Direct string comparison in multiple places is prone to typos. It
also means that a consumer of the class needs to know that whether
a user is pending or active is held in the `state` property, which
is an implementation detail.
To keep the conditionals in the Jinja template more readable, this
commit moves the logic into a method on the model, where it can
be split over multiple statements and lines.
We fixed a problem with the focus styles of list
items in this pull request:
https://github.com/alphagov/notifications-admin/pull/4141
This missed out pages for areas with counties in
them. This adds the fix to those pages.
These changes also include some extra spacing
between the end of the list and the button which
is lost by the new styles we're giving the list
items.
So we don’t have to deploy a change on a Saturday.
In the future this could pull from the rates in the database, but while
that code is shifting around I didn’t want to touoch it. We’d also have
to think about caching so as not to have a non-authenticated route
hitting the database.
Note: I've removed the pricing assertion in the "0_free_allowance"
test as it's covered elsewhere - the value of the test is really to
check that we don't show the remainder if there never was any.
The previous, manual calculation could be incorrect depending on
which SMS rates the free allowance was attributed to.
The new field also supersedes the old "letter_total" bolt-on so we
can get cost information consistently for both types.
This adds missing assertions for email and SMS usage, as well as
letters with the help of some additional test data.
Previously we were only checking monthly usage (in other tests).
The "with_letters" was mostly a duplicate of the one before - no
change in test setup - bar the three assertions at the end.
Having the assertions in a separate test will help keep the one
above manageable as we add more assertions for the annual usage.
This will make the following changes clearer.
In the next commits we'll go into more detail about "billing_units"
and how it differs for SMS vs. emails and letters.
At the moment, we put the sms rate on the usage page for each
months billing data by taking the single sms rate for the year.
The assumption that there will be a single sms rate for the year is
no longer going to be true. Therefore, instead we take the sms
rate from the monthly data itself which tells us the rate for
a months worth of sent SMS.
This will allow both prometheis (the shared one and our own) to scrape
the /metrics endpoint, each with their own authentication
See alphagov/gds_metrics_python#1 for more details
