Takes an input layer, existing field and a new name for the field, and
outputs a new layer with the selected field renamed.
While this result could also be achieved with the Refactor Fields
algorithm, Refactor Fields isn't particularly model friendly. It
relies on a constant, fixed table structure, and can't adapt to
input tables with different field structures.
In constrast, this simple Rename Field algorithm adapts nicely for
model use, because it operates on a single field only and leaves
all the other fields untouched.
Offers the following benefits over the GRASS/SAGA versions:
- Full support for z/m values and handling curved geometries without loss
of curves
- Works with all native data types, no need for format transformation
- Supports dynamic (data defined, per feature) translate/scale/rotate parameters
- Allows transformation and scaling of both Z and M values (if present)
- Supports in-place edit mode
Fixes#33550
This algorithm splits features into multiple output features by
splitting a field's value with a specified character.
For instance, if a layer contains features with multiple comma
separated values contained in a single field, this algorithm can
be used to split these values up across multiple output features.
Geometries and other attributes remain unchanged in the output.
Optionally, the separator string can be a regular expression for
added flexibility.
Designed for use in models which need to process input files
with multiple concatenated values in a single attribute, e.g.
geocoding a table with "address1,address2,address3" format strings
This algorithm calculates the area and percentage cover
by which features from an input layer are overlapped by
features from a selection of overlay layers.
New attributes are added to the output layer reporting
the total area of overlap and percentage of the input
feature overlapped by each of the selected overlay layers.
This is quite a common GIS task request, yet is full
of traps for inexperienced users, and the amount of
manual data work usually done by users to calculate
these figures can often lead to mistakes and inaccurate
results. It's nice to have a robust, fast, inbuilt
algorithm which allows this task to be done in a
single step without risk of human error.
There's two motivations for this:
- the existing one was getting massive and took ages to run, which was
a pain when developing. Smaller batches allow just a subset of test to
be run which is much faster.
- There's a random segfault on test exit which occurs on Travis. Rather
then disabling these absolutely critical tests altogether, I'm using
this as a method of bisecting exactly which alg is causing this.
