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Useful code for satellite data processing

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# python_code

## Viewing the exercises
The 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 on this page the linked name of each exercise. A non-executable version of the exercise will open in a new browser window.

If you want an executable version of a Notebook, you can save each individual Notebook to your computer (use the save menu in your browser) bring the file up in your Jupyter Notebook software. To get all of the Notebooks, go to the GitHub repository, and download a zip file with all of the exercises included:

* On the green "Code" dropdown above, select "Download Zip"
* Unzip to a location on your computer
* In a terminal, navigate to the unzipped folder and launch Jupyter Notebook by entering:
```
jupyter notebook 
```

## Exercises
1. **[Emulating the R rerddapXtracto functions](py_xtractomatic.ipynb)**  
This exercise shows you how to duplicate the rerddapXtracto functions demonstrated in R tutorial section of the course.  
  * Extract environmental data from an ERDDAP server along an x,y and time trajectory, e.g. an animal or cruise track.
  * Extract environmental data from an ERDDAP server in an rectangular bounding box (polygon) over time.
  * Extract environmental data from an ERDDAP server in an irregular bounding box (polygon) over time, e.g. a marine protected area.

2. **[Creating a virtual buoy data](virtual_buoy_example_geopolar.ipynb)**   
Create a virtual buoy from satellite data for locations where in-situ buoy data may not be available or has been discontinued.

3. **[Comparing timeseries from different sensors](compare_sensor_data.ipynb)**
Several ocean color sensors have been launched since 1997 to provide continuous global ocean color data. Chlorophyll-a values can vary among the sensors during periods where measurements overlap. This exercise examines that variability.

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