mirror of
https://github.com/chanzuckerberg/cellxgene.git
synced 2026-09-23 07:18:12 +08:00
Severiano Badajoz
941c297363
* 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
70 lines
2.0 KiB
JavaScript
70 lines
2.0 KiB
JavaScript
import * as Dataframe from "../../../src/util/dataframe";
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describe("Dataframe column histogram", () => {
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test("categorical by categorical", () => {
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const df = new Dataframe.Dataframe(
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[3, 3],
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[["n1", "n2", "n3"], ["c1", "c2", "c3"], new Int32Array([0, 1, 2])],
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null,
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new Dataframe.KeyIndex(["name", "cat", "value"])
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);
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const h1 = df.col("cat").histogram(df.col("name"));
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expect(h1).toMatchObject(
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new Map([
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["n1", new Map([["c1", 1]])],
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["n2", new Map([["c2", 1]])],
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["n3", new Map([["c3", 1]])]
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])
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);
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// memoized?
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expect(df.col("cat").histogram(df.col("name"))).toMatchObject(h1);
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});
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test("continuous by categorical", () => {
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const df = new Dataframe.Dataframe(
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[3, 3],
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[["n1", "n2", "n3"], ["c1", "c2", "c3"], new Int32Array([0, 1, 2])],
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null,
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new Dataframe.KeyIndex(["name", "cat", "value"])
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);
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const h1 = df.col("value").histogram(3, [0, 2], df.col("name"));
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expect(h1).toMatchObject(
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new Map([["n1", [1, 0, 0]], ["n2", [0, 1, 0]], ["n3", [0, 0, 1]]])
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);
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// memoized?
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expect(df.col("value").histogram(3, [0, 2], df.col("name"))).toMatchObject(
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h1
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);
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});
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test("categorical", () => {
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const df = new Dataframe.Dataframe(
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[3, 3],
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[["n1", "n2", "n3"], ["c1", "c2", "c3"], new Int32Array([0, 1, 2])],
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null,
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new Dataframe.KeyIndex(["name", "cat", "value"])
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);
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const h1 = df.col("cat").histogram();
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expect(h1).toMatchObject(new Map([["c1", 1], ["c2", 1], ["c3", 1]]));
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// memoized?
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expect(df.col("value").histogram(3, [0, 2])).toMatchObject(h1);
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});
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test("continuous", () => {
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const df = new Dataframe.Dataframe(
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[3, 3],
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[["n1", "n2", "n3"], ["c1", "c2", "c3"], new Int32Array([0, 1, 2])],
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null,
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new Dataframe.KeyIndex(["name", "cat", "value"])
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);
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const h1 = df.col("value").histogram(3, [0, 2]);
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expect(h1).toMatchObject([1, 1, 1]);
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// memoized?
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expect(df.col("value").histogram(3, [0, 2])).toMatchObject(h1);
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});
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});
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