAz56le/CmjDtKuvvhrf92lubuaXv/zltLYLBwrqckvHEoQS0ubkxUjLErieVrG4/swGYk0NBn2j0D3gk0sbGEIgpcQNBLmUiWMLNm8PyJdCFLqTVbUmQYFSBtv6Aw5vhI29Hn5o0ZLVKZh8SVKuBhQrIa0NJjUXOlpMmnMO3QMBQ/mA4WKIUlDxFEkbRmsgpO6OFQroHwpwXcWSDl1Bu7HXpVSFRe0moM3Xtg3qp41AQiYhOGyxHrM9oZetHygqVUlTzmTlMgM1Aiu6EiSTUbQt5F5R6iilKJVKFAoFpJRY1gEkRpvO7riGtkZIoaWdvYxLIycWblUZnygq6ElgEB3pr0VH/xcxt9TMbL1/YuwTmPOn7M477+Tkk08e+/3jH/84l1xyCU899RSnnnoqn/jEJ+Z1gPsa6nJLqTSp++HkNYsgUJimjoKnW5Ss1CQbtnms3+ZTKIZs7Pb4/TNVHn+hytqtAaEnUUpSqgUMjPiMFCXFkiRfkdRcff1c1sAPtXpn7VZfTwpKkksbNOW0r81zG10e/FOZ9d0efcMhz250MYTAtg2qVegdDqi6Oq8vogVeP4BSRXvsV6oh5WrIY2tqlKq6c1WpItjW79M/EuBYgkTCoL3RIJe1GK1IihW9MOx6klAqKrWQgUJIW5PB0qjJ+cTMSr1F4nwqdVzXpa+vj9HRUaTcUSx+AGA6u+M8sAWtgd+Ernp9Ab2gSrRvkXEFUAV4Hm2H4KAXYFuAp4GHmdoecTpM9P6ZTYvFGPsE5kz4vb29LF26FID169fzwgsv8IlPfIJjjjmG6667jscee2zeB7kvoS63rHnQkDYoR/lo0JFluarIpQ1qnqK1wZwUudYXe7sHA6RS1DyF4xjkUibphIEldD1L0jEIAihWFUqFSCWxDLAsMAyoVhV1LhNKUSiFDBYkW/t8hvOS3sGAQjkkDGH5QpuOVpORgmRDj0fNlbQ0ag97lKLigpQghF7ATST0d9eH/hG9rpBK6nRNqRbQ3R9QiBaAE7YgmzFZ2GbSkNYOnKFUeJEX/3BRsqDJ5IglDonE1EbqXqDmTakThiFDQ0P09/fj+wewacvEaljQpL4BnaeHcYfLXjSp1/vRGmj5ZBJtfFYAOtEpGBNoRBP/AJPbI86Eid4/u2qxGGOfwZyfdRsbG8cald977720tLSMpXASiQTVanVnhx8QqMstBzxtQzA4qjXvnqdwbE2mDWmDBW2T/7wTPfG7BwJqHnS26H3WbdVeM4ctcbS5WjUEpRCGwPUhk4w6WXmSmi8xDT3JSKBWU2TTOvovVkIqNUUoNcHKEFqyFo7jUShJbMugKWfRWFT4foghwA0gDME2IZUwsS39muMoyjVJQ2DQPaCbk1smmJaBAKquouYHZNMmyxY6DI2GoAQNWYM2W9DeZCGVboI+XRP1QlmyqM0as3WYS9vHiSgWi2Ppm+kQBAFr165l5cqV027fp7FjfnzHatgeNKEvR8s5fTSRL0GncbrRJL8IPSnk0WmcBqZW0tpAO7t2ttxT758YLxvmTPhnnnkm//RP/0RfXx9f+tKXuOKKK8a2vfDCC2PR/4GMdNJg5VKHXNqgZyhgYHiiDt8c0+FPVJ9M9NbxA8iXwrEFXz/QunRNolAohfSPaAWMHyhMQ5FwDNqaTLr7wfUl5aiRuJKKUEJzg0lTzqJ/WPvtZFOC0ZJk3VaXXNZChQLD1Au1FU/SkDWo1BSyqghdbaeQSRokEgLfV6ST2oZhYCSkUAwwDBNDSEIlSNm6wMzzQ/JlxVDB55CFNo05k/Zm3ZmrMasXqis1SRB4DI7qCS0MwQskhbKkIW3QkDXYsM3brbaPnucxPDw8Y0QvpeThhx/mxz/+MeVyme9973vkcvtJcnlnapp6NexadFomi07NbIuOzaIjAQcd5b8aXQW7Eb1AGzA5x67QtglL0NW0g+zct+el9P6JMa+YM+H/3//7f3n729/ORz/6UU488UQ+97nPjW37zne+wxlnnDGvA9xXkU4aWm4ZmZvtWGm7IyZ663i+TslYlg6PwhAQUZHTNpfugYBKTeva80VBqSoZKYYkLEE2a2BWoVbTx5ZrOi8uABlK/AAMQzcoQSncQPeyTSUNfCnxfGhPGCxqN+juE1SqHqapI2vT1PYOCMgmBMIQpFOCak2SSStcX59boSeZqgtJCwpFxbMbXHJpk4RjYVvjf4P65NjdFzKyFfLlEMc2WNRm0ZA1dIpojm0f6+qbcrk8ZRtoon/ssce444476O3tHXv9f//3f3n3u9+9h//5lwCz6R51InoSWIdO1ySBU9A3QgVN4AKdqlmCTtscgp5AXpywre6R04huijIb3559xfsnxpwxZ8JftGgRv/rVr6bd9otf/IJkciZv1QMT4+S+82h0oreOaepK2GpUiCWVIvChe9AjX9I2xn6g8BWoQDtcugEM5ANSCZ3wbmzQ320TpJJsHwzZTkCoBEEoAUEuazBSlFSqWjsvhKLmS4by0NFs0tpkUyyHeH4YSTJ1DUAqYWCYAtPQUX7NVaQcaMyZDI3ocxlozkindR/diqfIZRUowdbtOvxzoieBdNJgeZfDc0/C0SsSJBM2SUdH9nNp+6iUolwuk8/np03fKKV48sknueOOO9iyZcukbYZhUKvVphyzT2K23aNOQOfcLXRkXv/o1RhfyC0Ba9DR/wLgWPSE8jQ6srej63Sh0y9b2bVvz77k/RNjTtgjvdq2bdvo7e1l4cKFLF68mIaGuAJjJkz01mlIC2quonvAjwzNBJt6awzmtfWwbevOUcWyGvu8WyZ4ASgVkkqZY4VXjiOwLAPPl+TLCiUlni9IJwXZlFbBGEJ/L1UUji1xfYNCWbGiy6Yll+S3T1ZwfQUKfB8sA0jq3yWCzhaL9kYLxwEIyRe03bLjCBI2VFyFLRUL2yyWdJqs3eqxabtHR7M1lqJpa9LMkEka2LYxxT56IqYzV6vVaoyOjk6bvlFK8dxzz3H77bezfv36Ked6zWtew/ve9z4WLVo0z//VvYAqU2WXdUyXHz8EHfknJuxXRkfxW9BNThYz+QmhrrcfQOfsm6LtdcO02fj27CvePzHmhN0i/JtvvpnPfvazdHd3j73W1dXFJz7xCd773vfO2+AONCxosxgYDXhqnYsfQCohcF0YKfiUXYmSWtZpWwKE/ud4oW4/2JQT1FxJuQYNlqAS5fDTDvQXFErqvL1p6Kg+CGH7UEAmpVMlxYrOnTu2oDGrJ4iNvT6phKCj2aJ/VGIaEsOAIJAM5iHlCJYvtDhieZKRoqQpJ1jU4TAw6rNhW4DrS4pVnaZavMBmaafNwIh+QpFSkE5oK+buwYB8cXJEvqN99I6om6u5Xki5OEKlUpl2v7Vr13L77bezZs2aKdtOOeUU3vjGN7Js2TIWLly4Z/+8lwrTyS4nYsf8+ER3yyS6SckTaLJfhJ4kXCY/IQyiSb9ukFZvbzgXi4Td9f6J8bJizoT/+c9/nn/8x3/k7W9/O1deeSWdnZ309fXxwx/+kPe///0MDw/zsY99bG+Mdb9HOmmQSxkkE4JsWpByBaOlkHwZGrMWQkm8QFKqhfi+lkoKwA0U5UqUPjEV7Y2a2AGUEhiAlRDYlqBag1zGxPMVXrQYmy9KwlDLRP0Qhke1kidpS0xLG7ctaDEoewYogbQVUkkSlmDFIoczTkjz6LNVtvUHLGwz6Wq1KZQUhQooqSekpCPY2hcQBIq2ZpPRoqTiasuFjmaTvqHJq3zT2UdPhOtLvFqJoQF3krVyHRs3buSOO+7gqaeemrLt+OOP501vehPLly+fh//aS4ydNSGBqfnxur/NM+j2g/3olM2R6GrbIfTkcCSa8Cc+IcymPeKuMFvvnxj7BOZM+DfeeCMf/vCH+Zd/+ZdJr1922WV0dnZy4403HnSEP5NF8nT7lV3F4UsTmIYgDBWur1izyWAkH1JMh6zv9qi5kHR0DlwIQaUWUqophBHSmLEYHFXkUjpilgoaMgIpdK9az4fGhCBhCRKNAoSgWgtJJXUl7MBIgGnqycEQIKSg5sOKxQ6WKSiVtVwoldCVxIZpkE0bHL8ygecptg8FCENQKIUIQ+f1MyndpWvzdp9MQitzal7U8MQRNGZM0lHKod7Tdmf20dVqhS3bhmlrANuavPK3bds2fvSjH01b73HUUUdx5ZVX7p/yyzpSzNyEZKb8eC7adzGa2NegUzUhOtUziJZvrmTqE8J8EHRM8vsN5kz4hUKB888/f9ptF154ITfddNMeD2p/wbhFsk//aEC1CumkoL3FoqvVmiItnJjGME0BtiCQIaYABBimQKooshcCISAMJWGoVXaW0G0GR4uSesshw4BU0kJJxYgfkkrqiBulTdNqUWFVLi0ZzitqnkQYJkFJ++dbJrQ2mQzlQ1IJY2y8uYz20s+XQp7bUCOUgnRGkPUNhvIhiYR2xHQc7bQ5WlAUSyF9gx6OY3DksgQtORM/VAyMhiRtHeEHEzI7O9pHGwQMj4wwPFojmxK0N4+T/fbt27nzzjt55JFHpmj6Dz30UK688kqOPvrovfJ/fskxUxOSmfLjdbuFhehPtECna6po0g/RJN+KlmruqKB5qb1wYu+dlw1zJvyLLrqI++67jwsuuGDKtnvvvZfzzjtvXga2r2NHi2TPh2RSUPUUfUMBNVcylA8n9YydmMYQkS9+vhRSrsmx86QSAIKaB2GoK3dDCZk0GJbBaEkSShBKM2fN11YOEu1OWalKBkdDlnbaWI6gWPExFPSP6M5UDRmDbNLA9RWGIaN8v2JBu41lSFKNJqAN4EYLASVXsmW7ScLWnvy+D8OjOkrXC8u6X69tgOspCmVFA4pMSit9EqZu6tI3qGggWhCOUJds9g74bO0doVgsYhrQ2WzS3mySTBgMDg5y11138dBDD01R5ixdupQrr7yS448/fsrC70Q4zn7WRnGmJiQz5cfz0VcSTe4F9ILqIjShumi/m2fRaZ5jGPe33xPnzLnipb5ejCmYFeE/8cQTYz9fe+21vPe976W/v58rrriCjo4O+vv7ufPOO/nVr37F17/+9TkNYN26dXzpS1/ikUce4ZlnnuHII4/kmWeemdu7eBkwZpEsdOTeOsEiuXvAp1gJ2BjodoKLO+2xYqLWBpMNPR5VV1KpQSYlWNhmMVoM2bDNQylFNm3g+pKqqz15EjYkTBPfk0hDSzgjvqd/OCBhCxa2m2TSOmIWQpNs4ELSMqh6ElfXPWGbegJxbEEubWrZpqutGwxDICDysxc8t9HDNgWh1Pn6TEo7eCIkm7eHGIZiYasdVfAKneLJ6naGpYqkeaJoS0zfJ0Eol7Q1yuI2j7DZwTQhYRuMjo7yw/+9mwceeIAgmJz/7+rq4o1vfCMnn3wyhjH9oq8QgnQ6TTab3f8IH2ZuQjIRdQLdiC6w2oKe+RWa7MuMq4Wz0fYGNMHORus/nyT8Ul8vxrSYFeGffPLJkyIopRTf+ta3+Na3vqVJbsIj9p/92Z8RhrN3w3r22Wf52c9+xmmnnYaUcr8wvBq3SBaTLJI9T6cnNvX4VFxJR7ONlD5tzQbdg7opyqIOi3LkQd/VZmGbBmVPL6425gzKFYkMwUATrWFAOqlthetGZ3XpJuhGJ+Waom9YImUICtqbDFzfQCpJNiMIlC7O8gIolBWGqQm6XNHnB20P0d4iIodPSdXV1b9OUlB1oaPFwPcVw4WQmqflmFIKHEvg2DoV1ZIz8ALGGsDUPKnbL1YUaUeAP57SCYKAkZGRMW18wjbA1jYJd97xM+677z48z5v0d+/o6OANb3gDp59++oxEbxgGpp0ilc6RcEyc/b3Bys4aoEwk0BVoKWYPWma5LNpnFJ3DX4CePDLoJ4YtzE7rP1+YbW1BjL2KWRH+Aw88sNcGcNlll3H55ZcD8I53vGO/MF+r5+ITthirmPU8yfahgN7BAIWiIWOQTgryZUn/sOSwxbqx98BowHBB2xxv7QtJOCFSQkPW5LRXpNjYGzBc8GlutKjUAkYK2k1SGFGNS51kLT3JNqQNGrMG24d1Ksc0ASFoaoClzTbd/SGFstbfS6moumCUQhrSBqZp0NxgUq2GDOUDhKGrfk2hr5dJ6jRUKqEntNFiiB9Ac86gVBHYlvbTsUzFUEFS80JdjJUyKJYlm3o8pNQLtwKFDdTcgLysUSwWJwUKlUqFe+65h3vuuWdKgVRLSwuXX345Z5xxxoxWx5ZlYVhpitUEI3lFEPpYZjBrm4b9DjsS6FK0ImcrmuQT6GIq0NH+cvQE8AI6xVMvxGpFk24de8MLZzpL5715vRgzYlaEf9ZZZ+21AcwUqe3LqOfiQ0mkW1fki5JiRTcSSRoGhqH3acwYVD1F72BAuaYYHA1JJbXc0fUVI8WQQkmydIGNYQiGCpJt/WAIhWkYJJyQchXSKQCBZQkqNYXn6zRHoSyxHYNcRuB6IZZpUnUV/UN6UTZp68mpWtOpHNBpIi9QpGxFGOgqXhSoUBduBQJUIChUtblaW1OAYUCpqsfuWAbbLah6ClkKo0kPyqMKwwQpQ4YDiW3ZLO+yacgaDA7raP2ZNb0sWzjeitF1XX75y1+yatWqKVYJDQ0NXHbZZZxzzjkzpmUSiQS5XA4lEqzd4s3ZpmG/xHQEmkGT+lOMm6YNoiP/Jegcfj+aUFuj4wfQufS6ZLOO+fbCmWttQYy9hgOoM8RLh0lVsxmD7YO64UjCEZRrmmAbkyZ+oGhuMGnMGmzp90k52knSQMsps2nd23b7YMDWPh8ldVPyUjWk5obUPD2h2BYkbBPPDxDo/Hs9a1YLQ6RnICXUPINcky7W8kJJoaQ97kWk7qnU9LmUAgS4NUXFkxhC0N6szRIG8zoN49iKfEUhQ8XTuHQ0B6STBm2NFlZC0JAxqXoBvq/PVa1KEo5OMeVLuhLX9UOGCwbVmkeCPADDRR9DwMJ2xe8e+jV33303hUJh0t83k8lw6aWXcsEFF5BIJNgRQghSqRS5XG5sIpirTcN+jekItIwm9cbodwdN9gJ4Mvo9gZZuNqEJ3kYv9m5ETxYW47n1upJnPhQ1c60tiLHXsFuE/53vfIevf/3rvPjii9P6k+z4AZ5vuK6L67pTruf7/m57odePm+3xbU06qh+sBQgCCqWAVEJQqwW6DWAgSScMGtMCJSXlik8uaZFyFEnHIF/ysU2DUEr8wKd7uy52qtUk7Y0Gpq0YHQ1x/cjYjCj69nW6xTY141tSUvV9hIBsWpDJWFSGJYmEoOaF+gnAFNimwjJ0zt42AAmh0k8A6YQRLT4rZKgVR54PjUkDhMSr+fQO+GRSBgnbRghBW6OiVtPXSTqglKQxCwlbTxhNliIMQ4aHC7TlXFrbBLIMIyNVHnn416x96peUiiOT/qbJZJILL7yQiy66iHRas8vE9SDDMMhkMmQymbEWj77v43qSgRGPbFIg5dTF4WxSMTDi0tGsXUfncj/siL1x70283qzOIdBmaFXGI/MedJR8FNpQrYAmaButy8+gfXQWosm/DZ3eqaKbpgxE+zejc4fL0GsC2xlX1HRGr2fnON76sRvRdQbT1RYcEu23F6L8uX62X25MHO9892IWakdR8y7w3e9+l2uvvZZ3vOMd3HzzzbzrXe8iDEPuvvtumpqauOaaa/inf/qn3RpMPYe/K5XOJz/5ST71qU9Nef22224bI4oY+xaklLzwwgs88sgj5PP5Sdssy+L444/n5JNPJpVKzXCGlwf19aU64nsvxkuJHe+/PcWcCf+Vr3wlV155JR/96EexbZvHHnuME088kWKxyIUXXshVV13FBz/4wd0azGwJf7ooa8mSJQwODu62gZvv+9x7771ccMEFc55VXU/y3AaX1etrmEJohYvSqRfPUxQqIYYQHNLlcOSyBImEwHUV24cDXtziMloI2NQTkMsatLeYuJ6ipz9gtKzN1DIJXQ2btKDs6ig95Uhef/wz3PXk0QwXTQygq90ikxL0DPqUKrqAK5Osp0AMkALP1xWySVtgOQalkqSzxcR2DPqHA8zIJhkEKUsghGJpl0OxGOgCqZzBonaH9maTSlWhBGzt8cllBF3tNoYRsmHzEGGgc/ZtTfDU6idZ+9QqiqN9k/5upmlxzAmv5aKLL+Wow9qm/F2TyeSktM3O/v7PbvBwbEFyms5aNVfh+YqjVzhzivDvvffeKR+4vXHvTbzerO+/ErAaLWl00NYKDnpBthVtd2CgUz3PouWadfvkZxhvgRiio/i6OK4FHWW3AWcz7qIJOhrvBg4B//Dd+LyU0PUBfYyniSY8Newt7Mln++XAxPHOdxAx55TO2rVrec1rXoNpmpimOfZIm8vl+Id/+Aeuv/763Sb82SKRSEyb27Vte4//obtzDtuGTEaRdLTcsalBUI4KoIbyAZWaoClrMFwSDBWh3TRJpw2aAwPDDKkFYDlgmga9gzrHbhgWmaT2tw8QeIGkIWPR0mxQLIcUSppQyzUTwzBJOYJAmlRcgWmCH+rJwpdC2xwYWs2jhIkwtJGa9LRz5WhZ0JGwdCoq1Hp8AF9BwjJxHJuWJhvMgKWRSRoI+pTP0GhIc6Oj6xAoUy2XsM2QwRGFKq/hdz//GSOD2yb9vYQwOPHk13DWeZeRzDTj+4pAChK2gWEYpNNpcrncrJuP2za0Nwu6BwPSKXOKhLhUC1nUZpHN7HkOf2/ee3M6TzNwEpq8H2Wc8DujL8F4h6xmNNF66Hz+GnTu3mC8YCsV/Wyi00Hbo/3qvjz11FELevF3xQ7jnU2uvzn6epkqbefrf/RSYW+MdbdaHNYjnEWLFvHcc89x9tlnA+N9RQ821DxJpaY4YqlDqarIl3W3qiBUOLZBNmWCUJQrksfX1FjaabGsy2btVo/NvT4qVAgBxXKIEoJsZLDWbJkUS7o3bQUYKQYEoX4CEFGgmnAEQVU3U8+Xgsh/R0spbQukUqSjxT3Pg1zawBCK7cN6e1ODgedBMqEXOEcK2hco4Qgc0yCd0uZsQyXdkcrz6w3aYUmHTXujyZbeEtv7RmnOaJ19X/cLPPvHuxkd3DTlb7XiiFO46HVXsHDhAkBXE9dcBZg0NjZMys/PBTvaNDiWrmiud9basd3kAQMPTfCnoaP9HQ3T0mjyzwG/Qqtzsuhoux99Y7WhI/xF6Jx+Nvqu0JNA3YcHpipqSmgp6FyqZ+PM18uGOX8KTj75ZJ566ikuuugiXv/61/OpT30KKSW2bfOFL3yBV73qVXM6X6VSYdWqVQBs3ryZQqHA7bffDmg5aHt7+1yH+JKjrstvypo0NQjafZN1W/Wk2Jyrtw2ElkaD4XzI5t6AvuGAQkVhGRAKgWkKRoqSRKSXTyV0oZNhwFAhJAzADSGQIY4pqBef5ksKRwfcuB4oJLZpkEqB5yndAUuAH0g8X0Ek92xrFKxY4lCpaH+dziYD3zPpH5EgFa0NAichSDmCfEl33MokDTqaTNqbLLxAMZx38aqjtKU9igXJc8+vY+3qn9Hf8+KUv9GSQ47n7Ncex5LDXkMyOR65KGHR1JRl0aKmseYuu4O6TcP2wYChQkglai6zqG2qp9EBg7oW/zB06uV5NEE3osn/aXTaxkUTdB+a4EV0XBFN4FvQE0WdoCVa0VNGTyZDaGnnjgoeGE8r7ap6NvbP2ScwZ8L/2Mc+xubNmwH49Kc/zebNm7n++uuRUnLKKafM2Vqhv7+fq666atJr9d8feOCBsaeHfRk7Wv0KBIMjmnAac1qeiaVozFq0Nlqs2+ZFzpLQ0WJRrEoqNUkYKIq+9skZETKK0KNudDmDqivxAnBDxcSnPdPQ+XpfQeDrzlWB7oGuK2JV9Bl2FIahSFiKdMqgVlV4gW54PlyEBe0W2bSgbzjEC8C0FAaCYiXEsQVHLnNY1GEjhCJwC5hhkUpVEpS38fRv7+S5Z5+e8rd5xdHH8eqzXo+TWYjjrx3LoTtOknQ6w2jFZlGbtUdkX8dY28k9aIi+32BHLb6Jllwm0FF3Hm2X3IhW5hSifUuM96ytRcd56BSOgXbZrBuwiQnb61F9L9oSub62vh0d0QfR+Sz05FCvnj2E2D9nH8KcCf9Vr3rVWBTf1NTEXXfdNbaQtTuLVsuXL5/ifrg/IuUI+kZDFrcLKm5IsSrJpXUuuVrTenypYGAkpFgO6enz6Wg2SSVNUJBMGDTmTIqVkHINba8gIJkA29Rk7/r6M+gHjNkN2yaMljTp1wurJLo1oh1F/papn0JcF/qHJI6jSJUV2wdDlnSavOqYNP0jIUoKjlhmsXSBYjAfoJTANMCyTZZ22qxY5KBklaHBAlKGDPT1cO8vfswLzz0x5e9xxJFH8eartFVxzZVs7KlRHQHMFE1NTUgsRvdSuuWAJfk6KmjSrpuqrY1+D9Gf6IBxqWUHOj2TR6doquiIvhUdaYdoUq9X3wq0tDNATxJD6CeB+iSSQxP2xmgs69EWD5XofJ3o1FAjsAFN9BXiJ4B9BPPySZtpIetAR90eeaigHS+HhkP6h32yKYMgkPi+ietJUklBOiHYPuhrPxwECoXjGPQPh3iBojFrUKlKihWtmfeVVthYtkG1pihXo0jeBNuBUlWPwfW1R45AF1WFUtshG0IXbfkBYOl9ZAi+1M3ShZKEKF7cInH9MiuXJPACRaVmcMyhCTpbM6jIYnlTj09TRlIpDeL7HkODfTxw3094+sk/zMqqOJO2OXpllscehUSykVLNwDLVgZ1u2RsoMt6ftoZW2lTQhmgL0GRZAB5Ea+i7GCfUIppQh9GRvEJH86OMR/ACTer16D8FPMe4Pt5CTww/QU8U50XH1/etP3VU0RNKORrXkczsnxM/AbykOEBXsvY+6vbI9VJ+yzDIOyHregI8V1smjJYkhy226WyxGC3ppiApB4YLEtsWlGsS15W4vmJgRBOnQGEKCAWUa+BGrQENoZuiyMjOoRLVIympLYel1OSuon1BEzyRP45jgTJ1QVWgwLH1wmyhDJt6AoJA8MojEkil6B0K8HxYudQh6SjWbczTV65QK4/w6/vv5k+P/26KyV1bxxLOPPdyTnzlCXS06NvKcRxyuRypVGrM8fLoFQ4Y9oGdbtkb6AF+jlbbZNAEO4AmzmOifaxo+yY0sftooneibd1ocm5DR9tZNMmW0ZNGnQ1KaDXNIHoyWIF+ClDRvj1ogj4P/QSwnPGF4aHo57p1wwr0JDGxKrjun1Mn+tk8AaAN9/L5PE1NTbu1sB8jJvzdRt0euaNZq2a6BwJqPhy+xGG0KGnMGfSPhORLkkxKu2uWKyF9Q9pJUkgYLQSYpoEfWSEnbJ168QOtdpG+zskbAjIp/XmreYAAVY+YBJF2Poruwyj9GuX0CaJcfkov6voBGCbkKwpR1duaMoJSRbJ9KGTpQpPmnEmhItmwJU9zpoJX7mPVqrt5bvVvCcPJVsWZhk5edcbrec3ppxAowUBeEgqb445ooalhqnlKwtFS0BhzQBG4Bx211+0SBtCEXCd5G03gD6PJvp4WqaFJOokm/v7o+HZ0rr2RcYuFeupnEJ0XdNFk/BQ6NeRE13DQ6wJE114Ynb9O+gMTzvMCmtRb0U8cdXlnEj1ZNbHzJ4DjdNFesVgcM9xramrarT9jjJjwdwt1e+SGjG5BODiqjdGaGwyCUNsZm6agpcFkQ7fPxm0efaMBUmqvekMIEo5BtRxQLId4uqugzrNHhJ5KaBKvSU3KNR9UNAGEEhLRf05qUY3O1yuQutkVlqknilDqY0oKgkB/jk30xBCEemIYKSmas5rwF7RLiuUaI0MjbC6NsnnNfTz44P0E/mSr4lSmlc7DLuLQI05lxaIEEpOWpiyLurIMFWC4YNA0zZKO60ncQMYR/lzwBFqB04GOrregI+lB9D+zFb14OoqOzute+EU0gdroiDmDnggKaCK3o3OOMjlt0xbtH6CfJErop4MF0fGK8QnFQ1s5HIaeCCroiakVTeaJ6NzbouvWNf356PeV7NRBs7SwRMEvzMlyPcbMiAl/NzCxVaHrSwbzIVJJegclNVd3kKpUFc2NBqaheHGLS6GstfappEE2YdDUqBdxy5UAzxsncNBEbVmalOtPrmEAfj2NQ2SAhib7+uumEe2vImVOqAlfgXbEBCyht9WiJwdLgO9DviyRssrzL47y4po8G1/4Fd1rHyQMxqtKAexkE63Lzqdt0Wks7UrQ3pIkEBkqQYb2RALLMmjI6Amxy5NjpF51dQro2Q0ekhDLFAeudfF8oIKO1NejUzkb0RWzZTSJZ9FkWkNH1PWaskPR+fW65NIlumHQkwWM98ptRJN5ecK+Aj2RZNAkLaPXFkXXKqDXAOoPes1oFqlEx/ejJ4nj0JNGDf00kULLQnvQk0M/Oo3UNP3bd02XkcER/AE/zuXPI2LC3w3UZZhDowED+ZD1W2vUfB1tN2YNLFtQqkpGSiHD+WDMpdLztZXyoBtSciXZtElTDqpD2sjMNHSk7gd6kTVlC5RU1AJtmJYyx1M9O7pKK6WvX+/ANTHDPsmrSujPq5Q6tSOEXjeoVcu4xWHKW35PvudBAq8y6fyJVI4jjruQhoWnI4RFvmrR0NjCokUNKKUYKejK4sWduuipUtPSSNDrHeu36kodbX9gHrjWxXuKeiHTerTGfQuaJPvQkbhCk2f96amusTfQ5NuGTqEoNIlbjC+s1tAKnKVoMn+B8Q5ZI+ioPYUm2DDav4fxNEu9iCuDnhRATzqt6IlGoZ88Dom2rYzGtp1xh85tjC8YZ5jioBmGIaPlUSrDlUiJMPVPVK1WyWb3ohfDAYx5JfzPfvazKKX4P//n/8znafc5SKmrXh9fU0Mp7XEvJbQ0GBQrerHWsQTVWkj/sE6q59Lay74a+bqUK4pqTTN00tFNTlx//PxuDWyhCJX+LKcTUbQegiXHCd+OJJf1TlImelt9TbWu3pHRhBBGEZsQOsUTeDVMOYw//DBy5AEqYWnSezXtFO3Lz+WIY84im02RTGVwEhn8XsVgQbAkkNiWQSYlyJdD2n1T++ObAit6Otk+GFCs6gElE7rI7IC1Lt5T1AuZehlXrgwzrp4J0JPCKJpsg+j1ugJnlPGoPURH3jX0jdABXIAm2oXop4Ay42qdleinBaLf608CLnpiKESvL2F8wklMuEaJ8QXiRvQkAHrSGIr2yaNdO09AT2Lr9XUUikKlQLESNcbJoyemCctAQ0ND/OIXv+DXv/41X/3qVzn88MPn8peNwTwT/ic/+ckDnvDr6pzB4VB7zijderBYVVRc3Ri8WIHGDFpKaekOUn4ggKiy1oBAQMUFy1BaJx89IhuGJvEw1NJL2wLH0U8ArqsjdKUiuSVRxD9hfCHa/rghqcm93hM3CKI1gkCfy1ABIhxBjT6MO3Q/hJMdLA0zQeOiMzns2HOxrAzlMEPaypDMOKAEDWmf4WJItSaxswa2qT17gkBRqmrJZdIxxtY7cmmDEXRNgu1IErZe/2jIGFPSPwc1RtAE/Ch6ps8yrpev2x2AJuoq+p+fRBNkEH1vQROxF23PoieEpehoPA8cj47mH4nO14EmZD/aXu+Huyx6bRQ9AbjoqHtxdFxdDuqjJwGFnhDq7RRBTyQt6Ny+ybjxW06PvbK+wqgzSmiH+vz1auHoGlu2bGHVqlX84Q9/GMvl/+AHPzigeWZvYV4Jf8OGDQdEEdXOoHX3AcmkYPkCiw09PjLylfcDhRISJQWlqqLmQjYpQBhUahKplK4oFRI/UFo/b2oCtiz9uam3MyRKz9QnANfXGnqU3l6nxh3/3Gbkd1/3D7N0XRcyOrdSClMVMQoPEQzeh/KHJ59A2GQXvIbWZWdT9hoYKmdpbMxQrCgqXkD/sKIha2hfnUAxlA9IOAahVIShlqO2NlhjxVRBqLX8XrSIsKHHwzQljRmTtiZzSvrnoEVUV0EzmmxH0DfEpuj1AB1JO4wraOq5uhrjk4OLjpxTjKdlAnQOPovW8R+FVswkmNxecAQdxfejydhCk26dpAfRhVUTVTrno2WhyWi8z6Mj+onFU2X0GsRW4IjonDXwtnmMhqO4GVefvxRdcymoRYrnNj3HqlWrePrpqRXcTzzxBNVqdZ+z097XMa+Ev3Tp0vk83T6HerSaThgMFyRuoEglTRZ1aM+akYLP0IggkIqKByhosHT06/mCmgvlql6wTCZ01K3Q0bxlQmujSbUWUqmBmYh6y5pQ9fQCayqhid80ILVDBsRATw4JWy/Ihkrr9g1Dky4KQr+MKDxMOPwLQn9g8gmEidFwGukF59K5oJNsJkt52GCwAFU/AASBIyi5IV4Y4pgmmaSO0isVSaEm6WgyWb7AmbQQ6/mSvsEAzw9pALJJQ8s3R7XfTUezOSn9c9CibkaWYDzCzqNTIVl09DyxKEqh/+kCTeyC8cXX+gwf3YNjnat60VF3e3SeEprA3eg6ndE5RxlfnK0/+DWgc/MLonN2Rq+fhp6k6nhFNL6tjGvr16GrblcAh0FohORVnnK2rMfUhlbv+Hrbo089yqrvrhqzcJmItrY23vKWt3DppZfGZL8bmDPh//KXv+TCCy+ccfu//du/8Td/8zd7NKh9FXV1TiohkAoKJW2fEIQmIwUd6YdyPF8e+LB9MGRBm0EmZRCEegHXMpV+EndAWPqzWo/EbctACKnz7ejgyrZ01e1wUUf0oWQsIjYNrbSpPxE4tm5xWI/wQwlCuZB/FHpXodyeHd6VgdNyEjSfh7S7aO3I4qSTeCEk7QDXVYwUoSGjaG22KJcVgyOSTDLkpKMSJBMGgVIcc0iCw5c5tDRMvqVGC7rPrwMQgGEKEoaBYyuG8yE1T3HiEck4nVNfnKxHyGvRkXYCHf3Xq1AnpnXqZE/0Wj3SdxhfZPXQ0X4GTa59aHKvtx84Bp226Ubn9OsVuWX0hGCgSb4LTfpO9PPpwB+Y6mOfQxdMbUQ/OQxG348AdaiiKIoUh4tIJceVQgNQ66zxm0d/wz333MPg4CA7Yvny5VxyySWccsopLFmyJC682k3MmfAvvvhi3v/+9/PFL35x0gy7bds2/uIv/oLf/va3Byzh19U5QgiyKcGmmtTWx0ovxI4UopaEUd7eVdqSeFu/JJuSY5G87+vPZyKhidswdMVsqRqSsAwyaQESAqmfHCwTmrIGxaocK67yIlm8EHohF4V2zIwixTCEahhiVh7H3X43YWXLDu9GYDacgNVxASKxFCnS2KaJJw2cQFf/hiriE6XPNzyibZ8zKTCFoOZBc4MglRDTkn39iWjpApv+IYmsah2+7ehcvx9qZ7imhoOc7GGcuJ9hXA+fRBPvKJq464qcidBLQ+OoTTi+E024xWhbncjdaNuhjEstR6Kv7ejo3EUra1rQef+6Pn8DOtJv3cl7yaFlmYcxpuaptFfI1/IE1UBPXhaQgHyQ594/3Mv9372fcqU85VTHHnssl1xyCa94xSsm9TmIsXuYM+HfdtttfOADH+Dee+/lO9/5Dqeeeirf+ta3+Nu//Vu6urp4+OGH98Y49wlMbF7e1miQShoM5kOtMVeKMNQdour2B+mI0GsuFCqAhIQDmTQ0ZkxamyyGRnQnKanACHVFbKUGnq/0pOBoCwTbMkjaCsfQvjr13H09XWMIPWm4HkipkOWnYeBOqG2Y8j6M3DE4HZdAegWB1CWSAmjKauVMtaaounpyySQgjEzYKjV90UxKk3QuY3BIl00owZmmenaibXTCtNk8AqFUeBWJacKCVgvbnP7Ygw717EUWvbjZhE7DlNAEWU/TwGSS33HJrL5P3QLBn/B6/StETyJ9jEsua+gov4/xyWUE/XRQl1ZW0Qu/FuNrDjtDGrwGj1E5irvV1RPPiB5Db62Xe3ru4bebf4sfTp7FTNPktNNO45JLLjng08QvNeZM+FdffTVnnnkm7373u3nta1/LiSeeyGOPPcbf/M3f8PnPf/6AN1GrN9ooVCRLOyxe3OYyPOozXJTYtiKTNqKGKHrx1LQ0+XtBFI1HBVHphMAUguacSdWVDBX0Iq5dlQQyytsbOkWTSQrKNQVKUapL7KIPumlEn+FIe29UX0T23wGV56eMXaSPxGy/FLvhCJTQ/6eUo3P+lgDH0U8ubqTqcRzd4Hy0qK2eW3IGwhSUq4rRYsDQqEWhHOLYBkEoGV9K1phoG52IWg8ubncIpIFjG5imntgO+vx9BU2oMG5nvB4tsxSM2xWbaPKt//9nCnglmpCrjNsc+9ExDuP+Oauj6zSiI/4exn16coy7cvrotE8netFVsstm42EYks/nKZfL+knlT4ANa8O1rNqwiid6n0DtMFslk0nOOussLr74Ylpbd/YIEWN3sVuLtl1dXfzVX/0VDz74II8++ignnHAC//AP/3DAkz2MN9rY2OOxsSegbyhg+0iIG1XLBijCQOCYCpROtwQByACSSZ2PF0JH4qHUXjq5jEm+HGCI8YpYsy7PlDBUkNp6QWpiB8ZIUkXpFlnbDP23E5afmjro5AqM9tdjZI8lmbQwxHilrSKycTDAQFBx9YcwmRIgBVVPV++mkwbJpL5oEEiCQNEz6MLzipVLEqzd4tPaICct2E58IsomdOi5td9DKitaTFYcvTwR5+/r+nrQ0bfPuOFYI/qfNBq9Xne4iNZtiBRdM6JeZetP+H0ITeoiOq+DXjOoa98T0bXrZmb1iaUputbEBig7Xk4piv1FiiNFpCkhCVJJ/jT8J1b1rmJtYe2UYxozjVx4yYWce+65ZDKZac6qG92n0+nd7ogWQ2POhF8ul7n++uu59dZbueaaa3jrW9/KBz7wAY455hj+8z//kyuvvHJvjHOfQqUmeW69y1NraxQrCoEm0GpNNw1xLK2W8QJN4EpF+vkQlKF/lkr77VRrEi+UZFKQSRiEUuocvwGOaVCuSSrV8UVdwxi3SwAIvV5kz51QfJwpz/eJpdD2esidqHvZmnoSQUDWikQeBqSTAoGh9f6hroY1DMFIQVGtKppzBqmEoFyRmmOEwgsV5aqBgcHCNhPHFtNWzi5osxgYDXh6g0sjYAiBYUKhpJmo3vzloK60tdGfxBBNulvRZFtXydSjdIiMkqJ9J6Z5dgU54bvBuJlaPYdfl3AOo1U3efQibRL9ZFFvPF5EK3GmEchU+iqMPjtK2BdCAB4eD299mJ//4ef0Fnqn7L8wt5BLTriEVx/+auzT7MmOmjDW3ziTyeyykX2M2WHOhH/cccdRKpW44447uOKKKwBYvXo1H/zgB3nzm9/Mn//5n/Pd7353vse5z6BSk/zqj2V+82RZG50BCcfEroVUo0itGhVN1T3p61WwfmSLIAIYyAfkPAsptVJFRMVZgdI5fMeGMNRNzJXUap66z70SoNwh7rvvPrwXnmPKp97pioj+VIQxHg3JaFzJBGRTAtPQzdFdF5pygo4WCz+UjBZDylVwLAEphR9CWJVj6wZulG5qbzdoazVIJkxSCWPaytl00iCbNkgnDAh0710TWNRu0dpoUKiouNI2jVbCbEZH9iU0AdcVN3nGtfcTSX62ZD8dquhPv4EmfMl4W8N6odcA+gkjhX4qKKPTTYdMOE8BPNNjZHgE708e5KGcKHP/i/dz7xP3kq9MLugDOKLrCC45+RKOP/J4jNDQeX0fSIIQgmQySSaTIZlMxgu184zdIvybb755Uq/ZdDrNTTfdxBVXXMG11147rwPc1/DCZpfHXqji+1pW6QUCW2gPnHIlkkGiPz/WhC5U9XXJsQIjBTIZUK5BJWpuUvNDfC9qTORppY8XRp9xPzJQc0dh8C7U6K95Ru1QrWR3aqJvOH0S0dch0ZOHds3UEhw/ANNUWLYilzbwfMiXJA1pPflklKAWgOdGVb+2LoNPJQ0UAt9XhFKHjdNVztYbvC/ptNm+ARZ32GTSNon6H0RMNVo7KLEMTfg+mmwj11Sq0e8B4743de+c3UV9oqhF3+ua/gTjFb0p9ARTV/2k0VLME6NjntXfhu8bxpMelGFwZJB7+u7h10/9GtefbLonEJx0+ElcetqlHNp16PgGF7DASTtkmjOkUqk4ZbMXMWfCv/POO2fcdvHFF09bFXegoOZJntvo4rqQSgpqnsT1oorZqFq2bkEMmuzrKRRDqzf1epvSRmpDef05M01NvG60IGdbmtzdiRGcVyIY+AmM/kr7JE+E1aqJvulMhNg5Eyj0WCo1beRmGtCQEfgBbOj2KFVCEo7B0sU2vUMhPYMhQimasgalGhTLuiq2UkPLUF2JApZ22HS121MqZwtlydZ+DxWGWMD2IZ8G16CtUaeTgkCN9eA9qFHXsy9Bk2kTWodfL4CK7qOxrN2eRPcwniaqm6xJxnvcCvSCbRI9CbQBr0Y3PAkhfCwk35OHLFTTVbZt2saqB1fxh5E/aH39BNiWzRmLz+B12dfReV7npLSNZVik3TTpI9PYy2dYFNgRcSvEPcK8u2U2Nzfveqf9FOWqpFiW2CaUapLRgtarE6lvkg74Ikrd1M3Moly5VLrrlGFAMXoSMCVYjs7Ph6FuWCKUjsLHVHdhBYZ+TjDyC5CTo6ZsNks1dzlh47kIMbt/pWnoySQU+gliQSs0ZC0KZclIMcCyBY4FZVcriQSKYhmKFUkqGdX6SD3GtANeoBjOB5QqikpN0d5ioNCqneGC5PmNNYZGQ1oa6tcX9Az4bO7xSKcMfS4F7U0+yxbaB3cuH3Tl6tPoP3R9QVYx7ouzo+5+d2ERNUZg3Agtj04hCTTR59BkfzRwPqiUovhIkeKmIl6Hx+b1m/nlE7/kuc3PTTl9Jpnh/BPP54ITL6BBNugmKt1gLDRINaRIqzTJUlJbPqycxXiLxK0Q5wG7Rfi/+c1vuPnmm3nxxRep1WpTtj/11DRKkQMEwgA/lBQrMpJK6ojZD/XnMww1yaOi4ipb5/ANqY+1LR3RG5F3fcKO0j+WjvIleqFXSBeGfwlDq0BOtirGzGK2X8I7ruzglgdPRIazewS2TT0p1e0ZWhoEmZSNQJG0BUbOwA90FXH/UEAyofX/GJIg0Kkny9Lpq0zKIOXoJwND6IlhzaYqm7abLGq3GCkEDI2GuL5+iugfCuhyNJfUXMlgXtKB9hZqatD1DJ6vYqvkQ9BR/h/Ruvj6ImvdJgH2nPQNxtNEZnRuB22g1oyOwiuMV9m+GkqHlChsKOBucHm091FW3bOKLf07FvNBe6qdi0+7mDNfeSYJR6v2REWQPCJJZlmGZCWJcIUm7EOZHWEX0U1ghplVK8QYM2POhP+LX/yCSy+9lPPPP5/HHnuM173udVSrVX73u9+xePFizjrrrL0xzn0CQkCpLClVJL6n8CLiNKyo6CnQkbwVjOfugxDSKaGtFgJdNBWGOr+vnwwESiid7hFA6KFGfoUa+imExckDMNLQ+jpouQjLtrCs2U+sdduVmh89RaCfLBK20DJRoXBsgQLyJUU6qat5C2Vt84DQE5epIJsE2zSQClCK0VKAEAaFUkhHi4Eb5e23D4c0ZgTNOYvtgz440DvoU/UtGrMGwwXFoYsFi9ttErFVskZ9ATfDZCuFeoQ/H9F9fQHYYpwBFjNunVBvaFIG90SXkRUjFAYK/PrXv+YX9/yCofLQlFMe0nwIr+t8HacsPAXzaO2lkbSTpJwUqXIK8zhTV9/uTkpmI5rsl7DTVogxdo05E/4///M/c/311/Mv//Iv2LbNZz7zGU488UQ2b97MRRddxLnnnrs3xvmyo97EI5SKpGNQdRWWqX3sg5om8fpn0TDAiQjSC0DUtC1yvcm4ZY27WkoUgQ+uFxAMPwSDP4ZgdPLFjQQ0XwitlyDM+qdkbklvIcY7YxlG5NFjatdLgaRcldgGuIHU9hCmYLigcH3GJyOinruhwLF1Zy8QdA9KBCGmgL5hD5RDV7tJNikIpSAM4dDFNnIE+kZCGrIWAkUuLehqs0gmdEQfWyWjF26bgbPRpFxfyN2V3n6uqK8LZNFEuiD6OVLp+KFPPpWn1+vl3v++l/ufv5/KDk1xAI5dcSyXnnopR7UdhXheYBdsMlaGdGMa0zN14VYb48qeuebd66ZvLey0FSKH7ca5D0LMmfDXrFnD5z73OQxDqzLKZe1/sWzZMj75yU/yyU9+kre97W3zPtCXG9sHAwbyPtm0rhCNgtuxnrITA6961atUEPqMPQk4tk7p+J52s0xbUHEl1YGHCfvuBH8H0yhhQ/P50PpnCGvPOvyEEwYYSh29DxVC3EDiBeD7evG57tXjuloq6kQLynXSNwTUXIWSCscxcEwIlSLl6MYmfgBVTzKYF3i+tlUoVUOackLX7FjQ3myQdEyEUGRS4+movWWVXPP0OfeLPrrb0XntxdHPz6MlkXVJ5nyhbryWR3fVakMXSfmSwrYCL6ZfZJWxit+t+h2BnNy43hQmp73iNFa8cgXndZ1HwkyQTqRJLU3hpByt8BlmbmmbmVAvSkvOsD2JlrHuovI3hsacCT+ZTI414164cCHr16/njDPOACCXy7F169Z5H+TLjeFCwDMbagzlQ9Zt9RkcDUEqrcqRkXnZBATRB3NiUBZKqLr6Sy/iSqoDj8HAHeBtn3wCYULT2dD2eoTVtFfeU6igVAPX0++jruL03KifbqT3rm8TKqr0VTrKD0MIpSSRNbBNg4SjF7JlKBgpaoKQUlfTtjSa9A+FtAD5oqTXCkgmJId02Tj2eNjmBWperZIrNRn1LwgJQrV/9NGtc2sJnZ/Oo6th5xtWdK16R62noHRUiT82/JG7jbv5U/FPUw5JOknOWXoOF3VdRPOiZv7U9ifaM+1kVEafYyk6n163gJgPJU29KG2HVohjqLHTyt8YkzFnwj/++ON54YUXuOCCCzjvvPP43Oc+R1tbG7Zt84lPfIJjjz12b4zzZUOlJnlxs0f3QECpElKsBGNSzLrmfiZM9wSulCIsrYaB28HdcXI0oPG10H4Fwt77XiIyBE/p9+E44+sRoKP5UI0vLLu+LviiTvrR+zdNiSkko9Fyg2kYuJ7C90Iac4JSVaubFrYKWnKQTguKFb0oUK0paq4kmTBQSlEoy7FOWXuKemeyQkXSkDHIpoz9o49uN9rT5nngN+jofm+gbshmQsWscL91Pz8p/oR13ropuzZnmrnwhAs5t/NcWkdbSefTmKMmdILT54y3M9wbipk0495C9ZaLdSj0RHMocTpnlpgz4V9//fVs3LgRgBtuuIHLLruM17/+9QAsXrx4pzr9/RHbBwNGiiE1T0Y+8AaliqTsatIzjNmLJlTpWRj4IdQ27rBFQMNpGO1vRDmd0x67NyAjeaUyAB+62kwaAkm+onBdTfah0vJLqXTxl6q/3+gN58t64rAMbZVsWQpfQtkNMU1DL1onDLIp/Ulty1kkkjaWqQ3h+oYDOlq0LLQhbYx1ytpTbB8MKFR0g5V6teY+3Ue33kp4I1qHvw6tw5/PNM5ESCiJEncvvJu7Ft5FX7Jvyi6LxCIuabqEcxaeQ66aI92fxpAGFMBvjHIofeio/tAdDp5Pvfwh6KediU1V6jYQLUyu/I2xU8z503XJJZeM/bxo0SIef/xx1q1bR7Va5cgjjzygPC/qfu66EFEQKolCYVqa4KQRkeYuzqPKL+iIvvri1I3ZE6HjTYjE4r322Z4J9SeQQIIIoVAOx5q3CKH1+kqCb2spZr2mKwh0Lj6X1gu7Ifr3QIIIBBKd/8+XJbalaG5MkIxa8514VIpkwmYwHzI4EtI7FJBJGSxqs+Yt1VL/vzVkjCml+ftsH926lL2eq+5jfhdpJ2DEGuH2Jbdzd9fdFO3ilO1Hlo/ksuAyXtP+GrJNWcx+U5NrKzqCz6EXeUE/jfwaLenMsXf08js2VSkxP+sDByH2OJwSQrBy5WwqJ/Y/6IpSSb4osS2tuBkcDqm44y6VOyNpVd0A/T+EytTCFDLHQPtViNTyvTX8OUEpKJQjBY+hPe9LNU36gYxSvn5UU2Dooq3Rkhpz70RESp7Il9+xBIYNodROcW1NFsEw5DIGpmmwuEM7aQ4XAo5Y5tDaOH81gHUf/mxqejLf5/roVtCNT+qdpR5jPOKfR/Qke/je0u9xb+e9uOYO1gdKcMrwKbxp4E2cUDoBJxctvtbHEQDb0AR7KDpPD3qBeTN6wloejb0QvY9d6eXn8hQwsalKXGm729itT9nWrVv58Y9/zNatW6cUXgkh+H//7//Ny+BebliRX3v3YIAQWuHhh1CuRQ6YMxynalug/3Yor566MbUS2t+MyBy+V8c+V9S19kb0c83VpG4n9KJ0qaYXcFMJrd33Agg9pRVKUhuqOY7CEIKGlIFlCRpzBsWSpLnRZEmnw8bhHS4qIJs2ycxAzLuLiT78KXPqKst8Lw7vMfJoo7IFaMXMs8yr6uSF7Avctuw2Hmp7CCUm37VO6HB2/9m8edubWVFZgUjX9bfolJKL1ubXlTDJaJzDwBloT/0A+BWaTfrQOXcP3Tu3ES37nKiX35OngJjk9whzJvz//d//5e1vfztSSjo6OqakcA4kwk86OmGdL4a6Obgrx3rKTgfldmvVTfEJpkwHyeU6os8es7eHvVuoF3SaBqD004xlQnuTQS2Q+HnIpSFhmQQKbBSmoVswQpQCCsEUSrdsVIaWpyqdYgl3+KPtapF2T6SUE334k46YlNaZ78XheUPd5/4PaPLfQygUf2z5I99b8j2ebH5yyvacn+Oynst407Y30eK3jG8oM654qRup1Zubp9CR+u/Qk8AZwCPRa39AWyS0oYm8iib8w9FPAY3odEwnekE6rpp9WTBnwv/4xz/OFVdcwc0330xjY+PeGNM+g5onSTl6wXFdt8vQqKQ0TWs35fbqgqnCo0xJvCYWQfubIHviPm31ajDekUsBad35kFzaoMEw8VwfzxdU3RDb1jr+QEZVu0rr8x1T5/lN08CxoVZTLGqzaEibY374YQheIGdcpJ0vKWW9M1n/iM7lO5aO+Od7cXhe0Mg44W9ij6L7QAT8quNXfH/J99mY3VEcAAuqC7hy25Vc2nspSTmDuL1+fRNN/knGK37LaLKeKJGsRdsl46keAz1xNUXHjaAng3XEVbMvI+Z81w8MDPCXf/mXBzzZQ5TD90JMUzGcl+RLk+N25fXD4F2Q/z1TKl/tTmh/AzSctksHy30BZuSzI6Py/Xp+e6QYIoTANqHiKlyv3q1Lq3OImrkoFbl9hgrXkyhlsqTD4rUnpGjImKzdUoUA8lFLxOkWaedTSlnvTFafPCo13aZxPheH9wry7JYyp2yW+dnCn3H74tsZSE59RDi8cDhv3fpWzhg4A5NZ5LLq6RsPHZnXGI/+E4w/BVTRJG+g0zkCeBX6CWAQnRY6HB31+9HrcdXsy4Y5E/7FF1/MI488wnnnnbc3xrNPwfMlW7f7rN3qU63Vq2sV+AMw9DPI/26PrIr3JQRS+/mDLrSq2zYPF3TqJuEI0kmdwvGit2xZkYGjnNDFC0g5BicckeDYw5I05fQtduhihy0vwGGLbRpz07c1nG8pZTppsGKxQ9e+XmmbZ7zgymNOEf6gM8gdi+/g7q67KVvlKdtPGzqNq7dczfH546NW9XNAwLgffhLNFnWbh/qlPHSqJ4Um/BF0LcFixlU79cXZUnTcAqZHXDW71zFnwr/pppt4y1veQqVS4bzzzqOpqWnKPieeeOLUA/dDbB8ItA4/H+IFEuUOwcg9MPoQqMkqB8xGaL0Ums9FGPtf2d/EqmAVPZrX0+4NGb14HcqoAYqKUjdGVLsj9M+WBUs7bY5ekeSko9KTqmhtS/+ccIwZc/Z7S0q5T5L8RJTQEfAcsCm9iR8s+QH3dd5HYEy2PrCkxfl95/OWrW9heWX57o+rroapR+WN6Ii97p8Pmujrt3si+hqJXm9gXIFUP08t+joMncaZiLhqdq9jzoRfLBapVCp8/vOf5wtf+MKkbUophBCE4b6id9t91DzJ2m4P0xQI4SL7f6btiuUOSXwzC80XQMtFCHOaRp/7CZSKrJzD8Zw8BiRtyCQtTCtkOK9wTEin9KQggZQjSCcMPQEIQVuTScIxCUMFEwjfj/wmrBm4d7+TUs4ntqGjfNhpOkehWN24mh8s/QGPtD4yZXsmyIwtxLZ5bbs/HovIDCr68tCRep3oS+gJAHQUX2K8124abf5WQsszFZroFzPeR/cF9GRyFOOkH1fNviSYM+Ffc801bNmyhRtvvJHDDz/8gCq0mohyVTI0GlIYfJ6hJ76E8gqTdzBS0HwOtFyCsPZfWUFdtVjnGTsqkLIsXUyVsKAaKGwhSCeUDuwiwzgZGapZFuTSFsmEoOqCQGGak5Ux2k5BR/jTYb+TUs4XKmgCrC+JTZPOCAn5bdtv+f7S7/N8w/NTtre5bVy19Sou7b2UTLhj2LwbCBhvoehGXx56Abae4jEnbC8xnpIKGbdfTqO1+aeg0z1NaOVOAGxAR/JHE1fNvoSYM+E/+uij3HbbbWMNzA9khAoyDQtR4YT0jUhA0xnQ8jqEswdR1D4AgX6PBlGzdQVmZBehIkdN1wXLDclltZ2zYQgsS4y1YsxlLKo1hWlCNinwIqfQUKpIkaOVMbmUwchOxjJbKSVAqSr33Xz8XJEH1qDVKTvANVzuWXAPP1jyA3pTU3M+K0oreMvWt3BO/znYap7zIHXFTf3f4KFN3OpmZvUIoZ/x3rjN6DTOKJr4c+jIfgvjPXpH0YSfjl5vjPaLq2ZfEsyZ8FeuXEkQBLvecT9HJmXQkjPYPthA52EX0fviz6Hh1dB6MTgL9qsF2ZlQz7+HKvol1Au1dZ8clF6MzSQhaRsUXEXgK9ozgjAUGIaBkpDLCDxPUqjAyUelOHyJQ9lVk5QxbU2KLdM4S0zEzqSUjqVtl59e5+4/zpezwXq0rn2Cj17ezvOjrh/x48U/pmAXphzyypFXcvXWqzll+JS5L8TOFgbjCzsOmqTrrQ+b0U8mdThomWVdVy+jfeoSzhx6omiMvtctj110Wmc5cRrnJcKcCf8rX/kKH/rQhzjmmGM48sgj52UQzz//PNdddx0PP/wwuVyOa665hs9+9rMva7oo6RgcuTzB5l6fhsV/RjXxWop+M5IDJ4VlCK3ICaNOXabQ3veIqIlLlNOXClJJg6obgoBCUTtcphwdfXuewDIN2ptNTjk6yaJ2h5qnm6q4niThGBhicvJ9usKqmaSUrTmDYlX3yN2vnC9ng4fQKZEqjI6O8tXlX+W+tvvwTG/SboYyOGvgLN6y5S0cUTpi747JQhN3nZhDxlM4Eh3Rd0W/N6Lz8CvQBVZ9aAI/HXgSrcVPoK2T63OThU7v9KDfe0z2Lxl2yy1z+/btHHPMMXR1dU1R6QghWL16GkuBGTAyMsK5557LypUr+dGPfkR3dzcf/OAHqVQq/Pu///tchzevOKTL4aSjUgyMBPSMJDDq2vMDBFLBxN4W4YTIvuJqlU7C1p2yevoD3Oix3DK1F74fRE9CjQaHLLRY0unQmLWo1CSbuj3WdnvkSzp335jR34fyAZWamrGwajopZU9/wFBxP3K+nC22oPP3Dnx5yZf55bd+ieqcvGqbCBNc0nsJV227ioW1hS/NuAK0vh6mdtpKoCPzcMK+o+iUVJ3Y29BGa4vQ6arDmGprXASWRcdWiEn/JcKcCf+kk06a14rRm266iUKhwJ133klLi176D/5/9s48Pq6q/P/vu82aZbI3abrQUnaQfRGkgLQsKhREBJRNQBEElUVZ/ErZ4SsiiugXVARBwG8RUECgBcEfaPkCyg6llLZ0TdNsM5n9Luf3x7l3JmmSZm/S5r5fr7ySuXOXMzdnPue5z3nO81gWF1xwAVdddRUNDQ39nGH0iIRU9tslTDZv8/GaPKme9dq3WVSk2Aqk1W94efKRIZiarhAMQjSsUF2uoaoq7UmHeNJizQaLj1blURSIRTWEIkh02pQBL76RoqYyRE2FvllL3bP6t8rMlwMlgVyctBaiNVG5xsOlPF/OCWtPYN7aeZRbY7DI0Uaqg2fZe7VuQXYOb0CIuvt6cfoKxcIq5cjwzIT72xssEkhf/3T3PH7c/RZj0IJ/3333jWgDnnnmGY488siC2AOcfPLJnH/++SxcuJCzzjprRK83WCIhWY5v1pQA7y3Lk8r1f8zWiDdx66VG1nUIukaz7Qq+oSmkczJXTklYQdc0VFUhlXUIGAplUY1lq02a2y2ChkJFWdEiDxkO2WZY22xRWwVht45tf5b6Nh2uWYaMWe+AE+0TebzhcSZlJnHyqpM5pukYgk5wbNsXQAq+jRTwIEVr3/selCHdMiVIKz1BsV6uiUyTUIYU9gxScRqRLiGBH3e/hRnzhCJLlizhG9/4RrdtsViM+vp6lizpGYIGkMvlyOWKyptIyIkt0zQxzaGZC95xmx4fT1msacqyfaNKexzWbrQLCcPGEkOzu/0eDpomhV7VZGZM1a1yZdsQ0EEzAAGGqhDUBIaGXFQlLBxLIZVWKA0LKkpVVq7No2sKZSVyVZZntJruTTN0m9Z4nppyBaPLF70kJNjYnqO2QnQP3XQcVGy3MlbPJ8tsTqAiq7OY5shZ+H31oxHte95kaBAqtAq++tWvctpVpxHKhCAI5liYvgrFVAmG+1qXbSzE1FtghtzvS5kpBT2CdNNEkEK/Bpk1cyoykmd7ipE/Ifdca5GROd5E7ijR13d7vNK1vYYxsqOhIro+R44BhmFw/fXXc8UVV3Tbvttuu/HZz36We+65p8cx8+fP59prr+2x/aGHHiIS8Z2BPiPH8ccf3+213/d8tiSb9r/hMuYW/lC48sorueSSSwqvE4kEU6ZMYe7cuZSVlQ3pnKZpsmjRIubMmdNtVI2nLJ77Vxpdh+Vrc6xtttnQbtHZM23JFsXQbM49/H1+++KumHbfq5G62sSBACCKsfemVUyR4DgyeVowKFNmCiCbl5a+cGvbVpbpMpRTg2hAwXLkpG1FqUZDjcGkKpXla6yChd/VUs/nTez2f7IytS/1tWF2mhrsZuFnc4K8Kdh1RqDH4qxMzuGT1SadGYfSiIqhK5iWXMxVGlaZOcUouIhGCq8/bMqI9r1W4Grgf8EMmCy6dxFzvjEHIzNGPo6Q+6MhrfMQ0p1T4742kOsG2sH8jMmifRcx5405GHmjmNd+d6R753Ck3x6ky+dTZASPl66hDjlp21th8hGmr+/2eKVre0eaMRf8iooK4vF4j+3t7e3d/PpdCQaDBIM9/ZuGYQz7H7rpOapjBo2THD76NE9dZZCWRJ5QUKEz5ZAfB35j09bI9yH4KlLQHcct5mIVffWKJpOeqe6IEAxCMKgSNKSvPpkW1FUBikIu7xAyFMIhGYHj2AqmA6oiC50Yhk5lWQAUlemTDZrbLdI5CAaLPnzDkKUQTUujqjxAKFS8x0IIklmbydU6JdGe0TaGATtuZxTCNbMZga6pNNYGtngc/oj2vUnAMcCTFCpLGRljbAQ/gLQOVKTANyCFP+3+LnV/dwL7UFggZWxnYJiGHLy2Qw4S2yHdPB4VFGP3x7Ba1Ujow5ZkNNo65oK/00479fDVx+Nx1q9fP2Jx/sNl1xlBWtptmtoc6mIGuZwglXYwU6NXY3qweIXUve9soV2OzFOv6W6eHDe+XtegPFKs3hUKKNi2wFIFjlCJBKE8omELhWhQK6x2VRUHgUoyLSiNKBiaSmWphgDKIiqTa3VCaxQ+WpVnQ5tFeaR7lM7kWh0hpNU+mBz1W03my8HyWeAo4C9j2IYQ0hr34uN3QVryCWRo5W7IOPsk0jIvpZgt011DQATZ6TaXHsH3eI05Yy74xxxzDDfddBMdHR2FmP4FCxagqipz584d28a5VMd0Zu8T4f3lOVasM0GBUFChJW7SFodMrmcVLBU5+am4FbL6q0ft5YjpK9pEQUbOeMJuuPuHQm7CQr04Qaq7S9/zNmjCPU5TCAdkREwoqGJoCigqjmOTzcu0x45brzYaVqivNiiPqoRDKrYFG+MWHUmbXE4FFIIBiJVqTK4xmFxr0FCld4ulLw2rfLw2T0dSfqDyEgU64PB9o6Sz2pBz1G8TIt+VBuBWZOz6libgXr+R4gKqMLKD1SPFv4Fixaod3Xa2UEwFkXGPa0QOCn56hHHNmAv++eefz5133sm8efO46qqrWLt2LZdffjnnn3/+mMbgb0p1TGf23jqf2cFdgISDrqkkUhbNbQ7NHSamJQgY0uItCStomkoybWHbUvQ1Vf62HUEq6+DYEA4qVJXrUlgdh2QaWuMmG1pN8pZCNASTqgyqYjqhoIrjCJIZG9OyaV0JP7moFl3TSeccgoZCwFDImwKhKAR1mdY4ZwoiQZVAQCEUULFsr6Ch/AwAqYyN7ShoqiAalqOJV2tWhkY62LaMi1eQSdA0DXRN7WFtR0Iqu8wMMWNKgFTGTZqm27zwPFSV60yqNrY9S304NAD/DfwN+DXwCnKFajvSh74SaW2nKRYlCSMt5lJ3e4W7LeRuq3DfT7l/e5kq48hFUfsg89eAdLFUufvHKS6E6prQrasbpgEZQ/8icCbFFMm+BT/uGXPBr6io4IUXXuCiiy5i3rx5lJaWcu6553LjjTeOddN6JVbS/ZZVletsNwbjkmma/G0lTK8PjIivr6q8v64weFEOdcl9b5qix3s+vXAaUkTHioGKtjchW4UfR78VMeaCD7Dzzjvz/PPPj3UzfHx8fLZpfDPLx8fHZ4LgC76Pj4/PBGFcuHSGi7dY2FvmPhRM0ySdTpNIJLaKWF2/vaNL1/aWlpb2mTBwJPreptfb2u7PeG/v1tRW6NnezfW/wbJNCH5nZycAU6ZMGeOW+GyLxOPxPlfR+n3PZ7TZXP8bLGOeS2ckcByHdevWDWsk9JbIr169esRu7mjit3d06dreyZMn99mvRqLvbXq9re3+jPf2bk1thZ7t9S38TVBVlcbGxhE5V1lZ2VbRKTz89o4uZWVlm/2yjWTf8663td2fraW9W1NbYXTa60/a+vj4+EwQfMH38fHxmSD4gu8SDAa55ppres2EOB7x2zu6bOn2+vdn9Nia2gqj295tYtLWx8fHx6d/fAvfx8fHZ4LgC76Pj4/PBMEXfB8fH58Jgi/4wJIlS5gzZw7RaJRJkybxgx/8gHw+v9W1Z/r06SiK0uMnm82OanuXLVvG+eefz5577omu6+y2224DOk4IwS233MLUqVMJh8McdNBBvPrqq+OyraN1b8db3xtOm8ai/21NfQ/Gvv9tEwuvhkN7eztHHHEEs2bN4rHHHmPt2rVccsklpNNpfvnLX2517TnppJO49NJLu20b7eiE999/n6effpoDDjgAx3FwnP7qe0luvfVWrrnmGm655Rb22GMP7rrrLubOnctbb73FjBkzxlVbYeTv7XjreyPRpi3d/7amvjec9sII3VsxwbnppptENBoVra2thW1333230DRNrF27dqtqz7Rp08SFF1442k3sgW3bhb/PPPNMseuuu/Z7TCaTEWVlZeLKK68sbMvlcmLatGni29/+9qi0U4ihtVWI0bm3463vDbdNY9H/tqa+J8TY978J79J55plnOPLII6msrCxsO/nkk3Ech4ULF0749gwEVR18N/rXv/5FIpHg5JNPLmwLBAKceOKJ/O1vfxvJ5nVjKG0dLcbj/3o8tmlzbE19D8a+/42f3j9GLFmyhJ122qnbtlgsRn19PUuWLNnq2vPHP/6RYDBISUkJxx57LO++++5oNXVYeJ9l08+68847s2rVKjKZzFg0a7OM9L0db31vJNq0NfS/rbHvwcjcW9+H395OLBbrsb2iooK2tratqj3HHXccBxxwAFOnTmX58uXceOONHHLIIbz55puj6pccCu3t7QSDQUKhULftFRUVCCFob28nHA73cfSWZzTu7XjrezAx+t/W1vdg5O7thBf8bYlf/OIXhb8/97nPMXfuXHbaaSduu+02fvWrX41hy7Z+/HvbP/49Gj1G6t5OeJdORUUF8Xi8x/b29vZufsytsT319fUccsgh/Pvf/x6p5o0YFRUV5HK5HmFl7e3tKIpCRUXFGLVsYIzEvR1vfQ8mRv/b2vseDP3eTnjB32mnnXr4JuPxOOvXr+/h45uI7RktvM/y0Ucfddu+ZMmSQmz0ts54/F+PxzaNNBO57014wT/mmGN4/vnn6ejoKGxbsGABqqoyd+7crbo969at45VXXmG//fYb4VYOn89+9rOUlZWxYMGCwjbTNHnsscc49thjx7BlA2Mk7u1463sj3abx2v+29r4Hw7i3ww7s3Mppa2sT9fX1Yvbs2eK5554T9957r4jFYmMSzz6Y9hxxxBFi5syZhdcPPfSQOO2008SDDz4o/v73v4vf/va3YubMmaKiokIsX758VNucSqXEggULxIIFC8Rhhx0mpkyZUnjd3Nzca3uFEOLmm28WwWBQ3HHHHeKFF14QX/7yl0Vpaan45JNPxlVbR+vejre+N5g2jZf+tzX1vaG2dyTv7YQXfCGE+OCDD8TnP/95EQ6HRW1trbjssstELpcb1+2ZPXu2mDZtWuH14sWLxWGHHSaqq6uFruuiurpanHzyyWLJkiWj3t4VK1YIoNefF198sdf2CiGE4zjipptuEo2NjSIYDIoDDjhA/Otf/xp3bR3Nezve+t5A2zRe+t/W1PeG2t6RvLd+PnwfHx+fCcKE9+H7+Pj4TBR8wffx8fGZIPiC7+Pj4zNB8AXfx8fHZ4LgC76Pj4/PBMEXfB8fH58Jgi/4Pj4+PhMEX/B9fHx8Jgi+4Pv4uLz00ksoisIbb7wx1k0B4LDDDuOLX/ziZvdZuXIliqLw6KOPFrbl83nOPvtsampqUBSFO+64g/vuu4+HHnpotJvsM87x8+H7+GzF1NfXs3jxYnbYYYfCtj/84Q888MAD3H///cycOZPp06dzyimnUFJSwmmnnTaGrfUZa3zB9/HZigkGgxx44IHdti1ZsoSGhga+9rWvjVGrfMYrvkvHZ8icddZZ7Lbbbjz//PPssccehMNhZs+ezcqVK2lra+Pkk0+mrKyMmTNn8qc//anbsU8//TRz5syhtraWsrIyDjjgAJ599tlu+8ybN48ZM2bQ2dlZ2PbII4+gKEqPfftj8eLFHHfccTQ0NBCNRtlzzz154IEHet23ubmZE088kWg0Sn19PTfddFO399esWcPJJ59MXV0doVCI7bbbju9///uDag/AP//5Tw499FDKy8spLS1l99135/777++x36OPPsqOO+5ISUkJRxxxBJ988knhvU1dOtOnT+enP/0pq1evRlEUFEVh+vTp/OMf/+Dpp58ubJs/f/6g2+uz9eNb+D7DoqmpiUsvvZSrr74awzC4+OKL+drXvkYkEuHQQw/lvPPO4ze/+Q1f//rXOfDAA5k2bRoAK1as4Etf+hKXXXYZqqryzDPPcOyxx/L3v/+dww47DIB77rmH3Xbbje9973v87ne/Y926dVxwwQWcf/75HH300YNq56effsrBBx/M+eefTygU4p///CfnnHMOjuNw5plndtv3m9/8JqeeeiqPPfYYzz//PFdffTWVlZWcf/75AJxxxhmsW7eOX/ziF9TV1bFq1apB+/0TiQRf+MIXOOSQQ3j44YcJBoN88MEH3fLQA7z11lv85Cc/4ZZbbsG2bS655BK+/vWvs3jx4l7P+/jjj3Prrbfyj3/8g8cffxyAcDjM2WefTSQS4bbbbgOgsbFxUO312UYYkZyfPhOSM888UyiKIt57773CtjvvvFMA4oc//GFhW3t7u9A0Tdxxxx29nse2bWGappg7d6449dRTu7332GOPCUA88cQT4uijjxbbb7+9SCaTw2q34zjCNE3xzW9+Uxx00EGF7S+++KIAxOmnn95t/9NPP11MnjxZ2LYthBAiGo2KX/ziF8Nqw+uvvy4A8c477/S5z+zZs0U0Gi3kSRdCiN///vcCEKtXrxZCFNPtLliwoLDPd7/73R7pgGfPni2+8IUvDKvNPls/vkvHZ1g0NDSw6667Fl57k4dHHnlkYVssFqO2tpbVq1cXtq1Zs4YzzzyTyZMno+s6hmGwcOFCli5d2u38J5xwAmeccQZf/epXWbRoEX/4wx+IRqODbmd7ezsXX3wx06ZNwzAMDMPgnnvu6XE975pdOemkk1i7di1r1qwBYO+99+a2227j17/+NcuWLRt0WwBmzpxJWVkZ3/72t/nf//1fNm7c2Ot+e+65JzU1NYXXu+yyC0ChLT4+g8EXfJ9hEYvFur0OBAJ9bveKRjuOw3HHHccrr7zCddddx4svvsjrr7/OMccc06OwNMBpp51GLpdj77335qCDDhpSO8866ywefvhhLrvsMhYuXMjrr7/ON77xjV6vV1tb2+11XV0dAOvXrwfgT3/6E5///Oe5+uqrmTVrFjvttBOPPfbYoNpTUVHBokWLKC0t5fTTT2fSpEkcdthhvPvuu9326+v+9tZuH5/+8AXfZ4uzbNky3nzzTW6//XbOOeccZs+ezb777ksmk+mxbyqV4sILL+Qzn/kMb7zxBr///e8Hfb1sNstTTz3Fj370Iy666CKOOOII9t13XxzH6XX/5ubmbq83bNgAyBBI7/e9995LS0sLr732GjvuuCNf/epXWb58+aDatf/++/PMM8/Q0dHBk08+SXNzM/PmzRv05/PxGSi+4PtscTxh96xVkJOq//znP3vse+mll9Le3s4zzzzDd7/7Xb73ve+xatWqQV0vl8vhOE6363V2dvLXv/611/29yU6PRx99lIaGhh4Tnaqqst9++3HDDTdgWdaQ3TvhcJhjjz2Wb3/726xYsWJUrPeuT1g+Exdf8H22ODvttBONjY1cccUVPPXUUzzyyCPMnTuXyZMnd9vv2Wef5e677+ZXv/oV9fX13HzzzTQ0NHDWWWch3Mqcn376Kbquc9111/V5vfLycvbbbz9uueUWHn30UZ544gnmzJlDeXl5r/v//e9/5/LLL2fhwoVcfvnlPPDAA1x99dWoqko8HufAAw/krrvu4vnnn+dvf/sbP/jBD4jFYuy9994A/OMf/0DXdf7whz/02aann36aE088kQceeIB//OMf/OlPf+LOO+/k4IMPJhQKDfaW9svOO+/MG2+8wZNPPskbb7zBunXrRvwaPuMfX/B9tjjBYJDHHnuMYDDIV77yFX784x9z9dVXM3v27MI+bW1tnHPOOZxyyil89atfBSAUCvHAAw/w8ssv8/Of/xwAIQS2bffpnvF46KGH2H777TnzzDO5+OKLOemkkzjjjDN63ffuu+9m6dKlnHDCCTzwwANcf/31XHDBBYU27L777tx5550cd9xxnH766TiOw8KFC6murh5wm7bffntUVeXqq6/mqKOO4pJLLuHggw9mwYIFA7+Rg+AHP/gBBx98MGeccQb77bcf99xzz6hcx2d84xcx9/Hx8Zkg+Ba+j4+PzwTBF3wfHx+fCYIv+D4+Pj4TBF/wfXx8fCYIvuD79Mtbb72Foii89NJLY92UrZ7p06fzne98Z1SO21IFXAZSmGVL0tvnnj9/Pv/617/GsFXjEz9bpo/PFuTxxx+noqJirJsxLH71q1+hadpYN6PA3nvvzeLFi9l5550L26699lpKSkr47Gc/O4YtG3/4gj8O8WK4DcMY66aMCzKZDOFweKybMSLstddeY92EIeP9H7wEbuOFsrKyHkVgfHrHd+n0Qn/FMlKpFNFotJBbvCsnnXRStwRfHR0dXHDBBdTX1xMMBtlnn31YuHBht2O8R+T777+fHXfckWAwyNtvv8369ev5xje+wYwZMwiHw8yaNYurrrqKXC7X7fh4PM7Xv/51SktLqa2t5aqrruKnP/0piqJ0228gbQG44YYbmDRpEiUlJZx44ok9csv0xRVXXMHuu+9OSUkJkydP5tRTTy0kHOvK008/zcEHH0wkEqGiooLDDjuMN998Eyg+nj/99NOcdNJJlJWV8ZWvfAWQq2pPOukkysvLiUajHHXUUT2Sjf31r39l3333paSkhFgsxr777svf/va3Ab/fG4qi8N///d/Mnz+furo6qqurOfvss0mlUt32W7NmDV//+teprq4mHA5z6KGH8u9//7vbPr25Zu6++26mTZtGJBJhzpw5vPnmmyiKwn333dejLXfddRfTpk2jvLycefPm9Zpls78CLgD/7//9Pz772c8SDoeprq7mG9/4Bm1tbYX3vcIq9913H+eddx5VVVXsv//+QE+Xzvz58ykpKeHdd9/lkEMOIRKJsNtuu/Hcc891u2Y+n+fiiy+msrKSWCzGt771LR566CEURWHlypW933zANE0uv/xypk6dSjAYpL6+ni996UvE43Ggp0vH6/eXX355oeCL544UQnDbbbexww47EAwGmTFjBj/72c+6XW+kCtyMS8YyN/N45eGHHxa33HKLePrpp8ULL7wgrrvuOmEYhrjvvvsK+5xyyilin3326XZcIpEQoVCokCs9l8uJfffdV0yZMkX87ne/E88++6z4+te/LnRd75YHffbs2aKurk7suuuu4pFHHhHPPfecaGpqEu+884649NJLxeOPPy5eeuklcc8994iGhgZx1llndbvuCSecIMrLy8WvfvUr8fTTT4t58+aJKVOmiK7/3oG2xctnf9lll4lnn31WXHrppaKxsVEA4sUXX9zsfTv77LPFQw89JF566SWxYMECceCBB4pZs2YJ0zQL+zzyyCNCURQxb9488fjjj4unn35aXHXVVeLJJ58UQhRz0jc0NIgf/OAH4vnnnxcvvviiSCQSYvr06WLGjBnioYceEo899pjYZ599RCwWE6tWrRJCCLFs2TJhGIY444wzxMKFC8Wzzz4rbr31VvHggw8O6P2+AMSUKVPEaaedJp555hnx85//XAQCgW45/9va2sS0adPErrvuKh566CHx9NNPi6OPPlqUlZWJDRs2FPabNm2auPDCCwuv//KXvwhAnHvuueLZZ58Vt9xyi9h+++0FIH7/+993O27KlCli7ty54sknnxS///3vRSwWE1/96lcL+3j3bvLkyYX/32WXXSYA8etf/7qw3xtvvCECgUDhXL/97W9FdXW12H///YVlWUKIYp79SZMmiXPOOUc899xz4plnnhFC9Mytf80114hAICB233138Zvf/EY8++yz4vDDDxfRaFS0tLQU9vv+978vAoGAuOWWW8Szzz4rzj333EI/XbFiRZ/3/9prrxUlJSXirrvuEi+99JJ49NFHxTe/+c3CffU+9+uvvy6EEGLx4sUCEBdddJFYvHixWLx4sYjH40IIIS666CIRDofFDTfcIBYtWiSuvfZaYRhGt/tz+OGHix133FE88sgj4sUXXxT333+/uOiiizbTQ7YefMHvh76KZXhf1KVLlxa23X///ULTNNHU1CSEEOLee+8Vuq6L999/v9s5DzjgAPGVr3yl8Hr27NnCMIyCcPWFaZrij3/8o9B1XaRSKSGEEO+//74AxB/+8IfCfrZti1mzZnUT/IG0xbIs0dDQ0GsBkIEIflcsyxJr1qwRgHjuueeEEPJeNjY2iqOOOqrP47wv7/nnn99t+89//nOhKIr44IMPCttaW1tFNBoVl1xyiRBCiAULFghAJBKJXs/d3/t9AYj999+/27YzzzxTzJw5s/D6xz/+sSgvL+8m7tlsVkydOlVcfvnlhW2bCv5+++0njjjiiG7nvv7663sV/MbGRpHNZgvbrrnmGmEYRqEwy0ALuJxwwgli6tSpIp/PF/Z57rnnBCD++te/CiGKgn/00Uf3uB+9CT4gnn766cI27/gHHnhACCH/V6FQSFx33XXdzvX5z3++X8H/whe+IE488cQ+399U8IWQ/7Of/OQn3fZbtmyZUBRF3H333d22//CHPxSTJk0a0QI34xXfpdMLAymWcfTRRxOLxXjkkUcK2x555BEOP/zwQv70hQsXsvvuu7PDDjtgWVbhZ86cObz++uvdrrnHHnswZcqUbtuEENxxxx3ssssuhMNhDMPga1/7GpZlFVLxeuc57rjjCsepqsqXvvSlbucaSFvWrFnDunXrei0AMhCeeeYZPvvZz1JeXo6u64Xskt59++ijj1izZg3f+MY3+j3XF77whW6vX375ZXbbbbduE3OVlZXMmTOHV155BZD3UNM0TjvtNJ588snCI79Hf+9vjjlz5nR7vcsuu3QrQrJw4UIOP/xwKisrC/dW0zRmz57d43/tYds2b775Zrf/HcDxxx/f6/6zZ88mGAx2a4Npmj1cbv0VcHn55Zc5/vjju80RzZ07l1gsVriXHpv+H/pCVdVuRW+mT59OOBwuXPPdd98lm80O+LN2Ze+99+Zvf/sb8+fP5/XXX+83b1JfPP/88wB8+ctf7vYdOPLII2lqaioU6BmJAjfjFV/we2EgxTICgQBf/vKXC4Lf2trKokWLOO200wr7tLS08OabbxYGDe/nhhtu6Fb9CYpFNrpyxx13cOmll3L88cfzl7/8hddee4277roLKBbAWL9+PYZh9Mj8uGkRj4G0xfO391UAZHO8/vrrhXmPBx54gMWLF/Pqq692a2traysgq2T1x6bXbG9v77UddXV1Bd/zDjvswFNPPUU8HueEE06gpqaG4447rpBOub/3N0dvhUi6zqW0tLTwxBNP9Li/DzzwQI//tcfGjRuxLKtbRSvoef831wboWQylvwIuA7mXmx7bH+FwuFv6aa99XfspMODP2pWrr76aH/7wh9x///3sv//+TJo0iWuvvbaQMXWgtLS0IISgurq62//IG8y9/9NIFLgZr/hROpvgFcu4/fbbueiiiwrbe7MqTj31VH73u9/xzjvvsHjxYjRN48QTTyy8X1lZyR577MHvfve7fq+76QQrwIIFCzjuuOO4+eabC9s++OCDbvvU19djmibxeLyb6G9q9Q2kLV6Bj74KgGyOxx9/nPLycv73f/8XVZV2xKefftptn6qqKoABpebd9H5UVlby0Ucf9dhvw4YNVFZWFl4fffTRHH300SQSCZ599lm+//3vc/bZZ/PCCy8M6P2hUllZydFHH83111/f472uVnlXampq0HW9x8TrQCfJ+6K/Ai6VlZW9XmPTewm998uh4F1748aN3Qb8gXzWYDDI/PnzmT9/PsuWLePee+9l/vz5zJgxg9NPP33AbaisrERRFF555ZUegxPAjjvuWGjrvffey29/+1v+/e9/c8MNN/DVr36Vjz76iBkzZgz4euMR38LfhMEUyzjssMOYNGkSDz/8MA8//DDHHHNMN9E98sgjWb58OQ0NDey77749fvojk8n06Jh//OMfu732zvOXv/ylsM1xHJ588slu+w2kLY2NjdTX1/daAGQgbTUMo5tAbNrWHXfckcbGxiFVrTrkkEN49913u4l+e3s7zz//PIccckiP/cvKyjj55JM55ZRT+PDDDwf9/mA58sgj+eCDD9h555173Nvdd9+912M0TWOvvfbq9r8DeOKJJ4bVlv4KuBxyyCE88cQTWJZV2GfRokV0dHT0ei9Hgt12241QKDTsz7r99ttz0003UVlZudn/m2EYPZ58Pv/5zwPySbO370BpaWm3/UeqwM14wrfwN6FrsQzPArvlllsoLy/vYY1omsbJJ5/MfffdR3Nzczd/PsAZZ5zB3XffzWGHHcZll13GDjvsQEdHB2+++Sb5fL6b5d4bc+bM4ec//zm//OUv2WGHHXjwwQd7dLpdd92VE044gYsvvph0Os20adO45557yGQy3cR3IG3RNI0rrriC7373u9TV1TFnzhwWLlzIiy++2O99mzNnDnfccQcXXXQRJ5xwAosXL+4WygrSWrzttts49dRT+fKXv8wZZ5xBMBhk8eLF7LfffptdvXn22Wfzs5/9jC984QvccMMNhEIhbrzxRnRd53vf+x4gwxsXL17M0UcfTX19PStWrODBBx9k7ty5A3p/OFxyySX88Y9/ZPbs2Xz3u99l6tSpbNy4kf/7v/+joaGhz7C+H/3oRxx//PGcd955fOUrX+HNN9/k/vvvByg8KQ0Wr4DLnDlzWLRoEQ888AB33XVX4XxXX301n/3sZ/niF7/IRRddxIYNG7jiiivYf//9OfbYY4d2A/qhqqqKb3/729x4442EQiH23HNPFixYUJjf2dxnnTdvHvvssw977bUX0WiUJ598kvb2do444og+j9l55535y1/+wuc+9zmi0Sg77rgjO+ywAxdeeCGnn346l19+OQcccACmabJ06VJefPFFnnjiCeLxOEcddRSnn346O+64I/l8njvvvLNbgZutmjGeNB6XfPzxx+KII44QkUhETJkyRfzkJz8R11xzjYhGoz329ULASkpKRDqd7vF+PB4X3//+98XUqVOFYRiivr5eHHvsseKpp54q7LNp1INHZ2enOOuss0RFRYWoqKgQ5513nnjyySd7RCS0t7eLr33tayIajYqqqipxySWXiB/96EciFosNui2O44hrr71W1NbWikgkIo477jjx7LPPDihK59ZbbxWNjY0iEomIOXPmiKVLl/YaLfHXv/5VHHDAASIUColYLCaOOOII8eabbwoheo+48Fi5cqU48cQTRWlpaeEaXUNK//Wvf4kvfOELor6+XgQCATF16lTx3e9+txCV09/7fdHbZ/jZz34mNv36rF+/XpxzzjmF8zc2NoqTTjpJ/POf/yzss2mUjhBC/PrXvxZTpkwRoVBIzJ49WyxcuFAA4oknntjscY8//ni3CBfv3j311FPiuOOOE5FIRNTV1Ynrr7++x2d66aWXxEEHHSSCwaCorKwUZ511lmhtbS2870XZLFiwoMexvUXp9PbdKC8vF9dcc03hdS6XE9/5zndELBYTZWVl4swzzxS//OUvBSA6Ojp6HO/x3//932LfffcV5eXlIhqNir333ls89NBDhfd76zMvv/yy2HvvvUU4HO7Wdx3HEXfeeafYbbfdRCAQEJWVleKggw4St99+uxBCRlade+65YscddxThcFhUVlaKuXPnitdee63P9m1N+AVQtlEOPfRQNE0bkHXuM7743e9+x7nnnsuKFSuYPn36WDdnVDn99NN55ZVXWLFixVg3ZULgu3S2Af785z+zatUqdt99d9LpNA899BAvv/xyD1+uz/ijra2Na6+9liOOOILS0lJef/11brzxRo4//vhtTuz/8Y9/8M9//pN99tkHx3F46qmn+OMf/8jtt98+1k2bMPiCvw1QUlLCAw88wMcff0w+n2ennXbiwQcfZN68eWPdNJ9+MAyDTz75hIceeoiOjg5qamo4/fTTufXWW8e6aSNOSUkJTz31FLfeeiuZTIbtttuO22+/vTAH4zP6+C4dHx8fnwmCH5bp4+PjM0HwBd/Hx8dnguALvo+Pj88EwRd8Hx8fnwmCL/g+Pj4+EwRf8H18fHwmCL7g+/j4+EwQfMH38fHxmSD4gu/j4+MzQfAF38fHx2eC4Au+j4+PzwTBF3wfHx+fCYIv+D4+Pj4TBF/wfXx8fCYIvuD7+Pj4TBB8wffx8fGZIPiC7+Pj4zNB8AXfx8fHZ4LgC76Pj4/PBMEXfB8fH58Jgi/4Pj4+PhMEX/B9fHx8Jgi+4Pv4+PhMEHzB9/Hx8Zkg+ILv4+PjM0HwBd/Hx8dnguALvo+Pj88EwRd8Hx8fnwmCL/g+Pj4+EwRf8H18fHwmCL7g+/j4+EwQfMH38fHxmSD4gu/j4+MzQfAF38fHx2eC4Au+j4+PzwTBF3wfHx+fCYIv+D4+Pj4TBF/wfcaUJUuWMGfOHKLRKJMmTeIHP/gB+Xx+xI5dsGABxx9/PI2NjUSjUfbcc0/uvfdehBCj8XF8tiJGu+8N9xqjgT5mV/aZ8LS3t3PEEUcwa9YsHnvsMdauXcsll1xCOp3ml7/85Ygce/vttzN9+nR++tOfUlNTw6JFizjvvPNYvXo111xzzWh/RJ9xypboe8O5xqghfHwGQT6fF47jjMi5brrpJhGNRkVra2th29133y00TRNr164dkWM3btzY49jzzjtPlJWVCdu2R+BT+Gwptra+N5xrjBbbhEtHCEEikfAf00eBmpoabrrpJq688koaGhqorKwcsXM/88wzHHnkkd3OefLJJ+M4DgsXLhyRY6urq3scu9dee5FIJEilUsP+DH7fGz229r43nGuMFtuE4Hd2dlJeXk5nZyemafKXv/wF0zSHdK5s3uH1DzK8/XGWpavyPX7e/jjL6x9kyOYd0lmHt5dmefmtNG9/nOXDFTne/th9vTRLOuuM8CcdHMO9F+vWraOlpYU777yT9evXc++99/LnP/8ZRVEK+wghsCyr35/eWLJkCTvttFO3bbFYjPr6epYsWbLZtg322K734pVXXmHy5MmUlpYO9Fb0yUj2vW0Jv+8V9+t6LwZ6jdFiwvjws3kHywZdg1Cg73EuFFCpKtNY22IRCig9Olgi5TC5WicUUFm+Jk8i7VBboRX2C2sKoYBCc7tNU4vFjMbAFmv7SPP2228DcNJJJ3HnnXf2us/999/P2Wef3e+5VqxYwfTp07tta29vJxaL9di3oqKCtra2zZ5vqMf+85//5JFHHuGnP/1pv232GTu2hb43nGuMFtu84KezDk0tFq0JG8sW6JpCVZnGpGqdSKh38ZxUrdOZdmhutymLqgR0hbwlxb4sojKpWiebd2hNyPe7DgoAiqJQFlVpTdg05J0hi/RQ2j6SvPPOOxiGwfXXX9/nPl/60pd4/fXX+z1XQ0PDSDZtSLS0tHDBBRdw+OGHc/HFF491c3w2w7bW98YL27Tgp7MOH6+SVnhZVKUkrJK3BGtbLDrTDrOmBnoVzkhIZdbUQEFs01kHXVOYXK0XxDaZcbBsQUm4d+EN6ArprLTMt2TbR5K3336bgw46qFcrxaOyspLy8vJ+z6XrPbtaRUUF8Xi8x/b29vZ+/bWDPbajo4PrrruOyspK/vznP6Oq24Q3c5tlW+h7w7nGaLFN9/qmFqvgcgkHVTRNIRxUqa3QSKSl9dwXkZDKjMYAu28fZPftQ+y+fZAZjUWR1TXQNWn590bekha5rm35to8Ub7/9Nvvss89m97n//vsxDKPfn5UrV/Y4dqeddurhy4zH46xfv76Hj3Q4x2YyGebNm0c6nebJJ58ckEj4jC3bQt8bzjVGi23Wws/lHVoTzrBdLn29Nxhf/2DZEu6i/sjlcixdupQf/OAHm91vOI/VxxxzDDfddBMdHR0FS27BggWoqsrcuXM3e76BHmtZFieffDJLlizh2muvZfLkyf221Wds2Vb6Xtf9PAZ6jVFjTIJBR5h4PC4AEY/HRT6fF0888YRoT2TFv95JiQ+WZ8VHn+Z6/HywXL7fmR56LHYqY4u3PsqI//dmSry1NCM+WJ4Vby11X3+UEanM0M7dmbZHpO3evcjn84Nuw7///W8BiLfeemtIn2EgtLW1ifr6ejF79mzx3HPPiXvvvVfEYjFx4YUXdtvv/vvvF5qmiZdeemnQx5533nkCEP/93/8tbr31VvHyyy+LxYsXi8WLF4tsNjvsz9Bb3xvK/d7W8Ptecb9DDz1UXHPNNeI3v/lNr/ttSbZZC19Xiy6XsKb0eH+4LhcYmK9/SG3XRr/t/fH2228TCATYZZddRu0aFRUVvPDCC1x00UXMmzeP0tJSzj33XG688cZu+zmOg23b3WLdB3qsF+/cm7XYW/SGz9izrfQ9b7/vfOc73HzzzcRisV7325IoQmz9K0YSiQTl5eXE43HC4TB/+9vfOPbYY1m9QU5ydg2bBOlyaW63mVytj0jYJIx86OTyNflht900zcK9MAxj2G3amhmte9FX3/Pvt9/3PMbTvdhmLXwYWHhlV4Yj2iPtSx9s2318fHz6Y5tWjYG6XEYy3n2kLP3Rchf5+PhMXLZpwYdieGVDH0I8UvHuo7FIqr+2+/j4+AyGbV7wPfoSyq7x7kNNjzCYQWMoTwC+yPv4+IwEE0bwPboKLjAi8e4DGTQmVetjmibBx8fHZ8IIfm8ul3BAIZV1KAn3fhsGkh5hIIuk1rVatMZtsqYYszQJPj4+PoNWmeuuu45169b1+t769eu57rrrht2okcZzuaxtsQgYCrESjYChsKHDZkOLRWeqd0UfSLy7ZYNlCwJ6z3h5kIPGxjaLjpQ94mkSsnmHZMYhmx/bNMw+Pj5bB4MW/GuvvZY1a9b0+t66deu49tprh92okaavvDSNNTqqBqs2WD0KWAg3PUJVmbZZd87mcurkTcHGDpNEyiFWom3WbTQY0U5nHZavyfPushzvLsvy7rIcy9fkxzz//kgwlBqg9913H4qi9Pi54ooret3//vvvZ6+99iIUClFdXc0xxxxDJpMZjY/jsxUx1PqzTz31FHvvvTfBYJApU6ZwzTXXYNu9G5Fj3fcG7dIRQvQQLo/169dvNrvdWLCpyyVnOtg2aBoEDZWpkwxWrDNZu9GkqlwfdLx7bzl1sjmHlrhNR6dNU6tF3nLoSOoEDYVQsPvgMdismuMhi+ZoMdwaoM8++2y3xGi95c25+eabue2227jqqqs46KCDaGlp4YUXXujzC+ozMRhq33v11Vc5/vjjOfXUU7n55pt5//33+dGPfkQqleK2227rtu946HsDEvyHH36Yhx9+uPD60ksv7SHs2WyWN954g4MPPnhEGzhcPJeLrsKaDSbxlI3tgONASVihskxlUqVGdUwnkxNDinfvukgqYBSfKBAKNRUauZxKU6tFLi+YWmd0E/3BpkkYiaii8cr//M//kEgkePzxxwvpYy3L4oILLuCqq67qN6/5Pvvs02tJQ4+1a9dy/fXX89e//pVjjjmmsP3LX/7yyHwAn62Wofa9+fPns+eee/Lggw8CcNRRRyGE4Morr+Tyyy+nrq4OGD99b0CmYD6fp7Ozk87OTgBSqVThtfejKApnnHEGd99996g2eLDoGtg2LF9nsrHDRghpJTe3Wby3PMer72ZpiTs01Oh9pkLuD2+R1ORqnaZWi40dNtGQQm2FypQ6g5oKDUNTSGWk5e8xULeRx0CzaI6kT7+xsZEf/OAHnHvuudTW1lJWVsZJJ51Ee3v7iF3DY7RrgL7wwgtMnz692xfOZ/yyNfS9N998s0fmy6OOOgrTNHnuuecK28ZL3xuQosXjcf70pz/x4osvMm3aNO677z5efPHFbj/PPfccd9xxx6CryyxYsIDjjz+exsZGotEoe+65J/fee++IFYUOBVRsR9Aat4kEBe2dNomkQzikUB1TSWYELe0Wq5ss1+pXhxT3HgmpNNTqVMd0Zk0xiIY00jnBmmaLeNIhk3VIph1a2m0yWYdMzk2bMIg0CQOZILZsMeSiK5vS0tLC2rVrueuuuxBC8PDDD3Pdddfx5JNPcuWVV/bYXwyjxigMr84owK677oqmacyYMYObb765x6PyRx99xG677cYNN9xAbW0tgUCAgw8+mP/7v/8b4B3x2VJsLX0vm80SDAa7bfNef/jhh4Vt46XvDUhpvv/973PQQQdRW1vLp59+OqKTDLfffjvTp0/npz/9KTU1NSxatIjzzjuP1atXc8011wz7/Nm8g6ZCVbnG6mbpVqkoU7FshUxOUBVTiZWqtCYsSlvUIblDvNj+XN7BtATxTodsHqJhhYimYNqCnCnI5R06MzZtCYuSiEZ1uUpVTGegxZe2dBbNt956C4CzzjqLu+66C4DPf/7zvPvuuzzzzDM99h9OjVEYeg3Q+vp6rr32Wg444AAUReGvf/0rP/rRj1i7dm03/2tHRwfPP/8877//Pr/61a+IRCLcdNNNzJ07l48//pja2tp+2+6zZdha+t6sWbN47bXXum179dVXAbodN1763oAEv6qqik8++YT99tsPoM9J26Hw5JNPdvO7HnHEEbS2tnL77bfzX//1X8MuRWfZoGkKjbU6bQkLx1FI56RPv6JUozQiJz0jwZ6LrPpbFbtpbL/jyCyXQsCUSUbhPgU1hboqhQ2t0tUza0qAzrQgmXHoSOYHvAhrNIuu9MZbb71FIBDg6quv7rZ9hx124E9/+lOP/ceqxuhRRx3FUUcdVXg9d+5cwuEwP/vZz7j66qupr68H5D1KJpM8+uij7LHHHgAceOCBTJ8+nV/+8pfjMqR4orK19L0LLriAc845h5///OecfvrpfPDBB1x99dVoWs8st+Oh7w1I8L/whS9wxhlnFMLc5s2b1+MxxkNRFD755JMBN6C3Sba99tqL3/zmN6RSKUpLSwd8rt7wrGLbEVSW6QQC8p+gqQqGrpBznwBCQWnxW/bA8uL0Fi2TzDi0d9rYNtRVagSDm5jaisAB1rcMfRHWlsyi+dZbb7H//vv3+JKsX7++1y/OcGqMwsjWAD355JO57bbbeOuttwqCH41GqaqqKnzhvDbvtddevP/++4M6v8/osrX0vbPOOot3332Xyy67jO9973sEAgGuueYa7rjjjkK/g/HT9wakDvfccw+HHHIIH374IbfffjuHHnookyZNGrVGvfLKK0yePHnYYg9Fq3hFk4migAIEXQtYCEEqI6iJydFY1yBvOqxusvoNe+wtWiagK9RXG6xrsVi1wWLqJAVdV7AseZ2yiEo2J+hI2UytM4YUZbMls2i+9dZb7Lrrrt222bbNX//6124WtcdwH6tHuwbo1KlT+3w8z2azwz6/z8ixtfQ9VVX52c9+xvz58/n000+ZOnUqpmly9dVXc+CBBxb2Gy99b0CCbxgG55xzDgB//vOfueKKK/jMZz4zKg165ZVXeOSRR/jpT3/a5z65XI5cLld4nUgkAFlowBvBTdMkl3ewHCiJOFRGBW0dJu0ZQXWZhuVAOiuIhBQqShTinTb1VRqt7TlaOkxiJRoIB1AI6FBVButbsihYNNRqbGy3KAkpOE5xclkBwgGbydWQylgkUg44CrqhUF2uYeiC1WmbsrCK4/ScQCoJCTa256itEIVBqTcMDabUySggy5HVvYIBBbAxzeJEpWma3X4Phmw2y0cffUQwGOx2/B/+8AdWrVrFt7/97R7nPfroo1m8eHG/566pqem1TXPnzuXWW29l48aNBX/qI488gqqqHH744YP6HA899BCaprHbbrthmiamabLvvvvywgsv8Prrr7PnnnsC0Nrayn/+8x8uvvjiXs+/acGKgfa9ic5E63uRSISdd94ZkKGa2223HbNnzx5W34Oe/W+4jKuKV2vWrOGAAw5g5513ZuHChX367+fPn9/rit6HHnqISCQy2s2cECxbtozLLruMqqoqDjvsMPbcc08+/PBDHn30UU499VROOOGEEb9mMpnkoosuoqGhgZNOOom2tjbuvfdeZs+ezTe/+c3Cfi+++CJ33nkn1113Hbvtthvz589njz32YOrUqQC8/vrrLFy4kC9+8YsFQwVkubof/vCHdHZ28vWvf51AIMCf//xn1q1bxy9+8QsqKip6tOn444/v9trve6PP1tT3li5dyvvvv892221HPp/ntdde46WXXuLHP/5xN/fNUPoe9Ox/w2VAgv/YY49xxBFHEIvFeOyxx/o96YknnjjohnR0dPC5z30ORVF4+eWXN+uP683KmjJlCi0tLShaiJf/8TyxyZ+jrCSAoSsk0zZrmi0sRxAJqSTTNoqiUFmuEovqVJaplEZVlq81+XBljuqYRlBXMW1BR6dNKuMQDauUlajk8tI/v3ytSVlUZebkAMFgcXImnrR5/f0MHUmHHaYGKAmrZE1BW9wmFIBYmU5tTKOsRHM/i6Cp1aIjaZM3BZqm8JlZIaZO0gkHh+eeMU2TRYsWMWfOnEFbCvfeey/f/e53efXVVzn//PN56623mDJlCpdffvmAHp2Hyocffsj3v/99Fi9eTGlpKV//+te57rrrCASKbq4//OEPnHvuuSxatIjZs2dzySWX8Oyzz7J27Vocx2HWrFl84xvf4MILLyy4zbx7sddee3HllVfy9NNPk8/nOeSQQ/jJT37SZ/3UgVj4Xt8Lh8NDvt/bGhOl77311lt85zvf4YMPPgBg//33Z/78+d3cOUPtezBGFr6qqrz66qvsv//+/UbNKIoy6KXCmUyGOXPmsGrVKhYvXtzrkvjN0bWu6IZ2jQ/eep5Zux+Jrhtkcw6rmkySGYd01iEYUNA1hfakTTSgsdv2AXacFqSpxWJFk0lrh4WqKkRCCoausqHVZE2zRWONTqxMw7IF2zcG2dBm8skak5mTDaZMKnaI1U15lnyap7JMJRxSsW3cH4fOjAOKwNBUptbJf+Sy1Xnakw7hoEIuL6ir0phcY1AT04edJmE4tTS/853v8Oqrr/LGG28M+frjCb+m7ZbF73tFtrqatitWrCjMOK9YsWJEG2BZFieffDIffvghL7/88qDFvivZvENbQlp0XkRUS4dNKisoCSt0JAVtCYeZjQbTJhm0tDt8ssbEsiCRkhZ2a9ymLeEQK1EoCWsk0w7lpSrJjI2iQH2NQcBQqInpdHQ6rNpgUlaiyn0zNqs2mEyq0pk1JYCqKqQyNus2WghUqmIqZl6QtwWvvpehud1CALFSDdOEWJmGpiokUg5Z0yRgKOy8Xe/RUCNdNH1T3nrrrYKv0cdnS+L3vdFjQII/bdq0Xv8eCS644AKeeuopfvrTn5JIJAqLFkCGZ/YV/tkblg1Wl0nUnOkQT9lEwwrxpEM+LydpdV1B11QqymRUzpqNJivX5TF0BdNytzULdE0K8qRKnXRWUB1TqS6XrphQUGXGZIMV6wWZPFi2jWlDeanKdvXFfDnNbYK8BRVlKo6A5rSNcBxMW668jZWpBHRQkCNU3hI0rc0TDCjEOx1AMK0+MCr1d/tCCME777zDKaecMiLn8/EZKH7fG12GHLS9bt061qxZ02tI0aGHHjrg83h5Ki699NIe7/UVQtUXuga6WvSn2zbYDhgCkmmbQEBBVRQcB3I5mW/GETK75dqNFnUVOpUxuRirIykt/ba4hYrClEk60xr0bonPVFVhSm2AWVMNdE3Fsh0+XqWgum3oOuCYpqC53Wb1hjyqomBZUBJRiYY0ait1NEWmaU4kHapiGo4Nti1TM+RNmDVVuo36y5SpqhQs/14W4w4IRVEK0Sc+PlsSv++NLoMW/OXLl3P66acXLPFNpwAG68NfuXLlYJvQJ6GASmWZShMghEyBrKmQMwW2I8jmBYam0NRi4ghwbFB1Kf6GrqDpcpGWoivUVqpUljt8oghSWZsptSHKo8Xb1XVla6zE265SVeYUVsJ6A47mCJrbLTa02mgqlJdqNLcLhIDOlEzXnHGjslRVtiFtCgKGQl2VRjxZLJLSNfY/ZzoIAeUlCs1tcuI3ElILln8suvXnx/fx8Rk5Bi345513HmvWrOHee+9ll1126TaDPR6oq9L5AOm7Ly/VKAmrrG62SCQFecuhNKIRCSuoKnQkBMJ0aGq3qS5TiYRVOtMytYKugePIDJSWDVkTMjmn28rWoAGGAa1xi6ibdK3rSthQAECwrsWmo9OROXwcucgqFFCJhBwSKYdON2VzJKRg2ZC3HPKmTP0QCmiIqMP6VgsUKIuq5PKClg6rkOrZdqOJFFVhrx2CxEo08pZgfasceDM5hwk+h+jj48MQBP+1117j/vvvH1Lo5ZbAC2Wsr9LoSAkMQ6EkqNKMgyMgVqLgAKm0oCyqEAlprFxvopZrNNYGaO1wF0wBQUOhulynvEQwqVIjnnJACFRVIZN1WLPB4p1lOVQVqss1ZjYG2K4h0G0lrOMotHTYNFTrVJTrNLdagEIkCB0JOaC0dzoYhuI+FQja4oLJ1Rr1bpqEgK6QNx1QFJyAYO1Gi1RWFJKzrWsxaYk77kIwmTsorCkYmkb7atjQalFWMvC5EJ9xTBowAQPww/59BsmgBX/y5Mlo2gilYxxFpjcEsIWGZUNnykLVBKs3WDS1ydDMihKF8lKNTNaholwjmbVpaTPJmSAQqO4K25wpMDSwHQXhCEzLobnVZl2LielA2NBQdUFn0qGlwyaZdth9+xAzGgM05KX7JZt10A0FQ5NWfIublz9WqqJrCqmcjBBKZwSREGxXq7HrzGBhviBvCQKGCgo0tUqxryiTOfFNS5A3obJMJZUR7pOEvAdepFJbQta9HY1oHp9BMlTB7gRWAOsByz1+ErAdMPwMJD4ThEErwI033sgtt9yy2ZSh44VQQE5slkZ1qit0tmswKIvKnPHtScGGNumKmTXZoDMjWN1sIRCUhFV0HT5tyrN0dR4HOcFaW6GTzAjeXpajud2mokQlVqYSCWqoGnR0OixZmWf52nxBYOuqdGY0BqgoUTEtubDK0L0onwBV5SqlYY2SsEJZicI+O4fYZ+cwsVI5FntzBfVVOpWlKk1tFpFQMWOp7cj5CdMUVJarpHMOObO7795yRi5Hvs8Q6QTeAV4GXnF/3nG3D+TY/wCfACGgGgi6r/8zwHP4+DBAC/+4447r9nrNmjVMnz6dPffcs0cOaUVR+Mtf/jJiDRwJ8qbDhhaLvAXT6gPk84KNHRbtnQ6maWHaDroqCAfBtiCRc0ilHRnKaYOZl/H5ZVGV9RtNVBU306ZCJKwQUGVY5eq4RUenje0I4kkp0pOqdRqqddYCk0sUHEfBtA06U4J40iaZVdiuwWBavUEmK0BREEi//KZZMJMZh4ChkEwLFMVBAKm0Q2dKrhWoLtfIWzI6iS4+e10duRz5PkPAE+w2oBIp2lmkYLcDe7N5K32Fe+wUwIu8KgGiwGr3/T16P9THpysDEvxEItEtt/P2229f+Nsrezie6Ug4qBoEFDAtQVO7Rd4UVMc04kmb1oRNbcygskwDRaE1boGqUFmuURrREMD6jRbrNsrQSl1XiIYV0jmbUlsBAa0JB8cBS7ra0TQK4ZKTa3U60yrxpENZVKEkpBHQBQKor9LZYVqAyjK9W4x9b1kwVRW5YCxu09RqkkgJFEBRIe8WX9FUBc/j5gVQVZYNrYqXzwgxHMFOI904lV2O9VDc7U3A9vg+fZ9+GZDgv/TSS6PcjNHDqwNbV6nxyRqT95eb5E1BaVSmLc5bAlCYVq9jOyo506a2UicaVtjQZhMyFJIZQda0Wb/RZkO7RVAD29YJBWQx9FRGkMsLIkHoTAt0Q668NXRobrcpTTq9pjTebpLRbcFUJKQWfP+9raINBVQqSzWWrs6jKQoN1RrRiIppCj5dn+eDFXn2mBlEVxUyOYd4p/Tj1FWNXI58n0HSl2Bnkb74KJsXbBNIIr+pWeTTQVdC7vt+gk6fATBiSpDP58ddiCbIRUipjE086UhrWAFVgUxWDgbhoEJJWEFRVAKGQlOrw9RJBsGAgqbItAzxpI3jaMTKVNqTKqbp0JG0iIRUqvKCdNYmaCgkMw6arlAb0wgY8tvtFRZvqNU3K+Zd6Wt7OuuwpsVkQ6uFpipEQgLTEkTDKlXlGoriYDqCjqSNrinUV8koneEmYfMZBiZS2D03ThzYiHTz2MhBQAV2oafgdwIfAUsADen2qQIakAMF7jkNurnwfHz6YtBK8MADD3DnnXcWXr/33nvMmjWLSCTCYYcdRnNz84g2cLjoGsRTDk2tFq1xi7a4TWfGIZ1xCgVRdFWKv6JIMZY+epVoSGV9i4WqyAnRkrBGrEQjHFIRArI5h3hS1sntTNtkc4LJ1Tr1XSzqTQuLexPJoYBKNu+QzMgImt7o+n466/DeJ1nWNltsVx9wryFY32KxbqNJTYXBZ3cP01BtsMPUALtvH2R6w/gbgCccBpAH3gcWA88gJ2ybkROvAjkAvE/3yVfP778WqEd2VAPpAloCpNxj25DROr47x2cADNrC/8lPfsK3vvWtwuuLLrqIQCDAHXfcwZ133slVV13Fb3/72xFt5HBJpx2WrzURuFEymkBRFDoSNm0OzJhsEAmqtCUchBBYJuRUh7wtQzIDhoZpCRwbGcGjKQQ1qeBtnTbCUQgGFGZOCbDXDqFu6Rd6KyzeXz4c7/31bXKuIWjI8Mtk2qEkolIW0VA1haoKDcsSdKZkqGlpVKMjaRMMyAGlazEUnzHCRoryUsAbf2uQlr4FhIGdkK6frr78rn7/KnffBFCGnOhdBlQgXUXbbYHP4bNNMGjBX7lyZSF/c0tLCy+//DJPPfUURx99NDU1NVx22WUj3sjhYNnQ3mljWg6aBpYlfe6GboMi0ytkcg711Rq2A8JRaU/aVKoqk6p0NFVBUSCdsUmkZPz7TtOladaZFLTELYLuF1mmMy4qe2+FxXurhds1H87kWp1P1uRZvcHCdgQoFPLw1JRrBAMyT39Qk+mbDV3m9EmkbEojao/BxWeMWYEU9UZkGGY5UvgVYA0wDZiMtNY9Xz509/tHkYPCOqDVfX8DsCvSFeTH4fsMkEELvqqq5PN5QFZ/MQyDww8/HID6+npaW1s3d/gWJ5WR4ZehoMxNHzQEliMwLenuMQwZP7+xw2bWlADVFSFWbbDIZB1KI9LaVhQFTYHqmMq0Br2QU2dDm0necRCOYF2Lxbr/pKkq05lWL+P+8yY9Cov3Vgu3a03bDR9l2NBqY+gKJREVQ5NzA5Zl0dxuUVOpIdAIGKJwvK4rZPPSdz+zIeBH5IwXvAnbyUAM6Y5xkJOsGlLsy92/dbpPvnp+f48oMAtp8WeBDuQg0JvY+6txffpg0IL/mc98hl/96lc0Njbyi1/8giOOOKKQwnjVqlXU1taOeCOHh1zwZNoy3YLtSIs5k3ewLGiN2ygIJlUWC45UlOo0tVg0tVlYFnRmLGY0BKiJFbNlZrI2S1flSaYcykpUGmsNOtM27QmHtz7OM3WSwz47hJg+uZja2IsYKouq3cJcQa5fCAUU3vwoT0lEo66qy4AQktW2EklBMi0oqYL2hEM0rGBoCqmsjMWfVmd0G1x8xpiuE7Y6MHWT9w2K0Tre6llv8tWLyinZ5JiQu28pPSdq/dW4Pv0waHW46aab+OIXv8gee+xBaWkpzz//fOG9xx9/nP33339EGzh8ZCbMsBCksoKgrrpVr1TSOZvqmPR3T63vmU9epj9QsGyV9oRDaVieK28Jlq7O05Gw0TTI5R2yeQVNVaivlmmSdVUhFFS7ndOywbLlSt6cKbNkahoEDdV936Ez7TCpSu82IBi6QmlEI5U1yeQENRWyYEo8ZZPJybz6MyYH2HVmcMRy4vuMACaQQ1rj1Ugr/V2kRS8o+vCzSKt8JkWLvB65MCtK93BOb6K2674w/MVdPhOCQQv+wQcfzKpVq1i6dCkzZ87sttL2nHPO6bYoazwQDevUVKo0bYSSkELOFJh5uUCpPKrSmVKYVK0SDetk89JSXrnOJGsKyqIqlWVBKspsVjWZrFxnUVstUyxblsBxBEIoOAGFgCFX2ybTcmLYtgVNbRYNtUX/va7JVbAr1+XJmkKmTlahPKpRHZMiriigqZuusJEplTs6bdoSNgJBfbVBaVSho9Nh2iSD3WaGfLEfLySR7pv1SL/7RqT7xgvHdJAunixyUHgd2Ifuk6/bIYV6Nd0F3BP0TSdq/dW4PgNgSM//paWl7LPPPj22H3vsscNu0EijazCzIUg2myOdk6UOdV0WIElmBLFSlcaaAKub8iQzgjUbTDqSDlMn6Sio2A6EDJWdpgVp7rCpLtOoLFf5cGWW5g6baAjSORVDV4gENUoikMpCIm2TM7vnsHEcWUpxTbNFfbVGJCgnYDd22KQyNpquUFehkc45lG7i9gnoEA4pNAQ0dFUtxNrPnBwY0WpXPiPA20ixrkROqr4L/B05ENQiQyrXIf33uyFFOUZ3C7wUaZWvQE7mJpEumpn0dNH4q3F9Bsg27/ANBVS2awiQNQWJpO2KKxg6TJskffIK0BJ3CAXAFoKyEhl/v6rJJBKSkS+OEDgONLeaBA34z4dZEmnHTc6mEAzIAis5UyUcVElnQVFEt4iZphaLgKFQX63J98MCXVeIhGB9i8WUWp29dgjx3oo87QmbaFhFIMjlIZuTAr/vLmFmTAmMWD3b0a6NOyFpp7ulPR14FRmK6UXsbIf89sWQIt6JFO6uglyKtMq3Z/OTsF3nCnrDX43r47LNCz5QKEqSiGpMnQTSEargCOhM2RiGQm2FRiYntxsatOcdmlodYiUOkbDMg7O+xSJnyugdyxaEQwqmLVfaOo5KKKiQzDpk8zaRoEZ1edGd403Y1lTo7gBjk0jZ5PICTYPGOp3SiMr0yQFMGz5Zk2fVepN03sFxBXnm5AC1VfqICPOWqI074ci4vyvobmm3IoV+b2Rc/gxkPL0XitnqHtOXIPdnlRv0PckL/mpcnwITQvAjIZVZUwN8ut5kY4eFEDIvfUlYWvfBAGRyAttx0FSZc76pxSJr2mxoE4X4dk2Vk67N7RahkIquypj+TA5UzcHJyrKFhq4wpU6loaZ4e7tO2GqaQmOtSt7UsW3p89dU6EjaBAyVmY0BWuMyrUN1WCccUCgtUQnoCmubZUqHwYpyV0vecfqvjeuL/hDwBLtrrZks0uLvGlXjdQsFaeE3IQeAoQpyhMFP8vpMSCaE4HvWbFunTTYvI2hKwjLj5ZKVFraQlr2mQnObyTvLcmiqgmk5ZPMQMgTJlMBRBIamoimy1KEAais02pMWqbRMrxwwFMojCrtvH6KyrKvgO5gWJDM25W4N3IChgJtzJ5NzCoumEkmH0qjGgXUGjkNhcldVocOtbzujsXvahM25Zlauy9ORKta6TWdlZs0pdUavawF6O7/PAPAEO0dxVa2F/JZVI/32pcgoHQ8dOSBUMTxBHuwkr8+EZJsX/HTW4d1l2W4rVxHSn245gkweqmMaJSGVVNbh0yZZ4jASkqtyNQVSOUE2KwiHQNUEiqYQCalkcg5CgdKoBsImHFKxLEFJtFiesKvrpCVuEu90mFrnUFNRjOnvuiIXKMTqCyH/lsnbZAhnwFBY1yIK0T+bc81YlszRs761WN83mXH4ZK1JeVSltkIQChbNQUVRisne/ApZgyfs/m5HulYU5DdMQ1rw65ADgO3+5JA5dSoorrAdKoOZ5PWZsIyo4D/44IM4jsMZZ5wxkqcdFivW5Vm6Kt9t5Wo65/DpRpOOlMN29TqWJXPKd6ZtcnkHHIf2BGRzMrNmMOi4C6NkQfOgIbOuhUMqeRPSbpbMoGutBwMKK9aaWLasJ5szZdbM7eoDLHdMPlkrI4FmNBiomtKtyInn+tE1ZO3ajKxdKyOLBPFOh/aEzQ7Tgv26ZlTFAuSAputSvAO6FPWcKWjpsGms6y7qAV0+AfgVsoZBBd0t7Qgy6+WOyMicNNLfryFdMAcCdSNw3YFO8vpMWEZU8M8+++xxJfjZvMMna/KALGZiuiLXkbRpSzpksg5rW2xmNGg0t9msbbaIJ23yjozXV1XQdJA5yAQtCZtwwGFybQBdFbTEBSUhhZKIzIjZEpcpEdZutFjVlKQ0rFBdoTNzcgAlqhINa8yaohArtVjVZPHx6jyTqnVqYjrT6g0iIZlBU9cU1rdKsfdq1wJoAQUhBC0dDq0dFsmU2meahjUbLZIpmQKj66Jeb74gGFCIp2xqTK2w8At6T/bmM0g+gxR8z9KuQop9CJlmAWRoZhyZ6ni3Eb7+RBH5gaaQ8FNNFBhRwX/hhRcQXpmlcUBr3GJDu01FqSwS0tRqkcnJqJigrqBHFDoSDvkauciqpUOmOg7qsjC4roGmqBiGkEVOcqAiyGRtkilZZCSTBdN2UFApCytEIwq2I4//NC4TtBmGQiYnmDrJIBRUqYnJcoWtHTZ5U6ZAbmqxChEyJWGVdz+xqYlp3WLxhRCkszCpUqct4YDi9JmmIRpSWLvBoXyTexIwFMpLNDa0Wuh693KIvSV78xkCJfS0tG2K7hZv26707m7xBWrzDDSFhJ9qogcjKviHHnroSJ5uWKxcl+fTZkFzq0k6K4XTtgRVMc11V9g4AoyAguW40TWGSlW5zsa4TSSkEAlAOi8wLQVVE1iO9Ol3JB0qyjR0S8G0HfJ5QSAgQJMrbQMGZPKCjqSFukGgqgqZrMARgrpKnXUtFtmcIBSUeXt0XekWIVMd0wgZCp1pmbPf0GT4ZyojiIYUJlXpdCRtUBQCpT1X5QKENyPY1eUa7QkZFmpaDrat9Kif6zMCbCrW/blbfIHqn4GmkPBTTfTKiHyzV65cybJly9h7772prKwciVMOmUyuy0Rl1KA6ptPe6ZBMW26ueYVcTkbLtHTYlJeo2JZOW8LG0EDo4DiCkKEQDquoqkMibZPPgSXAFIDl4Ngqmi6LjkeCCnlbsLHNodQtVJ7PCxQUsjmH9rh0zzRttKiMqVgOVJSqlIY1AgGFoKF2i5BpqNWZOsmgLW6RTMti5aGAQk1MpmAQQMBQQZEumLDWi+grUF6iQkextq1HMKBQXqJSHdMApbBqt2v9XJ8RpqvVvuljF2w5gdranx4GmkLCTzXRK4MW/EsvvRTbtrnjjjsAmTDtlFNOwTRNKioqWLhwYa9pF7YUG1qLE5W2UKir1GlL5HCEIJlxSKRsNFcgIyG5KjaRFlimTIRmOaBrKqoq6EhYpHPSPaOoEFRkioOsCcmsoDSkkEzbpDICR4CqCpIpGbap6W6IZUaQM01qKqSLSE9BTYXO+hYbKyYXftkhGYHjRcjEylRsR9CScAgHQUUlHFKojmkE3YHBi+hZ22IRCiiYFoWYfkOHRMphRn2A5R3Iga1UI6AXLfmamMwOqqpsdqWtvxJ3mAzUah9tgdoWnh76SyERQQ6QsX72m8CpJgYt+I8//jjXXXdd4fVVV13Fsccey/XXX8/ll1/Oj370I5555pkRbeRAyeYd2hLyP7x+o0UiY5PJyWRnqYxDJifIZAWlJTJapzqmUVdlkMtL33gkqIIDmZxNMlvsK7oGeVNa+EKAqsmngEQG8qbAFjKax7ahM+/I/DsBOfmZz4Npgq5bBHUV04S8LcCGT5sEG1ot6qp0Ksp0KkpVhBAs/VTWqi13o2n0ALTHHVLpPOUlcg7Ac7ts7LB4Z1kWx5G+ewQoqmBqncG0BoPlH8q00B0pUSiePhBL3l+JOwIM1Gof7Vw424p7o68UEl5uog3Iz5hCZijdpY/zTOBUE4MW/PXr1zN1qkzs/cknn/DRRx/x4IMPsttuu3HRRRdx5plnjngjB4plQ9p16bTEbaIRWbpQVWD1xjwdndJ1oSjSreE4CrblUBLWaKzVCQUVPlqZY32bhXBssnnI5eXCJ10D23WNaAok0w6aqhANqziOtIJNU+6LKiNjbEdIH7wBCOhMO7R22lRZGtGghqo6qGiksgJHWHSmFHJ5wXaTYcbkALUVMqoonrLRdZl4rTqmFVbCprPysyru4gKBbKC6iWpMbwhgC23Alnp/Vbn8lbgDZKBW+2jnwtlW3Bu9pZBIIWv8xpGL3aqQax6WIifK96JY8N1jAqeaGLTgl5eXFwqVL1q0iMrKyoILJxgMkslkNnf4qKJrUhQBYqUqtpC+9WTaJhxQ0TUIBKCqTCMUUMjmHVass5nZqDC9PsjGdptgUKW+SmdDu0I6YxWKmgcDMjxTEdK9I2P0BYEAREMqHSkHtSD08okgEJAuIEWR4ZARHZy0QBMysZqiQM50CAd0LEtgmgLTFtJKVxRCQYXGOpUaU8O2wbQcQEF1tbapxSJvwe7bB8lbopBfP6C71bNc9xYMzh3TX1UufyXuABiM1T6SuXA29dFvS5k0e0shsQ4p9nXIRWyNyNDXFPAWMjvpDl3OMcFTTQxa8A899FB+/OMfs2HDBm677TbmzZtXeO+jjz4qWP9jzcZ2i1RWkLMc1jRZZExHTnQK6Ew5JDMKwYC09G1HPh10JG1U4KDdIzR3WLz6TpqWhE1AUxCKguNIwQWBogICGW0TkJa15rl+HLcRDtK3D5iWcBc9yapbjlAwVIV83iGRdAgEFLfgikoq55AznUJ8fNBQwQDblhOslt1b9azizKy3YrYtMXiTsL+qXP5K3AEyGKu9nOHnwunLR185iHZsDXRNIRFBunECSLEvQ65rACn6G5DWfxXyHvupJgYv+D/72c84/fTTueKKK9h777258cYbC+898MADfO5znxvRBg4Gy5bx9Tlg3UaLSFjFtgXxlEU27+AIlVBARq9oqoItFBQE7Z0Wq5ryaKpCrFTDcqCyVGdSjUHekt+5XE765jVFLsiyLUBAOASKqqCqgkhQxXEEnRkvzFMhqMtBIGfKfD3lJXKACRuCnKkQMOSTRk2ljqbJuYW82T0+3qProqjCilwV1mwwiafsbgVVKkpVLGfwayK6JnnrDX8l7gDpzWr3yhnq9CxpOJxcOJvz0a8H8mw7mTS7ppD4BPmZq5CWfQNF900U6ab6AGnte089EzzVxKAFf/Lkyfz973/v9b3nnnuOUKgvU2L00TXIWVLkQiGF1rhFOivrwOoKGIbMLb+hzUHXpThmcgIhBFXlOhWlGtGwQjwpXUABN1VCR6dNXi7YRdfA9Dwl7kStZSuEQ1BfpZNIO6SyNqGgQNNUdMPBchSCqiBnCVJpC1XTSGahLAJ1VTqKqlBeosuBxBG0dthYto18NpBsuigqm3fI5KRLSghBeYlGxI3X39hh095pU1MxtHuoa0qf4Z7+StwB0tX9AFJ4W5B+ZQ0pQAdStNqHkwunPx+97b6/rWTS9FJINCA/QwlS9DdFQ7pz9qY4qG1Nn3MUGFYc/po1a1i/fj319fU0NjZSVlY2Uu0aMpYpZL4qRS5QylmC1rhJIgV2zimkOM6ZIBxBQIfaSo3pDTqr1lm0tAvyliCTE7TGZXZNVZWROUKAowAOlITk90jXFcpLVTRFTuCatqCsRMGxpcvIESqaYiMUBUOBvKlQEYFQUEHXZXrlsqhKIm0T0hUSGenLf/OjHDXlNvXVOqGQ2m1RlBdBs2Jtnk83WNRXaSAUYqUqwYCKoTt8ut6iqmzwLpdQQKWqTCuEe2660tdfiTsItgPWAC+7r6uQ37gW93UH0jr3xHwouXAG4qPvcF8vQfq0txX3RjXyXn1Cz8/fdTCr3vJNG68MSfDvuecebrjhBtauXVvY1tDQwI9+9CO+9a1vjVjjBotlu3H0yP+3riuoioKmKWRNB8sE3YCIIlAFCEVOxKqqSsSd1F3TbDO5VkNVFdoSDqqioCoQCkAwINBVBSEUdANKIxrZvKA8IounZHKCfB6qyw2yeZt01iHnumc0FSZVqJi2Ql2FjqKpWJbNxjaLdNam0tQJBhQqy3SCMZWmVpt3l+dYujrPjEaDXaYFmT5ZTpR+vCov5xYMlboKnWxOkM1ZpLMKVTEd04Kqcg1VHVqaC69gTHO79OV3jd/3V+IOglJkIrUoUrzzyM65PdL6b6P3CJnBWKH9zRXYSCs/hhT+1W67prBtuDf8tNCDYtDf3Jtvvpmrr76a008/nZNOOom6ujo2bNjAggULuOCCC2hra+PKK68cjbb2i2U7WK67paJEI5kVpHIyD73qumMUISdSvcVXKIJcXvDJGhPD0JhULVMn6JrCpCoNsEmkIW/aWJaK6ubBV1BwLEE642A7grpyA1WDaEQlGlSIhHQs2yRnOijIgaU14dBQo1NdpdHZKWhplwNCvtNB1RxmlktHqmkpTJ1kMKVOZ0ObjWl6CdyKETRVZSodCZWyUo1kyiGZtmnvFKDYzGwMUFGqks4ObSbOKxjjxeEPJn7fpwtpIIF0KXh+e53u4jzcCJnNRfikkPV024BZ7k8Hsph6iK1f7MFPCz1IBi34d955J5dffjm33nprt+1f+tKXqKur48477xwzwdc1lXDYBhOqKzRKTJV4p4Om5jBUEKoMmbTd3PKhkIJlyWRnG9otZkwOoKoqGztMEp0CyxF0pgSBoEJFmUE+Z+MIBU2Tk7CmO0kqp34FibRDrFTD0BUc4aAoKtVlKooqsB1BRye0ddgEDJugrjCtIUBZRKW5zaSh1iCZFrQlLLZ3V8DatkJVuUbeFKxtMRE4mJaMlNFUGZ6pKVBbqVNRplGddcjmRSF7p672nmdnIERCKjMaAzT4K22HTlfru7dvWgjp3mlBuh2GIvqbq3a1Fhm2uCdFt0Y10rW0NcXf94efFnrADFrwE4kERx55ZK/vzZ07l//5n/8ZdqOGiq5BdblOewKWr81jOxqpjE0ybZMzQdehJKy46YJlOgJNBcsStMQtDE2hI+WQSsuIl1BQJRjQSWccHEtgCwXHkb5s2wYtqDKpWsXQFTo6HWzHQUWlPeEQTwqCbsx/c7tFZ1pg5h06bYXO1TnKSnR2nh5w1wZolIQ10hkTyxasXJMnEFDImZDMWOTzMLlGp6nVJhqGHaYECQdVyks0NrbbBAyBcASprENLhw0I8qZgu/rhC7Qv8sOgP+t7GdIqBSlaA011kEbGnoP0x/fm1ogDHyIHg8mbHL+1xd8PlG3lc4wigxb8o446iueff545c+b0eG/RokV8/vOfH5GGDYVQQKWiVKUd3DTNCrou/fGoAk2R8e+OG3oYNOTkrQo4tkY652CaDtGwRkenQzrrUFuho6AQT8rJW0fIaJtISKUkpBIKquRNQSJlkc0C2OTycsFVJKDJqJ2cIJ12UHWZaC2VFeTzNqubTJp0hfoqHSEgbynk8zaJFFTFVOmqycokbDlTENIcOhKwfK3JrCkK1eUa6axDc5tFR6dNMiNX/9qOoCyquQu1ZEI5Y2sJu9uW6Mv6TiHFeDkyT34jA0t10Am8B7yNdMuAnITdA+muaaHo1si77+1Cz5WmsPXF348GW3siuSEwIMH/z3/+U/j73HPP5Vvf+hbNzc3MmzeP2tpampubefzxx/n73//O3XffPagGLFu2jNtuu41XX32V9957j5122on33ntvcJ+iK+48ZVW5TiCgk80LVq5TMU2bTB6E6hAKKDgI8pYMsdQ0KI0qxJMy/XA2D4YuiCdhdd4kFFAJByGeFGTzDllTDh7ZoApZ6YYRAlRdWtmGCoahkbMcGQsvHEwBISEQioqmgXBkPh7bVnCEIJMXpDM2yZyDELB+oyBjOqhARZlGJisIBlSmTdJZtcEiVmoxpS5AbYXGyvX5QgnHsqiKoUv/f0lY8PEGmVCurCS42dvmM0r0Zn0vQ4r9DKSFrdN/qoNO4BXg3+7rGmRf3wi8gBwkPkfRrWG6+/YVPru1xd+PJNtCIrkhMiDB33fffXuE591///3cf//9KIrSrejJF7/4RWx74Kty3n//fZ5++mkOOOAAHMdxV7MOjWzeIZ2T7ayO6XRmZChladSgJW6jmuDY0krXVMjZ0p8fDikYmkrQkJO82Zz07wd0QTzlkM56TwfQUKMRT8oShPm8Q3uHjaLKSWJHyDQOHUlBOivImnJhlOUolITl00A255DLCwIRVUbZVMq0Cao7B9DRKSdINfeJJGDIxVwdKYdoRKEqJnPvrGqyCBgKza02ybQgVqJRFpUVtkwLVm+wmFIj78W6FouaKotYiR9ds8XZdFLRs8J3RIpzV+t7c66WFciwyiBSnLrG2zchSyg20n2g2Nzq3XXAtOF+uK2QbSWR3BAZkAK8+OKLo9aAL33pSxx//PEAnHXWWbzxxhtDPpcMy5SDT0ONTian0RY30TQZXqnrgqyXvVKTSc3UgMxNr2neilpBPi9IJGXBE1VVCAddl0pAQVU1SsMOoaBKzpI+c0MTtCZAQbp7hIBU1i2YEpLx/oYuq1DpmorAJhRUCYdA0xXyWYfKco1wEFrbBUIFgUJZWJMTy7b00dsOGLrKjAaDletN1rdYtHRYGLpMuVwaVknnBMmMzaomhxVrLWaVwdJVORTNoLHaGZUoGz+Fcj90nVT0YvAb6Xsid1NXSxop+BYyvLKreCtIP34CWEn3gaK3p4t1yIHDdq+fZMJYt8C2k0huiAxI8GfPnj1qDVDVkRMIXaMQmbJuo8WGNos1zSYbWizypkA4xfBMVZOVpHJ5KZBgEAoqCKHR3pkjlYFQEFRNxtrbDgQDDqmsg23KXDfNHTa5nIznDwdsAhqAQjQs0yWInEycFgwqrp/fIRiQvvdMzmFDK6QzFqoKLe0WjqMwc0qAkrBGU7uFrsp1BCVhFRGSKRpsR650ravSyZmCSdUaTa0OugqtCZtsXqCpMkoonrSgTK4mVnBGPNuln0J5kESQUTIlDC7VgQnkkJNNvblggu57GboPFF2fLlYAHyOfBEBa9zmk8E8Q63abSiQ3RLapZ/xQQCUSlP/Jj1Zl2dAqCn73TBYcoLxEIRyUeeZN0wEFLEumQQ6HFDa0miQzcrFUOgvRsI0QMgFxR0IQCpqkM5C35I+wQdFAVwFFIRZVcVAoi8psml46hmwODN0hFjVwgFTaJloCeVOGcrYnBQqCyjKdqfUBohGV5jYLVZEWdGfaIagrNLWYBAMqk6rk4qhYiY5lWixZmcMWcrK2PWFjOwphV3QNXcE0FSbXaiOW7dJPoTxENhdG2VeqAwMp6g5S0Df91ubc98J0HxDS7vY6ijl16pFzB3mkuKWR7qW+FoFtS4x2Guq+yHT5PcZzJkMS/AceeIC7776bpUuXkpWhKd1IJBLDbtjmyOVy5HK5HtczTZN0Vn6DPlmVIZ5WEcjFUQgw3IVXlqWCEKiKwAjIOPp0NodtaeRyJmFDIAzp71dQUBSBqoClgGUWUx7rbrUoVYFwUM4HZHMKsTINyxLESmVUTklUoSMuSOccMnmTlFurNptXCeoKpWGFkqBFHJu2uEPQkIXUc9k88YxDQFeJBgVhQ2NVU5bamM4OUxRSaYdszqEkJBDCwrYEubxNNmujqQLTXa1VF4N4MkdVuaAkBBvbc9RWyEngobJ2Q554UubnVxR5nYAOVWXQ0pFn7Qab6Q3jJ4WyaZrdfg8UY5Pwps31PV3XB3aNKUiBXY1ciRtEina7+3oK3UXHQFrkS5EZIOvdY0AOEp3Ib/JUd9924FOkoFtIN07CPabW3VdHDhAbkYPBJPf3dHf7MBnq/R5VFOQkdobeI5cyyPuiMDKinwQ+BXO9ey/+Zcr7PI3en+56YdP+N1wUITateLp5HnzwQc4991zOOuss7rnnHr7xjW9g2zZPPvkksViMM844gx//+MdDaoznw+8vSmf+/Plce+21PbY/9NBDRCLb6LOYz5jgzS95+H3PZ0uyaf8bLoMW/L322ouTTjqJK664AsMweOONN9h7773p7Oxk7ty5fOUrX+GSSy4ZUmMGKvi9WVlTpkzh4xVNPPL3PLtX/YdP4vvQkpDZI5vbbZJph7y7+KqiVMVxZMrjcFDBcuPWy0tVVq633ElWmS0zGFBo67TI5+Qkr2VDaVhWOsmbDpmsvH2GDqi4eXc0qspU6ip1HCFdRYmkjEDaGHdkcXEEuqYypVZH11U6U7ItHZ0W8aSDZQtqKw2CAUhn5KKvqbU6O04LIpClFbebrLN2g01rp01rh0UooJIzHZavzVMSUZlWp+J0/Au75CDaOqGsRMOyZOTSZ2YFmTrJIBwcvJWfyjq8vzxHeVRD6yXsz7YhnrLZdUaQ6Dhx65imyaJFi5gzZ86grKaBWPhTpkyhpaWFcDg8sGu4lh9NyJh8FWn5NQKtFC1zA2kJxpFPAEGktb4UaY2Hgd2AfZDx9iXA+0jXzGSkpZoC3kG6K/6DfDqY2aUttnv+mUjr9iB3uxefPkRrf6j3e9RJItcxeE9Tmz5dfYYBW9+bpcv/wcyaLGpfxJyKORghQ66A3g7Ytf/TjPS9G7RL5+OPP+bggw9G0zQ0TSs80paWlvLDH/6Q733ve0MW/IESDAYJBnvGlaezKvFOBapA0QMEgwq5tCxaLnAQyCiddMbNeolGe1JWsFJ1jUBIRVHAxMHKg4PMnRPQDfJ5h2xeVr+yhIqhQjCgEzAcklkHXVfQVPdHg6n1IcpKVNriNm0JQUlYI2c6VJTJASeZcUjnBB1JjeoKnVBIhn8ahkYkYtPRKUDRUFWVumqVshKVdEbQ1qlQX62TNW0i4SA7bifz66SyWTa221SW6Uyr17EdWY0rC6zaIAgEDMJBg07bobJUpT2pYq8TzJqqDdrXHhIOAcPBFgoBreexeUsWmwkFDQxjfAi+h2EYw/oS9dX3up53s9foRAqwFxZYgfwnrQD+D+lymUwxXPA1pGh/DilE5UiXTwY5OOwO7O+eO410+VRSjL83KGbZrkVm7/TOD8XJ4ATSnbOSEY1PH+79HnEqkAOkFyabYuRz73j/hxByvUUr0AjGewZGpSH/h83I1M1b+KFwSCUOPQtn8uTJfPDBBxx22GEA2LZNa2vriDZwMGi6QqDwiQThkMxDo2tS7C1L1qVNZaUP3rRkds1wEBzLIZeTIZh2RkE1oCyqEgoqmHmnUE+qPKpgIzNwooBjK4QCGoriYOgKpRHplzct4frxdabUyaInnzZZmLbNinV5OUkcVFiXl5VUSiIqnWmbsqjGzMYAy9ea1FTohdw8AAoO8ZRNaVQp5KQPBWTOm1iZyker8mSyDtGwSlOLRXN7njLkuoO6Co10VlAWUZlcaxAcRrlCP4XyMOgrLFBBCm0dRQvT87WDnOSNUMyp7/ng1yEFJkLvk5IhZGTQGqR13+4eU++eoxnpzy5HJlZrYtuPTx/t3DsmcgBtQd67rvMta9xt1YzJKudBC/6+++7LO++8w1FHHcVxxx3Htddei+M4GIbBLbfcwoEHHjio86XTaf72t78B8Omnn5JIJHj00UcBGQ5aU1Mz4HOVRlSqK+RHSmVtArpOWYlKzlRoapWTqkJAznVrIKSLx9Bl6KKDQ3VMRVU1hOOgCFkcxXZsENK61zQFHTBNga7LDJ2qcKN28jbZjEMkoqHrNoYOoRAIIdMnIwS2W3s2lRE4jiAUguY2QUuHSqxUDhgBTaWiTMNxi6d7GJpCJicXZ82cHOgmqJVlOp/ZXi2ESZaXqJiWAiZUluuomkJNmUZ1TCPkunGGU67QT6E8BPoKC+xACkE1cjDIIgXXQkbZRJFPBTVIK91AisVGpGDvixSsvnL3NCAFqBlpsU9Cio7nxvC+sk1sufj08ZDWYLSuayDdNu8jP2MKObi8gby/cfoOsR1lBv2tvPLKK/n0008BuO666/j000/53ve+h+M47LfffoNOrdDc3MxXvvKVbtu81y+++GLh6WEgxEp0dtlOIdssF0FlcjaOkPlzAjrkpOudcAhwpLUP4LirbTVV4AgoCau0xh2SGRsnIYWwJCJ9+oahkjdtbFumNgYVRXHQNXk+Q1MwVId4CpJpm3ROEA4oTG8I0BI3aWl3CAVB1xxaEgIjZVNboWMLh8qYQdCAeNqmrlLDzMPGNpvyUhVdV0hlHZJph2mTjF4FddMMl6m0xquvwK7Tg4TCRqFGrsdwyhWOVArlbXrR1qaitqkFnkJa26uRMfJe3vwZFDNsakihbkW6XFSKln8EOVAsR/r/+wr5jCJDL1PuPpOQg0cVUohKkUVaBhqfPhyx3prTGgz0c3dSXPMQcH8AVrk/tYxZUZZBC/6BBx5YsOJjsRh/+ctfChNZQ6l4NX36dAY5b7xZPjMrxP81y7j6rCkwTYvWuEPOzYwZDEDQkNY+lpxgzOZB0wRRXaG13cIwFGxHJRxUCRhgOzqOI9MXoIBpqlgCNFVgGIJ0GmxFTurKYiugumGfybRDZ0pBVXLkbQBBOiezbnryZjmyxGI2K1i53kLTwNAMhIBM3ibd6hANyuyZMyYH2G1maLOCWhBOR/7WdaWH2MPwyxUOJ4XyNr1oK4kU8U1FrY6iBa4gV7zGka6ZUmRsfJKiYEfd/d9xfy939w0h3QPrkcLzT3fbzkjhXE/v1a32BnZy9+0qWnE2H58O8ilkLXLuYKhivbWmNRjsIOV9HkFxIR1I4d+AHDQ62TpcOr3R10TWWOCJxeQanZaESmu76mbKFOhBmbQskSzmxVcUORDk82BoclI3rAjCQQgGZI6djqSNYzu0xqGmUmPXGSFM0yKeFqxclyORlgnY1Dwo2ASDYGiy7qzjQDgoWNNio6sqZSWQydik8wqRsEIooMqIIF0WNHccWYrLtKCsRCEaMmhNWKRzgh2nBthn53DhM/ZnHXtx9p1ph0hYjJqvfbDHb/OLtrwokAhF14wnAmVIazlO0cUSQgrgUmTWywzS8m9ACm0CaZFHkIPCh+7vHdyfpHtsC9JFk2Zw1a36cgV1fQJpQWbq9IqDVzN4sR6vaQ02Z7kPdpBKI5+6skiB9wZ/3O3eArq1bL2CP57Y0CpLXn1urxKWr3NY22xS0WbywcocqbRX0tBbVAUIaeXnBASCcnFWPgeq6qCqsgxhPi/I5GTVq46UTSJpEzAUNsZtOuJy4NA16RqyHZlkTQlJsc9m5XVUTfr7O5OQteTTga7KFbN1lTp5U84lRMMqkZBKrESma7YdQXmJiuMoVJbLiJrBWselYXVc+dq9ql21FVphEAprCqFhTCSPK9Yh/+lrKU6wVrqvyylG5MQoWvgCKaKK+/MpckBoRoZclrn7tCIfDSvd473QzRrgdaR47k1xsBhIdaveXEEp5FPCBqRQJZFCVeK+91n3mgMV6/GY1mAglvtgBykvDUaWYnit9+SkIf/fceTiK1/wh0c279CWkP8VywLTEkyuNehM2bIkoSMwTSn4dpeknLrqJU6DaFiRFroOqirIZqXrI2/Lid5MFta1yAlZ05TbNEVO2irISWDblpFAXtHznAmGkE8StigODh1Jh0jIoa4SsjmHYECjqkwnbwvqqnQ0za2H6+6fdlMxr26yBmUdz5xi0NKhjItyhdm8Q2tCDj5dnzgAFEUZ1kTymOMtod+ItMBjFCdYP6EYtRFGikLa3b8DKbh7I8V1GVLYA0gLfnuKkThZpGso4P7dgvT5t7vn8qx1ncFVt9o00dpqimmck8hBphT5RfnYPeZIpPANRKw3l9Yg6763Jd0cA7HcNQY/SBnuce3uNTz3Gcj/ieNep2PkP9JA2KYEv2u2TNsWsjoVAk2T5QBzprzfm2Zgthxp2du2jL7J5eU+qipkPVw3V44tIzGx3Tw6KjKGH+GeU4CwpIXv5OW1gro8PpNzF2e5g4TjDgBtCYu3PhY01miEAhpCkambNQ3pd3c7i20L0lmHlevytMYd6qq0giD2Zx2HgyozGo1xUa7QcovPlIR7v/5wJpLHHE+s8nRPYWwiBftjd9uRyEf7OFK4pyNFYAnyCWAmcgAwKbqEQD4h5N19TKTYT6boDqpy3/f2h4Fbz10Tra1027LSPd/2SGEMIq17DTn5uAK58GsgOWh6cxt5LqMW93iBtKR3ZvR9+QOx3Kcx+Nw7XoK8BMUkeEn3vaR7rTA9E91tIbYpwe+aLbMg8nloTznkTVGIyumNoCGFOWvJfDkCaa07NmiiUFcF3ZBCbpvSeleQYm95g8gmg0nekk8AQkixU3DDQwvvC9JtFoYqCAVUOtOC7RoM6WbCKUy2JtI2azeaJDOCgK6QzDiUR51CmOVArOPxYDHrGuiadCuFtU3NpuFPJI8p3hc4SHeLcCNS3N3oMIT7ux1pySeRwp9DDhStSMFPIgX3Y/d9h2Icd6W7787u9TzXUdfYfY++BHlT37UXnx5Fuoc0pKBrSAHzUjG3IYVwI0XrvL9iKpu6jdIUJ63L3XPXUpwYHs0J3IG6lxrou0Ql9F1Epgr5/4oj/3/egJF3fyLudTJscbYpwQ8FVCrLVJqQYh0JqXyyOsfqdTkyOXmPe9N8melSCrKugOUKvCf84PrhdQhq7tOpu2hLUbqIfS8IIG3KJwjVTbamKTLDJkKmPNZU6d5x1udlyuacLG0YDiiURzUMXfDRKhNDkxO7sRIVR8DGDumimTrJIBRUtwrreJtetOV98XPIf7zi/t2B/NJ7X/Y1SJEsRYpPu/s76+5TSnFStwkpHhXIFbUhipb3LKS4ZJGPm+uQTwubsqkw9eW7rkZa20uQE8MdSHGe5LapzW1LEClWOeSXoZ2eGT57o6vbyFsHEEMOJhUUn0BGewJ3oFkzDQaf2RTkZ4khB+4sRRGxKM7RbC1x+JvjhhtuQAjBf/3Xf43kaQdFXZXOB8D7y7Nk8xprNpq0xAXpbO9iD9JCz+alKAdC0rWTt6S7RnEP8hZombZroInu8wADwXL/4Q7SaBJI1004IH9btkMooLO+xSYUsqiOaaxab7KhXYrjzIYAHSlbGlqlKhVlsmB6S4dNY526Ra3j4cTPb7OLtrwvcBA52VmGtLwzyC+7F8iWQQpJFVIUVlFcQevF02eQopNBCtBGZFx3PdIijnc5b8I9R3OXY6uQFmqE7sLUl+/6Pff4Uve1iRT8JW7bpiFdEV5eHwPZkZvcNm03gPvjuY0+QA4omtv+RretXgbLkZrA7Sv6ZnPF5aH7ALnp3IZ3v7z719vnLnPP7z11bfoU4bl2Bh/FPmxG9Js1f/78MRd8D5nWWPq+TatvsQf3KduWbh1FgcoKhVRKkM65k7Ka65LxziPYrHvIQ1Pc/d1RXTgQMuQTgRAypUNQh9ISDcVRcHCoLJOLvj5alWPtRg1dlStzK0t18has3WDyST7PlFqDmphOOKjQEreJRuTq3foqHcuWgtyLx2TYjET8/Egt2hp3eInGvGibNqSf2nG3CaSoxtz3g0iRs5EWexVSXPMU893EkMKVQ4pkKzLp1g5I8f2YYjoEr86tRTE5m5ebp87dfwlF33XcvV7Ibc9KpCiHkQLsPZ1scD/Ddu42xW1zOTKCaCBx+F3FdyfkU07MvfamlvZA5gQ2R3/RN4OtSeDNbTRRtPw3F+ZquufMu6+9fcLuuWHkUjAPkhEV/OXLl4/oIqqh4IVl7jIjSEtc8M7HxQVOmyNkSAs/ElZQhUoo6IAiwzERkDWl60emU5Di3R8CmT1TRvzIiVvLkQNLzpQ/qrsGwLJsBAqZnKA2ptPcYRENyeLpLXGbVc0mQcPCEZDJyoyYrXHLnRRWaWpV5SRw3GZDu000pBKLDvIRpB82jZ83dMjmBCub8oOOnx/Ooq1xTwPSKpyFtGLLkIIxHdkpEshOaSGt3Q6kKKtIUWlHiqznAsgjffU1yAHEdPed4m6zkRZ4HikorcjBpJ2iP/ktpFh9hBTUdykuAlLd30H3+ge4n8ObNPbq8JYiI4La3PZ8BTmQbI7eFqGVu/dFp++onf7mBPpioHHzg7Hc+8u9s+mTRBr5f/IG81SX/ULuZ09SjNLagoyo4E+dOnUkTzdouoZlKgokkjJ9saKDahXnU1V6zK0SDMqShopQMR1BaUQjHBIonTaZjDs560bjeOGSquK6djYZ47y5AsVth65BaVgeI4QbFur+HTQUIiGF9rgARS64ylsOjpB5eCxbsH6jRSrjUFqiEQ2pctCxBC1xG9OEqhhUlqkEAiqZHMQ7bSIBhfWt0pmfyTmMRMJCL36+LKLQ2mETT9nYjqzs1RZ30HXYdcbmlmv2xNDkJPRIlroccz6DFJImpIjUI0VXQYqe565ZieyIjUiReZei4LQhBwaQou1NdLYgheVTd78qZGfb4O5ThRxoPMFfhvyWexOQSWAxcnDY0T3HRqTlr7rHOhQHlwBywGp2t9vIbJOzkeLlhYv2hbcIrauorqM4b7EDA/eN90VXwR1o3HzXqKSBWu69Lcrq7Ukijby/Xhimpw+CYmqMvHsvtjCDFvyFCxcyd+7cPt//xS9+wcUXXzysRg2VrmGZeVOQzEA4qvSw8Ht7BlFVNyQwZ6OpKqblkHcEQUPBtgSKBgHDjb13F1oJXB//JpOkCnIi1nGKi64cVMpLHbI5GdYJ8omiLKohcLAchXAA4imH5nZ3OGpycByFRNrBNiGgywLpji3XGIQMMEIKquJQGtGoqZT/zvaEQzIjmFSl0b4aVjWZTJ9sDMuK9uLngzqs3mCRygqiYYWIJmsOtCcc3l+Wp75ap7Ks2K1s28ayrMJv72/vtRCC6upqwuERKLM0Xiihp0VoI8Uhj/S31yFFwkYKjrdSFopuoAxSIOLu+5VIAS5HWvGvueee5W4LUKxetQdSxNqRydW8Orob3DZUuOe3KT5xrEV26p3da01HCmUzxbj+A5GDwTLkIGEgXTMN7jGbimI7vYtvxv0ZjG98UzYVXBs5mEyjp9+8t/DU4WTN3NyTRKfbnri7r2cDqV0+dzljEjIz6EseffTRXHDBBfzkJz/p9iVds2YNZ555Jq+88sqYCX7XsEzbhkzOIqyrqIqb897pPuB6aEBAkwnSLNsmaEj3RdYsWukgxV1TZbZN25bRPL3hAKqQrpyQ66fXdYFtq9jCIZ+XbQ0EwLJkXnndjdfP5iySaWTOHkshHFBwbBkOms4qlEWEzBHk+s9Lggq2raLpxYiXaFghnrKJuh3t7Y9ztCYUIiF1yPlqvPj5ZNohlRVUlKkI4eDYFoqwKQmZrG3OseTjJLOmGgVhH2sX35iyqXhsj/Tjvo5c0LSW4oKjTyiGa+bc/VT3pwPpv98RKcJt7jaF4gIskJZ6gGLopoIUdu/pLoscBLzFQKuQ1nup+3slUoBfQbpuaijOPcxy//bcPpXIgelj93rlSN/8DKRYe//2CnoX38luG70VwQOxsD3S7j143/3bE9xWt/2CYi6irvQ1NzCUieHNPUn8u8t1VIoGoTcouXm8RqKU5GAZtOA/9NBDXHjhhSxatIgHHniA/fffn/vvv5/vfve7NDQ08K9//Ws02jkguoZlvvdJho9XCzo6bSy774gaVYGyKJRENXaaFiSZdliz0SSVhbABwZBColPg2JBxRwvL7n/SVtNkojbvb+HI1MYKG0z6YgAAPZVJREFU8klBVd2Vu6agJKxg6CrJlC2jhQyFgC7IWdDe6aCoMjGbaTm0J93UzkBNhYauq1iW6FZ5ytDkU8GaZlsGCSgydbSmKQPOV+M4skKXZ4lnMiaJeIo1G7KEAoJ2U3QT87zpEECwsc2ktiLQa7K2CYfnavAs6+VIcVyLtA5XI61xDSkEnpWpIK1ui6JwpZGWdjXFEK9G5Dd4pbvds+IdpBCbwBEULcwsxfw+TciBZhLFFL7eCuA40mrudNu4I1K4NYoCuxw5L9BJMbzUS//QTjE8tK8UW96gsyPF1cje5+8ryqyrRf8xcoDbyb1fOnLQ8dYxrEMOUh7e6lab4YdD9hfHX0b3EMyuLh1vm8PWMWl7yimncOihh3LOOedwyCGHsPfee/PGG29w8cUXc/PNN495EjXD/UTL15qEgzrJtCAYgEy+9/11V5dCAQVVlVZwKuWQy0MgoJBIClJZN4bewl2925NNY/x1HWKlkM7JkE/HcVAVqImpcvARgpwp8+NXlOqYtoNpaqiajH6xbAXhCGlZC1DyECtTsS35efKWjCzKOXLVqvdkA2DaglTaxgkKGZQQUggYMr1zKKDQ1JpnTZMtK2P14mqxbRtn0+XIgKHkiHfmCFdqCFEUdCEEmaygvFQtzE+MRYzxuKHrRGUC6W83KQp1DikINRQXV3mdJ40UJRsplnnkvfTcHGUUF+xUIIW60z3eW9HZgRwkAu77SYoTpBHcR9Au5/fer3Hbvcptez1yIKlzr510j/8I6c7JIwunm0hrdynSQvfi9XE/a29pkbpOzNruPdpcTptO4F9IkffCXSuRg2cnUvij7mfwoo+mUHTzbEQOclOR7qiBRBb1RX9x/F4dAyhWFPO25yhGRSV7HjraDMmL1NDQwLe//W1eeuklXnvtNfbcc09++MMfjrnYA6xcLx3kMxsN2pMaLR024aBDKiMwu/jaVUDT5aSrUEBRBJ+sybG+xZbFUIQM6czmZWUsBekW0jS6ncejsEBLcdMwOJBIyde5HAgVSkKQzDoycZsiVwMnM4KNbXkm1wbIR2zWt8iVqLoq3Uym5bqiHMjnbEJBjfISlWzezcUThmmTAuRNOagIx6G1I4dpWlREZK9T7E6SibQr5Db5vMOyuEB1BmeJ11bqREJ52jptyqKynZYtxT4cUigJS/dZb3VuJxRdJyq91ajeIpxdkSJURjH2PknxMd/7HUJa7BZyEChFCqeOtPS91alrkJZ5BcUwSm9BUzXwtHvtScgJ3Tqke0hHupgqkFZzxm1PrfvTTtGdtAQppGsphogG3PNp7k8tcrBYixR9b8K5nWJFL4+uE7M2/UfVADyHdJWUufcojhTtSuTTk2fRexFSa9zPvNH9/CCfiKYy/HTM/cXxe64bkIOq933QKGbLNOn7SWYUGbTgp1Ipvve973HvvfdyxhlncOqpp3LhhRey22678etf/5qTTjppNNo5IDqSFmubBSVAXVUAVRW0l2qk0iahAFiZojA7AJa7UtaC9S026ayb6sL1rXUmpb8e3OMGEOWo4lbGUmScfTorB5RwQJ4knZWTvpoqBxnLgvVtgs5sjqAmc9eXhjVyFiTTlozXNxwsy8G0HDQVOpMOQcNBsW10B8qDGVJJm4+bLRCKdP/kBC2WQ30EIkYW0yya3IamkM2JgiWeM51CkrbNDQDlJRo7TgmwbG0e03LIZh0QJpGgIGrYtGw0iUUdNqxXME0T0zSxLKvX36ZpYts2pmliGAaxWIxTTjllcP/w8Yo3Uem5RiIUJ/Ca3d95ZGcpp5hIzaEo+F6ddAvZ+TqRFmoVUqSmIl057e7f1RRX8HpuFc/yt4A9kYLuCW8HUsRNpCB67dwZKWgV7jmXu9dd4X6GNRQnwnaj6LKxkGLsfU7vqSHM5idm+4uqec+9/iL3fW818nr3793d67a654hSLBSznuJ8Q8x934tqGs5q3v7i+LMU3XRQ1A3v/+v58bcGH/4ee+xBMpnkz3/+M/PmzQPg7bff5pJLLuHkk0/mtNNO48EHHxzpdg6IfF6GNAIs/TTL+jaF5rY8rQmBaSnd0iR482MISGZkjLxwZIpk05RWfFdL3jumv6wFtnATqGkyzt4W0kq3bflEoSIHmbwpEDjYlg0IUsJBCzuEgwI764CwMdMWluWg6gJVFQjHIW/Z6LYgGrVpjEFQt2ndqGCZeZKpPMK2UFSbRGeegG7SZq8jGN2IZdnYthTcXM4knzd5PWiTyVhksiaWZWLbJgo2KjaO07dQm6Z0AW1+OdvgaGho2PoF33O1BJFug9VIX7OXKM1z4XhffDelRyHSxfOlex1NQQqH5/Ndi7Rgvfj99Ujxi7s/bRQncA2KfvIs0t3S6J67iqKfP0Cxru0s93ccKZJWl58Nbvt1pMB+jHTt7EUx73uN2573kIPNzsiBy0YOQJ4Lx5uY1eg/p81ryEHGS0gXdNvT4X6mmHs+b9WxJ7h7IQVdpThZ/bF7j7yIouGs5t1cHL9XpMZ02+Wd3xvYdfezbw25dPbYYw/uueeebrVmI5EI//M//8O8efM499xzR7SBgyEQkJEkqNKH7wiNfKaZdMJyq03Jb5hQvG+a4obgqFjua0uArSlYpiz+jTBB2AhscCz3ddffVvfXTh5HSCdfHBNF5DEt+b4qTATWJueRzngbiwwWCjbCseSPkL8z7j4jKbDjDdMcgxmskcb7CKuQghdGiqNJcRLWC5+03d9puvvfu1olXuy+l18HiqUQ4xQzZ3pRO56rSEFayQmkGDlIwW52r6chxT+GFFAvJ45CMT9/CdJN0vVJpAEp0DmkuG9EDkJl7v4B97WGtLxxj2t3z7kr3UM34/RfaWupe+5qihEvAhnl04mM1CmjmHzOe3rwFrt5kUre51zj3hcvFNNkaCUbNxfHbyLvW9dBHYo5dLyQ260hl87jjz/e53tHH30077777rAaNBxiJTq5bB4i0LbhPRJN/yGfTWBmTFdk7YKAdxVbKAq32W370AS2ryeBzXmEthUp1zQdXTdQNQ1V0wkYhvtaR1F1AoZOSSRAMGhgGAa6rlNSUkJVVdVYN334eF/gri4Kz8dcjRSnEqTLIY8Umgr3/QyyE3jpFrzMigGkCIUpRvxMRlrp7yEFNuceryGfEBSkuAeRop6nGOMv3LZ54pl3z51AiuGOFNM5bER2Wi/+vwK5UGqZe602pIW7v9smL0zyQGR4Zof7WUrc/dqQA03X+7U5X3gLUtR3cI9dRnEC1BuEOinOG0Dx6eEDty1ebiK63McNSHdYJfIppZ2hlWzsK47/Lffa3hNaVx++l2MnzdZh4fdHRUXFSJ9ywHQkLZJZByLQ2baK9lWLxqwtWwpN0zEMvSCehlEUUlXVMM08erAMVdXRdZ1IOEA4ZJDJawQCOsFAAE2X7+m6gabp/P/2zjw4ruLa/5977+wjabR5kW3ZxiBjMGYN+8sz8UJiEwcoEjAp9vfs5yIFBYFQxHGeycsDnErgl7w4+REXcUhe2GJCUgQC2BAXBBsIOz8IxgbbWJa8yNau0Wz39u+Pvq07oxlJM1qRdL8q1czcpft0376nT58+i8AAzaB6cpBgwIfH48Hn82EYRkY9lmVQ3wixpEGk2EvA5yVlwd76BPGE4PgZPgJ+Z2dKCCFj+ZcZVE92xJsx53iVjglIZnUYyZgNpERbjGSyzUhmp7JeHcLxbhVIhlyCI5kr9Yzy1FTem0rKSN8MVIy8wz6nulhDbt522Peq+tWEshsnUUcMObkoG/xpSOZ4GCc0swe5sRsFzrSPB+22KTPRMNkqlL504S12P1k2rWpfoxhnn6ITuZq4EGf1oFI8zkTq9oN2f5l23xTbfTjTLqe3EAz5IJcHroXD8BWXVaocpdJry7P8QUS/GP7LL7/Mhg0b2LlzJ7FYtn/w+++/P2DC+oO2qEVb1IJyyQiHBgZoHvmve5zvmgfwZJ5DfmqaB6EZaedlGZr9KTDQdA+Gx4PX4yGWNNB1D/6AB0P3oOkGKdODJWwp2e9h8kQ/VeUBSos9HDvNx/HTfQT8mRuupmny3nvvMefEeYDRtSkbjVns3Jfoss3vDtMUtEUtZvdhq197MInQTaZMdLJXiaRAt01Em9sEk9MMtzRNoyik0dxuMjFpjD1bfaXSUVK9ipuiNjlnIJlWPY7nagNy01UgmVejXUbE/mzDCUdspv1WppudOLbd6VY+Sl2E/VvtBQgcVYaOoy7y4Jg4TsHRlTfY9SjmF7TboRJ4qE3IChwVFml1v49jk64jg62lM8jedOFTkRPWLpu+43GctJS+XnkEp68clNnkTLs979g0qolRTaQehia/rppYlOpOjQvVX8raqq8NwSFAwVzx+eef56KLLmLRokW8+eabLFmyhM7OTrZt28a0adOYP3/+UNCZF8yUIBqXypFQ6Uwi079CMpEgltCxRCazdRizFzQfaF7Qu31qPvu786n1M+ZLNlvNPKcJC80Aj19gdArQLExdeuh6DAsrJfBogqDXIpYQWMIgltJobIPUPouSkODYah0hRJZ3q9+rYxgO3YYhrYSSpsjJ8JOmk3WrJ8STFs3tJkWhzJj2piXDPxSFNNo6TcqTOr40xt7dQmhMQbVnApJRKam3Acm8wjjqnWOQjO0NHP19I45aRsXG8eDo+1pxTPrSHXjSmb1S1Wg49vGhtPs9SKn3Hfu7btMbsOlS1kUxm8YK+/rDSGYet8uZgZy4IsjQDX7kButenLy705HMrQgnv+5bNi1KGu9NF36Mff87yMmk2KZVlRe071Wmq6qdSlWkIntC5guoYvlX4Fj+pHB8FcJISX+K3ScK+er6J9j90Ua24xU4+xYTsm8dahTM8NeuXcstt9zCj370I7xeLz/84Q85/fTT+eyzz/jyl7/MggULhoLOvFASNjDtWDqe8CwC02qwOprRYkizGXuDVga6l8q17nlVBwtqb6a7Q1aP1xvShl0gQyzoup1sXYOE/SLrHlsw82lUVHgI+Dz4fZBEY1+Th5NOLCIU0EmmBLpmoQkpWlRUVKBpmrTTFwLTNJkcjVN/JEk4KFUtlp2jUQhBe1Qwqax3Cdy0vZdD3SYMQ5eZxjQhcwh393DOZzIZtVAqk49xwgAnkAxrL47NvLJcmYlknIeQ0rVidI04DlhKKmzB2YxVx5SKQ60gvPZ3JT2qQZi0z6sVh1oZKHVDo132mWn1K6neh7Tb/9gupwxnI7YYGUhNqXWqkdLxXvv3dBxVVRuSSb6GVKeoMBFKX6504cqsM2JfX4OzCmjHeaHCdt/NITtdoFIVvYhkrifirIQCyImqFrlHEMfxh1D283G7LuU3UWlf05tjWDqUGehBnEmYtLJ15ORZnuPeIUbBDP+jjz7i7rvvRtflMr6jQ8b+nDFjBnfddRd33XUXV1111aATmg9SpoyrDtDYKrCEwNIiWDaPH04oPpfvZqxlSe9chJL4pQOXbkAsJj9TtgNYpEijtFj64JumRTAAR1tMXnq7nXDQg65DJGxQGpa1BwIBvN3CZQZCFobPCXXs82jEEiYt7SbTpwpmTfPi94qu8Aoq1EJXyAWRwusxSZpWxirB69UoDhkcakwS9GukLSzynkzGDJRa5TOkRGohpcoAUk/ehJTy6pFMYCZOYLR6JJNRsXZMJJNT4Q3iOOoUpRrQ7fPpqhb1hitHLjXZBOxy1b5ApU3jLhxb/FakpNtif/8Yx3krgWSqu9LqCSEH6CEcRhhDMr6D9m9l4tmGnEjmAV+0z+0hm6lOQkYfbcJJvuK3656CM9El7fNK+p6EZNoHkZNpulwy1S77bZwQ0ynkpvBn9vdy5GS11753Ok6Ogu66ftXnSvpXTmcajs09ac/EwAlJPcwomOEHAgEsy0LTNKqqqvj000/54hflEysuLqa2tnbQicwfeheHtUzpNNVb+sHPEwR2btyUnJxSJiQ7weuz4+5YELdsD10dYjFBwC9kQLMOQXO7ye46jQllBqGATI1YXixkgL4c4ZF7SkIyfZI/7+Bqlp6g7kiKyoh8myxLBkvzB5K0xDrxeyEQNDB0QTxh0hpNESnyMKnCIJGysmIAjXooq4tTkS99PdJiI4ZkKkra8yMZ0mdI5qesVOJp50uQjOsozspAw0mFqNQ66XrgdLWE0uV7cfYH7NwOXZYxalNxEk7Y4ohNgwqMVoeUhg0kk1Wqklq7jXNxkqrX4SQkVxuSe9Pq9+GkRFSqqn045qo9edtOtsuajhOaoNS+f6dd7ltkThRKf+7BWeFodt967TY2ICeToN3WWrte5aDWjLMKmIhjSaR0/TvtZwPOc1RmsY12HT4yLYt8dl+2MTqsdE455RQ+/vhjFi9ezMKFC7n77ruprKzE6/WyZs0a5s2b13chQ4R40uzS4esGGDYDHS0wLUhamWog044BpFS2hmHnv2438cdlXPy2qIWmydAHpUU6rVGLusNxigIWp1fB2ztinHWSN4uJDzQJiUpVeKRFrRK8JFKCuGUwryZAUUgnGpOTUqhIo3qKQUmRTmu7DLWcSJoYuoAGjSkTrdGb6UpBSWxqo7odyTDU0l2pJdqQDPUojvWNslLZgWRIFTgbk36cQaGsTbDPp0NtyKZvFCuJ2Icj1cdwvGCL7foacHTUSlquxTH5nIZU50RtGk0k42u326fs2ZU5qEqM4kUyRkVTG85+AUip/gngS0hpP9cGarHdd+/gmFYqS502JDMOkDlRHEJOSCU4m7yqXw/Zz6UIOUmV2TQ22PUrb2YdJ9bRP5FqKOUzcAQ5me/FWc1U2v3QgON3oV5c0j6V38Vhu8xhRMEM/5ZbbmHPnj0A3HPPPSxbtoyvfe1rAEybNq1XO/0hh6YRizsivWmNLvv23tRAAindaxq0Ry0ggaEbaMLC8OhMneDBMHTaojJMwsQyg9Z2yRn21CcJB+PMPdafk6n2N0Z+PqkK03PfWhYZGbOKgnKCONBo0RFLFJQx63MJtYr6EMkkFLMEyWQOIhmC0rErBmAhGUQIyTTK7HubcCRCZV6prEwge4NITQTK3FKdN+yyDZxgZepcO048H5BSrdpsVHFfNCQT22nTv9e+VoU5iCAniChSr/4xDmcpt+8N4zDLIhwrlilI6bw5rT+6b6K+i2TCM3Hy/CoJehqZSVRU2a/b/XfUvsZr98uH9r0HcSx0diMnsYB9bZFdR8LuA7USabev6UAGcqvFcQSbjDN5K69fZS2VDqXKiTMiKJjhL126tOv71KlTeeutt/jkk0/o7Oxkzpw5+Hy5QuMNDzo7LZJ23OJ4QqpFRolGp09o2JE9hQwDEYtDSZGJx4CKIg3L0tB06S8WDsq3vTgkmafHEDR3mBw8kmLWNOf5DCQRuUJfq4T077v3S2Y/sczZLA8aMoLn4aZs+kYd1KbtZ0gG+yGSUbTibCwqKd3AUTN4kMxBhSi2cDxwG3CYg9JVp+vs1SatRaZ6J33gJ2walE28MpFUnq5qolIbvcrCZwKO6aa6P2BfF8ORlJWtfhFysojYv1XdHqRuPogzgSVwVCReJDNtRKqVDuNszLbYx7+AlKSbcfwP/kGmBK2gNqVNHH8DtYrpsMtP4ISUVtK4WiWolVjKrt+yfzfb93yEdHpLICcUFRen1C5fbf6qFZZ6BVSohaGxE8kLAzZW1zSNmpqavi8cBhgerSublGnll2h8tEAgx5XfI3X8Qsj2RTsh2pmiLWJRUeylLKLeWJnBC0AIndIig6OtJlMSFpbFgBORd0dfE4bKmFUS1rMsozRNoySsd9E3anPbKmk+hZRKlVSu9NmKOaVv5HmRTNCPZPKtSIakpGrFONRGn3pjlcpGMf3ebLrTnZbUCkHp88FZdajJR91j4kioynkpZbdT7TOYyAluP1IfDo65J3ZblJpjOlL9ErTvO2r3jWK6LyAnET9OKOe9SEbajJTKlUWNUjs14ExECikcD+JpOPsRTXZfFdn/OpKBx+xr2+y2CPt4sU1j+gTejEwQcwhHim9HqpEmICc6xfR7glK9DZWrUC/oV5W1tbX8+c9/pra2NsvxStM0fvaznw0KcYXCNC3iYyAkS08QlkyqYloysFusUwZkU2qeRCKJEB4mVWgYht4VSK60RIYubm43ae2wONCQSlOr6CRSIu/EKP2FyphVFMxdts+jEY3JVcKohRp7aoN1D46LfbqlRjoUwy7CCaegHLPSzS79OCsE5alZiECT716WUjcoNU8QRx2CTadiWEr6LcJRV01FmkEqG3MVWsGHlPzVakKtblJIhv6OfawUJ8BYEslwvUgVTQVykvHbtLUj1UdTcezzwdmQVpZNRcgVho5UwyhHtBakmqrEbmcDkqHPwvEM7rTPqTpfRzJ+bFqVaWvCpqUcZ7LsDWo1N8womOH/4Q9/4Oqrr8ayLCZOnJilwhlZhi8jU45VWAISSWl5JAQEA1BaJBOqWKZFLCk41JjC79MpCQuOtEiv4+kTfSRSUpI/2pwaEbWKx5Dx8xMpQTCHs5eizzOarXYUwz+KEx/HixNIKxfi9r9iqOo6tcmpHKlUEDN1zWCvXlM4HrztOCsBNcEoXb9ixoqmEpzQBx8h296BNHG8CsekczrOXkX64y9D6tDrcUwalfTdYpelVghnIScNpf+3kJ68TTgRNyP2/xGkVP4ZjoTfjOM/MAWp7mlGqoxUZFEfkhEftdtcipxoJuI40aWQKwBVZ6PdF532+ab8upx/IC26hhEFM/zVq1dzySWXsGHDBiKRyFDQ1G8cPppkLKdQFUBHzEmg7tNhSqWXxlaT1g4I+QXRuMXBIwliCYOJEck9fT6No60WlRGD9k5rQGqV/ur9Az6ZT7fuSIqAL9M7VwhBa4fF1ErP6FXngCO9KilSSZn5rFq6Twhmt0+RZzkDQfpGotrUTa8fMtVHagJQsfwNpNXLYSSDvAq5uWkhpXADJxmJFyfcQwuSyUaQDF85finrokYkk96PZKpqklAObi04sYWUhZBKhPIOkuEr80rlxOZFSvFl9vnpOJvlZXYbKuxrqpAOaXX2tWGc1U87jtrOi1xJ5KujP5TndYOIghl+Q0MDK1eu/NwxewDdo48qq5z+QCDVOUEvhEI6nXGBrsuNWV3T0TSThAkzp3g5fbaXI5/BkWaTSJGPylJDhkPoh1olGrMGrPdXZpyHm8wuZ69ESjL7kpDO5MoRUGoOJpSEr8ONoRtJzUxhCQtNaHT9iW6ffXzXbbG+67fQ0WyOoiF/A13Hs+6zy0u/r6dru8pCRxN2HYZ93gDN0tB0TX4qWnX7P6KhpTQw5f1WkcXOf+ykpbMFr+mFT0ELaOh2aBI9qoMFmldDC2hoxRqaaZcTkwKBFtDQkhq0g35Ul98/BC2mocd00EALamjtGppHQyuS12u6hlaqoXVoaI0aTLDb1KRBHDSPhu7VoRl5b1JDq9LQS3S0kCa5YgfoxToUI48dAi0h206xvE9v1iXDtx3CtGZZt9ojUP2Z8qdoamqizl/HjM4ZXc+BEZBtCn7DvvKVr/Daa6+xcOHCoaBnQCgJy/AE4wEpAfGE4IAtMZeEPXi9Us0T8GscO83XJWlUVRhMneSzJwbJZHVLZGW56kmtEo1Z3cwp+6f3z8eMc1QjTZ34sfExVnis2Ij1E9the9n2vq9TGkQdKSWn67ZVZFGQ/Rukb913B3LTHKTErzxfy7pdl779uDdHOYdzHMuFyTmOVXX7/Vv47am/ZfNLm/EKeyk4AjJzwQz/gQce4IorriAajbJw4UJKS0uzrjn99NMHg7aCUR7xdCUxH+tIpqClXeD3CYrDBn6PRmdc4PVoTCrzMbXSx4wqjZf2wswpPrw2Uw8FND7ak0DXBZaQoQ8iYYOKiE5rVORUqxw8Mnh6/4E6e40KNIHwj/W1pov+oEu6Bycy6jCiYPbY1tZGNBrl3nvvZd26dRnnhBBomoZpjoypRWfM5HOQR33YkEiCrkFnpyBlyp1cv6VRHLKl9W7LnWjMoj1qEY1Lz9xISEdogrqGFHUNgtnTfVlqlaEypxxzTB6ct6kF1hprScVTmO0mwhIIW9ko7GBJ6relWV3H1V/6eaGJjHPqtzpn2cp/S7Myysx1TUZZPXzv9X5DIDwywJ4w0+41REaUVsuwsDwWdWfWUfVGFZh2ewwQurzHEnaZlnD6x7DrNu3yNPvfI7BsL0p1zBIWQhMymooQ8j6vPIewy/HLsgHnekCYdv0GCJ/ASllgSboxbJoSdrs1u92aXZZF5nPQwEIGJezqM5H5zNDA0q0uVVgX9jPsKJjhX3PNNezbt4+f//znzJ49e0QdrbrDMPSR9GkYNvg8dEWhTJnQmbQIh3SCPh2vT6czDs3tJikrW1JPpOCU4/wcbbVo7TAxUzKUccqE4qCepVYZF+aUgwWl0jFhfmK+3KBsYXS5e/cGHWn5ouNk6PLgmCUq/wIDksEkf137V5ZuWoo36XWCrQWRm6nKaziBs0GsuFHcKQcfsl/Tza3Th6IKU5zCSZaihL4qpDrHj1S7WEirHHAifyraw8i4QJ04zl8q7LIKS6HjmNlOxvEgLrbp+BQptauwDsjJIRFM8Nyjz7H0yqUYIk1fOgLvTMEM/x//+AePPPJIVwLzzxPMlBOOd4iiHo84dKRU7/HKoGp+LwR9OhPKPFREPPg9gkNNJkeaTDy6EzEtXVIP+nWmBQwSSQ+mHRPftAQdcUGsm6Q+LswpBwvp5njNSEYyVpg9OOaX4GTVUoHR0uOCKIckkHpyFTitHTkJKk9ZxfAUF1JMXZWlPGZz0aG8hVU9ygFKQUNa5uhIppxEWtKoOEYh+3gUJ2duG5Jp1+JkHVPBz1R7ZyEnhKk46R+Vjb7KtKWSv2BvrPe0Ozsp9+GhRMHr6pqaGlKf04hkhkfrapChkxGadyxAQ3rZyo1WKArp+HwaRWGd8hLZ2MPNKVo6BAePJtl7wBGLlKSuvG8BfF6NYEDH59XweTRSpsiS1JU5ZWuHlZVYRZlTVpQYY1NFUygUU1BhA9I9Y8cKlG2+ihMTJDObVk9I4NjVq9ADCla377mGUi4BTtWrPICVR3IK2f9lON7IKSQjP4AT+M2HlMRVQDtlY68hJ4ROm2a10lChpJUT3GTkSmA3cpM4iZPUJR/MyfO6QUTBb+n999/P3XffzY4dOwaNiB07drB48WLC4TCTJ0/mjjvuIJFIFFyOmbLQ7ZfOk6b2GCvQAJ8XKiOaPblZJJJSZ3m02eTg0QTtUcHEMp2yEg/1DXJi7oxbGZJ6LvQmqU+u9FAS0jncZNIZtzBNQWfcNq8swJwylrBo77SIJcbYg+kOxZxUqr2xiigOA8/nkSqm3B2KKSupPVdZuRi+8vgFJxSDclgzkFYwGk7uYGV3X2zTvg/JwI8incbqcUIyRHFWDSpgm4V05FITQDtSD78LRzWlHOXywWiww7/llls4ePAgJ510ElOmTMmy0tE0jffeey/v8pqamliwYAE1NTU8+eST1NXV8e1vf5toNMr69esLos3w6ARsa5TYCEWjG0pYQDIJTW2ChB0PXQgQVoqmNgufByoiHiaWGkSKdCaWa3x2GA4dTVEzw99vx6eBmlMOhg3/qEIMKfmNh32NwQhlonT5fV2joOFk7lLfNaTu34Nkxm3ICJhqUijDCQ+tslmpwHNqj+CAXb4K7qZUU0dx4vcLZMiF3chJQcXnSVczZaf5zo36PK8bRBTM8M8444xBTQv4wAMP0Nrayp/+9CfKy8sBSKVS3HjjjaxevZopU6bkXVbIr3Xt/o9VpCyIxuQehRDg88kxGPQJyiMedE3j0/oUZ5V58NvMu7FVStUDcXzqrznlYNnwjwqo7mtlfDD74YSKvaNs9RVKcdQwKg6Q0tt7cDyBk/YxL05sIBUeIokTClpFI1XhK1S5ahWjkrsfxInZo6DuzxejIZbOQw89NKgEPPvssyxatKiL2QNcfvnlrFq1is2bN3PdddflXVYwoBO1N3l0Terwx6JaxzBk+3QdKksBdHweCPoMvF5ojwpa2lQuNUhZUjdfFBy441OhuvrBtOH/3KPE/hQ4jMFl/IMHmdXTUZtEkDlvD+FY02hIlU05kpm342T9Ss8spoLVdeDsRYBjbaRyB6QzcVV/erRTcPYOoLA4RyV9XzLYGPEtpR07dnDDDTdkHCstLaWqqqrHfYJ4PE487uhsWlulDdQnn0XRNfmGhQMm7THIYVgyqhHyy/0JQ4dwyCAY0EklLYTQiMUTlEU8lJfoNLclaW2XI0/HBCtJMqnjNaB6ksbEMp2UJWPs+31yFCeTg8ud4gmLhqYERQGZ0Lw7igKChqY4E8tE12pkqJBMJjM+80X3XMA9jb1kMonndfk6JUuSTsC0MahazAfJYDLjsyCoMM3pEUY9yElU9WsFMvFJGCf8tJoEIDNbWPr+QAVSn2/gmGjqSKYfxglTrSYJtcGrcgUoekI4weYs+3oVgK5bk3vsiz8Dt/XeFd3H30Chie6mF8MMr9fLD3/4Q+68886M4yeddBLnnXceGzZsyLrnrrvu4gc/+EHW8UceeYRQKJR13IWL/uLiiy/O+O2OPRfDie7jb6AYlQw/l5RVXV3N9rf383//1MllZ3zIs//vZI60QXs06/ZRBU0Dr70ppetQXmxgCek5GA4YlBbrFIV0mttMAl6YNtmHaQliMZgzUyd5dDtfnL+I4vDwq03iCYsPdyfweTUC/uylViwuSCQFc2f5hkXC37JlC4sXLy5IaspHwq+urubIkSME3w2ypX0Li7+9GK/ldUwRx5haMR8kg0m2bNzC4hsW4+3so79V8hUVShqcGDpKyvYDxyHt4DuR6Q5PQm587kGaVH6KXFGlkNE6q+zzTch9FWEfP4KTFEZHbugaOLb6KvuVsrxRqw2VGD09bLXa/PXi+Ax0Wyj32BdLgUd775rBlvBHXKVTVlZGS0tL1vGmpqYMvX46/H4//hwxFOYeW8zUCXL+SloQCvqIJUyio2hZrSEZvGlbBHi9Nr8QUv3SEZfJTDy6TmdSY4LXgyV0wiGDimIDgcahxhQzJnupqfbxz6NQHPYN+sDJB14vTCjTqDuSIhQ0siyD2mMmUys9FA3jZOT1egfUFz2NPa/Xi/dfvfBX8Lam1eEj//joYxDemBdv3JuZzlExUg2pOy9CMlll3aLUJcr71odMcD4fyVQP2/dUI1U0IaQdvAdpUTMbOMa+X4VB/ieZ8e7VJqyyxAkhGb5ysoriePGq8M/KKsiHk8/Xh5OO0rKPpTP/9L7o9GYy/Cfy7MRBxIgz/Dlz5mTp6ltaWjhw4ABz5hTumXDxBUUc2g0tHeAxTLwe0OKff4dHtRmLkMm+NeU4JiTz93tlSAXLjtNREtJJmdICJxzQmDZRZ0Kph8Y2k+On+5l/epCJZRr/fHdk2zXmQyLnQjkydroPh2mNZXv83qD07uq7Fyk9R3FCNYSQm6tKN34CkinvRzLZY4DTcXTu59jX1SL7+kSktU4QmRAFpPQ+CelB24RMrq6yZc2wy25ASvQmjiOZhZN/WDnOKRv8OI71D2nnS236lQVRPovVs/O4Zggw4m/bkiVLuOeee2hubu6y6d+0aRO6rnPhhRcWXN4Zc8L8dTfMme5l90HwWIJIkQwF3GknNk+HAZSXQdCn0RETtEdlULLBnCAMIBSW+WiTKYjHpfWQJaQjlc8rUxUamm3qa0r1jaLBY0DAbxAOaISCGmGfQcj2sk3ELTRDw+/XMQXMneVn7iw/laWegjcohwJjPiRyLqwD/g8yfV4MySSU3bhSOShmp6xF2nBiyHweoWLmKC9bRadO5qao+lQWKEU4ErsfJ2WiilOjTCYrcFIjKuerOci+q8RR05yMVOWAVOUcRDLbMPBN4EqktL8XZ7P3YuRm7h7kM2lHqoROQzL5Q0gb/GRauzSbtrh9r0ovqfqhGjlxqeTzbTjxhYTdnv3kfp5nA6/lOD4MGHGGv2rVKn7+859zySWXsHr1aurq6vjOd77DqlWrCrLB7461KyexszZFY6tJeYnBsdP8HGhI8un+GHvqkhhenVlVXubVBOnoNDEtjZRp4ffp1B5M0tJmEo2b+L0GRWENv1fD0DU6YxaWJTCFRkmRBkIjljARQsfQBZYFTe0m7e0WhhcmlnkoL/Hg82iUlRgYBtQ1pGhqMyny60SKDUJBnXjCkiakAorDOm0dMlKfoYPfp2OmBH6fht+nYxgynSNAOKgTS1jEEzLfbWnRiD/SLIyLkMjp+CpwKbAVqUMuB85D6vPfQuqQJwNfQDKSRqQU2oxkMEfs4ypQVyOOjllJwdXIBN3vI52AWpHxXYqQEuxRHMl6GnKymYDD2AJI1YfSb3faNKn48S1p9/uQUrcKGbAHx9eg2i63Calqidj1Re26fm8fU2qbJJLhT7brqEMy4ClISR77vhSOukZpfCM4ahaQzP+4tDLVuVPsPlRqmUr7eNQuS+WpVRY9ybTjXuRklbR/dyK5ZBBHx++xafOSmXcXMlU5Hvv4HfbvpYyIGicdI84dysrKePHFF7npppu45JJLKC4u5t///d+5++67B1z23FmZng3HTPVzzNRs/WtFJLMbpk8aWp3ypPI8yp/Q9yUKo4V5jhY6Bw1f6vY7gkyl1x25juWLZQO4t784NY9rlOfquWQ6SqWjp/44ttvv3oyfejpXmeOYUh/1REt/0T3ZSXc8AfyVPjdohwMjzvABTjjhBF544YWRJsOFCxcuxjTGmcjlwoULF+MXLsN34cKFi3GCz4VKZ6BQvmOtra0kk0mi0Sitra0jYnv+eYLbFw4G0hfFxcU9Bgx0x15uuH3hYKB90dv4KxRjguG3tclgGtXV1SNMiYuxiJaWFkpKcke6cseei6FGb+OvUIx4aIXBgGVZ1NfXU1xcTFtbG9XV1dTW1g5aJ41WKLd/ty8G1he9SVju2MsNd+w5GGhfuBJ+N+i6zrRp0wC6OqakpGTcDzQFty8cDHZfuGOvd7h94eDz0Bfupq0LFy5cjBO4DN+FCxcuxgnGHMP3+/2sXbs2Z0TD8Qa3LxwMR1+4/e3A7QsHn6e+GBObti5cuHDhom+MOQnfhQsXLlzkhsvwXbhw4WKcwGX4Lly4cDFOMGYY/o4dO1i8eDHhcJjJkydzxx13kEgk+r5xFGPTpk1cfPHFTJs2jXA4zKmnnsrGjRtJ35a54IIL0DQt6797lrGxgIceeihnW7vnS/71r3/N7NmzCQQCnHLKKTz99NMDqnc8jj1wx186RmrsFYox4XjV1NTEggULqKmp4cknn6Suro5vf/vbRKNR1q9fP9LkDRnuv/9+Zs6cyX333ceECRPYsmULK1asoLa2lrVr13Zdd/755/OTn/wk496ZM2cOM7XDh+eee45IxAlwPnXq1K7vjz32GCtWrOB73/seCxYs4PHHH+fSSy/l73//O+ecc07BdY3XsQfu+MuF4Rx7/YIYA7jnnntEOBwWR48e7Tr2q1/9ShiGIerq6kaQsqFFQ0ND1rEVK1aIkpISYZqmEEKI+fPni4suumi4SRsR/OY3vxFAzn5RmD17trjyyiszjp177rliyZIl/apzvI49Idzxl46RGHv9wZhQ6Tz77LMsWrSI8vLyrmOXX345lmWxefPmEaRsaFFZmZ3W57TTTqO1tZWOjo4RoOjzjd27d7Nz504uv/zyjOPLly/nxRdfJB6PF1zmeB174I6/QjAUY68/GBMMf8eOHcyZMyfjWGlpKVVVVWNOV9gXXnnlFaZOnUpxcXHXsZdeeolwOEwgEGD+/Pm8/PLLI0jh0GPu3LkYhsGsWbO49957Me0EwGosdB8rJ5xwAolEgj179hRclzv2MjHex99wjr3+YMzo8EtLS7OOl5WV0djYOPwEjRBeeeUVHnvsMe67776uY/Pnz+eaa66hpqaG+vp6fvKTn7Bo0SJeeuklzj333BGkdvBRVVXFD37wA84++2w0TeOpp55izZo11NXVsX79epqamgCyxkpZWRlAv8aKO/YcjOfxNxJjr18YNuXREMLj8Yh777036/jcuXPFihUrRoCi4Udtba2YMmWKWLhwYZf+NBfa29vFjBkzhlVvOJK4/fbbhWEYor6+Xvz+978XgDhw4EDGNW+88YYAxLZt2wou3x17Eu74y8ZQj73+YEyodMrKymhpack63tTUlKFbHatobm5myZIlVFRU8Mc//hFd7/mxhsNhLrroIt56661hpHDkcPnll2OaJu+++26XNNV9rCjpqz9jZbyPPXDHX08Y6rHXH4wJhj9nzpwsfWlLSwsHDhzI0pmNNXR2dvLVr36VlpYWnn322QyTMBeZUGOh+1jZsWMHPp+PWbNm9avM8Tr2wB1/+WIoxl5/MCYY/pIlS3jhhRdobm7uOrZp0yZ0XefCCy8cOcKGGKlUissvv5yPPvqI5557LsPmtyd0dHTw9NNPc+aZZw4DhSOPxx57DMMwOO2005g1axazZ89m06ZNGdc8/vjjLFy4EJ/PV3D543XsgTv++sJQj71+YVgUR0OMxsZGUVVVJebPny+ef/55sXHjRlFaWiq+9a1vjTRpQ4oVK1YIQNx3333i1VdfzfiPxWLi5ZdfFsuWLRMbN24Uf/vb38Tvf/97cdpppwmfzydef/31kSZ/0HHhhReKdevWiWeeeUY888wz4j/+4z+Epmnilltu6brmkUceEZqmif/8z/8UW7duFatWrRIej0ds3769X3WO17EnhDv+0jESY68/GBMMXwgh/vnPf4qFCxeKYDAoJk6cKG6//XYRj8dHmqwhxYwZMwSQ83/Pnj1i165d4stf/rKYPHmy8Hq9orS0VCxdunTMvWwKN998s6ipqRHBYFD4/X4xb9488bOf/UxYlpVx3YMPPiiOO+444fP5xLx588Rf/vKXAdU7HseeEO74S8dIjb1C4cbDd+HChYtxgjGhw3fhwoULF33DZfguXLhwMU7gMnwXLly4GCdwGb4LFy5cjBO4DN+FCxcuxglchu/ChQsXQ4DOzk6WLVtGKBTisssuG7YQyL3BZfguXLhwMQTYsGEDhmHw3HPP0dLSwkMPPTTSJLkM34ULFy6GAo2NjRx33HGceeaZzJw5k1QqNdIkjS2G39zcjKZpXTPpddddx0knnZT3/X/+85/55S9/OUTUDT1GO/1DjdHYP/mM4e7jfrDRU78V+n7li8Eot3sZQ/XszzrrLH7xi1/kPHf11Vfz4IMPEgqF2LVrF9dff33G+RUrVrBixYpBp6lXDKtf7xCjqalJAOI3v/mNEEKITz75RLz33nt533/ttdeKuXPnDhF1Q4/RTv9QYzT2Tz5juPu4H2z01G+Fvl8Dra8QdKdtKJ79k08+KSZMmCCi0WjO8/v27ROBQEAA4sYbb8w6v2vXLuHz+cTOnTsHla7eMCYyXvWEY489dqRJcDFACCFIJBL4/f6RJmVE8Hkew+Odtp/+9KdceeWVBIPBnOfXrFlDJBKhpqaGDz74IOv8cccdx/nnn88vfvELfvrTnw4xtTaGbWoZAmzYsEHMmDFDBINBsWDBgq7sMUrS6T6rf/DBB2LJkiWivLxcBINBMXv2bPGjH/2o61q6BYC69tprhRBCbN++XSxbtkxUVVWJUCgkTjnlFPG73/0uix5V39atW8Wpp54qQqGQOPPMM8Wbb76Zcd327dvF4sWLRXFxsSgqKhJnnXWW2Lx5c9Y1X/rSl0QoFBIlJSXiyiuvFIcOHeqxL3qjXwgh/vjHP4pTTjlF+P1+UVVVJW699VbR2dnZa//m25586C20D5955hlx8sknC6/XKzZt2pRXHf19vgNte199W0hZPdGRjr7GfXqf99Zf+dDVW7/loq2vsZ3POMhHGu/tWXcvo69nX+i7JoQQu3fvFpqmiZdffjnn+XfffVfoui7Wr18vbrjhBlFRUZHzul/+8peisrJSJJPJXusbLIxaCf/pp59m5cqVXHfddSxfvpy33nqLb3zjG73es2zZMiZNmsSvf/1rIpEIn3zyCfv37wfg+9//Pg0NDezYsYOHH34YgAkTJgDw2Wefcf7557Nq1SoCgQDbtm3j3/7t37Asi2uvvTajjoMHD3LzzTdz5513EolE+O53v8ull17Kp59+itfrZdu2bSxYsIBzzjmHBx98kNLSUt5880327dvXVcarr77KBRdcwNKlS3n88cfp6OhgzZo1XHzxxbz66qs529Yb/U899RRf//rXWb58OevWrWPHjh2sXr2affv28cQTT/TaZ321J196C+nD+vp6br75ZtasWcP06dOZPn16XnX09/kOpO359m0+ZeWDfMd9vmOoL7oK6bd8xnYh46A39Pasu6O3NvTnXQN48cUX8Xg8nHXWWTnP33HHHRxzzDGsXLmS//mf/2Hjxo0cOHCAqqqqjOvOO+88jhw5wrvvvssXvvCFvNvfbwzLtDIEOPvss8UXv/jFjGPf//73e5TwGxoaBCCeeuqpHsvMR7KwLEskk0mxcuVKce6552bdr2ma+OCDD7qObd26VQDi73//uxBCiPPOO0+ceOKJIpVK9VjHv/7rv4rzzjsvI7Tqhx9+KDRNE88880zB9J922mlZtP7qV78SgHj//fd7La+v9vSH3r76EBCvvfZaxvG+6his51to2/Pp23zLyofmfMa9EPk9k3zp6qnfuh/PZ2yno6dx0Ndz6s+z7qnM/r5rK1eu7JHGLVu2CEA88sgjQgghnn32WQFkreKFECKZTArDMMT69et7rGswMSqtdEzT5K233uLSSy/NOP71r3+9x3sqKiqYMWMG3/3ud/ntb3/bozSQC01NTdx8883MmDEDr9eL1+tlw4YN7Ny5M+vaKVOmMHfu3K7fJ554IgD79+8nGo3y2muvce2112IYRs66otEo27Zt4xvf+AamaZJKpUilUsyePZvq6mreeOONvOkGaG9v5913383qmyuuuAKAV155pdf7e2tPIfQW0ocVFRWcffbZBfXJQJ5vf9teSN/2VZYQoqtdqVQK0zSz6Ml33BcyhvqiK1/kM7ahsHHQEwbrWQ/kXTtw4EDOlY4QgjvuuINTTz2V5cuXA3T1by49vsfjobS0lAMHDvSrDYViVDL8hoYGUqkUEydOzDg+adKkHu/RNI3Nmzdzwgkn8K1vfYvq6mq+8IUv8PLLL/dZ33XXXcejjz7K7bffzubNm3njjTe44YYbiMViWdeWlpZm/Fapy2KxGE1NTViWxZQpU3qsq6mpCdM0ufXWW7teCPW/b98+amtr+6Q3Hc3NzQghsvomEong9/tpbGzs9f7e2lMIvYX0YXda86ljIM+3v20vpG/7Kuull17KaNfChQuz6Ml33BcyhvqiK1/kM7ahsHHQEwbrWQ/kXYvFYjkNCR5++GHeeecd7rzzTlpaWmhubqa4uJhwOJyT4QP4/X46OzsLor2/GJU6/AkTJuDxeDh8+HDG8UOHDvV6n8opmUwm2b59O6tXr2bZsmXU1dVRVFSU855YLMbTTz/N/fffz0033dR13LKsgukuLS1F13Xq6+t7vUbTNFavXs0ll1ySdb6ysrLgOjVNy+qrlpYW4vE45eXlBZXXU/m90VtoH2qaVnAd0L/nOxAMZt+eccYZGRJlcXFx1jX5jvvBHkP5IJ+xPZjv0mA864H0U3l5OXv37s04Fo/HWbNmDUCXdJ+Onhh+c3MzFRUVedE8UIxKhm8YBqeffjp/+tOfuPXWW7uO97UBqeD1epk/fz533nknX/va16ivr2f27Nn4fL4sSSMej2NZVkaS4ba2Np566qmC6Q6Hw5x77rn87ne/47bbbsu59FXXfPTRR/z3f/93QeXnor+oqIhTTz2VJ554IqOv/vCHPwDwL//yLwW3o1B6W1paBtSHhfZJIc93IBjMvi0uLu5z0y7fcT+QMZQL+fRbPmN7MN8lhZ6edT5tGEg/HX/88WzdujXj2Pr169m3bx8PP/xw1krn/vvvZ+vWrQghMgSahoYGotEoxx9/fEH19xejkuEDfO973+Piiy/m+uuv77JW+N///d8er3///fe57bbbuOKKKzj22GNpaWnh3nvvZebMmV02uyeccAIbN27k0UcfpaamhsrKSmbOnMmZZ57JunXruiSsdevWEYlEsiStfLBu3ToWLFjAokWLuPHGGykrK+Ptt9+msrKSG264AYAf//jHLFiwgCuuuILly5dTVlbG/v372bJlC9dffz0XXHBBzrJ7ov+uu+7ikksu4aqrruKqq67i448/ZvXq1Vx22WXMmzev4DZ0Rz70DrQP+6qjvLy83893IBjqvu2OfMd9f8dQLuTbb32N7UgkMijvUj7vcr5t6G8/nX/++fzXf/0X+/fvZ9q0aTQ3N3PPPfdw9dVX881vfjPr+rfffpu//OUv7N27l2OOOabr+JtvvgkMXPDKG8OyNTxEeOCBB0R1dbUIBAJi/vz54vXXX+/RSufQoUPiqquuErNmzRJ+v19MnDhRXHbZZRlebi0tLWL58uWioqIiw1Z3165dYsGCBSIUConq6mrx4x//WKxdu1aEw+EMenJZAuTygty2bVuX3W9xcbE455xzxAsvvJBx3xtvvCGWLl0qIpGICAaDoqamRqxatUrU1tb22B890S+EEE888YQ4+eSThc/nE5MnTxa33HJL3nb4fbUnH3oH0of51DGQ5zvQtvfVt4WUlQ8dfY37fPqrELp66rdc9/c1tvMZB31Z6eTzrLuX0duz78+7Fo/HRUVFhdiwYYMQQojvfOc7IhQKif379+e8/vnnn89pWXTTTTdlWV0NJdwk5i5cuHDRD9x222288847/O1vf+vX/alUiunTp7Nu3TquueaaQaYuN0allY4LFy5cjDRuv/12Xn/9dd57771+3f/II49QVFSUUwU0VHAZvgsXLlz0A1VVVTz00EM0NDT0635d19m4cSMez/BtpboqHRcuXLgYJ3AlfBcuXLgYJ3AZvgsXLlyME7gM34ULFy7GCVyG78KFCxfjBC7Dd+HChYtxApfhu3DhwsU4gcvwXbhw4WKcwGX4Lly4cDFO4DJ8Fy5cuBgncBm+CxcuXIwT/H++K3Y67hL3CQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saveas=f\"structural_statistics_{metric_prefix}\"\n",
    "\n",
    "fig = plt.figure(figsize=[4, 5])\n",
    "axs = fig.subplot_mosaic(\n",
    "    \"\"\"\n",
    "    bbbBBB\n",
    "    cccCCC\n",
    "    \"\"\",\n",
    "    gridspec_kw={\n",
    "        \"hspace\": 0.75,\n",
    "    }\n",
    ")\n",
    "\n",
    "for homolog, iter_ax in zip([\"Delta\", \"BA2\"], [\"b\", \"B\"]):\n",
    "\n",
    "    sns.regplot(\n",
    "        data = nbr_score_df,\n",
    "        y = f\"{metric_prefix}_{homolog}\",\n",
    "        x = f\"nbr_{metric_prefix}_{homolog}\",\n",
    "        ax=axs[iter_ax],\n",
    "        scatter_kws = {\n",
    "            \"color\":'royalblue' if homolog == \"Delta\" else \"magenta\",\n",
    "            \"s\":35,\n",
    "            \"alpha\":0.2,\n",
    "        },\n",
    "        line_kws={\"color\": \"0.25\"}\n",
    "    )\n",
    "\n",
    "    corr, p = scipy.stats.pearsonr(\n",
    "        nbr_score_df[f\"nbr_{metric_prefix}_{homolog}\"], \n",
    "        nbr_score_df[f\"{metric_prefix}_{homolog}\"]\n",
    "    )\n",
    "    p = round(p, 3)\n",
    "    p = f\"p={p}\" if p != 0 else \"p<0.001\"\n",
    "    axs[iter_ax].annotate(\n",
    "        f\"$r = {corr:.2f}$\\n${p}$\", \n",
    "        (0.55, 0.8), \n",
    "        xycoords=\"axes fraction\", \n",
    "        fontsize=11\n",
    "    )\n",
    "    \n",
    "    axs[iter_ax].set_ylim([-0.2, 2.5])\n",
    "    axs[iter_ax].set_yticks([0, 1,2], labels = [0, 1,2])\n",
    "    axs[iter_ax].set_xlim([-0.2, 1.52])\n",
    "    axs[iter_ax].set_xticks([0, 0.5, 1.0, 1.5], labels = [0, 0.5, 1.0, 1.5])\n",
    "    axs[iter_ax].set_title(homolog+\"\\n\" if homolog == \"Delta\" else \"BA.2\\n\")\n",
    "    \n",
    "    if homolog == \"Delta\":\n",
    "        xlabel=f'{metric_prefix} \\naveraged across neighboring sites'\n",
    "        xlabel=f'max. abs. shift \\naveraged across neighboring sites'\n",
    "        axs[iter_ax].set_xlabel(xlabel)\n",
    "        axs[iter_ax].xaxis.set_label_coords(1.0, -0.22)\n",
    "        axs[iter_ax].set_ylabel(\"max. abs. shift\")\n",
    "    else:\n",
    "        axs[iter_ax].set_yticklabels([])\n",
    "        axs[iter_ax].set_xlabel(None)\n",
    "        axs[iter_ax].set_ylabel(None)\n",
    "        \n",
    "    sns.despine(ax=axs[iter_ax])\n",
    "    axs[iter_ax].grid()\n",
    "\n",
    "for homolog, iter_ax in zip([\"Delta\", \"BA2\"], [\"c\", \"C\"]):\n",
    "    \n",
    "    data = nbr_score_df.query(f\"dist_nearest_{homolog}_nis != 0\").copy()\n",
    "    data[f\"dist_nearest_{homolog}_nis\"].clip(upper=50, inplace=True)\n",
    "    sns.regplot(\n",
    "        data = data,\n",
    "        y = f\"{metric_prefix}_{homolog}\",\n",
    "        x = f\"dist_nearest_{homolog}_nis\",\n",
    "        ax=axs[iter_ax],\n",
    "        scatter_kws = {\n",
    "            \"color\":'royalblue' if homolog == \"Delta\" else \"magenta\",\n",
    "            \"s\":35,\n",
    "            \"alpha\":0.2,\n",
    "        },\n",
    "        line_kws={\"color\": \"0.25\"}\n",
    "    )\n",
    "    corr, p = scipy.stats.pearsonr(\n",
    "        nbr_score_df.query(f\"dist_nearest_{homolog}_nis != 0\")[f\"dist_nearest_{homolog}_nis\"], \n",
    "        nbr_score_df.query(f\"dist_nearest_{homolog}_nis != 0\")[f\"{metric_prefix}_{homolog}\"]\n",
    "    )\n",
    "    p = round(p, 2)\n",
    "    p = f\"p={p}\" if p != 0 else \"p<0.001\"\n",
    "    axs[iter_ax].annotate(\n",
    "        f\"$r={corr:.2f}$\\n${p}$\", \n",
    "        (0.55, 0.8),\n",
    "        xycoords=\"axes fraction\", \n",
    "        fontsize=11\n",
    "    )\n",
    "\n",
    "    axs[iter_ax].set_ylim([-0.2, 2.5])\n",
    "    axs[iter_ax].set_yticks([0, 1,2], labels = [0, 1,2])\n",
    "    \n",
    "    axs[iter_ax].set_xlim([-1, 55])\n",
    "    axs[iter_ax].set_xticks([0, 25, 50], labels = [0, 25, 50])\n",
    "    \n",
    "    \n",
    "    if homolog == \"Delta\":\n",
    "        xlabel = 'distance to nearest non-identical site ($\\AA$)'\n",
    "        axs[iter_ax].set_xlabel(xlabel)\n",
    "        axs[iter_ax].xaxis.set_label_coords(1.0, -0.18)\n",
    "        axs[iter_ax].set_ylabel(metric_prefix)\n",
    "        axs[iter_ax].set_ylabel(\"max. abs. shift\")\n",
    "        \n",
    "    else:\n",
    "        axs[iter_ax].set_yticklabels([])\n",
    "        axs[iter_ax].set_xlabel(None)\n",
    "        axs[iter_ax].set_ylabel(None)\n",
    "        \n",
    "    sns.despine(ax=axs[iter_ax])\n",
    "    axs[iter_ax].grid()\n",
    "\n",
    "    \n",
    "fig.savefig(f\"{output_dir}/{saveas}.pdf\",bbox_inches='tight')\n",
    "fig.savefig(f\"{output_dir}/{saveas}.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
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