PEP8 recommends keeping imports at the top of the file. Also, I had
to re-work the issn/isbn so they didn't conflict with the functions
in check.py (flake8 warned about them being redefined).
Imports sorted with isort.
See: https://www.python.org/dev/peps/pep-0008/#imports
By using df[column] = df[column].apply(check...) we were re-writing
the DataFrame every time we returned from a check. We don't actuall
y need to return a value at all, as the point of checks is to print
a warning to the screen. In Python a "return" statement without a v
ariable returns None.
I haven't measured the impact of this, but I assume it will mean we
are faster and use less memory.
Allow overriding the directory for the requests cache. In the case
of csv-metadata-quality-web, which currently runs on Google's App
Engine, we can only write to /tmp.
This is no longer class-ified as "unsafe" as I have yet to see a
case where this was intentional, and it always causes issues when
you import the data in a DSpace repository.
PEP8 recommends keeping imports at the top of the file. Also, I had
to re-work the issn/isbn so they didn't conflict with the functions
in check.py (flake8 warned about them being redefined).
Imports sorted with isort.
See: https://www.python.org/dev/peps/pep-0008/#imports
We used to only check fields that had "date" in their name because
we were using DSpace's default dc.date.* fields. Now we are using
dcterms.issued so I will add that one as well.
We should also allow ISO 8601 extended in combined date and time
format. DSpace does not have a problem with dates in this format
and I have found some metadata that uses this date format.
For example: 2020-08-31T11:04:56Z
See: https://en.wikipedia.org/wiki/ISO_8601
I just came across some metadata that had unnecessary multi-value
separators at the end of a field, causing a blank value to be used.
For example: "Kenya||Tanzania||"
According to PEP8 we should avoid scoped imports unless you have a
good reason. Here there are two cases where we do (issn and isbn),
but I will move the others to the global scope.
Python's built-in unicodedata library includes the is_normalized()
function starting with Python 3.8. This utility function allows us
to do the same thing with earlier Python versions.
See: https://docs.python.org/3/library/unicodedata.html
Works decenty well assuming the title, abstract, and citation fields
are an accurate representation of the language as identified by the
language field. Handles ISO 639-1 (alpha 2) and ISO 639-3 (alpha 3)
values seamlessly.
This includes updated pipenv environment, test data, pytest tests
for both correct and incorrect ISO 639-1 and ISO 639-3 languages,
and a new command line option "-e".
ISO 639-1 uses two-letter codes and ISO 639-3 uses three-letter codes.
Technically there ISO 639-2/T and ISO 639-2/B, which also uses three
letter codes, but those are not supported by the pycountry library
so I won't even worry about them.
See: https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes
This happens in names very often, for example in the contributor
and citation fields. I will limit this to those fields for now and
hide this fix behind the "unsafe fixes" option until I test it more.
I recycled this code from a separate agrovoc-lookup.py script that
checks lines in a text file to see if they are valid AGROVOC terms
or not. There I was concerned about skipping comments or something
I think, but we don't need to check that here. We simply check the
term that is in the field and inform the user if it's valid or not.
This makes it easier to understand where the error is in case a CSV
has multiple date fields, for example:
Missing date (dc.date.issued).
Missing date (dc.date.issued[]).
If you have 126 items and you get 126 "Missing date" messages then
it's likely that 100 of the items have dates in one field, and the
others have dates in other field.
Add a check for soft hyphens (U+00AD). In one sample CSV I have a
normal hyphen followed by a soft hyphen in an ISBN. This causes the
ISBN validation to fail.