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class Clock extends React.Component {
  constructor(props) {
    super(props);
    this.state = {date: new Date()};
  }
  
  componentDidMount() {
    this.timerID = setInterval(
    () => this.tick(),
    1000

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using System;
using System.Text.RegularExpressions;

public class Kata
{
  public static string CreatePhoneNumber(int[] numbers) =>
    new Regex("(...)(...)(....)").Replace(String.Concat(numbers), "($1) $2-$3");
}

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public class Kata
{
  public static string CreatePhoneNumber(int[] numbers)
  {
    return long.Parse(string.Concat(numbers)).ToString("(000) 000-0000");
  }
}

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def reverse_word_order(text):
    newText = ""
    position = 0

    for t in text:
        if t == " ":
            newText = text[position:text.find(t, position)] + " " + newText
            position = text.find(t, position) + 1

    newText = text[position:] + " " + newText 

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import sys

s = input()

# pdb uses stdin to be interactive; this switches stdin to be the teletype interface
# (terminal and keyboard) after getting the redirected input
sys.stdin = open("/dev/tty")
breakpoint()

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import pandas as pd

# 1. Read raw CSV
df = pd.read_csv("raw_data.csv")

# 2. Convert the timestamp column to datetime (adjust column name/format as necessary)
df["timestamp"] = pd.to_datetime(df["timestamp"])

# 3. Make the timestamp the index
df.set_index("timestamp", inplace=True)

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import pandas as pd

# 1. Read raw PresentMon CSV
df = pd.read_csv("presentmon_raw.csv")

# 2. Convert "TimeInSeconds" to a time-based index (Step 1)
df["timestamp"] = pd.to_timedelta(df["TimeInSeconds"], unit="s")
df.set_index("timestamp", inplace=True)

# ------------------

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# Goals
# Label each dataset with its day or config (e.g., “6 cores” vs. “10 cores”).
# Combine them into a single DataFrame (or keep them separate if you prefer).
# Align them by time so you can compare performance around similar test phases.

import pandas as pd

# Read each day, convert timestamp, label config
df_day1 = pd.read_csv("day1_raw.csv")
df_day1["timestamp"] = pd.to_datetime(df_day1["timestamp"])

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import pandas as pd

df_day1 = pd.read_csv("day1_processed.csv")
df_day2 = pd.read_csv("day2_processed.csv")

# Each has columns like: ["relative_time_s", "CPU_usage", "FPS", ...].

# We assume day1["relative_time_s"]=0 at the start of Day 1's test, day2["relative_time_s"]=0 at start of Day 2's test, etc.
# Hypothetical times in seconds from the start of each day's recording
STEP_TIMES = {

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Identifying Which ESXi TOP Columns Matter

ESXi “esxtop” data can be huge, especially if you capture all columns. Not all metrics are equally important. Typically, for diagnosing performance in a VM environment, you want to focus on:

• CPU Usage (e.g., %USED, %RDY, %CSTP / co-stop for SMP VMs, etc.)

  • %RDY (ready time) is a key metric to see if the VM is waiting for CPU.
  • %CSTP (co-stop) indicates scheduling delays for multi-vCPU VMs.

• Memory Usage (e.g., MEMCTL, MEM_ACTIVE, MEM_CONSUMED, MEM_OVERHEAD)

• If memory is overcommitted, ballooning or swapping might occur.