diff --git a/power-stats.ipynb b/power-stats.ipynb
index 24a2d965e03d42c9be7d80eb5be2e2b3f95e19d3..60efb6849dd42c3810ecacb7488a4b09fb9add74 100644
--- a/power-stats.ipynb
+++ b/power-stats.ipynb
@@ -86,8 +86,18 @@
"metadata": {},
"outputs": [],
"source": [
- "startdate = '2020/01/01 00:00:00'\n",
- "enddate = '2021/02/21 00:00:00'"
+ "startdate = '2021/01/01 00:00:00'\n",
+ "enddate = '2021/04/8 00:00:00'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "displaystart = '2021-02-01'\n",
+ "displaystop = '2021-04-08'"
]
},
{
@@ -109,7 +119,11 @@
"metadata": {},
"outputs": [],
"source": [
- "response = requests.get('https://master:8081/rest/v1/monitoring/dump', params=params, cert=cert, verify=False)"
+ "if os.path.exists(\"ipower_data.csv\"):\n",
+ " df = pd.read_csv(\"power_data.csv\")\n",
+ "else:\n",
+ " response = requests.get('https://master:8081/rest/v1/monitoring/dump', params=params, cert=cert, verify=False)\n",
+ " df = pd.DataFrame(response.json()[\"data\"])"
]
},
{
@@ -119,15 +133,6 @@
"Simply read the json response into a dataframe for futher parsing."
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df = pd.DataFrame(response.json()[\"data\"])"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -148,6 +153,15 @@
"#df = df.loc[df['raw'] < 10000]"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -192,7 +206,7 @@
"m6_hourly_pwr=pd.DataFrame(np.zeros((1,len(hourly_idx))).T, index=hourly_idx, columns=['sum'])\n",
"\n",
"for num, entity in enumerate(sorted(df.entity.unique())):\n",
- " if entity not in ['c0108', 'c0009']:\n",
+ " if entity not in ['c0009']:\n",
" node_pwr=df[df.entity==entity].set_index(\"datetime\")\n",
" node_pwr=node_pwr[['raw']].resample('H').mean()\n",
" node_pwr=node_pwr[startdate:enddate].fillna(method=\"ffill\")\n",
@@ -204,11 +218,20 @@
" m6_hourly_pwr[entity]= node_pwr[startdate:enddate]\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m6_hourly_pwr"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Plot Per-node Hourly\n",
+ "## Plot Per-node Hourly for Row 5 Rack 1\n",
"\n",
"This is just to see the data for each node in one plot and get a feel for how the nodes behave relative to each other. Plot nodes in individual subplotes to decern individual behavior of specific nodes. It does give a sense of how the total power adds up. \n",
"\n",
@@ -228,7 +251,7 @@
"num_nodes=36\n",
"fig, axes = plt.subplots(num_nodes,1, sharex=True, figsize=(20,30))\n",
"for i in range(num_nodes):\n",
- " m6_hourly_pwr['2020-02-01':'2021-02-21'].iloc[:,i+1:i+2].plot(ax=axes[i], legend=True)\n",
+ " m6_hourly_pwr[displaystart:displaystop].iloc[:,i+1:i+2].plot(ax=axes[i], legend=True)\n",
" axes[i].legend(loc='lower left')"
]
},
@@ -245,7 +268,61 @@
"metadata": {},
"outputs": [],
"source": [
- "m6_hourly_pwr['2020-02-01':'2021-02-21'].iloc[:,1:2].plot()"
+ "select_node=\"c0022\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df[df.entity==select_node].set_index(\"datetime\")[\"2020-09-01\":\"2020-10-04\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m6_hourly_pwr[displaystart:displaystop].iloc[:,3:4][\"2020-10-03\":\"2020-10-04\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m6_hourly_pwr[displaystart:displaystop].iloc[:,3:4][\"2020-09-28\":\"2020-10-14\"].plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m6_hourly_pwr[displaystart:displaystop].iloc[:,3:4].plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df[df[\"entity\"]==\"c0001\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df[df[\"entity\"]==\"c0001\"][\"datetime\"].max()"
]
},
{
@@ -263,7 +340,7 @@
"metadata": {},
"outputs": [],
"source": [
- "nan_mask = m6_hourly_pwr['2021-02-01':'2021-02-02'].isna()"
+ "nan_mask = m6_hourly_pwr[\"2021-03-22\":\"2021-03-23\"].isna()"
]
},
{
@@ -275,6 +352,15 @@
"power_missing = nan_mask[nan_mask].apply(lambda row: row[row == True].index, axis=1)[1]"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(*power_missing,sep=\", \")"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -288,6 +374,54 @@
" axes[i].legend(loc='lower left')"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "node"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df[df[\"entity\"]==node][\"datetime\"].max()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lastreport = pd.DataFrame(columns=('node', 'datetime'))\n",
+ "\n",
+ "for i, node in enumerate(power_missing):\n",
+ " lastreport.loc[i] = [node, df[df[\"entity\"]==node][\"datetime\"].max()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lastreport.sort_values(by=\"datetime\") #[\"datetime\"].sort()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"{}:\\t{}\".format(node, df[df[\"entity\"]==node][\"datetime\"].max()))"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -309,7 +443,7 @@
"for i, node in enumerate(m6_hourly_pwr.iloc[:,1:].columns):\n",
" if (i == num_nodes):\n",
" break\n",
- " m6_hourly_pwr[node]['2020-02-01':'2021-02-21'].plot(ax=axes[i], legend=True)\n",
+ " m6_hourly_pwr[node][displaystart:displaystop].plot(ax=axes[i], legend=True)\n",
" axes[i].legend(loc='lower left')\n"
]
},
@@ -331,7 +465,7 @@
"metadata": {},
"outputs": [],
"source": [
- "kW = m6_hourly_pwr['2020-02-01':'2021-02-21'].sum(axis=1)/1000"
+ "kW = m6_hourly_pwr[displaystart:displaystop].sum(axis=1)/1000"
]
},
{
@@ -388,6 +522,33 @@
"ax.set_ylabel('Power (kW)')\n",
"ax.set_title('Cheaha Trends in Electricity Consumption');"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Save Hourly Power to Dataframe\n",
+ "\n",
+ "This makes it easy to use the data in other analysis and learning efforts."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m6_hourly_pwr.to_pickle(\"m6_hourly_pwr.gz\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.to_pickle(\"power_stats_raw_df.gz\")"
+ ]
}
],
"metadata": {