08-1-DAZ1234-t2t-hg002-asm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Install the PGR-TK package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If neccessary, install the package into your python environment \n",
    "```\n",
    "pip install --force /code/pgrtk-0.3.2-cp38-cp38-linux_x86_64.whl\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pgrtk\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import SVG\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pgrtk 0.3.2 (main:00d8eb3, release build, linux [x86_64] [rustc 1.62.0 (a8314ef7d 2022-06-27)])'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pgrtk.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"/results\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Lazy load the the AGCfile for fetching sequences## Load the hg19 reference into `ref_seq`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_db =pgrtk.AGCFile(\"/data/pgr-tk-HGRP-y1-evaluation-set-v0.agc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the index into memory\n",
    "The loads the index into the memory. It takes about one minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 142 ms, sys: 269 ms, total: 411 ms\n",
      "Wall time: 116 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "sdb = pgrtk.SeqIndexDB()\n",
    "sdb.load_from_agc_index(\"/data/v2.7.XY.fasta\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the specification about how SHIMMERs are computed using the `get_shmmr_spec()` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80, 56, 4, 64, False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdb.get_shmmr_spec() # window_size, k_mer_size, hieraical_reduction_factor, min_span, use_k_mer_sketch_other_than_minimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query the database with the sequence of the region"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use a copy of the annotation for IGV hg19 to look up the chromosome and locations of the gene of interest.\n",
    "We can fetch the region of interest with `ref_db`. Let's fetch the whole region and do a dot plot to examine the repeat structure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "939\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 4777685.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ref_file_name, roi_chr, roi_b, roi_e = \\\n",
    "('hg38_tagged.fa', \n",
    "'chrY_hg38', \n",
    "23129355, \n",
    "24907040)\n",
    "#DAZ1_len = 23199094-23129355\n",
    "#DAZ2_len = 23291356-23219434\n",
    "\n",
    "padding = 1500000\n",
    "DAZ1_s = 23129355-23129355 + padding\n",
    "DAZ2_s = 23219434-23129355 + padding\n",
    "DAZ3_s = 24763069-23129355 + padding\n",
    "DAZ4_s = 24833919-23129355 + padding\n",
    "roi_len = roi_e - roi_b\n",
    "#get a segment of a reference\n",
    "roi_seq = ref_db.get_sub_seq(ref_file_name, roi_chr, roi_b-padding, roi_e+padding)\n",
    "x, y = pgrtk.get_shmmr_dots(roi_seq, roi_seq, 128, 56, 6, 32)\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.plot(x, y, \".\", markersize=0.5)\n",
    "print(x[0])\n",
    "plt.plot((DAZ1_s, DAZ1_s),(0,len(roi_seq)),'r-')\n",
    "plt.plot((DAZ2_s, DAZ2_s),(0,len(roi_seq)),'r-')\n",
    "plt.plot((DAZ3_s, DAZ3_s),(0,len(roi_seq)),'r-')\n",
    "plt.plot((DAZ4_s, DAZ4_s),(0,len(roi_seq)),'r-')\n",
    "\n",
    "plt.xlim(0,len(roi_seq))\n",
    "plt.ylim(0,len(roi_seq))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of hits is 1\n",
      "The total aligned regions is 5\n"
     ]
    }
   ],
   "source": [
    "#find hits in the pangenomic reference\n",
    "aln_range = pgrtk.query_sdb(sdb, roi_seq, merge_range_tol=100000)\n",
    "\n",
    "print(\"The number of hits is\", len(aln_range))\n",
    "\n",
    "count = 0\n",
    "for sid, rgns in aln_range.items():\n",
    "    count += len(rgns)\n",
    "\n",
    "print(\"The total aligned regions is\", count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can examine, the target region sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('chrY_hg002', 'v2.7.XY', 62460029) target region 7231462 - 7291494 len: 60032\n"
     ]
    }
   ],
   "source": [
    "rgn_lengths = []\n",
    "for k in list(aln_range.keys()):\n",
    "    b, e = aln_range[k][0][0:2]\n",
    "    print(sdb.seq_info[k], \"target region\", b, \"-\", e, \"len:\", e-b )\n",
    "    rgn_lengths.append(e-b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rgn_lengths, bins=32);\n",
    "plt.xlabel(\"ROI size\")\n",
    "plt.ylabel(\"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some mapped regions are larger, let's check the repeat / CNV by quick dot plots. We label the region of the ROI by two vertical grey lines. We observe that there are different number of intersections (~different copy number of the ROI) of the region the \"dots\" due to the different repeat structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1728 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seq_list = []\n",
    "i = 1\n",
    "plt.figure(figsize=(24, 24))\n",
    "for k in list(aln_range.keys()):\n",
    "    ctg_name, source, _ = sdb.seq_info[k]\n",
    "    seq_id = k\n",
    "    rgns = aln_range[k].copy()\n",
    "\n",
    "    rgns = pgrtk.merge_regions(rgns, tol=1000)\n",
    "\n",
    "    for rgn in rgns:\n",
    "        b, e, length, orientation, aln = rgn\n",
    "        aln.sort()\n",
    "  \n",
    "        #if aln[0][0][0] > padding or aln[-1][0][1] < padding + roi_len:\n",
    "        #    continue\n",
    "        \n",
    "        #for illustration purpose, we only plot with the sequnce spanned through the whole region\n",
    "\n",
    "        if e-b < 0.75 * (roi_len + 2 * padding):             \n",
    "            continue\n",
    "        #print(b, e)\n",
    "        seq =  sdb.get_sub_seq(source, ctg_name, b, e)\n",
    "        #seq =  sdb.get_seq(source, ctg_name)\n",
    "  \n",
    "        if orientation == 1:\n",
    "            seq = pgrtk.rc_byte_seq(seq)\n",
    "\n",
    "        seq_list.append((\"{}_{}_{}_{}\".format(ctg_name, b, e, orientation), seq))\n",
    "        x, y = pgrtk.get_shmmr_dots(roi_seq, seq, 128, 56, 4, 1)\n",
    "        \n",
    "        \n",
    "        if i <= 36:\n",
    "            plt.subplot(4,4,i)\n",
    "            plt.plot(x, y, \".\", markersize=0.1)\n",
    "            #plt.plot([padding, padding],[0, max(y)], color='grey')\n",
    "            #plt.plot([padding+roi_len, padding+roi_len],[0, max(y)], color='grey')\n",
    "        i += 1\n",
    "        plt.plot((DAZ1_s, DAZ1_s),(0,len(seq)),'r-')\n",
    "        plt.plot((DAZ2_s, DAZ2_s),(0,len(seq)),'r-')\n",
    "        plt.plot((DAZ3_s, DAZ3_s),(0,len(seq)),'r-')\n",
    "        plt.plot((DAZ4_s, DAZ4_s),(0,len(seq)),'r-')\n",
    "        \n",
    " \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a new shimmer database for the detailed study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_list = []\n",
    "seq_info = sdb.seq_info.copy()\n",
    "i = 0\n",
    "for k in list(aln_range.keys()):\n",
    "    ctg_name, source, _ = seq_info[k]\n",
    "    seq_id = k\n",
    "    rgns = aln_range[k].copy()\n",
    "    \n",
    "    rgns = pgrtk.merge_regions(rgns,tol=1000)\n",
    "\n",
    "    for rgn in rgns:\n",
    "        b, e, length, orientation, aln = rgn\n",
    "        \n",
    "        #for illustration purpose, we only plot with the sequnce spanned through the whole region\n",
    "\n",
    "        if e-b < 0.75 * (roi_len + 2 * padding):             \n",
    "            continue\n",
    "            \n",
    "        seq =  sdb.get_sub_seq(source, ctg_name, b, e)\n",
    "        if orientation == 1:\n",
    "            seq = pgrtk.rc_byte_seq(seq)\n",
    "  \n",
    "        seq_list.append((\"{}_{}_{}_{}\".format(ctg_name, b, e, orientation), seq))\n",
    "        \n",
    "        i += 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a shimmer indext database with smaller window (denser shimmers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_sdb = pgrtk.SeqIndexDB() \n",
    "new_sdb.load_from_seq_list(seq_list, w=80, k=56, r=6, min_span=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_sdb.seq_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of hits is 1\n",
      "The total aligned regions is 1\n"
     ]
    }
   ],
   "source": [
    "#find hits in the pangenomic reference\n",
    "aln_range0 = pgrtk.query_sdb(new_sdb, roi_seq, merge_range_tol=300000)\n",
    "\n",
    "aln_range = aln_range0\n",
    "\n",
    "print(\"The number of hits is\", len(aln_range))\n",
    "\n",
    "count = 0\n",
    "for sid, rgns in aln_range.items():\n",
    "    count += len(rgns)\n",
    "\n",
    "print(\"The total aligned regions is\", count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's loop through the hits (contigs that has alignments) and see the number of \"copies\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of copy: 1, number of haplotypes: 1\n"
     ]
    }
   ],
   "source": [
    "n_copy = {}\n",
    "for k in list(aln_range.keys()):\n",
    "    n_copy[k] = len(aln_range[k])\n",
    "copy_count = Counter(n_copy.values())\n",
    "for nc, nh in copy_count.items():\n",
    "    print(\"number of copy: {}, number of haplotypes: {}\".format(nc, nh))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rgn_lengths = []\n",
    "for k in list(aln_range.keys()):\n",
    "    for sid, rgns in aln_range.items():\n",
    "        for rgn in rgns:\n",
    "            b, e = rgn[0:2]\n",
    "            #print(seq_index[k], \"target region\", b, \"-\", e, \"len:\", e-b )\n",
    "            rgn_lengths.append(e-b)\n",
    "plt.hist(rgn_lengths, bins=32);\n",
    "plt.xlabel(\"ROI size\")\n",
    "plt.ylabel(\"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Repeat Pattern Using the Principal Bundles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "shmmrspec = {\"w\": 64, \"k\":56, \"r\":1, \"min_span\":28 }\n",
    "new_sdb = pgrtk.SeqIndexDB() \n",
    "new_sdb.load_from_seq_list(seq_list, \n",
    "                           w = shmmrspec[\"w\"], \n",
    "                           k = shmmrspec[\"k\"], \n",
    "                           r = shmmrspec[\"r\"], \n",
    "                           min_span = shmmrspec[\"min_span\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "176\n"
     ]
    }
   ],
   "source": [
    "principal_bundles, sid_smps = new_sdb.get_principal_bundle_decomposition(0,8)\n",
    "sid_smps = dict(sid_smps)\n",
    "print(len(principal_bundles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>nipy_spectral</strong> </div><div class=\"cmap\"><img alt=\"nipy_spectral colormap\" title=\"nipy_spectral\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#000000ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #000000ff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#ccccccff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #ccccccff;\"></div></div>"
      ],
      "text/plain": [
       "<matplotlib.colors.LinearSegmentedColormap at 0x7f4e06f4ff40>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmap=plt.get_cmap(\"nipy_spectral\")\n",
    "cmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0  91   6  97  12 103  18 109  24 115  30 121  36 127  42 133  48 139\n",
      "  54 145  60 151  66 157  72 163  78 169  84 175  90   5  96  11 102  17\n",
      " 108  23 114  29 120  35 126  41 132  47 138  53 144  59 150  65 156  71\n",
      " 162  77 168  83 174  89   4  95  10 101  16 107  22 113  28 119  34 125\n",
      "  40 131  46 137  52 143  58 149  64 155  70 161  76 167  82 173  88   3\n",
      "  94   9 100  15 106  21 112  27 118  33 124  39 130  45 136  51 142  57\n",
      " 148  63 154  69 160  75 166  81 172  87   2  93   8  99  14 105  20 111\n",
      "  26 117  32 123  38 129  44 135  50 141  56 147  62 153  68 159  74 165\n",
      "  80 171  86   1  92   7  98  13 104  19 110  25 116  31 122  37 128  43\n",
      " 134  49 140  55 146  61 152  67 158  73 164  79 170  85]\n"
     ]
    }
   ],
   "source": [
    "lpb = len(principal_bundles)\n",
    "color_theme0 = cmap(np.linspace(0.1, 0.9, lpb))\n",
    "#pseudo-randomize the colors\n",
    "idx = np.array([_ * (lpb+8011) for _ in range(lpb)]) % lpb\n",
    "print(idx)\n",
    "color_theme = color_theme0[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_sdb.generate_mapg_gfa(0, \"/results/test_48_56_4_28.gfa\")\n",
    "\n",
    "v_to_name = {}\n",
    "with open(\"/results/test_48_56_4_28.gfa\") as f:\n",
    "    for r in f:\n",
    "        r = r.strip().split(\"\\t\")\n",
    "        if r[0] != \"S\":\n",
    "            continue\n",
    "        n = r[1]\n",
    "        v = r[4].split(\":\")[-1]\n",
    "        v_to_name[v] = n\n",
    "\n",
    "f = open(\"/results/test_48_56_4_28_color.csv\", \"w\")\n",
    "print(\"Name,Color\", file=f)\n",
    "for bundle_id, bundle in enumerate(principal_bundles):\n",
    "    for v in bundle[2]:\n",
    "        vertex = tuple(v[:2])\n",
    "        color = color_theme[bundle_id]\n",
    "  \n",
    "        print(v_to_name[\"{:016x}_{:016x}\".format(*vertex)], color, file=f, sep=\",\")\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "b_vertex_to_position = {}\n",
    "i = 0\n",
    "for bundle in principal_bundles:\n",
    "    for v in bundle[2]:\n",
    "        vertex = tuple(v[:2])\n",
    "        b_vertex_to_position[vertex] = i, bundle[0]\n",
    "        i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cn/oA13oOsOn2JE6+agY5CT1BnCDZJAy6jgLyceLkqJij+L5zgNc51EPMv/pT2uJh0Dbc8NI7bPy6JyWlMcTGFPOfr06l65AeQe0dv/Q2Fh5aRunwleDwkFECzVKw8kkhLmoaSzPv58KP9pFfqjgd8PLomQxr+zpQgpBIK8/5kD+fFdH5nJ0KhQ5IIIHbuZ07uCPgdYd8NpTTx3ZhzbSjyCn18O1zx7N53KV8dekB5mwYysedl1u7eTgsO+JL4ik4pSBgWnXFJ7+t4OENF/G2buOb11vwSPpIFvc/ByQatJS0AfeQ6dyECxdJksS2rttoGVVpqxAGzzyX77sfDe5RXP7dUvY+3Y7iklhiY4qZenczxt8xMagNz/yygBtWPUbhCZ9BdCnPZsIfOlBWXh5PO6ISd9D+hR0Uua0qbXKf9/m/429HyQdiSGEcsbmLyJICjmkNuQKxEsvJnMxiFnP5R5t56dDnHNf+e+4e9gzxzpKg9pziCPnAH5Y4VGu2kt3dMwhr7Zk0Vd0NYP9vYwdrT8Ulm3fabu3tc3/3CnHUWqI3G2vjmKqsQR0KOIgudePw2BniUVwFVa7yG5ACVwFujyKAODyU+D3BukvAbRes263k5bkpKspCpLwgCl1+layrhPx4KIhuhsPeDkAdURwqKCGusIhD5dvp4lYHe/ZZdnsOHqQ4DnJiwGWXUKlAlmaVhS92/YY4PNYNCDhE8Wg+xTEC9lOOU1w4HOUrUXtwkesqv6kUJc8V/CY7EpTsbkbLqFLi4rOIKYLoUmieKeTnu6jwACuwsWBjrdiwc2chTqeVJ243ZGTkBQ370959DHCVkCAeOuwrJDc+3RIGgJhMXHY5unCxsTSwve6SfErTfwNNA6I4lJFKSal1bzgcyrbVVe+VsypzA6WJ2aACHmju28gRKHYt5kChB++jk8sDMVF7sNYHBHCjpd8D+XwXDR77HimggE/LF5GtxK7CvXR37yOzMIWElANofixx0YW03OPmYHpRxdpCoEiKqvwedcH3+3fQKzafKJT4HW42pHW0hAFAojng2IYL63clCDtKdwRMZ3P8NtA+QBwttudSXGKVV3FJLD8v31+lDcv3r6NQ8iDaBQ4Y7ldeInvIKanYNRwblQl466kScGUAsDWqPF4xxay0F6T9bO9eAI5qsblKYTiShC0OIpKEtePVDaqaU1XQAG5ahXtVcfxtmCoiq0RkVezBQ8ztMJljPY+yanhbDjWPoyTaQXzLOI6/Y3Cor1OJK3tfSaekZF46KpXkrJaMyvuMOPkNcCOU8vg9nUlLiyEx0UHr1tHceGN7BqZfx/heHxIXVURsVBEP/O79Cmkm3v4f7vwYNg/8hi6ZW0goySdOS7n/jLbodSM5w/U5aXqAJM0nIQ7+b4bVHIw/9VROyT2aQZ9Am0JIKoEUl5PpKeVPfJ1Su+HMTkO8y52VppDsGMvdxzaHEg8UeXAWxJDguBohASGBZtGXc0vv4cRHOUmMiuac9r1Ij0uiNjn9khzmFg6jY0wBzR3RfNcHMpOUroW9rKXnFPBYxz96/KNWbBg8uDnt2sWRlOQkISGK668PvrLzXaeM4vGoFuR7ovjuhOYM3rYctBS0FEdxGh2jO5AgCSRKIuclnRcwjajYZNptHgzyHVBI8vFZxMYWE+0sxeFQjh5T9f05qcso4nPblP0CMrzb09hlHR97NX/onkBaQhTJ0UKiU2gXfwdCIkIKDmlBbNyfgQROLhGiFBwqJJDARII/Ad/d53aeOHkAl7GAgpL2tJ/wBR/sPJ6PWnRj1LxCa8k/N2XlNSFpQpXfoy64pNepfLilHyU40JHwp+VflJUXWsopzt+TKIkkSAKtolrRN6ZvwHTGR50I8hFQyM8D2xIbU0K0s5TYmGJ+98eqN4q7oMtI4j2JkJcIbgfzC20Pu7yEQXROdnJWl3gSnUK8U+jbfDIOWiCkICSSFHcHiNDbLcQpONVBAgmcaa9wPrGLtQr+yr1H4XaDKtT2+8thdSvZO0+9j7Uj1mzbbSMR7FbqP+BovWv2LPIooVlpLmldh3NUdBtS2ifijA3cpxqKUk8pO/N3UlyobPtxJcUxMaTEpDJ08IkkxTgpKfGwc2cx7dvHEhtr6Wqpazub9r5Ccmw7OrS8GP8tF/aV7CHjhzeJyUvCk3g8Hft1Iz3JCpO96nMOrN7Bvn7j6NY7ibQ25XHV7Sb7p9VsLliDu3Nr+qWPItlRccMqjzuLPYeexOF00yb5VhwOq3tr454ift5dwtijk4h1Oij2/AR4iJGjERHW5xyg2O1mYPM2VFxOv3Z45ZeFbHhuLonRbvaPgomJacSnxvHImpW861hJclIybw55kxNanlBrNuTnu/joo720aRPLiSe2qjLsgk0rmfPjLAYnFHLc/oHMK4WSnoO4e9BptE5wMD9vPimOFM5NOpcoCXyvFRbkc8Mr97FW0pkweDTHff8d+7bkccKlo0nv3TlgHC+qyhvbl7G2aD1RaQfp5+xBn8xZNI/ZRfOkv5KS8lfrGi4PPx4ooUuKk7QEJy7dh8uTQYzjGBySiKv4Y9ylK1gb24lF0ds5hmM4m/I9i0YykgIK+IqvcNjPiQv3LOQ/817mpH3K2jaDyI3K5vwkJ57WWWR89wPP9N5MUXIxt/e6jVt6BttQrm7ZnrOXaxf/jQHxv3Cy6ziW7y5lXZe+XHHMSZzRtgvv5L1DlieLiUkTaR7VPHAiqjwy/1HePZjDoJ4jmVyUy84vttL31AH0HxN4nMKXr/et5dP9q8lpvZWuie0YfGApbZzLiIs9hTYtX0MkClXlhwMlJDiFXqkxeDSPEs9anI7uOKUN7tKfcBW/xx5nW+bF7aMlLbmcy3HiRFVxvPI8n1wwhagoCPazbc8mejiCd1nCERxzsGcUzcEafL7Bx30WkOkzIN1CVW8Vkf5Yewp7B6SXYC3T6xaRlcD1WN1SH2Jt6v6hiFwHHO0zIH2Oqp5flV1DhgxRM5XVYKg+EqChXkopzoY7s71Rcz7n88aLZ0AiLD1nCg6f/h7VcqEINdbgJVxxCOduOAFrw/i1IrLGdrsda1OT+SJyBbADa8tEVHWdiMwH1mN1ql2n1r7DYG3j9xLWxh8LKd9s/Hlgrohsxlr3fVIYdhkMhmoSSBgATud0lrGsjq0xhKKsvBLOAKDUBTHRliD4PteHKwzVIaQ4qLU/a7C+h5FB4twP3B/AfRXWVon+7kVQRWeowWA4LIKJgpd00uvIEkM4XM3VPM3T5Q5uwAmj//csi8+egtMJ6oGTo0prbfq3aUcaDI0cKZXyX3oQjZjHvDqzx1A1kifWxqtQXl6/OqGLNbvpjPeetaavT7yyVu0w4mAwNFJy9sTRrHmxtXEpBBUGrTwx0BABcnNzmZefAmm2g295Tb+87LSuysusymow1FNkhyC7hbsWC5kHy4/C4mfJYw8vI2XHBp6sEDdnjwDF4H31xlvR+NQrs5lthOEIImut8uq2VMjMLC+vrEPHAvAyjrLyeoeeFeLm7PkFzU/hWu8iRPVAyI04GAz1jN/9/g7kJ4FooARuONb2sCuM/IKp/I+Ky1as4NrAifm+D6q+p8qN3HikTG7yyIvHW2+PAys7YdWsYh2q3/EZF+FbALn4vwjZG4Abc+2P6nNglVddC7kRB4OhHjFK5vLpeV9Ac9vBRcWnSPvcTTHhkJ2F9dKat6LZa7qRjiSjZC7ywsmQaq9eICCxlcPlsSWs9O5yUf6iO0Bh5MrLiIPBUA/Ytm0bo2RuuYP3rehEKNxtn3vfuPX04jieqxC/LWP8UixfyiV7P2TvBXUomm6E4UhRVl5RQE5z61xh/i+UnaPgVhjJUr/Y/i9Plk8IyD5olVf23u/QhMiVlxEHgyHCjJK5XNn1i4qOq06yniAVOibAuz9DfgHkllxBy5Yb6c5kxrOBjpzDqSzg9LJXhixS0ovB+SGQBHxIihGFI8YomVtRyN2AxoAt4tf0g2lfQ14ebM5uTZsWShwJnE8BbTiVnlzNJVRc/y0lfRKOpANYTcZppKQrKemD6ugbBabaq7LWF8wb0obGQIVKxmZ/i938+I/3rS4kN+ifPq97wwwBCVReAEteeNZ61PbAw87J3HLxVXVrWDWotSW76wtGHAwNmWCVjD+f6MW1bIkhHBYuXMg/zjwQMlxDKK8juXyGwWA4goQjDJfNFf74xz/WgTWGUDRVITfiYDDUETk5OZzT7L2Q4RpbJdOQCUcYGmt5GXEwGOqAcCqZU26Du/7eOCuahsaotnOtjQNC0FiFAYw4GAy1zih5jsqvKlekMVcyDY1RSfdAvndDqKZbXmYqq8FQS8hLf0NeWokRhobBu+9+gry0AvK72S5Nu7xMy8FgqAXkVQFWBPAp3xm3qVQyDQF5RKDVt1S1y7GzLSza1XTKLGTLQUReEJF9IvKTj9tMEflNRNbYx5k+freJyGYR2Sgio33cjxWRtbbfo/YOc4hIrIi8brt/KyJdjvB3NBjqDNkkOH4WcPquuXzIPjfCUN+Yt2keskmgO3gXQxoz5lXb17vmiIdP9OImJQwQXrfSS1Dp3XyAf6rqQPv4EEBE+mHt4tbfjvOESNkmu08CU4Ge9uFN8wrgoKr2AP4JPFTD72IwRBTZZD11ti6Oguh821VZ8uJxjHrqba658VE6zmlrhKGeIJuEi7jIx8USg1suuJ/U537k6imPEv3UJj7RyyJkYWQJZye4z6vxND8eeE1Vi4Gt9rafw0RkG5Ciqt8AiMjLwASsbULHAzPt+G8C/xER0Yb6dp6hyeEVBS/7Et12I+Ez4BQAbou9FZ19POfWuXWGQPiXmbWO4fnAfADejJrIG888wnl1bVg94nDGHP4sIpcAq4CbVfUg0B5Y7hNmp+1Wap/7u2P//xVAVV0ikg20BEK/jmgwRJhKlYyXrgJrb4EEiHEmUzwhp24NMwQkaHkdC3y4G9oPh2zQK8yzaU3F4UngXqzno3uBfwB/oqrRnMDuhPCrgIhMxeqaolOnTtWz2GA4ggStZLwkKRwH2tNUMvWBbzd9ywhGVB3ozBJTXj7UaCqrqu5VVbeqeoBngWG2106go0/QDsAu271DAPcKcUTECTQDsoJc9xlVHaKqQ1q3bl0T0w2GwyakMAAXc7GpaOoJsklCCwNGyP2pkTiISFufj38AvDOZFgCT7BlIXbEGnleo6m4gV0RG2LOULgHe84lzqX1+HrDUjDcY6huD1w3mP+skLGHQnsrLPV+uA6sMwVi8ezE5a8Mrr6JORUYYAhCyW0lE5gGnAq1EZCdwD3CqiAzE6v7ZBlwFoKrrRGQ+sB5rD6vrVNVtJ3UN1syneKyBaO8C9M8Dc+3B6yys2U4GQ71BXn+Xff2/Z1KAHb78MZVM5On/+rvE9v8D01oBeVWHNeUVnHBmK10YwPn5KsLfD9wfwH0VcFQA9yJgYig7DIZIIHNfh+gSpkmQgTAbU8nUD+SRf0OHTrwI9IolqDh4eniwX7UyBMEsn2EwBGLp32nd7SOIjgGS6KBwaVLlYG1pa4ShnnC2PArtrSHP6xRO3x04nPZUIwxhYMTBYPBjyVJB9Q4OdLZnW4swKyOJyzLh9mi4HpiBVcns6rmrqqQMdYAsFXSJAMllbgXr/0u+BzqWwGPR1stUy0qWGSGvBmZtJYPBZv/cB2iz3IXnHOvzoG9/4PsrYyDKCYUvw0/wdxH0ggkRtdNQzlnP9qa0m49D4VZI6AYaD+ve5le3mz9PPo8/R8zChotpORgMQErSbFp5boeR1r4LInBM4Qqa3dMcDn4FxVk4X80wwlBPEHkbz0vCh9EzEbsxMGTo/Rw3tRVkfA4lWfDdOnRyU37H+fAwLQdDk0fkbfKfvtn6sOg68ifdQKLCyv+uI9tjvdjvlB3kvf1KBK00eBF5m7iYAkSAksXMcsB0hcIZcXxTsMAO9StLbrwnkmY2eIw4GJosIm+XnR/IhY6xkDH0BpJ3DuSlDmtYnb6ES/K70KPDfdzV/zxinTFVpGaobebMeZvLLrPOS1xW1aWxLyHvzuKzy2/hHVlH77i9/NhmFlf1PJ3ezdoHT8wQEmmo75sNGTJEV61aFWkzDA0UX2Hw4n7xXLyTWDweiLq8Yf42GiOByqv4uXOJth9vVcHh6AqXbKljyxoeIrJaVYeECmdaDoYmRcuWb5MVcHEWiLr8rbJz1XPqyCJDKAIJA0Dslaa8ahMjDoYmQ7BKxpfCwjOJi4urA2sMoQinvMAIQ21hZisZ6oyDqamUThEOivDMaEGW2cen1rGvcF+tXTucikb1nCYtDBvzNpaVhXwq/CqCe6pVXr8mC/JJxfK6YM0FtWZLuOVlhKH2MOJgqBNa9H0UzTtE3nPwVCFcdavt4fOiatq3abVy7b593wsZxlQycPSqowHo+8rvYPTLpPSFnGdhQRZ0HX4lRFGhvOYfmh8ZQzHlVRcYcTCEzV3r7kKWS9kRDp1mnojEfszdS6chLcEN3B8/36poPFjbKx8AwtwL5y/L/1JtGw4ccAf1M0+f5bgopf3vZrLj9Wt4ZPE1RHeDvTGx3NpiPu5jMqxAxUA2kI9VfmHgW167sg/vjfJPP21pyquOMOJgCJv7cu+r8FmWC7t3B1nAxmbX365n8BmbaNYadK/lVkSpVcHkYO0RqEAx9HX1rTItWS48xmMV3BzLQ9/Cu3b9PqC7qWQqMm5oNLnxI/jzE4/wU/wpFH0A61J6UByzibH3LLMWscsBSoACQq54GkjA2/9c8+mlqudwyimn1Di+oXoYcTAcFu22t6vyCV6B75cOw7EjmrM/t3olFI9Vwbgqhj2QH3hn2C+WfxH0GlrlWqkW0dHRlJaeTdeuMGJEvGktBMFVmkgP/Z4np13L2K4fcg0ncvKBdeSVNCPrIFDkF6E4eFrhtuqCoXoOF1yQSosWpnUXKcxsJcMRQZYLOqJyRS2tfyNu/3a2/9KN207ZyDseGHXzR3zfCvb7acGUblMCpnskcDqdbNliKpiqaFZyiMVF6XQfkcPuLcmM2b2BXtM+hC9/okWzyuH7JPWp5NZjeQ8yyDgi9rz22u+OSDqGmmFegjOEzaDlg1jDmpDhfEWixFNC+i0XkjP7HKCQUs8ViAj72U3PRe3IzrXCXdTxIv474r8V0glHGAIJkqHmXJAlvNXuSShO4MO98ZzRpnyrlRO2Cl/bP7m+SX1ZN2ZdhaWvwymvX1r+Qs+ePY+43YbwCfcluJDiICIvAOOAfap6lO3WAngd6IK1E9z5qnrQ9rsNuAJr7PEvqvqR7X4s5TvBfQhMU1UVkVjgZeBYIBO4QFW3hTLciEPkqO1Ke+TykSxlaa1ewwD9l/dnPevZ220vbdq0Oay0jJA3HMIVh3DGHF4Cxvi5zQCWqGpPYIn9GRHph7XNZ387zhMiEmXHeRKYirWvdE+fNK8ADqpqD+CfwENh2GSIIOH8yGW5MHF59Tf4k+USUhgy2mSYiuYwkeXCetYDkLYlrcbdd+HOGjPl1fAIKQ6q+jnW3s6+jAfm2OdzgAk+7q+parGqbgU2A8NEpC2QoqrfqNVUedkvjjetN4GRYrZpqvfoCKV4cBUjksCbvFmtSifcSqZbt24hwxmqT3UFItzyMsLQMKnpbKU0Vd0NYP/3tknbA7/6hNtpu7W3z/3dK8RRVRfWJMeWNbTLUIfExMSE3YoI5W+ePusHslwYsHxAWOFCYcqrYXOkp7IGumO0Cveq4lROXGSqiKwSkVX79++voYmGcJBMQTKFCZkTQoYNVyDGLR8X0D2c9MO6hm3z7MzZIcMagvMjPwYtl3CE/FRODau8umR2QTKF4zKPq5GdhtqlpuKw1+4qwv7vXRRnJ9DRJ1wHYJft3iGAe4U4IuIEmlG5GwsAVX1GVYeo6pDWrVvX0HRDKCSz/Mf/Hu9V+ByMcCrwD/iAkpKS8uscoadPryh4uZmbmZE5I2S8pkxNWnw9l4eeZaQjlGUjloVOO1PYznYAlrOc6MzokHEMdUtNxWEBcKl9finwno/7JBGJFZGuWAPPK+yup1wRGWGPJ1ziF8eb1nnAUm2o82sbMZIp3Jx5c8hwoSqdadumhXW9J3gibGEIxENmXkNIdITSilZhh9/M5pDphcJfyL24/N+INESckOIgIvOAb4DeIrJTRK4AHgROF5FNwOn2Z1R1HTAfWA8sAq5TVe/CNtcAz2ENUmcAC23354GWIrIZuAl75pOh/jGb2WG3Is7irIB+l7W+LKz414y4JmS4cGwxVM3+EfsPe2ygOt1+hoaDeQmuiTNs2ApWroS//jWOWbOOAewfcVUjQlj90ke3PDpk+v5dE76VyI6cHXRe3zmof9A0g1UyGy6FrMHQ519oz/IdwRISVlBUBNu396Jjx+Yh02+q+JfVoaMP0SyxWZVhwimvxMxECiio7LH7ONh6EbT7gCldOvJMy2cAGDRoDWvWlHDnnance695Ye5Ic8RegquvGHE4fERWVHJTHUbHd5ayM30k9MAShyB1cWmzUpzOul2BJagwfPYSFHsnzQkLTxnI2PY7KgXLyOhJt26ptWafoSI3Zd7EP/lnZY+Nl8H28+0Pwpj0lsxqHc/RR1cUkU6dHGzfHrIeM1SDI/kSnKEJIbKCnYUlsG0hfLLQWpY5yPPDRXkX1altVVLcBl8lG9s+8F7C3btvqjubDIGFAWD7ufaJVV6LTiusJAwAO3aEuS644YhjFt4zBMY7AXnJQjhprLXoiR8Tqf4b0LWH3aK4SIDmQUNFRQX1MtQljhLwJMC9wM/NCdo8NUQM03Joqtw2DDiIpQJ+TYPXY63/ChSAdtCyaYe+TGxe9+LwWfRnldy0pfLQMT3homaUC0PFyibvnOG4Jw/HddFw8JlOa6hdtGXlZqe2VPaMGw0XJcPPqQTqu3xn8AW4Jw/HffFw+Pj5ujHWUAEz5tAUeepK9GvrB+eY+zBwElUNLsyY0ZwHHuhVZ+bVhEDjJxY/4p48BRGosCjLyw3zvm8sBC8vKL1wuNXCE5878ql8SEioC9MaPWbMwRCQuR8/RKktDALoxbfiufg4yrdkq8xDDx2qK/OOOAGFoYmQkS/2cVKkTQkbrzCI+D2qPH9FpEyqMzLyE+zyqh8iaMShCZGRL1xySPHfb80SiZM4m/sDxhs7tn7crDXB4Wh6wpCRfy8Z+b5f+ksy8jtFzJ7q4HQGKa8xt9a5LXWJVV6F9qdCv/KLDEYcmggZ+YLbY91w7X7/YNkkJN+2wnsXf4jqsEpxP/jgqDqx8XDYsKFit9fkyQT8LgA4E+vAoshgVSp3B/D5NYBb5AhUNkHLC6DnoFq0JnJ4W3f1ETNbqR4ircqnYY4f6+DduV1qnJbvjRfl8EqB4vz9g/z2vxm0xWdlxJetJQyq/JHWU3r3bh7Y7jEzYNGD5Z8b6VjDwfyDZNEi0mZUi4Dl9WwRTIkr/5x+MjxceRJCY6C+ioIXMyBdz/AVBi/ZW9qSkhJgLmkI3G4324oq6n+PD6dTPqPHg068vfpGGuodoSqadPJITGy8LaaGRmhh6Eb3xCOzF7c/4Q5Im5ZDPSGQKHi5b3Y2D8+svjiUerZVctt8ZvmCdN0TG+aDgaF6mHJuaDxE98TIj7EYcagHVCUMALdNS6lRurHObhBwSv9auifW/3EEw+FjhKFhUZ/KywxIR5CtCbFIywwI0bWXmlqz2UIigoOKy2x3T1QjDI2QLnEVl7y2yrn+VDSGilQum6n1rrxMyyFC5CcI3WI3Wh+CzLX8+Uvo0+fw9kvumvgI8MhhpWGo/0RFRdW7ysVQNfW9vIw41DHyty2AC0+g+0K1TCj0wOGJgsFgMBwOh9WtJCLbRGStiKwRkVW2WwsR+VhENtn/U33C3yYim0Vko4iM9nE/1k5ns4g8au8W1+iwhAESHbkAfFM0xvJQLTuuutgIg8FgiDxHYszhd6o60Gdq1Axgiar2BJbYnxGRfsAkoD8wBnhCRLxrZD4JTMXaVrSn7d9oePKuM5B7NpcJQL4nlb0Kw9lKfkFPBhR9xezC69CsHjz1z25kiPBqN0GWCrKkUeqkwWCo59TGgPR4YI59PgeY4OP+mqoWq+pWrO1Ch4lIWyBFVb+x945+2SdOvceVcRuu9VJ2+PJs7rPIAuHa0lmULfBjN4ra3raBxZf158O445jyr1JuLFgEgMfjYVMLmPwSMOcV6AWyU5Bfhbz8vIrX9rmuK7/yxjaGxkGFcl5fs5lrhrqjYnk13Ie7wxUHBRaLyGoRmWq7panqbgD7v3d7rvZUfId/p+3W3j73d6+X7PdbhkCLHiybbKR4b4yhAEx9ahMJDsAZVzERVSCaMekLOO/gV1w3pXxRMVdREWPnABtGwH1/LF8sVSDlkFUxeG+6i7Pas4rWrEuKpXR7+XabxRSzv0KWGhoirvVLyiqX/BjIT4X8+FxcpaVlYTLZhQbbjclQ55SVl9jl1Qxc65uX+R/gN4opjpB11eNwB6RPUNVdItIG+FhENlQRNpCEahXulROwBGgqQKdOdbuQ2Le8zotMquB2+/YJtKV8spHg/UL2m9vrTqBg6Cz+8cqV3HzZS5WmrOo9lccWYhIS6LAVdm4ZC2cuL88dAUVx7XgKgEtzk7lj4G+47RL8pQ10K97JLbEdK6T3OC6iMDvcNExGAVYlU1aEiZB/KIXprYsqhOzKyUyncS4z0VAoE4ZYINl2jIL8ltlMD1DNPVXPRf2wWg6qusv+vw94BxgG7LW7irD/77OD7wR8a64OwC7bvUMA90DXe0ZVh6jqkNatWx+O6dXGXxgA7l3R2rar3M17C7hK8+GqvwBw2vYv+eeLk8HlguIidGb3gMLg5dMRK2D0TCiKLV8dTyFN0iDvGgBa//YHSxjsVsWTwC0xHSul9ReSqv1dDfUMe28D7801vUVRpSBb+bxOTTJUgfcnZ5fZ9OYRtOUwqLE4iEiiiCR7z4EzgJ+ABcCldrBLgffs8wXAJBGJFZGuWAPPK+yup1wRGWHPUrrEJ069pl/qLvp6Kkw2KnsWcEYn4hmxjZjSExn0Ptz40rd0HT0WfaB/yHS7Dx2KjvHAmvZwIA0U+jr6sqfdHoixeu8KEnaXhX9SgNjAabkbSBPWEAKF6cD01phXVxsIXwHTWwIxkbakZhxOt1Ia8I4969QJvKqqi0RkJTBfRK4AdoC10bCqrhOR+cB6wAVcp6puO61rgJewdipeaB8R59nVO5n6tbX+xFVnt0Q7ZQI+76wN+YCijanEJhykQMHpsB/unP3tcELx6V/U+Pp6XuWFt5w9nsa1/hme6PQxK10wJ4EKXU+FBcLcZotRl5PO4z8je+TFPI01hXbdean0a5taKU1DfeU0YCmUwPQ0yn+tdnln/tCdXxcNo9Wxv9Bh1GrkMauc28XCb1PNdOg6J/0n2HMUZMH09pR3LQUZk/aWF4BeX//Ky6zKGoSM/bn0eG1/ucNtOxmz8hQ69bE+lglEKXQvhIwoILFu+xGvtgYiKtx8T8uycofZbSnbVsumPt6EhqopK2eoIAzvHv847pJodGYLSKk4i6lrImz5kynrSLCV73mIwUFHVJ9+7J/A2ZXc6+q3aVZlPUwG+woDQD4s6mcN+F3pOqV8h7FoyIiue/vKqHTzCZwncGI7Au2Pue1QMV2aB+mDMtRf/Mr518VDcZc40Vkdre3ufN6uB9iaX8f2Gcp4yuosCSIM6wnaB1zPML2XQZjQxc/Bp6Cfc35GQUFF76cn/sDTEzcjE7cgE6teZfVwGDF5S9k1PJ4Aa/ZNbw4ntQ8oDAAdk83zQGMgoc9G9JHO5fugNs5FBRokk/h3QPenH8ugoQgDGHEIypzf+zXx/pFe4eMrj6xlOH8FwO0SIBFfBakNkZCJW/jWZwnuOVcuqSAOT6HQ3u7oDFJZREWZaa0NjUBdlcu2vVnlBtmm+zByDOKsSm6dl++pMs6XZ9U/cTdjDiHwHTTyx/sD3HuwmPSpvwUP98bh/VDnzdvCRW8H9/dNP5i9Q4EVpsJoNIRzXxrqD6Pf3M7i3e6AfnVdXmbMoQY8KE+Xnc/Qq1i5uYjr4lPo2LyYGbsrTgkd2rK80ZWWWnVTUSZuYUBbWPNo9W+CI9H6MJVF42Nab/j3xopuk4B5pqzrJR9MaE/0k5WXuKnPv00jDlQUBS83J8/lqfEnUlCsJMQKM8Y2Y0V0Hp/udTPz2CTuOr5NhfAZD0H36cGv8cPu4H7B2HMgN2QY/1aJXt+NoXO2sCoHbj8mmvtPqfxinKHh868zutGvxS6u+sZ6Ia4+VzIGcDqdZP8pjWYv7AUaRnk1eXEIJAwAO1NTcNjdgAXFyqI1hXw/q3PAsADdunVD3zgyT/pebn8tu0r/YN1VKy+t/zee4fCZOqQdU0N2DhjqCymJiQ1CFLw02QHpB+97OqgwAHTMyi4b7E2IFc4cFN5WnfpGN06u2a6elZg1uUXQaxzuOIbBYDBURZNsOVQlCl4eybuECzOKeeXzXPp0iGbKyPCXSv5sjlVx+7Yivryn+kstt0xN4Piu8PXWcjcjCgaDoS5oUrOVHrznafi/0OFm6FU1tMpgMBjqN2a2kh/htBbACIPBYDBAExCHB1s/DQdChzOiYDAYDOU0anEwrQWDwWCoGY1SHB5s8TQcDB3OiILBYDAEptGJg2ktGAwGw+HTaMTh7bff5pdz94cMZ0TBYDAYQlNvXoITkTEislFENovIjHDi7M3N5i/LnqDXKf3ZeO4+NMRGO6GEQZ4X+0hEnh9E3IupyPNC7IuxLNu1LPwv40NpaSnPbfiJ4uKabdf526GDzMvYUKO4BoPBUFPqxXsOIhIF/AKcDuwEVgIXqur6oHG6ddbjzzidr4c8z/1TnkDsv0CE01po/nxzssmmf/bNrIsaR4+sm9jc6fsy/4SoZPIvy6nW9+r6xv/Yn7Oa/OREABwuN+6LwtI9AGTeM7BjCEQ3A5QF41L4fa82IeMZDAZDMMJ9z6G+tByGAZtVdYuqlgCvAeOrjFEaz4rjBxCoseDbggi3GymbbMhbxrrUFiQlf8rmTsdW8C9wV+/Jf/mvuZz4/lxLGOzNWDzOKGb+8FlY8V8e0o+UjZ1tYQAQzn4/9EJ8BoPBcCSoL+LQHvjV5/NO260KHDz28L+gGD4f8DFgiYKiePAwQ6+q/vhCr2VAd9Jz3MDFQJyP55+qldT0Lw/SLiuzkvvcHeF1EQ3c+hs5Kd6lMgTvRkJuj6dadhgMBkNNqC/iEKg/qFKbQESmisgqEVlF/iFStAjcm/loyjs8MP0OMjnA62PmkPPZ9mob8Pjgx2H3YIavW8Brt98HjnhgGTALeBMcU6qV3l0jmvFNmq1vqmX7eX5y8nlhxf/bE/fjKPbdi9SKH+WoL0VmMBgaM/VlzOE4YKaqjrY/3wagqg8Ei9M2OUb33Lsc5AfgZNu1EJ121GHZsjJVGDb9VTbfdhE9XlgJRIFHyZ7Um5TExGqlNfCOhVz/7/O58rk7QYQXu43gsqEnh45o85d7b+KxxKstG4DJveN45awQDSqDwWCognDHHOqLODixBqRHAr9hDUhfpKrrgsWpq21CDQaDoTHRoBbeU1WXiPwZ+AjrMfmFqoTBYDAYDLVLvRAHAFX9EPgw0nYYDAaDoZ50K9UEEdkP5BPWmqtNglaYvACTD76YvCjH5EU5vVU1OVSgetNyqC6q2lpEVoXTd9YUMHlhYfKhHJMX5Zi8KEdEwhqsNfMiDQaDwVAJIw4Gg8FgqERDF4dnIm1APcLkhYXJh3JMXpRj8qKcsPKiwQ5IGwwGg6H2aOgtB4PBYDDUAg1SHGqy90NjRUReEJF9IvJTpG2JJCLSUUSWicjPIrJORKZF2qZIISJxIrJCRH6w8+JvkbYpkohIlIh8LyLvR9qWSCMi20RkrYisCTVrqcF1K9Vk74fGjIicDOQBL6vq4S0s1YARkbZAW1X9TkSSgdXAhKZ4X4iIAImqmici0cCXwDRVXR5h0yKCiNwEDAFSVHVcpO2JJCKyDRiiqiHf+WiILYfq7/3QiFHVz4GsSNsRaVR1t6p+Z5/nAj8Tctn3xola5Nkfo+2jYT0FHiFEpANwFvBcpG1paDREcajB3g+GpoSIdAEGAd9G2JSIYXelrAH2AR+ralPNi38BtwJmIxQLBRaLyGoRmVpVwIYoDmHt/WBomohIEvAWcIOqVm9f10aEqrpVdSDQARgmIk2uy1FExgH7VHV1pG2pR5ygqoOBscB1drd0QBqiOOwEOvp87gDsipAthnqE3b/+FvBfVX070vbUB1T1EPApMCaylkSEE4Cz7X7214DTROSVyJoUWVR1l/1/H/AOVjd9QBqiOKwEeopIVxGJASYBCyJskyHC2IOwzwM/q+rsSNsTSUSktYg0t8/jgVFAePvTNiJU9TZV7aCqXbDqiaWq+scImxUxRCTRnqyBiCQCZwBBZzk2OHFQVRfg3fvhZ2B+U977QUTmAd8AvUVkp4hcEWmbIsQJWBt/n2ZP01sjImdG2qgI0RZYJiI/Yj1MfayqTX4ap4E04EsR+QFYAXygqouCBW5wU1kNBoPBUPs0uJaDwWAwGGofIw4Gg8FgqIQRB4PBYDBUwoiDwWAwGCphxMFgMBgaANVdZFNEzheR9fbii69W+3pmtpLBYDDUf6qzyKaI9ATmA6ep6kERaWO/+BY2puVgMBgMDYBAi2yKSHcRWWSvlfSFiPSxvaYAj6vqQTtutYQBjDgYDAZDQ+YZ4HpVPRb4K/CE7d4L6CUiX4nIchGp9vIpziNopMFgMBjqCHuRyeOBN6zVYwCItf87gZ7AqVjrz30hIkfZa22FhREHg8FgaJg4gEP26rv+7ASWq2opsFVENmKJxcrqJG4wGAyGBoa9JP1WEZkI1uKTIjLA9n4X+J3t3gqrm2lLddI34mAwGAwNgCCLbE4GrrAX01tH+a6YHwGZIrIeWAbcoqqZ1bqemcpqMBgMBn9My8FgMBgMlTDiYDAYDIZKGHEwGAwGQyWMOBgMBoOhEkYcDAaDwVAJIw4Gg8FgqIQRB4PBYDBUwoiDwWAwGCrx/yVVp1+QXbUTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seq_info = new_sdb.seq_info.copy()\n",
    "sinfo = list(seq_info.items())\n",
    "sinfo.sort(key=lambda x: x[1][0])\n",
    "\n",
    "#fig = plt.figure(figsize=(24,96))\n",
    "pidx = 1\n",
    "for sid, data in sinfo[:10]:\n",
    "    ctg, _, _ = data\n",
    "    smps = sid_smps[sid]\n",
    "    #print(seq_info[sid])\n",
    "    #print(sid, len(smps))\n",
    "    x, rx = [], []\n",
    "    y, ry = [], []\n",
    "    c, rc = [], []\n",
    "\n",
    "   \n",
    "    for s in smps:\n",
    "        if s[1] is None:\n",
    "            continue\n",
    "        vertex = tuple(s[0][:2])\n",
    "        x.append(s[0][2])\n",
    "        y.append(b_vertex_to_position[vertex][0]) # same direction\n",
    "        c.append(color_theme[s[1][0]])\n",
    "            \n",
    "\n",
    "    fig = plt.figure(figsize=(6,2))\n",
    "    plt.scatter(x, y, marker=\".\", color=c, s=40)\n",
    "    \n",
    "    \n",
    "    plt.xlim(-10000,5000000)\n",
    "    plt.title(ctg)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_file_name, daz1_chr, daz1_b, daz1_e = \\\n",
    "('hg38_tagged.fa', \n",
    "'chrY_hg38', 23129355, \n",
    "23199094)\n",
    "DAZ1_len = 23199094-23129355\n",
    "DAZ2_len = 23291356-23219434\n",
    "padding = 100\n",
    "#roi_len = roi_e - roi_b\n",
    "#get a segment of a reference\n",
    "DAZ1_seq = ref_db.get_sub_seq(ref_file_name, daz1_chr, daz1_b-padding, daz1_e+padding)\n",
    "\n",
    "ref_file_name, daz2_chr, daz2_b, daz2_e = \\\n",
    "('hg38_tagged.fa', \n",
    "'chrY_hg38', 23219434, \n",
    "23291356)\n",
    "DAZ1_len = 23199094-23129355\n",
    "DAZ2_len = 23291356-23291356\n",
    "padding = 100\n",
    "#roi_len = roi_e - roi_b\n",
    "#get a segment of a reference\n",
    "DAZ2_seq = ref_db.get_sub_seq(ref_file_name, daz2_chr, daz2_b-padding, daz2_e+padding)\n",
    "\n",
    "ref_file_name, daz3_chr, daz3_b, daz3_e = \\\n",
    "('hg38_tagged.fa', \n",
    "'chrY_hg38', 24763069, \n",
    "24813492)\n",
    "\n",
    "padding = 100\n",
    "#roi_len = roi_e - roi_b\n",
    "#get a segment of a reference\n",
    "DAZ3_seq = ref_db.get_sub_seq(ref_file_name, daz3_chr, daz3_b-padding, daz3_e+padding)\n",
    "\n",
    "\n",
    "ref_file_name, daz4_chr, daz4_b, daz4_e = \\\n",
    "('hg38_tagged.fa', \n",
    "'chrY_hg38', 24833919, \n",
    "24907040)\n",
    "\n",
    "padding = 100\n",
    "#roi_len = roi_e - roi_b\n",
    "#get a segment of a reference\n",
    "DAZ4_seq = ref_db.get_sub_seq(ref_file_name, daz4_chr, daz4_b-padding, daz4_e+padding)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_matches1 = new_sdb.get_match_positions_with_fragment(DAZ1_seq)\n",
    "gene_matches2 = new_sdb.get_match_positions_with_fragment(DAZ2_seq)\n",
    "gene_matches3 = new_sdb.get_match_positions_with_fragment(DAZ3_seq)\n",
    "gene_matches4 = new_sdb.get_match_positions_with_fragment(DAZ4_seq)\n",
    "gene_matches = [gene_matches1, gene_matches2, gene_matches3, gene_matches4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1728x4608 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seq_info = new_sdb.seq_info.copy()\n",
    "sinfo = list(seq_info.items())\n",
    "sinfo.sort(key=lambda x: x[1][0])\n",
    "\n",
    "fig = plt.figure(figsize=(24,64))\n",
    "# pidx = 1\n",
    "for sid, data in sinfo:\n",
    "    ctg, _, _ = data\n",
    "    smps = sid_smps[sid]\n",
    "    #print(seq_info[sid])\n",
    "    #print(sid, len(smps))\n",
    "    x, rx = [], []\n",
    "    y, ry = [], []\n",
    "    c, rc = [], []\n",
    "    for s in smps:\n",
    "        if s[1] is None:\n",
    "            continue\n",
    "        \n",
    "        if s[0][4] == s[1][1]:\n",
    "            x.append(s[0][2])\n",
    "            y.append(0) # same direction\n",
    "            c.append(color_theme[s[1][0]])\n",
    "        else:\n",
    "            rx.append(s[0][2])\n",
    "            ry.append(6)\n",
    "            rc.append(color_theme[s[1][0]])\n",
    "        # if tuple(s[0][2:4]) in matches:\n",
    "        #     track0_x.append(s[0][2])\n",
    "        #     track0_c.append(fcolors[s[1][0]])\n",
    "            \n",
    "    y = np.array(y) \n",
    "    ry = np.array(ry)\n",
    "    x.reverse()\n",
    "    c.reverse()\n",
    "    \n",
    "    # plt.subplot(len(sid_smps), 1, pidx)\n",
    "    # pidx += 1\n",
    "    \n",
    "    plt.figure(figsize=(24,3))\n",
    "    plt.scatter(x, y, marker=\">\", color=c, s=80)\n",
    "    plt.scatter(rx, ry, marker=\"<\", color=rc, s=80)\n",
    "    \n",
    "    \n",
    "    for mi in range(4):\n",
    "        matches = set([(_[0], _[1]) for _ in gene_matches[mi][sid]])\n",
    "        track_x = [_[0] for _ in matches]\n",
    "        plt.scatter(track_x, [-4-mi*2]*len(track_x), marker=\"s\", color=\"black\", s=2)\n",
    "  \n",
    "    \n",
    "    \n",
    "    #track0_x = [_[0] for _ in gene_matches[sid]]\n",
    "    #plt.scatter(track0_x, [-6]*len(track0_x), marker=\"s\", color=\"black\", s=20)\n",
    "    \n",
    "    #plt.axis(\"off\")\n",
    "    plt.yticks([])\n",
    "    plt.ylim(-16,12)\n",
    "    plt.xlim(-50000,5200000)\n",
    "    plt.text(0, -15, ctg, fontsize=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}