categorical vs continuous mini histograms (#827)

* comment * add histogram functionality to Dataframe; port category occupancy to use it * fix binning and create histogram for continous by catagorical * Remove unnecessary logs * Begin work on KDE * Replace broken KDE with working histogram * Define domain and range based on data from histogram * Fix occupancy * Add continuous obs and switch to canvas * Stop value from always rerendering * clear before render * Clear canvas on render * refactor categorical occupancy to canvas * Remove log * simplify finding max * refactor kde->histogram and occupancy->bins * refactor svg -> canvas * rename to occupancy stack * create popup * add metadata and categorical values to popup * fix overflow * remove zeros info * style graph * fix shouldComponentUpdate to look for world changes * change categorySelected -> categoryValueSelected * refactor out render * remove comment * conditionally have bottom border * remove diff comp * remove comments * remove unnecessary mapping * Add comments describing drawing functions * comments * flip comparison order * remove logging * move default to parameter * move defaults to parameter * disable popover if not showing histogram * fix wording and styling * add line break
2026-10-05 13:08:11 +08:00 · 2019-07-09 11:19:01 -07:00
parent 722a91f1d2
commit 941c297363
6 changed files with 465 additions and 64 deletions
@@ -0,0 +1,69 @@
+import * as Dataframe from "../../../src/util/dataframe";
+
+describe("Dataframe column histogram", () => {
+  test("categorical by categorical", () => {
+    const df = new Dataframe.Dataframe(
+      [3, 3],
+      [["n1", "n2", "n3"], ["c1", "c2", "c3"], new Int32Array([0, 1, 2])],
+      null,
+      new Dataframe.KeyIndex(["name", "cat", "value"])
+    );
+
+    const h1 = df.col("cat").histogram(df.col("name"));
+    expect(h1).toMatchObject(
+      new Map([
+        ["n1", new Map([["c1", 1]])],
+        ["n2", new Map([["c2", 1]])],
+        ["n3", new Map([["c3", 1]])]
+      ])
+    );
+    // memoized?
+    expect(df.col("cat").histogram(df.col("name"))).toMatchObject(h1);
+  });
+
+  test("continuous by categorical", () => {
+    const df = new Dataframe.Dataframe(
+      [3, 3],
+      [["n1", "n2", "n3"], ["c1", "c2", "c3"], new Int32Array([0, 1, 2])],
+      null,
+      new Dataframe.KeyIndex(["name", "cat", "value"])
+    );
+
+    const h1 = df.col("value").histogram(3, [0, 2], df.col("name"));
+    expect(h1).toMatchObject(
+      new Map([["n1", [1, 0, 0]], ["n2", [0, 1, 0]], ["n3", [0, 0, 1]]])
+    );
+    // memoized?
+    expect(df.col("value").histogram(3, [0, 2], df.col("name"))).toMatchObject(
+      h1
+    );
+  });
+
+  test("categorical", () => {
+    const df = new Dataframe.Dataframe(
+      [3, 3],
+      [["n1", "n2", "n3"], ["c1", "c2", "c3"], new Int32Array([0, 1, 2])],
+      null,
+      new Dataframe.KeyIndex(["name", "cat", "value"])
+    );
+
+    const h1 = df.col("cat").histogram();
+    expect(h1).toMatchObject(new Map([["c1", 1], ["c2", 1], ["c3", 1]]));
+    // memoized?
+    expect(df.col("value").histogram(3, [0, 2])).toMatchObject(h1);
+  });
+
+  test("continuous", () => {
+    const df = new Dataframe.Dataframe(
+      [3, 3],
+      [["n1", "n2", "n3"], ["c1", "c2", "c3"], new Int32Array([0, 1, 2])],
+      null,
+      new Dataframe.KeyIndex(["name", "cat", "value"])
+    );
+
+    const h1 = df.col("value").histogram(3, [0, 2]);
+    expect(h1).toMatchObject([1, 1, 1]);
+    // memoized?
+    expect(df.col("value").histogram(3, [0, 2])).toMatchObject(h1);
+  });
+});