Add validation of per-node power data to ensure power report is accurate

Plot power data on a per-node bases and create a plot for all nodes missing data in the past month.

Add validation of per-node power data to ensure power report is accurate
Plot power data on a per-node bases and create a plot for all nodes missing data in the past month.
a5b9275b · John-Paul Robinson · 3143fc83 · a5b9275b
Commit a5b9275b authored 4 years ago by John-Paul Robinson
--- a/power-stats.ipynb
+++ b/power-stats.ipynb
@@ -121,7 +121,11 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "Some of data values report unrealistic power values.  Any reading over 10kW is considered invalid."
+    "## Clean Data and Resample\n",
+    "\n",
+    "Some of data values report unrealistic power values.  Any reading over 10kW is considered invalid.  \n",
+    "\n",
+    "Shouldn't do that until later since it implicitly filters out NaN"
   ]
  },
  {
@@ -130,7 +134,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "df = df.loc[df['raw'] <  10000]"
+    "#df = df.loc[df['raw'] <  10000]"
   ]
  },
  {
@@ -176,11 +180,12 @@
    "# prepare data frame to append to, use zeros for default column \n",
    "m6_hourly_pwr=pd.DataFrame(np.zeros((1,len(hourly_idx))).T, index=hourly_idx, columns=['sum'])\n",
    "\n",
-    "for num, entity in enumerate(df.entity.unique()):\n",
+    "for num, entity in enumerate(sorted(df.entity.unique())):\n",
    "    if entity not in ['c0108', '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",
+    "        node_pwr=node_pwr[startdate:enddate].fillna(method=\"bfill\")\n",
    "        if debug:\n",
    "          print(node_pwr)\n",
    "          missing = node_pwr['raw'].isnull().sum()\n",
@@ -194,8 +199,64 @@
   "source": [
    "## Plot Per-node Hourly\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.  There is too much data to decern individual behavior of specific nodes\n",
-    "but it does give a sense of how the total power adds up."
+    "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",
+    "Inspect the nodes in the first rack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_nodes=36\n",
+    "fig, axes = plt.subplots(num_nodes,1, 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=\"left\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Overview plot reveals missing power data for a number of nodes.  Inspect one up close."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m6_hourly_pwr['2020-02-01':'2021-02-21'].iloc[:,1:2].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Identify nodes that have missing data\n",
+    "\n",
+    "Identify nodes by ones that have NaN values over the past month."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nan_mask = m6_hourly_pwr['2021-02-01':'2021-02-02'].isna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "power_missing = nan_mask[nan_mask].apply(lambda row: row[row == True].index, axis=1)[1]"
   ]
  },
  {
@@ -204,7 +265,10 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "m6_hourly_pwr['2020-02-01':'2020-07-09'].iloc[:,1:].plot(legend=False)"
+    "num_nodes=len(power_missing)\n",
+    "fig, axes = plt.subplots(num_nodes,1, figsize=(20,30))\n",
+    "for i, node in enumerate(power_missing):\n",
+    "    m6_hourly_pwr[node].plot(ax=axes[i], legend=\"left\")"
   ]
  },
  {

 %% Cell type:markdown id: tags:

 # Power Stats

 Use RestAPI to read power consumption info for cluster nodes and generate usage reports.  this is based on the [pandas time series tutorial by Jennifer Walker](https://www.dataquest.io/blog/tutorial-time-series-analysis-with-pandas/)

 %% Cell type:code id: tags:

 ``` 
 import requests
 import pprint
 import datetime
 import os
 import numpy as np
 import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
 ```

 %% Cell type:code id: tags:

 ``` 
 plt.rcParams["figure.figsize"] = (20,6)
 ```

 %% Cell type:markdown id: tags:

 Set up credentials to query RestAPI. Bright controls access based on the user identity.  The user's cert.pem and cert.key are automatically generated but the cacert.pem needs to be constructed from the  certs returned by the master.

 %% Cell type:code id: tags:

 ``` 
 cert_file='~/.cm/cert.pem'
 key_file='~/.cm/cert.key'
 ca_file='cacert.pem'
 ```

 %% Cell type:code id: tags:

 ``` 
 cert=(os.path.expanduser(cert_file), os.path.expanduser(key_file))
 ```

 %% Cell type:markdown id: tags:

 ## Gather Cluster Power Data

 %% Cell type:code id: tags:

 ``` 
 startdate = '2020/01/01 00:00:00'
 enddate = '2021/02/21 00:00:00'
 ```

 %% Cell type:code id: tags:

 ``` 
 params = (
    ('start', startdate),
    ('measurable', 'Pwr_Consumption'),
    ('indent', '1'),
 )
 ```

 %% Cell type:code id: tags:

 ``` 
 response = requests.get('https://master:8081/rest/v1/monitoring/dump', params=params, cert=cert, verify=False)
 ```

 %% Cell type:markdown id: tags:

 Simply read the json response into a dataframe for futher parsing.

 %% Cell type:code id: tags:

 ``` 
 df = pd.DataFrame(response.json()["data"])
 ```

 %% Cell type:markdown id: tags:

+## Clean Data and Resample
+
 Some of data values report unrealistic power values.  Any reading over 10kW is considered invalid.

+Shouldn't do that until later since it implicitly filters out NaN
+
 %% Cell type:code id: tags:

 ``` 
-df = df.loc[df['raw'] <  10000]
+#df = df.loc[df['raw'] <  10000]
 ```

 %% Cell type:markdown id: tags:

 Create a datatime type column from the reported sample times.

 %% Cell type:code id: tags:

 ``` 
 df['datetime'] = pd.to_datetime(df.time, format="%Y/%m/%d %H:%M:%S")
 ```

 %% Cell type:markdown id: tags:

 Create an index for the hourly

 %% Cell type:code id: tags:

 ``` 
 hourly_idx=pd.date_range(startdate, enddate, freq='H')
 ```

 %% Cell type:code id: tags:

 ``` 
 debug=False

 # prepare data frame to append to, use zeros for default column
 m6_hourly_pwr=pd.DataFrame(np.zeros((1,len(hourly_idx))).T, index=hourly_idx, columns=['sum'])

-for num, entity in enumerate(df.entity.unique()):
+for num, entity in enumerate(sorted(df.entity.unique())):
    if entity not in ['c0108', 'c0009']:
        node_pwr=df[df.entity==entity].set_index("datetime")
        node_pwr=node_pwr[['raw']].resample('H').mean()
        node_pwr=node_pwr[startdate:enddate].fillna(method="ffill")
+        node_pwr=node_pwr[startdate:enddate].fillna(method="bfill")
        if debug:
          print(node_pwr)
          missing = node_pwr['raw'].isnull().sum()
          print("{}: {} missing {}\n".format(num, entity, missing))
        m6_hourly_pwr[entity]= node_pwr[startdate:enddate]
 ```

 %% Cell type:markdown id: tags:

 ## Plot Per-node Hourly

-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.  There is too much data to decern individual behavior of specific nodes
-but it does give a sense of how the total power adds up.
+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.
+
+Inspect the nodes in the first rack.
+
+%% Cell type:code id: tags:
+
+``` 
+num_nodes=36
+fig, axes = plt.subplots(num_nodes,1, figsize=(20,30))
+for i in range(num_nodes):
+    m6_hourly_pwr['2020-02-01':'2021-02-21'].iloc[:,i+1:i+2].plot(ax=axes[i], legend="left")
+```
+
+%% Cell type:markdown id: tags:
+
+Overview plot reveals missing power data for a number of nodes.  Inspect one up close.
+
+%% Cell type:code id: tags:
+
+``` 
+m6_hourly_pwr['2020-02-01':'2021-02-21'].iloc[:,1:2].plot()
+```
+
+%% Cell type:markdown id: tags:
+
+## Identify nodes that have missing data
+
+Identify nodes by ones that have NaN values over the past month.
+
+%% Cell type:code id: tags:
+
+``` 
+nan_mask = m6_hourly_pwr['2021-02-01':'2021-02-02'].isna()
+```
+
+%% Cell type:code id: tags:
+
+``` 
+power_missing = nan_mask[nan_mask].apply(lambda row: row[row == True].index, axis=1)[1]
+```

 %% Cell type:code id: tags:

 ``` 
-m6_hourly_pwr['2020-02-01':'2020-07-09'].iloc[:,1:].plot(legend=False)
+num_nodes=len(power_missing)
+fig, axes = plt.subplots(num_nodes,1, figsize=(20,30))
+for i, node in enumerate(power_missing):
+    m6_hourly_pwr[node].plot(ax=axes[i], legend="left")
 ```

 %% Cell type:markdown id: tags:

 # Plot Power Usage Graph


 Pick the start and end date for the plots from the data range selected above. Generate the sum and plot only it's values.

 We skip over the first month of collection because it is uncommonly noisy.

 %% Cell type:code id: tags:

 ``` 
 kW = m6_hourly_pwr['2020-02-01':'2021-02-21'].sum(axis=1)/1000
 ```

 %% Cell type:code id: tags:

 ``` 
 ax = kW.plot()
 ax.set_ylabel("Power (kW)")
 ax.set_title("Cheaha compute and login node hourly power use")
 ```

 %% Cell type:markdown id: tags:

 Resample hourly sum to support the seven day average.

 %% Cell type:code id: tags:

 ``` 
 kW_d = kW.resample('D').mean()
 ```

 %% Cell type:code id: tags:

 ``` 
 # Compute the centered 7-day rolling mean
 # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html
 kW_7d = kW_d.rolling(7, center=True).mean()
 ```

 %% Cell type:code id: tags:

 ``` 
 # Plot houry, daily, 7-day rolling mean
 fig, ax = plt.subplots()
 ax.plot(kW, marker='.', markersize=2, color='gray', linestyle='None', label='Hourly Average')
 ax.plot(kW_d, color='brown', linewidth=2, label='1-day Average')
 ax.plot(kW_7d, color='black', linewidth=4, label='7-day Rolling Average')
 label='Trend (7 day Rolling Mean)'
 ax.legend()
 ax.set_ylabel('Power (kW)')
 ax.set_title('Cheaha Trends in Electricity Consumption');
 ```