require all input layers to be in the same CRS
The default behaviour is to assume that algorithms are well behaved
and can handle multi-CRS inputs, but algs have the option to
flag that they do not allow this and require the input CRS check.
Those algs should document that they require all inputs to have
matching CRS - processing 3.0 behaviour is to assume that algs
can handle this.
flexible QVariantMap creatOptions parameters which include an
optional fileEncoding value
More flexible, allows sinks to be created using any creation
option which is passed to the underlying provider
This somewhat changes the meaning of outputs from processing 2.x.
In 2.x processing outputs were used both as a method of specifying
inputs to algorithms (file paths to destination layers created
by the algorithm) AND pure outputs (such as statistics calculated
by the algorithm).
This is now split. The old input-type-outputs (destination layers)
are now input parameters (since the parameter value IS an input to the
algorithm). To differentiate them from parameters indicating pure
input layers a new "isDestination()" method was added to
QgsProcessingParameterDefinition.
Output definitions are now purely indications of values CREATED
by the algorithms. Suitable candidates are the existing calculated
stats and actual file path/URI of any layers created by the algorithm.
Moving forward we should ensure all algorithms output as much
useful information as possible - e.g. number of features processed,
number of skipped features, count null geometries encountered, etc...
While the processing API is in such a state of flux it's
become unmaintainable to port these algorithms change-by-change.
Instead, disable them all until the API is fixed, and then we
can port them algorithm by algorithm and reactivate each
test in turn.
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.
