diff --git a/notebooks/minimal-viable-alteration.ipynb b/notebooks/minimal-viable-alteration.ipynb
index 15d8866..bd7d933 100644
--- a/notebooks/minimal-viable-alteration.ipynb
+++ b/notebooks/minimal-viable-alteration.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 66,
    "metadata": {
     "collapsed": true
    },
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 67,
    "outputs": [],
    "source": [
     "def read_xy(path: str):\n",
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 68,
    "outputs": [],
    "source": [
     "def read_all(load_dir: str):\n",
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 69,
    "outputs": [
     {
      "name": "stderr",
@@ -63,7 +63,7 @@
       "text/plain": "      x   y     N          r         cr        fd\n0     0   0     1   0.000000   0.000000 -0.000000\n1    -1   0     2   1.000000   1.000000       inf\n2    -2   0     3   2.000000   2.000000  1.584963\n3     1   0     4   1.000000   2.000000  2.000000\n4    -3   0     5   3.000000   3.000000  1.464974\n..   ..  ..   ...        ...        ...       ...\n995 -18  -4   996  18.439089  54.120237  1.729739\n996 -47 -19   997  50.695167  54.120237  1.729990\n997  -3  20   998  20.223748  54.120237  1.730241\n998 -40  14   999  42.379240  54.120237  1.730492\n999  35  -3  1000  35.128336  54.120237  1.730743\n\n[1000 rows x 6 columns]",
       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>x</th>\n      <th>y</th>\n      <th>N</th>\n      <th>r</th>\n      <th>cr</th>\n      <th>fd</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>inf</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-2</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>1.584963</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-3</td>\n      <td>0</td>\n      <td>5</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>1.464974</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>995</th>\n      <td>-18</td>\n      <td>-4</td>\n      <td>996</td>\n      <td>18.439089</td>\n      <td>54.120237</td>\n      <td>1.729739</td>\n    </tr>\n    <tr>\n      <th>996</th>\n      <td>-47</td>\n      <td>-19</td>\n      <td>997</td>\n      <td>50.695167</td>\n      <td>54.120237</td>\n      <td>1.729990</td>\n    </tr>\n    <tr>\n      <th>997</th>\n      <td>-3</td>\n      <td>20</td>\n      <td>998</td>\n      <td>20.223748</td>\n      <td>54.120237</td>\n      <td>1.730241</td>\n    </tr>\n    <tr>\n      <th>998</th>\n      <td>-40</td>\n      <td>14</td>\n      <td>999</td>\n      <td>42.379240</td>\n      <td>54.120237</td>\n      <td>1.730492</td>\n    </tr>\n    <tr>\n      <th>999</th>\n      <td>35</td>\n      <td>-3</td>\n      <td>1000</td>\n      <td>35.128336</td>\n      <td>54.120237</td>\n      <td>1.730743</td>\n    </tr>\n  </tbody>\n</table>\n<p>1000 rows × 6 columns</p>\n</div>"
      },
-     "execution_count": 24,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 70,
    "outputs": [
     {
      "name": "stderr",
@@ -296,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 71,
    "outputs": [
     {
      "data": {
@@ -319,11 +319,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 120,
    "outputs": [],
    "source": [
     "def convergent_tail_index(series, tol):\n",
-    "    diffs = np.abs(np.ediff1d(a.fd))\n",
+    "    diffs = np.abs(np.ediff1d(series))\n",
     "    for i in range(0, len(diffs)):\n",
     "        if np.max(diffs[i:]) <= tol:\n",
     "            return i\n",
@@ -337,12 +337,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 121,
    "outputs": [],
    "source": [
-    "def compute_fd(df):\n",
-    "    tail_index = convergent_tail_index(df.fd, 0.05)\n",
-    "    return np.mean(a.fd[tail_index:])"
+    "def compute_fd(df, tol = 0.05):\n",
+    "    tail_index = convergent_tail_index(df.fd, tol)\n",
+    "    return np.mean(df.fd[tail_index:])"
    ],
    "metadata": {
     "collapsed": false
@@ -350,36 +350,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 122,
    "outputs": [
     {
      "data": {
-      "text/plain": "[<matplotlib.lines.Line2D at 0x13718f520>]"
+      "text/plain": "[1.6972188780546005,\n 1.741736844787361,\n 1.732135584521786,\n 1.7152613146304656,\n 1.728836880434697,\n 1.7300514834031597,\n 1.748421194691431,\n 1.7287640430180986,\n 1.7123919318562837,\n 1.7739930888022366,\n 1.7584717697837762,\n 1.7009044749656368,\n 1.7316986067539795,\n 1.7397504422026655,\n 1.714607675170586,\n 1.7541336557600284,\n 1.6944449486837003,\n 1.7413017386225582,\n 1.7586824633086955,\n 1.7621333897577298,\n 1.731409604427308,\n 1.7040785354660994,\n 1.720970435833901,\n 1.7493817381438628,\n 1.7194584712458039,\n 1.7371705652881426,\n 1.7476735580577902,\n 1.7453410966666576,\n 1.7405812458466108,\n 1.7318723528446751,\n 1.7363887686944106,\n 1.7082895630235775,\n 1.7398588474316838,\n 1.7071844051847547,\n 1.730417361421785,\n 1.7562821657346117,\n 1.6970380009288513,\n 1.727847271689809,\n 1.7344987347694254,\n 1.6917005260543316,\n 1.736465228691966,\n 1.7463067343476375,\n 1.7327131263137254,\n 1.7320567534823674,\n 1.7119737361873444,\n 1.7157273693563924,\n 1.7290841054382464,\n 1.7647982477471926,\n 1.7562017718859915,\n 1.6950468500096474,\n 1.747725564486872,\n 1.7252896867263523,\n 1.7671445124216143,\n 1.7693744566230807,\n 1.7059184138844365,\n 1.7090900521487329,\n 1.7231096018794785,\n 1.7209694721543376,\n 1.7678252294196048,\n 1.7316721866238294,\n 1.7262902265337214,\n 1.7377991339529295,\n 1.770592027972686,\n 1.6973207132946366,\n 1.7180222330856505,\n 1.7350278763310734,\n 1.7371312165639443,\n 1.7199230950584823,\n 1.7102785432584966,\n 1.7244790713772096,\n 1.7351634054665168,\n 1.713486860504405,\n 1.7568987349317238,\n 1.7136332033996875,\n 1.7343403134354431,\n 1.7238926108882633,\n 1.7090981817183717,\n 1.703302194004836,\n 1.7446644371287279,\n 1.7379078424212124,\n 1.7644224392368066,\n 1.7411749383649393,\n 1.7387938040773043,\n 1.7587053030420463,\n 1.7723295858624455,\n 1.7207158077979385,\n 1.712275781859355,\n 1.684724599409851,\n 1.7184172924395567,\n 1.7291500326282283,\n 1.764249287056465,\n 1.6995609808553767,\n 1.7141798221829627,\n 1.7337439700388064,\n 1.7367204835781522,\n 1.7532684630386097,\n 1.722663761530065,\n 1.7281346013788716]"
      },
-     "execution_count": 52,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.plot([compute_fd(df) for df in dfs])"
+    "fds = [compute_fd(df, 0.001) for df in dfs]\n",
+    "fds_clean = [f for f in fds if f < np.inf]\n",
+    "fds_clean"
    ],
    "metadata": {
     "collapsed": false
    }
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 124,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(1.7311567309540636, 0.020648406595153657)"
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "Interesting that this produces the same value, I suppose we have convergence to the accuracy of a double?"
+    "np.mean(fds_clean), np.std(fds_clean)"
    ],
    "metadata": {
     "collapsed": false