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
Like the main Join Attributes by Location algorithm, this algorithm
takes two layers and combines the attributes based on a spatial
criteria.
However this algorithm calculates summaries for the attributes for
all matching features, e.g. calculating the mean/min/max/etc.
The list of fields to summaries, and the summaries to
calculate for those, can be selected.
- allow non spatial inputs
- allow calculation of stats on any field type, with specific
string and datetime stats calculated when field type matches
- output a full set of stats for numeric fields (including median
, quartiles, etc)
- also calculate stats for 'null' category
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.
have specific icons, instead of generic qgis icon
We consider these 'top level' algorithms, and using the
standard algorithm icon should help reflect this and
differentiate them from 3rd party algorithms.
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)
