Changes:
- handle different CRS transparently
- don't build a spatial index on the selection layer. Instead
only use feature requests to fetch features which are within
the desired bounds, and rely on the presence of an appropriate
spatial index at the provider's backend. Otherwise, we force
every user of this algorithm to have a full iteration of the
source table, regardless of how large the table is. That means
that trying to select a set of addresses which fall within
a specific locality from a table which contains the addresses
for a whole state will FORCE every address in the state to
be initially read before any calculation begins. With this
change only those features within the bounding box of the
selected localities will ever be fetched from the provider,
resulting in huge speed improvements for the algorithm.
- use prepared geometries for the spatial relation tests.
This dramatically speeds up the algorithm in the case
where the intersection layer features cover multiple
features from the 'selection' layer.
- Add a 'select within current selection' mode
- Optimise feature requests for efficiency (especially
with respect to the 'disjoint' selection mode)
initAlgorithm() method
This allows 2 benefits:
- algorithms can be subclassed and have subclasses add additional
parameters/outputs to the algorithm. With the previous approach
of declaring parameters/outputs in the constructor, it's not
possible to call virtual methods to add additional parameters/
outputs (since you can't call virtual methods from a constructor).
- initAlgorithm takes a variant map argument, allowing the algorithm
to dynamically adjust its declared parameters and outputs according
to this configuration map. This potentially allows model algorithms which
can be configured to have variable numbers of parameters and
outputs at run time. E.g. a "router" algorithm which directs
features to one of any number of output sinks depending on some
user configured criteria.
Algorithms and other processing code should use this method
(instead of dataobjects.getLayerFromString) to
retrieve layers from a string, as it considers the processing
context and allows resolving strings to temporarily stored layers.
This permits processing models to function correctly when
intermediate results are stored as memory layers. Subsequent
model algorithms can then access these temporary layers as inputs.
All temporary layers will be removed when the context object
is destroyed after the model algorithm is run.
Also
- simplify and add tests
- remove large memory leak (persistant store of all non-project layers)
- remove broken support for direct loading postgres/virtual layers
by string (Python version was very broken and would never match
a postgres/virtual layer)
- don't use setFilterFid() within loops to fetch features one
at time (as it's extremely slow), instead use setFilterFids()
outside the loop
- don't fetch unused attributes/geometry when it can be
avoided
