",
+ "license": "BSD-3-Clause",
+ "bugs": {
+ "url": "https://github.com/rustwasm/create-wasm-app/issues"
+ },
+ "homepage": "https://github.com/rustwasm/create-wasm-app#readme",
+ "dependencies": {
+ "bootstrap": "^5.1.3",
+ "follow-redirects": ">=1.14.7",
+ "lodash": "^4.17.21",
+ "monaco-editor-webpack-plugin": "^6.0.0",
+ "node-forge": ">=1.0.0",
+ "vegafusion-wasm": "../../vegafusion-wasm/pkg",
+ "grpc-web": "^1.5.0"
+ },
+ "devDependencies": {
+ "copy-webpack-plugin": "^10.0.0",
+ "css-loader": "^6.5.1",
+ "file-loader": "^6.2.0",
+ "postcss-loader": "^6.2.0",
+ "sass-loader": "^12.3.0",
+ "style-loader": "^3.3.1",
+ "svg-inline-loader": "^0.8.2",
+ "webpack": "^5.64.2",
+ "webpack-cli": "^4.9.1",
+ "webpack-dev-server": "^4.5.0"
+ }
+}
\ No newline at end of file
diff --git a/examples/editor-demo/src/bootstrap.js b/examples/editor-demo/src/bootstrap.js
new file mode 100644
index 000000000..7934d627e
--- /dev/null
+++ b/examples/editor-demo/src/bootstrap.js
@@ -0,0 +1,5 @@
+// A dependency graph that contains any wasm must all be imported
+// asynchronously. This `bootstrap.js` file does the single async import, so
+// that no one else needs to worry about it again.
+import("./index.js")
+ .catch(e => console.error("Error importing `index.js`:", e));
diff --git a/examples/editor-demo/src/index.html b/examples/editor-demo/src/index.html
new file mode 100644
index 000000000..3f9b8d1e4
--- /dev/null
+++ b/examples/editor-demo/src/index.html
@@ -0,0 +1,60 @@
+
+
+
+ Hello wasm-pack!
+
+
+
+
+This page contains webassembly and javascript content, please enable javascript in your browser.
+
+
+
diff --git a/examples/editor-demo/src/index.js b/examples/editor-demo/src/index.js
new file mode 100644
index 000000000..a002179f5
--- /dev/null
+++ b/examples/editor-demo/src/index.js
@@ -0,0 +1,1501 @@
+const { vegaFusionEmbed, getColumnUsage, makeGrpcSendMessageFn } = await import("vegafusion-wasm");
+
+import * as Monaco from 'monaco-editor/esm/vs/editor/editor.api';
+import _ from "lodash"
+import * as grpcWeb from 'grpc-web';
+import 'bootstrap';
+import 'bootstrap/dist/css/bootstrap.min.css';
+import './style.css';
+
+async function init() {
+ monaco_init()
+
+ let initial_spec = JSON.stringify(flights_spec, null, 2);
+
+ let editor = Monaco.editor.create(document.getElementById('editor-monaco'), {
+ value: initial_spec,
+ language: 'json',
+ theme: 'vs-dark',
+ automaticLayout: true,
+ fixedOverflowWidgets: true,
+ });
+ let spec_model = editor.getModel();
+
+ let server_spec_monaco = Monaco.editor.create(document.getElementById('server-spec-monaco'), {
+ value: "",
+ language: 'json',
+ theme: 'vs-dark',
+ automaticLayout: true,
+ readOnly: true,
+ });
+
+ let client_spec_monaco = Monaco.editor.create(document.getElementById('client-spec-monaco'), {
+ value: "",
+ language: 'json',
+ theme: 'vs-dark',
+ automaticLayout: true,
+ readOnly: true,
+ });
+
+ let comm_plan_monaco = Monaco.editor.create(document.getElementById('comm-plan-monaco'), {
+ value: "",
+ language: 'json',
+ theme: 'vs-dark',
+ automaticLayout: true,
+ readOnly: true,
+ });
+
+ let column_usage_monaco = Monaco.editor.create(document.getElementById('column-usage-monaco'), {
+ value: "",
+ language: 'json',
+ theme: 'vs-dark',
+ automaticLayout: true,
+ readOnly: true,
+ });
+
+ // Add checkbox for toggling VegaFusion server
+ const container = document.createElement('div');
+ container.className = 'pb-3'; // Add padding-bottom
+
+ const serverCheckbox = document.createElement('input');
+ serverCheckbox.type = 'checkbox';
+ serverCheckbox.id = 'use-server';
+ serverCheckbox.checked = false;
+ serverCheckbox.className = 'form-check-input me-2';
+
+ const label = document.createElement('label');
+ label.htmlFor = 'use-server';
+ label.textContent = 'Use VegaFusion Server';
+ label.className = 'form-check-label';
+
+ // Add elements to container
+ container.appendChild(serverCheckbox);
+ container.appendChild(label);
+
+ // Insert container before the chart element
+ document.getElementById('vega-chart').insertAdjacentElement("beforebegin", container);
+
+ async function update_chart() {
+ try {
+ let element = document.getElementById("vega-chart");
+ let config = {
+ verbose: false,
+ debounce_wait: 30,
+ debounce_max_wait: 60,
+ embed_opts: {
+ mode: "vega",
+ },
+ };
+
+ let spec = editor.getValue();
+
+ let chart_handle;
+ if (serverCheckbox.checked) {
+ const hostname = 'http://127.0.0.1:50051';
+ try {
+ let client = new grpcWeb.GrpcWebClientBase({format: "binary"});
+ let send_message_grpc = makeGrpcSendMessageFn(client, hostname);
+
+ chart_handle = await vegaFusionEmbed(
+ element,
+ spec,
+ config,
+ send_message_grpc,
+ );
+
+ } catch (e) {
+ // Clear the chart and display an error message
+ element.innerHTML = `
+
+ Failed to connect to VegaFusion server using gRPC at ${hostname}.
+ Please ensure the server is running and accessible.
+
`;
+ return;
+ }
+ } else {
+ chart_handle = await vegaFusionEmbed(
+ element,
+ spec,
+ config,
+ );
+ }
+
+ // Get column usage
+ const usage = getColumnUsage(spec);
+
+ server_spec_monaco.setValue(JSON.stringify(chart_handle.serverSpec(), null, 2));
+ client_spec_monaco.setValue(JSON.stringify(chart_handle.clientSpec(), null, 2));
+ comm_plan_monaco.setValue(JSON.stringify(chart_handle.commPlan(), null, 2));
+ column_usage_monaco.setValue(JSON.stringify(usage, null, 2));
+ } catch (e) {
+ console.error("Failed to render spec");
+ server_spec_monaco.setValue("");
+ client_spec_monaco.setValue("");
+ comm_plan_monaco.setValue("");
+ column_usage_monaco.setValue("");
+ console.log(e);
+ return;
+ }
+ }
+
+ // Update chart when checkbox changes
+ serverCheckbox.addEventListener('change', async () => {
+ let cardElement = document.getElementById('vega-chart').closest('.card'); // Find the parent card
+ cardElement.classList.toggle('border-success');
+ await update_chart();
+ });
+
+ // Update chart (with debounce) when editor value changes
+ await update_chart()
+ let content_change_listener = _.debounce(async (content) => {
+ await update_chart()
+ }, 750);
+
+ spec_model.onDidChangeContent(content_change_listener)
+}
+
+function monaco_init() {
+ Monaco.languages.json.jsonDefaults.setDiagnosticsOptions(
+ {
+ validate: true,
+ allowComments: true,
+ trailingCommas: true,
+ schemas: [],
+ enableSchemaRequest: true
+ }
+ );
+}
+
+let flights_spec = {
+ "$schema": "https://vega.github.io/schema/vega/v5.json",
+ "background": "white",
+ "padding": 5,
+ "data": [
+ {"name": "brush_store"},
+ {
+ "name": "source_0",
+ "url": "https://raw.githubusercontent.com/vega/vega-datasets/main/data/flights-2k.json",
+ // "url": "https://vegafusion-datasets.s3.amazonaws.com/vega/flights_200k.json",
+ // "url": "https://vegafusion-datasets.s3.amazonaws.com/vega/flights_200k.parquet",
+ "format": {"type": "json", "parse": {"date": "date"}},
+ "transform": [
+ {
+ "type": "extent",
+ "field": "delay",
+ "signal": "child__column_delay_layer_1_bin_maxbins_20_delay_extent"
+ },
+ {
+ "type": "bin",
+ "field": "delay",
+ "as": ["bin_maxbins_20_delay", "bin_maxbins_20_delay_end"],
+ "signal": "child__column_delay_layer_1_bin_maxbins_20_delay_bins",
+ "extent": {
+ "signal": "child__column_delay_layer_1_bin_maxbins_20_delay_extent"
+ },
+ "maxbins": 20
+ },
+ {
+ "type": "extent",
+ "field": "distance",
+ "signal": "child__column_distance_layer_0_bin_maxbins_20_distance_extent"
+ },
+ {
+ "type": "bin",
+ "field": "distance",
+ "as": ["bin_maxbins_20_distance", "bin_maxbins_20_distance_end"],
+ "signal": "child__column_distance_layer_0_bin_maxbins_20_distance_bins",
+ "extent": {
+ "signal": "child__column_distance_layer_0_bin_maxbins_20_distance_extent"
+ },
+ "maxbins": 20
+ },
+ {"type": "formula", "expr": "hours(datum.date)", "as": "time"}
+ ]
+ },
+ {
+ "name": "data_0",
+ "source": "source_0",
+ "transform": [
+ {
+ "type": "extent",
+ "field": "time",
+ "signal": "child__column_time_layer_1_bin_maxbins_20_time_extent"
+ },
+ {
+ "type": "bin",
+ "field": "time",
+ "as": ["bin_maxbins_20_time", "bin_maxbins_20_time_end"],
+ "signal": "child__column_time_layer_1_bin_maxbins_20_time_bins",
+ "extent": {
+ "signal": "child__column_time_layer_1_bin_maxbins_20_time_extent"
+ },
+ "maxbins": 20
+ }
+ ]
+ },
+ {
+ "name": "data_1",
+ "source": "data_0",
+ "transform": [
+ {
+ "type": "filter",
+ "expr": "!length(data(\"brush_store\")) || vlSelectionTest(\"brush_store\", datum)"
+ },
+ {
+ "type": "aggregate",
+ "groupby": ["bin_maxbins_20_time", "bin_maxbins_20_time_end"],
+ "ops": ["count"],
+ "fields": [null],
+ "as": ["__count"]
+ },
+ {
+ "type": "filter",
+ "expr": "isValid(datum[\"bin_maxbins_20_time\"]) && isFinite(+datum[\"bin_maxbins_20_time\"])"
+ }
+ ]
+ },
+ {
+ "name": "data_2",
+ "source": "data_0",
+ "transform": [
+ {
+ "type": "aggregate",
+ "groupby": ["bin_maxbins_20_time", "bin_maxbins_20_time_end"],
+ "ops": ["count"],
+ "fields": [null],
+ "as": ["__count"]
+ },
+ {
+ "type": "filter",
+ "expr": "isValid(datum[\"bin_maxbins_20_time\"]) && isFinite(+datum[\"bin_maxbins_20_time\"])"
+ }
+ ]
+ },
+ {
+ "name": "data_3",
+ "source": "source_0",
+ "transform": [
+ {
+ "type": "filter",
+ "expr": "!length(data(\"brush_store\")) || vlSelectionTest(\"brush_store\", datum)"
+ }
+ ]
+ },
+ {
+ "name": "data_4",
+ "source": "data_3",
+ "transform": [
+ {
+ "type": "aggregate",
+ "groupby": ["bin_maxbins_20_delay", "bin_maxbins_20_delay_end"],
+ "ops": ["count"],
+ "fields": [null],
+ "as": ["__count"]
+ },
+ {
+ "type": "filter",
+ "expr": "isValid(datum[\"bin_maxbins_20_delay\"]) && isFinite(+datum[\"bin_maxbins_20_delay\"])"
+ }
+ ]
+ },
+ {
+ "name": "data_5",
+ "source": "data_3",
+ "transform": [
+ {
+ "type": "aggregate",
+ "groupby": ["bin_maxbins_20_distance", "bin_maxbins_20_distance_end"],
+ "ops": ["count"],
+ "fields": [null],
+ "as": ["__count"]
+ },
+ {
+ "type": "filter",
+ "expr": "isValid(datum[\"bin_maxbins_20_distance\"]) && isFinite(+datum[\"bin_maxbins_20_distance\"])"
+ }
+ ]
+ },
+ {
+ "name": "data_6",
+ "source": "source_0",
+ "transform": [
+ {
+ "type": "aggregate",
+ "groupby": ["bin_maxbins_20_distance", "bin_maxbins_20_distance_end"],
+ "ops": ["count"],
+ "fields": [null],
+ "as": ["__count"]
+ },
+ {
+ "type": "filter",
+ "expr": "isValid(datum[\"bin_maxbins_20_distance\"]) && isFinite(+datum[\"bin_maxbins_20_distance\"])"
+ }
+ ]
+ },
+ {
+ "name": "data_7",
+ "source": "source_0",
+ "transform": [
+ {
+ "type": "aggregate",
+ "groupby": ["bin_maxbins_20_delay", "bin_maxbins_20_delay_end"],
+ "ops": ["count"],
+ "fields": [null],
+ "as": ["__count"]
+ },
+ {
+ "type": "filter",
+ "expr": "isValid(datum[\"bin_maxbins_20_delay\"]) && isFinite(+datum[\"bin_maxbins_20_delay\"])"
+ }
+ ]
+ }
+ ],
+ "signals": [
+ {"name": "childWidth", "value": 200},
+ {"name": "childHeight", "value": 200},
+ {
+ "name": "unit",
+ "value": {},
+ "on": [
+ {"events": "mousemove", "update": "isTuple(group()) ? group() : unit"}
+ ]
+ },
+ {
+ "name": "brush",
+ "update": "vlSelectionResolve(\"brush_store\", \"union\")"
+ }
+ ],
+ "layout": {"padding": 20, "columns": 3, "bounds": "full", "align": "all"},
+ "marks": [
+ {
+ "type": "group",
+ "name": "child__column_distance_group",
+ "style": "cell",
+ "encode": {
+ "update": {
+ "width": {"signal": "childWidth"},
+ "height": {"signal": "childHeight"}
+ }
+ },
+ "signals": [
+ {
+ "name": "brush_x",
+ "value": [],
+ "on": [
+ {
+ "events": {
+ "source": "scope",
+ "type": "mousedown",
+ "filter": [
+ "!event.item || event.item.mark.name !== \"brush_brush\""
+ ]
+ },
+ "update": "[x(unit), x(unit)]"
+ },
+ {
+ "events": {
+ "source": "window",
+ "type": "mousemove",
+ "consume": true,
+ "between": [
+ {
+ "source": "scope",
+ "type": "mousedown",
+ "filter": [
+ "!event.item || event.item.mark.name !== \"brush_brush\""
+ ]
+ },
+ {"source": "window", "type": "mouseup"}
+ ]
+ },
+ "update": "[brush_x[0], clamp(x(unit), 0, childWidth)]"
+ },
+ {
+ "events": {"signal": "brush_scale_trigger"},
+ "update": "[scale(\"child__column_distance_x\", brush_distance[0]), scale(\"child__column_distance_x\", brush_distance[1])]"
+ },
+ {
+ "events": [{"source": "view", "type": "dblclick"}],
+ "update": "[0, 0]"
+ },
+ {
+ "events": {"signal": "brush_translate_delta"},
+ "update": "clampRange(panLinear(brush_translate_anchor.extent_x, brush_translate_delta.x / span(brush_translate_anchor.extent_x)), 0, childWidth)"
+ },
+ {
+ "events": {"signal": "brush_zoom_delta"},
+ "update": "clampRange(zoomLinear(brush_x, brush_zoom_anchor.x, brush_zoom_delta), 0, childWidth)"
+ }
+ ]
+ },
+ {
+ "name": "brush_distance",
+ "on": [
+ {
+ "events": {"signal": "brush_x"},
+ "update": "brush_x[0] === brush_x[1] ? null : invert(\"child__column_distance_x\", brush_x)"
+ }
+ ]
+ },
+ {
+ "name": "brush_scale_trigger",
+ "value": {},
+ "on": [
+ {
+ "events": [{"scale": "child__column_distance_x"}],
+ "update": "(!isArray(brush_distance) || (+invert(\"child__column_distance_x\", brush_x)[0] === +brush_distance[0] && +invert(\"child__column_distance_x\", brush_x)[1] === +brush_distance[1])) ? brush_scale_trigger : {}"
+ }
+ ]
+ },
+ {
+ "name": "brush_tuple",
+ "on": [
+ {
+ "events": [{"signal": "brush_distance"}],
+ "update": "brush_distance ? {unit: \"child__column_distance_layer_0\", fields: brush_tuple_fields, values: [brush_distance]} : null"
+ }
+ ]
+ },
+ {
+ "name": "brush_tuple_fields",
+ "value": [{"field": "distance", "channel": "x", "type": "R"}]
+ },
+ {
+ "name": "brush_translate_anchor",
+ "value": {},
+ "on": [
+ {
+ "events": [
+ {
+ "source": "scope",
+ "type": "mousedown",
+ "markname": "brush_brush"
+ }
+ ],
+ "update": "{x: x(unit), y: y(unit), extent_x: slice(brush_x)}"
+ }
+ ]
+ },
+ {
+ "name": "brush_translate_delta",
+ "value": {},
+ "on": [
+ {
+ "events": [
+ {
+ "source": "window",
+ "type": "mousemove",
+ "consume": true,
+ "between": [
+ {
+ "source": "scope",
+ "type": "mousedown",
+ "markname": "brush_brush"
+ },
+ {"source": "window", "type": "mouseup"}
+ ]
+ }
+ ],
+ "update": "{x: brush_translate_anchor.x - x(unit), y: brush_translate_anchor.y - y(unit)}"
+ }
+ ]
+ },
+ {
+ "name": "brush_zoom_anchor",
+ "on": [
+ {
+ "events": [
+ {
+ "source": "scope",
+ "type": "wheel",
+ "consume": true,
+ "markname": "brush_brush"
+ }
+ ],
+ "update": "{x: x(unit), y: y(unit)}"
+ }
+ ]
+ },
+ {
+ "name": "brush_zoom_delta",
+ "on": [
+ {
+ "events": [
+ {
+ "source": "scope",
+ "type": "wheel",
+ "consume": true,
+ "markname": "brush_brush"
+ }
+ ],
+ "force": true,
+ "update": "pow(1.001, event.deltaY * pow(16, event.deltaMode))"
+ }
+ ]
+ },
+ {
+ "name": "brush_modify",
+ "on": [
+ {
+ "events": {"signal": "brush_tuple"},
+ "update": "modify(\"brush_store\", brush_tuple, true)"
+ }
+ ]
+ }
+ ],
+ "marks": [
+ {
+ "name": "brush_brush_bg",
+ "type": "rect",
+ "clip": true,
+ "encode": {
+ "enter": {
+ "fill": {"value": "#333"},
+ "fillOpacity": {"value": 0.125}
+ },
+ "update": {
+ "x": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_distance_layer_0\"",
+ "signal": "brush_x[0]"
+ },
+ {"value": 0}
+ ],
+ "y": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_distance_layer_0\"",
+ "value": 0
+ },
+ {"value": 0}
+ ],
+ "x2": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_distance_layer_0\"",
+ "signal": "brush_x[1]"
+ },
+ {"value": 0}
+ ],
+ "y2": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_distance_layer_0\"",
+ "field": {"group": "height"}
+ },
+ {"value": 0}
+ ]
+ }
+ }
+ },
+ {
+ "name": "child__column_distance_layer_0_marks",
+ "type": "rect",
+ "style": ["bar"],
+ "interactive": true,
+ "from": {"data": "data_6"},
+ "encode": {
+ "update": {
+ "fill": {"value": "#ddd"},
+ "ariaRoleDescription": {"value": "bar"},
+ "description": {
+ "signal": "\"distance (binned): \" + (!isValid(datum[\"bin_maxbins_20_distance\"]) || !isFinite(+datum[\"bin_maxbins_20_distance\"]) ? \"null\" : format(datum[\"bin_maxbins_20_distance\"], \"\") + \" – \" + format(datum[\"bin_maxbins_20_distance_end\"], \"\")) + \"; Count of Records: \" + (format(datum[\"__count\"], \"\"))"
+ },
+ "x2": {
+ "scale": "child__column_distance_x",
+ "field": "bin_maxbins_20_distance",
+ "offset": 1
+ },
+ "x": {
+ "scale": "child__column_distance_x",
+ "field": "bin_maxbins_20_distance_end"
+ },
+ "y": {"scale": "child__column_distance_y", "field": "__count"},
+ "y2": {"scale": "child__column_distance_y", "value": 0}
+ }
+ }
+ },
+ {
+ "name": "child__column_distance_layer_1_marks",
+ "type": "rect",
+ "style": ["bar"],
+ "interactive": false,
+ "from": {"data": "data_5"},
+ "encode": {
+ "update": {
+ "fill": {"value": "#4c78a8"},
+ "ariaRoleDescription": {"value": "bar"},
+ "description": {
+ "signal": "\"distance (binned): \" + (!isValid(datum[\"bin_maxbins_20_distance\"]) || !isFinite(+datum[\"bin_maxbins_20_distance\"]) ? \"null\" : format(datum[\"bin_maxbins_20_distance\"], \"\") + \" – \" + format(datum[\"bin_maxbins_20_distance_end\"], \"\")) + \"; Count of Records: \" + (format(datum[\"__count\"], \"\"))"
+ },
+ "x2": {
+ "scale": "child__column_distance_x",
+ "field": "bin_maxbins_20_distance",
+ "offset": 1
+ },
+ "x": {
+ "scale": "child__column_distance_x",
+ "field": "bin_maxbins_20_distance_end"
+ },
+ "y": {"scale": "child__column_distance_y", "field": "__count"},
+ "y2": {"scale": "child__column_distance_y", "value": 0}
+ }
+ }
+ },
+ {
+ "name": "brush_brush",
+ "type": "rect",
+ "clip": true,
+ "encode": {
+ "enter": {"fill": {"value": "transparent"}},
+ "update": {
+ "x": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_distance_layer_0\"",
+ "signal": "brush_x[0]"
+ },
+ {"value": 0}
+ ],
+ "y": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_distance_layer_0\"",
+ "value": 0
+ },
+ {"value": 0}
+ ],
+ "x2": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_distance_layer_0\"",
+ "signal": "brush_x[1]"
+ },
+ {"value": 0}
+ ],
+ "y2": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_distance_layer_0\"",
+ "field": {"group": "height"}
+ },
+ {"value": 0}
+ ],
+ "stroke": [
+ {"test": "brush_x[0] !== brush_x[1]", "value": "white"},
+ {"value": null}
+ ]
+ }
+ }
+ }
+ ],
+ "axes": [
+ {
+ "scale": "child__column_distance_y",
+ "orient": "left",
+ "gridScale": "child__column_distance_x",
+ "grid": true,
+ "tickCount": {"signal": "ceil(childHeight/40)"},
+ "domain": false,
+ "labels": false,
+ "aria": false,
+ "maxExtent": 0,
+ "minExtent": 0,
+ "ticks": false,
+ "zindex": 0
+ },
+ {
+ "scale": "child__column_distance_x",
+ "orient": "bottom",
+ "grid": false,
+ "title": "distance (binned)",
+ "labelFlush": true,
+ "labelOverlap": true,
+ "tickCount": {"signal": "ceil(childWidth/10)"},
+ "zindex": 0
+ },
+ {
+ "scale": "child__column_distance_y",
+ "orient": "left",
+ "grid": false,
+ "title": "Count of Records",
+ "labelOverlap": true,
+ "tickCount": {"signal": "ceil(childHeight/40)"},
+ "zindex": 0
+ }
+ ]
+ },
+ {
+ "type": "group",
+ "name": "child__column_delay_group",
+ "style": "cell",
+ "encode": {
+ "update": {
+ "width": {"signal": "childWidth"},
+ "height": {"signal": "childHeight"}
+ }
+ },
+ "signals": [
+ {
+ "name": "brush_x",
+ "value": [],
+ "on": [
+ {
+ "events": {
+ "source": "scope",
+ "type": "mousedown",
+ "filter": [
+ "!event.item || event.item.mark.name !== \"brush_brush\""
+ ]
+ },
+ "update": "[x(unit), x(unit)]"
+ },
+ {
+ "events": {
+ "source": "window",
+ "type": "mousemove",
+ "consume": true,
+ "between": [
+ {
+ "source": "scope",
+ "type": "mousedown",
+ "filter": [
+ "!event.item || event.item.mark.name !== \"brush_brush\""
+ ]
+ },
+ {"source": "window", "type": "mouseup"}
+ ]
+ },
+ "update": "[brush_x[0], clamp(x(unit), 0, childWidth)]"
+ },
+ {
+ "events": {"signal": "brush_scale_trigger"},
+ "update": "[scale(\"child__column_delay_x\", brush_delay[0]), scale(\"child__column_delay_x\", brush_delay[1])]"
+ },
+ {
+ "events": [{"source": "view", "type": "dblclick"}],
+ "update": "[0, 0]"
+ },
+ {
+ "events": {"signal": "brush_translate_delta"},
+ "update": "clampRange(panLinear(brush_translate_anchor.extent_x, brush_translate_delta.x / span(brush_translate_anchor.extent_x)), 0, childWidth)"
+ },
+ {
+ "events": {"signal": "brush_zoom_delta"},
+ "update": "clampRange(zoomLinear(brush_x, brush_zoom_anchor.x, brush_zoom_delta), 0, childWidth)"
+ }
+ ]
+ },
+ {
+ "name": "brush_delay",
+ "on": [
+ {
+ "events": {"signal": "brush_x"},
+ "update": "brush_x[0] === brush_x[1] ? null : invert(\"child__column_delay_x\", brush_x)"
+ }
+ ]
+ },
+ {
+ "name": "brush_scale_trigger",
+ "value": {},
+ "on": [
+ {
+ "events": [{"scale": "child__column_delay_x"}],
+ "update": "(!isArray(brush_delay) || (+invert(\"child__column_delay_x\", brush_x)[0] === +brush_delay[0] && +invert(\"child__column_delay_x\", brush_x)[1] === +brush_delay[1])) ? brush_scale_trigger : {}"
+ }
+ ]
+ },
+ {
+ "name": "brush_tuple",
+ "on": [
+ {
+ "events": [{"signal": "brush_delay"}],
+ "update": "brush_delay ? {unit: \"child__column_delay_layer_0\", fields: brush_tuple_fields, values: [brush_delay]} : null"
+ }
+ ]
+ },
+ {
+ "name": "brush_tuple_fields",
+ "value": [{"field": "delay", "channel": "x", "type": "R"}]
+ },
+ {
+ "name": "brush_translate_anchor",
+ "value": {},
+ "on": [
+ {
+ "events": [
+ {
+ "source": "scope",
+ "type": "mousedown",
+ "markname": "brush_brush"
+ }
+ ],
+ "update": "{x: x(unit), y: y(unit), extent_x: slice(brush_x)}"
+ }
+ ]
+ },
+ {
+ "name": "brush_translate_delta",
+ "value": {},
+ "on": [
+ {
+ "events": [
+ {
+ "source": "window",
+ "type": "mousemove",
+ "consume": true,
+ "between": [
+ {
+ "source": "scope",
+ "type": "mousedown",
+ "markname": "brush_brush"
+ },
+ {"source": "window", "type": "mouseup"}
+ ]
+ }
+ ],
+ "update": "{x: brush_translate_anchor.x - x(unit), y: brush_translate_anchor.y - y(unit)}"
+ }
+ ]
+ },
+ {
+ "name": "brush_zoom_anchor",
+ "on": [
+ {
+ "events": [
+ {
+ "source": "scope",
+ "type": "wheel",
+ "consume": true,
+ "markname": "brush_brush"
+ }
+ ],
+ "update": "{x: x(unit), y: y(unit)}"
+ }
+ ]
+ },
+ {
+ "name": "brush_zoom_delta",
+ "on": [
+ {
+ "events": [
+ {
+ "source": "scope",
+ "type": "wheel",
+ "consume": true,
+ "markname": "brush_brush"
+ }
+ ],
+ "force": true,
+ "update": "pow(1.001, event.deltaY * pow(16, event.deltaMode))"
+ }
+ ]
+ },
+ {
+ "name": "brush_modify",
+ "on": [
+ {
+ "events": {"signal": "brush_tuple"},
+ "update": "modify(\"brush_store\", brush_tuple, true)"
+ }
+ ]
+ }
+ ],
+ "marks": [
+ {
+ "name": "brush_brush_bg",
+ "type": "rect",
+ "clip": true,
+ "encode": {
+ "enter": {
+ "fill": {"value": "#333"},
+ "fillOpacity": {"value": 0.125}
+ },
+ "update": {
+ "x": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_delay_layer_0\"",
+ "signal": "brush_x[0]"
+ },
+ {"value": 0}
+ ],
+ "y": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_delay_layer_0\"",
+ "value": 0
+ },
+ {"value": 0}
+ ],
+ "x2": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_delay_layer_0\"",
+ "signal": "brush_x[1]"
+ },
+ {"value": 0}
+ ],
+ "y2": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_delay_layer_0\"",
+ "field": {"group": "height"}
+ },
+ {"value": 0}
+ ]
+ }
+ }
+ },
+ {
+ "name": "child__column_delay_layer_0_marks",
+ "type": "rect",
+ "style": ["bar"],
+ "interactive": true,
+ "from": {"data": "data_7"},
+ "encode": {
+ "update": {
+ "fill": {"value": "#ddd"},
+ "ariaRoleDescription": {"value": "bar"},
+ "description": {
+ "signal": "\"delay (binned): \" + (!isValid(datum[\"bin_maxbins_20_delay\"]) || !isFinite(+datum[\"bin_maxbins_20_delay\"]) ? \"null\" : format(datum[\"bin_maxbins_20_delay\"], \"\") + \" – \" + format(datum[\"bin_maxbins_20_delay_end\"], \"\")) + \"; Count of Records: \" + (format(datum[\"__count\"], \"\"))"
+ },
+ "x2": {
+ "scale": "child__column_delay_x",
+ "field": "bin_maxbins_20_delay",
+ "offset": 1
+ },
+ "x": {
+ "scale": "child__column_delay_x",
+ "field": "bin_maxbins_20_delay_end"
+ },
+ "y": {"scale": "child__column_delay_y", "field": "__count"},
+ "y2": {"scale": "child__column_delay_y", "value": 0}
+ }
+ }
+ },
+ {
+ "name": "child__column_delay_layer_1_marks",
+ "type": "rect",
+ "style": ["bar"],
+ "interactive": false,
+ "from": {"data": "data_4"},
+ "encode": {
+ "update": {
+ "fill": {"value": "#4c78a8"},
+ "ariaRoleDescription": {"value": "bar"},
+ "description": {
+ "signal": "\"delay (binned): \" + (!isValid(datum[\"bin_maxbins_20_delay\"]) || !isFinite(+datum[\"bin_maxbins_20_delay\"]) ? \"null\" : format(datum[\"bin_maxbins_20_delay\"], \"\") + \" – \" + format(datum[\"bin_maxbins_20_delay_end\"], \"\")) + \"; Count of Records: \" + (format(datum[\"__count\"], \"\"))"
+ },
+ "x2": {
+ "scale": "child__column_delay_x",
+ "field": "bin_maxbins_20_delay",
+ "offset": 1
+ },
+ "x": {
+ "scale": "child__column_delay_x",
+ "field": "bin_maxbins_20_delay_end"
+ },
+ "y": {"scale": "child__column_delay_y", "field": "__count"},
+ "y2": {"scale": "child__column_delay_y", "value": 0}
+ }
+ }
+ },
+ {
+ "name": "brush_brush",
+ "type": "rect",
+ "clip": true,
+ "encode": {
+ "enter": {"fill": {"value": "transparent"}},
+ "update": {
+ "x": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_delay_layer_0\"",
+ "signal": "brush_x[0]"
+ },
+ {"value": 0}
+ ],
+ "y": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_delay_layer_0\"",
+ "value": 0
+ },
+ {"value": 0}
+ ],
+ "x2": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_delay_layer_0\"",
+ "signal": "brush_x[1]"
+ },
+ {"value": 0}
+ ],
+ "y2": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_delay_layer_0\"",
+ "field": {"group": "height"}
+ },
+ {"value": 0}
+ ],
+ "stroke": [
+ {"test": "brush_x[0] !== brush_x[1]", "value": "white"},
+ {"value": null}
+ ]
+ }
+ }
+ }
+ ],
+ "axes": [
+ {
+ "scale": "child__column_delay_y",
+ "orient": "left",
+ "gridScale": "child__column_delay_x",
+ "grid": true,
+ "tickCount": {"signal": "ceil(childHeight/40)"},
+ "domain": false,
+ "labels": false,
+ "aria": false,
+ "maxExtent": 0,
+ "minExtent": 0,
+ "ticks": false,
+ "zindex": 0
+ },
+ {
+ "scale": "child__column_delay_x",
+ "orient": "bottom",
+ "grid": false,
+ "title": "delay (binned)",
+ "labelFlush": true,
+ "labelOverlap": true,
+ "tickCount": {"signal": "ceil(childWidth/10)"},
+ "zindex": 0
+ },
+ {
+ "scale": "child__column_delay_y",
+ "orient": "left",
+ "grid": false,
+ "title": "Count of Records",
+ "labelOverlap": true,
+ "tickCount": {"signal": "ceil(childHeight/40)"},
+ "zindex": 0
+ }
+ ]
+ },
+ {
+ "type": "group",
+ "name": "child__column_time_group",
+ "style": "cell",
+ "encode": {
+ "update": {
+ "width": {"signal": "childWidth"},
+ "height": {"signal": "childHeight"}
+ }
+ },
+ "signals": [
+ {
+ "name": "brush_x",
+ "value": [],
+ "on": [
+ {
+ "events": {
+ "source": "scope",
+ "type": "mousedown",
+ "filter": [
+ "!event.item || event.item.mark.name !== \"brush_brush\""
+ ]
+ },
+ "update": "[x(unit), x(unit)]"
+ },
+ {
+ "events": {
+ "source": "window",
+ "type": "mousemove",
+ "consume": true,
+ "between": [
+ {
+ "source": "scope",
+ "type": "mousedown",
+ "filter": [
+ "!event.item || event.item.mark.name !== \"brush_brush\""
+ ]
+ },
+ {"source": "window", "type": "mouseup"}
+ ]
+ },
+ "update": "[brush_x[0], clamp(x(unit), 0, childWidth)]"
+ },
+ {
+ "events": {"signal": "brush_scale_trigger"},
+ "update": "[scale(\"child__column_time_x\", brush_time[0]), scale(\"child__column_time_x\", brush_time[1])]"
+ },
+ {
+ "events": [{"source": "view", "type": "dblclick"}],
+ "update": "[0, 0]"
+ },
+ {
+ "events": {"signal": "brush_translate_delta"},
+ "update": "clampRange(panLinear(brush_translate_anchor.extent_x, brush_translate_delta.x / span(brush_translate_anchor.extent_x)), 0, childWidth)"
+ },
+ {
+ "events": {"signal": "brush_zoom_delta"},
+ "update": "clampRange(zoomLinear(brush_x, brush_zoom_anchor.x, brush_zoom_delta), 0, childWidth)"
+ }
+ ]
+ },
+ {
+ "name": "brush_time",
+ "on": [
+ {
+ "events": {"signal": "brush_x"},
+ "update": "brush_x[0] === brush_x[1] ? null : invert(\"child__column_time_x\", brush_x)"
+ }
+ ]
+ },
+ {
+ "name": "brush_scale_trigger",
+ "value": {},
+ "on": [
+ {
+ "events": [{"scale": "child__column_time_x"}],
+ "update": "(!isArray(brush_time) || (+invert(\"child__column_time_x\", brush_x)[0] === +brush_time[0] && +invert(\"child__column_time_x\", brush_x)[1] === +brush_time[1])) ? brush_scale_trigger : {}"
+ }
+ ]
+ },
+ {
+ "name": "brush_tuple",
+ "on": [
+ {
+ "events": [{"signal": "brush_time"}],
+ "update": "brush_time ? {unit: \"child__column_time_layer_0\", fields: brush_tuple_fields, values: [brush_time]} : null"
+ }
+ ]
+ },
+ {
+ "name": "brush_tuple_fields",
+ "value": [{"field": "time", "channel": "x", "type": "R"}]
+ },
+ {
+ "name": "brush_translate_anchor",
+ "value": {},
+ "on": [
+ {
+ "events": [
+ {
+ "source": "scope",
+ "type": "mousedown",
+ "markname": "brush_brush"
+ }
+ ],
+ "update": "{x: x(unit), y: y(unit), extent_x: slice(brush_x)}"
+ }
+ ]
+ },
+ {
+ "name": "brush_translate_delta",
+ "value": {},
+ "on": [
+ {
+ "events": [
+ {
+ "source": "window",
+ "type": "mousemove",
+ "consume": true,
+ "between": [
+ {
+ "source": "scope",
+ "type": "mousedown",
+ "markname": "brush_brush"
+ },
+ {"source": "window", "type": "mouseup"}
+ ]
+ }
+ ],
+ "update": "{x: brush_translate_anchor.x - x(unit), y: brush_translate_anchor.y - y(unit)}"
+ }
+ ]
+ },
+ {
+ "name": "brush_zoom_anchor",
+ "on": [
+ {
+ "events": [
+ {
+ "source": "scope",
+ "type": "wheel",
+ "consume": true,
+ "markname": "brush_brush"
+ }
+ ],
+ "update": "{x: x(unit), y: y(unit)}"
+ }
+ ]
+ },
+ {
+ "name": "brush_zoom_delta",
+ "on": [
+ {
+ "events": [
+ {
+ "source": "scope",
+ "type": "wheel",
+ "consume": true,
+ "markname": "brush_brush"
+ }
+ ],
+ "force": true,
+ "update": "pow(1.001, event.deltaY * pow(16, event.deltaMode))"
+ }
+ ]
+ },
+ {
+ "name": "brush_modify",
+ "on": [
+ {
+ "events": {"signal": "brush_tuple"},
+ "update": "modify(\"brush_store\", brush_tuple, true)"
+ }
+ ]
+ }
+ ],
+ "marks": [
+ {
+ "name": "brush_brush_bg",
+ "type": "rect",
+ "clip": true,
+ "encode": {
+ "enter": {
+ "fill": {"value": "#333"},
+ "fillOpacity": {"value": 0.125}
+ },
+ "update": {
+ "x": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_time_layer_0\"",
+ "signal": "brush_x[0]"
+ },
+ {"value": 0}
+ ],
+ "y": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_time_layer_0\"",
+ "value": 0
+ },
+ {"value": 0}
+ ],
+ "x2": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_time_layer_0\"",
+ "signal": "brush_x[1]"
+ },
+ {"value": 0}
+ ],
+ "y2": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_time_layer_0\"",
+ "field": {"group": "height"}
+ },
+ {"value": 0}
+ ]
+ }
+ }
+ },
+ {
+ "name": "child__column_time_layer_0_marks",
+ "type": "rect",
+ "style": ["bar"],
+ "interactive": true,
+ "from": {"data": "data_2"},
+ "encode": {
+ "update": {
+ "fill": {"value": "#ddd"},
+ "ariaRoleDescription": {"value": "bar"},
+ "description": {
+ "signal": "\"time (binned): \" + (!isValid(datum[\"bin_maxbins_20_time\"]) || !isFinite(+datum[\"bin_maxbins_20_time\"]) ? \"null\" : format(datum[\"bin_maxbins_20_time\"], \"\") + \" – \" + format(datum[\"bin_maxbins_20_time_end\"], \"\")) + \"; Count of Records: \" + (format(datum[\"__count\"], \"\"))"
+ },
+ "x2": {
+ "scale": "child__column_time_x",
+ "field": "bin_maxbins_20_time",
+ "offset": 1
+ },
+ "x": {
+ "scale": "child__column_time_x",
+ "field": "bin_maxbins_20_time_end"
+ },
+ "y": {"scale": "child__column_time_y", "field": "__count"},
+ "y2": {"scale": "child__column_time_y", "value": 0}
+ }
+ }
+ },
+ {
+ "name": "child__column_time_layer_1_marks",
+ "type": "rect",
+ "style": ["bar"],
+ "interactive": false,
+ "from": {"data": "data_1"},
+ "encode": {
+ "update": {
+ "fill": {"value": "#4c78a8"},
+ "ariaRoleDescription": {"value": "bar"},
+ "description": {
+ "signal": "\"time (binned): \" + (!isValid(datum[\"bin_maxbins_20_time\"]) || !isFinite(+datum[\"bin_maxbins_20_time\"]) ? \"null\" : format(datum[\"bin_maxbins_20_time\"], \"\") + \" – \" + format(datum[\"bin_maxbins_20_time_end\"], \"\")) + \"; Count of Records: \" + (format(datum[\"__count\"], \"\"))"
+ },
+ "x2": {
+ "scale": "child__column_time_x",
+ "field": "bin_maxbins_20_time",
+ "offset": 1
+ },
+ "x": {
+ "scale": "child__column_time_x",
+ "field": "bin_maxbins_20_time_end"
+ },
+ "y": {"scale": "child__column_time_y", "field": "__count"},
+ "y2": {"scale": "child__column_time_y", "value": 0}
+ }
+ }
+ },
+ {
+ "name": "brush_brush",
+ "type": "rect",
+ "clip": true,
+ "encode": {
+ "enter": {"fill": {"value": "transparent"}},
+ "update": {
+ "x": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_time_layer_0\"",
+ "signal": "brush_x[0]"
+ },
+ {"value": 0}
+ ],
+ "y": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_time_layer_0\"",
+ "value": 0
+ },
+ {"value": 0}
+ ],
+ "x2": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_time_layer_0\"",
+ "signal": "brush_x[1]"
+ },
+ {"value": 0}
+ ],
+ "y2": [
+ {
+ "test": "data(\"brush_store\").length && data(\"brush_store\")[0].unit === \"child__column_time_layer_0\"",
+ "field": {"group": "height"}
+ },
+ {"value": 0}
+ ],
+ "stroke": [
+ {"test": "brush_x[0] !== brush_x[1]", "value": "white"},
+ {"value": null}
+ ]
+ }
+ }
+ }
+ ],
+ "axes": [
+ {
+ "scale": "child__column_time_y",
+ "orient": "left",
+ "gridScale": "child__column_time_x",
+ "grid": true,
+ "tickCount": {"signal": "ceil(childHeight/40)"},
+ "domain": false,
+ "labels": false,
+ "aria": false,
+ "maxExtent": 0,
+ "minExtent": 0,
+ "ticks": false,
+ "zindex": 0
+ },
+ {
+ "scale": "child__column_time_x",
+ "orient": "bottom",
+ "grid": false,
+ "title": "time (binned)",
+ "labelFlush": true,
+ "labelOverlap": true,
+ "tickCount": {"signal": "ceil(childWidth/10)"},
+ "zindex": 0
+ },
+ {
+ "scale": "child__column_time_y",
+ "orient": "left",
+ "grid": false,
+ "title": "Count of Records",
+ "labelOverlap": true,
+ "tickCount": {"signal": "ceil(childHeight/40)"},
+ "zindex": 0
+ }
+ ]
+ }
+ ],
+ "scales": [
+ {
+ "name": "child__column_distance_x",
+ "type": "linear",
+ "domain": {
+ "signal": "[child__column_distance_layer_0_bin_maxbins_20_distance_bins.start, child__column_distance_layer_0_bin_maxbins_20_distance_bins.stop]"
+ },
+ "range": [0, {"signal": "childWidth"}],
+ "bins": {
+ "signal": "child__column_distance_layer_0_bin_maxbins_20_distance_bins"
+ },
+ "zero": false
+ },
+ {
+ "name": "child__column_distance_y",
+ "type": "linear",
+ "domain": {
+ "fields": [
+ {"data": "data_6", "field": "__count"},
+ {"data": "data_5", "field": "__count"}
+ ]
+ },
+ "range": [{"signal": "childHeight"}, 0],
+ "nice": true,
+ "zero": true
+ },
+ {
+ "name": "child__column_delay_x",
+ "type": "linear",
+ "domain": {
+ "signal": "[child__column_delay_layer_1_bin_maxbins_20_delay_bins.start, child__column_delay_layer_1_bin_maxbins_20_delay_bins.stop]"
+ },
+ "range": [0, {"signal": "childWidth"}],
+ "bins": {
+ "signal": "child__column_delay_layer_1_bin_maxbins_20_delay_bins"
+ },
+ "zero": false
+ },
+ {
+ "name": "child__column_delay_y",
+ "type": "linear",
+ "domain": {
+ "fields": [
+ {"data": "data_7", "field": "__count"},
+ {"data": "data_4", "field": "__count"}
+ ]
+ },
+ "range": [{"signal": "childHeight"}, 0],
+ "nice": true,
+ "zero": true
+ },
+ {
+ "name": "child__column_time_x",
+ "type": "linear",
+ "domain": {
+ "signal": "[child__column_time_layer_1_bin_maxbins_20_time_bins.start, child__column_time_layer_1_bin_maxbins_20_time_bins.stop]"
+ },
+ "range": [0, {"signal": "childWidth"}],
+ "bins": {"signal": "child__column_time_layer_1_bin_maxbins_20_time_bins"},
+ "zero": false
+ },
+ {
+ "name": "child__column_time_y",
+ "type": "linear",
+ "domain": {
+ "fields": [
+ {"data": "data_2", "field": "__count"},
+ {"data": "data_1", "field": "__count"}
+ ]
+ },
+ "range": [{"signal": "childHeight"}, 0],
+ "nice": true,
+ "zero": true
+ }
+ ]
+}
+
+await init();
\ No newline at end of file
diff --git a/examples/editor-demo/src/style.css b/examples/editor-demo/src/style.css
new file mode 100644
index 000000000..80f0430da
--- /dev/null
+++ b/examples/editor-demo/src/style.css
@@ -0,0 +1,3 @@
+.tab-pane .monaco-editor {
+ height: 500px;
+}
diff --git a/examples/editor-demo/webpack.config.js b/examples/editor-demo/webpack.config.js
new file mode 100644
index 000000000..27ace46c0
--- /dev/null
+++ b/examples/editor-demo/webpack.config.js
@@ -0,0 +1,33 @@
+const CopyWebpackPlugin = require("copy-webpack-plugin");
+const MonacoWebpackPlugin = require('monaco-editor-webpack-plugin');
+const path = require('path');
+
+module.exports = {
+ entry: "./src/bootstrap.js",
+ output: {
+ path: path.resolve(__dirname, "dist"),
+ filename: "bootstrap.js",
+ },
+ mode: "development",
+ module: {
+ rules: [
+ {
+ test: /\.css$/,
+ use: ['style-loader', 'css-loader']
+ }, {
+ test: /\.svg$/,
+ loader: 'svg-inline-loader'
+ },
+ ]
+ },
+ plugins: [
+ new CopyWebpackPlugin({patterns: ['src/index.html']}),
+ new MonacoWebpackPlugin({
+ languages: ['typescript', 'javascript', 'css', "json"]
+ })
+ ],
+ experiments: {
+ topLevelAwait: true,
+ asyncWebAssembly: true,
+ }
+};
diff --git a/examples/python-examples/chart_state.py b/examples/python-examples/chart_state.py
new file mode 100644
index 000000000..722fd98e7
--- /dev/null
+++ b/examples/python-examples/chart_state.py
@@ -0,0 +1,441 @@
+from typing import Any
+import json
+import vegafusion as vf
+
+
+# This example demonstrates how to use a chart state, and update it in response to simulated interactive
+# updates to the chart
+def main():
+ spec = get_spec()
+
+ # Build chart state
+ chart_state = vf.runtime.new_chart_state(spec)
+
+ # Get the initial pre-transformed spec that can be rendered
+ _init_spec = chart_state.get_client_spec()
+
+ # Get the watch plan, which includes which signals and data variables that should be listened to
+ # and relayed from the displayed vega chart back to the chart state.
+ watch_plan = chart_state.get_comm_plan()
+ print("Watch Plan:\n" + json.dumps(watch_plan, indent=2), end="\n\n")
+
+ # Report an update to the maxbins signal. Update will return the signal and dataset updates that should
+ # but updated in the displayed chart.
+ updates = chart_state.update(
+ [
+ {
+ "name": "maxbins",
+ "namespace": "signal",
+ "scope": [],
+ "value": 4,
+ }
+ ]
+ )
+
+ print("Server to Client Updates:\n" + json.dumps(updates, indent=2), end="\n\n")
+
+
+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,
+ "bind": {"input": "select", "options": [5, 10, 20]}
+ },
+ {
+ "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": {"signal": "maxbins"}
+ }
+ ]
+ },
+ {
+ "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)
+
+
+def expected_spec() -> dict[str, Any]:
+ return json.loads("""
+ {
+ "$schema": "https://vega.github.io/schema/vega/v5.json",
+ "data": [
+ {
+ "name": "table"
+ },
+ {
+ "name": "counts",
+ "values": [
+ {
+ "bin0": 6.0,
+ "bin1": 7.0,
+ "count": 985
+ },
+ {
+ "bin0": 3.0,
+ "bin1": 4.0,
+ "count": 100
+ },
+ {
+ "bin0": 7.0,
+ "bin1": 8.0,
+ "count": 741
+ },
+ {
+ "bin0": 5.0,
+ "bin1": 6.0,
+ "count": 633
+ },
+ {
+ "bin0": 8.0,
+ "bin1": 9.0,
+ "count": 204
+ },
+ {
+ "bin0": 2.0,
+ "bin1": 3.0,
+ "count": 43
+ },
+ {
+ "bin0": 4.0,
+ "bin1": 5.0,
+ "count": 273
+ },
+ {
+ "bin0": 9.0,
+ "bin1": 10.0,
+ "count": 4
+ },
+ {
+ "bin0": 1.0,
+ "bin1": 2.0,
+ "count": 5
+ }
+ ]
+ },
+ {
+ "name": "nulls",
+ "values": [
+ {
+ "count": 213
+ }
+ ]
+ }
+ ],
+ "signals": [
+ {
+ "name": "bins",
+ "value": {
+ "fields": [
+ "IMDB Rating"
+ ],
+ "fname": "bin_IMDB Rating",
+ "start": 1.0,
+ "step": 1.0,
+ "stop": 10.0
+ }
+ },
+ {
+ "name": "maxbins",
+ "value": 10
+ },
+ {
+ "name": "binCount",
+ "update": "(bins.stop - bins.start) / bins.step"
+ },
+ {
+ "name": "nullGap",
+ "value": 10
+ },
+ {
+ "name": "barStep",
+ "update": "(width - nullGap) / (1 + binCount)"
+ }
+ ],
+ "marks": [
+ {
+ "type": "rect",
+ "from": {
+ "data": "counts"
+ },
+ "encode": {
+ "update": {
+ "y": {
+ "field": "count",
+ "scale": "yscale"
+ },
+ "fill": {
+ "value": "steelblue"
+ },
+ "x2": {
+ "field": "bin1",
+ "scale": "xscale"
+ },
+ "x": {
+ "field": "bin0",
+ "scale": "xscale",
+ "offset": 1
+ },
+ "y2": {
+ "value": 0,
+ "scale": "yscale"
+ }
+ },
+ "hover": {
+ "fill": {
+ "value": "firebrick"
+ }
+ }
+ }
+ },
+ {
+ "type": "rect",
+ "from": {
+ "data": "nulls"
+ },
+ "encode": {
+ "hover": {
+ "fill": {
+ "value": "firebrick"
+ }
+ },
+ "update": {
+ "x2": {
+ "scale": "xscale-null",
+ "band": 1
+ },
+ "y": {
+ "field": "count",
+ "scale": "yscale"
+ },
+ "y2": {
+ "value": 0,
+ "scale": "yscale"
+ },
+ "fill": {
+ "value": "#aaa"
+ },
+ "x": {
+ "scale": "xscale-null",
+ "offset": 1
+ }
+ }
+ }
+ }
+ ],
+ "scales": [
+ {
+ "name": "yscale",
+ "type": "linear",
+ "domain": {
+ "fields": [
+ {
+ "data": "counts",
+ "field": "count"
+ },
+ {
+ "data": "nulls",
+ "field": "count"
+ }
+ ]
+ },
+ "range": "height",
+ "nice": true,
+ "round": true
+ },
+ {
+ "name": "xscale",
+ "type": "linear",
+ "domain": {
+ "signal": "[bins.start, bins.stop]"
+ },
+ "range": [
+ {
+ "signal": "barStep + nullGap"
+ },
+ {
+ "signal": "width"
+ }
+ ],
+ "bins": {
+ "signal": "bins"
+ },
+ "round": true
+ },
+ {
+ "name": "xscale-null",
+ "type": "band",
+ "domain": [
+ null
+ ],
+ "range": [
+ 0,
+ {
+ "signal": "barStep"
+ }
+ ],
+ "round": true
+ }
+ ],
+ "axes": [
+ {
+ "scale": "xscale",
+ "tickMinStep": 0.5,
+ "orient": "bottom"
+ },
+ {
+ "scale": "xscale-null",
+ "orient": "bottom"
+ },
+ {
+ "scale": "yscale",
+ "tickCount": 5,
+ "offset": 5,
+ "orient": "left"
+ }
+ ],
+ "width": 400,
+ "height": 200,
+ "description": "A histogram of film ratings, modified to include null values.",
+ "padding": 5,
+ "autosize": {
+ "type": "fit",
+ "resize": true
+ }
+}
+ """)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/python-examples/column_usage.py b/examples/python-examples/column_usage.py
new file mode 100644
index 000000000..756e27166
--- /dev/null
+++ b/examples/python-examples/column_usage.py
@@ -0,0 +1,124 @@
+import json
+from typing import Any
+import vegafusion as vf
+
+
+def main():
+ spec = get_spec()
+ column_usage = vf.get_column_usage(spec)
+ print(json.dumps(column_usage, indent=2))
+
+ assert column_usage == {
+ "source": ["Acceleration", "Horsepower", "Miles_per_Gallon"]
+ }
+
+
+def get_spec() -> dict[str, Any]:
+ spec_str = """
+ {
+ "$schema": "https://vega.github.io/schema/vega/v5.json",
+ "description": "A basic scatter plot example depicting automobile statistics.",
+ "width": 200,
+ "height": 200,
+ "padding": 5,
+
+ "data": [
+ {
+ "name": "source",
+ "url": "data/cars.json",
+ "transform": [
+ {
+ "type": "filter",
+ "expr": "datum['Horsepower'] != null && datum['Miles_per_Gallon'] != null && datum['Acceleration'] != null"
+ }
+ ]
+ }
+ ],
+
+ "scales": [
+ {
+ "name": "x",
+ "type": "linear",
+ "round": true,
+ "nice": true,
+ "zero": true,
+ "domain": {"data": "source", "field": "Horsepower"},
+ "range": "width"
+ },
+ {
+ "name": "y",
+ "type": "linear",
+ "round": true,
+ "nice": true,
+ "zero": true,
+ "domain": {"data": "source", "field": "Miles_per_Gallon"},
+ "range": "height"
+ },
+ {
+ "name": "size",
+ "type": "linear",
+ "round": true,
+ "nice": false,
+ "zero": true,
+ "domain": {"data": "source", "field": "Acceleration"},
+ "range": [4,361]
+ }
+ ],
+
+ "axes": [
+ {
+ "scale": "x",
+ "grid": true,
+ "domain": false,
+ "orient": "bottom",
+ "tickCount": 5,
+ "title": "Horsepower"
+ },
+ {
+ "scale": "y",
+ "grid": true,
+ "domain": false,
+ "orient": "left",
+ "titlePadding": 5,
+ "title": "Miles_per_Gallon"
+ }
+ ],
+
+ "legends": [
+ {
+ "size": "size",
+ "title": "Acceleration",
+ "format": "s",
+ "symbolStrokeColor": "#4682b4",
+ "symbolStrokeWidth": 2,
+ "symbolOpacity": 0.5,
+ "symbolType": "circle"
+ }
+ ],
+
+ "marks": [
+ {
+ "name": "marks",
+ "type": "symbol",
+ "from": {"data": "source"},
+ "encode": {
+ "update": {
+ "x": {"scale": "x", "field": "Horsepower"},
+ "y": {"scale": "y", "field": "Miles_per_Gallon"},
+ "size": {"scale": "size", "field": "Acceleration"},
+ "shape": {"value": "circle"},
+ "strokeWidth": {"value": 2},
+ "opacity": {"value": 0.5},
+ "stroke": {"value": "#4682b4"},
+ "fill": {"value": "transparent"}
+ }
+ }
+ }
+ ]
+ }
+ """
+ return json.loads(spec_str)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/python-examples/grpc.py b/examples/python-examples/grpc.py
new file mode 100644
index 000000000..55f2480a0
--- /dev/null
+++ b/examples/python-examples/grpc.py
@@ -0,0 +1,177 @@
+import json
+from typing import Any
+
+import vegafusion as vf
+
+
+# Example that connects to a VegaFusion Server instance over gRPC and performs
+# pre_transform_datasets
+def main():
+ # Connect to local VegaFusion Server instance
+ try:
+ vf.runtime.grpc_connect("http://127.0.0.1:50051")
+ except ValueError as e:
+ raise ValueError(
+ "Try launching a VegaFusion Server instance on port 50051 with "
+ "`vegafusion-server --port 50051`"
+ ) from e
+ 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},
+ "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": []
+ }
+ ]
+ }
+ ],
+ "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)"
+ }
+ ],
+ "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()
diff --git a/examples/python-examples/inline_datasets.py b/examples/python-examples/inline_datasets.py
new file mode 100644
index 000000000..2d5d37c8c
--- /dev/null
+++ b/examples/python-examples/inline_datasets.py
@@ -0,0 +1,423 @@
+import json
+from typing import Any
+
+import vegafusion as vf
+import pandas as pd
+
+
+# This example demonstrates how to use the `pre_transform_spec` method with an inline dataset
+# (a pandas DataFrame in this case) to create a new spec with supported transforms pre-evaluated.
+def main():
+ movies_df = pd.read_json(
+ "https://raw.githubusercontent.com/vega/vega-datasets/refs/heads/main/data/movies.json"
+ )
+ spec = get_spec()
+ transformed_spec, warnings = vf.runtime.pre_transform_spec(
+ spec, inline_datasets={"movies": movies_df}
+ )
+ assert warnings == []
+ assert transformed_spec == expected_spec()
+
+
+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},
+ "data": [
+ {
+ "name": "table",
+ "url": "vegafusion+dataset://movies",
+ "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": []
+ }
+ ]
+ }
+ ],
+ "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)"
+ }
+ ],
+ "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)
+
+
+def expected_spec() -> dict[str, Any]:
+ return json.loads("""
+ {
+ "$schema": "https://vega.github.io/schema/vega/v5.json",
+ "data": [
+ {
+ "name": "table"
+ },
+ {
+ "name": "counts",
+ "values": [
+ {
+ "bin0": 6.0,
+ "bin1": 7.0,
+ "count": 985
+ },
+ {
+ "bin0": 3.0,
+ "bin1": 4.0,
+ "count": 100
+ },
+ {
+ "bin0": 7.0,
+ "bin1": 8.0,
+ "count": 741
+ },
+ {
+ "bin0": 5.0,
+ "bin1": 6.0,
+ "count": 633
+ },
+ {
+ "bin0": 8.0,
+ "bin1": 9.0,
+ "count": 204
+ },
+ {
+ "bin0": 2.0,
+ "bin1": 3.0,
+ "count": 43
+ },
+ {
+ "bin0": 4.0,
+ "bin1": 5.0,
+ "count": 273
+ },
+ {
+ "bin0": 9.0,
+ "bin1": 10.0,
+ "count": 4
+ },
+ {
+ "bin0": 1.0,
+ "bin1": 2.0,
+ "count": 5
+ }
+ ]
+ },
+ {
+ "name": "nulls",
+ "values": [
+ {
+ "count": 213
+ }
+ ]
+ }
+ ],
+ "signals": [
+ {
+ "name": "bins",
+ "value": {
+ "fields": [
+ "IMDB Rating"
+ ],
+ "fname": "bin_IMDB Rating",
+ "start": 1.0,
+ "step": 1.0,
+ "stop": 10.0
+ }
+ },
+ {
+ "name": "maxbins",
+ "value": 10
+ },
+ {
+ "name": "binCount",
+ "update": "(bins.stop - bins.start) / bins.step"
+ },
+ {
+ "name": "nullGap",
+ "value": 10
+ },
+ {
+ "name": "barStep",
+ "update": "(width - nullGap) / (1 + binCount)"
+ }
+ ],
+ "marks": [
+ {
+ "type": "rect",
+ "from": {
+ "data": "counts"
+ },
+ "encode": {
+ "update": {
+ "y": {
+ "field": "count",
+ "scale": "yscale"
+ },
+ "fill": {
+ "value": "steelblue"
+ },
+ "x2": {
+ "field": "bin1",
+ "scale": "xscale"
+ },
+ "x": {
+ "field": "bin0",
+ "scale": "xscale",
+ "offset": 1
+ },
+ "y2": {
+ "value": 0,
+ "scale": "yscale"
+ }
+ },
+ "hover": {
+ "fill": {
+ "value": "firebrick"
+ }
+ }
+ }
+ },
+ {
+ "type": "rect",
+ "from": {
+ "data": "nulls"
+ },
+ "encode": {
+ "hover": {
+ "fill": {
+ "value": "firebrick"
+ }
+ },
+ "update": {
+ "x2": {
+ "scale": "xscale-null",
+ "band": 1
+ },
+ "y": {
+ "field": "count",
+ "scale": "yscale"
+ },
+ "y2": {
+ "value": 0,
+ "scale": "yscale"
+ },
+ "fill": {
+ "value": "#aaa"
+ },
+ "x": {
+ "scale": "xscale-null",
+ "offset": 1
+ }
+ }
+ }
+ }
+ ],
+ "scales": [
+ {
+ "name": "yscale",
+ "type": "linear",
+ "domain": {
+ "fields": [
+ {
+ "data": "counts",
+ "field": "count"
+ },
+ {
+ "data": "nulls",
+ "field": "count"
+ }
+ ]
+ },
+ "range": "height",
+ "nice": true,
+ "round": true
+ },
+ {
+ "name": "xscale",
+ "type": "linear",
+ "domain": {
+ "signal": "[bins.start, bins.stop]"
+ },
+ "range": [
+ {
+ "signal": "barStep + nullGap"
+ },
+ {
+ "signal": "width"
+ }
+ ],
+ "bins": {
+ "signal": "bins"
+ },
+ "round": true
+ },
+ {
+ "name": "xscale-null",
+ "type": "band",
+ "domain": [
+ null
+ ],
+ "range": [
+ 0,
+ {
+ "signal": "barStep"
+ }
+ ],
+ "round": true
+ }
+ ],
+ "axes": [
+ {
+ "scale": "xscale",
+ "tickMinStep": 0.5,
+ "orient": "bottom"
+ },
+ {
+ "scale": "xscale-null",
+ "orient": "bottom"
+ },
+ {
+ "scale": "yscale",
+ "tickCount": 5,
+ "offset": 5,
+ "orient": "left"
+ }
+ ],
+ "width": 400,
+ "height": 200,
+ "description": "A histogram of film ratings, modified to include null values.",
+ "padding": 5,
+ "autosize": {
+ "type": "fit",
+ "resize": true
+ }
+}
+ """)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/python-examples/pre_transform_data.py b/examples/python-examples/pre_transform_data.py
new file mode 100644
index 000000000..bf634a79c
--- /dev/null
+++ b/examples/python-examples/pre_transform_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()
diff --git a/examples/python-examples/pre_transform_extract.py b/examples/python-examples/pre_transform_extract.py
new file mode 100644
index 000000000..5d44ae710
--- /dev/null
+++ b/examples/python-examples/pre_transform_extract.py
@@ -0,0 +1,384 @@
+import json
+from typing import Any
+
+
+import vegafusion as vf
+
+
+# This example demonstrates how to use the `pre_transform_extract` method to create a new
+# spec with supported transforms pre-evaluated and the transformed datasets extract in arrow format
+def main():
+ spec = get_spec()
+ transformed_spec, datasets, warnings = vf.runtime.pre_transform_extract(
+ spec, extract_threshold=4
+ )
+ print(datasets)
+ assert warnings == []
+ assert transformed_spec == expected_spec()
+ assert len(datasets) == 1
+
+ name, scope, data = datasets[0]
+ assert name == "counts"
+ assert scope == []
+ assert data.num_rows == 9
+ assert data.column_names == ["bin0", "bin1", "count"]
+
+
+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)
+
+
+def expected_spec() -> dict[str, Any]:
+ return json.loads("""
+ {
+ "$schema": "https://vega.github.io/schema/vega/v5.json",
+ "data": [
+ {
+ "name": "table"
+ },
+ {
+ "name": "counts"
+ },
+ {
+ "name": "nulls",
+ "values": [
+ {
+ "count": 213
+ }
+ ]
+ }
+ ],
+ "signals": [
+ {
+ "name": "bins",
+ "value": {
+ "fields": [
+ "IMDB Rating"
+ ],
+ "fname": "bin_IMDB Rating",
+ "start": 1.0,
+ "step": 1.0,
+ "stop": 10.0
+ }
+ },
+ {
+ "name": "maxbins",
+ "value": 10
+ },
+ {
+ "name": "binCount",
+ "update": "(bins.stop - bins.start) / bins.step"
+ },
+ {
+ "name": "nullGap",
+ "value": 10
+ },
+ {
+ "name": "barStep",
+ "update": "(width - nullGap) / (1 + binCount)"
+ }
+ ],
+ "marks": [
+ {
+ "type": "rect",
+ "from": {
+ "data": "counts"
+ },
+ "encode": {
+ "update": {
+ "y": {
+ "field": "count",
+ "scale": "yscale"
+ },
+ "fill": {
+ "value": "steelblue"
+ },
+ "x2": {
+ "field": "bin1",
+ "scale": "xscale"
+ },
+ "x": {
+ "field": "bin0",
+ "scale": "xscale",
+ "offset": 1
+ },
+ "y2": {
+ "value": 0,
+ "scale": "yscale"
+ }
+ },
+ "hover": {
+ "fill": {
+ "value": "firebrick"
+ }
+ }
+ }
+ },
+ {
+ "type": "rect",
+ "from": {
+ "data": "nulls"
+ },
+ "encode": {
+ "hover": {
+ "fill": {
+ "value": "firebrick"
+ }
+ },
+ "update": {
+ "x2": {
+ "scale": "xscale-null",
+ "band": 1
+ },
+ "y": {
+ "field": "count",
+ "scale": "yscale"
+ },
+ "y2": {
+ "value": 0,
+ "scale": "yscale"
+ },
+ "fill": {
+ "value": "#aaa"
+ },
+ "x": {
+ "scale": "xscale-null",
+ "offset": 1
+ }
+ }
+ }
+ }
+ ],
+ "scales": [
+ {
+ "name": "yscale",
+ "type": "linear",
+ "domain": {
+ "fields": [
+ {
+ "data": "counts",
+ "field": "count"
+ },
+ {
+ "data": "nulls",
+ "field": "count"
+ }
+ ]
+ },
+ "range": "height",
+ "nice": true,
+ "round": true
+ },
+ {
+ "name": "xscale",
+ "type": "linear",
+ "domain": {
+ "signal": "[bins.start, bins.stop]"
+ },
+ "range": [
+ {
+ "signal": "barStep + nullGap"
+ },
+ {
+ "signal": "width"
+ }
+ ],
+ "bins": {
+ "signal": "bins"
+ },
+ "round": true
+ },
+ {
+ "name": "xscale-null",
+ "type": "band",
+ "domain": [
+ null
+ ],
+ "range": [
+ 0,
+ {
+ "signal": "barStep"
+ }
+ ],
+ "round": true
+ }
+ ],
+ "axes": [
+ {
+ "scale": "xscale",
+ "tickMinStep": 0.5,
+ "orient": "bottom"
+ },
+ {
+ "scale": "xscale-null",
+ "orient": "bottom"
+ },
+ {
+ "scale": "yscale",
+ "tickCount": 5,
+ "offset": 5,
+ "orient": "left"
+ }
+ ],
+ "width": 400,
+ "height": 200,
+ "description": "A histogram of film ratings, modified to include null values.",
+ "padding": 5,
+ "autosize": {
+ "type": "fit",
+ "resize": true
+ }
+}
+ """)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/python-examples/pre_transform_spec.py b/examples/python-examples/pre_transform_spec.py
new file mode 100644
index 000000000..80d99359e
--- /dev/null
+++ b/examples/python-examples/pre_transform_spec.py
@@ -0,0 +1,420 @@
+import json
+from typing import Any
+
+import vegafusion as vf
+
+
+# This example demonstrates how to use the `pre_transform_spec` method to create a new
+# spec with supported transforms pre-evaluated.
+def main():
+ spec = get_spec()
+ transformed_spec, warnings = vf.runtime.pre_transform_spec(spec)
+ assert warnings == []
+ assert transformed_spec == expected_spec()
+
+
+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)
+
+
+def expected_spec() -> dict[str, Any]:
+ return json.loads("""
+ {
+ "$schema": "https://vega.github.io/schema/vega/v5.json",
+ "data": [
+ {
+ "name": "table"
+ },
+ {
+ "name": "counts",
+ "values": [
+ {
+ "bin0": 6.0,
+ "bin1": 7.0,
+ "count": 985
+ },
+ {
+ "bin0": 3.0,
+ "bin1": 4.0,
+ "count": 100
+ },
+ {
+ "bin0": 7.0,
+ "bin1": 8.0,
+ "count": 741
+ },
+ {
+ "bin0": 5.0,
+ "bin1": 6.0,
+ "count": 633
+ },
+ {
+ "bin0": 8.0,
+ "bin1": 9.0,
+ "count": 204
+ },
+ {
+ "bin0": 2.0,
+ "bin1": 3.0,
+ "count": 43
+ },
+ {
+ "bin0": 4.0,
+ "bin1": 5.0,
+ "count": 273
+ },
+ {
+ "bin0": 9.0,
+ "bin1": 10.0,
+ "count": 4
+ },
+ {
+ "bin0": 1.0,
+ "bin1": 2.0,
+ "count": 5
+ }
+ ]
+ },
+ {
+ "name": "nulls",
+ "values": [
+ {
+ "count": 213
+ }
+ ]
+ }
+ ],
+ "signals": [
+ {
+ "name": "bins",
+ "value": {
+ "fields": [
+ "IMDB Rating"
+ ],
+ "fname": "bin_IMDB Rating",
+ "start": 1.0,
+ "step": 1.0,
+ "stop": 10.0
+ }
+ },
+ {
+ "name": "maxbins",
+ "value": 10
+ },
+ {
+ "name": "binCount",
+ "update": "(bins.stop - bins.start) / bins.step"
+ },
+ {
+ "name": "nullGap",
+ "value": 10
+ },
+ {
+ "name": "barStep",
+ "update": "(width - nullGap) / (1 + binCount)"
+ }
+ ],
+ "marks": [
+ {
+ "type": "rect",
+ "from": {
+ "data": "counts"
+ },
+ "encode": {
+ "update": {
+ "y": {
+ "field": "count",
+ "scale": "yscale"
+ },
+ "fill": {
+ "value": "steelblue"
+ },
+ "x2": {
+ "field": "bin1",
+ "scale": "xscale"
+ },
+ "x": {
+ "field": "bin0",
+ "scale": "xscale",
+ "offset": 1
+ },
+ "y2": {
+ "value": 0,
+ "scale": "yscale"
+ }
+ },
+ "hover": {
+ "fill": {
+ "value": "firebrick"
+ }
+ }
+ }
+ },
+ {
+ "type": "rect",
+ "from": {
+ "data": "nulls"
+ },
+ "encode": {
+ "hover": {
+ "fill": {
+ "value": "firebrick"
+ }
+ },
+ "update": {
+ "x2": {
+ "scale": "xscale-null",
+ "band": 1
+ },
+ "y": {
+ "field": "count",
+ "scale": "yscale"
+ },
+ "y2": {
+ "value": 0,
+ "scale": "yscale"
+ },
+ "fill": {
+ "value": "#aaa"
+ },
+ "x": {
+ "scale": "xscale-null",
+ "offset": 1
+ }
+ }
+ }
+ }
+ ],
+ "scales": [
+ {
+ "name": "yscale",
+ "type": "linear",
+ "domain": {
+ "fields": [
+ {
+ "data": "counts",
+ "field": "count"
+ },
+ {
+ "data": "nulls",
+ "field": "count"
+ }
+ ]
+ },
+ "range": "height",
+ "nice": true,
+ "round": true
+ },
+ {
+ "name": "xscale",
+ "type": "linear",
+ "domain": {
+ "signal": "[bins.start, bins.stop]"
+ },
+ "range": [
+ {
+ "signal": "barStep + nullGap"
+ },
+ {
+ "signal": "width"
+ }
+ ],
+ "bins": {
+ "signal": "bins"
+ },
+ "round": true
+ },
+ {
+ "name": "xscale-null",
+ "type": "band",
+ "domain": [
+ null
+ ],
+ "range": [
+ 0,
+ {
+ "signal": "barStep"
+ }
+ ],
+ "round": true
+ }
+ ],
+ "axes": [
+ {
+ "scale": "xscale",
+ "tickMinStep": 0.5,
+ "orient": "bottom"
+ },
+ {
+ "scale": "xscale-null",
+ "orient": "bottom"
+ },
+ {
+ "scale": "yscale",
+ "tickCount": 5,
+ "offset": 5,
+ "orient": "left"
+ }
+ ],
+ "width": 400,
+ "height": 200,
+ "description": "A histogram of film ratings, modified to include null values.",
+ "padding": 5,
+ "autosize": {
+ "type": "fit",
+ "resize": true
+ }
+}
+ """)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/rust-examples/Cargo.toml b/examples/rust-examples/Cargo.toml
new file mode 100644
index 000000000..3805f20c7
--- /dev/null
+++ b/examples/rust-examples/Cargo.toml
@@ -0,0 +1,14 @@
+[package]
+name = "rust-examples"
+version = "0.1.0"
+edition = "2021"
+
+[dev-dependencies]
+serde_json = { workspace = true }
+vegafusion-common = { path = "../../vegafusion-common" }
+vegafusion-core = { path = "../../vegafusion-core" }
+vegafusion-runtime = { path = "../../vegafusion-runtime" }
+datafusion = { workspace = true }
+reqwest = { workspace = true }
+tonic = { workspace = true }
+tokio = { workspace = true }
\ No newline at end of file
diff --git a/examples/rust-examples/examples/chart_state.rs b/examples/rust-examples/examples/chart_state.rs
new file mode 100644
index 000000000..7756cb0c6
--- /dev/null
+++ b/examples/rust-examples/examples/chart_state.rs
@@ -0,0 +1,196 @@
+use vegafusion_core::chart_state::ChartState;
+use vegafusion_core::planning::watch::{ExportUpdateJSON, ExportUpdateNamespace};
+use vegafusion_core::spec::chart::ChartSpec;
+use vegafusion_runtime::task_graph::runtime::VegaFusionRuntime;
+
+/// This example demonstrates how to use the `pre_transform_spec` method to create a new
+/// spec with supported transforms pre-evaluated.
+#[tokio::main]
+async fn main() {
+ let spec = get_spec();
+
+ // Make runtime
+ let runtime = VegaFusionRuntime::new(None);
+
+ // Construct ChartState
+ let chart_state = ChartState::try_new(
+ &runtime,
+ spec,
+ Default::default(), // Inline datasets
+ Default::default(), // Options
+ )
+ .await
+ .unwrap();
+
+ // Get initial transformed spec for display
+ let _transformed_spec = chart_state.get_client_spec();
+
+ // Get comm plan
+ let comm_plan = chart_state.get_comm_plan();
+ println!("{:#?}", comm_plan);
+
+ // Apply an update to the maxbins signal
+ let updates = chart_state
+ .update(
+ &runtime,
+ vec![ExportUpdateJSON {
+ namespace: ExportUpdateNamespace::Signal,
+ name: "maxbins".to_string(),
+ scope: vec![],
+ value: 4.into(),
+ }],
+ )
+ .await
+ .unwrap();
+
+ // Print updates that should be applied to the rendered Vega chart
+ println!("{}", serde_json::to_string_pretty(&updates).unwrap());
+}
+
+fn get_spec() -> ChartSpec {
+ let spec_str = r##"
+ {
+ "$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,
+ "bind": {"input": "select", "options": [5, 10, 20]}
+ },
+ {
+ "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": {"signal": "maxbins"}
+ }
+ ]
+ },
+ {
+ "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"}
+ }
+ }
+ }
+ ]
+ }
+ "##;
+ serde_json::from_str(spec_str).unwrap()
+}
diff --git a/examples/rust-examples/examples/column_usage.rs b/examples/rust-examples/examples/column_usage.rs
new file mode 100644
index 000000000..8f38204db
--- /dev/null
+++ b/examples/rust-examples/examples/column_usage.rs
@@ -0,0 +1,128 @@
+use vegafusion_core::{get_column_usage, spec::chart::ChartSpec};
+
+/// This example demonstrates how to use the `get_column_usage` function to get the names
+/// of columns of each root dataset that are referenced in a Vega specification.
+fn main() {
+ let spec = get_spec();
+ let column_usage = get_column_usage(&spec).unwrap();
+ println!("{:#?}", column_usage);
+
+ assert_eq!(
+ column_usage,
+ std::collections::HashMap::from([(
+ "source".to_string(),
+ Some(vec![
+ "Acceleration".to_string(),
+ "Horsepower".to_string(),
+ "Miles_per_Gallon".to_string(),
+ ])
+ )])
+ );
+}
+
+fn get_spec() -> ChartSpec {
+ let spec_str = r##"
+ {
+ "$schema": "https://vega.github.io/schema/vega/v5.json",
+ "description": "A basic scatter plot example depicting automobile statistics.",
+ "width": 200,
+ "height": 200,
+ "padding": 5,
+
+ "data": [
+ {
+ "name": "source",
+ "url": "data/cars.json",
+ "transform": [
+ {
+ "type": "filter",
+ "expr": "datum['Horsepower'] != null && datum['Miles_per_Gallon'] != null && datum['Acceleration'] != null"
+ }
+ ]
+ }
+ ],
+
+ "scales": [
+ {
+ "name": "x",
+ "type": "linear",
+ "round": true,
+ "nice": true,
+ "zero": true,
+ "domain": {"data": "source", "field": "Horsepower"},
+ "range": "width"
+ },
+ {
+ "name": "y",
+ "type": "linear",
+ "round": true,
+ "nice": true,
+ "zero": true,
+ "domain": {"data": "source", "field": "Miles_per_Gallon"},
+ "range": "height"
+ },
+ {
+ "name": "size",
+ "type": "linear",
+ "round": true,
+ "nice": false,
+ "zero": true,
+ "domain": {"data": "source", "field": "Acceleration"},
+ "range": [4,361]
+ }
+ ],
+
+ "axes": [
+ {
+ "scale": "x",
+ "grid": true,
+ "domain": false,
+ "orient": "bottom",
+ "tickCount": 5,
+ "title": "Horsepower"
+ },
+ {
+ "scale": "y",
+ "grid": true,
+ "domain": false,
+ "orient": "left",
+ "titlePadding": 5,
+ "title": "Miles_per_Gallon"
+ }
+ ],
+
+ "legends": [
+ {
+ "size": "size",
+ "title": "Acceleration",
+ "format": "s",
+ "symbolStrokeColor": "#4682b4",
+ "symbolStrokeWidth": 2,
+ "symbolOpacity": 0.5,
+ "symbolType": "circle"
+ }
+ ],
+
+ "marks": [
+ {
+ "name": "marks",
+ "type": "symbol",
+ "from": {"data": "source"},
+ "encode": {
+ "update": {
+ "x": {"scale": "x", "field": "Horsepower"},
+ "y": {"scale": "y", "field": "Miles_per_Gallon"},
+ "size": {"scale": "size", "field": "Acceleration"},
+ "shape": {"value": "circle"},
+ "strokeWidth": {"value": 2},
+ "opacity": {"value": 0.5},
+ "stroke": {"value": "#4682b4"},
+ "fill": {"value": "transparent"}
+ }
+ }
+ }
+ ]
+ }
+ "##;
+ serde_json::from_str(spec_str).unwrap()
+}
diff --git a/examples/rust-examples/examples/grpc.rs b/examples/rust-examples/examples/grpc.rs
new file mode 100644
index 000000000..23ef0f460
--- /dev/null
+++ b/examples/rust-examples/examples/grpc.rs
@@ -0,0 +1,211 @@
+use std::str::FromStr;
+use tonic::transport::{Channel, Uri};
+use vegafusion_core::proto::gen::tasks::Variable;
+use vegafusion_core::runtime::VegaFusionRuntimeTrait;
+use vegafusion_core::spec::chart::ChartSpec;
+use vegafusion_core::task_graph::task_value::TaskValue;
+use vegafusion_runtime::task_graph::GrpcVegaFusionRuntime;
+
+/// This example demonstrates how to connect to a VegaFusion Server instance over gRPC then use
+/// the `pre_transform_values` method to get transformed datasets as Arrow tables.
+///
+/// Be sure to start an instance of VegaFusionServer with
+///
+/// ```
+/// vegafusion-server --port 50051
+/// ```
+#[tokio::main]
+async fn main() {
+ let spec = get_spec();
+
+ // Build URI
+ let uri = Uri::from_str("http://127.0.0.1:50051").unwrap();
+
+ // Build gRPC channel
+ let channel = Channel::builder(uri).connect().await.unwrap();
+
+ // Build GrpcVegaFusionRuntime
+ let runtime = GrpcVegaFusionRuntime::try_new(channel).await.unwrap();
+
+ let (values, warnings) = runtime
+ .pre_transform_values(
+ &spec,
+ &[(Variable::new_data("counts"), vec![])],
+ &Default::default(), // Inline datasets
+ &Default::default(), // Options
+ )
+ .await
+ .unwrap();
+
+ assert_eq!(values.len(), 1);
+ assert_eq!(warnings.len(), 0);
+
+ let TaskValue::Table(counts_table) = &values[0] else {
+ panic!("Expected a table")
+ };
+
+ let tbl_repr = counts_table.pretty_format(None).unwrap();
+
+ assert_eq!(
+ tbl_repr,
+ "\
++------+------+-------+
+| bin0 | bin1 | count |
++------+------+-------+
+| 6.0 | 7.0 | 985 |
+| 3.0 | 4.0 | 100 |
+| 7.0 | 8.0 | 741 |
+| 5.0 | 6.0 | 633 |
+| 8.0 | 9.0 | 204 |
+| 2.0 | 3.0 | 43 |
+| 4.0 | 5.0 | 273 |
+| 9.0 | 10.0 | 4 |
+| 1.0 | 2.0 | 5 |
++------+------+-------+"
+ )
+}
+
+fn get_spec() -> ChartSpec {
+ let spec_str = r##"
+ {
+ "$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},
+ "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": []
+ }
+ ]
+ }
+ ],
+ "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)"
+ }
+ ],
+ "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"}
+ }
+ }
+ }
+ ]
+ }
+ "##;
+ serde_json::from_str(spec_str).unwrap()
+}
diff --git a/examples/rust-examples/examples/inline_datasets.rs b/examples/rust-examples/examples/inline_datasets.rs
new file mode 100644
index 000000000..d36ba09da
--- /dev/null
+++ b/examples/rust-examples/examples/inline_datasets.rs
@@ -0,0 +1,194 @@
+use std::collections::HashMap;
+use vegafusion_common::data::table::VegaFusionTable;
+use vegafusion_core::data::dataset::VegaFusionDataset;
+use vegafusion_core::runtime::VegaFusionRuntimeTrait;
+use vegafusion_core::spec::chart::ChartSpec;
+use vegafusion_runtime::task_graph::runtime::VegaFusionRuntime;
+
+/// This example demonstrates how to use the `pre_transform_spec` method with an inline
+/// Arrow table to create a new spec with supported transforms pre-evaluated.
+#[tokio::main]
+async fn main() {
+ let spec = get_spec();
+
+ // Fetch movies dataset as json
+ let client = reqwest::ClientBuilder::new().build().unwrap();
+ let movies_json: serde_json::Value = client
+ .get(
+ "https://raw.githubusercontent.com/vega/vega-datasets/refs/heads/main/data/movies.json",
+ )
+ .send()
+ .await
+ .unwrap()
+ .json()
+ .await
+ .unwrap();
+
+ // Construct VegaFusionTable and wrap in VegaFusionDataset
+ let table = VegaFusionTable::from_json(&movies_json).unwrap();
+ let dataset = VegaFusionDataset::from_table(table, None).unwrap();
+ let inline_datasets: HashMap =
+ vec![("movies".to_string(), dataset)].into_iter().collect();
+
+ let runtime = VegaFusionRuntime::new(None);
+
+ let (transformed_spec, warnings) = runtime
+ .pre_transform_spec(
+ &spec,
+ &inline_datasets, // Inline datasets
+ &Default::default(), // Options
+ )
+ .await
+ .unwrap();
+
+ assert_eq!(warnings.len(), 0);
+ println!(
+ "{}",
+ serde_json::to_string_pretty(&transformed_spec).unwrap()
+ );
+}
+
+fn get_spec() -> ChartSpec {
+ let spec_str = r##"
+ {
+ "$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},
+ "data": [
+ {
+ "name": "table",
+ "url": "vegafusion+dataset://movies",
+ "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": []
+ }
+ ]
+ }
+ ],
+ "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)"
+ }
+ ],
+ "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"}
+ }
+ }
+ }
+ ]
+ }
+ "##;
+ serde_json::from_str(spec_str).unwrap()
+}
diff --git a/examples/rust-examples/examples/inline_datasets_plan.rs b/examples/rust-examples/examples/inline_datasets_plan.rs
new file mode 100644
index 000000000..d867a939a
--- /dev/null
+++ b/examples/rust-examples/examples/inline_datasets_plan.rs
@@ -0,0 +1,209 @@
+use datafusion::logical_expr::lit;
+use datafusion::prelude::{col, SessionContext};
+use std::collections::HashMap;
+use std::path::PathBuf;
+use vegafusion_core::data::dataset::VegaFusionDataset;
+use vegafusion_core::runtime::VegaFusionRuntimeTrait;
+use vegafusion_core::spec::chart::ChartSpec;
+use vegafusion_runtime::task_graph::runtime::VegaFusionRuntime;
+
+/// This example demonstrates how to use the `pre_transform_spec` method with an inline
+/// dataset that wraps a DataFusion logical plan to create a new spec with supported
+/// transforms pre-evaluated.
+#[tokio::main]
+async fn main() -> vegafusion_common::error::Result<()> {
+ // Construct default DataFusion session context
+ let ctx = SessionContext::new();
+
+ // Build path to parquet file
+ let parquet_path = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
+ .parent()
+ .unwrap()
+ .join("datasets")
+ .join("movies.parquet")
+ .display()
+ .to_string();
+
+ // Read parquet file into dataframe
+ let df = ctx
+ .read_parquet(parquet_path, Default::default())
+ .await?
+ .filter(col("MPAA Rating").eq(lit("PG")))?;
+
+ // Extract logical plan from DataFrame
+ // let plan = df.into_optimized_plan()?;
+ let plan = df.logical_plan().clone();
+
+ // Load chart spec
+ let spec = get_spec();
+
+ // Create VegaFusionDataset from the logical plan
+ let dataset = VegaFusionDataset::from_plan(plan);
+
+ let inline_datasets: HashMap =
+ vec![("movies".to_string(), dataset)].into_iter().collect();
+
+ let runtime = VegaFusionRuntime::new(None);
+
+ let (transformed_spec, warnings) = runtime
+ .pre_transform_spec(
+ &spec,
+ &inline_datasets, // Inline datasets
+ &Default::default(), // Options
+ )
+ .await
+ .unwrap();
+
+ assert_eq!(warnings.len(), 0);
+ println!(
+ "{}",
+ serde_json::to_string_pretty(&transformed_spec).unwrap()
+ );
+
+ Ok(())
+}
+
+fn get_spec() -> ChartSpec {
+ let spec_str = r##"
+ {
+ "$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},
+ "data": [
+ {
+ "name": "table",
+ "url": "vegafusion+dataset://movies",
+ "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": []
+ }
+ ]
+ }
+ ],
+ "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)"
+ }
+ ],
+ "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"}
+ }
+ }
+ }
+ ]
+ }
+ "##;
+ serde_json::from_str(spec_str).unwrap()
+}
diff --git a/examples/rust-examples/examples/pre_transform_data.rs b/examples/rust-examples/examples/pre_transform_data.rs
new file mode 100644
index 000000000..4285120e4
--- /dev/null
+++ b/examples/rust-examples/examples/pre_transform_data.rs
@@ -0,0 +1,196 @@
+use vegafusion_core::proto::gen::tasks::Variable;
+use vegafusion_core::runtime::VegaFusionRuntimeTrait;
+use vegafusion_core::spec::chart::ChartSpec;
+use vegafusion_core::task_graph::task_value::TaskValue;
+use vegafusion_runtime::task_graph::runtime::VegaFusionRuntime;
+
+/// This example demonstrates how to use the `pre_transform_values` method to get
+/// transformed datasets as Arrow tables.
+#[tokio::main]
+async fn main() {
+ let spec = get_spec();
+
+ let runtime = VegaFusionRuntime::new(None);
+
+ let (values, warnings) = runtime
+ .pre_transform_values(
+ &spec,
+ &[(Variable::new_data("counts"), vec![])],
+ &Default::default(), // Inline datasets
+ &Default::default(), // Options
+ )
+ .await
+ .unwrap();
+
+ assert_eq!(values.len(), 1);
+ assert_eq!(warnings.len(), 0);
+
+ let TaskValue::Table(counts_table) = &values[0] else {
+ panic!("Expected a table")
+ };
+
+ let tbl_repr = counts_table.pretty_format(None).unwrap();
+
+ assert_eq!(
+ tbl_repr,
+ "\
++------+------+-------+
+| bin0 | bin1 | count |
++------+------+-------+
+| 6.0 | 7.0 | 985 |
+| 3.0 | 4.0 | 100 |
+| 7.0 | 8.0 | 741 |
+| 5.0 | 6.0 | 633 |
+| 8.0 | 9.0 | 204 |
+| 2.0 | 3.0 | 43 |
+| 4.0 | 5.0 | 273 |
+| 9.0 | 10.0 | 4 |
+| 1.0 | 2.0 | 5 |
++------+------+-------+"
+ )
+}
+
+fn get_spec() -> ChartSpec {
+ let spec_str = r##"
+ {
+ "$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},
+ "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": []
+ }
+ ]
+ }
+ ],
+ "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)"
+ }
+ ],
+ "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"}
+ }
+ }
+ }
+ ]
+ }
+ "##;
+ serde_json::from_str(spec_str).unwrap()
+}
diff --git a/examples/rust-examples/examples/pre_transform_extract.rs b/examples/rust-examples/examples/pre_transform_extract.rs
new file mode 100644
index 000000000..e9d93010b
--- /dev/null
+++ b/examples/rust-examples/examples/pre_transform_extract.rs
@@ -0,0 +1,180 @@
+use vegafusion_core::proto::gen::pretransform::PreTransformExtractOpts;
+use vegafusion_core::runtime::{PreTransformExtractTable, VegaFusionRuntimeTrait};
+use vegafusion_core::spec::chart::ChartSpec;
+use vegafusion_runtime::task_graph::runtime::VegaFusionRuntime;
+
+/// This example demonstrates how to use the `pre_transform_extract` method to create a new
+/// spec with supported transforms pre-evaluated and the transformed datasets extract in arrow format
+#[tokio::main]
+async fn main() {
+ let spec = get_spec();
+
+ let runtime = VegaFusionRuntime::new(None);
+
+ let (transformed_spec, datasets, warnings) = runtime
+ .pre_transform_extract(
+ &spec,
+ &Default::default(), // Inline datasets
+ &PreTransformExtractOpts {
+ extract_threshold: 4,
+ ..Default::default()
+ },
+ )
+ .await
+ .unwrap();
+
+ assert_eq!(warnings.len(), 0);
+ assert_eq!(datasets.len(), 1);
+
+ let PreTransformExtractTable { name, scope, table } = datasets[0].clone();
+ println!(
+ "{name}({scope:?})\n{}\n{}",
+ table.pretty_format(None).unwrap(),
+ serde_json::to_string_pretty(&transformed_spec).unwrap()
+ );
+}
+
+fn get_spec() -> ChartSpec {
+ let spec_str = r##"
+ {
+ "$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},
+ "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": []
+ }
+ ]
+ }
+ ],
+ "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)"
+ }
+ ],
+ "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"}
+ }
+ }
+ }
+ ]
+ }
+ "##;
+ serde_json::from_str(spec_str).unwrap()
+}
diff --git a/examples/rust-examples/examples/pre_transform_spec.rs b/examples/rust-examples/examples/pre_transform_spec.rs
new file mode 100644
index 000000000..fb035a038
--- /dev/null
+++ b/examples/rust-examples/examples/pre_transform_spec.rs
@@ -0,0 +1,172 @@
+use vegafusion_core::runtime::VegaFusionRuntimeTrait;
+use vegafusion_core::spec::chart::ChartSpec;
+use vegafusion_runtime::task_graph::runtime::VegaFusionRuntime;
+
+/// This example demonstrates how to use the `pre_transform_spec` method to create a new
+/// spec with supported transforms pre-evaluated.
+#[tokio::main]
+async fn main() {
+ let spec = get_spec();
+
+ let runtime = VegaFusionRuntime::new(None);
+
+ let (transformed_spec, warnings) = runtime
+ .pre_transform_spec(
+ &spec,
+ &Default::default(), // Inline datasets
+ &Default::default(), // Options
+ )
+ .await
+ .unwrap();
+
+ assert_eq!(warnings.len(), 0);
+ println!(
+ "{}",
+ serde_json::to_string_pretty(&transformed_spec).unwrap()
+ );
+}
+
+fn get_spec() -> ChartSpec {
+ let spec_str = r##"
+ {
+ "$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},
+ "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": []
+ }
+ ]
+ }
+ ],
+ "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)"
+ }
+ ],
+ "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"}
+ }
+ }
+ }
+ ]
+ }
+ "##;
+ serde_json::from_str(spec_str).unwrap()
+}
diff --git a/java/.gitignore b/java/.gitignore
deleted file mode 100644
index 9e2a1bf21..000000000
--- a/java/.gitignore
+++ /dev/null
@@ -1,6 +0,0 @@
-# Ignore Gradle project-specific cache directory
-.gradle
-
-# Ignore Gradle build output directory
-build
-/native/
diff --git a/java/README.md b/java/README.md
deleted file mode 100644
index ca0c8e8ce..000000000
--- a/java/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
-# VegaFusion-java
-This directory contains the Java interface to VegaFusion. This is the pure-Java logic that relies on the native jni library generated by the `vegafusion-jni` Rust crate.
-
-# Notes / Limitations
- - Vega JSON specifications are input and output as strings.
- - `VegaFusionRuntime.preTransformSpec` doesn't support inline datasets yet.
- - `preTransformValues` isn't supported yet
- - `preTransformExtract` isn't supported yet
- - Custom SQL connections aren't supported yet (The default DataFusion connection is used)
-
-# Running tests
-In order to run tests:
-
-```
-pixi run test-java
-```
-
-# Releasing
-See [RELEASE.md](../RELEASE.md) for instructions on publishing to Maven Central.
diff --git a/java/gradle/wrapper/gradle-wrapper.jar b/java/gradle/wrapper/gradle-wrapper.jar
deleted file mode 100644
index c1962a79e..000000000
Binary files a/java/gradle/wrapper/gradle-wrapper.jar and /dev/null differ
diff --git a/java/gradle/wrapper/gradle-wrapper.properties b/java/gradle/wrapper/gradle-wrapper.properties
deleted file mode 100644
index 37aef8d3f..000000000
--- a/java/gradle/wrapper/gradle-wrapper.properties
+++ /dev/null
@@ -1,6 +0,0 @@
-distributionBase=GRADLE_USER_HOME
-distributionPath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-8.1.1-bin.zip
-networkTimeout=10000
-zipStoreBase=GRADLE_USER_HOME
-zipStorePath=wrapper/dists
diff --git a/java/gradlew b/java/gradlew
deleted file mode 100755
index aeb74cbb4..000000000
--- a/java/gradlew
+++ /dev/null
@@ -1,245 +0,0 @@
-#!/bin/sh
-
-#
-# Copyright © 2015-2021 the original authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# https://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-##############################################################################
-#
-# Gradle start up script for POSIX generated by Gradle.
-#
-# Important for running:
-#
-# (1) You need a POSIX-compliant shell to run this script. If your /bin/sh is
-# noncompliant, but you have some other compliant shell such as ksh or
-# bash, then to run this script, type that shell name before the whole
-# command line, like:
-#
-# ksh Gradle
-#
-# Busybox and similar reduced shells will NOT work, because this script
-# requires all of these POSIX shell features:
-# * functions;
-# * expansions «$var», «${var}», «${var:-default}», «${var+SET}»,
-# «${var#prefix}», «${var%suffix}», and «$( cmd )»;
-# * compound commands having a testable exit status, especially «case»;
-# * various built-in commands including «command», «set», and «ulimit».
-#
-# Important for patching:
-#
-# (2) This script targets any POSIX shell, so it avoids extensions provided
-# by Bash, Ksh, etc; in particular arrays are avoided.
-#
-# The "traditional" practice of packing multiple parameters into a
-# space-separated string is a well documented source of bugs and security
-# problems, so this is (mostly) avoided, by progressively accumulating
-# options in "$@", and eventually passing that to Java.
-#
-# Where the inherited environment variables (DEFAULT_JVM_OPTS, JAVA_OPTS,
-# and GRADLE_OPTS) rely on word-splitting, this is performed explicitly;
-# see the in-line comments for details.
-#
-# There are tweaks for specific operating systems such as AIX, CygWin,
-# Darwin, MinGW, and NonStop.
-#
-# (3) This script is generated from the Groovy template
-# https://github.com/gradle/gradle/blob/HEAD/subprojects/plugins/src/main/resources/org/gradle/api/internal/plugins/unixStartScript.txt
-# within the Gradle project.
-#
-# You can find Gradle at https://github.com/gradle/gradle/.
-#
-##############################################################################
-
-# Attempt to set APP_HOME
-
-# Resolve links: $0 may be a link
-app_path=$0
-
-# Need this for daisy-chained symlinks.
-while
- APP_HOME=${app_path%"${app_path##*/}"} # leaves a trailing /; empty if no leading path
- [ -h "$app_path" ]
-do
- ls=$( ls -ld "$app_path" )
- link=${ls#*' -> '}
- case $link in #(
- /*) app_path=$link ;; #(
- *) app_path=$APP_HOME$link ;;
- esac
-done
-
-# This is normally unused
-# shellcheck disable=SC2034
-APP_BASE_NAME=${0##*/}
-APP_HOME=$( cd "${APP_HOME:-./}" && pwd -P ) || exit
-
-# Use the maximum available, or set MAX_FD != -1 to use that value.
-MAX_FD=maximum
-
-warn () {
- echo "$*"
-} >&2
-
-die () {
- echo
- echo "$*"
- echo
- exit 1
-} >&2
-
-# OS specific support (must be 'true' or 'false').
-cygwin=false
-msys=false
-darwin=false
-nonstop=false
-case "$( uname )" in #(
- CYGWIN* ) cygwin=true ;; #(
- Darwin* ) darwin=true ;; #(
- MSYS* | MINGW* ) msys=true ;; #(
- NONSTOP* ) nonstop=true ;;
-esac
-
-CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
-
-
-# Determine the Java command to use to start the JVM.
-if [ -n "$JAVA_HOME" ] ; then
- if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
- # IBM's JDK on AIX uses strange locations for the executables
- JAVACMD=$JAVA_HOME/jre/sh/java
- else
- JAVACMD=$JAVA_HOME/bin/java
- fi
- if [ ! -x "$JAVACMD" ] ; then
- die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
-
-Please set the JAVA_HOME variable in your environment to match the
-location of your Java installation."
- fi
-else
- JAVACMD=java
- which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
-
-Please set the JAVA_HOME variable in your environment to match the
-location of your Java installation."
-fi
-
-# Increase the maximum file descriptors if we can.
-if ! "$cygwin" && ! "$darwin" && ! "$nonstop" ; then
- case $MAX_FD in #(
- max*)
- # In POSIX sh, ulimit -H is undefined. That's why the result is checked to see if it worked.
- # shellcheck disable=SC3045
- MAX_FD=$( ulimit -H -n ) ||
- warn "Could not query maximum file descriptor limit"
- esac
- case $MAX_FD in #(
- '' | soft) :;; #(
- *)
- # In POSIX sh, ulimit -n is undefined. That's why the result is checked to see if it worked.
- # shellcheck disable=SC3045
- ulimit -n "$MAX_FD" ||
- warn "Could not set maximum file descriptor limit to $MAX_FD"
- esac
-fi
-
-# Collect all arguments for the java command, stacking in reverse order:
-# * args from the command line
-# * the main class name
-# * -classpath
-# * -D...appname settings
-# * --module-path (only if needed)
-# * DEFAULT_JVM_OPTS, JAVA_OPTS, and GRADLE_OPTS environment variables.
-
-# For Cygwin or MSYS, switch paths to Windows format before running java
-if "$cygwin" || "$msys" ; then
- APP_HOME=$( cygpath --path --mixed "$APP_HOME" )
- CLASSPATH=$( cygpath --path --mixed "$CLASSPATH" )
-
- JAVACMD=$( cygpath --unix "$JAVACMD" )
-
- # Now convert the arguments - kludge to limit ourselves to /bin/sh
- for arg do
- if
- case $arg in #(
- -*) false ;; # don't mess with options #(
- /?*) t=${arg#/} t=/${t%%/*} # looks like a POSIX filepath
- [ -e "$t" ] ;; #(
- *) false ;;
- esac
- then
- arg=$( cygpath --path --ignore --mixed "$arg" )
- fi
- # Roll the args list around exactly as many times as the number of
- # args, so each arg winds up back in the position where it started, but
- # possibly modified.
- #
- # NB: a `for` loop captures its iteration list before it begins, so
- # changing the positional parameters here affects neither the number of
- # iterations, nor the values presented in `arg`.
- shift # remove old arg
- set -- "$@" "$arg" # push replacement arg
- done
-fi
-
-
-# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
-DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"'
-
-# Collect all arguments for the java command;
-# * $DEFAULT_JVM_OPTS, $JAVA_OPTS, and $GRADLE_OPTS can contain fragments of
-# shell script including quotes and variable substitutions, so put them in
-# double quotes to make sure that they get re-expanded; and
-# * put everything else in single quotes, so that it's not re-expanded.
-
-set -- \
- "-Dorg.gradle.appname=$APP_BASE_NAME" \
- -classpath "$CLASSPATH" \
- org.gradle.wrapper.GradleWrapperMain \
- "$@"
-
-# Stop when "xargs" is not available.
-if ! command -v xargs >/dev/null 2>&1
-then
- die "xargs is not available"
-fi
-
-# Use "xargs" to parse quoted args.
-#
-# With -n1 it outputs one arg per line, with the quotes and backslashes removed.
-#
-# In Bash we could simply go:
-#
-# readarray ARGS < <( xargs -n1 <<<"$var" ) &&
-# set -- "${ARGS[@]}" "$@"
-#
-# but POSIX shell has neither arrays nor command substitution, so instead we
-# post-process each arg (as a line of input to sed) to backslash-escape any
-# character that might be a shell metacharacter, then use eval to reverse
-# that process (while maintaining the separation between arguments), and wrap
-# the whole thing up as a single "set" statement.
-#
-# This will of course break if any of these variables contains a newline or
-# an unmatched quote.
-#
-
-eval "set -- $(
- printf '%s\n' "$DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS" |
- xargs -n1 |
- sed ' s~[^-[:alnum:]+,./:=@_]~\\&~g; ' |
- tr '\n' ' '
- )" '"$@"'
-
-exec "$JAVACMD" "$@"
diff --git a/java/gradlew.bat b/java/gradlew.bat
deleted file mode 100644
index 6689b85be..000000000
--- a/java/gradlew.bat
+++ /dev/null
@@ -1,92 +0,0 @@
-@rem
-@rem Copyright 2015 the original author or authors.
-@rem
-@rem Licensed under the Apache License, Version 2.0 (the "License");
-@rem you may not use this file except in compliance with the License.
-@rem You may obtain a copy of the License at
-@rem
-@rem https://www.apache.org/licenses/LICENSE-2.0
-@rem
-@rem Unless required by applicable law or agreed to in writing, software
-@rem distributed under the License is distributed on an "AS IS" BASIS,
-@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-@rem See the License for the specific language governing permissions and
-@rem limitations under the License.
-@rem
-
-@if "%DEBUG%"=="" @echo off
-@rem ##########################################################################
-@rem
-@rem Gradle startup script for Windows
-@rem
-@rem ##########################################################################
-
-@rem Set local scope for the variables with windows NT shell
-if "%OS%"=="Windows_NT" setlocal
-
-set DIRNAME=%~dp0
-if "%DIRNAME%"=="" set DIRNAME=.
-@rem This is normally unused
-set APP_BASE_NAME=%~n0
-set APP_HOME=%DIRNAME%
-
-@rem Resolve any "." and ".." in APP_HOME to make it shorter.
-for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi
-
-@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
-set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m"
-
-@rem Find java.exe
-if defined JAVA_HOME goto findJavaFromJavaHome
-
-set JAVA_EXE=java.exe
-%JAVA_EXE% -version >NUL 2>&1
-if %ERRORLEVEL% equ 0 goto execute
-
-echo.
-echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:findJavaFromJavaHome
-set JAVA_HOME=%JAVA_HOME:"=%
-set JAVA_EXE=%JAVA_HOME%/bin/java.exe
-
-if exist "%JAVA_EXE%" goto execute
-
-echo.
-echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:execute
-@rem Setup the command line
-
-set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
-
-
-@rem Execute Gradle
-"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %*
-
-:end
-@rem End local scope for the variables with windows NT shell
-if %ERRORLEVEL% equ 0 goto mainEnd
-
-:fail
-rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
-rem the _cmd.exe /c_ return code!
-set EXIT_CODE=%ERRORLEVEL%
-if %EXIT_CODE% equ 0 set EXIT_CODE=1
-if not ""=="%GRADLE_EXIT_CONSOLE%" exit %EXIT_CODE%
-exit /b %EXIT_CODE%
-
-:mainEnd
-if "%OS%"=="Windows_NT" endlocal
-
-:omega
diff --git a/java/lib/build.gradle b/java/lib/build.gradle
deleted file mode 100644
index 79798c1bd..000000000
--- a/java/lib/build.gradle
+++ /dev/null
@@ -1,138 +0,0 @@
-plugins {
- // Apply the java-library plugin for API and implementation separation.
- id 'java-library'
- id 'maven-publish'
- id 'signing'
-}
-
-repositories {
- // Use Maven Central for resolving dependencies.
- mavenCentral()
-}
-
-dependencies {
- // Use JUnit Jupiter for testing.
- testImplementation 'org.junit.jupiter:junit-jupiter:5.9.1'
- testImplementation 'com.fasterxml.jackson.core:jackson-databind:2.15.0'
-}
-
-// Apply a specific Java toolchain to ease working on different environments.
-java {
- toolchain {
- languageVersion = JavaLanguageVersion.of(17)
- }
-}
-
-tasks.named('test') {
- // Use JUnit Platform for unit tests.
- useJUnitPlatform()
-}
-
-def versionId = new File('version.txt').text.trim()
-
-tasks.register('sourcesJar', Jar) {
- dependsOn classes
- archiveClassifier.set('sources')
- from sourceSets.main.allSource
-}
-
-tasks.register('javadocJar', Jar) {
- dependsOn javadoc
- archiveClassifier.set('javadoc')
- from javadoc.destinationDir
-}
-
-artifacts {
- archives sourcesJar
- archives javadocJar
-}
-
-publishing {
- publications {
- mavenJava(MavenPublication) {
- from components.java
- groupId = 'io.vegafusion'
- artifactId = 'vegafusion'
- version = versionId
- artifact sourcesJar
- artifact javadocJar
- pom {
- name = 'VegaFusion'
- description = 'Server-side scaling for Vega visualizations'
- url = 'https://vegafusion.io/'
- licenses {
- license {
- name = 'BSD-3-Clause'
- url = 'https://spdx.org/licenses/BSD-3-Clause.html'
- }
- }
- developers {
- developer {
- id = 'jonmmease'
- name = 'Jon Mease'
- email = 'jonmmease@gmail.com'
- }
- }
- scm {
- connection = 'scm:git:https://github.com/hex-inc/vegafusion.git'
- developerConnection = 'scm:git:git@github.com:hex-inc/vegafusion.git'
- url = 'https://github.com/hex-inc/vegafusion'
- }
- }
- }
- }
- repositories {
- maven {
- name = 'ossrh'
- url = uri("https://s01.oss.sonatype.org/service/local/staging/deploy/maven2/")
- credentials {
- def ossrhUsernameProvider = providers.gradleProperty('ossrhUsername')
- def ossrhPasswordProvider = providers.gradleProperty('ossrhPassword')
-
- username = ossrhUsernameProvider.orNull
- password = ossrhPasswordProvider.orNull
- }
- }
- }
-}
-
-signing {
- sign publishing.publications.mavenJava
-}
-
-jar {
- manifest {
- attributes "Main-Class": "io.vegafusion.VegaFusionRuntime"
- }
- archiveBaseName.set('vegafusion')
- archiveVersion.set(versionId)
-
- // Validate that VEGAFUSION_JNI_LIBRARIES is set and valid
- doFirst {
- def nativeLibDir = System.getenv('VEGAFUSION_JNI_LIBRARIES')
- if (nativeLibDir == null) {
- throw new GradleException(
- 'VEGAFUSION_JNI_LIBRARIES environment variable is not set'
- )
- }
-
- // Validate the expected subdirectories
- def subdirs = [
- 'linux-64',
- 'osx-64',
- 'osx-arm64',
-// 'win-64'
- ]
- subdirs.each { subdir ->
- def subdirPath = new File(nativeLibDir, subdir)
- if (!subdirPath.isDirectory()) {
- throw new GradleException("Expected subdirectory $subdir does not exist in $nativeLibDir")
- }
- }
- }
-
- def nativeLibDir = System.getenv('VEGAFUSION_JNI_LIBRARIES')
- from(nativeLibDir) {
- into "native"
- }
-}
diff --git a/java/lib/src/main/java/io/vegafusion/VegaFusionException.java b/java/lib/src/main/java/io/vegafusion/VegaFusionException.java
deleted file mode 100644
index 91c82fc3a..000000000
--- a/java/lib/src/main/java/io/vegafusion/VegaFusionException.java
+++ /dev/null
@@ -1,16 +0,0 @@
-package io.vegafusion;
-
-/**
- * This class represents exceptions specific to VegaFusion
- */
-public class VegaFusionException extends RuntimeException {
- /**
- * Constructs a new VegaFusionException with the specified detail message.
- *
- * @param message the detail message. The detail message is saved for
- * later retrieval by the {@link #getMessage()} method.
- */
- public VegaFusionException(String message) {
- super(message);
- }
-}
diff --git a/java/lib/src/main/java/io/vegafusion/VegaFusionRuntime.java b/java/lib/src/main/java/io/vegafusion/VegaFusionRuntime.java
deleted file mode 100644
index ecd43ff1e..000000000
--- a/java/lib/src/main/java/io/vegafusion/VegaFusionRuntime.java
+++ /dev/null
@@ -1,239 +0,0 @@
-package io.vegafusion;
-
-import java.io.InputStream;
-import java.nio.file.Files;
-import java.nio.file.Paths;
-import java.nio.file.StandardCopyOption;
-import java.io.IOException;
-
-/**
- * This class provides a VegaFusion runtime that may be used to perform
- * Vega transform operations.
- */
-public class VegaFusionRuntime {
- /**
- * Returns the version of the VegaFusion
- *
- * @return A string storing the version of the VegaFusion
- */
- public static native String version();
-
- /**
- * Creates a native VegaFusionRuntime object with specified capacity
- * and memory limit. This is a native method, implemented in native code.
- *
- * @param capacity The capacity of the runtime cache
- * @param memoryLimit The memory limit for the runtime cache
- * @param numThreads The number of worker threads
- * @return A long representing a pointer to the native runtime.
- */
- private static native long innerCreate(long capacity, long memoryLimit, int numThreads);
-
- /**
- * Destroys a native VegaFusionRuntime object.
- * This is a native method, implemented in native code.
- *
- * @param pointer The pointer to the state to be destroyed.
- */
- private static native void innerDestroy(long pointer);
-
- /**
- * Patches a previously pre-transformed Vega specification.
- * This is a native method, implemented in native code.
- *
- * @param spec1 Previous input to preTransformSpec
- * @param preTransformedSpec1 Previous result of preTransformSpec
- * @param spec2 New Vega spec that is potentially similar to spec1
- * @return The pre-transformed version of spec2, or null if patching
- * is not possible.
- */
- private static native String innerPatchPreTransformedSpec(
- String spec1, String preTransformedSpec1, String spec2
- );
-
- /**
- * Evaluate the transforms in a Vega specification, returning a new
- * specification with transformed data included inline.
- * This is a native method, implemented in native code.
- *
- * @param pointer The pointer to the native VegaFusionRuntime
- * @param spec The Vega specification to be transformed
- * @param localTz The local time zone (defaults to "UTC" if null)
- * @param defaultInputTz The default input time zone (defaults to localTz if null)
- * @param rowLimit The row limit for the inline transformed data.
- * If zero then no limit is imposed.
- * @param preserveInteractivity Whether to preserve interactivity in the resulting
- * Vega specification
- * @return String containing the transformed specification.
- Any warnings are added to the spec under usermeta.vegafusion_warnings
- */
- private static native String innerPreTransformSpec(
- long pointer, String spec, String localTz, String defaultInputTz, int rowLimit, boolean preserveInteractivity
- );
-
- static {
- String libPath = System.getenv("VEGAFUSION_JNI_LIBRARY");
- if (libPath != null) {
- // Use explicit path to jni library
- System.load(libPath);
- } else {
- // Use library bundled in jar
- try {
- System.loadLibrary("vegafusion_jni");
- } catch (LinkageError e) {
- // Build path based on os and architecture
-
- String osName = System.getProperty("os.name").toLowerCase();
- String osArch = System.getProperty("os.arch").toLowerCase();
-
- String libName;
- String directory;
- if (osName.contains("win")) {
- libName = "vegafusion_jni.dll";
- if (osArch.equals("amd64") || osArch.equals("x86_64")) {
- directory = "win-64";
- } else {
- throw new UnsupportedOperationException("Unsupported architecture for Windows: " + osArch);
- }
- } else if (osName.contains("mac")) {
- libName = "libvegafusion_jni.dylib";
- if (osArch.equals("amd64") || osArch.equals("x86_64")) {
- directory = "osx-64";
- } else if (osArch.equals("aarch64") || osArch.equals("arm64")) {
- directory = "osx-arm64";
- } else {
- throw new UnsupportedOperationException("Unsupported architecture for macOS: " + osArch);
- }
- } else if (osName.contains("nix") || osName.contains("nux")) {
- libName = "libvegafusion_jni.so";
- if (osArch.equals("amd64") || osArch.equals("x86_64")) {
- directory = "linux-64";
- } else {
- throw new UnsupportedOperationException("Unsupported architecture for Linux: " + osArch);
- }
- } else {
- throw new UnsupportedOperationException("Unsupported operating system: " + osName);
- }
-
- // Path in the jar file to the compiled library
- String libPathInJar = "/native/" + directory + "/" + libName;
-
- // Extract the library to a temporary file
- InputStream libStream = VegaFusionRuntime.class.getResourceAsStream(libPathInJar);
- if (libStream == null) {
- throw new RuntimeException("Failed to find " + libPathInJar + " in jar file");
- }
-
- // Create a temp file and get its path
- try {
- String tempFileName = Paths.get(libName).getFileName().toString();
- java.nio.file.Path temp = Files.createTempFile(tempFileName, "");
-
- // Copy the library to the temp file
- Files.copy(libStream, temp, StandardCopyOption.REPLACE_EXISTING);
-
- // Load the library
- System.load(temp.toAbsolutePath().toString());
-
- // Schedule the temp file to be deleted on exit
- temp.toFile().deleteOnExit();
- } catch (IOException ioe) {
- e.printStackTrace();
- }
- }
- }
- }
-
- private long state_ptr;
-
- /**
- * Constructs a new VegaFusionRuntime with the specified cache capacity and memory limit.
- *
- * @param capacity The cache capacity (in number of cache entries)
- * @param memoryLimit The cache memory limit (in bytes)
- * @param numThreads The number of worker threads
- */
- public VegaFusionRuntime(long capacity, long memoryLimit, int numThreads) {
- state_ptr = VegaFusionRuntime.innerCreate(capacity, memoryLimit, numThreads);
- }
-
- /**
- * Destroys the native VegaFusionRuntime.
- * The class instance should not be used after destroy is called.
- */
- public void destroy() {
- if (state_ptr != 0) {
- innerDestroy(state_ptr);
- state_ptr = 0;
- }
- }
-
- /**
- * Patches a previously pre-transformed Vega specification.
- *
- * @param spec1 Previous input to preTransformSpec
- * @param preTransformedSpec1 Previous result of preTransformSpec
- * @param spec2 New Vega spec that is potentially similar to spec1
- * @return The pre-transformed version of spec2, or null if patching
- * is not possible.
- * @throws IllegalStateException if the destroy method was called previously
- */
- public String patchPreTransformedSpec(String spec1, String preTransformedSpec1, String spec2) {
- validate();
- return innerPatchPreTransformedSpec(spec1, preTransformedSpec1, spec2);
- }
-
- /**
- * Evaluate the transforms in a Vega specification, returning a new
- * specification with transformed data included inline.
- *
- * @param spec The Vega specification to be transformed
- * @param localTz The local time zone (defaults to "UTC" if null)
- * @param defaultInputTz The default input time zone (defaults to localTz if null)
- * @param rowLimit The row limit for the inline transformed data.
- * If zero then no limit is imposed.
- * @param preserveInteractivity Whether to preserve interactivity in the resulting
- * Vega specification
- * @return String containing the transformed specification.
- Any warnings are added to the spec under usermeta.vegafusion_warnings.
- * @throws IllegalStateException if the destroy method was called previously
- */
- public String preTransformSpec(String spec, String localTz, String defaultInputTz, int rowLimit, boolean preserveInteractivity) {
- validate();
- return innerPreTransformSpec(state_ptr, spec, localTz, defaultInputTz, rowLimit, preserveInteractivity);
- }
-
- /**
- * Checks if the VegaFusionRuntime state is valid
- * (if the destroy method was not called previously)
- *
- * @return A boolean indicating whether the runtime is valid.
- */
- public boolean isValid() {
- return state_ptr != 0;
- }
-
- /**
- * Validates the native VegaFusionRuntime
- *
- * @throws IllegalStateException If the native VegaFusionRuntime is invalid.
- */
- public void validate() throws IllegalStateException {
- if (state_ptr == 0) {
- throw new IllegalStateException("VegaFusionRuntime may not be used after calling destroy()");
- }
- }
-
- /**
- * The main entry point for the program.
- * This method retrieves the version of the VegaFusionRuntime and prints it out,
- * along with a brief description of VegaFusion.
- *
- * @param args An array of command-line arguments for the application.
- */
- public static void main(String[] args) {
- String version = VegaFusionRuntime.version();
- System.out.println("VegaFusion: Server-side scaling for Vega visualizations");
- System.out.println("Version: " + version);
- }
-}
diff --git a/java/lib/src/test/java/io/vegafusion/VegaFusionRuntimeTest.java b/java/lib/src/test/java/io/vegafusion/VegaFusionRuntimeTest.java
deleted file mode 100644
index 0b41b38fc..000000000
--- a/java/lib/src/test/java/io/vegafusion/VegaFusionRuntimeTest.java
+++ /dev/null
@@ -1,347 +0,0 @@
-package io.vegafusion;
-
-import java.io.IOException;
-import java.nio.file.*;
-import java.util.ArrayList;
-import java.util.Map;
-import java.util.List;
-
-import com.fasterxml.jackson.core.JsonProcessingException;
-import org.junit.jupiter.api.Test;
-
-import com.fasterxml.jackson.databind.ObjectMapper;
-import com.fasterxml.jackson.core.type.TypeReference;
-import com.fasterxml.jackson.databind.JsonNode;
-
-import static org.junit.jupiter.api.Assertions.*;
-
-public class VegaFusionRuntimeTest {
- private VegaFusionRuntime makeRuntime() {
- return new VegaFusionRuntime(32, 1000000000, 4);
- }
-
- @Test
- void testVersion() throws IOException {
- String expectedVersion = new String(Files.readAllBytes(Paths.get("../version.txt"))).trim();
- var version = VegaFusionRuntime.version();
- assertEquals(version, expectedVersion);
- }
-
- @Test
- void testCreate() {
- VegaFusionRuntime runtime = makeRuntime();
- assertTrue(runtime.isValid());
-
- // Destroy should invalidate
- runtime.destroy();
- assertFalse(runtime.isValid());
-
- // Destroy should be idempotent
- runtime.destroy();
- assertFalse(runtime.isValid());
- }
-
- @Test
- void testPatchPretransformedSpec() throws JsonProcessingException {
- // Create runtime
- VegaFusionRuntime runtime = makeRuntime();
-
- // Define simple specs that are compatible with patching
- String spec1 = "{\"width\": 100, \"height\": 200}";
- String preTransformedSpec1 = "{\"width\": 100, \"height\": 150}";
- String spec2 = "{\"width\": 150, \"height\": 200}";
-
- // Perform patch
- String preTransformedSpec2 = runtime.patchPreTransformedSpec(spec1, preTransformedSpec1, spec2);
-
- // Validate results
- ObjectMapper mapper = new ObjectMapper();
- Map specMap = mapper.readValue(
- preTransformedSpec2, new TypeReference<>(){}
- );
- assertEquals(specMap.get("width").asInt(), 150);
- assertEquals(specMap.get("height").asInt(), 150);
-
- // Cleanup
- runtime.destroy();
-
- // Calling again after destroy should raise an exception
- assertThrows(IllegalStateException.class, () ->
- runtime.patchPreTransformedSpec(spec1, preTransformedSpec1, spec2)
- );
- }
-
- @Test
- void testUnsuccessfulPatchPretransformedSpec() {
- // Create runtime
- VegaFusionRuntime runtime = makeRuntime();
-
- // Define specs that are not compatible with patching
- String spec1 = "{\"data\": [{\"name\": \"foo\"}]}";
- String preTransformedSpec1 = "{\"data\": []}";
- String spec2 = "{\"data\": [{\"name\": \"bar\"}]}";
- String preTransformedSpec2 = runtime.patchPreTransformedSpec(spec1, preTransformedSpec1, spec2);
- assertNull(preTransformedSpec2);
-
- // Cleanup
- runtime.destroy();
-
- // Calling again after destroy should raise an exception
- assertThrows(IllegalStateException.class, () ->
- runtime.patchPreTransformedSpec(spec1, preTransformedSpec1, spec2)
- );
- }
-
- @Test
- void testInvalidPatchPretransformedSpec1() {
- // Create runtime
- VegaFusionRuntime runtime = makeRuntime();
-
- // Define spec strings that are not valid JSON
- String spec1 = "{\"data\"";
- String preTransformedSpec1 = "{\"data\"";
- String spec2 = "{\"data\"";
-
- VegaFusionException e = assertThrows(VegaFusionException.class, () ->
- runtime.patchPreTransformedSpec(spec1, preTransformedSpec1, spec2)
- );
-
- assertTrue(e.getMessage().toLowerCase().contains("serde"));
-
- // Cleanup
- runtime.destroy();
- }
-
- @Test
- void testInvalidPatchPretransformedSpec2() {
- // Create runtime
- VegaFusionRuntime runtime = makeRuntime();
-
- // Define spec strings that are valid JSON but invalid Vega specs
- String spec1 = "{\"data\": 23}";
- String preTransformedSpec1 = "{}";
- String spec2 = "{}";
-
- VegaFusionException e = assertThrows(VegaFusionException.class, () ->
- runtime.patchPreTransformedSpec(spec1, preTransformedSpec1, spec2)
- );
-
- assertTrue(e.getMessage().toLowerCase().contains("serde"));
-
- // Cleanup
- runtime.destroy();
- }
-
-
- @Test
- void testPretransformSpec() throws JsonProcessingException {
- // Build VegaFusionRuntime
- VegaFusionRuntime runtime = makeRuntime();
-
- // Construct histogram spec
- String spec = histSpec();
-
- // Pre-transform spec with rowLimit of 3 so that inline data is truncated
- String preTransformedSpecResult = runtime.preTransformSpec(
- spec, "UTC", null, 3, true
- );
-
- // Parse spec as JSON
- ObjectMapper mapper = new ObjectMapper();
- Map preTransformedSpec = mapper.readValue(
- preTransformedSpecResult, new TypeReference<>(){}
- );
-
- assertEquals(
- preTransformedSpec.get("$schema").asText(),
- "https://vega.github.io/schema/vega/v5.json"
- );
-
- // Collect list of warnings
- JsonNode usermetaNode = preTransformedSpec.get("usermeta");
- JsonNode vegafusionWarningsNode = usermetaNode.get("vegafusion_warnings");
- assertTrue(vegafusionWarningsNode.isArray());
- List warningList = new ArrayList<>();
- var elements = vegafusionWarningsNode.elements();
- while (elements.hasNext()) {
- warningList.add(elements.next());
- }
-
- // We should have 1 RowLimitExceeded warning
- assertEquals(warningList.size(), 1);
- var firstWarningType = warningList.get(0).get("type").asText();
- assertEquals(firstWarningType, "RowLimitExceeded");
-
- // Clean up Runtime
- runtime.destroy();
-
- // Calling again after destroy should raise an exception
- assertThrows(IllegalStateException.class, () ->
- runtime.preTransformSpec(
- spec, "UTC", "UTC", 3, true
- )
- );
- }
-
- @Test
- void testInvalidPretransformSpec() {
- // Build VegaFusionRuntime
- VegaFusionRuntime runtime = makeRuntime();
-
- // Construct invalid Vega spec
- String spec = "{\"data\": \"foo\"}";
-
- // Check that exception is raised by preTransformSpec
- VegaFusionException e = assertThrows(VegaFusionException.class, () ->
- runtime.preTransformSpec(
- spec, "UTC", "UTC", 0, true
- )
- );
-
- assertTrue(e.getMessage().toLowerCase().contains("serde"));
-
- // Clean up Runtime
- runtime.destroy();
- }
-
- String histSpec() {
- return """
- {
- "$schema": "https://vega.github.io/schema/vega/v5.json",
- "background": "white",
- "description": "https://vega.github.io/vega-lite/examples/histogram.html",
- "padding": 5,
- "width": 200,
- "height": 200,
- "style": "cell",
- "data": [
- {
- "name": "source_0",
- "url": "https://raw.githubusercontent.com/vega/vega-datasets/master/data/movies.json",
- "format": {"type": "json"},
- "transform": [
- {
- "type": "extent",
- "field": "IMDB Rating",
- "signal": "bin_maxbins_10_IMDB_Rating_extent"
- },
- {
- "type": "bin",
- "field": "IMDB Rating",
- "as": [
- "bin_maxbins_10_IMDB Rating",
- "bin_maxbins_10_IMDB Rating_end"
- ],
- "signal": "bin_maxbins_10_IMDB_Rating_bins",
- "extent": {"signal": "bin_maxbins_10_IMDB_Rating_extent"},
- "maxbins": 10
- },
- {
- "type": "aggregate",
- "groupby": [
- "bin_maxbins_10_IMDB Rating",
- "bin_maxbins_10_IMDB Rating_end"
- ],
- "ops": ["count"],
- "fields": [null],
- "as": ["__count"]
- },
- {
- "type": "filter",
- "expr": "isValid(datum[\\"bin_maxbins_10_IMDB Rating\\"]) && isFinite(+datum[\\"bin_maxbins_10_IMDB Rating\\"])"
- }
- ]
- }
- ],
- "marks": [
- {
- "name": "marks",
- "type": "rect",
- "style": ["bar"],
- "from": {"data": "source_0"},
- "encode": {
- "update": {
- "fill": {"value": "#4c78a8"},
- "ariaRoleDescription": {"value": "bar"},
- "description": {
- "signal": "\\"IMDB Rating (binned): \\" + (!isValid(datum[\\"bin_maxbins_10_IMDB Rating\\"]) || !isFinite(+datum[\\"bin_maxbins_10_IMDB Rating\\"]) ? \\"null\\" : format(datum[\\"bin_maxbins_10_IMDB Rating\\"], \\"\\") + \\" – \\" + format(datum[\\"bin_maxbins_10_IMDB Rating_end\\"], \\"\\")) + \\"; Count of Records: \\" + (format(datum[\\"__count\\"], \\"\\"))"
- },
- "x2": [
- {
- "test": "!isValid(datum[\\"bin_maxbins_10_IMDB Rating\\"]) || !isFinite(+datum[\\"bin_maxbins_10_IMDB Rating\\"])",
- "value": 0
- },
- {"scale": "x", "field": "bin_maxbins_10_IMDB Rating", "offset": 1}
- ],
- "x": [
- {
- "test": "!isValid(datum[\\"bin_maxbins_10_IMDB Rating\\"]) || !isFinite(+datum[\\"bin_maxbins_10_IMDB Rating\\"])",
- "value": 0
- },
- {"scale": "x", "field": "bin_maxbins_10_IMDB Rating_end"}
- ],
- "y": {"scale": "y", "field": "__count"},
- "y2": {"scale": "y", "value": 0}
- }
- }
- }
- ],
- "scales": [
- {
- "name": "x",
- "type": "linear",
- "domain": {
- "signal": "[bin_maxbins_10_IMDB_Rating_bins.start, bin_maxbins_10_IMDB_Rating_bins.stop]"
- },
- "range": [0, {"signal": "width"}],
- "bins": {"signal": "bin_maxbins_10_IMDB_Rating_bins"},
- "zero": false
- },
- {
- "name": "y",
- "type": "linear",
- "domain": {"data": "source_0", "field": "__count"},
- "range": [{"signal": "height"}, 0],
- "nice": true,
- "zero": true
- }
- ],
- "axes": [
- {
- "scale": "y",
- "orient": "left",
- "gridScale": "x",
- "grid": true,
- "tickCount": {"signal": "ceil(height/40)"},
- "domain": false,
- "labels": false,
- "aria": false,
- "maxExtent": 0,
- "minExtent": 0,
- "ticks": false,
- "zindex": 0
- },
- {
- "scale": "x",
- "orient": "bottom",
- "grid": false,
- "title": "IMDB Rating (binned)",
- "labelFlush": true,
- "labelOverlap": true,
- "tickCount": {"signal": "ceil(width/10)"},
- "zindex": 0
- },
- {
- "scale": "y",
- "orient": "left",
- "grid": false,
- "title": "Count of Records",
- "labelOverlap": true,
- "tickCount": {"signal": "ceil(height/40)"},
- "zindex": 0
- }
- ]
- }
- """;
- }
-}
diff --git a/java/settings.gradle b/java/settings.gradle
deleted file mode 100644
index d51a9a313..000000000
--- a/java/settings.gradle
+++ /dev/null
@@ -1,16 +0,0 @@
-/*
- * This file was generated by the Gradle 'init' task.
- *
- * The settings file is used to specify which projects to include in your build.
- *
- * Detailed information about configuring a multi-project build in Gradle can be found
- * in the user manual at https://docs.gradle.org/8.1.1/userguide/multi_project_builds.html
- */
-
-plugins {
- // Apply the foojay-resolver plugin to allow automatic download of JDKs
- id 'org.gradle.toolchains.foojay-resolver-convention' version '0.4.0'
-}
-
-rootProject.name = 'VegaFusion'
-include('lib')
diff --git a/java/version.txt b/java/version.txt
deleted file mode 100644
index e184d8477..000000000
--- a/java/version.txt
+++ /dev/null
@@ -1 +0,0 @@
-1.6.9
\ No newline at end of file
diff --git a/javascript/vegafusion-embed/images/VegaFusionLogo-SmallGrey.svg b/javascript/vegafusion-embed/images/VegaFusionLogo-SmallGrey.svg
deleted file mode 100644
index e5aaabacd..000000000
--- a/javascript/vegafusion-embed/images/VegaFusionLogo-SmallGrey.svg
+++ /dev/null
@@ -1 +0,0 @@
-/tsconfig.json',
- },
- },
-};
diff --git a/python/vegafusion-jupyter/package-lock.json b/python/vegafusion-jupyter/package-lock.json
deleted file mode 100644
index 05d2067bb..000000000
--- a/python/vegafusion-jupyter/package-lock.json
+++ /dev/null
@@ -1,26918 +0,0 @@
-{
- "name": "vegafusion-jupyter",
- "version": "1.6.9",
- "lockfileVersion": 2,
- "requires": true,
- "packages": {
- "": {
- "name": "vegafusion-jupyter",
- "version": "1.5.0",
- "license": "BSD-3-Clause",
- "dependencies": {
- "@jupyter-widgets/base": "^4 || ^5 || ^6",
- "@jupyterlab/notebook": "^3 || ^4",
- "marked": "^4.0.10",
- "vega": "^5.25.0",
- "vega-lite": "^5.8.0",
- "vega-themes": "^2.13.0",
- "vegafusion-embed": "../../javascript/vegafusion-embed",
- "vegafusion-wasm": "../../vegafusion-wasm/pkg"
- },
- "devDependencies": {
- "@babel/core": "^7.5.0",
- "@babel/preset-env": "^7.5.0",
- "@jupyterlab/builder": "^4.0.5",
- "@phosphor/application": "^1.6.0",
- "@phosphor/widgets": "^1.6.0",
- "@types/jest": "^26.0.0",
- "@types/webpack-env": "^1.16.3",
- "@typescript-eslint/eslint-plugin": "^3.6.0",
- "@typescript-eslint/parser": "^3.6.0",
- "acorn": "^7.2.0",
- "css-loader": "6.5.1",
- "eslint": "^7.4.0",
- "eslint-config-prettier": "^6.11.0",
- "eslint-plugin-prettier": "^3.1.4",
- "fs-extra": "^7.0.0",
- "identity-obj-proxy": "^3.0.0",
- "jest": "^26.0.0",
- "mkdirp": "^0.5.1",
- "npm-run-all": "^4.1.3",
- "prettier": "^2.0.5",
- "rimraf": "^2.6.2",
- "sass": "^1.45.2",
- "source-map-loader": "^1.1.3",
- "style-loader": "^1.0.0",
- "svg-inline-loader": "^0.8.2",
- "ts-jest": "^26.0.0",
- "ts-loader": "^8.0.0",
- "typescript": "~4.1.3",
- "webpack": "^5.65.0",
- "webpack-cli": "^4.9.1",
- "webpack-require-from": "^1.8.6"
- }
- },
- "../../javascript/vegafusion-embed": {
- "version": "1.5.0",
- "license": "BSD-3-Clause",
- "dependencies": {
- "grpc-web": "^1.3.1",
- "vega-lite": "^4.17.0",
- "vegafusion-wasm": "../../vegafusion-wasm/pkg"
- },
- "devDependencies": {
- "@babel/core": "^7.5.0",
- "@babel/preset-env": "^7.5.0",
- "@types/node": "17.0.21",
- "@typescript-eslint/eslint-plugin": "^3.6.0",
- "@typescript-eslint/parser": "^3.6.0",
- "acorn": "^7.2.0",
- "css-loader": "6.5.1",
- "eslint": "^7.4.0",
- "eslint-config-prettier": "^6.11.0",
- "eslint-plugin-prettier": "^3.1.4",
- "fs-extra": "^7.0.0",
- "identity-obj-proxy": "^3.0.0",
- "mkdirp": "^0.5.1",
- "npm-run-all": "^4.1.3",
- "prettier": "^2.0.5",
- "rimraf": "^2.6.2",
- "sass": "^1.45.2",
- "source-map-loader": "^1.1.3",
- "style-loader": "^1.0.0",
- "svg-inline-loader": "^0.8.2",
- "ts-loader": "^8.0.0",
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\ No newline at end of file
diff --git a/python/vegafusion-jupyter/package.json b/python/vegafusion-jupyter/package.json
deleted file mode 100644
index a16c81288..000000000
--- a/python/vegafusion-jupyter/package.json
+++ /dev/null
@@ -1,109 +0,0 @@
-{
- "name": "vegafusion-jupyter",
- "version": "1.6.9",
- "description": "Altair Jupyter Widget library that relies on VegaFusion for serverside calculations",
- "keywords": [
- "jupyter",
- "jupyterlab",
- "jupyterlab-extension",
- "widgets"
- ],
- "files": [
- "lib/**/*.js",
- "dist/*.js",
- "dist/*.wasm",
- "css/*.css",
- "css/*.scss",
- "images/*.svg"
- ],
- "homepage": "https://github.com/hex-inc/vegafusion",
- "bugs": {
- "url": "https://github.com/hex-inc/vegafusion/issues"
- },
- "license": "BSD-3-Clause",
- "author": {
- "name": "Jon Mease",
- "email": "jon@vegafusion.io"
- },
- "main": "lib/index.js",
- "types": "./lib/index.d.ts",
- "repository": {
- "type": "git",
- "url": "https://github.com/jonmmease/vegafusion"
- },
- "scripts": {
- "build:dev": "npm run build:lib && npm run build:nbextension && npm run build:labextension:dev && pip install --force-reinstall --no-deps .",
- "build:prod": "npm run clean && npm run build:lib && npm run build:nbextension && npm run build:labextension",
- "build:labextension": "jupyter-labextension build .",
- "build:labextension:dev": "jupyter-labextension build --development True .",
- "build:lib": "tsc && sass scss:css",
- "build:nbextension": "webpack",
- "clean": "npm run clean:lib && npm run clean:dist && npm run clean:nbextension && npm run clean:labextension",
- "clean:dist": "rimraf dist",
- "clean:lib": "rimraf lib",
- "clean:labextension": "rimraf vegafusion_jupyter/labextension",
- "clean:nbextension": "rimraf vegafusion_jupyter/nbextension/* && cp src/nbextension/extension.js vegafusion_jupyter/nbextension/",
- "lint": "eslint . --ext .ts,.tsx --fix",
- "lint:check": "eslint . --ext .ts,.tsx",
- "prepack": "npm run build:prod",
- "test": "jest",
- "watch": "npm-run-all -p watch:*",
- "watch:lib": "tsc -w",
- "watch:nbextension": "webpack --watch --mode=development",
- "watch:labextension": "jupyter-labextension watch ."
- },
- "dependencies": {
- "@jupyter-widgets/base": "^4 || ^5 || ^6",
- "@jupyterlab/notebook": "^3 || ^4",
- "marked": "^4.0.10",
- "vega": "^5.25.0",
- "vega-lite": "^5.8.0",
- "vega-themes": "^2.13.0",
- "vegafusion-embed": "../../javascript/vegafusion-embed",
- "vegafusion-wasm": "../../vegafusion-wasm/pkg"
- },
- "devDependencies": {
- "@babel/core": "^7.5.0",
- "@babel/preset-env": "^7.5.0",
- "@jupyterlab/builder": "^4.0.5",
- "@phosphor/application": "^1.6.0",
- "@phosphor/widgets": "^1.6.0",
- "@types/jest": "^26.0.0",
- "@types/webpack-env": "^1.16.3",
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- "acorn": "^7.2.0",
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- "identity-obj-proxy": "^3.0.0",
- "jest": "^26.0.0",
- "mkdirp": "^0.5.1",
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- "prettier": "^2.0.5",
- "rimraf": "^2.6.2",
- "sass": "^1.45.2",
- "source-map-loader": "^1.1.3",
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- "svg-inline-loader": "^0.8.2",
- "ts-jest": "^26.0.0",
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- "typescript": "~4.1.3",
- "webpack": "^5.65.0",
- "webpack-cli": "^4.9.1",
- "webpack-require-from": "^1.8.6"
- },
- "jupyterlab": {
- "extension": "lib/plugin",
- "outputDir": "vegafusion_jupyter/labextension/",
- "webpackConfig": "webpack.config.experimental.js",
- "sharedPackages": {
- "@jupyter-widgets/base": {
- "bundled": false,
- "singleton": true
- }
- }
- }
-}
\ No newline at end of file
diff --git a/python/vegafusion-jupyter/pyproject.toml b/python/vegafusion-jupyter/pyproject.toml
deleted file mode 100644
index d288f0b28..000000000
--- a/python/vegafusion-jupyter/pyproject.toml
+++ /dev/null
@@ -1,3 +0,0 @@
-[build-system]
-requires = ["jupyter_packaging==0.12.3", "jupyterlab==4.*", "setuptools>=40.8.0", "wheel"]
-build-backend = "setuptools.build_meta"
diff --git a/python/vegafusion-jupyter/readthedocs.yml b/python/vegafusion-jupyter/readthedocs.yml
deleted file mode 100644
index c972d9ccf..000000000
--- a/python/vegafusion-jupyter/readthedocs.yml
+++ /dev/null
@@ -1,9 +0,0 @@
-type: sphinx
-python:
- version: 3.5
- pip_install: true
- extra_requirements:
- - examples
- - docs
-conda:
- file: docs/environment.yml
diff --git a/python/vegafusion-jupyter/scss/vegafusion-embed.scss b/python/vegafusion-jupyter/scss/vegafusion-embed.scss
deleted file mode 100644
index 289dc7326..000000000
--- a/python/vegafusion-jupyter/scss/vegafusion-embed.scss
+++ /dev/null
@@ -1,162 +0,0 @@
-// Taken from vega-embed: https://github.com/vega/vega-embed which is released
-// under the BSD-3-Clause License: https://github.com/vega/vega-embed/blob/next/LICENSE
-.vegafusion-embed {
- min-height: 40px; // Ensure there's enough room for the menu button before plot is ready
- position: relative;
- display: inline-block;
- box-sizing: border-box;
- overflow: visible;
-
- &.has-actions {
- padding-right: 38px;
- }
-
- details:not([open]) > :not(summary) {
- display: none !important;
- }
-
- summary {
- list-style: none;
- position: absolute;
- top: 0;
- right: 0;
- padding: 6px;
- z-index: 1000;
- background: white;
- box-shadow: 1px 1px 3px rgba(0, 0, 0, 0.1);
- color: #1b1e23;
- border: 1px solid #aaa;
- border-radius: 999px;
- opacity: 0.2;
- transition: opacity 0.4s ease-in;
- outline: none;
- cursor: pointer;
- line-height: 0px; // For Safari
-
- &::-webkit-details-marker {
- display: none;
- }
-
- &:active {
- box-shadow: #aaa 0px 0px 0px 1px inset;
- }
-
- svg {
- width: 16px;
- height: 16px;
- }
- }
-
- details[open] summary {
- opacity: 0.5;
- }
-
- &:hover summary,
- &:focus summary {
- opacity: 0.7 !important;
- transition: opacity 0.2s ease;
- }
-
- .vegafusion-actions {
- position: absolute;
- z-index: 1001;
- top: 35px;
- right: -9px;
- display: flex;
- flex-direction: column;
- padding-bottom: 8px;
- padding-top: 8px;
- border-radius: 4px;
- box-shadow: 0 2px 8px 0 rgba(0, 0, 0, 0.2);
- border: 1px solid #d9d9d9;
- background: white;
- animation-duration: 0.15s;
- animation-name: scale-in;
- animation-timing-function: cubic-bezier(0.2, 0, 0.13, 1.5);
- text-align: left; // only to make sure this is not a a different value
-
- hr {
- width: auto;
- height: 1px;
- border: none;
- background-color: #434a56;
- margin: 4px 10px 4px 10px;
- opacity: 50%;
- }
-
- .source-msg {
- padding: 4px 16px;
- font-family: sans-serif;
- font-size: 10px;
- font-weight: 400;
- max-width: 180px;
- color: #CD5C5C;
- }
-
- a {
- padding: 4px 16px;
- font-family: sans-serif;
- font-size: 12px;
- font-weight: 600;
- white-space: nowrap;
- color: #434a56;
- text-decoration: none;
-
- &:hover {
- background-color: #f7f7f9;
- color: black;
- }
- }
-
- &::before,
- &::after {
- content: "";
- display: inline-block;
- position: absolute;
- pointer-events: none;
- }
-
- &::before {
- left: auto;
- right: 14px;
- top: -16px;
- border: 8px solid #0000;
- border-bottom-color: #d9d9d9;
- }
-
- &::after {
- left: auto;
- right: 15px;
- top: -14px;
- border: 7px solid #0000;
- border-bottom-color: #fff;
- }
- }
-
- .chart-wrapper {
- &.fit-x {
- width: 100%;
- }
- &.fit-y {
- height: 100%;
- }
- }
-}
-
-.vegafusion-embed-wrapper {
- max-width: 100%;
- overflow: auto;
- padding-right: 14px;
-}
-
-@keyframes scale-in {
- from {
- opacity: 0;
- transform: scale(0.6);
- }
-
- to {
- opacity: 1;
- transform: scale(1);
- }
-}
diff --git a/python/vegafusion-jupyter/scss/widget.scss b/python/vegafusion-jupyter/scss/widget.scss
deleted file mode 100644
index d9fc50489..000000000
--- a/python/vegafusion-jupyter/scss/widget.scss
+++ /dev/null
@@ -1,7 +0,0 @@
-.vegafusion-loading {
- color: red;
- background: aqua;
- .inner {
- width: 100px;
- }
-}
diff --git a/python/vegafusion-jupyter/setup.cfg b/python/vegafusion-jupyter/setup.cfg
deleted file mode 100644
index 735f4a2a6..000000000
--- a/python/vegafusion-jupyter/setup.cfg
+++ /dev/null
@@ -1,15 +0,0 @@
-[bdist_wheel]
-universal = 0
-
-[metadata]
-name = vegafusion-jupyter
-description = Altair Jupyter Widget library that relies on VegaFusion for serverside calculations
-version = 1.6.9
-author = Jon Mease
-author_email = jonmmease@gmail.com
-url = https://vegafusion.io
-long_description = file: README.md
-long_description_content_type = text/markdown
-license = BSD-3-Clause
-license_files = [ LICENSE.txt ]
-
diff --git a/python/vegafusion-jupyter/setup.py b/python/vegafusion-jupyter/setup.py
deleted file mode 100644
index 6c25ae782..000000000
--- a/python/vegafusion-jupyter/setup.py
+++ /dev/null
@@ -1,108 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# Copyright (c) Jupyter Development Team.
-# Distributed under the terms of the Modified BSD License.
-
-from __future__ import print_function
-from glob import glob
-import os
-from os.path import join as pjoin
-from setuptools import setup, find_packages
-
-
-from jupyter_packaging import (
- create_cmdclass,
- install_npm,
- ensure_targets,
- combine_commands,
- get_version,
- skip_if_exists
-)
-
-HERE = os.path.dirname(os.path.abspath(__file__))
-
-# The name of the project
-name = 'vegafusion-jupyter'
-module = name.replace('-', '_')
-
-# Get the version
-version = get_version(pjoin(module, '_version.py'))
-
-# Representative files that should exist after a successful build
-jstargets = [
- pjoin(HERE, module, 'nbextension', 'index.js'),
- pjoin(HERE, module, 'labextension', 'package.json'),
-]
-
-package_data_spec = {
- name: [
- 'nbextension/**js*',
- 'labextension/**'
- ]
-}
-
-
-data_files_spec = [
- ('share/jupyter/nbextensions/vegafusion-jupyter', 'vegafusion_jupyter/nbextension', '**'),
- ('share/jupyter/labextensions/vegafusion-jupyter', 'vegafusion_jupyter/labextension', '**'),
- ('share/jupyter/labextensions/vegafusion-jupyter', '.', 'install.json'),
- ('etc/jupyter/nbconfig/notebook.d', '.', 'vegafusion-jupyter.json'),
-]
-
-
-cmdclass = create_cmdclass('jsdeps', package_data_spec=package_data_spec,
- data_files_spec=data_files_spec)
-
-npm_install = combine_commands(
- install_npm(HERE, build_cmd='build:prod'),
- ensure_targets(jstargets),
-)
-
-cmdclass['jsdeps'] = skip_if_exists(jstargets, npm_install)
-
-
-setup_args = dict(
- name = name,
- description = 'Altair Jupyter Widget library that relies on VegaFusion for serverside calculations',
- version = version,
- scripts = glob(pjoin('scripts', '*')),
- cmdclass = cmdclass,
- packages = find_packages(".", exclude=["tests"]),
- author = 'Jon Mease',
- author_email = 'jonmmease@gmail.com',
- url = 'https://vegafusion.io',
- license = 'BSD-3-Clause',
- platforms = "Linux, Mac OS X, Windows",
- keywords = ['Jupyter', 'Widgets', 'IPython'],
- classifiers = [
- 'Intended Audience :: Developers',
- 'Intended Audience :: Science/Research',
- 'License :: OSI Approved :: BSD License',
- 'Programming Language :: Python',
- 'Programming Language :: Python :: 3',
- 'Programming Language :: Python :: 3.7',
- 'Programming Language :: Python :: 3.8',
- 'Programming Language :: Python :: 3.9',
- 'Programming Language :: Python :: 3.10',
- 'Framework :: Jupyter',
- ],
- include_package_data = True,
- python_requires=">=3.7",
- install_requires = [
- 'ipywidgets>=7.0.0,<9',
- 'altair>=4.2.0',
- f'vegafusion=={version}', # [vegafusion VERSION]
- ],
- extras_require={
- 'embed': [
- f"vegafusion-python-embed=={version}",
- "vl-convert-python>=0.7.0"
- ],
- },
- entry_points = {
- },
-)
-
-if __name__ == '__main__':
- setup(**setup_args)
diff --git a/python/vegafusion-jupyter/src/extension.ts b/python/vegafusion-jupyter/src/extension.ts
deleted file mode 100644
index c54c4035b..000000000
--- a/python/vegafusion-jupyter/src/extension.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-// Entry point for the notebook bundle containing custom model definitions.
-//
-// Setup notebook base URL
-//
-// Some static assets may be required by the custom widget javascript. The base
-// url for the notebook is not known at build time and is therefore computed
-// dynamically.
-// eslint-disable-next-line @typescript-eslint/no-non-null-assertion
-(window as any).__webpack_public_path__ =
- document.querySelector('body')!.getAttribute('data-base-url') +
- 'nbextensions/vegafusion-jupyter/';
-
-export * from './index';
diff --git a/python/vegafusion-jupyter/src/index.ts b/python/vegafusion-jupyter/src/index.ts
deleted file mode 100644
index 41c7f0105..000000000
--- a/python/vegafusion-jupyter/src/index.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-export * from './version';
-export * from './widget';
diff --git a/python/vegafusion-jupyter/src/nbextension/extension.js b/python/vegafusion-jupyter/src/nbextension/extension.js
deleted file mode 100644
index 4fa79d598..000000000
--- a/python/vegafusion-jupyter/src/nbextension/extension.js
+++ /dev/null
@@ -1,17 +0,0 @@
-// Entry point for the notebook bundle containing custom model definitions.
-//
-define(function() {
- "use strict";
-
- window['requirejs'].config({
- map: {
- '*': {
- 'vegafusion-jupyter': 'nbextensions/vegafusion-jupyter/index',
- },
- }
- });
- // Export the required load_ipython_extension function
- return {
- load_ipython_extension : function() {}
- };
-});
diff --git a/python/vegafusion-jupyter/src/plugin.ts b/python/vegafusion-jupyter/src/plugin.ts
deleted file mode 100644
index 7017a725d..000000000
--- a/python/vegafusion-jupyter/src/plugin.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-import { Application, IPlugin } from '@phosphor/application';
-
-import { Widget } from '@phosphor/widgets';
-
-import { IJupyterWidgetRegistry } from '@jupyter-widgets/base';
-
-import * as widgetExports from './widget';
-
-import { MODULE_NAME, MODULE_VERSION } from './version';
-
-const EXTENSION_ID = 'vegafusion-jupyter:plugin';
-
-/**
- * The example plugin.
- */
-const examplePlugin: IPlugin, void> = {
- id: EXTENSION_ID,
- requires: [IJupyterWidgetRegistry],
- activate: activateWidgetExtension,
- autoStart: true,
-} as unknown as IPlugin, void>;
-// the "as unknown as ..." typecast above is solely to support JupyterLab 1
-// and 2 in the same codebase and should be removed when we migrate to Lumino.
-
-export default examplePlugin;
-
-/**
- * Activate the widget extension.
- */
-function activateWidgetExtension(
- app: Application,
- registry: IJupyterWidgetRegistry
-): void {
- registry.registerWidget({
- name: MODULE_NAME,
- version: MODULE_VERSION,
- exports: widgetExports,
- });
-}
diff --git a/python/vegafusion-jupyter/src/version.ts b/python/vegafusion-jupyter/src/version.ts
deleted file mode 100644
index 1e34941e7..000000000
--- a/python/vegafusion-jupyter/src/version.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-// eslint-disable-next-line @typescript-eslint/ban-ts-comment
-// @ts-ignore
-// eslint-disable-next-line @typescript-eslint/no-var-requires
-const data = require('../package.json');
-
-/**
- * The _model_module_version/_view_module_version this package implements.
- *
- * The html widget manager assumes that this is the same as the npm package
- * version number.
- */
-export const MODULE_VERSION = data.version;
-
-/*
- * The current package name.
- */
-export const MODULE_NAME = data.name;
diff --git a/python/vegafusion-jupyter/src/widget.ts b/python/vegafusion-jupyter/src/widget.ts
deleted file mode 100644
index 84aaeaf63..000000000
--- a/python/vegafusion-jupyter/src/widget.ts
+++ /dev/null
@@ -1,156 +0,0 @@
-// -----------------------------------------------------------
-// Dropdown menu implementation is based heavily on vega-embed
-// (https://github.com/vega/vega-embed) which is released
-// under the BSD-3-Clause License: https://github.com/vega/vega-embed/blob/next/LICENSE
-
-import {DOMWidgetModel, DOMWidgetView, ISerializers,} from '@jupyter-widgets/base';
-import * as vegaThemes from 'vega-themes';
-import {mergeConfig} from 'vega';
-import {TopLevelSpec, Config} from 'vega-lite';
-import {MODULE_NAME, MODULE_VERSION} from './version';
-
-export class VegaFusionModel extends DOMWidgetModel {
- defaults() {
- return {
- ...super.defaults(),
- _model_name: VegaFusionModel.model_name,
- _model_module: VegaFusionModel.model_module,
- _model_module_version: VegaFusionModel.model_module_version,
- _view_name: VegaFusionModel.view_name,
- _view_module: VegaFusionModel.view_module,
- _view_module_version: VegaFusionModel.view_module_version,
- spec: null,
- full_vega_spec: null,
- client_vega_spec: null,
- server_vega_spec: null,
- vegafusion_handle: null,
- verbose: null,
- debounce_wait: 30,
- debounce_max_wait: 60,
- _request_msg: null,
- _response_msg: null,
- };
- }
-
- static serializers: ISerializers = {
- ...DOMWidgetModel.serializers,
- // Add any extra serializers here
- _request_msg: {
- serialize: (value: any): DataView | null => {
- if (value.buffer) {
- return new DataView(value.buffer.slice(0));
- } else {
- return null;
- }
- },
- },
- };
-
- static model_name = 'VegaFusionModel';
- static model_module = MODULE_NAME;
- static model_module_version = MODULE_VERSION;
- static view_name = 'VegaFusionView'; // Set to null if no view
- static view_module = MODULE_NAME; // Set to null if no view
- static view_module_version = MODULE_VERSION;
-}
-
-export class VegaFusionView extends DOMWidgetView {
- vegafusion_handle: import('vegafusion-embed').MsgReceiver;
- embedVegaFusion: typeof import('vegafusion-embed').embedVegaFusion;
- vegalite_compile: typeof import('vega-lite').compile;
- vegaThemes: Record;
-
- async render() {
- const { embedVegaFusion } = await import('vegafusion-embed');
- this.embedVegaFusion = embedVegaFusion;
-
- const { compile } = await import('vega-lite');
- this.vegalite_compile = compile;
- this.vegaThemes = Object.assign({}, vegaThemes) as Record;
-
- this.value_changed();
- this.model.on('change:spec', this.value_changed, this);
- this.model.on('change:verbose', this.value_changed, this);
- this.model.on('change:debounce_wait', this.value_changed, this);
- this.model.on('change:debounce_max_wait', this.value_changed, this);
- this.model.on('change:_response_msg', () => {
- const msgBytes: DataView = this.model.get('_response_msg');
- if (msgBytes !== null) {
- if (this.model.get('verbose')) {
- console.log('VegaFusion(js): Received response');
- console.log(msgBytes.buffer);
- }
- const bytes = new Uint8Array(msgBytes.buffer);
- this.vegafusion_handle.receive(bytes);
- }
- });
- }
-
- value_changed() {
- const spec = this.model.get('spec');
- if (spec !== null) {
- const parsed = JSON.parse(spec) as TopLevelSpec;
-
- // Apply Vega embed theme
- const usermeta = parsed.usermeta ?? {};
- const embedOptions = (usermeta.embedOptions ?? {}) as Record<
- string,
- string
- >;
- const usermetaTheme = embedOptions.theme as string | null;
- // eslint-disable-next-line eqeqeq
- if (usermetaTheme != null) {
- const themeConfig = this.vegaThemes[usermetaTheme];
- // eslint-disable-next-line eqeqeq
- if (themeConfig != null) {
- parsed['config'] = mergeConfig(parsed.config ?? {}, themeConfig);
- }
- }
-
- let vega_spec_json;
- if ((parsed['$schema'] ?? '').endsWith('schema/vega/v5.json')) {
- vega_spec_json = spec;
- } else {
- // Assume we have a Vega-Lite spec, compile to vega
- const vega_spec = this.vegalite_compile(parsed);
- vega_spec_json = JSON.stringify(vega_spec.spec, null, 2);
- }
-
- const config = {
- verbose: this.model.get('verbose') || false,
- debounce_wait: this.model.get('debounce_wait') || 30,
- debounce_max_wait: this.model.get('debounce_max_wait'),
- };
-
- // this.vegafusion_handle = this.embedVegaFusion(
- this.vegafusion_handle = this.embedVegaFusion(
- this.el,
- vega_spec_json,
- (request: Uint8Array) => {
- if (this.model.get('verbose')) {
- console.log('VegaFusion(js): Send request');
- }
-
- this.model.set('_request_msg', new DataView(request.buffer));
- this.touch();
- this.model.set('_request_msg', {});
- },
- config
- );
-
- // Update vega spec properties
- this.model.set('full_vega_spec', vega_spec_json);
- this.model.set(
- 'client_vega_spec',
- this.vegafusion_handle.client_spec_json()
- );
- this.model.set(
- 'server_vega_spec',
- this.vegafusion_handle.server_spec_json()
- );
- this.model.set('comm_plan', this.vegafusion_handle.comm_plan_json());
-
- this.touch();
- }
- }
-}
diff --git a/python/vegafusion-jupyter/tests/conftest.py b/python/vegafusion-jupyter/tests/conftest.py
deleted file mode 100644
index 25a11a14b..000000000
--- a/python/vegafusion-jupyter/tests/conftest.py
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-import pytest
-
-from ipykernel.comm import Comm
-from ipywidgets import Widget
-
-class MockComm(Comm):
- """A mock Comm object.
-
- Can be used to inspect calls to Comm's open/send/close methods.
- """
- comm_id = 'a-b-c-d'
- kernel = 'Truthy'
-
- def __init__(self, *args, **kwargs):
- self.log_open = []
- self.log_send = []
- self.log_close = []
- super(MockComm, self).__init__(*args, **kwargs)
-
- def open(self, *args, **kwargs):
- self.log_open.append((args, kwargs))
-
- def send(self, *args, **kwargs):
- self.log_send.append((args, kwargs))
-
- def close(self, *args, **kwargs):
- self.log_close.append((args, kwargs))
-
-_widget_attrs = {}
-undefined = object()
-
-
-@pytest.fixture
-def mock_comm():
- _widget_attrs['_comm_default'] = getattr(Widget, '_comm_default', undefined)
- Widget._comm_default = lambda self: MockComm()
- _widget_attrs['_ipython_display_'] = Widget._ipython_display_
- def raise_not_implemented(*args, **kwargs):
- raise NotImplementedError()
- Widget._ipython_display_ = raise_not_implemented
-
- yield MockComm()
-
- for attr, value in _widget_attrs.items():
- if value is undefined:
- delattr(Widget, attr)
- else:
- setattr(Widget, attr, value)
diff --git a/python/vegafusion-jupyter/tests/test_conext_manager.py b/python/vegafusion-jupyter/tests/test_conext_manager.py
deleted file mode 100644
index cb6d50d0d..000000000
--- a/python/vegafusion-jupyter/tests/test_conext_manager.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import altair as alt
-import vegafusion as vf
-
-
-def test_widget_enabler_context_manager():
- alt.data_transformers.enable("json")
- alt.renderers.enable("mimetype")
-
- assert alt.data_transformers.active == "json"
- assert alt.renderers.active == "mimetype"
-
- with vf.enable_widget():
- assert alt.data_transformers.active == "vegafusion-feather"
- assert alt.renderers.active == "vegafusion-widget"
-
- assert alt.data_transformers.active == "json"
- assert alt.renderers.active == "mimetype"
-
- ctx = vf.enable_widget()
- assert alt.data_transformers.active == "vegafusion-feather"
- assert alt.renderers.active == "vegafusion-widget"
- assert repr(ctx) == "vegafusion.enable_widget()"
-
-
-def test_widget_enabler_context_manager_preserves_options():
- # No options
- with vf.enable_widget():
- assert alt.data_transformers.active == "vegafusion-feather"
- assert alt.data_transformers.options == {"data_dir": "_vegafusion_data"}
-
- assert alt.renderers.active == "vegafusion-widget"
- assert alt.renderers.options == {
- 'debounce_max_wait': 60, 'debounce_wait': 30, 'download_source_link': None
- }
-
- # Check that Widget uses default options
- widget = vf.jupyter.VegaFusionWidget()
- assert widget.debounce_max_wait == 60
-
- # Options in the enable call
- with vf.enable_widget(debounce_max_wait=200, data_dir="my_data_dir"):
- assert alt.data_transformers.active == "vegafusion-feather"
- assert alt.data_transformers.options == {"data_dir": "my_data_dir"}
-
- assert alt.renderers.active == "vegafusion-widget"
- assert alt.renderers.options == {
- 'debounce_max_wait': 200, 'debounce_wait': 30, 'download_source_link': None
- }
-
- # Check that widget picks up options from context manager
- widget = vf.jupyter.VegaFusionWidget()
- assert widget.debounce_max_wait == 200
diff --git a/python/vegafusion-jupyter/tests/test_nbextension_path.py b/python/vegafusion-jupyter/tests/test_nbextension_path.py
deleted file mode 100644
index b9bb474f3..000000000
--- a/python/vegafusion-jupyter/tests/test_nbextension_path.py
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-def test_nbextension_path():
- # Check that magic function can be imported from package root:
- from vegafusion_jupyter import _jupyter_nbextension_paths
- # Ensure that it can be called without incident:
- path = _jupyter_nbextension_paths()
- # Some sanity checks:
- assert len(path) == 1
- assert isinstance(path[0], dict)
diff --git a/python/vegafusion-jupyter/tsconfig.eslint.json b/python/vegafusion-jupyter/tsconfig.eslint.json
deleted file mode 100644
index 737e3e680..000000000
--- a/python/vegafusion-jupyter/tsconfig.eslint.json
+++ /dev/null
@@ -1,5 +0,0 @@
-{
- "extends": "./tsconfig.json",
- "include": ["src/**/*.ts", "src/**/*.tsx"],
- "exclude": []
-}
\ No newline at end of file
diff --git a/python/vegafusion-jupyter/tsconfig.json b/python/vegafusion-jupyter/tsconfig.json
deleted file mode 100644
index f7386f098..000000000
--- a/python/vegafusion-jupyter/tsconfig.json
+++ /dev/null
@@ -1,25 +0,0 @@
-{
- "compilerOptions": {
- "declaration": true,
- "esModuleInterop":true,
- "lib": ["es2015", "dom"],
- "module": "esnext",
- "moduleResolution": "node",
- "noEmitOnError": true,
- "noUnusedLocals": true,
- "outDir": "lib",
- "resolveJsonModule": true,
- "rootDir": "src",
- "skipLibCheck": true,
- "sourceMap": true,
- "strict": true,
- "strictPropertyInitialization": false,
- "target": "es2017",
- "types": ["jest"]
- },
- "include": [
- "src/**/*.ts",
- "src/**/*.tsx",
- ],
- "exclude": ["src/**/__tests__"]
-}
diff --git a/python/vegafusion-jupyter/vegafusion-jupyter.json b/python/vegafusion-jupyter/vegafusion-jupyter.json
deleted file mode 100644
index 6442f642a..000000000
--- a/python/vegafusion-jupyter/vegafusion-jupyter.json
+++ /dev/null
@@ -1,5 +0,0 @@
-{
- "load_extensions": {
- "vegafusion-jupyter/extension": true
- }
-}
diff --git a/python/vegafusion-jupyter/vegafusion_jupyter/__init__.py b/python/vegafusion-jupyter/vegafusion_jupyter/__init__.py
deleted file mode 100644
index bea9e97ba..000000000
--- a/python/vegafusion-jupyter/vegafusion_jupyter/__init__.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import altair as alt
-
-from vegafusion import RendererTransformerEnabler
-from . import renderer
-from . import widget
-from .widget import VegaFusionWidget
-from ._version import __version__
-
-
-def enable(
- download_source_link=None,
- debounce_wait=30,
- debounce_max_wait=60,
- data_dir="_vegafusion_data"
-):
- """
- Enable the VegaFusion data transformer and renderer so that all Charts
- are displayed using VegaFusion.
-
- This isn't necessary in order to use the VegaFusionWidget directly
- """
- # Import vegafusion.transformer so that vegafusion-feather transform
- # will be registered
- import vegafusion.transformer
- return RendererTransformerEnabler(
- renderer_ctx=alt.renderers.enable(
- 'vegafusion-widget',
- debounce_wait=debounce_wait,
- debounce_max_wait=debounce_max_wait,
- download_source_link=download_source_link,
- ),
- data_transformer_ctx=alt.data_transformers.enable(
- 'vegafusion-feather', data_dir=data_dir
- ),
- repr_str=f"vegafusion.enable_widget()"
- )
-
-
-def disable():
- """
- Disable the VegaFusion data transformers and renderers so that Charts
- are not displayed using VegaFusion
-
- Equivalent to
-
- ```python
- import altair as alt
- alt.renderers.enable('default')
- alt.data_transformers.enable('default')
- ```
-
- This does not affect the behavior of VegaFusionWidget
- """
- from vegafusion import disable
- disable()
-
-
-def _jupyter_labextension_paths():
- """Called by Jupyter Lab Server to detect if it is a valid labextension and
- to install the widget
- Returns
- =======
- src: Source directory name to copy files from. Webpack outputs generated files
- into this directory and Jupyter Lab copies from this directory during
- widget installation
- dest: Destination directory name to install widget files to. Jupyter Lab copies
- from `src` directory into /labextensions/ directory
- during widget installation
- """
- return [{
- 'src': 'labextension',
- 'dest': 'vegafusion-jupyter',
- }]
-
-
-def _jupyter_nbextension_paths():
- """Called by Jupyter Notebook Server to detect if it is a valid nbextension and
- to install the widget
- Returns
- =======
- section: The section of the Jupyter Notebook Server to change.
- Must be 'notebook' for widget extensions
- src: Source directory name to copy files from. Webpack outputs generated files
- into this directory and Jupyter Notebook copies from this directory during
- widget installation
- dest: Destination directory name to install widget files to. Jupyter Notebook copies
- from `src` directory into /nbextensions/ directory
- during widget installation
- require: Path to importable AMD Javascript module inside the
- /nbextensions/ directory
- """
- return [{
- 'section': 'notebook',
- 'src': 'nbextension',
- 'dest': 'vegafusion_jupyter',
- 'require': 'vegafusion_jupyter/extension'
- }]
diff --git a/python/vegafusion-jupyter/vegafusion_jupyter/_frontend.py b/python/vegafusion-jupyter/vegafusion_jupyter/_frontend.py
deleted file mode 100644
index ad3309da3..000000000
--- a/python/vegafusion-jupyter/vegafusion_jupyter/_frontend.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""
-Information about the frontend package of the widgets.
-"""
-module_name = "vegafusion-jupyter"
-module_version = "^1.6.9"
diff --git a/python/vegafusion-jupyter/vegafusion_jupyter/_version.py b/python/vegafusion-jupyter/vegafusion_jupyter/_version.py
deleted file mode 100644
index 4c2dc20a7..000000000
--- a/python/vegafusion-jupyter/vegafusion_jupyter/_version.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = '1.6.9'
diff --git a/python/vegafusion-jupyter/vegafusion_jupyter/nbextension/extension.js b/python/vegafusion-jupyter/vegafusion_jupyter/nbextension/extension.js
deleted file mode 100644
index 4fa79d598..000000000
--- a/python/vegafusion-jupyter/vegafusion_jupyter/nbextension/extension.js
+++ /dev/null
@@ -1,17 +0,0 @@
-// Entry point for the notebook bundle containing custom model definitions.
-//
-define(function() {
- "use strict";
-
- window['requirejs'].config({
- map: {
- '*': {
- 'vegafusion-jupyter': 'nbextensions/vegafusion-jupyter/index',
- },
- }
- });
- // Export the required load_ipython_extension function
- return {
- load_ipython_extension : function() {}
- };
-});
diff --git a/python/vegafusion-jupyter/vegafusion_jupyter/renderer.py b/python/vegafusion-jupyter/vegafusion_jupyter/renderer.py
deleted file mode 100644
index 17ba0f0e9..000000000
--- a/python/vegafusion-jupyter/vegafusion_jupyter/renderer.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import altair as alt
-
-def vegafusion_renderer(spec, **widget_options):
- """
- Altair renderer that displays charts using a VegaFusionWidget
- """
- from IPython.display import display
- from vegafusion_jupyter import VegaFusionWidget
-
- # Display widget as a side effect, then return empty string text representation
- # so that Altair doesn't also display a string representation
- widget = VegaFusionWidget(spec, **widget_options)
- display(widget)
- return {'text/plain': ""}
-
-alt.renderers.register('vegafusion-widget', vegafusion_renderer)
diff --git a/python/vegafusion-jupyter/vegafusion_jupyter/widget.py b/python/vegafusion-jupyter/vegafusion_jupyter/widget.py
deleted file mode 100644
index f6c5b1af1..000000000
--- a/python/vegafusion-jupyter/vegafusion_jupyter/widget.py
+++ /dev/null
@@ -1,107 +0,0 @@
-from ipywidgets import DOMWidget
-from traitlets import Unicode, Bool, Float, CBytes, observe
-import time
-
-import logging
-logger = logging.getLogger("vegafusion")
-
-from ._frontend import module_name, module_version
-import altair as alt
-import json
-
-
-class VegaFusionWidget(DOMWidget):
- _model_name = Unicode('VegaFusionModel').tag(sync=True)
- _model_module = Unicode(module_name).tag(sync=True)
- _model_module_version = Unicode(module_version).tag(sync=True)
- _view_name = Unicode('VegaFusionView').tag(sync=True)
- _view_module = Unicode(module_name).tag(sync=True)
- _view_module_version = Unicode(module_version).tag(sync=True)
-
- spec = Unicode(None, allow_none=True).tag(sync=True)
- full_vega_spec = Unicode(None, allow_none=True, read_only=True).tag(sync=True)
- client_vega_spec = Unicode(None, allow_none=True, read_only=True).tag(sync=True)
- server_vega_spec = Unicode(None, allow_none=True, read_only=True).tag(sync=True)
- comm_plan = Unicode(None, allow_none=True, read_only=True).tag(sync=True)
- verbose = Bool(False).tag(sync=True)
- debounce_wait = Float(30, allow_none=False).tag(sync=True)
- debounce_max_wait = Float(60, allow_none=True).tag(sync=True)
-
- # Message transport properties
- _request_msg = CBytes(allow_none=True, read_only=True).tag(sync=True)
- _response_msg = CBytes(allow_none=True).tag(sync=True)
-
- def __init__(self, *args, **kwargs):
- # Make sure transformer and renderer and registered
- import vegafusion as vf
- import vegafusion.transformer
- import vegafusion_jupyter.renderer
-
- # Support altair object or spec as the single positional argument
- if len(args) == 1:
- # Use single positional argument as spec
- kwargs.setdefault("spec", args[0])
-
- # Handle spec as Altair chart
- if isinstance(kwargs.get("spec", None), alt.TopLevelMixin):
- spec = kwargs["spec"]
-
- # If vegafusion-feather renderer is already enabled, use the same options
- if alt.data_transformers.active == "vegafusion-feather":
- data_transformer_opts = alt.data_transformers.options
- else:
- data_transformer_opts = dict()
-
- with vf.enable_widget(**data_transformer_opts):
- # Temporarily enable the vegafusion renderer and transformer so
- # that we use them even if they are not enabled globally
- spec = spec.to_dict()
-
- # Set spec as a dict, which will be converted to string below
- kwargs["spec"] = spec
-
- # Handle spec as dict
- if isinstance(kwargs.get("spec", None), dict):
- kwargs["spec"] = json.dumps(kwargs["spec"], indent=2)
-
- # If vegafusion renderer is already enabled, use the configured debounce options as the default
- if alt.renderers.active == "vegafusion-widget":
- # Use configured debounce options, if any
- renderer_opts = alt.renderers.options
- for opt in ["debounce_wait", "debounce_max_wait"]:
- if opt in renderer_opts:
- kwargs.setdefault(opt, renderer_opts[opt])
-
- super().__init__(**kwargs)
-
- # Wire up widget message callback
- self.on_msg(self._handle_message)
-
- def _log(self, msg):
- if self.verbose:
- # Use print to show up in JupyterLab Log pane
- print(f"VegaFusionWidget(py): {msg}")
-
- @observe("_request_msg")
- def _handle_message(self, change):
- from vegafusion.runtime import runtime
- change_new = change["new"]
- if change_new is not None:
- msg_bytes = change["new"]
- start = time.time()
- self._log("Received request")
-
- # Build response
- response_bytes = runtime.process_request_bytes(
- msg_bytes
- )
-
- message_len = len(response_bytes)
- self._response_msg = response_bytes
-
- duration = (time.time() - start) * 1000
- self._log(f"Sent response in {duration:.1f}ms ({message_len} bytes)")
-
- # Clean up temporary tables
- if runtime._connection is not None:
- runtime._connection.unregister_temporary_tables()
diff --git a/python/vegafusion-jupyter/webpack.config.experimental.js b/python/vegafusion-jupyter/webpack.config.experimental.js
deleted file mode 100644
index eb764a8cf..000000000
--- a/python/vegafusion-jupyter/webpack.config.experimental.js
+++ /dev/null
@@ -1,8 +0,0 @@
-const experiments = {
- syncWebAssembly: true,
- topLevelAwait: true,
-};
-
-module.exports = {
- experiments
-}
\ No newline at end of file
diff --git a/python/vegafusion-jupyter/webpack.config.js b/python/vegafusion-jupyter/webpack.config.js
deleted file mode 100644
index d76344c71..000000000
--- a/python/vegafusion-jupyter/webpack.config.js
+++ /dev/null
@@ -1,97 +0,0 @@
-const path = require('path');
-const version = require('./package.json').version;
-const WebpackRequireFrom = require("webpack-require-from");
-
-// Custom webpack rules
-const rules = [
- { test: /\.ts$/, loader: 'ts-loader' },
- { test: /\.js$/, loader: 'source-map-loader' },
- { test: /\.svg$/, loader: 'svg-inline-loader' },
- { test: /\.css$/, use: [
- // Not sure why it's necessary to not include these loaders.
- // It looks like JupyterLab is automatically including these somewhere, so it's an error to duplicate
- // them
- 'style-loader', 'css-loader'
- ]
- }
-];
-
-// Packages that shouldn't be bundled but loaded at runtime
-const externals = ['@jupyter-widgets/base'];
-
-const resolve = {
- // Add '.ts' and '.tsx' as resolvable extensions.
- extensions: [".webpack.js", ".web.js", ".ts", ".js"]
-};
-
-const experiments = {
- syncWebAssembly: true,
- topLevelAwait: true,
-};
-
-module.exports = [
- /**
- * Notebook extension
- *
- * This bundle only contains the part of the JavaScript that is run on load of
- * the notebook.
- */
- {
- entry: './src/extension.ts',
- output: {
- filename: 'index.js',
- path: path.resolve(__dirname, 'vegafusion_jupyter', 'nbextension'),
- libraryTarget: 'amd',
- publicPath: '',
- },
- module: {
- rules: rules
- },
- mode: "production",
- externals,
- resolve,
- experiments,
- plugins: [
- /**
- * The __webpack_public_path__ is set in extension.ts, but for some reason
- * delayed imports (await import("foo")) do not honor this path. But when
- * the path is set using WebpackRequireFrom, the resource path is computed
- * correctly. The embeddable bundle below, used in JupyterLab, does not
- * have this issue.
- *
- * More investigation needed here.
- * */
- new WebpackRequireFrom({
- variableName: "__webpack_public_path__"
- })
- ]
- },
-
- /**
- * Embeddable vegafusion-jupyter bundle
- *
- * This bundle is almost identical to the notebook extension bundle. The only
- * difference is in the configuration of the webpack public path for the
- * static assets.
- *
- * The target bundle is always `dist/index.js`, which is the path required by
- * the custom widget embedder.
- */
- {
- entry: './src/index.ts',
- output: {
- filename: 'index.js',
- path: path.resolve(__dirname, 'dist'),
- libraryTarget: 'amd',
- library: "vegafusion-jupyter",
- publicPath: 'https://unpkg.com/vegafusion-jupyter@' + version + '/dist/'
- },
- module: {
- rules: rules
- },
- mode: "production",
- externals,
- resolve,
- experiments,
- },
-];
diff --git a/python/vegafusion/LICENSE.txt b/python/vegafusion/LICENSE.txt
deleted file mode 100644
index b765d1206..000000000
--- a/python/vegafusion/LICENSE.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Copyright (c) 2023, Hex Technologies, Inc
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright notice, this
- list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright notice,
- this list of conditions and the following disclaimer in the documentation
- and/or other materials provided with the distribution.
-
-3. Neither the name of the copyright holder nor the names of its contributors
- may be used to endorse or promote products derived from this software
- without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/python/vegafusion/README.md b/python/vegafusion/README.md
deleted file mode 100644
index 3e8af9b97..000000000
--- a/python/vegafusion/README.md
+++ /dev/null
@@ -1,4 +0,0 @@
-## VegaFusion
-This package is part of the VegaFusion project: https://vegafusion.io.
-
-In particular, the `vegafusion` package provides a pure Python interface to a VegaFusion Runtime. Initially, the only runtime available is provided by the `vegafusion-python-embed` package. Eventually, the VegaFusion runtime will be available as a standalone gRPC server and this package will be updated to support communication with a VegaFusion Runtime over gRPC as an alternative to the Runtime provided by `vegafusion-python-embed`.
diff --git a/python/vegafusion/checks/check_lazy_imports.py b/python/vegafusion/checks/check_lazy_imports.py
deleted file mode 100644
index 299cfb416..000000000
--- a/python/vegafusion/checks/check_lazy_imports.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import sys
-import vegafusion as vf
-import vegafusion_embed
-
-for mod in ["polars", "pandas", "pyarrow", "duckdb", "altair"]:
- assert mod not in sys.modules, f"{mod} module should be imported lazily"
diff --git a/python/vegafusion/pyproject.toml b/python/vegafusion/pyproject.toml
deleted file mode 100644
index e4b7ecef1..000000000
--- a/python/vegafusion/pyproject.toml
+++ /dev/null
@@ -1,10 +0,0 @@
-[build-system]
-requires = [
- "setuptools >= 35.0.2",
- "setuptools_scm >= 2.0.0, <3"
-]
-build-backend = "setuptools.build_meta"
-
-[tool.black]
-line-length = 88
-target_version = ['py37']
diff --git a/python/vegafusion/pytest.ini b/python/vegafusion/pytest.ini
deleted file mode 100644
index 5ee647716..000000000
--- a/python/vegafusion/pytest.ini
+++ /dev/null
@@ -1,2 +0,0 @@
-[pytest]
-testpaths = tests
diff --git a/python/vegafusion/setup.cfg b/python/vegafusion/setup.cfg
deleted file mode 100644
index 8da2b7153..000000000
--- a/python/vegafusion/setup.cfg
+++ /dev/null
@@ -1,47 +0,0 @@
-[bdist_wheel]
-universal = 0
-
-[metadata]
-name = vegafusion
-description = Core tools for using VegaFusion from Python
-version = 1.6.9
-long_description = file: README.md
-long_description_content_type = text/markdown
-keywords = vega, altair, vegafusion, arrow
-license = BSD-3-Clause
-license_files = [LICENSE.txt]
-python = "^3.7"
-homepage = "https://vegafusion.io"
-repository = "https://github.com/hex-inc/vegafusion"
-documentation = "https://vegafusion.io"
-classifiers =
- Programming Language :: Python :: 3.7
- Programming Language :: Python :: 3.8
- Programming Language :: Python :: 3.9
- Programming Language :: Python :: 3.10
- License :: OSI Approved :: BSD License
- Topic :: Scientific/Engineering :: Visualization
-
-[options]
-packages = find:
-python_requires = >=3.7
-include_package_data = True
-install_requires =
- altair>=5.2.0
- pyarrow>=5
- pandas
- psutil
- protobuf
-
-[options.extras_require]
-embed =
- vegafusion-python-embed==1.6.9
- vl-convert-python>=0.7.0
-
-[options.entry_points]
-altair.vegalite.v5.renderer =
- vegafusion-mime = vegafusion.renderer:vegafusion_mime_renderer
-altair.vegalite.v5.data_transformer =
- vegafusion-feather = vegafusion.transformer:feather_transformer
- vegafusion-inline = vegafusion.transformer:inline_data_transformer
-
diff --git a/python/vegafusion/setup.py b/python/vegafusion/setup.py
deleted file mode 100644
index b908cbe55..000000000
--- a/python/vegafusion/setup.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import setuptools
-
-setuptools.setup()
diff --git a/python/vegafusion/tests/altair_mocks/area/density_facet/mock.py b/python/vegafusion/tests/altair_mocks/area/density_facet/mock.py
deleted file mode 100644
index 30bf3d986..000000000
--- a/python/vegafusion/tests/altair_mocks/area/density_facet/mock.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# https://altair-viz.github.io/gallery/density_facet.html
-import altair as alt
-from vega_datasets import data
-
-source = data.iris()
-
-alt.Chart(source).transform_fold(
- ['petalWidth',
- 'petalLength',
- 'sepalWidth',
- 'sepalLength'],
- as_ = ['Measurement_type', 'value']
-).transform_density(
- density='value',
- bandwidth=0.3,
- groupby=['Measurement_type'],
- extent= [0, 8]
-).mark_area().encode(
- alt.X('value:Q'),
- alt.Y('density:Q'),
- alt.Row('Measurement_type:N')
-).properties(width=300, height=50)
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/area/density_stack/mock.py b/python/vegafusion/tests/altair_mocks/area/density_stack/mock.py
deleted file mode 100644
index ad0d0273e..000000000
--- a/python/vegafusion/tests/altair_mocks/area/density_stack/mock.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# https://altair-viz.github.io/gallery/density_stack.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.iris()
-
-alt.Chart(source).transform_fold(
- ['petalWidth',
- 'petalLength',
- 'sepalWidth',
- 'sepalLength'],
- as_ = ['Measurement_type', 'value']
-).transform_density(
- density='value',
- bandwidth=0.3,
- groupby=['Measurement_type'],
- extent= [0, 8],
- counts = True,
- steps=200
-).mark_area().encode(
- alt.X('value:Q'),
- alt.Y('density:Q', stack='zero'),
- alt.Color('Measurement_type:N')
-).properties(width=400, height=100)
diff --git a/python/vegafusion/tests/altair_mocks/area/gradient/mock.py b/python/vegafusion/tests/altair_mocks/area/gradient/mock.py
deleted file mode 100644
index d2b63bc5e..000000000
--- a/python/vegafusion/tests/altair_mocks/area/gradient/mock.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# https://altair-viz.github.io/gallery/area_chart_gradient.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.stocks()
-
-alt.Chart(source).transform_filter(
- 'datum.symbol==="GOOG"'
-).mark_area(
- line={'color':'darkgreen'},
- color=alt.Gradient(
- gradient='linear',
- stops=[alt.GradientStop(color='white', offset=0),
- alt.GradientStop(color='darkgreen', offset=1)],
- x1=1,
- x2=1,
- y1=1,
- y2=0
- )
-).encode(
- alt.X('date:T'),
- alt.Y('price:Q')
-)
diff --git a/python/vegafusion/tests/altair_mocks/area/horizon_graph/mock.py b/python/vegafusion/tests/altair_mocks/area/horizon_graph/mock.py
deleted file mode 100644
index 0a628a76a..000000000
--- a/python/vegafusion/tests/altair_mocks/area/horizon_graph/mock.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# https://altair-viz.github.io/gallery/horizon_graph.html
-
-import altair as alt
-import pandas as pd
-
-source = pd.DataFrame([
- {"x": 1, "y": 28}, {"x": 2, "y": 55},
- {"x": 3, "y": 43}, {"x": 4, "y": 91},
- {"x": 5, "y": 81}, {"x": 6, "y": 53},
- {"x": 7, "y": 19}, {"x": 8, "y": 87},
- {"x": 9, "y": 52}, {"x": 10, "y": 48},
- {"x": 11, "y": 24}, {"x": 12, "y": 49},
- {"x": 13, "y": 87}, {"x": 14, "y": 66},
- {"x": 15, "y": 17}, {"x": 16, "y": 27},
- {"x": 17, "y": 68}, {"x": 18, "y": 16},
- {"x": 19, "y": 49}, {"x": 20, "y": 15}
-])
-
-area1 = alt.Chart(source).mark_area(
- clip=True,
- interpolate='monotone'
-).encode(
- alt.X('x', scale=alt.Scale(zero=False, nice=False)),
- alt.Y('y', scale=alt.Scale(domain=[0, 50]), title='y'),
- opacity=alt.value(0.6)
-).properties(
- width=500,
- height=75
-)
-
-area2 = area1.encode(
- alt.Y('ny:Q', scale=alt.Scale(domain=[0, 50]))
-).transform_calculate(
- "ny", alt.datum.y - 50
-)
-
-area1 + area2
diff --git a/python/vegafusion/tests/altair_mocks/area/streamgraph/mock.py b/python/vegafusion/tests/altair_mocks/area/streamgraph/mock.py
deleted file mode 100644
index 56624680b..000000000
--- a/python/vegafusion/tests/altair_mocks/area/streamgraph/mock.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# https://altair-viz.github.io/gallery/streamgraph.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.unemployment_across_industries.url
-
-alt.Chart(source).mark_area().encode(
- alt.X('yearmonth(date):T',
- axis=alt.Axis(format='%Y', domain=False, tickSize=0)
- ),
- alt.Y('sum(count):Q', stack='center', axis=None),
- alt.Color('series:N',
- scale=alt.Scale(scheme='category20b')
- )
-).interactive()
diff --git a/python/vegafusion/tests/altair_mocks/area/trellis_sort_array/mock.py b/python/vegafusion/tests/altair_mocks/area/trellis_sort_array/mock.py
deleted file mode 100644
index 25e3f4c25..000000000
--- a/python/vegafusion/tests/altair_mocks/area/trellis_sort_array/mock.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# https://altair-viz.github.io/gallery/trellis_area_sort_array.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.stocks()
-
-alt.Chart(source).transform_filter(
- alt.datum.symbol != 'GOOG'
-).mark_area().encode(
- x='date:T',
- y='price:Q',
- color='symbol:N',
- row=alt.Row('symbol:N', sort=['MSFT', 'AAPL', 'IBM', 'AMZN'])
-).properties(height=50, width=400)
diff --git a/python/vegafusion/tests/altair_mocks/bar/and_tick_chart/mock.py b/python/vegafusion/tests/altair_mocks/bar/and_tick_chart/mock.py
deleted file mode 100644
index b2669dd38..000000000
--- a/python/vegafusion/tests/altair_mocks/bar/and_tick_chart/mock.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# https://altair-viz.github.io/gallery/layered_chart_bar_mark.html
-
-import altair as alt
-import pandas as pd
-
-source = pd.DataFrame({
- 'project': ['a', 'b', 'c', 'd', 'e', 'f', 'g'],
- 'score': [25, 57, 23, 19, 8, 47, 8],
- 'goal': [25, 47, 30, 27, 38, 19, 4]
-})
-
-bar = alt.Chart(source).mark_bar().encode(
- x='project',
- y='score'
-).properties(
- width=alt.Step(40) # controls width of bar.
-)
-
-tick = alt.Chart(source).mark_tick(
- color='red',
- thickness=2,
- size=40 * 0.9, # controls width of tick.
-).encode(
- x='project',
- y='goal'
-)
-
-bar + tick
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/bar/diverging_stacked/mock.py b/python/vegafusion/tests/altair_mocks/bar/diverging_stacked/mock.py
deleted file mode 100644
index 7e1bd976f..000000000
--- a/python/vegafusion/tests/altair_mocks/bar/diverging_stacked/mock.py
+++ /dev/null
@@ -1,364 +0,0 @@
-# https://altair-viz.github.io/gallery/diverging_stacked_bar_chart.html
-
-import altair as alt
-import pandas as pd
-
-source = pd.DataFrame([
- {
- "question": "Question 1",
- "type": "Strongly disagree",
- "value": 24,
- "percentage": 0.7,
- "percentage_start": -19.1,
- "percentage_end": -18.4
- },
- {
- "question": "Question 1",
- "type": "Disagree",
- "value": 294,
- "percentage": 9.1,
- "percentage_start": -18.4,
- "percentage_end": -9.2
- },
- {
- "question": "Question 1",
- "type": "Neither agree nor disagree",
- "value": 594,
- "percentage": 18.5,
- "percentage_start": -9.2,
- "percentage_end": 9.2
- },
- {
- "question": "Question 1",
- "type": "Agree",
- "value": 1927,
- "percentage": 59.9,
- "percentage_start": 9.2,
- "percentage_end": 69.2
- },
- {
- "question": "Question 1",
- "type": "Strongly agree",
- "value": 376,
- "percentage": 11.7,
- "percentage_start": 69.2,
- "percentage_end": 80.9
- },
-
- {
- "question": "Question 2",
- "type": "Strongly disagree",
- "value": 2,
- "percentage": 18.2,
- "percentage_start": -36.4,
- "percentage_end": -18.2
- },
- {
- "question": "Question 2",
- "type": "Disagree",
- "value": 2,
- "percentage": 18.2,
- "percentage_start": -18.2,
- "percentage_end": 0
- },
- {
- "question": "Question 2",
- "type": "Neither agree nor disagree",
- "value": 0,
- "percentage": 0,
- "percentage_start": 0,
- "percentage_end": 0
- },
- {
- "question": "Question 2",
- "type": "Agree",
- "value": 7,
- "percentage": 63.6,
- "percentage_start": 0,
- "percentage_end": 63.6
- },
- {
- "question": "Question 2",
- "type": "Strongly agree",
- "value": 11,
- "percentage": 0,
- "percentage_start": 63.6,
- "percentage_end": 63.6
- },
-
- {
- "question": "Question 3",
- "type": "Strongly disagree",
- "value": 2,
- "percentage": 20,
- "percentage_start": -30,
- "percentage_end": -10
- },
- {
- "question": "Question 3",
- "type": "Disagree",
- "value": 0,
- "percentage": 0,
- "percentage_start": -10,
- "percentage_end": -10
- },
- {
- "question": "Question 3",
- "type": "Neither agree nor disagree",
- "value": 2,
- "percentage": 20,
- "percentage_start": -10,
- "percentage_end": 10
- },
- {
- "question": "Question 3",
- "type": "Agree",
- "value": 4,
- "percentage": 40,
- "percentage_start": 10,
- "percentage_end": 50
- },
- {
- "question": "Question 3",
- "type": "Strongly agree",
- "value": 2,
- "percentage": 20,
- "percentage_start": 50,
- "percentage_end": 70
- },
-
- {
- "question": "Question 4",
- "type": "Strongly disagree",
- "value": 0,
- "percentage": 0,
- "percentage_start": -15.6,
- "percentage_end": -15.6
- },
- {
- "question": "Question 4",
- "type": "Disagree",
- "value": 2,
- "percentage": 12.5,
- "percentage_start": -15.6,
- "percentage_end": -3.1
- },
- {
- "question": "Question 4",
- "type": "Neither agree nor disagree",
- "value": 1,
- "percentage": 6.3,
- "percentage_start": -3.1,
- "percentage_end": 3.1
- },
- {
- "question": "Question 4",
- "type": "Agree",
- "value": 7,
- "percentage": 43.8,
- "percentage_start": 3.1,
- "percentage_end": 46.9
- },
- {
- "question": "Question 4",
- "type": "Strongly agree",
- "value": 6,
- "percentage": 37.5,
- "percentage_start": 46.9,
- "percentage_end": 84.4
- },
-
- {
- "question": "Question 5",
- "type": "Strongly disagree",
- "value": 0,
- "percentage": 0,
- "percentage_start": -10.4,
- "percentage_end": -10.4
- },
- {
- "question": "Question 5",
- "type": "Disagree",
- "value": 1,
- "percentage": 4.2,
- "percentage_start": -10.4,
- "percentage_end": -6.3
- },
- {
- "question": "Question 5",
- "type": "Neither agree nor disagree",
- "value": 3,
- "percentage": 12.5,
- "percentage_start": -6.3,
- "percentage_end": 6.3
- },
- {
- "question": "Question 5",
- "type": "Agree",
- "value": 16,
- "percentage": 66.7,
- "percentage_start": 6.3,
- "percentage_end": 72.9
- },
- {
- "question": "Question 5",
- "type": "Strongly agree",
- "value": 4,
- "percentage": 16.7,
- "percentage_start": 72.9,
- "percentage_end": 89.6
- },
-
- {
- "question": "Question 6",
- "type": "Strongly disagree",
- "value": 1,
- "percentage": 6.3,
- "percentage_start": -18.8,
- "percentage_end": -12.5
- },
- {
- "question": "Question 6",
- "type": "Disagree",
- "value": 1,
- "percentage": 6.3,
- "percentage_start": -12.5,
- "percentage_end": -6.3
- },
- {
- "question": "Question 6",
- "type": "Neither agree nor disagree",
- "value": 2,
- "percentage": 12.5,
- "percentage_start": -6.3,
- "percentage_end": 6.3
- },
- {
- "question": "Question 6",
- "type": "Agree",
- "value": 9,
- "percentage": 56.3,
- "percentage_start": 6.3,
- "percentage_end": 62.5
- },
- {
- "question": "Question 6",
- "type": "Strongly agree",
- "value": 3,
- "percentage": 18.8,
- "percentage_start": 62.5,
- "percentage_end": 81.3
- },
-
- {
- "question": "Question 7",
- "type": "Strongly disagree",
- "value": 0,
- "percentage": 0,
- "percentage_start": -10,
- "percentage_end": -10
- },
- {
- "question": "Question 7",
- "type": "Disagree",
- "value": 0,
- "percentage": 0,
- "percentage_start": -10,
- "percentage_end": -10
- },
- {
- "question": "Question 7",
- "type": "Neither agree nor disagree",
- "value": 1,
- "percentage": 20,
- "percentage_start": -10,
- "percentage_end": 10
- },
- {
- "question": "Question 7",
- "type": "Agree",
- "value": 4,
- "percentage": 80,
- "percentage_start": 10,
- "percentage_end": 90
- },
- {
- "question": "Question 7",
- "type": "Strongly agree",
- "value": 0,
- "percentage": 0,
- "percentage_start": 90,
- "percentage_end": 90
- },
-
- {
- "question": "Question 8",
- "type": "Strongly disagree",
- "value": 0,
- "percentage": 0,
- "percentage_start": 0,
- "percentage_end": 0
- },
- {
- "question": "Question 8",
- "type": "Disagree",
- "value": 0,
- "percentage": 0,
- "percentage_start": 0,
- "percentage_end": 0
- },
- {
- "question": "Question 8",
- "type": "Neither agree nor disagree",
- "value": 0,
- "percentage": 0,
- "percentage_start": 0,
- "percentage_end": 0
- },
- {
- "question": "Question 8",
- "type": "Agree",
- "value": 0,
- "percentage": 0,
- "percentage_start": 0,
- "percentage_end": 0
- },
- {
- "question": "Question 8",
- "type": "Strongly agree",
- "value": 2,
- "percentage": 100,
- "percentage_start": 0,
- "percentage_end": 100
- }
-])
-
-color_scale = alt.Scale(
- domain=[
- "Strongly disagree",
- "Disagree",
- "Neither agree nor disagree",
- "Agree",
- "Strongly agree"
- ],
- range=["#c30d24", "#f3a583", "#cccccc", "#94c6da", "#1770ab"]
-)
-
-y_axis = alt.Axis(
- title='Question',
- offset=5,
- ticks=False,
- minExtent=60,
- domain=False
-)
-
-alt.Chart(source).mark_bar().encode(
- x='percentage_start:Q',
- x2='percentage_end:Q',
- y=alt.Y('question:N', axis=y_axis),
- color=alt.Color(
- 'type:N',
- legend=alt.Legend( title='Response'),
- scale=color_scale,
- )
-)
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/bar/percentage_of_total/mock.py b/python/vegafusion/tests/altair_mocks/bar/percentage_of_total/mock.py
deleted file mode 100644
index 9c232c665..000000000
--- a/python/vegafusion/tests/altair_mocks/bar/percentage_of_total/mock.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# https://altair-viz.github.io/gallery/percentage_of_total.html
-
-import altair as alt
-import pandas as pd
-
-source = pd.DataFrame({'Activity': ['Sleeping', 'Eating', 'TV', 'Work', 'Exercise'],
- 'Time': [8, 2, 4, 8, 2]})
-
-alt.Chart(source).transform_joinaggregate(
- TotalTime='sum(Time)',
-).transform_calculate(
- PercentOfTotal="datum.Time / datum.TotalTime"
-).mark_bar().encode(
- alt.X('PercentOfTotal:Q', axis=alt.Axis(format='.0%')),
- y='Activity:N'
-)
diff --git a/python/vegafusion/tests/altair_mocks/bar/stacked_with_text_overlay/mock.py b/python/vegafusion/tests/altair_mocks/bar/stacked_with_text_overlay/mock.py
deleted file mode 100644
index aa44349cd..000000000
--- a/python/vegafusion/tests/altair_mocks/bar/stacked_with_text_overlay/mock.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# https://altair-viz.github.io/gallery/stacked_bar_chart_with_text.html
-
-import altair as alt
-from vega_datasets import data
-
-source=data.barley()
-
-bars = alt.Chart(source).mark_bar().encode(
- x=alt.X('sum(yield):Q', stack='zero'),
- y=alt.Y('variety:N'),
- color=alt.Color('site')
-)
-
-text = alt.Chart(source).mark_text(dx=-15, dy=3, color='white').encode(
- x=alt.X('sum(yield):Q', stack='zero'),
- y=alt.Y('variety:N'),
- detail='site:N',
- text=alt.Text('sum(yield):Q', format='.1f')
-)
-
-bars + text
diff --git a/python/vegafusion/tests/altair_mocks/bar/with_error_bars/mock.py b/python/vegafusion/tests/altair_mocks/bar/with_error_bars/mock.py
deleted file mode 100644
index a0f561a73..000000000
--- a/python/vegafusion/tests/altair_mocks/bar/with_error_bars/mock.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# https://altair-viz.github.io/gallery/grouped_bar_chart_with_error_bars.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.barley()
-
-bars = alt.Chart().mark_bar().encode(
- x='year:O',
- y=alt.Y('mean(yield):Q', title='Mean Yield'),
- color='year:N',
-)
-
-error_bars = alt.Chart().mark_errorbar(extent='ci').encode(
- x='year:O',
- y='yield:Q'
-)
-
-alt.layer(bars, error_bars, data=source).facet(
- column='site:N'
-)
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/bar/with_rolling_mean/mock.py b/python/vegafusion/tests/altair_mocks/bar/with_rolling_mean/mock.py
deleted file mode 100644
index a7a10b903..000000000
--- a/python/vegafusion/tests/altair_mocks/bar/with_rolling_mean/mock.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# https://altair-viz.github.io/gallery/bar_with_rolling_mean.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.wheat()
-
-bar = alt.Chart(source).mark_bar().encode(
- x='year:O',
- y='wheat:Q'
-)
-
-line = alt.Chart(source).mark_line(color='red').transform_window(
- # The field to average
- rolling_mean='mean(wheat)',
- # The number of values before and after the current value to include.
- frame=[-9, 0]
-).encode(
- x='year:O',
- y='rolling_mean:Q'
-)
-
-(bar + line).properties(width=600)
diff --git a/python/vegafusion/tests/altair_mocks/bar/with_rounded_edges/mock.py b/python/vegafusion/tests/altair_mocks/bar/with_rounded_edges/mock.py
deleted file mode 100644
index fb996d2ba..000000000
--- a/python/vegafusion/tests/altair_mocks/bar/with_rounded_edges/mock.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# https://altair-viz.github.io/gallery/bar_rounded.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.seattle_weather()
-
-alt.Chart(source).mark_bar(
- cornerRadiusTopLeft=3,
- cornerRadiusTopRight=3
-).encode(
- x='month(date):O',
- y='count():Q',
- color='weather:N'
-)
diff --git a/python/vegafusion/tests/altair_mocks/casestudy/airports/mock.py b/python/vegafusion/tests/altair_mocks/casestudy/airports/mock.py
deleted file mode 100644
index d76fc5914..000000000
--- a/python/vegafusion/tests/altair_mocks/casestudy/airports/mock.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# https://altair-viz.github.io/gallery/airports.html
-
-import altair as alt
-from vega_datasets import data
-
-airports = data.airports()
-states = alt.topo_feature(data.us_10m.url, feature='states')
-
-# US states background
-background = alt.Chart(states).mark_geoshape(
- fill='lightgray',
- stroke='white'
-).properties(
- width=500,
- height=300
-).project('albersUsa')
-
-# airport positions on background
-points = alt.Chart(airports).mark_circle(
- size=10,
- color='steelblue'
-).encode(
- longitude='longitude:Q',
- latitude='latitude:Q',
- tooltip=['name', 'city', 'state']
-)
-
-background + points
diff --git a/python/vegafusion/tests/altair_mocks/casestudy/co2_concentration/mock.py b/python/vegafusion/tests/altair_mocks/casestudy/co2_concentration/mock.py
deleted file mode 100644
index e301b852a..000000000
--- a/python/vegafusion/tests/altair_mocks/casestudy/co2_concentration/mock.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# https://altair-viz.github.io/gallery/co2_concentration.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.co2_concentration.url
-
-base = alt.Chart(
- source,
- title="Carbon Dioxide in the Atmosphere"
-).transform_calculate(
- year="year(datum.Date)"
-).transform_calculate(
- decade="floor(datum.year / 10)"
-).transform_calculate(
- scaled_date="(datum.year % 10) + (month(datum.Date)/12)"
-).transform_window(
- first_date='first_value(scaled_date)',
- last_date='last_value(scaled_date)',
- sort=[{"field": "scaled_date", "order": "ascending"}],
- groupby=['decade'],
- frame=[None, None]
-).transform_calculate(
- end="datum.first_date === datum.scaled_date ? 'first' : datum.last_date === datum.scaled_date ? 'last' : null"
-).encode(
- x=alt.X(
- "scaled_date:Q",
- axis=alt.Axis(title="Year into Decade", tickCount=11)
- ),
- y=alt.Y(
- "CO2:Q",
- title="CO2 concentration in ppm",
- scale=alt.Scale(zero=False)
- )
-)
-
-line = base.mark_line().encode(
- color=alt.Color(
- "decade:O",
- scale=alt.Scale(scheme="magma"),
- legend=None
- )
-)
-
-text = base.encode(text="year:N")
-
-start_year = text.transform_filter(
- alt.datum.end == 'first'
-).mark_text(baseline="top")
-
-end_year = text.transform_filter(
- alt.datum.end == 'last'
-).mark_text(baseline="bottom")
-
-(line + start_year + end_year).configure_text(
- align="left",
- dx=1,
- dy=3
-).properties(width=600, height=375)
diff --git a/python/vegafusion/tests/altair_mocks/casestudy/falkensee/mock.py b/python/vegafusion/tests/altair_mocks/casestudy/falkensee/mock.py
deleted file mode 100644
index 1dc85856e..000000000
--- a/python/vegafusion/tests/altair_mocks/casestudy/falkensee/mock.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# https://altair-viz.github.io/gallery/falkensee.html
-
-import altair as alt
-import pandas as pd
-
-source = [
- {"year": "1875", "population": 1309},
- {"year": "1890", "population": 1558},
- {"year": "1910", "population": 4512},
- {"year": "1925", "population": 8180},
- {"year": "1933", "population": 15915},
- {"year": "1939", "population": 24824},
- {"year": "1946", "population": 28275},
- {"year": "1950", "population": 29189},
- {"year": "1964", "population": 29881},
- {"year": "1971", "population": 26007},
- {"year": "1981", "population": 24029},
- {"year": "1985", "population": 23340},
- {"year": "1989", "population": 22307},
- {"year": "1990", "population": 22087},
- {"year": "1991", "population": 22139},
- {"year": "1992", "population": 22105},
- {"year": "1993", "population": 22242},
- {"year": "1994", "population": 22801},
- {"year": "1995", "population": 24273},
- {"year": "1996", "population": 25640},
- {"year": "1997", "population": 27393},
- {"year": "1998", "population": 29505},
- {"year": "1999", "population": 32124},
- {"year": "2000", "population": 33791},
- {"year": "2001", "population": 35297},
- {"year": "2002", "population": 36179},
- {"year": "2003", "population": 36829},
- {"year": "2004", "population": 37493},
- {"year": "2005", "population": 38376},
- {"year": "2006", "population": 39008},
- {"year": "2007", "population": 39366},
- {"year": "2008", "population": 39821},
- {"year": "2009", "population": 40179},
- {"year": "2010", "population": 40511},
- {"year": "2011", "population": 40465},
- {"year": "2012", "population": 40905},
- {"year": "2013", "population": 41258},
- {"year": "2014", "population": 41777}
-]
-
-source2 = [{
- "start": "1933",
- "end": "1945",
- "event": "Nazi Rule"
-},
- {
- "start": "1948",
- "end": "1989",
- "event": "GDR (East Germany)"
- }]
-
-
-source = pd.DataFrame(source)
-source2 = pd.DataFrame(source2)
-
-
-line = alt.Chart(source).mark_line(color='#333').encode(
- alt.X('year:T', axis=alt.Axis(format='%Y')),
- y='population'
-).properties(
- width=500,
- height=300
-)
-
-point = line.mark_point(color='#333')
-
-rect = alt.Chart(source2).mark_rect().encode(
- x='start:T',
- x2='end:T',
- color='event:N'
-)
-
-rect + line + point
diff --git a/python/vegafusion/tests/altair_mocks/casestudy/isotype_emoji/mock.py b/python/vegafusion/tests/altair_mocks/casestudy/isotype_emoji/mock.py
deleted file mode 100644
index 927885702..000000000
--- a/python/vegafusion/tests/altair_mocks/casestudy/isotype_emoji/mock.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# https://altair-viz.github.io/gallery/isotype_emoji.html
-
-import altair as alt
-import pandas as pd
-
-source = pd.DataFrame([
- {'country': 'Great Britain', 'animal': 'cattle'},
- {'country': 'Great Britain', 'animal': 'cattle'},
- {'country': 'Great Britain', 'animal': 'cattle'},
- {'country': 'Great Britain', 'animal': 'pigs'},
- {'country': 'Great Britain', 'animal': 'pigs'},
- {'country': 'Great Britain', 'animal': 'sheep'},
- {'country': 'Great Britain', 'animal': 'sheep'},
- {'country': 'Great Britain', 'animal': 'sheep'},
- {'country': 'Great Britain', 'animal': 'sheep'},
- {'country': 'Great Britain', 'animal': 'sheep'},
- {'country': 'Great Britain', 'animal': 'sheep'},
- {'country': 'Great Britain', 'animal': 'sheep'},
- {'country': 'Great Britain', 'animal': 'sheep'},
- {'country': 'Great Britain', 'animal': 'sheep'},
- {'country': 'Great Britain', 'animal': 'sheep'},
- {'country': 'United States', 'animal': 'cattle'},
- {'country': 'United States', 'animal': 'cattle'},
- {'country': 'United States', 'animal': 'cattle'},
- {'country': 'United States', 'animal': 'cattle'},
- {'country': 'United States', 'animal': 'cattle'},
- {'country': 'United States', 'animal': 'cattle'},
- {'country': 'United States', 'animal': 'cattle'},
- {'country': 'United States', 'animal': 'cattle'},
- {'country': 'United States', 'animal': 'cattle'},
- {'country': 'United States', 'animal': 'pigs'},
- {'country': 'United States', 'animal': 'pigs'},
- {'country': 'United States', 'animal': 'pigs'},
- {'country': 'United States', 'animal': 'pigs'},
- {'country': 'United States', 'animal': 'pigs'},
- {'country': 'United States', 'animal': 'pigs'},
- {'country': 'United States', 'animal': 'sheep'},
- {'country': 'United States', 'animal': 'sheep'},
- {'country': 'United States', 'animal': 'sheep'},
- {'country': 'United States', 'animal': 'sheep'},
- {'country': 'United States', 'animal': 'sheep'},
- {'country': 'United States', 'animal': 'sheep'},
- {'country': 'United States', 'animal': 'sheep'}
-])
-
-
-alt.Chart(source).mark_text(size=45, baseline='middle').encode(
- alt.X('x:O', axis=None),
- alt.Y('animal:O', axis=None),
- alt.Row('country:N', header=alt.Header(title='')),
- alt.Text('emoji:N')
-).transform_calculate(
- emoji="{'cattle': '🐄', 'pigs': '🐖', 'sheep': '🐏'}[datum.animal]"
-).transform_window(
- x='rank()',
- groupby=['country', 'animal']
-).properties(width=550, height=140)
diff --git a/python/vegafusion/tests/altair_mocks/casestudy/london_tube/mock.py b/python/vegafusion/tests/altair_mocks/casestudy/london_tube/mock.py
deleted file mode 100644
index a98441b20..000000000
--- a/python/vegafusion/tests/altair_mocks/casestudy/london_tube/mock.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# https://altair-viz.github.io/gallery/london_tube.html
-
-import altair as alt
-from vega_datasets import data
-
-boroughs = alt.topo_feature(data.londonBoroughs.url, 'boroughs')
-tubelines = alt.topo_feature(data.londonTubeLines.url, 'line')
-centroids = data.londonCentroids.url
-
-background = alt.Chart(boroughs).mark_geoshape(
- stroke='white',
- strokeWidth=2
-).encode(
- color=alt.value('#eee'),
-).properties(
- width=600,
- height=400
-)
-
-labels = alt.Chart(centroids).mark_text().encode(
- longitude='cx:Q',
- latitude='cy:Q',
- text='bLabel:N',
- size=alt.value(8),
- opacity=alt.value(0.6)
-).transform_calculate(
- "bLabel", "indexof (datum.name,' ') > 0 ? substring(datum.name,0,indexof(datum.name, ' ')) : datum.name"
-)
-
-line_scale = alt.Scale(domain=["Bakerloo", "Central", "Circle", "District", "DLR",
- "Hammersmith & City", "Jubilee", "Metropolitan", "Northern",
- "Piccadilly", "Victoria", "Waterloo & City"],
- range=["rgb(137,78,36)", "rgb(220,36,30)", "rgb(255,206,0)",
- "rgb(1,114,41)", "rgb(0,175,173)", "rgb(215,153,175)",
- "rgb(106,114,120)", "rgb(114,17,84)", "rgb(0,0,0)",
- "rgb(0,24,168)", "rgb(0,160,226)", "rgb(106,187,170)"])
-
-lines = alt.Chart(tubelines).mark_geoshape(
- filled=False,
- strokeWidth=2
-).encode(
- alt.Color(
- 'id:N',
- legend=alt.Legend(
- title=None,
- orient='bottom-right',
- offset=0
- ),
- scale=line_scale
- )
-)
-
-background + labels + lines
diff --git a/python/vegafusion/tests/altair_mocks/casestudy/natural_disasters/mock.py b/python/vegafusion/tests/altair_mocks/casestudy/natural_disasters/mock.py
deleted file mode 100644
index 4afaea5fc..000000000
--- a/python/vegafusion/tests/altair_mocks/casestudy/natural_disasters/mock.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# https://altair-viz.github.io/gallery/natural_disasters.html
-
-import altair as alt
-from vega_datasets import data
-
-source = "https://raw.githubusercontent.com/vega/vega-datasets/main/data/disasters.csv"
-
-alt.Chart(source).mark_circle(
- opacity=0.8,
- stroke='black',
- strokeWidth=1
-).encode(
- alt.X('Year:O', axis=alt.Axis(labelAngle=0)),
- alt.Y('Entity:N'),
- alt.Size('Deaths:Q',
- scale=alt.Scale(range=[0, 4000]),
- legend=alt.Legend(title='Annual Global Deaths')
- ),
- alt.Color('Entity:N', legend=None)
-).properties(
- width=300,
- height=300
-).transform_filter(
- alt.datum.Entity != 'All natural disasters'
-)
-
diff --git a/python/vegafusion/tests/altair_mocks/casestudy/top_k_items/mock.py b/python/vegafusion/tests/altair_mocks/casestudy/top_k_items/mock.py
deleted file mode 100644
index 54c877a9b..000000000
--- a/python/vegafusion/tests/altair_mocks/casestudy/top_k_items/mock.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# https://altair-viz.github.io/gallery/top_k_items.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.movies.url
-
-# Top 10 movies by IMBD rating
-alt.Chart(
- source,
-).mark_bar().encode(
- x=alt.X('Title:N', sort='-y'),
- y=alt.Y('IMDB_Rating:Q'),
- color=alt.Color('IMDB_Rating:Q')
-
-).transform_window(
- rank='rank(IMDB_Rating)',
- sort=[
- alt.SortField('IMDB_Rating', order='descending'),
- alt.SortField('Title', order='ascending'),
- ]
-).transform_filter(
- (alt.datum.rank < 10)
-)
diff --git a/python/vegafusion/tests/altair_mocks/casestudy/us_employment/mock.py b/python/vegafusion/tests/altair_mocks/casestudy/us_employment/mock.py
deleted file mode 100644
index 69b6ab454..000000000
--- a/python/vegafusion/tests/altair_mocks/casestudy/us_employment/mock.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# https://altair-viz.github.io/gallery/us_employment.html
-
-import altair as alt
-import pandas as pd
-from vega_datasets import data
-
-source = data.us_employment()
-presidents = pd.DataFrame([
- {
- "start": "2006-01-01",
- "end": "2009-01-19",
- "president": "Bush"
- },
- {
- "start": "2009-01-20",
- "end": "2015-12-31",
- "president": "Obama"
- }
-])
-
-bars = alt.Chart(
- source,
- title="The U.S. employment crash during the Great Recession"
-).mark_bar().encode(
- x=alt.X("month:T", title=""),
- y=alt.Y("nonfarm_change:Q", title="Change in non-farm employment (in thousands)"),
- color=alt.condition(
- alt.datum.nonfarm_change > 0,
- alt.value("steelblue"),
- alt.value("orange")
- )
-)
-
-rule = alt.Chart(presidents).mark_rule(
- color="black",
- strokeWidth=2
-).encode(
- x='end:T'
-).transform_filter(alt.datum.president == "Bush")
-
-text = alt.Chart(presidents).mark_text(
- align='left',
- baseline='middle',
- dx=7,
- dy=-135,
- size=11
-).encode(
- x='start:T',
- x2='end:T',
- text='president',
- color=alt.value('#000000')
-)
-
-(bars + rule + text).properties(width=600)
diff --git a/python/vegafusion/tests/altair_mocks/casestudy/us_population_over_time_facet/mock.py b/python/vegafusion/tests/altair_mocks/casestudy/us_population_over_time_facet/mock.py
deleted file mode 100644
index 79c4fafe4..000000000
--- a/python/vegafusion/tests/altair_mocks/casestudy/us_population_over_time_facet/mock.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# https://altair-viz.github.io/gallery/us_population_over_time_facet.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.population.url
-
-alt.Chart(source).mark_area().encode(
- x='age:O',
- y=alt.Y(
- 'sum(people):Q',
- title='Population',
- axis=alt.Axis(format='~s')
- ),
- facet=alt.Facet('year:O', columns=5),
-).properties(
- title='US Age Distribution By Year',
- width=80,
- height=80
-)
diff --git a/python/vegafusion/tests/altair_mocks/casestudy/us_state_capitals/mock.py b/python/vegafusion/tests/altair_mocks/casestudy/us_state_capitals/mock.py
deleted file mode 100644
index 6090d3f44..000000000
--- a/python/vegafusion/tests/altair_mocks/casestudy/us_state_capitals/mock.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# https://altair-viz.github.io/gallery/us_state_capitals.html
-
-import altair as alt
-from vega_datasets import data
-
-states = alt.topo_feature(data.us_10m.url, 'states')
-capitals = data.us_state_capitals.url
-
-# US states background
-background = alt.Chart(states).mark_geoshape(
- fill='lightgray',
- stroke='white'
-).properties(
- title='US State Capitols',
- width=650,
- height=400
-).project('albersUsa')
-
-# Points and text
-hover = alt.selection(type='single', on='mouseover', nearest=True,
- fields=['lat', 'lon'])
-
-base = alt.Chart(capitals).encode(
- longitude='lon:Q',
- latitude='lat:Q',
-)
-
-text = base.mark_text(dy=-5, align='right').encode(
- alt.Text('city', type='nominal'),
- opacity=alt.condition(~hover, alt.value(0), alt.value(1))
-)
-
-points = base.mark_point().encode(
- color=alt.value('black'),
- size=alt.condition(~hover, alt.value(30), alt.value(100))
-).add_selection(hover)
-
-background + points + text
diff --git a/python/vegafusion/tests/altair_mocks/histogram/layered/mock.py b/python/vegafusion/tests/altair_mocks/histogram/layered/mock.py
deleted file mode 100644
index b31d58964..000000000
--- a/python/vegafusion/tests/altair_mocks/histogram/layered/mock.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# https://altair-viz.github.io/gallery/layered_histogram.html
-
-import pandas as pd
-import altair as alt
-import numpy as np
-np.random.seed(42)
-
-# Generating Data
-source = pd.DataFrame({
- 'Trial A': np.random.normal(0, 0.8, 1000),
- 'Trial B': np.random.normal(-2, 1, 1000),
- 'Trial C': np.random.normal(3, 2, 1000)
-})
-
-alt.Chart(source).transform_fold(
- ['Trial A', 'Trial B', 'Trial C'],
- as_=['Experiment', 'Measurement']
-).mark_bar(
- opacity=0.3,
- binSpacing=0
-).encode(
- alt.X('Measurement:Q', bin=alt.Bin(maxbins=100)),
- alt.Y('count()', stack=None),
- alt.Color('Experiment:N')
-)
diff --git a/python/vegafusion/tests/altair_mocks/histogram/with_a_global_mean_overlay/mock.py b/python/vegafusion/tests/altair_mocks/histogram/with_a_global_mean_overlay/mock.py
deleted file mode 100644
index 2f0dfecd0..000000000
--- a/python/vegafusion/tests/altair_mocks/histogram/with_a_global_mean_overlay/mock.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# https://altair-viz.github.io/gallery/histogram_with_a_global_mean_overlay.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.movies.url
-
-base = alt.Chart(source)
-
-bar = base.mark_bar().encode(
- x=alt.X('IMDB_Rating:Q', bin=True, axis=None),
- y='count()'
-)
-
-rule = base.mark_rule(color='red').encode(
- x='mean(IMDB_Rating):Q',
- size=alt.value(5)
-)
-
-bar + rule
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/interactive/area-interval_selection/mock.py b/python/vegafusion/tests/altair_mocks/interactive/area-interval_selection/mock.py
deleted file mode 100644
index 52e2160a5..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/area-interval_selection/mock.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# https://altair-viz.github.io/gallery/interval_selection.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.sp500.url
-
-brush = alt.selection(type='interval', encodings=['x'])
-
-base = alt.Chart(source).mark_area().encode(
- x = 'date:T',
- y = 'price:Q'
-).properties(
- width=600,
- height=200
-)
-
-upper = base.encode(
- alt.X('date:T', scale=alt.Scale(domain=brush))
-)
-
-lower = base.properties(
- height=60
-).add_selection(brush)
-
-upper & lower
diff --git a/python/vegafusion/tests/altair_mocks/interactive/casestudy-airport_connections/mock.py b/python/vegafusion/tests/altair_mocks/interactive/casestudy-airport_connections/mock.py
deleted file mode 100644
index 66782f786..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/casestudy-airport_connections/mock.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# https://altair-viz.github.io/gallery/airport_connections.html
-
-import altair as alt
-from vega_datasets import data
-
-# Since these data are each more than 5,000 rows we'll import from the URLs
-airports = data.airports.url
-flights_airport = data.flights_airport.url
-
-states = alt.topo_feature(data.us_10m.url, feature="states")
-
-# Create mouseover selection
-select_city = alt.selection_single(
- on="mouseover", nearest=True, fields=["origin"], empty="none"
-)
-
-# Define which attributes to lookup from airports.csv
-lookup_data = alt.LookupData(
- airports, key="iata", fields=["state", "latitude", "longitude"]
-)
-
-background = alt.Chart(states).mark_geoshape(
- fill="lightgray",
- stroke="white"
-).properties(
- width=600,
- height=400
-).project("albersUsa")
-
-connections = alt.Chart(flights_airport).mark_rule(opacity=0.35).encode(
- latitude="latitude:Q",
- longitude="longitude:Q",
- latitude2="lat2:Q",
- longitude2="lon2:Q"
-).transform_lookup(
- lookup="origin",
- from_=lookup_data
-).transform_lookup(
- lookup="destination",
- from_=lookup_data,
- as_=["state", "lat2", "lon2"]
-).transform_filter(
- select_city
-)
-
-points = alt.Chart(flights_airport).mark_circle().encode(
- latitude="latitude:Q",
- longitude="longitude:Q",
- size=alt.Size("routes:Q", scale=alt.Scale(range=[0, 1000]), legend=None),
- order=alt.Order("routes:Q", sort="descending"),
- tooltip=["origin:N", "routes:Q"]
-).transform_aggregate(
- routes="count()",
- groupby=["origin"]
-).transform_lookup(
- lookup="origin",
- from_=lookup_data
-).transform_filter(
- (alt.datum.state != "PR") & (alt.datum.state != "VI")
-).add_selection(
- select_city
-)
-
-(background + connections + points).configure_view(stroke=None)
diff --git a/python/vegafusion/tests/altair_mocks/interactive/casestudy-seattle_weather_interactive/mock.py b/python/vegafusion/tests/altair_mocks/interactive/casestudy-seattle_weather_interactive/mock.py
deleted file mode 100644
index 7d5573db5..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/casestudy-seattle_weather_interactive/mock.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import altair as alt
-from vega_datasets import data
-
-source = data.seattle_weather()
-
-scale = alt.Scale(domain=['sun', 'fog', 'drizzle', 'rain', 'snow'],
- range=['#e7ba52', '#a7a7a7', '#aec7e8', '#1f77b4', '#9467bd'])
-color = alt.Color('weather:N', scale=scale)
-
-# We create two selections:
-# - a brush that is active on the top panel
-# - a multi-click that is active on the bottom panel
-brush = alt.selection_interval(encodings=['x'])
-click = alt.selection_point(encodings=['color'])
-
-# Top panel is scatter plot of temperature vs time
-points = alt.Chart().mark_point().encode(
- alt.X('monthdate(date):T', title='Date'),
- alt.Y('temp_max:Q',
- title='Maximum Daily Temperature (C)',
- scale=alt.Scale(domain=[-5, 40])
- ),
- color=alt.condition(brush, color, alt.value('lightgray')),
- size=alt.Size('precipitation:Q', scale=alt.Scale(range=[5, 200]))
-).properties(
- width=550,
- height=300
-).add_params(
- brush
-).transform_filter(
- click
-)
-
-# Bottom panel is a bar chart of weather type
-bars = alt.Chart().mark_bar().encode(
- x='count()',
- y='weather:N',
- color=alt.condition(click, color, alt.value('lightgray')),
-).transform_filter(
- brush
-).properties(
- width=550,
-).add_params(
- click
-)
-
-alt.vconcat(
- points,
- bars,
- data=source,
- title="Seattle Weather: 2012-2015"
-)
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/interactive/casestudy-us_population_pyramid_over_time/mock.py b/python/vegafusion/tests/altair_mocks/interactive/casestudy-us_population_pyramid_over_time/mock.py
deleted file mode 100644
index d3423fa0f..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/casestudy-us_population_pyramid_over_time/mock.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# https://altair-viz.github.io/gallery/us_population_pyramid_over_time.html
-import altair as alt
-from vega_datasets import data
-
-source = data.population.url
-
-slider = alt.binding_range(min=1850, max=2000, step=10)
-select_year = alt.selection_point(name='year', fields=['year'],
- bind=slider, value={'year': 2000})
-
-base = alt.Chart(source).add_params(
- select_year
-).transform_filter(
- select_year
-).transform_calculate(
- gender=alt.expr.if_(alt.datum.sex == 1, 'Male', 'Female')
-).properties(
- width=250
-)
-
-
-color_scale = alt.Scale(domain=['Male', 'Female'],
- range=['#1f77b4', '#e377c2'])
-
-left = base.transform_filter(
- alt.datum.gender == 'Female'
-).encode(
- y=alt.Y('age:O', axis=None),
- x=alt.X('sum(people):Q',
- title='population',
- sort=alt.SortOrder('descending')),
- color=alt.Color('gender:N', scale=color_scale, legend=None)
-).mark_bar().properties(title='Female')
-
-middle = base.encode(
- y=alt.Y('age:O', axis=None),
- text=alt.Text('age:Q'),
-).mark_text().properties(width=20)
-
-right = base.transform_filter(
- alt.datum.gender == 'Male'
-).encode(
- y=alt.Y('age:O', axis=None),
- x=alt.X('sum(people):Q', title='population'),
- color=alt.Color('gender:N', scale=color_scale, legend=None)
-).mark_bar().properties(title='Male')
-
-alt.concat(left, middle, right, spacing=5)
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/interactive/casestudy-weather_heatmap/mock.py b/python/vegafusion/tests/altair_mocks/interactive/casestudy-weather_heatmap/mock.py
deleted file mode 100644
index 75d14ef76..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/casestudy-weather_heatmap/mock.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# https://altair-viz.github.io/gallery/weather_heatmap.html
-
-import altair as alt
-from vega_datasets import data
-
-# Since the data is more than 5,000 rows we'll import it from a URL
-source = "https://raw.githubusercontent.com/vega/vega-datasets/v1.31.1/data/seattle-temps.csv"
-
-alt.Chart(
- source,
- title="2010 Daily High Temperature (F) in Seattle, WA"
-).mark_rect().encode(
- x='date(date):O',
- y='month(date):O',
- color=alt.Color('max(temp):Q', scale=alt.Scale(scheme="inferno")),
- tooltip=[
- alt.Tooltip('monthdate(date):T', title='Date'),
- alt.Tooltip('max(temp):Q', title='Max Temp')
- ]
-).properties(width=550)
diff --git a/python/vegafusion/tests/altair_mocks/interactive/cross_highlight/mock.py b/python/vegafusion/tests/altair_mocks/interactive/cross_highlight/mock.py
deleted file mode 100644
index 0939ae894..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/cross_highlight/mock.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import altair as alt
-from vega_datasets import data
-
-source = data.movies.url
-
-pts = alt.selection_point(encodings=['x'])
-
-rect = alt.Chart(data.movies.url).mark_rect().encode(
- alt.X('IMDB_Rating:Q', bin=True),
- alt.Y('Rotten_Tomatoes_Rating:Q', bin=True),
- alt.Color('count()',
- scale=alt.Scale(scheme='greenblue'),
- legend=alt.Legend(title='Total Records')
- )
-)
-
-circ = rect.mark_point().encode(
- alt.ColorValue('grey'),
- alt.Size('count()',
- legend=alt.Legend(title='Records in Selection')
- )
-).transform_filter(
- pts
-)
-
-bar = alt.Chart(source).mark_bar().encode(
- x='Major_Genre:N',
- y='count()',
- color=alt.condition(pts, alt.ColorValue("steelblue"), alt.ColorValue("grey"))
-).properties(
- width=550,
- height=200
-).add_params(pts)
-
-alt.vconcat(
- rect + circ,
- bar
-).resolve_legend(
- color="independent",
- size="independent"
-)
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/interactive/histogram-responsive/mock.py b/python/vegafusion/tests/altair_mocks/interactive/histogram-responsive/mock.py
deleted file mode 100644
index fffbb2aa2..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/histogram-responsive/mock.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# https://altair-viz.github.io/gallery/histogram_responsive.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.flights_5k.url
-
-brush = alt.selection_interval(encodings=['x'])
-
-base = alt.Chart(source).transform_calculate(
- time="hours(datum.date) + minutes(datum.date) / 60"
-).mark_bar().encode(
- y='count():Q'
-).properties(
- width=600,
- height=100
-)
-
-alt.vconcat(
- base.encode(
- alt.X('time:Q',
- bin=alt.Bin(maxbins=30, extent=brush),
- scale=alt.Scale(domain=brush)
- )
- ),
- base.encode(
- alt.X('time:Q', bin=alt.Bin(maxbins=30)),
- ).add_selection(brush)
-)
diff --git a/python/vegafusion/tests/altair_mocks/interactive/layered_crossfilter/mock.py b/python/vegafusion/tests/altair_mocks/interactive/layered_crossfilter/mock.py
deleted file mode 100644
index f5084ff7a..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/layered_crossfilter/mock.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# https://altair-viz.github.io/gallery/interactive_layered_crossfilter.html
-
-import altair as alt
-from vega_datasets import data
-
-source = alt.UrlData(
- data.flights_2k.url,
- format={'parse': {'date': 'date'}}
-)
-
-brush = alt.selection(type='interval', encodings=['x'])
-
-# Define the base chart, with the common parts of the
-# background and highlights
-base = alt.Chart().mark_bar().encode(
- x=alt.X(alt.repeat('column'), type='quantitative', bin=alt.Bin(maxbins=20)),
- y='count()'
-).properties(
- width=130,
- height=130
-)
-
-# gray background with selection
-background = base.encode(
- color=alt.value('#ddd')
-).add_selection(brush)
-
-# blue highlights on the transformed data
-highlight = base.transform_filter(brush)
-
-# layer the two charts & repeat
-alt.layer(
- background,
- highlight,
- data=source
-).transform_calculate(
- "time",
- "hours(datum.date)"
-).repeat(column=["distance", "delay", "time"])
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/interactive/legend/mock.py b/python/vegafusion/tests/altair_mocks/interactive/legend/mock.py
deleted file mode 100644
index 7a76d0125..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/legend/mock.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# https://altair-viz.github.io/gallery/interactive_legend.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.unemployment_across_industries.url
-
-selection = alt.selection_multi(fields=['series'], bind='legend')
-
-alt.Chart(source).mark_area().encode(
- alt.X('yearmonth(date):T', axis=alt.Axis(domain=False, format='%Y', tickSize=0)),
- alt.Y('sum(count):Q', stack='center', axis=None),
- alt.Color('series:N', scale=alt.Scale(scheme='category20b')),
- opacity=alt.condition(selection, alt.value(1), alt.value(0.2))
-).add_selection(
- selection
-)
diff --git a/python/vegafusion/tests/altair_mocks/interactive/multiline_highlight/mock.py b/python/vegafusion/tests/altair_mocks/interactive/multiline_highlight/mock.py
deleted file mode 100644
index 8b867f91e..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/multiline_highlight/mock.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import altair as alt
-from vega_datasets import data
-
-source = data.stocks()
-
-highlight = alt.selection_point(on='mouseover',
- fields=['symbol'], nearest=True)
-
-base = alt.Chart(source).encode(
- x='date:T',
- y='price:Q',
- color='symbol:N'
-)
-
-points = base.mark_circle().encode(
- opacity=alt.value(0)
-).add_params(
- highlight
-).properties(
- width=600
-)
-
-lines = base.mark_line().encode(
- size=alt.condition(~highlight, alt.value(1), alt.value(3))
-)
-
-points + lines
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/interactive/multiline_tooltip/mock.py b/python/vegafusion/tests/altair_mocks/interactive/multiline_tooltip/mock.py
deleted file mode 100644
index 7fd59ff51..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/multiline_tooltip/mock.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import altair as alt
-import pandas as pd
-import numpy as np
-
-np.random.seed(42)
-source = pd.DataFrame(np.cumsum(np.random.randn(100, 3), 0).round(2),
- columns=['A', 'B', 'C'], index=pd.RangeIndex(100, name='x'))
-source = source.reset_index().melt('x', var_name='category', value_name='y')
-
-# Create a selection that chooses the nearest point & selects based on x-value
-nearest = alt.selection_point(nearest=True, on='mouseover',
- fields=['x'], empty=False)
-
-# The basic line
-line = alt.Chart(source).mark_line(interpolate='basis').encode(
- x='x:Q',
- y='y:Q',
- color='category:N'
-)
-
-# Transparent selectors across the chart. This is what tells us
-# the x-value of the cursor
-selectors = alt.Chart(source).mark_point().encode(
- x='x:Q',
- opacity=alt.value(0),
-).add_params(
- nearest
-)
-
-# Draw points on the line, and highlight based on selection
-points = line.mark_point().encode(
- opacity=alt.condition(nearest, alt.value(1), alt.value(0))
-)
-
-# Draw text labels near the points, and highlight based on selection
-text = line.mark_text(align='left', dx=5, dy=-5).encode(
- text=alt.condition(nearest, 'y:Q', alt.value(' '))
-)
-
-# Draw a rule at the location of the selection
-rules = alt.Chart(source).mark_rule(color='gray').encode(
- x='x:Q',
-).transform_filter(
- nearest
-)
-
-# Put the five layers into a chart and bind the data
-alt.layer(
- line, selectors, points, rules, text
-).properties(
- width=600, height=300
-)
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/interactive/other-image_tooltip/mock.py b/python/vegafusion/tests/altair_mocks/interactive/other-image_tooltip/mock.py
deleted file mode 100644
index 8b0da07df..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/other-image_tooltip/mock.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# https://altair-viz.github.io/gallery/image_tooltip.html
-
-import altair as alt
-import pandas as pd
-
-source = pd.DataFrame.from_records(
- [{'a': 1, 'b': 1, 'image': 'https://altair-viz.github.io/_static/altair-logo-light.png'},
- {'a': 2, 'b': 2, 'image': 'https://avatars.githubusercontent.com/u/11796929?s=200&v=4'}]
-)
-alt.Chart(source).mark_circle(size=200).encode(
- x='a',
- y='b',
- tooltip=['image'] # Must be a list for the image to render
-)
diff --git a/python/vegafusion/tests/altair_mocks/interactive/scatter-with_linked_table/mock.py b/python/vegafusion/tests/altair_mocks/interactive/scatter-with_linked_table/mock.py
deleted file mode 100644
index c936f42c2..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/scatter-with_linked_table/mock.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import altair as alt
-from vega_datasets import data
-
-source = data.cars()
-
-# Brush for selection
-brush = alt.selection_interval()
-
-# Scatter Plot
-points = alt.Chart(source).mark_point().encode(
- x='Horsepower:Q',
- y='Miles_per_Gallon:Q',
- color=alt.condition(brush, 'Cylinders:O', alt.value('grey'))
-).add_params(brush)
-
-# Base chart for data tables
-ranked_text = alt.Chart(source).mark_text().encode(
- y=alt.Y('row_number:O',axis=None)
-).transform_window(
- row_number='row_number()'
-).transform_filter(
- brush
-).transform_window(
- rank='rank(row_number)'
-).transform_filter(
- alt.datum.rank<20
-)
-
-# Data Tables
-horsepower = ranked_text.encode(text='Horsepower:N').properties(title='Horsepower')
-mpg = ranked_text.encode(text='Miles_per_Gallon:N').properties(title='MPG')
-origin = ranked_text.encode(text='Origin:N').properties(title='Origin')
-text = alt.hconcat(horsepower, mpg, origin) # Combine data tables
-
-# Build chart
-alt.hconcat(
- points,
- text
-).resolve_legend(
- color="independent"
-)
diff --git a/python/vegafusion/tests/altair_mocks/interactive/scatter_linked_brush/mock.py b/python/vegafusion/tests/altair_mocks/interactive/scatter_linked_brush/mock.py
deleted file mode 100644
index 04a887303..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/scatter_linked_brush/mock.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# https://altair-viz.github.io/gallery/scatter_linked_brush.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.cars()
-
-brush = alt.selection(type='interval', resolve='global')
-
-base = alt.Chart(source).mark_point().encode(
- y='Miles_per_Gallon',
- color=alt.condition(brush, 'Origin', alt.ColorValue('gray')),
-).add_selection(
- brush
-).properties(
- width=200,
- height=250
-)
-
-base.encode(x='Horsepower') | base.encode(x='Acceleration')
diff --git a/python/vegafusion/tests/altair_mocks/interactive/scatter_with_histogram/mock.py b/python/vegafusion/tests/altair_mocks/interactive/scatter_with_histogram/mock.py
deleted file mode 100644
index eb036ad1e..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/scatter_with_histogram/mock.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import altair as alt
-import pandas as pd
-import numpy as np
-
-np.random.seed(42)
-
-x = np.random.normal(size=100)
-y = np.random.normal(size=100)
-
-m = np.random.normal(15, 1, size=100)
-
-source = pd.DataFrame({"x": x, "y":y, "m":m})
-
-# interval selection in the scatter plot
-pts = alt.selection_interval(encodings=["x"])
-
-# left panel: scatter plot
-points = alt.Chart().mark_point(filled=True, color="black").encode(
- x='x',
- y='y'
-).transform_filter(
- pts
-).properties(
- width=300,
- height=300
-)
-
-# right panel: histogram
-mag = alt.Chart().mark_bar().encode(
- x='mbin:N',
- y="count()",
- color=alt.condition(pts, alt.value("black"), alt.value("lightgray"))
-).properties(
- width=300,
- height=300
-).add_params(pts)
-
-# build the chart:
-alt.hconcat(
- points,
- mag,
- data=source
-).transform_bin(
- "mbin",
- field="m",
- bin=alt.Bin(maxbins=20)
-)
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/interactive/scatter_with_layered_histogram/mock.py b/python/vegafusion/tests/altair_mocks/interactive/scatter_with_layered_histogram/mock.py
deleted file mode 100644
index b23c0d4ec..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/scatter_with_layered_histogram/mock.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import altair as alt
-import pandas as pd
-import numpy as np
-
-np.random.seed(1)
-
-# generate fake data
-source = pd.DataFrame({
- 'gender': ['M']*1000 + ['F']*1000,
- 'height':np.concatenate((
- np.random.normal(69, 7, 1000), np.random.normal(64, 6, 1000)
- )),
- 'weight': np.concatenate((
- np.random.normal(195.8, 144, 1000), np.random.normal(167, 100, 1000)
- )),
- 'age': np.concatenate((
- np.random.normal(45, 8, 1000), np.random.normal(51, 6, 1000)
- ))
-})
-
-selector = alt.selection_point(fields=['gender'])
-
-color_scale = alt.Scale(domain=['M', 'F'],
- range=['#1FC3AA', '#8624F5'])
-
-base = alt.Chart(source).properties(
- width=250,
- height=250
-).add_params(selector)
-
-points = base.mark_point(filled=True, size=200).encode(
- x=alt.X('mean(height):Q').scale(domain=[0,84]),
- y=alt.Y('mean(weight):Q').scale(domain=[0,250]),
- color=alt.condition(
- selector,
- 'gender:N',
- alt.value('lightgray'),
- scale=color_scale),
-)
-
-hists = base.mark_bar(opacity=0.5, thickness=100).encode(
- x=alt.X('age')
- .bin(step=5) # step keeps bin size the same
- .scale(domain=[0,100]),
- y=alt.Y('count()')
- .stack(None)
- .scale(domain=[0,350]),
- color=alt.Color('gender:N', scale=color_scale)
-).transform_filter(
- selector
-)
-
-points | hists
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/interactive/select_detail/mock.py b/python/vegafusion/tests/altair_mocks/interactive/select_detail/mock.py
deleted file mode 100644
index 055b4f094..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/select_detail/mock.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# https://altair-viz.github.io/gallery/select_detail.html
-
-import altair as alt
-import pandas as pd
-import numpy as np
-
-np.random.seed(0)
-
-n_objects = 20
-n_times = 50
-
-# Create one (x, y) pair of metadata per object
-locations = pd.DataFrame({
- 'id': range(n_objects),
- 'x': np.random.randn(n_objects),
- 'y': np.random.randn(n_objects)
-})
-
-# Create a 50-element time-series for each object
-timeseries = pd.DataFrame(np.random.randn(n_times, n_objects).cumsum(0),
- columns=locations['id'],
- index=pd.RangeIndex(0, n_times, name='time'))
-
-# Melt the wide-form timeseries into a long-form view
-timeseries = timeseries.reset_index().melt('time')
-
-# Merge the (x, y) metadata into the long-form view
-timeseries['id'] = timeseries['id'].astype(int) # make merge not complain
-data = pd.merge(timeseries, locations, on='id')
-
-# Data is prepared, now make a chart
-
-selector = alt.selection_single(empty='all', fields=['id'])
-
-base = alt.Chart(data).properties(
- width=200,
- height=200
-).add_selection(selector)
-
-points = base.mark_point(filled=True, size=200).encode(
- x='mean(x)',
- y='mean(y)',
- color=alt.condition(selector, 'id:O', alt.value('lightgray'), legend=None),
-)
-
-timeseries = base.mark_line().encode(
- x='time',
- y=alt.Y('value', scale=alt.Scale(domain=(-15, 15))),
- color=alt.Color('id:O', legend=None)
-).transform_filter(
- selector
-)
-
-points | timeseries
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/interactive/select_mark_area/mock.py b/python/vegafusion/tests/altair_mocks/interactive/select_mark_area/mock.py
deleted file mode 100644
index 1e535edf9..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/select_mark_area/mock.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import altair as alt
-from vega_datasets import data
-
-source = data.unemployment_across_industries.url
-
-base = alt.Chart(source).mark_area(
- color='goldenrod',
- opacity=0.3
-).encode(
- x='yearmonth(date):T',
- y='sum(count):Q',
-)
-
-brush = alt.selection_interval(encodings=['x'])
-background = base.add_params(brush)
-selected = base.transform_filter(brush).mark_area(color='goldenrod')
-
-background + selected
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/interactive/selection_histogram/mock.py b/python/vegafusion/tests/altair_mocks/interactive/selection_histogram/mock.py
deleted file mode 100644
index ffb9733f6..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/selection_histogram/mock.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import altair as alt
-from vega_datasets import data
-
-source = data.cars()
-
-brush = alt.selection_interval()
-
-points = alt.Chart(source).mark_point().encode(
- x='Horsepower:Q',
- y='Miles_per_Gallon:Q',
- color=alt.condition(brush, 'Origin:N', alt.value('lightgray'))
-).add_params(
- brush
-)
-
-bars = alt.Chart(source).mark_bar().encode(
- y='Origin:N',
- color='Origin:N',
- x='count(Origin):Q'
-).transform_filter(
- brush
-)
-
-points & bars
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/interactive/selection_layer_bar_month/mock.py b/python/vegafusion/tests/altair_mocks/interactive/selection_layer_bar_month/mock.py
deleted file mode 100644
index 9def26a55..000000000
--- a/python/vegafusion/tests/altair_mocks/interactive/selection_layer_bar_month/mock.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import altair as alt
-from vega_datasets import data
-
-source = data.seattle_weather()
-brush = alt.selection_interval(encodings=['x'])
-
-bars = alt.Chart().mark_bar().encode(
- x='month(date):O',
- y='mean(precipitation):Q',
- opacity=alt.condition(brush, alt.OpacityValue(1), alt.OpacityValue(0.7)),
-).add_params(
- brush
-)
-
-line = alt.Chart().mark_rule(color='firebrick').encode(
- y='mean(precipitation):Q',
- size=alt.SizeValue(3)
-).transform_filter(
- brush
-)
-
-alt.layer(bars, line, data=source)
diff --git a/python/vegafusion/tests/altair_mocks/line/bump_chart/mock.py b/python/vegafusion/tests/altair_mocks/line/bump_chart/mock.py
deleted file mode 100644
index 20a549106..000000000
--- a/python/vegafusion/tests/altair_mocks/line/bump_chart/mock.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# https://altair-viz.github.io/gallery/bump_chart.html
-# Width set to 500
-
-import altair as alt
-from vega_datasets import data
-import pandas as pd
-
-stocks = data.stocks()
-source = stocks.groupby([pd.Grouper(key="date", freq="6M"),"symbol"]).mean().reset_index()
-
-alt.Chart(source).mark_line(point = True).encode(
- x = alt.X("date:O", timeUnit="yearmonth", title="date"),
- y="rank:O",
- color=alt.Color("symbol:N")
-).transform_window(
- rank="rank()",
- sort=[alt.SortField("price", order="descending")],
- groupby=["date"]
-).properties(
- title="Bump Chart for Stock Prices",
- width=500,
- height=150,
-)
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/line/step_chart/mock.py b/python/vegafusion/tests/altair_mocks/line/step_chart/mock.py
deleted file mode 100644
index 3feda7925..000000000
--- a/python/vegafusion/tests/altair_mocks/line/step_chart/mock.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# https://altair-viz.github.io/gallery/step_chart.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.stocks()
-
-alt.Chart(source).mark_line(interpolate='step-after').encode(
- x='date',
- y='price'
-).transform_filter(
- alt.datum.symbol == 'GOOG'
-)
diff --git a/python/vegafusion/tests/altair_mocks/line/trail_marker/mock.py b/python/vegafusion/tests/altair_mocks/line/trail_marker/mock.py
deleted file mode 100644
index ea43669c2..000000000
--- a/python/vegafusion/tests/altair_mocks/line/trail_marker/mock.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# https://altair-viz.github.io/gallery/trail_marker.html
-# With year column converted to a string. VegaFusion doesn't need this, but the altair case isn't handling
-# an integer year column correctly.
-
-import altair as alt
-from vega_datasets import data
-
-source = data.wheat()
-source["year"] = source.year.astype(str)
-
-alt.Chart(source).mark_trail().encode(
- x='year:T',
- y='wheat:Q',
- size='wheat:Q'
-)
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/line/with_ci/mock.py b/python/vegafusion/tests/altair_mocks/line/with_ci/mock.py
deleted file mode 100644
index a215c2c39..000000000
--- a/python/vegafusion/tests/altair_mocks/line/with_ci/mock.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# https://altair-viz.github.io/gallery/line_with_ci.html
-# extent changed for "ci" to "stdev" to be deterministic
-
-import altair as alt
-from vega_datasets import data
-
-source = data.cars()
-
-line = alt.Chart(source).mark_line().encode(
- x='Year',
- y='mean(Miles_per_Gallon)'
-)
-
-band = alt.Chart(source).mark_errorband(extent='stdev').encode(
- x='Year',
- y=alt.Y('Miles_per_Gallon', title='Miles/Gallon'),
-)
-
-band + line
diff --git a/python/vegafusion/tests/altair_mocks/line/with_datum/mock.py b/python/vegafusion/tests/altair_mocks/line/with_datum/mock.py
deleted file mode 100644
index 177179182..000000000
--- a/python/vegafusion/tests/altair_mocks/line/with_datum/mock.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# https://altair-viz.github.io/gallery/line_chart_with_datum.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.stocks()
-
-lines = (
- alt.Chart(source)
- .mark_line()
- .encode(x="date", y="price", color="symbol")
-)
-
-xrule = (
- alt.Chart()
- .mark_rule(color="cyan", strokeWidth=2)
- .encode(x=alt.datum(alt.DateTime(year=2006, month="November")))
-)
-
-yrule = (
- alt.Chart().mark_rule(strokeDash=[12, 6], size=2).encode(y=alt.datum(350))
-)
-
-
-lines + yrule + xrule
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/line/with_generator/mock.py b/python/vegafusion/tests/altair_mocks/line/with_generator/mock.py
deleted file mode 100644
index c3f491da5..000000000
--- a/python/vegafusion/tests/altair_mocks/line/with_generator/mock.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# https://altair-viz.github.io/gallery/line_chart_with_generator.html
-
-import altair as alt
-
-source = alt.sequence(start=0, stop=12.71, step=0.1, as_='x')
-
-alt.Chart(source).mark_line().transform_calculate(
- sin='sin(datum.x)',
- cos='cos(datum.x)'
-).transform_fold(
- ['sin', 'cos']
-).encode(
- x='x:Q',
- y='value:Q',
- color='key:N'
-)
diff --git a/python/vegafusion/tests/altair_mocks/line/with_points/mock.py b/python/vegafusion/tests/altair_mocks/line/with_points/mock.py
deleted file mode 100644
index 3ddc0be7e..000000000
--- a/python/vegafusion/tests/altair_mocks/line/with_points/mock.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# https://altair-viz.github.io/gallery/line_chart_with_points.html
-
-import altair as alt
-import numpy as np
-import pandas as pd
-
-x = np.arange(100)
-source = pd.DataFrame({
- 'x': x,
- 'f(x)': np.sin(x / 5)
-})
-
-alt.Chart(source).mark_line(
- point=alt.OverlayMarkDef(color="red")
-).encode(
- x='x',
- y='f(x)'
-)
diff --git a/python/vegafusion/tests/altair_mocks/maps/airports_count/mock.py b/python/vegafusion/tests/altair_mocks/maps/airports_count/mock.py
deleted file mode 100644
index 7736762f8..000000000
--- a/python/vegafusion/tests/altair_mocks/maps/airports_count/mock.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# https://altair-viz.github.io/gallery/airports_count.html
-
-import altair as alt
-from vega_datasets import data
-
-airports = data.airports.url
-states = alt.topo_feature(data.us_10m.url, feature='states')
-
-# US states background
-background = alt.Chart(states).mark_geoshape(
- fill='lightgray',
- stroke='white'
-).properties(
- width=500,
- height=300
-).project('albersUsa')
-
-# airport positions on background
-points = alt.Chart(airports).transform_aggregate(
- latitude='mean(latitude)',
- longitude='mean(longitude)',
- count='count()',
- groupby=['state']
-).mark_circle().encode(
- longitude='longitude:Q',
- latitude='latitude:Q',
- size=alt.Size('count:Q', title='Number of Airports'),
- color=alt.value('steelblue'),
- tooltip=['state:N','count:Q']
-).properties(
- title='Number of airports in US'
-)
-
-background + points
diff --git a/python/vegafusion/tests/altair_mocks/maps/choropleth/mock.py b/python/vegafusion/tests/altair_mocks/maps/choropleth/mock.py
deleted file mode 100644
index 439ce0efb..000000000
--- a/python/vegafusion/tests/altair_mocks/maps/choropleth/mock.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# https://altair-viz.github.io/gallery/choropleth.html
-
-import altair as alt
-from vega_datasets import data
-
-counties = alt.topo_feature(data.us_10m.url, 'counties')
-source = data.unemployment.url
-
-alt.Chart(counties).mark_geoshape().encode(
- color='rate:Q'
-).transform_lookup(
- lookup='id',
- from_=alt.LookupData(source, 'id', ['rate'])
-).project(
- type='albersUsa'
-).properties(
- width=500,
- height=300
-)
diff --git a/python/vegafusion/tests/altair_mocks/maps/choropleth_repeat/mock.py b/python/vegafusion/tests/altair_mocks/maps/choropleth_repeat/mock.py
deleted file mode 100644
index 0712910d7..000000000
--- a/python/vegafusion/tests/altair_mocks/maps/choropleth_repeat/mock.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# https://altair-viz.github.io/gallery/choropleth_repeat.html
-
-import altair as alt
-from vega_datasets import data
-
-states = alt.topo_feature(data.us_10m.url, 'states')
-source = data.population_engineers_hurricanes.url
-variable_list = ['population', 'engineers', 'hurricanes']
-
-alt.Chart(states).mark_geoshape().encode(
- alt.Color(alt.repeat('row'), type='quantitative')
-).transform_lookup(
- lookup='id',
- from_=alt.LookupData(source, 'id', variable_list)
-).properties(
- width=500,
- height=150
-).project(
- type='albersUsa'
-).repeat(
- row=variable_list
-).resolve_scale(
- color='independent'
-)
diff --git a/python/vegafusion/tests/altair_mocks/maps/world/mock.py b/python/vegafusion/tests/altair_mocks/maps/world/mock.py
deleted file mode 100644
index dd83054c0..000000000
--- a/python/vegafusion/tests/altair_mocks/maps/world/mock.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# https://altair-viz.github.io/gallery/world_map.html
-
-import altair as alt
-from vega_datasets import data
-
-# Data generators for the background
-sphere = alt.sphere()
-graticule = alt.graticule()
-
-# Source of land data
-source = alt.topo_feature(data.world_110m.url, 'countries')
-
-# Layering and configuring the components
-alt.layer(
- alt.Chart(sphere).mark_geoshape(fill='lightblue'),
- alt.Chart(graticule).mark_geoshape(stroke='white', strokeWidth=0.5),
- alt.Chart(source).mark_geoshape(fill='ForestGreen', stroke='black')
-).project(
- 'naturalEarth1'
-).properties(width=600, height=400).configure_view(stroke=None)
diff --git a/python/vegafusion/tests/altair_mocks/maps/world_projections/mock.py b/python/vegafusion/tests/altair_mocks/maps/world_projections/mock.py
deleted file mode 100644
index d55546322..000000000
--- a/python/vegafusion/tests/altair_mocks/maps/world_projections/mock.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# https://altair-viz.github.io/gallery/world_projections.html
-
-import altair as alt
-from vega_datasets import data
-
-source = alt.topo_feature(data.world_110m.url, 'countries')
-
-base = alt.Chart(source).mark_geoshape(
- fill='#666666',
- stroke='white'
-).properties(
- width=300,
- height=180
-)
-
-projections = ['equirectangular', 'mercator', 'orthographic', 'gnomonic']
-charts = [base.project(proj).properties(title=proj)
- for proj in projections]
-
-alt.concat(*charts, columns=2)
diff --git a/python/vegafusion/tests/altair_mocks/other/bar_chart_with_highlighted_segment/mock.py b/python/vegafusion/tests/altair_mocks/other/bar_chart_with_highlighted_segment/mock.py
deleted file mode 100644
index 1fa582e0a..000000000
--- a/python/vegafusion/tests/altair_mocks/other/bar_chart_with_highlighted_segment/mock.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# https://altair-viz.github.io/gallery/bar_chart_with_highlighted_segment.html
-
-import altair as alt
-import pandas as pd
-from vega_datasets import data
-
-source = data.wheat()
-threshold = pd.DataFrame([{"threshold": 90}])
-
-bars = alt.Chart(source).mark_bar().encode(
- x="year:O",
- y="wheat:Q",
-)
-
-highlight = alt.Chart(source).mark_bar(color="#e45755").encode(
- x='year:O',
- y='baseline:Q',
- y2='wheat:Q'
-).transform_filter(
- alt.datum.wheat > 90
-).transform_calculate("baseline", "90")
-
-rule = alt.Chart(threshold).mark_rule().encode(
- y='threshold:Q'
-)
-
-(bars + highlight + rule).properties(width=500)
diff --git a/python/vegafusion/tests/altair_mocks/other/binned_heatmap/mock.py b/python/vegafusion/tests/altair_mocks/other/binned_heatmap/mock.py
deleted file mode 100644
index 93d86d2b7..000000000
--- a/python/vegafusion/tests/altair_mocks/other/binned_heatmap/mock.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# https://altair-viz.github.io/gallery/binned_heatmap.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.movies.url
-
-alt.Chart(source).mark_rect().encode(
- alt.X('IMDB_Rating:Q', bin=alt.Bin(maxbins=60)),
- alt.Y('Rotten_Tomatoes_Rating:Q', bin=alt.Bin(maxbins=40)),
- alt.Color('count():Q', scale=alt.Scale(scheme='greenblue'))
-)
diff --git a/python/vegafusion/tests/altair_mocks/other/candlestick_chart/mock.py b/python/vegafusion/tests/altair_mocks/other/candlestick_chart/mock.py
deleted file mode 100644
index e59bfe139..000000000
--- a/python/vegafusion/tests/altair_mocks/other/candlestick_chart/mock.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# https://altair-viz.github.io/gallery/candlestick_chart.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.ohlc()
-
-open_close_color = alt.condition("datum.open <= datum.close",
- alt.value("#06982d"),
- alt.value("#ae1325"))
-
-base = alt.Chart(source).encode(
- alt.X('date:T',
- axis=alt.Axis(
- format='%m/%d',
- labelAngle=-45,
- title='Date in 2009'
- )
- ),
- color=open_close_color
-)
-
-rule = base.mark_rule().encode(
- alt.Y(
- 'low:Q',
- title='Price',
- scale=alt.Scale(zero=False),
- ),
- alt.Y2('high:Q')
-)
-
-bar = base.mark_bar().encode(
- alt.Y('open:Q'),
- alt.Y2('close:Q')
-)
-
-rule + bar
diff --git a/python/vegafusion/tests/altair_mocks/other/comet_chart/mock.py b/python/vegafusion/tests/altair_mocks/other/comet_chart/mock.py
deleted file mode 100644
index e764cf306..000000000
--- a/python/vegafusion/tests/altair_mocks/other/comet_chart/mock.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# https://altair-viz.github.io/gallery/comet_chart.html
-
-import altair as alt
-import vega_datasets
-
-(
- alt.Chart(vega_datasets.data.barley.url)
- .transform_pivot("year", value="yield", groupby=["variety", "site"])
- .transform_fold(["1931", "1932"], as_=["year", "yield"])
- .transform_calculate(calculate="datum['1932'] - datum['1931']", as_="delta")
- .mark_trail()
- .encode(
- x=alt.X('year:O', title=None),
- y=alt.Y('variety:N', title='Variety'),
- size=alt.Size('yield:Q', scale=alt.Scale(range=[0, 12]), legend=alt.Legend(values=[20, 60], title='Barley Yield (bushels/acre)')),
- color=alt.Color('delta:Q', scale=alt.Scale(domainMid=0), legend=alt.Legend(title='Yield Delta (%)')),
- tooltip=alt.Tooltip(['year:O', 'yield:Q']),
- column=alt.Column('site:N', title='Site')
-
- )
- .configure_view(stroke=None)
- .configure_legend(orient='bottom', direction='horizontal')
- .properties(title='Barley Yield comparison between 1932 and 1931')
-)
diff --git a/python/vegafusion/tests/altair_mocks/other/errorbars_with_ci/mock.py b/python/vegafusion/tests/altair_mocks/other/errorbars_with_ci/mock.py
deleted file mode 100644
index 39fbea73d..000000000
--- a/python/vegafusion/tests/altair_mocks/other/errorbars_with_ci/mock.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# https://altair-viz.github.io/gallery/errorbars_with_ci.html
-# Padding updates to make size deterministic even when data is not
-
-import altair as alt
-from vega_datasets import data
-
-source = data.barley()
-
-error_bars = alt.Chart(source).mark_errorbar(extent='ci').encode(
- x=alt.X('yield:Q', scale=alt.Scale(zero=False)),
- y=alt.Y('variety:N')
-)
-
-points = alt.Chart(source).mark_point(filled=True, color='black').encode(
- x=alt.X('yield:Q', aggregate='mean'),
- y=alt.Y('variety:N'),
-)
-
-(error_bars + points).properties(
- padding={"left": 50, "top": 5, "right": 50, "bottom": 5}
-)
diff --git a/python/vegafusion/tests/altair_mocks/other/errorbars_with_std/mock.py b/python/vegafusion/tests/altair_mocks/other/errorbars_with_std/mock.py
deleted file mode 100644
index 0a5e21f87..000000000
--- a/python/vegafusion/tests/altair_mocks/other/errorbars_with_std/mock.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# https://altair-viz.github.io/gallery/errorbars_with_std.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.barley()
-
-error_bars = alt.Chart(source).mark_errorbar(extent='stdev').encode(
- x=alt.X('yield:Q', scale=alt.Scale(zero=False)),
- y=alt.Y('variety:N')
-)
-
-points = alt.Chart(source).mark_point(filled=True, color='black').encode(
- x=alt.X('yield:Q', aggregate='mean'),
- y=alt.Y('variety:N'),
-)
-
-error_bars + points
diff --git a/python/vegafusion/tests/altair_mocks/other/gantt_chart/mock.py b/python/vegafusion/tests/altair_mocks/other/gantt_chart/mock.py
deleted file mode 100644
index da0f7f44c..000000000
--- a/python/vegafusion/tests/altair_mocks/other/gantt_chart/mock.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# https://altair-viz.github.io/gallery/gantt_chart.html
-
-import altair as alt
-import pandas as pd
-
-source = pd.DataFrame([
- {"task": "A", "start": 1, "end": 3},
- {"task": "B", "start": 3, "end": 8},
- {"task": "C", "start": 8, "end": 10}
-])
-
-alt.Chart(source).mark_bar().encode(
- x='start',
- x2='end',
- y='task'
-)
diff --git a/python/vegafusion/tests/altair_mocks/other/layered_chart_with_dual_axis/mock.py b/python/vegafusion/tests/altair_mocks/other/layered_chart_with_dual_axis/mock.py
deleted file mode 100644
index f11f2040d..000000000
--- a/python/vegafusion/tests/altair_mocks/other/layered_chart_with_dual_axis/mock.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# https://altair-viz.github.io/gallery/layered_chart_with_dual_axis.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.seattle_weather()
-
-base = alt.Chart(source).encode(
- alt.X('month(date):T', axis=alt.Axis(title=None))
-)
-
-area = base.mark_area(opacity=0.3, color='#57A44C').encode(
- alt.Y('average(temp_max)',
- axis=alt.Axis(title='Avg. Temperature (°C)', titleColor='#57A44C')),
- alt.Y2('average(temp_min)')
-)
-
-line = base.mark_line(stroke='#5276A7', interpolate='monotone').encode(
- alt.Y('average(precipitation)',
- axis=alt.Axis(title='Precipitation (inches)', titleColor='#5276A7'))
-)
-
-alt.layer(area, line).resolve_scale(
- y = 'independent'
-)
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/other/layered_heatmap_text/mock.py b/python/vegafusion/tests/altair_mocks/other/layered_heatmap_text/mock.py
deleted file mode 100644
index 746e3eb0b..000000000
--- a/python/vegafusion/tests/altair_mocks/other/layered_heatmap_text/mock.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# https://altair-viz.github.io/gallery/layered_heatmap_text.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.cars()
-
-# Configure common options
-base = alt.Chart(source).transform_aggregate(
- num_cars='count()',
- groupby=['Origin', 'Cylinders']
-).encode(
- alt.X('Cylinders:O', scale=alt.Scale(paddingInner=0)),
- alt.Y('Origin:O', scale=alt.Scale(paddingInner=0)),
-)
-
-# Configure heatmap
-heatmap = base.mark_rect().encode(
- color=alt.Color('num_cars:Q',
- scale=alt.Scale(scheme='viridis'),
- legend=alt.Legend(direction='horizontal')
- )
-)
-
-# Configure text
-text = base.mark_text(baseline='middle').encode(
- text='num_cars:Q',
- color=alt.condition(
- alt.datum.num_cars > 100,
- alt.value('black'),
- alt.value('white')
- )
-)
-
-# Draw the chart
-heatmap + text
diff --git a/python/vegafusion/tests/altair_mocks/other/normed_parallel_coordinates/mock.py b/python/vegafusion/tests/altair_mocks/other/normed_parallel_coordinates/mock.py
deleted file mode 100644
index 2b984d091..000000000
--- a/python/vegafusion/tests/altair_mocks/other/normed_parallel_coordinates/mock.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# https://altair-viz.github.io/gallery/normed_parallel_coordinates.html
-
-import altair as alt
-from vega_datasets import data
-from altair import datum
-
-source = data.iris()
-
-alt.Chart(source).transform_window(
- index='count()'
-).transform_fold(
- ['petalLength', 'petalWidth', 'sepalLength', 'sepalWidth']
-).transform_joinaggregate(
- min='min(value)',
- max='max(value)',
- groupby=['key']
-).transform_calculate(
- minmax_value=(datum.value-datum.min)/(datum.max-datum.min),
- mid=(datum.min+datum.max)/2
-).mark_line().encode(
- x='key:N',
- y='minmax_value:Q',
- color='species:N',
- detail='index:N',
- opacity=alt.value(0.5)
-).properties(width=500)
diff --git a/python/vegafusion/tests/altair_mocks/other/parallel_coordinates/mock.py b/python/vegafusion/tests/altair_mocks/other/parallel_coordinates/mock.py
deleted file mode 100644
index 6210ee669..000000000
--- a/python/vegafusion/tests/altair_mocks/other/parallel_coordinates/mock.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# https://altair-viz.github.io/gallery/parallel_coordinates.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.iris()
-
-alt.Chart(source).transform_window(
- index='count()'
-).transform_fold(
- ['petalLength', 'petalWidth', 'sepalLength', 'sepalWidth']
-).mark_line().encode(
- x='key:N',
- y='value:Q',
- color='species:N',
- detail='index:N',
- opacity=alt.value(0.5)
-).properties(width=500)
diff --git a/python/vegafusion/tests/altair_mocks/other/ranged_dot_plot/mock.py b/python/vegafusion/tests/altair_mocks/other/ranged_dot_plot/mock.py
deleted file mode 100644
index 00d930a9c..000000000
--- a/python/vegafusion/tests/altair_mocks/other/ranged_dot_plot/mock.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# https://altair-viz.github.io/gallery/ranged_dot_plot.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.countries.url
-
-chart = alt.layer(
- data=source
-).transform_filter(
- filter={"field": 'country',
- "oneOf": ["China", "India", "United States", "Indonesia", "Brazil"]}
-).transform_filter(
- filter={'field': 'year',
- "oneOf": [1955, 2000]}
-)
-
-chart += alt.Chart().mark_line(color='#db646f').encode(
- x='life_expect:Q',
- y='country:N',
- detail='country:N'
-)
-# Add points for life expectancy in 1955 & 2000
-chart += alt.Chart().mark_point(
- size=100,
- opacity=1,
- filled=True
-).encode(
- x='life_expect:Q',
- y='country:N',
- color=alt.Color('year:O',
- scale=alt.Scale(
- domain=[1955, 2000],
- range=['#e6959c', '#911a24']
- )
- )
-).interactive()
-
-chart
diff --git a/python/vegafusion/tests/altair_mocks/other/ridgeline_plot/mock.py b/python/vegafusion/tests/altair_mocks/other/ridgeline_plot/mock.py
deleted file mode 100644
index 2a164c85a..000000000
--- a/python/vegafusion/tests/altair_mocks/other/ridgeline_plot/mock.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# https://altair-viz.github.io/gallery/ridgeline_plot.html
-
-import altair as alt
-from vega_datasets import data
-
-source = "https://raw.githubusercontent.com/vega/vega-datasets/v1.31.1/data/seattle-weather.csv"
-
-step = 20
-overlap = 1
-
-alt.Chart(source, height=step).transform_timeunit(
- Month='month(date)'
-).transform_joinaggregate(
- mean_temp='mean(temp_max)', groupby=['Month']
-).transform_bin(
- ['bin_max', 'bin_min'], 'temp_max'
-).transform_aggregate(
- value='count()', groupby=['Month', 'mean_temp', 'bin_min', 'bin_max']
-).transform_impute(
- impute='value', groupby=['Month', 'mean_temp'], key='bin_min', value=0
-).mark_area(
- interpolate='monotone',
- fillOpacity=0.8,
- stroke='lightgray',
- strokeWidth=0.5
-).encode(
- alt.X('bin_min:Q', bin='binned', title='Maximum Daily Temperature (C)'),
- alt.Y(
- 'value:Q',
- scale=alt.Scale(range=[step, -step * overlap]),
- axis=None
- ),
- alt.Fill(
- 'mean_temp:Q',
- legend=None,
- scale=alt.Scale(domain=[30, 5], scheme='redyellowblue')
- )
-).facet(
- row=alt.Row(
- 'Month:T',
- title=None,
- header=alt.Header(labelAngle=0, labelAlign='right', format='%B')
- )
-).properties(
- title='Seattle Weather',
- bounds='flush'
-).configure_facet(
- spacing=0
-).configure_view(
- stroke=None
-).configure_title(
- anchor='end'
-)
diff --git a/python/vegafusion/tests/altair_mocks/other/scatter_marginal_hist/mock.py b/python/vegafusion/tests/altair_mocks/other/scatter_marginal_hist/mock.py
deleted file mode 100644
index f4621d087..000000000
--- a/python/vegafusion/tests/altair_mocks/other/scatter_marginal_hist/mock.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# https://altair-viz.github.io/gallery/scatter_marginal_hist.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.iris()
-
-base = alt.Chart(source)
-
-xscale = alt.Scale(domain=(4.0, 8.0))
-yscale = alt.Scale(domain=(1.9, 4.55))
-
-bar_args = {'opacity': .3, 'binSpacing': 0}
-
-points = base.mark_circle().encode(
- alt.X('sepalLength', scale=xscale),
- alt.Y('sepalWidth', scale=yscale),
- color='species',
-)
-
-top_hist = base.mark_bar(**bar_args).encode(
- alt.X('sepalLength:Q',
- # when using bins, the axis scale is set through
- # the bin extent, so we do not specify the scale here
- # (which would be ignored anyway)
- bin=alt.Bin(maxbins=20, extent=xscale.domain),
- stack=None,
- title=''
- ),
- alt.Y('count()', stack=None, title=''),
- alt.Color('species:N'),
-).properties(height=60)
-
-right_hist = base.mark_bar(**bar_args).encode(
- alt.Y('sepalWidth:Q',
- bin=alt.Bin(maxbins=20, extent=yscale.domain),
- stack=None,
- title='',
- ),
- alt.X('count()', stack=None, title=''),
- alt.Color('species:N'),
-).properties(width=60)
-
-top_hist & (points | right_hist)
diff --git a/python/vegafusion/tests/altair_mocks/other/sorted_error_bars_with_ci/mock.py b/python/vegafusion/tests/altair_mocks/other/sorted_error_bars_with_ci/mock.py
deleted file mode 100644
index 6b987a5ff..000000000
--- a/python/vegafusion/tests/altair_mocks/other/sorted_error_bars_with_ci/mock.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# https://altair-viz.github.io/gallery/sorted_error_bars_with_ci.html
-# Padding updates to make size deterministic even when data is not
-
-import altair as alt
-from vega_datasets import data
-
-source = data.barley()
-
-points = alt.Chart(source).mark_point(
- filled=True,
- color='black'
-).encode(
- x=alt.X('mean(yield)', title='Barley Yield'),
- y=alt.Y(
- 'variety',
- sort=alt.EncodingSortField(
- field='yield',
- op='mean',
- order='descending'
- )
- )
-).properties(
- width=400,
- height=250
-)
-
-error_bars = points.mark_rule().encode(
- x='ci0(yield)',
- x2='ci1(yield)',
-)
-
-(points + error_bars).properties(
- padding={"left": 50, "top": 5, "right": 50, "bottom": 5}
-)
diff --git a/python/vegafusion/tests/altair_mocks/other/stem_and_leaf/mock.py b/python/vegafusion/tests/altair_mocks/other/stem_and_leaf/mock.py
deleted file mode 100644
index fe954f4f7..000000000
--- a/python/vegafusion/tests/altair_mocks/other/stem_and_leaf/mock.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# https://altair-viz.github.io/gallery/stem_and_leaf.html
-
-import altair as alt
-import pandas as pd
-import numpy as np
-np.random.seed(42)
-
-# Generating random data
-source = pd.DataFrame({'samples': np.random.normal(50, 15, 100).astype(int).astype(str)})
-
-# Splitting stem and leaf
-source['stem'] = source['samples'].str[:-1]
-source['leaf'] = source['samples'].str[-1]
-
-source = source.sort_values(by=['stem', 'leaf'])
-
-# Determining leaf position
-source['position'] = source.groupby('stem').cumcount().add(1)
-
-# Creating stem and leaf plot
-alt.Chart(source).mark_text(
- align='left',
- baseline='middle',
- dx=-5
-).encode(
- alt.X('position:Q', title='',
- axis=alt.Axis(ticks=False, labels=False, grid=False)
- ),
- alt.Y('stem:N', title='', axis=alt.Axis(tickSize=0)),
- text='leaf:N',
-).configure_axis(
- labelFontSize=20
-).configure_text(
- fontSize=20
-)
diff --git a/python/vegafusion/tests/altair_mocks/other/violin_plot/mock.py b/python/vegafusion/tests/altair_mocks/other/violin_plot/mock.py
deleted file mode 100644
index 4cdc79864..000000000
--- a/python/vegafusion/tests/altair_mocks/other/violin_plot/mock.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# https://altair-viz.github.io/gallery/violin_plot.html
-
-import altair as alt
-from vega_datasets import data
-
-alt.Chart(data.cars()).transform_density(
- 'Miles_per_Gallon',
- as_=['Miles_per_Gallon', 'density'],
- extent=[5, 50],
- groupby=['Origin']
-).mark_area(orient='horizontal').encode(
- y='Miles_per_Gallon:Q',
- color='Origin:N',
- x=alt.X(
- 'density:Q',
- stack='center',
- impute=None,
- title=None,
- axis=alt.Axis(labels=False, values=[0], grid=False, ticks=True),
- ),
- column=alt.Column(
- 'Origin:N',
- header=alt.Header(
- titleOrient='bottom',
- labelOrient='bottom',
- labelPadding=0,
- ),
- )
-).properties(
- width=100
-).configure_facet(
- spacing=0
-).configure_view(
- stroke=None
-)
diff --git a/python/vegafusion/tests/altair_mocks/other/wilkinson_dot_plot/mock.py b/python/vegafusion/tests/altair_mocks/other/wilkinson_dot_plot/mock.py
deleted file mode 100644
index 5469282b9..000000000
--- a/python/vegafusion/tests/altair_mocks/other/wilkinson_dot_plot/mock.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# https://altair-viz.github.io/gallery/wilkinson-dot-plot.html
-
-import altair as alt
-import pandas as pd
-
-source = pd.DataFrame(
- {"data":[1,1,1,1,1,1,1,1,1,1,
- 2,2,2,
- 3,3,
- 4,4,4,4,4,4]
- }
-)
-
-alt.Chart(source).mark_circle(opacity=1).transform_window(
- id='rank()',
- groupby=['data']
-).encode(
- alt.X('data:O'),
- alt.Y('id:O',
- axis=None,
- sort='descending')
-).properties(height=100)
diff --git a/python/vegafusion/tests/altair_mocks/scatter/dot_dash_plot/mock.py b/python/vegafusion/tests/altair_mocks/scatter/dot_dash_plot/mock.py
deleted file mode 100644
index b1cc02a32..000000000
--- a/python/vegafusion/tests/altair_mocks/scatter/dot_dash_plot/mock.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import altair as alt
-from vega_datasets import data
-
-source = data.cars()
-
-# Configure the options common to all layers
-brush = alt.selection_interval()
-base = alt.Chart(source).add_params(brush)
-
-# Configure the points
-points = base.mark_point().encode(
- x=alt.X('Miles_per_Gallon', title=''),
- y=alt.Y('Horsepower', title=''),
- color=alt.condition(brush, 'Origin', alt.value('grey'))
-)
-
-# Configure the ticks
-tick_axis = alt.Axis(labels=False, domain=False, ticks=False)
-
-x_ticks = base.mark_tick().encode(
- alt.X('Miles_per_Gallon', axis=tick_axis),
- alt.Y('Origin', title='', axis=tick_axis),
- color=alt.condition(brush, 'Origin', alt.value('lightgrey'))
-)
-
-y_ticks = base.mark_tick().encode(
- alt.X('Origin', title='', axis=tick_axis),
- alt.Y('Horsepower', axis=tick_axis),
- color=alt.condition(brush, 'Origin', alt.value('lightgrey'))
-)
-
-# Build the chart
-y_ticks | (points & x_ticks)
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/scatter/matrix/mock.py b/python/vegafusion/tests/altair_mocks/scatter/matrix/mock.py
deleted file mode 100644
index 353f686d0..000000000
--- a/python/vegafusion/tests/altair_mocks/scatter/matrix/mock.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# https://altair-viz.github.io/gallery/area_chart_gradient.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.cars()
-
-alt.Chart(source).mark_circle().encode(
- alt.X(alt.repeat("column"), type='quantitative'),
- alt.Y(alt.repeat("row"), type='quantitative'),
- color='Origin:N'
-).properties(
- width=120,
- height=120
-).repeat(
- row=['Horsepower', 'Acceleration', 'Miles_per_Gallon'],
- column=['Miles_per_Gallon', 'Acceleration', 'Horsepower']
-).interactive()
\ No newline at end of file
diff --git a/python/vegafusion/tests/altair_mocks/scatter/qq/mock.py b/python/vegafusion/tests/altair_mocks/scatter/qq/mock.py
deleted file mode 100644
index 539143d95..000000000
--- a/python/vegafusion/tests/altair_mocks/scatter/qq/mock.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# https://altair-viz.github.io/gallery/area_chart_gradient.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.normal_2d.url
-
-base = alt.Chart(source).transform_quantile(
- 'u',
- step=0.01,
- as_ = ['p', 'v']
-).transform_calculate(
- uniform = 'quantileUniform(datum.p)',
- normal = 'quantileNormal(datum.p)'
-).mark_point().encode(
- alt.Y('v:Q')
-).properties(width=300)
-
-base.encode(x='uniform:Q') | base.encode(x='normal:Q')
diff --git a/python/vegafusion/tests/altair_mocks/scatter/stripplot/mock.py b/python/vegafusion/tests/altair_mocks/scatter/stripplot/mock.py
deleted file mode 100644
index 199bcddcb..000000000
--- a/python/vegafusion/tests/altair_mocks/scatter/stripplot/mock.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# https://altair-viz.github.io/gallery/stripplot.html
-# Padding and bounds updates to make size deterministic even when data is not
-
-import altair as alt
-from vega_datasets import data
-
-source = data.movies.url
-
-stripplot = alt.Chart(source, width=40, padding=60, bounds="flush").mark_circle(size=8).encode(
- x=alt.X(
- 'jitter:Q',
- title=None,
- axis=alt.Axis(values=[0], ticks=True, grid=False, labels=False),
- scale=alt.Scale(),
- ),
- y=alt.Y('IMDB_Rating:Q'),
- color=alt.Color('Major_Genre:N', legend=None),
- column=alt.Column(
- 'Major_Genre:N',
- header=alt.Header(
- labelAngle=-90,
- titleOrient='top',
- labelOrient='bottom',
- labelAlign='right',
- labelPadding=3,
- ),
- ),
-).transform_calculate(
- # Generate Gaussian jitter with a Box-Muller transform
- jitter='sqrt(-2*log(random()))*cos(2*PI*random())'
-).configure_facet(
- spacing=0
-).configure_view(
- stroke=None
-)
-
-stripplot
diff --git a/python/vegafusion/tests/altair_mocks/scatter/with_labels/mock.py b/python/vegafusion/tests/altair_mocks/scatter/with_labels/mock.py
deleted file mode 100644
index d33e95754..000000000
--- a/python/vegafusion/tests/altair_mocks/scatter/with_labels/mock.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# https://altair-viz.github.io/gallery/scatter_with_labels.html
-
-import altair as alt
-import pandas as pd
-
-source = pd.DataFrame({
- 'x': [1, 3, 5, 7, 9],
- 'y': [1, 3, 5, 7, 9],
- 'label': ['A', 'B', 'C', 'D', 'E']
-})
-
-points = alt.Chart(source).mark_point().encode(
- x='x:Q',
- y='y:Q'
-)
-
-text = points.mark_text(
- align='left',
- baseline='middle',
- dx=7
-).encode(
- text='label'
-)
-
-points + text
diff --git a/python/vegafusion/tests/altair_mocks/scatter/with_lowess/mock.py b/python/vegafusion/tests/altair_mocks/scatter/with_lowess/mock.py
deleted file mode 100644
index d05e710a7..000000000
--- a/python/vegafusion/tests/altair_mocks/scatter/with_lowess/mock.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# https://altair-viz.github.io/gallery/scatter_with_loess.html
-
-import altair as alt
-import pandas as pd
-import numpy as np
-
-np.random.seed(1)
-
-source = pd.DataFrame({
- 'x': np.arange(100),
- 'A': np.random.randn(100).cumsum(),
- 'B': np.random.randn(100).cumsum(),
- 'C': np.random.randn(100).cumsum(),
-})
-
-base = alt.Chart(source).mark_circle(opacity=0.5).transform_fold(
- fold=['A', 'B', 'C'],
- as_=['category', 'y']
-).encode(
- alt.X('x:Q'),
- alt.Y('y:Q'),
- alt.Color('category:N')
-)
-
-base + base.transform_loess('x', 'y', groupby=['category']).mark_line(size=4)
diff --git a/python/vegafusion/tests/altair_mocks/scatter/with_rolling_mean/mock.py b/python/vegafusion/tests/altair_mocks/scatter/with_rolling_mean/mock.py
deleted file mode 100644
index 19ef84f80..000000000
--- a/python/vegafusion/tests/altair_mocks/scatter/with_rolling_mean/mock.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# https://altair-viz.github.io/gallery/scatter_with_rolling_mean.html
-
-import altair as alt
-from vega_datasets import data
-
-source = data.seattle_weather()
-
-line = alt.Chart(source).mark_line(
- color='red',
- size=3
-).transform_window(
- rolling_mean='mean(temp_max)',
- # sort=[{"field": "date"}], # Shouldn't need this
- frame=[-15, 15]
-).encode(
- x='date:T',
- y='rolling_mean:Q'
-)
-
-points = alt.Chart(source).mark_point().encode(
- x='date:T',
- y=alt.Y('temp_max:Q',
- axis=alt.Axis(title='Max Temp'))
-)
-
-points + line
diff --git a/python/vegafusion/tests/altair_mocks/simple/bar_chart/mock.py b/python/vegafusion/tests/altair_mocks/simple/bar_chart/mock.py
deleted file mode 100644
index 69e27af64..000000000
--- a/python/vegafusion/tests/altair_mocks/simple/bar_chart/mock.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# https://altair-viz.github.io/gallery/simple_bar_chart.html
-
-import altair as alt
-import pandas as pd
-
-source = pd.DataFrame({
- 'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
- 'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
-})
-
-alt.Chart(source).mark_bar().encode(
- x='a',
- y='b'
-)
\ No newline at end of file
diff --git a/python/vegafusion/tests/test_context_manager.py b/python/vegafusion/tests/test_context_manager.py
deleted file mode 100644
index cbc8d4325..000000000
--- a/python/vegafusion/tests/test_context_manager.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import altair as alt
-import vegafusion as vf
-
-
-def test_mime_enabler_context_manager():
- alt.data_transformers.enable("json")
- alt.renderers.enable("mimetype")
-
- assert alt.data_transformers.active == "json"
- assert alt.renderers.active == "mimetype"
-
- with vf.disable():
- assert alt.data_transformers.active == "default"
- assert alt.renderers.active == "default"
-
- assert alt.data_transformers.active == "json"
- assert alt.renderers.active == "mimetype"
-
- ctx = vf.disable()
- assert alt.data_transformers.active == "default"
- assert alt.renderers.active == "default"
- assert repr(ctx) == "vegafusion.disable()"
-
- with vf.enable():
- assert alt.data_transformers.active == "vegafusion-inline"
- assert alt.renderers.active == "vegafusion-mime"
-
- assert alt.data_transformers.active == "default"
- assert alt.renderers.active == "default"
-
- ctx = vf.enable()
- assert alt.data_transformers.active == "vegafusion-inline"
- assert alt.renderers.active == "vegafusion-mime"
- assert repr(ctx) == "vegafusion.enable(mimetype='html', row_limit=10000, embed_options={})"
diff --git a/python/vegafusion/tests/test_datasource.py b/python/vegafusion/tests/test_datasource.py
deleted file mode 100644
index 853b8ce05..000000000
--- a/python/vegafusion/tests/test_datasource.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import pandas as pd
-import pyarrow as pa
-from vegafusion.datasource import PandasDatasource
-
-def test_mixed_col_type_inference():
- df = pd.DataFrame({
- 'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
- 'b': [28, 55, 43, 91, 81, 53, 19, 87, 52],
- 'c': [28, 55, 43, 91, 81, 53, 19, 87, 52],
- 'd': [28, 55, 43, 91, 81, 53, 19, 87, 52],
- 'e': [28, 55, 43, 91, 81, 53, 19, 87, 52],
- 'f': [28, 55, 43, 91, '81.5', 53, 19, 87, 52],
- })
- datasource = PandasDatasource(df)
-
- expected_schema = pa.schema([
- pa.field("a", pa.string()),
- pa.field("b", pa.int64()),
- pa.field("c", pa.int64()),
- pa.field("d", pa.int64()),
- pa.field("e", pa.int64()),
- pa.field("f", pa.string()),
- ])
-
- assert datasource.schema() == expected_schema
diff --git a/python/vegafusion/tests/test_row_limit.py b/python/vegafusion/tests/test_row_limit.py
deleted file mode 100644
index f9bbbd1a5..000000000
--- a/python/vegafusion/tests/test_row_limit.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import pytest
-import vegafusion as vf
-import altair as alt
-from altair.utils.execeval import eval_block
-from pathlib import Path
-here = Path(__file__).parent
-altair_mocks_dir = here / "altair_mocks"
-
-
-def test_row_limit():
- mock_path = altair_mocks_dir / "scatter" / "bubble_plot" / "mock.py"
- mock_src = mock_path.read_text("utf8")
- chart = eval_block(mock_src)
-
- # Dataset has 406 rows (before filtering out nulls) and this chart is not aggregated.
- # Limit of 500 rows should be fine
- with vf.enable(row_limit=500):
- chart._repr_mimebundle_()
-
- # Limit of 300 rows should raise RowLimitError
- with vf.enable(row_limit=300):
- with pytest.raises(vf.RowLimitError):
- chart._repr_mimebundle_()
-
- # Adding an aggregation should allow row limit to be much lower
- chart = chart.encode(
- alt.X("Horsepower", bin=True),
- alt.Y("Miles_per_Gallon", bin=True),
- alt.Size("count()")
- )
- with vf.enable(row_limit=50):
- chart._repr_mimebundle_()
diff --git a/python/vegafusion/tests/test_save.py b/python/vegafusion/tests/test_save.py
deleted file mode 100644
index 1c639a2d0..000000000
--- a/python/vegafusion/tests/test_save.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import altair as alt
-from vega_datasets import data
-import vegafusion as vf
-from io import StringIO, BytesIO
-import json
-
-
-def make_histogram():
- source = data.movies.url
- return alt.Chart(source).mark_bar().encode(
- alt.X("IMDB_Rating:Q", bin=True),
- y='count()',
- )
-
-
-def test_save_html():
- chart = make_histogram()
- f = StringIO()
- vf.save_html(chart, f)
- html = f.getvalue().strip()
- assert html.startswith("")
- assert "https://cdn.jsdelivr.net/npm/vega@5" in html
- assert "https://cdn.jsdelivr.net/npm/vega-embed@6" in html
-
-
-# We can't test inline HTML until after a new release of Altair Viewer
-# def test_save_html_inline():
-# chart = make_histogram()
-# f = StringIO()
-# vf.save_html(chart, f, inline=True)
-# html = f.getvalue().strip()
-# assert html.startswith("")
-# assert " 
---
+# Overview
+VegaFusion provides Rust, Python, and JavaScript libraries for analyzing and scaling [Vega](https://vega.github.io/vega/) visualizations. The goal is to provide low-level building blocks that higher level Vega systems (such as [Vega-Altair](https://altair-viz.github.io/) in Python) can integrate with.
-VegaFusion provides serverside acceleration for the [Vega](https://vega.github.io/) visualization grammar. While not limited to Python, an initial application of VegaFusion is the acceleration of the [Vega-Altair](https://altair-viz.github.io/) Python interface to [Vega-Lite](https://vega.github.io/vega-lite/).
+> [!NOTE]
+> If you've arrived here looking for information on how to scale Vega-Altair visualizations to support larger datasets,
+> see the Vega-Altair documentation on the [`"vegafusion"` data transformer](https://altair-viz.github.io/user_guide/large_datasets.html#vegafusion-data-transformer).
-The core VegaFusion algorithms are implemented in Rust. Python integration is provided using [PyO3](https://pyo3.rs/v0.15.1/) and JavaScript integration is provided using [wasm-bindgen](https://github.com/rustwasm/wasm-bindgen).
+## Python Installation
-[](https://mybinder.org/v2/gh/hex-inc/vegafusion-demos/HEAD?labpath=notebooks)
-
-## Documentation
-See the documentation at https://vegafusion.io
-
-## History
-VegaFusion was developed by Jon Mease and acquired by [Hex Technologies](https://hex.tech/) in 2022. Hex donated VegaFusion to the Vega Project in 2024 and continues to support its development and maintenance.
-
-VegaFusion's integration with Vega-Altair was initially developed outside of Altair in the `vegafusion` Python package. As of Vega-Altair version 5.3, all of these integrations have been incorporated into the upstream Vega-Altair package.
-
-## Quickstart 1: Overcome `MaxRowsError` with VegaFusion
-The Vega-Altair [`"vegafusion"` data transformer](https://altair-viz.github.io/user_guide/large_datasets.html#vegafusion-data-transformer) can be used to overcome the Altair [`MaxRowsError`](https://altair-viz.github.io/user_guide/faq.html#maxrowserror-how-can-i-plot-large-datasets) by performing data-intensive aggregations on the server and pruning unused columns from the source dataset. First install the `altair` Python package with the `all` extras enabled
+The VegaFusion Python package can be installed into a Python environment using pip
```bash
-pip install "altair[all]>=5.3"
-```
-
-Then open a Jupyter notebook (either the classic notebook or a notebook inside JupyterLab), and create an Altair histogram of a 1 million row flights dataset
-
-```python
-import pandas as pd
-import altair as alt
-
-flights = pd.read_parquet(
- "https://vegafusion-datasets.s3.amazonaws.com/vega/flights_1m.parquet"
-)
-
-delay_hist = alt.Chart(flights).mark_bar().encode(
- alt.X("delay", bin=alt.Bin(maxbins=30)),
- alt.Y("count()")
-)
-delay_hist
-```
-```
----------------------------------------------------------------------------
-MaxRowsError: The number of rows in your dataset is greater than the maximum allowed (5000).
-
-Try enabling the VegaFusion data transformer which raises this limit by pre-evaluating data
-transformations in Python.
- >> import altair as alt
- >> alt.data_transformers.enable("vegafusion")
-
-Or, see https://altair-viz.github.io/user_guide/large_datasets.html for additional information
-on how to plot large datasets.
-```
-
-This results in an Altair `MaxRowsError`, as by default Altair is configured to allow no more than 5,000 rows of data to be sent to the browser. This is a safety measure to avoid crashing the user's browser. The `"vegafusion"` data transformer can be used to overcome this limitation by performing data intensive transforms (e.g. filtering, binning, aggregation, etc.) in the Python kernel before the resulting data is sent to the web browser.
-
-The `"vegafusion"` data transformer is enabled like this:
-
-```python
-alt.data_transformers.enable("vegafusion")
-```
-
-Now the chart displays quickly without errors
-```
-delay_hist
-```
-
-
-## Quickstart 2: Extract transformed data
-By default, data transforms in an Altair chart (e.g. filtering, binning, aggregation, etc.) are performed by the Vega JavaScript library running in the browser. This has the advantage of making the charts produced by Altair fully standalone, not requiring access to a running Python kernel to render properly. But it has the disadvantage of making it difficult to access the transformed data (e.g. the histogram bin edges and count values) from Python. Since VegaFusion evaluates these transforms in the Python kernel, it's possible to access them from Python using the `chart.transformed_data()` function.
-
-For example, the following code demonstrates how to access the histogram bin edges and counts for the example above:
-
-```python
-import pandas as pd
-import altair as alt
-
-flights = pd.read_parquet(
- "https://vegafusion-datasets.s3.amazonaws.com/vega/flights_1m.parquet"
-)
-
-delay_hist = alt.Chart(flights).mark_bar().encode(
- alt.X("delay", bin=alt.Bin(maxbins=30)),
- alt.Y("count()")
-)
-delay_hist.transformed_data()
+pip install vegafusion
```
-| | bin_maxbins_30_delay | bin_maxbins_30_delay_end | __count |
-|---:|-----------------------:|---------------------------:|----------:|
-| 0 | -20 | 0 | 419400 |
-| 1 | 80 | 100 | 11000 |
-| 2 | 0 | 20 | 392700 |
-| 3 | 40 | 60 | 38400 |
-| 4 | 60 | 80 | 21800 |
-| 5 | 20 | 40 | 92700 |
-| 6 | 100 | 120 | 5300 |
-| 7 | -40 | -20 | 9900 |
-| 8 | 120 | 140 | 3300 |
-| 9 | 140 | 160 | 2000 |
-| 10 | 160 | 180 | 1800 |
-| 11 | 320 | 340 | 100 |
-| 12 | 180 | 200 | 900 |
-| 13 | 240 | 260 | 100 |
-| 14 | -60 | -40 | 100 |
-| 15 | 260 | 280 | 100 |
-| 16 | 200 | 220 | 300 |
-| 17 | 360 | 380 | 100 |
-
-## Quickstart 3: Accelerate interactive charts
-
-As shown above, the `"vegafusion"` data transformer can be combined with Vega-Altair's standard [renderers](https://altair-viz.github.io/user_guide/display_frontends.html), and this configuration will work well to scale non-interactive charts. However, because the standard renderers do not support passing information from the browser back to the Python kernel, VegaFusion is unable to evaluate transforms that are referenced by selections. To support this use case, the `"vegafusion"` data transformer may be combined with Altair's [`JupyterChart`](https://altair-viz.github.io/user_guide/jupyter_chart.html). Because `JupyterChart` (when used in an environment that support Jupyter Widgets) provides a two-way connection between the browser and the Python kernel, selection operations (e.g. filtering to the extents of a brush selection) can be evaluated interactively in the Python kernel, which eliminates the need to transfer the full dataset to the browser in order to maintain interactivity.
-
-This mode is activate by enabling the `"vegafusion"` data transformer and either enabling the `"jupyter"` renderer, or using `JupyterChart` directly.
-
-```python
-import pandas as pd
-import altair as alt
-
-alt.data_transformers.enable("vegafusion")
-alt.renderers.enable("jupyter")
-
-flights = pd.read_parquet(
- "https://vegafusion-datasets.s3.amazonaws.com/vega/flights_1m.parquet"
-)
-
-brush = alt.selection_interval(encodings=['x'])
-
-# Define the base chart, with the common parts of the
-# background and highlights
-base = alt.Chart().mark_bar().encode(
- x=alt.X(alt.repeat('column'), type='quantitative', bin=alt.Bin(maxbins=20)),
- y='count()'
-).properties(
- width=160,
- height=130
-)
-
-# gray background with selection
-background = base.encode(
- color=alt.value('#ddd')
-).add_params(brush)
-# blue highlights on the selected data
-highlight = base.transform_filter(brush)
+or conda
-# layer the two charts & repeat
-chart = alt.layer(
- background,
- highlight,
- data=flights
-).transform_calculate(
- "time",
- "hours(datum.date)"
-).repeat(column=["distance", "delay", "time"])
-
-chart
+```bash
+conda install -c conda-forge vegafusion
```
-https://user-images.githubusercontent.com/15064365/209974420-480121b4-b206-4bb2-b473-0c663e38ea5e.mov
-
-Histogram binning, aggregation, and selection filtering are now evaluated in the Python kernel process with efficient parallelization, and only the aggregated data (one row per histogram bar) is sent to the browser.
-
-You can see that the JupyterChart widget maintains a live connection to the Python kernel by noticing that the Python [kernel is running](https://experienceleague.adobe.com/docs/experience-platform/data-science-workspace/jupyterlab/overview.html?lang=en#kernel-sessions) as the selection region is created or moved.
-
-## Motivation for VegaFusion
-Vega makes it possible to create declarative JSON specifications for rich interactive visualizations that are fully self-contained. They can run entirely in a web browser without requiring access to an external database or a Python kernel.
-
-For datasets of a few thousand rows or fewer, this architecture results in extremely smooth and responsive interactivity. However, this architecture does not scale very well to datasets of hundreds of thousands of rows or more. This is the problem that VegaFusion aims to solve.
-
-## DataFusion integration
-[Apache Arrow DataFusion](https://github.com/apache/arrow-datafusion) is an SQL compatible query engine that integrates with the Rust implementation of Apache Arrow. VegaFusion uses DataFusion to implement many of the Vega transforms, and it compiles the Vega expression language directly into the DataFusion expression language. In addition to being quite fast, a particularly powerful characteristic of DataFusion is that it provides many interfaces that can be extended with custom Rust logic. For example, VegaFusion defines many custom UDFs that are designed to implement the precise semantics of the Vega expression language and Vega expression functions.
-
-# License
-VegaFusion is licensed under the [BSD-3](https://opensource.org/licenses/BSD-3-Clause) license. This is the same license used by Vega, Vega-Lite, and Vega-Altair.
-
-# About the Name
-There are two meanings behind the name "VegaFusion"
-- It's a reference to the [Apache Arrow DataFusion](https://github.com/apache/arrow-datafusion) library which is used to implement many of the supported Vega transforms
-- Vega and Altair are named after stars, and stars are powered by nuclear fusion
-
-# Building VegaFusion
-If you're interested in building VegaFusion from source, see [BUILD.md](BUILD.md)
-
-# Roadmap
-Supporting serverside acceleration for Altair in Jupyter was chosen as the first application of VegaFusion, but there are a lot of exciting ways that VegaFusion can be extended in the future. For more information, see the [Roadmap](https://vegafusion.io/roadmap.html).
+## Documentation
+See [Documentation](https://vegafusion.io/) and [Examples](https://github.com/vega/vegafusion/tree/v2/examples/python-examples/chart_state.py).
\ No newline at end of file
diff --git a/RELEASE.md b/RELEASE.md
index bf0af6901..d8f131759 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -20,24 +20,10 @@ python automation/bump_version.py $VF_VERSION
Open a pull request to merge this branch into `main`. This will start the continuous integration jobs on GitHub Actions. These jobs will test VegaFusion and build packages for publication.
### Publish Python packages
-To publish the `vegafusion-python-embed` packages to PyPI, first download and unzip the `vegafusion-python-embed-wheels` artifacts from GitHub Actions. Then `cd` into the directory of `*.whl` and `*.tar.gz` files and upload the packages to PyPI using [twine](https://pypi.org/project/twine/).
+To publish the `vegafusion` packages to PyPI, first download and unzip the `vegafusion-python-wheels-all` artifacts from GitHub Actions. Then `cd` into the directory of `*.whl` and `*.tar.gz` files and upload the packages to PyPI using [twine](https://pypi.org/project/twine/).
```bash
-cd vegafusion-python-embed-wheels
-twine upload *
-```
-
-To publish the `vegafusion` packages, download and unzip the `vegafusion-wheel` artifacts. Then upload with twine.
-
-```bash
-cd vegafusion-packages
-twine upload *
-```
-
-To publish the `vegafusion-jupyter` packages, download and unzip the `vegafusion-jupyter-packages` artifacts. Then upload with twine.
-
-```bash
-cd vegafusion-jupyter-packages
+cd vegafusion-python-wheels-all
twine upload *
```
@@ -45,58 +31,22 @@ twine upload *
First, download and unzip the `vegafusion-wasm-packages` artifact. Then publish the `vegafusion-wasm-X.Y.Z.tgz` package to NPM. If this is a release candidate, include the `--pre` flag to `npm publish`.
```bash
-unzip vegafusion-wasm-packages.zip
+cd vegafusion-wasm-package
npm publish vegafusion-wasm-$VF_VERSION.tgz
```
-Next, change the version of `vegafusion-wasm` in `javascript/vegafusion-embed/package.json` from `"../../vegafusion-wasm/pkg"` to `"~X.Y.Z"`
-
-Then update `package.lock`, and build package, then publish to NPM (include the `--pre` flag to `npm publish` if this is a release candidate)
-```bash
-cd javascript/vegafusion-embed/
-npm install
-npm run build
-npm publish
-```
-
-Next, change the version of `vegafusion-wasm` and `vegafusion-embed` in `python/vegafusion-jupyter/package.json` from local paths to `"~X.Y.Z"`
-
-Then build and publish the packages (include the `--pre` flag if this is a release candidate)
-
-```bash
-cd python/vegafusion-jupyter/
-npm install
-npm run build:prod
-npm publish
-```
-
-### Publish Java library
-First, download and unzip the `jni-native` CI artifact. This artifact contains the compiled JNI libraries for each supported operating system and architecture.
-
-Set the `VEGAFUSION_JNI_LIBRARIES` environment variable to the unzipped `jni-native` directory and publish the jar with `pixi run publish-java`:
-
-```
-VEGAFUSION_JNI_LIBRARIES=/path/to/jni-native pixi run publish-java
-```
-
-This publishes the jar files to OSSRH at https://s01.oss.sonatype.org/. In order to sync these files to the public maven central repository, follow the steps described in https://central.sonatype.org/publish/release/.
-
-#### Java publication config
-Publishing the Java library to maven central requires configuring the `~/.gradle/gradle.properties` file with:
-```
-signing.keyId=YourKeyId
-signing.password=YourPublicKeyPassword
-signing.secretKeyRingFile=PathToYourKeyRingFile
-
-ossrhUsername=your-jira-id
-ossrhPassword=your-jira-password
-```
-
-
-
### Publish Rust crates
The Rust crates should be published in the following order
```
pixi run publish-rs
```
+
+### Create GitHub Release
+Create a [GitHub Release](https://github.com/vega/vegafusion/releases) with tag `vX.Y.Z` and title `Release X.Y.Z`. Attach:
+ 1. the wheels from the `vegafusion-python-wheels-all` artifact
+ 2. The server binary archives from the `vegafusion-server-all` artifact
+ 2. the `vegafusion-wasm-X.Y.Z.tgz` package from the `vegafusion-wasm-package` artifact
+
+### Publish documentation
+Publish the documentation to the `gh-pages` branch of the https://github.com/vegafusion/vegafusion.github.io repository by running `pixi run docs-publish`, then confirm the push by entering `y` at the prompt.
diff --git a/automation/bump_version.py b/automation/bump_version.py
index 3c39b5bc1..6d893cf75 100644
--- a/automation/bump_version.py
+++ b/automation/bump_version.py
@@ -3,6 +3,7 @@
import toml
import json
import configparser
+import subprocess
root = Path(__file__).parent.parent.absolute()
@@ -17,14 +18,10 @@ def bump_version(version):
cargo_packages = [
"vegafusion-common",
"vegafusion-core",
- "vegafusion-datafusion-udfs",
- "vegafusion-dataframe",
- "vegafusion-sql",
"vegafusion-runtime",
- "vegafusion-python-embed",
+ "vegafusion-python",
"vegafusion-server",
"vegafusion-wasm",
- "vegafusion-jni",
]
for package in cargo_packages:
@@ -51,11 +48,7 @@ def bump_version(version):
print(f"Updated version in {cargo_toml_path}")
# Handle package.json files
- package_json_dirs = [
- root / "vegafusion-wasm",
- root / "javascript" / "vegafusion-embed",
- root / "python" / "vegafusion-jupyter"
- ]
+ package_json_dirs = [root / "vegafusion-wasm"]
for package_json_dir in package_json_dirs:
for fname in ["package.json", "package-lock.json"]:
package_json_path = package_json_dir / fname
@@ -64,64 +57,28 @@ def bump_version(version):
package_json_path.write_text(json.dumps(package_json, indent=2))
print(f"Updated version in {package_json_path}")
- # Handle _version.py files
- version_py_dirs = [
- root / "python" / "vegafusion" / "vegafusion",
- root / "python" / "vegafusion-jupyter" / "vegafusion_jupyter",
- ]
- for version_py_dir in version_py_dirs:
- version_py_path = version_py_dir / "_version.py"
- version_py_path.write_text(f'__version__ = {repr(version)}\n')
- print(f"Updated version in {version_py_path}")
- # Handle cfg files
- cfg_dirs = [
- root / "python" / "vegafusion",
- root / "python" / "vegafusion-jupyter",
+ # Handle pyproject.toml files
+ pyproject_toml_dirs = [
+ root / "vegafusion-python",
]
- for cfg_dir in cfg_dirs:
- cgf_path = cfg_dir / "setup.cfg"
- parser = configparser.ConfigParser()
- parser.read_string(cgf_path.read_text())
-
- # Set package version
- parser.set("metadata", "version", version)
-
- # Check for embed dependencies
- if parser.has_option("options.extras_require", "embed"):
- deps = parser.get("options.extras_require", "embed").split("\n")
- new_deps = []
- for dep in deps:
- if dep.strip().startswith("vegafusion-python-embed"):
- new_deps.append(f"vegafusion-python-embed=={version}")
- elif dep.strip().startswith("vegafusion"):
- new_deps.append(f"vegafusion=={version}")
- else:
- new_deps.append(dep)
-
- deps_str = "\n".join(new_deps)
- parser.set("options.extras_require", "embed", deps_str)
-
- with cgf_path.open("wt") as f:
- parser.write(f)
- print(f"Updated version in {cgf_path}")
-
- # Handle _frontend.py
- frontend_py_path = root / "python" / "vegafusion-jupyter" / "vegafusion_jupyter" / "_frontend.py"
- frontend_py_path.write_text(f"""\
-\"\"\"
-Information about the frontend package of the widgets.
-\"\"\"
-module_name = "vegafusion-jupyter"
-module_version = "^{version}"
-""")
- print(f"Updated version in {frontend_py_path}")
-
- # Handle java/version.txt
- version_txt_path = root / "java" / "version.txt"
- with open(version_txt_path, "wt") as f:
- f.write(version)
- print(f"Updated version in {version_txt_path}")
+ for pyproject_toml_dir in pyproject_toml_dirs:
+ for fname in ["pyproject.toml"]:
+ pyproject_toml_path = pyproject_toml_dir / fname
+ pyproject_toml = toml.loads(pyproject_toml_path.read_text())
+ pyproject_toml["project"]["version"] = version
+ pyproject_toml_path.write_text(toml.dumps(pyproject_toml))
+ print(f"Updated version in {pyproject_toml_path}")
+
+ # Run taplo fmt *.toml
+ print("Formatting TOML files...")
+ try:
+ subprocess.run(["taplo", "fmt", "vegafusion-*/**/*.toml"], cwd=root, check=True)
+ print("TOML files formatted successfully.")
+ except subprocess.CalledProcessError as e:
+ print(f"Error formatting TOML files: {e}")
+ except FileNotFoundError:
+ print("taplo command not found. Make sure it's installed and in your PATH.")
if __name__ == '__main__':
diff --git a/automation/set_default_release_profile.py b/automation/set_default_release_profile.py
new file mode 100644
index 000000000..e7b5e8a04
--- /dev/null
+++ b/automation/set_default_release_profile.py
@@ -0,0 +1,43 @@
+import sys
+import toml
+from pathlib import Path
+import copy
+def set_default_release_profile(profile_type: str):
+ # Compute project root path (2 levels up from script location)
+ script_path = Path(__file__).resolve()
+ project_root = script_path.parent.parent
+ cargo_path = project_root / 'Cargo.toml'
+
+ if not cargo_path.exists():
+ print(f"Error: Cargo.toml not found at {cargo_path}")
+ sys.exit(1)
+
+ # Parse toml file
+ with open(cargo_path) as f:
+ cargo_config = toml.load(f)
+
+ # Get source profile
+ source_profile = f'release-{profile_type}'
+ if source_profile not in cargo_config['profile']:
+ print(f"Error: profile.{source_profile} not found in Cargo.toml")
+ sys.exit(1)
+
+ # Replace release profile with selected profile
+ cargo_config['profile']['release'] = copy.deepcopy(cargo_config['profile'][source_profile])
+ del cargo_config['profile']["release"]["inherits"]
+
+ # Write updated config back to file
+ with open(cargo_path, 'w') as f:
+ toml.dump(cargo_config, f)
+
+ print(f"Successfully updated release profile with {source_profile} configuration")
+
+if __name__ == "__main__":
+ # set_default_release_profile('opt')
+
+ if len(sys.argv) != 2:
+ print("Usage: python set_default_release_profile.py 
+
+
+## Debouncing
+The `VegaFusionWidget` provides configurable [debouncing](https://css-tricks.com/debouncing-throttling-explained-examples/) to control how frequently user interaction updates are sent from the browser to the Python kernel.
+
+The widget's `debounce_wait` and `debounce_max_wait` properties correspond to the `wait` and `max_wait` properties of the [lodash debounce](https://lodash.com/docs/#debounce) function. VegaFusion uses sensible defaults (`debounce_wait` of 30ms and `debounce_max_wait` of 60ms), but it can be useful to increase the default values to handle interactions on large datasets.
+
+## Planner Results
+The `VegaFusionWidget` also supports displaying the results of the planner. See [How it Works](../about/how_it_works.md) for more details.
+
+The following properties are available:
+
+ - `widget.spec`: This is the original Vega specification
+ - `widget.transformed_spec`: This is the initial transformed Vega specification produced by VegaFusion
+ - `widget.server_spec`: This is the portion of the full Vega spec that was planned to run on the server (The Python kernel in this case)
+ - `widget.client_spec`: This is the portion of the full Vega spec that was planned to run on the client and be rendered by the Vega JavaScript Library.
+ - `widget.comm_plan`: This is the specification of which signals and datasets must be transfered between the client and server in order to preserve the interactive behavior of the original specification.
diff --git a/docs/source/features/transform_data.md b/docs/source/features/transform_data.md
new file mode 100644
index 000000000..a3ab661f2
--- /dev/null
+++ b/docs/source/features/transform_data.md
@@ -0,0 +1,16 @@
+# Transform 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.
diff --git a/docs/source/features/transform_extract.md b/docs/source/features/transform_extract.md
new file mode 100644
index 000000000..6cea26735
--- /dev/null
+++ b/docs/source/features/transform_extract.md
@@ -0,0 +1,15 @@
+# Transform Extract
+
+The `pre_transform_extract` method generates a transformed spec like the [`pre_transform_spec`](./transform_spec.md) method, but instead of inlining the transformed datasets in the spec, these datasets are returned separately in arrow table format. This can be useful in contexts where the inline datasets are large, and it's possible to transmit them more efficiently in arrow format.
+
+## Python
+
+```{eval-rst}
+.. automethod:: vegafusion.runtime.VegaFusionRuntime.pre_transform_extract
+```
+
+**Example**: See [pre_transform_extract.py](https://github.com/vega/vegafusion/tree/v2/examples/python-examples/pre_transform_extract.py) for a complete example.
+
+## Rust
+
+See [pre_transform_extract.rs](https://github.com/vega/vegafusion/tree/v2/examples/rust-examples/examples/pre_transform_extract.rs) for a complete example.
diff --git a/docs/source/features/transform_spec.md b/docs/source/features/transform_spec.md
new file mode 100644
index 000000000..7055528f4
--- /dev/null
+++ b/docs/source/features/transform_spec.md
@@ -0,0 +1,21 @@
+# Transform Spec
+
+VegaFusion can be used to evaluate datasets in a Vega spec, remove unused columns, and inline the results in a transformed Vega spec. This transformed Vega spec is self-contained and may be displayed with the standard Vega JavaScript library.
+
+This is the foundation of Vega-Altair's [``"vegafusion"`` data transformer](https://altair-viz.github.io/user_guide/large_datasets.html#vegafusion-data-transformer) when used with the default HTML or static image renderers.
+
+:::{warning}
+The pre-transform process will, by default, preserve the interactive behavior of the input Vega specification. For interactive charts that perform filtering, this may result in the generation of a spec containing the full input dataset. If interactivity does not need to be preserved (e.g. if the resulting chart is used in a static context) then the ``preserve_interactivity`` option should be set to False. If interactivity is needed, then the [Chart State](./chart_state) workflow may be more appropriate.
+:::
+
+## Python
+
+```{eval-rst}
+.. automethod:: vegafusion.runtime.VegaFusionRuntime.pre_transform_spec
+```
+
+**Example**: See [pre_transform_spec.py](https://github.com/vega/vegafusion/tree/v2/examples/python-examples/pre_transform_spec.py) for a complete example.
+
+## Rust
+
+See [pre_transform_spec.rs](https://github.com/vega/vegafusion/tree/v2/examples/rust-examples/examples/pre_transform_spec.rs) for a complete example.
diff --git a/docs/source/features/vega-lite.md b/docs/source/features/vega-lite.md
new file mode 100644
index 000000000..04e39d7f3
--- /dev/null
+++ b/docs/source/features/vega-lite.md
@@ -0,0 +1,2 @@
+# Vega-Lite
+[Vega-Lite](https://vega.github.io/vega-lite/) is a high-level specification for charts that is compiled to standard Vega specification by the Vega-Lite TypeScript library. VegaFusion operates exclusively on Vega specifications, so it does not directly support Vega-Lite. The [vl-convert](https://github.com/vega/vl-convert) project provides Rust and Python libraries that can be used to convert Vega-Lite chart specifications to Vega specifications that are compatible with VegaFusion. Vega-Altair's `"vegafusion"` data transformer uses this approach.
diff --git a/docs/source/index.md b/docs/source/index.md
new file mode 100644
index 000000000..53cd2e56c
--- /dev/null
+++ b/docs/source/index.md
@@ -0,0 +1,70 @@
+```{raw} html
+
+ 
+
+ 
+
+
+### DataFusion integration
+[Apache Arrow DataFusion](https://github.com/apache/arrow-datafusion) is an SQL compatible query engine that integrates with the Rust implementation of Apache Arrow. VegaFusion uses DataFusion to implement many of the Vega transforms, and it compiles the Vega expression language directly into the DataFusion expression language. In addition to being really fast, a particularly powerful characteristic of DataFusion is that it provides many interfaces that can be extended with custom Rust logic. For example, VegaFusion defines many custom UDFs that are designed to implement the precise semantics of the Vega expression language and the Vega expression functions.
+
+
+
+## Examples
+VegaFusion is compatible with all of the examples in the [Altair gallery](https://altair-viz.github.io/gallery/index.html). Here are a few additional examples that demonstrate the benefits of server-side acceleration.
+
+### Large Altair Gallery Examples
+The following examples are modifications of Altair Gallery examples that use larger datasets. Each example displays the same plot with plain Altair and with Altair + VegaFusion to make is easy to compare performance. You can try them out using the binder links below.
+
+#### Interactive Crossfilter
+
+ - [Notebook file](https://github.com/vegafusion/demos/blob/main/notebooks/large_altair_examples/flights_crossfilter.ipynb)
+ - [](https://mybinder.org/v2/gh/vegafusion/demos/HEAD?urlpath=voila%2Frender%2Fnotebooks%2Flarge_altair_examples%2Fflights_crossfilter.ipynb) - Voila Dashboard
+ - [](https://gesis.mybinder.org/binder/v2/gh/vegafusion/demos/93a9a68100de0d6282b114f8cf01cbb30c672267?urlpath=lab%2Ftree%2Fnotebooks%2Flarge_altair_examples%2Fflights_crossfilter.ipynb) - Jupyterlab
+
+
+
+#### Interactive Average
+
+ - [Notebook file](https://github.com/vegafusion/demos/blob/main/notebooks/large_altair_examples/interactive_average.ipynb)
+ - [](https://mybinder.org/v2/gh/vegafusion/demos/HEAD?urlpath=voila%2Frender%2Fnotebooks%2Flarge_altair_examples%2Finteractive_average.ipynb) - Voila Dashboard
+ - [](https://gesis.mybinder.org/binder/v2/gh/vegafusion/demos/93a9a68100de0d6282b114f8cf01cbb30c672267?urlpath=lab%2Ftree%2Fnotebooks%2Flarge_altair_examples%2Finteractive_average.ipynb) - JupyterLab
+
+
+
+### 10 Million Taxi Rides
+This example creates a dashboard to explore 10 million taxi rides in New York City from January 2015. It supports zoom-based rasterization of the pickup locations (similar to [Datashader](https://datashader.org/)), and combines that with cross filtering across a histogram of trip distances and a heatmap of tip rate by hour and day of the week.
+
+See the [PyViz documentation example](https://examples.pyviz.org/nyc_taxi/nyc_taxi.html) for more information on the dataset.
+
+ - [Notebook file](https://github.com/vegafusion/demos/blob/main/notebooks/nyc_taxi/nyc_taxi.ipynb)
+
+
+
+## About the Name
+There are two meanings behind the name "VegaFusion"
+ - It's a reference to the [Apache Arrow DataFusion](https://github.com/apache/arrow-datafusion) library described above
+ - Vega and Altair are named after stars, and stars are powered by nuclear fusion
+
+## Licensing and Funding
+At least until a sustainable funding model is established, VegaFusion will be developed under the [AGPLv3 license](https://www.gnu.org/licenses/agpl-3.0.en.html). This is a copy-left license in the GPL family of licenses. As with all [OSI approved licenses](https://opensource.org/licenses/alphabetical), there are no restrictions on what code licensed under the AGPL can be used for. However, the requirements for what must be shared publicly are greater than for licenses that are more commonly used in the Python ecosystem like [Apache-2](https://opensource.org/licenses/Apache-2.0), [MIT](https://opensource.org/licenses/MIT), and [BSD-3](https://opensource.org/licenses/BSD-3-Clause).
+
+## Roadmap
+This is only the beginning for VegaFusion. There are a lot of possible future directions, and many of them are detailed in the roadmap.
+
+## Learn more
+Here are some ways that you can get in touch to learn more.
+ - Check out the code on [GitHub](https://github.com/hex-inc/vegafusion/)
+ - Start a discussion on [GitHub Discussions](https://github.com/hex-inc/vegafusion/discussions)
+ - And of course, if something doesn't work please [open an issue](https://github.com/hex-inc/vegafusion/issues)
+
+Thanks!
diff --git a/docs/source/posts/2023/2023-01-21_Release_1.0.0.md b/docs/source/posts/2023/2023-01-21_Release_1.0.0.md
new file mode 100644
index 000000000..286735237
--- /dev/null
+++ b/docs/source/posts/2023/2023-01-21_Release_1.0.0.md
@@ -0,0 +1,284 @@
+---
+date: 2023-01-21
+category: Release
+author: Jon Mease
+---
+
+# VegaFusion 1.0
+**Mime renderer, transform evaluation, and a permissive license**
+
+By: Jon Mease
+
+---
+
+The VegaFusion team is happy to announce the release of VegaFusion version 1.0. This release is the culmination of several months of work on both VegaFusion itself and its new dependency: [VlConvert](https://medium.com/@jonmmease/introducing-vlconvert-c763f0076e89). This version introduces a brand new mimetype based renderer that is compatible with many notebook and compute environments without requiring custom extensions. It also includes a new `transformed_data()` function that makes it possible to evaluate the data transforms associated with an Altair `Chart` and return the result as a pandas DataFrame.
+
+Finally, we're thrilled to announce that VegaFusion has been adopted by Hex and is now licensed under the same permissive BSD-3 license used by Vega, Vega-Lite, and Altair.
+
+## Background
+VegaFusion scales Vega, Vega-Lite, and Altair visualizations by moving data transformations from the browser to a server (e.g. a Python kernel).
+
+The initial focus was on supporting complex interactive visualizations that require a live two-way connection between the client and server. This approach works well, but it relies on a custom Jupyter widget extension which adds complexity and limits the environments it can be deployed in.
+
+To reduce complexity while addressing the more common scenario of non-interactive charts, VegaFusion 1.0 introduces an entirely new renderer that does not require any custom extensions but instead leverages the built-in rendering capabilities of modern notebooks and compute environments.
+
+## VegaFusion mime renderer
+Altair comes with a default limit of 5,000 input rows to avoid crashing the user's browser. Because all data transformations are applied in the browser by the Vega JavaScript library, the full input dataset must be loaded into the browser. The VegaFusion mime renderer performs most common Vega transforms in the Python kernel, and so the row limit is applied to the transformed data rather than the input data. When the chart includes any form of aggregation, the transformed dataset typically has many fewer rows than the input dataset.
+
+To use the VegaFusion mime renderer, first install the `vegafusion` Python package with the `embed` extras enabled:
+
+```bash
+pip install -U "vegafusion[embed]"
+```
+
+Then open a Jupyter notebook (either the classic notebook or a notebook inside JupyterLab), and create an Altair histogram of a 1 million row flights dataset
+
+```python
+import pandas as pd
+import altair as alt
+
+flights = pd.read_parquet(
+ "https://vegafusion-datasets.s3.amazonaws.com/vega/flights_1m.parquet"
+)
+
+delay_hist = alt.Chart(flights).mark_bar().encode(
+ alt.X("delay", bin=alt.Bin(maxbins=30)),
+ alt.Y("count()")
+)
+delay_hist
+```
+```
+---------------------------------------------------------------------------
+MaxRowsError Traceback (most recent call last)
+...
+MaxRowsError: The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation
+```
+
+As expected, this results in an Altair `MaxRowsError`. The VegaFusion mime renderer can be used to overcome this limitation by performing data intensive transforms (e.g. filtering, binning, aggregation, etc.) in the Python kernel before the resulting data is sent to the web browser.
+
+Run these two lines to import and enable the new VegaFusion mime renderer:
+
+```python
+import vegafusion as vf
+vf.enable()
+```
+
+Now the chart displays quickly without errors:
+```
+delay_hist
+```
+
+
+## Transform evaluation
+
+VegaFusion now supports extracting the transformed data for an Altair Chart using the `vegafusion.transformed_data()` function. This is particularly useful when building a chart that includes a pipeline of transforms, as it's now possible to see the intermediate results of each transform step.
+
+### Example: Top K
+Here is an example, based on the [Top-K plot with Others](https://altair-viz.github.io/gallery/top_k_with_others.html) example from the Altair documentation, of how `transformed_data()` can be helpful when building a complex chart.
+
+First, create an Altair `Chart` wrapping the data source URL.
+```python
+import altair as alt
+import vegafusion as vf
+
+source = "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json"
+chart = alt.Chart(source)
+```
+
+The `transformed_data()` function can be used on this empty chart to access a preview of the data that is available at the URL. Here the `row_limit` argument is used to limit the result to 3 rows and the DataFrame is transposed to make it easier to read.
+
+```python
+vf.transformed_data(chart, row_limit=3).T
+```
+
+| | 0 | 1 | 2 |
+|:-----------------------|:---------------|:-----------------------|:---------------------------|
+| Title | The Land Girls | First Love, Last Rites | I Married a Strange Person |
+| US_Gross | 146083 | 10876 | 203134 |
+| Worldwide_Gross | 146083 | 10876 | 203134 |
+| Production_Budget | 8000000 | 300000 | 250000 |
+| Release_Date | Jun 12 1998 | Aug 07 1998 | Aug 28 1998 |
+| MPAA_Rating | R | R | |
+| Distributor | Gramercy | Strand | Lionsgate |
+| IMDB_Rating | 6.1 | 6.9 | 6.8 |
+| IMDB_Votes | 1071.0 | 207.0 | 865.0 |
+| Major_Genre | | Drama | Comedy |
+| Rotten_Tomatoes_Rating | nan | nan | nan |
+| Source | | | |
+| Creative_Type | | | |
+| Director | | | |
+| US_DVD_Sales | nan | nan | nan |
+| Running_Time_min | nan | nan | nan |
+
+The first step of making this chart is to compute the average worldwide gross of all the movies for each director. This can be accomplished with the Altair [Aggregate Transform](https://altair-viz.github.io/user_guide/transform/aggregate.html).
+
+```python
+chart = (
+ alt.Chart(source)
+ .transform_aggregate(
+ aggregate_gross='mean(Worldwide_Gross)',
+ groupby=["Director"],
+ )
+)
+vf.transformed_data(chart, row_limit=5)
+```
+
+| | Director | aggregate_gross |
+|---:|:------------------|------------------:|
+| 0 | | 3.59284e+07 |
+| 1 | Christopher Nolan | 3.44251e+08 |
+| 2 | Roman Polanski | 5.13407e+07 |
+| 3 | Richard Fleischer | 2.27635e+07 |
+| 4 | Blake Edwards | 5e+06 |
+
+Next, the directors are ranked by average gross in descending order. This can be accomplished with the Altair [Window Transform](https://altair-viz.github.io/user_guide/transform/window.html)
+
+```python
+chart = (
+ alt.Chart(source)
+ .transform_aggregate(
+ aggregate_gross='mean(Worldwide_Gross)',
+ groupby=["Director"],
+ ).transform_window(
+ rank='row_number()',
+ sort=[alt.SortField("aggregate_gross", order="descending")],
+ )
+)
+vf.transformed_data(chart, row_limit=5)
+```
+
+| | Director | aggregate_gross | rank |
+|---:|:----------------|------------------:|-------:|
+| 0 | David Yates | 9.37984e+08 | 1 |
+| 1 | James Cameron | 8.29781e+08 | 2 |
+| 2 | Carlos Saldanha | 7.69293e+08 | 3 |
+| 3 | Pete Docter | 7.31305e+08 | 4 |
+| 4 | Andrew Stanton | 7.00319e+08 | 5 |
+
+Then, a new column is added that contains the director's name for the top 9 ranked directors and "All Others" for the remaining directors. This can be accomplished using the Altair [Calculate Transform](https://altair-viz.github.io/user_guide/transform/calculate.html).
+
+```python
+chart = (
+ alt.Chart(source)
+ .transform_aggregate(
+ aggregate_gross='mean(Worldwide_Gross)',
+ groupby=["Director"],
+ ).transform_window(
+ rank='row_number()',
+ sort=[alt.SortField("aggregate_gross", order="descending")],
+ ).transform_calculate(
+ ranked_director="datum.rank < 10 ? datum.Director : 'All Others'"
+ )
+)
+vf.transformed_data(chart, row_limit=12)
+```
+| | Director | aggregate_gross | rank | ranked_director |
+|---:|:-----------------|------------------:|-------:|:------------------|
+| 0 | David Yates | 9.37984e+08 | 1 | David Yates |
+| 1 | James Cameron | 8.29781e+08 | 2 | James Cameron |
+| 2 | Carlos Saldanha | 7.69293e+08 | 3 | Carlos Saldanha |
+| 3 | Pete Docter | 7.31305e+08 | 4 | Pete Docter |
+| 4 | Andrew Stanton | 7.00319e+08 | 5 | Andrew Stanton |
+| 5 | David Slade | 6.88155e+08 | 6 | David Slade |
+| 6 | George Lucas | 6.73577e+08 | 7 | George Lucas |
+| 7 | Andrew Adamson | 6.43134e+08 | 8 | Andrew Adamson |
+| 8 | Peter Jackson | 5.95566e+08 | 9 | Peter Jackson |
+| 9 | Richard Marquand | 5.727e+08 | 10 | All Others |
+| 10 | Eric Darnell | 5.66099e+08 | 11 | All Others |
+| 11 | Roland Emmerich | 4.5506e+08 | 12 | All Others |
+
+Finally, this dataset is ready to be encoded as a bar mark:
+
+```python
+import altair as alt
+import vegafusion as vf
+
+vf.enable()
+
+source = "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json"
+chart = (
+ alt.Chart(source)
+ .transform_aggregate(
+ aggregate_gross='mean(Worldwide_Gross)',
+ groupby=["Director"],
+ ).transform_window(
+ rank='row_number()',
+ sort=[alt.SortField("aggregate_gross", order="descending")],
+ ).transform_calculate(
+ ranked_director="datum.rank < 10 ? datum.Director : 'All Others'"
+ ).mark_bar().encode(
+ x=alt.X("aggregate_gross:Q", aggregate="mean", title=None),
+ y=alt.Y(
+ "ranked_director:N",
+ sort=alt.Sort(op="mean", field="aggregate_gross", order="descending"),
+ title=None,
+ ),
+ )
+)
+chart
+```
+
+
+
+The exact value of each bar can be accessed by applying `transformed_data()` to the final chart (which includes the implicit transforms in the bar mark encoding).
+
+```python
+vf.transformed_data(chart)
+```
+
+| | ranked_director | mean_aggregate_gross |
+|---:|:------------------|-----------------------:|
+| 0 | David Yates | 9.37984e+08 |
+| 1 | James Cameron | 8.29781e+08 |
+| 2 | Carlos Saldanha | 7.69293e+08 |
+| 3 | Pete Docter | 7.31305e+08 |
+| 4 | Andrew Stanton | 7.00319e+08 |
+| 5 | David Slade | 6.88155e+08 |
+| 6 | George Lucas | 6.73577e+08 |
+| 7 | Andrew Adamson | 6.43134e+08 |
+| 8 | Peter Jackson | 5.95566e+08 |
+| 9 | All Others | 8.87602e+07 |
+
+## Increased transform coverage
+VegaFusion's coverage of Vega-Lite's transforms is not yet complete, but it's growing with each release. Support for the [Pivot](https://vega.github.io/vega-lite/docs/pivot.html), [Impute](https://vega.github.io/vega-lite/docs/impute.html), [Fold](https://vega.github.io/vega-lite/docs/fold.html), and [Sequence](https://vega.github.io/vega/docs/transforms/sequence/) transforms was added in this release. Here is the full set of supported Vega-Lite/Vega transforms:
+
+ - [Aggregate](https://vega.github.io/vega-lite/docs/aggregate.html)
+ - [Bin](https://vega.github.io/vega-lite/docs/bin.html)
+ - [Calculate](https://vega.github.io/vega-lite/docs/calculate.html)
+ - [Collect](https://vega.github.io/vega/docs/transforms/collect/)
+ - [Extent](https://vega.github.io/vega/docs/transforms/extent/)
+ - [Filter](https://vega.github.io/vega-lite/docs/filter.html)
+ - [Fold](https://vega.github.io/vega-lite/docs/fold.html)
+ - [Identifier](https://vega.github.io/vega/docs/transforms/identifier/)
+ - [Impute](https://vega.github.io/vega-lite/docs/impute.html)
+ - [Join Aggregate](https://vega.github.io/vega-lite/docs/joinaggregate.html)
+ - [Pivot](https://vega.github.io/vega-lite/docs/pivot.html)
+ - [Project](https://vega.github.io/vega/docs/transforms/project/)
+ - [Sequence](https://vega.github.io/vega/docs/transforms/sequence/)
+ - [Stack](https://vega.github.io/vega-lite/docs/stack.html)
+ - [Time Unit](https://vega.github.io/vega-lite/docs/timeunit.html)
+ - [Window](https://vega.github.io/vega-lite/docs/window.html)
+
+More on the way!
+
+## Leveraging VlConvert
+
+Both the new mime renderer and the `transfomed_data()` function require the ability to convert Vega-Lite specifications (as produced by Altair) to Vega specifications (as consumed by VegaFusion). This functionality is provided by the Vega-Lite JavaScript library, which presents a challenge: How can we perform this conversion in Python without a web browser?
+
+To meet this challenge, we developed a new Python library called [VlConvert](https://github.com/vega/vl-convert). You can read all about VlConvert in its [announcement post](https://medium.com/@jonmmease/introducing-vlconvert-c763f0076e89), but in short it embeds the [Deno](https://deno.land/) JavaScript runtime in a Python library for the purpose of running the Vega-Lite JavaScript library.
+
+While motivated by the needs of VegaFusion, VlConvert turned out to be a great foundation for implementing static image export, as it is a simple Python package that doesn't require external dependencies on a web browser or Node.js. In fact, VlConvert has been adopted by the Vega community and will be the default image export engine for the coming release of Altair 5!
+
+## A new partnership and a new license
+VegaFusion was first released in January 2022 under the copyleft AGPL license. I chose to use the AGPL license initially because I wanted to share the work with the Vega community, but I also wanted to leave open the possibility of building a company with a dual-license business model around the project.
+
+The day of the initial release, Barry McCardel (the CEO of [Hex Technologies](https://hex.tech/)) reached out to me, and we started talking about how VegaFusion could help scale the [Hex chart cell](https://learn.hex.tech/docs/logic-cell-types/display-cells/chart-cells) (which is itself built on Vega-Lite). I granted them a commercial license exception, and started working with the Hex team to integrate VegaFusion into Hex. After months of working together with Barry and the Hex team, and lots of conversations around the future of the project, we decided that the best path forward was for me to continue working on VegaFusion as a member of Hex team, and for VegaFusion to be re-licensed to a permissive license that is compatible with the rest of the Vega ecosystem.
+
+The end result is that VegaFusion 1.0 is licensed under the [BSD-3](https://opensource.org/licenses/BSD-3-Clause) license. Also, to make it easier to take advantage of Hex's testing infrastructure the VegaFusion repositories have been moved under the [hex-inc](https://github.com/hex-inc) GitHub organization.
+
+## Learn more
+Check out these resources if you'd like to learn more:
+ - [VegaFusion Documentation](https://vegafusion.io/)
+ - [VegaFusion GitHub](https://github.com/hex-inc/vegafusion)
+ - [Report and Issue](https://github.com/hex-inc/vegafusion/issues)
+ - [Start a Discussions](https://github.com/hex-inc/vegafusion/discussions)
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+---
+date: 2023-03-25
+category: Release
+author: Jon Mease
+---
+
+# VegaFusion 1.1
+**DuckDB and Polars support, Altair 5 compatibility, and lots of bug fixes**
+
+By: Jon Mease
+
+---
+
+The VegaFusion team is happy to announce the release of version 1.1. In addition to the usual slew of bug fixes, this release includes support for evaluating Vega transforms in DuckDB, support for Polars and the DataFrame Interchange Protocol, and forward compatibility with the coming release of Altair 5.
+
+## DuckDB support
+[DuckDB](https://duckdb.org/) is an in-process SQL OLAP query engine and database that provides bindings for a wide variety of languages, including Python. The VegaFusion 1.1 Python library now includes two forms of integration with DuckDB. First, it can use DuckDB in place of DataFusion to power Vega transforms over the pandas DataFrames that are referenced by Altair charts. Second, VegaFusion 1.1 makes it possible to reference externally defined DuckDB tables and views in Altair charts.
+
+### Vega transforms with DuckDB
+
+VegaFusion can now be configured to evaluate Vega transforms using the DuckDB Python library by calling `vegafusion.runtime.set_connection("duckdb")`.
+
+Once the DuckDB connection is enabled, pandas DataFrames referenced by Altair charts are automatically registered with DuckDB and Vega transforms are translated into DuckDB SQL queries. Here is a full example, adapted from the [2D Histogram Heatmap](https://altair-viz.github.io/gallery/histogram_heatmap.html) Altair gallery example.
+
+```python
+import vegafusion as vf
+import pandas as pd
+import altair as alt
+
+# Configure DuckDB connection
+vf.runtime.set_connection("duckdb")
+
+# Enable Mime Renderer
+vf.enable()
+
+# Load 201k row version of the Vega movies dataset with pandas
+movies = pd.read_parquet(
+ "https://vegafusion-datasets.s3.amazonaws.com/vega/movies_201k.parquet"
+)
+
+# Create an Altair chart from the pandas DataFrame as usual.
+# Binning and aggregation will be evaluated against the DataFrame with DuckDB
+chart = alt.Chart(movies).mark_rect().encode(
+ alt.X('IMDB_Rating:Q', bin=alt.Bin(maxbins=60)),
+ alt.Y('Rotten_Tomatoes_Rating:Q', bin=alt.Bin(maxbins=40)),
+ alt.Color('count():Q', scale=alt.Scale(scheme='greenblue'))
+)
+chart
+```
+
+
+Additionally, the transformed data can be extracted as usual:
+```python
+vf.transformed_data(chart, row_limit=5).T
+```
+| | 0 | 1 | 2 | 3 | 4 |
+|:------------------------------------------|-----:|------:|------:|-------:|-------:|
+| bin_maxbins_60_IMDB_Rating | 3.4 | 5.8 | 7 | 7 | 7.4 |
+| bin_maxbins_60_IMDB_Rating_end | 3.6 | 6 | 7.2 | 7.2 | 7.6 |
+| bin_maxbins_40_Rotten_Tomatoes_Rating | 60 | 25 | 85 | 80 | 80 |
+| bin_maxbins_40_Rotten_Tomatoes_Rating_end | 65 | 30 | 90 | 85 | 85 |
+| __count | 63 | 441 | 504 | 1260 | 1134 |
+
+The default DataFusion connection can be re-enabled by calling `vegafusion.runtime.set_connection("datafusion")`
+
+#### Performance benefit
+DuckDB has the ability to perform queries against Pandas DataFrames without first serializing them to Arrow. This can result in 10x+ speedups when visualizing large Pandas DataFrames with the VegaFusion Mime Renderer. Presently, the VegaFusion Widget Renderer always serializes DataFrames to Arrow and writes them to disk, so significant performance gains are not expected for the Widget Renderer.
+
+### Access DuckDB tables
+VegaFusion 1.1 also supports integration with external DuckDB connections, making it possible to reference DuckDB tables and views from Altair charts. To begin, import `duckdb` and create a new connection.
+
+```python
+import duckdb
+conn = duckdb.connect()
+```
+
+Pass this DuckDB connection to `vegafusion.runtime.set_connection` (instead of the string `"duckdb"` as in the previous example).
+
+```python
+vf.runtime.set_connection(conn)
+```
+
+Next, use the DuckDB connection to create a table or view. Here, the DuckDB `read_parquet` method is used to load the 201k row movies dataset, and a DuckDB query is used to filter NULL values. The result of this query is registered as a table named `movies`.
+
+```python
+relation = conn.read_parquet(
+ "https://vegafusion-datasets.s3.amazonaws.com/vega/movies_201k.parquet"
+)
+
+relation.query("tbl", """
+ SELECT * FROM tbl
+ WHERE Rotten_Tomatoes_Rating IS NOT NULL AND Imdb_Rating IS NOT NULL
+""").to_table("movies")
+```
+
+DuckDB tables and views registered with `conn` may be referenced from Altair charts with a special URL syntax using `table://` as the prefix. To reference the DuckDB table named `movies`, the Altair chart should be passed the url string `"table://movies"`. Here is a full example
+
+```python
+import duckdb
+import vegafusion as vf
+import pandas as pd
+import altair as alt
+
+# Create DuckDB connection
+conn = duckdb.connect()
+
+# Pass DuckDB connection to VegaFusion's set_connection method
+vf.runtime.set_connection(conn)
+
+# Enable Mime Renderer
+vf.enable()
+
+# Read parquet file using the DuckDB connection
+relation = conn.read_parquet(
+ "https://vegafusion-datasets.s3.amazonaws.com/vega/movies_201k.parquet"
+)
+
+# Filter NULL values and register the result as a table named "movies"
+relation.query("tbl", """
+ SELECT * FROM tbl
+ WHERE Rotten_Tomatoes_Rating IS NOT NULL AND Imdb_Rating IS NOT NULL
+""").to_table("movies")
+
+# Create an Altair chart that references the registered DuckDB table
+chart = alt.Chart("table://movies").mark_rect().encode(
+ alt.X('IMDB_Rating:Q', bin=alt.Bin(maxbins=60)),
+ alt.Y('Rotten_Tomatoes_Rating:Q', bin=alt.Bin(maxbins=40)),
+ alt.Color('count():Q', scale=alt.Scale(scheme='greenblue'))
+)
+chart
+```
+
+
+See the DuckDB connection docs for more information.
+
+## Polars support
+
+[Polars](https://www.pola.rs/) describes itself as a "Lightning-fast DataFrame library for Rust and Python". Polars has quickly [gained popularity](https://star-history.com/#pola-rs/polars&Date) as a faster alternative to pandas that also supports larger datasets.
+
+VegaFusion's new Polars integration makes it possible to input and output Polars DataFrames without conversion through pandas.
+
+### Polars as Input
+Here is a full example that uses Polars to read the 201k row movies dataset from a remote parquet file. The DataFrame's `filter` method is then used to remove rows with NULL movie rating values. This filtered DataFrame is passed as the input to an Altair chart.
+
+```python
+import polars as pl
+import vegafusion as vf
+import altair as alt
+
+# Enable Mime Renderer
+vf.enable()
+
+# Load 201k movies parquet dataset
+source = pl.read_parquet(
+ "https://vegafusion-datasets.s3.amazonaws.com/vega/movies_201k.parquet"
+)
+
+# Fitler out rows with null ratings
+source = source.filter(
+ pl.col("IMDB_Rating").is_not_null() & pl.col("Rotten_Tomatoes_Rating").is_not_null()
+)
+
+# Create chart
+chart = alt.Chart(source).mark_circle().encode(
+ alt.X('IMDB_Rating:Q', bin=True),
+ alt.Y('Rotten_Tomatoes_Rating:Q', bin=True),
+ size='count()'
+)
+chart
+```
+
+
+
+### Polars as Output
+When a Chart that references a Polars DataFrame is passed to `vegafusion.transformed_data`, the result will also be a Polars DataFrame. Here is a Polars version of the example from the Transformed Data section.
+
+```python
+import altair as alt
+import vegafusion as vf
+import polars a pl
+
+# Enable mime renderer
+vf.enable()
+
+# Load 201k movies parquet dataset with Polars
+source = pl.read_parquet(
+ "https://vegafusion-datasets.s3.amazonaws.com/vega/movies_201k.parquet"
+)
+
+# Build chart
+chart = (
+ alt.Chart(source)
+ .transform_aggregate(
+ aggregate_gross='mean(Worldwide_Gross)',
+ groupby=["Director"],
+ ).transform_window(
+ rank='row_number()',
+ sort=[alt.SortField("aggregate_gross", order="descending")],
+ ).transform_calculate(
+ ranked_director="datum.rank < 10 ? datum.Director : 'All Others'"
+ ).mark_bar().encode(
+ x=alt.X("aggregate_gross:Q", aggregate="mean", title=None),
+ y=alt.Y(
+ "ranked_director:N",
+ sort=alt.Sort(op="mean", field="aggregate_gross", order="descending"),
+ title=None,
+ ),
+ )
+).properties(
+ title="Top Directors by Average Worldwide Gross",
+)
+chart
+```
+
+
+Now retrieve the chart's transformed data as a Polars DataFrame.
+
+```python
+transformed = vf.transformed_data(chart)
+print(type(transformed))
+transformed
+```
+```
+