The Spark Challenge Student Check-In/Out Tracking Script

Overview

The Spark Challenge Student Check-In/Out Tracking Script

This Python Script uses the Student ID Database to match the entries with the ID Card Swipe and records the entries and swipe times.

Big thanks to Claire Poukey!!

The verification file is not included in this repository since it includes sensitive and confidential information about students

What Do You Need To Run This Script?

  • Python3
  • ID Number Database (verification.txt)
  • Magnetic Card Strip Reader

How to Run This Script?

  1. Clone the script to your machine
  2. Connect the magnetic card strip reader to your machine
  3. Run the command make check-in in terminal
  4. Swipe card once for check-in
  5. Swipe card second time for check-out
  6. When you are done with all entries exit the program safely using exit command when SWIPE PUID prompted.
  7. Run the attandance script by running the command make attendance

Note that the script stops recording after 2 swipes and the entries need to be deleted manually from the log file.

What This Script Does?:

  1. Records the check-in time with the first card swipe
  2. Records the check-out time with the second card swipes
  3. Ignores the entries after second card swipe
  4. Takes the difference between the check-in and check-out time to see how long the student stayed at the event
  5. Prints the ID card number and the time stayed to a CSV File

Parts of This Script:

  1. Python Script (checkin.py)
  2. Attendance Script (attandance.py)
  3. Makefile (to make things a little easier)
  4. Log Files

Things to be Careful About:

  1. Running make clean clears the log files so use with caution
  2. If the program crashes for some reason you have to manually rerun the script using make check-in command
  3. You have to manually run make attendance after the event is over to process the data.

Things That Still Need Attention:

  1. Nothing. (To be updated)
Owner
Electrical Engineering Student at Purdue University
In this project, ETL pipeline is build on data warehouse hosted on AWS Redshift.

ETL Pipeline for AWS Project Description In this project, ETL pipeline is build on data warehouse hosted on AWS Redshift. The data is loaded from S3 t

Mobeen Ahmed 1 Nov 01, 2021
pyhsmm MITpyhsmm - Bayesian inference in HSMMs and HMMs. MIT

Bayesian inference in HSMMs and HMMs This is a Python library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and expli

Matthew Johnson 527 Dec 04, 2022
This python script allows you to manipulate the audience data from Sl.ido surveys

Slido-Automated-VoteBot This python script allows you to manipulate the audience data from Sl.ido surveys Since Slido blocks interference from automat

Pranav Menon 1 Jan 24, 2022
BigDL - Evaluate the performance of BigDL (Distributed Deep Learning on Apache Spark) in big data analysis problems

Evaluate the performance of BigDL (Distributed Deep Learning on Apache Spark) in big data analysis problems.

Vo Cong Thanh 1 Jan 06, 2022
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods

Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods Introduction Graph Neural Networks (GNNs) have demonstrated

37 Dec 15, 2022
An Aspiring Drop-In Replacement for NumPy at Scale

Legate NumPy is a Legate library that aims to provide a distributed and accelerated drop-in replacement for the NumPy API on top of the Legion runtime. Using Legate NumPy you do things like run the f

Legate 502 Jan 03, 2023
t-SNE and hierarchical clustering are popular methods of exploratory data analysis, particularly in biology.

tree-SNE t-SNE and hierarchical clustering are popular methods of exploratory data analysis, particularly in biology. Building on recent advances in s

Isaac Robinson 61 Nov 21, 2022
Synthetic data need to preserve the statistical properties of real data in terms of their individual behavior and (inter-)dependences

Synthetic data need to preserve the statistical properties of real data in terms of their individual behavior and (inter-)dependences. Copula and functional Principle Component Analysis (fPCA) are st

32 Dec 20, 2022
An implementation of the largeVis algorithm for visualizing large, high-dimensional datasets, for R

largeVis This is an implementation of the largeVis algorithm described in (https://arxiv.org/abs/1602.00370). It also incorporates: A very fast algori

336 May 25, 2022
Toolchest provides APIs for scientific and bioinformatic data analysis.

Toolchest Python Client Toolchest provides APIs for scientific and bioinformatic data analysis. It allows you to abstract away the costliness of runni

Toolchest 11 Jun 30, 2022
Python implementation of Principal Component Analysis

Principal Component Analysis Principal Component Analysis (PCA) is a dimension-reduction algorithm. The idea is to use the singular value decompositio

Ignacio Darago 1 Nov 06, 2021
NumPy aware dynamic Python compiler using LLVM

Numba A Just-In-Time Compiler for Numerical Functions in Python Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaco

Numba 8.2k Jan 07, 2023
Python utility to extract differences between two pandas dataframes.

Python utility to extract differences between two pandas dataframes.

Jaime Valero 8 Jan 07, 2023
Data imputations library to preprocess datasets with missing data

Impyute is a library of missing data imputation algorithms. This library was designed to be super lightweight, here's a sneak peak at what impyute can do.

Elton Law 329 Dec 05, 2022
Conduits - A Declarative Pipelining Tool For Pandas

Conduits - A Declarative Pipelining Tool For Pandas Traditional tools for declaring pipelines in Python suck. They are mostly imperative, and can some

Kale Miller 7 Nov 21, 2021
A fast, flexible, and performant feature selection package for python.

linselect A fast, flexible, and performant feature selection package for python. Package in a nutshell It's built on stepwise linear regression When p

88 Dec 06, 2022
Integrate bus data from a variety of sources (batch processing and real time processing).

Purpose: This is integrate bus data from a variety of sources such as: csv, json api, sensor data ... into Relational Database (batch processing and r

1 Nov 25, 2021
A DSL for data-driven computational pipelines

"Dataflow variables are spectacularly expressive in concurrent programming" Henri E. Bal , Jennifer G. Steiner , Andrew S. Tanenbaum Quick overview Ne

1.9k Jan 03, 2023
TextDescriptives - A Python library for calculating a large variety of statistics from text

A Python library for calculating a large variety of statistics from text(s) using spaCy v.3 pipeline components and extensions. TextDescriptives can be used to calculate several descriptive statistic

150 Dec 30, 2022
Vaex library for Big Data Analytics of an Airline dataset

Vaex-Big-Data-Analytics-for-Airline-data A Python notebook (ipynb) created in Jupyter Notebook, which utilizes the Vaex library for Big Data Analytics

Nikolas Petrou 1 Feb 13, 2022