Metric update (#205)

* Updated metrics to consistently be ms

* Updated metrics to consistently be ms

* Updated metric documentation on the site

* Updated some tests to work with the updated metrics

1 files changed, 49 insertions, 64 deletions

diff --git a/opendc-experiments/opendc-experiments-greenifier/src/main/Python_scripts/OpenDCdemo.ipynb b/opendc-experiments/opendc-experiments-greenifier/src/main/Python_scripts/OpenDCdemo.ipynb
index ecef829e..09ff26d6 100644
--- a/opendc-experiments/opendc-experiments-greenifier/src/main/Python_scripts/OpenDCdemo.ipynb
+++ b/opendc-experiments/opendc-experiments-greenifier/src/main/Python_scripts/OpenDCdemo.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "18170001",
    "metadata": {},
    "outputs": [],
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "a2d05361",
    "metadata": {},
    "outputs": [
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "fd17d88a",
    "metadata": {},
    "outputs": [
@@ -251,7 +251,7 @@
        "4  1019 2013-08-12 14:15:46+00:00    900000          1   0.000000"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "346f097f",
    "metadata": {
     "scrolled": true
@@ -364,7 +364,7 @@
        "4   2599.999649        179306  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -392,10 +392,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "0d400ffd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Addition/subtraction of integers and integer-arrays with Timestamp is no longer supported.  Instead of adding/subtracting `n`, use `n * obj.freq`",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 17\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_absolute_timestamp\u001b[39m(df, start_dt):\n\u001b[1;32m     15\u001b[0m     df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mabsolute_timestamp\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m start_dt \u001b[38;5;241m+\u001b[39m (df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimestamp\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m-\u001b[39m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimestamp\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmin())\n\u001b[0;32m---> 17\u001b[0m \u001b[43madd_absolute_timestamp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_host_single\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_meta\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstart_time\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     18\u001b[0m add_absolute_timestamp(df_host_single, df_meta[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstart_time\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmin())\n\u001b[1;32m     20\u001b[0m add_absolute_timestamp(df_server_single, df_meta[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstart_time\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmin())\n",
+      "Cell \u001b[0;32mIn[8], line 15\u001b[0m, in \u001b[0;36madd_absolute_timestamp\u001b[0;34m(df, start_dt)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_absolute_timestamp\u001b[39m(df, start_dt):\n\u001b[0;32m---> 15\u001b[0m     df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mabsolute_timestamp\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mstart_dt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtimestamp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtimestamp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/ops/common.py:72\u001b[0m, in \u001b[0;36m_unpack_zerodim_and_defer.<locals>.new_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m     70\u001b[0m other \u001b[38;5;241m=\u001b[39m item_from_zerodim(other)\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:107\u001b[0m, in \u001b[0;36mOpsMixin.__radd__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[38;5;129m@unpack_zerodim_and_defer\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__radd__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    106\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__radd__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[0;32m--> 107\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_arith_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mroperator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mradd\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/series.py:6262\u001b[0m, in \u001b[0;36mSeries._arith_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m   6260\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_arith_method\u001b[39m(\u001b[38;5;28mself\u001b[39m, other, op):\n\u001b[1;32m   6261\u001b[0m     \u001b[38;5;28mself\u001b[39m, other \u001b[38;5;241m=\u001b[39m ops\u001b[38;5;241m.\u001b[39malign_method_SERIES(\u001b[38;5;28mself\u001b[39m, other)\n\u001b[0;32m-> 6262\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbase\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mIndexOpsMixin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_arith_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/base.py:1325\u001b[0m, in \u001b[0;36mIndexOpsMixin._arith_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m   1322\u001b[0m rvalues \u001b[38;5;241m=\u001b[39m ensure_wrapped_if_datetimelike(rvalues)\n\u001b[1;32m   1324\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(\u001b[38;5;28mall\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 1325\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marithmetic_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1327\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_construct_result(result, name\u001b[38;5;241m=\u001b[39mres_name)\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/ops/array_ops.py:218\u001b[0m, in \u001b[0;36marithmetic_op\u001b[0;34m(left, right, op)\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[38;5;66;03m# NB: We assume that extract_array and ensure_wrapped_if_datetimelike\u001b[39;00m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;66;03m#  have already been called on `left` and `right`,\u001b[39;00m\n\u001b[1;32m    207\u001b[0m \u001b[38;5;66;03m#  and `maybe_prepare_scalar_for_op` has already been called on `right`\u001b[39;00m\n\u001b[1;32m    208\u001b[0m \u001b[38;5;66;03m# We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy\u001b[39;00m\n\u001b[1;32m    209\u001b[0m \u001b[38;5;66;03m# casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)\u001b[39;00m\n\u001b[1;32m    211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    212\u001b[0m     should_extension_dispatch(left, right)\n\u001b[1;32m    213\u001b[0m     \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(right, (Timedelta, BaseOffset, Timestamp))\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;66;03m# Timedelta/Timestamp and other custom scalars are included in the check\u001b[39;00m\n\u001b[1;32m    217\u001b[0m     \u001b[38;5;66;03m# because numexpr will fail on it, see GH#31457\u001b[39;00m\n\u001b[0;32m--> 218\u001b[0m     res_values \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    219\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;66;03m# TODO we should handle EAs consistently and move this check before the if/else\u001b[39;00m\n\u001b[1;32m    221\u001b[0m     \u001b[38;5;66;03m# (https://github.com/pandas-dev/pandas/issues/41165)\u001b[39;00m\n\u001b[1;32m    222\u001b[0m     _bool_arith_check(op, left, right)\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/roperator.py:11\u001b[0m, in \u001b[0;36mradd\u001b[0;34m(left, right)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mradd\u001b[39m(left, right):\n\u001b[0;32m---> 11\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mright\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mleft\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/_libs/tslibs/timestamps.pyx:504\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.timestamps._Timestamp.__add__\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Addition/subtraction of integers and integer-arrays with Timestamp is no longer supported.  Instead of adding/subtracting `n`, use `n * obj.freq`"
+     ]
+    }
+   ],
    "source": [
     "output_folder = f\"{base_folder}/output\"\n",
     "workload = \"workload=bitbrains-small\"\n",
@@ -425,93 +445,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "a9a61332",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['timestamp', 'host_id', 'cpu_count', 'mem_capacity',\n",
-       "       'guests_terminated', 'guests_running', 'guests_error', 'guests_invalid',\n",
-       "       'cpu_limit', 'cpu_usage', 'cpu_demand', 'cpu_utilization',\n",
-       "       'cpu_time_active', 'cpu_time_idle', 'cpu_time_steal', 'cpu_time_lost',\n",
-       "       'power_draw', 'energy_usage', 'uptime', 'downtime', 'boot_time',\n",
-       "       'absolute_timestamp'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df_host_single.columns"
+    "df_host_single = pd.read_parquet(f\"{output_folder}/topology=single/{workload}/{seed}/host.parquet\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 27,
    "id": "d6fb41d9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0        350.000000\n",
-       "1        350.000000\n",
-       "2        350.000000\n",
-       "3        291.779985\n",
-       "4        297.482719\n",
-       "            ...    \n",
-       "25918    267.445769\n",
-       "25919    276.532201\n",
-       "25920    263.564494\n",
-       "25921    273.482545\n",
-       "25922    200.000186\n",
-       "Name: power_draw, Length: 25923, dtype: float64"
+       "Timedelta('0 days 00:05:00')"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_host_single.power_draw"
+    "pd.Timedelta(300000, unit=\"ms\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 30,
    "id": "3c271734",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0        105000.000000\n",
-       "1        105000.000000\n",
-       "2        105000.000000\n",
-       "3         87533.995628\n",
-       "4         89244.815826\n",
-       "             ...      \n",
-       "25918     79980.733894\n",
-       "25919     80669.879478\n",
-       "25920     82337.210297\n",
-       "25921     79545.414650\n",
-       "25922     68917.601292\n",
-       "Name: energy_usage, Length: 25923, dtype: float64"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "AttributeError",
+     "evalue": "Can only use .dt accessor with datetimelike values",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf_host_single\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimestamp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[38;5;241m.\u001b[39mto_pytimedelta()\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/generic.py:5907\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5900\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   5901\u001b[0m     name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[1;32m   5902\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[1;32m   5903\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[1;32m   5904\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[1;32m   5905\u001b[0m ):\n\u001b[1;32m   5906\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[0;32m-> 5907\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/accessor.py:183\u001b[0m, in \u001b[0;36mCachedAccessor.__get__\u001b[0;34m(self, obj, cls)\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    181\u001b[0m     \u001b[38;5;66;03m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[39;00m\n\u001b[1;32m    182\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessor\n\u001b[0;32m--> 183\u001b[0m accessor_obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_accessor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;66;03m# Replace the property with the accessor object. Inspired by:\u001b[39;00m\n\u001b[1;32m    185\u001b[0m \u001b[38;5;66;03m# https://www.pydanny.com/cached-property.html\u001b[39;00m\n\u001b[1;32m    186\u001b[0m \u001b[38;5;66;03m# We need to use object.__setattr__ because we overwrite __setattr__ on\u001b[39;00m\n\u001b[1;32m    187\u001b[0m \u001b[38;5;66;03m# NDFrame\u001b[39;00m\n\u001b[1;32m    188\u001b[0m \u001b[38;5;28mobject\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__setattr__\u001b[39m(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name, accessor_obj)\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/accessors.py:513\u001b[0m, in \u001b[0;36mCombinedDatetimelikeProperties.__new__\u001b[0;34m(cls, data)\u001b[0m\n\u001b[1;32m    510\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_period_dtype(data\u001b[38;5;241m.\u001b[39mdtype):\n\u001b[1;32m    511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m PeriodProperties(data, orig)\n\u001b[0;32m--> 513\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan only use .dt accessor with datetimelike values\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mAttributeError\u001b[0m: Can only use .dt accessor with datetimelike values"
+     ]
     }
    ],
    "source": [
-    "df_host_single.energy_usage"
+    "df_host_single.timestamp.to_pytimedelta()"
    ]
   },
   {