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
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.
The previous naming was too easily confused with processing outputs.
Rename them to QgsProcessingParameterFileDestination, etc... to
make it clearer what they are used for.
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)
Replaces the existing 'Basic Stats for Numeric Fields' and
'Basic Stats for String Fields' algorithms and adds support
for date/time/datetime fields.
Having a single unified algorithm allows more flexible models
where a field type may not be known in advance.
Deprecate existing basic stats algorithms
