chausen

import java.awt.*;
import java.awt.image.BufferedImage;

public class CrosshatchDrawer {

    /**
     * Draws a crosshatch pattern (two sets of diagonal lines) on the given image.
     *
     * @param image   the BufferedImage to draw on
     * @param spacing distance (in pixels) between diagonal lines

chausen

import java.awt.*;
import java.awt.image.BufferedImage;
import java.util.HashSet;
import java.util.List;
import java.util.Set;

public class LayeredCrosshatchDemo {

    // Suppose we have enumerated density levels
    public enum DensityLevel {

chausen

// Instead of 4 loops, do 2 loops per diagonal direction.
// For "/" diagonals, let's start from:
//   - The bottom edge (y = bounds.y + bounds.height), x from bounds.x..bounds.x + width
//   - The left edge (x = bounds.x), y from bounds.y..bounds.y + height

// For "\" diagonals, let's start from:
//   - The top edge (y = bounds.y), x from bounds.x..bounds.x + width
//   - The left edge (x = bounds.x), y from bounds.y..bounds.y + height

//  Example for "/" diagonals:

chausen

import java.awt.*;
import java.awt.image.BufferedImage;
import java.util.List;
import java.util.HashSet;
import java.util.Set;

public class CrosshatchMultipleShapesExample {

    public static class Region {
        // In your case, each Region might correspond to 

chausen

import java.awt.*;
import java.awt.image.BufferedImage;
import java.util.List;

public class CrosshatchExample {

    // A simple data structure to hold a region’s pixel coordinates plus a “density” value
    public static class Region {
        private List<Point> pixelCoordinates;
        private float density; // e.g. 0.1 = sparse, 0.9 = dense

chausen

I need help understanding what each element of a header from a ESXi top output represents. Here is a header: \abcd1\Group Cpu(10:drivers)% Used ChatGPT said: Reasoned about ESXi top header elements for 6 seconds First, it’s worth noting that esxtop (or resxtop) output can come from interactive sessions or batch/CSV exports, and the exact formatting can vary. However, the line you provided,

scss Copy Edit \abcd1\Group Cpu(10:drivers)% Used

chausen

I need help understanding what each element of a header from a ESXi top output represents. Here is a header:
\\abcd1\Group Cpu(10:drivers)\% Used
ChatGPT said:
Reasoned about ESXi top header elements for 6 seconds
First, it’s worth noting that esxtop (or resxtop) output can come from interactive sessions or batch/CSV exports, and the exact formatting can vary. However, the line you provided,

scss
Copy
Edit
\\abcd1\Group Cpu(10:drivers)\% Used

chausen

You said: Can you give me a general overview of how to understand the output of this command? I basically want to see cpu/memory/gpu/disk/network utilization/wait time for all of my vms

ChatGPT said: The esxtop command in ESXi provides real-time performance metrics for your VMs and hosts. When exported to a CSV file, it includes various performance metrics for CPU, memory, GPU (if applicable), disk, and network. Here's a general overview of how to interpret the output:

1. Understanding the Columns

The CSV file will have multiple columns corresponding to different metrics. While the exact column names depend on the version of ESXi, here are some key ones:

CPU Metrics

chausen

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.

chausen

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 = {