Even if the projection is assign, the QgsRasterLayer and the QgsRasterDataProvider was not updated.
The fix reloads the QgsRasterDataProvider's data and updates the QgsRasterLayer's crs.
Fixed#37920
(e.g. parameters) when they are run in the "edit in-place" mode
This allows algorithms to dynamically adapt their behavior to make them compatible
with in-place mode. Previously, some useful algorithms could not be
run in-place because they alter a layer's structure (e.g. adding new
fields).
Now, these algorithms have a means to detect that they are being
run in-place and change their input parameters accordingly. E.g.
an algorithm which usually adds new fields to store calculated
values (such as "add xy fields to layer") could instead expose
field parameter choices to ask the user to pick from existing
fields in which to store the calculated values, thereby avoiding
the need to change the table structure and making them eligable
for running in-place mode.
Note that this needs to be handled algorithm-by-algorithm, it's
not automatic! It's just the raw api to allow this...
This algorithm calculates the rotation required to align point features
with their nearest feature from another reference layer. A new field is
added to the output layer which is filled with the angle (in degrees,
clockwise) to the nearest reference feature.
Optionally, the output layer's symbology can be set to automatically
use the calculated rotation field to rotate marker symbols.
If desired, a maximum distance to use when aligning points can be set,
to avoid aligning isolated points to distant features.
Designed for use cases like aligning building point symbols to follow
the nearest road direction!
Offers the same functionality as the Random points inside polygons
algorithm, and in addition it includes (possible to opt-out) the attributes
from the containing polygon and the ability to specify a seed for the random
number generator.
Could replace the Python algorithm Random points inside polygons, but
this C++ implementation only supports "Points count" ("number of points
for each feature") directly, while "Points density" is supported by allowing the
point count to be data defined (using the polygon area to calculate the
number of points).
