17. Learn to Write Pseudocode for Python Programming#
Attribution This lecture is designed based on great resources available on Earth Data Science Workflows from Earth Lab CU Boulder.
Pseudcode can help you design data workflows through listing out the individual steps of a workflow in plain language, so the focus is on the overall data process, rather than on the specific code needed.
17.1. Design an Efficient Data Workflow Using Pseudocode#
You have now identified your challenge - to calculate and plot average normalized difference vegetation index (NDVI) for every Landsat scene (individually) across one year for two study locations:
You have been given a study area boundary for each site. Your next step is to write out the steps that you need to follow to get to your end goal plot and csv file.
You know you will need to do the following:
Find Landsat scenes.
Access the scenes on the cloud or download them.
Read the data.
Calculate NDVI.
Save NDVI values to pandas dataframe with the associated date and site name
Plot NDVI time-series
Export NDVI values to a csv file
Begin your process by pseudocoding - or writing out steps in plain English - everything that you will need to do in order to get to your end goal. The steps below will walk you through beginning this process of writing pseudocode.
17.2. Begin With the Workflow for One Landsat Scene#
Search for one Landsat scene across your area of interest (aoi)
Get a list of relevant files for the resulting Landsat scene.
Recall that Landsat data is provided as a series of GeoTIFF files - one for each band (e.g. red, near-infrared) and one with the quality assurance information (e.g. cloud cover and shadows).
Steps required to get data for one Landsat scene for a site
Query an API to find the target Landsat scene
Get a list of files/assets for that scene.
Subset the list to just the files/assets required to calculate NDVI.
Open and crop the data for one Landsat scene.
Now that you have the steps to get the data for one Landsat scene, you can expand your pseudocode steps to include opening and cropping the data to the site boundary.
Steps required to process one Landsat scene for a site
Query an API to find the target Landsat scene
Get a list of files/assets for that scene.
Subset the list to just the files/assets required to calculate NDVI.
Open and crop the bands needed to calculate NDVI.
Calculate NDVI for one Landsat scene.
Last, you can expand your pseudocode to include using the bands that you opened and cropped to calculate NDVI.
Steps required to process one Landsat scene for a site
Query an API to find the target Landsat scene
Get a list of files/assets for that scene.
Subset the list to just the files/assets required to calculate NDVI.
Open and crop the bands needed to calculate NDVI.
Calculate average NDVI for that scene.
17.3. Expand Workflow to Include All Landsat Scenes for a Site#
You have now identified the steps required to process a single Landsat scene. Those steps now need to be repeated across all of the scenes for each site for a year.
Steps required to process all Landsat scenes for a site
Query an API to find all Landsat scenes for your aoi
For each scene, use the steps outlined previously for one scene to calculate NDVI for the data in that scene.
Save NDVI values and the date for that scene (there are some steps here that you need to flesh out as well) to a list or dataframe that contains average NDVI for each scene at this site.
OK - now you are ready to put the workflow for a single site together. Of course, remember there are some sub steps that have not been fleshed out just yet, but start with the basics and build from there.
Steps required to process all Landsat scenes for a site
Query an API to find all Landsat scenes for your site
For each scene, use the steps outlined previously for one site to calculate NDVI:
Get a list of files/assets for that scene.
Subset the list to just the files/assets required to calculate NDVI.
Open and crop the bands needed to calculate NDVI.
Calculate average NDVI for that scene.
Save NDVI values and the date for that scene (there are some steps here that you need to flesh out as well) to a list or dataframe that contains average NDVI for each scene at this site.
Export dataframe with mean NDVI values to csv. (i.e. a “data product” output that you can share with others)
Plot the NDVI for each Landsat scene
17.4. Add Multiple Sites Worth of Data to Your Workflow#
Above you begin to think about the steps associated with creating a workflow for:
A single Landsat scene.
A set of Landsat scenes for a particular site.
But you want to do this for two or more sites. You are not sure if you will have more than two sites but in this case you want to design a workflow that allows for two or more sites. Add an additional layer to your pseudo code.
Modular workflow for many sites
Get list of all sites.
For each site, use the steps outlined previously for one site to calculate NDVI:
Query an API to find all Landsat scenes for that site
For each scene, use the steps outlined previously for one scene to calculate NDVI for the data in that scene:
Get a list of files/assets for that scene.
Subset the list to just the files/assets required to calculate NDVI.
Open and crop the bands needed to calculate NDVI.
Calculate average NDVI for that scene.
Save NDVI values and the date for that scene (there are some steps here that you need to flesh out as well) to a list or dataframe that contains average NDVI for each scene at this site.
Export dataframe with mean NDVI values to csv. (i.e. a “data product” output that you can share with others)
Plot the NDVI for each Landsat scene
You have now designed a workflow using pseudocode to process several sites worth of landsat data.
Of course, the pseudocode above is just beginning. For each of the steps above, you need to flesh out how you can accomplish each task.
The next lesson in this chapter focuses on data workflow best practices that can help you implement your workflow efficiently and effectively.