This branch does away with the hard coded match_queue dummy results presented to the user upon first log in.
Instead, filtering/scoring logic is utilized to present the user with results based on their first set of survey answers (answers including presence, zip_code, and is_active (participant or candidate).
Testing this can be finicky, as it actually does filter based off of zip_code now, so please input a zip code that the mock data populates with at least some users to test (I originally used a zip code in Alaska and got no results). For reference I utilized the zip code, 90291, to get a decent size initial dataset to then further filter down.
This branch does away with the hard coded match_queue dummy results presented to the user upon first log in.
Instead, filtering/scoring logic is utilized to present the user with results based on their first set of survey answers (answers including presence, zip_code, and is_active (participant or candidate).
Testing this can be finicky, as it actually does filter based off of zip_code now, so please input a zip code that the mock data populates with at least some users to test (I originally used a zip code in Alaska and got no results). For reference I utilized the zip code, 90291, to get a decent size initial dataset to then further filter down.
maeda
This branch does away with the hard coded match_queue dummy results presented to the user upon first log in.
Instead, filtering/scoring logic is utilized to present the user with results based on their first set of survey answers (answers including presence, zip_code, and is_active (participant or candidate).
Testing this can be finicky, as it actually does filter based off of zip_code now, so please input a zip code that the mock data populates with at least some users to test (I originally used a zip code in Alaska and got no results). For reference I utilized the zip code, 90291, to get a decent size initial dataset to then further filter down.