satellite_course_python_code

Viewing the exercises

The python exercises are a series of Jupyter Notebooks that are hosted on a GitHub repository. You can view a single exercise to see if it is of interest by clicking the linked name of each exercise in the Exercises section below. A non-executable version of the exercise will open in a new browser window.

Running the exercises

Setup and test your python environment:

1. Make sure you have conda installed on your machine
2. Download this repository. Use the green Code dropdown to select Download Zip and unzip to a location on your computer
3. Use a terminal window to navigate to the unzipped folder

The following commands:

• Create a new conda environment named 'coastwatch' and load the required modules to it
• Activate the environment
• Runs a script that checks for any missing modules
• Launches jupyter-lab for displaying the jupyter notebook tutorials

conda env create -f environment.yml
conda activate coastwatch
python check_modules.py
jupyter-lab

Exercises

The first three exercises demonstrate the CoastWatch R code tutorials as python code. They show you how to extract gridded data from ERDDAP inside a box or polygon, and along a track. Additional examples demonstrate practical applications of working with time-series of satellite data.

1. Get Data Using a Rectangular Bounding Box
   Demonstrates how to extract environmental data from an ERDDAP server in an rectangular bounding box (polygon) over time.
2. Get Data Along a Track
   Extract environmental data from an ERDDAP server along an x,y and time trajectory, e.g. an animal or cruise track.
3. Get Data Using an Irregular Shape
   Extract environmental data from an ERDDAP server in an irregular bounding box (polygon) over time, e.g. a marine protected area.
4. Comparing time-series of different satellite datasets
   Several satellite ocean color sensors have been launched since 1997 to provide a continuous record of global ocean color data. This exercise examines the variability of Chlorophyll-a values during time periods where the satellite measurements overlap.
5. Creating a virtual buoy
   Create a virtual buoy from satellite data for locations where in-situ buoy data may not be available or has been discontinued.