multipoint datasets
There's no single approach to use for multi-point inputs, so instead
of overloading this algorithm with extra options to control how these
are handled, we raise a helpful error and push the reponsibility back
to the user to get the data into the right format before running the
tool.
Fixes#20799
Replace with just f[idx]. Calling QgsFeature.attributes()
allocates a list of all attributes, which is inefficient
when only a single attribute value is needed.
Remove a bunch of manual "max" values for numeric parameters
where the maximum just represents a 'large number' and not a real
constraint, and let the default parameter max value handling kick in instead.
In the case of random selection the max value exceeded the possible
range for integers in spin boxes and broke the widget.
Fixes#20015
Include descriptive text with the specified parameter value
in error, and always check that sources were loaded to avoid
raw Python exceptions when they are not
Enhancements:
- support source/target layers in different CRS
- output layers with geometry (i.e. keep input point geometry - avoids
need to rejoin result back to original table to get geometry)
- keep original data types for id fields
- don't fire off many single feature requests - instead request
multiple features at once to improve speed
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
