77 lines
3.7 KiB
Rust
77 lines
3.7 KiB
Rust
use core_types::table::Table;
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use core_types::{Color, ExtractVarArgs};
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use core_types::{Ctx, ExtractIndex, ExtractPosition};
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use glam::DVec2;
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use graphic_types::vector_types::GradientStops;
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use graphic_types::{Graphic, Vector};
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use raster_types::{CPU, Raster};
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#[node_macro::node(category("Context"), path(graphene_core::vector))]
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fn read_graphic(ctx: impl Ctx + ExtractVarArgs) -> Table<Graphic> {
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let Ok(var_arg) = ctx.vararg(0) else { return Default::default() };
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let var_arg = var_arg as &dyn std::any::Any;
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var_arg.downcast_ref().cloned().unwrap_or_default()
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}
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#[node_macro::node(category("Context"), path(graphene_core::vector))]
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fn read_vector(ctx: impl Ctx + ExtractVarArgs) -> Table<Vector> {
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let Ok(var_arg) = ctx.vararg(0) else { return Default::default() };
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let var_arg = var_arg as &dyn std::any::Any;
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var_arg.downcast_ref().cloned().unwrap_or_default()
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}
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#[node_macro::node(category("Context"), path(graphene_core::vector))]
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fn read_raster(ctx: impl Ctx + ExtractVarArgs) -> Table<Raster<CPU>> {
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let Ok(var_arg) = ctx.vararg(0) else { return Default::default() };
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let var_arg = var_arg as &dyn std::any::Any;
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var_arg.downcast_ref().cloned().unwrap_or_default()
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}
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#[node_macro::node(category("Context"), path(graphene_core::vector))]
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fn read_color(ctx: impl Ctx + ExtractVarArgs) -> Table<Color> {
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let Ok(var_arg) = ctx.vararg(0) else { return Default::default() };
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let var_arg = var_arg as &dyn std::any::Any;
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var_arg.downcast_ref().cloned().unwrap_or_default()
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}
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#[node_macro::node(category("Context"), path(graphene_core::vector))]
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fn read_gradient(ctx: impl Ctx + ExtractVarArgs) -> Table<GradientStops> {
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let Ok(var_arg) = ctx.vararg(0) else { return Default::default() };
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let var_arg = var_arg as &dyn std::any::Any;
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var_arg.downcast_ref().cloned().unwrap_or_default()
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}
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#[node_macro::node(category("Context"), path(core_types::vector))]
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async fn read_position(
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ctx: impl Ctx + ExtractPosition,
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_primary: (),
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/// The number of nested loops to traverse outwards (from the innermost loop) to get the position from. The most upstream loop is level 0, and downstream loops add levels.
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///
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/// In programming terms: inside the double loop `i { j { ... } }`, *Loop Level* 0 = `j` and 1 = `i`. After inserting a third loop `k { ... }`, inside it, levels would be 0 = `k`, 1 = `j`, and 2 = `i`.
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loop_level: u32,
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) -> DVec2 {
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ctx.try_position().and_then(|mut iter| iter.nth(loop_level as usize).or_else(|| iter.last())).unwrap_or(DVec2::ZERO)
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}
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// TODO: Return u32, u64, or usize instead of f64 after #1621 is resolved and has allowed us to implement automatic type conversion in the node graph for nodes with generic type inputs.
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// TODO: (Currently automatic type conversion only works for concrete types, via the Graphene preprocessor and not the full Graphene type system.)
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/// Produces the index of the current iteration of a loop by reading from the evaluation context, which is supplied by downstream nodes such as *Instance Repeat*.
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///
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/// Nested loops can enable 2D or higher-dimensional iteration by using the *Loop Level* parameter to read the index from outer levels of loops.
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#[node_macro::node(category("Context"), path(core_types::vector))]
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async fn read_index(
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ctx: impl Ctx + ExtractIndex,
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_primary: (),
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/// The number of nested loops to traverse outwards (from the innermost loop) to get the index from. The most upstream loop is level 0, and downstream loops add levels.
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///
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/// In programming terms: inside the double loop `i { j { ... } }`, *Loop Level* 0 = `j` and 1 = `i`. After inserting a third loop `k { ... }`, inside it, levels would be 0 = `k`, 1 = `j`, and 2 = `i`.
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loop_level: u32,
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) -> f64 {
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ctx.try_index().and_then(|mut iter| iter.nth(loop_level as usize).or_else(|| iter.last())).unwrap_or(0) as f64
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}
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