Dataframe view update and blueprint API (part 4): blueprint API (#7551)

### What

This PR implements the blueprint API for the dataframe view:

```python
blueprint = rrb.Blueprint(
    rrb.DataframeView(
        origin="/trig",
        query=rrb.archetypes.DataframeQueryV2(
            timeline="t",
            filter_by_range=(rr.TimeInt(seconds=0), rr.TimeInt(seconds=20)),
            filter_by_event="/trig/tan_sparse:Scalar",
            select=["t", "log_tick", "/trig/sin:Scalar", "/trig/cos:Scalar", "/trig/tan_sparse:Scalar"],
        ),
    ),
)
```

The blueprint API is now functional and can be used to configure
everything in the new UI, which is _still_ mostly inoperative (except
for column visibility—this can be tested end-to-end).

<hr>

Part of a series to address #6896 and #7498.

All PRs:
- #7515
- #7516
- #7527 
- #7545
- #7551
- #7572
- #7573

### Checklist
* [x] I have read and agree to [Contributor
Guide](https://github.com/rerun-io/rerun/blob/main/CONTRIBUTING.md) and
the [Code of
Conduct](https://github.com/rerun-io/rerun/blob/main/CODE_OF_CONDUCT.md)
* [x] I've included a screenshot or gif (if applicable)
* [x] I have tested the web demo (if applicable):
* Using examples from latest `main` build:
[rerun.io/viewer](https://rerun.io/viewer/pr/7551?manifest_url=https://app.rerun.io/version/main/examples_manifest.json)
* Using full set of examples from `nightly` build:
[rerun.io/viewer](https://rerun.io/viewer/pr/7551?manifest_url=https://app.rerun.io/version/nightly/examples_manifest.json)
* [x] The PR title and labels are set such as to maximize their
usefulness for the next release's CHANGELOG
* [x] If applicable, add a new check to the [release
checklist](https://github.com/rerun-io/rerun/blob/main/tests/python/release_checklist)!
* [x] If have noted any breaking changes to the log API in
`CHANGELOG.md` and the migration guide

- [PR Build Summary](https://build.rerun.io/pr/7551)
- [Recent benchmark results](https://build.rerun.io/graphs/crates.html)
- [Wasm size tracking](https://build.rerun.io/graphs/sizes.html)

To run all checks from `main`, comment on the PR with `@rerun-bot
full-check`.

crates/store/re_types/definitions/rerun/blueprint/datatypes/component_column_selector.fbs

@@ -5,6 +5,7 @@ namespace rerun.blueprint.datatypes;
 //TODO(ab, jleibs): this probably needs reunification with whatever structure the data out API uses.
 // Has to be a table because flatbuffer doesn't support strings in structs.
 table ComponentColumnSelector (
+  "attr.python.aliases": "str",
   "attr.rust.derive": "Default, PartialEq, Eq, Hash",
   "attr.rerun.scope": "blueprint"
 ) {

crates/store/re_types/definitions/rerun/blueprint/datatypes/filter_by_event.fbs

@@ -4,8 +4,9 @@ namespace rerun.blueprint.datatypes;
 /// Configuration for the filter by event feature of the dataframe view.
 // Has to be a table because flatbuffer doesn't support strings in structs.
 table FilterByEvent (
-  "attr.rust.derive": "Default, PartialEq, Eq",
-  "attr.rerun.scope": "blueprint"
+    "attr.python.aliases": "blueprint_datatypes.ComponentColumnSelectorLike",
+    "attr.rust.derive": "Default, PartialEq, Eq",
+    "attr.rerun.scope": "blueprint"
 ) {
     /// Whether the filter by event feature is active.
     active: rerun.datatypes.Bool (order: 100);

crates/store/re_types/definitions/rerun/blueprint/datatypes/selected_columns.fbs

@@ -4,6 +4,7 @@ namespace rerun.blueprint.datatypes;
 /// List of selected columns in a dataframe.
 // Has to be a table because flatbuffer doesn't support strings in structs.
 table SelectedColumns (
+  "attr.python.aliases": "Sequence[Union[blueprint_datatypes.ComponentColumnSelectorLike, datatypes.Utf8Like]]",
   "attr.rust.derive": "Default, PartialEq, Eq",
   "attr.rerun.scope": "blueprint"
 ) {

crates/viewer/re_space_view_dataframe/src/view_query_v2/blueprint.rs

@@ -3,7 +3,7 @@ use std::collections::HashSet;
 use crate::dataframe_ui::HideColumnAction;
 use crate::view_query_v2::QueryV2;
 use re_chunk_store::{ColumnDescriptor, ColumnSelector};
-use re_log_types::{TimeInt, TimelineName};
+use re_log_types::{EntityPath, TimeInt, TimelineName};
 use re_types::blueprint::{components, datatypes};
 use re_viewer_context::{SpaceViewSystemExecutionError, ViewerContext};
 
@@ -168,7 +168,15 @@ impl QueryV2 {
             .iter()
             .map(|timeline_name| timeline_name.as_str().into())
             .collect();
-        let selected_component_columns = component_columns.iter().cloned().collect::<HashSet<_>>();
+        let selected_component_columns = component_columns
+            .iter()
+            .map(|selector| {
+                (
+                    EntityPath::from(selector.entity_path.as_str()),
+                    selector.component.as_str(),
+                )
+            })
+            .collect::<HashSet<_>>();
 
         let query_timeline_name = *self.timeline(ctx)?.name();
         let result = view_columns
@@ -181,12 +189,16 @@ impl QueryV2 {
                         || selected_time_columns.contains(desc.timeline.name())
                 }
                 ColumnDescriptor::Component(desc) => {
-                    let blueprint_component_descriptor = components::ComponentColumnSelector::new(
-                        &desc.entity_path,
-                        desc.component_name,
-                    );
-
-                    selected_component_columns.contains(&blueprint_component_descriptor)
+                    // Check against both the full name and short name, as the user might have used
+                    // the latter in the blueprint API.
+                    //
+                    // TODO(ab): this means that if the user chooses `"/foo/bar:Scalar"`, it will
+                    // select both `rerun.components.Scalar` and `Scalar`, should both of these
+                    // exist.
+                    selected_component_columns
+                        .contains(&(desc.entity_path.clone(), desc.component_name.full_name()))
+                        || selected_component_columns
+                            .contains(&(desc.entity_path.clone(), desc.component_name.short_name()))
                 }
             })
             .cloned()

docs/snippets/all/views/dataframe.py

@@ -8,21 +8,24 @@
 rr.init("rerun_example_dataframe", spawn=True)
 
 # Log some data.
-rr.log("trig/sin", rr.SeriesLine(color=[255, 0, 0], name="sin(0.01t)"), static=True)
-rr.log("trig/cos", rr.SeriesLine(color=[0, 255, 0], name="cos(0.01t)"), static=True)
 for t in range(0, int(math.pi * 4 * 100.0)):
     rr.set_time_seconds("t", t)
     rr.log("trig/sin", rr.Scalar(math.sin(float(t) / 100.0)))
     rr.log("trig/cos", rr.Scalar(math.cos(float(t) / 100.0)))
 
+    # some sparse data
+    if t % 5 == 0:
+        rr.log("trig/tan_sparse", rr.Scalar(math.tan(float(t) / 100.0)))
+
 # Create a Dataframe View
 blueprint = rrb.Blueprint(
     rrb.DataframeView(
         origin="/trig",
-        # TODO(#6896): improve `DataframeQueryV2` API and showcase more features
         query=rrb.archetypes.DataframeQueryV2(
             timeline="t",
-            range_filter=rrb.components.RangeFilter(start=rr.TimeInt(seconds=0), end=rr.TimeInt(seconds=20)),
+            filter_by_range=(rr.TimeInt(seconds=0), rr.TimeInt(seconds=20)),
+            filter_by_event="/trig/tan_sparse:Scalar",
+            select=["t", "log_tick", "/trig/sin:Scalar", "/trig/cos:Scalar", "/trig/tan_sparse:Scalar"],
         ),
     ),
 )

rerun_py/rerun_sdk/rerun/blueprint/archetypes/dataframe_query_v2_ext.py

@@ -0,0 +1,70 @@
+from __future__ import annotations
+
+from typing import Any
+
+from ... import datatypes
+from ...blueprint import components as blueprint_components, datatypes as blueprint_datatypes
+from ...error_utils import catch_and_log_exceptions
+
+
+class DataframeQueryV2Ext:
+    """Extension for [DataframeQueryV2][rerun.blueprint.archetypes.DataframeQueryV2]."""
+
+    def __init__(
+        self: Any,
+        *,
+        timeline: datatypes.Utf8Like | None = None,
+        filter_by_range: tuple[datatypes.TimeInt, datatypes.TimeInt]
+        | blueprint_datatypes.FilterByRangeLike
+        | None = None,
+        filter_by_event: blueprint_datatypes.ComponentColumnSelectorLike | None = None,
+        apply_latest_at: bool = False,
+        select: list[blueprint_datatypes.ComponentColumnSelectorLike | datatypes.Utf8Like | str] | None = None,
+    ):
+        """
+        Create a new instance of the DataframeQueryV2 archetype.
+
+        Parameters
+        ----------
+        timeline:
+            The timeline for this query.
+
+        filter_by_range:
+            If set, a range filter is applied.
+
+        filter_by_event:
+            If provided, the dataframe will only contain rows corresponding to timestamps at which an event was logged
+            for the provided column.
+
+        apply_latest_at:
+            Should empty cells be filled with latest-at queries?
+
+        select:
+            Selected columns. If unset, all columns are selected.
+
+        """
+
+        if isinstance(filter_by_range, tuple):
+            start, end = filter_by_range
+            filter_by_range = blueprint_components.FilterByRange(start, end)
+
+        if filter_by_event is not None:
+            if isinstance(filter_by_event, str):
+                column = blueprint_datatypes.ComponentColumnSelector(spec=filter_by_event)
+            else:
+                column = filter_by_event
+
+            new_filter_by_event = blueprint_components.FilterByEvent(active=True, column=column)
+        else:
+            new_filter_by_event = None
+
+        with catch_and_log_exceptions(context=self.__class__.__name__):
+            self.__attrs_init__(
+                timeline=timeline,
+                filter_by_range=filter_by_range,
+                filter_by_event=new_filter_by_event,
+                apply_latest_at=apply_latest_at,
+                select=select,
+            )
+            return
+        self.__attrs_clear__()