Tutorial

• Using the command line tool geoslurp
  • Getting info and help
  • Administrative tasks
  • Dataset Management
  • View management
  • Database Function Management
• Python Examples
  • Establish a database connection and retrieve the inventory and User settings
    • Read-only user versus priviliged user
    • Using geoslurpConnect
    • Alternatively one can use the class GeoslurpConnector and provide all connections details manually
    • Loading the Dataset Inventory
    • Setting and getting usersettings from the database
      • Default settings
      • Storing and retrieving Authentication details
  • Downloading and registering “catalogue” datasets
  • Download and register a dynamic dataset of (crowd-sourced?) US Marijuana street prices
    • Example: US street prices of Marijuana
      • Create the stucture of the database table using sqalchemy ORM methods
      • Create a helper function to extract relevant information from the file
      • Inherit from DataSet
      • Create an instance of the class and call the pull, and register methods to download and register the data
    • Avoid reinventing the wheel
  • Discovery techniques
    • Query a single table and visualize the result
      • Pick a state and plot the results using matplotlib
    • A more complicated query which joins data between tables
      • Visualize the result geographically using matplotlib
  • Exporting parts of the database to portable formats
    • Exporting a subset of the PSMSL table
    • Exporting an Argo query and the profile files in the South China Sea for the first 2 weeks of December 2010
  • Exporting a selection of GRACE datasets and auxiliary datasets (without geographical information) to a local sqlite database.
  • Applying the Ocean function to geometries and generating grids
    • Example 1: Generate a global 0.125 degree equidistant raster
    • Example 2: Use the optimized table for other queries
  • Conclusion: An indexed and subdivided geometry table speeds up the ocean queries considerably