jmbde ist ein Programm für das Management von Ressourcen in Unternehmen. Mit diesem Programm können die Mitarbeiter und die Ausrüstung, die sie für ihre Arbeit benötigen erfasst werden. Das sind unter anderem Computer, Drucker und Telefone.

Features | Documentation | Changelog | Contributing | FAQ | english

Das Programm verwendet das Microsoft dotnet Framework und ist somit auf fast allen Plattformen ausführbar. Es wird die aktuelle Version 6.x verwendet. Um das Programm zu compilieren muss das dotnet-sdk-framework installiert werden. Für den Start des gebauten Programms reicht der Download und die Installation der Runtime. Der Download wird von Microsoft hier angeboten

Im Folgenden finden Sie einige hilfreiche Hinweise, wie Sie jmbde auf Ihrer nativen Plattform ausführen können.

• GitHub issues (preferred)

• Main Page
• User Manual

In dem master branch befindet sich der aktuellste Pre-Release Code.

• Releases

• Milestones

Im Folgenden finden Sie Hinweise für Entwickler, wie Sie jmbde auf Ihrem nativen System aufbauen können. Sie sind keine vollständigen Leitfäden, sondern enthalten Hinweise zu den notwendigen Maßnahmen. Bibliotheken, Kompilierungs-Flags, etc.

Zum erstellen des Programms wird das dotnet-sdk in der Version 6.0.100 benötigt. Das Framework ist im Internet bei der Adresse https://dotnet.microsoft.com/download/dotnet/thank-you/sdk-6.0.100-macos-x64-installer zu finden. Das Framework muss nach dem Download installiert werden.

        cd src/jmbde
        dotnet build -c Release
        dotnet ef database update

        cd src/jmbde
        dotnet build -c Debug
        dotnet ef database update

Sie können nun jmbde mit dem Befehl bin/jmbde aus der Kommandozeile starten.

EUPL-1.2 © Jürgen Mülbert

Deepak Pathak, Philipp Krähenbühl, Trevor Darrell

CCNN is a framework for optimizing convolutional neural networks with linear constraints.

• It has been shown to achieve state-of-the-art results on the task of weakly-supervised semantic segmentation.
• It is written in Python and C++, and based on Caffe.
• It has been published at ICCV 2015. It was initially described in the arXiv report.

If you find CCNN useful in your research, please cite:

@inproceedings{pathakICCV15ccnn,
    Author = {Pathak, Deepak and Kr\"ahenb\"uhl, Philipp and Darrell, Trevor},
    Title = {Constrained Convolutional Neural Networks for Weakly Supervised Segmentation},
    Booktitle = {International Conference on Computer Vision ({ICCV})},
    Year = {2015}
}

CCNN is released under academic, non-commercial UC Berkeley license (see LICENSE file for details).

1. Requirements
2. Installation
3. Usage
4. Scripts Information
5. Extra Downloads

1. Requirements for Caffe and pycaffe (see: Caffe installation instructions)
2. GCC version more than 4.7
3. Boost version more than 1.53 (recommended). If system dependencies give issues, install anaconda dependencies:

$ conda install boost
$ conda install protobuf

4. A good GPU (e.g., Titan, K20, K40, …) with at least 3G of memory is sufficient.

1. Clone the CCNN repository

# Make sure to clone with --recursive
git clone --recursive https://github.com/pathak22/ccnn.git

2. Build Caffe and pycaffe

• Now follow the Caffe installation instructions here
• Caffe must be built with support for Python layers!
• In your Makefile.config, make sure to have this line uncommented
  WITH_PYTHON_LAYER := 1
• You can download my Makefile.config for reference.

cd ccnn/caffe-ccnn
# If you have all caffe requirements installed
# and your Makefile.config in place, then simply do:
make -j8 && make pycaffe

3. Now build CCNN

   cd ccnn
   mkdir build
   cd build
   cmake ..
   make -j8

• Note: If anaconda is installed, then python paths may have been messed b/w anaconda and system python.
• I usually run this command :

cmake .. -DBOOST_ROOT=/home/pathak/anaconda -DPYTHON_LIBRARY=/home/pathak/anaconda/lib/libpython2.7.so -DPYTHON_INCLUDE_DIR=/home/pathak/anaconda/include/python2.7/ -DCMAKE_C_COMPILER=gcc-4.8 -DCMAKE_CXX_COMPILER=g++-4.8

• To verify this do : ccmake ./ inside the build folder and manually check the following things :
  MAKE_CXX_COMPILER, CMAKE_C_COMPILER , PYTHON_EXECUTABLE , PYTHON_INCLUDE_DIR , PYTHON_LIBRARY
• Make sure that cmake doesn’t mess the anaconda boost to system boost.

4. Configure path (if needed) in src/user_config.py.

5. (Optional — I don’t do it) If everything runs fine, set CMAKE_BUILD_TYPE using ccmake . to Release. This prevents eigen from checking all assertions etc. and works faster.

Demo CCNN.

cd ccnn
bash ./models/scripts/download_ccnn_models.sh
# This will populate the `ccnn/models/` folder with trained models.
python ./src/demo.py

Train CCNN.

cd ccnn
bash ./models/scripts/download_pretrained_models.sh
# This will populate the `ccnn/models/` folder with imagenet pre-trained models.
python ./src/train.py 2> log.txt

Test CCNN.

cd ccnn
python ./src/test.py  # To test IOU with CRF post-processing
python ./src/test_argmax.py  # To test IOU without CRF

Model Prototxts:

• models/fcn_8s/ : Atrous algorithm based 8-strided VGG, described here.
• models/fcn_32s/ : 32-strided VGG

Configure:

• src/config.py : Set glog-minlevel accordingly to get desired caffe output to terminal

Helper Scripts:

• src/extras/ : These scripts are not needed to run the code. They are simple helper scripts to create data, to prepare pascal test server file, to add pascal cmap to segmentation outputs etc.

• Pascal VOC Image List: train, val, trainval, test
• Training image-level label indicator files
• Pascal VOC 2012 validation result images
• Pascal VOC 2012 test result images

📢 Use this project, contribute to it or open issues to help evolve it using Store Discussion.

Cookie Script app brings the solution to make your website cookies comply with GDPR and CCPA.

Learn how to install and configure the app on the following sections.

It is possible to install the Cookie Script in your store either by using App Store or the VTEX IO CLI.

1. Access the Apps section in your account’s admin page and look for the Cookie Script box;
2. Then, click on the Install button;
3. You’ll see a warning message about needing to enter the necessary configurations. Scroll down and type in your Cookie Script ID.
4. Click on Save.

1. In your terminal, install the vtex.cookie-script@0.x app.
2. To confirm that the app has now been installed, run in your terminal vtex ls and check the installed apps’ list.
3. Access the Apps section in your account’s admin page and look for the Cookie Script box. Once you find it, click on it.
4. Fill in the Cookie Script ID field.
5. Click on Save.

After installing the app, you must create an account in Cookie Script to make the app work on your store. Follow the steps on the Cookie Script Configuration section to create an account and configure the app.

Once you have installed the app, you need to create an account in Cookie Script to be able to configure the app.

⚠️ You must follow the steps described in this section to guarantee the cookies will work. Otherwise, they will break the purchases flow from your store.

1. Go to the Cookie Script page and create your account.
2. After creating your account, go to the Dashboard tab and click on Add website.

3. Once you have added your website, go to the Cookie scanner tab and run a scan.
4. After the scan is complete, go to Cookies tab and make sure the following cookies are categorized as Stricly Necessary: ASPXAUTH, checkout.vtex.com, CookieConsent, device, vtex_segment, vtex_session, VtexFingerPrint, VtexRCMacIdv7, VtexRCRequestCounter, VtexRCSessionIdv7 and VtexWorkspace.

Thanks goes to these wonderful people:

This project follows the all-contributors specification. Contributions of any kind are welcome!

📢 Use this project, contribute to it or open issues to help evolve it using Store Discussion.

Cookie Script app brings the solution to make your website cookies comply with GDPR and CCPA.

Learn how to install and configure the app on the following sections.

It is possible to install the Cookie Script in your store either by using App Store or the VTEX IO CLI.

1. Access the Apps section in your account’s admin page and look for the Cookie Script box;
2. Then, click on the Install button;
3. You’ll see a warning message about needing to enter the necessary configurations. Scroll down and type in your Cookie Script ID.
4. Click on Save.

1. In your terminal, install the vtex.cookie-script@0.x app.
2. To confirm that the app has now been installed, run in your terminal vtex ls and check the installed apps’ list.
3. Access the Apps section in your account’s admin page and look for the Cookie Script box. Once you find it, click on it.
4. Fill in the Cookie Script ID field.
5. Click on Save.

After installing the app, you must create an account in Cookie Script to make the app work on your store. Follow the steps on the Cookie Script Configuration section to create an account and configure the app.

Once you have installed the app, you need to create an account in Cookie Script to be able to configure the app.

⚠️ You must follow the steps described in this section to guarantee the cookies will work. Otherwise, they will break the purchases flow from your store.

1. Go to the Cookie Script page and create your account.
2. After creating your account, go to the Dashboard tab and click on Add website.

3. Once you have added your website, go to the Cookie scanner tab and run a scan.
4. After the scan is complete, go to Cookies tab and make sure the following cookies are categorized as Stricly Necessary: ASPXAUTH, checkout.vtex.com, CookieConsent, device, vtex_segment, vtex_session, VtexFingerPrint, VtexRCMacIdv7, VtexRCRequestCounter, VtexRCSessionIdv7 and VtexWorkspace.

Thanks goes to these wonderful people:

This project follows the all-contributors specification. Contributions of any kind are welcome!

A beautifully designed, interactive analog clock web application featuring a sleek analog clock crafted with Adobe Illustrator, customizable themes, and sound effects. Perfect for adding elegance to your browser.

• Smooth Analog Clock: Real-time hour, minute, and second hands.
• Customizable Themes: Switch between light and dark modes.
• Optional Sound Effects: Toggle ticking sounds for an immersive experience.
• Responsive Design: Seamless performance across all screen sizes.
• Intuitive Controls: Easily toggle themes and sounds via tooltips.
• Well-Structured CSS: Modular styles for maintainability.

• A modern web browser (Chrome, Firefox, Edge, Brave).
• Node.js (optional, for local development).

1. Clone the repository:

   git clone https://github.com/waiz3ple/wall-clock.git

2. Navigate to the project directory:

   cd wall-clock

3. Install dependencies (if applicable):

   npm install

4. Run locally:

   npm run dev

• Toggle Theme: Click the settings icon (⚙️) to switch between light and dark modes.
• Enable/Disable Sound: Use the sound toggle in the tooltip.
• Close Tooltip: Click outside the tooltip or the close button to dismiss it.

Modify variables.css to adjust light and dark modes.

Replace clock-ticking.wav in the assets/sounds folder with your preferred audio file.

• fonts.css: Custom fonts.
• variables.css: Theme and color variables.
• base.css: Global styles.
• clock.css: Clock-specific styles.
• checkbox.css: Toggle switch styles.
• tooltip.css: Tooltip styles.
• media-queries.css: Responsive adjustments.
• animations.css: Keyframe animations.
• settings-icon.css: Settings icon styles.

Tested on:

• Google Chrome
• Mozilla Firefox
• Brave
• Opera
• Microsoft Edge

Automatically deploys to GitHub Pages when the main branch is built. The dist folder is generated and deployed.

• Desktop Screen Saver Mode: Convert the app into a screensaver for Windows and macOS.
• Alarm Feature: Add an alarm clock option with customizable alerts.
• Additional Themes: Introduce more theme options, including seasonal and custom backgrounds.
• Widget Support: Develop a widget version for desktops and mobile devices.

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

Licensed under the MIT License.
Please do not claim this project as your own.

• Wasiu Ramoni
• GitHub: @waiz3ple
• Twitter: @waiz3ple
• Telegram: t.me/waiz3ple

Enjoy your time with the Analog Clock App! ⏰

Allows user to take full control of the terminal of their computer through Facebook’s messaging service.

I shall not collect user’s content or information, or otherwise access Facebook, using automated means (such as harvesting bots, robots, spiders, or scrapers) without user’s prior permission.

To use the script to full extent, make sure that you keep it at the home directory. Run it like this :

python ~/main.py

Enter your facebook username and password when prompted.

Alternatively, you can automate authentication. Create settings.txt file in repo’s folder, and write email and password there in following format:

[main]
email = addresswithoutquotes@gmail.com
password = passwordwithoutquotes

Wait till it sets up. To make sure that it has setup, your url should be 'https://facebook.com/messages/*your own username*.

To send the commands, search for your own name on the messenger and send commands to it.

While using the set ... as ... command, you can create a file named commands.txt and write the Alias name in the following format (seperated by single space):

Alias_command_without_quotes  actual_command_without_quotes

The file has been included in the repo, which has some useful commands for Mac.

pip install selenium

Link for proper installation.

Any other command you might normally use on your CLI.

• Added save img and save file command.
• File permssion changes for settings.txt on log-in and quitting
• Added senddir command.
• Fixed misc. bugs.
• CPU and Chill.
• Added set ... as command.
• Reduced dependencies on machine generated id’s and classes.
• Auto-authentification from settings file (Pushed by @tedmx).
• Disabled Chrome Notifications (Pushed by @mmplisskin).
• Added show command.

show URL (https://www.foo.bar) /Relative FilePath 'Foo/Bar/main.py'

• Shifted to Chrome.
• Faster Log-In .
• Added memory command, to get current memory stats of the machine.
• Added quit and help commands.

help : Displays the commands which can be used
quit : quit session

• Addded condition for proper log-in.
• Using getpass() to hide password (Pushed by @idoqo).
• Support for Python 2.7 (Pushed by @amitt001).
• Added support for sending files and cd. Type following commands on.Messenger :

cd __dirPath
send __filePath

• Add support for cd.
• Send files.
• Switch to PhantomJS. (Chucking the idea, as error in the build of PhantomJS which doesn’t allow file upload.)
• Error Logs.
• Running in backgroud thread.
• Fix all the bugs.

Apache-2.0

Painless Docker book git repository.

Painless Docker is a practical guide to master Docker & its ecosystem with real world examples.

This book is a detailed guide to create, deploy, optimize,secure, trace, debug, log, orchestrate & monitor Docker, Docker clusters & modern Docker-based microservices from development to production.

Docker is a powerful tool, however learning how to use it the right way could take a long time. Engineers and developers are confused in front of the rapidly growing cloud and containers ecosystem, many of them have found some difficulties to enter the containers world.

Painless Docker is a gate to enter Docker’s world and master it, you will find that using Docker is easy and efficient for your development, operations & production environments.

Painless Docker is a complete and detailed guide to master Docker and a great part of its ecosystem. The book is designed for beginners and for intermediate levels : You will be guided step by step from the simple basic concepts to the advanced powerful features in order to master Docker and microServices from development to production using Docker, Docker Compose, Docker Swarm, Kubernetes and other interesting tools.

The 10 most important things you’ll learn

• Basics & Advanced Concepts Of Docker
• Building Your Own Images & Running Containers In Production
• Docker Volumes & Networking
• Optimizing Docker
• Building Your Own Docker Monitoring Tool Using Docker API
• Docker Logging & Debugging
• Docker Orchestration Using Swarm, Kubernetes, Marathon & Rancher
• Using Docker To Build Microservices Architecture
• Docker Security
• Docker Best Practices

Docker and the Docker logo are trademarks or registered trademarks of Docker, Inc. in the United States and/or other countries.

This script automates the installation, uninstallation, cleanup, and updating of pre-commit hooks in your Git repositories.

Pre-commit hooks help ensure that your commits meet certain standards and protect against secret leakages.

• Python must be installed on your system.

You can download and run the script directly using curl. Execute the following command in your terminal:

curl -fsSL https://raw.githubusercontent.com/Andygol/check-leaks/main/pre-commit.sh | sh -s install

This will download and run the script with the install command.

Alternatively, you can download the script and make it executable manually:

curl -O https://raw.githubusercontent.com/Andygol/check-leaks/main/pre-commit.sh

chmod +x pre-commit.sh

Run the following command to set up pre-commit hooks for your repository:

./pre-commit.sh install

This will create a virtual environment, install pre-commit, configure hooks including one for detecting secrets, and run pre-commit.

To remove pre-commit from your repository, use the following command:

./pre-commit.sh uninstall

This will uninstall pre-commit and leave your repository without any hooks.

If you want to remove the virtual environment and configuration files, run:

./pre-commit.sh cleanup

Do note that this will not remove the hooks from your repository. Use the uninstall command to remove them. You can combine uninstall and cleanup to remove the hooks and the virtual environment.

./pre-commit.sh uninstall cleanup

curl -fsSL https://raw.githubusercontent.com/Andygol/check-leaks/main/pre-commit.sh | sh -s uninstall cleanup

To update your pre-commit hooks to the latest versions, use:

./pre-commit.sh update

This will automatically update the hooks using pre-commit autoupdate.

• This script assumes that you have Python installed on your system.
• For more details on pre-commit, visit the official documentation.

Feel free to customize the script based on your needs.

Note: Always review scripts before running them, and use them at your own risk.

This script is licensed under the MIT License. See the LICENSE file for details.

This script was created by Andrii Holovin (Andygol).

A comprehensive one-stop platform for all your JEE preparation needs, featuring AI-powered tutoring, study materials, official papers, and more.

• Features
  • Core Features
  • User Experience
  • Study Resources
  • AI Tutor Features
  • News & Updates
  • Platform Integrations
• Technologies Used
  • Frontend
  • Backend & APIs
  • Development & Deployment
• Getting Started
• Building for Production
• Project Structure
• All versions
• Contributing
• License
• Support

• AI Tutor: Personalized JEE preparation assistance with Google OAuth integration
• Study Materials: Comprehensive resources for Physics, Chemistry, and Mathematics
• Official Papers: Direct access to JEE Main and Advanced official papers and answer keys
• Chapter-wise PYQs: Solved previous year questions organized by chapters
• Real-time News: Latest JEE-related news and updates powered by GNews API
• Contact Form: Email integration for user queries and feedback

• Modern and responsive UI built with Next.js and Tailwind CSS
• Dark/Light theme support with system preference detection
• Mobile-first design approach
• Smooth animations and hover effects
• SEO optimized with automatic sitemap generation

• Physics Resources: Complete study materials and reference books
• Chemistry Resources: Comprehensive chemistry study guides
• Mathematics Resources: Extensive math preparation materials
• Additional Platforms: Integration with Unacademy, Physics Wallah, and Apni Kaksha

• Google OAuth authentication
• File upload and attachment support
• Markdown and LaTeX rendering for mathematical expressions
• Chat history persistence
• Real-time message streaming
• Subscription management system

• Real-time JEE news from GNews API
• Categorized news cards
• Automatic content refresh
• Mobile-responsive news layout

• Unacademy: Direct links to Unacademy JEE courses
• Physics Wallah: Access to PW study materials
• Apni Kaksha: Additional study resources

• Framework: Next.js 15 with App Router
• UI Library: React 19
• Styling: Tailwind CSS with custom animations
• Icons: React Icons
• Theme Management: next-themes
• Markdown Rendering: React Markdown with KaTeX support
• Math Rendering: KaTeX for mathematical expressions

• Email Service: Nodemailer for contact form
• News API: GNews API for real-time updates
• Authentication: Google OAuth with @react-oauth/google
• File Handling: Custom file upload system
• API Routes: Next.js API routes with rewrites

• Build Tool: Turbopack for faster development
• SEO: next-sitemap for automatic sitemap generation
• Analytics: Google Analytics integration
• Ad Integration: Google AdSense support
• Performance: Image optimization and caching

1. Clone the repository:

git clone https://github.com/Samya-S/jeechallenger-2.0.git
cd jeechallenger-2.0

2. Install dependencies:

npm install

3. Create a .env.local file in the root directory and add your environment variables:

# Email Configuration for Contact form
AUTH_EMAIL=your-email@example.com
AUTH_PASS=your-email-password
SENDER_EMAIL=your-email@example.com
RECEIVER_EMAIL=recipient@example.com

# GNews API Configuration
GNEWS_API_KEY=your-gnews-api-key

# Google OAuth Configuration
NEXT_PUBLIC_GOOGLE_CLIENT_ID=your-google-client-id

# AI Tutor Backend (for production)
# The app uses API rewrites to connect to the AI tutor backend

4. Run the development server:

npm run dev

5. Open http://localhost:3000 in your browser to see the result.

npm run build
npm start

jeechallenger-2.0/
├── app/                          # Next.js App Router pages
│   ├── ai-tutor/                # AI Tutor functionality
│   ├── contact-us/              # Contact form
│   ├── materials/               # Study materials
│   │   ├── physics/            # Physics resources
│   │   ├── chemistry/          # Chemistry resources
│   │   ├── mathematics/        # Mathematics resources
│   │   └── chapterwise-solved-pyqs/  # PYQs by chapter
│   ├── more-platforms/         # External platform links
│   ├── news/                   # News section
│   └── official-links/         # JEE official papers
├── components/                  # Reusable React components
│   ├── AiTutorComponents/      # AI Tutor specific components
│   ├── common/                 # Shared components
│   ├── home/                   # Home page components
│   └── utils/                  # Utility components
├── lib/                        # Utility functions and configs
├── server/                     # Server actions
└── public/                     # Static assets

Upgraded the vanilla JavaScript project to a modern Next.js application with AI-powered features.

This is version 1.2, made using HTML, CSS and vanilla JavaScript. The code is available at the main branch of the repository Samya-S/jeechallenger (archived) and at Samya-S/jeechallenger-v1.2 (archived).

This version includes PHP. The code is available at v1.1 branch of the repository Samya-S/jeechallenger (archived) and at Samya-S/jeechallenger-v1.1 (archived).

This version is made using HTML, CSS and vanilla JavaScript. The code is available at v1.0 branch of the repository Samya-S/jeechallenger (archived).

Contributions are welcome! Please feel free to submit a Pull Request.

• Follow the existing code structure and naming conventions
• Ensure responsive design for all new components
• Add proper TypeScript types if applicable
• Test on both light and dark themes
• Update documentation for new features

This project is licensed under the MIT License – see the LICENSE file for details.

If you find this project helpful and would like to support its development, consider becoming a sponsor. Your support helps maintain and improve the project.

My name is Brendan Willis and I am currently a senior at the University of Massachusetts Amherst. I will be graduating in May with a degree in Mathematics and concentrations in statistics and actuarial science. I also have an economics minor.

As a Mathematics major, I have taken several classes involving data analysis. Telling stories with data and learning about different things through analysis is a passion of mine. I have analyzed data on Python, R, and Stata and am confident I could quickly pick up any other software. This repository is meant to display some of the projects I have completed in school.

Enjoy!

This small project displays my ability to wrangle and clean data on R. I used the ‘covidcast’ package on R to query over data and pull out observations I was interested in. In this case I pulled out covid data for counties near where I live between 2020 and 2022. I then made some interactive tables to investigate covid rates in these places. Then, I pulled out data for all counties in the United States in the month of January 2022, so I could make an interactive table with the county from each state with the max covid rate from that month. This project isn’t so much about getting results from the data; its main purpose is to display my ability to work with data on R.

This project consists of 3 parts:

1. Making simple word game.
2. Programming computer player for word game by utilizing monte carlo simulation.
3. analyzing results of simulation.

The initial creation of the word game is contained in the file ‘ps2.py’ above. When this program is run it deals a hand of 15 random letters to the player. The player makes as many words with the letters as they can until they either run out of letters or can’t make any more words. The goal is to score as many points as possible. The ‘ps3a.py’ contains the second part of this project. This program implements a computer player for the word game that uses monte carlo simulation in order to take N samples from the hand and choose the word that provides the best score. The third part of the project in the file ‘ps3c.ipynb’ runs the simulation for different values of N. This file displays histograms that show how the score gotten by the computer program tends to increase as N increases.

In this project I utilize the sqlite3 library on Python to connect to databases and write SQL queries that clean and filter tables in order to get the data I want. I then use the pandas library to read tables into python as dataframes. I also join\merge tables based on specific columns.

This group project consisted of two parts. The first part was to create an optimal schedule for 60 professors based on their course and time preferences and adhering to several constraints. All of the constraints are explained in more detail in the presentation file in the above folder; but one example is that a proffessor could not be scheduled to teach all five days of the week. The second part of this project is to take the schedule and enact changes that need to be made so that it minimized how much the overall schedule is changed. Again, this is all explained in more detail in the folder above. The python code we wrote to generate the schedule can be seen in file ‘456project2.ipynb’. I had a hand in this code but my biggest contribution to this project was cleaning the data. The project all depended on making a schedule that optimized the professors’ time and course preferences. The original data set can be seen in the ‘preference_data3’ file above. This data set was not even close to the format we needed to write our program. In the ‘time_preferences.rmd’ file you can see how I cleaned and transformed the data so that we could work with it. The original data we got just had professors rank whether they like teaching early, middle or late in the day on a scale from 1-3. For our program we needed the ranking to be for each hour of each day and on a scale from 0-1. My code takes the original data set and puts it in this format so that we could work with it. The final report for this project is rather lengthy but at the end of it you can find our results. Our code successfully generated a schedule that seemed to maximize each professor’s preferences while adhering to all of the constraints.