The values in the raster are already normalized between 0 and 2**16,
so a simple conversion to [0, 1] will do.
Check for existance of secondary_importance table statically when
creating the SQL function. For that to work importance tables need
to be created before the functions.
The index over geometry_sectors are mainly used for ordering
the places which need indexing. That means they function effectively
as a TODO list. Consolodate them so that they always only contain
the places which are still to do. Also add the appropriate index
for the boundary indexing phase.
In offline mode no attempts are made to download data from the internet.
At the moment that only concerns the computation of the database date.
It contacts the main API to get the date.
The Python code now takes care of reading postcodes from placex,
enhancing them with potentially existing external postcodes and
updating location_postcodes accordingly. The initial setup and
updates use exactly the same function.
External postcode handling has been generalized. External postcodes
for any country are now accepted. The format of the external postcode
file has changed. We now expect CSV, potentially gzipped. The
postcodes are no longer saved in the database.
Creating and populating the word table is now the responsibility
of the tokenizer.
The get_maxwordfreq() function has been replaced with a
simple template parameter to the SQL during function installation.
The number is taken from the parameter list in the database to
ensure that it is not changed after installation.
This adds the boilerplate for selecting configurable tokenizers.
A tokenizer can be chosen at import time and will then install
itself such that it is fixed for the given database import even
when the software itself is updated.
The legacy tokenizer implements Nominatim's traditional algorithms.
Indexing scans the placex table sequentially during indexing
on the initial import. That is okay because we know that all
rows need to be processed anywhere. When continuing the import,
however, a large part might already be indexed, so that the
process spends a lot of time going through rows that are no
longer of interest. Create a supporting index for all unindexed
rows to speed up the scan. This is the same index as used later
for updates.
Also switches to jinja-based preprocessing, which allows to
simplify the SQL files. Use 'if not exists' where possible
so that the step can be rerun to fix missing indexes.
