Looking up the coordinate reference system for a given polygon’s bounds
is surprisingly expensive.
We can make things run faster by precomputing it at the time of
generating the simplified polygons, then storing it in the SQLite
database alongside the polygon data.
Transforming full resolution polygons to linear coordinates is somewhat
expensive. We can make things run faster by:
- looking at `simple_polygons` which have fewer points and are therefore
faster to transform.
- reusing polygons that have already been transformed where possible
Brings in https://github.com/alphagov/notifications-utils/pull/889/files
At the moment, we are not doing any transformation of features before
applying geometric algorithms to them. This is, in effect, assuming that
the earth is flat.
This new version of utils implements the transformation of our polygons
to a Cartesian plane. In other words, it converts them from being
defined in spherical degrees to metres.
For the admin app this means we need to convert places where the code
expects things to be measured in degrees to work in metres instead.
WTForms versions less than 3.0.0 have a security vulnerability where
arbitrary HTML can be inserted into the label of a form, allowing the
possibility of a cross-site scripting attack.
I don’t know if there’s anywhere we put user-generated content into form
labels but it’s possible we are vulnerable somewhere.
This require moving some imports because as of
https://github.com/wtforms/wtforms/pull/614/files
there is no longer a separate module for HTML 5 fields, they are now
considered core fields.
As of https://github.com/wtforms/wtforms/issues/445/files custom
implementations of `pre_validate` or `post_validate` must raise
`ValidationError` to trigger a validation message, where we were raising
`ValueError` this was no longer being caught.
As of https://github.com/wtforms/wtforms/pull/355/files `StringField`
returns `None` for empty data, not `''` but our `validate_email_address`
function only accepts strings.
We think that the API is returning incorrect data for this column.
It’s going to take a while to figure out what’s going on with the
queries in the API, so this pull request temporarily removes the column
so we’ve not giving people incorrect data.
We are starting to see lots of 100.0%s in the current table
and we think this looks suspiciously too good so think it is
beneficial to change it to be 2dp such that we get a few more
non 100.0% values.
This will put all the values in to having 2dp, however it will
also require the API to have a change to
https://github.com/alphagov/notifications-api/blob/master/app/performance_dashboard/rest.py#L81
where it is currently losing the granularity down to a single
decimal place (meaning that if we were really at 99.894% then that
would be shown on the page as 99.90% rather than 99.89%). However,
I don't think it is a blocker that we get that sorted before this
can be merged.
This means that the data in the report will match what’s on the page,
where the values are rounded to the nearest penny.
This uses the same string formatting to round the numbers which the
`big_number` component does, so it should round the numbers in the same
way.
It’s a pain having to remember to update both of them. Looks like `pip`
now supports a different syntax without the `egg` bit which means a git
dependency only needs the version number changing in one place.
This is so org level users can use this data easier for things
like determining spending per service.
We do not include sms fragments sent column and remove other sms columns
consistency.
Do not add sms fragments sent column for now until we agree on an
unambiguous name for it. The data in this column is sms billing units
multiplied by international sms weighing. My favourite for a clear
name would be 'text message credits used', but we need a naming
strategy for this.
The sentences in the key to the map have 2 forms:
1. the visual form, with an graphic pattern
alongside text saying what the pattern means
in the map
2. the semantic form, with text describing what
the pattern means and how big an area it covers
for this alert
This is achieved by making the graphic
non-semantic and having hidden words in the
sentence for each paragraph.
While the spaces between words are correct for
both forms of these sentences, we observed screen
readers speaking some groups of words as one when
they spoke the sentence. Most noticably, 'the
alert' was spoken as 'thealert'.
Swapping the class used to hide the visually
hidden words from ours to the GOVUK one seemed to
fix this. It's unclear why it does but the
`govuk-visually-hidden` class adds the following
styles to those in ours:
- width: 1px
- height: 1px
- white-space: nowrap
It also has `margin: 0` instead of `margin: -1px`.
At the moment, the icons in the key of the map
showing what each type of area drawn on the map
means change colour in high contrast mode. This
causes a mismatch between the areas on the map and
the key.
These changes remake the images in SVG to prevent
the colours changing in high contrast modes. They
also add a white background to the icons, to make
sure they match the areas they refer to in the
map.
Previously when trying to bring in
https://github.com/alphagov/notifications-utils/pull/889 we had trouble
because there were no wheels available for the version of Pip that we
were using. Now that we have upgraded to Python 3.9 we are using a
newer buildpack which should have the necessary wheels available.
This commit is just installing the package and nothing else, to give us
more confidence that we can merge
https://github.com/alphagov/notifications-utils/pull/915/files without
having to subsequently revert it.
