- alias WKT_2018* to new WKT_2019* values, since the spec is actually
2019, not 2018
- add WKT_PREFERRED value which currently aliases to WKT2_2019, but
can be changed if/when future bumps to the WKT spec happen
- add WKT_PREFERRED_GDAL which should be used whenever a CRS is
exported to WKT for use with GDAL API. Aliases to WKT2_2019 currently,
but can be changed if/when a new spec is released and GDAL supports it
This allows defered setting of parameter values, e.g. if you add an algorithm, fill in
half the parameter values, then realise you need to add a new input to the model, you
don't have to lose all your filled in values...
This change "dims" the results from the SAGA provider when a search
is made in the toolbox, to visually push users towards picking alternative
algorithms instead.
The Processing implementation of SAGA algorithms are a constant source
of critical bugs for users, causing incorrect analysis results. There's
zero community interest in actively maintaining this provider, so we
need to take steps to push users to stop picking these algorithms
wherever alternative (QGIS/GRASS/GDAL based) equivalents exist.
And for 4.0, seriously re-consider dropping this provider from the
out of the box install. We are causing more harm then good by offering
it to users.
This allows a range of new possibilities, including:
- models with static outputs for child algorithms, e.g. always saving
a child algorithm's output to a geopackage or postgres layer
- models with expression based output values for child algorithms, e.g.
generating an automatic file name based on today's date and saving
outputs to that file
The Random points on lines algorithm supplements the existing "Random points along line" algorithm, and will prove to be more useful to the majority of users than the "original".
Features:
The points are distributed randomly over the lines based on "along the line" distance, meaning that the distribution of the points will be flat over the length of the line (each place on the feature has the same probability of being "hit").
The Random points along line, on the other hand, uses a line segment based approach, meaning that the density will depend on the segment length (short segments will have a higher point density than longer ones).
