Quisby

Overview

Quisby is an advanced data preprocessing and visualization tool designed to transform benchmark regression data into comprehensible formats within Google Spreadsheets. It simplifies the intricate process of benchmark data analysis by offering intuitive functionalities, allowing users to obtain actionable insights more effortlessly.

Target Audience

Quisby is tailored for individuals, data scientists, researchers, and professionals who seek to plot and analyze benchmark results.

Main Features

Benchmark Data Plotting

Quisby supports a variety of popular benchmarks including but not limited to:

• linpack
• streams
• specjbb
• speccpu
• fio
• uperf
• coremark
• coremark_pro
• passmark
• pypref
• phoronix
• etcd
• auto_hpl
• hammerdb
• aim
• pig
• reboot

Spreadsheet Comparison

Users have the capability to compare two benchmark data spreadsheets, facilitating a holistic analysis.

Prerequisites

Software

• Python version 3.9 or above.

Google Service Account

To utilize Google Sheets API, you need to set up a Google Service Account. Follow the steps mentioned here.

Other Requirements

• Install additional dependencies from the requirements.txt file provided in the Quisby repository.
• Create a config.ini file with the specified content.

Installation Process

1. Clone the Quisby Repository:

git clone https://github.com/sousinha1997/Quisby.git

2. Navigate to cloned Repository:

cd Quisby

3. Install Required Deppendecies:

pip install -r requirements.txt

Usage Instructions

Using Quisby involves two primary phases: Data Collection and Running the Application. Let's delve deeper into each phase:

Data Collection

Ensure optimal performance and accurate visualization with Quisby by formatting and collecting your benchmark data appropriately. See detailed instructions here.

Running the Application

Before running Quisby, ensure that you have your data appropriately formatted, stored in a Google Spreadsheet, and that you've filled out the config.ini file with the required parameters. See detailed instructions here. For command line assistance, run:

python quisby.py --help

Post-Execution Steps and Data Management

Analyzing the Logs:

Dive into the quisby.log to get detailed information about the operations. This can be particularly helpful if you encounter any issues or want to understand the underlying processes better.

Managing Spreadsheets:

The charts.json file serves as a repository for your spreadsheet names and IDs. Regularly back up this file to prevent any data loss. Additionally, this file can be used to quickly access or reference your spreadsheets without manually navigating through Google Sheets.

Feedback and Troubleshooting:

If you encounter any issues or anomalies, first check the logs for any specific error messages. The log details combined with the structured data in charts.json will often provide clues to address the issue. If problems persist, consider reaching out to the application's support or development team.

These additions provide the user with a more holistic view of the application's operations and the data it generates. If there's anything else you'd like to add or modify, do let me know!