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https://github.com/chanzuckerberg/cellxgene.git
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* Added TS. Updated build and linting config. Added types. * [ts-migrate][.] Rename files from JS/JSX to TS/TSX Co-authored-by: ts-migrate <> * [ts-migrate][.] Run TS Migrate Co-authored-by: ts-migrate <> * Corrected files mangled by ts-migrate. * Updated lint config, minor linting. * Re-enabled Husky. * Updated tests and config. * Reverted webpack devtool config. * Removed obsolete snapshots. * Added annotations snap. * Updated tsconfig includes wrt linting. * Removed ts-migrate. Co-authored-by: Timmy Huang <tihuan@users.noreply.github.com>
This commit is contained in:
Mim Hastie
co-authored by
ts-migrate
Timmy Huang
ts-migrate
Timmy Huang
parent
7328cbdbd5
commit
934cc5c69b
@@ -1,206 +0,0 @@
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import every from "lodash.every";
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import map from "lodash.map";
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import isNumber from "lodash.isnumber";
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import zip from "lodash.zip";
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import _ from "lodash";
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import { flatbuffers } from "flatbuffers";
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import { NetEncoding } from "../../../src/util/stateManager/matrix_generated";
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/*
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test data mocking REST 0.2 API responses. Used in several tests.
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*/
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const nObs = 10;
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const nVar = 32;
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const field4Categories = [83, true, "foo", 2.222222];
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const fieldDCategories = [99, false, "mumble", 3.1415];
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const aConfigResponse = {
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config: {
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features: [
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{ method: "POST", path: "/cluster/", available: false },
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{ method: "POST", path: "/layout/", available: false },
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{ method: "POST", path: "/diffexp/", available: false },
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{ method: "POST", path: "/saveLocal/", available: false },
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],
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displayNames: {
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engine: "the little engine that could",
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dataset: "all your zeros are mine",
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},
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},
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};
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const aSchemaResponse = {
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schema: {
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dataframe: {
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nObs,
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nVar,
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type: "float32",
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},
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annotations: {
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obs: {
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index: "name",
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columns: [
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{ name: "name", type: "string" },
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{ name: "field1", type: "int32" },
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{ name: "field2", type: "float32" },
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{ name: "field3", type: "boolean" },
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{
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name: "field4",
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type: "categorical",
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categories: field4Categories,
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},
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],
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},
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var: {
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index: "name",
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columns: [
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{ name: "name", type: "string" },
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{ name: "fieldA", type: "int32" },
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{ name: "fieldB", type: "float32" },
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{ name: "fieldC", type: "boolean" },
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{
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name: "fieldD",
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type: "categorical",
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categories: fieldDCategories,
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},
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],
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},
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},
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layout: {
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obs: [{ name: "umap", type: "float32", dims: ["umap_0", "umap_1"] }],
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var: [],
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},
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},
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};
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const anAnnotationsObsJSONResponse = {
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names: ["name", "field1", "field2", "field3", "field4"],
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data: _()
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.range(nObs)
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.map((idx) => [
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idx,
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`obs${idx}`,
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2 * idx,
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idx + 0.0133,
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// eslint-disable-next-line no-bitwise -- idx & 1 to check for odd numbers
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!!(idx & 1),
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field4Categories[idx % field4Categories.length],
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])
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.value(),
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};
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const anAnnotationsVarJSONResponse = {
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names: ["fieldA", "fieldB", "fieldC", "fieldD", "name"],
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data: _()
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.range(nVar)
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.map((idx) => [
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idx,
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10 * idx,
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idx + 2.90143,
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// eslint-disable-next-line no-bitwise -- idx & 1 to check for odd numbers
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!!(idx & 1),
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fieldDCategories[idx % fieldDCategories.length],
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`var${idx}`,
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])
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.value(),
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};
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function encodeTypedArray(builder, uType, uData) {
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const uTypeName = NetEncoding.TypedArray[uType];
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const ArrayType = NetEncoding[uTypeName];
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const dv = ArrayType.createDataVector(builder, uData);
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builder.startObject(1);
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builder.addFieldOffset(0, dv, 0);
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return builder.endObject();
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}
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function encodeMatrix(columns, colIndex = undefined) {
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/*
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IMPORTANT: this is not a general purpose encoder. in particular,
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it doesn't correctly handle all column index types, nor does it
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handle all column typedarray types.
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encodeMatrixFBS in matrix.py is more general. This is used only
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as a testing santity check (alt implementation).
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*/
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const utf8Encoder = new TextEncoder("utf-8");
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const builder = new flatbuffers.Builder(1024);
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const cols = map(columns, (carr) => {
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let uType;
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let tarr;
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if (every(carr, isNumber)) {
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uType = NetEncoding.TypedArray.Float32Array;
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tarr = encodeTypedArray(builder, uType, new Float32Array(carr));
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} else {
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uType = NetEncoding.TypedArray.JSONEncodedArray;
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const json = JSON.stringify(carr);
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const jsonUTF8 = utf8Encoder.encode(json);
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tarr = encodeTypedArray(builder, uType, jsonUTF8);
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}
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NetEncoding.Column.startColumn(builder);
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NetEncoding.Column.addUType(builder, uType);
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NetEncoding.Column.addU(builder, tarr);
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return NetEncoding.Column.endColumn(builder);
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});
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const encColumns = NetEncoding.Matrix.createColumnsVector(builder, cols);
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let encColIndex;
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if (colIndex) {
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encColIndex = encodeTypedArray(
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builder,
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NetEncoding.TypedArray.JSONEncodedArray,
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utf8Encoder.encode(JSON.stringify(colIndex))
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);
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}
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NetEncoding.Matrix.startMatrix(builder);
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NetEncoding.Matrix.addNRows(builder, columns[0].length);
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NetEncoding.Matrix.addNCols(builder, columns.length);
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NetEncoding.Matrix.addColumns(builder, encColumns);
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if (colIndex) {
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NetEncoding.Matrix.addColIndexType(
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builder,
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NetEncoding.TypedArray.JSONEncodedArray
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);
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NetEncoding.Matrix.addColIndex(builder, encColIndex);
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}
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const root = NetEncoding.Matrix.endMatrix(builder);
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builder.finish(root);
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return builder.asUint8Array();
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}
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const anAnnotationsObsFBSResponse = (() => {
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const columns = zip(...anAnnotationsObsJSONResponse.data).slice(1);
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return encodeMatrix(columns, anAnnotationsObsJSONResponse.names);
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})();
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const anAnnotationsVarFBSResponse = (() => {
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const columns = zip(...anAnnotationsVarJSONResponse.data).slice(1);
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return encodeMatrix(columns, anAnnotationsVarJSONResponse.names);
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})();
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const aLayoutFBSResponse = (() => {
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const coords = [
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new Float32Array(nObs).fill(Math.random()),
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new Float32Array(nObs).fill(Math.random()),
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];
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return encodeMatrix(coords, ["umap_0", "umap_1"]);
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})();
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const aDataObsResponse = {
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var: [2, 4, 29],
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obs: _()
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.range(nObs)
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.map((idx) => [idx, Math.random(), Math.random(), Math.random()])
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.value(),
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};
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export {
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aLayoutFBSResponse as layoutObs,
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aDataObsResponse as dataObs,
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anAnnotationsVarFBSResponse as annotationsVar,
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anAnnotationsObsFBSResponse as annotationsObs,
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aSchemaResponse as schema,
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aConfigResponse as config,
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};
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