Ancestor_Composition_Solver.ipynb

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        "<a href=\"https://colab.research.google.com/github/AncestorComposition/public/blob/main/Ancestor_Composition_Solver.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "*Estimate the ancestor composition of your genome using DNA match segment data and information about common ancestors.*\n",
        "### Step 1: Upload CSV files\n",
        "*   Click on the folder icon to the left to expand the \"Files\" pane.\n",
        "*   Click the \"Upload to session storage\" button in the upper left to upload CSV files for match shared segments (from MyHeritage, FamilyTree DNA, and/or 23andMe) and a CSV file with common ancestor information for some of your matches. [Detailed directions](#directions) are provided below."
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          "text": [
            "Ancestor Composition Solver completed succesfully.\n",
            "*** Coverage ***\n",
            "Coverage at grandparent generation is 90.6%\n",
            "Coverage at great-grandparent generation is 89.4%\n",
            "Coverage at 2nd great-grandparent generation is 84.5%\n",
            "Coverage at 3rd great-grandparent generation is 19.4%\n",
            "*** Crossovers ***\n",
            "Your genome has 29 paternal crossovers and 42 maternal crossovers.\n",
            "The average number of paternal crossovers is 26.4 and the average number of maternal crossovers is 41.1.\n",
            "*** Inheritance from grandparents ***\n",
            "From grandparent Anthony Irvine you inherited 26.4% of your genome.\n",
            "From grandparent Mary Larsson you inherited 23.5% of your genome.\n",
            "From grandparent Preston Newman you inherited 34.4% of your genome.\n",
            "From grandparent Elizabeth Hawthorn you inherited 15.7% of your genome.\n",
            "Typical range of inheritance from grandparents is 14% - 35%.\n",
            "*** Inheritance from great-grandparents ***\n",
            "From great-grandparent  Richard Irvine  you inherited 11.5% of your genome.\n",
            "From great-grandparent  Mary Jenson you inherited 15.1% of your genome.\n",
            "From great-grandparent  John Levi Larsson  you inherited 4.4% of your genome.\n",
            "From great-grandparent  Caroline Walton you inherited 19.1% of your genome.\n",
            "From great-grandparent  Preston Newman  you inherited 10.6% of your genome.\n",
            "From great-grandparent  Lucy Porter you inherited 23.2% of your genome.\n",
            "From great-grandparent  Robert Hamilton Hawthorn  you inherited 4.0% of your genome.\n",
            "From great-grandparent  Abrelia Clarissa Scott you inherited 12.0% of your genome.\n",
            "Typical range of inheritance from great-grandparents is 4% - 22%.\n"
          ]
        }
      ],
      "source": [
        "!pip install distinctipy -q\n",
        "#@title Step 2: Calculate your Ancestor Composition { display-mode: \"form\" }\n",
        "from bokeh.models import AnnularWedge, HoverTool, Range1d, Panel, Tabs\n",
        "from bokeh.plotting import figure, show\n",
        "from bokeh.io import output_notebook\n",
        "from bokeh.models.widgets.tables import ColumnDataSource\n",
        "import pandas as pd\n",
        "from distinctipy import distinctipy, get_hex\n",
        "from collections import defaultdict\n",
        "import requests\n",
        "import math\n",
        "import os\n",
        "Gender = \"Male\"  # @param [\"Male\", \"Female\"]\n",
        "OptionalAPIKey = \"\" # @param {type:\"string\"}\n",
        "\n",
        "output_notebook()\n",
        "\n",
        "sidemap = {}\n",
        "\n",
        "groups_rep = list(range(4, 64))\n",
        "\n",
        "allchromes = list(range(1, 23))\n",
        "allchromes.append('X')\n",
        "\n",
        "def gen(anc):\n",
        "    '''Generation of ancestor '''\n",
        "    return math.floor(math.log(anc, 2))\n",
        "\n",
        "\n",
        "def ahnentafel_descendants(a_val, a_min):\n",
        "    ''' Ahnentafel numbers of descendants '''\n",
        "    if not isinstance(a_val, list):\n",
        "        a_val = [a_val]\n",
        "    if not a_val[-1] % 2 == 0:\n",
        "        a_val.append(int((a_val[-1] - 1) / 2))\n",
        "    else:\n",
        "        a_val.append(int(a_val[-1] / 2))\n",
        "    if a_val[-1] <= a_min:\n",
        "        return a_val\n",
        "    return ahnentafel_descendants(a_val, a_min)\n",
        "\n",
        "\n",
        "def side(a_val):\n",
        "  ''' Determine whether ancestor is on paternal or maternal side '''\n",
        "  if a_val in sidemap:\n",
        "      return sidemap[a_val]\n",
        "  if 2 in ahnentafel_descendants(a_val, 0):\n",
        "      sidemap[a_val] = 'Paternal'\n",
        "      return 'Paternal'\n",
        "  sidemap[a_val] = 'Maternal'\n",
        "  return 'Maternal'\n",
        "\n",
        "\n",
        "def csv_reader():\n",
        "  ''' Parse CSV files '''\n",
        "  files = os.listdir()\n",
        "  segsmh = None\n",
        "  segsftdna = None\n",
        "  segs23 = None\n",
        "  mf = None\n",
        "  allnames = set()\n",
        "  for f in files:\n",
        "      if '.csv' in f:\n",
        "          try:\n",
        "              df = pd.read_csv(f)\n",
        "          except Exception:\n",
        "              pass\n",
        "          if 'Match name' in df and 'SNPs' in df:\n",
        "              segsmh = df\n",
        "              allnames = allnames.union(set(list(segsmh['Match name'])))\n",
        "          elif 'Match Name' in df and 'Matching SNPs' in df:\n",
        "              segsftdna = df\n",
        "              allnames = allnames.union(set(list(segsftdna['Match Name'])))\n",
        "          elif 'Display Name' in df and 'Genetic Distance' in df:\n",
        "              segs23 = df\n",
        "              allnames = allnames.union(set(list(segs23['Display Name'])))\n",
        "          elif 'Male Ancestor #' in df and len(df) > 4:\n",
        "              mf = df\n",
        "  if not allnames:\n",
        "    raise Exception(\"Did not find any valid match segment CSV files.\")\n",
        "  if not isinstance(mf, pd.DataFrame):\n",
        "    raise Exception(\"Did not find a valid common ancestor file.\")\n",
        "  #Obscure the match names\n",
        "  obscure_dict = dict(zip(allnames, range(len(allnames))))\n",
        "\n",
        "  # FTDNA matches and segs\n",
        "  matches = {}\n",
        "  if isinstance(segsftdna, pd.DataFrame):\n",
        "    for (index, row) in segsftdna.iterrows():\n",
        "        nmind = obscure_dict[row['Match Name']]\n",
        "        if not nmind in matches:\n",
        "            matches[nmind] = {}\n",
        "            matches[nmind]['cm'] = 0\n",
        "            matches[nmind]['Segs'] = defaultdict(list)\n",
        "        matches[nmind]['cm'] = matches[nmind]['cm'] \\\n",
        "            + row['Centimorgans']\n",
        "        matches[nmind]['Segs'][str(row['Chromosome'])].append((row['Start Location'], row['End Location'],\n",
        "                                                              row['Centimorgans'], row['Matching SNPs']))\n",
        "  # MH matches and segs\n",
        "  if isinstance(segsmh, pd.DataFrame):\n",
        "    for (index, row) in segsmh.iterrows():\n",
        "        nmind = obscure_dict[row['Match name']]\n",
        "        if not nmind in matches:\n",
        "            matches[nmind] = {}\n",
        "            matches[nmind]['cm'] = 0\n",
        "            matches[nmind]['Segs'] = defaultdict(list)\n",
        "        matches[nmind]['cm'] = matches[nmind]['cm'] \\\n",
        "            + row['Centimorgans']\n",
        "        matches[nmind]['Segs'][str(row['Chromosome'\n",
        "                                       ])].append((row['Start Location'], row['End Location'],\n",
        "                                                   row['Centimorgans'], row['SNPs']))\n",
        "  # 23andMe\n",
        "  if isinstance(segs23, pd.DataFrame):\n",
        "    for (index, row) in segs23.iterrows():\n",
        "        nmind = obscure_dict[row['Display Name']]\n",
        "        if not nmind in matches:\n",
        "            matches[nmind] = {}\n",
        "            matches[nmind]['cm'] = 0\n",
        "            matches[nmind]['Segs'] = defaultdict(list)\n",
        "        if isinstance(row['Chromosome Number'], str):\n",
        "            matches[nmind]['cm'] = matches[nmind]['cm'] + \\\n",
        "                row['Genetic Distance']\n",
        "            matches[nmind]['Segs'][row['Chromosome Number'\n",
        "                                       ]].append((row['Chromosome Start Point'],\n",
        "                                                  row['Chromosome End Point'],\n",
        "                                                  row['Genetic Distance'], row['# SNPs']))\n",
        "\n",
        "  # Common ancestor data\n",
        "  groupdict = dict.fromkeys(range(4, 128), 'Unnamed Ancestor')\n",
        "  commonancestors = dict()\n",
        "  for (index, row) in mf.iterrows():\n",
        "    if row['Male Ancestor #'] in range(4,128):\n",
        "      groupdict[row['Male Ancestor #']] = row['Male Ancestor Name']\n",
        "      groupdict[row['Female Ancestor #']] = row['Female Ancestor Name']\n",
        "      if row['Match Name'] in obscure_dict:\n",
        "        nmind = obscure_dict[row['Match Name']]\n",
        "        if nmind in matches:\n",
        "          if row['Male Ancestor #'] > 0 and row['Female Ancestor #'] > 0:\n",
        "            commonancestors[nmind] = int(row['Male Ancestor #']/2)\n",
        "          elif row['Male Ancestor #'] > 0:\n",
        "            commonancestors[nmind] = int(row['Male Ancestor #'])\n",
        "          elif row['Female Ancestor #'] > 0:\n",
        "            commonancestors[nmind] = int(row['Female Ancestor #'])\n",
        "\n",
        "  return matches, groupdict, commonancestors\n",
        "\n",
        "\n",
        "matches, groupdict, commonancestors = csv_reader()\n",
        "\n",
        "\n",
        "def call_ancestor_composition_solver(matches, n, Gender, commonancestors):\n",
        "  # Call the Ancestor Composition Solver API\n",
        "  token = OptionalAPIKey\n",
        "  sort_matches = sorted(matches.items(), key=lambda x: x[1]['cm']+1000*int(x[0] in commonancestors), reverse=True)\n",
        "  first_n = dict(list(sort_matches)[:n])\n",
        "  json_input = dict()\n",
        "  url = \"https://5if5u6ou58.execute-api.us-east-1.amazonaws.com/default/AncestorComposition\"\n",
        "  json_input[\"Matches\"] = first_n\n",
        "  json_input[\"Gender\"] = Gender\n",
        "  json_input[\"Token\"] = token\n",
        "  json_input[\"commonancestors\"] = commonancestors\n",
        "  r = requests.post(url, json=json_input)\n",
        "  json_output = r.json()\n",
        "  message = json_output[\"message\"]\n",
        "  print(message)\n",
        "  ancestor_content = json_output[\"ancestor_content\"]\n",
        "  ancestor_segments = json_output[\"ancestor_segments\"]\n",
        "  return ancestor_content, ancestor_segments, message\n",
        "\n",
        "if len(OptionalAPIKey) < 16:\n",
        "  n = 1000\n",
        "else:\n",
        "  n = 30000\n",
        "ancestor_content, ancestor_segments, message = call_ancestor_composition_solver(\n",
        "    matches, n, Gender, commonancestors)\n",
        "\n",
        "\n",
        "def plot_results(ancestor_content, ancestor_segments):\n",
        "  '''Display the results'''\n",
        "  if Gender == \"Male\":\n",
        "    totbp = 6.27e9\n",
        "  else:\n",
        "    totbp = 6.37e9\n",
        "  tups = []\n",
        "  tups.append((1, 'You', 100))\n",
        "  if Gender == \"Male\":\n",
        "    tups.append((2, 'Father', 49.23))\n",
        "    tups.append((3, 'Mother', 50.76))\n",
        "  elif Gender == \"Female\":\n",
        "    tups.append((2, 'Father', 50.0))\n",
        "    tups.append((3, 'Mother', 50.0))\n",
        "\n",
        "  for g in ancestor_content:\n",
        "      gn = int(g)\n",
        "      if gn % 2 == 0:\n",
        "          content = 100 * (62460029 + ancestor_content[g]) / totbp\n",
        "      else:\n",
        "          content = 100 * ancestor_content[g] / totbp\n",
        "      if gn in groupdict:\n",
        "          tups.append((gn, groupdict[gn], content))\n",
        "      else:\n",
        "          tups.append((gn, ' ', content))\n",
        "\n",
        "  colors = distinctipy.get_colors(len(groups_rep), rng=1)\n",
        "  colorhex = {}\n",
        "  for (ind, g) in enumerate(groups_rep):\n",
        "      colorhex[g] = get_hex(colors[ind])\n",
        "\n",
        "  # Plot chromosomes\n",
        "  p = figure(width=900, height=2000, tools='save')\n",
        "  p.toolbar.logo = None\n",
        "\n",
        "  for g, gcolor in colorhex.items():\n",
        "      ws = list()\n",
        "      ys = list()\n",
        "      xs = list()\n",
        "      gens = gen(g) - 1\n",
        "      if side(g) == 'Paternal':\n",
        "          MULT = 1\n",
        "      else:\n",
        "          MULT = -1\n",
        "      for c in allchromes + ['Y']:\n",
        "          if str(c) in ancestor_segments[str(g)]:\n",
        "              for seg in ancestor_segments[str(g)][str(c)]:\n",
        "                  if c in range(1, 23):\n",
        "                      ws.append((seg[1] - seg[0]) / 1e6)\n",
        "                      ys.append(c + MULT * gens / 12)\n",
        "                      xs.append((seg[0] + seg[1]) / 2e6)\n",
        "                  elif c == 'X':\n",
        "                      ws.append((seg[1] - seg[0]) / 1e6)\n",
        "                      ys.append(MULT * gens / 12)\n",
        "                      xs.append((seg[0] + seg[1]) / 2e6)\n",
        "                  elif c == 'Y':\n",
        "                      MULT = 1\n",
        "                      ws.append((seg[1] - seg[0]) / 1e6)\n",
        "                      ys.append(MULT * gens / 12)\n",
        "                      xs.append((seg[0] + seg[1]) / 2e6)\n",
        "      p.rect(\n",
        "          x=xs,\n",
        "          y=ys,\n",
        "          height=0.08,\n",
        "          width=ws,\n",
        "          color=gcolor,\n",
        "          name=groupdict[g],\n",
        "          legend_label=groupdict[g],\n",
        "      )\n",
        "\n",
        "  hover_tool = HoverTool(tooltips='$name')\n",
        "  hover_tool.point_policy = 'follow_mouse'\n",
        "  p.tools.append(hover_tool)\n",
        "  p.yaxis.axis_label = 'Chromosome #'\n",
        "  p.xaxis.axis_label = 'Mbp'\n",
        "  p.x_range = Range1d(0, 270)\n",
        "  p.legend.location = 'top_right'\n",
        "  p.legend.click_policy = 'hide'\n",
        "  hover_tool.names = list(groupdict.values())\n",
        "  p.y_range = Range1d(-0.5, 22.5)\n",
        "\n",
        "  # Plot  chromosome boundaries\n",
        "  ws = list()\n",
        "  ys = list()\n",
        "  xs = list()\n",
        "  cw = {\n",
        "      1: 255,\n",
        "      2: 246,\n",
        "      3: 201.1,\n",
        "      4: 193.5,\n",
        "      5: 182.0,\n",
        "      6: 172.1,\n",
        "      7: 160.6,\n",
        "      8: 146.3,\n",
        "      9: 150.6,\n",
        "      10: 138,\n",
        "      11: 136.1,\n",
        "      12: 138,\n",
        "      13: 120,\n",
        "      14: 110,\n",
        "      15: 105,\n",
        "      16: 96.3,\n",
        "      17: 84.3,\n",
        "      18: 80.5,\n",
        "      19: 61.7,\n",
        "      20: 66.2,\n",
        "      21: 48,\n",
        "      22: 51.3,\n",
        "  }\n",
        "  for c in range(1, 23):\n",
        "      ws.append(cw[c])\n",
        "      ys.append(c + 0.20)\n",
        "      xs.append(cw[c] / 2)\n",
        "\n",
        "  # X or Y chromosome, depending on gender\n",
        "  if Gender == \"Male\":\n",
        "    ws.append(58)\n",
        "    ys.append(0.20)\n",
        "    xs.append(58 / 2)\n",
        "    p.rect(\n",
        "        x=xs,\n",
        "        y=ys,\n",
        "        height=0.40,\n",
        "        width=ws,\n",
        "        line_color='white',\n",
        "        color='blue',\n",
        "        fill_alpha=0.05,\n",
        "        name='father',\n",
        "    )\n",
        "  else:\n",
        "    ws.append(155)\n",
        "    ys.append(0.20)\n",
        "    xs.append(155 / 2)\n",
        "    p.rect(\n",
        "        x=xs,\n",
        "        y=ys,\n",
        "        height=0.40,\n",
        "        width=ws,\n",
        "        line_color='white',\n",
        "        color='blue',\n",
        "        fill_alpha=0.05,\n",
        "        name='father',\n",
        "    )\n",
        "  # Plot maternal chromosome boundaries\n",
        "  ws = list()\n",
        "  ys = list()\n",
        "  xs = list()\n",
        "\n",
        "  for c in range(1, 23):\n",
        "      ws.append(cw[c])\n",
        "      ys.append(c - 0.20)\n",
        "      xs.append(cw[c] / 2)\n",
        "\n",
        "  # Maternal X chromosome\n",
        "\n",
        "  ws.append(155)\n",
        "  ys.append(-0.20)\n",
        "  xs.append(155 / 2)\n",
        "  p.rect(\n",
        "      x=xs,\n",
        "      y=ys,\n",
        "      height=0.40,\n",
        "      width=ws,\n",
        "      line_color='white',\n",
        "      color='red',\n",
        "      fill_alpha=0.05,\n",
        "      name='mother',\n",
        "  )\n",
        "\n",
        "  p.xgrid.grid_line_color = None\n",
        "  p.yaxis.ticker = list(range(23))\n",
        "  p.legend.title = 'Ancestors'\n",
        "  if Gender == \"Male\":\n",
        "    p.yaxis.major_label_overrides = {0: 'Y/X'}\n",
        "  else:\n",
        "    p.yaxis.major_label_overrides = {0: 'X'}\n",
        "  tabs = []\n",
        "  tab1 = Panel(child=p, title='Chromosomes')\n",
        "  tabs.append(tab1)\n",
        "\n",
        " #Annular wedge chart showing inheritance by generation\n",
        "  p2 = figure(\n",
        "      height=900,\n",
        "      width=900,\n",
        "      toolbar_location=None,\n",
        "      tools='hover',\n",
        "      tooltips='@name: @pct{1.1}%',\n",
        "      x_range=(-1.5, 1.5),\n",
        "      y_range=(-1.5, 1.5),\n",
        "  )\n",
        "  sadict = {}\n",
        "  eadict = {}\n",
        "\n",
        "  eadict[2] = math.pi\n",
        "  if Gender == \"Male\":\n",
        "    sadict[2] = math.pi - 49.23 * math.pi / 100\n",
        "    eadict[3] = 50.76 * math.pi / 100\n",
        "    tups.append((2, 'Father', 49.23))\n",
        "    tups.append((3, 'Mother', 50.76))\n",
        "  else:\n",
        "    sadict[2] = math.pi - 50.0 * math.pi / 100\n",
        "    eadict[3] = 50.00 * math.pi / 100\n",
        "    tups.append((2, 'Father', 50))\n",
        "    tups.append((3, 'Mother', 50))\n",
        "\n",
        "  sadict[3] = 0\n",
        "  # For each generation\n",
        "  for GENID in range(2, 6):\n",
        "      angle = []\n",
        "      color = []\n",
        "      name = []\n",
        "      startangle = []\n",
        "      endangle = []\n",
        "      pct = []\n",
        "      start = -math.pi\n",
        "      for (ind, t) in enumerate([t for t in tups if gen(t[0]) == GENID]):\n",
        "          if True:\n",
        "              if t[0] % 2 == 0:  # male\n",
        "                  end = eadict[int(t[0] / 2)]\n",
        "                  endangle.append(end)\n",
        "                  start = end - t[2] * math.pi / 100\n",
        "                  startangle.append(start)\n",
        "              else:\n",
        "                # female\n",
        "                  start = sadict[int((t[0] - 1) / 2)]\n",
        "                  startangle.append(start)\n",
        "                  end = start + t[2] * math.pi / 100\n",
        "                  endangle.append(end)\n",
        "              angle.append(t[2] * math.pi / 100)\n",
        "              color.append(colorhex[t[0]])\n",
        "              pct.append(t[2])\n",
        "              name.append(t[1])\n",
        "              sadict[t[0]] = start\n",
        "              eadict[t[0]] = end\n",
        "\n",
        "      md = {\n",
        "          'startangle': startangle,\n",
        "          'color': color,\n",
        "          'name': name,\n",
        "          'endangle': endangle,\n",
        "          'pct': pct,\n",
        "      }\n",
        "      df = pd.DataFrame(md)\n",
        "      source = ColumnDataSource(df)\n",
        "\n",
        "      glyph = AnnularWedge(\n",
        "          x=0,\n",
        "          y=0,\n",
        "          inner_radius=0.1 + 0.20 * GENID,\n",
        "          outer_radius=0.3 + 0.20 * GENID,\n",
        "          start_angle='startangle',\n",
        "          end_angle='endangle',\n",
        "          line_color='white',\n",
        "          fill_color='color',\n",
        "      )\n",
        "      p2.add_glyph(source, glyph)\n",
        "\n",
        "  angle = []\n",
        "  color = []\n",
        "  name = []\n",
        "  startangle = []\n",
        "  endangle = []\n",
        "  pct = []\n",
        "\n",
        "  startangle.append(0)\n",
        "  name.append('Mother')\n",
        "  if Gender == \"Male\":\n",
        "    pct.append(50.76)\n",
        "    endangle.append(50.76 * math.pi / 100)\n",
        "  else:\n",
        "    pct.append(50.00)\n",
        "    endangle.append(50.00 * math.pi / 100)\n",
        "  color.append('#FFC0CB')\n",
        "\n",
        "  endangle.append(math.pi)\n",
        "  name.append('Father')\n",
        "  if Gender == \"Male\":\n",
        "    pct.append(49.23)\n",
        "    startangle.append(50.76 * math.pi / 100)\n",
        "  else:\n",
        "    pct.append(50.00)\n",
        "    startangle.append(50.00 * math.pi / 100)\n",
        "  color.append('#ADD8E6')\n",
        "  md = {\n",
        "      'startangle': startangle,\n",
        "      'color': color,\n",
        "      'name': name,\n",
        "      'endangle': endangle,\n",
        "      'pct': pct,\n",
        "  }\n",
        "  df = pd.DataFrame(md)\n",
        "  source = ColumnDataSource(df)\n",
        "\n",
        "  GENID = 1\n",
        "  glyph = AnnularWedge(\n",
        "      x=0,\n",
        "      y=0,\n",
        "      inner_radius=0.1 + 0.20 * GENID,\n",
        "      outer_radius=0.3 + 0.20 * GENID,\n",
        "      start_angle='startangle',\n",
        "      end_angle='endangle',\n",
        "      line_color='white',\n",
        "      fill_color='color',\n",
        "  )\n",
        "  p2.add_glyph(source, glyph)\n",
        "\n",
        "  p2.axis.axis_label = None\n",
        "  p2.axis.visible = False\n",
        "  p2.grid.grid_line_color = None\n",
        "  tab = Panel(child=p2, title='Inheritance')\n",
        "  tabs.append(tab)\n",
        "\n",
        "  show(Tabs(tabs=tabs))\n",
        "\n",
        "plot_results(ancestor_content, ancestor_segments)\n",
        "\n",
        "print(message)\n",
        "\n",
        "def diagnostic_messages(ancestor_content, ancestor_segments, groupdict):\n",
        "  if Gender == \"Male\":\n",
        "    totbp = 6.27e9\n",
        "  else:\n",
        "    totbp = 6.37e9\n",
        "  print(\"*** Coverage ***\")\n",
        "  gp = 0\n",
        "  for c in range(4, 8):\n",
        "    gp = gp+ancestor_content[str(c)]\n",
        "  print('Coverage at grandparent generation is ' +\n",
        "        \"{:.1f}\".format(100*gp/totbp) + \"%\")\n",
        "  unallocgp = 100-100*gp/totbp\n",
        "\n",
        "  ggp = 0\n",
        "  for c in range(8, 16):\n",
        "    ggp = ggp+ancestor_content[str(c)]\n",
        "  print('Coverage at great-grandparent generation is ' +\n",
        "        \"{:.1f}\".format(100*ggp/totbp) + \"%\")\n",
        "  unallocggp = 100-100*ggp/totbp\n",
        "\n",
        "  gggp = 0\n",
        "  for c in range(16, 32):\n",
        "    gggp = gggp+ancestor_content[str(c)]\n",
        "  print('Coverage at 2nd great-grandparent generation is ' +\n",
        "        \"{:.1f}\".format(100*gggp/totbp) + \"%\")\n",
        "\n",
        "  ggggp = 0\n",
        "  for c in range(32, 64):\n",
        "    ggggp = ggggp+ancestor_content[str(c)]\n",
        "  print('Coverage at 3rd great-grandparent generation is ' +\n",
        "        \"{:.1f}\".format(100*ggggp/totbp) + \"%\")\n",
        "\n",
        "  if unallocgp < 15:\n",
        "    pcov = 0\n",
        "    for c in range(1, 23):\n",
        "      pcov = pcov+min([len(ancestor_segments['4'][str(c)]),\n",
        "                      len(ancestor_segments['5'][str(c)])])\n",
        "\n",
        "    mcov = 0\n",
        "    for c in range(1, 23):\n",
        "      mcov = mcov+min([len(ancestor_segments['6'][str(c)]),\n",
        "                      len(ancestor_segments['7'][str(c)])])\n",
        "    mcov = mcov+min([len(ancestor_segments['6']['X']),\n",
        "                    len(ancestor_segments['7']['X'])])\n",
        "\n",
        "    print(\"*** Crossovers ***\")\n",
        "    print(\"Your genome has \" + str(pcov) + \" paternal crossovers and \" +\n",
        "          str(mcov) + \" maternal crossovers.\")\n",
        "    print(\"The average number of paternal crossovers is 26.4 and the average number of maternal crossovers is 41.1.\")\n",
        "\n",
        "    print(\"*** Inheritance from grandparents ***\")\n",
        "    for c in range(4, 8):\n",
        "      print('From grandparent ' + groupdict[c] + \" you inherited \" + \"{:.1f}\".format(\n",
        "          100*ancestor_content[str(c)]/totbp+unallocgp/4) + \"% of your genome.\")\n",
        "    print('Typical range of inheritance from grandparents is 14% - 35%.')\n",
        "\n",
        "    print(\"*** Inheritance from great-grandparents ***\")\n",
        "    for c in range(8, 16):\n",
        "      print('From great-grandparent ' + groupdict[c] + \" you inherited \" + \"{:.1f}\".format(\n",
        "          100*ancestor_content[str(c)]/totbp+unallocggp/8) + \"% of your genome.\")\n",
        "    print('Typical range of inheritance from great-grandparents is 4% - 22%.')\n",
        "\n",
        "\n",
        "diagnostic_messages(ancestor_content, ancestor_segments, groupdict)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Export Results { display-mode: \"form\" }\n",
        "#@markdown Export a CSV file with the results\n",
        "from pandas.io.formats.style_render import DataFrame\n",
        "from google.colab import files\n",
        "try:\n",
        "  df=DataFrame\n",
        "  chr=list()\n",
        "  start=list()\n",
        "  end=list()\n",
        "  group=list()\n",
        "  color=list()\n",
        "  ancestryside=list()\n",
        "  colors = distinctipy.get_colors(64,rng=1)\n",
        "  colorhex = {}\n",
        "  for (ind, g) in enumerate(range(64)):\n",
        "      colorhex[g] = get_hex(colors[ind])\n",
        "  for g in  ancestor_segments:\n",
        "    if int(g)>3:\n",
        "      for c in ancestor_segments[g]:\n",
        "        for s in ancestor_segments[g][c]:\n",
        "          chr.append(c)\n",
        "          start.append(s[0])\n",
        "          end.append(s[1])\n",
        "          group.append(groupdict[int(g)])\n",
        "          color.append(colorhex[int(g)])\n",
        "          ancestryside.append(side(int(g)))\n",
        "  ddict = {'chr': chr, 'start': start, 'end': end, 'group':group,'color':color,'ancestry side':ancestryside} \n",
        "  df=DataFrame(ddict)\n",
        "  df.to_csv('export.csv',index=False) \n",
        "  files.download('export.csv')\n",
        "except Exception as e:\n",
        "  print(str(e))\n",
        "  print(\"Please run the 'Calculate your Ancestor Composition' cell first.\")"
      ],
      "metadata": {
        "id": "q4nvUKUku4i_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "844ff34a-1b53-4a61-8347-f51b503b53df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_bb5b524a-059b-4179-861d-2b4bd251ec36\", \"export.csv\", 47735)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "#@title Register for or recover an API key { display-mode: \"form\" }\n",
        "#@markdown An API key allows you to run the solver using up to 30,000 matches, up to five times per day.  Without an API key, the solver uses only 1,000 matches.\n",
        "EmailAddress = \"\" #@param {type:\"string\"}\n",
        "\n",
        "def request_api_key(email):\n",
        "  # Call the Ancestor Composition Solver API\n",
        "  json_input=dict()\n",
        "  json_input[\"Email\"]=email\n",
        "  url=\"https://5if5u6ou58.execute-api.us-east-1.amazonaws.com/default/AncestorComposition\"\n",
        "  r = requests.post(url, json=json_input)\n",
        "  json_output=r.json()\n",
        "  print(str(json_output[\"Message\"]))\n",
        "\n",
        "request_api_key(EmailAddress)"
      ],
      "metadata": {
        "id": "sjrwnZ3btu_U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown <a name=\"directions\"></a>\n",
        "#@title Detailed Directions\n",
        "#@markdown 1.   Download matching segment data CSV files from [23andMe](https://www.23andme.com/), [MyHeritage](https://www.myheritage.com/), and/or [FamilyTree DNA](https://www.familytreedna.com/).\n",
        "#@markdown 2.   For some of your matches (at least 25 - 300 individuals, for best results), identify the most recent common ancestor or ancestral couple that you share with the match.  This can be done using MyHeritage \"Theory of Relativity\", Ancestry \"ThruLines\", FamilyTree DNA, or 23AndMe \"Family Tree\".  Record this information in a \"Common Ancestor\" CSV file (download a template by running this cell) with five columns and a header row:\n",
        "#@markdown    *   \"Match Name\", a column for which each row contains the name of a matching individual as it appears in a matching segment CSV file.\n",
        "#@markdown    *   \"Male Ancestor Name\".  Each row of this column contains the name of the most recent male ancestor that you share with the matching individual.\n",
        "#@markdown    *  \"Male Ancestor #\", the ahntentafel number (see diagram below) of the common male ancestor.\n",
        "#@markdown    * \"Female Ancestor Name\".  Each row of this column contains the name of the most recent female ancestor that you share with the matching individual.\n",
        "#@markdown    * \"Female Ancestor #\", the ahnentafel number of the common female ancestor.\n",
        "#@markdown 3. Click on the folder icon to the left to expand the \"Files\" pane.\n",
        "#@markdown 4. Click the \"Upload to session storage\" button which will appear in the upper left to upload CSV files for match shared segments (from MyHeritage, FamilyTree DNA, and/or 23andMe) and the CSV file (from Step 2) with common ancestor information for some of your matches.\n",
        "#@markdown 5. Run the Ancestor Composition Solver.  The results will be displayed in:\n",
        "#@markdown    * An ideogram showing which regions of your chromosomes were inherited from specific ancestors.\n",
        "#@markdown   *  A fan chart showing the percentage of your genome inherited from ancestors in each generation.\n",
        "#@markdown 6. You may export the results to a CSV file.\n",
        " \n",
        "\n",
        "from pandas.io.formats.style_render import DataFrame\n",
        "from google.colab import files\n",
        "import pandas as pd\n",
        "from bokeh.io import show\n",
        "from bokeh.plotting import figure\n",
        "from bokeh.models import LabelSet, ColumnDataSource\n",
        "import math\n",
        "from bokeh.io import output_notebook\n",
        "output_notebook()\n",
        "\n",
        "df=DataFrame\n",
        "ddict = {'Match Name': [\"NameofMatch\"], 'Male Ancestor Name': [\"Father of Paternal Grandfater\"], 'Male Ancestor #': [8], 'Female Ancestor Name':[\"Mother of Paternal Grandfather\"],'Female Ancestor #':[9]} \n",
        "df=DataFrame(ddict)\n",
        "df.to_csv('template.csv',index=False) \n",
        "files.download('template.csv')\n",
        "\n",
        "def gen(anc):\n",
        "    '''Generation of ancestor '''\n",
        "    return math.floor(math.log(anc, 2))\n",
        "#%%\n",
        "gendata=list()\n",
        "for ahn in range(1,128):\n",
        "    if ahn == 1:\n",
        "        adder=\" (You)\"\n",
        "    elif ahn == 2:\n",
        "        adder=\" (Father)\"\n",
        "    elif ahn == 3:\n",
        "        adder=\" (Mother)\"\n",
        "    else:\n",
        "        adder = \"\"\n",
        "    thisgen=dict()\n",
        "    thisgen[\"gen\"]=gen(ahn)\n",
        "    thisgen[\"label\"]=str(ahn)+adder\n",
        "    thisgen[\"innerrad\"]=gen(ahn)\n",
        "    thisgen[\"outerrad\"]=gen(ahn)+1\n",
        "    increment=math.pi/(2**gen(ahn))\n",
        "    which=ahn-2**gen(ahn)\n",
        "    thisgen[\"startangle\"]=math.pi-(which+1)*increment \n",
        "    thisgen[\"endangle\"]=math.pi-which*increment\n",
        "    thisgen[\"midangle\"]=(which+0.5)*increment\n",
        "    thisgen['xlabel']=0.5*(thisgen[\"innerrad\"]+thisgen[\"outerrad\"])*math.cos(0.5*(thisgen[\"startangle\"]+thisgen[\"endangle\"]))\n",
        "    thisgen['ylabel']=0.5*(thisgen[\"innerrad\"]+thisgen[\"outerrad\"])*math.sin(0.5*(thisgen[\"startangle\"]+thisgen[\"endangle\"]))\n",
        "   \n",
        "    gendata.append(thisgen)\n",
        "data = pd.DataFrame(gendata)\n",
        "\n",
        "\n",
        "\n",
        "p = figure(plot_height=1000, plot_width=1000, title=\"Ahnenetafel Numbers\", toolbar_location=None,\n",
        "           tools=\"hover\", tooltips=\"@label\", x_range=(-8, 8),y_range=(-8,8))\n",
        "source = ColumnDataSource(data)\n",
        "p.annular_wedge(x=0, y=0, inner_radius=\"innerrad\", outer_radius=\"outerrad\",\n",
        "        start_angle=\"startangle\", end_angle=\"endangle\",\n",
        "        line_color=\"black\", fill_color=\"white\",  source=source)\n",
        "\n",
        "\n",
        "labels = LabelSet(x='xlabel', y='ylabel', text='label', source=source,\n",
        "                  text_align ='center',text_baseline='center',text_font_size=\"9pt\")\n",
        "\n",
        "p.add_layout(labels)\n",
        "\n",
        "p.axis.axis_label=None\n",
        "p.axis.visible=False\n",
        "p.grid.grid_line_color = None\n",
        "\n",
        "show(p)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "id": "mDXu7HcYQ72H",
        "outputId": "64079804-89b5-4d01-a404-388a9a4ef4e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_c9ca4d99-4729-4af6-82da-be4f5f305e1a\", \"template.csv\", 162)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "(function(root) {\n",
              "  function now() {\n",
              "    return new Date();\n",
              "  }\n",
              "\n",
              "  var force = true;\n",
              "\n",
              "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
              "    root._bokeh_onload_callbacks = [];\n",
              "    root._bokeh_is_loading = undefined;\n",
              "  }\n",
              "\n",
              "  var JS_MIME_TYPE = 'application/javascript';\n",
              "  var HTML_MIME_TYPE = 'text/html';\n",
              "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
              "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
              "\n",
              "  /**\n",
              "   * Render data to the DOM node\n",
              "   */\n",
              "  function render(props, node) {\n",
              "    var script = document.createElement(\"script\");\n",
              "    node.appendChild(script);\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when an output is cleared or removed\n",
              "   */\n",
              "  function handleClearOutput(event, handle) {\n",
              "    var cell = handle.cell;\n",
              "\n",
              "    var id = cell.output_area._bokeh_element_id;\n",
              "    var server_id = cell.output_area._bokeh_server_id;\n",
              "    // Clean up Bokeh references\n",
              "    if (id != null && id in Bokeh.index) {\n",
              "      Bokeh.index[id].model.document.clear();\n",
              "      delete Bokeh.index[id];\n",
              "    }\n",
              "\n",
              "    if (server_id !== undefined) {\n",
              "      // Clean up Bokeh references\n",
              "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
              "      cell.notebook.kernel.execute(cmd, {\n",
              "        iopub: {\n",
              "          output: function(msg) {\n",
              "            var id = msg.content.text.trim();\n",
              "            if (id in Bokeh.index) {\n",
              "              Bokeh.index[id].model.document.clear();\n",
              "              delete Bokeh.index[id];\n",
              "            }\n",
              "          }\n",
              "        }\n",
              "      });\n",
              "      // Destroy server and session\n",
              "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
              "      cell.notebook.kernel.execute(cmd);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when a new output is added\n",
              "   */\n",
              "  function handleAddOutput(event, handle) {\n",
              "    var output_area = handle.output_area;\n",
              "    var output = handle.output;\n",
              "\n",
              "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
              "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
              "      return\n",
              "    }\n",
              "\n",
              "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
              "\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
              "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
              "      // store reference to embed id on output_area\n",
              "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
              "    }\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
              "      var bk_div = document.createElement(\"div\");\n",
              "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
              "      var script_attrs = bk_div.children[0].attributes;\n",
              "      for (var i = 0; i < script_attrs.length; i++) {\n",
              "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
              "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
              "      }\n",
              "      // store reference to server id on output_area\n",
              "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function register_renderer(events, OutputArea) {\n",
              "\n",
              "    function append_mime(data, metadata, element) {\n",
              "      // create a DOM node to render to\n",
              "      var toinsert = this.create_output_subarea(\n",
              "        metadata,\n",
              "        CLASS_NAME,\n",
              "        EXEC_MIME_TYPE\n",
              "      );\n",
              "      this.keyboard_manager.register_events(toinsert);\n",
              "      // Render to node\n",
              "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
              "      render(props, toinsert[toinsert.length - 1]);\n",
              "      element.append(toinsert);\n",
              "      return toinsert\n",
              "    }\n",
              "\n",
              "    /* Handle when an output is cleared or removed */\n",
              "    events.on('clear_output.CodeCell', handleClearOutput);\n",
              "    events.on('delete.Cell', handleClearOutput);\n",
              "\n",
              "    /* Handle when a new output is added */\n",
              "    events.on('output_added.OutputArea', handleAddOutput);\n",
              "\n",
              "    /**\n",
              "     * Register the mime type and append_mime function with output_area\n",
              "     */\n",
              "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
              "      /* Is output safe? */\n",
              "      safe: true,\n",
              "      /* Index of renderer in `output_area.display_order` */\n",
              "      index: 0\n",
              "    });\n",
              "  }\n",
              "\n",
              "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
              "  if (root.Jupyter !== undefined) {\n",
              "    var events = require('base/js/events');\n",
              "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
              "\n",
              "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
              "      register_renderer(events, OutputArea);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  \n",
              "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
              "    root._bokeh_timeout = Date.now() + 5000;\n",
              "    root._bokeh_failed_load = false;\n",
              "  }\n",
              "\n",
              "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
              "     \"<div style='background-color: #fdd'>\\n\"+\n",
              "     \"<p>\\n\"+\n",
              "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
              "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
              "     \"</p>\\n\"+\n",
              "     \"<ul>\\n\"+\n",
              "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
              "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
              "     \"</ul>\\n\"+\n",
              "     \"<code>\\n\"+\n",
              "     \"from bokeh.resources import INLINE\\n\"+\n",
              "     \"output_notebook(resources=INLINE)\\n\"+\n",
              "     \"</code>\\n\"+\n",
              "     \"</div>\"}};\n",
              "\n",
              "  function display_loaded() {\n",
              "    var el = document.getElementById(null);\n",
              "    if (el != null) {\n",
              "      el.textContent = \"BokehJS is loading...\";\n",
              "    }\n",
              "    if (root.Bokeh !== undefined) {\n",
              "      if (el != null) {\n",
              "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
              "      }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(display_loaded, 100)\n",
              "    }\n",
              "  }\n",
              "\n",
              "\n",
              "  function run_callbacks() {\n",
              "    try {\n",
              "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
              "        if (callback != null)\n",
              "          callback();\n",
              "      });\n",
              "    } finally {\n",
              "      delete root._bokeh_onload_callbacks\n",
              "    }\n",
              "    console.debug(\"Bokeh: all callbacks have finished\");\n",
              "  }\n",
              "\n",
              "  function load_libs(css_urls, js_urls, callback) {\n",
              "    if (css_urls == null) css_urls = [];\n",
              "    if (js_urls == null) js_urls = [];\n",
              "\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
              "    }\n",
              "    if (js_urls == null || js_urls.length === 0) {\n",
              "      run_callbacks();\n",
              "      return null;\n",
              "    }\n",
              "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
              "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
              "\n",
              "    function on_load() {\n",
              "      root._bokeh_is_loading--;\n",
              "      if (root._bokeh_is_loading === 0) {\n",
              "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
              "        run_callbacks()\n",
              "      }\n",
              "    }\n",
              "\n",
              "    function on_error(url) {\n",
              "      console.error(\"failed to load \" + url);\n",
              "    }\n",
              "\n",
              "    for (let i = 0; i < css_urls.length; i++) {\n",
              "      const url = css_urls[i];\n",
              "      const element = document.createElement(\"link\");\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.rel = \"stylesheet\";\n",
              "      element.type = \"text/css\";\n",
              "      element.href = url;\n",
              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
              "      document.body.appendChild(element);\n",
              "    }\n",
              "\n",
              "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\": \"dM3QQsP+wXdHg42wTqW85BjZQdLNNIXqlPw/BgKoExPmTG7ZLML4EGqLMfqHT6ON\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\": \"8x57I4YuIfu8XyZfFo0XVr2WAT8EK4rh/uDe3wF7YuW2FNUSNEpJbsPaB1nJ2fz2\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\": \"3QTqdz9LyAm2i0sG5XTePsHec3UHWwVsrOL68SYRoAXsafvfAyqtQ+h440+qIBhS\"};\n",
              "\n",
              "    for (let i = 0; i < js_urls.length; i++) {\n",
              "      const url = js_urls[i];\n",
              "      const element = document.createElement('script');\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.async = false;\n",
              "      element.src = url;\n",
              "      if (url in hashes) {\n",
              "        element.crossOrigin = \"anonymous\";\n",
              "        element.integrity = \"sha384-\" + hashes[url];\n",
              "      }\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
              "\n",
              "  \n",
              "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\"];\n",
              "  var css_urls = [];\n",
              "  \n",
              "\n",
              "  var inline_js = [\n",
              "    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "    function(Bokeh) {\n",
              "    \n",
              "    \n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    \n",
              "    if (root.Bokeh !== undefined || force === true) {\n",
              "      \n",
              "    for (var i = 0; i < inline_js.length; i++) {\n",
              "      inline_js[i].call(root, root.Bokeh);\n",
              "    }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
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              "      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
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Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; 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Application\",\"version\":\"2.3.3\"}};\n",
              "  var render_items = [{\"docid\":\"2c2ed91b-e9ca-4c24-9941-531a5782b3ab\",\"root_ids\":[\"1002\"],\"roots\":{\"1002\":\"d7622a3a-dc72-4c36-b4ca-de479114b59e\"}}];\n",
              "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
              "\n",
              "  }\n",
              "  if (root.Bokeh !== undefined) {\n",
              "    embed_document(root);\n",
              "  } else {\n",
              "    var attempts = 0;\n",
              "    var timer = setInterval(function(root) {\n",
              "      if (root.Bokeh !== undefined) {\n",
              "        clearInterval(timer);\n",
              "        embed_document(root);\n",
              "      } else {\n",
              "        attempts++;\n",
              "        if (attempts > 100) {\n",
              "          clearInterval(timer);\n",
              "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
              "        }\n",
              "      }\n",
              "    }, 10, root)\n",
              "  }\n",
              "})(window);"
            ],
            "application/vnd.bokehjs_exec.v0+json": ""
          },
          "metadata": {
            "application/vnd.bokehjs_exec.v0+json": {
              "id": "1002"
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CVePnWRvWEQt"
      },
      "source": [
        "### FAQ\n",
        "1.   How does this notebook preserve my privacy?\n",
        "    *   The CSV files that you upload are stored in a temporary private Google Colab workspace and are not available to others.\n",
        "    *   The data is anonymized (all names removed) before a solver API is called to estimate your ancestral composition.\n",
        "    *   This Colab notebook with your ancestral composition will be private to you unless you share it with others.\n",
        "2.   How does the solver work?\n",
        "    *   The solver assigns DNA segments to ancestors in a way that is:\n",
        "        * **Self-consistent**.  Segments from one ancestor may overlap with segments from another ancestor only if the two ancestors are on the same line.\n",
        "        * **Maximally consistent with common ancestor matches**.  The solver assigns segments to ancestors in a way that it is 100% consistent with the maximum possible number of common ancestor matches. It is generally not possible to be consistent with all common ancestor matches, due to phasing errors, false positives, and DNA shared from multiple lines.\n",
        "        * **Supported by a large percentage of other match data**.  Ancestor segments from a match with an unknown common ancestor are used to expand the set of ancestor segments in a way that is self-consistent and fully consistent with known common ancestor matches.\n",
        "\n",
        "3. How has the solver been validated?\n",
        "    * Limited validation testing has shown that the results of the solver:\n",
        "        * Are self-consistent.\n",
        "        * Are maximally-consistent with common ancestor matches.\n",
        "        * Are supported by a large percentage of other match data.\n",
        "        * Predict a number of crossovers on the maternal and paternal lines that are within typical ranges, if sufficient common ancestor data is provided.\n",
        "        * Predict inheritance percentages for grandparents and great-grandparents that are within typical ranges, if sufficient common ancestor data is provided.\n",
        "    * Please note that the results can only be as accurate as the provided common ancestor information.\n",
        "\n",
        "4. Who is the developer?\n",
        "    * The developer is a professional data scientist and family history enthusiast.\n",
        "    \n",
        "5. Questions or problems running this notebook?\n",
        "     * Please report problems at https://github.com/AncestorComposition/public/issues\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LwxEe23CWICz"
      },
      "source": [
        "### License\n",
        "\n",
        "This notebook is licensed for personal, noncommercial use under the folllowing terms:\n",
        "https://creativecommons.org/licenses/by-nc-nd/4.0/\n",
        "\n",
        "The API called by this notebook may be used only for personal, noncommercial use.\n",
        "\n",
        "Inquiries for commercial licenses may be sent to info@ancestorcomposition.com."
      ]
    }
  ],
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