memv-package-nhunh 0.0.1

Visualize memory usage

Envs

• Python 3.8

Guide

To monitor memory usage in a specific time and visualize it in a graph, you can use a combination of tools and techniques as follows:

Use a system monitoring tool such as htop, top, or glances to track memory usage in real-time. These tools can display information about CPU, memory, disk usage, and network traffic in a user-friendly interface.

For a more detailed look at memory usage over time, use a command-line utility such as psrecord or pidstat to capture memory usage data at regular intervals. You can specify the time interval and duration for data collection.

Save the output of the monitoring tool to a file for further processing and use a Python script to analyze and visualize the collected data. Use a Python library such as matplotlib or seaborn to create a memory usage graph with the collected data.

Optionally, you can set up a continuous monitoring process using a task scheduler such as cron to capture memory usage data regularly and automate the data collection and analysis process.

Here is an example Python script to plot memory usage data collected using psrecord:

import matplotlib.pyplot as plt import numpy as np

Load data from file

data = np.loadtxt('memory-usage.txt')

Extract time and memory usage data

time = data[:, 0] memory = data[:, 1] / 1024 # Convert to MB for readability

Plot the data using matplotlib

fig, ax = plt.subplots() ax.plot(time, memory) ax.set(title='Memory Usage', xlabel='Time (s)', ylabel='Memory Usage (MB)')

Show the plot

plt.show() Note: The above script assumes that the memory-usage.txt file contains two columns of data: time in seconds and memory usage in bytes. You may need to adjust the script according to the format of the data captured by your monitoring tool