v2-docs: Add docs for transformed data

docs/source/column_usage.md

@@ -1,4 +1,4 @@
-# Get Column Usage
+# Column Usage
 VegaFusion provides a function for introspecting a Vega specification and determining which columns are referenced from each root dataset. A root dataset is one defined at the top-level of the spec that includes a `url` or `values` properties. This is useful in contexts where it's more efficient to minimize the number of columns provided to the Vega specification. For example, the Python library uses this function to determine how to downsample the input DataFrame columns prior to converting to Arrow.
 
 When VegaFusion cannot precisely determine which columns are referenced from each root dataset, this function returns `None` or `null` for the corresponding dataset.

docs/source/index.md

@@ -18,4 +18,5 @@ If you've arrived here looking for information on how to scale Vega-Altair visua
 :caption: Contents
 
 column_usage
+transformed_data
 ```

docs/source/transformed_data.md

@@ -0,0 +1,16 @@
+# Transformed Data
+
+VegaFusion can be used to evaluate datasets in a Vega spec and return them as arrow tables or DataFrames. This is the foundation for Vega-Altair's [`chart.transformed_data`](https://altair-viz.github.io/user_guide/transform/index.html#accessing-transformed-data) method.
+
+## Python
+
+```{eval-rst}
+.. automethod:: vegafusion.runtime.VegaFusionRuntime.pre_transform_datasets
+```
+
+**Example**: See [pre_transform_data.py](https://github.com/vega/vegafusion/tree/v2/examples/python-examples/pre_transform_data.py) for a complete example.
+
+## Rust
+The Rust API provides a slightly more general `pre_transform_values` method that can extract dataset or signal values.
+
+See [pre_transform_data.rs](https://github.com/vega/vegafusion/tree/v2/examples/rust-examples/examples/pre_transform_data.rs) for a complete example of extracting dataset values as arrow tables.

examples/python-examples/column_usage.py

@@ -1,12 +1,11 @@
 import json
 from typing import Any
-
-from vegafusion import get_column_usage
+import vegafusion as vf
 
 
 def main():
     spec = get_spec()
-    column_usage = get_column_usage(spec)
+    column_usage = vf.get_column_usage(spec)
     print(json.dumps(column_usage, indent=2))
 
     assert column_usage == {

examples/python-examples/pre_transformed_data.py

@@ -0,0 +1,170 @@
+import json
+from typing import Any
+
+import vegafusion as vf
+
+
+def main():
+    spec = get_spec()
+    res, warnings = vf.runtime.pre_transform_datasets(
+        spec, ["counts"], dataset_format="polars"
+    )
+    assert warnings == []
+    assert len(res) == 1
+    print(res[0])
+
+
+def get_spec() -> dict[str, Any]:
+    """
+    Based on https://vega.github.io/editor/#/examples/vega/histogram-null-values
+    """
+    spec_str = """
+{
+  "$schema": "https://vega.github.io/schema/vega/v5.json",
+  "description": "A histogram of film ratings, modified to include null values.",
+  "width": 400,
+  "height": 200,
+  "padding": 5,
+  "autosize": {"type": "fit", "resize": true},
+
+  "signals": [
+    {
+      "name": "maxbins", "value": 10
+    },
+    {
+      "name": "binCount",
+      "update": "(bins.stop - bins.start) / bins.step"
+    },
+    {
+      "name": "nullGap", "value": 10
+    },
+    {
+      "name": "barStep",
+      "update": "(width - nullGap) / (1 + binCount)"
+    }
+  ],
+
+  "data": [
+    {
+      "name": "table",
+      "url": "data/movies.json",
+      "transform": [
+        {
+          "type": "extent", "field": "IMDB Rating",
+          "signal": "extent"
+        },
+        {
+          "type": "bin", "signal": "bins",
+          "field": "IMDB Rating", "extent": {"signal": "extent"},
+          "maxbins": 10
+        }
+      ]
+    },
+    {
+      "name": "counts",
+      "source": "table",
+      "transform": [
+        {
+          "type": "filter",
+          "expr": "datum['IMDB Rating'] != null"
+        },
+        {
+          "type": "aggregate",
+          "groupby": ["bin0", "bin1"]
+        }
+      ]
+    },
+    {
+      "name": "nulls",
+      "source": "table",
+      "transform": [
+        {
+          "type": "filter",
+          "expr": "datum['IMDB Rating'] == null"
+        },
+        {
+          "type": "aggregate",
+          "groupby": []
+        }
+      ]
+    }
+  ],
+
+  "scales": [
+    {
+      "name": "yscale",
+      "type": "linear",
+      "range": "height",
+      "round": true, "nice": true,
+      "domain": {
+        "fields": [
+          {"data": "counts", "field": "count"},
+          {"data": "nulls", "field": "count"}
+        ]
+      }
+    },
+    {
+      "name": "xscale",
+      "type": "linear",
+      "range": [{"signal": "barStep + nullGap"}, {"signal": "width"}],
+      "round": true,
+      "domain": {"signal": "[bins.start, bins.stop]"},
+      "bins": {"signal": "bins"}
+    },
+    {
+      "name": "xscale-null",
+      "type": "band",
+      "range": [0, {"signal": "barStep"}],
+      "round": true,
+      "domain": [null]
+    }
+  ],
+
+  "axes": [
+    {"orient": "bottom", "scale": "xscale", "tickMinStep": 0.5},
+    {"orient": "bottom", "scale": "xscale-null"},
+    {"orient": "left", "scale": "yscale", "tickCount": 5, "offset": 5}
+  ],
+
+  "marks": [
+    {
+      "type": "rect",
+      "from": {"data": "counts"},
+      "encode": {
+        "update": {
+          "x": {"scale": "xscale", "field": "bin0", "offset": 1},
+          "x2": {"scale": "xscale", "field": "bin1"},
+          "y": {"scale": "yscale", "field": "count"},
+          "y2": {"scale": "yscale", "value": 0},
+          "fill": {"value": "steelblue"}
+        },
+        "hover": {
+          "fill": {"value": "firebrick"}
+        }
+      }
+    },
+    {
+      "type": "rect",
+      "from": {"data": "nulls"},
+      "encode": {
+        "update": {
+          "x": {"scale": "xscale-null", "value": null, "offset": 1},
+          "x2": {"scale": "xscale-null", "band": 1},
+          "y": {"scale": "yscale", "field": "count"},
+          "y2": {"scale": "yscale", "value": 0},
+          "fill": {"value": "#aaa"}
+        },
+        "hover": {
+          "fill": {"value": "firebrick"}
+        }
+      }
+    }
+  ]
+}
+
+    """
+    return json.loads(spec_str)
+
+
+if __name__ == "__main__":
+    main()

examples/rust-examples/Cargo.toml

@@ -6,3 +6,5 @@ edition = "2021"
 [dev-dependencies]
 serde_json = { workspace = true }
 vegafusion-core = { path = "../../vegafusion-core" }
+vegafusion-runtime = { path = "../../vegafusion-runtime" }
+tokio = "1.41.1"