diff --git a/machine_time_waste/statuses.dio b/machine_time_waste/statuses.dio
index 34b78615..63fcc39e 100644
--- a/machine_time_waste/statuses.dio
+++ b/machine_time_waste/statuses.dio
@@ -1 +1 @@
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diff --git a/machine_time_waste/statuses_total_time.ipynb b/machine_time_waste/statuses_total_time.ipynb
new file mode 100644
index 00000000..a0c053ad
--- /dev/null
+++ b/machine_time_waste/statuses_total_time.ipynb
@@ -0,0 +1,304 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "german-marathon",
+   "metadata": {},
+   "source": [
+    "# Statuses total time\n",
+    "Sums the times instances spend in one of each states in the diagram saved as\n",
+    "\"statuses.drawio\". Unknown times are summed as \"unknown\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "boolean-microwave",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import sys\n",
+    "import pandas\n",
+    "import seaborn as sns\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "activated-messenger",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# QUEUE = set([\"0-2\", \"1-2\", \"assumptions:\", \"1-1\", \"1-0\"])\n",
+    "# RESUB = set([\"4-1\", \"4-0\", \"5-1\", \"6-1\", \"7-1\", \"8-1\", \"assumptions:\", \"5-0\", \"6-0\", \"7-0\", \\\n",
+    "#   \"8-0\"])\n",
+    "# READY = set([\"0-3\", \"2-3\", \"0-9\", \"2-9\", \"9-3\", \"2-7\", \"2-8\", \"9-7\", \"9-8\", \\\n",
+    "#   \"9-9\", \"0-7\", \"0-8\", \"assumptions:\", \"2-0\", \"2-4\", \"9-4\", \"9-1\"])\n",
+    "# RUN = set([\"3-1\", \"3-10\", \"3-4\", \"3-5\", \"3-6\", \"3-7\", \"3-8\", \"10-5\", \"10-6\", \\\n",
+    "#   \"10-7\", \"10-8\", \"10-4\", \"10-10\", \"10-1\", \"assumptions:\", \"3-0\", \"10-0\", \"3-3\"])\n",
+    "\n",
+    "QUEUE = set([\"0-2\", \"1-2\"])\n",
+    "ENDED = set([\"5-1\", \"6-1\", \"7-1\", \"8-1\"])\n",
+    "READY = set([\"0-3\", \"0-9\", \"2-3\", \"2-9\", \"9-3\", \"9-9\"])\n",
+    "RUN = set([\"3-1\", \"3-4\", \"3-5\", \"3-6\", \"3-7\", \"3-8\", \"3-10\", \"10-1\", \"10-4\", \"10-5\", \"10-6\", \"10-7\", \"10-8\", \"10-10\"])\n",
+    "EVICT = set([\"4-1\", \"4-0\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "novel-commercial",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obj = {}\n",
+    "\n",
+    "filename=\"/home/claudio/google_2019/thesis_queries/machine_time_waste/a_state_changes.json\"\n",
+    "\n",
+    "with open(filename, 'r') as f:\n",
+    "    obj = json.loads(f.read())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "casual-oasis",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = {'Last termination': [], 'time_type': [], 'time_ms': []}\n",
+    "totals = {}\n",
+    "\n",
+    "def to_name(et):\n",
+    "    if et == 4: \n",
+    "        return 'EVICT'\n",
+    "    elif et == 5: \n",
+    "        return 'FAIL'\n",
+    "    elif et == 6:\n",
+    "        return 'FINISH'\n",
+    "    elif et == 7:\n",
+    "        return 'KILL'\n",
+    "    elif et == 8:\n",
+    "        return 'LOST'\n",
+    "    else:\n",
+    "        return 'NO_TERM'\n",
+    "\n",
+    "def add_record(et, tt, time):\n",
+    "    data['Last termination'].append(to_name(et))\n",
+    "    data['time_type'].append(tt)\n",
+    "    data['time_ms'].append(time)\n",
+    "    \n",
+    "    \n",
+    "for pair in obj[\"data\"]:\n",
+    "    s = set()\n",
+    "    qt = et = rt = xt = vt = ut = 0\n",
+    "  \n",
+    "    # print(\"End type \" + str(pair[0]))  \n",
+    "    \n",
+    "    pair[0] = 0 if pair[0] is None else pair[0]\n",
+    "  \n",
+    "    x = pair[1]\n",
+    "    for k in x.keys():\n",
+    "        if k in QUEUE:\n",
+    "            qt += x[k]\n",
+    "        elif k in ENDED:\n",
+    "            et += x[k]\n",
+    "        elif k in READY:\n",
+    "            rt += x[k]\n",
+    "        elif k in RUN:\n",
+    "            xt += x[k]\n",
+    "        elif k in EVICT:\n",
+    "            vt += x[k]\n",
+    "        else:\n",
+    "            s.add(k)\n",
+    "            ut += x[k]\n",
+    "    \n",
+    "    add_record(pair[0], 'queued', qt)\n",
+    "    add_record(pair[0], 'ended', et)\n",
+    "    add_record(pair[0], 'ready', rt)\n",
+    "    add_record(pair[0], 'running', xt)\n",
+    "    add_record(pair[0], 'evicted', vt)\n",
+    "    add_record(pair[0], 'unknown', ut)\n",
+    "    totals[pair[0]] = qt + et + rt + xt + vt + ut\n",
+    "    # print(s) \n",
+    "    \n",
+    "df = pandas.DataFrame(data, columns=['Last termination', 'time_type', 'time_ms'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "encouraging-acoustic",
+   "metadata": {},
+   "source": [
+    "## Graph 1: Absolute total time spent per status per \"last termination\" type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "romance-complex",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Last termination time_type       time_ms\n",
+      "0           NO_TERM    queued  1.515995e-02\n",
+      "1           NO_TERM     ended  0.000000e+00\n",
+      "2           NO_TERM     ready  3.365563e-02\n",
+      "3           NO_TERM   running  9.511017e-01\n",
+      "4           NO_TERM   evicted  0.000000e+00\n",
+      "5           NO_TERM   unknown  8.273546e-05\n",
+      "6             EVICT    queued  2.199883e-02\n",
+      "7             EVICT     ended  7.361000e-08\n",
+      "8             EVICT     ready  6.672357e-03\n",
+      "9             EVICT   running  8.789100e-01\n",
+      "10            EVICT   evicted  1.503149e-05\n",
+      "11            EVICT   unknown  9.240368e-02\n",
+      "12             FAIL    queued  8.517812e-02\n",
+      "13             FAIL     ended  0.000000e+00\n",
+      "14             FAIL     ready  9.300481e-04\n",
+      "15             FAIL   running  6.596810e-01\n",
+      "16             FAIL   evicted  6.492666e-12\n",
+      "17             FAIL   unknown  2.542109e-01\n",
+      "18           FINISH    queued  3.237908e-01\n",
+      "19           FINISH     ended  1.907057e-07\n",
+      "20           FINISH     ready  2.215952e-02\n",
+      "21           FINISH   running  3.951182e-01\n",
+      "22           FINISH   evicted  3.313964e-11\n",
+      "23           FINISH   unknown  2.589313e-01\n",
+      "24             KILL    queued  3.455137e-02\n",
+      "25             KILL     ended  3.509624e-05\n",
+      "26             KILL     ready  4.547064e-03\n",
+      "27             KILL   running  3.297613e-01\n",
+      "28             KILL   evicted  2.820098e-05\n",
+      "29             KILL   unknown  6.310770e-01\n",
+      "30             LOST    queued  3.119403e-05\n",
+      "31             LOST     ended  1.203386e-01\n",
+      "32             LOST     ready  8.201877e-04\n",
+      "33             LOST   running  2.690978e-04\n",
+      "34             LOST   evicted  1.693104e-13\n",
+      "35             LOST   unknown  8.785409e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Total microseconds')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"ticks\")\n",
+    "\n",
+    "print(df)\n",
+    "\n",
+    "g = sns.histplot(df, x=\"Last termination\", weights=\"time_ms\", shrink=.5,\n",
+    "             hue=\"time_type\", multiple=\"dodge\", discrete=True)\n",
+    "g.set_yscale(\"log\")\n",
+    "g.set_ylabel(\"Total microseconds\") "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "temporal-alabama",
+   "metadata": {},
+   "source": [
+    "## Graph 2: Relative total time spent per status per \"last termination\" type\n",
+    "\n",
+    "Values are proportions on total for each \"last termination\" type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "actual-penetration",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/claudio/python-venv/lib/python3.6/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  iloc._setitem_with_indexer(indexer, value)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in [0,4,5,6,7,8]:\n",
+    "    df[\"time_ms\"].loc[df[\"Last termination\"] == to_name(i)] = df[\"time_ms\"].loc[df[\"Last termination\"] == to_name(i)] / totals[i]\n",
+    "\n",
+    "h = sns.histplot(df, x=\"Last termination\", weights=\"time_ms\", shrink=.5, common_bins=True,\n",
+    "             hue=\"time_type\", multiple=\"stack\", discrete=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "disabled-relative",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "successful-guidance",
+   "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": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}