Unit tests review

[amhanson9]

I made a lot of unit tests at once and then edited a lot of those functions. Do a review to make sure each test has all the necessary variations and are as close as possible to how the function works in production (input data types, where it saves, etc.). Also look for things that could more efficient, like using functions for repetitive code (e.g., read CSV into pandas and df to list) or make seed_df within setUp and use row index to access the right row for each test, instead of a separate seed_df for each test. It makes it easier to update later when there are changes to seed_df.

Reviewed all unit tests and made some changes when moved to Linux. Additional ideas are below or in separate issues.

[amhanson9]

test_add_completeness: make the data closer to accurate, like blank cells after an error that would stop processing (only get one WARC error type at once)

[amhanson9]

test_download_metadata: the data variations is present but not explicit because the tests are named for the department instead of the variation. Make the variations more explicit: one each of the 4 metadata reports always there, 1 each of the 6 metadata reports, all metadata reports and more than one of metadata reports that can repeat.

[amhanson9]

test_metadata_csv:

• Where possible, include all departments in each test. No all departments have data that fits all tests.
• Split tests for no collector and no title
• Reorganize to group tests for errors and group correct functioning, so it is easier to see the tests are complete

[amhanson9]

test_verify_warc_fixity: would it be faster or a better test to save an extremely small WARC to the GitHub repo instead of downloading from Archive-It every time?