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.
destroyed by the python garbage collector
The code generated with the /Factory/ annotation was not sufficient
to correctly transfer the ownership of objects created in Python
back to c++ (despite mailing list messages which hint that it
is).
Anyway, this awful abomination works. Let's all move on to more
useful ways to spend our time...
and pure virtual createInstance
Allows us to add logic which always need applying within
create(), leaving createInstance() free to just return a
raw new instance of the class
The previous naming was too easily confused with processing outputs.
Rename them to QgsProcessingParameterFileDestination, etc... to
make it clearer what they are used for.
model execution
And use this function to determine in advance dependencies between
child algorithm parameters with expression based values and
which other child algorithms they depend upon.
Contains variables for model parameters, algorithm results for
other child algorithms which are not dependent on the
algorithm.
Allows removal of final pieces of ModelerAlgorithm code
The expression is evaluated just before the child algorithm is
executed, so can utilise results already calculated by other
children in the model through the use of expression context
functions
Since it's safe to evaluate parameters in background threads
now, it's usually going to be ok to evaluate everything in
the processAlgorithm step.
This keeps the algorithm code as simple as possible, and will
make porting faster.
Note that the prepare/postProcess virtual methods still exist
and can be used when an algorithm MUST do setup/cleanup work
in the main thread.
add to the current context
With appropriate note and tests to ensure that both the current
context and that which the results being taken from share the
same thread affinity
- take a clone of algs before running them. This avoids issues
if the algorithm is removed or edited while a background task
is running
- accept an optional existing feedback object
So that multiple input type parameters are correctly handled,
allowing models with child algorithms like merge to correctly
use any combination of static layers/model inputs/child outputs
as their input parameter
