diff --git a/.gitignore b/.gitignore
index 38c4422..3b10f54 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,6 +6,7 @@ output
 *egg*
 dist
 __pycache__
+*/.ipynb_checkpoints/*
 idealg_v
 build
 venv3
diff --git a/notebooks/squig_stats.ipynb b/notebooks/squig_stats.ipynb
new file mode 100644
index 0000000..10f1c2c
--- /dev/null
+++ b/notebooks/squig_stats.ipynb
@@ -0,0 +1,559 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e12bb0ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#notebook { padding-top:0px !important; } .container { width:100% !important; } .end_space { min-height:0px !important; } </style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML(\n",
+    "    '<style>'\n",
+    "        '#notebook { padding-top:0px !important; } ' \n",
+    "        '.container { width:100% !important; } '\n",
+    "        '.end_space { min-height:0px !important; } '\n",
+    "    '</style>'\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7cda617d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from io import StringIO\n",
+    "\n",
+    "POS_DWELL = 0\n",
+    "POS_MEAN = 1\n",
+    "POS_MEDIAN = 2\n",
+    "POS_STD = 3\n",
+    "POS_BOXPLOT = 4\n",
+    "POS_DWELL_STD = 5\n",
+    "\n",
+    "def get_boxplot_stats(data):\n",
+    "    return [round(np.min(data), 4), round(np.percentile(data, 25), 4), round(np.median(data), 4), round(np.percentile(data, 75), 4), round(np.max(data), 4)]\n",
+    "\n",
+    "def get_stats(file_path, base_shift=0):\n",
+    "    df = pd.read_csv(file_path, sep='\\t', header=None)\n",
+    "\n",
+    "    # Initialize an empty list to store cell means\n",
+    "    pos_dwell = []\n",
+    "    pos_mean = []\n",
+    "    pos_median = []\n",
+    "    pos_std = []\n",
+    "    pos_boxplot = []\n",
+    "    pos_dwell_std = []\n",
+    "    \n",
+    "    # Iterate over columns\n",
+    "    for col in df.columns[abs(base_shift):]:\n",
+    "        col_means = []\n",
+    "        col_dwells = []\n",
+    "        col_medians = []\n",
+    "        col_stds = []\n",
+    "        col_values = []\n",
+    "\n",
+    "        for cell in df[col]:\n",
+    "            if pd.isna(cell):\n",
+    "                continue\n",
+    "\n",
+    "            cell_values = str(cell).split(',')\n",
+    "            col_dwells.append(len(cell_values))\n",
+    "\n",
+    "            numeric_values = pd.to_numeric(cell_values, errors='coerce')\n",
+    "            col_values.extend(numeric_values)\n",
+    "            col_means.append(np.nanmean(numeric_values))\n",
+    "            col_medians.append(np.nanmedian(numeric_values))\n",
+    "            col_stds.append(np.std(numeric_values))\n",
+    "        \n",
+    "#         print(col_dwells)\n",
+    "#         print(col_values)\n",
+    "        \n",
+    "        pos_dwell.append(get_boxplot_stats(col_dwells))\n",
+    "        pos_mean.append(get_boxplot_stats(col_means))\n",
+    "        pos_median.append(get_boxplot_stats(col_medians))\n",
+    "        pos_std.append(get_boxplot_stats(col_stds))\n",
+    "        pos_boxplot.append(get_boxplot_stats(col_values))\n",
+    "        \n",
+    "        pos_dwell_std.append(np.std(col_dwells))\n",
+    "        \n",
+    "\n",
+    "#     print(pos_dwell)\n",
+    "#     print(pos_mean)\n",
+    "#     print(pos_median)\n",
+    "#     print(pos_std)\n",
+    "    return [pos_dwell, pos_mean, pos_median, pos_std, pos_boxplot, pos_dwell_std]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d7e27dde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %matplotlib notebook\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "colors = ['lightblue','lightcoral','lightcyan','lightgoldenrodyellow','lightgreen','lightpink','lightsalmon','lightseagreen','lightskyblue','lightslategray']\n",
+    "medianprops = dict(color='red', linewidth=2)\n",
+    "\n",
+    "def draw_boxplots(stats, ref, ref_index, title, labels):\n",
+    "\n",
+    "    plt.figure(figsize=(30,6))\n",
+    "\n",
+    "    num_stats = len(stats)\n",
+    "    \n",
+    "    all_stats = []\n",
+    "    x_ticks = []\n",
+    "    for i in range(len(stats[0])):\n",
+    "        for j in range(num_stats):\n",
+    "            all_stats.append(stats[j][i])\n",
+    "\n",
+    "    for i in range(len(stats[0])):\n",
+    "        x_ticks.append(ref[i])\n",
+    "        x_ticks.append(ref_index[i])\n",
+    "        for j in range(num_stats-2):\n",
+    "            x_ticks.append('')\n",
+    "\n",
+    "    distance_within_group = 1\n",
+    "    distance_between_groups = 3\n",
+    "    positions = [0]\n",
+    "    positions.append(positions[-1]+distance_within_group)\n",
+    "    for i in range(len(stats[0])):\n",
+    "        for j in range(num_stats-1):\n",
+    "            positions.append(positions[-1]+distance_within_group)\n",
+    "        positions.append(positions[-1]+distance_between_groups)\n",
+    "\n",
+    "    positions = positions[1:-1]\n",
+    "\n",
+    "    # Set box colors\n",
+    "    box_colors = colors[:num_stats] * (len(all_stats) // 2)\n",
+    "\n",
+    "    # Create paired box plots with different colors for experiments and controls\n",
+    "    bplot = plt.boxplot(all_stats, medianprops=medianprops, positions=positions, vert=True, patch_artist=True, flierprops=dict(marker='.', markerfacecolor='black', markersize=5))\n",
+    "\n",
+    "    for patch, color in zip(bplot['boxes'], box_colors):\n",
+    "        patch.set_facecolor(color)\n",
+    "        \n",
+    "    legend_elements = []\n",
+    "    for j in range(num_stats):\n",
+    "        legend_elements.append(Line2D([0], [0], marker='s', color='w', markerfacecolor=box_colors[j], markersize=10, label=labels[j]))\n",
+    "    \n",
+    "    plt.legend(handles=legend_elements, loc='upper right')\n",
+    "\n",
+    "    # Add labels and title\n",
+    "    plt.xticks(positions, x_ticks)\n",
+    "    plt.title(title)\n",
+    "\n",
+    "    # Show the plot\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dddfdc9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ref = \"AGGTTGCAGTGAACCAACGTCGCCACTGCACTCCAGTCTGGCGACAGAGCGAGACTCCCTGTCA\"\n",
+    "ref_index = list(range(7,100))\n",
+    "\n",
+    "file_path = '../test/data/raw/metric/eventalign.tsv'\n",
+    "f5c = get_stats(file_path, base_shift=-6)\n",
+    "\n",
+    "file_path = '../test/data/raw/metric/nanopolish.tsv'\n",
+    "nanopolish = get_stats(file_path, base_shift=-6)\n",
+    "\n",
+    "file_path = '../test/data/raw/metric/realign.tsv'\n",
+    "realign = get_stats(file_path, base_shift=-6)\n",
+    "\n",
+    "file_path = '../test/data/raw/metric/sigfish.tsv'\n",
+    "sigfish = get_stats(file_path, base_shift=-6)\n",
+    "\n",
+    "file_path = '../test/data/raw/metric/squigualator.tsv'\n",
+    "squigulator = get_stats(file_path, base_shift=-6)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a1d54a40",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2160x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "start_base = 0\n",
+    "end_base = 40\n",
+    "stats_index = POS_MEDIAN\n",
+    "stats = [squigulator[stats_index][start_base:end_base], nanopolish[stats_index][start_base:end_base], f5c[stats_index][start_base:end_base], sigfish[stats_index][start_base:end_base], realign[stats_index][start_base:end_base]]\n",
+    "legend_labels = ['pore model', 'nanopolish projection', 'f5c eventalign', 'sigfish dtw', 'squigualiser realign']\n",
+    "draw_boxplots(stats, ref, ref_index, \"event median\", legend_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "83f77dcb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2160x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "start_base = 0\n",
+    "end_base = 40\n",
+    "stats_index = POS_BOXPLOT\n",
+    "stats = [squigulator[stats_index][start_base:end_base], nanopolish[stats_index][start_base:end_base], f5c[stats_index][start_base:end_base], realign[stats_index][start_base:end_base]]\n",
+    "legend_labels = ['pore model', 'nanopolish projection', 'f5c eventalign', 'squigualiser realign']\n",
+    "draw_boxplots(stats, ref, ref_index, \"event median\", legend_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d7980959",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x_min = 0\n",
+    "x_max = 20\n",
+    "y_min = 0\n",
+    "y_max = 20\n",
+    "\n",
+    "MIN_INDEX = 0\n",
+    "Q1_INDEX = 1\n",
+    "MEDIAN_INDEX = 2\n",
+    "Q3_INDEX = 3\n",
+    "MAX_INDEX = 4\n",
+    "\n",
+    "def fix_range(arr):\n",
+    "    lower_limit = x_min\n",
+    "    upper_limit = x_max\n",
+    "    mask = (arr < lower_limit) | (arr > upper_limit)\n",
+    "    arr[mask] = np.nan\n",
+    "    return arr\n",
+    "\n",
+    "def draw_scatter(list_a, list_b, label_a, label_b):\n",
+    "    \n",
+    "    use_stat_index = MEDIAN_INDEX\n",
+    "    \n",
+    "    a_x = np.array([sublist[use_stat_index] for sublist in list_a[POS_DWELL]])\n",
+    "    a_y = np.array([sublist[use_stat_index] for sublist in list_a[POS_STD]])\n",
+    "    b_x = np.array([sublist[use_stat_index] for sublist in list_b[POS_DWELL]])\n",
+    "    b_y = np.array([sublist[use_stat_index] for sublist in list_b[POS_STD]])\n",
+    "    \n",
+    "    a_x = fix_range(a_x)\n",
+    "    b_x = fix_range(b_x)\n",
+    "    \n",
+    "    plt.scatter(a_x, a_y, color='blue', marker='o', label=label_a)\n",
+    "    plt.scatter(b_x, b_y, color='red', marker='o', label=label_b)\n",
+    "\n",
+    "    # Add labels and title\n",
+    "    plt.xlabel('dwell time')\n",
+    "    plt.ylabel('current level stddev')\n",
+    "    plt.title('dwell time Vs current level stddev')\n",
+    "    plt.legend()\n",
+    "    \n",
+    "    # Set x-axis limits\n",
+    "    plt.xlim(x_min, x_max)\n",
+    "    plt.ylim(y_min, y_max)\n",
+    "    \n",
+    "    fig_name = \"dwell_vs_current_{}.svg\".format(label_b) \n",
+    "    plt.savefig(fig_name, format='svg')\n",
+    "\n",
+    "    # Show the plot\n",
+    "    plt.show()\n",
+    "    \n",
+    "def draw_scatter_2(list_a, list_b, label_a, label_b):\n",
+    "    \n",
+    "    use_stat_index = MEDIAN_INDEX\n",
+    "    \n",
+    "    a_x = np.array(list_a[POS_DWELL_STD])\n",
+    "    a_y = np.array([sublist[use_stat_index] for sublist in list_a[POS_STD]])\n",
+    "    b_x = np.array(list_b[POS_DWELL_STD])\n",
+    "    b_y = np.array([sublist[use_stat_index] for sublist in list_b[POS_STD]])\n",
+    "    \n",
+    "    a_x = fix_range(a_x)\n",
+    "    b_x = fix_range(b_x)\n",
+    "    \n",
+    "    plt.scatter(a_x, a_y, color='blue', marker='o', label=label_a)\n",
+    "    plt.scatter(b_x, b_y, color='red', marker='o', label=label_b)\n",
+    "\n",
+    "    # Add labels and title\n",
+    "    plt.xlabel('dwell time stddev')\n",
+    "    plt.ylabel('current level stddev')\n",
+    "    plt.title('dwell time stddev Vs current level stddev')\n",
+    "    plt.legend()\n",
+    "    \n",
+    "    # Set x-axis limits\n",
+    "    plt.xlim(x_min, x_max)\n",
+    "    plt.ylim(y_min, y_max)\n",
+    "    \n",
+    "    fig_name = \"dwell_stddev_vs_current_stddev_{}.svg\".format(label_b) \n",
+    "    plt.savefig(fig_name, format='svg')\n",
+    "\n",
+    "    # Show the plot\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "0bf9a54f",
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "draw_scatter(squigulator, nanopolish, 'squigulator', 'nanopolish')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "2fd52dc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "draw_scatter(squigulator, f5c, 'squigulator', 'f5c')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "98d6dd4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "draw_scatter(squigulator, sigfish, 'squigulator', 'sigfish dtw')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ea358592",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DZJZMJE2WtFzSCzll9+TMB79I0twC+y6SND/dblZWMVo7/iVdWVncy2pWp7K8MrkNODG3ICI+FxHDImIYcD/wQJH9j0m3HZVdiLYZ/5KuLA9P0yPkDuBYzlD0jS6zZBIRTwFv5lunpO//Z4G78q23GvEv6crz8DQNJSI2GzKls+p9zpEs1arN5ChgWUS8UmB9ANMlzZY0vopx9Wz+JW31LoO7DRctWsQBBxzAxRdfzIgRI/jWt77Fxz72MQ4++GCuueaaTdudfvrpjBw5kiFDhjBp0qS871XqUPTXXHMNI0aM4KCDDuLll18u+2+oC5Xo+VjoAQwCXshTfgvwf4vst3v6/GFgHvDxItuOB2YBswYOHNj5LqFmVjOd6gF/550R/fptPppAv35ld4N/7bXXQlLMnDkzHnnkkfjSl74UGzdujA0bNsTJJ58cTz75ZERErFq1KiIi3nvvvRgyZEisXLkyIiKamppixYoVERGxzTbbRETEvffeGyeddFJs2LAhli5dGjvssEPce++9m7a/6aabIiLi5ptvjosuuqis+Dsjyx7wVb8ykbQVcAZwT6FtImJJ+rwceBAYXWTbSRExKiJG9e/fv9Lhmlm9yPBuw6amJg477DCmT5/O9OnTGT58OCNGjODll1/mlVeSCpSbbrqJQw45hMMOO4w33nhjU3k+HQ1Ff8YZZwAwcuTITRNsNbpaVHMdB7wcES35VkraRtJ2ra+B44EX8m1rVvfcCbRyMrzbsHXY9ojgqquuYu7cucydO5eFCxdy0UUX8cQTT/DYY48xc+ZM5s2bx/Dhw4sODx8djHTwgQ98AIDevXtXfXrdrGR5a/BdwExgP0ktki5KV51Nu4Z3SbtLmpYufgR4RtI84LfALyLi4aziNMuM5zOprCrcbXjCCScwefJk3nnnHQD+9Kc/sXz5clavXs2OO+5Iv379ePnll3nuueeKvk+xoei7q8x6wEfEOQXKL8hTtgQYm77+I3BIVnGZVU2xahnf1NB5VZhW+vjjj2fBggUcfvjhQNKgfuedd3LiiSdy6623cvDBB7Pffvtx2GGHFX2fM888k8cff5yhQ4ey7777bjYUfXelji7HGsmoUaNi1iz3cbQ60atX/oEdpeRWYWPBggUccMABpe/Q3Jwk49dfT65IJk6s28T8zjvvsO2227Jq1SpGjx7NjBkzaj6CcL7zLWl2VKA/n8fmMsvKwIFJ1Va+cuuaRhiDPtXThqJ3MjHLShWqZax+9YR2klwe6NEsK+4EWpLuVNVez7I+z04mZlYzffv2ZdWqVU4oGYsIVq1aRd++fTM7hqu5zLLi+WE6NGDAAFpaWlixYkWtQ+n2+vbty4ABAzJ7f9/NZZaVQYPyN8A3NSWDPprVgUrdzeVqLrOseH4Y60GcTMyy4vlhrAdxMjHLiueHsR7EycQsK7412HoQ381llqUG6rFtVg5fmZiZWdmcTMzMrGxOJmZmVjYnEzMzK5uTiZmZlc3JxMzMyuZkYmZmZcssmUiaLGm5pBdyyq6V9CdJc9PH2AL7nijp95IWSroyqxjNzKwysrwyuQ04MU/5DRExLH1Ma79SUm/gZuAk4EDgHEkHZhinmZmVKbNkEhFPAW92YdfRwMKI+GNErAXuBk6raHBWWHNzMnR6r17Jc3NzrSMyswZQizaTSyQ9n1aD7Zhn/R7AGznLLWlZXpLGS5olaZYn2ClT62ROixdDRNtkTk4oZtaBaieTW4B9gGHAUuD6PNsoT1nBGbwiYlJEjIqIUf37969IkD3W1Ve3zQrY6r33knIzsyKqmkwiYllEbIiIjcB/klRptdcC7JmzPABYUo34ejxP5mRmXVTVZCJpt5zFvwFeyLPZ74DBkvaS1Ac4G5hajfh6PE/mZGZdlOWtwXcBM4H9JLVIugi4TtJ8Sc8DxwB/l267u6RpABGxHrgEeARYAPw0Il7MKk7L4cmczKyLFFGwOaLhjBo1KmbNmlXrMBpbc3PSRvL668kVycSJno/DrBuTNDsiRpX7Ph1OjiVpKsntuT+LiHfLPaDVOU/mZGZdUEo11/XAGOAlSfdK+oykvhnHZWZmDaTDK5OIeBJ4Mu2Z/kngS8BkYPuMYzMzswZR0hzwkj4InAp8DhgBTMkyKDMzayyltJncAxwKPEwyZtYTaT8RMzMzoLQ2kx8D+0TEhIj4lROJWSd4rDPrIUpJJk8BV0maBCBpsKRTsg3LrBvwWGfWg5R6ZbIWOCJdbgG+nVlEZt2FxzqzHqSUZLJPRFwHrAOIiL+QfzBGM8vlsc6sByklmaxN7+YKAEn7AH/NNCqz7sBjnVkPUkoyuYbkTq49JTUDjwN/n2lUZt2BxzqzHqSUTouPSpoDHEZSvXVZRKzMPDKzRtc6LI3HOrMeoOBAj5JGFNsxIuZkElEZPNCjmVnnVGOgx9ZZEPsCo4B5JFcmBwO/IRmvy8zMrHCbSUQcExHHAIuBEenUuCOB4cDCagVoZmb1r5QG+P0jYn7rQkS8QDKHu5mZGVDaQI8LJP0IuJPk9uBzSWZANDMzA0q7Mvki8CJwGfBV4KW0zMw64rG5rIco5dbg94Eb0kfJJE0GTgGWR8TQtOy7JEPZrwVeBb4YEW/l2XcRsAbYAKyvxJ0GZlXXOjZX65AqrWNzgW8Ptm6n2K3B80l7vecTEQcXfWPp48A7wO05yeR44FcRsV7Sv6bvc0WefRcBozrbn8W3BltdGTQoSSDtNTXBokXVjsYsr2rcGtw6MvDfps93pM/jgPe23HxzEfGUpEHtyqbnLD4HfKa0MM0akMfmsh6k2K3BiyNiMXBkRPx9RMxPH1cCJ1Tg2BcCvyx0eGC6pNmSxhd7E0njJc2SNGvFihUVCMusQjw2l/UgpTTAbyNpUwdFSUcA25RzUElXA+uBQq2RR0bECOAk4G/TKrO8ImJS2gdmVP/+/csJy6yyPDaX9SCl3Bp8ETBZ0odIrhhWk1xVdImk80mq0I6NAg02EbEkfV4u6UFgNMkkXWaNw2NzWQ9SSjJ5MyIOkbQ9SYP9akl7deVgkk4ErgA+ERF5210kbQP0iog16evjgW925XhmNTdunJOH9QilVHPdDxARb0fE6rTsvo52knQXMBPYT1KLpIuA7wPbAY9Kmivp1nTb3SVNS3f9CPCMpHnAb4FfRMTDnfqrzOqF+5lYD1HwykTS/sAQ4EOSzshZtT3J4I9FRcQ5eYr/q8C2S4Cx6es/Aod09P5mdc/9TKwHKVbNtR9J28YOJB0NW60BvpRhTGbdQ7E54J1MrJspmEwi4mfAzyQdHhEzqxiTWfeQr8NisXKzBlZKm8nfSNpe0taSHpe0UtK5mUdm1uh69+5cuVkDKyWZHB8Rb5NUebUA+wJfzzQqs+5gw4bOlZs1sFKSydbp81jgroh4M8N4zLqPpqbOlZs1sFKSyc8lvUwyde/jkvoD72cbllk34B7w1oN0mEzSsbgOJxnFdx3JII+nZR2YWcMbNw7OP7+tjaR372TZd3JZN1TKlQkR8eeI2JC+fjci/jfbsMy6geZmmDKlrY1kw4Zk2R0XrRsqKZmYWRcU62di1s04mZhlxfOZWA9SbDiVEcV2jIg5lQ/HrBsZODB/B0XPZ2LdULHhVK4vsi6AT1Y4FrPuZeLEzcfmAt/NZd1WseFUjqlmIGbdjuczsR6kw/lMJPUDvgYMjIjxkgYD+0XEf2cenVmj83wm1kOU0gD/Y2AtcES63AJ8O7OIzMys4ZSSTPaJiOuAdQAR8RdAmUZlZmYNpZRkslbSB0ka3ZG0D/DXTKMyM8vHM1fWrVLmgL8WeBjYU1IzcCRwQYYxmZltyTNX1rVSxuaaDpxBkkDuIhmj64mO9pM0WdJySS/klO0k6VFJr6TPOxbY90RJv5e0UNKVpf4xZtaNeUSButZhMpE0FTgeeCIi/jsiVpb43rcBJ7YruxJ4PCIGA4+ny+2P1xu4GTgJOBA4R9KBJR7TzLorjyhQ10ppM7keOAp4SdK9kj4jqW9HO0XEU0D7uU9OA6akr6cAp+fZdTSwMCL+GBFrgbvxKMVmVmjkAI8oUBdKqeZ6MiIuBvYGJgGfBZZ38XgfiYil6fsuBT6cZ5s9gDdyllvSsrwkjZc0S9KsFStWdDEss4y4wbhyPD9MXStpoMf0bq4zgQnAx2i7ushCvtuOo9DGETEpIkZFxKj+/ftnGJZZJ7U2GC9eDBFtDcZOKF0zbhxMmpTMVCklz5MmufG9TpTSZnIPsIBkLK6bSfqdfKWLx1smabf0fXcj/xVOC7BnzvIAYEkXj2dWO24wrrxx42DRIti4MXl2IqkbpfaA3yciJkTEryJiYxnHmwqcn74+H/hZnm1+BwyWtJekPsDZ6X5mjcUNxtaDlJJMngKukjQJQNJgSad0tJOku4CZwH6SWiRdBHwH+JSkV4BPpctI2l3SNICIWA9cAjxCckX004h4sfN/mlmNucHYepBSOi3+GJjN5mNz3QsUHegxIs4psOrYPNsuAcbmLE8DppUQm1n98hD01oN4bC6zrLjB2HqQUq5MPDaXWVd5CHrrIUpJJtfgsbnMzKyIUjotPkoXxuYyM9xp0XqMglcmkka0K1qaPg+UNDAi5mQXllk30NwMX/wirFuXLC9enCyDq76s21FE/s7lkn5dZL+IiE9mE1LXjRo1KmbNmlXrMMwSu+wCq1ZtWb7zzrCy1PFSzbIlaXZEjCr3fQpWc0XEMUUedZdIzOpOvkRSrNw65mrDulVKA7yZWe15cqy6VtJAj2bWBb0K/PcqVG7FeayzuuZPtVlWNhYYxq5QuRXnsc7qWimjBj9eSpmZtdPU1LlyK85jndW1gslEUl9JOwG7SNoxnb99J0mDgN2rFqFVlxs4K2fs2M6VW3GeHKuuFWuA/zLwVZLEMZu28bjeJpnXxLobN3BW1rQCY5UWKrfiWj+DV1+dVG0NHJgkEn8260LBfiabNpC+EhH/UaV4yuJ+JmUaNChJIO01NSUTEVnn9OqVzLDYnuR2E6sblepn0uGtwRHxH5KOAAblbh8Rt5d7cKszbuCsrIED8ydn1/FbN1RKA/wdwPeAMSTzv38MKDuLWR1yA2dluY7fepBSOi2OAg6MjurDrPF5MqfKGjcOZsxI5jDZsAF694bzz3cdv3VLpfQzeQHYNetArA54MqfKam6GKVOSRALJ85QpvkPOuqVSGuB/DQwDfkvOpFgR8ekuHVDaD7gnp2hv4BsR8e852xwN/Ax4LS16ICK+2dF7uwHe6opvaLAGULUGeODacg+SKyJ+T5KckNQb+BPwYJ5Nn46IUyp5bLOqypdIipWbNbBSJsd6ElgEbJ2+/h1QqblMjgVejQj/76oX7rRYOb17d67crIGVcjfXl4D7gB+mRXsAD1Xo+GeTzN6Yz+GS5kn6paQhReIbL2mWpFkrVqyoUFg9VGunxcWLk/4RrZ0WnVC6prWtpNRyswZWSpvJXGA08JuIGJ6WzY+Ig8o6sNQHWAIMiYhl7dZtD2yMiHckjQVujIjBHb2n20zK5Dr+yvL5tAaQ+eRYOf4aEWtzDrwVUInbhE8C5rRPJAAR8XZEvJO+ngZsLWmXChzTinGnxcqaOBH69Nm8rE8f32pt3VIpyeRJSf8AfFDSp4B7gZ9X4NjnUKCKS9KukpS+Hp3G6enpsuZOi5XX/srf3bWsmyolmVwBrADmkwz+OA34f+UcVFI/4FPAAzllEyRNSBc/A7wgaR5wE3C2O01WgXtsV9bVV8O6dZuXrVvnyZysWyraZiKpF/B8RAytXkhd5zaTCrj44s17bI8fDz/4Qa2jakwe6NEaQFXaTCJiIzBPkus5egL32K4sVxtaD1JKNdduwIuSHpc0tfWRdWBWA55ju7JcbWg9SCk94P8p8yisPvhursryZE7WgxRNJmmbyc2N0mZiZfL8G5U3bpyTh/UIbjOxNq6WqTwPT2M9RCnVXK1tJr8F3m0t7OqowVbHXC1TWa3D07S2Q7UOTwM+p9btlDKcyifylaeDPtYV3xpsdcXDqVgDqOYc8HWXNMwagm9osB6klFGD10h6O328L2mDpLerEZxZQ9tpp86VmzWwUq5MtstdlnQ6ySjCZlbM++93rtysgZXSaXEzEfEQ8MnKh2LWzbz7bufKzRpYKdVcZ+Q8PiPpO1RmCHozs87xrdZ1q5Rbg0/Neb2eZArf0zKJxqw72XlnWJVn5oSdd65+LN2Bb7Wuax3eGtxIfGuw1ZXmZjjvvM1HDpbgjjv85dcVvtU6E1WbaVHSFEk75CzvKGlyuQc26/ZmzMg/OdaMGbWJp9H5Vuu6VkoD/MER8VbrQkT8GRieWURWW66Trpxbb+1cuRXnIf3rWinJpJekHVsXJO1EaW0t1mha66QXL05+QbfWSTuhdE2hKuRuVLVcVR47rq6VkkyuB56V9C1J3wSeBa7LNiyrCc9nYvVs3LhkFtCmpqTtqakpWXb7U10oqQFe0oEkfUsEPB4RL5V1UGkRsAbYAKxv3/gjScCNwFjgPeCCiJjT0fu6Ab5Mnma2sqTC63x1YnWiamNzAaTJo6wEkscxEbGywLqTgMHp41DglvTZsuT5TCqrV6/8SbhXp/sKm9W9ev1UnwbcHonngB0k7VbroLo910lX1jHHdK7crIHVKpkEMF3SbEnj86zfA3gjZ7klLduCpPGSZkmatWLFigxC7UFcJ11ZCxd2rtysgdXqrqwjI2KJpA8Dj0p6OSKeylmfr7I5byVzREwCJkHSZlL5UHsYTzNbOe4XYT1ITa5MImJJ+rwceJAtRyFuAfbMWR4ALKlOdGYV4n4R1oNUPZlI2kbSdq2vgeOBF9ptNhX4ghKHAasjYmmVQzUrz8SJWza29+rlNijrlmpxZfIR4BlJ84DfAr+IiIclTZA0Id1mGvBHYCHwn8DFNYjTrDwzZmx5N9fGjR5OxbolD/Rom2tuTjopvv56Uh0zcaLbULpqq61gw4Yty3v3hvXrqx+PWR5V7WdiPYSH+K6sfImkWLlZA6vXfiZWCx5OpbJ69+5cuVkDczKxNvl6vxcrt+LG5+tCVaTcrIG5msva9O5duI7fOu8HP0ieJ01Kzmvv3kkiaS0360Z8ZWJtXMdfeUceCQMGJCMKDBiQLJt1Q04m1qapqXPlVpznh7EexMnE2nigx8ryDQ3WgziZWBsP9FhZvqHBehA3wNvmPNCjmXWBr0zMrHE0N8OgQckYZ4MGuf2pjvjKxMwag0doqGu+MjGzxuAbGuqak4lt7uKLkwEKpeT5Yg/YbHXCNzTUNScTa3PxxXDLLW2dFDdsSJadULrG/XYqy2Od1TUnE2szaVLnyq24iRO3/KLr3dv9drrKIzTUNScTa+P/rJU1Y8aW527DBk+O1VW+0qtrTibWpv0Usx2VW3G+0qssj9BQ1/wtYW0++MHOlVtxvtKrLI/QUNeqnkwk7Snp15IWSHpR0mV5tjla0mpJc9PHN6odZ4/07rudKzertnHjYNEi2LgxeXYiqRu16LS4Hvi/ETFH0nbAbEmPRsRL7bZ7OiJOqUF8ZmbWSVW/MomIpRExJ329BlgA7FHtOMzMrHJq2mYiaRAwHPhNntWHS5on6ZeShlQ3MjMz64yajc0laVvgfuCrEfF2u9VzgKaIeEfSWOAhYHCB9xkPjAcYOHBgdgGbmVlBNbkykbQ1SSJpjogH2q+PiLcj4p309TRga0m75HuviJgUEaMiYlT//v0zjdusU6TOlZs1sFrczSXgv4AFEfFvBbbZNd0OSaNJ4lxVvSjNKiCic+VmDawWVyZHAucBn8y59XespAmSJqTbfAZ4QdI84Cbg7Aj/D7QcjTCvhceSsh6k6m0mEfEMUPQ6PyK+D3y/OhFZw2mUeS3cabHympuTIedffx0GDkx6v9fTv3kP5h7w1ngaZV6LnXfuXLkV1/ojYvHipKqw9UdEPV6V9kBOJtZ4Xn+9c+W18v77nSu34hrlR0QP5WRijafQLeD1dmu4h6epLE+OVdecTKzxePTYnsmjWtc1/ytY42mU0WPdZlJZGzd2rtyqysnEGlMjjB57443Qp8/mZX36JOVm3YyTiVlWxo2DyZM3v4KaPLk+E18j8JVeXXMyMctSI1xBNQpf6dU1JxNr84EPdK7crJp8pVfXajZqsNWhtWs7V25WbePGOXnUKV+ZWJtG6b9hZnXHycTauP+GmXWRk4m1aZT+G2ZWd9xmYptznbSZdYGvTMzMrGxOJmZmVjYnEzMzK5uTiZmZlc3JxMzMylaTZCLpREm/l7RQ0pV51kvSTen65yWNqEWcZuWStnyYdUdVTyaSegM3AycBBwLnSDqw3WYnAYPTx3jglqoGaVYBhRKHE4p1R7W4MhkNLIyIP0bEWuBu4LR225wG3B6J54AdJO1W7UDNzKw0tei0uAfwRs5yC3BoCdvsASxt/2aSxpNcvQD8VdILlQs1E7sAK2sdRAkcZ9lGjmx7vQLov2lJmj27+vGUpI7P52YcZ+XsV4k3qUUyyXeRH13YJimMmARMApA0KyJGlRdethohRnCclZbEubhB4myU8+k4K0HSrEq8Ty2quVqAPXOWBwBLurCNmZnViVokk98BgyXtJakPcDYwtd02U4EvpHd1HQasjogtqrjMzKw+VL2aKyLWS7oEeAToDUyOiBclTUjX3wpMA8YCC4H3gC+W+PaTMgi50hohRnCcleY4K8txVk5FYlRE3qYIMzOzkrkHvJmZlc3JxMzMytZwyaQRhmKRtKekX0taIOlFSZfl2eZoSaslzU0f36h2nGkciyTNT2PY4hbBOjmf++Wcp7mS3pb01Xbb1OR8SposaXlu/yZJO0l6VNIr6fOOBfYt+lmuQpzflfRy+u/6oKQdCuxb9DNShTivlfSnnH/bsQX2rcr5LBDjPTnxLZI0t8C+1TyXeb+HMvt8RkTDPEga7F8F9gb6APOAA9ttMxb4JUlflcOA39Qgzt2AEenr7YA/5InzaOC/6+CcLgJ2KbK+5uczz2fgf4GmejifwMeBEcALOWXXAVemr68E/rXA31H0s1yFOI8Htkpf/2u+OEv5jFQhzmuBy0v4XFTlfOaLsd3664Fv1MG5zPs9lNXns9GuTBpiKJaIWBoRc9LXa4AFJD34G1HNz2c7xwKvRsTiGsawSUQ8BbzZrvg0YEr6egpwep5dS/ksZxpnREyPiPXp4nMk/blqqsD5LEXVzmexGCUJ+CxwVxbH7owi30OZfD4bLZkUGmals9tUjaRBwHDgN3lWHy5pnqRfShpS3cg2CWC6pNlKhqZpr67OJ0m/pEL/UevhfAJ8JNJ+Uenzh/NsU2/n9UKSK9B8OvqMVMMlaXXc5ALVMvVyPo8ClkXEKwXW1+RctvseyuTz2WjJpKJDsWRN0rbA/cBXI+LtdqvnkFTVHAL8B/BQlcNrdWREjCAZqflvJX283fp6Op99gE8D9+ZZXS/ns1T1dF6vBtYDzQU26egzkrVbgH2AYSTj812fZ5t6OZ/nUPyqpOrnsoPvoYK75Skrej4bLZk0zFAskrYm+QdsjogH2q+PiLcj4p309TRga0m7VDlMImJJ+rwceJDk8jZXXZzP1EnAnIhY1n5FvZzP1LLWqsD0eXmeberivEo6HzgFGBdpZXl7JXxGMhURyyJiQ0RsBP6zwPFrfj4lbQWcAdxTaJtqn8sC30OZfD4bLZk0xFAsab3pfwELIuLfCmyza7odkkaT/Fusql6UIGkbSdu1viZpkG0/6nLNz2eOgr/66uF85pgKnJ++Ph/4WZ5tSvksZ0rSicAVwKcj4r0C25TyGclUuza6vylw/JqfT+A44OWIaMm3strnssj3UDafz2rcVVDhOxTGktyV8CpwdVo2AZiQvhbJ5FuvAvOBUTWIcQzJJeHzwNz0MbZdnJcAL5LcJfEccEQN4tw7Pf68NJa6PJ9pHP1IksOHcspqfj5JkttSYB3Jr7mLgJ2Bx4FX0ued0m13B6YV+yxXOc6FJPXirZ/RW9vHWegzUuU470g/e8+TfKHtVsvzmS/GtPy21s9jzra1PJeFvocy+Xx6OBUzMytbo1VzmZlZHXIyMTOzsjmZmJlZ2ZxMzMysbE4mZmZWNicTsxzpCLWXl/keF0j6frH3k3S6pANzlr8p6bhyjmtWS04mZrVxOskIrgBExDci4rHahWNWHicT6/EkXZ3O2/AYsF9a9mFJs9PXh0gKSQPT5Vcl9ZPUX9L9kn6XPo4s8XhHkIwx9t10Xot9JN0m6TPp+kWS/lnSTEmzJI2Q9Eh63Ak57/P19LjPS/qnCp8Ws07ZqtYBmNWSpJEkQ0UMJ/n/MAeYHRHLJfWVtD3JSLCzgKMkPQMsj4j3JP0IuCEinkkTzSPAAR0dMyKelTSVZP6V+9I42m/2RkQcLukGkp7VRwJ9SXpO3yrpeGAwydhOAqZK+ngkw6ObVZ2TifV0RwEPRjo2Vfol3+pZki/xjwP/DJxI8sX9dLr+OODAnESwfevYSxXQGsd8YNtI5qNYI+l9JTMiHp8+/ifdbluS5OJkYjXhZGJWeGjtp0mSTRPJYHhXpNv+d7q+F3B4RPwld6c8Vxld8df0eWPO69blrUiS2r9ExA8rcTCzcrnNxHq6p4C/kfTB9Kri1HbrzgVeiWT48zdJBr+bka6fTjLAJACShnXiuGtIplLtqkeAC9O5KpC0h6R8kxyZVYWTifVokUxreg/JiKr301aFRUQsSl+2Vh09A7wVEX9Oly8FRqUN4C+RjGJcqruBr0v6H0n7dCHu6cBPgJmS5gP3UV5yMiuLRw02M7Oy+crEzMzK5mRiZmZlczIxM7OyOZmYmVnZnEzMzKxsTiZmZlY2JxMzMyvb/weiNYfL3UG1XQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "draw_scatter(squigulator, realign, 'squigulator', 'realign')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "0a664c01",
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "draw_scatter_2(squigulator, nanopolish, 'squigulator', 'nanopolish')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "887c4634",
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "draw_scatter_2(squigulator, f5c, 'squigulator', 'f5c')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "16e8abfe",
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "draw_scatter_2(squigulator, sigfish, 'squigulator', 'sigfish dtw')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "ee52fcb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "draw_scatter_2(squigulator, realign, 'squigulator', 'realign')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c0bee3a",
+   "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": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/test/data/raw/metric/eventalign.tsv b/test/data/raw/metric/eventalign.tsv
new file mode 100644
index 0000000..f5273ae
--- /dev/null
+++ b/test/data/raw/metric/eventalign.tsv
@@ -0,0 +1,13 @@
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diff --git a/test/data/raw/metric/nanopolish.tsv b/test/data/raw/metric/nanopolish.tsv
new file mode 100644
index 0000000..4ad9f26
--- /dev/null
+++ b/test/data/raw/metric/nanopolish.tsv
@@ -0,0 +1,13 @@
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diff --git a/test/data/raw/metric/realign.tsv b/test/data/raw/metric/realign.tsv
new file mode 100644
index 0000000..cb19a68
--- /dev/null
+++ b/test/data/raw/metric/realign.tsv
@@ -0,0 +1,13 @@
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diff --git a/test/data/raw/metric/sigfish.tsv b/test/data/raw/metric/sigfish.tsv
new file mode 100644
index 0000000..a644bf3
--- /dev/null
+++ b/test/data/raw/metric/sigfish.tsv
@@ -0,0 +1,13 @@
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59.35,58.25,57.97,53.99	62.78,63.19,61.68,62.23	71.57,71.02,71.57,71.85,70.89,67.59,70.34,70.75,70.47,70.89,70.89,71.02,69.92	70.61,72.4,74.05,69.37,72.53,75.14,71.85,74.73,68.96,71.44	65.53,65.94,66.08,63.88,60.86,63.6,60.99	56.46,57.15,57.7,58.93,57.29,60.31,55.64,58.66,57.01,54.54,55.64	59.21,57.56,58.66,59.48,59.9,59.07,61.68,60.03,61.13,57.29	71.3,70.47,66.08,68.14,66.22,62.78	70.34,70.89,67.18,71.44,66.08,68.14	61.96,60.17,58.38,64.7,63.33,58.38	57.42,51.79,56.19,55.36,56.74,56.19,55.5,57.56,55.91	59.21,59.48,61.68,59.62,63.88,59.62,63.33,58.52,55.64,54.54,58.93,57.84,62.09,59.62,58.66,61.41,57.84,57.29,57.97,56.19,59.76	66.22,58.8,66.08,63.47,63.74,64.02,64.7,61.82	70.89,72.95,70.75,70.06	nan	64.43,59.21,61.96	57.7,58.25,57.84,55.5,56.19,53.99,55.09,57.42,55.23,56.6
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+nan	nan	nan	nan	nan	nan	123.91,124.74,125.42,125.01,128.58,123.64,120.62,118.83,117.73	114.3,116.36,112.65,112.65,114.98,108.66	88.33,92.73,91.49	81.74,82.43,79.54,82.29,80.78,80.23,78.99,79.95,80.23,82.01,112.24,81.05,84.62	152.07,152.49,149.88	138.75,127.62,131.33,122.13,115.95,114.3,113.33,110.18,116.91	84.76,70.2,67.86,69.65,70.89,72.12,68.55,64.98	75.83,78.99,77.48,78.17,87.51	117.04,118.01,115.12,115.67,115.67,115.12,117.46,119.24,116.91,116.36,116.22,116.22,116.08,114.71,109.9	99.32,101.8,101.66,101.25,104.54,100.28,99.87,101.38,98.09,98.77,97.81,96.3	87.65,79.82,76.79,74.05,69.79,68.28,80.36	nan	94.65,94.65,92.59,94.24,89.02	94.38,95.61,96.3,90.12,79.27,75.56,76.66,77.89,69.24,70.61,75.83	131.19,130.78,128.31,127.76,132.98,130.09,116.08,100.15	nan	76.24,68.83,69.24,69.79,66.35,68.28,66.63	89.16,101.38,106.33,100.01,102.07,103.44,104.82,101.52,98.77	nan	82.97,84.21,82.7,82.97,82.97,82.7,85.86,82.7,83.94,83.11,83.39,83.52,84.49,83.52,82.29,85.86,87.37	114.02,113.2,114.71,113.61,113.88,113.47,112.65,119.65	136.14,134.63,135.04,134.35,136.28,133.67,136.28,136.14	114.71,121.17,119.93,116.22,118.14,117.87,122.81,120.75,124.46,121.99,118.56,121.3,120.89	108.66,107.02,110.59,111.0,108.8,108.8,110.04,110.31,112.1,110.72,107.15,110.72,106.6,105.78	95.34,86.27,88.2,88.88,87.51,86.27,86.55,87.51,84.76,86.68,85.86	102.07,103.44,100.28,100.42,104.13	137.24,137.38,138.2,136.83,135.59,135.32,136.96	124.6,119.38,119.38,121.85,116.08,119.38,122.13,119.38,119.65,116.63,119.65,119.65,113.47	100.01,96.71,94.51,94.1,100.15,95.06	81.46,78.99,78.85,79.82,75.83,75.69,73.5,75.83,74.87	72.53,73.63,72.53,73.22,74.32,73.22,75.42,80.91	137.1,138.47,141.5,134.9,141.36,138.2,135.45,132.16,136.41,137.38,138.61,139.02,136.55,137.93	110.18,113.75,114.57,108.39,107.43,106.6,106.05	132.7,135.86,138.2,134.22,139.71,138.34,139.99,138.89,138.34,139.99,139.16,138.2,135.32	118.69,116.36,117.18,118.28,117.87,118.14,112.51	93.14,81.88,78.85,80.64,79.27,80.36,79.82,81.88,75.42,74.87	96.58,98.36,100.28,98.5,101.66,95.2,99.32,97.95,99.05,98.09,99.05,98.91,98.09,97.67,97.95,98.91,99.46,100.7,93.83,87.51	60.99,63.6,64.98,63.6,64.43,65.67,63.47,64.98	66.08,66.35,65.8,66.63,65.8,65.8,66.08,65.53,64.7,66.76,67.59,66.63,66.63,68.41,65.8,67.18,65.67,66.35,68.83,66.9,66.08,63.47,67.18,71.57,75.69,73.77,76.24,75.97,87.51,79.54,87.78	130.78,133.25,121.03	87.51,89.57,82.15	74.87,75.83,74.46,75.14,75.97,73.91,73.22,75.42	74.87,74.32,76.79,73.22,77.34,86.13,88.06,80.09,79.13,82.56,78.3,73.5,72.67,72.12,74.32,75.42,72.95,80.36,75.14,77.89,76.52,76.24,71.57,76.11,73.91	75.97,77.89,78.44,71.57,71.71,73.22	83.39,89.98,88.61,88.88	87.78,86.82,87.51,86.68,90.26,88.47,87.51,83.11,84.21,84.9,89.71,90.94,91.63,93.97,94.65	81.88,74.87,70.47,72.26	68.83,65.39,69.1,73.08,72.12	79.4,81.74,78.85,77.07,81.74,80.91,82.56,81.46,81.74,81.33,80.78,82.29,84.62,91.35,93.69	122.54,126.39,126.94,127.62,123.78	nan	105.09,98.77,110.45,105.78,111.14,111.0,110.31,111.82,110.86,110.31,111.55	106.05,102.07,102.21,103.72,100.83,101.25,101.25,99.6,100.15,101.25,114.85,119.24,115.4,109.9,101.52,99.6,99.32,99.32,101.11,100.7,99.87,99.46,98.22,99.87	93.97,92.45,93.69,93.69,96.44	137.24,138.75,137.93,138.2,138.2,136.96,139.3,137.51,139.71,135.73,137.93,134.49,136.0,138.2,136.69,134.9,137.65,138.61,136.41,135.32,134.77,137.79,137.1,137.65,136.69,117.04,125.15,116.49,116.36,113.47,122.54,142.32,138.2,138.61,140.4,139.3,139.85,138.47,138.2,138.2,140.67	117.59,108.66,111.69,108.94	140.54,139.57,139.3,139.71,139.57,137.65,140.26,139.71,139.3,136.69,138.06	116.77,117.04,117.18	94.38,79.54,76.24,69.92	65.12,63.6,63.88,65.94,64.15,63.74,63.33	65.53,64.29,64.43	67.31,66.63,65.8,65.53,65.39,63.47,62.78,65.25,65.53,69.24,66.63,63.47,64.29,63.06,65.8,72.67,67.31,65.53,62.64,63.47,68.28,65.94,66.08,65.53,68.41,67.18,67.86,67.86,69.79,71.02,70.61,67.45,68.83,67.18,67.31,66.9,66.08,68.14,71.3,67.31,70.34,69.79,66.63,68.83,69.37,68.83,67.86,67.86,68.28,68.69,70.89,67.45,66.35,66.63,68.55,67.86,71.02,68.14,69.24,69.92,71.02,70.75,72.26,71.02,70.2,70.61,71.3,71.71,70.2,72.67,69.92,75.28,70.47,68.28,70.61,67.45	65.8,64.98,64.84,69.1,64.02,67.86,65.39,63.06,64.15	62.09,61.68,63.06,63.6,64.43,65.94,62.51,63.6,64.84,60.03,59.48,63.19,63.74,64.43,61.82,65.39,66.08,65.12,60.72,62.09,64.43,62.23,63.47,64.15,66.08,62.78,62.92,61.68,62.23,63.33,62.23,66.63,64.29,65.53,63.88,65.8,65.12,65.53,64.43,61.54,63.19,66.49,65.25,65.8,62.51,61.54,64.98,62.78,62.09,59.9,61.54,62.78,62.92,64.98,65.67,66.08,66.76,61.96,61.54,64.43,65.67,64.43,64.7,66.22,66.08,63.6,65.39,64.29,64.7,66.63,66.35,64.84,64.7,64.7,64.15,64.98,66.49,64.02	60.72,59.62,62.23	nan	nan	nan	nan	nan	nan	nan	nan	nan	65.12,65.8,61.41,62.78,62.51,63.19,61.96,60.99,63.33,63.33,63.47,63.74,62.23,62.78,62.92,61.82,62.23,65.25,67.18,63.88	69.24,72.53,72.12,70.47,69.65,70.2,72.12	68.55,66.63,70.75,68.28,67.86,73.5,68.14,67.04,67.59	63.6,61.54,62.23,60.58,68.14,61.82,64.02,60.03,61.96,64.43,63.33,62.78	62.09,61.54,60.58,61.82	63.33,61.82,62.78,61.96	68.28,66.08,67.86,68.28,66.9	64.43,65.53,67.04,64.84,65.25	69.37,67.73,66.08,67.73,66.08,66.35,65.67,65.39	64.43,60.58,62.92,64.43,62.23,66.63	nan	71.57,77.07,78.44,76.11,79.13,78.58,79.27,77.62,80.64,79.13,79.82,79.82,83.25,77.62,80.36,74.73,79.27,77.48,79.27,77.89,78.85,77.48,78.99,77.34,78.58	76.38,78.58,74.87,80.09,74.18,77.62,78.03,75.97,78.03,75.01,75.42,83.66,78.17,74.32	68.83,70.2,65.8,64.29,66.49,66.9,64.43,65.12,63.88,65.53,64.98,63.6,66.63,64.84,63.47,66.63,63.47,66.76,66.63,62.51,65.12,66.35,67.18	64.02,63.06,63.88,63.06	64.84,64.15,64.7,64.43,62.23	65.25,67.86,65.39,63.74,64.43,66.9,66.63,64.15,65.67,63.6,65.12,63.19,64.98,63.74,63.74,64.15,64.43,63.6,65.53,65.39,64.98,62.64,63.33,61.96,64.29,62.51,59.76,57.56,57.97,67.45,62.78,66.9,63.6,63.33,64.43,60.03,63.47,67.59,71.02,73.08,73.22	78.17,77.07,80.09,76.66	71.85,71.57,72.81,73.91	68.14,64.84,65.67,64.98	60.03,63.06,63.47
diff --git a/test/data/raw/metric/squigualator.tsv b/test/data/raw/metric/squigualator.tsv
new file mode 100644
index 0000000..5218442
--- /dev/null
+++ b/test/data/raw/metric/squigualator.tsv
@@ -0,0 +1 @@
+nan	nan	nan	nan	nan	nan	110.39,110.39,110.39,110.39,110.39,110.39,110.39,110.39,110.39,110.39	103.39,103.39,103.39,103.39,103.39,103.39,103.39,103.39,103.39,103.39	79.21,79.21,79.21,79.21,79.21,79.21,79.21,79.21,79.21,79.21	71.24,71.24,71.24,71.24,71.24,71.24,71.24,71.24,71.24,71.24	143.5,143.5,143.5,143.5,143.5,143.5,143.5,143.5,143.5,143.5	110.67,110.67,110.67,110.67,110.67,110.67,110.67,110.67,110.67,110.67	62.45,62.45,62.45,62.45,62.45,62.45,62.45,62.45,62.45,62.45	73.99,73.99,73.99,73.99,73.99,73.99,73.99,73.99,73.99,73.99	111.77,111.77,111.77,111.77,111.77,111.77,111.77,111.77,111.77,111.77	93.77,93.77,93.77,93.77,93.77,93.77,93.77,93.77,93.77,93.77	67.67,67.67,67.67,67.67,67.67,67.67,67.67,67.67,67.67,67.67	60.39,60.39,60.39,60.39,60.39,60.39,60.39,60.39,60.39,60.39	109.02,109.02,109.02,109.02,109.02,109.02,109.02,109.02,109.02,109.02	77.97,77.97,77.97,77.97,77.97,77.97,77.97,77.97,77.97,77.97	124.82,124.82,124.82,124.82,124.82,124.82,124.82,124.82,124.82,124.82	103.53,103.53,103.53,103.53,103.53,103.53,103.53,103.53,103.53,103.53	74.4,74.4,74.4,74.4,74.4,74.4,74.4,74.4,74.4,74.4	96.93,96.93,96.93,96.93,96.93,96.93,96.93,96.93,96.93,96.93	90.61,90.61,90.61,90.61,90.61,90.61,90.61,90.61,90.61,90.61	75.23,75.23,75.23,75.23,75.23,75.23,75.23,75.23,75.23,75.23	101.88,101.88,101.88,101.88,101.88,101.88,101.88,101.88,101.88,101.88	126.33,126.33,126.33,126.33,126.33,126.33,126.33,126.33,126.33,126.33	114.24,114.24,114.24,114.24,114.24,114.24,114.24,114.24,114.24,114.24	101.05,101.05,101.05,101.05,101.05,101.05,101.05,101.05,101.05,101.05	75.78,75.78,75.78,75.78,75.78,75.78,75.78,75.78,75.78,75.78	97.21,97.21,97.21,97.21,97.21,97.21,97.21,97.21,97.21,97.21	124.82,124.82,124.82,124.82,124.82,124.82,124.82,124.82,124.82,124.82	112.59,112.59,112.59,112.59,112.59,112.59,112.59,112.59,112.59,112.59	88.96,88.96,88.96,88.96,88.96,88.96,88.96,88.96,88.96,88.96	73.17,73.17,73.17,73.17,73.17,73.17,73.17,73.17,73.17,73.17	66.85,66.85,66.85,66.85,66.85,66.85,66.85,66.85,66.85,66.85	126.88,126.88,126.88,126.88,126.88,126.88,126.88,126.88,126.88,126.88	104.49,104.49,104.49,104.49,104.49,104.49,104.49,104.49,104.49,104.49	122.48,122.48,122.48,122.48,122.48,122.48,122.48,122.48,122.48,122.48	107.51,107.51,107.51,107.51,107.51,107.51,107.51,107.51,107.51,107.51	71.52,71.52,71.52,71.52,71.52,71.52,71.52,71.52,71.52,71.52	100.37,100.37,100.37,100.37,100.37,100.37,100.37,100.37,100.37,100.37	70.01,70.01,70.01,70.01,70.01,70.01,70.01,70.01,70.01,70.01	66.43,66.43,66.43,66.43,66.43,66.43,66.43,66.43,66.43,66.43	123.17,123.17,123.17,123.17,123.17,123.17,123.17,123.17,123.17,123.17	84.57,84.57,84.57,84.57,84.57,84.57,84.57,84.57,84.57,84.57	65.88,65.88,65.88,65.88,65.88,65.88,65.88,65.88,65.88,65.88	71.93,71.93,71.93,71.93,71.93,71.93,71.93,71.93,71.93,71.93	72.89,72.89,72.89,72.89,72.89,72.89,72.89,72.89,72.89,72.89	102.15,102.15,102.15,102.15,102.15,102.15,102.15,102.15,102.15,102.15	73.3,73.3,73.3,73.3,73.3,73.3,73.3,73.3,73.3,73.3	64.37,64.37,64.37,64.37,64.37,64.37,64.37,64.37,64.37,64.37	73.3,73.3,73.3,73.3,73.3,73.3,73.3,73.3,73.3,73.3	72.48,72.48,72.48,72.48,72.48,72.48,72.48,72.48,72.48,72.48	116.99,116.99,116.99,116.99,116.99,116.99,116.99,116.99,116.99,116.99	120.7,120.7,120.7,120.7,120.7,120.7,120.7,120.7,120.7,120.7	110.53,110.53,110.53,110.53,110.53,110.53,110.53,110.53,110.53,110.53	89.38,89.38,89.38,89.38,89.38,89.38,89.38,89.38,89.38,89.38	84.57,84.57,84.57,84.57,84.57,84.57,84.57,84.57,84.57,84.57	126.05,126.05,126.05,126.05,126.05,126.05,126.05,126.05,126.05,126.05	108.47,108.47,108.47,108.47,108.47,108.47,108.47,108.47,108.47,108.47	126.33,126.33,126.33,126.33,126.33,126.33,126.33,126.33,126.33,126.33	106.27,106.27,106.27,106.27,106.27,106.27,106.27,106.27,106.27,106.27	69.32,69.32,69.32,69.32,69.32,69.32,69.32,69.32,69.32,69.32	56.68,56.68,56.68,56.68,56.68,56.68,56.68,56.68,56.68,56.68	59.02,59.02,59.02,59.02,59.02,59.02,59.02,59.02,59.02,59.02	62.45,62.45,62.45,62.45,62.45,62.45,62.45,62.45,62.45,62.45	61.35,61.35,61.35,61.35,61.35,61.35,61.35,61.35,61.35,61.35	57.92,57.92,57.92,57.92,57.92,57.92,57.92,57.92,57.92,57.92	54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21	54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21	54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21	54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21	54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21	54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21	54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21	54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21	54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21	54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21,54.21	55.31,55.31,55.31,55.31,55.31,55.31,55.31,55.31,55.31,55.31	59.15,59.15,59.15,59.15,59.15,59.15,59.15,59.15,59.15,59.15	62.59,62.59,62.59,62.59,62.59,62.59,62.59,62.59,62.59,62.59	56.68,56.68,56.68,56.68,56.68,56.68,56.68,56.68,56.68,56.68	55.17,55.17,55.17,55.17,55.17,55.17,55.17,55.17,55.17,55.17	56.54,56.54,56.54,56.54,56.54,56.54,56.54,56.54,56.54,56.54	65.47,65.47,65.47,65.47,65.47,65.47,65.47,65.47,65.47,65.47	69.73,69.73,69.73,69.73,69.73,69.73,69.73,69.73,69.73,69.73	60.39,60.39,60.39,60.39,60.39,60.39,60.39,60.39,60.39,60.39	55.17,55.17,55.17,55.17,55.17,55.17,55.17,55.17,55.17,55.17	56.54,56.54,56.54,56.54,56.54,56.54,56.54,56.54,56.54,56.54	65.47,65.47,65.47,65.47,65.47,65.47,65.47,65.47,65.47,65.47	69.73,69.73,69.73,69.73,69.73,69.73,69.73,69.73,69.73,69.73	60.39,60.39,60.39,60.39,60.39,60.39,60.39,60.39,60.39,60.39	55.17,55.17,55.17,55.17,55.17,55.17,55.17,55.17,55.17,55.17	55.31,55.31,55.31,55.31,55.31,55.31,55.31,55.31,55.31,55.31	61.08,61.08,61.08,61.08,61.08,61.08,61.08,61.08,61.08,61.08	66.43,66.43,66.43,66.43,66.43,66.43,66.43,66.43,66.43,66.43	66.57,66.57,66.57,66.57,66.57,66.57,66.57,66.57,66.57,66.57	56.68,56.68,56.68,56.68,56.68,56.68,56.68,56.68,56.68,56.68