* Add symbol dds attributes and adapt style code generation
* Update to mapbox-gl-js/master
* Refactor SymbolFeature as a subclass of GeometryTileFeature
Prepares for enabling DDS on symbol paint properties by allowing the
SymbolFeatures, which we keep around after constructing SymbolLayout,
to be used in evaluating data-driven paint properties later in the
layout process.
* Draft approach for splitting icon/text paint properties
The `Program` types are set up to bind GL attributes to each of the
data-driven paint properties specified in the `PaintProperties` type
provided. Since `SymbolPaintProperties` specifies both `Text*` and
`Icon*` properties, the symbolIcon, symbolIconSDF, and symbolGlyph
programs each attempt to bind roughly double the number of attributes
that they actually need.
This change addresses this by:
- Adding the more specific `IconPaintProperties` and `TextPaintProperties` types, which are subsets of the full `SymbolPaintProperties`.
- The symbol layer continues to use its `SymbolPaintProperties paint` member to track layer property state, but it provides helpers that construct objects of each the specific `{Icon,Text}PaintProperties::Evaluated` type, for use by the painter.
- The three symbol programs instantiate `Program<>` using the appropriate `{Icon,Text}PaintProperties` type.
* check in generated style code
* Populate paint buffers for symbol DDS properties
* Address first round of review comments
* Refactor VectorTile{Layer,Feature} to explicitly share data
* Update submodule
This converts the style property classes (CirclePaintProperties and so on) to the same tuple-based approach as gl::Attribute and gl::Uniform. The approach is outlined in https://github.com/mapbox/cpp/blob/master/C%2B%2B%20Structural%20Metaprogramming.md.
The main advantage of this approach is it allows writing algorithms that work on sets of style properties, without resorting to code generation or manually repetitive code. This lets us iterate on approaches to data-driven properties more easily.
Another advantage is that the cascading, unevaluated, and evaluated states of a set of properties exist as independent structures, instead of individual properties holding their own state. This is a more functional approach that makes data flow clearer and reduces state.
