This repository is being formed to demonstrate the solution to the Cloud Mask Replacement Task

• Code can be tested via the following methods described
  • Virtual environment:
  • Docker Method:
  • Jupyter solution:
    • Local
    • Via Docker
  • AWS Cloud Method

• All the sample data must be copied into the data folder
• the data folder path can be changed by updating the processing_path variable within the code.
• Virtual environment method requires debian based linux distribution in order to work properly.
• Docker methods can be used in mac and windows operating systems

• python3 -m venv venv
• source venv/activate
• ./install.sh # For GDAL
• pip install -r requirements.txt

If all the steps are successful you can use the virtual environment as your projects default interpreter to test the codebase. From the settings menu of your favorite Python IDE change the interpreter to the newly created venv folder.

• Make sure in your operating system docker is installed and running.
• For the docker method, a dedicated ubuntu base image has been used. All the necessary libraries and virtual environments are being handled via docker image. Required python libraries, necessary local libraries, and the virtual environment is being prepared for you within the image.
• docker-compose -f docker-compose.yml up -d
• Change interpreter from the virtual environment to docker environment
• After updating the interpreter options, you can run the main.py to see the results.
• Outputs are also exported to the data folder with a date tag of YYYYMMDD-HHMM.
• Docker build install following libraries:
• xarray
• gdal
• rasterio
• rioxarray
• jupyter

• Define your python interpreter using the above methods
• from the command line run the following code:
  • jupyter notebook --ip 0.0.0.0 --port 8888 --allow-root --NotebookApp.token='' --NotebookApp.password=''
• You can run the individual cells to review the task
• Please be sure you have the test data copied into the data folder.

• Make sure in your operating system docker is installed and running.
• run the code docker-compose -f docker-compose.yml up -d
• from any internet browser go to the following page:
  • http://localhost:8999/
• You can run the individual cells to review the task
• Please be sure you have the test data copied into the data folder.

• The container created for this task has been uploaded to AWS Cloud solutions in order to give remote access rather than the local one.
• You can reach from the following address:
  • http://ec2-18-117-109-195.us-east-2.compute.amazonaws.com/
  • http://18.117.109.195/
• Similar to the previous Jupyter solutions you can run the individual cells to review the task